diff --git a/09_up_and_running_with_tensorflow.ipynb b/09_up_and_running_with_tensorflow.ipynb
index bff2006..304bbb4 100644
--- a/09_up_and_running_with_tensorflow.ipynb
+++ b/09_up_and_running_with_tensorflow.ipynb
@@ -2,9 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -14,10 +14,10 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1473,7 +1473,7 @@
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input: &quot;gradients/predictions_grad/tuple/control_dependency_1&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@theta&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: false\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;GradientDescent&quot;\\n  op: &quot;NoOp&quot;\\n  input: &quot;^GradientDescent/update_theta/ApplyGradientDescent&quot;\\n}\\nnode {\\n  name: &quot;gradients/predictions_grad/tuple/control_dependency&quot;\\n  op: &quot;Identity&quot;\\n  input: &quot;gradients/predictions_grad/MatMul&quot;\\n  input: &quot;^gradients/predictions_grad/tuple/group_deps&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@gradients/predictions_grad/MatMul&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;Square&quot;\\n  op: &quot;Square&quot;\\n  input: &quot;sub&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;mse&quot;\\n  op: &quot;Mean&quot;\\n  input: &quot;Square&quot;\\n  input: &quot;Const&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;Tidx&quot;\\n    value {\\n      type: DT_INT32\\n    }\\n  }\\n  attr {\\n    key: &quot;keep_dims&quot;\\n    value {\\n      b: false\\n    }\\n  }\\n}\\n';\n",
        "          }\n",
        "        </script>\n",
        "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
@@ -2696,7 +2696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2715,7 +2715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
@@ -2745,7 +2745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2761,7 +2761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -2774,7 +2774,7 @@
        "       [ 1.        , -0.83343911,  0.53505665]])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2792,7 +2792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2808,7 +2808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2829,7 +2829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2849,7 +2849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -2862,7 +2862,7 @@
        "       [ 1.        , -0.98941204,  0.02473116]])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2874,7 +2874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -2887,7 +2887,7 @@
        "       [0]])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2912,7 +2912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2928,7 +2928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2959,7 +2959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2979,7 +2979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2999,7 +2999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3016,7 +3016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3032,7 +3032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3050,7 +3050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3073,7 +3073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
@@ -3128,7 +3128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
@@ -3141,7 +3141,7 @@
        "       [0.50859046]], dtype=float32)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3159,7 +3159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
@@ -3172,7 +3172,7 @@
        "       [ True]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3198,7 +3198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -3207,7 +3207,7 @@
        "0.8627450980392157"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3220,7 +3220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
@@ -3229,7 +3229,7 @@
        "0.8888888888888888"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3247,7 +3247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
@@ -3298,7 +3298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3323,7 +3323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
@@ -3346,7 +3346,7 @@
        "         1.53179024e-01]])"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3364,7 +3364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3380,7 +3380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3414,7 +3414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3438,7 +3438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3464,7 +3464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
@@ -3541,7 +3541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3550,7 +3550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -3559,7 +3559,7 @@
        "0.9797979797979798"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3570,7 +3570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -3579,7 +3579,7 @@
        "0.9797979797979798"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3590,7 +3590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -3639,7 +3639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -3647,70 +3647,70 @@
      "output_type": "stream",
      "text": [
       "반복 0\n",
-      "  logdir: tf_logs/logreg-run-20180409085423/\n",
+      "  logdir: tf_logs/logreg-run-20180430082412/\n",
       "  batch_size: 54\n",
       "  learning_rate: 0.004430375245218265\n",
       "  훈련: .....................\n",
       "  정밀도: 0.9797979797979798\n",
       "  재현율: 0.9797979797979798\n",
       "반복 1\n",
-      "  logdir: tf_logs/logreg-run-20180409090102/\n",
+      "  logdir: tf_logs/logreg-run-20180430082554/\n",
       "  batch_size: 22\n",
       "  learning_rate: 0.0017826497151386947\n",
       "  훈련: .....................\n",
       "  정밀도: 0.9797979797979798\n",
       "  재현율: 0.9797979797979798\n",
       "반복 2\n",
-      "  logdir: tf_logs/logreg-run-20180409091435/\n",
+      "  logdir: tf_logs/logreg-run-20180430082952/\n",
       "  batch_size: 74\n",
       "  learning_rate: 0.00203228544324115\n",
       "  훈련: .....................\n",
       "  정밀도: 0.9696969696969697\n",
       "  재현율: 0.9696969696969697\n",
       "반복 3\n",
-      "  logdir: tf_logs/logreg-run-20180409091959/\n",
+      "  logdir: tf_logs/logreg-run-20180430083111/\n",
       "  batch_size: 58\n",
       "  learning_rate: 0.004491523825137997\n",
       "  훈련: .....................\n",
       "  정밀도: 0.9797979797979798\n",
       "  재현율: 0.9797979797979798\n",
       "반복 4\n",
-      "  logdir: tf_logs/logreg-run-20180409092604/\n",
+      "  logdir: tf_logs/logreg-run-20180430083254/\n",
       "  batch_size: 61\n",
       "  learning_rate: 0.07963234721775589\n",
       "  훈련: .....................\n",
       "  정밀도: 0.9801980198019802\n",
       "  재현율: 1.0\n",
       "반복 5\n",
-      "  logdir: tf_logs/logreg-run-20180409093019/\n",
+      "  logdir: tf_logs/logreg-run-20180430083430/\n",
       "  batch_size: 92\n",
       "  learning_rate: 0.0004634250583294876\n",
       "  훈련: .....................\n",
       "  정밀도: 0.912621359223301\n",
       "  재현율: 0.9494949494949495\n",
       "반복 6\n",
-      "  logdir: tf_logs/logreg-run-20180409093334/\n",
+      "  logdir: tf_logs/logreg-run-20180430083531/\n",
       "  batch_size: 74\n",
       "  learning_rate: 0.047706818419354494\n",
       "  훈련: .....................\n",
       "  정밀도: 0.98\n",
       "  재현율: 0.98989898989899\n",
       "반복 7\n",
-      "  logdir: tf_logs/logreg-run-20180409093631/\n",
+      "  logdir: tf_logs/logreg-run-20180430083649/\n",
       "  batch_size: 58\n",
       "  learning_rate: 0.0001694044709524274\n",
       "  훈련: .....................\n",
       "  정밀도: 0.9\n",
       "  재현율: 0.9090909090909091\n",
       "반복 8\n",
-      "  logdir: tf_logs/logreg-run-20180409094251/\n",
+      "  logdir: tf_logs/logreg-run-20180430083828/\n",
       "  batch_size: 61\n",
       "  learning_rate: 0.04171461199412461\n",
       "  훈련: .....................\n",
       "  정밀도: 0.9801980198019802\n",
       "  재현율: 1.0\n",
       "반복 9\n",
-      "  logdir: tf_logs/logreg-run-20180409094921/\n",
+      "  logdir: tf_logs/logreg-run-20180430084000/\n",
       "  batch_size: 92\n",
       "  learning_rate: 0.00010742922968438615\n",
       "  훈련: .....................\n",
diff --git a/10_introduction_to_artificial_neural_networks.ipynb b/10_introduction_to_artificial_neural_networks.ipynb
index 573000b..6be3860 100644
--- a/10_introduction_to_artificial_neural_networks.ipynb
+++ b/10_introduction_to_artificial_neural_networks.ipynb
@@ -12,10 +12,10 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -340,16 +340,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
       "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
+      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
       "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
+      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
       "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
+      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
       "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
      ]
     }
@@ -362,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,849 +378,855 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py:102: extract_pandas_labels (from tensorflow.contrib.learn.python.learn.learn_io.pandas_io) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please access pandas data directly.\n",
+      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.__new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n",
       "INFO:tensorflow:Create CheckpointSaverHook.\n",
       "INFO:tensorflow:Graph was finalized.\n",
       "INFO:tensorflow:Running local_init_op.\n",
       "INFO:tensorflow:Done running local_init_op.\n",
-      "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmp8zcl_r8x/model.ckpt.\n",
-      "INFO:tensorflow:step = 0, loss = 2.2865593\n",
+      "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpe5h7pn1c/model.ckpt.\n",
+      "INFO:tensorflow:step = 0, loss = 2.4005778\n",
+      "INFO:tensorflow:global_step/sec: 750.317\n",
+      "INFO:tensorflow:step = 100, loss = 0.31267592 (0.135 sec)\n",
+      "INFO:tensorflow:global_step/sec: 792.405\n",
+      "INFO:tensorflow:step = 200, loss = 0.29636884 (0.125 sec)\n",
+      "INFO:tensorflow:global_step/sec: 852.452\n",
+      "INFO:tensorflow:step = 300, loss = 0.4080767 (0.118 sec)\n",
+      "INFO:tensorflow:global_step/sec: 799.931\n",
+      "INFO:tensorflow:step = 400, loss = 0.23435771 (0.125 sec)\n",
+      "INFO:tensorflow:global_step/sec: 830.96\n",
+      "INFO:tensorflow:step = 500, loss = 0.2913078 (0.120 sec)\n",
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+      "INFO:tensorflow:step = 39000, loss = 0.00012208936 (0.103 sec)\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "INFO:tensorflow:step = 39900, loss = 0.00077618135 (0.162 sec)\n",
-      "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmp8zcl_r8x/model.ckpt.\n",
-      "INFO:tensorflow:Loss for final step: 0.0004767616.\n"
+      "INFO:tensorflow:global_step/sec: 998.005\n",
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+      "INFO:tensorflow:step = 39700, loss = 0.00017348562 (0.104 sec)\n",
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+      "INFO:tensorflow:step = 39800, loss = 0.0010935392 (0.105 sec)\n",
+      "INFO:tensorflow:global_step/sec: 1010.07\n",
+      "INFO:tensorflow:step = 39900, loss = 0.00081650575 (0.099 sec)\n",
+      "INFO:tensorflow:Saving checkpoints for 40000 into /tmp/tmpe5h7pn1c/model.ckpt.\n",
+      "INFO:tensorflow:Loss for final step: 0.00038293868.\n"
      ]
     },
     {
@@ -1225,7 +1235,7 @@
        "SKCompat()"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1253,7 +1263,7 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Graph was finalized.\n",
-      "INFO:tensorflow:Restoring parameters from /tmp/tmpupshz99y/model.ckpt-40000\n",
+      "INFO:tensorflow:Restoring parameters from /tmp/tmpe5h7pn1c/model.ckpt-40000\n",
       "INFO:tensorflow:Running local_init_op.\n",
       "INFO:tensorflow:Done running local_init_op.\n"
      ]
@@ -1261,7 +1271,7 @@
     {
      "data": {
       "text/plain": [
-       "0.9823"
+       "0.9836"
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@@ -1284,7 +1294,7 @@
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      "data": {
       "text/plain": [
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+       "0.07057629913444195"
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      "execution_count": 15,
@@ -1433,46 +1443,46 @@
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-      "3 훈련 정확도: 0.96 검증 세트 정확도: 0.9484\n",
-      "4 훈련 정확도: 0.92 검증 세트 정확도: 0.9536\n",
-      "5 훈련 정확도: 0.94 검증 세트 정확도: 0.9584\n",
-      "6 훈련 정확도: 0.96 검증 세트 정확도: 0.9596\n",
-      "7 훈련 정확도: 0.96 검증 세트 정확도: 0.9634\n",
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-      "12 훈련 정확도: 0.98 검증 세트 정확도: 0.9718\n",
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-      "24 훈련 정확도: 1.0 검증 세트 정확도: 0.9752\n",
-      "25 훈련 정확도: 1.0 검증 세트 정확도: 0.976\n",
-      "26 훈련 정확도: 0.98 검증 세트 정확도: 0.9764\n",
-      "27 훈련 정확도: 1.0 검증 세트 정확도: 0.9752\n",
-      "28 훈련 정확도: 0.98 검증 세트 정확도: 0.9764\n",
-      "29 훈련 정확도: 1.0 검증 세트 정확도: 0.9768\n",
-      "30 훈련 정확도: 1.0 검증 세트 정확도: 0.9774\n",
-      "31 훈련 정확도: 1.0 검증 세트 정확도: 0.9764\n",
-      "32 훈련 정확도: 0.98 검증 세트 정확도: 0.978\n",
-      "33 훈련 정확도: 0.98 검증 세트 정확도: 0.9782\n",
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+      "4 훈련 정확도: 0.92 검증 세트 정확도: 0.9584\n",
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+      "8 훈련 정확도: 0.92 검증 세트 정확도: 0.9638\n",
+      "9 훈련 정확도: 0.96 검증 세트 정확도: 0.965\n",
+      "10 훈련 정확도: 0.98 검증 세트 정확도: 0.9688\n",
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+      "13 훈련 정확도: 0.94 검증 세트 정확도: 0.968\n",
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+      "20 훈련 정확도: 0.98 검증 세트 정확도: 0.9748\n",
+      "21 훈련 정확도: 1.0 검증 세트 정확도: 0.9748\n",
+      "22 훈련 정확도: 1.0 검증 세트 정확도: 0.9756\n",
+      "23 훈련 정확도: 1.0 검증 세트 정확도: 0.9778\n",
+      "24 훈련 정확도: 1.0 검증 세트 정확도: 0.9778\n",
+      "25 훈련 정확도: 1.0 검증 세트 정확도: 0.9774\n",
+      "26 훈련 정확도: 1.0 검증 세트 정확도: 0.9776\n",
+      "27 훈련 정확도: 1.0 검증 세트 정확도: 0.9766\n",
+      "28 훈련 정확도: 0.98 검증 세트 정확도: 0.9776\n",
+      "29 훈련 정확도: 1.0 검증 세트 정확도: 0.978\n",
+      "30 훈련 정확도: 1.0 검증 세트 정확도: 0.9772\n",
+      "31 훈련 정확도: 1.0 검증 세트 정확도: 0.9786\n",
+      "32 훈련 정확도: 0.98 검증 세트 정확도: 0.9774\n",
+      "33 훈련 정확도: 0.98 검증 세트 정확도: 0.9786\n",
       "34 훈련 정확도: 0.98 검증 세트 정확도: 0.9782\n",
-      "35 훈련 정확도: 1.0 검증 세트 정확도: 0.9784\n",
-      "36 훈련 정확도: 0.98 검증 세트 정확도: 0.9788\n",
-      "37 훈련 정확도: 0.98 검증 세트 정확도: 0.978\n",
-      "38 훈련 정확도: 1.0 검증 세트 정확도: 0.979\n",
-      "39 훈련 정확도: 1.0 검증 세트 정확도: 0.9792\n"
+      "35 훈련 정확도: 0.98 검증 세트 정확도: 0.9772\n",
+      "36 훈련 정확도: 0.98 검증 세트 정확도: 0.9794\n",
+      "37 훈련 정확도: 1.0 검증 세트 정확도: 0.9786\n",
+      "38 훈련 정확도: 1.0 검증 세트 정확도: 0.9792\n",
+      "39 훈련 정확도: 1.0 검증 세트 정확도: 0.9788\n"
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-      "1 훈련 정확도: 0.88 테스트 정확도: 0.9175\n",
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-      "3 훈련 정확도: 0.9 테스트 정확도: 0.9384\n",
-      "4 훈련 정확도: 0.92 테스트 정확도: 0.9437\n",
-      "5 훈련 정확도: 0.96 테스트 정확도: 0.948\n",
-      "6 훈련 정확도: 0.92 테스트 정확도: 0.9517\n",
-      "7 훈련 정확도: 1.0 테스트 정확도: 0.9535\n",
-      "8 훈련 정확도: 0.96 테스트 정확도: 0.9554\n",
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-      "10 훈련 정확도: 0.94 테스트 정확도: 0.961\n",
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-      "12 훈련 정확도: 1.0 테스트 정확도: 0.9639\n",
-      "13 훈련 정확도: 0.96 테스트 정확도: 0.9658\n",
-      "14 훈련 정확도: 0.98 테스트 정확도: 0.9669\n",
-      "15 훈련 정확도: 0.94 테스트 정확도: 0.968\n",
-      "16 훈련 정확도: 0.92 테스트 정확도: 0.9688\n",
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-      "18 훈련 정확도: 1.0 테스트 정확도: 0.9703\n",
-      "19 훈련 정확도: 1.0 테스트 정확도: 0.9702\n"
+      "0 훈련 정확도: 0.9 테스트 정확도: 0.9053\n",
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+      "5 훈련 정확도: 0.94 테스트 정확도: 0.9477\n",
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+      "7 훈련 정확도: 0.98 테스트 정확도: 0.9543\n",
+      "8 훈련 정확도: 0.96 테스트 정확도: 0.9569\n",
+      "9 훈련 정확도: 0.94 테스트 정확도: 0.9605\n",
+      "10 훈련 정확도: 0.92 테스트 정확도: 0.9619\n",
+      "11 훈련 정확도: 0.96 테스트 정확도: 0.9631\n",
+      "12 훈련 정확도: 1.0 테스트 정확도: 0.9659\n",
+      "13 훈련 정확도: 0.94 테스트 정확도: 0.9656\n",
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+      "16 훈련 정확도: 0.96 테스트 정확도: 0.97\n",
+      "17 훈련 정확도: 0.98 테스트 정확도: 0.9696\n",
+      "18 훈련 정확도: 1.0 테스트 정확도: 0.9698\n",
+      "19 훈련 정확도: 1.0 테스트 정확도: 0.9705\n"
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&quot;hidden2/bias&quot;\\n  input: &quot;save/RestoreV2:2&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@hidden2/bias&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_3&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;hidden2/kernel&quot;\\n  input: &quot;save/RestoreV2:3&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@hidden2/kernel&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_4&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;outputs/bias&quot;\\n  input: &quot;save/RestoreV2:4&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@outputs/bias&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/Assign_5&quot;\\n  op: &quot;Assign&quot;\\n  input: &quot;outputs/kernel&quot;\\n  input: &quot;save/RestoreV2:5&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;_class&quot;\\n    value {\\n      list {\\n        s: &quot;loc:@outputs/kernel&quot;\\n      }\\n    }\\n  }\\n  attr {\\n    key: &quot;use_locking&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n  attr {\\n    key: &quot;validate_shape&quot;\\n    value {\\n      b: true\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/restore_all&quot;\\n  op: &quot;NoOp&quot;\\n  input: &quot;^save/Assign&quot;\\n  input: &quot;^save/Assign_1&quot;\\n  input: &quot;^save/Assign_2&quot;\\n  input: &quot;^save/Assign_3&quot;\\n  input: &quot;^save/Assign_4&quot;\\n  input: &quot;^save/Assign_5&quot;\\n}\\n';\n",
        "          }\n",
        "        </script>\n",
        "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
@@ -1997,29 +2007,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "에포크: 0 \t검증 세트 정확도: 90.540% \t손실: 0.35307\n",
-      "에포크: 5 \t검증 세트 정확도: 95.040% \t손실: 0.17874\n",
-      "에포크: 10 \t검증 세트 정확도: 96.620% \t손실: 0.12884\n",
-      "에포크: 15 \t검증 세트 정확도: 97.120% \t손실: 0.10528\n",
-      "에포크: 20 \t검증 세트 정확도: 97.480% \t손실: 0.09091\n",
-      "에포크: 25 \t검증 세트 정확도: 97.740% \t손실: 0.08149\n",
-      "에포크: 30 \t검증 세트 정확도: 97.860% \t손실: 0.07682\n",
-      "에포크: 35 \t검증 세트 정확도: 97.940% \t손실: 0.07219\n",
-      "에포크: 40 \t검증 세트 정확도: 98.100% \t손실: 0.07086\n",
-      "에포크: 45 \t검증 세트 정확도: 98.080% \t손실: 0.06832\n",
-      "에포크: 50 \t검증 세트 정확도: 98.100% \t손실: 0.06627\n",
-      "에포크: 55 \t검증 세트 정확도: 98.140% \t손실: 0.06754\n",
-      "에포크: 60 \t검증 세트 정확도: 98.200% \t손실: 0.06616\n",
-      "에포크: 65 \t검증 세트 정확도: 98.060% \t손실: 0.06676\n",
-      "에포크: 70 \t검증 세트 정확도: 98.120% \t손실: 0.06751\n",
-      "에포크: 75 \t검증 세트 정확도: 98.140% \t손실: 0.06712\n",
-      "에포크: 80 \t검증 세트 정확도: 98.200% \t손실: 0.06759\n",
-      "에포크: 85 \t검증 세트 정확도: 98.160% \t손실: 0.06789\n",
-      "에포크: 90 \t검증 세트 정확도: 98.220% \t손실: 0.06897\n",
-      "에포크: 95 \t검증 세트 정확도: 98.240% \t손실: 0.07033\n",
-      "에포크: 100 \t검증 세트 정확도: 98.260% \t손실: 0.06976\n",
-      "에포크: 105 \t검증 세트 정확도: 98.240% \t손실: 0.07060\n",
-      "에포크: 110 \t검증 세트 정확도: 98.320% \t손실: 0.07099\n",
+      "에포크: 0 \t검증 세트 정확도: 90.440% \t손실: 0.35228\n",
+      "에포크: 5 \t검증 세트 정확도: 95.060% \t손실: 0.17543\n",
+      "에포크: 10 \t검증 세트 정확도: 96.700% \t손실: 0.12549\n",
+      "에포크: 15 \t검증 세트 정확도: 97.220% \t손실: 0.10439\n",
+      "에포크: 20 \t검증 세트 정확도: 97.580% \t손실: 0.08918\n",
+      "에포크: 25 \t검증 세트 정확도: 97.740% \t손실: 0.08118\n",
+      "에포크: 30 \t검증 세트 정확도: 97.800% \t손실: 0.07795\n",
+      "에포크: 35 \t검증 세트 정확도: 97.900% \t손실: 0.07108\n",
+      "에포크: 40 \t검증 세트 정확도: 97.940% \t손실: 0.06991\n",
+      "에포크: 45 \t검증 세트 정확도: 97.900% \t손실: 0.06789\n",
+      "에포크: 50 \t검증 세트 정확도: 98.100% \t손실: 0.06652\n",
+      "에포크: 55 \t검증 세트 정확도: 98.100% \t손실: 0.06645\n",
+      "에포크: 60 \t검증 세트 정확도: 98.140% \t손실: 0.06523\n",
+      "에포크: 65 \t검증 세트 정확도: 98.080% \t손실: 0.06597\n",
+      "에포크: 70 \t검증 세트 정확도: 98.080% \t손실: 0.06768\n",
+      "에포크: 75 \t검증 세트 정확도: 98.140% \t손실: 0.06720\n",
+      "에포크: 80 \t검증 세트 정확도: 98.100% \t손실: 0.06712\n",
+      "에포크: 85 \t검증 세트 정확도: 98.200% \t손실: 0.06725\n",
+      "에포크: 90 \t검증 세트 정확도: 98.180% \t손실: 0.06716\n",
+      "에포크: 95 \t검증 세트 정확도: 98.160% \t손실: 0.06904\n",
+      "에포크: 100 \t검증 세트 정확도: 98.220% \t손실: 0.06847\n",
+      "에포크: 105 \t검증 세트 정확도: 98.140% \t손실: 0.06907\n",
+      "에포크: 110 \t검증 세트 정확도: 98.180% \t손실: 0.06993\n",
+      "에포크: 115 \t검증 세트 정확도: 98.220% \t손실: 0.07063\n",
       "조기 종료\n"
      ]
     }
@@ -2108,7 +2119,7 @@
     {
      "data": {
       "text/plain": [
-       "0.98"
+       "0.9788"
       ]
      },
      "execution_count": 54,
diff --git a/11_deep_learning.ipynb b/11_deep_learning.ipynb
index ac64382..7e212c8 100644
--- a/11_deep_learning.ipynb
+++ b/11_deep_learning.ipynb
@@ -14,11 +14,11 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,17 +184,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use the retry module or similar alternatives.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "he_init = tf.contrib.layers.variance_scaling_initializer()\n",
     "hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu,\n",
@@ -421,14 +411,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 배치 정확도: 0.86 검증 세트 정확도: 0.9018\n",
-      "5 배치 정확도: 0.92 검증 세트 정확도: 0.9484\n",
-      "10 배치 정확도: 0.98 검증 세트 정확도: 0.9638\n",
-      "15 배치 정확도: 1.0 검증 세트 정확도: 0.9706\n",
+      "0 배치 정확도: 0.86 검증 세트 정확도: 0.9044\n",
+      "5 배치 정확도: 0.94 검증 세트 정확도: 0.951\n",
+      "10 배치 정확도: 0.96 검증 세트 정확도: 0.9662\n",
+      "15 배치 정확도: 1.0 검증 세트 정확도: 0.9722\n",
       "20 배치 정확도: 1.0 검증 세트 정확도: 0.9746\n",
-      "25 배치 정확도: 1.0 검증 세트 정확도: 0.976\n",
-      "30 배치 정확도: 1.0 검증 세트 정확도: 0.9784\n",
-      "35 배치 정확도: 0.96 검증 세트 정확도: 0.9786\n"
+      "25 배치 정확도: 1.0 검증 세트 정확도: 0.9764\n",
+      "30 배치 정확도: 0.98 검증 세트 정확도: 0.978\n",
+      "35 배치 정확도: 0.96 검증 세트 정확도: 0.9794\n"
      ]
     }
    ],
@@ -710,14 +700,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 배치 정확도: 0.92 검증 세트 정확도: 0.9226\n",
-      "5 배치 정확도: 1.0 검증 세트 정확도: 0.9544\n",
-      "10 배치 정확도: 0.94 검증 세트 정확도: 0.9632\n",
-      "15 배치 정확도: 0.96 검증 세트 정확도: 0.9648\n",
-      "20 배치 정확도: 1.0 검증 세트 정확도: 0.9666\n",
-      "25 배치 정확도: 1.0 검증 세트 정확도: 0.9682\n",
-      "30 배치 정확도: 1.0 검증 세트 정확도: 0.9684\n",
-      "35 배치 정확도: 1.0 검증 세트 정확도: 0.9682\n"
+      "0 배치 정확도: 0.96 검증 세트 정확도: 0.924\n",
+      "5 배치 정확도: 1.0 검증 세트 정확도: 0.9568\n",
+      "10 배치 정확도: 0.94 검증 세트 정확도: 0.967\n",
+      "15 배치 정확도: 0.98 검증 세트 정확도: 0.9686\n",
+      "20 배치 정확도: 1.0 검증 세트 정확도: 0.971\n",
+      "25 배치 정확도: 1.0 검증 세트 정확도: 0.9694\n",
+      "30 배치 정확도: 1.0 검증 세트 정확도: 0.97\n",
+      "35 배치 정확도: 1.0 검증 세트 정확도: 0.9712\n"
      ]
     }
    ],
@@ -911,26 +901,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 테스트 정확도: 0.8738\n",
-      "1 테스트 정확도: 0.8976\n",
-      "2 테스트 정확도: 0.9108\n",
-      "3 테스트 정확도: 0.9208\n",
-      "4 테스트 정확도: 0.9287\n",
-      "5 테스트 정확도: 0.9352\n",
-      "6 테스트 정확도: 0.9406\n",
-      "7 테스트 정확도: 0.9444\n",
-      "8 테스트 정확도: 0.947\n",
-      "9 테스트 정확도: 0.9491\n",
-      "10 테스트 정확도: 0.953\n",
-      "11 테스트 정확도: 0.9542\n",
-      "12 테스트 정확도: 0.9572\n",
-      "13 테스트 정확도: 0.959\n",
-      "14 테스트 정확도: 0.9602\n",
-      "15 테스트 정확도: 0.9605\n",
-      "16 테스트 정확도: 0.9618\n",
-      "17 테스트 정확도: 0.9633\n",
-      "18 테스트 정확도: 0.9636\n",
-      "19 테스트 정확도: 0.9644\n"
+      "0 테스트 정확도: 0.8666\n",
+      "1 테스트 정확도: 0.894\n",
+      "2 테스트 정확도: 0.9088\n",
+      "3 테스트 정확도: 0.9189\n",
+      "4 테스트 정확도: 0.9262\n",
+      "5 테스트 정확도: 0.9334\n",
+      "6 테스트 정확도: 0.9379\n",
+      "7 테스트 정확도: 0.9419\n",
+      "8 테스트 정확도: 0.9444\n",
+      "9 테스트 정확도: 0.9477\n",
+      "10 테스트 정확도: 0.9499\n",
+      "11 테스트 정확도: 0.952\n",
+      "12 테스트 정확도: 0.9546\n",
+      "13 테스트 정확도: 0.956\n",
+      "14 테스트 정확도: 0.9567\n",
+      "15 테스트 정확도: 0.9591\n",
+      "16 테스트 정확도: 0.9609\n",
+      "17 테스트 정확도: 0.9612\n",
+      "18 테스트 정확도: 0.9618\n",
+      "19 테스트 정확도: 0.9635\n"
      ]
     }
    ],
@@ -1178,26 +1168,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 테스트 정확도: 0.4986\n",
-      "1 테스트 정확도: 0.8282\n",
-      "2 테스트 정확도: 0.8829\n",
-      "3 테스트 정확도: 0.9022\n",
-      "4 테스트 정확도: 0.9118\n",
-      "5 테스트 정확도: 0.9211\n",
-      "6 테스트 정확도: 0.9267\n",
-      "7 테스트 정확도: 0.9367\n",
-      "8 테스트 정확도: 0.9376\n",
-      "9 테스트 정확도: 0.941\n",
-      "10 테스트 정확도: 0.9465\n",
-      "11 테스트 정확도: 0.9469\n",
-      "12 테스트 정확도: 0.9465\n",
-      "13 테스트 정확도: 0.9528\n",
-      "14 테스트 정확도: 0.9538\n",
-      "15 테스트 정확도: 0.9549\n",
-      "16 테스트 정확도: 0.9579\n",
-      "17 테스트 정확도: 0.9587\n",
-      "18 테스트 정확도: 0.957\n",
-      "19 테스트 정확도: 0.9604\n"
+      "0 테스트 정확도: 0.3138\n",
+      "1 테스트 정확도: 0.8001\n",
+      "2 테스트 정확도: 0.881\n",
+      "3 테스트 정확도: 0.904\n",
+      "4 테스트 정확도: 0.9122\n",
+      "5 테스트 정확도: 0.9195\n",
+      "6 테스트 정확도: 0.9245\n",
+      "7 테스트 정확도: 0.9301\n",
+      "8 테스트 정확도: 0.9331\n",
+      "9 테스트 정확도: 0.9389\n",
+      "10 테스트 정확도: 0.943\n",
+      "11 테스트 정확도: 0.9449\n",
+      "12 테스트 정확도: 0.9456\n",
+      "13 테스트 정확도: 0.9485\n",
+      "14 테스트 정확도: 0.9524\n",
+      "15 테스트 정확도: 0.9519\n",
+      "16 테스트 정확도: 0.9563\n",
+      "17 테스트 정확도: 0.9583\n",
+      "18 테스트 정확도: 0.956\n",
+      "19 테스트 정확도: 0.9608\n"
      ]
     }
    ],
@@ -1270,296 +1260,272 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "X\n",
-      "y\n",
-      "hidden1/kernel/Initializer/random_uniform/shape\n",
-      "hidden1/kernel/Initializer/random_uniform/min\n",
+      "save/RestoreV2/shape_and_slices\n",
+      "save/RestoreV2/tensor_names\n",
+      "save/SaveV2/shape_and_slices\n",
+      "save/SaveV2/tensor_names\n",
+      "save/Const\n",
+      "save/RestoreV2\n",
+      "eval/Const\n",
+      "eval/in_top_k/InTopKV2/k\n",
+      "GradientDescent/learning_rate\n",
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+      "clip_by_value_11/clip_value_min\n",
+      "clip_by_value_10/clip_value_max\n",
+      "clip_by_value_10/clip_value_min\n",
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+      "clip_by_value_9/clip_value_min\n",
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+      "clip_by_value_5/clip_value_min\n",
+      "clip_by_value_4/clip_value_max\n",
+      "clip_by_value_4/clip_value_min\n",
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+      "clip_by_value_3/clip_value_min\n",
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+      "clip_by_value_1/clip_value_min\n",
+      "clip_by_value/clip_value_max\n",
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+      "gradients/loss/loss_grad/Const_1\n",
+      "gradients/loss/loss_grad/Const\n",
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+      "gradients/grad_ys_0\n",
+      "gradients/Shape\n",
+      "gradients/Fill\n",
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+      "outputs/bias\n",
+      "save/Assign_10\n",
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+      "outputs/bias/Assign\n",
+      "outputs/kernel\n",
+      "save/Assign_11\n",
+      "outputs/kernel/read\n",
+      "outputs/kernel/Initializer/random_uniform/max\n",
+      "outputs/kernel/Initializer/random_uniform/min\n",
+      "outputs/kernel/Initializer/random_uniform/sub\n",
+      "outputs/kernel/Initializer/random_uniform/shape\n",
+      "outputs/kernel/Initializer/random_uniform/RandomUniform\n",
+      "outputs/kernel/Initializer/random_uniform/mul\n",
+      "outputs/kernel/Initializer/random_uniform\n",
+      "outputs/kernel/Assign\n",
+      "hidden5/bias\n",
+      "save/Assign_8\n",
+      "hidden5/bias/read\n",
+      "hidden5/bias/Initializer/zeros\n",
+      "hidden5/bias/Assign\n",
+      "hidden5/kernel\n",
+      "save/Assign_9\n",
+      "hidden5/kernel/read\n",
+      "hidden5/kernel/Initializer/random_uniform/max\n",
+      "hidden5/kernel/Initializer/random_uniform/min\n",
+      "hidden5/kernel/Initializer/random_uniform/sub\n",
+      "hidden5/kernel/Initializer/random_uniform/shape\n",
+      "hidden5/kernel/Initializer/random_uniform/RandomUniform\n",
+      "hidden5/kernel/Initializer/random_uniform/mul\n",
+      "hidden5/kernel/Initializer/random_uniform\n",
+      "hidden5/kernel/Assign\n",
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+      "hidden4/bias/Assign\n",
+      "hidden4/kernel\n",
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+      "hidden4/kernel/read\n",
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+      "hidden4/kernel/Initializer/random_uniform/sub\n",
+      "hidden4/kernel/Initializer/random_uniform/shape\n",
+      "hidden4/kernel/Initializer/random_uniform/RandomUniform\n",
+      "hidden4/kernel/Initializer/random_uniform/mul\n",
+      "hidden4/kernel/Initializer/random_uniform\n",
+      "hidden4/kernel/Assign\n",
+      "hidden3/bias\n",
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+      "hidden3/bias/Initializer/zeros\n",
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+      "hidden3/kernel\n",
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+      "hidden3/kernel/read\n",
+      "hidden3/kernel/Initializer/random_uniform/max\n",
+      "hidden3/kernel/Initializer/random_uniform/min\n",
+      "hidden3/kernel/Initializer/random_uniform/sub\n",
+      "hidden3/kernel/Initializer/random_uniform/shape\n",
+      "hidden3/kernel/Initializer/random_uniform/RandomUniform\n",
+      "hidden3/kernel/Initializer/random_uniform/mul\n",
+      "hidden3/kernel/Initializer/random_uniform\n",
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+      "hidden2/kernel/Initializer/random_uniform/min\n",
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+      "hidden2/kernel/Initializer/random_uniform/shape\n",
+      "hidden2/kernel/Initializer/random_uniform/RandomUniform\n",
+      "hidden2/kernel/Initializer/random_uniform/mul\n",
+      "hidden2/kernel/Initializer/random_uniform\n",
+      "hidden2/kernel/Assign\n",
+      "hidden1/bias\n",
+      "save/Assign\n",
+      "hidden1/bias/read\n",
+      "hidden1/bias/Initializer/zeros\n",
+      "hidden1/bias/Assign\n",
+      "hidden1/kernel\n",
+      "save/Assign_1\n",
+      "save/restore_all\n",
+      "save/SaveV2\n",
+      "save/control_dependency\n",
+      "hidden1/kernel/read\n",
       "hidden1/kernel/Initializer/random_uniform/max\n",
-      "hidden1/kernel/Initializer/random_uniform/RandomUniform\n",
+      "hidden1/kernel/Initializer/random_uniform/min\n",
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+      "hidden1/kernel/Initializer/random_uniform/shape\n",
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       "hidden1/kernel/Initializer/random_uniform/mul\n",
       "hidden1/kernel/Initializer/random_uniform\n",
-      "hidden1/kernel\n",
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-      "hidden1/bias/Initializer/zeros/shape_as_tensor\n",
-      "hidden1/bias/Initializer/zeros/Const\n",
-      "hidden1/bias/Initializer/zeros\n",
-      "hidden1/bias\n",
-      "hidden1/bias/Assign\n",
-      "hidden1/bias/read\n",
+      "init\n",
+      "y\n",
+      "loss/SparseSoftmaxCrossEntropyWithLogits/Shape\n",
+      "X\n",
       "dnn/hidden1/MatMul\n",
       "dnn/hidden1/BiasAdd\n",
       "dnn/hidden1/Relu\n",
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-      "loss/SparseSoftmaxCrossEntropyWithLogits/Shape\n",
+      "eval/in_top_k/InTopKV2\n",
+      "eval/Cast\n",
+      "eval/accuracy\n",
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+      "gradients/dnn/outputs/BiasAdd_grad/tuple/control_dependency\n",
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+      "clip_by_value_10\n",
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+      "GradientDescent/update_hidden5/bias/ApplyGradientDescent\n",
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+      "clip_by_value_6\n",
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+      "clip_by_value_2\n",
+      "GradientDescent/update_hidden2/kernel/ApplyGradientDescent\n",
+      "gradients/dnn/hidden2/MatMul_grad/tuple/control_dependency\n",
       "gradients/dnn/hidden1/Relu_grad/ReluGrad\n",
       "gradients/dnn/hidden1/BiasAdd_grad/BiasAddGrad\n",
       "gradients/dnn/hidden1/BiasAdd_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden1/BiasAdd_grad/tuple/control_dependency\n",
       "gradients/dnn/hidden1/BiasAdd_grad/tuple/control_dependency_1\n",
-      "gradients/dnn/hidden1/MatMul_grad/MatMul\n",
-      "gradients/dnn/hidden1/MatMul_grad/MatMul_1\n",
-      "gradients/dnn/hidden1/MatMul_grad/tuple/group_deps\n",
-      "gradients/dnn/hidden1/MatMul_grad/tuple/control_dependency\n",
-      "gradients/dnn/hidden1/MatMul_grad/tuple/control_dependency_1\n",
-      "clip_by_value/Minimum/y\n",
-      "clip_by_value/Minimum\n",
-      "clip_by_value/y\n",
-      "clip_by_value\n",
-      "clip_by_value_1/Minimum/y\n",
-      "clip_by_value_1/Minimum\n",
-      "clip_by_value_1/y\n",
       "clip_by_value_1\n",
-      "clip_by_value_2/Minimum/y\n",
-      "clip_by_value_2/Minimum\n",
-      "clip_by_value_2/y\n",
-      "clip_by_value_2\n",
-      "clip_by_value_3/Minimum/y\n",
-      "clip_by_value_3/Minimum\n",
-      "clip_by_value_3/y\n",
-      "clip_by_value_3\n",
-      "clip_by_value_4/Minimum/y\n",
-      "clip_by_value_4/Minimum\n",
-      "clip_by_value_4/y\n",
-      "clip_by_value_4\n",
-      "clip_by_value_5/Minimum/y\n",
-      "clip_by_value_5/Minimum\n",
-      "clip_by_value_5/y\n",
-      "clip_by_value_5\n",
-      "clip_by_value_6/Minimum/y\n",
-      "clip_by_value_6/Minimum\n",
-      "clip_by_value_6/y\n",
-      "clip_by_value_6\n",
-      "clip_by_value_7/Minimum/y\n",
-      "clip_by_value_7/Minimum\n",
-      "clip_by_value_7/y\n",
-      "clip_by_value_7\n",
-      "clip_by_value_8/Minimum/y\n",
-      "clip_by_value_8/Minimum\n",
-      "clip_by_value_8/y\n",
-      "clip_by_value_8\n",
-      "clip_by_value_9/Minimum/y\n",
-      "clip_by_value_9/Minimum\n",
-      "clip_by_value_9/y\n",
-      "clip_by_value_9\n",
-      "clip_by_value_10/Minimum/y\n",
-      "clip_by_value_10/Minimum\n",
-      "clip_by_value_10/y\n",
-      "clip_by_value_10\n",
-      "clip_by_value_11/Minimum/y\n",
-      "clip_by_value_11/Minimum\n",
-      "clip_by_value_11/y\n",
-      "clip_by_value_11\n",
-      "GradientDescent/learning_rate\n",
-      "GradientDescent/update_hidden1/kernel/ApplyGradientDescent\n",
       "GradientDescent/update_hidden1/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden2/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden2/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden3/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden3/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden4/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden4/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden5/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_hidden5/bias/ApplyGradientDescent\n",
-      "GradientDescent/update_outputs/kernel/ApplyGradientDescent\n",
-      "GradientDescent/update_outputs/bias/ApplyGradientDescent\n",
+      "gradients/dnn/hidden1/BiasAdd_grad/tuple/control_dependency\n",
+      "gradients/dnn/hidden1/MatMul_grad/MatMul_1\n",
+      "gradients/dnn/hidden1/MatMul_grad/MatMul\n",
+      "gradients/dnn/hidden1/MatMul_grad/tuple/group_deps\n",
+      "gradients/dnn/hidden1/MatMul_grad/tuple/control_dependency_1\n",
+      "clip_by_value\n",
+      "GradientDescent/update_hidden1/kernel/ApplyGradientDescent\n",
       "GradientDescent\n",
-      "eval/in_top_k/InTopKV2/k\n",
-      "eval/in_top_k/InTopKV2\n",
-      "eval/Cast\n",
-      "eval/Const\n",
-      "eval/accuracy\n",
-      "init\n",
-      "save/Const\n",
-      "save/SaveV2/tensor_names\n",
-      "save/SaveV2/shape_and_slices\n",
-      "save/SaveV2\n",
-      "save/control_dependency\n",
-      "save/RestoreV2/tensor_names\n",
-      "save/RestoreV2/shape_and_slices\n",
-      "save/RestoreV2\n",
-      "save/Assign\n",
-      "save/Assign_1\n",
-      "save/Assign_2\n",
-      "save/Assign_3\n",
-      "save/Assign_4\n",
-      "save/Assign_5\n",
-      "save/Assign_6\n",
-      "save/Assign_7\n",
-      "save/Assign_8\n",
-      "save/Assign_9\n",
-      "save/Assign_10\n",
-      "save/Assign_11\n",
-      "save/restore_all\n"
+      "gradients/dnn/hidden1/MatMul_grad/tuple/control_dependency\n",
+      "loss/loss\n"
      ]
     }
    ],
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@@ -1750,26 +1716,26 @@
      "output_type": "stream",
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       "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 테스트 정확도: 0.9596\n",
-      "1 테스트 정확도: 0.9618\n",
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-      "4 테스트 정확도: 0.9641\n",
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-      "6 테스트 정확도: 0.9639\n",
-      "7 테스트 정확도: 0.9652\n",
-      "8 테스트 정확도: 0.9648\n",
-      "9 테스트 정확도: 0.9665\n",
-      "10 테스트 정확도: 0.9669\n",
-      "11 테스트 정확도: 0.9665\n",
-      "12 테스트 정확도: 0.9683\n",
-      "13 테스트 정확도: 0.9683\n",
+      "0 테스트 정확도: 0.9607\n",
+      "1 테스트 정확도: 0.9611\n",
+      "2 테스트 정확도: 0.962\n",
+      "3 테스트 정확도: 0.961\n",
+      "4 테스트 정확도: 0.9643\n",
+      "5 테스트 정확도: 0.9653\n",
+      "6 테스트 정확도: 0.9665\n",
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       "14 테스트 정확도: 0.9688\n",
-      "15 테스트 정확도: 0.9678\n",
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-      "17 테스트 정확도: 0.9686\n",
-      "18 테스트 정확도: 0.9689\n",
-      "19 테스트 정확도: 0.9692\n"
+      "15 테스트 정확도: 0.9685\n",
+      "16 테스트 정확도: 0.9687\n",
+      "17 테스트 정확도: 0.9701\n",
+      "18 테스트 정확도: 0.9678\n",
+      "19 테스트 정확도: 0.9687\n"
      ]
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@@ -1859,26 +1825,26 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 테스트 정확도: 0.9614\n",
-      "1 테스트 정확도: 0.9625\n",
-      "2 테스트 정확도: 0.9627\n",
-      "3 테스트 정확도: 0.9584\n",
-      "4 테스트 정확도: 0.9649\n",
-      "5 테스트 정확도: 0.9642\n",
-      "6 테스트 정확도: 0.9646\n",
-      "7 테스트 정확도: 0.9641\n",
-      "8 테스트 정확도: 0.9641\n",
-      "9 테스트 정확도: 0.9634\n",
-      "10 테스트 정확도: 0.9663\n",
-      "11 테스트 정확도: 0.9674\n",
-      "12 테스트 정확도: 0.9691\n",
-      "13 테스트 정확도: 0.967\n",
-      "14 테스트 정확도: 0.968\n",
-      "15 테스트 정확도: 0.9691\n",
-      "16 테스트 정확도: 0.9686\n",
-      "17 테스트 정확도: 0.9702\n",
-      "18 테스트 정확도: 0.9689\n",
-      "19 테스트 정확도: 0.9704\n"
+      "0 테스트 정확도: 0.9612\n",
+      "1 테스트 정확도: 0.9617\n",
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+      "3 테스트 정확도: 0.9617\n",
+      "4 테스트 정확도: 0.9646\n",
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+      "7 테스트 정확도: 0.9648\n",
+      "8 테스트 정확도: 0.9669\n",
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+      "12 테스트 정확도: 0.9687\n",
+      "13 테스트 정확도: 0.9686\n",
+      "14 테스트 정확도: 0.9685\n",
+      "15 테스트 정확도: 0.9694\n",
+      "16 테스트 정확도: 0.9682\n",
+      "17 테스트 정확도: 0.9694\n",
+      "18 테스트 정확도: 0.9695\n",
+      "19 테스트 정확도: 0.9707\n"
      ]
     }
    ],
@@ -1958,26 +1924,26 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 테스트 정확도: 0.9172\n",
-      "1 테스트 정확도: 0.9339\n",
-      "2 테스트 정확도: 0.9476\n",
-      "3 테스트 정확도: 0.9514\n",
-      "4 테스트 정확도: 0.9564\n",
-      "5 테스트 정확도: 0.9583\n",
-      "6 테스트 정확도: 0.9597\n",
-      "7 테스트 정확도: 0.9607\n",
-      "8 테스트 정확도: 0.9628\n",
-      "9 테스트 정확도: 0.9645\n",
-      "10 테스트 정확도: 0.9648\n",
-      "11 테스트 정확도: 0.9647\n",
-      "12 테스트 정확도: 0.9655\n",
-      "13 테스트 정확도: 0.9664\n",
-      "14 테스트 정확도: 0.9664\n",
-      "15 테스트 정확도: 0.9666\n",
-      "16 테스트 정확도: 0.9681\n",
-      "17 테스트 정확도: 0.9675\n",
-      "18 테스트 정확도: 0.9683\n",
-      "19 테스트 정확도: 0.9677\n"
+      "0 테스트 정확도: 0.9236\n",
+      "1 테스트 정확도: 0.9383\n",
+      "2 테스트 정확도: 0.9492\n",
+      "3 테스트 정확도: 0.9526\n",
+      "4 테스트 정확도: 0.9566\n",
+      "5 테스트 정확도: 0.9577\n",
+      "6 테스트 정확도: 0.9596\n",
+      "7 테스트 정확도: 0.961\n",
+      "8 테스트 정확도: 0.9623\n",
+      "9 테스트 정확도: 0.9626\n",
+      "10 테스트 정확도: 0.9642\n",
+      "11 테스트 정확도: 0.9628\n",
+      "12 테스트 정확도: 0.9641\n",
+      "13 테스트 정확도: 0.9655\n",
+      "14 테스트 정확도: 0.9655\n",
+      "15 테스트 정확도: 0.9662\n",
+      "16 테스트 정확도: 0.9676\n",
+      "17 테스트 정확도: 0.9686\n",
+      "18 테스트 정확도: 0.9687\n",
+      "19 테스트 정확도: 0.9693\n"
      ]
     }
    ],
@@ -2059,26 +2025,26 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 테스트 정확도: 0.9024\n",
-      "1 테스트 정확도: 0.9285\n",
-      "2 테스트 정확도: 0.9403\n",
-      "3 테스트 정확도: 0.946\n",
-      "4 테스트 정확도: 0.9511\n",
-      "5 테스트 정확도: 0.9527\n",
-      "6 테스트 정확도: 0.9544\n",
-      "7 테스트 정확도: 0.9574\n",
-      "8 테스트 정확도: 0.9576\n",
-      "9 테스트 정확도: 0.9603\n",
-      "10 테스트 정확도: 0.9595\n",
-      "11 테스트 정확도: 0.962\n",
-      "12 테스트 정확도: 0.9622\n",
-      "13 테스트 정확도: 0.9638\n",
-      "14 테스트 정확도: 0.9635\n",
-      "15 테스트 정확도: 0.9643\n",
-      "16 테스트 정확도: 0.9637\n",
-      "17 테스트 정확도: 0.9643\n",
-      "18 테스트 정확도: 0.9643\n",
-      "19 테스트 정확도: 0.9653\n"
+      "0 테스트 정확도: 0.9026\n",
+      "1 테스트 정확도: 0.9307\n",
+      "2 테스트 정확도: 0.9394\n",
+      "3 테스트 정확도: 0.943\n",
+      "4 테스트 정확도: 0.9482\n",
+      "5 테스트 정확도: 0.951\n",
+      "6 테스트 정확도: 0.9514\n",
+      "7 테스트 정확도: 0.9539\n",
+      "8 테스트 정확도: 0.9545\n",
+      "9 테스트 정확도: 0.9569\n",
+      "10 테스트 정확도: 0.9597\n",
+      "11 테스트 정확도: 0.9599\n",
+      "12 테스트 정확도: 0.9604\n",
+      "13 테스트 정확도: 0.962\n",
+      "14 테스트 정확도: 0.9619\n",
+      "15 테스트 정확도: 0.9634\n",
+      "16 테스트 정확도: 0.963\n",
+      "17 테스트 정확도: 0.9647\n",
+      "18 테스트 정확도: 0.9653\n",
+      "19 테스트 정확도: 0.9659\n"
      ]
     }
    ],
@@ -2370,26 +2336,26 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 테스트 정확도: 0.8918\n",
-      "1 테스트 정확도: 0.9229\n",
-      "2 테스트 정확도: 0.9325\n",
-      "3 테스트 정확도: 0.9389\n",
-      "4 테스트 정확도: 0.9426\n",
-      "5 테스트 정확도: 0.9466\n",
-      "6 테스트 정확도: 0.9492\n",
-      "7 테스트 정확도: 0.9505\n",
-      "8 테스트 정확도: 0.952\n",
+      "0 테스트 정확도: 0.8987\n",
+      "1 테스트 정확도: 0.931\n",
+      "2 테스트 정확도: 0.9371\n",
+      "3 테스트 정확도: 0.9418\n",
+      "4 테스트 정확도: 0.9437\n",
+      "5 테스트 정확도: 0.9477\n",
+      "6 테스트 정확도: 0.9494\n",
+      "7 테스트 정확도: 0.9524\n",
+      "8 테스트 정확도: 0.9513\n",
       "9 테스트 정확도: 0.9522\n",
-      "10 테스트 정확도: 0.9532\n",
-      "11 테스트 정확도: 0.9535\n",
-      "12 테스트 정확도: 0.9527\n",
-      "13 테스트 정확도: 0.9549\n",
-      "14 테스트 정확도: 0.9542\n",
-      "15 테스트 정확도: 0.9548\n",
-      "16 테스트 정확도: 0.9567\n",
-      "17 테스트 정확도: 0.9564\n",
-      "18 테스트 정확도: 0.9567\n",
-      "19 테스트 정확도: 0.957\n"
+      "10 테스트 정확도: 0.9536\n",
+      "11 테스트 정확도: 0.9538\n",
+      "12 테스트 정확도: 0.9536\n",
+      "13 테스트 정확도: 0.9548\n",
+      "14 테스트 정확도: 0.9537\n",
+      "15 테스트 정확도: 0.9552\n",
+      "16 테스트 정확도: 0.9552\n",
+      "17 테스트 정확도: 0.9551\n",
+      "18 테스트 정확도: 0.9552\n",
+      "19 테스트 정확도: 0.9554\n"
      ]
     }
    ],
@@ -2490,26 +2456,26 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 테스트 정확도: 0.9149\n",
-      "1 테스트 정확도: 0.9385\n",
-      "2 테스트 정확도: 0.9438\n",
-      "3 테스트 정확도: 0.9473\n",
-      "4 테스트 정확도: 0.9481\n",
-      "5 테스트 정확도: 0.9506\n",
-      "6 테스트 정확도: 0.9513\n",
-      "7 테스트 정확도: 0.9521\n",
-      "8 테스트 정확도: 0.9529\n",
-      "9 테스트 정확도: 0.9537\n",
-      "10 테스트 정확도: 0.9535\n",
-      "11 테스트 정확도: 0.954\n",
-      "12 테스트 정확도: 0.9541\n",
-      "13 테스트 정확도: 0.9547\n",
-      "14 테스트 정확도: 0.9555\n",
-      "15 테스트 정확도: 0.9562\n",
-      "16 테스트 정확도: 0.9556\n",
-      "17 테스트 정확도: 0.9566\n",
-      "18 테스트 정확도: 0.9561\n",
-      "19 테스트 정확도: 0.9571\n"
+      "0 테스트 정확도: 0.9035\n",
+      "1 테스트 정확도: 0.932\n",
+      "2 테스트 정확도: 0.9398\n",
+      "3 테스트 정확도: 0.9435\n",
+      "4 테스트 정확도: 0.9471\n",
+      "5 테스트 정확도: 0.949\n",
+      "6 테스트 정확도: 0.9499\n",
+      "7 테스트 정확도: 0.9492\n",
+      "8 테스트 정확도: 0.9518\n",
+      "9 테스트 정확도: 0.9521\n",
+      "10 테스트 정확도: 0.9529\n",
+      "11 테스트 정확도: 0.9536\n",
+      "12 테스트 정확도: 0.953\n",
+      "13 테스트 정확도: 0.9535\n",
+      "14 테스트 정확도: 0.9525\n",
+      "15 테스트 정확도: 0.9538\n",
+      "16 테스트 정확도: 0.953\n",
+      "17 테스트 정확도: 0.9551\n",
+      "18 테스트 정확도: 0.9549\n",
+      "19 테스트 정확도: 0.9555\n"
      ]
     }
    ],
@@ -2610,26 +2576,26 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n",
-      "0 테스트 정확도: 0.914\n",
-      "1 테스트 정확도: 0.9395\n",
-      "2 테스트 정확도: 0.9444\n",
-      "3 테스트 정확도: 0.9464\n",
-      "4 테스트 정확도: 0.9489\n",
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-      "11 테스트 정확도: 0.9539\n",
-      "12 테스트 정확도: 0.9546\n",
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-      "14 테스트 정확도: 0.9551\n",
-      "15 테스트 정확도: 0.9563\n",
-      "16 테스트 정확도: 0.9557\n",
-      "17 테스트 정확도: 0.956\n",
-      "18 테스트 정확도: 0.9561\n",
-      "19 테스트 정확도: 0.9565\n"
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+      "2 테스트 정확도: 0.9425\n",
+      "3 테스트 정확도: 0.945\n",
+      "4 테스트 정확도: 0.9473\n",
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+      "7 테스트 정확도: 0.9507\n",
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+      "9 테스트 정확도: 0.9524\n",
+      "10 테스트 정확도: 0.9513\n",
+      "11 테스트 정확도: 0.9523\n",
+      "12 테스트 정확도: 0.9522\n",
+      "13 테스트 정확도: 0.954\n",
+      "14 테스트 정확도: 0.9539\n",
+      "15 테스트 정확도: 0.9537\n",
+      "16 테스트 정확도: 0.9547\n",
+      "17 테스트 정확도: 0.954\n",
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+      "19 테스트 정확도: 0.9547\n"
      ]
     }
    ],
@@ -2822,11 +2788,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 테스트 정확도: 0.9617\n",
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-      "2 테스트 정확도: 0.9749\n",
-      "3 테스트 정확도: 0.9784\n",
-      "4 테스트 정확도: 0.9819\n"
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+      "2 테스트 정확도: 0.9731\n",
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+      "4 테스트 정확도: 0.9798\n"
      ]
     }
    ],
@@ -2952,26 +2918,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "4 테스트 정확도: 0.8964\n",
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-      "7 테스트 정확도: 0.9039\n",
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-      "11 테스트 정확도: 0.9085\n",
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+      "14 테스트 정확도: 0.9069\n",
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+      "16 테스트 정확도: 0.9064\n",
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+      "19 테스트 정확도: 0.9063\n"
      ]
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    ],
@@ -3108,26 +3074,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "11 테스트 정확도: 0.9148\n",
-      "12 테스트 정확도: 0.9158\n",
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-      "19 테스트 정확도: 0.9158\n"
+      "0 테스트 정확도: 0.8299\n",
+      "1 테스트 정확도: 0.8778\n",
+      "2 테스트 정확도: 0.8915\n",
+      "3 테스트 정확도: 0.9016\n",
+      "4 테스트 정확도: 0.9068\n",
+      "5 테스트 정확도: 0.9103\n",
+      "6 테스트 정확도: 0.9124\n",
+      "7 테스트 정확도: 0.9137\n",
+      "8 테스트 정확도: 0.9149\n",
+      "9 테스트 정확도: 0.9175\n",
+      "10 테스트 정확도: 0.9176\n",
+      "11 테스트 정확도: 0.9184\n",
+      "12 테스트 정확도: 0.9191\n",
+      "13 테스트 정확도: 0.9182\n",
+      "14 테스트 정확도: 0.9195\n",
+      "15 테스트 정확도: 0.9201\n",
+      "16 테스트 정확도: 0.918\n",
+      "17 테스트 정확도: 0.9184\n",
+      "18 테스트 정확도: 0.9181\n",
+      "19 테스트 정확도: 0.9174\n"
      ]
     }
    ],
@@ -3221,26 +3187,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 테스트 정확도: 0.9165\n",
-      "1 테스트 정확도: 0.9398\n",
-      "2 테스트 정확도: 0.948\n",
-      "3 테스트 정확도: 0.9495\n",
-      "4 테스트 정확도: 0.9555\n",
-      "5 테스트 정확도: 0.9558\n",
-      "6 테스트 정확도: 0.96\n",
-      "7 테스트 정확도: 0.9617\n",
-      "8 테스트 정확도: 0.9623\n",
-      "9 테스트 정확도: 0.965\n",
-      "10 테스트 정확도: 0.9642\n",
-      "11 테스트 정확도: 0.9631\n",
-      "12 테스트 정확도: 0.9636\n",
-      "13 테스트 정확도: 0.9658\n",
-      "14 테스트 정확도: 0.9686\n",
-      "15 테스트 정확도: 0.9673\n",
-      "16 테스트 정확도: 0.9698\n",
-      "17 테스트 정확도: 0.9658\n",
+      "0 테스트 정확도: 0.9205\n",
+      "1 테스트 정확도: 0.942\n",
+      "2 테스트 정확도: 0.9469\n",
+      "3 테스트 정확도: 0.9511\n",
+      "4 테스트 정확도: 0.9542\n",
+      "5 테스트 정확도: 0.9569\n",
+      "6 테스트 정확도: 0.961\n",
+      "7 테스트 정확도: 0.9598\n",
+      "8 테스트 정확도: 0.9612\n",
+      "9 테스트 정확도: 0.9656\n",
+      "10 테스트 정확도: 0.9662\n",
+      "11 테스트 정확도: 0.9616\n",
+      "12 테스트 정확도: 0.9663\n",
+      "13 테스트 정확도: 0.9668\n",
+      "14 테스트 정확도: 0.965\n",
+      "15 테스트 정확도: 0.9678\n",
+      "16 테스트 정확도: 0.9696\n",
+      "17 테스트 정확도: 0.9674\n",
       "18 테스트 정확도: 0.9694\n",
-      "19 테스트 정확도: 0.9676\n"
+      "19 테스트 정확도: 0.9683\n"
      ]
     }
    ],
@@ -3391,26 +3357,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 테스트 정확도: 0.9465\n",
-      "1 테스트 정확도: 0.9659\n",
-      "2 테스트 정확도: 0.9685\n",
-      "3 테스트 정확도: 0.9749\n",
-      "4 테스트 정확도: 0.9743\n",
-      "5 테스트 정확도: 0.977\n",
-      "6 테스트 정확도: 0.977\n",
-      "7 테스트 정확도: 0.9788\n",
-      "8 테스트 정확도: 0.9791\n",
-      "9 테스트 정확도: 0.9792\n",
-      "10 테스트 정확도: 0.9797\n",
-      "11 테스트 정확도: 0.9798\n",
-      "12 테스트 정확도: 0.9801\n",
-      "13 테스트 정확도: 0.9794\n",
-      "14 테스트 정확도: 0.9809\n",
-      "15 테스트 정확도: 0.9817\n",
-      "16 테스트 정확도: 0.9806\n",
-      "17 테스트 정확도: 0.9807\n",
-      "18 테스트 정확도: 0.9808\n",
-      "19 테스트 정확도: 0.9817\n"
+      "0 테스트 정확도: 0.9512\n",
+      "1 테스트 정확도: 0.9671\n",
+      "2 테스트 정확도: 0.972\n",
+      "3 테스트 정확도: 0.9757\n",
+      "4 테스트 정확도: 0.975\n",
+      "5 테스트 정확도: 0.9757\n",
+      "6 테스트 정확도: 0.9777\n",
+      "7 테스트 정확도: 0.9796\n",
+      "8 테스트 정확도: 0.9806\n",
+      "9 테스트 정확도: 0.9798\n",
+      "10 테스트 정확도: 0.9813\n",
+      "11 테스트 정확도: 0.9809\n",
+      "12 테스트 정확도: 0.9817\n",
+      "13 테스트 정확도: 0.9804\n",
+      "14 테스트 정확도: 0.9819\n",
+      "15 테스트 정확도: 0.9823\n",
+      "16 테스트 정확도: 0.9833\n",
+      "17 테스트 정확도: 0.9827\n",
+      "18 테스트 정확도: 0.9825\n",
+      "19 테스트 정확도: 0.9822\n"
      ]
     }
    ],
@@ -3546,26 +3512,26 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 테스트 정확도: 0.9529\n",
-      "1 테스트 정확도: 0.9653\n",
-      "2 테스트 정확도: 0.9704\n",
-      "3 테스트 정확도: 0.9747\n",
-      "4 테스트 정확도: 0.9749\n",
-      "5 테스트 정확도: 0.9753\n",
-      "6 테스트 정확도: 0.9772\n",
-      "7 테스트 정확도: 0.9793\n",
-      "8 테스트 정확도: 0.9768\n",
-      "9 테스트 정확도: 0.9778\n",
-      "10 테스트 정확도: 0.98\n",
-      "11 테스트 정확도: 0.9807\n",
-      "12 테스트 정확도: 0.9803\n",
-      "13 테스트 정확도: 0.9809\n",
-      "14 테스트 정확도: 0.9812\n",
-      "15 테스트 정확도: 0.981\n",
-      "16 테스트 정확도: 0.9813\n",
-      "17 테스트 정확도: 0.9808\n",
-      "18 테스트 정확도: 0.9811\n",
-      "19 테스트 정확도: 0.9809\n"
+      "0 테스트 정확도: 0.954\n",
+      "1 테스트 정확도: 0.9657\n",
+      "2 테스트 정확도: 0.9707\n",
+      "3 테스트 정확도: 0.9743\n",
+      "4 테스트 정확도: 0.9761\n",
+      "5 테스트 정확도: 0.9777\n",
+      "6 테스트 정확도: 0.9775\n",
+      "7 테스트 정확도: 0.979\n",
+      "8 테스트 정확도: 0.9769\n",
+      "9 테스트 정확도: 0.9803\n",
+      "10 테스트 정확도: 0.9803\n",
+      "11 테스트 정확도: 0.9798\n",
+      "12 테스트 정확도: 0.9818\n",
+      "13 테스트 정확도: 0.9813\n",
+      "14 테스트 정확도: 0.9811\n",
+      "15 테스트 정확도: 0.9806\n",
+      "16 테스트 정확도: 0.9812\n",
+      "17 테스트 정확도: 0.9819\n",
+      "18 테스트 정확도: 0.9822\n",
+      "19 테스트 정확도: 0.982\n"
      ]
     }
    ],
diff --git a/11_deep_learning_exercises.ipynb b/11_deep_learning_exercises.ipynb
index d952743..cd5a737 100644
--- a/11_deep_learning_exercises.ipynb
+++ b/11_deep_learning_exercises.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "scrolled": true
    },
@@ -14,11 +14,11 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,19 +159,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use the retry module or similar alternatives.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "he_init = tf.contrib.layers.variance_scaling_initializer()\n",
     "\n",
@@ -187,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -230,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -288,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,38 +292,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.144007\t최선의 손실: 0.144007\t정확도: 97.34%\n",
-      "1\t검증 세트 손실: 0.124397\t최선의 손실: 0.124397\t정확도: 96.95%\n",
-      "2\t검증 세트 손실: 0.185427\t최선의 손실: 0.124397\t정확도: 96.52%\n",
-      "3\t검증 세트 손실: 0.147826\t최선의 손실: 0.124397\t정확도: 96.25%\n",
-      "4\t검증 세트 손실: 0.145386\t최선의 손실: 0.124397\t정확도: 97.22%\n",
-      "5\t검증 세트 손실: 0.134986\t최선의 손실: 0.124397\t정확도: 96.95%\n",
-      "6\t검증 세트 손실: 0.259464\t최선의 손실: 0.124397\t정확도: 95.39%\n",
-      "7\t검증 세트 손실: 0.230232\t최선의 손실: 0.124397\t정확도: 94.29%\n",
-      "8\t검증 세트 손실: 0.158316\t최선의 손실: 0.124397\t정확도: 97.42%\n",
-      "9\t검증 세트 손실: 0.268429\t최선의 손실: 0.124397\t정확도: 97.07%\n",
-      "10\t검증 세트 손실: 0.147843\t최선의 손실: 0.124397\t정확도: 97.77%\n",
-      "11\t검증 세트 손실: 0.393391\t최선의 손실: 0.124397\t정확도: 96.91%\n",
-      "12\t검증 세트 손실: 0.180520\t최선의 손실: 0.124397\t정확도: 97.85%\n",
-      "13\t검증 세트 손실: 0.145679\t최선의 손실: 0.124397\t정확도: 97.38%\n",
-      "14\t검증 세트 손실: 0.649295\t최선의 손실: 0.124397\t정확도: 97.34%\n",
-      "15\t검증 세트 손실: 0.179004\t최선의 손실: 0.124397\t정확도: 98.08%\n",
-      "16\t검증 세트 손실: 0.228197\t최선의 손실: 0.124397\t정확도: 96.52%\n",
-      "17\t검증 세트 손실: 0.263742\t최선의 손실: 0.124397\t정확도: 96.36%\n",
-      "18\t검증 세트 손실: 1.248711\t최선의 손실: 0.124397\t정확도: 65.21%\n",
-      "19\t검증 세트 손실: 2.936332\t최선의 손실: 0.124397\t정확도: 95.31%\n",
-      "20\t검증 세트 손실: 0.590014\t최선의 손실: 0.124397\t정확도: 89.05%\n",
-      "21\t검증 세트 손실: 0.467989\t최선의 손실: 0.124397\t정확도: 95.23%\n",
+      "0\t검증 세트 손실: 0.142580\t최선의 손실: 0.142580\t정확도: 96.64%\n",
+      "1\t검증 세트 손실: 0.136356\t최선의 손실: 0.136356\t정확도: 96.33%\n",
+      "2\t검증 세트 손실: 0.105153\t최선의 손실: 0.105153\t정확도: 97.15%\n",
+      "3\t검증 세트 손실: 0.110939\t최선의 손실: 0.105153\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.315193\t최선의 손실: 0.105153\t정확도: 90.23%\n",
+      "5\t검증 세트 손실: 1.352539\t최선의 손실: 0.105153\t정확도: 95.93%\n",
+      "6\t검증 세트 손실: 0.404159\t최선의 손실: 0.105153\t정확도: 90.58%\n",
+      "7\t검증 세트 손실: 0.478956\t최선의 손실: 0.105153\t정확도: 76.66%\n",
+      "8\t검증 세트 손실: 0.296281\t최선의 손실: 0.105153\t정확도: 94.14%\n",
+      "9\t검증 세트 손실: 0.474146\t최선의 손실: 0.105153\t정확도: 86.47%\n",
+      "10\t검증 세트 손실: 0.272404\t최선의 손실: 0.105153\t정확도: 93.47%\n",
+      "11\t검증 세트 손실: 0.262523\t최선의 손실: 0.105153\t정확도: 96.05%\n",
+      "12\t검증 세트 손실: 0.228771\t최선의 손실: 0.105153\t정확도: 97.42%\n",
+      "13\t검증 세트 손실: 0.240359\t최선의 손실: 0.105153\t정확도: 97.38%\n",
+      "14\t검증 세트 손실: 0.234160\t최선의 손실: 0.105153\t정확도: 97.54%\n",
+      "15\t검증 세트 손실: 0.334344\t최선의 손실: 0.105153\t정확도: 97.77%\n",
+      "16\t검증 세트 손실: 0.286479\t최선의 손실: 0.105153\t정확도: 97.46%\n",
+      "17\t검증 세트 손실: 2.433587\t최선의 손실: 0.105153\t정확도: 94.29%\n",
+      "18\t검증 세트 손실: 1.035606\t최선의 손실: 0.105153\t정확도: 85.54%\n",
+      "19\t검증 세트 손실: 0.272855\t최선의 손실: 0.105153\t정확도: 96.21%\n",
+      "20\t검증 세트 손실: 0.304605\t최선의 손실: 0.105153\t정확도: 96.21%\n",
+      "21\t검증 세트 손실: 0.953484\t최선의 손실: 0.105153\t정확도: 72.67%\n",
+      "22\t검증 세트 손실: 0.219466\t최선의 손실: 0.105153\t정확도: 96.95%\n",
       "조기 종료!\n",
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_0_to_4.ckpt\n",
-      "최종 테스트 정확도: 97.00%\n"
+      "최종 테스트 정확도: 97.10%\n"
      ]
     }
    ],
@@ -410,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,51 +591,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.461491\t최선의 손실: 0.461491\t정확도: 91.13%\n",
-      "1\t검증 세트 손실: 0.100868\t최선의 손실: 0.100868\t정확도: 97.89%\n",
-      "2\t검증 세트 손실: 1.099176\t최선의 손실: 0.100868\t정확도: 54.77%\n",
-      "3\t검증 세트 손실: 1.359620\t최선의 손실: 0.100868\t정확도: 36.98%\n",
-      "4\t검증 세트 손실: 1.630533\t최선의 손실: 0.100868\t정확도: 24.00%\n",
-      "5\t검증 세트 손실: 1.642829\t최선의 손실: 0.100868\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.676497\t최선의 손실: 0.100868\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 1.767159\t최선의 손실: 0.100868\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.700400\t최선의 손실: 0.100868\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.763634\t최선의 손실: 0.100868\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.629541\t최선의 손실: 0.100868\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.812458\t최선의 손실: 0.100868\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.675868\t최선의 손실: 0.100868\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.633252\t최선의 손실: 0.100868\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.652904\t최선의 손실: 0.100868\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.635938\t최선의 손실: 0.100868\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.718920\t최선의 손실: 0.100868\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.682459\t최선의 손실: 0.100868\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.675367\t최선의 손실: 0.100868\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.645803\t최선의 손실: 0.100868\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.722334\t최선의 손실: 0.100868\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.656415\t최선의 손실: 0.100868\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.643529\t최선의 손실: 0.100868\t정확도: 18.73%\n",
+      "0\t검증 세트 손실: 0.825829\t최선의 손실: 0.825829\t정확도: 84.36%\n",
+      "1\t검증 세트 손실: 0.226767\t최선의 손실: 0.226767\t정확도: 95.39%\n",
+      "2\t검증 세트 손실: 0.094620\t최선의 손실: 0.094620\t정확도: 97.50%\n",
+      "3\t검증 세트 손실: 1.769363\t최선의 손실: 0.094620\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 1.639235\t최선의 손실: 0.094620\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.641189\t최선의 손실: 0.094620\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.676724\t최선의 손실: 0.094620\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.775086\t최선의 손실: 0.094620\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.699567\t최선의 손실: 0.094620\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.767559\t최선의 손실: 0.094620\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.629341\t최선의 손실: 0.094620\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.812651\t최선의 손실: 0.094620\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.675936\t최선의 손실: 0.094620\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 1.633258\t최선의 손실: 0.094620\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.652906\t최선의 손실: 0.094620\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.635939\t최선의 손실: 0.094620\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.718920\t최선의 손실: 0.094620\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.682459\t최선의 손실: 0.094620\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.675367\t최선의 손실: 0.094620\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.645803\t최선의 손실: 0.094620\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1.722334\t최선의 손실: 0.094620\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.656415\t최선의 손실: 0.094620\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.643529\t최선의 손실: 0.094620\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.644234\t최선의 손실: 0.094620\t정확도: 19.27%\n",
       "조기 종료!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "DNNClassifier(activation=<function elu at 0x7f99417cf6a8>,\n",
+       "DNNClassifier(activation=<function elu at 0x7f738a2bd1e0>,\n",
        "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -663,16 +655,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9836544074722708"
+       "0.9793734189531037"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -693,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -701,747 +693,785 @@
      "output_type": "stream",
      "text": [
       "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.152108\t최선의 손실: 0.152108\t정확도: 95.86%\n",
-      "1\t검증 세트 손실: 0.135257\t최선의 손실: 0.135257\t정확도: 96.36%\n",
-      "2\t검증 세트 손실: 0.124339\t최선의 손실: 0.124339\t정확도: 96.36%\n",
-      "3\t검증 세트 손실: 0.150407\t최선의 손실: 0.124339\t정확도: 95.82%\n",
-      "4\t검증 세트 손실: 0.187580\t최선의 손실: 0.124339\t정확도: 95.50%\n",
-      "5\t검증 세트 손실: 0.114265\t최선의 손실: 0.114265\t정확도: 97.19%\n",
-      "6\t검증 세트 손실: 0.106723\t최선의 손실: 0.106723\t정확도: 96.76%\n",
-      "7\t검증 세트 손실: 1.613546\t최선의 손실: 0.106723\t정확도: 22.09%\n",
-      "8\t검증 세트 손실: 1.577145\t최선의 손실: 0.106723\t정확도: 21.62%\n",
-      "9\t검증 세트 손실: 1.616387\t최선의 손실: 0.106723\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.616002\t최선의 손실: 0.106723\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.610402\t최선의 손실: 0.106723\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.624974\t최선의 손실: 0.106723\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.643835\t최선의 손실: 0.106723\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 1.614718\t최선의 손실: 0.106723\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.612610\t최선의 손실: 0.106723\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.620854\t최선의 손실: 0.106723\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.617509\t최선의 손실: 0.106723\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.619164\t최선의 손실: 0.106723\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.650435\t최선의 손실: 0.106723\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.618648\t최선의 손실: 0.106723\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.629365\t최선의 손실: 0.106723\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.621644\t최선의 손실: 0.106723\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.613017\t최선의 손실: 0.106723\t정확도: 20.91%\n",
-      "24\t검증 세트 손실: 1.623499\t최선의 손실: 0.106723\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.618789\t최선의 손실: 0.106723\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.638315\t최선의 손실: 0.106723\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 1.616616\t최선의 손실: 0.106723\t정확도: 19.27%\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.131358\t최선의 손실: 0.131358\t정확도: 96.68%\n",
+      "1\t검증 세트 손실: 0.136887\t최선의 손실: 0.131358\t정확도: 96.52%\n",
+      "2\t검증 세트 손실: 0.122056\t최선의 손실: 0.122056\t정확도: 96.79%\n",
+      "3\t검증 세트 손실: 0.131960\t최선의 손실: 0.122056\t정확도: 96.79%\n",
+      "4\t검증 세트 손실: 0.173777\t최선의 손실: 0.122056\t정확도: 95.97%\n",
+      "5\t검증 세트 손실: 0.133734\t최선의 손실: 0.122056\t정확도: 96.68%\n",
+      "6\t검증 세트 손실: 0.120576\t최선의 손실: 0.120576\t정확도: 96.99%\n",
+      "7\t검증 세트 손실: 0.147677\t최선의 손실: 0.120576\t정확도: 96.68%\n",
+      "8\t검증 세트 손실: 0.129115\t최선의 손실: 0.120576\t정확도: 96.79%\n",
+      "9\t검증 세트 손실: 0.207614\t최선의 손실: 0.120576\t정확도: 95.62%\n",
+      "10\t검증 세트 손실: 1.389598\t최선의 손실: 0.120576\t정확도: 34.79%\n",
+      "11\t검증 세트 손실: 1.069314\t최선의 손실: 0.120576\t정확도: 38.58%\n",
+      "12\t검증 세트 손실: 1.061086\t최선의 손실: 0.120576\t정확도: 39.76%\n",
+      "13\t검증 세트 손실: 1.026689\t최선의 손실: 0.120576\t정확도: 39.37%\n",
+      "14\t검증 세트 손실: 1.043682\t최선의 손실: 0.120576\t정확도: 40.42%\n",
+      "15\t검증 세트 손실: 1.083720\t최선의 손실: 0.120576\t정확도: 40.89%\n",
+      "16\t검증 세트 손실: 1.031031\t최선의 손실: 0.120576\t정확도: 40.62%\n",
+      "17\t검증 세트 손실: 1.046122\t최선의 손실: 0.120576\t정확도: 39.56%\n",
+      "18\t검증 세트 손실: 1.050750\t최선의 손실: 0.120576\t정확도: 40.70%\n",
+      "19\t검증 세트 손실: 1.052398\t최선의 손실: 0.120576\t정확도: 40.07%\n",
+      "20\t검증 세트 손실: 1.030613\t최선의 손실: 0.120576\t정확도: 40.42%\n",
+      "21\t검증 세트 손실: 1.623587\t최선의 손실: 0.120576\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.616578\t최선의 손실: 0.120576\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.614933\t최선의 손실: 0.120576\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 1.623768\t최선의 손실: 0.120576\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.615650\t최선의 손실: 0.120576\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.631997\t최선의 손실: 0.120576\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 1.631281\t최선의 손실: 0.120576\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=   8.8s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=10, total=   5.5s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=10 \n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    8.8s remaining:    0.0s\n"
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.6s remaining:    0.0s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.189355\t최선의 손실: 0.189355\t정확도: 94.57%\n",
-      "1\t검증 세트 손실: 0.117609\t최선의 손실: 0.117609\t정확도: 97.11%\n",
-      "2\t검증 세트 손실: 0.187319\t최선의 손실: 0.117609\t정확도: 95.62%\n",
-      "3\t검증 세트 손실: 0.128511\t최선의 손실: 0.117609\t정확도: 97.03%\n",
-      "4\t검증 세트 손실: 0.109509\t최선의 손실: 0.109509\t정확도: 97.62%\n",
-      "5\t검증 세트 손실: 0.147996\t최선의 손실: 0.109509\t정확도: 96.09%\n",
-      "6\t검증 세트 손실: 0.124949\t최선의 손실: 0.109509\t정확도: 97.15%\n",
-      "7\t검증 세트 손실: 0.238166\t최선의 손실: 0.109509\t정확도: 95.27%\n",
-      "8\t검증 세트 손실: 0.134434\t최선의 손실: 0.109509\t정확도: 97.38%\n",
-      "9\t검증 세트 손실: 1.457385\t최선의 손실: 0.109509\t정확도: 39.33%\n",
-      "10\t검증 세트 손실: 1.230790\t최선의 손실: 0.109509\t정확도: 40.38%\n",
-      "11\t검증 세트 손실: 1.177325\t최선의 손실: 0.109509\t정확도: 40.15%\n",
-      "12\t검증 세트 손실: 1.160145\t최선의 손실: 0.109509\t정확도: 40.03%\n",
-      "13\t검증 세트 손실: 1.162522\t최선의 손실: 0.109509\t정확도: 40.03%\n",
-      "14\t검증 세트 손실: 1.170691\t최선의 손실: 0.109509\t정확도: 41.71%\n",
-      "15\t검증 세트 손실: 1.167971\t최선의 손실: 0.109509\t정확도: 40.42%\n",
-      "16\t검증 세트 손실: 1.166986\t최선의 손실: 0.109509\t정확도: 40.38%\n",
-      "17\t검증 세트 손실: 1.154227\t최선의 손실: 0.109509\t정확도: 40.38%\n",
-      "18\t검증 세트 손실: 1.159322\t최선의 손실: 0.109509\t정확도: 40.19%\n",
-      "19\t검증 세트 손실: 1.157019\t최선의 손실: 0.109509\t정확도: 40.34%\n",
-      "20\t검증 세트 손실: 1.160094\t최선의 손실: 0.109509\t정확도: 40.23%\n",
-      "21\t검증 세트 손실: 1.215104\t최선의 손실: 0.109509\t정확도: 40.03%\n",
-      "22\t검증 세트 손실: 1.167137\t최선의 손실: 0.109509\t정확도: 40.23%\n",
-      "23\t검증 세트 손실: 1.158918\t최선의 손실: 0.109509\t정확도: 40.15%\n",
-      "24\t검증 세트 손실: 1.170212\t최선의 손실: 0.109509\t정확도: 40.34%\n",
-      "25\t검증 세트 손실: 1.151162\t최선의 손실: 0.109509\t정확도: 42.42%\n",
+      "0\t검증 세트 손실: 0.160212\t최선의 손실: 0.160212\t정확도: 95.39%\n",
+      "1\t검증 세트 손실: 0.152015\t최선의 손실: 0.152015\t정확도: 96.13%\n",
+      "2\t검증 세트 손실: 0.116358\t최선의 손실: 0.116358\t정확도: 97.19%\n",
+      "3\t검증 세트 손실: 0.212592\t최선의 손실: 0.116358\t정확도: 94.84%\n",
+      "4\t검증 세트 손실: 0.118002\t최선의 손실: 0.116358\t정확도: 96.76%\n",
+      "5\t검증 세트 손실: 0.153413\t최선의 손실: 0.116358\t정확도: 95.86%\n",
+      "6\t검증 세트 손실: 0.134702\t최선의 손실: 0.116358\t정확도: 96.95%\n",
+      "7\t검증 세트 손실: 0.140082\t최선의 손실: 0.116358\t정확도: 96.52%\n",
+      "8\t검증 세트 손실: 0.165304\t최선의 손실: 0.116358\t정확도: 93.71%\n",
+      "9\t검증 세트 손실: 1.620656\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.609132\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.632971\t최선의 손실: 0.116358\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 1.628353\t최선의 손실: 0.116358\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 1.625960\t최선의 손실: 0.116358\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.614784\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.614323\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.620646\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.613835\t최선의 손실: 0.116358\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.612884\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.610178\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.613204\t최선의 손실: 0.116358\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.666974\t최선의 손실: 0.116358\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.627591\t최선의 손실: 0.116358\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.630242\t최선의 손실: 0.116358\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=   5.8s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.185078\t최선의 손실: 0.185078\t정확도: 94.33%\n",
-      "1\t검증 세트 손실: 0.145538\t최선의 손실: 0.145538\t정확도: 96.60%\n",
-      "2\t검증 세트 손실: 0.130201\t최선의 손실: 0.130201\t정확도: 96.83%\n",
-      "3\t검증 세트 손실: 0.119384\t최선의 손실: 0.119384\t정확도: 96.95%\n",
-      "4\t검증 세트 손실: 0.144287\t최선의 손실: 0.119384\t정확도: 96.87%\n",
-      "5\t검증 세트 손실: 0.118167\t최선의 손실: 0.118167\t정확도: 97.11%\n",
-      "6\t검증 세트 손실: 0.149176\t최선의 손실: 0.118167\t정확도: 95.78%\n",
-      "7\t검증 세트 손실: 0.146681\t최선의 손실: 0.118167\t정확도: 96.56%\n",
-      "8\t검증 세트 손실: 0.212406\t최선의 손실: 0.118167\t정확도: 95.50%\n",
-      "9\t검증 세트 손실: 0.133058\t최선의 손실: 0.118167\t정확도: 96.83%\n",
-      "10\t검증 세트 손실: 1.048793\t최선의 손실: 0.118167\t정확도: 79.44%\n",
-      "11\t검증 세트 손실: 0.962885\t최선의 손실: 0.118167\t정확도: 60.83%\n",
-      "12\t검증 세트 손실: 0.985428\t최선의 손실: 0.118167\t정확도: 54.73%\n",
-      "13\t검증 세트 손실: 0.942681\t최선의 손실: 0.118167\t정확도: 48.79%\n",
-      "14\t검증 세트 손실: 0.974854\t최선의 손실: 0.118167\t정확도: 56.06%\n",
-      "15\t검증 세트 손실: 0.965942\t최선의 손실: 0.118167\t정확도: 49.57%\n",
-      "16\t검증 세트 손실: 0.838436\t최선의 손실: 0.118167\t정확도: 58.37%\n",
-      "17\t검증 세트 손실: 0.863985\t최선의 손실: 0.118167\t정확도: 59.93%\n",
-      "18\t검증 세트 손실: 0.792665\t최선의 손실: 0.118167\t정확도: 62.20%\n",
-      "19\t검증 세트 손실: 0.742582\t최선의 손실: 0.118167\t정확도: 63.45%\n",
-      "20\t검증 세트 손실: 0.817602\t최선의 손실: 0.118167\t정확도: 65.29%\n",
-      "21\t검증 세트 손실: 1.638920\t최선의 손실: 0.118167\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.621425\t최선의 손실: 0.118167\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.627051\t최선의 손실: 0.118167\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.611009\t최선의 손실: 0.118167\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.617701\t최선의 손실: 0.118167\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.609761\t최선의 손실: 0.118167\t정확도: 22.01%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=10, total=   4.8s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.196744\t최선의 손실: 0.196744\t정확도: 94.88%\n",
+      "1\t검증 세트 손실: 0.193064\t최선의 손실: 0.193064\t정확도: 94.49%\n",
+      "2\t검증 세트 손실: 0.176615\t최선의 손실: 0.176615\t정확도: 95.66%\n",
+      "3\t검증 세트 손실: 0.189284\t최선의 손실: 0.176615\t정확도: 95.58%\n",
+      "4\t검증 세트 손실: 0.150652\t최선의 손실: 0.150652\t정확도: 95.97%\n",
+      "5\t검증 세트 손실: 0.158509\t최선의 손실: 0.150652\t정확도: 96.17%\n",
+      "6\t검증 세트 손실: 0.161489\t최선의 손실: 0.150652\t정확도: 96.29%\n",
+      "7\t검증 세트 손실: 0.147854\t최선의 손실: 0.147854\t정확도: 96.25%\n",
+      "8\t검증 세트 손실: 0.169249\t최선의 손실: 0.147854\t정확도: 96.40%\n",
+      "9\t검증 세트 손실: 0.154190\t최선의 손실: 0.147854\t정확도: 96.60%\n",
+      "10\t검증 세트 손실: 0.143528\t최선의 손실: 0.143528\t정확도: 95.86%\n",
+      "11\t검증 세트 손실: 0.257454\t최선의 손실: 0.143528\t정확도: 94.18%\n",
+      "12\t검증 세트 손실: 0.168330\t최선의 손실: 0.143528\t정확도: 96.64%\n",
+      "13\t검증 세트 손실: 0.147234\t최선의 손실: 0.143528\t정확도: 96.01%\n",
+      "14\t검증 세트 손실: 0.197637\t최선의 손실: 0.143528\t정확도: 94.76%\n",
+      "15\t검증 세트 손실: 1.797953\t최선의 손실: 0.143528\t정확도: 38.94%\n",
+      "16\t검증 세트 손실: 1.074162\t최선의 손실: 0.143528\t정확도: 40.23%\n",
+      "17\t검증 세트 손실: 1.032647\t최선의 손실: 0.143528\t정확도: 36.40%\n",
+      "18\t검증 세트 손실: 1.010367\t최선의 손실: 0.143528\t정확도: 38.19%\n",
+      "19\t검증 세트 손실: 1.028174\t최선의 손실: 0.143528\t정확도: 42.38%\n",
+      "20\t검증 세트 손실: 0.995176\t최선의 손실: 0.143528\t정확도: 45.93%\n",
+      "21\t검증 세트 손실: 1.011931\t최선의 손실: 0.143528\t정확도: 50.59%\n",
+      "22\t검증 세트 손실: 0.907021\t최선의 손실: 0.143528\t정확도: 61.77%\n",
+      "23\t검증 세트 손실: 0.797462\t최선의 손실: 0.143528\t정확도: 66.93%\n",
+      "24\t검증 세트 손실: 0.777895\t최선의 손실: 0.143528\t정확도: 66.89%\n",
+      "25\t검증 세트 손실: 0.762751\t최선의 손실: 0.143528\t정확도: 68.57%\n",
+      "26\t검증 세트 손실: 0.767683\t최선의 손실: 0.143528\t정확도: 68.45%\n",
+      "27\t검증 세트 손실: 0.745557\t최선의 손실: 0.143528\t정확도: 67.63%\n",
+      "28\t검증 세트 손실: 0.769768\t최선의 손실: 0.143528\t정확도: 66.97%\n",
+      "29\t검증 세트 손실: 0.781149\t최선의 손실: 0.143528\t정확도: 68.84%\n",
+      "30\t검증 세트 손실: 0.741308\t최선의 손실: 0.143528\t정확도: 66.26%\n",
+      "31\t검증 세트 손실: 0.709860\t최선의 손실: 0.143528\t정확도: 68.69%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=   6.3s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.133993\t최선의 손실: 0.133993\t정확도: 95.78%\n",
-      "1\t검증 세트 손실: 0.083684\t최선의 손실: 0.083684\t정확도: 97.42%\n",
-      "2\t검증 세트 손실: 0.089795\t최선의 손실: 0.083684\t정확도: 97.26%\n",
-      "3\t검증 세트 손실: 0.086861\t최선의 손실: 0.083684\t정확도: 97.22%\n",
-      "4\t검증 세트 손실: 0.081328\t최선의 손실: 0.081328\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.066929\t최선의 손실: 0.066929\t정확도: 98.16%\n",
-      "6\t검증 세트 손실: 0.080990\t최선의 손실: 0.066929\t정확도: 97.97%\n",
-      "7\t검증 세트 손실: 0.088480\t최선의 손실: 0.066929\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.083254\t최선의 손실: 0.066929\t정확도: 97.85%\n",
-      "9\t검증 세트 손실: 0.078953\t최선의 손실: 0.066929\t정확도: 98.01%\n",
-      "10\t검증 세트 손실: 0.084964\t최선의 손실: 0.066929\t정확도: 98.08%\n",
-      "11\t검증 세트 손실: 0.082606\t최선의 손실: 0.066929\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.090441\t최선의 손실: 0.066929\t정확도: 97.73%\n",
-      "13\t검증 세트 손실: 0.094558\t최선의 손실: 0.066929\t정확도: 97.69%\n",
-      "14\t검증 세트 손실: 0.100627\t최선의 손실: 0.066929\t정확도: 97.97%\n",
-      "15\t검증 세트 손실: 0.088135\t최선의 손실: 0.066929\t정확도: 98.05%\n",
-      "16\t검증 세트 손실: 0.090596\t최선의 손실: 0.066929\t정확도: 98.20%\n",
-      "17\t검증 세트 손실: 0.102386\t최선의 손실: 0.066929\t정확도: 97.89%\n",
-      "18\t검증 세트 손실: 0.079426\t최선의 손실: 0.066929\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.073499\t최선의 손실: 0.066929\t정확도: 98.32%\n",
-      "20\t검증 세트 손실: 0.090814\t최선의 손실: 0.066929\t정확도: 98.12%\n",
-      "21\t검증 세트 손실: 0.097851\t최선의 손실: 0.066929\t정확도: 98.16%\n",
-      "22\t검증 세트 손실: 0.094713\t최선의 손실: 0.066929\t정확도: 98.24%\n",
-      "23\t검증 세트 손실: 0.103381\t최선의 손실: 0.066929\t정확도: 98.24%\n",
-      "24\t검증 세트 손실: 0.129995\t최선의 손실: 0.066929\t정확도: 98.24%\n",
-      "25\t검증 세트 손실: 0.116384\t최선의 손실: 0.066929\t정확도: 98.20%\n",
-      "26\t검증 세트 손실: 0.071880\t최선의 손실: 0.066929\t정확도: 98.28%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=10, total=   6.3s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.172310\t최선의 손실: 0.172310\t정확도: 95.15%\n",
+      "1\t검증 세트 손실: 0.094362\t최선의 손실: 0.094362\t정확도: 96.99%\n",
+      "2\t검증 세트 손실: 0.101984\t최선의 손실: 0.094362\t정확도: 96.99%\n",
+      "3\t검증 세트 손실: 0.082851\t최선의 손실: 0.082851\t정확도: 97.30%\n",
+      "4\t검증 세트 손실: 0.079570\t최선의 손실: 0.079570\t정확도: 97.81%\n",
+      "5\t검증 세트 손실: 0.071549\t최선의 손실: 0.071549\t정확도: 97.93%\n",
+      "6\t검증 세트 손실: 0.075239\t최선의 손실: 0.071549\t정확도: 98.05%\n",
+      "7\t검증 세트 손실: 0.081123\t최선의 손실: 0.071549\t정확도: 97.65%\n",
+      "8\t검증 세트 손실: 0.076917\t최선의 손실: 0.071549\t정확도: 97.73%\n",
+      "9\t검증 세트 손실: 0.061917\t최선의 손실: 0.061917\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.073441\t최선의 손실: 0.061917\t정확도: 98.08%\n",
+      "11\t검증 세트 손실: 0.074805\t최선의 손실: 0.061917\t정확도: 98.16%\n",
+      "12\t검증 세트 손실: 0.083041\t최선의 손실: 0.061917\t정확도: 97.65%\n",
+      "13\t검증 세트 손실: 0.067938\t최선의 손실: 0.061917\t정확도: 98.28%\n",
+      "14\t검증 세트 손실: 0.078205\t최선의 손실: 0.061917\t정확도: 97.89%\n",
+      "15\t검증 세트 손실: 0.082053\t최선의 손실: 0.061917\t정확도: 98.01%\n",
+      "16\t검증 세트 손실: 0.069031\t최선의 손실: 0.061917\t정확도: 98.16%\n",
+      "17\t검증 세트 손실: 0.076230\t최선의 손실: 0.061917\t정확도: 97.93%\n",
+      "18\t검증 세트 손실: 0.088186\t최선의 손실: 0.061917\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.089445\t최선의 손실: 0.061917\t정확도: 97.58%\n",
+      "20\t검증 세트 손실: 0.085073\t최선의 손실: 0.061917\t정확도: 97.97%\n",
+      "21\t검증 세트 손실: 0.084958\t최선의 손실: 0.061917\t정확도: 97.93%\n",
+      "22\t검증 세트 손실: 0.090365\t최선의 손실: 0.061917\t정확도: 98.05%\n",
+      "23\t검증 세트 손실: 0.084356\t최선의 손실: 0.061917\t정확도: 98.24%\n",
+      "24\t검증 세트 손실: 0.079886\t최선의 손실: 0.061917\t정확도: 98.08%\n",
+      "25\t검증 세트 손실: 0.117730\t최선의 손실: 0.061917\t정확도: 98.05%\n",
+      "26\t검증 세트 손실: 0.085933\t최선의 손실: 0.061917\t정확도: 98.08%\n",
+      "27\t검증 세트 손실: 0.094323\t최선의 손실: 0.061917\t정확도: 98.05%\n",
+      "28\t검증 세트 손실: 0.098955\t최선의 손실: 0.061917\t정확도: 98.12%\n",
+      "29\t검증 세트 손실: 0.117525\t최선의 손실: 0.061917\t정확도: 97.85%\n",
+      "30\t검증 세트 손실: 0.106434\t최선의 손실: 0.061917\t정확도: 97.81%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=   1.8s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.114157\t최선의 손실: 0.114157\t정확도: 96.91%\n",
-      "1\t검증 세트 손실: 0.082857\t최선의 손실: 0.082857\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.073590\t최선의 손실: 0.073590\t정확도: 97.97%\n",
-      "3\t검증 세트 손실: 0.069253\t최선의 손실: 0.069253\t정확도: 97.85%\n",
-      "4\t검증 세트 손실: 0.083177\t최선의 손실: 0.069253\t정확도: 97.77%\n",
-      "5\t검증 세트 손실: 0.062727\t최선의 손실: 0.062727\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.087472\t최선의 손실: 0.062727\t정확도: 97.65%\n",
-      "7\t검증 세트 손실: 0.101244\t최선의 손실: 0.062727\t정확도: 96.99%\n",
-      "8\t검증 세트 손실: 0.071010\t최선의 손실: 0.062727\t정확도: 98.24%\n",
-      "9\t검증 세트 손실: 0.085385\t최선의 손실: 0.062727\t정확도: 97.85%\n",
-      "10\t검증 세트 손실: 0.084067\t최선의 손실: 0.062727\t정확도: 97.93%\n",
-      "11\t검증 세트 손실: 0.072116\t최선의 손실: 0.062727\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.073560\t최선의 손실: 0.062727\t정확도: 98.40%\n",
-      "13\t검증 세트 손실: 0.093819\t최선의 손실: 0.062727\t정확도: 97.97%\n",
-      "14\t검증 세트 손실: 0.099304\t최선의 손실: 0.062727\t정확도: 97.81%\n",
-      "15\t검증 세트 손실: 0.116346\t최선의 손실: 0.062727\t정확도: 97.97%\n",
-      "16\t검증 세트 손실: 0.092528\t최선의 손실: 0.062727\t정확도: 98.08%\n",
-      "17\t검증 세트 손실: 0.090187\t최선의 손실: 0.062727\t정확도: 97.89%\n",
-      "18\t검증 세트 손실: 0.118451\t최선의 손실: 0.062727\t정확도: 97.77%\n",
-      "19\t검증 세트 손실: 0.097046\t최선의 손실: 0.062727\t정확도: 98.16%\n",
-      "20\t검증 세트 손실: 0.092530\t최선의 손실: 0.062727\t정확도: 98.32%\n",
-      "21\t검증 세트 손실: 0.081043\t최선의 손실: 0.062727\t정확도: 98.16%\n",
-      "22\t검증 세트 손실: 0.076591\t최선의 손실: 0.062727\t정확도: 98.20%\n",
-      "23\t검증 세트 손실: 0.119946\t최선의 손실: 0.062727\t정확도: 98.28%\n",
-      "24\t검증 세트 손실: 0.094382\t최선의 손실: 0.062727\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.105111\t최선의 손실: 0.062727\t정확도: 98.48%\n",
-      "26\t검증 세트 손실: 0.111304\t최선의 손실: 0.062727\t정확도: 97.89%\n",
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=   2.5s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.113188\t최선의 손실: 0.113188\t정확도: 96.60%\n",
+      "1\t검증 세트 손실: 0.081384\t최선의 손실: 0.081384\t정확도: 97.58%\n",
+      "2\t검증 세트 손실: 0.068770\t최선의 손실: 0.068770\t정확도: 98.12%\n",
+      "3\t검증 세트 손실: 0.077317\t최선의 손실: 0.068770\t정확도: 97.73%\n",
+      "4\t검증 세트 손실: 0.074333\t최선의 손실: 0.068770\t정확도: 97.97%\n",
+      "5\t검증 세트 손실: 0.084735\t최선의 손실: 0.068770\t정확도: 97.30%\n",
+      "6\t검증 세트 손실: 0.082230\t최선의 손실: 0.068770\t정확도: 97.73%\n",
+      "7\t검증 세트 손실: 0.071615\t최선의 손실: 0.068770\t정확도: 97.77%\n",
+      "8\t검증 세트 손실: 0.108529\t최선의 손실: 0.068770\t정확도: 97.11%\n",
+      "9\t검증 세트 손실: 0.082649\t최선의 손실: 0.068770\t정확도: 97.50%\n",
+      "10\t검증 세트 손실: 0.068166\t최선의 손실: 0.068166\t정확도: 97.93%\n",
+      "11\t검증 세트 손실: 0.075183\t최선의 손실: 0.068166\t정확도: 98.01%\n",
+      "12\t검증 세트 손실: 0.095576\t최선의 손실: 0.068166\t정확도: 97.50%\n",
+      "13\t검증 세트 손실: 0.073035\t최선의 손실: 0.068166\t정확도: 98.16%\n",
+      "14\t검증 세트 손실: 0.084064\t최선의 손실: 0.068166\t정확도: 98.24%\n",
+      "15\t검증 세트 손실: 0.089926\t최선의 손실: 0.068166\t정확도: 98.05%\n",
+      "16\t검증 세트 손실: 0.088055\t최선의 손실: 0.068166\t정확도: 97.81%\n",
+      "17\t검증 세트 손실: 0.075185\t최선의 손실: 0.068166\t정확도: 98.20%\n",
+      "18\t검증 세트 손실: 0.090796\t최선의 손실: 0.068166\t정확도: 98.08%\n",
+      "19\t검증 세트 손실: 0.102710\t최선의 손실: 0.068166\t정확도: 98.16%\n",
+      "20\t검증 세트 손실: 0.080605\t최선의 손실: 0.068166\t정확도: 97.85%\n",
+      "21\t검증 세트 손실: 0.078738\t최선의 손실: 0.068166\t정확도: 98.32%\n",
+      "22\t검증 세트 손실: 0.081123\t최선의 손실: 0.068166\t정확도: 98.08%\n",
+      "23\t검증 세트 손실: 0.075498\t최선의 손실: 0.068166\t정확도: 98.48%\n",
+      "24\t검증 세트 손실: 0.105926\t최선의 손실: 0.068166\t정확도: 97.85%\n",
+      "25\t검증 세트 손실: 0.080662\t최선의 손실: 0.068166\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.071509\t최선의 손실: 0.068166\t정확도: 98.16%\n",
+      "27\t검증 세트 손실: 0.074344\t최선의 손실: 0.068166\t정확도: 98.08%\n",
+      "28\t검증 세트 손실: 0.076510\t최선의 손실: 0.068166\t정확도: 98.44%\n",
+      "29\t검증 세트 손실: 0.087910\t최선의 손실: 0.068166\t정확도: 98.12%\n",
+      "30\t검증 세트 손실: 0.522572\t최선의 손실: 0.068166\t정확도: 94.96%\n",
+      "31\t검증 세트 손실: 0.117455\t최선의 손실: 0.068166\t정확도: 96.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=   1.8s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.121246\t최선의 손실: 0.121246\t정확도: 96.33%\n",
-      "1\t검증 세트 손실: 0.088884\t최선의 손실: 0.088884\t정확도: 97.46%\n",
-      "2\t검증 세트 손실: 0.084427\t최선의 손실: 0.084427\t정확도: 97.50%\n",
-      "3\t검증 세트 손실: 0.077795\t최선의 손실: 0.077795\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.068468\t최선의 손실: 0.068468\t정확도: 98.01%\n",
-      "5\t검증 세트 손실: 0.061770\t최선의 손실: 0.061770\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.073451\t최선의 손실: 0.061770\t정확도: 98.28%\n",
-      "7\t검증 세트 손실: 0.066004\t최선의 손실: 0.061770\t정확도: 98.16%\n",
-      "8\t검증 세트 손실: 0.071487\t최선의 손실: 0.061770\t정확도: 97.93%\n",
-      "9\t검증 세트 손실: 0.065490\t최선의 손실: 0.061770\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.082158\t최선의 손실: 0.061770\t정확도: 98.12%\n",
-      "11\t검증 세트 손실: 0.072519\t최선의 손실: 0.061770\t정확도: 97.85%\n",
-      "12\t검증 세트 손실: 0.063134\t최선의 손실: 0.061770\t정확도: 98.40%\n",
-      "13\t검증 세트 손실: 0.067306\t최선의 손실: 0.061770\t정확도: 98.36%\n",
-      "14\t검증 세트 손실: 0.077465\t최선의 손실: 0.061770\t정확도: 98.32%\n",
-      "15\t검증 세트 손실: 0.062908\t최선의 손실: 0.061770\t정확도: 98.63%\n",
-      "16\t검증 세트 손실: 0.109157\t최선의 손실: 0.061770\t정확도: 97.73%\n",
-      "17\t검증 세트 손실: 0.067520\t최선의 손실: 0.061770\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.062284\t최선의 손실: 0.061770\t정확도: 98.87%\n",
-      "19\t검증 세트 손실: 0.073150\t최선의 손실: 0.061770\t정확도: 98.40%\n",
-      "20\t검증 세트 손실: 0.070878\t최선의 손실: 0.061770\t정확도: 98.36%\n",
-      "21\t검증 세트 손실: 0.068003\t최선의 손실: 0.061770\t정확도: 98.63%\n",
-      "22\t검증 세트 손실: 0.079152\t최선의 손실: 0.061770\t정확도: 98.28%\n",
-      "23\t검증 세트 손실: 0.088453\t최선의 손실: 0.061770\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.075195\t최선의 손실: 0.061770\t정확도: 98.59%\n",
-      "25\t검증 세트 손실: 0.095734\t최선의 손실: 0.061770\t정확도: 97.85%\n",
-      "26\t검증 세트 손실: 0.079913\t최선의 손실: 0.061770\t정확도: 98.67%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=   1.9s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 95.578964\t최선의 손실: 95.578964\t정확도: 35.93%\n",
-      "1\t검증 세트 손실: 456.949524\t최선의 손실: 95.578964\t정확도: 61.14%\n",
-      "2\t검증 세트 손실: 13.798228\t최선의 손실: 13.798228\t정확도: 89.29%\n",
-      "3\t검증 세트 손실: 6.584868\t최선의 손실: 6.584868\t정확도: 89.56%\n",
-      "4\t검증 세트 손실: 6.784085\t최선의 손실: 6.584868\t정확도: 91.79%\n"
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=   2.5s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.121159\t최선의 손실: 0.121159\t정확도: 96.68%\n",
+      "1\t검증 세트 손실: 0.093012\t최선의 손실: 0.093012\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.084517\t최선의 손실: 0.084517\t정확도: 97.50%\n",
+      "3\t검증 세트 손실: 0.070080\t최선의 손실: 0.070080\t정확도: 97.58%\n",
+      "4\t검증 세트 손실: 0.073499\t최선의 손실: 0.070080\t정확도: 97.69%\n",
+      "5\t검증 세트 손실: 0.077686\t최선의 손실: 0.070080\t정확도: 97.93%\n",
+      "6\t검증 세트 손실: 0.063006\t최선의 손실: 0.063006\t정확도: 98.24%\n",
+      "7\t검증 세트 손실: 0.080366\t최선의 손실: 0.063006\t정확도: 97.50%\n",
+      "8\t검증 세트 손실: 0.082043\t최선의 손실: 0.063006\t정확도: 97.42%\n",
+      "9\t검증 세트 손실: 0.068878\t최선의 손실: 0.063006\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.072873\t최선의 손실: 0.063006\t정확도: 97.85%\n",
+      "11\t검증 세트 손실: 0.074462\t최선의 손실: 0.063006\t정확도: 98.24%\n",
+      "12\t검증 세트 손실: 0.073173\t최선의 손실: 0.063006\t정확도: 98.16%\n",
+      "13\t검증 세트 손실: 0.067475\t최선의 손실: 0.063006\t정확도: 98.28%\n",
+      "14\t검증 세트 손실: 0.062992\t최선의 손실: 0.062992\t정확도: 98.63%\n",
+      "15\t검증 세트 손실: 0.062149\t최선의 손실: 0.062149\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.091188\t최선의 손실: 0.062149\t정확도: 98.28%\n",
+      "17\t검증 세트 손실: 0.094647\t최선의 손실: 0.062149\t정확도: 97.81%\n",
+      "18\t검증 세트 손실: 0.089870\t최선의 손실: 0.062149\t정확도: 98.28%\n",
+      "19\t검증 세트 손실: 0.073986\t최선의 손실: 0.062149\t정확도: 98.24%\n",
+      "20\t검증 세트 손실: 0.082416\t최선의 손실: 0.062149\t정확도: 98.24%\n",
+      "21\t검증 세트 손실: 0.079643\t최선의 손실: 0.062149\t정확도: 98.16%\n",
+      "22\t검증 세트 손실: 0.075323\t최선의 손실: 0.062149\t정확도: 98.32%\n",
+      "23\t검증 세트 손실: 0.064139\t최선의 손실: 0.062149\t정확도: 98.40%\n",
+      "24\t검증 세트 손실: 0.088675\t최선의 손실: 0.062149\t정확도: 98.28%\n",
+      "25\t검증 세트 손실: 0.073147\t최선의 손실: 0.062149\t정확도: 98.48%\n",
+      "26\t검증 세트 손실: 0.081069\t최선의 손실: 0.062149\t정확도: 97.97%\n",
+      "27\t검증 세트 손실: 0.081614\t최선의 손실: 0.062149\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 0.091744\t최선의 손실: 0.062149\t정확도: 98.12%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5\t검증 세트 손실: 7.155737\t최선의 손실: 6.584868\t정확도: 85.11%\n",
-      "6\t검증 세트 손실: 4.890808\t최선의 손실: 4.890808\t정확도: 92.03%\n",
-      "7\t검증 세트 손실: 12.860675\t최선의 손실: 4.890808\t정확도: 81.86%\n",
-      "8\t검증 세트 손실: 859.041443\t최선의 손실: 4.890808\t정확도: 27.76%\n",
-      "9\t검증 세트 손실: 190.513977\t최선의 손실: 4.890808\t정확도: 36.90%\n",
-      "10\t검증 세트 손실: 79.406662\t최선의 손실: 4.890808\t정확도: 41.59%\n",
-      "11\t검증 세트 손실: 25.008566\t최선의 손실: 4.890808\t정확도: 39.68%\n",
-      "12\t검증 세트 손실: 21.378765\t최선의 손실: 4.890808\t정확도: 45.82%\n",
-      "13\t검증 세트 손실: 3055.117188\t최선의 손실: 4.890808\t정확도: 66.97%\n",
-      "14\t검증 세트 손실: 1005.395020\t최선의 손실: 4.890808\t정확도: 84.60%\n",
-      "15\t검증 세트 손실: 652.815125\t최선의 손실: 4.890808\t정확도: 84.60%\n",
-      "16\t검증 세트 손실: 990.136841\t최선의 손실: 4.890808\t정확도: 79.16%\n",
-      "17\t검증 세트 손실: 300.188873\t최선의 손실: 4.890808\t정확도: 89.60%\n",
-      "18\t검증 세트 손실: 423.836945\t최선의 손실: 4.890808\t정확도: 85.93%\n",
-      "19\t검증 세트 손실: 334.590240\t최선의 손실: 4.890808\t정확도: 90.62%\n",
-      "20\t검증 세트 손실: 518.449402\t최선의 손실: 4.890808\t정확도: 84.60%\n",
-      "21\t검증 세트 손실: 875.507996\t최선의 손실: 4.890808\t정확도: 79.44%\n",
-      "22\t검증 세트 손실: 314.489838\t최선의 손실: 4.890808\t정확도: 90.70%\n",
-      "23\t검증 세트 손실: 266.187439\t최선의 손실: 4.890808\t정확도: 89.33%\n",
-      "24\t검증 세트 손실: 252.348190\t최선의 손실: 4.890808\t정확도: 89.87%\n",
-      "25\t검증 세트 손실: 1940.884644\t최선의 손실: 4.890808\t정확도: 77.99%\n",
-      "26\t검증 세트 손실: 354.183594\t최선의 손실: 4.890808\t정확도: 92.14%\n",
-      "27\t검증 세트 손실: 450.675232\t최선의 손실: 4.890808\t정확도: 92.22%\n",
+      "29\t검증 세트 손실: 0.105981\t최선의 손실: 0.062149\t정확도: 98.05%\n",
+      "30\t검증 세트 손실: 0.100353\t최선의 손실: 0.062149\t정확도: 97.97%\n",
+      "31\t검증 세트 손실: 0.106992\t최선의 손실: 0.062149\t정확도: 98.05%\n",
+      "32\t검증 세트 손실: 0.079171\t최선의 손실: 0.062149\t정확도: 98.12%\n",
+      "33\t검증 세트 손실: 0.091985\t최선의 손실: 0.062149\t정확도: 98.05%\n",
+      "34\t검증 세트 손실: 0.093240\t최선의 손실: 0.062149\t정확도: 98.36%\n",
+      "35\t검증 세트 손실: 0.088248\t최선의 손실: 0.062149\t정확도: 98.44%\n",
+      "36\t검증 세트 손실: 0.108965\t최선의 손실: 0.062149\t정확도: 98.16%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90, total=  15.8s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 49.433842\t최선의 손실: 49.433842\t정확도: 22.60%\n",
-      "1\t검증 세트 손실: 0.704112\t최선의 손실: 0.704112\t정확도: 84.48%\n",
-      "2\t검증 세트 손실: 0.543954\t최선의 손실: 0.543954\t정확도: 87.92%\n",
-      "3\t검증 세트 손실: 3.077376\t최선의 손실: 0.543954\t정확도: 69.43%\n",
-      "4\t검증 세트 손실: 0.759342\t최선의 손실: 0.543954\t정확도: 82.96%\n",
-      "5\t검증 세트 손실: 153961.937500\t최선의 손실: 0.543954\t정확도: 40.42%\n",
-      "6\t검증 세트 손실: 254.387115\t최선의 손실: 0.543954\t정확도: 74.59%\n",
-      "7\t검증 세트 손실: 34.732498\t최선의 손실: 0.543954\t정확도: 87.72%\n",
-      "8\t검증 세트 손실: 24.145407\t최선의 손실: 0.543954\t정확도: 88.74%\n",
-      "9\t검증 세트 손실: 20.078070\t최선의 손실: 0.543954\t정확도: 88.62%\n",
-      "10\t검증 세트 손실: 14.909928\t최선의 손실: 0.543954\t정확도: 90.27%\n",
-      "11\t검증 세트 손실: 16.545815\t최선의 손실: 0.543954\t정확도: 89.29%\n",
-      "12\t검증 세트 손실: 33.942226\t최선의 손실: 0.543954\t정확도: 81.39%\n",
-      "13\t검증 세트 손실: 19.153067\t최선의 손실: 0.543954\t정확도: 87.33%\n",
-      "14\t검증 세트 손실: 12.376163\t최선의 손실: 0.543954\t정확도: 92.34%\n",
-      "15\t검증 세트 손실: 28.868040\t최선의 손실: 0.543954\t정확도: 86.32%\n",
-      "16\t검증 세트 손실: 20.618740\t최선의 손실: 0.543954\t정확도: 91.40%\n",
-      "17\t검증 세트 손실: 11.160923\t최선의 손실: 0.543954\t정확도: 93.82%\n",
-      "18\t검증 세트 손실: 32.816063\t최선의 손실: 0.543954\t정확도: 86.20%\n",
-      "19\t검증 세트 손실: 13.982798\t최선의 손실: 0.543954\t정확도: 91.05%\n",
-      "20\t검증 세트 손실: 11.455084\t최선의 손실: 0.543954\t정확도: 94.92%\n",
-      "21\t검증 세트 손실: 6.452749\t최선의 손실: 0.543954\t정확도: 95.23%\n",
-      "22\t검증 세트 손실: 17.444902\t최선의 손실: 0.543954\t정확도: 91.40%\n",
-      "23\t검증 세트 손실: 501.551300\t최선의 손실: 0.543954\t정확도: 66.11%\n",
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=   2.8s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=90 \n",
+      "0\t검증 세트 손실: 4959.016602\t최선의 손실: 4959.016602\t정확도: 58.95%\n",
+      "1\t검증 세트 손실: 5.446846\t최선의 손실: 5.446846\t정확도: 65.68%\n",
+      "2\t검증 세트 손실: 1392.261108\t최선의 손실: 5.446846\t정확도: 39.21%\n",
+      "3\t검증 세트 손실: 68.069595\t최선의 손실: 5.446846\t정확도: 76.94%\n",
+      "4\t검증 세트 손실: 27.544319\t최선의 손실: 5.446846\t정확도: 81.16%\n",
+      "5\t검증 세트 손실: 12.511521\t최선의 손실: 5.446846\t정확도: 88.19%\n",
+      "6\t검증 세트 손실: 16.485355\t최선의 손실: 5.446846\t정확도: 76.66%\n",
+      "7\t검증 세트 손실: 6.624844\t최선의 손실: 5.446846\t정확도: 86.83%\n",
+      "8\t검증 세트 손실: 23.746469\t최선의 손실: 5.446846\t정확도: 81.31%\n",
+      "9\t검증 세트 손실: 9.279375\t최선의 손실: 5.446846\t정확도: 79.63%\n",
+      "10\t검증 세트 손실: 4.899003\t최선의 손실: 4.899003\t정확도: 91.01%\n",
+      "11\t검증 세트 손실: 24.955046\t최선의 손실: 4.899003\t정확도: 84.75%\n",
+      "12\t검증 세트 손실: 2644.093506\t최선의 손실: 4.899003\t정확도: 36.55%\n",
+      "13\t검증 세트 손실: 40.783417\t최선의 손실: 4.899003\t정확도: 78.34%\n",
+      "14\t검증 세트 손실: 26.221090\t최선의 손실: 4.899003\t정확도: 74.28%\n",
+      "15\t검증 세트 손실: 11.327071\t최선의 손실: 4.899003\t정확도: 85.93%\n",
+      "16\t검증 세트 손실: 7.320235\t최선의 손실: 4.899003\t정확도: 84.13%\n",
+      "17\t검증 세트 손실: 12.503627\t최선의 손실: 4.899003\t정확도: 85.34%\n",
+      "18\t검증 세트 손실: 11.629165\t최선의 손실: 4.899003\t정확도: 82.96%\n",
+      "19\t검증 세트 손실: 307.863617\t최선의 손실: 4.899003\t정확도: 40.77%\n",
+      "20\t검증 세트 손실: 17.127947\t최선의 손실: 4.899003\t정확도: 76.51%\n",
+      "21\t검증 세트 손실: 83.561554\t최선의 손실: 4.899003\t정확도: 45.54%\n",
+      "22\t검증 세트 손실: 17.610264\t최선의 손실: 4.899003\t정확도: 77.01%\n",
+      "23\t검증 세트 손실: 1647.858643\t최선의 손실: 4.899003\t정확도: 40.54%\n",
+      "24\t검증 세트 손실: 50.775074\t최선의 손실: 4.899003\t정확도: 52.78%\n",
+      "25\t검증 세트 손실: 25.715580\t최선의 손실: 4.899003\t정확도: 74.08%\n",
+      "26\t검증 세트 손실: 29.263557\t최선의 손실: 4.899003\t정확도: 69.39%\n",
+      "27\t검증 세트 손실: 182.563919\t최선의 손실: 4.899003\t정확도: 70.56%\n",
+      "28\t검증 세트 손실: 37.389202\t최선의 손실: 4.899003\t정확도: 68.41%\n",
+      "29\t검증 세트 손실: 115.808907\t최선의 손실: 4.899003\t정확도: 47.54%\n",
+      "30\t검증 세트 손실: 18.930515\t최선의 손실: 4.899003\t정확도: 76.54%\n",
+      "31\t검증 세트 손실: 3627.237305\t최선의 손실: 4.899003\t정확도: 47.38%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90, total=  23.6s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 333.559967\t최선의 손실: 333.559967\t정확도: 30.65%\n",
-      "1\t검증 세트 손실: 34.197578\t최선의 손실: 34.197578\t정확도: 47.19%\n",
-      "2\t검증 세트 손실: 5.089581\t최선의 손실: 5.089581\t정확도: 61.10%\n",
-      "3\t검증 세트 손실: 9.958323\t최선의 손실: 5.089581\t정확도: 51.92%\n",
-      "4\t검증 세트 손실: 2.825920\t최선의 손실: 2.825920\t정확도: 72.13%\n",
-      "5\t검증 세트 손실: 1.368483\t최선의 손실: 1.368483\t정확도: 79.44%\n",
-      "6\t검증 세트 손실: 0.751443\t최선의 손실: 0.751443\t정확도: 86.67%\n",
-      "7\t검증 세트 손실: 0.583905\t최선의 손실: 0.583905\t정확도: 87.72%\n",
-      "8\t검증 세트 손실: 57811.753906\t최선의 손실: 0.583905\t정확도: 21.31%\n",
-      "9\t검증 세트 손실: 702.962280\t최선의 손실: 0.583905\t정확도: 74.04%\n",
-      "10\t검증 세트 손실: 514.108398\t최선의 손실: 0.583905\t정확도: 74.28%\n",
-      "11\t검증 세트 손실: 591.473755\t최선의 손실: 0.583905\t정확도: 63.29%\n",
-      "12\t검증 세트 손실: 179.754562\t최선의 손실: 0.583905\t정확도: 87.45%\n",
-      "13\t검증 세트 손실: 136.654999\t최선의 손실: 0.583905\t정확도: 91.09%\n",
-      "14\t검증 세트 손실: 105.555527\t최선의 손실: 0.583905\t정확도: 91.28%\n",
-      "15\t검증 세트 손실: 156.502197\t최선의 손실: 0.583905\t정확도: 83.23%\n",
-      "16\t검증 세트 손실: 60.464817\t최선의 손실: 0.583905\t정확도: 93.59%\n",
-      "17\t검증 세트 손실: 60.698246\t최선의 손실: 0.583905\t정확도: 93.98%\n",
-      "18\t검증 세트 손실: 78.608902\t최선의 손실: 0.583905\t정확도: 90.34%\n",
-      "19\t검증 세트 손실: 60.484241\t최선의 손실: 0.583905\t정확도: 93.67%\n",
-      "20\t검증 세트 손실: 57.342499\t최선의 손실: 0.583905\t정확도: 95.31%\n",
-      "21\t검증 세트 손실: 257.624329\t최선의 손실: 0.583905\t정확도: 81.31%\n",
-      "22\t검증 세트 손실: 1069.247803\t최선의 손실: 0.583905\t정확도: 72.56%\n",
-      "23\t검증 세트 손실: 233.642365\t최선의 손실: 0.583905\t정확도: 92.18%\n",
-      "24\t검증 세트 손실: 109.390945\t최선의 손실: 0.583905\t정확도: 95.35%\n",
-      "25\t검증 세트 손실: 81.199173\t최선의 손실: 0.583905\t정확도: 95.19%\n",
-      "26\t검증 세트 손실: 83.744453\t최선의 손실: 0.583905\t정확도: 94.88%\n",
-      "27\t검증 세트 손실: 66.247078\t최선의 손실: 0.583905\t정확도: 94.25%\n",
-      "28\t검증 세트 손실: 774.748657\t최선의 손실: 0.583905\t정확도: 77.95%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=90, total=  16.8s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.147230\t최선의 손실: 0.147230\t정확도: 96.60%\n",
+      "1\t검증 세트 손실: 4.722157\t최선의 손실: 0.147230\t정확도: 69.19%\n",
+      "2\t검증 세트 손실: 1.250429\t최선의 손실: 0.147230\t정확도: 82.49%\n",
+      "3\t검증 세트 손실: 1.098807\t최선의 손실: 0.147230\t정확도: 80.92%\n",
+      "4\t검증 세트 손실: 0.658860\t최선의 손실: 0.147230\t정확도: 87.53%\n",
+      "5\t검증 세트 손실: 0.666731\t최선의 손실: 0.147230\t정확도: 89.87%\n",
+      "6\t검증 세트 손실: 0.501160\t최선의 손실: 0.147230\t정확도: 89.41%\n",
+      "7\t검증 세트 손실: 0.730787\t최선의 손실: 0.147230\t정확도: 87.65%\n",
+      "8\t검증 세트 손실: 2.203176\t최선의 손실: 0.147230\t정확도: 80.61%\n",
+      "9\t검증 세트 손실: 0.364436\t최선의 손실: 0.147230\t정확도: 93.08%\n",
+      "10\t검증 세트 손실: 0.670909\t최선의 손실: 0.147230\t정확도: 91.20%\n",
+      "11\t검증 세트 손실: 8708.349609\t최선의 손실: 0.147230\t정확도: 22.67%\n",
+      "12\t검증 세트 손실: 352.935913\t최선의 손실: 0.147230\t정확도: 72.44%\n",
+      "13\t검증 세트 손실: 177.381348\t최선의 손실: 0.147230\t정확도: 84.68%\n",
+      "14\t검증 세트 손실: 282.876190\t최선의 손실: 0.147230\t정확도: 70.41%\n",
+      "15\t검증 세트 손실: 181.573318\t최선의 손실: 0.147230\t정확도: 77.01%\n",
+      "16\t검증 세트 손실: 112.160133\t최선의 손실: 0.147230\t정확도: 82.06%\n",
+      "17\t검증 세트 손실: 65.078499\t최선의 손실: 0.147230\t정확도: 92.46%\n",
+      "18\t검증 세트 손실: 127.249916\t최선의 손실: 0.147230\t정확도: 82.02%\n",
+      "19\t검증 세트 손실: 79.489655\t최선의 손실: 0.147230\t정확도: 89.13%\n",
+      "20\t검증 세트 손실: 46.940380\t최선의 손실: 0.147230\t정확도: 89.29%\n",
+      "21\t검증 세트 손실: 310.315857\t최선의 손실: 0.147230\t정확도: 85.38%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90, total=  26.2s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 248.288559\t최선의 손실: 248.288559\t정확도: 72.95%\n",
-      "1\t검증 세트 손실: 246.913452\t최선의 손실: 246.913452\t정확도: 90.38%\n",
-      "2\t검증 세트 손실: 70.185951\t최선의 손실: 70.185951\t정확도: 93.43%\n",
-      "3\t검증 세트 손실: 55.937859\t최선의 손실: 55.937859\t정확도: 89.91%\n",
-      "4\t검증 세트 손실: 30.211781\t최선의 손실: 30.211781\t정확도: 94.96%\n",
-      "5\t검증 세트 손실: 15.288311\t최선의 손실: 15.288311\t정확도: 95.50%\n",
-      "6\t검증 세트 손실: 19.610983\t최선의 손실: 15.288311\t정확도: 93.08%\n",
-      "7\t검증 세트 손실: 9533.393555\t최선의 손실: 15.288311\t정확도: 29.98%\n",
-      "8\t검증 세트 손실: 270498.000000\t최선의 손실: 15.288311\t정확도: 62.16%\n",
-      "9\t검증 세트 손실: 51069.253906\t최선의 손실: 15.288311\t정확도: 86.32%\n",
-      "10\t검증 세트 손실: 44974.000000\t최선의 손실: 15.288311\t정확도: 89.21%\n",
-      "11\t검증 세트 손실: 31619.927734\t최선의 손실: 15.288311\t정확도: 90.19%\n",
-      "12\t검증 세트 손실: 25629.871094\t최선의 손실: 15.288311\t정확도: 92.22%\n",
-      "13\t검증 세트 손실: 21345.863281\t최선의 손실: 15.288311\t정확도: 91.99%\n",
-      "14\t검증 세트 손실: 32277.392578\t최선의 손실: 15.288311\t정확도: 88.15%\n",
-      "15\t검증 세트 손실: 18639.580078\t최선의 손실: 15.288311\t정확도: 94.10%\n",
-      "16\t검증 세트 손실: 16932.640625\t최선의 손실: 15.288311\t정확도: 93.86%\n",
-      "17\t검증 세트 손실: 9917.537109\t최선의 손실: 15.288311\t정확도: 95.27%\n",
-      "18\t검증 세트 손실: 109466.898438\t최선의 손실: 15.288311\t정확도: 91.83%\n",
-      "19\t검증 세트 손실: 58856.683594\t최선의 손실: 15.288311\t정확도: 89.52%\n",
-      "20\t검증 세트 손실: 43752.148438\t최선의 손실: 15.288311\t정확도: 92.42%\n",
-      "21\t검증 세트 손실: 59894.562500\t최선의 손실: 15.288311\t정확도: 89.84%\n",
-      "22\t검증 세트 손실: 18224.046875\t최선의 손실: 15.288311\t정확도: 94.53%\n",
-      "23\t검증 세트 손실: 16439.027344\t최선의 손실: 15.288311\t정확도: 95.23%\n",
-      "24\t검증 세트 손실: 19259.230469\t최선의 손실: 15.288311\t정확도: 95.23%\n",
-      "25\t검증 세트 손실: 21648.716797\t최선의 손실: 15.288311\t정확도: 90.97%\n",
-      "26\t검증 세트 손실: 9587.924805\t최선의 손실: 15.288311\t정확도: 95.74%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=90, total=  11.6s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.229090\t최선의 손실: 0.229090\t정확도: 92.81%\n",
+      "1\t검증 세트 손실: 251.224731\t최선의 손실: 0.229090\t정확도: 41.48%\n",
+      "2\t검증 세트 손실: 88.049706\t최선의 손실: 0.229090\t정확도: 64.50%\n",
+      "3\t검증 세트 손실: 17.839800\t최선의 손실: 0.229090\t정확도: 87.22%\n",
+      "4\t검증 세트 손실: 12.472750\t최선의 손실: 0.229090\t정확도: 88.82%\n",
+      "5\t검증 세트 손실: 22.833015\t최선의 손실: 0.229090\t정확도: 78.62%\n",
+      "6\t검증 세트 손실: 86.344406\t최선의 손실: 0.229090\t정확도: 61.42%\n",
+      "7\t검증 세트 손실: 14.758308\t최선의 손실: 0.229090\t정확도: 86.86%\n",
+      "8\t검증 세트 손실: 12.333218\t최선의 손실: 0.229090\t정확도: 85.89%\n",
+      "9\t검증 세트 손실: 4.636055\t최선의 손실: 0.229090\t정확도: 93.98%\n",
+      "10\t검증 세트 손실: 11.301844\t최선의 손실: 0.229090\t정확도: 92.22%\n",
+      "11\t검증 세트 손실: 60003.097656\t최선의 손실: 0.229090\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 831.429138\t최선의 손실: 0.229090\t정확도: 40.97%\n",
+      "13\t검증 세트 손실: 299.983765\t최선의 손실: 0.229090\t정확도: 47.50%\n",
+      "14\t검증 세트 손실: 414.385986\t최선의 손실: 0.229090\t정확도: 46.64%\n",
+      "15\t검증 세트 손실: 136.976868\t최선의 손실: 0.229090\t정확도: 56.33%\n",
+      "16\t검증 세트 손실: 125.706047\t최선의 손실: 0.229090\t정확도: 59.42%\n",
+      "17\t검증 세트 손실: 181.108658\t최선의 손실: 0.229090\t정확도: 48.32%\n",
+      "18\t검증 세트 손실: 58.884644\t최선의 손실: 0.229090\t정확도: 69.55%\n",
+      "19\t검증 세트 손실: 74.829948\t최선의 손실: 0.229090\t정확도: 61.42%\n",
+      "20\t검증 세트 손실: 42.261604\t최선의 손실: 0.229090\t정확도: 76.54%\n",
+      "21\t검증 세트 손실: 49.808132\t최선의 손실: 0.229090\t정확도: 76.04%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=  30.9s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 4.316842\t최선의 손실: 4.316842\t정확도: 75.45%\n",
-      "1\t검증 세트 손실: 0.631359\t최선의 손실: 0.631359\t정확도: 91.52%\n",
-      "2\t검증 세트 손실: 1.717827\t최선의 손실: 0.631359\t정확도: 85.61%\n",
-      "3\t검증 세트 손실: 4.171156\t최선의 손실: 0.631359\t정확도: 83.35%\n",
-      "4\t검증 세트 손실: 0.719589\t최선의 손실: 0.631359\t정확도: 94.45%\n",
-      "5\t검증 세트 손실: 0.718259\t최선의 손실: 0.631359\t정확도: 91.52%\n",
-      "6\t검증 세트 손실: 0.544879\t최선의 손실: 0.544879\t정확도: 91.71%\n",
-      "7\t검증 세트 손실: 0.289657\t최선의 손실: 0.289657\t정확도: 95.86%\n",
-      "8\t검증 세트 손실: 0.243266\t최선의 손실: 0.243266\t정확도: 96.72%\n",
-      "9\t검증 세트 손실: 137200.218750\t최선의 손실: 0.243266\t정확도: 82.33%\n",
-      "10\t검증 세트 손실: 14452.642578\t최선의 손실: 0.243266\t정확도: 92.26%\n",
-      "11\t검증 세트 손실: 8673.256836\t최선의 손실: 0.243266\t정확도: 93.16%\n",
-      "12\t검증 세트 손실: 6657.038086\t최선의 손실: 0.243266\t정확도: 92.46%\n",
-      "13\t검증 세트 손실: 14093.375977\t최선의 손실: 0.243266\t정확도: 90.77%\n",
-      "14\t검증 세트 손실: 14948.821289\t최선의 손실: 0.243266\t정확도: 94.37%\n",
-      "15\t검증 세트 손실: 6042.347168\t최선의 손실: 0.243266\t정확도: 94.57%\n",
-      "16\t검증 세트 손실: 3680.402344\t최선의 손실: 0.243266\t정확도: 95.97%\n",
-      "17\t검증 세트 손실: 5296.261719\t최선의 손실: 0.243266\t정확도: 93.04%\n",
-      "18\t검증 세트 손실: 2350.711914\t최선의 손실: 0.243266\t정확도: 96.25%\n",
-      "19\t검증 세트 손실: 14574.782227\t최선의 손실: 0.243266\t정확도: 93.00%\n",
-      "20\t검증 세트 손실: 2438.916260\t최선의 손실: 0.243266\t정확도: 95.78%\n",
-      "21\t검증 세트 손실: 2365.428955\t최선의 손실: 0.243266\t정확도: 96.33%\n",
-      "22\t검증 세트 손실: 3202.943604\t최선의 손실: 0.243266\t정확도: 95.93%\n",
-      "23\t검증 세트 손실: 23447.957031\t최선의 손실: 0.243266\t정확도: 94.18%\n",
-      "24\t검증 세트 손실: 2221.499268\t최선의 손실: 0.243266\t정확도: 96.33%\n",
-      "25\t검증 세트 손실: 1284.244141\t최선의 손실: 0.243266\t정확도: 96.09%\n",
-      "26\t검증 세트 손실: 3731326.250000\t최선의 손실: 0.243266\t정확도: 60.01%\n",
-      "27\t검증 세트 손실: 140845.531250\t최선의 손실: 0.243266\t정확도: 94.92%\n",
-      "28\t검증 세트 손실: 53531.851562\t최선의 손실: 0.243266\t정확도: 95.54%\n",
-      "29\t검증 세트 손실: 37405.890625\t최선의 손실: 0.243266\t정확도: 96.48%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=90, total=  11.1s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 132433.281250\t최선의 손실: 132433.281250\t정확도: 50.16%\n",
+      "1\t검증 세트 손실: 3770.931885\t최선의 손실: 3770.931885\t정확도: 88.66%\n",
+      "2\t검증 세트 손실: 1526.988037\t최선의 손실: 1526.988037\t정확도: 94.25%\n",
+      "3\t검증 세트 손실: 574.409302\t최선의 손실: 574.409302\t정확도: 95.00%\n",
+      "4\t검증 세트 손실: 1113.290161\t최선의 손실: 574.409302\t정확도: 93.55%\n",
+      "5\t검증 세트 손실: 305.444550\t최선의 손실: 305.444550\t정확도: 96.48%\n",
+      "6\t검증 세트 손실: 1057.970093\t최선의 손실: 305.444550\t정확도: 94.41%\n",
+      "7\t검증 세트 손실: 562.626953\t최선의 손실: 305.444550\t정확도: 94.64%\n",
+      "8\t검증 세트 손실: 235.488754\t최선의 손실: 235.488754\t정확도: 96.33%\n",
+      "9\t검증 세트 손실: 550.002563\t최선의 손실: 235.488754\t정확도: 94.02%\n",
+      "10\t검증 세트 손실: 503.020081\t최선의 손실: 235.488754\t정확도: 94.18%\n",
+      "11\t검증 세트 손실: 276.377625\t최선의 손실: 235.488754\t정확도: 95.93%\n",
+      "12\t검증 세트 손실: 719950.187500\t최선의 손실: 235.488754\t정확도: 83.42%\n",
+      "13\t검증 세트 손실: 25419.740234\t최선의 손실: 235.488754\t정확도: 89.60%\n",
+      "14\t검증 세트 손실: 14069.107422\t최선의 손실: 235.488754\t정확도: 92.81%\n",
+      "15\t검증 세트 손실: 8124.010254\t최선의 손실: 235.488754\t정확도: 94.80%\n",
+      "16\t검증 세트 손실: 14208.000000\t최선의 손실: 235.488754\t정확도: 94.76%\n",
+      "17\t검증 세트 손실: 7569.164062\t최선의 손실: 235.488754\t정확도: 93.20%\n",
+      "18\t검증 세트 손실: 4437.259277\t최선의 손실: 235.488754\t정확도: 96.60%\n",
+      "19\t검증 세트 손실: 3471.979736\t최선의 손실: 235.488754\t정확도: 96.56%\n",
+      "20\t검증 세트 손실: 5827.518555\t최선의 손실: 235.488754\t정확도: 95.23%\n",
+      "21\t검증 세트 손실: 122180.601562\t최선의 손실: 235.488754\t정확도: 92.49%\n",
+      "22\t검증 세트 손실: 21209.189453\t최선의 손실: 235.488754\t정확도: 96.48%\n",
+      "23\t검증 세트 손실: 19500.445312\t최선의 손실: 235.488754\t정확도: 96.29%\n",
+      "24\t검증 세트 손실: 22690.796875\t최선의 손실: 235.488754\t정확도: 94.72%\n",
+      "25\t검증 세트 손실: 9974.504883\t최선의 손실: 235.488754\t정확도: 95.23%\n",
+      "26\t검증 세트 손실: 11218.433594\t최선의 손실: 235.488754\t정확도: 95.47%\n",
+      "27\t검증 세트 손실: 6999.335449\t최선의 손실: 235.488754\t정확도: 96.83%\n",
+      "28\t검증 세트 손실: 13278.951172\t최선의 손실: 235.488754\t정확도: 96.56%\n",
+      "29\t검증 세트 손실: 9023.555664\t최선의 손실: 235.488754\t정확도: 96.17%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=  33.2s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70, total=  14.9s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 1210.428101\t최선의 손실: 1210.428101\t정확도: 84.32%\n",
+      "1\t검증 세트 손실: 250.879883\t최선의 손실: 250.879883\t정확도: 91.95%\n",
+      "2\t검증 세트 손실: 361.333099\t최선의 손실: 250.879883\t정확도: 90.73%\n",
+      "3\t검증 세트 손실: 175.191010\t최선의 손실: 175.191010\t정확도: 93.20%\n",
+      "4\t검증 세트 손실: 1293.845825\t최선의 손실: 175.191010\t정확도: 92.77%\n",
+      "5\t검증 세트 손실: 139.306168\t최선의 손실: 139.306168\t정확도: 94.96%\n",
+      "6\t검증 세트 손실: 65.168983\t최선의 손실: 65.168983\t정확도: 95.00%\n",
+      "7\t검증 세트 손실: 51.962337\t최선의 손실: 51.962337\t정확도: 94.61%\n",
+      "8\t검증 세트 손실: 61.159500\t최선의 손실: 51.962337\t정확도: 95.78%\n",
+      "9\t검증 세트 손실: 24.346964\t최선의 손실: 24.346964\t정확도: 96.25%\n",
+      "10\t검증 세트 손실: 658.503601\t최선의 손실: 24.346964\t정확도: 76.51%\n",
+      "11\t검증 세트 손실: 120.321312\t최선의 손실: 24.346964\t정확도: 92.34%\n",
+      "12\t검증 세트 손실: 44.175312\t최선의 손실: 24.346964\t정확도: 95.07%\n",
+      "13\t검증 세트 손실: 68.956612\t최선의 손실: 24.346964\t정확도: 93.59%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.726552\t최선의 손실: 0.726552\t정확도: 94.10%\n",
-      "1\t검증 세트 손실: 0.199025\t최선의 손실: 0.199025\t정확도: 95.66%\n",
-      "2\t검증 세트 손실: 0.299734\t최선의 손실: 0.199025\t정확도: 95.90%\n",
-      "3\t검증 세트 손실: 244759.000000\t최선의 손실: 0.199025\t정확도: 65.52%\n",
-      "4\t검증 세트 손실: 30024.287109\t최선의 손실: 0.199025\t정확도: 84.48%\n",
-      "5\t검증 세트 손실: 14391.659180\t최선의 손실: 0.199025\t정확도: 86.40%\n",
-      "6\t검증 세트 손실: 9431.748047\t최선의 손실: 0.199025\t정확도: 87.69%\n",
-      "7\t검증 세트 손실: 20141.632812\t최선의 손실: 0.199025\t정확도: 78.69%\n",
-      "8\t검증 세트 손실: 8865.558594\t최선의 손실: 0.199025\t정확도: 88.47%\n",
-      "9\t검증 세트 손실: 3610.484863\t최선의 손실: 0.199025\t정확도: 94.41%\n",
-      "10\t검증 세트 손실: 6304.683594\t최선의 손실: 0.199025\t정확도: 93.71%\n",
-      "11\t검증 세트 손실: 3026.992676\t최선의 손실: 0.199025\t정확도: 94.88%\n",
-      "12\t검증 세트 손실: 6243.002441\t최선의 손실: 0.199025\t정확도: 89.80%\n",
-      "13\t검증 세트 손실: 5048.999023\t최선의 손실: 0.199025\t정확도: 93.12%\n",
-      "14\t검증 세트 손실: 2894.541260\t최선의 손실: 0.199025\t정확도: 94.29%\n",
-      "15\t검증 세트 손실: 2206.543213\t최선의 손실: 0.199025\t정확도: 95.15%\n",
-      "16\t검증 세트 손실: 1975.770142\t최선의 손실: 0.199025\t정확도: 95.74%\n",
-      "17\t검증 세트 손실: 20622.431641\t최선의 손실: 0.199025\t정확도: 88.39%\n",
-      "18\t검증 세트 손실: 5495.878906\t최선의 손실: 0.199025\t정확도: 94.18%\n",
-      "19\t검증 세트 손실: 140227.359375\t최선의 손실: 0.199025\t정확도: 86.43%\n",
-      "20\t검증 세트 손실: 74270.929688\t최선의 손실: 0.199025\t정확도: 92.10%\n",
-      "21\t검증 세트 손실: 50651.078125\t최선의 손실: 0.199025\t정확도: 90.85%\n",
-      "22\t검증 세트 손실: 67420.812500\t최선의 손실: 0.199025\t정확도: 91.79%\n",
+      "14\t검증 세트 손실: 94.236565\t최선의 손실: 24.346964\t정확도: 96.01%\n",
+      "15\t검증 세트 손실: 158.345917\t최선의 손실: 24.346964\t정확도: 93.16%\n",
+      "16\t검증 세트 손실: 162.887070\t최선의 손실: 24.346964\t정확도: 93.00%\n",
+      "17\t검증 세트 손실: 97.908829\t최선의 손실: 24.346964\t정확도: 96.17%\n",
+      "18\t검증 세트 손실: 368280.000000\t최선의 손실: 24.346964\t정확도: 85.07%\n",
+      "19\t검증 세트 손실: 31994.333984\t최선의 손실: 24.346964\t정확도: 93.94%\n",
+      "20\t검증 세트 손실: 20165.306641\t최선의 손실: 24.346964\t정확도: 94.49%\n",
+      "21\t검증 세트 손실: 16833.039062\t최선의 손실: 24.346964\t정확도: 95.62%\n",
+      "22\t검증 세트 손실: 13721.860352\t최선의 손실: 24.346964\t정확도: 94.64%\n",
+      "23\t검증 세트 손실: 8218.649414\t최선의 손실: 24.346964\t정확도: 94.45%\n",
+      "24\t검증 세트 손실: 42806.761719\t최선의 손실: 24.346964\t정확도: 93.59%\n",
+      "25\t검증 세트 손실: 6759.045898\t최선의 손실: 24.346964\t정확도: 96.25%\n",
+      "26\t검증 세트 손실: 4524.816895\t최선의 손실: 24.346964\t정확도: 96.36%\n",
+      "27\t검증 세트 손실: 4394.646973\t최선의 손실: 24.346964\t정확도: 96.21%\n",
+      "28\t검증 세트 손실: 5590.129883\t최선의 손실: 24.346964\t정확도: 95.70%\n",
+      "29\t검증 세트 손실: 28643.064453\t최선의 손실: 24.346964\t정확도: 91.48%\n",
+      "30\t검증 세트 손실: 7282.187012\t최선의 손실: 24.346964\t정확도: 96.99%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=  26.4s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.097183\t최선의 손실: 0.097183\t정확도: 97.26%\n",
-      "1\t검증 세트 손실: 0.064187\t최선의 손실: 0.064187\t정확도: 98.16%\n",
-      "2\t검증 세트 손실: 0.078971\t최선의 손실: 0.064187\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 0.052083\t최선의 손실: 0.052083\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.044358\t최선의 손실: 0.044358\t정확도: 98.79%\n",
-      "5\t검증 세트 손실: 0.078136\t최선의 손실: 0.044358\t정확도: 98.08%\n",
-      "6\t검증 세트 손실: 0.050722\t최선의 손실: 0.044358\t정확도: 98.94%\n",
-      "7\t검증 세트 손실: 0.063873\t최선의 손실: 0.044358\t정확도: 98.55%\n",
-      "8\t검증 세트 손실: 0.047796\t최선의 손실: 0.044358\t정확도: 98.59%\n",
-      "9\t검증 세트 손실: 0.055671\t최선의 손실: 0.044358\t정확도: 98.79%\n",
-      "10\t검증 세트 손실: 0.047219\t최선의 손실: 0.044358\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.035715\t최선의 손실: 0.035715\t정확도: 99.10%\n",
-      "12\t검증 세트 손실: 0.053054\t최선의 손실: 0.035715\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.040820\t최선의 손실: 0.035715\t정확도: 99.10%\n",
-      "14\t검증 세트 손실: 0.054431\t최선의 손실: 0.035715\t정확도: 98.67%\n",
-      "15\t검증 세트 손실: 0.061538\t최선의 손실: 0.035715\t정확도: 99.02%\n",
-      "16\t검증 세트 손실: 0.062488\t최선의 손실: 0.035715\t정확도: 98.36%\n",
-      "17\t검증 세트 손실: 0.053677\t최선의 손실: 0.035715\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.044973\t최선의 손실: 0.035715\t정확도: 99.02%\n",
-      "19\t검증 세트 손실: 0.050552\t최선의 손실: 0.035715\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.049795\t최선의 손실: 0.035715\t정확도: 99.10%\n",
-      "21\t검증 세트 손실: 0.067863\t최선의 손실: 0.035715\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.067747\t최선의 손실: 0.035715\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.074176\t최선의 손실: 0.035715\t정확도: 99.02%\n",
-      "24\t검증 세트 손실: 0.055398\t최선의 손실: 0.035715\t정확도: 99.02%\n",
-      "25\t검증 세트 손실: 0.082723\t최선의 손실: 0.035715\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.065284\t최선의 손실: 0.035715\t정확도: 98.67%\n",
-      "27\t검증 세트 손실: 0.054484\t최선의 손실: 0.035715\t정확도: 98.87%\n",
-      "28\t검증 세트 손실: 0.065777\t최선의 손실: 0.035715\t정확도: 98.55%\n",
-      "29\t검증 세트 손실: 0.062188\t최선의 손실: 0.035715\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.047662\t최선의 손실: 0.035715\t정확도: 99.06%\n",
-      "31\t검증 세트 손실: 0.066010\t최선의 손실: 0.035715\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.054179\t최선의 손실: 0.035715\t정확도: 98.91%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70, total=  15.6s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 80718.687500\t최선의 손실: 80718.687500\t정확도: 50.74%\n",
+      "1\t검증 세트 손실: 539.508118\t최선의 손실: 539.508118\t정확도: 77.52%\n",
+      "2\t검증 세트 손실: 226.713760\t최선의 손실: 226.713760\t정확도: 89.64%\n",
+      "3\t검증 세트 손실: 131.542404\t최선의 손실: 131.542404\t정확도: 89.87%\n",
+      "4\t검증 세트 손실: 64.967957\t최선의 손실: 64.967957\t정확도: 93.98%\n",
+      "5\t검증 세트 손실: 82.670006\t최선의 손실: 64.967957\t정확도: 93.39%\n",
+      "6\t검증 세트 손실: 177.502884\t최선의 손실: 64.967957\t정확도: 86.51%\n",
+      "7\t검증 세트 손실: 58.526741\t최선의 손실: 58.526741\t정확도: 94.06%\n",
+      "8\t검증 세트 손실: 62.443504\t최선의 손실: 58.526741\t정확도: 94.33%\n",
+      "9\t검증 세트 손실: 861.628235\t최선의 손실: 58.526741\t정확도: 81.47%\n",
+      "10\t검증 세트 손실: 54784.886719\t최선의 손실: 58.526741\t정확도: 87.72%\n",
+      "11\t검증 세트 손실: 15499.198242\t최선의 손실: 58.526741\t정확도: 91.20%\n",
+      "12\t검증 세트 손실: 47138.777344\t최선의 손실: 58.526741\t정확도: 75.92%\n",
+      "13\t검증 세트 손실: 16010.203125\t최선의 손실: 58.526741\t정확도: 94.18%\n",
+      "14\t검증 세트 손실: 16123.461914\t최선의 손실: 58.526741\t정확도: 88.08%\n",
+      "15\t검증 세트 손실: 11741.057617\t최선의 손실: 58.526741\t정확도: 92.69%\n",
+      "16\t검증 세트 손실: 7792.813965\t최선의 손실: 58.526741\t정확도: 94.25%\n",
+      "17\t검증 세트 손실: 8034.357910\t최선의 손실: 58.526741\t정확도: 95.90%\n",
+      "18\t검증 세트 손실: 8918.059570\t최선의 손실: 58.526741\t정확도: 92.22%\n",
+      "19\t검증 세트 손실: 25640.595703\t최선의 손실: 58.526741\t정확도: 86.04%\n",
+      "20\t검증 세트 손실: 9147.901367\t최선의 손실: 58.526741\t정확도: 94.45%\n",
+      "21\t검증 세트 손실: 8412.023438\t최선의 손실: 58.526741\t정확도: 92.57%\n",
+      "22\t검증 세트 손실: 5322.297852\t최선의 손실: 58.526741\t정확도: 95.97%\n",
+      "23\t검증 세트 손실: 10231.715820\t최선의 손실: 58.526741\t정확도: 95.93%\n",
+      "24\t검증 세트 손실: 8643.117188\t최선의 손실: 58.526741\t정확도: 93.78%\n",
+      "25\t검증 세트 손실: 3290.678711\t최선의 손실: 58.526741\t정확도: 96.56%\n",
+      "26\t검증 세트 손실: 11431.452148\t최선의 손실: 58.526741\t정확도: 93.98%\n",
+      "27\t검증 세트 손실: 4360.528320\t최선의 손실: 58.526741\t정확도: 95.04%\n",
+      "28\t검증 세트 손실: 13306722.000000\t최선의 손실: 58.526741\t정확도: 61.38%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=120, total=   2.8s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.089573\t최선의 손실: 0.089573\t정확도: 97.03%\n",
-      "1\t검증 세트 손실: 0.055397\t최선의 손실: 0.055397\t정확도: 98.36%\n",
-      "2\t검증 세트 손실: 0.048335\t최선의 손실: 0.048335\t정확도: 98.63%\n",
-      "3\t검증 세트 손실: 0.060539\t최선의 손실: 0.048335\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.047387\t최선의 손실: 0.047387\t정확도: 98.67%\n",
-      "5\t검증 세트 손실: 0.062816\t최선의 손실: 0.047387\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.064074\t최선의 손실: 0.047387\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.063918\t최선의 손실: 0.047387\t정확도: 98.05%\n",
-      "8\t검증 세트 손실: 0.055057\t최선의 손실: 0.047387\t정확도: 98.59%\n",
-      "9\t검증 세트 손실: 0.067551\t최선의 손실: 0.047387\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.056522\t최선의 손실: 0.047387\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.054450\t최선의 손실: 0.047387\t정확도: 98.63%\n",
-      "12\t검증 세트 손실: 0.072799\t최선의 손실: 0.047387\t정확도: 98.44%\n",
-      "13\t검증 세트 손실: 0.061446\t최선의 손실: 0.047387\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.077624\t최선의 손실: 0.047387\t정확도: 98.40%\n",
-      "15\t검증 세트 손실: 0.057517\t최선의 손실: 0.047387\t정확도: 98.94%\n",
-      "16\t검증 세트 손실: 0.060800\t최선의 손실: 0.047387\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.058616\t최선의 손실: 0.047387\t정확도: 98.63%\n",
-      "18\t검증 세트 손실: 0.065950\t최선의 손실: 0.047387\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.043009\t최선의 손실: 0.043009\t정확도: 99.26%\n",
-      "20\t검증 세트 손실: 0.066217\t최선의 손실: 0.043009\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.063874\t최선의 손실: 0.043009\t정확도: 98.91%\n",
-      "22\t검증 세트 손실: 0.063629\t최선의 손실: 0.043009\t정확도: 99.18%\n",
-      "23\t검증 세트 손실: 0.073902\t최선의 손실: 0.043009\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.047016\t최선의 손실: 0.043009\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.049214\t최선의 손실: 0.043009\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.056414\t최선의 손실: 0.043009\t정확도: 98.98%\n",
-      "27\t검증 세트 손실: 0.113518\t최선의 손실: 0.043009\t정확도: 98.63%\n",
-      "28\t검증 세트 손실: 0.052673\t최선의 손실: 0.043009\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.074228\t최선의 손실: 0.043009\t정확도: 98.71%\n",
-      "30\t검증 세트 손실: 0.054646\t최선의 손실: 0.043009\t정확도: 98.98%\n",
-      "31\t검증 세트 손실: 0.061315\t최선의 손실: 0.043009\t정확도: 99.06%\n",
-      "32\t검증 세트 손실: 0.070595\t최선의 손실: 0.043009\t정확도: 99.10%\n",
-      "33\t검증 세트 손실: 0.076136\t최선의 손실: 0.043009\t정확도: 99.02%\n",
-      "34\t검증 세트 손실: 0.077359\t최선의 손실: 0.043009\t정확도: 98.59%\n",
-      "35\t검증 세트 손실: 0.061617\t최선의 손실: 0.043009\t정확도: 99.37%\n",
-      "36\t검증 세트 손실: 0.065231\t최선의 손실: 0.043009\t정확도: 99.18%\n",
-      "37\t검증 세트 손실: 0.057983\t최선의 손실: 0.043009\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.067298\t최선의 손실: 0.043009\t정확도: 98.87%\n",
-      "39\t검증 세트 손실: 0.066046\t최선의 손실: 0.043009\t정확도: 98.71%\n",
-      "40\t검증 세트 손실: 0.075476\t최선의 손실: 0.043009\t정확도: 98.28%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70, total=  15.3s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.107274\t최선의 손실: 0.107274\t정확도: 96.83%\n",
+      "1\t검증 세트 손실: 0.069711\t최선의 손실: 0.069711\t정확도: 98.05%\n",
+      "2\t검증 세트 손실: 0.066326\t최선의 손실: 0.066326\t정확도: 98.08%\n",
+      "3\t검증 세트 손실: 0.047015\t최선의 손실: 0.047015\t정확도: 98.55%\n",
+      "4\t검증 세트 손실: 0.055278\t최선의 손실: 0.047015\t정확도: 98.32%\n",
+      "5\t검증 세트 손실: 0.052022\t최선의 손실: 0.047015\t정확도: 98.48%\n",
+      "6\t검증 세트 손실: 0.044637\t최선의 손실: 0.044637\t정확도: 98.75%\n",
+      "7\t검증 세트 손실: 0.043804\t최선의 손실: 0.043804\t정확도: 98.48%\n",
+      "8\t검증 세트 손실: 0.046090\t최선의 손실: 0.043804\t정확도: 98.87%\n",
+      "9\t검증 세트 손실: 0.062197\t최선의 손실: 0.043804\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.054584\t최선의 손실: 0.043804\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.064657\t최선의 손실: 0.043804\t정확도: 98.94%\n",
+      "12\t검증 세트 손실: 0.063401\t최선의 손실: 0.043804\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.070756\t최선의 손실: 0.043804\t정확도: 98.51%\n",
+      "14\t검증 세트 손실: 0.066664\t최선의 손실: 0.043804\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.054236\t최선의 손실: 0.043804\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.063984\t최선의 손실: 0.043804\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.069500\t최선의 손실: 0.043804\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.065138\t최선의 손실: 0.043804\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.073720\t최선의 손실: 0.043804\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.078482\t최선의 손실: 0.043804\t정확도: 98.63%\n",
+      "21\t검증 세트 손실: 0.062891\t최선의 손실: 0.043804\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.053290\t최선의 손실: 0.043804\t정확도: 98.83%\n",
+      "23\t검증 세트 손실: 0.069758\t최선의 손실: 0.043804\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.107299\t최선의 손실: 0.043804\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.055343\t최선의 손실: 0.043804\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.078952\t최선의 손실: 0.043804\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.063310\t최선의 손실: 0.043804\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.067477\t최선의 손실: 0.043804\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=120, total=   3.4s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.084515\t최선의 손실: 0.084515\t정확도: 97.38%\n",
-      "1\t검증 세트 손실: 0.063706\t최선의 손실: 0.063706\t정확도: 98.08%\n",
-      "2\t검증 세트 손실: 0.069576\t최선의 손실: 0.063706\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.058311\t최선의 손실: 0.058311\t정확도: 98.40%\n",
-      "4\t검증 세트 손실: 0.047344\t최선의 손실: 0.047344\t정확도: 98.79%\n",
-      "5\t검증 세트 손실: 0.056416\t최선의 손실: 0.047344\t정확도: 98.40%\n",
-      "6\t검증 세트 손실: 0.105556\t최선의 손실: 0.047344\t정확도: 97.93%\n",
-      "7\t검증 세트 손실: 0.069413\t최선의 손실: 0.047344\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.054398\t최선의 손실: 0.047344\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.060772\t최선의 손실: 0.047344\t정확도: 98.94%\n",
-      "10\t검증 세트 손실: 0.065015\t최선의 손실: 0.047344\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.060596\t최선의 손실: 0.047344\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.067129\t최선의 손실: 0.047344\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.066027\t최선의 손실: 0.047344\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.056540\t최선의 손실: 0.047344\t정확도: 98.75%\n",
-      "15\t검증 세트 손실: 0.071981\t최선의 손실: 0.047344\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.076697\t최선의 손실: 0.047344\t정확도: 98.98%\n",
-      "17\t검증 세트 손실: 0.064456\t최선의 손실: 0.047344\t정확도: 98.67%\n",
-      "18\t검증 세트 손실: 0.058075\t최선의 손실: 0.047344\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.059089\t최선의 손실: 0.047344\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.058641\t최선의 손실: 0.047344\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.066358\t최선의 손실: 0.047344\t정확도: 98.59%\n",
-      "22\t검증 세트 손실: 0.057566\t최선의 손실: 0.047344\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.053103\t최선의 손실: 0.047344\t정확도: 98.59%\n",
-      "24\t검증 세트 손실: 0.072208\t최선의 손실: 0.047344\t정확도: 98.75%\n",
-      "25\t검증 세트 손실: 0.064587\t최선의 손실: 0.047344\t정확도: 98.83%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=120, total=   2.9s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.123813\t최선의 손실: 0.123813\t정확도: 95.93%\n",
+      "1\t검증 세트 손실: 0.080252\t최선의 손실: 0.080252\t정확도: 97.73%\n",
+      "2\t검증 세트 손실: 0.066413\t최선의 손실: 0.066413\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.064630\t최선의 손실: 0.064630\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.069865\t최선의 손실: 0.064630\t정확도: 98.16%\n",
+      "5\t검증 세트 손실: 0.056303\t최선의 손실: 0.056303\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.045329\t최선의 손실: 0.045329\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.052976\t최선의 손실: 0.045329\t정확도: 98.51%\n",
+      "8\t검증 세트 손실: 0.056145\t최선의 손실: 0.045329\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.069189\t최선의 손실: 0.045329\t정확도: 98.32%\n",
+      "10\t검증 세트 손실: 0.062726\t최선의 손실: 0.045329\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.072085\t최선의 손실: 0.045329\t정확도: 97.77%\n",
+      "12\t검증 세트 손실: 0.050544\t최선의 손실: 0.045329\t정확도: 98.59%\n",
+      "13\t검증 세트 손실: 0.060281\t최선의 손실: 0.045329\t정확도: 98.44%\n",
+      "14\t검증 세트 손실: 0.065029\t최선의 손실: 0.045329\t정확도: 98.75%\n",
+      "15\t검증 세트 손실: 0.073835\t최선의 손실: 0.045329\t정확도: 98.83%\n",
+      "16\t검증 세트 손실: 0.053914\t최선의 손실: 0.045329\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.053996\t최선의 손실: 0.045329\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.083269\t최선의 손실: 0.045329\t정확도: 98.59%\n",
+      "19\t검증 세트 손실: 0.051260\t최선의 손실: 0.045329\t정확도: 98.63%\n",
+      "20\t검증 세트 손실: 0.060272\t최선의 손실: 0.045329\t정확도: 98.71%\n",
+      "21\t검증 세트 손실: 0.066821\t최선의 손실: 0.045329\t정확도: 98.71%\n",
+      "22\t검증 세트 손실: 0.060669\t최선의 손실: 0.045329\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.071502\t최선의 손실: 0.045329\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.065432\t최선의 손실: 0.045329\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.075932\t최선의 손실: 0.045329\t정확도: 98.59%\n",
+      "26\t검증 세트 손실: 0.060810\t최선의 손실: 0.045329\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.060271\t최선의 손실: 0.045329\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=120, total=   2.2s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.097322\t최선의 손실: 0.097322\t정확도: 97.22%\n",
-      "1\t검증 세트 손실: 0.069966\t최선의 손실: 0.069966\t정확도: 97.89%\n",
-      "2\t검증 세트 손실: 0.065172\t최선의 손실: 0.065172\t정확도: 97.97%\n",
-      "3\t검증 세트 손실: 0.067270\t최선의 손실: 0.065172\t정확도: 98.08%\n",
-      "4\t검증 세트 손실: 0.046900\t최선의 손실: 0.046900\t정확도: 98.40%\n",
-      "5\t검증 세트 손실: 0.058165\t최선의 손실: 0.046900\t정확도: 98.28%\n",
-      "6\t검증 세트 손실: 0.050262\t최선의 손실: 0.046900\t정확도: 98.40%\n",
-      "7\t검증 세트 손실: 0.066775\t최선의 손실: 0.046900\t정확도: 97.89%\n",
-      "8\t검증 세트 손실: 0.054660\t최선의 손실: 0.046900\t정확도: 98.48%\n",
-      "9\t검증 세트 손실: 0.040801\t최선의 손실: 0.040801\t정확도: 98.91%\n",
-      "10\t검증 세트 손실: 0.041018\t최선의 손실: 0.040801\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.074018\t최선의 손실: 0.040801\t정확도: 98.08%\n",
-      "12\t검증 세트 손실: 0.054530\t최선의 손실: 0.040801\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.063592\t최선의 손실: 0.040801\t정확도: 98.59%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=120, total=   2.7s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.106828\t최선의 손실: 0.106828\t정확도: 97.03%\n",
+      "1\t검증 세트 손실: 0.072934\t최선의 손실: 0.072934\t정확도: 97.73%\n",
+      "2\t검증 세트 손실: 0.057485\t최선의 손실: 0.057485\t정확도: 98.05%\n",
+      "3\t검증 세트 손실: 0.053167\t최선의 손실: 0.053167\t정확도: 98.36%\n",
+      "4\t검증 세트 손실: 0.049069\t최선의 손실: 0.049069\t정확도: 98.55%\n",
+      "5\t검증 세트 손실: 0.054874\t최선의 손실: 0.049069\t정확도: 98.24%\n",
+      "6\t검증 세트 손실: 0.057360\t최선의 손실: 0.049069\t정확도: 98.63%\n",
+      "7\t검증 세트 손실: 0.062357\t최선의 손실: 0.049069\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.061323\t최선의 손실: 0.049069\t정확도: 98.48%\n",
+      "9\t검증 세트 손실: 0.053943\t최선의 손실: 0.049069\t정확도: 98.79%\n",
+      "10\t검증 세트 손실: 0.044623\t최선의 손실: 0.044623\t정확도: 98.83%\n",
+      "11\t검증 세트 손실: 0.053998\t최선의 손실: 0.044623\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.060959\t최선의 손실: 0.044623\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.061352\t최선의 손실: 0.044623\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.062252\t최선의 손실: 0.044623\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.056174\t최선의 손실: 0.044623\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.052866\t최선의 손실: 0.044623\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.049789\t최선의 손실: 0.044623\t정확도: 99.02%\n",
+      "18\t검증 세트 손실: 0.080001\t최선의 손실: 0.044623\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.052114\t최선의 손실: 0.044623\t정확도: 99.02%\n",
+      "20\t검증 세트 손실: 0.072481\t최선의 손실: 0.044623\t정확도: 98.87%\n",
+      "21\t검증 세트 손실: 0.076079\t최선의 손실: 0.044623\t정확도: 98.67%\n",
+      "22\t검증 세트 손실: 0.057769\t최선의 손실: 0.044623\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.079892\t최선의 손실: 0.044623\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.077849\t최선의 손실: 0.044623\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.071438\t최선의 손실: 0.044623\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.070310\t최선의 손실: 0.044623\t정확도: 98.36%\n",
+      "27\t검증 세트 손실: 0.083621\t최선의 손실: 0.044623\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.073572\t최선의 손실: 0.044623\t정확도: 98.71%\n",
+      "29\t검증 세트 손실: 0.077124\t최선의 손실: 0.044623\t정확도: 98.63%\n",
+      "30\t검증 세트 손실: 0.057518\t최선의 손실: 0.044623\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.083708\t최선의 손실: 0.044623\t정확도: 98.79%\n",
+      "조기 종료!\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "14\t검증 세트 손실: 0.060439\t최선의 손실: 0.040801\t정확도: 98.63%\n",
-      "15\t검증 세트 손실: 0.054385\t최선의 손실: 0.040801\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.054502\t최선의 손실: 0.040801\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.059543\t최선의 손실: 0.040801\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.059768\t최선의 손실: 0.040801\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.060284\t최선의 손실: 0.040801\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.056557\t최선의 손실: 0.040801\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.052929\t최선의 손실: 0.040801\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.067156\t최선의 손실: 0.040801\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.090359\t최선의 손실: 0.040801\t정확도: 98.55%\n",
-      "24\t검증 세트 손실: 0.059252\t최선의 손실: 0.040801\t정확도: 98.79%\n",
-      "25\t검증 세트 손실: 0.041968\t최선의 손실: 0.040801\t정확도: 99.14%\n",
-      "26\t검증 세트 손실: 0.058532\t최선의 손실: 0.040801\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.057262\t최선의 손실: 0.040801\t정확도: 98.94%\n",
-      "28\t검증 세트 손실: 0.077395\t최선의 손실: 0.040801\t정확도: 98.63%\n",
-      "29\t검증 세트 손실: 0.069821\t최선의 손실: 0.040801\t정확도: 98.83%\n",
-      "30\t검증 세트 손실: 0.060456\t최선의 손실: 0.040801\t정확도: 98.87%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=120, total=   3.0s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.104860\t최선의 손실: 0.104860\t정확도: 96.83%\n",
+      "1\t검증 세트 손실: 0.063637\t최선의 손실: 0.063637\t정확도: 98.01%\n",
+      "2\t검증 세트 손실: 0.056582\t최선의 손실: 0.056582\t정확도: 98.16%\n",
+      "3\t검증 세트 손실: 0.075052\t최선의 손실: 0.056582\t정확도: 98.05%\n",
+      "4\t검증 세트 손실: 0.057609\t최선의 손실: 0.056582\t정확도: 98.01%\n",
+      "5\t검증 세트 손실: 0.045507\t최선의 손실: 0.045507\t정확도: 98.48%\n",
+      "6\t검증 세트 손실: 0.049195\t최선의 손실: 0.045507\t정확도: 98.51%\n",
+      "7\t검증 세트 손실: 0.069034\t최선의 손실: 0.045507\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.049007\t최선의 손실: 0.045507\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.052319\t최선의 손실: 0.045507\t정확도: 98.36%\n",
+      "10\t검증 세트 손실: 0.043772\t최선의 손실: 0.043772\t정확도: 98.91%\n",
+      "11\t검증 세트 손실: 0.045685\t최선의 손실: 0.043772\t정확도: 98.91%\n",
+      "12\t검증 세트 손실: 0.056483\t최선의 손실: 0.043772\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.044548\t최선의 손실: 0.043772\t정확도: 99.02%\n",
+      "14\t검증 세트 손실: 0.068830\t최선의 손실: 0.043772\t정확도: 98.51%\n",
+      "15\t검증 세트 손실: 0.058750\t최선의 손실: 0.043772\t정확도: 98.75%\n",
+      "16\t검증 세트 손실: 0.046024\t최선의 손실: 0.043772\t정확도: 98.87%\n",
+      "17\t검증 세트 손실: 0.052285\t최선의 손실: 0.043772\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.049637\t최선의 손실: 0.043772\t정확도: 98.94%\n",
+      "19\t검증 세트 손실: 0.049103\t최선의 손실: 0.043772\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.049867\t최선의 손실: 0.043772\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.061481\t최선의 손실: 0.043772\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.080012\t최선의 손실: 0.043772\t정확도: 98.40%\n",
+      "23\t검증 세트 손실: 0.044825\t최선의 손실: 0.043772\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.053555\t최선의 손실: 0.043772\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.064046\t최선의 손실: 0.043772\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.060272\t최선의 손실: 0.043772\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.057582\t최선의 손실: 0.043772\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.069431\t최선의 손실: 0.043772\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.064404\t최선의 손실: 0.043772\t정확도: 98.83%\n",
+      "30\t검증 세트 손실: 0.054434\t최선의 손실: 0.043772\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.047485\t최선의 손실: 0.043772\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90, total=   2.5s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.097005\t최선의 손실: 0.097005\t정확도: 97.15%\n",
-      "1\t검증 세트 손실: 0.066300\t최선의 손실: 0.066300\t정확도: 98.16%\n",
-      "2\t검증 세트 손실: 0.059779\t최선의 손실: 0.059779\t정확도: 98.16%\n",
-      "3\t검증 세트 손실: 0.060148\t최선의 손실: 0.059779\t정확도: 97.73%\n",
-      "4\t검증 세트 손실: 0.056491\t최선의 손실: 0.056491\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.055258\t최선의 손실: 0.055258\t정확도: 98.40%\n",
-      "6\t검증 세트 손실: 0.045552\t최선의 손실: 0.045552\t정확도: 98.75%\n",
-      "7\t검증 세트 손실: 0.072891\t최선의 손실: 0.045552\t정확도: 98.24%\n",
-      "8\t검증 세트 손실: 0.057539\t최선의 손실: 0.045552\t정확도: 98.55%\n",
-      "9\t검증 세트 손실: 0.065618\t최선의 손실: 0.045552\t정확도: 98.32%\n",
-      "10\t검증 세트 손실: 0.048580\t최선의 손실: 0.045552\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.049901\t최선의 손실: 0.045552\t정확도: 98.83%\n",
-      "12\t검증 세트 손실: 0.056862\t최선의 손실: 0.045552\t정확도: 98.71%\n",
-      "13\t검증 세트 손실: 0.049920\t최선의 손실: 0.045552\t정확도: 98.44%\n",
-      "14\t검증 세트 손실: 0.048864\t최선의 손실: 0.045552\t정확도: 98.87%\n",
-      "15\t검증 세트 손실: 0.049378\t최선의 손실: 0.045552\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.041168\t최선의 손실: 0.041168\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.041468\t최선의 손실: 0.041168\t정확도: 99.14%\n",
-      "18\t검증 세트 손실: 0.039555\t최선의 손실: 0.039555\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.052697\t최선의 손실: 0.039555\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.048338\t최선의 손실: 0.039555\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.046633\t최선의 손실: 0.039555\t정확도: 99.02%\n",
-      "22\t검증 세트 손실: 0.067136\t최선의 손실: 0.039555\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.053884\t최선의 손실: 0.039555\t정확도: 99.10%\n",
-      "24\t검증 세트 손실: 0.070523\t최선의 손실: 0.039555\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.055275\t최선의 손실: 0.039555\t정확도: 98.83%\n",
-      "26\t검증 세트 손실: 0.051659\t최선의 손실: 0.039555\t정확도: 98.67%\n",
-      "27\t검증 세트 손실: 0.055999\t최선의 손실: 0.039555\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.060500\t최선의 손실: 0.039555\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.063608\t최선의 손실: 0.039555\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.088478\t최선의 손실: 0.039555\t정확도: 98.48%\n",
-      "31\t검증 세트 손실: 0.054590\t최선의 손실: 0.039555\t정확도: 98.71%\n",
-      "32\t검증 세트 손실: 0.053615\t최선의 손실: 0.039555\t정확도: 98.94%\n",
-      "33\t검증 세트 손실: 0.055092\t최선의 손실: 0.039555\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.047265\t최선의 손실: 0.039555\t정확도: 99.10%\n",
-      "35\t검증 세트 손실: 0.066102\t최선의 손실: 0.039555\t정확도: 98.94%\n",
-      "36\t검증 세트 손실: 0.068572\t최선의 손실: 0.039555\t정확도: 98.63%\n",
-      "37\t검증 세트 손실: 0.039650\t최선의 손실: 0.039555\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.056645\t최선의 손실: 0.039555\t정확도: 99.06%\n",
-      "39\t검증 세트 손실: 0.052016\t최선의 손실: 0.039555\t정확도: 99.14%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=90, total=   3.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.098019\t최선의 손실: 0.098019\t정확도: 96.52%\n",
+      "1\t검증 세트 손실: 0.064768\t최선의 손실: 0.064768\t정확도: 98.05%\n",
+      "2\t검증 세트 손실: 0.073431\t최선의 손실: 0.064768\t정확도: 97.81%\n",
+      "3\t검증 세트 손실: 0.061744\t최선의 손실: 0.061744\t정확도: 98.40%\n",
+      "4\t검증 세트 손실: 0.073912\t최선의 손실: 0.061744\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 0.061302\t최선의 손실: 0.061302\t정확도: 98.32%\n",
+      "6\t검증 세트 손실: 0.056094\t최선의 손실: 0.056094\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.053869\t최선의 손실: 0.053869\t정확도: 98.63%\n",
+      "8\t검증 세트 손실: 0.088458\t최선의 손실: 0.053869\t정확도: 98.28%\n",
+      "9\t검증 세트 손실: 0.061973\t최선의 손실: 0.053869\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.075527\t최선의 손실: 0.053869\t정확도: 98.44%\n",
+      "11\t검증 세트 손실: 0.070365\t최선의 손실: 0.053869\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.070385\t최선의 손실: 0.053869\t정확도: 98.79%\n",
+      "13\t검증 세트 손실: 0.070137\t최선의 손실: 0.053869\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.081736\t최선의 손실: 0.053869\t정확도: 98.16%\n",
+      "15\t검증 세트 손실: 0.070439\t최선의 손실: 0.053869\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.055508\t최선의 손실: 0.053869\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.055728\t최선의 손실: 0.053869\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.088348\t최선의 손실: 0.053869\t정확도: 98.44%\n",
+      "19\t검증 세트 손실: 0.104571\t최선의 손실: 0.053869\t정확도: 97.97%\n",
+      "20\t검증 세트 손실: 0.069832\t최선의 손실: 0.053869\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.066238\t최선의 손실: 0.053869\t정확도: 98.32%\n",
+      "22\t검증 세트 손실: 0.077768\t최선의 손실: 0.053869\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.076351\t최선의 손실: 0.053869\t정확도: 98.59%\n",
+      "24\t검증 세트 손실: 0.066137\t최선의 손실: 0.053869\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.095149\t최선의 손실: 0.053869\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.102762\t최선의 손실: 0.053869\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.060888\t최선의 손실: 0.053869\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.065960\t최선의 손실: 0.053869\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90, total=   3.2s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.100662\t최선의 손실: 0.100662\t정확도: 96.95%\n",
-      "1\t검증 세트 손실: 0.070559\t최선의 손실: 0.070559\t정확도: 97.93%\n",
-      "2\t검증 세트 손실: 0.064840\t최선의 손실: 0.064840\t정확도: 98.05%\n",
-      "3\t검증 세트 손실: 0.052647\t최선의 손실: 0.052647\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.050295\t최선의 손실: 0.050295\t정확도: 98.44%\n",
-      "5\t검증 세트 손실: 0.058745\t최선의 손실: 0.050295\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.051734\t최선의 손실: 0.050295\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.069309\t최선의 손실: 0.050295\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.058909\t최선의 손실: 0.050295\t정확도: 98.28%\n",
-      "9\t검증 세트 손실: 0.042293\t최선의 손실: 0.042293\t정확도: 98.98%\n",
-      "10\t검증 세트 손실: 0.058786\t최선의 손실: 0.042293\t정확도: 98.79%\n",
-      "11\t검증 세트 손실: 0.064917\t최선의 손실: 0.042293\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.052526\t최선의 손실: 0.042293\t정확도: 98.94%\n",
-      "13\t검증 세트 손실: 0.061298\t최선의 손실: 0.042293\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.058547\t최선의 손실: 0.042293\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.061197\t최선의 손실: 0.042293\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.064453\t최선의 손실: 0.042293\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.068181\t최선의 손실: 0.042293\t정확도: 98.28%\n",
-      "18\t검증 세트 손실: 0.067622\t최선의 손실: 0.042293\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.048094\t최선의 손실: 0.042293\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.073656\t최선의 손실: 0.042293\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.063076\t최선의 손실: 0.042293\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.059329\t최선의 손실: 0.042293\t정확도: 98.91%\n",
-      "23\t검증 세트 손실: 0.068430\t최선의 손실: 0.042293\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.074491\t최선의 손실: 0.042293\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.060618\t최선의 손실: 0.042293\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.063230\t최선의 손실: 0.042293\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.077404\t최선의 손실: 0.042293\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.111447\t최선의 손실: 0.042293\t정확도: 97.65%\n",
-      "29\t검증 세트 손실: 0.073138\t최선의 손실: 0.042293\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.075339\t최선의 손실: 0.042293\t정확도: 98.63%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=90, total=   3.1s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.081412\t최선의 손실: 0.081412\t정확도: 97.54%\n",
+      "1\t검증 세트 손실: 0.070878\t최선의 손실: 0.070878\t정확도: 97.89%\n",
+      "2\t검증 세트 손실: 0.066145\t최선의 손실: 0.066145\t정확도: 98.01%\n",
+      "3\t검증 세트 손실: 0.060662\t최선의 손실: 0.060662\t정확도: 98.36%\n",
+      "4\t검증 세트 손실: 0.045459\t최선의 손실: 0.045459\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.047400\t최선의 손실: 0.045459\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.057726\t최선의 손실: 0.045459\t정확도: 98.32%\n",
+      "7\t검증 세트 손실: 0.060481\t최선의 손실: 0.045459\t정확도: 98.51%\n",
+      "8\t검증 세트 손실: 0.080229\t최선의 손실: 0.045459\t정확도: 98.05%\n",
+      "9\t검증 세트 손실: 0.046444\t최선의 손실: 0.045459\t정확도: 98.87%\n",
+      "10\t검증 세트 손실: 0.068748\t최선의 손실: 0.045459\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.054034\t최선의 손실: 0.045459\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.053028\t최선의 손실: 0.045459\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.064798\t최선의 손실: 0.045459\t정확도: 98.71%\n",
+      "14\t검증 세트 손실: 0.060597\t최선의 손실: 0.045459\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.067788\t최선의 손실: 0.045459\t정확도: 98.55%\n",
+      "16\t검증 세트 손실: 0.059558\t최선의 손실: 0.045459\t정확도: 98.63%\n",
+      "17\t검증 세트 손실: 0.054059\t최선의 손실: 0.045459\t정확도: 98.44%\n",
+      "18\t검증 세트 손실: 0.056300\t최선의 손실: 0.045459\t정확도: 98.94%\n",
+      "19\t검증 세트 손실: 0.066750\t최선의 손실: 0.045459\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.066124\t최선의 손실: 0.045459\t정확도: 98.87%\n",
+      "21\t검증 세트 손실: 0.085799\t최선의 손실: 0.045459\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.059779\t최선의 손실: 0.045459\t정확도: 98.75%\n",
+      "23\t검증 세트 손실: 0.062951\t최선의 손실: 0.045459\t정확도: 98.55%\n",
+      "24\t검증 세트 손실: 0.070578\t최선의 손실: 0.045459\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.071260\t최선의 손실: 0.045459\t정확도: 98.75%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=90, total=   2.5s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.124217\t최선의 손실: 0.124217\t정확도: 96.09%\n",
-      "1\t검증 세트 손실: 0.094650\t최선의 손실: 0.094650\t정확도: 97.03%\n",
-      "2\t검증 세트 손실: 0.081571\t최선의 손실: 0.081571\t정확도: 97.50%\n",
-      "3\t검증 세트 손실: 0.061818\t최선의 손실: 0.061818\t정확도: 98.01%\n",
-      "4\t검증 세트 손실: 0.071965\t최선의 손실: 0.061818\t정확도: 97.81%\n",
-      "5\t검증 세트 손실: 0.061999\t최선의 손실: 0.061818\t정확도: 98.28%\n",
-      "6\t검증 세트 손실: 0.054793\t최선의 손실: 0.054793\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.064100\t최선의 손실: 0.054793\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.062329\t최선의 손실: 0.054793\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.067211\t최선의 손실: 0.054793\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.060865\t최선의 손실: 0.054793\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.062362\t최선의 손실: 0.054793\t정확도: 98.55%\n",
-      "12\t검증 세트 손실: 0.072066\t최선의 손실: 0.054793\t정확도: 98.55%\n",
-      "13\t검증 세트 손실: 0.079338\t최선의 손실: 0.054793\t정확도: 98.55%\n",
-      "14\t검증 세트 손실: 0.089459\t최선의 손실: 0.054793\t정확도: 98.63%\n",
-      "15\t검증 세트 손실: 0.079441\t최선의 손실: 0.054793\t정확도: 98.44%\n",
-      "16\t검증 세트 손실: 0.062901\t최선의 손실: 0.054793\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.063921\t최선의 손실: 0.054793\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.075805\t최선의 손실: 0.054793\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.072677\t최선의 손실: 0.054793\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.092266\t최선의 손실: 0.054793\t정확도: 98.32%\n",
-      "21\t검증 세트 손실: 0.090763\t최선의 손실: 0.054793\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.071661\t최선의 손실: 0.054793\t정확도: 98.28%\n",
-      "23\t검증 세트 손실: 0.058486\t최선의 손실: 0.054793\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.057853\t최선의 손실: 0.054793\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.059634\t최선의 손실: 0.054793\t정확도: 98.91%\n",
-      "26\t검증 세트 손실: 0.062273\t최선의 손실: 0.054793\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.057789\t최선의 손실: 0.054793\t정확도: 99.02%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.01, n_neurons=90, total=   2.6s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.140914\t최선의 손실: 0.140914\t정확도: 95.39%\n",
+      "1\t검증 세트 손실: 0.082843\t최선의 손실: 0.082843\t정확도: 97.62%\n",
+      "2\t검증 세트 손실: 0.086208\t최선의 손실: 0.082843\t정확도: 97.38%\n",
+      "3\t검증 세트 손실: 0.073511\t최선의 손실: 0.073511\t정확도: 97.54%\n",
+      "4\t검증 세트 손실: 0.062359\t최선의 손실: 0.062359\t정확도: 98.05%\n",
+      "5\t검증 세트 손실: 0.055234\t최선의 손실: 0.055234\t정확도: 98.36%\n",
+      "6\t검증 세트 손실: 0.058129\t최선의 손실: 0.055234\t정확도: 98.28%\n",
+      "7\t검증 세트 손실: 0.058522\t최선의 손실: 0.055234\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.072050\t최선의 손실: 0.055234\t정확도: 98.05%\n",
+      "9\t검증 세트 손실: 0.060180\t최선의 손실: 0.055234\t정확도: 98.32%\n",
+      "10\t검증 세트 손실: 0.060058\t최선의 손실: 0.055234\t정확도: 98.44%\n",
+      "11\t검증 세트 손실: 0.059316\t최선의 손실: 0.055234\t정확도: 98.59%\n",
+      "12\t검증 세트 손실: 0.067020\t최선의 손실: 0.055234\t정확도: 98.48%\n",
+      "13\t검증 세트 손실: 0.089801\t최선의 손실: 0.055234\t정확도: 97.85%\n",
+      "14\t검증 세트 손실: 0.057018\t최선의 손실: 0.055234\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.075723\t최선의 손실: 0.055234\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.055992\t최선의 손실: 0.055234\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.063138\t최선의 손실: 0.055234\t정확도: 98.67%\n",
+      "18\t검증 세트 손실: 0.083351\t최선의 손실: 0.055234\t정확도: 98.48%\n",
+      "19\t검증 세트 손실: 0.097937\t최선의 손실: 0.055234\t정확도: 98.28%\n",
+      "20\t검증 세트 손실: 0.088433\t최선의 손실: 0.055234\t정확도: 97.30%\n",
+      "21\t검증 세트 손실: 0.058375\t최선의 손실: 0.055234\t정확도: 98.59%\n",
+      "22\t검증 세트 손실: 0.054606\t최선의 손실: 0.054606\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.055023\t최선의 손실: 0.054606\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.065253\t최선의 손실: 0.054606\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.054023\t최선의 손실: 0.054023\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.053618\t최선의 손실: 0.053618\t정확도: 98.91%\n",
+      "27\t검증 세트 손실: 0.057054\t최선의 손실: 0.053618\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.057692\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "29\t검증 세트 손실: 0.057957\t최선의 손실: 0.053618\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.059506\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "31\t검증 세트 손실: 0.060461\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "32\t검증 세트 손실: 0.061301\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "33\t검증 세트 손실: 0.062103\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "34\t검증 세트 손실: 0.062749\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "35\t검증 세트 손실: 0.063474\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "36\t검증 세트 손실: 0.064156\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "37\t검증 세트 손실: 0.064709\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "38\t검증 세트 손실: 0.065248\t최선의 손실: 0.053618\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.065800\t최선의 손실: 0.053618\t정확도: 98.94%\n",
+      "40\t검증 세트 손실: 0.066506\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "41\t검증 세트 손실: 0.066900\t최선의 손실: 0.053618\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.067465\t최선의 손실: 0.053618\t정확도: 99.02%\n",
+      "43\t검증 세트 손실: 0.067890\t최선의 손실: 0.053618\t정확도: 98.94%\n",
+      "44\t검증 세트 손실: 0.068309\t최선의 손실: 0.053618\t정확도: 98.94%\n",
+      "45\t검증 세트 손실: 0.068612\t최선의 손실: 0.053618\t정확도: 98.94%\n",
+      "46\t검증 세트 손실: 0.069083\t최선의 손실: 0.053618\t정확도: 98.98%\n",
+      "47\t검증 세트 손실: 0.069538\t최선의 손실: 0.053618\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.1s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.141076\t최선의 손실: 0.141076\t정확도: 95.11%\n",
-      "1\t검증 세트 손실: 0.093234\t최선의 손실: 0.093234\t정확도: 97.07%\n",
-      "2\t검증 세트 손실: 0.082130\t최선의 손실: 0.082130\t정확도: 97.54%\n",
-      "3\t검증 세트 손실: 0.071495\t최선의 손실: 0.071495\t정확도: 97.77%\n",
-      "4\t검증 세트 손실: 0.065252\t최선의 손실: 0.065252\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.063368\t최선의 손실: 0.063368\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.056380\t최선의 손실: 0.056380\t정확도: 98.36%\n",
-      "7\t검증 세트 손실: 0.046885\t최선의 손실: 0.046885\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.049022\t최선의 손실: 0.046885\t정확도: 98.55%\n",
-      "9\t검증 세트 손실: 0.046623\t최선의 손실: 0.046623\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.051268\t최선의 손실: 0.046623\t정확도: 98.55%\n",
-      "11\t검증 세트 손실: 0.048732\t최선의 손실: 0.046623\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.049078\t최선의 손실: 0.046623\t정확도: 98.67%\n",
-      "13\t검증 세트 손실: 0.064367\t최선의 손실: 0.046623\t정확도: 98.51%\n",
-      "14\t검증 세트 손실: 0.067968\t최선의 손실: 0.046623\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.050671\t최선의 손실: 0.046623\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.051368\t최선의 손실: 0.046623\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.069792\t최선의 손실: 0.046623\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.056970\t최선의 손실: 0.046623\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.070868\t최선의 손실: 0.046623\t정확도: 98.83%\n",
-      "20\t검증 세트 손실: 0.059233\t최선의 손실: 0.046623\t정확도: 99.02%\n",
-      "21\t검증 세트 손실: 0.052301\t최선의 손실: 0.046623\t정확도: 98.75%\n",
-      "22\t검증 세트 손실: 0.058403\t최선의 손실: 0.046623\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.064116\t최선의 손실: 0.046623\t정확도: 98.75%\n",
-      "24\t검증 세트 손실: 0.060469\t최선의 손실: 0.046623\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.078180\t최선의 손실: 0.046623\t정확도: 98.79%\n",
-      "26\t검증 세트 손실: 0.087886\t최선의 손실: 0.046623\t정확도: 98.36%\n",
-      "27\t검증 세트 손실: 0.085679\t최선의 손실: 0.046623\t정확도: 98.08%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, n_neurons=140, total=   4.1s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, n_neurons=140 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "28\t검증 세트 손실: 0.074703\t최선의 손실: 0.046623\t정확도: 98.59%\n",
-      "29\t검증 세트 손실: 0.054141\t최선의 손실: 0.046623\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.048089\t최선의 손실: 0.046623\t정확도: 98.98%\n",
+      "0\t검증 세트 손실: 0.127971\t최선의 손실: 0.127971\t정확도: 95.93%\n",
+      "1\t검증 세트 손실: 0.076743\t최선의 손실: 0.076743\t정확도: 97.73%\n",
+      "2\t검증 세트 손실: 0.059686\t최선의 손실: 0.059686\t정확도: 98.16%\n",
+      "3\t검증 세트 손실: 0.055500\t최선의 손실: 0.055500\t정확도: 98.40%\n",
+      "4\t검증 세트 손실: 0.049846\t최선의 손실: 0.049846\t정확도: 98.36%\n",
+      "5\t검증 세트 손실: 0.050812\t최선의 손실: 0.049846\t정확도: 98.44%\n",
+      "6\t검증 세트 손실: 0.036367\t최선의 손실: 0.036367\t정확도: 98.91%\n",
+      "7\t검증 세트 손실: 0.040803\t최선의 손실: 0.036367\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.055379\t최선의 손실: 0.036367\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.053489\t최선의 손실: 0.036367\t정확도: 98.48%\n",
+      "10\t검증 세트 손실: 0.054987\t최선의 손실: 0.036367\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.060149\t최선의 손실: 0.036367\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.047267\t최선의 손실: 0.036367\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.053801\t최선의 손실: 0.036367\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.049535\t최선의 손실: 0.036367\t정확도: 98.91%\n",
+      "15\t검증 세트 손실: 0.043959\t최선의 손실: 0.036367\t정확도: 99.10%\n",
+      "16\t검증 세트 손실: 0.059245\t최선의 손실: 0.036367\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.053413\t최선의 손실: 0.036367\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.072318\t최선의 손실: 0.036367\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.055238\t최선의 손실: 0.036367\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.057511\t최선의 손실: 0.036367\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.066092\t최선의 손실: 0.036367\t정확도: 98.55%\n",
+      "22\t검증 세트 손실: 0.057374\t최선의 손실: 0.036367\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.063126\t최선의 손실: 0.036367\t정확도: 98.71%\n",
+      "24\t검증 세트 손실: 0.044008\t최선의 손실: 0.036367\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.064212\t최선의 손실: 0.036367\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.051263\t최선의 손실: 0.036367\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.057779\t최선의 손실: 0.036367\t정확도: 98.75%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.4s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.139013\t최선의 손실: 0.139013\t정확도: 95.47%\n",
-      "1\t검증 세트 손실: 0.081791\t최선의 손실: 0.081791\t정확도: 97.65%\n",
-      "2\t검증 세트 손실: 0.065703\t최선의 손실: 0.065703\t정확도: 97.93%\n",
-      "3\t검증 세트 손실: 0.054955\t최선의 손실: 0.054955\t정확도: 98.40%\n",
-      "4\t검증 세트 손실: 0.056072\t최선의 손실: 0.054955\t정확도: 98.28%\n",
-      "5\t검증 세트 손실: 0.052713\t최선의 손실: 0.052713\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.053862\t최선의 손실: 0.052713\t정확도: 98.40%\n",
-      "7\t검증 세트 손실: 0.049631\t최선의 손실: 0.049631\t정확도: 98.51%\n",
-      "8\t검증 세트 손실: 0.046011\t최선의 손실: 0.046011\t정확도: 98.67%\n",
-      "9\t검증 세트 손실: 0.047032\t최선의 손실: 0.046011\t정확도: 98.91%\n",
-      "10\t검증 세트 손실: 0.068970\t최선의 손실: 0.046011\t정확도: 98.32%\n",
-      "11\t검증 세트 손실: 0.054171\t최선의 손실: 0.046011\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.061753\t최선의 손실: 0.046011\t정확도: 98.59%\n",
-      "13\t검증 세트 손실: 0.054669\t최선의 손실: 0.046011\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.069172\t최선의 손실: 0.046011\t정확도: 98.32%\n",
-      "15\t검증 세트 손실: 0.063854\t최선의 손실: 0.046011\t정확도: 98.63%\n",
-      "16\t검증 세트 손실: 0.062371\t최선의 손실: 0.046011\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.044229\t최선의 손실: 0.044229\t정확도: 98.91%\n",
-      "18\t검증 세트 손실: 0.061519\t최선의 손실: 0.044229\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.050834\t최선의 손실: 0.044229\t정확도: 98.91%\n",
-      "20\t검증 세트 손실: 0.060048\t최선의 손실: 0.044229\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.052309\t최선의 손실: 0.044229\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.050177\t최선의 손실: 0.044229\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.056486\t최선의 손실: 0.044229\t정확도: 98.71%\n",
-      "24\t검증 세트 손실: 0.050995\t최선의 손실: 0.044229\t정확도: 99.02%\n",
-      "25\t검증 세트 손실: 0.052955\t최선의 손실: 0.044229\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.048071\t최선의 손실: 0.044229\t정확도: 99.10%\n",
-      "27\t검증 세트 손실: 0.060625\t최선의 손실: 0.044229\t정확도: 98.98%\n",
-      "28\t검증 세트 손실: 0.066287\t최선의 손실: 0.044229\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.067314\t최선의 손실: 0.044229\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.055359\t최선의 손실: 0.044229\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.048776\t최선의 손실: 0.044229\t정확도: 98.94%\n",
-      "32\t검증 세트 손실: 0.082135\t최선의 손실: 0.044229\t정확도: 98.51%\n",
-      "33\t검증 세트 손실: 0.053732\t최선의 손실: 0.044229\t정확도: 98.91%\n",
-      "34\t검증 세트 손실: 0.053210\t최선의 손실: 0.044229\t정확도: 98.91%\n",
-      "35\t검증 세트 손실: 0.081394\t최선의 손실: 0.044229\t정확도: 98.48%\n",
-      "36\t검증 세트 손실: 0.061642\t최선의 손실: 0.044229\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.050465\t최선의 손실: 0.044229\t정확도: 99.14%\n",
-      "38\t검증 세트 손실: 0.093587\t최선의 손실: 0.044229\t정확도: 98.48%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, n_neurons=140, total=   2.7s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.122151\t최선의 손실: 0.122151\t정확도: 95.93%\n",
+      "1\t검증 세트 손실: 0.085570\t최선의 손실: 0.085570\t정확도: 97.03%\n",
+      "2\t검증 세트 손실: 0.070517\t최선의 손실: 0.070517\t정확도: 98.01%\n",
+      "3\t검증 세트 손실: 0.049195\t최선의 손실: 0.049195\t정확도: 98.48%\n",
+      "4\t검증 세트 손실: 0.051055\t최선의 손실: 0.049195\t정확도: 98.71%\n",
+      "5\t검증 세트 손실: 0.045652\t최선의 손실: 0.045652\t정확도: 98.55%\n",
+      "6\t검증 세트 손실: 0.050153\t최선의 손실: 0.045652\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.043203\t최선의 손실: 0.043203\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.060054\t최선의 손실: 0.043203\t정확도: 98.28%\n",
+      "9\t검증 세트 손실: 0.042490\t최선의 손실: 0.042490\t정확도: 98.94%\n",
+      "10\t검증 세트 손실: 0.054238\t최선의 손실: 0.042490\t정확도: 98.83%\n",
+      "11\t검증 세트 손실: 0.049426\t최선의 손실: 0.042490\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.047042\t최선의 손실: 0.042490\t정확도: 98.98%\n",
+      "13\t검증 세트 손실: 0.045060\t최선의 손실: 0.042490\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.070199\t최선의 손실: 0.042490\t정확도: 98.48%\n",
+      "15\t검증 세트 손실: 0.089735\t최선의 손실: 0.042490\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.047618\t최선의 손실: 0.042490\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.048529\t최선의 손실: 0.042490\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.058544\t최선의 손실: 0.042490\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 0.053611\t최선의 손실: 0.042490\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.040155\t최선의 손실: 0.040155\t정확도: 99.10%\n",
+      "21\t검증 세트 손실: 0.056456\t최선의 손실: 0.040155\t정확도: 98.67%\n",
+      "22\t검증 세트 손실: 0.043374\t최선의 손실: 0.040155\t정확도: 99.02%\n",
+      "23\t검증 세트 손실: 0.040023\t최선의 손실: 0.040023\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.062493\t최선의 손실: 0.040023\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.057333\t최선의 손실: 0.040023\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.069200\t최선의 손실: 0.040023\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.076313\t최선의 손실: 0.040023\t정확도: 98.83%\n",
+      "28\t검증 세트 손실: 0.049476\t최선의 손실: 0.040023\t정확도: 98.83%\n",
+      "29\t검증 세트 손실: 0.056255\t최선의 손실: 0.040023\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.046781\t최선의 손실: 0.040023\t정확도: 99.14%\n",
+      "31\t검증 세트 손실: 0.045267\t최선의 손실: 0.040023\t정확도: 99.14%\n",
+      "32\t검증 세트 손실: 0.043644\t최선의 손실: 0.040023\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.053942\t최선의 손실: 0.040023\t정확도: 98.98%\n",
+      "34\t검증 세트 손실: 0.062772\t최선의 손실: 0.040023\t정확도: 98.67%\n",
+      "35\t검증 세트 손실: 0.058241\t최선의 손실: 0.040023\t정확도: 99.10%\n",
+      "36\t검증 세트 손실: 0.078497\t최선의 손실: 0.040023\t정확도: 98.67%\n",
+      "37\t검증 세트 손실: 0.037670\t최선의 손실: 0.037670\t정확도: 99.06%\n",
+      "38\t검증 세트 손실: 0.057495\t최선의 손실: 0.037670\t정확도: 99.02%\n",
+      "39\t검증 세트 손실: 0.069040\t최선의 손실: 0.037670\t정확도: 98.87%\n",
+      "40\t검증 세트 손실: 0.060888\t최선의 손실: 0.037670\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.051018\t최선의 손실: 0.037670\t정확도: 98.79%\n",
+      "42\t검증 세트 손실: 0.050367\t최선의 손실: 0.037670\t정확도: 99.14%\n",
+      "43\t검증 세트 손실: 0.058271\t최선의 손실: 0.037670\t정확도: 99.02%\n",
+      "44\t검증 세트 손실: 0.059966\t최선의 손실: 0.037670\t정확도: 99.14%\n",
+      "45\t검증 세트 손실: 0.064093\t최선의 손실: 0.037670\t정확도: 99.22%\n",
+      "46\t검증 세트 손실: 0.080976\t최선의 손실: 0.037670\t정확도: 98.55%\n",
+      "47\t검증 세트 손실: 0.068146\t최선의 손실: 0.037670\t정확도: 98.71%\n",
+      "48\t검증 세트 손실: 0.065466\t최선의 손실: 0.037670\t정확도: 98.91%\n",
+      "49\t검증 세트 손실: 0.058348\t최선의 손실: 0.037670\t정확도: 98.91%\n",
+      "50\t검증 세트 손실: 0.073661\t최선의 손실: 0.037670\t정확도: 98.94%\n",
+      "51\t검증 세트 손실: 0.047907\t최선의 손실: 0.037670\t정확도: 99.26%\n",
+      "52\t검증 세트 손실: 0.065923\t최선의 손실: 0.037670\t정확도: 99.02%\n",
+      "53\t검증 세트 손실: 0.062081\t최선의 손실: 0.037670\t정확도: 98.59%\n",
+      "54\t검증 세트 손실: 0.048828\t최선의 손실: 0.037670\t정확도: 98.98%\n",
+      "55\t검증 세트 손실: 0.046497\t최선의 손실: 0.037670\t정확도: 99.02%\n",
+      "56\t검증 세트 손실: 0.049490\t최선의 손실: 0.037670\t정확도: 99.06%\n",
+      "57\t검증 세트 손실: 0.067472\t최선의 손실: 0.037670\t정확도: 98.91%\n",
+      "58\t검증 세트 손실: 0.063062\t최선의 손실: 0.037670\t정확도: 98.55%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.8s\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.637751\t최선의 손실: 1.637751\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.621888\t최선의 손실: 1.621888\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.619830\t최선의 손실: 1.619830\t정확도: 19.27%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, n_neurons=140, total=   5.0s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 1.637586\t최선의 손실: 1.637586\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.621897\t최선의 손실: 1.621897\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.619832\t최선의 손실: 1.619832\t정확도: 19.27%\n",
       "3\t검증 세트 손실: 1.616522\t최선의 손실: 1.616522\t정확도: 19.27%\n",
       "4\t검증 세트 손실: 1.654961\t최선의 손실: 1.616522\t정확도: 22.01%\n",
       "5\t검증 세트 손실: 1.621477\t최선의 손실: 1.616522\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.632694\t최선의 손실: 1.616522\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.614864\t최선의 손실: 1.614864\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.642610\t최선의 손실: 1.614864\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.639825\t최선의 손실: 1.614864\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.615379\t최선의 손실: 1.614864\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.630566\t최선의 손실: 1.614864\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.636741\t최선의 손실: 1.614864\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.613106\t최선의 손실: 1.613106\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.629265\t최선의 손실: 1.613106\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.617145\t최선의 손실: 1.613106\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.626926\t최선의 손실: 1.613106\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 1.632695\t최선의 손실: 1.616522\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.614865\t최선의 손실: 1.614865\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.642610\t최선의 손실: 1.614865\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.639825\t최선의 손실: 1.614865\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.615379\t최선의 손실: 1.614865\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.630566\t최선의 손실: 1.614865\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.636741\t최선의 손실: 1.614865\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.613105\t최선의 손실: 1.613105\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.629265\t최선의 손실: 1.613105\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.617145\t최선의 손실: 1.613105\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.626926\t최선의 손실: 1.613105\t정확도: 18.73%\n",
       "17\t검증 세트 손실: 1.608595\t최선의 손실: 1.608595\t정확도: 22.01%\n",
       "18\t검증 세트 손실: 1.624733\t최선의 손실: 1.608595\t정확도: 19.27%\n",
       "19\t검증 세트 손실: 1.624033\t최선의 손실: 1.608595\t정확도: 20.91%\n",
@@ -1451,7 +1481,7 @@
       "23\t검증 세트 손실: 1.621078\t최선의 손실: 1.608595\t정확도: 22.01%\n",
       "24\t검증 세트 손실: 1.633986\t최선의 손실: 1.608595\t정확도: 22.01%\n",
       "25\t검증 세트 손실: 1.647180\t최선의 손실: 1.608595\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.614126\t최선의 손실: 1.608595\t정확도: 19.08%\n",
+      "26\t검증 세트 손실: 1.614127\t최선의 손실: 1.608595\t정확도: 19.08%\n",
       "27\t검증 세트 손실: 1.638544\t최선의 손실: 1.608595\t정확도: 19.27%\n",
       "28\t검증 세트 손실: 1.656266\t최선의 손실: 1.608595\t정확도: 22.01%\n",
       "29\t검증 세트 손실: 1.617746\t최선의 손실: 1.608595\t정확도: 18.73%\n",
@@ -1465,21 +1495,21 @@
       "37\t검증 세트 손실: 1.632279\t최선의 손실: 1.608595\t정확도: 18.73%\n",
       "38\t검증 세트 손실: 1.631023\t최선의 손실: 1.608595\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=50, total= 2.6min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.631881\t최선의 손실: 1.631881\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 1.644828\t최선의 손실: 1.631881\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.611659\t최선의 손실: 1.611659\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.614181\t최선의 손실: 1.611659\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.617812\t최선의 손실: 1.611659\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.624094\t최선의 손실: 1.611659\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.635103\t최선의 손실: 1.611659\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.630915\t최선의 손실: 1.611659\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.639864\t최선의 손실: 1.611659\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.611896\t최선의 손실: 1.611659\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.613292\t최선의 손실: 1.611659\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.614463\t최선의 손실: 1.611659\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.615520\t최선의 손실: 1.611659\t정확도: 20.91%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=50, total= 1.0min\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 1.631925\t최선의 손실: 1.631925\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.644837\t최선의 손실: 1.631925\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.611660\t최선의 손실: 1.611660\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.614181\t최선의 손실: 1.611660\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.617812\t최선의 손실: 1.611660\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.624094\t최선의 손실: 1.611660\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.635103\t최선의 손실: 1.611660\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.630915\t최선의 손실: 1.611660\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.639864\t최선의 손실: 1.611660\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.611896\t최선의 손실: 1.611660\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.613292\t최선의 손실: 1.611660\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.614463\t최선의 손실: 1.611660\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.615520\t최선의 손실: 1.611660\t정확도: 20.91%\n",
       "13\t검증 세트 손실: 1.609592\t최선의 손실: 1.609592\t정확도: 22.01%\n",
       "14\t검증 세트 손실: 1.628132\t최선의 손실: 1.609592\t정확도: 22.01%\n",
       "15\t검증 세트 손실: 1.613725\t최선의 손실: 1.609592\t정확도: 19.08%\n",
@@ -1489,11 +1519,17 @@
       "19\t검증 세트 손실: 1.611823\t최선의 손실: 1.609592\t정확도: 22.01%\n",
       "20\t검증 세트 손실: 1.612159\t최선의 손실: 1.609592\t정확도: 20.91%\n",
       "21\t검증 세트 손실: 1.657980\t최선의 손실: 1.609592\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.643385\t최선의 손실: 1.609592\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.643385\t최선의 손실: 1.609592\t정확도: 19.27%\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "23\t검증 세트 손실: 1.647554\t최선의 손실: 1.609592\t정확도: 22.01%\n",
       "24\t검증 세트 손실: 1.652203\t최선의 손실: 1.609592\t정확도: 18.73%\n",
       "25\t검증 세트 손실: 1.610976\t최선의 손실: 1.609592\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.614100\t최선의 손실: 1.609592\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.614099\t최선의 손실: 1.609592\t정확도: 20.91%\n",
       "27\t검증 세트 손실: 1.614368\t최선의 손실: 1.609592\t정확도: 20.91%\n",
       "28\t검증 세트 손실: 1.642985\t최선의 손실: 1.609592\t정확도: 19.08%\n",
       "29\t검증 세트 손실: 1.614041\t최선의 손실: 1.609592\t정확도: 19.08%\n",
@@ -1511,7 +1547,7 @@
       "41\t검증 세트 손실: 1.634035\t최선의 손실: 1.607884\t정확도: 22.01%\n",
       "42\t검증 세트 손실: 1.618333\t최선의 손실: 1.607884\t정확도: 20.91%\n",
       "43\t검증 세트 손실: 1.618746\t최선의 손실: 1.607884\t정확도: 22.01%\n",
-      "44\t검증 세트 손실: 1.621662\t최선의 손실: 1.607884\t정확도: 19.08%\n",
+      "44\t검증 세트 손실: 1.621663\t최선의 손실: 1.607884\t정확도: 19.08%\n",
       "45\t검증 세트 손실: 1.629384\t최선의 손실: 1.607884\t정확도: 22.01%\n",
       "46\t검증 세트 손실: 1.637659\t최선의 손실: 1.607884\t정확도: 18.73%\n",
       "47\t검증 세트 손실: 1.609606\t최선의 손실: 1.607884\t정확도: 22.01%\n",
@@ -1524,35 +1560,29 @@
       "54\t검증 세트 손실: 1.654810\t최선의 손실: 1.607884\t정확도: 18.73%\n",
       "55\t검증 세트 손실: 1.645115\t최선의 손실: 1.607884\t정확도: 18.73%\n",
       "56\t검증 세트 손실: 1.669472\t최선의 손실: 1.607884\t정확도: 19.08%\n",
-      "57\t검증 세트 손실: 1.619669\t최선의 손실: 1.607884\t정확도: 18.73%\n",
+      "57\t검증 세트 손실: 1.619668\t최선의 손실: 1.607884\t정확도: 18.73%\n",
       "58\t검증 세트 손실: 1.612547\t최선의 손실: 1.607884\t정확도: 22.01%\n",
       "59\t검증 세트 손실: 1.619514\t최선의 손실: 1.607884\t정확도: 22.01%\n",
       "60\t검증 세트 손실: 1.635965\t최선의 손실: 1.607884\t정확도: 18.73%\n",
-      "61\t검증 세트 손실: 1.627400\t최선의 손실: 1.607884\t정확도: 22.01%\n",
+      "61\t검증 세트 손실: 1.627399\t최선의 손실: 1.607884\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=50, total= 4.5min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.658578\t최선의 손실: 1.658578\t정확도: 19.27%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\t검증 세트 손실: 1.624506\t최선의 손실: 1.624506\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.614148\t최선의 손실: 1.614148\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.616997\t최선의 손실: 1.614148\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.614503\t최선의 손실: 1.614148\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.627702\t최선의 손실: 1.614148\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.648679\t최선의 손실: 1.614148\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.627600\t최선의 손실: 1.614148\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.625684\t최선의 손실: 1.614148\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.615555\t최선의 손실: 1.614148\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.619122\t최선의 손실: 1.614148\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.639598\t최선의 손실: 1.614148\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.627612\t최선의 손실: 1.614148\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.615013\t최선의 손실: 1.614148\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.624332\t최선의 손실: 1.614148\t정확도: 19.08%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=50, total= 1.6min\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 1.644320\t최선의 손실: 1.644320\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.624583\t최선의 손실: 1.624583\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.614159\t최선의 손실: 1.614159\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.616997\t최선의 손실: 1.614159\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.614503\t최선의 손실: 1.614159\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.627702\t최선의 손실: 1.614159\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.648679\t최선의 손실: 1.614159\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.627600\t최선의 손실: 1.614159\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.625684\t최선의 손실: 1.614159\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.615556\t최선의 손실: 1.614159\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.619122\t최선의 손실: 1.614159\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.639598\t최선의 손실: 1.614159\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.627612\t최선의 손실: 1.614159\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.615013\t최선의 손실: 1.614159\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.624332\t최선의 손실: 1.614159\t정확도: 19.08%\n",
       "15\t검증 세트 손실: 1.612442\t최선의 손실: 1.612442\t정확도: 22.01%\n",
       "16\t검증 세트 손실: 1.627837\t최선의 손실: 1.612442\t정확도: 22.01%\n",
       "17\t검증 세트 손실: 1.613230\t최선의 손실: 1.612442\t정확도: 18.73%\n",
@@ -1566,7 +1596,7 @@
       "25\t검증 세트 손실: 1.619330\t최선의 손실: 1.609109\t정확도: 19.27%\n",
       "26\t검증 세트 손실: 1.611331\t최선의 손실: 1.609109\t정확도: 22.01%\n",
       "27\t검증 세트 손실: 1.614303\t최선의 손실: 1.609109\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 1.628118\t최선의 손실: 1.609109\t정확도: 19.08%\n",
+      "28\t검증 세트 손실: 1.628117\t최선의 손실: 1.609109\t정확도: 19.08%\n",
       "29\t검증 세트 손실: 1.610351\t최선의 손실: 1.609109\t정확도: 20.91%\n",
       "30\t검증 세트 손실: 1.626937\t최선의 손실: 1.609109\t정확도: 20.91%\n",
       "31\t검증 세트 손실: 1.633268\t최선의 손실: 1.609109\t정확도: 18.73%\n",
@@ -1593,3594 +1623,3589 @@
       "52\t검증 세트 손실: 1.620595\t최선의 손실: 1.609064\t정확도: 18.73%\n",
       "53\t검증 세트 손실: 1.617682\t최선의 손실: 1.609064\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=50, total= 3.7min\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.108735\t최선의 손실: 0.108735\t정확도: 97.42%\n",
-      "1\t검증 세트 손실: 0.101997\t최선의 손실: 0.101997\t정확도: 97.38%\n",
-      "2\t검증 세트 손실: 0.090526\t최선의 손실: 0.090526\t정확도: 97.65%\n",
-      "3\t검증 세트 손실: 0.085668\t최선의 손실: 0.085668\t정확도: 97.65%\n",
-      "4\t검증 세트 손실: 0.086532\t최선의 손실: 0.085668\t정확도: 98.01%\n",
-      "5\t검증 세트 손실: 0.101848\t최선의 손실: 0.085668\t정확도: 97.85%\n",
-      "6\t검증 세트 손실: 0.096091\t최선의 손실: 0.085668\t정확도: 97.89%\n",
-      "7\t검증 세트 손실: 0.088031\t최선의 손실: 0.085668\t정확도: 97.50%\n",
-      "8\t검증 세트 손실: 0.092105\t최선의 손실: 0.085668\t정확도: 97.54%\n",
-      "9\t검증 세트 손실: 0.064690\t최선의 손실: 0.064690\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.082943\t최선의 손실: 0.064690\t정확도: 97.77%\n",
-      "11\t검증 세트 손실: 0.091618\t최선의 손실: 0.064690\t정확도: 97.97%\n",
-      "12\t검증 세트 손실: 0.076132\t최선의 손실: 0.064690\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.150091\t최선의 손실: 0.064690\t정확도: 96.52%\n",
-      "14\t검증 세트 손실: 0.088967\t최선의 손실: 0.064690\t정확도: 97.77%\n",
-      "15\t검증 세트 손실: 0.101856\t최선의 손실: 0.064690\t정확도: 98.05%\n",
-      "16\t검증 세트 손실: 0.080795\t최선의 손실: 0.064690\t정확도: 97.89%\n",
-      "17\t검증 세트 손실: 0.088673\t최선의 손실: 0.064690\t정확도: 97.97%\n",
-      "18\t검증 세트 손실: 0.092856\t최선의 손실: 0.064690\t정확도: 97.54%\n",
-      "19\t검증 세트 손실: 0.125516\t최선의 손실: 0.064690\t정확도: 96.68%\n",
-      "20\t검증 세트 손실: 0.145792\t최선의 손실: 0.064690\t정확도: 97.73%\n",
-      "21\t검증 세트 손실: 0.248565\t최선의 손실: 0.064690\t정확도: 96.33%\n",
-      "22\t검증 세트 손실: 0.115422\t최선의 손실: 0.064690\t정확도: 97.42%\n",
-      "23\t검증 세트 손실: 0.152344\t최선의 손실: 0.064690\t정확도: 97.69%\n",
-      "24\t검증 세트 손실: 0.102415\t최선의 손실: 0.064690\t정확도: 97.85%\n",
-      "25\t검증 세트 손실: 0.104257\t최선의 손실: 0.064690\t정확도: 97.62%\n",
-      "26\t검증 세트 손실: 0.107024\t최선의 손실: 0.064690\t정확도: 97.81%\n",
-      "27\t검증 세트 손실: 0.092586\t최선의 손실: 0.064690\t정확도: 97.93%\n",
-      "28\t검증 세트 손실: 0.078250\t최선의 손실: 0.064690\t정확도: 98.20%\n",
-      "29\t검증 세트 손실: 0.088415\t최선의 손실: 0.064690\t정확도: 98.20%\n",
-      "30\t검증 세트 손실: 0.096415\t최선의 손실: 0.064690\t정확도: 98.01%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=50, total= 1.4min\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.107880\t최선의 손실: 0.107880\t정확도: 96.99%\n",
+      "1\t검증 세트 손실: 0.116085\t최선의 손실: 0.107880\t정확도: 96.99%\n",
+      "2\t검증 세트 손실: 0.101172\t최선의 손실: 0.101172\t정확도: 97.26%\n",
+      "3\t검증 세트 손실: 0.105067\t최선의 손실: 0.101172\t정확도: 97.19%\n",
+      "4\t검증 세트 손실: 0.084716\t최선의 손실: 0.084716\t정확도: 97.62%\n",
+      "5\t검증 세트 손실: 0.089455\t최선의 손실: 0.084716\t정확도: 97.81%\n",
+      "6\t검증 세트 손실: 0.079002\t최선의 손실: 0.079002\t정확도: 97.73%\n",
+      "7\t검증 세트 손실: 0.078476\t최선의 손실: 0.078476\t정확도: 97.50%\n",
+      "8\t검증 세트 손실: 0.067152\t최선의 손실: 0.067152\t정확도: 98.12%\n",
+      "9\t검증 세트 손실: 0.091263\t최선의 손실: 0.067152\t정확도: 97.69%\n",
+      "10\t검증 세트 손실: 0.111605\t최선의 손실: 0.067152\t정확도: 97.69%\n",
+      "11\t검증 세트 손실: 0.088740\t최선의 손실: 0.067152\t정확도: 97.85%\n",
+      "12\t검증 세트 손실: 0.082147\t최선의 손실: 0.067152\t정확도: 97.69%\n",
+      "13\t검증 세트 손실: 0.077458\t최선의 손실: 0.067152\t정확도: 97.93%\n",
+      "14\t검증 세트 손실: 0.115961\t최선의 손실: 0.067152\t정확도: 97.22%\n",
+      "15\t검증 세트 손실: 0.131673\t최선의 손실: 0.067152\t정확도: 97.50%\n",
+      "16\t검증 세트 손실: 0.081151\t최선의 손실: 0.067152\t정확도: 97.97%\n",
+      "17\t검증 세트 손실: 0.075369\t최선의 손실: 0.067152\t정확도: 98.05%\n",
+      "18\t검증 세트 손실: 0.088948\t최선의 손실: 0.067152\t정확도: 97.58%\n",
+      "19\t검증 세트 손실: 0.080407\t최선의 손실: 0.067152\t정확도: 98.05%\n",
+      "20\t검증 세트 손실: 0.099786\t최선의 손실: 0.067152\t정확도: 97.97%\n",
+      "21\t검증 세트 손실: 0.080877\t최선의 손실: 0.067152\t정확도: 97.89%\n",
+      "22\t검증 세트 손실: 0.123441\t최선의 손실: 0.067152\t정확도: 97.03%\n",
+      "23\t검증 세트 손실: 0.114029\t최선의 손실: 0.067152\t정확도: 97.15%\n",
+      "24\t검증 세트 손실: 0.149343\t최선의 손실: 0.067152\t정확도: 96.79%\n",
+      "25\t검증 세트 손실: 0.095381\t최선의 손실: 0.067152\t정확도: 98.01%\n",
+      "26\t검증 세트 손실: 0.098636\t최선의 손실: 0.067152\t정확도: 97.58%\n",
+      "27\t검증 세트 손실: 0.083231\t최선의 손실: 0.067152\t정확도: 97.62%\n",
+      "28\t검증 세트 손실: 0.082398\t최선의 손실: 0.067152\t정확도: 98.16%\n",
+      "29\t검증 세트 손실: 0.093786\t최선의 손실: 0.067152\t정확도: 98.28%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  13.0s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.140799\t최선의 손실: 0.140799\t정확도: 96.52%\n",
-      "1\t검증 세트 손실: 0.097943\t최선의 손실: 0.097943\t정확도: 97.50%\n",
-      "2\t검증 세트 손실: 0.087052\t최선의 손실: 0.087052\t정확도: 97.58%\n",
-      "3\t검증 세트 손실: 0.085939\t최선의 손실: 0.085939\t정확도: 97.73%\n",
-      "4\t검증 세트 손실: 0.094447\t최선의 손실: 0.085939\t정확도: 97.19%\n",
-      "5\t검증 세트 손실: 0.097743\t최선의 손실: 0.085939\t정확도: 97.22%\n",
-      "6\t검증 세트 손실: 0.094395\t최선의 손실: 0.085939\t정확도: 97.26%\n",
-      "7\t검증 세트 손실: 0.165194\t최선의 손실: 0.085939\t정확도: 97.46%\n",
-      "8\t검증 세트 손실: 0.085659\t최선의 손실: 0.085659\t정확도: 98.01%\n",
-      "9\t검증 세트 손실: 0.086360\t최선의 손실: 0.085659\t정확도: 97.50%\n",
-      "10\t검증 세트 손실: 0.104427\t최선의 손실: 0.085659\t정확도: 97.30%\n",
-      "11\t검증 세트 손실: 0.081346\t최선의 손실: 0.081346\t정확도: 97.93%\n",
-      "12\t검증 세트 손실: 0.095409\t최선의 손실: 0.081346\t정확도: 97.81%\n",
-      "13\t검증 세트 손실: 0.104306\t최선의 손실: 0.081346\t정확도: 97.42%\n",
-      "14\t검증 세트 손실: 0.100191\t최선의 손실: 0.081346\t정확도: 97.58%\n",
-      "15\t검증 세트 손실: 0.094879\t최선의 손실: 0.081346\t정확도: 97.73%\n",
-      "16\t검증 세트 손실: 0.107761\t최선의 손실: 0.081346\t정확도: 97.62%\n",
-      "17\t검증 세트 손실: 0.107148\t최선의 손실: 0.081346\t정확도: 97.77%\n",
-      "18\t검증 세트 손실: 0.108115\t최선의 손실: 0.081346\t정확도: 96.91%\n",
-      "19\t검증 세트 손실: 0.146881\t최선의 손실: 0.081346\t정확도: 96.09%\n",
-      "20\t검증 세트 손실: 0.126310\t최선의 손실: 0.081346\t정확도: 96.60%\n",
-      "21\t검증 세트 손실: 0.100648\t최선의 손실: 0.081346\t정확도: 97.85%\n",
-      "22\t검증 세트 손실: 0.100795\t최선의 손실: 0.081346\t정확도: 97.65%\n",
-      "23\t검증 세트 손실: 0.103273\t최선의 손실: 0.081346\t정확도: 97.85%\n",
-      "24\t검증 세트 손실: 0.106565\t최선의 손실: 0.081346\t정확도: 97.85%\n",
-      "25\t검증 세트 손실: 0.117352\t최선의 손실: 0.081346\t정확도: 97.89%\n",
-      "26\t검증 세트 손실: 0.099601\t최선의 손실: 0.081346\t정확도: 98.28%\n",
-      "27\t검증 세트 손실: 0.084647\t최선의 손실: 0.081346\t정확도: 97.97%\n",
-      "28\t검증 세트 손실: 0.102326\t최선의 손실: 0.081346\t정확도: 97.46%\n",
-      "29\t검증 세트 손실: 0.109051\t최선의 손실: 0.081346\t정확도: 97.85%\n",
-      "30\t검증 세트 손실: 0.098815\t최선의 손실: 0.081346\t정확도: 97.81%\n",
-      "31\t검증 세트 손실: 0.109224\t최선의 손실: 0.081346\t정확도: 97.69%\n",
-      "32\t검증 세트 손실: 0.091412\t최선의 손실: 0.081346\t정확도: 97.93%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  13.8s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.090772\t최선의 손실: 0.090772\t정확도: 97.11%\n",
-      "1\t검증 세트 손실: 0.085344\t최선의 손실: 0.085344\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.078549\t최선의 손실: 0.078549\t정확도: 97.77%\n",
-      "3\t검증 세트 손실: 0.087578\t최선의 손실: 0.078549\t정확도: 97.50%\n",
-      "4\t검증 세트 손실: 0.095201\t최선의 손실: 0.078549\t정확도: 97.81%\n",
-      "5\t검증 세트 손실: 0.091876\t최선의 손실: 0.078549\t정확도: 97.26%\n",
-      "6\t검증 세트 손실: 0.081552\t최선의 손실: 0.078549\t정확도: 98.32%\n",
-      "7\t검증 세트 손실: 0.110901\t최선의 손실: 0.078549\t정확도: 97.30%\n",
-      "8\t검증 세트 손실: 0.107864\t최선의 손실: 0.078549\t정확도: 97.93%\n",
-      "9\t검증 세트 손실: 0.095715\t최선의 손실: 0.078549\t정확도: 97.81%\n",
-      "10\t검증 세트 손실: 0.088153\t최선의 손실: 0.078549\t정확도: 98.01%\n",
-      "11\t검증 세트 손실: 0.081059\t최선의 손실: 0.078549\t정확도: 98.05%\n",
-      "12\t검증 세트 손실: 0.075677\t최선의 손실: 0.075677\t정확도: 98.24%\n",
-      "13\t검증 세트 손실: 0.077484\t최선의 손실: 0.075677\t정확도: 98.16%\n",
-      "14\t검증 세트 손실: 0.093282\t최선의 손실: 0.075677\t정확도: 98.24%\n",
-      "15\t검증 세트 손실: 0.093351\t최선의 손실: 0.075677\t정확도: 98.08%\n",
-      "16\t검증 세트 손실: 0.072093\t최선의 손실: 0.072093\t정확도: 98.32%\n",
-      "17\t검증 세트 손실: 0.093091\t최선의 손실: 0.072093\t정확도: 98.16%\n",
-      "18\t검증 세트 손실: 0.102723\t최선의 손실: 0.072093\t정확도: 98.08%\n",
-      "19\t검증 세트 손실: 0.092918\t최선의 손실: 0.072093\t정확도: 98.48%\n",
-      "20\t검증 세트 손실: 0.151514\t최선의 손실: 0.072093\t정확도: 98.08%\n",
-      "21\t검증 세트 손실: 0.092552\t최선의 손실: 0.072093\t정확도: 98.48%\n",
-      "22\t검증 세트 손실: 0.127942\t최선의 손실: 0.072093\t정확도: 97.19%\n",
-      "23\t검증 세트 손실: 0.118038\t최선의 손실: 0.072093\t정확도: 97.50%\n",
-      "24\t검증 세트 손실: 0.083242\t최선의 손실: 0.072093\t정확도: 98.16%\n",
-      "25\t검증 세트 손실: 0.107958\t최선의 손실: 0.072093\t정확도: 98.32%\n",
-      "26\t검증 세트 손실: 0.089555\t최선의 손실: 0.072093\t정확도: 98.24%\n",
-      "27\t검증 세트 손실: 0.088590\t최선의 손실: 0.072093\t정확도: 98.44%\n",
-      "28\t검증 세트 손실: 0.105149\t최선의 손실: 0.072093\t정확도: 98.05%\n",
-      "29\t검증 세트 손실: 0.078396\t최선의 손실: 0.072093\t정확도: 98.36%\n",
-      "30\t검증 세트 손실: 0.085697\t최선의 손실: 0.072093\t정확도: 98.20%\n",
-      "31\t검증 세트 손실: 0.079487\t최선의 손실: 0.072093\t정확도: 97.97%\n"
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=   5.9s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.105974\t최선의 손실: 0.105974\t정확도: 96.48%\n",
+      "1\t검증 세트 손실: 0.096178\t최선의 손실: 0.096178\t정확도: 96.83%\n",
+      "2\t검증 세트 손실: 0.109713\t최선의 손실: 0.096178\t정확도: 97.11%\n",
+      "3\t검증 세트 손실: 0.086026\t최선의 손실: 0.086026\t정확도: 97.54%\n",
+      "4\t검증 세트 손실: 0.089247\t최선의 손실: 0.086026\t정확도: 97.58%\n",
+      "5\t검증 세트 손실: 0.083310\t최선의 손실: 0.083310\t정확도: 97.65%\n",
+      "6\t검증 세트 손실: 0.078221\t최선의 손실: 0.078221\t정확도: 97.46%\n",
+      "7\t검증 세트 손실: 0.147870\t최선의 손실: 0.078221\t정확도: 97.03%\n",
+      "8\t검증 세트 손실: 0.072606\t최선의 손실: 0.072606\t정확도: 98.12%\n",
+      "9\t검증 세트 손실: 0.105270\t최선의 손실: 0.072606\t정확도: 97.62%\n",
+      "10\t검증 세트 손실: 0.092718\t최선의 손실: 0.072606\t정확도: 97.54%\n",
+      "11\t검증 세트 손실: 0.079112\t최선의 손실: 0.072606\t정확도: 98.16%\n",
+      "12\t검증 세트 손실: 0.067963\t최선의 손실: 0.067963\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.087775\t최선의 손실: 0.067963\t정확도: 98.01%\n",
+      "14\t검증 세트 손실: 0.089214\t최선의 손실: 0.067963\t정확도: 97.85%\n",
+      "15\t검증 세트 손실: 0.122234\t최선의 손실: 0.067963\t정확도: 97.50%\n",
+      "16\t검증 세트 손실: 0.101987\t최선의 손실: 0.067963\t정확도: 97.73%\n",
+      "17\t검증 세트 손실: 0.095182\t최선의 손실: 0.067963\t정확도: 97.50%\n",
+      "18\t검증 세트 손실: 0.089304\t최선의 손실: 0.067963\t정확도: 98.01%\n",
+      "19\t검증 세트 손실: 0.089892\t최선의 손실: 0.067963\t정확도: 97.77%\n",
+      "20\t검증 세트 손실: 0.070597\t최선의 손실: 0.067963\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.139731\t최선의 손실: 0.067963\t정확도: 97.85%\n",
+      "22\t검증 세트 손실: 0.129323\t최선의 손실: 0.067963\t정확도: 97.77%\n",
+      "23\t검증 세트 손실: 0.108652\t최선의 손실: 0.067963\t정확도: 97.77%\n",
+      "24\t검증 세트 손실: 0.109841\t최선의 손실: 0.067963\t정확도: 97.69%\n",
+      "25\t검증 세트 손실: 0.089324\t최선의 손실: 0.067963\t정확도: 98.20%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "32\t검증 세트 손실: 0.082798\t최선의 손실: 0.072093\t정확도: 98.24%\n",
-      "33\t검증 세트 손실: 0.088828\t최선의 손실: 0.072093\t정확도: 98.24%\n",
-      "34\t검증 세트 손실: 0.144066\t최선의 손실: 0.072093\t정확도: 97.93%\n",
-      "35\t검증 세트 손실: 0.111487\t최선의 손실: 0.072093\t정확도: 98.16%\n",
-      "36\t검증 세트 손실: 0.087607\t최선의 손실: 0.072093\t정확도: 98.44%\n",
-      "37\t검증 세트 손실: 0.110404\t최선의 손실: 0.072093\t정확도: 98.24%\n",
+      "26\t검증 세트 손실: 0.108104\t최선의 손실: 0.067963\t정확도: 97.85%\n",
+      "27\t검증 세트 손실: 0.219004\t최선의 손실: 0.067963\t정확도: 97.07%\n",
+      "28\t검증 세트 손실: 0.108362\t최선의 손실: 0.067963\t정확도: 97.77%\n",
+      "29\t검증 세트 손실: 0.098935\t최선의 손실: 0.067963\t정확도: 97.89%\n",
+      "30\t검증 세트 손실: 0.100086\t최선의 손실: 0.067963\t정확도: 97.54%\n",
+      "31\t검증 세트 손실: 0.091291\t최선의 손실: 0.067963\t정확도: 98.24%\n",
+      "32\t검증 세트 손실: 0.371474\t최선의 손실: 0.067963\t정확도: 98.32%\n",
+      "33\t검증 세트 손실: 0.111307\t최선의 손실: 0.067963\t정확도: 97.54%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  18.5s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.152535\t최선의 손실: 0.152535\t정확도: 95.90%\n",
-      "1\t검증 세트 손실: 0.139352\t최선의 손실: 0.139352\t정확도: 96.13%\n",
-      "2\t검증 세트 손실: 0.196786\t최선의 손실: 0.139352\t정확도: 94.64%\n",
-      "3\t검증 세트 손실: 3.236673\t최선의 손실: 0.139352\t정확도: 35.77%\n",
-      "4\t검증 세트 손실: 1.121514\t최선의 손실: 0.139352\t정확도: 54.73%\n",
-      "5\t검증 세트 손실: 0.843739\t최선의 손실: 0.139352\t정확도: 66.77%\n",
-      "6\t검증 세트 손실: 0.950402\t최선의 손실: 0.139352\t정확도: 65.17%\n",
-      "7\t검증 세트 손실: 0.633643\t최선의 손실: 0.139352\t정확도: 78.11%\n",
-      "8\t검증 세트 손실: 0.607917\t최선의 손실: 0.139352\t정확도: 78.81%\n",
-      "9\t검증 세트 손실: 0.450111\t최선의 손실: 0.139352\t정확도: 85.69%\n",
-      "10\t검증 세트 손실: 0.456769\t최선의 손실: 0.139352\t정확도: 85.57%\n",
-      "11\t검증 세트 손실: 0.397621\t최선의 손실: 0.139352\t정확도: 86.71%\n",
-      "12\t검증 세트 손실: 0.368468\t최선의 손실: 0.139352\t정확도: 88.31%\n",
-      "13\t검증 세트 손실: 0.341828\t최선의 손실: 0.139352\t정확도: 89.80%\n",
-      "14\t검증 세트 손실: 0.341308\t최선의 손실: 0.139352\t정확도: 89.21%\n",
-      "15\t검증 세트 손실: 0.323737\t최선의 손실: 0.139352\t정확도: 90.38%\n",
-      "16\t검증 세트 손실: 0.646640\t최선의 손실: 0.139352\t정확도: 79.20%\n",
-      "17\t검증 세트 손실: 0.947087\t최선의 손실: 0.139352\t정확도: 64.19%\n",
-      "18\t검증 세트 손실: 0.826915\t최선의 손실: 0.139352\t정확도: 69.04%\n",
-      "19\t검증 세트 손실: 0.808745\t최선의 손실: 0.139352\t정확도: 69.94%\n",
-      "20\t검증 세트 손실: 0.801041\t최선의 손실: 0.139352\t정확도: 68.26%\n",
-      "21\t검증 세트 손실: 0.742561\t최선의 손실: 0.139352\t정확도: 72.52%\n",
-      "22\t검증 세트 손실: 0.641935\t최선의 손실: 0.139352\t정확도: 77.33%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=   6.4s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.089817\t최선의 손실: 0.089817\t정확도: 97.65%\n",
+      "1\t검증 세트 손실: 0.099206\t최선의 손실: 0.089817\t정확도: 97.30%\n",
+      "2\t검증 세트 손실: 0.093683\t최선의 손실: 0.089817\t정확도: 97.46%\n",
+      "3\t검증 세트 손실: 0.093219\t최선의 손실: 0.089817\t정확도: 97.54%\n",
+      "4\t검증 세트 손실: 0.081220\t최선의 손실: 0.081220\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 0.085970\t최선의 손실: 0.081220\t정확도: 97.77%\n",
+      "6\t검증 세트 손실: 0.097083\t최선의 손실: 0.081220\t정확도: 96.95%\n",
+      "7\t검증 세트 손실: 0.093486\t최선의 손실: 0.081220\t정확도: 97.50%\n",
+      "8\t검증 세트 손실: 0.102408\t최선의 손실: 0.081220\t정확도: 97.77%\n",
+      "9\t검증 세트 손실: 0.115371\t최선의 손실: 0.081220\t정확도: 97.89%\n",
+      "10\t검증 세트 손실: 0.086811\t최선의 손실: 0.081220\t정확도: 97.89%\n",
+      "11\t검증 세트 손실: 0.114072\t최선의 손실: 0.081220\t정확도: 96.79%\n",
+      "12\t검증 세트 손실: 0.076541\t최선의 손실: 0.076541\t정확도: 97.69%\n",
+      "13\t검증 세트 손실: 0.230332\t최선의 손실: 0.076541\t정확도: 94.92%\n",
+      "14\t검증 세트 손실: 0.082088\t최선의 손실: 0.076541\t정확도: 97.46%\n",
+      "15\t검증 세트 손실: 0.078963\t최선의 손실: 0.076541\t정확도: 98.05%\n",
+      "16\t검증 세트 손실: 0.094094\t최선의 손실: 0.076541\t정확도: 97.93%\n",
+      "17\t검증 세트 손실: 0.122267\t최선의 손실: 0.076541\t정확도: 97.81%\n",
+      "18\t검증 세트 손실: 0.092557\t최선의 손실: 0.076541\t정확도: 98.08%\n",
+      "19\t검증 세트 손실: 0.084450\t최선의 손실: 0.076541\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.110771\t최선의 손실: 0.076541\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.080215\t최선의 손실: 0.076541\t정확도: 98.01%\n",
+      "22\t검증 세트 손실: 0.067613\t최선의 손실: 0.067613\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.077307\t최선의 손실: 0.067613\t정확도: 98.20%\n",
+      "24\t검증 세트 손실: 0.076811\t최선의 손실: 0.067613\t정확도: 98.20%\n",
+      "25\t검증 세트 손실: 0.097404\t최선의 손실: 0.067613\t정확도: 98.24%\n",
+      "26\t검증 세트 손실: 0.081096\t최선의 손실: 0.067613\t정확도: 98.20%\n",
+      "27\t검증 세트 손실: 0.082617\t최선의 손실: 0.067613\t정확도: 98.28%\n",
+      "28\t검증 세트 손실: 0.130741\t최선의 손실: 0.067613\t정확도: 97.58%\n",
+      "29\t검증 세트 손실: 0.073137\t최선의 손실: 0.067613\t정확도: 98.16%\n",
+      "30\t검증 세트 손실: 0.136117\t최선의 손실: 0.067613\t정확도: 97.42%\n",
+      "31\t검증 세트 손실: 0.068615\t최선의 손실: 0.067613\t정확도: 98.16%\n",
+      "32\t검증 세트 손실: 0.084258\t최선의 손실: 0.067613\t정확도: 97.97%\n",
+      "33\t검증 세트 손실: 0.097888\t최선의 손실: 0.067613\t정확도: 97.58%\n",
+      "34\t검증 세트 손실: 0.089018\t최선의 손실: 0.067613\t정확도: 97.97%\n",
+      "35\t검증 세트 손실: 0.085503\t최선의 손실: 0.067613\t정확도: 98.40%\n",
+      "36\t검증 세트 손실: 0.099707\t최선의 손실: 0.067613\t정확도: 98.12%\n",
+      "37\t검증 세트 손실: 0.079744\t최선의 손실: 0.067613\t정확도: 98.44%\n",
+      "38\t검증 세트 손실: 0.075756\t최선의 손실: 0.067613\t정확도: 97.85%\n",
+      "39\t검증 세트 손실: 0.065852\t최선의 손실: 0.065852\t정확도: 97.89%\n",
+      "40\t검증 세트 손실: 0.070889\t최선의 손실: 0.065852\t정확도: 98.28%\n",
+      "41\t검증 세트 손실: 0.078918\t최선의 손실: 0.065852\t정확도: 98.36%\n",
+      "42\t검증 세트 손실: 0.135299\t최선의 손실: 0.065852\t정확도: 98.24%\n",
+      "43\t검증 세트 손실: 0.209663\t최선의 손실: 0.065852\t정확도: 97.42%\n",
+      "44\t검증 세트 손실: 0.102574\t최선의 손실: 0.065852\t정확도: 97.73%\n",
+      "45\t검증 세트 손실: 0.190972\t최선의 손실: 0.065852\t정확도: 95.62%\n",
+      "46\t검증 세트 손실: 0.124340\t최선의 손실: 0.065852\t정확도: 97.03%\n",
+      "47\t검증 세트 손실: 0.118770\t최선의 손실: 0.065852\t정확도: 97.11%\n",
+      "48\t검증 세트 손실: 0.150175\t최선의 손실: 0.065852\t정확도: 96.21%\n",
+      "49\t검증 세트 손실: 0.126142\t최선의 손실: 0.065852\t정확도: 97.03%\n",
+      "50\t검증 세트 손실: 0.163138\t최선의 손실: 0.065852\t정확도: 95.82%\n",
+      "51\t검증 세트 손실: 0.116130\t최선의 손실: 0.065852\t정확도: 97.77%\n",
+      "52\t검증 세트 손실: 0.108413\t최선의 손실: 0.065852\t정확도: 97.65%\n",
+      "53\t검증 세트 손실: 0.219239\t최선의 손실: 0.065852\t정확도: 97.77%\n",
+      "54\t검증 세트 손실: 0.118316\t최선의 손실: 0.065852\t정확도: 97.26%\n",
+      "55\t검증 세트 손실: 0.126674\t최선의 손실: 0.065852\t정확도: 97.19%\n",
+      "56\t검증 세트 손실: 0.150349\t최선의 손실: 0.065852\t정확도: 96.17%\n",
+      "57\t검증 세트 손실: 0.119968\t최선의 손실: 0.065852\t정확도: 97.65%\n",
+      "58\t검증 세트 손실: 0.209942\t최선의 손실: 0.065852\t정확도: 96.29%\n",
+      "59\t검증 세트 손실: 0.122797\t최선의 손실: 0.065852\t정확도: 97.15%\n",
+      "60\t검증 세트 손실: 0.109410\t최선의 손실: 0.065852\t정확도: 97.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50, total=   8.6s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.165030\t최선의 손실: 0.165030\t정확도: 96.21%\n",
-      "1\t검증 세트 손실: 0.727631\t최선의 손실: 0.165030\t정확도: 96.21%\n",
-      "2\t검증 세트 손실: 3.621723\t최선의 손실: 0.165030\t정확도: 32.10%\n",
-      "3\t검증 세트 손실: 0.819815\t최선의 손실: 0.165030\t정확도: 69.31%\n",
-      "4\t검증 세트 손실: 0.951488\t최선의 손실: 0.165030\t정확도: 60.05%\n",
-      "5\t검증 세트 손실: 0.671907\t최선의 손실: 0.165030\t정확도: 74.39%\n",
-      "6\t검증 세트 손실: 0.563235\t최선의 손실: 0.165030\t정확도: 79.95%\n",
-      "7\t검증 세트 손실: 0.935405\t최선의 손실: 0.165030\t정확도: 66.42%\n",
-      "8\t검증 세트 손실: 0.686519\t최선의 손실: 0.165030\t정확도: 77.60%\n",
-      "9\t검증 세트 손실: 0.568691\t최선의 손실: 0.165030\t정확도: 81.39%\n",
-      "10\t검증 세트 손실: 0.477355\t최선의 손실: 0.165030\t정확도: 84.75%\n",
-      "11\t검증 세트 손실: 0.421604\t최선의 손실: 0.165030\t정확도: 86.51%\n",
-      "12\t검증 세트 손실: 0.729095\t최선의 손실: 0.165030\t정확도: 78.07%\n",
-      "13\t검증 세트 손실: 0.397903\t최선의 손실: 0.165030\t정확도: 89.05%\n",
-      "14\t검증 세트 손실: 0.362663\t최선의 손실: 0.165030\t정확도: 89.44%\n",
-      "15\t검증 세트 손실: 1.078201\t최선의 손실: 0.165030\t정확도: 79.16%\n",
-      "16\t검증 세트 손실: 0.389230\t최선의 손실: 0.165030\t정확도: 87.65%\n",
-      "17\t검증 세트 손실: 0.354517\t최선의 손실: 0.165030\t정확도: 88.66%\n",
-      "18\t검증 세트 손실: 0.392793\t최선의 손실: 0.165030\t정확도: 88.94%\n",
-      "19\t검증 세트 손실: 0.316272\t최선의 손실: 0.165030\t정확도: 91.79%\n",
-      "20\t검증 세트 손실: 0.446910\t최선의 손실: 0.165030\t정확도: 86.04%\n",
-      "21\t검증 세트 손실: 0.280924\t최선의 손실: 0.165030\t정확도: 93.32%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=  11.3s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.132927\t최선의 손실: 0.132927\t정확도: 95.90%\n",
+      "1\t검증 세트 손실: 0.111200\t최선의 손실: 0.111200\t정확도: 96.79%\n",
+      "2\t검증 세트 손실: 37.050770\t최선의 손실: 0.111200\t정확도: 39.48%\n",
+      "3\t검증 세트 손실: 1.138917\t최선의 손실: 0.111200\t정확도: 55.04%\n",
+      "4\t검증 세트 손실: 1.157438\t최선의 손실: 0.111200\t정확도: 57.15%\n",
+      "5\t검증 세트 손실: 0.975950\t최선의 손실: 0.111200\t정확도: 63.92%\n",
+      "6\t검증 세트 손실: 0.902163\t최선의 손실: 0.111200\t정확도: 70.09%\n",
+      "7\t검증 세트 손실: 0.732203\t최선의 손실: 0.111200\t정확도: 72.83%\n",
+      "8\t검증 세트 손실: 0.601421\t최선의 손실: 0.111200\t정확도: 79.98%\n",
+      "9\t검증 세트 손실: 0.542954\t최선의 손실: 0.111200\t정확도: 81.67%\n",
+      "10\t검증 세트 손실: 0.514194\t최선의 손실: 0.111200\t정확도: 83.03%\n",
+      "11\t검증 세트 손실: 0.399974\t최선의 손실: 0.111200\t정확도: 88.23%\n",
+      "12\t검증 세트 손실: 18.918188\t최선의 손실: 0.111200\t정확도: 19.12%\n",
+      "13\t검증 세트 손실: 1.381926\t최선의 손실: 0.111200\t정확도: 50.31%\n",
+      "14\t검증 세트 손실: 0.918248\t최선의 손실: 0.111200\t정확도: 63.25%\n",
+      "15\t검증 세트 손실: 0.616825\t최선의 손실: 0.111200\t정확도: 77.95%\n",
+      "16\t검증 세트 손실: 0.562620\t최선의 손실: 0.111200\t정확도: 78.50%\n",
+      "17\t검증 세트 손실: 0.505384\t최선의 손실: 0.111200\t정확도: 81.59%\n",
+      "18\t검증 세트 손실: 0.636875\t최선의 손실: 0.111200\t정확도: 77.48%\n",
+      "19\t검증 세트 손실: 0.448416\t최선의 손실: 0.111200\t정확도: 84.91%\n",
+      "20\t검증 세트 손실: 0.419162\t최선의 손실: 0.111200\t정확도: 85.97%\n",
+      "21\t검증 세트 손실: 1554.238892\t최선의 손실: 0.111200\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1050.457397\t최선의 손실: 0.111200\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50, total=   5.6s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.218129\t최선의 손실: 0.218129\t정확도: 94.53%\n",
-      "1\t검증 세트 손실: 6.918653\t최선의 손실: 0.218129\t정확도: 35.18%\n",
-      "2\t검증 세트 손실: 0.861112\t최선의 손실: 0.218129\t정확도: 62.04%\n",
-      "3\t검증 세트 손실: 0.454873\t최선의 손실: 0.218129\t정확도: 87.02%\n",
-      "4\t검증 세트 손실: 0.316567\t최선의 손실: 0.218129\t정확도: 91.24%\n",
-      "5\t검증 세트 손실: 0.994009\t최선의 손실: 0.218129\t정확도: 61.69%\n",
-      "6\t검증 세트 손실: 0.509249\t최선의 손실: 0.218129\t정확도: 81.47%\n",
-      "7\t검증 세트 손실: 0.387635\t최선의 손실: 0.218129\t정확도: 88.47%\n",
-      "8\t검증 세트 손실: 0.338761\t최선의 손실: 0.218129\t정확도: 88.86%\n",
-      "9\t검증 세트 손실: 0.293384\t최선의 손실: 0.218129\t정확도: 91.56%\n",
-      "10\t검증 세트 손실: 0.248082\t최선의 손실: 0.218129\t정확도: 93.08%\n",
-      "11\t검증 세트 손실: 0.227857\t최선의 손실: 0.218129\t정확도: 93.08%\n",
-      "12\t검증 세트 손실: 0.231077\t최선의 손실: 0.218129\t정확도: 92.92%\n",
-      "13\t검증 세트 손실: 0.181535\t최선의 손실: 0.181535\t정확도: 94.64%\n",
-      "14\t검증 세트 손실: 0.187303\t최선의 손실: 0.181535\t정확도: 95.15%\n",
-      "15\t검증 세트 손실: 0.166437\t최선의 손실: 0.166437\t정확도: 95.62%\n",
-      "16\t검증 세트 손실: 3321.664551\t최선의 손실: 0.166437\t정확도: 19.12%\n",
-      "17\t검증 세트 손실: 89.064911\t최선의 손실: 0.166437\t정확도: 21.03%\n",
-      "18\t검증 세트 손실: 3145.370117\t최선의 손실: 0.166437\t정확도: 18.80%\n",
-      "19\t검증 세트 손실: 98.788887\t최선의 손실: 0.166437\t정확도: 28.85%\n",
-      "20\t검증 세트 손실: 160.241440\t최선의 손실: 0.166437\t정확도: 23.30%\n",
-      "21\t검증 세트 손실: 26.126627\t최선의 손실: 0.166437\t정확도: 34.36%\n",
-      "22\t검증 세트 손실: 54.756786\t최선의 손실: 0.166437\t정확도: 28.62%\n",
-      "23\t검증 세트 손실: 25.029139\t최선의 손실: 0.166437\t정확도: 31.74%\n",
-      "24\t검증 세트 손실: 19.616251\t최선의 손실: 0.166437\t정확도: 33.74%\n",
-      "25\t검증 세트 손실: 70.107224\t최선의 손실: 0.166437\t정확도: 26.15%\n",
-      "26\t검증 세트 손실: 64.121429\t최선의 손실: 0.166437\t정확도: 35.93%\n",
-      "27\t검증 세트 손실: 47.646088\t최선의 손실: 0.166437\t정확도: 30.06%\n",
-      "28\t검증 세트 손실: 20.624407\t최선의 손실: 0.166437\t정확도: 47.69%\n",
-      "29\t검증 세트 손실: 27.086983\t최선의 손실: 0.166437\t정확도: 41.01%\n",
-      "30\t검증 세트 손실: 24.091425\t최선의 손실: 0.166437\t정확도: 26.04%\n",
-      "31\t검증 세트 손실: 24.204378\t최선의 손실: 0.166437\t정확도: 39.41%\n",
-      "32\t검증 세트 손실: 28.001499\t최선의 손실: 0.166437\t정확도: 44.76%\n",
-      "33\t검증 세트 손실: 10.820848\t최선의 손실: 0.166437\t정확도: 50.08%\n",
-      "34\t검증 세트 손실: 12.645007\t최선의 손실: 0.166437\t정확도: 38.39%\n",
-      "35\t검증 세트 손실: 30.409821\t최선의 손실: 0.166437\t정확도: 40.81%\n",
-      "36\t검증 세트 손실: 11.152120\t최선의 손실: 0.166437\t정확도: 57.35%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=50, total=   6.0s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.158531\t최선의 손실: 0.158531\t정확도: 95.62%\n",
+      "1\t검증 세트 손실: 0.113408\t최선의 손실: 0.113408\t정확도: 96.52%\n",
+      "2\t검증 세트 손실: 229.098495\t최선의 손실: 0.113408\t정확도: 22.99%\n",
+      "3\t검증 세트 손실: 2.362132\t최선의 손실: 0.113408\t정확도: 58.68%\n",
+      "4\t검증 세트 손실: 1.079743\t최선의 손실: 0.113408\t정확도: 70.48%\n",
+      "5\t검증 세트 손실: 1.070271\t최선의 손실: 0.113408\t정확도: 70.80%\n",
+      "6\t검증 세트 손실: 0.634102\t최선의 손실: 0.113408\t정확도: 78.19%\n",
+      "7\t검증 세트 손실: 1.711234\t최선의 손실: 0.113408\t정확도: 64.66%\n",
+      "8\t검증 세트 손실: 0.498184\t최선의 손실: 0.113408\t정확도: 83.97%\n",
+      "9\t검증 세트 손실: 0.538831\t최선의 손실: 0.113408\t정확도: 80.65%\n",
+      "10\t검증 세트 손실: 0.809379\t최선의 손실: 0.113408\t정확도: 72.75%\n",
+      "11\t검증 세트 손실: 1.353109\t최선의 손실: 0.113408\t정확도: 75.10%\n",
+      "12\t검증 세트 손실: 0.458012\t최선의 손실: 0.113408\t정확도: 85.93%\n",
+      "13\t검증 세트 손실: 0.448484\t최선의 손실: 0.113408\t정확도: 86.94%\n",
+      "14\t검증 세트 손실: 0.312879\t최선의 손실: 0.113408\t정확도: 91.67%\n",
+      "15\t검증 세트 손실: 0.367759\t최선의 손실: 0.113408\t정확도: 90.50%\n",
+      "16\t검증 세트 손실: 0.345461\t최선의 손실: 0.113408\t정확도: 90.38%\n",
+      "17\t검증 세트 손실: 410.613312\t최선의 손실: 0.113408\t정확도: 21.77%\n",
+      "18\t검증 세트 손실: 15.462116\t최선의 손실: 0.113408\t정확도: 48.51%\n",
+      "19\t검증 세트 손실: 4.375600\t최선의 손실: 0.113408\t정확도: 65.36%\n",
+      "20\t검증 세트 손실: 7.017228\t최선의 손실: 0.113408\t정확도: 59.54%\n",
+      "21\t검증 세트 손실: 4.358530\t최선의 손실: 0.113408\t정확도: 66.38%\n",
+      "22\t검증 세트 손실: 6.039410\t최선의 손실: 0.113408\t정확도: 58.17%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50, total=  23.8s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.100192\t최선의 손실: 0.100192\t정확도: 96.83%\n",
-      "1\t검증 세트 손실: 0.075679\t최선의 손실: 0.075679\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.072786\t최선의 손실: 0.072786\t정확도: 97.93%\n",
-      "3\t검증 세트 손실: 0.154871\t최선의 손실: 0.072786\t정확도: 96.48%\n",
-      "4\t검증 세트 손실: 0.076819\t최선의 손실: 0.072786\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.294537\t최선의 손실: 0.072786\t정확도: 94.49%\n",
-      "6\t검증 세트 손실: 0.094928\t최선의 손실: 0.072786\t정확도: 97.73%\n",
-      "7\t검증 세트 손실: 0.073651\t최선의 손실: 0.072786\t정확도: 98.36%\n",
-      "8\t검증 세트 손실: 0.069052\t최선의 손실: 0.069052\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.101538\t최선의 손실: 0.069052\t정확도: 97.54%\n",
-      "10\t검증 세트 손실: 13.638007\t최선의 손실: 0.069052\t정확도: 93.86%\n",
-      "11\t검증 세트 손실: 0.270728\t최선의 손실: 0.069052\t정확도: 97.46%\n",
-      "12\t검증 세트 손실: 0.185818\t최선의 손실: 0.069052\t정확도: 97.93%\n",
-      "13\t검증 세트 손실: 0.183542\t최선의 손실: 0.069052\t정확도: 96.60%\n",
-      "14\t검증 세트 손실: 0.136134\t최선의 손실: 0.069052\t정확도: 98.12%\n",
-      "15\t검증 세트 손실: 0.208421\t최선의 손실: 0.069052\t정확도: 98.08%\n",
-      "16\t검증 세트 손실: 0.113156\t최선의 손실: 0.069052\t정확도: 98.16%\n",
-      "17\t검증 세트 손실: 0.118731\t최선의 손실: 0.069052\t정확도: 98.08%\n",
-      "18\t검증 세트 손실: 0.135589\t최선의 손실: 0.069052\t정확도: 97.54%\n",
-      "19\t검증 세트 손실: 0.197157\t최선의 손실: 0.069052\t정확도: 97.69%\n",
-      "20\t검증 세트 손실: 0.143162\t최선의 손실: 0.069052\t정확도: 98.08%\n",
-      "21\t검증 세트 손실: 0.206223\t최선의 손실: 0.069052\t정확도: 98.48%\n",
-      "22\t검증 세트 손실: 0.857168\t최선의 손실: 0.069052\t정확도: 96.95%\n",
-      "23\t검증 세트 손실: 0.455034\t최선의 손실: 0.069052\t정확도: 98.01%\n",
-      "24\t검증 세트 손실: 0.475461\t최선의 손실: 0.069052\t정확도: 97.38%\n",
-      "25\t검증 세트 손실: 0.283880\t최선의 손실: 0.069052\t정확도: 98.24%\n",
-      "26\t검증 세트 손실: 0.361382\t최선의 손실: 0.069052\t정확도: 98.05%\n",
-      "27\t검증 세트 손실: 0.303715\t최선의 손실: 0.069052\t정확도: 98.40%\n",
-      "28\t검증 세트 손실: 0.238251\t최선의 손실: 0.069052\t정확도: 98.08%\n",
-      "29\t검증 세트 손실: 0.336467\t최선의 손실: 0.069052\t정확도: 96.64%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=50, total=   6.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.145438\t최선의 손실: 0.145438\t정확도: 95.82%\n",
+      "1\t검증 세트 손실: 3.877617\t최선의 손실: 0.145438\t정확도: 50.39%\n",
+      "2\t검증 세트 손실: 27.619528\t최선의 손실: 0.145438\t정확도: 48.51%\n",
+      "3\t검증 세트 손실: 2.913589\t최선의 손실: 0.145438\t정확도: 47.65%\n",
+      "4\t검증 세트 손실: 0.876097\t최선의 손실: 0.145438\t정확도: 73.53%\n",
+      "5\t검증 세트 손실: 0.925830\t최선의 손실: 0.145438\t정확도: 70.02%\n",
+      "6\t검증 세트 손실: 0.674014\t최선의 손실: 0.145438\t정확도: 77.95%\n",
+      "7\t검증 세트 손실: 0.630606\t최선의 손실: 0.145438\t정확도: 79.32%\n",
+      "8\t검증 세트 손실: 0.575127\t최선의 손실: 0.145438\t정확도: 83.62%\n",
+      "9\t검증 세트 손실: 0.806705\t최선의 손실: 0.145438\t정확도: 76.54%\n",
+      "10\t검증 세트 손실: 0.535571\t최선의 손실: 0.145438\t정확도: 83.46%\n",
+      "11\t검증 세트 손실: 0.642189\t최선의 손실: 0.145438\t정확도: 83.46%\n",
+      "12\t검증 세트 손실: 0.507052\t최선의 손실: 0.145438\t정확도: 86.36%\n",
+      "13\t검증 세트 손실: 0.465312\t최선의 손실: 0.145438\t정확도: 88.08%\n",
+      "14\t검증 세트 손실: 0.369507\t최선의 손실: 0.145438\t정확도: 90.70%\n",
+      "15\t검증 세트 손실: 0.403651\t최선의 손실: 0.145438\t정확도: 89.68%\n",
+      "16\t검증 세트 손실: 0.301971\t최선의 손실: 0.145438\t정확도: 91.75%\n",
+      "17\t검증 세트 손실: 30.116100\t최선의 손실: 0.145438\t정확도: 22.79%\n",
+      "18\t검증 세트 손실: 20.000879\t최선의 손실: 0.145438\t정확도: 18.80%\n",
+      "19\t검증 세트 손실: 3.623142\t최선의 손실: 0.145438\t정확도: 45.00%\n",
+      "20\t검증 세트 손실: 2.489800\t최선의 손실: 0.145438\t정확도: 43.94%\n",
+      "21\t검증 세트 손실: 2.083584\t최선의 손실: 0.145438\t정확도: 47.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=  33.3s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.087997\t최선의 손실: 0.087997\t정확도: 97.50%\n",
-      "1\t검증 세트 손실: 0.064545\t최선의 손실: 0.064545\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.088338\t최선의 손실: 0.064545\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 0.068261\t최선의 손실: 0.064545\t정확도: 98.36%\n",
-      "4\t검증 세트 손실: 0.094763\t최선의 손실: 0.064545\t정확도: 97.93%\n",
-      "5\t검증 세트 손실: 0.112378\t최선의 손실: 0.064545\t정확도: 96.64%\n",
-      "6\t검증 세트 손실: 0.084959\t최선의 손실: 0.064545\t정확도: 97.81%\n",
-      "7\t검증 세트 손실: 0.081065\t최선의 손실: 0.064545\t정확도: 97.93%\n",
-      "8\t검증 세트 손실: 0.073495\t최선의 손실: 0.064545\t정확도: 97.97%\n",
-      "9\t검증 세트 손실: 0.091784\t최선의 손실: 0.064545\t정확도: 98.01%\n",
-      "10\t검증 세트 손실: 0.129992\t최선의 손실: 0.064545\t정확도: 97.03%\n",
-      "11\t검증 세트 손실: 0.111728\t최선의 손실: 0.064545\t정확도: 97.69%\n",
-      "12\t검증 세트 손실: 0.093739\t최선의 손실: 0.064545\t정확도: 98.05%\n",
-      "13\t검증 세트 손실: 0.082904\t최선의 손실: 0.064545\t정확도: 98.28%\n",
-      "14\t검증 세트 손실: 0.057669\t최선의 손실: 0.057669\t정확도: 98.40%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=50, total=   5.7s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=50 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15\t검증 세트 손실: 0.091740\t최선의 손실: 0.057669\t정확도: 97.97%\n",
-      "16\t검증 세트 손실: 0.815509\t최선의 손실: 0.057669\t정확도: 96.21%\n",
-      "17\t검증 세트 손실: 0.687379\t최선의 손실: 0.057669\t정확도: 93.55%\n",
-      "18\t검증 세트 손실: 0.134047\t최선의 손실: 0.057669\t정확도: 97.73%\n",
-      "19\t검증 세트 손실: 0.110551\t최선의 손실: 0.057669\t정확도: 98.12%\n",
-      "20\t검증 세트 손실: 0.188607\t최선의 손실: 0.057669\t정확도: 97.54%\n",
-      "21\t검증 세트 손실: 0.090257\t최선의 손실: 0.057669\t정확도: 98.28%\n",
-      "22\t검증 세트 손실: 0.109700\t최선의 손실: 0.057669\t정확도: 97.46%\n",
-      "23\t검증 세트 손실: 0.407096\t최선의 손실: 0.057669\t정확도: 94.80%\n",
-      "24\t검증 세트 손실: 0.127601\t최선의 손실: 0.057669\t정확도: 97.65%\n",
-      "25\t검증 세트 손실: 0.093802\t최선의 손실: 0.057669\t정확도: 98.05%\n",
-      "26\t검증 세트 손실: 0.092848\t최선의 손실: 0.057669\t정확도: 98.28%\n",
-      "27\t검증 세트 손실: 0.085425\t최선의 손실: 0.057669\t정확도: 98.20%\n",
-      "28\t검증 세트 손실: 0.091840\t최선의 손실: 0.057669\t정확도: 98.16%\n",
-      "29\t검증 세트 손실: 0.110013\t최선의 손실: 0.057669\t정확도: 98.16%\n",
-      "30\t검증 세트 손실: 0.211727\t최선의 손실: 0.057669\t정확도: 97.19%\n",
-      "31\t검증 세트 손실: 0.441088\t최선의 손실: 0.057669\t정확도: 97.89%\n",
-      "32\t검증 세트 손실: 0.523083\t최선의 손실: 0.057669\t정확도: 98.01%\n",
-      "33\t검증 세트 손실: 0.296272\t최선의 손실: 0.057669\t정확도: 98.01%\n",
-      "34\t검증 세트 손실: 0.271759\t최선의 손실: 0.057669\t정확도: 97.93%\n",
-      "35\t검증 세트 손실: 0.232677\t최선의 손실: 0.057669\t정확도: 98.20%\n",
+      "0\t검증 세트 손실: 0.121137\t최선의 손실: 0.121137\t정확도: 97.30%\n",
+      "1\t검증 세트 손실: 0.070906\t최선의 손실: 0.070906\t정확도: 98.08%\n",
+      "2\t검증 세트 손실: 0.085365\t최선의 손실: 0.070906\t정확도: 97.69%\n",
+      "3\t검증 세트 손실: 0.085952\t최선의 손실: 0.070906\t정확도: 97.73%\n",
+      "4\t검증 세트 손실: 0.095008\t최선의 손실: 0.070906\t정확도: 97.97%\n",
+      "5\t검증 세트 손실: 0.057167\t최선의 손실: 0.057167\t정확도: 98.55%\n",
+      "6\t검증 세트 손실: 0.144764\t최선의 손실: 0.057167\t정확도: 97.30%\n",
+      "7\t검증 세트 손실: 0.217731\t최선의 손실: 0.057167\t정확도: 96.17%\n",
+      "8\t검증 세트 손실: 0.090277\t최선의 손실: 0.057167\t정확도: 97.62%\n",
+      "9\t검증 세트 손실: 0.090789\t최선의 손실: 0.057167\t정확도: 97.62%\n",
+      "10\t검증 세트 손실: 0.095652\t최선의 손실: 0.057167\t정확도: 98.08%\n",
+      "11\t검증 세트 손실: 0.073820\t최선의 손실: 0.057167\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.110818\t최선의 손실: 0.057167\t정확도: 98.05%\n",
+      "13\t검증 세트 손실: 0.100247\t최선의 손실: 0.057167\t정확도: 97.73%\n",
+      "14\t검증 세트 손실: 0.099558\t최선의 손실: 0.057167\t정확도: 98.05%\n",
+      "15\t검증 세트 손실: 0.126182\t최선의 손실: 0.057167\t정확도: 97.77%\n",
+      "16\t검증 세트 손실: 0.072048\t최선의 손실: 0.057167\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.099430\t최선의 손실: 0.057167\t정확도: 98.24%\n",
+      "18\t검증 세트 손실: 0.131276\t최선의 손실: 0.057167\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 4.010344\t최선의 손실: 0.057167\t정확도: 92.92%\n",
+      "20\t검증 세트 손실: 0.283552\t최선의 손실: 0.057167\t정확도: 97.46%\n",
+      "21\t검증 세트 손실: 0.298757\t최선의 손실: 0.057167\t정확도: 97.77%\n",
+      "22\t검증 세트 손실: 0.241401\t최선의 손실: 0.057167\t정확도: 98.05%\n",
+      "23\t검증 세트 손실: 0.268764\t최선의 손실: 0.057167\t정확도: 97.77%\n",
+      "24\t검증 세트 손실: 0.203203\t최선의 손실: 0.057167\t정확도: 98.20%\n",
+      "25\t검증 세트 손실: 0.208211\t최선의 손실: 0.057167\t정확도: 98.32%\n",
+      "26\t검증 세트 손실: 0.224445\t최선의 손실: 0.057167\t정확도: 98.28%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=  43.3s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.120073\t최선의 손실: 0.120073\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.086446\t최선의 손실: 0.086446\t정확도: 97.77%\n",
-      "2\t검증 세트 손실: 0.083693\t최선의 손실: 0.083693\t정확도: 97.81%\n",
-      "3\t검증 세트 손실: 0.082181\t최선의 손실: 0.082181\t정확도: 98.12%\n",
-      "4\t검증 세트 손실: 0.087609\t최선의 손실: 0.082181\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.068959\t최선의 손실: 0.068959\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.106647\t최선의 손실: 0.068959\t정확도: 97.73%\n",
-      "7\t검증 세트 손실: 0.221187\t최선의 손실: 0.068959\t정확도: 96.52%\n",
-      "8\t검증 세트 손실: 0.667271\t최선의 손실: 0.068959\t정확도: 95.70%\n",
-      "9\t검증 세트 손실: 0.147900\t최선의 손실: 0.068959\t정확도: 97.26%\n",
-      "10\t검증 세트 손실: 0.113266\t최선의 손실: 0.068959\t정확도: 97.58%\n",
-      "11\t검증 세트 손실: 0.101582\t최선의 손실: 0.068959\t정확도: 98.12%\n",
-      "12\t검증 세트 손실: 0.113848\t최선의 손실: 0.068959\t정확도: 97.89%\n",
-      "13\t검증 세트 손실: 0.096135\t최선의 손실: 0.068959\t정확도: 98.24%\n",
-      "14\t검증 세트 손실: 0.142978\t최선의 손실: 0.068959\t정확도: 96.91%\n",
-      "15\t검증 세트 손실: 0.090707\t최선의 손실: 0.068959\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.115819\t최선의 손실: 0.068959\t정확도: 98.55%\n",
-      "17\t검증 세트 손실: 0.104773\t최선의 손실: 0.068959\t정확도: 97.77%\n",
-      "18\t검증 세트 손실: 0.103647\t최선의 손실: 0.068959\t정확도: 98.28%\n",
-      "19\t검증 세트 손실: 0.100393\t최선의 손실: 0.068959\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.070226\t최선의 손실: 0.068959\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.074961\t최선의 손실: 0.068959\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.113539\t최선의 손실: 0.068959\t정확도: 97.22%\n",
-      "23\t검증 세트 손실: 0.082213\t최선의 손실: 0.068959\t정확도: 98.40%\n",
-      "24\t검증 세트 손실: 0.946240\t최선의 손실: 0.068959\t정확도: 96.13%\n",
-      "25\t검증 세트 손실: 0.234501\t최선의 손실: 0.068959\t정확도: 97.30%\n",
-      "26\t검증 세트 손실: 0.180743\t최선의 손실: 0.068959\t정확도: 97.26%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=50, total=  13.7s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.105112\t최선의 손실: 0.105112\t정확도: 96.83%\n",
+      "1\t검증 세트 손실: 0.119679\t최선의 손실: 0.105112\t정확도: 96.72%\n",
+      "2\t검증 세트 손실: 0.070298\t최선의 손실: 0.070298\t정확도: 97.65%\n",
+      "3\t검증 세트 손실: 0.061979\t최선의 손실: 0.061979\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.063616\t최선의 손실: 0.061979\t정확도: 98.48%\n",
+      "5\t검증 세트 손실: 0.159490\t최선의 손실: 0.061979\t정확도: 96.83%\n",
+      "6\t검증 세트 손실: 0.174766\t최선의 손실: 0.061979\t정확도: 95.97%\n",
+      "7\t검증 세트 손실: 0.084139\t최선의 손실: 0.061979\t정확도: 97.50%\n",
+      "8\t검증 세트 손실: 0.103106\t최선의 손실: 0.061979\t정확도: 97.50%\n",
+      "9\t검증 세트 손실: 0.069663\t최선의 손실: 0.061979\t정확도: 98.20%\n",
+      "10\t검증 세트 손실: 0.070249\t최선의 손실: 0.061979\t정확도: 98.48%\n",
+      "11\t검증 세트 손실: 0.070775\t최선의 손실: 0.061979\t정확도: 98.36%\n",
+      "12\t검증 세트 손실: 0.077920\t최선의 손실: 0.061979\t정확도: 98.20%\n",
+      "13\t검증 세트 손실: 0.080037\t최선의 손실: 0.061979\t정확도: 98.36%\n",
+      "14\t검증 세트 손실: 0.068783\t최선의 손실: 0.061979\t정확도: 98.44%\n",
+      "15\t검증 세트 손실: 0.123447\t최선의 손실: 0.061979\t정확도: 97.81%\n",
+      "16\t검증 세트 손실: 0.401022\t최선의 손실: 0.061979\t정확도: 97.07%\n",
+      "17\t검증 세트 손실: 0.205209\t최선의 손실: 0.061979\t정확도: 97.38%\n",
+      "18\t검증 세트 손실: 0.175490\t최선의 손실: 0.061979\t정확도: 97.97%\n",
+      "19\t검증 세트 손실: 0.132414\t최선의 손실: 0.061979\t정확도: 98.16%\n",
+      "20\t검증 세트 손실: 0.183627\t최선의 손실: 0.061979\t정확도: 98.36%\n",
+      "21\t검증 세트 손실: 0.119784\t최선의 손실: 0.061979\t정확도: 98.28%\n",
+      "22\t검증 세트 손실: 0.181573\t최선의 손실: 0.061979\t정확도: 97.89%\n",
+      "23\t검증 세트 손실: 0.172342\t최선의 손실: 0.061979\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.172872\t최선의 손실: 0.061979\t정확도: 97.85%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=  21.1s\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.368491\t최선의 손실: 0.368491\t정확도: 95.39%\n",
-      "1\t검증 세트 손실: 130.917419\t최선의 손실: 0.368491\t정확도: 94.76%\n",
-      "2\t검증 세트 손실: 101.268257\t최선의 손실: 0.368491\t정확도: 94.18%\n",
-      "3\t검증 세트 손실: 85.619148\t최선의 손실: 0.368491\t정확도: 93.12%\n",
-      "4\t검증 세트 손실: 554.709290\t최선의 손실: 0.368491\t정확도: 95.04%\n",
-      "5\t검증 세트 손실: 272.838379\t최선의 손실: 0.368491\t정확도: 95.50%\n",
-      "6\t검증 세트 손실: 960.711670\t최선의 손실: 0.368491\t정확도: 89.80%\n",
-      "7\t검증 세트 손실: 301.481445\t최선의 손실: 0.368491\t정확도: 92.61%\n",
-      "8\t검증 세트 손실: 245.840530\t최선의 손실: 0.368491\t정확도: 94.92%\n",
-      "9\t검증 세트 손실: 1603.068481\t최선의 손실: 0.368491\t정확도: 96.33%\n",
-      "10\t검증 세트 손실: 789.690063\t최선의 손실: 0.368491\t정확도: 96.95%\n",
-      "11\t검증 세트 손실: 719.812134\t최선의 손실: 0.368491\t정확도: 97.19%\n",
-      "12\t검증 세트 손실: 52544.398438\t최선의 손실: 0.368491\t정확도: 94.06%\n",
-      "13\t검증 세트 손실: 700.174377\t최선의 손실: 0.368491\t정확도: 96.95%\n",
-      "14\t검증 세트 손실: 1356.004639\t최선의 손실: 0.368491\t정확도: 96.83%\n",
-      "15\t검증 세트 손실: 2131.025879\t최선의 손실: 0.368491\t정확도: 96.21%\n",
-      "16\t검증 세트 손실: 2501.643799\t최선의 손실: 0.368491\t정확도: 97.22%\n",
-      "17\t검증 세트 손실: 1653.145020\t최선의 손실: 0.368491\t정확도: 96.33%\n",
-      "18\t검증 세트 손실: 2946.200928\t최선의 손실: 0.368491\t정확도: 97.46%\n",
-      "19\t검증 세트 손실: 5734.861816\t최선의 손실: 0.368491\t정확도: 96.64%\n",
-      "20\t검증 세트 손실: 2869.549561\t최선의 손실: 0.368491\t정확도: 97.11%\n",
-      "21\t검증 세트 손실: 14769.299805\t최선의 손실: 0.368491\t정확도: 96.72%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=50, total=  13.2s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.091645\t최선의 손실: 0.091645\t정확도: 97.73%\n",
+      "1\t검증 세트 손실: 0.098688\t최선의 손실: 0.091645\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.074697\t최선의 손실: 0.074697\t정확도: 97.85%\n",
+      "3\t검증 세트 손실: 0.080217\t최선의 손실: 0.074697\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.172856\t최선의 손실: 0.074697\t정확도: 95.70%\n",
+      "5\t검증 세트 손실: 0.226557\t최선의 손실: 0.074697\t정확도: 96.29%\n",
+      "6\t검증 세트 손실: 0.223732\t최선의 손실: 0.074697\t정확도: 96.48%\n",
+      "7\t검증 세트 손실: 0.093877\t최선의 손실: 0.074697\t정확도: 98.01%\n",
+      "8\t검증 세트 손실: 0.106671\t최선의 손실: 0.074697\t정확도: 98.16%\n",
+      "9\t검증 세트 손실: 0.113766\t최선의 손실: 0.074697\t정확도: 98.01%\n",
+      "10\t검증 세트 손실: 0.101972\t최선의 손실: 0.074697\t정확도: 98.36%\n",
+      "11\t검증 세트 손실: 0.158243\t최선의 손실: 0.074697\t정확도: 98.16%\n",
+      "12\t검증 세트 손실: 0.114188\t최선의 손실: 0.074697\t정확도: 98.28%\n",
+      "13\t검증 세트 손실: 0.192354\t최선의 손실: 0.074697\t정확도: 97.62%\n",
+      "14\t검증 세트 손실: 0.300916\t최선의 손실: 0.074697\t정확도: 95.50%\n",
+      "15\t검증 세트 손실: 0.331469\t최선의 손실: 0.074697\t정확도: 96.87%\n",
+      "16\t검증 세트 손실: 0.090687\t최선의 손실: 0.074697\t정확도: 98.08%\n",
+      "17\t검증 세트 손실: 0.113787\t최선의 손실: 0.074697\t정확도: 97.97%\n",
+      "18\t검증 세트 손실: 0.093577\t최선의 손실: 0.074697\t정확도: 98.44%\n",
+      "19\t검증 세트 손실: 0.941514\t최선의 손실: 0.074697\t정확도: 98.44%\n",
+      "20\t검증 세트 손실: 0.284207\t최선의 손실: 0.074697\t정확도: 98.12%\n",
+      "21\t검증 세트 손실: 0.587284\t최선의 손실: 0.074697\t정확도: 97.97%\n",
+      "22\t검증 세트 손실: 0.250640\t최선의 손실: 0.074697\t정확도: 97.73%\n",
+      "23\t검증 세트 손실: 0.134260\t최선의 손실: 0.074697\t정확도: 98.20%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=120, total=  57.9s\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 1.196132\t최선의 손실: 1.196132\t정확도: 92.22%\n",
-      "1\t검증 세트 손실: 211.565506\t최선의 손실: 1.196132\t정확도: 91.67%\n",
-      "2\t검증 세트 손실: 157.571716\t최선의 손실: 1.196132\t정확도: 94.10%\n",
-      "3\t검증 세트 손실: 617.658203\t최선의 손실: 1.196132\t정확도: 94.49%\n",
-      "4\t검증 세트 손실: 218.745331\t최선의 손실: 1.196132\t정확도: 94.02%\n",
-      "5\t검증 세트 손실: 302.661652\t최선의 손실: 1.196132\t정확도: 95.39%\n",
-      "6\t검증 세트 손실: 2339.665771\t최선의 손실: 1.196132\t정확도: 95.39%\n",
-      "7\t검증 세트 손실: 1324.275269\t최선의 손실: 1.196132\t정확도: 94.41%\n",
-      "8\t검증 세트 손실: 3169.243408\t최선의 손실: 1.196132\t정확도: 87.69%\n",
-      "9\t검증 세트 손실: 1091.192505\t최선의 손실: 1.196132\t정확도: 96.44%\n",
-      "10\t검증 세트 손실: 1587.667847\t최선의 손실: 1.196132\t정확도: 96.09%\n",
-      "11\t검증 세트 손실: 11011.303711\t최선의 손실: 1.196132\t정확도: 89.87%\n",
-      "12\t검증 세트 손실: 7379.000977\t최선의 손실: 1.196132\t정확도: 95.00%\n",
-      "13\t검증 세트 손실: 5042.047363\t최선의 손실: 1.196132\t정확도: 95.62%\n",
-      "14\t검증 세트 손실: 3205.275391\t최선의 손실: 1.196132\t정확도: 97.50%\n",
-      "15\t검증 세트 손실: 2566.455322\t최선의 손실: 1.196132\t정확도: 95.31%\n",
-      "16\t검증 세트 손실: 2727.923828\t최선의 손실: 1.196132\t정확도: 96.91%\n",
-      "17\t검증 세트 손실: 1985.775269\t최선의 손실: 1.196132\t정확도: 96.87%\n",
-      "18\t검증 세트 손실: 1466.854858\t최선의 손실: 1.196132\t정확도: 97.03%\n",
-      "19\t검증 세트 손실: 4966.490234\t최선의 손실: 1.196132\t정확도: 96.68%\n",
-      "20\t검증 세트 손실: 4089.767090\t최선의 손실: 1.196132\t정확도: 96.56%\n",
-      "21\t검증 세트 손실: 2750.019287\t최선의 손실: 1.196132\t정확도: 97.38%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=50, total=  12.4s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=120 \n",
+      "0\t검증 세트 손실: 2.191029\t최선의 손실: 2.191029\t정확도: 94.29%\n",
+      "1\t검증 세트 손실: 0.550576\t최선의 손실: 0.550576\t정확도: 94.64%\n",
+      "2\t검증 세트 손실: 468.513306\t최선의 손실: 0.550576\t정확도: 89.68%\n",
+      "3\t검증 세트 손실: 143.625900\t최선의 손실: 0.550576\t정확도: 95.11%\n",
+      "4\t검증 세트 손실: 1367.518921\t최선의 손실: 0.550576\t정확도: 93.82%\n",
+      "5\t검증 세트 손실: 549.279602\t최선의 손실: 0.550576\t정확도: 90.11%\n",
+      "6\t검증 세트 손실: 70.266670\t최선의 손실: 0.550576\t정확도: 96.33%\n",
+      "7\t검증 세트 손실: 2655.786621\t최선의 손실: 0.550576\t정확도: 93.35%\n",
+      "8\t검증 세트 손실: 1381.712769\t최선의 손실: 0.550576\t정확도: 95.78%\n",
+      "9\t검증 세트 손실: 1554.111816\t최선의 손실: 0.550576\t정확도: 97.03%\n",
+      "10\t검증 세트 손실: 24602.771484\t최선의 손실: 0.550576\t정확도: 81.63%\n",
+      "11\t검증 세트 손실: 2799.895508\t최선의 손실: 0.550576\t정확도: 95.43%\n",
+      "12\t검증 세트 손실: 7181.537109\t최선의 손실: 0.550576\t정확도: 96.01%\n",
+      "13\t검증 세트 손실: 4905.388672\t최선의 손실: 0.550576\t정확도: 96.52%\n",
+      "14\t검증 세트 손실: 1159.943115\t최선의 손실: 0.550576\t정확도: 93.90%\n",
+      "15\t검증 세트 손실: 2589.167236\t최선의 손실: 0.550576\t정확도: 97.11%\n",
+      "16\t검증 세트 손실: 3253.500000\t최선의 손실: 0.550576\t정확도: 94.37%\n",
+      "17\t검증 세트 손실: 5564.344238\t최선의 손실: 0.550576\t정확도: 96.40%\n",
+      "18\t검증 세트 손실: 3185.916992\t최선의 손실: 0.550576\t정확도: 95.70%\n",
+      "19\t검증 세트 손실: 6553.982422\t최선의 손실: 0.550576\t정확도: 94.14%\n",
+      "20\t검증 세트 손실: 1450.538574\t최선의 손실: 0.550576\t정확도: 97.73%\n",
+      "21\t검증 세트 손실: 4040.645020\t최선의 손실: 0.550576\t정확도: 97.62%\n",
+      "22\t검증 세트 손실: 4721.737793\t최선의 손실: 0.550576\t정확도: 97.85%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=120, total= 1.8min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.707342\t최선의 손실: 0.707342\t정확도: 96.01%\n",
-      "1\t검증 세트 손실: 372.567780\t최선의 손실: 0.707342\t정확도: 95.15%\n",
-      "2\t검증 세트 손실: 156.859711\t최선의 손실: 0.707342\t정확도: 94.02%\n",
-      "3\t검증 세트 손실: 2493.623047\t최선의 손실: 0.707342\t정확도: 95.04%\n",
-      "4\t검증 세트 손실: 285.295258\t최선의 손실: 0.707342\t정확도: 96.72%\n",
-      "5\t검증 세트 손실: 452.294617\t최선의 손실: 0.707342\t정확도: 96.48%\n",
-      "6\t검증 세트 손실: 443.692841\t최선의 손실: 0.707342\t정확도: 96.13%\n",
-      "7\t검증 세트 손실: 1463.569824\t최선의 손실: 0.707342\t정확도: 96.40%\n",
-      "8\t검증 세트 손실: 545.505981\t최선의 손실: 0.707342\t정확도: 92.34%\n",
-      "9\t검증 세트 손실: 306.539917\t최선의 손실: 0.707342\t정확도: 97.22%\n",
-      "10\t검증 세트 손실: 32519.474609\t최선의 손실: 0.707342\t정확도: 96.33%\n",
-      "11\t검증 세트 손실: 2268.039551\t최선의 손실: 0.707342\t정확도: 96.48%\n",
-      "12\t검증 세트 손실: 1123.819580\t최선의 손실: 0.707342\t정확도: 97.15%\n",
-      "13\t검증 세트 손실: 1196.488037\t최선의 손실: 0.707342\t정확도: 96.91%\n",
-      "14\t검증 세트 손실: 2017.207642\t최선의 손실: 0.707342\t정확도: 96.21%\n",
-      "15\t검증 세트 손실: 340024.250000\t최선의 손실: 0.707342\t정확도: 94.61%\n",
-      "16\t검증 세트 손실: 7882.217285\t최선의 손실: 0.707342\t정확도: 95.54%\n",
-      "17\t검증 세트 손실: 22297.777344\t최선의 손실: 0.707342\t정확도: 96.91%\n",
-      "18\t검증 세트 손실: 11864.982422\t최선의 손실: 0.707342\t정확도: 94.57%\n",
-      "19\t검증 세트 손실: 1416.363770\t최선의 손실: 0.707342\t정확도: 96.48%\n",
-      "20\t검증 세트 손실: 10868.095703\t최선의 손실: 0.707342\t정확도: 95.43%\n",
-      "21\t검증 세트 손실: 7095.385254\t최선의 손실: 0.707342\t정확도: 97.81%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=120, total=  52.3s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.888117\t최선의 손실: 0.888117\t정확도: 93.04%\n",
+      "1\t검증 세트 손실: 1451.503662\t최선의 손실: 0.888117\t정확도: 86.40%\n",
+      "2\t검증 세트 손실: 902.686279\t최선의 손실: 0.888117\t정확도: 84.56%\n",
+      "3\t검증 세트 손실: 116.941811\t최선의 손실: 0.888117\t정확도: 95.27%\n",
+      "4\t검증 세트 손실: 1044.688965\t최선의 손실: 0.888117\t정확도: 90.30%\n",
+      "5\t검증 세트 손실: 60.188244\t최선의 손실: 0.888117\t정확도: 96.76%\n",
+      "6\t검증 세트 손실: 582.721558\t최선의 손실: 0.888117\t정확도: 95.78%\n",
+      "7\t검증 세트 손실: 161.514236\t최선의 손실: 0.888117\t정확도: 96.52%\n",
+      "8\t검증 세트 손실: 23808.359375\t최선의 손실: 0.888117\t정확도: 93.00%\n",
+      "9\t검증 세트 손실: 1830.198608\t최선의 손실: 0.888117\t정확도: 94.61%\n",
+      "10\t검증 세트 손실: 1752.950195\t최선의 손실: 0.888117\t정확도: 97.07%\n",
+      "11\t검증 세트 손실: 64718.109375\t최선의 손실: 0.888117\t정확도: 90.73%\n",
+      "12\t검증 세트 손실: 2440.778564\t최선의 손실: 0.888117\t정확도: 96.72%\n",
+      "13\t검증 세트 손실: 2417.018066\t최선의 손실: 0.888117\t정확도: 95.35%\n",
+      "14\t검증 세트 손실: 3608.109863\t최선의 손실: 0.888117\t정확도: 96.95%\n",
+      "15\t검증 세트 손실: 3748.943359\t최선의 손실: 0.888117\t정확도: 96.87%\n",
+      "16\t검증 세트 손실: 3390.311279\t최선의 손실: 0.888117\t정확도: 91.52%\n",
+      "17\t검증 세트 손실: 10835.370117\t최선의 손실: 0.888117\t정확도: 96.40%\n",
+      "18\t검증 세트 손실: 3144.320801\t최선의 손실: 0.888117\t정확도: 97.15%\n",
+      "19\t검증 세트 손실: 2038.895630\t최선의 손실: 0.888117\t정확도: 97.30%\n",
+      "20\t검증 세트 손실: 67374.390625\t최선의 손실: 0.888117\t정확도: 96.40%\n",
+      "21\t검증 세트 손실: 11923.332031\t최선의 손실: 0.888117\t정확도: 97.15%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=120, total= 1.4min\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.105957\t최선의 손실: 1.105957\t정확도: 55.00%\n",
-      "1\t검증 세트 손실: 0.848733\t최선의 손실: 0.848733\t정확도: 55.82%\n",
-      "2\t검증 세트 손실: 0.796563\t최선의 손실: 0.796563\t정확도: 56.76%\n",
-      "3\t검증 세트 손실: 0.816182\t최선의 손실: 0.796563\t정확도: 56.84%\n",
-      "4\t검증 세트 손실: 0.781674\t최선의 손실: 0.781674\t정확도: 57.51%\n",
-      "5\t검증 세트 손실: 0.772134\t최선의 손실: 0.772134\t정확도: 59.42%\n",
-      "6\t검증 세트 손실: 0.688018\t최선의 손실: 0.688018\t정확도: 67.36%\n",
-      "7\t검증 세트 손실: 0.539013\t최선의 손실: 0.539013\t정확도: 74.20%\n",
-      "8\t검증 세트 손실: 0.464832\t최선의 손실: 0.464832\t정확도: 79.98%\n",
-      "9\t검증 세트 손실: 0.366063\t최선의 손실: 0.366063\t정확도: 88.08%\n",
-      "10\t검증 세트 손실: 0.295389\t최선의 손실: 0.295389\t정확도: 92.73%\n",
-      "11\t검증 세트 손실: 0.207188\t최선의 손실: 0.207188\t정확도: 95.23%\n",
-      "12\t검증 세트 손실: 0.190751\t최선의 손실: 0.190751\t정확도: 95.50%\n",
-      "13\t검증 세트 손실: 0.172705\t최선의 손실: 0.172705\t정확도: 96.21%\n",
-      "14\t검증 세트 손실: 0.159283\t최선의 손실: 0.159283\t정확도: 96.60%\n",
-      "15\t검증 세트 손실: 0.149292\t최선의 손실: 0.149292\t정확도: 96.29%\n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=120, total=  48.3s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=120 \n",
+      "0\t검증 세트 손실: 2.861630\t최선의 손실: 2.861630\t정확도: 84.91%\n",
+      "1\t검증 세트 손실: 7026.324707\t최선의 손실: 2.861630\t정확도: 65.56%\n",
+      "2\t검증 세트 손실: 28.677971\t최선의 손실: 2.861630\t정확도: 95.47%\n",
+      "3\t검증 세트 손실: 22.225904\t최선의 손실: 2.861630\t정확도: 95.00%\n",
+      "4\t검증 세트 손실: 712.787292\t최선의 손실: 2.861630\t정확도: 94.45%\n",
+      "5\t검증 세트 손실: 505.192535\t최선의 손실: 2.861630\t정확도: 91.52%\n",
+      "6\t검증 세트 손실: 581.789978\t최선의 손실: 2.861630\t정확도: 94.96%\n",
+      "7\t검증 세트 손실: 60.615391\t최선의 손실: 2.861630\t정확도: 96.48%\n",
+      "8\t검증 세트 손실: 4728.928711\t최선의 손실: 2.861630\t정확도: 90.38%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "16\t검증 세트 손실: 0.153978\t최선의 손실: 0.149292\t정확도: 96.33%\n",
-      "17\t검증 세트 손실: 0.169993\t최선의 손실: 0.149292\t정확도: 96.83%\n",
-      "18\t검증 세트 손실: 0.159837\t최선의 손실: 0.149292\t정확도: 96.29%\n",
-      "19\t검증 세트 손실: 0.137205\t최선의 손실: 0.137205\t정확도: 96.83%\n",
-      "20\t검증 세트 손실: 0.140931\t최선의 손실: 0.137205\t정확도: 96.83%\n",
-      "21\t검증 세트 손실: 0.141669\t최선의 손실: 0.137205\t정확도: 96.48%\n",
-      "22\t검증 세트 손실: 0.127462\t최선의 손실: 0.127462\t정확도: 96.91%\n",
-      "23\t검증 세트 손실: 0.145811\t최선의 손실: 0.127462\t정확도: 96.76%\n",
-      "24\t검증 세트 손실: 0.142831\t최선의 손실: 0.127462\t정확도: 96.91%\n",
-      "25\t검증 세트 손실: 0.146590\t최선의 손실: 0.127462\t정확도: 97.03%\n",
-      "26\t검증 세트 손실: 0.158859\t최선의 손실: 0.127462\t정확도: 96.68%\n",
-      "27\t검증 세트 손실: 0.172991\t최선의 손실: 0.127462\t정확도: 96.29%\n",
-      "28\t검증 세트 손실: 0.135166\t최선의 손실: 0.127462\t정확도: 96.99%\n",
-      "29\t검증 세트 손실: 0.170418\t최선의 손실: 0.127462\t정확도: 96.64%\n",
-      "30\t검증 세트 손실: 0.149580\t최선의 손실: 0.127462\t정확도: 96.72%\n",
-      "31\t검증 세트 손실: 0.141238\t최선의 손실: 0.127462\t정확도: 96.68%\n",
-      "32\t검증 세트 손실: 0.144747\t최선의 손실: 0.127462\t정확도: 96.91%\n",
-      "33\t검증 세트 손실: 0.137689\t최선의 손실: 0.127462\t정확도: 96.87%\n",
-      "34\t검증 세트 손실: 0.128189\t최선의 손실: 0.127462\t정확도: 97.11%\n",
-      "35\t검증 세트 손실: 0.138738\t최선의 손실: 0.127462\t정확도: 96.76%\n",
-      "36\t검증 세트 손실: 0.151199\t최선의 손실: 0.127462\t정확도: 96.68%\n",
-      "37\t검증 세트 손실: 0.174538\t최선의 손실: 0.127462\t정확도: 96.60%\n",
-      "38\t검증 세트 손실: 0.139028\t최선의 손실: 0.127462\t정확도: 96.95%\n",
-      "39\t검증 세트 손실: 0.135343\t최선의 손실: 0.127462\t정확도: 96.79%\n",
-      "40\t검증 세트 손실: 0.149727\t최선의 손실: 0.127462\t정확도: 96.68%\n",
-      "41\t검증 세트 손실: 0.184557\t최선의 손실: 0.127462\t정확도: 96.33%\n",
-      "42\t검증 세트 손실: 0.131643\t최선의 손실: 0.127462\t정확도: 96.83%\n",
-      "43\t검증 세트 손실: 0.124518\t최선의 손실: 0.124518\t정확도: 97.07%\n",
-      "44\t검증 세트 손실: 0.152576\t최선의 손실: 0.124518\t정확도: 97.19%\n",
-      "45\t검증 세트 손실: 0.141789\t최선의 손실: 0.124518\t정확도: 97.38%\n",
-      "46\t검증 세트 손실: 0.137955\t최선의 손실: 0.124518\t정확도: 96.79%\n",
-      "47\t검증 세트 손실: 0.127617\t최선의 손실: 0.124518\t정확도: 97.15%\n",
-      "48\t검증 세트 손실: 0.135483\t최선의 손실: 0.124518\t정확도: 97.15%\n",
-      "49\t검증 세트 손실: 0.143366\t최선의 손실: 0.124518\t정확도: 96.95%\n",
-      "50\t검증 세트 손실: 0.155156\t최선의 손실: 0.124518\t정확도: 96.99%\n",
-      "51\t검증 세트 손실: 0.125690\t최선의 손실: 0.124518\t정확도: 97.19%\n",
-      "52\t검증 세트 손실: 0.148659\t최선의 손실: 0.124518\t정확도: 97.15%\n",
-      "53\t검증 세트 손실: 0.132011\t최선의 손실: 0.124518\t정확도: 97.03%\n",
-      "54\t검증 세트 손실: 0.150102\t최선의 손실: 0.124518\t정확도: 96.91%\n",
-      "55\t검증 세트 손실: 0.166547\t최선의 손실: 0.124518\t정확도: 96.99%\n",
-      "56\t검증 세트 손실: 0.154281\t최선의 손실: 0.124518\t정확도: 96.95%\n",
-      "57\t검증 세트 손실: 0.167424\t최선의 손실: 0.124518\t정확도: 96.64%\n",
-      "58\t검증 세트 손실: 0.179358\t최선의 손실: 0.124518\t정확도: 96.64%\n",
-      "59\t검증 세트 손실: 0.160859\t최선의 손실: 0.124518\t정확도: 96.79%\n",
-      "60\t검증 세트 손실: 0.208024\t최선의 손실: 0.124518\t정확도: 95.62%\n",
-      "61\t검증 세트 손실: 0.140103\t최선의 손실: 0.124518\t정확도: 97.07%\n",
-      "62\t검증 세트 손실: 0.124330\t최선의 손실: 0.124330\t정확도: 97.38%\n",
-      "63\t검증 세트 손실: 0.149156\t최선의 손실: 0.124330\t정확도: 97.34%\n",
-      "64\t검증 세트 손실: 0.169225\t최선의 손실: 0.124330\t정확도: 96.95%\n",
-      "65\t검증 세트 손실: 0.149570\t최선의 손실: 0.124330\t정확도: 97.07%\n",
-      "66\t검증 세트 손실: 0.471711\t최선의 손실: 0.124330\t정확도: 89.25%\n",
-      "67\t검증 세트 손실: 3.525298\t최선의 손실: 0.124330\t정확도: 20.91%\n",
-      "68\t검증 세트 손실: 1.692360\t최선의 손실: 0.124330\t정확도: 19.08%\n",
-      "69\t검증 세트 손실: 1.626297\t최선의 손실: 0.124330\t정확도: 22.01%\n",
-      "70\t검증 세트 손실: 1.621915\t최선의 손실: 0.124330\t정확도: 19.08%\n",
-      "71\t검증 세트 손실: 1.682144\t최선의 손실: 0.124330\t정확도: 22.01%\n",
-      "72\t검증 세트 손실: 1.627203\t최선의 손실: 0.124330\t정확도: 22.01%\n",
-      "73\t검증 세트 손실: 1.614225\t최선의 손실: 0.124330\t정확도: 22.01%\n",
-      "74\t검증 세트 손실: 1.615130\t최선의 손실: 0.124330\t정확도: 19.27%\n",
-      "75\t검증 세트 손실: 1.613111\t최선의 손실: 0.124330\t정확도: 20.91%\n",
-      "76\t검증 세트 손실: 1.626649\t최선의 손실: 0.124330\t정확도: 19.08%\n",
-      "77\t검증 세트 손실: 1.634623\t최선의 손실: 0.124330\t정확도: 22.01%\n",
-      "78\t검증 세트 손실: 1.618031\t최선의 손실: 0.124330\t정확도: 22.01%\n",
-      "79\t검증 세트 손실: 1.613620\t최선의 손실: 0.124330\t정확도: 18.73%\n",
-      "80\t검증 세트 손실: 1.628467\t최선의 손실: 0.124330\t정확도: 22.01%\n",
-      "81\t검증 세트 손실: 1.626809\t최선의 손실: 0.124330\t정확도: 18.73%\n",
-      "82\t검증 세트 손실: 1.611831\t최선의 손실: 0.124330\t정확도: 19.27%\n",
-      "83\t검증 세트 손실: 1.634447\t최선의 손실: 0.124330\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 526.303162\t최선의 손실: 2.861630\t정확도: 96.21%\n",
+      "10\t검증 세트 손실: 558.767883\t최선의 손실: 2.861630\t정확도: 94.25%\n",
+      "11\t검증 세트 손실: 1046.698486\t최선의 손실: 2.861630\t정확도: 96.17%\n",
+      "12\t검증 세트 손실: 445.640015\t최선의 손실: 2.861630\t정확도: 95.82%\n",
+      "13\t검증 세트 손실: 650.660645\t최선의 손실: 2.861630\t정확도: 96.29%\n",
+      "14\t검증 세트 손실: 633.673401\t최선의 손실: 2.861630\t정확도: 96.99%\n",
+      "15\t검증 세트 손실: 4291.384766\t최선의 손실: 2.861630\t정확도: 96.56%\n",
+      "16\t검증 세트 손실: 1141.078003\t최선의 손실: 2.861630\t정확도: 96.99%\n",
+      "17\t검증 세트 손실: 1373.047363\t최선의 손실: 2.861630\t정확도: 97.46%\n",
+      "18\t검증 세트 손실: 3618.889404\t최선의 손실: 2.861630\t정확도: 96.64%\n",
+      "19\t검증 세트 손실: 3446.796387\t최선의 손실: 2.861630\t정확도: 95.35%\n",
+      "20\t검증 세트 손실: 2572.293701\t최선의 손실: 2.861630\t정확도: 96.95%\n",
+      "21\t검증 세트 손실: 2632.004395\t최선의 손실: 2.861630\t정확도: 96.76%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=   5.4s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.962133\t최선의 손실: 0.962133\t정확도: 59.66%\n",
-      "1\t검증 세트 손실: 0.444314\t최선의 손실: 0.444314\t정확도: 85.54%\n",
-      "2\t검증 세트 손실: 0.277367\t최선의 손실: 0.277367\t정확도: 92.03%\n",
-      "3\t검증 세트 손실: 0.238493\t최선의 손실: 0.238493\t정확도: 93.51%\n",
-      "4\t검증 세트 손실: 0.204228\t최선의 손실: 0.204228\t정확도: 94.41%\n",
-      "5\t검증 세트 손실: 0.194329\t최선의 손실: 0.194329\t정확도: 94.72%\n",
-      "6\t검증 세트 손실: 0.174222\t최선의 손실: 0.174222\t정확도: 95.04%\n",
-      "7\t검증 세트 손실: 0.190384\t최선의 손실: 0.174222\t정확도: 94.33%\n",
-      "8\t검증 세트 손실: 0.161643\t최선의 손실: 0.161643\t정확도: 95.74%\n",
-      "9\t검증 세트 손실: 0.169720\t최선의 손실: 0.161643\t정확도: 95.97%\n",
-      "10\t검증 세트 손실: 0.150086\t최선의 손실: 0.150086\t정확도: 96.21%\n",
-      "11\t검증 세트 손실: 0.154739\t최선의 손실: 0.150086\t정확도: 96.40%\n",
-      "12\t검증 세트 손실: 0.168504\t최선의 손실: 0.150086\t정확도: 96.01%\n",
-      "13\t검증 세트 손실: 0.157923\t최선의 손실: 0.150086\t정확도: 96.64%\n",
-      "14\t검증 세트 손실: 0.197367\t최선의 손실: 0.150086\t정확도: 96.09%\n",
-      "15\t검증 세트 손실: 0.157006\t최선의 손실: 0.150086\t정확도: 96.60%\n",
-      "16\t검증 세트 손실: 0.166260\t최선의 손실: 0.150086\t정확도: 96.56%\n",
-      "17\t검증 세트 손실: 0.201479\t최선의 손실: 0.150086\t정확도: 96.44%\n",
-      "18\t검증 세트 손실: 0.132141\t최선의 손실: 0.132141\t정확도: 96.95%\n",
-      "19\t검증 세트 손실: 0.157115\t최선의 손실: 0.132141\t정확도: 96.72%\n",
-      "20\t검증 세트 손실: 0.175099\t최선의 손실: 0.132141\t정확도: 96.21%\n",
-      "21\t검증 세트 손실: 0.141371\t최선의 손실: 0.132141\t정확도: 96.72%\n",
-      "22\t검증 세트 손실: 0.134869\t최선의 손실: 0.132141\t정확도: 96.91%\n",
-      "23\t검증 세트 손실: 0.160978\t최선의 손실: 0.132141\t정확도: 96.56%\n",
-      "24\t검증 세트 손실: 0.152659\t최선의 손실: 0.132141\t정확도: 96.79%\n",
-      "25\t검증 세트 손실: 0.155337\t최선의 손실: 0.132141\t정확도: 97.26%\n",
-      "26\t검증 세트 손실: 0.161242\t최선의 손실: 0.132141\t정확도: 97.22%\n",
-      "27\t검증 세트 손실: 0.181685\t최선의 손실: 0.132141\t정확도: 97.03%\n",
-      "28\t검증 세트 손실: 0.166956\t최선의 손실: 0.132141\t정확도: 96.36%\n",
-      "29\t검증 세트 손실: 0.153614\t최선의 손실: 0.132141\t정확도: 97.19%\n",
-      "30\t검증 세트 손실: 0.211743\t최선의 손실: 0.132141\t정확도: 96.60%\n",
-      "31\t검증 세트 손실: 0.161358\t최선의 손실: 0.132141\t정확도: 97.42%\n",
-      "32\t검증 세트 손실: 0.154086\t최선의 손실: 0.132141\t정확도: 96.95%\n",
-      "33\t검증 세트 손실: 0.152525\t최선의 손실: 0.132141\t정확도: 97.34%\n",
-      "34\t검증 세트 손실: 0.129073\t최선의 손실: 0.129073\t정확도: 97.69%\n",
-      "35\t검증 세트 손실: 0.161623\t최선의 손실: 0.129073\t정확도: 96.79%\n",
-      "36\t검증 세트 손실: 0.134228\t최선의 손실: 0.129073\t정확도: 97.42%\n",
-      "37\t검증 세트 손실: 0.166492\t최선의 손실: 0.129073\t정확도: 97.07%\n",
-      "38\t검증 세트 손실: 0.180484\t최선의 손실: 0.129073\t정확도: 96.91%\n",
-      "39\t검증 세트 손실: 0.142439\t최선의 손실: 0.129073\t정확도: 97.65%\n",
-      "40\t검증 세트 손실: 0.155151\t최선의 손실: 0.129073\t정확도: 97.46%\n",
-      "41\t검증 세트 손실: 0.209832\t최선의 손실: 0.129073\t정확도: 96.91%\n",
-      "42\t검증 세트 손실: 0.179334\t최선의 손실: 0.129073\t정확도: 97.07%\n",
-      "43\t검증 세트 손실: 0.126103\t최선의 손실: 0.126103\t정확도: 97.69%\n",
-      "44\t검증 세트 손실: 0.149265\t최선의 손실: 0.126103\t정확도: 97.19%\n",
-      "45\t검증 세트 손실: 0.124602\t최선의 손실: 0.124602\t정확도: 97.50%\n",
-      "46\t검증 세트 손실: 0.139364\t최선의 손실: 0.124602\t정확도: 97.34%\n",
-      "47\t검증 세트 손실: 0.131678\t최선의 손실: 0.124602\t정확도: 97.89%\n",
-      "48\t검증 세트 손실: 0.126034\t최선의 손실: 0.124602\t정확도: 97.73%\n",
-      "49\t검증 세트 손실: 0.129732\t최선의 손실: 0.124602\t정확도: 97.50%\n",
-      "50\t검증 세트 손실: 0.119051\t최선의 손실: 0.119051\t정확도: 97.73%\n",
-      "51\t검증 세트 손실: 0.132279\t최선의 손실: 0.119051\t정확도: 97.19%\n",
-      "52\t검증 세트 손실: 0.161544\t최선의 손실: 0.119051\t정확도: 97.50%\n",
-      "53\t검증 세트 손실: 0.125891\t최선의 손실: 0.119051\t정확도: 97.93%\n",
-      "54\t검증 세트 손실: 0.141739\t최선의 손실: 0.119051\t정확도: 97.62%\n",
-      "55\t검증 세트 손실: 0.145868\t최선의 손실: 0.119051\t정확도: 97.65%\n",
-      "56\t검증 세트 손실: 0.131565\t최선의 손실: 0.119051\t정확도: 97.58%\n",
-      "57\t검증 세트 손실: 0.152181\t최선의 손실: 0.119051\t정확도: 97.30%\n",
-      "58\t검증 세트 손실: 0.148251\t최선의 손실: 0.119051\t정확도: 97.50%\n",
-      "59\t검증 세트 손실: 0.138614\t최선의 손실: 0.119051\t정확도: 97.77%\n",
-      "60\t검증 세트 손실: 0.151327\t최선의 손실: 0.119051\t정확도: 97.69%\n",
-      "61\t검증 세트 손실: 0.178624\t최선의 손실: 0.119051\t정확도: 97.26%\n",
-      "62\t검증 세트 손실: 0.174152\t최선의 손실: 0.119051\t정확도: 97.30%\n",
-      "63\t검증 세트 손실: 0.241129\t최선의 손실: 0.119051\t정확도: 96.48%\n",
-      "64\t검증 세트 손실: 0.146934\t최선의 손실: 0.119051\t정확도: 97.46%\n",
-      "65\t검증 세트 손실: 0.129420\t최선의 손실: 0.119051\t정확도: 97.58%\n",
-      "66\t검증 세트 손실: 0.131170\t최선의 손실: 0.119051\t정확도: 97.65%\n",
-      "67\t검증 세트 손실: 0.169099\t최선의 손실: 0.119051\t정확도: 97.62%\n",
-      "68\t검증 세트 손실: 0.187016\t최선의 손실: 0.119051\t정확도: 97.38%\n",
-      "69\t검증 세트 손실: 0.154167\t최선의 손실: 0.119051\t정확도: 97.62%\n",
-      "70\t검증 세트 손실: 0.228406\t최선의 손실: 0.119051\t정확도: 96.99%\n",
-      "71\t검증 세트 손실: 0.176527\t최선의 손실: 0.119051\t정확도: 96.91%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=120, total=  52.5s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 1.806977\t최선의 손실: 1.806977\t정확도: 59.19%\n",
+      "1\t검증 세트 손실: 0.809881\t최선의 손실: 0.809881\t정확도: 67.40%\n",
+      "2\t검증 세트 손실: 0.559785\t최선의 손실: 0.559785\t정확도: 83.78%\n",
+      "3\t검증 세트 손실: 0.492852\t최선의 손실: 0.492852\t정확도: 84.83%\n",
+      "4\t검증 세트 손실: 0.379005\t최선의 손실: 0.379005\t정확도: 89.13%\n",
+      "5\t검증 세트 손실: 0.379707\t최선의 손실: 0.379005\t정확도: 89.52%\n",
+      "6\t검증 세트 손실: 0.323126\t최선의 손실: 0.323126\t정확도: 91.28%\n",
+      "7\t검증 세트 손실: 0.346183\t최선의 손실: 0.323126\t정확도: 90.66%\n",
+      "8\t검증 세트 손실: 0.295331\t최선의 손실: 0.295331\t정확도: 92.06%\n",
+      "9\t검증 세트 손실: 0.269320\t최선의 손실: 0.269320\t정확도: 93.28%\n",
+      "10\t검증 세트 손실: 0.258228\t최선의 손실: 0.258228\t정확도: 93.59%\n",
+      "11\t검증 세트 손실: 0.294986\t최선의 손실: 0.258228\t정확도: 93.20%\n",
+      "12\t검증 세트 손실: 0.234410\t최선의 손실: 0.234410\t정확도: 94.80%\n",
+      "13\t검증 세트 손실: 0.244054\t최선의 손실: 0.234410\t정확도: 95.39%\n",
+      "14\t검증 세트 손실: 0.269746\t최선의 손실: 0.234410\t정확도: 94.06%\n",
+      "15\t검증 세트 손실: 0.240322\t최선의 손실: 0.234410\t정확도: 95.11%\n",
+      "16\t검증 세트 손실: 0.226761\t최선의 손실: 0.226761\t정확도: 95.74%\n",
+      "17\t검증 세트 손실: 0.254592\t최선의 손실: 0.226761\t정확도: 95.43%\n",
+      "18\t검증 세트 손실: 0.193520\t최선의 손실: 0.193520\t정확도: 96.17%\n",
+      "19\t검증 세트 손실: 0.176751\t최선의 손실: 0.176751\t정확도: 96.60%\n",
+      "20\t검증 세트 손실: 0.158227\t최선의 손실: 0.158227\t정확도: 97.19%\n",
+      "21\t검증 세트 손실: 0.191871\t최선의 손실: 0.158227\t정확도: 96.68%\n",
+      "22\t검증 세트 손실: 0.163650\t최선의 손실: 0.158227\t정확도: 96.68%\n",
+      "23\t검증 세트 손실: 0.161560\t최선의 손실: 0.158227\t정확도: 96.99%\n",
+      "24\t검증 세트 손실: 0.194619\t최선의 손실: 0.158227\t정확도: 96.36%\n",
+      "25\t검증 세트 손실: 0.131908\t최선의 손실: 0.131908\t정확도: 96.95%\n",
+      "26\t검증 세트 손실: 0.174713\t최선의 손실: 0.131908\t정확도: 96.48%\n",
+      "27\t검증 세트 손실: 0.148331\t최선의 손실: 0.131908\t정확도: 97.50%\n",
+      "28\t검증 세트 손실: 0.151780\t최선의 손실: 0.131908\t정확도: 97.07%\n",
+      "29\t검증 세트 손실: 0.180111\t최선의 손실: 0.131908\t정확도: 96.83%\n",
+      "30\t검증 세트 손실: 0.152027\t최선의 손실: 0.131908\t정확도: 97.22%\n",
+      "31\t검증 세트 손실: 0.138725\t최선의 손실: 0.131908\t정확도: 97.26%\n",
+      "32\t검증 세트 손실: 0.169938\t최선의 손실: 0.131908\t정확도: 97.22%\n",
+      "33\t검증 세트 손실: 0.135861\t최선의 손실: 0.131908\t정확도: 97.34%\n",
+      "34\t검증 세트 손실: 0.152764\t최선의 손실: 0.131908\t정확도: 97.15%\n",
+      "35\t검증 세트 손실: 0.168375\t최선의 손실: 0.131908\t정확도: 96.72%\n",
+      "36\t검증 세트 손실: 0.156932\t최선의 손실: 0.131908\t정확도: 97.62%\n",
+      "37\t검증 세트 손실: 0.143252\t최선의 손실: 0.131908\t정확도: 97.15%\n",
+      "38\t검증 세트 손실: 0.154851\t최선의 손실: 0.131908\t정확도: 97.07%\n",
+      "39\t검증 세트 손실: 0.136753\t최선의 손실: 0.131908\t정확도: 97.50%\n",
+      "40\t검증 세트 손실: 0.154372\t최선의 손실: 0.131908\t정확도: 97.19%\n",
+      "41\t검증 세트 손실: 0.146505\t최선의 손실: 0.131908\t정확도: 97.46%\n",
+      "42\t검증 세트 손실: 0.151017\t최선의 손실: 0.131908\t정확도: 97.22%\n",
+      "43\t검증 세트 손실: 0.153815\t최선의 손실: 0.131908\t정확도: 97.42%\n",
+      "44\t검증 세트 손실: 0.178019\t최선의 손실: 0.131908\t정확도: 97.07%\n",
+      "45\t검증 세트 손실: 0.159920\t최선의 손실: 0.131908\t정확도: 97.34%\n",
+      "46\t검증 세트 손실: 0.130484\t최선의 손실: 0.130484\t정확도: 97.77%\n",
+      "47\t검증 세트 손실: 0.150000\t최선의 손실: 0.130484\t정확도: 97.38%\n",
+      "48\t검증 세트 손실: 0.145951\t최선의 손실: 0.130484\t정확도: 97.38%\n",
+      "49\t검증 세트 손실: 0.163293\t최선의 손실: 0.130484\t정확도: 96.95%\n",
+      "50\t검증 세트 손실: 0.145887\t최선의 손실: 0.130484\t정확도: 97.42%\n",
+      "51\t검증 세트 손실: 0.147842\t최선의 손실: 0.130484\t정확도: 97.15%\n",
+      "52\t검증 세트 손실: 0.139897\t최선의 손실: 0.130484\t정확도: 97.34%\n",
+      "53\t검증 세트 손실: 0.163539\t최선의 손실: 0.130484\t정확도: 97.26%\n",
+      "54\t검증 세트 손실: 0.163130\t최선의 손실: 0.130484\t정확도: 96.79%\n",
+      "55\t검증 세트 손실: 0.142913\t최선의 손실: 0.130484\t정확도: 97.38%\n",
+      "56\t검증 세트 손실: 0.152211\t최선의 손실: 0.130484\t정확도: 96.95%\n",
+      "57\t검증 세트 손실: 0.191550\t최선의 손실: 0.130484\t정확도: 96.79%\n",
+      "58\t검증 세트 손실: 0.138296\t최선의 손실: 0.130484\t정확도: 97.26%\n",
+      "59\t검증 세트 손실: 0.174876\t최선의 손실: 0.130484\t정확도: 96.95%\n",
+      "60\t검증 세트 손실: 0.146157\t최선의 손실: 0.130484\t정확도: 97.65%\n",
+      "61\t검증 세트 손실: 0.169633\t최선의 손실: 0.130484\t정확도: 97.34%\n",
+      "62\t검증 세트 손실: 0.180838\t최선의 손실: 0.130484\t정확도: 97.07%\n",
+      "63\t검증 세트 손실: 0.179646\t최선의 손실: 0.130484\t정확도: 97.26%\n",
+      "64\t검증 세트 손실: 0.178301\t최선의 손실: 0.130484\t정확도: 97.54%\n",
+      "65\t검증 세트 손실: 0.170125\t최선의 손실: 0.130484\t정확도: 97.30%\n",
+      "66\t검증 세트 손실: 0.199896\t최선의 손실: 0.130484\t정확도: 97.38%\n",
+      "67\t검증 세트 손실: 0.188461\t최선의 손실: 0.130484\t정확도: 97.22%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=   4.5s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.508641\t최선의 손실: 1.508641\t정확도: 45.35%\n",
-      "1\t검증 세트 손실: 0.591078\t최선의 손실: 0.591078\t정확도: 79.01%\n",
-      "2\t검증 세트 손실: 0.515768\t최선의 손실: 0.515768\t정확도: 89.13%\n",
-      "3\t검증 세트 손실: 0.433902\t최선의 손실: 0.433902\t정확도: 90.81%\n",
-      "4\t검증 세트 손실: 0.786073\t최선의 손실: 0.433902\t정확도: 74.75%\n",
-      "5\t검증 세트 손실: 0.457616\t최선의 손실: 0.433902\t정확도: 92.81%\n",
-      "6\t검증 세트 손실: 0.485790\t최선의 손실: 0.433902\t정확도: 93.04%\n",
-      "7\t검증 세트 손실: 0.391872\t최선의 손실: 0.391872\t정확도: 94.53%\n",
-      "8\t검증 세트 손실: 0.399642\t최선의 손실: 0.391872\t정확도: 95.27%\n",
-      "9\t검증 세트 손실: 0.404569\t최선의 손실: 0.391872\t정확도: 95.62%\n",
-      "10\t검증 세트 손실: 0.479930\t최선의 손실: 0.391872\t정확도: 95.11%\n",
-      "11\t검증 세트 손실: 0.393690\t최선의 손실: 0.391872\t정확도: 95.19%\n",
-      "12\t검증 세트 손실: 0.438934\t최선의 손실: 0.391872\t정확도: 95.50%\n",
-      "13\t검증 세트 손실: 1.073666\t최선의 손실: 0.391872\t정확도: 88.82%\n",
-      "14\t검증 세트 손실: 2.090673\t최선의 손실: 0.391872\t정확도: 40.30%\n",
-      "15\t검증 세트 손실: 11.220267\t최선의 손실: 0.391872\t정확도: 37.57%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=50, total=   5.4s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.781046\t최선의 손실: 0.781046\t정확도: 75.84%\n",
+      "1\t검증 세트 손실: 0.291295\t최선의 손실: 0.291295\t정확도: 91.28%\n",
+      "2\t검증 세트 손실: 0.203184\t최선의 손실: 0.203184\t정확도: 94.41%\n",
+      "3\t검증 세트 손실: 0.177062\t최선의 손실: 0.177062\t정확도: 95.43%\n",
+      "4\t검증 세트 손실: 0.177654\t최선의 손실: 0.177062\t정확도: 95.27%\n",
+      "5\t검증 세트 손실: 0.251902\t최선의 손실: 0.177062\t정확도: 96.52%\n",
+      "6\t검증 세트 손실: 0.137736\t최선의 손실: 0.137736\t정확도: 96.52%\n",
+      "7\t검증 세트 손실: 0.138122\t최선의 손실: 0.137736\t정확도: 96.68%\n",
+      "8\t검증 세트 손실: 0.220462\t최선의 손실: 0.137736\t정확도: 96.05%\n",
+      "9\t검증 세트 손실: 0.193657\t최선의 손실: 0.137736\t정확도: 96.99%\n",
+      "10\t검증 세트 손실: 0.157444\t최선의 손실: 0.137736\t정확도: 96.36%\n",
+      "11\t검증 세트 손실: 0.159819\t최선의 손실: 0.137736\t정확도: 95.78%\n",
+      "12\t검증 세트 손실: 0.131291\t최선의 손실: 0.131291\t정확도: 96.60%\n",
+      "13\t검증 세트 손실: 0.146173\t최선의 손실: 0.131291\t정확도: 96.48%\n",
+      "14\t검증 세트 손실: 0.124065\t최선의 손실: 0.124065\t정확도: 96.91%\n",
+      "15\t검증 세트 손실: 0.133700\t최선의 손실: 0.124065\t정확도: 96.72%\n",
+      "16\t검증 세트 손실: 0.140085\t최선의 손실: 0.124065\t정확도: 96.64%\n",
+      "17\t검증 세트 손실: 0.118738\t최선의 손실: 0.118738\t정확도: 96.99%\n",
+      "18\t검증 세트 손실: 0.145952\t최선의 손실: 0.118738\t정확도: 96.64%\n",
+      "19\t검증 세트 손실: 0.127597\t최선의 손실: 0.118738\t정확도: 97.07%\n",
+      "20\t검증 세트 손실: 0.132488\t최선의 손실: 0.118738\t정확도: 96.79%\n",
+      "21\t검증 세트 손실: 0.120681\t최선의 손실: 0.118738\t정확도: 97.11%\n",
+      "22\t검증 세트 손실: 0.171823\t최선의 손실: 0.118738\t정확도: 96.21%\n",
+      "23\t검증 세트 손실: 0.131154\t최선의 손실: 0.118738\t정확도: 96.95%\n",
+      "24\t검증 세트 손실: 0.120292\t최선의 손실: 0.118738\t정확도: 96.87%\n",
+      "25\t검증 세트 손실: 0.128713\t최선의 손실: 0.118738\t정확도: 96.87%\n",
+      "26\t검증 세트 손실: 0.123390\t최선의 손실: 0.118738\t정확도: 97.07%\n",
+      "27\t검증 세트 손실: 0.134952\t최선의 손실: 0.118738\t정확도: 96.95%\n",
+      "28\t검증 세트 손실: 0.142447\t최선의 손실: 0.118738\t정확도: 96.95%\n",
+      "29\t검증 세트 손실: 0.139623\t최선의 손실: 0.118738\t정확도: 97.26%\n",
+      "30\t검증 세트 손실: 0.136778\t최선의 손실: 0.118738\t정확도: 97.07%\n",
+      "31\t검증 세트 손실: 0.166253\t최선의 손실: 0.118738\t정확도: 96.83%\n",
+      "32\t검증 세트 손실: 0.231107\t최선의 손실: 0.118738\t정확도: 96.29%\n",
+      "33\t검증 세트 손실: 0.145661\t최선의 손실: 0.118738\t정확도: 96.05%\n",
+      "34\t검증 세트 손실: 0.127940\t최선의 손실: 0.118738\t정확도: 96.91%\n",
+      "35\t검증 세트 손실: 0.133446\t최선의 손실: 0.118738\t정확도: 97.26%\n",
+      "36\t검증 세트 손실: 0.139976\t최선의 손실: 0.118738\t정확도: 96.72%\n",
+      "37\t검증 세트 손실: 0.160298\t최선의 손실: 0.118738\t정확도: 96.64%\n",
+      "38\t검증 세트 손실: 0.153905\t최선의 손실: 0.118738\t정확도: 96.87%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=50, total=   3.0s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.839977\t최선의 손실: 0.839977\t정확도: 69.47%\n",
+      "1\t검증 세트 손실: 0.398734\t최선의 손실: 0.398734\t정확도: 85.54%\n",
+      "2\t검증 세트 손실: 0.289568\t최선의 손실: 0.289568\t정확도: 91.24%\n",
+      "3\t검증 세트 손실: 0.225639\t최선의 손실: 0.225639\t정확도: 93.51%\n",
+      "4\t검증 세트 손실: 0.181285\t최선의 손실: 0.181285\t정확도: 95.11%\n",
+      "5\t검증 세트 손실: 0.163609\t최선의 손실: 0.163609\t정확도: 95.39%\n",
+      "6\t검증 세트 손실: 0.162064\t최선의 손실: 0.162064\t정확도: 95.97%\n",
+      "7\t검증 세트 손실: 0.136860\t최선의 손실: 0.136860\t정확도: 95.82%\n",
+      "8\t검증 세트 손실: 0.159193\t최선의 손실: 0.136860\t정확도: 95.70%\n",
+      "9\t검증 세트 손실: 0.125925\t최선의 손실: 0.125925\t정확도: 96.56%\n",
+      "10\t검증 세트 손실: 0.139119\t최선의 손실: 0.125925\t정확도: 95.86%\n",
+      "11\t검증 세트 손실: 0.131127\t최선의 손실: 0.125925\t정확도: 96.56%\n",
+      "12\t검증 세트 손실: 0.129788\t최선의 손실: 0.125925\t정확도: 96.56%\n",
+      "13\t검증 세트 손실: 0.134593\t최선의 손실: 0.125925\t정확도: 96.29%\n",
+      "14\t검증 세트 손실: 0.133004\t최선의 손실: 0.125925\t정확도: 96.40%\n",
+      "15\t검증 세트 손실: 0.124415\t최선의 손실: 0.124415\t정확도: 96.64%\n",
+      "16\t검증 세트 손실: 0.126594\t최선의 손실: 0.124415\t정확도: 96.56%\n",
+      "17\t검증 세트 손실: 0.142581\t최선의 손실: 0.124415\t정확도: 96.33%\n",
+      "18\t검증 세트 손실: 0.134940\t최선의 손실: 0.124415\t정확도: 96.76%\n",
+      "19\t검증 세트 손실: 0.165898\t최선의 손실: 0.124415\t정확도: 96.13%\n",
+      "20\t검증 세트 손실: 0.143502\t최선의 손실: 0.124415\t정확도: 96.56%\n",
+      "21\t검증 세트 손실: 3.112810\t최선의 손실: 0.124415\t정확도: 22.99%\n",
+      "22\t검증 세트 손실: 1.694839\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.610992\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.614075\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.628404\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.619276\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.613198\t최선의 손실: 0.124415\t정확도: 19.27%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "16\t검증 세트 손실: 6.801213\t최선의 손실: 0.391872\t정확도: 37.57%\n",
-      "17\t검증 세트 손실: 3.793837\t최선의 손실: 0.391872\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.618412\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.610003\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.643316\t최선의 손실: 0.391872\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.610561\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.609754\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.612017\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.611107\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.615096\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.615750\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "27\t검증 세트 손실: 1.629283\t최선의 손실: 0.391872\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 1.636984\t최선의 손실: 0.391872\t정확도: 18.73%\n",
+      "28\t검증 세트 손실: 1.631046\t최선의 손실: 0.124415\t정확도: 18.73%\n",
+      "29\t검증 세트 손실: 1.618762\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "30\t검증 세트 손실: 1.615479\t최선의 손실: 0.124415\t정확도: 20.91%\n",
+      "31\t검증 세트 손실: 1.616488\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 1.610171\t최선의 손실: 0.124415\t정확도: 22.01%\n",
+      "33\t검증 세트 손실: 1.623160\t최선의 손실: 0.124415\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 1.627331\t최선의 손실: 0.124415\t정확도: 20.91%\n",
+      "35\t검증 세트 손실: 1.627435\t최선의 손실: 0.124415\t정확도: 19.27%\n",
+      "36\t검증 세트 손실: 1.618342\t최선의 손실: 0.124415\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=   2.1s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1331.952637\t최선의 손실: 1331.952637\t정확도: 39.41%\n",
-      "1\t검증 세트 손실: 6422.535156\t최선의 손실: 1331.952637\t정확도: 54.81%\n",
-      "2\t검증 세트 손실: 1866.986694\t최선의 손실: 1331.952637\t정확도: 78.89%\n",
-      "3\t검증 세트 손실: 26640.218750\t최선의 손실: 1331.952637\t정확도: 38.62%\n",
-      "4\t검증 세트 손실: 1600.338013\t최선의 손실: 1331.952637\t정확도: 75.49%\n",
-      "5\t검증 세트 손실: 3036.848633\t최선의 손실: 1331.952637\t정확도: 74.43%\n",
-      "6\t검증 세트 손실: 2534.325439\t최선의 손실: 1331.952637\t정확도: 64.03%\n",
-      "7\t검증 세트 손실: 8677.793945\t최선의 손실: 1331.952637\t정확도: 67.47%\n",
-      "8\t검증 세트 손실: 4149.645020\t최선의 손실: 1331.952637\t정확도: 70.09%\n",
-      "9\t검증 세트 손실: 1073.308594\t최선의 손실: 1073.308594\t정확도: 82.33%\n",
-      "10\t검증 세트 손실: 4662.416504\t최선의 손실: 1073.308594\t정확도: 87.45%\n",
-      "11\t검증 세트 손실: 502.162964\t최선의 손실: 502.162964\t정확도: 90.85%\n",
-      "12\t검증 세트 손실: 2188.209229\t최선의 손실: 502.162964\t정확도: 87.41%\n",
-      "13\t검증 세트 손실: 19823.478516\t최선의 손실: 502.162964\t정확도: 31.74%\n",
-      "14\t검증 세트 손실: 5550.531738\t최선의 손실: 502.162964\t정확도: 31.70%\n",
-      "15\t검증 세트 손실: 1601266.625000\t최선의 손실: 502.162964\t정확도: 35.89%\n",
-      "16\t검증 세트 손실: 179877.015625\t최선의 손실: 502.162964\t정확도: 45.04%\n",
-      "17\t검증 세트 손실: 85881.828125\t최선의 손실: 502.162964\t정확도: 56.84%\n",
-      "18\t검증 세트 손실: 48829.757812\t최선의 손실: 502.162964\t정확도: 61.34%\n",
-      "19\t검증 세트 손실: 12449.135742\t최선의 손실: 502.162964\t정확도: 72.24%\n",
-      "20\t검증 세트 손실: 23228.798828\t최선의 손실: 502.162964\t정확도: 64.78%\n",
-      "21\t검증 세트 손실: 11967.949219\t최선의 손실: 502.162964\t정확도: 85.03%\n",
-      "22\t검증 세트 손실: 8473.166992\t최선의 손실: 502.162964\t정확도: 88.51%\n",
-      "23\t검증 세트 손실: 12806.001953\t최선의 손실: 502.162964\t정확도: 83.82%\n",
-      "24\t검증 세트 손실: 4942.685059\t최선의 손실: 502.162964\t정확도: 92.96%\n",
-      "25\t검증 세트 손실: 7400.000000\t최선의 손실: 502.162964\t정확도: 93.28%\n",
-      "26\t검증 세트 손실: 7249.056152\t최선의 손실: 502.162964\t정확도: 90.66%\n",
-      "27\t검증 세트 손실: 19888.722656\t최선의 손실: 502.162964\t정확도: 81.94%\n",
-      "28\t검증 세트 손실: 498275.062500\t최선의 손실: 502.162964\t정확도: 49.73%\n",
-      "29\t검증 세트 손실: 104418.328125\t최선의 손실: 502.162964\t정확도: 65.56%\n",
-      "30\t검증 세트 손실: 43277.023438\t최선의 손실: 502.162964\t정확도: 76.66%\n",
-      "31\t검증 세트 손실: 40254.226562\t최선의 손실: 502.162964\t정확도: 83.74%\n",
-      "32\t검증 세트 손실: 46655.179688\t최선의 손실: 502.162964\t정확도: 86.83%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=50, total=   3.0s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 6.036595\t최선의 손실: 6.036595\t정확도: 69.59%\n",
+      "1\t검증 세트 손실: 2.043573\t최선의 손실: 2.043573\t정확도: 72.87%\n",
+      "2\t검증 세트 손실: 0.484242\t최선의 손실: 0.484242\t정확도: 87.65%\n",
+      "3\t검증 세트 손실: 1.541157\t최선의 손실: 0.484242\t정확도: 73.81%\n",
+      "4\t검증 세트 손실: 699320.437500\t최선의 손실: 0.484242\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 90903.320312\t최선의 손실: 0.484242\t정확도: 19.16%\n",
+      "6\t검증 세트 손실: 88216.609375\t최선의 손실: 0.484242\t정확도: 18.80%\n",
+      "7\t검증 세트 손실: 73468.218750\t최선의 손실: 0.484242\t정확도: 22.95%\n",
+      "8\t검증 세트 손실: 23802.373047\t최선의 손실: 0.484242\t정확도: 31.20%\n",
+      "9\t검증 세트 손실: 327091.750000\t최선의 손실: 0.484242\t정확도: 25.92%\n",
+      "10\t검증 세트 손실: 5122195.500000\t최선의 손실: 0.484242\t정확도: 32.76%\n",
+      "11\t검증 세트 손실: 26600.587891\t최선의 손실: 0.484242\t정확도: 16.85%\n",
+      "12\t검증 세트 손실: 25212.904297\t최선의 손실: 0.484242\t정확도: 23.61%\n",
+      "13\t검증 세트 손실: 9658.112305\t최선의 손실: 0.484242\t정확도: 43.82%\n",
+      "14\t검증 세트 손실: 7743.038086\t최선의 손실: 0.484242\t정확도: 37.61%\n",
+      "15\t검증 세트 손실: 17401.955078\t최선의 손실: 0.484242\t정확도: 39.84%\n",
+      "16\t검증 세트 손실: 4059.947510\t최선의 손실: 0.484242\t정확도: 47.26%\n",
+      "17\t검증 세트 손실: 4344.253906\t최선의 손실: 0.484242\t정확도: 50.98%\n",
+      "18\t검증 세트 손실: 7808.818848\t최선의 손실: 0.484242\t정확도: 41.95%\n",
+      "19\t검증 세트 손실: 6955.620605\t최선의 손실: 0.484242\t정확도: 54.26%\n",
+      "20\t검증 세트 손실: 1842.103394\t최선의 손실: 0.484242\t정확도: 64.74%\n",
+      "21\t검증 세트 손실: 3608.283936\t최선의 손실: 0.484242\t정확도: 47.97%\n",
+      "22\t검증 세트 손실: 4201.949707\t최선의 손실: 0.484242\t정확도: 62.71%\n",
+      "23\t검증 세트 손실: 2242.165039\t최선의 손실: 0.484242\t정확도: 70.68%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=  37.5s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.514261\t최선의 손실: 0.514261\t정확도: 82.25%\n",
-      "1\t검증 세트 손실: 0.238895\t최선의 손실: 0.238895\t정확도: 93.47%\n",
-      "2\t검증 세트 손실: 0.203131\t최선의 손실: 0.203131\t정확도: 94.92%\n",
-      "3\t검증 세트 손실: 315650.437500\t최선의 손실: 0.203131\t정확도: 20.91%\n",
-      "4\t검증 세트 손실: 1116.908081\t최선의 손실: 0.203131\t정확도: 41.52%\n",
-      "5\t검증 세트 손실: 11249.695312\t최선의 손실: 0.203131\t정확도: 42.14%\n",
-      "6\t검증 세트 손실: 1845.703735\t최선의 손실: 0.203131\t정확도: 44.25%\n",
-      "7\t검증 세트 손실: 922.755493\t최선의 손실: 0.203131\t정확도: 46.79%\n",
-      "8\t검증 세트 손실: 679.572876\t최선의 손실: 0.203131\t정확도: 57.94%\n",
-      "9\t검증 세트 손실: 209.430420\t최선의 손실: 0.203131\t정확도: 62.86%\n",
-      "10\t검증 세트 손실: 240.647522\t최선의 손실: 0.203131\t정확도: 61.53%\n",
-      "11\t검증 세트 손실: 304.618073\t최선의 손실: 0.203131\t정확도: 67.16%\n",
-      "12\t검증 세트 손실: 129.182907\t최선의 손실: 0.203131\t정확도: 64.00%\n",
-      "13\t검증 세트 손실: 101.573006\t최선의 손실: 0.203131\t정확도: 73.57%\n",
-      "14\t검증 세트 손실: 89.611748\t최선의 손실: 0.203131\t정확도: 77.01%\n",
-      "15\t검증 세트 손실: 54.555519\t최선의 손실: 0.203131\t정확도: 78.46%\n",
-      "16\t검증 세트 손실: 58.656212\t최선의 손실: 0.203131\t정확도: 78.38%\n",
-      "17\t검증 세트 손실: 297.403168\t최선의 손실: 0.203131\t정확도: 84.01%\n",
-      "18\t검증 세트 손실: 2486129.500000\t최선의 손실: 0.203131\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 55284.691406\t최선의 손실: 0.203131\t정확도: 61.45%\n",
-      "20\t검증 세트 손실: 26018.662109\t최선의 손실: 0.203131\t정확도: 71.07%\n",
-      "21\t검증 세트 손실: 27339.531250\t최선의 손실: 0.203131\t정확도: 73.42%\n",
-      "22\t검증 세트 손실: 27410.183594\t최선의 손실: 0.203131\t정확도: 71.89%\n",
-      "23\t검증 세트 손실: 46958.832031\t최선의 손실: 0.203131\t정확도: 69.78%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=100, total=  12.8s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.164671\t최선의 손실: 1.164671\t정확도: 48.71%\n",
+      "1\t검증 세트 손실: 0.790552\t최선의 손실: 0.790552\t정확도: 69.74%\n",
+      "2\t검증 세트 손실: 0.970793\t최선의 손실: 0.790552\t정확도: 57.35%\n",
+      "3\t검증 세트 손실: 2941387.500000\t최선의 손실: 0.790552\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 7641.762207\t최선의 손실: 0.790552\t정확도: 53.67%\n",
+      "5\t검증 세트 손실: 12138.183594\t최선의 손실: 0.790552\t정확도: 63.14%\n",
+      "6\t검증 세트 손실: 5672.643066\t최선의 손실: 0.790552\t정확도: 64.89%\n",
+      "7\t검증 세트 손실: 8377.610352\t최선의 손실: 0.790552\t정확도: 62.31%\n",
+      "8\t검증 세트 손실: 1999.374756\t최선의 손실: 0.790552\t정확도: 86.63%\n",
+      "9\t검증 세트 손실: 3335.918701\t최선의 손실: 0.790552\t정확도: 87.45%\n",
+      "10\t검증 세트 손실: 3753.828369\t최선의 손실: 0.790552\t정확도: 86.24%\n",
+      "11\t검증 세트 손실: 4737.047852\t최선의 손실: 0.790552\t정확도: 85.93%\n",
+      "12\t검증 세트 손실: 2261.474121\t최선의 손실: 0.790552\t정확도: 92.77%\n",
+      "13\t검증 세트 손실: 2852.490479\t최선의 손실: 0.790552\t정확도: 89.29%\n",
+      "14\t검증 세트 손실: 1982.741821\t최선의 손실: 0.790552\t정확도: 94.76%\n",
+      "15\t검증 세트 손실: 916.144470\t최선의 손실: 0.790552\t정확도: 95.07%\n",
+      "16\t검증 세트 손실: 13890.856445\t최선의 손실: 0.790552\t정확도: 86.20%\n",
+      "17\t검증 세트 손실: 2817.363281\t최선의 손실: 0.790552\t정확도: 90.97%\n",
+      "18\t검증 세트 손실: 2156.271729\t최선의 손실: 0.790552\t정확도: 94.68%\n",
+      "19\t검증 세트 손실: 1415.136963\t최선의 손실: 0.790552\t정확도: 95.62%\n",
+      "20\t검증 세트 손실: 28429.826172\t최선의 손실: 0.790552\t정확도: 88.15%\n",
+      "21\t검증 세트 손실: 8520.758789\t최선의 손실: 0.790552\t정확도: 90.23%\n",
+      "22\t검증 세트 손실: 3743.801758\t최선의 손실: 0.790552\t정확도: 93.90%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=  28.7s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 6362.806641\t최선의 손실: 6362.806641\t정확도: 19.16%\n",
-      "1\t검증 세트 손실: 658.557983\t최선의 손실: 658.557983\t정확도: 19.16%\n",
-      "2\t검증 세트 손실: 224.465408\t최선의 손실: 224.465408\t정확도: 20.88%\n",
-      "3\t검증 세트 손실: 147.993637\t최선의 손실: 147.993637\t정확도: 22.48%\n",
-      "4\t검증 세트 손실: 76.824715\t최선의 손실: 76.824715\t정확도: 26.78%\n",
-      "5\t검증 세트 손실: 101.480392\t최선의 손실: 76.824715\t정확도: 35.22%\n",
-      "6\t검증 세트 손실: 65.893311\t최선의 손실: 65.893311\t정확도: 34.13%\n",
-      "7\t검증 세트 손실: 285.092560\t최선의 손실: 65.893311\t정확도: 21.07%\n",
-      "8\t검증 세트 손실: 244744.078125\t최선의 손실: 65.893311\t정확도: 50.04%\n",
-      "9\t검증 세트 손실: 64929.625000\t최선의 손실: 65.893311\t정확도: 65.60%\n",
-      "10\t검증 세트 손실: 30611.500000\t최선의 손실: 65.893311\t정확도: 62.71%\n",
-      "11\t검증 세트 손실: 25789.529297\t최선의 손실: 65.893311\t정확도: 70.21%\n",
-      "12\t검증 세트 손실: 80212.375000\t최선의 손실: 65.893311\t정확도: 55.59%\n",
-      "13\t검증 세트 손실: 14900.758789\t최선의 손실: 65.893311\t정확도: 72.79%\n",
-      "14\t검증 세트 손실: 69291.804688\t최선의 손실: 65.893311\t정확도: 64.82%\n",
-      "15\t검증 세트 손실: 11501.311523\t최선의 손실: 65.893311\t정확도: 73.89%\n",
-      "16\t검증 세트 손실: 19053.410156\t최선의 손실: 65.893311\t정확도: 76.78%\n",
-      "17\t검증 세트 손실: 17419.580078\t최선의 손실: 65.893311\t정확도: 72.83%\n",
-      "18\t검증 세트 손실: 1747775.000000\t최선의 손실: 65.893311\t정확도: 20.88%\n",
-      "19\t검증 세트 손실: 1026978.312500\t최선의 손실: 65.893311\t정확도: 19.12%\n",
-      "20\t검증 세트 손실: 535804.875000\t최선의 손실: 65.893311\t정확도: 19.23%\n",
-      "21\t검증 세트 손실: 197924.765625\t최선의 손실: 65.893311\t정확도: 19.74%\n",
-      "22\t검증 세트 손실: 1089384.125000\t최선의 손실: 65.893311\t정확도: 22.71%\n",
-      "23\t검증 세트 손실: 278057.781250\t최선의 손실: 65.893311\t정확도: 22.24%\n",
-      "24\t검증 세트 손실: 112573.171875\t최선의 손실: 65.893311\t정확도: 27.09%\n",
-      "25\t검증 세트 손실: 50943.160156\t최선의 손실: 65.893311\t정확도: 34.21%\n",
-      "26\t검증 세트 손실: 62708.859375\t최선의 손실: 65.893311\t정확도: 36.79%\n",
-      "27\t검증 세트 손실: 67864.093750\t최선의 손실: 65.893311\t정확도: 51.29%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=100, total=  11.9s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 142.923111\t최선의 손실: 142.923111\t정확도: 24.20%\n",
+      "1\t검증 세트 손실: 29.855112\t최선의 손실: 29.855112\t정확도: 35.26%\n",
+      "2\t검증 세트 손실: 14.329179\t최선의 손실: 14.329179\t정확도: 55.12%\n",
+      "3\t검증 세트 손실: 7.141100\t최선의 손실: 7.141100\t정확도: 46.95%\n",
+      "4\t검증 세트 손실: 15564.836914\t최선의 손실: 7.141100\t정확도: 37.76%\n",
+      "5\t검증 세트 손실: 49132.343750\t최선의 손실: 7.141100\t정확도: 33.93%\n",
+      "6\t검증 세트 손실: 5029.900391\t최선의 손실: 7.141100\t정확도: 56.02%\n",
+      "7\t검증 세트 손실: 4776.240723\t최선의 손실: 7.141100\t정확도: 37.10%\n",
+      "8\t검증 세트 손실: 6642.529297\t최선의 손실: 7.141100\t정확도: 35.81%\n",
+      "9\t검증 세트 손실: 2295.722412\t최선의 손실: 7.141100\t정확도: 69.04%\n",
+      "10\t검증 세트 손실: 3680.871094\t최선의 손실: 7.141100\t정확도: 51.60%\n",
+      "11\t검증 세트 손실: 1622.592896\t최선의 손실: 7.141100\t정확도: 76.04%\n",
+      "12\t검증 세트 손실: 783.341919\t최선의 손실: 7.141100\t정확도: 86.20%\n",
+      "13\t검증 세트 손실: 1776.053955\t최선의 손실: 7.141100\t정확도: 75.33%\n",
+      "14\t검증 세트 손실: 1809.907593\t최선의 손실: 7.141100\t정확도: 77.95%\n",
+      "15\t검증 세트 손실: 893.129578\t최선의 손실: 7.141100\t정확도: 88.98%\n",
+      "16\t검증 세트 손실: 1499.173340\t최선의 손실: 7.141100\t정확도: 77.87%\n",
+      "17\t검증 세트 손실: 1079123.250000\t최선의 손실: 7.141100\t정확도: 40.03%\n",
+      "18\t검증 세트 손실: 11388.935547\t최선의 손실: 7.141100\t정확도: 70.80%\n",
+      "19\t검증 세트 손실: 2554.880859\t최선의 손실: 7.141100\t정확도: 84.79%\n",
+      "20\t검증 세트 손실: 2799.998779\t최선의 손실: 7.141100\t정확도: 82.45%\n",
+      "21\t검증 세트 손실: 2527.630859\t최선의 손실: 7.141100\t정확도: 81.51%\n",
+      "22\t검증 세트 손실: 1364.217896\t최선의 손실: 7.141100\t정확도: 91.36%\n",
+      "23\t검증 세트 손실: 2490.092773\t최선의 손실: 7.141100\t정확도: 82.84%\n",
+      "24\t검증 세트 손실: 1355.910400\t최선의 손실: 7.141100\t정확도: 91.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=  28.4s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 21.287699\t최선의 손실: 21.287699\t정확도: 67.36%\n",
-      "1\t검증 세트 손실: 2.110445\t최선의 손실: 2.110445\t정확도: 84.17%\n",
-      "2\t검증 세트 손실: 1.498328\t최선의 손실: 1.498328\t정확도: 92.57%\n",
-      "3\t검증 세트 손실: 1.870462\t최선의 손실: 1.498328\t정확도: 90.81%\n",
-      "4\t검증 세트 손실: 0.615253\t최선의 손실: 0.615253\t정확도: 94.80%\n",
-      "5\t검증 세트 손실: 1.178986\t최선의 손실: 0.615253\t정확도: 91.67%\n",
-      "6\t검증 세트 손실: 0.768491\t최선의 손실: 0.615253\t정확도: 92.30%\n",
-      "7\t검증 세트 손실: 0.668250\t최선의 손실: 0.615253\t정확도: 94.29%\n",
-      "8\t검증 세트 손실: 1.644635\t최선의 손실: 0.615253\t정확도: 92.10%\n",
-      "9\t검증 세트 손실: 1.294488\t최선의 손실: 0.615253\t정확도: 94.41%\n",
-      "10\t검증 세트 손실: 1.320778\t최선의 손실: 0.615253\t정확도: 94.49%\n",
-      "11\t검증 세트 손실: 0.890391\t최선의 손실: 0.615253\t정확도: 95.31%\n",
-      "12\t검증 세트 손실: 0.596060\t최선의 손실: 0.596060\t정확도: 96.40%\n",
-      "13\t검증 세트 손실: 0.601668\t최선의 손실: 0.596060\t정확도: 95.78%\n",
-      "14\t검증 세트 손실: 0.461635\t최선의 손실: 0.461635\t정확도: 97.11%\n",
-      "15\t검증 세트 손실: 0.670643\t최선의 손실: 0.461635\t정확도: 94.49%\n",
-      "16\t검증 세트 손실: 114284.148438\t최선의 손실: 0.461635\t정확도: 85.46%\n",
-      "17\t검증 세트 손실: 25282.388672\t최선의 손실: 0.461635\t정확도: 92.34%\n",
-      "18\t검증 세트 손실: 14807.373047\t최선의 손실: 0.461635\t정확도: 93.32%\n",
-      "19\t검증 세트 손실: 9794.808594\t최선의 손실: 0.461635\t정확도: 93.51%\n",
-      "20\t검증 세트 손실: 7501.621582\t최선의 손실: 0.461635\t정확도: 93.67%\n",
-      "21\t검증 세트 손실: 5704.536133\t최선의 손실: 0.461635\t정확도: 94.68%\n",
-      "22\t검증 세트 손실: 8673.655273\t최선의 손실: 0.461635\t정확도: 92.92%\n",
-      "23\t검증 세트 손실: 6517.557617\t최선의 손실: 0.461635\t정확도: 94.18%\n",
-      "24\t검증 세트 손실: 7589.434082\t최선의 손실: 0.461635\t정확도: 93.43%\n",
-      "25\t검증 세트 손실: 4997.328613\t최선의 손실: 0.461635\t정확도: 95.35%\n",
-      "26\t검증 세트 손실: 4285.012207\t최선의 손실: 0.461635\t정확도: 95.07%\n",
-      "27\t검증 세트 손실: 3591.486328\t최선의 손실: 0.461635\t정확도: 95.82%\n",
-      "28\t검증 세트 손실: 3215.497314\t최선의 손실: 0.461635\t정확도: 95.62%\n",
-      "29\t검증 세트 손실: 3403.066406\t최선의 손실: 0.461635\t정확도: 95.90%\n",
-      "30\t검증 세트 손실: 3302.111816\t최선의 손실: 0.461635\t정확도: 95.97%\n",
-      "31\t검증 세트 손실: 9855.057617\t최선의 손실: 0.461635\t정확도: 93.43%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=100, total=  12.8s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 83.126709\t최선의 손실: 83.126709\t정확도: 69.82%\n",
+      "1\t검증 세트 손실: 12.327357\t최선의 손실: 12.327357\t정확도: 90.30%\n",
+      "2\t검증 세트 손실: 8.727623\t최선의 손실: 8.727623\t정확도: 91.95%\n",
+      "3\t검증 세트 손실: 5.870103\t최선의 손실: 5.870103\t정확도: 94.02%\n",
+      "4\t검증 세트 손실: 6.386273\t최선의 손실: 5.870103\t정확도: 94.80%\n",
+      "5\t검증 세트 손실: 6.119045\t최선의 손실: 5.870103\t정확도: 95.62%\n",
+      "6\t검증 세트 손실: 6.039952\t최선의 손실: 5.870103\t정확도: 93.12%\n",
+      "7\t검증 세트 손실: 7.730052\t최선의 손실: 5.870103\t정확도: 93.63%\n",
+      "8\t검증 세트 손실: 7.727905\t최선의 손실: 5.870103\t정확도: 90.19%\n",
+      "9\t검증 세트 손실: 2.480627\t최선의 손실: 2.480627\t정확도: 94.49%\n",
+      "10\t검증 세트 손실: 5.616520\t최선의 손실: 2.480627\t정확도: 94.72%\n",
+      "11\t검증 세트 손실: 19383864.000000\t최선의 손실: 2.480627\t정확도: 44.88%\n",
+      "12\t검증 세트 손실: 150023.953125\t최선의 손실: 2.480627\t정확도: 84.44%\n",
+      "13\t검증 세트 손실: 280061.906250\t최선의 손실: 2.480627\t정확도: 77.87%\n",
+      "14\t검증 세트 손실: 50015.339844\t최선의 손실: 2.480627\t정확도: 92.77%\n",
+      "15\t검증 세트 손실: 59763.019531\t최선의 손실: 2.480627\t정확도: 92.61%\n",
+      "16\t검증 세트 손실: 50495.222656\t최선의 손실: 2.480627\t정확도: 91.48%\n",
+      "17\t검증 세트 손실: 40739.222656\t최선의 손실: 2.480627\t정확도: 92.53%\n",
+      "18\t검증 세트 손실: 30827.220703\t최선의 손실: 2.480627\t정확도: 94.88%\n",
+      "19\t검증 세트 손실: 28979.734375\t최선의 손실: 2.480627\t정확도: 94.92%\n",
+      "20\t검증 세트 손실: 27522.001953\t최선의 손실: 2.480627\t정확도: 95.86%\n",
+      "21\t검증 세트 손실: 51740.640625\t최선의 손실: 2.480627\t정확도: 91.56%\n",
+      "22\t검증 세트 손실: 29873.216797\t최선의 손실: 2.480627\t정확도: 93.94%\n",
+      "23\t검증 세트 손실: 24277.392578\t최선의 손실: 2.480627\t정확도: 94.02%\n",
+      "24\t검증 세트 손실: 65393.214844\t최선의 손실: 2.480627\t정확도: 88.62%\n",
+      "25\t검증 세트 손실: 23980.199219\t최선의 손실: 2.480627\t정확도: 95.07%\n",
+      "26\t검증 세트 손실: 31407.361328\t최선의 손실: 2.480627\t정확도: 95.97%\n",
+      "27\t검증 세트 손실: 42031.664062\t최선의 손실: 2.480627\t정확도: 92.38%\n",
+      "28\t검증 세트 손실: 15289.069336\t최선의 손실: 2.480627\t정확도: 96.72%\n",
+      "29\t검증 세트 손실: 13410.017578\t최선의 손실: 2.480627\t정확도: 97.03%\n",
+      "30\t검증 세트 손실: 24539.708984\t최선의 손실: 2.480627\t정확도: 93.32%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70, total=   8.2s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.655982\t최선의 손실: 0.655982\t정확도: 93.43%\n",
+      "1\t검증 세트 손실: 0.247061\t최선의 손실: 0.247061\t정확도: 95.43%\n",
+      "2\t검증 세트 손실: 0.251590\t최선의 손실: 0.247061\t정확도: 96.05%\n",
+      "3\t검증 세트 손실: 1.551615\t최선의 손실: 0.247061\t정확도: 92.14%\n",
+      "4\t검증 세트 손실: 0.573421\t최선의 손실: 0.247061\t정확도: 92.96%\n",
+      "5\t검증 세트 손실: 0.729028\t최선의 손실: 0.247061\t정확도: 90.23%\n",
+      "6\t검증 세트 손실: 0.148618\t최선의 손실: 0.148618\t정확도: 96.64%\n",
+      "7\t검증 세트 손실: 0.188756\t최선의 손실: 0.148618\t정확도: 96.25%\n",
+      "8\t검증 세트 손실: 0.191839\t최선의 손실: 0.148618\t정확도: 96.29%\n",
+      "9\t검증 세트 손실: 0.131412\t최선의 손실: 0.131412\t정확도: 97.54%\n",
+      "10\t검증 세트 손실: 0.180036\t최선의 손실: 0.131412\t정확도: 97.58%\n",
+      "11\t검증 세트 손실: 0.121361\t최선의 손실: 0.121361\t정확도: 97.26%\n",
+      "12\t검증 세트 손실: 26.170311\t최선의 손실: 0.121361\t정확도: 87.29%\n",
+      "13\t검증 세트 손실: 26316.294922\t최선의 손실: 0.121361\t정확도: 87.49%\n",
+      "14\t검증 세트 손실: 7366.489258\t최선의 손실: 0.121361\t정확도: 93.90%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "32\t검증 세트 손실: 5501.453125\t최선의 손실: 0.461635\t정확도: 94.92%\n",
-      "33\t검증 세트 손실: 2935.468994\t최선의 손실: 0.461635\t정확도: 96.68%\n",
-      "34\t검증 세트 손실: 5006.396973\t최선의 손실: 0.461635\t정확도: 94.18%\n",
-      "35\t검증 세트 손실: 13359.974609\t최선의 손실: 0.461635\t정확도: 95.31%\n",
+      "15\t검증 세트 손실: 10705.510742\t최선의 손실: 0.121361\t정확도: 92.06%\n",
+      "16\t검증 세트 손실: 5004.265137\t최선의 손실: 0.121361\t정확도: 95.39%\n",
+      "17\t검증 세트 손실: 5891.799805\t최선의 손실: 0.121361\t정확도: 95.62%\n",
+      "18\t검증 세트 손실: 3836.821777\t최선의 손실: 0.121361\t정확도: 96.48%\n",
+      "19\t검증 세트 손실: 3843.629883\t최선의 손실: 0.121361\t정확도: 96.83%\n",
+      "20\t검증 세트 손실: 2667.820312\t최선의 손실: 0.121361\t정확도: 96.83%\n",
+      "21\t검증 세트 손실: 3475.297852\t최선의 손실: 0.121361\t정확도: 95.58%\n",
+      "22\t검증 세트 손실: 2551.543701\t최선의 손실: 0.121361\t정확도: 96.05%\n",
+      "23\t검증 세트 손실: 3031.558594\t최선의 손실: 0.121361\t정확도: 95.23%\n",
+      "24\t검증 세트 손실: 1891.486084\t최선의 손실: 0.121361\t정확도: 97.19%\n",
+      "25\t검증 세트 손실: 2338.679932\t최선의 손실: 0.121361\t정확도: 96.64%\n",
+      "26\t검증 세트 손실: 2014.508545\t최선의 손실: 0.121361\t정확도: 95.66%\n",
+      "27\t검증 세트 손실: 2405.554443\t최선의 손실: 0.121361\t정확도: 96.60%\n",
+      "28\t검증 세트 손실: 3458.688477\t최선의 손실: 0.121361\t정확도: 94.64%\n",
+      "29\t검증 세트 손실: 17461.615234\t최선의 손실: 0.121361\t정확도: 94.06%\n",
+      "30\t검증 세트 손실: 2813.408203\t최선의 손실: 0.121361\t정확도: 96.17%\n",
+      "31\t검증 세트 손실: 5106.535156\t최선의 손실: 0.121361\t정확도: 93.20%\n",
+      "32\t검증 세트 손실: 5045.147949\t최선의 손실: 0.121361\t정확도: 93.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=  20.1s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.079257\t최선의 손실: 1.079257\t정확도: 93.28%\n",
-      "1\t검증 세트 손실: 0.297304\t최선의 손실: 0.297304\t정확도: 95.31%\n",
-      "2\t검증 세트 손실: 0.248445\t최선의 손실: 0.248445\t정확도: 94.96%\n",
-      "3\t검증 세트 손실: 0.166490\t최선의 손실: 0.166490\t정확도: 96.44%\n",
-      "4\t검증 세트 손실: 0.234731\t최선의 손실: 0.166490\t정확도: 95.19%\n",
-      "5\t검증 세트 손실: 0.186308\t최선의 손실: 0.166490\t정확도: 96.40%\n",
-      "6\t검증 세트 손실: 0.290339\t최선의 손실: 0.166490\t정확도: 95.43%\n",
-      "7\t검증 세트 손실: 0.121503\t최선의 손실: 0.121503\t정확도: 97.26%\n",
-      "8\t검증 세트 손실: 0.178362\t최선의 손실: 0.121503\t정확도: 96.01%\n",
-      "9\t검증 세트 손실: 0.143887\t최선의 손실: 0.121503\t정확도: 97.26%\n",
-      "10\t검증 세트 손실: 0.141421\t최선의 손실: 0.121503\t정확도: 97.46%\n",
-      "11\t검증 세트 손실: 0.134212\t최선의 손실: 0.121503\t정확도: 97.30%\n",
-      "12\t검증 세트 손실: 0.120654\t최선의 손실: 0.120654\t정확도: 97.50%\n",
-      "13\t검증 세트 손실: 0.144255\t최선의 손실: 0.120654\t정확도: 96.68%\n",
-      "14\t검증 세트 손실: 0.201947\t최선의 손실: 0.120654\t정확도: 97.81%\n",
-      "15\t검증 세트 손실: 0.361525\t최선의 손실: 0.120654\t정확도: 97.19%\n",
-      "16\t검증 세트 손실: 240629.187500\t최선의 손실: 0.120654\t정확도: 73.65%\n",
-      "17\t검증 세트 손실: 14664.394531\t최선의 손실: 0.120654\t정확도: 90.42%\n",
-      "18\t검증 세트 손실: 11565.112305\t최선의 손실: 0.120654\t정확도: 91.79%\n",
-      "19\t검증 세트 손실: 6073.204102\t최선의 손실: 0.120654\t정확도: 94.37%\n",
-      "20\t검증 세트 손실: 7164.896973\t최선의 손실: 0.120654\t정확도: 93.67%\n",
-      "21\t검증 세트 손실: 7848.322266\t최선의 손실: 0.120654\t정확도: 93.43%\n",
-      "22\t검증 세트 손실: 5050.926270\t최선의 손실: 0.120654\t정확도: 94.72%\n",
-      "23\t검증 세트 손실: 4218.679199\t최선의 손실: 0.120654\t정확도: 93.24%\n",
-      "24\t검증 세트 손실: 7895.297852\t최선의 손실: 0.120654\t정확도: 92.03%\n",
-      "25\t검증 세트 손실: 4021.369385\t최선의 손실: 0.120654\t정확도: 95.04%\n",
-      "26\t검증 세트 손실: 8796.249023\t최선의 손실: 0.120654\t정확도: 95.70%\n",
-      "27\t검증 세트 손실: 8003.501953\t최선의 손실: 0.120654\t정확도: 93.24%\n",
-      "28\t검증 세트 손실: 5479.166504\t최선의 손실: 0.120654\t정확도: 94.68%\n",
-      "29\t검증 세트 손실: 1985.568237\t최선의 손실: 0.120654\t정확도: 96.05%\n",
-      "30\t검증 세트 손실: 2622.328125\t최선의 손실: 0.120654\t정확도: 95.70%\n",
-      "31\t검증 세트 손실: 2946.321533\t최선의 손실: 0.120654\t정확도: 94.64%\n",
-      "32\t검증 세트 손실: 4272.594727\t최선의 손실: 0.120654\t정확도: 91.32%\n",
-      "33\t검증 세트 손실: 3960.039062\t최선의 손실: 0.120654\t정확도: 95.39%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70, total=   8.7s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.475914\t최선의 손실: 0.475914\t정확도: 92.03%\n",
+      "1\t검증 세트 손실: 0.276809\t최선의 손실: 0.276809\t정확도: 94.14%\n",
+      "2\t검증 세트 손실: 0.147532\t최선의 손실: 0.147532\t정확도: 96.79%\n",
+      "3\t검증 세트 손실: 0.193318\t최선의 손실: 0.147532\t정확도: 95.70%\n",
+      "4\t검증 세트 손실: 0.131158\t최선의 손실: 0.131158\t정확도: 96.52%\n",
+      "5\t검증 세트 손실: 0.124747\t최선의 손실: 0.124747\t정확도: 96.52%\n",
+      "6\t검증 세트 손실: 0.143715\t최선의 손실: 0.124747\t정확도: 96.91%\n",
+      "7\t검증 세트 손실: 0.455844\t최선의 손실: 0.124747\t정확도: 89.60%\n",
+      "8\t검증 세트 손실: 38552.222656\t최선의 손실: 0.124747\t정확도: 80.06%\n",
+      "9\t검증 세트 손실: 6882.469727\t최선의 손실: 0.124747\t정확도: 93.43%\n",
+      "10\t검증 세트 손실: 6768.856934\t최선의 손실: 0.124747\t정확도: 91.48%\n",
+      "11\t검증 세트 손실: 5060.610840\t최선의 손실: 0.124747\t정확도: 93.55%\n",
+      "12\t검증 세트 손실: 5545.289062\t최선의 손실: 0.124747\t정확도: 94.84%\n",
+      "13\t검증 세트 손실: 6497.045410\t최선의 손실: 0.124747\t정확도: 95.11%\n",
+      "14\t검증 세트 손실: 7212.971191\t최선의 손실: 0.124747\t정확도: 92.81%\n",
+      "15\t검증 세트 손실: 2851.519531\t최선의 손실: 0.124747\t정확도: 95.19%\n",
+      "16\t검증 세트 손실: 1474.055542\t최선의 손실: 0.124747\t정확도: 96.09%\n",
+      "17\t검증 세트 손실: 2161.396240\t최선의 손실: 0.124747\t정확도: 94.10%\n",
+      "18\t검증 세트 손실: 5285.481445\t최선의 손실: 0.124747\t정확도: 87.92%\n",
+      "19\t검증 세트 손실: 3471.203369\t최선의 손실: 0.124747\t정확도: 89.44%\n",
+      "20\t검증 세트 손실: 1261.343994\t최선의 손실: 0.124747\t정확도: 96.95%\n",
+      "21\t검증 세트 손실: 1978.592285\t최선의 손실: 0.124747\t정확도: 95.19%\n",
+      "22\t검증 세트 손실: 1779.017456\t최선의 손실: 0.124747\t정확도: 95.15%\n",
+      "23\t검증 세트 손실: 1385.271118\t최선의 손실: 0.124747\t정확도: 96.05%\n",
+      "24\t검증 세트 손실: 2275.356934\t최선의 손실: 0.124747\t정확도: 96.95%\n",
+      "25\t검증 세트 손실: 5435.272461\t최선의 손실: 0.124747\t정확도: 95.04%\n",
+      "26\t검증 세트 손실: 8076.832031\t최선의 손실: 0.124747\t정확도: 94.96%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=  13.1s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.690383\t최선의 손실: 1.690383\t정확도: 86.16%\n",
-      "1\t검증 세트 손실: 0.839198\t최선의 손실: 0.839198\t정확도: 90.73%\n",
-      "2\t검증 세트 손실: 8511.845703\t최선의 손실: 0.839198\t정확도: 53.95%\n",
-      "3\t검증 세트 손실: 4713.507324\t최선의 손실: 0.839198\t정확도: 63.06%\n",
-      "4\t검증 세트 손실: 2391.249512\t최선의 손실: 0.839198\t정확도: 61.34%\n",
-      "5\t검증 세트 손실: 742.780579\t최선의 손실: 0.839198\t정확도: 81.04%\n",
-      "6\t검증 세트 손실: 1052.956665\t최선의 손실: 0.839198\t정확도: 84.75%\n",
-      "7\t검증 세트 손실: 738.233521\t최선의 손실: 0.839198\t정확도: 83.07%\n",
-      "8\t검증 세트 손실: 592.370728\t최선의 손실: 0.839198\t정확도: 82.92%\n",
-      "9\t검증 세트 손실: 661.272949\t최선의 손실: 0.839198\t정확도: 83.78%\n",
-      "10\t검증 세트 손실: 1865.488281\t최선의 손실: 0.839198\t정확도: 81.55%\n",
-      "11\t검증 세트 손실: 576.916687\t최선의 손실: 0.839198\t정확도: 93.75%\n",
-      "12\t검증 세트 손실: 1509.700195\t최선의 손실: 0.839198\t정확도: 80.73%\n",
-      "13\t검증 세트 손실: 603.189697\t최선의 손실: 0.839198\t정확도: 94.21%\n",
-      "14\t검증 세트 손실: 489.511719\t최선의 손실: 0.839198\t정확도: 89.29%\n",
-      "15\t검증 세트 손실: 396.781372\t최선의 손실: 0.839198\t정확도: 95.15%\n",
-      "16\t검증 세트 손실: 418.344666\t최선의 손실: 0.839198\t정확도: 92.69%\n",
-      "17\t검증 세트 손실: 422.605652\t최선의 손실: 0.839198\t정확도: 92.92%\n",
-      "18\t검증 세트 손실: 1480.280762\t최선의 손실: 0.839198\t정확도: 91.24%\n",
-      "19\t검증 세트 손실: 435.743805\t최선의 손실: 0.839198\t정확도: 94.45%\n",
-      "20\t검증 세트 손실: 472.042511\t최선의 손실: 0.839198\t정확도: 90.81%\n",
-      "21\t검증 세트 손실: 348.102753\t최선의 손실: 0.839198\t정확도: 94.25%\n",
-      "22\t검증 세트 손실: 227.657761\t최선의 손실: 0.839198\t정확도: 96.21%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=70, total=   8.1s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=100 \n",
+      "0\t검증 세트 손실: 2.088119\t최선의 손실: 2.088119\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 2.474890\t최선의 손실: 2.088119\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 2.227653\t최선의 손실: 2.088119\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 2.281022\t최선의 손실: 2.088119\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 2.067660\t최선의 손실: 2.067660\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 1.691264\t최선의 손실: 1.691264\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.097506\t최선의 손실: 1.691264\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.991475\t최선의 손실: 1.691264\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.886126\t최선의 손실: 1.691264\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 2.482887\t최선의 손실: 1.691264\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.726879\t최선의 손실: 1.691264\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.677547\t최선의 손실: 1.677547\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.999843\t최선의 손실: 1.677547\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.756568\t최선의 손실: 1.677547\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 2.995760\t최선의 손실: 1.677547\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.696038\t최선의 손실: 1.677547\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 2.644184\t최선의 손실: 1.677547\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 2.019513\t최선의 손실: 1.677547\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 2.568111\t최선의 손실: 1.677547\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 2.833771\t최선의 손실: 1.677547\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.283541\t최선의 손실: 1.677547\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 2.377193\t최선의 손실: 1.677547\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 1.983343\t최선의 손실: 1.677547\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 2.143953\t최선의 손실: 1.677547\t정확도: 19.27%\n",
+      "24\t검증 세트 손실: 2.032369\t최선의 손실: 1.677547\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.827990\t최선의 손실: 1.677547\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.893215\t최선의 손실: 1.677547\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 2.163732\t최선의 손실: 1.677547\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 2.621585\t최선의 손실: 1.677547\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 2.281029\t최선의 손실: 1.677547\t정확도: 20.91%\n",
+      "30\t검증 세트 손실: 1.913919\t최선의 손실: 1.677547\t정확도: 19.27%\n",
+      "31\t검증 세트 손실: 2.141618\t최선의 손실: 1.677547\t정확도: 19.27%\n",
+      "32\t검증 세트 손실: 2.017598\t최선의 손실: 1.677547\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=   5.8s\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 2.138421\t최선의 손실: 2.138421\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 2.294655\t최선의 손실: 2.138421\t정확도: 22.01%\n",
-      "2\t검증 세트 손실: 2.186422\t최선의 손실: 2.138421\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 2.281094\t최선의 손실: 2.138421\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 2.071735\t최선의 손실: 2.071735\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.698517\t최선의 손실: 1.698517\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 2.098029\t최선의 손실: 1.698517\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 1.991318\t최선의 손실: 1.698517\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 1.886116\t최선의 손실: 1.698517\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 2.482890\t최선의 손실: 1.698517\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.726894\t최선의 손실: 1.698517\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.677546\t최선의 손실: 1.677546\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.999843\t최선의 손실: 1.677546\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.756568\t최선의 손실: 1.677546\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 2.995760\t최선의 손실: 1.677546\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.696039\t최선의 손실: 1.677546\t정확도: 19.27%\n",
-      "16\t검증 세트 손실: 2.644183\t최선의 손실: 1.677546\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 2.019506\t최선의 손실: 1.677546\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 2.568144\t최선의 손실: 1.677546\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 2.833771\t최선의 손실: 1.677546\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.283547\t최선의 손실: 1.677546\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 2.377192\t최선의 손실: 1.677546\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 1.983367\t최선의 손실: 1.677546\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2.143980\t최선의 손실: 1.677546\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 2.032378\t최선의 손실: 1.677546\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.827984\t최선의 손실: 1.677546\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.893213\t최선의 손실: 1.677546\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 2.163722\t최선의 손실: 1.677546\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 2.621582\t최선의 손실: 1.677546\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 2.281034\t최선의 손실: 1.677546\t정확도: 20.91%\n",
-      "30\t검증 세트 손실: 1.913924\t최선의 손실: 1.677546\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 2.141611\t최선의 손실: 1.677546\t정확도: 19.27%\n",
-      "32\t검증 세트 손실: 2.017543\t최선의 손실: 1.677546\t정확도: 19.27%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=100, total=  52.4s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=100 \n",
+      "0\t검증 세트 손실: 2.002813\t최선의 손실: 2.002813\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.969606\t최선의 손실: 1.969606\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 2.180912\t최선의 손실: 1.969606\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 2.180711\t최선의 손실: 1.969606\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 2.003936\t최선의 손실: 1.969606\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 2.124589\t최선의 손실: 1.969606\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1.798478\t최선의 손실: 1.798478\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 2.029039\t최선의 손실: 1.798478\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 2.358032\t최선의 손실: 1.798478\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.772402\t최선의 손실: 1.772402\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 2.146578\t최선의 손실: 1.772402\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 2.741047\t최선의 손실: 1.772402\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 2.146332\t최선의 손실: 1.772402\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 2.504782\t최선의 손실: 1.772402\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 2.295664\t최선의 손실: 1.772402\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 2.064213\t최선의 손실: 1.772402\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 1.657115\t최선의 손실: 1.657115\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.927626\t최선의 손실: 1.657115\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 1.869214\t최선의 손실: 1.657115\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 2.003770\t최선의 손실: 1.657115\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 2.406379\t최선의 손실: 1.657115\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.857646\t최선의 손실: 1.657115\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 2.096557\t최선의 손실: 1.657115\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 2.520510\t최선의 손실: 1.657115\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.893650\t최선의 손실: 1.657115\t정확도: 19.08%\n",
+      "25\t검증 세트 손실: 2.078130\t최선의 손실: 1.657115\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 2.804218\t최선의 손실: 1.657115\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 2.394269\t최선의 손실: 1.657115\t정확도: 20.91%\n",
+      "28\t검증 세트 손실: 1.977030\t최선의 손실: 1.657115\t정확도: 20.91%\n",
+      "29\t검증 세트 손실: 1.721669\t최선의 손실: 1.657115\t정확도: 19.27%\n",
+      "30\t검증 세트 손실: 2.203090\t최선의 손실: 1.657115\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 2.605250\t최선의 손실: 1.657115\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 2.066093\t최선의 손실: 1.657115\t정확도: 18.73%\n",
+      "33\t검증 세트 손실: 2.527796\t최선의 손실: 1.657115\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 2.730633\t최선의 손실: 1.657115\t정확도: 18.73%\n",
+      "35\t검증 세트 손실: 2.582782\t최선의 손실: 1.657115\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 2.845665\t최선의 손실: 1.657115\t정확도: 19.27%\n",
+      "37\t검증 세트 손실: 1.998367\t최선의 손실: 1.657115\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total= 2.6min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.747549\t최선의 손실: 1.747549\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 1.904110\t최선의 손실: 1.747549\t정확도: 20.91%\n",
-      "2\t검증 세트 손실: 2.160414\t최선의 손실: 1.747549\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 2.264995\t최선의 손실: 1.747549\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.950098\t최선의 손실: 1.747549\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 2.214798\t최선의 손실: 1.747549\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.789345\t최선의 손실: 1.747549\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 2.049994\t최선의 손실: 1.747549\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 2.362208\t최선의 손실: 1.747549\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.772666\t최선의 손실: 1.747549\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 2.139758\t최선의 손실: 1.747549\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 2.742522\t최선의 손실: 1.747549\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 2.157409\t최선의 손실: 1.747549\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 2.521263\t최선의 손실: 1.747549\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 2.301965\t최선의 손실: 1.747549\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 2.036252\t최선의 손실: 1.747549\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 1.658193\t최선의 손실: 1.658193\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.941444\t최선의 손실: 1.658193\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.873620\t최선의 손실: 1.658193\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 2.003679\t최선의 손실: 1.658193\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 2.406615\t최선의 손실: 1.658193\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.857468\t최선의 손실: 1.658193\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 2.096715\t최선의 손실: 1.658193\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 2.521003\t최선의 손실: 1.658193\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.893639\t최선의 손실: 1.658193\t정확도: 19.08%\n",
-      "25\t검증 세트 손실: 2.078127\t최선의 손실: 1.658193\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 2.804216\t최선의 손실: 1.658193\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 2.394250\t최선의 손실: 1.658193\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 1.977028\t최선의 손실: 1.658193\t정확도: 20.91%\n",
-      "29\t검증 세트 손실: 1.721669\t최선의 손실: 1.658193\t정확도: 19.27%\n",
-      "30\t검증 세트 손실: 2.204971\t최선의 손실: 1.658193\t정확도: 19.08%\n",
-      "31\t검증 세트 손실: 2.605102\t최선의 손실: 1.658193\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 2.066093\t최선의 손실: 1.658193\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 2.527804\t최선의 손실: 1.658193\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 2.730635\t최선의 손실: 1.658193\t정확도: 18.73%\n",
-      "35\t검증 세트 손실: 2.582781\t최선의 손실: 1.658193\t정확도: 22.01%\n",
-      "36\t검증 세트 손실: 2.845670\t최선의 손실: 1.658193\t정확도: 19.27%\n",
-      "37\t검증 세트 손실: 1.998366\t최선의 손실: 1.658193\t정확도: 19.27%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total= 2.8min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.809967\t최선의 손실: 1.809967\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 2.190592\t최선의 손실: 1.809967\t정확도: 22.01%\n"
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=100, total=  59.9s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.802910\t최선의 손실: 1.802910\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 2.206211\t최선의 손실: 1.802910\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 2.173965\t최선의 손실: 1.802910\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 2.142203\t최선의 손실: 1.802910\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 3.100131\t최선의 손실: 1.802910\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 2.463806\t최선의 손실: 1.802910\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.003303\t최선의 손실: 1.802910\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.763765\t최선의 손실: 1.763765\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.700590\t최선의 손실: 1.700590\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 2.334472\t최선의 손실: 1.700590\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.853944\t최선의 손실: 1.700590\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.842179\t최선의 손실: 1.700590\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 2.091017\t최선의 손실: 1.700590\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 3.280353\t최선의 손실: 1.700590\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 2.147483\t최선의 손실: 1.700590\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.816309\t최선의 손실: 1.700590\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 1.802172\t최선의 손실: 1.700590\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 2.435375\t최선의 손실: 1.700590\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.970478\t최선의 손실: 1.700590\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 2.368727\t최선의 손실: 1.700590\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.211055\t최선의 손실: 1.700590\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 2.549932\t최선의 손실: 1.700590\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.926627\t최선의 손실: 1.700590\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 2.675274\t최선의 손실: 1.700590\t정확도: 19.08%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2\t검증 세트 손실: 2.174850\t최선의 손실: 1.809967\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 2.142107\t최선의 손실: 1.809967\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 3.099808\t최선의 손실: 1.809967\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 2.463803\t최선의 손실: 1.809967\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 2.003186\t최선의 손실: 1.809967\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.763757\t최선의 손실: 1.763757\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.700596\t최선의 손실: 1.700596\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 2.334471\t최선의 손실: 1.700596\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.853951\t최선의 손실: 1.700596\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 1.842174\t최선의 손실: 1.700596\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 2.091021\t최선의 손실: 1.700596\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 3.280363\t최선의 손실: 1.700596\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 2.147484\t최선의 손실: 1.700596\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.816310\t최선의 손실: 1.700596\t정확도: 19.27%\n",
-      "16\t검증 세트 손실: 1.802176\t최선의 손실: 1.700596\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 2.435381\t최선의 손실: 1.700596\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.970493\t최선의 손실: 1.700596\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 2.368728\t최선의 손실: 1.700596\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.211060\t최선의 손실: 1.700596\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 2.549932\t최선의 손실: 1.700596\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.926906\t최선의 손실: 1.700596\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 2.681846\t최선의 손실: 1.700596\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 2.492188\t최선의 손실: 1.700596\t정확도: 19.27%\n",
-      "25\t검증 세트 손실: 1.703645\t최선의 손실: 1.700596\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 2.281184\t최선의 손실: 1.700596\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 1.998524\t최선의 손실: 1.700596\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 2.451838\t최선의 손실: 1.700596\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 2.781381\t최선의 손실: 1.700596\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 2.497146\t최선의 손실: 1.700590\t정확도: 19.27%\n",
+      "25\t검증 세트 손실: 1.703904\t최선의 손실: 1.700590\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 2.280939\t최선의 손실: 1.700590\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 1.996808\t최선의 손실: 1.700590\t정확도: 18.73%\n",
+      "28\t검증 세트 손실: 2.451432\t최선의 손실: 1.700590\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 2.785381\t최선의 손실: 1.700590\t정확도: 20.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total= 2.3min\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.142171\t최선의 손실: 0.142171\t정확도: 95.58%\n",
-      "1\t검증 세트 손실: 0.101119\t최선의 손실: 0.101119\t정확도: 96.76%\n",
-      "2\t검증 세트 손실: 0.087742\t최선의 손실: 0.087742\t정확도: 97.46%\n",
-      "3\t검증 세트 손실: 0.080839\t최선의 손실: 0.080839\t정확도: 97.58%\n",
-      "4\t검증 세트 손실: 0.067274\t최선의 손실: 0.067274\t정확도: 98.24%\n",
-      "5\t검증 세트 손실: 0.069337\t최선의 손실: 0.067274\t정확도: 97.97%\n",
-      "6\t검증 세트 손실: 0.064414\t최선의 손실: 0.064414\t정확도: 98.01%\n",
-      "7\t검증 세트 손실: 0.054993\t최선의 손실: 0.054993\t정확도: 98.20%\n",
-      "8\t검증 세트 손실: 0.073216\t최선의 손실: 0.054993\t정확도: 98.08%\n",
-      "9\t검증 세트 손실: 0.059547\t최선의 손실: 0.054993\t정확도: 98.28%\n",
-      "10\t검증 세트 손실: 0.064511\t최선의 손실: 0.054993\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.060513\t최선의 손실: 0.054993\t정확도: 98.20%\n",
-      "12\t검증 세트 손실: 0.067830\t최선의 손실: 0.054993\t정확도: 98.51%\n",
-      "13\t검증 세트 손실: 0.067984\t최선의 손실: 0.054993\t정확도: 98.51%\n",
-      "14\t검증 세트 손실: 0.055973\t최선의 손실: 0.054993\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.068918\t최선의 손실: 0.054993\t정확도: 98.59%\n",
-      "16\t검증 세트 손실: 0.062575\t최선의 손실: 0.054993\t정확도: 98.44%\n",
-      "17\t검증 세트 손실: 0.069534\t최선의 손실: 0.054993\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.069324\t최선의 손실: 0.054993\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.070893\t최선의 손실: 0.054993\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.072902\t최선의 손실: 0.054993\t정확도: 98.32%\n",
-      "21\t검증 세트 손실: 0.099037\t최선의 손실: 0.054993\t정확도: 97.89%\n",
-      "22\t검증 세트 손실: 0.064810\t최선의 손실: 0.054993\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.066945\t최선의 손실: 0.054993\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.061167\t최선의 손실: 0.054993\t정확도: 98.79%\n",
-      "25\t검증 세트 손실: 0.061794\t최선의 손실: 0.054993\t정확도: 98.79%\n",
-      "26\t검증 세트 손실: 0.070548\t최선의 손실: 0.054993\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.085596\t최선의 손실: 0.054993\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.096883\t최선의 손실: 0.054993\t정확도: 98.24%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=100, total=  47.2s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.146550\t최선의 손실: 0.146550\t정확도: 94.84%\n",
+      "1\t검증 세트 손실: 0.094867\t최선의 손실: 0.094867\t정확도: 96.87%\n",
+      "2\t검증 세트 손실: 0.095423\t최선의 손실: 0.094867\t정확도: 96.56%\n",
+      "3\t검증 세트 손실: 0.064081\t최선의 손실: 0.064081\t정확도: 98.05%\n",
+      "4\t검증 세트 손실: 0.064736\t최선의 손실: 0.064081\t정확도: 97.89%\n",
+      "5\t검증 세트 손실: 0.066322\t최선의 손실: 0.064081\t정확도: 97.89%\n",
+      "6\t검증 세트 손실: 0.063930\t최선의 손실: 0.063930\t정확도: 98.12%\n",
+      "7\t검증 세트 손실: 0.064293\t최선의 손실: 0.063930\t정확도: 98.08%\n",
+      "8\t검증 세트 손실: 0.055577\t최선의 손실: 0.055577\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.058049\t최선의 손실: 0.055577\t정확도: 98.20%\n",
+      "10\t검증 세트 손실: 0.069408\t최선의 손실: 0.055577\t정확도: 98.32%\n",
+      "11\t검증 세트 손실: 0.067740\t최선의 손실: 0.055577\t정확도: 98.24%\n",
+      "12\t검증 세트 손실: 0.077918\t최선의 손실: 0.055577\t정확도: 98.20%\n",
+      "13\t검증 세트 손실: 0.072366\t최선의 손실: 0.055577\t정확도: 98.12%\n",
+      "14\t검증 세트 손실: 0.062674\t최선의 손실: 0.055577\t정확도: 98.44%\n",
+      "15\t검증 세트 손실: 0.075901\t최선의 손실: 0.055577\t정확도: 98.40%\n",
+      "16\t검증 세트 손실: 0.077503\t최선의 손실: 0.055577\t정확도: 98.48%\n",
+      "17\t검증 세트 손실: 0.062158\t최선의 손실: 0.055577\t정확도: 98.36%\n",
+      "18\t검증 세트 손실: 0.067350\t최선의 손실: 0.055577\t정확도: 98.36%\n",
+      "19\t검증 세트 손실: 0.078718\t최선의 손실: 0.055577\t정확도: 98.28%\n",
+      "20\t검증 세트 손실: 0.074724\t최선의 손실: 0.055577\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.105794\t최선의 손실: 0.055577\t정확도: 98.12%\n",
+      "22\t검증 세트 손실: 0.064985\t최선의 손실: 0.055577\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.063155\t최선의 손실: 0.055577\t정확도: 98.59%\n",
+      "24\t검증 세트 손실: 0.073929\t최선의 손실: 0.055577\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.101777\t최선의 손실: 0.055577\t정확도: 98.28%\n",
+      "26\t검증 세트 손실: 0.088768\t최선의 손실: 0.055577\t정확도: 98.20%\n",
+      "27\t검증 세트 손실: 0.093053\t최선의 손실: 0.055577\t정확도: 98.44%\n",
+      "28\t검증 세트 손실: 0.097961\t최선의 손실: 0.055577\t정확도: 98.48%\n",
+      "29\t검증 세트 손실: 0.117461\t최선의 손실: 0.055577\t정확도: 98.12%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=   2.1s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.187420\t최선의 손실: 0.187420\t정확도: 94.64%\n",
-      "1\t검증 세트 손실: 0.092286\t최선의 손실: 0.092286\t정확도: 97.19%\n",
-      "2\t검증 세트 손실: 0.080031\t최선의 손실: 0.080031\t정확도: 97.38%\n",
-      "3\t검증 세트 손실: 0.065068\t최선의 손실: 0.065068\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.086103\t최선의 손실: 0.065068\t정확도: 97.62%\n",
-      "5\t검증 세트 손실: 0.061247\t최선의 손실: 0.061247\t정확도: 98.16%\n",
-      "6\t검증 세트 손실: 0.062105\t최선의 손실: 0.061247\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.060643\t최선의 손실: 0.060643\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.064919\t최선의 손실: 0.060643\t정확도: 97.93%\n",
-      "9\t검증 세트 손실: 0.056016\t최선의 손실: 0.056016\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.056698\t최선의 손실: 0.056016\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.071026\t최선의 손실: 0.056016\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.058708\t최선의 손실: 0.056016\t정확도: 98.28%\n",
-      "13\t검증 세트 손실: 0.050799\t최선의 손실: 0.050799\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.063219\t최선의 손실: 0.050799\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.060944\t최선의 손실: 0.050799\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.119937\t최선의 손실: 0.050799\t정확도: 98.16%\n",
-      "17\t검증 세트 손실: 0.077194\t최선의 손실: 0.050799\t정확도: 98.36%\n",
-      "18\t검증 세트 손실: 0.057790\t최선의 손실: 0.050799\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.086906\t최선의 손실: 0.050799\t정확도: 98.28%\n",
-      "20\t검증 세트 손실: 0.070374\t최선의 손실: 0.050799\t정확도: 98.44%\n",
-      "21\t검증 세트 손실: 0.063394\t최선의 손실: 0.050799\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.062591\t최선의 손실: 0.050799\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.074555\t최선의 손실: 0.050799\t정확도: 98.79%\n",
-      "24\t검증 세트 손실: 0.101477\t최선의 손실: 0.050799\t정확도: 98.44%\n",
-      "25\t검증 세트 손실: 0.079119\t최선의 손실: 0.050799\t정확도: 98.63%\n",
-      "26\t검증 세트 손실: 0.056071\t최선의 손실: 0.050799\t정확도: 98.83%\n",
-      "27\t검증 세트 손실: 0.068221\t최선의 손실: 0.050799\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.070973\t최선의 손실: 0.050799\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.066251\t최선의 손실: 0.050799\t정확도: 98.83%\n",
-      "30\t검증 세트 손실: 0.095603\t최선의 손실: 0.050799\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.077472\t최선의 손실: 0.050799\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.084664\t최선의 손실: 0.050799\t정확도: 98.63%\n",
-      "33\t검증 세트 손실: 0.085159\t최선의 손실: 0.050799\t정확도: 98.79%\n",
-      "34\t검증 세트 손실: 0.082461\t최선의 손실: 0.050799\t정확도: 98.71%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=90, total=   2.4s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.158881\t최선의 손실: 0.158881\t정확도: 95.23%\n",
+      "1\t검증 세트 손실: 0.096742\t최선의 손실: 0.096742\t정확도: 96.79%\n",
+      "2\t검증 세트 손실: 0.076127\t최선의 손실: 0.076127\t정확도: 97.85%\n",
+      "3\t검증 세트 손실: 0.072875\t최선의 손실: 0.072875\t정확도: 97.77%\n",
+      "4\t검증 세트 손실: 0.067349\t최선의 손실: 0.067349\t정확도: 97.97%\n",
+      "5\t검증 세트 손실: 0.059423\t최선의 손실: 0.059423\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.059172\t최선의 손실: 0.059172\t정확도: 98.36%\n",
+      "7\t검증 세트 손실: 0.053208\t최선의 손실: 0.053208\t정확도: 98.48%\n",
+      "8\t검증 세트 손실: 0.058086\t최선의 손실: 0.053208\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.048196\t최선의 손실: 0.048196\t정확도: 98.63%\n",
+      "10\t검증 세트 손실: 0.047804\t최선의 손실: 0.047804\t정확도: 98.91%\n",
+      "11\t검증 세트 손실: 0.073028\t최선의 손실: 0.047804\t정확도: 98.28%\n",
+      "12\t검증 세트 손실: 0.055050\t최선의 손실: 0.047804\t정확도: 98.51%\n",
+      "13\t검증 세트 손실: 0.079629\t최선의 손실: 0.047804\t정확도: 98.16%\n",
+      "14\t검증 세트 손실: 0.046765\t최선의 손실: 0.046765\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.058802\t최선의 손실: 0.046765\t정확도: 98.55%\n",
+      "16\t검증 세트 손실: 0.071269\t최선의 손실: 0.046765\t정확도: 98.48%\n",
+      "17\t검증 세트 손실: 0.063493\t최선의 손실: 0.046765\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.043272\t최선의 손실: 0.043272\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.052531\t최선의 손실: 0.043272\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.050159\t최선의 손실: 0.043272\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.067074\t최선의 손실: 0.043272\t정확도: 98.71%\n",
+      "22\t검증 세트 손실: 0.051822\t최선의 손실: 0.043272\t정확도: 98.91%\n",
+      "23\t검증 세트 손실: 0.050157\t최선의 손실: 0.043272\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.058223\t최선의 손실: 0.043272\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.062174\t최선의 손실: 0.043272\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.056458\t최선의 손실: 0.043272\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.059603\t최선의 손실: 0.043272\t정확도: 98.94%\n",
+      "28\t검증 세트 손실: 0.060543\t최선의 손실: 0.043272\t정확도: 99.02%\n",
+      "29\t검증 세트 손실: 0.060816\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.061481\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "31\t검증 세트 손실: 0.061855\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "32\t검증 세트 손실: 0.062419\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.062981\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "34\t검증 세트 손실: 0.063243\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "35\t검증 세트 손실: 0.063618\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "36\t검증 세트 손실: 0.064015\t최선의 손실: 0.043272\t정확도: 99.10%\n",
+      "37\t검증 세트 손실: 0.064373\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "38\t검증 세트 손실: 0.064755\t최선의 손실: 0.043272\t정확도: 99.06%\n",
+      "39\t검증 세트 손실: 0.065099\t최선의 손실: 0.043272\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=   2.5s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.150950\t최선의 손실: 0.150950\t정확도: 95.15%\n",
-      "1\t검증 세트 손실: 0.097475\t최선의 손실: 0.097475\t정확도: 97.30%\n",
-      "2\t검증 세트 손실: 0.075428\t최선의 손실: 0.075428\t정확도: 97.69%\n",
-      "3\t검증 세트 손실: 0.062067\t최선의 손실: 0.062067\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.055305\t최선의 손실: 0.055305\t정확도: 98.16%\n",
-      "5\t검증 세트 손실: 0.073231\t최선의 손실: 0.055305\t정확도: 97.58%\n",
-      "6\t검증 세트 손실: 0.063232\t최선의 손실: 0.055305\t정확도: 98.16%\n",
-      "7\t검증 세트 손실: 0.048504\t최선의 손실: 0.048504\t정확도: 98.51%\n",
-      "8\t검증 세트 손실: 0.085259\t최선의 손실: 0.048504\t정확도: 97.62%\n",
-      "9\t검증 세트 손실: 0.052102\t최선의 손실: 0.048504\t정확도: 98.32%\n",
-      "10\t검증 세트 손실: 0.072052\t최선의 손실: 0.048504\t정확도: 98.20%\n",
-      "11\t검증 세트 손실: 0.056372\t최선의 손실: 0.048504\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.068997\t최선의 손실: 0.048504\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.062729\t최선의 손실: 0.048504\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.060668\t최선의 손실: 0.048504\t정확도: 98.59%\n",
-      "15\t검증 세트 손실: 0.075149\t최선의 손실: 0.048504\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.083582\t최선의 손실: 0.048504\t정확도: 98.08%\n",
-      "17\t검증 세트 손실: 0.090015\t최선의 손실: 0.048504\t정확도: 97.62%\n",
-      "18\t검증 세트 손실: 0.058635\t최선의 손실: 0.048504\t정확도: 98.59%\n",
-      "19\t검증 세트 손실: 0.084502\t최선의 손실: 0.048504\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.072341\t최선의 손실: 0.048504\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.079912\t최선의 손실: 0.048504\t정확도: 98.59%\n",
-      "22\t검증 세트 손실: 0.070450\t최선의 손실: 0.048504\t정확도: 98.98%\n",
-      "23\t검증 세트 손실: 0.089152\t최선의 손실: 0.048504\t정확도: 98.67%\n",
-      "24\t검증 세트 손실: 0.084106\t최선의 손실: 0.048504\t정확도: 98.67%\n",
-      "25\t검증 세트 손실: 0.074789\t최선의 손실: 0.048504\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.082783\t최선의 손실: 0.048504\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.063084\t최선의 손실: 0.048504\t정확도: 98.87%\n",
-      "28\t검증 세트 손실: 0.057225\t최선의 손실: 0.048504\t정확도: 98.98%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=90, total=   3.3s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.154424\t최선의 손실: 0.154424\t정확도: 94.88%\n",
+      "1\t검증 세트 손실: 0.099753\t최선의 손실: 0.099753\t정확도: 96.91%\n",
+      "2\t검증 세트 손실: 0.092431\t최선의 손실: 0.092431\t정확도: 97.42%\n",
+      "3\t검증 세트 손실: 0.067389\t최선의 손실: 0.067389\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.058412\t최선의 손실: 0.058412\t정확도: 98.16%\n",
+      "5\t검증 세트 손실: 0.059779\t최선의 손실: 0.058412\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.055561\t최선의 손실: 0.055561\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.047204\t최선의 손실: 0.047204\t정확도: 98.44%\n",
+      "8\t검증 세트 손실: 0.060022\t최선의 손실: 0.047204\t정확도: 98.08%\n",
+      "9\t검증 세트 손실: 0.046195\t최선의 손실: 0.046195\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.055300\t최선의 손실: 0.046195\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.051454\t최선의 손실: 0.046195\t정확도: 98.91%\n",
+      "12\t검증 세트 손실: 0.054987\t최선의 손실: 0.046195\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.068825\t최선의 손실: 0.046195\t정확도: 98.36%\n",
+      "14\t검증 세트 손실: 0.059701\t최선의 손실: 0.046195\t정확도: 98.83%\n",
+      "15\t검증 세트 손실: 0.069635\t최선의 손실: 0.046195\t정확도: 98.08%\n",
+      "16\t검증 세트 손실: 0.068311\t최선의 손실: 0.046195\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.056853\t최선의 손실: 0.046195\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.060310\t최선의 손실: 0.046195\t정확도: 98.55%\n",
+      "19\t검증 세트 손실: 0.059858\t최선의 손실: 0.046195\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.088564\t최선의 손실: 0.046195\t정확도: 98.44%\n",
+      "21\t검증 세트 손실: 0.063994\t최선의 손실: 0.046195\t정확도: 98.20%\n",
+      "22\t검증 세트 손실: 0.054595\t최선의 손실: 0.046195\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.065719\t최선의 손실: 0.046195\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.066579\t최선의 손실: 0.046195\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.064999\t최선의 손실: 0.046195\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.065419\t최선의 손실: 0.046195\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.066912\t최선의 손실: 0.046195\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.067712\t최선의 손실: 0.046195\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.068452\t최선의 손실: 0.046195\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.069111\t최선의 손실: 0.046195\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=   2.1s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.456164\t최선의 손실: 0.456164\t정확도: 85.50%\n",
-      "1\t검증 세트 손실: 1.081003\t최선의 손실: 0.456164\t정확도: 42.73%\n",
-      "2\t검증 세트 손실: 1.009942\t최선의 손실: 0.456164\t정확도: 45.07%\n",
-      "3\t검증 세트 손실: 0.684777\t최선의 손실: 0.456164\t정확도: 59.50%\n",
-      "4\t검증 세트 손실: 0.691017\t최선의 손실: 0.456164\t정확도: 69.51%\n",
-      "5\t검증 세트 손실: 0.673710\t최선의 손실: 0.456164\t정확도: 68.14%\n",
-      "6\t검증 세트 손실: 0.596713\t최선의 손실: 0.456164\t정확도: 73.57%\n",
-      "7\t검증 세트 손실: 0.713732\t최선의 손실: 0.456164\t정확도: 68.37%\n",
-      "8\t검증 세트 손실: 0.736117\t최선의 손실: 0.456164\t정확도: 66.85%\n",
-      "9\t검증 세트 손실: 0.655985\t최선의 손실: 0.456164\t정확도: 68.18%\n",
-      "10\t검증 세트 손실: 0.678524\t최선의 손실: 0.456164\t정확도: 68.37%\n",
-      "11\t검증 세트 손실: 0.898289\t최선의 손실: 0.456164\t정확도: 57.15%\n",
-      "12\t검증 세트 손실: 0.863088\t최선의 손실: 0.456164\t정확도: 56.61%\n",
-      "13\t검증 세트 손실: 0.890658\t최선의 손실: 0.456164\t정확도: 58.68%\n",
-      "14\t검증 세트 손실: 0.914111\t최선의 손실: 0.456164\t정확도: 54.42%\n",
-      "15\t검증 세트 손실: 0.854778\t최선의 손실: 0.456164\t정확도: 56.25%\n",
-      "16\t검증 세트 손실: 0.828151\t최선의 손실: 0.456164\t정확도: 57.66%\n",
-      "17\t검증 세트 손실: 0.816001\t최선의 손실: 0.456164\t정확도: 60.91%\n",
-      "18\t검증 세트 손실: 0.858396\t최선의 손실: 0.456164\t정확도: 60.63%\n",
-      "19\t검증 세트 손실: 0.764936\t최선의 손실: 0.456164\t정확도: 59.77%\n",
-      "20\t검증 세트 손실: 0.760427\t최선의 손실: 0.456164\t정확도: 60.91%\n",
-      "21\t검증 세트 손실: 0.762697\t최선의 손실: 0.456164\t정확도: 60.91%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=90, total=   2.5s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.154114\t최선의 손실: 0.154114\t정확도: 96.68%\n",
+      "1\t검증 세트 손실: 0.222299\t최선의 손실: 0.154114\t정확도: 93.47%\n",
+      "2\t검증 세트 손실: 0.729852\t최선의 손실: 0.154114\t정확도: 79.98%\n",
+      "3\t검증 세트 손실: 0.868618\t최선의 손실: 0.154114\t정확도: 76.08%\n",
+      "4\t검증 세트 손실: 0.438869\t최선의 손실: 0.154114\t정확도: 89.64%\n",
+      "5\t검증 세트 손실: 0.526391\t최선의 손실: 0.154114\t정확도: 87.22%\n",
+      "6\t검증 세트 손실: 0.448938\t최선의 손실: 0.154114\t정확도: 89.01%\n",
+      "7\t검증 세트 손실: 0.596725\t최선의 손실: 0.154114\t정확도: 80.18%\n",
+      "8\t검증 세트 손실: 0.576775\t최선의 손실: 0.154114\t정확도: 80.69%\n",
+      "9\t검증 세트 손실: 0.532032\t최선의 손실: 0.154114\t정확도: 84.40%\n",
+      "10\t검증 세트 손실: 0.545526\t최선의 손실: 0.154114\t정확도: 82.06%\n",
+      "11\t검증 세트 손실: 0.644569\t최선의 손실: 0.154114\t정확도: 79.91%\n",
+      "12\t검증 세트 손실: 0.522775\t최선의 손실: 0.154114\t정확도: 84.79%\n",
+      "13\t검증 세트 손실: 0.427011\t최선의 손실: 0.154114\t정확도: 89.44%\n",
+      "14\t검증 세트 손실: 0.558450\t최선의 손실: 0.154114\t정확도: 71.74%\n",
+      "15\t검증 세트 손실: 0.551022\t최선의 손실: 0.154114\t정확도: 72.13%\n",
+      "16\t검증 세트 손실: 0.562261\t최선의 손실: 0.154114\t정확도: 71.89%\n",
+      "17\t검증 세트 손실: 0.624466\t최선의 손실: 0.154114\t정확도: 70.80%\n",
+      "18\t검증 세트 손실: 0.802376\t최선의 손실: 0.154114\t정확도: 65.01%\n",
+      "19\t검증 세트 손실: 0.781837\t최선의 손실: 0.154114\t정확도: 65.13%\n",
+      "20\t검증 세트 손실: 0.876148\t최선의 손실: 0.154114\t정확도: 57.11%\n",
+      "21\t검증 세트 손실: 1.516996\t최선의 손실: 0.154114\t정확도: 27.52%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  24.7s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n"
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=30, total=   7.9s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.170579\t최선의 손실: 0.170579\t정확도: 95.27%\n",
+      "1\t검증 세트 손실: 0.304907\t최선의 손실: 0.170579\t정확도: 93.43%\n",
+      "2\t검증 세트 손실: 0.761635\t최선의 손실: 0.170579\t정확도: 66.46%\n",
+      "3\t검증 세트 손실: 0.593629\t최선의 손실: 0.170579\t정확도: 81.20%\n",
+      "4\t검증 세트 손실: 1.281537\t최선의 손실: 0.170579\t정확도: 37.41%\n",
+      "5\t검증 세트 손실: 1.587119\t최선의 손실: 0.170579\t정확도: 23.38%\n",
+      "6\t검증 세트 손실: 1.588985\t최선의 손실: 0.170579\t정확도: 20.91%\n",
+      "7\t검증 세트 손실: 1.585827\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "8\t검증 세트 손실: 1.581620\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "9\t검증 세트 손실: 1.580047\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "10\t검증 세트 손실: 1.587766\t최선의 손실: 0.170579\t정확도: 23.65%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.557426\t최선의 손실: 0.557426\t정확도: 87.18%\n",
-      "1\t검증 세트 손실: 1.111108\t최선의 손실: 0.557426\t정확도: 63.29%\n",
-      "2\t검증 세트 손실: 0.400892\t최선의 손실: 0.400892\t정확도: 84.56%\n",
-      "3\t검증 세트 손실: 0.411816\t최선의 손실: 0.400892\t정확도: 86.90%\n",
-      "4\t검증 세트 손실: 0.359184\t최선의 손실: 0.359184\t정확도: 88.19%\n",
-      "5\t검증 세트 손실: 1.099694\t최선의 손실: 0.359184\t정확도: 59.15%\n",
-      "6\t검증 세트 손실: 0.622663\t최선의 손실: 0.359184\t정확도: 74.94%\n",
-      "7\t검증 세트 손실: 0.764234\t최선의 손실: 0.359184\t정확도: 68.96%\n",
-      "8\t검증 세트 손실: 0.626890\t최선의 손실: 0.359184\t정확도: 74.51%\n",
-      "9\t검증 세트 손실: 0.963785\t최선의 손실: 0.359184\t정확도: 66.42%\n",
-      "10\t검증 세트 손실: 0.718695\t최선의 손실: 0.359184\t정확도: 69.27%\n",
-      "11\t검증 세트 손실: 0.672607\t최선의 손실: 0.359184\t정확도: 77.52%\n",
-      "12\t검증 세트 손실: 1.081294\t최선의 손실: 0.359184\t정확도: 60.56%\n",
-      "13\t검증 세트 손실: 0.840021\t최선의 손실: 0.359184\t정확도: 70.52%\n",
-      "14\t검증 세트 손실: 0.748317\t최선의 손실: 0.359184\t정확도: 76.08%\n",
-      "15\t검증 세트 손실: 0.744548\t최선의 손실: 0.359184\t정확도: 74.08%\n",
-      "16\t검증 세트 손실: 0.761264\t최선의 손실: 0.359184\t정확도: 72.20%\n",
-      "17\t검증 세트 손실: 0.733307\t최선의 손실: 0.359184\t정확도: 71.97%\n",
-      "18\t검증 세트 손실: 0.724354\t최선의 손실: 0.359184\t정확도: 72.56%\n",
-      "19\t검증 세트 손실: 0.787100\t최선의 손실: 0.359184\t정확도: 72.17%\n",
-      "20\t검증 세트 손실: 0.967451\t최선의 손실: 0.359184\t정확도: 53.09%\n",
-      "21\t검증 세트 손실: 0.947732\t최선의 손실: 0.359184\t정확도: 65.05%\n",
-      "22\t검증 세트 손실: 0.852222\t최선의 손실: 0.359184\t정확도: 66.97%\n",
-      "23\t검증 세트 손실: 0.805841\t최선의 손실: 0.359184\t정확도: 70.33%\n",
-      "24\t검증 세트 손실: 0.716761\t최선의 손실: 0.359184\t정확도: 76.00%\n",
-      "25\t검증 세트 손실: 0.672304\t최선의 손실: 0.359184\t정확도: 76.58%\n",
+      "11\t검증 세트 손실: 1.582595\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "12\t검증 세트 손실: 1.580916\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "13\t검증 세트 손실: 1.583850\t최선의 손실: 0.170579\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.581269\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "15\t검증 세트 손실: 1.581416\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "16\t검증 세트 손실: 1.585085\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "17\t검증 세트 손실: 1.587295\t최선의 손실: 0.170579\t정확도: 20.72%\n",
+      "18\t검증 세트 손실: 1.582088\t최선의 손실: 0.170579\t정확도: 23.65%\n",
+      "19\t검증 세트 손실: 1.270253\t최선의 손실: 0.170579\t정확도: 39.56%\n",
+      "20\t검증 세트 손실: 1.575266\t최선의 손실: 0.170579\t정확도: 24.32%\n",
+      "21\t검증 세트 손실: 1.576395\t최선의 손실: 0.170579\t정확도: 24.32%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  23.8s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.592539\t최선의 손실: 0.592539\t정확도: 91.63%\n",
-      "1\t검증 세트 손실: 0.754116\t최선의 손실: 0.592539\t정확도: 70.84%\n",
-      "2\t검증 세트 손실: 0.685524\t최선의 손실: 0.592539\t정확도: 66.97%\n",
-      "3\t검증 세트 손실: 1.455357\t최선의 손실: 0.592539\t정확도: 38.55%\n",
-      "4\t검증 세트 손실: 1.166625\t최선의 손실: 0.592539\t정확도: 39.91%\n",
-      "5\t검증 세트 손실: 1.168464\t최선의 손실: 0.592539\t정확도: 39.91%\n",
-      "6\t검증 세트 손실: 1.170603\t최선의 손실: 0.592539\t정확도: 39.91%\n",
-      "7\t검증 세트 손실: 1.173944\t최선의 손실: 0.592539\t정확도: 39.44%\n",
-      "8\t검증 세트 손실: 1.167578\t최선의 손실: 0.592539\t정확도: 39.91%\n",
-      "9\t검증 세트 손실: 1.169736\t최선의 손실: 0.592539\t정확도: 39.44%\n",
-      "10\t검증 세트 손실: 1.172070\t최선의 손실: 0.592539\t정확도: 39.44%\n",
-      "11\t검증 세트 손실: 1.173677\t최선의 손실: 0.592539\t정확도: 39.44%\n",
-      "12\t검증 세트 손실: 1.166643\t최선의 손실: 0.592539\t정확도: 41.59%\n",
-      "13\t검증 세트 손실: 1.169766\t최선의 손실: 0.592539\t정확도: 39.91%\n",
-      "14\t검증 세트 손실: 1.167633\t최선의 손실: 0.592539\t정확도: 41.59%\n",
-      "15\t검증 세트 손실: 1.171206\t최선의 손실: 0.592539\t정확도: 39.48%\n",
-      "16\t검증 세트 손실: 1.179566\t최선의 손실: 0.592539\t정확도: 39.44%\n",
-      "17\t검증 세트 손실: 1.173942\t최선의 손실: 0.592539\t정확도: 39.48%\n",
-      "18\t검증 세트 손실: 1.166832\t최선의 손실: 0.592539\t정확도: 41.59%\n",
-      "19\t검증 세트 손실: 1.167686\t최선의 손실: 0.592539\t정확도: 39.48%\n",
-      "20\t검증 세트 손실: 1.175319\t최선의 손실: 0.592539\t정확도: 39.91%\n",
-      "21\t검증 세트 손실: 1.170894\t최선의 손실: 0.592539\t정확도: 39.44%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=30, total=   7.9s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.213169\t최선의 손실: 0.213169\t정확도: 95.50%\n",
+      "1\t검증 세트 손실: 0.517129\t최선의 손실: 0.213169\t정확도: 74.75%\n",
+      "2\t검증 세트 손실: 1.409116\t최선의 손실: 0.213169\t정확도: 34.40%\n",
+      "3\t검증 세트 손실: 1.311955\t최선의 손실: 0.213169\t정확도: 35.38%\n",
+      "4\t검증 세트 손실: 1.265677\t최선의 손실: 0.213169\t정확도: 37.45%\n",
+      "5\t검증 세트 손실: 1.257055\t최선의 손실: 0.213169\t정확도: 37.22%\n",
+      "6\t검증 세트 손실: 1.018267\t최선의 손실: 0.213169\t정확도: 47.93%\n",
+      "7\t검증 세트 손실: 0.991482\t최선의 손실: 0.213169\t정확도: 53.75%\n",
+      "8\t검증 세트 손실: 0.943871\t최선의 손실: 0.213169\t정확도: 53.21%\n",
+      "9\t검증 세트 손실: 0.927410\t최선의 손실: 0.213169\t정확도: 57.27%\n",
+      "10\t검증 세트 손실: 1.062522\t최선의 손실: 0.213169\t정확도: 54.57%\n",
+      "11\t검증 세트 손실: 0.883424\t최선의 손실: 0.213169\t정확도: 59.23%\n",
+      "12\t검증 세트 손실: 0.898039\t최선의 손실: 0.213169\t정확도: 56.10%\n",
+      "13\t검증 세트 손실: 0.862216\t최선의 손실: 0.213169\t정확도: 55.24%\n",
+      "14\t검증 세트 손실: 0.828746\t최선의 손실: 0.213169\t정확도: 59.73%\n",
+      "15\t검증 세트 손실: 0.827358\t최선의 손실: 0.213169\t정확도: 59.73%\n",
+      "16\t검증 세트 손실: 0.833474\t최선의 손실: 0.213169\t정확도: 59.73%\n",
+      "17\t검증 세트 손실: 0.840201\t최선의 손실: 0.213169\t정확도: 55.75%\n",
+      "18\t검증 세트 손실: 0.823117\t최선의 손실: 0.213169\t정확도: 59.73%\n",
+      "19\t검증 세트 손실: 0.846446\t최선의 손실: 0.213169\t정확도: 55.75%\n",
+      "20\t검증 세트 손실: 0.823826\t최선의 손실: 0.213169\t정확도: 56.72%\n",
+      "21\t검증 세트 손실: 0.839755\t최선의 손실: 0.213169\t정확도: 59.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  14.0s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.214429\t최선의 손실: 0.214429\t정확도: 93.47%\n",
-      "1\t검증 세트 손실: 0.095454\t최선의 손실: 0.095454\t정확도: 97.07%\n",
-      "2\t검증 세트 손실: 0.110252\t최선의 손실: 0.095454\t정확도: 97.26%\n",
-      "3\t검증 세트 손실: 124.600945\t최선의 손실: 0.095454\t정확도: 75.45%\n",
-      "4\t검증 세트 손실: 18.740988\t최선의 손실: 0.095454\t정확도: 84.40%\n",
-      "5\t검증 세트 손실: 8.521291\t최선의 손실: 0.095454\t정확도: 92.42%\n",
-      "6\t검증 세트 손실: 7.660351\t최선의 손실: 0.095454\t정확도: 90.54%\n",
-      "7\t검증 세트 손실: 4.248132\t최선의 손실: 0.095454\t정확도: 93.24%\n",
-      "8\t검증 세트 손실: 5.812137\t최선의 손실: 0.095454\t정확도: 93.24%\n",
-      "9\t검증 세트 손실: 4.946031\t최선의 손실: 0.095454\t정확도: 93.35%\n",
-      "10\t검증 세트 손실: 9.710240\t최선의 손실: 0.095454\t정확도: 90.58%\n",
-      "11\t검증 세트 손실: 6.983078\t최선의 손실: 0.095454\t정확도: 92.03%\n",
-      "12\t검증 세트 손실: 5.864136\t최선의 손실: 0.095454\t정확도: 91.56%\n",
-      "13\t검증 세트 손실: 4.128540\t최선의 손실: 0.095454\t정확도: 92.26%\n",
-      "14\t검증 세트 손실: 2.293397\t최선의 손실: 0.095454\t정확도: 95.31%\n",
-      "15\t검증 세트 손실: 3.417061\t최선의 손실: 0.095454\t정확도: 95.54%\n",
-      "16\t검증 세트 손실: 2.521930\t최선의 손실: 0.095454\t정확도: 96.72%\n",
-      "17\t검증 세트 손실: 2.163189\t최선의 손실: 0.095454\t정확도: 95.07%\n",
-      "18\t검증 세트 손실: 1.744772\t최선의 손실: 0.095454\t정확도: 95.74%\n",
-      "19\t검증 세트 손실: 2.174726\t최선의 손실: 0.095454\t정확도: 94.72%\n",
-      "20\t검증 세트 손실: 2.315196\t최선의 손실: 0.095454\t정확도: 95.27%\n",
-      "21\t검증 세트 손실: 2.199471\t최선의 손실: 0.095454\t정확도: 95.23%\n",
-      "22\t검증 세트 손실: 2.269139\t최선의 손실: 0.095454\t정확도: 95.58%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=30, total=   7.8s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.207863\t최선의 손실: 0.207863\t정확도: 93.32%\n",
+      "1\t검증 세트 손실: 0.116150\t최선의 손실: 0.116150\t정확도: 96.60%\n",
+      "2\t검증 세트 손실: 4865.910645\t최선의 손실: 0.116150\t정확도: 20.25%\n",
+      "3\t검증 세트 손실: 16.174971\t최선의 손실: 0.116150\t정확도: 72.71%\n",
+      "4\t검증 세트 손실: 3.151557\t최선의 손실: 0.116150\t정확도: 88.39%\n",
+      "5\t검증 세트 손실: 1.583676\t최선의 손실: 0.116150\t정확도: 93.59%\n",
+      "6\t검증 세트 손실: 1.383922\t최선의 손실: 0.116150\t정확도: 92.57%\n",
+      "7\t검증 세트 손실: 0.940549\t최선의 손실: 0.116150\t정확도: 94.45%\n",
+      "8\t검증 세트 손실: 0.797701\t최선의 손실: 0.116150\t정확도: 94.96%\n",
+      "9\t검증 세트 손실: 1.165885\t최선의 손실: 0.116150\t정확도: 93.12%\n",
+      "10\t검증 세트 손실: 1.159082\t최선의 손실: 0.116150\t정확도: 93.43%\n",
+      "11\t검증 세트 손실: 1.846297\t최선의 손실: 0.116150\t정확도: 87.84%\n",
+      "12\t검증 세트 손실: 0.508150\t최선의 손실: 0.116150\t정확도: 95.54%\n",
+      "13\t검증 세트 손실: 0.566183\t최선의 손실: 0.116150\t정확도: 95.54%\n",
+      "14\t검증 세트 손실: 0.731023\t최선의 손실: 0.116150\t정확도: 93.51%\n",
+      "15\t검증 세트 손실: 0.720464\t최선의 손실: 0.116150\t정확도: 94.53%\n",
+      "16\t검증 세트 손실: 0.780148\t최선의 손실: 0.116150\t정확도: 93.94%\n",
+      "17\t검증 세트 손실: 0.459359\t최선의 손실: 0.116150\t정확도: 95.74%\n",
+      "18\t검증 세트 손실: 0.864795\t최선의 손실: 0.116150\t정확도: 93.12%\n",
+      "19\t검증 세트 손실: 0.455302\t최선의 손실: 0.116150\t정확도: 95.66%\n",
+      "20\t검증 세트 손실: 0.345717\t최선의 손실: 0.116150\t정확도: 96.52%\n",
+      "21\t검증 세트 손실: 0.358819\t최선의 손실: 0.116150\t정확도: 95.82%\n",
+      "22\t검증 세트 손실: 0.368642\t최선의 손실: 0.116150\t정확도: 96.95%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=  13.5s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.120025\t최선의 손실: 0.120025\t정확도: 96.76%\n",
-      "1\t검증 세트 손실: 0.093775\t최선의 손실: 0.093775\t정확도: 97.11%\n",
-      "2\t검증 세트 손실: 2790.809326\t최선의 손실: 0.093775\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 21.045246\t최선의 손실: 0.093775\t정확도: 74.78%\n",
-      "4\t검증 세트 손실: 15.357703\t최선의 손실: 0.093775\t정확도: 74.75%\n",
-      "5\t검증 세트 손실: 5.305198\t최선의 손실: 0.093775\t정확도: 83.27%\n",
-      "6\t검증 세트 손실: 5.641426\t최선의 손실: 0.093775\t정확도: 85.93%\n",
-      "7\t검증 세트 손실: 4.699309\t최선의 손실: 0.093775\t정확도: 85.18%\n",
-      "8\t검증 세트 손실: 7.321022\t최선의 손실: 0.093775\t정확도: 84.48%\n",
-      "9\t검증 세트 손실: 6.518161\t최선의 손실: 0.093775\t정확도: 78.30%\n",
-      "10\t검증 세트 손실: 2.068504\t최선의 손실: 0.093775\t정확도: 91.24%\n",
-      "11\t검증 세트 손실: 3.273045\t최선의 손실: 0.093775\t정확도: 81.78%\n",
-      "12\t검증 세트 손실: 1.912147\t최선의 손실: 0.093775\t정확도: 90.93%\n",
-      "13\t검증 세트 손실: 3.240192\t최선의 손실: 0.093775\t정확도: 83.35%\n",
-      "14\t검증 세트 손실: 6.037939\t최선의 손실: 0.093775\t정확도: 76.58%\n",
-      "15\t검증 세트 손실: 123.847107\t최선의 손실: 0.093775\t정확도: 72.60%\n",
-      "16\t검증 세트 손실: 9.087336\t최선의 손실: 0.093775\t정확도: 77.33%\n",
-      "17\t검증 세트 손실: 4.271200\t최선의 손실: 0.093775\t정확도: 85.14%\n",
-      "18\t검증 세트 손실: 11.562712\t최선의 손실: 0.093775\t정확도: 75.76%\n",
-      "19\t검증 세트 손실: 2.360671\t최선의 손실: 0.093775\t정확도: 89.13%\n",
-      "20\t검증 세트 손실: 2.100498\t최선의 손실: 0.093775\t정확도: 88.82%\n",
-      "21\t검증 세트 손실: 3.126916\t최선의 손실: 0.093775\t정확도: 90.81%\n",
-      "22\t검증 세트 손실: 3.445858\t최선의 손실: 0.093775\t정확도: 89.52%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50, total=   6.6s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.128376\t최선의 손실: 0.128376\t정확도: 96.29%\n",
+      "1\t검증 세트 손실: 0.180738\t최선의 손실: 0.128376\t정확도: 95.35%\n",
+      "2\t검증 세트 손실: 0.095937\t최선의 손실: 0.095937\t정확도: 97.58%\n",
+      "3\t검증 세트 손실: 58.235878\t최선의 손실: 0.095937\t정확도: 87.29%\n",
+      "4\t검증 세트 손실: 20.941236\t최선의 손실: 0.095937\t정확도: 91.95%\n",
+      "5\t검증 세트 손실: 17.023081\t최선의 손실: 0.095937\t정확도: 84.99%\n",
+      "6\t검증 세트 손실: 12.311157\t최선의 손실: 0.095937\t정확도: 91.28%\n",
+      "7\t검증 세트 손실: 4.557021\t최선의 손실: 0.095937\t정확도: 93.67%\n",
+      "8\t검증 세트 손실: 3.954118\t최선의 손실: 0.095937\t정확도: 94.61%\n",
+      "9\t검증 세트 손실: 3.380944\t최선의 손실: 0.095937\t정확도: 95.54%\n",
+      "10\t검증 세트 손실: 2.290258\t최선의 손실: 0.095937\t정확도: 96.33%\n",
+      "11\t검증 세트 손실: 2.672034\t최선의 손실: 0.095937\t정확도: 95.70%\n",
+      "12\t검증 세트 손실: 2.849624\t최선의 손실: 0.095937\t정확도: 96.01%\n",
+      "13\t검증 세트 손실: 2.584081\t최선의 손실: 0.095937\t정확도: 93.35%\n",
+      "14\t검증 세트 손실: 3.317739\t최선의 손실: 0.095937\t정확도: 93.16%\n",
+      "15\t검증 세트 손실: 1.830962\t최선의 손실: 0.095937\t정확도: 95.47%\n",
+      "16\t검증 세트 손실: 3.222162\t최선의 손실: 0.095937\t정확도: 93.28%\n",
+      "17\t검증 세트 손실: 3.994379\t최선의 손실: 0.095937\t정확도: 93.00%\n",
+      "18\t검증 세트 손실: 2.577537\t최선의 손실: 0.095937\t정확도: 95.23%\n",
+      "19\t검증 세트 손실: 2.272468\t최선의 손실: 0.095937\t정확도: 95.47%\n",
+      "20\t검증 세트 손실: 1.970981\t최선의 손실: 0.095937\t정확도: 96.76%\n",
+      "21\t검증 세트 손실: 3.708265\t최선의 손실: 0.095937\t정확도: 94.45%\n",
+      "22\t검증 세트 손실: 32.994152\t최선의 손실: 0.095937\t정확도: 84.36%\n",
+      "23\t검증 세트 손실: 295.254303\t최선의 손실: 0.095937\t정확도: 88.90%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=   6.1s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.127556\t최선의 손실: 0.127556\t정확도: 96.56%\n",
-      "1\t검증 세트 손실: 0.135753\t최선의 손실: 0.127556\t정확도: 95.86%\n",
-      "2\t검증 세트 손실: 0.115426\t최선의 손실: 0.115426\t정확도: 96.33%\n",
-      "3\t검증 세트 손실: 309.774811\t최선의 손실: 0.115426\t정확도: 68.61%\n",
-      "4\t검증 세트 손실: 74.924477\t최선의 손실: 0.115426\t정확도: 77.80%\n",
-      "5\t검증 세트 손실: 28.577547\t최선의 손실: 0.115426\t정확도: 91.87%\n",
-      "6\t검증 세트 손실: 33.354176\t최선의 손실: 0.115426\t정확도: 88.43%\n",
-      "7\t검증 세트 손실: 24.824554\t최선의 손실: 0.115426\t정확도: 88.55%\n",
-      "8\t검증 세트 손실: 15.054883\t최선의 손실: 0.115426\t정확도: 92.22%\n",
-      "9\t검증 세트 손실: 21.285221\t최선의 손실: 0.115426\t정확도: 89.87%\n",
-      "10\t검증 세트 손실: 17.629885\t최선의 손실: 0.115426\t정확도: 89.33%\n",
-      "11\t검증 세트 손실: 16.261175\t최선의 손실: 0.115426\t정확도: 92.42%\n",
-      "12\t검증 세트 손실: 7.856236\t최선의 손실: 0.115426\t정확도: 93.59%\n",
-      "13\t검증 세트 손실: 7.812813\t최선의 손실: 0.115426\t정확도: 92.89%\n",
-      "14\t검증 세트 손실: 7.976117\t최선의 손실: 0.115426\t정확도: 93.12%\n",
-      "15\t검증 세트 손실: 8.839465\t최선의 손실: 0.115426\t정확도: 92.85%\n",
-      "16\t검증 세트 손실: 8.156719\t최선의 손실: 0.115426\t정확도: 92.03%\n",
-      "17\t검증 세트 손실: 5.851768\t최선의 손실: 0.115426\t정확도: 94.64%\n",
-      "18\t검증 세트 손실: 11.550165\t최선의 손실: 0.115426\t정확도: 87.69%\n",
-      "19\t검증 세트 손실: 8.431129\t최선의 손실: 0.115426\t정확도: 92.57%\n",
-      "20\t검증 세트 손실: 15.551073\t최선의 손실: 0.115426\t정확도: 84.25%\n",
-      "21\t검증 세트 손실: 7.964292\t최선의 손실: 0.115426\t정확도: 94.21%\n",
-      "22\t검증 세트 손실: 7.786922\t최선의 손실: 0.115426\t정확도: 92.18%\n",
-      "23\t검증 세트 손실: 118.694023\t최선의 손실: 0.115426\t정확도: 66.97%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50, total=   6.8s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.135876\t최선의 손실: 0.135876\t정확도: 96.29%\n",
+      "1\t검증 세트 손실: 0.115954\t최선의 손실: 0.115954\t정확도: 96.87%\n",
+      "2\t검증 세트 손실: 0.154973\t최선의 손실: 0.115954\t정확도: 96.79%\n",
+      "3\t검증 세트 손실: 4283.950195\t최선의 손실: 0.115954\t정확도: 40.03%\n",
+      "4\t검증 세트 손실: 16.348341\t최선의 손실: 0.115954\t정확도: 92.18%\n",
+      "5\t검증 세트 손실: 14.281738\t최선의 손실: 0.115954\t정확도: 90.54%\n",
+      "6\t검증 세트 손실: 6.059865\t최선의 손실: 0.115954\t정확도: 95.78%\n",
+      "7\t검증 세트 손실: 5.353794\t최선의 손실: 0.115954\t정확도: 95.78%\n",
+      "8\t검증 세트 손실: 4.916859\t최선의 손실: 0.115954\t정확도: 94.45%\n",
+      "9\t검증 세트 손실: 6.142323\t최선의 손실: 0.115954\t정확도: 90.42%\n",
+      "10\t검증 세트 손실: 5.301787\t최선의 손실: 0.115954\t정확도: 92.10%\n",
+      "11\t검증 세트 손실: 2.801515\t최선의 손실: 0.115954\t정확도: 96.01%\n",
+      "12\t검증 세트 손실: 3.696591\t최선의 손실: 0.115954\t정확도: 94.92%\n",
+      "13\t검증 세트 손실: 2.594696\t최선의 손실: 0.115954\t정확도: 94.29%\n",
+      "14\t검증 세트 손실: 2.553940\t최선의 손실: 0.115954\t정확도: 95.50%\n",
+      "15\t검증 세트 손실: 3.046704\t최선의 손실: 0.115954\t정확도: 94.18%\n",
+      "16\t검증 세트 손실: 2.616475\t최선의 손실: 0.115954\t정확도: 95.78%\n",
+      "17\t검증 세트 손실: 1.756856\t최선의 손실: 0.115954\t정확도: 97.03%\n",
+      "18\t검증 세트 손실: 2.703667\t최선의 손실: 0.115954\t정확도: 94.72%\n",
+      "19\t검증 세트 손실: 4.286672\t최선의 손실: 0.115954\t정확도: 95.50%\n",
+      "20\t검증 세트 손실: 6.111000\t최선의 손실: 0.115954\t정확도: 95.47%\n",
+      "21\t검증 세트 손실: 6251.285645\t최선의 손실: 0.115954\t정확도: 88.66%\n",
+      "22\t검증 세트 손실: 1339.132080\t최선의 손실: 0.115954\t정확도: 93.12%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=   6.2s\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.281448\t최선의 손실: 0.281448\t정확도: 93.90%\n",
-      "1\t검증 세트 손실: 0.141311\t최선의 손실: 0.141311\t정확도: 95.70%\n",
-      "2\t검증 세트 손실: 0.193231\t최선의 손실: 0.141311\t정확도: 95.19%\n",
-      "3\t검증 세트 손실: 0.182039\t최선의 손실: 0.141311\t정확도: 96.72%\n",
-      "4\t검증 세트 손실: 0.166596\t최선의 손실: 0.141311\t정확도: 95.07%\n",
-      "5\t검증 세트 손실: 0.168967\t최선의 손실: 0.141311\t정확도: 95.82%\n",
-      "6\t검증 세트 손실: 0.146280\t최선의 손실: 0.141311\t정확도: 96.48%\n",
-      "7\t검증 세트 손실: 0.327146\t최선의 손실: 0.141311\t정확도: 94.18%\n",
-      "8\t검증 세트 손실: 0.272934\t최선의 손실: 0.141311\t정확도: 93.35%\n",
-      "9\t검증 세트 손실: 0.659857\t최선의 손실: 0.141311\t정확도: 74.98%\n",
-      "10\t검증 세트 손실: 0.563928\t최선의 손실: 0.141311\t정확도: 81.82%\n",
-      "11\t검증 세트 손실: 0.228714\t최선의 손실: 0.141311\t정확도: 94.72%\n",
-      "12\t검증 세트 손실: 0.890232\t최선의 손실: 0.141311\t정확도: 53.52%\n",
-      "13\t검증 세트 손실: 0.529571\t최선의 손실: 0.141311\t정확도: 76.11%\n",
-      "14\t검증 세트 손실: 0.648899\t최선의 손실: 0.141311\t정확도: 69.35%\n",
-      "15\t검증 세트 손실: 0.493483\t최선의 손실: 0.141311\t정확도: 75.25%\n",
-      "16\t검증 세트 손실: 0.476965\t최선의 손실: 0.141311\t정확도: 74.12%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50, total=   6.1s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.187205\t최선의 손실: 0.187205\t정확도: 95.31%\n",
+      "1\t검증 세트 손실: 0.155600\t최선의 손실: 0.155600\t정확도: 95.54%\n",
+      "2\t검증 세트 손실: 0.201718\t최선의 손실: 0.155600\t정확도: 95.27%\n",
+      "3\t검증 세트 손실: 0.193190\t최선의 손실: 0.155600\t정확도: 95.07%\n",
+      "4\t검증 세트 손실: 0.339775\t최선의 손실: 0.155600\t정확도: 93.51%\n",
+      "5\t검증 세트 손실: 0.257080\t최선의 손실: 0.155600\t정확도: 92.22%\n",
+      "6\t검증 세트 손실: 0.230981\t최선의 손실: 0.155600\t정확도: 93.90%\n",
+      "7\t검증 세트 손실: 0.323723\t최선의 손실: 0.155600\t정확도: 92.57%\n",
+      "8\t검증 세트 손실: 0.213642\t최선의 손실: 0.155600\t정확도: 94.64%\n",
+      "9\t검증 세트 손실: 0.561179\t최선의 손실: 0.155600\t정확도: 74.39%\n",
+      "10\t검증 세트 손실: 0.582037\t최선의 손실: 0.155600\t정확도: 76.58%\n",
+      "11\t검증 세트 손실: 0.257203\t최선의 손실: 0.155600\t정확도: 91.87%\n",
+      "12\t검증 세트 손실: 0.217545\t최선의 손실: 0.155600\t정확도: 95.15%\n",
+      "13\t검증 세트 손실: 0.336808\t최선의 손실: 0.155600\t정확도: 94.76%\n",
+      "14\t검증 세트 손실: 0.496656\t최선의 손실: 0.155600\t정확도: 75.25%\n",
+      "15\t검증 세트 손실: 0.534746\t최선의 손실: 0.155600\t정확도: 75.37%\n",
+      "16\t검증 세트 손실: 0.539472\t최선의 손실: 0.155600\t정확도: 76.43%\n",
+      "17\t검증 세트 손실: 0.292615\t최선의 손실: 0.155600\t정확도: 94.49%\n",
+      "18\t검증 세트 손실: 0.309884\t최선의 손실: 0.155600\t정확도: 93.55%\n",
+      "19\t검증 세트 손실: 0.378482\t최선의 손실: 0.155600\t정확도: 92.30%\n",
+      "20\t검증 세트 손실: 0.912774\t최선의 손실: 0.155600\t정확도: 63.76%\n",
+      "21\t검증 세트 손실: 0.685795\t최선의 손실: 0.155600\t정확도: 71.62%\n",
+      "22\t검증 세트 손실: 0.501723\t최선의 손실: 0.155600\t정확도: 77.29%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=10, total=  36.4s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.146041\t최선의 손실: 0.146041\t정확도: 96.64%\n",
+      "1\t검증 세트 손실: 0.143019\t최선의 손실: 0.143019\t정확도: 96.99%\n",
+      "2\t검증 세트 손실: 0.124966\t최선의 손실: 0.124966\t정확도: 96.91%\n",
+      "3\t검증 세트 손실: 0.179542\t최선의 손실: 0.124966\t정확도: 95.23%\n",
+      "4\t검증 세트 손실: 0.159705\t최선의 손실: 0.124966\t정확도: 95.82%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "17\t검증 세트 손실: 0.884090\t최선의 손실: 0.141311\t정확도: 55.20%\n",
-      "18\t검증 세트 손실: 0.257031\t최선의 손실: 0.141311\t정확도: 94.61%\n",
-      "19\t검증 세트 손실: 0.291304\t최선의 손실: 0.141311\t정확도: 93.51%\n",
-      "20\t검증 세트 손실: 0.238871\t최선의 손실: 0.141311\t정확도: 95.70%\n",
-      "21\t검증 세트 손실: 0.275975\t최선의 손실: 0.141311\t정확도: 92.92%\n",
-      "22\t검증 세트 손실: 0.392057\t최선의 손실: 0.141311\t정확도: 88.27%\n",
+      "5\t검증 세트 손실: 0.185205\t최선의 손실: 0.124966\t정확도: 95.86%\n",
+      "6\t검증 세트 손실: 0.151646\t최선의 손실: 0.124966\t정확도: 96.40%\n",
+      "7\t검증 세트 손실: 0.164112\t최선의 손실: 0.124966\t정확도: 96.29%\n",
+      "8\t검증 세트 손실: 0.683742\t최선의 손실: 0.124966\t정확도: 78.81%\n",
+      "9\t검증 세트 손실: 1.137868\t최선의 손실: 0.124966\t정확도: 47.46%\n",
+      "10\t검증 세트 손실: 0.245956\t최선의 손실: 0.124966\t정확도: 92.65%\n",
+      "11\t검증 세트 손실: 0.459690\t최선의 손실: 0.124966\t정확도: 80.30%\n",
+      "12\t검증 세트 손실: 1.955078\t최선의 손실: 0.124966\t정확도: 91.52%\n",
+      "13\t검증 세트 손실: 0.582435\t최선의 손실: 0.124966\t정확도: 73.85%\n",
+      "14\t검증 세트 손실: 0.520729\t최선의 손실: 0.124966\t정확도: 77.83%\n",
+      "15\t검증 세트 손실: 0.762683\t최선의 손실: 0.124966\t정확도: 71.70%\n",
+      "16\t검증 세트 손실: 0.591740\t최선의 손실: 0.124966\t정확도: 74.28%\n",
+      "17\t검증 세트 손실: 1.212704\t최선의 손실: 0.124966\t정확도: 45.97%\n",
+      "18\t검증 세트 손실: 0.785290\t최선의 손실: 0.124966\t정확도: 70.68%\n",
+      "19\t검증 세트 손실: 0.664190\t최선의 손실: 0.124966\t정확도: 68.53%\n",
+      "20\t검증 세트 손실: 0.903928\t최선의 손실: 0.124966\t정확도: 56.84%\n",
+      "21\t검증 세트 손실: 1.120231\t최선의 손실: 0.124966\t정확도: 54.10%\n",
+      "22\t검증 세트 손실: 1.421821\t최선의 손실: 0.124966\t정확도: 57.43%\n",
+      "23\t검증 세트 손실: 1.060686\t최선의 손실: 0.124966\t정확도: 53.99%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total= 1.7min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.175297\t최선의 손실: 0.175297\t정확도: 95.74%\n",
-      "1\t검증 세트 손실: 0.175310\t최선의 손실: 0.175297\t정확도: 95.23%\n",
-      "2\t검증 세트 손실: 0.170198\t최선의 손실: 0.170198\t정확도: 95.82%\n",
-      "3\t검증 세트 손실: 0.158704\t최선의 손실: 0.158704\t정확도: 96.48%\n",
-      "4\t검증 세트 손실: 0.174055\t최선의 손실: 0.158704\t정확도: 95.50%\n",
-      "5\t검증 세트 손실: 0.273801\t최선의 손실: 0.158704\t정확도: 94.61%\n",
-      "6\t검증 세트 손실: 0.187173\t최선의 손실: 0.158704\t정확도: 95.82%\n",
-      "7\t검증 세트 손실: 0.206855\t최선의 손실: 0.158704\t정확도: 95.47%\n",
-      "8\t검증 세트 손실: 0.193623\t최선의 손실: 0.158704\t정확도: 96.21%\n",
-      "9\t검증 세트 손실: 0.182015\t최선의 손실: 0.158704\t정확도: 96.21%\n",
-      "10\t검증 세트 손실: 0.433208\t최선의 손실: 0.158704\t정확도: 89.05%\n",
-      "11\t검증 세트 손실: 0.459390\t최선의 손실: 0.158704\t정확도: 87.76%\n",
-      "12\t검증 세트 손실: 0.263914\t최선의 손실: 0.158704\t정확도: 93.63%\n",
-      "13\t검증 세트 손실: 0.274253\t최선의 손실: 0.158704\t정확도: 93.59%\n",
-      "14\t검증 세트 손실: 0.308080\t최선의 손실: 0.158704\t정확도: 94.25%\n",
-      "15\t검증 세트 손실: 0.279430\t최선의 손실: 0.158704\t정확도: 94.49%\n",
-      "16\t검증 세트 손실: 0.330866\t최선의 손실: 0.158704\t정확도: 92.61%\n",
-      "17\t검증 세트 손실: 0.266120\t최선의 손실: 0.158704\t정확도: 94.33%\n",
-      "18\t검증 세트 손실: 0.324825\t최선의 손실: 0.158704\t정확도: 92.65%\n",
-      "19\t검증 세트 손실: 0.384181\t최선의 손실: 0.158704\t정확도: 91.20%\n",
-      "20\t검증 세트 손실: 0.342653\t최선의 손실: 0.158704\t정확도: 92.81%\n",
-      "21\t검증 세트 손실: 0.689463\t최선의 손실: 0.158704\t정확도: 74.78%\n",
-      "22\t검증 세트 손실: 0.851573\t최선의 손실: 0.158704\t정확도: 53.56%\n",
-      "23\t검증 세트 손실: 0.782400\t최선의 손실: 0.158704\t정확도: 57.66%\n",
-      "24\t검증 세트 손실: 0.821679\t최선의 손실: 0.158704\t정확도: 57.11%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=10, total=  38.0s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.154843\t최선의 손실: 0.154843\t정확도: 96.48%\n",
+      "1\t검증 세트 손실: 0.166200\t최선의 손실: 0.154843\t정확도: 95.62%\n",
+      "2\t검증 세트 손실: 0.148874\t최선의 손실: 0.148874\t정확도: 96.17%\n",
+      "3\t검증 세트 손실: 0.171479\t최선의 손실: 0.148874\t정확도: 96.91%\n",
+      "4\t검증 세트 손실: 0.151820\t최선의 손실: 0.148874\t정확도: 96.25%\n",
+      "5\t검증 세트 손실: 0.168374\t최선의 손실: 0.148874\t정확도: 94.92%\n",
+      "6\t검증 세트 손실: 0.172633\t최선의 손실: 0.148874\t정확도: 96.13%\n",
+      "7\t검증 세트 손실: 0.236397\t최선의 손실: 0.148874\t정확도: 94.68%\n",
+      "8\t검증 세트 손실: 0.201140\t최선의 손실: 0.148874\t정확도: 95.62%\n",
+      "9\t검증 세트 손실: 0.231211\t최선의 손실: 0.148874\t정확도: 95.54%\n",
+      "10\t검증 세트 손실: 0.198220\t최선의 손실: 0.148874\t정확도: 96.64%\n",
+      "11\t검증 세트 손실: 0.213465\t최선의 손실: 0.148874\t정확도: 96.48%\n",
+      "12\t검증 세트 손실: 0.156489\t최선의 손실: 0.148874\t정확도: 96.87%\n",
+      "13\t검증 세트 손실: 0.162654\t최선의 손실: 0.148874\t정확도: 96.60%\n",
+      "14\t검증 세트 손실: 0.154136\t최선의 손실: 0.148874\t정확도: 96.91%\n",
+      "15\t검증 세트 손실: 0.589684\t최선의 손실: 0.148874\t정확도: 92.18%\n",
+      "16\t검증 세트 손실: 0.168494\t최선의 손실: 0.148874\t정확도: 96.33%\n",
+      "17\t검증 세트 손실: 0.154388\t최선의 손실: 0.148874\t정확도: 96.33%\n",
+      "18\t검증 세트 손실: 0.231472\t최선의 손실: 0.148874\t정확도: 93.55%\n",
+      "19\t검증 세트 손실: 0.273040\t최선의 손실: 0.148874\t정확도: 95.47%\n",
+      "20\t검증 세트 손실: 0.204334\t최선의 손실: 0.148874\t정확도: 95.23%\n",
+      "21\t검증 세트 손실: 0.210322\t최선의 손실: 0.148874\t정확도: 95.47%\n",
+      "22\t검증 세트 손실: 0.550468\t최선의 손실: 0.148874\t정확도: 77.87%\n",
+      "23\t검증 세트 손실: 0.436300\t최선의 손실: 0.148874\t정확도: 93.24%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total= 1.9min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.188546\t최선의 손실: 0.188546\t정확도: 95.58%\n",
-      "1\t검증 세트 손실: 0.216965\t최선의 손실: 0.188546\t정확도: 95.78%\n",
-      "2\t검증 세트 손실: 0.202688\t최선의 손실: 0.188546\t정확도: 94.57%\n",
-      "3\t검증 세트 손실: 0.161543\t최선의 손실: 0.161543\t정확도: 96.36%\n",
-      "4\t검증 세트 손실: 0.166941\t최선의 손실: 0.161543\t정확도: 95.70%\n",
-      "5\t검증 세트 손실: 0.390400\t최선의 손실: 0.161543\t정확도: 88.19%\n",
-      "6\t검증 세트 손실: 0.670298\t최선의 손실: 0.161543\t정확도: 78.30%\n",
-      "7\t검증 세트 손실: 0.213355\t최선의 손실: 0.161543\t정확도: 94.80%\n",
-      "8\t검증 세트 손실: 0.319307\t최선의 손실: 0.161543\t정확도: 92.85%\n",
-      "9\t검증 세트 손실: 0.194003\t최선의 손실: 0.161543\t정확도: 95.19%\n",
-      "10\t검증 세트 손실: 0.319074\t최선의 손실: 0.161543\t정확도: 90.19%\n",
-      "11\t검증 세트 손실: 0.206689\t최선의 손실: 0.161543\t정확도: 95.04%\n",
-      "12\t검증 세트 손실: 0.197539\t최선의 손실: 0.161543\t정확도: 95.47%\n",
-      "13\t검증 세트 손실: 0.185998\t최선의 손실: 0.161543\t정확도: 95.66%\n",
-      "14\t검증 세트 손실: 0.225085\t최선의 손실: 0.161543\t정확도: 95.07%\n",
-      "15\t검증 세트 손실: 0.245285\t최선의 손실: 0.161543\t정확도: 95.11%\n",
-      "16\t검증 세트 손실: 0.176609\t최선의 손실: 0.161543\t정확도: 96.13%\n",
-      "17\t검증 세트 손실: 0.217232\t최선의 손실: 0.161543\t정확도: 96.01%\n",
-      "18\t검증 세트 손실: 0.187590\t최선의 손실: 0.161543\t정확도: 96.52%\n",
-      "19\t검증 세트 손실: 0.237370\t최선의 손실: 0.161543\t정확도: 95.19%\n",
-      "20\t검증 세트 손실: 0.184698\t최선의 손실: 0.161543\t정확도: 96.36%\n",
-      "21\t검증 세트 손실: 0.207457\t최선의 손실: 0.161543\t정확도: 95.62%\n",
-      "22\t검증 세트 손실: 0.204589\t최선의 손실: 0.161543\t정확도: 95.66%\n",
-      "23\t검증 세트 손실: 0.215471\t최선의 손실: 0.161543\t정확도: 94.96%\n",
-      "24\t검증 세트 손실: 0.186354\t최선의 손실: 0.161543\t정확도: 96.09%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=10, total=  37.8s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.157443\t최선의 손실: 1.157443\t정확도: 84.71%\n",
+      "1\t검증 세트 손실: 0.232183\t최선의 손실: 0.232183\t정확도: 94.53%\n",
+      "2\t검증 세트 손실: 0.145029\t최선의 손실: 0.145029\t정확도: 95.93%\n",
+      "3\t검증 세트 손실: 0.124508\t최선의 손실: 0.124508\t정확도: 96.52%\n",
+      "4\t검증 세트 손실: 0.126395\t최선의 손실: 0.124508\t정확도: 96.64%\n",
+      "5\t검증 세트 손실: 0.098194\t최선의 손실: 0.098194\t정확도: 96.99%\n",
+      "6\t검증 세트 손실: 0.107421\t최선의 손실: 0.098194\t정확도: 96.91%\n",
+      "7\t검증 세트 손실: 0.182254\t최선의 손실: 0.098194\t정확도: 96.99%\n",
+      "8\t검증 세트 손실: 0.374182\t최선의 손실: 0.098194\t정확도: 97.30%\n",
+      "9\t검증 세트 손실: 0.481884\t최선의 손실: 0.098194\t정확도: 97.65%\n",
+      "10\t검증 세트 손실: 0.740136\t최선의 손실: 0.098194\t정확도: 97.73%\n",
+      "11\t검증 세트 손실: 0.118991\t최선의 손실: 0.098194\t정확도: 97.26%\n",
+      "12\t검증 세트 손실: 0.104574\t최선의 손실: 0.098194\t정확도: 96.99%\n",
+      "13\t검증 세트 손실: 0.099898\t최선의 손실: 0.098194\t정확도: 97.73%\n",
+      "14\t검증 세트 손실: 0.113839\t최선의 손실: 0.098194\t정확도: 97.73%\n",
+      "15\t검증 세트 손실: 0.141195\t최선의 손실: 0.098194\t정확도: 97.69%\n",
+      "16\t검증 세트 손실: 0.186763\t최선의 손실: 0.098194\t정확도: 97.97%\n",
+      "17\t검증 세트 손실: 0.221368\t최선의 손실: 0.098194\t정확도: 98.01%\n",
+      "18\t검증 세트 손실: 0.126441\t최선의 손실: 0.098194\t정확도: 97.50%\n",
+      "19\t검증 세트 손실: 0.220052\t최선의 손실: 0.098194\t정확도: 97.85%\n",
+      "20\t검증 세트 손실: 0.218932\t최선의 손실: 0.098194\t정확도: 98.01%\n",
+      "21\t검증 세트 손실: 0.371587\t최선의 손실: 0.098194\t정확도: 98.01%\n",
+      "22\t검증 세트 손실: 0.327157\t최선의 손실: 0.098194\t정확도: 97.85%\n",
+      "23\t검증 세트 손실: 0.391303\t최선의 손실: 0.098194\t정확도: 97.81%\n",
+      "24\t검증 세트 손실: 0.763993\t최선의 손실: 0.098194\t정확도: 97.58%\n",
+      "25\t검증 세트 손실: 0.443893\t최선의 손실: 0.098194\t정확도: 98.12%\n",
+      "26\t검증 세트 손실: 1.643594\t최선의 손실: 0.098194\t정확도: 98.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total= 1.8min\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 4.006384\t최선의 손실: 4.006384\t정확도: 87.06%\n",
-      "1\t검증 세트 손실: 0.916902\t최선의 손실: 0.916902\t정확도: 91.48%\n",
-      "2\t검증 세트 손실: 0.256638\t최선의 손실: 0.256638\t정확도: 95.86%\n",
-      "3\t검증 세트 손실: 0.257483\t최선의 손실: 0.256638\t정확도: 94.76%\n",
-      "4\t검증 세트 손실: 0.154216\t최선의 손실: 0.154216\t정확도: 96.25%\n",
-      "5\t검증 세트 손실: 0.127134\t최선의 손실: 0.127134\t정확도: 97.11%\n",
-      "6\t검증 세트 손실: 0.105854\t최선의 손실: 0.105854\t정확도: 97.22%\n",
-      "7\t검증 세트 손실: 0.138453\t최선의 손실: 0.105854\t정확도: 97.34%\n",
-      "8\t검증 세트 손실: 0.151709\t최선의 손실: 0.105854\t정확도: 96.72%\n",
-      "9\t검증 세트 손실: 0.113966\t최선의 손실: 0.105854\t정확도: 97.81%\n",
-      "10\t검증 세트 손실: 0.149938\t최선의 손실: 0.105854\t정확도: 96.91%\n",
-      "11\t검증 세트 손실: 0.106593\t최선의 손실: 0.105854\t정확도: 97.69%\n",
-      "12\t검증 세트 손실: 0.115312\t최선의 손실: 0.105854\t정확도: 97.65%\n",
-      "13\t검증 세트 손실: 0.106911\t최선의 손실: 0.105854\t정확도: 97.81%\n",
-      "14\t검증 세트 손실: 0.117680\t최선의 손실: 0.105854\t정확도: 97.77%\n",
-      "15\t검증 세트 손실: 0.099152\t최선의 손실: 0.099152\t정확도: 97.97%\n",
-      "16\t검증 세트 손실: 0.101210\t최선의 손실: 0.099152\t정확도: 98.08%\n",
-      "17\t검증 세트 손실: 0.109231\t최선의 손실: 0.099152\t정확도: 98.05%\n",
-      "18\t검증 세트 손실: 0.120415\t최선의 손실: 0.099152\t정확도: 97.50%\n",
-      "19\t검증 세트 손실: 0.098051\t최선의 손실: 0.098051\t정확도: 98.05%\n",
-      "20\t검증 세트 손실: 0.105460\t최선의 손실: 0.098051\t정확도: 98.28%\n",
-      "21\t검증 세트 손실: 0.090138\t최선의 손실: 0.090138\t정확도: 97.93%\n",
-      "22\t검증 세트 손실: 0.082608\t최선의 손실: 0.082608\t정확도: 98.36%\n",
-      "23\t검증 세트 손실: 0.096469\t최선의 손실: 0.082608\t정확도: 97.81%\n",
-      "24\t검증 세트 손실: 0.100019\t최선의 손실: 0.082608\t정확도: 97.89%\n",
-      "25\t검증 세트 손실: 0.094899\t최선의 손실: 0.082608\t정확도: 98.01%\n",
-      "26\t검증 세트 손실: 0.111149\t최선의 손실: 0.082608\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.139824\t최선의 손실: 0.082608\t정확도: 98.12%\n",
-      "28\t검증 세트 손실: 0.114585\t최선의 손실: 0.082608\t정확도: 98.12%\n",
-      "29\t검증 세트 손실: 0.102686\t최선의 손실: 0.082608\t정확도: 98.08%\n",
-      "30\t검증 세트 손실: 0.096147\t최선의 손실: 0.082608\t정확도: 98.32%\n",
-      "31\t검증 세트 손실: 0.111991\t최선의 손실: 0.082608\t정확도: 98.24%\n",
-      "32\t검증 세트 손실: 0.122890\t최선의 손실: 0.082608\t정확도: 97.50%\n",
-      "33\t검증 세트 손실: 0.147747\t최선의 손실: 0.082608\t정확도: 97.50%\n",
-      "34\t검증 세트 손실: 0.159671\t최선의 손실: 0.082608\t정확도: 97.42%\n",
-      "35\t검증 세트 손실: 0.115794\t최선의 손실: 0.082608\t정확도: 98.20%\n",
-      "36\t검증 세트 손실: 0.109021\t최선의 손실: 0.082608\t정확도: 98.24%\n",
-      "37\t검증 세트 손실: 0.102080\t최선의 손실: 0.082608\t정확도: 98.28%\n",
-      "38\t검증 세트 손실: 0.090342\t최선의 손실: 0.082608\t정확도: 98.51%\n",
-      "39\t검증 세트 손실: 0.100750\t최선의 손실: 0.082608\t정확도: 98.48%\n",
-      "40\t검증 세트 손실: 0.095902\t최선의 손실: 0.082608\t정확도: 98.67%\n",
-      "41\t검증 세트 손실: 0.112576\t최선의 손실: 0.082608\t정확도: 98.48%\n",
-      "42\t검증 세트 손실: 0.109631\t최선의 손실: 0.082608\t정확도: 98.40%\n",
-      "43\t검증 세트 손실: 0.108885\t최선의 손실: 0.082608\t정확도: 98.48%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=140, total=   2.8s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=140 \n",
+      "0\t검증 세트 손실: 5.672205\t최선의 손실: 5.672205\t정확도: 76.11%\n",
+      "1\t검증 세트 손실: 0.386186\t최선의 손실: 0.386186\t정확도: 94.64%\n",
+      "2\t검증 세트 손실: 0.231733\t최선의 손실: 0.231733\t정확도: 94.33%\n",
+      "3\t검증 세트 손실: 0.170750\t최선의 손실: 0.170750\t정확도: 96.25%\n",
+      "4\t검증 세트 손실: 0.141344\t최선의 손실: 0.141344\t정확도: 96.76%\n",
+      "5\t검증 세트 손실: 0.159313\t최선의 손실: 0.141344\t정확도: 96.05%\n",
+      "6\t검증 세트 손실: 0.122773\t최선의 손실: 0.122773\t정확도: 96.60%\n",
+      "7\t검증 세트 손실: 0.175810\t최선의 손실: 0.122773\t정확도: 95.74%\n",
+      "8\t검증 세트 손실: 0.140915\t최선의 손실: 0.122773\t정확도: 96.76%\n",
+      "9\t검증 세트 손실: 0.107318\t최선의 손실: 0.107318\t정확도: 97.03%\n",
+      "10\t검증 세트 손실: 0.129378\t최선의 손실: 0.107318\t정확도: 97.11%\n",
+      "11\t검증 세트 손실: 0.117297\t최선의 손실: 0.107318\t정확도: 97.19%\n",
+      "12\t검증 세트 손실: 0.125791\t최선의 손실: 0.107318\t정확도: 97.22%\n",
+      "13\t검증 세트 손실: 0.109519\t최선의 손실: 0.107318\t정확도: 97.38%\n",
+      "14\t검증 세트 손실: 0.114122\t최선의 손실: 0.107318\t정확도: 97.30%\n",
+      "15\t검증 세트 손실: 0.126638\t최선의 손실: 0.107318\t정확도: 96.91%\n",
+      "16\t검증 세트 손실: 0.102796\t최선의 손실: 0.102796\t정확도: 97.30%\n",
+      "17\t검증 세트 손실: 0.118342\t최선의 손실: 0.102796\t정확도: 97.54%\n",
+      "18\t검증 세트 손실: 0.103873\t최선의 손실: 0.102796\t정확도: 97.77%\n",
+      "19\t검증 세트 손실: 0.105319\t최선의 손실: 0.102796\t정확도: 97.73%\n",
+      "20\t검증 세트 손실: 0.096005\t최선의 손실: 0.096005\t정확도: 97.73%\n",
+      "21\t검증 세트 손실: 0.099639\t최선의 손실: 0.096005\t정확도: 97.77%\n",
+      "22\t검증 세트 손실: 0.130762\t최선의 손실: 0.096005\t정확도: 97.30%\n",
+      "23\t검증 세트 손실: 0.120652\t최선의 손실: 0.096005\t정확도: 97.85%\n",
+      "24\t검증 세트 손실: 0.107850\t최선의 손실: 0.096005\t정확도: 97.58%\n",
+      "25\t검증 세트 손실: 0.114519\t최선의 손실: 0.096005\t정확도: 97.93%\n",
+      "26\t검증 세트 손실: 0.105212\t최선의 손실: 0.096005\t정확도: 98.05%\n",
+      "27\t검증 세트 손실: 0.120313\t최선의 손실: 0.096005\t정확도: 97.38%\n",
+      "28\t검증 세트 손실: 0.110441\t최선의 손실: 0.096005\t정확도: 97.62%\n",
+      "29\t검증 세트 손실: 0.098783\t최선의 손실: 0.096005\t정확도: 98.08%\n",
+      "30\t검증 세트 손실: 0.103371\t최선의 손실: 0.096005\t정확도: 97.69%\n",
+      "31\t검증 세트 손실: 0.135528\t최선의 손실: 0.096005\t정확도: 97.85%\n",
+      "32\t검증 세트 손실: 0.129523\t최선의 손실: 0.096005\t정확도: 97.26%\n",
+      "33\t검증 세트 손실: 0.123512\t최선의 손실: 0.096005\t정확도: 98.20%\n",
+      "34\t검증 세트 손실: 0.083394\t최선의 손실: 0.083394\t정확도: 98.16%\n",
+      "35\t검증 세트 손실: 0.082598\t최선의 손실: 0.082598\t정확도: 98.08%\n",
+      "36\t검증 세트 손실: 0.096071\t최선의 손실: 0.082598\t정확도: 98.36%\n",
+      "37\t검증 세트 손실: 0.088197\t최선의 손실: 0.082598\t정확도: 98.32%\n",
+      "38\t검증 세트 손실: 0.123205\t최선의 손실: 0.082598\t정확도: 97.89%\n",
+      "39\t검증 세트 손실: 0.121883\t최선의 손실: 0.082598\t정확도: 98.08%\n",
+      "40\t검증 세트 손실: 0.115641\t최선의 손실: 0.082598\t정확도: 97.97%\n",
+      "41\t검증 세트 손실: 0.146359\t최선의 손실: 0.082598\t정확도: 97.81%\n",
+      "42\t검증 세트 손실: 0.145872\t최선의 손실: 0.082598\t정확도: 97.97%\n",
+      "43\t검증 세트 손실: 0.151714\t최선의 손실: 0.082598\t정확도: 98.01%\n",
+      "44\t검증 세트 손실: 0.155411\t최선의 손실: 0.082598\t정확도: 97.93%\n",
+      "45\t검증 세트 손실: 0.131183\t최선의 손실: 0.082598\t정확도: 98.48%\n",
+      "46\t검증 세트 손실: 0.166692\t최선의 손실: 0.082598\t정확도: 98.16%\n",
+      "47\t검증 세트 손실: 0.151311\t최선의 손실: 0.082598\t정확도: 97.73%\n",
+      "48\t검증 세트 손실: 0.129393\t최선의 손실: 0.082598\t정확도: 97.58%\n",
+      "49\t검증 세트 손실: 728971.562500\t최선의 손실: 0.082598\t정확도: 32.45%\n",
+      "50\t검증 세트 손실: 138147.328125\t최선의 손실: 0.082598\t정확도: 63.68%\n",
+      "51\t검증 세트 손실: 9092.852539\t최선의 손실: 0.082598\t정확도: 90.85%\n",
+      "52\t검증 세트 손실: 3705.204102\t최선의 손실: 0.082598\t정확도: 94.41%\n",
+      "53\t검증 세트 손실: 2758.589844\t최선의 손실: 0.082598\t정확도: 93.59%\n",
+      "54\t검증 세트 손실: 2018.196777\t최선의 손실: 0.082598\t정확도: 95.39%\n",
+      "55\t검증 세트 손실: 1641.911987\t최선의 손실: 0.082598\t정확도: 95.93%\n",
+      "56\t검증 세트 손실: 1519.194336\t최선의 손실: 0.082598\t정확도: 95.50%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140, total=   3.9s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 9.453047\t최선의 손실: 9.453047\t정확도: 84.36%\n",
-      "1\t검증 세트 손실: 1.233515\t최선의 손실: 1.233515\t정확도: 94.76%\n",
-      "2\t검증 세트 손실: 0.323692\t최선의 손실: 0.323692\t정확도: 95.47%\n",
-      "3\t검증 세트 손실: 0.217715\t최선의 손실: 0.217715\t정확도: 95.15%\n",
-      "4\t검증 세트 손실: 0.174236\t최선의 손실: 0.174236\t정확도: 96.29%\n",
-      "5\t검증 세트 손실: 0.141281\t최선의 손실: 0.141281\t정확도: 96.72%\n",
-      "6\t검증 세트 손실: 0.130232\t최선의 손실: 0.130232\t정확도: 96.60%\n",
-      "7\t검증 세트 손실: 0.227588\t최선의 손실: 0.130232\t정확도: 95.31%\n",
-      "8\t검증 세트 손실: 0.129455\t최선의 손실: 0.129455\t정확도: 97.19%\n",
-      "9\t검증 세트 손실: 0.108489\t최선의 손실: 0.108489\t정확도: 97.11%\n",
-      "10\t검증 세트 손실: 0.129668\t최선의 손실: 0.108489\t정확도: 97.26%\n",
-      "11\t검증 세트 손실: 0.105641\t최선의 손실: 0.105641\t정확도: 97.30%\n",
-      "12\t검증 세트 손실: 0.115163\t최선의 손실: 0.105641\t정확도: 97.07%\n",
-      "13\t검증 세트 손실: 0.097624\t최선의 손실: 0.097624\t정확도: 97.46%\n",
-      "14\t검증 세트 손실: 0.137750\t최선의 손실: 0.097624\t정확도: 96.48%\n",
-      "15\t검증 세트 손실: 0.095068\t최선의 손실: 0.095068\t정확도: 97.58%\n",
-      "16\t검증 세트 손실: 0.102500\t최선의 손실: 0.095068\t정확도: 97.46%\n",
-      "17\t검증 세트 손실: 0.094829\t최선의 손실: 0.094829\t정확도: 97.93%\n",
-      "18\t검증 세트 손실: 0.113929\t최선의 손실: 0.094829\t정확도: 98.05%\n",
-      "19\t검증 세트 손실: 0.100728\t최선의 손실: 0.094829\t정확도: 97.73%\n",
-      "20\t검증 세트 손실: 0.103466\t최선의 손실: 0.094829\t정확도: 97.77%\n",
-      "21\t검증 세트 손실: 0.104894\t최선의 손실: 0.094829\t정확도: 97.97%\n",
-      "22\t검증 세트 손실: 0.095438\t최선의 손실: 0.094829\t정확도: 97.85%\n",
-      "23\t검증 세트 손실: 0.091487\t최선의 손실: 0.091487\t정확도: 98.16%\n",
-      "24\t검증 세트 손실: 0.111558\t최선의 손실: 0.091487\t정확도: 98.28%\n",
-      "25\t검증 세트 손실: 0.117672\t최선의 손실: 0.091487\t정확도: 97.97%\n",
-      "26\t검증 세트 손실: 0.097556\t최선의 손실: 0.091487\t정확도: 98.12%\n",
-      "27\t검증 세트 손실: 0.103669\t최선의 손실: 0.091487\t정확도: 97.93%\n",
-      "28\t검증 세트 손실: 0.107139\t최선의 손실: 0.091487\t정확도: 98.40%\n",
-      "29\t검증 세트 손실: 0.090941\t최선의 손실: 0.090941\t정확도: 98.32%\n",
-      "30\t검증 세트 손실: 0.132760\t최선의 손실: 0.090941\t정확도: 97.58%\n",
-      "31\t검증 세트 손실: 0.127162\t최선의 손실: 0.090941\t정확도: 97.81%\n",
-      "32\t검증 세트 손실: 0.105985\t최선의 손실: 0.090941\t정확도: 98.24%\n",
-      "33\t검증 세트 손실: 0.090290\t최선의 손실: 0.090290\t정확도: 98.12%\n",
-      "34\t검증 세트 손실: 0.099409\t최선의 손실: 0.090290\t정확도: 98.24%\n",
-      "35\t검증 세트 손실: 0.097831\t최선의 손실: 0.090290\t정확도: 98.12%\n",
-      "36\t검증 세트 손실: 0.112829\t최선의 손실: 0.090290\t정확도: 98.12%\n",
-      "37\t검증 세트 손실: 0.117093\t최선의 손실: 0.090290\t정확도: 98.20%\n",
-      "38\t검증 세트 손실: 0.116239\t최선의 손실: 0.090290\t정확도: 98.36%\n",
-      "39\t검증 세트 손실: 0.112478\t최선의 손실: 0.090290\t정확도: 98.28%\n",
-      "40\t검증 세트 손실: 0.140442\t최선의 손실: 0.090290\t정확도: 98.32%\n",
-      "41\t검증 세트 손실: 0.119835\t최선의 손실: 0.090290\t정확도: 98.24%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=140, total=   5.1s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=140 \n",
+      "0\t검증 세트 손실: 4.388824\t최선의 손실: 4.388824\t정확도: 65.13%\n",
+      "1\t검증 세트 손실: 0.373368\t최선의 손실: 0.373368\t정확도: 93.00%\n",
+      "2\t검증 세트 손실: 0.265943\t최선의 손실: 0.265943\t정확도: 95.27%\n",
+      "3\t검증 세트 손실: 0.161400\t최선의 손실: 0.161400\t정확도: 96.09%\n",
+      "4\t검증 세트 손실: 0.147365\t최선의 손실: 0.147365\t정확도: 96.13%\n",
+      "5\t검증 세트 손실: 0.164545\t최선의 손실: 0.147365\t정확도: 96.01%\n",
+      "6\t검증 세트 손실: 0.110799\t최선의 손실: 0.110799\t정확도: 96.79%\n",
+      "7\t검증 세트 손실: 0.136099\t최선의 손실: 0.110799\t정확도: 96.17%\n",
+      "8\t검증 세트 손실: 0.140970\t최선의 손실: 0.110799\t정확도: 96.52%\n",
+      "9\t검증 세트 손실: 0.123529\t최선의 손실: 0.110799\t정확도: 96.25%\n",
+      "10\t검증 세트 손실: 0.127387\t최선의 손실: 0.110799\t정확도: 96.79%\n",
+      "11\t검증 세트 손실: 0.082641\t최선의 손실: 0.082641\t정확도: 97.54%\n",
+      "12\t검증 세트 손실: 0.083702\t최선의 손실: 0.082641\t정확도: 97.69%\n",
+      "13\t검증 세트 손실: 0.079665\t최선의 손실: 0.079665\t정확도: 97.85%\n",
+      "14\t검증 세트 손실: 0.080880\t최선의 손실: 0.079665\t정확도: 97.62%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "42\t검증 세트 손실: 0.151141\t최선의 손실: 0.090290\t정확도: 98.32%\n",
-      "43\t검증 세트 손실: 0.147770\t최선의 손실: 0.090290\t정확도: 98.12%\n",
-      "44\t검증 세트 손실: 0.147178\t최선의 손실: 0.090290\t정확도: 98.01%\n",
-      "45\t검증 세트 손실: 0.159367\t최선의 손실: 0.090290\t정확도: 98.24%\n",
-      "46\t검증 세트 손실: 0.138232\t최선의 손실: 0.090290\t정확도: 98.24%\n",
-      "47\t검증 세트 손실: 0.169357\t최선의 손실: 0.090290\t정확도: 98.08%\n",
-      "48\t검증 세트 손실: 0.132380\t최선의 손실: 0.090290\t정확도: 98.05%\n",
-      "49\t검증 세트 손실: 0.187758\t최선의 손실: 0.090290\t정확도: 97.11%\n",
-      "50\t검증 세트 손실: 0.125078\t최선의 손실: 0.090290\t정확도: 98.01%\n",
-      "51\t검증 세트 손실: 0.122774\t최선의 손실: 0.090290\t정확도: 98.16%\n",
-      "52\t검증 세트 손실: 0.150378\t최선의 손실: 0.090290\t정확도: 98.12%\n",
-      "53\t검증 세트 손실: 0.127897\t최선의 손실: 0.090290\t정확도: 98.28%\n",
-      "54\t검증 세트 손실: 0.146594\t최선의 손실: 0.090290\t정확도: 98.16%\n",
+      "15\t검증 세트 손실: 0.093466\t최선의 손실: 0.079665\t정확도: 97.54%\n",
+      "16\t검증 세트 손실: 0.079792\t최선의 손실: 0.079665\t정확도: 97.89%\n",
+      "17\t검증 세트 손실: 0.109453\t최선의 손실: 0.079665\t정확도: 97.38%\n",
+      "18\t검증 세트 손실: 0.080300\t최선의 손실: 0.079665\t정확도: 98.24%\n",
+      "19\t검증 세트 손실: 0.110411\t최선의 손실: 0.079665\t정확도: 97.46%\n",
+      "20\t검증 세트 손실: 0.075459\t최선의 손실: 0.075459\t정확도: 97.85%\n",
+      "21\t검증 세트 손실: 0.074641\t최선의 손실: 0.074641\t정확도: 98.32%\n",
+      "22\t검증 세트 손실: 0.062378\t최선의 손실: 0.062378\t정확도: 98.24%\n",
+      "23\t검증 세트 손실: 0.068209\t최선의 손실: 0.062378\t정확도: 98.32%\n",
+      "24\t검증 세트 손실: 0.076480\t최선의 손실: 0.062378\t정확도: 98.08%\n",
+      "25\t검증 세트 손실: 0.087372\t최선의 손실: 0.062378\t정확도: 98.28%\n",
+      "26\t검증 세트 손실: 0.120130\t최선의 손실: 0.062378\t정확도: 97.58%\n",
+      "27\t검증 세트 손실: 0.078843\t최선의 손실: 0.062378\t정확도: 98.24%\n",
+      "28\t검증 세트 손실: 0.066052\t최선의 손실: 0.062378\t정확도: 98.51%\n",
+      "29\t검증 세트 손실: 0.077615\t최선의 손실: 0.062378\t정확도: 98.28%\n",
+      "30\t검증 세트 손실: 0.086158\t최선의 손실: 0.062378\t정확도: 98.28%\n",
+      "31\t검증 세트 손실: 0.138006\t최선의 손실: 0.062378\t정확도: 98.16%\n",
+      "32\t검증 세트 손실: 0.125814\t최선의 손실: 0.062378\t정확도: 98.28%\n",
+      "33\t검증 세트 손실: 0.105276\t최선의 손실: 0.062378\t정확도: 98.20%\n",
+      "34\t검증 세트 손실: 0.423094\t최선의 손실: 0.062378\t정확도: 97.30%\n",
+      "35\t검증 세트 손실: 0.329065\t최선의 손실: 0.062378\t정확도: 95.39%\n",
+      "36\t검증 세트 손실: 0.106528\t최선의 손실: 0.062378\t정확도: 98.08%\n",
+      "37\t검증 세트 손실: 0.104373\t최선의 손실: 0.062378\t정확도: 98.01%\n",
+      "38\t검증 세트 손실: 0.102380\t최선의 손실: 0.062378\t정확도: 98.24%\n",
+      "39\t검증 세트 손실: 0.112255\t최선의 손실: 0.062378\t정확도: 98.36%\n",
+      "40\t검증 세트 손실: 0.125890\t최선의 손실: 0.062378\t정확도: 98.08%\n",
+      "41\t검증 세트 손실: 0.106239\t최선의 손실: 0.062378\t정확도: 98.48%\n",
+      "42\t검증 세트 손실: 0.109116\t최선의 손실: 0.062378\t정확도: 98.32%\n",
+      "43\t검증 세트 손실: 0.126178\t최선의 손실: 0.062378\t정확도: 98.48%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140, total=   4.6s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.649060\t최선의 손실: 1.649060\t정확도: 90.11%\n",
-      "1\t검증 세트 손실: 0.333499\t최선의 손실: 0.333499\t정확도: 94.29%\n",
-      "2\t검증 세트 손실: 0.151405\t최선의 손실: 0.151405\t정확도: 96.21%\n",
-      "3\t검증 세트 손실: 0.171386\t최선의 손실: 0.151405\t정확도: 95.66%\n",
-      "4\t검증 세트 손실: 0.095544\t최선의 손실: 0.095544\t정확도: 97.30%\n",
-      "5\t검증 세트 손실: 0.093500\t최선의 손실: 0.093500\t정확도: 97.30%\n",
-      "6\t검증 세트 손실: 0.090487\t최선의 손실: 0.090487\t정확도: 97.54%\n",
-      "7\t검증 세트 손실: 0.077849\t최선의 손실: 0.077849\t정확도: 97.73%\n",
-      "8\t검증 세트 손실: 0.078425\t최선의 손실: 0.077849\t정확도: 97.62%\n",
-      "9\t검증 세트 손실: 0.069363\t최선의 손실: 0.069363\t정확도: 98.05%\n",
-      "10\t검증 세트 손실: 0.093595\t최선의 손실: 0.069363\t정확도: 97.65%\n",
-      "11\t검증 세트 손실: 0.115415\t최선의 손실: 0.069363\t정확도: 97.26%\n",
-      "12\t검증 세트 손실: 0.083760\t최선의 손실: 0.069363\t정확도: 97.69%\n",
-      "13\t검증 세트 손실: 0.129764\t최선의 손실: 0.069363\t정확도: 97.62%\n",
-      "14\t검증 세트 손실: 0.179094\t최선의 손실: 0.069363\t정확도: 97.50%\n",
-      "15\t검증 세트 손실: 0.104890\t최선의 손실: 0.069363\t정확도: 97.22%\n",
-      "16\t검증 세트 손실: 0.076357\t최선의 손실: 0.069363\t정확도: 98.08%\n",
-      "17\t검증 세트 손실: 0.064434\t최선의 손실: 0.064434\t정확도: 98.28%\n",
-      "18\t검증 세트 손실: 0.057050\t최선의 손실: 0.057050\t정확도: 98.40%\n",
-      "19\t검증 세트 손실: 0.070662\t최선의 손실: 0.057050\t정확도: 98.36%\n",
-      "20\t검증 세트 손실: 0.074808\t최선의 손실: 0.057050\t정확도: 97.93%\n",
-      "21\t검증 세트 손실: 0.077262\t최선의 손실: 0.057050\t정확도: 97.97%\n",
-      "22\t검증 세트 손실: 0.069737\t최선의 손실: 0.057050\t정확도: 98.28%\n",
-      "23\t검증 세트 손실: 0.056673\t최선의 손실: 0.056673\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.061921\t최선의 손실: 0.056673\t정확도: 98.40%\n",
-      "25\t검증 세트 손실: 0.068397\t최선의 손실: 0.056673\t정확도: 98.32%\n",
-      "26\t검증 세트 손실: 0.065127\t최선의 손실: 0.056673\t정확도: 98.48%\n",
-      "27\t검증 세트 손실: 0.075965\t최선의 손실: 0.056673\t정확도: 98.36%\n",
-      "28\t검증 세트 손실: 0.084780\t최선의 손실: 0.056673\t정확도: 98.24%\n",
-      "29\t검증 세트 손실: 0.106900\t최선의 손실: 0.056673\t정확도: 98.01%\n",
-      "30\t검증 세트 손실: 0.070881\t최선의 손실: 0.056673\t정확도: 98.12%\n",
-      "31\t검증 세트 손실: 0.062803\t최선의 손실: 0.056673\t정확도: 98.44%\n",
-      "32\t검증 세트 손실: 0.063434\t최선의 손실: 0.056673\t정확도: 98.63%\n",
-      "33\t검증 세트 손실: 0.068963\t최선의 손실: 0.056673\t정확도: 98.51%\n",
-      "34\t검증 세트 손실: 0.066063\t최선의 손실: 0.056673\t정확도: 98.67%\n",
-      "35\t검증 세트 손실: 0.085743\t최선의 손실: 0.056673\t정확도: 98.48%\n",
-      "36\t검증 세트 손실: 0.076747\t최선의 손실: 0.056673\t정확도: 98.48%\n",
-      "37\t검증 세트 손실: 0.119533\t최선의 손실: 0.056673\t정확도: 97.38%\n",
-      "38\t검증 세트 손실: 0.078195\t최선의 손실: 0.056673\t정확도: 98.36%\n",
-      "39\t검증 세트 손실: 0.120229\t최선의 손실: 0.056673\t정확도: 97.54%\n",
-      "40\t검증 세트 손실: 0.080171\t최선의 손실: 0.056673\t정확도: 98.40%\n",
-      "41\t검증 세트 손실: 0.058011\t최선의 손실: 0.056673\t정확도: 98.83%\n",
-      "42\t검증 세트 손실: 0.063754\t최선의 손실: 0.056673\t정확도: 98.75%\n",
-      "43\t검증 세트 손실: 0.059640\t최선의 손실: 0.056673\t정확도: 98.79%\n",
-      "44\t검증 세트 손실: 0.058603\t최선의 손실: 0.056673\t정확도: 98.79%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=140, total=   4.3s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.170523\t최선의 손실: 0.170523\t정확도: 95.78%\n",
+      "1\t검증 세트 손실: 0.174640\t최선의 손실: 0.170523\t정확도: 95.62%\n",
+      "2\t검증 세트 손실: 0.329979\t최선의 손실: 0.170523\t정확도: 95.23%\n",
+      "3\t검증 세트 손실: 0.187569\t최선의 손실: 0.170523\t정확도: 95.50%\n",
+      "4\t검증 세트 손실: 0.202614\t최선의 손실: 0.170523\t정확도: 95.82%\n",
+      "5\t검증 세트 손실: 0.156649\t최선의 손실: 0.156649\t정확도: 96.13%\n",
+      "6\t검증 세트 손실: 0.150534\t최선의 손실: 0.150534\t정확도: 96.52%\n",
+      "7\t검증 세트 손실: 0.187594\t최선의 손실: 0.150534\t정확도: 95.39%\n",
+      "8\t검증 세트 손실: 0.182636\t최선의 손실: 0.150534\t정확도: 95.58%\n",
+      "9\t검증 세트 손실: 0.182917\t최선의 손실: 0.150534\t정확도: 95.82%\n",
+      "10\t검증 세트 손실: 0.160721\t최선의 손실: 0.150534\t정확도: 96.29%\n",
+      "11\t검증 세트 손실: 0.232980\t최선의 손실: 0.150534\t정확도: 94.68%\n",
+      "12\t검증 세트 손실: 0.527393\t최선의 손실: 0.150534\t정확도: 85.34%\n",
+      "13\t검증 세트 손실: 0.198471\t최선의 손실: 0.150534\t정확도: 95.62%\n",
+      "14\t검증 세트 손실: 0.226465\t최선의 손실: 0.150534\t정확도: 94.72%\n",
+      "15\t검증 세트 손실: 0.209665\t최선의 손실: 0.150534\t정확도: 95.15%\n",
+      "16\t검증 세트 손실: 0.551681\t최선의 손실: 0.150534\t정확도: 74.39%\n",
+      "17\t검증 세트 손실: 0.494566\t최선의 손실: 0.150534\t정확도: 74.86%\n",
+      "18\t검증 세트 손실: 0.743307\t최선의 손실: 0.150534\t정확도: 68.84%\n",
+      "19\t검증 세트 손실: 0.862795\t최선의 손실: 0.150534\t정확도: 53.95%\n",
+      "20\t검증 세트 손실: 0.927397\t최선의 손실: 0.150534\t정확도: 57.43%\n",
+      "21\t검증 세트 손실: 0.798097\t최선의 손실: 0.150534\t정확도: 61.42%\n",
+      "22\t검증 세트 손실: 0.632958\t최선의 손실: 0.150534\t정확도: 73.69%\n",
+      "23\t검증 세트 손실: 0.663212\t최선의 손실: 0.150534\t정확도: 72.67%\n",
+      "24\t검증 세트 손실: 0.622458\t최선의 손실: 0.150534\t정확도: 71.42%\n",
+      "25\t검증 세트 손실: 0.729291\t최선의 손실: 0.150534\t정확도: 69.86%\n",
+      "26\t검증 세트 손실: 0.822939\t최선의 손실: 0.150534\t정확도: 64.43%\n",
+      "27\t검증 세트 손실: 0.629102\t최선의 손실: 0.150534\t정확도: 74.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140, total=   4.0s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.379187\t최선의 손실: 0.379187\t정확도: 88.62%\n",
-      "1\t검증 세트 손실: 0.220197\t최선의 손실: 0.220197\t정확도: 94.61%\n",
-      "2\t검증 세트 손실: 0.226756\t최선의 손실: 0.220197\t정확도: 94.88%\n",
-      "3\t검증 세트 손실: 0.294906\t최선의 손실: 0.220197\t정확도: 93.67%\n",
-      "4\t검증 세트 손실: 0.276646\t최선의 손실: 0.220197\t정확도: 93.59%\n",
-      "5\t검증 세트 손실: 0.450164\t최선의 손실: 0.220197\t정확도: 87.33%\n",
-      "6\t검증 세트 손실: 0.497254\t최선의 손실: 0.220197\t정확도: 77.25%\n",
-      "7\t검증 세트 손실: 0.432554\t최선의 손실: 0.220197\t정확도: 75.53%\n",
-      "8\t검증 세트 손실: 0.404863\t최선의 손실: 0.220197\t정확도: 80.02%\n",
-      "9\t검증 세트 손실: 0.415739\t최선의 손실: 0.220197\t정확도: 79.98%\n",
-      "10\t검증 세트 손실: 0.317598\t최선의 손실: 0.220197\t정확도: 91.63%\n",
-      "11\t검증 세트 손실: 0.261596\t최선의 손실: 0.220197\t정확도: 92.73%\n",
-      "12\t검증 세트 손실: 0.253682\t최선의 손실: 0.220197\t정확도: 93.63%\n",
-      "13\t검증 세트 손실: 0.333394\t최선의 손실: 0.220197\t정확도: 91.44%\n",
-      "14\t검증 세트 손실: 0.312941\t최선의 손실: 0.220197\t정확도: 92.73%\n",
-      "15\t검증 세트 손실: 0.314253\t최선의 손실: 0.220197\t정확도: 92.18%\n",
-      "16\t검증 세트 손실: 0.390307\t최선의 손실: 0.220197\t정확도: 87.26%\n",
-      "17\t검증 세트 손실: 0.268072\t최선의 손실: 0.220197\t정확도: 93.28%\n",
-      "18\t검증 세트 손실: 0.794061\t최선의 손실: 0.220197\t정확도: 58.95%\n",
-      "19\t검증 세트 손실: 0.589314\t최선의 손실: 0.220197\t정확도: 73.18%\n",
-      "20\t검증 세트 손실: 0.391504\t최선의 손실: 0.220197\t정확도: 84.56%\n",
-      "21\t검증 세트 손실: 0.362778\t최선의 손실: 0.220197\t정확도: 88.39%\n",
-      "22\t검증 세트 손실: 0.424897\t최선의 손실: 0.220197\t정확도: 84.40%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=10, total=   5.5s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.684786\t최선의 손실: 0.684786\t정확도: 64.97%\n",
+      "1\t검증 세트 손실: 0.664361\t최선의 손실: 0.664361\t정확도: 66.30%\n",
+      "2\t검증 세트 손실: 0.655848\t최선의 손실: 0.655848\t정확도: 68.65%\n",
+      "3\t검증 세트 손실: 0.625136\t최선의 손실: 0.625136\t정확도: 69.04%\n",
+      "4\t검증 세트 손실: 0.643380\t최선의 손실: 0.625136\t정확도: 67.08%\n",
+      "5\t검증 세트 손실: 0.640493\t최선의 손실: 0.625136\t정확도: 67.87%\n",
+      "6\t검증 세트 손실: 0.625290\t최선의 손실: 0.625136\t정확도: 68.41%\n",
+      "7\t검증 세트 손실: 0.699419\t최선의 손실: 0.625136\t정확도: 68.57%\n",
+      "8\t검증 세트 손실: 0.780990\t최선의 손실: 0.625136\t정확도: 56.88%\n",
+      "9\t검증 세트 손실: 0.755508\t최선의 손실: 0.625136\t정확도: 57.27%\n",
+      "10\t검증 세트 손실: 0.711334\t최선의 손실: 0.625136\t정확도: 58.41%\n",
+      "11\t검증 세트 손실: 0.800180\t최선의 손실: 0.625136\t정확도: 54.65%\n",
+      "12\t검증 세트 손실: 0.753357\t최선의 손실: 0.625136\t정확도: 58.21%\n",
+      "13\t검증 세트 손실: 0.768297\t최선의 손실: 0.625136\t정확도: 56.88%\n",
+      "14\t검증 세트 손실: 0.728352\t최선의 손실: 0.625136\t정확도: 57.15%\n",
+      "15\t검증 세트 손실: 0.710964\t최선의 손실: 0.625136\t정확도: 58.48%\n",
+      "16\t검증 세트 손실: 0.754282\t최선의 손실: 0.625136\t정확도: 56.80%\n",
+      "17\t검증 세트 손실: 0.706291\t최선의 손실: 0.625136\t정확도: 57.82%\n",
+      "18\t검증 세트 손실: 0.692489\t최선의 손실: 0.625136\t정확도: 58.60%\n",
+      "19\t검증 세트 손실: 0.721068\t최선의 손실: 0.625136\t정확도: 58.17%\n",
+      "20\t검증 세트 손실: 0.713464\t최선의 손실: 0.625136\t정확도: 58.09%\n",
+      "21\t검증 세트 손실: 0.556656\t최선의 손실: 0.556656\t정확도: 73.61%\n",
+      "22\t검증 세트 손실: 0.738637\t최선의 손실: 0.556656\t정확도: 70.64%\n",
+      "23\t검증 세트 손실: 0.518988\t최선의 손실: 0.518988\t정확도: 76.97%\n",
+      "24\t검증 세트 손실: 0.476505\t최선의 손실: 0.476505\t정확도: 78.11%\n",
+      "25\t검증 세트 손실: 0.518704\t최선의 손실: 0.476505\t정확도: 76.90%\n",
+      "26\t검증 세트 손실: 0.689445\t최선의 손실: 0.476505\t정확도: 67.87%\n",
+      "27\t검증 세트 손실: 0.499047\t최선의 손실: 0.476505\t정확도: 75.57%\n",
+      "28\t검증 세트 손실: 0.554122\t최선의 손실: 0.476505\t정확도: 74.24%\n",
+      "29\t검증 세트 손실: 0.481840\t최선의 손실: 0.476505\t정확도: 77.40%\n",
+      "30\t검증 세트 손실: 0.467037\t최선의 손실: 0.467037\t정확도: 77.87%\n",
+      "31\t검증 세트 손실: 0.493988\t최선의 손실: 0.467037\t정확도: 78.30%\n",
+      "32\t검증 세트 손실: 0.505964\t최선의 손실: 0.467037\t정확도: 76.04%\n",
+      "33\t검증 세트 손실: 0.503623\t최선의 손실: 0.467037\t정확도: 77.52%\n",
+      "34\t검증 세트 손실: 0.559446\t최선의 손실: 0.467037\t정확도: 73.26%\n",
+      "35\t검증 세트 손실: 0.496419\t최선의 손실: 0.467037\t정확도: 76.90%\n",
+      "36\t검증 세트 손실: 0.519173\t최선의 손실: 0.467037\t정확도: 76.74%\n",
+      "37\t검증 세트 손실: 0.467910\t최선의 손실: 0.467037\t정확도: 75.80%\n",
+      "38\t검증 세트 손실: 0.486617\t최선의 손실: 0.467037\t정확도: 76.39%\n",
+      "39\t검증 세트 손실: 0.504105\t최선의 손실: 0.467037\t정확도: 76.94%\n",
+      "40\t검증 세트 손실: 0.492015\t최선의 손실: 0.467037\t정확도: 76.19%\n",
+      "41\t검증 세트 손실: 0.536777\t최선의 손실: 0.467037\t정확도: 75.22%\n",
+      "42\t검증 세트 손실: 0.490061\t최선의 손실: 0.467037\t정확도: 76.47%\n",
+      "43\t검증 세트 손실: 0.498000\t최선의 손실: 0.467037\t정확도: 75.72%\n",
+      "44\t검증 세트 손실: 0.517160\t최선의 손실: 0.467037\t정확도: 76.43%\n",
+      "45\t검증 세트 손실: 0.569707\t최선의 손실: 0.467037\t정확도: 73.18%\n",
+      "46\t검증 세트 손실: 0.507376\t최선의 손실: 0.467037\t정확도: 77.13%\n",
+      "47\t검증 세트 손실: 0.497331\t최선의 손실: 0.467037\t정확도: 76.08%\n",
+      "48\t검증 세트 손실: 0.473522\t최선의 손실: 0.467037\t정확도: 77.37%\n",
+      "49\t검증 세트 손실: 0.503434\t최선의 손실: 0.467037\t정확도: 77.05%\n",
+      "50\t검증 세트 손실: 0.491839\t최선의 손실: 0.467037\t정확도: 77.17%\n",
+      "51\t검증 세트 손실: 0.479974\t최선의 손실: 0.467037\t정확도: 77.37%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=10, total=   9.0s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.513219\t최선의 손실: 0.513219\t정확도: 83.50%\n",
-      "1\t검증 세트 손실: 0.425513\t최선의 손실: 0.425513\t정확도: 88.51%\n",
-      "2\t검증 세트 손실: 0.459874\t최선의 손실: 0.425513\t정확도: 86.04%\n",
-      "3\t검증 세트 손실: 0.743735\t최선의 손실: 0.425513\t정확도: 68.69%\n",
-      "4\t검증 세트 손실: 0.615024\t최선의 손실: 0.425513\t정확도: 80.26%\n",
-      "5\t검증 세트 손실: 0.618761\t최선의 손실: 0.425513\t정확도: 83.78%\n",
-      "6\t검증 세트 손실: 0.654848\t최선의 손실: 0.425513\t정확도: 85.18%\n",
-      "7\t검증 세트 손실: 0.513764\t최선의 손실: 0.425513\t정확도: 83.07%\n",
-      "8\t검증 세트 손실: 0.552035\t최선의 손실: 0.425513\t정확도: 83.97%\n",
-      "9\t검증 세트 손실: 0.524418\t최선의 손실: 0.425513\t정확도: 86.55%\n",
-      "10\t검증 세트 손실: 0.535456\t최선의 손실: 0.425513\t정확도: 84.83%\n",
-      "11\t검증 세트 손실: 0.732857\t최선의 손실: 0.425513\t정확도: 82.56%\n",
-      "12\t검증 세트 손실: 0.572359\t최선의 손실: 0.425513\t정확도: 81.35%\n",
-      "13\t검증 세트 손실: 0.629179\t최선의 손실: 0.425513\t정확도: 78.73%\n",
-      "14\t검증 세트 손실: 0.540800\t최선의 손실: 0.425513\t정확도: 85.69%\n",
-      "15\t검증 세트 손실: 0.581087\t최선의 손실: 0.425513\t정확도: 88.15%\n",
-      "16\t검증 세트 손실: 0.523799\t최선의 손실: 0.425513\t정확도: 86.47%\n",
-      "17\t검증 세트 손실: 0.652196\t최선의 손실: 0.425513\t정확도: 89.05%\n",
-      "18\t검증 세트 손실: 0.597805\t최선의 손실: 0.425513\t정확도: 86.75%\n",
-      "19\t검증 세트 손실: 0.850923\t최선의 손실: 0.425513\t정확도: 88.82%\n",
-      "20\t검증 세트 손실: 1.026379\t최선의 손실: 0.425513\t정확도: 75.68%\n",
-      "21\t검증 세트 손실: 0.687647\t최선의 손실: 0.425513\t정확도: 88.58%\n",
-      "22\t검증 세트 손실: 0.810320\t최선의 손실: 0.425513\t정확도: 88.94%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=10, total=   9.6s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.624638\t최선의 손실: 0.624638\t정확도: 68.92%\n",
+      "1\t검증 세트 손실: 0.497803\t최선의 손실: 0.497803\t정확도: 81.55%\n",
+      "2\t검증 세트 손실: 0.442841\t최선의 손실: 0.442841\t정확도: 86.32%\n",
+      "3\t검증 세트 손실: 0.387590\t최선의 손실: 0.387590\t정확도: 87.76%\n",
+      "4\t검증 세트 손실: 0.913214\t최선의 손실: 0.387590\t정확도: 60.99%\n",
+      "5\t검증 세트 손실: 0.565457\t최선의 손실: 0.387590\t정확도: 76.39%\n",
+      "6\t검증 세트 손실: 0.552457\t최선의 손실: 0.387590\t정확도: 73.85%\n",
+      "7\t검증 세트 손실: 1.203449\t최선의 손실: 0.387590\t정확도: 41.32%\n",
+      "8\t검증 세트 손실: 0.907918\t최선의 손실: 0.387590\t정확도: 56.65%\n",
+      "9\t검증 세트 손실: 0.837292\t최선의 손실: 0.387590\t정확도: 59.66%\n",
+      "10\t검증 세트 손실: 0.822480\t최선의 손실: 0.387590\t정확도: 62.20%\n",
+      "11\t검증 세트 손실: 0.887609\t최선의 손실: 0.387590\t정확도: 63.96%\n",
+      "12\t검증 세트 손실: 1.103977\t최선의 손실: 0.387590\t정확도: 51.13%\n",
+      "13\t검증 세트 손실: 0.908210\t최선의 손실: 0.387590\t정확도: 60.99%\n",
+      "14\t검증 세트 손실: 0.922159\t최선의 손실: 0.387590\t정확도: 59.81%\n",
+      "15\t검증 세트 손실: 0.809119\t최선의 손실: 0.387590\t정확도: 64.97%\n",
+      "16\t검증 세트 손실: 0.724027\t최선의 손실: 0.387590\t정확도: 69.74%\n",
+      "17\t검증 세트 손실: 0.701775\t최선의 손실: 0.387590\t정확도: 69.78%\n",
+      "18\t검증 세트 손실: 0.742970\t최선의 손실: 0.387590\t정확도: 67.63%\n",
+      "19\t검증 세트 손실: 0.645720\t최선의 손실: 0.387590\t정확도: 74.63%\n",
+      "20\t검증 세트 손실: 0.626291\t최선의 손실: 0.387590\t정확도: 75.33%\n",
+      "21\t검증 세트 손실: 0.644477\t최선의 손실: 0.387590\t정확도: 72.56%\n",
+      "22\t검증 세트 손실: 0.668837\t최선의 손실: 0.387590\t정확도: 73.96%\n",
+      "23\t검증 세트 손실: 0.610075\t최선의 손실: 0.387590\t정확도: 76.00%\n",
+      "24\t검증 세트 손실: 0.612418\t최선의 손실: 0.387590\t정확도: 75.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=10, total=   7.6s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.198663\t최선의 손실: 0.198663\t정확도: 95.27%\n",
-      "1\t검증 세트 손실: 0.239447\t최선의 손실: 0.198663\t정확도: 93.16%\n",
-      "2\t검증 세트 손실: 0.262546\t최선의 손실: 0.198663\t정확도: 94.14%\n",
-      "3\t검증 세트 손실: 0.481278\t최선의 손실: 0.198663\t정확도: 79.09%\n",
-      "4\t검증 세트 손실: 0.638713\t최선의 손실: 0.198663\t정확도: 73.73%\n",
-      "5\t검증 세트 손실: 0.580559\t최선의 손실: 0.198663\t정확도: 76.27%\n",
-      "6\t검증 세트 손실: 0.626433\t최선의 손실: 0.198663\t정확도: 74.24%\n",
-      "7\t검증 세트 손실: 0.503762\t최선의 손실: 0.198663\t정확도: 83.42%\n",
-      "8\t검증 세트 손실: 0.440275\t최선의 손실: 0.198663\t정확도: 88.31%\n",
-      "9\t검증 세트 손실: 0.846131\t최선의 손실: 0.198663\t정확도: 67.47%\n",
-      "10\t검증 세트 손실: 0.443511\t최선의 손실: 0.198663\t정확도: 87.80%\n",
-      "11\t검증 세트 손실: 0.355902\t최선의 손실: 0.198663\t정확도: 91.05%\n",
-      "12\t검증 세트 손실: 0.399738\t최선의 손실: 0.198663\t정확도: 87.72%\n",
-      "13\t검증 세트 손실: 0.359021\t최선의 손실: 0.198663\t정확도: 88.82%\n",
-      "14\t검증 세트 손실: 0.353994\t최선의 손실: 0.198663\t정확도: 89.41%\n",
-      "15\t검증 세트 손실: 0.264728\t최선의 손실: 0.198663\t정확도: 93.94%\n",
-      "16\t검증 세트 손실: 0.336917\t최선의 손실: 0.198663\t정확도: 91.40%\n",
-      "17\t검증 세트 손실: 0.264454\t최선의 손실: 0.198663\t정확도: 92.85%\n",
-      "18\t검증 세트 손실: 0.256318\t최선의 손실: 0.198663\t정확도: 93.08%\n",
-      "19\t검증 세트 손실: 0.312692\t최선의 손실: 0.198663\t정확도: 92.38%\n",
-      "20\t검증 세트 손실: 0.267820\t최선의 손실: 0.198663\t정확도: 94.18%\n",
-      "21\t검증 세트 손실: 0.313231\t최선의 손실: 0.198663\t정확도: 91.99%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=10, total=   9.7s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.242396\t최선의 손실: 0.242396\t정확도: 94.45%\n",
-      "1\t검증 세트 손실: 0.135866\t최선의 손실: 0.135866\t정확도: 96.48%\n",
-      "2\t검증 세트 손실: 0.112811\t최선의 손실: 0.112811\t정확도: 97.03%\n",
-      "3\t검증 세트 손실: 0.134959\t최선의 손실: 0.112811\t정확도: 96.09%\n",
-      "4\t검증 세트 손실: 0.097997\t최선의 손실: 0.097997\t정확도: 97.46%\n",
-      "5\t검증 세트 손실: 0.099839\t최선의 손실: 0.097997\t정확도: 97.34%\n",
-      "6\t검증 세트 손실: 0.089402\t최선의 손실: 0.089402\t정확도: 97.34%\n",
-      "7\t검증 세트 손실: 0.125962\t최선의 손실: 0.089402\t정확도: 96.87%\n",
-      "8\t검증 세트 손실: 0.105609\t최선의 손실: 0.089402\t정확도: 97.15%\n",
-      "9\t검증 세트 손실: 0.094637\t최선의 손실: 0.089402\t정확도: 97.46%\n",
-      "10\t검증 세트 손실: 0.093300\t최선의 손실: 0.089402\t정확도: 97.42%\n",
-      "11\t검증 세트 손실: 0.100736\t최선의 손실: 0.089402\t정확도: 97.46%\n",
-      "12\t검증 세트 손실: 0.089812\t최선의 손실: 0.089402\t정확도: 97.42%\n"
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, n_neurons=10, total=   4.9s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.167856\t최선의 손실: 0.167856\t정확도: 95.62%\n",
+      "1\t검증 세트 손실: 0.108457\t최선의 손실: 0.108457\t정확도: 96.64%\n",
+      "2\t검증 세트 손실: 0.115244\t최선의 손실: 0.108457\t정확도: 96.68%\n",
+      "3\t검증 세트 손실: 0.103707\t최선의 손실: 0.103707\t정확도: 97.11%\n",
+      "4\t검증 세트 손실: 0.088900\t최선의 손실: 0.088900\t정확도: 97.07%\n",
+      "5\t검증 세트 손실: 0.108243\t최선의 손실: 0.088900\t정확도: 96.44%\n",
+      "6\t검증 세트 손실: 0.102634\t최선의 손실: 0.088900\t정확도: 96.60%\n",
+      "7\t검증 세트 손실: 0.115583\t최선의 손실: 0.088900\t정확도: 96.44%\n",
+      "8\t검증 세트 손실: 0.115068\t최선의 손실: 0.088900\t정확도: 96.48%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "13\t검증 세트 손실: 0.092214\t최선의 손실: 0.089402\t정확도: 97.62%\n",
-      "14\t검증 세트 손실: 0.079624\t최선의 손실: 0.079624\t정확도: 97.42%\n",
-      "15\t검증 세트 손실: 0.086305\t최선의 손실: 0.079624\t정확도: 97.58%\n",
-      "16\t검증 세트 손실: 0.096964\t최선의 손실: 0.079624\t정확도: 97.46%\n",
-      "17\t검증 세트 손실: 0.101025\t최선의 손실: 0.079624\t정확도: 97.58%\n",
-      "18\t검증 세트 손실: 0.090879\t최선의 손실: 0.079624\t정확도: 97.42%\n",
-      "19\t검증 세트 손실: 0.091883\t최선의 손실: 0.079624\t정확도: 97.34%\n",
-      "20\t검증 세트 손실: 0.089803\t최선의 손실: 0.079624\t정확도: 97.46%\n",
-      "21\t검증 세트 손실: 0.095743\t최선의 손실: 0.079624\t정확도: 97.77%\n",
-      "22\t검증 세트 손실: 0.090633\t최선의 손실: 0.079624\t정확도: 97.77%\n",
-      "23\t검증 세트 손실: 0.099427\t최선의 손실: 0.079624\t정확도: 97.42%\n",
-      "24\t검증 세트 손실: 0.117027\t최선의 손실: 0.079624\t정확도: 97.11%\n",
-      "25\t검증 세트 손실: 0.090101\t최선의 손실: 0.079624\t정확도: 97.58%\n",
-      "26\t검증 세트 손실: 0.095941\t최선의 손실: 0.079624\t정확도: 97.73%\n",
-      "27\t검증 세트 손실: 0.112449\t최선의 손실: 0.079624\t정확도: 97.46%\n",
-      "28\t검증 세트 손실: 0.089798\t최선의 손실: 0.079624\t정확도: 97.73%\n",
-      "29\t검증 세트 손실: 0.110599\t최선의 손실: 0.079624\t정확도: 97.65%\n",
-      "30\t검증 세트 손실: 0.098960\t최선의 손실: 0.079624\t정확도: 97.65%\n",
-      "31\t검증 세트 손실: 0.104743\t최선의 손실: 0.079624\t정확도: 97.93%\n",
-      "32\t검증 세트 손실: 0.120302\t최선의 손실: 0.079624\t정확도: 97.26%\n",
-      "33\t검증 세트 손실: 0.103994\t최선의 손실: 0.079624\t정확도: 97.38%\n",
-      "34\t검증 세트 손실: 0.116423\t최선의 손실: 0.079624\t정확도: 97.58%\n",
-      "35\t검증 세트 손실: 0.119716\t최선의 손실: 0.079624\t정확도: 97.58%\n",
+      "9\t검증 세트 손실: 0.087827\t최선의 손실: 0.087827\t정확도: 97.58%\n",
+      "10\t검증 세트 손실: 0.094932\t최선의 손실: 0.087827\t정확도: 97.19%\n",
+      "11\t검증 세트 손실: 0.112793\t최선의 손실: 0.087827\t정확도: 96.83%\n",
+      "12\t검증 세트 손실: 0.084992\t최선의 손실: 0.084992\t정확도: 97.62%\n",
+      "13\t검증 세트 손실: 0.101290\t최선의 손실: 0.084992\t정확도: 97.03%\n",
+      "14\t검증 세트 손실: 0.095391\t최선의 손실: 0.084992\t정확도: 97.46%\n",
+      "15\t검증 세트 손실: 0.089654\t최선의 손실: 0.084992\t정확도: 97.50%\n",
+      "16\t검증 세트 손실: 0.083526\t최선의 손실: 0.083526\t정확도: 97.89%\n",
+      "17\t검증 세트 손실: 0.104814\t최선의 손실: 0.083526\t정확도: 97.42%\n",
+      "18\t검증 세트 손실: 0.105825\t최선의 손실: 0.083526\t정확도: 97.54%\n",
+      "19\t검증 세트 손실: 0.106660\t최선의 손실: 0.083526\t정확도: 97.15%\n",
+      "20\t검증 세트 손실: 0.110320\t최선의 손실: 0.083526\t정확도: 97.26%\n",
+      "21\t검증 세트 손실: 0.093945\t최선의 손실: 0.083526\t정확도: 97.69%\n",
+      "22\t검증 세트 손실: 0.101414\t최선의 손실: 0.083526\t정확도: 97.69%\n",
+      "23\t검증 세트 손실: 0.125447\t최선의 손실: 0.083526\t정확도: 97.19%\n",
+      "24\t검증 세트 손실: 0.100788\t최선의 손실: 0.083526\t정확도: 97.50%\n",
+      "25\t검증 세트 손실: 0.127719\t최선의 손실: 0.083526\t정확도: 97.22%\n",
+      "26\t검증 세트 손실: 0.136075\t최선의 손실: 0.083526\t정확도: 97.46%\n",
+      "27\t검증 세트 손실: 0.124739\t최선의 손실: 0.083526\t정확도: 97.26%\n",
+      "28\t검증 세트 손실: 0.107940\t최선의 손실: 0.083526\t정확도: 97.34%\n",
+      "29\t검증 세트 손실: 0.104835\t최선의 손실: 0.083526\t정확도: 97.46%\n",
+      "30\t검증 세트 손실: 0.111142\t최선의 손실: 0.083526\t정확도: 97.22%\n",
+      "31\t검증 세트 손실: 0.117282\t최선의 손실: 0.083526\t정확도: 97.73%\n",
+      "32\t검증 세트 손실: 0.127079\t최선의 손실: 0.083526\t정확도: 97.34%\n",
+      "33\t검증 세트 손실: 0.129730\t최선의 손실: 0.083526\t정확도: 97.34%\n",
+      "34\t검증 세트 손실: 0.124026\t최선의 손실: 0.083526\t정확도: 97.62%\n",
+      "35\t검증 세트 손실: 0.113576\t최선의 손실: 0.083526\t정확도: 97.77%\n",
+      "36\t검증 세트 손실: 0.127601\t최선의 손실: 0.083526\t정확도: 97.65%\n",
+      "37\t검증 세트 손실: 0.140860\t최선의 손실: 0.083526\t정확도: 97.22%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=10, total=   2.9s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.243685\t최선의 손실: 0.243685\t정확도: 93.63%\n",
-      "1\t검증 세트 손실: 0.121529\t최선의 손실: 0.121529\t정확도: 96.40%\n",
-      "2\t검증 세트 손실: 0.117983\t최선의 손실: 0.117983\t정확도: 97.07%\n",
-      "3\t검증 세트 손실: 0.112240\t최선의 손실: 0.112240\t정확도: 97.22%\n",
-      "4\t검증 세트 손실: 0.097154\t최선의 손실: 0.097154\t정확도: 97.38%\n",
-      "5\t검증 세트 손실: 0.095386\t최선의 손실: 0.095386\t정확도: 97.19%\n",
-      "6\t검증 세트 손실: 0.101290\t최선의 손실: 0.095386\t정확도: 97.42%\n",
-      "7\t검증 세트 손실: 0.109133\t최선의 손실: 0.095386\t정확도: 96.99%\n",
-      "8\t검증 세트 손실: 0.089035\t최선의 손실: 0.089035\t정확도: 97.77%\n",
-      "9\t검증 세트 손실: 0.095609\t최선의 손실: 0.089035\t정확도: 97.46%\n",
-      "10\t검증 세트 손실: 0.093067\t최선의 손실: 0.089035\t정확도: 97.62%\n",
-      "11\t검증 세트 손실: 0.099579\t최선의 손실: 0.089035\t정확도: 97.54%\n",
-      "12\t검증 세트 손실: 0.114539\t최선의 손실: 0.089035\t정확도: 97.50%\n",
-      "13\t검증 세트 손실: 0.095014\t최선의 손실: 0.089035\t정확도: 97.50%\n",
-      "14\t검증 세트 손실: 0.122938\t최선의 손실: 0.089035\t정확도: 97.26%\n",
-      "15\t검증 세트 손실: 0.107589\t최선의 손실: 0.089035\t정확도: 97.65%\n",
-      "16\t검증 세트 손실: 0.114373\t최선의 손실: 0.089035\t정확도: 97.73%\n",
-      "17\t검증 세트 손실: 0.095480\t최선의 손실: 0.089035\t정확도: 97.42%\n",
-      "18\t검증 세트 손실: 0.116851\t최선의 손실: 0.089035\t정확도: 97.73%\n",
-      "19\t검증 세트 손실: 0.108270\t최선의 손실: 0.089035\t정확도: 97.65%\n",
-      "20\t검증 세트 손실: 0.110943\t최선의 손실: 0.089035\t정확도: 97.65%\n",
-      "21\t검증 세트 손실: 0.106456\t최선의 손실: 0.089035\t정확도: 97.73%\n",
-      "22\t검증 세트 손실: 0.117352\t최선의 손실: 0.089035\t정확도: 97.62%\n",
-      "23\t검증 세트 손실: 0.118869\t최선의 손실: 0.089035\t정확도: 97.62%\n",
-      "24\t검증 세트 손실: 0.124601\t최선의 손실: 0.089035\t정확도: 97.50%\n",
-      "25\t검증 세트 손실: 0.119623\t최선의 손실: 0.089035\t정확도: 97.50%\n",
-      "26\t검증 세트 손실: 0.108625\t최선의 손실: 0.089035\t정확도: 97.62%\n",
-      "27\t검증 세트 손실: 0.105453\t최선의 손실: 0.089035\t정확도: 97.46%\n",
-      "28\t검증 세트 손실: 0.121576\t최선의 손실: 0.089035\t정확도: 97.54%\n",
-      "29\t검증 세트 손실: 0.119689\t최선의 손실: 0.089035\t정확도: 97.11%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=10, total=   3.8s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.213472\t최선의 손실: 0.213472\t정확도: 94.06%\n",
+      "1\t검증 세트 손실: 0.145661\t최선의 손실: 0.145661\t정확도: 96.05%\n",
+      "2\t검증 세트 손실: 0.123632\t최선의 손실: 0.123632\t정확도: 96.36%\n",
+      "3\t검증 세트 손실: 0.124052\t최선의 손실: 0.123632\t정확도: 96.44%\n",
+      "4\t검증 세트 손실: 0.144889\t최선의 손실: 0.123632\t정확도: 96.13%\n",
+      "5\t검증 세트 손실: 0.110220\t최선의 손실: 0.110220\t정확도: 97.11%\n",
+      "6\t검증 세트 손실: 0.120046\t최선의 손실: 0.110220\t정확도: 96.83%\n",
+      "7\t검증 세트 손실: 0.104886\t최선의 손실: 0.104886\t정확도: 97.34%\n",
+      "8\t검증 세트 손실: 0.110804\t최선의 손실: 0.104886\t정확도: 96.91%\n",
+      "9\t검증 세트 손실: 0.114845\t최선의 손실: 0.104886\t정확도: 96.76%\n",
+      "10\t검증 세트 손실: 0.116173\t최선의 손실: 0.104886\t정확도: 96.95%\n",
+      "11\t검증 세트 손실: 0.124587\t최선의 손실: 0.104886\t정확도: 96.76%\n",
+      "12\t검증 세트 손실: 0.108523\t최선의 손실: 0.104886\t정확도: 97.15%\n",
+      "13\t검증 세트 손실: 0.107945\t최선의 손실: 0.104886\t정확도: 97.15%\n",
+      "14\t검증 세트 손실: 0.100707\t최선의 손실: 0.100707\t정확도: 97.30%\n",
+      "15\t검증 세트 손실: 0.106083\t최선의 손실: 0.100707\t정확도: 97.11%\n",
+      "16\t검증 세트 손실: 0.099621\t최선의 손실: 0.099621\t정확도: 97.46%\n",
+      "17\t검증 세트 손실: 0.101612\t최선의 손실: 0.099621\t정확도: 97.03%\n",
+      "18\t검증 세트 손실: 0.121625\t최선의 손실: 0.099621\t정확도: 96.60%\n",
+      "19\t검증 세트 손실: 0.111096\t최선의 손실: 0.099621\t정확도: 96.87%\n",
+      "20\t검증 세트 손실: 0.109175\t최선의 손실: 0.099621\t정확도: 97.22%\n",
+      "21\t검증 세트 손실: 0.110920\t최선의 손실: 0.099621\t정확도: 97.42%\n",
+      "22\t검증 세트 손실: 0.096081\t최선의 손실: 0.096081\t정확도: 97.54%\n",
+      "23\t검증 세트 손실: 0.114144\t최선의 손실: 0.096081\t정확도: 97.19%\n",
+      "24\t검증 세트 손실: 0.105850\t최선의 손실: 0.096081\t정확도: 97.34%\n",
+      "25\t검증 세트 손실: 0.110524\t최선의 손실: 0.096081\t정확도: 97.50%\n",
+      "26\t검증 세트 손실: 0.097885\t최선의 손실: 0.096081\t정확도: 97.69%\n",
+      "27\t검증 세트 손실: 0.123684\t최선의 손실: 0.096081\t정확도: 97.46%\n",
+      "28\t검증 세트 손실: 0.113832\t최선의 손실: 0.096081\t정확도: 97.26%\n",
+      "29\t검증 세트 손실: 0.143234\t최선의 손실: 0.096081\t정확도: 97.22%\n",
+      "30\t검증 세트 손실: 0.145033\t최선의 손실: 0.096081\t정확도: 97.26%\n",
+      "31\t검증 세트 손실: 0.115267\t최선의 손실: 0.096081\t정확도: 97.38%\n",
+      "32\t검증 세트 손실: 0.131718\t최선의 손실: 0.096081\t정확도: 97.30%\n",
+      "33\t검증 세트 손실: 0.125927\t최선의 손실: 0.096081\t정확도: 97.38%\n",
+      "34\t검증 세트 손실: 0.096799\t최선의 손실: 0.096081\t정확도: 97.46%\n",
+      "35\t검증 세트 손실: 0.117402\t최선의 손실: 0.096081\t정확도: 97.54%\n",
+      "36\t검증 세트 손실: 0.112056\t최선의 손실: 0.096081\t정확도: 97.46%\n",
+      "37\t검증 세트 손실: 0.105629\t최선의 손실: 0.096081\t정확도: 97.62%\n",
+      "38\t검증 세트 손실: 0.117222\t최선의 손실: 0.096081\t정확도: 97.42%\n",
+      "39\t검증 세트 손실: 0.132882\t최선의 손실: 0.096081\t정확도: 97.38%\n",
+      "40\t검증 세트 손실: 0.157051\t최선의 손실: 0.096081\t정확도: 97.19%\n",
+      "41\t검증 세트 손실: 0.105921\t최선의 손실: 0.096081\t정확도: 97.77%\n",
+      "42\t검증 세트 손실: 0.120951\t최선의 손실: 0.096081\t정확도: 97.54%\n",
+      "43\t검증 세트 손실: 0.127499\t최선의 손실: 0.096081\t정확도: 97.26%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=10, total=   2.5s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.278816\t최선의 손실: 0.278816\t정확도: 92.30%\n",
-      "1\t검증 세트 손실: 0.200693\t최선의 손실: 0.200693\t정확도: 94.18%\n",
-      "2\t검증 세트 손실: 0.169359\t최선의 손실: 0.169359\t정확도: 95.23%\n",
-      "3\t검증 세트 손실: 0.139187\t최선의 손실: 0.139187\t정확도: 96.09%\n",
-      "4\t검증 세트 손실: 0.143174\t최선의 손실: 0.139187\t정확도: 96.40%\n",
-      "5\t검증 세트 손실: 0.113422\t최선의 손실: 0.113422\t정확도: 96.91%\n",
-      "6\t검증 세트 손실: 0.105227\t최선의 손실: 0.105227\t정확도: 97.38%\n",
-      "7\t검증 세트 손실: 0.124250\t최선의 손실: 0.105227\t정확도: 96.44%\n",
-      "8\t검증 세트 손실: 0.115332\t최선의 손실: 0.105227\t정확도: 96.95%\n",
-      "9\t검증 세트 손실: 0.112531\t최선의 손실: 0.105227\t정확도: 97.22%\n",
-      "10\t검증 세트 손실: 0.113772\t최선의 손실: 0.105227\t정확도: 97.19%\n",
-      "11\t검증 세트 손실: 0.123709\t최선의 손실: 0.105227\t정확도: 97.26%\n",
-      "12\t검증 세트 손실: 0.113645\t최선의 손실: 0.105227\t정확도: 97.46%\n",
-      "13\t검증 세트 손실: 0.107108\t최선의 손실: 0.105227\t정확도: 97.22%\n",
-      "14\t검증 세트 손실: 0.115973\t최선의 손실: 0.105227\t정확도: 97.30%\n",
-      "15\t검증 세트 손실: 0.112200\t최선의 손실: 0.105227\t정확도: 97.62%\n",
-      "16\t검증 세트 손실: 0.104191\t최선의 손실: 0.104191\t정확도: 97.42%\n",
-      "17\t검증 세트 손실: 0.118397\t최선의 손실: 0.104191\t정확도: 97.07%\n",
-      "18\t검증 세트 손실: 0.115540\t최선의 손실: 0.104191\t정확도: 97.38%\n",
-      "19\t검증 세트 손실: 0.102513\t최선의 손실: 0.102513\t정확도: 97.50%\n",
-      "20\t검증 세트 손실: 0.095432\t최선의 손실: 0.095432\t정확도: 97.62%\n",
-      "21\t검증 세트 손실: 0.111335\t최선의 손실: 0.095432\t정확도: 97.38%\n",
-      "22\t검증 세트 손실: 0.098926\t최선의 손실: 0.095432\t정확도: 97.38%\n",
-      "23\t검증 세트 손실: 0.104599\t최선의 손실: 0.095432\t정확도: 97.46%\n",
-      "24\t검증 세트 손실: 0.096523\t최선의 손실: 0.095432\t정확도: 97.58%\n",
-      "25\t검증 세트 손실: 0.104308\t최선의 손실: 0.095432\t정확도: 97.77%\n",
-      "26\t검증 세트 손실: 0.111252\t최선의 손실: 0.095432\t정확도: 97.54%\n",
-      "27\t검증 세트 손실: 0.141068\t최선의 손실: 0.095432\t정확도: 97.15%\n",
-      "28\t검증 세트 손실: 0.106456\t최선의 손실: 0.095432\t정확도: 97.69%\n",
-      "29\t검증 세트 손실: 0.103339\t최선의 손실: 0.095432\t정확도: 97.54%\n",
-      "30\t검증 세트 손실: 0.101375\t최선의 손실: 0.095432\t정확도: 97.65%\n",
-      "31\t검증 세트 손실: 0.129214\t최선의 손실: 0.095432\t정확도: 97.22%\n",
-      "32\t검증 세트 손실: 0.127868\t최선의 손실: 0.095432\t정확도: 97.58%\n",
-      "33\t검증 세트 손실: 0.119747\t최선의 손실: 0.095432\t정확도: 97.65%\n",
-      "34\t검증 세트 손실: 0.103284\t최선의 손실: 0.095432\t정확도: 97.62%\n",
-      "35\t검증 세트 손실: 0.104018\t최선의 손실: 0.095432\t정확도: 97.65%\n",
-      "36\t검증 세트 손실: 0.092864\t최선의 손실: 0.092864\t정확도: 97.73%\n",
-      "37\t검증 세트 손실: 0.105280\t최선의 손실: 0.092864\t정확도: 97.65%\n",
-      "38\t검증 세트 손실: 0.128475\t최선의 손실: 0.092864\t정확도: 97.22%\n",
-      "39\t검증 세트 손실: 0.120634\t최선의 손실: 0.092864\t정확도: 97.62%\n",
-      "40\t검증 세트 손실: 0.122174\t최선의 손실: 0.092864\t정확도: 97.26%\n",
-      "41\t검증 세트 손실: 0.101580\t최선의 손실: 0.092864\t정확도: 97.73%\n",
-      "42\t검증 세트 손실: 0.104868\t최선의 손실: 0.092864\t정확도: 97.93%\n",
-      "43\t검증 세트 손실: 0.108611\t최선의 손실: 0.092864\t정확도: 97.62%\n",
-      "44\t검증 세트 손실: 0.110853\t최선의 손실: 0.092864\t정확도: 97.65%\n",
-      "45\t검증 세트 손실: 0.136845\t최선의 손실: 0.092864\t정확도: 97.69%\n",
-      "46\t검증 세트 손실: 0.135661\t최선의 손실: 0.092864\t정확도: 97.50%\n",
-      "47\t검증 세트 손실: 0.129200\t최선의 손실: 0.092864\t정확도: 97.42%\n",
-      "48\t검증 세트 손실: 0.162981\t최선의 손실: 0.092864\t정확도: 96.95%\n",
-      "49\t검증 세트 손실: 0.112627\t최선의 손실: 0.092864\t정확도: 97.73%\n",
-      "50\t검증 세트 손실: 0.104317\t최선의 손실: 0.092864\t정확도: 97.58%\n",
-      "51\t검증 세트 손실: 0.124665\t최선의 손실: 0.092864\t정확도: 97.58%\n",
-      "52\t검증 세트 손실: 0.119053\t최선의 손실: 0.092864\t정확도: 97.58%\n",
-      "53\t검증 세트 손실: 0.119615\t최선의 손실: 0.092864\t정확도: 97.58%\n",
-      "54\t검증 세트 손실: 0.130052\t최선의 손실: 0.092864\t정확도: 97.50%\n",
-      "55\t검증 세트 손실: 0.133811\t최선의 손실: 0.092864\t정확도: 97.65%\n",
-      "56\t검증 세트 손실: 0.135184\t최선의 손실: 0.092864\t정확도: 97.34%\n",
-      "57\t검증 세트 손실: 0.138678\t최선의 손실: 0.092864\t정확도: 97.65%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=10, total=   4.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.229440\t최선의 손실: 0.229440\t정확도: 94.02%\n",
+      "1\t검증 세트 손실: 0.148883\t최선의 손실: 0.148883\t정확도: 95.58%\n",
+      "2\t검증 세트 손실: 0.141299\t최선의 손실: 0.141299\t정확도: 96.29%\n",
+      "3\t검증 세트 손실: 0.124889\t최선의 손실: 0.124889\t정확도: 96.48%\n",
+      "4\t검증 세트 손실: 0.135961\t최선의 손실: 0.124889\t정확도: 96.79%\n",
+      "5\t검증 세트 손실: 0.119037\t최선의 손실: 0.119037\t정확도: 96.87%\n",
+      "6\t검증 세트 손실: 0.109765\t최선의 손실: 0.109765\t정확도: 97.03%\n",
+      "7\t검증 세트 손실: 0.119925\t최선의 손실: 0.109765\t정확도: 96.76%\n",
+      "8\t검증 세트 손실: 0.108676\t최선의 손실: 0.108676\t정확도: 97.15%\n",
+      "9\t검증 세트 손실: 0.104914\t최선의 손실: 0.104914\t정확도: 96.99%\n",
+      "10\t검증 세트 손실: 0.107098\t최선의 손실: 0.104914\t정확도: 97.50%\n",
+      "11\t검증 세트 손실: 0.106003\t최선의 손실: 0.104914\t정확도: 97.50%\n",
+      "12\t검증 세트 손실: 0.095941\t최선의 손실: 0.095941\t정확도: 97.50%\n",
+      "13\t검증 세트 손실: 0.100579\t최선의 손실: 0.095941\t정확도: 97.62%\n",
+      "14\t검증 세트 손실: 0.131193\t최선의 손실: 0.095941\t정확도: 97.07%\n",
+      "15\t검증 세트 손실: 0.082981\t최선의 손실: 0.082981\t정확도: 97.85%\n",
+      "16\t검증 세트 손실: 0.091641\t최선의 손실: 0.082981\t정확도: 97.69%\n",
+      "17\t검증 세트 손실: 0.084437\t최선의 손실: 0.082981\t정확도: 98.08%\n",
+      "18\t검증 세트 손실: 0.100398\t최선의 손실: 0.082981\t정확도: 97.46%\n",
+      "19\t검증 세트 손실: 0.098047\t최선의 손실: 0.082981\t정확도: 97.93%\n",
+      "20\t검증 세트 손실: 0.101944\t최선의 손실: 0.082981\t정확도: 97.69%\n",
+      "21\t검증 세트 손실: 0.093215\t최선의 손실: 0.082981\t정확도: 97.62%\n",
+      "22\t검증 세트 손실: 0.093235\t최선의 손실: 0.082981\t정확도: 97.50%\n",
+      "23\t검증 세트 손실: 0.084818\t최선의 손실: 0.082981\t정확도: 97.77%\n",
+      "24\t검증 세트 손실: 0.094919\t최선의 손실: 0.082981\t정확도: 98.01%\n",
+      "25\t검증 세트 손실: 0.095856\t최선의 손실: 0.082981\t정확도: 97.81%\n",
+      "26\t검증 세트 손실: 0.109810\t최선의 손실: 0.082981\t정확도: 97.77%\n",
+      "27\t검증 세트 손실: 0.103441\t최선의 손실: 0.082981\t정확도: 97.42%\n",
+      "28\t검증 세트 손실: 0.089491\t최선의 손실: 0.082981\t정확도: 97.89%\n",
+      "29\t검증 세트 손실: 0.093624\t최선의 손실: 0.082981\t정확도: 97.73%\n",
+      "30\t검증 세트 손실: 0.097720\t최선의 손실: 0.082981\t정확도: 97.77%\n",
+      "31\t검증 세트 손실: 0.093149\t최선의 손실: 0.082981\t정확도: 97.93%\n",
+      "32\t검증 세트 손실: 0.095089\t최선의 손실: 0.082981\t정확도: 97.65%\n",
+      "33\t검증 세트 손실: 0.126067\t최선의 손실: 0.082981\t정확도: 97.34%\n",
+      "34\t검증 세트 손실: 0.106720\t최선의 손실: 0.082981\t정확도: 97.50%\n",
+      "35\t검증 세트 손실: 0.106553\t최선의 손실: 0.082981\t정확도: 97.65%\n",
+      "36\t검증 세트 손실: 0.102629\t최선의 손실: 0.082981\t정확도: 97.97%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=10, total=   4.6s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.101116\t최선의 손실: 0.101116\t정확도: 96.64%\n",
-      "1\t검증 세트 손실: 0.096342\t최선의 손실: 0.096342\t정확도: 97.69%\n",
-      "2\t검증 세트 손실: 0.084857\t최선의 손실: 0.084857\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.081569\t최선의 손실: 0.081569\t정확도: 97.77%\n",
-      "4\t검증 세트 손실: 0.067351\t최선의 손실: 0.067351\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.089557\t최선의 손실: 0.067351\t정확도: 98.05%\n",
-      "6\t검증 세트 손실: 0.057874\t최선의 손실: 0.057874\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.108594\t최선의 손실: 0.057874\t정확도: 97.34%\n",
-      "8\t검증 세트 손실: 0.122266\t최선의 손실: 0.057874\t정확도: 97.89%\n",
-      "9\t검증 세트 손실: 0.058454\t최선의 손실: 0.057874\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.136170\t최선의 손실: 0.057874\t정확도: 98.20%\n",
-      "11\t검증 세트 손실: 0.125346\t최선의 손실: 0.057874\t정확도: 97.19%\n",
-      "12\t검증 세트 손실: 0.147048\t최선의 손실: 0.057874\t정확도: 96.68%\n",
-      "13\t검증 세트 손실: 0.120763\t최선의 손실: 0.057874\t정확도: 97.77%\n",
-      "14\t검증 세트 손실: 0.106452\t최선의 손실: 0.057874\t정확도: 97.62%\n",
-      "15\t검증 세트 손실: 0.093804\t최선의 손실: 0.057874\t정확도: 97.93%\n",
-      "16\t검증 세트 손실: 0.131801\t최선의 손실: 0.057874\t정확도: 96.87%\n",
-      "17\t검증 세트 손실: 0.099748\t최선의 손실: 0.057874\t정확도: 97.93%\n",
-      "18\t검증 세트 손실: 0.161332\t최선의 손실: 0.057874\t정확도: 97.22%\n",
-      "19\t검증 세트 손실: 0.107834\t최선의 손실: 0.057874\t정확도: 97.73%\n",
-      "20\t검증 세트 손실: 0.111569\t최선의 손실: 0.057874\t정확도: 97.97%\n",
-      "21\t검증 세트 손실: 0.079869\t최선의 손실: 0.057874\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.372129\t최선의 손실: 0.057874\t정확도: 96.40%\n",
-      "23\t검증 세트 손실: 0.115609\t최선의 손실: 0.057874\t정확도: 98.12%\n",
-      "24\t검증 세트 손실: 0.124579\t최선의 손실: 0.057874\t정확도: 97.65%\n",
-      "25\t검증 세트 손실: 0.135093\t최선의 손실: 0.057874\t정확도: 97.07%\n",
-      "26\t검증 세트 손실: 0.141190\t최선의 손실: 0.057874\t정확도: 97.54%\n",
-      "27\t검증 세트 손실: 0.140617\t최선의 손실: 0.057874\t정확도: 97.26%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=10, total=   3.5s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.087686\t최선의 손실: 0.087686\t정확도: 97.38%\n",
+      "1\t검증 세트 손실: 0.085174\t최선의 손실: 0.085174\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.085704\t최선의 손실: 0.085174\t정확도: 97.62%\n",
+      "3\t검증 세트 손실: 0.092375\t최선의 손실: 0.085174\t정확도: 97.38%\n",
+      "4\t검증 세트 손실: 0.063319\t최선의 손실: 0.063319\t정확도: 98.28%\n",
+      "5\t검증 세트 손실: 0.094933\t최선의 손실: 0.063319\t정확도: 97.62%\n",
+      "6\t검증 세트 손실: 0.102323\t최선의 손실: 0.063319\t정확도: 97.81%\n",
+      "7\t검증 세트 손실: 0.136986\t최선의 손실: 0.063319\t정확도: 97.97%\n",
+      "8\t검증 세트 손실: 0.217947\t최선의 손실: 0.063319\t정확도: 94.53%\n",
+      "9\t검증 세트 손실: 1.658626\t최선의 손실: 0.063319\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.649404\t최선의 손실: 0.063319\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.662151\t최선의 손실: 0.063319\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.612890\t최선의 손실: 0.063319\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.614011\t최선의 손실: 0.063319\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.642151\t최선의 손실: 0.063319\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.623434\t최선의 손실: 0.063319\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.641410\t최선의 손실: 0.063319\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.651077\t최선의 손실: 0.063319\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.664958\t최선의 손실: 0.063319\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.621747\t최선의 손실: 0.063319\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.648838\t최선의 손실: 0.063319\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.653800\t최선의 손실: 0.063319\t정확도: 19.08%\n",
+      "22\t검증 세트 손실: 1.613054\t최선의 손실: 0.063319\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.621140\t최선의 손실: 0.063319\t정확도: 19.08%\n",
+      "24\t검증 세트 손실: 1.654621\t최선의 손실: 0.063319\t정확도: 19.27%\n",
+      "25\t검증 세트 손실: 1.664644\t최선의 손실: 0.063319\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=  12.7s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.098279\t최선의 손실: 0.098279\t정확도: 96.72%\n",
-      "1\t검증 세트 손실: 0.082256\t최선의 손실: 0.082256\t정확도: 97.65%\n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=70, total=   5.1s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.100504\t최선의 손실: 0.100504\t정확도: 97.34%\n",
+      "1\t검증 세트 손실: 0.077670\t최선의 손실: 0.077670\t정확도: 97.89%\n",
+      "2\t검증 세트 손실: 0.070908\t최선의 손실: 0.070908\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.080686\t최선의 손실: 0.070908\t정확도: 97.77%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2\t검증 세트 손실: 0.062222\t최선의 손실: 0.062222\t정확도: 98.05%\n",
-      "3\t검증 세트 손실: 0.104538\t최선의 손실: 0.062222\t정확도: 97.62%\n",
-      "4\t검증 세트 손실: 0.084856\t최선의 손실: 0.062222\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.076951\t최선의 손실: 0.062222\t정확도: 98.16%\n",
-      "6\t검증 세트 손실: 0.093877\t최선의 손실: 0.062222\t정확도: 97.73%\n",
-      "7\t검증 세트 손실: 0.177094\t최선의 손실: 0.062222\t정확도: 97.42%\n",
-      "8\t검증 세트 손실: 0.343613\t최선의 손실: 0.062222\t정확도: 97.50%\n",
-      "9\t검증 세트 손실: 2.886327\t최선의 손실: 0.062222\t정확도: 34.75%\n",
-      "10\t검증 세트 손실: 1.619923\t최선의 손실: 0.062222\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 1.654726\t최선의 손실: 0.062222\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.643674\t최선의 손실: 0.062222\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.658978\t최선의 손실: 0.062222\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 1.645270\t최선의 손실: 0.062222\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.628464\t최선의 손실: 0.062222\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.619229\t최선의 손실: 0.062222\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 1.655719\t최선의 손실: 0.062222\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.683712\t최선의 손실: 0.062222\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.633460\t최선의 손실: 0.062222\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.635544\t최선의 손실: 0.062222\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 1.696720\t최선의 손실: 0.062222\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.628040\t최선의 손실: 0.062222\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.702383\t최선의 손실: 0.062222\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 0.092688\t최선의 손실: 0.070908\t정확도: 98.01%\n",
+      "5\t검증 세트 손실: 0.065849\t최선의 손실: 0.065849\t정확도: 98.40%\n",
+      "6\t검증 세트 손실: 0.049691\t최선의 손실: 0.049691\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.083899\t최선의 손실: 0.049691\t정확도: 97.77%\n",
+      "8\t검증 세트 손실: 0.128899\t최선의 손실: 0.049691\t정확도: 97.97%\n",
+      "9\t검증 세트 손실: 1.060302\t최선의 손실: 0.049691\t정확도: 77.25%\n",
+      "10\t검증 세트 손실: 1.609462\t최선의 손실: 0.049691\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.622200\t최선의 손실: 0.049691\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.622142\t최선의 손실: 0.049691\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 1.638234\t최선의 손실: 0.049691\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.632090\t최선의 손실: 0.049691\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.627630\t최선의 손실: 0.049691\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.631667\t최선의 손실: 0.049691\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1.652224\t최선의 손실: 0.049691\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.670237\t최선의 손실: 0.049691\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.639060\t최선의 손실: 0.049691\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.627109\t최선의 손실: 0.049691\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.689527\t최선의 손실: 0.049691\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.646617\t최선의 손실: 0.049691\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.669662\t최선의 손실: 0.049691\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.732146\t최선의 손실: 0.049691\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.637256\t최선의 손실: 0.049691\t정확도: 18.73%\n",
+      "26\t검증 세트 손실: 1.623066\t최선의 손실: 0.049691\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 1.644380\t최선의 손실: 0.049691\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=   8.7s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.116984\t최선의 손실: 0.116984\t정확도: 96.76%\n",
-      "1\t검증 세트 손실: 0.077740\t최선의 손실: 0.077740\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.071885\t최선의 손실: 0.071885\t정확도: 97.77%\n",
-      "3\t검증 세트 손실: 0.075591\t최선의 손실: 0.071885\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.081161\t최선의 손실: 0.071885\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.059382\t최선의 손실: 0.059382\t정확도: 98.59%\n",
-      "6\t검증 세트 손실: 0.073682\t최선의 손실: 0.059382\t정확도: 97.97%\n",
-      "7\t검증 세트 손실: 6.212564\t최선의 손실: 0.059382\t정확도: 94.10%\n",
-      "8\t검증 세트 손실: 1.670928\t최선의 손실: 0.059382\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.688671\t최선의 손실: 0.059382\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.634994\t최선의 손실: 0.059382\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.662258\t최선의 손실: 0.059382\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.668326\t최선의 손실: 0.059382\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.617838\t최선의 손실: 0.059382\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.666623\t최선의 손실: 0.059382\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.669570\t최선의 손실: 0.059382\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.671378\t최선의 손실: 0.059382\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.654236\t최선의 손실: 0.059382\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.662004\t최선의 손실: 0.059382\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.620586\t최선의 손실: 0.059382\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.608856\t최선의 손실: 0.059382\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.679412\t최선의 손실: 0.059382\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.619289\t최선의 손실: 0.059382\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.705554\t최선의 손실: 0.059382\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.759143\t최선의 손실: 0.059382\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.622644\t최선의 손실: 0.059382\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.674581\t최선의 손실: 0.059382\t정확도: 19.08%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=70, total=   5.4s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.093523\t최선의 손실: 0.093523\t정확도: 98.08%\n",
+      "1\t검증 세트 손실: 0.082070\t최선의 손실: 0.082070\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.076321\t최선의 손실: 0.076321\t정확도: 97.85%\n",
+      "3\t검증 세트 손실: 0.069173\t최선의 손실: 0.069173\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.096466\t최선의 손실: 0.069173\t정확도: 97.69%\n",
+      "5\t검증 세트 손실: 0.136112\t최선의 손실: 0.069173\t정확도: 97.03%\n",
+      "6\t검증 세트 손실: 0.074059\t최선의 손실: 0.069173\t정확도: 98.01%\n",
+      "7\t검증 세트 손실: 0.348167\t최선의 손실: 0.069173\t정확도: 94.92%\n",
+      "8\t검증 세트 손실: 1.539912\t최선의 손실: 0.069173\t정확도: 22.09%\n",
+      "9\t검증 세트 손실: 1.222259\t최선의 손실: 0.069173\t정확도: 40.73%\n",
+      "10\t검증 세트 손실: 1.261736\t최선의 손실: 0.069173\t정확도: 40.62%\n",
+      "11\t검증 세트 손실: 1.172883\t최선의 손실: 0.069173\t정확도: 55.00%\n",
+      "12\t검증 세트 손실: 1.100172\t최선의 손실: 0.069173\t정확도: 49.73%\n",
+      "13\t검증 세트 손실: 1.174639\t최선의 손실: 0.069173\t정확도: 39.33%\n",
+      "14\t검증 세트 손실: 1.079120\t최선의 손실: 0.069173\t정확도: 50.39%\n",
+      "15\t검증 세트 손실: 4.192696\t최선의 손실: 0.069173\t정확도: 39.37%\n",
+      "16\t검증 세트 손실: 1.606188\t최선의 손실: 0.069173\t정확도: 23.30%\n",
+      "17\t검증 세트 손실: 1.523751\t최선의 손실: 0.069173\t정확도: 29.24%\n",
+      "18\t검증 세트 손실: 1.474042\t최선의 손실: 0.069173\t정확도: 39.56%\n",
+      "19\t검증 세트 손실: 1.298944\t최선의 손실: 0.069173\t정확도: 41.75%\n",
+      "20\t검증 세트 손실: 1.467899\t최선의 손실: 0.069173\t정확도: 40.54%\n",
+      "21\t검증 세트 손실: 1.353439\t최선의 손실: 0.069173\t정확도: 40.58%\n",
+      "22\t검증 세트 손실: 1.472696\t최선의 손실: 0.069173\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.710593\t최선의 손실: 0.069173\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.732331\t최선의 손실: 0.069173\t정확도: 20.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=  16.0s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.109383\t최선의 손실: 0.109383\t정확도: 96.99%\n",
-      "1\t검증 세트 손실: 0.192791\t최선의 손실: 0.109383\t정확도: 94.84%\n",
-      "2\t검증 세트 손실: 0.089592\t최선의 손실: 0.089592\t정확도: 97.81%\n",
-      "3\t검증 세트 손실: 0.102684\t최선의 손실: 0.089592\t정확도: 97.58%\n",
-      "4\t검증 세트 손실: 0.088971\t최선의 손실: 0.088971\t정확도: 97.46%\n",
-      "5\t검증 세트 손실: 0.092640\t최선의 손실: 0.088971\t정확도: 97.65%\n",
-      "6\t검증 세트 손실: 0.113068\t최선의 손실: 0.088971\t정확도: 97.22%\n",
-      "7\t검증 세트 손실: 0.133528\t최선의 손실: 0.088971\t정확도: 95.82%\n",
-      "8\t검증 세트 손실: 0.281054\t최선의 손실: 0.088971\t정확도: 91.83%\n",
-      "9\t검증 세트 손실: 0.134933\t최선의 손실: 0.088971\t정확도: 96.44%\n",
-      "10\t검증 세트 손실: 0.114529\t최선의 손실: 0.088971\t정확도: 96.99%\n",
-      "11\t검증 세트 손실: 0.130821\t최선의 손실: 0.088971\t정확도: 96.83%\n",
-      "12\t검증 세트 손실: 0.133958\t최선의 손실: 0.088971\t정확도: 97.15%\n",
-      "13\t검증 세트 손실: 0.118840\t최선의 손실: 0.088971\t정확도: 97.22%\n",
-      "14\t검증 세트 손실: 0.282917\t최선의 손실: 0.088971\t정확도: 95.78%\n",
-      "15\t검증 세트 손실: 0.127427\t최선의 손실: 0.088971\t정확도: 97.15%\n",
-      "16\t검증 세트 손실: 0.131306\t최선의 손실: 0.088971\t정확도: 97.46%\n",
-      "17\t검증 세트 손실: 0.190191\t최선의 손실: 0.088971\t정확도: 97.46%\n",
-      "18\t검증 세트 손실: 0.289641\t최선의 손실: 0.088971\t정확도: 96.72%\n",
-      "19\t검증 세트 손실: 0.230134\t최선의 손실: 0.088971\t정확도: 97.07%\n",
-      "20\t검증 세트 손실: 0.228336\t최선의 손실: 0.088971\t정확도: 97.81%\n",
-      "21\t검증 세트 손실: 0.371991\t최선의 손실: 0.088971\t정확도: 97.46%\n",
-      "22\t검증 세트 손실: 0.385261\t최선의 손실: 0.088971\t정확도: 97.62%\n",
-      "23\t검증 세트 손실: 0.201835\t최선의 손실: 0.088971\t정확도: 97.85%\n",
-      "24\t검증 세트 손실: 0.276092\t최선의 손실: 0.088971\t정확도: 97.54%\n",
-      "25\t검증 세트 손실: 0.123427\t최선의 손실: 0.088971\t정확도: 97.54%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=70, total=   4.8s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.101146\t최선의 손실: 0.101146\t정확도: 97.07%\n",
+      "1\t검증 세트 손실: 0.130169\t최선의 손실: 0.101146\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.100457\t최선의 손실: 0.100457\t정확도: 97.34%\n",
+      "3\t검증 세트 손실: 0.117355\t최선의 손실: 0.100457\t정확도: 96.17%\n",
+      "4\t검증 세트 손실: 0.081583\t최선의 손실: 0.081583\t정확도: 98.20%\n",
+      "5\t검증 세트 손실: 0.096482\t최선의 손실: 0.081583\t정확도: 98.24%\n",
+      "6\t검증 세트 손실: 0.091361\t최선의 손실: 0.081583\t정확도: 97.69%\n",
+      "7\t검증 세트 손실: 0.094196\t최선의 손실: 0.081583\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.107934\t최선의 손실: 0.081583\t정확도: 98.20%\n",
+      "9\t검증 세트 손실: 8.287700\t최선의 손실: 0.081583\t정확도: 73.85%\n",
+      "10\t검증 세트 손실: 0.173934\t최선의 손실: 0.081583\t정확도: 95.93%\n",
+      "11\t검증 세트 손실: 0.176870\t최선의 손실: 0.081583\t정확도: 95.54%\n",
+      "12\t검증 세트 손실: 0.125311\t최선의 손실: 0.081583\t정확도: 96.95%\n",
+      "13\t검증 세트 손실: 0.124417\t최선의 손실: 0.081583\t정확도: 96.76%\n",
+      "14\t검증 세트 손실: 0.127395\t최선의 손실: 0.081583\t정확도: 96.76%\n",
+      "15\t검증 세트 손실: 0.116030\t최선의 손실: 0.081583\t정확도: 97.15%\n",
+      "16\t검증 세트 손실: 0.108955\t최선의 손실: 0.081583\t정확도: 97.38%\n",
+      "17\t검증 세트 손실: 0.116104\t최선의 손실: 0.081583\t정확도: 96.68%\n",
+      "18\t검증 세트 손실: 0.105036\t최선의 손실: 0.081583\t정확도: 97.15%\n",
+      "19\t검증 세트 손실: 0.122407\t최선의 손실: 0.081583\t정확도: 96.95%\n",
+      "20\t검증 세트 손실: 0.102630\t최선의 손실: 0.081583\t정확도: 97.46%\n",
+      "21\t검증 세트 손실: 0.098233\t최선의 손실: 0.081583\t정확도: 97.50%\n",
+      "22\t검증 세트 손실: 0.264302\t최선의 손실: 0.081583\t정확도: 94.06%\n",
+      "23\t검증 세트 손실: 0.126336\t최선의 손실: 0.081583\t정확도: 97.19%\n",
+      "24\t검증 세트 손실: 0.121883\t최선의 손실: 0.081583\t정확도: 96.68%\n",
+      "25\t검증 세트 손실: 0.104375\t최선의 손실: 0.081583\t정확도: 97.03%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=30, total=   7.2s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.113502\t최선의 손실: 0.113502\t정확도: 96.99%\n",
-      "1\t검증 세트 손실: 0.093098\t최선의 손실: 0.093098\t정확도: 97.15%\n",
-      "2\t검증 세트 손실: 0.088339\t최선의 손실: 0.088339\t정확도: 97.26%\n",
-      "3\t검증 세트 손실: 0.091445\t최선의 손실: 0.088339\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.087649\t최선의 손실: 0.087649\t정확도: 97.46%\n",
-      "5\t검증 세트 손실: 0.073500\t최선의 손실: 0.073500\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.064490\t최선의 손실: 0.064490\t정확도: 98.16%\n",
-      "7\t검증 세트 손실: 0.094124\t최선의 손실: 0.064490\t정확도: 97.77%\n",
-      "8\t검증 세트 손실: 2.859280\t최선의 손실: 0.064490\t정확도: 80.69%\n",
-      "9\t검증 세트 손실: 0.168902\t최선의 손실: 0.064490\t정확도: 96.87%\n",
-      "10\t검증 세트 손실: 0.108051\t최선의 손실: 0.064490\t정확도: 97.62%\n",
-      "11\t검증 세트 손실: 0.106929\t최선의 손실: 0.064490\t정확도: 97.38%\n",
-      "12\t검증 세트 손실: 0.094406\t최선의 손실: 0.064490\t정확도: 97.85%\n",
-      "13\t검증 세트 손실: 0.112241\t최선의 손실: 0.064490\t정확도: 97.93%\n",
-      "14\t검증 세트 손실: 0.107816\t최선의 손실: 0.064490\t정확도: 97.85%\n",
-      "15\t검증 세트 손실: 0.140458\t최선의 손실: 0.064490\t정확도: 97.46%\n",
-      "16\t검증 세트 손실: 0.142587\t최선의 손실: 0.064490\t정확도: 97.97%\n",
-      "17\t검증 세트 손실: 0.157258\t최선의 손실: 0.064490\t정확도: 97.89%\n",
-      "18\t검증 세트 손실: 0.169502\t최선의 손실: 0.064490\t정확도: 97.85%\n",
-      "19\t검증 세트 손실: 1.366497\t최선의 손실: 0.064490\t정확도: 89.68%\n",
-      "20\t검증 세트 손실: 0.517381\t최선의 손실: 0.064490\t정확도: 96.29%\n",
-      "21\t검증 세트 손실: 0.522313\t최선의 손실: 0.064490\t정확도: 97.07%\n",
-      "22\t검증 세트 손실: 0.417625\t최선의 손실: 0.064490\t정확도: 97.26%\n",
-      "23\t검증 세트 손실: 0.358935\t최선의 손실: 0.064490\t정확도: 97.46%\n",
-      "24\t검증 세트 손실: 0.412064\t최선의 손실: 0.064490\t정확도: 97.62%\n",
-      "25\t검증 세트 손실: 0.515022\t최선의 손실: 0.064490\t정확도: 97.58%\n",
-      "26\t검증 세트 손실: 0.632119\t최선의 손실: 0.064490\t정확도: 97.42%\n",
-      "27\t검증 세트 손실: 0.558595\t최선의 손실: 0.064490\t정확도: 97.81%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=30, total=   6.7s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.124405\t최선의 손실: 0.124405\t정확도: 96.48%\n",
+      "1\t검증 세트 손실: 0.110886\t최선의 손실: 0.110886\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.082193\t최선의 손실: 0.082193\t정확도: 97.69%\n",
+      "3\t검증 세트 손실: 0.139313\t최선의 손실: 0.082193\t정확도: 95.97%\n",
+      "4\t검증 세트 손실: 0.070937\t최선의 손실: 0.070937\t정확도: 98.16%\n",
+      "5\t검증 세트 손실: 0.185676\t최선의 손실: 0.070937\t정확도: 96.44%\n",
+      "6\t검증 세트 손실: 0.092606\t최선의 손실: 0.070937\t정확도: 97.65%\n",
+      "7\t검증 세트 손실: 0.091678\t최선의 손실: 0.070937\t정확도: 97.93%\n",
+      "8\t검증 세트 손실: 0.106870\t최선의 손실: 0.070937\t정확도: 97.50%\n",
+      "9\t검증 세트 손실: 0.084350\t최선의 손실: 0.070937\t정확도: 97.97%\n",
+      "10\t검증 세트 손실: 0.201747\t최선의 손실: 0.070937\t정확도: 97.26%\n",
+      "11\t검증 세트 손실: 0.259863\t최선의 손실: 0.070937\t정확도: 97.54%\n",
+      "12\t검증 세트 손실: 0.267050\t최선의 손실: 0.070937\t정확도: 95.39%\n",
+      "13\t검증 세트 손실: 0.157687\t최선의 손실: 0.070937\t정확도: 96.56%\n",
+      "14\t검증 세트 손실: 0.112575\t최선의 손실: 0.070937\t정확도: 97.34%\n",
+      "15\t검증 세트 손실: 0.111127\t최선의 손실: 0.070937\t정확도: 97.62%\n",
+      "16\t검증 세트 손실: 0.127304\t최선의 손실: 0.070937\t정확도: 97.19%\n",
+      "17\t검증 세트 손실: 0.223683\t최선의 손실: 0.070937\t정확도: 97.62%\n",
+      "18\t검증 세트 손실: 0.120968\t최선의 손실: 0.070937\t정확도: 97.65%\n",
+      "19\t검증 세트 손실: 0.122463\t최선의 손실: 0.070937\t정확도: 97.46%\n",
+      "20\t검증 세트 손실: 0.133595\t최선의 손실: 0.070937\t정확도: 97.93%\n",
+      "21\t검증 세트 손실: 0.152281\t최선의 손실: 0.070937\t정확도: 97.69%\n",
+      "22\t검증 세트 손실: 0.185707\t최선의 손실: 0.070937\t정확도: 97.46%\n",
+      "23\t검증 세트 손실: 0.198358\t최선의 손실: 0.070937\t정확도: 97.19%\n",
+      "24\t검증 세트 손실: 0.118452\t최선의 손실: 0.070937\t정확도: 97.93%\n",
+      "25\t검증 세트 손실: 0.122375\t최선의 손실: 0.070937\t정확도: 97.19%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=30, total=   6.9s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.113333\t최선의 손실: 0.113333\t정확도: 96.68%\n",
-      "1\t검증 세트 손실: 0.112020\t최선의 손실: 0.112020\t정확도: 96.79%\n",
-      "2\t검증 세트 손실: 0.106437\t최선의 손실: 0.106437\t정확도: 96.91%\n",
-      "3\t검증 세트 손실: 0.105874\t최선의 손실: 0.105874\t정확도: 97.65%\n",
-      "4\t검증 세트 손실: 0.108900\t최선의 손실: 0.105874\t정확도: 97.34%\n",
-      "5\t검증 세트 손실: 0.186891\t최선의 손실: 0.105874\t정확도: 96.87%\n",
-      "6\t검증 세트 손실: 0.279187\t최선의 손실: 0.105874\t정확도: 95.39%\n",
-      "7\t검증 세트 손실: 0.104874\t최선의 손실: 0.104874\t정확도: 97.54%\n",
-      "8\t검증 세트 손실: 0.082334\t최선의 손실: 0.082334\t정확도: 98.01%\n",
-      "9\t검증 세트 손실: 0.098448\t최선의 손실: 0.082334\t정확도: 97.50%\n",
-      "10\t검증 세트 손실: 0.093269\t최선의 손실: 0.082334\t정확도: 97.89%\n",
-      "11\t검증 세트 손실: 0.104946\t최선의 손실: 0.082334\t정확도: 98.01%\n",
-      "12\t검증 세트 손실: 0.127378\t최선의 손실: 0.082334\t정확도: 98.24%\n",
-      "13\t검증 세트 손실: 0.151562\t최선의 손실: 0.082334\t정확도: 98.05%\n",
-      "14\t검증 세트 손실: 2.648582\t최선의 손실: 0.082334\t정확도: 90.58%\n",
-      "15\t검증 세트 손실: 0.303118\t최선의 손실: 0.082334\t정확도: 95.78%\n",
-      "16\t검증 세트 손실: 0.195080\t최선의 손실: 0.082334\t정확도: 96.52%\n",
-      "17\t검증 세트 손실: 0.193104\t최선의 손실: 0.082334\t정확도: 96.25%\n",
-      "18\t검증 세트 손실: 0.166404\t최선의 손실: 0.082334\t정확도: 96.33%\n",
-      "19\t검증 세트 손실: 0.292862\t최선의 손실: 0.082334\t정확도: 94.64%\n",
-      "20\t검증 세트 손실: 0.196028\t최선의 손실: 0.082334\t정확도: 96.29%\n",
-      "21\t검증 세트 손실: 0.174412\t최선의 손실: 0.082334\t정확도: 96.68%\n",
-      "22\t검증 세트 손실: 0.147722\t최선의 손실: 0.082334\t정확도: 97.26%\n",
-      "23\t검증 세트 손실: 0.166427\t최선의 손실: 0.082334\t정확도: 96.99%\n",
-      "24\t검증 세트 손실: 0.175106\t최선의 손실: 0.082334\t정확도: 97.34%\n",
-      "25\t검증 세트 손실: 0.266416\t최선의 손실: 0.082334\t정확도: 96.76%\n",
-      "26\t검증 세트 손실: 0.201879\t최선의 손실: 0.082334\t정확도: 97.50%\n",
-      "27\t검증 세트 손실: 0.203171\t최선의 손실: 0.082334\t정확도: 97.19%\n",
-      "28\t검증 세트 손실: 0.201282\t최선의 손실: 0.082334\t정확도: 97.77%\n",
-      "29\t검증 세트 손실: 0.183198\t최선의 손실: 0.082334\t정확도: 97.38%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=30, total=   7.0s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.106767\t최선의 손실: 0.106767\t정확도: 96.79%\n",
+      "1\t검증 세트 손실: 0.094559\t최선의 손실: 0.094559\t정확도: 97.22%\n",
+      "2\t검증 세트 손실: 0.114843\t최선의 손실: 0.094559\t정확도: 96.40%\n",
+      "3\t검증 세트 손실: 0.124342\t최선의 손실: 0.094559\t정확도: 97.22%\n",
+      "4\t검증 세트 손실: 0.095788\t최선의 손실: 0.094559\t정확도: 97.46%\n",
+      "5\t검증 세트 손실: 0.115398\t최선의 손실: 0.094559\t정확도: 97.77%\n",
+      "6\t검증 세트 손실: 0.126915\t최선의 손실: 0.094559\t정확도: 97.69%\n",
+      "7\t검증 세트 손실: 0.142426\t최선의 손실: 0.094559\t정확도: 97.42%\n",
+      "8\t검증 세트 손실: 0.668274\t최선의 손실: 0.094559\t정확도: 86.24%\n",
+      "9\t검증 세트 손실: 0.345686\t최선의 손실: 0.094559\t정확도: 90.77%\n",
+      "10\t검증 세트 손실: 0.282065\t최선의 손실: 0.094559\t정확도: 94.96%\n",
+      "11\t검증 세트 손실: 0.296174\t최선의 손실: 0.094559\t정확도: 95.31%\n",
+      "12\t검증 세트 손실: 0.250170\t최선의 손실: 0.094559\t정확도: 95.50%\n",
+      "13\t검증 세트 손실: 0.230425\t최선의 손실: 0.094559\t정확도: 97.15%\n",
+      "14\t검증 세트 손실: 0.257916\t최선의 손실: 0.094559\t정확도: 96.95%\n",
+      "15\t검증 세트 손실: 0.225920\t최선의 손실: 0.094559\t정확도: 97.62%\n",
+      "16\t검증 세트 손실: 0.257288\t최선의 손실: 0.094559\t정확도: 97.46%\n",
+      "17\t검증 세트 손실: 0.233440\t최선의 손실: 0.094559\t정확도: 97.30%\n",
+      "18\t검증 세트 손실: 0.234715\t최선의 손실: 0.094559\t정확도: 97.34%\n",
+      "19\t검증 세트 손실: 0.293865\t최선의 손실: 0.094559\t정확도: 95.74%\n",
+      "20\t검증 세트 손실: 0.303360\t최선의 손실: 0.094559\t정확도: 97.03%\n",
+      "21\t검증 세트 손실: 0.286693\t최선의 손실: 0.094559\t정확도: 97.38%\n",
+      "22\t검증 세트 손실: 0.283414\t최선의 손실: 0.094559\t정확도: 97.62%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=30, total=  17.1s\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.963861\t최선의 손실: 1.963861\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 2.239836\t최선의 손실: 1.963861\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 2.648025\t최선의 손실: 1.963861\t정확도: 19.27%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=30, total=   6.1s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 2.072199\t최선의 손실: 2.072199\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 2.129733\t최선의 손실: 2.072199\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 2.579006\t최선의 손실: 2.072199\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 3.483662\t최선의 손실: 2.072199\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 2.872097\t최선의 손실: 2.072199\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 3.209298\t최선의 손실: 2.072199\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.687177\t최선의 손실: 2.072199\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 2.489157\t최선의 손실: 2.072199\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 2.409631\t최선의 손실: 2.072199\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 2.627393\t최선의 손실: 2.072199\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.897247\t최선의 손실: 1.897247\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 2.330847\t최선의 손실: 1.897247\t정확도: 18.73%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3\t검증 세트 손실: 3.274834\t최선의 손실: 1.963861\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 2.903732\t최선의 손실: 1.963861\t정확도: 18.73%\n",
-      "5\t검증 세트 손실: 3.447991\t최선의 손실: 1.963861\t정확도: 18.73%\n",
-      "6\t검증 세트 손실: 2.066833\t최선의 손실: 1.963861\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 2.595544\t최선의 손실: 1.963861\t정확도: 20.91%\n",
-      "8\t검증 세트 손실: 3.108186\t최선의 손실: 1.963861\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 2.555270\t최선의 손실: 1.963861\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 2.184132\t최선의 손실: 1.963861\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 3.077903\t최선의 손실: 1.963861\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2.305145\t최선의 손실: 1.963861\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 2.891106\t최선의 손실: 1.963861\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 3.594179\t최선의 손실: 1.963861\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 3.116541\t최선의 손실: 1.963861\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 4.077157\t최선의 손실: 1.963861\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 3.712220\t최선의 손실: 1.963861\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 2.785445\t최선의 손실: 1.963861\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 3.980928\t최선의 손실: 1.963861\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 3.714163\t최선의 손실: 1.963861\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 2.121120\t최선의 손실: 1.963861\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 2.193029\t최선의 손실: 1.897247\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 2.432747\t최선의 손실: 1.897247\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 2.763578\t최선의 손실: 1.897247\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 2.097944\t최선의 손실: 1.897247\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 3.267366\t최선의 손실: 1.897247\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 2.947588\t최선의 손실: 1.897247\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 3.994301\t최선의 손실: 1.897247\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 4.488717\t최선의 손실: 1.897247\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.885106\t최선의 손실: 1.897247\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 2.420197\t최선의 손실: 1.897247\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 1.643378\t최선의 손실: 1.643378\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.877509\t최선의 손실: 1.643378\t정확도: 19.08%\n",
+      "24\t검증 세트 손실: 2.305290\t최선의 손실: 1.643378\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 2.186714\t최선의 손실: 1.643378\t정확도: 19.08%\n",
+      "26\t검증 세트 손실: 2.791283\t최선의 손실: 1.643378\t정확도: 20.91%\n",
+      "27\t검증 세트 손실: 3.082478\t최선의 손실: 1.643378\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 3.760586\t최선의 손실: 1.643378\t정확도: 18.73%\n",
+      "29\t검증 세트 손실: 3.059407\t최선의 손실: 1.643378\t정확도: 19.08%\n",
+      "30\t검증 세트 손실: 2.367162\t최선의 손실: 1.643378\t정확도: 22.01%\n",
+      "31\t검증 세트 손실: 2.324404\t최선의 손실: 1.643378\t정확도: 18.73%\n",
+      "32\t검증 세트 손실: 2.482554\t최선의 손실: 1.643378\t정확도: 22.01%\n",
+      "33\t검증 세트 손실: 1.772722\t최선의 손실: 1.643378\t정확도: 19.27%\n",
+      "34\t검증 세트 손실: 1.831538\t최선의 손실: 1.643378\t정확도: 18.73%\n",
+      "35\t검증 세트 손실: 3.448979\t최선의 손실: 1.643378\t정확도: 18.73%\n",
+      "36\t검증 세트 손실: 2.424606\t최선의 손실: 1.643378\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 3.191264\t최선의 손실: 1.643378\t정확도: 20.91%\n",
+      "38\t검증 세트 손실: 2.796863\t최선의 손실: 1.643378\t정확도: 18.73%\n",
+      "39\t검증 세트 손실: 3.745407\t최선의 손실: 1.643378\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 2.954590\t최선의 손실: 1.643378\t정확도: 22.01%\n",
+      "41\t검증 세트 손실: 2.808969\t최선의 손실: 1.643378\t정확도: 19.27%\n",
+      "42\t검증 세트 손실: 3.376358\t최선의 손실: 1.643378\t정확도: 19.27%\n",
+      "43\t검증 세트 손실: 4.511799\t최선의 손실: 1.643378\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total= 1.6min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 2.116524\t최선의 손실: 2.116524\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.992368\t최선의 손실: 1.992368\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.896428\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 2.665803\t최선의 손실: 1.896428\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 2.129385\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.945731\t최선의 손실: 1.896428\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 3.033203\t최선의 손실: 1.896428\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 1.921383\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 2.396555\t최선의 손실: 1.896428\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 2.577152\t최선의 손실: 1.896428\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 2.904552\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 4.546922\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2.447152\t최선의 손실: 1.896428\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 3.800289\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.987069\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 2.315655\t최선의 손실: 1.896428\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 2.101521\t최선의 손실: 1.896428\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 3.347872\t최선의 손실: 1.896428\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 2.944885\t최선의 손실: 1.896428\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 2.961946\t최선의 손실: 1.896428\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.817912\t최선의 손실: 1.817912\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 2.097931\t최선의 손실: 1.817912\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 3.847214\t최선의 손실: 1.817912\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2.941053\t최선의 손실: 1.817912\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 3.075153\t최선의 손실: 1.817912\t정확도: 19.08%\n",
-      "25\t검증 세트 손실: 1.900365\t최선의 손실: 1.817912\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 4.125238\t최선의 손실: 1.817912\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 2.507520\t최선의 손실: 1.817912\t정확도: 19.27%\n",
-      "28\t검증 세트 손실: 2.743090\t최선의 손실: 1.817912\t정확도: 20.91%\n",
-      "29\t검증 세트 손실: 2.497759\t최선의 손실: 1.817912\t정확도: 18.73%\n",
-      "30\t검증 세트 손실: 3.471140\t최선의 손실: 1.817912\t정확도: 22.01%\n",
-      "31\t검증 세트 손실: 3.402673\t최선의 손실: 1.817912\t정확도: 18.73%\n",
-      "32\t검증 세트 손실: 2.494294\t최선의 손실: 1.817912\t정확도: 19.27%\n",
-      "33\t검증 세트 손실: 4.720769\t최선의 손실: 1.817912\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 2.958045\t최선의 손실: 1.817912\t정확도: 20.91%\n",
-      "35\t검증 세트 손실: 4.610086\t최선의 손실: 1.817912\t정확도: 22.01%\n",
-      "36\t검증 세트 손실: 2.021252\t최선의 손실: 1.817912\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 1.844255\t최선의 손실: 1.817912\t정확도: 22.01%\n",
-      "38\t검증 세트 손실: 2.579932\t최선의 손실: 1.817912\t정확도: 18.73%\n",
-      "39\t검증 세트 손실: 4.483880\t최선의 손실: 1.817912\t정확도: 18.73%\n",
-      "40\t검증 세트 손실: 3.368157\t최선의 손실: 1.817912\t정확도: 18.73%\n",
-      "41\t검증 세트 손실: 3.996012\t최선의 손실: 1.817912\t정확도: 19.08%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=100, total= 1.2min\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.851923\t최선의 손실: 1.851923\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 1.705224\t최선의 손실: 1.705224\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 2.119510\t최선의 손실: 1.705224\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 2.388535\t최선의 손실: 1.705224\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 2.475481\t최선의 손실: 1.705224\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 2.531415\t최선의 손실: 1.705224\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 3.690760\t최선의 손실: 1.705224\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 3.477469\t최선의 손실: 1.705224\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 3.788414\t최선의 손실: 1.705224\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 2.709978\t최선의 손실: 1.705224\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 3.514318\t최선의 손실: 1.705224\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 5.001879\t최선의 손실: 1.705224\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 2.262856\t최선의 손실: 1.705224\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 2.445182\t최선의 손실: 1.705224\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 3.636562\t최선의 손실: 1.705224\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 3.161936\t최선의 손실: 1.705224\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 2.248353\t최선의 손실: 1.705224\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 2.833916\t최선의 손실: 1.705224\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 2.085172\t최선의 손실: 1.705224\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 2.978865\t최선의 손실: 1.705224\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 2.268603\t최선의 손실: 1.705224\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 2.397529\t최선의 손실: 1.705224\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 2.821512\t최선의 손실: 1.705224\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total= 2.8min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 9.643633\t최선의 손실: 9.643633\t정확도: 20.88%\n",
-      "1\t검증 세트 손실: 9.468699\t최선의 손실: 9.468699\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 11.056254\t최선의 손실: 9.468699\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 8.960897\t최선의 손실: 8.960897\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 9.456238\t최선의 손실: 8.960897\t정확도: 20.88%\n",
-      "5\t검증 세트 손실: 10.858907\t최선의 손실: 8.960897\t정확도: 20.88%\n",
-      "6\t검증 세트 손실: 10.956953\t최선의 손실: 8.960897\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 9.146707\t최선의 손실: 8.960897\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 8.953437\t최선의 손실: 8.953437\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 11.535048\t최선의 손실: 8.953437\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 11.124145\t최선의 손실: 8.953437\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 9.058640\t최선의 손실: 8.953437\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 12.763225\t최선의 손실: 8.953437\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 10.646526\t최선의 손실: 8.953437\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 10.011097\t최선의 손실: 8.953437\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 10.587687\t최선의 손실: 8.953437\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 12.183850\t최선의 손실: 8.953437\t정확도: 20.88%\n",
-      "17\t검증 세트 손실: 10.780311\t최선의 손실: 8.953437\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 11.295723\t최선의 손실: 8.953437\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 10.516144\t최선의 손실: 8.953437\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 10.919409\t최선의 손실: 8.953437\t정확도: 20.88%\n",
-      "21\t검증 세트 손실: 8.884756\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 9.777854\t최선의 손실: 8.884756\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 11.526637\t최선의 손실: 8.884756\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 9.707176\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "25\t검증 세트 손실: 9.047187\t최선의 손실: 8.884756\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 9.821705\t최선의 손실: 8.884756\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 11.288241\t최선의 손실: 8.884756\t정확도: 20.88%\n",
-      "28\t검증 세트 손실: 9.859500\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "29\t검증 세트 손실: 14.410431\t최선의 손실: 8.884756\t정확도: 20.88%\n",
-      "30\t검증 세트 손실: 10.869306\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 10.841506\t최선의 손실: 8.884756\t정확도: 19.08%\n",
-      "32\t검증 세트 손실: 10.049554\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "33\t검증 세트 손실: 9.389754\t최선의 손실: 8.884756\t정확도: 18.73%\n",
-      "34\t검증 세트 손실: 10.115581\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "35\t검증 세트 손실: 9.171180\t최선의 손실: 8.884756\t정확도: 19.08%\n",
-      "36\t검증 세트 손실: 12.319985\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "37\t검증 세트 손실: 10.379531\t최선의 손실: 8.884756\t정확도: 19.08%\n",
-      "38\t검증 세트 손실: 10.868767\t최선의 손실: 8.884756\t정확도: 20.88%\n",
-      "39\t검증 세트 손실: 9.283783\t최선의 손실: 8.884756\t정확도: 18.73%\n",
-      "40\t검증 세트 손실: 9.019840\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "41\t검증 세트 손실: 9.041245\t최선의 손실: 8.884756\t정확도: 19.27%\n",
-      "42\t검증 세트 손실: 10.533521\t최선의 손실: 8.884756\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=100, total=  36.2s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.735050\t최선의 손실: 1.735050\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 2.166456\t최선의 손실: 1.735050\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 2.624226\t최선의 손실: 1.735050\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 2.824949\t최선의 손실: 1.735050\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 2.040699\t최선의 손실: 1.735050\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 3.497959\t최선의 손실: 1.735050\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.562282\t최선의 손실: 1.735050\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 2.212391\t최선의 손실: 1.735050\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 2.163023\t최선의 손실: 1.735050\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 2.342201\t최선의 손실: 1.735050\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 3.025177\t최선의 손실: 1.735050\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 3.098560\t최선의 손실: 1.735050\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 3.595425\t최선의 손실: 1.735050\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 5.890924\t최선의 손실: 1.735050\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 3.466570\t최선의 손실: 1.735050\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 2.526669\t최선의 손실: 1.735050\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 2.534619\t최선의 손실: 1.735050\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 3.098251\t최선의 손실: 1.735050\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 2.420249\t최선의 손실: 1.735050\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 1.810114\t최선의 손실: 1.735050\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.071054\t최선의 손실: 1.735050\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 3.331684\t최선의 손실: 1.735050\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total= 3.2min\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.521713\t최선의 손실: 0.521713\t정확도: 90.23%\n",
-      "1\t검증 세트 손실: 0.252185\t최선의 손실: 0.252185\t정확도: 93.86%\n",
-      "2\t검증 세트 손실: 0.204104\t최선의 손실: 0.204104\t정확도: 95.11%\n",
-      "3\t검증 세트 손실: 0.155696\t최선의 손실: 0.155696\t정확도: 96.01%\n",
-      "4\t검증 세트 손실: 0.273306\t최선의 손실: 0.155696\t정확도: 96.79%\n",
-      "5\t검증 세트 손실: 0.158184\t최선의 손실: 0.155696\t정확도: 96.87%\n",
-      "6\t검증 세트 손실: 0.131808\t최선의 손실: 0.131808\t정확도: 96.44%\n",
-      "7\t검증 세트 손실: 0.187080\t최선의 손실: 0.131808\t정확도: 96.83%\n",
-      "8\t검증 세트 손실: 0.140221\t최선의 손실: 0.131808\t정확도: 97.22%\n",
-      "9\t검증 세트 손실: 0.327715\t최선의 손실: 0.131808\t정확도: 94.76%\n",
-      "10\t검증 세트 손실: 999.143555\t최선의 손실: 0.131808\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.620525\t최선의 손실: 0.131808\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.630546\t최선의 손실: 0.131808\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 1.615589\t최선의 손실: 0.131808\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.642511\t최선의 손실: 0.131808\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.637842\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.635307\t최선의 손실: 0.131808\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 1.674186\t최선의 손실: 0.131808\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.645329\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.636713\t최선의 손실: 0.131808\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.723335\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.639705\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.667653\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.638479\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.655467\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.756797\t최선의 손실: 0.131808\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.860113\t최선의 손실: 0.131808\t정확도: 22.01%\n",
-      "27\t검증 세트 손실: 1.728304\t최선의 손실: 0.131808\t정확도: 19.27%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=100, total=  35.9s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.610146\t최선의 손실: 1.610146\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.623749\t최선의 손실: 1.610146\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 1.618509\t최선의 손실: 1.610146\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.610027\t최선의 손실: 1.610027\t정확도: 20.91%\n",
+      "4\t검증 세트 손실: 1.622788\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.618430\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.625848\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.637762\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.626076\t최선의 손실: 1.610027\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.676955\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.633169\t최선의 손실: 1.610027\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.613407\t최선의 손실: 1.610027\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 1.626840\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.613033\t최선의 손실: 1.610027\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.624439\t최선의 손실: 1.610027\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.638262\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.650929\t최선의 손실: 1.610027\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 1.689331\t최선의 손실: 1.610027\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.641437\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.619905\t최선의 손실: 1.610027\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.631764\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.662911\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.646962\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.639880\t최선의 손실: 1.610027\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.659072\t최선의 손실: 1.610027\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=   8.1s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.436407\t최선의 손실: 0.436407\t정확도: 85.73%\n",
-      "1\t검증 세트 손실: 0.322576\t최선의 손실: 0.322576\t정확도: 90.85%\n",
-      "2\t검증 세트 손실: 0.322521\t최선의 손실: 0.322521\t정확도: 90.42%\n",
-      "3\t검증 세트 손실: 0.189469\t최선의 손실: 0.189469\t정확도: 94.57%\n",
-      "4\t검증 세트 손실: 0.338633\t최선의 손실: 0.189469\t정확도: 91.44%\n",
-      "5\t검증 세트 손실: 0.232659\t최선의 손실: 0.189469\t정확도: 94.21%\n",
-      "6\t검증 세트 손실: 0.190864\t최선의 손실: 0.189469\t정확도: 95.23%\n",
-      "7\t검증 세트 손실: 1.798630\t최선의 손실: 0.189469\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.621283\t최선의 손실: 0.189469\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.615426\t최선의 손실: 0.189469\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.613769\t최선의 손실: 0.189469\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.623129\t최선의 손실: 0.189469\t정확도: 22.01%\n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=90, total=   4.9s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.475791\t최선의 손실: 0.475791\t정확도: 83.19%\n",
+      "1\t검증 세트 손실: 0.434308\t최선의 손실: 0.434308\t정확도: 85.50%\n",
+      "2\t검증 세트 손실: 0.230502\t최선의 손실: 0.230502\t정확도: 93.59%\n",
+      "3\t검증 세트 손실: 0.236499\t최선의 손실: 0.230502\t정확도: 92.81%\n",
+      "4\t검증 세트 손실: 0.287307\t최선의 손실: 0.230502\t정확도: 92.46%\n",
+      "5\t검증 세트 손실: 0.195621\t최선의 손실: 0.195621\t정확도: 95.31%\n",
+      "6\t검증 세트 손실: 0.199034\t최선의 손실: 0.195621\t정확도: 94.53%\n",
+      "7\t검증 세트 손실: 0.181908\t최선의 손실: 0.181908\t정확도: 95.04%\n",
+      "8\t검증 세트 손실: 0.183070\t최선의 손실: 0.181908\t정확도: 96.33%\n",
+      "9\t검증 세트 손실: 0.365571\t최선의 손실: 0.181908\t정확도: 92.34%\n",
+      "10\t검증 세트 손실: 0.160700\t최선의 손실: 0.160700\t정확도: 96.09%\n",
+      "11\t검증 세트 손실: 329.330139\t최선의 손실: 0.160700\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.619296\t최선의 손실: 0.160700\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.625582\t최선의 손실: 0.160700\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.618828\t최선의 손실: 0.160700\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.623297\t최선의 손실: 0.160700\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.644161\t최선의 손실: 0.160700\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1.621000\t최선의 손실: 0.160700\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.635412\t최선의 손실: 0.160700\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.620329\t최선의 손실: 0.160700\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.623312\t최선의 손실: 0.160700\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.730854\t최선의 손실: 0.160700\t정확도: 18.73%\n",
+      "22\t검증 세트 손실: 1.623593\t최선의 손실: 0.160700\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.626812\t최선의 손실: 0.160700\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.686106\t최선의 손실: 0.160700\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.617290\t최선의 손실: 0.160700\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.608587\t최선의 손실: 0.160700\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.618588\t최선의 손실: 0.160700\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.655280\t최선의 손실: 0.160700\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.631796\t최선의 손실: 0.160700\t정확도: 22.01%\n",
+      "30\t검증 세트 손실: 1.624421\t최선의 손실: 0.160700\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 1.707929\t최선의 손실: 0.160700\t정확도: 18.73%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=90, total=   6.1s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.610373\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.622654\t최선의 손실: 1.610373\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.612585\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.664878\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.618409\t최선의 손실: 1.610373\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.682015\t최선의 손실: 1.610373\t정확도: 22.01%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 1.628301\t최선의 손실: 0.189469\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.629994\t최선의 손실: 0.189469\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.621768\t최선의 손실: 0.189469\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.616969\t최선의 손실: 0.189469\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.626010\t최선의 손실: 0.189469\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.609404\t최선의 손실: 0.189469\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.666713\t최선의 손실: 0.189469\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.634140\t최선의 손실: 0.189469\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.658169\t최선의 손실: 0.189469\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.670559\t최선의 손실: 0.189469\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.645971\t최선의 손실: 0.189469\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.710074\t최선의 손실: 0.189469\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.684467\t최선의 손실: 0.189469\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 1.612779\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.654525\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.659124\t최선의 손실: 1.610373\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.686131\t최선의 손실: 1.610373\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.633308\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.654168\t최선의 손실: 1.610373\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.624906\t최선의 손실: 1.610373\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.680345\t최선의 손실: 1.610373\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.644989\t최선의 손실: 1.610373\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.653713\t최선의 손실: 1.610373\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 1.729175\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.625388\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 1.632917\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.657624\t최선의 손실: 1.610373\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.648483\t최선의 손실: 1.610373\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 2.145180\t최선의 손실: 1.610373\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=   9.8s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.271728\t최선의 손실: 0.271728\t정확도: 91.87%\n",
-      "1\t검증 세트 손실: 0.166530\t최선의 손실: 0.166530\t정확도: 95.04%\n",
-      "2\t검증 세트 손실: 0.158315\t최선의 손실: 0.158315\t정확도: 95.78%\n",
-      "3\t검증 세트 손실: 0.123501\t최선의 손실: 0.123501\t정확도: 96.36%\n",
-      "4\t검증 세트 손실: 0.127386\t최선의 손실: 0.123501\t정확도: 96.56%\n",
-      "5\t검증 세트 손실: 0.112227\t최선의 손실: 0.112227\t정확도: 97.07%\n",
-      "6\t검증 세트 손실: 0.121880\t최선의 손실: 0.112227\t정확도: 96.17%\n",
-      "7\t검증 세트 손실: 0.129387\t최선의 손실: 0.112227\t정확도: 97.26%\n",
-      "8\t검증 세트 손실: 0.102182\t최선의 손실: 0.102182\t정확도: 97.26%\n",
-      "9\t검증 세트 손실: 0.112237\t최선의 손실: 0.102182\t정확도: 96.76%\n",
-      "10\t검증 세트 손실: 0.097491\t최선의 손실: 0.097491\t정확도: 97.34%\n",
-      "11\t검증 세트 손실: 1.682909\t최선의 손실: 0.097491\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.642390\t최선의 손실: 0.097491\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 1.704439\t최선의 손실: 0.097491\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.631732\t최선의 손실: 0.097491\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.652068\t최선의 손실: 0.097491\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.741586\t최선의 손실: 0.097491\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.709882\t최선의 손실: 0.097491\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.684922\t최선의 손실: 0.097491\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.691524\t최선의 손실: 0.097491\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.617895\t최선의 손실: 0.097491\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 2.278021\t최선의 손실: 0.097491\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.714548\t최선의 손실: 0.097491\t정확도: 20.91%\n",
-      "23\t검증 세트 손실: 1.654276\t최선의 손실: 0.097491\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.815093\t최선의 손실: 0.097491\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 1.818063\t최선의 손실: 0.097491\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.737230\t최선의 손실: 0.097491\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 1.706169\t최선의 손실: 0.097491\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 1.859058\t최선의 손실: 0.097491\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.872163\t최선의 손실: 0.097491\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 1.971718\t최선의 손실: 0.097491\t정확도: 20.91%\n",
-      "31\t검증 세트 손실: 2.081059\t최선의 손실: 0.097491\t정확도: 22.01%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=90, total=   4.4s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.534450\t최선의 손실: 0.534450\t정확도: 80.02%\n",
+      "1\t검증 세트 손실: 0.242853\t최선의 손실: 0.242853\t정확도: 93.08%\n",
+      "2\t검증 세트 손실: 0.164465\t최선의 손실: 0.164465\t정확도: 95.35%\n",
+      "3\t검증 세트 손실: 0.179451\t최선의 손실: 0.164465\t정확도: 95.58%\n",
+      "4\t검증 세트 손실: 0.163037\t최선의 손실: 0.163037\t정확도: 96.25%\n",
+      "5\t검증 세트 손실: 0.137344\t최선의 손실: 0.137344\t정확도: 96.33%\n",
+      "6\t검증 세트 손실: 1.288620\t최선의 손실: 0.137344\t정확도: 38.27%\n",
+      "7\t검증 세트 손실: 2101.928467\t최선의 손실: 0.137344\t정확도: 42.22%\n",
+      "8\t검증 세트 손실: 90.610878\t최선의 손실: 0.137344\t정확도: 35.93%\n",
+      "9\t검증 세트 손실: 1246.037964\t최선의 손실: 0.137344\t정확도: 29.52%\n",
+      "10\t검증 세트 손실: 114.796776\t최선의 손실: 0.137344\t정확도: 75.68%\n",
+      "11\t검증 세트 손실: 43.102406\t최선의 손실: 0.137344\t정확도: 89.09%\n",
+      "12\t검증 세트 손실: 28.707685\t최선의 손실: 0.137344\t정확도: 89.99%\n",
+      "13\t검증 세트 손실: 44.073612\t최선의 손실: 0.137344\t정확도: 76.97%\n",
+      "14\t검증 세트 손실: 19.661518\t최선의 손실: 0.137344\t정확도: 89.33%\n",
+      "15\t검증 세트 손실: 23.418596\t최선의 손실: 0.137344\t정확도: 86.12%\n",
+      "16\t검증 세트 손실: 16.700291\t최선의 손실: 0.137344\t정확도: 82.92%\n",
+      "17\t검증 세트 손실: 11.804067\t최선의 손실: 0.137344\t정확도: 82.72%\n",
+      "18\t검증 세트 손실: 20.685619\t최선의 손실: 0.137344\t정확도: 86.75%\n",
+      "19\t검증 세트 손실: 10.927954\t최선의 손실: 0.137344\t정확도: 91.79%\n",
+      "20\t검증 세트 손실: 7.516058\t최선의 손실: 0.137344\t정확도: 92.46%\n",
+      "21\t검증 세트 손실: 15.552411\t최선의 손실: 0.137344\t정확도: 77.60%\n",
+      "22\t검증 세트 손실: 7.115134\t최선의 손실: 0.137344\t정확도: 92.03%\n",
+      "23\t검증 세트 손실: 7.460151\t최선의 손실: 0.137344\t정확도: 92.18%\n",
+      "24\t검증 세트 손실: 5.436759\t최선의 손실: 0.137344\t정확도: 92.30%\n",
+      "25\t검증 세트 손실: 11.147212\t최선의 손실: 0.137344\t정확도: 88.62%\n",
+      "26\t검증 세트 손실: 6.502192\t최선의 손실: 0.137344\t정확도: 92.42%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=  16.8s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.489270\t최선의 손실: 0.489270\t정확도: 87.06%\n",
-      "1\t검증 세트 손실: 0.288864\t최선의 손실: 0.288864\t정확도: 91.95%\n",
-      "2\t검증 세트 손실: 0.200709\t최선의 손실: 0.200709\t정확도: 94.84%\n",
-      "3\t검증 세트 손실: 0.273328\t최선의 손실: 0.200709\t정확도: 93.24%\n",
-      "4\t검증 세트 손실: 26.573284\t최선의 손실: 0.200709\t정확도: 10.01%\n",
-      "5\t검증 세트 손실: 464.956421\t최선의 손실: 0.200709\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 79.371407\t최선의 손실: 0.200709\t정확도: 27.44%\n",
-      "7\t검증 세트 손실: 7.111516\t최선의 손실: 0.200709\t정확도: 30.10%\n",
-      "8\t검증 세트 손실: 2.093973\t최선의 손실: 0.200709\t정확도: 44.37%\n",
-      "9\t검증 세트 손실: 1.630846\t최선의 손실: 0.200709\t정확도: 55.00%\n",
-      "10\t검증 세트 손실: 0.868100\t최선의 손실: 0.200709\t정확도: 66.54%\n",
-      "11\t검증 세트 손실: 0.836032\t최선의 손실: 0.200709\t정확도: 64.74%\n",
-      "12\t검증 세트 손실: 0.766347\t최선의 손실: 0.200709\t정확도: 70.88%\n",
-      "13\t검증 세트 손실: 0.754287\t최선의 손실: 0.200709\t정확도: 69.19%\n",
-      "14\t검증 세트 손실: 0.663557\t최선의 손실: 0.200709\t정확도: 76.54%\n",
-      "15\t검증 세트 손실: 0.769779\t최선의 손실: 0.200709\t정확도: 68.73%\n",
-      "16\t검증 세트 손실: 0.789064\t최선의 손실: 0.200709\t정확도: 68.73%\n",
-      "17\t검증 세트 손실: 0.600946\t최선의 손실: 0.200709\t정확도: 79.01%\n",
-      "18\t검증 세트 손실: 0.681962\t최선의 손실: 0.200709\t정확도: 74.16%\n",
-      "19\t검증 세트 손실: 0.615904\t최선의 손실: 0.200709\t정확도: 79.24%\n",
-      "20\t검증 세트 손실: 0.788042\t최선의 손실: 0.200709\t정확도: 71.38%\n",
-      "21\t검증 세트 손실: 0.529729\t최선의 손실: 0.200709\t정확도: 81.70%\n",
-      "22\t검증 세트 손실: 0.579015\t최선의 손실: 0.200709\t정확도: 77.83%\n",
-      "23\t검증 세트 손실: 0.498796\t최선의 손실: 0.200709\t정확도: 83.11%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=50, total=   2.9s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.422188\t최선의 손실: 0.422188\t정확도: 86.00%\n",
+      "1\t검증 세트 손실: 0.244317\t최선의 손실: 0.244317\t정확도: 93.47%\n",
+      "2\t검증 세트 손실: 0.166225\t최선의 손실: 0.166225\t정확도: 95.27%\n",
+      "3\t검증 세트 손실: 0.151278\t최선의 손실: 0.151278\t정확도: 95.58%\n",
+      "4\t검증 세트 손실: 0.150296\t최선의 손실: 0.150296\t정확도: 95.86%\n",
+      "5\t검증 세트 손실: 0.131763\t최선의 손실: 0.131763\t정확도: 95.90%\n",
+      "6\t검증 세트 손실: 0.129852\t최선의 손실: 0.129852\t정확도: 96.33%\n",
+      "7\t검증 세트 손실: 0.145528\t최선의 손실: 0.129852\t정확도: 95.90%\n",
+      "8\t검증 세트 손실: 0.119571\t최선의 손실: 0.119571\t정확도: 96.33%\n",
+      "9\t검증 세트 손실: 0.120829\t최선의 손실: 0.119571\t정확도: 96.60%\n",
+      "10\t검증 세트 손실: 0.156666\t최선의 손실: 0.119571\t정확도: 96.21%\n",
+      "11\t검증 세트 손실: 0.115083\t최선의 손실: 0.115083\t정확도: 96.68%\n",
+      "12\t검증 세트 손실: 0.114017\t최선의 손실: 0.114017\t정확도: 96.56%\n",
+      "13\t검증 세트 손실: 0.121444\t최선의 손실: 0.114017\t정확도: 96.21%\n",
+      "14\t검증 세트 손실: 0.152109\t최선의 손실: 0.114017\t정확도: 96.48%\n",
+      "15\t검증 세트 손실: 0.142726\t최선의 손실: 0.114017\t정확도: 96.52%\n",
+      "16\t검증 세트 손실: 0.133899\t최선의 손실: 0.114017\t정확도: 96.64%\n",
+      "17\t검증 세트 손실: 0.142289\t최선의 손실: 0.114017\t정확도: 96.79%\n",
+      "18\t검증 세트 손실: 0.138076\t최선의 손실: 0.114017\t정확도: 95.90%\n",
+      "19\t검증 세트 손실: 0.199469\t최선의 손실: 0.114017\t정확도: 96.33%\n",
+      "20\t검증 세트 손실: 0.155188\t최선의 손실: 0.114017\t정확도: 96.72%\n",
+      "21\t검증 세트 손실: 0.142691\t최선의 손실: 0.114017\t정확도: 96.64%\n",
+      "22\t검증 세트 손실: 0.160068\t최선의 손실: 0.114017\t정확도: 96.83%\n",
+      "23\t검증 세트 손실: 0.118238\t최선의 손실: 0.114017\t정확도: 97.03%\n",
+      "24\t검증 세트 손실: 0.135756\t최선의 손실: 0.114017\t정확도: 96.83%\n",
+      "25\t검증 세트 손실: 0.144372\t최선의 손실: 0.114017\t정확도: 96.76%\n",
+      "26\t검증 세트 손실: 0.114687\t최선의 손실: 0.114017\t정확도: 96.99%\n",
+      "27\t검증 세트 손실: 0.117526\t최선의 손실: 0.114017\t정확도: 96.91%\n",
+      "28\t검증 세트 손실: 0.156386\t최선의 손실: 0.114017\t정확도: 96.83%\n",
+      "29\t검증 세트 손실: 0.181003\t최선의 손실: 0.114017\t정확도: 96.25%\n",
+      "30\t검증 세트 손실: 0.144495\t최선의 손실: 0.114017\t정확도: 96.60%\n",
+      "31\t검증 세트 손실: 0.152948\t최선의 손실: 0.114017\t정확도: 96.52%\n",
+      "32\t검증 세트 손실: 86775.179688\t최선의 손실: 0.114017\t정확도: 19.27%\n",
+      "33\t검증 세트 손실: 133690.546875\t최선의 손실: 0.114017\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50, total=   2.1s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.158590\t최선의 손실: 1.158590\t정확도: 51.06%\n",
-      "1\t검증 세트 손실: 0.390698\t최선의 손실: 0.390698\t정확도: 87.45%\n",
-      "2\t검증 세트 손실: 8.004457\t최선의 손실: 0.390698\t정확도: 52.27%\n",
-      "3\t검증 세트 손실: 274.579865\t최선의 손실: 0.390698\t정확도: 37.57%\n",
-      "4\t검증 세트 손실: 162.619675\t최선의 손실: 0.390698\t정확도: 43.35%\n",
-      "5\t검증 세트 손실: 38.107117\t최선의 손실: 0.390698\t정확도: 60.87%\n",
-      "6\t검증 세트 손실: 4.495638\t최선의 손실: 0.390698\t정확도: 70.05%\n",
-      "7\t검증 세트 손실: 2.685226\t최선의 손실: 0.390698\t정확도: 78.11%\n",
-      "8\t검증 세트 손실: 2.702629\t최선의 손실: 0.390698\t정확도: 84.25%\n",
-      "9\t검증 세트 손실: 1.442871\t최선의 손실: 0.390698\t정확도: 82.02%\n",
-      "10\t검증 세트 손실: 1.234612\t최선의 손실: 0.390698\t정확도: 84.44%\n",
-      "11\t검증 세트 손실: 1.322111\t최선의 손실: 0.390698\t정확도: 79.28%\n",
-      "12\t검증 세트 손실: 0.999903\t최선의 손실: 0.390698\t정확도: 84.87%\n",
-      "13\t검증 세트 손실: 0.848702\t최선의 손실: 0.390698\t정확도: 86.86%\n",
-      "14\t검증 세트 손실: 0.752219\t최선의 손실: 0.390698\t정확도: 86.90%\n",
-      "15\t검증 세트 손실: 0.797808\t최선의 손실: 0.390698\t정확도: 87.26%\n",
-      "16\t검증 세트 손실: 0.735270\t최선의 손실: 0.390698\t정확도: 84.91%\n",
-      "17\t검증 세트 손실: 0.639442\t최선의 손실: 0.390698\t정확도: 87.80%\n",
-      "18\t검증 세트 손실: 0.682845\t최선의 손실: 0.390698\t정확도: 85.85%\n",
-      "19\t검증 세트 손실: 0.572476\t최선의 손실: 0.390698\t정확도: 88.70%\n",
-      "20\t검증 세트 손실: 0.700951\t최선의 손실: 0.390698\t정확도: 85.07%\n",
-      "21\t검증 세트 손실: 0.709110\t최선의 손실: 0.390698\t정확도: 82.64%\n",
-      "22\t검증 세트 손실: 0.475639\t최선의 손실: 0.390698\t정확도: 88.86%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=50, total=   3.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.505769\t최선의 손실: 0.505769\t정확도: 84.68%\n",
+      "1\t검증 세트 손실: 0.225978\t최선의 손실: 0.225978\t정확도: 95.15%\n",
+      "2\t검증 세트 손실: 0.173461\t최선의 손실: 0.173461\t정확도: 95.07%\n",
+      "3\t검증 세트 손실: 0.134323\t최선의 손실: 0.134323\t정확도: 96.56%\n",
+      "4\t검증 세트 손실: 0.144994\t최선의 손실: 0.134323\t정확도: 96.13%\n",
+      "5\t검증 세트 손실: 0.135564\t최선의 손실: 0.134323\t정확도: 97.03%\n",
+      "6\t검증 세트 손실: 0.120251\t최선의 손실: 0.120251\t정확도: 96.68%\n",
+      "7\t검증 세트 손실: 0.125025\t최선의 손실: 0.120251\t정확도: 96.33%\n",
+      "8\t검증 세트 손실: 0.122969\t최선의 손실: 0.120251\t정확도: 96.95%\n",
+      "9\t검증 세트 손실: 0.107851\t최선의 손실: 0.107851\t정확도: 97.19%\n",
+      "10\t검증 세트 손실: 0.120032\t최선의 손실: 0.107851\t정확도: 96.91%\n",
+      "11\t검증 세트 손실: 0.115541\t최선의 손실: 0.107851\t정확도: 96.95%\n",
+      "12\t검증 세트 손실: 0.132142\t최선의 손실: 0.107851\t정확도: 96.95%\n",
+      "13\t검증 세트 손실: 0.121362\t최선의 손실: 0.107851\t정확도: 97.03%\n",
+      "14\t검증 세트 손실: 0.127986\t최선의 손실: 0.107851\t정확도: 97.54%\n",
+      "15\t검증 세트 손실: 0.117486\t최선의 손실: 0.107851\t정확도: 97.30%\n",
+      "16\t검증 세트 손실: 0.129763\t최선의 손실: 0.107851\t정확도: 96.56%\n",
+      "17\t검증 세트 손실: 0.126127\t최선의 손실: 0.107851\t정확도: 97.30%\n",
+      "18\t검증 세트 손실: 0.130760\t최선의 손실: 0.107851\t정확도: 96.72%\n",
+      "19\t검증 세트 손실: 0.149424\t최선의 손실: 0.107851\t정확도: 96.99%\n",
+      "20\t검증 세트 손실: 0.159733\t최선의 손실: 0.107851\t정확도: 96.44%\n",
+      "21\t검증 세트 손실: 0.161975\t최선의 손실: 0.107851\t정확도: 96.91%\n",
+      "22\t검증 세트 손실: 0.132446\t최선의 손실: 0.107851\t정확도: 96.95%\n",
+      "23\t검증 세트 손실: 0.120073\t최선의 손실: 0.107851\t정확도: 97.73%\n",
+      "24\t검증 세트 손실: 0.181879\t최선의 손실: 0.107851\t정확도: 95.93%\n",
+      "25\t검증 세트 손실: 62456.683594\t최선의 손실: 0.107851\t정확도: 22.05%\n",
+      "26\t검증 세트 손실: 113864.531250\t최선의 손실: 0.107851\t정확도: 27.68%\n",
+      "27\t검증 세트 손실: 7692.474609\t최선의 손실: 0.107851\t정확도: 21.11%\n",
+      "28\t검증 세트 손실: 853.537842\t최선의 손실: 0.107851\t정확도: 33.97%\n",
+      "29\t검증 세트 손실: 338.833282\t최선의 손실: 0.107851\t정확도: 38.35%\n",
+      "30\t검증 세트 손실: 117.795815\t최선의 손실: 0.107851\t정확도: 38.35%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50, total=   2.0s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.209320\t최선의 손실: 1.209320\t정확도: 58.84%\n",
-      "1\t검증 세트 손실: 0.787802\t최선의 손실: 0.787802\t정확도: 71.38%\n",
-      "2\t검증 세트 손실: 0.456914\t최선의 손실: 0.456914\t정확도: 87.18%\n",
-      "3\t검증 세트 손실: 0.561122\t최선의 손실: 0.456914\t정확도: 85.46%\n",
-      "4\t검증 세트 손실: 0.352339\t최선의 손실: 0.352339\t정확도: 91.28%\n",
-      "5\t검증 세트 손실: 0.468756\t최선의 손실: 0.352339\t정확도: 85.93%\n",
-      "6\t검증 세트 손실: 0.790995\t최선의 손실: 0.352339\t정확도: 64.62%\n",
-      "7\t검증 세트 손실: 0.295953\t최선의 손실: 0.295953\t정확도: 92.69%\n",
-      "8\t검증 세트 손실: 0.354652\t최선의 손실: 0.295953\t정확도: 91.32%\n",
-      "9\t검증 세트 손실: 0.185636\t최선의 손실: 0.185636\t정확도: 95.19%\n",
-      "10\t검증 세트 손실: 0.250770\t최선의 손실: 0.185636\t정확도: 95.31%\n",
-      "11\t검증 세트 손실: 0.160074\t최선의 손실: 0.160074\t정확도: 95.62%\n",
-      "12\t검증 세트 손실: 0.147832\t최선의 손실: 0.147832\t정확도: 95.97%\n",
-      "13\t검증 세트 손실: 0.146074\t최선의 손실: 0.146074\t정확도: 96.09%\n",
-      "14\t검증 세트 손실: 0.129274\t최선의 손실: 0.129274\t정확도: 96.29%\n",
-      "15\t검증 세트 손실: 0.147291\t최선의 손실: 0.129274\t정확도: 95.90%\n",
-      "16\t검증 세트 손실: 0.139895\t최선의 손실: 0.129274\t정확도: 96.56%\n",
-      "17\t검증 세트 손실: 0.133608\t최선의 손실: 0.129274\t정확도: 96.05%\n",
-      "18\t검증 세트 손실: 0.134994\t최선의 손실: 0.129274\t정확도: 96.36%\n",
-      "19\t검증 세트 손실: 0.145616\t최선의 손실: 0.129274\t정확도: 96.21%\n",
-      "20\t검증 세트 손실: 0.140773\t최선의 손실: 0.129274\t정확도: 96.44%\n",
-      "21\t검증 세트 손실: 0.173514\t최선의 손실: 0.129274\t정확도: 96.33%\n",
-      "22\t검증 세트 손실: 0.135084\t최선의 손실: 0.129274\t정확도: 96.36%\n",
-      "23\t검증 세트 손실: 0.142889\t최선의 손실: 0.129274\t정확도: 96.60%\n",
-      "24\t검증 세트 손실: 3477.625977\t최선의 손실: 0.129274\t정확도: 20.99%\n",
-      "25\t검증 세트 손실: 5557.346191\t최선의 손실: 0.129274\t정확도: 15.91%\n",
-      "26\t검증 세트 손실: 4956.949707\t최선의 손실: 0.129274\t정확도: 30.61%\n",
-      "27\t검증 세트 손실: 177.028671\t최선의 손실: 0.129274\t정확도: 69.19%\n",
-      "28\t검증 세트 손실: 103.310738\t최선의 손실: 0.129274\t정확도: 66.58%\n",
-      "29\t검증 세트 손실: 72.088646\t최선의 손실: 0.129274\t정확도: 64.62%\n",
-      "30\t검증 세트 손실: 51.669029\t최선의 손실: 0.129274\t정확도: 74.12%\n",
-      "31\t검증 세트 손실: 39.500965\t최선의 손실: 0.129274\t정확도: 64.46%\n",
-      "32\t검증 세트 손실: 27.839560\t최선의 손실: 0.129274\t정확도: 69.70%\n",
-      "33\t검증 세트 손실: 20.963121\t최선의 손실: 0.129274\t정확도: 73.96%\n",
-      "34\t검증 세트 손실: 18.471773\t최선의 손실: 0.129274\t정확도: 74.12%\n",
-      "35\t검증 세트 손실: 16.843431\t최선의 손실: 0.129274\t정확도: 74.00%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=50, total=   2.9s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.178188\t최선의 손실: 0.178188\t정확도: 94.80%\n",
-      "1\t검증 세트 손실: 0.107596\t최선의 손실: 0.107596\t정확도: 96.33%\n",
-      "2\t검증 세트 손실: 0.096855\t최선의 손실: 0.096855\t정확도: 97.11%\n",
-      "3\t검증 세트 손실: 0.075554\t최선의 손실: 0.075554\t정확도: 97.73%\n",
-      "4\t검증 세트 손실: 0.079884\t최선의 손실: 0.075554\t정확도: 97.69%\n",
-      "5\t검증 세트 손실: 0.087444\t최선의 손실: 0.075554\t정확도: 97.07%\n",
-      "6\t검증 세트 손실: 0.064190\t최선의 손실: 0.064190\t정확도: 98.12%\n",
-      "7\t검증 세트 손실: 0.065503\t최선의 손실: 0.064190\t정확도: 98.08%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.1, n_neurons=50, total=   2.9s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.239338\t최선의 손실: 0.239338\t정확도: 92.81%\n",
+      "1\t검증 세트 손실: 0.114066\t최선의 손실: 0.114066\t정확도: 96.36%\n",
+      "2\t검증 세트 손실: 0.101452\t최선의 손실: 0.101452\t정확도: 96.87%\n",
+      "3\t검증 세트 손실: 0.082100\t최선의 손실: 0.082100\t정확도: 97.11%\n",
+      "4\t검증 세트 손실: 0.088032\t최선의 손실: 0.082100\t정확도: 97.30%\n",
+      "5\t검증 세트 손실: 0.072678\t최선의 손실: 0.072678\t정확도: 97.65%\n",
+      "6\t검증 세트 손실: 0.071484\t최선의 손실: 0.071484\t정확도: 97.93%\n",
+      "7\t검증 세트 손실: 0.070591\t최선의 손실: 0.070591\t정확도: 97.89%\n",
+      "8\t검증 세트 손실: 0.085810\t최선의 손실: 0.070591\t정확도: 98.05%\n",
+      "9\t검증 세트 손실: 0.067289\t최선의 손실: 0.067289\t정확도: 98.16%\n",
+      "10\t검증 세트 손실: 0.067073\t최선의 손실: 0.067073\t정확도: 98.20%\n",
+      "11\t검증 세트 손실: 0.058687\t최선의 손실: 0.058687\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.069050\t최선의 손실: 0.058687\t정확도: 98.20%\n",
+      "13\t검증 세트 손실: 0.069751\t최선의 손실: 0.058687\t정확도: 98.44%\n",
+      "14\t검증 세트 손실: 0.065718\t최선의 손실: 0.058687\t정확도: 98.32%\n",
+      "15\t검증 세트 손실: 0.079307\t최선의 손실: 0.058687\t정확도: 98.20%\n",
+      "16\t검증 세트 손실: 0.067547\t최선의 손실: 0.058687\t정확도: 98.55%\n",
+      "17\t검증 세트 손실: 0.075381\t최선의 손실: 0.058687\t정확도: 98.51%\n",
+      "18\t검증 세트 손실: 0.091851\t최선의 손실: 0.058687\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.084878\t최선의 손실: 0.058687\t정확도: 98.48%\n",
+      "20\t검증 세트 손실: 0.116638\t최선의 손실: 0.058687\t정확도: 97.81%\n",
+      "21\t검증 세트 손실: 0.063745\t최선의 손실: 0.058687\t정확도: 98.32%\n",
+      "22\t검증 세트 손실: 0.066115\t최선의 손실: 0.058687\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.059691\t최선의 손실: 0.058687\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.097625\t최선의 손실: 0.058687\t정확도: 98.05%\n",
+      "25\t검증 세트 손실: 0.075381\t최선의 손실: 0.058687\t정확도: 98.55%\n",
+      "26\t검증 세트 손실: 0.087848\t최선의 손실: 0.058687\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.074701\t최선의 손실: 0.058687\t정확도: 98.36%\n",
+      "28\t검증 세트 손실: 0.075509\t최선의 손실: 0.058687\t정확도: 98.48%\n",
+      "29\t검증 세트 손실: 0.064600\t최선의 손실: 0.058687\t정확도: 98.55%\n",
+      "30\t검증 세트 손실: 0.093949\t최선의 손실: 0.058687\t정확도: 98.20%\n",
+      "31\t검증 세트 손실: 0.080279\t최선의 손실: 0.058687\t정확도: 98.44%\n",
+      "32\t검증 세트 손실: 0.068379\t최선의 손실: 0.058687\t정확도: 98.51%\n",
+      "조기 종료!\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8\t검증 세트 손실: 0.059329\t최선의 손실: 0.059329\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.059631\t최선의 손실: 0.059329\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.061598\t최선의 손실: 0.059329\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.065848\t최선의 손실: 0.059329\t정확도: 98.24%\n",
-      "12\t검증 세트 손실: 0.069765\t최선의 손실: 0.059329\t정확도: 98.24%\n",
-      "13\t검증 세트 손실: 0.071521\t최선의 손실: 0.059329\t정확도: 98.05%\n",
-      "14\t검증 세트 손실: 0.068203\t최선의 손실: 0.059329\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.070240\t최선의 손실: 0.059329\t정확도: 98.16%\n",
-      "16\t검증 세트 손실: 0.088610\t최선의 손실: 0.059329\t정확도: 97.89%\n",
-      "17\t검증 세트 손실: 0.058584\t최선의 손실: 0.058584\t정확도: 98.36%\n",
-      "18\t검증 세트 손실: 0.091615\t최선의 손실: 0.058584\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.069207\t최선의 손실: 0.058584\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.066283\t최선의 손실: 0.058584\t정확도: 98.51%\n",
-      "21\t검증 세트 손실: 0.108184\t최선의 손실: 0.058584\t정확도: 97.97%\n",
-      "22\t검증 세트 손실: 0.071178\t최선의 손실: 0.058584\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.105811\t최선의 손실: 0.058584\t정확도: 98.36%\n",
-      "24\t검증 세트 손실: 0.115762\t최선의 손실: 0.058584\t정확도: 97.54%\n",
-      "25\t검증 세트 손실: 0.073354\t최선의 손실: 0.058584\t정확도: 98.36%\n",
-      "26\t검증 세트 손실: 0.075891\t최선의 손실: 0.058584\t정확도: 98.59%\n",
-      "27\t검증 세트 손실: 0.079764\t최선의 손실: 0.058584\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.078885\t최선의 손실: 0.058584\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.068161\t최선의 손실: 0.058584\t정확도: 98.67%\n",
-      "30\t검증 세트 손실: 0.076801\t최선의 손실: 0.058584\t정확도: 98.63%\n",
-      "31\t검증 세트 손실: 0.081340\t최선의 손실: 0.058584\t정확도: 98.44%\n",
-      "32\t검증 세트 손실: 0.071056\t최선의 손실: 0.058584\t정확도: 98.83%\n",
-      "33\t검증 세트 손실: 0.085320\t최선의 손실: 0.058584\t정확도: 98.63%\n",
-      "34\t검증 세트 손실: 0.089315\t최선의 손실: 0.058584\t정확도: 98.55%\n",
-      "35\t검증 세트 손실: 0.088326\t최선의 손실: 0.058584\t정확도: 98.67%\n",
-      "36\t검증 세트 손실: 0.091025\t최선의 손실: 0.058584\t정확도: 98.59%\n",
-      "37\t검증 세트 손실: 0.091058\t최선의 손실: 0.058584\t정확도: 98.75%\n",
-      "38\t검증 세트 손실: 0.091692\t최선의 손실: 0.058584\t정확도: 98.75%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=140, total=   2.9s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.197067\t최선의 손실: 0.197067\t정확도: 94.18%\n",
+      "1\t검증 세트 손실: 0.106381\t최선의 손실: 0.106381\t정확도: 96.44%\n",
+      "2\t검증 세트 손실: 0.091681\t최선의 손실: 0.091681\t정확도: 97.07%\n",
+      "3\t검증 세트 손실: 0.074427\t최선의 손실: 0.074427\t정확도: 97.81%\n",
+      "4\t검증 세트 손실: 0.068278\t최선의 손실: 0.068278\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.061890\t최선의 손실: 0.061890\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.055001\t최선의 손실: 0.055001\t정확도: 98.20%\n",
+      "7\t검증 세트 손실: 0.054396\t최선의 손실: 0.054396\t정확도: 98.40%\n",
+      "8\t검증 세트 손실: 0.081775\t최선의 손실: 0.054396\t정확도: 97.89%\n",
+      "9\t검증 세트 손실: 0.049693\t최선의 손실: 0.049693\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.062691\t최선의 손실: 0.049693\t정확도: 98.44%\n",
+      "11\t검증 세트 손실: 0.048321\t최선의 손실: 0.048321\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.053928\t최선의 손실: 0.048321\t정확도: 98.40%\n",
+      "13\t검증 세트 손실: 0.045204\t최선의 손실: 0.045204\t정확도: 98.71%\n",
+      "14\t검증 세트 손실: 0.047268\t최선의 손실: 0.045204\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.065757\t최선의 손실: 0.045204\t정확도: 98.40%\n",
+      "16\t검증 세트 손실: 0.054667\t최선의 손실: 0.045204\t정확도: 98.59%\n",
+      "17\t검증 세트 손실: 0.065387\t최선의 손실: 0.045204\t정확도: 98.36%\n",
+      "18\t검증 세트 손실: 0.057643\t최선의 손실: 0.045204\t정확도: 98.63%\n",
+      "19\t검증 세트 손실: 0.069373\t최선의 손실: 0.045204\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.055061\t최선의 손실: 0.045204\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.060174\t최선의 손실: 0.045204\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.071010\t최선의 손실: 0.045204\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.087202\t최선의 손실: 0.045204\t정확도: 98.05%\n",
+      "24\t검증 세트 손실: 0.054379\t최선의 손실: 0.045204\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.068473\t최선의 손실: 0.045204\t정확도: 98.67%\n",
+      "26\t검증 세트 손실: 0.069386\t최선의 손실: 0.045204\t정확도: 98.55%\n",
+      "27\t검증 세트 손실: 0.087186\t최선의 손실: 0.045204\t정확도: 98.36%\n",
+      "28\t검증 세트 손실: 0.083339\t최선의 손실: 0.045204\t정확도: 98.51%\n",
+      "29\t검증 세트 손실: 0.064417\t최선의 손실: 0.045204\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.065300\t최선의 손실: 0.045204\t정확도: 98.59%\n",
+      "31\t검증 세트 손실: 0.070114\t최선의 손실: 0.045204\t정확도: 98.94%\n",
+      "32\t검증 세트 손실: 0.106997\t최선의 손실: 0.045204\t정확도: 98.24%\n",
+      "33\t검증 세트 손실: 0.063659\t최선의 손실: 0.045204\t정확도: 98.83%\n",
+      "34\t검증 세트 손실: 0.079765\t최선의 손실: 0.045204\t정확도: 98.55%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.9s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.170381\t최선의 손실: 0.170381\t정확도: 94.53%\n",
-      "1\t검증 세트 손실: 0.104063\t최선의 손실: 0.104063\t정확도: 97.30%\n",
-      "2\t검증 세트 손실: 0.089897\t최선의 손실: 0.089897\t정확도: 97.15%\n",
-      "3\t검증 세트 손실: 0.068806\t최선의 손실: 0.068806\t정확도: 97.85%\n",
-      "4\t검증 세트 손실: 0.069163\t최선의 손실: 0.068806\t정확도: 97.93%\n",
-      "5\t검증 세트 손실: 0.069247\t최선의 손실: 0.068806\t정확도: 97.85%\n",
-      "6\t검증 세트 손실: 0.068930\t최선의 손실: 0.068806\t정확도: 98.05%\n",
-      "7\t검증 세트 손실: 0.049346\t최선의 손실: 0.049346\t정확도: 98.40%\n",
-      "8\t검증 세트 손실: 0.059513\t최선의 손실: 0.049346\t정확도: 98.20%\n",
-      "9\t검증 세트 손실: 0.050510\t최선의 손실: 0.049346\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.064705\t최선의 손실: 0.049346\t정확도: 98.36%\n",
-      "11\t검증 세트 손실: 0.044707\t최선의 손실: 0.044707\t정확도: 98.87%\n",
-      "12\t검증 세트 손실: 0.049088\t최선의 손실: 0.044707\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.059723\t최선의 손실: 0.044707\t정확도: 98.32%\n",
-      "14\t검증 세트 손실: 0.055833\t최선의 손실: 0.044707\t정확도: 98.63%\n",
-      "15\t검증 세트 손실: 0.055988\t최선의 손실: 0.044707\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.068090\t최선의 손실: 0.044707\t정확도: 98.67%\n",
-      "17\t검증 세트 손실: 0.077307\t최선의 손실: 0.044707\t정확도: 98.32%\n",
-      "18\t검증 세트 손실: 0.059881\t최선의 손실: 0.044707\t정확도: 98.51%\n",
-      "19\t검증 세트 손실: 0.059879\t최선의 손실: 0.044707\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.072215\t최선의 손실: 0.044707\t정확도: 98.36%\n",
-      "21\t검증 세트 손실: 0.077999\t최선의 손실: 0.044707\t정확도: 98.28%\n",
-      "22\t검증 세트 손실: 0.063794\t최선의 손실: 0.044707\t정확도: 98.44%\n",
-      "23\t검증 세트 손실: 0.051719\t최선의 손실: 0.044707\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.060042\t최선의 손실: 0.044707\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.056132\t최선의 손실: 0.044707\t정확도: 98.83%\n",
-      "26\t검증 세트 손실: 0.073849\t최선의 손실: 0.044707\t정확도: 98.59%\n",
-      "27\t검증 세트 손실: 0.071304\t최선의 손실: 0.044707\t정확도: 98.63%\n",
-      "28\t검증 세트 손실: 0.088351\t최선의 손실: 0.044707\t정확도: 98.24%\n",
-      "29\t검증 세트 손실: 0.093488\t최선의 손실: 0.044707\t정확도: 98.44%\n",
-      "30\t검증 세트 손실: 0.064556\t최선의 손실: 0.044707\t정확도: 98.63%\n",
-      "31\t검증 세트 손실: 0.078119\t최선의 손실: 0.044707\t정확도: 98.67%\n",
-      "32\t검증 세트 손실: 0.076984\t최선의 손실: 0.044707\t정확도: 98.67%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=140, total=   3.1s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.176398\t최선의 손실: 0.176398\t정확도: 94.37%\n",
+      "1\t검증 세트 손실: 0.106044\t최선의 손실: 0.106044\t정확도: 96.40%\n",
+      "2\t검증 세트 손실: 0.068445\t최선의 손실: 0.068445\t정확도: 98.01%\n",
+      "3\t검증 세트 손실: 0.058436\t최선의 손실: 0.058436\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.053733\t최선의 손실: 0.053733\t정확도: 98.55%\n",
+      "5\t검증 세트 손실: 0.061811\t최선의 손실: 0.053733\t정확도: 98.24%\n",
+      "6\t검증 세트 손실: 0.053496\t최선의 손실: 0.053496\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.045420\t최선의 손실: 0.045420\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.060170\t최선의 손실: 0.045420\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.056050\t최선의 손실: 0.045420\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.050274\t최선의 손실: 0.045420\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.070461\t최선의 손실: 0.045420\t정확도: 98.32%\n",
+      "12\t검증 세트 손실: 0.045541\t최선의 손실: 0.045420\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.056959\t최선의 손실: 0.045420\t정확도: 98.71%\n",
+      "14\t검증 세트 손실: 0.058894\t최선의 손실: 0.045420\t정확도: 98.75%\n",
+      "15\t검증 세트 손실: 0.054790\t최선의 손실: 0.045420\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.071234\t최선의 손실: 0.045420\t정확도: 98.48%\n",
+      "17\t검증 세트 손실: 0.076426\t최선의 손실: 0.045420\t정확도: 98.24%\n",
+      "18\t검증 세트 손실: 0.065854\t최선의 손실: 0.045420\t정확도: 98.83%\n",
+      "19\t검증 세트 손실: 0.082783\t최선의 손실: 0.045420\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.049891\t최선의 손실: 0.045420\t정확도: 99.06%\n",
+      "21\t검증 세트 손실: 0.053952\t최선의 손실: 0.045420\t정확도: 98.59%\n",
+      "22\t검증 세트 손실: 0.071388\t최선의 손실: 0.045420\t정확도: 98.67%\n",
+      "23\t검증 세트 손실: 0.060695\t최선의 손실: 0.045420\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.072785\t최선의 손실: 0.045420\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.055535\t최선의 손실: 0.045420\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.058247\t최선의 손실: 0.045420\t정확도: 98.67%\n",
+      "27\t검증 세트 손실: 0.061225\t최선의 손실: 0.045420\t정확도: 98.71%\n",
+      "28\t검증 세트 손실: 0.059180\t최선의 손실: 0.045420\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.4s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.173753\t최선의 손실: 0.173753\t정확도: 94.72%\n",
-      "1\t검증 세트 손실: 0.107101\t최선의 손실: 0.107101\t정확도: 96.95%\n",
-      "2\t검증 세트 손실: 0.087230\t최선의 손실: 0.087230\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 0.068220\t최선의 손실: 0.068220\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.081026\t최선의 손실: 0.068220\t정확도: 97.54%\n",
-      "5\t검증 세트 손실: 0.061419\t최선의 손실: 0.061419\t정확도: 98.28%\n",
-      "6\t검증 세트 손실: 0.058177\t최선의 손실: 0.058177\t정확도: 98.28%\n",
-      "7\t검증 세트 손실: 0.067226\t최선의 손실: 0.058177\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.066021\t최선의 손실: 0.058177\t정확도: 98.12%\n",
-      "9\t검증 세트 손실: 0.055885\t최선의 손실: 0.055885\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.055245\t최선의 손실: 0.055245\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.048769\t최선의 손실: 0.048769\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.067149\t최선의 손실: 0.048769\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.046818\t최선의 손실: 0.046818\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.053119\t최선의 손실: 0.046818\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.056688\t최선의 손실: 0.046818\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.062298\t최선의 손실: 0.046818\t정확도: 98.55%\n",
-      "17\t검증 세트 손실: 0.100927\t최선의 손실: 0.046818\t정확도: 97.85%\n",
-      "18\t검증 세트 손실: 0.072150\t최선의 손실: 0.046818\t정확도: 98.59%\n",
-      "19\t검증 세트 손실: 0.055622\t최선의 손실: 0.046818\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.071964\t최선의 손실: 0.046818\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.060776\t최선의 손실: 0.046818\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.075168\t최선의 손실: 0.046818\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.079154\t최선의 손실: 0.046818\t정확도: 98.59%\n",
-      "24\t검증 세트 손실: 0.069773\t최선의 손실: 0.046818\t정확도: 98.63%\n",
-      "25\t검증 세트 손실: 0.060585\t최선의 손실: 0.046818\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.064822\t최선의 손실: 0.046818\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.087618\t최선의 손실: 0.046818\t정확도: 98.51%\n",
-      "28\t검증 세트 손실: 0.073733\t최선의 손실: 0.046818\t정확도: 98.59%\n",
-      "29\t검증 세트 손실: 0.091079\t최선의 손실: 0.046818\t정확도: 98.59%\n",
-      "30\t검증 세트 손실: 0.055200\t최선의 손실: 0.046818\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.085863\t최선의 손실: 0.046818\t정확도: 98.32%\n",
-      "32\t검증 세트 손실: 0.071217\t최선의 손실: 0.046818\t정확도: 99.06%\n",
-      "33\t검증 세트 손실: 0.067165\t최선의 손실: 0.046818\t정확도: 98.83%\n",
-      "34\t검증 세트 손실: 0.065542\t최선의 손실: 0.046818\t정확도: 98.98%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=140, total=   2.6s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.080899\t최선의 손실: 0.080899\t정확도: 97.62%\n",
+      "1\t검증 세트 손실: 0.086929\t최선의 손실: 0.080899\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.067645\t최선의 손실: 0.067645\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.097675\t최선의 손실: 0.067645\t정확도: 97.15%\n",
+      "4\t검증 세트 손실: 0.065275\t최선의 손실: 0.065275\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.063906\t최선의 손실: 0.063906\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.064461\t최선의 손실: 0.063906\t정확도: 98.55%\n",
+      "7\t검증 세트 손실: 0.069317\t최선의 손실: 0.063906\t정확도: 98.12%\n",
+      "8\t검증 세트 손실: 0.077867\t최선의 손실: 0.063906\t정확도: 98.16%\n",
+      "9\t검증 세트 손실: 0.070425\t최선의 손실: 0.063906\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.058823\t최선의 손실: 0.058823\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.068192\t최선의 손실: 0.058823\t정확도: 98.48%\n",
+      "12\t검증 세트 손실: 0.081103\t최선의 손실: 0.058823\t정확도: 98.36%\n",
+      "13\t검증 세트 손실: 0.068341\t최선의 손실: 0.058823\t정확도: 98.32%\n",
+      "14\t검증 세트 손실: 0.058458\t최선의 손실: 0.058458\t정확도: 98.44%\n",
+      "15\t검증 세트 손실: 0.082741\t최선의 손실: 0.058458\t정확도: 98.01%\n",
+      "16\t검증 세트 손실: 0.081735\t최선의 손실: 0.058458\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.090354\t최선의 손실: 0.058458\t정확도: 98.48%\n",
+      "18\t검증 세트 손실: 0.085913\t최선의 손실: 0.058458\t정확도: 98.44%\n",
+      "19\t검증 세트 손실: 0.072368\t최선의 손실: 0.058458\t정확도: 98.20%\n",
+      "20\t검증 세트 손실: 0.100350\t최선의 손실: 0.058458\t정확도: 98.32%\n",
+      "21\t검증 세트 손실: 0.080326\t최선의 손실: 0.058458\t정확도: 98.55%\n",
+      "22\t검증 세트 손실: 0.064068\t최선의 손실: 0.058458\t정확도: 98.12%\n",
+      "23\t검증 세트 손실: 0.092098\t최선의 손실: 0.058458\t정확도: 98.28%\n",
+      "24\t검증 세트 손실: 0.068865\t최선의 손실: 0.058458\t정확도: 98.40%\n",
+      "25\t검증 세트 손실: 0.081062\t최선의 손실: 0.058458\t정확도: 98.55%\n",
+      "26\t검증 세트 손실: 0.084749\t최선의 손실: 0.058458\t정확도: 98.75%\n",
+      "27\t검증 세트 손실: 0.079991\t최선의 손실: 0.058458\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.102478\t최선의 손실: 0.058458\t정확도: 98.55%\n",
+      "29\t검증 세트 손실: 0.096927\t최선의 손실: 0.058458\t정확도: 98.36%\n",
+      "30\t검증 세트 손실: 0.082304\t최선의 손실: 0.058458\t정확도: 98.12%\n",
+      "31\t검증 세트 손실: 0.091970\t최선의 손실: 0.058458\t정확도: 98.20%\n",
+      "32\t검증 세트 손실: 0.117607\t최선의 손실: 0.058458\t정확도: 98.59%\n",
+      "33\t검증 세트 손실: 0.086787\t최선의 손실: 0.058458\t정확도: 98.67%\n",
+      "34\t검증 세트 손실: 0.094951\t최선의 손실: 0.058458\t정확도: 98.36%\n",
+      "35\t검증 세트 손실: 0.125504\t최선의 손실: 0.058458\t정확도: 97.97%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.6s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.079150\t최선의 손실: 0.079150\t정확도: 97.58%\n",
-      "1\t검증 세트 손실: 0.095827\t최선의 손실: 0.079150\t정확도: 97.19%\n",
-      "2\t검증 세트 손실: 0.074945\t최선의 손실: 0.074945\t정확도: 97.85%\n",
-      "3\t검증 세트 손실: 0.074505\t최선의 손실: 0.074505\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.065397\t최선의 손실: 0.065397\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 0.088068\t최선의 손실: 0.065397\t정확도: 97.73%\n",
-      "6\t검증 세트 손실: 0.077272\t최선의 손실: 0.065397\t정확도: 97.73%\n",
-      "7\t검증 세트 손실: 0.080167\t최선의 손실: 0.065397\t정확도: 97.97%\n",
-      "8\t검증 세트 손실: 0.066630\t최선의 손실: 0.065397\t정확도: 98.55%\n",
-      "9\t검증 세트 손실: 0.060636\t최선의 손실: 0.060636\t정확도: 98.16%\n",
-      "10\t검증 세트 손실: 0.092101\t최선의 손실: 0.060636\t정확도: 97.89%\n",
-      "11\t검증 세트 손실: 0.060531\t최선의 손실: 0.060531\t정확도: 98.40%\n",
-      "12\t검증 세트 손실: 0.073181\t최선의 손실: 0.060531\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.063899\t최선의 손실: 0.060531\t정확도: 98.32%\n",
-      "14\t검증 세트 손실: 0.075383\t최선의 손실: 0.060531\t정확도: 98.24%\n",
-      "15\t검증 세트 손실: 0.063200\t최선의 손실: 0.060531\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.074794\t최선의 손실: 0.060531\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.088033\t최선의 손실: 0.060531\t정확도: 97.93%\n",
-      "18\t검증 세트 손실: 0.084869\t최선의 손실: 0.060531\t정확도: 97.77%\n",
-      "19\t검증 세트 손실: 0.070163\t최선의 손실: 0.060531\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.071534\t최선의 손실: 0.060531\t정확도: 98.51%\n",
-      "21\t검증 세트 손실: 0.088551\t최선의 손실: 0.060531\t정확도: 98.36%\n",
-      "22\t검증 세트 손실: 0.071261\t최선의 손실: 0.060531\t정확도: 98.51%\n",
-      "23\t검증 세트 손실: 0.084528\t최선의 손실: 0.060531\t정확도: 98.55%\n",
-      "24\t검증 세트 손실: 0.082686\t최선의 손실: 0.060531\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.068594\t최선의 손실: 0.060531\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.076502\t최선의 손실: 0.060531\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.089733\t최선의 손실: 0.060531\t정확도: 98.51%\n",
-      "28\t검증 세트 손실: 0.075271\t최선의 손실: 0.060531\t정확도: 98.40%\n",
-      "29\t검증 세트 손실: 0.065003\t최선의 손실: 0.060531\t정확도: 98.67%\n",
-      "30\t검증 세트 손실: 0.087265\t최선의 손실: 0.060531\t정확도: 98.44%\n",
-      "31\t검증 세트 손실: 0.093315\t최선의 손실: 0.060531\t정확도: 98.44%\n",
-      "32\t검증 세트 손실: 0.079851\t최선의 손실: 0.060531\t정확도: 98.08%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=30, total=   6.9s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.133944\t최선의 손실: 0.133944\t정확도: 96.29%\n",
+      "1\t검증 세트 손실: 0.071065\t최선의 손실: 0.071065\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.080428\t최선의 손실: 0.071065\t정확도: 97.46%\n",
+      "3\t검증 세트 손실: 0.073572\t최선의 손실: 0.071065\t정확도: 97.81%\n",
+      "4\t검증 세트 손실: 0.078816\t최선의 손실: 0.071065\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.070823\t최선의 손실: 0.070823\t정확도: 97.97%\n",
+      "6\t검증 세트 손실: 0.068107\t최선의 손실: 0.068107\t정확도: 97.93%\n",
+      "7\t검증 세트 손실: 0.064171\t최선의 손실: 0.064171\t정확도: 98.08%\n",
+      "8\t검증 세트 손실: 0.061481\t최선의 손실: 0.061481\t정확도: 98.20%\n",
+      "9\t검증 세트 손실: 0.065495\t최선의 손실: 0.061481\t정확도: 98.32%\n",
+      "10\t검증 세트 손실: 0.074713\t최선의 손실: 0.061481\t정확도: 97.93%\n",
+      "11\t검증 세트 손실: 0.093392\t최선의 손실: 0.061481\t정확도: 98.16%\n",
+      "12\t검증 세트 손실: 0.069908\t최선의 손실: 0.061481\t정확도: 98.36%\n",
+      "13\t검증 세트 손실: 0.062453\t최선의 손실: 0.061481\t정확도: 98.24%\n",
+      "14\t검증 세트 손실: 0.080277\t최선의 손실: 0.061481\t정확도: 98.28%\n",
+      "15\t검증 세트 손실: 0.074390\t최선의 손실: 0.061481\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.089892\t최선의 손실: 0.061481\t정확도: 97.69%\n",
+      "17\t검증 세트 손실: 0.095613\t최선의 손실: 0.061481\t정확도: 97.81%\n",
+      "18\t검증 세트 손실: 0.087863\t최선의 손실: 0.061481\t정확도: 98.20%\n",
+      "19\t검증 세트 손실: 0.091383\t최선의 손실: 0.061481\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.076829\t최선의 손실: 0.061481\t정확도: 98.40%\n",
+      "21\t검증 세트 손실: 0.079899\t최선의 손실: 0.061481\t정확도: 98.36%\n",
+      "22\t검증 세트 손실: 0.126113\t최선의 손실: 0.061481\t정확도: 97.46%\n",
+      "23\t검증 세트 손실: 0.075031\t최선의 손실: 0.061481\t정확도: 98.32%\n",
+      "24\t검증 세트 손실: 0.079582\t최선의 손실: 0.061481\t정확도: 98.63%\n",
+      "25\t검증 세트 손실: 0.106970\t최선의 손실: 0.061481\t정확도: 98.20%\n",
+      "26\t검증 세트 손실: 0.081104\t최선의 손실: 0.061481\t정확도: 98.24%\n",
+      "27\t검증 세트 손실: 0.096702\t최선의 손실: 0.061481\t정확도: 98.08%\n",
+      "28\t검증 세트 손실: 0.134037\t최선의 손실: 0.061481\t정확도: 97.85%\n",
+      "29\t검증 세트 손실: 0.095820\t최선의 손실: 0.061481\t정확도: 97.93%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  11.2s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.096602\t최선의 손실: 0.096602\t정확도: 97.26%\n",
-      "1\t검증 세트 손실: 0.083669\t최선의 손실: 0.083669\t정확도: 97.77%\n",
-      "2\t검증 세트 손실: 0.075943\t최선의 손실: 0.075943\t정확도: 97.54%\n",
-      "3\t검증 세트 손실: 0.067363\t최선의 손실: 0.067363\t정확도: 98.01%\n",
-      "4\t검증 세트 손실: 0.061604\t최선의 손실: 0.061604\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.079038\t최선의 손실: 0.061604\t정확도: 98.01%\n",
-      "6\t검증 세트 손실: 0.057206\t최선의 손실: 0.057206\t정확도: 98.36%\n",
-      "7\t검증 세트 손실: 0.055678\t최선의 손실: 0.055678\t정확도: 98.44%\n",
-      "8\t검증 세트 손실: 0.136101\t최선의 손실: 0.055678\t정확도: 97.46%\n",
-      "9\t검증 세트 손실: 0.077405\t최선의 손실: 0.055678\t정확도: 98.28%\n",
-      "10\t검증 세트 손실: 0.080401\t최선의 손실: 0.055678\t정확도: 98.32%\n",
-      "11\t검증 세트 손실: 0.060691\t최선의 손실: 0.055678\t정확도: 98.32%\n"
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=30, total=   5.8s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.089724\t최선의 손실: 0.089724\t정확도: 97.50%\n",
+      "1\t검증 세트 손실: 0.083544\t최선의 손실: 0.083544\t정확도: 97.30%\n",
+      "2\t검증 세트 손실: 0.071631\t최선의 손실: 0.071631\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.077266\t최선의 손실: 0.071631\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.061176\t최선의 손실: 0.061176\t정확도: 98.32%\n",
+      "5\t검증 세트 손실: 0.070229\t최선의 손실: 0.061176\t정확도: 98.32%\n",
+      "6\t검증 세트 손실: 0.071452\t최선의 손실: 0.061176\t정확도: 98.36%\n",
+      "7\t검증 세트 손실: 0.058201\t최선의 손실: 0.058201\t정확도: 98.28%\n",
+      "8\t검증 세트 손실: 0.077724\t최선의 손실: 0.058201\t정확도: 98.48%\n",
+      "9\t검증 세트 손실: 0.072952\t최선의 손실: 0.058201\t정확도: 98.20%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 0.072005\t최선의 손실: 0.055678\t정확도: 98.05%\n",
-      "13\t검증 세트 손실: 0.100109\t최선의 손실: 0.055678\t정확도: 98.05%\n",
-      "14\t검증 세트 손실: 0.087967\t최선의 손실: 0.055678\t정확도: 98.01%\n",
-      "15\t검증 세트 손실: 0.058833\t최선의 손실: 0.055678\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.074368\t최선의 손실: 0.055678\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.074334\t최선의 손실: 0.055678\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.079316\t최선의 손실: 0.055678\t정확도: 98.40%\n",
-      "19\t검증 세트 손실: 0.086600\t최선의 손실: 0.055678\t정확도: 98.08%\n",
-      "20\t검증 세트 손실: 0.175397\t최선의 손실: 0.055678\t정확도: 98.51%\n",
-      "21\t검증 세트 손실: 0.089505\t최선의 손실: 0.055678\t정확도: 98.16%\n",
-      "22\t검증 세트 손실: 0.075199\t최선의 손실: 0.055678\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.119482\t최선의 손실: 0.055678\t정확도: 98.36%\n",
-      "24\t검증 세트 손실: 0.074329\t최선의 손실: 0.055678\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.099184\t최선의 손실: 0.055678\t정확도: 98.20%\n",
-      "26\t검증 세트 손실: 0.126611\t최선의 손실: 0.055678\t정확도: 96.99%\n",
-      "27\t검증 세트 손실: 0.091845\t최선의 손실: 0.055678\t정확도: 98.24%\n",
-      "28\t검증 세트 손실: 0.143131\t최선의 손실: 0.055678\t정확도: 98.16%\n",
+      "10\t검증 세트 손실: 0.078914\t최선의 손실: 0.058201\t정확도: 98.16%\n",
+      "11\t검증 세트 손실: 0.065126\t최선의 손실: 0.058201\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.084432\t최선의 손실: 0.058201\t정확도: 97.69%\n",
+      "13\t검증 세트 손실: 0.077058\t최선의 손실: 0.058201\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.092233\t최선의 손실: 0.058201\t정확도: 98.20%\n",
+      "15\t검증 세트 손실: 0.090472\t최선의 손실: 0.058201\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.105631\t최선의 손실: 0.058201\t정확도: 97.81%\n",
+      "17\t검증 세트 손실: 0.082115\t최선의 손실: 0.058201\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.085675\t최선의 손실: 0.058201\t정확도: 98.48%\n",
+      "19\t검증 세트 손실: 0.075742\t최선의 손실: 0.058201\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.069197\t최선의 손실: 0.058201\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.077932\t최선의 손실: 0.058201\t정확도: 98.55%\n",
+      "22\t검증 세트 손실: 0.083667\t최선의 손실: 0.058201\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.076434\t최선의 손실: 0.058201\t정확도: 98.32%\n",
+      "24\t검증 세트 손실: 0.105803\t최선의 손실: 0.058201\t정확도: 98.28%\n",
+      "25\t검증 세트 손실: 0.092556\t최선의 손실: 0.058201\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.109842\t최선의 손실: 0.058201\t정확도: 97.93%\n",
+      "27\t검증 세트 손실: 0.086381\t최선의 손실: 0.058201\t정확도: 98.59%\n",
+      "28\t검증 세트 손실: 0.081117\t최선의 손실: 0.058201\t정확도: 98.67%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  10.2s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.081740\t최선의 손실: 0.081740\t정확도: 97.81%\n",
-      "1\t검증 세트 손실: 0.070659\t최선의 손실: 0.070659\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.113457\t최선의 손실: 0.070659\t정확도: 96.72%\n",
-      "3\t검증 세트 손실: 0.072762\t최선의 손실: 0.070659\t정확도: 98.08%\n",
-      "4\t검증 세트 손실: 0.051770\t최선의 손실: 0.051770\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.061317\t최선의 손실: 0.051770\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.062819\t최선의 손실: 0.051770\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.070712\t최선의 손실: 0.051770\t정확도: 98.20%\n",
-      "8\t검증 세트 손실: 0.080278\t최선의 손실: 0.051770\t정확도: 98.24%\n",
-      "9\t검증 세트 손실: 0.083887\t최선의 손실: 0.051770\t정확도: 98.05%\n",
-      "10\t검증 세트 손실: 0.084211\t최선의 손실: 0.051770\t정확도: 98.12%\n",
-      "11\t검증 세트 손실: 0.082333\t최선의 손실: 0.051770\t정확도: 98.48%\n",
-      "12\t검증 세트 손실: 0.067751\t최선의 손실: 0.051770\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.059958\t최선의 손실: 0.051770\t정확도: 98.79%\n",
-      "14\t검증 세트 손실: 0.100918\t최선의 손실: 0.051770\t정확도: 97.85%\n",
-      "15\t검증 세트 손실: 0.072452\t최선의 손실: 0.051770\t정확도: 98.67%\n",
-      "16\t검증 세트 손실: 0.051044\t최선의 손실: 0.051044\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.054611\t최선의 손실: 0.051044\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.074576\t최선의 손실: 0.051044\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.076053\t최선의 손실: 0.051044\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.058827\t최선의 손실: 0.051044\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.086179\t최선의 손실: 0.051044\t정확도: 97.97%\n",
-      "22\t검증 세트 손실: 0.089209\t최선의 손실: 0.051044\t정확도: 98.16%\n",
-      "23\t검증 세트 손실: 0.067416\t최선의 손실: 0.051044\t정확도: 98.75%\n",
-      "24\t검증 세트 손실: 0.059893\t최선의 손실: 0.051044\t정확도: 98.94%\n",
-      "25\t검증 세트 손실: 0.085712\t최선의 손실: 0.051044\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.093221\t최선의 손실: 0.051044\t정확도: 98.67%\n",
-      "27\t검증 세트 손실: 0.069557\t최선의 손실: 0.051044\t정확도: 98.48%\n",
-      "28\t검증 세트 손실: 0.092511\t최선의 손실: 0.051044\t정확도: 98.63%\n",
-      "29\t검증 세트 손실: 0.091845\t최선의 손실: 0.051044\t정확도: 98.24%\n",
-      "30\t검증 세트 손실: 0.097837\t최선의 손실: 0.051044\t정확도: 98.55%\n",
-      "31\t검증 세트 손실: 0.085698\t최선의 손실: 0.051044\t정확도: 98.51%\n",
-      "32\t검증 세트 손실: 0.076772\t최선의 손실: 0.051044\t정확도: 98.51%\n",
-      "33\t검증 세트 손실: 0.067900\t최선의 손실: 0.051044\t정확도: 98.59%\n",
-      "34\t검증 세트 손실: 0.076697\t최선의 손실: 0.051044\t정확도: 98.87%\n",
-      "35\t검증 세트 손실: 0.058845\t최선의 손실: 0.051044\t정확도: 98.91%\n",
-      "36\t검증 세트 손실: 0.083690\t최선의 손실: 0.051044\t정확도: 98.83%\n",
-      "37\t검증 세트 손실: 0.078264\t최선의 손실: 0.051044\t정확도: 98.55%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=30, total=   5.5s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.160262\t최선의 손실: 0.160262\t정확도: 95.23%\n",
+      "1\t검증 세트 손실: 0.100659\t최선의 손실: 0.100659\t정확도: 96.87%\n",
+      "2\t검증 세트 손실: 0.080187\t최선의 손실: 0.080187\t정확도: 97.38%\n",
+      "3\t검증 세트 손실: 0.081639\t최선의 손실: 0.080187\t정확도: 97.19%\n",
+      "4\t검증 세트 손실: 0.072798\t최선의 손실: 0.072798\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 0.077768\t최선의 손실: 0.072798\t정확도: 97.81%\n",
+      "6\t검증 세트 손실: 0.070504\t최선의 손실: 0.070504\t정확도: 97.85%\n",
+      "7\t검증 세트 손실: 0.070825\t최선의 손실: 0.070504\t정확도: 97.93%\n",
+      "8\t검증 세트 손실: 0.055643\t최선의 손실: 0.055643\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.076649\t최선의 손실: 0.055643\t정확도: 98.16%\n",
+      "10\t검증 세트 손실: 0.090530\t최선의 손실: 0.055643\t정확도: 97.81%\n",
+      "11\t검증 세트 손실: 0.077155\t최선의 손실: 0.055643\t정확도: 97.89%\n",
+      "12\t검증 세트 손실: 0.065595\t최선의 손실: 0.055643\t정확도: 98.36%\n",
+      "13\t검증 세트 손실: 0.075640\t최선의 손실: 0.055643\t정확도: 98.36%\n",
+      "14\t검증 세트 손실: 0.075739\t최선의 손실: 0.055643\t정확도: 98.05%\n",
+      "15\t검증 세트 손실: 0.101385\t최선의 손실: 0.055643\t정확도: 98.05%\n",
+      "16\t검증 세트 손실: 0.061411\t최선의 손실: 0.055643\t정확도: 98.20%\n",
+      "17\t검증 세트 손실: 0.121962\t최선의 손실: 0.055643\t정확도: 97.38%\n",
+      "18\t검증 세트 손실: 0.092972\t최선의 손실: 0.055643\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.083629\t최선의 손실: 0.055643\t정확도: 98.32%\n",
+      "20\t검증 세트 손실: 0.084502\t최선의 손실: 0.055643\t정확도: 98.24%\n",
+      "21\t검증 세트 손실: 0.124567\t최선의 손실: 0.055643\t정확도: 98.01%\n",
+      "22\t검증 세트 손실: 0.119193\t최선의 손실: 0.055643\t정확도: 98.16%\n",
+      "23\t검증 세트 손실: 0.110082\t최선의 손실: 0.055643\t정확도: 97.81%\n",
+      "24\t검증 세트 손실: 0.102506\t최선의 손실: 0.055643\t정확도: 97.93%\n",
+      "25\t검증 세트 손실: 0.089855\t최선의 손실: 0.055643\t정확도: 98.32%\n",
+      "26\t검증 세트 손실: 0.108907\t최선의 손실: 0.055643\t정확도: 97.85%\n",
+      "27\t검증 세트 손실: 0.107569\t최선의 손실: 0.055643\t정확도: 98.05%\n",
+      "28\t검증 세트 손실: 0.139499\t최선의 손실: 0.055643\t정확도: 98.08%\n",
+      "29\t검증 세트 손실: 0.099804\t최선의 손실: 0.055643\t정확도: 98.12%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total=  17.8s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.384559\t최선의 손실: 0.384559\t정확도: 85.69%\n",
-      "1\t검증 세트 손실: 0.141354\t최선의 손실: 0.141354\t정확도: 96.21%\n",
-      "2\t검증 세트 손실: 0.121376\t최선의 손실: 0.121376\t정확도: 96.44%\n",
-      "3\t검증 세트 손실: 0.111289\t최선의 손실: 0.111289\t정확도: 96.64%\n",
-      "4\t검증 세트 손실: 0.104053\t최선의 손실: 0.104053\t정확도: 96.79%\n",
-      "5\t검증 세트 손실: 0.091560\t최선의 손실: 0.091560\t정확도: 97.62%\n",
-      "6\t검증 세트 손실: 0.085408\t최선의 손실: 0.085408\t정확도: 97.73%\n",
-      "7\t검증 세트 손실: 0.109997\t최선의 손실: 0.085408\t정확도: 97.22%\n",
-      "8\t검증 세트 손실: 0.105647\t최선의 손실: 0.085408\t정확도: 96.83%\n",
-      "9\t검증 세트 손실: 0.119785\t최선의 손실: 0.085408\t정확도: 97.42%\n",
-      "10\t검증 세트 손실: 0.093925\t최선의 손실: 0.085408\t정확도: 97.58%\n",
-      "11\t검증 세트 손실: 0.119430\t최선의 손실: 0.085408\t정확도: 97.19%\n",
-      "12\t검증 세트 손실: 0.081382\t최선의 손실: 0.081382\t정확도: 98.01%\n",
-      "13\t검증 세트 손실: 0.087727\t최선의 손실: 0.081382\t정확도: 97.85%\n",
-      "14\t검증 세트 손실: 0.102109\t최선의 손실: 0.081382\t정확도: 97.62%\n",
-      "15\t검증 세트 손실: 0.094087\t최선의 손실: 0.081382\t정확도: 97.62%\n",
-      "16\t검증 세트 손실: 0.089556\t최선의 손실: 0.081382\t정확도: 98.01%\n",
-      "17\t검증 세트 손실: 0.104641\t최선의 손실: 0.081382\t정확도: 97.69%\n",
-      "18\t검증 세트 손실: 0.086995\t최선의 손실: 0.081382\t정확도: 97.73%\n",
-      "19\t검증 세트 손실: 0.079066\t최선의 손실: 0.079066\t정확도: 98.08%\n",
-      "20\t검증 세트 손실: 0.091216\t최선의 손실: 0.079066\t정확도: 97.69%\n",
-      "21\t검증 세트 손실: 0.107207\t최선의 손실: 0.079066\t정확도: 97.34%\n",
-      "22\t검증 세트 손실: 0.079644\t최선의 손실: 0.079066\t정확도: 98.05%\n",
-      "23\t검증 세트 손실: 0.089646\t최선의 손실: 0.079066\t정확도: 97.42%\n",
-      "24\t검증 세트 손실: 0.093155\t최선의 손실: 0.079066\t정확도: 97.73%\n",
-      "25\t검증 세트 손실: 0.099511\t최선의 손실: 0.079066\t정확도: 97.62%\n",
-      "26\t검증 세트 손실: 0.079076\t최선의 손실: 0.079066\t정확도: 97.97%\n",
-      "27\t검증 세트 손실: 0.093112\t최선의 손실: 0.079066\t정확도: 97.93%\n",
-      "28\t검증 세트 손실: 0.090607\t최선의 손실: 0.079066\t정확도: 97.89%\n",
-      "29\t검증 세트 손실: 0.105360\t최선의 손실: 0.079066\t정확도: 98.01%\n",
-      "30\t검증 세트 손실: 0.090559\t최선의 손실: 0.079066\t정확도: 98.01%\n",
-      "31\t검증 세트 손실: 0.105684\t최선의 손실: 0.079066\t정확도: 97.77%\n",
-      "32\t검증 세트 손실: 0.129214\t최선의 손실: 0.079066\t정확도: 97.93%\n",
-      "33\t검증 세트 손실: 0.113266\t최선의 손실: 0.079066\t정확도: 97.89%\n",
-      "34\t검증 세트 손실: 0.111541\t최선의 손실: 0.079066\t정확도: 97.89%\n",
-      "35\t검증 세트 손실: 0.123271\t최선의 손실: 0.079066\t정확도: 97.73%\n",
-      "36\t검증 세트 손실: 0.096761\t최선의 손실: 0.079066\t정확도: 97.77%\n",
-      "37\t검증 세트 손실: 0.116599\t최선의 손실: 0.079066\t정확도: 98.01%\n",
-      "38\t검증 세트 손실: 0.145225\t최선의 손실: 0.079066\t정확도: 98.12%\n",
-      "39\t검증 세트 손실: 0.112932\t최선의 손실: 0.079066\t정확도: 98.05%\n",
-      "40\t검증 세트 손실: 0.155266\t최선의 손실: 0.079066\t정확도: 98.05%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50, total=   2.9s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.153423\t최선의 손실: 0.153423\t정확도: 95.78%\n",
+      "1\t검증 세트 손실: 0.102476\t최선의 손실: 0.102476\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.079294\t최선의 손실: 0.079294\t정확도: 97.81%\n",
+      "3\t검증 세트 손실: 0.072379\t최선의 손실: 0.072379\t정확도: 97.89%\n",
+      "4\t검증 세트 손실: 0.082705\t최선의 손실: 0.072379\t정확도: 97.42%\n",
+      "5\t검증 세트 손실: 0.075357\t최선의 손실: 0.072379\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.068994\t최선의 손실: 0.068994\t정확도: 97.85%\n",
+      "7\t검증 세트 손실: 0.067877\t최선의 손실: 0.067877\t정확도: 97.81%\n",
+      "8\t검증 세트 손실: 0.072242\t최선의 손실: 0.067877\t정확도: 98.08%\n",
+      "9\t검증 세트 손실: 0.082041\t최선의 손실: 0.067877\t정확도: 97.89%\n",
+      "10\t검증 세트 손실: 0.061254\t최선의 손실: 0.061254\t정확도: 98.16%\n",
+      "11\t검증 세트 손실: 0.102034\t최선의 손실: 0.061254\t정확도: 97.54%\n",
+      "12\t검증 세트 손실: 0.077375\t최선의 손실: 0.061254\t정확도: 98.24%\n",
+      "13\t검증 세트 손실: 0.094210\t최선의 손실: 0.061254\t정확도: 97.85%\n",
+      "14\t검증 세트 손실: 0.064804\t최선의 손실: 0.061254\t정확도: 98.28%\n",
+      "15\t검증 세트 손실: 0.084054\t최선의 손실: 0.061254\t정확도: 98.01%\n",
+      "16\t검증 세트 손실: 0.083359\t최선의 손실: 0.061254\t정확도: 97.81%\n",
+      "17\t검증 세트 손실: 0.063826\t최선의 손실: 0.061254\t정확도: 98.01%\n",
+      "18\t검증 세트 손실: 0.080522\t최선의 손실: 0.061254\t정확도: 98.16%\n",
+      "19\t검증 세트 손실: 0.064327\t최선의 손실: 0.061254\t정확도: 98.16%\n",
+      "20\t검증 세트 손실: 0.069200\t최선의 손실: 0.061254\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.079504\t최선의 손실: 0.061254\t정확도: 98.20%\n",
+      "22\t검증 세트 손실: 0.064423\t최선의 손실: 0.061254\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.066099\t최선의 손실: 0.061254\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.067042\t최선의 손실: 0.061254\t정확도: 98.40%\n",
+      "25\t검증 세트 손실: 0.076443\t최선의 손실: 0.061254\t정확도: 98.48%\n",
+      "26\t검증 세트 손실: 0.082615\t최선의 손실: 0.061254\t정확도: 98.01%\n",
+      "27\t검증 세트 손실: 0.073807\t최선의 손실: 0.061254\t정확도: 98.59%\n",
+      "28\t검증 세트 손실: 0.079079\t최선의 손실: 0.061254\t정확도: 98.55%\n",
+      "29\t검증 세트 손실: 0.076373\t최선의 손실: 0.061254\t정확도: 98.24%\n",
+      "30\t검증 세트 손실: 0.086683\t최선의 손실: 0.061254\t정확도: 98.16%\n",
+      "31\t검증 세트 손실: 0.102502\t최선의 손실: 0.061254\t정확도: 98.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=   3.2s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.169024\t최선의 손실: 0.169024\t정확도: 94.64%\n",
-      "1\t검증 세트 손실: 0.125391\t최선의 손실: 0.125391\t정확도: 96.05%\n",
-      "2\t검증 세트 손실: 0.091691\t최선의 손실: 0.091691\t정확도: 97.03%\n",
-      "3\t검증 세트 손실: 0.101900\t최선의 손실: 0.091691\t정확도: 97.11%\n",
-      "4\t검증 세트 손실: 0.106521\t최선의 손실: 0.091691\t정확도: 96.99%\n",
-      "5\t검증 세트 손실: 0.083508\t최선의 손실: 0.083508\t정확도: 97.54%\n",
-      "6\t검증 세트 손실: 0.081106\t최선의 손실: 0.081106\t정확도: 97.42%\n",
-      "7\t검증 세트 손실: 0.091247\t최선의 손실: 0.081106\t정확도: 97.50%\n",
-      "8\t검증 세트 손실: 0.078133\t최선의 손실: 0.078133\t정확도: 97.85%\n",
-      "9\t검증 세트 손실: 0.070626\t최선의 손실: 0.070626\t정확도: 98.01%\n",
-      "10\t검증 세트 손실: 0.070875\t최선의 손실: 0.070626\t정확도: 97.77%\n",
-      "11\t검증 세트 손실: 0.080254\t최선의 손실: 0.070626\t정확도: 98.05%\n",
-      "12\t검증 세트 손실: 0.073580\t최선의 손실: 0.070626\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.104993\t최선의 손실: 0.070626\t정확도: 97.65%\n",
-      "14\t검증 세트 손실: 0.108379\t최선의 손실: 0.070626\t정확도: 97.89%\n",
-      "15\t검증 세트 손실: 0.158892\t최선의 손실: 0.070626\t정확도: 97.07%\n",
-      "16\t검증 세트 손실: 0.072345\t최선의 손실: 0.070626\t정확도: 98.08%\n",
-      "17\t검증 세트 손실: 0.074456\t최선의 손실: 0.070626\t정확도: 98.24%\n",
-      "18\t검증 세트 손실: 0.086364\t최선의 손실: 0.070626\t정확도: 97.93%\n",
-      "19\t검증 세트 손실: 0.084147\t최선의 손실: 0.070626\t정확도: 98.20%\n",
-      "20\t검증 세트 손실: 0.090314\t최선의 손실: 0.070626\t정확도: 97.97%\n",
-      "21\t검증 세트 손실: 0.090803\t최선의 손실: 0.070626\t정확도: 98.24%\n",
-      "22\t검증 세트 손실: 0.100446\t최선의 손실: 0.070626\t정확도: 97.65%\n",
-      "23\t검증 세트 손실: 0.100645\t최선의 손실: 0.070626\t정확도: 97.73%\n",
-      "24\t검증 세트 손실: 0.088065\t최선의 손실: 0.070626\t정확도: 98.16%\n",
-      "25\t검증 세트 손실: 0.128137\t최선의 손실: 0.070626\t정확도: 98.24%\n",
-      "26\t검증 세트 손실: 0.092960\t최선의 손실: 0.070626\t정확도: 98.28%\n",
-      "27\t검증 세트 손실: 0.115977\t최선의 손실: 0.070626\t정확도: 98.01%\n",
-      "28\t검증 세트 손실: 0.084471\t최선의 손실: 0.070626\t정확도: 98.28%\n",
-      "29\t검증 세트 손실: 0.091828\t최선의 손실: 0.070626\t정확도: 98.40%\n",
-      "30\t검증 세트 손실: 0.085286\t최선의 손실: 0.070626\t정확도: 98.44%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50, total=   3.1s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.197061\t최선의 손실: 0.197061\t정확도: 94.61%\n",
+      "1\t검증 세트 손실: 0.123727\t최선의 손실: 0.123727\t정확도: 96.36%\n",
+      "2\t검증 세트 손실: 0.098659\t최선의 손실: 0.098659\t정확도: 97.42%\n",
+      "3\t검증 세트 손실: 0.090791\t최선의 손실: 0.090791\t정확도: 97.46%\n",
+      "4\t검증 세트 손실: 0.085404\t최선의 손실: 0.085404\t정확도: 97.58%\n",
+      "5\t검증 세트 손실: 0.077603\t최선의 손실: 0.077603\t정확도: 97.73%\n",
+      "6\t검증 세트 손실: 0.078992\t최선의 손실: 0.077603\t정확도: 97.85%\n",
+      "7\t검증 세트 손실: 0.077411\t최선의 손실: 0.077411\t정확도: 97.54%\n",
+      "8\t검증 세트 손실: 0.075559\t최선의 손실: 0.075559\t정확도: 97.62%\n",
+      "9\t검증 세트 손실: 0.066374\t최선의 손실: 0.066374\t정확도: 98.32%\n",
+      "10\t검증 세트 손실: 0.075748\t최선의 손실: 0.066374\t정확도: 98.01%\n",
+      "11\t검증 세트 손실: 0.070875\t최선의 손실: 0.066374\t정확도: 98.12%\n",
+      "12\t검증 세트 손실: 0.087676\t최선의 손실: 0.066374\t정확도: 97.77%\n",
+      "13\t검증 세트 손실: 0.137549\t최선의 손실: 0.066374\t정확도: 97.07%\n",
+      "14\t검증 세트 손실: 0.089437\t최선의 손실: 0.066374\t정확도: 98.20%\n",
+      "15\t검증 세트 손실: 0.083123\t최선의 손실: 0.066374\t정확도: 97.85%\n",
+      "16\t검증 세트 손실: 0.093365\t최선의 손실: 0.066374\t정확도: 97.97%\n",
+      "17\t검증 세트 손실: 0.082568\t최선의 손실: 0.066374\t정확도: 98.08%\n",
+      "18\t검증 세트 손실: 0.076299\t최선의 손실: 0.066374\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.063424\t최선의 손실: 0.063424\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.060626\t최선의 손실: 0.060626\t정확도: 98.63%\n",
+      "21\t검증 세트 손실: 0.100935\t최선의 손실: 0.060626\t정확도: 97.73%\n",
+      "22\t검증 세트 손실: 0.078307\t최선의 손실: 0.060626\t정확도: 98.40%\n",
+      "23\t검증 세트 손실: 0.068938\t최선의 손실: 0.060626\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.074617\t최선의 손실: 0.060626\t정확도: 98.40%\n",
+      "25\t검증 세트 손실: 0.083491\t최선의 손실: 0.060626\t정확도: 98.01%\n",
+      "26\t검증 세트 손실: 0.071231\t최선의 손실: 0.060626\t정확도: 98.44%\n",
+      "27\t검증 세트 손실: 0.092019\t최선의 손실: 0.060626\t정확도: 98.05%\n",
+      "28\t검증 세트 손실: 0.100253\t최선의 손실: 0.060626\t정확도: 98.16%\n",
+      "29\t검증 세트 손실: 0.103174\t최선의 손실: 0.060626\t정확도: 97.93%\n",
+      "30\t검증 세트 손실: 0.086439\t최선의 손실: 0.060626\t정확도: 98.08%\n",
+      "31\t검증 세트 손실: 0.073938\t최선의 손실: 0.060626\t정확도: 98.51%\n",
+      "32\t검증 세트 손실: 0.074669\t최선의 손실: 0.060626\t정확도: 98.59%\n",
+      "33\t검증 세트 손실: 0.078257\t최선의 손실: 0.060626\t정확도: 98.87%\n",
+      "34\t검증 세트 손실: 0.082806\t최선의 손실: 0.060626\t정확도: 98.63%\n",
+      "35\t검증 세트 손실: 0.094371\t최선의 손실: 0.060626\t정확도: 98.48%\n",
+      "36\t검증 세트 손실: 0.083087\t최선의 손실: 0.060626\t정확도: 98.24%\n",
+      "37\t검증 세트 손실: 0.082361\t최선의 손실: 0.060626\t정확도: 98.44%\n",
+      "38\t검증 세트 손실: 0.092762\t최선의 손실: 0.060626\t정확도: 98.24%\n",
+      "39\t검증 세트 손실: 0.079691\t최선의 손실: 0.060626\t정확도: 98.67%\n",
+      "40\t검증 세트 손실: 0.089438\t최선의 손실: 0.060626\t정확도: 98.44%\n",
+      "41\t검증 세트 손실: 0.091520\t최선의 손실: 0.060626\t정확도: 98.32%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=   2.5s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.202724\t최선의 손실: 0.202724\t정확도: 94.06%\n",
-      "1\t검증 세트 손실: 0.120850\t최선의 손실: 0.120850\t정확도: 96.87%\n",
-      "2\t검증 세트 손실: 0.091687\t최선의 손실: 0.091687\t정확도: 97.26%\n",
-      "3\t검증 세트 손실: 0.071419\t최선의 손실: 0.071419\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.079583\t최선의 손실: 0.071419\t정확도: 97.62%\n",
-      "5\t검증 세트 손실: 0.081134\t최선의 손실: 0.071419\t정확도: 97.81%\n",
-      "6\t검증 세트 손실: 0.089086\t최선의 손실: 0.071419\t정확도: 97.46%\n",
-      "7\t검증 세트 손실: 0.073021\t최선의 손실: 0.071419\t정확도: 98.05%\n",
-      "8\t검증 세트 손실: 0.089828\t최선의 손실: 0.071419\t정확도: 97.30%\n",
-      "9\t검증 세트 손실: 0.075703\t최선의 손실: 0.071419\t정확도: 97.93%\n",
-      "10\t검증 세트 손실: 0.106328\t최선의 손실: 0.071419\t정확도: 97.46%\n",
-      "11\t검증 세트 손실: 0.098564\t최선의 손실: 0.071419\t정확도: 97.54%\n",
-      "12\t검증 세트 손실: 0.092319\t최선의 손실: 0.071419\t정확도: 97.42%\n",
-      "13\t검증 세트 손실: 0.068599\t최선의 손실: 0.068599\t정확도: 98.05%\n",
-      "14\t검증 세트 손실: 0.076371\t최선의 손실: 0.068599\t정확도: 97.97%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.05, n_neurons=50, total=   3.8s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.098050\t최선의 손실: 0.098050\t정확도: 97.11%\n",
+      "1\t검증 세트 손실: 0.121009\t최선의 손실: 0.098050\t정확도: 96.48%\n",
+      "2\t검증 세트 손실: 0.082243\t최선의 손실: 0.082243\t정확도: 97.58%\n",
+      "3\t검증 세트 손실: 0.090979\t최선의 손실: 0.082243\t정확도: 97.65%\n",
+      "4\t검증 세트 손실: 0.069501\t최선의 손실: 0.069501\t정확도: 98.12%\n",
+      "5\t검증 세트 손실: 3.556787\t최선의 손실: 0.069501\t정확도: 87.80%\n",
+      "6\t검증 세트 손실: 0.889255\t최선의 손실: 0.069501\t정확도: 94.10%\n",
+      "7\t검증 세트 손실: 0.404884\t최선의 손실: 0.069501\t정확도: 94.84%\n",
+      "8\t검증 세트 손실: 0.319005\t최선의 손실: 0.069501\t정확도: 96.36%\n",
+      "9\t검증 세트 손실: 0.259473\t최선의 손실: 0.069501\t정확도: 95.23%\n",
+      "10\t검증 세트 손실: 0.317733\t최선의 손실: 0.069501\t정확도: 95.90%\n",
+      "11\t검증 세트 손실: 0.790930\t최선의 손실: 0.069501\t정확도: 93.71%\n",
+      "12\t검증 세트 손실: 0.200749\t최선의 손실: 0.069501\t정확도: 96.01%\n",
+      "13\t검증 세트 손실: 0.177936\t최선의 손실: 0.069501\t정확도: 96.44%\n",
+      "14\t검증 세트 손실: 0.253150\t최선의 손실: 0.069501\t정확도: 95.50%\n",
+      "15\t검증 세트 손실: 0.158879\t최선의 손실: 0.069501\t정확도: 96.95%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15\t검증 세트 손실: 0.079539\t최선의 손실: 0.068599\t정확도: 98.12%\n",
-      "16\t검증 세트 손실: 0.084038\t최선의 손실: 0.068599\t정확도: 98.12%\n",
-      "17\t검증 세트 손실: 0.076966\t최선의 손실: 0.068599\t정확도: 98.01%\n",
-      "18\t검증 세트 손실: 0.071891\t최선의 손실: 0.068599\t정확도: 98.24%\n",
-      "19\t검증 세트 손실: 0.117910\t최선의 손실: 0.068599\t정확도: 97.97%\n",
-      "20\t검증 세트 손실: 0.090368\t최선의 손실: 0.068599\t정확도: 98.24%\n",
-      "21\t검증 세트 손실: 0.085518\t최선의 손실: 0.068599\t정확도: 98.16%\n",
-      "22\t검증 세트 손실: 0.082713\t최선의 손실: 0.068599\t정확도: 98.08%\n",
-      "23\t검증 세트 손실: 0.100054\t최선의 손실: 0.068599\t정확도: 98.01%\n",
-      "24\t검증 세트 손실: 0.096238\t최선의 손실: 0.068599\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.111319\t최선의 손실: 0.068599\t정확도: 97.93%\n",
-      "26\t검증 세트 손실: 0.098903\t최선의 손실: 0.068599\t정확도: 97.93%\n",
-      "27\t검증 세트 손실: 0.100849\t최선의 손실: 0.068599\t정확도: 98.44%\n",
-      "28\t검증 세트 손실: 0.076003\t최선의 손실: 0.068599\t정확도: 98.44%\n",
-      "29\t검증 세트 손실: 0.083883\t최선의 손실: 0.068599\t정확도: 98.51%\n",
-      "30\t검증 세트 손실: 0.107264\t최선의 손실: 0.068599\t정확도: 98.08%\n",
-      "31\t검증 세트 손실: 0.086958\t최선의 손실: 0.068599\t정확도: 98.48%\n",
-      "32\t검증 세트 손실: 0.077220\t최선의 손실: 0.068599\t정확도: 98.32%\n",
-      "33\t검증 세트 손실: 0.085715\t최선의 손실: 0.068599\t정확도: 98.48%\n",
-      "34\t검증 세트 손실: 0.106794\t최선의 손실: 0.068599\t정확도: 98.36%\n",
+      "16\t검증 세트 손실: 0.144042\t최선의 손실: 0.069501\t정확도: 96.48%\n",
+      "17\t검증 세트 손실: 0.170115\t최선의 손실: 0.069501\t정확도: 97.03%\n",
+      "18\t검증 세트 손실: 0.140658\t최선의 손실: 0.069501\t정확도: 96.95%\n",
+      "19\t검증 세트 손실: 0.245837\t최선의 손실: 0.069501\t정확도: 94.21%\n",
+      "20\t검증 세트 손실: 0.180271\t최선의 손실: 0.069501\t정확도: 96.56%\n",
+      "21\t검증 세트 손실: 0.169416\t최선의 손실: 0.069501\t정확도: 96.33%\n",
+      "22\t검증 세트 손실: 0.203975\t최선의 손실: 0.069501\t정확도: 96.99%\n",
+      "23\t검증 세트 손실: 0.133772\t최선의 손실: 0.069501\t정확도: 96.99%\n",
+      "24\t검증 세트 손실: 0.446753\t최선의 손실: 0.069501\t정확도: 94.80%\n",
+      "25\t검증 세트 손실: 0.411719\t최선의 손실: 0.069501\t정확도: 95.58%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=50, total=   3.1s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.139597\t최선의 손실: 0.139597\t정확도: 95.70%\n",
-      "1\t검증 세트 손실: 0.106883\t최선의 손실: 0.106883\t정확도: 97.19%\n",
-      "2\t검증 세트 손실: 177.867950\t최선의 손실: 0.106883\t정확도: 19.12%\n",
-      "3\t검증 세트 손실: 0.603640\t최선의 손실: 0.106883\t정확도: 93.98%\n",
-      "4\t검증 세트 손실: 0.267589\t최선의 손실: 0.106883\t정확도: 95.78%\n",
-      "5\t검증 세트 손실: 0.187867\t최선의 손실: 0.106883\t정확도: 96.79%\n",
-      "6\t검증 세트 손실: 0.163603\t최선의 손실: 0.106883\t정확도: 97.19%\n",
-      "7\t검증 세트 손실: 0.195018\t최선의 손실: 0.106883\t정확도: 96.52%\n",
-      "8\t검증 세트 손실: 0.188671\t최선의 손실: 0.106883\t정확도: 96.17%\n",
-      "9\t검증 세트 손실: 0.167114\t최선의 손실: 0.106883\t정확도: 95.43%\n",
-      "10\t검증 세트 손실: 0.142075\t최선의 손실: 0.106883\t정확도: 96.64%\n",
-      "11\t검증 세트 손실: 0.230815\t최선의 손실: 0.106883\t정확도: 96.68%\n",
-      "12\t검증 세트 손실: 0.157506\t최선의 손실: 0.106883\t정확도: 96.56%\n",
-      "13\t검증 세트 손실: 0.181203\t최선의 손실: 0.106883\t정확도: 95.00%\n",
-      "14\t검증 세트 손실: 0.124207\t최선의 손실: 0.106883\t정확도: 97.34%\n",
-      "15\t검증 세트 손실: 0.107004\t최선의 손실: 0.106883\t정확도: 97.58%\n",
-      "16\t검증 세트 손실: 0.111620\t최선의 손실: 0.106883\t정확도: 97.15%\n",
-      "17\t검증 세트 손실: 0.113718\t최선의 손실: 0.106883\t정확도: 97.62%\n",
-      "18\t검증 세트 손실: 0.159602\t최선의 손실: 0.106883\t정확도: 95.93%\n",
-      "19\t검증 세트 손실: 0.116548\t최선의 손실: 0.106883\t정확도: 97.93%\n",
-      "20\t검증 세트 손실: 0.108821\t최선의 손실: 0.106883\t정확도: 97.89%\n",
-      "21\t검증 세트 손실: 0.113536\t최선의 손실: 0.106883\t정확도: 97.97%\n",
-      "22\t검증 세트 손실: 1.821871\t최선의 손실: 0.106883\t정확도: 78.89%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=70, total=   7.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.095429\t최선의 손실: 0.095429\t정확도: 97.11%\n",
+      "1\t검증 세트 손실: 0.091871\t최선의 손실: 0.091871\t정확도: 97.30%\n",
+      "2\t검증 세트 손실: 0.075460\t최선의 손실: 0.075460\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.118922\t최선의 손실: 0.075460\t정확도: 96.95%\n",
+      "4\t검증 세트 손실: 0.100172\t최선의 손실: 0.075460\t정확도: 97.30%\n",
+      "5\t검증 세트 손실: 4.459483\t최선의 손실: 0.075460\t정확도: 95.82%\n",
+      "6\t검증 세트 손실: 1.384358\t최선의 손실: 0.075460\t정확도: 95.97%\n",
+      "7\t검증 세트 손실: 1.083951\t최선의 손실: 0.075460\t정확도: 96.72%\n",
+      "8\t검증 세트 손실: 0.610901\t최선의 손실: 0.075460\t정확도: 96.87%\n",
+      "9\t검증 세트 손실: 0.850514\t최선의 손실: 0.075460\t정확도: 94.33%\n",
+      "10\t검증 세트 손실: 0.739289\t최선의 손실: 0.075460\t정확도: 96.76%\n",
+      "11\t검증 세트 손실: 0.597405\t최선의 손실: 0.075460\t정확도: 96.64%\n",
+      "12\t검증 세트 손실: 0.273587\t최선의 손실: 0.075460\t정확도: 97.26%\n",
+      "13\t검증 세트 손실: 0.314634\t최선의 손실: 0.075460\t정확도: 97.30%\n",
+      "14\t검증 세트 손실: 0.432981\t최선의 손실: 0.075460\t정확도: 96.64%\n",
+      "15\t검증 세트 손실: 0.344430\t최선의 손실: 0.075460\t정확도: 97.22%\n",
+      "16\t검증 세트 손실: 0.233093\t최선의 손실: 0.075460\t정확도: 97.19%\n",
+      "17\t검증 세트 손실: 0.296393\t최선의 손실: 0.075460\t정확도: 97.19%\n",
+      "18\t검증 세트 손실: 0.405623\t최선의 손실: 0.075460\t정확도: 96.79%\n",
+      "19\t검증 세트 손실: 0.373033\t최선의 손실: 0.075460\t정확도: 96.56%\n",
+      "20\t검증 세트 손실: 0.419655\t최선의 손실: 0.075460\t정확도: 97.73%\n",
+      "21\t검증 세트 손실: 0.368952\t최선의 손실: 0.075460\t정확도: 96.95%\n",
+      "22\t검증 세트 손실: 0.294249\t최선의 손실: 0.075460\t정확도: 98.01%\n",
+      "23\t검증 세트 손실: 0.289043\t최선의 손실: 0.075460\t정확도: 97.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=  17.3s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.102614\t최선의 손실: 0.102614\t정확도: 97.34%\n",
-      "1\t검증 세트 손실: 0.086442\t최선의 손실: 0.086442\t정확도: 97.58%\n",
-      "2\t검증 세트 손실: 0.092693\t최선의 손실: 0.086442\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.110322\t최선의 손실: 0.086442\t정확도: 97.19%\n",
-      "4\t검증 세트 손실: 0.072053\t최선의 손실: 0.072053\t정확도: 98.01%\n",
-      "5\t검증 세트 손실: 6.556969\t최선의 손실: 0.072053\t정확도: 94.57%\n",
-      "6\t검증 세트 손실: 0.544334\t최선의 손실: 0.072053\t정확도: 96.29%\n",
-      "7\t검증 세트 손실: 0.243270\t최선의 손실: 0.072053\t정확도: 96.40%\n",
-      "8\t검증 세트 손실: 0.217119\t최선의 손실: 0.072053\t정확도: 96.52%\n",
-      "9\t검증 세트 손실: 0.174145\t최선의 손실: 0.072053\t정확도: 97.11%\n",
-      "10\t검증 세트 손실: 0.148942\t최선의 손실: 0.072053\t정확도: 97.38%\n",
-      "11\t검증 세트 손실: 0.172448\t최선의 손실: 0.072053\t정확도: 97.54%\n",
-      "12\t검증 세트 손실: 0.141484\t최선의 손실: 0.072053\t정확도: 97.62%\n",
-      "13\t검증 세트 손실: 0.151800\t최선의 손실: 0.072053\t정확도: 97.38%\n",
-      "14\t검증 세트 손실: 0.122853\t최선의 손실: 0.072053\t정확도: 97.42%\n",
-      "15\t검증 세트 손실: 0.120691\t최선의 손실: 0.072053\t정확도: 97.19%\n",
-      "16\t검증 세트 손실: 0.523393\t최선의 손실: 0.072053\t정확도: 97.30%\n",
-      "17\t검증 세트 손실: 0.347528\t최선의 손실: 0.072053\t정확도: 96.56%\n",
-      "18\t검증 세트 손실: 0.221324\t최선의 손실: 0.072053\t정확도: 97.11%\n",
-      "19\t검증 세트 손실: 0.170370\t최선의 손실: 0.072053\t정확도: 97.11%\n",
-      "20\t검증 세트 손실: 0.196139\t최선의 손실: 0.072053\t정확도: 97.62%\n",
-      "21\t검증 세트 손실: 0.199942\t최선의 손실: 0.072053\t정확도: 97.54%\n",
-      "22\t검증 세트 손실: 0.165955\t최선의 손실: 0.072053\t정확도: 98.12%\n",
-      "23\t검증 세트 손실: 0.226891\t최선의 손실: 0.072053\t정확도: 97.62%\n",
-      "24\t검증 세트 손실: 0.237778\t최선의 손실: 0.072053\t정확도: 98.05%\n",
-      "25\t검증 세트 손실: 0.261576\t최선의 손실: 0.072053\t정확도: 97.62%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=70, total=   6.5s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.112196\t최선의 손실: 0.112196\t정확도: 97.50%\n",
+      "1\t검증 세트 손실: 0.111844\t최선의 손실: 0.111844\t정확도: 97.03%\n",
+      "2\t검증 세트 손실: 0.078418\t최선의 손실: 0.078418\t정확도: 97.85%\n",
+      "3\t검증 세트 손실: 0.090784\t최선의 손실: 0.078418\t정확도: 97.65%\n",
+      "4\t검증 세트 손실: 313.628784\t최선의 손실: 0.078418\t정확도: 55.94%\n",
+      "5\t검증 세트 손실: 2.176649\t최선의 손실: 0.078418\t정확도: 92.30%\n",
+      "6\t검증 세트 손실: 0.888484\t최선의 손실: 0.078418\t정확도: 94.84%\n",
+      "7\t검증 세트 손실: 0.541041\t최선의 손실: 0.078418\t정확도: 95.82%\n",
+      "8\t검증 세트 손실: 0.462705\t최선의 손실: 0.078418\t정확도: 95.31%\n",
+      "9\t검증 세트 손실: 0.574607\t최선의 손실: 0.078418\t정확도: 92.77%\n",
+      "10\t검증 세트 손실: 0.449993\t최선의 손실: 0.078418\t정확도: 95.39%\n",
+      "11\t검증 세트 손실: 0.766099\t최선의 손실: 0.078418\t정확도: 96.17%\n",
+      "12\t검증 세트 손실: 0.460248\t최선의 손실: 0.078418\t정확도: 95.54%\n",
+      "13\t검증 세트 손실: 0.666092\t최선의 손실: 0.078418\t정확도: 96.21%\n",
+      "14\t검증 세트 손실: 0.502363\t최선의 손실: 0.078418\t정확도: 95.93%\n",
+      "15\t검증 세트 손실: 0.628674\t최선의 손실: 0.078418\t정확도: 93.63%\n",
+      "16\t검증 세트 손실: 0.618984\t최선의 손실: 0.078418\t정확도: 95.86%\n",
+      "17\t검증 세트 손실: 0.376936\t최선의 손실: 0.078418\t정확도: 96.87%\n",
+      "18\t검증 세트 손실: 0.503911\t최선의 손실: 0.078418\t정확도: 96.40%\n",
+      "19\t검증 세트 손실: 0.496745\t최선의 손실: 0.078418\t정확도: 97.03%\n",
+      "20\t검증 세트 손실: 0.455397\t최선의 손실: 0.078418\t정확도: 96.87%\n",
+      "21\t검증 세트 손실: 0.585883\t최선의 손실: 0.078418\t정확도: 96.91%\n",
+      "22\t검증 세트 손실: 0.239998\t최선의 손실: 0.078418\t정확도: 97.15%\n",
+      "23\t검증 세트 손실: 0.262028\t최선의 손실: 0.078418\t정확도: 96.52%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=  12.1s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.102446\t최선의 손실: 0.102446\t정확도: 97.22%\n",
-      "1\t검증 세트 손실: 0.085459\t최선의 손실: 0.085459\t정확도: 97.42%\n",
-      "2\t검증 세트 손실: 16.349220\t최선의 손실: 0.085459\t정확도: 87.76%\n",
-      "3\t검증 세트 손실: 0.465897\t최선의 손실: 0.085459\t정확도: 96.21%\n",
-      "4\t검증 세트 손실: 0.397796\t최선의 손실: 0.085459\t정확도: 95.78%\n",
-      "5\t검증 세트 손실: 0.481536\t최선의 손실: 0.085459\t정확도: 94.68%\n",
-      "6\t검증 세트 손실: 0.196007\t최선의 손실: 0.085459\t정확도: 96.83%\n",
-      "7\t검증 세트 손실: 0.251098\t최선의 손실: 0.085459\t정확도: 95.78%\n",
-      "8\t검증 세트 손실: 0.191508\t최선의 손실: 0.085459\t정확도: 96.25%\n",
-      "9\t검증 세트 손실: 0.181342\t최선의 손실: 0.085459\t정확도: 96.29%\n",
-      "10\t검증 세트 손실: 0.189284\t최선의 손실: 0.085459\t정확도: 96.87%\n",
-      "11\t검증 세트 손실: 0.155376\t최선의 손실: 0.085459\t정확도: 97.19%\n",
-      "12\t검증 세트 손실: 0.146131\t최선의 손실: 0.085459\t정확도: 97.03%\n",
-      "13\t검증 세트 손실: 0.154825\t최선의 손실: 0.085459\t정확도: 97.22%\n",
-      "14\t검증 세트 손실: 0.165241\t최선의 손실: 0.085459\t정확도: 97.26%\n",
-      "15\t검증 세트 손실: 0.161821\t최선의 손실: 0.085459\t정확도: 97.26%\n",
-      "16\t검증 세트 손실: 0.171326\t최선의 손실: 0.085459\t정확도: 97.30%\n",
-      "17\t검증 세트 손실: 0.523251\t최선의 손실: 0.085459\t정확도: 94.45%\n",
-      "18\t검증 세트 손실: 0.263522\t최선의 손실: 0.085459\t정확도: 96.48%\n",
-      "19\t검증 세트 손실: 0.151020\t최선의 손실: 0.085459\t정확도: 97.11%\n",
-      "20\t검증 세트 손실: 0.177090\t최선의 손실: 0.085459\t정확도: 96.13%\n",
-      "21\t검증 세트 손실: 0.197114\t최선의 손실: 0.085459\t정확도: 97.26%\n",
-      "22\t검증 세트 손실: 0.115307\t최선의 손실: 0.085459\t정확도: 97.62%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.02, n_neurons=70, total=   6.8s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.575684\t최선의 손실: 0.575684\t정확도: 91.05%\n",
+      "1\t검증 세트 손실: 0.198513\t최선의 손실: 0.198513\t정확도: 95.70%\n",
+      "2\t검증 세트 손실: 1.369517\t최선의 손실: 0.198513\t정확도: 96.72%\n",
+      "3\t검증 세트 손실: 0.662526\t최선의 손실: 0.198513\t정확도: 95.74%\n",
+      "4\t검증 세트 손실: 2.235500\t최선의 손실: 0.198513\t정확도: 89.44%\n",
+      "5\t검증 세트 손실: 18.934570\t최선의 손실: 0.198513\t정확도: 92.81%\n",
+      "6\t검증 세트 손실: 1.405595\t최선의 손실: 0.198513\t정확도: 94.88%\n",
+      "7\t검증 세트 손실: 1.899426\t최선의 손실: 0.198513\t정확도: 96.48%\n",
+      "8\t검증 세트 손실: 112.991264\t최선의 손실: 0.198513\t정확도: 90.15%\n",
+      "9\t검증 세트 손실: 5.134137\t최선의 손실: 0.198513\t정확도: 96.56%\n",
+      "10\t검증 세트 손실: 2.259147\t최선의 손실: 0.198513\t정확도: 97.30%\n",
+      "11\t검증 세트 손실: 2.016696\t최선의 손실: 0.198513\t정확도: 96.13%\n",
+      "12\t검증 세트 손실: 1373.081055\t최선의 손실: 0.198513\t정확도: 84.95%\n",
+      "13\t검증 세트 손실: 19.274576\t최선의 손실: 0.198513\t정확도: 97.11%\n",
+      "14\t검증 세트 손실: 11.491969\t최선의 손실: 0.198513\t정확도: 96.99%\n",
+      "15\t검증 세트 손실: 18.545557\t최선의 손실: 0.198513\t정확도: 97.46%\n",
+      "16\t검증 세트 손실: 37.446453\t최선의 손실: 0.198513\t정확도: 96.33%\n",
+      "17\t검증 세트 손실: 15.326568\t최선의 손실: 0.198513\t정확도: 97.46%\n",
+      "18\t검증 세트 손실: 62.875854\t최선의 손실: 0.198513\t정확도: 96.79%\n",
+      "19\t검증 세트 손실: 33.146793\t최선의 손실: 0.198513\t정확도: 96.33%\n",
+      "20\t검증 세트 손실: 35.567013\t최선의 손실: 0.198513\t정확도: 95.35%\n",
+      "21\t검증 세트 손실: 98.442978\t최선의 손실: 0.198513\t정확도: 97.62%\n",
+      "22\t검증 세트 손실: 26.157816\t최선의 손실: 0.198513\t정확도: 95.97%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=70, total=   6.3s\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.390774\t최선의 손실: 0.390774\t정확도: 91.71%\n",
-      "1\t검증 세트 손실: 0.274003\t최선의 손실: 0.274003\t정확도: 93.24%\n",
-      "2\t검증 세트 손실: 7.962043\t최선의 손실: 0.274003\t정확도: 94.10%\n",
-      "3\t검증 세트 손실: 2.153220\t최선의 손실: 0.274003\t정확도: 96.40%\n",
-      "4\t검증 세트 손실: 1.464430\t최선의 손실: 0.274003\t정확도: 95.70%\n",
-      "5\t검증 세트 손실: 0.488308\t최선의 손실: 0.274003\t정확도: 96.56%\n",
-      "6\t검증 세트 손실: 5.521015\t최선의 손실: 0.274003\t정확도: 94.25%\n",
-      "7\t검증 세트 손실: 1.174495\t최선의 손실: 0.274003\t정확도: 95.66%\n",
-      "8\t검증 세트 손실: 1.214483\t최선의 손실: 0.274003\t정확도: 95.90%\n",
-      "9\t검증 세트 손실: 4.020058\t최선의 손실: 0.274003\t정확도: 95.54%\n",
-      "10\t검증 세트 손실: 1.972624\t최선의 손실: 0.274003\t정확도: 96.05%\n",
-      "11\t검증 세트 손실: 5.653223\t최선의 손실: 0.274003\t정확도: 96.68%\n",
-      "12\t검증 세트 손실: 2.346063\t최선의 손실: 0.274003\t정확도: 96.68%\n",
-      "13\t검증 세트 손실: 26.148129\t최선의 손실: 0.274003\t정확도: 96.56%\n",
-      "14\t검증 세트 손실: 8.486547\t최선의 손실: 0.274003\t정확도: 96.52%\n",
-      "15\t검증 세트 손실: 6.314254\t최선의 손실: 0.274003\t정확도: 96.72%\n",
-      "16\t검증 세트 손실: 643.986511\t최선의 손실: 0.274003\t정확도: 91.44%\n",
-      "17\t검증 세트 손실: 36.965202\t최선의 손실: 0.274003\t정확도: 93.67%\n",
-      "18\t검증 세트 손실: 22.680779\t최선의 손실: 0.274003\t정확도: 97.07%\n",
-      "19\t검증 세트 손실: 8.213774\t최선의 손실: 0.274003\t정확도: 97.46%\n",
-      "20\t검증 세트 손실: 22.481865\t최선의 손실: 0.274003\t정확도: 93.04%\n",
-      "21\t검증 세트 손실: 132.578873\t최선의 손실: 0.274003\t정확도: 91.05%\n",
-      "22\t검증 세트 손실: 27.266813\t최선의 손실: 0.274003\t정확도: 90.58%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=90, total=  51.9s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.157493\t최선의 손실: 0.157493\t정확도: 95.58%\n",
+      "1\t검증 세트 손실: 2.944275\t최선의 손실: 0.157493\t정확도: 94.29%\n",
+      "2\t검증 세트 손실: 0.581938\t최선의 손실: 0.157493\t정확도: 95.07%\n",
+      "3\t검증 세트 손실: 0.294728\t최선의 손실: 0.157493\t정확도: 95.86%\n",
+      "4\t검증 세트 손실: 7.793715\t최선의 손실: 0.157493\t정확도: 95.50%\n",
+      "5\t검증 세트 손실: 1.583940\t최선의 손실: 0.157493\t정확도: 96.95%\n",
+      "6\t검증 세트 손실: 10.767104\t최선의 손실: 0.157493\t정확도: 95.39%\n",
+      "7\t검증 세트 손실: 5.439963\t최선의 손실: 0.157493\t정확도: 95.62%\n",
+      "8\t검증 세트 손실: 9.976748\t최선의 손실: 0.157493\t정확도: 91.99%\n",
+      "9\t검증 세트 손실: 11.453062\t최선의 손실: 0.157493\t정확도: 94.29%\n",
+      "10\t검증 세트 손실: 1.557506\t최선의 손실: 0.157493\t정확도: 96.17%\n",
+      "11\t검증 세트 손실: 32.106136\t최선의 손실: 0.157493\t정확도: 95.31%\n",
+      "12\t검증 세트 손실: 3.802172\t최선의 손실: 0.157493\t정확도: 96.83%\n",
+      "13\t검증 세트 손실: 4.084211\t최선의 손실: 0.157493\t정확도: 95.54%\n",
+      "14\t검증 세트 손실: 4.434893\t최선의 손실: 0.157493\t정확도: 94.80%\n",
+      "15\t검증 세트 손실: 11.291752\t최선의 손실: 0.157493\t정확도: 94.06%\n",
+      "16\t검증 세트 손실: 36.411655\t최선의 손실: 0.157493\t정확도: 80.26%\n",
+      "17\t검증 세트 손실: 23.102913\t최선의 손실: 0.157493\t정확도: 96.05%\n",
+      "18\t검증 세트 손실: 19.616882\t최선의 손실: 0.157493\t정확도: 97.58%\n",
+      "19\t검증 세트 손실: 17.267912\t최선의 손실: 0.157493\t정확도: 97.42%\n",
+      "20\t검증 세트 손실: 89.420662\t최선의 손실: 0.157493\t정확도: 95.66%\n",
+      "21\t검증 세트 손실: 40.262978\t최선의 손실: 0.157493\t정확도: 97.62%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90, total= 1.8min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1.825121\t최선의 손실: 1.825121\t정확도: 93.98%\n",
-      "1\t검증 세트 손실: 1.411333\t최선의 손실: 1.411333\t정확도: 89.56%\n",
-      "2\t검증 세트 손실: 159.956833\t최선의 손실: 1.411333\t정확도: 78.07%\n",
-      "3\t검증 세트 손실: 0.257276\t최선의 손실: 0.257276\t정확도: 95.82%\n",
-      "4\t검증 세트 손실: 0.372907\t최선의 손실: 0.257276\t정확도: 94.57%\n",
-      "5\t검증 세트 손실: 5.488899\t최선의 손실: 0.257276\t정확도: 95.39%\n",
-      "6\t검증 세트 손실: 0.769424\t최선의 손실: 0.257276\t정확도: 96.76%\n",
-      "7\t검증 세트 손실: 147.564529\t최선의 손실: 0.257276\t정확도: 88.19%\n",
-      "8\t검증 세트 손실: 3.901420\t최선의 손실: 0.257276\t정확도: 94.80%\n",
-      "9\t검증 세트 손실: 2.723315\t최선의 손실: 0.257276\t정확도: 95.58%\n",
-      "10\t검증 세트 손실: 65.757637\t최선의 손실: 0.257276\t정확도: 88.08%\n",
-      "11\t검증 세트 손실: 7.617427\t최선의 손실: 0.257276\t정확도: 96.76%\n",
-      "12\t검증 세트 손실: 7.766799\t최선의 손실: 0.257276\t정확도: 95.58%\n",
-      "13\t검증 세트 손실: 7.725477\t최선의 손실: 0.257276\t정확도: 94.68%\n",
-      "14\t검증 세트 손실: 67.241936\t최선의 손실: 0.257276\t정확도: 95.23%\n",
-      "15\t검증 세트 손실: 5.936694\t최선의 손실: 0.257276\t정확도: 96.09%\n",
-      "16\t검증 세트 손실: 12.767251\t최선의 손실: 0.257276\t정확도: 94.92%\n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=90, total=  49.4s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.154444\t최선의 손실: 0.154444\t정확도: 96.17%\n",
+      "1\t검증 세트 손실: 0.149705\t최선의 손실: 0.149705\t정확도: 96.21%\n",
+      "2\t검증 세트 손실: 4.493134\t최선의 손실: 0.149705\t정확도: 91.75%\n",
+      "3\t검증 세트 손실: 0.870666\t최선의 손실: 0.149705\t정확도: 96.83%\n",
+      "4\t검증 세트 손실: 4.619765\t최선의 손실: 0.149705\t정확도: 94.02%\n",
+      "5\t검증 세트 손실: 0.862156\t최선의 손실: 0.149705\t정확도: 96.25%\n",
+      "6\t검증 세트 손실: 1.252086\t최선의 손실: 0.149705\t정확도: 96.72%\n",
+      "7\t검증 세트 손실: 11.049644\t최선의 손실: 0.149705\t정확도: 93.90%\n",
+      "8\t검증 세트 손실: 3.773036\t최선의 손실: 0.149705\t정확도: 94.61%\n",
+      "9\t검증 세트 손실: 15.751504\t최선의 손실: 0.149705\t정확도: 93.63%\n",
+      "10\t검증 세트 손실: 7.203225\t최선의 손실: 0.149705\t정확도: 96.68%\n",
+      "11\t검증 세트 손실: 3.495100\t최선의 손실: 0.149705\t정확도: 97.19%\n",
+      "12\t검증 세트 손실: 2.774927\t최선의 손실: 0.149705\t정확도: 95.90%\n",
+      "13\t검증 세트 손실: 10.205169\t최선의 손실: 0.149705\t정확도: 97.38%\n",
+      "14\t검증 세트 손실: 128.774429\t최선의 손실: 0.149705\t정확도: 95.86%\n",
+      "15\t검증 세트 손실: 18.837996\t최선의 손실: 0.149705\t정확도: 96.72%\n",
+      "16\t검증 세트 손실: 11.180440\t최선의 손실: 0.149705\t정확도: 97.58%\n",
+      "17\t검증 세트 손실: 7.504907\t최선의 손실: 0.149705\t정확도: 95.78%\n",
+      "18\t검증 세트 손실: 24.789751\t최선의 손실: 0.149705\t정확도: 97.11%\n",
+      "19\t검증 세트 손실: 27.146902\t최선의 손실: 0.149705\t정확도: 97.42%\n",
+      "20\t검증 세트 손실: 21.985619\t최선의 손실: 0.149705\t정확도: 97.19%\n",
+      "21\t검증 세트 손실: 68.813683\t최선의 손실: 0.149705\t정확도: 95.15%\n",
+      "22\t검증 세트 손실: 49.376770\t최선의 손실: 0.149705\t정확도: 95.00%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=90, total=  52.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=160 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "17\t검증 세트 손실: 37.190468\t최선의 손실: 0.257276\t정확도: 96.17%\n",
-      "18\t검증 세트 손실: 100.990433\t최선의 손실: 0.257276\t정확도: 95.04%\n",
-      "19\t검증 세트 손실: 26.503275\t최선의 손실: 0.257276\t정확도: 95.00%\n",
-      "20\t검증 세트 손실: 36.062477\t최선의 손실: 0.257276\t정확도: 97.77%\n",
-      "21\t검증 세트 손실: 11.017694\t최선의 손실: 0.257276\t정확도: 97.15%\n",
-      "22\t검증 세트 손실: 17.413185\t최선의 손실: 0.257276\t정확도: 96.25%\n",
-      "23\t검증 세트 손실: 51.923813\t최선의 손실: 0.257276\t정확도: 96.17%\n",
-      "24\t검증 세트 손실: 74.670090\t최선의 손실: 0.257276\t정확도: 93.16%\n",
+      "0\t검증 세트 손실: 17.200743\t최선의 손실: 17.200743\t정확도: 95.74%\n",
+      "1\t검증 세트 손실: 14.253987\t최선의 손실: 14.253987\t정확도: 95.97%\n",
+      "2\t검증 세트 손실: 5.206242\t최선의 손실: 5.206242\t정확도: 96.36%\n",
+      "3\t검증 세트 손실: 8.541135\t최선의 손실: 5.206242\t정확도: 95.31%\n",
+      "4\t검증 세트 손실: 15893812.000000\t최선의 손실: 5.206242\t정확도: 58.84%\n",
+      "5\t검증 세트 손실: 472927.375000\t최선의 손실: 5.206242\t정확도: 92.18%\n",
+      "6\t검증 세트 손실: 438728.750000\t최선의 손실: 5.206242\t정확도: 91.13%\n",
+      "7\t검증 세트 손실: 270036.406250\t최선의 손실: 5.206242\t정확도: 93.67%\n",
+      "8\t검증 세트 손실: 249133.562500\t최선의 손실: 5.206242\t정확도: 92.46%\n",
+      "9\t검증 세트 손실: 225087.406250\t최선의 손실: 5.206242\t정확도: 92.22%\n",
+      "10\t검증 세트 손실: 143029.937500\t최선의 손실: 5.206242\t정확도: 95.11%\n",
+      "11\t검증 세트 손실: 119648.921875\t최선의 손실: 5.206242\t정확도: 94.45%\n",
+      "12\t검증 세트 손실: 157800.843750\t최선의 손실: 5.206242\t정확도: 95.04%\n",
+      "13\t검증 세트 손실: 158027.390625\t최선의 손실: 5.206242\t정확도: 93.20%\n",
+      "14\t검증 세트 손실: 87231.921875\t최선의 손실: 5.206242\t정확도: 96.05%\n",
+      "15\t검증 세트 손실: 370445.281250\t최선의 손실: 5.206242\t정확도: 84.64%\n",
+      "16\t검증 세트 손실: 107828.867188\t최선의 손실: 5.206242\t정확도: 93.59%\n",
+      "17\t검증 세트 손실: 100835.015625\t최선의 손실: 5.206242\t정확도: 94.92%\n",
+      "18\t검증 세트 손실: 98477.226562\t최선의 손실: 5.206242\t정확도: 94.02%\n",
+      "19\t검증 세트 손실: 128852.343750\t최선의 손실: 5.206242\t정확도: 92.65%\n",
+      "20\t검증 세트 손실: 77158.421875\t최선의 손실: 5.206242\t정확도: 95.23%\n",
+      "21\t검증 세트 손실: 70546.921875\t최선의 손실: 5.206242\t정확도: 96.33%\n",
+      "22\t검증 세트 손실: 61966.484375\t최선의 손실: 5.206242\t정확도: 95.47%\n",
+      "23\t검증 세트 손실: 42823.332031\t최선의 손실: 5.206242\t정확도: 95.82%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90, total= 2.0min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.123462\t최선의 손실: 0.123462\t정확도: 96.44%\n",
-      "1\t검증 세트 손실: 0.130036\t최선의 손실: 0.123462\t정확도: 97.19%\n",
-      "2\t검증 세트 손실: 1.331930\t최선의 손실: 0.123462\t정확도: 95.54%\n",
-      "3\t검증 세트 손실: 0.682557\t최선의 손실: 0.123462\t정확도: 94.53%\n",
-      "4\t검증 세트 손실: 811.273804\t최선의 손실: 0.123462\t정확도: 89.80%\n",
-      "5\t검증 세트 손실: 2.688985\t최선의 손실: 0.123462\t정확도: 96.60%\n",
-      "6\t검증 세트 손실: 51.836357\t최선의 손실: 0.123462\t정확도: 85.46%\n",
-      "7\t검증 세트 손실: 1.497834\t최선의 손실: 0.123462\t정확도: 96.83%\n",
-      "8\t검증 세트 손실: 15.357615\t최선의 손실: 0.123462\t정확도: 96.52%\n",
-      "9\t검증 세트 손실: 6.811410\t최선의 손실: 0.123462\t정확도: 97.38%\n",
-      "10\t검증 세트 손실: 3.986437\t최선의 손실: 0.123462\t정확도: 96.25%\n",
-      "11\t검증 세트 손실: 65.692848\t최선의 손실: 0.123462\t정확도: 94.84%\n",
-      "12\t검증 세트 손실: 38.177429\t최선의 손실: 0.123462\t정확도: 97.03%\n",
-      "13\t검증 세트 손실: 58.058815\t최선의 손실: 0.123462\t정확도: 96.79%\n",
-      "14\t검증 세트 손실: 38.789989\t최선의 손실: 0.123462\t정확도: 95.74%\n",
-      "15\t검증 세트 손실: 21.427187\t최선의 손실: 0.123462\t정확도: 97.73%\n",
-      "16\t검증 세트 손실: 39.031902\t최선의 손실: 0.123462\t정확도: 97.62%\n",
-      "17\t검증 세트 손실: 19.691397\t최선의 손실: 0.123462\t정확도: 96.68%\n",
-      "18\t검증 세트 손실: 14.453270\t최선의 손실: 0.123462\t정확도: 97.65%\n",
-      "19\t검증 세트 손실: 50.139332\t최선의 손실: 0.123462\t정확도: 97.46%\n",
-      "20\t검증 세트 손실: 31.654808\t최선의 손실: 0.123462\t정확도: 96.87%\n",
-      "21\t검증 세트 손실: 66.504585\t최선의 손실: 0.123462\t정확도: 96.79%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=160, total=   7.0s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=160 \n",
+      "0\t검증 세트 손실: 29.554945\t최선의 손실: 29.554945\t정확도: 95.58%\n",
+      "1\t검증 세트 손실: 13.937226\t최선의 손실: 13.937226\t정확도: 95.27%\n",
+      "2\t검증 세트 손실: 7.242129\t최선의 손실: 7.242129\t정확도: 96.01%\n",
+      "3\t검증 세트 손실: 8.979114\t최선의 손실: 7.242129\t정확도: 95.54%\n",
+      "4\t검증 세트 손실: 6.077828\t최선의 손실: 6.077828\t정확도: 95.11%\n",
+      "5\t검증 세트 손실: 8.596805\t최선의 손실: 6.077828\t정확도: 94.25%\n",
+      "6\t검증 세트 손실: 2.560314\t최선의 손실: 2.560314\t정확도: 96.79%\n",
+      "7\t검증 세트 손실: 3.615873\t최선의 손실: 2.560314\t정확도: 97.11%\n",
+      "8\t검증 세트 손실: 3.827514\t최선의 손실: 2.560314\t정확도: 96.52%\n",
+      "9\t검증 세트 손실: 1.723601\t최선의 손실: 1.723601\t정확도: 96.36%\n",
+      "10\t검증 세트 손실: 1.404683\t최선의 손실: 1.404683\t정확도: 97.38%\n",
+      "11\t검증 세트 손실: 4.070507\t최선의 손실: 1.404683\t정확도: 97.03%\n",
+      "12\t검증 세트 손실: 7296493.000000\t최선의 손실: 1.404683\t정확도: 89.95%\n",
+      "13\t검증 세트 손실: 4265878.500000\t최선의 손실: 1.404683\t정확도: 88.12%\n",
+      "14\t검증 세트 손실: 2050702.125000\t최선의 손실: 1.404683\t정확도: 89.44%\n",
+      "15\t검증 세트 손실: 1480763.375000\t최선의 손실: 1.404683\t정확도: 94.14%\n",
+      "16\t검증 세트 손실: 1228521.625000\t최선의 손실: 1.404683\t정확도: 94.57%\n",
+      "17\t검증 세트 손실: 1054135.750000\t최선의 손실: 1.404683\t정확도: 93.28%\n",
+      "18\t검증 세트 손실: 541723.125000\t최선의 손실: 1.404683\t정확도: 95.70%\n",
+      "19\t검증 세트 손실: 592967.125000\t최선의 손실: 1.404683\t정확도: 94.96%\n",
+      "20\t검증 세트 손실: 655705.250000\t최선의 손실: 1.404683\t정확도: 95.39%\n",
+      "21\t검증 세트 손실: 461276.718750\t최선의 손실: 1.404683\t정확도: 96.29%\n",
+      "22\t검증 세트 손실: 449558.406250\t최선의 손실: 1.404683\t정확도: 95.50%\n",
+      "23\t검증 세트 손실: 382192.750000\t최선의 손실: 1.404683\t정확도: 96.44%\n",
+      "24\t검증 세트 손실: 420873.562500\t최선의 손실: 1.404683\t정확도: 96.33%\n",
+      "25\t검증 세트 손실: 486280.156250\t최선의 손실: 1.404683\t정확도: 96.29%\n",
+      "26\t검증 세트 손실: 475292.562500\t최선의 손실: 1.404683\t정확도: 94.96%\n",
+      "27\t검증 세트 손실: 334601.593750\t최선의 손실: 1.404683\t정확도: 96.17%\n",
+      "28\t검증 세트 손실: 299380.718750\t최선의 손실: 1.404683\t정확도: 95.74%\n",
+      "29\t검증 세트 손실: 295286.000000\t최선의 손실: 1.404683\t정확도: 96.83%\n",
+      "30\t검증 세트 손실: 379212.406250\t최선의 손실: 1.404683\t정확도: 95.62%\n",
+      "31\t검증 세트 손실: 236672.843750\t최선의 손실: 1.404683\t정확도: 96.76%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90, total= 2.1min\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 128.369171\t최선의 손실: 128.369171\t정확도: 74.51%\n",
-      "1\t검증 세트 손실: 8.494885\t최선의 손실: 8.494885\t정확도: 93.67%\n",
-      "2\t검증 세트 손실: 6.093445\t최선의 손실: 6.093445\t정확도: 92.42%\n",
-      "3\t검증 세트 손실: 5.135828\t최선의 손실: 5.135828\t정확도: 96.05%\n",
-      "4\t검증 세트 손실: 3.286034\t최선의 손실: 3.286034\t정확도: 94.61%\n",
-      "5\t검증 세트 손실: 30.980230\t최선의 손실: 3.286034\t정확도: 94.14%\n",
-      "6\t검증 세트 손실: 2.885873\t최선의 손실: 2.885873\t정확도: 95.78%\n",
-      "7\t검증 세트 손실: 1.862644\t최선의 손실: 1.862644\t정확도: 95.66%\n",
-      "8\t검증 세트 손실: 1.383299\t최선의 손실: 1.383299\t정확도: 96.72%\n",
-      "9\t검증 세트 손실: 1.159837\t최선의 손실: 1.159837\t정확도: 95.31%\n",
-      "10\t검증 세트 손실: 1.938892\t최선의 손실: 1.159837\t정확도: 95.11%\n",
-      "11\t검증 세트 손실: 28.134012\t최선의 손실: 1.159837\t정확도: 88.31%\n",
-      "12\t검증 세트 손실: 904812.687500\t최선의 손실: 1.159837\t정확도: 84.25%\n",
-      "13\t검증 세트 손실: 468843.625000\t최선의 손실: 1.159837\t정확도: 85.73%\n",
-      "14\t검증 세트 손실: 286814.593750\t최선의 손실: 1.159837\t정확도: 92.10%\n",
-      "15\t검증 세트 손실: 291412.156250\t최선의 손실: 1.159837\t정확도: 92.65%\n",
-      "16\t검증 세트 손실: 211564.234375\t최선의 손실: 1.159837\t정확도: 92.46%\n",
-      "17\t검증 세트 손실: 265793.593750\t최선의 손실: 1.159837\t정확도: 88.08%\n",
-      "18\t검증 세트 손실: 151061.890625\t최선의 손실: 1.159837\t정확도: 92.26%\n",
-      "19\t검증 세트 손실: 113265.562500\t최선의 손실: 1.159837\t정확도: 94.45%\n",
-      "20\t검증 세트 손실: 103292.656250\t최선의 손실: 1.159837\t정확도: 94.37%\n",
-      "21\t검증 세트 손실: 174068.093750\t최선의 손실: 1.159837\t정확도: 93.08%\n",
-      "22\t검증 세트 손실: 107886.460938\t최선의 손실: 1.159837\t정확도: 94.49%\n",
-      "23\t검증 세트 손실: 101017.296875\t최선의 손실: 1.159837\t정확도: 94.92%\n",
-      "24\t검증 세트 손실: 129782.742188\t최선의 손실: 1.159837\t정확도: 92.14%\n",
-      "25\t검증 세트 손실: 122373.351562\t최선의 손실: 1.159837\t정확도: 93.04%\n",
-      "26\t검증 세트 손실: 136155.078125\t최선의 손실: 1.159837\t정확도: 93.16%\n",
-      "27\t검증 세트 손실: 91902.890625\t최선의 손실: 1.159837\t정확도: 95.07%\n",
-      "28\t검증 세트 손실: 73012.179688\t최선의 손실: 1.159837\t정확도: 94.45%\n",
-      "29\t검증 세트 손실: 123694.601562\t최선의 손실: 1.159837\t정확도: 93.20%\n",
-      "30\t검증 세트 손실: 53242.046875\t최선의 손실: 1.159837\t정확도: 95.97%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=160, total=   9.6s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=160 \n",
+      "0\t검증 세트 손실: 116.743477\t최선의 손실: 116.743477\t정확도: 94.61%\n",
+      "1\t검증 세트 손실: 30.564833\t최선의 손실: 30.564833\t정확도: 95.39%\n",
+      "2\t검증 세트 손실: 26.874285\t최선의 손실: 26.874285\t정확도: 96.25%\n",
+      "3\t검증 세트 손실: 18.797020\t최선의 손실: 18.797020\t정확도: 96.29%\n",
+      "4\t검증 세트 손실: 16.209648\t최선의 손실: 16.209648\t정확도: 95.62%\n",
+      "5\t검증 세트 손실: 16.517054\t최선의 손실: 16.209648\t정확도: 96.72%\n",
+      "6\t검증 세트 손실: 19.190168\t최선의 손실: 16.209648\t정확도: 96.05%\n",
+      "7\t검증 세트 손실: 21.205551\t최선의 손실: 16.209648\t정확도: 95.70%\n",
+      "8\t검증 세트 손실: 19.361776\t최선의 손실: 16.209648\t정확도: 97.42%\n",
+      "9\t검증 세트 손실: 24.419006\t최선의 손실: 16.209648\t정확도: 96.68%\n",
+      "10\t검증 세트 손실: 26.337101\t최선의 손실: 16.209648\t정확도: 97.34%\n",
+      "11\t검증 세트 손실: 21.814873\t최선의 손실: 16.209648\t정확도: 97.50%\n",
+      "12\t검증 세트 손실: 21.433725\t최선의 손실: 16.209648\t정확도: 97.11%\n",
+      "13\t검증 세트 손실: 20.486774\t최선의 손실: 16.209648\t정확도: 95.54%\n",
+      "14\t검증 세트 손실: 23.209517\t최선의 손실: 16.209648\t정확도: 96.87%\n",
+      "15\t검증 세트 손실: 20.408636\t최선의 손실: 16.209648\t정확도: 97.89%\n",
+      "16\t검증 세트 손실: 19.918737\t최선의 손실: 16.209648\t정확도: 97.22%\n",
+      "17\t검증 세트 손실: 10.074617\t최선의 손실: 10.074617\t정확도: 97.46%\n",
+      "18\t검증 세트 손실: 3.541973\t최선의 손실: 3.541973\t정확도: 98.08%\n",
+      "19\t검증 세트 손실: 2.308402\t최선의 손실: 2.308402\t정확도: 98.12%\n",
+      "20\t검증 세트 손실: 12.178642\t최선의 손실: 2.308402\t정확도: 97.50%\n",
+      "21\t검증 세트 손실: 118.751663\t최선의 손실: 2.308402\t정확도: 96.29%\n",
+      "22\t검증 세트 손실: 5090885.000000\t최선의 손실: 2.308402\t정확도: 91.52%\n",
+      "23\t검증 세트 손실: 579070.687500\t최선의 손실: 2.308402\t정확도: 93.12%\n",
+      "24\t검증 세트 손실: 499409.968750\t최선의 손실: 2.308402\t정확도: 92.85%\n",
+      "25\t검증 세트 손실: 328022.531250\t최선의 손실: 2.308402\t정확도: 94.29%\n",
+      "26\t검증 세트 손실: 381176.781250\t최선의 손실: 2.308402\t정확도: 93.82%\n",
+      "27\t검증 세트 손실: 219421.140625\t최선의 손실: 2.308402\t정확도: 95.27%\n",
+      "28\t검증 세트 손실: 235648.578125\t최선의 손실: 2.308402\t정확도: 94.06%\n",
+      "29\t검증 세트 손실: 482583.312500\t최선의 손실: 2.308402\t정확도: 90.15%\n",
+      "30\t검증 세트 손실: 329153.062500\t최선의 손실: 2.308402\t정확도: 90.77%\n",
+      "31\t검증 세트 손실: 206714.390625\t최선의 손실: 2.308402\t정확도: 94.45%\n",
+      "32\t검증 세트 손실: 112242.187500\t최선의 손실: 2.308402\t정확도: 96.44%\n",
+      "33\t검증 세트 손실: 84812.335938\t최선의 손실: 2.308402\t정확도: 96.05%\n",
+      "34\t검증 세트 손실: 590410.812500\t최선의 손실: 2.308402\t정확도: 85.22%\n",
+      "35\t검증 세트 손실: 1251438.625000\t최선의 손실: 2.308402\t정확도: 88.00%\n",
+      "36\t검증 세트 손실: 419227.875000\t최선의 손실: 2.308402\t정확도: 93.67%\n",
+      "37\t검증 세트 손실: 458831.250000\t최선의 손실: 2.308402\t정확도: 94.33%\n",
+      "38\t검증 세트 손실: 196684.703125\t최선의 손실: 2.308402\t정확도: 95.82%\n",
+      "39\t검증 세트 손실: 132196.625000\t최선의 손실: 2.308402\t정확도: 96.83%\n",
+      "40\t검증 세트 손실: 148503.468750\t최선의 손실: 2.308402\t정확도: 96.68%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=160, total=  19.4s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 495.431183\t최선의 손실: 495.431183\t정확도: 88.66%\n",
-      "1\t검증 세트 손실: 92.563553\t최선의 손실: 92.563553\t정확도: 94.02%\n",
-      "2\t검증 세트 손실: 118.078979\t최선의 손실: 92.563553\t정확도: 94.02%\n",
-      "3\t검증 세트 손실: 73.097351\t최선의 손실: 73.097351\t정확도: 94.57%\n",
-      "4\t검증 세트 손실: 44.249088\t최선의 손실: 44.249088\t정확도: 96.72%\n",
-      "5\t검증 세트 손실: 81.988220\t최선의 손실: 44.249088\t정확도: 91.28%\n",
-      "6\t검증 세트 손실: 408.362152\t최선의 손실: 44.249088\t정확도: 88.35%\n",
-      "7\t검증 세트 손실: 77.312637\t최선의 손실: 44.249088\t정확도: 96.87%\n",
-      "8\t검증 세트 손실: 87.770508\t최선의 손실: 44.249088\t정확도: 94.96%\n",
-      "9\t검증 세트 손실: 31.523285\t최선의 손실: 31.523285\t정확도: 96.29%\n",
-      "10\t검증 세트 손실: 20.678562\t최선의 손실: 20.678562\t정확도: 96.83%\n",
-      "11\t검증 세트 손실: 33080980.000000\t최선의 손실: 20.678562\t정확도: 75.25%\n",
-      "12\t검증 세트 손실: 6077034.000000\t최선의 손실: 20.678562\t정확도: 92.69%\n",
-      "13\t검증 세트 손실: 7761286.500000\t최선의 손실: 20.678562\t정확도: 92.34%\n",
-      "14\t검증 세트 손실: 4842542.500000\t최선의 손실: 20.678562\t정확도: 93.51%\n",
-      "15\t검증 세트 손실: 1524424.125000\t최선의 손실: 20.678562\t정확도: 94.02%\n",
-      "16\t검증 세트 손실: 604992.875000\t최선의 손실: 20.678562\t정확도: 94.92%\n",
-      "17\t검증 세트 손실: 556327.187500\t최선의 손실: 20.678562\t정확도: 95.07%\n",
-      "18\t검증 세트 손실: 967908.187500\t최선의 손실: 20.678562\t정확도: 93.35%\n",
-      "19\t검증 세트 손실: 454266.781250\t최선의 손실: 20.678562\t정확도: 95.90%\n",
-      "20\t검증 세트 손실: 498216.187500\t최선의 손실: 20.678562\t정확도: 95.27%\n",
-      "21\t검증 세트 손실: 418752.500000\t최선의 손실: 20.678562\t정확도: 96.52%\n",
-      "22\t검증 세트 손실: 531750.875000\t최선의 손실: 20.678562\t정확도: 93.78%\n",
-      "23\t검증 세트 손실: 2517796.500000\t최선의 손실: 20.678562\t정확도: 91.05%\n",
-      "24\t검증 세트 손실: 1058208.375000\t최선의 손실: 20.678562\t정확도: 92.85%\n",
-      "25\t검증 세트 손실: 359224.656250\t최선의 손실: 20.678562\t정확도: 96.99%\n",
-      "26\t검증 세트 손실: 390875.031250\t최선의 손실: 20.678562\t정확도: 95.23%\n",
-      "27\t검증 세트 손실: 315813.843750\t최선의 손실: 20.678562\t정확도: 96.83%\n",
-      "28\t검증 세트 손실: 301325.093750\t최선의 손실: 20.678562\t정확도: 96.56%\n",
-      "29\t검증 세트 손실: 508952.218750\t최선의 손실: 20.678562\t정확도: 95.35%\n",
-      "30\t검증 세트 손실: 225302.421875\t최선의 손실: 20.678562\t정확도: 97.03%\n",
-      "31\t검증 세트 손실: 538931.812500\t최선의 손실: 20.678562\t정확도: 95.27%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=160, total=  11.4s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.113342\t최선의 손실: 0.113342\t정확도: 96.56%\n",
+      "1\t검증 세트 손실: 0.080433\t최선의 손실: 0.080433\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.071787\t최선의 손실: 0.071787\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.096215\t최선의 손실: 0.071787\t정확도: 97.65%\n",
+      "4\t검증 세트 손실: 0.084797\t최선의 손실: 0.071787\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 4.327129\t최선의 손실: 0.071787\t정확도: 38.78%\n",
+      "6\t검증 세트 손실: 0.715438\t최선의 손실: 0.071787\t정확도: 67.94%\n",
+      "7\t검증 세트 손실: 0.419203\t최선의 손실: 0.071787\t정확도: 90.58%\n",
+      "8\t검증 세트 손실: 0.411197\t최선의 손실: 0.071787\t정확도: 92.85%\n",
+      "9\t검증 세트 손실: 0.399615\t최선의 손실: 0.071787\t정확도: 95.07%\n",
+      "10\t검증 세트 손실: 0.362663\t최선의 손실: 0.071787\t정확도: 94.49%\n",
+      "11\t검증 세트 손실: 0.318348\t최선의 손실: 0.071787\t정확도: 95.27%\n",
+      "12\t검증 세트 손실: 0.304515\t최선의 손실: 0.071787\t정확도: 96.40%\n",
+      "13\t검증 세트 손실: 0.303814\t최선의 손실: 0.071787\t정확도: 96.60%\n",
+      "14\t검증 세트 손실: 0.335364\t최선의 손실: 0.071787\t정확도: 95.66%\n",
+      "15\t검증 세트 손실: 0.391131\t최선의 손실: 0.071787\t정확도: 95.07%\n",
+      "16\t검증 세트 손실: 0.365249\t최선의 손실: 0.071787\t정확도: 95.04%\n",
+      "17\t검증 세트 손실: 0.296959\t최선의 손실: 0.071787\t정확도: 96.48%\n",
+      "18\t검증 세트 손실: 0.241468\t최선의 손실: 0.071787\t정확도: 96.25%\n",
+      "19\t검증 세트 손실: 0.253837\t최선의 손실: 0.071787\t정확도: 97.07%\n",
+      "20\t검증 세트 손실: 0.382106\t최선의 손실: 0.071787\t정확도: 97.19%\n",
+      "21\t검증 세트 손실: 0.191115\t최선의 손실: 0.071787\t정확도: 97.26%\n",
+      "22\t검증 세트 손실: 0.284780\t최선의 손실: 0.071787\t정확도: 97.03%\n",
+      "23\t검증 세트 손실: 0.230705\t최선의 손실: 0.071787\t정확도: 97.03%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=160, total=   8.6s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 18.784498\t최선의 손실: 18.784498\t정확도: 94.21%\n",
-      "1\t검증 세트 손실: 9.211025\t최선의 손실: 9.211025\t정확도: 94.25%\n",
-      "2\t검증 세트 손실: 6.116770\t최선의 손실: 6.116770\t정확도: 96.95%\n",
-      "3\t검증 세트 손실: 17.490591\t최선의 손실: 6.116770\t정확도: 93.86%\n",
-      "4\t검증 세트 손실: 3770543.000000\t최선의 손실: 6.116770\t정확도: 61.06%\n",
-      "5\t검증 세트 손실: 139084.421875\t최선의 손실: 6.116770\t정확도: 92.38%\n",
-      "6\t검증 세트 손실: 115693.359375\t최선의 손실: 6.116770\t정확도: 90.54%\n",
-      "7\t검증 세트 손실: 64108.117188\t최선의 손실: 6.116770\t정확도: 94.53%\n",
-      "8\t검증 세트 손실: 81441.492188\t최선의 손실: 6.116770\t정확도: 95.74%\n",
-      "9\t검증 세트 손실: 108514.125000\t최선의 손실: 6.116770\t정확도: 89.17%\n",
-      "10\t검증 세트 손실: 37006.187500\t최선의 손실: 6.116770\t정확도: 95.93%\n",
-      "11\t검증 세트 손실: 22738.439453\t최선의 손실: 6.116770\t정확도: 96.36%\n",
-      "12\t검증 세트 손실: 20531.867188\t최선의 손실: 6.116770\t정확도: 96.29%\n",
-      "13\t검증 세트 손실: 51833.531250\t최선의 손실: 6.116770\t정확도: 89.64%\n",
-      "14\t검증 세트 손실: 20063.193359\t최선의 손실: 6.116770\t정확도: 95.93%\n",
-      "15\t검증 세트 손실: 27340.003906\t최선의 손실: 6.116770\t정확도: 95.23%\n",
-      "16\t검증 세트 손실: 16353.160156\t최선의 손실: 6.116770\t정확도: 96.83%\n",
-      "17\t검증 세트 손실: 144302.312500\t최선의 손실: 6.116770\t정확도: 92.34%\n",
-      "18\t검증 세트 손실: 75895.500000\t최선의 손실: 6.116770\t정확도: 92.26%\n",
-      "19\t검증 세트 손실: 22643.607422\t최선의 손실: 6.116770\t정확도: 97.34%\n",
-      "20\t검증 세트 손실: 73874.328125\t최선의 손실: 6.116770\t정확도: 95.47%\n",
-      "21\t검증 세트 손실: 33788.207031\t최선의 손실: 6.116770\t정확도: 96.64%\n",
-      "22\t검증 세트 손실: 25380.992188\t최선의 손실: 6.116770\t정확도: 96.76%\n",
-      "23\t검증 세트 손실: 15640.231445\t최선의 손실: 6.116770\t정확도: 97.73%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=160, total=  16.4s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.084260\t최선의 손실: 0.084260\t정확도: 97.11%\n",
-      "1\t검증 세트 손실: 0.089959\t최선의 손실: 0.084260\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.081528\t최선의 손실: 0.081528\t정확도: 97.69%\n",
-      "3\t검증 세트 손실: 0.073965\t최선의 손실: 0.073965\t정확도: 97.42%\n",
-      "4\t검증 세트 손실: 0.220072\t최선의 손실: 0.073965\t정확도: 94.80%\n",
-      "5\t검증 세트 손실: 1.623947\t최선의 손실: 0.073965\t정확도: 19.55%\n",
-      "6\t검증 세트 손실: 1.647751\t최선의 손실: 0.073965\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.634392\t최선의 손실: 0.073965\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.621102\t최선의 손실: 0.073965\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.674855\t최선의 손실: 0.073965\t정확도: 22.01%\n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=50, total=   4.7s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.088133\t최선의 손실: 0.088133\t정확도: 96.68%\n",
+      "1\t검증 세트 손실: 0.083034\t최선의 손실: 0.083034\t정확도: 97.65%\n",
+      "2\t검증 세트 손실: 0.098712\t최선의 손실: 0.083034\t정확도: 97.54%\n",
+      "3\t검증 세트 손실: 0.063780\t최선의 손실: 0.063780\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.069571\t최선의 손실: 0.063780\t정확도: 98.05%\n",
+      "5\t검증 세트 손실: 0.068939\t최선의 손실: 0.063780\t정확도: 98.24%\n",
+      "6\t검증 세트 손실: 0.093549\t최선의 손실: 0.063780\t정확도: 97.89%\n",
+      "7\t검증 세트 손실: 0.128129\t최선의 손실: 0.063780\t정확도: 97.54%\n",
+      "8\t검증 세트 손실: 0.158568\t최선의 손실: 0.063780\t정확도: 96.64%\n",
+      "9\t검증 세트 손실: 0.199788\t최선의 손실: 0.063780\t정확도: 96.76%\n",
+      "10\t검증 세트 손실: 1.283732\t최선의 손실: 0.063780\t정확도: 40.70%\n",
+      "11\t검증 세트 손실: 1.365949\t최선의 손실: 0.063780\t정확도: 35.03%\n",
+      "12\t검증 세트 손실: 1.373866\t최선의 손실: 0.063780\t정확도: 35.54%\n",
+      "13\t검증 세트 손실: 1.451926\t최선의 손실: 0.063780\t정확도: 39.29%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10\t검증 세트 손실: 1.622938\t최선의 손실: 0.073965\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.618586\t최선의 손실: 0.073965\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.621902\t최선의 손실: 0.073965\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 1.615610\t최선의 손실: 0.073965\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.621776\t최선의 손실: 0.073965\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.628085\t최선의 손실: 0.073965\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.658137\t최선의 손실: 0.073965\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.701497\t최선의 손실: 0.073965\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.648183\t최선의 손실: 0.073965\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.634995\t최선의 손실: 0.073965\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.623821\t최선의 손실: 0.073965\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.630844\t최선의 손실: 0.073965\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.658025\t최선의 손실: 0.073965\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.619369\t최선의 손실: 0.073965\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 1.641589\t최선의 손실: 0.073965\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.263125\t최선의 손실: 0.063780\t정확도: 47.26%\n",
+      "15\t검증 세트 손실: 0.863230\t최선의 손실: 0.063780\t정확도: 60.52%\n",
+      "16\t검증 세트 손실: 0.745303\t최선의 손실: 0.063780\t정확도: 60.59%\n",
+      "17\t검증 세트 손실: 0.780116\t최선의 손실: 0.063780\t정확도: 75.92%\n",
+      "18\t검증 세트 손실: 0.610404\t최선의 손실: 0.063780\t정확도: 75.72%\n",
+      "19\t검증 세트 손실: 0.296462\t최선의 손실: 0.063780\t정확도: 94.06%\n",
+      "20\t검증 세트 손실: 0.418174\t최선의 손실: 0.063780\t정확도: 90.77%\n",
+      "21\t검증 세트 손실: 0.209804\t최선의 손실: 0.063780\t정확도: 96.56%\n",
+      "22\t검증 세트 손실: 0.292427\t최선의 손실: 0.063780\t정확도: 97.07%\n",
+      "23\t검증 세트 손실: 0.209261\t최선의 손실: 0.063780\t정확도: 97.81%\n",
+      "24\t검증 세트 손실: 0.159998\t최선의 손실: 0.063780\t정확도: 97.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=   4.9s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.085723\t최선의 손실: 0.085723\t정확도: 97.30%\n",
-      "1\t검증 세트 손실: 0.083973\t최선의 손실: 0.083973\t정확도: 97.62%\n",
-      "2\t검증 세트 손실: 0.087153\t최선의 손실: 0.083973\t정확도: 97.97%\n",
-      "3\t검증 세트 손실: 0.060900\t최선의 손실: 0.060900\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.081806\t최선의 손실: 0.060900\t정확도: 97.85%\n",
-      "5\t검증 세트 손실: 0.243020\t최선의 손실: 0.060900\t정확도: 96.36%\n",
-      "6\t검증 세트 손실: 0.600253\t최선의 손실: 0.060900\t정확도: 88.12%\n",
-      "7\t검증 세트 손실: 0.149549\t최선의 손실: 0.060900\t정확도: 96.05%\n",
-      "8\t검증 세트 손실: 0.111032\t최선의 손실: 0.060900\t정확도: 97.15%\n",
-      "9\t검증 세트 손실: 0.098501\t최선의 손실: 0.060900\t정확도: 97.58%\n",
-      "10\t검증 세트 손실: 0.114124\t최선의 손실: 0.060900\t정확도: 97.07%\n",
-      "11\t검증 세트 손실: 0.082109\t최선의 손실: 0.060900\t정확도: 98.05%\n",
-      "12\t검증 세트 손실: 0.215107\t최선의 손실: 0.060900\t정확도: 94.68%\n",
-      "13\t검증 세트 손실: 0.107657\t최선의 손실: 0.060900\t정확도: 97.42%\n",
-      "14\t검증 세트 손실: 0.076587\t최선의 손실: 0.060900\t정확도: 97.97%\n",
-      "15\t검증 세트 손실: 0.289241\t최선의 손실: 0.060900\t정확도: 92.26%\n",
-      "16\t검증 세트 손실: 0.130826\t최선의 손실: 0.060900\t정확도: 97.19%\n",
-      "17\t검증 세트 손실: 0.114789\t최선의 손실: 0.060900\t정확도: 97.30%\n",
-      "18\t검증 세트 손실: 0.213738\t최선의 손실: 0.060900\t정확도: 95.31%\n",
-      "19\t검증 세트 손실: 0.106432\t최선의 손실: 0.060900\t정확도: 97.11%\n",
-      "20\t검증 세트 손실: 0.098626\t최선의 손실: 0.060900\t정확도: 97.62%\n",
-      "21\t검증 세트 손실: 0.113315\t최선의 손실: 0.060900\t정확도: 97.69%\n",
-      "22\t검증 세트 손실: 0.078158\t최선의 손실: 0.060900\t정확도: 97.85%\n",
-      "23\t검증 세트 손실: 0.078744\t최선의 손실: 0.060900\t정확도: 97.85%\n",
-      "24\t검증 세트 손실: 0.174594\t최선의 손실: 0.060900\t정확도: 95.58%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=50, total=   4.9s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.088381\t최선의 손실: 0.088381\t정확도: 97.34%\n",
+      "1\t검증 세트 손실: 0.094022\t최선의 손실: 0.088381\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.072776\t최선의 손실: 0.072776\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.117047\t최선의 손실: 0.072776\t정확도: 97.81%\n",
+      "4\t검증 세트 손실: 0.094600\t최선의 손실: 0.072776\t정확도: 97.73%\n",
+      "5\t검증 세트 손실: 0.125898\t최선의 손실: 0.072776\t정확도: 97.81%\n",
+      "6\t검증 세트 손실: 0.096533\t최선의 손실: 0.072776\t정확도: 97.81%\n",
+      "7\t검증 세트 손실: 1.959116\t최선의 손실: 0.072776\t정확도: 75.25%\n",
+      "8\t검증 세트 손실: 0.248488\t최선의 손실: 0.072776\t정확도: 93.59%\n",
+      "9\t검증 세트 손실: 0.139645\t최선의 손실: 0.072776\t정확도: 95.66%\n",
+      "10\t검증 세트 손실: 0.130222\t최선의 손실: 0.072776\t정확도: 96.40%\n",
+      "11\t검증 세트 손실: 0.116270\t최선의 손실: 0.072776\t정확도: 96.87%\n",
+      "12\t검증 세트 손실: 0.100375\t최선의 손실: 0.072776\t정확도: 97.38%\n",
+      "13\t검증 세트 손실: 0.096093\t최선의 손실: 0.072776\t정확도: 97.42%\n",
+      "14\t검증 세트 손실: 0.107601\t최선의 손실: 0.072776\t정확도: 97.81%\n",
+      "15\t검증 세트 손실: 0.133184\t최선의 손실: 0.072776\t정확도: 97.81%\n",
+      "16\t검증 세트 손실: 0.207566\t최선의 손실: 0.072776\t정확도: 97.22%\n",
+      "17\t검증 세트 손실: 0.110256\t최선의 손실: 0.072776\t정확도: 97.34%\n",
+      "18\t검증 세트 손실: 0.095349\t최선의 손실: 0.072776\t정확도: 97.62%\n",
+      "19\t검증 세트 손실: 0.986982\t최선의 손실: 0.072776\t정확도: 84.68%\n",
+      "20\t검증 세트 손실: 0.772586\t최선의 손실: 0.072776\t정확도: 97.34%\n",
+      "21\t검증 세트 손실: 0.140419\t최선의 손실: 0.072776\t정확도: 96.95%\n",
+      "22\t검증 세트 손실: 0.921984\t최선의 손실: 0.072776\t정확도: 79.05%\n",
+      "23\t검증 세트 손실: 0.296639\t최선의 손실: 0.072776\t정확도: 95.04%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=   9.1s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.087227\t최선의 손실: 0.087227\t정확도: 97.34%\n",
-      "1\t검증 세트 손실: 0.087987\t최선의 손실: 0.087227\t정확도: 97.50%\n",
-      "2\t검증 세트 손실: 0.068450\t최선의 손실: 0.068450\t정확도: 98.20%\n",
-      "3\t검증 세트 손실: 0.093859\t최선의 손실: 0.068450\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.058422\t최선의 손실: 0.058422\t정확도: 98.16%\n",
-      "5\t검증 세트 손실: 0.065535\t최선의 손실: 0.058422\t정확도: 98.24%\n",
-      "6\t검증 세트 손실: 0.120203\t최선의 손실: 0.058422\t정확도: 98.12%\n",
-      "7\t검증 세트 손실: 0.140604\t최선의 손실: 0.058422\t정확도: 97.85%\n",
-      "8\t검증 세트 손실: 0.371111\t최선의 손실: 0.058422\t정확도: 95.86%\n",
-      "9\t검증 세트 손실: 1.656615\t최선의 손실: 0.058422\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.637914\t최선의 손실: 0.058422\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.628356\t최선의 손실: 0.058422\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.664625\t최선의 손실: 0.058422\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.631098\t최선의 손실: 0.058422\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.645762\t최선의 손실: 0.058422\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.643786\t최선의 손실: 0.058422\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.697384\t최선의 손실: 0.058422\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.634629\t최선의 손실: 0.058422\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.620549\t최선의 손실: 0.058422\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.630479\t최선의 손실: 0.058422\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.666352\t최선의 손실: 0.058422\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.719559\t최선의 손실: 0.058422\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.619382\t최선의 손실: 0.058422\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.629884\t최선의 손실: 0.058422\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.666217\t최선의 손실: 0.058422\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.610782\t최선의 손실: 0.058422\t정확도: 20.91%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=50, total=   4.7s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.142274\t최선의 손실: 0.142274\t정확도: 96.36%\n",
+      "1\t검증 세트 손실: 0.133510\t최선의 손실: 0.133510\t정확도: 96.91%\n",
+      "2\t검증 세트 손실: 0.122981\t최선의 손실: 0.122981\t정확도: 96.48%\n",
+      "3\t검증 세트 손실: 0.128198\t최선의 손실: 0.122981\t정확도: 96.76%\n",
+      "4\t검증 세트 손실: 0.139517\t최선의 손실: 0.122981\t정확도: 96.87%\n",
+      "5\t검증 세트 손실: 0.118783\t최선의 손실: 0.118783\t정확도: 96.79%\n",
+      "6\t검증 세트 손실: 0.117338\t최선의 손실: 0.117338\t정확도: 96.56%\n",
+      "7\t검증 세트 손실: 0.118613\t최선의 손실: 0.117338\t정확도: 96.76%\n",
+      "8\t검증 세트 손실: 0.112223\t최선의 손실: 0.112223\t정확도: 97.26%\n",
+      "9\t검증 세트 손실: 0.130085\t최선의 손실: 0.112223\t정확도: 96.87%\n",
+      "10\t검증 세트 손실: 0.126656\t최선의 손실: 0.112223\t정확도: 96.44%\n",
+      "11\t검증 세트 손실: 0.117869\t최선의 손실: 0.112223\t정확도: 97.07%\n",
+      "12\t검증 세트 손실: 0.182284\t최선의 손실: 0.112223\t정확도: 95.39%\n",
+      "13\t검증 세트 손실: 0.135290\t최선의 손실: 0.112223\t정확도: 96.52%\n",
+      "14\t검증 세트 손실: 0.120524\t최선의 손실: 0.112223\t정확도: 96.99%\n",
+      "15\t검증 세트 손실: 0.152629\t최선의 손실: 0.112223\t정확도: 96.76%\n",
+      "16\t검증 세트 손실: 0.127829\t최선의 손실: 0.112223\t정확도: 96.52%\n",
+      "17\t검증 세트 손실: 0.134285\t최선의 손실: 0.112223\t정확도: 96.87%\n",
+      "18\t검증 세트 손실: 0.165808\t최선의 손실: 0.112223\t정확도: 96.09%\n",
+      "19\t검증 세트 손실: 0.143477\t최선의 손실: 0.112223\t정확도: 96.76%\n",
+      "20\t검증 세트 손실: 0.130859\t최선의 손실: 0.112223\t정확도: 96.60%\n",
+      "21\t검증 세트 손실: 0.116497\t최선의 손실: 0.112223\t정확도: 96.95%\n",
+      "22\t검증 세트 손실: 0.122594\t최선의 손실: 0.112223\t정확도: 96.95%\n",
+      "23\t검증 세트 손실: 0.105619\t최선의 손실: 0.105619\t정확도: 97.19%\n",
+      "24\t검증 세트 손실: 0.106889\t최선의 손실: 0.105619\t정확도: 97.42%\n",
+      "25\t검증 세트 손실: 0.128727\t최선의 손실: 0.105619\t정확도: 96.76%\n",
+      "26\t검증 세트 손실: 0.146713\t최선의 손실: 0.105619\t정확도: 96.87%\n",
+      "27\t검증 세트 손실: 0.129831\t최선의 손실: 0.105619\t정확도: 97.15%\n",
+      "28\t검증 세트 손실: 0.177439\t최선의 손실: 0.105619\t정확도: 96.52%\n",
+      "29\t검증 세트 손실: 0.123852\t최선의 손실: 0.105619\t정확도: 97.19%\n",
+      "30\t검증 세트 손실: 0.125675\t최선의 손실: 0.105619\t정확도: 97.22%\n",
+      "31\t검증 세트 손실: 0.181843\t최선의 손실: 0.105619\t정확도: 96.40%\n",
+      "32\t검증 세트 손실: 0.130455\t최선의 손실: 0.105619\t정확도: 96.83%\n",
+      "33\t검증 세트 손실: 0.127908\t최선의 손실: 0.105619\t정확도: 97.19%\n",
+      "34\t검증 세트 손실: 0.151321\t최선의 손실: 0.105619\t정확도: 96.56%\n",
+      "35\t검증 세트 손실: 0.138253\t최선의 손실: 0.105619\t정확도: 96.52%\n",
+      "36\t검증 세트 손실: 0.142900\t최선의 손실: 0.105619\t정확도: 96.79%\n",
+      "37\t검증 세트 손실: 0.140734\t최선의 손실: 0.105619\t정확도: 96.33%\n",
+      "38\t검증 세트 손실: 0.170714\t최선의 손실: 0.105619\t정확도: 96.99%\n",
+      "39\t검증 세트 손실: 0.135179\t최선의 손실: 0.105619\t정확도: 97.03%\n",
+      "40\t검증 세트 손실: 0.136301\t최선의 손실: 0.105619\t정확도: 96.64%\n",
+      "41\t검증 세트 손실: 0.148185\t최선의 손실: 0.105619\t정확도: 96.76%\n",
+      "42\t검증 세트 손실: 0.125137\t최선의 손실: 0.105619\t정확도: 96.76%\n",
+      "43\t검증 세트 손실: 0.138498\t최선의 손실: 0.105619\t정확도: 96.83%\n",
+      "44\t검증 세트 손실: 0.148452\t최선의 손실: 0.105619\t정확도: 96.60%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=  11.9s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.135666\t최선의 손실: 0.135666\t정확도: 96.33%\n",
-      "1\t검증 세트 손실: 0.110627\t최선의 손실: 0.110627\t정확도: 96.95%\n",
-      "2\t검증 세트 손실: 0.118004\t최선의 손실: 0.110627\t정확도: 97.11%\n",
-      "3\t검증 세트 손실: 0.102309\t최선의 손실: 0.102309\t정확도: 96.79%\n",
-      "4\t검증 세트 손실: 0.109981\t최선의 손실: 0.102309\t정확도: 97.15%\n",
-      "5\t검증 세트 손실: 0.109082\t최선의 손실: 0.102309\t정확도: 97.07%\n",
-      "6\t검증 세트 손실: 0.132418\t최선의 손실: 0.102309\t정확도: 96.83%\n",
-      "7\t검증 세트 손실: 0.123247\t최선의 손실: 0.102309\t정확도: 96.60%\n",
-      "8\t검증 세트 손실: 0.104454\t최선의 손실: 0.102309\t정확도: 97.50%\n",
-      "9\t검증 세트 손실: 0.126719\t최선의 손실: 0.102309\t정확도: 97.34%\n",
-      "10\t검증 세트 손실: 0.113776\t최선의 손실: 0.102309\t정확도: 96.91%\n",
-      "11\t검증 세트 손실: 0.130552\t최선의 손실: 0.102309\t정확도: 96.13%\n",
-      "12\t검증 세트 손실: 0.114300\t최선의 손실: 0.102309\t정확도: 96.99%\n",
-      "13\t검증 세트 손실: 0.139138\t최선의 손실: 0.102309\t정확도: 96.76%\n",
-      "14\t검증 세트 손실: 0.132606\t최선의 손실: 0.102309\t정확도: 96.99%\n",
-      "15\t검증 세트 손실: 0.128943\t최선의 손실: 0.102309\t정확도: 97.03%\n",
-      "16\t검증 세트 손실: 0.115076\t최선의 손실: 0.102309\t정확도: 97.11%\n",
-      "17\t검증 세트 손실: 0.123501\t최선의 손실: 0.102309\t정확도: 96.76%\n",
-      "18\t검증 세트 손실: 0.179310\t최선의 손실: 0.102309\t정확도: 96.52%\n",
-      "19\t검증 세트 손실: 0.108632\t최선의 손실: 0.102309\t정확도: 97.62%\n",
-      "20\t검증 세트 손실: 0.103727\t최선의 손실: 0.102309\t정확도: 97.42%\n",
-      "21\t검증 세트 손실: 0.154918\t최선의 손실: 0.102309\t정확도: 97.19%\n",
-      "22\t검증 세트 손실: 0.113262\t최선의 손실: 0.102309\t정확도: 97.22%\n",
-      "23\t검증 세트 손실: 0.108446\t최선의 손실: 0.102309\t정확도: 97.22%\n",
-      "24\t검증 세트 손실: 0.109897\t최선의 손실: 0.102309\t정확도: 97.30%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=10, total=   8.6s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.303208\t최선의 손실: 0.303208\t정확도: 88.70%\n",
+      "1\t검증 세트 손실: 0.283273\t최선의 손실: 0.283273\t정확도: 90.42%\n",
+      "2\t검증 세트 손실: 0.222847\t최선의 손실: 0.222847\t정확도: 93.28%\n",
+      "3\t검증 세트 손실: 0.239942\t최선의 손실: 0.222847\t정확도: 92.57%\n",
+      "4\t검증 세트 손실: 0.220328\t최선의 손실: 0.220328\t정확도: 93.55%\n",
+      "5\t검증 세트 손실: 0.191916\t최선의 손실: 0.191916\t정확도: 94.76%\n",
+      "6\t검증 세트 손실: 0.167315\t최선의 손실: 0.167315\t정확도: 95.04%\n",
+      "7\t검증 세트 손실: 0.159181\t최선의 손실: 0.159181\t정확도: 95.31%\n",
+      "8\t검증 세트 손실: 0.143442\t최선의 손실: 0.143442\t정확도: 96.17%\n",
+      "9\t검증 세트 손실: 0.131389\t최선의 손실: 0.131389\t정확도: 96.44%\n",
+      "10\t검증 세트 손실: 0.161128\t최선의 손실: 0.131389\t정확도: 96.13%\n",
+      "11\t검증 세트 손실: 0.130165\t최선의 손실: 0.130165\t정확도: 96.56%\n",
+      "12\t검증 세트 손실: 0.131124\t최선의 손실: 0.130165\t정확도: 96.36%\n",
+      "13\t검증 세트 손실: 0.137689\t최선의 손실: 0.130165\t정확도: 96.33%\n",
+      "14\t검증 세트 손실: 0.126581\t최선의 손실: 0.126581\t정확도: 96.87%\n",
+      "15\t검증 세트 손실: 0.123323\t최선의 손실: 0.123323\t정확도: 96.44%\n",
+      "16\t검증 세트 손실: 0.131466\t최선의 손실: 0.123323\t정확도: 96.95%\n",
+      "17\t검증 세트 손실: 0.138757\t최선의 손실: 0.123323\t정확도: 96.44%\n",
+      "18\t검증 세트 손실: 0.133373\t최선의 손실: 0.123323\t정확도: 96.44%\n",
+      "19\t검증 세트 손실: 0.129778\t최선의 손실: 0.123323\t정확도: 96.68%\n",
+      "20\t검증 세트 손실: 0.156229\t최선의 손실: 0.123323\t정확도: 96.01%\n",
+      "21\t검증 세트 손실: 0.326346\t최선의 손실: 0.123323\t정확도: 92.38%\n",
+      "22\t검증 세트 손실: 0.146211\t최선의 손실: 0.123323\t정확도: 96.29%\n",
+      "23\t검증 세트 손실: 0.136110\t최선의 손실: 0.123323\t정확도: 96.40%\n",
+      "24\t검증 세트 손실: 0.144782\t최선의 손실: 0.123323\t정확도: 96.76%\n",
+      "25\t검증 세트 손실: 0.147591\t최선의 손실: 0.123323\t정확도: 96.05%\n",
+      "26\t검증 세트 손실: 0.124485\t최선의 손실: 0.123323\t정확도: 96.79%\n",
+      "27\t검증 세트 손실: 0.164708\t최선의 손실: 0.123323\t정확도: 96.21%\n",
+      "28\t검증 세트 손실: 0.128645\t최선의 손실: 0.123323\t정확도: 96.72%\n",
+      "29\t검증 세트 손실: 0.145412\t최선의 손실: 0.123323\t정확도: 95.97%\n",
+      "30\t검증 세트 손실: 0.137178\t최선의 손실: 0.123323\t정확도: 96.79%\n",
+      "31\t검증 세트 손실: 0.112817\t최선의 손실: 0.112817\t정확도: 96.72%\n",
+      "32\t검증 세트 손실: 0.149258\t최선의 손실: 0.112817\t정확도: 96.40%\n",
+      "33\t검증 세트 손실: 0.156759\t최선의 손실: 0.112817\t정확도: 96.72%\n",
+      "34\t검증 세트 손실: 0.156666\t최선의 손실: 0.112817\t정확도: 96.29%\n",
+      "35\t검증 세트 손실: 0.166439\t최선의 손실: 0.112817\t정확도: 96.36%\n",
+      "36\t검증 세트 손실: 0.145728\t최선의 손실: 0.112817\t정확도: 96.25%\n",
+      "37\t검증 세트 손실: 0.149319\t최선의 손실: 0.112817\t정확도: 96.83%\n",
+      "38\t검증 세트 손실: 0.152830\t최선의 손실: 0.112817\t정확도: 96.29%\n",
+      "39\t검증 세트 손실: 0.141351\t최선의 손실: 0.112817\t정확도: 96.72%\n",
+      "40\t검증 세트 손실: 0.157660\t최선의 손실: 0.112817\t정확도: 96.64%\n",
+      "41\t검증 세트 손실: 0.133846\t최선의 손실: 0.112817\t정확도: 96.83%\n",
+      "42\t검증 세트 손실: 0.144165\t최선의 손실: 0.112817\t정확도: 96.64%\n",
+      "43\t검증 세트 손실: 0.142172\t최선의 손실: 0.112817\t정확도: 96.64%\n",
+      "44\t검증 세트 손실: 0.128800\t최선의 손실: 0.112817\t정확도: 96.79%\n",
+      "45\t검증 세트 손실: 0.132444\t최선의 손실: 0.112817\t정확도: 96.99%\n",
+      "46\t검증 세트 손실: 0.123527\t최선의 손실: 0.112817\t정확도: 96.91%\n",
+      "47\t검증 세트 손실: 0.171308\t최선의 손실: 0.112817\t정확도: 95.93%\n",
+      "48\t검증 세트 손실: 0.142094\t최선의 손실: 0.112817\t정확도: 96.95%\n",
+      "49\t검증 세트 손실: 0.138835\t최선의 손실: 0.112817\t정확도: 96.83%\n",
+      "50\t검증 세트 손실: 0.147881\t최선의 손실: 0.112817\t정확도: 97.15%\n",
+      "51\t검증 세트 손실: 0.131372\t최선의 손실: 0.112817\t정확도: 96.95%\n",
+      "52\t검증 세트 손실: 0.138544\t최선의 손실: 0.112817\t정확도: 96.95%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=10, total=   9.5s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.198822\t최선의 손실: 0.198822\t정확도: 94.96%\n",
-      "1\t검증 세트 손실: 0.146453\t최선의 손실: 0.146453\t정확도: 95.93%\n",
-      "2\t검증 세트 손실: 0.114432\t최선의 손실: 0.114432\t정확도: 96.64%\n",
-      "3\t검증 세트 손실: 0.117532\t최선의 손실: 0.114432\t정확도: 96.83%\n",
-      "4\t검증 세트 손실: 0.132134\t최선의 손실: 0.114432\t정확도: 96.44%\n",
-      "5\t검증 세트 손실: 0.118366\t최선의 손실: 0.114432\t정확도: 96.44%\n",
-      "6\t검증 세트 손실: 0.129357\t최선의 손실: 0.114432\t정확도: 96.48%\n",
-      "7\t검증 세트 손실: 0.143947\t최선의 손실: 0.114432\t정확도: 95.97%\n",
-      "8\t검증 세트 손실: 0.103425\t최선의 손실: 0.103425\t정확도: 97.15%\n",
-      "9\t검증 세트 손실: 0.112685\t최선의 손실: 0.103425\t정확도: 96.95%\n",
-      "10\t검증 세트 손실: 0.099507\t최선의 손실: 0.099507\t정확도: 96.95%\n",
-      "11\t검증 세트 손실: 0.123091\t최선의 손실: 0.099507\t정확도: 96.79%\n",
-      "12\t검증 세트 손실: 0.140728\t최선의 손실: 0.099507\t정확도: 96.95%\n",
-      "13\t검증 세트 손실: 0.141677\t최선의 손실: 0.099507\t정확도: 95.97%\n",
-      "14\t검증 세트 손실: 0.143394\t최선의 손실: 0.099507\t정확도: 97.15%\n",
-      "15\t검증 세트 손실: 0.104855\t최선의 손실: 0.099507\t정확도: 97.22%\n",
-      "16\t검증 세트 손실: 0.115790\t최선의 손실: 0.099507\t정확도: 97.30%\n",
-      "17\t검증 세트 손실: 0.112810\t최선의 손실: 0.099507\t정확도: 97.15%\n",
-      "18\t검증 세트 손실: 0.116928\t최선의 손실: 0.099507\t정확도: 97.03%\n",
-      "19\t검증 세트 손실: 0.119195\t최선의 손실: 0.099507\t정확도: 96.91%\n",
-      "20\t검증 세트 손실: 0.111744\t최선의 손실: 0.099507\t정확도: 97.26%\n",
-      "21\t검증 세트 손실: 0.139696\t최선의 손실: 0.099507\t정확도: 96.83%\n",
-      "22\t검증 세트 손실: 0.164993\t최선의 손실: 0.099507\t정확도: 96.60%\n",
-      "23\t검증 세트 손실: 0.134938\t최선의 손실: 0.099507\t정확도: 97.07%\n",
-      "24\t검증 세트 손실: 0.146705\t최선의 손실: 0.099507\t정확도: 97.11%\n",
-      "25\t검증 세트 손실: 0.134780\t최선의 손실: 0.099507\t정확도: 97.03%\n",
-      "26\t검증 세트 손실: 0.137446\t최선의 손실: 0.099507\t정확도: 96.87%\n",
-      "27\t검증 세트 손실: 0.130177\t최선의 손실: 0.099507\t정확도: 97.03%\n",
-      "28\t검증 세트 손실: 0.177340\t최선의 손실: 0.099507\t정확도: 95.58%\n",
-      "29\t검증 세트 손실: 0.124595\t최선의 손실: 0.099507\t정확도: 97.11%\n",
-      "30\t검증 세트 손실: 0.134116\t최선의 손실: 0.099507\t정확도: 96.87%\n",
-      "31\t검증 세트 손실: 0.124160\t최선의 손실: 0.099507\t정확도: 97.15%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=10, total=  17.1s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.103641\t최선의 손실: 0.103641\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.119976\t최선의 손실: 0.103641\t정확도: 96.56%\n",
-      "2\t검증 세트 손실: 0.123396\t최선의 손실: 0.103641\t정확도: 96.64%\n",
-      "3\t검증 세트 손실: 0.102881\t최선의 손실: 0.102881\t정확도: 97.69%\n",
-      "4\t검증 세트 손실: 0.121298\t최선의 손실: 0.102881\t정확도: 96.83%\n",
-      "5\t검증 세트 손실: 0.133374\t최선의 손실: 0.102881\t정확도: 96.83%\n",
-      "6\t검증 세트 손실: 0.093039\t최선의 손실: 0.093039\t정확도: 97.46%\n",
-      "7\t검증 세트 손실: 0.095257\t최선의 손실: 0.093039\t정확도: 97.69%\n",
-      "8\t검증 세트 손실: 0.096162\t최선의 손실: 0.093039\t정확도: 97.58%\n",
-      "9\t검증 세트 손실: 0.141542\t최선의 손실: 0.093039\t정확도: 96.29%\n",
-      "10\t검증 세트 손실: 0.116543\t최선의 손실: 0.093039\t정확도: 96.52%\n",
-      "11\t검증 세트 손실: 0.145715\t최선의 손실: 0.093039\t정확도: 96.99%\n",
-      "12\t검증 세트 손실: 0.113521\t최선의 손실: 0.093039\t정확도: 96.33%\n",
-      "13\t검증 세트 손실: 0.103363\t최선의 손실: 0.093039\t정확도: 97.30%\n",
-      "14\t검증 세트 손실: 0.099521\t최선의 손실: 0.093039\t정확도: 97.34%\n",
-      "15\t검증 세트 손실: 0.114970\t최선의 손실: 0.093039\t정확도: 97.65%\n",
-      "16\t검증 세트 손실: 0.100733\t최선의 손실: 0.093039\t정확도: 97.73%\n",
-      "17\t검증 세트 손실: 0.141481\t최선의 손실: 0.093039\t정확도: 97.42%\n"
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=10, total=  10.0s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.142011\t최선의 손실: 0.142011\t정확도: 95.70%\n",
+      "1\t검증 세트 손실: 0.132000\t최선의 손실: 0.132000\t정확도: 95.62%\n",
+      "2\t검증 세트 손실: 0.117085\t최선의 손실: 0.117085\t정확도: 96.44%\n",
+      "3\t검증 세트 손실: 0.122689\t최선의 손실: 0.117085\t정확도: 96.68%\n",
+      "4\t검증 세트 손실: 0.125578\t최선의 손실: 0.117085\t정확도: 96.64%\n",
+      "5\t검증 세트 손실: 0.146806\t최선의 손실: 0.117085\t정확도: 96.36%\n",
+      "6\t검증 세트 손실: 0.118707\t최선의 손실: 0.117085\t정확도: 96.87%\n",
+      "7\t검증 세트 손실: 0.141140\t최선의 손실: 0.117085\t정확도: 96.17%\n",
+      "8\t검증 세트 손실: 0.120196\t최선의 손실: 0.117085\t정확도: 96.83%\n",
+      "9\t검증 세트 손실: 0.109335\t최선의 손실: 0.109335\t정확도: 96.95%\n",
+      "10\t검증 세트 손실: 0.177834\t최선의 손실: 0.109335\t정확도: 95.07%\n",
+      "11\t검증 세트 손실: 0.132460\t최선의 손실: 0.109335\t정확도: 96.91%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "18\t검증 세트 손실: 0.117808\t최선의 손실: 0.093039\t정확도: 97.22%\n",
-      "19\t검증 세트 손실: 0.155116\t최선의 손실: 0.093039\t정확도: 96.99%\n",
-      "20\t검증 세트 손실: 0.129246\t최선의 손실: 0.093039\t정확도: 97.34%\n",
-      "21\t검증 세트 손실: 0.154954\t최선의 손실: 0.093039\t정확도: 96.21%\n",
-      "22\t검증 세트 손실: 0.101678\t최선의 손실: 0.093039\t정확도: 97.34%\n",
-      "23\t검증 세트 손실: 0.123998\t최선의 손실: 0.093039\t정확도: 97.26%\n",
-      "24\t검증 세트 손실: 0.115114\t최선의 손실: 0.093039\t정확도: 97.11%\n",
-      "25\t검증 세트 손실: 0.099806\t최선의 손실: 0.093039\t정확도: 97.69%\n",
-      "26\t검증 세트 손실: 0.099427\t최선의 손실: 0.093039\t정확도: 97.54%\n",
-      "27\t검증 세트 손실: 0.085041\t최선의 손실: 0.085041\t정확도: 97.54%\n",
-      "28\t검증 세트 손실: 0.125900\t최선의 손실: 0.085041\t정확도: 96.95%\n",
-      "29\t검증 세트 손실: 0.109399\t최선의 손실: 0.085041\t정확도: 97.69%\n",
-      "30\t검증 세트 손실: 0.095834\t최선의 손실: 0.085041\t정확도: 97.65%\n",
-      "31\t검증 세트 손실: 0.109296\t최선의 손실: 0.085041\t정확도: 97.58%\n",
-      "32\t검증 세트 손실: 0.138597\t최선의 손실: 0.085041\t정확도: 96.99%\n",
-      "33\t검증 세트 손실: 0.110600\t최선의 손실: 0.085041\t정확도: 97.30%\n",
-      "34\t검증 세트 손실: 0.147475\t최선의 손실: 0.085041\t정확도: 96.91%\n",
-      "35\t검증 세트 손실: 0.098015\t최선의 손실: 0.085041\t정확도: 97.50%\n",
-      "36\t검증 세트 손실: 0.112714\t최선의 손실: 0.085041\t정확도: 97.30%\n",
-      "37\t검증 세트 손실: 0.096248\t최선의 손실: 0.085041\t정확도: 97.34%\n",
-      "38\t검증 세트 손실: 0.103097\t최선의 손실: 0.085041\t정확도: 97.54%\n",
-      "39\t검증 세트 손실: 0.158588\t최선의 손실: 0.085041\t정확도: 97.07%\n",
-      "40\t검증 세트 손실: 0.158934\t최선의 손실: 0.085041\t정확도: 96.95%\n",
-      "41\t검증 세트 손실: 0.115280\t최선의 손실: 0.085041\t정확도: 97.03%\n",
-      "42\t검증 세트 손실: 0.102344\t최선의 손실: 0.085041\t정확도: 97.58%\n",
-      "43\t검증 세트 손실: 0.108645\t최선의 손실: 0.085041\t정확도: 97.30%\n",
-      "44\t검증 세트 손실: 0.138716\t최선의 손실: 0.085041\t정확도: 96.52%\n",
-      "45\t검증 세트 손실: 0.142500\t최선의 손실: 0.085041\t정확도: 95.70%\n",
-      "46\t검증 세트 손실: 0.112439\t최선의 손실: 0.085041\t정확도: 97.22%\n",
-      "47\t검증 세트 손실: 0.096400\t최선의 손실: 0.085041\t정확도: 97.42%\n",
-      "48\t검증 세트 손실: 0.122456\t최선의 손실: 0.085041\t정확도: 97.50%\n",
+      "12\t검증 세트 손실: 0.167044\t최선의 손실: 0.109335\t정확도: 96.25%\n",
+      "13\t검증 세트 손실: 0.119785\t최선의 손실: 0.109335\t정확도: 96.87%\n",
+      "14\t검증 세트 손실: 0.124444\t최선의 손실: 0.109335\t정확도: 96.68%\n",
+      "15\t검증 세트 손실: 0.149647\t최선의 손실: 0.109335\t정확도: 96.21%\n",
+      "16\t검증 세트 손실: 0.123429\t최선의 손실: 0.109335\t정확도: 96.44%\n",
+      "17\t검증 세트 손실: 0.120085\t최선의 손실: 0.109335\t정확도: 97.03%\n",
+      "18\t검증 세트 손실: 0.120813\t최선의 손실: 0.109335\t정확도: 96.44%\n",
+      "19\t검증 세트 손실: 0.134334\t최선의 손실: 0.109335\t정확도: 96.17%\n",
+      "20\t검증 세트 손실: 0.145839\t최선의 손실: 0.109335\t정확도: 96.52%\n",
+      "21\t검증 세트 손실: 0.106488\t최선의 손실: 0.106488\t정확도: 97.11%\n",
+      "22\t검증 세트 손실: 0.121791\t최선의 손실: 0.106488\t정확도: 96.79%\n",
+      "23\t검증 세트 손실: 0.143375\t최선의 손실: 0.106488\t정확도: 96.33%\n",
+      "24\t검증 세트 손실: 0.122682\t최선의 손실: 0.106488\t정확도: 96.87%\n",
+      "25\t검증 세트 손실: 0.146276\t최선의 손실: 0.106488\t정확도: 96.79%\n",
+      "26\t검증 세트 손실: 0.150099\t최선의 손실: 0.106488\t정확도: 96.76%\n",
+      "27\t검증 세트 손실: 0.128638\t최선의 손실: 0.106488\t정확도: 96.72%\n",
+      "28\t검증 세트 손실: 0.169055\t최선의 손실: 0.106488\t정확도: 95.78%\n",
+      "29\t검증 세트 손실: 0.153824\t최선의 손실: 0.106488\t정확도: 96.13%\n",
+      "30\t검증 세트 손실: 0.143584\t최선의 손실: 0.106488\t정확도: 95.93%\n",
+      "31\t검증 세트 손실: 0.111478\t최선의 손실: 0.106488\t정확도: 96.56%\n",
+      "32\t검증 세트 손실: 0.115927\t최선의 손실: 0.106488\t정확도: 96.13%\n",
+      "33\t검증 세트 손실: 0.125903\t최선의 손실: 0.106488\t정확도: 96.87%\n",
+      "34\t검증 세트 손실: 0.133483\t최선의 손실: 0.106488\t정확도: 96.68%\n",
+      "35\t검증 세트 손실: 0.139168\t최선의 손실: 0.106488\t정확도: 96.68%\n",
+      "36\t검증 세트 손실: 0.146673\t최선의 손실: 0.106488\t정확도: 96.48%\n",
+      "37\t검증 세트 손실: 0.190075\t최선의 손실: 0.106488\t정확도: 94.61%\n",
+      "38\t검증 세트 손실: 0.132726\t최선의 손실: 0.106488\t정확도: 96.52%\n",
+      "39\t검증 세트 손실: 0.127567\t최선의 손실: 0.106488\t정확도: 96.48%\n",
+      "40\t검증 세트 손실: 0.140544\t최선의 손실: 0.106488\t정확도: 96.44%\n",
+      "41\t검증 세트 손실: 0.124606\t최선의 손실: 0.106488\t정확도: 96.56%\n",
+      "42\t검증 세트 손실: 0.110304\t최선의 손실: 0.106488\t정확도: 97.46%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=10, total=  17.0s\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.157320\t최선의 손실: 0.157320\t정확도: 95.70%\n",
-      "1\t검증 세트 손실: 0.240017\t최선의 손실: 0.157320\t정확도: 95.39%\n",
-      "2\t검증 세트 손실: 0.908707\t최선의 손실: 0.157320\t정확도: 51.09%\n",
-      "3\t검증 세트 손실: 1.618340\t최선의 손실: 0.157320\t정확도: 43.67%\n",
-      "4\t검증 세트 손실: 1.150683\t최선의 손실: 0.157320\t정확도: 40.50%\n",
-      "5\t검증 세트 손실: 1.030974\t최선의 손실: 0.157320\t정확도: 55.55%\n",
-      "6\t검증 세트 손실: 1.237740\t최선의 손실: 0.157320\t정확도: 34.48%\n",
-      "7\t검증 세트 손실: 1.348727\t최선의 손실: 0.157320\t정확도: 34.40%\n",
-      "8\t검증 세트 손실: 1.373794\t최선의 손실: 0.157320\t정확도: 34.40%\n",
-      "9\t검증 세트 손실: 1.357962\t최선의 손실: 0.157320\t정확도: 33.85%\n",
-      "10\t검증 세트 손실: 1.354785\t최선의 손실: 0.157320\t정확도: 36.28%\n",
-      "11\t검증 세트 손실: 1.345401\t최선의 손실: 0.157320\t정확도: 33.85%\n",
-      "12\t검증 세트 손실: 1.352069\t최선의 손실: 0.157320\t정확도: 33.85%\n",
-      "13\t검증 세트 손실: 1.348234\t최선의 손실: 0.157320\t정확도: 34.13%\n",
-      "14\t검증 세트 손실: 1.353216\t최선의 손실: 0.157320\t정확도: 34.13%\n",
-      "15\t검증 세트 손실: 1.353749\t최선의 손실: 0.157320\t정확도: 34.40%\n",
-      "16\t검증 세트 손실: 1.344880\t최선의 손실: 0.157320\t정확도: 34.13%\n",
-      "17\t검증 세트 손실: 1.346335\t최선의 손실: 0.157320\t정확도: 34.40%\n",
-      "18\t검증 세트 손실: 1.357418\t최선의 손실: 0.157320\t정확도: 34.13%\n",
-      "19\t검증 세트 손실: 1.348730\t최선의 손실: 0.157320\t정확도: 36.28%\n",
-      "20\t검증 세트 손실: 1.351453\t최선의 손실: 0.157320\t정확도: 36.28%\n",
-      "21\t검증 세트 손실: 1.353368\t최선의 손실: 0.157320\t정확도: 34.40%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=10, total=   8.2s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.209698\t최선의 손실: 0.209698\t정확도: 94.61%\n",
+      "1\t검증 세트 손실: 0.188132\t최선의 손실: 0.188132\t정확도: 94.76%\n",
+      "2\t검증 세트 손실: 0.303091\t최선의 손실: 0.188132\t정확도: 92.06%\n",
+      "3\t검증 세트 손실: 0.342957\t최선의 손실: 0.188132\t정확도: 89.44%\n",
+      "4\t검증 세트 손실: 0.345057\t최선의 손실: 0.188132\t정확도: 92.65%\n",
+      "5\t검증 세트 손실: 0.311282\t최선의 손실: 0.188132\t정확도: 92.96%\n",
+      "6\t검증 세트 손실: 0.428300\t최선의 손실: 0.188132\t정확도: 89.17%\n",
+      "7\t검증 세트 손실: 0.891059\t최선의 손실: 0.188132\t정확도: 60.09%\n",
+      "8\t검증 세트 손실: 1.292694\t최선의 손실: 0.188132\t정확도: 41.99%\n",
+      "9\t검증 세트 손실: 1.148692\t최선의 손실: 0.188132\t정확도: 47.89%\n",
+      "10\t검증 세트 손실: 1.365890\t최선의 손실: 0.188132\t정확도: 37.02%\n",
+      "11\t검증 세트 손실: 0.955915\t최선의 손실: 0.188132\t정확도: 57.70%\n",
+      "12\t검증 세트 손실: 0.883937\t최선의 손실: 0.188132\t정확도: 55.39%\n",
+      "13\t검증 세트 손실: 1.410480\t최선의 손실: 0.188132\t정확도: 30.02%\n",
+      "14\t검증 세트 손실: 1.409139\t최선의 손실: 0.188132\t정확도: 33.23%\n",
+      "15\t검증 세트 손실: 1.409129\t최선의 손실: 0.188132\t정확도: 33.23%\n",
+      "16\t검증 세트 손실: 1.409049\t최선의 손실: 0.188132\t정확도: 33.23%\n",
+      "17\t검증 세트 손실: 1.196379\t최선의 손실: 0.188132\t정확도: 38.74%\n",
+      "18\t검증 세트 손실: 1.179280\t최선의 손실: 0.188132\t정확도: 42.06%\n",
+      "19\t검증 세트 손실: 1.175023\t최선의 손실: 0.188132\t정확도: 38.74%\n",
+      "20\t검증 세트 손실: 1.173956\t최선의 손실: 0.188132\t정확도: 42.06%\n",
+      "21\t검증 세트 손실: 1.174710\t최선의 손실: 0.188132\t정확도: 39.21%\n",
+      "22\t검증 세트 손실: 1.173433\t최선의 손실: 0.188132\t정확도: 39.21%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total= 1.7min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.249724\t최선의 손실: 0.249724\t정확도: 94.06%\n",
-      "1\t검증 세트 손실: 0.196979\t최선의 손실: 0.196979\t정확도: 95.00%\n",
-      "2\t검증 세트 손실: 0.249376\t최선의 손실: 0.196979\t정확도: 93.98%\n",
-      "3\t검증 세트 손실: 0.223938\t최선의 손실: 0.196979\t정확도: 92.38%\n",
-      "4\t검증 세트 손실: 0.250900\t최선의 손실: 0.196979\t정확도: 93.67%\n",
-      "5\t검증 세트 손실: 0.478246\t최선의 손실: 0.196979\t정확도: 75.68%\n",
-      "6\t검증 세트 손실: 0.335070\t최선의 손실: 0.196979\t정확도: 91.24%\n",
-      "7\t검증 세트 손실: 0.374289\t최선의 손실: 0.196979\t정확도: 91.75%\n",
-      "8\t검증 세트 손실: 0.569542\t최선의 손실: 0.196979\t정확도: 74.55%\n",
-      "9\t검증 세트 손실: 0.569353\t최선의 손실: 0.196979\t정확도: 86.36%\n",
-      "10\t검증 세트 손실: 0.563465\t최선의 손실: 0.196979\t정확도: 76.11%\n",
-      "11\t검증 세트 손실: 0.252684\t최선의 손실: 0.196979\t정확도: 95.00%\n",
-      "12\t검증 세트 손실: 0.330634\t최선의 손실: 0.196979\t정확도: 92.38%\n",
-      "13\t검증 세트 손실: 1.263097\t최선의 손실: 0.196979\t정확도: 39.52%\n",
-      "14\t검증 세트 손실: 1.229293\t최선의 손실: 0.196979\t정확도: 40.66%\n",
-      "15\t검증 세트 손실: 1.249588\t최선의 손실: 0.196979\t정확도: 36.59%\n",
-      "16\t검증 세트 손실: 1.208979\t최선의 손실: 0.196979\t정확도: 41.09%\n",
-      "17\t검증 세트 손실: 1.262963\t최선의 손실: 0.196979\t정확도: 36.63%\n",
-      "18\t검증 세트 손실: 1.228174\t최선의 손실: 0.196979\t정확도: 40.85%\n",
-      "19\t검증 세트 손실: 1.208529\t최선의 손실: 0.196979\t정확도: 41.09%\n",
-      "20\t검증 세트 손실: 1.215091\t최선의 손실: 0.196979\t정확도: 38.12%\n",
-      "21\t검증 세트 손실: 1.238637\t최선의 손실: 0.196979\t정확도: 41.16%\n",
-      "22\t검증 세트 손실: 1.219505\t최선의 손실: 0.196979\t정확도: 38.12%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=  36.7s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.288984\t최선의 손실: 0.288984\t정확도: 92.92%\n",
+      "1\t검증 세트 손실: 0.273478\t최선의 손실: 0.273478\t정확도: 93.24%\n",
+      "2\t검증 세트 손실: 0.379908\t최선의 손실: 0.273478\t정확도: 87.53%\n",
+      "3\t검증 세트 손실: 0.694924\t최선의 손실: 0.273478\t정확도: 68.26%\n",
+      "4\t검증 세트 손실: 0.593704\t최선의 손실: 0.273478\t정확도: 76.58%\n",
+      "5\t검증 세트 손실: 0.545762\t최선의 손실: 0.273478\t정확도: 77.91%\n",
+      "6\t검증 세트 손실: 0.636678\t최선의 손실: 0.273478\t정확도: 69.86%\n",
+      "7\t검증 세트 손실: 0.587154\t최선의 손실: 0.273478\t정확도: 72.20%\n",
+      "8\t검증 세트 손실: 0.738391\t최선의 손실: 0.273478\t정확도: 74.16%\n",
+      "9\t검증 세트 손실: 0.515256\t최선의 손실: 0.273478\t정확도: 77.25%\n",
+      "10\t검증 세트 손실: 0.478646\t최선의 손실: 0.273478\t정확도: 85.89%\n",
+      "11\t검증 세트 손실: 0.464411\t최선의 손실: 0.273478\t정확도: 87.10%\n",
+      "12\t검증 세트 손실: 0.415218\t최선의 손실: 0.273478\t정확도: 92.14%\n",
+      "13\t검증 세트 손실: 0.358096\t최선의 손실: 0.273478\t정확도: 93.08%\n",
+      "14\t검증 세트 손실: 0.317838\t최선의 손실: 0.273478\t정확도: 94.14%\n",
+      "15\t검증 세트 손실: 0.434255\t최선의 손실: 0.273478\t정확도: 92.34%\n",
+      "16\t검증 세트 손실: 0.401900\t최선의 손실: 0.273478\t정확도: 94.68%\n",
+      "17\t검증 세트 손실: 0.858923\t최선의 손실: 0.273478\t정확도: 91.56%\n",
+      "18\t검증 세트 손실: 0.808469\t최선의 손실: 0.273478\t정확도: 59.58%\n",
+      "19\t검증 세트 손실: 1.051956\t최선의 손실: 0.273478\t정확도: 57.66%\n",
+      "20\t검증 세트 손실: 0.960327\t최선의 손실: 0.273478\t정확도: 54.89%\n",
+      "21\t검증 세트 손실: 0.873785\t최선의 손실: 0.273478\t정확도: 59.85%\n",
+      "22\t검증 세트 손실: 0.896759\t최선의 손실: 0.273478\t정확도: 57.66%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total= 1.8min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.221224\t최선의 손실: 0.221224\t정확도: 94.88%\n",
-      "1\t검증 세트 손실: 0.207292\t최선의 손실: 0.207292\t정확도: 92.73%\n",
-      "2\t검증 세트 손실: 0.217219\t최선의 손실: 0.207292\t정확도: 94.80%\n",
-      "3\t검증 세트 손실: 0.498039\t최선의 손실: 0.207292\t정확도: 73.10%\n",
-      "4\t검증 세트 손실: 1.614660\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.622447\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.632921\t최선의 손실: 0.207292\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.616715\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.611729\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.610272\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.610119\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.615095\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.609143\t최선의 손실: 0.207292\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.612667\t최선의 손실: 0.207292\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.610130\t최선의 손실: 0.207292\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.611327\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.620625\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.614154\t최선의 손실: 0.207292\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.607983\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.610182\t최선의 손실: 0.207292\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.612591\t최선의 손실: 0.207292\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.620057\t최선의 손실: 0.207292\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.611555\t최선의 손실: 0.207292\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=  38.0s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.201873\t최선의 손실: 0.201873\t정확도: 94.61%\n",
+      "1\t검증 세트 손실: 0.278906\t최선의 손실: 0.201873\t정확도: 94.33%\n",
+      "2\t검증 세트 손실: 0.179366\t최선의 손실: 0.179366\t정확도: 95.43%\n",
+      "3\t검증 세트 손실: 0.209879\t최선의 손실: 0.179366\t정확도: 95.50%\n",
+      "4\t검증 세트 손실: 0.150550\t최선의 손실: 0.150550\t정확도: 96.33%\n",
+      "5\t검증 세트 손실: 0.316041\t최선의 손실: 0.150550\t정확도: 95.62%\n",
+      "6\t검증 세트 손실: 0.243724\t최선의 손실: 0.150550\t정확도: 95.11%\n",
+      "7\t검증 세트 손실: 0.261831\t최선의 손실: 0.150550\t정확도: 95.66%\n",
+      "8\t검증 세트 손실: 0.455155\t최선의 손실: 0.150550\t정확도: 77.37%\n",
+      "9\t검증 세트 손실: 0.480061\t최선의 손실: 0.150550\t정확도: 80.57%\n",
+      "10\t검증 세트 손실: 0.521370\t최선의 손실: 0.150550\t정확도: 86.83%\n",
+      "11\t검증 세트 손실: 0.532341\t최선의 손실: 0.150550\t정확도: 74.90%\n",
+      "12\t검증 세트 손실: 0.652826\t최선의 손실: 0.150550\t정확도: 70.72%\n",
+      "13\t검증 세트 손실: 0.617716\t최선의 손실: 0.150550\t정확도: 71.03%\n",
+      "14\t검증 세트 손실: 0.591330\t최선의 손실: 0.150550\t정확도: 72.01%\n",
+      "15\t검증 세트 손실: 0.556862\t최선의 손실: 0.150550\t정확도: 74.24%\n",
+      "16\t검증 세트 손실: 0.591681\t최선의 손실: 0.150550\t정확도: 72.79%\n",
+      "17\t검증 세트 손실: 0.622786\t최선의 손실: 0.150550\t정확도: 70.60%\n",
+      "18\t검증 세트 손실: 0.545044\t최선의 손실: 0.150550\t정확도: 74.59%\n",
+      "19\t검증 세트 손실: 0.547901\t최선의 손실: 0.150550\t정확도: 74.24%\n",
+      "20\t검증 세트 손실: 0.810745\t최선의 손실: 0.150550\t정확도: 55.94%\n",
+      "21\t검증 세트 손실: 0.801731\t최선의 손실: 0.150550\t정확도: 56.80%\n",
+      "22\t검증 세트 손실: 0.914241\t최선의 손실: 0.150550\t정확도: 48.87%\n",
+      "23\t검증 세트 손실: 1.386802\t최선의 손실: 0.150550\t정확도: 32.99%\n",
+      "24\t검증 세트 손실: 1.088921\t최선의 손실: 0.150550\t정확도: 39.48%\n",
+      "25\t검증 세트 손실: 1.060776\t최선의 손실: 0.150550\t정확도: 40.38%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=30, total= 1.6min\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.120158\t최선의 손실: 0.120158\t정확도: 97.07%\n",
-      "1\t검증 세트 손실: 0.071366\t최선의 손실: 0.071366\t정확도: 98.16%\n",
-      "2\t검증 세트 손실: 0.076233\t최선의 손실: 0.071366\t정확도: 97.97%\n",
-      "3\t검증 세트 손실: 0.055587\t최선의 손실: 0.055587\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.069383\t최선의 손실: 0.055587\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.043767\t최선의 손실: 0.043767\t정확도: 98.91%\n",
-      "6\t검증 세트 손실: 0.052748\t최선의 손실: 0.043767\t정확도: 98.67%\n",
-      "7\t검증 세트 손실: 0.075534\t최선의 손실: 0.043767\t정확도: 98.55%\n",
-      "8\t검증 세트 손실: 0.117780\t최선의 손실: 0.043767\t정확도: 97.89%\n",
-      "9\t검증 세트 손실: 0.064375\t최선의 손실: 0.043767\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.056109\t최선의 손실: 0.043767\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.074570\t최선의 손실: 0.043767\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.067239\t최선의 손실: 0.043767\t정확도: 98.40%\n",
-      "13\t검증 세트 손실: 0.078493\t최선의 손실: 0.043767\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.071414\t최선의 손실: 0.043767\t정확도: 98.32%\n",
-      "15\t검증 세트 손실: 0.088129\t최선의 손실: 0.043767\t정확도: 98.20%\n",
-      "16\t검증 세트 손실: 0.069130\t최선의 손실: 0.043767\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.074763\t최선의 손실: 0.043767\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.076867\t최선의 손실: 0.043767\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.063648\t최선의 손실: 0.043767\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.092026\t최선의 손실: 0.043767\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.130808\t최선의 손실: 0.043767\t정확도: 98.63%\n",
-      "22\t검증 세트 손실: 0.130654\t최선의 손실: 0.043767\t정확도: 97.93%\n",
-      "23\t검증 세트 손실: 0.143704\t최선의 손실: 0.043767\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.064002\t최선의 손실: 0.043767\t정확도: 98.63%\n",
-      "25\t검증 세트 손실: 0.045030\t최선의 손실: 0.043767\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.043505\t최선의 손실: 0.043505\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.095036\t최선의 손실: 0.043505\t정확도: 98.83%\n",
-      "28\t검증 세트 손실: 0.074999\t최선의 손실: 0.043505\t정확도: 98.79%\n",
-      "29\t검증 세트 손실: 0.066700\t최선의 손실: 0.043505\t정확도: 98.67%\n",
-      "30\t검증 세트 손실: 0.086559\t최선의 손실: 0.043505\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.097509\t최선의 손실: 0.043505\t정확도: 98.44%\n",
-      "32\t검증 세트 손실: 0.169619\t최선의 손실: 0.043505\t정확도: 97.65%\n",
-      "33\t검증 세트 손실: 0.128630\t최선의 손실: 0.043505\t정확도: 98.12%\n",
-      "34\t검증 세트 손실: 0.101688\t최선의 손실: 0.043505\t정확도: 98.59%\n",
-      "35\t검증 세트 손실: 0.126437\t최선의 손실: 0.043505\t정확도: 98.63%\n",
-      "36\t검증 세트 손실: 0.077922\t최선의 손실: 0.043505\t정확도: 98.75%\n",
-      "37\t검증 세트 손실: 0.155221\t최선의 손실: 0.043505\t정확도: 98.08%\n",
-      "38\t검증 세트 손실: 0.446893\t최선의 손실: 0.043505\t정확도: 98.40%\n",
-      "39\t검증 세트 손실: 0.081665\t최선의 손실: 0.043505\t정확도: 98.20%\n",
-      "40\t검증 세트 손실: 0.086624\t최선의 손실: 0.043505\t정확도: 98.75%\n",
-      "41\t검증 세트 손실: 0.106303\t최선의 손실: 0.043505\t정확도: 98.75%\n",
-      "42\t검증 세트 손실: 0.114733\t최선의 손실: 0.043505\t정확도: 98.75%\n",
-      "43\t검증 세트 손실: 0.111955\t최선의 손실: 0.043505\t정확도: 98.87%\n",
-      "44\t검증 세트 손실: 0.310864\t최선의 손실: 0.043505\t정확도: 97.77%\n"
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, n_neurons=30, total=  41.5s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.099403\t최선의 손실: 0.099403\t정확도: 97.42%\n",
+      "1\t검증 세트 손실: 0.106865\t최선의 손실: 0.099403\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.070944\t최선의 손실: 0.070944\t정확도: 98.40%\n",
+      "3\t검증 세트 손실: 0.088405\t최선의 손실: 0.070944\t정확도: 97.54%\n",
+      "4\t검증 세트 손실: 0.124723\t최선의 손실: 0.070944\t정확도: 97.85%\n",
+      "5\t검증 세트 손실: 0.065126\t최선의 손실: 0.065126\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.105031\t최선의 손실: 0.065126\t정확도: 98.08%\n",
+      "7\t검증 세트 손실: 0.063968\t최선의 손실: 0.063968\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.152365\t최선의 손실: 0.063968\t정확도: 98.12%\n",
+      "9\t검증 세트 손실: 0.065310\t최선의 손실: 0.063968\t정확도: 98.98%\n",
+      "10\t검증 세트 손실: 0.103839\t최선의 손실: 0.063968\t정확도: 98.91%\n",
+      "11\t검증 세트 손실: 0.059531\t최선의 손실: 0.059531\t정확도: 98.87%\n",
+      "12\t검증 세트 손실: 0.052848\t최선의 손실: 0.052848\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.094883\t최선의 손실: 0.052848\t정확도: 98.32%\n",
+      "14\t검증 세트 손실: 0.062268\t최선의 손실: 0.052848\t정확도: 98.51%\n",
+      "15\t검증 세트 손실: 0.058920\t최선의 손실: 0.052848\t정확도: 99.02%\n",
+      "16\t검증 세트 손실: 0.110803\t최선의 손실: 0.052848\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.131475\t최선의 손실: 0.052848\t정확도: 98.40%\n",
+      "18\t검증 세트 손실: 0.128299\t최선의 손실: 0.052848\t정확도: 98.48%\n",
+      "19\t검증 세트 손실: 0.151737\t최선의 손실: 0.052848\t정확도: 98.24%\n",
+      "20\t검증 세트 손실: 0.092345\t최선의 손실: 0.052848\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.102235\t최선의 손실: 0.052848\t정확도: 98.75%\n",
+      "22\t검증 세트 손실: 0.154458\t최선의 손실: 0.052848\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.160669\t최선의 손실: 0.052848\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.265314\t최선의 손실: 0.052848\t정확도: 95.74%\n",
+      "25\t검증 세트 손실: 0.168266\t최선의 손실: 0.052848\t정확도: 97.81%\n",
+      "26\t검증 세트 손실: 0.109742\t최선의 손실: 0.052848\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.140405\t최선의 손실: 0.052848\t정확도: 98.32%\n",
+      "28\t검증 세트 손실: 0.093673\t최선의 손실: 0.052848\t정확도: 98.59%\n",
+      "29\t검증 세트 손실: 0.100620\t최선의 손실: 0.052848\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.199646\t최선의 손실: 0.052848\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.149391\t최선의 손실: 0.052848\t정확도: 98.36%\n",
+      "32\t검증 세트 손실: 0.165423\t최선의 손실: 0.052848\t정확도: 97.77%\n",
+      "33\t검증 세트 손실: 0.193753\t최선의 손실: 0.052848\t정확도: 98.71%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120, total=   6.6s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.098281\t최선의 손실: 0.098281\t정확도: 97.07%\n",
+      "1\t검증 세트 손실: 0.068742\t최선의 손실: 0.068742\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.068669\t최선의 손실: 0.068669\t정확도: 98.20%\n",
+      "3\t검증 세트 손실: 0.058016\t최선의 손실: 0.058016\t정확도: 98.36%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "45\t검증 세트 손실: 0.286172\t최선의 손실: 0.043505\t정확도: 98.16%\n",
-      "46\t검증 세트 손실: 0.707332\t최선의 손실: 0.043505\t정확도: 96.01%\n",
-      "47\t검증 세트 손실: 0.146919\t최선의 손실: 0.043505\t정확도: 97.73%\n",
+      "4\t검증 세트 손실: 0.058197\t최선의 손실: 0.058016\t정확도: 98.51%\n",
+      "5\t검증 세트 손실: 0.087033\t최선의 손실: 0.058016\t정확도: 98.01%\n",
+      "6\t검증 세트 손실: 0.066855\t최선의 손실: 0.058016\t정확도: 98.05%\n",
+      "7\t검증 세트 손실: 0.108692\t최선의 손실: 0.058016\t정확도: 96.91%\n",
+      "8\t검증 세트 손실: 0.402230\t최선의 손실: 0.058016\t정확도: 98.32%\n",
+      "9\t검증 세트 손실: 0.082898\t최선의 손실: 0.058016\t정확도: 98.05%\n",
+      "10\t검증 세트 손실: 0.080534\t최선의 손실: 0.058016\t정확도: 98.36%\n",
+      "11\t검증 세트 손실: 0.065200\t최선의 손실: 0.058016\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.111653\t최선의 손실: 0.058016\t정확도: 98.55%\n",
+      "13\t검증 세트 손실: 0.080090\t최선의 손실: 0.058016\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.091369\t최선의 손실: 0.058016\t정확도: 98.75%\n",
+      "15\t검증 세트 손실: 0.091037\t최선의 손실: 0.058016\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.262611\t최선의 손실: 0.058016\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.089861\t최선의 손실: 0.058016\t정확도: 98.24%\n",
+      "18\t검증 세트 손실: 0.135155\t최선의 손실: 0.058016\t정확도: 97.97%\n",
+      "19\t검증 세트 손실: 0.146337\t최선의 손실: 0.058016\t정확도: 98.48%\n",
+      "20\t검증 세트 손실: 0.151961\t최선의 손실: 0.058016\t정확도: 98.08%\n",
+      "21\t검증 세트 손실: 0.166575\t최선의 손실: 0.058016\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.126371\t최선의 손실: 0.058016\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.223717\t최선의 손실: 0.058016\t정확도: 98.94%\n",
+      "24\t검증 세트 손실: 0.190619\t최선의 손실: 0.058016\t정확도: 98.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120, total=  25.9s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.067246\t최선의 손실: 0.067246\t정확도: 98.08%\n",
-      "1\t검증 세트 손실: 0.073896\t최선의 손실: 0.067246\t정확도: 97.69%\n",
-      "2\t검증 세트 손실: 0.055876\t최선의 손실: 0.055876\t정확도: 98.20%\n",
-      "3\t검증 세트 손실: 0.091007\t최선의 손실: 0.055876\t정확도: 97.58%\n",
-      "4\t검증 세트 손실: 0.080946\t최선의 손실: 0.055876\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 0.064551\t최선의 손실: 0.055876\t정확도: 98.55%\n",
-      "6\t검증 세트 손실: 0.095393\t최선의 손실: 0.055876\t정확도: 97.58%\n",
-      "7\t검증 세트 손실: 0.151538\t최선의 손실: 0.055876\t정확도: 97.81%\n",
-      "8\t검증 세트 손실: 0.075075\t최선의 손실: 0.055876\t정확도: 98.44%\n",
-      "9\t검증 세트 손실: 0.152018\t최선의 손실: 0.055876\t정확도: 97.69%\n",
-      "10\t검증 세트 손실: 0.106562\t최선의 손실: 0.055876\t정확도: 98.01%\n",
-      "11\t검증 세트 손실: 0.078981\t최선의 손실: 0.055876\t정확도: 98.48%\n",
-      "12\t검증 세트 손실: 0.065631\t최선의 손실: 0.055876\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.095457\t최선의 손실: 0.055876\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.254828\t최선의 손실: 0.055876\t정확도: 98.01%\n",
-      "15\t검증 세트 손실: 0.101265\t최선의 손실: 0.055876\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.086630\t최선의 손실: 0.055876\t정확도: 98.67%\n",
-      "17\t검증 세트 손실: 0.099340\t최선의 손실: 0.055876\t정확도: 98.28%\n",
-      "18\t검증 세트 손실: 0.055995\t최선의 손실: 0.055876\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.055159\t최선의 손실: 0.055159\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.057496\t최선의 손실: 0.055159\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.077435\t최선의 손실: 0.055159\t정확도: 98.63%\n",
-      "22\t검증 세트 손실: 0.135955\t최선의 손실: 0.055159\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.325772\t최선의 손실: 0.055159\t정확도: 97.42%\n",
-      "24\t검증 세트 손실: 0.075199\t최선의 손실: 0.055159\t정확도: 98.01%\n",
-      "25\t검증 세트 손실: 0.093766\t최선의 손실: 0.055159\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.148443\t최선의 손실: 0.055159\t정확도: 98.20%\n",
-      "27\t검증 세트 손실: 0.151517\t최선의 손실: 0.055159\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.099230\t최선의 손실: 0.055159\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.106178\t최선의 손실: 0.055159\t정확도: 98.94%\n",
-      "30\t검증 세트 손실: 0.109847\t최선의 손실: 0.055159\t정확도: 98.83%\n",
-      "31\t검증 세트 손실: 0.126557\t최선의 손실: 0.055159\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.106449\t최선의 손실: 0.055159\t정확도: 98.59%\n",
-      "33\t검증 세트 손실: 0.125655\t최선의 손실: 0.055159\t정확도: 98.51%\n",
-      "34\t검증 세트 손실: 0.101301\t최선의 손실: 0.055159\t정확도: 98.51%\n",
-      "35\t검증 세트 손실: 0.075254\t최선의 손실: 0.055159\t정확도: 98.91%\n",
-      "36\t검증 세트 손실: 0.060080\t최선의 손실: 0.055159\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.127021\t최선의 손실: 0.055159\t정확도: 98.79%\n",
-      "38\t검증 세트 손실: 0.140531\t최선의 손실: 0.055159\t정확도: 98.79%\n",
-      "39\t검증 세트 손실: 0.287493\t최선의 손실: 0.055159\t정확도: 98.12%\n",
-      "40\t검증 세트 손실: 0.078646\t최선의 손실: 0.055159\t정확도: 98.79%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120, total=   4.9s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.085552\t최선의 손실: 0.085552\t정확도: 97.85%\n",
+      "1\t검증 세트 손실: 0.089608\t최선의 손실: 0.085552\t정확도: 97.89%\n",
+      "2\t검증 세트 손실: 0.070552\t최선의 손실: 0.070552\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.082482\t최선의 손실: 0.070552\t정확도: 98.44%\n",
+      "4\t검증 세트 손실: 0.079973\t최선의 손실: 0.070552\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.088804\t최선의 손실: 0.070552\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.096330\t최선의 손실: 0.070552\t정확도: 98.16%\n",
+      "7\t검증 세트 손실: 0.095007\t최선의 손실: 0.070552\t정확도: 98.24%\n",
+      "8\t검증 세트 손실: 0.089123\t최선의 손실: 0.070552\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.082870\t최선의 손실: 0.070552\t정확도: 98.20%\n",
+      "10\t검증 세트 손실: 0.074764\t최선의 손실: 0.070552\t정확도: 98.40%\n",
+      "11\t검증 세트 손실: 0.082377\t최선의 손실: 0.070552\t정확도: 98.08%\n",
+      "12\t검증 세트 손실: 0.063850\t최선의 손실: 0.063850\t정확도: 98.79%\n",
+      "13\t검증 세트 손실: 0.065651\t최선의 손실: 0.063850\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.074433\t최선의 손실: 0.063850\t정확도: 99.14%\n",
+      "15\t검증 세트 손실: 0.094154\t최선의 손실: 0.063850\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.139724\t최선의 손실: 0.063850\t정확도: 98.12%\n",
+      "17\t검증 세트 손실: 0.109124\t최선의 손실: 0.063850\t정확도: 97.97%\n",
+      "18\t검증 세트 손실: 0.076964\t최선의 손실: 0.063850\t정확도: 98.48%\n",
+      "19\t검증 세트 손실: 0.090597\t최선의 손실: 0.063850\t정확도: 98.55%\n",
+      "20\t검증 세트 손실: 0.103935\t최선의 손실: 0.063850\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.879723\t최선의 손실: 0.063850\t정확도: 98.44%\n",
+      "22\t검증 세트 손실: 0.198376\t최선의 손실: 0.063850\t정확도: 97.34%\n",
+      "23\t검증 세트 손실: 0.339309\t최선의 손실: 0.063850\t정확도: 98.55%\n",
+      "24\t검증 세트 손실: 0.112018\t최선의 손실: 0.063850\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.146320\t최선의 손실: 0.063850\t정확도: 98.63%\n",
+      "26\t검증 세트 손실: 0.206771\t최선의 손실: 0.063850\t정확도: 98.32%\n",
+      "27\t검증 세트 손실: 0.072395\t최선의 손실: 0.063850\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.115217\t최선의 손실: 0.063850\t정확도: 98.40%\n",
+      "29\t검증 세트 손실: 0.107765\t최선의 손실: 0.063850\t정확도: 98.63%\n",
+      "30\t검증 세트 손실: 0.141313\t최선의 손실: 0.063850\t정확도: 98.75%\n",
+      "31\t검증 세트 손실: 0.069053\t최선의 손실: 0.063850\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.083974\t최선의 손실: 0.063850\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.073239\t최선의 손실: 0.063850\t정확도: 98.79%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120, total=  18.4s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.065767\t최선의 손실: 0.065767\t정확도: 98.08%\n",
-      "1\t검증 세트 손실: 0.070170\t최선의 손실: 0.065767\t정확도: 98.08%\n",
-      "2\t검증 세트 손실: 0.074742\t최선의 손실: 0.065767\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 0.088692\t최선의 손실: 0.065767\t정확도: 97.77%\n",
-      "4\t검증 세트 손실: 0.080009\t최선의 손실: 0.065767\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.060895\t최선의 손실: 0.060895\t정확도: 98.36%\n",
-      "6\t검증 세트 손실: 0.081106\t최선의 손실: 0.060895\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.053233\t최선의 손실: 0.053233\t정확도: 98.55%\n",
-      "8\t검증 세트 손실: 0.454540\t최선의 손실: 0.053233\t정확도: 93.24%\n",
-      "9\t검증 세트 손실: 0.111023\t최선의 손실: 0.053233\t정확도: 97.93%\n",
-      "10\t검증 세트 손실: 0.078336\t최선의 손실: 0.053233\t정확도: 98.36%\n",
-      "11\t검증 세트 손실: 0.105994\t최선의 손실: 0.053233\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.060525\t최선의 손실: 0.053233\t정확도: 98.55%\n",
-      "13\t검증 세트 손실: 0.077578\t최선의 손실: 0.053233\t정확도: 98.51%\n",
-      "14\t검증 세트 손실: 0.088879\t최선의 손실: 0.053233\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.064710\t최선의 손실: 0.053233\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.075178\t최선의 손실: 0.053233\t정확도: 98.44%\n",
-      "17\t검증 세트 손실: 0.066707\t최선의 손실: 0.053233\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.084640\t최선의 손실: 0.053233\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.090649\t최선의 손실: 0.053233\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.075895\t최선의 손실: 0.053233\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.060686\t최선의 손실: 0.053233\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.070502\t최선의 손실: 0.053233\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.106062\t최선의 손실: 0.053233\t정확도: 98.91%\n",
-      "24\t검증 세트 손실: 0.062335\t최선의 손실: 0.053233\t정확도: 98.67%\n",
-      "25\t검증 세트 손실: 0.079347\t최선의 손실: 0.053233\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.106836\t최선의 손실: 0.053233\t정확도: 99.02%\n",
-      "27\t검증 세트 손실: 0.081122\t최선의 손실: 0.053233\t정확도: 98.05%\n",
-      "28\t검증 세트 손실: 0.074058\t최선의 손실: 0.053233\t정확도: 98.79%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120, total=   6.7s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.170717\t최선의 손실: 0.170717\t정확도: 95.70%\n",
+      "1\t검증 세트 손실: 0.112153\t최선의 손실: 0.112153\t정확도: 97.22%\n",
+      "2\t검증 세트 손실: 0.356301\t최선의 손실: 0.112153\t정확도: 97.07%\n",
+      "3\t검증 세트 손실: 0.165519\t최선의 손실: 0.112153\t정확도: 95.54%\n",
+      "4\t검증 세트 손실: 0.117512\t최선의 손실: 0.112153\t정확도: 96.56%\n",
+      "5\t검증 세트 손실: 0.236505\t최선의 손실: 0.112153\t정확도: 95.97%\n",
+      "6\t검증 세트 손실: 0.122924\t최선의 손실: 0.112153\t정확도: 96.68%\n",
+      "7\t검증 세트 손실: 0.094885\t최선의 손실: 0.094885\t정확도: 97.65%\n",
+      "8\t검증 세트 손실: 0.125687\t최선의 손실: 0.094885\t정확도: 97.11%\n",
+      "9\t검증 세트 손실: 0.166539\t최선의 손실: 0.094885\t정확도: 97.69%\n",
+      "10\t검증 세트 손실: 0.445109\t최선의 손실: 0.094885\t정확도: 77.72%\n",
+      "11\t검증 세트 손실: 0.579614\t최선의 손실: 0.094885\t정확도: 71.85%\n",
+      "12\t검증 세트 손실: 0.237894\t최선의 손실: 0.094885\t정확도: 93.59%\n",
+      "13\t검증 세트 손실: 0.991413\t최선의 손실: 0.094885\t정확도: 52.81%\n",
+      "14\t검증 세트 손실: 1.285432\t최선의 손실: 0.094885\t정확도: 37.37%\n",
+      "15\t검증 세트 손실: 0.953269\t최선의 손실: 0.094885\t정확도: 53.87%\n",
+      "16\t검증 세트 손실: 0.927930\t최선의 손실: 0.094885\t정확도: 55.24%\n",
+      "17\t검증 세트 손실: 0.853495\t최선의 손실: 0.094885\t정확도: 58.25%\n",
+      "18\t검증 세트 손실: 0.664458\t최선의 손실: 0.094885\t정확도: 66.97%\n",
+      "19\t검증 세트 손실: 1.103874\t최선의 손실: 0.094885\t정확도: 45.11%\n",
+      "20\t검증 세트 손실: 1.370126\t최선의 손실: 0.094885\t정확도: 34.83%\n",
+      "21\t검증 세트 손실: 0.897738\t최선의 손실: 0.094885\t정확도: 58.21%\n",
+      "22\t검증 세트 손실: 1.218518\t최선의 손실: 0.094885\t정확도: 38.58%\n",
+      "23\t검증 세트 손실: 1.238646\t최선의 손실: 0.094885\t정확도: 37.84%\n",
+      "24\t검증 세트 손실: 1.189381\t최선의 손실: 0.094885\t정확도: 39.99%\n",
+      "25\t검증 세트 손실: 1.227096\t최선의 손실: 0.094885\t정확도: 39.72%\n",
+      "26\t검증 세트 손실: 1.168134\t최선의 손실: 0.094885\t정확도: 39.99%\n",
+      "27\t검증 세트 손실: 1.173124\t최선의 손실: 0.094885\t정확도: 40.42%\n",
+      "28\t검증 세트 손실: 1.162961\t최선의 손실: 0.094885\t정확도: 39.72%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120, total=   8.2s\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.221267\t최선의 손실: 0.221267\t정확도: 93.63%\n",
-      "1\t검증 세트 손실: 0.289041\t최선의 손실: 0.221267\t정확도: 91.13%\n",
-      "2\t검증 세트 손실: 0.179430\t최선의 손실: 0.179430\t정확도: 96.33%\n",
-      "3\t검증 세트 손실: 0.230503\t최선의 손실: 0.179430\t정확도: 96.21%\n",
-      "4\t검증 세트 손실: 0.207437\t최선의 손실: 0.179430\t정확도: 94.41%\n",
-      "5\t검증 세트 손실: 0.182269\t최선의 손실: 0.179430\t정확도: 95.86%\n",
-      "6\t검증 세트 손실: 0.494662\t최선의 손실: 0.179430\t정확도: 89.52%\n",
-      "7\t검증 세트 손실: 0.187349\t최선의 손실: 0.179430\t정확도: 96.01%\n",
-      "8\t검증 세트 손실: 0.827735\t최선의 손실: 0.179430\t정확도: 57.39%\n",
-      "9\t검증 세트 손실: 0.841292\t최선의 손실: 0.179430\t정확도: 66.15%\n",
-      "10\t검증 세트 손실: 1.134344\t최선의 손실: 0.179430\t정확도: 48.20%\n",
-      "11\t검증 세트 손실: 1.369024\t최선의 손실: 0.179430\t정확도: 33.35%\n",
-      "12\t검증 세트 손실: 1.358167\t최선의 손실: 0.179430\t정확도: 42.06%\n",
-      "13\t검증 세트 손실: 1.285436\t최선의 손실: 0.179430\t정확도: 40.27%\n",
-      "14\t검증 세트 손실: 0.961020\t최선의 손실: 0.179430\t정확도: 52.62%\n",
-      "15\t검증 세트 손실: 1.201801\t최선의 손실: 0.179430\t정확도: 38.00%\n",
-      "16\t검증 세트 손실: 1.214425\t최선의 손실: 0.179430\t정확도: 39.01%\n",
-      "17\t검증 세트 손실: 1.370331\t최선의 손실: 0.179430\t정확도: 40.42%\n",
-      "18\t검증 세트 손실: 1.536335\t최선의 손실: 0.179430\t정확도: 39.01%\n",
-      "19\t검증 세트 손실: 1.358865\t최선의 손실: 0.179430\t정확도: 35.07%\n",
-      "20\t검증 세트 손실: 1.177379\t최선의 손실: 0.179430\t정확도: 41.24%\n",
-      "21\t검증 세트 손실: 1.460414\t최선의 손실: 0.179430\t정확도: 32.33%\n",
-      "22\t검증 세트 손실: 1.611078\t최선의 손실: 0.179430\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.611237\t최선의 손실: 0.179430\t정확도: 19.27%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120, total=  46.1s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.168923\t최선의 손실: 0.168923\t정확도: 96.64%\n",
+      "1\t검증 세트 손실: 0.668021\t최선의 손실: 0.168923\t정확도: 78.26%\n",
+      "2\t검증 세트 손실: 0.135307\t최선의 손실: 0.135307\t정확도: 96.44%\n",
+      "3\t검증 세트 손실: 0.223210\t최선의 손실: 0.135307\t정확도: 95.70%\n",
+      "4\t검증 세트 손실: 0.244548\t최선의 손실: 0.135307\t정확도: 95.31%\n",
+      "5\t검증 세트 손실: 0.355595\t최선의 손실: 0.135307\t정확도: 88.23%\n",
+      "6\t검증 세트 손실: 0.215950\t최선의 손실: 0.135307\t정확도: 95.54%\n",
+      "7\t검증 세트 손실: 0.215260\t최선의 손실: 0.135307\t정확도: 94.18%\n",
+      "8\t검증 세트 손실: 0.431250\t최선의 손실: 0.135307\t정확도: 88.74%\n",
+      "9\t검증 세트 손실: 0.586349\t최선의 손실: 0.135307\t정확도: 75.33%\n",
+      "10\t검증 세트 손실: 0.467814\t최선의 손실: 0.135307\t정확도: 77.33%\n",
+      "11\t검증 세트 손실: 0.595968\t최선의 손실: 0.135307\t정확도: 77.99%\n",
+      "12\t검증 세트 손실: 0.597795\t최선의 손실: 0.135307\t정확도: 73.73%\n",
+      "13\t검증 세트 손실: 0.721484\t최선의 손실: 0.135307\t정확도: 70.02%\n",
+      "14\t검증 세트 손실: 1.045896\t최선의 손실: 0.135307\t정확도: 50.78%\n",
+      "15\t검증 세트 손실: 0.978818\t최선의 손실: 0.135307\t정확도: 59.03%\n",
+      "16\t검증 세트 손실: 0.712122\t최선의 손실: 0.135307\t정확도: 60.13%\n",
+      "17\t검증 세트 손실: 0.980456\t최선의 손실: 0.135307\t정확도: 54.89%\n",
+      "18\t검증 세트 손실: 1.260593\t최선의 손실: 0.135307\t정확도: 39.01%\n",
+      "19\t검증 세트 손실: 1.232449\t최선의 손실: 0.135307\t정확도: 39.80%\n",
+      "20\t검증 세트 손실: 0.785241\t최선의 손실: 0.135307\t정확도: 60.05%\n",
+      "21\t검증 세트 손실: 0.821024\t최선의 손실: 0.135307\t정확도: 53.99%\n",
+      "22\t검증 세트 손실: 1.102662\t최선의 손실: 0.135307\t정확도: 47.93%\n",
+      "23\t검증 세트 손실: 0.818214\t최선의 손실: 0.135307\t정확도: 57.19%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120, total= 1.1min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.339182\t최선의 손실: 0.339182\t정확도: 92.81%\n",
-      "1\t검증 세트 손실: 0.228361\t최선의 손실: 0.228361\t정확도: 95.47%\n",
-      "2\t검증 세트 손실: 0.266401\t최선의 손실: 0.228361\t정확도: 93.78%\n",
-      "3\t검증 세트 손실: 0.206344\t최선의 손실: 0.206344\t정확도: 93.35%\n",
-      "4\t검증 세트 손실: 0.352000\t최선의 손실: 0.206344\t정확도: 93.43%\n",
-      "5\t검증 세트 손실: 0.548482\t최선의 손실: 0.206344\t정확도: 76.15%\n",
-      "6\t검증 세트 손실: 1.317818\t최선의 손실: 0.206344\t정확도: 35.89%\n",
-      "7\t검증 세트 손실: 0.858590\t최선의 손실: 0.206344\t정확도: 57.58%\n",
-      "8\t검증 세트 손실: 0.586514\t최선의 손실: 0.206344\t정확도: 72.67%\n",
-      "9\t검증 세트 손실: 0.481579\t최선의 손실: 0.206344\t정확도: 77.01%\n",
-      "10\t검증 세트 손실: 1.317995\t최선의 손실: 0.206344\t정확도: 40.15%\n",
-      "11\t검증 세트 손실: 1.138342\t최선의 손실: 0.206344\t정확도: 42.57%\n",
-      "12\t검증 세트 손실: 1.677455\t최선의 손실: 0.206344\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.610888\t최선의 손실: 0.206344\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.608781\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.609206\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.610895\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.611615\t최선의 손실: 0.206344\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.610137\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.609073\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.609575\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.612212\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.617150\t최선의 손실: 0.206344\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.613451\t최선의 손실: 0.206344\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.611077\t최선의 손실: 0.206344\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120, total=  38.1s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.211541\t최선의 손실: 0.211541\t정확도: 94.14%\n",
+      "1\t검증 세트 손실: 0.156464\t최선의 손실: 0.156464\t정확도: 97.22%\n",
+      "2\t검증 세트 손실: 0.143931\t최선의 손실: 0.143931\t정확도: 96.13%\n",
+      "3\t검증 세트 손실: 0.388200\t최선의 손실: 0.143931\t정확도: 85.97%\n",
+      "4\t검증 세트 손실: 0.506145\t최선의 손실: 0.143931\t정확도: 89.29%\n",
+      "5\t검증 세트 손실: 0.386040\t최선의 손실: 0.143931\t정확도: 94.53%\n",
+      "6\t검증 세트 손실: 0.467338\t최선의 손실: 0.143931\t정확도: 90.93%\n",
+      "7\t검증 세트 손실: 1.227406\t최선의 손실: 0.143931\t정확도: 39.80%\n",
+      "8\t검증 세트 손실: 0.365700\t최선의 손실: 0.143931\t정확도: 95.19%\n",
+      "9\t검증 세트 손실: 0.489615\t최선의 손실: 0.143931\t정확도: 84.95%\n",
+      "10\t검증 세트 손실: 0.361486\t최선의 손실: 0.143931\t정확도: 90.23%\n",
+      "11\t검증 세트 손실: 0.542525\t최선의 손실: 0.143931\t정확도: 75.57%\n",
+      "12\t검증 세트 손실: 0.407821\t최선의 손실: 0.143931\t정확도: 89.05%\n",
+      "13\t검증 세트 손실: 0.516631\t최선의 손실: 0.143931\t정확도: 85.61%\n",
+      "14\t검증 세트 손실: 0.514917\t최선의 손실: 0.143931\t정확도: 79.83%\n",
+      "15\t검증 세트 손실: 0.548003\t최선의 손실: 0.143931\t정확도: 82.84%\n",
+      "16\t검증 세트 손실: 0.317007\t최선의 손실: 0.143931\t정확도: 91.95%\n",
+      "17\t검증 세트 손실: 0.425324\t최선의 손실: 0.143931\t정확도: 88.90%\n",
+      "18\t검증 세트 손실: 0.416569\t최선의 손실: 0.143931\t정확도: 86.94%\n",
+      "19\t검증 세트 손실: 0.572535\t최선의 손실: 0.143931\t정확도: 78.77%\n",
+      "20\t검증 세트 손실: 0.440767\t최선의 손실: 0.143931\t정확도: 76.43%\n",
+      "21\t검증 세트 손실: 0.838530\t최선의 손실: 0.143931\t정확도: 58.33%\n",
+      "22\t검증 세트 손실: 0.849207\t최선의 손실: 0.143931\t정확도: 54.53%\n",
+      "23\t검증 세트 손실: 1.302065\t최선의 손실: 0.143931\t정확도: 39.72%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120, total= 1.9min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.213156\t최선의 손실: 0.213156\t정확도: 95.97%\n",
-      "1\t검증 세트 손실: 0.211482\t최선의 손실: 0.211482\t정확도: 95.66%\n",
-      "2\t검증 세트 손실: 0.461584\t최선의 손실: 0.211482\t정확도: 85.97%\n",
-      "3\t검증 세트 손실: 0.137723\t최선의 손실: 0.137723\t정확도: 96.99%\n",
-      "4\t검증 세트 손실: 0.327331\t최선의 손실: 0.137723\t정확도: 95.07%\n",
-      "5\t검증 세트 손실: 0.249639\t최선의 손실: 0.137723\t정확도: 95.04%\n",
-      "6\t검증 세트 손실: 0.303737\t최선의 손실: 0.137723\t정확도: 96.01%\n",
-      "7\t검증 세트 손실: 0.551523\t최선의 손실: 0.137723\t정확도: 95.04%\n",
-      "8\t검증 세트 손실: 2.204783\t최선의 손실: 0.137723\t정확도: 93.08%\n",
-      "9\t검증 세트 손실: 0.664218\t최선의 손실: 0.137723\t정확도: 68.22%\n",
-      "10\t검증 세트 손실: 1.151074\t최선의 손실: 0.137723\t정확도: 92.65%\n",
-      "11\t검증 세트 손실: 0.851433\t최선의 손실: 0.137723\t정확도: 59.11%\n",
-      "12\t검증 세트 손실: 1.221146\t최선의 손실: 0.137723\t정확도: 37.26%\n",
-      "13\t검증 세트 손실: 1.198441\t최선의 손실: 0.137723\t정확도: 38.58%\n",
-      "14\t검증 세트 손실: 1.210691\t최선의 손실: 0.137723\t정확도: 40.15%\n",
-      "15\t검증 세트 손실: 1.202851\t최선의 손실: 0.137723\t정확도: 40.15%\n",
-      "16\t검증 세트 손실: 1.205243\t최선의 손실: 0.137723\t정확도: 39.99%\n",
-      "17\t검증 세트 손실: 1.203615\t최선의 손실: 0.137723\t정확도: 36.63%\n"
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=120, total=  38.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1314.942383\t최선의 손실: 1314.942383\t정확도: 25.49%\n",
+      "1\t검증 세트 손실: 40.076431\t최선의 손실: 40.076431\t정확도: 81.04%\n",
+      "2\t검증 세트 손실: 6.392291\t최선의 손실: 6.392291\t정확도: 92.42%\n",
+      "3\t검증 세트 손실: 2.217434\t최선의 손실: 2.217434\t정확도: 94.76%\n",
+      "4\t검증 세트 손실: 2.452962\t최선의 손실: 2.217434\t정확도: 92.26%\n",
+      "5\t검증 세트 손실: 2.313586\t최선의 손실: 2.217434\t정확도: 94.76%\n",
+      "6\t검증 세트 손실: 1.443193\t최선의 손실: 1.443193\t정확도: 95.27%\n",
+      "7\t검증 세트 손실: 1.204808\t최선의 손실: 1.204808\t정확도: 95.43%\n",
+      "8\t검증 세트 손실: 1.057029\t최선의 손실: 1.057029\t정확도: 93.67%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "18\t검증 세트 손실: 1.201473\t최선의 손실: 0.137723\t정확도: 39.99%\n",
-      "19\t검증 세트 손실: 1.203602\t최선의 손실: 0.137723\t정확도: 39.99%\n",
-      "20\t검증 세트 손실: 1.204841\t최선의 손실: 0.137723\t정확도: 36.75%\n",
-      "21\t검증 세트 손실: 1.203644\t최선의 손실: 0.137723\t정확도: 39.99%\n",
-      "22\t검증 세트 손실: 1.204627\t최선의 손실: 0.137723\t정확도: 39.99%\n",
-      "23\t검증 세트 손실: 1.204855\t최선의 손실: 0.137723\t정확도: 39.99%\n",
-      "24\t검증 세트 손실: 1.201652\t최선의 손실: 0.137723\t정확도: 38.90%\n",
+      "9\t검증 세트 손실: 1.192031\t최선의 손실: 1.057029\t정확도: 95.47%\n",
+      "10\t검증 세트 손실: 0.920280\t최선의 손실: 0.920280\t정확도: 94.92%\n",
+      "11\t검증 세트 손실: 0.971789\t최선의 손실: 0.920280\t정확도: 95.90%\n",
+      "12\t검증 세트 손실: 1.129442\t최선의 손실: 0.920280\t정확도: 94.29%\n",
+      "13\t검증 세트 손실: 0.714328\t최선의 손실: 0.714328\t정확도: 96.05%\n",
+      "14\t검증 세트 손실: 1.070054\t최선의 손실: 0.714328\t정확도: 95.54%\n",
+      "15\t검증 세트 손실: 1.970670\t최선의 손실: 0.714328\t정확도: 94.84%\n",
+      "16\t검증 세트 손실: 0.698383\t최선의 손실: 0.698383\t정확도: 96.25%\n",
+      "17\t검증 세트 손실: 0.869765\t최선의 손실: 0.698383\t정확도: 96.60%\n",
+      "18\t검증 세트 손실: 0.671454\t최선의 손실: 0.671454\t정확도: 96.87%\n",
+      "19\t검증 세트 손실: 0.675267\t최선의 손실: 0.671454\t정확도: 93.35%\n",
+      "20\t검증 세트 손실: 0.674204\t최선의 손실: 0.671454\t정확도: 96.48%\n",
+      "21\t검증 세트 손실: 0.731187\t최선의 손실: 0.671454\t정확도: 96.21%\n",
+      "22\t검증 세트 손실: 1.082663\t최선의 손실: 0.671454\t정확도: 96.09%\n",
+      "23\t검증 세트 손실: 0.827655\t최선의 손실: 0.671454\t정확도: 94.64%\n",
+      "24\t검증 세트 손실: 1.153016\t최선의 손실: 0.671454\t정확도: 96.25%\n",
+      "25\t검증 세트 손실: 0.944627\t최선의 손실: 0.671454\t정확도: 96.87%\n",
+      "26\t검증 세트 손실: 0.894790\t최선의 손실: 0.671454\t정확도: 96.40%\n",
+      "27\t검증 세트 손실: 0.819723\t최선의 손실: 0.671454\t정확도: 95.66%\n",
+      "28\t검증 세트 손실: 0.638434\t최선의 손실: 0.638434\t정확도: 97.30%\n",
+      "29\t검증 세트 손실: 50772136.000000\t최선의 손실: 0.638434\t정확도: 20.91%\n",
+      "30\t검증 세트 손실: 4854678.500000\t최선의 손실: 0.638434\t정확도: 39.25%\n",
+      "31\t검증 세트 손실: 117958.554688\t최선의 손실: 0.638434\t정확도: 76.00%\n",
+      "32\t검증 세트 손실: 29899.611328\t최선의 손실: 0.638434\t정확도: 92.61%\n",
+      "33\t검증 세트 손실: 17746.064453\t최선의 손실: 0.638434\t정확도: 94.14%\n",
+      "34\t검증 세트 손실: 19501.957031\t최선의 손실: 0.638434\t정확도: 92.18%\n",
+      "35\t검증 세트 손실: 17806.558594\t최선의 손실: 0.638434\t정확도: 91.40%\n",
+      "36\t검증 세트 손실: 11128.076172\t최선의 손실: 0.638434\t정확도: 94.68%\n",
+      "37\t검증 세트 손실: 10683.347656\t최선의 손실: 0.638434\t정확도: 94.33%\n",
+      "38\t검증 세트 손실: 12163.385742\t최선의 손실: 0.638434\t정확도: 92.89%\n",
+      "39\t검증 세트 손실: 10424.551758\t최선의 손실: 0.638434\t정확도: 93.55%\n",
+      "40\t검증 세트 손실: 9157.805664\t최선의 손실: 0.638434\t정확도: 94.25%\n",
+      "41\t검증 세트 손실: 10931.814453\t최선의 손실: 0.638434\t정확도: 88.19%\n",
+      "42\t검증 세트 손실: 7521.845215\t최선의 손실: 0.638434\t정확도: 94.25%\n",
+      "43\t검증 세트 손실: 7186.455078\t최선의 손실: 0.638434\t정확도: 95.47%\n",
+      "44\t검증 세트 손실: 10835.562500\t최선의 손실: 0.638434\t정확도: 87.92%\n",
+      "45\t검증 세트 손실: 9688.641602\t최선의 손실: 0.638434\t정확도: 91.20%\n",
+      "46\t검증 세트 손실: 8415.784180\t최선의 손실: 0.638434\t정확도: 91.09%\n",
+      "47\t검증 세트 손실: 5121.711426\t최선의 손실: 0.638434\t정확도: 95.39%\n",
+      "48\t검증 세트 손실: 5273.121094\t최선의 손실: 0.638434\t정확도: 94.92%\n",
+      "49\t검증 세트 손실: 4206.107910\t최선의 손실: 0.638434\t정확도: 95.93%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=120, total= 2.1min\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 294.072998\t최선의 손실: 294.072998\t정확도: 55.36%\n",
-      "1\t검증 세트 손실: 9.411483\t최선의 손실: 9.411483\t정확도: 94.53%\n",
-      "2\t검증 세트 손실: 3.487891\t최선의 손실: 3.487891\t정확도: 95.04%\n",
-      "3\t검증 세트 손실: 1.285705\t최선의 손실: 1.285705\t정확도: 95.27%\n",
-      "4\t검증 세트 손실: 0.828561\t최선의 손실: 0.828561\t정확도: 96.64%\n",
-      "5\t검증 세트 손실: 1.328397\t최선의 손실: 0.828561\t정확도: 95.78%\n",
-      "6\t검증 세트 손실: 0.768011\t최선의 손실: 0.768011\t정확도: 96.48%\n",
-      "7\t검증 세트 손실: 2.023814\t최선의 손실: 0.768011\t정확도: 93.24%\n",
-      "8\t검증 세트 손실: 0.951476\t최선의 손실: 0.768011\t정확도: 96.76%\n",
-      "9\t검증 세트 손실: 0.825493\t최선의 손실: 0.768011\t정확도: 96.29%\n",
-      "10\t검증 세트 손실: 1.174926\t최선의 손실: 0.768011\t정확도: 96.21%\n",
-      "11\t검증 세트 손실: 0.863615\t최선의 손실: 0.768011\t정확도: 96.99%\n",
-      "12\t검증 세트 손실: 1.090430\t최선의 손실: 0.768011\t정확도: 93.24%\n",
-      "13\t검증 세트 손실: 0.734882\t최선의 손실: 0.734882\t정확도: 96.29%\n",
-      "14\t검증 세트 손실: 1.181044\t최선의 손실: 0.734882\t정확도: 92.18%\n",
-      "15\t검증 세트 손실: 0.670621\t최선의 손실: 0.670621\t정확도: 96.79%\n",
-      "16\t검증 세트 손실: 0.783295\t최선의 손실: 0.670621\t정확도: 96.60%\n",
-      "17\t검증 세트 손실: 1.023281\t최선의 손실: 0.670621\t정확도: 94.41%\n",
-      "18\t검증 세트 손실: 0.652984\t최선의 손실: 0.652984\t정확도: 97.38%\n",
-      "19\t검증 세트 손실: 0.602854\t최선의 손실: 0.602854\t정확도: 97.11%\n",
-      "20\t검증 세트 손실: 0.631285\t최선의 손실: 0.602854\t정확도: 97.11%\n",
-      "21\t검증 세트 손실: 0.421012\t최선의 손실: 0.421012\t정확도: 96.91%\n",
-      "22\t검증 세트 손실: 0.694989\t최선의 손실: 0.421012\t정확도: 95.70%\n",
-      "23\t검증 세트 손실: 0.751794\t최선의 손실: 0.421012\t정확도: 96.68%\n",
-      "24\t검증 세트 손실: 0.507754\t최선의 손실: 0.421012\t정확도: 97.46%\n",
-      "25\t검증 세트 손실: 0.575431\t최선의 손실: 0.421012\t정확도: 97.46%\n",
-      "26\t검증 세트 손실: 0.496305\t최선의 손실: 0.421012\t정확도: 97.26%\n",
-      "27\t검증 세트 손실: 0.549811\t최선의 손실: 0.421012\t정확도: 97.11%\n",
-      "28\t검증 세트 손실: 0.547750\t최선의 손실: 0.421012\t정확도: 97.11%\n",
-      "29\t검증 세트 손실: 11.552811\t최선의 손실: 0.421012\t정확도: 86.36%\n",
-      "30\t검증 세트 손실: 217755408.000000\t최선의 손실: 0.421012\t정확도: 20.91%\n",
-      "31\t검증 세트 손실: 5290664.000000\t최선의 손실: 0.421012\t정확도: 48.20%\n",
-      "32\t검증 세트 손실: 298539.500000\t최선의 손실: 0.421012\t정확도: 84.87%\n",
-      "33\t검증 세트 손실: 287788.531250\t최선의 손실: 0.421012\t정확도: 78.30%\n",
-      "34\t검증 세트 손실: 102085.953125\t최선의 손실: 0.421012\t정확도: 86.67%\n",
-      "35\t검증 세트 손실: 162748.703125\t최선의 손실: 0.421012\t정확도: 89.99%\n",
-      "36\t검증 세트 손실: 74364.820312\t최선의 손실: 0.421012\t정확도: 92.92%\n",
-      "37\t검증 세트 손실: 107251.640625\t최선의 손실: 0.421012\t정확도: 87.80%\n",
-      "38\t검증 세트 손실: 51531.953125\t최선의 손실: 0.421012\t정확도: 94.25%\n",
-      "39\t검증 세트 손실: 37527.601562\t최선의 손실: 0.421012\t정확도: 94.49%\n",
-      "40\t검증 세트 손실: 178241.921875\t최선의 손실: 0.421012\t정확도: 75.76%\n",
-      "41\t검증 세트 손실: 70224.445312\t최선의 손실: 0.421012\t정확도: 93.59%\n",
-      "42\t검증 세트 손실: 51960.617188\t최선의 손실: 0.421012\t정확도: 92.61%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=100, total=   3.6s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 600.989075\t최선의 손실: 600.989075\t정확도: 56.72%\n",
-      "1\t검증 세트 손실: 1256.166382\t최선의 손실: 600.989075\t정확도: 69.27%\n",
-      "2\t검증 세트 손실: 50.853348\t최선의 손실: 50.853348\t정확도: 80.65%\n",
-      "3\t검증 세트 손실: 6.459490\t최선의 손실: 6.459490\t정확도: 92.73%\n",
-      "4\t검증 세트 손실: 5.059019\t최선의 손실: 5.059019\t정확도: 92.18%\n",
-      "5\t검증 세트 손실: 4.482785\t최선의 손실: 4.482785\t정확도: 93.94%\n",
-      "6\t검증 세트 손실: 12.717978\t최선의 손실: 4.482785\t정확도: 79.59%\n",
-      "7\t검증 세트 손실: 5.989799\t최선의 손실: 4.482785\t정확도: 95.04%\n",
-      "8\t검증 세트 손실: 4.843441\t최선의 손실: 4.482785\t정확도: 93.59%\n",
-      "9\t검증 세트 손실: 3.658738\t최선의 손실: 3.658738\t정확도: 95.07%\n",
-      "10\t검증 세트 손실: 3.296386\t최선의 손실: 3.296386\t정확도: 94.33%\n",
-      "11\t검증 세트 손실: 2.281672\t최선의 손실: 2.281672\t정확도: 96.44%\n",
-      "12\t검증 세트 손실: 2.791848\t최선의 손실: 2.281672\t정확도: 94.64%\n",
-      "13\t검증 세트 손실: 1.759879\t최선의 손실: 1.759879\t정확도: 96.87%\n",
-      "14\t검증 세트 손실: 1.647222\t최선의 손실: 1.647222\t정확도: 96.09%\n",
-      "15\t검증 세트 손실: 1.439582\t최선의 손실: 1.439582\t정확도: 96.76%\n",
-      "16\t검증 세트 손실: 1.310678\t최선의 손실: 1.310678\t정확도: 96.52%\n",
-      "17\t검증 세트 손실: 1.540824\t최선의 손실: 1.310678\t정확도: 96.33%\n",
-      "18\t검증 세트 손실: 1.619995\t최선의 손실: 1.310678\t정확도: 96.83%\n",
-      "19\t검증 세트 손실: 1.312162\t최선의 손실: 1.310678\t정확도: 96.64%\n",
-      "20\t검증 세트 손실: 1.379920\t최선의 손실: 1.310678\t정확도: 96.13%\n",
-      "21\t검증 세트 손실: 1.431065\t최선의 손실: 1.310678\t정확도: 96.13%\n",
-      "22\t검증 세트 손실: 2.534339\t최선의 손실: 1.310678\t정확도: 96.25%\n",
-      "23\t검증 세트 손실: 2.259780\t최선의 손실: 1.310678\t정확도: 96.99%\n",
-      "24\t검증 세트 손실: 2.512620\t최선의 손실: 1.310678\t정확도: 96.29%\n",
-      "25\t검증 세트 손실: 2.500117\t최선의 손실: 1.310678\t정확도: 96.13%\n",
-      "26\t검증 세트 손실: 2.785220\t최선의 손실: 1.310678\t정확도: 95.97%\n",
-      "27\t검증 세트 손실: 2.501270\t최선의 손실: 1.310678\t정확도: 96.33%\n",
-      "28\t검증 세트 손실: 3.183125\t최선의 손실: 1.310678\t정확도: 95.90%\n",
-      "29\t검증 세트 손실: 2.625275\t최선의 손실: 1.310678\t정확도: 96.76%\n",
-      "30\t검증 세트 손실: 5.396920\t최선의 손실: 1.310678\t정확도: 96.29%\n",
-      "31\t검증 세트 손실: 2.929878\t최선의 손실: 1.310678\t정확도: 96.91%\n",
-      "32\t검증 세트 손실: 3.158629\t최선의 손실: 1.310678\t정확도: 96.83%\n",
-      "33\t검증 세트 손실: 3.178306\t최선의 손실: 1.310678\t정확도: 96.48%\n",
-      "34\t검증 세트 손실: 3.339588\t최선의 손실: 1.310678\t정확도: 96.87%\n",
-      "35\t검증 세트 손실: 3.349923\t최선의 손실: 1.310678\t정확도: 96.68%\n",
-      "36\t검증 세트 손실: 4.169959\t최선의 손실: 1.310678\t정확도: 96.36%\n",
-      "37\t검증 세트 손실: 4.605066\t최선의 손실: 1.310678\t정확도: 96.68%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=100, total=   3.3s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 203.479935\t최선의 손실: 203.479935\t정확도: 51.92%\n",
-      "1\t검증 세트 손실: 4.444092\t최선의 손실: 4.444092\t정확도: 89.48%\n",
-      "2\t검증 세트 손실: 2.151964\t최선의 손실: 2.151964\t정확도: 94.45%\n",
-      "3\t검증 세트 손실: 0.955432\t최선의 손실: 0.955432\t정확도: 94.29%\n",
-      "4\t검증 세트 손실: 1.799778\t최선의 손실: 0.955432\t정확도: 90.66%\n",
-      "5\t검증 세트 손실: 0.700927\t최선의 손실: 0.700927\t정확도: 95.07%\n",
-      "6\t검증 세트 손실: 0.678202\t최선의 손실: 0.678202\t정확도: 95.97%\n",
-      "7\t검증 세트 손실: 0.500969\t최선의 손실: 0.500969\t정확도: 95.23%\n",
-      "8\t검증 세트 손실: 0.346542\t최선의 손실: 0.346542\t정확도: 95.86%\n",
-      "9\t검증 세트 손실: 0.561927\t최선의 손실: 0.346542\t정확도: 93.90%\n",
-      "10\t검증 세트 손실: 0.281770\t최선의 손실: 0.281770\t정확도: 96.64%\n",
-      "11\t검증 세트 손실: 0.254701\t최선의 손실: 0.254701\t정확도: 97.19%\n",
-      "12\t검증 세트 손실: 0.276660\t최선의 손실: 0.254701\t정확도: 96.40%\n",
-      "13\t검증 세트 손실: 0.303234\t최선의 손실: 0.254701\t정확도: 97.19%\n",
-      "14\t검증 세트 손실: 0.249991\t최선의 손실: 0.249991\t정확도: 96.40%\n",
-      "15\t검증 세트 손실: 0.253157\t최선의 손실: 0.249991\t정확도: 96.36%\n",
-      "16\t검증 세트 손실: 0.157344\t최선의 손실: 0.157344\t정확도: 97.19%\n",
-      "17\t검증 세트 손실: 0.166277\t최선의 손실: 0.157344\t정확도: 96.83%\n",
-      "18\t검증 세트 손실: 0.151138\t최선의 손실: 0.151138\t정확도: 97.30%\n",
-      "19\t검증 세트 손실: 0.190650\t최선의 손실: 0.151138\t정확도: 96.68%\n",
-      "20\t검증 세트 손실: 0.158107\t최선의 손실: 0.151138\t정확도: 96.87%\n",
-      "21\t검증 세트 손실: 0.209009\t최선의 손실: 0.151138\t정확도: 96.52%\n",
-      "22\t검증 세트 손실: 0.133390\t최선의 손실: 0.133390\t정확도: 97.54%\n",
-      "23\t검증 세트 손실: 0.143064\t최선의 손실: 0.133390\t정확도: 97.58%\n",
-      "24\t검증 세트 손실: 0.132432\t최선의 손실: 0.132432\t정확도: 97.58%\n",
-      "25\t검증 세트 손실: 0.132022\t최선의 손실: 0.132022\t정확도: 97.22%\n",
-      "26\t검증 세트 손실: 0.133451\t최선의 손실: 0.132022\t정확도: 97.46%\n",
-      "27\t검증 세트 손실: 0.458598\t최선의 손실: 0.132022\t정확도: 95.43%\n",
-      "28\t검증 세트 손실: 0.308590\t최선의 손실: 0.132022\t정확도: 96.48%\n",
-      "29\t검증 세트 손실: 0.132361\t최선의 손실: 0.132022\t정확도: 97.85%\n",
-      "30\t검증 세트 손실: 0.132157\t최선의 손실: 0.132022\t정확도: 97.85%\n",
-      "31\t검증 세트 손실: 0.141118\t최선의 손실: 0.132022\t정확도: 97.58%\n",
-      "32\t검증 세트 손실: 0.116553\t최선의 손실: 0.116553\t정확도: 97.77%\n",
-      "33\t검증 세트 손실: 0.211887\t최선의 손실: 0.116553\t정확도: 96.79%\n",
-      "34\t검증 세트 손실: 0.165419\t최선의 손실: 0.116553\t정확도: 97.65%\n",
-      "35\t검증 세트 손실: 0.160416\t최선의 손실: 0.116553\t정확도: 97.46%\n",
-      "36\t검증 세트 손실: 0.123662\t최선의 손실: 0.116553\t정확도: 97.97%\n",
-      "37\t검증 세트 손실: 0.133340\t최선의 손실: 0.116553\t정확도: 97.89%\n",
-      "38\t검증 세트 손실: 0.143114\t최선의 손실: 0.116553\t정확도: 97.77%\n",
-      "39\t검증 세트 손실: 0.135759\t최선의 손실: 0.116553\t정확도: 97.46%\n",
-      "40\t검증 세트 손실: 0.155820\t최선의 손실: 0.116553\t정확도: 97.89%\n",
-      "41\t검증 세트 손실: 0.130784\t최선의 손실: 0.116553\t정확도: 97.97%\n",
-      "42\t검증 세트 손실: 0.138670\t최선의 손실: 0.116553\t정확도: 98.08%\n",
-      "43\t검증 세트 손실: 0.147361\t최선의 손실: 0.116553\t정확도: 98.01%\n",
-      "44\t검증 세트 손실: 0.164930\t최선의 손실: 0.116553\t정확도: 97.81%\n",
-      "45\t검증 세트 손실: 0.253358\t최선의 손실: 0.116553\t정확도: 97.07%\n",
-      "46\t검증 세트 손실: 0.211472\t최선의 손실: 0.116553\t정확도: 97.19%\n",
-      "47\t검증 세트 손실: 0.156707\t최선의 손실: 0.116553\t정확도: 97.85%\n",
-      "48\t검증 세트 손실: 0.168487\t최선의 손실: 0.116553\t정확도: 97.77%\n",
-      "49\t검증 세트 손실: 0.153485\t최선의 손실: 0.116553\t정확도: 98.01%\n",
-      "50\t검증 세트 손실: 0.146136\t최선의 손실: 0.116553\t정확도: 97.54%\n",
-      "51\t검증 세트 손실: 0.164273\t최선의 손실: 0.116553\t정확도: 97.81%\n",
-      "52\t검증 세트 손실: 0.157017\t최선의 손실: 0.116553\t정확도: 98.08%\n",
-      "53\t검증 세트 손실: 0.160814\t최선의 손실: 0.116553\t정확도: 97.97%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=100, total=   4.3s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140 \n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=100, total=   5.0s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 3010.684814\t최선의 손실: 3010.684814\t정확도: 27.91%\n",
+      "1\t검증 세트 손실: 39787.558594\t최선의 손실: 3010.684814\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 15723.130859\t최선의 손실: 3010.684814\t정확도: 40.42%\n",
+      "3\t검증 세트 손실: 421.288300\t최선의 손실: 421.288300\t정확도: 80.18%\n",
+      "4\t검증 세트 손실: 109.856506\t최선의 손실: 109.856506\t정확도: 89.05%\n",
+      "5\t검증 세트 손실: 86.636215\t최선의 손실: 86.636215\t정확도: 89.80%\n",
+      "6\t검증 세트 손실: 52.966919\t최선의 손실: 52.966919\t정확도: 92.69%\n",
+      "7\t검증 세트 손실: 38.056244\t최선의 손실: 38.056244\t정확도: 93.90%\n",
+      "8\t검증 세트 손실: 36.983814\t최선의 손실: 36.983814\t정확도: 93.08%\n",
+      "9\t검증 세트 손실: 61.762638\t최선의 손실: 36.983814\t정확도: 89.95%\n",
+      "10\t검증 세트 손실: 33.161491\t최선의 손실: 33.161491\t정확도: 94.84%\n",
+      "11\t검증 세트 손실: 22.876093\t최선의 손실: 22.876093\t정확도: 94.88%\n",
+      "12\t검증 세트 손실: 19.158258\t최선의 손실: 19.158258\t정확도: 95.11%\n",
+      "13\t검증 세트 손실: 17.907173\t최선의 손실: 17.907173\t정확도: 95.43%\n",
+      "14\t검증 세트 손실: 31.376652\t최선의 손실: 17.907173\t정확도: 92.46%\n",
+      "15\t검증 세트 손실: 17.319159\t최선의 손실: 17.319159\t정확도: 95.62%\n",
+      "16\t검증 세트 손실: 13.636541\t최선의 손실: 13.636541\t정확도: 95.39%\n",
+      "17\t검증 세트 손실: 12.445709\t최선의 손실: 12.445709\t정확도: 95.54%\n",
+      "18\t검증 세트 손실: 16.339996\t최선의 손실: 12.445709\t정확도: 95.15%\n",
+      "19\t검증 세트 손실: 16.906164\t최선의 손실: 12.445709\t정확도: 94.18%\n",
+      "20\t검증 세트 손실: 11.117499\t최선의 손실: 11.117499\t정확도: 95.93%\n",
+      "21\t검증 세트 손실: 22.524979\t최선의 손실: 11.117499\t정확도: 92.06%\n",
+      "22\t검증 세트 손실: 21.848267\t최선의 손실: 11.117499\t정확도: 95.58%\n",
+      "23\t검증 세트 손실: 14.381080\t최선의 손실: 11.117499\t정확도: 96.01%\n",
+      "24\t검증 세트 손실: 13.651818\t최선의 손실: 11.117499\t정확도: 95.90%\n",
+      "25\t검증 세트 손실: 14.168403\t최선의 손실: 11.117499\t정확도: 95.43%\n",
+      "26\t검증 세트 손실: 11.178098\t최선의 손실: 11.117499\t정확도: 95.82%\n",
+      "27\t검증 세트 손실: 12.076550\t최선의 손실: 11.117499\t정확도: 95.19%\n",
+      "28\t검증 세트 손실: 8.197154\t최선의 손실: 8.197154\t정확도: 96.25%\n",
+      "29\t검증 세트 손실: 7.442372\t최선의 손실: 7.442372\t정확도: 96.25%\n",
+      "30\t검증 세트 손실: 169.350891\t최선의 손실: 7.442372\t정확도: 74.04%\n",
+      "31\t검증 세트 손실: 77.672989\t최선의 손실: 7.442372\t정확도: 94.14%\n",
+      "32\t검증 세트 손실: 29.025915\t최선의 손실: 7.442372\t정확도: 95.50%\n",
+      "33\t검증 세트 손실: 20.929600\t최선의 손실: 7.442372\t정확도: 95.47%\n",
+      "34\t검증 세트 손실: 14.592470\t최선의 손실: 7.442372\t정확도: 96.17%\n",
+      "35\t검증 세트 손실: 13.325763\t최선의 손실: 7.442372\t정확도: 95.82%\n",
+      "36\t검증 세트 손실: 17.170300\t최선의 손실: 7.442372\t정확도: 94.84%\n",
+      "37\t검증 세트 손실: 11.226819\t최선의 손실: 7.442372\t정확도: 95.86%\n",
+      "38\t검증 세트 손실: 9.433427\t최선의 손실: 7.442372\t정확도: 96.33%\n",
+      "39\t검증 세트 손실: 10.258090\t최선의 손실: 7.442372\t정확도: 95.90%\n",
+      "40\t검증 세트 손실: 10.984679\t최선의 손실: 7.442372\t정확도: 95.27%\n",
+      "41\t검증 세트 손실: 7.551307\t최선의 손실: 7.442372\t정확도: 96.48%\n",
+      "42\t검증 세트 손실: 7.164837\t최선의 손실: 7.164837\t정확도: 96.60%\n",
+      "43\t검증 세트 손실: 9.383512\t최선의 손실: 7.164837\t정확도: 95.00%\n",
+      "44\t검증 세트 손실: 7.794334\t최선의 손실: 7.164837\t정확도: 96.29%\n",
+      "45\t검증 세트 손실: 5.979932\t최선의 손실: 5.979932\t정확도: 96.60%\n",
+      "46\t검증 세트 손실: 6.012082\t최선의 손실: 5.979932\t정확도: 96.60%\n",
+      "47\t검증 세트 손실: 7.830351\t최선의 손실: 5.979932\t정확도: 95.97%\n",
+      "48\t검증 세트 손실: 7.804088\t최선의 손실: 5.979932\t정확도: 95.47%\n",
+      "49\t검증 세트 손실: 5.313087\t최선의 손실: 5.313087\t정확도: 96.83%\n",
+      "50\t검증 세트 손실: 6.156207\t최선의 손실: 5.313087\t정확도: 96.25%\n",
+      "51\t검증 세트 손실: 5.433918\t최선의 손실: 5.313087\t정확도: 96.60%\n",
+      "52\t검증 세트 손실: 5.442094\t최선의 손실: 5.313087\t정확도: 96.60%\n",
+      "53\t검증 세트 손실: 5.530026\t최선의 손실: 5.313087\t정확도: 96.44%\n",
+      "54\t검증 세트 손실: 4.826842\t최선의 손실: 4.826842\t정확도: 96.60%\n",
+      "55\t검증 세트 손실: 5.366208\t최선의 손실: 4.826842\t정확도: 96.48%\n",
+      "56\t검증 세트 손실: 6.299458\t최선의 손실: 4.826842\t정확도: 96.29%\n",
+      "57\t검증 세트 손실: 5.111415\t최선의 손실: 4.826842\t정확도: 96.25%\n",
+      "58\t검증 세트 손실: 4.896547\t최선의 손실: 4.826842\t정확도: 96.87%\n",
+      "59\t검증 세트 손실: 5.353154\t최선의 손실: 4.826842\t정확도: 96.25%\n",
+      "60\t검증 세트 손실: 7.178024\t최선의 손실: 4.826842\t정확도: 96.21%\n",
+      "61\t검증 세트 손실: 5.517487\t최선의 손실: 4.826842\t정확도: 96.60%\n",
+      "62\t검증 세트 손실: 6.861269\t최선의 손실: 4.826842\t정확도: 96.13%\n",
+      "63\t검증 세트 손실: 10.075420\t최선의 손실: 4.826842\t정확도: 95.93%\n",
+      "64\t검증 세트 손실: 60.720966\t최선의 손실: 4.826842\t정확도: 89.17%\n",
+      "65\t검증 세트 손실: 17.637930\t최선의 손실: 4.826842\t정확도: 95.54%\n",
+      "66\t검증 세트 손실: 13.327236\t최선의 손실: 4.826842\t정확도: 95.27%\n",
+      "67\t검증 세트 손실: 8.237678\t최선의 손실: 4.826842\t정확도: 96.17%\n",
+      "68\t검증 세트 손실: 22.510513\t최선의 손실: 4.826842\t정확도: 93.08%\n",
+      "69\t검증 세트 손실: 6.725041\t최선의 손실: 4.826842\t정확도: 96.52%\n",
+      "70\t검증 세트 손실: 7.426890\t최선의 손실: 4.826842\t정확도: 95.58%\n",
+      "71\t검증 세트 손실: 9.362950\t최선의 손실: 4.826842\t정확도: 95.19%\n",
+      "72\t검증 세트 손실: 6.276609\t최선의 손실: 4.826842\t정확도: 96.72%\n",
+      "73\t검증 세트 손실: 6.578702\t최선의 손실: 4.826842\t정확도: 96.76%\n",
+      "74\t검증 세트 손실: 5.063471\t최선의 손실: 4.826842\t정확도: 96.48%\n",
+      "75\t검증 세트 손실: 4.715806\t최선의 손실: 4.715806\t정확도: 96.72%\n",
+      "76\t검증 세트 손실: 5.145185\t최선의 손실: 4.715806\t정확도: 96.56%\n",
+      "77\t검증 세트 손실: 8.953596\t최선의 손실: 4.715806\t정확도: 94.29%\n",
+      "78\t검증 세트 손실: 4.490973\t최선의 손실: 4.490973\t정확도: 96.99%\n",
+      "79\t검증 세트 손실: 4.752704\t최선의 손실: 4.490973\t정확도: 96.21%\n",
+      "80\t검증 세트 손실: 4.983382\t최선의 손실: 4.490973\t정확도: 96.83%\n",
+      "81\t검증 세트 손실: 4.455763\t최선의 손실: 4.455763\t정확도: 96.79%\n",
+      "82\t검증 세트 손실: 4.459873\t최선의 손실: 4.455763\t정확도: 96.64%\n",
+      "83\t검증 세트 손실: 4.762742\t최선의 손실: 4.455763\t정확도: 96.72%\n",
+      "84\t검증 세트 손실: 3.414814\t최선의 손실: 3.414814\t정확도: 97.26%\n",
+      "85\t검증 세트 손실: 3.512894\t최선의 손실: 3.414814\t정확도: 96.95%\n",
+      "86\t검증 세트 손실: 3.389872\t최선의 손실: 3.389872\t정확도: 97.11%\n",
+      "87\t검증 세트 손실: 3.489617\t최선의 손실: 3.389872\t정확도: 97.07%\n",
+      "88\t검증 세트 손실: 3.126602\t최선의 손실: 3.126602\t정확도: 97.07%\n",
+      "89\t검증 세트 손실: 4.551726\t최선의 손실: 3.126602\t정확도: 96.52%\n",
+      "90\t검증 세트 손실: 5.032935\t최선의 손실: 3.126602\t정확도: 96.25%\n",
+      "91\t검증 세트 손실: 3.117604\t최선의 손실: 3.117604\t정확도: 97.22%\n",
+      "92\t검증 세트 손실: 4.219283\t최선의 손실: 3.117604\t정확도: 96.83%\n",
+      "93\t검증 세트 손실: 5.118777\t최선의 손실: 3.117604\t정확도: 96.44%\n",
+      "94\t검증 세트 손실: 4.027419\t최선의 손실: 3.117604\t정확도: 96.68%\n",
+      "95\t검증 세트 손실: 4.552743\t최선의 손실: 3.117604\t정확도: 96.76%\n",
+      "96\t검증 세트 손실: 8.757862\t최선의 손실: 3.117604\t정확도: 96.29%\n",
+      "97\t검증 세트 손실: 4.236472\t최선의 손실: 3.117604\t정확도: 97.19%\n",
+      "98\t검증 세트 손실: 5.199555\t최선의 손실: 3.117604\t정확도: 97.38%\n",
+      "99\t검증 세트 손실: 7.812878\t최선의 손실: 3.117604\t정확도: 95.90%\n",
+      "100\t검증 세트 손실: 6.463937\t최선의 손실: 3.117604\t정확도: 97.22%\n",
+      "101\t검증 세트 손실: 5.379649\t최선의 손실: 3.117604\t정확도: 95.93%\n",
+      "102\t검증 세트 손실: 6.155845\t최선의 손실: 3.117604\t정확도: 95.58%\n",
+      "103\t검증 세트 손실: 3.807156\t최선의 손실: 3.117604\t정확도: 97.38%\n",
+      "104\t검증 세트 손실: 3.363041\t최선의 손실: 3.117604\t정확도: 97.54%\n",
+      "105\t검증 세트 손실: 3.377702\t최선의 손실: 3.117604\t정확도: 97.50%\n",
+      "106\t검증 세트 손실: 5.077661\t최선의 손실: 3.117604\t정확도: 96.83%\n",
+      "107\t검증 세트 손실: 9.299778\t최선의 손실: 3.117604\t정확도: 96.83%\n",
+      "108\t검증 세트 손실: 23.366348\t최선의 손실: 3.117604\t정확도: 95.27%\n",
+      "109\t검증 세트 손실: 22.040054\t최선의 손실: 3.117604\t정확도: 96.79%\n",
+      "110\t검증 세트 손실: 7.549925\t최선의 손실: 3.117604\t정확도: 96.99%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.136163\t최선의 손실: 0.136163\t정확도: 96.64%\n",
-      "1\t검증 세트 손실: 0.060144\t최선의 손실: 0.060144\t정확도: 98.05%\n",
-      "2\t검증 세트 손실: 0.070559\t최선의 손실: 0.060144\t정확도: 97.77%\n",
-      "3\t검증 세트 손실: 0.048422\t최선의 손실: 0.048422\t정확도: 98.36%\n",
-      "4\t검증 세트 손실: 0.062384\t최선의 손실: 0.048422\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 0.069186\t최선의 손실: 0.048422\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.063494\t최선의 손실: 0.048422\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.073359\t최선의 손실: 0.048422\t정확도: 98.44%\n",
-      "8\t검증 세트 손실: 0.062817\t최선의 손실: 0.048422\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.057873\t최선의 손실: 0.048422\t정확도: 98.67%\n",
-      "10\t검증 세트 손실: 0.080888\t최선의 손실: 0.048422\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.080761\t최선의 손실: 0.048422\t정확도: 98.40%\n",
-      "12\t검증 세트 손실: 0.077064\t최선의 손실: 0.048422\t정확도: 98.40%\n",
-      "13\t검증 세트 손실: 0.060489\t최선의 손실: 0.048422\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.069909\t최선의 손실: 0.048422\t정확도: 98.48%\n",
-      "15\t검증 세트 손실: 0.063728\t최선의 손실: 0.048422\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.057476\t최선의 손실: 0.048422\t정확도: 98.67%\n",
-      "17\t검증 세트 손실: 0.069101\t최선의 손실: 0.048422\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.087961\t최선의 손실: 0.048422\t정확도: 98.51%\n",
-      "19\t검증 세트 손실: 0.076209\t최선의 손실: 0.048422\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.073647\t최선의 손실: 0.048422\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.080354\t최선의 손실: 0.048422\t정확도: 98.51%\n",
-      "22\t검증 세트 손실: 0.060327\t최선의 손실: 0.048422\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.078404\t최선의 손실: 0.048422\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.081067\t최선의 손실: 0.048422\t정확도: 99.06%\n",
+      "111\t검증 세트 손실: 5.583642\t최선의 손실: 3.117604\t정확도: 96.52%\n",
+      "112\t검증 세트 손실: 22.477179\t최선의 손실: 3.117604\t정확도: 96.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140, total=   2.2s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.087402\t최선의 손실: 0.087402\t정확도: 97.30%\n",
-      "1\t검증 세트 손실: 0.068284\t최선의 손실: 0.068284\t정확도: 97.93%\n",
-      "2\t검증 세트 손실: 0.054329\t최선의 손실: 0.054329\t정확도: 98.55%\n",
-      "3\t검증 세트 손실: 0.087474\t최선의 손실: 0.054329\t정확도: 97.54%\n",
-      "4\t검증 세트 손실: 0.061637\t최선의 손실: 0.054329\t정확도: 98.44%\n",
-      "5\t검증 세트 손실: 0.058702\t최선의 손실: 0.054329\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.051159\t최선의 손실: 0.051159\t정확도: 98.67%\n",
-      "7\t검증 세트 손실: 0.071315\t최선의 손실: 0.051159\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.055292\t최선의 손실: 0.051159\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.061502\t최선의 손실: 0.051159\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.047776\t최선의 손실: 0.047776\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.044077\t최선의 손실: 0.044077\t정확도: 98.83%\n",
-      "12\t검증 세트 손실: 0.055008\t최선의 손실: 0.044077\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.057364\t최선의 손실: 0.044077\t정확도: 98.87%\n",
-      "14\t검증 세트 손실: 0.084691\t최선의 손실: 0.044077\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.065149\t최선의 손실: 0.044077\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.067139\t최선의 손실: 0.044077\t정확도: 98.55%\n",
-      "17\t검증 세트 손실: 0.065845\t최선의 손실: 0.044077\t정확도: 98.91%\n",
-      "18\t검증 세트 손실: 0.072256\t최선의 손실: 0.044077\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.066068\t최선의 손실: 0.044077\t정확도: 98.55%\n",
-      "20\t검증 세트 손실: 0.042607\t최선의 손실: 0.042607\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.042771\t최선의 손실: 0.042607\t정확도: 99.14%\n",
-      "22\t검증 세트 손실: 0.055172\t최선의 손실: 0.042607\t정확도: 99.02%\n",
-      "23\t검증 세트 손실: 0.076229\t최선의 손실: 0.042607\t정확도: 98.71%\n",
-      "24\t검증 세트 손실: 0.050738\t최선의 손실: 0.042607\t정확도: 98.91%\n",
-      "25\t검증 세트 손실: 0.054599\t최선의 손실: 0.042607\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.073753\t최선의 손실: 0.042607\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.060779\t최선의 손실: 0.042607\t정확도: 98.87%\n",
-      "28\t검증 세트 손실: 0.062978\t최선의 손실: 0.042607\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.049963\t최선의 손실: 0.042607\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.074445\t최선의 손실: 0.042607\t정확도: 98.48%\n",
-      "31\t검증 세트 손실: 0.087114\t최선의 손실: 0.042607\t정확도: 98.71%\n",
-      "32\t검증 세트 손실: 0.056230\t최선의 손실: 0.042607\t정확도: 98.98%\n",
-      "33\t검증 세트 손실: 0.060288\t최선의 손실: 0.042607\t정확도: 98.67%\n",
-      "34\t검증 세트 손실: 0.061999\t최선의 손실: 0.042607\t정확도: 98.87%\n",
-      "35\t검증 세트 손실: 0.068323\t최선의 손실: 0.042607\t정확도: 98.71%\n",
-      "36\t검증 세트 손실: 0.062425\t최선의 손실: 0.042607\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.082041\t최선의 손실: 0.042607\t정확도: 98.87%\n",
-      "38\t검증 세트 손실: 0.077846\t최선의 손실: 0.042607\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.080710\t최선의 손실: 0.042607\t정확도: 98.75%\n",
-      "40\t검증 세트 손실: 0.070743\t최선의 손실: 0.042607\t정확도: 99.02%\n",
-      "41\t검증 세트 손실: 0.058709\t최선의 손실: 0.042607\t정확도: 98.75%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=100, total=  10.1s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=100 \n",
+      "0\t검증 세트 손실: 5020.979492\t최선의 손실: 5020.979492\t정확도: 18.76%\n",
+      "1\t검증 세트 손실: 1976.238525\t최선의 손실: 1976.238525\t정확도: 44.10%\n",
+      "2\t검증 세트 손실: 69.379158\t최선의 손실: 69.379158\t정확도: 71.23%\n",
+      "3\t검증 세트 손실: 8.217289\t최선의 손실: 8.217289\t정확도: 87.96%\n",
+      "4\t검증 세트 손실: 4.344393\t최선의 손실: 4.344393\t정확도: 91.40%\n",
+      "5\t검증 세트 손실: 2.934256\t최선의 손실: 2.934256\t정확도: 93.59%\n",
+      "6\t검증 세트 손실: 2.509925\t최선의 손실: 2.509925\t정확도: 93.94%\n",
+      "7\t검증 세트 손실: 1.941523\t최선의 손실: 1.941523\t정확도: 92.18%\n",
+      "8\t검증 세트 손실: 4.377177\t최선의 손실: 1.941523\t정확도: 90.93%\n",
+      "9\t검증 세트 손실: 1.344884\t최선의 손실: 1.344884\t정확도: 95.15%\n",
+      "10\t검증 세트 손실: 1.135813\t최선의 손실: 1.135813\t정확도: 95.11%\n",
+      "11\t검증 세트 손실: 1.092989\t최선의 손실: 1.092989\t정확도: 95.74%\n",
+      "12\t검증 세트 손실: 1.148805\t최선의 손실: 1.092989\t정확도: 95.43%\n",
+      "13\t검증 세트 손실: 1.016746\t최선의 손실: 1.016746\t정확도: 94.21%\n",
+      "14\t검증 세트 손실: 0.849319\t최선의 손실: 0.849319\t정확도: 96.01%\n",
+      "15\t검증 세트 손실: 1.508190\t최선의 손실: 0.849319\t정확도: 92.53%\n",
+      "16\t검증 세트 손실: 0.851839\t최선의 손실: 0.849319\t정확도: 95.97%\n",
+      "17\t검증 세트 손실: 11.253705\t최선의 손실: 0.849319\t정확도: 78.23%\n",
+      "18\t검증 세트 손실: 4.258766\t최선의 손실: 0.849319\t정확도: 94.21%\n",
+      "19\t검증 세트 손실: 1.996792\t최선의 손실: 0.849319\t정확도: 95.43%\n",
+      "20\t검증 세트 손실: 1.111662\t최선의 손실: 0.849319\t정확도: 96.52%\n",
+      "21\t검증 세트 손실: 1.149388\t최선의 손실: 0.849319\t정확도: 96.13%\n",
+      "22\t검증 세트 손실: 0.888399\t최선의 손실: 0.849319\t정확도: 96.40%\n",
+      "23\t검증 세트 손실: 0.637672\t최선의 손실: 0.637672\t정확도: 96.99%\n",
+      "24\t검증 세트 손실: 0.574833\t최선의 손실: 0.574833\t정확도: 97.26%\n",
+      "25\t검증 세트 손실: 0.480164\t최선의 손실: 0.480164\t정확도: 97.30%\n",
+      "26\t검증 세트 손실: 0.747075\t최선의 손실: 0.480164\t정확도: 96.01%\n",
+      "27\t검증 세트 손실: 0.655856\t최선의 손실: 0.480164\t정확도: 96.87%\n",
+      "28\t검증 세트 손실: 0.831307\t최선의 손실: 0.480164\t정확도: 95.97%\n",
+      "29\t검증 세트 손실: 0.655496\t최선의 손실: 0.480164\t정확도: 97.15%\n",
+      "30\t검증 세트 손실: 0.780814\t최선의 손실: 0.480164\t정확도: 97.19%\n",
+      "31\t검증 세트 손실: 0.734302\t최선의 손실: 0.480164\t정확도: 96.33%\n",
+      "32\t검증 세트 손실: 1.561500\t최선의 손실: 0.480164\t정확도: 96.44%\n",
+      "33\t검증 세트 손실: 1.455134\t최선의 손실: 0.480164\t정확도: 96.87%\n",
+      "34\t검증 세트 손실: 1.658954\t최선의 손실: 0.480164\t정확도: 96.91%\n",
+      "35\t검증 세트 손실: 1.184403\t최선의 손실: 0.480164\t정확도: 96.09%\n",
+      "36\t검증 세트 손실: 1.798230\t최선의 손실: 0.480164\t정확도: 96.95%\n",
+      "37\t검증 세트 손실: 1.607311\t최선의 손실: 0.480164\t정확도: 97.26%\n",
+      "38\t검증 세트 손실: 1.100999\t최선의 손실: 0.480164\t정확도: 97.03%\n",
+      "39\t검증 세트 손실: 1.278384\t최선의 손실: 0.480164\t정확도: 97.62%\n",
+      "40\t검증 세트 손실: 1.422520\t최선의 손실: 0.480164\t정확도: 97.07%\n",
+      "41\t검증 세트 손실: 1.482012\t최선의 손실: 0.480164\t정확도: 96.79%\n",
+      "42\t검증 세트 손실: 1.954535\t최선의 손실: 0.480164\t정확도: 97.34%\n",
+      "43\t검증 세트 손실: 140.594574\t최선의 손실: 0.480164\t정확도: 61.45%\n",
+      "44\t검증 세트 손실: 29700838.000000\t최선의 손실: 0.480164\t정확도: 19.27%\n",
+      "45\t검증 세트 손실: 403161.656250\t최선의 손실: 0.480164\t정확도: 53.17%\n",
+      "46\t검증 세트 손실: 96768.375000\t최선의 손실: 0.480164\t정확도: 73.42%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140, total=   3.4s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.080937\t최선의 손실: 0.080937\t정확도: 97.69%\n",
-      "1\t검증 세트 손실: 0.060636\t최선의 손실: 0.060636\t정확도: 98.20%\n",
-      "2\t검증 세트 손실: 0.063217\t최선의 손실: 0.060636\t정확도: 98.20%\n",
-      "3\t검증 세트 손실: 0.054022\t최선의 손실: 0.054022\t정확도: 98.48%\n",
-      "4\t검증 세트 손실: 0.064862\t최선의 손실: 0.054022\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.056632\t최선의 손실: 0.054022\t정확도: 98.51%\n",
-      "6\t검증 세트 손실: 0.049413\t최선의 손실: 0.049413\t정확도: 98.71%\n",
-      "7\t검증 세트 손실: 0.058420\t최선의 손실: 0.049413\t정확도: 98.28%\n",
-      "8\t검증 세트 손실: 0.115330\t최선의 손실: 0.049413\t정확도: 97.69%\n",
-      "9\t검증 세트 손실: 0.047273\t최선의 손실: 0.047273\t정확도: 98.48%\n",
-      "10\t검증 세트 손실: 0.064510\t최선의 손실: 0.047273\t정확도: 98.59%\n",
-      "11\t검증 세트 손실: 0.063522\t최선의 손실: 0.047273\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.066435\t최선의 손실: 0.047273\t정확도: 98.55%\n",
-      "13\t검증 세트 손실: 0.062076\t최선의 손실: 0.047273\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.066999\t최선의 손실: 0.047273\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.058137\t최선의 손실: 0.047273\t정확도: 98.91%\n",
-      "16\t검증 세트 손실: 0.067030\t최선의 손실: 0.047273\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.070856\t최선의 손실: 0.047273\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.083069\t최선의 손실: 0.047273\t정확도: 98.59%\n",
-      "19\t검증 세트 손실: 0.070730\t최선의 손실: 0.047273\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.066847\t최선의 손실: 0.047273\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.069423\t최선의 손실: 0.047273\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.083826\t최선의 손실: 0.047273\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.052645\t최선의 손실: 0.047273\t정확도: 98.63%\n",
-      "24\t검증 세트 손실: 0.052708\t최선의 손실: 0.047273\t정확도: 99.14%\n",
-      "25\t검증 세트 손실: 0.057833\t최선의 손실: 0.047273\t정확도: 99.18%\n",
-      "26\t검증 세트 손실: 0.057703\t최선의 손실: 0.047273\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.054356\t최선의 손실: 0.047273\t정확도: 99.02%\n",
-      "28\t검증 세트 손실: 0.052261\t최선의 손실: 0.047273\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.058344\t최선의 손실: 0.047273\t정확도: 98.83%\n",
-      "30\t검증 세트 손실: 0.060717\t최선의 손실: 0.047273\t정확도: 98.94%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=100, total=   4.7s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.113825\t최선의 손실: 0.113825\t정확도: 96.52%\n",
+      "1\t검증 세트 손실: 0.085296\t최선의 손실: 0.085296\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.070906\t최선의 손실: 0.070906\t정확도: 97.77%\n",
+      "3\t검증 세트 손실: 0.062628\t최선의 손실: 0.062628\t정확도: 98.28%\n",
+      "4\t검증 세트 손실: 0.069291\t최선의 손실: 0.062628\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.055527\t최선의 손실: 0.055527\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.053126\t최선의 손실: 0.053126\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.064649\t최선의 손실: 0.053126\t정확도: 98.40%\n",
+      "8\t검증 세트 손실: 0.078424\t최선의 손실: 0.053126\t정확도: 98.16%\n",
+      "9\t검증 세트 손실: 0.075120\t최선의 손실: 0.053126\t정확도: 98.36%\n",
+      "10\t검증 세트 손실: 0.061990\t최선의 손실: 0.053126\t정확도: 98.75%\n",
+      "11\t검증 세트 손실: 0.100819\t최선의 손실: 0.053126\t정확도: 97.93%\n",
+      "12\t검증 세트 손실: 0.088329\t최선의 손실: 0.053126\t정확도: 98.05%\n",
+      "13\t검증 세트 손실: 0.067940\t최선의 손실: 0.053126\t정확도: 98.75%\n",
+      "14\t검증 세트 손실: 0.069515\t최선의 손실: 0.053126\t정확도: 98.48%\n",
+      "15\t검증 세트 손실: 0.069333\t최선의 손실: 0.053126\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.084390\t최선의 손실: 0.053126\t정확도: 98.48%\n",
+      "17\t검증 세트 손실: 0.068616\t최선의 손실: 0.053126\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.069949\t최선의 손실: 0.053126\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.068754\t최선의 손실: 0.053126\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.071720\t최선의 손실: 0.053126\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.094218\t최선의 손실: 0.053126\t정확도: 98.40%\n",
+      "22\t검증 세트 손실: 0.064649\t최선의 손실: 0.053126\t정확도: 98.67%\n",
+      "23\t검증 세트 손실: 0.058254\t최선의 손실: 0.053126\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.076868\t최선의 손실: 0.053126\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.075925\t최선의 손실: 0.053126\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.054092\t최선의 손실: 0.053126\t정확도: 98.79%\n",
+      "27\t검증 세트 손실: 0.074304\t최선의 손실: 0.053126\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=140, total=   2.9s\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.827730\t최선의 손실: 1.827730\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 1.635679\t최선의 손실: 1.635679\t정확도: 20.91%\n",
-      "2\t검증 세트 손실: 1.752968\t최선의 손실: 1.635679\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 1.668025\t최선의 손실: 1.635679\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.640283\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.907970\t최선의 손실: 1.635679\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 1.861142\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.646455\t최선의 손실: 1.635679\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.682898\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.800022\t최선의 손실: 1.635679\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 1.787573\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.982571\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.943092\t최선의 손실: 1.635679\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.777602\t최선의 손실: 1.635679\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.791736\t최선의 손실: 1.635679\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.913675\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.850441\t최선의 손실: 1.635679\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.676427\t최선의 손실: 1.635679\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.803298\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.727334\t최선의 손실: 1.635679\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.709793\t최선의 손실: 1.635679\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 1.744467\t최선의 손실: 1.635679\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.651264\t최선의 손실: 1.635679\t정확도: 20.91%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=140, total=   2.9s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.114602\t최선의 손실: 0.114602\t정확도: 96.33%\n",
+      "1\t검증 세트 손실: 0.075141\t최선의 손실: 0.075141\t정확도: 97.81%\n",
+      "2\t검증 세트 손실: 0.060133\t최선의 손실: 0.060133\t정확도: 98.05%\n",
+      "3\t검증 세트 손실: 0.057651\t최선의 손실: 0.057651\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.073752\t최선의 손실: 0.057651\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.051506\t최선의 손실: 0.051506\t정확도: 98.51%\n",
+      "6\t검증 세트 손실: 0.043582\t최선의 손실: 0.043582\t정확도: 98.51%\n",
+      "7\t검증 세트 손실: 0.057917\t최선의 손실: 0.043582\t정확도: 98.28%\n",
+      "8\t검증 세트 손실: 0.068923\t최선의 손실: 0.043582\t정확도: 97.81%\n",
+      "9\t검증 세트 손실: 0.070654\t최선의 손실: 0.043582\t정확도: 98.40%\n",
+      "10\t검증 세트 손실: 0.067213\t최선의 손실: 0.043582\t정확도: 98.40%\n",
+      "11\t검증 세트 손실: 0.060256\t최선의 손실: 0.043582\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.044530\t최선의 손실: 0.043582\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.048885\t최선의 손실: 0.043582\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.067358\t최선의 손실: 0.043582\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.056037\t최선의 손실: 0.043582\t정확도: 98.51%\n",
+      "16\t검증 세트 손실: 0.072115\t최선의 손실: 0.043582\t정확도: 98.55%\n",
+      "17\t검증 세트 손실: 0.052748\t최선의 손실: 0.043582\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.077543\t최선의 손실: 0.043582\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.050224\t최선의 손실: 0.043582\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.062844\t최선의 손실: 0.043582\t정확도: 98.44%\n",
+      "21\t검증 세트 손실: 0.102190\t최선의 손실: 0.043582\t정확도: 97.81%\n",
+      "22\t검증 세트 손실: 0.061326\t최선의 손실: 0.043582\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.098202\t최선의 손실: 0.043582\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.074070\t최선의 손실: 0.043582\t정확도: 98.83%\n",
+      "25\t검증 세트 손실: 0.070223\t최선의 손실: 0.043582\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.069980\t최선의 손실: 0.043582\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.074269\t최선의 손실: 0.043582\t정확도: 98.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total= 1.4min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.758383\t최선의 손실: 1.758383\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 1.652472\t최선의 손실: 1.652472\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.791855\t최선의 손실: 1.652472\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 1.838270\t최선의 손실: 1.652472\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.800166\t최선의 손실: 1.652472\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.669709\t최선의 손실: 1.652472\t정확도: 18.73%\n",
-      "6\t검증 세트 손실: 1.851122\t최선의 손실: 1.652472\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 1.649280\t최선의 손실: 1.649280\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.899553\t최선의 손실: 1.649280\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 1.914811\t최선의 손실: 1.649280\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.686123\t최선의 손실: 1.649280\t정확도: 22.01%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=140, total=   2.8s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.115913\t최선의 손실: 0.115913\t정확도: 96.33%\n",
+      "1\t검증 세트 손실: 0.067352\t최선의 손실: 0.067352\t정확도: 98.01%\n",
+      "2\t검증 세트 손실: 0.064063\t최선의 손실: 0.064063\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.046716\t최선의 손실: 0.046716\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.050072\t최선의 손실: 0.046716\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.057662\t최선의 손실: 0.046716\t정확도: 98.36%\n",
+      "6\t검증 세트 손실: 0.052138\t최선의 손실: 0.046716\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.049542\t최선의 손실: 0.046716\t정확도: 98.79%\n",
+      "8\t검증 세트 손실: 0.054165\t최선의 손실: 0.046716\t정확도: 98.20%\n",
+      "9\t검증 세트 손실: 0.059066\t최선의 손실: 0.046716\t정확도: 97.93%\n",
+      "10\t검증 세트 손실: 0.057015\t최선의 손실: 0.046716\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.071340\t최선의 손실: 0.046716\t정확도: 98.16%\n",
+      "12\t검증 세트 손실: 0.053844\t최선의 손실: 0.046716\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.048327\t최선의 손실: 0.046716\t정확도: 98.94%\n",
+      "14\t검증 세트 손실: 0.062958\t최선의 손실: 0.046716\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.052573\t최선의 손실: 0.046716\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.067082\t최선의 손실: 0.046716\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.067930\t최선의 손실: 0.046716\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.069941\t최선의 손실: 0.046716\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.091365\t최선의 손실: 0.046716\t정확도: 98.32%\n",
+      "20\t검증 세트 손실: 0.057197\t최선의 손실: 0.046716\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.082179\t최선의 손실: 0.046716\t정확도: 98.44%\n",
+      "22\t검증 세트 손실: 0.090204\t최선의 손실: 0.046716\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.092029\t최선의 손실: 0.046716\t정확도: 98.44%\n",
+      "24\t검증 세트 손실: 0.068891\t최선의 손실: 0.046716\t정확도: 98.51%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.01, n_neurons=140, total=   2.5s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.851537\t최선의 손실: 1.851537\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 1.635981\t최선의 손실: 1.635981\t정확도: 20.91%\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\t검증 세트 손실: 1.747335\t최선의 손실: 1.635981\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 1.666753\t최선의 손실: 1.635981\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.640455\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.907968\t최선의 손실: 1.635981\t정확도: 19.08%\n",
+      "6\t검증 세트 손실: 1.861143\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.646455\t최선의 손실: 1.635981\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.682898\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.800022\t최선의 손실: 1.635981\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 1.787573\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.982571\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.943092\t최선의 손실: 1.635981\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.777602\t최선의 손실: 1.635981\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.791736\t최선의 손실: 1.635981\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.913676\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.850441\t최선의 손실: 1.635981\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.676427\t최선의 손실: 1.635981\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.803298\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.727334\t최선의 손실: 1.635981\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.709793\t최선의 손실: 1.635981\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.744467\t최선의 손실: 1.635981\t정확도: 18.73%\n",
+      "22\t검증 세트 손실: 1.651264\t최선의 손실: 1.635981\t정확도: 20.91%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=30, total=  36.3s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.898193\t최선의 손실: 1.898193\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 1.715824\t최선의 손실: 1.715824\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.695348\t최선의 손실: 1.695348\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1.790663\t최선의 손실: 1.695348\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.842701\t최선의 손실: 1.695348\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.672896\t최선의 손실: 1.672896\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 1.855113\t최선의 손실: 1.672896\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 1.649565\t최선의 손실: 1.649565\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.899382\t최선의 손실: 1.649565\t정확도: 20.91%\n",
+      "9\t검증 세트 손실: 1.914842\t최선의 손실: 1.649565\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.686124\t최선의 손실: 1.649565\t정확도: 22.01%\n",
       "11\t검증 세트 손실: 1.647474\t최선의 손실: 1.647474\t정확도: 20.91%\n",
-      "12\t검증 세트 손실: 1.853964\t최선의 손실: 1.647474\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 1.853963\t최선의 손실: 1.647474\t정확도: 20.91%\n",
       "13\t검증 세트 손실: 1.669570\t최선의 손실: 1.647474\t정확도: 22.01%\n",
       "14\t검증 세트 손실: 1.703601\t최선의 손실: 1.647474\t정확도: 19.27%\n",
       "15\t검증 세트 손실: 1.998326\t최선의 손실: 1.647474\t정확도: 19.08%\n",
       "16\t검증 세트 손실: 1.656298\t최선의 손실: 1.647474\t정확도: 20.91%\n",
       "17\t검증 세트 손실: 1.947681\t최선의 손실: 1.647474\t정확도: 20.91%\n",
       "18\t검증 세트 손실: 2.143588\t최선의 손실: 1.647474\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.900223\t최선의 손실: 1.647474\t정확도: 20.91%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "19\t검증 세트 손실: 1.900223\t최선의 손실: 1.647474\t정확도: 20.91%\n",
       "20\t검증 세트 손실: 1.930350\t최선의 손실: 1.647474\t정확도: 18.73%\n",
       "21\t검증 세트 손실: 1.900985\t최선의 손실: 1.647474\t정확도: 22.01%\n",
       "22\t검증 세트 손실: 1.750338\t최선의 손실: 1.647474\t정확도: 19.27%\n",
@@ -5189,44 +5214,44 @@
       "25\t검증 세트 손실: 1.836215\t최선의 손실: 1.647474\t정확도: 22.01%\n",
       "26\t검증 세트 손실: 1.777828\t최선의 손실: 1.647474\t정확도: 22.01%\n",
       "27\t검증 세트 손실: 1.763804\t최선의 손실: 1.647474\t정확도: 19.27%\n",
-      "28\t검증 세트 손실: 1.860309\t최선의 손실: 1.647474\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.860308\t최선의 손실: 1.647474\t정확도: 19.27%\n",
       "29\t검증 세트 손실: 1.954945\t최선의 손실: 1.647474\t정확도: 18.73%\n",
       "30\t검증 세트 손실: 1.838278\t최선의 손실: 1.647474\t정확도: 18.73%\n",
       "31\t검증 세트 손실: 1.988403\t최선의 손실: 1.647474\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 2.001386\t최선의 손실: 1.647474\t정확도: 19.27%\n",
+      "32\t검증 세트 손실: 2.001385\t최선의 손실: 1.647474\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total= 2.3min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.642422\t최선의 손실: 1.642422\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.853892\t최선의 손실: 1.642422\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.737606\t최선의 손실: 1.642422\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 1.995679\t최선의 손실: 1.642422\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 2.141553\t최선의 손실: 1.642422\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.850775\t최선의 손실: 1.642422\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.746681\t최선의 손실: 1.642422\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 1.830489\t최선의 손실: 1.642422\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.649747\t최선의 손실: 1.642422\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 2.048925\t최선의 손실: 1.642422\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.832475\t최선의 손실: 1.642422\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 1.816107\t최선의 손실: 1.642422\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.836798\t최선의 손실: 1.642422\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.796182\t최선의 손실: 1.642422\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.792563\t최선의 손실: 1.642422\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.879796\t최선의 손실: 1.642422\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.704596\t최선의 손실: 1.642422\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.803579\t최선의 손실: 1.642422\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 2.113099\t최선의 손실: 1.642422\t정확도: 19.08%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=30, total=  52.4s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.647423\t최선의 손실: 1.647423\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.840158\t최선의 손실: 1.647423\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.738320\t최선의 손실: 1.647423\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 1.999720\t최선의 손실: 1.647423\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 2.132829\t최선의 손실: 1.647423\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.850594\t최선의 손실: 1.647423\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1.746712\t최선의 손실: 1.647423\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 2.006028\t최선의 손실: 1.647423\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.823843\t최선의 손실: 1.647423\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.867809\t최선의 손실: 1.647423\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.797798\t최선의 손실: 1.647423\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.810957\t최선의 손실: 1.647423\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.836460\t최선의 손실: 1.647423\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 1.796240\t최선의 손실: 1.647423\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.792560\t최선의 손실: 1.647423\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.879794\t최선의 손실: 1.647423\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.704596\t최선의 손실: 1.647423\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.803578\t최선의 손실: 1.647423\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 2.113099\t최선의 손실: 1.647423\t정확도: 19.08%\n",
       "19\t검증 세트 손실: 1.628995\t최선의 손실: 1.628995\t정확도: 19.27%\n",
       "20\t검증 세트 손실: 1.801368\t최선의 손실: 1.628995\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.647708\t최선의 손실: 1.628995\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.647709\t최선의 손실: 1.628995\t정확도: 22.01%\n",
       "22\t검증 세트 손실: 1.653073\t최선의 손실: 1.628995\t정확도: 20.91%\n",
       "23\t검증 세트 손실: 1.739612\t최선의 손실: 1.628995\t정확도: 22.01%\n",
       "24\t검증 세트 손실: 1.861974\t최선의 손실: 1.628995\t정확도: 22.01%\n",
       "25\t검증 세트 손실: 1.744546\t최선의 손실: 1.628995\t정확도: 19.27%\n",
       "26\t검증 세트 손실: 1.719069\t최선의 손실: 1.628995\t정확도: 19.08%\n",
       "27\t검증 세트 손실: 1.685109\t최선의 손실: 1.628995\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 2.323919\t최선의 손실: 1.628995\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 2.050603\t최선의 손실: 1.628995\t정확도: 18.73%\n",
+      "28\t검증 세트 손실: 2.323920\t최선의 손실: 1.628995\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 2.050604\t최선의 손실: 1.628995\t정확도: 18.73%\n",
       "30\t검증 세트 손실: 1.854671\t최선의 손실: 1.628995\t정확도: 19.27%\n",
       "31\t검증 세트 손실: 1.956272\t최선의 손실: 1.628995\t정확도: 18.73%\n",
       "32\t검증 세트 손실: 1.666341\t최선의 손실: 1.628995\t정확도: 19.08%\n",
@@ -5235,1017 +5260,1012 @@
       "35\t검증 세트 손실: 1.761389\t최선의 손실: 1.628995\t정확도: 22.01%\n",
       "36\t검증 세트 손실: 1.971672\t최선의 손실: 1.628995\t정확도: 22.01%\n",
       "37\t검증 세트 손실: 1.921872\t최선의 손실: 1.628995\t정확도: 22.01%\n",
-      "38\t검증 세트 손실: 1.989986\t최선의 손실: 1.628995\t정확도: 18.73%\n",
+      "38\t검증 세트 손실: 1.989987\t최선의 손실: 1.628995\t정확도: 18.73%\n",
       "39\t검증 세트 손실: 1.904658\t최선의 손실: 1.628995\t정확도: 19.08%\n",
       "40\t검증 세트 손실: 1.730791\t최선의 손실: 1.628995\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total= 2.7min\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.138911\t최선의 손실: 0.138911\t정확도: 95.93%\n",
-      "1\t검증 세트 손실: 0.170981\t최선의 손실: 0.138911\t정확도: 96.17%\n",
-      "2\t검증 세트 손실: 0.129292\t최선의 손실: 0.129292\t정확도: 96.99%\n",
-      "3\t검증 세트 손실: 94854.359375\t최선의 손실: 0.129292\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 361.763336\t최선의 손실: 0.129292\t정확도: 43.63%\n",
-      "5\t검증 세트 손실: 55.220924\t최선의 손실: 0.129292\t정확도: 65.68%\n",
-      "6\t검증 세트 손실: 195.612640\t최선의 손실: 0.129292\t정확도: 58.09%\n",
-      "7\t검증 세트 손실: 110.936630\t최선의 손실: 0.129292\t정확도: 59.93%\n",
-      "8\t검증 세트 손실: 53.411636\t최선의 손실: 0.129292\t정확도: 74.32%\n",
-      "9\t검증 세트 손실: 41.322144\t최선의 손실: 0.129292\t정확도: 76.97%\n",
-      "10\t검증 세트 손실: 58.330109\t최선의 손실: 0.129292\t정확도: 74.90%\n",
-      "11\t검증 세트 손실: 22.411245\t최선의 손실: 0.129292\t정확도: 85.22%\n",
-      "12\t검증 세트 손실: 13.047668\t최선의 손실: 0.129292\t정확도: 89.25%\n",
-      "13\t검증 세트 손실: 62.490543\t최선의 손실: 0.129292\t정확도: 73.73%\n",
-      "14\t검증 세트 손실: 55.076561\t최선의 손실: 0.129292\t정확도: 72.83%\n",
-      "15\t검증 세트 손실: 10.388856\t최선의 손실: 0.129292\t정확도: 89.25%\n",
-      "16\t검증 세트 손실: 56.814827\t최선의 손실: 0.129292\t정확도: 72.24%\n",
-      "17\t검증 세트 손실: 21.056829\t최선의 손실: 0.129292\t정확도: 84.71%\n",
-      "18\t검증 세트 손실: 31.702936\t최선의 손실: 0.129292\t정확도: 78.58%\n",
-      "19\t검증 세트 손실: 26.630791\t최선의 손실: 0.129292\t정확도: 77.56%\n",
-      "20\t검증 세트 손실: 9.647603\t최선의 손실: 0.129292\t정확도: 88.94%\n",
-      "21\t검증 세트 손실: 12.168832\t최선의 손실: 0.129292\t정확도: 87.96%\n",
-      "22\t검증 세트 손실: 7.722749\t최선의 손실: 0.129292\t정확도: 91.01%\n",
-      "23\t검증 세트 손실: 10.074813\t최선의 손실: 0.129292\t정확도: 88.00%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=30, total= 1.1min\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.145859\t최선의 손실: 0.145859\t정확도: 95.97%\n",
+      "1\t검증 세트 손실: 5468.776855\t최선의 손실: 0.145859\t정확도: 18.06%\n",
+      "2\t검증 세트 손실: 20.570230\t최선의 손실: 0.145859\t정확도: 73.38%\n",
+      "3\t검증 세트 손실: 1359.967773\t최선의 손실: 0.145859\t정확도: 29.05%\n",
+      "4\t검증 세트 손실: 34.903469\t최선의 손실: 0.145859\t정확도: 55.24%\n",
+      "5\t검증 세트 손실: 8.598099\t최선의 손실: 0.145859\t정확도: 70.68%\n",
+      "6\t검증 세트 손실: 5.304133\t최선의 손실: 0.145859\t정확도: 81.31%\n",
+      "7\t검증 세트 손실: 5.076799\t최선의 손실: 0.145859\t정확도: 84.60%\n",
+      "8\t검증 세트 손실: 5.873591\t최선의 손실: 0.145859\t정확도: 80.77%\n",
+      "9\t검증 세트 손실: 3.773102\t최선의 손실: 0.145859\t정확도: 83.93%\n",
+      "10\t검증 세트 손실: 4.683840\t최선의 손실: 0.145859\t정확도: 81.24%\n",
+      "11\t검증 세트 손실: 2.157884\t최선의 손실: 0.145859\t정확도: 91.44%\n",
+      "12\t검증 세트 손실: 2.490200\t최선의 손실: 0.145859\t정확도: 89.60%\n",
+      "13\t검증 세트 손실: 2.630225\t최선의 손실: 0.145859\t정확도: 90.07%\n",
+      "14\t검증 세트 손실: 86674.000000\t최선의 손실: 0.145859\t정확도: 21.89%\n",
+      "15\t검증 세트 손실: 293.210419\t최선의 손실: 0.145859\t정확도: 65.32%\n",
+      "16\t검증 세트 손실: 100.000206\t최선의 손실: 0.145859\t정확도: 72.52%\n",
+      "17\t검증 세트 손실: 125.600319\t최선의 손실: 0.145859\t정확도: 72.05%\n",
+      "18\t검증 세트 손실: 52.328102\t최선의 손실: 0.145859\t정확도: 77.91%\n",
+      "19\t검증 세트 손실: 103.615860\t최선의 손실: 0.145859\t정확도: 71.58%\n",
+      "20\t검증 세트 손실: 338.410400\t최선의 손실: 0.145859\t정확도: 63.49%\n",
+      "21\t검증 세트 손실: 56.173965\t최선의 손실: 0.145859\t정확도: 83.62%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=   9.2s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.155893\t최선의 손실: 0.155893\t정확도: 95.86%\n",
-      "1\t검증 세트 손실: 0.133092\t최선의 손실: 0.133092\t정확도: 96.05%\n",
-      "2\t검증 세트 손실: 0.133403\t최선의 손실: 0.133092\t정확도: 96.13%\n",
-      "3\t검증 세트 손실: 0.171060\t최선의 손실: 0.133092\t정확도: 96.21%\n",
-      "4\t검증 세트 손실: 0.173314\t최선의 손실: 0.133092\t정확도: 95.62%\n",
-      "5\t검증 세트 손실: 8816.419922\t최선의 손실: 0.133092\t정확도: 25.02%\n",
-      "6\t검증 세트 손실: 1733.415161\t최선의 손실: 0.133092\t정확도: 52.23%\n",
-      "7\t검증 세트 손실: 390.712494\t최선의 손실: 0.133092\t정확도: 61.96%\n",
-      "8\t검증 세트 손실: 3468.233887\t최선의 손실: 0.133092\t정확도: 41.13%\n",
-      "9\t검증 세트 손실: 173.103668\t최선의 손실: 0.133092\t정확도: 66.65%\n",
-      "10\t검증 세트 손실: 76.144615\t최선의 손실: 0.133092\t정확도: 81.27%\n",
-      "11\t검증 세트 손실: 72.141418\t최선의 손실: 0.133092\t정확도: 78.30%\n",
-      "12\t검증 세트 손실: 56.497173\t최선의 손실: 0.133092\t정확도: 82.88%\n",
-      "13\t검증 세트 손실: 70.076172\t최선의 손실: 0.133092\t정확도: 81.24%\n",
-      "14\t검증 세트 손실: 32.980091\t최선의 손실: 0.133092\t정확도: 89.72%\n",
-      "15\t검증 세트 손실: 70.705528\t최선의 손실: 0.133092\t정확도: 70.02%\n",
-      "16\t검증 세트 손실: 53.895084\t최선의 손실: 0.133092\t정확도: 87.53%\n",
-      "17\t검증 세트 손실: 31.674978\t최선의 손실: 0.133092\t정확도: 90.30%\n",
-      "18\t검증 세트 손실: 44.900822\t최선의 손실: 0.133092\t정확도: 87.80%\n",
-      "19\t검증 세트 손실: 40.564159\t최선의 손실: 0.133092\t정확도: 87.92%\n",
-      "20\t검증 세트 손실: 30.230900\t최선의 손실: 0.133092\t정확도: 90.93%\n",
-      "21\t검증 세트 손실: 25.049728\t최선의 손실: 0.133092\t정확도: 90.89%\n",
-      "22\t검증 세트 손실: 30.273878\t최선의 손실: 0.133092\t정확도: 88.70%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=100, total=   6.0s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.208030\t최선의 손실: 0.208030\t정확도: 94.37%\n",
+      "1\t검증 세트 손실: 0.196613\t최선의 손실: 0.196613\t정확도: 95.93%\n",
+      "2\t검증 세트 손실: 247.472397\t최선의 손실: 0.196613\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 6.159650\t최선의 손실: 0.196613\t정확도: 50.90%\n",
+      "4\t검증 세트 손실: 3.203585\t최선의 손실: 0.196613\t정확도: 55.82%\n",
+      "5\t검증 세트 손실: 2.594485\t최선의 손실: 0.196613\t정확도: 63.06%\n",
+      "6\t검증 세트 손실: 3.166553\t최선의 손실: 0.196613\t정확도: 69.31%\n",
+      "7\t검증 세트 손실: 1.700563\t최선의 손실: 0.196613\t정확도: 60.79%\n",
+      "8\t검증 세트 손실: 1.947698\t최선의 손실: 0.196613\t정확도: 57.58%\n",
+      "9\t검증 세트 손실: 2.496299\t최선의 손실: 0.196613\t정확도: 69.78%\n",
+      "10\t검증 세트 손실: 0.851627\t최선의 손실: 0.196613\t정확도: 76.11%\n",
+      "11\t검증 세트 손실: 1.270312\t최선의 손실: 0.196613\t정확도: 63.92%\n",
+      "12\t검증 세트 손실: 0.880078\t최선의 손실: 0.196613\t정확도: 72.83%\n",
+      "13\t검증 세트 손실: 0.646642\t최선의 손실: 0.196613\t정확도: 78.69%\n",
+      "14\t검증 세트 손실: 0.585163\t최선의 손실: 0.196613\t정확도: 80.02%\n",
+      "15\t검증 세트 손실: 56574.492188\t최선의 손실: 0.196613\t정확도: 17.87%\n",
+      "16\t검증 세트 손실: 147.873306\t최선의 손실: 0.196613\t정확도: 32.92%\n",
+      "17\t검증 세트 손실: 46.359734\t최선의 손실: 0.196613\t정확도: 58.60%\n",
+      "18\t검증 세트 손실: 19.051437\t최선의 손실: 0.196613\t정확도: 65.36%\n",
+      "19\t검증 세트 손실: 63.853989\t최선의 손실: 0.196613\t정확도: 55.16%\n",
+      "20\t검증 세트 손실: 13.370034\t최선의 손실: 0.196613\t정확도: 74.00%\n",
+      "21\t검증 세트 손실: 11.819901\t최선의 손실: 0.196613\t정확도: 73.85%\n",
+      "22\t검증 세트 손실: 10.628263\t최선의 손실: 0.196613\t정확도: 79.40%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=   6.0s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.150384\t최선의 손실: 0.150384\t정확도: 95.86%\n",
-      "1\t검증 세트 손실: 0.146128\t최선의 손실: 0.146128\t정확도: 95.82%\n",
-      "2\t검증 세트 손실: 8522.495117\t최선의 손실: 0.146128\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 62.042568\t최선의 손실: 0.146128\t정확도: 36.43%\n",
-      "4\t검증 세트 손실: 11.416627\t최선의 손실: 0.146128\t정확도: 57.19%\n",
-      "5\t검증 세트 손실: 17.149200\t최선의 손실: 0.146128\t정확도: 57.35%\n",
-      "6\t검증 세트 손실: 4.676721\t최선의 손실: 0.146128\t정확도: 73.42%\n",
-      "7\t검증 세트 손실: 6.025187\t최선의 손실: 0.146128\t정확도: 71.19%\n",
-      "8\t검증 세트 손실: 2.503750\t최선의 손실: 0.146128\t정확도: 79.71%\n",
-      "9\t검증 세트 손실: 2.374610\t최선의 손실: 0.146128\t정확도: 79.98%\n",
-      "10\t검증 세트 손실: 2.750906\t최선의 손실: 0.146128\t정확도: 77.72%\n",
-      "11\t검증 세트 손실: 3.585639\t최선의 손실: 0.146128\t정확도: 78.46%\n",
-      "12\t검증 세트 손실: 1.581524\t최선의 손실: 0.146128\t정확도: 89.13%\n",
-      "13\t검증 세트 손실: 1.903270\t최선의 손실: 0.146128\t정확도: 87.61%\n",
-      "14\t검증 세트 손실: 3.187950\t최선의 손실: 0.146128\t정확도: 83.78%\n",
-      "15\t검증 세트 손실: 1.456897\t최선의 손실: 0.146128\t정확도: 89.48%\n",
-      "16\t검증 세트 손실: 801420.937500\t최선의 손실: 0.146128\t정확도: 36.71%\n",
-      "17\t검증 세트 손실: 507009.843750\t최선의 손실: 0.146128\t정확도: 51.52%\n",
-      "18\t검증 세트 손실: 2445.016846\t최선의 손실: 0.146128\t정확도: 36.86%\n",
-      "19\t검증 세트 손실: 2168.480957\t최선의 손실: 0.146128\t정확도: 49.34%\n",
-      "20\t검증 세트 손실: 1522.503784\t최선의 손실: 0.146128\t정확도: 69.62%\n",
-      "21\t검증 세트 손실: 267.074493\t최선의 손실: 0.146128\t정확도: 75.49%\n",
-      "22\t검증 세트 손실: 491.346802\t최선의 손실: 0.146128\t정확도: 72.75%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=   8.6s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.145668\t최선의 손실: 0.145668\t정확도: 96.83%\n",
-      "1\t검증 세트 손실: 0.254363\t최선의 손실: 0.145668\t정확도: 92.77%\n",
-      "2\t검증 세트 손실: 18.811153\t최선의 손실: 0.145668\t정확도: 82.76%\n",
-      "3\t검증 세트 손실: 1.003305\t최선의 손실: 0.145668\t정확도: 91.87%\n",
-      "4\t검증 세트 손실: 0.711709\t최선의 손실: 0.145668\t정확도: 94.33%\n",
-      "5\t검증 세트 손실: 0.527077\t최선의 손실: 0.145668\t정확도: 94.76%\n",
-      "6\t검증 세트 손실: 0.373374\t최선의 손실: 0.145668\t정확도: 96.05%\n",
-      "7\t검증 세트 손실: 0.339637\t최선의 손실: 0.145668\t정확도: 95.70%\n",
-      "8\t검증 세트 손실: 0.777586\t최선의 손실: 0.145668\t정확도: 95.19%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=100, total=   6.2s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=100 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9\t검증 세트 손실: 0.726363\t최선의 손실: 0.145668\t정확도: 87.96%\n",
-      "10\t검증 세트 손실: 0.391932\t최선의 손실: 0.145668\t정확도: 95.54%\n",
-      "11\t검증 세트 손실: 0.198357\t최선의 손실: 0.145668\t정확도: 95.54%\n",
-      "12\t검증 세트 손실: 0.226283\t최선의 손실: 0.145668\t정확도: 96.05%\n",
-      "13\t검증 세트 손실: 0.161128\t최선의 손실: 0.145668\t정확도: 96.29%\n",
-      "14\t검증 세트 손실: 0.162096\t최선의 손실: 0.145668\t정확도: 96.36%\n",
-      "15\t검증 세트 손실: 0.351059\t최선의 손실: 0.145668\t정확도: 96.48%\n",
-      "16\t검증 세트 손실: 9.810344\t최선의 손실: 0.145668\t정확도: 85.57%\n",
-      "17\t검증 세트 손실: 4.489713\t최선의 손실: 0.145668\t정확도: 93.78%\n",
-      "18\t검증 세트 손실: 2.079704\t최선의 손실: 0.145668\t정확도: 94.53%\n",
-      "19\t검증 세트 손실: 2.270699\t최선의 손실: 0.145668\t정확도: 93.78%\n",
-      "20\t검증 세트 손실: 1.559015\t최선의 손실: 0.145668\t정확도: 94.41%\n",
-      "21\t검증 세트 손실: 2.625832\t최선의 손실: 0.145668\t정확도: 91.63%\n",
+      "0\t검증 세트 손실: 18.338503\t최선의 손실: 18.338503\t정확도: 39.29%\n",
+      "1\t검증 세트 손실: 425.615509\t최선의 손실: 18.338503\t정확도: 33.78%\n",
+      "2\t검증 세트 손실: 185.916489\t최선의 손실: 18.338503\t정확도: 31.43%\n",
+      "3\t검증 세트 손실: 166.548126\t최선의 손실: 18.338503\t정확도: 24.28%\n",
+      "4\t검증 세트 손실: 51.209503\t최선의 손실: 18.338503\t정확도: 46.25%\n",
+      "5\t검증 세트 손실: 28.571503\t최선의 손실: 18.338503\t정확도: 54.93%\n",
+      "6\t검증 세트 손실: 68.079857\t최선의 손실: 18.338503\t정확도: 35.54%\n",
+      "7\t검증 세트 손실: 23.278650\t최선의 손실: 18.338503\t정확도: 54.77%\n",
+      "8\t검증 세트 손실: 29.167734\t최선의 손실: 18.338503\t정확도: 58.64%\n",
+      "9\t검증 세트 손실: 54.082554\t최선의 손실: 18.338503\t정확도: 58.05%\n",
+      "10\t검증 세트 손실: 185.491760\t최선의 손실: 18.338503\t정확도: 64.54%\n",
+      "11\t검증 세트 손실: 15.751663\t최선의 손실: 15.751663\t정확도: 61.49%\n",
+      "12\t검증 세트 손실: 312.584137\t최선의 손실: 15.751663\t정확도: 47.73%\n",
+      "13\t검증 세트 손실: 996.449402\t최선의 손실: 15.751663\t정확도: 64.66%\n",
+      "14\t검증 세트 손실: 5.246263\t최선의 손실: 5.246263\t정확도: 75.53%\n",
+      "15\t검증 세트 손실: 16.334307\t최선의 손실: 5.246263\t정확도: 52.78%\n",
+      "16\t검증 세트 손실: 6.229960\t최선의 손실: 5.246263\t정확도: 72.28%\n",
+      "17\t검증 세트 손실: 90.696144\t최선의 손실: 5.246263\t정확도: 27.95%\n",
+      "18\t검증 세트 손실: 7.062860\t최선의 손실: 5.246263\t정확도: 68.45%\n",
+      "19\t검증 세트 손실: 8.526780\t최선의 손실: 5.246263\t정확도: 65.75%\n",
+      "20\t검증 세트 손실: 2.316842\t최선의 손실: 2.316842\t정확도: 80.53%\n",
+      "21\t검증 세트 손실: 6.353879\t최선의 손실: 2.316842\t정확도: 74.12%\n",
+      "22\t검증 세트 손실: 5.215212\t최선의 손실: 2.316842\t정확도: 78.07%\n",
+      "23\t검증 세트 손실: 5.451184\t최선의 손실: 2.316842\t정확도: 69.62%\n",
+      "24\t검증 세트 손실: 4.846220\t최선의 손실: 2.316842\t정확도: 72.60%\n",
+      "25\t검증 세트 손실: 4.784024\t최선의 손실: 2.316842\t정확도: 74.04%\n",
+      "26\t검증 세트 손실: 37.108181\t최선의 손실: 2.316842\t정확도: 76.11%\n",
+      "27\t검증 세트 손실: 52439.007812\t최선의 손실: 2.316842\t정확도: 20.99%\n",
+      "28\t검증 세트 손실: 5034.442871\t최선의 손실: 2.316842\t정확도: 45.70%\n",
+      "29\t검증 세트 손실: 6722.470703\t최선의 손실: 2.316842\t정확도: 48.24%\n",
+      "30\t검증 세트 손실: 3570.641846\t최선의 손실: 2.316842\t정확도: 55.32%\n",
+      "31\t검증 세트 손실: 2112.906006\t최선의 손실: 2.316842\t정확도: 59.07%\n",
+      "32\t검증 세트 손실: 1652.064453\t최선의 손실: 2.316842\t정확도: 62.55%\n",
+      "33\t검증 세트 손실: 1379.428711\t최선의 손실: 2.316842\t정확도: 63.45%\n",
+      "34\t검증 세트 손실: 725.681274\t최선의 손실: 2.316842\t정확도: 71.19%\n",
+      "35\t검증 세트 손실: 2062.173096\t최선의 손실: 2.316842\t정확도: 70.29%\n",
+      "36\t검증 세트 손실: 433.131836\t최선의 손실: 2.316842\t정확도: 77.33%\n",
+      "37\t검증 세트 손실: 1508.218994\t최선의 손실: 2.316842\t정확도: 70.88%\n",
+      "38\t검증 세트 손실: 531.776917\t최선의 손실: 2.316842\t정확도: 79.16%\n",
+      "39\t검증 세트 손실: 466.234222\t최선의 손실: 2.316842\t정확도: 76.43%\n",
+      "40\t검증 세트 손실: 592.140747\t최선의 손실: 2.316842\t정확도: 75.68%\n",
+      "41\t검증 세트 손실: 351.140656\t최선의 손실: 2.316842\t정확도: 81.24%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=  20.6s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.154353\t최선의 손실: 0.154353\t정확도: 95.97%\n",
-      "1\t검증 세트 손실: 0.116159\t최선의 손실: 0.116159\t정확도: 96.95%\n",
-      "2\t검증 세트 손실: 0.844457\t최선의 손실: 0.116159\t정확도: 84.36%\n",
-      "3\t검증 세트 손실: 0.152178\t최선의 손실: 0.116159\t정확도: 95.23%\n",
-      "4\t검증 세트 손실: 0.118351\t최선의 손실: 0.116159\t정확도: 96.64%\n",
-      "5\t검증 세트 손실: 0.138608\t최선의 손실: 0.116159\t정확도: 96.33%\n",
-      "6\t검증 세트 손실: 3.533624\t최선의 손실: 0.116159\t정확도: 87.49%\n",
-      "7\t검증 세트 손실: 0.614404\t최선의 손실: 0.116159\t정확도: 92.10%\n",
-      "8\t검증 세트 손실: 0.559318\t최선의 손실: 0.116159\t정확도: 94.37%\n",
-      "9\t검증 세트 손실: 21.891829\t최선의 손실: 0.116159\t정확도: 95.43%\n",
-      "10\t검증 세트 손실: 0.457160\t최선의 손실: 0.116159\t정확도: 93.47%\n",
-      "11\t검증 세트 손실: 4.749017\t최선의 손실: 0.116159\t정확도: 73.53%\n",
-      "12\t검증 세트 손실: 0.537248\t최선의 손실: 0.116159\t정확도: 94.61%\n",
-      "13\t검증 세트 손실: 0.594200\t최선의 손실: 0.116159\t정확도: 94.57%\n",
-      "14\t검증 세트 손실: 0.328511\t최선의 손실: 0.116159\t정확도: 95.62%\n",
-      "15\t검증 세트 손실: 3.909336\t최선의 손실: 0.116159\t정확도: 95.19%\n",
-      "16\t검증 세트 손실: 0.392431\t최선의 손실: 0.116159\t정확도: 94.88%\n",
-      "17\t검증 세트 손실: 0.389865\t최선의 손실: 0.116159\t정확도: 94.41%\n",
-      "18\t검증 세트 손실: 0.325837\t최선의 손실: 0.116159\t정확도: 95.97%\n",
-      "19\t검증 세트 손실: 3.983140\t최선의 손실: 0.116159\t정확도: 90.38%\n",
-      "20\t검증 세트 손실: 1.789338\t최선의 손실: 0.116159\t정확도: 88.74%\n",
-      "21\t검증 세트 손실: 1.031155\t최선의 손실: 0.116159\t정확도: 94.02%\n",
-      "22\t검증 세트 손실: 1.107690\t최선의 손실: 0.116159\t정확도: 93.94%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.05, n_neurons=100, total=  11.9s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.189298\t최선의 손실: 0.189298\t정확도: 95.39%\n",
+      "1\t검증 세트 손실: 0.117694\t최선의 손실: 0.117694\t정확도: 97.07%\n",
+      "2\t검증 세트 손실: 0.158282\t최선의 손실: 0.117694\t정확도: 96.60%\n",
+      "3\t검증 세트 손실: 2.502646\t최선의 손실: 0.117694\t정확도: 83.50%\n",
+      "4\t검증 세트 손실: 0.375008\t최선의 손실: 0.117694\t정확도: 91.87%\n",
+      "5\t검증 세트 손실: 0.190433\t최선의 손실: 0.117694\t정확도: 96.36%\n",
+      "6\t검증 세트 손실: 0.216534\t최선의 손실: 0.117694\t정확도: 94.53%\n",
+      "7\t검증 세트 손실: 0.235945\t최선의 손실: 0.117694\t정확도: 93.98%\n",
+      "8\t검증 세트 손실: 0.136139\t최선의 손실: 0.117694\t정확도: 96.83%\n",
+      "9\t검증 세트 손실: 0.233759\t최선의 손실: 0.117694\t정확도: 96.48%\n",
+      "10\t검증 세트 손실: 16.444185\t최선의 손실: 0.117694\t정확도: 72.20%\n",
+      "11\t검증 세트 손실: 1.594100\t최선의 손실: 0.117694\t정확도: 93.51%\n",
+      "12\t검증 세트 손실: 0.587239\t최선의 손실: 0.117694\t정확도: 95.43%\n",
+      "13\t검증 세트 손실: 0.644546\t최선의 손실: 0.117694\t정확도: 90.93%\n",
+      "14\t검증 세트 손실: 0.649053\t최선의 손실: 0.117694\t정확도: 93.51%\n",
+      "15\t검증 세트 손실: 0.406479\t최선의 손실: 0.117694\t정확도: 95.78%\n",
+      "16\t검증 세트 손실: 0.334222\t최선의 손실: 0.117694\t정확도: 96.48%\n",
+      "17\t검증 세트 손실: 0.405307\t최선의 손실: 0.117694\t정확도: 96.48%\n",
+      "18\t검증 세트 손실: 1.200766\t최선의 손실: 0.117694\t정확도: 96.25%\n",
+      "19\t검증 세트 손실: 20.597158\t최선의 손실: 0.117694\t정확도: 74.98%\n",
+      "20\t검증 세트 손실: 0.770737\t최선의 손실: 0.117694\t정확도: 94.06%\n",
+      "21\t검증 세트 손실: 2.604972\t최선의 손실: 0.117694\t정확도: 85.85%\n",
+      "22\t검증 세트 손실: 0.528899\t최선의 손실: 0.117694\t정확도: 94.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=  17.5s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.141316\t최선의 손실: 0.141316\t정확도: 96.44%\n",
-      "1\t검증 세트 손실: 0.839137\t최선의 손실: 0.141316\t정확도: 76.90%\n",
-      "2\t검증 세트 손실: 0.725516\t최선의 손실: 0.141316\t정확도: 75.18%\n",
-      "3\t검증 세트 손실: 0.184344\t최선의 손실: 0.141316\t정확도: 94.02%\n",
-      "4\t검증 세트 손실: 0.138444\t최선의 손실: 0.138444\t정확도: 95.86%\n",
-      "5\t검증 세트 손실: 0.147773\t최선의 손실: 0.138444\t정확도: 95.74%\n",
-      "6\t검증 세트 손실: 0.156881\t최선의 손실: 0.138444\t정확도: 95.07%\n",
-      "7\t검증 세트 손실: 0.122229\t최선의 손실: 0.122229\t정확도: 96.64%\n",
-      "8\t검증 세트 손실: 0.115837\t최선의 손실: 0.115837\t정확도: 97.11%\n",
-      "9\t검증 세트 손실: 0.143795\t최선의 손실: 0.115837\t정확도: 96.40%\n",
-      "10\t검증 세트 손실: 0.126030\t최선의 손실: 0.115837\t정확도: 96.52%\n",
-      "11\t검증 세트 손실: 47.140617\t최선의 손실: 0.115837\t정확도: 65.83%\n",
-      "12\t검증 세트 손실: 2.263320\t최선의 손실: 0.115837\t정확도: 92.61%\n",
-      "13\t검증 세트 손실: 2.020555\t최선의 손실: 0.115837\t정확도: 92.77%\n",
-      "14\t검증 세트 손실: 1.283074\t최선의 손실: 0.115837\t정확도: 93.04%\n",
-      "15\t검증 세트 손실: 1.106213\t최선의 손실: 0.115837\t정확도: 94.49%\n",
-      "16\t검증 세트 손실: 8.624186\t최선의 손실: 0.115837\t정확도: 88.66%\n",
-      "17\t검증 세트 손실: 2.330008\t최선의 손실: 0.115837\t정확도: 95.11%\n",
-      "18\t검증 세트 손실: 1.252477\t최선의 손실: 0.115837\t정확도: 96.13%\n",
-      "19\t검증 세트 손실: 0.901375\t최선의 손실: 0.115837\t정확도: 95.39%\n",
-      "20\t검증 세트 손실: 1.412964\t최선의 손실: 0.115837\t정확도: 95.70%\n",
-      "21\t검증 세트 손실: 1.007553\t최선의 손실: 0.115837\t정확도: 95.39%\n",
-      "22\t검증 세트 손실: 3.461746\t최선의 손실: 0.115837\t정확도: 95.58%\n",
-      "23\t검증 세트 손실: 5.380153\t최선의 손실: 0.115837\t정확도: 97.22%\n",
-      "24\t검증 세트 손실: 5.098266\t최선의 손실: 0.115837\t정확도: 88.39%\n",
-      "25\t검증 세트 손실: 4.597194\t최선의 손실: 0.115837\t정확도: 95.27%\n",
-      "26\t검증 세트 손실: 1.976285\t최선의 손실: 0.115837\t정확도: 92.10%\n",
-      "27\t검증 세트 손실: 392.158936\t최선의 손실: 0.115837\t정확도: 73.73%\n",
-      "28\t검증 세트 손실: 17.374557\t최선의 손실: 0.115837\t정확도: 89.95%\n",
-      "29\t검증 세트 손실: 3.956095\t최선의 손실: 0.115837\t정확도: 95.23%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=100, total=  12.0s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.204705\t최선의 손실: 0.204705\t정확도: 95.54%\n",
+      "1\t검증 세트 손실: 0.380572\t최선의 손실: 0.204705\t정확도: 87.26%\n",
+      "2\t검증 세트 손실: 129.182007\t최선의 손실: 0.204705\t정확도: 51.41%\n",
+      "3\t검증 세트 손실: 3.044452\t최선의 손실: 0.204705\t정확도: 91.44%\n",
+      "4\t검증 세트 손실: 1.522623\t최선의 손실: 0.204705\t정확도: 92.26%\n",
+      "5\t검증 세트 손실: 0.931617\t최선의 손실: 0.204705\t정확도: 93.51%\n",
+      "6\t검증 세트 손실: 0.644692\t최선의 손실: 0.204705\t정확도: 92.26%\n",
+      "7\t검증 세트 손실: 0.455474\t최선의 손실: 0.204705\t정확도: 95.50%\n",
+      "8\t검증 세트 손실: 0.432333\t최선의 손실: 0.204705\t정확도: 95.66%\n",
+      "9\t검증 세트 손실: 0.644138\t최선의 손실: 0.204705\t정확도: 91.59%\n",
+      "10\t검증 세트 손실: 0.367756\t최선의 손실: 0.204705\t정확도: 95.93%\n",
+      "11\t검증 세트 손실: 0.274936\t최선의 손실: 0.204705\t정확도: 95.93%\n",
+      "12\t검증 세트 손실: 0.321725\t최선의 손실: 0.204705\t정확도: 96.05%\n",
+      "13\t검증 세트 손실: 0.307084\t최선의 손실: 0.204705\t정확도: 96.99%\n",
+      "14\t검증 세트 손실: 14.159133\t최선의 손실: 0.204705\t정확도: 87.88%\n",
+      "15\t검증 세트 손실: 1.684021\t최선의 손실: 0.204705\t정확도: 94.06%\n",
+      "16\t검증 세트 손실: 1.010862\t최선의 손실: 0.204705\t정확도: 94.68%\n",
+      "17\t검증 세트 손실: 0.853353\t최선의 손실: 0.204705\t정확도: 95.82%\n",
+      "18\t검증 세트 손실: 1044.452515\t최선의 손실: 0.204705\t정확도: 58.44%\n",
+      "19\t검증 세트 손실: 22.361332\t최선의 손실: 0.204705\t정확도: 92.14%\n",
+      "20\t검증 세트 손실: 6.544475\t최선의 손실: 0.204705\t정확도: 95.90%\n",
+      "21\t검증 세트 손실: 5.810543\t최선의 손실: 0.204705\t정확도: 95.93%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, n_neurons=100, total=  19.7s\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 2.097275\t최선의 손실: 2.097275\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.858785\t최선의 손실: 1.858785\t정확도: 22.01%\n",
-      "2\t검증 세트 손실: 1.796979\t최선의 손실: 1.796979\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 1.873194\t최선의 손실: 1.796979\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 1.731643\t최선의 손실: 1.731643\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.666485\t최선의 손실: 1.666485\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 2.220949\t최선의 손실: 1.666485\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 2.015727\t최선의 손실: 1.666485\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 2.090927\t최선의 손실: 1.666485\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 1.947276\t최선의 손실: 1.666485\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.792378\t최선의 손실: 1.666485\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.672828\t최선의 손실: 1.666485\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.912345\t최선의 손실: 1.666485\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 2.118674\t최선의 손실: 1.666485\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 2.516823\t최선의 손실: 1.666485\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.914144\t최선의 손실: 1.666485\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 2.225628\t최선의 손실: 1.666485\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.658233\t최선의 손실: 1.658233\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 2.221773\t최선의 손실: 1.658233\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 2.665044\t최선의 손실: 1.658233\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.008371\t최선의 손실: 1.658233\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 2.444497\t최선의 손실: 1.658233\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 2.119731\t최선의 손실: 1.658233\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 2.245319\t최선의 손실: 1.658233\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 1.920224\t최선의 손실: 1.658233\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.993324\t최선의 손실: 1.658233\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.718370\t최선의 손실: 1.658233\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 2.015471\t최선의 손실: 1.658233\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 2.296115\t최선의 손실: 1.658233\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.739970\t최선의 손실: 1.658233\t정확도: 20.91%\n",
-      "30\t검증 세트 손실: 1.709733\t최선의 손실: 1.658233\t정확도: 18.73%\n",
-      "31\t검증 세트 손실: 1.664723\t최선의 손실: 1.658233\t정확도: 20.91%\n",
-      "32\t검증 세트 손실: 1.779046\t최선의 손실: 1.658233\t정확도: 22.01%\n",
-      "33\t검증 세트 손실: 1.714498\t최선의 손실: 1.658233\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 2.160547\t최선의 손실: 1.658233\t정확도: 19.08%\n",
-      "35\t검증 세트 손실: 2.389713\t최선의 손실: 1.658233\t정확도: 19.27%\n",
-      "36\t검증 세트 손실: 1.843754\t최선의 손실: 1.658233\t정확도: 18.73%\n",
-      "37\t검증 세트 손실: 1.860697\t최선의 손실: 1.658233\t정확도: 19.08%\n",
-      "38\t검증 세트 손실: 2.205716\t최선의 손실: 1.658233\t정확도: 19.08%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=100, total=  11.5s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.175472\t최선의 손실: 0.175472\t정확도: 96.33%\n",
+      "1\t검증 세트 손실: 50.099869\t최선의 손실: 0.175472\t정확도: 85.07%\n",
+      "2\t검증 세트 손실: 0.191368\t최선의 손실: 0.175472\t정확도: 95.74%\n",
+      "3\t검증 세트 손실: 0.136587\t최선의 손실: 0.136587\t정확도: 97.19%\n",
+      "4\t검증 세트 손실: 0.126187\t최선의 손실: 0.126187\t정확도: 96.64%\n",
+      "5\t검증 세트 손실: 0.114800\t최선의 손실: 0.114800\t정확도: 96.83%\n",
+      "6\t검증 세트 손실: 0.161920\t최선의 손실: 0.114800\t정확도: 95.93%\n",
+      "7\t검증 세트 손실: 0.093160\t최선의 손실: 0.093160\t정확도: 97.65%\n",
+      "8\t검증 세트 손실: 0.099834\t최선의 손실: 0.093160\t정확도: 97.65%\n",
+      "9\t검증 세트 손실: 0.870338\t최선의 손실: 0.093160\t정확도: 93.63%\n",
+      "10\t검증 세트 손실: 3.955570\t최선의 손실: 0.093160\t정확도: 95.70%\n",
+      "11\t검증 세트 손실: 1.708703\t최선의 손실: 0.093160\t정확도: 95.27%\n",
+      "12\t검증 세트 손실: 1.371071\t최선의 손실: 0.093160\t정확도: 93.82%\n",
+      "13\t검증 세트 손실: 0.906444\t최선의 손실: 0.093160\t정확도: 96.05%\n",
+      "14\t검증 세트 손실: 1.052944\t최선의 손실: 0.093160\t정확도: 94.21%\n",
+      "15\t검증 세트 손실: 1.663604\t최선의 손실: 0.093160\t정확도: 91.75%\n",
+      "16\t검증 세트 손실: 0.707526\t최선의 손실: 0.093160\t정확도: 95.62%\n",
+      "17\t검증 세트 손실: 0.366497\t최선의 손실: 0.093160\t정확도: 96.68%\n",
+      "18\t검증 세트 손실: 0.776876\t최선의 손실: 0.093160\t정확도: 93.00%\n",
+      "19\t검증 세트 손실: 1.017818\t최선의 손실: 0.093160\t정확도: 96.44%\n",
+      "20\t검증 세트 손실: 1.567619\t최선의 손실: 0.093160\t정확도: 95.74%\n",
+      "21\t검증 세트 손실: 0.269926\t최선의 손실: 0.093160\t정확도: 96.68%\n",
+      "22\t검증 세트 손실: 0.264357\t최선의 손실: 0.093160\t정확도: 96.64%\n",
+      "23\t검증 세트 손실: 0.208538\t최선의 손실: 0.093160\t정확도: 97.07%\n",
+      "24\t검증 세트 손실: 5.485671\t최선의 손실: 0.093160\t정확도: 91.48%\n",
+      "25\t검증 세트 손실: 2.244076\t최선의 손실: 0.093160\t정확도: 93.78%\n",
+      "26\t검증 세트 손실: 0.855001\t최선의 손실: 0.093160\t정확도: 95.82%\n",
+      "27\t검증 세트 손실: 0.718094\t최선의 손실: 0.093160\t정확도: 95.15%\n",
+      "28\t검증 세트 손실: 0.419366\t최선의 손실: 0.093160\t정확도: 97.07%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total= 2.8min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.783194\t최선의 손실: 1.783194\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.877121\t최선의 손실: 1.783194\t정확도: 22.01%\n",
-      "2\t검증 세트 손실: 2.011948\t최선의 손실: 1.783194\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 2.011266\t최선의 손실: 1.783194\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.971342\t최선의 손실: 1.783194\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.647388\t최선의 손실: 1.647388\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.701500\t최선의 손실: 1.647388\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 2.395790\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 1.941441\t최선의 손실: 1.647388\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.963342\t최선의 손실: 1.647388\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.835827\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.930027\t최선의 손실: 1.647388\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.885896\t최선의 손실: 1.647388\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 2.390906\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.961099\t최선의 손실: 1.647388\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 1.962755\t최선의 손실: 1.647388\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.886139\t최선의 손실: 1.647388\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.892864\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.981895\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 1.750127\t최선의 손실: 1.647388\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.134340\t최선의 손실: 1.647388\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.879304\t최선의 손실: 1.647388\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 2.148499\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 2.088906\t최선의 손실: 1.647388\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.838559\t최선의 손실: 1.647388\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.905609\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "26\t검증 세트 손실: 2.406298\t최선의 손실: 1.647388\t정확도: 19.08%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total= 1.7min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.783779\t최선의 손실: 1.783779\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 2.016555\t최선의 손실: 1.783779\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.936229\t최선의 손실: 1.783779\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 1.853899\t최선의 손실: 1.783779\t정확도: 19.27%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, learning_rate=0.02, n_neurons=100, total=  14.2s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=70 \n",
+      "0\t검증 세트 손실: 2.138208\t최선의 손실: 2.138208\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.808719\t최선의 손실: 1.808719\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 1.792734\t최선의 손실: 1.792734\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 1.871393\t최선의 손실: 1.792734\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 1.731558\t최선의 손실: 1.731558\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 1.666486\t최선의 손실: 1.666486\t정확도: 19.08%\n",
+      "6\t검증 세트 손실: 2.220947\t최선의 손실: 1.666486\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 2.015726\t최선의 손실: 1.666486\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 2.090927\t최선의 손실: 1.666486\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 1.713748\t최선의 손실: 1.666486\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 2.046488\t최선의 손실: 1.666486\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.737402\t최선의 손실: 1.666486\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.826834\t최선의 손실: 1.666486\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 2.120012\t최선의 손실: 1.666486\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 2.178529\t최선의 손실: 1.666486\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 2.246719\t최선의 손실: 1.666486\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 2.159572\t최선의 손실: 1.666486\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.650901\t최선의 손실: 1.650901\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 2.165642\t최선의 손실: 1.650901\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 2.591237\t최선의 손실: 1.650901\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.997003\t최선의 손실: 1.650901\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 2.441002\t최선의 손실: 1.650901\t정확도: 20.91%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4\t검증 세트 손실: 2.686582\t최선의 손실: 1.783779\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.852321\t최선의 손실: 1.783779\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.812496\t최선의 손실: 1.783779\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.757771\t최선의 손실: 1.757771\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 1.902283\t최선의 손실: 1.757771\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 2.087057\t최선의 손실: 1.757771\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.779784\t최선의 손실: 1.757771\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 2.227396\t최선의 손실: 1.757771\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.950018\t최선의 손실: 1.757771\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 3.099453\t최선의 손실: 1.757771\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.830344\t최선의 손실: 1.757771\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.733783\t최선의 손실: 1.733783\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 2.189536\t최선의 손실: 1.733783\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.943920\t최선의 손실: 1.733783\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.848899\t최선의 손실: 1.733783\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 1.984283\t최선의 손실: 1.733783\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.919440\t최선의 손실: 1.733783\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 2.156083\t최선의 손실: 1.733783\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.834820\t최선의 손실: 1.733783\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 2.444077\t최선의 손실: 1.733783\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 1.866027\t최선의 손실: 1.733783\t정확도: 19.27%\n",
-      "25\t검증 세트 손실: 1.884747\t최선의 손실: 1.733783\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.738111\t최선의 손실: 1.733783\t정확도: 18.73%\n",
+      "22\t검증 세트 손실: 2.119686\t최선의 손실: 1.650901\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 2.245298\t최선의 손실: 1.650901\t정확도: 19.27%\n",
+      "24\t검증 세트 손실: 1.920224\t최선의 손실: 1.650901\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.993323\t최선의 손실: 1.650901\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.718371\t최선의 손실: 1.650901\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 2.015470\t최선의 손실: 1.650901\t정확도: 18.73%\n",
+      "28\t검증 세트 손실: 2.296115\t최선의 손실: 1.650901\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.739971\t최선의 손실: 1.650901\t정확도: 20.91%\n",
+      "30\t검증 세트 손실: 1.709734\t최선의 손실: 1.650901\t정확도: 18.73%\n",
+      "31\t검증 세트 손실: 1.664723\t최선의 손실: 1.650901\t정확도: 20.91%\n",
+      "32\t검증 세트 손실: 1.779047\t최선의 손실: 1.650901\t정확도: 22.01%\n",
+      "33\t검증 세트 손실: 1.714498\t최선의 손실: 1.650901\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 2.160549\t최선의 손실: 1.650901\t정확도: 19.08%\n",
+      "35\t검증 세트 손실: 2.389712\t최선의 손실: 1.650901\t정확도: 19.27%\n",
+      "36\t검증 세트 손실: 1.843753\t최선의 손실: 1.650901\t정확도: 18.73%\n",
+      "37\t검증 세트 손실: 1.860698\t최선의 손실: 1.650901\t정확도: 19.08%\n",
+      "38\t검증 세트 손실: 2.205716\t최선의 손실: 1.650901\t정확도: 19.08%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=70, total= 1.0min\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=70 \n",
+      "0\t검증 세트 손실: 1.719961\t최선의 손실: 1.719961\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.848444\t최선의 손실: 1.719961\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 2.004003\t최선의 손실: 1.719961\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 2.015480\t최선의 손실: 1.719961\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.970654\t최선의 손실: 1.719961\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.647382\t최선의 손실: 1.647382\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.701499\t최선의 손실: 1.647382\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 2.395790\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.941441\t최선의 손실: 1.647382\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.963343\t최선의 손실: 1.647382\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.835827\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.930027\t최선의 손실: 1.647382\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.885896\t최선의 손실: 1.647382\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 2.390906\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.961099\t최선의 손실: 1.647382\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 1.962755\t최선의 손실: 1.647382\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.886139\t최선의 손실: 1.647382\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.892864\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.981895\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 1.750126\t최선의 손실: 1.647382\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.134341\t최선의 손실: 1.647382\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.879304\t최선의 손실: 1.647382\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 2.148499\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "23\t검증 세트 손실: 2.088906\t최선의 손실: 1.647382\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.838559\t최선의 손실: 1.647382\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.905607\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "26\t검증 세트 손실: 2.406297\t최선의 손실: 1.647382\t정확도: 19.08%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=70, total=  43.0s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=70 \n",
+      "0\t검증 세트 손실: 1.758163\t최선의 손실: 1.758163\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 1.776485\t최선의 손실: 1.758163\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.911588\t최선의 손실: 1.758163\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1.864851\t최선의 손실: 1.758163\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 2.814934\t최선의 손실: 1.758163\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.853260\t최선의 손실: 1.758163\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.812867\t최선의 손실: 1.758163\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.757710\t최선의 손실: 1.757710\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.902283\t최선의 손실: 1.757710\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 2.087056\t최선의 손실: 1.757710\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.779784\t최선의 손실: 1.757710\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 2.227395\t최선의 손실: 1.757710\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.950018\t최선의 손실: 1.757710\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 3.099451\t최선의 손실: 1.757710\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.830345\t최선의 손실: 1.757710\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.733784\t최선의 손실: 1.733784\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 2.189536\t최선의 손실: 1.733784\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.943920\t최선의 손실: 1.733784\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 1.848899\t최선의 손실: 1.733784\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 1.984281\t최선의 손실: 1.733784\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.919440\t최선의 손실: 1.733784\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 2.156084\t최선의 손실: 1.733784\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.834819\t최선의 손실: 1.733784\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 2.444077\t최선의 손실: 1.733784\t정확도: 19.08%\n",
+      "24\t검증 세트 손실: 1.866025\t최선의 손실: 1.733784\t정확도: 19.27%\n",
+      "25\t검증 세트 손실: 1.884747\t최선의 손실: 1.733784\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.738112\t최선의 손실: 1.733784\t정확도: 18.73%\n",
       "27\t검증 세트 손실: 1.679295\t최선의 손실: 1.679295\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 2.567141\t최선의 손실: 1.679295\t정확도: 19.08%\n",
+      "28\t검증 세트 손실: 2.567142\t최선의 손실: 1.679295\t정확도: 19.08%\n",
       "29\t검증 세트 손실: 2.661162\t최선의 손실: 1.679295\t정확도: 20.91%\n",
       "30\t검증 세트 손실: 2.013063\t최선의 손실: 1.679295\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 1.886540\t최선의 손실: 1.679295\t정확도: 22.01%\n",
+      "31\t검증 세트 손실: 1.886539\t최선의 손실: 1.679295\t정확도: 22.01%\n",
       "32\t검증 세트 손실: 1.891879\t최선의 손실: 1.679295\t정확도: 19.08%\n",
-      "33\t검증 세트 손실: 2.216809\t최선의 손실: 1.679295\t정확도: 22.01%\n",
+      "33\t검증 세트 손실: 2.216810\t최선의 손실: 1.679295\t정확도: 22.01%\n",
       "34\t검증 세트 손실: 2.191176\t최선의 손실: 1.679295\t정확도: 19.27%\n",
       "35\t검증 세트 손실: 1.704170\t최선의 손실: 1.679295\t정확도: 19.08%\n",
-      "36\t검증 세트 손실: 1.731656\t최선의 손실: 1.679295\t정확도: 19.27%\n",
+      "36\t검증 세트 손실: 1.731655\t최선의 손실: 1.679295\t정확도: 19.27%\n",
       "37\t검증 세트 손실: 1.813347\t최선의 손실: 1.679295\t정확도: 22.01%\n",
       "38\t검증 세트 손실: 3.032770\t최선의 손실: 1.679295\t정확도: 18.73%\n",
-      "39\t검증 세트 손실: 2.401284\t최선의 손실: 1.679295\t정확도: 19.27%\n",
+      "39\t검증 세트 손실: 2.401281\t최선의 손실: 1.679295\t정확도: 19.27%\n",
       "40\t검증 세트 손실: 1.645940\t최선의 손실: 1.645940\t정확도: 19.08%\n",
-      "41\t검증 세트 손실: 2.166196\t최선의 손실: 1.645940\t정확도: 22.01%\n",
-      "42\t검증 세트 손실: 2.384702\t최선의 손실: 1.645940\t정확도: 22.01%\n",
+      "41\t검증 세트 손실: 2.166197\t최선의 손실: 1.645940\t정확도: 22.01%\n",
+      "42\t검증 세트 손실: 2.384700\t최선의 손실: 1.645940\t정확도: 22.01%\n",
       "43\t검증 세트 손실: 2.204781\t최선의 손실: 1.645940\t정확도: 20.91%\n",
       "44\t검증 세트 손실: 1.765179\t최선의 손실: 1.645940\t정확도: 18.73%\n",
       "45\t검증 세트 손실: 1.696824\t최선의 손실: 1.645940\t정확도: 18.73%\n",
       "46\t검증 세트 손실: 2.521093\t최선의 손실: 1.645940\t정확도: 20.91%\n",
-      "47\t검증 세트 손실: 2.133250\t최선의 손실: 1.645940\t정확도: 19.08%\n",
+      "47\t검증 세트 손실: 2.133248\t최선의 손실: 1.645940\t정확도: 19.08%\n",
       "48\t검증 세트 손실: 1.695982\t최선의 손실: 1.645940\t정확도: 22.01%\n",
       "49\t검증 세트 손실: 1.936503\t최선의 손실: 1.645940\t정확도: 19.08%\n",
-      "50\t검증 세트 손실: 2.176908\t최선의 손실: 1.645940\t정확도: 18.73%\n",
-      "51\t검증 세트 손실: 2.179085\t최선의 손실: 1.645940\t정확도: 19.27%\n",
-      "52\t검증 세트 손실: 2.013385\t최선의 손실: 1.645940\t정확도: 20.91%\n",
-      "53\t검증 세트 손실: 2.239874\t최선의 손실: 1.645940\t정확도: 19.27%\n",
-      "54\t검증 세트 손실: 1.942736\t최선의 손실: 1.645940\t정확도: 20.91%\n",
+      "50\t검증 세트 손실: 2.176905\t최선의 손실: 1.645940\t정확도: 18.73%\n",
+      "51\t검증 세트 손실: 2.179087\t최선의 손실: 1.645940\t정확도: 19.27%\n",
+      "52\t검증 세트 손실: 2.013388\t최선의 손실: 1.645940\t정확도: 20.91%\n",
+      "53\t검증 세트 손실: 2.239873\t최선의 손실: 1.645940\t정확도: 19.27%\n",
+      "54\t검증 세트 손실: 1.942737\t최선의 손실: 1.645940\t정확도: 20.91%\n",
       "55\t검증 세트 손실: 1.686158\t최선의 손실: 1.645940\t정확도: 20.91%\n",
-      "56\t검증 세트 손실: 2.281745\t최선의 손실: 1.645940\t정확도: 19.08%\n",
-      "57\t검증 세트 손실: 2.154368\t최선의 손실: 1.645940\t정확도: 22.01%\n",
+      "56\t검증 세트 손실: 2.281744\t최선의 손실: 1.645940\t정확도: 19.08%\n",
+      "57\t검증 세트 손실: 2.154366\t최선의 손실: 1.645940\t정확도: 22.01%\n",
       "58\t검증 세트 손실: 1.815150\t최선의 손실: 1.645940\t정확도: 19.27%\n",
-      "59\t검증 세트 손실: 1.998512\t최선의 손실: 1.645940\t정확도: 19.27%\n",
+      "59\t검증 세트 손실: 1.998514\t최선의 손실: 1.645940\t정확도: 19.27%\n",
       "60\t검증 세트 손실: 1.969455\t최선의 손실: 1.645940\t정확도: 19.08%\n",
-      "61\t검증 세트 손실: 2.026055\t최선의 손실: 1.645940\t정확도: 19.27%\n",
+      "61\t검증 세트 손실: 2.026059\t최선의 손실: 1.645940\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total= 5.0min\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.107000\t최선의 손실: 0.107000\t정확도: 96.64%\n",
-      "1\t검증 세트 손실: 0.062275\t최선의 손실: 0.062275\t정확도: 98.05%\n",
-      "2\t검증 세트 손실: 0.064971\t최선의 손실: 0.062275\t정확도: 98.28%\n",
-      "3\t검증 세트 손실: 0.060944\t최선의 손실: 0.060944\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.057274\t최선의 손실: 0.057274\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 0.060075\t최선의 손실: 0.057274\t정확도: 98.12%\n",
-      "6\t검증 세트 손실: 0.062446\t최선의 손실: 0.057274\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.055982\t최선의 손실: 0.055982\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.053537\t최선의 손실: 0.053537\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.082670\t최선의 손실: 0.053537\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.067450\t최선의 손실: 0.053537\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.068145\t최선의 손실: 0.053537\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.053314\t최선의 손실: 0.053314\t정확도: 98.59%\n",
-      "13\t검증 세트 손실: 0.074160\t최선의 손실: 0.053314\t정확도: 98.20%\n",
-      "14\t검증 세트 손실: 0.068750\t최선의 손실: 0.053314\t정확도: 98.79%\n",
-      "15\t검증 세트 손실: 0.075723\t최선의 손실: 0.053314\t정확도: 98.28%\n",
-      "16\t검증 세트 손실: 0.069299\t최선의 손실: 0.053314\t정확도: 98.48%\n",
-      "17\t검증 세트 손실: 0.068101\t최선의 손실: 0.053314\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.084456\t최선의 손실: 0.053314\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.077960\t최선의 손실: 0.053314\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.082896\t최선의 손실: 0.053314\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.091179\t최선의 손실: 0.053314\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.066026\t최선의 손실: 0.053314\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.076133\t최선의 손실: 0.053314\t정확도: 98.67%\n",
-      "24\t검증 세트 손실: 0.091173\t최선의 손실: 0.053314\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.068447\t최선의 손실: 0.053314\t정확도: 98.44%\n",
-      "26\t검증 세트 손실: 0.084980\t최선의 손실: 0.053314\t정확도: 98.51%\n",
-      "27\t검증 세트 손실: 0.098757\t최선의 손실: 0.053314\t정확도: 98.44%\n",
-      "28\t검증 세트 손실: 0.086767\t최선의 손실: 0.053314\t정확도: 98.24%\n",
-      "29\t검증 세트 손실: 0.080819\t최선의 손실: 0.053314\t정확도: 98.59%\n",
-      "30\t검증 세트 손실: 0.078652\t최선의 손실: 0.053314\t정확도: 98.40%\n",
-      "31\t검증 세트 손실: 0.086897\t최선의 손실: 0.053314\t정확도: 98.40%\n",
-      "32\t검증 세트 손실: 0.065696\t최선의 손실: 0.053314\t정확도: 98.59%\n",
-      "33\t검증 세트 손실: 0.080745\t최선의 손실: 0.053314\t정확도: 98.71%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, n_neurons=70, total= 1.6min\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.114358\t최선의 손실: 0.114358\t정확도: 96.48%\n",
+      "1\t검증 세트 손실: 0.060477\t최선의 손실: 0.060477\t정확도: 98.16%\n",
+      "2\t검증 세트 손실: 0.069311\t최선의 손실: 0.060477\t정확도: 98.01%\n",
+      "3\t검증 세트 손실: 0.057225\t최선의 손실: 0.057225\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.053348\t최선의 손실: 0.053348\t정확도: 98.51%\n",
+      "5\t검증 세트 손실: 0.070816\t최선의 손실: 0.053348\t정확도: 97.89%\n",
+      "6\t검증 세트 손실: 0.059080\t최선의 손실: 0.053348\t정확도: 98.36%\n",
+      "7\t검증 세트 손실: 0.056534\t최선의 손실: 0.053348\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.083831\t최선의 손실: 0.053348\t정확도: 98.05%\n",
+      "9\t검증 세트 손실: 0.078649\t최선의 손실: 0.053348\t정확도: 98.40%\n",
+      "10\t검증 세트 손실: 0.046982\t최선의 손실: 0.046982\t정확도: 98.71%\n",
+      "11\t검증 세트 손실: 0.046235\t최선의 손실: 0.046235\t정확도: 99.22%\n",
+      "12\t검증 세트 손실: 0.071229\t최선의 손실: 0.046235\t정확도: 98.51%\n",
+      "13\t검증 세트 손실: 0.076433\t최선의 손실: 0.046235\t정확도: 98.24%\n",
+      "14\t검증 세트 손실: 0.048894\t최선의 손실: 0.046235\t정확도: 98.83%\n",
+      "15\t검증 세트 손실: 0.075544\t최선의 손실: 0.046235\t정확도: 98.28%\n",
+      "16\t검증 세트 손실: 0.067587\t최선의 손실: 0.046235\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.056984\t최선의 손실: 0.046235\t정확도: 98.51%\n",
+      "18\t검증 세트 손실: 0.078113\t최선의 손실: 0.046235\t정확도: 98.44%\n",
+      "19\t검증 세트 손실: 0.056438\t최선의 손실: 0.046235\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.060104\t최선의 손실: 0.046235\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.063634\t최선의 손실: 0.046235\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.063741\t최선의 손실: 0.046235\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.056883\t최선의 손실: 0.046235\t정확도: 98.91%\n",
+      "24\t검증 세트 손실: 0.081599\t최선의 손실: 0.046235\t정확도: 98.63%\n",
+      "25\t검증 세트 손실: 0.075620\t최선의 손실: 0.046235\t정확도: 98.67%\n",
+      "26\t검증 세트 손실: 0.084908\t최선의 손실: 0.046235\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.088520\t최선의 손실: 0.046235\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.089091\t최선의 손실: 0.046235\t정확도: 98.12%\n",
+      "29\t검증 세트 손실: 0.066591\t최선의 손실: 0.046235\t정확도: 98.71%\n",
+      "30\t검증 세트 손실: 0.054922\t최선의 손실: 0.046235\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.057893\t최선의 손실: 0.046235\t정확도: 98.91%\n",
+      "32\t검증 세트 손실: 0.061195\t최선의 손실: 0.046235\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=50, total=   2.3s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.085884\t최선의 손실: 0.085884\t정확도: 97.38%\n",
-      "1\t검증 세트 손실: 0.067593\t최선의 손실: 0.067593\t정확도: 98.08%\n",
-      "2\t검증 세트 손실: 0.056049\t최선의 손실: 0.056049\t정확도: 98.28%\n",
-      "3\t검증 세트 손실: 0.060275\t최선의 손실: 0.056049\t정확도: 98.32%\n",
-      "4\t검증 세트 손실: 0.076345\t최선의 손실: 0.056049\t정확도: 97.97%\n",
-      "5\t검증 세트 손실: 0.059927\t최선의 손실: 0.056049\t정확도: 98.08%\n",
-      "6\t검증 세트 손실: 0.088460\t최선의 손실: 0.056049\t정확도: 97.69%\n",
-      "7\t검증 세트 손실: 0.057587\t최선의 손실: 0.056049\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.069987\t최선의 손실: 0.056049\t정확도: 97.97%\n",
-      "9\t검증 세트 손실: 0.058974\t최선의 손실: 0.056049\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.063505\t최선의 손실: 0.056049\t정확도: 98.59%\n",
-      "11\t검증 세트 손실: 0.091075\t최선의 손실: 0.056049\t정확도: 97.58%\n",
-      "12\t검증 세트 손실: 0.076401\t최선의 손실: 0.056049\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.070285\t최선의 손실: 0.056049\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.081582\t최선의 손실: 0.056049\t정확도: 98.20%\n",
-      "15\t검증 세트 손실: 0.064593\t최선의 손실: 0.056049\t정확도: 98.71%\n",
-      "16\t검증 세트 손실: 0.061504\t최선의 손실: 0.056049\t정확도: 98.75%\n",
-      "17\t검증 세트 손실: 0.073123\t최선의 손실: 0.056049\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.073903\t최선의 손실: 0.056049\t정확도: 98.48%\n",
-      "19\t검증 세트 손실: 0.066385\t최선의 손실: 0.056049\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.115916\t최선의 손실: 0.056049\t정확도: 98.16%\n",
-      "21\t검증 세트 손실: 0.068217\t최선의 손실: 0.056049\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.071681\t최선의 손실: 0.056049\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.070247\t최선의 손실: 0.056049\t정확도: 98.79%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=50, total=   1.7s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.090026\t최선의 손실: 0.090026\t정확도: 97.22%\n",
-      "1\t검증 세트 손실: 0.059447\t최선의 손실: 0.059447\t정확도: 98.12%\n",
-      "2\t검증 세트 손실: 0.050250\t최선의 손실: 0.050250\t정확도: 98.48%\n",
-      "3\t검증 세트 손실: 0.051104\t최선의 손실: 0.050250\t정확도: 98.55%\n",
-      "4\t검증 세트 손실: 0.058065\t최선의 손실: 0.050250\t정확도: 98.24%\n",
-      "5\t검증 세트 손실: 0.060043\t최선의 손실: 0.050250\t정확도: 98.40%\n",
-      "6\t검증 세트 손실: 0.046047\t최선의 손실: 0.046047\t정확도: 98.63%\n",
-      "7\t검증 세트 손실: 0.052395\t최선의 손실: 0.046047\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.051778\t최선의 손실: 0.046047\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.051082\t최선의 손실: 0.046047\t정확도: 98.79%\n",
-      "10\t검증 세트 손실: 0.072258\t최선의 손실: 0.046047\t정확도: 98.55%\n",
-      "11\t검증 세트 손실: 0.084302\t최선의 손실: 0.046047\t정확도: 98.08%\n",
-      "12\t검증 세트 손실: 0.046922\t최선의 손실: 0.046047\t정확도: 98.87%\n",
-      "13\t검증 세트 손실: 0.062193\t최선의 손실: 0.046047\t정확도: 98.79%\n",
-      "14\t검증 세트 손실: 0.050692\t최선의 손실: 0.046047\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.053188\t최선의 손실: 0.046047\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.080184\t최선의 손실: 0.046047\t정확도: 98.48%\n",
-      "17\t검증 세트 손실: 0.071998\t최선의 손실: 0.046047\t정확도: 98.55%\n",
-      "18\t검증 세트 손실: 0.068269\t최선의 손실: 0.046047\t정확도: 98.55%\n",
-      "19\t검증 세트 손실: 0.078570\t최선의 손실: 0.046047\t정확도: 98.51%\n",
-      "20\t검증 세트 손실: 0.079461\t최선의 손실: 0.046047\t정확도: 98.24%\n",
-      "21\t검증 세트 손실: 0.065375\t최선의 손실: 0.046047\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.068840\t최선의 손실: 0.046047\t정확도: 98.71%\n",
-      "23\t검증 세트 손실: 0.087299\t최선의 손실: 0.046047\t정확도: 98.48%\n",
-      "24\t검증 세트 손실: 0.054171\t최선의 손실: 0.046047\t정확도: 98.75%\n",
-      "25\t검증 세트 손실: 0.055536\t최선의 손실: 0.046047\t정확도: 98.67%\n",
-      "26\t검증 세트 손실: 0.058189\t최선의 손실: 0.046047\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.065519\t최선의 손실: 0.046047\t정확도: 98.87%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=50, total=   2.1s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n"
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=50, total=   2.6s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.091162\t최선의 손실: 0.091162\t정확도: 97.19%\n",
+      "1\t검증 세트 손실: 0.067173\t최선의 손실: 0.067173\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.055353\t최선의 손실: 0.055353\t정확도: 98.24%\n",
+      "3\t검증 세트 손실: 0.066485\t최선의 손실: 0.055353\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.049233\t최선의 손실: 0.049233\t정확도: 98.48%\n",
+      "5\t검증 세트 손실: 0.054100\t최선의 손실: 0.049233\t정확도: 98.32%\n",
+      "6\t검증 세트 손실: 0.060087\t최선의 손실: 0.049233\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.053904\t최선의 손실: 0.049233\t정확도: 98.36%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.771589\t최선의 손실: 0.771589\t정확도: 74.98%\n",
-      "1\t검증 세트 손실: 26.960949\t최선의 손실: 0.771589\t정확도: 70.25%\n",
-      "2\t검증 세트 손실: 1.635355\t최선의 손실: 0.771589\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 1.619329\t최선의 손실: 0.771589\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 1.689969\t최선의 손실: 0.771589\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.628324\t최선의 손실: 0.771589\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.671623\t최선의 손실: 0.771589\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.636309\t최선의 손실: 0.771589\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.625584\t최선의 손실: 0.771589\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 1.736851\t최선의 손실: 0.771589\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.694749\t최선의 손실: 0.771589\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.811687\t최선의 손실: 0.771589\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.690879\t최선의 손실: 0.771589\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.760164\t최선의 손실: 0.771589\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.826802\t최선의 손실: 0.771589\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.757212\t최선의 손실: 0.771589\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.793381\t최선의 손실: 0.771589\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.944023\t최선의 손실: 0.771589\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.742110\t최선의 손실: 0.771589\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.753960\t최선의 손실: 0.771589\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.807641\t최선의 손실: 0.771589\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 2.290135\t최선의 손실: 0.771589\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 0.059023\t최선의 손실: 0.049233\t정확도: 98.55%\n",
+      "9\t검증 세트 손실: 0.056148\t최선의 손실: 0.049233\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.050187\t최선의 손실: 0.049233\t정확도: 98.83%\n",
+      "11\t검증 세트 손실: 0.071004\t최선의 손실: 0.049233\t정확도: 98.28%\n",
+      "12\t검증 세트 손실: 0.044647\t최선의 손실: 0.044647\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.070554\t최선의 손실: 0.044647\t정확도: 98.32%\n",
+      "14\t검증 세트 손실: 0.041783\t최선의 손실: 0.041783\t정확도: 98.91%\n",
+      "15\t검증 세트 손실: 0.062721\t최선의 손실: 0.041783\t정확도: 98.87%\n",
+      "16\t검증 세트 손실: 0.051807\t최선의 손실: 0.041783\t정확도: 99.02%\n",
+      "17\t검증 세트 손실: 0.066709\t최선의 손실: 0.041783\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.061977\t최선의 손실: 0.041783\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.062951\t최선의 손실: 0.041783\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.067577\t최선의 손실: 0.041783\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.065615\t최선의 손실: 0.041783\t정확도: 98.91%\n",
+      "22\t검증 세트 손실: 0.064310\t최선의 손실: 0.041783\t정확도: 98.87%\n",
+      "23\t검증 세트 손실: 0.055371\t최선의 손실: 0.041783\t정확도: 98.94%\n",
+      "24\t검증 세트 손실: 0.055397\t최선의 손실: 0.041783\t정확도: 98.87%\n",
+      "25\t검증 세트 손실: 0.064382\t최선의 손실: 0.041783\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.084317\t최선의 손실: 0.041783\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.072014\t최선의 손실: 0.041783\t정확도: 98.94%\n",
+      "28\t검증 세트 손실: 0.059824\t최선의 손실: 0.041783\t정확도: 98.91%\n",
+      "29\t검증 세트 손실: 0.072655\t최선의 손실: 0.041783\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.044234\t최선의 손실: 0.041783\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.050295\t최선의 손실: 0.041783\t정확도: 98.98%\n",
+      "32\t검증 세트 손실: 0.059645\t최선의 손실: 0.041783\t정확도: 99.10%\n",
+      "33\t검증 세트 손실: 0.063843\t최선의 손실: 0.041783\t정확도: 99.02%\n",
+      "34\t검증 세트 손실: 0.047605\t최선의 손실: 0.041783\t정확도: 99.22%\n",
+      "35\t검증 세트 손실: 0.050846\t최선의 손실: 0.041783\t정확도: 99.22%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=  19.3s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.229658\t최선의 손실: 0.229658\t정확도: 92.92%\n",
-      "1\t검증 세트 손실: 0.297511\t최선의 손실: 0.229658\t정확도: 91.05%\n",
-      "2\t검증 세트 손실: 0.155690\t최선의 손실: 0.155690\t정확도: 95.54%\n",
-      "3\t검증 세트 손실: 0.168562\t최선의 손실: 0.155690\t정확도: 95.15%\n",
-      "4\t검증 세트 손실: 0.258109\t최선의 손실: 0.155690\t정확도: 93.71%\n",
-      "5\t검증 세트 손실: 1.625565\t최선의 손실: 0.155690\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 1.766487\t최선의 손실: 0.155690\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.747193\t최선의 손실: 0.155690\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.889008\t최선의 손실: 0.155690\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.770646\t최선의 손실: 0.155690\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.965527\t최선의 손실: 0.155690\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.737248\t최선의 손실: 0.155690\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.836914\t최선의 손실: 0.155690\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.688705\t최선의 손실: 0.155690\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.635479\t최선의 손실: 0.155690\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.758781\t최선의 손실: 0.155690\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.713174\t최선의 손실: 0.155690\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.947669\t최선의 손실: 0.155690\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.711011\t최선의 손실: 0.155690\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.623090\t최선의 손실: 0.155690\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.783531\t최선의 손실: 0.155690\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.695248\t최선의 손실: 0.155690\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.873146\t최선의 손실: 0.155690\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.708539\t최선의 손실: 0.155690\t정확도: 19.27%\n",
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=50, total=   2.9s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.091095\t최선의 손실: 0.091095\t정확도: 97.11%\n",
+      "1\t검증 세트 손실: 0.065859\t최선의 손실: 0.065859\t정확도: 98.36%\n",
+      "2\t검증 세트 손실: 0.063147\t최선의 손실: 0.063147\t정확도: 98.12%\n",
+      "3\t검증 세트 손실: 0.048955\t최선의 손실: 0.048955\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.044121\t최선의 손실: 0.044121\t정확도: 98.55%\n",
+      "5\t검증 세트 손실: 0.046659\t최선의 손실: 0.044121\t정확도: 98.48%\n",
+      "6\t검증 세트 손실: 0.068328\t최선의 손실: 0.044121\t정확도: 97.97%\n",
+      "7\t검증 세트 손실: 0.065214\t최선의 손실: 0.044121\t정확도: 98.48%\n",
+      "8\t검증 세트 손실: 0.044875\t최선의 손실: 0.044121\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.056871\t최선의 손실: 0.044121\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.054276\t최선의 손실: 0.044121\t정확도: 98.44%\n",
+      "11\t검증 세트 손실: 0.054838\t최선의 손실: 0.044121\t정확도: 98.59%\n",
+      "12\t검증 세트 손실: 0.097442\t최선의 손실: 0.044121\t정확도: 97.77%\n",
+      "13\t검증 세트 손실: 0.044847\t최선의 손실: 0.044121\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.050028\t최선의 손실: 0.044121\t정확도: 98.79%\n",
+      "15\t검증 세트 손실: 0.064182\t최선의 손실: 0.044121\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.055423\t최선의 손실: 0.044121\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.071993\t최선의 손실: 0.044121\t정확도: 98.48%\n",
+      "18\t검증 세트 손실: 0.057762\t최선의 손실: 0.044121\t정확도: 98.87%\n",
+      "19\t검증 세트 손실: 0.068004\t최선의 손실: 0.044121\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.057072\t최선의 손실: 0.044121\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.078571\t최선의 손실: 0.044121\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.061078\t최선의 손실: 0.044121\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.048459\t최선의 손실: 0.044121\t정확도: 99.10%\n",
+      "24\t검증 세트 손실: 0.052760\t최선의 손실: 0.044121\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.071075\t최선의 손실: 0.044121\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=  26.2s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.671588\t최선의 손실: 1.671588\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 1.672945\t최선의 손실: 1.671588\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.688667\t최선의 손실: 1.671588\t정확도: 20.91%\n",
-      "3\t검증 세트 손실: 1.667923\t최선의 손실: 1.667923\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.815471\t최선의 손실: 1.667923\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.822381\t최선의 손실: 1.667923\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.696205\t최선의 손실: 1.667923\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.661083\t최선의 손실: 1.661083\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.665628\t최선의 손실: 1.661083\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.892490\t최선의 손실: 1.661083\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.635558\t최선의 손실: 1.635558\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.947889\t최선의 손실: 1.635558\t정확도: 20.91%\n",
-      "12\t검증 세트 손실: 1.923997\t최선의 손실: 1.635558\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.858474\t최선의 손실: 1.635558\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.790646\t최선의 손실: 1.635558\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.831532\t최선의 손실: 1.635558\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 1.994293\t최선의 손실: 1.635558\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 2.505163\t최선의 손실: 1.635558\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.729989\t최선의 손실: 1.635558\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 1.675596\t최선의 손실: 1.635558\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.977618\t최선의 손실: 1.635558\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.848394\t최선의 손실: 1.635558\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.842910\t최선의 손실: 1.635558\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.694068\t최선의 손실: 1.635558\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.770762\t최선의 손실: 1.635558\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 2.034660\t최선의 손실: 1.635558\t정확도: 19.27%\n",
-      "26\t검증 세트 손실: 1.838430\t최선의 손실: 1.635558\t정확도: 19.27%\n",
-      "27\t검증 세트 손실: 2.754782\t최선의 손실: 1.635558\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 1.821044\t최선의 손실: 1.635558\t정확도: 19.27%\n",
-      "29\t검증 세트 손실: 2.093417\t최선의 손실: 1.635558\t정확도: 20.91%\n",
-      "30\t검증 세트 손실: 2.094291\t최선의 손실: 1.635558\t정확도: 19.08%\n",
-      "31\t검증 세트 손실: 1.622222\t최선의 손실: 1.622222\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 1.942160\t최선의 손실: 1.622222\t정확도: 19.27%\n",
-      "33\t검증 세트 손실: 1.819090\t최선의 손실: 1.622222\t정확도: 20.91%\n",
-      "34\t검증 세트 손실: 1.753124\t최선의 손실: 1.622222\t정확도: 18.73%\n",
-      "35\t검증 세트 손실: 1.672571\t최선의 손실: 1.622222\t정확도: 19.08%\n",
-      "36\t검증 세트 손실: 1.966116\t최선의 손실: 1.622222\t정확도: 18.73%\n",
-      "37\t검증 세트 손실: 1.720796\t최선의 손실: 1.622222\t정확도: 22.01%\n",
-      "38\t검증 세트 손실: 1.653188\t최선의 손실: 1.622222\t정확도: 22.01%\n",
-      "39\t검증 세트 손실: 2.175611\t최선의 손실: 1.622222\t정확도: 19.27%\n",
-      "40\t검증 세트 손실: 1.952669\t최선의 손실: 1.622222\t정확도: 19.08%\n",
-      "41\t검증 세트 손실: 1.866459\t최선의 손실: 1.622222\t정확도: 19.08%\n",
-      "42\t검증 세트 손실: 1.948127\t최선의 손실: 1.622222\t정확도: 18.73%\n",
-      "43\t검증 세트 손실: 2.071376\t최선의 손실: 1.622222\t정확도: 18.73%\n",
-      "44\t검증 세트 손실: 2.231695\t최선의 손실: 1.622222\t정확도: 19.08%\n",
-      "45\t검증 세트 손실: 1.649554\t최선의 손실: 1.622222\t정확도: 22.01%\n",
-      "46\t검증 세트 손실: 2.119060\t최선의 손실: 1.622222\t정확도: 19.08%\n",
-      "47\t검증 세트 손실: 2.026964\t최선의 손실: 1.622222\t정확도: 19.08%\n",
-      "48\t검증 세트 손실: 1.730794\t최선의 손실: 1.622222\t정확도: 22.01%\n",
-      "49\t검증 세트 손실: 1.698287\t최선의 손실: 1.622222\t정확도: 22.01%\n",
-      "50\t검증 세트 손실: 1.691545\t최선의 손실: 1.622222\t정확도: 20.91%\n",
-      "51\t검증 세트 손실: 1.818575\t최선의 손실: 1.622222\t정확도: 19.08%\n",
-      "52\t검증 세트 손실: 1.815513\t최선의 손실: 1.622222\t정확도: 19.08%\n",
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, n_neurons=50, total=   2.3s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.686885\t최선의 손실: 0.686885\t정확도: 70.48%\n",
+      "1\t검증 세트 손실: 0.665067\t최선의 손실: 0.665067\t정확도: 75.29%\n",
+      "2\t검증 세트 손실: 0.592836\t최선의 손실: 0.592836\t정확도: 75.37%\n",
+      "3\t검증 세트 손실: 1.618263\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.800830\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.668317\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.742478\t최선의 손실: 0.592836\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.821391\t최선의 손실: 0.592836\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 1.685867\t최선의 손실: 0.592836\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.914875\t최선의 손실: 0.592836\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.662377\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.948868\t최선의 손실: 0.592836\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.751566\t최선의 손실: 0.592836\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.731403\t최선의 손실: 0.592836\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 2.137684\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.801604\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.730228\t최선의 손실: 0.592836\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 2.261269\t최선의 손실: 0.592836\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.791214\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.677419\t최선의 손실: 0.592836\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.745686\t최선의 손실: 0.592836\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 2.603042\t최선의 손실: 0.592836\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 1.677082\t최선의 손실: 0.592836\t정확도: 20.91%\n",
+      "23\t검증 세트 손실: 1.705256\t최선의 손실: 0.592836\t정확도: 20.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=  58.9s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.097617\t최선의 손실: 0.097617\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.107861\t최선의 손실: 0.097617\t정확도: 97.62%\n",
-      "2\t검증 세트 손실: 0.115668\t최선의 손실: 0.097617\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 4.182774\t최선의 손실: 0.097617\t정확도: 34.52%\n",
-      "4\t검증 세트 손실: 1.684147\t최선의 손실: 0.097617\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.659361\t최선의 손실: 0.097617\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.627893\t최선의 손실: 0.097617\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.698372\t최선의 손실: 0.097617\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 1.745275\t최선의 손실: 0.097617\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.874469\t최선의 손실: 0.097617\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.746328\t최선의 손실: 0.097617\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.766853\t최선의 손실: 0.097617\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.697403\t최선의 손실: 0.097617\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.755309\t최선의 손실: 0.097617\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.683068\t최선의 손실: 0.097617\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.665968\t최선의 손실: 0.097617\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.810268\t최선의 손실: 0.097617\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.643192\t최선의 손실: 0.097617\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.686457\t최선의 손실: 0.097617\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.631886\t최선의 손실: 0.097617\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.659873\t최선의 손실: 0.097617\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.726670\t최선의 손실: 0.097617\t정확도: 19.27%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=70, total=   8.6s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 1.699185\t최선의 손실: 1.699185\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.751781\t최선의 손실: 1.699185\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.626875\t최선의 손실: 1.626875\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.707134\t최선의 손실: 1.626875\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 1.730580\t최선의 손실: 1.626875\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 2.008147\t최선의 손실: 1.626875\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.746560\t최선의 손실: 1.626875\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.778596\t최선의 손실: 1.626875\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.653974\t최선의 손실: 1.626875\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 1.836840\t최선의 손실: 1.626875\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.697078\t최선의 손실: 1.626875\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.809081\t최선의 손실: 1.626875\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.734146\t최선의 손실: 1.626875\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.828305\t최선의 손실: 1.626875\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.749131\t최선의 손실: 1.626875\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.741981\t최선의 손실: 1.626875\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 1.833165\t최선의 손실: 1.626875\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 2.675101\t최선의 손실: 1.626875\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.796279\t최선의 손실: 1.626875\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.759636\t최선의 손실: 1.626875\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.771903\t최선의 손실: 1.626875\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.792966\t최선의 손실: 1.626875\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 2.183041\t최선의 손실: 1.626875\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.770285\t최선의 손실: 1.626875\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120, total=  22.2s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.110857\t최선의 손실: 0.110857\t정확도: 96.40%\n",
-      "1\t검증 세트 손실: 0.081192\t최선의 손실: 0.081192\t정확도: 97.93%\n",
-      "2\t검증 세트 손실: 0.080805\t최선의 손실: 0.080805\t정확도: 97.97%\n",
-      "3\t검증 세트 손실: 0.110956\t최선의 손실: 0.080805\t정확도: 97.46%\n",
-      "4\t검증 세트 손실: 0.576953\t최선의 손실: 0.080805\t정확도: 93.20%\n",
-      "5\t검증 세트 손실: 1.696308\t최선의 손실: 0.080805\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.697116\t최선의 손실: 0.080805\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.810633\t최선의 손실: 0.080805\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.830539\t최선의 손실: 0.080805\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.744079\t최선의 손실: 0.080805\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.733988\t최선의 손실: 0.080805\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.646445\t최선의 손실: 0.080805\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.799598\t최선의 손실: 0.080805\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.646820\t최선의 손실: 0.080805\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.675084\t최선의 손실: 0.080805\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.701996\t최선의 손실: 0.080805\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.656502\t최선의 손실: 0.080805\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.669253\t최선의 손실: 0.080805\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.633581\t최선의 손실: 0.080805\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.632738\t최선의 손실: 0.080805\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.697655\t최선의 손실: 0.080805\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 1.767829\t최선의 손실: 0.080805\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.870493\t최선의 손실: 0.080805\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.721550\t최선의 손실: 0.080805\t정확도: 22.01%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=70, total=   9.0s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.470658\t최선의 손실: 0.470658\t정확도: 85.57%\n",
+      "1\t검증 세트 손실: 0.279000\t최선의 손실: 0.279000\t정확도: 93.47%\n",
+      "2\t검증 세트 손실: 0.231258\t최선의 손실: 0.231258\t정확도: 94.64%\n",
+      "3\t검증 세트 손실: 0.179164\t최선의 손실: 0.179164\t정확도: 95.90%\n",
+      "4\t검증 세트 손실: 0.178250\t최선의 손실: 0.178250\t정확도: 96.05%\n",
+      "5\t검증 세트 손실: 1.684420\t최선의 손실: 0.178250\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.780468\t최선의 손실: 0.178250\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.716533\t최선의 손실: 0.178250\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.780900\t최선의 손실: 0.178250\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.932595\t최선의 손실: 0.178250\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.624806\t최선의 손실: 0.178250\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.630108\t최선의 손실: 0.178250\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 2.146630\t최선의 손실: 0.178250\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.855734\t최선의 손실: 0.178250\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.753762\t최선의 손실: 0.178250\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 2.007253\t최선의 손실: 0.178250\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 1.966294\t최선의 손실: 0.178250\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 2.309704\t최선의 손실: 0.178250\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.820705\t최선의 손실: 0.178250\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 1.727309\t최선의 손실: 0.178250\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.826717\t최선의 손실: 0.178250\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.936386\t최선의 손실: 0.178250\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.861397\t최선의 손실: 0.178250\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.931087\t최선의 손실: 0.178250\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.748093\t최선의 손실: 0.178250\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.995049\t최선의 손실: 0.178250\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120, total=  22.9s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120 \n"
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, n_neurons=70, total=   9.2s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.100756\t최선의 손실: 0.100756\t정확도: 96.95%\n",
+      "1\t검증 세트 손실: 0.109700\t최선의 손실: 0.100756\t정확도: 97.07%\n",
+      "2\t검증 세트 손실: 1.696449\t최선의 손실: 0.100756\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 1.686458\t최선의 손실: 0.100756\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 1.686715\t최선의 손실: 0.100756\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.675942\t최선의 손실: 0.100756\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.637443\t최선의 손실: 0.100756\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.698726\t최선의 손실: 0.100756\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.733199\t최선의 손실: 0.100756\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.883424\t최선의 손실: 0.100756\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.731890\t최선의 손실: 0.100756\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.762735\t최선의 손실: 0.100756\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.700323\t최선의 손실: 0.100756\t정확도: 19.08%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.123865\t최선의 손실: 0.123865\t정확도: 96.72%\n",
-      "1\t검증 세트 손실: 0.389060\t최선의 손실: 0.123865\t정확도: 93.78%\n",
-      "2\t검증 세트 손실: 0.135307\t최선의 손실: 0.123865\t정확도: 96.68%\n",
-      "3\t검증 세트 손실: 0.135571\t최선의 손실: 0.123865\t정확도: 97.22%\n",
-      "4\t검증 세트 손실: 0.171853\t최선의 손실: 0.123865\t정확도: 97.07%\n",
-      "5\t검증 세트 손실: 0.084773\t최선의 손실: 0.084773\t정확도: 97.62%\n",
-      "6\t검증 세트 손실: 0.454981\t최선의 손실: 0.084773\t정확도: 91.28%\n",
-      "7\t검증 세트 손실: 1.709035\t최선의 손실: 0.084773\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.747327\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.892869\t최선의 손실: 0.084773\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.695854\t최선의 손실: 0.084773\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 1.906526\t최선의 손실: 0.084773\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.722814\t최선의 손실: 0.084773\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.619644\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.678113\t최선의 손실: 0.084773\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.624077\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.632909\t최선의 손실: 0.084773\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.668706\t최선의 손실: 0.084773\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.633563\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.754125\t최선의 손실: 0.084773\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.717416\t최선의 손실: 0.084773\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 1.829763\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.675003\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.685262\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.725638\t최선의 손실: 0.084773\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.662402\t최선의 손실: 0.084773\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.677925\t최선의 손실: 0.084773\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.767699\t최선의 손실: 0.100756\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.682552\t최선의 손실: 0.100756\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.667079\t최선의 손실: 0.100756\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.815681\t최선의 손실: 0.100756\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.642721\t최선의 손실: 0.100756\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.686189\t최선의 손실: 0.100756\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 1.631973\t최선의 손실: 0.100756\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.659709\t최선의 손실: 0.100756\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.727266\t최선의 손실: 0.100756\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120, total=  27.8s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.610512\t최선의 손실: 1.610512\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 1.619129\t최선의 손실: 1.610512\t정확도: 22.01%\n",
-      "2\t검증 세트 손실: 1.611361\t최선의 손실: 1.610512\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.608293\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.618361\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.615836\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.618150\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.617732\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.617140\t최선의 손실: 1.608293\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.608702\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.609206\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.608782\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.616529\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.625477\t최선의 손실: 1.608293\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 1.614388\t최선의 손실: 1.608293\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.612091\t최선의 손실: 1.608293\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.608088\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.615100\t최선의 손실: 1.608088\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.616771\t최선의 손실: 1.608088\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.612291\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.615546\t최선의 손실: 1.608088\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 1.619602\t최선의 손실: 1.608088\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.616395\t최선의 손실: 1.608088\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.615318\t최선의 손실: 1.608088\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 1.615881\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.613409\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.619709\t최선의 손실: 1.608088\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 1.620597\t최선의 손실: 1.608088\t정확도: 19.08%\n",
-      "28\t검증 세트 손실: 1.610771\t최선의 손실: 1.608088\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.610737\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "30\t검증 세트 손실: 1.620082\t최선의 손실: 1.608088\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 1.614028\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 1.614077\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "33\t검증 세트 손실: 1.615155\t최선의 손실: 1.608088\t정확도: 19.27%\n",
-      "34\t검증 세트 손실: 1.617152\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "35\t검증 세트 손실: 1.615051\t최선의 손실: 1.608088\t정확도: 22.01%\n",
-      "36\t검증 세트 손실: 1.608877\t최선의 손실: 1.608088\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 1.611355\t최선의 손실: 1.608088\t정확도: 22.01%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=120, total=   8.0s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.113396\t최선의 손실: 0.113396\t정확도: 95.90%\n",
+      "1\t검증 세트 손실: 0.094950\t최선의 손실: 0.094950\t정확도: 97.38%\n",
+      "2\t검증 세트 손실: 0.084801\t최선의 손실: 0.084801\t정확도: 98.12%\n",
+      "3\t검증 세트 손실: 1.988617\t최선의 손실: 0.084801\t정확도: 76.90%\n",
+      "4\t검증 세트 손실: 1.681228\t최선의 손실: 0.084801\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 1.637818\t최선의 손실: 0.084801\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.672144\t최선의 손실: 0.084801\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.799376\t최선의 손실: 0.084801\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.778339\t최선의 손실: 0.084801\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.766598\t최선의 손실: 0.084801\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.769501\t최선의 손실: 0.084801\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.647730\t최선의 손실: 0.084801\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.801164\t최선의 손실: 0.084801\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.663191\t최선의 손실: 0.084801\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.664481\t최선의 손실: 0.084801\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.721701\t최선의 손실: 0.084801\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.662973\t최선의 손실: 0.084801\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.663475\t최선의 손실: 0.084801\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.634805\t최선의 손실: 0.084801\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.646442\t최선의 손실: 0.084801\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.686122\t최선의 손실: 0.084801\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.770724\t최선의 손실: 0.084801\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.874919\t최선의 손실: 0.084801\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.726069\t최선의 손실: 0.084801\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total=  41.7s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.825838\t최선의 손실: 0.825838\t정확도: 62.08%\n",
-      "1\t검증 세트 손실: 0.916889\t최선의 손실: 0.825838\t정확도: 57.23%\n",
-      "2\t검증 세트 손실: 0.861542\t최선의 손실: 0.825838\t정확도: 61.65%\n",
-      "3\t검증 세트 손실: 0.976102\t최선의 손실: 0.825838\t정확도: 55.94%\n",
-      "4\t검증 세트 손실: 1.218402\t최선의 손실: 0.825838\t정확도: 42.38%\n",
-      "5\t검증 세트 손실: 1.028282\t최선의 손실: 0.825838\t정확도: 52.89%\n",
-      "6\t검증 세트 손실: 0.953912\t최선의 손실: 0.825838\t정확도: 56.45%\n",
-      "7\t검증 세트 손실: 1.003688\t최선의 손실: 0.825838\t정확도: 57.27%\n",
-      "8\t검증 세트 손실: 1.005022\t최선의 손실: 0.825838\t정확도: 54.50%\n",
-      "9\t검증 세트 손실: 0.967141\t최선의 손실: 0.825838\t정확도: 54.38%\n",
-      "10\t검증 세트 손실: 1.004999\t최선의 손실: 0.825838\t정확도: 55.04%\n",
-      "11\t검증 세트 손실: 0.927891\t최선의 손실: 0.825838\t정확도: 59.50%\n",
-      "12\t검증 세트 손실: 0.957200\t최선의 손실: 0.825838\t정확도: 53.87%\n",
-      "13\t검증 세트 손실: 0.950389\t최선의 손실: 0.825838\t정확도: 51.29%\n",
-      "14\t검증 세트 손실: 0.923430\t최선의 손실: 0.825838\t정확도: 59.58%\n",
-      "15\t검증 세트 손실: 1.247266\t최선의 손실: 0.825838\t정확도: 41.48%\n",
-      "16\t검증 세트 손실: 1.474475\t최선의 손실: 0.825838\t정확도: 31.00%\n",
-      "17\t검증 세트 손실: 1.428339\t최선의 손실: 0.825838\t정확도: 30.34%\n",
-      "18\t검증 세트 손실: 1.424499\t최선의 손실: 0.825838\t정확도: 30.88%\n",
-      "19\t검증 세트 손실: 1.409230\t최선의 손실: 0.825838\t정확도: 32.49%\n",
-      "20\t검증 세트 손실: 1.415816\t최선의 손실: 0.825838\t정확도: 30.88%\n",
-      "21\t검증 세트 손실: 1.437233\t최선의 손실: 0.825838\t정확도: 30.88%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=120, total=   8.8s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.084141\t최선의 손실: 0.084141\t정확도: 97.77%\n",
+      "1\t검증 세트 손실: 0.102494\t최선의 손실: 0.084141\t정확도: 97.30%\n",
+      "2\t검증 세트 손실: 0.088281\t최선의 손실: 0.084141\t정확도: 97.77%\n",
+      "3\t검증 세트 손실: 0.122603\t최선의 손실: 0.084141\t정확도: 97.89%\n",
+      "4\t검증 세트 손실: 3.825336\t최선의 손실: 0.084141\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 1.660504\t최선의 손실: 0.084141\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.675614\t최선의 손실: 0.084141\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.756210\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.742413\t최선의 손실: 0.084141\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.795882\t최선의 손실: 0.084141\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.684198\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.887752\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.781898\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 1.616503\t최선의 손실: 0.084141\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.682783\t최선의 손실: 0.084141\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.626697\t최선의 손실: 0.084141\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.637564\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.677987\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 1.636392\t최선의 손실: 0.084141\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.766090\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.711775\t최선의 손실: 0.084141\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.833128\t최선의 손실: 0.084141\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total=  21.4s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.790682\t최선의 손실: 0.790682\t정확도: 61.49%\n",
-      "1\t검증 세트 손실: 1.373413\t최선의 손실: 0.790682\t정확도: 33.82%\n",
-      "2\t검증 세트 손실: 1.614275\t최선의 손실: 0.790682\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.628534\t최선의 손실: 0.790682\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.607451\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "5\t검증 세트 손실: 1.614628\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "6\t검증 세트 손실: 1.614262\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "7\t검증 세트 손실: 1.614891\t최선의 손실: 0.790682\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.611218\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "9\t검증 세트 손실: 1.609978\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "10\t검증 세트 손실: 1.610853\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "11\t검증 세트 손실: 1.611889\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "12\t검증 세트 손실: 1.609510\t최선의 손실: 0.790682\t정확도: 20.99%\n",
-      "13\t검증 세트 손실: 1.618919\t최선의 손실: 0.790682\t정확도: 19.35%\n",
-      "14\t검증 세트 손실: 1.611675\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "15\t검증 세트 손실: 1.614186\t최선의 손실: 0.790682\t정확도: 22.09%\n",
-      "16\t검증 세트 손실: 1.627519\t최선의 손실: 0.790682\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.624155\t최선의 손실: 0.790682\t정확도: 19.16%\n",
-      "18\t검증 세트 손실: 1.607973\t최선의 손실: 0.790682\t정확도: 20.99%\n",
-      "19\t검증 세트 손실: 1.611853\t최선의 손실: 0.790682\t정확도: 19.16%\n",
-      "20\t검증 세트 손실: 1.611933\t최선의 손실: 0.790682\t정확도: 19.35%\n",
-      "21\t검증 세트 손실: 1.621637\t최선의 손실: 0.790682\t정확도: 22.09%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, n_neurons=120, total=   8.3s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.183183\t최선의 손실: 1.183183\t정확도: 39.37%\n",
+      "1\t검증 세트 손실: 1.156975\t최선의 손실: 1.156975\t정확도: 40.15%\n",
+      "2\t검증 세트 손실: 1.142294\t최선의 손실: 1.142294\t정확도: 39.91%\n",
+      "3\t검증 세트 손실: 1.712495\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.722970\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.720111\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.720981\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.721960\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.721319\t최선의 손실: 1.142294\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.713006\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.713486\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.713101\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.720850\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.729657\t최선의 손실: 1.142294\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.718734\t최선의 손실: 1.142294\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.716366\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.712418\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.719463\t최선의 손실: 1.142294\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.721072\t최선의 손실: 1.142294\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.716616\t최선의 손실: 1.142294\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.719879\t최선의 손실: 1.142294\t정확도: 19.04%\n",
+      "21\t검증 세트 손실: 1.723837\t최선의 손실: 1.142294\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.720670\t최선의 손실: 1.142294\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.719644\t최선의 손실: 1.142294\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total=  23.3s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 190.050507\t최선의 손실: 190.050507\t정확도: 62.86%\n",
-      "1\t검증 세트 손실: 17.941080\t최선의 손실: 17.941080\t정확도: 81.04%\n",
-      "2\t검증 세트 손실: 2.589796\t최선의 손실: 2.589796\t정확도: 91.99%\n",
-      "3\t검증 세트 손실: 1.724113\t최선의 손실: 1.724113\t정확도: 92.85%\n",
-      "4\t검증 세트 손실: 0.920454\t최선의 손실: 0.920454\t정확도: 94.61%\n",
-      "5\t검증 세트 손실: 1.106638\t최선의 손실: 0.920454\t정확도: 94.61%\n",
-      "6\t검증 세트 손실: 1.465958\t최선의 손실: 0.920454\t정확도: 93.16%\n",
-      "7\t검증 세트 손실: 0.949555\t최선의 손실: 0.920454\t정확도: 94.25%\n",
-      "8\t검증 세트 손실: 0.683924\t최선의 손실: 0.683924\t정확도: 93.08%\n",
-      "9\t검증 세트 손실: 0.417844\t최선의 손실: 0.417844\t정확도: 95.00%\n",
-      "10\t검증 세트 손실: 0.311016\t최선의 손실: 0.311016\t정확도: 95.78%\n",
-      "11\t검증 세트 손실: 1.409881\t최선의 손실: 0.311016\t정확도: 90.19%\n",
-      "12\t검증 세트 손실: 0.630825\t최선의 손실: 0.311016\t정확도: 94.57%\n",
-      "13\t검증 세트 손실: 0.319570\t최선의 손실: 0.311016\t정확도: 96.36%\n",
-      "14\t검증 세트 손실: 1.221422\t최선의 손실: 0.311016\t정확도: 87.96%\n",
-      "15\t검증 세트 손실: 0.422543\t최선의 손실: 0.311016\t정확도: 94.96%\n",
-      "16\t검증 세트 손실: 0.307564\t최선의 손실: 0.307564\t정확도: 95.86%\n",
-      "17\t검증 세트 손실: 0.635537\t최선의 손실: 0.307564\t정확도: 92.53%\n",
-      "18\t검증 세트 손실: 0.340673\t최선의 손실: 0.307564\t정확도: 95.97%\n",
-      "19\t검증 세트 손실: 0.211373\t최선의 손실: 0.211373\t정확도: 97.07%\n",
-      "20\t검증 세트 손실: 0.200511\t최선의 손실: 0.200511\t정확도: 96.40%\n",
-      "21\t검증 세트 손실: 0.203896\t최선의 손실: 0.200511\t정확도: 96.52%\n",
-      "22\t검증 세트 손실: 0.229534\t최선의 손실: 0.200511\t정확도: 95.66%\n",
-      "23\t검증 세트 손실: 0.203291\t최선의 손실: 0.200511\t정확도: 96.25%\n",
-      "24\t검증 세트 손실: 0.258155\t최선의 손실: 0.200511\t정확도: 95.70%\n",
-      "25\t검증 세트 손실: 0.195711\t최선의 손실: 0.195711\t정확도: 96.13%\n",
-      "26\t검증 세트 손실: 0.224064\t최선의 손실: 0.195711\t정확도: 96.17%\n",
-      "27\t검증 세트 손실: 0.202712\t최선의 손실: 0.195711\t정확도: 96.29%\n",
-      "28\t검증 세트 손실: 0.246122\t최선의 손실: 0.195711\t정확도: 95.58%\n",
-      "29\t검증 세트 손실: 0.436484\t최선의 손실: 0.195711\t정확도: 93.39%\n",
-      "30\t검증 세트 손실: 0.406520\t최선의 손실: 0.195711\t정확도: 96.25%\n",
-      "31\t검증 세트 손실: 0.218840\t최선의 손실: 0.195711\t정확도: 96.95%\n",
-      "32\t검증 세트 손실: 0.159342\t최선의 손실: 0.159342\t정확도: 96.95%\n",
-      "33\t검증 세트 손실: 0.150097\t최선의 손실: 0.150097\t정확도: 97.19%\n",
-      "34\t검증 세트 손실: 0.260048\t최선의 손실: 0.150097\t정확도: 96.25%\n",
-      "35\t검증 세트 손실: 0.197931\t최선의 손실: 0.150097\t정확도: 96.29%\n",
-      "36\t검증 세트 손실: 0.284565\t최선의 손실: 0.150097\t정확도: 95.27%\n",
-      "37\t검증 세트 손실: 0.186990\t최선의 손실: 0.150097\t정확도: 96.64%\n"
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=140, total=   8.8s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.611838\t최선의 손실: 1.611838\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.619869\t최선의 손실: 1.611838\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.609144\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.620026\t최선의 손실: 1.609144\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 1.611113\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.612420\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.632195\t최선의 손실: 1.609144\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 1.622895\t최선의 손실: 1.609144\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.613112\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.611249\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.610247\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.611971\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.615685\t최선의 손실: 1.609144\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 1.617306\t최선의 손실: 1.609144\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.610562\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.612048\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.619843\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.614758\t최선의 손실: 1.609144\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.611528\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.609967\t최선의 손실: 1.609144\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.608913\t최선의 손실: 1.608913\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.629896\t최선의 손실: 1.608913\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.620674\t최선의 손실: 1.608913\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.622630\t최선의 손실: 1.608913\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.607977\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.610930\t최선의 손실: 1.607977\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.611142\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.621740\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 1.630300\t최선의 손실: 1.607977\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.609746\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "30\t검증 세트 손실: 1.609461\t최선의 손실: 1.607977\t정확도: 20.91%\n",
+      "31\t검증 세트 손실: 1.614957\t최선의 손실: 1.607977\t정확도: 19.27%\n",
+      "32\t검증 세트 손실: 1.615025\t최선의 손실: 1.607977\t정확도: 19.08%\n",
+      "33\t검증 세트 손실: 1.610046\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "34\t검증 세트 손실: 1.610016\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 1.612438\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 1.609452\t최선의 손실: 1.607977\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 1.615658\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "38\t검증 세트 손실: 1.611836\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "39\t검증 세트 손실: 1.617720\t최선의 손실: 1.607977\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 1.609951\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "41\t검증 세트 손실: 1.613536\t최선의 손실: 1.607977\t정확도: 19.08%\n",
+      "42\t검증 세트 손실: 1.615567\t최선의 손실: 1.607977\t정확도: 19.27%\n",
+      "43\t검증 세트 손실: 1.619919\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "44\t검증 세트 손실: 1.622907\t최선의 손실: 1.607977\t정확도: 18.73%\n",
+      "45\t검증 세트 손실: 1.612809\t최선의 손실: 1.607977\t정확도: 22.01%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=140, total=  16.6s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.297705\t최선의 손실: 1.297705\t정확도: 38.47%\n",
+      "1\t검증 세트 손실: 1.334553\t최선의 손실: 1.297705\t정확도: 32.99%\n",
+      "2\t검증 세트 손실: 1.376659\t최선의 손실: 1.297705\t정확도: 31.12%\n",
+      "3\t검증 세트 손실: 1.290620\t최선의 손실: 1.290620\t정확도: 34.71%\n",
+      "4\t검증 세트 손실: 1.234635\t최선의 손실: 1.234635\t정확도: 39.48%\n",
+      "5\t검증 세트 손실: 1.327098\t최선의 손실: 1.234635\t정확도: 36.00%\n",
+      "6\t검증 세트 손실: 1.251889\t최선의 손실: 1.234635\t정확도: 35.26%\n",
+      "7\t검증 세트 손실: 1.235189\t최선의 손실: 1.234635\t정확도: 40.03%\n",
+      "8\t검증 세트 손실: 1.232602\t최선의 손실: 1.232602\t정확도: 36.43%\n",
+      "9\t검증 세트 손실: 1.341155\t최선의 손실: 1.232602\t정확도: 39.95%\n",
+      "10\t검증 세트 손실: 1.272914\t최선의 손실: 1.232602\t정확도: 38.43%\n",
+      "11\t검증 세트 손실: 1.242171\t최선의 손실: 1.232602\t정확도: 39.09%\n",
+      "12\t검증 세트 손실: 1.214482\t최선의 손실: 1.214482\t정확도: 39.13%\n",
+      "13\t검증 세트 손실: 1.235632\t최선의 손실: 1.214482\t정확도: 38.08%\n",
+      "14\t검증 세트 손실: 1.614427\t최선의 손실: 1.214482\t정확도: 20.91%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "38\t검증 세트 손실: 0.473195\t최선의 손실: 0.150097\t정확도: 93.00%\n",
-      "39\t검증 세트 손실: 0.215336\t최선의 손실: 0.150097\t정확도: 96.33%\n",
-      "40\t검증 세트 손실: 0.203254\t최선의 손실: 0.150097\t정확도: 96.68%\n",
-      "41\t검증 세트 손실: 0.197062\t최선의 손실: 0.150097\t정확도: 96.60%\n",
-      "42\t검증 세트 손실: 0.208500\t최선의 손실: 0.150097\t정확도: 96.52%\n",
-      "43\t검증 세트 손실: 0.190903\t최선의 손실: 0.150097\t정확도: 96.09%\n",
-      "44\t검증 세트 손실: 0.145707\t최선의 손실: 0.145707\t정확도: 96.95%\n",
-      "45\t검증 세트 손실: 0.153758\t최선의 손실: 0.145707\t정확도: 97.07%\n",
-      "46\t검증 세트 손실: 0.177501\t최선의 손실: 0.145707\t정확도: 96.99%\n",
-      "47\t검증 세트 손실: 0.183252\t최선의 손실: 0.145707\t정확도: 96.72%\n",
-      "48\t검증 세트 손실: 0.186533\t최선의 손실: 0.145707\t정확도: 96.72%\n",
-      "49\t검증 세트 손실: 0.187364\t최선의 손실: 0.145707\t정확도: 96.87%\n",
-      "50\t검증 세트 손실: 0.203390\t최선의 손실: 0.145707\t정확도: 96.87%\n",
-      "51\t검증 세트 손실: 0.175550\t최선의 손실: 0.145707\t정확도: 97.19%\n",
-      "52\t검증 세트 손실: 0.190425\t최선의 손실: 0.145707\t정확도: 96.68%\n",
-      "53\t검증 세트 손실: 0.182552\t최선의 손실: 0.145707\t정확도: 96.87%\n",
-      "54\t검증 세트 손실: 0.177382\t최선의 손실: 0.145707\t정확도: 97.11%\n",
-      "55\t검증 세트 손실: 0.241041\t최선의 손실: 0.145707\t정확도: 96.17%\n",
-      "56\t검증 세트 손실: 0.228743\t최선의 손실: 0.145707\t정확도: 96.83%\n",
-      "57\t검증 세트 손실: 0.207951\t최선의 손실: 0.145707\t정확도: 96.95%\n",
-      "58\t검증 세트 손실: 0.364212\t최선의 손실: 0.145707\t정확도: 95.31%\n",
-      "59\t검증 세트 손실: 6.490515\t최선의 손실: 0.145707\t정확도: 74.47%\n",
-      "60\t검증 세트 손실: 11040115.000000\t최선의 손실: 0.145707\t정확도: 58.52%\n",
-      "61\t검증 세트 손실: 317097.937500\t최선의 손실: 0.145707\t정확도: 93.75%\n",
-      "62\t검증 세트 손실: 118534.117188\t최선의 손실: 0.145707\t정확도: 93.82%\n",
-      "63\t검증 세트 손실: 105242.367188\t최선의 손실: 0.145707\t정확도: 93.00%\n",
-      "64\t검증 세트 손실: 84274.273438\t최선의 손실: 0.145707\t정확도: 92.73%\n",
-      "65\t검증 세트 손실: 51568.363281\t최선의 손실: 0.145707\t정확도: 94.49%\n",
+      "15\t검증 세트 손실: 1.617456\t최선의 손실: 1.214482\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.626728\t최선의 손실: 1.214482\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.625576\t최선의 손실: 1.214482\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.609318\t최선의 손실: 1.214482\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.613103\t최선의 손실: 1.214482\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1.613209\t최선의 손실: 1.214482\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.623181\t최선의 손실: 1.214482\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.617826\t최선의 손실: 1.214482\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.617140\t최선의 손실: 1.214482\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.608747\t최선의 손실: 1.214482\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.615628\t최선의 손실: 1.214482\t정확도: 18.73%\n",
+      "26\t검증 세트 손실: 1.608631\t최선의 손실: 1.214482\t정확도: 20.91%\n",
+      "27\t검증 세트 손실: 1.614458\t최선의 손실: 1.214482\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 1.623593\t최선의 손실: 1.214482\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.620651\t최선의 손실: 1.214482\t정확도: 19.27%\n",
+      "30\t검증 세트 손실: 1.614126\t최선의 손실: 1.214482\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 1.608861\t최선의 손실: 1.214482\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 1.612235\t최선의 손실: 1.214482\t정확도: 18.73%\n",
+      "33\t검증 세트 손실: 1.618439\t최선의 손실: 1.214482\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90, total=   4.9s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 117.371674\t최선의 손실: 117.371674\t정확도: 45.23%\n",
-      "1\t검증 세트 손실: 6.691076\t최선의 손실: 6.691076\t정확도: 80.10%\n",
-      "2\t검증 세트 손실: 1.302685\t최선의 손실: 1.302685\t정확도: 93.43%\n",
-      "3\t검증 세트 손실: 0.724224\t최선의 손실: 0.724224\t정확도: 94.33%\n",
-      "4\t검증 세트 손실: 0.493935\t최선의 손실: 0.493935\t정확도: 94.92%\n",
-      "5\t검증 세트 손실: 0.440370\t최선의 손실: 0.440370\t정확도: 95.19%\n",
-      "6\t검증 세트 손실: 0.507205\t최선의 손실: 0.440370\t정확도: 93.90%\n",
-      "7\t검증 세트 손실: 0.316383\t최선의 손실: 0.316383\t정확도: 95.58%\n",
-      "8\t검증 세트 손실: 0.444067\t최선의 손실: 0.316383\t정확도: 93.71%\n",
-      "9\t검증 세트 손실: 0.471499\t최선의 손실: 0.316383\t정확도: 95.27%\n",
-      "10\t검증 세트 손실: 0.343757\t최선의 손실: 0.316383\t정확도: 95.54%\n",
-      "11\t검증 세트 손실: 0.273896\t최선의 손실: 0.273896\t정확도: 96.01%\n",
-      "12\t검증 세트 손실: 0.449954\t최선의 손실: 0.273896\t정확도: 96.40%\n",
-      "13\t검증 세트 손실: 0.390708\t최선의 손실: 0.273896\t정확도: 94.88%\n",
-      "14\t검증 세트 손실: 0.443756\t최선의 손실: 0.273896\t정확도: 96.60%\n",
-      "15\t검증 세트 손실: 0.404167\t최선의 손실: 0.273896\t정확도: 93.55%\n",
-      "16\t검증 세트 손실: 0.687086\t최선의 손실: 0.273896\t정확도: 95.23%\n",
-      "17\t검증 세트 손실: 0.376961\t최선의 손실: 0.273896\t정확도: 96.52%\n",
-      "18\t검증 세트 손실: 0.625960\t최선의 손실: 0.273896\t정확도: 96.56%\n",
-      "19\t검증 세트 손실: 0.701077\t최선의 손실: 0.273896\t정확도: 96.05%\n",
-      "20\t검증 세트 손실: 0.666570\t최선의 손실: 0.273896\t정확도: 95.74%\n",
-      "21\t검증 세트 손실: 0.337010\t최선의 손실: 0.273896\t정확도: 95.35%\n",
-      "22\t검증 세트 손실: 0.649159\t최선의 손실: 0.273896\t정확도: 94.53%\n",
-      "23\t검증 세트 손실: 0.449315\t최선의 손실: 0.273896\t정확도: 96.87%\n",
-      "24\t검증 세트 손실: 0.619225\t최선의 손실: 0.273896\t정확도: 96.56%\n",
-      "25\t검증 세트 손실: 0.250382\t최선의 손실: 0.250382\t정확도: 96.25%\n",
-      "26\t검증 세트 손실: 0.340263\t최선의 손실: 0.250382\t정확도: 96.76%\n",
-      "27\t검증 세트 손실: 0.293067\t최선의 손실: 0.250382\t정확도: 96.99%\n",
-      "28\t검증 세트 손실: 0.316008\t최선의 손실: 0.250382\t정확도: 97.15%\n",
-      "29\t검증 세트 손실: 0.366221\t최선의 손실: 0.250382\t정확도: 96.68%\n",
-      "30\t검증 세트 손실: 0.481669\t최선의 손실: 0.250382\t정확도: 97.38%\n",
-      "31\t검증 세트 손실: 0.414414\t최선의 손실: 0.250382\t정확도: 96.99%\n",
-      "32\t검증 세트 손실: 0.345587\t최선의 손실: 0.250382\t정확도: 97.42%\n",
-      "33\t검증 세트 손실: 0.477521\t최선의 손실: 0.250382\t정확도: 97.26%\n",
-      "34\t검증 세트 손실: 0.924604\t최선의 손실: 0.250382\t정확도: 97.03%\n",
-      "35\t검증 세트 손실: 0.240121\t최선의 손실: 0.240121\t정확도: 97.58%\n",
-      "36\t검증 세트 손실: 0.403958\t최선의 손실: 0.240121\t정확도: 97.73%\n",
-      "37\t검증 세트 손실: 0.189163\t최선의 손실: 0.189163\t정확도: 97.03%\n",
-      "38\t검증 세트 손실: 0.268884\t최선의 손실: 0.189163\t정확도: 97.15%\n",
-      "39\t검증 세트 손실: 0.290507\t최선의 손실: 0.189163\t정확도: 97.15%\n",
-      "40\t검증 세트 손실: 0.359130\t최선의 손실: 0.189163\t정확도: 97.46%\n",
-      "41\t검증 세트 손실: 0.298072\t최선의 손실: 0.189163\t정확도: 94.88%\n",
-      "42\t검증 세트 손실: 0.485423\t최선의 손실: 0.189163\t정확도: 93.86%\n",
-      "43\t검증 세트 손실: 0.705601\t최선의 손실: 0.189163\t정확도: 95.43%\n",
-      "44\t검증 세트 손실: 0.408883\t최선의 손실: 0.189163\t정확도: 96.72%\n",
-      "45\t검증 세트 손실: 0.454758\t최선의 손실: 0.189163\t정확도: 97.50%\n",
-      "46\t검증 세트 손실: 0.302884\t최선의 손실: 0.189163\t정확도: 97.15%\n",
-      "47\t검증 세트 손실: 0.227342\t최선의 손실: 0.189163\t정확도: 97.07%\n",
-      "48\t검증 세트 손실: 0.237216\t최선의 손실: 0.189163\t정확도: 97.19%\n",
-      "49\t검증 세트 손실: 0.226533\t최선의 손실: 0.189163\t정확도: 98.01%\n",
-      "50\t검증 세트 손실: 0.178515\t최선의 손실: 0.178515\t정확도: 97.77%\n",
-      "51\t검증 세트 손실: 0.193977\t최선의 손실: 0.178515\t정확도: 97.46%\n",
-      "52\t검증 세트 손실: 0.200170\t최선의 손실: 0.178515\t정확도: 97.58%\n",
-      "53\t검증 세트 손실: 0.274949\t최선의 손실: 0.178515\t정확도: 97.69%\n",
-      "54\t검증 세트 손실: 0.207962\t최선의 손실: 0.178515\t정확도: 97.73%\n",
-      "55\t검증 세트 손실: 0.235335\t최선의 손실: 0.178515\t정확도: 97.89%\n",
-      "56\t검증 세트 손실: 0.282621\t최선의 손실: 0.178515\t정확도: 97.58%\n",
-      "57\t검증 세트 손실: 0.255886\t최선의 손실: 0.178515\t정확도: 97.50%\n",
-      "58\t검증 세트 손실: 0.333379\t최선의 손실: 0.178515\t정확도: 97.19%\n",
-      "59\t검증 세트 손실: 0.288626\t최선의 손실: 0.178515\t정확도: 97.65%\n",
-      "60\t검증 세트 손실: 0.370064\t최선의 손실: 0.178515\t정확도: 97.54%\n",
-      "61\t검증 세트 손실: 0.451385\t최선의 손실: 0.178515\t정확도: 97.07%\n",
-      "62\t검증 세트 손실: 0.454155\t최선의 손실: 0.178515\t정확도: 96.64%\n",
-      "63\t검증 세트 손실: 0.461850\t최선의 손실: 0.178515\t정확도: 97.19%\n",
-      "64\t검증 세트 손실: 0.494679\t최선의 손실: 0.178515\t정확도: 97.42%\n",
-      "65\t검증 세트 손실: 0.124899\t최선의 손실: 0.124899\t정확도: 97.77%\n",
-      "66\t검증 세트 손실: 0.121632\t최선의 손실: 0.121632\t정확도: 97.81%\n",
-      "67\t검증 세트 손실: 0.116527\t최선의 손실: 0.116527\t정확도: 98.01%\n",
-      "68\t검증 세트 손실: 0.159491\t최선의 손실: 0.116527\t정확도: 97.89%\n",
-      "69\t검증 세트 손실: 0.167134\t최선의 손실: 0.116527\t정확도: 97.54%\n",
-      "70\t검증 세트 손실: 0.183106\t최선의 손실: 0.116527\t정확도: 97.58%\n",
-      "71\t검증 세트 손실: 0.191654\t최선의 손실: 0.116527\t정확도: 97.81%\n",
-      "72\t검증 세트 손실: 0.220577\t최선의 손실: 0.116527\t정확도: 97.85%\n",
-      "73\t검증 세트 손실: 0.258518\t최선의 손실: 0.116527\t정확도: 97.69%\n",
-      "74\t검증 세트 손실: 0.201793\t최선의 손실: 0.116527\t정확도: 98.08%\n",
-      "75\t검증 세트 손실: 0.276650\t최선의 손실: 0.116527\t정확도: 97.54%\n",
-      "76\t검증 세트 손실: 0.429051\t최선의 손실: 0.116527\t정확도: 97.58%\n",
-      "77\t검증 세트 손실: 0.498747\t최선의 손실: 0.116527\t정확도: 96.21%\n",
-      "78\t검증 세트 손실: 0.539477\t최선의 손실: 0.116527\t정확도: 96.56%\n",
-      "79\t검증 세트 손실: 15884179.000000\t최선의 손실: 0.116527\t정확도: 34.75%\n",
-      "80\t검증 세트 손실: 92947408.000000\t최선의 손실: 0.116527\t정확도: 19.39%\n",
-      "81\t검증 세트 손실: 3485558.250000\t최선의 손실: 0.116527\t정확도: 45.50%\n",
-      "82\t검증 세트 손실: 640732.625000\t최선의 손실: 0.116527\t정확도: 78.26%\n",
-      "83\t검증 세트 손실: 177609.531250\t최선의 손실: 0.116527\t정확도: 86.94%\n",
-      "84\t검증 세트 손실: 167277.203125\t최선의 손실: 0.116527\t정확도: 79.20%\n",
-      "85\t검증 세트 손실: 114869.367188\t최선의 손실: 0.116527\t정확도: 89.29%\n",
-      "86\t검증 세트 손실: 135661.859375\t최선의 손실: 0.116527\t정확도: 79.55%\n",
-      "87\t검증 세트 손실: 97423.460938\t최선의 손실: 0.116527\t정확도: 89.72%\n",
-      "88\t검증 세트 손실: 76302.828125\t최선의 손실: 0.116527\t정확도: 91.79%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, n_neurons=140, total=  12.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=90 \n",
+      "0\t검증 세트 손실: 27.651333\t최선의 손실: 27.651333\t정확도: 76.11%\n",
+      "1\t검증 세트 손실: 1.027318\t최선의 손실: 1.027318\t정확도: 94.06%\n",
+      "2\t검증 세트 손실: 0.498753\t최선의 손실: 0.498753\t정확도: 94.37%\n",
+      "3\t검증 세트 손실: 0.650802\t최선의 손실: 0.498753\t정확도: 92.14%\n",
+      "4\t검증 세트 손실: 0.531051\t최선의 손실: 0.498753\t정확도: 94.10%\n",
+      "5\t검증 세트 손실: 0.261925\t최선의 손실: 0.261925\t정확도: 96.09%\n",
+      "6\t검증 세트 손실: 0.233486\t최선의 손실: 0.233486\t정확도: 96.60%\n",
+      "7\t검증 세트 손실: 0.235923\t최선의 손실: 0.233486\t정확도: 95.62%\n",
+      "8\t검증 세트 손실: 0.250061\t최선의 손실: 0.233486\t정확도: 95.04%\n",
+      "9\t검증 세트 손실: 0.193153\t최선의 손실: 0.193153\t정확도: 97.15%\n",
+      "10\t검증 세트 손실: 0.229657\t최선의 손실: 0.193153\t정확도: 95.90%\n",
+      "11\t검증 세트 손실: 0.219174\t최선의 손실: 0.193153\t정확도: 95.93%\n",
+      "12\t검증 세트 손실: 0.174832\t최선의 손실: 0.174832\t정확도: 97.19%\n",
+      "13\t검증 세트 손실: 0.166164\t최선의 손실: 0.166164\t정확도: 97.19%\n",
+      "14\t검증 세트 손실: 0.178789\t최선의 손실: 0.166164\t정확도: 97.15%\n",
+      "15\t검증 세트 손실: 0.194410\t최선의 손실: 0.166164\t정확도: 96.79%\n",
+      "16\t검증 세트 손실: 0.186633\t최선의 손실: 0.166164\t정확도: 97.30%\n",
+      "17\t검증 세트 손실: 0.196186\t최선의 손실: 0.166164\t정확도: 97.50%\n",
+      "18\t검증 세트 손실: 0.262891\t최선의 손실: 0.166164\t정확도: 97.03%\n",
+      "19\t검증 세트 손실: 0.260892\t최선의 손실: 0.166164\t정확도: 96.64%\n",
+      "20\t검증 세트 손실: 0.153048\t최선의 손실: 0.153048\t정확도: 97.50%\n",
+      "21\t검증 세트 손실: 0.225180\t최선의 손실: 0.153048\t정확도: 96.87%\n",
+      "22\t검증 세트 손실: 0.171731\t최선의 손실: 0.153048\t정확도: 97.11%\n",
+      "23\t검증 세트 손실: 0.158410\t최선의 손실: 0.153048\t정확도: 97.58%\n",
+      "24\t검증 세트 손실: 0.177999\t최선의 손실: 0.153048\t정확도: 97.62%\n",
+      "25\t검증 세트 손실: 0.154367\t최선의 손실: 0.153048\t정확도: 97.46%\n",
+      "26\t검증 세트 손실: 0.225585\t최선의 손실: 0.153048\t정확도: 97.54%\n",
+      "27\t검증 세트 손실: 0.209234\t최선의 손실: 0.153048\t정확도: 96.87%\n",
+      "28\t검증 세트 손실: 0.154025\t최선의 손실: 0.153048\t정확도: 97.42%\n",
+      "29\t검증 세트 손실: 0.111588\t최선의 손실: 0.111588\t정확도: 97.46%\n",
+      "30\t검증 세트 손실: 0.134136\t최선의 손실: 0.111588\t정확도: 97.42%\n",
+      "31\t검증 세트 손실: 0.117081\t최선의 손실: 0.111588\t정확도: 97.58%\n",
+      "32\t검증 세트 손실: 0.124013\t최선의 손실: 0.111588\t정확도: 97.65%\n",
+      "33\t검증 세트 손실: 0.122537\t최선의 손실: 0.111588\t정확도: 97.93%\n",
+      "34\t검증 세트 손실: 0.157946\t최선의 손실: 0.111588\t정확도: 97.34%\n",
+      "35\t검증 세트 손실: 0.109569\t최선의 손실: 0.109569\t정확도: 97.85%\n",
+      "36\t검증 세트 손실: 0.153450\t최선의 손실: 0.109569\t정확도: 97.81%\n",
+      "37\t검증 세트 손실: 0.178541\t최선의 손실: 0.109569\t정확도: 97.89%\n",
+      "38\t검증 세트 손실: 0.150441\t최선의 손실: 0.109569\t정확도: 97.46%\n",
+      "39\t검증 세트 손실: 0.146705\t최선의 손실: 0.109569\t정확도: 97.73%\n",
+      "40\t검증 세트 손실: 0.143541\t최선의 손실: 0.109569\t정확도: 97.85%\n",
+      "41\t검증 세트 손실: 0.139371\t최선의 손실: 0.109569\t정확도: 98.01%\n",
+      "42\t검증 세트 손실: 0.128523\t최선의 손실: 0.109569\t정확도: 97.85%\n",
+      "43\t검증 세트 손실: 0.122834\t최선의 손실: 0.109569\t정확도: 97.50%\n",
+      "44\t검증 세트 손실: 0.207059\t최선의 손실: 0.109569\t정확도: 97.38%\n",
+      "45\t검증 세트 손실: 0.158751\t최선의 손실: 0.109569\t정확도: 97.77%\n",
+      "46\t검증 세트 손실: 0.163218\t최선의 손실: 0.109569\t정확도: 97.97%\n",
+      "47\t검증 세트 손실: 0.222932\t최선의 손실: 0.109569\t정확도: 97.46%\n",
+      "48\t검증 세트 손실: 0.177142\t최선의 손실: 0.109569\t정확도: 97.97%\n",
+      "49\t검증 세트 손실: 0.189048\t최선의 손실: 0.109569\t정확도: 98.12%\n",
+      "50\t검증 세트 손실: 0.212369\t최선의 손실: 0.109569\t정확도: 97.22%\n",
+      "51\t검증 세트 손실: 0.136718\t최선의 손실: 0.109569\t정확도: 98.01%\n",
+      "52\t검증 세트 손실: 0.392633\t최선의 손실: 0.109569\t정확도: 95.35%\n",
+      "53\t검증 세트 손실: 0.185505\t최선의 손실: 0.109569\t정확도: 96.09%\n",
+      "54\t검증 세트 손실: 0.169381\t최선의 손실: 0.109569\t정확도: 97.34%\n",
+      "55\t검증 세트 손실: 0.123495\t최선의 손실: 0.109569\t정확도: 97.15%\n",
+      "56\t검증 세트 손실: 0.222544\t최선의 손실: 0.109569\t정확도: 97.65%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90, total=   6.7s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 21.638136\t최선의 손실: 21.638136\t정확도: 81.27%\n",
-      "1\t검증 세트 손실: 10.983274\t최선의 손실: 10.983274\t정확도: 67.55%\n",
-      "2\t검증 세트 손실: 2.091864\t최선의 손실: 2.091864\t정확도: 85.54%\n",
-      "3\t검증 세트 손실: 0.807268\t최선의 손실: 0.807268\t정확도: 92.92%\n",
-      "4\t검증 세트 손실: 1.003669\t최선의 손실: 0.807268\t정확도: 89.48%\n",
-      "5\t검증 세트 손실: 0.555904\t최선의 손실: 0.555904\t정확도: 93.35%\n",
-      "6\t검증 세트 손실: 0.788397\t최선의 손실: 0.555904\t정확도: 90.03%\n",
-      "7\t검증 세트 손실: 0.593549\t최선의 손실: 0.555904\t정확도: 92.14%\n",
-      "8\t검증 세트 손실: 0.418600\t최선의 손실: 0.418600\t정확도: 94.29%\n",
-      "9\t검증 세트 손실: 0.892962\t최선의 손실: 0.418600\t정확도: 92.14%\n",
-      "10\t검증 세트 손실: 0.487105\t최선의 손실: 0.418600\t정확도: 95.43%\n",
-      "11\t검증 세트 손실: 0.732779\t최선의 손실: 0.418600\t정확도: 93.90%\n",
-      "12\t검증 세트 손실: 0.435569\t최선의 손실: 0.418600\t정확도: 93.94%\n",
-      "13\t검증 세트 손실: 0.409047\t최선의 손실: 0.409047\t정확도: 96.09%\n",
-      "14\t검증 세트 손실: 0.294021\t최선의 손실: 0.294021\t정확도: 95.93%\n",
-      "15\t검증 세트 손실: 0.292550\t최선의 손실: 0.292550\t정확도: 95.70%\n",
-      "16\t검증 세트 손실: 0.327437\t최선의 손실: 0.292550\t정확도: 95.43%\n",
-      "17\t검증 세트 손실: 0.264882\t최선의 손실: 0.264882\t정확도: 96.83%\n",
-      "18\t검증 세트 손실: 0.271789\t최선의 손실: 0.264882\t정확도: 96.72%\n",
-      "19\t검증 세트 손실: 0.262128\t최선의 손실: 0.262128\t정확도: 96.76%\n",
-      "20\t검증 세트 손실: 0.326099\t최선의 손실: 0.262128\t정확도: 95.97%\n",
-      "21\t검증 세트 손실: 0.329504\t최선의 손실: 0.262128\t정확도: 96.29%\n",
-      "22\t검증 세트 손실: 0.268984\t최선의 손실: 0.262128\t정확도: 96.95%\n",
-      "23\t검증 세트 손실: 0.311832\t최선의 손실: 0.262128\t정확도: 96.83%\n",
-      "24\t검증 세트 손실: 0.289305\t최선의 손실: 0.262128\t정확도: 96.25%\n",
-      "25\t검증 세트 손실: 0.307952\t최선의 손실: 0.262128\t정확도: 96.13%\n",
-      "26\t검증 세트 손실: 0.321027\t최선의 손실: 0.262128\t정확도: 95.66%\n",
-      "27\t검증 세트 손실: 0.371572\t최선의 손실: 0.262128\t정확도: 95.23%\n",
-      "28\t검증 세트 손실: 0.363256\t최선의 손실: 0.262128\t정확도: 96.83%\n",
-      "29\t검증 세트 손실: 0.338067\t최선의 손실: 0.262128\t정확도: 96.76%\n",
-      "30\t검증 세트 손실: 0.304915\t최선의 손실: 0.262128\t정확도: 95.23%\n",
-      "31\t검증 세트 손실: 0.274552\t최선의 손실: 0.262128\t정확도: 97.07%\n",
-      "32\t검증 세트 손실: 0.295838\t최선의 손실: 0.262128\t정확도: 97.11%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=90, total=   5.4s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=90 \n",
+      "0\t검증 세트 손실: 282.127258\t최선의 손실: 282.127258\t정확도: 32.33%\n",
+      "1\t검증 세트 손실: 13.180264\t최선의 손실: 13.180264\t정확도: 89.37%\n",
+      "2\t검증 세트 손실: 4.518518\t최선의 손실: 4.518518\t정확도: 90.03%\n",
+      "3\t검증 세트 손실: 2.014270\t최선의 손실: 2.014270\t정확도: 89.52%\n",
+      "4\t검증 세트 손실: 0.922805\t최선의 손실: 0.922805\t정확도: 93.94%\n",
+      "5\t검증 세트 손실: 0.713453\t최선의 손실: 0.713453\t정확도: 94.57%\n",
+      "6\t검증 세트 손실: 0.741638\t최선의 손실: 0.713453\t정확도: 95.27%\n",
+      "7\t검증 세트 손실: 0.677397\t최선의 손실: 0.677397\t정확도: 94.84%\n",
+      "8\t검증 세트 손실: 0.607238\t최선의 손실: 0.607238\t정확도: 93.63%\n",
+      "9\t검증 세트 손실: 0.673068\t최선의 손실: 0.607238\t정확도: 95.04%\n",
+      "10\t검증 세트 손실: 1.119752\t최선의 손실: 0.607238\t정확도: 95.97%\n",
+      "11\t검증 세트 손실: 0.805660\t최선의 손실: 0.607238\t정확도: 94.68%\n",
+      "12\t검증 세트 손실: 0.533558\t최선의 손실: 0.533558\t정확도: 96.64%\n",
+      "13\t검증 세트 손실: 0.491368\t최선의 손실: 0.491368\t정확도: 96.25%\n",
+      "14\t검증 세트 손실: 0.282638\t최선의 손실: 0.282638\t정확도: 96.91%\n",
+      "15\t검증 세트 손실: 0.257320\t최선의 손실: 0.257320\t정확도: 96.64%\n",
+      "16\t검증 세트 손실: 0.288632\t최선의 손실: 0.257320\t정확도: 95.43%\n",
+      "17\t검증 세트 손실: 0.203745\t최선의 손실: 0.203745\t정확도: 96.87%\n",
+      "18\t검증 세트 손실: 0.197672\t최선의 손실: 0.197672\t정확도: 97.30%\n",
+      "19\t검증 세트 손실: 0.192116\t최선의 손실: 0.192116\t정확도: 96.79%\n",
+      "20\t검증 세트 손실: 0.290392\t최선의 손실: 0.192116\t정확도: 95.90%\n",
+      "21\t검증 세트 손실: 0.187792\t최선의 손실: 0.187792\t정확도: 97.19%\n",
+      "22\t검증 세트 손실: 0.188861\t최선의 손실: 0.187792\t정확도: 96.95%\n",
+      "23\t검증 세트 손실: 0.205599\t최선의 손실: 0.187792\t정확도: 96.56%\n",
+      "24\t검증 세트 손실: 0.149950\t최선의 손실: 0.149950\t정확도: 97.15%\n",
+      "25\t검증 세트 손실: 0.182672\t최선의 손실: 0.149950\t정확도: 96.40%\n",
+      "26\t검증 세트 손실: 0.179624\t최선의 손실: 0.149950\t정확도: 97.07%\n",
+      "27\t검증 세트 손실: 0.215875\t최선의 손실: 0.149950\t정확도: 96.01%\n",
+      "28\t검증 세트 손실: 0.137885\t최선의 손실: 0.137885\t정확도: 97.85%\n",
+      "29\t검증 세트 손실: 0.145454\t최선의 손실: 0.137885\t정확도: 97.62%\n",
+      "30\t검증 세트 손실: 0.159099\t최선의 손실: 0.137885\t정확도: 97.42%\n",
+      "31\t검증 세트 손실: 0.129990\t최선의 손실: 0.129990\t정확도: 97.58%\n",
+      "32\t검증 세트 손실: 0.138724\t최선의 손실: 0.129990\t정확도: 97.62%\n",
+      "33\t검증 세트 손실: 0.151110\t최선의 손실: 0.129990\t정확도: 97.89%\n",
+      "34\t검증 세트 손실: 0.146401\t최선의 손실: 0.129990\t정확도: 97.85%\n",
+      "35\t검증 세트 손실: 0.129052\t최선의 손실: 0.129052\t정확도: 97.73%\n",
+      "36\t검증 세트 손실: 0.175818\t최선의 손실: 0.129052\t정확도: 97.34%\n",
+      "37\t검증 세트 손실: 0.175967\t최선의 손실: 0.129052\t정확도: 97.15%\n",
+      "38\t검증 세트 손실: 0.149950\t최선의 손실: 0.129052\t정확도: 97.62%\n",
+      "39\t검증 세트 손실: 0.129325\t최선의 손실: 0.129052\t정확도: 98.05%\n",
+      "40\t검증 세트 손실: 0.133003\t최선의 손실: 0.129052\t정확도: 97.89%\n",
+      "41\t검증 세트 손실: 0.188904\t최선의 손실: 0.129052\t정확도: 97.03%\n",
+      "42\t검증 세트 손실: 0.263754\t최선의 손실: 0.129052\t정확도: 96.64%\n",
+      "43\t검증 세트 손실: 0.173546\t최선의 손실: 0.129052\t정확도: 97.62%\n",
+      "44\t검증 세트 손실: 0.253551\t최선의 손실: 0.129052\t정확도: 96.29%\n",
+      "45\t검증 세트 손실: 0.212221\t최선의 손실: 0.129052\t정확도: 97.26%\n",
+      "46\t검증 세트 손실: 0.161625\t최선의 손실: 0.129052\t정확도: 97.62%\n",
+      "47\t검증 세트 손실: 0.142971\t최선의 손실: 0.129052\t정확도: 98.24%\n",
+      "48\t검증 세트 손실: 0.169362\t최선의 손실: 0.129052\t정확도: 97.58%\n",
+      "49\t검증 세트 손실: 0.201055\t최선의 손실: 0.129052\t정확도: 97.30%\n",
+      "50\t검증 세트 손실: 0.172198\t최선의 손실: 0.129052\t정확도: 97.93%\n",
+      "51\t검증 세트 손실: 0.215838\t최선의 손실: 0.129052\t정확도: 98.01%\n",
+      "52\t검증 세트 손실: 0.210989\t최선의 손실: 0.129052\t정확도: 97.65%\n",
+      "53\t검증 세트 손실: 0.262592\t최선의 손실: 0.129052\t정확도: 96.79%\n",
+      "54\t검증 세트 손실: 0.324196\t최선의 손실: 0.129052\t정확도: 96.56%\n",
+      "55\t검증 세트 손실: 0.205696\t최선의 손실: 0.129052\t정확도: 97.65%\n",
+      "56\t검증 세트 손실: 0.213988\t최선의 손실: 0.129052\t정확도: 97.30%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=90, total=   5.1s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=90 \n",
+      "0\t검증 세트 손실: 873.370972\t최선의 손실: 873.370972\t정확도: 20.45%\n",
+      "1\t검증 세트 손실: 27.610022\t최선의 손실: 27.610022\t정확도: 77.17%\n",
+      "2\t검증 세트 손실: 4.068854\t최선의 손실: 4.068854\t정확도: 91.48%\n",
+      "3\t검증 세트 손실: 1.597515\t최선의 손실: 1.597515\t정확도: 94.45%\n",
+      "4\t검증 세트 손실: 1.535136\t최선의 손실: 1.535136\t정확도: 92.10%\n",
+      "5\t검증 세트 손실: 0.924582\t최선의 손실: 0.924582\t정확도: 94.64%\n",
+      "6\t검증 세트 손실: 0.891371\t최선의 손실: 0.891371\t정확도: 93.94%\n",
+      "7\t검증 세트 손실: 0.986235\t최선의 손실: 0.891371\t정확도: 94.25%\n",
+      "8\t검증 세트 손실: 0.951238\t최선의 손실: 0.891371\t정확도: 92.30%\n",
+      "9\t검증 세트 손실: 2.126219\t최선의 손실: 0.891371\t정확도: 84.79%\n",
+      "10\t검증 세트 손실: 0.460685\t최선의 손실: 0.460685\t정확도: 95.82%\n",
+      "11\t검증 세트 손실: 0.474828\t최선의 손실: 0.460685\t정확도: 95.74%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "33\t검증 세트 손실: 0.273056\t최선의 손실: 0.262128\t정확도: 97.15%\n",
-      "34\t검증 세트 손실: 0.278628\t최선의 손실: 0.262128\t정확도: 97.58%\n",
-      "35\t검증 세트 손실: 0.275180\t최선의 손실: 0.262128\t정확도: 95.82%\n",
-      "36\t검증 세트 손실: 0.248080\t최선의 손실: 0.248080\t정확도: 96.95%\n",
-      "37\t검증 세트 손실: 0.270179\t최선의 손실: 0.248080\t정확도: 96.68%\n",
-      "38\t검증 세트 손실: 0.351644\t최선의 손실: 0.248080\t정확도: 95.66%\n",
-      "39\t검증 세트 손실: 0.277407\t최선의 손실: 0.248080\t정확도: 97.81%\n",
-      "40\t검증 세트 손실: 0.372831\t최선의 손실: 0.248080\t정확도: 95.70%\n",
-      "41\t검증 세트 손실: 0.406695\t최선의 손실: 0.248080\t정확도: 95.54%\n",
-      "42\t검증 세트 손실: 0.405936\t최선의 손실: 0.248080\t정확도: 97.38%\n",
-      "43\t검증 세트 손실: 0.336664\t최선의 손실: 0.248080\t정확도: 97.34%\n",
-      "44\t검증 세트 손실: 0.350621\t최선의 손실: 0.248080\t정확도: 97.26%\n",
-      "45\t검증 세트 손실: 0.550765\t최선의 손실: 0.248080\t정확도: 94.92%\n",
-      "46\t검증 세트 손실: 0.251712\t최선의 손실: 0.248080\t정확도: 96.52%\n",
-      "47\t검증 세트 손실: 0.191296\t최선의 손실: 0.191296\t정확도: 97.42%\n",
-      "48\t검증 세트 손실: 0.213644\t최선의 손실: 0.191296\t정확도: 97.38%\n",
-      "49\t검증 세트 손실: 0.267468\t최선의 손실: 0.191296\t정확도: 96.83%\n",
-      "50\t검증 세트 손실: 0.255339\t최선의 손실: 0.191296\t정확도: 97.15%\n",
-      "51\t검증 세트 손실: 0.211983\t최선의 손실: 0.191296\t정확도: 97.58%\n",
-      "52\t검증 세트 손실: 0.310858\t최선의 손실: 0.191296\t정확도: 97.38%\n",
-      "53\t검증 세트 손실: 0.310168\t최선의 손실: 0.191296\t정확도: 97.19%\n",
-      "54\t검증 세트 손실: 0.297499\t최선의 손실: 0.191296\t정확도: 97.34%\n",
-      "55\t검증 세트 손실: 0.229321\t최선의 손실: 0.191296\t정확도: 97.46%\n",
-      "56\t검증 세트 손실: 0.218966\t최선의 손실: 0.191296\t정확도: 97.42%\n",
-      "57\t검증 세트 손실: 0.244614\t최선의 손실: 0.191296\t정확도: 97.30%\n",
-      "58\t검증 세트 손실: 0.258493\t최선의 손실: 0.191296\t정확도: 97.38%\n",
-      "59\t검증 세트 손실: 0.261146\t최선의 손실: 0.191296\t정확도: 97.30%\n",
-      "60\t검증 세트 손실: 0.294660\t최선의 손실: 0.191296\t정확도: 97.26%\n",
-      "61\t검증 세트 손실: 0.263153\t최선의 손실: 0.191296\t정확도: 97.34%\n",
-      "62\t검증 세트 손실: 0.333223\t최선의 손실: 0.191296\t정확도: 97.26%\n",
-      "63\t검증 세트 손실: 0.287419\t최선의 손실: 0.191296\t정확도: 97.38%\n",
-      "64\t검증 세트 손실: 0.249618\t최선의 손실: 0.191296\t정확도: 97.22%\n",
-      "65\t검증 세트 손실: 0.276081\t최선의 손실: 0.191296\t정확도: 97.58%\n",
-      "66\t검증 세트 손실: 0.181720\t최선의 손실: 0.181720\t정확도: 96.76%\n",
-      "67\t검증 세트 손실: 0.209461\t최선의 손실: 0.181720\t정확도: 96.48%\n",
-      "68\t검증 세트 손실: 0.418183\t최선의 손실: 0.181720\t정확도: 94.57%\n",
-      "69\t검증 세트 손실: 0.282450\t최선의 손실: 0.181720\t정확도: 96.99%\n",
-      "70\t검증 세트 손실: 0.227782\t최선의 손실: 0.181720\t정확도: 97.42%\n",
-      "71\t검증 세트 손실: 0.232042\t최선의 손실: 0.181720\t정확도: 97.54%\n",
-      "72\t검증 세트 손실: 0.215191\t최선의 손실: 0.181720\t정확도: 97.73%\n",
-      "73\t검증 세트 손실: 0.203225\t최선의 손실: 0.181720\t정확도: 97.54%\n",
-      "74\t검증 세트 손실: 0.211399\t최선의 손실: 0.181720\t정확도: 97.50%\n",
-      "75\t검증 세트 손실: 0.224905\t최선의 손실: 0.181720\t정확도: 97.34%\n",
-      "76\t검증 세트 손실: 0.334400\t최선의 손실: 0.181720\t정확도: 97.38%\n",
-      "77\t검증 세트 손실: 0.219173\t최선의 손실: 0.181720\t정확도: 97.30%\n",
-      "78\t검증 세트 손실: 0.223847\t최선의 손실: 0.181720\t정확도: 97.54%\n",
-      "79\t검증 세트 손실: 0.270548\t최선의 손실: 0.181720\t정확도: 97.50%\n",
-      "80\t검증 세트 손실: 0.349744\t최선의 손실: 0.181720\t정확도: 97.07%\n",
-      "81\t검증 세트 손실: 0.432475\t최선의 손실: 0.181720\t정확도: 96.76%\n",
-      "82\t검증 세트 손실: 0.384205\t최선의 손실: 0.181720\t정확도: 97.15%\n",
-      "83\t검증 세트 손실: 0.488265\t최선의 손실: 0.181720\t정확도: 97.62%\n",
-      "84\t검증 세트 손실: 0.487970\t최선의 손실: 0.181720\t정확도: 97.38%\n",
-      "85\t검증 세트 손실: 0.376749\t최선의 손실: 0.181720\t정확도: 97.42%\n",
-      "86\t검증 세트 손실: 0.384709\t최선의 손실: 0.181720\t정확도: 97.85%\n",
-      "87\t검증 세트 손실: 0.564745\t최선의 손실: 0.181720\t정확도: 97.73%\n",
+      "12\t검증 세트 손실: 0.569282\t최선의 손실: 0.460685\t정확도: 95.86%\n",
+      "13\t검증 세트 손실: 0.446970\t최선의 손실: 0.446970\t정확도: 96.87%\n",
+      "14\t검증 세트 손실: 0.552905\t최선의 손실: 0.446970\t정확도: 95.47%\n",
+      "15\t검증 세트 손실: 0.676743\t최선의 손실: 0.446970\t정확도: 94.29%\n",
+      "16\t검증 세트 손실: 0.781570\t최선의 손실: 0.446970\t정확도: 96.48%\n",
+      "17\t검증 세트 손실: 0.545105\t최선의 손실: 0.446970\t정확도: 96.91%\n",
+      "18\t검증 세트 손실: 0.449043\t최선의 손실: 0.446970\t정확도: 96.64%\n",
+      "19\t검증 세트 손실: 0.272469\t최선의 손실: 0.272469\t정확도: 97.07%\n",
+      "20\t검증 세트 손실: 0.427910\t최선의 손실: 0.272469\t정확도: 93.63%\n",
+      "21\t검증 세트 손실: 0.382450\t최선의 손실: 0.272469\t정확도: 95.66%\n",
+      "22\t검증 세트 손실: 0.284913\t최선의 손실: 0.272469\t정확도: 96.33%\n",
+      "23\t검증 세트 손실: 0.283387\t최선의 손실: 0.272469\t정확도: 97.19%\n",
+      "24\t검증 세트 손실: 0.233463\t최선의 손실: 0.233463\t정확도: 97.26%\n",
+      "25\t검증 세트 손실: 0.348983\t최선의 손실: 0.233463\t정확도: 95.70%\n",
+      "26\t검증 세트 손실: 0.240162\t최선의 손실: 0.233463\t정확도: 96.79%\n",
+      "27\t검증 세트 손실: 0.237886\t최선의 손실: 0.233463\t정확도: 97.42%\n",
+      "28\t검증 세트 손실: 0.229221\t최선의 손실: 0.229221\t정확도: 97.42%\n",
+      "29\t검증 세트 손실: 0.288951\t최선의 손실: 0.229221\t정확도: 95.93%\n",
+      "30\t검증 세트 손실: 0.285358\t최선의 손실: 0.229221\t정확도: 96.91%\n",
+      "31\t검증 세트 손실: 2.052584\t최선의 손실: 0.229221\t정확도: 83.46%\n",
+      "32\t검증 세트 손실: 0.404090\t최선의 손실: 0.229221\t정확도: 97.22%\n",
+      "33\t검증 세트 손실: 0.319337\t최선의 손실: 0.229221\t정확도: 96.68%\n",
+      "34\t검증 세트 손실: 0.399099\t최선의 손실: 0.229221\t정확도: 97.46%\n",
+      "35\t검증 세트 손실: 0.328975\t최선의 손실: 0.229221\t정확도: 97.15%\n",
+      "36\t검증 세트 손실: 0.237103\t최선의 손실: 0.229221\t정확도: 97.22%\n",
+      "37\t검증 세트 손실: 0.578590\t최선의 손실: 0.229221\t정확도: 96.36%\n",
+      "38\t검증 세트 손실: 0.723128\t최선의 손실: 0.229221\t정확도: 96.56%\n",
+      "39\t검증 세트 손실: 0.423164\t최선의 손실: 0.229221\t정확도: 97.19%\n",
+      "40\t검증 세트 손실: 0.281639\t최선의 손실: 0.229221\t정확도: 97.54%\n",
+      "41\t검증 세트 손실: 0.195646\t최선의 손실: 0.195646\t정확도: 96.87%\n",
+      "42\t검증 세트 손실: 0.214120\t최선의 손실: 0.195646\t정확도: 97.46%\n",
+      "43\t검증 세트 손실: 0.190019\t최선의 손실: 0.190019\t정확도: 97.22%\n",
+      "44\t검증 세트 손실: 0.166766\t최선의 손실: 0.166766\t정확도: 97.58%\n",
+      "45\t검증 세트 손실: 0.167684\t최선의 손실: 0.166766\t정확도: 97.46%\n",
+      "46\t검증 세트 손실: 0.178557\t최선의 손실: 0.166766\t정확도: 97.46%\n",
+      "47\t검증 세트 손실: 0.252749\t최선의 손실: 0.166766\t정확도: 96.60%\n",
+      "48\t검증 세트 손실: 0.167429\t최선의 손실: 0.166766\t정확도: 97.34%\n",
+      "49\t검증 세트 손실: 0.163534\t최선의 손실: 0.163534\t정확도: 97.34%\n",
+      "50\t검증 세트 손실: 0.182053\t최선의 손실: 0.163534\t정확도: 97.54%\n",
+      "51\t검증 세트 손실: 0.144452\t최선의 손실: 0.144452\t정확도: 97.77%\n",
+      "52\t검증 세트 손실: 0.145313\t최선의 손실: 0.144452\t정확도: 97.62%\n",
+      "53\t검증 세트 손실: 0.182866\t최선의 손실: 0.144452\t정확도: 97.19%\n",
+      "54\t검증 세트 손실: 0.208894\t최선의 손실: 0.144452\t정확도: 96.79%\n",
+      "55\t검증 세트 손실: 0.195044\t최선의 손실: 0.144452\t정확도: 96.95%\n",
+      "56\t검증 세트 손실: 0.137982\t최선의 손실: 0.137982\t정확도: 97.73%\n",
+      "57\t검증 세트 손실: 0.139858\t최선의 손실: 0.137982\t정확도: 97.89%\n",
+      "58\t검증 세트 손실: 0.154100\t최선의 손실: 0.137982\t정확도: 97.42%\n",
+      "59\t검증 세트 손실: 0.198994\t최선의 손실: 0.137982\t정확도: 97.22%\n",
+      "60\t검증 세트 손실: 0.150858\t최선의 손실: 0.137982\t정확도: 97.65%\n",
+      "61\t검증 세트 손실: 0.174443\t최선의 손실: 0.137982\t정확도: 97.11%\n",
+      "62\t검증 세트 손실: 0.142614\t최선의 손실: 0.137982\t정확도: 97.22%\n",
+      "63\t검증 세트 손실: 0.160285\t최선의 손실: 0.137982\t정확도: 97.50%\n",
+      "64\t검증 세트 손실: 0.147266\t최선의 손실: 0.137982\t정확도: 97.73%\n",
+      "65\t검증 세트 손실: 0.218413\t최선의 손실: 0.137982\t정확도: 97.30%\n",
+      "66\t검증 세트 손실: 0.139224\t최선의 손실: 0.137982\t정확도: 98.05%\n",
+      "67\t검증 세트 손실: 0.180549\t최선의 손실: 0.137982\t정확도: 97.38%\n",
+      "68\t검증 세트 손실: 0.167369\t최선의 손실: 0.137982\t정확도: 97.62%\n",
+      "69\t검증 세트 손실: 0.183546\t최선의 손실: 0.137982\t정확도: 97.26%\n",
+      "70\t검증 세트 손실: 0.196321\t최선의 손실: 0.137982\t정확도: 97.26%\n",
+      "71\t검증 세트 손실: 0.161517\t최선의 손실: 0.137982\t정확도: 97.77%\n",
+      "72\t검증 세트 손실: 0.150964\t최선의 손실: 0.137982\t정확도: 97.69%\n",
+      "73\t검증 세트 손실: 0.135124\t최선의 손실: 0.135124\t정확도: 97.65%\n",
+      "74\t검증 세트 손실: 0.158261\t최선의 손실: 0.135124\t정확도: 97.77%\n",
+      "75\t검증 세트 손실: 0.138820\t최선의 손실: 0.135124\t정확도: 97.69%\n",
+      "76\t검증 세트 손실: 0.274215\t최선의 손실: 0.135124\t정확도: 96.60%\n",
+      "77\t검증 세트 손실: 0.294717\t최선의 손실: 0.135124\t정확도: 97.34%\n",
+      "78\t검증 세트 손실: 0.264875\t최선의 손실: 0.135124\t정확도: 97.26%\n",
+      "79\t검증 세트 손실: 0.286378\t최선의 손실: 0.135124\t정확도: 96.48%\n",
+      "80\t검증 세트 손실: 0.367494\t최선의 손실: 0.135124\t정확도: 96.95%\n",
+      "81\t검증 세트 손실: 0.619192\t최선의 손실: 0.135124\t정확도: 96.56%\n",
+      "82\t검증 세트 손실: 0.602679\t최선의 손실: 0.135124\t정확도: 97.26%\n",
+      "83\t검증 세트 손실: 0.551204\t최선의 손실: 0.135124\t정확도: 97.77%\n",
+      "84\t검증 세트 손실: 0.412516\t최선의 손실: 0.135124\t정확도: 97.85%\n",
+      "85\t검증 세트 손실: 0.312202\t최선의 손실: 0.135124\t정확도: 97.65%\n",
+      "86\t검증 세트 손실: 0.403900\t최선의 손실: 0.135124\t정확도: 97.73%\n",
+      "87\t검증 세트 손실: 0.270737\t최선의 손실: 0.135124\t정확도: 97.58%\n",
+      "88\t검증 세트 손실: 0.337094\t최선의 손실: 0.135124\t정확도: 97.34%\n",
+      "89\t검증 세트 손실: 2.248111\t최선의 손실: 0.135124\t정확도: 95.31%\n",
+      "90\t검증 세트 손실: 1.746803\t최선의 손실: 0.135124\t정확도: 95.93%\n",
+      "91\t검증 세트 손실: 0.672275\t최선의 손실: 0.135124\t정확도: 97.46%\n",
+      "92\t검증 세트 손실: 1.469252\t최선의 손실: 0.135124\t정확도: 97.26%\n",
+      "93\t검증 세트 손실: 0.413384\t최선의 손실: 0.135124\t정확도: 95.23%\n",
+      "94\t검증 세트 손실: 0.489472\t최선의 손실: 0.135124\t정확도: 97.22%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>, n_neurons=90, total=   6.5s\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>, learning_rate=0.1, n_neurons=90, total=   8.6s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 91.5min finished\n"
+      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 39.4min finished\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.067337\t최선의 손실: 0.067337\t정확도: 97.93%\n",
-      "1\t검증 세트 손실: 0.054187\t최선의 손실: 0.054187\t정확도: 98.24%\n",
-      "2\t검증 세트 손실: 0.053684\t최선의 손실: 0.053684\t정확도: 98.63%\n",
-      "3\t검증 세트 손실: 0.041214\t최선의 손실: 0.041214\t정확도: 98.63%\n",
-      "4\t검증 세트 손실: 0.057057\t최선의 손실: 0.041214\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.046840\t최선의 손실: 0.041214\t정확도: 98.67%\n",
-      "6\t검증 세트 손실: 0.039114\t최선의 손실: 0.039114\t정확도: 99.10%\n",
-      "7\t검증 세트 손실: 0.026181\t최선의 손실: 0.026181\t정확도: 99.14%\n",
-      "8\t검증 세트 손실: 0.042696\t최선의 손실: 0.026181\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.035419\t최선의 손실: 0.026181\t정확도: 98.94%\n",
-      "10\t검증 세트 손실: 0.041890\t최선의 손실: 0.026181\t정확도: 98.79%\n",
-      "11\t검증 세트 손실: 0.044646\t최선의 손실: 0.026181\t정확도: 98.87%\n",
-      "12\t검증 세트 손실: 0.048809\t최선의 손실: 0.026181\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.049727\t최선의 손실: 0.026181\t정확도: 98.94%\n",
-      "14\t검증 세트 손실: 0.036089\t최선의 손실: 0.026181\t정확도: 99.30%\n",
-      "15\t검증 세트 손실: 0.058307\t최선의 손실: 0.026181\t정확도: 98.98%\n",
-      "16\t검증 세트 손실: 0.043529\t최선의 손실: 0.026181\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.054985\t최선의 손실: 0.026181\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.043630\t최선의 손실: 0.026181\t정확도: 99.02%\n",
-      "19\t검증 세트 손실: 0.061039\t최선의 손실: 0.026181\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.043826\t최선의 손실: 0.026181\t정확도: 99.18%\n",
-      "21\t검증 세트 손실: 0.070892\t최선의 손실: 0.026181\t정확도: 99.06%\n",
-      "22\t검증 세트 손실: 0.051166\t최선의 손실: 0.026181\t정확도: 99.02%\n",
-      "23\t검증 세트 손실: 0.045468\t최선의 손실: 0.026181\t정확도: 99.10%\n",
-      "24\t검증 세트 손실: 0.071197\t최선의 손실: 0.026181\t정확도: 98.91%\n",
-      "25\t검증 세트 손실: 0.070717\t최선의 손실: 0.026181\t정확도: 98.71%\n",
-      "26\t검증 세트 손실: 0.075408\t최선의 손실: 0.026181\t정확도: 99.10%\n",
-      "27\t검증 세트 손실: 0.038304\t최선의 손실: 0.026181\t정확도: 99.18%\n",
-      "28\t검증 세트 손실: 0.072007\t최선의 손실: 0.026181\t정확도: 98.98%\n",
+      "0\t검증 세트 손실: 0.110219\t최선의 손실: 0.110219\t정확도: 96.64%\n",
+      "1\t검증 세트 손실: 0.073379\t최선의 손실: 0.073379\t정확도: 97.73%\n",
+      "2\t검증 세트 손실: 0.062589\t최선의 손실: 0.062589\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.060639\t최선의 손실: 0.060639\t정확도: 98.01%\n",
+      "4\t검증 세트 손실: 0.046205\t최선의 손실: 0.046205\t정확도: 98.28%\n",
+      "5\t검증 세트 손실: 0.066354\t최선의 손실: 0.046205\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.035539\t최선의 손실: 0.035539\t정확도: 98.91%\n",
+      "7\t검증 세트 손실: 0.034738\t최선의 손실: 0.034738\t정확도: 99.02%\n",
+      "8\t검증 세트 손실: 0.041379\t최선의 손실: 0.034738\t정확도: 98.94%\n",
+      "9\t검증 세트 손실: 0.045923\t최선의 손실: 0.034738\t정확도: 98.91%\n",
+      "10\t검증 세트 손실: 0.039392\t최선의 손실: 0.034738\t정확도: 98.87%\n",
+      "11\t검증 세트 손실: 0.044926\t최선의 손실: 0.034738\t정확도: 99.06%\n",
+      "12\t검증 세트 손실: 0.057086\t최선의 손실: 0.034738\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.045179\t최선의 손실: 0.034738\t정확도: 98.91%\n",
+      "14\t검증 세트 손실: 0.050866\t최선의 손실: 0.034738\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.070704\t최선의 손실: 0.034738\t정확도: 98.55%\n",
+      "16\t검증 세트 손실: 0.058181\t최선의 손실: 0.034738\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.035154\t최선의 손실: 0.034738\t정확도: 99.10%\n",
+      "18\t검증 세트 손실: 0.036058\t최선의 손실: 0.034738\t정확도: 99.10%\n",
+      "19\t검증 세트 손실: 0.031061\t최선의 손실: 0.031061\t정확도: 99.26%\n",
+      "20\t검증 세트 손실: 0.038540\t최선의 손실: 0.031061\t정확도: 99.22%\n",
+      "21\t검증 세트 손실: 0.034978\t최선의 손실: 0.031061\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.035325\t최선의 손실: 0.031061\t정확도: 99.26%\n",
+      "23\t검증 세트 손실: 0.047618\t최선의 손실: 0.031061\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.056481\t최선의 손실: 0.031061\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.037995\t최선의 손실: 0.031061\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.035194\t최선의 손실: 0.031061\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.031699\t최선의 손실: 0.031061\t정확도: 99.45%\n",
+      "28\t검증 세트 손실: 0.036009\t최선의 손실: 0.031061\t정확도: 99.30%\n",
+      "29\t검증 세트 손실: 0.041670\t최선의 손실: 0.031061\t정확도: 99.18%\n",
+      "30\t검증 세트 손실: 0.058693\t최선의 손실: 0.031061\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.034367\t최선의 손실: 0.031061\t정확도: 99.26%\n",
+      "32\t검증 세트 손실: 0.041851\t최선의 손실: 0.031061\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.049129\t최선의 손실: 0.031061\t정확도: 99.14%\n",
+      "34\t검증 세트 손실: 0.056744\t최선의 손실: 0.031061\t정확도: 98.75%\n",
+      "35\t검증 세트 손실: 0.046107\t최선의 손실: 0.031061\t정확도: 99.02%\n",
+      "36\t검증 세트 손실: 0.044106\t최선의 손실: 0.031061\t정확도: 98.98%\n",
+      "37\t검증 세트 손실: 0.050291\t최선의 손실: 0.031061\t정확도: 98.98%\n",
+      "38\t검증 세트 손실: 0.051503\t최선의 손실: 0.031061\t정확도: 98.79%\n",
+      "39\t검증 세트 손실: 0.052207\t최선의 손실: 0.031061\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.051642\t최선의 손실: 0.031061\t정확도: 98.87%\n",
       "조기 종료!\n"
      ]
     },
@@ -6253,19 +6273,19 @@
      "data": {
       "text/plain": [
        "RandomizedSearchCV(cv=None, error_score='raise',\n",
-       "          estimator=DNNClassifier(activation=<function elu at 0x7f99417cf6a8>,\n",
+       "          estimator=DNNClassifier(activation=<function elu at 0x7f738a2bd1e0>,\n",
        "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42),\n",
        "          fit_params=None, iid=True, n_iter=50, n_jobs=1,\n",
-       "          param_distributions={'batch_size': [10, 50, 100, 500], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x7f994177e9d8>, <function elu at 0x7f99417cf6a8>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed97268>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991ed086a8>], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160]},\n",
+       "          param_distributions={'batch_size': [10, 50, 100, 500], 'activation': [<function relu at 0x7f738a2ee510>, <function elu at 0x7f738a2bd1e0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f737fb55048>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d745d08>], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160]},\n",
        "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
        "          return_train_score='warn', scoring=None, verbose=2)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6298,19 +6318,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'activation': <function __main__.leaky_relu.<locals>.parametrized_leaky_relu(z, name=None)>,\n",
+       "{'activation': <function tensorflow.python.ops.gen_nn_ops.elu(features, name=None)>,\n",
        " 'batch_size': 500,\n",
        " 'learning_rate': 0.01,\n",
-       " 'n_neurons': 120}"
+       " 'n_neurons': 140}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6321,16 +6341,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9920217941233703"
+       "0.9918272037361354"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6356,7 +6376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6386,57 +6406,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.079047\t최선의 손실: 0.079047\t정확도: 97.50%\n",
-      "1\t검증 세트 손실: 0.060790\t최선의 손실: 0.060790\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.049416\t최선의 손실: 0.049416\t정확도: 98.36%\n",
-      "3\t검증 세트 손실: 0.072482\t최선의 손실: 0.049416\t정확도: 97.93%\n",
-      "4\t검증 세트 손실: 0.049397\t최선의 손실: 0.049397\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.043662\t최선의 손실: 0.043662\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.050105\t최선의 손실: 0.043662\t정확도: 98.71%\n",
-      "7\t검증 세트 손실: 0.027069\t최선의 손실: 0.027069\t정확도: 99.18%\n",
-      "8\t검증 세트 손실: 0.040721\t최선의 손실: 0.027069\t정확도: 98.87%\n",
-      "9\t검증 세트 손실: 0.053530\t최선의 손실: 0.027069\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.056037\t최선의 손실: 0.027069\t정확도: 98.48%\n",
-      "11\t검증 세트 손실: 0.058392\t최선의 손실: 0.027069\t정확도: 98.91%\n",
-      "12\t검증 세트 손실: 0.031265\t최선의 손실: 0.027069\t정확도: 99.30%\n",
-      "13\t검증 세트 손실: 0.043205\t최선의 손실: 0.027069\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.037472\t최선의 손실: 0.027069\t정확도: 99.14%\n",
-      "15\t검증 세트 손실: 0.050415\t최선의 손실: 0.027069\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.040397\t최선의 손실: 0.027069\t정확도: 99.06%\n",
-      "17\t검증 세트 손실: 0.040210\t최선의 손실: 0.027069\t정확도: 99.30%\n",
-      "18\t검증 세트 손실: 0.058857\t최선의 손실: 0.027069\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.056906\t최선의 손실: 0.027069\t정확도: 99.18%\n",
-      "20\t검증 세트 손실: 0.044915\t최선의 손실: 0.027069\t정확도: 99.10%\n",
-      "21\t검증 세트 손실: 0.039712\t최선의 손실: 0.027069\t정확도: 99.37%\n",
-      "22\t검증 세트 손실: 0.051272\t최선의 손실: 0.027069\t정확도: 99.02%\n",
-      "23\t검증 세트 손실: 0.042959\t최선의 손실: 0.027069\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.039972\t최선의 손실: 0.027069\t정확도: 99.30%\n",
-      "25\t검증 세트 손실: 0.044658\t최선의 손실: 0.027069\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.047855\t최선의 손실: 0.027069\t정확도: 99.22%\n",
-      "27\t검증 세트 손실: 0.054638\t최선의 손실: 0.027069\t정확도: 99.18%\n",
-      "28\t검증 세트 손실: 0.071942\t최선의 손실: 0.027069\t정확도: 98.83%\n",
+      "0\t검증 세트 손실: 0.086888\t최선의 손실: 0.086888\t정확도: 97.26%\n",
+      "1\t검증 세트 손실: 0.064788\t최선의 손실: 0.064788\t정확도: 98.01%\n",
+      "2\t검증 세트 손실: 0.061523\t최선의 손실: 0.061523\t정확도: 98.28%\n",
+      "3\t검증 세트 손실: 0.058581\t최선의 손실: 0.058581\t정확도: 98.16%\n",
+      "4\t검증 세트 손실: 0.053322\t최선의 손실: 0.053322\t정확도: 98.20%\n",
+      "5\t검증 세트 손실: 0.046818\t최선의 손실: 0.046818\t정확도: 98.75%\n",
+      "6\t검증 세트 손실: 0.044525\t최선의 손실: 0.044525\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.049822\t최선의 손실: 0.044525\t정확도: 98.32%\n",
+      "8\t검증 세트 손실: 0.048372\t최선의 손실: 0.044525\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.044103\t최선의 손실: 0.044103\t정확도: 98.63%\n",
+      "10\t검증 세트 손실: 0.047062\t최선의 손실: 0.044103\t정확도: 98.67%\n",
+      "11\t검증 세트 손실: 0.073834\t최선의 손실: 0.044103\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.038117\t최선의 손실: 0.038117\t정확도: 99.10%\n",
+      "13\t검증 세트 손실: 0.060503\t최선의 손실: 0.038117\t정확도: 98.59%\n",
+      "14\t검증 세트 손실: 0.053996\t최선의 손실: 0.038117\t정확도: 98.48%\n",
+      "15\t검증 세트 손실: 0.060920\t최선의 손실: 0.038117\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.043309\t최선의 손실: 0.038117\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.063278\t최선의 손실: 0.038117\t정확도: 98.55%\n",
+      "18\t검증 세트 손실: 0.063923\t최선의 손실: 0.038117\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.056511\t최선의 손실: 0.038117\t정확도: 98.63%\n",
+      "20\t검증 세트 손실: 0.057804\t최선의 손실: 0.038117\t정확도: 98.87%\n",
+      "21\t검증 세트 손실: 0.040331\t최선의 손실: 0.038117\t정확도: 99.10%\n",
+      "22\t검증 세트 손실: 0.031264\t최선의 손실: 0.031264\t정확도: 99.26%\n",
+      "23\t검증 세트 손실: 0.058548\t최선의 손실: 0.031264\t정확도: 99.10%\n",
+      "24\t검증 세트 손실: 0.088708\t최선의 손실: 0.031264\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.049553\t최선의 손실: 0.031264\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.046667\t최선의 손실: 0.031264\t정확도: 99.26%\n",
+      "27\t검증 세트 손실: 0.044422\t최선의 손실: 0.031264\t정확도: 99.22%\n",
+      "28\t검증 세트 손실: 0.073061\t최선의 손실: 0.031264\t정확도: 99.02%\n",
+      "29\t검증 세트 손실: 0.040139\t최선의 손실: 0.031264\t정확도: 99.26%\n",
+      "30\t검증 세트 손실: 0.050345\t최선의 손실: 0.031264\t정확도: 99.18%\n",
+      "31\t검증 세트 손실: 0.051355\t최선의 손실: 0.031264\t정확도: 99.06%\n",
+      "32\t검증 세트 손실: 0.057797\t최선의 손실: 0.031264\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.057988\t최선의 손실: 0.031264\t정확도: 98.87%\n",
+      "34\t검증 세트 손실: 0.084701\t최선의 손실: 0.031264\t정확도: 98.87%\n",
+      "35\t검증 세트 손실: 0.057586\t최선의 손실: 0.031264\t정확도: 98.75%\n",
+      "36\t검증 세트 손실: 0.062692\t최선의 손실: 0.031264\t정확도: 99.02%\n",
+      "37\t검증 세트 손실: 0.065022\t최선의 손실: 0.031264\t정확도: 98.83%\n",
+      "38\t검증 세트 손실: 0.064432\t최선의 손실: 0.031264\t정확도: 98.94%\n",
+      "39\t검증 세트 손실: 0.064081\t최선의 손실: 0.031264\t정확도: 98.98%\n",
+      "40\t검증 세트 손실: 0.102783\t최선의 손실: 0.031264\t정확도: 99.06%\n",
+      "41\t검증 세트 손실: 0.076302\t최선의 손실: 0.031264\t정확도: 98.87%\n",
+      "42\t검증 세트 손실: 0.057563\t최선의 손실: 0.031264\t정확도: 98.98%\n",
+      "43\t검증 세트 손실: 0.061898\t최선의 손실: 0.031264\t정확도: 99.10%\n",
       "조기 종료!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991b28c510>,\n",
+       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d757a60>,\n",
        "       batch_norm_momentum=None, batch_size=500, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=5, n_neurons=120,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6463,16 +6498,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9902704806382565"
+       "0.9922163845106052"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6491,57 +6526,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.036333\t최선의 손실: 0.036333\t정확도: 98.75%\n",
-      "1\t검증 세트 손실: 0.052010\t최선의 손실: 0.036333\t정확도: 98.24%\n",
-      "2\t검증 세트 손실: 0.038845\t최선의 손실: 0.036333\t정확도: 98.87%\n",
-      "3\t검증 세트 손실: 0.048249\t최선의 손실: 0.036333\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.039629\t최선의 손실: 0.036333\t정확도: 98.87%\n",
-      "5\t검증 세트 손실: 0.035323\t최선의 손실: 0.035323\t정확도: 98.94%\n",
-      "6\t검증 세트 손실: 0.023557\t최선의 손실: 0.023557\t정확도: 99.41%\n",
-      "7\t검증 세트 손실: 0.022157\t최선의 손실: 0.022157\t정확도: 99.30%\n",
-      "8\t검증 세트 손실: 0.037116\t최선의 손실: 0.022157\t정확도: 99.14%\n",
-      "9\t검증 세트 손실: 0.045302\t최선의 손실: 0.022157\t정확도: 98.94%\n",
-      "10\t검증 세트 손실: 0.036152\t최선의 손실: 0.022157\t정확도: 99.18%\n",
-      "11\t검증 세트 손실: 0.036711\t최선의 손실: 0.022157\t정확도: 99.02%\n",
-      "12\t검증 세트 손실: 0.039405\t최선의 손실: 0.022157\t정확도: 98.94%\n",
-      "13\t검증 세트 손실: 0.033997\t최선의 손실: 0.022157\t정확도: 99.14%\n",
-      "14\t검증 세트 손실: 0.047193\t최선의 손실: 0.022157\t정확도: 99.06%\n",
-      "15\t검증 세트 손실: 0.028874\t최선의 손실: 0.022157\t정확도: 99.22%\n",
-      "16\t검증 세트 손실: 0.036701\t최선의 손실: 0.022157\t정확도: 99.10%\n",
-      "17\t검증 세트 손실: 0.029823\t최선의 손실: 0.022157\t정확도: 99.22%\n",
-      "18\t검증 세트 손실: 0.064695\t최선의 손실: 0.022157\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.039404\t최선의 손실: 0.022157\t정확도: 99.10%\n",
-      "20\t검증 세트 손실: 0.029483\t최선의 손실: 0.022157\t정확도: 99.30%\n",
-      "21\t검증 세트 손실: 0.027619\t최선의 손실: 0.022157\t정확도: 99.41%\n",
-      "22\t검증 세트 손실: 0.038648\t최선의 손실: 0.022157\t정확도: 98.98%\n",
-      "23\t검증 세트 손실: 0.049202\t최선의 손실: 0.022157\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.054787\t최선의 손실: 0.022157\t정확도: 98.75%\n",
-      "25\t검증 세트 손실: 0.050222\t최선의 손실: 0.022157\t정확도: 98.71%\n",
-      "26\t검증 세트 손실: 0.043764\t최선의 손실: 0.022157\t정확도: 98.98%\n",
-      "27\t검증 세트 손실: 0.040656\t최선의 손실: 0.022157\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.034767\t최선의 손실: 0.022157\t정확도: 99.14%\n",
+      "0\t검증 세트 손실: 0.037753\t최선의 손실: 0.037753\t정확도: 98.91%\n",
+      "1\t검증 세트 손실: 0.041754\t최선의 손실: 0.037753\t정확도: 98.75%\n",
+      "2\t검증 세트 손실: 0.039994\t최선의 손실: 0.037753\t정확도: 98.71%\n",
+      "3\t검증 세트 손실: 0.041313\t최선의 손실: 0.037753\t정확도: 98.98%\n",
+      "4\t검증 세트 손실: 0.041555\t최선의 손실: 0.037753\t정확도: 98.75%\n",
+      "5\t검증 세트 손실: 0.040498\t최선의 손실: 0.037753\t정확도: 99.02%\n",
+      "6\t검증 세트 손실: 0.043453\t최선의 손실: 0.037753\t정확도: 98.94%\n",
+      "7\t검증 세트 손실: 0.037884\t최선의 손실: 0.037753\t정확도: 99.06%\n",
+      "8\t검증 세트 손실: 0.031270\t최선의 손실: 0.031270\t정확도: 99.18%\n",
+      "9\t검증 세트 손실: 0.037382\t최선의 손실: 0.031270\t정확도: 99.14%\n",
+      "10\t검증 세트 손실: 0.037925\t최선의 손실: 0.031270\t정확도: 98.87%\n",
+      "11\t검증 세트 손실: 0.044685\t최선의 손실: 0.031270\t정확도: 98.91%\n",
+      "12\t검증 세트 손실: 0.032196\t최선의 손실: 0.031270\t정확도: 99.18%\n",
+      "13\t검증 세트 손실: 0.046368\t최선의 손실: 0.031270\t정확도: 98.94%\n",
+      "14\t검증 세트 손실: 0.037728\t최선의 손실: 0.031270\t정확도: 99.10%\n",
+      "15\t검증 세트 손실: 0.040095\t최선의 손실: 0.031270\t정확도: 99.18%\n",
+      "16\t검증 세트 손실: 0.034966\t최선의 손실: 0.031270\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.041942\t최선의 손실: 0.031270\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.039248\t최선의 손실: 0.031270\t정확도: 99.22%\n",
+      "19\t검증 세트 손실: 0.043054\t최선의 손실: 0.031270\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.030199\t최선의 손실: 0.030199\t정확도: 99.18%\n",
+      "21\t검증 세트 손실: 0.034276\t최선의 손실: 0.030199\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.046206\t최선의 손실: 0.030199\t정확도: 98.91%\n",
+      "23\t검증 세트 손실: 0.035547\t최선의 손실: 0.030199\t정확도: 99.14%\n",
+      "24\t검증 세트 손실: 0.038880\t최선의 손실: 0.030199\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.042313\t최선의 손실: 0.030199\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.047189\t최선의 손실: 0.030199\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.052189\t최선의 손실: 0.030199\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.025986\t최선의 손실: 0.025986\t정확도: 99.53%\n",
+      "29\t검증 세트 손실: 0.021600\t최선의 손실: 0.021600\t정확도: 99.45%\n",
+      "30\t검증 세트 손실: 0.030939\t최선의 손실: 0.021600\t정확도: 99.10%\n",
+      "31\t검증 세트 손실: 0.037651\t최선의 손실: 0.021600\t정확도: 99.26%\n",
+      "32\t검증 세트 손실: 0.036357\t최선의 손실: 0.021600\t정확도: 99.18%\n",
+      "33\t검증 세트 손실: 0.032924\t최선의 손실: 0.021600\t정확도: 99.18%\n",
+      "34\t검증 세트 손실: 0.037478\t최선의 손실: 0.021600\t정확도: 99.22%\n",
+      "35\t검증 세트 손실: 0.050882\t최선의 손실: 0.021600\t정확도: 98.98%\n",
+      "36\t검증 세트 손실: 0.041661\t최선의 손실: 0.021600\t정확도: 99.18%\n",
+      "37\t검증 세트 손실: 0.057605\t최선의 손실: 0.021600\t정확도: 98.67%\n",
+      "38\t검증 세트 손실: 0.045729\t최선의 손실: 0.021600\t정확도: 99.02%\n",
+      "39\t검증 세트 손실: 0.044777\t최선의 손실: 0.021600\t정확도: 99.06%\n",
+      "40\t검증 세트 손실: 0.046448\t최선의 손실: 0.021600\t정확도: 99.18%\n",
+      "41\t검증 세트 손실: 0.044785\t최선의 손실: 0.021600\t정확도: 99.22%\n",
+      "42\t검증 세트 손실: 0.043182\t최선의 손실: 0.021600\t정확도: 99.22%\n",
+      "43\t검증 세트 손실: 0.023467\t최선의 손실: 0.021600\t정확도: 99.49%\n",
+      "44\t검증 세트 손실: 0.035105\t최선의 손실: 0.021600\t정확도: 99.30%\n",
+      "45\t검증 세트 손실: 0.040262\t최선의 손실: 0.021600\t정확도: 99.22%\n",
+      "46\t검증 세트 손실: 0.046612\t최선의 손실: 0.021600\t정확도: 99.14%\n",
+      "47\t검증 세트 손실: 0.054809\t최선의 손실: 0.021600\t정확도: 99.06%\n",
+      "48\t검증 세트 손실: 0.033765\t최선의 손실: 0.021600\t정확도: 99.14%\n",
+      "49\t검증 세트 손실: 0.024501\t최선의 손실: 0.021600\t정확도: 99.30%\n",
+      "50\t검증 세트 손실: 0.039177\t최선의 손실: 0.021600\t정확도: 99.14%\n",
       "조기 종료!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f991b28cd90>,\n",
+       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f736d757d90>,\n",
        "       batch_norm_momentum=0.95, batch_size=500, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=5, n_neurons=120,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6562,16 +6619,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9920217941233703"
+       "0.9933839268340144"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6590,7 +6647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -6598,7196 +6655,7489 @@
      "output_type": "stream",
      "text": [
       "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.083375\t최선의 손실: 0.083375\t정확도: 97.77%\n",
-      "1\t검증 세트 손실: 0.074788\t최선의 손실: 0.074788\t정확도: 98.05%\n",
-      "2\t검증 세트 손실: 0.071817\t최선의 손실: 0.071817\t정확도: 98.40%\n",
-      "3\t검증 세트 손실: 0.050368\t최선의 손실: 0.050368\t정확도: 98.55%\n",
-      "4\t검증 세트 손실: 0.040893\t최선의 손실: 0.040893\t정확도: 98.87%\n",
-      "5\t검증 세트 손실: 0.030559\t최선의 손실: 0.030559\t정확도: 99.10%\n",
-      "6\t검증 세트 손실: 0.029231\t최선의 손실: 0.029231\t정확도: 98.98%\n",
-      "7\t검증 세트 손실: 0.041471\t최선의 손실: 0.029231\t정확도: 98.94%\n",
-      "8\t검증 세트 손실: 0.054116\t최선의 손실: 0.029231\t정확도: 98.59%\n",
-      "9\t검증 세트 손실: 0.035588\t최선의 손실: 0.029231\t정확도: 99.10%\n",
-      "10\t검증 세트 손실: 0.030628\t최선의 손실: 0.029231\t정확도: 99.14%\n",
-      "11\t검증 세트 손실: 0.041834\t최선의 손실: 0.029231\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.078051\t최선의 손실: 0.029231\t정확도: 98.12%\n",
-      "13\t검증 세트 손실: 0.051671\t최선의 손실: 0.029231\t정확도: 98.44%\n",
-      "14\t검증 세트 손실: 0.065961\t최선의 손실: 0.029231\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.043774\t최선의 손실: 0.029231\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.042503\t최선의 손실: 0.029231\t정확도: 98.87%\n",
-      "17\t검증 세트 손실: 0.045913\t최선의 손실: 0.029231\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.047385\t최선의 손실: 0.029231\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.042997\t최선의 손실: 0.029231\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.038428\t최선의 손실: 0.029231\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.055829\t최선의 손실: 0.029231\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.052120\t최선의 손실: 0.029231\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.043227\t최선의 손실: 0.029231\t정확도: 98.75%\n",
-      "24\t검증 세트 손실: 0.054613\t최선의 손실: 0.029231\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.044133\t최선의 손실: 0.029231\t정확도: 98.91%\n",
-      "26\t검증 세트 손실: 0.035949\t최선의 손실: 0.029231\t정확도: 99.02%\n",
-      "27\t검증 세트 손실: 0.057662\t최선의 손실: 0.029231\t정확도: 98.87%\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.063628\t최선의 손실: 0.063628\t정확도: 98.16%\n",
+      "1\t검증 세트 손실: 0.078497\t최선의 손실: 0.063628\t정확도: 97.81%\n",
+      "2\t검증 세트 손실: 0.081407\t최선의 손실: 0.063628\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.062405\t최선의 손실: 0.062405\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.039643\t최선의 손실: 0.039643\t정확도: 98.98%\n",
+      "5\t검증 세트 손실: 0.043090\t최선의 손실: 0.039643\t정확도: 98.83%\n",
+      "6\t검증 세트 손실: 0.039838\t최선의 손실: 0.039643\t정확도: 98.94%\n",
+      "7\t검증 세트 손실: 0.041517\t최선의 손실: 0.039643\t정확도: 99.10%\n",
+      "8\t검증 세트 손실: 0.062114\t최선의 손실: 0.039643\t정확도: 98.51%\n",
+      "9\t검증 세트 손실: 0.046949\t최선의 손실: 0.039643\t정확도: 98.94%\n",
+      "10\t검증 세트 손실: 0.045700\t최선의 손실: 0.039643\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.034234\t최선의 손실: 0.034234\t정확도: 99.10%\n",
+      "12\t검증 세트 손실: 0.037071\t최선의 손실: 0.034234\t정확도: 99.06%\n",
+      "13\t검증 세트 손실: 0.046539\t최선의 손실: 0.034234\t정확도: 98.83%\n",
+      "14\t검증 세트 손실: 0.042388\t최선의 손실: 0.034234\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.034174\t최선의 손실: 0.034174\t정확도: 99.22%\n",
+      "16\t검증 세트 손실: 0.039555\t최선의 손실: 0.034174\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.036716\t최선의 손실: 0.034174\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.045392\t최선의 손실: 0.034174\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.038646\t최선의 손실: 0.034174\t정확도: 99.06%\n",
+      "20\t검증 세트 손실: 0.039447\t최선의 손실: 0.034174\t정확도: 99.14%\n",
+      "21\t검증 세트 손실: 0.051483\t최선의 손실: 0.034174\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.037612\t최선의 손실: 0.034174\t정확도: 99.06%\n",
+      "23\t검증 세트 손실: 0.044852\t최선의 손실: 0.034174\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.047999\t최선의 손실: 0.034174\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.042913\t최선의 손실: 0.034174\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.044003\t최선의 손실: 0.034174\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.046793\t최선의 손실: 0.034174\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.035474\t최선의 손실: 0.034174\t정확도: 99.18%\n",
+      "29\t검증 세트 손실: 0.036406\t최선의 손실: 0.034174\t정확도: 99.22%\n",
+      "30\t검증 세트 손실: 0.045975\t최선의 손실: 0.034174\t정확도: 99.18%\n",
+      "31\t검증 세트 손실: 0.039438\t최선의 손실: 0.034174\t정확도: 99.22%\n",
+      "32\t검증 세트 손실: 0.055773\t최선의 손실: 0.034174\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.050222\t최선의 손실: 0.034174\t정확도: 98.91%\n",
+      "34\t검증 세트 손실: 0.041436\t최선의 손실: 0.034174\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.051475\t최선의 손실: 0.034174\t정확도: 98.79%\n",
+      "36\t검증 세트 손실: 0.049580\t최선의 손실: 0.034174\t정확도: 99.14%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70, total=  38.0s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70 \n"
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=70, total=  50.7s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=70 \n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   38.1s remaining:    0.0s\n"
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   50.8s remaining:    0.0s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.095481\t최선의 손실: 0.095481\t정확도: 97.15%\n",
-      "1\t검증 세트 손실: 0.061352\t최선의 손실: 0.061352\t정확도: 98.48%\n",
-      "2\t검증 세트 손실: 0.074936\t최선의 손실: 0.061352\t정확도: 98.01%\n",
-      "3\t검증 세트 손실: 0.055220\t최선의 손실: 0.055220\t정확도: 98.48%\n",
-      "4\t검증 세트 손실: 0.048025\t최선의 손실: 0.048025\t정확도: 98.67%\n",
-      "5\t검증 세트 손실: 0.063975\t최선의 손실: 0.048025\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.047399\t최선의 손실: 0.047399\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.071903\t최선의 손실: 0.047399\t정확도: 98.24%\n",
-      "8\t검증 세트 손실: 0.044756\t최선의 손실: 0.044756\t정확도: 98.87%\n",
-      "9\t검증 세트 손실: 0.056765\t최선의 손실: 0.044756\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.057513\t최선의 손실: 0.044756\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.058188\t최선의 손실: 0.044756\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.049270\t최선의 손실: 0.044756\t정확도: 98.59%\n",
-      "13\t검증 세트 손실: 0.046668\t최선의 손실: 0.044756\t정확도: 98.87%\n",
-      "14\t검증 세트 손실: 0.044805\t최선의 손실: 0.044756\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.048343\t최선의 손실: 0.044756\t정확도: 98.98%\n",
-      "16\t검증 세트 손실: 0.048303\t최선의 손실: 0.044756\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.063295\t최선의 손실: 0.044756\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.049594\t최선의 손실: 0.044756\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.050819\t최선의 손실: 0.044756\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.059087\t최선의 손실: 0.044756\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.064357\t최선의 손실: 0.044756\t정확도: 98.55%\n",
-      "22\t검증 세트 손실: 0.057521\t최선의 손실: 0.044756\t정확도: 98.55%\n",
-      "23\t검증 세트 손실: 0.041910\t최선의 손실: 0.041910\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.036776\t최선의 손실: 0.036776\t정확도: 99.06%\n",
-      "25\t검증 세트 손실: 0.046450\t최선의 손실: 0.036776\t정확도: 98.98%\n",
-      "26\t검증 세트 손실: 0.051996\t최선의 손실: 0.036776\t정확도: 99.06%\n",
-      "27\t검증 세트 손실: 0.045451\t최선의 손실: 0.036776\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.057058\t최선의 손실: 0.036776\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.060575\t최선의 손실: 0.036776\t정확도: 98.63%\n",
-      "30\t검증 세트 손실: 0.052564\t최선의 손실: 0.036776\t정확도: 99.14%\n",
-      "31\t검증 세트 손실: 0.043336\t최선의 손실: 0.036776\t정확도: 99.10%\n",
-      "32\t검증 세트 손실: 0.057582\t최선의 손실: 0.036776\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.045996\t최선의 손실: 0.036776\t정확도: 99.02%\n",
-      "34\t검증 세트 손실: 0.062942\t최선의 손실: 0.036776\t정확도: 98.94%\n",
-      "35\t검증 세트 손실: 0.038491\t최선의 손실: 0.036776\t정확도: 99.18%\n",
-      "36\t검증 세트 손실: 0.055696\t최선의 손실: 0.036776\t정확도: 98.83%\n",
-      "37\t검증 세트 손실: 0.043323\t최선의 손실: 0.036776\t정확도: 99.02%\n",
-      "38\t검증 세트 손실: 0.045819\t최선의 손실: 0.036776\t정확도: 98.87%\n",
-      "39\t검증 세트 손실: 0.066450\t최선의 손실: 0.036776\t정확도: 98.94%\n",
-      "40\t검증 세트 손실: 0.045899\t최선의 손실: 0.036776\t정확도: 99.06%\n",
-      "41\t검증 세트 손실: 0.048590\t최선의 손실: 0.036776\t정확도: 99.10%\n",
-      "42\t검증 세트 손실: 0.049459\t최선의 손실: 0.036776\t정확도: 99.06%\n",
-      "43\t검증 세트 손실: 0.050563\t최선의 손실: 0.036776\t정확도: 99.10%\n",
-      "44\t검증 세트 손실: 0.048455\t최선의 손실: 0.036776\t정확도: 99.14%\n",
-      "45\t검증 세트 손실: 0.049165\t최선의 손실: 0.036776\t정확도: 98.98%\n",
+      "0\t검증 세트 손실: 0.083991\t최선의 손실: 0.083991\t정확도: 97.69%\n",
+      "1\t검증 세트 손실: 0.054791\t최선의 손실: 0.054791\t정확도: 98.59%\n",
+      "2\t검증 세트 손실: 0.059230\t최선의 손실: 0.054791\t정확도: 98.20%\n",
+      "3\t검증 세트 손실: 0.059522\t최선의 손실: 0.054791\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.043569\t최선의 손실: 0.043569\t정확도: 98.71%\n",
+      "5\t검증 세트 손실: 0.055622\t최선의 손실: 0.043569\t정확도: 98.79%\n",
+      "6\t검증 세트 손실: 0.054838\t최선의 손실: 0.043569\t정확도: 98.51%\n",
+      "7\t검증 세트 손실: 0.044712\t최선의 손실: 0.043569\t정확도: 98.79%\n",
+      "8\t검증 세트 손실: 0.033677\t최선의 손실: 0.033677\t정확도: 99.10%\n",
+      "9\t검증 세트 손실: 0.049924\t최선의 손실: 0.033677\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.040808\t최선의 손실: 0.033677\t정확도: 98.98%\n",
+      "11\t검증 세트 손실: 0.058955\t최선의 손실: 0.033677\t정확도: 98.48%\n",
+      "12\t검증 세트 손실: 0.050546\t최선의 손실: 0.033677\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.037833\t최선의 손실: 0.033677\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.051192\t최선의 손실: 0.033677\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.038225\t최선의 손실: 0.033677\t정확도: 99.06%\n",
+      "16\t검증 세트 손실: 0.053009\t최선의 손실: 0.033677\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.047198\t최선의 손실: 0.033677\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.042114\t최선의 손실: 0.033677\t정확도: 99.06%\n",
+      "19\t검증 세트 손실: 0.040017\t최선의 손실: 0.033677\t정확도: 99.06%\n",
+      "20\t검증 세트 손실: 0.053782\t최선의 손실: 0.033677\t정확도: 98.63%\n",
+      "21\t검증 세트 손실: 0.036929\t최선의 손실: 0.033677\t정확도: 99.10%\n",
+      "22\t검증 세트 손실: 0.052195\t최선의 손실: 0.033677\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.045641\t최선의 손실: 0.033677\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.047816\t최선의 손실: 0.033677\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.041355\t최선의 손실: 0.033677\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.044486\t최선의 손실: 0.033677\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.045875\t최선의 손실: 0.033677\t정확도: 98.83%\n",
+      "28\t검증 세트 손실: 0.062991\t최선의 손실: 0.033677\t정확도: 98.91%\n",
+      "29\t검증 세트 손실: 0.056856\t최선의 손실: 0.033677\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70, total= 1.0min\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.136669\t최선의 손실: 0.136669\t정확도: 96.91%\n",
-      "1\t검증 세트 손실: 0.050651\t최선의 손실: 0.050651\t정확도: 98.28%\n",
-      "2\t검증 세트 손실: 0.107808\t최선의 손실: 0.050651\t정확도: 97.07%\n",
-      "3\t검증 세트 손실: 0.045355\t최선의 손실: 0.045355\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.045858\t최선의 손실: 0.045355\t정확도: 98.63%\n",
-      "5\t검증 세트 손실: 0.038292\t최선의 손실: 0.038292\t정확도: 98.91%\n",
-      "6\t검증 세트 손실: 0.037235\t최선의 손실: 0.037235\t정확도: 98.98%\n",
-      "7\t검증 세트 손실: 0.039951\t최선의 손실: 0.037235\t정확도: 98.94%\n",
-      "8\t검증 세트 손실: 0.043425\t최선의 손실: 0.037235\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.044586\t최선의 손실: 0.037235\t정확도: 98.55%\n",
-      "10\t검증 세트 손실: 0.048427\t최선의 손실: 0.037235\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.033496\t최선의 손실: 0.033496\t정확도: 99.06%\n",
-      "12\t검증 세트 손실: 0.072946\t최선의 손실: 0.033496\t정확도: 98.01%\n",
-      "13\t검증 세트 손실: 0.029820\t최선의 손실: 0.029820\t정확도: 99.06%\n",
-      "14\t검증 세트 손실: 0.050886\t최선의 손실: 0.029820\t정확도: 98.63%\n",
-      "15\t검증 세트 손실: 0.045621\t최선의 손실: 0.029820\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.033847\t최선의 손실: 0.029820\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.078657\t최선의 손실: 0.029820\t정확도: 98.05%\n",
-      "18\t검증 세트 손실: 0.042608\t최선의 손실: 0.029820\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.043272\t최선의 손실: 0.029820\t정확도: 99.18%\n",
-      "20\t검증 세트 손실: 0.032040\t최선의 손실: 0.029820\t정확도: 99.14%\n",
-      "21\t검증 세트 손실: 0.032142\t최선의 손실: 0.029820\t정확도: 99.26%\n",
-      "22\t검증 세트 손실: 0.029244\t최선의 손실: 0.029244\t정확도: 99.30%\n",
-      "23\t검증 세트 손실: 0.033465\t최선의 손실: 0.029244\t정확도: 99.22%\n",
-      "24\t검증 세트 손실: 0.029918\t최선의 손실: 0.029244\t정확도: 99.18%\n",
-      "25\t검증 세트 손실: 0.035292\t최선의 손실: 0.029244\t정확도: 99.26%\n",
-      "26\t검증 세트 손실: 0.034231\t최선의 손실: 0.029244\t정확도: 99.14%\n",
-      "27\t검증 세트 손실: 0.037567\t최선의 손실: 0.029244\t정확도: 99.10%\n",
-      "28\t검증 세트 손실: 0.049046\t최선의 손실: 0.029244\t정확도: 98.75%\n",
-      "29\t검증 세트 손실: 0.040679\t최선의 손실: 0.029244\t정확도: 98.98%\n",
-      "30\t검증 세트 손실: 0.042833\t최선의 손실: 0.029244\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.045991\t최선의 손실: 0.029244\t정확도: 99.14%\n",
-      "32\t검증 세트 손실: 0.051594\t최선의 손실: 0.029244\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.025425\t최선의 손실: 0.025425\t정확도: 99.41%\n",
-      "34\t검증 세트 손실: 0.032751\t최선의 손실: 0.025425\t정확도: 99.10%\n",
-      "35\t검증 세트 손실: 0.032722\t최선의 손실: 0.025425\t정확도: 99.22%\n",
-      "36\t검증 세트 손실: 0.034432\t최선의 손실: 0.025425\t정확도: 99.22%\n",
-      "37\t검증 세트 손실: 0.032372\t최선의 손실: 0.025425\t정확도: 99.26%\n",
-      "38\t검증 세트 손실: 0.025472\t최선의 손실: 0.025425\t정확도: 99.30%\n",
-      "39\t검증 세트 손실: 0.041039\t최선의 손실: 0.025425\t정확도: 99.26%\n",
-      "40\t검증 세트 손실: 0.029108\t최선의 손실: 0.025425\t정확도: 99.26%\n",
-      "41\t검증 세트 손실: 0.021917\t최선의 손실: 0.021917\t정확도: 99.45%\n",
-      "42\t검증 세트 손실: 0.028807\t최선의 손실: 0.021917\t정확도: 99.34%\n",
-      "43\t검증 세트 손실: 0.027081\t최선의 손실: 0.021917\t정확도: 99.30%\n",
-      "44\t검증 세트 손실: 0.046828\t최선의 손실: 0.021917\t정확도: 98.98%\n",
-      "45\t검증 세트 손실: 0.031189\t최선의 손실: 0.021917\t정확도: 99.18%\n",
-      "46\t검증 세트 손실: 0.042587\t최선의 손실: 0.021917\t정확도: 98.98%\n",
-      "47\t검증 세트 손실: 0.033603\t최선의 손실: 0.021917\t정확도: 99.22%\n",
-      "48\t검증 세트 손실: 0.045095\t최선의 손실: 0.021917\t정확도: 99.06%\n",
-      "49\t검증 세트 손실: 0.046637\t최선의 손실: 0.021917\t정확도: 99.06%\n",
-      "50\t검증 세트 손실: 0.039005\t최선의 손실: 0.021917\t정확도: 99.10%\n",
-      "51\t검증 세트 손실: 0.028744\t최선의 손실: 0.021917\t정확도: 99.30%\n",
-      "52\t검증 세트 손실: 0.071046\t최선의 손실: 0.021917\t정확도: 98.67%\n",
-      "53\t검증 세트 손실: 0.032383\t최선의 손실: 0.021917\t정확도: 99.18%\n",
-      "54\t검증 세트 손실: 0.032703\t최선의 손실: 0.021917\t정확도: 99.10%\n",
-      "55\t검증 세트 손실: 0.034681\t최선의 손실: 0.021917\t정확도: 99.02%\n",
-      "56\t검증 세트 손실: 0.042203\t최선의 손실: 0.021917\t정확도: 99.02%\n",
-      "57\t검증 세트 손실: 0.039646\t최선의 손실: 0.021917\t정확도: 98.98%\n",
-      "58\t검증 세트 손실: 0.076522\t최선의 손실: 0.021917\t정확도: 98.83%\n",
-      "59\t검증 세트 손실: 0.040491\t최선의 손실: 0.021917\t정확도: 99.14%\n",
-      "60\t검증 세트 손실: 0.038741\t최선의 손실: 0.021917\t정확도: 99.26%\n",
-      "61\t검증 세트 손실: 0.034363\t최선의 손실: 0.021917\t정확도: 99.10%\n",
-      "62\t검증 세트 손실: 0.034222\t최선의 손실: 0.021917\t정확도: 99.34%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=70, total=  41.4s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.103484\t최선의 손실: 0.103484\t정확도: 97.26%\n",
+      "1\t검증 세트 손실: 0.069015\t최선의 손실: 0.069015\t정확도: 98.20%\n",
+      "2\t검증 세트 손실: 0.050168\t최선의 손실: 0.050168\t정확도: 98.51%\n",
+      "3\t검증 세트 손실: 0.047037\t최선의 손실: 0.047037\t정확도: 98.24%\n",
+      "4\t검증 세트 손실: 0.047041\t최선의 손실: 0.047037\t정확도: 98.79%\n",
+      "5\t검증 세트 손실: 0.042584\t최선의 손실: 0.042584\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.048306\t최선의 손실: 0.042584\t정확도: 98.75%\n",
+      "7\t검증 세트 손실: 0.074728\t최선의 손실: 0.042584\t정확도: 98.40%\n",
+      "8\t검증 세트 손실: 0.055586\t최선의 손실: 0.042584\t정확도: 98.51%\n",
+      "9\t검증 세트 손실: 0.042523\t최선의 손실: 0.042523\t정확도: 99.06%\n",
+      "10\t검증 세트 손실: 0.045513\t최선의 손실: 0.042523\t정확도: 98.98%\n",
+      "11\t검증 세트 손실: 0.044564\t최선의 손실: 0.042523\t정확도: 98.87%\n",
+      "12\t검증 세트 손실: 0.053247\t최선의 손실: 0.042523\t정확도: 98.59%\n",
+      "13\t검증 세트 손실: 0.043113\t최선의 손실: 0.042523\t정확도: 99.02%\n",
+      "14\t검증 세트 손실: 0.044170\t최선의 손실: 0.042523\t정확도: 98.98%\n",
+      "15\t검증 세트 손실: 0.036422\t최선의 손실: 0.036422\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.044292\t최선의 손실: 0.036422\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.044433\t최선의 손실: 0.036422\t정확도: 99.06%\n",
+      "18\t검증 세트 손실: 0.059611\t최선의 손실: 0.036422\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 0.046239\t최선의 손실: 0.036422\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.034206\t최선의 손실: 0.034206\t정확도: 99.22%\n",
+      "21\t검증 세트 손실: 0.038934\t최선의 손실: 0.034206\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.050841\t최선의 손실: 0.034206\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.048152\t최선의 손실: 0.034206\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.044766\t최선의 손실: 0.034206\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.042161\t최선의 손실: 0.034206\t정확도: 99.02%\n",
+      "26\t검증 세트 손실: 0.053046\t최선의 손실: 0.034206\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.038413\t최선의 손실: 0.034206\t정확도: 99.14%\n",
+      "28\t검증 세트 손실: 0.057736\t최선의 손실: 0.034206\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.067301\t최선의 손실: 0.034206\t정확도: 98.67%\n",
+      "30\t검증 세트 손실: 0.053833\t최선의 손실: 0.034206\t정확도: 99.10%\n",
+      "31\t검증 세트 손실: 0.074894\t최선의 손실: 0.034206\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.060371\t최선의 손실: 0.034206\t정확도: 98.71%\n",
+      "33\t검증 세트 손실: 0.060536\t최선의 손실: 0.034206\t정확도: 98.87%\n",
+      "34\t검증 세트 손실: 0.044409\t최선의 손실: 0.034206\t정확도: 99.22%\n",
+      "35\t검증 세트 손실: 0.058418\t최선의 손실: 0.034206\t정확도: 98.91%\n",
+      "36\t검증 세트 손실: 0.045458\t최선의 손실: 0.034206\t정확도: 99.06%\n",
+      "37\t검증 세트 손실: 0.045492\t최선의 손실: 0.034206\t정확도: 98.87%\n",
+      "38\t검증 세트 손실: 0.054853\t최선의 손실: 0.034206\t정확도: 99.14%\n",
+      "39\t검증 세트 손실: 0.037868\t최선의 손실: 0.034206\t정확도: 99.18%\n",
+      "40\t검증 세트 손실: 0.038539\t최선의 손실: 0.034206\t정확도: 99.22%\n",
+      "41\t검증 세트 손실: 0.044495\t최선의 손실: 0.034206\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70, total= 1.4min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.117387\t최선의 손실: 0.117387\t정확도: 96.99%\n",
-      "1\t검증 세트 손실: 0.074382\t최선의 손실: 0.074382\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.071500\t최선의 손실: 0.071500\t정확도: 97.77%\n",
-      "3\t검증 세트 손실: 0.055125\t최선의 손실: 0.055125\t정확도: 98.48%\n",
-      "4\t검증 세트 손실: 0.046396\t최선의 손실: 0.046396\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.081079\t최선의 손실: 0.046396\t정확도: 97.30%\n",
-      "6\t검증 세트 손실: 0.060277\t최선의 손실: 0.046396\t정확도: 98.08%\n",
-      "7\t검증 세트 손실: 0.041411\t최선의 손실: 0.041411\t정확도: 98.79%\n",
-      "8\t검증 세트 손실: 0.034733\t최선의 손실: 0.034733\t정확도: 98.67%\n",
-      "9\t검증 세트 손실: 0.041292\t최선의 손실: 0.034733\t정확도: 98.79%\n",
-      "10\t검증 세트 손실: 0.048101\t최선의 손실: 0.034733\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.036449\t최선의 손실: 0.034733\t정확도: 98.94%\n",
-      "12\t검증 세트 손실: 0.053014\t최선의 손실: 0.034733\t정확도: 98.48%\n",
-      "13\t검증 세트 손실: 0.033297\t최선의 손실: 0.033297\t정확도: 99.02%\n",
-      "14\t검증 세트 손실: 0.034902\t최선의 손실: 0.033297\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.046130\t최선의 손실: 0.033297\t정확도: 98.40%\n",
-      "16\t검증 세트 손실: 0.043398\t최선의 손실: 0.033297\t정확도: 98.75%\n",
-      "17\t검증 세트 손실: 0.031512\t최선의 손실: 0.031512\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.060811\t최선의 손실: 0.031512\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.042267\t최선의 손실: 0.031512\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.044838\t최선의 손실: 0.031512\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.044206\t최선의 손실: 0.031512\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.044140\t최선의 손실: 0.031512\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.041208\t최선의 손실: 0.031512\t정확도: 99.02%\n",
-      "24\t검증 세트 손실: 0.045285\t최선의 손실: 0.031512\t정확도: 98.79%\n",
-      "25\t검증 세트 손실: 0.039412\t최선의 손실: 0.031512\t정확도: 98.79%\n",
-      "26\t검증 세트 손실: 0.044108\t최선의 손실: 0.031512\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.057664\t최선의 손실: 0.031512\t정확도: 98.44%\n",
-      "28\t검증 세트 손실: 0.056764\t최선의 손실: 0.031512\t정확도: 98.67%\n",
-      "29\t검증 세트 손실: 0.046898\t최선의 손실: 0.031512\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.038529\t최선의 손실: 0.031512\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.044857\t최선의 손실: 0.031512\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.045340\t최선의 손실: 0.031512\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.068960\t최선의 손실: 0.031512\t정확도: 98.12%\n",
-      "34\t검증 세트 손실: 0.043831\t최선의 손실: 0.031512\t정확도: 98.91%\n",
-      "35\t검증 세트 손실: 0.044732\t최선의 손실: 0.031512\t정확도: 98.67%\n",
-      "36\t검증 세트 손실: 0.048380\t최선의 손실: 0.031512\t정확도: 98.67%\n",
-      "37\t검증 세트 손실: 0.064374\t최선의 손실: 0.031512\t정확도: 98.79%\n",
-      "38\t검증 세트 손실: 0.047593\t최선의 손실: 0.031512\t정확도: 98.91%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=70, total=  57.5s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.092394\t최선의 손실: 0.092394\t정확도: 97.30%\n",
+      "1\t검증 세트 손실: 0.089528\t최선의 손실: 0.089528\t정확도: 96.91%\n",
+      "2\t검증 세트 손실: 0.068315\t최선의 손실: 0.068315\t정확도: 98.08%\n",
+      "3\t검증 세트 손실: 0.052507\t최선의 손실: 0.052507\t정확도: 98.48%\n",
+      "4\t검증 세트 손실: 0.050697\t최선의 손실: 0.050697\t정확도: 98.67%\n",
+      "5\t검증 세트 손실: 0.051487\t최선의 손실: 0.050697\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.054557\t최선의 손실: 0.050697\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.037659\t최선의 손실: 0.037659\t정확도: 98.75%\n",
+      "8\t검증 세트 손실: 0.041472\t최선의 손실: 0.037659\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.037465\t최선의 손실: 0.037465\t정확도: 98.94%\n",
+      "10\t검증 세트 손실: 0.036320\t최선의 손실: 0.036320\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.036870\t최선의 손실: 0.036320\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.051396\t최선의 손실: 0.036320\t정확도: 98.48%\n",
+      "13\t검증 세트 손실: 0.037073\t최선의 손실: 0.036320\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.034271\t최선의 손실: 0.034271\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.036546\t최선의 손실: 0.034271\t정확도: 98.75%\n",
+      "16\t검증 세트 손실: 0.035157\t최선의 손실: 0.034271\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.036715\t최선의 손실: 0.034271\t정확도: 99.02%\n",
+      "18\t검증 세트 손실: 0.035001\t최선의 손실: 0.034271\t정확도: 99.14%\n",
+      "19\t검증 세트 손실: 0.041194\t최선의 손실: 0.034271\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.031068\t최선의 손실: 0.031068\t정확도: 98.98%\n",
+      "21\t검증 세트 손실: 0.038653\t최선의 손실: 0.031068\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.054458\t최선의 손실: 0.031068\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.045002\t최선의 손실: 0.031068\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.048187\t최선의 손실: 0.031068\t정확도: 98.63%\n",
+      "25\t검증 세트 손실: 0.044286\t최선의 손실: 0.031068\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.048029\t최선의 손실: 0.031068\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.048141\t최선의 손실: 0.031068\t정확도: 98.51%\n",
+      "28\t검증 세트 손실: 0.050455\t최선의 손실: 0.031068\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.041856\t최선의 손실: 0.031068\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.043156\t최선의 손실: 0.031068\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.040332\t최선의 손실: 0.031068\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.031913\t최선의 손실: 0.031068\t정확도: 99.18%\n",
+      "33\t검증 세트 손실: 0.064254\t최선의 손실: 0.031068\t정확도: 98.01%\n",
+      "34\t검증 세트 손실: 0.045916\t최선의 손실: 0.031068\t정확도: 98.87%\n",
+      "35\t검증 세트 손실: 0.043439\t최선의 손실: 0.031068\t정확도: 98.79%\n",
+      "36\t검증 세트 손실: 0.039758\t최선의 손실: 0.031068\t정확도: 98.91%\n",
+      "37\t검증 세트 손실: 0.040424\t최선의 손실: 0.031068\t정확도: 98.87%\n",
+      "38\t검증 세트 손실: 0.045537\t최선의 손실: 0.031068\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.042858\t최선의 손실: 0.031068\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.041651\t최선의 손실: 0.031068\t정확도: 98.98%\n",
+      "41\t검증 세트 손실: 0.052592\t최선의 손실: 0.031068\t정확도: 98.75%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=90, total= 4.3min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=90 \n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=90, total= 4.7min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.100689\t최선의 손실: 0.100689\t정확도: 96.72%\n",
+      "1\t검증 세트 손실: 0.069924\t최선의 손실: 0.069924\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.075781\t최선의 손실: 0.069924\t정확도: 97.69%\n",
+      "3\t검증 세트 손실: 0.059858\t최선의 손실: 0.059858\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.061743\t최선의 손실: 0.059858\t정확도: 98.12%\n",
+      "5\t검증 세트 손실: 0.056824\t최선의 손실: 0.056824\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.058518\t최선의 손실: 0.056824\t정확도: 98.12%\n",
+      "7\t검증 세트 손실: 0.042964\t최선의 손실: 0.042964\t정확도: 98.55%\n",
+      "8\t검증 세트 손실: 0.037063\t최선의 손실: 0.037063\t정확도: 98.79%\n",
+      "9\t검증 세트 손실: 0.053006\t최선의 손실: 0.037063\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.057195\t최선의 손실: 0.037063\t정확도: 98.24%\n",
+      "11\t검증 세트 손실: 0.041464\t최선의 손실: 0.037063\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.063526\t최선의 손실: 0.037063\t정확도: 98.36%\n",
+      "13\t검증 세트 손실: 0.048881\t최선의 손실: 0.037063\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.041142\t최선의 손실: 0.037063\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.047467\t최선의 손실: 0.037063\t정확도: 98.40%\n",
+      "16\t검증 세트 손실: 0.052968\t최선의 손실: 0.037063\t정확도: 98.36%\n",
+      "17\t검증 세트 손실: 0.068615\t최선의 손실: 0.037063\t정확도: 98.32%\n",
+      "18\t검증 세트 손실: 0.040651\t최선의 손실: 0.037063\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.050385\t최선의 손실: 0.037063\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.038707\t최선의 손실: 0.037063\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.040185\t최선의 손실: 0.037063\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.052721\t최선의 손실: 0.037063\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.046426\t최선의 손실: 0.037063\t정확도: 98.55%\n",
+      "24\t검증 세트 손실: 0.043158\t최선의 손실: 0.037063\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.043784\t최선의 손실: 0.037063\t정확도: 98.48%\n",
+      "26\t검증 세트 손실: 0.044302\t최선의 손실: 0.037063\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.038341\t최선의 손실: 0.037063\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.051967\t최선의 손실: 0.037063\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.042402\t최선의 손실: 0.037063\t정확도: 98.71%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=90, total= 3.4min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=90 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.200795\t최선의 손실: 0.200795\t정확도: 93.51%\n",
-      "1\t검증 세트 손실: 0.091690\t최선의 손실: 0.091690\t정확도: 97.50%\n",
-      "2\t검증 세트 손실: 0.061808\t최선의 손실: 0.061808\t정확도: 98.59%\n",
-      "3\t검증 세트 손실: 0.054355\t최선의 손실: 0.054355\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.060210\t최선의 손실: 0.054355\t정확도: 98.16%\n",
-      "5\t검증 세트 손실: 0.053961\t최선의 손실: 0.053961\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.045113\t최선의 손실: 0.045113\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.042532\t최선의 손실: 0.042532\t정확도: 98.67%\n",
-      "8\t검증 세트 손실: 0.050096\t최선의 손실: 0.042532\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.066149\t최선의 손실: 0.042532\t정확도: 97.89%\n",
-      "10\t검증 세트 손실: 0.056959\t최선의 손실: 0.042532\t정확도: 98.48%\n",
-      "11\t검증 세트 손실: 0.030098\t최선의 손실: 0.030098\t정확도: 99.10%\n",
-      "12\t검증 세트 손실: 0.035244\t최선의 손실: 0.030098\t정확도: 98.83%\n",
-      "13\t검증 세트 손실: 0.061658\t최선의 손실: 0.030098\t정확도: 98.44%\n",
-      "14\t검증 세트 손실: 0.052417\t최선의 손실: 0.030098\t정확도: 98.24%\n",
-      "15\t검증 세트 손실: 0.047441\t최선의 손실: 0.030098\t정확도: 98.63%\n",
-      "16\t검증 세트 손실: 0.062804\t최선의 손실: 0.030098\t정확도: 98.05%\n",
-      "17\t검증 세트 손실: 0.066598\t최선의 손실: 0.030098\t정확도: 98.05%\n",
-      "18\t검증 세트 손실: 0.037437\t최선의 손실: 0.030098\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.045407\t최선의 손실: 0.030098\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.044763\t최선의 손실: 0.030098\t정확도: 98.91%\n",
-      "21\t검증 세트 손실: 0.045794\t최선의 손실: 0.030098\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.040266\t최선의 손실: 0.030098\t정확도: 98.71%\n",
-      "23\t검증 세트 손실: 0.043152\t최선의 손실: 0.030098\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.030189\t최선의 손실: 0.030098\t정확도: 98.91%\n",
-      "25\t검증 세트 손실: 0.037462\t최선의 손실: 0.030098\t정확도: 98.98%\n",
-      "26\t검증 세트 손실: 0.037172\t최선의 손실: 0.030098\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.043433\t최선의 손실: 0.030098\t정확도: 98.71%\n",
-      "28\t검증 세트 손실: 0.044343\t최선의 손실: 0.030098\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.056648\t최선의 손실: 0.030098\t정확도: 98.51%\n",
-      "30\t검증 세트 손실: 0.048444\t최선의 손실: 0.030098\t정확도: 98.48%\n",
-      "31\t검증 세트 손실: 0.050972\t최선의 손실: 0.030098\t정확도: 98.67%\n",
-      "32\t검증 세트 손실: 0.046328\t최선의 손실: 0.030098\t정확도: 98.75%\n",
+      "0\t검증 세트 손실: 0.091876\t최선의 손실: 0.091876\t정확도: 97.65%\n",
+      "1\t검증 세트 손실: 0.073604\t최선의 손실: 0.073604\t정확도: 97.89%\n",
+      "2\t검증 세트 손실: 0.093396\t최선의 손실: 0.073604\t정확도: 97.46%\n",
+      "3\t검증 세트 손실: 0.055939\t최선의 손실: 0.055939\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.066660\t최선의 손실: 0.055939\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.063245\t최선의 손실: 0.055939\t정확도: 97.89%\n",
+      "6\t검증 세트 손실: 0.064115\t최선의 손실: 0.055939\t정확도: 97.85%\n",
+      "7\t검증 세트 손실: 0.054974\t최선의 손실: 0.054974\t정확도: 98.01%\n",
+      "8\t검증 세트 손실: 0.050848\t최선의 손실: 0.050848\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.050869\t최선의 손실: 0.050848\t정확도: 98.24%\n",
+      "10\t검증 세트 손실: 0.042244\t최선의 손실: 0.042244\t정확도: 98.67%\n",
+      "11\t검증 세트 손실: 0.039359\t최선의 손실: 0.039359\t정확도: 98.79%\n",
+      "12\t검증 세트 손실: 0.040671\t최선의 손실: 0.039359\t정확도: 98.63%\n",
+      "13\t검증 세트 손실: 0.042599\t최선의 손실: 0.039359\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.047775\t최선의 손실: 0.039359\t정확도: 98.63%\n",
+      "15\t검증 세트 손실: 0.042566\t최선의 손실: 0.039359\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.030219\t최선의 손실: 0.030219\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.036437\t최선의 손실: 0.030219\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.036485\t최선의 손실: 0.030219\t정확도: 99.02%\n",
+      "19\t검증 세트 손실: 0.036268\t최선의 손실: 0.030219\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.052054\t최선의 손실: 0.030219\t정확도: 98.28%\n",
+      "21\t검증 세트 손실: 0.038959\t최선의 손실: 0.030219\t정확도: 99.02%\n",
+      "22\t검증 세트 손실: 0.029678\t최선의 손실: 0.029678\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.048211\t최선의 손실: 0.029678\t정확도: 97.93%\n",
+      "24\t검증 세트 손실: 0.042849\t최선의 손실: 0.029678\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.032228\t최선의 손실: 0.029678\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.041824\t최선의 손실: 0.029678\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.038938\t최선의 손실: 0.029678\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.029859\t최선의 손실: 0.029678\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.036125\t최선의 손실: 0.029678\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.051026\t최선의 손실: 0.029678\t정확도: 98.59%\n",
+      "31\t검증 세트 손실: 0.039493\t최선의 손실: 0.029678\t정확도: 98.71%\n",
+      "32\t검증 세트 손실: 0.039933\t최선의 손실: 0.029678\t정확도: 98.67%\n",
+      "33\t검증 세트 손실: 0.039581\t최선의 손실: 0.029678\t정확도: 98.87%\n",
+      "34\t검증 세트 손실: 0.041988\t최선의 손실: 0.029678\t정확도: 98.71%\n",
+      "35\t검증 세트 손실: 0.048734\t최선의 손실: 0.029678\t정확도: 98.55%\n",
+      "36\t검증 세트 손실: 0.045163\t최선의 손실: 0.029678\t정확도: 98.51%\n",
+      "37\t검증 세트 손실: 0.031747\t최선의 손실: 0.029678\t정확도: 99.02%\n",
+      "38\t검증 세트 손실: 0.037678\t최선의 손실: 0.029678\t정확도: 98.91%\n",
+      "39\t검증 세트 손실: 0.050969\t최선의 손실: 0.029678\t정확도: 98.75%\n",
+      "40\t검증 세트 손실: 0.052393\t최선의 손실: 0.029678\t정확도: 98.59%\n",
+      "41\t검증 세트 손실: 0.042938\t최선의 손실: 0.029678\t정확도: 98.94%\n",
+      "42\t검증 세트 손실: 0.037984\t최선의 손실: 0.029678\t정확도: 98.91%\n",
+      "43\t검증 세트 손실: 0.033761\t최선의 손실: 0.029678\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=90, total= 3.7min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.099519\t최선의 손실: 0.099519\t정확도: 97.07%\n",
-      "1\t검증 세트 손실: 0.071460\t최선의 손실: 0.071460\t정확도: 97.93%\n",
-      "2\t검증 세트 손실: 0.076633\t최선의 손실: 0.071460\t정확도: 97.42%\n",
-      "3\t검증 세트 손실: 0.059037\t최선의 손실: 0.059037\t정확도: 98.08%\n",
-      "4\t검증 세트 손실: 0.087567\t최선의 손실: 0.059037\t정확도: 96.95%\n",
-      "5\t검증 세트 손실: 0.068752\t최선의 손실: 0.059037\t정확도: 97.93%\n",
-      "6\t검증 세트 손실: 0.062684\t최선의 손실: 0.059037\t정확도: 97.81%\n",
-      "7\t검증 세트 손실: 0.042307\t최선의 손실: 0.042307\t정확도: 98.67%\n",
-      "8\t검증 세트 손실: 0.042615\t최선의 손실: 0.042307\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.046215\t최선의 손실: 0.042307\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.047018\t최선의 손실: 0.042307\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.051076\t최선의 손실: 0.042307\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.044801\t최선의 손실: 0.042307\t정확도: 98.71%\n",
-      "13\t검증 세트 손실: 0.038723\t최선의 손실: 0.038723\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.039400\t최선의 손실: 0.038723\t정확도: 98.79%\n",
-      "15\t검증 세트 손실: 0.047013\t최선의 손실: 0.038723\t정확도: 98.40%\n",
-      "16\t검증 세트 손실: 0.051492\t최선의 손실: 0.038723\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.054847\t최선의 손실: 0.038723\t정확도: 98.24%\n",
-      "18\t검증 세트 손실: 0.039648\t최선의 손실: 0.038723\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.033861\t최선의 손실: 0.033861\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.042352\t최선의 손실: 0.033861\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.029798\t최선의 손실: 0.029798\t정확도: 99.10%\n",
-      "22\t검증 세트 손실: 0.038243\t최선의 손실: 0.029798\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.051725\t최선의 손실: 0.029798\t정확도: 98.75%\n",
-      "24\t검증 세트 손실: 0.036005\t최선의 손실: 0.029798\t정확도: 98.79%\n",
-      "25\t검증 세트 손실: 0.030007\t최선의 손실: 0.029798\t정확도: 98.98%\n",
-      "26\t검증 세트 손실: 0.030617\t최선의 손실: 0.029798\t정확도: 99.10%\n",
-      "27\t검증 세트 손실: 0.038029\t최선의 손실: 0.029798\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.030975\t최선의 손실: 0.029798\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.032595\t최선의 손실: 0.029798\t정확도: 99.22%\n",
-      "30\t검증 세트 손실: 0.051580\t최선의 손실: 0.029798\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.039747\t최선의 손실: 0.029798\t정확도: 98.91%\n",
-      "32\t검증 세트 손실: 0.033771\t최선의 손실: 0.029798\t정확도: 99.02%\n",
-      "33\t검증 세트 손실: 0.040343\t최선의 손실: 0.029798\t정확도: 98.98%\n",
-      "34\t검증 세트 손실: 0.025858\t최선의 손실: 0.025858\t정확도: 99.26%\n",
-      "35\t검증 세트 손실: 0.037761\t최선의 손실: 0.025858\t정확도: 99.02%\n",
-      "36\t검증 세트 손실: 0.057972\t최선의 손실: 0.025858\t정확도: 98.48%\n",
-      "37\t검증 세트 손실: 0.041166\t최선의 손실: 0.025858\t정확도: 98.91%\n",
-      "38\t검증 세트 손실: 0.037857\t최선의 손실: 0.025858\t정확도: 98.75%\n",
-      "39\t검증 세트 손실: 0.032188\t최선의 손실: 0.025858\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.035530\t최선의 손실: 0.025858\t정확도: 98.83%\n",
-      "41\t검증 세트 손실: 0.045209\t최선의 손실: 0.025858\t정확도: 98.87%\n",
-      "42\t검증 세트 손실: 0.032893\t최선의 손실: 0.025858\t정확도: 99.02%\n",
-      "43\t검증 세트 손실: 0.044428\t최선의 손실: 0.025858\t정확도: 98.87%\n",
-      "44\t검증 세트 손실: 0.030121\t최선의 손실: 0.025858\t정확도: 99.02%\n",
-      "45\t검증 세트 손실: 0.039506\t최선의 손실: 0.025858\t정확도: 99.18%\n",
-      "46\t검증 세트 손실: 0.121871\t최선의 손실: 0.025858\t정확도: 96.95%\n",
-      "47\t검증 세트 손실: 0.038278\t최선의 손실: 0.025858\t정확도: 99.22%\n",
-      "48\t검증 세트 손실: 0.038588\t최선의 손실: 0.025858\t정확도: 98.83%\n",
-      "49\t검증 세트 손실: 0.041254\t최선의 손실: 0.025858\t정확도: 98.87%\n",
-      "50\t검증 세트 손실: 0.046248\t최선의 손실: 0.025858\t정확도: 98.71%\n",
-      "51\t검증 세트 손실: 0.040598\t최선의 손실: 0.025858\t정확도: 99.06%\n",
-      "52\t검증 세트 손실: 0.046054\t최선의 손실: 0.025858\t정확도: 98.83%\n",
-      "53\t검증 세트 손실: 0.043912\t최선의 손실: 0.025858\t정확도: 99.02%\n",
-      "54\t검증 세트 손실: 0.053743\t최선의 손실: 0.025858\t정확도: 98.75%\n",
-      "55\t검증 세트 손실: 0.056174\t최선의 손실: 0.025858\t정확도: 98.71%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=90, total= 4.9min\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.95, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.076647\t최선의 손실: 0.076647\t정확도: 97.50%\n",
+      "1\t검증 세트 손실: 0.070688\t최선의 손실: 0.070688\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.049971\t최선의 손실: 0.049971\t정확도: 98.48%\n",
+      "3\t검증 세트 손실: 0.055747\t최선의 손실: 0.049971\t정확도: 98.16%\n",
+      "4\t검증 세트 손실: 0.032690\t최선의 손실: 0.032690\t정확도: 98.98%\n",
+      "5\t검증 세트 손실: 0.048317\t최선의 손실: 0.032690\t정확도: 98.55%\n",
+      "6\t검증 세트 손실: 0.041392\t최선의 손실: 0.032690\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.047000\t최선의 손실: 0.032690\t정확도: 98.75%\n",
+      "8\t검증 세트 손실: 0.037567\t최선의 손실: 0.032690\t정확도: 98.98%\n",
+      "9\t검증 세트 손실: 0.047863\t최선의 손실: 0.032690\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.047219\t최선의 손실: 0.032690\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.059121\t최선의 손실: 0.032690\t정확도: 98.48%\n",
+      "12\t검증 세트 손실: 0.060568\t최선의 손실: 0.032690\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.038968\t최선의 손실: 0.032690\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.068293\t최선의 손실: 0.032690\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.062840\t최선의 손실: 0.032690\t정확도: 98.44%\n",
+      "16\t검증 세트 손실: 0.050111\t최선의 손실: 0.032690\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.050792\t최선의 손실: 0.032690\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.044606\t최선의 손실: 0.032690\t정확도: 98.87%\n",
+      "19\t검증 세트 손실: 0.044825\t최선의 손실: 0.032690\t정확도: 98.98%\n",
+      "20\t검증 세트 손실: 0.044571\t최선의 손실: 0.032690\t정확도: 98.87%\n",
+      "21\t검증 세트 손실: 0.042612\t최선의 손실: 0.032690\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.038893\t최선의 손실: 0.032690\t정확도: 99.02%\n",
+      "23\t검증 세트 손실: 0.048431\t최선의 손실: 0.032690\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.042242\t최선의 손실: 0.032690\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.045781\t최선의 손실: 0.032690\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=90, total= 6.2min\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.074129\t최선의 손실: 0.074129\t정확도: 97.54%\n",
-      "1\t검증 세트 손실: 0.059973\t최선의 손실: 0.059973\t정확도: 98.44%\n",
-      "2\t검증 세트 손실: 0.045952\t최선의 손실: 0.045952\t정확도: 98.48%\n",
-      "3\t검증 세트 손실: 0.054501\t최선의 손실: 0.045952\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.038640\t최선의 손실: 0.038640\t정확도: 98.83%\n",
-      "5\t검증 세트 손실: 0.056448\t최선의 손실: 0.038640\t정확도: 98.40%\n",
-      "6\t검증 세트 손실: 0.050317\t최선의 손실: 0.038640\t정확도: 98.48%\n",
-      "7\t검증 세트 손실: 0.048674\t최선의 손실: 0.038640\t정확도: 98.44%\n",
-      "8\t검증 세트 손실: 0.053496\t최선의 손실: 0.038640\t정확도: 98.48%\n",
-      "9\t검증 세트 손실: 0.051664\t최선의 손실: 0.038640\t정확도: 98.83%\n",
-      "10\t검증 세트 손실: 0.059121\t최선의 손실: 0.038640\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.043925\t최선의 손실: 0.038640\t정확도: 98.63%\n",
-      "12\t검증 세트 손실: 0.044179\t최선의 손실: 0.038640\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.052603\t최선의 손실: 0.038640\t정확도: 98.40%\n",
-      "14\t검증 세트 손실: 0.056689\t최선의 손실: 0.038640\t정확도: 98.59%\n",
-      "15\t검증 세트 손실: 0.046739\t최선의 손실: 0.038640\t정확도: 98.63%\n",
-      "16\t검증 세트 손실: 0.066740\t최선의 손실: 0.038640\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.049193\t최선의 손실: 0.038640\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.053110\t최선의 손실: 0.038640\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.057742\t최선의 손실: 0.038640\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.062006\t최선의 손실: 0.038640\t정확도: 98.36%\n",
-      "21\t검증 세트 손실: 0.057400\t최선의 손실: 0.038640\t정확도: 98.51%\n",
-      "22\t검증 세트 손실: 0.050357\t최선의 손실: 0.038640\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.043529\t최선의 손실: 0.038640\t정확도: 99.02%\n",
-      "24\t검증 세트 손실: 0.071083\t최선의 손실: 0.038640\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.060988\t최선의 손실: 0.038640\t정확도: 98.71%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.95, n_neurons=30, total=  20.5s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.95, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.092499\t최선의 손실: 0.092499\t정확도: 97.03%\n",
+      "1\t검증 세트 손실: 0.051763\t최선의 손실: 0.051763\t정확도: 98.44%\n",
+      "2\t검증 세트 손실: 0.051675\t최선의 손실: 0.051675\t정확도: 98.36%\n",
+      "3\t검증 세트 손실: 0.059938\t최선의 손실: 0.051675\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.048690\t최선의 손실: 0.048690\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.060875\t최선의 손실: 0.048690\t정확도: 98.05%\n",
+      "6\t검증 세트 손실: 0.059599\t최선의 손실: 0.048690\t정확도: 98.28%\n",
+      "7\t검증 세트 손실: 0.083244\t최선의 손실: 0.048690\t정확도: 97.65%\n",
+      "8\t검증 세트 손실: 0.047191\t최선의 손실: 0.047191\t정확도: 98.83%\n",
+      "9\t검증 세트 손실: 0.052848\t최선의 손실: 0.047191\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.058681\t최선의 손실: 0.047191\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.048012\t최선의 손실: 0.047191\t정확도: 98.79%\n",
+      "12\t검증 세트 손실: 0.073008\t최선의 손실: 0.047191\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.046092\t최선의 손실: 0.046092\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.055496\t최선의 손실: 0.046092\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.052925\t최선의 손실: 0.046092\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.052844\t최선의 손실: 0.046092\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.088690\t최선의 손실: 0.046092\t정확도: 98.01%\n",
+      "18\t검증 세트 손실: 0.056832\t최선의 손실: 0.046092\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.043968\t최선의 손실: 0.043968\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.042371\t최선의 손실: 0.042371\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.053754\t최선의 손실: 0.042371\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.047212\t최선의 손실: 0.042371\t정확도: 98.91%\n",
+      "23\t검증 세트 손실: 0.070855\t최선의 손실: 0.042371\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.058350\t최선의 손실: 0.042371\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.054577\t최선의 손실: 0.042371\t정확도: 98.71%\n",
+      "26\t검증 세트 손실: 0.057369\t최선의 손실: 0.042371\t정확도: 98.67%\n",
+      "27\t검증 세트 손실: 0.061614\t최선의 손실: 0.042371\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.050850\t최선의 손실: 0.042371\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.051178\t최선의 손실: 0.042371\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.049146\t최선의 손실: 0.042371\t정확도: 98.87%\n",
+      "31\t검증 세트 손실: 0.038524\t최선의 손실: 0.038524\t정확도: 98.94%\n",
+      "32\t검증 세트 손실: 0.053932\t최선의 손실: 0.038524\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.051450\t최선의 손실: 0.038524\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.056512\t최선의 손실: 0.038524\t정확도: 98.63%\n",
+      "35\t검증 세트 손실: 0.055473\t최선의 손실: 0.038524\t정확도: 98.79%\n",
+      "36\t검증 세트 손실: 0.071611\t최선의 손실: 0.038524\t정확도: 98.28%\n",
+      "37\t검증 세트 손실: 0.049631\t최선의 손실: 0.038524\t정확도: 98.91%\n",
+      "38\t검증 세트 손실: 0.057090\t최선의 손실: 0.038524\t정확도: 98.91%\n",
+      "39\t검증 세트 손실: 0.060016\t최선의 손실: 0.038524\t정확도: 98.91%\n",
+      "40\t검증 세트 손실: 0.063464\t최선의 손실: 0.038524\t정확도: 98.63%\n",
+      "41\t검증 세트 손실: 0.049496\t최선의 손실: 0.038524\t정확도: 98.83%\n",
+      "42\t검증 세트 손실: 0.070661\t최선의 손실: 0.038524\t정확도: 98.91%\n",
+      "43\t검증 세트 손실: 0.065194\t최선의 손실: 0.038524\t정확도: 98.67%\n",
+      "44\t검증 세트 손실: 0.069169\t최선의 손실: 0.038524\t정확도: 98.55%\n",
+      "45\t검증 세트 손실: 0.062831\t최선의 손실: 0.038524\t정확도: 98.83%\n",
+      "46\t검증 세트 손실: 0.068173\t최선의 손실: 0.038524\t정확도: 98.63%\n",
+      "47\t검증 세트 손실: 0.075707\t최선의 손실: 0.038524\t정확도: 98.75%\n",
+      "48\t검증 세트 손실: 0.079468\t최선의 손실: 0.038524\t정확도: 98.28%\n",
+      "49\t검증 세트 손실: 0.057800\t최선의 손실: 0.038524\t정확도: 98.79%\n",
+      "50\t검증 세트 손실: 0.053942\t최선의 손실: 0.038524\t정확도: 99.10%\n",
+      "51\t검증 세트 손실: 0.058705\t최선의 손실: 0.038524\t정확도: 98.83%\n",
+      "52\t검증 세트 손실: 0.055172\t최선의 손실: 0.038524\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=30, total=  19.8s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.082362\t최선의 손실: 0.082362\t정확도: 97.30%\n",
-      "1\t검증 세트 손실: 0.061810\t최선의 손실: 0.061810\t정확도: 97.89%\n",
-      "2\t검증 세트 손실: 0.047661\t최선의 손실: 0.047661\t정확도: 98.71%\n",
-      "3\t검증 세트 손실: 0.070829\t최선의 손실: 0.047661\t정확도: 97.85%\n",
-      "4\t검증 세트 손실: 0.072774\t최선의 손실: 0.047661\t정확도: 98.05%\n",
-      "5\t검증 세트 손실: 0.060080\t최선의 손실: 0.047661\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.065745\t최선의 손실: 0.047661\t정확도: 98.16%\n",
-      "7\t검증 세트 손실: 0.047788\t최선의 손실: 0.047661\t정확도: 98.71%\n",
-      "8\t검증 세트 손실: 0.064379\t최선의 손실: 0.047661\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.048066\t최선의 손실: 0.047661\t정확도: 98.87%\n",
-      "10\t검증 세트 손실: 0.075974\t최선의 손실: 0.047661\t정확도: 98.05%\n",
-      "11\t검증 세트 손실: 0.057605\t최선의 손실: 0.047661\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.049435\t최선의 손실: 0.047661\t정확도: 98.59%\n",
-      "13\t검증 세트 손실: 0.051673\t최선의 손실: 0.047661\t정확도: 98.59%\n",
-      "14\t검증 세트 손실: 0.056618\t최선의 손실: 0.047661\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.054848\t최선의 손실: 0.047661\t정확도: 98.40%\n",
-      "16\t검증 세트 손실: 0.045606\t최선의 손실: 0.045606\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.063649\t최선의 손실: 0.045606\t정확도: 98.44%\n",
-      "18\t검증 세트 손실: 0.052412\t최선의 손실: 0.045606\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.048580\t최선의 손실: 0.045606\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.055994\t최선의 손실: 0.045606\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.061485\t최선의 손실: 0.045606\t정확도: 98.36%\n",
-      "22\t검증 세트 손실: 0.053770\t최선의 손실: 0.045606\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.060681\t최선의 손실: 0.045606\t정확도: 98.71%\n",
-      "24\t검증 세트 손실: 0.068883\t최선의 손실: 0.045606\t정확도: 98.59%\n",
-      "25\t검증 세트 손실: 0.060294\t최선의 손실: 0.045606\t정확도: 98.48%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.95, n_neurons=30, total=  40.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.95, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.075296\t최선의 손실: 0.075296\t정확도: 97.58%\n",
+      "1\t검증 세트 손실: 0.071908\t최선의 손실: 0.071908\t정확도: 97.81%\n",
+      "2\t검증 세트 손실: 0.062550\t최선의 손실: 0.062550\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.044234\t최선의 손실: 0.044234\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.042548\t최선의 손실: 0.042548\t정확도: 98.51%\n",
+      "5\t검증 세트 손실: 0.040109\t최선의 손실: 0.040109\t정확도: 98.79%\n",
+      "6\t검증 세트 손실: 0.037041\t최선의 손실: 0.037041\t정확도: 99.10%\n",
+      "7\t검증 세트 손실: 0.049114\t최선의 손실: 0.037041\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.085176\t최선의 손실: 0.037041\t정확도: 97.65%\n",
+      "9\t검증 세트 손실: 0.047157\t최선의 손실: 0.037041\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.043190\t최선의 손실: 0.037041\t정확도: 98.71%\n",
+      "11\t검증 세트 손실: 0.043984\t최선의 손실: 0.037041\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.033917\t최선의 손실: 0.033917\t정확도: 99.06%\n",
+      "13\t검증 세트 손실: 0.056460\t최선의 손실: 0.033917\t정확도: 98.75%\n",
+      "14\t검증 세트 손실: 0.050176\t최선의 손실: 0.033917\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.051643\t최선의 손실: 0.033917\t정확도: 98.75%\n",
+      "16\t검증 세트 손실: 0.050040\t최선의 손실: 0.033917\t정확도: 98.48%\n",
+      "17\t검증 세트 손실: 0.048172\t최선의 손실: 0.033917\t정확도: 98.51%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "26\t검증 세트 손실: 0.059510\t최선의 손실: 0.045606\t정확도: 98.59%\n",
-      "27\t검증 세트 손실: 0.055914\t최선의 손실: 0.045606\t정확도: 98.48%\n",
-      "28\t검증 세트 손실: 0.072411\t최선의 손실: 0.045606\t정확도: 98.36%\n",
-      "29\t검증 세트 손실: 0.076583\t최선의 손실: 0.045606\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.059259\t최선의 손실: 0.045606\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.046304\t최선의 손실: 0.045606\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.053859\t최선의 손실: 0.045606\t정확도: 99.02%\n",
-      "33\t검증 세트 손실: 0.060213\t최선의 손실: 0.045606\t정확도: 98.98%\n",
-      "34\t검증 세트 손실: 0.053418\t최선의 손실: 0.045606\t정확도: 98.94%\n",
-      "35\t검증 세트 손실: 0.068954\t최선의 손실: 0.045606\t정확도: 98.59%\n",
-      "36\t검증 세트 손실: 0.059802\t최선의 손실: 0.045606\t정확도: 98.51%\n",
-      "37\t검증 세트 손실: 0.059496\t최선의 손실: 0.045606\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.052322\t최선의 손실: 0.033917\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.051675\t최선의 손실: 0.033917\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.054263\t최선의 손실: 0.033917\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.049284\t최선의 손실: 0.033917\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.045582\t최선의 손실: 0.033917\t정확도: 98.87%\n",
+      "23\t검증 세트 손실: 0.048945\t최선의 손실: 0.033917\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.034695\t최선의 손실: 0.033917\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.050671\t최선의 손실: 0.033917\t정확도: 98.67%\n",
+      "26\t검증 세트 손실: 0.050165\t최선의 손실: 0.033917\t정확도: 98.55%\n",
+      "27\t검증 세트 손실: 0.035421\t최선의 손실: 0.033917\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.038195\t최선의 손실: 0.033917\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.043858\t최선의 손실: 0.033917\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.042251\t최선의 손실: 0.033917\t정확도: 98.94%\n",
+      "31\t검증 세트 손실: 0.054718\t최선의 손실: 0.033917\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.050415\t최선의 손실: 0.033917\t정확도: 98.83%\n",
+      "33\t검증 세트 손실: 0.039215\t최선의 손실: 0.033917\t정확도: 99.14%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=30, total=  28.2s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.074546\t최선의 손실: 0.074546\t정확도: 97.77%\n",
-      "1\t검증 세트 손실: 0.071410\t최선의 손실: 0.071410\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.056182\t최선의 손실: 0.056182\t정확도: 98.24%\n",
-      "3\t검증 세트 손실: 0.048641\t최선의 손실: 0.048641\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.053387\t최선의 손실: 0.048641\t정확도: 98.40%\n",
-      "5\t검증 세트 손실: 0.050983\t최선의 손실: 0.048641\t정확도: 98.40%\n",
-      "6\t검증 세트 손실: 0.050810\t최선의 손실: 0.048641\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.069335\t최선의 손실: 0.048641\t정확도: 98.08%\n",
-      "8\t검증 세트 손실: 0.052878\t최선의 손실: 0.048641\t정확도: 98.40%\n",
-      "9\t검증 세트 손실: 0.045078\t최선의 손실: 0.045078\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.052477\t최선의 손실: 0.045078\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.082381\t최선의 손실: 0.045078\t정확도: 97.97%\n",
-      "12\t검증 세트 손실: 0.044482\t최선의 손실: 0.044482\t정확도: 98.94%\n",
-      "13\t검증 세트 손실: 0.060147\t최선의 손실: 0.044482\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.058493\t최선의 손실: 0.044482\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.055421\t최선의 손실: 0.044482\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.050288\t최선의 손실: 0.044482\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.050905\t최선의 손실: 0.044482\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.062071\t최선의 손실: 0.044482\t정확도: 98.51%\n",
-      "19\t검증 세트 손실: 0.056346\t최선의 손실: 0.044482\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.060890\t최선의 손실: 0.044482\t정확도: 98.40%\n",
-      "21\t검증 세트 손실: 0.045914\t최선의 손실: 0.044482\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.049901\t최선의 손실: 0.044482\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.046723\t최선의 손실: 0.044482\t정확도: 99.10%\n",
-      "24\t검증 세트 손실: 0.054217\t최선의 손실: 0.044482\t정확도: 98.91%\n",
-      "25\t검증 세트 손실: 0.060996\t최선의 손실: 0.044482\t정확도: 98.63%\n",
-      "26\t검증 세트 손실: 0.055036\t최선의 손실: 0.044482\t정확도: 98.83%\n",
-      "27\t검증 세트 손실: 0.083680\t최선의 손실: 0.044482\t정확도: 98.12%\n",
-      "28\t검증 세트 손실: 0.062230\t최선의 손실: 0.044482\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.057779\t최선의 손실: 0.044482\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.046031\t최선의 손실: 0.044482\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.075430\t최선의 손실: 0.044482\t정확도: 98.36%\n",
-      "32\t검증 세트 손실: 0.071037\t최선의 손실: 0.044482\t정확도: 98.67%\n",
-      "33\t검증 세트 손실: 0.061479\t최선의 손실: 0.044482\t정확도: 98.71%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.95, n_neurons=30, total=  26.2s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.091198\t최선의 손실: 0.091198\t정확도: 96.95%\n",
+      "1\t검증 세트 손실: 0.065468\t최선의 손실: 0.065468\t정확도: 98.05%\n",
+      "2\t검증 세트 손실: 0.063855\t최선의 손실: 0.063855\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.092156\t최선의 손실: 0.063855\t정확도: 97.11%\n",
+      "4\t검증 세트 손실: 0.056239\t최선의 손실: 0.056239\t정확도: 98.55%\n",
+      "5\t검증 세트 손실: 0.065626\t최선의 손실: 0.056239\t정확도: 98.05%\n",
+      "6\t검증 세트 손실: 0.048486\t최선의 손실: 0.048486\t정확도: 98.51%\n",
+      "7\t검증 세트 손실: 0.046699\t최선의 손실: 0.046699\t정확도: 98.75%\n",
+      "8\t검증 세트 손실: 0.043634\t최선의 손실: 0.043634\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.045380\t최선의 손실: 0.043634\t정확도: 98.75%\n",
+      "10\t검증 세트 손실: 0.041112\t최선의 손실: 0.041112\t정확도: 98.71%\n",
+      "11\t검증 세트 손실: 0.045459\t최선의 손실: 0.041112\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.039244\t최선의 손실: 0.039244\t정확도: 98.94%\n",
+      "13\t검증 세트 손실: 0.049873\t최선의 손실: 0.039244\t정확도: 98.48%\n",
+      "14\t검증 세트 손실: 0.036434\t최선의 손실: 0.036434\t정확도: 98.83%\n",
+      "15\t검증 세트 손실: 0.039192\t최선의 손실: 0.036434\t정확도: 98.75%\n",
+      "16\t검증 세트 손실: 0.046440\t최선의 손실: 0.036434\t정확도: 98.87%\n",
+      "17\t검증 세트 손실: 0.040923\t최선의 손실: 0.036434\t정확도: 99.02%\n",
+      "18\t검증 세트 손실: 0.039257\t최선의 손실: 0.036434\t정확도: 98.98%\n",
+      "19\t검증 세트 손실: 0.045210\t최선의 손실: 0.036434\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.042469\t최선의 손실: 0.036434\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.042265\t최선의 손실: 0.036434\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.050446\t최선의 손실: 0.036434\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.058288\t최선의 손실: 0.036434\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.048027\t최선의 손실: 0.036434\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.050348\t최선의 손실: 0.036434\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.046989\t최선의 손실: 0.036434\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.050095\t최선의 손실: 0.036434\t정확도: 98.79%\n",
+      "28\t검증 세트 손실: 0.055401\t최선의 손실: 0.036434\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.051051\t최선의 손실: 0.036434\t정확도: 98.83%\n",
+      "30\t검증 세트 손실: 0.042524\t최선의 손실: 0.036434\t정확도: 99.06%\n",
+      "31\t검증 세트 손실: 0.034795\t최선의 손실: 0.034795\t정확도: 99.22%\n",
+      "32\t검증 세트 손실: 0.041811\t최선의 손실: 0.034795\t정확도: 99.02%\n",
+      "33\t검증 세트 손실: 0.042502\t최선의 손실: 0.034795\t정확도: 99.06%\n",
+      "34\t검증 세트 손실: 0.045683\t최선의 손실: 0.034795\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.058879\t최선의 손실: 0.034795\t정확도: 98.67%\n",
+      "36\t검증 세트 손실: 0.032840\t최선의 손실: 0.032840\t정확도: 99.14%\n",
+      "37\t검증 세트 손실: 0.056627\t최선의 손실: 0.032840\t정확도: 98.83%\n",
+      "38\t검증 세트 손실: 0.047572\t최선의 손실: 0.032840\t정확도: 99.02%\n",
+      "39\t검증 세트 손실: 0.047597\t최선의 손실: 0.032840\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.060994\t최선의 손실: 0.032840\t정확도: 98.71%\n",
+      "41\t검증 세트 손실: 0.044269\t최선의 손실: 0.032840\t정확도: 98.71%\n",
+      "42\t검증 세트 손실: 0.044705\t최선의 손실: 0.032840\t정확도: 98.83%\n",
+      "43\t검증 세트 손실: 0.048607\t최선의 손실: 0.032840\t정확도: 99.02%\n",
+      "44\t검증 세트 손실: 0.042279\t최선의 손실: 0.032840\t정확도: 99.06%\n",
+      "45\t검증 세트 손실: 0.052749\t최선의 손실: 0.032840\t정확도: 99.22%\n",
+      "46\t검증 세트 손실: 0.056668\t최선의 손실: 0.032840\t정확도: 99.06%\n",
+      "47\t검증 세트 손실: 0.068900\t최선의 손실: 0.032840\t정확도: 98.71%\n",
+      "48\t검증 세트 손실: 0.051167\t최선의 손실: 0.032840\t정확도: 99.06%\n",
+      "49\t검증 세트 손실: 0.058872\t최선의 손실: 0.032840\t정확도: 98.98%\n",
+      "50\t검증 세트 손실: 0.047029\t최선의 손실: 0.032840\t정확도: 99.14%\n",
+      "51\t검증 세트 손실: 0.046757\t최선의 손실: 0.032840\t정확도: 98.98%\n",
+      "52\t검증 세트 손실: 0.048155\t최선의 손실: 0.032840\t정확도: 99.06%\n",
+      "53\t검증 세트 손실: 0.055792\t최선의 손실: 0.032840\t정확도: 98.83%\n",
+      "54\t검증 세트 손실: 0.051190\t최선의 손실: 0.032840\t정확도: 99.06%\n",
+      "55\t검증 세트 손실: 0.061267\t최선의 손실: 0.032840\t정확도: 99.02%\n",
+      "56\t검증 세트 손실: 0.054755\t최선의 손실: 0.032840\t정확도: 99.10%\n",
+      "57\t검증 세트 손실: 0.059316\t최선의 손실: 0.032840\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=30, total=  25.5s\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.95, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.107669\t최선의 손실: 0.107669\t정확도: 96.83%\n",
-      "1\t검증 세트 손실: 0.081908\t최선의 손실: 0.081908\t정확도: 97.30%\n",
-      "2\t검증 세트 손실: 0.060492\t최선의 손실: 0.060492\t정확도: 98.12%\n",
-      "3\t검증 세트 손실: 0.059477\t최선의 손실: 0.059477\t정확도: 98.28%\n",
-      "4\t검증 세트 손실: 0.050289\t최선의 손실: 0.050289\t정확도: 98.63%\n",
-      "5\t검증 세트 손실: 0.051325\t최선의 손실: 0.050289\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.052272\t최선의 손실: 0.050289\t정확도: 98.51%\n",
-      "7\t검증 세트 손실: 0.055225\t최선의 손실: 0.050289\t정확도: 98.28%\n",
-      "8\t검증 세트 손실: 0.043180\t최선의 손실: 0.043180\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.045096\t최선의 손실: 0.043180\t정확도: 98.79%\n",
-      "10\t검증 세트 손실: 0.038900\t최선의 손실: 0.038900\t정확도: 98.87%\n",
-      "11\t검증 세트 손실: 0.051299\t최선의 손실: 0.038900\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.044814\t최선의 손실: 0.038900\t정확도: 98.87%\n",
-      "13\t검증 세트 손실: 0.047801\t최선의 손실: 0.038900\t정확도: 98.44%\n",
-      "14\t검증 세트 손실: 0.041483\t최선의 손실: 0.038900\t정확도: 98.75%\n",
-      "15\t검증 세트 손실: 0.048159\t최선의 손실: 0.038900\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.049734\t최선의 손실: 0.038900\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.052242\t최선의 손실: 0.038900\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.047899\t최선의 손실: 0.038900\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.046496\t최선의 손실: 0.038900\t정확도: 98.98%\n",
-      "20\t검증 세트 손실: 0.046711\t최선의 손실: 0.038900\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.060351\t최선의 손실: 0.038900\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.034011\t최선의 손실: 0.034011\t정확도: 99.14%\n",
-      "23\t검증 세트 손실: 0.041540\t최선의 손실: 0.034011\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.048366\t최선의 손실: 0.034011\t정확도: 98.51%\n",
-      "25\t검증 세트 손실: 0.037740\t최선의 손실: 0.034011\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.041407\t최선의 손실: 0.034011\t정확도: 98.71%\n",
-      "27\t검증 세트 손실: 0.066007\t최선의 손실: 0.034011\t정확도: 98.36%\n",
-      "28\t검증 세트 손실: 0.058093\t최선의 손실: 0.034011\t정확도: 98.71%\n",
-      "29\t검증 세트 손실: 0.040684\t최선의 손실: 0.034011\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.056813\t최선의 손실: 0.034011\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.051393\t최선의 손실: 0.034011\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.054122\t최선의 손실: 0.034011\t정확도: 98.55%\n",
-      "33\t검증 세트 손실: 0.051048\t최선의 손실: 0.034011\t정확도: 98.75%\n",
-      "34\t검증 세트 손실: 0.054388\t최선의 손실: 0.034011\t정확도: 98.79%\n",
-      "35\t검증 세트 손실: 0.057339\t최선의 손실: 0.034011\t정확도: 99.02%\n",
-      "36\t검증 세트 손실: 0.067697\t최선의 손실: 0.034011\t정확도: 98.48%\n",
-      "37\t검증 세트 손실: 0.055452\t최선의 손실: 0.034011\t정확도: 98.87%\n",
-      "38\t검증 세트 손실: 0.064118\t최선의 손실: 0.034011\t정확도: 98.71%\n",
-      "39\t검증 세트 손실: 0.057045\t최선의 손실: 0.034011\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.074760\t최선의 손실: 0.034011\t정확도: 98.71%\n",
-      "41\t검증 세트 손실: 0.054232\t최선의 손실: 0.034011\t정확도: 98.87%\n",
-      "42\t검증 세트 손실: 0.054107\t최선의 손실: 0.034011\t정확도: 99.06%\n",
-      "43\t검증 세트 손실: 0.054477\t최선의 손실: 0.034011\t정확도: 98.98%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=160, total= 6.5min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.116320\t최선의 손실: 0.116320\t정확도: 96.44%\n",
+      "1\t검증 세트 손실: 0.079460\t최선의 손실: 0.079460\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.050014\t최선의 손실: 0.050014\t정확도: 98.63%\n",
+      "3\t검증 세트 손실: 0.052302\t최선의 손실: 0.050014\t정확도: 98.20%\n",
+      "4\t검증 세트 손실: 0.056383\t최선의 손실: 0.050014\t정확도: 98.40%\n",
+      "5\t검증 세트 손실: 0.057231\t최선의 손실: 0.050014\t정확도: 98.20%\n",
+      "6\t검증 세트 손실: 0.051295\t최선의 손실: 0.050014\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.064319\t최선의 손실: 0.050014\t정확도: 97.97%\n",
+      "8\t검증 세트 손실: 0.046548\t최선의 손실: 0.046548\t정확도: 98.91%\n",
+      "9\t검증 세트 손실: 0.064707\t최선의 손실: 0.046548\t정확도: 98.48%\n",
+      "10\t검증 세트 손실: 0.058704\t최선의 손실: 0.046548\t정확도: 98.28%\n",
+      "11\t검증 세트 손실: 0.039684\t최선의 손실: 0.039684\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.041206\t최선의 손실: 0.039684\t정확도: 98.98%\n",
+      "13\t검증 세트 손실: 0.042333\t최선의 손실: 0.039684\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.038146\t최선의 손실: 0.038146\t정확도: 98.79%\n",
+      "15\t검증 세트 손실: 0.061221\t최선의 손실: 0.038146\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.072197\t최선의 손실: 0.038146\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.051361\t최선의 손실: 0.038146\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.040605\t최선의 손실: 0.038146\t정확도: 98.87%\n",
+      "19\t검증 세트 손실: 0.039526\t최선의 손실: 0.038146\t정확도: 99.10%\n",
+      "20\t검증 세트 손실: 0.038151\t최선의 손실: 0.038146\t정확도: 99.10%\n",
+      "21\t검증 세트 손실: 0.045658\t최선의 손실: 0.038146\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.036344\t최선의 손실: 0.036344\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.044660\t최선의 손실: 0.036344\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.042676\t최선의 손실: 0.036344\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.047500\t최선의 손실: 0.036344\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.032769\t최선의 손실: 0.032769\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.040619\t최선의 손실: 0.032769\t정확도: 98.79%\n",
+      "28\t검증 세트 손실: 0.035751\t최선의 손실: 0.032769\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.041951\t최선의 손실: 0.032769\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.047085\t최선의 손실: 0.032769\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.042319\t최선의 손실: 0.032769\t정확도: 98.59%\n",
+      "32\t검증 세트 손실: 0.040337\t최선의 손실: 0.032769\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.043397\t최선의 손실: 0.032769\t정확도: 98.87%\n",
+      "34\t검증 세트 손실: 0.053035\t최선의 손실: 0.032769\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.031111\t최선의 손실: 0.031111\t정확도: 99.14%\n",
+      "36\t검증 세트 손실: 0.038809\t최선의 손실: 0.031111\t정확도: 99.18%\n",
+      "37\t검증 세트 손실: 0.066265\t최선의 손실: 0.031111\t정확도: 98.71%\n",
+      "38\t검증 세트 손실: 0.042943\t최선의 손실: 0.031111\t정확도: 99.14%\n",
+      "39\t검증 세트 손실: 0.031180\t최선의 손실: 0.031111\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.036544\t최선의 손실: 0.031111\t정확도: 98.83%\n",
+      "41\t검증 세트 손실: 0.040800\t최선의 손실: 0.031111\t정확도: 98.63%\n",
+      "42\t검증 세트 손실: 0.044463\t최선의 손실: 0.031111\t정확도: 98.87%\n",
+      "43\t검증 세트 손실: 0.038522\t최선의 손실: 0.031111\t정확도: 98.98%\n",
+      "44\t검증 세트 손실: 0.030484\t최선의 손실: 0.030484\t정확도: 99.14%\n",
+      "45\t검증 세트 손실: 0.053659\t최선의 손실: 0.030484\t정확도: 98.67%\n",
+      "46\t검증 세트 손실: 0.061877\t최선의 손실: 0.030484\t정확도: 98.98%\n",
+      "47\t검증 세트 손실: 0.053947\t최선의 손실: 0.030484\t정확도: 99.10%\n",
+      "48\t검증 세트 손실: 0.042910\t최선의 손실: 0.030484\t정확도: 99.10%\n",
+      "49\t검증 세트 손실: 0.046934\t최선의 손실: 0.030484\t정확도: 98.87%\n",
+      "50\t검증 세트 손실: 0.049552\t최선의 손실: 0.030484\t정확도: 98.83%\n",
+      "51\t검증 세트 손실: 0.023309\t최선의 손실: 0.023309\t정확도: 99.45%\n",
+      "52\t검증 세트 손실: 0.033160\t최선의 손실: 0.023309\t정확도: 99.26%\n",
+      "53\t검증 세트 손실: 0.031126\t최선의 손실: 0.023309\t정확도: 99.06%\n",
+      "54\t검증 세트 손실: 0.041478\t최선의 손실: 0.023309\t정확도: 98.98%\n",
+      "55\t검증 세트 손실: 0.037447\t최선의 손실: 0.023309\t정확도: 99.06%\n",
+      "56\t검증 세트 손실: 0.042296\t최선의 손실: 0.023309\t정확도: 98.94%\n",
+      "57\t검증 세트 손실: 0.032843\t최선의 손실: 0.023309\t정확도: 99.22%\n",
+      "58\t검증 세트 손실: 0.048084\t최선의 손실: 0.023309\t정확도: 98.94%\n",
+      "59\t검증 세트 손실: 0.049161\t최선의 손실: 0.023309\t정확도: 98.98%\n",
+      "60\t검증 세트 손실: 0.042808\t최선의 손실: 0.023309\t정확도: 99.10%\n",
+      "61\t검증 세트 손실: 0.055363\t최선의 손실: 0.023309\t정확도: 98.98%\n",
+      "62\t검증 세트 손실: 0.059813\t최선의 손실: 0.023309\t정확도: 98.59%\n",
+      "63\t검증 세트 손실: 0.069216\t최선의 손실: 0.023309\t정확도: 98.75%\n",
+      "64\t검증 세트 손실: 0.044518\t최선의 손실: 0.023309\t정확도: 99.02%\n",
+      "65\t검증 세트 손실: 0.071636\t최선의 손실: 0.023309\t정확도: 98.59%\n",
+      "66\t검증 세트 손실: 0.042240\t최선의 손실: 0.023309\t정확도: 98.63%\n",
+      "67\t검증 세트 손실: 0.048922\t최선의 손실: 0.023309\t정확도: 98.91%\n",
+      "68\t검증 세트 손실: 0.043027\t최선의 손실: 0.023309\t정확도: 99.06%\n",
+      "69\t검증 세트 손실: 0.049371\t최선의 손실: 0.023309\t정확도: 98.83%\n",
+      "70\t검증 세트 손실: 0.052327\t최선의 손실: 0.023309\t정확도: 99.18%\n",
+      "71\t검증 세트 손실: 0.059115\t최선의 손실: 0.023309\t정확도: 98.75%\n",
+      "72\t검증 세트 손실: 0.050615\t최선의 손실: 0.023309\t정확도: 98.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.95, n_neurons=160, total= 4.9min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.95, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.124598\t최선의 손실: 0.124598\t정확도: 96.05%\n",
-      "1\t검증 세트 손실: 0.079308\t최선의 손실: 0.079308\t정확도: 97.65%\n",
-      "2\t검증 세트 손실: 0.064161\t최선의 손실: 0.064161\t정확도: 97.93%\n",
-      "3\t검증 세트 손실: 0.071249\t최선의 손실: 0.064161\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.049271\t최선의 손실: 0.049271\t정확도: 98.28%\n",
-      "5\t검증 세트 손실: 0.050016\t최선의 손실: 0.049271\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.064692\t최선의 손실: 0.049271\t정확도: 98.28%\n",
-      "7\t검증 세트 손실: 0.065300\t최선의 손실: 0.049271\t정확도: 97.89%\n",
-      "8\t검증 세트 손실: 0.047525\t최선의 손실: 0.047525\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.046820\t최선의 손실: 0.046820\t정확도: 98.48%\n",
-      "10\t검증 세트 손실: 0.078378\t최선의 손실: 0.046820\t정확도: 97.73%\n",
-      "11\t검증 세트 손실: 0.034637\t최선의 손실: 0.034637\t정확도: 98.91%\n",
-      "12\t검증 세트 손실: 0.049177\t최선의 손실: 0.034637\t정확도: 98.44%\n",
-      "13\t검증 세트 손실: 0.045031\t최선의 손실: 0.034637\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.046446\t최선의 손실: 0.034637\t정확도: 98.63%\n",
-      "15\t검증 세트 손실: 0.074107\t최선의 손실: 0.034637\t정확도: 97.93%\n",
-      "16\t검증 세트 손실: 0.044894\t최선의 손실: 0.034637\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.040090\t최선의 손실: 0.034637\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.043000\t최선의 손실: 0.034637\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.044729\t최선의 손실: 0.034637\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.041505\t최선의 손실: 0.034637\t정확도: 98.91%\n",
-      "21\t검증 세트 손실: 0.043182\t최선의 손실: 0.034637\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.038863\t최선의 손실: 0.034637\t정확도: 99.14%\n",
-      "23\t검증 세트 손실: 0.058923\t최선의 손실: 0.034637\t정확도: 98.48%\n",
-      "24\t검증 세트 손실: 0.053854\t최선의 손실: 0.034637\t정확도: 98.79%\n",
-      "25\t검증 세트 손실: 0.046655\t최선의 손실: 0.034637\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.048234\t최선의 손실: 0.034637\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.057158\t최선의 손실: 0.034637\t정확도: 98.28%\n",
-      "28\t검증 세트 손실: 0.042578\t최선의 손실: 0.034637\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.051412\t최선의 손실: 0.034637\t정확도: 98.59%\n",
-      "30\t검증 세트 손실: 0.058962\t최선의 손실: 0.034637\t정확도: 98.59%\n",
-      "31\t검증 세트 손실: 0.050264\t최선의 손실: 0.034637\t정확도: 98.75%\n",
-      "32\t검증 세트 손실: 0.042758\t최선의 손실: 0.034637\t정확도: 98.75%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.95, n_neurons=160, total= 3.7min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.95, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.098743\t최선의 손실: 0.098743\t정확도: 97.38%\n",
-      "1\t검증 세트 손실: 0.060568\t최선의 손실: 0.060568\t정확도: 98.40%\n",
-      "2\t검증 세트 손실: 0.069208\t최선의 손실: 0.060568\t정확도: 97.73%\n",
-      "3\t검증 세트 손실: 0.047820\t최선의 손실: 0.047820\t정확도: 98.48%\n",
-      "4\t검증 세트 손실: 0.056255\t최선의 손실: 0.047820\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.045592\t최선의 손실: 0.045592\t정확도: 98.59%\n",
-      "6\t검증 세트 손실: 0.058133\t최선의 손실: 0.045592\t정확도: 98.05%\n",
-      "7\t검증 세트 손실: 0.037030\t최선의 손실: 0.037030\t정확도: 98.79%\n",
-      "8\t검증 세트 손실: 0.055521\t최선의 손실: 0.037030\t정확도: 98.40%\n",
-      "9\t검증 세트 손실: 0.037220\t최선의 손실: 0.037030\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.037635\t최선의 손실: 0.037030\t정확도: 98.87%\n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=160, total= 8.2min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=160 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11\t검증 세트 손실: 0.044192\t최선의 손실: 0.037030\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.041667\t최선의 손실: 0.037030\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.032838\t최선의 손실: 0.032838\t정확도: 99.02%\n",
-      "14\t검증 세트 손실: 0.053266\t최선의 손실: 0.032838\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.036054\t최선의 손실: 0.032838\t정확도: 99.02%\n",
-      "16\t검증 세트 손실: 0.041054\t최선의 손실: 0.032838\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.038742\t최선의 손실: 0.032838\t정확도: 99.02%\n",
-      "18\t검증 세트 손실: 0.047823\t최선의 손실: 0.032838\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.036744\t최선의 손실: 0.032838\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.046247\t최선의 손실: 0.032838\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.037393\t최선의 손실: 0.032838\t정확도: 99.06%\n",
-      "22\t검증 세트 손실: 0.035310\t최선의 손실: 0.032838\t정확도: 99.18%\n",
-      "23\t검증 세트 손실: 0.038782\t최선의 손실: 0.032838\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.051444\t최선의 손실: 0.032838\t정확도: 98.51%\n",
-      "25\t검증 세트 손실: 0.036264\t최선의 손실: 0.032838\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.033756\t최선의 손실: 0.032838\t정확도: 99.30%\n",
-      "27\t검증 세트 손실: 0.029895\t최선의 손실: 0.029895\t정확도: 99.37%\n",
-      "28\t검증 세트 손실: 0.025545\t최선의 손실: 0.025545\t정확도: 99.30%\n",
-      "29\t검증 세트 손실: 0.044658\t최선의 손실: 0.025545\t정확도: 98.63%\n",
-      "30\t검증 세트 손실: 0.034507\t최선의 손실: 0.025545\t정확도: 99.10%\n",
-      "31\t검증 세트 손실: 0.037429\t최선의 손실: 0.025545\t정확도: 98.98%\n",
-      "32\t검증 세트 손실: 0.032580\t최선의 손실: 0.025545\t정확도: 99.06%\n",
-      "33\t검증 세트 손실: 0.044165\t최선의 손실: 0.025545\t정확도: 99.02%\n",
-      "34\t검증 세트 손실: 0.034193\t최선의 손실: 0.025545\t정확도: 98.94%\n",
-      "35\t검증 세트 손실: 0.043885\t최선의 손실: 0.025545\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.035633\t최선의 손실: 0.025545\t정확도: 99.14%\n",
-      "37\t검증 세트 손실: 0.035488\t최선의 손실: 0.025545\t정확도: 99.22%\n",
-      "38\t검증 세트 손실: 0.039216\t최선의 손실: 0.025545\t정확도: 99.10%\n",
-      "39\t검증 세트 손실: 0.035729\t최선의 손실: 0.025545\t정확도: 99.02%\n",
-      "40\t검증 세트 손실: 0.033338\t최선의 손실: 0.025545\t정확도: 99.14%\n",
-      "41\t검증 세트 손실: 0.039714\t최선의 손실: 0.025545\t정확도: 99.22%\n",
-      "42\t검증 세트 손실: 0.041296\t최선의 손실: 0.025545\t정확도: 99.18%\n",
-      "43\t검증 세트 손실: 0.033037\t최선의 손실: 0.025545\t정확도: 99.34%\n",
-      "44\t검증 세트 손실: 0.027955\t최선의 손실: 0.025545\t정확도: 99.18%\n",
-      "45\t검증 세트 손실: 0.044810\t최선의 손실: 0.025545\t정확도: 99.02%\n",
-      "46\t검증 세트 손실: 0.052915\t최선의 손실: 0.025545\t정확도: 98.94%\n",
-      "47\t검증 세트 손실: 0.040527\t최선의 손실: 0.025545\t정확도: 99.06%\n",
-      "48\t검증 세트 손실: 0.044526\t최선의 손실: 0.025545\t정확도: 98.71%\n",
-      "49\t검증 세트 손실: 0.045634\t최선의 손실: 0.025545\t정확도: 99.02%\n",
+      "0\t검증 세트 손실: 0.080993\t최선의 손실: 0.080993\t정확도: 97.58%\n",
+      "1\t검증 세트 손실: 0.065657\t최선의 손실: 0.065657\t정확도: 98.20%\n",
+      "2\t검증 세트 손실: 0.090192\t최선의 손실: 0.065657\t정확도: 97.54%\n",
+      "3\t검증 세트 손실: 0.051412\t최선의 손실: 0.051412\t정확도: 98.63%\n",
+      "4\t검증 세트 손실: 0.065449\t최선의 손실: 0.051412\t정확도: 98.08%\n",
+      "5\t검증 세트 손실: 0.060002\t최선의 손실: 0.051412\t정확도: 98.05%\n",
+      "6\t검증 세트 손실: 0.047430\t최선의 손실: 0.047430\t정확도: 98.55%\n",
+      "7\t검증 세트 손실: 0.041980\t최선의 손실: 0.041980\t정확도: 98.79%\n",
+      "8\t검증 세트 손실: 0.040703\t최선의 손실: 0.040703\t정확도: 98.87%\n",
+      "9\t검증 세트 손실: 0.039710\t최선의 손실: 0.039710\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.034910\t최선의 손실: 0.034910\t정확도: 98.83%\n",
+      "11\t검증 세트 손실: 0.037037\t최선의 손실: 0.034910\t정확도: 98.79%\n",
+      "12\t검증 세트 손실: 0.035956\t최선의 손실: 0.034910\t정확도: 98.79%\n",
+      "13\t검증 세트 손실: 0.036476\t최선의 손실: 0.034910\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.040442\t최선의 손실: 0.034910\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.041826\t최선의 손실: 0.034910\t정확도: 98.83%\n",
+      "16\t검증 세트 손실: 0.047319\t최선의 손실: 0.034910\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.037770\t최선의 손실: 0.034910\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.041799\t최선의 손실: 0.034910\t정확도: 98.63%\n",
+      "19\t검증 세트 손실: 0.043587\t최선의 손실: 0.034910\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.036169\t최선의 손실: 0.034910\t정확도: 98.83%\n",
+      "21\t검증 세트 손실: 0.033629\t최선의 손실: 0.033629\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.033980\t최선의 손실: 0.033629\t정확도: 99.18%\n",
+      "23\t검증 세트 손실: 0.036364\t최선의 손실: 0.033629\t정확도: 99.10%\n",
+      "24\t검증 세트 손실: 0.031083\t최선의 손실: 0.031083\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.047780\t최선의 손실: 0.031083\t정확도: 98.51%\n",
+      "26\t검증 세트 손실: 0.041796\t최선의 손실: 0.031083\t정확도: 99.06%\n",
+      "27\t검증 세트 손실: 0.028064\t최선의 손실: 0.028064\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.037152\t최선의 손실: 0.028064\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.047100\t최선의 손실: 0.028064\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.045828\t최선의 손실: 0.028064\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.039920\t최선의 손실: 0.028064\t정확도: 99.02%\n",
+      "32\t검증 세트 손실: 0.041275\t최선의 손실: 0.028064\t정확도: 98.83%\n",
+      "33\t검증 세트 손실: 0.039371\t최선의 손실: 0.028064\t정확도: 98.87%\n",
+      "34\t검증 세트 손실: 0.033128\t최선의 손실: 0.028064\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.055724\t최선의 손실: 0.028064\t정확도: 98.55%\n",
+      "36\t검증 세트 손실: 0.047866\t최선의 손실: 0.028064\t정확도: 98.83%\n",
+      "37\t검증 세트 손실: 0.044287\t최선의 손실: 0.028064\t정확도: 99.22%\n",
+      "38\t검증 세트 손실: 0.038409\t최선의 손실: 0.028064\t정확도: 99.06%\n",
+      "39\t검증 세트 손실: 0.028334\t최선의 손실: 0.028064\t정확도: 99.34%\n",
+      "40\t검증 세트 손실: 0.034251\t최선의 손실: 0.028064\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.035092\t최선의 손실: 0.028064\t정확도: 99.10%\n",
+      "42\t검증 세트 손실: 0.048595\t최선의 손실: 0.028064\t정확도: 98.63%\n",
+      "43\t검증 세트 손실: 0.042829\t최선의 손실: 0.028064\t정확도: 98.94%\n",
+      "44\t검증 세트 손실: 0.042131\t최선의 손실: 0.028064\t정확도: 99.10%\n",
+      "45\t검증 세트 손실: 0.032703\t최선의 손실: 0.028064\t정확도: 98.91%\n",
+      "46\t검증 세트 손실: 0.040683\t최선의 손실: 0.028064\t정확도: 99.02%\n",
+      "47\t검증 세트 손실: 0.049562\t최선의 손실: 0.028064\t정확도: 98.91%\n",
+      "48\t검증 세트 손실: 0.041742\t최선의 손실: 0.028064\t정확도: 99.10%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.95, n_neurons=160, total= 5.6min\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.090636\t최선의 손실: 0.090636\t정확도: 97.22%\n",
-      "1\t검증 세트 손실: 0.072621\t최선의 손실: 0.072621\t정확도: 97.65%\n",
-      "2\t검증 세트 손실: 0.083637\t최선의 손실: 0.072621\t정확도: 97.34%\n",
-      "3\t검증 세트 손실: 0.056457\t최선의 손실: 0.056457\t정확도: 98.40%\n",
-      "4\t검증 세트 손실: 0.052031\t최선의 손실: 0.052031\t정확도: 98.48%\n",
-      "5\t검증 세트 손실: 0.054600\t최선의 손실: 0.052031\t정확도: 98.59%\n",
-      "6\t검증 세트 손실: 0.046673\t최선의 손실: 0.046673\t정확도: 98.51%\n",
-      "7\t검증 세트 손실: 0.080476\t최선의 손실: 0.046673\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.082801\t최선의 손실: 0.046673\t정확도: 98.01%\n",
-      "9\t검증 세트 손실: 0.058925\t최선의 손실: 0.046673\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.062136\t최선의 손실: 0.046673\t정확도: 98.24%\n",
-      "11\t검증 세트 손실: 0.042324\t최선의 손실: 0.042324\t정확도: 98.94%\n",
-      "12\t검증 세트 손실: 0.059649\t최선의 손실: 0.042324\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.046359\t최선의 손실: 0.042324\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.048188\t최선의 손실: 0.042324\t정확도: 98.87%\n",
-      "15\t검증 세트 손실: 0.062846\t최선의 손실: 0.042324\t정확도: 98.67%\n",
-      "16\t검증 세트 손실: 0.083587\t최선의 손실: 0.042324\t정확도: 98.32%\n",
-      "17\t검증 세트 손실: 0.053963\t최선의 손실: 0.042324\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.087287\t최선의 손실: 0.042324\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.048571\t최선의 손실: 0.042324\t정확도: 98.98%\n",
-      "20\t검증 세트 손실: 0.061668\t최선의 손실: 0.042324\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.059896\t최선의 손실: 0.042324\t정확도: 98.75%\n",
-      "22\t검증 세트 손실: 0.049091\t최선의 손실: 0.042324\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.048532\t최선의 손실: 0.042324\t정확도: 99.10%\n",
-      "24\t검증 세트 손실: 0.072629\t최선의 손실: 0.042324\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.047512\t최선의 손실: 0.042324\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.062067\t최선의 손실: 0.042324\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.041560\t최선의 손실: 0.041560\t정확도: 99.10%\n",
-      "28\t검증 세트 손실: 0.080288\t최선의 손실: 0.041560\t정확도: 98.44%\n",
-      "29\t검증 세트 손실: 0.063680\t최선의 손실: 0.041560\t정확도: 98.98%\n",
-      "30\t검증 세트 손실: 0.059862\t최선의 손실: 0.041560\t정확도: 98.71%\n",
-      "31\t검증 세트 손실: 0.059109\t최선의 손실: 0.041560\t정확도: 98.98%\n",
-      "32\t검증 세트 손실: 0.077017\t최선의 손실: 0.041560\t정확도: 98.51%\n",
-      "33\t검증 세트 손실: 0.049986\t최선의 손실: 0.041560\t정확도: 98.71%\n",
-      "34\t검증 세트 손실: 0.089954\t최선의 손실: 0.041560\t정확도: 98.44%\n",
-      "35\t검증 세트 손실: 0.076876\t최선의 손실: 0.041560\t정확도: 98.40%\n",
-      "36\t검증 세트 손실: 0.067376\t최선의 손실: 0.041560\t정확도: 98.83%\n",
-      "37\t검증 세트 손실: 0.068929\t최선의 손실: 0.041560\t정확도: 98.79%\n",
-      "38\t검증 세트 손실: 0.075592\t최선의 손실: 0.041560\t정확도: 98.67%\n",
-      "39\t검증 세트 손실: 0.070125\t최선의 손실: 0.041560\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.063774\t최선의 손실: 0.041560\t정확도: 98.75%\n",
-      "41\t검증 세트 손실: 0.062487\t최선의 손실: 0.041560\t정확도: 98.91%\n",
-      "42\t검증 세트 손실: 0.068292\t최선의 손실: 0.041560\t정확도: 98.75%\n",
-      "43\t검증 세트 손실: 0.096655\t최선의 손실: 0.041560\t정확도: 98.51%\n",
-      "44\t검증 세트 손실: 0.074974\t최선의 손실: 0.041560\t정확도: 98.75%\n",
-      "45\t검증 세트 손실: 0.064730\t최선의 손실: 0.041560\t정확도: 98.71%\n",
-      "46\t검증 세트 손실: 0.053433\t최선의 손실: 0.041560\t정확도: 99.02%\n",
-      "47\t검증 세트 손실: 0.064993\t최선의 손실: 0.041560\t정확도: 98.91%\n",
-      "48\t검증 세트 손실: 0.050525\t최선의 손실: 0.041560\t정확도: 99.10%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=160, total= 5.5min\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.081862\t최선의 손실: 0.081862\t정확도: 97.22%\n",
+      "1\t검증 세트 손실: 0.070255\t최선의 손실: 0.070255\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.064241\t최선의 손실: 0.064241\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.051164\t최선의 손실: 0.051164\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.069002\t최선의 손실: 0.051164\t정확도: 97.89%\n",
+      "5\t검증 세트 손실: 0.053198\t최선의 손실: 0.051164\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.045905\t최선의 손실: 0.045905\t정확도: 98.63%\n",
+      "7\t검증 세트 손실: 0.048864\t최선의 손실: 0.045905\t정확도: 98.51%\n",
+      "8\t검증 세트 손실: 0.043354\t최선의 손실: 0.043354\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.047967\t최선의 손실: 0.043354\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.076099\t최선의 손실: 0.043354\t정확도: 98.01%\n",
+      "11\t검증 세트 손실: 0.051235\t최선의 손실: 0.043354\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.058537\t최선의 손실: 0.043354\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.049373\t최선의 손실: 0.043354\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.084456\t최선의 손실: 0.043354\t정확도: 98.16%\n",
+      "15\t검증 세트 손실: 0.052971\t최선의 손실: 0.043354\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.061618\t최선의 손실: 0.043354\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.065421\t최선의 손실: 0.043354\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.058587\t최선의 손실: 0.043354\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.059426\t최선의 손실: 0.043354\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.071169\t최선의 손실: 0.043354\t정확도: 98.40%\n",
+      "21\t검증 세트 손실: 0.066676\t최선의 손실: 0.043354\t정확도: 98.48%\n",
+      "22\t검증 세트 손실: 0.062056\t최선의 손실: 0.043354\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.052431\t최선의 손실: 0.043354\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.058298\t최선의 손실: 0.043354\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.068543\t최선의 손실: 0.043354\t정확도: 98.71%\n",
+      "26\t검증 세트 손실: 0.054589\t최선의 손실: 0.043354\t정확도: 98.67%\n",
+      "27\t검증 세트 손실: 0.062246\t최선의 손실: 0.043354\t정확도: 98.71%\n",
+      "28\t검증 세트 손실: 0.068320\t최선의 손실: 0.043354\t정확도: 98.55%\n",
+      "29\t검증 세트 손실: 0.062270\t최선의 손실: 0.043354\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=100, total=  34.7s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.133138\t최선의 손실: 0.133138\t정확도: 96.29%\n",
-      "1\t검증 세트 손실: 0.064047\t최선의 손실: 0.064047\t정확도: 97.89%\n",
-      "2\t검증 세트 손실: 0.056512\t최선의 손실: 0.056512\t정확도: 98.44%\n",
-      "3\t검증 세트 손실: 0.089754\t최선의 손실: 0.056512\t정확도: 97.50%\n",
-      "4\t검증 세트 손실: 0.082472\t최선의 손실: 0.056512\t정확도: 97.97%\n",
-      "5\t검증 세트 손실: 0.046876\t최선의 손실: 0.046876\t정확도: 98.51%\n",
-      "6\t검증 세트 손실: 0.074518\t최선의 손실: 0.046876\t정확도: 98.28%\n",
-      "7\t검증 세트 손실: 0.062318\t최선의 손실: 0.046876\t정확도: 98.40%\n",
-      "8\t검증 세트 손실: 0.051812\t최선의 손실: 0.046876\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.066126\t최선의 손실: 0.046876\t정확도: 98.16%\n",
-      "10\t검증 세트 손실: 0.055068\t최선의 손실: 0.046876\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.077275\t최선의 손실: 0.046876\t정확도: 97.97%\n",
-      "12\t검증 세트 손실: 0.054821\t최선의 손실: 0.046876\t정확도: 98.67%\n",
-      "13\t검증 세트 손실: 0.064086\t최선의 손실: 0.046876\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.051071\t최선의 손실: 0.046876\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.049813\t최선의 손실: 0.046876\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.063841\t최선의 손실: 0.046876\t정확도: 98.98%\n",
-      "17\t검증 세트 손실: 0.062229\t최선의 손실: 0.046876\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.059476\t최선의 손실: 0.046876\t정확도: 98.63%\n",
-      "19\t검증 세트 손실: 0.058619\t최선의 손실: 0.046876\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.038836\t최선의 손실: 0.038836\t정확도: 99.18%\n",
-      "21\t검증 세트 손실: 0.039712\t최선의 손실: 0.038836\t정확도: 99.18%\n",
-      "22\t검증 세트 손실: 0.046015\t최선의 손실: 0.038836\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.080761\t최선의 손실: 0.038836\t정확도: 98.71%\n",
-      "24\t검증 세트 손실: 0.040464\t최선의 손실: 0.038836\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.063298\t최선의 손실: 0.038836\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.040774\t최선의 손실: 0.038836\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.067690\t최선의 손실: 0.038836\t정확도: 98.40%\n",
-      "28\t검증 세트 손실: 0.062398\t최선의 손실: 0.038836\t정확도: 98.75%\n",
-      "29\t검증 세트 손실: 0.043671\t최선의 손실: 0.038836\t정확도: 98.98%\n",
-      "30\t검증 세트 손실: 0.039500\t최선의 손실: 0.038836\t정확도: 99.10%\n",
-      "31\t검증 세트 손실: 0.039144\t최선의 손실: 0.038836\t정확도: 99.06%\n",
-      "32\t검증 세트 손실: 0.051054\t최선의 손실: 0.038836\t정확도: 99.10%\n",
-      "33\t검증 세트 손실: 0.046288\t최선의 손실: 0.038836\t정확도: 98.98%\n",
-      "34\t검증 세트 손실: 0.054889\t최선의 손실: 0.038836\t정확도: 98.79%\n",
-      "35\t검증 세트 손실: 0.066178\t최선의 손실: 0.038836\t정확도: 98.55%\n",
-      "36\t검증 세트 손실: 0.052046\t최선의 손실: 0.038836\t정확도: 99.14%\n",
-      "37\t검증 세트 손실: 0.051652\t최선의 손실: 0.038836\t정확도: 98.75%\n",
-      "38\t검증 세트 손실: 0.069955\t최선의 손실: 0.038836\t정확도: 98.67%\n",
-      "39\t검증 세트 손실: 0.056474\t최선의 손실: 0.038836\t정확도: 98.87%\n",
-      "40\t검증 세트 손실: 0.075449\t최선의 손실: 0.038836\t정확도: 98.71%\n",
-      "41\t검증 세트 손실: 0.091972\t최선의 손실: 0.038836\t정확도: 98.63%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100, total=  21.9s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.167876\t최선의 손실: 0.167876\t정확도: 95.39%\n",
+      "1\t검증 세트 손실: 0.063097\t최선의 손실: 0.063097\t정확도: 98.08%\n",
+      "2\t검증 세트 손실: 0.071609\t최선의 손실: 0.063097\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.068691\t최선의 손실: 0.063097\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.098614\t최선의 손실: 0.063097\t정확도: 97.38%\n",
+      "5\t검증 세트 손실: 0.048212\t최선의 손실: 0.048212\t정확도: 98.55%\n",
+      "6\t검증 세트 손실: 0.061751\t최선의 손실: 0.048212\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.043305\t최선의 손실: 0.043305\t정확도: 98.75%\n",
+      "8\t검증 세트 손실: 0.046952\t최선의 손실: 0.043305\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.136076\t최선의 손실: 0.043305\t정확도: 97.03%\n",
+      "10\t검증 세트 손실: 0.045884\t최선의 손실: 0.043305\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.075396\t최선의 손실: 0.043305\t정확도: 98.12%\n",
+      "12\t검증 세트 손실: 0.047580\t최선의 손실: 0.043305\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.060831\t최선의 손실: 0.043305\t정확도: 98.28%\n",
+      "14\t검증 세트 손실: 0.039337\t최선의 손실: 0.039337\t정확도: 99.06%\n",
+      "15\t검증 세트 손실: 0.040293\t최선의 손실: 0.039337\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.043898\t최선의 손실: 0.039337\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.055085\t최선의 손실: 0.039337\t정확도: 98.67%\n",
+      "18\t검증 세트 손실: 0.068534\t최선의 손실: 0.039337\t정확도: 98.16%\n",
+      "19\t검증 세트 손실: 0.048347\t최선의 손실: 0.039337\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.049943\t최선의 손실: 0.039337\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.041360\t최선의 손실: 0.039337\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.125538\t최선의 손실: 0.039337\t정확도: 97.22%\n",
+      "23\t검증 세트 손실: 0.060884\t최선의 손실: 0.039337\t정확도: 98.63%\n",
+      "24\t검증 세트 손실: 0.048962\t최선의 손실: 0.039337\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.041645\t최선의 손실: 0.039337\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.052996\t최선의 손실: 0.039337\t정확도: 98.24%\n",
+      "27\t검증 세트 손실: 0.066677\t최선의 손실: 0.039337\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.047366\t최선의 손실: 0.039337\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.043280\t최선의 손실: 0.039337\t정확도: 99.10%\n",
+      "30\t검증 세트 손실: 0.072161\t최선의 손실: 0.039337\t정확도: 98.55%\n",
+      "31\t검증 세트 손실: 0.062901\t최선의 손실: 0.039337\t정확도: 98.71%\n",
+      "32\t검증 세트 손실: 0.070910\t최선의 손실: 0.039337\t정확도: 98.67%\n",
+      "33\t검증 세트 손실: 0.056249\t최선의 손실: 0.039337\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.054640\t최선의 손실: 0.039337\t정확도: 98.75%\n",
+      "35\t검증 세트 손실: 0.061723\t최선의 손실: 0.039337\t정확도: 98.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=100, total=  29.3s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.119004\t최선의 손실: 0.119004\t정확도: 96.76%\n",
-      "1\t검증 세트 손실: 0.069847\t최선의 손실: 0.069847\t정확도: 97.81%\n",
-      "2\t검증 세트 손실: 0.056360\t최선의 손실: 0.056360\t정확도: 98.08%\n",
-      "3\t검증 세트 손실: 0.062063\t최선의 손실: 0.056360\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.064753\t최선의 손실: 0.056360\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.045822\t최선의 손실: 0.045822\t정확도: 98.48%\n",
-      "6\t검증 세트 손실: 0.047323\t최선의 손실: 0.045822\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.053155\t최선의 손실: 0.045822\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.068904\t최선의 손실: 0.045822\t정확도: 98.28%\n",
-      "9\t검증 세트 손실: 0.043366\t최선의 손실: 0.043366\t정확도: 98.83%\n",
-      "10\t검증 세트 손실: 0.034546\t최선의 손실: 0.034546\t정확도: 98.94%\n",
-      "11\t검증 세트 손실: 0.051516\t최선의 손실: 0.034546\t정확도: 98.59%\n",
-      "12\t검증 세트 손실: 0.039155\t최선의 손실: 0.034546\t정확도: 99.02%\n",
-      "13\t검증 세트 손실: 0.047730\t최선의 손실: 0.034546\t정확도: 98.59%\n",
-      "14\t검증 세트 손실: 0.061512\t최선의 손실: 0.034546\t정확도: 98.55%\n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100, total=  25.9s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.119960\t최선의 손실: 0.119960\t정확도: 96.95%\n",
+      "1\t검증 세트 손실: 0.074024\t최선의 손실: 0.074024\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.064185\t최선의 손실: 0.064185\t정확도: 97.73%\n",
+      "3\t검증 세트 손실: 0.058739\t최선의 손실: 0.058739\t정확도: 98.24%\n",
+      "4\t검증 세트 손실: 0.070764\t최선의 손실: 0.058739\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.038561\t최선의 손실: 0.038561\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.042209\t최선의 손실: 0.038561\t정확도: 98.71%\n",
+      "7\t검증 세트 손실: 0.065389\t최선의 손실: 0.038561\t정확도: 98.24%\n",
+      "8\t검증 세트 손실: 0.054796\t최선의 손실: 0.038561\t정확도: 98.59%\n",
+      "9\t검증 세트 손실: 0.063752\t최선의 손실: 0.038561\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.054501\t최선의 손실: 0.038561\t정확도: 98.44%\n",
+      "11\t검증 세트 손실: 0.063553\t최선의 손실: 0.038561\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.041340\t최선의 손실: 0.038561\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.043117\t최선의 손실: 0.038561\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.051491\t최선의 손실: 0.038561\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.057156\t최선의 손실: 0.038561\t정확도: 98.87%\n",
+      "16\t검증 세트 손실: 0.061733\t최선의 손실: 0.038561\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.037437\t최선의 손실: 0.037437\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.067148\t최선의 손실: 0.037437\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.048706\t최선의 손실: 0.037437\t정확도: 99.10%\n",
+      "20\t검증 세트 손실: 0.059282\t최선의 손실: 0.037437\t정확도: 98.98%\n",
+      "21\t검증 세트 손실: 0.057973\t최선의 손실: 0.037437\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.048654\t최선의 손실: 0.037437\t정확도: 98.87%\n",
+      "23\t검증 세트 손실: 0.046990\t최선의 손실: 0.037437\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.051595\t최선의 손실: 0.037437\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.035380\t최선의 손실: 0.035380\t정확도: 99.14%\n",
+      "26\t검증 세트 손실: 0.050929\t최선의 손실: 0.035380\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.048902\t최선의 손실: 0.035380\t정확도: 98.91%\n",
+      "28\t검증 세트 손실: 0.046297\t최선의 손실: 0.035380\t정확도: 99.30%\n",
+      "29\t검증 세트 손실: 0.052129\t최선의 손실: 0.035380\t정확도: 98.94%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15\t검증 세트 손실: 0.046358\t최선의 손실: 0.034546\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.044567\t최선의 손실: 0.034546\t정확도: 98.87%\n",
-      "17\t검증 세트 손실: 0.055646\t최선의 손실: 0.034546\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.062079\t최선의 손실: 0.034546\t정확도: 98.55%\n",
-      "19\t검증 세트 손실: 0.045181\t최선의 손실: 0.034546\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.086080\t최선의 손실: 0.034546\t정확도: 98.32%\n",
-      "21\t검증 세트 손실: 0.062363\t최선의 손실: 0.034546\t정확도: 98.59%\n",
-      "22\t검증 세트 손실: 0.052238\t최선의 손실: 0.034546\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.053775\t최선의 손실: 0.034546\t정확도: 98.79%\n",
-      "24\t검증 세트 손실: 0.054605\t최선의 손실: 0.034546\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.036085\t최선의 손실: 0.034546\t정확도: 99.14%\n",
-      "26\t검증 세트 손실: 0.039719\t최선의 손실: 0.034546\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.065774\t최선의 손실: 0.034546\t정확도: 98.48%\n",
-      "28\t검증 세트 손실: 0.039298\t최선의 손실: 0.034546\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.040088\t최선의 손실: 0.034546\t정확도: 99.18%\n",
-      "30\t검증 세트 손실: 0.046029\t최선의 손실: 0.034546\t정확도: 99.06%\n",
-      "31\t검증 세트 손실: 0.044339\t최선의 손실: 0.034546\t정확도: 99.14%\n",
+      "30\t검증 세트 손실: 0.048024\t최선의 손실: 0.035380\t정확도: 99.06%\n",
+      "31\t검증 세트 손실: 0.058064\t최선의 손실: 0.035380\t정확도: 98.98%\n",
+      "32\t검증 세트 손실: 0.051286\t최선의 손실: 0.035380\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.080228\t최선의 손실: 0.035380\t정확도: 98.51%\n",
+      "34\t검증 세트 손실: 0.051052\t최선의 손실: 0.035380\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.058742\t최선의 손실: 0.035380\t정확도: 98.91%\n",
+      "36\t검증 세트 손실: 0.063548\t최선의 손실: 0.035380\t정확도: 98.91%\n",
+      "37\t검증 세트 손실: 0.058361\t최선의 손실: 0.035380\t정확도: 98.79%\n",
+      "38\t검증 세트 손실: 0.073720\t최선의 손실: 0.035380\t정확도: 98.79%\n",
+      "39\t검증 세트 손실: 0.089845\t최선의 손실: 0.035380\t정확도: 98.24%\n",
+      "40\t검증 세트 손실: 0.050312\t최선의 손실: 0.035380\t정확도: 98.91%\n",
+      "41\t검증 세트 손실: 0.034238\t최선의 손실: 0.034238\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.039486\t최선의 손실: 0.034238\t정확도: 99.14%\n",
+      "43\t검증 세트 손실: 0.059658\t최선의 손실: 0.034238\t정확도: 99.02%\n",
+      "44\t검증 세트 손실: 0.071439\t최선의 손실: 0.034238\t정확도: 98.91%\n",
+      "45\t검증 세트 손실: 0.042942\t최선의 손실: 0.034238\t정확도: 99.10%\n",
+      "46\t검증 세트 손실: 0.048536\t최선의 손실: 0.034238\t정확도: 99.06%\n",
+      "47\t검증 세트 손실: 0.051885\t최선의 손실: 0.034238\t정확도: 99.14%\n",
+      "48\t검증 세트 손실: 0.072594\t최선의 손실: 0.034238\t정확도: 98.83%\n",
+      "49\t검증 세트 손실: 0.041460\t최선의 손실: 0.034238\t정확도: 99.06%\n",
+      "50\t검증 세트 손실: 0.058374\t최선의 손실: 0.034238\t정확도: 98.91%\n",
+      "51\t검증 세트 손실: 0.032418\t최선의 손실: 0.032418\t정확도: 99.30%\n",
+      "52\t검증 세트 손실: 0.062834\t최선의 손실: 0.032418\t정확도: 98.91%\n",
+      "53\t검증 세트 손실: 0.053102\t최선의 손실: 0.032418\t정확도: 99.10%\n",
+      "54\t검증 세트 손실: 0.054361\t최선의 손실: 0.032418\t정확도: 99.06%\n",
+      "55\t검증 세트 손실: 0.046185\t최선의 손실: 0.032418\t정확도: 99.22%\n",
+      "56\t검증 세트 손실: 0.042961\t최선의 손실: 0.032418\t정확도: 99.26%\n",
+      "57\t검증 세트 손실: 0.044639\t최선의 손실: 0.032418\t정확도: 99.22%\n",
+      "58\t검증 세트 손실: 0.055760\t최선의 손실: 0.032418\t정확도: 98.83%\n",
+      "59\t검증 세트 손실: 0.054755\t최선의 손실: 0.032418\t정확도: 99.02%\n",
+      "60\t검증 세트 손실: 0.071301\t최선의 손실: 0.032418\t정확도: 98.59%\n",
+      "61\t검증 세트 손실: 0.049676\t최선의 손실: 0.032418\t정확도: 98.94%\n",
+      "62\t검증 세트 손실: 0.059672\t최선의 손실: 0.032418\t정확도: 98.87%\n",
+      "63\t검증 세트 손실: 0.046113\t최선의 손실: 0.032418\t정확도: 98.83%\n",
+      "64\t검증 세트 손실: 0.058924\t최선의 손실: 0.032418\t정확도: 99.10%\n",
+      "65\t검증 세트 손실: 0.045565\t최선의 손실: 0.032418\t정확도: 99.06%\n",
+      "66\t검증 세트 손실: 0.066494\t최선의 손실: 0.032418\t정확도: 98.91%\n",
+      "67\t검증 세트 손실: 0.053688\t최선의 손실: 0.032418\t정확도: 99.02%\n",
+      "68\t검증 세트 손실: 0.047278\t최선의 손실: 0.032418\t정확도: 99.02%\n",
+      "69\t검증 세트 손실: 0.059956\t최선의 손실: 0.032418\t정확도: 98.67%\n",
+      "70\t검증 세트 손실: 0.048300\t최선의 손실: 0.032418\t정확도: 99.26%\n",
+      "71\t검증 세트 손실: 0.068859\t최선의 손실: 0.032418\t정확도: 98.67%\n",
+      "72\t검증 세트 손실: 0.057515\t최선의 손실: 0.032418\t정확도: 99.10%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=100, total=  22.6s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.129614\t최선의 손실: 0.129614\t정확도: 96.36%\n",
-      "1\t검증 세트 손실: 0.091170\t최선의 손실: 0.091170\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.096968\t최선의 손실: 0.091170\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.058828\t최선의 손실: 0.058828\t정확도: 98.28%\n",
-      "4\t검증 세트 손실: 0.212912\t최선의 손실: 0.058828\t정확도: 95.82%\n",
-      "5\t검증 세트 손실: 0.112315\t최선의 손실: 0.058828\t정확도: 97.73%\n",
-      "6\t검증 세트 손실: 0.084302\t최선의 손실: 0.058828\t정확도: 98.24%\n",
-      "7\t검증 세트 손실: 0.079986\t최선의 손실: 0.058828\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.099733\t최선의 손실: 0.058828\t정확도: 97.81%\n",
-      "9\t검증 세트 손실: 0.080764\t최선의 손실: 0.058828\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.090275\t최선의 손실: 0.058828\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.128091\t최선의 손실: 0.058828\t정확도: 98.08%\n",
-      "12\t검증 세트 손실: 0.060891\t최선의 손실: 0.058828\t정확도: 98.44%\n",
-      "13\t검증 세트 손실: 0.053354\t최선의 손실: 0.053354\t정확도: 98.91%\n",
-      "14\t검증 세트 손실: 0.206332\t최선의 손실: 0.053354\t정확도: 97.85%\n",
-      "15\t검증 세트 손실: 0.134740\t최선의 손실: 0.053354\t정확도: 98.28%\n",
-      "16\t검증 세트 손실: 0.097256\t최선의 손실: 0.053354\t정확도: 98.16%\n",
-      "17\t검증 세트 손실: 0.074112\t최선의 손실: 0.053354\t정확도: 98.91%\n",
-      "18\t검증 세트 손실: 0.059093\t최선의 손실: 0.053354\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.058916\t최선의 손실: 0.053354\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.090472\t최선의 손실: 0.053354\t정확도: 98.20%\n",
-      "21\t검증 세트 손실: 0.164464\t최선의 손실: 0.053354\t정확도: 98.01%\n",
-      "22\t검증 세트 손실: 1.127154\t최선의 손실: 0.053354\t정확도: 96.76%\n",
-      "23\t검증 세트 손실: 0.913075\t최선의 손실: 0.053354\t정확도: 97.58%\n",
-      "24\t검증 세트 손실: 0.203635\t최선의 손실: 0.053354\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.102421\t최선의 손실: 0.053354\t정확도: 98.91%\n",
-      "26\t검증 세트 손실: 0.097965\t최선의 손실: 0.053354\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.122188\t최선의 손실: 0.053354\t정확도: 98.83%\n",
-      "28\t검증 세트 손실: 0.104975\t최선의 손실: 0.053354\t정확도: 98.98%\n",
-      "29\t검증 세트 손실: 0.100748\t최선의 손실: 0.053354\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.094514\t최선의 손실: 0.053354\t정확도: 98.87%\n",
-      "31\t검증 세트 손실: 0.098528\t최선의 손실: 0.053354\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.097534\t최선의 손실: 0.053354\t정확도: 98.98%\n",
-      "33\t검증 세트 손실: 0.116270\t최선의 손실: 0.053354\t정확도: 98.91%\n",
-      "34\t검증 세트 손실: 0.114458\t최선의 손실: 0.053354\t정확도: 98.75%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100, total=  51.4s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.128765\t최선의 손실: 0.128765\t정확도: 96.95%\n",
+      "1\t검증 세트 손실: 0.109230\t최선의 손실: 0.109230\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.106861\t최선의 손실: 0.106861\t정확도: 97.65%\n",
+      "3\t검증 세트 손실: 0.105287\t최선의 손실: 0.105287\t정확도: 97.07%\n",
+      "4\t검증 세트 손실: 0.097041\t최선의 손실: 0.097041\t정확도: 97.30%\n",
+      "5\t검증 세트 손실: 0.101194\t최선의 손실: 0.097041\t정확도: 98.20%\n",
+      "6\t검증 세트 손실: 0.070162\t최선의 손실: 0.070162\t정확도: 97.89%\n",
+      "7\t검증 세트 손실: 0.110723\t최선의 손실: 0.070162\t정확도: 97.62%\n",
+      "8\t검증 세트 손실: 0.084668\t최선의 손실: 0.070162\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.066144\t최선의 손실: 0.066144\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.068287\t최선의 손실: 0.066144\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.078140\t최선의 손실: 0.066144\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.256938\t최선의 손실: 0.066144\t정확도: 97.93%\n",
+      "13\t검증 세트 손실: 0.744854\t최선의 손실: 0.066144\t정확도: 96.76%\n",
+      "14\t검증 세트 손실: 0.377391\t최선의 손실: 0.066144\t정확도: 97.11%\n",
+      "15\t검증 세트 손실: 0.068453\t최선의 손실: 0.066144\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.055766\t최선의 손실: 0.055766\t정확도: 98.87%\n",
+      "17\t검증 세트 손실: 0.084038\t최선의 손실: 0.055766\t정확도: 98.67%\n",
+      "18\t검증 세트 손실: 0.054554\t최선의 손실: 0.054554\t정확도: 99.06%\n",
+      "19\t검증 세트 손실: 0.067436\t최선의 손실: 0.054554\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.070345\t최선의 손실: 0.054554\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.144514\t최선의 손실: 0.054554\t정확도: 98.12%\n",
+      "22\t검증 세트 손실: 0.069750\t최선의 손실: 0.054554\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.092699\t최선의 손실: 0.054554\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.067160\t최선의 손실: 0.054554\t정확도: 98.83%\n",
+      "25\t검증 세트 손실: 0.415006\t최선의 손실: 0.054554\t정확도: 95.90%\n",
+      "26\t검증 세트 손실: 1.138411\t최선의 손실: 0.054554\t정확도: 97.77%\n",
+      "27\t검증 세트 손실: 0.166700\t최선의 손실: 0.054554\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.224209\t최선의 손실: 0.054554\t정확도: 98.59%\n",
+      "29\t검증 세트 손실: 0.125089\t최선의 손실: 0.054554\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.148741\t최선의 손실: 0.054554\t정확도: 98.67%\n",
+      "31\t검증 세트 손실: 0.210633\t최선의 손실: 0.054554\t정확도: 98.20%\n",
+      "32\t검증 세트 손실: 0.165081\t최선의 손실: 0.054554\t정확도: 98.51%\n",
+      "33\t검증 세트 손실: 0.284772\t최선의 손실: 0.054554\t정확도: 98.48%\n",
+      "34\t검증 세트 손실: 0.165289\t최선의 손실: 0.054554\t정확도: 98.75%\n",
+      "35\t검증 세트 손실: 0.173287\t최선의 손실: 0.054554\t정확도: 98.67%\n",
+      "36\t검증 세트 손실: 0.129511\t최선의 손실: 0.054554\t정확도: 98.79%\n",
+      "37\t검증 세트 손실: 0.127490\t최선의 손실: 0.054554\t정확도: 98.63%\n",
+      "38\t검증 세트 손실: 0.138601\t최선의 손실: 0.054554\t정확도: 98.67%\n",
+      "39\t검증 세트 손실: 0.121465\t최선의 손실: 0.054554\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=90, total=  24.6s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.135932\t최선의 손실: 0.135932\t정확도: 96.83%\n",
-      "1\t검증 세트 손실: 0.071364\t최선의 손실: 0.071364\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.077112\t최선의 손실: 0.071364\t정확도: 97.85%\n",
-      "3\t검증 세트 손실: 0.110510\t최선의 손실: 0.071364\t정확도: 97.11%\n",
-      "4\t검증 세트 손실: 0.076001\t최선의 손실: 0.071364\t정확도: 98.16%\n",
-      "5\t검증 세트 손실: 0.052847\t최선의 손실: 0.052847\t정확도: 98.51%\n",
-      "6\t검증 세트 손실: 0.095253\t최선의 손실: 0.052847\t정확도: 98.05%\n",
-      "7\t검증 세트 손실: 0.098554\t최선의 손실: 0.052847\t정확도: 97.81%\n",
-      "8\t검증 세트 손실: 0.064224\t최선의 손실: 0.052847\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.161043\t최선의 손실: 0.052847\t정확도: 96.87%\n",
-      "10\t검증 세트 손실: 0.118186\t최선의 손실: 0.052847\t정확도: 98.28%\n",
-      "11\t검증 세트 손실: 0.139484\t최선의 손실: 0.052847\t정확도: 97.97%\n",
-      "12\t검증 세트 손실: 0.207130\t최선의 손실: 0.052847\t정확도: 97.81%\n",
-      "13\t검증 세트 손실: 0.119540\t최선의 손실: 0.052847\t정확도: 98.01%\n",
-      "14\t검증 세트 손실: 0.041092\t최선의 손실: 0.041092\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.119121\t최선의 손실: 0.041092\t정확도: 97.46%\n",
-      "16\t검증 세트 손실: 0.119001\t최선의 손실: 0.041092\t정확도: 97.81%\n",
-      "17\t검증 세트 손실: 13.950522\t최선의 손실: 0.041092\t정확도: 79.24%\n",
-      "18\t검증 세트 손실: 0.161590\t최선의 손실: 0.041092\t정확도: 98.59%\n",
-      "19\t검증 세트 손실: 0.099294\t최선의 손실: 0.041092\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.104133\t최선의 손실: 0.041092\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.136227\t최선의 손실: 0.041092\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.112590\t최선의 손실: 0.041092\t정확도: 98.51%\n",
-      "23\t검증 세트 손실: 0.103317\t최선의 손실: 0.041092\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.090096\t최선의 손실: 0.041092\t정확도: 98.67%\n",
-      "25\t검증 세트 손실: 0.072276\t최선의 손실: 0.041092\t정확도: 99.14%\n",
-      "26\t검증 세트 손실: 0.130532\t최선의 손실: 0.041092\t정확도: 98.63%\n",
-      "27\t검증 세트 손실: 0.104004\t최선의 손실: 0.041092\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.094635\t최선의 손실: 0.041092\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.071498\t최선의 손실: 0.041092\t정확도: 99.10%\n",
-      "30\t검증 세트 손실: 0.095797\t최선의 손실: 0.041092\t정확도: 98.87%\n",
-      "31\t검증 세트 손실: 0.073516\t최선의 손실: 0.041092\t정확도: 99.02%\n",
-      "32\t검증 세트 손실: 0.169024\t최선의 손실: 0.041092\t정확도: 98.63%\n",
-      "33\t검증 세트 손실: 0.176421\t최선의 손실: 0.041092\t정확도: 98.55%\n",
-      "34\t검증 세트 손실: 0.425699\t최선의 손실: 0.041092\t정확도: 98.51%\n",
-      "35\t검증 세트 손실: 0.107119\t최선의 손실: 0.041092\t정확도: 98.87%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=90, total=  29.0s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.200460\t최선의 손실: 0.200460\t정확도: 95.74%\n",
+      "1\t검증 세트 손실: 0.078041\t최선의 손실: 0.078041\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.120519\t최선의 손실: 0.078041\t정확도: 96.91%\n",
+      "3\t검증 세트 손실: 0.105682\t최선의 손실: 0.078041\t정확도: 97.19%\n",
+      "4\t검증 세트 손실: 0.102752\t최선의 손실: 0.078041\t정확도: 97.65%\n",
+      "5\t검증 세트 손실: 0.072649\t최선의 손실: 0.072649\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.108762\t최선의 손실: 0.072649\t정확도: 97.69%\n",
+      "7\t검증 세트 손실: 0.097297\t최선의 손실: 0.072649\t정확도: 98.01%\n",
+      "8\t검증 세트 손실: 0.079924\t최선의 손실: 0.072649\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.195067\t최선의 손실: 0.072649\t정확도: 97.19%\n",
+      "10\t검증 세트 손실: 0.129881\t최선의 손실: 0.072649\t정확도: 98.08%\n",
+      "11\t검증 세트 손실: 0.060155\t최선의 손실: 0.060155\t정확도: 98.59%\n",
+      "12\t검증 세트 손실: 0.053662\t최선의 손실: 0.053662\t정확도: 98.91%\n",
+      "13\t검증 세트 손실: 0.112037\t최선의 손실: 0.053662\t정확도: 98.20%\n",
+      "14\t검증 세트 손실: 0.080377\t최선의 손실: 0.053662\t정확도: 98.16%\n",
+      "15\t검증 세트 손실: 0.079755\t최선의 손실: 0.053662\t정확도: 98.36%\n",
+      "16\t검증 세트 손실: 0.058739\t최선의 손실: 0.053662\t정확도: 99.06%\n",
+      "17\t검증 세트 손실: 0.099610\t최선의 손실: 0.053662\t정확도: 98.51%\n",
+      "18\t검증 세트 손실: 0.350080\t최선의 손실: 0.053662\t정확도: 97.58%\n",
+      "19\t검증 세트 손실: 0.135290\t최선의 손실: 0.053662\t정확도: 98.32%\n",
+      "20\t검증 세트 손실: 0.115173\t최선의 손실: 0.053662\t정확도: 97.65%\n",
+      "21\t검증 세트 손실: 0.069374\t최선의 손실: 0.053662\t정확도: 98.71%\n",
+      "22\t검증 세트 손실: 0.076693\t최선의 손실: 0.053662\t정확도: 98.83%\n",
+      "23\t검증 세트 손실: 0.076522\t최선의 손실: 0.053662\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.070103\t최선의 손실: 0.053662\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.085081\t최선의 손실: 0.053662\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.091652\t최선의 손실: 0.053662\t정확도: 98.44%\n",
+      "27\t검증 세트 손실: 0.099273\t최선의 손실: 0.053662\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 9.969027\t최선의 손실: 0.053662\t정확도: 91.75%\n",
+      "29\t검증 세트 손실: 0.200338\t최선의 손실: 0.053662\t정확도: 98.51%\n",
+      "30\t검증 세트 손실: 0.305656\t최선의 손실: 0.053662\t정확도: 97.77%\n",
+      "31\t검증 세트 손실: 0.168912\t최선의 손실: 0.053662\t정확도: 98.67%\n",
+      "32\t검증 세트 손실: 0.144018\t최선의 손실: 0.053662\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.245742\t최선의 손실: 0.053662\t정확도: 98.55%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=90, total=  25.2s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.189836\t최선의 손실: 0.189836\t정확도: 95.62%\n",
-      "1\t검증 세트 손실: 0.095061\t최선의 손실: 0.095061\t정확도: 97.15%\n",
-      "2\t검증 세트 손실: 0.087732\t최선의 손실: 0.087732\t정확도: 97.65%\n",
-      "3\t검증 세트 손실: 0.141436\t최선의 손실: 0.087732\t정확도: 96.44%\n",
-      "4\t검증 세트 손실: 0.078159\t최선의 손실: 0.078159\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.067946\t최선의 손실: 0.067946\t정확도: 97.77%\n",
-      "6\t검증 세트 손실: 0.069333\t최선의 손실: 0.067946\t정확도: 97.97%\n",
-      "7\t검증 세트 손실: 0.101641\t최선의 손실: 0.067946\t정확도: 98.36%\n",
-      "8\t검증 세트 손실: 0.098746\t최선의 손실: 0.067946\t정확도: 97.77%\n",
-      "9\t검증 세트 손실: 0.056218\t최선의 손실: 0.056218\t정확도: 98.24%\n",
-      "10\t검증 세트 손실: 0.080033\t최선의 손실: 0.056218\t정확도: 98.16%\n",
-      "11\t검증 세트 손실: 0.244248\t최선의 손실: 0.056218\t정확도: 97.69%\n",
-      "12\t검증 세트 손실: 0.060152\t최선의 손실: 0.056218\t정확도: 98.83%\n",
-      "13\t검증 세트 손실: 0.060118\t최선의 손실: 0.056218\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.083029\t최선의 손실: 0.056218\t정확도: 98.40%\n",
-      "15\t검증 세트 손실: 7.346083\t최선의 손실: 0.056218\t정확도: 95.19%\n",
-      "16\t검증 세트 손실: 0.511338\t최선의 손실: 0.056218\t정확도: 95.47%\n",
-      "17\t검증 세트 손실: 0.210103\t최선의 손실: 0.056218\t정확도: 97.85%\n",
-      "18\t검증 세트 손실: 0.135497\t최선의 손실: 0.056218\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.091861\t최선의 손실: 0.056218\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.125557\t최선의 손실: 0.056218\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.324336\t최선의 손실: 0.056218\t정확도: 98.20%\n",
-      "22\t검증 세트 손실: 0.123898\t최선의 손실: 0.056218\t정확도: 98.71%\n",
-      "23\t검증 세트 손실: 0.125966\t최선의 손실: 0.056218\t정확도: 98.79%\n",
-      "24\t검증 세트 손실: 0.141039\t최선의 손실: 0.056218\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.241843\t최선의 손실: 0.056218\t정확도: 98.08%\n",
-      "26\t검증 세트 손실: 0.168513\t최선의 손실: 0.056218\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.204078\t최선의 손실: 0.056218\t정확도: 98.05%\n",
-      "28\t검증 세트 손실: 0.151762\t최선의 손실: 0.056218\t정확도: 98.75%\n",
-      "29\t검증 세트 손실: 0.188834\t최선의 손실: 0.056218\t정확도: 98.40%\n",
-      "30\t검증 세트 손실: 0.122915\t최선의 손실: 0.056218\t정확도: 98.79%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=90, total=  22.2s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.194773\t최선의 손실: 0.194773\t정확도: 96.79%\n",
-      "1\t검증 세트 손실: 0.077545\t최선의 손실: 0.077545\t정확도: 97.50%\n",
-      "2\t검증 세트 손실: 0.067367\t최선의 손실: 0.067367\t정확도: 98.08%\n",
-      "3\t검증 세트 손실: 0.051447\t최선의 손실: 0.051447\t정확도: 98.48%\n",
-      "4\t검증 세트 손실: 0.056587\t최선의 손실: 0.051447\t정확도: 98.44%\n",
-      "5\t검증 세트 손실: 0.077897\t최선의 손실: 0.051447\t정확도: 97.85%\n",
-      "6\t검증 세트 손실: 0.056480\t최선의 손실: 0.051447\t정확도: 98.05%\n",
-      "7\t검증 세트 손실: 0.075205\t최선의 손실: 0.051447\t정확도: 98.20%\n",
-      "8\t검증 세트 손실: 0.060857\t최선의 손실: 0.051447\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.051656\t최선의 손실: 0.051447\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.056602\t최선의 손실: 0.051447\t정확도: 98.59%\n",
-      "11\t검증 세트 손실: 0.052860\t최선의 손실: 0.051447\t정확도: 98.59%\n",
-      "12\t검증 세트 손실: 0.043399\t최선의 손실: 0.043399\t정확도: 98.91%\n",
-      "13\t검증 세트 손실: 0.059473\t최선의 손실: 0.043399\t정확도: 98.59%\n",
-      "14\t검증 세트 손실: 0.050591\t최선의 손실: 0.043399\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.076999\t최선의 손실: 0.043399\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.090480\t최선의 손실: 0.043399\t정확도: 98.01%\n",
-      "17\t검증 세트 손실: 0.066614\t최선의 손실: 0.043399\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.070953\t최선의 손실: 0.043399\t정확도: 98.63%\n",
-      "19\t검증 세트 손실: 0.073684\t최선의 손실: 0.043399\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.056462\t최선의 손실: 0.043399\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.058668\t최선의 손실: 0.043399\t정확도: 98.98%\n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=90, total=  24.6s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.179192\t최선의 손실: 0.179192\t정확도: 96.21%\n",
+      "1\t검증 세트 손실: 0.107784\t최선의 손실: 0.107784\t정확도: 97.26%\n",
+      "2\t검증 세트 손실: 0.117730\t최선의 손실: 0.107784\t정확도: 96.72%\n",
+      "3\t검증 세트 손실: 0.073185\t최선의 손실: 0.073185\t정확도: 98.28%\n",
+      "4\t검증 세트 손실: 0.103491\t최선의 손실: 0.073185\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 0.091637\t최선의 손실: 0.073185\t정확도: 97.89%\n",
+      "6\t검증 세트 손실: 0.060660\t최선의 손실: 0.060660\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.080753\t최선의 손실: 0.060660\t정확도: 97.85%\n",
+      "8\t검증 세트 손실: 0.071843\t최선의 손실: 0.060660\t정확도: 98.28%\n",
+      "9\t검증 세트 손실: 0.053894\t최선의 손실: 0.053894\t정확도: 98.75%\n",
+      "10\t검증 세트 손실: 0.261849\t최선의 손실: 0.053894\t정확도: 96.40%\n",
+      "11\t검증 세트 손실: 0.108982\t최선의 손실: 0.053894\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.103779\t최선의 손실: 0.053894\t정확도: 98.08%\n",
+      "13\t검증 세트 손실: 0.688804\t최선의 손실: 0.053894\t정확도: 97.19%\n",
+      "14\t검증 세트 손실: 0.147013\t최선의 손실: 0.053894\t정확도: 98.24%\n",
+      "15\t검증 세트 손실: 0.078057\t최선의 손실: 0.053894\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.086667\t최선의 손실: 0.053894\t정확도: 98.32%\n",
+      "17\t검증 세트 손실: 0.067731\t최선의 손실: 0.053894\t정확도: 98.94%\n",
+      "18\t검증 세트 손실: 0.121521\t최선의 손실: 0.053894\t정확도: 98.20%\n",
+      "19\t검증 세트 손실: 0.076988\t최선의 손실: 0.053894\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.069042\t최선의 손실: 0.053894\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.089692\t최선의 손실: 0.053894\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.080063\t최선의 손실: 0.053894\t정확도: 98.67%\n",
+      "23\t검증 세트 손실: 0.074204\t최선의 손실: 0.053894\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.107091\t최선의 손실: 0.053894\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.306583\t최선의 손실: 0.053894\t정확도: 97.73%\n",
+      "26\t검증 세트 손실: 0.260135\t최선의 손실: 0.053894\t정확도: 97.81%\n",
+      "27\t검증 세트 손실: 0.068766\t최선의 손실: 0.053894\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.060516\t최선의 손실: 0.053894\t정확도: 99.10%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "22\t검증 세트 손실: 0.058935\t최선의 손실: 0.043399\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.073786\t최선의 손실: 0.043399\t정확도: 98.55%\n",
-      "24\t검증 세트 손실: 0.065987\t최선의 손실: 0.043399\t정확도: 98.67%\n",
-      "25\t검증 세트 손실: 0.071629\t최선의 손실: 0.043399\t정확도: 98.83%\n",
-      "26\t검증 세트 손실: 0.054794\t최선의 손실: 0.043399\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.069758\t최선의 손실: 0.043399\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.074406\t최선의 손실: 0.043399\t정확도: 98.44%\n",
-      "29\t검증 세트 손실: 0.065840\t최선의 손실: 0.043399\t정확도: 98.67%\n",
-      "30\t검증 세트 손실: 0.077554\t최선의 손실: 0.043399\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.074336\t최선의 손실: 0.043399\t정확도: 98.59%\n",
-      "32\t검증 세트 손실: 0.094563\t최선의 손실: 0.043399\t정확도: 98.16%\n",
-      "33\t검증 세트 손실: 0.060390\t최선의 손실: 0.043399\t정확도: 98.91%\n",
+      "29\t검증 세트 손실: 0.058014\t최선의 손실: 0.053894\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.077688\t최선의 손실: 0.053894\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=70, total=   6.7s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.248603\t최선의 손실: 0.248603\t정확도: 95.82%\n",
-      "1\t검증 세트 손실: 0.080798\t최선의 손실: 0.080798\t정확도: 97.89%\n",
-      "2\t검증 세트 손실: 0.065363\t최선의 손실: 0.065363\t정확도: 97.93%\n",
-      "3\t검증 세트 손실: 0.062382\t최선의 손실: 0.062382\t정확도: 98.12%\n",
-      "4\t검증 세트 손실: 0.085972\t최선의 손실: 0.062382\t정확도: 97.38%\n",
-      "5\t검증 세트 손실: 0.073710\t최선의 손실: 0.062382\t정확도: 97.89%\n",
-      "6\t검증 세트 손실: 0.048596\t최선의 손실: 0.048596\t정확도: 98.67%\n",
-      "7\t검증 세트 손실: 0.061401\t최선의 손실: 0.048596\t정확도: 98.44%\n",
-      "8\t검증 세트 손실: 0.055596\t최선의 손실: 0.048596\t정확도: 98.48%\n",
-      "9\t검증 세트 손실: 0.063431\t최선의 손실: 0.048596\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.054427\t최선의 손실: 0.048596\t정확도: 98.59%\n",
-      "11\t검증 세트 손실: 0.076134\t최선의 손실: 0.048596\t정확도: 98.24%\n",
-      "12\t검증 세트 손실: 0.054631\t최선의 손실: 0.048596\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.058194\t최선의 손실: 0.048596\t정확도: 98.40%\n",
-      "14\t검증 세트 손실: 0.058228\t최선의 손실: 0.048596\t정확도: 98.75%\n",
-      "15\t검증 세트 손실: 0.048272\t최선의 손실: 0.048272\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.062301\t최선의 손실: 0.048272\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.056942\t최선의 손실: 0.048272\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.074948\t최선의 손실: 0.048272\t정확도: 98.63%\n",
-      "19\t검증 세트 손실: 0.058288\t최선의 손실: 0.048272\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.050247\t최선의 손실: 0.048272\t정확도: 98.91%\n",
-      "21\t검증 세트 손실: 0.086412\t최선의 손실: 0.048272\t정확도: 98.16%\n",
-      "22\t검증 세트 손실: 0.080910\t최선의 손실: 0.048272\t정확도: 98.63%\n",
-      "23\t검증 세트 손실: 0.092128\t최선의 손실: 0.048272\t정확도: 98.40%\n",
-      "24\t검증 세트 손실: 0.080097\t최선의 손실: 0.048272\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.057133\t최선의 손실: 0.048272\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.061928\t최선의 손실: 0.048272\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.048856\t최선의 손실: 0.048272\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.047911\t최선의 손실: 0.047911\t정확도: 98.79%\n",
-      "29\t검증 세트 손실: 0.046883\t최선의 손실: 0.046883\t정확도: 98.75%\n",
-      "30\t검증 세트 손실: 0.098631\t최선의 손실: 0.046883\t정확도: 97.93%\n",
-      "31\t검증 세트 손실: 0.059968\t최선의 손실: 0.046883\t정확도: 98.67%\n",
-      "32\t검증 세트 손실: 0.110838\t최선의 손실: 0.046883\t정확도: 98.01%\n",
-      "33\t검증 세트 손실: 0.097279\t최선의 손실: 0.046883\t정확도: 98.28%\n",
-      "34\t검증 세트 손실: 0.062698\t최선의 손실: 0.046883\t정확도: 98.79%\n",
-      "35\t검증 세트 손실: 0.071800\t최선의 손실: 0.046883\t정확도: 98.40%\n",
-      "36\t검증 세트 손실: 0.081824\t최선의 손실: 0.046883\t정확도: 98.55%\n",
-      "37\t검증 세트 손실: 0.080716\t최선의 손실: 0.046883\t정확도: 98.67%\n",
-      "38\t검증 세트 손실: 0.076471\t최선의 손실: 0.046883\t정확도: 98.63%\n",
-      "39\t검증 세트 손실: 0.056460\t최선의 손실: 0.046883\t정확도: 98.94%\n",
-      "40\t검증 세트 손실: 0.066606\t최선의 손실: 0.046883\t정확도: 98.79%\n",
-      "41\t검증 세트 손실: 0.064084\t최선의 손실: 0.046883\t정확도: 98.79%\n",
-      "42\t검증 세트 손실: 0.059847\t최선의 손실: 0.046883\t정확도: 98.83%\n",
-      "43\t검증 세트 손실: 0.066599\t최선의 손실: 0.046883\t정확도: 98.71%\n",
-      "44\t검증 세트 손실: 0.059943\t최선의 손실: 0.046883\t정확도: 98.83%\n",
-      "45\t검증 세트 손실: 0.063150\t최선의 손실: 0.046883\t정확도: 98.83%\n",
-      "46\t검증 세트 손실: 0.062073\t최선의 손실: 0.046883\t정확도: 98.83%\n",
-      "47\t검증 세트 손실: 0.068705\t최선의 손실: 0.046883\t정확도: 98.75%\n",
-      "48\t검증 세트 손실: 0.068634\t최선의 손실: 0.046883\t정확도: 98.79%\n",
-      "49\t검증 세트 손실: 0.067485\t최선의 손실: 0.046883\t정확도: 98.79%\n",
-      "50\t검증 세트 손실: 0.067968\t최선의 손실: 0.046883\t정확도: 98.79%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=90, total=  22.5s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.180757\t최선의 손실: 0.180757\t정확도: 96.87%\n",
+      "1\t검증 세트 손실: 0.084867\t최선의 손실: 0.084867\t정확도: 97.58%\n",
+      "2\t검증 세트 손실: 0.076456\t최선의 손실: 0.076456\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.069824\t최선의 손실: 0.069824\t정확도: 98.16%\n",
+      "4\t검증 세트 손실: 0.054059\t최선의 손실: 0.054059\t정확도: 98.40%\n",
+      "5\t검증 세트 손실: 0.069224\t최선의 손실: 0.054059\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.065480\t최선의 손실: 0.054059\t정확도: 97.97%\n",
+      "7\t검증 세트 손실: 0.058515\t최선의 손실: 0.054059\t정확도: 98.44%\n",
+      "8\t검증 세트 손실: 0.068231\t최선의 손실: 0.054059\t정확도: 98.08%\n",
+      "9\t검증 세트 손실: 0.101827\t최선의 손실: 0.054059\t정확도: 97.62%\n",
+      "10\t검증 세트 손실: 0.067064\t최선의 손실: 0.054059\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.084453\t최선의 손실: 0.054059\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.082210\t최선의 손실: 0.054059\t정확도: 98.12%\n",
+      "13\t검증 세트 손실: 0.077878\t최선의 손실: 0.054059\t정확도: 98.36%\n",
+      "14\t검증 세트 손실: 0.089336\t최선의 손실: 0.054059\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.074467\t최선의 손실: 0.054059\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.083832\t최선의 손실: 0.054059\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.078087\t최선의 손실: 0.054059\t정확도: 98.44%\n",
+      "18\t검증 세트 손실: 0.092584\t최선의 손실: 0.054059\t정확도: 98.32%\n",
+      "19\t검증 세트 손실: 0.077227\t최선의 손실: 0.054059\t정확도: 98.59%\n",
+      "20\t검증 세트 손실: 0.063012\t최선의 손실: 0.054059\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.055073\t최선의 손실: 0.054059\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.067046\t최선의 손실: 0.054059\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.070101\t최선의 손실: 0.054059\t정확도: 98.55%\n",
+      "24\t검증 세트 손실: 0.071371\t최선의 손실: 0.054059\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.088405\t최선의 손실: 0.054059\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=70, total=   9.4s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.227437\t최선의 손실: 0.227437\t정확도: 96.05%\n",
-      "1\t검증 세트 손실: 0.076199\t최선의 손실: 0.076199\t정확도: 98.08%\n",
-      "2\t검증 세트 손실: 0.057159\t최선의 손실: 0.057159\t정확도: 98.24%\n",
-      "3\t검증 세트 손실: 0.074126\t최선의 손실: 0.057159\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.072090\t최선의 손실: 0.057159\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.070738\t최선의 손실: 0.057159\t정확도: 97.81%\n",
-      "6\t검증 세트 손실: 0.063844\t최선의 손실: 0.057159\t정확도: 98.51%\n",
-      "7\t검증 세트 손실: 0.046396\t최선의 손실: 0.046396\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.059848\t최선의 손실: 0.046396\t정확도: 98.36%\n",
-      "9\t검증 세트 손실: 0.058500\t최선의 손실: 0.046396\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.058148\t최선의 손실: 0.046396\t정확도: 98.75%\n",
-      "11\t검증 세트 손실: 0.071850\t최선의 손실: 0.046396\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.066604\t최선의 손실: 0.046396\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.063818\t최선의 손실: 0.046396\t정확도: 98.55%\n",
-      "14\t검증 세트 손실: 0.073294\t최선의 손실: 0.046396\t정확도: 98.32%\n",
-      "15\t검증 세트 손실: 0.056874\t최선의 손실: 0.046396\t정확도: 98.59%\n",
-      "16\t검증 세트 손실: 0.060067\t최선의 손실: 0.046396\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.057291\t최선의 손실: 0.046396\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.063052\t최선의 손실: 0.046396\t정확도: 98.87%\n",
-      "19\t검증 세트 손실: 0.067986\t최선의 손실: 0.046396\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.057197\t최선의 손실: 0.046396\t정확도: 98.98%\n",
-      "21\t검증 세트 손실: 0.052492\t최선의 손실: 0.046396\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.066387\t최선의 손실: 0.046396\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.072502\t최선의 손실: 0.046396\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.059915\t최선의 손실: 0.046396\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.068404\t최선의 손실: 0.046396\t정확도: 98.79%\n",
-      "26\t검증 세트 손실: 0.055132\t최선의 손실: 0.046396\t정확도: 98.83%\n",
-      "27\t검증 세트 손실: 0.073424\t최선의 손실: 0.046396\t정확도: 98.51%\n",
-      "28\t검증 세트 손실: 0.096211\t최선의 손실: 0.046396\t정확도: 98.16%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=70, total=   6.0s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.238777\t최선의 손실: 0.238777\t정확도: 96.09%\n",
+      "1\t검증 세트 손실: 0.084124\t최선의 손실: 0.084124\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.072420\t최선의 손실: 0.072420\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.077255\t최선의 손실: 0.072420\t정확도: 97.93%\n",
+      "4\t검증 세트 손실: 0.086163\t최선의 손실: 0.072420\t정확도: 97.81%\n",
+      "5\t검증 세트 손실: 0.055903\t최선의 손실: 0.055903\t정확도: 98.40%\n",
+      "6\t검증 세트 손실: 0.060110\t최선의 손실: 0.055903\t정확도: 98.28%\n",
+      "7\t검증 세트 손실: 0.069010\t최선의 손실: 0.055903\t정확도: 98.28%\n",
+      "8\t검증 세트 손실: 0.068578\t최선의 손실: 0.055903\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.055525\t최선의 손실: 0.055525\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.068984\t최선의 손실: 0.055525\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.091190\t최선의 손실: 0.055525\t정확도: 98.05%\n",
+      "12\t검증 세트 손실: 0.059331\t최선의 손실: 0.055525\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.066820\t최선의 손실: 0.055525\t정확도: 98.48%\n",
+      "14\t검증 세트 손실: 0.058275\t최선의 손실: 0.055525\t정확도: 98.32%\n",
+      "15\t검증 세트 손실: 0.072034\t최선의 손실: 0.055525\t정확도: 98.36%\n",
+      "16\t검증 세트 손실: 0.057350\t최선의 손실: 0.055525\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.073128\t최선의 손실: 0.055525\t정확도: 98.44%\n",
+      "18\t검증 세트 손실: 0.085329\t최선의 손실: 0.055525\t정확도: 98.24%\n",
+      "19\t검증 세트 손실: 0.095423\t최선의 손실: 0.055525\t정확도: 98.01%\n",
+      "20\t검증 세트 손실: 0.100209\t최선의 손실: 0.055525\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.094351\t최선의 손실: 0.055525\t정확도: 98.05%\n",
+      "22\t검증 세트 손실: 0.086857\t최선의 손실: 0.055525\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.068587\t최선의 손실: 0.055525\t정확도: 98.71%\n",
+      "24\t검증 세트 손실: 0.074157\t최선의 손실: 0.055525\t정확도: 98.83%\n",
+      "25\t검증 세트 손실: 0.068431\t최선의 손실: 0.055525\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.070078\t최선의 손실: 0.055525\t정확도: 98.67%\n",
+      "27\t검증 세트 손실: 0.062776\t최선의 손실: 0.055525\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.062677\t최선의 손실: 0.055525\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.064563\t최선의 손실: 0.055525\t정확도: 98.71%\n",
+      "30\t검증 세트 손실: 0.073195\t최선의 손실: 0.055525\t정확도: 98.75%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=70, total=   6.0s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.084062\t최선의 손실: 0.084062\t정확도: 97.54%\n",
-      "1\t검증 세트 손실: 0.071922\t최선의 손실: 0.071922\t정확도: 97.85%\n",
-      "2\t검증 세트 손실: 0.063998\t최선의 손실: 0.063998\t정확도: 98.24%\n",
-      "3\t검증 세트 손실: 0.063668\t최선의 손실: 0.063668\t정확도: 98.24%\n",
-      "4\t검증 세트 손실: 0.056555\t최선의 손실: 0.056555\t정확도: 98.40%\n",
-      "5\t검증 세트 손실: 0.082867\t최선의 손실: 0.056555\t정확도: 98.24%\n",
-      "6\t검증 세트 손실: 0.051940\t최선의 손실: 0.051940\t정확도: 98.32%\n",
-      "7\t검증 세트 손실: 0.044013\t최선의 손실: 0.044013\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.043150\t최선의 손실: 0.043150\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.055731\t최선의 손실: 0.043150\t정확도: 98.48%\n",
-      "10\t검증 세트 손실: 0.054150\t최선의 손실: 0.043150\t정확도: 98.36%\n",
-      "11\t검증 세트 손실: 0.048467\t최선의 손실: 0.043150\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.051338\t최선의 손실: 0.043150\t정확도: 98.98%\n",
-      "13\t검증 세트 손실: 0.058139\t최선의 손실: 0.043150\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.068121\t최선의 손실: 0.043150\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.065366\t최선의 손실: 0.043150\t정확도: 98.44%\n",
-      "16\t검증 세트 손실: 0.057391\t최선의 손실: 0.043150\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.054255\t최선의 손실: 0.043150\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.052463\t최선의 손실: 0.043150\t정확도: 98.87%\n",
-      "19\t검증 세트 손실: 0.070246\t최선의 손실: 0.043150\t정확도: 98.40%\n",
-      "20\t검증 세트 손실: 0.044258\t최선의 손실: 0.043150\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.044096\t최선의 손실: 0.043150\t정확도: 98.98%\n",
-      "22\t검증 세트 손실: 0.053833\t최선의 손실: 0.043150\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.057250\t최선의 손실: 0.043150\t정확도: 98.67%\n",
-      "24\t검증 세트 손실: 0.065152\t최선의 손실: 0.043150\t정확도: 98.24%\n",
-      "25\t검증 세트 손실: 0.067450\t최선의 손실: 0.043150\t정확도: 98.67%\n",
-      "26\t검증 세트 손실: 0.082443\t최선의 손실: 0.043150\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.049826\t최선의 손실: 0.043150\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.067473\t최선의 손실: 0.043150\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.071836\t최선의 손실: 0.043150\t정확도: 98.55%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=70, total=   6.8s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.196650\t최선의 손실: 0.196650\t정확도: 96.40%\n",
+      "1\t검증 세트 손실: 0.085189\t최선의 손실: 0.085189\t정확도: 97.81%\n",
+      "2\t검증 세트 손실: 0.062124\t최선의 손실: 0.062124\t정확도: 98.40%\n",
+      "3\t검증 세트 손실: 0.058201\t최선의 손실: 0.058201\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.054694\t최선의 손실: 0.054694\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.081935\t최선의 손실: 0.054694\t정확도: 97.73%\n",
+      "6\t검증 세트 손실: 0.055264\t최선의 손실: 0.054694\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.052535\t최선의 손실: 0.052535\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.052626\t최선의 손실: 0.052535\t정확도: 98.59%\n",
+      "9\t검증 세트 손실: 0.057204\t최선의 손실: 0.052535\t정확도: 98.79%\n",
+      "10\t검증 세트 손실: 0.046867\t최선의 손실: 0.046867\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.067172\t최선의 손실: 0.046867\t정확도: 98.59%\n",
+      "12\t검증 세트 손실: 0.064424\t최선의 손실: 0.046867\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.072622\t최선의 손실: 0.046867\t정확도: 98.71%\n",
+      "14\t검증 세트 손실: 0.069293\t최선의 손실: 0.046867\t정확도: 98.44%\n",
+      "15\t검증 세트 손실: 0.077501\t최선의 손실: 0.046867\t정확도: 98.12%\n",
+      "16\t검증 세트 손실: 0.058976\t최선의 손실: 0.046867\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.055445\t최선의 손실: 0.046867\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.065526\t최선의 손실: 0.046867\t정확도: 98.87%\n",
+      "19\t검증 세트 손실: 0.076662\t최선의 손실: 0.046867\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.057090\t최선의 손실: 0.046867\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.064278\t최선의 손실: 0.046867\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.059920\t최선의 손실: 0.046867\t정확도: 98.75%\n",
+      "23\t검증 세트 손실: 0.061062\t최선의 손실: 0.046867\t정확도: 98.94%\n",
+      "24\t검증 세트 손실: 0.057498\t최선의 손실: 0.046867\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.066788\t최선의 손실: 0.046867\t정확도: 98.55%\n",
+      "26\t검증 세트 손실: 0.070715\t최선의 손실: 0.046867\t정확도: 98.63%\n",
+      "27\t검증 세트 손실: 0.064959\t최선의 손실: 0.046867\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.046098\t최선의 손실: 0.046098\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.061308\t최선의 손실: 0.046098\t정확도: 99.02%\n",
+      "30\t검증 세트 손실: 0.055047\t최선의 손실: 0.046098\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.064623\t최선의 손실: 0.046098\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.044539\t최선의 손실: 0.044539\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.045530\t최선의 손실: 0.044539\t정확도: 98.98%\n",
+      "34\t검증 세트 손실: 0.046554\t최선의 손실: 0.044539\t정확도: 99.06%\n",
+      "35\t검증 세트 손실: 0.053516\t최선의 손실: 0.044539\t정확도: 99.10%\n",
+      "36\t검증 세트 손실: 0.073186\t최선의 손실: 0.044539\t정확도: 98.63%\n",
+      "37\t검증 세트 손실: 0.104298\t최선의 손실: 0.044539\t정확도: 98.40%\n",
+      "38\t검증 세트 손실: 0.107996\t최선의 손실: 0.044539\t정확도: 98.16%\n",
+      "39\t검증 세트 손실: 0.079892\t최선의 손실: 0.044539\t정확도: 98.51%\n",
+      "40\t검증 세트 손실: 0.097744\t최선의 손실: 0.044539\t정확도: 98.36%\n",
+      "41\t검증 세트 손실: 0.069988\t최선의 손실: 0.044539\t정확도: 98.55%\n",
+      "42\t검증 세트 손실: 0.063090\t최선의 손실: 0.044539\t정확도: 98.63%\n",
+      "43\t검증 세트 손실: 0.072710\t최선의 손실: 0.044539\t정확도: 98.75%\n",
+      "44\t검증 세트 손실: 0.051018\t최선의 손실: 0.044539\t정확도: 98.75%\n",
+      "45\t검증 세트 손실: 0.045012\t최선의 손실: 0.044539\t정확도: 98.91%\n",
+      "46\t검증 세트 손실: 0.051731\t최선의 손실: 0.044539\t정확도: 98.83%\n",
+      "47\t검증 세트 손실: 0.045377\t최선의 손실: 0.044539\t정확도: 98.98%\n",
+      "48\t검증 세트 손실: 0.046650\t최선의 손실: 0.044539\t정확도: 98.91%\n",
+      "49\t검증 세트 손실: 0.050845\t최선의 손실: 0.044539\t정확도: 98.94%\n",
+      "50\t검증 세트 손실: 0.073915\t최선의 손실: 0.044539\t정확도: 98.63%\n",
+      "51\t검증 세트 손실: 0.057294\t최선의 손실: 0.044539\t정확도: 99.02%\n",
+      "52\t검증 세트 손실: 0.083171\t최선의 손실: 0.044539\t정확도: 98.83%\n",
+      "53\t검증 세트 손실: 0.063489\t최선의 손실: 0.044539\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=50, total=  22.6s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.087921\t최선의 손실: 0.087921\t정확도: 97.54%\n",
-      "1\t검증 세트 손실: 0.068416\t최선의 손실: 0.068416\t정확도: 98.05%\n",
-      "2\t검증 세트 손실: 0.062104\t최선의 손실: 0.062104\t정확도: 98.05%\n",
-      "3\t검증 세트 손실: 0.087785\t최선의 손실: 0.062104\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.064702\t최선의 손실: 0.062104\t정확도: 97.93%\n",
-      "5\t검증 세트 손실: 0.041024\t최선의 손실: 0.041024\t정확도: 98.79%\n",
-      "6\t검증 세트 손실: 0.056222\t최선의 손실: 0.041024\t정확도: 98.48%\n",
-      "7\t검증 세트 손실: 0.082735\t최선의 손실: 0.041024\t정확도: 97.89%\n",
-      "8\t검증 세트 손실: 0.042873\t최선의 손실: 0.041024\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.068383\t최선의 손실: 0.041024\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.076383\t최선의 손실: 0.041024\t정확도: 98.12%\n",
-      "11\t검증 세트 손실: 0.056984\t최선의 손실: 0.041024\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.059972\t최선의 손실: 0.041024\t정확도: 98.51%\n",
-      "13\t검증 세트 손실: 0.056745\t최선의 손실: 0.041024\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.052281\t최선의 손실: 0.041024\t정확도: 98.87%\n",
-      "15\t검증 세트 손실: 0.055086\t최선의 손실: 0.041024\t정확도: 98.44%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=70, total=  10.8s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.082665\t최선의 손실: 0.082665\t정확도: 97.46%\n",
+      "1\t검증 세트 손실: 0.060302\t최선의 손실: 0.060302\t정확도: 98.05%\n",
+      "2\t검증 세트 손실: 0.071451\t최선의 손실: 0.060302\t정확도: 97.81%\n",
+      "3\t검증 세트 손실: 0.047338\t최선의 손실: 0.047338\t정확도: 98.40%\n",
+      "4\t검증 세트 손실: 0.040440\t최선의 손실: 0.040440\t정확도: 98.48%\n",
+      "5\t검증 세트 손실: 0.046700\t최선의 손실: 0.040440\t정확도: 98.44%\n",
+      "6\t검증 세트 손실: 0.051747\t최선의 손실: 0.040440\t정확도: 98.55%\n",
+      "7\t검증 세트 손실: 0.052674\t최선의 손실: 0.040440\t정확도: 98.55%\n",
+      "8\t검증 세트 손실: 0.062728\t최선의 손실: 0.040440\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.045128\t최선의 손실: 0.040440\t정확도: 98.79%\n",
+      "10\t검증 세트 손실: 0.047719\t최선의 손실: 0.040440\t정확도: 98.71%\n",
+      "11\t검증 세트 손실: 0.048127\t최선의 손실: 0.040440\t정확도: 98.59%\n",
+      "12\t검증 세트 손실: 0.058701\t최선의 손실: 0.040440\t정확도: 98.55%\n",
+      "13\t검증 세트 손실: 0.048414\t최선의 손실: 0.040440\t정확도: 98.83%\n",
+      "14\t검증 세트 손실: 0.064757\t최선의 손실: 0.040440\t정확도: 98.40%\n",
+      "15\t검증 세트 손실: 0.078266\t최선의 손실: 0.040440\t정확도: 98.51%\n",
+      "16\t검증 세트 손실: 0.071695\t최선의 손실: 0.040440\t정확도: 98.36%\n",
+      "17\t검증 세트 손실: 0.064979\t최선의 손실: 0.040440\t정확도: 98.40%\n",
+      "18\t검증 세트 손실: 0.048306\t최선의 손실: 0.040440\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.060988\t최선의 손실: 0.040440\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.070403\t최선의 손실: 0.040440\t정확도: 98.36%\n",
+      "21\t검증 세트 손실: 0.061471\t최선의 손실: 0.040440\t정확도: 98.67%\n",
+      "22\t검증 세트 손실: 0.065767\t최선의 손실: 0.040440\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.058258\t최선의 손실: 0.040440\t정확도: 98.94%\n",
+      "24\t검증 세트 손실: 0.050103\t최선의 손실: 0.040440\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.054577\t최선의 손실: 0.040440\t정확도: 98.75%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=50, total=  20.5s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=50 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "16\t검증 세트 손실: 0.043198\t최선의 손실: 0.041024\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.051297\t최선의 손실: 0.041024\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.044260\t최선의 손실: 0.041024\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.052738\t최선의 손실: 0.041024\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.051812\t최선의 손실: 0.041024\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.048263\t최선의 손실: 0.041024\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.041113\t최선의 손실: 0.041024\t정확도: 98.91%\n",
-      "23\t검증 세트 손실: 0.056621\t최선의 손실: 0.041024\t정확도: 98.59%\n",
-      "24\t검증 세트 손실: 0.043315\t최선의 손실: 0.041024\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.068357\t최선의 손실: 0.041024\t정확도: 98.28%\n",
-      "26\t검증 세트 손실: 0.067516\t최선의 손실: 0.041024\t정확도: 98.44%\n",
+      "0\t검증 세트 손실: 0.086246\t최선의 손실: 0.086246\t정확도: 97.42%\n",
+      "1\t검증 세트 손실: 0.058083\t최선의 손실: 0.058083\t정확도: 98.44%\n",
+      "2\t검증 세트 손실: 0.053848\t최선의 손실: 0.053848\t정확도: 98.51%\n",
+      "3\t검증 세트 손실: 0.054959\t최선의 손실: 0.053848\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.066866\t최선의 손실: 0.053848\t정확도: 98.12%\n",
+      "5\t검증 세트 손실: 0.043207\t최선의 손실: 0.043207\t정확도: 98.75%\n",
+      "6\t검증 세트 손실: 0.053675\t최선의 손실: 0.043207\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.053676\t최선의 손실: 0.043207\t정확도: 98.63%\n",
+      "8\t검증 세트 손실: 0.044923\t최선의 손실: 0.043207\t정확도: 98.59%\n",
+      "9\t검증 세트 손실: 0.173405\t최선의 손실: 0.043207\t정확도: 95.50%\n",
+      "10\t검증 세트 손실: 0.054545\t최선의 손실: 0.043207\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.058687\t최선의 손실: 0.043207\t정확도: 98.83%\n",
+      "12\t검증 세트 손실: 0.047626\t최선의 손실: 0.043207\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.039282\t최선의 손실: 0.039282\t정확도: 99.10%\n",
+      "14\t검증 세트 손실: 0.042646\t최선의 손실: 0.039282\t정확도: 98.98%\n",
+      "15\t검증 세트 손실: 0.063650\t최선의 손실: 0.039282\t정확도: 98.67%\n",
+      "16\t검증 세트 손실: 0.051720\t최선의 손실: 0.039282\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.063068\t최선의 손실: 0.039282\t정확도: 98.44%\n",
+      "18\t검증 세트 손실: 0.073633\t최선의 손실: 0.039282\t정확도: 98.48%\n",
+      "19\t검증 세트 손실: 0.050965\t최선의 손실: 0.039282\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.056682\t최선의 손실: 0.039282\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.045295\t최선의 손실: 0.039282\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.059904\t최선의 손실: 0.039282\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.064584\t최선의 손실: 0.039282\t정확도: 98.83%\n",
+      "24\t검증 세트 손실: 0.062633\t최선의 손실: 0.039282\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.061092\t최선의 손실: 0.039282\t정확도: 98.51%\n",
+      "26\t검증 세트 손실: 0.056516\t최선의 손실: 0.039282\t정확도: 98.63%\n",
+      "27\t검증 세트 손실: 0.064466\t최선의 손실: 0.039282\t정확도: 98.59%\n",
+      "28\t검증 세트 손실: 0.070460\t최선의 손실: 0.039282\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.059736\t최선의 손실: 0.039282\t정확도: 98.63%\n",
+      "30\t검증 세트 손실: 0.064964\t최선의 손실: 0.039282\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.070674\t최선의 손실: 0.039282\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.061749\t최선의 손실: 0.039282\t정확도: 98.79%\n",
+      "33\t검증 세트 손실: 0.095090\t최선의 손실: 0.039282\t정확도: 98.59%\n",
+      "34\t검증 세트 손실: 0.080904\t최선의 손실: 0.039282\t정확도: 98.75%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=50, total=  20.5s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.086443\t최선의 손실: 0.086443\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.093282\t최선의 손실: 0.086443\t정확도: 97.22%\n",
-      "2\t검증 세트 손실: 0.063857\t최선의 손실: 0.063857\t정확도: 98.12%\n",
-      "3\t검증 세트 손실: 0.052829\t최선의 손실: 0.052829\t정확도: 98.40%\n",
-      "4\t검증 세트 손실: 0.058156\t최선의 손실: 0.052829\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.041545\t최선의 손실: 0.041545\t정확도: 98.63%\n",
-      "6\t검증 세트 손실: 0.042285\t최선의 손실: 0.041545\t정확도: 98.87%\n",
-      "7\t검증 세트 손실: 0.065843\t최선의 손실: 0.041545\t정확도: 98.40%\n",
-      "8\t검증 세트 손실: 0.099342\t최선의 손실: 0.041545\t정확도: 97.42%\n",
-      "9\t검증 세트 손실: 0.105914\t최선의 손실: 0.041545\t정확도: 97.93%\n",
-      "10\t검증 세트 손실: 0.043107\t최선의 손실: 0.041545\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.054182\t최선의 손실: 0.041545\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.049190\t최선의 손실: 0.041545\t정확도: 98.71%\n",
-      "13\t검증 세트 손실: 0.052578\t최선의 손실: 0.041545\t정확도: 98.91%\n",
-      "14\t검증 세트 손실: 0.048856\t최선의 손실: 0.041545\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.057896\t최선의 손실: 0.041545\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.049075\t최선의 손실: 0.041545\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.049798\t최선의 손실: 0.041545\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.063291\t최선의 손실: 0.041545\t정확도: 98.44%\n",
-      "19\t검증 세트 손실: 0.073217\t최선의 손실: 0.041545\t정확도: 98.32%\n",
-      "20\t검증 세트 손실: 0.060686\t최선의 손실: 0.041545\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.050754\t최선의 손실: 0.041545\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.059266\t최선의 손실: 0.041545\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.045283\t최선의 손실: 0.041545\t정확도: 99.02%\n",
-      "24\t검증 세트 손실: 0.059744\t최선의 손실: 0.041545\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.047360\t최선의 손실: 0.041545\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.073991\t최선의 손실: 0.041545\t정확도: 98.48%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=50, total=  27.2s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.103225\t최선의 손실: 0.103225\t정확도: 97.03%\n",
+      "1\t검증 세트 손실: 0.083427\t최선의 손실: 0.083427\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.053552\t최선의 손실: 0.053552\t정확도: 98.12%\n",
+      "3\t검증 세트 손실: 0.051051\t최선의 손실: 0.051051\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.065512\t최선의 손실: 0.051051\t정확도: 98.16%\n",
+      "5\t검증 세트 손실: 0.053775\t최선의 손실: 0.051051\t정확도: 98.44%\n",
+      "6\t검증 세트 손실: 0.063962\t최선의 손실: 0.051051\t정확도: 97.97%\n",
+      "7\t검증 세트 손실: 0.056034\t최선의 손실: 0.051051\t정확도: 98.20%\n",
+      "8\t검증 세트 손실: 0.055516\t최선의 손실: 0.051051\t정확도: 98.48%\n",
+      "9\t검증 세트 손실: 0.049177\t최선의 손실: 0.049177\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.051355\t최선의 손실: 0.049177\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.053650\t최선의 손실: 0.049177\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.052506\t최선의 손실: 0.049177\t정확도: 98.51%\n",
+      "13\t검증 세트 손실: 0.058173\t최선의 손실: 0.049177\t정확도: 98.83%\n",
+      "14\t검증 세트 손실: 0.045370\t최선의 손실: 0.045370\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.063449\t최선의 손실: 0.045370\t정확도: 98.75%\n",
+      "16\t검증 세트 손실: 0.036760\t최선의 손실: 0.036760\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.051880\t최선의 손실: 0.036760\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.053960\t최선의 손실: 0.036760\t정확도: 98.59%\n",
+      "19\t검증 세트 손실: 0.056463\t최선의 손실: 0.036760\t정확도: 98.48%\n",
+      "20\t검증 세트 손실: 0.039044\t최선의 손실: 0.036760\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.046477\t최선의 손실: 0.036760\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.042744\t최선의 손실: 0.036760\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.044919\t최선의 손실: 0.036760\t정확도: 99.14%\n",
+      "24\t검증 세트 손실: 0.053870\t최선의 손실: 0.036760\t정확도: 98.98%\n",
+      "25\t검증 세트 손실: 0.048826\t최선의 손실: 0.036760\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.049775\t최선의 손실: 0.036760\t정확도: 98.79%\n",
+      "27\t검증 세트 손실: 0.051606\t최선의 손실: 0.036760\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.047535\t최선의 손실: 0.036760\t정확도: 99.02%\n",
+      "29\t검증 세트 손실: 0.052209\t최선의 손실: 0.036760\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.061072\t최선의 손실: 0.036760\t정확도: 98.59%\n",
+      "31\t검증 세트 손실: 0.054165\t최선의 손실: 0.036760\t정확도: 99.02%\n",
+      "32\t검증 세트 손실: 0.052764\t최선의 손실: 0.036760\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.071356\t최선의 손실: 0.036760\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.052011\t최선의 손실: 0.036760\t정확도: 98.91%\n",
+      "35\t검증 세트 손실: 0.061120\t최선의 손실: 0.036760\t정확도: 98.71%\n",
+      "36\t검증 세트 손실: 0.037108\t최선의 손실: 0.036760\t정확도: 98.98%\n",
+      "37\t검증 세트 손실: 0.044305\t최선의 손실: 0.036760\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=50, total=  20.5s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.098351\t최선의 손실: 0.098351\t정확도: 96.91%\n",
-      "1\t검증 세트 손실: 0.080201\t최선의 손실: 0.080201\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.093511\t최선의 손실: 0.080201\t정확도: 97.03%\n",
-      "3\t검증 세트 손실: 0.067448\t최선의 손실: 0.067448\t정확도: 97.85%\n",
-      "4\t검증 세트 손실: 0.083526\t최선의 손실: 0.067448\t정확도: 97.69%\n",
-      "5\t검증 세트 손실: 0.080162\t최선의 손실: 0.067448\t정확도: 97.89%\n",
-      "6\t검증 세트 손실: 0.083411\t최선의 손실: 0.067448\t정확도: 97.65%\n",
-      "7\t검증 세트 손실: 0.062153\t최선의 손실: 0.062153\t정확도: 98.28%\n",
-      "8\t검증 세트 손실: 0.102713\t최선의 손실: 0.062153\t정확도: 96.91%\n",
-      "9\t검증 세트 손실: 0.069313\t최선의 손실: 0.062153\t정확도: 97.85%\n",
-      "10\t검증 세트 손실: 0.071428\t최선의 손실: 0.062153\t정확도: 98.08%\n",
-      "11\t검증 세트 손실: 0.073975\t최선의 손실: 0.062153\t정확도: 97.81%\n",
-      "12\t검증 세트 손실: 0.061599\t최선의 손실: 0.061599\t정확도: 98.16%\n",
-      "13\t검증 세트 손실: 0.074061\t최선의 손실: 0.061599\t정확도: 98.05%\n",
-      "14\t검증 세트 손실: 0.068672\t최선의 손실: 0.061599\t정확도: 98.16%\n",
-      "15\t검증 세트 손실: 0.077236\t최선의 손실: 0.061599\t정확도: 97.81%\n",
-      "16\t검증 세트 손실: 0.083025\t최선의 손실: 0.061599\t정확도: 97.81%\n",
-      "17\t검증 세트 손실: 0.077448\t최선의 손실: 0.061599\t정확도: 97.77%\n",
-      "18\t검증 세트 손실: 0.076160\t최선의 손실: 0.061599\t정확도: 98.01%\n",
-      "19\t검증 세트 손실: 0.071695\t최선의 손실: 0.061599\t정확도: 98.05%\n",
-      "20\t검증 세트 손실: 0.079754\t최선의 손실: 0.061599\t정확도: 97.85%\n",
-      "21\t검증 세트 손실: 0.065763\t최선의 손실: 0.061599\t정확도: 98.20%\n",
-      "22\t검증 세트 손실: 0.085975\t최선의 손실: 0.061599\t정확도: 97.46%\n",
-      "23\t검증 세트 손실: 0.066962\t최선의 손실: 0.061599\t정확도: 98.16%\n",
-      "24\t검증 세트 손실: 0.071259\t최선의 손실: 0.061599\t정확도: 97.77%\n",
-      "25\t검증 세트 손실: 0.073873\t최선의 손실: 0.061599\t정확도: 98.01%\n",
-      "26\t검증 세트 손실: 0.082384\t최선의 손실: 0.061599\t정확도: 97.89%\n",
-      "27\t검증 세트 손실: 0.071802\t최선의 손실: 0.061599\t정확도: 98.20%\n",
-      "28\t검증 세트 손실: 0.075957\t최선의 손실: 0.061599\t정확도: 97.93%\n",
-      "29\t검증 세트 손실: 0.073730\t최선의 손실: 0.061599\t정확도: 98.12%\n",
-      "30\t검증 세트 손실: 0.076355\t최선의 손실: 0.061599\t정확도: 97.93%\n",
-      "31\t검증 세트 손실: 0.087189\t최선의 손실: 0.061599\t정확도: 98.01%\n",
-      "32\t검증 세트 손실: 0.078520\t최선의 손실: 0.061599\t정확도: 98.05%\n",
-      "33\t검증 세트 손실: 0.088515\t최선의 손실: 0.061599\t정확도: 97.58%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=50, total=  29.2s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.134514\t최선의 손실: 0.134514\t정확도: 95.90%\n",
+      "1\t검증 세트 손실: 0.081585\t최선의 손실: 0.081585\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.092019\t최선의 손실: 0.081585\t정확도: 96.99%\n",
+      "3\t검증 세트 손실: 0.087169\t최선의 손실: 0.081585\t정확도: 97.11%\n",
+      "4\t검증 세트 손실: 0.098632\t최선의 손실: 0.081585\t정확도: 96.87%\n",
+      "5\t검증 세트 손실: 0.080218\t최선의 손실: 0.080218\t정확도: 97.26%\n",
+      "6\t검증 세트 손실: 0.063809\t최선의 손실: 0.063809\t정확도: 97.97%\n",
+      "7\t검증 세트 손실: 0.063664\t최선의 손실: 0.063664\t정확도: 98.05%\n",
+      "8\t검증 세트 손실: 0.097072\t최선의 손실: 0.063664\t정확도: 97.19%\n",
+      "9\t검증 세트 손실: 0.076377\t최선의 손실: 0.063664\t정확도: 97.97%\n",
+      "10\t검증 세트 손실: 0.070486\t최선의 손실: 0.063664\t정확도: 98.01%\n",
+      "11\t검증 세트 손실: 0.063156\t최선의 손실: 0.063156\t정확도: 98.12%\n",
+      "12\t검증 세트 손실: 0.077876\t최선의 손실: 0.063156\t정확도: 97.46%\n",
+      "13\t검증 세트 손실: 0.061934\t최선의 손실: 0.061934\t정확도: 98.16%\n",
+      "14\t검증 세트 손실: 0.066849\t최선의 손실: 0.061934\t정확도: 97.97%\n",
+      "15\t검증 세트 손실: 0.062584\t최선의 손실: 0.061934\t정확도: 98.28%\n",
+      "16\t검증 세트 손실: 0.064055\t최선의 손실: 0.061934\t정확도: 98.32%\n",
+      "17\t검증 세트 손실: 0.059122\t최선의 손실: 0.059122\t정확도: 98.48%\n",
+      "18\t검증 세트 손실: 0.071083\t최선의 손실: 0.059122\t정확도: 97.97%\n",
+      "19\t검증 세트 손실: 0.060996\t최선의 손실: 0.059122\t정확도: 98.32%\n",
+      "20\t검증 세트 손실: 0.070064\t최선의 손실: 0.059122\t정확도: 98.16%\n",
+      "21\t검증 세트 손실: 0.062813\t최선의 손실: 0.059122\t정확도: 98.12%\n",
+      "22\t검증 세트 손실: 0.068625\t최선의 손실: 0.059122\t정확도: 98.01%\n",
+      "23\t검증 세트 손실: 0.080963\t최선의 손실: 0.059122\t정확도: 97.73%\n",
+      "24\t검증 세트 손실: 0.071521\t최선의 손실: 0.059122\t정확도: 98.40%\n",
+      "25\t검증 세트 손실: 0.074071\t최선의 손실: 0.059122\t정확도: 98.20%\n",
+      "26\t검증 세트 손실: 0.064502\t최선의 손실: 0.059122\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.060679\t최선의 손실: 0.059122\t정확도: 98.44%\n",
+      "28\t검증 세트 손실: 0.057001\t최선의 손실: 0.057001\t정확도: 98.59%\n",
+      "29\t검증 세트 손실: 0.058308\t최선의 손실: 0.057001\t정확도: 98.36%\n",
+      "30\t검증 세트 손실: 0.071026\t최선의 손실: 0.057001\t정확도: 98.20%\n",
+      "31\t검증 세트 손실: 0.084238\t최선의 손실: 0.057001\t정확도: 97.77%\n",
+      "32\t검증 세트 손실: 0.068050\t최선의 손실: 0.057001\t정확도: 98.24%\n",
+      "33\t검증 세트 손실: 0.090901\t최선의 손실: 0.057001\t정확도: 97.77%\n",
+      "34\t검증 세트 손실: 0.094247\t최선의 손실: 0.057001\t정확도: 97.93%\n",
+      "35\t검증 세트 손실: 0.077411\t최선의 손실: 0.057001\t정확도: 98.36%\n",
+      "36\t검증 세트 손실: 0.075878\t최선의 손실: 0.057001\t정확도: 98.32%\n",
+      "37\t검증 세트 손실: 0.081057\t최선의 손실: 0.057001\t정확도: 98.12%\n",
+      "38\t검증 세트 손실: 0.084254\t최선의 손실: 0.057001\t정확도: 98.20%\n",
+      "39\t검증 세트 손실: 0.097609\t최선의 손실: 0.057001\t정확도: 98.12%\n",
+      "40\t검증 세트 손실: 0.079385\t최선의 손실: 0.057001\t정확도: 98.16%\n",
+      "41\t검증 세트 손실: 0.085914\t최선의 손실: 0.057001\t정확도: 98.20%\n",
+      "42\t검증 세트 손실: 0.082795\t최선의 손실: 0.057001\t정확도: 98.28%\n",
+      "43\t검증 세트 손실: 0.074909\t최선의 손실: 0.057001\t정확도: 98.16%\n",
+      "44\t검증 세트 손실: 0.093735\t최선의 손실: 0.057001\t정확도: 97.97%\n",
+      "45\t검증 세트 손실: 0.085049\t최선의 손실: 0.057001\t정확도: 97.89%\n",
+      "46\t검증 세트 손실: 0.083702\t최선의 손실: 0.057001\t정확도: 98.20%\n",
+      "47\t검증 세트 손실: 0.076748\t최선의 손실: 0.057001\t정확도: 98.24%\n",
+      "48\t검증 세트 손실: 0.081754\t최선의 손실: 0.057001\t정확도: 98.40%\n",
+      "49\t검증 세트 손실: 0.076726\t최선의 손실: 0.057001\t정확도: 98.36%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=10, total=  42.8s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.095015\t최선의 손실: 0.095015\t정확도: 97.22%\n",
-      "1\t검증 세트 손실: 0.071164\t최선의 손실: 0.071164\t정확도: 97.65%\n",
-      "2\t검증 세트 손실: 0.082597\t최선의 손실: 0.071164\t정확도: 97.46%\n",
-      "3\t검증 세트 손실: 0.097461\t최선의 손실: 0.071164\t정확도: 96.95%\n",
-      "4\t검증 세트 손실: 0.072557\t최선의 손실: 0.071164\t정확도: 97.85%\n",
-      "5\t검증 세트 손실: 0.073786\t최선의 손실: 0.071164\t정확도: 97.62%\n",
-      "6\t검증 세트 손실: 0.070840\t최선의 손실: 0.070840\t정확도: 98.08%\n",
-      "7\t검증 세트 손실: 0.081610\t최선의 손실: 0.070840\t정확도: 97.30%\n",
-      "8\t검증 세트 손실: 0.066585\t최선의 손실: 0.066585\t정확도: 98.28%\n",
-      "9\t검증 세트 손실: 0.091307\t최선의 손실: 0.066585\t정확도: 97.46%\n",
-      "10\t검증 세트 손실: 0.096124\t최선의 손실: 0.066585\t정확도: 97.42%\n",
-      "11\t검증 세트 손실: 0.081218\t최선의 손실: 0.066585\t정확도: 97.69%\n",
-      "12\t검증 세트 손실: 0.067661\t최선의 손실: 0.066585\t정확도: 98.01%\n",
-      "13\t검증 세트 손실: 0.083330\t최선의 손실: 0.066585\t정확도: 97.69%\n",
-      "14\t검증 세트 손실: 0.073991\t최선의 손실: 0.066585\t정확도: 97.89%\n",
-      "15\t검증 세트 손실: 0.065067\t최선의 손실: 0.065067\t정확도: 98.08%\n",
-      "16\t검증 세트 손실: 0.071566\t최선의 손실: 0.065067\t정확도: 97.69%\n",
-      "17\t검증 세트 손실: 0.089489\t최선의 손실: 0.065067\t정확도: 97.34%\n",
-      "18\t검증 세트 손실: 0.096858\t최선의 손실: 0.065067\t정확도: 97.15%\n",
-      "19\t검증 세트 손실: 0.075712\t최선의 손실: 0.065067\t정확도: 98.01%\n",
-      "20\t검증 세트 손실: 0.062858\t최선의 손실: 0.062858\t정확도: 98.12%\n",
-      "21\t검증 세트 손실: 0.078082\t최선의 손실: 0.062858\t정확도: 98.12%\n",
-      "22\t검증 세트 손실: 0.096646\t최선의 손실: 0.062858\t정확도: 97.26%\n",
-      "23\t검증 세트 손실: 0.069137\t최선의 손실: 0.062858\t정확도: 98.20%\n",
-      "24\t검증 세트 손실: 0.081727\t최선의 손실: 0.062858\t정확도: 97.89%\n",
-      "25\t검증 세트 손실: 0.077290\t최선의 손실: 0.062858\t정확도: 97.77%\n",
-      "26\t검증 세트 손실: 0.066250\t최선의 손실: 0.062858\t정확도: 97.97%\n",
-      "27\t검증 세트 손실: 0.071986\t최선의 손실: 0.062858\t정확도: 98.12%\n",
-      "28\t검증 세트 손실: 0.086801\t최선의 손실: 0.062858\t정확도: 97.89%\n",
-      "29\t검증 세트 손실: 0.081100\t최선의 손실: 0.062858\t정확도: 97.69%\n",
-      "30\t검증 세트 손실: 0.060346\t최선의 손실: 0.060346\t정확도: 98.40%\n",
-      "31\t검증 세트 손실: 0.070866\t최선의 손실: 0.060346\t정확도: 98.05%\n",
-      "32\t검증 세트 손실: 0.077941\t최선의 손실: 0.060346\t정확도: 98.01%\n",
-      "33\t검증 세트 손실: 0.084355\t최선의 손실: 0.060346\t정확도: 97.93%\n",
-      "34\t검증 세트 손실: 0.093780\t최선의 손실: 0.060346\t정확도: 97.81%\n",
-      "35\t검증 세트 손실: 0.076006\t최선의 손실: 0.060346\t정확도: 98.24%\n",
-      "36\t검증 세트 손실: 0.088208\t최선의 손실: 0.060346\t정확도: 97.77%\n",
-      "37\t검증 세트 손실: 0.083594\t최선의 손실: 0.060346\t정확도: 98.16%\n",
-      "38\t검증 세트 손실: 0.087853\t최선의 손실: 0.060346\t정확도: 98.05%\n",
-      "39\t검증 세트 손실: 0.098490\t최선의 손실: 0.060346\t정확도: 97.77%\n",
-      "40\t검증 세트 손실: 0.085096\t최선의 손실: 0.060346\t정확도: 98.20%\n",
-      "41\t검증 세트 손실: 0.095924\t최선의 손실: 0.060346\t정확도: 97.81%\n",
-      "42\t검증 세트 손실: 0.085094\t최선의 손실: 0.060346\t정확도: 98.05%\n",
-      "43\t검증 세트 손실: 0.097812\t최선의 손실: 0.060346\t정확도: 97.93%\n",
-      "44\t검증 세트 손실: 0.101121\t최선의 손실: 0.060346\t정확도: 97.69%\n",
-      "45\t검증 세트 손실: 0.097728\t최선의 손실: 0.060346\t정확도: 97.69%\n",
-      "46\t검증 세트 손실: 0.074514\t최선의 손실: 0.060346\t정확도: 98.16%\n",
-      "47\t검증 세트 손실: 0.083531\t최선의 손실: 0.060346\t정확도: 98.20%\n",
-      "48\t검증 세트 손실: 0.101891\t최선의 손실: 0.060346\t정확도: 97.81%\n",
-      "49\t검증 세트 손실: 0.091112\t최선의 손실: 0.060346\t정확도: 98.08%\n",
-      "50\t검증 세트 손실: 0.097057\t최선의 손실: 0.060346\t정확도: 97.93%\n",
-      "51\t검증 세트 손실: 0.104641\t최선의 손실: 0.060346\t정확도: 97.77%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=10, total=  35.9s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.117530\t최선의 손실: 0.117530\t정확도: 96.68%\n",
-      "1\t검증 세트 손실: 0.129234\t최선의 손실: 0.117530\t정확도: 95.78%\n",
-      "2\t검증 세트 손실: 0.078716\t최선의 손실: 0.078716\t정확도: 97.54%\n",
-      "3\t검증 세트 손실: 0.071197\t최선의 손실: 0.071197\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.067831\t최선의 손실: 0.067831\t정확도: 98.16%\n",
-      "5\t검증 세트 손실: 0.065352\t최선의 손실: 0.065352\t정확도: 97.93%\n",
-      "6\t검증 세트 손실: 0.094477\t최선의 손실: 0.065352\t정확도: 97.38%\n",
-      "7\t검증 세트 손실: 0.091726\t최선의 손실: 0.065352\t정확도: 97.19%\n",
-      "8\t검증 세트 손실: 0.068596\t최선의 손실: 0.065352\t정확도: 98.20%\n",
-      "9\t검증 세트 손실: 0.075266\t최선의 손실: 0.065352\t정확도: 97.85%\n",
-      "10\t검증 세트 손실: 0.064224\t최선의 손실: 0.064224\t정확도: 98.20%\n",
-      "11\t검증 세트 손실: 0.070997\t최선의 손실: 0.064224\t정확도: 97.69%\n",
-      "12\t검증 세트 손실: 0.062089\t최선의 손실: 0.062089\t정확도: 98.51%\n",
-      "13\t검증 세트 손실: 0.061412\t최선의 손실: 0.061412\t정확도: 98.20%\n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=10, total=  35.6s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.101731\t최선의 손실: 0.101731\t정확도: 96.64%\n",
+      "1\t검증 세트 손실: 0.086409\t최선의 손실: 0.086409\t정확도: 97.42%\n",
+      "2\t검증 세트 손실: 0.064543\t최선의 손실: 0.064543\t정확도: 98.48%\n",
+      "3\t검증 세트 손실: 0.084126\t최선의 손실: 0.064543\t정확도: 97.19%\n",
+      "4\t검증 세트 손실: 0.078599\t최선의 손실: 0.064543\t정확도: 98.01%\n",
+      "5\t검증 세트 손실: 0.074887\t최선의 손실: 0.064543\t정확도: 97.65%\n",
+      "6\t검증 세트 손실: 0.067409\t최선의 손실: 0.064543\t정확도: 98.08%\n",
+      "7\t검증 세트 손실: 0.076101\t최선의 손실: 0.064543\t정확도: 97.69%\n",
+      "8\t검증 세트 손실: 0.050465\t최선의 손실: 0.050465\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.081629\t최선의 손실: 0.050465\t정확도: 97.81%\n",
+      "10\t검증 세트 손실: 0.060985\t최선의 손실: 0.050465\t정확도: 98.08%\n",
+      "11\t검증 세트 손실: 0.061632\t최선의 손실: 0.050465\t정확도: 98.28%\n",
+      "12\t검증 세트 손실: 0.066436\t최선의 손실: 0.050465\t정확도: 98.28%\n",
+      "13\t검증 세트 손실: 0.065559\t최선의 손실: 0.050465\t정확도: 98.48%\n",
+      "14\t검증 세트 손실: 0.060153\t최선의 손실: 0.050465\t정확도: 98.36%\n",
+      "15\t검증 세트 손실: 0.063997\t최선의 손실: 0.050465\t정확도: 98.16%\n",
+      "16\t검증 세트 손실: 0.055867\t최선의 손실: 0.050465\t정확도: 98.36%\n",
+      "17\t검증 세트 손실: 0.064924\t최선의 손실: 0.050465\t정확도: 98.48%\n",
+      "18\t검증 세트 손실: 0.081871\t최선의 손실: 0.050465\t정확도: 97.93%\n",
+      "19\t검증 세트 손실: 0.067985\t최선의 손실: 0.050465\t정확도: 98.16%\n",
+      "20\t검증 세트 손실: 0.090049\t최선의 손실: 0.050465\t정확도: 97.65%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "14\t검증 세트 손실: 0.077788\t최선의 손실: 0.061412\t정확도: 97.93%\n",
-      "15\t검증 세트 손실: 0.060510\t최선의 손실: 0.060510\t정확도: 98.44%\n",
-      "16\t검증 세트 손실: 0.060162\t최선의 손실: 0.060162\t정확도: 98.48%\n",
-      "17\t검증 세트 손실: 0.067720\t최선의 손실: 0.060162\t정확도: 98.20%\n",
-      "18\t검증 세트 손실: 0.070199\t최선의 손실: 0.060162\t정확도: 98.36%\n",
-      "19\t검증 세트 손실: 0.067771\t최선의 손실: 0.060162\t정확도: 98.20%\n",
-      "20\t검증 세트 손실: 0.072444\t최선의 손실: 0.060162\t정확도: 98.44%\n",
-      "21\t검증 세트 손실: 0.056102\t최선의 손실: 0.056102\t정확도: 98.55%\n",
-      "22\t검증 세트 손실: 0.061431\t최선의 손실: 0.056102\t정확도: 98.44%\n",
-      "23\t검증 세트 손실: 0.062497\t최선의 손실: 0.056102\t정확도: 98.40%\n",
-      "24\t검증 세트 손실: 0.073957\t최선의 손실: 0.056102\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.072038\t최선의 손실: 0.056102\t정확도: 98.28%\n",
-      "26\t검증 세트 손실: 0.063246\t최선의 손실: 0.056102\t정확도: 98.36%\n",
-      "27\t검증 세트 손실: 0.080345\t최선의 손실: 0.056102\t정확도: 98.08%\n",
-      "28\t검증 세트 손실: 0.079366\t최선의 손실: 0.056102\t정확도: 98.32%\n",
-      "29\t검증 세트 손실: 0.079801\t최선의 손실: 0.056102\t정확도: 98.40%\n",
-      "30\t검증 세트 손실: 0.070670\t최선의 손실: 0.056102\t정확도: 98.32%\n",
-      "31\t검증 세트 손실: 0.079827\t최선의 손실: 0.056102\t정확도: 98.51%\n",
-      "32\t검증 세트 손실: 0.080529\t최선의 손실: 0.056102\t정확도: 98.36%\n",
-      "33\t검증 세트 손실: 0.072509\t최선의 손실: 0.056102\t정확도: 98.44%\n",
-      "34\t검증 세트 손실: 0.103512\t최선의 손실: 0.056102\t정확도: 97.77%\n",
-      "35\t검증 세트 손실: 0.058599\t최선의 손실: 0.056102\t정확도: 98.44%\n",
-      "36\t검증 세트 손실: 0.060402\t최선의 손실: 0.056102\t정확도: 98.59%\n",
-      "37\t검증 세트 손실: 0.067756\t최선의 손실: 0.056102\t정확도: 98.51%\n",
-      "38\t검증 세트 손실: 0.089954\t최선의 손실: 0.056102\t정확도: 98.01%\n",
-      "39\t검증 세트 손실: 0.087687\t최선의 손실: 0.056102\t정확도: 98.12%\n",
-      "40\t검증 세트 손실: 0.082879\t최선의 손실: 0.056102\t정확도: 98.28%\n",
-      "41\t검증 세트 손실: 0.079288\t최선의 손실: 0.056102\t정확도: 98.28%\n",
-      "42\t검증 세트 손실: 0.075829\t최선의 손실: 0.056102\t정확도: 98.28%\n",
+      "21\t검증 세트 손실: 0.064523\t최선의 손실: 0.050465\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.087388\t최선의 손실: 0.050465\t정확도: 97.89%\n",
+      "23\t검증 세트 손실: 0.061250\t최선의 손실: 0.050465\t정확도: 98.44%\n",
+      "24\t검증 세트 손실: 0.092467\t최선의 손실: 0.050465\t정확도: 97.65%\n",
+      "25\t검증 세트 손실: 0.068438\t최선의 손실: 0.050465\t정확도: 98.28%\n",
+      "26\t검증 세트 손실: 0.060756\t최선의 손실: 0.050465\t정확도: 98.32%\n",
+      "27\t검증 세트 손실: 0.057426\t최선의 손실: 0.050465\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 0.076512\t최선의 손실: 0.050465\t정확도: 98.28%\n",
+      "29\t검증 세트 손실: 0.084764\t최선의 손실: 0.050465\t정확도: 98.40%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=10, total=  29.9s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.115049\t최선의 손실: 0.115049\t정확도: 96.44%\n",
-      "1\t검증 세트 손실: 0.077833\t최선의 손실: 0.077833\t정확도: 97.81%\n",
-      "2\t검증 세트 손실: 0.080859\t최선의 손실: 0.077833\t정확도: 97.69%\n",
-      "3\t검증 세트 손실: 0.063424\t최선의 손실: 0.063424\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.065269\t최선의 손실: 0.063424\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.092000\t최선의 손실: 0.063424\t정확도: 97.46%\n",
-      "6\t검증 세트 손실: 0.064760\t최선의 손실: 0.063424\t정확도: 97.81%\n",
-      "7\t검증 세트 손실: 0.051642\t최선의 손실: 0.051642\t정확도: 98.55%\n",
-      "8\t검증 세트 손실: 0.069036\t최선의 손실: 0.051642\t정확도: 98.05%\n",
-      "9\t검증 세트 손실: 0.063504\t최선의 손실: 0.051642\t정확도: 98.20%\n",
-      "10\t검증 세트 손실: 0.068010\t최선의 손실: 0.051642\t정확도: 98.01%\n",
-      "11\t검증 세트 손실: 0.053356\t최선의 손실: 0.051642\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.085000\t최선의 손실: 0.051642\t정확도: 97.81%\n",
-      "13\t검증 세트 손실: 0.063676\t최선의 손실: 0.051642\t정확도: 98.01%\n",
-      "14\t검증 세트 손실: 0.074976\t최선의 손실: 0.051642\t정확도: 97.85%\n",
-      "15\t검증 세트 손실: 0.054396\t최선의 손실: 0.051642\t정확도: 98.40%\n",
-      "16\t검증 세트 손실: 0.074660\t최선의 손실: 0.051642\t정확도: 97.89%\n",
-      "17\t검증 세트 손실: 0.072665\t최선의 손실: 0.051642\t정확도: 98.01%\n",
-      "18\t검증 세트 손실: 0.079072\t최선의 손실: 0.051642\t정확도: 98.16%\n",
-      "19\t검증 세트 손실: 0.074225\t최선의 손실: 0.051642\t정확도: 97.93%\n",
-      "20\t검증 세트 손실: 0.059617\t최선의 손실: 0.051642\t정확도: 98.28%\n",
-      "21\t검증 세트 손실: 0.074249\t최선의 손실: 0.051642\t정확도: 97.85%\n",
-      "22\t검증 세트 손실: 0.069520\t최선의 손실: 0.051642\t정확도: 98.05%\n",
-      "23\t검증 세트 손실: 0.068194\t최선의 손실: 0.051642\t정확도: 98.08%\n",
-      "24\t검증 세트 손실: 0.060654\t최선의 손실: 0.051642\t정확도: 98.28%\n",
-      "25\t검증 세트 손실: 0.068734\t최선의 손실: 0.051642\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.060449\t최선의 손실: 0.051642\t정확도: 98.36%\n",
-      "27\t검증 세트 손실: 0.063361\t최선의 손실: 0.051642\t정확도: 98.40%\n",
-      "28\t검증 세트 손실: 0.073832\t최선의 손실: 0.051642\t정확도: 98.16%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=10, total=  21.9s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.132167\t최선의 손실: 0.132167\t정확도: 96.05%\n",
+      "1\t검증 세트 손실: 0.099537\t최선의 손실: 0.099537\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.080030\t최선의 손실: 0.080030\t정확도: 97.50%\n",
+      "3\t검증 세트 손실: 0.071593\t최선의 손실: 0.071593\t정확도: 97.65%\n",
+      "4\t검증 세트 손실: 0.064518\t최선의 손실: 0.064518\t정확도: 98.08%\n",
+      "5\t검증 세트 손실: 0.061577\t최선의 손실: 0.061577\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.064413\t최선의 손실: 0.061577\t정확도: 98.05%\n",
+      "7\t검증 세트 손실: 0.068468\t최선의 손실: 0.061577\t정확도: 97.69%\n",
+      "8\t검증 세트 손실: 0.064003\t최선의 손실: 0.061577\t정확도: 98.08%\n",
+      "9\t검증 세트 손실: 0.056095\t최선의 손실: 0.056095\t정확도: 98.28%\n",
+      "10\t검증 세트 손실: 0.056232\t최선의 손실: 0.056095\t정확도: 98.28%\n",
+      "11\t검증 세트 손실: 0.058225\t최선의 손실: 0.056095\t정확도: 98.12%\n",
+      "12\t검증 세트 손실: 0.063825\t최선의 손실: 0.056095\t정확도: 98.28%\n",
+      "13\t검증 세트 손실: 0.068194\t최선의 손실: 0.056095\t정확도: 97.89%\n",
+      "14\t검증 세트 손실: 0.070116\t최선의 손실: 0.056095\t정확도: 98.08%\n",
+      "15\t검증 세트 손실: 0.070622\t최선의 손실: 0.056095\t정확도: 97.85%\n",
+      "16\t검증 세트 손실: 0.070959\t최선의 손실: 0.056095\t정확도: 98.01%\n",
+      "17\t검증 세트 손실: 0.060632\t최선의 손실: 0.056095\t정확도: 98.44%\n",
+      "18\t검증 세트 손실: 0.066540\t최선의 손실: 0.056095\t정확도: 98.28%\n",
+      "19\t검증 세트 손실: 0.087158\t최선의 손실: 0.056095\t정확도: 97.50%\n",
+      "20\t검증 세트 손실: 0.061374\t최선의 손실: 0.056095\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.053974\t최선의 손실: 0.053974\t정확도: 98.40%\n",
+      "22\t검증 세트 손실: 0.058466\t최선의 손실: 0.053974\t정확도: 98.16%\n",
+      "23\t검증 세트 손실: 0.055485\t최선의 손실: 0.053974\t정확도: 98.28%\n",
+      "24\t검증 세트 손실: 0.057702\t최선의 손실: 0.053974\t정확도: 98.51%\n",
+      "25\t검증 세트 손실: 0.069253\t최선의 손실: 0.053974\t정확도: 98.24%\n",
+      "26\t검증 세트 손실: 0.066295\t최선의 손실: 0.053974\t정확도: 98.16%\n",
+      "27\t검증 세트 손실: 0.066670\t최선의 손실: 0.053974\t정확도: 97.81%\n",
+      "28\t검증 세트 손실: 0.060873\t최선의 손실: 0.053974\t정확도: 98.32%\n",
+      "29\t검증 세트 손실: 0.067732\t최선의 손실: 0.053974\t정확도: 98.40%\n",
+      "30\t검증 세트 손실: 0.054688\t최선의 손실: 0.053974\t정확도: 98.48%\n",
+      "31\t검증 세트 손실: 0.054034\t최선의 손실: 0.053974\t정확도: 98.48%\n",
+      "32\t검증 세트 손실: 0.066638\t최선의 손실: 0.053974\t정확도: 98.16%\n",
+      "33\t검증 세트 손실: 0.058947\t최선의 손실: 0.053974\t정확도: 98.36%\n",
+      "34\t검증 세트 손실: 0.081918\t최선의 손실: 0.053974\t정확도: 97.85%\n",
+      "35\t검증 세트 손실: 0.057187\t최선의 손실: 0.053974\t정확도: 98.40%\n",
+      "36\t검증 세트 손실: 0.071879\t최선의 손실: 0.053974\t정확도: 98.32%\n",
+      "37\t검증 세트 손실: 0.077703\t최선의 손실: 0.053974\t정확도: 98.40%\n",
+      "38\t검증 세트 손실: 0.079690\t최선의 손실: 0.053974\t정확도: 97.93%\n",
+      "39\t검증 세트 손실: 0.076092\t최선의 손실: 0.053974\t정확도: 98.32%\n",
+      "40\t검증 세트 손실: 0.062806\t최선의 손실: 0.053974\t정확도: 98.36%\n",
+      "41\t검증 세트 손실: 0.081574\t최선의 손실: 0.053974\t정확도: 98.16%\n",
+      "42\t검증 세트 손실: 0.068271\t최선의 손실: 0.053974\t정확도: 98.36%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=10, total=  21.9s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.144877\t최선의 손실: 0.144877\t정확도: 96.01%\n",
-      "1\t검증 세트 손실: 0.097001\t최선의 손실: 0.097001\t정확도: 97.30%\n",
-      "2\t검증 세트 손실: 0.100256\t최선의 손실: 0.097001\t정확도: 97.22%\n",
-      "3\t검증 세트 손실: 0.094846\t최선의 손실: 0.094846\t정확도: 97.65%\n",
-      "4\t검증 세트 손실: 0.083199\t최선의 손실: 0.083199\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.074588\t최선의 손실: 0.074588\t정확도: 97.46%\n",
-      "6\t검증 세트 손실: 0.075854\t최선의 손실: 0.074588\t정확도: 97.77%\n",
-      "7\t검증 세트 손실: 0.084459\t최선의 손실: 0.074588\t정확도: 97.46%\n",
-      "8\t검증 세트 손실: 0.076166\t최선의 손실: 0.074588\t정확도: 97.62%\n",
-      "9\t검증 세트 손실: 0.100541\t최선의 손실: 0.074588\t정확도: 97.19%\n",
-      "10\t검증 세트 손실: 0.064420\t최선의 손실: 0.064420\t정확도: 98.12%\n",
-      "11\t검증 세트 손실: 0.071461\t최선의 손실: 0.064420\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.070355\t최선의 손실: 0.064420\t정확도: 98.01%\n",
-      "13\t검증 세트 손실: 0.070415\t최선의 손실: 0.064420\t정확도: 98.05%\n",
-      "14\t검증 세트 손실: 0.073106\t최선의 손실: 0.064420\t정확도: 98.12%\n",
-      "15\t검증 세트 손실: 0.069573\t최선의 손실: 0.064420\t정확도: 98.08%\n",
-      "16\t검증 세트 손실: 0.063871\t최선의 손실: 0.063871\t정확도: 98.24%\n",
-      "17\t검증 세트 손실: 0.090664\t최선의 손실: 0.063871\t정확도: 97.69%\n",
-      "18\t검증 세트 손실: 0.084155\t최선의 손실: 0.063871\t정확도: 97.58%\n",
-      "19\t검증 세트 손실: 0.083979\t최선의 손실: 0.063871\t정확도: 97.65%\n",
-      "20\t검증 세트 손실: 0.064385\t최선의 손실: 0.063871\t정확도: 98.36%\n",
-      "21\t검증 세트 손실: 0.068665\t최선의 손실: 0.063871\t정확도: 98.16%\n",
-      "22\t검증 세트 손실: 0.069871\t최선의 손실: 0.063871\t정확도: 98.32%\n",
-      "23\t검증 세트 손실: 0.064721\t최선의 손실: 0.063871\t정확도: 98.36%\n",
-      "24\t검증 세트 손실: 0.071842\t최선의 손실: 0.063871\t정확도: 98.20%\n",
-      "25\t검증 세트 손실: 0.082699\t최선의 손실: 0.063871\t정확도: 97.85%\n",
-      "26\t검증 세트 손실: 0.086008\t최선의 손실: 0.063871\t정확도: 97.85%\n",
-      "27\t검증 세트 손실: 0.075640\t최선의 손실: 0.063871\t정확도: 98.36%\n",
-      "28\t검증 세트 손실: 0.072680\t최선의 손실: 0.063871\t정확도: 98.28%\n",
-      "29\t검증 세트 손실: 0.078733\t최선의 손실: 0.063871\t정확도: 98.16%\n",
-      "30\t검증 세트 손실: 0.064511\t최선의 손실: 0.063871\t정확도: 98.36%\n",
-      "31\t검증 세트 손실: 0.063774\t최선의 손실: 0.063774\t정확도: 98.32%\n",
-      "32\t검증 세트 손실: 0.075072\t최선의 손실: 0.063774\t정확도: 98.12%\n",
-      "33\t검증 세트 손실: 0.062263\t최선의 손실: 0.062263\t정확도: 98.48%\n",
-      "34\t검증 세트 손실: 0.073525\t최선의 손실: 0.062263\t정확도: 98.48%\n",
-      "35\t검증 세트 손실: 0.067314\t최선의 손실: 0.062263\t정확도: 98.28%\n",
-      "36\t검증 세트 손실: 0.078653\t최선의 손실: 0.062263\t정확도: 98.12%\n",
-      "37\t검증 세트 손실: 0.069073\t최선의 손실: 0.062263\t정확도: 98.40%\n",
-      "38\t검증 세트 손실: 0.073081\t최선의 손실: 0.062263\t정확도: 98.40%\n",
-      "39\t검증 세트 손실: 0.075646\t최선의 손실: 0.062263\t정확도: 98.40%\n",
-      "40\t검증 세트 손실: 0.075965\t최선의 손실: 0.062263\t정확도: 98.12%\n",
-      "41\t검증 세트 손실: 0.086760\t최선의 손실: 0.062263\t정확도: 97.69%\n",
-      "42\t검증 세트 손실: 0.079547\t최선의 손실: 0.062263\t정확도: 98.20%\n",
-      "43\t검증 세트 손실: 0.085569\t최선의 손실: 0.062263\t정확도: 98.20%\n",
-      "44\t검증 세트 손실: 0.080923\t최선의 손실: 0.062263\t정확도: 98.16%\n",
-      "45\t검증 세트 손실: 0.097697\t최선의 손실: 0.062263\t정확도: 98.16%\n",
-      "46\t검증 세트 손실: 0.084029\t최선의 손실: 0.062263\t정확도: 97.77%\n",
-      "47\t검증 세트 손실: 0.069890\t최선의 손실: 0.062263\t정확도: 98.36%\n",
-      "48\t검증 세트 손실: 0.090972\t최선의 손실: 0.062263\t정확도: 98.36%\n",
-      "49\t검증 세트 손실: 0.074848\t최선의 손실: 0.062263\t정확도: 97.93%\n",
-      "50\t검증 세트 손실: 0.084123\t최선의 손실: 0.062263\t정확도: 98.12%\n",
-      "51\t검증 세트 손실: 0.066841\t최선의 손실: 0.062263\t정확도: 98.44%\n",
-      "52\t검증 세트 손실: 0.097564\t최선의 손실: 0.062263\t정확도: 97.93%\n",
-      "53\t검증 세트 손실: 0.096227\t최선의 손실: 0.062263\t정확도: 97.89%\n",
-      "54\t검증 세트 손실: 0.095636\t최선의 손실: 0.062263\t정확도: 97.77%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=10, total=  30.9s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.116169\t최선의 손실: 0.116169\t정확도: 96.83%\n",
+      "1\t검증 세트 손실: 0.095470\t최선의 손실: 0.095470\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.081989\t최선의 손실: 0.081989\t정확도: 97.42%\n",
+      "3\t검증 세트 손실: 0.071254\t최선의 손실: 0.071254\t정확도: 97.62%\n",
+      "4\t검증 세트 손실: 0.080372\t최선의 손실: 0.071254\t정확도: 97.34%\n",
+      "5\t검증 세트 손실: 0.101148\t최선의 손실: 0.071254\t정확도: 96.95%\n",
+      "6\t검증 세트 손실: 0.075994\t최선의 손실: 0.071254\t정확도: 97.54%\n",
+      "7\t검증 세트 손실: 0.066632\t최선의 손실: 0.066632\t정확도: 97.77%\n",
+      "8\t검증 세트 손실: 0.111524\t최선의 손실: 0.066632\t정확도: 96.56%\n",
+      "9\t검증 세트 손실: 0.073151\t최선의 손실: 0.066632\t정확도: 97.81%\n",
+      "10\t검증 세트 손실: 0.078713\t최선의 손실: 0.066632\t정확도: 98.01%\n",
+      "11\t검증 세트 손실: 0.064326\t최선의 손실: 0.064326\t정확도: 98.12%\n",
+      "12\t검증 세트 손실: 0.077346\t최선의 손실: 0.064326\t정확도: 97.50%\n",
+      "13\t검증 세트 손실: 0.089582\t최선의 손실: 0.064326\t정확도: 97.81%\n",
+      "14\t검증 세트 손실: 0.062679\t최선의 손실: 0.062679\t정확도: 98.05%\n",
+      "15\t검증 세트 손실: 0.085070\t최선의 손실: 0.062679\t정확도: 97.77%\n",
+      "16\t검증 세트 손실: 0.074750\t최선의 손실: 0.062679\t정확도: 97.81%\n",
+      "17\t검증 세트 손실: 0.077926\t최선의 손실: 0.062679\t정확도: 98.01%\n",
+      "18\t검증 세트 손실: 0.077916\t최선의 손실: 0.062679\t정확도: 97.89%\n",
+      "19\t검증 세트 손실: 0.069158\t최선의 손실: 0.062679\t정확도: 98.05%\n",
+      "20\t검증 세트 손실: 0.074622\t최선의 손실: 0.062679\t정확도: 98.08%\n",
+      "21\t검증 세트 손실: 0.080547\t최선의 손실: 0.062679\t정확도: 97.73%\n",
+      "22\t검증 세트 손실: 0.075792\t최선의 손실: 0.062679\t정확도: 97.93%\n",
+      "23\t검증 세트 손실: 0.083545\t최선의 손실: 0.062679\t정확도: 98.20%\n",
+      "24\t검증 세트 손실: 0.069391\t최선의 손실: 0.062679\t정확도: 98.48%\n",
+      "25\t검증 세트 손실: 0.084157\t최선의 손실: 0.062679\t정확도: 98.05%\n",
+      "26\t검증 세트 손실: 0.077677\t최선의 손실: 0.062679\t정확도: 98.01%\n",
+      "27\t검증 세트 손실: 0.070602\t최선의 손실: 0.062679\t정확도: 98.05%\n",
+      "28\t검증 세트 손실: 0.074261\t최선의 손실: 0.062679\t정확도: 97.89%\n",
+      "29\t검증 세트 손실: 0.074854\t최선의 손실: 0.062679\t정확도: 98.40%\n",
+      "30\t검증 세트 손실: 0.070797\t최선의 손실: 0.062679\t정확도: 97.97%\n",
+      "31\t검증 세트 손실: 0.088122\t최선의 손실: 0.062679\t정확도: 97.85%\n",
+      "32\t검증 세트 손실: 0.087391\t최선의 손실: 0.062679\t정확도: 97.85%\n",
+      "33\t검증 세트 손실: 0.074986\t최선의 손실: 0.062679\t정확도: 98.32%\n",
+      "34\t검증 세트 손실: 0.083052\t최선의 손실: 0.062679\t정확도: 98.28%\n",
+      "35\t검증 세트 손실: 0.105431\t최선의 손실: 0.062679\t정확도: 97.77%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=10, total=  40.3s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.121131\t최선의 손실: 0.121131\t정확도: 96.36%\n",
-      "1\t검증 세트 손실: 0.090973\t최선의 손실: 0.090973\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.084742\t최선의 손실: 0.084742\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 0.072448\t최선의 손실: 0.072448\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.066831\t최선의 손실: 0.066831\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.074175\t최선의 손실: 0.066831\t정확도: 97.85%\n",
-      "6\t검증 세트 손실: 0.067513\t최선의 손실: 0.066831\t정확도: 98.12%\n",
-      "7\t검증 세트 손실: 0.069403\t최선의 손실: 0.066831\t정확도: 98.05%\n",
-      "8\t검증 세트 손실: 0.069106\t최선의 손실: 0.066831\t정확도: 98.01%\n",
-      "9\t검증 세트 손실: 0.072179\t최선의 손실: 0.066831\t정확도: 98.08%\n",
-      "10\t검증 세트 손실: 0.067787\t최선의 손실: 0.066831\t정확도: 98.24%\n",
-      "11\t검증 세트 손실: 0.063762\t최선의 손실: 0.063762\t정확도: 97.97%\n",
-      "12\t검증 세트 손실: 0.077249\t최선의 손실: 0.063762\t정확도: 98.16%\n",
-      "13\t검증 세트 손실: 0.071889\t최선의 손실: 0.063762\t정확도: 97.85%\n",
-      "14\t검증 세트 손실: 0.062495\t최선의 손실: 0.062495\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.087349\t최선의 손실: 0.062495\t정확도: 97.69%\n",
-      "16\t검증 세트 손실: 0.068854\t최선의 손실: 0.062495\t정확도: 98.32%\n",
-      "17\t검증 세트 손실: 0.075881\t최선의 손실: 0.062495\t정확도: 97.97%\n",
-      "18\t검증 세트 손실: 0.065521\t최선의 손실: 0.062495\t정확도: 98.20%\n",
-      "19\t검증 세트 손실: 0.070254\t최선의 손실: 0.062495\t정확도: 98.12%\n",
-      "20\t검증 세트 손실: 0.075191\t최선의 손실: 0.062495\t정확도: 98.20%\n",
-      "21\t검증 세트 손실: 0.066304\t최선의 손실: 0.062495\t정확도: 98.28%\n",
-      "22\t검증 세트 손실: 0.065000\t최선의 손실: 0.062495\t정확도: 98.08%\n",
-      "23\t검증 세트 손실: 0.063678\t최선의 손실: 0.062495\t정확도: 98.28%\n",
-      "24\t검증 세트 손실: 0.067932\t최선의 손실: 0.062495\t정확도: 98.32%\n",
-      "25\t검증 세트 손실: 0.068921\t최선의 손실: 0.062495\t정확도: 98.32%\n",
-      "26\t검증 세트 손실: 0.077988\t최선의 손실: 0.062495\t정확도: 98.01%\n",
-      "27\t검증 세트 손실: 0.083956\t최선의 손실: 0.062495\t정확도: 97.97%\n",
-      "28\t검증 세트 손실: 0.075767\t최선의 손실: 0.062495\t정확도: 98.28%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=10, total=  27.7s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.131693\t최선의 손실: 0.131693\t정확도: 96.36%\n",
+      "1\t검증 세트 손실: 0.098332\t최선의 손실: 0.098332\t정확도: 96.99%\n",
+      "2\t검증 세트 손실: 0.073208\t최선의 손실: 0.073208\t정확도: 97.81%\n",
+      "3\t검증 세트 손실: 0.080219\t최선의 손실: 0.073208\t정확도: 97.46%\n",
+      "4\t검증 세트 손실: 0.072171\t최선의 손실: 0.072171\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.071263\t최선의 손실: 0.071263\t정확도: 97.81%\n",
+      "6\t검증 세트 손실: 0.073132\t최선의 손실: 0.071263\t정확도: 98.05%\n",
+      "7\t검증 세트 손실: 0.068598\t최선의 손실: 0.068598\t정확도: 97.89%\n",
+      "8\t검증 세트 손실: 0.059948\t최선의 손실: 0.059948\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.079204\t최선의 손실: 0.059948\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.058507\t최선의 손실: 0.058507\t정확도: 98.40%\n",
+      "11\t검증 세트 손실: 0.059104\t최선의 손실: 0.058507\t정확도: 98.28%\n",
+      "12\t검증 세트 손실: 0.067320\t최선의 손실: 0.058507\t정확도: 97.65%\n",
+      "13\t검증 세트 손실: 0.059695\t최선의 손실: 0.058507\t정확도: 98.40%\n",
+      "14\t검증 세트 손실: 0.061725\t최선의 손실: 0.058507\t정확도: 98.36%\n",
+      "15\t검증 세트 손실: 0.075816\t최선의 손실: 0.058507\t정확도: 97.93%\n",
+      "16\t검증 세트 손실: 0.061593\t최선의 손실: 0.058507\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.063518\t최선의 손실: 0.058507\t정확도: 98.32%\n",
+      "18\t검증 세트 손실: 0.079756\t최선의 손실: 0.058507\t정확도: 97.97%\n",
+      "19\t검증 세트 손실: 0.067111\t최선의 손실: 0.058507\t정확도: 98.28%\n",
+      "20\t검증 세트 손실: 0.069989\t최선의 손실: 0.058507\t정확도: 97.97%\n",
+      "21\t검증 세트 손실: 0.074330\t최선의 손실: 0.058507\t정확도: 98.28%\n",
+      "22\t검증 세트 손실: 0.070777\t최선의 손실: 0.058507\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.078676\t최선의 손실: 0.058507\t정확도: 98.20%\n",
+      "24\t검증 세트 손실: 0.062486\t최선의 손실: 0.058507\t정확도: 98.40%\n",
+      "25\t검증 세트 손실: 0.077409\t최선의 손실: 0.058507\t정확도: 97.97%\n",
+      "26\t검증 세트 손실: 0.054626\t최선의 손실: 0.054626\t정확도: 98.48%\n",
+      "27\t검증 세트 손실: 0.060684\t최선의 손실: 0.054626\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 0.070787\t최선의 손실: 0.054626\t정확도: 98.24%\n",
+      "29\t검증 세트 손실: 0.077992\t최선의 손실: 0.054626\t정확도: 98.20%\n",
+      "30\t검증 세트 손실: 0.065693\t최선의 손실: 0.054626\t정확도: 98.20%\n",
+      "31\t검증 세트 손실: 0.081795\t최선의 손실: 0.054626\t정확도: 97.97%\n",
+      "32\t검증 세트 손실: 0.082780\t최선의 손실: 0.054626\t정확도: 98.12%\n",
+      "33\t검증 세트 손실: 0.079374\t최선의 손실: 0.054626\t정확도: 98.01%\n",
+      "34\t검증 세트 손실: 0.067044\t최선의 손실: 0.054626\t정확도: 98.44%\n",
+      "35\t검증 세트 손실: 0.057532\t최선의 손실: 0.054626\t정확도: 98.51%\n",
+      "36\t검증 세트 손실: 0.069986\t최선의 손실: 0.054626\t정확도: 98.28%\n",
+      "37\t검증 세트 손실: 0.063438\t최선의 손실: 0.054626\t정확도: 98.63%\n",
+      "38\t검증 세트 손실: 0.082112\t최선의 손실: 0.054626\t정확도: 98.36%\n",
+      "39\t검증 세트 손실: 0.087065\t최선의 손실: 0.054626\t정확도: 98.20%\n",
+      "40\t검증 세트 손실: 0.073974\t최선의 손실: 0.054626\t정확도: 98.36%\n",
+      "41\t검증 세트 손실: 0.076677\t최선의 손실: 0.054626\t정확도: 98.44%\n",
+      "42\t검증 세트 손실: 0.072091\t최선의 손실: 0.054626\t정확도: 98.59%\n",
+      "43\t검증 세트 손실: 0.076558\t최선의 손실: 0.054626\t정확도: 98.40%\n",
+      "44\t검증 세트 손실: 0.067303\t최선의 손실: 0.054626\t정확도: 98.48%\n",
+      "45\t검증 세트 손실: 0.067299\t최선의 손실: 0.054626\t정확도: 98.40%\n",
+      "46\t검증 세트 손실: 0.101668\t최선의 손실: 0.054626\t정확도: 98.24%\n",
+      "47\t검증 세트 손실: 0.080537\t최선의 손실: 0.054626\t정확도: 98.16%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=10, total=  36.5s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=10 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "29\t검증 세트 손실: 0.067749\t최선의 손실: 0.062495\t정확도: 98.28%\n",
-      "30\t검증 세트 손실: 0.069107\t최선의 손실: 0.062495\t정확도: 98.36%\n",
-      "31\t검증 세트 손실: 0.087054\t최선의 손실: 0.062495\t정확도: 98.12%\n",
-      "32\t검증 세트 손실: 0.077569\t최선의 손실: 0.062495\t정확도: 97.89%\n",
-      "33\t검증 세트 손실: 0.076726\t최선의 손실: 0.062495\t정확도: 98.20%\n",
-      "34\t검증 세트 손실: 0.073970\t최선의 손실: 0.062495\t정확도: 98.08%\n",
-      "35\t검증 세트 손실: 0.068366\t최선의 손실: 0.062495\t정확도: 98.24%\n",
+      "0\t검증 세트 손실: 0.110073\t최선의 손실: 0.110073\t정확도: 96.99%\n",
+      "1\t검증 세트 손실: 0.103993\t최선의 손실: 0.103993\t정확도: 97.30%\n",
+      "2\t검증 세트 손실: 0.076519\t최선의 손실: 0.076519\t정확도: 97.62%\n",
+      "3\t검증 세트 손실: 0.062687\t최선의 손실: 0.062687\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.058639\t최선의 손실: 0.058639\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.059712\t최선의 손실: 0.058639\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.052621\t최선의 손실: 0.052621\t정확도: 98.16%\n",
+      "7\t검증 세트 손실: 0.068912\t최선의 손실: 0.052621\t정확도: 97.97%\n",
+      "8\t검증 세트 손실: 0.059364\t최선의 손실: 0.052621\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.055714\t최선의 손실: 0.052621\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.056494\t최선의 손실: 0.052621\t정확도: 98.48%\n",
+      "11\t검증 세트 손실: 0.063154\t최선의 손실: 0.052621\t정확도: 98.08%\n",
+      "12\t검증 세트 손실: 0.080215\t최선의 손실: 0.052621\t정확도: 97.38%\n",
+      "13\t검증 세트 손실: 0.069968\t최선의 손실: 0.052621\t정확도: 98.05%\n",
+      "14\t검증 세트 손실: 0.055614\t최선의 손실: 0.052621\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.065322\t최선의 손실: 0.052621\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.063421\t최선의 손실: 0.052621\t정확도: 98.08%\n",
+      "17\t검증 세트 손실: 0.074311\t최선의 손실: 0.052621\t정확도: 98.20%\n",
+      "18\t검증 세트 손실: 0.063910\t최선의 손실: 0.052621\t정확도: 98.40%\n",
+      "19\t검증 세트 손실: 0.067374\t최선의 손실: 0.052621\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.067972\t최선의 손실: 0.052621\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.063738\t최선의 손실: 0.052621\t정확도: 98.40%\n",
+      "22\t검증 세트 손실: 0.059061\t최선의 손실: 0.052621\t정확도: 98.24%\n",
+      "23\t검증 세트 손실: 0.063323\t최선의 손실: 0.052621\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.064741\t최선의 손실: 0.052621\t정확도: 98.36%\n",
+      "25\t검증 세트 손실: 0.069619\t최선의 손실: 0.052621\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.072030\t최선의 손실: 0.052621\t정확도: 98.08%\n",
+      "27\t검증 세트 손실: 0.077301\t최선의 손실: 0.052621\t정확도: 98.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=10, total=  26.8s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.410911\t최선의 손실: 0.410911\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.388402\t최선의 손실: 0.388402\t정확도: 96.60%\n",
-      "2\t검증 세트 손실: 0.570372\t최선의 손실: 0.388402\t정확도: 96.25%\n",
-      "3\t검증 세트 손실: 0.158652\t최선의 손실: 0.158652\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.129941\t최선의 손실: 0.129941\t정확도: 98.63%\n",
-      "5\t검증 세트 손실: 0.112978\t최선의 손실: 0.112978\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.065549\t최선의 손실: 0.065549\t정확도: 98.63%\n",
-      "7\t검증 세트 손실: 0.119735\t최선의 손실: 0.065549\t정확도: 98.28%\n",
-      "8\t검증 세트 손실: 0.094353\t최선의 손실: 0.065549\t정확도: 98.36%\n",
-      "9\t검증 세트 손실: 0.084783\t최선의 손실: 0.065549\t정확도: 98.28%\n",
-      "10\t검증 세트 손실: 0.078459\t최선의 손실: 0.065549\t정확도: 98.87%\n",
-      "11\t검증 세트 손실: 0.108699\t최선의 손실: 0.065549\t정확도: 98.59%\n",
-      "12\t검증 세트 손실: 0.110910\t최선의 손실: 0.065549\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.089220\t최선의 손실: 0.065549\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.059864\t최선의 손실: 0.059864\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.107837\t최선의 손실: 0.059864\t정확도: 97.58%\n",
-      "16\t검증 세트 손실: 0.062374\t최선의 손실: 0.059864\t정확도: 98.83%\n",
-      "17\t검증 세트 손실: 0.080713\t최선의 손실: 0.059864\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.072387\t최선의 손실: 0.059864\t정확도: 99.02%\n",
-      "19\t검증 세트 손실: 0.064161\t최선의 손실: 0.059864\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.084898\t최선의 손실: 0.059864\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.098352\t최선의 손실: 0.059864\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.086521\t최선의 손실: 0.059864\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.149258\t최선의 손실: 0.059864\t정확도: 96.91%\n",
-      "24\t검증 세트 손실: 0.113715\t최선의 손실: 0.059864\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.054738\t최선의 손실: 0.054738\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.070989\t최선의 손실: 0.054738\t정확도: 98.98%\n",
-      "27\t검증 세트 손실: 0.069452\t최선의 손실: 0.054738\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.065957\t최선의 손실: 0.054738\t정확도: 98.75%\n",
-      "29\t검증 세트 손실: 0.068515\t최선의 손실: 0.054738\t정확도: 98.75%\n",
-      "30\t검증 세트 손실: 0.092912\t최선의 손실: 0.054738\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.072814\t최선의 손실: 0.054738\t정확도: 98.79%\n",
-      "32\t검증 세트 손실: 0.072254\t최선의 손실: 0.054738\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.081353\t최선의 손실: 0.054738\t정확도: 99.02%\n",
-      "34\t검증 세트 손실: 0.157113\t최선의 손실: 0.054738\t정확도: 98.36%\n",
-      "35\t검증 세트 손실: 0.061710\t최선의 손실: 0.054738\t정확도: 98.98%\n",
-      "36\t검증 세트 손실: 0.071858\t최선의 손실: 0.054738\t정확도: 98.55%\n",
-      "37\t검증 세트 손실: 0.069073\t최선의 손실: 0.054738\t정확도: 98.55%\n",
-      "38\t검증 세트 손실: 0.065352\t최선의 손실: 0.054738\t정확도: 98.91%\n",
-      "39\t검증 세트 손실: 0.061305\t최선의 손실: 0.054738\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.063698\t최선의 손실: 0.054738\t정확도: 98.94%\n",
-      "41\t검증 세트 손실: 0.064711\t최선의 손실: 0.054738\t정확도: 98.79%\n",
-      "42\t검증 세트 손실: 0.072485\t최선의 손실: 0.054738\t정확도: 98.98%\n",
-      "43\t검증 세트 손실: 0.072051\t최선의 손실: 0.054738\t정확도: 98.67%\n",
-      "44\t검증 세트 손실: 0.064030\t최선의 손실: 0.054738\t정확도: 98.87%\n",
-      "45\t검증 세트 손실: 0.073773\t최선의 손실: 0.054738\t정확도: 98.91%\n",
-      "46\t검증 세트 손실: 0.058439\t최선의 손실: 0.054738\t정확도: 99.06%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=10, total=  21.9s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.359829\t최선의 손실: 0.359829\t정확도: 97.77%\n",
+      "1\t검증 세트 손실: 0.364421\t최선의 손실: 0.359829\t정확도: 97.85%\n",
+      "2\t검증 세트 손실: 0.231389\t최선의 손실: 0.231389\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.195181\t최선의 손실: 0.195181\t정확도: 97.50%\n",
+      "4\t검증 세트 손실: 0.112750\t최선의 손실: 0.112750\t정확도: 98.48%\n",
+      "5\t검증 세트 손실: 0.111981\t최선의 손실: 0.111981\t정확도: 98.32%\n",
+      "6\t검증 세트 손실: 0.082034\t최선의 손실: 0.082034\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.104430\t최선의 손실: 0.082034\t정확도: 98.44%\n",
+      "8\t검증 세트 손실: 0.130796\t최선의 손실: 0.082034\t정확도: 97.89%\n",
+      "9\t검증 세트 손실: 0.096560\t최선의 손실: 0.082034\t정확도: 98.16%\n",
+      "10\t검증 세트 손실: 0.111537\t최선의 손실: 0.082034\t정확도: 98.40%\n",
+      "11\t검증 세트 손실: 0.070427\t최선의 손실: 0.070427\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.125368\t최선의 손실: 0.070427\t정확도: 98.28%\n",
+      "13\t검증 세트 손실: 0.076785\t최선의 손실: 0.070427\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.136310\t최선의 손실: 0.070427\t정확도: 98.01%\n",
+      "15\t검증 세트 손실: 0.071623\t최선의 손실: 0.070427\t정확도: 98.67%\n",
+      "16\t검증 세트 손실: 0.057971\t최선의 손실: 0.057971\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.078935\t최선의 손실: 0.057971\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.086379\t최선의 손실: 0.057971\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 0.055365\t최선의 손실: 0.055365\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.088441\t최선의 손실: 0.055365\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.117875\t최선의 손실: 0.055365\t정확도: 98.44%\n",
+      "22\t검증 세트 손실: 0.093601\t최선의 손실: 0.055365\t정확도: 98.40%\n",
+      "23\t검증 세트 손실: 0.094867\t최선의 손실: 0.055365\t정확도: 98.20%\n",
+      "24\t검증 세트 손실: 0.083480\t최선의 손실: 0.055365\t정확도: 98.48%\n",
+      "25\t검증 세트 손실: 0.068320\t최선의 손실: 0.055365\t정확도: 98.71%\n",
+      "26\t검증 세트 손실: 0.077581\t최선의 손실: 0.055365\t정확도: 98.75%\n",
+      "27\t검증 세트 손실: 0.072071\t최선의 손실: 0.055365\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.080163\t최선의 손실: 0.055365\t정확도: 98.75%\n",
+      "29\t검증 세트 손실: 0.056289\t최선의 손실: 0.055365\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.084576\t최선의 손실: 0.055365\t정확도: 98.63%\n",
+      "31\t검증 세트 손실: 0.110309\t최선의 손실: 0.055365\t정확도: 98.20%\n",
+      "32\t검증 세트 손실: 0.122288\t최선의 손실: 0.055365\t정확도: 98.36%\n",
+      "33\t검증 세트 손실: 0.071027\t최선의 손실: 0.055365\t정확도: 98.75%\n",
+      "34\t검증 세트 손실: 0.069620\t최선의 손실: 0.055365\t정확도: 98.87%\n",
+      "35\t검증 세트 손실: 0.058101\t최선의 손실: 0.055365\t정확도: 98.83%\n",
+      "36\t검증 세트 손실: 0.075786\t최선의 손실: 0.055365\t정확도: 98.63%\n",
+      "37\t검증 세트 손실: 0.066282\t최선의 손실: 0.055365\t정확도: 98.71%\n",
+      "38\t검증 세트 손실: 0.058758\t최선의 손실: 0.055365\t정확도: 98.83%\n",
+      "39\t검증 세트 손실: 0.052552\t최선의 손실: 0.052552\t정확도: 98.94%\n",
+      "40\t검증 세트 손실: 0.106118\t최선의 손실: 0.052552\t정확도: 98.40%\n",
+      "41\t검증 세트 손실: 0.075016\t최선의 손실: 0.052552\t정확도: 98.55%\n",
+      "42\t검증 세트 손실: 0.058250\t최선의 손실: 0.052552\t정확도: 98.83%\n",
+      "43\t검증 세트 손실: 0.054776\t최선의 손실: 0.052552\t정확도: 98.79%\n",
+      "44\t검증 세트 손실: 0.058226\t최선의 손실: 0.052552\t정확도: 98.83%\n",
+      "45\t검증 세트 손실: 0.067598\t최선의 손실: 0.052552\t정확도: 98.83%\n",
+      "46\t검증 세트 손실: 0.071955\t최선의 손실: 0.052552\t정확도: 98.91%\n",
+      "47\t검증 세트 손실: 0.087799\t최선의 손실: 0.052552\t정확도: 98.32%\n",
+      "48\t검증 세트 손실: 0.064175\t최선의 손실: 0.052552\t정확도: 98.75%\n",
+      "49\t검증 세트 손실: 0.073855\t최선의 손실: 0.052552\t정확도: 98.71%\n",
+      "50\t검증 세트 손실: 0.057091\t최선의 손실: 0.052552\t정확도: 99.02%\n",
+      "51\t검증 세트 손실: 0.065071\t최선의 손실: 0.052552\t정확도: 98.79%\n",
+      "52\t검증 세트 손실: 0.121043\t최선의 손실: 0.052552\t정확도: 97.30%\n",
+      "53\t검증 세트 손실: 0.068543\t최선의 손실: 0.052552\t정확도: 98.44%\n",
+      "54\t검증 세트 손실: 0.102280\t최선의 손실: 0.052552\t정확도: 98.28%\n",
+      "55\t검증 세트 손실: 0.059961\t최선의 손실: 0.052552\t정확도: 98.87%\n",
+      "56\t검증 세트 손실: 0.083062\t최선의 손실: 0.052552\t정확도: 98.36%\n",
+      "57\t검증 세트 손실: 0.065376\t최선의 손실: 0.052552\t정확도: 98.94%\n",
+      "58\t검증 세트 손실: 0.068387\t최선의 손실: 0.052552\t정확도: 98.75%\n",
+      "59\t검증 세트 손실: 0.062752\t최선의 손실: 0.052552\t정확도: 98.94%\n",
+      "60\t검증 세트 손실: 0.065153\t최선의 손실: 0.052552\t정확도: 98.79%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=50, total= 1.1min\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.810349\t최선의 손실: 0.810349\t정확도: 93.32%\n",
-      "1\t검증 세트 손실: 0.304420\t최선의 손실: 0.304420\t정확도: 97.42%\n",
-      "2\t검증 세트 손실: 0.223985\t최선의 손실: 0.223985\t정확도: 97.73%\n",
-      "3\t검증 세트 손실: 0.187151\t최선의 손실: 0.187151\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.138121\t최선의 손실: 0.138121\t정확도: 98.01%\n",
-      "5\t검증 세트 손실: 0.139540\t최선의 손실: 0.138121\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.088718\t최선의 손실: 0.088718\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.136528\t최선의 손실: 0.088718\t정확도: 98.16%\n",
-      "8\t검증 세트 손실: 0.095468\t최선의 손실: 0.088718\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.107640\t최선의 손실: 0.088718\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.088798\t최선의 손실: 0.088718\t정확도: 98.75%\n",
-      "11\t검증 세트 손실: 0.100986\t최선의 손실: 0.088718\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.097060\t최선의 손실: 0.088718\t정확도: 97.93%\n",
-      "13\t검증 세트 손실: 0.059837\t최선의 손실: 0.059837\t정확도: 98.91%\n",
-      "14\t검증 세트 손실: 0.087951\t최선의 손실: 0.059837\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.068212\t최선의 손실: 0.059837\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.063382\t최선의 손실: 0.059837\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.082606\t최선의 손실: 0.059837\t정확도: 98.63%\n",
-      "18\t검증 세트 손실: 0.059001\t최선의 손실: 0.059001\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.090550\t최선의 손실: 0.059001\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.060201\t최선의 손실: 0.059001\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.055893\t최선의 손실: 0.055893\t정확도: 99.06%\n",
-      "22\t검증 세트 손실: 0.063601\t최선의 손실: 0.055893\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.080799\t최선의 손실: 0.055893\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.056042\t최선의 손실: 0.055893\t정확도: 99.06%\n",
-      "25\t검증 세트 손실: 0.096135\t최선의 손실: 0.055893\t정확도: 98.51%\n",
-      "26\t검증 세트 손실: 0.137186\t최선의 손실: 0.055893\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.119972\t최선의 손실: 0.055893\t정확도: 98.40%\n",
-      "28\t검증 세트 손실: 0.075698\t최선의 손실: 0.055893\t정확도: 98.79%\n",
-      "29\t검증 세트 손실: 0.061491\t최선의 손실: 0.055893\t정확도: 98.94%\n",
-      "30\t검증 세트 손실: 0.064280\t최선의 손실: 0.055893\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.061573\t최선의 손실: 0.055893\t정확도: 98.94%\n",
-      "32\t검증 세트 손실: 0.085675\t최선의 손실: 0.055893\t정확도: 98.67%\n",
-      "33\t검증 세트 손실: 0.103697\t최선의 손실: 0.055893\t정확도: 98.40%\n",
-      "34\t검증 세트 손실: 0.080901\t최선의 손실: 0.055893\t정확도: 98.98%\n",
-      "35\t검증 세트 손실: 0.067049\t최선의 손실: 0.055893\t정확도: 98.98%\n",
-      "36\t검증 세트 손실: 0.047963\t최선의 손실: 0.047963\t정확도: 99.30%\n",
-      "37\t검증 세트 손실: 0.050923\t최선의 손실: 0.047963\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.111339\t최선의 손실: 0.047963\t정확도: 98.24%\n",
-      "39\t검증 세트 손실: 0.065273\t최선의 손실: 0.047963\t정확도: 98.83%\n",
-      "40\t검증 세트 손실: 0.054996\t최선의 손실: 0.047963\t정확도: 99.02%\n",
-      "41\t검증 세트 손실: 0.118657\t최선의 손실: 0.047963\t정확도: 98.05%\n",
-      "42\t검증 세트 손실: 0.078051\t최선의 손실: 0.047963\t정확도: 99.10%\n",
-      "43\t검증 세트 손실: 0.078598\t최선의 손실: 0.047963\t정확도: 98.59%\n",
-      "44\t검증 세트 손실: 0.082108\t최선의 손실: 0.047963\t정확도: 98.55%\n",
-      "45\t검증 세트 손실: 0.055564\t최선의 손실: 0.047963\t정확도: 98.98%\n",
-      "46\t검증 세트 손실: 0.079369\t최선의 손실: 0.047963\t정확도: 98.63%\n",
-      "47\t검증 세트 손실: 0.067146\t최선의 손실: 0.047963\t정확도: 98.79%\n",
-      "48\t검증 세트 손실: 0.063510\t최선의 손실: 0.047963\t정확도: 98.87%\n",
-      "49\t검증 세트 손실: 0.068451\t최선의 손실: 0.047963\t정확도: 98.83%\n",
-      "50\t검증 세트 손실: 0.081339\t최선의 손실: 0.047963\t정확도: 98.75%\n",
-      "51\t검증 세트 손실: 0.057323\t최선의 손실: 0.047963\t정확도: 98.98%\n",
-      "52\t검증 세트 손실: 0.062104\t최선의 손실: 0.047963\t정확도: 98.75%\n",
-      "53\t검증 세트 손실: 0.129986\t최선의 손실: 0.047963\t정확도: 98.08%\n",
-      "54\t검증 세트 손실: 0.076618\t최선의 손실: 0.047963\t정확도: 98.83%\n",
-      "55\t검증 세트 손실: 0.094106\t최선의 손실: 0.047963\t정확도: 98.75%\n",
-      "56\t검증 세트 손실: 0.084276\t최선의 손실: 0.047963\t정확도: 98.67%\n",
-      "57\t검증 세트 손실: 0.059692\t최선의 손실: 0.047963\t정확도: 98.75%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=50, total= 1.5min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.600511\t최선의 손실: 0.600511\t정확도: 95.35%\n",
+      "1\t검증 세트 손실: 0.614152\t최선의 손실: 0.600511\t정확도: 96.05%\n",
+      "2\t검증 세트 손실: 0.189359\t최선의 손실: 0.189359\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.192983\t최선의 손실: 0.189359\t정확도: 98.01%\n",
+      "4\t검증 세트 손실: 0.156893\t최선의 손실: 0.156893\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.153724\t최선의 손실: 0.153724\t정확도: 97.73%\n",
+      "6\t검증 세트 손실: 0.124345\t최선의 손실: 0.124345\t정확도: 98.12%\n",
+      "7\t검증 세트 손실: 0.132286\t최선의 손실: 0.124345\t정확도: 98.05%\n",
+      "8\t검증 세트 손실: 0.095898\t최선의 손실: 0.095898\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.089488\t최선의 손실: 0.089488\t정확도: 98.75%\n",
+      "10\t검증 세트 손실: 0.097735\t최선의 손실: 0.089488\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.077987\t최선의 손실: 0.077987\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.095204\t최선의 손실: 0.077987\t정확도: 98.08%\n",
+      "13\t검증 세트 손실: 0.083689\t최선의 손실: 0.077987\t정확도: 98.83%\n",
+      "14\t검증 세트 손실: 0.087024\t최선의 손실: 0.077987\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.057374\t최선의 손실: 0.057374\t정확도: 99.06%\n",
+      "16\t검증 세트 손실: 0.120517\t최선의 손실: 0.057374\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.082610\t최선의 손실: 0.057374\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.095454\t최선의 손실: 0.057374\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.062754\t최선의 손실: 0.057374\t정확도: 99.14%\n",
+      "20\t검증 세트 손실: 0.082952\t최선의 손실: 0.057374\t정확도: 98.83%\n",
+      "21\t검증 세트 손실: 0.091160\t최선의 손실: 0.057374\t정확도: 98.59%\n",
+      "22\t검증 세트 손실: 0.078122\t최선의 손실: 0.057374\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.085244\t최선의 손실: 0.057374\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.052602\t최선의 손실: 0.052602\t정확도: 98.87%\n",
+      "25\t검증 세트 손실: 0.127962\t최선의 손실: 0.052602\t정확도: 98.01%\n",
+      "26\t검증 세트 손실: 0.086028\t최선의 손실: 0.052602\t정확도: 98.32%\n",
+      "27\t검증 세트 손실: 0.075617\t최선의 손실: 0.052602\t정확도: 98.83%\n",
+      "28\t검증 세트 손실: 0.075564\t최선의 손실: 0.052602\t정확도: 98.75%\n",
+      "29\t검증 세트 손실: 0.051420\t최선의 손실: 0.051420\t정확도: 98.67%\n",
+      "30\t검증 세트 손실: 0.056507\t최선의 손실: 0.051420\t정확도: 99.18%\n",
+      "31\t검증 세트 손실: 0.062134\t최선의 손실: 0.051420\t정확도: 98.91%\n",
+      "32\t검증 세트 손실: 0.125835\t최선의 손실: 0.051420\t정확도: 97.93%\n",
+      "33\t검증 세트 손실: 0.059745\t최선의 손실: 0.051420\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.050565\t최선의 손실: 0.050565\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.115264\t최선의 손실: 0.050565\t정확도: 98.51%\n",
+      "36\t검증 세트 손실: 0.086740\t최선의 손실: 0.050565\t정확도: 98.75%\n",
+      "37\t검증 세트 손실: 0.050624\t최선의 손실: 0.050565\t정확도: 99.06%\n",
+      "38\t검증 세트 손실: 0.060088\t최선의 손실: 0.050565\t정확도: 98.67%\n",
+      "39\t검증 세트 손실: 0.088459\t최선의 손실: 0.050565\t정확도: 98.91%\n",
+      "40\t검증 세트 손실: 0.051412\t최선의 손실: 0.050565\t정확도: 98.98%\n",
+      "41\t검증 세트 손실: 0.067413\t최선의 손실: 0.050565\t정확도: 98.87%\n",
+      "42\t검증 세트 손실: 0.100030\t최선의 손실: 0.050565\t정확도: 98.40%\n",
+      "43\t검증 세트 손실: 0.083006\t최선의 손실: 0.050565\t정확도: 98.71%\n",
+      "44\t검증 세트 손실: 0.073599\t최선의 손실: 0.050565\t정확도: 98.59%\n",
+      "45\t검증 세트 손실: 0.056947\t최선의 손실: 0.050565\t정확도: 98.94%\n",
+      "46\t검증 세트 손실: 0.080081\t최선의 손실: 0.050565\t정확도: 98.63%\n",
+      "47\t검증 세트 손실: 0.077132\t최선의 손실: 0.050565\t정확도: 98.79%\n",
+      "48\t검증 세트 손실: 0.070984\t최선의 손실: 0.050565\t정확도: 98.59%\n",
+      "49\t검증 세트 손실: 0.065118\t최선의 손실: 0.050565\t정확도: 98.79%\n",
+      "50\t검증 세트 손실: 0.093204\t최선의 손실: 0.050565\t정확도: 98.79%\n",
+      "51\t검증 세트 손실: 0.053782\t최선의 손실: 0.050565\t정확도: 99.22%\n",
+      "52\t검증 세트 손실: 0.059016\t최선의 손실: 0.050565\t정확도: 98.98%\n",
+      "53\t검증 세트 손실: 0.071706\t최선의 손실: 0.050565\t정확도: 98.94%\n",
+      "54\t검증 세트 손실: 0.072927\t최선의 손실: 0.050565\t정확도: 98.87%\n",
+      "55\t검증 세트 손실: 0.066558\t최선의 손실: 0.050565\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=50, total= 1.4min\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.575078\t최선의 손실: 0.575078\t정확도: 96.64%\n",
-      "1\t검증 세트 손실: 0.304499\t최선의 손실: 0.304499\t정확도: 97.58%\n",
-      "2\t검증 세트 손실: 0.244638\t최선의 손실: 0.244638\t정확도: 97.65%\n",
-      "3\t검증 세트 손실: 0.137236\t최선의 손실: 0.137236\t정확도: 98.32%\n",
-      "4\t검증 세트 손실: 0.149942\t최선의 손실: 0.137236\t정확도: 98.16%\n",
-      "5\t검증 세트 손실: 0.207436\t최선의 손실: 0.137236\t정확도: 96.95%\n",
-      "6\t검증 세트 손실: 0.133272\t최선의 손실: 0.133272\t정확도: 98.05%\n",
-      "7\t검증 세트 손실: 0.146085\t최선의 손실: 0.133272\t정확도: 98.28%\n",
-      "8\t검증 세트 손실: 0.114953\t최선의 손실: 0.114953\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.140855\t최선의 손실: 0.114953\t정확도: 98.12%\n",
-      "10\t검증 세트 손실: 0.106660\t최선의 손실: 0.106660\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.123568\t최선의 손실: 0.106660\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.138227\t최선의 손실: 0.106660\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.079988\t최선의 손실: 0.079988\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.087987\t최선의 손실: 0.079988\t정확도: 98.44%\n",
-      "15\t검증 세트 손실: 0.070121\t최선의 손실: 0.070121\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.079924\t최선의 손실: 0.070121\t정확도: 98.83%\n",
-      "17\t검증 세트 손실: 0.092519\t최선의 손실: 0.070121\t정확도: 98.63%\n",
-      "18\t검증 세트 손실: 0.087444\t최선의 손실: 0.070121\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.110576\t최선의 손실: 0.070121\t정확도: 98.40%\n",
-      "20\t검증 세트 손실: 0.089848\t최선의 손실: 0.070121\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.082915\t최선의 손실: 0.070121\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.084487\t최선의 손실: 0.070121\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.071363\t최선의 손실: 0.070121\t정확도: 98.75%\n",
-      "24\t검증 세트 손실: 0.068504\t최선의 손실: 0.068504\t정확도: 98.75%\n",
-      "25\t검증 세트 손실: 0.079768\t최선의 손실: 0.068504\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.073163\t최선의 손실: 0.068504\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.046648\t최선의 손실: 0.046648\t정확도: 98.94%\n",
-      "28\t검증 세트 손실: 0.153105\t최선의 손실: 0.046648\t정확도: 97.73%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=50, total= 1.4min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=50 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "29\t검증 세트 손실: 0.067710\t최선의 손실: 0.046648\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.066284\t최선의 손실: 0.046648\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.086048\t최선의 손실: 0.046648\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.126248\t최선의 손실: 0.046648\t정확도: 97.69%\n",
-      "33\t검증 세트 손실: 0.074229\t최선의 손실: 0.046648\t정확도: 98.79%\n",
-      "34\t검증 세트 손실: 0.078397\t최선의 손실: 0.046648\t정확도: 98.63%\n",
-      "35\t검증 세트 손실: 0.098795\t최선의 손실: 0.046648\t정확도: 98.48%\n",
-      "36\t검증 세트 손실: 0.058875\t최선의 손실: 0.046648\t정확도: 98.98%\n",
-      "37\t검증 세트 손실: 0.068994\t최선의 손실: 0.046648\t정확도: 98.67%\n",
-      "38\t검증 세트 손실: 0.059208\t최선의 손실: 0.046648\t정확도: 98.94%\n",
-      "39\t검증 세트 손실: 0.060417\t최선의 손실: 0.046648\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.097532\t최선의 손실: 0.046648\t정확도: 98.63%\n",
-      "41\t검증 세트 손실: 0.069687\t최선의 손실: 0.046648\t정확도: 98.98%\n",
-      "42\t검증 세트 손실: 0.051236\t최선의 손실: 0.046648\t정확도: 98.98%\n",
-      "43\t검증 세트 손실: 0.054491\t최선의 손실: 0.046648\t정확도: 98.94%\n",
-      "44\t검증 세트 손실: 0.064683\t최선의 손실: 0.046648\t정확도: 98.87%\n",
-      "45\t검증 세트 손실: 0.086131\t최선의 손실: 0.046648\t정확도: 98.32%\n",
-      "46\t검증 세트 손실: 0.056321\t최선의 손실: 0.046648\t정확도: 98.98%\n",
-      "47\t검증 세트 손실: 0.063493\t최선의 손실: 0.046648\t정확도: 98.79%\n",
-      "48\t검증 세트 손실: 0.057117\t최선의 손실: 0.046648\t정확도: 99.02%\n",
+      "0\t검증 세트 손실: 0.433448\t최선의 손실: 0.433448\t정확도: 96.87%\n",
+      "1\t검증 세트 손실: 0.296668\t최선의 손실: 0.296668\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.271451\t최선의 손실: 0.271451\t정확도: 97.50%\n",
+      "3\t검증 세트 손실: 0.168923\t최선의 손실: 0.168923\t정확도: 98.36%\n",
+      "4\t검증 세트 손실: 0.107488\t최선의 손실: 0.107488\t정확도: 98.94%\n",
+      "5\t검증 세트 손실: 0.147033\t최선의 손실: 0.107488\t정확도: 97.65%\n",
+      "6\t검증 세트 손실: 0.091610\t최선의 손실: 0.091610\t정확도: 98.79%\n",
+      "7\t검증 세트 손실: 0.119546\t최선의 손실: 0.091610\t정확도: 98.32%\n",
+      "8\t검증 세트 손실: 0.082931\t최선의 손실: 0.082931\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.074126\t최선의 손실: 0.074126\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.081139\t최선의 손실: 0.074126\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.094756\t최선의 손실: 0.074126\t정확도: 98.51%\n",
+      "12\t검증 세트 손실: 0.081141\t최선의 손실: 0.074126\t정확도: 98.36%\n",
+      "13\t검증 세트 손실: 0.070765\t최선의 손실: 0.070765\t정확도: 98.91%\n",
+      "14\t검증 세트 손실: 0.062611\t최선의 손실: 0.062611\t정확도: 98.91%\n",
+      "15\t검증 세트 손실: 0.067694\t최선의 손실: 0.062611\t정확도: 98.51%\n",
+      "16\t검증 세트 손실: 0.067856\t최선의 손실: 0.062611\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.072161\t최선의 손실: 0.062611\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.071769\t최선의 손실: 0.062611\t정확도: 98.94%\n",
+      "19\t검증 세트 손실: 0.166590\t최선의 손실: 0.062611\t정확도: 98.05%\n",
+      "20\t검증 세트 손실: 0.047676\t최선의 손실: 0.047676\t정확도: 99.22%\n",
+      "21\t검증 세트 손실: 0.053783\t최선의 손실: 0.047676\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.042736\t최선의 손실: 0.042736\t정확도: 99.26%\n",
+      "23\t검증 세트 손실: 0.043484\t최선의 손실: 0.042736\t정확도: 99.18%\n",
+      "24\t검증 세트 손실: 0.076440\t최선의 손실: 0.042736\t정확도: 98.87%\n",
+      "25\t검증 세트 손실: 0.066690\t최선의 손실: 0.042736\t정확도: 98.63%\n",
+      "26\t검증 세트 손실: 0.151983\t최선의 손실: 0.042736\t정확도: 98.01%\n",
+      "27\t검증 세트 손실: 0.079544\t최선의 손실: 0.042736\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.080673\t최선의 손실: 0.042736\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.110736\t최선의 손실: 0.042736\t정확도: 98.36%\n",
+      "30\t검증 세트 손실: 0.082631\t최선의 손실: 0.042736\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.083951\t최선의 손실: 0.042736\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.073412\t최선의 손실: 0.042736\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.084679\t최선의 손실: 0.042736\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.074954\t최선의 손실: 0.042736\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.079212\t최선의 손실: 0.042736\t정확도: 98.94%\n",
+      "36\t검증 세트 손실: 0.135929\t최선의 손실: 0.042736\t정확도: 98.40%\n",
+      "37\t검증 세트 손실: 0.058608\t최선의 손실: 0.042736\t정확도: 99.22%\n",
+      "38\t검증 세트 손실: 0.088154\t최선의 손실: 0.042736\t정확도: 98.55%\n",
+      "39\t검증 세트 손실: 0.071432\t최선의 손실: 0.042736\t정확도: 99.22%\n",
+      "40\t검증 세트 손실: 0.085492\t최선의 손실: 0.042736\t정확도: 98.32%\n",
+      "41\t검증 세트 손실: 0.094601\t최선의 손실: 0.042736\t정확도: 98.87%\n",
+      "42\t검증 세트 손실: 0.068677\t최선의 손실: 0.042736\t정확도: 98.87%\n",
+      "43\t검증 세트 손실: 0.062977\t최선의 손실: 0.042736\t정확도: 99.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=50, total= 1.2min\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=10 \n",
-      "0\t검증 세트 손실: 14.598553\t최선의 손실: 14.598553\t정확도: 55.47%\n",
-      "1\t검증 세트 손실: 1.035781\t최선의 손실: 1.035781\t정확도: 91.99%\n",
-      "2\t검증 세트 손실: 0.500554\t최선의 손실: 0.500554\t정확도: 93.08%\n",
-      "3\t검증 세트 손실: 0.273066\t최선의 손실: 0.273066\t정확도: 95.00%\n",
-      "4\t검증 세트 손실: 0.191963\t최선의 손실: 0.191963\t정확도: 96.01%\n",
-      "5\t검증 세트 손실: 0.305322\t최선의 손실: 0.191963\t정확도: 93.75%\n",
-      "6\t검증 세트 손실: 0.216431\t최선의 손실: 0.191963\t정확도: 95.58%\n",
-      "7\t검증 세트 손실: 0.161410\t최선의 손실: 0.161410\t정확도: 96.13%\n",
-      "8\t검증 세트 손실: 0.252005\t최선의 손실: 0.161410\t정확도: 96.13%\n",
-      "9\t검증 세트 손실: 0.157737\t최선의 손실: 0.157737\t정확도: 96.72%\n",
-      "10\t검증 세트 손실: 0.152428\t최선의 손실: 0.152428\t정확도: 96.87%\n",
-      "11\t검증 세트 손실: 0.151233\t최선의 손실: 0.151233\t정확도: 96.44%\n",
-      "12\t검증 세트 손실: 0.138748\t최선의 손실: 0.138748\t정확도: 96.91%\n",
-      "13\t검증 세트 손실: 0.173327\t최선의 손실: 0.138748\t정확도: 96.17%\n",
-      "14\t검증 세트 손실: 0.141857\t최선의 손실: 0.138748\t정확도: 97.58%\n",
-      "15\t검증 세트 손실: 0.127443\t최선의 손실: 0.127443\t정확도: 97.30%\n",
-      "16\t검증 세트 손실: 0.135721\t최선의 손실: 0.127443\t정확도: 97.07%\n",
-      "17\t검증 세트 손실: 0.109661\t최선의 손실: 0.109661\t정확도: 97.54%\n",
-      "18\t검증 세트 손실: 0.111660\t최선의 손실: 0.109661\t정확도: 97.81%\n",
-      "19\t검증 세트 손실: 0.192464\t최선의 손실: 0.109661\t정확도: 96.87%\n",
-      "20\t검증 세트 손실: 0.129298\t최선의 손실: 0.109661\t정확도: 97.19%\n",
-      "21\t검증 세트 손실: 0.176315\t최선의 손실: 0.109661\t정확도: 96.52%\n",
-      "22\t검증 세트 손실: 0.159546\t최선의 손실: 0.109661\t정확도: 97.03%\n",
-      "23\t검증 세트 손실: 0.183199\t최선의 손실: 0.109661\t정확도: 96.76%\n",
-      "24\t검증 세트 손실: 0.150356\t최선의 손실: 0.109661\t정확도: 97.19%\n",
-      "25\t검증 세트 손실: 0.137115\t최선의 손실: 0.109661\t정확도: 97.62%\n",
-      "26\t검증 세트 손실: 0.176502\t최선의 손실: 0.109661\t정확도: 96.76%\n",
-      "27\t검증 세트 손실: 0.187546\t최선의 손실: 0.109661\t정확도: 97.07%\n",
-      "28\t검증 세트 손실: 0.158962\t최선의 손실: 0.109661\t정확도: 97.42%\n",
-      "29\t검증 세트 손실: 0.147727\t최선의 손실: 0.109661\t정확도: 97.19%\n",
-      "30\t검증 세트 손실: 0.127167\t최선의 손실: 0.109661\t정확도: 97.62%\n",
-      "31\t검증 세트 손실: 0.112720\t최선의 손실: 0.109661\t정확도: 97.69%\n",
-      "32\t검증 세트 손실: 0.133338\t최선의 손실: 0.109661\t정확도: 97.73%\n",
-      "33\t검증 세트 손실: 0.113947\t최선의 손실: 0.109661\t정확도: 97.81%\n",
-      "34\t검증 세트 손실: 0.167466\t최선의 손실: 0.109661\t정확도: 97.11%\n",
-      "35\t검증 세트 손실: 0.282511\t최선의 손실: 0.109661\t정확도: 95.23%\n",
-      "36\t검증 세트 손실: 0.143537\t최선의 손실: 0.109661\t정확도: 97.69%\n",
-      "37\t검증 세트 손실: 0.155448\t최선의 손실: 0.109661\t정확도: 97.50%\n",
-      "38\t검증 세트 손실: 0.166862\t최선의 손실: 0.109661\t정확도: 96.79%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=50, total= 1.1min\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=10 \n",
+      "0\t검증 세트 손실: 7.052859\t최선의 손실: 7.052859\t정확도: 76.74%\n",
+      "1\t검증 세트 손실: 13.063363\t최선의 손실: 7.052859\t정확도: 43.28%\n",
+      "2\t검증 세트 손실: 5.073461\t최선의 손실: 5.073461\t정확도: 57.19%\n",
+      "3\t검증 세트 손실: 1.638235\t최선의 손실: 1.638235\t정확도: 80.61%\n",
+      "4\t검증 세트 손실: 2.871516\t최선의 손실: 1.638235\t정확도: 65.64%\n",
+      "5\t검증 세트 손실: 1.694853\t최선의 손실: 1.638235\t정확도: 78.34%\n",
+      "6\t검증 세트 손실: 0.910604\t최선의 손실: 0.910604\t정확도: 88.70%\n",
+      "7\t검증 세트 손실: 0.666115\t최선의 손실: 0.666115\t정확도: 90.23%\n",
+      "8\t검증 세트 손실: 0.435403\t최선의 손실: 0.435403\t정확도: 92.14%\n",
+      "9\t검증 세트 손실: 0.317309\t최선의 손실: 0.317309\t정확도: 94.25%\n",
+      "10\t검증 세트 손실: 0.378442\t최선의 손실: 0.317309\t정확도: 93.86%\n",
+      "11\t검증 세트 손실: 0.178882\t최선의 손실: 0.178882\t정확도: 96.48%\n",
+      "12\t검증 세트 손실: 0.419340\t최선의 손실: 0.178882\t정확도: 92.69%\n",
+      "13\t검증 세트 손실: 0.311352\t최선의 손실: 0.178882\t정확도: 94.21%\n",
+      "14\t검증 세트 손실: 0.385119\t최선의 손실: 0.178882\t정확도: 92.57%\n",
+      "15\t검증 세트 손실: 0.311142\t최선의 손실: 0.178882\t정확도: 94.57%\n",
+      "16\t검증 세트 손실: 0.458151\t최선의 손실: 0.178882\t정확도: 91.44%\n",
+      "17\t검증 세트 손실: 0.479321\t최선의 손실: 0.178882\t정확도: 93.35%\n",
+      "18\t검증 세트 손실: 0.709976\t최선의 손실: 0.178882\t정확도: 87.92%\n",
+      "19\t검증 세트 손실: 0.252005\t최선의 손실: 0.178882\t정확도: 96.29%\n",
+      "20\t검증 세트 손실: 0.331431\t최선의 손실: 0.178882\t정확도: 94.14%\n",
+      "21\t검증 세트 손실: 0.144309\t최선의 손실: 0.144309\t정확도: 97.54%\n",
+      "22\t검증 세트 손실: 0.447498\t최선의 손실: 0.144309\t정확도: 91.56%\n",
+      "23\t검증 세트 손실: 0.364762\t최선의 손실: 0.144309\t정확도: 94.29%\n",
+      "24\t검증 세트 손실: 0.152351\t최선의 손실: 0.144309\t정확도: 97.22%\n",
+      "25\t검증 세트 손실: 0.187180\t최선의 손실: 0.144309\t정확도: 96.56%\n",
+      "26\t검증 세트 손실: 0.156412\t최선의 손실: 0.144309\t정확도: 97.46%\n",
+      "27\t검증 세트 손실: 0.201175\t최선의 손실: 0.144309\t정확도: 96.60%\n",
+      "28\t검증 세트 손실: 0.200675\t최선의 손실: 0.144309\t정확도: 97.07%\n",
+      "29\t검증 세트 손실: 0.627635\t최선의 손실: 0.144309\t정확도: 91.05%\n",
+      "30\t검증 세트 손실: 0.177366\t최선의 손실: 0.144309\t정확도: 96.91%\n",
+      "31\t검증 세트 손실: 0.235548\t최선의 손실: 0.144309\t정확도: 96.33%\n",
+      "32\t검증 세트 손실: 0.111953\t최선의 손실: 0.111953\t정확도: 97.65%\n",
+      "33\t검증 세트 손실: 0.157762\t최선의 손실: 0.111953\t정확도: 97.50%\n",
+      "34\t검증 세트 손실: 0.301864\t최선의 손실: 0.111953\t정확도: 95.50%\n",
+      "35\t검증 세트 손실: 0.158183\t최선의 손실: 0.111953\t정확도: 97.46%\n",
+      "36\t검증 세트 손실: 0.139712\t최선의 손실: 0.111953\t정확도: 97.42%\n",
+      "37\t검증 세트 손실: 0.225694\t최선의 손실: 0.111953\t정확도: 96.13%\n",
+      "38\t검증 세트 손실: 0.222555\t최선의 손실: 0.111953\t정확도: 96.79%\n",
+      "39\t검증 세트 손실: 0.200973\t최선의 손실: 0.111953\t정확도: 96.56%\n",
+      "40\t검증 세트 손실: 0.200040\t최선의 손실: 0.111953\t정확도: 97.38%\n",
+      "41\t검증 세트 손실: 0.198404\t최선의 손실: 0.111953\t정확도: 96.99%\n",
+      "42\t검증 세트 손실: 0.182030\t최선의 손실: 0.111953\t정확도: 97.15%\n",
+      "43\t검증 세트 손실: 0.167423\t최선의 손실: 0.111953\t정확도: 97.38%\n",
+      "44\t검증 세트 손실: 0.203263\t최선의 손실: 0.111953\t정확도: 96.72%\n",
+      "45\t검증 세트 손실: 0.257995\t최선의 손실: 0.111953\t정확도: 96.05%\n",
+      "46\t검증 세트 손실: 0.159231\t최선의 손실: 0.111953\t정확도: 97.69%\n",
+      "47\t검증 세트 손실: 0.183876\t최선의 손실: 0.111953\t정확도: 96.99%\n",
+      "48\t검증 세트 손실: 0.154769\t최선의 손실: 0.111953\t정확도: 97.15%\n",
+      "49\t검증 세트 손실: 0.200801\t최선의 손실: 0.111953\t정확도: 97.07%\n",
+      "50\t검증 세트 손실: 0.176726\t최선의 손실: 0.111953\t정확도: 97.42%\n",
+      "51\t검증 세트 손실: 0.138653\t최선의 손실: 0.111953\t정확도: 97.38%\n",
+      "52\t검증 세트 손실: 0.154672\t최선의 손실: 0.111953\t정확도: 97.46%\n",
+      "53\t검증 세트 손실: 0.149071\t최선의 손실: 0.111953\t정확도: 97.77%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=10, total=   8.0s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=10 \n",
-      "0\t검증 세트 손실: 9.549815\t최선의 손실: 9.549815\t정확도: 77.87%\n",
-      "1\t검증 세트 손실: 2.680438\t최선의 손실: 2.680438\t정확도: 79.44%\n",
-      "2\t검증 세트 손실: 1.044536\t최선의 손실: 1.044536\t정확도: 84.68%\n",
-      "3\t검증 세트 손실: 0.393837\t최선의 손실: 0.393837\t정확도: 93.08%\n",
-      "4\t검증 세트 손실: 0.276538\t최선의 손실: 0.276538\t정확도: 94.64%\n",
-      "5\t검증 세트 손실: 0.334599\t최선의 손실: 0.276538\t정확도: 93.98%\n",
-      "6\t검증 세트 손실: 0.355878\t최선의 손실: 0.276538\t정확도: 92.42%\n",
-      "7\t검증 세트 손실: 0.244126\t최선의 손실: 0.244126\t정확도: 95.82%\n",
-      "8\t검증 세트 손실: 0.201105\t최선의 손실: 0.201105\t정확도: 95.74%\n",
-      "9\t검증 세트 손실: 0.331399\t최선의 손실: 0.201105\t정확도: 93.16%\n",
-      "10\t검증 세트 손실: 0.344309\t최선의 손실: 0.201105\t정확도: 93.71%\n",
-      "11\t검증 세트 손실: 0.213708\t최선의 손실: 0.201105\t정확도: 94.53%\n",
-      "12\t검증 세트 손실: 0.271767\t최선의 손실: 0.201105\t정확도: 95.04%\n",
-      "13\t검증 세트 손실: 0.192132\t최선의 손실: 0.192132\t정확도: 96.17%\n",
-      "14\t검증 세트 손실: 0.292027\t최선의 손실: 0.192132\t정확도: 95.27%\n",
-      "15\t검증 세트 손실: 0.269663\t최선의 손실: 0.192132\t정확도: 95.15%\n",
-      "16\t검증 세트 손실: 0.173167\t최선의 손실: 0.173167\t정확도: 96.33%\n",
-      "17\t검증 세트 손실: 0.237848\t최선의 손실: 0.173167\t정확도: 94.96%\n",
-      "18\t검증 세트 손실: 0.301777\t최선의 손실: 0.173167\t정확도: 95.27%\n",
-      "19\t검증 세트 손실: 0.167977\t최선의 손실: 0.167977\t정확도: 96.52%\n",
-      "20\t검증 세트 손실: 0.164549\t최선의 손실: 0.164549\t정확도: 97.34%\n",
-      "21\t검증 세트 손실: 0.158403\t최선의 손실: 0.158403\t정확도: 97.11%\n",
-      "22\t검증 세트 손실: 0.123523\t최선의 손실: 0.123523\t정확도: 97.54%\n",
-      "23\t검증 세트 손실: 0.171289\t최선의 손실: 0.123523\t정확도: 97.26%\n",
-      "24\t검증 세트 손실: 0.189330\t최선의 손실: 0.123523\t정확도: 96.44%\n",
-      "25\t검증 세트 손실: 0.176947\t최선의 손실: 0.123523\t정확도: 96.95%\n",
-      "26\t검증 세트 손실: 0.235734\t최선의 손실: 0.123523\t정확도: 96.05%\n",
-      "27\t검증 세트 손실: 0.192702\t최선의 손실: 0.123523\t정확도: 96.79%\n",
-      "28\t검증 세트 손실: 0.184886\t최선의 손실: 0.123523\t정확도: 96.72%\n",
-      "29\t검증 세트 손실: 0.162950\t최선의 손실: 0.123523\t정확도: 97.38%\n",
-      "30\t검증 세트 손실: 0.291058\t최선의 손실: 0.123523\t정확도: 95.23%\n",
-      "31\t검증 세트 손실: 0.146607\t최선의 손실: 0.123523\t정확도: 97.65%\n",
-      "32\t검증 세트 손실: 0.120944\t최선의 손실: 0.120944\t정확도: 97.69%\n",
-      "33\t검증 세트 손실: 0.142238\t최선의 손실: 0.120944\t정확도: 97.58%\n",
-      "34\t검증 세트 손실: 0.148444\t최선의 손실: 0.120944\t정확도: 97.77%\n",
-      "35\t검증 세트 손실: 0.215279\t최선의 손실: 0.120944\t정확도: 96.25%\n",
-      "36\t검증 세트 손실: 0.129874\t최선의 손실: 0.120944\t정확도: 97.50%\n",
-      "37\t검증 세트 손실: 0.158171\t최선의 손실: 0.120944\t정확도: 97.19%\n",
-      "38\t검증 세트 손실: 0.159481\t최선의 손실: 0.120944\t정확도: 97.38%\n",
-      "39\t검증 세트 손실: 0.223581\t최선의 손실: 0.120944\t정확도: 96.33%\n",
-      "40\t검증 세트 손실: 0.128624\t최선의 손실: 0.120944\t정확도: 97.89%\n",
-      "41\t검증 세트 손실: 0.151008\t최선의 손실: 0.120944\t정확도: 97.54%\n",
-      "42\t검증 세트 손실: 0.148477\t최선의 손실: 0.120944\t정확도: 97.46%\n",
-      "43\t검증 세트 손실: 0.157206\t최선의 손실: 0.120944\t정확도: 97.65%\n",
-      "44\t검증 세트 손실: 0.207800\t최선의 손실: 0.120944\t정확도: 96.91%\n",
-      "45\t검증 세트 손실: 0.214656\t최선의 손실: 0.120944\t정확도: 96.87%\n",
-      "46\t검증 세트 손실: 0.440834\t최선의 손실: 0.120944\t정확도: 93.75%\n",
-      "47\t검증 세트 손실: 0.247687\t최선의 손실: 0.120944\t정확도: 96.68%\n",
-      "48\t검증 세트 손실: 0.189420\t최선의 손실: 0.120944\t정확도: 96.76%\n",
-      "49\t검증 세트 손실: 0.165617\t최선의 손실: 0.120944\t정확도: 97.65%\n",
-      "50\t검증 세트 손실: 0.208882\t최선의 손실: 0.120944\t정확도: 97.22%\n",
-      "51\t검증 세트 손실: 0.155238\t최선의 손실: 0.120944\t정확도: 97.50%\n",
-      "52\t검증 세트 손실: 0.191163\t최선의 손실: 0.120944\t정확도: 97.11%\n",
-      "53\t검증 세트 손실: 0.189210\t최선의 손실: 0.120944\t정확도: 97.34%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=10, total=  11.4s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=10 \n",
+      "0\t검증 세트 손실: 19.930344\t최선의 손실: 19.930344\t정확도: 62.39%\n",
+      "1\t검증 세트 손실: 10.896272\t최선의 손실: 10.896272\t정확도: 55.82%\n",
+      "2\t검증 세트 손실: 1.826073\t최선의 손실: 1.826073\t정확도: 79.71%\n",
+      "3\t검증 세트 손실: 0.649916\t최선의 손실: 0.649916\t정확도: 90.23%\n",
+      "4\t검증 세트 손실: 0.415710\t최선의 손실: 0.415710\t정확도: 92.89%\n",
+      "5\t검증 세트 손실: 0.448443\t최선의 손실: 0.415710\t정확도: 92.96%\n",
+      "6\t검증 세트 손실: 0.213386\t최선의 손실: 0.213386\t정확도: 95.90%\n",
+      "7\t검증 세트 손실: 0.175297\t최선의 손실: 0.175297\t정확도: 96.01%\n",
+      "8\t검증 세트 손실: 0.203133\t최선의 손실: 0.175297\t정확도: 95.31%\n",
+      "9\t검증 세트 손실: 0.250368\t최선의 손실: 0.175297\t정확도: 95.00%\n",
+      "10\t검증 세트 손실: 0.188137\t최선의 손실: 0.175297\t정확도: 96.01%\n",
+      "11\t검증 세트 손실: 0.171474\t최선의 손실: 0.171474\t정확도: 96.33%\n",
+      "12\t검증 세트 손실: 0.238515\t최선의 손실: 0.171474\t정확도: 95.58%\n",
+      "13\t검증 세트 손실: 0.186545\t최선의 손실: 0.171474\t정확도: 95.93%\n",
+      "14\t검증 세트 손실: 0.145301\t최선의 손실: 0.145301\t정확도: 96.91%\n",
+      "15\t검증 세트 손실: 0.238824\t최선의 손실: 0.145301\t정확도: 96.09%\n",
+      "16\t검증 세트 손실: 0.180919\t최선의 손실: 0.145301\t정확도: 96.91%\n",
+      "17\t검증 세트 손실: 0.166174\t최선의 손실: 0.145301\t정확도: 97.11%\n",
+      "18\t검증 세트 손실: 0.152984\t최선의 손실: 0.145301\t정확도: 97.19%\n",
+      "19\t검증 세트 손실: 0.125913\t최선의 손실: 0.125913\t정확도: 97.81%\n",
+      "20\t검증 세트 손실: 0.113101\t최선의 손실: 0.113101\t정확도: 98.01%\n",
+      "21\t검증 세트 손실: 0.184494\t최선의 손실: 0.113101\t정확도: 96.09%\n",
+      "22\t검증 세트 손실: 0.201067\t최선의 손실: 0.113101\t정확도: 96.76%\n",
+      "23\t검증 세트 손실: 0.212952\t최선의 손실: 0.113101\t정확도: 96.64%\n",
+      "24\t검증 세트 손실: 0.143011\t최선의 손실: 0.113101\t정확도: 97.46%\n",
+      "25\t검증 세트 손실: 0.279099\t최선의 손실: 0.113101\t정확도: 94.84%\n",
+      "26\t검증 세트 손실: 0.168420\t최선의 손실: 0.113101\t정확도: 97.19%\n",
+      "27\t검증 세트 손실: 0.154719\t최선의 손실: 0.113101\t정확도: 97.73%\n",
+      "28\t검증 세트 손실: 0.182870\t최선의 손실: 0.113101\t정확도: 96.99%\n",
+      "29\t검증 세트 손실: 0.156189\t최선의 손실: 0.113101\t정확도: 97.46%\n",
+      "30\t검증 세트 손실: 0.209875\t최선의 손실: 0.113101\t정확도: 96.36%\n",
+      "31\t검증 세트 손실: 0.130683\t최선의 손실: 0.113101\t정확도: 97.93%\n",
+      "32\t검증 세트 손실: 0.136181\t최선의 손실: 0.113101\t정확도: 97.50%\n",
+      "33\t검증 세트 손실: 0.165776\t최선의 손실: 0.113101\t정확도: 97.22%\n",
+      "34\t검증 세트 손실: 0.161023\t최선의 손실: 0.113101\t정확도: 97.42%\n",
+      "35\t검증 세트 손실: 0.158594\t최선의 손실: 0.113101\t정확도: 97.42%\n",
+      "36\t검증 세트 손실: 0.147174\t최선의 손실: 0.113101\t정확도: 97.69%\n",
+      "37\t검증 세트 손실: 0.167847\t최선의 손실: 0.113101\t정확도: 97.54%\n",
+      "38\t검증 세트 손실: 0.190588\t최선의 손실: 0.113101\t정확도: 97.07%\n",
+      "39\t검증 세트 손실: 0.186567\t최선의 손실: 0.113101\t정확도: 97.19%\n",
+      "40\t검증 세트 손실: 0.223221\t최선의 손실: 0.113101\t정확도: 97.03%\n",
+      "41\t검증 세트 손실: 0.161292\t최선의 손실: 0.113101\t정확도: 97.69%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=10, total=  10.4s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=10 \n",
-      "0\t검증 세트 손실: 3.254761\t최선의 손실: 3.254761\t정확도: 88.27%\n",
-      "1\t검증 세트 손실: 1.295470\t최선의 손실: 1.295470\t정확도: 92.26%\n",
-      "2\t검증 세트 손실: 0.570490\t최선의 손실: 0.570490\t정확도: 92.34%\n",
-      "3\t검증 세트 손실: 0.354441\t최선의 손실: 0.354441\t정확도: 94.29%\n",
-      "4\t검증 세트 손실: 0.327354\t최선의 손실: 0.327354\t정확도: 94.76%\n",
-      "5\t검증 세트 손실: 0.363427\t최선의 손실: 0.327354\t정확도: 94.61%\n",
-      "6\t검증 세트 손실: 0.276593\t최선의 손실: 0.276593\t정확도: 95.31%\n",
-      "7\t검증 세트 손실: 0.186905\t최선의 손실: 0.186905\t정확도: 96.01%\n",
-      "8\t검증 세트 손실: 0.153996\t최선의 손실: 0.153996\t정확도: 97.11%\n",
-      "9\t검증 세트 손실: 0.131436\t최선의 손실: 0.131436\t정확도: 97.22%\n",
-      "10\t검증 세트 손실: 0.148323\t최선의 손실: 0.131436\t정확도: 97.19%\n",
-      "11\t검증 세트 손실: 0.277091\t최선의 손실: 0.131436\t정확도: 95.27%\n",
-      "12\t검증 세트 손실: 0.168875\t최선의 손실: 0.131436\t정확도: 96.87%\n",
-      "13\t검증 세트 손실: 0.138479\t최선의 손실: 0.131436\t정확도: 97.11%\n",
-      "14\t검증 세트 손실: 0.390685\t최선의 손실: 0.131436\t정확도: 95.15%\n",
-      "15\t검증 세트 손실: 0.156910\t최선의 손실: 0.131436\t정확도: 96.83%\n",
-      "16\t검증 세트 손실: 0.142775\t최선의 손실: 0.131436\t정확도: 97.26%\n",
-      "17\t검증 세트 손실: 0.117687\t최선의 손실: 0.117687\t정확도: 97.38%\n",
-      "18\t검증 세트 손실: 0.122040\t최선의 손실: 0.117687\t정확도: 97.58%\n",
-      "19\t검증 세트 손실: 0.121028\t최선의 손실: 0.117687\t정확도: 97.73%\n",
-      "20\t검증 세트 손실: 0.154387\t최선의 손실: 0.117687\t정확도: 97.07%\n",
-      "21\t검증 세트 손실: 0.101735\t최선의 손실: 0.101735\t정확도: 97.62%\n",
-      "22\t검증 세트 손실: 0.144537\t최선의 손실: 0.101735\t정확도: 97.07%\n",
-      "23\t검증 세트 손실: 0.145354\t최선의 손실: 0.101735\t정확도: 97.03%\n",
-      "24\t검증 세트 손실: 0.133520\t최선의 손실: 0.101735\t정확도: 97.26%\n",
-      "25\t검증 세트 손실: 0.236379\t최선의 손실: 0.101735\t정확도: 96.33%\n",
-      "26\t검증 세트 손실: 0.133909\t최선의 손실: 0.101735\t정확도: 97.22%\n",
-      "27\t검증 세트 손실: 0.132794\t최선의 손실: 0.101735\t정확도: 97.22%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=10, total=   9.0s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=10 \n",
+      "0\t검증 세트 손실: 10.151381\t최선의 손실: 10.151381\t정확도: 78.46%\n",
+      "1\t검증 세트 손실: 2.776223\t최선의 손실: 2.776223\t정확도: 79.75%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "28\t검증 세트 손실: 0.130231\t최선의 손실: 0.101735\t정확도: 97.65%\n",
-      "29\t검증 세트 손실: 0.163009\t최선의 손실: 0.101735\t정확도: 96.79%\n",
-      "30\t검증 세트 손실: 0.139892\t최선의 손실: 0.101735\t정확도: 97.42%\n",
-      "31\t검증 세트 손실: 0.189623\t최선의 손실: 0.101735\t정확도: 96.33%\n",
-      "32\t검증 세트 손실: 0.115245\t최선의 손실: 0.101735\t정확도: 97.46%\n",
-      "33\t검증 세트 손실: 0.136469\t최선의 손실: 0.101735\t정확도: 97.54%\n",
-      "34\t검증 세트 손실: 0.161897\t최선의 손실: 0.101735\t정확도: 96.91%\n",
-      "35\t검증 세트 손실: 0.156172\t최선의 손실: 0.101735\t정확도: 96.99%\n",
-      "36\t검증 세트 손실: 0.105712\t최선의 손실: 0.101735\t정확도: 97.77%\n",
-      "37\t검증 세트 손실: 0.129321\t최선의 손실: 0.101735\t정확도: 97.30%\n",
-      "38\t검증 세트 손실: 0.158442\t최선의 손실: 0.101735\t정확도: 97.07%\n",
-      "39\t검증 세트 손실: 0.135372\t최선의 손실: 0.101735\t정확도: 97.26%\n",
-      "40\t검증 세트 손실: 0.132786\t최선의 손실: 0.101735\t정확도: 97.50%\n",
-      "41\t검증 세트 손실: 0.120880\t최선의 손실: 0.101735\t정확도: 97.54%\n",
-      "42\t검증 세트 손실: 0.151742\t최선의 손실: 0.101735\t정확도: 97.38%\n",
+      "2\t검증 세트 손실: 0.503057\t최선의 손실: 0.503057\t정확도: 93.90%\n",
+      "3\t검증 세트 손실: 0.476858\t최선의 손실: 0.476858\t정확도: 93.04%\n",
+      "4\t검증 세트 손실: 0.386102\t최선의 손실: 0.386102\t정확도: 94.41%\n",
+      "5\t검증 세트 손실: 0.392953\t최선의 손실: 0.386102\t정확도: 93.24%\n",
+      "6\t검증 세트 손실: 0.253081\t최선의 손실: 0.253081\t정확도: 95.39%\n",
+      "7\t검증 세트 손실: 0.229552\t최선의 손실: 0.229552\t정확도: 95.86%\n",
+      "8\t검증 세트 손실: 0.193943\t최선의 손실: 0.193943\t정확도: 95.70%\n",
+      "9\t검증 세트 손실: 0.103853\t최선의 손실: 0.103853\t정확도: 97.42%\n",
+      "10\t검증 세트 손실: 0.117728\t최선의 손실: 0.103853\t정확도: 97.73%\n",
+      "11\t검증 세트 손실: 0.163906\t최선의 손실: 0.103853\t정확도: 96.33%\n",
+      "12\t검증 세트 손실: 0.116410\t최선의 손실: 0.103853\t정확도: 97.54%\n",
+      "13\t검증 세트 손실: 0.168469\t최선의 손실: 0.103853\t정확도: 96.91%\n",
+      "14\t검증 세트 손실: 0.113451\t최선의 손실: 0.103853\t정확도: 97.50%\n",
+      "15\t검증 세트 손실: 0.161063\t최선의 손실: 0.103853\t정확도: 97.15%\n",
+      "16\t검증 세트 손실: 0.119229\t최선의 손실: 0.103853\t정확도: 97.58%\n",
+      "17\t검증 세트 손실: 0.133826\t최선의 손실: 0.103853\t정확도: 97.50%\n",
+      "18\t검증 세트 손실: 0.121301\t최선의 손실: 0.103853\t정확도: 97.77%\n",
+      "19\t검증 세트 손실: 0.114776\t최선의 손실: 0.103853\t정확도: 97.93%\n",
+      "20\t검증 세트 손실: 0.149931\t최선의 손실: 0.103853\t정확도: 97.50%\n",
+      "21\t검증 세트 손실: 0.121008\t최선의 손실: 0.103853\t정확도: 97.58%\n",
+      "22\t검증 세트 손실: 0.184128\t최선의 손실: 0.103853\t정확도: 96.56%\n",
+      "23\t검증 세트 손실: 0.141400\t최선의 손실: 0.103853\t정확도: 97.54%\n",
+      "24\t검증 세트 손실: 0.106147\t최선의 손실: 0.103853\t정확도: 97.89%\n",
+      "25\t검증 세트 손실: 0.204525\t최선의 손실: 0.103853\t정확도: 96.09%\n",
+      "26\t검증 세트 손실: 0.109633\t최선의 손실: 0.103853\t정확도: 98.05%\n",
+      "27\t검증 세트 손실: 0.111297\t최선의 손실: 0.103853\t정확도: 97.89%\n",
+      "28\t검증 세트 손실: 0.135570\t최선의 손실: 0.103853\t정확도: 97.65%\n",
+      "29\t검증 세트 손실: 0.173906\t최선의 손실: 0.103853\t정확도: 97.19%\n",
+      "30\t검증 세트 손실: 0.177988\t최선의 손실: 0.103853\t정확도: 97.15%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=10, total=   8.6s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.107533\t최선의 손실: 0.107533\t정확도: 97.65%\n",
-      "1\t검증 세트 손실: 0.080167\t최선의 손실: 0.080167\t정확도: 97.89%\n",
-      "2\t검증 세트 손실: 0.096975\t최선의 손실: 0.080167\t정확도: 97.42%\n",
-      "3\t검증 세트 손실: 0.062795\t최선의 손실: 0.062795\t정확도: 98.55%\n",
-      "4\t검증 세트 손실: 0.039766\t최선의 손실: 0.039766\t정확도: 98.91%\n",
-      "5\t검증 세트 손실: 0.046581\t최선의 손실: 0.039766\t정확도: 98.94%\n",
-      "6\t검증 세트 손실: 0.040352\t최선의 손실: 0.039766\t정확도: 98.87%\n",
-      "7\t검증 세트 손실: 0.046314\t최선의 손실: 0.039766\t정확도: 98.87%\n",
-      "8\t검증 세트 손실: 0.040432\t최선의 손실: 0.039766\t정확도: 99.10%\n",
-      "9\t검증 세트 손실: 0.059799\t최선의 손실: 0.039766\t정확도: 98.55%\n",
-      "10\t검증 세트 손실: 0.072162\t최선의 손실: 0.039766\t정확도: 98.59%\n",
-      "11\t검증 세트 손실: 0.049059\t최선의 손실: 0.039766\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.057679\t최선의 손실: 0.039766\t정확도: 98.67%\n",
-      "13\t검증 세트 손실: 0.050879\t최선의 손실: 0.039766\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.041384\t최선의 손실: 0.039766\t정확도: 98.79%\n",
-      "15\t검증 세트 손실: 0.049447\t최선의 손실: 0.039766\t정확도: 98.98%\n",
-      "16\t검증 세트 손실: 0.047744\t최선의 손실: 0.039766\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.047801\t최선의 손실: 0.039766\t정확도: 99.14%\n",
-      "18\t검증 세트 손실: 0.043172\t최선의 손실: 0.039766\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.060523\t최선의 손실: 0.039766\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.060766\t최선의 손실: 0.039766\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.048143\t최선의 손실: 0.039766\t정확도: 98.98%\n",
-      "22\t검증 세트 손실: 0.049607\t최선의 손실: 0.039766\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.056536\t최선의 손실: 0.039766\t정확도: 98.67%\n",
-      "24\t검증 세트 손실: 0.052231\t최선의 손실: 0.039766\t정확도: 99.06%\n",
-      "25\t검증 세트 손실: 0.042282\t최선의 손실: 0.039766\t정확도: 99.22%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=10, total=   7.1s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.071181\t최선의 손실: 0.071181\t정확도: 97.85%\n",
+      "1\t검증 세트 손실: 0.067456\t최선의 손실: 0.067456\t정확도: 98.16%\n",
+      "2\t검증 세트 손실: 0.057569\t최선의 손실: 0.057569\t정확도: 98.08%\n",
+      "3\t검증 세트 손실: 0.044773\t최선의 손실: 0.044773\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.039859\t최선의 손실: 0.039859\t정확도: 98.71%\n",
+      "5\t검증 세트 손실: 0.046495\t최선의 손실: 0.039859\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.040521\t최선의 손실: 0.039859\t정확도: 98.87%\n",
+      "7\t검증 세트 손실: 0.040171\t최선의 손실: 0.039859\t정확도: 98.83%\n",
+      "8\t검증 세트 손실: 0.060084\t최선의 손실: 0.039859\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.042842\t최선의 손실: 0.039859\t정확도: 99.06%\n",
+      "10\t검증 세트 손실: 0.047493\t최선의 손실: 0.039859\t정확도: 99.10%\n",
+      "11\t검증 세트 손실: 0.055118\t최선의 손실: 0.039859\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.038138\t최선의 손실: 0.038138\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.062902\t최선의 손실: 0.038138\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.063634\t최선의 손실: 0.038138\t정확도: 98.63%\n",
+      "15\t검증 세트 손실: 0.082238\t최선의 손실: 0.038138\t정확도: 98.08%\n",
+      "16\t검증 세트 손실: 0.050023\t최선의 손실: 0.038138\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.046987\t최선의 손실: 0.038138\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.043891\t최선의 손실: 0.038138\t정확도: 99.02%\n",
+      "19\t검증 세트 손실: 0.044171\t최선의 손실: 0.038138\t정확도: 99.06%\n",
+      "20\t검증 세트 손실: 0.037839\t최선의 손실: 0.037839\t정확도: 99.10%\n",
+      "21\t검증 세트 손실: 0.037877\t최선의 손실: 0.037839\t정확도: 99.14%\n",
+      "22\t검증 세트 손실: 0.040567\t최선의 손실: 0.037839\t정확도: 99.02%\n",
+      "23\t검증 세트 손실: 0.065361\t최선의 손실: 0.037839\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.066811\t최선의 손실: 0.037839\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.048681\t최선의 손실: 0.037839\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.043441\t최선의 손실: 0.037839\t정확도: 99.14%\n",
+      "27\t검증 세트 손실: 0.043755\t최선의 손실: 0.037839\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.074767\t최선의 손실: 0.037839\t정확도: 98.28%\n",
+      "29\t검증 세트 손실: 0.054195\t최선의 손실: 0.037839\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.057416\t최선의 손실: 0.037839\t정확도: 99.06%\n",
+      "31\t검증 세트 손실: 0.054263\t최선의 손실: 0.037839\t정확도: 98.91%\n",
+      "32\t검증 세트 손실: 0.061338\t최선의 손실: 0.037839\t정확도: 98.83%\n",
+      "33\t검증 세트 손실: 0.060662\t최선의 손실: 0.037839\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.044580\t최선의 손실: 0.037839\t정확도: 98.91%\n",
+      "35\t검증 세트 손실: 0.051787\t최선의 손실: 0.037839\t정확도: 98.94%\n",
+      "36\t검증 세트 손실: 0.052558\t최선의 손실: 0.037839\t정확도: 98.83%\n",
+      "37\t검증 세트 손실: 0.046682\t최선의 손실: 0.037839\t정확도: 98.94%\n",
+      "38\t검증 세트 손실: 0.056991\t최선의 손실: 0.037839\t정확도: 98.83%\n",
+      "39\t검증 세트 손실: 0.065732\t최선의 손실: 0.037839\t정확도: 98.87%\n",
+      "40\t검증 세트 손실: 0.083961\t최선의 손실: 0.037839\t정확도: 98.59%\n",
+      "41\t검증 세트 손실: 0.057180\t최선의 손실: 0.037839\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160, total=  20.9s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.085810\t최선의 손실: 0.085810\t정확도: 97.54%\n",
-      "1\t검증 세트 손실: 0.071865\t최선의 손실: 0.071865\t정확도: 98.20%\n",
-      "2\t검증 세트 손실: 0.043509\t최선의 손실: 0.043509\t정확도: 98.55%\n",
-      "3\t검증 세트 손실: 0.042863\t최선의 손실: 0.042863\t정확도: 98.67%\n",
-      "4\t검증 세트 손실: 0.048150\t최선의 손실: 0.042863\t정확도: 98.75%\n",
-      "5\t검증 세트 손실: 0.054470\t최선의 손실: 0.042863\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.070436\t최선의 손실: 0.042863\t정확도: 98.48%\n",
-      "7\t검증 세트 손실: 0.061203\t최선의 손실: 0.042863\t정확도: 98.55%\n",
-      "8\t검증 세트 손실: 0.071439\t최선의 손실: 0.042863\t정확도: 98.08%\n",
-      "9\t검증 세트 손실: 0.054249\t최선의 손실: 0.042863\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.055128\t최선의 손실: 0.042863\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.050130\t최선의 손실: 0.042863\t정확도: 98.91%\n",
-      "12\t검증 세트 손실: 0.052587\t최선의 손실: 0.042863\t정확도: 98.59%\n",
-      "13\t검증 세트 손실: 0.056764\t최선의 손실: 0.042863\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.040417\t최선의 손실: 0.040417\t정확도: 98.98%\n",
-      "15\t검증 세트 손실: 0.075227\t최선의 손실: 0.040417\t정확도: 98.59%\n",
-      "16\t검증 세트 손실: 0.035770\t최선의 손실: 0.035770\t정확도: 99.10%\n",
-      "17\t검증 세트 손실: 0.045274\t최선의 손실: 0.035770\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.040399\t최선의 손실: 0.035770\t정확도: 99.10%\n",
-      "19\t검증 세트 손실: 0.048042\t최선의 손실: 0.035770\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.078840\t최선의 손실: 0.035770\t정확도: 98.28%\n",
-      "21\t검증 세트 손실: 0.040536\t최선의 손실: 0.035770\t정확도: 99.14%\n",
-      "22\t검증 세트 손실: 0.037241\t최선의 손실: 0.035770\t정확도: 99.18%\n",
-      "23\t검증 세트 손실: 0.045079\t최선의 손실: 0.035770\t정확도: 99.14%\n",
-      "24\t검증 세트 손실: 0.033520\t최선의 손실: 0.033520\t정확도: 99.22%\n",
-      "25\t검증 세트 손실: 0.032191\t최선의 손실: 0.032191\t정확도: 98.98%\n",
-      "26\t검증 세트 손실: 0.046454\t최선의 손실: 0.032191\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.048026\t최선의 손실: 0.032191\t정확도: 98.94%\n",
-      "28\t검증 세트 손실: 0.044552\t최선의 손실: 0.032191\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.064901\t최선의 손실: 0.032191\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.037272\t최선의 손실: 0.032191\t정확도: 99.10%\n",
-      "31\t검증 세트 손실: 0.043823\t최선의 손실: 0.032191\t정확도: 99.10%\n",
-      "32\t검증 세트 손실: 0.045407\t최선의 손실: 0.032191\t정확도: 99.14%\n",
-      "33\t검증 세트 손실: 0.048777\t최선의 손실: 0.032191\t정확도: 98.91%\n",
-      "34\t검증 세트 손실: 0.049619\t최선의 손실: 0.032191\t정확도: 99.26%\n",
-      "35\t검증 세트 손실: 0.041444\t최선의 손실: 0.032191\t정확도: 99.14%\n",
-      "36\t검증 세트 손실: 0.045260\t최선의 손실: 0.032191\t정확도: 99.02%\n",
-      "37\t검증 세트 손실: 0.039860\t최선의 손실: 0.032191\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.052609\t최선의 손실: 0.032191\t정확도: 99.10%\n",
-      "39\t검증 세트 손실: 0.040877\t최선의 손실: 0.032191\t정확도: 99.26%\n",
-      "40\t검증 세트 손실: 0.042791\t최선의 손실: 0.032191\t정확도: 99.14%\n",
-      "41\t검증 세트 손실: 0.051645\t최선의 손실: 0.032191\t정확도: 98.94%\n",
-      "42\t검증 세트 손실: 0.042517\t최선의 손실: 0.032191\t정확도: 99.02%\n",
-      "43\t검증 세트 손실: 0.059111\t최선의 손실: 0.032191\t정확도: 98.83%\n",
-      "44\t검증 세트 손실: 0.058045\t최선의 손실: 0.032191\t정확도: 99.26%\n",
-      "45\t검증 세트 손실: 0.052249\t최선의 손실: 0.032191\t정확도: 98.94%\n",
-      "46\t검증 세트 손실: 0.056544\t최선의 손실: 0.032191\t정확도: 98.79%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160, total=  30.5s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.102413\t최선의 손실: 0.102413\t정확도: 97.58%\n",
+      "1\t검증 세트 손실: 0.052670\t최선의 손실: 0.052670\t정확도: 98.36%\n",
+      "2\t검증 세트 손실: 0.039125\t최선의 손실: 0.039125\t정확도: 98.59%\n",
+      "3\t검증 세트 손실: 0.068599\t최선의 손실: 0.039125\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.050092\t최선의 손실: 0.039125\t정확도: 98.40%\n",
+      "5\t검증 세트 손실: 0.037934\t최선의 손실: 0.037934\t정확도: 98.87%\n",
+      "6\t검증 세트 손실: 0.054497\t최선의 손실: 0.037934\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.045929\t최선의 손실: 0.037934\t정확도: 98.71%\n",
+      "8\t검증 세트 손실: 0.037146\t최선의 손실: 0.037146\t정확도: 98.83%\n",
+      "9\t검증 세트 손실: 0.042345\t최선의 손실: 0.037146\t정확도: 98.91%\n",
+      "10\t검증 세트 손실: 0.049618\t최선의 손실: 0.037146\t정확도: 98.67%\n",
+      "11\t검증 세트 손실: 0.060479\t최선의 손실: 0.037146\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.045485\t최선의 손실: 0.037146\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.033993\t최선의 손실: 0.033993\t정확도: 99.18%\n",
+      "14\t검증 세트 손실: 0.068269\t최선의 손실: 0.033993\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.045579\t최선의 손실: 0.033993\t정확도: 99.02%\n",
+      "16\t검증 세트 손실: 0.045313\t최선의 손실: 0.033993\t정확도: 98.87%\n",
+      "17\t검증 세트 손실: 0.055338\t최선의 손실: 0.033993\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.047906\t최선의 손실: 0.033993\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.039394\t최선의 손실: 0.033993\t정확도: 98.98%\n",
+      "20\t검증 세트 손실: 0.044197\t최선의 손실: 0.033993\t정확도: 98.98%\n",
+      "21\t검증 세트 손실: 0.039072\t최선의 손실: 0.033993\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.043315\t최선의 손실: 0.033993\t정확도: 99.22%\n",
+      "23\t검증 세트 손실: 0.066644\t최선의 손실: 0.033993\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.047627\t최선의 손실: 0.033993\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.052578\t최선의 손실: 0.033993\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.053004\t최선의 손실: 0.033993\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.045526\t최선의 손실: 0.033993\t정확도: 99.02%\n",
+      "28\t검증 세트 손실: 0.040564\t최선의 손실: 0.033993\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.043224\t최선의 손실: 0.033993\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.047727\t최선의 손실: 0.033993\t정확도: 99.02%\n",
+      "31\t검증 세트 손실: 0.049360\t최선의 손실: 0.033993\t정확도: 99.02%\n",
+      "32\t검증 세트 손실: 0.059143\t최선의 손실: 0.033993\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.082714\t최선의 손실: 0.033993\t정확도: 98.05%\n",
+      "34\t검증 세트 손실: 0.037357\t최선의 손실: 0.033993\t정확도: 99.22%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160, total=  32.7s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.057082\t최선의 손실: 0.057082\t정확도: 98.28%\n",
-      "1\t검증 세트 손실: 0.053504\t최선의 손실: 0.053504\t정확도: 98.55%\n",
-      "2\t검증 세트 손실: 0.056202\t최선의 손실: 0.053504\t정확도: 98.48%\n",
-      "3\t검증 세트 손실: 0.032565\t최선의 손실: 0.032565\t정확도: 98.63%\n",
-      "4\t검증 세트 손실: 0.044521\t최선의 손실: 0.032565\t정확도: 98.63%\n",
-      "5\t검증 세트 손실: 0.037018\t최선의 손실: 0.032565\t정확도: 98.91%\n",
-      "6\t검증 세트 손실: 0.052856\t최선의 손실: 0.032565\t정확도: 98.40%\n",
-      "7\t검증 세트 손실: 0.056774\t최선의 손실: 0.032565\t정확도: 98.83%\n",
-      "8\t검증 세트 손실: 0.029305\t최선의 손실: 0.029305\t정확도: 99.22%\n",
-      "9\t검증 세트 손실: 0.042449\t최선의 손실: 0.029305\t정확도: 98.91%\n",
-      "10\t검증 세트 손실: 0.052258\t최선의 손실: 0.029305\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.060479\t최선의 손실: 0.029305\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.036164\t최선의 손실: 0.029305\t정확도: 99.26%\n",
-      "13\t검증 세트 손실: 0.042908\t최선의 손실: 0.029305\t정확도: 99.06%\n",
-      "14\t검증 세트 손실: 0.041993\t최선의 손실: 0.029305\t정확도: 98.94%\n",
-      "15\t검증 세트 손실: 0.028771\t최선의 손실: 0.028771\t정확도: 99.22%\n",
-      "16\t검증 세트 손실: 0.042611\t최선의 손실: 0.028771\t정확도: 99.06%\n",
-      "17\t검증 세트 손실: 0.033897\t최선의 손실: 0.028771\t정확도: 99.02%\n",
-      "18\t검증 세트 손실: 0.040785\t최선의 손실: 0.028771\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.048160\t최선의 손실: 0.028771\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.073455\t최선의 손실: 0.028771\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.043961\t최선의 손실: 0.028771\t정확도: 98.91%\n",
-      "22\t검증 세트 손실: 0.035433\t최선의 손실: 0.028771\t정확도: 99.18%\n",
-      "23\t검증 세트 손실: 0.047869\t최선의 손실: 0.028771\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.040685\t최선의 손실: 0.028771\t정확도: 99.18%\n",
-      "25\t검증 세트 손실: 0.046115\t최선의 손실: 0.028771\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.052197\t최선의 손실: 0.028771\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.040662\t최선의 손실: 0.028771\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.045026\t최선의 손실: 0.028771\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.053738\t최선의 손실: 0.028771\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.040095\t최선의 손실: 0.028771\t정확도: 99.22%\n",
-      "31\t검증 세트 손실: 0.028581\t최선의 손실: 0.028581\t정확도: 99.18%\n",
-      "32\t검증 세트 손실: 0.043274\t최선의 손실: 0.028581\t정확도: 99.14%\n",
-      "33\t검증 세트 손실: 0.054561\t최선의 손실: 0.028581\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.038504\t최선의 손실: 0.028581\t정확도: 99.14%\n",
-      "35\t검증 세트 손실: 0.033040\t최선의 손실: 0.028581\t정확도: 99.22%\n",
-      "36\t검증 세트 손실: 0.029818\t최선의 손실: 0.028581\t정확도: 99.26%\n",
-      "37\t검증 세트 손실: 0.033664\t최선의 손실: 0.028581\t정확도: 99.18%\n",
-      "38\t검증 세트 손실: 0.046023\t최선의 손실: 0.028581\t정확도: 99.22%\n",
-      "39\t검증 세트 손실: 0.031232\t최선의 손실: 0.028581\t정확도: 99.22%\n",
-      "40\t검증 세트 손실: 0.033618\t최선의 손실: 0.028581\t정확도: 99.22%\n",
-      "41\t검증 세트 손실: 0.031883\t최선의 손실: 0.028581\t정확도: 99.22%\n",
-      "42\t검증 세트 손실: 0.065488\t최선의 손실: 0.028581\t정확도: 98.91%\n",
-      "43\t검증 세트 손실: 0.041290\t최선의 손실: 0.028581\t정확도: 99.22%\n",
-      "44\t검증 세트 손실: 0.048189\t최선의 손실: 0.028581\t정확도: 99.14%\n",
-      "45\t검증 세트 손실: 0.031524\t최선의 손실: 0.028581\t정확도: 99.14%\n",
-      "46\t검증 세트 손실: 0.035298\t최선의 손실: 0.028581\t정확도: 99.18%\n",
-      "47\t검증 세트 손실: 0.036457\t최선의 손실: 0.028581\t정확도: 99.37%\n",
-      "48\t검증 세트 손실: 0.037449\t최선의 손실: 0.028581\t정확도: 99.26%\n",
-      "49\t검증 세트 손실: 0.038124\t최선의 손실: 0.028581\t정확도: 99.30%\n",
-      "50\t검증 세트 손실: 0.032524\t최선의 손실: 0.028581\t정확도: 99.18%\n",
-      "51\t검증 세트 손실: 0.046167\t최선의 손실: 0.028581\t정확도: 99.14%\n",
-      "52\t검증 세트 손실: 0.030789\t최선의 손실: 0.028581\t정확도: 99.22%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160, total=  25.4s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.088132\t최선의 손실: 0.088132\t정확도: 97.69%\n",
+      "1\t검증 세트 손실: 0.051754\t최선의 손실: 0.051754\t정확도: 98.40%\n",
+      "2\t검증 세트 손실: 0.052524\t최선의 손실: 0.051754\t정확도: 98.01%\n",
+      "3\t검증 세트 손실: 0.039532\t최선의 손실: 0.039532\t정확도: 98.63%\n",
+      "4\t검증 세트 손실: 0.031391\t최선의 손실: 0.031391\t정확도: 98.98%\n",
+      "5\t검증 세트 손실: 0.038576\t최선의 손실: 0.031391\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.049726\t최선의 손실: 0.031391\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.062434\t최선의 손실: 0.031391\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.044130\t최선의 손실: 0.031391\t정확도: 98.75%\n",
+      "9\t검증 세트 손실: 0.043320\t최선의 손실: 0.031391\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.042790\t최선의 손실: 0.031391\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.068755\t최선의 손실: 0.031391\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.036225\t최선의 손실: 0.031391\t정확도: 99.06%\n",
+      "13\t검증 세트 손실: 0.039659\t최선의 손실: 0.031391\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.038061\t최선의 손실: 0.031391\t정확도: 99.14%\n",
+      "15\t검증 세트 손실: 0.054892\t최선의 손실: 0.031391\t정확도: 98.51%\n",
+      "16\t검증 세트 손실: 0.047018\t최선의 손실: 0.031391\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.042140\t최선의 손실: 0.031391\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.034033\t최선의 손실: 0.031391\t정확도: 99.06%\n",
+      "19\t검증 세트 손실: 0.044748\t최선의 손실: 0.031391\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.041574\t최선의 손실: 0.031391\t정확도: 99.22%\n",
+      "21\t검증 세트 손실: 0.052476\t최선의 손실: 0.031391\t정확도: 99.02%\n",
+      "22\t검증 세트 손실: 0.045503\t최선의 손실: 0.031391\t정확도: 99.14%\n",
+      "23\t검증 세트 손실: 0.041496\t최선의 손실: 0.031391\t정확도: 99.18%\n",
+      "24\t검증 세트 손실: 0.041971\t최선의 손실: 0.031391\t정확도: 99.22%\n",
+      "25\t검증 세트 손실: 0.043924\t최선의 손실: 0.031391\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160, total=  36.8s\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100 \n"
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160, total=  19.4s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.231731\t최선의 손실: 0.231731\t정확도: 93.71%\n",
+      "1\t검증 세트 손실: 0.157520\t최선의 손실: 0.157520\t정확도: 95.54%\n",
+      "2\t검증 세트 손실: 0.109393\t최선의 손실: 0.109393\t정확도: 97.11%\n",
+      "3\t검증 세트 손실: 0.103250\t최선의 손실: 0.103250\t정확도: 97.26%\n",
+      "4\t검증 세트 손실: 0.108879\t최선의 손실: 0.103250\t정확도: 96.95%\n",
+      "5\t검증 세트 손실: 0.098352\t최선의 손실: 0.098352\t정확도: 97.62%\n",
+      "6\t검증 세트 손실: 0.085183\t최선의 손실: 0.085183\t정확도: 97.77%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.211328\t최선의 손실: 0.211328\t정확도: 95.07%\n",
-      "1\t검증 세트 손실: 0.156527\t최선의 손실: 0.156527\t정확도: 95.62%\n",
-      "2\t검증 세트 손실: 0.153674\t최선의 손실: 0.153674\t정확도: 96.52%\n",
-      "3\t검증 세트 손실: 0.098270\t최선의 손실: 0.098270\t정확도: 97.22%\n",
-      "4\t검증 세트 손실: 0.088835\t최선의 손실: 0.088835\t정확도: 97.62%\n",
-      "5\t검증 세트 손실: 0.139075\t최선의 손실: 0.088835\t정확도: 96.05%\n",
-      "6\t검증 세트 손실: 0.079404\t최선의 손실: 0.079404\t정확도: 97.38%\n",
-      "7\t검증 세트 손실: 0.085108\t최선의 손실: 0.079404\t정확도: 97.69%\n",
-      "8\t검증 세트 손실: 0.074737\t최선의 손실: 0.074737\t정확도: 98.08%\n",
-      "9\t검증 세트 손실: 0.073027\t최선의 손실: 0.073027\t정확도: 98.08%\n",
-      "10\t검증 세트 손실: 0.062658\t최선의 손실: 0.062658\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.069846\t최선의 손실: 0.062658\t정확도: 98.28%\n",
-      "12\t검증 세트 손실: 0.057026\t최선의 손실: 0.057026\t정확도: 98.59%\n",
-      "13\t검증 세트 손실: 0.063116\t최선의 손실: 0.057026\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.077750\t최선의 손실: 0.057026\t정확도: 98.32%\n",
-      "15\t검증 세트 손실: 0.089887\t최선의 손실: 0.057026\t정확도: 97.89%\n",
-      "16\t검증 세트 손실: 0.070576\t최선의 손실: 0.057026\t정확도: 98.40%\n",
-      "17\t검증 세트 손실: 0.065110\t최선의 손실: 0.057026\t정확도: 98.48%\n",
-      "18\t검증 세트 손실: 0.200989\t최선의 손실: 0.057026\t정확도: 94.88%\n",
-      "19\t검증 세트 손실: 0.063296\t최선의 손실: 0.057026\t정확도: 98.36%\n",
-      "20\t검증 세트 손실: 0.067534\t최선의 손실: 0.057026\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.060558\t최선의 손실: 0.057026\t정확도: 98.55%\n",
-      "22\t검증 세트 손실: 0.058984\t최선의 손실: 0.057026\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.091913\t최선의 손실: 0.057026\t정확도: 98.48%\n",
-      "24\t검증 세트 손실: 0.081241\t최선의 손실: 0.057026\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.073448\t최선의 손실: 0.057026\t정확도: 98.28%\n",
-      "26\t검증 세트 손실: 0.062935\t최선의 손실: 0.057026\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.073146\t최선의 손실: 0.057026\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.067866\t최선의 손실: 0.057026\t정확도: 98.67%\n",
-      "29\t검증 세트 손실: 0.091592\t최선의 손실: 0.057026\t정확도: 98.44%\n",
-      "30\t검증 세트 손실: 0.061153\t최선의 손실: 0.057026\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.077181\t최선의 손실: 0.057026\t정확도: 98.51%\n",
-      "32\t검증 세트 손실: 0.071064\t최선의 손실: 0.057026\t정확도: 98.63%\n",
-      "33\t검증 세트 손실: 0.264344\t최선의 손실: 0.057026\t정확도: 94.06%\n",
+      "7\t검증 세트 손실: 0.075878\t최선의 손실: 0.075878\t정확도: 98.01%\n",
+      "8\t검증 세트 손실: 0.085244\t최선의 손실: 0.075878\t정확도: 97.77%\n",
+      "9\t검증 세트 손실: 0.176430\t최선의 손실: 0.075878\t정확도: 95.58%\n",
+      "10\t검증 세트 손실: 0.070517\t최선의 손실: 0.070517\t정확도: 98.08%\n",
+      "11\t검증 세트 손실: 0.130834\t최선의 손실: 0.070517\t정확도: 96.17%\n",
+      "12\t검증 세트 손실: 0.067546\t최선의 손실: 0.067546\t정확도: 98.55%\n",
+      "13\t검증 세트 손실: 0.058482\t최선의 손실: 0.058482\t정확도: 98.48%\n",
+      "14\t검증 세트 손실: 0.072861\t최선의 손실: 0.058482\t정확도: 98.05%\n",
+      "15\t검증 세트 손실: 0.081471\t최선의 손실: 0.058482\t정확도: 98.16%\n",
+      "16\t검증 세트 손실: 0.068911\t최선의 손실: 0.058482\t정확도: 98.16%\n",
+      "17\t검증 세트 손실: 0.058401\t최선의 손실: 0.058401\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.083487\t최선의 손실: 0.058401\t정확도: 98.01%\n",
+      "19\t검증 세트 손실: 0.078428\t최선의 손실: 0.058401\t정확도: 98.20%\n",
+      "20\t검증 세트 손실: 0.066711\t최선의 손실: 0.058401\t정확도: 98.55%\n",
+      "21\t검증 세트 손실: 0.073400\t최선의 손실: 0.058401\t정확도: 98.32%\n",
+      "22\t검증 세트 손실: 0.119341\t최선의 손실: 0.058401\t정확도: 97.58%\n",
+      "23\t검증 세트 손실: 0.063473\t최선의 손실: 0.058401\t정확도: 98.48%\n",
+      "24\t검증 세트 손실: 0.065908\t최선의 손실: 0.058401\t정확도: 98.48%\n",
+      "25\t검증 세트 손실: 0.067188\t최선의 손실: 0.058401\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.050294\t최선의 손실: 0.050294\t정확도: 98.79%\n",
+      "27\t검증 세트 손실: 0.136331\t최선의 손실: 0.050294\t정확도: 96.68%\n",
+      "28\t검증 세트 손실: 0.095547\t최선의 손실: 0.050294\t정확도: 97.93%\n",
+      "29\t검증 세트 손실: 0.081338\t최선의 손실: 0.050294\t정확도: 98.32%\n",
+      "30\t검증 세트 손실: 0.072268\t최선의 손실: 0.050294\t정확도: 98.32%\n",
+      "31\t검증 세트 손실: 0.055478\t최선의 손실: 0.050294\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.090056\t최선의 손실: 0.050294\t정확도: 98.55%\n",
+      "33\t검증 세트 손실: 0.095716\t최선의 손실: 0.050294\t정확도: 98.05%\n",
+      "34\t검증 세트 손실: 0.081588\t최선의 손실: 0.050294\t정확도: 98.63%\n",
+      "35\t검증 세트 손실: 0.126287\t최선의 손실: 0.050294\t정확도: 97.93%\n",
+      "36\t검증 세트 손실: 0.069573\t최선의 손실: 0.050294\t정확도: 98.67%\n",
+      "37\t검증 세트 손실: 0.094590\t최선의 손실: 0.050294\t정확도: 98.32%\n",
+      "38\t검증 세트 손실: 0.068940\t최선의 손실: 0.050294\t정확도: 98.59%\n",
+      "39\t검증 세트 손실: 0.061477\t최선의 손실: 0.050294\t정확도: 98.87%\n",
+      "40\t검증 세트 손실: 0.069103\t최선의 손실: 0.050294\t정확도: 98.44%\n",
+      "41\t검증 세트 손실: 0.115995\t최선의 손실: 0.050294\t정확도: 97.89%\n",
+      "42\t검증 세트 손실: 0.052463\t최선의 손실: 0.050294\t정확도: 98.98%\n",
+      "43\t검증 세트 손실: 0.074376\t최선의 손실: 0.050294\t정확도: 98.12%\n",
+      "44\t검증 세트 손실: 0.066484\t최선의 손실: 0.050294\t정확도: 98.75%\n",
+      "45\t검증 세트 손실: 0.065911\t최선의 손실: 0.050294\t정확도: 98.75%\n",
+      "46\t검증 세트 손실: 0.093904\t최선의 손실: 0.050294\t정확도: 97.54%\n",
+      "47\t검증 세트 손실: 0.084615\t최선의 손실: 0.050294\t정확도: 98.12%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100, total= 3.8min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.142691\t최선의 손실: 0.142691\t정확도: 96.29%\n",
-      "1\t검증 세트 손실: 0.130640\t최선의 손실: 0.130640\t정확도: 96.79%\n",
-      "2\t검증 세트 손실: 0.113146\t최선의 손실: 0.113146\t정확도: 96.99%\n",
-      "3\t검증 세트 손실: 0.117691\t최선의 손실: 0.113146\t정확도: 96.60%\n",
-      "4\t검증 세트 손실: 0.112255\t최선의 손실: 0.112255\t정확도: 97.22%\n",
-      "5\t검증 세트 손실: 0.220661\t최선의 손실: 0.112255\t정확도: 95.50%\n",
-      "6\t검증 세트 손실: 0.176282\t최선의 손실: 0.112255\t정확도: 94.80%\n",
-      "7\t검증 세트 손실: 0.078374\t최선의 손실: 0.078374\t정확도: 97.89%\n",
-      "8\t검증 세트 손실: 0.080442\t최선의 손실: 0.078374\t정확도: 98.24%\n",
-      "9\t검증 세트 손실: 0.095610\t최선의 손실: 0.078374\t정확도: 97.50%\n",
-      "10\t검증 세트 손실: 0.159705\t최선의 손실: 0.078374\t정확도: 96.05%\n",
-      "11\t검증 세트 손실: 0.086861\t최선의 손실: 0.078374\t정확도: 98.16%\n",
-      "12\t검증 세트 손실: 0.088928\t최선의 손실: 0.078374\t정확도: 97.89%\n",
-      "13\t검증 세트 손실: 0.256864\t최선의 손실: 0.078374\t정확도: 96.36%\n",
-      "14\t검증 세트 손실: 0.063606\t최선의 손실: 0.063606\t정확도: 98.32%\n",
-      "15\t검증 세트 손실: 0.112274\t최선의 손실: 0.063606\t정확도: 97.62%\n",
-      "16\t검증 세트 손실: 0.164434\t최선의 손실: 0.063606\t정확도: 96.29%\n",
-      "17\t검증 세트 손실: 0.088809\t최선의 손실: 0.063606\t정확도: 98.28%\n",
-      "18\t검증 세트 손실: 0.113204\t최선의 손실: 0.063606\t정확도: 97.11%\n",
-      "19\t검증 세트 손실: 0.141859\t최선의 손실: 0.063606\t정확도: 97.77%\n",
-      "20\t검증 세트 손실: 0.064781\t최선의 손실: 0.063606\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.124430\t최선의 손실: 0.063606\t정확도: 96.95%\n",
-      "22\t검증 세트 손실: 0.076584\t최선의 손실: 0.063606\t정확도: 98.12%\n",
-      "23\t검증 세트 손실: 0.046603\t최선의 손실: 0.046603\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.059657\t최선의 손실: 0.046603\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.067692\t최선의 손실: 0.046603\t정확도: 98.44%\n",
-      "26\t검증 세트 손실: 0.049439\t최선의 손실: 0.046603\t정확도: 98.63%\n",
-      "27\t검증 세트 손실: 0.246642\t최선의 손실: 0.046603\t정확도: 95.66%\n",
-      "28\t검증 세트 손실: 0.075825\t최선의 손실: 0.046603\t정확도: 98.75%\n",
-      "29\t검증 세트 손실: 0.088204\t최선의 손실: 0.046603\t정확도: 98.44%\n",
-      "30\t검증 세트 손실: 0.071925\t최선의 손실: 0.046603\t정확도: 98.12%\n",
-      "31\t검증 세트 손실: 0.074707\t최선의 손실: 0.046603\t정확도: 98.20%\n",
-      "32\t검증 세트 손실: 0.084033\t최선의 손실: 0.046603\t정확도: 98.16%\n",
-      "33\t검증 세트 손실: 0.051562\t최선의 손실: 0.046603\t정확도: 98.59%\n",
-      "34\t검증 세트 손실: 0.103087\t최선의 손실: 0.046603\t정확도: 98.05%\n",
-      "35\t검증 세트 손실: 0.129018\t최선의 손실: 0.046603\t정확도: 96.79%\n",
-      "36\t검증 세트 손실: 0.072808\t최선의 손실: 0.046603\t정확도: 98.59%\n",
-      "37\t검증 세트 손실: 0.123008\t최선의 손실: 0.046603\t정확도: 98.08%\n",
-      "38\t검증 세트 손실: 0.062449\t최선의 손실: 0.046603\t정확도: 98.48%\n",
-      "39\t검증 세트 손실: 0.054648\t최선의 손실: 0.046603\t정확도: 98.75%\n",
-      "40\t검증 세트 손실: 0.074329\t최선의 손실: 0.046603\t정확도: 98.32%\n",
-      "41\t검증 세트 손실: 0.078840\t최선의 손실: 0.046603\t정확도: 98.71%\n",
-      "42\t검증 세트 손실: 0.060227\t최선의 손실: 0.046603\t정확도: 98.59%\n",
-      "43\t검증 세트 손실: 0.048364\t최선의 손실: 0.046603\t정확도: 98.91%\n",
-      "44\t검증 세트 손실: 0.056780\t최선의 손실: 0.046603\t정확도: 98.55%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.98, n_neurons=100, total= 5.4min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.126109\t최선의 손실: 0.126109\t정확도: 96.56%\n",
+      "1\t검증 세트 손실: 0.163839\t최선의 손실: 0.126109\t정확도: 96.09%\n",
+      "2\t검증 세트 손실: 0.181517\t최선의 손실: 0.126109\t정확도: 95.43%\n",
+      "3\t검증 세트 손실: 0.109545\t최선의 손실: 0.109545\t정확도: 97.22%\n",
+      "4\t검증 세트 손실: 0.095043\t최선의 손실: 0.095043\t정확도: 97.07%\n",
+      "5\t검증 세트 손실: 1.071779\t최선의 손실: 0.095043\t정확도: 77.68%\n",
+      "6\t검증 세트 손실: 0.083921\t최선의 손실: 0.083921\t정확도: 97.62%\n",
+      "7\t검증 세트 손실: 0.079194\t최선의 손실: 0.079194\t정확도: 97.81%\n",
+      "8\t검증 세트 손실: 0.063628\t최선의 손실: 0.063628\t정확도: 98.48%\n",
+      "9\t검증 세트 손실: 0.161354\t최선의 손실: 0.063628\t정확도: 97.46%\n",
+      "10\t검증 세트 손실: 0.079807\t최선의 손실: 0.063628\t정확도: 97.73%\n",
+      "11\t검증 세트 손실: 0.087508\t최선의 손실: 0.063628\t정확도: 97.50%\n",
+      "12\t검증 세트 손실: 0.081408\t최선의 손실: 0.063628\t정확도: 97.89%\n",
+      "13\t검증 세트 손실: 0.043469\t최선의 손실: 0.043469\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.047896\t최선의 손실: 0.043469\t정확도: 98.83%\n",
+      "15\t검증 세트 손실: 0.106865\t최선의 손실: 0.043469\t정확도: 97.30%\n",
+      "16\t검증 세트 손실: 0.272490\t최선의 손실: 0.043469\t정확도: 96.44%\n",
+      "17\t검증 세트 손실: 0.102018\t최선의 손실: 0.043469\t정확도: 98.08%\n",
+      "18\t검증 세트 손실: 0.069989\t최선의 손실: 0.043469\t정확도: 98.28%\n",
+      "19\t검증 세트 손실: 0.089270\t최선의 손실: 0.043469\t정확도: 98.16%\n",
+      "20\t검증 세트 손실: 0.084765\t최선의 손실: 0.043469\t정확도: 98.01%\n",
+      "21\t검증 세트 손실: 0.093893\t최선의 손실: 0.043469\t정확도: 97.97%\n",
+      "22\t검증 세트 손실: 0.065592\t최선의 손실: 0.043469\t정확도: 98.12%\n",
+      "23\t검증 세트 손실: 0.053247\t최선의 손실: 0.043469\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.052241\t최선의 손실: 0.043469\t정확도: 98.51%\n",
+      "25\t검증 세트 손실: 0.085246\t최선의 손실: 0.043469\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.074352\t최선의 손실: 0.043469\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.065167\t최선의 손실: 0.043469\t정확도: 98.59%\n",
+      "28\t검증 세트 손실: 0.136274\t최선의 손실: 0.043469\t정확도: 98.01%\n",
+      "29\t검증 세트 손실: 0.156089\t최선의 손실: 0.043469\t정확도: 97.58%\n",
+      "30\t검증 세트 손실: 0.135385\t최선의 손실: 0.043469\t정확도: 98.24%\n",
+      "31\t검증 세트 손실: 0.126160\t최선의 손실: 0.043469\t정확도: 98.32%\n",
+      "32\t검증 세트 손실: 0.083859\t최선의 손실: 0.043469\t정확도: 98.67%\n",
+      "33\t검증 세트 손실: 0.074401\t최선의 손실: 0.043469\t정확도: 98.44%\n",
+      "34\t검증 세트 손실: 0.167507\t최선의 손실: 0.043469\t정확도: 95.54%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100, total= 5.0min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.173741\t최선의 손실: 0.173741\t정확도: 95.19%\n",
-      "1\t검증 세트 손실: 6.813097\t최선의 손실: 0.173741\t정확도: 60.01%\n",
-      "2\t검증 세트 손실: 0.090958\t최선의 손실: 0.090958\t정확도: 97.58%\n",
-      "3\t검증 세트 손실: 0.169533\t최선의 손실: 0.090958\t정확도: 96.60%\n",
-      "4\t검증 세트 손실: 0.127037\t최선의 손실: 0.090958\t정확도: 96.56%\n",
-      "5\t검증 세트 손실: 0.214496\t최선의 손실: 0.090958\t정확도: 92.77%\n",
-      "6\t검증 세트 손실: 0.127282\t최선의 손실: 0.090958\t정확도: 97.15%\n",
-      "7\t검증 세트 손실: 0.076849\t최선의 손실: 0.076849\t정확도: 97.81%\n",
-      "8\t검증 세트 손실: 0.068538\t최선의 손실: 0.068538\t정확도: 98.05%\n",
-      "9\t검증 세트 손실: 0.088739\t최선의 손실: 0.068538\t정확도: 97.81%\n",
-      "10\t검증 세트 손실: 0.059519\t최선의 손실: 0.059519\t정확도: 98.32%\n",
-      "11\t검증 세트 손실: 0.055515\t최선의 손실: 0.055515\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.117598\t최선의 손실: 0.055515\t정확도: 97.30%\n",
-      "13\t검증 세트 손실: 0.101029\t최선의 손실: 0.055515\t정확도: 98.24%\n",
-      "14\t검증 세트 손실: 0.067018\t최선의 손실: 0.055515\t정확도: 98.01%\n",
-      "15\t검증 세트 손실: 0.068875\t최선의 손실: 0.055515\t정확도: 98.40%\n",
-      "16\t검증 세트 손실: 0.080123\t최선의 손실: 0.055515\t정확도: 97.69%\n",
-      "17\t검증 세트 손실: 0.066710\t최선의 손실: 0.055515\t정확도: 98.01%\n",
-      "18\t검증 세트 손실: 0.098528\t최선의 손실: 0.055515\t정확도: 98.28%\n",
-      "19\t검증 세트 손실: 0.060753\t최선의 손실: 0.055515\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.065738\t최선의 손실: 0.055515\t정확도: 98.12%\n",
-      "21\t검증 세트 손실: 0.113205\t최선의 손실: 0.055515\t정확도: 97.50%\n",
-      "22\t검증 세트 손실: 0.064737\t최선의 손실: 0.055515\t정확도: 98.16%\n",
-      "23\t검증 세트 손실: 0.063385\t최선의 손실: 0.055515\t정확도: 98.32%\n",
-      "24\t검증 세트 손실: 0.053536\t최선의 손실: 0.053536\t정확도: 98.63%\n",
-      "25\t검증 세트 손실: 0.064362\t최선의 손실: 0.053536\t정확도: 98.20%\n",
-      "26\t검증 세트 손실: 0.050614\t최선의 손실: 0.050614\t정확도: 98.55%\n",
-      "27\t검증 세트 손실: 0.053106\t최선의 손실: 0.050614\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.059598\t최선의 손실: 0.050614\t정확도: 98.48%\n",
-      "29\t검증 세트 손실: 0.052688\t최선의 손실: 0.050614\t정확도: 98.32%\n",
-      "30\t검증 세트 손실: 0.074404\t최선의 손실: 0.050614\t정확도: 98.55%\n",
-      "31\t검증 세트 손실: 0.061300\t최선의 손실: 0.050614\t정확도: 98.16%\n",
-      "32\t검증 세트 손실: 0.074920\t최선의 손실: 0.050614\t정확도: 98.12%\n",
-      "33\t검증 세트 손실: 0.064219\t최선의 손실: 0.050614\t정확도: 98.63%\n",
-      "34\t검증 세트 손실: 0.054154\t최선의 손실: 0.050614\t정확도: 98.63%\n",
-      "35\t검증 세트 손실: 0.093771\t최선의 손실: 0.050614\t정확도: 98.44%\n",
-      "36\t검증 세트 손실: 0.082757\t최선의 손실: 0.050614\t정확도: 98.36%\n",
-      "37\t검증 세트 손실: 0.062576\t최선의 손실: 0.050614\t정확도: 98.55%\n",
-      "38\t검증 세트 손실: 0.054242\t최선의 손실: 0.050614\t정확도: 98.36%\n",
-      "39\t검증 세트 손실: 0.062907\t최선의 손실: 0.050614\t정확도: 98.40%\n",
-      "40\t검증 세트 손실: 0.047377\t최선의 손실: 0.047377\t정확도: 98.63%\n",
-      "41\t검증 세트 손실: 0.067064\t최선의 손실: 0.047377\t정확도: 98.91%\n",
-      "42\t검증 세트 손실: 0.066431\t최선의 손실: 0.047377\t정확도: 98.32%\n",
-      "43\t검증 세트 손실: 0.078557\t최선의 손실: 0.047377\t정확도: 97.89%\n",
-      "44\t검증 세트 손실: 0.062662\t최선의 손실: 0.047377\t정확도: 98.55%\n",
-      "45\t검증 세트 손실: 0.080537\t최선의 손실: 0.047377\t정확도: 98.67%\n",
-      "46\t검증 세트 손실: 0.077232\t최선의 손실: 0.047377\t정확도: 98.51%\n",
-      "47\t검증 세트 손실: 0.058230\t최선의 손실: 0.047377\t정확도: 98.59%\n",
-      "48\t검증 세트 손실: 0.070025\t최선의 손실: 0.047377\t정확도: 98.40%\n",
-      "49\t검증 세트 손실: 0.054102\t최선의 손실: 0.047377\t정확도: 98.67%\n",
-      "50\t검증 세트 손실: 0.075908\t최선의 손실: 0.047377\t정확도: 98.59%\n",
-      "51\t검증 세트 손실: 0.058648\t최선의 손실: 0.047377\t정확도: 98.83%\n",
-      "52\t검증 세트 손실: 0.075089\t최선의 손실: 0.047377\t정확도: 98.67%\n",
-      "53\t검증 세트 손실: 0.068781\t최선의 손실: 0.047377\t정확도: 98.63%\n",
-      "54\t검증 세트 손실: 0.070485\t최선의 손실: 0.047377\t정확도: 98.51%\n",
-      "55\t검증 세트 손실: 0.105725\t최선의 손실: 0.047377\t정확도: 97.62%\n",
-      "56\t검증 세트 손실: 0.084280\t최선의 손실: 0.047377\t정확도: 98.59%\n",
-      "57\t검증 세트 손실: 0.094828\t최선의 손실: 0.047377\t정확도: 98.48%\n",
-      "58\t검증 세트 손실: 0.082533\t최선의 손실: 0.047377\t정확도: 98.55%\n",
-      "59\t검증 세트 손실: 0.085313\t최선의 손실: 0.047377\t정확도: 98.51%\n",
-      "60\t검증 세트 손실: 0.055632\t최선의 손실: 0.047377\t정확도: 98.71%\n",
-      "61\t검증 세트 손실: 0.066767\t최선의 손실: 0.047377\t정확도: 98.36%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.98, n_neurons=100, total= 3.9min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.146222\t최선의 손실: 0.146222\t정확도: 96.29%\n",
+      "1\t검증 세트 손실: 0.256701\t최선의 손실: 0.146222\t정확도: 91.36%\n",
+      "2\t검증 세트 손실: 0.326301\t최선의 손실: 0.146222\t정확도: 93.32%\n",
+      "3\t검증 세트 손실: 0.140409\t최선의 손실: 0.140409\t정확도: 96.25%\n",
+      "4\t검증 세트 손실: 0.068104\t최선의 손실: 0.068104\t정확도: 98.28%\n",
+      "5\t검증 세트 손실: 0.107473\t최선의 손실: 0.068104\t정확도: 96.68%\n",
+      "6\t검증 세트 손실: 0.096906\t최선의 손실: 0.068104\t정확도: 97.11%\n",
+      "7\t검증 세트 손실: 0.053777\t최선의 손실: 0.053777\t정확도: 98.51%\n",
+      "8\t검증 세트 손실: 0.064571\t최선의 손실: 0.053777\t정확도: 98.20%\n",
+      "9\t검증 세트 손실: 0.182540\t최선의 손실: 0.053777\t정확도: 96.91%\n",
+      "10\t검증 세트 손실: 0.145309\t최선의 손실: 0.053777\t정확도: 95.93%\n",
+      "11\t검증 세트 손실: 0.059678\t최선의 손실: 0.053777\t정확도: 98.28%\n",
+      "12\t검증 세트 손실: 0.086401\t최선의 손실: 0.053777\t정확도: 98.16%\n",
+      "13\t검증 세트 손실: 0.095717\t최선의 손실: 0.053777\t정확도: 97.50%\n",
+      "14\t검증 세트 손실: 0.071382\t최선의 손실: 0.053777\t정확도: 98.40%\n",
+      "15\t검증 세트 손실: 0.090047\t최선의 손실: 0.053777\t정확도: 97.42%\n",
+      "16\t검증 세트 손실: 0.077557\t최선의 손실: 0.053777\t정확도: 98.55%\n",
+      "17\t검증 세트 손실: 0.059410\t최선의 손실: 0.053777\t정확도: 98.44%\n",
+      "18\t검증 세트 손실: 0.211875\t최선의 손실: 0.053777\t정확도: 95.04%\n",
+      "19\t검증 세트 손실: 0.076627\t최선의 손실: 0.053777\t정확도: 97.93%\n",
+      "20\t검증 세트 손실: 0.050249\t최선의 손실: 0.050249\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.054263\t최선의 손실: 0.050249\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.095076\t최선의 손실: 0.050249\t정확도: 98.12%\n",
+      "23\t검증 세트 손실: 0.037912\t최선의 손실: 0.037912\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.092834\t최선의 손실: 0.037912\t정확도: 97.65%\n",
+      "25\t검증 세트 손실: 0.036107\t최선의 손실: 0.036107\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.104105\t최선의 손실: 0.036107\t정확도: 97.15%\n",
+      "27\t검증 세트 손실: 0.040680\t최선의 손실: 0.036107\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.047868\t최선의 손실: 0.036107\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.043056\t최선의 손실: 0.036107\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.071384\t최선의 손실: 0.036107\t정확도: 98.08%\n",
+      "31\t검증 세트 손실: 0.078650\t최선의 손실: 0.036107\t정확도: 97.81%\n",
+      "32\t검증 세트 손실: 0.042707\t최선의 손실: 0.036107\t정확도: 99.02%\n",
+      "33\t검증 세트 손실: 0.054201\t최선의 손실: 0.036107\t정확도: 98.63%\n",
+      "34\t검증 세트 손실: 0.036133\t최선의 손실: 0.036107\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.104686\t최선의 손실: 0.036107\t정확도: 97.22%\n",
+      "36\t검증 세트 손실: 0.134384\t최선의 손실: 0.036107\t정확도: 97.77%\n",
+      "37\t검증 세트 손실: 0.050642\t최선의 손실: 0.036107\t정확도: 98.91%\n",
+      "38\t검증 세트 손실: 0.067899\t최선의 손실: 0.036107\t정확도: 98.51%\n",
+      "39\t검증 세트 손실: 0.057274\t최선의 손실: 0.036107\t정확도: 98.79%\n",
+      "40\t검증 세트 손실: 0.084119\t최선의 손실: 0.036107\t정확도: 98.75%\n",
+      "41\t검증 세트 손실: 0.053514\t최선의 손실: 0.036107\t정확도: 99.06%\n",
+      "42\t검증 세트 손실: 0.048903\t최선의 손실: 0.036107\t정확도: 98.94%\n",
+      "43\t검증 세트 손실: 0.074492\t최선의 손실: 0.036107\t정확도: 98.59%\n",
+      "44\t검증 세트 손실: 0.064297\t최선의 손실: 0.036107\t정확도: 98.55%\n",
+      "45\t검증 세트 손실: 0.075940\t최선의 손실: 0.036107\t정확도: 98.12%\n",
+      "46\t검증 세트 손실: 0.054782\t최선의 손실: 0.036107\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100, total= 6.9min\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.093786\t최선의 손실: 0.093786\t정확도: 96.95%\n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.98, n_neurons=100, total= 5.2min\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.108025\t최선의 손실: 0.108025\t정확도: 96.33%\n",
+      "1\t검증 세트 손실: 0.076635\t최선의 손실: 0.076635\t정확도: 97.97%\n",
+      "2\t검증 세트 손실: 0.074013\t최선의 손실: 0.074013\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.055829\t최선의 손실: 0.055829\t정확도: 98.44%\n",
+      "4\t검증 세트 손실: 0.053956\t최선의 손실: 0.053956\t정확도: 98.28%\n",
+      "5\t검증 세트 손실: 0.056101\t최선의 손실: 0.053956\t정확도: 98.67%\n",
+      "6\t검증 세트 손실: 0.044115\t최선의 손실: 0.044115\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.060508\t최선의 손실: 0.044115\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.044909\t최선의 손실: 0.044115\t정확도: 98.91%\n",
+      "9\t검증 세트 손실: 0.060889\t최선의 손실: 0.044115\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.036749\t최선의 손실: 0.036749\t정확도: 98.91%\n",
+      "11\t검증 세트 손실: 0.040409\t최선의 손실: 0.036749\t정확도: 99.10%\n",
+      "12\t검증 세트 손실: 0.051520\t최선의 손실: 0.036749\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.107114\t최선의 손실: 0.036749\t정확도: 98.16%\n",
+      "14\t검증 세트 손실: 0.045694\t최선의 손실: 0.036749\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.050639\t최선의 손실: 0.036749\t정확도: 98.83%\n",
+      "16\t검증 세트 손실: 0.049120\t최선의 손실: 0.036749\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.034938\t최선의 손실: 0.034938\t정확도: 99.22%\n",
+      "18\t검증 세트 손실: 0.041426\t최선의 손실: 0.034938\t정확도: 99.14%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1\t검증 세트 손실: 0.072289\t최선의 손실: 0.072289\t정확도: 97.77%\n",
-      "2\t검증 세트 손실: 0.064119\t최선의 손실: 0.064119\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.063192\t최선의 손실: 0.063192\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.044016\t최선의 손실: 0.044016\t정확도: 98.79%\n",
-      "5\t검증 세트 손실: 0.052489\t최선의 손실: 0.044016\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.040642\t최선의 손실: 0.040642\t정확도: 98.71%\n",
-      "7\t검증 세트 손실: 0.052248\t최선의 손실: 0.040642\t정확도: 98.55%\n",
-      "8\t검증 세트 손실: 0.059176\t최선의 손실: 0.040642\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.055699\t최선의 손실: 0.040642\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.053287\t최선의 손실: 0.040642\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.054732\t최선의 손실: 0.040642\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.040028\t최선의 손실: 0.040028\t정확도: 99.10%\n",
-      "13\t검증 세트 손실: 0.106279\t최선의 손실: 0.040028\t정확도: 98.01%\n",
-      "14\t검증 세트 손실: 0.076503\t최선의 손실: 0.040028\t정확도: 98.24%\n",
-      "15\t검증 세트 손실: 0.041039\t최선의 손실: 0.040028\t정확도: 99.14%\n",
-      "16\t검증 세트 손실: 0.055984\t최선의 손실: 0.040028\t정확도: 98.75%\n",
-      "17\t검증 세트 손실: 0.067446\t최선의 손실: 0.040028\t정확도: 98.44%\n",
-      "18\t검증 세트 손실: 0.053441\t최선의 손실: 0.040028\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.062524\t최선의 손실: 0.040028\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.057273\t최선의 손실: 0.040028\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.060561\t최선의 손실: 0.040028\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.133104\t최선의 손실: 0.040028\t정확도: 98.05%\n",
-      "23\t검증 세트 손실: 0.078302\t최선의 손실: 0.040028\t정확도: 98.71%\n",
-      "24\t검증 세트 손실: 0.045828\t최선의 손실: 0.040028\t정확도: 99.14%\n",
-      "25\t검증 세트 손실: 0.047588\t최선의 손실: 0.040028\t정확도: 99.18%\n",
-      "26\t검증 세트 손실: 0.046107\t최선의 손실: 0.040028\t정확도: 99.02%\n",
-      "27\t검증 세트 손실: 0.045046\t최선의 손실: 0.040028\t정확도: 99.14%\n",
-      "28\t검증 세트 손실: 0.052180\t최선의 손실: 0.040028\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.056234\t최선의 손실: 0.040028\t정확도: 98.94%\n",
-      "30\t검증 세트 손실: 0.085138\t최선의 손실: 0.040028\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.043546\t최선의 손실: 0.040028\t정확도: 99.26%\n",
-      "32\t검증 세트 손실: 0.078184\t최선의 손실: 0.040028\t정확도: 98.71%\n",
-      "33\t검증 세트 손실: 0.103661\t최선의 손실: 0.040028\t정확도: 98.28%\n",
+      "19\t검증 세트 손실: 0.086902\t최선의 손실: 0.034938\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.080846\t최선의 손실: 0.034938\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.040564\t최선의 손실: 0.034938\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.050681\t최선의 손실: 0.034938\t정확도: 98.87%\n",
+      "23\t검증 세트 손실: 0.029690\t최선의 손실: 0.029690\t정확도: 99.45%\n",
+      "24\t검증 세트 손실: 0.082578\t최선의 손실: 0.029690\t정확도: 98.55%\n",
+      "25\t검증 세트 손실: 0.073668\t최선의 손실: 0.029690\t정확도: 98.67%\n",
+      "26\t검증 세트 손실: 0.047765\t최선의 손실: 0.029690\t정확도: 99.02%\n",
+      "27\t검증 세트 손실: 0.050707\t최선의 손실: 0.029690\t정확도: 98.94%\n",
+      "28\t검증 세트 손실: 0.054398\t최선의 손실: 0.029690\t정확도: 98.91%\n",
+      "29\t검증 세트 손실: 0.090548\t최선의 손실: 0.029690\t정확도: 98.63%\n",
+      "30\t검증 세트 손실: 0.183887\t최선의 손실: 0.029690\t정확도: 98.01%\n",
+      "31\t검증 세트 손실: 0.114752\t최선의 손실: 0.029690\t정확도: 98.08%\n",
+      "32\t검증 세트 손실: 0.067549\t최선의 손실: 0.029690\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.059744\t최선의 손실: 0.029690\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.037072\t최선의 손실: 0.029690\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.044778\t최선의 손실: 0.029690\t정확도: 99.02%\n",
+      "36\t검증 세트 손실: 0.041730\t최선의 손실: 0.029690\t정확도: 99.14%\n",
+      "37\t검증 세트 손실: 0.047610\t최선의 손실: 0.029690\t정확도: 99.02%\n",
+      "38\t검증 세트 손실: 0.071458\t최선의 손실: 0.029690\t정확도: 98.91%\n",
+      "39\t검증 세트 손실: 0.091496\t최선의 손실: 0.029690\t정확도: 98.75%\n",
+      "40\t검증 세트 손실: 0.240279\t최선의 손실: 0.029690\t정확도: 98.40%\n",
+      "41\t검증 세트 손실: 0.050614\t최선의 손실: 0.029690\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.064126\t최선의 손실: 0.029690\t정확도: 99.14%\n",
+      "43\t검증 세트 손실: 0.049459\t최선의 손실: 0.029690\t정확도: 99.34%\n",
+      "44\t검증 세트 손실: 0.050877\t최선의 손실: 0.029690\t정확도: 99.14%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140, total=  24.1s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.135886\t최선의 손실: 0.135886\t정확도: 96.17%\n",
-      "1\t검증 세트 손실: 0.065168\t최선의 손실: 0.065168\t정확도: 98.08%\n",
-      "2\t검증 세트 손실: 0.064778\t최선의 손실: 0.064778\t정확도: 97.81%\n",
-      "3\t검증 세트 손실: 0.067086\t최선의 손실: 0.064778\t정확도: 97.85%\n",
-      "4\t검증 세트 손실: 0.076101\t최선의 손실: 0.064778\t정확도: 97.58%\n",
-      "5\t검증 세트 손실: 0.049099\t최선의 손실: 0.049099\t정확도: 98.51%\n",
-      "6\t검증 세트 손실: 0.074627\t최선의 손실: 0.049099\t정확도: 98.01%\n",
-      "7\t검증 세트 손실: 0.047967\t최선의 손실: 0.047967\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.043179\t최선의 손실: 0.043179\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.052924\t최선의 손실: 0.043179\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.053577\t최선의 손실: 0.043179\t정확도: 98.94%\n",
-      "11\t검증 세트 손실: 0.065159\t최선의 손실: 0.043179\t정확도: 98.20%\n",
-      "12\t검증 세트 손실: 0.044701\t최선의 손실: 0.043179\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.092816\t최선의 손실: 0.043179\t정확도: 98.16%\n",
-      "14\t검증 세트 손실: 0.055996\t최선의 손실: 0.043179\t정확도: 98.75%\n",
-      "15\t검증 세트 손실: 0.054320\t최선의 손실: 0.043179\t정확도: 98.91%\n",
-      "16\t검증 세트 손실: 0.061527\t최선의 손실: 0.043179\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.038790\t최선의 손실: 0.038790\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.053761\t최선의 손실: 0.038790\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.038174\t최선의 손실: 0.038174\t정확도: 99.18%\n",
-      "20\t검증 세트 손실: 0.047089\t최선의 손실: 0.038174\t정확도: 98.98%\n",
-      "21\t검증 세트 손실: 0.042901\t최선의 손실: 0.038174\t정확도: 99.22%\n",
-      "22\t검증 세트 손실: 0.065401\t최선의 손실: 0.038174\t정확도: 98.63%\n",
-      "23\t검증 세트 손실: 0.085617\t최선의 손실: 0.038174\t정확도: 98.59%\n",
-      "24\t검증 세트 손실: 0.054048\t최선의 손실: 0.038174\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.045904\t최선의 손실: 0.038174\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.067163\t최선의 손실: 0.038174\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.061845\t최선의 손실: 0.038174\t정확도: 98.83%\n",
-      "28\t검증 세트 손실: 0.046965\t최선의 손실: 0.038174\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.047166\t최선의 손실: 0.038174\t정확도: 99.06%\n",
-      "30\t검증 세트 손실: 0.078956\t최선의 손실: 0.038174\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.067004\t최선의 손실: 0.038174\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.063701\t최선의 손실: 0.038174\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.072812\t최선의 손실: 0.038174\t정확도: 98.67%\n",
-      "34\t검증 세트 손실: 0.072147\t최선의 손실: 0.038174\t정확도: 99.02%\n",
-      "35\t검증 세트 손실: 0.117880\t최선의 손실: 0.038174\t정확도: 98.71%\n",
-      "36\t검증 세트 손실: 0.057485\t최선의 손실: 0.038174\t정확도: 98.83%\n",
-      "37\t검증 세트 손실: 0.038975\t최선의 손실: 0.038174\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.050297\t최선의 손실: 0.038174\t정확도: 98.98%\n",
-      "39\t검증 세트 손실: 0.045188\t최선의 손실: 0.038174\t정확도: 99.02%\n",
-      "40\t검증 세트 손실: 0.036051\t최선의 손실: 0.036051\t정확도: 99.26%\n",
-      "41\t검증 세트 손실: 0.034870\t최선의 손실: 0.034870\t정확도: 99.14%\n",
-      "42\t검증 세트 손실: 0.046554\t최선의 손실: 0.034870\t정확도: 99.22%\n",
-      "43\t검증 세트 손실: 0.050083\t최선의 손실: 0.034870\t정확도: 99.02%\n",
-      "44\t검증 세트 손실: 0.054308\t최선의 손실: 0.034870\t정확도: 98.94%\n",
-      "45\t검증 세트 손실: 0.047029\t최선의 손실: 0.034870\t정확도: 99.14%\n",
-      "46\t검증 세트 손실: 0.071842\t최선의 손실: 0.034870\t정확도: 98.79%\n",
-      "47\t검증 세트 손실: 0.059985\t최선의 손실: 0.034870\t정확도: 98.94%\n",
-      "48\t검증 세트 손실: 0.069927\t최선의 손실: 0.034870\t정확도: 98.79%\n",
-      "49\t검증 세트 손실: 0.120465\t최선의 손실: 0.034870\t정확도: 98.83%\n",
-      "50\t검증 세트 손실: 0.107597\t최선의 손실: 0.034870\t정확도: 98.83%\n",
-      "51\t검증 세트 손실: 0.064595\t최선의 손실: 0.034870\t정확도: 99.02%\n",
-      "52\t검증 세트 손실: 0.042214\t최선의 손실: 0.034870\t정확도: 99.22%\n",
-      "53\t검증 세트 손실: 0.045633\t최선의 손실: 0.034870\t정확도: 99.22%\n",
-      "54\t검증 세트 손실: 0.044037\t최선의 손실: 0.034870\t정확도: 99.34%\n",
-      "55\t검증 세트 손실: 0.040538\t최선의 손실: 0.034870\t정확도: 99.22%\n",
-      "56\t검증 세트 손실: 0.051001\t최선의 손실: 0.034870\t정확도: 99.14%\n",
-      "57\t검증 세트 손실: 0.050152\t최선의 손실: 0.034870\t정확도: 99.22%\n",
-      "58\t검증 세트 손실: 0.051717\t최선의 손실: 0.034870\t정확도: 98.98%\n",
-      "59\t검증 세트 손실: 0.144445\t최선의 손실: 0.034870\t정확도: 98.71%\n",
-      "60\t검증 세트 손실: 0.165739\t최선의 손실: 0.034870\t정확도: 98.59%\n",
-      "61\t검증 세트 손실: 0.090116\t최선의 손실: 0.034870\t정확도: 99.02%\n",
-      "62\t검증 세트 손실: 0.134587\t최선의 손실: 0.034870\t정확도: 98.67%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140, total=  32.4s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.119130\t최선의 손실: 0.119130\t정확도: 96.21%\n",
+      "1\t검증 세트 손실: 0.069518\t최선의 손실: 0.069518\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.066960\t최선의 손실: 0.066960\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.070500\t최선의 손실: 0.066960\t정확도: 97.93%\n",
+      "4\t검증 세트 손실: 0.074278\t최선의 손실: 0.066960\t정확도: 97.65%\n",
+      "5\t검증 세트 손실: 0.041822\t최선의 손실: 0.041822\t정확도: 98.75%\n",
+      "6\t검증 세트 손실: 0.085031\t최선의 손실: 0.041822\t정확도: 97.58%\n",
+      "7\t검증 세트 손실: 0.073763\t최선의 손실: 0.041822\t정확도: 97.93%\n",
+      "8\t검증 세트 손실: 0.050045\t최선의 손실: 0.041822\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.095699\t최선의 손실: 0.041822\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.050977\t최선의 손실: 0.041822\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.054034\t최선의 손실: 0.041822\t정확도: 98.36%\n",
+      "12\t검증 세트 손실: 0.056480\t최선의 손실: 0.041822\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.038037\t최선의 손실: 0.038037\t정확도: 99.10%\n",
+      "14\t검증 세트 손실: 0.053569\t최선의 손실: 0.038037\t정확도: 98.40%\n",
+      "15\t검증 세트 손실: 0.060095\t최선의 손실: 0.038037\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.051083\t최선의 손실: 0.038037\t정확도: 98.59%\n",
+      "17\t검증 세트 손실: 0.092406\t최선의 손실: 0.038037\t정확도: 98.05%\n",
+      "18\t검증 세트 손실: 0.057667\t최선의 손실: 0.038037\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.065463\t최선의 손실: 0.038037\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.072211\t최선의 손실: 0.038037\t정확도: 98.71%\n",
+      "21\t검증 세트 손실: 0.051581\t최선의 손실: 0.038037\t정확도: 98.91%\n",
+      "22\t검증 세트 손실: 0.066303\t최선의 손실: 0.038037\t정확도: 98.32%\n",
+      "23\t검증 세트 손실: 0.074959\t최선의 손실: 0.038037\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.061271\t최선의 손실: 0.038037\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.063458\t최선의 손실: 0.038037\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.062921\t최선의 손실: 0.038037\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.058778\t최선의 손실: 0.038037\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.070389\t최선의 손실: 0.038037\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.080855\t최선의 손실: 0.038037\t정확도: 98.67%\n",
+      "30\t검증 세트 손실: 0.161552\t최선의 손실: 0.038037\t정확도: 98.40%\n",
+      "31\t검증 세트 손실: 0.088001\t최선의 손실: 0.038037\t정확도: 98.51%\n",
+      "32\t검증 세트 손실: 0.057790\t최선의 손실: 0.038037\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.061750\t최선의 손실: 0.038037\t정확도: 98.94%\n",
+      "34\t검증 세트 손실: 0.051804\t최선의 손실: 0.038037\t정확도: 99.10%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140, total=  43.3s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.094680\t최선의 손실: 0.094680\t정확도: 97.26%\n",
-      "1\t검증 세트 손실: 0.073300\t최선의 손실: 0.073300\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.067478\t최선의 손실: 0.067478\t정확도: 97.58%\n",
-      "3\t검증 세트 손실: 0.050895\t최선의 손실: 0.050895\t정확도: 98.08%\n",
-      "4\t검증 세트 손실: 0.062481\t최선의 손실: 0.050895\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.046010\t최선의 손실: 0.046010\t정확도: 98.75%\n",
-      "6\t검증 세트 손실: 0.039650\t최선의 손실: 0.039650\t정확도: 98.91%\n",
-      "7\t검증 세트 손실: 0.063575\t최선의 손실: 0.039650\t정확도: 98.16%\n",
-      "8\t검증 세트 손실: 0.046361\t최선의 손실: 0.039650\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.050890\t최선의 손실: 0.039650\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.040331\t최선의 손실: 0.039650\t정확도: 98.79%\n",
-      "11\t검증 세트 손실: 0.058958\t최선의 손실: 0.039650\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.038555\t최선의 손실: 0.038555\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.058222\t최선의 손실: 0.038555\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.054772\t최선의 손실: 0.038555\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.052751\t최선의 손실: 0.038555\t정확도: 98.87%\n",
-      "16\t검증 세트 손실: 0.072053\t최선의 손실: 0.038555\t정확도: 98.44%\n",
-      "17\t검증 세트 손실: 0.061906\t최선의 손실: 0.038555\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.048370\t최선의 손실: 0.038555\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.046604\t최선의 손실: 0.038555\t정확도: 99.06%\n",
-      "20\t검증 세트 손실: 0.048478\t최선의 손실: 0.038555\t정확도: 99.02%\n",
-      "21\t검증 세트 손실: 0.072029\t최선의 손실: 0.038555\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.030976\t최선의 손실: 0.030976\t정확도: 99.26%\n",
-      "23\t검증 세트 손실: 0.035017\t최선의 손실: 0.030976\t정확도: 99.02%\n",
-      "24\t검증 세트 손실: 0.059024\t최선의 손실: 0.030976\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.035215\t최선의 손실: 0.030976\t정확도: 99.22%\n",
-      "26\t검증 세트 손실: 0.049200\t최선의 손실: 0.030976\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.153849\t최선의 손실: 0.030976\t정확도: 98.44%\n",
-      "28\t검증 세트 손실: 0.050171\t최선의 손실: 0.030976\t정확도: 99.10%\n",
-      "29\t검증 세트 손실: 0.044031\t최선의 손실: 0.030976\t정확도: 99.18%\n",
-      "30\t검증 세트 손실: 0.026211\t최선의 손실: 0.026211\t정확도: 99.37%\n",
-      "31\t검증 세트 손실: 0.030787\t최선의 손실: 0.026211\t정확도: 99.37%\n",
-      "32\t검증 세트 손실: 0.028762\t최선의 손실: 0.026211\t정확도: 99.57%\n",
-      "33\t검증 세트 손실: 0.038048\t최선의 손실: 0.026211\t정확도: 99.30%\n",
-      "34\t검증 세트 손실: 0.037197\t최선의 손실: 0.026211\t정확도: 99.37%\n",
-      "35\t검증 세트 손실: 0.045084\t최선의 손실: 0.026211\t정확도: 99.26%\n",
-      "36\t검증 세트 손실: 0.035204\t최선의 손실: 0.026211\t정확도: 99.22%\n",
-      "37\t검증 세트 손실: 0.041051\t최선의 손실: 0.026211\t정확도: 99.26%\n",
-      "38\t검증 세트 손실: 0.042754\t최선의 손실: 0.026211\t정확도: 99.30%\n",
-      "39\t검증 세트 손실: 0.045307\t최선의 손실: 0.026211\t정확도: 99.26%\n",
-      "40\t검증 세트 손실: 0.034354\t최선의 손실: 0.026211\t정확도: 99.30%\n",
-      "41\t검증 세트 손실: 0.077110\t최선의 손실: 0.026211\t정확도: 98.59%\n",
-      "42\t검증 세트 손실: 0.048552\t최선의 손실: 0.026211\t정확도: 99.06%\n",
-      "43\t검증 세트 손실: 0.059350\t최선의 손실: 0.026211\t정확도: 98.98%\n",
-      "44\t검증 세트 손실: 0.058384\t최선의 손실: 0.026211\t정확도: 98.83%\n",
-      "45\t검증 세트 손실: 0.038003\t최선의 손실: 0.026211\t정확도: 99.34%\n",
-      "46\t검증 세트 손실: 0.065110\t최선의 손실: 0.026211\t정확도: 99.06%\n",
-      "47\t검증 세트 손실: 0.114981\t최선의 손실: 0.026211\t정확도: 98.71%\n",
-      "48\t검증 세트 손실: 0.189793\t최선의 손실: 0.026211\t정확도: 98.63%\n",
-      "49\t검증 세트 손실: 0.068446\t최선의 손실: 0.026211\t정확도: 98.94%\n",
-      "50\t검증 세트 손실: 0.035656\t최선의 손실: 0.026211\t정확도: 99.30%\n",
-      "51\t검증 세트 손실: 0.040827\t최선의 손실: 0.026211\t정확도: 99.30%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140, total=  25.3s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.087454\t최선의 손실: 0.087454\t정확도: 97.19%\n",
+      "1\t검증 세트 손실: 0.071518\t최선의 손실: 0.071518\t정확도: 97.30%\n",
+      "2\t검증 세트 손실: 0.061442\t최선의 손실: 0.061442\t정확도: 98.08%\n",
+      "3\t검증 세트 손실: 0.056095\t최선의 손실: 0.056095\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.048185\t최선의 손실: 0.048185\t정확도: 98.48%\n",
+      "5\t검증 세트 손실: 0.046425\t최선의 손실: 0.046425\t정확도: 98.44%\n",
+      "6\t검증 세트 손실: 0.089852\t최선의 손실: 0.046425\t정확도: 97.58%\n",
+      "7\t검증 세트 손실: 0.051369\t최선의 손실: 0.046425\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.050223\t최선의 손실: 0.046425\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.052587\t최선의 손실: 0.046425\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.043762\t최선의 손실: 0.043762\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.046483\t최선의 손실: 0.043762\t정확도: 98.98%\n",
+      "12\t검증 세트 손실: 0.052347\t최선의 손실: 0.043762\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.040203\t최선의 손실: 0.040203\t정확도: 98.94%\n",
+      "14\t검증 세트 손실: 0.048978\t최선의 손실: 0.040203\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.048855\t최선의 손실: 0.040203\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.040879\t최선의 손실: 0.040203\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.053719\t최선의 손실: 0.040203\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.050158\t최선의 손실: 0.040203\t정확도: 98.98%\n",
+      "19\t검증 세트 손실: 0.053702\t최선의 손실: 0.040203\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.060874\t최선의 손실: 0.040203\t정확도: 99.06%\n",
+      "21\t검증 세트 손실: 0.072925\t최선의 손실: 0.040203\t정확도: 98.59%\n",
+      "22\t검증 세트 손실: 0.038608\t최선의 손실: 0.038608\t정확도: 99.18%\n",
+      "23\t검증 세트 손실: 0.043937\t최선의 손실: 0.038608\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.049469\t최선의 손실: 0.038608\t정확도: 99.18%\n",
+      "25\t검증 세트 손실: 0.052569\t최선의 손실: 0.038608\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.068104\t최선의 손실: 0.038608\t정확도: 99.02%\n",
+      "27\t검증 세트 손실: 0.062374\t최선의 손실: 0.038608\t정확도: 98.55%\n",
+      "28\t검증 세트 손실: 0.048803\t최선의 손실: 0.038608\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.063343\t최선의 손실: 0.038608\t정확도: 98.63%\n",
+      "30\t검증 세트 손실: 0.135585\t최선의 손실: 0.038608\t정확도: 98.01%\n",
+      "31\t검증 세트 손실: 0.034800\t최선의 손실: 0.034800\t정확도: 99.37%\n",
+      "32\t검증 세트 손실: 0.053341\t최선의 손실: 0.034800\t정확도: 99.18%\n",
+      "33\t검증 세트 손실: 0.044114\t최선의 손실: 0.034800\t정확도: 99.18%\n",
+      "34\t검증 세트 손실: 0.042160\t최선의 손실: 0.034800\t정확도: 99.18%\n",
+      "35\t검증 세트 손실: 0.043861\t최선의 손실: 0.034800\t정확도: 99.37%\n",
+      "36\t검증 세트 손실: 0.039182\t최선의 손실: 0.034800\t정확도: 99.22%\n",
+      "37\t검증 세트 손실: 0.058285\t최선의 손실: 0.034800\t정확도: 98.91%\n",
+      "38\t검증 세트 손실: 0.032671\t최선의 손실: 0.032671\t정확도: 99.26%\n",
+      "39\t검증 세트 손실: 0.049419\t최선의 손실: 0.032671\t정확도: 99.06%\n",
+      "40\t검증 세트 손실: 0.045961\t최선의 손실: 0.032671\t정확도: 98.98%\n",
+      "41\t검증 세트 손실: 0.056387\t최선의 손실: 0.032671\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.048859\t최선의 손실: 0.032671\t정확도: 99.26%\n",
+      "43\t검증 세트 손실: 0.072222\t최선의 손실: 0.032671\t정확도: 98.48%\n",
+      "44\t검증 세트 손실: 0.144786\t최선의 손실: 0.032671\t정확도: 98.67%\n",
+      "45\t검증 세트 손실: 0.064285\t최선의 손실: 0.032671\t정확도: 98.91%\n",
+      "46\t검증 세트 손실: 0.050635\t최선의 손실: 0.032671\t정확도: 98.91%\n",
+      "47\t검증 세트 손실: 0.043545\t최선의 손실: 0.032671\t정확도: 99.34%\n",
+      "48\t검증 세트 손실: 0.039729\t최선의 손실: 0.032671\t정확도: 99.30%\n",
+      "49\t검증 세트 손실: 0.044882\t최선의 손실: 0.032671\t정확도: 99.26%\n",
+      "50\t검증 세트 손실: 0.068495\t최선의 손실: 0.032671\t정확도: 99.06%\n",
+      "51\t검증 세트 손실: 0.064603\t최선의 손실: 0.032671\t정확도: 98.91%\n",
+      "52\t검증 세트 손실: 0.064984\t최선의 손실: 0.032671\t정확도: 99.02%\n",
+      "53\t검증 세트 손실: 0.060143\t최선의 손실: 0.032671\t정확도: 99.06%\n",
+      "54\t검증 세트 손실: 0.068774\t최선의 손실: 0.032671\t정확도: 98.91%\n",
+      "55\t검증 세트 손실: 0.054389\t최선의 손실: 0.032671\t정확도: 99.18%\n",
+      "56\t검증 세트 손실: 0.047623\t최선의 손실: 0.032671\t정확도: 99.06%\n",
+      "57\t검증 세트 손실: 0.053877\t최선의 손실: 0.032671\t정확도: 98.71%\n",
+      "58\t검증 세트 손실: 0.117762\t최선의 손실: 0.032671\t정확도: 98.28%\n",
+      "59\t검증 세트 손실: 0.083230\t최선의 손실: 0.032671\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140, total=  36.0s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140 \n"
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140, total=  42.6s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.133103\t최선의 손실: 0.133103\t정확도: 96.13%\n",
+      "1\t검증 세트 손실: 0.066106\t최선의 손실: 0.066106\t정확도: 98.16%\n",
+      "2\t검증 세트 손실: 0.063599\t최선의 손실: 0.063599\t정확도: 98.05%\n",
+      "3\t검증 세트 손실: 0.095986\t최선의 손실: 0.063599\t정확도: 97.42%\n",
+      "4\t검증 세트 손실: 0.064321\t최선의 손실: 0.063599\t정확도: 98.51%\n",
+      "5\t검증 세트 손실: 0.050575\t최선의 손실: 0.050575\t정확도: 98.67%\n",
+      "6\t검증 세트 손실: 0.050400\t최선의 손실: 0.050400\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.142201\t최선의 손실: 0.050400\t정확도: 96.76%\n",
+      "8\t검증 세트 손실: 0.058525\t최선의 손실: 0.050400\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.058320\t최선의 손실: 0.050400\t정확도: 98.36%\n",
+      "10\t검증 세트 손실: 0.043177\t최선의 손실: 0.043177\t정확도: 98.91%\n",
+      "11\t검증 세트 손실: 0.043477\t최선의 손실: 0.043177\t정확도: 98.94%\n",
+      "12\t검증 세트 손실: 0.042576\t최선의 손실: 0.042576\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.078864\t최선의 손실: 0.042576\t정확도: 97.85%\n",
+      "14\t검증 세트 손실: 0.117818\t최선의 손실: 0.042576\t정확도: 98.28%\n",
+      "15\t검증 세트 손실: 0.054176\t최선의 손실: 0.042576\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.044311\t최선의 손실: 0.042576\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.041128\t최선의 손실: 0.041128\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.059210\t최선의 손실: 0.041128\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.049836\t최선의 손실: 0.041128\t정확도: 99.06%\n",
+      "20\t검증 세트 손실: 0.057770\t최선의 손실: 0.041128\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.238198\t최선의 손실: 0.041128\t정확도: 98.20%\n",
+      "22\t검증 세트 손실: 0.082284\t최선의 손실: 0.041128\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.058684\t최선의 손실: 0.041128\t정확도: 98.91%\n",
+      "24\t검증 세트 손실: 0.052726\t최선의 손실: 0.041128\t정확도: 99.18%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.101946\t최선의 손실: 0.101946\t정확도: 97.07%\n",
-      "1\t검증 세트 손실: 0.070038\t최선의 손실: 0.070038\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.073905\t최선의 손실: 0.070038\t정확도: 97.85%\n",
-      "3\t검증 세트 손실: 0.073032\t최선의 손실: 0.070038\t정확도: 98.08%\n",
-      "4\t검증 세트 손실: 0.045020\t최선의 손실: 0.045020\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.069852\t최선의 손실: 0.045020\t정확도: 98.24%\n",
-      "6\t검증 세트 손실: 0.054792\t최선의 손실: 0.045020\t정확도: 98.71%\n",
-      "7\t검증 세트 손실: 0.082769\t최선의 손실: 0.045020\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.061471\t최선의 손실: 0.045020\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.055051\t최선의 손실: 0.045020\t정확도: 98.79%\n",
-      "10\t검증 세트 손실: 0.061764\t최선의 손실: 0.045020\t정확도: 98.20%\n",
-      "11\t검증 세트 손실: 0.063941\t최선의 손실: 0.045020\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.033384\t최선의 손실: 0.033384\t정확도: 99.26%\n",
-      "13\t검증 세트 손실: 0.059961\t최선의 손실: 0.033384\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.069096\t최선의 손실: 0.033384\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.143355\t최선의 손실: 0.033384\t정확도: 97.93%\n",
-      "16\t검증 세트 손실: 0.055252\t최선의 손실: 0.033384\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.053497\t최선의 손실: 0.033384\t정확도: 99.02%\n",
-      "18\t검증 세트 손실: 0.040320\t최선의 손실: 0.033384\t정확도: 99.10%\n",
-      "19\t검증 세트 손실: 0.064870\t최선의 손실: 0.033384\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.048702\t최선의 손실: 0.033384\t정확도: 99.02%\n",
-      "21\t검증 세트 손실: 0.063125\t최선의 손실: 0.033384\t정확도: 98.98%\n",
-      "22\t검증 세트 손실: 0.052645\t최선의 손실: 0.033384\t정확도: 99.10%\n",
-      "23\t검증 세트 손실: 0.077829\t최선의 손실: 0.033384\t정확도: 98.67%\n",
-      "24\t검증 세트 손실: 0.086826\t최선의 손실: 0.033384\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.047070\t최선의 손실: 0.033384\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.053195\t최선의 손실: 0.033384\t정확도: 99.06%\n",
-      "27\t검증 세트 손실: 0.066284\t최선의 손실: 0.033384\t정확도: 98.87%\n",
-      "28\t검증 세트 손실: 0.077271\t최선의 손실: 0.033384\t정확도: 98.67%\n",
-      "29\t검증 세트 손실: 0.183949\t최선의 손실: 0.033384\t정확도: 98.40%\n",
-      "30\t검증 세트 손실: 0.080059\t최선의 손실: 0.033384\t정확도: 98.59%\n",
-      "31\t검증 세트 손실: 0.065788\t최선의 손실: 0.033384\t정확도: 99.14%\n",
-      "32\t검증 세트 손실: 0.066196\t최선의 손실: 0.033384\t정확도: 98.94%\n",
-      "33\t검증 세트 손실: 0.069029\t최선의 손실: 0.033384\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.056744\t최선의 손실: 0.041128\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.072535\t최선의 손실: 0.041128\t정확도: 98.75%\n",
+      "27\t검증 세트 손실: 0.065889\t최선의 손실: 0.041128\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.063956\t최선의 손실: 0.041128\t정확도: 99.10%\n",
+      "29\t검증 세트 손실: 0.127281\t최선의 손실: 0.041128\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.215989\t최선의 손실: 0.041128\t정확도: 98.51%\n",
+      "31\t검증 세트 손실: 0.070305\t최선의 손실: 0.041128\t정확도: 98.75%\n",
+      "32\t검증 세트 손실: 0.069855\t최선의 손실: 0.041128\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.079615\t최선의 손실: 0.041128\t정확도: 98.94%\n",
+      "34\t검증 세트 손실: 0.065296\t최선의 손실: 0.041128\t정확도: 99.06%\n",
+      "35\t검증 세트 손실: 0.074146\t최선의 손실: 0.041128\t정확도: 98.94%\n",
+      "36\t검증 세트 손실: 0.073557\t최선의 손실: 0.041128\t정확도: 98.87%\n",
+      "37\t검증 세트 손실: 0.114279\t최선의 손실: 0.041128\t정확도: 98.75%\n",
+      "38\t검증 세트 손실: 0.092351\t최선의 손실: 0.041128\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140, total=  46.1s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.120977\t최선의 손실: 0.120977\t정확도: 96.48%\n",
-      "1\t검증 세트 손실: 0.079886\t최선의 손실: 0.079886\t정확도: 97.81%\n",
-      "2\t검증 세트 손실: 0.071202\t최선의 손실: 0.071202\t정확도: 97.65%\n",
-      "3\t검증 세트 손실: 0.056642\t최선의 손실: 0.056642\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.054857\t최선의 손실: 0.054857\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.045591\t최선의 손실: 0.045591\t정확도: 98.98%\n",
-      "6\t검증 세트 손실: 0.060888\t최선의 손실: 0.045591\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.071067\t최선의 손실: 0.045591\t정확도: 98.28%\n",
-      "8\t검증 세트 손실: 0.059326\t최선의 손실: 0.045591\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.067046\t최선의 손실: 0.045591\t정확도: 98.55%\n",
-      "10\t검증 세트 손실: 0.154068\t최선의 손실: 0.045591\t정확도: 97.07%\n",
-      "11\t검증 세트 손실: 0.059244\t최선의 손실: 0.045591\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.057271\t최선의 손실: 0.045591\t정확도: 98.55%\n",
-      "13\t검증 세트 손실: 0.040724\t최선의 손실: 0.040724\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.098598\t최선의 손실: 0.040724\t정확도: 98.20%\n",
-      "15\t검증 세트 손실: 0.086227\t최선의 손실: 0.040724\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.049521\t최선의 손실: 0.040724\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.066531\t최선의 손실: 0.040724\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.071470\t최선의 손실: 0.040724\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.137632\t최선의 손실: 0.040724\t정확도: 98.48%\n",
-      "20\t검증 세트 손실: 0.061324\t최선의 손실: 0.040724\t정확도: 98.59%\n",
-      "21\t검증 세트 손실: 0.052182\t최선의 손실: 0.040724\t정확도: 98.91%\n",
-      "22\t검증 세트 손실: 0.045094\t최선의 손실: 0.040724\t정확도: 99.06%\n",
-      "23\t검증 세트 손실: 0.128230\t최선의 손실: 0.040724\t정확도: 98.08%\n",
-      "24\t검증 세트 손실: 0.075684\t최선의 손실: 0.040724\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.046533\t최선의 손실: 0.040724\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.076050\t최선의 손실: 0.040724\t정확도: 98.71%\n",
-      "27\t검증 세트 손실: 0.094915\t최선의 손실: 0.040724\t정확도: 98.67%\n",
-      "28\t검증 세트 손실: 0.114807\t최선의 손실: 0.040724\t정확도: 98.71%\n",
-      "29\t검증 세트 손실: 0.069154\t최선의 손실: 0.040724\t정확도: 98.55%\n",
-      "30\t검증 세트 손실: 0.052269\t최선의 손실: 0.040724\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.065356\t최선의 손실: 0.040724\t정확도: 98.75%\n",
-      "32\t검증 세트 손실: 0.064201\t최선의 손실: 0.040724\t정확도: 98.91%\n",
-      "33\t검증 세트 손실: 0.065124\t최선의 손실: 0.040724\t정확도: 98.83%\n",
-      "34\t검증 세트 손실: 0.060516\t최선의 손실: 0.040724\t정확도: 98.83%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140, total=  53.8s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.105705\t최선의 손실: 0.105705\t정확도: 97.07%\n",
+      "1\t검증 세트 손실: 0.086468\t최선의 손실: 0.086468\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.059534\t최선의 손실: 0.059534\t정확도: 98.24%\n",
+      "3\t검증 세트 손실: 0.048997\t최선의 손실: 0.048997\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.063811\t최선의 손실: 0.048997\t정확도: 98.28%\n",
+      "5\t검증 세트 손실: 0.076997\t최선의 손실: 0.048997\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.065164\t최선의 손실: 0.048997\t정확도: 98.20%\n",
+      "7\t검증 세트 손실: 0.051127\t최선의 손실: 0.048997\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.060290\t최선의 손실: 0.048997\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.053828\t최선의 손실: 0.048997\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.071058\t최선의 손실: 0.048997\t정확도: 98.24%\n",
+      "11\t검증 세트 손실: 0.080245\t최선의 손실: 0.048997\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.048256\t최선의 손실: 0.048256\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.046914\t최선의 손실: 0.046914\t정확도: 98.71%\n",
+      "14\t검증 세트 손실: 0.049635\t최선의 손실: 0.046914\t정확도: 99.02%\n",
+      "15\t검증 세트 손실: 0.050362\t최선의 손실: 0.046914\t정확도: 98.91%\n",
+      "16\t검증 세트 손실: 0.049864\t최선의 손실: 0.046914\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.084479\t최선의 손실: 0.046914\t정확도: 98.05%\n",
+      "18\t검증 세트 손실: 0.072766\t최선의 손실: 0.046914\t정확도: 98.55%\n",
+      "19\t검증 세트 손실: 0.114762\t최선의 손실: 0.046914\t정확도: 98.48%\n",
+      "20\t검증 세트 손실: 0.050973\t최선의 손실: 0.046914\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.053116\t최선의 손실: 0.046914\t정확도: 98.67%\n",
+      "22\t검증 세트 손실: 0.053094\t최선의 손실: 0.046914\t정확도: 98.87%\n",
+      "23\t검증 세트 손실: 0.047140\t최선의 손실: 0.046914\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.047444\t최선의 손실: 0.046914\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.057759\t최선의 손실: 0.046914\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.079742\t최선의 손실: 0.046914\t정확도: 98.67%\n",
+      "27\t검증 세트 손실: 0.186857\t최선의 손실: 0.046914\t정확도: 97.65%\n",
+      "28\t검증 세트 손실: 0.067016\t최선의 손실: 0.046914\t정확도: 98.83%\n",
+      "29\t검증 세트 손실: 0.055902\t최선의 손실: 0.046914\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.057876\t최선의 손실: 0.046914\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.052385\t최선의 손실: 0.046914\t정확도: 98.98%\n",
+      "32\t검증 세트 손실: 0.061360\t최선의 손실: 0.046914\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.103357\t최선의 손실: 0.046914\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.064397\t최선의 손실: 0.046914\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140, total=  47.6s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.131736\t최선의 손실: 0.131736\t정확도: 96.40%\n",
-      "1\t검증 세트 손실: 0.069265\t최선의 손실: 0.069265\t정확도: 98.05%\n",
-      "2\t검증 세트 손실: 0.049468\t최선의 손실: 0.049468\t정확도: 98.44%\n",
-      "3\t검증 세트 손실: 0.077580\t최선의 손실: 0.049468\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.048263\t최선의 손실: 0.048263\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.081651\t최선의 손실: 0.048263\t정확도: 97.62%\n",
-      "6\t검증 세트 손실: 0.053073\t최선의 손실: 0.048263\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.042582\t최선의 손실: 0.042582\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.046393\t최선의 손실: 0.042582\t정확도: 98.87%\n",
-      "9\t검증 세트 손실: 0.060170\t최선의 손실: 0.042582\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.040423\t최선의 손실: 0.040423\t정확도: 98.94%\n",
-      "11\t검증 세트 손실: 0.043495\t최선의 손실: 0.040423\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.062669\t최선의 손실: 0.040423\t정확도: 98.48%\n",
-      "13\t검증 세트 손실: 0.037925\t최선의 손실: 0.037925\t정확도: 99.18%\n",
-      "14\t검증 세트 손실: 0.048244\t최선의 손실: 0.037925\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.066171\t최선의 손실: 0.037925\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.043400\t최선의 손실: 0.037925\t정확도: 98.75%\n",
-      "17\t검증 세트 손실: 0.066901\t최선의 손실: 0.037925\t정확도: 98.44%\n",
-      "18\t검증 세트 손실: 0.099514\t최선의 손실: 0.037925\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.058542\t최선의 손실: 0.037925\t정확도: 98.98%\n",
-      "20\t검증 세트 손실: 0.051227\t최선의 손실: 0.037925\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.036499\t최선의 손실: 0.036499\t정확도: 99.14%\n",
-      "22\t검증 세트 손실: 0.040370\t최선의 손실: 0.036499\t정확도: 99.18%\n",
-      "23\t검증 세트 손실: 0.047640\t최선의 손실: 0.036499\t정확도: 99.14%\n",
-      "24\t검증 세트 손실: 0.052787\t최선의 손실: 0.036499\t정확도: 99.14%\n",
-      "25\t검증 세트 손실: 0.055241\t최선의 손실: 0.036499\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.101349\t최선의 손실: 0.036499\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.066350\t최선의 손실: 0.036499\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.088695\t최선의 손실: 0.036499\t정확도: 98.67%\n",
-      "29\t검증 세트 손실: 0.045796\t최선의 손실: 0.036499\t정확도: 99.30%\n",
-      "30\t검증 세트 손실: 0.047226\t최선의 손실: 0.036499\t정확도: 99.10%\n",
-      "31\t검증 세트 손실: 0.071162\t최선의 손실: 0.036499\t정확도: 98.91%\n",
-      "32\t검증 세트 손실: 0.437619\t최선의 손실: 0.036499\t정확도: 97.15%\n",
-      "33\t검증 세트 손실: 0.126681\t최선의 손실: 0.036499\t정확도: 98.44%\n",
-      "34\t검증 세트 손실: 0.093536\t최선의 손실: 0.036499\t정확도: 98.79%\n",
-      "35\t검증 세트 손실: 0.064816\t최선의 손실: 0.036499\t정확도: 98.98%\n",
-      "36\t검증 세트 손실: 0.066505\t최선의 손실: 0.036499\t정확도: 99.02%\n",
-      "37\t검증 세트 손실: 0.054403\t최선의 손실: 0.036499\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.363131\t최선의 손실: 0.036499\t정확도: 98.24%\n",
-      "39\t검증 세트 손실: 0.117666\t최선의 손실: 0.036499\t정확도: 99.10%\n",
-      "40\t검증 세트 손실: 0.096642\t최선의 손실: 0.036499\t정확도: 99.22%\n",
-      "41\t검증 세트 손실: 0.075912\t최선의 손실: 0.036499\t정확도: 99.22%\n",
-      "42\t검증 세트 손실: 0.075917\t최선의 손실: 0.036499\t정확도: 99.34%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140, total=  48.2s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.135143\t최선의 손실: 0.135143\t정확도: 96.48%\n",
+      "1\t검증 세트 손실: 0.078869\t최선의 손실: 0.078869\t정확도: 97.85%\n",
+      "2\t검증 세트 손실: 0.062899\t최선의 손실: 0.062899\t정확도: 98.20%\n",
+      "3\t검증 세트 손실: 0.115810\t최선의 손실: 0.062899\t정확도: 97.38%\n",
+      "4\t검증 세트 손실: 0.042165\t최선의 손실: 0.042165\t정확도: 98.79%\n",
+      "5\t검증 세트 손실: 0.058716\t최선의 손실: 0.042165\t정확도: 98.71%\n",
+      "6\t검증 세트 손실: 0.180417\t최선의 손실: 0.042165\t정확도: 94.92%\n",
+      "7\t검증 세트 손실: 0.044667\t최선의 손실: 0.042165\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.032070\t최선의 손실: 0.032070\t정확도: 98.98%\n",
+      "9\t검증 세트 손실: 0.079101\t최선의 손실: 0.032070\t정확도: 97.50%\n",
+      "10\t검증 세트 손실: 0.045293\t최선의 손실: 0.032070\t정확도: 98.98%\n",
+      "11\t검증 세트 손실: 0.046010\t최선의 손실: 0.032070\t정확도: 98.83%\n",
+      "12\t검증 세트 손실: 0.042143\t최선의 손실: 0.032070\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.043920\t최선의 손실: 0.032070\t정확도: 98.83%\n",
+      "14\t검증 세트 손실: 0.053784\t최선의 손실: 0.032070\t정확도: 98.79%\n",
+      "15\t검증 세트 손실: 0.042763\t최선의 손실: 0.032070\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.038263\t최선의 손실: 0.032070\t정확도: 98.94%\n",
+      "17\t검증 세트 손실: 0.071085\t최선의 손실: 0.032070\t정확도: 98.12%\n",
+      "18\t검증 세트 손실: 0.047308\t최선의 손실: 0.032070\t정확도: 98.83%\n",
+      "19\t검증 세트 손실: 0.075823\t최선의 손실: 0.032070\t정확도: 98.55%\n",
+      "20\t검증 세트 손실: 0.056294\t최선의 손실: 0.032070\t정확도: 98.63%\n",
+      "21\t검증 세트 손실: 0.037461\t최선의 손실: 0.032070\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.043791\t최선의 손실: 0.032070\t정확도: 99.18%\n",
+      "23\t검증 세트 손실: 0.065643\t최선의 손실: 0.032070\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.158140\t최선의 손실: 0.032070\t정확도: 98.51%\n",
+      "25\t검증 세트 손실: 0.043592\t최선의 손실: 0.032070\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.047428\t최선의 손실: 0.032070\t정확도: 98.91%\n",
+      "27\t검증 세트 손실: 0.041175\t최선의 손실: 0.032070\t정확도: 99.10%\n",
+      "28\t검증 세트 손실: 0.057545\t최선의 손실: 0.032070\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.097339\t최선의 손실: 0.032070\t정확도: 98.51%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=140, total=  59.0s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.241139\t최선의 손실: 0.241139\t정확도: 96.25%\n",
-      "1\t검증 세트 손실: 0.116025\t최선의 손실: 0.116025\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.092420\t최선의 손실: 0.092420\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.083172\t최선의 손실: 0.083172\t정확도: 97.93%\n",
-      "4\t검증 세트 손실: 0.067830\t최선의 손실: 0.067830\t정확도: 98.55%\n",
-      "5\t검증 세트 손실: 0.079189\t최선의 손실: 0.067830\t정확도: 98.05%\n",
-      "6\t검증 세트 손실: 0.060521\t최선의 손실: 0.060521\t정확도: 98.51%\n",
-      "7\t검증 세트 손실: 0.083330\t최선의 손실: 0.060521\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.059690\t최선의 손실: 0.059690\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.069885\t최선의 손실: 0.059690\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.071203\t최선의 손실: 0.059690\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.094000\t최선의 손실: 0.059690\t정확도: 97.93%\n",
-      "12\t검증 세트 손실: 0.078130\t최선의 손실: 0.059690\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.068290\t최선의 손실: 0.059690\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.079925\t최선의 손실: 0.059690\t정확도: 98.28%\n",
-      "15\t검증 세트 손실: 0.063062\t최선의 손실: 0.059690\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.052380\t최선의 손실: 0.052380\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.074383\t최선의 손실: 0.052380\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.055423\t최선의 손실: 0.052380\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.072917\t최선의 손실: 0.052380\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.076034\t최선의 손실: 0.052380\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.078506\t최선의 손실: 0.052380\t정확도: 98.55%\n",
-      "22\t검증 세트 손실: 0.093286\t최선의 손실: 0.052380\t정확도: 98.08%\n",
-      "23\t검증 세트 손실: 0.112655\t최선의 손실: 0.052380\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.052631\t최선의 손실: 0.052380\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.069536\t최선의 손실: 0.052380\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.064658\t최선의 손실: 0.052380\t정확도: 98.67%\n",
-      "27\t검증 세트 손실: 0.153665\t최선의 손실: 0.052380\t정확도: 97.85%\n",
-      "28\t검증 세트 손실: 0.062414\t최선의 손실: 0.052380\t정확도: 98.59%\n",
-      "29\t검증 세트 손실: 0.085054\t최선의 손실: 0.052380\t정확도: 98.75%\n",
-      "30\t검증 세트 손실: 0.071632\t최선의 손실: 0.052380\t정확도: 98.87%\n",
-      "31\t검증 세트 손실: 0.082752\t최선의 손실: 0.052380\t정확도: 98.36%\n",
-      "32\t검증 세트 손실: 0.082239\t최선의 손실: 0.052380\t정확도: 98.83%\n",
-      "33\t검증 세트 손실: 0.104686\t최선의 손실: 0.052380\t정확도: 98.16%\n"
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.9, n_neurons=140, total=  41.5s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.151724\t최선의 손실: 0.151724\t정확도: 96.95%\n",
+      "1\t검증 세트 손실: 0.120594\t최선의 손실: 0.120594\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.090844\t최선의 손실: 0.090844\t정확도: 97.42%\n",
+      "3\t검증 세트 손실: 0.114561\t최선의 손실: 0.090844\t정확도: 97.42%\n",
+      "4\t검증 세트 손실: 0.080502\t최선의 손실: 0.080502\t정확도: 97.89%\n",
+      "5\t검증 세트 손실: 0.072344\t최선의 손실: 0.072344\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.101349\t최선의 손실: 0.072344\t정확도: 97.69%\n",
+      "7\t검증 세트 손실: 0.154126\t최선의 손실: 0.072344\t정확도: 97.58%\n",
+      "8\t검증 세트 손실: 0.074440\t최선의 손실: 0.072344\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.073239\t최선의 손실: 0.072344\t정확도: 98.40%\n",
+      "10\t검증 세트 손실: 0.086200\t최선의 손실: 0.072344\t정확도: 98.20%\n",
+      "11\t검증 세트 손실: 0.085255\t최선의 손실: 0.072344\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.064739\t최선의 손실: 0.064739\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.106004\t최선의 손실: 0.064739\t정확도: 97.93%\n",
+      "14\t검증 세트 손실: 0.054256\t최선의 손실: 0.054256\t정확도: 98.79%\n",
+      "15\t검증 세트 손실: 0.053818\t최선의 손실: 0.053818\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.050924\t최선의 손실: 0.050924\t정확도: 99.02%\n",
+      "17\t검증 세트 손실: 0.046462\t최선의 손실: 0.046462\t정확도: 99.02%\n",
+      "18\t검증 세트 손실: 0.086777\t최선의 손실: 0.046462\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.090960\t최선의 손실: 0.046462\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.052801\t최선의 손실: 0.046462\t정확도: 98.71%\n",
+      "21\t검증 세트 손실: 0.054146\t최선의 손실: 0.046462\t정확도: 98.75%\n",
+      "22\t검증 세트 손실: 0.044171\t최선의 손실: 0.044171\t정확도: 98.83%\n",
+      "23\t검증 세트 손실: 0.054817\t최선의 손실: 0.044171\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.060715\t최선의 손실: 0.044171\t정확도: 98.83%\n",
+      "25\t검증 세트 손실: 0.049981\t최선의 손실: 0.044171\t정확도: 98.79%\n",
+      "26\t검증 세트 손실: 0.048551\t최선의 손실: 0.044171\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.100187\t최선의 손실: 0.044171\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.046144\t최선의 손실: 0.044171\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.050754\t최선의 손실: 0.044171\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.056756\t최선의 손실: 0.044171\t정확도: 98.94%\n",
+      "31\t검증 세트 손실: 0.065392\t최선의 손실: 0.044171\t정확도: 98.48%\n",
+      "32\t검증 세트 손실: 0.053494\t최선의 손실: 0.044171\t정확도: 99.02%\n",
+      "33\t검증 세트 손실: 0.050250\t최선의 손실: 0.044171\t정확도: 99.18%\n",
+      "34\t검증 세트 손실: 0.069268\t최선의 손실: 0.044171\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.049337\t최선의 손실: 0.044171\t정확도: 98.75%\n",
+      "36\t검증 세트 손실: 0.084173\t최선의 손실: 0.044171\t정확도: 98.48%\n",
+      "37\t검증 세트 손실: 0.092654\t최선의 손실: 0.044171\t정확도: 98.28%\n",
+      "38\t검증 세트 손실: 0.066534\t최선의 손실: 0.044171\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.051616\t최선의 손실: 0.044171\t정확도: 99.18%\n",
+      "40\t검증 세트 손실: 0.051745\t최선의 손실: 0.044171\t정확도: 99.18%\n",
+      "41\t검증 세트 손실: 0.064010\t최선의 손실: 0.044171\t정확도: 98.67%\n",
+      "42\t검증 세트 손실: 0.053334\t최선의 손실: 0.044171\t정확도: 98.83%\n",
+      "43\t검증 세트 손실: 0.052871\t최선의 손실: 0.044171\t정확도: 98.98%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=70, total=  59.6s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.243234\t최선의 손실: 0.243234\t정확도: 95.54%\n",
+      "1\t검증 세트 손실: 0.093165\t최선의 손실: 0.093165\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.088342\t최선의 손실: 0.088342\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.088780\t최선의 손실: 0.088342\t정확도: 98.08%\n",
+      "4\t검증 세트 손실: 0.085085\t최선의 손실: 0.085085\t정확도: 98.32%\n",
+      "5\t검증 세트 손실: 0.081780\t최선의 손실: 0.081780\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.115873\t최선의 손실: 0.081780\t정확도: 96.64%\n",
+      "7\t검증 세트 손실: 0.063978\t최선의 손실: 0.063978\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.064050\t최선의 손실: 0.063978\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.071514\t최선의 손실: 0.063978\t정확도: 98.28%\n",
+      "10\t검증 세트 손실: 0.053416\t최선의 손실: 0.053416\t정확도: 98.67%\n",
+      "11\t검증 세트 손실: 0.053904\t최선의 손실: 0.053416\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.120021\t최선의 손실: 0.053416\t정확도: 97.73%\n",
+      "13\t검증 세트 손실: 0.065555\t최선의 손실: 0.053416\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.082776\t최선의 손실: 0.053416\t정확도: 98.12%\n",
+      "15\t검증 세트 손실: 0.064429\t최선의 손실: 0.053416\t정확도: 98.28%\n",
+      "16\t검증 세트 손실: 0.066875\t최선의 손실: 0.053416\t정확도: 98.36%\n",
+      "17\t검증 세트 손실: 0.125460\t최선의 손실: 0.053416\t정확도: 97.81%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "34\t검증 세트 손실: 0.097178\t최선의 손실: 0.052380\t정확도: 98.51%\n",
-      "35\t검증 세트 손실: 0.104476\t최선의 손실: 0.052380\t정확도: 98.48%\n",
-      "36\t검증 세트 손실: 0.065429\t최선의 손실: 0.052380\t정확도: 98.75%\n",
-      "37\t검증 세트 손실: 0.078362\t최선의 손실: 0.052380\t정확도: 98.36%\n",
+      "18\t검증 세트 손실: 0.056473\t최선의 손실: 0.053416\t정확도: 98.55%\n",
+      "19\t검증 세트 손실: 0.054321\t최선의 손실: 0.053416\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.080832\t최선의 손실: 0.053416\t정확도: 98.36%\n",
+      "21\t검증 세트 손실: 0.086669\t최선의 손실: 0.053416\t정확도: 98.36%\n",
+      "22\t검증 세트 손실: 0.104901\t최선의 손실: 0.053416\t정확도: 98.32%\n",
+      "23\t검증 세트 손실: 0.095346\t최선의 손실: 0.053416\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.092183\t최선의 손실: 0.053416\t정확도: 98.24%\n",
+      "25\t검증 세트 손실: 0.113185\t최선의 손실: 0.053416\t정확도: 98.48%\n",
+      "26\t검증 세트 손실: 0.104748\t최선의 손실: 0.053416\t정확도: 97.85%\n",
+      "27\t검증 세트 손실: 0.080750\t최선의 손실: 0.053416\t정확도: 98.79%\n",
+      "28\t검증 세트 손실: 0.074065\t최선의 손실: 0.053416\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.083262\t최선의 손실: 0.053416\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.086033\t최선의 손실: 0.053416\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.109489\t최선의 손실: 0.053416\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70, total=  51.3s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.263729\t최선의 손실: 0.263729\t정확도: 94.92%\n",
-      "1\t검증 세트 손실: 0.093922\t최선의 손실: 0.093922\t정확도: 97.93%\n",
-      "2\t검증 세트 손실: 0.072657\t최선의 손실: 0.072657\t정확도: 98.32%\n",
-      "3\t검증 세트 손실: 0.081906\t최선의 손실: 0.072657\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.163200\t최선의 손실: 0.072657\t정확도: 96.60%\n",
-      "5\t검증 세트 손실: 0.058498\t최선의 손실: 0.058498\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.196048\t최선의 손실: 0.058498\t정확도: 96.21%\n",
-      "7\t검증 세트 손실: 0.086365\t최선의 손실: 0.058498\t정확도: 97.93%\n",
-      "8\t검증 세트 손실: 0.091766\t최선의 손실: 0.058498\t정확도: 97.58%\n",
-      "9\t검증 세트 손실: 0.098409\t최선의 손실: 0.058498\t정확도: 97.65%\n",
-      "10\t검증 세트 손실: 0.075593\t최선의 손실: 0.058498\t정확도: 98.16%\n",
-      "11\t검증 세트 손실: 0.075268\t최선의 손실: 0.058498\t정확도: 98.40%\n",
-      "12\t검증 세트 손실: 0.080298\t최선의 손실: 0.058498\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.063989\t최선의 손실: 0.058498\t정확도: 98.32%\n",
-      "14\t검증 세트 손실: 0.077775\t최선의 손실: 0.058498\t정확도: 97.97%\n",
-      "15\t검증 세트 손실: 0.053974\t최선의 손실: 0.053974\t정확도: 98.59%\n",
-      "16\t검증 세트 손실: 0.076785\t최선의 손실: 0.053974\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.082723\t최선의 손실: 0.053974\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.060798\t최선의 손실: 0.053974\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.054081\t최선의 손실: 0.053974\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.063941\t최선의 손실: 0.053974\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.070046\t최선의 손실: 0.053974\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.074057\t최선의 손실: 0.053974\t정확도: 98.51%\n",
-      "23\t검증 세트 손실: 0.080297\t최선의 손실: 0.053974\t정확도: 98.63%\n",
-      "24\t검증 세트 손실: 0.088728\t최선의 손실: 0.053974\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.053130\t최선의 손실: 0.053130\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.095476\t최선의 손실: 0.053130\t정확도: 98.28%\n",
-      "27\t검증 세트 손실: 0.072798\t최선의 손실: 0.053130\t정확도: 98.63%\n",
-      "28\t검증 세트 손실: 0.096414\t최선의 손실: 0.053130\t정확도: 98.63%\n",
-      "29\t검증 세트 손실: 0.094239\t최선의 손실: 0.053130\t정확도: 98.48%\n",
-      "30\t검증 세트 손실: 0.096754\t최선의 손실: 0.053130\t정확도: 98.63%\n",
-      "31\t검증 세트 손실: 0.089892\t최선의 손실: 0.053130\t정확도: 98.55%\n",
-      "32\t검증 세트 손실: 0.131463\t최선의 손실: 0.053130\t정확도: 98.32%\n",
-      "33\t검증 세트 손실: 0.067870\t최선의 손실: 0.053130\t정확도: 98.63%\n",
-      "34\t검증 세트 손실: 0.062553\t최선의 손실: 0.053130\t정확도: 98.67%\n",
-      "35\t검증 세트 손실: 0.070258\t최선의 손실: 0.053130\t정확도: 98.98%\n",
-      "36\t검증 세트 손실: 0.080727\t최선의 손실: 0.053130\t정확도: 98.87%\n",
-      "37\t검증 세트 손실: 0.066601\t최선의 손실: 0.053130\t정확도: 98.71%\n",
-      "38\t검증 세트 손실: 0.071818\t최선의 손실: 0.053130\t정확도: 98.75%\n",
-      "39\t검증 세트 손실: 22.981682\t최선의 손실: 0.053130\t정확도: 52.15%\n",
-      "40\t검증 세트 손실: 0.057804\t최선의 손실: 0.053130\t정확도: 98.71%\n",
-      "41\t검증 세트 손실: 0.057641\t최선의 손실: 0.053130\t정확도: 98.79%\n",
-      "42\t검증 세트 손실: 0.081963\t최선의 손실: 0.053130\t정확도: 98.79%\n",
-      "43\t검증 세트 손실: 0.086167\t최선의 손실: 0.053130\t정확도: 98.28%\n",
-      "44\t검증 세트 손실: 0.093006\t최선의 손실: 0.053130\t정확도: 98.67%\n",
-      "45\t검증 세트 손실: 0.092545\t최선의 손실: 0.053130\t정확도: 98.48%\n",
-      "46\t검증 세트 손실: 0.075059\t최선의 손실: 0.053130\t정확도: 98.87%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=70, total=  43.6s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.139484\t최선의 손실: 0.139484\t정확도: 97.65%\n",
+      "1\t검증 세트 손실: 0.205313\t최선의 손실: 0.139484\t정확도: 95.58%\n",
+      "2\t검증 세트 손실: 0.092522\t최선의 손실: 0.092522\t정확도: 97.77%\n",
+      "3\t검증 세트 손실: 0.109513\t최선의 손실: 0.092522\t정확도: 98.20%\n",
+      "4\t검증 세트 손실: 0.091671\t최선의 손실: 0.091671\t정확도: 98.12%\n",
+      "5\t검증 세트 손실: 0.078452\t최선의 손실: 0.078452\t정확도: 98.05%\n",
+      "6\t검증 세트 손실: 0.055458\t최선의 손실: 0.055458\t정확도: 98.55%\n",
+      "7\t검증 세트 손실: 0.066974\t최선의 손실: 0.055458\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.074104\t최선의 손실: 0.055458\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.086296\t최선의 손실: 0.055458\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.048557\t최선의 손실: 0.048557\t정확도: 98.67%\n",
+      "11\t검증 세트 손실: 0.052265\t최선의 손실: 0.048557\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.058146\t최선의 손실: 0.048557\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.057652\t최선의 손실: 0.048557\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.068515\t최선의 손실: 0.048557\t정확도: 98.32%\n",
+      "15\t검증 세트 손실: 0.044264\t최선의 손실: 0.044264\t정확도: 98.98%\n",
+      "16\t검증 세트 손실: 0.058751\t최선의 손실: 0.044264\t정확도: 98.55%\n",
+      "17\t검증 세트 손실: 0.061649\t최선의 손실: 0.044264\t정확도: 98.48%\n",
+      "18\t검증 세트 손실: 0.078520\t최선의 손실: 0.044264\t정확도: 98.24%\n",
+      "19\t검증 세트 손실: 0.055092\t최선의 손실: 0.044264\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.064456\t최선의 손실: 0.044264\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.090630\t최선의 손실: 0.044264\t정확도: 98.36%\n",
+      "22\t검증 세트 손실: 0.064418\t최선의 손실: 0.044264\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.071621\t최선의 손실: 0.044264\t정확도: 98.83%\n",
+      "24\t검증 세트 손실: 0.059437\t최선의 손실: 0.044264\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.049754\t최선의 손실: 0.044264\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.076987\t최선의 손실: 0.044264\t정확도: 98.55%\n",
+      "27\t검증 세트 손실: 0.094377\t최선의 손실: 0.044264\t정확도: 98.24%\n",
+      "28\t검증 세트 손실: 0.076110\t최선의 손실: 0.044264\t정확도: 99.10%\n",
+      "29\t검증 세트 손실: 0.060822\t최선의 손실: 0.044264\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.038511\t최선의 손실: 0.038511\t정확도: 99.18%\n",
+      "31\t검증 세트 손실: 0.128417\t최선의 손실: 0.038511\t정확도: 98.32%\n",
+      "32\t검증 세트 손실: 0.084394\t최선의 손실: 0.038511\t정확도: 98.08%\n",
+      "33\t검증 세트 손실: 0.079480\t최선의 손실: 0.038511\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.063050\t최선의 손실: 0.038511\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.066022\t최선의 손실: 0.038511\t정확도: 98.79%\n",
+      "36\t검증 세트 손실: 0.051503\t최선의 손실: 0.038511\t정확도: 98.94%\n",
+      "37\t검증 세트 손실: 0.079582\t최선의 손실: 0.038511\t정확도: 98.67%\n",
+      "38\t검증 세트 손실: 0.059348\t최선의 손실: 0.038511\t정확도: 98.63%\n",
+      "39\t검증 세트 손실: 0.053009\t최선의 손실: 0.038511\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.074133\t최선의 손실: 0.038511\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.060819\t최선의 손실: 0.038511\t정확도: 99.02%\n",
+      "42\t검증 세트 손실: 0.069637\t최선의 손실: 0.038511\t정확도: 98.98%\n",
+      "43\t검증 세트 손실: 0.069228\t최선의 손실: 0.038511\t정확도: 98.94%\n",
+      "44\t검증 세트 손실: 0.074390\t최선의 손실: 0.038511\t정확도: 98.71%\n",
+      "45\t검증 세트 손실: 0.076109\t최선의 손실: 0.038511\t정확도: 98.94%\n",
+      "46\t검증 세트 손실: 0.074772\t최선의 손실: 0.038511\t정확도: 98.59%\n",
+      "47\t검증 세트 손실: 0.101177\t최선의 손실: 0.038511\t정확도: 98.63%\n",
+      "48\t검증 세트 손실: 0.051860\t최선의 손실: 0.038511\t정확도: 99.18%\n",
+      "49\t검증 세트 손실: 0.055809\t최선의 손실: 0.038511\t정확도: 98.91%\n",
+      "50\t검증 세트 손실: 0.051966\t최선의 손실: 0.038511\t정확도: 98.94%\n",
+      "51\t검증 세트 손실: 0.089286\t최선의 손실: 0.038511\t정확도: 99.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70, total= 1.1min\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.206556\t최선의 손실: 0.206556\t정확도: 96.52%\n",
-      "1\t검증 세트 손실: 0.104201\t최선의 손실: 0.104201\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.076651\t최선의 손실: 0.076651\t정확도: 97.85%\n",
-      "3\t검증 세트 손실: 0.111370\t최선의 손실: 0.076651\t정확도: 97.69%\n",
-      "4\t검증 세트 손실: 0.090014\t최선의 손실: 0.076651\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 0.057162\t최선의 손실: 0.057162\t정확도: 98.59%\n",
-      "6\t검증 세트 손실: 0.065792\t최선의 손실: 0.057162\t정확도: 98.67%\n",
-      "7\t검증 세트 손실: 0.059767\t최선의 손실: 0.057162\t정확도: 98.71%\n",
-      "8\t검증 세트 손실: 0.072501\t최선의 손실: 0.057162\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.047872\t최선의 손실: 0.047872\t정확도: 98.75%\n",
-      "10\t검증 세트 손실: 0.056412\t최선의 손실: 0.047872\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.083647\t최선의 손실: 0.047872\t정확도: 98.48%\n",
-      "12\t검증 세트 손실: 0.047986\t최선의 손실: 0.047872\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.043746\t최선의 손실: 0.043746\t정확도: 98.94%\n",
-      "14\t검증 세트 손실: 0.057332\t최선의 손실: 0.043746\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.090892\t최선의 손실: 0.043746\t정확도: 98.01%\n",
-      "16\t검증 세트 손실: 0.065646\t최선의 손실: 0.043746\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.069219\t최선의 손실: 0.043746\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.062999\t최선의 손실: 0.043746\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.059984\t최선의 손실: 0.043746\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.055256\t최선의 손실: 0.043746\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.110535\t최선의 손실: 0.043746\t정확도: 98.05%\n",
-      "22\t검증 세트 손실: 0.073171\t최선의 손실: 0.043746\t정확도: 98.63%\n",
-      "23\t검증 세트 손실: 0.053630\t최선의 손실: 0.043746\t정확도: 98.91%\n",
-      "24\t검증 세트 손실: 0.045999\t최선의 손실: 0.043746\t정확도: 98.98%\n",
-      "25\t검증 세트 손실: 0.067172\t최선의 손실: 0.043746\t정확도: 98.48%\n",
-      "26\t검증 세트 손실: 0.039782\t최선의 손실: 0.039782\t정확도: 99.02%\n",
-      "27\t검증 세트 손실: 0.094641\t최선의 손실: 0.039782\t정확도: 96.40%\n",
-      "28\t검증 세트 손실: 0.075318\t최선의 손실: 0.039782\t정확도: 98.71%\n",
-      "29\t검증 세트 손실: 0.061263\t최선의 손실: 0.039782\t정확도: 98.51%\n",
-      "30\t검증 세트 손실: 0.058751\t최선의 손실: 0.039782\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.087569\t최선의 손실: 0.039782\t정확도: 98.59%\n",
-      "32\t검증 세트 손실: 0.055770\t최선의 손실: 0.039782\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.075362\t최선의 손실: 0.039782\t정확도: 98.44%\n",
-      "34\t검증 세트 손실: 0.100451\t최선의 손실: 0.039782\t정확도: 98.75%\n",
-      "35\t검증 세트 손실: 0.076267\t최선의 손실: 0.039782\t정확도: 98.83%\n",
-      "36\t검증 세트 손실: 0.086123\t최선의 손실: 0.039782\t정확도: 98.44%\n",
-      "37\t검증 세트 손실: 0.050199\t최선의 손실: 0.039782\t정확도: 99.02%\n",
-      "38\t검증 세트 손실: 0.073766\t최선의 손실: 0.039782\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.057000\t최선의 손실: 0.039782\t정확도: 98.71%\n",
-      "40\t검증 세트 손실: 0.060591\t최선의 손실: 0.039782\t정확도: 98.98%\n",
-      "41\t검증 세트 손실: 0.069028\t최선의 손실: 0.039782\t정확도: 98.79%\n",
-      "42\t검증 세트 손실: 0.054384\t최선의 손실: 0.039782\t정확도: 98.98%\n",
-      "43\t검증 세트 손실: 0.066174\t최선의 손실: 0.039782\t정확도: 98.98%\n",
-      "44\t검증 세트 손실: 0.075508\t최선의 손실: 0.039782\t정확도: 98.59%\n",
-      "45\t검증 세트 손실: 0.051924\t최선의 손실: 0.039782\t정확도: 99.14%\n",
-      "46\t검증 세트 손실: 0.067170\t최선의 손실: 0.039782\t정확도: 98.98%\n",
-      "47\t검증 세트 손실: 0.075440\t최선의 손실: 0.039782\t정확도: 98.91%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=70, total= 1.2min\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, batch_norm_momentum=0.95, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.065931\t최선의 손실: 0.065931\t정확도: 97.77%\n",
+      "1\t검증 세트 손실: 0.077932\t최선의 손실: 0.065931\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.113987\t최선의 손실: 0.065931\t정확도: 97.07%\n",
+      "3\t검증 세트 손실: 0.043770\t최선의 손실: 0.043770\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.058327\t최선의 손실: 0.043770\t정확도: 98.28%\n",
+      "5\t검증 세트 손실: 0.038164\t최선의 손실: 0.038164\t정확도: 98.87%\n",
+      "6\t검증 세트 손실: 0.046073\t최선의 손실: 0.038164\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.045450\t최선의 손실: 0.038164\t정확도: 98.83%\n",
+      "8\t검증 세트 손실: 0.046028\t최선의 손실: 0.038164\t정확도: 98.98%\n",
+      "9\t검증 세트 손실: 0.050136\t최선의 손실: 0.038164\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.065400\t최선의 손실: 0.038164\t정확도: 98.36%\n",
+      "11\t검증 세트 손실: 0.055231\t최선의 손실: 0.038164\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.049704\t최선의 손실: 0.038164\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.060561\t최선의 손실: 0.038164\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.048627\t최선의 손실: 0.038164\t정확도: 98.98%\n",
+      "15\t검증 세트 손실: 0.072868\t최선의 손실: 0.038164\t정확도: 98.55%\n",
+      "16\t검증 세트 손실: 0.032939\t최선의 손실: 0.032939\t정확도: 99.06%\n",
+      "17\t검증 세트 손실: 0.054737\t최선의 손실: 0.032939\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.050624\t최선의 손실: 0.032939\t정확도: 98.91%\n",
+      "19\t검증 세트 손실: 0.048838\t최선의 손실: 0.032939\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.039965\t최선의 손실: 0.032939\t정확도: 98.87%\n",
+      "21\t검증 세트 손실: 0.068348\t최선의 손실: 0.032939\t정확도: 98.44%\n",
+      "22\t검증 세트 손실: 0.038694\t최선의 손실: 0.032939\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.040279\t최선의 손실: 0.032939\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.044575\t최선의 손실: 0.032939\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.041173\t최선의 손실: 0.032939\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.050473\t최선의 손실: 0.032939\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.040656\t최선의 손실: 0.032939\t정확도: 99.10%\n",
+      "28\t검증 세트 손실: 0.047303\t최선의 손실: 0.032939\t정확도: 99.10%\n",
+      "29\t검증 세트 손실: 0.042964\t최선의 손실: 0.032939\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.068818\t최선의 손실: 0.032939\t정확도: 98.40%\n",
+      "31\t검증 세트 손실: 0.047899\t최선의 손실: 0.032939\t정확도: 99.02%\n",
+      "32\t검증 세트 손실: 0.048816\t최선의 손실: 0.032939\t정확도: 99.10%\n",
+      "33\t검증 세트 손실: 0.046880\t최선의 손실: 0.032939\t정확도: 98.98%\n",
+      "34\t검증 세트 손실: 0.056414\t최선의 손실: 0.032939\t정확도: 98.91%\n",
+      "35\t검증 세트 손실: 0.058005\t최선의 손실: 0.032939\t정확도: 98.75%\n",
+      "36\t검증 세트 손실: 0.062161\t최선의 손실: 0.032939\t정확도: 98.63%\n",
+      "37\t검증 세트 손실: 0.054496\t최선의 손실: 0.032939\t정확도: 99.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.99, n_neurons=70, total= 1.1min\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.95, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.089630\t최선의 손실: 0.089630\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.080361\t최선의 손실: 0.080361\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.079515\t최선의 손실: 0.079515\t정확도: 97.69%\n",
-      "3\t검증 세트 손실: 0.058259\t최선의 손실: 0.058259\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.044540\t최선의 손실: 0.044540\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.042028\t최선의 손실: 0.042028\t정확도: 98.94%\n",
-      "6\t검증 세트 손실: 0.052000\t최선의 손실: 0.042028\t정확도: 98.63%\n",
-      "7\t검증 세트 손실: 0.048369\t최선의 손실: 0.042028\t정확도: 98.83%\n",
-      "8\t검증 세트 손실: 0.074659\t최선의 손실: 0.042028\t정확도: 97.97%\n",
-      "9\t검증 세트 손실: 0.051664\t최선의 손실: 0.042028\t정확도: 98.79%\n",
-      "10\t검증 세트 손실: 0.056078\t최선의 손실: 0.042028\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.056570\t최선의 손실: 0.042028\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.036846\t최선의 손실: 0.036846\t정확도: 99.10%\n",
-      "13\t검증 세트 손실: 0.039070\t최선의 손실: 0.036846\t정확도: 99.06%\n",
-      "14\t검증 세트 손실: 0.044090\t최선의 손실: 0.036846\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.037505\t최선의 손실: 0.036846\t정확도: 98.98%\n",
-      "16\t검증 세트 손실: 0.046397\t최선의 손실: 0.036846\t정확도: 98.98%\n",
-      "17\t검증 세트 손실: 0.045207\t최선의 손실: 0.036846\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.040068\t최선의 손실: 0.036846\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.036189\t최선의 손실: 0.036189\t정확도: 99.18%\n",
-      "20\t검증 세트 손실: 0.053231\t최선의 손실: 0.036189\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.058567\t최선의 손실: 0.036189\t정확도: 98.48%\n",
-      "22\t검증 세트 손실: 0.045009\t최선의 손실: 0.036189\t정확도: 98.67%\n",
-      "23\t검증 세트 손실: 0.036695\t최선의 손실: 0.036189\t정확도: 99.18%\n",
-      "24\t검증 세트 손실: 0.069715\t최선의 손실: 0.036189\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.045596\t최선의 손실: 0.036189\t정확도: 98.91%\n",
-      "26\t검증 세트 손실: 0.044806\t최선의 손실: 0.036189\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.079060\t최선의 손실: 0.036189\t정확도: 98.67%\n",
-      "28\t검증 세트 손실: 0.049510\t최선의 손실: 0.036189\t정확도: 99.06%\n",
-      "29\t검증 세트 손실: 0.060963\t최선의 손실: 0.036189\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.057084\t최선의 손실: 0.036189\t정확도: 98.87%\n",
-      "31\t검증 세트 손실: 0.058231\t최선의 손실: 0.036189\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.080963\t최선의 손실: 0.036189\t정확도: 98.40%\n",
-      "33\t검증 세트 손실: 0.047711\t최선의 손실: 0.036189\t정확도: 99.18%\n",
-      "34\t검증 세트 손실: 0.073844\t최선의 손실: 0.036189\t정확도: 98.48%\n",
-      "35\t검증 세트 손실: 0.055972\t최선의 손실: 0.036189\t정확도: 98.91%\n",
-      "36\t검증 세트 손실: 0.066374\t최선의 손실: 0.036189\t정확도: 98.83%\n",
-      "37\t검증 세트 손실: 0.056151\t최선의 손실: 0.036189\t정확도: 98.91%\n",
-      "38\t검증 세트 손실: 0.043945\t최선의 손실: 0.036189\t정확도: 98.94%\n",
-      "39\t검증 세트 손실: 0.060899\t최선의 손실: 0.036189\t정확도: 98.83%\n",
-      "40\t검증 세트 손실: 0.059604\t최선의 손실: 0.036189\t정확도: 98.91%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, batch_norm_momentum=0.95, n_neurons=100, total=  27.3s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, batch_norm_momentum=0.95, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.075930\t최선의 손실: 0.075930\t정확도: 97.85%\n",
+      "1\t검증 세트 손실: 0.058358\t최선의 손실: 0.058358\t정확도: 98.48%\n",
+      "2\t검증 세트 손실: 0.059321\t최선의 손실: 0.058358\t정확도: 98.24%\n",
+      "3\t검증 세트 손실: 0.081308\t최선의 손실: 0.058358\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.063581\t최선의 손실: 0.058358\t정확도: 98.08%\n",
+      "5\t검증 세트 손실: 0.040411\t최선의 손실: 0.040411\t정확도: 98.87%\n",
+      "6\t검증 세트 손실: 0.053229\t최선의 손실: 0.040411\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.059809\t최선의 손실: 0.040411\t정확도: 98.20%\n",
+      "8\t검증 세트 손실: 0.059564\t최선의 손실: 0.040411\t정확도: 98.55%\n",
+      "9\t검증 세트 손실: 0.079756\t최선의 손실: 0.040411\t정확도: 98.05%\n",
+      "10\t검증 세트 손실: 0.061207\t최선의 손실: 0.040411\t정확도: 98.40%\n",
+      "11\t검증 세트 손실: 0.042723\t최선의 손실: 0.040411\t정확도: 98.98%\n",
+      "12\t검증 세트 손실: 0.054858\t최선의 손실: 0.040411\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.036684\t최선의 손실: 0.036684\t정확도: 99.02%\n",
+      "14\t검증 세트 손실: 0.053147\t최선의 손실: 0.036684\t정확도: 98.79%\n",
+      "15\t검증 세트 손실: 0.065505\t최선의 손실: 0.036684\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.058023\t최선의 손실: 0.036684\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.070331\t최선의 손실: 0.036684\t정확도: 98.32%\n",
+      "18\t검증 세트 손실: 0.065821\t최선의 손실: 0.036684\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.048101\t최선의 손실: 0.036684\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.053236\t최선의 손실: 0.036684\t정확도: 99.18%\n",
+      "21\t검증 세트 손실: 0.049569\t최선의 손실: 0.036684\t정확도: 99.10%\n",
+      "22\t검증 세트 손실: 0.048080\t최선의 손실: 0.036684\t정확도: 99.06%\n",
+      "23\t검증 세트 손실: 0.048068\t최선의 손실: 0.036684\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.057166\t최선의 손실: 0.036684\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.038320\t최선의 손실: 0.036684\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.042075\t최선의 손실: 0.036684\t정확도: 99.26%\n",
+      "27\t검증 세트 손실: 0.069710\t최선의 손실: 0.036684\t정확도: 98.51%\n",
+      "28\t검증 세트 손실: 0.066504\t최선의 손실: 0.036684\t정확도: 98.75%\n",
+      "29\t검증 세트 손실: 0.071332\t최선의 손실: 0.036684\t정확도: 98.55%\n",
+      "30\t검증 세트 손실: 0.047619\t최선의 손실: 0.036684\t정확도: 98.91%\n",
+      "31\t검증 세트 손실: 0.058090\t최선의 손실: 0.036684\t정확도: 98.91%\n",
+      "32\t검증 세트 손실: 0.069312\t최선의 손실: 0.036684\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.074857\t최선의 손실: 0.036684\t정확도: 98.44%\n",
+      "34\t검증 세트 손실: 0.097190\t최선의 손실: 0.036684\t정확도: 98.67%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.95, n_neurons=100, total=  28.6s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.95, n_neurons=100 \n"
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, batch_norm_momentum=0.95, n_neurons=100, total=  25.1s\n",
+      "[CV] batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, batch_norm_momentum=0.95, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.154798\t최선의 손실: 0.154798\t정확도: 96.60%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.084144\t최선의 손실: 0.084144\t정확도: 97.65%\n",
-      "1\t검증 세트 손실: 0.071230\t최선의 손실: 0.071230\t정확도: 97.89%\n",
-      "2\t검증 세트 손실: 0.049606\t최선의 손실: 0.049606\t정확도: 98.67%\n",
-      "3\t검증 세트 손실: 0.058644\t최선의 손실: 0.049606\t정확도: 98.48%\n",
-      "4\t검증 세트 손실: 0.095931\t최선의 손실: 0.049606\t정확도: 97.69%\n",
-      "5\t검증 세트 손실: 0.052520\t최선의 손실: 0.049606\t정확도: 98.36%\n",
-      "6\t검증 세트 손실: 0.039519\t최선의 손실: 0.039519\t정확도: 98.98%\n",
-      "7\t검증 세트 손실: 0.059842\t최선의 손실: 0.039519\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.044206\t최선의 손실: 0.039519\t정확도: 98.87%\n",
-      "9\t검증 세트 손실: 0.050276\t최선의 손실: 0.039519\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.049326\t최선의 손실: 0.039519\t정확도: 98.75%\n",
-      "11\t검증 세트 손실: 0.057902\t최선의 손실: 0.039519\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.072666\t최선의 손실: 0.039519\t정확도: 98.48%\n",
-      "13\t검증 세트 손실: 0.048867\t최선의 손실: 0.039519\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.055056\t최선의 손실: 0.039519\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.043293\t최선의 손실: 0.039519\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.041629\t최선의 손실: 0.039519\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.065293\t최선의 손실: 0.039519\t정확도: 98.48%\n",
-      "18\t검증 세트 손실: 0.060448\t최선의 손실: 0.039519\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.040352\t최선의 손실: 0.039519\t정확도: 98.91%\n",
-      "20\t검증 세트 손실: 0.047260\t최선의 손실: 0.039519\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.054017\t최선의 손실: 0.039519\t정확도: 98.75%\n",
-      "22\t검증 세트 손실: 0.034892\t최선의 손실: 0.034892\t정확도: 99.06%\n",
-      "23\t검증 세트 손실: 0.055746\t최선의 손실: 0.034892\t정확도: 98.91%\n",
-      "24\t검증 세트 손실: 0.038024\t최선의 손실: 0.034892\t정확도: 99.14%\n",
-      "25\t검증 세트 손실: 0.037572\t최선의 손실: 0.034892\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.043418\t최선의 손실: 0.034892\t정확도: 99.14%\n",
-      "27\t검증 세트 손실: 0.047784\t최선의 손실: 0.034892\t정확도: 99.10%\n",
-      "28\t검증 세트 손실: 0.050636\t최선의 손실: 0.034892\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.051563\t최선의 손실: 0.034892\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.058028\t최선의 손실: 0.034892\t정확도: 98.71%\n",
-      "31\t검증 세트 손실: 0.041297\t최선의 손실: 0.034892\t정확도: 98.79%\n",
-      "32\t검증 세트 손실: 0.044943\t최선의 손실: 0.034892\t정확도: 98.91%\n",
-      "33\t검증 세트 손실: 0.069492\t최선의 손실: 0.034892\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.043558\t최선의 손실: 0.034892\t정확도: 99.10%\n",
-      "35\t검증 세트 손실: 0.047640\t최선의 손실: 0.034892\t정확도: 99.02%\n",
-      "36\t검증 세트 손실: 0.048953\t최선의 손실: 0.034892\t정확도: 99.10%\n",
-      "37\t검증 세트 손실: 0.052611\t최선의 손실: 0.034892\t정확도: 99.10%\n",
-      "38\t검증 세트 손실: 0.048676\t최선의 손실: 0.034892\t정확도: 99.06%\n",
-      "39\t검증 세트 손실: 0.056990\t최선의 손실: 0.034892\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.049569\t최선의 손실: 0.034892\t정확도: 99.26%\n",
-      "41\t검증 세트 손실: 0.044335\t최선의 손실: 0.034892\t정확도: 99.14%\n",
-      "42\t검증 세트 손실: 0.048046\t최선의 손실: 0.034892\t정확도: 99.14%\n",
-      "43\t검증 세트 손실: 0.052791\t최선의 손실: 0.034892\t정확도: 98.98%\n",
+      "1\t검증 세트 손실: 0.062540\t최선의 손실: 0.062540\t정확도: 98.08%\n",
+      "2\t검증 세트 손실: 0.055164\t최선의 손실: 0.055164\t정확도: 98.36%\n",
+      "3\t검증 세트 손실: 0.065984\t최선의 손실: 0.055164\t정확도: 98.01%\n",
+      "4\t검증 세트 손실: 0.051667\t최선의 손실: 0.051667\t정확도: 98.59%\n",
+      "5\t검증 세트 손실: 0.044697\t최선의 손실: 0.044697\t정확도: 98.40%\n",
+      "6\t검증 세트 손실: 0.047738\t최선의 손실: 0.044697\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.068400\t최선의 손실: 0.044697\t정확도: 98.32%\n",
+      "8\t검증 세트 손실: 0.044785\t최선의 손실: 0.044697\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.060203\t최선의 손실: 0.044697\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.035961\t최선의 손실: 0.035961\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.060677\t최선의 손실: 0.035961\t정확도: 98.79%\n",
+      "12\t검증 세트 손실: 0.043136\t최선의 손실: 0.035961\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.050055\t최선의 손실: 0.035961\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.058701\t최선의 손실: 0.035961\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.040683\t최선의 손실: 0.035961\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.045400\t최선의 손실: 0.035961\t정확도: 99.02%\n",
+      "17\t검증 세트 손실: 0.045145\t최선의 손실: 0.035961\t정확도: 98.59%\n",
+      "18\t검증 세트 손실: 0.033817\t최선의 손실: 0.033817\t정확도: 98.94%\n",
+      "19\t검증 세트 손실: 0.068858\t최선의 손실: 0.033817\t정확도: 98.63%\n",
+      "20\t검증 세트 손실: 0.040037\t최선의 손실: 0.033817\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.037063\t최선의 손실: 0.033817\t정확도: 99.10%\n",
+      "22\t검증 세트 손실: 0.027374\t최선의 손실: 0.027374\t정확도: 99.30%\n",
+      "23\t검증 세트 손실: 0.048398\t최선의 손실: 0.027374\t정확도: 98.83%\n",
+      "24\t검증 세트 손실: 0.058838\t최선의 손실: 0.027374\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.056238\t최선의 손실: 0.027374\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.048062\t최선의 손실: 0.027374\t정확도: 98.91%\n",
+      "27\t검증 세트 손실: 0.049432\t최선의 손실: 0.027374\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.060831\t최선의 손실: 0.027374\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.076985\t최선의 손실: 0.027374\t정확도: 98.83%\n",
+      "30\t검증 세트 손실: 0.037587\t최선의 손실: 0.027374\t정확도: 99.22%\n",
+      "31\t검증 세트 손실: 0.042006\t최선의 손실: 0.027374\t정확도: 99.26%\n",
+      "32\t검증 세트 손실: 0.052185\t최선의 손실: 0.027374\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.055611\t최선의 손실: 0.027374\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.048830\t최선의 손실: 0.027374\t정확도: 98.98%\n",
+      "35\t검증 세트 손실: 0.046155\t최선의 손실: 0.027374\t정확도: 99.06%\n",
+      "36\t검증 세트 손실: 0.044093\t최선의 손실: 0.027374\t정확도: 99.10%\n",
+      "37\t검증 세트 손실: 0.040054\t최선의 손실: 0.027374\t정확도: 99.30%\n",
+      "38\t검증 세트 손실: 0.056405\t최선의 손실: 0.027374\t정확도: 99.18%\n",
+      "39\t검증 세트 손실: 0.034278\t최선의 손실: 0.027374\t정확도: 99.37%\n",
+      "40\t검증 세트 손실: 0.048722\t최선의 손실: 0.027374\t정확도: 99.02%\n",
+      "41\t검증 세트 손실: 0.042103\t최선의 손실: 0.027374\t정확도: 99.22%\n",
+      "42\t검증 세트 손실: 0.029475\t최선의 손실: 0.027374\t정확도: 99.34%\n",
+      "43\t검증 세트 손실: 0.052710\t최선의 손실: 0.027374\t정확도: 99.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.95, n_neurons=100, total=  30.7s\n",
-      "[CV] batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.95, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.073060\t최선의 손실: 0.073060\t정확도: 97.81%\n",
-      "1\t검증 세트 손실: 0.072289\t최선의 손실: 0.072289\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.080829\t최선의 손실: 0.072289\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 0.056608\t최선의 손실: 0.056608\t정확도: 98.44%\n",
-      "4\t검증 세트 손실: 0.049235\t최선의 손실: 0.049235\t정확도: 98.75%\n",
-      "5\t검증 세트 손실: 0.041254\t최선의 손실: 0.041254\t정확도: 98.91%\n",
-      "6\t검증 세트 손실: 0.051087\t최선의 손실: 0.041254\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.057906\t최선의 손실: 0.041254\t정확도: 98.55%\n",
-      "8\t검증 세트 손실: 0.043413\t최선의 손실: 0.041254\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.040378\t최선의 손실: 0.040378\t정확도: 99.06%\n",
-      "10\t검증 세트 손실: 0.052485\t최선의 손실: 0.040378\t정확도: 98.55%\n",
-      "11\t검증 세트 손실: 0.038838\t최선의 손실: 0.038838\t정확도: 99.10%\n",
-      "12\t검증 세트 손실: 0.048008\t최선의 손실: 0.038838\t정확도: 98.98%\n",
-      "13\t검증 세트 손실: 0.035621\t최선의 손실: 0.035621\t정확도: 98.94%\n",
-      "14\t검증 세트 손실: 0.050873\t최선의 손실: 0.035621\t정확도: 99.10%\n",
-      "15\t검증 세트 손실: 0.052030\t최선의 손실: 0.035621\t정확도: 98.91%\n",
-      "16\t검증 세트 손실: 0.039592\t최선의 손실: 0.035621\t정확도: 98.98%\n",
-      "17\t검증 세트 손실: 0.042565\t최선의 손실: 0.035621\t정확도: 99.10%\n",
-      "18\t검증 세트 손실: 0.043944\t최선의 손실: 0.035621\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.087699\t최선의 손실: 0.035621\t정확도: 98.20%\n",
-      "20\t검증 세트 손실: 0.041656\t최선의 손실: 0.035621\t정확도: 98.98%\n",
-      "21\t검증 세트 손실: 0.046441\t최선의 손실: 0.035621\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.040743\t최선의 손실: 0.035621\t정확도: 99.10%\n",
-      "23\t검증 세트 손실: 0.037096\t최선의 손실: 0.035621\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.042183\t최선의 손실: 0.035621\t정확도: 99.14%\n",
-      "25\t검증 세트 손실: 0.034152\t최선의 손실: 0.034152\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.047652\t최선의 손실: 0.034152\t정확도: 99.02%\n",
-      "27\t검증 세트 손실: 0.034417\t최선의 손실: 0.034152\t정확도: 99.30%\n",
-      "28\t검증 세트 손실: 0.053778\t최선의 손실: 0.034152\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.050380\t최선의 손실: 0.034152\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.031364\t최선의 손실: 0.031364\t정확도: 99.02%\n",
-      "31\t검증 세트 손실: 0.037012\t최선의 손실: 0.031364\t정확도: 99.06%\n",
-      "32\t검증 세트 손실: 0.059236\t최선의 손실: 0.031364\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.083257\t최선의 손실: 0.031364\t정확도: 98.51%\n",
-      "34\t검증 세트 손실: 0.046318\t최선의 손실: 0.031364\t정확도: 99.10%\n",
-      "35\t검증 세트 손실: 0.062063\t최선의 손실: 0.031364\t정확도: 99.14%\n",
-      "36\t검증 세트 손실: 0.048767\t최선의 손실: 0.031364\t정확도: 99.26%\n",
-      "37\t검증 세트 손실: 0.040511\t최선의 손실: 0.031364\t정확도: 99.22%\n",
-      "38\t검증 세트 손실: 0.055843\t최선의 손실: 0.031364\t정확도: 99.18%\n",
-      "39\t검증 세트 손실: 0.055099\t최선의 손실: 0.031364\t정확도: 99.14%\n",
-      "40\t검증 세트 손실: 0.044822\t최선의 손실: 0.031364\t정확도: 99.06%\n",
-      "41\t검증 세트 손실: 0.058774\t최선의 손실: 0.031364\t정확도: 98.67%\n",
-      "42\t검증 세트 손실: 0.039207\t최선의 손실: 0.031364\t정확도: 99.18%\n",
-      "43\t검증 세트 손실: 0.050659\t최선의 손실: 0.031364\t정확도: 99.06%\n",
-      "44\t검증 세트 손실: 0.087732\t최선의 손실: 0.031364\t정확도: 98.63%\n",
-      "45\t검증 세트 손실: 0.050720\t최선의 손실: 0.031364\t정확도: 98.98%\n",
-      "46\t검증 세트 손실: 0.060987\t최선의 손실: 0.031364\t정확도: 98.67%\n",
-      "47\t검증 세트 손실: 0.048017\t최선의 손실: 0.031364\t정확도: 98.98%\n",
-      "48\t검증 세트 손실: 0.044595\t최선의 손실: 0.031364\t정확도: 99.06%\n",
-      "49\t검증 세트 손실: 0.070803\t최선의 손실: 0.031364\t정확도: 98.44%\n",
-      "50\t검증 세트 손실: 0.054406\t최선의 손실: 0.031364\t정확도: 99.18%\n",
-      "51\t검증 세트 손실: 0.034039\t최선의 손실: 0.031364\t정확도: 99.34%\n",
+      "[CV]  batch_size=100, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.05, batch_norm_momentum=0.95, n_neurons=100, total=  31.4s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.120728\t최선의 손실: 0.120728\t정확도: 96.99%\n",
+      "1\t검증 세트 손실: 0.089678\t최선의 손실: 0.089678\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.057672\t최선의 손실: 0.057672\t정확도: 98.32%\n",
+      "3\t검증 세트 손실: 0.049541\t최선의 손실: 0.049541\t정확도: 98.63%\n",
+      "4\t검증 세트 손실: 0.032449\t최선의 손실: 0.032449\t정확도: 99.22%\n",
+      "5\t검증 세트 손실: 0.052080\t최선의 손실: 0.032449\t정확도: 98.55%\n",
+      "6\t검증 세트 손실: 0.035967\t최선의 손실: 0.032449\t정확도: 98.91%\n",
+      "7\t검증 세트 손실: 0.050800\t최선의 손실: 0.032449\t정확도: 98.71%\n",
+      "8\t검증 세트 손실: 0.042688\t최선의 손실: 0.032449\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.044966\t최선의 손실: 0.032449\t정확도: 98.94%\n",
+      "10\t검증 세트 손실: 0.032032\t최선의 손실: 0.032032\t정확도: 99.10%\n",
+      "11\t검증 세트 손실: 0.042707\t최선의 손실: 0.032032\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.043551\t최선의 손실: 0.032032\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.045382\t최선의 손실: 0.032032\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.043365\t최선의 손실: 0.032032\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.040248\t최선의 손실: 0.032032\t정확도: 99.02%\n",
+      "16\t검증 세트 손실: 0.039445\t최선의 손실: 0.032032\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.044653\t최선의 손실: 0.032032\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.050344\t최선의 손실: 0.032032\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 0.050435\t최선의 손실: 0.032032\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.031531\t최선의 손실: 0.031531\t정확도: 99.22%\n",
+      "21\t검증 세트 손실: 0.046859\t최선의 손실: 0.031531\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.040226\t최선의 손실: 0.031531\t정확도: 99.34%\n",
+      "23\t검증 세트 손실: 0.050093\t최선의 손실: 0.031531\t정확도: 98.91%\n",
+      "24\t검증 세트 손실: 0.036308\t최선의 손실: 0.031531\t정확도: 99.22%\n",
+      "25\t검증 세트 손실: 0.056806\t최선의 손실: 0.031531\t정확도: 99.02%\n",
+      "26\t검증 세트 손실: 0.037584\t최선의 손실: 0.031531\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.035145\t최선의 손실: 0.031531\t정확도: 99.10%\n",
+      "28\t검증 세트 손실: 0.040897\t최선의 손실: 0.031531\t정확도: 99.22%\n",
+      "29\t검증 세트 손실: 0.045633\t최선의 손실: 0.031531\t정확도: 99.18%\n",
+      "30\t검증 세트 손실: 0.037583\t최선의 손실: 0.031531\t정확도: 99.22%\n",
+      "31\t검증 세트 손실: 0.057074\t최선의 손실: 0.031531\t정확도: 99.26%\n",
+      "32\t검증 세트 손실: 0.050496\t최선의 손실: 0.031531\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.042895\t최선의 손실: 0.031531\t정확도: 98.98%\n",
+      "34\t검증 세트 손실: 0.045969\t최선의 손실: 0.031531\t정확도: 98.98%\n",
+      "35\t검증 세트 손실: 0.052275\t최선의 손실: 0.031531\t정확도: 99.14%\n",
+      "36\t검증 세트 손실: 0.046039\t최선의 손실: 0.031531\t정확도: 99.02%\n",
+      "37\t검증 세트 손실: 0.039924\t최선의 손실: 0.031531\t정확도: 99.10%\n",
+      "38\t검증 세트 손실: 0.037728\t최선의 손실: 0.031531\t정확도: 99.02%\n",
+      "39\t검증 세트 손실: 0.049730\t최선의 손실: 0.031531\t정확도: 99.18%\n",
+      "40\t검증 세트 손실: 0.039820\t최선의 손실: 0.031531\t정확도: 99.18%\n",
+      "41\t검증 세트 손실: 0.044517\t최선의 손실: 0.031531\t정확도: 99.18%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.95, n_neurons=100, total=  36.0s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.089559\t최선의 손실: 0.089559\t정확도: 97.65%\n",
-      "1\t검증 세트 손실: 0.085435\t최선의 손실: 0.085435\t정확도: 97.58%\n",
-      "2\t검증 세트 손실: 0.080662\t최선의 손실: 0.080662\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.039865\t최선의 손실: 0.039865\t정확도: 98.83%\n",
-      "4\t검증 세트 손실: 0.038742\t최선의 손실: 0.038742\t정확도: 98.83%\n",
-      "5\t검증 세트 손실: 0.032230\t최선의 손실: 0.032230\t정확도: 98.91%\n",
-      "6\t검증 세트 손실: 0.050201\t최선의 손실: 0.032230\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.044080\t최선의 손실: 0.032230\t정확도: 99.02%\n",
-      "8\t검증 세트 손실: 0.051874\t최선의 손실: 0.032230\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.065916\t최선의 손실: 0.032230\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.038084\t최선의 손실: 0.032230\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.041194\t최선의 손실: 0.032230\t정확도: 98.87%\n",
-      "12\t검증 세트 손실: 0.047798\t최선의 손실: 0.032230\t정확도: 98.87%\n",
-      "13\t검증 세트 손실: 0.061613\t최선의 손실: 0.032230\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.055782\t최선의 손실: 0.032230\t정확도: 98.79%\n",
-      "15\t검증 세트 손실: 0.039605\t최선의 손실: 0.032230\t정확도: 99.02%\n",
-      "16\t검증 세트 손실: 0.041481\t최선의 손실: 0.032230\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.038507\t최선의 손실: 0.032230\t정확도: 99.10%\n",
-      "18\t검증 세트 손실: 0.046375\t최선의 손실: 0.032230\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.037076\t최선의 손실: 0.032230\t정확도: 99.22%\n",
-      "20\t검증 세트 손실: 0.065205\t최선의 손실: 0.032230\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.048373\t최선의 손실: 0.032230\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.032757\t최선의 손실: 0.032230\t정확도: 99.22%\n",
-      "23\t검증 세트 손실: 0.045649\t최선의 손실: 0.032230\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.037992\t최선의 손실: 0.032230\t정확도: 99.14%\n",
-      "25\t검증 세트 손실: 0.050054\t최선의 손실: 0.032230\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.030954\t최선의 손실: 0.030954\t정확도: 99.18%\n",
-      "27\t검증 세트 손실: 0.027909\t최선의 손실: 0.027909\t정확도: 99.30%\n",
-      "28\t검증 세트 손실: 0.055606\t최선의 손실: 0.027909\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.032664\t최선의 손실: 0.027909\t정확도: 99.22%\n",
-      "30\t검증 세트 손실: 0.052190\t최선의 손실: 0.027909\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.037907\t최선의 손실: 0.027909\t정확도: 98.98%\n",
-      "32\t검증 세트 손실: 0.059856\t최선의 손실: 0.027909\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.039883\t최선의 손실: 0.027909\t정확도: 99.02%\n",
-      "34\t검증 세트 손실: 0.044943\t최선의 손실: 0.027909\t정확도: 99.06%\n",
-      "35\t검증 세트 손실: 0.037851\t최선의 손실: 0.027909\t정확도: 99.14%\n",
-      "36\t검증 세트 손실: 0.039134\t최선의 손실: 0.027909\t정확도: 99.14%\n",
-      "37\t검증 세트 손실: 0.032808\t최선의 손실: 0.027909\t정확도: 99.26%\n",
-      "38\t검증 세트 손실: 0.035879\t최선의 손실: 0.027909\t정확도: 99.26%\n",
-      "39\t검증 세트 손실: 0.040825\t최선의 손실: 0.027909\t정확도: 99.26%\n",
-      "40\t검증 세트 손실: 0.045070\t최선의 손실: 0.027909\t정확도: 99.26%\n",
-      "41\t검증 세트 손실: 0.033641\t최선의 손실: 0.027909\t정확도: 99.10%\n",
-      "42\t검증 세트 손실: 0.029633\t최선의 손실: 0.027909\t정확도: 99.41%\n",
-      "43\t검증 세트 손실: 0.034018\t최선의 손실: 0.027909\t정확도: 99.41%\n",
-      "44\t검증 세트 손실: 0.037357\t최선의 손실: 0.027909\t정확도: 99.26%\n",
-      "45\t검증 세트 손실: 0.046430\t최선의 손실: 0.027909\t정확도: 99.06%\n",
-      "46\t검증 세트 손실: 0.035151\t최선의 손실: 0.027909\t정확도: 99.41%\n",
-      "47\t검증 세트 손실: 0.047288\t최선의 손실: 0.027909\t정확도: 99.18%\n",
-      "48\t검증 세트 손실: 0.045369\t최선의 손실: 0.027909\t정확도: 99.10%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=140, total=  57.6s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.076922\t최선의 손실: 0.076922\t정확도: 97.58%\n",
+      "1\t검증 세트 손실: 0.047699\t최선의 손실: 0.047699\t정확도: 98.71%\n",
+      "2\t검증 세트 손실: 0.076837\t최선의 손실: 0.047699\t정확도: 97.85%\n",
+      "3\t검증 세트 손실: 0.049386\t최선의 손실: 0.047699\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.051959\t최선의 손실: 0.047699\t정확도: 98.40%\n",
+      "5\t검증 세트 손실: 0.063258\t최선의 손실: 0.047699\t정확도: 98.44%\n",
+      "6\t검증 세트 손실: 0.058751\t최선의 손실: 0.047699\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.053461\t최선의 손실: 0.047699\t정확도: 98.55%\n",
+      "8\t검증 세트 손실: 0.040989\t최선의 손실: 0.040989\t정확도: 99.06%\n",
+      "9\t검증 세트 손실: 0.044446\t최선의 손실: 0.040989\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.057169\t최선의 손실: 0.040989\t정확도: 98.67%\n",
+      "11\t검증 세트 손실: 0.029686\t최선의 손실: 0.029686\t정확도: 99.06%\n",
+      "12\t검증 세트 손실: 0.037495\t최선의 손실: 0.029686\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.036500\t최선의 손실: 0.029686\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.040993\t최선의 손실: 0.029686\t정확도: 99.06%\n",
+      "15\t검증 세트 손실: 0.031942\t최선의 손실: 0.029686\t정확도: 99.22%\n",
+      "16\t검증 세트 손실: 0.038559\t최선의 손실: 0.029686\t정확도: 99.02%\n",
+      "17\t검증 세트 손실: 0.047316\t최선의 손실: 0.029686\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.064495\t최선의 손실: 0.029686\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.041156\t최선의 손실: 0.029686\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.051389\t최선의 손실: 0.029686\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.041599\t최선의 손실: 0.029686\t정확도: 99.10%\n",
+      "22\t검증 세트 손실: 0.054735\t최선의 손실: 0.029686\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.041397\t최선의 손실: 0.029686\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.043333\t최선의 손실: 0.029686\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.042686\t최선의 손실: 0.029686\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.052627\t최선의 손실: 0.029686\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.042165\t최선의 손실: 0.029686\t정확도: 99.18%\n",
+      "28\t검증 세트 손실: 0.040906\t최선의 손실: 0.029686\t정확도: 99.14%\n",
+      "29\t검증 세트 손실: 0.048716\t최선의 손실: 0.029686\t정확도: 99.02%\n",
+      "30\t검증 세트 손실: 0.050454\t최선의 손실: 0.029686\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.043774\t최선의 손실: 0.029686\t정확도: 98.83%\n",
+      "32\t검증 세트 손실: 0.053104\t최선의 손실: 0.029686\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=140, total= 1.1min\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=140 \n"
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=140, total=  45.5s\n",
+      "[CV] batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.077960\t최선의 손실: 0.077960\t정확도: 97.77%\n",
+      "1\t검증 세트 손실: 0.049934\t최선의 손실: 0.049934\t정확도: 98.40%\n",
+      "2\t검증 세트 손실: 0.072009\t최선의 손실: 0.049934\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.044691\t최선의 손실: 0.044691\t정확도: 98.44%\n",
+      "4\t검증 세트 손실: 0.043032\t최선의 손실: 0.043032\t정확도: 98.75%\n",
+      "5\t검증 세트 손실: 0.043981\t최선의 손실: 0.043032\t정확도: 98.71%\n",
+      "6\t검증 세트 손실: 0.039328\t최선의 손실: 0.039328\t정확도: 98.79%\n",
+      "7\t검증 세트 손실: 0.058083\t최선의 손실: 0.039328\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.059025\t최선의 손실: 0.039328\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.041651\t최선의 손실: 0.039328\t정확도: 98.75%\n",
+      "10\t검증 세트 손실: 0.048105\t최선의 손실: 0.039328\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.056053\t최선의 손실: 0.039328\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.033695\t최선의 손실: 0.033695\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.033184\t최선의 손실: 0.033184\t정확도: 99.14%\n",
+      "14\t검증 세트 손실: 0.031534\t최선의 손실: 0.031534\t정확도: 99.02%\n",
+      "15\t검증 세트 손실: 0.040954\t최선의 손실: 0.031534\t정확도: 99.10%\n",
+      "16\t검증 세트 손실: 0.039815\t최선의 손실: 0.031534\t정확도: 99.06%\n",
+      "17\t검증 세트 손실: 0.041942\t최선의 손실: 0.031534\t정확도: 99.14%\n",
+      "18\t검증 세트 손실: 0.041736\t최선의 손실: 0.031534\t정확도: 98.98%\n",
+      "19\t검증 세트 손실: 0.041029\t최선의 손실: 0.031534\t정확도: 99.06%\n",
+      "20\t검증 세트 손실: 0.025829\t최선의 손실: 0.025829\t정확도: 99.26%\n",
+      "21\t검증 세트 손실: 0.031497\t최선의 손실: 0.025829\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.034219\t최선의 손실: 0.025829\t정확도: 99.22%\n",
+      "23\t검증 세트 손실: 0.041883\t최선의 손실: 0.025829\t정확도: 99.14%\n",
+      "24\t검증 세트 손실: 0.029971\t최선의 손실: 0.025829\t정확도: 99.26%\n",
+      "25\t검증 세트 손실: 0.036772\t최선의 손실: 0.025829\t정확도: 99.18%\n",
+      "26\t검증 세트 손실: 0.030113\t최선의 손실: 0.025829\t정확도: 99.30%\n",
+      "27\t검증 세트 손실: 0.030234\t최선의 손실: 0.025829\t정확도: 99.22%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.094887\t최선의 손실: 0.094887\t정확도: 96.91%\n",
-      "1\t검증 세트 손실: 0.059152\t최선의 손실: 0.059152\t정확도: 98.48%\n",
-      "2\t검증 세트 손실: 0.064659\t최선의 손실: 0.059152\t정확도: 98.20%\n",
-      "3\t검증 세트 손실: 0.041172\t최선의 손실: 0.041172\t정확도: 98.71%\n",
-      "4\t검증 세트 손실: 0.063362\t최선의 손실: 0.041172\t정확도: 98.40%\n",
-      "5\t검증 세트 손실: 0.084988\t최선의 손실: 0.041172\t정확도: 97.89%\n",
-      "6\t검증 세트 손실: 0.053450\t최선의 손실: 0.041172\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.048330\t최선의 손실: 0.041172\t정확도: 98.83%\n",
-      "8\t검증 세트 손실: 0.038851\t최선의 손실: 0.038851\t정확도: 99.02%\n",
-      "9\t검증 세트 손실: 0.039103\t최선의 손실: 0.038851\t정확도: 98.94%\n",
-      "10\t검증 세트 손실: 0.043044\t최선의 손실: 0.038851\t정확도: 98.98%\n",
-      "11\t검증 세트 손실: 0.050750\t최선의 손실: 0.038851\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.029104\t최선의 손실: 0.029104\t정확도: 99.26%\n",
-      "13\t검증 세트 손실: 0.042967\t최선의 손실: 0.029104\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.034125\t최선의 손실: 0.029104\t정확도: 99.10%\n",
-      "15\t검증 세트 손실: 0.028392\t최선의 손실: 0.028392\t정확도: 99.18%\n",
-      "16\t검증 세트 손실: 0.038463\t최선의 손실: 0.028392\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.034523\t최선의 손실: 0.028392\t정확도: 98.98%\n",
-      "18\t검증 세트 손실: 0.027578\t최선의 손실: 0.027578\t정확도: 99.37%\n",
-      "19\t검증 세트 손실: 0.033163\t최선의 손실: 0.027578\t정확도: 99.26%\n",
-      "20\t검증 세트 손실: 0.056958\t최선의 손실: 0.027578\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.036803\t최선의 손실: 0.027578\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.035721\t최선의 손실: 0.027578\t정확도: 99.10%\n",
-      "23\t검증 세트 손실: 0.045006\t최선의 손실: 0.027578\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.050523\t최선의 손실: 0.027578\t정확도: 98.91%\n",
-      "25\t검증 세트 손실: 0.044799\t최선의 손실: 0.027578\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.040805\t최선의 손실: 0.027578\t정확도: 98.98%\n",
-      "27\t검증 세트 손실: 0.046990\t최선의 손실: 0.027578\t정확도: 99.18%\n",
-      "28\t검증 세트 손실: 0.053135\t최선의 손실: 0.027578\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.042638\t최선의 손실: 0.027578\t정확도: 99.26%\n",
-      "30\t검증 세트 손실: 0.050238\t최선의 손실: 0.027578\t정확도: 99.26%\n",
-      "31\t검증 세트 손실: 0.054705\t최선의 손실: 0.027578\t정확도: 98.55%\n",
-      "32\t검증 세트 손실: 0.042561\t최선의 손실: 0.027578\t정확도: 99.10%\n",
-      "33\t검증 세트 손실: 0.056005\t최선의 손실: 0.027578\t정확도: 98.67%\n",
-      "34\t검증 세트 손실: 0.041645\t최선의 손실: 0.027578\t정확도: 99.18%\n",
-      "35\t검증 세트 손실: 0.040014\t최선의 손실: 0.027578\t정확도: 99.22%\n",
-      "36\t검증 세트 손실: 0.043339\t최선의 손실: 0.027578\t정확도: 99.14%\n",
-      "37\t검증 세트 손실: 0.043593\t최선의 손실: 0.027578\t정확도: 98.91%\n",
-      "38\t검증 세트 손실: 0.055205\t최선의 손실: 0.027578\t정확도: 98.91%\n",
-      "39\t검증 세트 손실: 0.077120\t최선의 손실: 0.027578\t정확도: 98.55%\n",
+      "28\t검증 세트 손실: 0.039314\t최선의 손실: 0.025829\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.038838\t최선의 손실: 0.025829\t정확도: 99.14%\n",
+      "30\t검증 세트 손실: 0.057772\t최선의 손실: 0.025829\t정확도: 98.94%\n",
+      "31\t검증 세트 손실: 0.051400\t최선의 손실: 0.025829\t정확도: 99.10%\n",
+      "32\t검증 세트 손실: 0.046439\t최선의 손실: 0.025829\t정확도: 99.14%\n",
+      "33\t검증 세트 손실: 0.036503\t최선의 손실: 0.025829\t정확도: 99.41%\n",
+      "34\t검증 세트 손실: 0.046981\t최선의 손실: 0.025829\t정확도: 99.06%\n",
+      "35\t검증 세트 손실: 0.034807\t최선의 손실: 0.025829\t정확도: 99.45%\n",
+      "36\t검증 세트 손실: 0.042073\t최선의 손실: 0.025829\t정확도: 99.14%\n",
+      "37\t검증 세트 손실: 0.028926\t최선의 손실: 0.025829\t정확도: 99.14%\n",
+      "38\t검증 세트 손실: 0.031296\t최선의 손실: 0.025829\t정확도: 99.34%\n",
+      "39\t검증 세트 손실: 0.038672\t최선의 손실: 0.025829\t정확도: 99.30%\n",
+      "40\t검증 세트 손실: 0.047932\t최선의 손실: 0.025829\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.035917\t최선의 손실: 0.025829\t정확도: 99.22%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=140, total=  54.0s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.076223\t최선의 손실: 0.076223\t정확도: 97.97%\n",
-      "1\t검증 세트 손실: 0.061043\t최선의 손실: 0.061043\t정확도: 98.05%\n",
-      "2\t검증 세트 손실: 0.054684\t최선의 손실: 0.054684\t정확도: 98.24%\n",
-      "3\t검증 세트 손실: 0.039826\t최선의 손실: 0.039826\t정확도: 98.75%\n",
-      "4\t검증 세트 손실: 0.042562\t최선의 손실: 0.039826\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.033397\t최선의 손실: 0.033397\t정확도: 98.91%\n",
-      "6\t검증 세트 손실: 0.040267\t최선의 손실: 0.033397\t정확도: 98.91%\n",
-      "7\t검증 세트 손실: 0.029846\t최선의 손실: 0.029846\t정확도: 98.94%\n",
-      "8\t검증 세트 손실: 0.039670\t최선의 손실: 0.029846\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.032618\t최선의 손실: 0.029846\t정확도: 99.10%\n",
-      "10\t검증 세트 손실: 0.050199\t최선의 손실: 0.029846\t정확도: 98.59%\n",
-      "11\t검증 세트 손실: 0.035416\t최선의 손실: 0.029846\t정확도: 99.14%\n",
-      "12\t검증 세트 손실: 0.029272\t최선의 손실: 0.029272\t정확도: 99.37%\n",
-      "13\t검증 세트 손실: 0.042632\t최선의 손실: 0.029272\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.053035\t최선의 손실: 0.029272\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.038965\t최선의 손실: 0.029272\t정확도: 99.14%\n",
-      "16\t검증 세트 손실: 0.038077\t최선의 손실: 0.029272\t정확도: 99.10%\n",
-      "17\t검증 세트 손실: 0.045851\t최선의 손실: 0.029272\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.036896\t최선의 손실: 0.029272\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.029934\t최선의 손실: 0.029272\t정확도: 99.26%\n",
-      "20\t검증 세트 손실: 0.057594\t최선의 손실: 0.029272\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.029742\t최선의 손실: 0.029272\t정확도: 99.22%\n",
-      "22\t검증 세트 손실: 0.038546\t최선의 손실: 0.029272\t정확도: 99.10%\n",
-      "23\t검증 세트 손실: 0.046748\t최선의 손실: 0.029272\t정확도: 99.02%\n",
-      "24\t검증 세트 손실: 0.034875\t최선의 손실: 0.029272\t정확도: 99.26%\n",
-      "25\t검증 세트 손실: 0.024620\t최선의 손실: 0.024620\t정확도: 99.26%\n",
-      "26\t검증 세트 손실: 0.036435\t최선의 손실: 0.024620\t정확도: 99.26%\n",
-      "27\t검증 세트 손실: 0.037765\t최선의 손실: 0.024620\t정확도: 99.18%\n",
-      "28\t검증 세트 손실: 0.042079\t최선의 손실: 0.024620\t정확도: 99.18%\n",
-      "29\t검증 세트 손실: 0.033512\t최선의 손실: 0.024620\t정확도: 99.22%\n",
-      "30\t검증 세트 손실: 0.033169\t최선의 손실: 0.024620\t정확도: 99.02%\n",
-      "31\t검증 세트 손실: 0.071622\t최선의 손실: 0.024620\t정확도: 98.32%\n",
-      "32\t검증 세트 손실: 0.039231\t최선의 손실: 0.024620\t정확도: 98.98%\n",
-      "33\t검증 세트 손실: 0.035208\t최선의 손실: 0.024620\t정확도: 99.26%\n",
-      "34\t검증 세트 손실: 0.074237\t최선의 손실: 0.024620\t정확도: 98.83%\n",
-      "35\t검증 세트 손실: 0.050062\t최선의 손실: 0.024620\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.040115\t최선의 손실: 0.024620\t정확도: 99.22%\n",
-      "37\t검증 세트 손실: 0.029954\t최선의 손실: 0.024620\t정확도: 99.26%\n",
-      "38\t검증 세트 손실: 0.049628\t최선의 손실: 0.024620\t정확도: 99.14%\n",
-      "39\t검증 세트 손실: 0.042190\t최선의 손실: 0.024620\t정확도: 99.18%\n",
-      "40\t검증 세트 손실: 0.038912\t최선의 손실: 0.024620\t정확도: 99.41%\n",
-      "41\t검증 세트 손실: 0.044728\t최선의 손실: 0.024620\t정확도: 99.18%\n",
-      "42\t검증 세트 손실: 0.044403\t최선의 손실: 0.024620\t정확도: 99.30%\n",
-      "43\t검증 세트 손실: 0.045726\t최선의 손실: 0.024620\t정확도: 99.22%\n",
-      "44\t검증 세트 손실: 0.048149\t최선의 손실: 0.024620\t정확도: 99.22%\n",
-      "45\t검증 세트 손실: 0.027995\t최선의 손실: 0.024620\t정확도: 99.45%\n",
-      "46\t검증 세트 손실: 0.033623\t최선의 손실: 0.024620\t정확도: 99.02%\n",
+      "[CV]  batch_size=50, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=140, total=  57.6s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.090237\t최선의 손실: 0.090237\t정확도: 97.15%\n",
+      "1\t검증 세트 손실: 0.080839\t최선의 손실: 0.080839\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.083763\t최선의 손실: 0.080839\t정확도: 97.38%\n",
+      "3\t검증 세트 손실: 0.053788\t최선의 손실: 0.053788\t정확도: 98.05%\n",
+      "4\t검증 세트 손실: 0.057611\t최선의 손실: 0.053788\t정확도: 98.20%\n",
+      "5\t검증 세트 손실: 0.082613\t최선의 손실: 0.053788\t정확도: 97.73%\n",
+      "6\t검증 세트 손실: 0.051755\t최선의 손실: 0.051755\t정확도: 98.16%\n",
+      "7\t검증 세트 손실: 0.060546\t최선의 손실: 0.051755\t정확도: 98.40%\n",
+      "8\t검증 세트 손실: 0.059191\t최선의 손실: 0.051755\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.069791\t최선의 손실: 0.051755\t정확도: 98.20%\n",
+      "10\t검증 세트 손실: 0.059267\t최선의 손실: 0.051755\t정확도: 98.71%\n",
+      "11\t검증 세트 손실: 0.079011\t최선의 손실: 0.051755\t정확도: 98.16%\n",
+      "12\t검증 세트 손실: 0.069839\t최선의 손실: 0.051755\t정확도: 98.40%\n",
+      "13\t검증 세트 손실: 0.061013\t최선의 손실: 0.051755\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.044893\t최선의 손실: 0.044893\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.060636\t최선의 손실: 0.044893\t정확도: 98.59%\n",
+      "16\t검증 세트 손실: 0.071628\t최선의 손실: 0.044893\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.070100\t최선의 손실: 0.044893\t정확도: 98.48%\n",
+      "18\t검증 세트 손실: 0.057732\t최선의 손실: 0.044893\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.069883\t최선의 손실: 0.044893\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.069310\t최선의 손실: 0.044893\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.076700\t최선의 손실: 0.044893\t정확도: 98.24%\n",
+      "22\t검증 세트 손실: 0.066810\t최선의 손실: 0.044893\t정확도: 98.55%\n",
+      "23\t검증 세트 손실: 0.069755\t최선의 손실: 0.044893\t정확도: 98.59%\n",
+      "24\t검증 세트 손실: 0.066601\t최선의 손실: 0.044893\t정확도: 98.44%\n",
+      "25\t검증 세트 손실: 0.055007\t최선의 손실: 0.044893\t정확도: 98.59%\n",
+      "26\t검증 세트 손실: 0.071136\t최선의 손실: 0.044893\t정확도: 98.55%\n",
+      "27\t검증 세트 손실: 0.062546\t최선의 손실: 0.044893\t정확도: 98.55%\n",
+      "28\t검증 세트 손실: 0.069028\t최선의 손실: 0.044893\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.064673\t최선의 손실: 0.044893\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.071354\t최선의 손실: 0.044893\t정확도: 98.32%\n",
+      "31\t검증 세트 손실: 0.087218\t최선의 손실: 0.044893\t정확도: 98.24%\n",
+      "32\t검증 세트 손실: 0.078688\t최선의 손실: 0.044893\t정확도: 98.44%\n",
+      "33\t검증 세트 손실: 0.124537\t최선의 손실: 0.044893\t정확도: 97.46%\n",
+      "34\t검증 세트 손실: 0.132266\t최선의 손실: 0.044893\t정확도: 96.99%\n",
+      "35\t검증 세트 손실: 0.054596\t최선의 손실: 0.044893\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=140, total= 1.1min\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.099963\t최선의 손실: 0.099963\t정확도: 96.36%\n",
-      "1\t검증 세트 손실: 0.074627\t최선의 손실: 0.074627\t정확도: 97.34%\n",
-      "2\t검증 세트 손실: 0.082796\t최선의 손실: 0.074627\t정확도: 97.34%\n",
-      "3\t검증 세트 손실: 0.067983\t최선의 손실: 0.067983\t정확도: 97.69%\n",
-      "4\t검증 세트 손실: 0.061731\t최선의 손실: 0.061731\t정확도: 97.97%\n",
-      "5\t검증 세트 손실: 0.067676\t최선의 손실: 0.061731\t정확도: 97.73%\n",
-      "6\t검증 세트 손실: 0.071704\t최선의 손실: 0.061731\t정확도: 97.58%\n",
-      "7\t검증 세트 손실: 0.061428\t최선의 손실: 0.061428\t정확도: 98.08%\n",
-      "8\t검증 세트 손실: 0.059379\t최선의 손실: 0.059379\t정확도: 98.16%\n",
-      "9\t검증 세트 손실: 0.054492\t최선의 손실: 0.054492\t정확도: 98.87%\n",
-      "10\t검증 세트 손실: 0.049814\t최선의 손실: 0.049814\t정확도: 98.48%\n",
-      "11\t검증 세트 손실: 0.062924\t최선의 손실: 0.049814\t정확도: 98.40%\n",
-      "12\t검증 세트 손실: 0.065630\t최선의 손실: 0.049814\t정확도: 98.28%\n",
-      "13\t검증 세트 손실: 0.072134\t최선의 손실: 0.049814\t정확도: 97.89%\n",
-      "14\t검증 세트 손실: 0.068499\t최선의 손실: 0.049814\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.064401\t최선의 손실: 0.049814\t정확도: 98.28%\n",
-      "16\t검증 세트 손실: 0.064301\t최선의 손실: 0.049814\t정확도: 98.36%\n",
-      "17\t검증 세트 손실: 0.068350\t최선의 손실: 0.049814\t정확도: 98.08%\n",
-      "18\t검증 세트 손실: 0.055788\t최선의 손실: 0.049814\t정확도: 98.48%\n",
-      "19\t검증 세트 손실: 0.070710\t최선의 손실: 0.049814\t정확도: 98.20%\n",
-      "20\t검증 세트 손실: 0.056979\t최선의 손실: 0.049814\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.067842\t최선의 손실: 0.049814\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.076234\t최선의 손실: 0.049814\t정확도: 98.40%\n",
-      "23\t검증 세트 손실: 0.061975\t최선의 손실: 0.049814\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.065544\t최선의 손실: 0.049814\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.094019\t최선의 손실: 0.049814\t정확도: 98.20%\n",
-      "26\t검증 세트 손실: 0.077219\t최선의 손실: 0.049814\t정확도: 98.63%\n",
-      "27\t검증 세트 손실: 0.071801\t최선의 손실: 0.049814\t정확도: 98.48%\n",
-      "28\t검증 세트 손실: 0.062018\t최선의 손실: 0.049814\t정확도: 98.71%\n",
-      "29\t검증 세트 손실: 0.057769\t최선의 손실: 0.049814\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.063498\t최선의 손실: 0.049814\t정확도: 98.79%\n",
-      "31\t검증 세트 손실: 0.088268\t최선의 손실: 0.049814\t정확도: 98.24%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=30, total=   7.3s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.089812\t최선의 손실: 0.089812\t정확도: 97.38%\n",
+      "1\t검증 세트 손실: 0.070987\t최선의 손실: 0.070987\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.060570\t최선의 손실: 0.060570\t정확도: 97.77%\n",
+      "3\t검증 세트 손실: 0.055580\t최선의 손실: 0.055580\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.066589\t최선의 손실: 0.055580\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 0.055780\t최선의 손실: 0.055580\t정확도: 98.40%\n",
+      "6\t검증 세트 손실: 0.056136\t최선의 손실: 0.055580\t정확도: 98.32%\n",
+      "7\t검증 세트 손실: 0.058078\t최선의 손실: 0.055580\t정확도: 98.20%\n",
+      "8\t검증 세트 손실: 0.053919\t최선의 손실: 0.053919\t정확도: 98.59%\n",
+      "9\t검증 세트 손실: 0.053761\t최선의 손실: 0.053761\t정확도: 98.48%\n",
+      "10\t검증 세트 손실: 0.051593\t최선의 손실: 0.051593\t정확도: 98.48%\n",
+      "11\t검증 세트 손실: 0.089890\t최선의 손실: 0.051593\t정확도: 98.08%\n",
+      "12\t검증 세트 손실: 0.065910\t최선의 손실: 0.051593\t정확도: 98.48%\n",
+      "13\t검증 세트 손실: 0.054158\t최선의 손실: 0.051593\t정확도: 98.51%\n",
+      "14\t검증 세트 손실: 0.061032\t최선의 손실: 0.051593\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.072106\t최선의 손실: 0.051593\t정확도: 98.28%\n",
+      "16\t검증 세트 손실: 0.054282\t최선의 손실: 0.051593\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.060386\t최선의 손실: 0.051593\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.060527\t최선의 손실: 0.051593\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.057975\t최선의 손실: 0.051593\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.046821\t최선의 손실: 0.046821\t정확도: 99.10%\n",
+      "21\t검증 세트 손실: 0.059871\t최선의 손실: 0.046821\t정확도: 98.75%\n",
+      "22\t검증 세트 손실: 0.072651\t최선의 손실: 0.046821\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.068982\t최선의 손실: 0.046821\t정확도: 98.59%\n",
+      "24\t검증 세트 손실: 0.070575\t최선의 손실: 0.046821\t정확도: 98.63%\n",
+      "25\t검증 세트 손실: 0.070847\t최선의 손실: 0.046821\t정확도: 98.24%\n",
+      "26\t검증 세트 손실: 0.063566\t최선의 손실: 0.046821\t정확도: 98.55%\n",
+      "27\t검증 세트 손실: 0.058483\t최선의 손실: 0.046821\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.077500\t최선의 손실: 0.046821\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.067177\t최선의 손실: 0.046821\t정확도: 98.67%\n",
+      "30\t검증 세트 손실: 0.067068\t최선의 손실: 0.046821\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.086174\t최선의 손실: 0.046821\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.063737\t최선의 손실: 0.046821\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.081414\t최선의 손실: 0.046821\t정확도: 98.67%\n",
+      "34\t검증 세트 손실: 0.064474\t최선의 손실: 0.046821\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.068381\t최선의 손실: 0.046821\t정확도: 98.55%\n",
+      "36\t검증 세트 손실: 0.097818\t최선의 손실: 0.046821\t정확도: 98.28%\n",
+      "37\t검증 세트 손실: 0.072690\t최선의 손실: 0.046821\t정확도: 98.67%\n",
+      "38\t검증 세트 손실: 0.069451\t최선의 손실: 0.046821\t정확도: 98.67%\n",
+      "39\t검증 세트 손실: 0.062061\t최선의 손실: 0.046821\t정확도: 98.67%\n",
+      "40\t검증 세트 손실: 0.083101\t최선의 손실: 0.046821\t정확도: 98.44%\n",
+      "41\t검증 세트 손실: 0.082603\t최선의 손실: 0.046821\t정확도: 98.63%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=30, total=   6.4s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.093841\t최선의 손실: 0.093841\t정확도: 96.79%\n",
-      "1\t검증 세트 손실: 0.071806\t최선의 손실: 0.071806\t정확도: 97.62%\n",
-      "2\t검증 세트 손실: 0.062077\t최선의 손실: 0.062077\t정확도: 97.97%\n",
-      "3\t검증 세트 손실: 0.053344\t최선의 손실: 0.053344\t정확도: 98.28%\n",
-      "4\t검증 세트 손실: 0.071642\t최선의 손실: 0.053344\t정확도: 97.77%\n",
-      "5\t검증 세트 손실: 0.063599\t최선의 손실: 0.053344\t정확도: 97.97%\n",
-      "6\t검증 세트 손실: 0.052136\t최선의 손실: 0.052136\t정확도: 98.36%\n",
-      "7\t검증 세트 손실: 0.056452\t최선의 손실: 0.052136\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.073832\t최선의 손실: 0.052136\t정확도: 98.12%\n",
-      "9\t검증 세트 손실: 0.068648\t최선의 손실: 0.052136\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.051504\t최선의 손실: 0.051504\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.064048\t최선의 손실: 0.051504\t정확도: 98.28%\n",
-      "12\t검증 세트 손실: 0.065522\t최선의 손실: 0.051504\t정확도: 98.44%\n",
-      "13\t검증 세트 손실: 0.060514\t최선의 손실: 0.051504\t정확도: 98.40%\n",
-      "14\t검증 세트 손실: 0.058635\t최선의 손실: 0.051504\t정확도: 98.44%\n",
-      "15\t검증 세트 손실: 0.068924\t최선의 손실: 0.051504\t정확도: 98.59%\n",
-      "16\t검증 세트 손실: 0.073640\t최선의 손실: 0.051504\t정확도: 98.24%\n",
-      "17\t검증 세트 손실: 0.076705\t최선의 손실: 0.051504\t정확도: 98.44%\n",
-      "18\t검증 세트 손실: 0.066045\t최선의 손실: 0.051504\t정확도: 98.20%\n",
-      "19\t검증 세트 손실: 0.063588\t최선의 손실: 0.051504\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.054605\t최선의 손실: 0.051504\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.087370\t최선의 손실: 0.051504\t정확도: 97.85%\n",
-      "22\t검증 세트 손실: 0.049159\t최선의 손실: 0.049159\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.065457\t최선의 손실: 0.049159\t정확도: 98.48%\n",
-      "24\t검증 세트 손실: 0.069486\t최선의 손실: 0.049159\t정확도: 98.63%\n",
-      "25\t검증 세트 손실: 0.088930\t최선의 손실: 0.049159\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.064930\t최선의 손실: 0.049159\t정확도: 98.59%\n",
-      "27\t검증 세트 손실: 0.064584\t최선의 손실: 0.049159\t정확도: 98.79%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=30, total=   8.4s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.094133\t최선의 손실: 0.094133\t정확도: 96.95%\n",
+      "1\t검증 세트 손실: 0.071113\t최선의 손실: 0.071113\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.056921\t최선의 손실: 0.056921\t정확도: 98.01%\n",
+      "3\t검증 세트 손실: 0.054044\t최선의 손실: 0.054044\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.048515\t최선의 손실: 0.048515\t정확도: 98.20%\n",
+      "5\t검증 세트 손실: 0.068610\t최선의 손실: 0.048515\t정확도: 97.89%\n",
+      "6\t검증 세트 손실: 0.057992\t최선의 손실: 0.048515\t정확도: 98.51%\n",
+      "7\t검증 세트 손실: 0.054169\t최선의 손실: 0.048515\t정확도: 98.48%\n",
+      "8\t검증 세트 손실: 0.054871\t최선의 손실: 0.048515\t정확도: 98.55%\n",
+      "9\t검증 세트 손실: 0.057000\t최선의 손실: 0.048515\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.060326\t최선의 손실: 0.048515\t정확도: 98.36%\n",
+      "11\t검증 세트 손실: 0.054561\t최선의 손실: 0.048515\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.052130\t최선의 손실: 0.048515\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.043861\t최선의 손실: 0.043861\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.057703\t최선의 손실: 0.043861\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.053402\t최선의 손실: 0.043861\t정확도: 98.51%\n",
+      "16\t검증 세트 손실: 0.064207\t최선의 손실: 0.043861\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.059179\t최선의 손실: 0.043861\t정확도: 98.48%\n",
+      "18\t검증 세트 손실: 0.040435\t최선의 손실: 0.040435\t정확도: 99.10%\n",
+      "19\t검증 세트 손실: 0.037912\t최선의 손실: 0.037912\t정확도: 98.98%\n",
+      "20\t검증 세트 손실: 0.033243\t최선의 손실: 0.033243\t정확도: 99.18%\n",
+      "21\t검증 세트 손실: 0.035636\t최선의 손실: 0.033243\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.035391\t최선의 손실: 0.033243\t정확도: 99.14%\n",
+      "23\t검증 세트 손실: 0.032128\t최선의 손실: 0.032128\t정확도: 99.30%\n",
+      "24\t검증 세트 손실: 0.037616\t최선의 손실: 0.032128\t정확도: 99.22%\n",
+      "25\t검증 세트 손실: 0.039664\t최선의 손실: 0.032128\t정확도: 99.18%\n",
+      "26\t검증 세트 손실: 0.038103\t최선의 손실: 0.032128\t정확도: 99.30%\n",
+      "27\t검증 세트 손실: 0.047311\t최선의 손실: 0.032128\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.050507\t최선의 손실: 0.032128\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.047142\t최선의 손실: 0.032128\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.077076\t최선의 손실: 0.032128\t정확도: 98.51%\n",
+      "31\t검증 세트 손실: 0.073118\t최선의 손실: 0.032128\t정확도: 98.40%\n",
+      "32\t검증 세트 손실: 0.050524\t최선의 손실: 0.032128\t정확도: 98.71%\n",
+      "33\t검증 세트 손실: 0.049633\t최선의 손실: 0.032128\t정확도: 98.83%\n",
+      "34\t검증 세트 손실: 0.055527\t최선의 손실: 0.032128\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.047670\t최선의 손실: 0.032128\t정확도: 98.94%\n",
+      "36\t검증 세트 손실: 0.056846\t최선의 손실: 0.032128\t정확도: 98.98%\n",
+      "37\t검증 세트 손실: 0.048292\t최선의 손실: 0.032128\t정확도: 98.98%\n",
+      "38\t검증 세트 손실: 0.056828\t최선의 손실: 0.032128\t정확도: 98.87%\n",
+      "39\t검증 세트 손실: 0.055099\t최선의 손실: 0.032128\t정확도: 98.79%\n",
+      "40\t검증 세트 손실: 0.063287\t최선의 손실: 0.032128\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.069727\t최선의 손실: 0.032128\t정확도: 98.63%\n",
+      "42\t검증 세트 손실: 0.071060\t최선의 손실: 0.032128\t정확도: 98.67%\n",
+      "43\t검증 세트 손실: 0.066284\t최선의 손실: 0.032128\t정확도: 98.87%\n",
+      "44\t검증 세트 손실: 0.068756\t최선의 손실: 0.032128\t정확도: 98.55%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=30, total=   8.9s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.103256\t최선의 손실: 0.103256\t정확도: 97.07%\n",
+      "1\t검증 세트 손실: 0.076025\t최선의 손실: 0.076025\t정확도: 97.73%\n",
+      "2\t검증 세트 손실: 0.054421\t최선의 손실: 0.054421\t정확도: 98.36%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "28\t검증 세트 손실: 0.080723\t최선의 손실: 0.049159\t정확도: 98.63%\n",
-      "29\t검증 세트 손실: 0.076185\t최선의 손실: 0.049159\t정확도: 98.59%\n",
-      "30\t검증 세트 손실: 0.065683\t최선의 손실: 0.049159\t정확도: 98.51%\n",
-      "31\t검증 세트 손실: 0.073492\t최선의 손실: 0.049159\t정확도: 98.51%\n",
-      "32\t검증 세트 손실: 0.065158\t최선의 손실: 0.049159\t정확도: 98.94%\n",
-      "33\t검증 세트 손실: 0.070979\t최선의 손실: 0.049159\t정확도: 98.79%\n",
-      "34\t검증 세트 손실: 0.067718\t최선의 손실: 0.049159\t정확도: 98.51%\n",
-      "35\t검증 세트 손실: 0.060331\t최선의 손실: 0.049159\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.068064\t최선의 손실: 0.049159\t정확도: 98.75%\n",
-      "37\t검증 세트 손실: 0.077263\t최선의 손실: 0.049159\t정확도: 98.44%\n",
-      "38\t검증 세트 손실: 0.063675\t최선의 손실: 0.049159\t정확도: 98.75%\n",
-      "39\t검증 세트 손실: 0.070341\t최선의 손실: 0.049159\t정확도: 98.51%\n",
-      "40\t검증 세트 손실: 0.069054\t최선의 손실: 0.049159\t정확도: 98.63%\n",
-      "41\t검증 세트 손실: 0.070470\t최선의 손실: 0.049159\t정확도: 98.75%\n",
-      "42\t검증 세트 손실: 0.067235\t최선의 손실: 0.049159\t정확도: 98.79%\n",
-      "43\t검증 세트 손실: 0.069362\t최선의 손실: 0.049159\t정확도: 98.75%\n",
+      "3\t검증 세트 손실: 0.044547\t최선의 손실: 0.044547\t정확도: 98.67%\n",
+      "4\t검증 세트 손실: 0.056698\t최선의 손실: 0.044547\t정확도: 98.20%\n",
+      "5\t검증 세트 손실: 0.063571\t최선의 손실: 0.044547\t정확도: 98.24%\n",
+      "6\t검증 세트 손실: 0.052533\t최선의 손실: 0.044547\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.040790\t최선의 손실: 0.040790\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.060944\t최선의 손실: 0.040790\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.069921\t최선의 손실: 0.040790\t정확도: 98.40%\n",
+      "10\t검증 세트 손실: 0.051709\t최선의 손실: 0.040790\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.041268\t최선의 손실: 0.040790\t정확도: 99.02%\n",
+      "12\t검증 세트 손실: 0.060717\t최선의 손실: 0.040790\t정확도: 98.48%\n",
+      "13\t검증 세트 손실: 0.061758\t최선의 손실: 0.040790\t정확도: 98.59%\n",
+      "14\t검증 세트 손실: 0.047861\t최선의 손실: 0.040790\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.123977\t최선의 손실: 0.040790\t정확도: 97.65%\n",
+      "16\t검증 세트 손실: 0.052441\t최선의 손실: 0.040790\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.062721\t최선의 손실: 0.040790\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.131143\t최선의 손실: 0.040790\t정확도: 97.07%\n",
+      "19\t검증 세트 손실: 0.084946\t최선의 손실: 0.040790\t정확도: 98.05%\n",
+      "20\t검증 세트 손실: 0.058462\t최선의 손실: 0.040790\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.070593\t최선의 손실: 0.040790\t정확도: 98.59%\n",
+      "22\t검증 세트 손실: 0.067643\t최선의 손실: 0.040790\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.059054\t최선의 손실: 0.040790\t정확도: 98.83%\n",
+      "24\t검증 세트 손실: 0.047584\t최선의 손실: 0.040790\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.047819\t최선의 손실: 0.040790\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.047251\t최선의 손실: 0.040790\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.053085\t최선의 손실: 0.040790\t정확도: 99.10%\n",
+      "28\t검증 세트 손실: 0.052323\t최선의 손실: 0.040790\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=30, total=   8.3s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.102406\t최선의 손실: 0.102406\t정확도: 96.60%\n",
-      "1\t검증 세트 손실: 0.073082\t최선의 손실: 0.073082\t정확도: 97.58%\n",
-      "2\t검증 세트 손실: 0.073357\t최선의 손실: 0.073082\t정확도: 97.54%\n",
-      "3\t검증 세트 손실: 0.070479\t최선의 손실: 0.070479\t정확도: 97.62%\n",
-      "4\t검증 세트 손실: 0.056906\t최선의 손실: 0.056906\t정확도: 98.01%\n",
-      "5\t검증 세트 손실: 0.068903\t최선의 손실: 0.056906\t정확도: 97.77%\n",
-      "6\t검증 세트 손실: 0.054467\t최선의 손실: 0.054467\t정확도: 98.24%\n",
-      "7\t검증 세트 손실: 0.057947\t최선의 손실: 0.054467\t정확도: 98.16%\n",
-      "8\t검증 세트 손실: 0.076130\t최선의 손실: 0.054467\t정확도: 97.77%\n",
-      "9\t검증 세트 손실: 0.052465\t최선의 손실: 0.052465\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.067745\t최선의 손실: 0.052465\t정확도: 98.12%\n",
-      "11\t검증 세트 손실: 0.058365\t최선의 손실: 0.052465\t정확도: 98.40%\n",
-      "12\t검증 세트 손실: 0.073015\t최선의 손실: 0.052465\t정확도: 97.93%\n",
-      "13\t검증 세트 손실: 0.064160\t최선의 손실: 0.052465\t정확도: 98.32%\n",
-      "14\t검증 세트 손실: 0.053537\t최선의 손실: 0.052465\t정확도: 98.32%\n",
-      "15\t검증 세트 손실: 0.072532\t최선의 손실: 0.052465\t정확도: 98.28%\n",
-      "16\t검증 세트 손실: 0.076833\t최선의 손실: 0.052465\t정확도: 98.08%\n",
-      "17\t검증 세트 손실: 0.085550\t최선의 손실: 0.052465\t정확도: 98.16%\n",
-      "18\t검증 세트 손실: 0.061083\t최선의 손실: 0.052465\t정확도: 98.40%\n",
-      "19\t검증 세트 손실: 0.063664\t최선의 손실: 0.052465\t정확도: 98.36%\n",
-      "20\t검증 세트 손실: 0.064253\t최선의 손실: 0.052465\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.060563\t최선의 손실: 0.052465\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.056619\t최선의 손실: 0.052465\t정확도: 98.67%\n",
-      "23\t검증 세트 손실: 0.063501\t최선의 손실: 0.052465\t정확도: 98.59%\n",
-      "24\t검증 세트 손실: 0.060408\t최선의 손실: 0.052465\t정확도: 98.51%\n",
-      "25\t검증 세트 손실: 0.076040\t최선의 손실: 0.052465\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.086027\t최선의 손실: 0.052465\t정확도: 98.08%\n",
-      "27\t검증 세트 손실: 0.081232\t최선의 손실: 0.052465\t정확도: 98.36%\n",
-      "28\t검증 세트 손실: 0.058142\t최선의 손실: 0.052465\t정확도: 98.44%\n",
-      "29\t검증 세트 손실: 0.063319\t최선의 손실: 0.052465\t정확도: 98.48%\n",
-      "30\t검증 세트 손실: 0.065441\t최선의 손실: 0.052465\t정확도: 98.55%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=140, total=  21.2s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.126947\t최선의 손실: 0.126947\t정확도: 95.97%\n",
+      "1\t검증 세트 손실: 0.058402\t최선의 손실: 0.058402\t정확도: 97.81%\n",
+      "2\t검증 세트 손실: 0.049810\t최선의 손실: 0.049810\t정확도: 98.51%\n",
+      "3\t검증 세트 손실: 0.089860\t최선의 손실: 0.049810\t정확도: 97.50%\n",
+      "4\t검증 세트 손실: 0.065571\t최선의 손실: 0.049810\t정확도: 97.89%\n",
+      "5\t검증 세트 손실: 0.044949\t최선의 손실: 0.044949\t정확도: 98.79%\n",
+      "6\t검증 세트 손실: 0.056184\t최선의 손실: 0.044949\t정확도: 98.51%\n",
+      "7\t검증 세트 손실: 0.051249\t최선의 손실: 0.044949\t정확도: 98.63%\n",
+      "8\t검증 세트 손실: 0.091856\t최선의 손실: 0.044949\t정확도: 97.46%\n",
+      "9\t검증 세트 손실: 0.066062\t최선의 손실: 0.044949\t정확도: 98.28%\n",
+      "10\t검증 세트 손실: 0.046314\t최선의 손실: 0.044949\t정확도: 98.83%\n",
+      "11\t검증 세트 손실: 0.056074\t최선의 손실: 0.044949\t정확도: 98.59%\n",
+      "12\t검증 세트 손실: 0.040493\t최선의 손실: 0.040493\t정확도: 98.91%\n",
+      "13\t검증 세트 손실: 0.051138\t최선의 손실: 0.040493\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.050071\t최선의 손실: 0.040493\t정확도: 98.75%\n",
+      "15\t검증 세트 손실: 0.059666\t최선의 손실: 0.040493\t정확도: 98.44%\n",
+      "16\t검증 세트 손실: 0.048234\t최선의 손실: 0.040493\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.064873\t최선의 손실: 0.040493\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.052878\t최선의 손실: 0.040493\t정확도: 98.59%\n",
+      "19\t검증 세트 손실: 0.063264\t최선의 손실: 0.040493\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.058935\t최선의 손실: 0.040493\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.081826\t최선의 손실: 0.040493\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.072053\t최선의 손실: 0.040493\t정확도: 98.75%\n",
+      "23\t검증 세트 손실: 0.064803\t최선의 손실: 0.040493\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.071976\t최선의 손실: 0.040493\t정확도: 98.48%\n",
+      "25\t검증 세트 손실: 0.086582\t최선의 손실: 0.040493\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.060946\t최선의 손실: 0.040493\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.071983\t최선의 손실: 0.040493\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.054421\t최선의 손실: 0.040493\t정확도: 98.63%\n",
+      "29\t검증 세트 손실: 0.047705\t최선의 손실: 0.040493\t정확도: 99.14%\n",
+      "30\t검증 세트 손실: 0.043038\t최선의 손실: 0.040493\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.076180\t최선의 손실: 0.040493\t정확도: 98.51%\n",
+      "32\t검증 세트 손실: 0.045610\t최선의 손실: 0.040493\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.067253\t최선의 손실: 0.040493\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=30, total=   6.2s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.084026\t최선의 손실: 0.084026\t정확도: 97.46%\n",
-      "1\t검증 세트 손실: 0.068324\t최선의 손실: 0.068324\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.055685\t최선의 손실: 0.055685\t정확도: 98.20%\n",
-      "3\t검증 세트 손실: 0.052494\t최선의 손실: 0.052494\t정확도: 98.40%\n",
-      "4\t검증 세트 손실: 0.049114\t최선의 손실: 0.049114\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.052737\t최선의 손실: 0.049114\t정확도: 98.51%\n",
-      "6\t검증 세트 손실: 0.046164\t최선의 손실: 0.046164\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.058204\t최선의 손실: 0.046164\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.076614\t최선의 손실: 0.046164\t정확도: 98.05%\n",
-      "9\t검증 세트 손실: 0.057684\t최선의 손실: 0.046164\t정확도: 98.55%\n",
-      "10\t검증 세트 손실: 0.045802\t최선의 손실: 0.045802\t정확도: 99.02%\n",
-      "11\t검증 세트 손실: 0.049937\t최선의 손실: 0.045802\t정확도: 98.63%\n",
-      "12\t검증 세트 손실: 0.090229\t최선의 손실: 0.045802\t정확도: 97.85%\n",
-      "13\t검증 세트 손실: 0.042624\t최선의 손실: 0.042624\t정확도: 98.87%\n",
-      "14\t검증 세트 손실: 0.056058\t최선의 손실: 0.042624\t정확도: 98.59%\n",
-      "15\t검증 세트 손실: 0.077361\t최선의 손실: 0.042624\t정확도: 98.32%\n",
-      "16\t검증 세트 손실: 0.040771\t최선의 손실: 0.040771\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.044276\t최선의 손실: 0.040771\t정확도: 99.02%\n",
-      "18\t검증 세트 손실: 0.061478\t최선의 손실: 0.040771\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.049073\t최선의 손실: 0.040771\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.044162\t최선의 손실: 0.040771\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.053426\t최선의 손실: 0.040771\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.047262\t최선의 손실: 0.040771\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.048812\t최선의 손실: 0.040771\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.040568\t최선의 손실: 0.040568\t정확도: 98.94%\n",
-      "25\t검증 세트 손실: 0.048171\t최선의 손실: 0.040568\t정확도: 98.91%\n",
-      "26\t검증 세트 손실: 0.045670\t최선의 손실: 0.040568\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.041970\t최선의 손실: 0.040568\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.051937\t최선의 손실: 0.040568\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.052723\t최선의 손실: 0.040568\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.067472\t최선의 손실: 0.040568\t정확도: 98.87%\n",
-      "31\t검증 세트 손실: 0.061037\t최선의 손실: 0.040568\t정확도: 98.71%\n",
-      "32\t검증 세트 손실: 0.079516\t최선의 손실: 0.040568\t정확도: 98.71%\n",
-      "33\t검증 세트 손실: 0.069244\t최선의 손실: 0.040568\t정확도: 98.79%\n",
-      "34\t검증 세트 손실: 0.049844\t최선의 손실: 0.040568\t정확도: 98.91%\n",
-      "35\t검증 세트 손실: 0.066568\t최선의 손실: 0.040568\t정확도: 98.67%\n",
-      "36\t검증 세트 손실: 0.051771\t최선의 손실: 0.040568\t정확도: 98.91%\n",
-      "37\t검증 세트 손실: 0.045667\t최선의 손실: 0.040568\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.040656\t최선의 손실: 0.040568\t정확도: 99.02%\n",
-      "39\t검증 세트 손실: 0.091938\t최선의 손실: 0.040568\t정확도: 98.51%\n",
-      "40\t검증 세트 손실: 0.042743\t최선의 손실: 0.040568\t정확도: 99.06%\n",
-      "41\t검증 세트 손실: 0.049951\t최선의 손실: 0.040568\t정확도: 98.91%\n",
-      "42\t검증 세트 손실: 0.044913\t최선의 손실: 0.040568\t정확도: 99.14%\n",
-      "43\t검증 세트 손실: 0.068562\t최선의 손실: 0.040568\t정확도: 98.75%\n",
-      "44\t검증 세트 손실: 0.060558\t최선의 손실: 0.040568\t정확도: 98.98%\n",
-      "45\t검증 세트 손실: 0.069242\t최선의 손실: 0.040568\t정확도: 99.02%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=140, total=  24.5s\n",
+      "[CV] batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.088178\t최선의 손실: 0.088178\t정확도: 97.58%\n",
+      "1\t검증 세트 손실: 0.065057\t최선의 손실: 0.065057\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.071156\t최선의 손실: 0.065057\t정확도: 97.73%\n",
+      "3\t검증 세트 손실: 0.055781\t최선의 손실: 0.055781\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.043707\t최선의 손실: 0.043707\t정확도: 98.51%\n",
+      "5\t검증 세트 손실: 0.037202\t최선의 손실: 0.037202\t정확도: 98.83%\n",
+      "6\t검증 세트 손실: 0.045095\t최선의 손실: 0.037202\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.075240\t최선의 손실: 0.037202\t정확도: 97.97%\n",
+      "8\t검증 세트 손실: 0.080535\t최선의 손실: 0.037202\t정확도: 97.93%\n",
+      "9\t검증 세트 손실: 0.058697\t최선의 손실: 0.037202\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.065776\t최선의 손실: 0.037202\t정확도: 98.16%\n",
+      "11\t검증 세트 손실: 0.061931\t최선의 손실: 0.037202\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.050542\t최선의 손실: 0.037202\t정확도: 98.91%\n",
+      "13\t검증 세트 손실: 0.039279\t최선의 손실: 0.037202\t정확도: 98.94%\n",
+      "14\t검증 세트 손실: 0.033180\t최선의 손실: 0.033180\t정확도: 99.02%\n",
+      "15\t검증 세트 손실: 0.042096\t최선의 손실: 0.033180\t정확도: 99.14%\n",
+      "16\t검증 세트 손실: 0.045527\t최선의 손실: 0.033180\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.049220\t최선의 손실: 0.033180\t정확도: 99.10%\n",
+      "18\t검증 세트 손실: 0.042349\t최선의 손실: 0.033180\t정확도: 98.98%\n",
+      "19\t검증 세트 손실: 0.052105\t최선의 손실: 0.033180\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.045601\t최선의 손실: 0.033180\t정확도: 99.06%\n",
+      "21\t검증 세트 손실: 0.048569\t최선의 손실: 0.033180\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.048545\t최선의 손실: 0.033180\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.047211\t최선의 손실: 0.033180\t정확도: 99.18%\n",
+      "24\t검증 세트 손실: 0.038611\t최선의 손실: 0.033180\t정확도: 99.34%\n",
+      "25\t검증 세트 손실: 0.044315\t최선의 손실: 0.033180\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.075902\t최선의 손실: 0.033180\t정확도: 98.44%\n",
+      "27\t검증 세트 손실: 0.054093\t최선의 손실: 0.033180\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.053632\t최선의 손실: 0.033180\t정확도: 98.71%\n",
+      "29\t검증 세트 손실: 0.040795\t최선의 손실: 0.033180\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.058563\t최선의 손실: 0.033180\t정확도: 98.59%\n",
+      "31\t검증 세트 손실: 0.042442\t최선의 손실: 0.033180\t정확도: 99.14%\n",
+      "32\t검증 세트 손실: 0.064959\t최선의 손실: 0.033180\t정확도: 98.67%\n",
+      "33\t검증 세트 손실: 0.043057\t최선의 손실: 0.033180\t정확도: 99.22%\n",
+      "34\t검증 세트 손실: 0.052280\t최선의 손실: 0.033180\t정확도: 98.87%\n",
+      "35\t검증 세트 손실: 0.040216\t최선의 손실: 0.033180\t정확도: 99.26%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=140, total=  32.2s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.117593\t최선의 손실: 0.117593\t정확도: 96.29%\n",
-      "1\t검증 세트 손실: 0.057048\t최선의 손실: 0.057048\t정확도: 98.24%\n",
-      "2\t검증 세트 손실: 0.062026\t최선의 손실: 0.057048\t정확도: 98.12%\n",
-      "3\t검증 세트 손실: 0.063081\t최선의 손실: 0.057048\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.084061\t최선의 손실: 0.057048\t정확도: 97.89%\n",
-      "5\t검증 세트 손실: 0.048044\t최선의 손실: 0.048044\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.057617\t최선의 손실: 0.048044\t정확도: 98.40%\n",
-      "7\t검증 세트 손실: 0.063735\t최선의 손실: 0.048044\t정확도: 98.16%\n",
-      "8\t검증 세트 손실: 0.061923\t최선의 손실: 0.048044\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.064174\t최선의 손실: 0.048044\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.059796\t최선의 손실: 0.048044\t정확도: 98.79%\n",
-      "11\t검증 세트 손실: 0.068173\t최선의 손실: 0.048044\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.037175\t최선의 손실: 0.037175\t정확도: 99.06%\n",
-      "13\t검증 세트 손실: 0.051264\t최선의 손실: 0.037175\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.039107\t최선의 손실: 0.037175\t정확도: 99.14%\n",
-      "15\t검증 세트 손실: 0.048975\t최선의 손실: 0.037175\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.065018\t최선의 손실: 0.037175\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.048153\t최선의 손실: 0.037175\t정확도: 98.87%\n",
-      "18\t검증 세트 손실: 0.060504\t최선의 손실: 0.037175\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.041141\t최선의 손실: 0.037175\t정확도: 98.98%\n",
-      "20\t검증 세트 손실: 0.044133\t최선의 손실: 0.037175\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.056394\t최선의 손실: 0.037175\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.050100\t최선의 손실: 0.037175\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.064565\t최선의 손실: 0.037175\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.039418\t최선의 손실: 0.037175\t정확도: 99.18%\n",
-      "25\t검증 세트 손실: 0.052199\t최선의 손실: 0.037175\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.037295\t최선의 손실: 0.037175\t정확도: 99.30%\n",
-      "27\t검증 세트 손실: 0.056839\t최선의 손실: 0.037175\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.049086\t최선의 손실: 0.037175\t정확도: 99.14%\n",
-      "29\t검증 세트 손실: 0.065054\t최선의 손실: 0.037175\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.062407\t최선의 손실: 0.037175\t정확도: 98.71%\n",
-      "31\t검증 세트 손실: 0.082382\t최선의 손실: 0.037175\t정확도: 98.63%\n",
-      "32\t검증 세트 손실: 0.057896\t최선의 손실: 0.037175\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.067302\t최선의 손실: 0.037175\t정확도: 98.63%\n",
+      "[CV]  batch_size=100, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.95, n_neurons=140, total=  26.0s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.131393\t최선의 손실: 0.131393\t정확도: 96.72%\n",
+      "1\t검증 세트 손실: 0.086170\t최선의 손실: 0.086170\t정확도: 97.97%\n",
+      "2\t검증 세트 손실: 0.099843\t최선의 손실: 0.086170\t정확도: 97.77%\n",
+      "3\t검증 세트 손실: 0.090931\t최선의 손실: 0.086170\t정확도: 97.93%\n",
+      "4\t검증 세트 손실: 0.065998\t최선의 손실: 0.065998\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.175663\t최선의 손실: 0.065998\t정확도: 97.81%\n",
+      "6\t검증 세트 손실: 0.098703\t최선의 손실: 0.065998\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.069446\t최선의 손실: 0.065998\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.047317\t최선의 손실: 0.047317\t정확도: 98.75%\n",
+      "9\t검증 세트 손실: 0.247580\t최선의 손실: 0.047317\t정확도: 98.20%\n",
+      "10\t검증 세트 손실: 0.315585\t최선의 손실: 0.047317\t정확도: 98.05%\n",
+      "11\t검증 세트 손실: 0.087994\t최선의 손실: 0.047317\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.083825\t최선의 손실: 0.047317\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.055323\t최선의 손실: 0.047317\t정확도: 98.94%\n",
+      "14\t검증 세트 손실: 0.062809\t최선의 손실: 0.047317\t정확도: 98.91%\n",
+      "15\t검증 세트 손실: 0.105958\t최선의 손실: 0.047317\t정확도: 98.48%\n",
+      "16\t검증 세트 손실: 0.058466\t최선의 손실: 0.047317\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.553205\t최선의 손실: 0.047317\t정확도: 98.51%\n",
+      "18\t검증 세트 손실: 0.278615\t최선의 손실: 0.047317\t정확도: 97.03%\n",
+      "19\t검증 세트 손실: 0.154007\t최선의 손실: 0.047317\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.121135\t최선의 손실: 0.047317\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.144831\t최선의 손실: 0.047317\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.102446\t최선의 손실: 0.047317\t정확도: 98.91%\n",
+      "23\t검증 세트 손실: 0.139566\t최선의 손실: 0.047317\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.092886\t최선의 손실: 0.047317\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.131183\t최선의 손실: 0.047317\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.830137\t최선의 손실: 0.047317\t정확도: 97.34%\n",
+      "27\t검증 세트 손실: 0.141744\t최선의 손실: 0.047317\t정확도: 98.83%\n",
+      "28\t검증 세트 손실: 0.147363\t최선의 손실: 0.047317\t정확도: 99.02%\n",
+      "29\t검증 세트 손실: 0.301479\t최선의 손실: 0.047317\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=140, total=  24.3s\n",
-      "[CV] batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.100224\t최선의 손실: 0.100224\t정확도: 96.79%\n",
-      "1\t검증 세트 손실: 0.069201\t최선의 손실: 0.069201\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.053464\t최선의 손실: 0.053464\t정확도: 98.32%\n",
-      "3\t검증 세트 손실: 0.054689\t최선의 손실: 0.053464\t정확도: 98.24%\n",
-      "4\t검증 세트 손실: 0.048648\t최선의 손실: 0.048648\t정확도: 98.59%\n",
-      "5\t검증 세트 손실: 0.038047\t최선의 손실: 0.038047\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.036007\t최선의 손실: 0.036007\t정확도: 98.94%\n",
-      "7\t검증 세트 손실: 0.080599\t최선의 손실: 0.036007\t정확도: 97.85%\n",
-      "8\t검증 세트 손실: 0.038741\t최선의 손실: 0.036007\t정확도: 98.87%\n",
-      "9\t검증 세트 손실: 0.053011\t최선의 손실: 0.036007\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.068390\t최선의 손실: 0.036007\t정확도: 98.20%\n",
-      "11\t검증 세트 손실: 0.067664\t최선의 손실: 0.036007\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.037401\t최선의 손실: 0.036007\t정확도: 99.02%\n"
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=120, total=  41.5s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.140135\t최선의 손실: 0.140135\t정확도: 96.29%\n",
+      "1\t검증 세트 손실: 0.106388\t최선의 손실: 0.106388\t정확도: 97.62%\n",
+      "2\t검증 세트 손실: 0.112675\t최선의 손실: 0.106388\t정확도: 97.77%\n",
+      "3\t검증 세트 손실: 0.080585\t최선의 손실: 0.080585\t정확도: 97.58%\n",
+      "4\t검증 세트 손실: 0.123463\t최선의 손실: 0.080585\t정확도: 97.50%\n",
+      "5\t검증 세트 손실: 0.104837\t최선의 손실: 0.080585\t정확도: 97.38%\n",
+      "6\t검증 세트 손실: 1.745454\t최선의 손실: 0.080585\t정확도: 95.74%\n",
+      "7\t검증 세트 손실: 0.123549\t최선의 손실: 0.080585\t정확도: 98.16%\n",
+      "8\t검증 세트 손실: 0.049737\t최선의 손실: 0.049737\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.052383\t최선의 손실: 0.049737\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.062731\t최선의 손실: 0.049737\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.054424\t최선의 손실: 0.049737\t정확도: 98.83%\n",
+      "12\t검증 세트 손실: 0.064067\t최선의 손실: 0.049737\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.117782\t최선의 손실: 0.049737\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 4.063282\t최선의 손실: 0.049737\t정확도: 91.20%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "13\t검증 세트 손실: 0.051858\t최선의 손실: 0.036007\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.035653\t최선의 손실: 0.035653\t정확도: 99.10%\n",
-      "15\t검증 세트 손실: 0.063271\t최선의 손실: 0.035653\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.065900\t최선의 손실: 0.035653\t정확도: 98.48%\n",
-      "17\t검증 세트 손실: 0.056347\t최선의 손실: 0.035653\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.038739\t최선의 손실: 0.035653\t정확도: 98.87%\n",
-      "19\t검증 세트 손실: 0.055631\t최선의 손실: 0.035653\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.057954\t최선의 손실: 0.035653\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.040936\t최선의 손실: 0.035653\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.037441\t최선의 손실: 0.035653\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.041896\t최선의 손실: 0.035653\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.043264\t최선의 손실: 0.035653\t정확도: 99.02%\n",
-      "25\t검증 세트 손실: 0.056893\t최선의 손실: 0.035653\t정확도: 98.55%\n",
-      "26\t검증 세트 손실: 0.044320\t최선의 손실: 0.035653\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.149766\t최선의 손실: 0.035653\t정확도: 96.56%\n",
-      "28\t검증 세트 손실: 0.042880\t최선의 손실: 0.035653\t정확도: 99.14%\n",
-      "29\t검증 세트 손실: 0.050210\t최선의 손실: 0.035653\t정확도: 99.10%\n",
-      "30\t검증 세트 손실: 0.048102\t최선의 손실: 0.035653\t정확도: 99.06%\n",
-      "31\t검증 세트 손실: 0.040555\t최선의 손실: 0.035653\t정확도: 99.06%\n",
-      "32\t검증 세트 손실: 0.044875\t최선의 손실: 0.035653\t정확도: 98.91%\n",
-      "33\t검증 세트 손실: 0.076922\t최선의 손실: 0.035653\t정확도: 98.55%\n",
-      "34\t검증 세트 손실: 0.069735\t최선의 손실: 0.035653\t정확도: 98.87%\n",
-      "35\t검증 세트 손실: 0.056772\t최선의 손실: 0.035653\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.208509\t최선의 손실: 0.049737\t정확도: 97.15%\n",
+      "16\t검증 세트 손실: 0.118026\t최선의 손실: 0.049737\t정확도: 98.20%\n",
+      "17\t검증 세트 손실: 0.153792\t최선의 손실: 0.049737\t정확도: 98.32%\n",
+      "18\t검증 세트 손실: 0.082346\t최선의 손실: 0.049737\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.084494\t최선의 손실: 0.049737\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.128540\t최선의 손실: 0.049737\t정확도: 98.63%\n",
+      "21\t검증 세트 손실: 0.090399\t최선의 손실: 0.049737\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.240111\t최선의 손실: 0.049737\t정확도: 98.20%\n",
+      "23\t검증 세트 손실: 0.442379\t최선의 손실: 0.049737\t정확도: 98.01%\n",
+      "24\t검증 세트 손실: 0.168355\t최선의 손실: 0.049737\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.147873\t최선의 손실: 0.049737\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.164725\t최선의 손실: 0.049737\t정확도: 98.75%\n",
+      "27\t검증 세트 손실: 0.269011\t최선의 손실: 0.049737\t정확도: 98.59%\n",
+      "28\t검증 세트 손실: 0.171039\t최선의 손실: 0.049737\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.258062\t최선의 손실: 0.049737\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.95, n_neurons=140, total=  25.5s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.205435\t최선의 손실: 0.205435\t정확도: 95.07%\n",
-      "1\t검증 세트 손실: 0.125315\t최선의 손실: 0.125315\t정확도: 97.22%\n",
-      "2\t검증 세트 손실: 0.193342\t최선의 손실: 0.125315\t정확도: 96.87%\n",
-      "3\t검증 세트 손실: 0.069316\t최선의 손실: 0.069316\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.076039\t최선의 손실: 0.069316\t정확도: 98.05%\n",
-      "5\t검증 세트 손실: 0.717163\t최선의 손실: 0.069316\t정확도: 95.43%\n",
-      "6\t검증 세트 손실: 0.310161\t최선의 손실: 0.069316\t정확도: 97.89%\n",
-      "7\t검증 세트 손실: 0.076939\t최선의 손실: 0.069316\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.051878\t최선의 손실: 0.051878\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.054981\t최선의 손실: 0.051878\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.044501\t최선의 손실: 0.044501\t정확도: 98.91%\n",
-      "11\t검증 세트 손실: 0.093890\t최선의 손실: 0.044501\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.333138\t최선의 손실: 0.044501\t정확도: 97.97%\n",
-      "13\t검증 세트 손실: 1.007168\t최선의 손실: 0.044501\t정확도: 97.15%\n",
-      "14\t검증 세트 손실: 0.121775\t최선의 손실: 0.044501\t정확도: 98.48%\n",
-      "15\t검증 세트 손실: 0.119321\t최선의 손실: 0.044501\t정확도: 98.51%\n",
-      "16\t검증 세트 손실: 0.089557\t최선의 손실: 0.044501\t정확도: 98.75%\n",
-      "17\t검증 세트 손실: 0.079865\t최선의 손실: 0.044501\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.065300\t최선의 손실: 0.044501\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.113857\t최선의 손실: 0.044501\t정확도: 98.36%\n",
-      "20\t검증 세트 손실: 0.160572\t최선의 손실: 0.044501\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.807039\t최선의 손실: 0.044501\t정확도: 97.11%\n",
-      "22\t검증 세트 손실: 0.158210\t최선의 손실: 0.044501\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.128744\t최선의 손실: 0.044501\t정확도: 98.71%\n",
-      "24\t검증 세트 손실: 0.131805\t최선의 손실: 0.044501\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.072013\t최선의 손실: 0.044501\t정확도: 98.98%\n",
-      "26\t검증 세트 손실: 0.108326\t최선의 손실: 0.044501\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.098625\t최선의 손실: 0.044501\t정확도: 98.94%\n",
-      "28\t검증 세트 손실: 1.544607\t최선의 손실: 0.044501\t정확도: 97.54%\n",
-      "29\t검증 세트 손실: 0.375742\t최선의 손실: 0.044501\t정확도: 98.40%\n",
-      "30\t검증 세트 손실: 0.355431\t최선의 손실: 0.044501\t정확도: 98.71%\n",
-      "31\t검증 세트 손실: 0.337121\t최선의 손실: 0.044501\t정확도: 98.32%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=120, total=  41.5s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.246822\t최선의 손실: 0.246822\t정확도: 94.37%\n",
+      "1\t검증 세트 손실: 0.116875\t최선의 손실: 0.116875\t정확도: 97.03%\n",
+      "2\t검증 세트 손실: 0.151346\t최선의 손실: 0.116875\t정확도: 96.79%\n",
+      "3\t검증 세트 손실: 0.157655\t최선의 손실: 0.116875\t정확도: 97.58%\n",
+      "4\t검증 세트 손실: 0.062602\t최선의 손실: 0.062602\t정확도: 98.59%\n",
+      "5\t검증 세트 손실: 0.055035\t최선의 손실: 0.055035\t정확도: 98.36%\n",
+      "6\t검증 세트 손실: 0.054329\t최선의 손실: 0.054329\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.121180\t최선의 손실: 0.054329\t정확도: 97.89%\n",
+      "8\t검증 세트 손실: 17.985313\t최선의 손실: 0.054329\t정확도: 88.12%\n",
+      "9\t검증 세트 손실: 0.227063\t최선의 손실: 0.054329\t정확도: 98.36%\n",
+      "10\t검증 세트 손실: 0.121303\t최선의 손실: 0.054329\t정확도: 98.44%\n",
+      "11\t검증 세트 손실: 0.065822\t최선의 손실: 0.054329\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.064392\t최선의 손실: 0.054329\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.052692\t최선의 손실: 0.052692\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.096671\t최선의 손실: 0.052692\t정확도: 98.28%\n",
+      "15\t검증 세트 손실: 0.084376\t최선의 손실: 0.052692\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.687461\t최선의 손실: 0.052692\t정확도: 95.93%\n",
+      "17\t검증 세트 손실: 0.197026\t최선의 손실: 0.052692\t정확도: 97.93%\n",
+      "18\t검증 세트 손실: 0.072965\t최선의 손실: 0.052692\t정확도: 98.59%\n",
+      "19\t검증 세트 손실: 0.053382\t최선의 손실: 0.052692\t정확도: 99.10%\n",
+      "20\t검증 세트 손실: 0.051101\t최선의 손실: 0.051101\t정확도: 99.06%\n",
+      "21\t검증 세트 손실: 0.289944\t최선의 손실: 0.051101\t정확도: 97.81%\n",
+      "22\t검증 세트 손실: 0.299140\t최선의 손실: 0.051101\t정확도: 98.55%\n",
+      "23\t검증 세트 손실: 0.129550\t최선의 손실: 0.051101\t정확도: 98.71%\n",
+      "24\t검증 세트 손실: 0.102241\t최선의 손실: 0.051101\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.064825\t최선의 손실: 0.051101\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.100166\t최선의 손실: 0.051101\t정확도: 98.79%\n",
+      "27\t검증 세트 손실: 0.857064\t최선의 손실: 0.051101\t정확도: 96.21%\n",
+      "28\t검증 세트 손실: 0.249181\t최선의 손실: 0.051101\t정확도: 98.71%\n",
+      "29\t검증 세트 손실: 0.116856\t최선의 손실: 0.051101\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.125780\t최선의 손실: 0.051101\t정확도: 98.67%\n",
+      "31\t검증 세트 손실: 0.113756\t최선의 손실: 0.051101\t정확도: 98.55%\n",
+      "32\t검증 세트 손실: 0.179182\t최선의 손실: 0.051101\t정확도: 98.48%\n",
+      "33\t검증 세트 손실: 1.722452\t최선의 손실: 0.051101\t정확도: 98.08%\n",
+      "34\t검증 세트 손실: 0.357987\t최선의 손실: 0.051101\t정확도: 98.91%\n",
+      "35\t검증 세트 손실: 0.232358\t최선의 손실: 0.051101\t정확도: 99.14%\n",
+      "36\t검증 세트 손실: 0.147826\t최선의 손실: 0.051101\t정확도: 99.02%\n",
+      "37\t검증 세트 손실: 0.216837\t최선의 손실: 0.051101\t정확도: 98.51%\n",
+      "38\t검증 세트 손실: 0.223515\t최선의 손실: 0.051101\t정확도: 98.59%\n",
+      "39\t검증 세트 손실: 0.134025\t최선의 손실: 0.051101\t정확도: 99.14%\n",
+      "40\t검증 세트 손실: 0.168186\t최선의 손실: 0.051101\t정확도: 99.30%\n",
+      "41\t검증 세트 손실: 0.456839\t최선의 손실: 0.051101\t정확도: 98.44%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=120, total=  43.6s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.148405\t최선의 손실: 0.148405\t정확도: 95.78%\n",
-      "1\t검증 세트 손실: 0.109574\t최선의 손실: 0.109574\t정확도: 97.81%\n",
-      "2\t검증 세트 손실: 0.102540\t최선의 손실: 0.102540\t정확도: 97.62%\n",
-      "3\t검증 세트 손실: 0.201356\t최선의 손실: 0.102540\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.074214\t최선의 손실: 0.074214\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.059153\t최선의 손실: 0.059153\t정확도: 98.59%\n",
-      "6\t검증 세트 손실: 0.055269\t최선의 손실: 0.055269\t정확도: 98.63%\n",
-      "7\t검증 세트 손실: 0.059481\t최선의 손실: 0.055269\t정확도: 98.28%\n",
-      "8\t검증 세트 손실: 0.045428\t최선의 손실: 0.045428\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.169558\t최선의 손실: 0.045428\t정확도: 97.85%\n",
-      "10\t검증 세트 손실: 0.838808\t최선의 손실: 0.045428\t정확도: 96.17%\n",
-      "11\t검증 세트 손실: 0.122129\t최선의 손실: 0.045428\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.082823\t최선의 손실: 0.045428\t정확도: 98.67%\n",
-      "13\t검증 세트 손실: 0.074101\t최선의 손실: 0.045428\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.088103\t최선의 손실: 0.045428\t정확도: 98.48%\n",
-      "15\t검증 세트 손실: 0.270982\t최선의 손실: 0.045428\t정확도: 96.64%\n",
-      "16\t검증 세트 손실: 0.064891\t최선의 손실: 0.045428\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.151706\t최선의 손실: 0.045428\t정확도: 98.32%\n",
-      "18\t검증 세트 손실: 0.204376\t최선의 손실: 0.045428\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.249633\t최선의 손실: 0.045428\t정확도: 98.20%\n",
-      "20\t검증 세트 손실: 0.068436\t최선의 손실: 0.045428\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.141080\t최선의 손실: 0.045428\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.829899\t최선의 손실: 0.045428\t정확도: 97.93%\n",
-      "23\t검증 세트 손실: 0.112875\t최선의 손실: 0.045428\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.099792\t최선의 손실: 0.045428\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.084129\t최선의 손실: 0.045428\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.085119\t최선의 손실: 0.045428\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.086070\t최선의 손실: 0.045428\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.089914\t최선의 손실: 0.045428\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.739627\t최선의 손실: 0.045428\t정확도: 96.87%\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=120, total=  57.4s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.99, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.307358\t최선의 손실: 0.307358\t정확도: 92.49%\n",
+      "1\t검증 세트 손실: 0.163064\t최선의 손실: 0.163064\t정확도: 95.54%\n",
+      "2\t검증 세트 손실: 0.170965\t최선의 손실: 0.163064\t정확도: 95.07%\n",
+      "3\t검증 세트 손실: 0.179141\t최선의 손실: 0.163064\t정확도: 94.61%\n",
+      "4\t검증 세트 손실: 0.255565\t최선의 손실: 0.163064\t정확도: 91.44%\n",
+      "5\t검증 세트 손실: 0.163561\t최선의 손실: 0.163064\t정확도: 95.31%\n",
+      "6\t검증 세트 손실: 0.099528\t최선의 손실: 0.099528\t정확도: 97.30%\n",
+      "7\t검증 세트 손실: 0.158738\t최선의 손실: 0.099528\t정확도: 96.05%\n",
+      "8\t검증 세트 손실: 0.169150\t최선의 손실: 0.099528\t정확도: 95.74%\n",
+      "9\t검증 세트 손실: 0.079953\t최선의 손실: 0.079953\t정확도: 97.97%\n",
+      "10\t검증 세트 손실: 0.083141\t최선의 손실: 0.079953\t정확도: 97.77%\n",
+      "11\t검증 세트 손실: 0.096199\t최선의 손실: 0.079953\t정확도: 97.73%\n",
+      "12\t검증 세트 손실: 0.094466\t최선의 손실: 0.079953\t정확도: 97.62%\n",
+      "13\t검증 세트 손실: 0.102373\t최선의 손실: 0.079953\t정확도: 97.34%\n",
+      "14\t검증 세트 손실: 0.084664\t최선의 손실: 0.079953\t정확도: 97.93%\n",
+      "15\t검증 세트 손실: 0.108413\t최선의 손실: 0.079953\t정확도: 97.77%\n",
+      "16\t검증 세트 손실: 0.098793\t최선의 손실: 0.079953\t정확도: 97.93%\n",
+      "17\t검증 세트 손실: 0.091030\t최선의 손실: 0.079953\t정확도: 97.93%\n",
+      "18\t검증 세트 손실: 0.099024\t최선의 손실: 0.079953\t정확도: 98.01%\n",
+      "19\t검증 세트 손실: 0.148670\t최선의 손실: 0.079953\t정확도: 96.95%\n",
+      "20\t검증 세트 손실: 0.090588\t최선의 손실: 0.079953\t정확도: 98.05%\n",
+      "21\t검증 세트 손실: 0.093964\t최선의 손실: 0.079953\t정확도: 97.58%\n",
+      "22\t검증 세트 손실: 0.079994\t최선의 손실: 0.079953\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.090565\t최선의 손실: 0.079953\t정확도: 98.05%\n",
+      "24\t검증 세트 손실: 0.089805\t최선의 손실: 0.079953\t정확도: 98.12%\n",
+      "25\t검증 세트 손실: 0.090508\t최선의 손실: 0.079953\t정확도: 97.97%\n",
+      "26\t검증 세트 손실: 0.092237\t최선의 손실: 0.079953\t정확도: 98.44%\n",
+      "27\t검증 세트 손실: 0.097789\t최선의 손실: 0.079953\t정확도: 98.05%\n",
+      "28\t검증 세트 손실: 0.109668\t최선의 손실: 0.079953\t정확도: 97.65%\n",
+      "29\t검증 세트 손실: 0.138211\t최선의 손실: 0.079953\t정확도: 97.34%\n",
+      "30\t검증 세트 손실: 0.079243\t최선의 손실: 0.079243\t정확도: 98.24%\n",
+      "31\t검증 세트 손실: 0.104088\t최선의 손실: 0.079243\t정확도: 98.01%\n",
+      "32\t검증 세트 손실: 0.117829\t최선의 손실: 0.079243\t정확도: 97.58%\n",
+      "33\t검증 세트 손실: 0.126569\t최선의 손실: 0.079243\t정확도: 97.30%\n",
+      "34\t검증 세트 손실: 0.084593\t최선의 손실: 0.079243\t정확도: 98.08%\n",
+      "35\t검증 세트 손실: 0.122675\t최선의 손실: 0.079243\t정확도: 97.97%\n",
+      "36\t검증 세트 손실: 0.106003\t최선의 손실: 0.079243\t정확도: 98.12%\n",
+      "37\t검증 세트 손실: 0.123669\t최선의 손실: 0.079243\t정확도: 98.01%\n",
+      "38\t검증 세트 손실: 0.133142\t최선의 손실: 0.079243\t정확도: 97.58%\n",
+      "39\t검증 세트 손실: 0.117292\t최선의 손실: 0.079243\t정확도: 97.73%\n",
+      "40\t검증 세트 손실: 0.120116\t최선의 손실: 0.079243\t정확도: 97.77%\n",
+      "41\t검증 세트 손실: 0.099833\t최선의 손실: 0.079243\t정확도: 98.05%\n",
+      "42\t검증 세트 손실: 0.093745\t최선의 손실: 0.079243\t정확도: 98.01%\n",
+      "43\t검증 세트 손실: 0.105952\t최선의 손실: 0.079243\t정확도: 98.08%\n",
+      "44\t검증 세트 손실: 0.125163\t최선의 손실: 0.079243\t정확도: 97.54%\n",
+      "45\t검증 세트 손실: 0.106699\t최선의 손실: 0.079243\t정확도: 98.16%\n",
+      "46\t검증 세트 손실: 0.122824\t최선의 손실: 0.079243\t정확도: 98.05%\n",
+      "47\t검증 세트 손실: 0.121392\t최선의 손실: 0.079243\t정확도: 98.01%\n",
+      "48\t검증 세트 손실: 0.097285\t최선의 손실: 0.079243\t정확도: 98.16%\n",
+      "49\t검증 세트 손실: 0.104275\t최선의 손실: 0.079243\t정확도: 98.20%\n",
+      "50\t검증 세트 손실: 0.118939\t최선의 손실: 0.079243\t정확도: 97.93%\n",
+      "51\t검증 세트 손실: 0.123236\t최선의 손실: 0.079243\t정확도: 98.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=120, total=  41.3s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.141210\t최선의 손실: 0.141210\t정확도: 96.52%\n",
-      "1\t검증 세트 손실: 0.172529\t최선의 손실: 0.141210\t정확도: 95.62%\n",
-      "2\t검증 세트 손실: 0.068392\t최선의 손실: 0.068392\t정확도: 97.85%\n",
-      "3\t검증 세트 손실: 0.076567\t최선의 손실: 0.068392\t정확도: 98.24%\n",
-      "4\t검증 세트 손실: 0.201391\t최선의 손실: 0.068392\t정확도: 96.33%\n",
-      "5\t검증 세트 손실: 0.132176\t최선의 손실: 0.068392\t정확도: 97.34%\n",
-      "6\t검증 세트 손실: 0.151114\t최선의 손실: 0.068392\t정확도: 96.17%\n",
-      "7\t검증 세트 손실: 0.099289\t최선의 손실: 0.068392\t정확도: 98.20%\n",
-      "8\t검증 세트 손실: 0.077257\t최선의 손실: 0.068392\t정확도: 98.94%\n",
-      "9\t검증 세트 손실: 0.300675\t최선의 손실: 0.068392\t정확도: 97.34%\n",
-      "10\t검증 세트 손실: 0.686839\t최선의 손실: 0.068392\t정확도: 94.53%\n",
-      "11\t검증 세트 손실: 0.077999\t최선의 손실: 0.068392\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.083131\t최선의 손실: 0.068392\t정확도: 98.67%\n",
-      "13\t검증 세트 손실: 0.069312\t최선의 손실: 0.068392\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.059260\t최선의 손실: 0.059260\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.067264\t최선의 손실: 0.059260\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.062470\t최선의 손실: 0.059260\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.093765\t최선의 손실: 0.059260\t정확도: 98.36%\n",
-      "18\t검증 세트 손실: 0.639377\t최선의 손실: 0.059260\t정확도: 94.37%\n",
-      "19\t검증 세트 손실: 0.417886\t최선의 손실: 0.059260\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.228000\t최선의 손실: 0.059260\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.112298\t최선의 손실: 0.059260\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.122541\t최선의 손실: 0.059260\t정확도: 98.98%\n",
-      "23\t검증 세트 손실: 0.123667\t최선의 손실: 0.059260\t정확도: 98.91%\n",
-      "24\t검증 세트 손실: 0.153618\t최선의 손실: 0.059260\t정확도: 98.44%\n",
-      "25\t검증 세트 손실: 0.109966\t최선의 손실: 0.059260\t정확도: 98.91%\n",
-      "26\t검증 세트 손실: 0.179422\t최선의 손실: 0.059260\t정확도: 98.71%\n",
-      "27\t검증 세트 손실: 0.262298\t최선의 손실: 0.059260\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.274689\t최선의 손실: 0.059260\t정확도: 98.05%\n",
-      "29\t검증 세트 손실: 0.937729\t최선의 손실: 0.059260\t정확도: 97.07%\n",
-      "30\t검증 세트 손실: 0.172954\t최선의 손실: 0.059260\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.098748\t최선의 손실: 0.059260\t정확도: 99.06%\n",
-      "32\t검증 세트 손실: 0.154720\t최선의 손실: 0.059260\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.106856\t최선의 손실: 0.059260\t정확도: 98.87%\n",
-      "34\t검증 세트 손실: 0.113143\t최선의 손실: 0.059260\t정확도: 98.94%\n",
-      "35\t검증 세트 손실: 0.466508\t최선의 손실: 0.059260\t정확도: 98.08%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=120, total=  49.0s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.360502\t최선의 손실: 0.360502\t정확도: 92.42%\n",
-      "1\t검증 세트 손실: 0.343097\t최선의 손실: 0.343097\t정확도: 91.63%\n",
-      "2\t검증 세트 손실: 0.243100\t최선의 손실: 0.243100\t정확도: 93.43%\n",
-      "3\t검증 세트 손실: 0.136294\t최선의 손실: 0.136294\t정확도: 96.52%\n",
-      "4\t검증 세트 손실: 0.192735\t최선의 손실: 0.136294\t정확도: 94.33%\n",
-      "5\t검증 세트 손실: 0.123165\t최선의 손실: 0.123165\t정확도: 96.68%\n",
-      "6\t검증 세트 손실: 0.088052\t최선의 손실: 0.088052\t정확도: 97.73%\n",
-      "7\t검증 세트 손실: 0.122838\t최선의 손실: 0.088052\t정확도: 97.11%\n",
-      "8\t검증 세트 손실: 0.108896\t최선의 손실: 0.088052\t정확도: 97.15%\n",
-      "9\t검증 세트 손실: 0.109761\t최선의 손실: 0.088052\t정확도: 97.34%\n",
-      "10\t검증 세트 손실: 0.095247\t최선의 손실: 0.088052\t정확도: 97.65%\n",
-      "11\t검증 세트 손실: 0.106301\t최선의 손실: 0.088052\t정확도: 97.54%\n",
-      "12\t검증 세트 손실: 0.134393\t최선의 손실: 0.088052\t정확도: 96.64%\n",
-      "13\t검증 세트 손실: 0.104522\t최선의 손실: 0.088052\t정확도: 97.38%\n",
-      "14\t검증 세트 손실: 0.116746\t최선의 손실: 0.088052\t정확도: 97.38%\n",
-      "15\t검증 세트 손실: 0.090053\t최선의 손실: 0.088052\t정확도: 97.85%\n",
-      "16\t검증 세트 손실: 0.086312\t최선의 손실: 0.086312\t정확도: 98.01%\n",
-      "17\t검증 세트 손실: 0.080156\t최선의 손실: 0.080156\t정확도: 98.16%\n",
-      "18\t검증 세트 손실: 0.077468\t최선의 손실: 0.077468\t정확도: 98.36%\n",
-      "19\t검증 세트 손실: 0.092049\t최선의 손실: 0.077468\t정확도: 97.89%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.99, n_neurons=10, total=  10.0s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.99, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.263664\t최선의 손실: 0.263664\t정확도: 93.39%\n",
+      "1\t검증 세트 손실: 0.148657\t최선의 손실: 0.148657\t정확도: 95.74%\n",
+      "2\t검증 세트 손실: 0.098209\t최선의 손실: 0.098209\t정확도: 97.11%\n",
+      "3\t검증 세트 손실: 0.098677\t최선의 손실: 0.098209\t정확도: 97.11%\n",
+      "4\t검증 세트 손실: 0.088901\t최선의 손실: 0.088901\t정확도: 97.38%\n",
+      "5\t검증 세트 손실: 0.102906\t최선의 손실: 0.088901\t정확도: 96.83%\n",
+      "6\t검증 세트 손실: 0.092341\t최선의 손실: 0.088901\t정확도: 97.65%\n",
+      "7\t검증 세트 손실: 0.138194\t최선의 손실: 0.088901\t정확도: 96.36%\n",
+      "8\t검증 세트 손실: 0.097222\t최선의 손실: 0.088901\t정확도: 97.54%\n",
+      "9\t검증 세트 손실: 0.093328\t최선의 손실: 0.088901\t정확도: 97.26%\n",
+      "10\t검증 세트 손실: 0.112154\t최선의 손실: 0.088901\t정확도: 97.07%\n",
+      "11\t검증 세트 손실: 0.140055\t최선의 손실: 0.088901\t정확도: 96.48%\n",
+      "12\t검증 세트 손실: 0.104311\t최선의 손실: 0.088901\t정확도: 97.54%\n",
+      "13\t검증 세트 손실: 0.104837\t최선의 손실: 0.088901\t정확도: 97.85%\n",
+      "14\t검증 세트 손실: 0.108646\t최선의 손실: 0.088901\t정확도: 97.50%\n",
+      "15\t검증 세트 손실: 0.104913\t최선의 손실: 0.088901\t정확도: 97.30%\n",
+      "16\t검증 세트 손실: 0.080082\t최선의 손실: 0.080082\t정확도: 97.97%\n",
+      "17\t검증 세트 손실: 0.099181\t최선의 손실: 0.080082\t정확도: 98.01%\n",
+      "18\t검증 세트 손실: 0.102931\t최선의 손실: 0.080082\t정확도: 97.81%\n",
+      "19\t검증 세트 손실: 0.099718\t최선의 손실: 0.080082\t정확도: 97.97%\n",
+      "20\t검증 세트 손실: 0.109208\t최선의 손실: 0.080082\t정확도: 97.81%\n",
+      "21\t검증 세트 손실: 0.134621\t최선의 손실: 0.080082\t정확도: 97.34%\n",
+      "22\t검증 세트 손실: 0.110280\t최선의 손실: 0.080082\t정확도: 97.77%\n",
+      "23\t검증 세트 손실: 0.127218\t최선의 손실: 0.080082\t정확도: 97.50%\n",
+      "24\t검증 세트 손실: 0.112238\t최선의 손실: 0.080082\t정확도: 97.54%\n",
+      "25\t검증 세트 손실: 0.115524\t최선의 손실: 0.080082\t정확도: 97.97%\n",
+      "26\t검증 세트 손실: 0.111746\t최선의 손실: 0.080082\t정확도: 98.08%\n",
+      "27\t검증 세트 손실: 0.105992\t최선의 손실: 0.080082\t정확도: 97.89%\n",
+      "28\t검증 세트 손실: 0.084062\t최선의 손실: 0.080082\t정확도: 98.16%\n",
+      "29\t검증 세트 손실: 0.109162\t최선의 손실: 0.080082\t정확도: 97.77%\n",
+      "30\t검증 세트 손실: 0.108548\t최선의 손실: 0.080082\t정확도: 97.97%\n",
+      "31\t검증 세트 손실: 0.109851\t최선의 손실: 0.080082\t정확도: 98.01%\n",
+      "32\t검증 세트 손실: 0.123812\t최선의 손실: 0.080082\t정확도: 97.62%\n",
+      "33\t검증 세트 손실: 0.166110\t최선의 손실: 0.080082\t정확도: 97.11%\n",
+      "34\t검증 세트 손실: 0.128761\t최선의 손실: 0.080082\t정확도: 97.77%\n",
+      "35\t검증 세트 손실: 0.104277\t최선의 손실: 0.080082\t정확도: 98.08%\n",
+      "36\t검증 세트 손실: 0.121962\t최선의 손실: 0.080082\t정확도: 97.69%\n",
+      "37\t검증 세트 손실: 0.133628\t최선의 손실: 0.080082\t정확도: 97.73%\n",
+      "조기 종료!\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20\t검증 세트 손실: 0.093805\t최선의 손실: 0.077468\t정확도: 97.77%\n",
-      "21\t검증 세트 손실: 0.096472\t최선의 손실: 0.077468\t정확도: 98.20%\n",
-      "22\t검증 세트 손실: 0.111435\t최선의 손실: 0.077468\t정확도: 97.69%\n",
-      "23\t검증 세트 손실: 0.104948\t최선의 손실: 0.077468\t정확도: 97.62%\n",
-      "24\t검증 세트 손실: 0.099339\t최선의 손실: 0.077468\t정확도: 98.12%\n",
-      "25\t검증 세트 손실: 0.120037\t최선의 손실: 0.077468\t정확도: 97.77%\n",
-      "26\t검증 세트 손실: 0.104775\t최선의 손실: 0.077468\t정확도: 97.85%\n",
-      "27\t검증 세트 손실: 0.100497\t최선의 손실: 0.077468\t정확도: 97.81%\n",
-      "28\t검증 세트 손실: 0.084751\t최선의 손실: 0.077468\t정확도: 97.89%\n",
-      "29\t검증 세트 손실: 0.091135\t최선의 손실: 0.077468\t정확도: 98.12%\n",
-      "30\t검증 세트 손실: 0.106428\t최선의 손실: 0.077468\t정확도: 97.93%\n",
-      "31\t검증 세트 손실: 0.093221\t최선의 손실: 0.077468\t정확도: 97.97%\n",
-      "32\t검증 세트 손실: 0.145545\t최선의 손실: 0.077468\t정확도: 97.46%\n",
-      "33\t검증 세트 손실: 0.132916\t최선의 손실: 0.077468\t정확도: 97.81%\n",
-      "34\t검증 세트 손실: 0.164454\t최선의 손실: 0.077468\t정확도: 97.19%\n",
-      "35\t검증 세트 손실: 0.132329\t최선의 손실: 0.077468\t정확도: 97.69%\n",
-      "36\t검증 세트 손실: 0.110397\t최선의 손실: 0.077468\t정확도: 98.01%\n",
-      "37\t검증 세트 손실: 0.117590\t최선의 손실: 0.077468\t정확도: 97.62%\n",
-      "38\t검증 세트 손실: 0.104272\t최선의 손실: 0.077468\t정확도: 97.81%\n",
-      "39\t검증 세트 손실: 0.103389\t최선의 손실: 0.077468\t정확도: 97.54%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.99, n_neurons=10, total=   7.7s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.99, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.261918\t최선의 손실: 0.261918\t정확도: 93.43%\n",
+      "1\t검증 세트 손실: 0.178758\t최선의 손실: 0.178758\t정확도: 94.61%\n",
+      "2\t검증 세트 손실: 0.235439\t최선의 손실: 0.178758\t정확도: 92.81%\n",
+      "3\t검증 세트 손실: 0.236573\t최선의 손실: 0.178758\t정확도: 92.30%\n",
+      "4\t검증 세트 손실: 0.115192\t최선의 손실: 0.115192\t정확도: 96.40%\n",
+      "5\t검증 세트 손실: 0.096719\t최선의 손실: 0.096719\t정확도: 97.07%\n",
+      "6\t검증 세트 손실: 0.096867\t최선의 손실: 0.096719\t정확도: 96.72%\n",
+      "7\t검증 세트 손실: 0.142681\t최선의 손실: 0.096719\t정확도: 95.27%\n",
+      "8\t검증 세트 손실: 0.103823\t최선의 손실: 0.096719\t정확도: 96.79%\n",
+      "9\t검증 세트 손실: 0.076379\t최선의 손실: 0.076379\t정확도: 98.05%\n",
+      "10\t검증 세트 손실: 0.086660\t최선의 손실: 0.076379\t정확도: 97.62%\n",
+      "11\t검증 세트 손실: 0.085268\t최선의 손실: 0.076379\t정확도: 97.42%\n",
+      "12\t검증 세트 손실: 0.093674\t최선의 손실: 0.076379\t정확도: 97.65%\n",
+      "13\t검증 세트 손실: 0.084467\t최선의 손실: 0.076379\t정확도: 97.46%\n",
+      "14\t검증 세트 손실: 0.098277\t최선의 손실: 0.076379\t정확도: 97.50%\n",
+      "15\t검증 세트 손실: 0.085251\t최선의 손실: 0.076379\t정확도: 98.05%\n",
+      "16\t검증 세트 손실: 0.075786\t최선의 손실: 0.075786\t정확도: 98.36%\n",
+      "17\t검증 세트 손실: 0.112947\t최선의 손실: 0.075786\t정확도: 97.85%\n",
+      "18\t검증 세트 손실: 0.086268\t최선의 손실: 0.075786\t정확도: 97.81%\n",
+      "19\t검증 세트 손실: 0.073723\t최선의 손실: 0.073723\t정확도: 97.97%\n",
+      "20\t검증 세트 손실: 0.071825\t최선의 손실: 0.071825\t정확도: 98.08%\n",
+      "21\t검증 세트 손실: 0.107399\t최선의 손실: 0.071825\t정확도: 97.46%\n",
+      "22\t검증 세트 손실: 0.096187\t최선의 손실: 0.071825\t정확도: 98.40%\n",
+      "23\t검증 세트 손실: 0.096331\t최선의 손실: 0.071825\t정확도: 98.05%\n",
+      "24\t검증 세트 손실: 0.088417\t최선의 손실: 0.071825\t정확도: 98.05%\n",
+      "25\t검증 세트 손실: 0.078061\t최선의 손실: 0.071825\t정확도: 98.55%\n",
+      "26\t검증 세트 손실: 0.092300\t최선의 손실: 0.071825\t정확도: 98.20%\n",
+      "27\t검증 세트 손실: 0.082235\t최선의 손실: 0.071825\t정확도: 98.28%\n",
+      "28\t검증 세트 손실: 0.084093\t최선의 손실: 0.071825\t정확도: 98.12%\n",
+      "29\t검증 세트 손실: 0.098268\t최선의 손실: 0.071825\t정확도: 98.24%\n",
+      "30\t검증 세트 손실: 0.098040\t최선의 손실: 0.071825\t정확도: 97.89%\n",
+      "31\t검증 세트 손실: 0.088822\t최선의 손실: 0.071825\t정확도: 98.24%\n",
+      "32\t검증 세트 손실: 0.134231\t최선의 손실: 0.071825\t정확도: 97.81%\n",
+      "33\t검증 세트 손실: 0.116151\t최선의 손실: 0.071825\t정확도: 98.05%\n",
+      "34\t검증 세트 손실: 0.122445\t최선의 손실: 0.071825\t정확도: 97.89%\n",
+      "35\t검증 세트 손실: 0.108890\t최선의 손실: 0.071825\t정확도: 97.93%\n",
+      "36\t검증 세트 손실: 0.119269\t최선의 손실: 0.071825\t정확도: 97.85%\n",
+      "37\t검증 세트 손실: 0.102331\t최선의 손실: 0.071825\t정확도: 98.08%\n",
+      "38\t검증 세트 손실: 0.099484\t최선의 손실: 0.071825\t정확도: 98.24%\n",
+      "39\t검증 세트 손실: 0.112686\t최선의 손실: 0.071825\t정확도: 97.89%\n",
+      "40\t검증 세트 손실: 0.102856\t최선의 손실: 0.071825\t정확도: 97.97%\n",
+      "41\t검증 세트 손실: 0.099985\t최선의 손실: 0.071825\t정확도: 98.24%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=10, total=   7.7s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.343275\t최선의 손실: 0.343275\t정확도: 91.99%\n",
-      "1\t검증 세트 손실: 0.162813\t최선의 손실: 0.162813\t정확도: 95.74%\n",
-      "2\t검증 세트 손실: 0.238956\t최선의 손실: 0.162813\t정확도: 93.12%\n",
-      "3\t검증 세트 손실: 0.238368\t최선의 손실: 0.162813\t정확도: 92.85%\n",
-      "4\t검증 세트 손실: 0.173938\t최선의 손실: 0.162813\t정확도: 95.15%\n",
-      "5\t검증 세트 손실: 0.132890\t최선의 손실: 0.132890\t정확도: 95.50%\n",
-      "6\t검증 세트 손실: 0.287246\t최선의 손실: 0.132890\t정확도: 92.22%\n",
-      "7\t검증 세트 손실: 0.140473\t최선의 손실: 0.132890\t정확도: 96.33%\n",
-      "8\t검증 세트 손실: 0.123130\t최선의 손실: 0.123130\t정확도: 96.64%\n",
-      "9\t검증 세트 손실: 0.096285\t최선의 손실: 0.096285\t정확도: 97.65%\n",
-      "10\t검증 세트 손실: 0.115114\t최선의 손실: 0.096285\t정확도: 96.99%\n",
-      "11\t검증 세트 손실: 0.176163\t최선의 손실: 0.096285\t정확도: 95.54%\n",
-      "12\t검증 세트 손실: 0.102842\t최선의 손실: 0.096285\t정확도: 97.54%\n",
-      "13\t검증 세트 손실: 0.115453\t최선의 손실: 0.096285\t정확도: 97.30%\n",
-      "14\t검증 세트 손실: 0.126017\t최선의 손실: 0.096285\t정확도: 97.19%\n",
-      "15\t검증 세트 손실: 0.183101\t최선의 손실: 0.096285\t정확도: 95.74%\n",
-      "16\t검증 세트 손실: 0.101813\t최선의 손실: 0.096285\t정확도: 97.81%\n",
-      "17\t검증 세트 손실: 0.104523\t최선의 손실: 0.096285\t정확도: 97.65%\n",
-      "18\t검증 세트 손실: 0.111313\t최선의 손실: 0.096285\t정확도: 97.34%\n",
-      "19\t검증 세트 손실: 0.144627\t최선의 손실: 0.096285\t정확도: 97.22%\n",
-      "20\t검증 세트 손실: 0.103948\t최선의 손실: 0.096285\t정확도: 97.77%\n",
-      "21\t검증 세트 손실: 0.171916\t최선의 손실: 0.096285\t정확도: 96.36%\n",
-      "22\t검증 세트 손실: 0.134424\t최선의 손실: 0.096285\t정확도: 97.30%\n",
-      "23\t검증 세트 손실: 0.101524\t최선의 손실: 0.096285\t정확도: 97.85%\n",
-      "24\t검증 세트 손실: 0.115933\t최선의 손실: 0.096285\t정확도: 97.50%\n",
-      "25\t검증 세트 손실: 0.095199\t최선의 손실: 0.095199\t정확도: 97.73%\n",
-      "26\t검증 세트 손실: 0.115094\t최선의 손실: 0.095199\t정확도: 97.69%\n",
-      "27\t검증 세트 손실: 0.110376\t최선의 손실: 0.095199\t정확도: 97.81%\n",
-      "28\t검증 세트 손실: 0.121112\t최선의 손실: 0.095199\t정확도: 97.38%\n",
-      "29\t검증 세트 손실: 0.104667\t최선의 손실: 0.095199\t정확도: 97.69%\n",
-      "30\t검증 세트 손실: 0.133063\t최선의 손실: 0.095199\t정확도: 97.38%\n",
-      "31\t검증 세트 손실: 0.117781\t최선의 손실: 0.095199\t정확도: 97.62%\n",
-      "32\t검증 세트 손실: 0.108504\t최선의 손실: 0.095199\t정확도: 98.05%\n",
-      "33\t검증 세트 손실: 0.112106\t최선의 손실: 0.095199\t정확도: 97.93%\n",
-      "34\t검증 세트 손실: 0.139049\t최선의 손실: 0.095199\t정확도: 97.19%\n",
-      "35\t검증 세트 손실: 0.115719\t최선의 손실: 0.095199\t정확도: 97.73%\n",
-      "36\t검증 세트 손실: 0.113736\t최선의 손실: 0.095199\t정확도: 97.62%\n",
-      "37\t검증 세트 손실: 0.142997\t최선의 손실: 0.095199\t정확도: 96.83%\n",
-      "38\t검증 세트 손실: 0.126905\t최선의 손실: 0.095199\t정확도: 97.77%\n",
-      "39\t검증 세트 손실: 0.093525\t최선의 손실: 0.093525\t정확도: 98.01%\n",
-      "40\t검증 세트 손실: 0.130778\t최선의 손실: 0.093525\t정확도: 97.58%\n",
-      "41\t검증 세트 손실: 0.115927\t최선의 손실: 0.093525\t정확도: 97.93%\n",
-      "42\t검증 세트 손실: 0.119450\t최선의 손실: 0.093525\t정확도: 97.77%\n",
-      "43\t검증 세트 손실: 0.111787\t최선의 손실: 0.093525\t정확도: 98.05%\n",
-      "44\t검증 세트 손실: 0.122516\t최선의 손실: 0.093525\t정확도: 97.58%\n",
-      "45\t검증 세트 손실: 0.153348\t최선의 손실: 0.093525\t정확도: 97.46%\n",
-      "46\t검증 세트 손실: 0.127067\t최선의 손실: 0.093525\t정확도: 98.01%\n",
-      "47\t검증 세트 손실: 0.130530\t최선의 손실: 0.093525\t정확도: 97.58%\n",
-      "48\t검증 세트 손실: 0.164128\t최선의 손실: 0.093525\t정확도: 97.22%\n",
-      "49\t검증 세트 손실: 0.102302\t최선의 손실: 0.093525\t정확도: 97.89%\n",
-      "50\t검증 세트 손실: 0.167593\t최선의 손실: 0.093525\t정확도: 97.34%\n",
-      "51\t검증 세트 손실: 0.138538\t최선의 손실: 0.093525\t정확도: 97.46%\n",
-      "52\t검증 세트 손실: 0.149440\t최선의 손실: 0.093525\t정확도: 97.42%\n",
-      "53\t검증 세트 손실: 0.128892\t최선의 손실: 0.093525\t정확도: 97.65%\n",
-      "54\t검증 세트 손실: 0.126627\t최선의 손실: 0.093525\t정확도: 97.93%\n",
-      "55\t검증 세트 손실: 0.117593\t최선의 손실: 0.093525\t정확도: 97.65%\n",
-      "56\t검증 세트 손실: 0.139475\t최선의 손실: 0.093525\t정확도: 97.42%\n",
-      "57\t검증 세트 손실: 0.147776\t최선의 손실: 0.093525\t정확도: 97.42%\n",
-      "58\t검증 세트 손실: 0.114883\t최선의 손실: 0.093525\t정확도: 97.89%\n",
-      "59\t검증 세트 손실: 0.141886\t최선의 손실: 0.093525\t정확도: 97.65%\n",
-      "60\t검증 세트 손실: 0.145109\t최선의 손실: 0.093525\t정확도: 97.62%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.02, batch_norm_momentum=0.99, n_neurons=10, total=   8.4s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.131292\t최선의 손실: 0.131292\t정확도: 96.68%\n",
+      "1\t검증 세트 손실: 0.082291\t최선의 손실: 0.082291\t정확도: 98.05%\n",
+      "2\t검증 세트 손실: 0.085017\t최선의 손실: 0.082291\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.076425\t최선의 손실: 0.076425\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.055331\t최선의 손실: 0.055331\t정확도: 98.55%\n",
+      "5\t검증 세트 손실: 0.107172\t최선의 손실: 0.055331\t정확도: 97.69%\n",
+      "6\t검증 세트 손실: 0.049600\t최선의 손실: 0.049600\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.085594\t최선의 손실: 0.049600\t정확도: 98.01%\n",
+      "8\t검증 세트 손실: 0.061842\t최선의 손실: 0.049600\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.078949\t최선의 손실: 0.049600\t정확도: 98.24%\n",
+      "10\t검증 세트 손실: 0.103661\t최선의 손실: 0.049600\t정확도: 98.28%\n",
+      "11\t검증 세트 손실: 0.059533\t최선의 손실: 0.049600\t정확도: 98.79%\n",
+      "12\t검증 세트 손실: 0.097808\t최선의 손실: 0.049600\t정확도: 98.16%\n",
+      "13\t검증 세트 손실: 0.244844\t최선의 손실: 0.049600\t정확도: 97.46%\n",
+      "14\t검증 세트 손실: 0.055056\t최선의 손실: 0.049600\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.039415\t최선의 손실: 0.039415\t정확도: 99.10%\n",
+      "16\t검증 세트 손실: 0.047199\t최선의 손실: 0.039415\t정확도: 99.02%\n",
+      "17\t검증 세트 손실: 0.053203\t최선의 손실: 0.039415\t정확도: 98.94%\n",
+      "18\t검증 세트 손실: 0.178250\t최선의 손실: 0.039415\t정확도: 98.48%\n",
+      "19\t검증 세트 손실: 0.064748\t최선의 손실: 0.039415\t정확도: 98.63%\n",
+      "20\t검증 세트 손실: 0.052132\t최선의 손실: 0.039415\t정확도: 98.71%\n",
+      "21\t검증 세트 손실: 0.099498\t최선의 손실: 0.039415\t정확도: 97.93%\n",
+      "22\t검증 세트 손실: 0.070346\t최선의 손실: 0.039415\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.068477\t최선의 손실: 0.039415\t정확도: 98.94%\n",
+      "24\t검증 세트 손실: 0.078886\t최선의 손실: 0.039415\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.062279\t최선의 손실: 0.039415\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.056129\t최선의 손실: 0.039415\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.151441\t최선의 손실: 0.039415\t정확도: 97.93%\n",
+      "28\t검증 세트 손실: 0.199817\t최선의 손실: 0.039415\t정확도: 98.63%\n",
+      "29\t검증 세트 손실: 0.180960\t최선의 손실: 0.039415\t정확도: 98.51%\n",
+      "30\t검증 세트 손실: 0.093772\t최선의 손실: 0.039415\t정확도: 98.87%\n",
+      "31\t검증 세트 손실: 0.110970\t최선의 손실: 0.039415\t정확도: 98.71%\n",
+      "32\t검증 세트 손실: 0.081844\t최선의 손실: 0.039415\t정확도: 99.02%\n",
+      "33\t검증 세트 손실: 0.138687\t최선의 손실: 0.039415\t정확도: 98.63%\n",
+      "34\t검증 세트 손실: 0.124552\t최선의 손실: 0.039415\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.169394\t최선의 손실: 0.039415\t정확도: 98.55%\n",
+      "36\t검증 세트 손실: 0.424007\t최선의 손실: 0.039415\t정확도: 97.65%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=10, total=  11.1s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.664235\t최선의 손실: 0.664235\t정확도: 83.78%\n",
-      "1\t검증 세트 손실: 0.168976\t최선의 손실: 0.168976\t정확도: 95.47%\n",
-      "2\t검증 세트 손실: 0.187005\t최선의 손실: 0.168976\t정확도: 94.68%\n",
-      "3\t검증 세트 손실: 0.097839\t최선의 손실: 0.097839\t정확도: 97.58%\n",
-      "4\t검증 세트 손실: 0.147835\t최선의 손실: 0.097839\t정확도: 95.86%\n",
-      "5\t검증 세트 손실: 0.086918\t최선의 손실: 0.086918\t정확도: 97.69%\n",
-      "6\t검증 세트 손실: 0.123809\t최선의 손실: 0.086918\t정확도: 96.48%\n",
-      "7\t검증 세트 손실: 0.089504\t최선의 손실: 0.086918\t정확도: 97.69%\n",
-      "8\t검증 세트 손실: 0.090311\t최선의 손실: 0.086918\t정확도: 97.54%\n",
-      "9\t검증 세트 손실: 0.090093\t최선의 손실: 0.086918\t정확도: 97.69%\n",
-      "10\t검증 세트 손실: 0.107612\t최선의 손실: 0.086918\t정확도: 97.07%\n",
-      "11\t검증 세트 손실: 0.096351\t최선의 손실: 0.086918\t정확도: 97.77%\n",
-      "12\t검증 세트 손실: 0.090381\t최선의 손실: 0.086918\t정확도: 97.77%\n",
-      "13\t검증 세트 손실: 0.086344\t최선의 손실: 0.086344\t정확도: 97.85%\n",
-      "14\t검증 세트 손실: 0.105254\t최선의 손실: 0.086344\t정확도: 97.54%\n",
-      "15\t검증 세트 손실: 0.090773\t최선의 손실: 0.086344\t정확도: 97.69%\n",
-      "16\t검증 세트 손실: 0.094184\t최선의 손실: 0.086344\t정확도: 97.85%\n",
-      "17\t검증 세트 손실: 0.091938\t최선의 손실: 0.086344\t정확도: 97.77%\n",
-      "18\t검증 세트 손실: 0.104184\t최선의 손실: 0.086344\t정확도: 97.69%\n",
-      "19\t검증 세트 손실: 0.116767\t최선의 손실: 0.086344\t정확도: 97.46%\n",
-      "20\t검증 세트 손실: 0.099674\t최선의 손실: 0.086344\t정확도: 97.85%\n",
-      "21\t검증 세트 손실: 0.104412\t최선의 손실: 0.086344\t정확도: 97.69%\n",
-      "22\t검증 세트 손실: 0.117995\t최선의 손실: 0.086344\t정확도: 97.38%\n",
-      "23\t검증 세트 손실: 0.084129\t최선의 손실: 0.084129\t정확도: 97.93%\n",
-      "24\t검증 세트 손실: 0.090977\t최선의 손실: 0.084129\t정확도: 98.01%\n",
-      "25\t검증 세트 손실: 0.094132\t최선의 손실: 0.084129\t정확도: 97.85%\n",
-      "26\t검증 세트 손실: 0.098232\t최선의 손실: 0.084129\t정확도: 97.93%\n",
-      "27\t검증 세트 손실: 0.097770\t최선의 손실: 0.084129\t정확도: 98.16%\n",
-      "28\t검증 세트 손실: 0.102611\t최선의 손실: 0.084129\t정확도: 97.85%\n",
-      "29\t검증 세트 손실: 0.112397\t최선의 손실: 0.084129\t정확도: 97.81%\n",
-      "30\t검증 세트 손실: 0.084955\t최선의 손실: 0.084129\t정확도: 97.97%\n",
-      "31\t검증 세트 손실: 0.100127\t최선의 손실: 0.084129\t정확도: 97.85%\n",
-      "32\t검증 세트 손실: 0.103061\t최선의 손실: 0.084129\t정확도: 98.01%\n",
-      "33\t검증 세트 손실: 0.094529\t최선의 손실: 0.084129\t정확도: 98.12%\n",
-      "34\t검증 세트 손실: 0.093261\t최선의 손실: 0.084129\t정확도: 98.28%\n",
-      "35\t검증 세트 손실: 0.090767\t최선의 손실: 0.084129\t정확도: 98.12%\n",
-      "36\t검증 세트 손실: 0.097927\t최선의 손실: 0.084129\t정확도: 98.16%\n",
-      "37\t검증 세트 손실: 0.098650\t최선의 손실: 0.084129\t정확도: 97.97%\n",
-      "38\t검증 세트 손실: 0.107392\t최선의 손실: 0.084129\t정확도: 98.05%\n",
-      "39\t검증 세트 손실: 0.127225\t최선의 손실: 0.084129\t정확도: 97.50%\n",
-      "40\t검증 세트 손실: 0.091490\t최선의 손실: 0.084129\t정확도: 98.16%\n",
-      "41\t검증 세트 손실: 0.107564\t최선의 손실: 0.084129\t정확도: 98.12%\n",
-      "42\t검증 세트 손실: 0.099949\t최선의 손실: 0.084129\t정확도: 97.93%\n",
-      "43\t검증 세트 손실: 0.090862\t최선의 손실: 0.084129\t정확도: 98.24%\n",
-      "44\t검증 세트 손실: 0.109995\t최선의 손실: 0.084129\t정확도: 97.85%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=160, total=  54.6s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.143584\t최선의 손실: 0.143584\t정확도: 96.17%\n",
+      "1\t검증 세트 손실: 0.131566\t최선의 손실: 0.131566\t정확도: 96.36%\n",
+      "2\t검증 세트 손실: 0.154665\t최선의 손실: 0.131566\t정확도: 96.29%\n",
+      "3\t검증 세트 손실: 0.072913\t최선의 손실: 0.072913\t정확도: 98.28%\n",
+      "4\t검증 세트 손실: 0.141587\t최선의 손실: 0.072913\t정확도: 97.50%\n",
+      "5\t검증 세트 손실: 0.155106\t최선의 손실: 0.072913\t정확도: 96.60%\n",
+      "6\t검증 세트 손실: 0.101494\t최선의 손실: 0.072913\t정확도: 97.89%\n",
+      "7\t검증 세트 손실: 0.076734\t최선의 손실: 0.072913\t정확도: 97.97%\n",
+      "8\t검증 세트 손실: 0.062659\t최선의 손실: 0.062659\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.130313\t최선의 손실: 0.062659\t정확도: 96.68%\n",
+      "10\t검증 세트 손실: 0.155874\t최선의 손실: 0.062659\t정확도: 96.91%\n",
+      "11\t검증 세트 손실: 0.051767\t최선의 손실: 0.051767\t정확도: 98.59%\n",
+      "12\t검증 세트 손실: 0.063017\t최선의 손실: 0.051767\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.125207\t최선의 손실: 0.051767\t정확도: 98.08%\n",
+      "14\t검증 세트 손실: 0.090422\t최선의 손실: 0.051767\t정확도: 98.01%\n",
+      "15\t검증 세트 손실: 0.056263\t최선의 손실: 0.051767\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.063551\t최선의 손실: 0.051767\t정확도: 98.48%\n",
+      "17\t검증 세트 손실: 0.073275\t최선의 손실: 0.051767\t정확도: 98.59%\n",
+      "18\t검증 세트 손실: 0.082253\t최선의 손실: 0.051767\t정확도: 98.63%\n",
+      "19\t검증 세트 손실: 0.136064\t최선의 손실: 0.051767\t정확도: 97.69%\n",
+      "20\t검증 세트 손실: 0.084632\t최선의 손실: 0.051767\t정확도: 98.59%\n",
+      "21\t검증 세트 손실: 0.093283\t최선의 손실: 0.051767\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.083744\t최선의 손실: 0.051767\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.082607\t최선의 손실: 0.051767\t정확도: 98.63%\n",
+      "24\t검증 세트 손실: 0.090446\t최선의 손실: 0.051767\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.227800\t최선의 손실: 0.051767\t정확도: 97.89%\n",
+      "26\t검증 세트 손실: 0.167068\t최선의 손실: 0.051767\t정확도: 98.36%\n",
+      "27\t검증 세트 손실: 0.075256\t최선의 손실: 0.051767\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.084884\t최선의 손실: 0.051767\t정확도: 98.83%\n",
+      "29\t검증 세트 손실: 0.150394\t최선의 손실: 0.051767\t정확도: 98.55%\n",
+      "30\t검증 세트 손실: 0.077806\t최선의 손실: 0.051767\t정확도: 98.51%\n",
+      "31\t검증 세트 손실: 0.070894\t최선의 손실: 0.051767\t정확도: 98.71%\n",
+      "32\t검증 세트 손실: 0.095400\t최선의 손실: 0.051767\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=10, total=   8.5s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.135196\t최선의 손실: 0.135196\t정확도: 96.36%\n",
-      "1\t검증 세트 손실: 0.079983\t최선의 손실: 0.079983\t정확도: 97.85%\n",
-      "2\t검증 세트 손실: 0.084647\t최선의 손실: 0.079983\t정확도: 97.73%\n",
-      "3\t검증 세트 손실: 0.069346\t최선의 손실: 0.069346\t정확도: 98.05%\n",
-      "4\t검증 세트 손실: 0.059371\t최선의 손실: 0.059371\t정확도: 98.24%\n",
-      "5\t검증 세트 손실: 0.067333\t최선의 손실: 0.059371\t정확도: 98.28%\n",
-      "6\t검증 세트 손실: 0.040764\t최선의 손실: 0.040764\t정확도: 98.63%\n",
-      "7\t검증 세트 손실: 0.088359\t최선의 손실: 0.040764\t정확도: 97.62%\n",
-      "8\t검증 세트 손실: 0.070836\t최선의 손실: 0.040764\t정확도: 98.48%\n",
-      "9\t검증 세트 손실: 0.153734\t최선의 손실: 0.040764\t정확도: 96.29%\n",
-      "10\t검증 세트 손실: 0.143188\t최선의 손실: 0.040764\t정확도: 97.38%\n",
-      "11\t검증 세트 손실: 0.151150\t최선의 손실: 0.040764\t정확도: 97.73%\n",
-      "12\t검증 세트 손실: 0.092037\t최선의 손실: 0.040764\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.074253\t최선의 손실: 0.040764\t정확도: 98.40%\n",
-      "14\t검증 세트 손실: 0.068336\t최선의 손실: 0.040764\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.170849\t최선의 손실: 0.040764\t정확도: 98.40%\n",
-      "16\t검증 세트 손실: 0.092909\t최선의 손실: 0.040764\t정확도: 98.32%\n",
-      "17\t검증 세트 손실: 0.065087\t최선의 손실: 0.040764\t정확도: 98.63%\n",
-      "18\t검증 세트 손실: 0.518812\t최선의 손실: 0.040764\t정확도: 95.90%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=160, total=  48.8s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.125555\t최선의 손실: 0.125555\t정확도: 97.11%\n",
+      "1\t검증 세트 손실: 0.127064\t최선의 손실: 0.125555\t정확도: 97.03%\n",
+      "2\t검증 세트 손실: 0.137598\t최선의 손실: 0.125555\t정확도: 97.03%\n",
+      "3\t검증 세트 손실: 0.085083\t최선의 손실: 0.085083\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.057459\t최선의 손실: 0.057459\t정확도: 98.51%\n",
+      "5\t검증 세트 손실: 0.168158\t최선의 손실: 0.057459\t정확도: 96.87%\n",
+      "6\t검증 세트 손실: 0.162524\t최선의 손실: 0.057459\t정확도: 95.39%\n",
+      "7\t검증 세트 손실: 0.091804\t최선의 손실: 0.057459\t정확도: 98.63%\n",
+      "8\t검증 세트 손실: 0.054431\t최선의 손실: 0.054431\t정확도: 98.79%\n",
+      "9\t검증 세트 손실: 0.076868\t최선의 손실: 0.054431\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.077509\t최선의 손실: 0.054431\t정확도: 98.83%\n",
+      "11\t검증 세트 손실: 0.047990\t최선의 손실: 0.047990\t정확도: 98.98%\n",
+      "12\t검증 세트 손실: 0.161903\t최선의 손실: 0.047990\t정확도: 97.58%\n",
+      "13\t검증 세트 손실: 0.086317\t최선의 손실: 0.047990\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.064305\t최선의 손실: 0.047990\t정확도: 98.48%\n",
+      "15\t검증 세트 손실: 0.051832\t최선의 손실: 0.047990\t정확도: 98.91%\n",
+      "16\t검증 세트 손실: 0.061797\t최선의 손실: 0.047990\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.054310\t최선의 손실: 0.047990\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.057864\t최선의 손실: 0.047990\t정확도: 98.59%\n",
+      "19\t검증 세트 손실: 0.085490\t최선의 손실: 0.047990\t정확도: 98.48%\n",
+      "20\t검증 세트 손실: 0.141062\t최선의 손실: 0.047990\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.055621\t최선의 손실: 0.047990\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.043619\t최선의 손실: 0.043619\t정확도: 99.06%\n",
+      "23\t검증 세트 손실: 0.072820\t최선의 손실: 0.043619\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.040890\t최선의 손실: 0.040890\t정확도: 99.06%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "19\t검증 세트 손실: 0.059133\t최선의 손실: 0.040764\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.055154\t최선의 손실: 0.040764\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.058164\t최선의 손실: 0.040764\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.087640\t최선의 손실: 0.040764\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.075590\t최선의 손실: 0.040764\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.096082\t최선의 손실: 0.040764\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.067968\t최선의 손실: 0.040764\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.068542\t최선의 손실: 0.040764\t정확도: 99.02%\n",
-      "27\t검증 세트 손실: 0.095908\t최선의 손실: 0.040764\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.044905\t최선의 손실: 0.040890\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.063882\t최선의 손실: 0.040890\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.084354\t최선의 손실: 0.040890\t정확도: 98.44%\n",
+      "28\t검증 세트 손실: 0.084978\t최선의 손실: 0.040890\t정확도: 98.63%\n",
+      "29\t검증 세트 손실: 0.273063\t최선의 손실: 0.040890\t정확도: 97.73%\n",
+      "30\t검증 세트 손실: 0.240195\t최선의 손실: 0.040890\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.123615\t최선의 손실: 0.040890\t정확도: 98.79%\n",
+      "32\t검증 세트 손실: 0.079183\t최선의 손실: 0.040890\t정확도: 99.10%\n",
+      "33\t검증 세트 손실: 0.086839\t최선의 손실: 0.040890\t정확도: 98.75%\n",
+      "34\t검증 세트 손실: 0.080101\t최선의 손실: 0.040890\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.086596\t최선의 손실: 0.040890\t정확도: 99.30%\n",
+      "36\t검증 세트 손실: 0.150241\t최선의 손실: 0.040890\t정확도: 98.51%\n",
+      "37\t검증 세트 손실: 0.069625\t최선의 손실: 0.040890\t정확도: 99.10%\n",
+      "38\t검증 세트 손실: 0.203317\t최선의 손실: 0.040890\t정확도: 98.51%\n",
+      "39\t검증 세트 손실: 0.135664\t최선의 손실: 0.040890\t정확도: 99.06%\n",
+      "40\t검증 세트 손실: 0.097024\t최선의 손실: 0.040890\t정확도: 99.10%\n",
+      "41\t검증 세트 손실: 0.095055\t최선의 손실: 0.040890\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.109368\t최선의 손실: 0.040890\t정확도: 99.14%\n",
+      "43\t검증 세트 손실: 0.091120\t최선의 손실: 0.040890\t정확도: 98.94%\n",
+      "44\t검증 세트 손실: 0.076429\t최선의 손실: 0.040890\t정확도: 98.98%\n",
+      "45\t검증 세트 손실: 0.084663\t최선의 손실: 0.040890\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=160, total=  40.9s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.100757\t최선의 손실: 0.100757\t정확도: 97.38%\n",
-      "1\t검증 세트 손실: 0.074661\t최선의 손실: 0.074661\t정확도: 97.81%\n",
-      "2\t검증 세트 손실: 0.089473\t최선의 손실: 0.074661\t정확도: 97.81%\n",
-      "3\t검증 세트 손실: 0.061471\t최선의 손실: 0.061471\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.059648\t최선의 손실: 0.059648\t정확도: 98.59%\n",
-      "5\t검증 세트 손실: 0.056074\t최선의 손실: 0.056074\t정확도: 98.36%\n",
-      "6\t검증 세트 손실: 0.062955\t최선의 손실: 0.056074\t정확도: 98.48%\n",
-      "7\t검증 세트 손실: 0.126958\t최선의 손실: 0.056074\t정확도: 97.62%\n",
-      "8\t검증 세트 손실: 0.146402\t최선의 손실: 0.056074\t정확도: 98.01%\n",
-      "9\t검증 세트 손실: 0.066236\t최선의 손실: 0.056074\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.106078\t최선의 손실: 0.056074\t정확도: 97.77%\n",
-      "11\t검증 세트 손실: 0.070665\t최선의 손실: 0.056074\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.056769\t최선의 손실: 0.056074\t정확도: 98.51%\n",
-      "13\t검증 세트 손실: 0.064572\t최선의 손실: 0.056074\t정확도: 98.24%\n",
-      "14\t검증 세트 손실: 0.553410\t최선의 손실: 0.056074\t정확도: 96.29%\n",
-      "15\t검증 세트 손실: 0.081588\t최선의 손실: 0.056074\t정확도: 98.44%\n",
-      "16\t검증 세트 손실: 0.107277\t최선의 손실: 0.056074\t정확도: 98.16%\n",
-      "17\t검증 세트 손실: 0.076503\t최선의 손실: 0.056074\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.063122\t최선의 손실: 0.056074\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.081513\t최선의 손실: 0.056074\t정확도: 98.55%\n",
-      "20\t검증 세트 손실: 0.086022\t최선의 손실: 0.056074\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.079650\t최선의 손실: 0.056074\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.069727\t최선의 손실: 0.056074\t정확도: 98.67%\n",
-      "23\t검증 세트 손실: 0.074930\t최선의 손실: 0.056074\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.288487\t최선의 손실: 0.056074\t정확도: 95.15%\n",
-      "25\t검증 세트 손실: 0.107758\t최선의 손실: 0.056074\t정확도: 98.24%\n",
-      "26\t검증 세트 손실: 0.068261\t최선의 손실: 0.056074\t정확도: 98.36%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=160, total= 1.1min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.103826\t최선의 손실: 0.103826\t정확도: 97.38%\n",
+      "1\t검증 세트 손실: 0.074195\t최선의 손실: 0.074195\t정확도: 98.12%\n",
+      "2\t검증 세트 손실: 0.051645\t최선의 손실: 0.051645\t정확도: 98.48%\n",
+      "3\t검증 세트 손실: 0.057844\t최선의 손실: 0.051645\t정확도: 98.36%\n",
+      "4\t검증 세트 손실: 0.039945\t최선의 손실: 0.039945\t정확도: 98.94%\n",
+      "5\t검증 세트 손실: 0.041250\t최선의 손실: 0.039945\t정확도: 98.91%\n",
+      "6\t검증 세트 손실: 0.046527\t최선의 손실: 0.039945\t정확도: 98.63%\n",
+      "7\t검증 세트 손실: 0.046481\t최선의 손실: 0.039945\t정확도: 98.75%\n",
+      "8\t검증 세트 손실: 0.043871\t최선의 손실: 0.039945\t정확도: 98.79%\n",
+      "9\t검증 세트 손실: 0.078152\t최선의 손실: 0.039945\t정확도: 98.24%\n",
+      "10\t검증 세트 손실: 0.055889\t최선의 손실: 0.039945\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.032877\t최선의 손실: 0.032877\t정확도: 99.06%\n",
+      "12\t검증 세트 손실: 0.045403\t최선의 손실: 0.032877\t정확도: 98.91%\n",
+      "13\t검증 세트 손실: 0.043842\t최선의 손실: 0.032877\t정확도: 98.75%\n",
+      "14\t검증 세트 손실: 0.048140\t최선의 손실: 0.032877\t정확도: 98.63%\n",
+      "15\t검증 세트 손실: 0.050625\t최선의 손실: 0.032877\t정확도: 98.67%\n",
+      "16\t검증 세트 손실: 0.061452\t최선의 손실: 0.032877\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.036711\t최선의 손실: 0.032877\t정확도: 99.10%\n",
+      "18\t검증 세트 손실: 0.029693\t최선의 손실: 0.029693\t정확도: 99.02%\n",
+      "19\t검증 세트 손실: 0.034834\t최선의 손실: 0.029693\t정확도: 99.10%\n",
+      "20\t검증 세트 손실: 0.064291\t최선의 손실: 0.029693\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.047280\t최선의 손실: 0.029693\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.042938\t최선의 손실: 0.029693\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.053452\t최선의 손실: 0.029693\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.042687\t최선의 손실: 0.029693\t정확도: 99.14%\n",
+      "25\t검증 세트 손실: 0.057023\t최선의 손실: 0.029693\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.053165\t최선의 손실: 0.029693\t정확도: 98.75%\n",
+      "27\t검증 세트 손실: 0.035902\t최선의 손실: 0.029693\t정확도: 99.18%\n",
+      "28\t검증 세트 손실: 0.040296\t최선의 손실: 0.029693\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.045511\t최선의 손실: 0.029693\t정확도: 99.02%\n",
+      "30\t검증 세트 손실: 0.043047\t최선의 손실: 0.029693\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.043463\t최선의 손실: 0.029693\t정확도: 99.06%\n",
+      "32\t검증 세트 손실: 0.040384\t최선의 손실: 0.029693\t정확도: 99.18%\n",
+      "33\t검증 세트 손실: 0.041974\t최선의 손실: 0.029693\t정확도: 99.14%\n",
+      "34\t검증 세트 손실: 0.051840\t최선의 손실: 0.029693\t정확도: 99.02%\n",
+      "35\t검증 세트 손실: 0.050005\t최선의 손실: 0.029693\t정확도: 98.94%\n",
+      "36\t검증 세트 손실: 0.044436\t최선의 손실: 0.029693\t정확도: 98.91%\n",
+      "37\t검증 세트 손실: 0.047727\t최선의 손실: 0.029693\t정확도: 99.34%\n",
+      "38\t검증 세트 손실: 0.035632\t최선의 손실: 0.029693\t정확도: 99.26%\n",
+      "39\t검증 세트 손실: 0.040308\t최선의 손실: 0.029693\t정확도: 99.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=160, total=  39.5s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.128789\t최선의 손실: 0.128789\t정확도: 97.11%\n",
-      "1\t검증 세트 손실: 0.129315\t최선의 손실: 0.128789\t정확도: 97.22%\n",
-      "2\t검증 세트 손실: 0.085663\t최선의 손실: 0.085663\t정확도: 97.46%\n",
-      "3\t검증 세트 손실: 0.065953\t최선의 손실: 0.065953\t정확도: 98.36%\n",
-      "4\t검증 세트 손실: 0.075887\t최선의 손실: 0.065953\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.080486\t최선의 손실: 0.065953\t정확도: 97.89%\n",
-      "6\t검증 세트 손실: 0.353471\t최선의 손실: 0.065953\t정확도: 93.63%\n",
-      "7\t검증 세트 손실: 0.063123\t최선의 손실: 0.063123\t정확도: 98.40%\n",
-      "8\t검증 세트 손실: 0.046715\t최선의 손실: 0.046715\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.061253\t최선의 손실: 0.046715\t정확도: 98.12%\n",
-      "10\t검증 세트 손실: 0.052182\t최선의 손실: 0.046715\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.048522\t최선의 손실: 0.046715\t정확도: 98.48%\n",
-      "12\t검증 세트 손실: 0.111892\t최선의 손실: 0.046715\t정확도: 97.97%\n",
-      "13\t검증 세트 손실: 0.049489\t최선의 손실: 0.046715\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.408967\t최선의 손실: 0.046715\t정확도: 95.43%\n",
-      "15\t검증 세트 손실: 0.135527\t최선의 손실: 0.046715\t정확도: 98.01%\n",
-      "16\t검증 세트 손실: 0.048995\t최선의 손실: 0.046715\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.087302\t최선의 손실: 0.046715\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.071919\t최선의 손실: 0.046715\t정확도: 98.40%\n",
-      "19\t검증 세트 손실: 0.113465\t최선의 손실: 0.046715\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.078366\t최선의 손실: 0.046715\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.064303\t최선의 손실: 0.046715\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.082085\t최선의 손실: 0.046715\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.079118\t최선의 손실: 0.046715\t정확도: 98.48%\n",
-      "24\t검증 세트 손실: 0.117677\t최선의 손실: 0.046715\t정확도: 98.20%\n",
-      "25\t검증 세트 손실: 0.147481\t최선의 손실: 0.046715\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.088321\t최선의 손실: 0.046715\t정확도: 98.67%\n",
-      "27\t검증 세트 손실: 0.109918\t최선의 손실: 0.046715\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.090625\t최선의 손실: 0.046715\t정확도: 98.79%\n",
-      "29\t검증 세트 손실: 0.160380\t최선의 손실: 0.046715\t정확도: 98.40%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100, total=  58.6s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.142975\t최선의 손실: 0.142975\t정확도: 95.50%\n",
+      "1\t검증 세트 손실: 0.054392\t최선의 손실: 0.054392\t정확도: 98.59%\n",
+      "2\t검증 세트 손실: 0.080081\t최선의 손실: 0.054392\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.040324\t최선의 손실: 0.040324\t정확도: 98.67%\n",
+      "4\t검증 세트 손실: 0.060270\t최선의 손실: 0.040324\t정확도: 98.59%\n",
+      "5\t검증 세트 손실: 0.042700\t최선의 손실: 0.040324\t정확도: 98.91%\n",
+      "6\t검증 세트 손실: 0.042516\t최선의 손실: 0.040324\t정확도: 98.79%\n",
+      "7\t검증 세트 손실: 0.044706\t최선의 손실: 0.040324\t정확도: 98.94%\n",
+      "8\t검증 세트 손실: 0.045781\t최선의 손실: 0.040324\t정확도: 98.94%\n",
+      "9\t검증 세트 손실: 0.039452\t최선의 손실: 0.039452\t정확도: 99.06%\n",
+      "10\t검증 세트 손실: 0.064771\t최선의 손실: 0.039452\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.043055\t최선의 손실: 0.039452\t정확도: 98.87%\n",
+      "12\t검증 세트 손실: 0.065348\t최선의 손실: 0.039452\t정확도: 98.28%\n",
+      "13\t검증 세트 손실: 0.043307\t최선의 손실: 0.039452\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.043359\t최선의 손실: 0.039452\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.043042\t최선의 손실: 0.039452\t정확도: 98.87%\n",
+      "16\t검증 세트 손실: 0.047767\t최선의 손실: 0.039452\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.037415\t최선의 손실: 0.037415\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.056376\t최선의 손실: 0.037415\t정확도: 98.83%\n",
+      "19\t검증 세트 손실: 0.043631\t최선의 손실: 0.037415\t정확도: 99.06%\n",
+      "20\t검증 세트 손실: 0.049210\t최선의 손실: 0.037415\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.044375\t최선의 손실: 0.037415\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.067222\t최선의 손실: 0.037415\t정확도: 98.87%\n",
+      "23\t검증 세트 손실: 0.047910\t최선의 손실: 0.037415\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.050672\t최선의 손실: 0.037415\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.046088\t최선의 손실: 0.037415\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.075514\t최선의 손실: 0.037415\t정확도: 98.28%\n",
+      "27\t검증 세트 손실: 0.039417\t최선의 손실: 0.037415\t정확도: 99.14%\n",
+      "28\t검증 세트 손실: 0.035568\t최선의 손실: 0.035568\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.056665\t최선의 손실: 0.035568\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.043797\t최선의 손실: 0.035568\t정확도: 99.18%\n",
+      "31\t검증 세트 손실: 0.048093\t최선의 손실: 0.035568\t정확도: 98.94%\n",
+      "32\t검증 세트 손실: 0.037790\t최선의 손실: 0.035568\t정확도: 99.10%\n",
+      "33\t검증 세트 손실: 0.035579\t최선의 손실: 0.035568\t정확도: 99.18%\n",
+      "34\t검증 세트 손실: 0.045644\t최선의 손실: 0.035568\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.047025\t최선의 손실: 0.035568\t정확도: 99.14%\n",
+      "36\t검증 세트 손실: 0.065017\t최선의 손실: 0.035568\t정확도: 98.83%\n",
+      "37\t검증 세트 손실: 0.043838\t최선의 손실: 0.035568\t정확도: 98.98%\n",
+      "38\t검증 세트 손실: 0.048180\t최선의 손실: 0.035568\t정확도: 99.06%\n",
+      "39\t검증 세트 손실: 0.064780\t최선의 손실: 0.035568\t정확도: 98.71%\n",
+      "40\t검증 세트 손실: 0.049724\t최선의 손실: 0.035568\t정확도: 98.87%\n",
+      "41\t검증 세트 손실: 0.049605\t최선의 손실: 0.035568\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.045270\t최선의 손실: 0.035568\t정확도: 99.18%\n",
+      "43\t검증 세트 손실: 0.040992\t최선의 손실: 0.035568\t정확도: 99.18%\n",
+      "44\t검증 세트 손실: 0.040899\t최선의 손실: 0.035568\t정확도: 99.10%\n",
+      "45\t검증 세트 손실: 0.056193\t최선의 손실: 0.035568\t정확도: 99.10%\n",
+      "46\t검증 세트 손실: 0.055284\t최선의 손실: 0.035568\t정확도: 99.14%\n",
+      "47\t검증 세트 손실: 0.064897\t최선의 손실: 0.035568\t정확도: 98.87%\n",
+      "48\t검증 세트 손실: 0.035509\t최선의 손실: 0.035509\t정확도: 99.26%\n",
+      "49\t검증 세트 손실: 0.065829\t최선의 손실: 0.035509\t정확도: 98.91%\n",
+      "50\t검증 세트 손실: 0.044642\t최선의 손실: 0.035509\t정확도: 99.06%\n",
+      "51\t검증 세트 손실: 0.036089\t최선의 손실: 0.035509\t정확도: 99.26%\n",
+      "52\t검증 세트 손실: 0.044617\t최선의 손실: 0.035509\t정확도: 99.18%\n",
+      "53\t검증 세트 손실: 0.039619\t최선의 손실: 0.035509\t정확도: 99.30%\n",
+      "54\t검증 세트 손실: 0.064983\t최선의 손실: 0.035509\t정확도: 98.91%\n",
+      "55\t검증 세트 손실: 0.051789\t최선의 손실: 0.035509\t정확도: 98.94%\n",
+      "56\t검증 세트 손실: 0.064147\t최선의 손실: 0.035509\t정확도: 98.91%\n",
+      "57\t검증 세트 손실: 0.059825\t최선의 손실: 0.035509\t정확도: 98.94%\n",
+      "58\t검증 세트 손실: 0.057077\t최선의 손실: 0.035509\t정확도: 98.98%\n",
+      "59\t검증 세트 손실: 0.053895\t최선의 손실: 0.035509\t정확도: 99.06%\n",
+      "60\t검증 세트 손실: 0.054331\t최선의 손실: 0.035509\t정확도: 99.02%\n",
+      "61\t검증 세트 손실: 0.071560\t최선의 손실: 0.035509\t정확도: 98.98%\n",
+      "62\t검증 세트 손실: 0.058655\t최선의 손실: 0.035509\t정확도: 98.87%\n",
+      "63\t검증 세트 손실: 0.046037\t최선의 손실: 0.035509\t정확도: 99.06%\n",
+      "64\t검증 세트 손실: 0.058089\t최선의 손실: 0.035509\t정확도: 98.94%\n",
+      "65\t검증 세트 손실: 0.066232\t최선의 손실: 0.035509\t정확도: 99.02%\n",
+      "66\t검증 세트 손실: 0.063755\t최선의 손실: 0.035509\t정확도: 98.94%\n",
+      "67\t검증 세트 손실: 0.078273\t최선의 손실: 0.035509\t정확도: 98.55%\n",
+      "68\t검증 세트 손실: 0.058069\t최선의 손실: 0.035509\t정확도: 99.14%\n",
+      "69\t검증 세트 손실: 0.057971\t최선의 손실: 0.035509\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=160, total=  43.7s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.076464\t최선의 손실: 0.076464\t정확도: 98.12%\n",
-      "1\t검증 세트 손실: 0.063218\t최선의 손실: 0.063218\t정확도: 98.40%\n",
-      "2\t검증 세트 손실: 0.073896\t최선의 손실: 0.063218\t정확도: 97.81%\n",
-      "3\t검증 세트 손실: 0.049405\t최선의 손실: 0.049405\t정확도: 98.71%\n",
-      "4\t검증 세트 손실: 0.058214\t최선의 손실: 0.049405\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 0.054943\t최선의 손실: 0.049405\t정확도: 98.63%\n",
-      "6\t검증 세트 손실: 0.037935\t최선의 손실: 0.037935\t정확도: 99.02%\n",
-      "7\t검증 세트 손실: 0.058830\t최선의 손실: 0.037935\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.051020\t최선의 손실: 0.037935\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.052414\t최선의 손실: 0.037935\t정확도: 98.83%\n",
-      "10\t검증 세트 손실: 0.047939\t최선의 손실: 0.037935\t정확도: 98.98%\n",
-      "11\t검증 세트 손실: 0.033686\t최선의 손실: 0.033686\t정확도: 99.02%\n",
-      "12\t검증 세트 손실: 0.051146\t최선의 손실: 0.033686\t정확도: 98.91%\n",
-      "13\t검증 세트 손실: 0.057833\t최선의 손실: 0.033686\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.050129\t최선의 손실: 0.033686\t정확도: 98.59%\n",
-      "15\t검증 세트 손실: 0.073158\t최선의 손실: 0.033686\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.050694\t최선의 손실: 0.033686\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.049524\t최선의 손실: 0.033686\t정확도: 98.98%\n",
-      "18\t검증 세트 손실: 0.046098\t최선의 손실: 0.033686\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.043835\t최선의 손실: 0.033686\t정확도: 99.10%\n",
-      "20\t검증 세트 손실: 0.049283\t최선의 손실: 0.033686\t정확도: 99.02%\n",
-      "21\t검증 세트 손실: 0.033924\t최선의 손실: 0.033686\t정확도: 99.26%\n",
-      "22\t검증 세트 손실: 0.067250\t최선의 손실: 0.033686\t정확도: 98.55%\n",
-      "23\t검증 세트 손실: 0.046133\t최선의 손실: 0.033686\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.046883\t최선의 손실: 0.033686\t정확도: 99.10%\n",
-      "25\t검증 세트 손실: 0.038019\t최선의 손실: 0.033686\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.048427\t최선의 손실: 0.033686\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.046316\t최선의 손실: 0.033686\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.057227\t최선의 손실: 0.033686\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.057019\t최선의 손실: 0.033686\t정확도: 98.94%\n",
-      "30\t검증 세트 손실: 0.063827\t최선의 손실: 0.033686\t정확도: 98.75%\n",
-      "31\t검증 세트 손실: 0.085374\t최선의 손실: 0.033686\t정확도: 98.55%\n",
-      "32\t검증 세트 손실: 0.049745\t최선의 손실: 0.033686\t정확도: 98.94%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100, total=  47.7s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.084277\t최선의 손실: 0.084277\t정확도: 97.34%\n",
-      "1\t검증 세트 손실: 0.077700\t최선의 손실: 0.077700\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.061934\t최선의 손실: 0.061934\t정확도: 98.16%\n",
-      "3\t검증 세트 손실: 0.050947\t최선의 손실: 0.050947\t정확도: 98.32%\n",
-      "4\t검증 세트 손실: 0.054845\t최선의 손실: 0.050947\t정확도: 98.40%\n",
-      "5\t검증 세트 손실: 0.041500\t최선의 손실: 0.041500\t정확도: 98.67%\n",
-      "6\t검증 세트 손실: 0.057974\t최선의 손실: 0.041500\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.054029\t최선의 손실: 0.041500\t정확도: 98.51%\n",
-      "8\t검증 세트 손실: 0.051037\t최선의 손실: 0.041500\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.058109\t최선의 손실: 0.041500\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.071076\t최선의 손실: 0.041500\t정확도: 98.28%\n",
-      "11\t검증 세트 손실: 0.048255\t최선의 손실: 0.041500\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.044432\t최선의 손실: 0.041500\t정확도: 98.87%\n",
-      "13\t검증 세트 손실: 0.046699\t최선의 손실: 0.041500\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.048367\t최선의 손실: 0.041500\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.052393\t최선의 손실: 0.041500\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.034231\t최선의 손실: 0.034231\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.049000\t최선의 손실: 0.034231\t정확도: 98.91%\n",
-      "18\t검증 세트 손실: 0.045933\t최선의 손실: 0.034231\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.038933\t최선의 손실: 0.034231\t정확도: 99.10%\n",
-      "20\t검증 세트 손실: 0.040158\t최선의 손실: 0.034231\t정확도: 99.14%\n",
-      "21\t검증 세트 손실: 0.046457\t최선의 손실: 0.034231\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.038522\t최선의 손실: 0.034231\t정확도: 99.02%\n",
-      "23\t검증 세트 손실: 0.036221\t최선의 손실: 0.034231\t정확도: 99.26%\n",
-      "24\t검증 세트 손실: 0.039026\t최선의 손실: 0.034231\t정확도: 99.06%\n",
-      "25\t검증 세트 손실: 0.044349\t최선의 손실: 0.034231\t정확도: 98.98%\n",
-      "26\t검증 세트 손실: 0.056250\t최선의 손실: 0.034231\t정확도: 99.10%\n",
-      "27\t검증 세트 손실: 0.038951\t최선의 손실: 0.034231\t정확도: 99.22%\n",
-      "28\t검증 세트 손실: 0.050206\t최선의 손실: 0.034231\t정확도: 99.06%\n",
-      "29\t검증 세트 손실: 0.034977\t최선의 손실: 0.034231\t정확도: 99.22%\n",
-      "30\t검증 세트 손실: 0.054833\t최선의 손실: 0.034231\t정확도: 98.55%\n",
-      "31\t검증 세트 손실: 0.052390\t최선의 손실: 0.034231\t정확도: 99.10%\n",
-      "32\t검증 세트 손실: 0.063026\t최선의 손실: 0.034231\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.043766\t최선의 손실: 0.034231\t정확도: 99.06%\n",
-      "34\t검증 세트 손실: 0.043129\t최선의 손실: 0.034231\t정확도: 99.06%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100, total= 1.7min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.108367\t최선의 손실: 0.108367\t정확도: 96.87%\n",
+      "1\t검증 세트 손실: 0.071209\t최선의 손실: 0.071209\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.052676\t최선의 손실: 0.052676\t정확도: 98.51%\n",
+      "3\t검증 세트 손실: 0.053342\t최선의 손실: 0.052676\t정확도: 98.44%\n",
+      "4\t검증 세트 손실: 0.068954\t최선의 손실: 0.052676\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.042823\t최선의 손실: 0.042823\t정확도: 98.71%\n",
+      "6\t검증 세트 손실: 0.042508\t최선의 손실: 0.042508\t정확도: 98.71%\n",
+      "7\t검증 세트 손실: 0.049598\t최선의 손실: 0.042508\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.055941\t최선의 손실: 0.042508\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.037251\t최선의 손실: 0.037251\t정확도: 98.91%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "35\t검증 세트 손실: 0.053022\t최선의 손실: 0.034231\t정확도: 98.98%\n",
-      "36\t검증 세트 손실: 0.043812\t최선의 손실: 0.034231\t정확도: 99.02%\n",
-      "37\t검증 세트 손실: 0.044872\t최선의 손실: 0.034231\t정확도: 99.06%\n",
+      "10\t검증 세트 손실: 0.033508\t최선의 손실: 0.033508\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.032493\t최선의 손실: 0.032493\t정확도: 99.22%\n",
+      "12\t검증 세트 손실: 0.041632\t최선의 손실: 0.032493\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.040585\t최선의 손실: 0.032493\t정확도: 98.75%\n",
+      "14\t검증 세트 손실: 0.043409\t최선의 손실: 0.032493\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.043190\t최선의 손실: 0.032493\t정확도: 98.91%\n",
+      "16\t검증 세트 손실: 0.031681\t최선의 손실: 0.031681\t정확도: 99.14%\n",
+      "17\t검증 세트 손실: 0.043424\t최선의 손실: 0.031681\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.036272\t최선의 손실: 0.031681\t정확도: 99.14%\n",
+      "19\t검증 세트 손실: 0.042642\t최선의 손실: 0.031681\t정확도: 99.18%\n",
+      "20\t검증 세트 손실: 0.036506\t최선의 손실: 0.031681\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.044711\t최선의 손실: 0.031681\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.049469\t최선의 손실: 0.031681\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.040381\t최선의 손실: 0.031681\t정확도: 98.94%\n",
+      "24\t검증 세트 손실: 0.041399\t최선의 손실: 0.031681\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.033236\t최선의 손실: 0.031681\t정확도: 99.22%\n",
+      "26\t검증 세트 손실: 0.040205\t최선의 손실: 0.031681\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.036119\t최선의 손실: 0.031681\t정확도: 99.10%\n",
+      "28\t검증 세트 손실: 0.037971\t최선의 손실: 0.031681\t정확도: 99.22%\n",
+      "29\t검증 세트 손실: 0.041096\t최선의 손실: 0.031681\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.038645\t최선의 손실: 0.031681\t정확도: 99.14%\n",
+      "31\t검증 세트 손실: 0.040769\t최선의 손실: 0.031681\t정확도: 99.34%\n",
+      "32\t검증 세트 손실: 0.040687\t최선의 손실: 0.031681\t정확도: 99.10%\n",
+      "33\t검증 세트 손실: 0.055514\t최선의 손실: 0.031681\t정확도: 99.06%\n",
+      "34\t검증 세트 손실: 0.045935\t최선의 손실: 0.031681\t정확도: 99.02%\n",
+      "35\t검증 세트 손실: 0.039866\t최선의 손실: 0.031681\t정확도: 99.18%\n",
+      "36\t검증 세트 손실: 0.059583\t최선의 손실: 0.031681\t정확도: 98.71%\n",
+      "37\t검증 세트 손실: 0.039395\t최선의 손실: 0.031681\t정확도: 99.14%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100, total=  54.6s\n",
-      "[CV] batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.110408\t최선의 손실: 0.110408\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.056853\t최선의 손실: 0.056853\t정확도: 98.20%\n",
-      "2\t검증 세트 손실: 0.056448\t최선의 손실: 0.056448\t정확도: 98.40%\n",
-      "3\t검증 세트 손실: 0.058362\t최선의 손실: 0.056448\t정확도: 98.48%\n",
-      "4\t검증 세트 손실: 0.055988\t최선의 손실: 0.055988\t정확도: 98.28%\n",
-      "5\t검증 세트 손실: 0.044035\t최선의 손실: 0.044035\t정확도: 98.59%\n",
-      "6\t검증 세트 손실: 0.059388\t최선의 손실: 0.044035\t정확도: 98.40%\n",
-      "7\t검증 세트 손실: 0.089407\t최선의 손실: 0.044035\t정확도: 98.01%\n",
-      "8\t검증 세트 손실: 0.054444\t최선의 손실: 0.044035\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.061645\t최선의 손실: 0.044035\t정확도: 98.16%\n",
-      "10\t검증 세트 손실: 0.051525\t최선의 손실: 0.044035\t정확도: 98.75%\n",
-      "11\t검증 세트 손실: 0.058584\t최선의 손실: 0.044035\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.052633\t최선의 손실: 0.044035\t정확도: 98.48%\n",
-      "13\t검증 세트 손실: 0.046017\t최선의 손실: 0.044035\t정확도: 98.87%\n",
-      "14\t검증 세트 손실: 0.078919\t최선의 손실: 0.044035\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.047161\t최선의 손실: 0.044035\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.051087\t최선의 손실: 0.044035\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.060055\t최선의 손실: 0.044035\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.058938\t최선의 손실: 0.044035\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.051126\t최선의 손실: 0.044035\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.044238\t최선의 손실: 0.044035\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.048290\t최선의 손실: 0.044035\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.045204\t최선의 손실: 0.044035\t정확도: 98.98%\n",
-      "23\t검증 세트 손실: 0.039439\t최선의 손실: 0.039439\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.048227\t최선의 손실: 0.039439\t정확도: 98.94%\n",
-      "25\t검증 세트 손실: 0.042087\t최선의 손실: 0.039439\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.045581\t최선의 손실: 0.039439\t정확도: 99.22%\n",
-      "27\t검증 세트 손실: 0.034910\t최선의 손실: 0.034910\t정확도: 99.10%\n",
-      "28\t검증 세트 손실: 0.029560\t최선의 손실: 0.029560\t정확도: 99.30%\n",
-      "29\t검증 세트 손실: 0.041668\t최선의 손실: 0.029560\t정확도: 99.10%\n",
-      "30\t검증 세트 손실: 0.044432\t최선의 손실: 0.029560\t정확도: 99.02%\n",
-      "31\t검증 세트 손실: 0.044414\t최선의 손실: 0.029560\t정확도: 99.02%\n",
-      "32\t검증 세트 손실: 0.043285\t최선의 손실: 0.029560\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.038925\t최선의 손실: 0.029560\t정확도: 99.30%\n",
-      "34\t검증 세트 손실: 0.033194\t최선의 손실: 0.029560\t정확도: 99.26%\n",
-      "35\t검증 세트 손실: 0.039431\t최선의 손실: 0.029560\t정확도: 99.37%\n",
-      "36\t검증 세트 손실: 0.047997\t최선의 손실: 0.029560\t정확도: 99.02%\n",
-      "37\t검증 세트 손실: 0.047186\t최선의 손실: 0.029560\t정확도: 99.10%\n",
-      "38\t검증 세트 손실: 0.029873\t최선의 손실: 0.029560\t정확도: 99.22%\n",
-      "39\t검증 세트 손실: 0.035714\t최선의 손실: 0.029560\t정확도: 99.26%\n",
-      "40\t검증 세트 손실: 0.028211\t최선의 손실: 0.028211\t정확도: 99.22%\n",
-      "41\t검증 세트 손실: 0.029228\t최선의 손실: 0.028211\t정확도: 99.10%\n",
-      "42\t검증 세트 손실: 0.053766\t최선의 손실: 0.028211\t정확도: 99.02%\n",
-      "43\t검증 세트 손실: 0.045468\t최선의 손실: 0.028211\t정확도: 99.02%\n",
-      "44\t검증 세트 손실: 0.067825\t최선의 손실: 0.028211\t정확도: 98.71%\n",
-      "45\t검증 세트 손실: 0.037547\t최선의 손실: 0.028211\t정확도: 99.02%\n",
-      "46\t검증 세트 손실: 0.042632\t최선의 손실: 0.028211\t정확도: 98.91%\n",
-      "47\t검증 세트 손실: 0.038618\t최선의 손실: 0.028211\t정확도: 99.22%\n",
-      "48\t검증 세트 손실: 0.039219\t최선의 손실: 0.028211\t정확도: 99.02%\n",
-      "49\t검증 세트 손실: 0.048312\t최선의 손실: 0.028211\t정확도: 99.06%\n",
-      "50\t검증 세트 손실: 0.032787\t최선의 손실: 0.028211\t정확도: 99.22%\n",
-      "51\t검증 세트 손실: 0.034475\t최선의 손실: 0.028211\t정확도: 99.30%\n",
-      "52\t검증 세트 손실: 0.035332\t최선의 손실: 0.028211\t정확도: 99.37%\n",
-      "53\t검증 세트 손실: 0.047125\t최선의 손실: 0.028211\t정확도: 98.98%\n",
-      "54\t검증 세트 손실: 0.079003\t최선의 손실: 0.028211\t정확도: 98.75%\n",
-      "55\t검증 세트 손실: 0.034592\t최선의 손실: 0.028211\t정확도: 99.14%\n",
-      "56\t검증 세트 손실: 0.036480\t최선의 손실: 0.028211\t정확도: 99.18%\n",
-      "57\t검증 세트 손실: 0.040866\t최선의 손실: 0.028211\t정확도: 99.06%\n",
-      "58\t검증 세트 손실: 0.035280\t최선의 손실: 0.028211\t정확도: 99.30%\n",
-      "59\t검증 세트 손실: 0.034811\t최선의 손실: 0.028211\t정확도: 99.41%\n",
-      "60\t검증 세트 손실: 0.025797\t최선의 손실: 0.025797\t정확도: 99.61%\n",
-      "61\t검증 세트 손실: 0.050237\t최선의 손실: 0.025797\t정확도: 98.91%\n",
-      "62\t검증 세트 손실: 0.022429\t최선의 손실: 0.022429\t정확도: 99.53%\n",
-      "63\t검증 세트 손실: 0.033803\t최선의 손실: 0.022429\t정확도: 99.26%\n",
-      "64\t검증 세트 손실: 0.035789\t최선의 손실: 0.022429\t정확도: 99.45%\n",
-      "65\t검증 세트 손실: 0.036351\t최선의 손실: 0.022429\t정확도: 99.37%\n",
-      "66\t검증 세트 손실: 0.036849\t최선의 손실: 0.022429\t정확도: 99.10%\n",
-      "67\t검증 세트 손실: 0.029286\t최선의 손실: 0.022429\t정확도: 99.30%\n",
-      "68\t검증 세트 손실: 0.036497\t최선의 손실: 0.022429\t정확도: 99.18%\n",
-      "69\t검증 세트 손실: 0.045176\t최선의 손실: 0.022429\t정확도: 99.22%\n",
-      "70\t검증 세트 손실: 0.038993\t최선의 손실: 0.022429\t정확도: 99.34%\n",
-      "71\t검증 세트 손실: 0.043369\t최선의 손실: 0.022429\t정확도: 99.02%\n",
-      "72\t검증 세트 손실: 0.037018\t최선의 손실: 0.022429\t정확도: 99.22%\n",
-      "73\t검증 세트 손실: 0.037951\t최선의 손실: 0.022429\t정확도: 99.26%\n",
-      "74\t검증 세트 손실: 0.043249\t최선의 손실: 0.022429\t정확도: 99.14%\n",
-      "75\t검증 세트 손실: 0.043162\t최선의 손실: 0.022429\t정확도: 99.14%\n",
-      "76\t검증 세트 손실: 0.041343\t최선의 손실: 0.022429\t정확도: 99.18%\n",
-      "77\t검증 세트 손실: 0.047325\t최선의 손실: 0.022429\t정확도: 99.22%\n",
-      "78\t검증 세트 손실: 0.052501\t최선의 손실: 0.022429\t정확도: 99.22%\n",
-      "79\t검증 세트 손실: 0.033107\t최선의 손실: 0.022429\t정확도: 99.37%\n",
-      "80\t검증 세트 손실: 0.030502\t최선의 손실: 0.022429\t정확도: 99.26%\n",
-      "81\t검증 세트 손실: 0.039055\t최선의 손실: 0.022429\t정확도: 99.26%\n",
-      "82\t검증 세트 손실: 0.029830\t최선의 손실: 0.022429\t정확도: 99.10%\n",
-      "83\t검증 세트 손실: 0.027725\t최선의 손실: 0.022429\t정확도: 99.37%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=100, total=  55.9s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.071823\t최선의 손실: 0.071823\t정확도: 97.81%\n",
+      "1\t검증 세트 손실: 0.083366\t최선의 손실: 0.071823\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.047470\t최선의 손실: 0.047470\t정확도: 98.51%\n",
+      "3\t검증 세트 손실: 0.046650\t최선의 손실: 0.046650\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.033990\t최선의 손실: 0.033990\t정확도: 98.67%\n",
+      "5\t검증 세트 손실: 0.049786\t최선의 손실: 0.033990\t정확도: 98.75%\n",
+      "6\t검증 세트 손실: 0.036069\t최선의 손실: 0.033990\t정확도: 98.98%\n",
+      "7\t검증 세트 손실: 0.061283\t최선의 손실: 0.033990\t정확도: 98.75%\n",
+      "8\t검증 세트 손실: 0.053271\t최선의 손실: 0.033990\t정확도: 99.02%\n",
+      "9\t검증 세트 손실: 0.051755\t최선의 손실: 0.033990\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.046289\t최선의 손실: 0.033990\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.043208\t최선의 손실: 0.033990\t정확도: 98.98%\n",
+      "12\t검증 세트 손실: 0.038304\t최선의 손실: 0.033990\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.041323\t최선의 손실: 0.033990\t정확도: 99.06%\n",
+      "14\t검증 세트 손실: 0.066310\t최선의 손실: 0.033990\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.040970\t최선의 손실: 0.033990\t정확도: 99.06%\n",
+      "16\t검증 세트 손실: 0.047575\t최선의 손실: 0.033990\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.066451\t최선의 손실: 0.033990\t정확도: 98.51%\n",
+      "18\t검증 세트 손실: 0.038625\t최선의 손실: 0.033990\t정확도: 99.06%\n",
+      "19\t검증 세트 손실: 0.058420\t최선의 손실: 0.033990\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.048692\t최선의 손실: 0.033990\t정확도: 98.98%\n",
+      "21\t검증 세트 손실: 0.049047\t최선의 손실: 0.033990\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.042355\t최선의 손실: 0.033990\t정확도: 99.06%\n",
+      "23\t검증 세트 손실: 0.052079\t최선의 손실: 0.033990\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.051709\t최선의 손실: 0.033990\t정확도: 99.06%\n",
+      "25\t검증 세트 손실: 0.077638\t최선의 손실: 0.033990\t정확도: 98.51%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=100, total= 2.0min\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.074487\t최선의 손실: 0.074487\t정확도: 97.73%\n",
-      "1\t검증 세트 손실: 0.056946\t최선의 손실: 0.056946\t정확도: 98.44%\n",
-      "2\t검증 세트 손실: 0.049072\t최선의 손실: 0.049072\t정확도: 98.55%\n",
-      "3\t검증 세트 손실: 0.036962\t최선의 손실: 0.036962\t정확도: 98.59%\n",
-      "4\t검증 세트 손실: 0.042367\t최선의 손실: 0.036962\t정확도: 98.55%\n",
-      "5\t검증 세트 손실: 0.035617\t최선의 손실: 0.035617\t정확도: 99.06%\n",
-      "6\t검증 세트 손실: 0.036657\t최선의 손실: 0.035617\t정확도: 98.94%\n",
-      "7\t검증 세트 손실: 0.045442\t최선의 손실: 0.035617\t정확도: 98.91%\n",
-      "8\t검증 세트 손실: 0.049550\t최선의 손실: 0.035617\t정확도: 99.02%\n",
-      "9\t검증 세트 손실: 0.052316\t최선의 손실: 0.035617\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.050684\t최선의 손실: 0.035617\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.057479\t최선의 손실: 0.035617\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.036802\t최선의 손실: 0.035617\t정확도: 99.02%\n",
-      "13\t검증 세트 손실: 0.044266\t최선의 손실: 0.035617\t정확도: 98.98%\n",
-      "14\t검증 세트 손실: 0.039277\t최선의 손실: 0.035617\t정확도: 98.98%\n",
-      "15\t검증 세트 손실: 0.038948\t최선의 손실: 0.035617\t정확도: 99.22%\n",
-      "16\t검증 세트 손실: 0.046577\t최선의 손실: 0.035617\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.042352\t최선의 손실: 0.035617\t정확도: 99.10%\n",
-      "18\t검증 세트 손실: 0.045417\t최선의 손실: 0.035617\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.047312\t최선의 손실: 0.035617\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.047234\t최선의 손실: 0.035617\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.051971\t최선의 손실: 0.035617\t정확도: 98.98%\n",
-      "22\t검증 세트 손실: 0.044136\t최선의 손실: 0.035617\t정확도: 99.10%\n",
-      "23\t검증 세트 손실: 0.044673\t최선의 손실: 0.035617\t정확도: 99.10%\n",
-      "24\t검증 세트 손실: 0.042937\t최선의 손실: 0.035617\t정확도: 98.94%\n",
-      "25\t검증 세트 손실: 0.047876\t최선의 손실: 0.035617\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.048676\t최선의 손실: 0.035617\t정확도: 98.94%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=100, total=  20.6s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.069902\t최선의 손실: 0.069902\t정확도: 98.01%\n",
+      "1\t검증 세트 손실: 0.049765\t최선의 손실: 0.049765\t정확도: 98.48%\n",
+      "2\t검증 세트 손실: 0.040777\t최선의 손실: 0.040777\t정확도: 98.67%\n",
+      "3\t검증 세트 손실: 0.063043\t최선의 손실: 0.040777\t정확도: 98.28%\n",
+      "4\t검증 세트 손실: 0.037033\t최선의 손실: 0.037033\t정확도: 98.59%\n",
+      "5\t검증 세트 손실: 0.033829\t최선의 손실: 0.033829\t정확도: 99.14%\n",
+      "6\t검증 세트 손실: 0.042398\t최선의 손실: 0.033829\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.041344\t최선의 손실: 0.033829\t정확도: 98.79%\n",
+      "8\t검증 세트 손실: 0.033271\t최선의 손실: 0.033271\t정확도: 99.02%\n",
+      "9\t검증 세트 손실: 0.055699\t최선의 손실: 0.033271\t정확도: 98.63%\n",
+      "10\t검증 세트 손실: 0.030584\t최선의 손실: 0.030584\t정확도: 99.18%\n",
+      "11\t검증 세트 손실: 0.037680\t최선의 손실: 0.030584\t정확도: 98.98%\n",
+      "12\t검증 세트 손실: 0.058179\t최선의 손실: 0.030584\t정확도: 98.48%\n",
+      "13\t검증 세트 손실: 0.038357\t최선의 손실: 0.030584\t정확도: 99.26%\n",
+      "14\t검증 세트 손실: 0.045922\t최선의 손실: 0.030584\t정확도: 98.75%\n",
+      "15\t검증 세트 손실: 0.032168\t최선의 손실: 0.030584\t정확도: 99.22%\n",
+      "16\t검증 세트 손실: 0.049442\t최선의 손실: 0.030584\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.049287\t최선의 손실: 0.030584\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.041647\t최선의 손실: 0.030584\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 0.044965\t최선의 손실: 0.030584\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.037785\t최선의 손실: 0.030584\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.039188\t최선의 손실: 0.030584\t정확도: 99.02%\n",
+      "22\t검증 세트 손실: 0.039249\t최선의 손실: 0.030584\t정확도: 99.10%\n",
+      "23\t검증 세트 손실: 0.042254\t최선의 손실: 0.030584\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.035878\t최선의 손실: 0.030584\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.044154\t최선의 손실: 0.030584\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.048598\t최선의 손실: 0.030584\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.039289\t최선의 손실: 0.030584\t정확도: 98.94%\n",
+      "28\t검증 세트 손실: 0.049076\t최선의 손실: 0.030584\t정확도: 98.83%\n",
+      "29\t검증 세트 손실: 0.035342\t최선의 손실: 0.030584\t정확도: 99.02%\n",
+      "30\t검증 세트 손실: 0.048635\t최선의 손실: 0.030584\t정확도: 99.02%\n",
+      "31\t검증 세트 손실: 0.043257\t최선의 손실: 0.030584\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100, total=  20.7s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.069697\t최선의 손실: 0.069697\t정확도: 98.08%\n",
-      "1\t검증 세트 손실: 0.055878\t최선의 손실: 0.055878\t정확도: 98.28%\n",
-      "2\t검증 세트 손실: 0.051890\t최선의 손실: 0.051890\t정확도: 98.36%\n",
-      "3\t검증 세트 손실: 0.050236\t최선의 손실: 0.050236\t정확도: 98.67%\n",
-      "4\t검증 세트 손실: 0.057423\t최선의 손실: 0.050236\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.052890\t최선의 손실: 0.050236\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.043074\t최선의 손실: 0.043074\t정확도: 98.91%\n",
-      "7\t검증 세트 손실: 0.049913\t최선의 손실: 0.043074\t정확도: 98.36%\n",
-      "8\t검증 세트 손실: 0.034845\t최선의 손실: 0.034845\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.051821\t최선의 손실: 0.034845\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.041216\t최선의 손실: 0.034845\t정확도: 98.91%\n",
-      "11\t검증 세트 손실: 0.041486\t최선의 손실: 0.034845\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.036626\t최선의 손실: 0.034845\t정확도: 99.18%\n",
-      "13\t검증 세트 손실: 0.042606\t최선의 손실: 0.034845\t정확도: 99.06%\n",
-      "14\t검증 세트 손실: 0.034140\t최선의 손실: 0.034140\t정확도: 98.94%\n",
-      "15\t검증 세트 손실: 0.047994\t최선의 손실: 0.034140\t정확도: 98.59%\n",
-      "16\t검증 세트 손실: 0.041980\t최선의 손실: 0.034140\t정확도: 99.14%\n",
-      "17\t검증 세트 손실: 0.059697\t최선의 손실: 0.034140\t정확도: 98.55%\n",
-      "18\t검증 세트 손실: 0.040287\t최선의 손실: 0.034140\t정확도: 99.10%\n",
-      "19\t검증 세트 손실: 0.038261\t최선의 손실: 0.034140\t정확도: 98.98%\n",
-      "20\t검증 세트 손실: 0.039988\t최선의 손실: 0.034140\t정확도: 98.98%\n",
-      "21\t검증 세트 손실: 0.059944\t최선의 손실: 0.034140\t정확도: 98.91%\n",
-      "22\t검증 세트 손실: 0.038613\t최선의 손실: 0.034140\t정확도: 99.14%\n",
-      "23\t검증 세트 손실: 0.050496\t최선의 손실: 0.034140\t정확도: 98.79%\n",
-      "24\t검증 세트 손실: 0.042252\t최선의 손실: 0.034140\t정확도: 98.91%\n",
-      "25\t검증 세트 손실: 0.035315\t최선의 손실: 0.034140\t정확도: 99.18%\n",
-      "26\t검증 세트 손실: 0.081541\t최선의 손실: 0.034140\t정확도: 98.59%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=100, total=  24.9s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.054882\t최선의 손실: 0.054882\t정확도: 98.24%\n",
+      "1\t검증 세트 손실: 0.064919\t최선의 손실: 0.054882\t정확도: 97.85%\n",
+      "2\t검증 세트 손실: 0.039456\t최선의 손실: 0.039456\t정확도: 98.63%\n",
+      "3\t검증 세트 손실: 0.036158\t최선의 손실: 0.036158\t정확도: 98.83%\n",
+      "4\t검증 세트 손실: 0.041383\t최선의 손실: 0.036158\t정확도: 98.63%\n",
+      "5\t검증 세트 손실: 0.028806\t최선의 손실: 0.028806\t정확도: 99.06%\n",
+      "6\t검증 세트 손실: 0.035948\t최선의 손실: 0.028806\t정확도: 99.06%\n",
+      "7\t검증 세트 손실: 0.055442\t최선의 손실: 0.028806\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.039821\t최선의 손실: 0.028806\t정확도: 98.91%\n",
+      "9\t검증 세트 손실: 0.050303\t최선의 손실: 0.028806\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.032258\t최선의 손실: 0.028806\t정확도: 99.14%\n",
+      "11\t검증 세트 손실: 0.041545\t최선의 손실: 0.028806\t정확도: 98.94%\n",
+      "12\t검증 세트 손실: 0.043764\t최선의 손실: 0.028806\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.037222\t최선의 손실: 0.028806\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.045524\t최선의 손실: 0.028806\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.038451\t최선의 손실: 0.028806\t정확도: 98.83%\n",
+      "16\t검증 세트 손실: 0.041416\t최선의 손실: 0.028806\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.031743\t최선의 손실: 0.028806\t정확도: 99.22%\n",
+      "18\t검증 세트 손실: 0.043602\t최선의 손실: 0.028806\t정확도: 99.18%\n",
+      "19\t검증 세트 손실: 0.045299\t최선의 손실: 0.028806\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.039791\t최선의 손실: 0.028806\t정확도: 99.02%\n",
+      "21\t검증 세트 손실: 0.045720\t최선의 손실: 0.028806\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.035187\t최선의 손실: 0.028806\t정확도: 99.10%\n",
+      "23\t검증 세트 손실: 0.034243\t최선의 손실: 0.028806\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.034647\t최선의 손실: 0.028806\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.043237\t최선의 손실: 0.028806\t정확도: 99.14%\n",
+      "26\t검증 세트 손실: 0.039599\t최선의 손실: 0.028806\t정확도: 99.18%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=100, total=  21.4s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.503221\t최선의 손실: 1.503221\t정확도: 94.88%\n",
+      "1\t검증 세트 손실: 0.343883\t최선의 손실: 0.343883\t정확도: 96.48%\n",
+      "2\t검증 세트 손실: 0.175401\t최선의 손실: 0.175401\t정확도: 97.19%\n",
+      "3\t검증 세트 손실: 0.102898\t최선의 손실: 0.102898\t정확도: 98.01%\n",
+      "4\t검증 세트 손실: 0.108465\t최선의 손실: 0.102898\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 0.107050\t최선의 손실: 0.102898\t정확도: 97.89%\n",
+      "6\t검증 세트 손실: 0.099710\t최선의 손실: 0.099710\t정확도: 97.89%\n",
+      "7\t검증 세트 손실: 0.118917\t최선의 손실: 0.099710\t정확도: 97.77%\n",
+      "8\t검증 세트 손실: 0.079577\t최선의 손실: 0.079577\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.057785\t최선의 손실: 0.057785\t정확도: 98.79%\n",
+      "10\t검증 세트 손실: 0.097087\t최선의 손실: 0.057785\t정확도: 98.05%\n",
+      "11\t검증 세트 손실: 0.063840\t최선의 손실: 0.057785\t정확도: 98.87%\n",
+      "12\t검증 세트 손실: 0.068493\t최선의 손실: 0.057785\t정확도: 98.59%\n",
+      "13\t검증 세트 손실: 0.070257\t최선의 손실: 0.057785\t정확도: 98.75%\n",
+      "14\t검증 세트 손실: 0.166567\t최선의 손실: 0.057785\t정확도: 97.65%\n",
+      "15\t검증 세트 손실: 0.074752\t최선의 손실: 0.057785\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.068044\t최선의 손실: 0.057785\t정확도: 98.63%\n",
+      "17\t검증 세트 손실: 0.066281\t최선의 손실: 0.057785\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.067086\t최선의 손실: 0.057785\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.079633\t최선의 손실: 0.057785\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.089050\t최선의 손실: 0.057785\t정확도: 98.40%\n",
+      "21\t검증 세트 손실: 0.067924\t최선의 손실: 0.057785\t정확도: 98.79%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "27\t검증 세트 손실: 0.035998\t최선의 손실: 0.034140\t정확도: 98.98%\n",
-      "28\t검증 세트 손실: 0.036080\t최선의 손실: 0.034140\t정확도: 98.98%\n",
-      "29\t검증 세트 손실: 0.040520\t최선의 손실: 0.034140\t정확도: 99.18%\n",
-      "30\t검증 세트 손실: 0.052765\t최선의 손실: 0.034140\t정확도: 99.02%\n",
-      "31\t검증 세트 손실: 0.042080\t최선의 손실: 0.034140\t정확도: 98.94%\n",
-      "32\t검증 세트 손실: 0.053804\t최선의 손실: 0.034140\t정확도: 98.94%\n",
-      "33\t검증 세트 손실: 0.063147\t최선의 손실: 0.034140\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.045585\t최선의 손실: 0.034140\t정확도: 99.22%\n",
-      "35\t검증 세트 손실: 0.035553\t최선의 손실: 0.034140\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.066985\t최선의 손실: 0.057785\t정확도: 98.87%\n",
+      "23\t검증 세트 손실: 0.062243\t최선의 손실: 0.057785\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.061462\t최선의 손실: 0.057785\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.075491\t최선의 손실: 0.057785\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.122943\t최선의 손실: 0.057785\t정확도: 97.93%\n",
+      "27\t검증 세트 손실: 0.082544\t최선의 손실: 0.057785\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.082735\t최선의 손실: 0.057785\t정확도: 98.59%\n",
+      "29\t검증 세트 손실: 0.058160\t최선의 손실: 0.057785\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.074064\t최선의 손실: 0.057785\t정확도: 98.79%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100, total=  26.9s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.059747\t최선의 손실: 0.059747\t정확도: 98.12%\n",
-      "1\t검증 세트 손실: 0.056706\t최선의 손실: 0.056706\t정확도: 98.28%\n",
-      "2\t검증 세트 손실: 0.042022\t최선의 손실: 0.042022\t정확도: 98.79%\n",
-      "3\t검증 세트 손실: 0.039173\t최선의 손실: 0.039173\t정확도: 98.94%\n",
-      "4\t검증 세트 손실: 0.037839\t최선의 손실: 0.037839\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.045254\t최선의 손실: 0.037839\t정확도: 98.63%\n",
-      "6\t검증 세트 손실: 0.036813\t최선의 손실: 0.036813\t정확도: 98.83%\n",
-      "7\t검증 세트 손실: 0.043248\t최선의 손실: 0.036813\t정확도: 98.79%\n",
-      "8\t검증 세트 손실: 0.038819\t최선의 손실: 0.036813\t정확도: 98.91%\n",
-      "9\t검증 세트 손실: 0.032638\t최선의 손실: 0.032638\t정확도: 99.18%\n",
-      "10\t검증 세트 손실: 0.036554\t최선의 손실: 0.032638\t정확도: 99.02%\n",
-      "11\t검증 세트 손실: 0.034805\t최선의 손실: 0.032638\t정확도: 98.98%\n",
-      "12\t검증 세트 손실: 0.052437\t최선의 손실: 0.032638\t정확도: 99.18%\n",
-      "13\t검증 세트 손실: 0.039538\t최선의 손실: 0.032638\t정확도: 99.14%\n",
-      "14\t검증 세트 손실: 0.030848\t최선의 손실: 0.030848\t정확도: 99.14%\n",
-      "15\t검증 세트 손실: 0.040194\t최선의 손실: 0.030848\t정확도: 98.98%\n",
-      "16\t검증 세트 손실: 0.039088\t최선의 손실: 0.030848\t정확도: 99.10%\n",
-      "17\t검증 세트 손실: 0.031332\t최선의 손실: 0.030848\t정확도: 99.14%\n",
-      "18\t검증 세트 손실: 0.037360\t최선의 손실: 0.030848\t정확도: 98.94%\n",
-      "19\t검증 세트 손실: 0.034171\t최선의 손실: 0.030848\t정확도: 99.18%\n",
-      "20\t검증 세트 손실: 0.029559\t최선의 손실: 0.029559\t정확도: 99.22%\n",
-      "21\t검증 세트 손실: 0.033957\t최선의 손실: 0.029559\t정확도: 99.02%\n",
-      "22\t검증 세트 손실: 0.037301\t최선의 손실: 0.029559\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.035501\t최선의 손실: 0.029559\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.049135\t최선의 손실: 0.029559\t정확도: 98.79%\n",
-      "25\t검증 세트 손실: 0.035784\t최선의 손실: 0.029559\t정확도: 98.98%\n",
-      "26\t검증 세트 손실: 0.046699\t최선의 손실: 0.029559\t정확도: 98.98%\n",
-      "27\t검증 세트 손실: 0.034216\t최선의 손실: 0.029559\t정확도: 99.02%\n",
-      "28\t검증 세트 손실: 0.042027\t최선의 손실: 0.029559\t정확도: 99.14%\n",
-      "29\t검증 세트 손실: 0.041319\t최선의 손실: 0.029559\t정확도: 99.02%\n",
-      "30\t검증 세트 손실: 0.042732\t최선의 손실: 0.029559\t정확도: 98.98%\n",
-      "31\t검증 세트 손실: 0.034711\t최선의 손실: 0.029559\t정확도: 99.18%\n",
-      "32\t검증 세트 손실: 0.030200\t최선의 손실: 0.029559\t정확도: 99.37%\n",
-      "33\t검증 세트 손실: 0.036655\t최선의 손실: 0.029559\t정확도: 99.14%\n",
-      "34\t검증 세트 손실: 0.052769\t최선의 손실: 0.029559\t정확도: 98.98%\n",
-      "35\t검증 세트 손실: 0.048973\t최선의 손실: 0.029559\t정확도: 98.94%\n",
-      "36\t검증 세트 손실: 0.029924\t최선의 손실: 0.029559\t정확도: 99.41%\n",
-      "37\t검증 세트 손실: 0.036962\t최선의 손실: 0.029559\t정확도: 99.26%\n",
-      "38\t검증 세트 손실: 0.035119\t최선의 손실: 0.029559\t정확도: 99.18%\n",
-      "39\t검증 세트 손실: 0.033252\t최선의 손실: 0.029559\t정확도: 99.06%\n",
-      "40\t검증 세트 손실: 0.046411\t최선의 손실: 0.029559\t정확도: 99.06%\n",
-      "41\t검증 세트 손실: 0.036907\t최선의 손실: 0.029559\t정확도: 99.22%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=90, total=   7.1s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=90 \n",
+      "0\t검증 세트 손실: 2.611147\t최선의 손실: 2.611147\t정확도: 92.89%\n",
+      "1\t검증 세트 손실: 0.329195\t최선의 손실: 0.329195\t정확도: 97.22%\n",
+      "2\t검증 세트 손실: 0.222968\t최선의 손실: 0.222968\t정확도: 97.15%\n",
+      "3\t검증 세트 손실: 0.108871\t최선의 손실: 0.108871\t정확도: 97.42%\n",
+      "4\t검증 세트 손실: 0.147907\t최선의 손실: 0.108871\t정확도: 98.05%\n",
+      "5\t검증 세트 손실: 0.128718\t최선의 손실: 0.108871\t정확도: 97.46%\n",
+      "6\t검증 세트 손실: 0.080175\t최선의 손실: 0.080175\t정확도: 98.20%\n",
+      "7\t검증 세트 손실: 0.064961\t최선의 손실: 0.064961\t정확도: 98.83%\n",
+      "8\t검증 세트 손실: 0.067571\t최선의 손실: 0.064961\t정확도: 98.55%\n",
+      "9\t검증 세트 손실: 0.062239\t최선의 손실: 0.062239\t정확도: 98.91%\n",
+      "10\t검증 세트 손실: 0.132144\t최선의 손실: 0.062239\t정확도: 97.58%\n",
+      "11\t검증 세트 손실: 0.090287\t최선의 손실: 0.062239\t정확도: 98.48%\n",
+      "12\t검증 세트 손실: 0.061763\t최선의 손실: 0.061763\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.084429\t최선의 손실: 0.061763\t정확도: 98.40%\n",
+      "14\t검증 세트 손실: 0.072295\t최선의 손실: 0.061763\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.079541\t최선의 손실: 0.061763\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.076270\t최선의 손실: 0.061763\t정확도: 98.59%\n",
+      "17\t검증 세트 손실: 0.073179\t최선의 손실: 0.061763\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.068456\t최선의 손실: 0.061763\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.065106\t최선의 손실: 0.061763\t정확도: 99.02%\n",
+      "20\t검증 세트 손실: 0.098396\t최선의 손실: 0.061763\t정확도: 98.36%\n",
+      "21\t검증 세트 손실: 0.062987\t최선의 손실: 0.061763\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.070724\t최선의 손실: 0.061763\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.060606\t최선의 손실: 0.060606\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.063149\t최선의 손실: 0.060606\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.086013\t최선의 손실: 0.060606\t정확도: 98.67%\n",
+      "26\t검증 세트 손실: 0.061112\t최선의 손실: 0.060606\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.079881\t최선의 손실: 0.060606\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.073265\t최선의 손실: 0.060606\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.070313\t최선의 손실: 0.060606\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.090095\t최선의 손실: 0.060606\t정확도: 98.36%\n",
+      "31\t검증 세트 손실: 0.072101\t최선의 손실: 0.060606\t정확도: 98.83%\n",
+      "32\t검증 세트 손실: 0.070241\t최선의 손실: 0.060606\t정확도: 98.71%\n",
+      "33\t검증 세트 손실: 0.085835\t최선의 손실: 0.060606\t정확도: 98.40%\n",
+      "34\t검증 세트 손실: 0.068177\t최선의 손실: 0.060606\t정확도: 98.98%\n",
+      "35\t검증 세트 손실: 0.069700\t최선의 손실: 0.060606\t정확도: 98.87%\n",
+      "36\t검증 세트 손실: 0.083005\t최선의 손실: 0.060606\t정확도: 98.67%\n",
+      "37\t검증 세트 손실: 0.065944\t최선의 손실: 0.060606\t정확도: 98.94%\n",
+      "38\t검증 세트 손실: 0.060114\t최선의 손실: 0.060114\t정확도: 99.06%\n",
+      "39\t검증 세트 손실: 0.067219\t최선의 손실: 0.060114\t정확도: 99.10%\n",
+      "40\t검증 세트 손실: 0.067212\t최선의 손실: 0.060114\t정확도: 98.71%\n",
+      "41\t검증 세트 손실: 0.062099\t최선의 손실: 0.060114\t정확도: 98.79%\n",
+      "42\t검증 세트 손실: 0.046679\t최선의 손실: 0.046679\t정확도: 99.18%\n",
+      "43\t검증 세트 손실: 0.061170\t최선의 손실: 0.046679\t정확도: 99.06%\n",
+      "44\t검증 세트 손실: 0.086005\t최선의 손실: 0.046679\t정확도: 98.67%\n",
+      "45\t검증 세트 손실: 0.071463\t최선의 손실: 0.046679\t정확도: 98.79%\n",
+      "46\t검증 세트 손실: 0.070531\t최선의 손실: 0.046679\t정확도: 98.75%\n",
+      "47\t검증 세트 손실: 0.087820\t최선의 손실: 0.046679\t정확도: 98.67%\n",
+      "48\t검증 세트 손실: 0.100402\t최선의 손실: 0.046679\t정확도: 98.51%\n",
+      "49\t검증 세트 손실: 0.076931\t최선의 손실: 0.046679\t정확도: 98.83%\n",
+      "50\t검증 세트 손실: 0.067172\t최선의 손실: 0.046679\t정확도: 98.98%\n",
+      "51\t검증 세트 손실: 0.074445\t최선의 손실: 0.046679\t정확도: 98.98%\n",
+      "52\t검증 세트 손실: 0.080193\t최선의 손실: 0.046679\t정확도: 98.75%\n",
+      "53\t검증 세트 손실: 0.074057\t최선의 손실: 0.046679\t정확도: 99.14%\n",
+      "54\t검증 세트 손실: 0.068816\t최선의 손실: 0.046679\t정확도: 99.10%\n",
+      "55\t검증 세트 손실: 0.072120\t최선의 손실: 0.046679\t정확도: 98.94%\n",
+      "56\t검증 세트 손실: 0.074556\t최선의 손실: 0.046679\t정확도: 99.14%\n",
+      "57\t검증 세트 손실: 0.067679\t최선의 손실: 0.046679\t정확도: 99.10%\n",
+      "58\t검증 세트 손실: 0.095872\t최선의 손실: 0.046679\t정확도: 98.71%\n",
+      "59\t검증 세트 손실: 0.096095\t최선의 손실: 0.046679\t정확도: 98.75%\n",
+      "60\t검증 세트 손실: 0.093343\t최선의 손실: 0.046679\t정확도: 98.87%\n",
+      "61\t검증 세트 손실: 0.105578\t최선의 손실: 0.046679\t정확도: 98.59%\n",
+      "62\t검증 세트 손실: 0.105666\t최선의 손실: 0.046679\t정확도: 98.16%\n",
+      "63\t검증 세트 손실: 0.084279\t최선의 손실: 0.046679\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100, total=  31.2s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=90 \n",
-      "0\t검증 세트 손실: 6.848903\t최선의 손실: 6.848903\t정확도: 84.83%\n",
-      "1\t검증 세트 손실: 0.976121\t최선의 손실: 0.976121\t정확도: 91.16%\n",
-      "2\t검증 세트 손실: 0.323467\t최선의 손실: 0.323467\t정확도: 95.62%\n",
-      "3\t검증 세트 손실: 0.128004\t최선의 손실: 0.128004\t정확도: 97.42%\n",
-      "4\t검증 세트 손실: 0.118432\t최선의 손실: 0.118432\t정확도: 97.58%\n",
-      "5\t검증 세트 손실: 0.118029\t최선의 손실: 0.118029\t정확도: 97.42%\n",
-      "6\t검증 세트 손실: 0.098889\t최선의 손실: 0.098889\t정확도: 98.08%\n",
-      "7\t검증 세트 손실: 0.099287\t최선의 손실: 0.098889\t정확도: 98.20%\n",
-      "8\t검증 세트 손실: 0.083139\t최선의 손실: 0.083139\t정확도: 98.36%\n",
-      "9\t검증 세트 손실: 0.044138\t최선의 손실: 0.044138\t정확도: 99.02%\n",
-      "10\t검증 세트 손실: 0.077018\t최선의 손실: 0.044138\t정확도: 98.40%\n",
-      "11\t검증 세트 손실: 0.054935\t최선의 손실: 0.044138\t정확도: 98.63%\n",
-      "12\t검증 세트 손실: 0.061412\t최선의 손실: 0.044138\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.058530\t최선의 손실: 0.044138\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.113652\t최선의 손실: 0.044138\t정확도: 98.01%\n",
-      "15\t검증 세트 손실: 0.062772\t최선의 손실: 0.044138\t정확도: 98.59%\n",
-      "16\t검증 세트 손실: 0.061866\t최선의 손실: 0.044138\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.072685\t최선의 손실: 0.044138\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.090404\t최선의 손실: 0.044138\t정확도: 98.24%\n",
-      "19\t검증 세트 손실: 0.093658\t최선의 손실: 0.044138\t정확도: 98.12%\n",
-      "20\t검증 세트 손실: 0.073357\t최선의 손실: 0.044138\t정확도: 98.51%\n",
-      "21\t검증 세트 손실: 0.069025\t최선의 손실: 0.044138\t정확도: 98.63%\n",
-      "22\t검증 세트 손실: 0.062751\t최선의 손실: 0.044138\t정확도: 98.67%\n",
-      "23\t검증 세트 손실: 0.049975\t최선의 손실: 0.044138\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.074758\t최선의 손실: 0.044138\t정확도: 98.36%\n",
-      "25\t검증 세트 손실: 0.097106\t최선의 손실: 0.044138\t정확도: 98.28%\n",
-      "26\t검증 세트 손실: 0.067042\t최선의 손실: 0.044138\t정확도: 98.59%\n",
-      "27\t검증 세트 손실: 0.072504\t최선의 손실: 0.044138\t정확도: 98.94%\n",
-      "28\t검증 세트 손실: 0.105426\t최선의 손실: 0.044138\t정확도: 98.01%\n",
-      "29\t검증 세트 손실: 0.062429\t최선의 손실: 0.044138\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.100682\t최선의 손실: 0.044138\t정확도: 98.59%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=90, total=   6.7s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=90 \n",
-      "0\t검증 세트 손실: 11.776729\t최선의 손실: 11.776729\t정확도: 75.84%\n",
-      "1\t검증 세트 손실: 1.538014\t최선의 손실: 1.538014\t정확도: 90.34%\n",
-      "2\t검증 세트 손실: 0.349582\t최선의 손실: 0.349582\t정확도: 94.96%\n",
-      "3\t검증 세트 손실: 0.150303\t최선의 손실: 0.150303\t정확도: 97.11%\n",
-      "4\t검증 세트 손실: 0.086043\t최선의 손실: 0.086043\t정확도: 98.40%\n",
-      "5\t검증 세트 손실: 0.083314\t최선의 손실: 0.083314\t정확도: 98.05%\n",
-      "6\t검증 세트 손실: 0.054646\t최선의 손실: 0.054646\t정확도: 98.79%\n",
-      "7\t검증 세트 손실: 0.096144\t최선의 손실: 0.054646\t정확도: 97.42%\n",
-      "8\t검증 세트 손실: 0.074278\t최선의 손실: 0.054646\t정확도: 98.55%\n",
-      "9\t검증 세트 손실: 0.057172\t최선의 손실: 0.054646\t정확도: 98.87%\n",
-      "10\t검증 세트 손실: 0.160687\t최선의 손실: 0.054646\t정확도: 96.79%\n",
-      "11\t검증 세트 손실: 0.069311\t최선의 손실: 0.054646\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.078684\t최선의 손실: 0.054646\t정확도: 98.51%\n",
-      "13\t검증 세트 손실: 0.061232\t최선의 손실: 0.054646\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.068181\t최선의 손실: 0.054646\t정확도: 98.67%\n",
-      "15\t검증 세트 손실: 0.095149\t최선의 손실: 0.054646\t정확도: 98.08%\n",
-      "16\t검증 세트 손실: 0.062277\t최선의 손실: 0.054646\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.068145\t최선의 손실: 0.054646\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.080930\t최선의 손실: 0.054646\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.080372\t최선의 손실: 0.054646\t정확도: 98.28%\n",
-      "20\t검증 세트 손실: 0.074591\t최선의 손실: 0.054646\t정확도: 98.55%\n",
-      "21\t검증 세트 손실: 0.047679\t최선의 손실: 0.047679\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.064500\t최선의 손실: 0.047679\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.062973\t최선의 손실: 0.047679\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.048530\t최선의 손실: 0.047679\t정확도: 99.10%\n",
-      "25\t검증 세트 손실: 0.066528\t최선의 손실: 0.047679\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.126221\t최선의 손실: 0.047679\t정확도: 97.77%\n",
-      "27\t검증 세트 손실: 0.061864\t최선의 손실: 0.047679\t정확도: 98.83%\n",
-      "28\t검증 세트 손실: 0.077159\t최선의 손실: 0.047679\t정확도: 98.87%\n",
-      "29\t검증 세트 손실: 0.160475\t최선의 손실: 0.047679\t정확도: 97.30%\n",
-      "30\t검증 세트 손실: 0.299752\t최선의 손실: 0.047679\t정확도: 97.34%\n",
-      "31\t검증 세트 손실: 0.099158\t최선의 손실: 0.047679\t정확도: 98.67%\n",
-      "32\t검증 세트 손실: 0.091721\t최선의 손실: 0.047679\t정확도: 98.67%\n",
-      "33\t검증 세트 손실: 0.099609\t최선의 손실: 0.047679\t정확도: 98.20%\n",
-      "34\t검증 세트 손실: 0.074846\t최선의 손실: 0.047679\t정확도: 98.71%\n",
-      "35\t검증 세트 손실: 0.064124\t최선의 손실: 0.047679\t정확도: 98.91%\n",
-      "36\t검증 세트 손실: 0.058051\t최선의 손실: 0.047679\t정확도: 98.87%\n",
-      "37\t검증 세트 손실: 0.056938\t최선의 손실: 0.047679\t정확도: 99.02%\n",
-      "38\t검증 세트 손실: 0.062434\t최선의 손실: 0.047679\t정확도: 98.98%\n",
-      "39\t검증 세트 손실: 0.059418\t최선의 손실: 0.047679\t정확도: 98.87%\n",
-      "40\t검증 세트 손실: 0.065624\t최선의 손실: 0.047679\t정확도: 98.63%\n",
-      "41\t검증 세트 손실: 0.065855\t최선의 손실: 0.047679\t정확도: 98.91%\n",
-      "42\t검증 세트 손실: 0.082443\t최선의 손실: 0.047679\t정확도: 98.98%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=90, total=   8.6s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=90 \n",
-      "0\t검증 세트 손실: 12.572893\t최선의 손실: 12.572893\t정확도: 72.79%\n",
-      "1\t검증 세트 손실: 1.229921\t최선의 손실: 1.229921\t정확도: 86.67%\n",
-      "2\t검증 세트 손실: 0.388788\t최선의 손실: 0.388788\t정확도: 93.39%\n",
-      "3\t검증 세트 손실: 0.154787\t최선의 손실: 0.154787\t정확도: 97.15%\n",
-      "4\t검증 세트 손실: 0.130227\t최선의 손실: 0.130227\t정확도: 97.58%\n",
-      "5\t검증 세트 손실: 0.108414\t최선의 손실: 0.108414\t정확도: 98.28%\n",
-      "6\t검증 세트 손실: 0.096556\t최선의 손실: 0.096556\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.092078\t최선의 손실: 0.092078\t정확도: 98.24%\n",
-      "8\t검증 세트 손실: 0.106798\t최선의 손실: 0.092078\t정확도: 98.12%\n",
-      "9\t검증 세트 손실: 0.082276\t최선의 손실: 0.082276\t정확도: 98.44%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=90, total=  12.8s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=90 \n",
+      "0\t검증 세트 손실: 4.188056\t최선의 손실: 4.188056\t정확도: 87.26%\n",
+      "1\t검증 세트 손실: 0.365153\t최선의 손실: 0.365153\t정확도: 95.90%\n",
+      "2\t검증 세트 손실: 0.112459\t최선의 손실: 0.112459\t정확도: 98.16%\n",
+      "3\t검증 세트 손실: 0.096663\t최선의 손실: 0.096663\t정확도: 98.32%\n",
+      "4\t검증 세트 손실: 0.106690\t최선의 손실: 0.096663\t정확도: 98.01%\n",
+      "5\t검증 세트 손실: 0.061962\t최선의 손실: 0.061962\t정확도: 98.75%\n",
+      "6\t검증 세트 손실: 0.072431\t최선의 손실: 0.061962\t정확도: 98.55%\n",
+      "7\t검증 세트 손실: 0.084203\t최선의 손실: 0.061962\t정확도: 98.55%\n",
+      "8\t검증 세트 손실: 0.070452\t최선의 손실: 0.061962\t정확도: 98.59%\n",
+      "9\t검증 세트 손실: 0.056691\t최선의 손실: 0.056691\t정확도: 98.98%\n",
+      "10\t검증 세트 손실: 0.099957\t최선의 손실: 0.056691\t정확도: 98.24%\n",
+      "11\t검증 세트 손실: 0.086883\t최선의 손실: 0.056691\t정확도: 98.32%\n",
+      "12\t검증 세트 손실: 0.058331\t최선의 손실: 0.056691\t정확도: 98.91%\n",
+      "13\t검증 세트 손실: 0.083885\t최선의 손실: 0.056691\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.070222\t최선의 손실: 0.056691\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.097227\t최선의 손실: 0.056691\t정확도: 98.55%\n",
+      "16\t검증 세트 손실: 0.068876\t최선의 손실: 0.056691\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.063989\t최선의 손실: 0.056691\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.062172\t최선의 손실: 0.056691\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 0.069789\t최선의 손실: 0.056691\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.049966\t최선의 손실: 0.049966\t정확도: 98.87%\n",
+      "21\t검증 세트 손실: 0.059355\t최선의 손실: 0.049966\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.083066\t최선의 손실: 0.049966\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.114039\t최선의 손실: 0.049966\t정확도: 98.24%\n",
+      "24\t검증 세트 손실: 0.074058\t최선의 손실: 0.049966\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.100097\t최선의 손실: 0.049966\t정확도: 98.16%\n",
+      "26\t검증 세트 손실: 0.112392\t최선의 손실: 0.049966\t정확도: 98.20%\n",
+      "27\t검증 세트 손실: 0.073895\t최선의 손실: 0.049966\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.065203\t최선의 손실: 0.049966\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.080323\t최선의 손실: 0.049966\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.085915\t최선의 손실: 0.049966\t정확도: 98.63%\n",
+      "31\t검증 세트 손실: 0.049056\t최선의 손실: 0.049056\t정확도: 99.02%\n",
+      "32\t검증 세트 손실: 0.061053\t최선의 손실: 0.049056\t정확도: 98.75%\n",
+      "33\t검증 세트 손실: 0.061734\t최선의 손실: 0.049056\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.070944\t최선의 손실: 0.049056\t정확도: 98.87%\n",
+      "35\t검증 세트 손실: 0.065535\t최선의 손실: 0.049056\t정확도: 98.71%\n",
+      "36\t검증 세트 손실: 0.084605\t최선의 손실: 0.049056\t정확도: 98.83%\n",
+      "37\t검증 세트 손실: 0.086431\t최선의 손실: 0.049056\t정확도: 98.87%\n",
+      "38\t검증 세트 손실: 0.067088\t최선의 손실: 0.049056\t정확도: 98.87%\n",
+      "39\t검증 세트 손실: 0.064376\t최선의 손실: 0.049056\t정확도: 98.91%\n",
+      "40\t검증 세트 손실: 0.064298\t최선의 손실: 0.049056\t정확도: 98.83%\n",
+      "41\t검증 세트 손실: 0.058764\t최선의 손실: 0.049056\t정확도: 99.10%\n",
+      "42\t검증 세트 손실: 0.064812\t최선의 손실: 0.049056\t정확도: 98.71%\n",
+      "43\t검증 세트 손실: 0.059420\t최선의 손실: 0.049056\t정확도: 98.79%\n",
+      "44\t검증 세트 손실: 0.074673\t최선의 손실: 0.049056\t정확도: 98.79%\n",
+      "45\t검증 세트 손실: 0.068775\t최선의 손실: 0.049056\t정확도: 98.71%\n",
+      "46\t검증 세트 손실: 0.066007\t최선의 손실: 0.049056\t정확도: 98.79%\n",
+      "47\t검증 세트 손실: 0.066143\t최선의 손실: 0.049056\t정확도: 98.87%\n",
+      "48\t검증 세트 손실: 0.059352\t최선의 손실: 0.049056\t정확도: 98.94%\n",
+      "49\t검증 세트 손실: 0.050026\t최선의 손실: 0.049056\t정확도: 99.14%\n",
+      "50\t검증 세트 손실: 0.046825\t최선의 손실: 0.046825\t정확도: 99.06%\n",
+      "51\t검증 세트 손실: 0.052854\t최선의 손실: 0.046825\t정확도: 98.87%\n",
+      "52\t검증 세트 손실: 0.051145\t최선의 손실: 0.046825\t정확도: 99.06%\n",
+      "53\t검증 세트 손실: 0.081614\t최선의 손실: 0.046825\t정확도: 98.55%\n",
+      "54\t검증 세트 손실: 0.077910\t최선의 손실: 0.046825\t정확도: 98.79%\n",
+      "55\t검증 세트 손실: 0.082054\t최선의 손실: 0.046825\t정확도: 98.91%\n",
+      "56\t검증 세트 손실: 0.101228\t최선의 손실: 0.046825\t정확도: 98.44%\n",
+      "57\t검증 세트 손실: 0.103586\t최선의 손실: 0.046825\t정확도: 98.28%\n",
+      "58\t검증 세트 손실: 0.082251\t최선의 손실: 0.046825\t정확도: 98.87%\n",
+      "59\t검증 세트 손실: 0.066926\t최선의 손실: 0.046825\t정확도: 99.02%\n",
+      "60\t검증 세트 손실: 0.084568\t최선의 손실: 0.046825\t정확도: 98.71%\n",
+      "61\t검증 세트 손실: 0.138635\t최선의 손실: 0.046825\t정확도: 98.32%\n",
+      "62\t검증 세트 손실: 0.091414\t최선의 손실: 0.046825\t정확도: 98.44%\n",
+      "63\t검증 세트 손실: 0.114701\t최선의 손실: 0.046825\t정확도: 98.48%\n",
+      "64\t검증 세트 손실: 0.082429\t최선의 손실: 0.046825\t정확도: 98.55%\n",
+      "65\t검증 세트 손실: 0.101483\t최선의 손실: 0.046825\t정확도: 98.75%\n",
+      "66\t검증 세트 손실: 0.138116\t최선의 손실: 0.046825\t정확도: 97.93%\n",
+      "67\t검증 세트 손실: 0.074277\t최선의 손실: 0.046825\t정확도: 98.75%\n",
+      "68\t검증 세트 손실: 0.058912\t최선의 손실: 0.046825\t정확도: 98.83%\n",
+      "69\t검증 세트 손실: 0.054195\t최선의 손실: 0.046825\t정확도: 99.06%\n",
+      "70\t검증 세트 손실: 0.047374\t최선의 손실: 0.046825\t정확도: 99.10%\n",
+      "71\t검증 세트 손실: 0.046731\t최선의 손실: 0.046731\t정확도: 99.18%\n",
+      "72\t검증 세트 손실: 0.047290\t최선의 손실: 0.046731\t정확도: 99.14%\n",
+      "73\t검증 세트 손실: 0.053576\t최선의 손실: 0.046731\t정확도: 98.98%\n",
+      "74\t검증 세트 손실: 0.105883\t최선의 손실: 0.046731\t정확도: 98.63%\n",
+      "75\t검증 세트 손실: 0.087025\t최선의 손실: 0.046731\t정확도: 98.87%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10\t검증 세트 손실: 0.081587\t최선의 손실: 0.081587\t정확도: 98.55%\n",
-      "11\t검증 세트 손실: 0.106478\t최선의 손실: 0.081587\t정확도: 98.12%\n",
-      "12\t검증 세트 손실: 0.068628\t최선의 손실: 0.068628\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.058967\t최선의 손실: 0.058967\t정확도: 98.91%\n",
-      "14\t검증 세트 손실: 0.072045\t최선의 손실: 0.058967\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.064432\t최선의 손실: 0.058967\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.068910\t최선의 손실: 0.058967\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.095385\t최선의 손실: 0.058967\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.065205\t최선의 손실: 0.058967\t정확도: 98.63%\n",
-      "19\t검증 세트 손실: 0.071404\t최선의 손실: 0.058967\t정확도: 98.40%\n",
-      "20\t검증 세트 손실: 0.055379\t최선의 손실: 0.055379\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.058592\t최선의 손실: 0.055379\t정확도: 98.75%\n",
-      "22\t검증 세트 손실: 0.056573\t최선의 손실: 0.055379\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.058814\t최선의 손실: 0.055379\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.066665\t최선의 손실: 0.055379\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.140215\t최선의 손실: 0.055379\t정확도: 97.81%\n",
-      "26\t검증 세트 손실: 0.119495\t최선의 손실: 0.055379\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.064205\t최선의 손실: 0.055379\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.073307\t최선의 손실: 0.055379\t정확도: 98.71%\n",
-      "29\t검증 세트 손실: 0.066316\t최선의 손실: 0.055379\t정확도: 98.83%\n",
-      "30\t검증 세트 손실: 0.094281\t최선의 손실: 0.055379\t정확도: 98.24%\n",
-      "31\t검증 세트 손실: 0.070205\t최선의 손실: 0.055379\t정확도: 98.63%\n",
-      "32\t검증 세트 손실: 0.089607\t최선의 손실: 0.055379\t정확도: 98.63%\n",
-      "33\t검증 세트 손실: 0.050142\t최선의 손실: 0.050142\t정확도: 99.10%\n",
-      "34\t검증 세트 손실: 0.107829\t최선의 손실: 0.050142\t정확도: 98.48%\n",
-      "35\t검증 세트 손실: 0.051786\t최선의 손실: 0.050142\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.056066\t최선의 손실: 0.050142\t정확도: 99.14%\n",
-      "37\t검증 세트 손실: 0.083100\t최선의 손실: 0.050142\t정확도: 98.55%\n",
-      "38\t검증 세트 손실: 0.065534\t최선의 손실: 0.050142\t정확도: 99.02%\n",
-      "39\t검증 세트 손실: 0.072487\t최선의 손실: 0.050142\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.056342\t최선의 손실: 0.050142\t정확도: 98.87%\n",
-      "41\t검증 세트 손실: 0.050082\t최선의 손실: 0.050082\t정확도: 99.18%\n",
-      "42\t검증 세트 손실: 0.044171\t최선의 손실: 0.044171\t정확도: 99.18%\n",
-      "43\t검증 세트 손실: 0.044343\t최선의 손실: 0.044171\t정확도: 99.10%\n",
-      "44\t검증 세트 손실: 0.094435\t최선의 손실: 0.044171\t정확도: 98.79%\n",
-      "45\t검증 세트 손실: 0.046996\t최선의 손실: 0.044171\t정확도: 99.34%\n",
-      "46\t검증 세트 손실: 0.059334\t최선의 손실: 0.044171\t정확도: 98.98%\n",
-      "47\t검증 세트 손실: 0.081244\t최선의 손실: 0.044171\t정확도: 98.75%\n",
-      "48\t검증 세트 손실: 0.077274\t최선의 손실: 0.044171\t정확도: 98.83%\n",
-      "49\t검증 세트 손실: 0.063117\t최선의 손실: 0.044171\t정확도: 98.71%\n",
-      "50\t검증 세트 손실: 0.075738\t최선의 손실: 0.044171\t정확도: 98.48%\n",
-      "51\t검증 세트 손실: 0.066740\t최선의 손실: 0.044171\t정확도: 98.83%\n",
-      "52\t검증 세트 손실: 0.067512\t최선의 손실: 0.044171\t정확도: 98.87%\n",
-      "53\t검증 세트 손실: 0.061266\t최선의 손실: 0.044171\t정확도: 98.94%\n",
-      "54\t검증 세트 손실: 0.042947\t최선의 손실: 0.042947\t정확도: 99.10%\n",
-      "55\t검증 세트 손실: 0.045675\t최선의 손실: 0.042947\t정확도: 98.94%\n",
-      "56\t검증 세트 손실: 0.048878\t최선의 손실: 0.042947\t정확도: 99.10%\n",
-      "57\t검증 세트 손실: 0.045978\t최선의 손실: 0.042947\t정확도: 99.26%\n",
-      "58\t검증 세트 손실: 0.044652\t최선의 손실: 0.042947\t정확도: 99.18%\n",
-      "59\t검증 세트 손실: 0.045835\t최선의 손실: 0.042947\t정확도: 99.14%\n",
-      "60\t검증 세트 손실: 0.046151\t최선의 손실: 0.042947\t정확도: 99.14%\n",
-      "61\t검증 세트 손실: 0.052059\t최선의 손실: 0.042947\t정확도: 99.02%\n",
-      "62\t검증 세트 손실: 0.050625\t최선의 손실: 0.042947\t정확도: 98.91%\n",
-      "63\t검증 세트 손실: 0.050349\t최선의 손실: 0.042947\t정확도: 99.10%\n",
-      "64\t검증 세트 손실: 0.081717\t최선의 손실: 0.042947\t정확도: 98.48%\n",
-      "65\t검증 세트 손실: 0.073340\t최선의 손실: 0.042947\t정확도: 98.48%\n",
-      "66\t검증 세트 손실: 0.167761\t최선의 손실: 0.042947\t정확도: 97.62%\n",
-      "67\t검증 세트 손실: 0.068591\t최선의 손실: 0.042947\t정확도: 98.55%\n",
-      "68\t검증 세트 손실: 0.094132\t최선의 손실: 0.042947\t정확도: 98.63%\n",
-      "69\t검증 세트 손실: 0.105328\t최선의 손실: 0.042947\t정확도: 98.63%\n",
-      "70\t검증 세트 손실: 0.084797\t최선의 손실: 0.042947\t정확도: 98.75%\n",
-      "71\t검증 세트 손실: 0.055012\t최선의 손실: 0.042947\t정확도: 98.91%\n",
-      "72\t검증 세트 손실: 0.070677\t최선의 손실: 0.042947\t정확도: 99.10%\n",
-      "73\t검증 세트 손실: 0.067921\t최선의 손실: 0.042947\t정확도: 98.87%\n",
-      "74\t검증 세트 손실: 0.068961\t최선의 손실: 0.042947\t정확도: 98.67%\n",
-      "75\t검증 세트 손실: 0.064499\t최선의 손실: 0.042947\t정확도: 98.91%\n",
+      "76\t검증 세트 손실: 0.065437\t최선의 손실: 0.046731\t정확도: 98.87%\n",
+      "77\t검증 세트 손실: 0.085684\t최선의 손실: 0.046731\t정확도: 98.44%\n",
+      "78\t검증 세트 손실: 0.077815\t최선의 손실: 0.046731\t정확도: 98.67%\n",
+      "79\t검증 세트 손실: 0.070025\t최선의 손실: 0.046731\t정확도: 98.55%\n",
+      "80\t검증 세트 손실: 0.078044\t최선의 손실: 0.046731\t정확도: 98.91%\n",
+      "81\t검증 세트 손실: 0.101573\t최선의 손실: 0.046731\t정확도: 98.44%\n",
+      "82\t검증 세트 손실: 0.074199\t최선의 손실: 0.046731\t정확도: 98.94%\n",
+      "83\t검증 세트 손실: 0.087504\t최선의 손실: 0.046731\t정확도: 98.75%\n",
+      "84\t검증 세트 손실: 0.104900\t최선의 손실: 0.046731\t정확도: 98.44%\n",
+      "85\t검증 세트 손실: 0.089540\t최선의 손실: 0.046731\t정확도: 98.87%\n",
+      "86\t검증 세트 손실: 0.085148\t최선의 손실: 0.046731\t정확도: 98.83%\n",
+      "87\t검증 세트 손실: 0.070849\t최선의 손실: 0.046731\t정확도: 98.91%\n",
+      "88\t검증 세트 손실: 0.085560\t최선의 손실: 0.046731\t정확도: 98.83%\n",
+      "89\t검증 세트 손실: 0.096922\t최선의 손실: 0.046731\t정확도: 98.55%\n",
+      "90\t검증 세트 손실: 0.079419\t최선의 손실: 0.046731\t정확도: 98.75%\n",
+      "91\t검증 세트 손실: 0.084000\t최선의 손실: 0.046731\t정확도: 98.91%\n",
+      "92\t검증 세트 손실: 0.075143\t최선의 손실: 0.046731\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=90, total=  14.1s\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.109632\t최선의 손실: 0.109632\t정확도: 96.56%\n",
-      "1\t검증 세트 손실: 0.082287\t최선의 손실: 0.082287\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.056091\t최선의 손실: 0.056091\t정확도: 98.32%\n",
-      "3\t검증 세트 손실: 0.092882\t최선의 손실: 0.056091\t정확도: 97.03%\n",
-      "4\t검증 세트 손실: 0.051771\t최선의 손실: 0.051771\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.059953\t최선의 손실: 0.051771\t정확도: 98.05%\n",
-      "6\t검증 세트 손실: 0.048778\t최선의 손실: 0.048778\t정확도: 98.48%\n",
-      "7\t검증 세트 손실: 0.050576\t최선의 손실: 0.048778\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.061966\t최선의 손실: 0.048778\t정확도: 98.05%\n",
-      "9\t검증 세트 손실: 0.043506\t최선의 손실: 0.043506\t정확도: 98.83%\n",
-      "10\t검증 세트 손실: 0.044583\t최선의 손실: 0.043506\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.035384\t최선의 손실: 0.035384\t정확도: 98.94%\n",
-      "12\t검증 세트 손실: 0.042942\t최선의 손실: 0.035384\t정확도: 98.59%\n",
-      "13\t검증 세트 손실: 0.064227\t최선의 손실: 0.035384\t정확도: 98.24%\n",
-      "14\t검증 세트 손실: 0.047417\t최선의 손실: 0.035384\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.044894\t최선의 손실: 0.035384\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.044028\t최선의 손실: 0.035384\t정확도: 98.83%\n",
-      "17\t검증 세트 손실: 0.038578\t최선의 손실: 0.035384\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.040566\t최선의 손실: 0.035384\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.038243\t최선의 손실: 0.035384\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.040657\t최선의 손실: 0.035384\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.038341\t최선의 손실: 0.035384\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.048151\t최선의 손실: 0.035384\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.040962\t최선의 손실: 0.035384\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.049998\t최선의 손실: 0.035384\t정확도: 98.79%\n",
-      "25\t검증 세트 손실: 0.041547\t최선의 손실: 0.035384\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.045742\t최선의 손실: 0.035384\t정확도: 98.75%\n",
-      "27\t검증 세트 손실: 0.045466\t최선의 손실: 0.035384\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.042139\t최선의 손실: 0.035384\t정확도: 98.79%\n",
-      "29\t검증 세트 손실: 0.040594\t최선의 손실: 0.035384\t정확도: 99.02%\n",
-      "30\t검증 세트 손실: 0.040489\t최선의 손실: 0.035384\t정확도: 98.83%\n",
-      "31\t검증 세트 손실: 0.040467\t최선의 손실: 0.035384\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.035788\t최선의 손실: 0.035384\t정확도: 98.94%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=90, total=  17.9s\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.096978\t최선의 손실: 0.096978\t정확도: 97.30%\n",
+      "1\t검증 세트 손실: 0.112854\t최선의 손실: 0.096978\t정확도: 96.44%\n",
+      "2\t검증 세트 손실: 0.055253\t최선의 손실: 0.055253\t정확도: 98.32%\n",
+      "3\t검증 세트 손실: 0.072001\t최선의 손실: 0.055253\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.053359\t최선의 손실: 0.053359\t정확도: 98.51%\n",
+      "5\t검증 세트 손실: 0.048680\t최선의 손실: 0.048680\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.057046\t최선의 손실: 0.048680\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.052644\t최선의 손실: 0.048680\t정확도: 98.55%\n",
+      "8\t검증 세트 손실: 0.044177\t최선의 손실: 0.044177\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.054447\t최선의 손실: 0.044177\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.050202\t최선의 손실: 0.044177\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.040109\t최선의 손실: 0.040109\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.039591\t최선의 손실: 0.039591\t정확도: 98.98%\n",
+      "13\t검증 세트 손실: 0.053356\t최선의 손실: 0.039591\t정확도: 98.36%\n",
+      "14\t검증 세트 손실: 0.054836\t최선의 손실: 0.039591\t정확도: 98.20%\n",
+      "15\t검증 세트 손실: 0.038576\t최선의 손실: 0.038576\t정확도: 99.02%\n",
+      "16\t검증 세트 손실: 0.049281\t최선의 손실: 0.038576\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.044680\t최선의 손실: 0.038576\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.048048\t최선의 손실: 0.038576\t정확도: 98.36%\n",
+      "19\t검증 세트 손실: 0.047258\t최선의 손실: 0.038576\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.049524\t최선의 손실: 0.038576\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.050672\t최선의 손실: 0.038576\t정확도: 98.75%\n",
+      "22\t검증 세트 손실: 0.047611\t최선의 손실: 0.038576\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.056928\t최선의 손실: 0.038576\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.051932\t최선의 손실: 0.038576\t정확도: 98.20%\n",
+      "25\t검증 세트 손실: 0.033995\t최선의 손실: 0.033995\t정확도: 99.10%\n",
+      "26\t검증 세트 손실: 0.044368\t최선의 손실: 0.033995\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.052397\t최선의 손실: 0.033995\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 0.047020\t최선의 손실: 0.033995\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.040858\t최선의 손실: 0.033995\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.036302\t최선의 손실: 0.033995\t정확도: 99.02%\n",
+      "31\t검증 세트 손실: 0.056876\t최선의 손실: 0.033995\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.037074\t최선의 손실: 0.033995\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.051404\t최선의 손실: 0.033995\t정확도: 98.32%\n",
+      "34\t검증 세트 손실: 0.056508\t최선의 손실: 0.033995\t정확도: 98.48%\n",
+      "35\t검증 세트 손실: 0.045123\t최선의 손실: 0.033995\t정확도: 98.75%\n",
+      "36\t검증 세트 손실: 0.039042\t최선의 손실: 0.033995\t정확도: 98.94%\n",
+      "37\t검증 세트 손실: 0.046528\t최선의 손실: 0.033995\t정확도: 98.71%\n",
+      "38\t검증 세트 손실: 0.038646\t최선의 손실: 0.033995\t정확도: 98.91%\n",
+      "39\t검증 세트 손실: 0.041377\t최선의 손실: 0.033995\t정확도: 98.87%\n",
+      "40\t검증 세트 손실: 0.039349\t최선의 손실: 0.033995\t정확도: 98.75%\n",
+      "41\t검증 세트 손실: 0.050051\t최선의 손실: 0.033995\t정확도: 98.79%\n",
+      "42\t검증 세트 손실: 0.046143\t최선의 손실: 0.033995\t정확도: 98.83%\n",
+      "43\t검증 세트 손실: 0.035233\t최선의 손실: 0.033995\t정확도: 98.98%\n",
+      "44\t검증 세트 손실: 0.031639\t최선의 손실: 0.031639\t정확도: 99.18%\n",
+      "45\t검증 세트 손실: 0.045330\t최선의 손실: 0.031639\t정확도: 98.63%\n",
+      "46\t검증 세트 손실: 0.033205\t최선의 손실: 0.031639\t정확도: 99.02%\n",
+      "47\t검증 세트 손실: 0.038999\t최선의 손실: 0.031639\t정확도: 98.83%\n",
+      "48\t검증 세트 손실: 0.037164\t최선의 손실: 0.031639\t정확도: 99.14%\n",
+      "49\t검증 세트 손실: 0.036868\t최선의 손실: 0.031639\t정확도: 99.22%\n",
+      "50\t검증 세트 손실: 0.041086\t최선의 손실: 0.031639\t정확도: 99.14%\n",
+      "51\t검증 세트 손실: 0.045962\t최선의 손실: 0.031639\t정확도: 98.48%\n",
+      "52\t검증 세트 손실: 0.045828\t최선의 손실: 0.031639\t정확도: 98.87%\n",
+      "53\t검증 세트 손실: 0.056214\t최선의 손실: 0.031639\t정확도: 98.48%\n",
+      "54\t검증 세트 손실: 0.042836\t최선의 손실: 0.031639\t정확도: 98.83%\n",
+      "55\t검증 세트 손실: 0.034372\t최선의 손실: 0.031639\t정확도: 99.18%\n",
+      "56\t검증 세트 손실: 0.035091\t최선의 손실: 0.031639\t정확도: 99.10%\n",
+      "57\t검증 세트 손실: 0.048046\t최선의 손실: 0.031639\t정확도: 98.75%\n",
+      "58\t검증 세트 손실: 0.046053\t최선의 손실: 0.031639\t정확도: 98.75%\n",
+      "59\t검증 세트 손실: 0.065015\t최선의 손실: 0.031639\t정확도: 98.48%\n",
+      "60\t검증 세트 손실: 0.041717\t최선의 손실: 0.031639\t정확도: 99.06%\n",
+      "61\t검증 세트 손실: 0.037916\t최선의 손실: 0.031639\t정확도: 99.02%\n",
+      "62\t검증 세트 손실: 0.045199\t최선의 손실: 0.031639\t정확도: 98.98%\n",
+      "63\t검증 세트 손실: 0.044284\t최선의 손실: 0.031639\t정확도: 98.91%\n",
+      "64\t검증 세트 손실: 0.050787\t최선의 손실: 0.031639\t정확도: 98.67%\n",
+      "65\t검증 세트 손실: 0.042304\t최선의 손실: 0.031639\t정확도: 98.79%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=50, total= 3.8min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.116964\t최선의 손실: 0.116964\t정확도: 96.25%\n",
-      "1\t검증 세트 손실: 0.086606\t최선의 손실: 0.086606\t정확도: 97.42%\n",
-      "2\t검증 세트 손실: 0.066926\t최선의 손실: 0.066926\t정확도: 98.08%\n",
-      "3\t검증 세트 손실: 0.067606\t최선의 손실: 0.066926\t정확도: 97.77%\n",
-      "4\t검증 세트 손실: 0.053605\t최선의 손실: 0.053605\t정확도: 98.48%\n",
-      "5\t검증 세트 손실: 0.060575\t최선의 손실: 0.053605\t정확도: 98.16%\n",
-      "6\t검증 세트 손실: 0.051586\t최선의 손실: 0.051586\t정확도: 98.24%\n",
-      "7\t검증 세트 손실: 0.039293\t최선의 손실: 0.039293\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.048447\t최선의 손실: 0.039293\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.054399\t최선의 손실: 0.039293\t정확도: 98.16%\n",
-      "10\t검증 세트 손실: 0.045265\t최선의 손실: 0.039293\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.042232\t최선의 손실: 0.039293\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.040412\t최선의 손실: 0.039293\t정확도: 98.83%\n",
-      "13\t검증 세트 손실: 0.040856\t최선의 손실: 0.039293\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.041476\t최선의 손실: 0.039293\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.043885\t최선의 손실: 0.039293\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.050015\t최선의 손실: 0.039293\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.037812\t최선의 손실: 0.037812\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.034946\t최선의 손실: 0.034946\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.068029\t최선의 손실: 0.034946\t정확도: 98.05%\n",
-      "20\t검증 세트 손실: 0.041176\t최선의 손실: 0.034946\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.034686\t최선의 손실: 0.034686\t정확도: 98.91%\n",
-      "22\t검증 세트 손실: 0.039770\t최선의 손실: 0.034686\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.038565\t최선의 손실: 0.034686\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.043850\t최선의 손실: 0.034686\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.036242\t최선의 손실: 0.034686\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.037472\t최선의 손실: 0.034686\t정확도: 98.83%\n",
-      "27\t검증 세트 손실: 0.049607\t최선의 손실: 0.034686\t정확도: 98.40%\n",
-      "28\t검증 세트 손실: 0.041041\t최선의 손실: 0.034686\t정확도: 99.06%\n",
-      "29\t검증 세트 손실: 0.038752\t최선의 손실: 0.034686\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.042623\t최선의 손실: 0.034686\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.039592\t최선의 손실: 0.034686\t정확도: 99.06%\n",
-      "32\t검증 세트 손실: 0.032731\t최선의 손실: 0.032731\t정확도: 99.02%\n",
-      "33\t검증 세트 손실: 0.036655\t최선의 손실: 0.032731\t정확도: 98.79%\n",
-      "34\t검증 세트 손실: 0.035212\t최선의 손실: 0.032731\t정확도: 98.98%\n",
-      "35\t검증 세트 손실: 0.044313\t최선의 손실: 0.032731\t정확도: 98.75%\n",
-      "36\t검증 세트 손실: 0.036867\t최선의 손실: 0.032731\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.036695\t최선의 손실: 0.032731\t정확도: 98.79%\n",
-      "38\t검증 세트 손실: 0.034796\t최선의 손실: 0.032731\t정확도: 99.02%\n",
-      "39\t검증 세트 손실: 0.033626\t최선의 손실: 0.032731\t정확도: 99.14%\n",
-      "40\t검증 세트 손실: 0.037812\t최선의 손실: 0.032731\t정확도: 98.91%\n",
-      "41\t검증 세트 손실: 0.028983\t최선의 손실: 0.028983\t정확도: 99.06%\n",
-      "42\t검증 세트 손실: 0.028842\t최선의 손실: 0.028842\t정확도: 99.18%\n",
-      "43\t검증 세트 손실: 0.033524\t최선의 손실: 0.028842\t정확도: 98.94%\n",
-      "44\t검증 세트 손실: 0.035624\t최선의 손실: 0.028842\t정확도: 98.98%\n",
-      "45\t검증 세트 손실: 0.042897\t최선의 손실: 0.028842\t정확도: 98.63%\n",
-      "46\t검증 세트 손실: 0.035231\t최선의 손실: 0.028842\t정확도: 99.06%\n",
-      "47\t검증 세트 손실: 0.039042\t최선의 손실: 0.028842\t정확도: 98.94%\n",
-      "48\t검증 세트 손실: 0.037753\t최선의 손실: 0.028842\t정확도: 98.94%\n",
-      "49\t검증 세트 손실: 0.043525\t최선의 손실: 0.028842\t정확도: 98.83%\n",
-      "50\t검증 세트 손실: 0.039830\t최선의 손실: 0.028842\t정확도: 98.83%\n"
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=50, total= 7.0min\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.095904\t최선의 손실: 0.095904\t정확도: 97.50%\n",
+      "1\t검증 세트 손실: 0.084861\t최선의 손실: 0.084861\t정확도: 97.26%\n",
+      "2\t검증 세트 손실: 0.081257\t최선의 손실: 0.081257\t정확도: 97.69%\n",
+      "3\t검증 세트 손실: 0.059652\t최선의 손실: 0.059652\t정확도: 98.05%\n",
+      "4\t검증 세트 손실: 0.064768\t최선의 손실: 0.059652\t정확도: 98.01%\n",
+      "5\t검증 세트 손실: 0.064485\t최선의 손실: 0.059652\t정확도: 98.32%\n",
+      "6\t검증 세트 손실: 0.048992\t최선의 손실: 0.048992\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.043932\t최선의 손실: 0.043932\t정확도: 98.51%\n",
+      "8\t검증 세트 손실: 0.055271\t최선의 손실: 0.043932\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.056423\t최선의 손실: 0.043932\t정확도: 98.24%\n",
+      "10\t검증 세트 손실: 0.057078\t최선의 손실: 0.043932\t정확도: 98.24%\n",
+      "11\t검증 세트 손실: 0.036437\t최선의 손실: 0.036437\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.054579\t최선의 손실: 0.036437\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.040932\t최선의 손실: 0.036437\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.041749\t최선의 손실: 0.036437\t정확도: 98.79%\n",
+      "15\t검증 세트 손실: 0.037473\t최선의 손실: 0.036437\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.047992\t최선의 손실: 0.036437\t정확도: 98.44%\n",
+      "17\t검증 세트 손실: 0.047136\t최선의 손실: 0.036437\t정확도: 98.55%\n",
+      "18\t검증 세트 손실: 0.049372\t최선의 손실: 0.036437\t정확도: 98.48%\n",
+      "19\t검증 세트 손실: 0.048360\t최선의 손실: 0.036437\t정확도: 98.63%\n",
+      "20\t검증 세트 손실: 0.044232\t최선의 손실: 0.036437\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.043071\t최선의 손실: 0.036437\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.039054\t최선의 손실: 0.036437\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.039258\t최선의 손실: 0.036437\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.053488\t최선의 손실: 0.036437\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.048313\t최선의 손실: 0.036437\t정확도: 98.63%\n",
+      "26\t검증 세트 손실: 0.046819\t최선의 손실: 0.036437\t정확도: 98.59%\n",
+      "27\t검증 세트 손실: 0.041600\t최선의 손실: 0.036437\t정확도: 98.51%\n",
+      "28\t검증 세트 손실: 0.040787\t최선의 손실: 0.036437\t정확도: 98.59%\n",
+      "29\t검증 세트 손실: 0.043573\t최선의 손실: 0.036437\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.045084\t최선의 손실: 0.036437\t정확도: 98.63%\n",
+      "31\t검증 세트 손실: 0.044898\t최선의 손실: 0.036437\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.038699\t최선의 손실: 0.036437\t정확도: 98.67%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=50, total= 3.4min\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.087870\t최선의 손실: 0.087870\t정확도: 97.69%\n",
+      "1\t검증 세트 손실: 0.059954\t최선의 손실: 0.059954\t정확도: 98.28%\n",
+      "2\t검증 세트 손실: 0.066038\t최선의 손실: 0.059954\t정확도: 97.81%\n",
+      "3\t검증 세트 손실: 0.066287\t최선의 손실: 0.059954\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.052358\t최선의 손실: 0.052358\t정확도: 98.32%\n",
+      "5\t검증 세트 손실: 0.049549\t최선의 손실: 0.049549\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.061688\t최선의 손실: 0.049549\t정확도: 97.93%\n",
+      "7\t검증 세트 손실: 0.038946\t최선의 손실: 0.038946\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.037313\t최선의 손실: 0.037313\t정확도: 98.87%\n",
+      "9\t검증 세트 손실: 0.038647\t최선의 손실: 0.037313\t정확도: 98.79%\n",
+      "10\t검증 세트 손실: 0.037596\t최선의 손실: 0.037313\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.049494\t최선의 손실: 0.037313\t정확도: 98.51%\n",
+      "12\t검증 세트 손실: 0.046538\t최선의 손실: 0.037313\t정확도: 98.36%\n",
+      "13\t검증 세트 손실: 0.034838\t최선의 손실: 0.034838\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.042017\t최선의 손실: 0.034838\t정확도: 98.63%\n",
+      "15\t검증 세트 손실: 0.037248\t최선의 손실: 0.034838\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.047638\t최선의 손실: 0.034838\t정확도: 98.40%\n",
+      "17\t검증 세트 손실: 0.041282\t최선의 손실: 0.034838\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.031195\t최선의 손실: 0.031195\t정확도: 99.02%\n",
+      "19\t검증 세트 손실: 0.029430\t최선의 손실: 0.029430\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.038785\t최선의 손실: 0.029430\t정확도: 98.83%\n",
+      "21\t검증 세트 손실: 0.041304\t최선의 손실: 0.029430\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.036261\t최선의 손실: 0.029430\t정확도: 98.83%\n",
+      "23\t검증 세트 손실: 0.036418\t최선의 손실: 0.029430\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.029054\t최선의 손실: 0.029054\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.035280\t최선의 손실: 0.029054\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.037893\t최선의 손실: 0.029054\t정확도: 98.91%\n",
+      "27\t검증 세트 손실: 0.033140\t최선의 손실: 0.029054\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.033235\t최선의 손실: 0.029054\t정확도: 99.02%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "51\t검증 세트 손실: 0.046109\t최선의 손실: 0.028842\t정확도: 98.71%\n",
-      "52\t검증 세트 손실: 0.050913\t최선의 손실: 0.028842\t정확도: 98.87%\n",
-      "53\t검증 세트 손실: 0.058690\t최선의 손실: 0.028842\t정확도: 97.93%\n",
-      "54\t검증 세트 손실: 0.048605\t최선의 손실: 0.028842\t정확도: 98.59%\n",
-      "55\t검증 세트 손실: 0.034247\t최선의 손실: 0.028842\t정확도: 99.02%\n",
-      "56\t검증 세트 손실: 0.038168\t최선의 손실: 0.028842\t정확도: 98.98%\n",
-      "57\t검증 세트 손실: 0.036757\t최선의 손실: 0.028842\t정확도: 99.06%\n",
-      "58\t검증 세트 손실: 0.051576\t최선의 손실: 0.028842\t정확도: 98.67%\n",
-      "59\t검증 세트 손실: 0.044314\t최선의 손실: 0.028842\t정확도: 99.10%\n",
-      "60\t검증 세트 손실: 0.038813\t최선의 손실: 0.028842\t정확도: 98.75%\n",
-      "61\t검증 세트 손실: 0.038835\t최선의 손실: 0.028842\t정확도: 98.87%\n",
-      "62\t검증 세트 손실: 0.044648\t최선의 손실: 0.028842\t정확도: 98.91%\n",
-      "63\t검증 세트 손실: 0.046698\t최선의 손실: 0.028842\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.039207\t최선의 손실: 0.029054\t정확도: 98.67%\n",
+      "30\t검증 세트 손실: 0.040858\t최선의 손실: 0.029054\t정확도: 98.59%\n",
+      "31\t검증 세트 손실: 0.030017\t최선의 손실: 0.029054\t정확도: 99.06%\n",
+      "32\t검증 세트 손실: 0.032208\t최선의 손실: 0.029054\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.042530\t최선의 손실: 0.029054\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.040067\t최선의 손실: 0.029054\t정확도: 98.67%\n",
+      "35\t검증 세트 손실: 0.043733\t최선의 손실: 0.029054\t정확도: 98.40%\n",
+      "36\t검증 세트 손실: 0.041819\t최선의 손실: 0.029054\t정확도: 98.83%\n",
+      "37\t검증 세트 손실: 0.031849\t최선의 손실: 0.029054\t정확도: 99.02%\n",
+      "38\t검증 세트 손실: 0.034916\t최선의 손실: 0.029054\t정확도: 98.87%\n",
+      "39\t검증 세트 손실: 0.030296\t최선의 손실: 0.029054\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.046381\t최선의 손실: 0.029054\t정확도: 98.79%\n",
+      "41\t검증 세트 손실: 0.034857\t최선의 손실: 0.029054\t정확도: 98.94%\n",
+      "42\t검증 세트 손실: 0.036712\t최선의 손실: 0.029054\t정확도: 98.67%\n",
+      "43\t검증 세트 손실: 0.066128\t최선의 손실: 0.029054\t정확도: 98.48%\n",
+      "44\t검증 세트 손실: 0.028506\t최선의 손실: 0.028506\t정확도: 98.98%\n",
+      "45\t검증 세트 손실: 0.032316\t최선의 손실: 0.028506\t정확도: 99.14%\n",
+      "46\t검증 세트 손실: 0.055302\t최선의 손실: 0.028506\t정확도: 98.51%\n",
+      "47\t검증 세트 손실: 0.030114\t최선의 손실: 0.028506\t정확도: 98.94%\n",
+      "48\t검증 세트 손실: 0.041489\t최선의 손실: 0.028506\t정확도: 98.75%\n",
+      "49\t검증 세트 손실: 0.044913\t최선의 손실: 0.028506\t정확도: 98.71%\n",
+      "50\t검증 세트 손실: 0.041306\t최선의 손실: 0.028506\t정확도: 98.83%\n",
+      "51\t검증 세트 손실: 0.044254\t최선의 손실: 0.028506\t정확도: 98.87%\n",
+      "52\t검증 세트 손실: 0.034836\t최선의 손실: 0.028506\t정확도: 98.94%\n",
+      "53\t검증 세트 손실: 0.039152\t최선의 손실: 0.028506\t정확도: 99.10%\n",
+      "54\t검증 세트 손실: 0.038198\t최선의 손실: 0.028506\t정확도: 99.02%\n",
+      "55\t검증 세트 손실: 0.045002\t최선의 손실: 0.028506\t정확도: 98.98%\n",
+      "56\t검증 세트 손실: 0.039446\t최선의 손실: 0.028506\t정확도: 99.06%\n",
+      "57\t검증 세트 손실: 0.043565\t최선의 손실: 0.028506\t정확도: 98.94%\n",
+      "58\t검증 세트 손실: 0.042302\t최선의 손실: 0.028506\t정확도: 98.71%\n",
+      "59\t검증 세트 손실: 0.036168\t최선의 손실: 0.028506\t정확도: 98.98%\n",
+      "60\t검증 세트 손실: 0.043855\t최선의 손실: 0.028506\t정확도: 98.83%\n",
+      "61\t검증 세트 손실: 0.039906\t최선의 손실: 0.028506\t정확도: 98.71%\n",
+      "62\t검증 세트 손실: 0.051557\t최선의 손실: 0.028506\t정확도: 98.51%\n",
+      "63\t검증 세트 손실: 0.041147\t최선의 손실: 0.028506\t정확도: 99.02%\n",
+      "64\t검증 세트 손실: 0.038545\t최선의 손실: 0.028506\t정확도: 98.94%\n",
+      "65\t검증 세트 손실: 0.030753\t최선의 손실: 0.028506\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=50, total= 6.9min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.086990\t최선의 손실: 0.086990\t정확도: 97.46%\n",
-      "1\t검증 세트 손실: 0.054578\t최선의 손실: 0.054578\t정확도: 98.36%\n",
-      "2\t검증 세트 손실: 0.070682\t최선의 손실: 0.054578\t정확도: 97.73%\n",
-      "3\t검증 세트 손실: 0.060162\t최선의 손실: 0.054578\t정확도: 98.01%\n",
-      "4\t검증 세트 손실: 0.056577\t최선의 손실: 0.054578\t정확도: 98.28%\n",
-      "5\t검증 세트 손실: 0.064014\t최선의 손실: 0.054578\t정확도: 98.01%\n",
-      "6\t검증 세트 손실: 0.057904\t최선의 손실: 0.054578\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.046493\t최선의 손실: 0.046493\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.041252\t최선의 손실: 0.041252\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.039917\t최선의 손실: 0.039917\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.038653\t최선의 손실: 0.038653\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.038996\t최선의 손실: 0.038653\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.039798\t최선의 손실: 0.038653\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.044341\t최선의 손실: 0.038653\t정확도: 98.44%\n",
-      "14\t검증 세트 손실: 0.040812\t최선의 손실: 0.038653\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.031132\t최선의 손실: 0.031132\t정확도: 98.98%\n",
-      "16\t검증 세트 손실: 0.030084\t최선의 손실: 0.030084\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.055509\t최선의 손실: 0.030084\t정확도: 98.12%\n",
-      "18\t검증 세트 손실: 0.039782\t최선의 손실: 0.030084\t정확도: 98.87%\n",
-      "19\t검증 세트 손실: 0.040288\t최선의 손실: 0.030084\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.039257\t최선의 손실: 0.030084\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.027467\t최선의 손실: 0.027467\t정확도: 99.10%\n",
-      "22\t검증 세트 손실: 0.032980\t최선의 손실: 0.027467\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.041880\t최선의 손실: 0.027467\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.031568\t최선의 손실: 0.027467\t정확도: 98.98%\n",
-      "25\t검증 세트 손실: 0.036093\t최선의 손실: 0.027467\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.034655\t최선의 손실: 0.027467\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.031778\t최선의 손실: 0.027467\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.026870\t최선의 손실: 0.026870\t정확도: 99.22%\n",
-      "29\t검증 세트 손실: 0.040769\t최선의 손실: 0.026870\t정확도: 98.59%\n",
-      "30\t검증 세트 손실: 0.034323\t최선의 손실: 0.026870\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.030779\t최선의 손실: 0.026870\t정확도: 98.91%\n",
-      "32\t검증 세트 손실: 0.036052\t최선의 손실: 0.026870\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.032838\t최선의 손실: 0.026870\t정확도: 99.10%\n",
-      "34\t검증 세트 손실: 0.032950\t최선의 손실: 0.026870\t정확도: 98.91%\n",
-      "35\t검증 세트 손실: 0.032252\t최선의 손실: 0.026870\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.052646\t최선의 손실: 0.026870\t정확도: 98.91%\n",
-      "37\t검증 세트 손실: 0.033353\t최선의 손실: 0.026870\t정확도: 98.87%\n",
-      "38\t검증 세트 손실: 0.039252\t최선의 손실: 0.026870\t정확도: 98.83%\n",
-      "39\t검증 세트 손실: 0.036262\t최선의 손실: 0.026870\t정확도: 98.94%\n",
-      "40\t검증 세트 손실: 0.031136\t최선의 손실: 0.026870\t정확도: 98.83%\n",
-      "41\t검증 세트 손실: 0.041140\t최선의 손실: 0.026870\t정확도: 98.87%\n",
-      "42\t검증 세트 손실: 0.032881\t최선의 손실: 0.026870\t정확도: 99.10%\n",
-      "43\t검증 세트 손실: 0.030642\t최선의 손실: 0.026870\t정확도: 99.14%\n",
-      "44\t검증 세트 손실: 0.033943\t최선의 손실: 0.026870\t정확도: 98.71%\n",
-      "45\t검증 세트 손실: 0.030514\t최선의 손실: 0.026870\t정확도: 99.18%\n",
-      "46\t검증 세트 손실: 0.032747\t최선의 손실: 0.026870\t정확도: 98.91%\n",
-      "47\t검증 세트 손실: 0.022035\t최선의 손실: 0.022035\t정확도: 99.18%\n",
-      "48\t검증 세트 손실: 0.041046\t최선의 손실: 0.022035\t정확도: 98.63%\n",
-      "49\t검증 세트 손실: 0.035213\t최선의 손실: 0.022035\t정확도: 99.14%\n",
-      "50\t검증 세트 손실: 0.040877\t최선의 손실: 0.022035\t정확도: 98.75%\n",
-      "51\t검증 세트 손실: 0.030412\t최선의 손실: 0.022035\t정확도: 98.98%\n",
-      "52\t검증 세트 손실: 0.048062\t최선의 손실: 0.022035\t정확도: 98.98%\n",
-      "53\t검증 세트 손실: 0.030974\t최선의 손실: 0.022035\t정확도: 99.10%\n",
-      "54\t검증 세트 손실: 0.028232\t최선의 손실: 0.022035\t정확도: 99.02%\n",
-      "55\t검증 세트 손실: 0.038045\t최선의 손실: 0.022035\t정확도: 99.06%\n",
-      "56\t검증 세트 손실: 0.034453\t최선의 손실: 0.022035\t정확도: 98.87%\n",
-      "57\t검증 세트 손실: 0.057875\t최선의 손실: 0.022035\t정확도: 98.71%\n",
-      "58\t검증 세트 손실: 0.033631\t최선의 손실: 0.022035\t정확도: 98.83%\n",
-      "59\t검증 세트 손실: 0.029160\t최선의 손실: 0.022035\t정확도: 99.14%\n",
-      "60\t검증 세트 손실: 0.036046\t최선의 손실: 0.022035\t정확도: 98.94%\n",
-      "61\t검증 세트 손실: 0.033771\t최선의 손실: 0.022035\t정확도: 98.94%\n",
-      "62\t검증 세트 손실: 0.036875\t최선의 손실: 0.022035\t정확도: 98.94%\n",
-      "63\t검증 세트 손실: 0.040176\t최선의 손실: 0.022035\t정확도: 98.98%\n",
-      "64\t검증 세트 손실: 0.037326\t최선의 손실: 0.022035\t정확도: 98.98%\n",
-      "65\t검증 세트 손실: 0.037951\t최선의 손실: 0.022035\t정확도: 98.98%\n",
-      "66\t검증 세트 손실: 0.029884\t최선의 손실: 0.022035\t정확도: 99.14%\n",
-      "67\t검증 세트 손실: 0.048256\t최선의 손실: 0.022035\t정확도: 98.79%\n",
-      "68\t검증 세트 손실: 0.033609\t최선의 손실: 0.022035\t정확도: 98.98%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=50, total= 6.8min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.98, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.116777\t최선의 손실: 0.116777\t정확도: 96.56%\n",
+      "1\t검증 세트 손실: 0.136757\t최선의 손실: 0.116777\t정확도: 95.86%\n",
+      "2\t검증 세트 손실: 0.068325\t최선의 손실: 0.068325\t정확도: 98.12%\n",
+      "3\t검증 세트 손실: 0.088507\t최선의 손실: 0.068325\t정확도: 97.46%\n",
+      "4\t검증 세트 손실: 0.063202\t최선의 손실: 0.063202\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.112752\t최선의 손실: 0.063202\t정확도: 97.50%\n",
+      "6\t검증 세트 손실: 0.091423\t최선의 손실: 0.063202\t정확도: 97.81%\n",
+      "7\t검증 세트 손실: 0.095170\t최선의 손실: 0.063202\t정확도: 96.99%\n",
+      "8\t검증 세트 손실: 0.052942\t최선의 손실: 0.052942\t정확도: 98.28%\n",
+      "9\t검증 세트 손실: 0.047004\t최선의 손실: 0.047004\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.067101\t최선의 손실: 0.047004\t정확도: 98.24%\n",
+      "11\t검증 세트 손실: 0.032081\t최선의 손실: 0.032081\t정확도: 99.02%\n",
+      "12\t검증 세트 손실: 0.047173\t최선의 손실: 0.032081\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.070338\t최선의 손실: 0.032081\t정확도: 98.08%\n",
+      "14\t검증 세트 손실: 0.047967\t최선의 손실: 0.032081\t정확도: 98.67%\n",
+      "15\t검증 세트 손실: 0.045652\t최선의 손실: 0.032081\t정확도: 98.98%\n",
+      "16\t검증 세트 손실: 0.085640\t최선의 손실: 0.032081\t정확도: 98.20%\n",
+      "17\t검증 세트 손실: 0.038552\t최선의 손실: 0.032081\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.082988\t최선의 손실: 0.032081\t정확도: 98.01%\n",
+      "19\t검증 세트 손실: 0.076097\t최선의 손실: 0.032081\t정확도: 98.08%\n",
+      "20\t검증 세트 손실: 0.056143\t최선의 손실: 0.032081\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.062211\t최선의 손실: 0.032081\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.042904\t최선의 손실: 0.032081\t정확도: 98.91%\n",
+      "23\t검증 세트 손실: 0.050685\t최선의 손실: 0.032081\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.048012\t최선의 손실: 0.032081\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.051300\t최선의 손실: 0.032081\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.046824\t최선의 손실: 0.032081\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.106126\t최선의 손실: 0.032081\t정확도: 97.65%\n",
+      "28\t검증 세트 손실: 0.056282\t최선의 손실: 0.032081\t정확도: 98.75%\n",
+      "29\t검증 세트 손실: 0.050158\t최선의 손실: 0.032081\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.069284\t최선의 손실: 0.032081\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.055215\t최선의 손실: 0.032081\t정확도: 98.75%\n",
+      "32\t검증 세트 손실: 0.054022\t최선의 손실: 0.032081\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.98, n_neurons=50, total= 8.9min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.136921\t최선의 손실: 0.136921\t정확도: 95.50%\n",
-      "1\t검증 세트 손실: 0.111187\t최선의 손실: 0.111187\t정확도: 97.19%\n",
-      "2\t검증 세트 손실: 0.077198\t최선의 손실: 0.077198\t정확도: 97.46%\n",
-      "3\t검증 세트 손실: 0.077858\t최선의 손실: 0.077198\t정확도: 97.77%\n",
-      "4\t검증 세트 손실: 0.049427\t최선의 손실: 0.049427\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.064725\t최선의 손실: 0.049427\t정확도: 98.01%\n",
-      "6\t검증 세트 손실: 0.059513\t최선의 손실: 0.049427\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.052244\t최선의 손실: 0.049427\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.056840\t최선의 손실: 0.049427\t정확도: 98.16%\n",
-      "9\t검증 세트 손실: 0.068115\t최선의 손실: 0.049427\t정확도: 98.16%\n",
-      "10\t검증 세트 손실: 0.072103\t최선의 손실: 0.049427\t정확도: 97.97%\n",
-      "11\t검증 세트 손실: 0.051561\t최선의 손실: 0.049427\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.050322\t최선의 손실: 0.049427\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.052938\t최선의 손실: 0.049427\t정확도: 98.08%\n",
-      "14\t검증 세트 손실: 0.056365\t최선의 손실: 0.049427\t정확도: 98.63%\n",
-      "15\t검증 세트 손실: 0.047761\t최선의 손실: 0.047761\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.048158\t최선의 손실: 0.047761\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.042696\t최선의 손실: 0.042696\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.066382\t최선의 손실: 0.042696\t정확도: 98.63%\n",
-      "19\t검증 세트 손실: 0.054776\t최선의 손실: 0.042696\t정확도: 98.24%\n",
-      "20\t검증 세트 손실: 0.074219\t최선의 손실: 0.042696\t정확도: 97.85%\n",
-      "21\t검증 세트 손실: 0.057120\t최선의 손실: 0.042696\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.063184\t최선의 손실: 0.042696\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.063003\t최선의 손실: 0.042696\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.037857\t최선의 손실: 0.037857\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.067004\t최선의 손실: 0.037857\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.053633\t최선의 손실: 0.037857\t정확도: 98.55%\n",
-      "27\t검증 세트 손실: 0.112278\t최선의 손실: 0.037857\t정확도: 97.97%\n",
-      "28\t검증 세트 손실: 0.059994\t최선의 손실: 0.037857\t정확도: 98.55%\n",
-      "29\t검증 세트 손실: 0.066394\t최선의 손실: 0.037857\t정확도: 98.28%\n",
-      "30\t검증 세트 손실: 0.051556\t최선의 손실: 0.037857\t정확도: 98.75%\n",
-      "31\t검증 세트 손실: 0.046648\t최선의 손실: 0.037857\t정확도: 98.67%\n",
-      "32\t검증 세트 손실: 0.048346\t최선의 손실: 0.037857\t정확도: 98.75%\n",
-      "33\t검증 세트 손실: 0.065239\t최선의 손실: 0.037857\t정확도: 98.12%\n",
-      "34\t검증 세트 손실: 0.061400\t최선의 손실: 0.037857\t정확도: 98.63%\n",
-      "35\t검증 세트 손실: 0.068604\t최선의 손실: 0.037857\t정확도: 98.32%\n",
-      "36\t검증 세트 손실: 0.059426\t최선의 손실: 0.037857\t정확도: 98.59%\n",
-      "37\t검증 세트 손실: 0.051562\t최선의 손실: 0.037857\t정확도: 98.87%\n",
-      "38\t검증 세트 손실: 0.069117\t최선의 손실: 0.037857\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.070908\t최선의 손실: 0.037857\t정확도: 98.48%\n",
-      "40\t검증 세트 손실: 0.062069\t최선의 손실: 0.037857\t정확도: 98.71%\n",
-      "41\t검증 세트 손실: 0.073680\t최선의 손실: 0.037857\t정확도: 98.16%\n",
-      "42\t검증 세트 손실: 0.057379\t최선의 손실: 0.037857\t정확도: 98.79%\n",
-      "43\t검증 세트 손실: 0.080308\t최선의 손실: 0.037857\t정확도: 98.20%\n",
-      "44\t검증 세트 손실: 0.070193\t최선의 손실: 0.037857\t정확도: 98.55%\n",
-      "45\t검증 세트 손실: 0.050786\t최선의 손실: 0.037857\t정확도: 98.83%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.98, n_neurons=120, total= 3.7min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.98, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.119656\t최선의 손실: 0.119656\t정확도: 96.52%\n",
+      "1\t검증 세트 손실: 0.083369\t최선의 손실: 0.083369\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.088037\t최선의 손실: 0.083369\t정확도: 97.73%\n",
+      "3\t검증 세트 손실: 0.084917\t최선의 손실: 0.083369\t정확도: 97.26%\n",
+      "4\t검증 세트 손실: 0.408806\t최선의 손실: 0.083369\t정확도: 88.82%\n",
+      "5\t검증 세트 손실: 0.054569\t최선의 손실: 0.054569\t정확도: 98.36%\n",
+      "6\t검증 세트 손실: 0.063159\t최선의 손실: 0.054569\t정확도: 98.16%\n",
+      "7\t검증 세트 손실: 0.059206\t최선의 손실: 0.054569\t정확도: 98.20%\n",
+      "8\t검증 세트 손실: 0.059815\t최선의 손실: 0.054569\t정확도: 98.59%\n",
+      "9\t검증 세트 손실: 0.066764\t최선의 손실: 0.054569\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.128602\t최선의 손실: 0.054569\t정확도: 96.52%\n",
+      "11\t검증 세트 손실: 0.059790\t최선의 손실: 0.054569\t정확도: 98.32%\n",
+      "12\t검증 세트 손실: 0.041282\t최선의 손실: 0.041282\t정확도: 98.79%\n",
+      "13\t검증 세트 손실: 0.053042\t최선의 손실: 0.041282\t정확도: 98.75%\n",
+      "14\t검증 세트 손실: 0.059184\t최선의 손실: 0.041282\t정확도: 98.63%\n",
+      "15\t검증 세트 손실: 0.127422\t최선의 손실: 0.041282\t정확도: 96.21%\n",
+      "16\t검증 세트 손실: 0.069843\t최선의 손실: 0.041282\t정확도: 98.24%\n",
+      "17\t검증 세트 손실: 0.051472\t최선의 손실: 0.041282\t정확도: 98.55%\n",
+      "18\t검증 세트 손실: 0.062314\t최선의 손실: 0.041282\t정확도: 98.32%\n",
+      "19\t검증 세트 손실: 0.060744\t최선의 손실: 0.041282\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.081740\t최선의 손실: 0.041282\t정확도: 98.28%\n",
+      "21\t검증 세트 손실: 0.047823\t최선의 손실: 0.041282\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.066791\t최선의 손실: 0.041282\t정확도: 98.44%\n",
+      "23\t검증 세트 손실: 0.049661\t최선의 손실: 0.041282\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.049166\t최선의 손실: 0.041282\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.050005\t최선의 손실: 0.041282\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.056656\t최선의 손실: 0.041282\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.051406\t최선의 손실: 0.041282\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.051145\t최선의 손실: 0.041282\t정확도: 98.63%\n",
+      "29\t검증 세트 손실: 0.063088\t최선의 손실: 0.041282\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.075978\t최선의 손실: 0.041282\t정확도: 98.55%\n",
+      "31\t검증 세트 손실: 0.049713\t최선의 손실: 0.041282\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.082211\t최선의 손실: 0.041282\t정확도: 98.32%\n",
+      "33\t검증 세트 손실: 0.072877\t최선의 손실: 0.041282\t정확도: 98.63%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=120, total= 5.1min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.155870\t최선의 손실: 0.155870\t정확도: 95.11%\n",
-      "1\t검증 세트 손실: 0.087835\t최선의 손실: 0.087835\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.080836\t최선의 손실: 0.080836\t정확도: 97.73%\n",
-      "3\t검증 세트 손실: 0.060371\t최선의 손실: 0.060371\t정확도: 98.32%\n",
-      "4\t검증 세트 손실: 0.049899\t최선의 손실: 0.049899\t정확도: 98.48%\n",
-      "5\t검증 세트 손실: 0.050969\t최선의 손실: 0.049899\t정확도: 98.40%\n",
-      "6\t검증 세트 손실: 0.079112\t최선의 손실: 0.049899\t정확도: 97.89%\n",
-      "7\t검증 세트 손실: 0.050360\t최선의 손실: 0.049899\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.056514\t최선의 손실: 0.049899\t정확도: 98.36%\n",
-      "9\t검증 세트 손실: 0.080381\t최선의 손실: 0.049899\t정확도: 98.08%\n",
-      "10\t검증 세트 손실: 0.066871\t최선의 손실: 0.049899\t정확도: 97.89%\n",
-      "11\t검증 세트 손실: 0.083025\t최선의 손실: 0.049899\t정확도: 98.08%\n",
-      "12\t검증 세트 손실: 0.040442\t최선의 손실: 0.040442\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.060037\t최선의 손실: 0.040442\t정확도: 98.55%\n",
-      "14\t검증 세트 손실: 0.057221\t최선의 손실: 0.040442\t정확도: 98.12%\n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.98, n_neurons=120, total= 3.8min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.98, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.134105\t최선의 손실: 0.134105\t정확도: 96.33%\n",
+      "1\t검증 세트 손실: 0.069297\t최선의 손실: 0.069297\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.077878\t최선의 손실: 0.069297\t정확도: 97.65%\n",
+      "3\t검증 세트 손실: 0.064498\t최선의 손실: 0.064498\t정확도: 98.05%\n",
+      "4\t검증 세트 손실: 0.195118\t최선의 손실: 0.064498\t정확도: 94.02%\n",
+      "5\t검증 세트 손실: 0.101867\t최선의 손실: 0.064498\t정확도: 97.19%\n",
+      "6\t검증 세트 손실: 0.053866\t최선의 손실: 0.053866\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.054605\t최선의 손실: 0.053866\t정확도: 98.08%\n",
+      "8\t검증 세트 손실: 0.076127\t최선의 손실: 0.053866\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.102795\t최선의 손실: 0.053866\t정확도: 97.34%\n",
+      "10\t검증 세트 손실: 0.057974\t최선의 손실: 0.053866\t정확도: 98.16%\n",
+      "11\t검증 세트 손실: 0.054923\t최선의 손실: 0.053866\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.068695\t최선의 손실: 0.053866\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.063184\t최선의 손실: 0.053866\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.041052\t최선의 손실: 0.041052\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.041951\t최선의 손실: 0.041052\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.043527\t최선의 손실: 0.041052\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.044612\t최선의 손실: 0.041052\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.049249\t최선의 손실: 0.041052\t정확도: 98.83%\n",
+      "19\t검증 세트 손실: 0.040270\t최선의 손실: 0.040270\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.061687\t최선의 손실: 0.040270\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.045733\t최선의 손실: 0.040270\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.119481\t최선의 손실: 0.040270\t정확도: 96.99%\n",
+      "23\t검증 세트 손실: 0.032249\t최선의 손실: 0.032249\t정확도: 99.10%\n",
+      "24\t검증 세트 손실: 0.051179\t최선의 손실: 0.032249\t정확도: 98.55%\n",
+      "25\t검증 세트 손실: 0.046957\t최선의 손실: 0.032249\t정확도: 98.67%\n",
+      "26\t검증 세트 손실: 0.051148\t최선의 손실: 0.032249\t정확도: 99.14%\n",
+      "27\t검증 세트 손실: 0.179504\t최선의 손실: 0.032249\t정확도: 96.99%\n",
+      "28\t검증 세트 손실: 0.052706\t최선의 손실: 0.032249\t정확도: 98.71%\n",
+      "29\t검증 세트 손실: 0.048682\t최선의 손실: 0.032249\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.061176\t최선의 손실: 0.032249\t정확도: 98.44%\n",
+      "31\t검증 세트 손실: 0.037638\t최선의 손실: 0.032249\t정확도: 99.14%\n",
+      "32\t검증 세트 손실: 0.065486\t최선의 손실: 0.032249\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.045886\t최선의 손실: 0.032249\t정확도: 98.87%\n",
+      "34\t검증 세트 손실: 0.051519\t최선의 손실: 0.032249\t정확도: 98.91%\n",
+      "35\t검증 세트 손실: 0.052959\t최선의 손실: 0.032249\t정확도: 98.83%\n",
+      "36\t검증 세트 손실: 0.058313\t최선의 손실: 0.032249\t정확도: 98.36%\n",
+      "37\t검증 세트 손실: 0.035738\t최선의 손실: 0.032249\t정확도: 99.18%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15\t검증 세트 손실: 0.044956\t최선의 손실: 0.040442\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.077890\t최선의 손실: 0.040442\t정확도: 98.20%\n",
-      "17\t검증 세트 손실: 0.034296\t최선의 손실: 0.034296\t정확도: 99.06%\n",
-      "18\t검증 세트 손실: 0.054501\t최선의 손실: 0.034296\t정확도: 98.59%\n",
-      "19\t검증 세트 손실: 0.048300\t최선의 손실: 0.034296\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.050176\t최선의 손실: 0.034296\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.049109\t최선의 손실: 0.034296\t정확도: 98.75%\n",
-      "22\t검증 세트 손실: 0.044172\t최선의 손실: 0.034296\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.060609\t최선의 손실: 0.034296\t정확도: 98.55%\n",
-      "24\t검증 세트 손실: 0.072302\t최선의 손실: 0.034296\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.053963\t최선의 손실: 0.034296\t정확도: 98.83%\n",
-      "26\t검증 세트 손실: 0.042794\t최선의 손실: 0.034296\t정확도: 98.98%\n",
-      "27\t검증 세트 손실: 0.040234\t최선의 손실: 0.034296\t정확도: 98.98%\n",
-      "28\t검증 세트 손실: 0.072077\t최선의 손실: 0.034296\t정확도: 98.36%\n",
-      "29\t검증 세트 손실: 0.039362\t최선의 손실: 0.034296\t정확도: 98.98%\n",
-      "30\t검증 세트 손실: 0.064439\t최선의 손실: 0.034296\t정확도: 98.63%\n",
-      "31\t검증 세트 손실: 0.081911\t최선의 손실: 0.034296\t정확도: 98.05%\n",
-      "32\t검증 세트 손실: 0.060578\t최선의 손실: 0.034296\t정확도: 98.67%\n",
-      "33\t검증 세트 손실: 0.049303\t최선의 손실: 0.034296\t정확도: 98.48%\n",
-      "34\t검증 세트 손실: 0.061973\t최선의 손실: 0.034296\t정확도: 98.44%\n",
-      "35\t검증 세트 손실: 0.048827\t최선의 손실: 0.034296\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.083405\t최선의 손실: 0.034296\t정확도: 98.40%\n",
-      "37\t검증 세트 손실: 0.044750\t최선의 손실: 0.034296\t정확도: 98.98%\n",
-      "38\t검증 세트 손실: 0.098340\t최선의 손실: 0.034296\t정확도: 98.28%\n",
+      "38\t검증 세트 손실: 0.060783\t최선의 손실: 0.032249\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.065021\t최선의 손실: 0.032249\t정확도: 98.44%\n",
+      "40\t검증 세트 손실: 0.065220\t최선의 손실: 0.032249\t정확도: 98.75%\n",
+      "41\t검증 세트 손실: 0.052252\t최선의 손실: 0.032249\t정확도: 98.83%\n",
+      "42\t검증 세트 손실: 0.043190\t최선의 손실: 0.032249\t정확도: 99.45%\n",
+      "43\t검증 세트 손실: 0.150628\t최선의 손실: 0.032249\t정확도: 97.69%\n",
+      "44\t검증 세트 손실: 0.052702\t최선의 손실: 0.032249\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=120, total= 4.3min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.105104\t최선의 손실: 0.105104\t정확도: 96.95%\n",
-      "1\t검증 세트 손실: 0.059731\t최선의 손실: 0.059731\t정확도: 98.24%\n",
-      "2\t검증 세트 손실: 0.082357\t최선의 손실: 0.059731\t정확도: 97.38%\n",
-      "3\t검증 세트 손실: 0.057203\t최선의 손실: 0.057203\t정확도: 98.32%\n",
-      "4\t검증 세트 손실: 0.098816\t최선의 손실: 0.057203\t정확도: 97.19%\n",
-      "5\t검증 세트 손실: 0.077768\t최선의 손실: 0.057203\t정확도: 97.97%\n",
-      "6\t검증 세트 손실: 0.059917\t최선의 손실: 0.057203\t정확도: 97.62%\n",
-      "7\t검증 세트 손실: 0.046887\t최선의 손실: 0.046887\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.118044\t최선의 손실: 0.046887\t정확도: 97.38%\n",
-      "9\t검증 세트 손실: 0.053991\t최선의 손실: 0.046887\t정확도: 98.24%\n",
-      "10\t검증 세트 손실: 0.041171\t최선의 손실: 0.041171\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.080629\t최선의 손실: 0.041171\t정확도: 97.46%\n",
-      "12\t검증 세트 손실: 0.050683\t최선의 손실: 0.041171\t정확도: 98.71%\n",
-      "13\t검증 세트 손실: 0.049894\t최선의 손실: 0.041171\t정확도: 98.51%\n",
-      "14\t검증 세트 손실: 0.083115\t최선의 손실: 0.041171\t정확도: 97.81%\n",
-      "15\t검증 세트 손실: 0.040305\t최선의 손실: 0.040305\t정확도: 98.71%\n",
-      "16\t검증 세트 손실: 0.049540\t최선의 손실: 0.040305\t정확도: 98.55%\n",
-      "17\t검증 세트 손실: 0.037601\t최선의 손실: 0.037601\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.039031\t최선의 손실: 0.037601\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.047751\t최선의 손실: 0.037601\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.047432\t최선의 손실: 0.037601\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.039127\t최선의 손실: 0.037601\t정확도: 98.91%\n",
-      "22\t검증 세트 손실: 0.029800\t최선의 손실: 0.029800\t정확도: 99.26%\n",
-      "23\t검증 세트 손실: 0.044562\t최선의 손실: 0.029800\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.038963\t최선의 손실: 0.029800\t정확도: 98.98%\n",
-      "25\t검증 세트 손실: 0.067436\t최선의 손실: 0.029800\t정확도: 98.44%\n",
-      "26\t검증 세트 손실: 0.054664\t최선의 손실: 0.029800\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.049633\t최선의 손실: 0.029800\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.056260\t최선의 손실: 0.029800\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.051211\t최선의 손실: 0.029800\t정확도: 99.02%\n",
-      "30\t검증 세트 손실: 0.045657\t최선의 손실: 0.029800\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.040327\t최선의 손실: 0.029800\t정확도: 98.98%\n",
-      "32\t검증 세트 손실: 0.065412\t최선의 손실: 0.029800\t정확도: 98.48%\n",
-      "33\t검증 세트 손실: 0.066998\t최선의 손실: 0.029800\t정확도: 98.48%\n",
-      "34\t검증 세트 손실: 0.044967\t최선의 손실: 0.029800\t정확도: 98.94%\n",
-      "35\t검증 세트 손실: 0.067164\t최선의 손실: 0.029800\t정확도: 98.44%\n",
-      "36\t검증 세트 손실: 0.045353\t최선의 손실: 0.029800\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.031472\t최선의 손실: 0.029800\t정확도: 99.14%\n",
-      "38\t검증 세트 손실: 0.047358\t최선의 손실: 0.029800\t정확도: 98.79%\n",
-      "39\t검증 세트 손실: 0.077847\t최선의 손실: 0.029800\t정확도: 98.71%\n",
-      "40\t검증 세트 손실: 0.045700\t최선의 손실: 0.029800\t정확도: 98.63%\n",
-      "41\t검증 세트 손실: 0.034549\t최선의 손실: 0.029800\t정확도: 98.94%\n",
-      "42\t검증 세트 손실: 0.032443\t최선의 손실: 0.029800\t정확도: 99.06%\n",
-      "43\t검증 세트 손실: 0.032859\t최선의 손실: 0.029800\t정확도: 99.10%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.05, batch_norm_momentum=0.98, n_neurons=120, total= 5.0min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.061343\t최선의 손실: 0.061343\t정확도: 98.24%\n",
+      "1\t검증 세트 손실: 0.092855\t최선의 손실: 0.061343\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.056016\t최선의 손실: 0.056016\t정확도: 98.63%\n",
+      "3\t검증 세트 손실: 0.036236\t최선의 손실: 0.036236\t정확도: 98.79%\n",
+      "4\t검증 세트 손실: 0.043870\t최선의 손실: 0.036236\t정확도: 98.67%\n",
+      "5\t검증 세트 손실: 0.053011\t최선의 손실: 0.036236\t정확도: 98.51%\n",
+      "6\t검증 세트 손실: 0.047777\t최선의 손실: 0.036236\t정확도: 98.63%\n",
+      "7\t검증 세트 손실: 0.052829\t최선의 손실: 0.036236\t정확도: 98.79%\n",
+      "8\t검증 세트 손실: 0.050344\t최선의 손실: 0.036236\t정확도: 98.51%\n",
+      "9\t검증 세트 손실: 0.035788\t최선의 손실: 0.035788\t정확도: 99.18%\n",
+      "10\t검증 세트 손실: 0.042669\t최선의 손실: 0.035788\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.041024\t최선의 손실: 0.035788\t정확도: 99.06%\n",
+      "12\t검증 세트 손실: 0.045321\t최선의 손실: 0.035788\t정확도: 99.06%\n",
+      "13\t검증 세트 손실: 0.056885\t최선의 손실: 0.035788\t정확도: 98.71%\n",
+      "14\t검증 세트 손실: 0.062329\t최선의 손실: 0.035788\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.050035\t최선의 손실: 0.035788\t정확도: 98.87%\n",
+      "16\t검증 세트 손실: 0.037412\t최선의 손실: 0.035788\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.041354\t최선의 손실: 0.035788\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.044420\t최선의 손실: 0.035788\t정확도: 99.10%\n",
+      "19\t검증 세트 손실: 0.052518\t최선의 손실: 0.035788\t정확도: 98.94%\n",
+      "20\t검증 세트 손실: 0.053107\t최선의 손실: 0.035788\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.050111\t최선의 손실: 0.035788\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.047973\t최선의 손실: 0.035788\t정확도: 99.18%\n",
+      "23\t검증 세트 손실: 0.052860\t최선의 손실: 0.035788\t정확도: 98.83%\n",
+      "24\t검증 세트 손실: 0.048854\t최선의 손실: 0.035788\t정확도: 98.98%\n",
+      "25\t검증 세트 손실: 0.068567\t최선의 손실: 0.035788\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.054979\t최선의 손실: 0.035788\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.051484\t최선의 손실: 0.035788\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.038310\t최선의 손실: 0.035788\t정확도: 99.10%\n",
+      "29\t검증 세트 손실: 0.049579\t최선의 손실: 0.035788\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.043928\t최선의 손실: 0.035788\t정확도: 99.14%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=120, total= 4.9min\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.104241\t최선의 손실: 0.104241\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.071296\t최선의 손실: 0.071296\t정확도: 97.58%\n",
-      "2\t검증 세트 손실: 0.074887\t최선의 손실: 0.071296\t정확도: 98.20%\n",
-      "3\t검증 세트 손실: 0.050070\t최선의 손실: 0.050070\t정확도: 98.67%\n",
-      "4\t검증 세트 손실: 0.040782\t최선의 손실: 0.040782\t정확도: 99.02%\n",
-      "5\t검증 세트 손실: 0.040223\t최선의 손실: 0.040223\t정확도: 98.98%\n",
-      "6\t검증 세트 손실: 0.037885\t최선의 손실: 0.037885\t정확도: 98.91%\n",
-      "7\t검증 세트 손실: 0.053601\t최선의 손실: 0.037885\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.032915\t최선의 손실: 0.032915\t정확도: 99.06%\n",
-      "9\t검증 세트 손실: 0.046620\t최선의 손실: 0.032915\t정확도: 98.75%\n",
-      "10\t검증 세트 손실: 0.041975\t최선의 손실: 0.032915\t정확도: 98.87%\n",
-      "11\t검증 세트 손실: 0.044633\t최선의 손실: 0.032915\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.063274\t최선의 손실: 0.032915\t정확도: 98.51%\n",
-      "13\t검증 세트 손실: 0.051093\t최선의 손실: 0.032915\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.043010\t최선의 손실: 0.032915\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.047451\t최선의 손실: 0.032915\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.059581\t최선의 손실: 0.032915\t정확도: 98.67%\n",
-      "17\t검증 세트 손실: 0.057833\t최선의 손실: 0.032915\t정확도: 98.63%\n",
-      "18\t검증 세트 손실: 0.046430\t최선의 손실: 0.032915\t정확도: 98.91%\n",
-      "19\t검증 세트 손실: 0.045251\t최선의 손실: 0.032915\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.084844\t최선의 손실: 0.032915\t정확도: 98.01%\n",
-      "21\t검증 세트 손실: 0.060332\t최선의 손실: 0.032915\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.039171\t최선의 손실: 0.032915\t정확도: 99.14%\n",
-      "23\t검증 세트 손실: 0.041412\t최선의 손실: 0.032915\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.046247\t최선의 손실: 0.032915\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.052176\t최선의 손실: 0.032915\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.061877\t최선의 손실: 0.032915\t정확도: 98.55%\n",
-      "27\t검증 세트 손실: 0.039576\t최선의 손실: 0.032915\t정확도: 99.14%\n",
-      "28\t검증 세트 손실: 0.060338\t최선의 손실: 0.032915\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.046330\t최선의 손실: 0.032915\t정확도: 98.87%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=120, total=  46.3s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.102751\t최선의 손실: 0.102751\t정확도: 97.58%\n",
+      "1\t검증 세트 손실: 0.058735\t최선의 손실: 0.058735\t정확도: 98.24%\n",
+      "2\t검증 세트 손실: 0.055085\t최선의 손실: 0.055085\t정확도: 98.48%\n",
+      "3\t검증 세트 손실: 0.051649\t최선의 손실: 0.051649\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.093476\t최선의 손실: 0.051649\t정확도: 97.46%\n",
+      "5\t검증 세트 손실: 0.039300\t최선의 손실: 0.039300\t정확도: 98.67%\n",
+      "6\t검증 세트 손실: 0.057596\t최선의 손실: 0.039300\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.067741\t최선의 손실: 0.039300\t정확도: 98.24%\n",
+      "8\t검증 세트 손실: 0.034599\t최선의 손실: 0.034599\t정확도: 99.02%\n",
+      "9\t검증 세트 손실: 0.061323\t최선의 손실: 0.034599\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.041029\t최선의 손실: 0.034599\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.049458\t최선의 손실: 0.034599\t정확도: 98.94%\n",
+      "12\t검증 세트 손실: 0.047928\t최선의 손실: 0.034599\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.035485\t최선의 손실: 0.034599\t정확도: 99.14%\n",
+      "14\t검증 세트 손실: 0.068019\t최선의 손실: 0.034599\t정확도: 98.51%\n",
+      "15\t검증 세트 손실: 0.048835\t최선의 손실: 0.034599\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.034219\t최선의 손실: 0.034219\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.052109\t최선의 손실: 0.034219\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.056327\t최선의 손실: 0.034219\t정확도: 98.44%\n",
+      "19\t검증 세트 손실: 0.039765\t최선의 손실: 0.034219\t정확도: 99.14%\n",
+      "20\t검증 세트 손실: 0.069643\t최선의 손실: 0.034219\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.052780\t최선의 손실: 0.034219\t정확도: 99.02%\n",
+      "22\t검증 세트 손실: 0.054317\t최선의 손실: 0.034219\t정확도: 98.91%\n",
+      "23\t검증 세트 손실: 0.048549\t최선의 손실: 0.034219\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.059831\t최선의 손실: 0.034219\t정확도: 98.98%\n",
+      "25\t검증 세트 손실: 0.054184\t최선의 손실: 0.034219\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.064758\t최선의 손실: 0.034219\t정확도: 98.48%\n",
+      "27\t검증 세트 손실: 0.055836\t최선의 손실: 0.034219\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.059483\t최선의 손실: 0.034219\t정확도: 98.75%\n",
+      "29\t검증 세트 손실: 0.055508\t최선의 손실: 0.034219\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.058736\t최선의 손실: 0.034219\t정확도: 98.94%\n",
+      "31\t검증 세트 손실: 0.067702\t최선의 손실: 0.034219\t정확도: 98.75%\n",
+      "32\t검증 세트 손실: 0.083678\t최선의 손실: 0.034219\t정확도: 98.24%\n",
+      "33\t검증 세트 손실: 0.036882\t최선의 손실: 0.034219\t정확도: 99.10%\n",
+      "34\t검증 세트 손실: 0.073335\t최선의 손실: 0.034219\t정확도: 98.55%\n",
+      "35\t검증 세트 손실: 0.051463\t최선의 손실: 0.034219\t정확도: 98.87%\n",
+      "36\t검증 세트 손실: 0.045987\t최선의 손실: 0.034219\t정확도: 99.06%\n",
+      "37\t검증 세트 손실: 0.054756\t최선의 손실: 0.034219\t정확도: 98.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=120, total=  43.4s\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.154872\t최선의 손실: 0.154872\t정확도: 96.44%\n",
-      "1\t검증 세트 손실: 0.065258\t최선의 손실: 0.065258\t정확도: 98.32%\n",
-      "2\t검증 세트 손실: 0.069482\t최선의 손실: 0.065258\t정확도: 98.01%\n",
-      "3\t검증 세트 손실: 0.043752\t최선의 손실: 0.043752\t정확도: 98.71%\n",
-      "4\t검증 세트 손실: 0.071698\t최선의 손실: 0.043752\t정확도: 98.08%\n",
-      "5\t검증 세트 손실: 0.044803\t최선의 손실: 0.043752\t정확도: 98.98%\n",
-      "6\t검증 세트 손실: 0.045822\t최선의 손실: 0.043752\t정확도: 98.83%\n",
-      "7\t검증 세트 손실: 0.048401\t최선의 손실: 0.043752\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.054617\t최선의 손실: 0.043752\t정확도: 98.51%\n",
-      "9\t검증 세트 손실: 0.075518\t최선의 손실: 0.043752\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.061807\t최선의 손실: 0.043752\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.078737\t최선의 손실: 0.043752\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.050279\t최선의 손실: 0.043752\t정확도: 98.83%\n",
-      "13\t검증 세트 손실: 0.051129\t최선의 손실: 0.043752\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.043069\t최선의 손실: 0.043069\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.043209\t최선의 손실: 0.043069\t정확도: 99.18%\n",
-      "16\t검증 세트 손실: 0.050240\t최선의 손실: 0.043069\t정확도: 98.67%\n",
-      "17\t검증 세트 손실: 0.036699\t최선의 손실: 0.036699\t정확도: 99.02%\n",
-      "18\t검증 세트 손실: 0.042274\t최선의 손실: 0.036699\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.043303\t최선의 손실: 0.036699\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.038516\t최선의 손실: 0.036699\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.082148\t최선의 손실: 0.036699\t정확도: 98.51%\n",
-      "22\t검증 세트 손실: 0.033182\t최선의 손실: 0.033182\t정확도: 99.02%\n",
-      "23\t검증 세트 손실: 0.041171\t최선의 손실: 0.033182\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.038803\t최선의 손실: 0.033182\t정확도: 99.06%\n",
-      "25\t검증 세트 손실: 0.040926\t최선의 손실: 0.033182\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.039620\t최선의 손실: 0.033182\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.050874\t최선의 손실: 0.033182\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.048050\t최선의 손실: 0.033182\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.038503\t최선의 손실: 0.033182\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.041733\t최선의 손실: 0.033182\t정확도: 99.06%\n",
-      "31\t검증 세트 손실: 0.047435\t최선의 손실: 0.033182\t정확도: 99.06%\n",
-      "32\t검증 세트 손실: 0.040722\t최선의 손실: 0.033182\t정확도: 99.10%\n",
-      "33\t검증 세트 손실: 0.049190\t최선의 손실: 0.033182\t정확도: 98.91%\n",
-      "34\t검증 세트 손실: 0.049897\t최선의 손실: 0.033182\t정확도: 99.02%\n",
-      "35\t검증 세트 손실: 0.041044\t최선의 손실: 0.033182\t정확도: 99.14%\n",
-      "36\t검증 세트 손실: 0.059206\t최선의 손실: 0.033182\t정확도: 98.71%\n",
-      "37\t검증 세트 손실: 0.038074\t최선의 손실: 0.033182\t정확도: 99.10%\n",
-      "38\t검증 세트 손실: 0.044926\t최선의 손실: 0.033182\t정확도: 99.14%\n",
-      "39\t검증 세트 손실: 0.055479\t최선의 손실: 0.033182\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.048528\t최선의 손실: 0.033182\t정확도: 98.83%\n",
-      "41\t검증 세트 손실: 0.033388\t최선의 손실: 0.033182\t정확도: 99.30%\n",
-      "42\t검증 세트 손실: 0.042851\t최선의 손실: 0.033182\t정확도: 99.14%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=120, total=  56.1s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.072214\t최선의 손실: 0.072214\t정확도: 98.20%\n",
+      "1\t검증 세트 손실: 0.078920\t최선의 손실: 0.072214\t정확도: 97.50%\n",
+      "2\t검증 세트 손실: 0.076877\t최선의 손실: 0.072214\t정확도: 97.58%\n",
+      "3\t검증 세트 손실: 0.051608\t최선의 손실: 0.051608\t정확도: 98.44%\n",
+      "4\t검증 세트 손실: 0.063791\t최선의 손실: 0.051608\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.039870\t최선의 손실: 0.039870\t정확도: 98.94%\n",
+      "6\t검증 세트 손실: 0.038435\t최선의 손실: 0.038435\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.060640\t최선의 손실: 0.038435\t정확도: 98.28%\n",
+      "8\t검증 세트 손실: 0.065411\t최선의 손실: 0.038435\t정확도: 98.55%\n",
+      "9\t검증 세트 손실: 0.044354\t최선의 손실: 0.038435\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.053004\t최선의 손실: 0.038435\t정확도: 98.91%\n",
+      "11\t검증 세트 손실: 0.039701\t최선의 손실: 0.038435\t정확도: 99.06%\n",
+      "12\t검증 세트 손실: 0.046147\t최선의 손실: 0.038435\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.039985\t최선의 손실: 0.038435\t정확도: 99.06%\n",
+      "14\t검증 세트 손실: 0.036480\t최선의 손실: 0.036480\t정확도: 99.02%\n",
+      "15\t검증 세트 손실: 0.033436\t최선의 손실: 0.033436\t정확도: 99.22%\n",
+      "16\t검증 세트 손실: 0.039697\t최선의 손실: 0.033436\t정확도: 99.06%\n",
+      "17\t검증 세트 손실: 0.036699\t최선의 손실: 0.033436\t정확도: 99.06%\n",
+      "18\t검증 세트 손실: 0.030363\t최선의 손실: 0.030363\t정확도: 99.18%\n",
+      "19\t검증 세트 손실: 0.029244\t최선의 손실: 0.029244\t정확도: 99.26%\n",
+      "20\t검증 세트 손실: 0.052562\t최선의 손실: 0.029244\t정확도: 98.98%\n",
+      "21\t검증 세트 손실: 0.042869\t최선의 손실: 0.029244\t정확도: 98.87%\n",
+      "22\t검증 세트 손실: 0.036901\t최선의 손실: 0.029244\t정확도: 99.18%\n",
+      "23\t검증 세트 손실: 0.033276\t최선의 손실: 0.029244\t정확도: 99.14%\n",
+      "24\t검증 세트 손실: 0.032447\t최선의 손실: 0.029244\t정확도: 99.26%\n",
+      "25\t검증 세트 손실: 0.031756\t최선의 손실: 0.029244\t정확도: 99.26%\n",
+      "26\t검증 세트 손실: 0.046752\t최선의 손실: 0.029244\t정확도: 98.91%\n",
+      "27\t검증 세트 손실: 0.062289\t최선의 손실: 0.029244\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.065642\t최선의 손실: 0.029244\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.038004\t최선의 손실: 0.029244\t정확도: 99.10%\n",
+      "30\t검증 세트 손실: 0.040710\t최선의 손실: 0.029244\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.038604\t최선의 손실: 0.029244\t정확도: 99.10%\n",
+      "32\t검증 세트 손실: 0.038764\t최선의 손실: 0.029244\t정확도: 99.02%\n",
+      "33\t검증 세트 손실: 0.035431\t최선의 손실: 0.029244\t정확도: 98.91%\n",
+      "34\t검증 세트 손실: 0.040256\t최선의 손실: 0.029244\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.031368\t최선의 손실: 0.029244\t정확도: 99.26%\n",
+      "36\t검증 세트 손실: 0.028022\t최선의 손실: 0.028022\t정확도: 99.34%\n",
+      "37\t검증 세트 손실: 0.029344\t최선의 손실: 0.028022\t정확도: 99.22%\n",
+      "38\t검증 세트 손실: 0.032442\t최선의 손실: 0.028022\t정확도: 99.22%\n",
+      "39\t검증 세트 손실: 0.030828\t최선의 손실: 0.028022\t정확도: 99.30%\n",
+      "40\t검증 세트 손실: 0.030077\t최선의 손실: 0.028022\t정확도: 99.26%\n",
+      "41\t검증 세트 손실: 0.034269\t최선의 손실: 0.028022\t정확도: 99.14%\n",
+      "42\t검증 세트 손실: 0.046591\t최선의 손실: 0.028022\t정확도: 99.02%\n",
+      "43\t검증 세트 손실: 0.033647\t최선의 손실: 0.028022\t정확도: 99.14%\n",
+      "44\t검증 세트 손실: 0.050448\t최선의 손실: 0.028022\t정확도: 98.98%\n",
+      "45\t검증 세트 손실: 0.037530\t최선의 손실: 0.028022\t정확도: 99.30%\n",
+      "46\t검증 세트 손실: 0.028601\t최선의 손실: 0.028022\t정확도: 99.22%\n",
+      "47\t검증 세트 손실: 0.037573\t최선의 손실: 0.028022\t정확도: 99.30%\n",
+      "48\t검증 세트 손실: 0.025227\t최선의 손실: 0.025227\t정확도: 99.45%\n",
+      "49\t검증 세트 손실: 0.031127\t최선의 손실: 0.025227\t정확도: 99.57%\n",
+      "50\t검증 세트 손실: 0.047075\t최선의 손실: 0.025227\t정확도: 99.06%\n",
+      "51\t검증 세트 손실: 0.030472\t최선의 손실: 0.025227\t정확도: 99.49%\n",
+      "52\t검증 세트 손실: 0.042335\t최선의 손실: 0.025227\t정확도: 99.37%\n",
+      "53\t검증 세트 손실: 0.059074\t최선의 손실: 0.025227\t정확도: 99.37%\n",
+      "54\t검증 세트 손실: 0.051192\t최선의 손실: 0.025227\t정확도: 98.94%\n",
+      "55\t검증 세트 손실: 0.050349\t최선의 손실: 0.025227\t정확도: 98.98%\n",
+      "56\t검증 세트 손실: 0.047739\t최선의 손실: 0.025227\t정확도: 98.87%\n",
+      "57\t검증 세트 손실: 0.043501\t최선의 손실: 0.025227\t정확도: 98.79%\n",
+      "58\t검증 세트 손실: 0.044531\t최선의 손실: 0.025227\t정확도: 99.02%\n",
+      "59\t검증 세트 손실: 0.045715\t최선의 손실: 0.025227\t정확도: 99.02%\n",
+      "60\t검증 세트 손실: 0.032235\t최선의 손실: 0.025227\t정확도: 99.34%\n",
+      "61\t검증 세트 손실: 0.037784\t최선의 손실: 0.025227\t정확도: 98.98%\n",
+      "62\t검증 세트 손실: 0.046434\t최선의 손실: 0.025227\t정확도: 99.06%\n",
+      "63\t검증 세트 손실: 0.047430\t최선의 손실: 0.025227\t정확도: 99.02%\n",
+      "64\t검증 세트 손실: 0.035369\t최선의 손실: 0.025227\t정확도: 99.02%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "43\t검증 세트 손실: 0.042191\t최선의 손실: 0.033182\t정확도: 99.06%\n",
+      "65\t검증 세트 손실: 0.037671\t최선의 손실: 0.025227\t정확도: 99.10%\n",
+      "66\t검증 세트 손실: 0.051221\t최선의 손실: 0.025227\t정확도: 98.91%\n",
+      "67\t검증 세트 손실: 0.026387\t최선의 손실: 0.025227\t정확도: 99.34%\n",
+      "68\t검증 세트 손실: 0.042005\t최선의 손실: 0.025227\t정확도: 99.18%\n",
+      "69\t검증 세트 손실: 0.042958\t최선의 손실: 0.025227\t정확도: 99.26%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=120, total= 1.0min\n",
-      "[CV] batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.257949\t최선의 손실: 0.257949\t정확도: 94.80%\n",
-      "1\t검증 세트 손실: 0.063618\t최선의 손실: 0.063618\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.050322\t최선의 손실: 0.050322\t정확도: 98.32%\n",
-      "3\t검증 세트 손실: 0.039706\t최선의 손실: 0.039706\t정확도: 98.98%\n",
-      "4\t검증 세트 손실: 0.032025\t최선의 손실: 0.032025\t정확도: 98.94%\n",
-      "5\t검증 세트 손실: 0.053475\t최선의 손실: 0.032025\t정확도: 98.63%\n",
-      "6\t검증 세트 손실: 0.059577\t최선의 손실: 0.032025\t정확도: 98.28%\n",
-      "7\t검증 세트 손실: 0.063362\t최선의 손실: 0.032025\t정확도: 98.94%\n",
-      "8\t검증 세트 손실: 0.078117\t최선의 손실: 0.032025\t정확도: 98.67%\n",
-      "9\t검증 세트 손실: 0.059932\t최선의 손실: 0.032025\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.067905\t최선의 손실: 0.032025\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.048761\t최선의 손실: 0.032025\t정확도: 98.87%\n",
-      "12\t검증 세트 손실: 0.049641\t최선의 손실: 0.032025\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.031384\t최선의 손실: 0.031384\t정확도: 99.06%\n",
-      "14\t검증 세트 손실: 0.054245\t최선의 손실: 0.031384\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.035790\t최선의 손실: 0.031384\t정확도: 99.18%\n",
-      "16\t검증 세트 손실: 0.048642\t최선의 손실: 0.031384\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.036184\t최선의 손실: 0.031384\t정확도: 99.14%\n",
-      "18\t검증 세트 손실: 0.044832\t최선의 손실: 0.031384\t정확도: 98.94%\n",
-      "19\t검증 세트 손실: 0.032816\t최선의 손실: 0.031384\t정확도: 99.26%\n",
-      "20\t검증 세트 손실: 0.032490\t최선의 손실: 0.031384\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.038400\t최선의 손실: 0.031384\t정확도: 99.02%\n",
-      "22\t검증 세트 손실: 0.033769\t최선의 손실: 0.031384\t정확도: 99.45%\n",
-      "23\t검증 세트 손실: 0.042433\t최선의 손실: 0.031384\t정확도: 99.10%\n",
-      "24\t검증 세트 손실: 0.046696\t최선의 손실: 0.031384\t정확도: 99.02%\n",
-      "25\t검증 세트 손실: 0.040760\t최선의 손실: 0.031384\t정확도: 99.30%\n",
-      "26\t검증 세트 손실: 0.031016\t최선의 손실: 0.031016\t정확도: 99.18%\n",
-      "27\t검증 세트 손실: 0.046692\t최선의 손실: 0.031016\t정확도: 99.10%\n",
-      "28\t검증 세트 손실: 0.062606\t최선의 손실: 0.031016\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.040286\t최선의 손실: 0.031016\t정확도: 99.18%\n",
-      "30\t검증 세트 손실: 0.046340\t최선의 손실: 0.031016\t정확도: 99.14%\n",
-      "31\t검증 세트 손실: 0.035251\t최선의 손실: 0.031016\t정확도: 99.34%\n",
-      "32\t검증 세트 손실: 0.052874\t최선의 손실: 0.031016\t정확도: 98.94%\n",
-      "33\t검증 세트 손실: 0.036702\t최선의 손실: 0.031016\t정확도: 99.06%\n",
-      "34\t검증 세트 손실: 0.057888\t최선의 손실: 0.031016\t정확도: 99.06%\n",
-      "35\t검증 세트 손실: 0.061352\t최선의 손실: 0.031016\t정확도: 98.91%\n",
-      "36\t검증 세트 손실: 0.026375\t최선의 손실: 0.026375\t정확도: 99.34%\n",
-      "37\t검증 세트 손실: 0.026426\t최선의 손실: 0.026375\t정확도: 99.45%\n",
-      "38\t검증 세트 손실: 0.045040\t최선의 손실: 0.026375\t정확도: 99.02%\n",
-      "39\t검증 세트 손실: 0.035541\t최선의 손실: 0.026375\t정확도: 99.22%\n",
-      "40\t검증 세트 손실: 0.045447\t최선의 손실: 0.026375\t정확도: 98.94%\n",
-      "41\t검증 세트 손실: 0.034033\t최선의 손실: 0.026375\t정확도: 99.30%\n",
-      "42\t검증 세트 손실: 0.036147\t최선의 손실: 0.026375\t정확도: 99.34%\n",
-      "43\t검증 세트 손실: 0.041758\t최선의 손실: 0.026375\t정확도: 98.87%\n",
-      "44\t검증 세트 손실: 0.039872\t최선의 손실: 0.026375\t정확도: 99.18%\n",
-      "45\t검증 세트 손실: 0.035012\t최선의 손실: 0.026375\t정확도: 99.30%\n",
-      "46\t검증 세트 손실: 0.027153\t최선의 손실: 0.026375\t정확도: 99.37%\n",
-      "47\t검증 세트 손실: 0.027347\t최선의 손실: 0.026375\t정확도: 99.49%\n",
-      "48\t검증 세트 손실: 0.040611\t최선의 손실: 0.026375\t정확도: 99.26%\n",
-      "49\t검증 세트 손실: 0.039277\t최선의 손실: 0.026375\t정확도: 99.30%\n",
-      "50\t검증 세트 손실: 0.033591\t최선의 손실: 0.026375\t정확도: 99.26%\n",
-      "51\t검증 세트 손실: 0.032997\t최선의 손실: 0.026375\t정확도: 99.34%\n",
-      "52\t검증 세트 손실: 0.034880\t최선의 손실: 0.026375\t정확도: 99.37%\n",
-      "53\t검증 세트 손실: 0.044452\t최선의 손실: 0.026375\t정확도: 99.06%\n",
-      "54\t검증 세트 손실: 0.049409\t최선의 손실: 0.026375\t정확도: 99.14%\n",
-      "55\t검증 세트 손실: 0.042134\t최선의 손실: 0.026375\t정확도: 99.22%\n",
-      "56\t검증 세트 손실: 0.047037\t최선의 손실: 0.026375\t정확도: 99.18%\n",
-      "57\t검증 세트 손실: 0.028987\t최선의 손실: 0.026375\t정확도: 99.37%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=120, total= 1.7min\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140 \n",
+      "0\t검증 세트 손실: 201.350540\t최선의 손실: 201.350540\t정확도: 85.93%\n",
+      "1\t검증 세트 손실: 23.290667\t최선의 손실: 23.290667\t정확도: 91.44%\n",
+      "2\t검증 세트 손실: 11.956513\t최선의 손실: 11.956513\t정확도: 91.24%\n",
+      "3\t검증 세트 손실: 4.508650\t최선의 손실: 4.508650\t정확도: 94.45%\n",
+      "4\t검증 세트 손실: 4.159642\t최선의 손실: 4.159642\t정확도: 93.12%\n",
+      "5\t검증 세트 손실: 1.385204\t최선의 손실: 1.385204\t정확도: 97.03%\n",
+      "6\t검증 세트 손실: 1.392454\t최선의 손실: 1.385204\t정확도: 96.76%\n",
+      "7\t검증 세트 손실: 1.095826\t최선의 손실: 1.095826\t정확도: 97.38%\n",
+      "8\t검증 세트 손실: 0.670510\t최선의 손실: 0.670510\t정확도: 97.89%\n",
+      "9\t검증 세트 손실: 0.684699\t최선의 손실: 0.670510\t정확도: 97.65%\n",
+      "10\t검증 세트 손실: 0.494251\t최선의 손실: 0.494251\t정확도: 98.05%\n",
+      "11\t검증 세트 손실: 0.892210\t최선의 손실: 0.494251\t정확도: 97.03%\n",
+      "12\t검증 세트 손실: 0.534450\t최선의 손실: 0.494251\t정확도: 97.77%\n",
+      "13\t검증 세트 손실: 0.530880\t최선의 손실: 0.494251\t정확도: 97.58%\n",
+      "14\t검증 세트 손실: 0.460215\t최선의 손실: 0.460215\t정확도: 98.20%\n",
+      "15\t검증 세트 손실: 0.476261\t최선의 손실: 0.460215\t정확도: 97.58%\n",
+      "16\t검증 세트 손실: 0.422915\t최선의 손실: 0.422915\t정확도: 97.81%\n",
+      "17\t검증 세트 손실: 0.340137\t최선의 손실: 0.340137\t정확도: 98.59%\n",
+      "18\t검증 세트 손실: 0.467914\t최선의 손실: 0.340137\t정확도: 97.73%\n",
+      "19\t검증 세트 손실: 0.508998\t최선의 손실: 0.340137\t정확도: 98.12%\n",
+      "20\t검증 세트 손실: 0.247746\t최선의 손실: 0.247746\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.416637\t최선의 손실: 0.247746\t정확도: 97.93%\n",
+      "22\t검증 세트 손실: 0.351719\t최선의 손실: 0.247746\t정확도: 98.08%\n",
+      "23\t검증 세트 손실: 0.185835\t최선의 손실: 0.185835\t정확도: 98.55%\n",
+      "24\t검증 세트 손실: 0.232326\t최선의 손실: 0.185835\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.231070\t최선의 손실: 0.185835\t정확도: 98.55%\n",
+      "26\t검증 세트 손실: 0.133417\t최선의 손실: 0.133417\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.160911\t최선의 손실: 0.133417\t정확도: 98.79%\n",
+      "28\t검증 세트 손실: 0.145715\t최선의 손실: 0.133417\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.290035\t최선의 손실: 0.133417\t정확도: 98.24%\n",
+      "30\t검증 세트 손실: 0.171381\t최선의 손실: 0.133417\t정확도: 98.75%\n",
+      "31\t검증 세트 손실: 0.162910\t최선의 손실: 0.133417\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.219055\t최선의 손실: 0.133417\t정확도: 98.44%\n",
+      "33\t검증 세트 손실: 0.290063\t최선의 손실: 0.133417\t정확도: 98.16%\n",
+      "34\t검증 세트 손실: 0.224369\t최선의 손실: 0.133417\t정확도: 98.63%\n",
+      "35\t검증 세트 손실: 0.218714\t최선의 손실: 0.133417\t정확도: 98.98%\n",
+      "36\t검증 세트 손실: 0.272537\t최선의 손실: 0.133417\t정확도: 98.44%\n",
+      "37\t검증 세트 손실: 0.213410\t최선의 손실: 0.133417\t정확도: 98.75%\n",
+      "38\t검증 세트 손실: 0.174696\t최선의 손실: 0.133417\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.156932\t최선의 손실: 0.133417\t정확도: 98.87%\n",
+      "40\t검증 세트 손실: 0.164182\t최선의 손실: 0.133417\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.187780\t최선의 손실: 0.133417\t정확도: 98.71%\n",
+      "42\t검증 세트 손실: 0.186965\t최선의 손실: 0.133417\t정확도: 98.83%\n",
+      "43\t검증 세트 손실: 0.250247\t최선의 손실: 0.133417\t정확도: 98.59%\n",
+      "44\t검증 세트 손실: 0.324077\t최선의 손실: 0.133417\t정확도: 98.28%\n",
+      "45\t검증 세트 손실: 0.148492\t최선의 손실: 0.133417\t정확도: 98.79%\n",
+      "46\t검증 세트 손실: 0.128782\t최선의 손실: 0.128782\t정확도: 99.10%\n",
+      "47\t검증 세트 손실: 0.128124\t최선의 손실: 0.128124\t정확도: 98.98%\n",
+      "48\t검증 세트 손실: 0.236159\t최선의 손실: 0.128124\t정확도: 98.59%\n",
+      "49\t검증 세트 손실: 0.168459\t최선의 손실: 0.128124\t정확도: 98.79%\n",
+      "50\t검증 세트 손실: 0.200698\t최선의 손실: 0.128124\t정확도: 98.36%\n",
+      "51\t검증 세트 손실: 0.169390\t최선의 손실: 0.128124\t정확도: 98.94%\n",
+      "52\t검증 세트 손실: 0.164397\t최선의 손실: 0.128124\t정확도: 98.98%\n",
+      "53\t검증 세트 손실: 0.185698\t최선의 손실: 0.128124\t정확도: 98.83%\n",
+      "54\t검증 세트 손실: 0.171223\t최선의 손실: 0.128124\t정확도: 98.79%\n",
+      "55\t검증 세트 손실: 0.469652\t최선의 손실: 0.128124\t정확도: 97.73%\n",
+      "56\t검증 세트 손실: 0.289348\t최선의 손실: 0.128124\t정확도: 98.40%\n",
+      "57\t검증 세트 손실: 0.197328\t최선의 손실: 0.128124\t정확도: 98.79%\n",
+      "58\t검증 세트 손실: 0.168868\t최선의 손실: 0.128124\t정확도: 98.87%\n",
+      "59\t검증 세트 손실: 0.215075\t최선의 손실: 0.128124\t정확도: 98.83%\n",
+      "60\t검증 세트 손실: 0.401371\t최선의 손실: 0.128124\t정확도: 97.93%\n",
+      "61\t검증 세트 손실: 0.160647\t최선의 손실: 0.128124\t정확도: 98.71%\n",
+      "62\t검증 세트 손실: 0.189838\t최선의 손실: 0.128124\t정확도: 98.55%\n",
+      "63\t검증 세트 손실: 0.218456\t최선의 손실: 0.128124\t정확도: 98.59%\n",
+      "64\t검증 세트 손실: 0.170575\t최선의 손실: 0.128124\t정확도: 98.79%\n",
+      "65\t검증 세트 손실: 0.204905\t최선의 손실: 0.128124\t정확도: 98.67%\n",
+      "66\t검증 세트 손실: 0.222708\t최선의 손실: 0.128124\t정확도: 98.67%\n",
+      "67\t검증 세트 손실: 0.215705\t최선의 손실: 0.128124\t정확도: 98.51%\n",
+      "68\t검증 세트 손실: 0.137549\t최선의 손실: 0.128124\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=120, total= 1.4min\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140 \n",
-      "0\t검증 세트 손실: 518.132690\t최선의 손실: 518.132690\t정확도: 69.98%\n",
-      "1\t검증 세트 손실: 86.332458\t최선의 손실: 86.332458\t정확도: 73.92%\n",
-      "2\t검증 세트 손실: 24.124783\t최선의 손실: 24.124783\t정확도: 79.36%\n",
-      "3\t검증 세트 손실: 11.427398\t최선의 손실: 11.427398\t정확도: 85.81%\n",
-      "4\t검증 세트 손실: 2.932703\t최선의 손실: 2.932703\t정확도: 92.49%\n",
-      "5\t검증 세트 손실: 1.949817\t최선의 손실: 1.949817\t정확도: 95.07%\n",
-      "6\t검증 세트 손실: 1.531445\t최선의 손실: 1.531445\t정확도: 96.09%\n",
-      "7\t검증 세트 손실: 1.598384\t최선의 손실: 1.531445\t정확도: 95.82%\n",
-      "8\t검증 세트 손실: 0.973470\t최선의 손실: 0.973470\t정확도: 96.48%\n",
-      "9\t검증 세트 손실: 0.591351\t최선의 손실: 0.591351\t정확도: 97.46%\n",
-      "10\t검증 세트 손실: 0.511053\t최선의 손실: 0.511053\t정확도: 98.05%\n",
-      "11\t검증 세트 손실: 0.694251\t최선의 손실: 0.511053\t정확도: 97.15%\n",
-      "12\t검증 세트 손실: 0.532136\t최선의 손실: 0.511053\t정확도: 97.77%\n",
-      "13\t검증 세트 손실: 0.653078\t최선의 손실: 0.511053\t정확도: 97.07%\n",
-      "14\t검증 세트 손실: 0.421892\t최선의 손실: 0.421892\t정확도: 98.05%\n",
-      "15\t검증 세트 손실: 0.384741\t최선의 손실: 0.384741\t정확도: 98.20%\n",
-      "16\t검증 세트 손실: 0.403766\t최선의 손실: 0.384741\t정확도: 98.08%\n",
-      "17\t검증 세트 손실: 0.313245\t최선의 손실: 0.313245\t정확도: 98.16%\n",
-      "18\t검증 세트 손실: 0.369784\t최선의 손실: 0.313245\t정확도: 98.20%\n",
-      "19\t검증 세트 손실: 0.584939\t최선의 손실: 0.313245\t정확도: 97.22%\n",
-      "20\t검증 세트 손실: 0.438028\t최선의 손실: 0.313245\t정확도: 97.97%\n",
-      "21\t검증 세트 손실: 0.365968\t최선의 손실: 0.313245\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.408912\t최선의 손실: 0.313245\t정확도: 98.01%\n",
-      "23\t검증 세트 손실: 0.297079\t최선의 손실: 0.297079\t정확도: 98.28%\n",
-      "24\t검증 세트 손실: 0.210016\t최선의 손실: 0.210016\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.270214\t최선의 손실: 0.210016\t정확도: 98.71%\n",
-      "26\t검증 세트 손실: 0.267186\t최선의 손실: 0.210016\t정확도: 98.51%\n",
-      "27\t검증 세트 손실: 0.210620\t최선의 손실: 0.210016\t정확도: 98.51%\n",
-      "28\t검증 세트 손실: 0.225810\t최선의 손실: 0.210016\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.277482\t최선의 손실: 0.210016\t정확도: 98.12%\n",
-      "30\t검증 세트 손실: 0.251515\t최선의 손실: 0.210016\t정확도: 98.05%\n",
-      "31\t검증 세트 손실: 0.364768\t최선의 손실: 0.210016\t정확도: 98.05%\n",
-      "32\t검증 세트 손실: 0.248835\t최선의 손실: 0.210016\t정확도: 98.51%\n",
-      "33\t검증 세트 손실: 0.199720\t최선의 손실: 0.199720\t정확도: 98.67%\n",
-      "34\t검증 세트 손실: 0.242073\t최선의 손실: 0.199720\t정확도: 98.48%\n",
-      "35\t검증 세트 손실: 0.224714\t최선의 손실: 0.199720\t정확도: 98.48%\n",
-      "36\t검증 세트 손실: 0.272943\t최선의 손실: 0.199720\t정확도: 98.24%\n",
-      "37\t검증 세트 손실: 0.224665\t최선의 손실: 0.199720\t정확도: 98.44%\n",
-      "38\t검증 세트 손실: 0.381095\t최선의 손실: 0.199720\t정확도: 98.12%\n",
-      "39\t검증 세트 손실: 0.276394\t최선의 손실: 0.199720\t정확도: 98.28%\n",
-      "40\t검증 세트 손실: 0.258326\t최선의 손실: 0.199720\t정확도: 98.08%\n",
-      "41\t검증 세트 손실: 0.284414\t최선의 손실: 0.199720\t정확도: 98.05%\n",
-      "42\t검증 세트 손실: 0.235767\t최선의 손실: 0.199720\t정확도: 98.40%\n",
-      "43\t검증 세트 손실: 0.329186\t최선의 손실: 0.199720\t정확도: 97.65%\n",
-      "44\t검증 세트 손실: 0.232733\t최선의 손실: 0.199720\t정확도: 98.24%\n",
-      "45\t검증 세트 손실: 0.280705\t최선의 손실: 0.199720\t정확도: 98.20%\n",
-      "46\t검증 세트 손실: 0.243029\t최선의 손실: 0.199720\t정확도: 98.32%\n",
-      "47\t검증 세트 손실: 0.252522\t최선의 손실: 0.199720\t정확도: 98.32%\n",
-      "48\t검증 세트 손실: 0.229280\t최선의 손실: 0.199720\t정확도: 98.48%\n",
-      "49\t검증 세트 손실: 0.220083\t최선의 손실: 0.199720\t정확도: 98.32%\n",
-      "50\t검증 세트 손실: 0.209065\t최선의 손실: 0.199720\t정확도: 98.48%\n",
-      "51\t검증 세트 손실: 0.152449\t최선의 손실: 0.152449\t정확도: 98.71%\n",
-      "52\t검증 세트 손실: 0.167093\t최선의 손실: 0.152449\t정확도: 98.55%\n",
-      "53\t검증 세트 손실: 0.137062\t최선의 손실: 0.137062\t정확도: 98.63%\n",
-      "54\t검증 세트 손실: 0.127995\t최선의 손실: 0.127995\t정확도: 98.67%\n",
-      "55\t검증 세트 손실: 0.132092\t최선의 손실: 0.127995\t정확도: 98.79%\n",
-      "56\t검증 세트 손실: 0.120137\t최선의 손실: 0.120137\t정확도: 98.75%\n",
-      "57\t검증 세트 손실: 0.170799\t최선의 손실: 0.120137\t정확도: 98.48%\n",
-      "58\t검증 세트 손실: 0.202778\t최선의 손실: 0.120137\t정확도: 98.48%\n",
-      "59\t검증 세트 손실: 0.180466\t최선의 손실: 0.120137\t정확도: 98.51%\n",
-      "60\t검증 세트 손실: 0.191019\t최선의 손실: 0.120137\t정확도: 98.24%\n",
-      "61\t검증 세트 손실: 0.213853\t최선의 손실: 0.120137\t정확도: 98.32%\n",
-      "62\t검증 세트 손실: 0.244971\t최선의 손실: 0.120137\t정확도: 98.16%\n",
-      "63\t검증 세트 손실: 0.209483\t최선의 손실: 0.120137\t정확도: 98.40%\n",
-      "64\t검증 세트 손실: 0.322266\t최선의 손실: 0.120137\t정확도: 97.65%\n",
-      "65\t검증 세트 손실: 0.212541\t최선의 손실: 0.120137\t정확도: 98.59%\n",
-      "66\t검증 세트 손실: 0.344537\t최선의 손실: 0.120137\t정확도: 98.20%\n",
-      "67\t검증 세트 손실: 0.228636\t최선의 손실: 0.120137\t정확도: 98.79%\n",
-      "68\t검증 세트 손실: 0.238465\t최선의 손실: 0.120137\t정확도: 98.75%\n",
-      "69\t검증 세트 손실: 0.322842\t최선의 손실: 0.120137\t정확도: 98.08%\n",
-      "70\t검증 세트 손실: 0.302909\t최선의 손실: 0.120137\t정확도: 98.05%\n",
-      "71\t검증 세트 손실: 0.387155\t최선의 손실: 0.120137\t정확도: 97.81%\n",
-      "72\t검증 세트 손실: 0.329252\t최선의 손실: 0.120137\t정확도: 98.32%\n",
-      "73\t검증 세트 손실: 0.342713\t최선의 손실: 0.120137\t정확도: 98.32%\n",
-      "74\t검증 세트 손실: 0.376250\t최선의 손실: 0.120137\t정확도: 97.81%\n",
-      "75\t검증 세트 손실: 0.245514\t최선의 손실: 0.120137\t정확도: 98.51%\n",
-      "76\t검증 세트 손실: 0.333198\t최선의 손실: 0.120137\t정확도: 98.01%\n",
-      "77\t검증 세트 손실: 0.262530\t최선의 손실: 0.120137\t정확도: 98.01%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140, total=  13.9s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140 \n",
-      "0\t검증 세트 손실: 235.666534\t최선의 손실: 235.666534\t정확도: 82.64%\n",
-      "1\t검증 세트 손실: 84.629532\t최선의 손실: 84.629532\t정확도: 73.46%\n",
-      "2\t검증 세트 손실: 19.969128\t최선의 손실: 19.969128\t정확도: 85.50%\n",
-      "3\t검증 세트 손실: 19.029993\t최선의 손실: 19.029993\t정확도: 80.57%\n",
-      "4\t검증 세트 손실: 6.702769\t최선의 손실: 6.702769\t정확도: 89.01%\n",
-      "5\t검증 세트 손실: 3.104403\t최선의 손실: 3.104403\t정확도: 93.28%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140, total=  13.1s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140 \n",
+      "0\t검증 세트 손실: 134.921585\t최선의 손실: 134.921585\t정확도: 88.00%\n",
+      "1\t검증 세트 손실: 28.357632\t최선의 손실: 28.357632\t정확도: 88.51%\n",
+      "2\t검증 세트 손실: 10.578081\t최선의 손실: 10.578081\t정확도: 89.76%\n",
+      "3\t검증 세트 손실: 2.963521\t최선의 손실: 2.963521\t정확도: 95.58%\n",
+      "4\t검증 세트 손실: 4.752062\t최선의 손실: 2.963521\t정확도: 91.79%\n",
+      "5\t검증 세트 손실: 6.468737\t최선의 손실: 2.963521\t정확도: 90.07%\n",
+      "6\t검증 세트 손실: 1.555966\t최선의 손실: 1.555966\t정확도: 96.29%\n",
+      "7\t검증 세트 손실: 1.195242\t최선의 손실: 1.195242\t정확도: 96.60%\n",
+      "8\t검증 세트 손실: 1.212138\t최선의 손실: 1.195242\t정확도: 96.33%\n",
+      "9\t검증 세트 손실: 0.834825\t최선의 손실: 0.834825\t정확도: 97.07%\n",
+      "10\t검증 세트 손실: 0.803072\t최선의 손실: 0.803072\t정확도: 97.42%\n",
+      "11\t검증 세트 손실: 0.600534\t최선의 손실: 0.600534\t정확도: 98.12%\n",
+      "12\t검증 세트 손실: 0.511673\t최선의 손실: 0.511673\t정확도: 97.62%\n",
+      "13\t검증 세트 손실: 0.510442\t최선의 손실: 0.510442\t정확도: 97.89%\n",
+      "14\t검증 세트 손실: 0.431712\t최선의 손실: 0.431712\t정확도: 97.69%\n",
+      "15\t검증 세트 손실: 0.788709\t최선의 손실: 0.431712\t정확도: 97.22%\n",
+      "16\t검증 세트 손실: 0.548901\t최선의 손실: 0.431712\t정확도: 97.30%\n",
+      "17\t검증 세트 손실: 0.334116\t최선의 손실: 0.334116\t정확도: 98.05%\n",
+      "18\t검증 세트 손실: 0.386638\t최선의 손실: 0.334116\t정확도: 98.20%\n",
+      "19\t검증 세트 손실: 0.400214\t최선의 손실: 0.334116\t정확도: 98.05%\n",
+      "20\t검증 세트 손실: 0.664318\t최선의 손실: 0.334116\t정확도: 97.26%\n",
+      "21\t검증 세트 손실: 0.772704\t최선의 손실: 0.334116\t정확도: 96.52%\n",
+      "22\t검증 세트 손실: 0.304629\t최선의 손실: 0.304629\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.416265\t최선의 손실: 0.304629\t정확도: 97.77%\n",
+      "24\t검증 세트 손실: 0.352522\t최선의 손실: 0.304629\t정확도: 98.28%\n",
+      "25\t검증 세트 손실: 0.291643\t최선의 손실: 0.291643\t정확도: 98.44%\n",
+      "26\t검증 세트 손실: 0.226281\t최선의 손실: 0.226281\t정확도: 98.75%\n",
+      "27\t검증 세트 손실: 0.235502\t최선의 손실: 0.226281\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 0.308371\t최선의 손실: 0.226281\t정확도: 98.55%\n",
+      "29\t검증 세트 손실: 0.250173\t최선의 손실: 0.226281\t정확도: 98.51%\n",
+      "30\t검증 세트 손실: 0.532588\t최선의 손실: 0.226281\t정확도: 97.54%\n",
+      "31\t검증 세트 손실: 0.338428\t최선의 손실: 0.226281\t정확도: 98.08%\n",
+      "32\t검증 세트 손실: 0.259142\t최선의 손실: 0.226281\t정확도: 98.51%\n",
+      "33\t검증 세트 손실: 0.407033\t최선의 손실: 0.226281\t정확도: 98.01%\n",
+      "34\t검증 세트 손실: 0.257465\t최선의 손실: 0.226281\t정확도: 98.55%\n",
+      "35\t검증 세트 손실: 0.285851\t최선의 손실: 0.226281\t정확도: 98.51%\n",
+      "36\t검증 세트 손실: 0.258555\t최선의 손실: 0.226281\t정확도: 98.44%\n",
+      "37\t검증 세트 손실: 0.272459\t최선의 손실: 0.226281\t정확도: 98.12%\n",
+      "38\t검증 세트 손실: 0.330893\t최선의 손실: 0.226281\t정확도: 98.12%\n",
+      "39\t검증 세트 손실: 0.268104\t최선의 손실: 0.226281\t정확도: 98.44%\n",
+      "40\t검증 세트 손실: 0.332536\t최선의 손실: 0.226281\t정확도: 98.01%\n",
+      "41\t검증 세트 손실: 0.199516\t최선의 손실: 0.199516\t정확도: 98.67%\n",
+      "42\t검증 세트 손실: 0.241852\t최선의 손실: 0.199516\t정확도: 98.63%\n",
+      "43\t검증 세트 손실: 0.165017\t최선의 손실: 0.165017\t정확도: 99.02%\n",
+      "44\t검증 세트 손실: 0.216662\t최선의 손실: 0.165017\t정확도: 98.67%\n",
+      "45\t검증 세트 손실: 0.316521\t최선의 손실: 0.165017\t정확도: 98.16%\n",
+      "46\t검증 세트 손실: 0.374136\t최선의 손실: 0.165017\t정확도: 97.69%\n",
+      "47\t검증 세트 손실: 0.268151\t최선의 손실: 0.165017\t정확도: 98.32%\n",
+      "48\t검증 세트 손실: 0.255296\t최선의 손실: 0.165017\t정확도: 98.36%\n",
+      "49\t검증 세트 손실: 0.235373\t최선의 손실: 0.165017\t정확도: 98.67%\n",
+      "50\t검증 세트 손실: 0.206292\t최선의 손실: 0.165017\t정확도: 98.83%\n",
+      "51\t검증 세트 손실: 0.167458\t최선의 손실: 0.165017\t정확도: 98.98%\n",
+      "52\t검증 세트 손실: 0.206354\t최선의 손실: 0.165017\t정확도: 98.63%\n",
+      "53\t검증 세트 손실: 0.155558\t최선의 손실: 0.155558\t정확도: 98.98%\n",
+      "54\t검증 세트 손실: 0.165199\t최선의 손실: 0.155558\t정확도: 98.87%\n",
+      "55\t검증 세트 손실: 0.156487\t최선의 손실: 0.155558\t정확도: 99.10%\n",
+      "56\t검증 세트 손실: 0.144681\t최선의 손실: 0.144681\t정확도: 99.18%\n",
+      "57\t검증 세트 손실: 0.137558\t최선의 손실: 0.137558\t정확도: 99.14%\n",
+      "58\t검증 세트 손실: 0.128760\t최선의 손실: 0.128760\t정확도: 99.18%\n",
+      "59\t검증 세트 손실: 0.124892\t최선의 손실: 0.124892\t정확도: 99.18%\n",
+      "60\t검증 세트 손실: 0.121118\t최선의 손실: 0.121118\t정확도: 99.18%\n",
+      "61\t검증 세트 손실: 0.117004\t최선의 손실: 0.117004\t정확도: 99.22%\n",
+      "62\t검증 세트 손실: 0.115524\t최선의 손실: 0.115524\t정확도: 99.22%\n",
+      "63\t검증 세트 손실: 0.112479\t최선의 손실: 0.112479\t정확도: 99.22%\n",
+      "64\t검증 세트 손실: 0.108524\t최선의 손실: 0.108524\t정확도: 99.22%\n",
+      "65\t검증 세트 손실: 0.106841\t최선의 손실: 0.106841\t정확도: 99.22%\n",
+      "66\t검증 세트 손실: 0.104639\t최선의 손실: 0.104639\t정확도: 99.22%\n",
+      "67\t검증 세트 손실: 0.102194\t최선의 손실: 0.102194\t정확도: 99.22%\n",
+      "68\t검증 세트 손실: 0.100551\t최선의 손실: 0.100551\t정확도: 99.18%\n",
+      "69\t검증 세트 손실: 0.099278\t최선의 손실: 0.099278\t정확도: 99.18%\n",
+      "70\t검증 세트 손실: 0.097757\t최선의 손실: 0.097757\t정확도: 99.18%\n",
+      "71\t검증 세트 손실: 0.095738\t최선의 손실: 0.095738\t정확도: 99.14%\n",
+      "72\t검증 세트 손실: 0.094153\t최선의 손실: 0.094153\t정확도: 99.14%\n",
+      "73\t검증 세트 손실: 0.095563\t최선의 손실: 0.094153\t정확도: 99.14%\n",
+      "74\t검증 세트 손실: 0.091780\t최선의 손실: 0.091780\t정확도: 99.18%\n",
+      "75\t검증 세트 손실: 0.091089\t최선의 손실: 0.091089\t정확도: 99.18%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6\t검증 세트 손실: 1.280923\t최선의 손실: 1.280923\t정확도: 96.79%\n",
-      "7\t검증 세트 손실: 1.397720\t최선의 손실: 1.280923\t정확도: 96.33%\n",
-      "8\t검증 세트 손실: 1.324901\t최선의 손실: 1.280923\t정확도: 95.50%\n",
-      "9\t검증 세트 손실: 1.500487\t최선의 손실: 1.280923\t정확도: 95.43%\n",
-      "10\t검증 세트 손실: 0.936406\t최선의 손실: 0.936406\t정확도: 96.91%\n",
-      "11\t검증 세트 손실: 0.715390\t최선의 손실: 0.715390\t정확도: 97.30%\n",
-      "12\t검증 세트 손실: 0.472359\t최선의 손실: 0.472359\t정확도: 98.16%\n",
-      "13\t검증 세트 손실: 0.636721\t최선의 손실: 0.472359\t정확도: 97.42%\n",
-      "14\t검증 세트 손실: 0.626883\t최선의 손실: 0.472359\t정확도: 97.89%\n",
-      "15\t검증 세트 손실: 0.437824\t최선의 손실: 0.437824\t정확도: 97.93%\n",
-      "16\t검증 세트 손실: 0.359432\t최선의 손실: 0.359432\t정확도: 98.32%\n",
-      "17\t검증 세트 손실: 0.334784\t최선의 손실: 0.334784\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.283747\t최선의 손실: 0.283747\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.355278\t최선의 손실: 0.283747\t정확도: 98.24%\n",
-      "20\t검증 세트 손실: 0.378555\t최선의 손실: 0.283747\t정확도: 98.12%\n",
-      "21\t검증 세트 손실: 0.558102\t최선의 손실: 0.283747\t정확도: 96.99%\n",
-      "22\t검증 세트 손실: 0.279037\t최선의 손실: 0.279037\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.271508\t최선의 손실: 0.271508\t정확도: 98.48%\n",
-      "24\t검증 세트 손실: 0.360441\t최선의 손실: 0.271508\t정확도: 98.20%\n",
-      "25\t검증 세트 손실: 0.269140\t최선의 손실: 0.269140\t정확도: 98.51%\n",
-      "26\t검증 세트 손실: 0.295946\t최선의 손실: 0.269140\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.348259\t최선의 손실: 0.269140\t정확도: 97.97%\n",
-      "28\t검증 세트 손실: 0.282761\t최선의 손실: 0.269140\t정확도: 98.48%\n",
-      "29\t검증 세트 손실: 0.342753\t최선의 손실: 0.269140\t정확도: 97.89%\n",
-      "30\t검증 세트 손실: 0.267705\t최선의 손실: 0.267705\t정확도: 98.55%\n",
-      "31\t검증 세트 손실: 0.271125\t최선의 손실: 0.267705\t정확도: 98.55%\n",
-      "32\t검증 세트 손실: 0.301142\t최선의 손실: 0.267705\t정확도: 98.32%\n",
-      "33\t검증 세트 손실: 0.270507\t최선의 손실: 0.267705\t정확도: 98.36%\n",
-      "34\t검증 세트 손실: 0.221455\t최선의 손실: 0.221455\t정확도: 98.67%\n",
-      "35\t검증 세트 손실: 0.209695\t최선의 손실: 0.209695\t정확도: 98.67%\n",
-      "36\t검증 세트 손실: 0.217189\t최선의 손실: 0.209695\t정확도: 98.40%\n",
-      "37\t검증 세트 손실: 0.165551\t최선의 손실: 0.165551\t정확도: 98.94%\n",
-      "38\t검증 세트 손실: 0.195199\t최선의 손실: 0.165551\t정확도: 98.75%\n",
-      "39\t검증 세트 손실: 0.180503\t최선의 손실: 0.165551\t정확도: 98.87%\n",
-      "40\t검증 세트 손실: 0.215834\t최선의 손실: 0.165551\t정확도: 98.63%\n",
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-      "48\t검증 세트 손실: 0.190222\t최선의 손실: 0.165551\t정확도: 98.59%\n",
-      "49\t검증 세트 손실: 0.236508\t최선의 손실: 0.165551\t정확도: 98.40%\n",
-      "50\t검증 세트 손실: 0.329390\t최선의 손실: 0.165551\t정확도: 97.93%\n",
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-      "58\t검증 세트 손실: 0.158473\t최선의 손실: 0.158473\t정확도: 99.02%\n",
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-      "60\t검증 세트 손실: 0.703938\t최선의 손실: 0.158473\t정확도: 96.72%\n",
-      "61\t검증 세트 손실: 0.333371\t최선의 손실: 0.158473\t정확도: 97.62%\n",
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-      "64\t검증 세트 손실: 0.239166\t최선의 손실: 0.158473\t정확도: 98.36%\n",
-      "65\t검증 세트 손실: 0.184245\t최선의 손실: 0.158473\t정확도: 98.75%\n",
-      "66\t검증 세트 손실: 0.187671\t최선의 손실: 0.158473\t정확도: 98.87%\n",
-      "67\t검증 세트 손실: 0.166918\t최선의 손실: 0.158473\t정확도: 99.02%\n",
-      "68\t검증 세트 손실: 0.170791\t최선의 손실: 0.158473\t정확도: 98.91%\n",
-      "69\t검증 세트 손실: 0.212418\t최선의 손실: 0.158473\t정확도: 98.71%\n",
-      "70\t검증 세트 손실: 0.238288\t최선의 손실: 0.158473\t정확도: 98.63%\n",
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-      "73\t검증 세트 손실: 0.211799\t최선의 손실: 0.158473\t정확도: 98.48%\n",
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-      "75\t검증 세트 손실: 0.265656\t최선의 손실: 0.158473\t정확도: 98.40%\n",
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-      "78\t검증 세트 손실: 0.198761\t최선의 손실: 0.158473\t정확도: 98.83%\n",
-      "79\t검증 세트 손실: 0.228906\t최선의 손실: 0.158473\t정확도: 98.75%\n",
+      "76\t검증 세트 손실: 0.090508\t최선의 손실: 0.090508\t정확도: 99.14%\n",
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+      "79\t검증 세트 손실: 0.087435\t최선의 손실: 0.087435\t정확도: 99.06%\n",
+      "80\t검증 세트 손실: 0.086629\t최선의 손실: 0.086629\t정확도: 99.10%\n",
+      "81\t검증 세트 손실: 0.085849\t최선의 손실: 0.085849\t정확도: 99.06%\n",
+      "82\t검증 세트 손실: 0.086975\t최선의 손실: 0.085849\t정확도: 99.10%\n",
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+      "84\t검증 세트 손실: 0.085723\t최선의 손실: 0.085723\t정확도: 99.14%\n",
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+      "86\t검증 세트 손실: 0.084023\t최선의 손실: 0.084023\t정확도: 99.10%\n",
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+      "88\t검증 세트 손실: 0.083023\t최선의 손실: 0.083023\t정확도: 99.10%\n",
+      "89\t검증 세트 손실: 0.082244\t최선의 손실: 0.082244\t정확도: 99.10%\n",
+      "90\t검증 세트 손실: 0.081631\t최선의 손실: 0.081631\t정확도: 99.10%\n",
+      "91\t검증 세트 손실: 0.081847\t최선의 손실: 0.081631\t정확도: 99.10%\n",
+      "92\t검증 세트 손실: 0.081353\t최선의 손실: 0.081353\t정확도: 99.10%\n",
+      "93\t검증 세트 손실: 0.081113\t최선의 손실: 0.081113\t정확도: 99.10%\n",
+      "94\t검증 세트 손실: 0.080383\t최선의 손실: 0.080383\t정확도: 99.06%\n",
+      "95\t검증 세트 손실: 0.079870\t최선의 손실: 0.079870\t정확도: 99.06%\n",
+      "96\t검증 세트 손실: 0.079793\t최선의 손실: 0.079793\t정확도: 99.06%\n",
+      "97\t검증 세트 손실: 0.079440\t최선의 손실: 0.079440\t정확도: 99.06%\n",
+      "98\t검증 세트 손실: 0.079052\t최선의 손실: 0.079052\t정확도: 99.06%\n",
+      "99\t검증 세트 손실: 0.079486\t최선의 손실: 0.079052\t정확도: 99.06%\n",
+      "100\t검증 세트 손실: 0.079354\t최선의 손실: 0.079052\t정확도: 99.06%\n",
+      "101\t검증 세트 손실: 0.078805\t최선의 손실: 0.078805\t정확도: 99.10%\n",
+      "102\t검증 세트 손실: 0.078285\t최선의 손실: 0.078285\t정확도: 99.10%\n",
+      "103\t검증 세트 손실: 0.097363\t최선의 손실: 0.078285\t정확도: 98.94%\n",
+      "104\t검증 세트 손실: 60.835102\t최선의 손실: 0.078285\t정확도: 84.95%\n",
+      "105\t검증 세트 손실: 34.450249\t최선의 손실: 0.078285\t정확도: 97.19%\n",
+      "106\t검증 세트 손실: 12.631807\t최선의 손실: 0.078285\t정확도: 97.19%\n",
+      "107\t검증 세트 손실: 6.185861\t최선의 손실: 0.078285\t정확도: 97.03%\n",
+      "108\t검증 세트 손실: 3.970058\t최선의 손실: 0.078285\t정확도: 97.42%\n",
+      "109\t검증 세트 손실: 2.696879\t최선의 손실: 0.078285\t정확도: 97.42%\n",
+      "110\t검증 세트 손실: 2.175081\t최선의 손실: 0.078285\t정확도: 97.62%\n",
+      "111\t검증 세트 손실: 2.328239\t최선의 손실: 0.078285\t정확도: 97.34%\n",
+      "112\t검증 세트 손실: 2.510819\t최선의 손실: 0.078285\t정확도: 96.44%\n",
+      "113\t검증 세트 손실: 1.558764\t최선의 손실: 0.078285\t정확도: 97.50%\n",
+      "114\t검증 세트 손실: 1.629484\t최선의 손실: 0.078285\t정확도: 97.07%\n",
+      "115\t검증 세트 손실: 1.045175\t최선의 손실: 0.078285\t정확도: 97.93%\n",
+      "116\t검증 세트 손실: 0.980876\t최선의 손실: 0.078285\t정확도: 97.81%\n",
+      "117\t검증 세트 손실: 0.696114\t최선의 손실: 0.078285\t정확도: 98.12%\n",
+      "118\t검증 세트 손실: 0.734240\t최선의 손실: 0.078285\t정확도: 98.08%\n",
+      "119\t검증 세트 손실: 0.634051\t최선의 손실: 0.078285\t정확도: 98.16%\n",
+      "120\t검증 세트 손실: 0.527846\t최선의 손실: 0.078285\t정확도: 98.20%\n",
+      "121\t검증 세트 손실: 0.440940\t최선의 손실: 0.078285\t정확도: 98.40%\n",
+      "122\t검증 세트 손실: 0.371384\t최선의 손실: 0.078285\t정확도: 98.55%\n",
+      "123\t검증 세트 손실: 0.330436\t최선의 손실: 0.078285\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140, total=  14.0s\n",
-      "[CV] batch_size=500, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140 \n",
-      "0\t검증 세트 손실: 424.834595\t최선의 손실: 424.834595\t정확도: 74.08%\n",
-      "1\t검증 세트 손실: 118.019135\t최선의 손실: 118.019135\t정확도: 74.47%\n",
-      "2\t검증 세트 손실: 16.322695\t최선의 손실: 16.322695\t정확도: 87.76%\n",
-      "3\t검증 세트 손실: 2.977660\t최선의 손실: 2.977660\t정확도: 95.97%\n",
-      "4\t검증 세트 손실: 6.594670\t최선의 손실: 2.977660\t정확도: 91.48%\n",
-      "5\t검증 세트 손실: 1.985722\t최선의 손실: 1.985722\t정확도: 96.21%\n",
-      "6\t검증 세트 손실: 2.299536\t최선의 손실: 1.985722\t정확도: 96.21%\n",
-      "7\t검증 세트 손실: 0.821022\t최선의 손실: 0.821022\t정확도: 97.54%\n",
-      "8\t검증 세트 손실: 0.886164\t최선의 손실: 0.821022\t정확도: 97.65%\n",
-      "9\t검증 세트 손실: 0.572664\t최선의 손실: 0.572664\t정확도: 98.51%\n",
-      "10\t검증 세트 손실: 0.840612\t최선의 손실: 0.572664\t정확도: 97.26%\n",
-      "11\t검증 세트 손실: 0.495065\t최선의 손실: 0.495065\t정확도: 97.93%\n",
-      "12\t검증 세트 손실: 0.679360\t최선의 손실: 0.495065\t정확도: 97.69%\n",
-      "13\t검증 세트 손실: 0.928938\t최선의 손실: 0.495065\t정확도: 97.38%\n",
-      "14\t검증 세트 손실: 0.574001\t최선의 손실: 0.495065\t정확도: 98.08%\n",
-      "15\t검증 세트 손실: 0.642493\t최선의 손실: 0.495065\t정확도: 97.81%\n",
-      "16\t검증 세트 손실: 0.470300\t최선의 손실: 0.470300\t정확도: 97.89%\n",
-      "17\t검증 세트 손실: 0.581590\t최선의 손실: 0.470300\t정확도: 97.38%\n",
-      "18\t검증 세트 손실: 0.684782\t최선의 손실: 0.470300\t정확도: 96.68%\n",
-      "19\t검증 세트 손실: 0.642663\t최선의 손실: 0.470300\t정확도: 96.87%\n",
-      "20\t검증 세트 손실: 0.297738\t최선의 손실: 0.297738\t정확도: 98.24%\n",
-      "21\t검증 세트 손실: 0.410976\t최선의 손실: 0.297738\t정확도: 98.05%\n",
-      "22\t검증 세트 손실: 0.385018\t최선의 손실: 0.297738\t정확도: 98.44%\n",
-      "23\t검증 세트 손실: 0.375993\t최선의 손실: 0.297738\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.422366\t최선의 손실: 0.297738\t정확도: 97.89%\n",
-      "25\t검증 세트 손실: 0.311161\t최선의 손실: 0.297738\t정확도: 98.32%\n",
-      "26\t검증 세트 손실: 0.298135\t최선의 손실: 0.297738\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.379256\t최선의 손실: 0.297738\t정확도: 97.62%\n",
-      "28\t검증 세트 손실: 0.333578\t최선의 손실: 0.297738\t정확도: 98.08%\n",
-      "29\t검증 세트 손실: 0.321180\t최선의 손실: 0.297738\t정확도: 98.20%\n",
-      "30\t검증 세트 손실: 0.258109\t최선의 손실: 0.258109\t정확도: 98.51%\n",
-      "31\t검증 세트 손실: 0.276189\t최선의 손실: 0.258109\t정확도: 98.24%\n",
-      "32\t검증 세트 손실: 0.257249\t최선의 손실: 0.257249\t정확도: 98.55%\n",
-      "33\t검증 세트 손실: 0.212665\t최선의 손실: 0.212665\t정확도: 98.36%\n",
-      "34\t검증 세트 손실: 0.181352\t최선의 손실: 0.181352\t정확도: 98.91%\n",
-      "35\t검증 세트 손실: 0.175905\t최선의 손실: 0.175905\t정확도: 98.79%\n",
-      "36\t검증 세트 손실: 0.240097\t최선의 손실: 0.175905\t정확도: 98.36%\n",
-      "37\t검증 세트 손실: 0.235217\t최선의 손실: 0.175905\t정확도: 98.51%\n",
-      "38\t검증 세트 손실: 0.269216\t최선의 손실: 0.175905\t정확도: 98.44%\n",
-      "39\t검증 세트 손실: 0.179533\t최선의 손실: 0.175905\t정확도: 98.71%\n",
-      "40\t검증 세트 손실: 0.264719\t최선의 손실: 0.175905\t정확도: 98.08%\n",
-      "41\t검증 세트 손실: 0.184441\t최선의 손실: 0.175905\t정확도: 98.63%\n",
-      "42\t검증 세트 손실: 0.299841\t최선의 손실: 0.175905\t정확도: 97.97%\n",
-      "43\t검증 세트 손실: 0.193064\t최선의 손실: 0.175905\t정확도: 98.75%\n",
-      "44\t검증 세트 손실: 0.364195\t최선의 손실: 0.175905\t정확도: 97.77%\n",
-      "45\t검증 세트 손실: 0.232871\t최선의 손실: 0.175905\t정확도: 98.51%\n",
-      "46\t검증 세트 손실: 0.341062\t최선의 손실: 0.175905\t정확도: 98.01%\n",
-      "47\t검증 세트 손실: 0.224618\t최선의 손실: 0.175905\t정확도: 98.51%\n",
-      "48\t검증 세트 손실: 0.213343\t최선의 손실: 0.175905\t정확도: 98.51%\n",
-      "49\t검증 세트 손실: 0.227190\t최선의 손실: 0.175905\t정확도: 98.51%\n",
-      "50\t검증 세트 손실: 0.193868\t최선의 손실: 0.175905\t정확도: 98.59%\n",
-      "51\t검증 세트 손실: 0.162075\t최선의 손실: 0.162075\t정확도: 98.79%\n",
-      "52\t검증 세트 손실: 0.127807\t최선의 손실: 0.127807\t정확도: 98.87%\n",
-      "53\t검증 세트 손실: 0.115854\t최선의 손실: 0.115854\t정확도: 98.94%\n",
-      "54\t검증 세트 손실: 0.108752\t최선의 손실: 0.108752\t정확도: 98.94%\n",
-      "55\t검증 세트 손실: 0.104262\t최선의 손실: 0.104262\t정확도: 98.91%\n",
-      "56\t검증 세트 손실: 0.099323\t최선의 손실: 0.099323\t정확도: 98.94%\n",
-      "57\t검증 세트 손실: 0.100984\t최선의 손실: 0.099323\t정확도: 98.94%\n",
-      "58\t검증 세트 손실: 0.098498\t최선의 손실: 0.098498\t정확도: 98.98%\n",
-      "59\t검증 세트 손실: 0.092758\t최선의 손실: 0.092758\t정확도: 99.02%\n",
-      "60\t검증 세트 손실: 0.087329\t최선의 손실: 0.087329\t정확도: 99.06%\n",
-      "61\t검증 세트 손실: 0.083819\t최선의 손실: 0.083819\t정확도: 99.06%\n",
-      "62\t검증 세트 손실: 0.080112\t최선의 손실: 0.080112\t정확도: 99.10%\n",
-      "63\t검증 세트 손실: 0.077811\t최선의 손실: 0.077811\t정확도: 99.10%\n",
-      "64\t검증 세트 손실: 0.076321\t최선의 손실: 0.076321\t정확도: 99.10%\n",
-      "65\t검증 세트 손실: 0.075316\t최선의 손실: 0.075316\t정확도: 99.10%\n",
-      "66\t검증 세트 손실: 0.073498\t최선의 손실: 0.073498\t정확도: 99.10%\n",
-      "67\t검증 세트 손실: 0.071888\t최선의 손실: 0.071888\t정확도: 99.10%\n",
-      "68\t검증 세트 손실: 0.071552\t최선의 손실: 0.071552\t정확도: 99.10%\n",
-      "69\t검증 세트 손실: 0.069788\t최선의 손실: 0.069788\t정확도: 99.10%\n",
-      "70\t검증 세트 손실: 0.069543\t최선의 손실: 0.069543\t정확도: 99.10%\n",
-      "71\t검증 세트 손실: 0.067643\t최선의 손실: 0.067643\t정확도: 99.10%\n",
-      "72\t검증 세트 손실: 0.066144\t최선의 손실: 0.066144\t정확도: 99.18%\n",
-      "73\t검증 세트 손실: 0.065254\t최선의 손실: 0.065254\t정확도: 99.18%\n",
-      "74\t검증 세트 손실: 0.064628\t최선의 손실: 0.064628\t정확도: 99.18%\n",
-      "75\t검증 세트 손실: 0.063334\t최선의 손실: 0.063334\t정확도: 99.14%\n",
-      "76\t검증 세트 손실: 0.062323\t최선의 손실: 0.062323\t정확도: 99.18%\n",
-      "77\t검증 세트 손실: 0.061267\t최선의 손실: 0.061267\t정확도: 99.18%\n",
-      "78\t검증 세트 손실: 0.060345\t최선의 손실: 0.060345\t정확도: 99.18%\n",
-      "79\t검증 세트 손실: 0.059597\t최선의 손실: 0.059597\t정확도: 99.18%\n",
-      "80\t검증 세트 손실: 0.058829\t최선의 손실: 0.058829\t정확도: 99.18%\n",
-      "81\t검증 세트 손실: 0.058269\t최선의 손실: 0.058269\t정확도: 99.14%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140, total=  22.4s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140 \n",
+      "0\t검증 세트 손실: 411.945770\t최선의 손실: 411.945770\t정확도: 70.84%\n",
+      "1\t검증 세트 손실: 78.689690\t최선의 손실: 78.689690\t정확도: 76.19%\n",
+      "2\t검증 세트 손실: 28.895317\t최선의 손실: 28.895317\t정확도: 76.39%\n",
+      "3\t검증 세트 손실: 11.732854\t최선의 손실: 11.732854\t정확도: 79.95%\n",
+      "4\t검증 세트 손실: 4.377892\t최선의 손실: 4.377892\t정확도: 91.67%\n",
+      "5\t검증 세트 손실: 2.187544\t최선의 손실: 2.187544\t정확도: 93.67%\n",
+      "6\t검증 세트 손실: 2.329332\t최선의 손실: 2.187544\t정확도: 94.14%\n",
+      "7\t검증 세트 손실: 1.297179\t최선의 손실: 1.297179\t정확도: 96.52%\n",
+      "8\t검증 세트 손실: 1.833754\t최선의 손실: 1.297179\t정확도: 95.35%\n",
+      "9\t검증 세트 손실: 0.790275\t최선의 손실: 0.790275\t정확도: 97.62%\n",
+      "10\t검증 세트 손실: 1.430014\t최선의 손실: 0.790275\t정확도: 95.50%\n",
+      "11\t검증 세트 손실: 1.059726\t최선의 손실: 0.790275\t정확도: 96.68%\n",
+      "12\t검증 세트 손실: 0.528436\t최선의 손실: 0.528436\t정확도: 97.85%\n",
+      "13\t검증 세트 손실: 0.547276\t최선의 손실: 0.528436\t정확도: 97.85%\n",
+      "14\t검증 세트 손실: 0.631464\t최선의 손실: 0.528436\t정확도: 97.03%\n",
+      "15\t검증 세트 손실: 0.782471\t최선의 손실: 0.528436\t정확도: 96.36%\n",
+      "16\t검증 세트 손실: 0.431340\t최선의 손실: 0.431340\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.541496\t최선의 손실: 0.431340\t정확도: 97.50%\n",
+      "18\t검증 세트 손실: 0.594799\t최선의 손실: 0.431340\t정확도: 97.15%\n",
+      "19\t검증 세트 손실: 0.493455\t최선의 손실: 0.431340\t정확도: 97.42%\n",
+      "20\t검증 세트 손실: 0.375671\t최선의 손실: 0.375671\t정확도: 97.85%\n",
+      "21\t검증 세트 손실: 0.374120\t최선의 손실: 0.374120\t정확도: 97.89%\n",
+      "22\t검증 세트 손실: 0.592766\t최선의 손실: 0.374120\t정확도: 97.11%\n",
+      "23\t검증 세트 손실: 0.365891\t최선의 손실: 0.365891\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.365421\t최선의 손실: 0.365421\t정확도: 97.97%\n",
+      "25\t검증 세트 손실: 0.270817\t최선의 손실: 0.270817\t정확도: 98.51%\n",
+      "26\t검증 세트 손실: 0.203994\t최선의 손실: 0.203994\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.212388\t최선의 손실: 0.203994\t정확도: 98.79%\n",
+      "28\t검증 세트 손실: 0.176637\t최선의 손실: 0.176637\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.320330\t최선의 손실: 0.176637\t정확도: 98.48%\n",
+      "30\t검증 세트 손실: 0.183519\t최선의 손실: 0.176637\t정확도: 98.87%\n",
+      "31\t검증 세트 손실: 0.236324\t최선의 손실: 0.176637\t정확도: 98.75%\n",
+      "32\t검증 세트 손실: 0.261319\t최선의 손실: 0.176637\t정확도: 98.44%\n",
+      "33\t검증 세트 손실: 0.281253\t최선의 손실: 0.176637\t정확도: 98.16%\n",
+      "34\t검증 세트 손실: 0.308074\t최선의 손실: 0.176637\t정확도: 98.44%\n",
+      "35\t검증 세트 손실: 0.202940\t최선의 손실: 0.176637\t정확도: 98.75%\n",
+      "36\t검증 세트 손실: 0.498216\t최선의 손실: 0.176637\t정확도: 97.22%\n",
+      "37\t검증 세트 손실: 0.305456\t최선의 손실: 0.176637\t정확도: 97.93%\n",
+      "38\t검증 세트 손실: 0.172822\t최선의 손실: 0.172822\t정확도: 98.71%\n",
+      "39\t검증 세트 손실: 0.189202\t최선의 손실: 0.172822\t정확도: 98.40%\n",
+      "40\t검증 세트 손실: 0.218709\t최선의 손실: 0.172822\t정확도: 98.51%\n",
+      "41\t검증 세트 손실: 0.214125\t최선의 손실: 0.172822\t정확도: 98.83%\n",
+      "42\t검증 세트 손실: 0.309242\t최선의 손실: 0.172822\t정확도: 98.48%\n",
+      "43\t검증 세트 손실: 0.208022\t최선의 손실: 0.172822\t정확도: 98.71%\n",
+      "44\t검증 세트 손실: 0.263536\t최선의 손실: 0.172822\t정확도: 98.48%\n",
+      "45\t검증 세트 손실: 0.250368\t최선의 손실: 0.172822\t정확도: 98.44%\n",
+      "46\t검증 세트 손실: 0.243050\t최선의 손실: 0.172822\t정확도: 98.24%\n",
+      "47\t검증 세트 손실: 0.216919\t최선의 손실: 0.172822\t정확도: 98.59%\n",
+      "48\t검증 세트 손실: 0.149300\t최선의 손실: 0.149300\t정확도: 98.75%\n",
+      "49\t검증 세트 손실: 0.134516\t최선의 손실: 0.134516\t정확도: 98.75%\n",
+      "50\t검증 세트 손실: 0.179788\t최선의 손실: 0.134516\t정확도: 98.71%\n",
+      "51\t검증 세트 손실: 0.136034\t최선의 손실: 0.134516\t정확도: 98.71%\n",
+      "52\t검증 세트 손실: 0.135949\t최선의 손실: 0.134516\t정확도: 98.67%\n",
+      "53\t검증 세트 손실: 0.142198\t최선의 손실: 0.134516\t정확도: 98.87%\n",
+      "54\t검증 세트 손실: 0.130875\t최선의 손실: 0.130875\t정확도: 98.87%\n",
+      "55\t검증 세트 손실: 0.117064\t최선의 손실: 0.117064\t정확도: 98.87%\n",
+      "56\t검증 세트 손실: 0.119088\t최선의 손실: 0.117064\t정확도: 98.87%\n",
+      "57\t검증 세트 손실: 0.113740\t최선의 손실: 0.113740\t정확도: 98.83%\n",
+      "58\t검증 세트 손실: 0.108375\t최선의 손실: 0.108375\t정확도: 98.91%\n",
+      "59\t검증 세트 손실: 0.104941\t최선의 손실: 0.104941\t정확도: 98.91%\n",
+      "60\t검증 세트 손실: 0.100131\t최선의 손실: 0.100131\t정확도: 98.94%\n",
+      "61\t검증 세트 손실: 0.097417\t최선의 손실: 0.097417\t정확도: 98.94%\n",
+      "62\t검증 세트 손실: 0.094886\t최선의 손실: 0.094886\t정확도: 98.94%\n",
+      "63\t검증 세트 손실: 0.093159\t최선의 손실: 0.093159\t정확도: 98.94%\n",
+      "64\t검증 세트 손실: 0.095403\t최선의 손실: 0.093159\t정확도: 98.91%\n",
+      "65\t검증 세트 손실: 0.090674\t최선의 손실: 0.090674\t정확도: 98.98%\n",
+      "66\t검증 세트 손실: 0.086175\t최선의 손실: 0.086175\t정확도: 98.98%\n",
+      "67\t검증 세트 손실: 0.085754\t최선의 손실: 0.085754\t정확도: 98.98%\n",
+      "68\t검증 세트 손실: 0.083465\t최선의 손실: 0.083465\t정확도: 98.98%\n",
+      "69\t검증 세트 손실: 0.081653\t최선의 손실: 0.081653\t정확도: 99.02%\n",
+      "70\t검증 세트 손실: 0.079844\t최선의 손실: 0.079844\t정확도: 99.02%\n",
+      "71\t검증 세트 손실: 0.077332\t최선의 손실: 0.077332\t정확도: 99.02%\n",
+      "72\t검증 세트 손실: 0.076103\t최선의 손실: 0.076103\t정확도: 99.06%\n",
+      "73\t검증 세트 손실: 0.074743\t최선의 손실: 0.074743\t정확도: 99.06%\n",
+      "74\t검증 세트 손실: 0.073872\t최선의 손실: 0.073872\t정확도: 99.10%\n",
+      "75\t검증 세트 손실: 0.072964\t최선의 손실: 0.072964\t정확도: 99.10%\n",
+      "76\t검증 세트 손실: 0.069501\t최선의 손실: 0.069501\t정확도: 99.18%\n",
+      "77\t검증 세트 손실: 0.069357\t최선의 손실: 0.069357\t정확도: 99.18%\n",
+      "78\t검증 세트 손실: 0.069594\t최선의 손실: 0.069357\t정확도: 99.18%\n",
+      "79\t검증 세트 손실: 0.069607\t최선의 손실: 0.069357\t정확도: 99.14%\n",
+      "80\t검증 세트 손실: 0.069608\t최선의 손실: 0.069357\t정확도: 99.10%\n",
+      "81\t검증 세트 손실: 0.069142\t최선의 손실: 0.069142\t정확도: 99.10%\n",
+      "82\t검증 세트 손실: 0.069486\t최선의 손실: 0.069142\t정확도: 99.10%\n",
+      "83\t검증 세트 손실: 0.067378\t최선의 손실: 0.067378\t정확도: 99.10%\n",
+      "84\t검증 세트 손실: 0.066940\t최선의 손실: 0.066940\t정확도: 99.14%\n",
+      "85\t검증 세트 손실: 0.066275\t최선의 손실: 0.066275\t정확도: 99.10%\n",
+      "86\t검증 세트 손실: 0.065731\t최선의 손실: 0.065731\t정확도: 99.10%\n",
+      "87\t검증 세트 손실: 0.065339\t최선의 손실: 0.065339\t정확도: 99.10%\n",
+      "88\t검증 세트 손실: 0.064999\t최선의 손실: 0.064999\t정확도: 99.10%\n",
+      "89\t검증 세트 손실: 0.064511\t최선의 손실: 0.064511\t정확도: 99.14%\n",
+      "90\t검증 세트 손실: 0.064218\t최선의 손실: 0.064218\t정확도: 99.14%\n",
+      "91\t검증 세트 손실: 0.063909\t최선의 손실: 0.063909\t정확도: 99.14%\n",
+      "92\t검증 세트 손실: 0.063513\t최선의 손실: 0.063513\t정확도: 99.14%\n",
+      "93\t검증 세트 손실: 0.063299\t최선의 손실: 0.063299\t정확도: 99.10%\n",
+      "94\t검증 세트 손실: 0.063112\t최선의 손실: 0.063112\t정확도: 99.10%\n",
+      "95\t검증 세트 손실: 0.062896\t최선의 손실: 0.062896\t정확도: 99.10%\n",
+      "96\t검증 세트 손실: 0.063434\t최선의 손실: 0.062896\t정확도: 99.06%\n",
+      "97\t검증 세트 손실: 0.063214\t최선의 손실: 0.062896\t정확도: 99.06%\n",
+      "98\t검증 세트 손실: 0.062970\t최선의 손실: 0.062896\t정확도: 99.06%\n",
+      "99\t검증 세트 손실: 0.062768\t최선의 손실: 0.062768\t정확도: 99.10%\n",
+      "100\t검증 세트 손실: 0.062646\t최선의 손실: 0.062646\t정확도: 99.10%\n",
+      "101\t검증 세트 손실: 0.063090\t최선의 손실: 0.062646\t정확도: 99.10%\n",
+      "102\t검증 세트 손실: 0.062917\t최선의 손실: 0.062646\t정확도: 99.10%\n",
+      "103\t검증 세트 손실: 0.062461\t최선의 손실: 0.062461\t정확도: 99.14%\n",
+      "104\t검증 세트 손실: 0.062278\t최선의 손실: 0.062278\t정확도: 99.10%\n",
+      "105\t검증 세트 손실: 0.092729\t최선의 손실: 0.062278\t정확도: 98.67%\n",
+      "106\t검증 세트 손실: 0.303472\t최선의 손실: 0.062278\t정확도: 97.11%\n",
+      "107\t검증 세트 손실: 0.526361\t최선의 손실: 0.062278\t정확도: 97.85%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "82\t검증 세트 손실: 0.057863\t최선의 손실: 0.057863\t정확도: 99.14%\n",
-      "83\t검증 세트 손실: 0.057275\t최선의 손실: 0.057275\t정확도: 99.18%\n",
-      "84\t검증 세트 손실: 0.056889\t최선의 손실: 0.056889\t정확도: 99.18%\n",
-      "85\t검증 세트 손실: 0.056431\t최선의 손실: 0.056431\t정확도: 99.18%\n",
-      "86\t검증 세트 손실: 0.055946\t최선의 손실: 0.055946\t정확도: 99.18%\n",
-      "87\t검증 세트 손실: 0.055341\t최선의 손실: 0.055341\t정확도: 99.18%\n",
-      "88\t검증 세트 손실: 0.057361\t최선의 손실: 0.055341\t정확도: 99.14%\n",
-      "89\t검증 세트 손실: 0.056997\t최선의 손실: 0.055341\t정확도: 99.10%\n",
-      "90\t검증 세트 손실: 0.056059\t최선의 손실: 0.055341\t정확도: 99.10%\n",
-      "91\t검증 세트 손실: 0.054553\t최선의 손실: 0.054553\t정확도: 99.14%\n",
-      "92\t검증 세트 손실: 0.054106\t최선의 손실: 0.054106\t정확도: 99.14%\n",
-      "93\t검증 세트 손실: 0.053862\t최선의 손실: 0.053862\t정확도: 99.14%\n",
-      "94\t검증 세트 손실: 0.053557\t최선의 손실: 0.053557\t정확도: 99.14%\n",
-      "95\t검증 세트 손실: 0.053401\t최선의 손실: 0.053401\t정확도: 99.18%\n",
-      "96\t검증 세트 손실: 0.053122\t최선의 손실: 0.053122\t정확도: 99.18%\n",
-      "97\t검증 세트 손실: 0.053339\t최선의 손실: 0.053122\t정확도: 99.18%\n",
-      "98\t검증 세트 손실: 0.053293\t최선의 손실: 0.053122\t정확도: 99.18%\n",
-      "99\t검증 세트 손실: 0.053169\t최선의 손실: 0.053122\t정확도: 99.18%\n",
-      "100\t검증 세트 손실: 0.053022\t최선의 손실: 0.053022\t정확도: 99.18%\n",
-      "101\t검증 세트 손실: 0.053005\t최선의 손실: 0.053005\t정확도: 99.18%\n",
-      "102\t검증 세트 손실: 0.080390\t최선의 손실: 0.053005\t정확도: 98.87%\n",
-      "103\t검증 세트 손실: 0.950177\t최선의 손실: 0.053005\t정확도: 96.60%\n",
-      "104\t검증 세트 손실: 1.115826\t최선의 손실: 0.053005\t정확도: 97.62%\n",
-      "105\t검증 세트 손실: 0.524203\t최선의 손실: 0.053005\t정확도: 98.48%\n",
-      "106\t검증 세트 손실: 0.989915\t최선의 손실: 0.053005\t정확도: 97.89%\n",
-      "107\t검증 세트 손실: 0.527596\t최선의 손실: 0.053005\t정확도: 98.71%\n",
-      "108\t검증 세트 손실: 0.545820\t최선의 손실: 0.053005\t정확도: 98.51%\n",
-      "109\t검증 세트 손실: 0.750120\t최선의 손실: 0.053005\t정확도: 98.12%\n",
-      "110\t검증 세트 손실: 0.502486\t최선의 손실: 0.053005\t정확도: 98.59%\n",
-      "111\t검증 세트 손실: 0.416274\t최선의 손실: 0.053005\t정확도: 98.67%\n",
-      "112\t검증 세트 손실: 0.540316\t최선의 손실: 0.053005\t정확도: 98.48%\n",
-      "113\t검증 세트 손실: 0.440730\t최선의 손실: 0.053005\t정확도: 98.48%\n",
-      "114\t검증 세트 손실: 0.409985\t최선의 손실: 0.053005\t정확도: 98.20%\n",
-      "115\t검증 세트 손실: 0.282234\t최선의 손실: 0.053005\t정확도: 98.83%\n",
-      "116\t검증 세트 손실: 0.287431\t최선의 손실: 0.053005\t정확도: 98.91%\n",
-      "117\t검증 세트 손실: 0.233625\t최선의 손실: 0.053005\t정확도: 98.83%\n",
-      "118\t검증 세트 손실: 0.292580\t최선의 손실: 0.053005\t정확도: 98.75%\n",
-      "119\t검증 세트 손실: 0.321435\t최선의 손실: 0.053005\t정확도: 98.48%\n",
-      "120\t검증 세트 손실: 0.337319\t최선의 손실: 0.053005\t정확도: 98.51%\n",
-      "121\t검증 세트 손실: 0.306473\t최선의 손실: 0.053005\t정확도: 98.59%\n",
-      "122\t검증 세트 손실: 0.292504\t최선의 손실: 0.053005\t정확도: 98.40%\n",
+      "108\t검증 세트 손실: 1.277664\t최선의 손실: 0.062278\t정확도: 96.40%\n",
+      "109\t검증 세트 손실: 0.767619\t최선의 손실: 0.062278\t정확도: 98.01%\n",
+      "110\t검증 세트 손실: 0.657303\t최선의 손실: 0.062278\t정확도: 98.16%\n",
+      "111\t검증 세트 손실: 0.703333\t최선의 손실: 0.062278\t정확도: 98.36%\n",
+      "112\t검증 세트 손실: 0.700934\t최선의 손실: 0.062278\t정확도: 98.01%\n",
+      "113\t검증 세트 손실: 0.820728\t최선의 손실: 0.062278\t정확도: 98.32%\n",
+      "114\t검증 세트 손실: 0.402025\t최선의 손실: 0.062278\t정확도: 98.63%\n",
+      "115\t검증 세트 손실: 0.729238\t최선의 손실: 0.062278\t정확도: 97.89%\n",
+      "116\t검증 세트 손실: 0.457011\t최선의 손실: 0.062278\t정확도: 98.55%\n",
+      "117\t검증 세트 손실: 0.556598\t최선의 손실: 0.062278\t정확도: 98.44%\n",
+      "118\t검증 세트 손실: 0.458114\t최선의 손실: 0.062278\t정확도: 98.55%\n",
+      "119\t검증 세트 손실: 0.283602\t최선의 손실: 0.062278\t정확도: 99.02%\n",
+      "120\t검증 세트 손실: 0.339813\t최선의 손실: 0.062278\t정확도: 98.75%\n",
+      "121\t검증 세트 손실: 0.275453\t최선의 손실: 0.062278\t정확도: 98.91%\n",
+      "122\t검증 세트 손실: 0.216435\t최선의 손실: 0.062278\t정확도: 99.14%\n",
+      "123\t검증 세트 손실: 0.189303\t최선의 손실: 0.062278\t정확도: 99.18%\n",
+      "124\t검증 세트 손실: 0.186706\t최선의 손실: 0.062278\t정확도: 99.14%\n",
+      "125\t검증 세트 손실: 0.189144\t최선의 손실: 0.062278\t정확도: 99.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140, total=  20.9s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.061674\t최선의 손실: 0.061674\t정확도: 98.20%\n",
-      "1\t검증 세트 손실: 0.053610\t최선의 손실: 0.053610\t정확도: 98.59%\n",
-      "2\t검증 세트 손실: 0.046277\t최선의 손실: 0.046277\t정확도: 98.87%\n",
-      "3\t검증 세트 손실: 0.041674\t최선의 손실: 0.041674\t정확도: 98.59%\n",
-      "4\t검증 세트 손실: 0.043119\t최선의 손실: 0.041674\t정확도: 98.83%\n",
-      "5\t검증 세트 손실: 0.040042\t최선의 손실: 0.040042\t정확도: 98.79%\n",
-      "6\t검증 세트 손실: 0.055480\t최선의 손실: 0.040042\t정확도: 98.71%\n",
-      "7\t검증 세트 손실: 0.069609\t최선의 손실: 0.040042\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.062799\t최선의 손실: 0.040042\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.055554\t최선의 손실: 0.040042\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.045426\t최선의 손실: 0.040042\t정확도: 98.91%\n",
-      "11\t검증 세트 손실: 0.053989\t최선의 손실: 0.040042\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.054117\t최선의 손실: 0.040042\t정확도: 98.83%\n",
-      "13\t검증 세트 손실: 0.054927\t최선의 손실: 0.040042\t정확도: 98.79%\n",
-      "14\t검증 세트 손실: 0.055778\t최선의 손실: 0.040042\t정확도: 98.75%\n",
-      "15\t검증 세트 손실: 0.069337\t최선의 손실: 0.040042\t정확도: 98.67%\n",
-      "16\t검증 세트 손실: 0.067153\t최선의 손실: 0.040042\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.056165\t최선의 손실: 0.040042\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.045297\t최선의 손실: 0.040042\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.055298\t최선의 손실: 0.040042\t정확도: 98.91%\n",
-      "20\t검증 세트 손실: 0.076362\t최선의 손실: 0.040042\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.059650\t최선의 손실: 0.040042\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.056163\t최선의 손실: 0.040042\t정확도: 98.71%\n",
-      "23\t검증 세트 손실: 0.049833\t최선의 손실: 0.040042\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.055410\t최선의 손실: 0.040042\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.054707\t최선의 손실: 0.040042\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.054706\t최선의 손실: 0.040042\t정확도: 99.14%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140, total=  22.9s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.065839\t최선의 손실: 0.065839\t정확도: 98.12%\n",
+      "1\t검증 세트 손실: 0.058368\t최선의 손실: 0.058368\t정확도: 98.08%\n",
+      "2\t검증 세트 손실: 0.050396\t최선의 손실: 0.050396\t정확도: 98.44%\n",
+      "3\t검증 세트 손실: 0.053600\t최선의 손실: 0.050396\t정확도: 98.63%\n",
+      "4\t검증 세트 손실: 0.043140\t최선의 손실: 0.043140\t정확도: 98.87%\n",
+      "5\t검증 세트 손실: 0.078758\t최선의 손실: 0.043140\t정확도: 98.20%\n",
+      "6\t검증 세트 손실: 0.041241\t최선의 손실: 0.041241\t정확도: 98.83%\n",
+      "7\t검증 세트 손실: 0.075107\t최선의 손실: 0.041241\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.042943\t최선의 손실: 0.041241\t정확도: 99.02%\n",
+      "9\t검증 세트 손실: 0.051796\t최선의 손실: 0.041241\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.065187\t최선의 손실: 0.041241\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.048593\t최선의 손실: 0.041241\t정확도: 98.87%\n",
+      "12\t검증 세트 손실: 0.051984\t최선의 손실: 0.041241\t정확도: 98.55%\n",
+      "13\t검증 세트 손실: 0.042787\t최선의 손실: 0.041241\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.052153\t최선의 손실: 0.041241\t정확도: 98.91%\n",
+      "15\t검증 세트 손실: 0.048714\t최선의 손실: 0.041241\t정확도: 98.87%\n",
+      "16\t검증 세트 손실: 0.054660\t최선의 손실: 0.041241\t정확도: 99.02%\n",
+      "17\t검증 세트 손실: 0.065688\t최선의 손실: 0.041241\t정확도: 98.59%\n",
+      "18\t검증 세트 손실: 0.056435\t최선의 손실: 0.041241\t정확도: 98.87%\n",
+      "19\t검증 세트 손실: 0.062315\t최선의 손실: 0.041241\t정확도: 98.75%\n",
+      "20\t검증 세트 손실: 0.048100\t최선의 손실: 0.041241\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.051774\t최선의 손실: 0.041241\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.053469\t최선의 손실: 0.041241\t정확도: 98.75%\n",
+      "23\t검증 세트 손실: 0.052349\t최선의 손실: 0.041241\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.056164\t최선의 손실: 0.041241\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.062666\t최선의 손실: 0.041241\t정확도: 98.79%\n",
+      "26\t검증 세트 손실: 0.043523\t최선의 손실: 0.041241\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.067653\t최선의 손실: 0.041241\t정확도: 98.51%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=140, total=   6.4s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.058704\t최선의 손실: 0.058704\t정확도: 98.36%\n",
-      "1\t검증 세트 손실: 0.046010\t최선의 손실: 0.046010\t정확도: 98.44%\n",
-      "2\t검증 세트 손실: 0.044771\t최선의 손실: 0.044771\t정확도: 98.71%\n",
-      "3\t검증 세트 손실: 0.043740\t최선의 손실: 0.043740\t정확도: 98.55%\n",
-      "4\t검증 세트 손실: 0.037309\t최선의 손실: 0.037309\t정확도: 99.06%\n",
-      "5\t검증 세트 손실: 0.040574\t최선의 손실: 0.037309\t정확도: 98.87%\n",
-      "6\t검증 세트 손실: 0.047584\t최선의 손실: 0.037309\t정확도: 98.94%\n",
-      "7\t검증 세트 손실: 0.048294\t최선의 손실: 0.037309\t정확도: 98.71%\n",
-      "8\t검증 세트 손실: 0.028874\t최선의 손실: 0.028874\t정확도: 99.10%\n",
-      "9\t검증 세트 손실: 0.043042\t최선의 손실: 0.028874\t정확도: 98.91%\n",
-      "10\t검증 세트 손실: 0.052378\t최선의 손실: 0.028874\t정확도: 98.79%\n",
-      "11\t검증 세트 손실: 0.062975\t최선의 손실: 0.028874\t정확도: 98.55%\n",
-      "12\t검증 세트 손실: 0.038150\t최선의 손실: 0.028874\t정확도: 98.94%\n",
-      "13\t검증 세트 손실: 0.044607\t최선의 손실: 0.028874\t정확도: 99.06%\n",
-      "14\t검증 세트 손실: 0.044402\t최선의 손실: 0.028874\t정확도: 99.06%\n",
-      "15\t검증 세트 손실: 0.042324\t최선의 손실: 0.028874\t정확도: 99.22%\n",
-      "16\t검증 세트 손실: 0.033737\t최선의 손실: 0.028874\t정확도: 99.14%\n",
-      "17\t검증 세트 손실: 0.041144\t최선의 손실: 0.028874\t정확도: 99.06%\n",
-      "18\t검증 세트 손실: 0.039992\t최선의 손실: 0.028874\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.054436\t최선의 손실: 0.028874\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.052973\t최선의 손실: 0.028874\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.065127\t최선의 손실: 0.028874\t정확도: 98.51%\n",
-      "22\t검증 세트 손실: 0.052606\t최선의 손실: 0.028874\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.055464\t최선의 손실: 0.028874\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.054686\t최선의 손실: 0.028874\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.048955\t최선의 손실: 0.028874\t정확도: 99.14%\n",
-      "26\t검증 세트 손실: 0.056970\t최선의 손실: 0.028874\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.048335\t최선의 손실: 0.028874\t정확도: 99.14%\n",
-      "28\t검증 세트 손실: 0.042380\t최선의 손실: 0.028874\t정확도: 99.06%\n",
-      "29\t검증 세트 손실: 0.053824\t최선의 손실: 0.028874\t정확도: 99.14%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140, total=   6.7s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.073382\t최선의 손실: 0.073382\t정확도: 97.85%\n",
+      "1\t검증 세트 손실: 0.044233\t최선의 손실: 0.044233\t정확도: 98.51%\n",
+      "2\t검증 세트 손실: 0.051768\t최선의 손실: 0.044233\t정확도: 98.63%\n",
+      "3\t검증 세트 손실: 0.043119\t최선의 손실: 0.043119\t정확도: 98.98%\n",
+      "4\t검증 세트 손실: 0.049286\t최선의 손실: 0.043119\t정확도: 98.71%\n",
+      "5\t검증 세트 손실: 0.040036\t최선의 손실: 0.040036\t정확도: 98.91%\n",
+      "6\t검증 세트 손실: 0.032444\t최선의 손실: 0.032444\t정확도: 98.87%\n",
+      "7\t검증 세트 손실: 0.038404\t최선의 손실: 0.032444\t정확도: 98.91%\n",
+      "8\t검증 세트 손실: 0.035691\t최선의 손실: 0.032444\t정확도: 99.06%\n",
+      "9\t검증 세트 손실: 0.046585\t최선의 손실: 0.032444\t정확도: 98.83%\n",
+      "10\t검증 세트 손실: 0.049466\t최선의 손실: 0.032444\t정확도: 98.98%\n",
+      "11\t검증 세트 손실: 0.039054\t최선의 손실: 0.032444\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.047378\t최선의 손실: 0.032444\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.040289\t최선의 손실: 0.032444\t정확도: 99.14%\n",
+      "14\t검증 세트 손실: 0.028456\t최선의 손실: 0.028456\t정확도: 99.18%\n",
+      "15\t검증 세트 손실: 0.042616\t최선의 손실: 0.028456\t정확도: 99.02%\n",
+      "16\t검증 세트 손실: 0.041677\t최선의 손실: 0.028456\t정확도: 98.87%\n",
+      "17\t검증 세트 손실: 0.042025\t최선의 손실: 0.028456\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.044929\t최선의 손실: 0.028456\t정확도: 98.83%\n",
+      "19\t검증 세트 손실: 0.046623\t최선의 손실: 0.028456\t정확도: 98.94%\n",
+      "20\t검증 세트 손실: 0.039430\t최선의 손실: 0.028456\t정확도: 99.02%\n",
+      "21\t검증 세트 손실: 0.044966\t최선의 손실: 0.028456\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.055965\t최선의 손실: 0.028456\t정확도: 98.83%\n",
+      "23\t검증 세트 손실: 0.046191\t최선의 손실: 0.028456\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.037214\t최선의 손실: 0.028456\t정확도: 99.26%\n",
+      "25\t검증 세트 손실: 0.045441\t최선의 손실: 0.028456\t정확도: 99.26%\n",
+      "26\t검증 세트 손실: 0.030518\t최선의 손실: 0.028456\t정확도: 99.34%\n",
+      "27\t검증 세트 손실: 0.041784\t최선의 손실: 0.028456\t정확도: 99.14%\n",
+      "28\t검증 세트 손실: 0.042543\t최선의 손실: 0.028456\t정확도: 99.26%\n",
+      "29\t검증 세트 손실: 0.055863\t최선의 손실: 0.028456\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.045880\t최선의 손실: 0.028456\t정확도: 98.94%\n",
+      "31\t검증 세트 손실: 0.067076\t최선의 손실: 0.028456\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.039665\t최선의 손실: 0.028456\t정확도: 99.10%\n",
+      "33\t검증 세트 손실: 0.053608\t최선의 손실: 0.028456\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.064706\t최선의 손실: 0.028456\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.056634\t최선의 손실: 0.028456\t정확도: 99.02%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=140, total=   7.0s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.051269\t최선의 손실: 0.051269\t정확도: 98.20%\n",
-      "1\t검증 세트 손실: 0.047164\t최선의 손실: 0.047164\t정확도: 98.51%\n",
-      "2\t검증 세트 손실: 0.046125\t최선의 손실: 0.046125\t정확도: 98.55%\n",
-      "3\t검증 세트 손실: 0.049926\t최선의 손실: 0.046125\t정확도: 98.71%\n",
-      "4\t검증 세트 손실: 0.040317\t최선의 손실: 0.040317\t정확도: 98.91%\n",
-      "5\t검증 세트 손실: 0.052370\t최선의 손실: 0.040317\t정확도: 98.51%\n",
-      "6\t검증 세트 손실: 0.056749\t최선의 손실: 0.040317\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.031556\t최선의 손실: 0.031556\t정확도: 99.10%\n",
-      "8\t검증 세트 손실: 0.039339\t최선의 손실: 0.031556\t정확도: 98.91%\n",
-      "9\t검증 세트 손실: 0.042519\t최선의 손실: 0.031556\t정확도: 98.98%\n",
-      "10\t검증 세트 손실: 0.045073\t최선의 손실: 0.031556\t정확도: 98.94%\n",
-      "11\t검증 세트 손실: 0.049371\t최선의 손실: 0.031556\t정확도: 98.94%\n",
-      "12\t검증 세트 손실: 0.037876\t최선의 손실: 0.031556\t정확도: 99.06%\n",
-      "13\t검증 세트 손실: 0.039028\t최선의 손실: 0.031556\t정확도: 99.10%\n",
-      "14\t검증 세트 손실: 0.047397\t최선의 손실: 0.031556\t정확도: 98.87%\n",
-      "15\t검증 세트 손실: 0.064740\t최선의 손실: 0.031556\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.042143\t최선의 손실: 0.031556\t정확도: 98.87%\n",
-      "17\t검증 세트 손실: 0.040339\t최선의 손실: 0.031556\t정확도: 99.14%\n",
-      "18\t검증 세트 손실: 0.038674\t최선의 손실: 0.031556\t정확도: 99.02%\n",
-      "19\t검증 세트 손실: 0.042569\t최선의 손실: 0.031556\t정확도: 99.14%\n",
-      "20\t검증 세트 손실: 0.038616\t최선의 손실: 0.031556\t정확도: 99.22%\n",
-      "21\t검증 세트 손실: 0.045731\t최선의 손실: 0.031556\t정확도: 99.14%\n",
-      "22\t검증 세트 손실: 0.059765\t최선의 손실: 0.031556\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.044471\t최선의 손실: 0.031556\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.051077\t최선의 손실: 0.031556\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.048577\t최선의 손실: 0.031556\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.050963\t최선의 손실: 0.031556\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.041409\t최선의 손실: 0.031556\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.047579\t최선의 손실: 0.031556\t정확도: 99.02%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140, total=   8.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.064143\t최선의 손실: 0.064143\t정확도: 98.24%\n",
+      "1\t검증 세트 손실: 0.056535\t최선의 손실: 0.056535\t정확도: 98.28%\n",
+      "2\t검증 세트 손실: 0.049990\t최선의 손실: 0.049990\t정확도: 98.36%\n",
+      "3\t검증 세트 손실: 0.042866\t최선의 손실: 0.042866\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.041918\t최선의 손실: 0.041918\t정확도: 98.79%\n",
+      "5\t검증 세트 손실: 0.050608\t최선의 손실: 0.041918\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.047772\t최선의 손실: 0.041918\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.048633\t최선의 손실: 0.041918\t정확도: 98.71%\n",
+      "8\t검증 세트 손실: 0.050027\t최선의 손실: 0.041918\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.039067\t최선의 손실: 0.039067\t정확도: 98.91%\n",
+      "10\t검증 세트 손실: 0.061984\t최선의 손실: 0.039067\t정확도: 98.75%\n",
+      "11\t검증 세트 손실: 0.050699\t최선의 손실: 0.039067\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.043775\t최선의 손실: 0.039067\t정확도: 98.98%\n",
+      "13\t검증 세트 손실: 0.048813\t최선의 손실: 0.039067\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.061707\t최선의 손실: 0.039067\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.051949\t최선의 손실: 0.039067\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.031228\t최선의 손실: 0.031228\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.037851\t최선의 손실: 0.031228\t정확도: 99.18%\n",
+      "18\t검증 세트 손실: 0.039309\t최선의 손실: 0.031228\t정확도: 99.18%\n",
+      "19\t검증 세트 손실: 0.039933\t최선의 손실: 0.031228\t정확도: 99.10%\n",
+      "20\t검증 세트 손실: 0.039944\t최선의 손실: 0.031228\t정확도: 99.18%\n",
+      "21\t검증 세트 손실: 0.049582\t최선의 손실: 0.031228\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.046761\t최선의 손실: 0.031228\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.041392\t최선의 손실: 0.031228\t정확도: 98.94%\n",
+      "24\t검증 세트 손실: 0.087825\t최선의 손실: 0.031228\t정확도: 98.20%\n",
+      "25\t검증 세트 손실: 0.042939\t최선의 손실: 0.031228\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.049064\t최선의 손실: 0.031228\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.045306\t최선의 손실: 0.031228\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.044702\t최선의 손실: 0.031228\t정확도: 99.02%\n",
+      "29\t검증 세트 손실: 0.045001\t최선의 손실: 0.031228\t정확도: 99.06%\n",
+      "30\t검증 세트 손실: 0.040985\t최선의 손실: 0.031228\t정확도: 98.94%\n",
+      "31\t검증 세트 손실: 0.039088\t최선의 손실: 0.031228\t정확도: 98.94%\n",
+      "32\t검증 세트 손실: 0.046764\t최선의 손실: 0.031228\t정확도: 98.83%\n",
+      "33\t검증 세트 손실: 0.038245\t최선의 손실: 0.031228\t정확도: 99.22%\n",
+      "34\t검증 세트 손실: 0.041262\t최선의 손실: 0.031228\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.034610\t최선의 손실: 0.031228\t정확도: 99.10%\n",
+      "36\t검증 세트 손실: 0.043077\t최선의 손실: 0.031228\t정확도: 99.02%\n",
+      "37\t검증 세트 손실: 0.035024\t최선의 손실: 0.031228\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=140, total=   6.4s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.125925\t최선의 손실: 0.125925\t정확도: 97.07%\n",
-      "1\t검증 세트 손실: 0.077905\t최선의 손실: 0.077905\t정확도: 97.77%\n",
-      "2\t검증 세트 손실: 0.095114\t최선의 손실: 0.077905\t정확도: 97.73%\n",
-      "3\t검증 세트 손실: 0.070433\t최선의 손실: 0.070433\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.076925\t최선의 손실: 0.070433\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.061846\t최선의 손실: 0.061846\t정확도: 98.20%\n",
-      "6\t검증 세트 손실: 0.068006\t최선의 손실: 0.061846\t정확도: 98.08%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140, total=   8.7s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.152293\t최선의 손실: 0.152293\t정확도: 96.48%\n",
+      "1\t검증 세트 손실: 0.084866\t최선의 손실: 0.084866\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.064534\t최선의 손실: 0.064534\t정확도: 97.62%\n",
+      "3\t검증 세트 손실: 0.073013\t최선의 손실: 0.064534\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.072185\t최선의 손실: 0.064534\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.095159\t최선의 손실: 0.064534\t정확도: 97.93%\n",
+      "6\t검증 세트 손실: 0.055176\t최선의 손실: 0.055176\t정확도: 98.28%\n",
+      "7\t검증 세트 손실: 0.062947\t최선의 손실: 0.055176\t정확도: 98.40%\n",
+      "8\t검증 세트 손실: 0.089588\t최선의 손실: 0.055176\t정확도: 97.65%\n",
+      "9\t검증 세트 손실: 0.050393\t최선의 손실: 0.050393\t정확도: 98.75%\n",
+      "10\t검증 세트 손실: 0.063977\t최선의 손실: 0.050393\t정확도: 98.36%\n",
+      "11\t검증 세트 손실: 0.070309\t최선의 손실: 0.050393\t정확도: 98.32%\n",
+      "12\t검증 세트 손실: 0.084222\t최선의 손실: 0.050393\t정확도: 98.16%\n",
+      "13\t검증 세트 손실: 0.046166\t최선의 손실: 0.046166\t정확도: 98.51%\n",
+      "14\t검증 세트 손실: 0.059984\t최선의 손실: 0.046166\t정확도: 98.63%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7\t검증 세트 손실: 0.065022\t최선의 손실: 0.061846\t정확도: 98.40%\n",
-      "8\t검증 세트 손실: 0.040875\t최선의 손실: 0.040875\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.119322\t최선의 손실: 0.040875\t정확도: 97.85%\n",
-      "10\t검증 세트 손실: 0.096044\t최선의 손실: 0.040875\t정확도: 98.01%\n",
-      "11\t검증 세트 손실: 0.059884\t최선의 손실: 0.040875\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.079992\t최선의 손실: 0.040875\t정확도: 98.48%\n",
-      "13\t검증 세트 손실: 0.044340\t최선의 손실: 0.040875\t정확도: 98.87%\n",
-      "14\t검증 세트 손실: 0.096129\t최선의 손실: 0.040875\t정확도: 98.44%\n",
-      "15\t검증 세트 손실: 0.050689\t최선의 손실: 0.040875\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.059984\t최선의 손실: 0.040875\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.059613\t최선의 손실: 0.040875\t정확도: 98.87%\n",
-      "18\t검증 세트 손실: 0.057226\t최선의 손실: 0.040875\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.082832\t최선의 손실: 0.040875\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.109906\t최선의 손실: 0.040875\t정확도: 98.20%\n",
-      "21\t검증 세트 손실: 0.085206\t최선의 손실: 0.040875\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.052726\t최선의 손실: 0.040875\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.068426\t최선의 손실: 0.040875\t정확도: 98.63%\n",
-      "24\t검증 세트 손실: 0.062305\t최선의 손실: 0.040875\t정확도: 98.51%\n",
-      "25\t검증 세트 손실: 0.061016\t최선의 손실: 0.040875\t정확도: 98.67%\n",
-      "26\t검증 세트 손실: 0.124686\t최선의 손실: 0.040875\t정확도: 98.36%\n",
-      "27\t검증 세트 손실: 0.084200\t최선의 손실: 0.040875\t정확도: 98.36%\n",
-      "28\t검증 세트 손실: 0.055904\t최선의 손실: 0.040875\t정확도: 98.75%\n",
-      "29\t검증 세트 손실: 0.104475\t최선의 손실: 0.040875\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.080247\t최선의 손실: 0.046166\t정확도: 97.93%\n",
+      "16\t검증 세트 손실: 0.061843\t최선의 손실: 0.046166\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.048337\t최선의 손실: 0.046166\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.060371\t최선의 손실: 0.046166\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 0.080577\t최선의 손실: 0.046166\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.068256\t최선의 손실: 0.046166\t정확도: 99.02%\n",
+      "21\t검증 세트 손실: 0.142209\t최선의 손실: 0.046166\t정확도: 98.40%\n",
+      "22\t검증 세트 손실: 0.110053\t최선의 손실: 0.046166\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.122068\t최선의 손실: 0.046166\t정확도: 98.55%\n",
+      "24\t검증 세트 손실: 0.084962\t최선의 손실: 0.046166\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.070149\t최선의 손실: 0.046166\t정확도: 98.59%\n",
+      "26\t검증 세트 손실: 0.059966\t최선의 손실: 0.046166\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.088774\t최선의 손실: 0.046166\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.082710\t최선의 손실: 0.046166\t정확도: 98.55%\n",
+      "29\t검증 세트 손실: 0.096589\t최선의 손실: 0.046166\t정확도: 98.63%\n",
+      "30\t검증 세트 손실: 0.093410\t최선의 손실: 0.046166\t정확도: 98.67%\n",
+      "31\t검증 세트 손실: 0.096619\t최선의 손실: 0.046166\t정확도: 98.67%\n",
+      "32\t검증 세트 손실: 0.118848\t최선의 손실: 0.046166\t정확도: 98.36%\n",
+      "33\t검증 세트 손실: 0.142411\t최선의 손실: 0.046166\t정확도: 98.24%\n",
+      "34\t검증 세트 손실: 0.135614\t최선의 손실: 0.046166\t정확도: 98.28%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=100, total=  43.2s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.125685\t최선의 손실: 0.125685\t정확도: 96.25%\n",
-      "1\t검증 세트 손실: 0.061134\t최선의 손실: 0.061134\t정확도: 98.40%\n",
-      "2\t검증 세트 손실: 0.066079\t최선의 손실: 0.061134\t정확도: 98.08%\n",
-      "3\t검증 세트 손실: 0.062834\t최선의 손실: 0.061134\t정확도: 98.08%\n",
-      "4\t검증 세트 손실: 0.076449\t최선의 손실: 0.061134\t정확도: 98.08%\n",
-      "5\t검증 세트 손실: 0.071534\t최선의 손실: 0.061134\t정확도: 98.12%\n",
-      "6\t검증 세트 손실: 0.070536\t최선의 손실: 0.061134\t정확도: 98.36%\n",
-      "7\t검증 세트 손실: 0.083587\t최선의 손실: 0.061134\t정확도: 97.89%\n",
-      "8\t검증 세트 손실: 0.117620\t최선의 손실: 0.061134\t정확도: 97.89%\n",
-      "9\t검증 세트 손실: 0.076557\t최선의 손실: 0.061134\t정확도: 97.89%\n",
-      "10\t검증 세트 손실: 0.058572\t최선의 손실: 0.058572\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.091310\t최선의 손실: 0.058572\t정확도: 97.38%\n",
-      "12\t검증 세트 손실: 0.053167\t최선의 손실: 0.053167\t정확도: 98.55%\n",
-      "13\t검증 세트 손실: 0.072989\t최선의 손실: 0.053167\t정확도: 98.08%\n",
-      "14\t검증 세트 손실: 0.084771\t최선의 손실: 0.053167\t정확도: 97.81%\n",
-      "15\t검증 세트 손실: 0.076088\t최선의 손실: 0.053167\t정확도: 98.24%\n",
-      "16\t검증 세트 손실: 0.059906\t최선의 손실: 0.053167\t정확도: 98.28%\n",
-      "17\t검증 세트 손실: 0.337509\t최선의 손실: 0.053167\t정확도: 97.07%\n",
-      "18\t검증 세트 손실: 0.100605\t최선의 손실: 0.053167\t정확도: 98.16%\n",
-      "19\t검증 세트 손실: 0.063647\t최선의 손실: 0.053167\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.064800\t최선의 손실: 0.053167\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.092501\t최선의 손실: 0.053167\t정확도: 98.28%\n",
-      "22\t검증 세트 손실: 0.093795\t최선의 손실: 0.053167\t정확도: 98.28%\n",
-      "23\t검증 세트 손실: 0.134156\t최선의 손실: 0.053167\t정확도: 97.73%\n",
-      "24\t검증 세트 손실: 0.058514\t최선의 손실: 0.053167\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.054654\t최선의 손실: 0.053167\t정확도: 98.71%\n",
-      "26\t검증 세트 손실: 0.104227\t최선의 손실: 0.053167\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.077419\t최선의 손실: 0.053167\t정확도: 98.51%\n",
-      "28\t검증 세트 손실: 0.093105\t최선의 손실: 0.053167\t정확도: 98.48%\n",
-      "29\t검증 세트 손실: 0.059942\t최선의 손실: 0.053167\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.049744\t최선의 손실: 0.049744\t정확도: 99.06%\n",
-      "31\t검증 세트 손실: 0.130860\t최선의 손실: 0.049744\t정확도: 98.79%\n",
-      "32\t검증 세트 손실: 0.119444\t최선의 손실: 0.049744\t정확도: 98.36%\n",
-      "33\t검증 세트 손실: 0.067690\t최선의 손실: 0.049744\t정확도: 98.91%\n",
-      "34\t검증 세트 손실: 0.048863\t최선의 손실: 0.048863\t정확도: 99.10%\n",
-      "35\t검증 세트 손실: 0.062975\t최선의 손실: 0.048863\t정확도: 98.98%\n",
-      "36\t검증 세트 손실: 0.133079\t최선의 손실: 0.048863\t정확도: 98.20%\n",
-      "37\t검증 세트 손실: 0.161600\t최선의 손실: 0.048863\t정확도: 98.63%\n",
-      "38\t검증 세트 손실: 0.138019\t최선의 손실: 0.048863\t정확도: 98.71%\n",
-      "39\t검증 세트 손실: 0.099676\t최선의 손실: 0.048863\t정확도: 99.06%\n",
-      "40\t검증 세트 손실: 0.115043\t최선의 손실: 0.048863\t정확도: 99.06%\n",
-      "41\t검증 세트 손실: 0.108251\t최선의 손실: 0.048863\t정확도: 99.06%\n",
-      "42\t검증 세트 손실: 0.133911\t최선의 손실: 0.048863\t정확도: 98.75%\n",
-      "43\t검증 세트 손실: 0.107184\t최선의 손실: 0.048863\t정확도: 98.87%\n",
-      "44\t검증 세트 손실: 0.138871\t최선의 손실: 0.048863\t정확도: 98.63%\n",
-      "45\t검증 세트 손실: 0.096518\t최선의 손실: 0.048863\t정확도: 99.02%\n",
-      "46\t검증 세트 손실: 0.165379\t최선의 손실: 0.048863\t정확도: 98.24%\n",
-      "47\t검증 세트 손실: 0.131490\t최선의 손실: 0.048863\t정확도: 98.87%\n",
-      "48\t검증 세트 손실: 0.107603\t최선의 손실: 0.048863\t정확도: 98.98%\n",
-      "49\t검증 세트 손실: 0.225228\t최선의 손실: 0.048863\t정확도: 97.89%\n",
-      "50\t검증 세트 손실: 0.124624\t최선의 손실: 0.048863\t정확도: 98.79%\n",
-      "51\t검증 세트 손실: 0.110810\t최선의 손실: 0.048863\t정확도: 98.94%\n",
-      "52\t검증 세트 손실: 0.150765\t최선의 손실: 0.048863\t정확도: 98.83%\n",
-      "53\t검증 세트 손실: 0.110546\t최선의 손실: 0.048863\t정확도: 98.87%\n",
-      "54\t검증 세트 손실: 0.200315\t최선의 손실: 0.048863\t정확도: 98.44%\n",
-      "55\t검증 세트 손실: 0.140897\t최선의 손실: 0.048863\t정확도: 98.87%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=100, total=  51.4s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.138441\t최선의 손실: 0.138441\t정확도: 96.21%\n",
+      "1\t검증 세트 손실: 0.083399\t최선의 손실: 0.083399\t정확도: 97.85%\n",
+      "2\t검증 세트 손실: 0.105974\t최선의 손실: 0.083399\t정확도: 97.03%\n",
+      "3\t검증 세트 손실: 0.101395\t최선의 손실: 0.083399\t정확도: 97.69%\n",
+      "4\t검증 세트 손실: 0.074190\t최선의 손실: 0.074190\t정확도: 98.08%\n",
+      "5\t검증 세트 손실: 0.066238\t최선의 손실: 0.066238\t정확도: 98.36%\n",
+      "6\t검증 세트 손실: 0.086413\t최선의 손실: 0.066238\t정확도: 97.97%\n",
+      "7\t검증 세트 손실: 0.084840\t최선의 손실: 0.066238\t정확도: 98.01%\n",
+      "8\t검증 세트 손실: 0.155621\t최선의 손실: 0.066238\t정확도: 98.05%\n",
+      "9\t검증 세트 손실: 0.086445\t최선의 손실: 0.066238\t정확도: 98.32%\n",
+      "10\t검증 세트 손실: 0.117953\t최선의 손실: 0.066238\t정확도: 97.65%\n",
+      "11\t검증 세트 손실: 0.087004\t최선의 손실: 0.066238\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.117125\t최선의 손실: 0.066238\t정확도: 97.07%\n",
+      "13\t검증 세트 손실: 0.062928\t최선의 손실: 0.062928\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.077043\t최선의 손실: 0.062928\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.081936\t최선의 손실: 0.062928\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.177484\t최선의 손실: 0.062928\t정확도: 96.99%\n",
+      "17\t검증 세트 손실: 0.070882\t최선의 손실: 0.062928\t정확도: 98.67%\n",
+      "18\t검증 세트 손실: 0.068643\t최선의 손실: 0.062928\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.059064\t최선의 손실: 0.059064\t정확도: 98.91%\n",
+      "20\t검증 세트 손실: 0.068133\t최선의 손실: 0.059064\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.078434\t최선의 손실: 0.059064\t정확도: 98.67%\n",
+      "22\t검증 세트 손실: 0.076682\t최선의 손실: 0.059064\t정확도: 98.01%\n",
+      "23\t검증 세트 손실: 0.064771\t최선의 손실: 0.059064\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.092372\t최선의 손실: 0.059064\t정확도: 98.51%\n",
+      "25\t검증 세트 손실: 0.068255\t최선의 손실: 0.059064\t정확도: 98.48%\n",
+      "26\t검증 세트 손실: 0.112308\t최선의 손실: 0.059064\t정확도: 98.24%\n",
+      "27\t검증 세트 손실: 0.056772\t최선의 손실: 0.056772\t정확도: 98.91%\n",
+      "28\t검증 세트 손실: 0.088694\t최선의 손실: 0.056772\t정확도: 98.63%\n",
+      "29\t검증 세트 손실: 0.156110\t최선의 손실: 0.056772\t정확도: 98.40%\n",
+      "30\t검증 세트 손실: 0.074796\t최선의 손실: 0.056772\t정확도: 98.51%\n",
+      "31\t검증 세트 손실: 0.069013\t최선의 손실: 0.056772\t정확도: 99.10%\n",
+      "32\t검증 세트 손실: 0.055825\t최선의 손실: 0.055825\t정확도: 98.79%\n",
+      "33\t검증 세트 손실: 0.138826\t최선의 손실: 0.055825\t정확도: 98.24%\n",
+      "34\t검증 세트 손실: 0.108524\t최선의 손실: 0.055825\t정확도: 97.77%\n",
+      "35\t검증 세트 손실: 0.092955\t최선의 손실: 0.055825\t정확도: 98.36%\n",
+      "36\t검증 세트 손실: 0.154812\t최선의 손실: 0.055825\t정확도: 98.44%\n",
+      "37\t검증 세트 손실: 0.143031\t최선의 손실: 0.055825\t정확도: 98.55%\n",
+      "38\t검증 세트 손실: 0.111527\t최선의 손실: 0.055825\t정확도: 98.59%\n",
+      "39\t검증 세트 손실: 0.071654\t최선의 손실: 0.055825\t정확도: 98.79%\n",
+      "40\t검증 세트 손실: 0.102922\t최선의 손실: 0.055825\t정확도: 98.59%\n",
+      "41\t검증 세트 손실: 0.099876\t최선의 손실: 0.055825\t정확도: 98.83%\n",
+      "42\t검증 세트 손실: 0.109394\t최선의 손실: 0.055825\t정확도: 98.63%\n",
+      "43\t검증 세트 손실: 0.111789\t최선의 손실: 0.055825\t정확도: 98.48%\n",
+      "44\t검증 세트 손실: 0.355538\t최선의 손실: 0.055825\t정확도: 97.73%\n",
+      "45\t검증 세트 손실: 0.121457\t최선의 손실: 0.055825\t정확도: 98.75%\n",
+      "46\t검증 세트 손실: 0.167768\t최선의 손실: 0.055825\t정확도: 98.36%\n",
+      "47\t검증 세트 손실: 0.117973\t최선의 손실: 0.055825\t정확도: 98.83%\n",
+      "48\t검증 세트 손실: 0.198363\t최선의 손실: 0.055825\t정확도: 98.24%\n",
+      "49\t검증 세트 손실: 0.148372\t최선의 손실: 0.055825\t정확도: 98.55%\n",
+      "50\t검증 세트 손실: 0.148182\t최선의 손실: 0.055825\t정확도: 98.98%\n",
+      "51\t검증 세트 손실: 0.077818\t최선의 손실: 0.055825\t정확도: 98.87%\n",
+      "52\t검증 세트 손실: 0.134905\t최선의 손실: 0.055825\t정확도: 98.83%\n",
+      "53\t검증 세트 손실: 0.155560\t최선의 손실: 0.055825\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=100, total= 1.3min\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.105757\t최선의 손실: 0.105757\t정확도: 96.99%\n",
-      "1\t검증 세트 손실: 0.087593\t최선의 손실: 0.087593\t정확도: 97.69%\n",
-      "2\t검증 세트 손실: 0.060962\t최선의 손실: 0.060962\t정확도: 98.40%\n",
-      "3\t검증 세트 손실: 0.092039\t최선의 손실: 0.060962\t정확도: 97.77%\n",
-      "4\t검증 세트 손실: 0.084125\t최선의 손실: 0.060962\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.050618\t최선의 손실: 0.050618\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.185500\t최선의 손실: 0.050618\t정확도: 94.96%\n",
-      "7\t검증 세트 손실: 0.085199\t최선의 손실: 0.050618\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.043463\t최선의 손실: 0.043463\t정확도: 98.91%\n",
-      "9\t검증 세트 손실: 0.095670\t최선의 손실: 0.043463\t정확도: 97.46%\n",
-      "10\t검증 세트 손실: 0.072196\t최선의 손실: 0.043463\t정확도: 98.28%\n",
-      "11\t검증 세트 손실: 0.051295\t최선의 손실: 0.043463\t정확도: 98.75%\n",
-      "12\t검증 세트 손실: 0.475705\t최선의 손실: 0.043463\t정확도: 95.82%\n",
-      "13\t검증 세트 손실: 0.053576\t최선의 손실: 0.043463\t정확도: 98.59%\n",
-      "14\t검증 세트 손실: 0.065067\t최선의 손실: 0.043463\t정확도: 98.48%\n",
-      "15\t검증 세트 손실: 0.048293\t최선의 손실: 0.043463\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.065668\t최선의 손실: 0.043463\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.073961\t최선의 손실: 0.043463\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.143459\t최선의 손실: 0.043463\t정확도: 97.54%\n",
-      "19\t검증 세트 손실: 0.076530\t최선의 손실: 0.043463\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.073236\t최선의 손실: 0.043463\t정확도: 98.71%\n",
-      "21\t검증 세트 손실: 0.051393\t최선의 손실: 0.043463\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.078716\t최선의 손실: 0.043463\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.075225\t최선의 손실: 0.043463\t정확도: 98.75%\n",
-      "24\t검증 세트 손실: 0.105468\t최선의 손실: 0.043463\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.084359\t최선의 손실: 0.043463\t정확도: 98.63%\n",
-      "26\t검증 세트 손실: 0.063248\t최선의 손실: 0.043463\t정확도: 99.14%\n",
-      "27\t검증 세트 손실: 0.077268\t최선의 손실: 0.043463\t정확도: 98.71%\n",
-      "28\t검증 세트 손실: 0.118293\t최선의 손실: 0.043463\t정확도: 98.44%\n",
-      "29\t검증 세트 손실: 0.091925\t최선의 손실: 0.043463\t정확도: 98.55%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=100, total= 1.3min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.122714\t최선의 손실: 0.122714\t정확도: 96.72%\n",
+      "1\t검증 세트 손실: 0.103852\t최선의 손실: 0.103852\t정확도: 97.38%\n",
+      "2\t검증 세트 손실: 0.071847\t최선의 손실: 0.071847\t정확도: 97.65%\n",
+      "3\t검증 세트 손실: 0.063721\t최선의 손실: 0.063721\t정확도: 98.28%\n",
+      "4\t검증 세트 손실: 0.067185\t최선의 손실: 0.063721\t정확도: 98.36%\n",
+      "5\t검증 세트 손실: 0.060555\t최선의 손실: 0.060555\t정확도: 98.40%\n",
+      "6\t검증 세트 손실: 0.052923\t최선의 손실: 0.052923\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.112254\t최선의 손실: 0.052923\t정확도: 98.16%\n",
+      "8\t검증 세트 손실: 0.064250\t최선의 손실: 0.052923\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.080084\t최선의 손실: 0.052923\t정확도: 98.36%\n",
+      "10\t검증 세트 손실: 0.041694\t최선의 손실: 0.041694\t정확도: 98.98%\n",
+      "11\t검증 세트 손실: 0.085240\t최선의 손실: 0.041694\t정확도: 98.32%\n",
+      "12\t검증 세트 손실: 0.083483\t최선의 손실: 0.041694\t정확도: 97.81%\n",
+      "13\t검증 세트 손실: 0.052523\t최선의 손실: 0.041694\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.065294\t최선의 손실: 0.041694\t정확도: 98.83%\n",
+      "15\t검증 세트 손실: 0.059307\t최선의 손실: 0.041694\t정확도: 98.59%\n",
+      "16\t검증 세트 손실: 0.086920\t최선의 손실: 0.041694\t정확도: 98.01%\n",
+      "17\t검증 세트 손실: 0.099108\t최선의 손실: 0.041694\t정확도: 97.81%\n",
+      "18\t검증 세트 손실: 0.085882\t최선의 손실: 0.041694\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.061762\t최선의 손실: 0.041694\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.046894\t최선의 손실: 0.041694\t정확도: 99.14%\n",
+      "21\t검증 세트 손실: 0.118690\t최선의 손실: 0.041694\t정확도: 98.24%\n",
+      "22\t검증 세트 손실: 0.075201\t최선의 손실: 0.041694\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.066469\t최선의 손실: 0.041694\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.079118\t최선의 손실: 0.041694\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.077932\t최선의 손실: 0.041694\t정확도: 98.51%\n",
+      "26\t검증 세트 손실: 0.084854\t최선의 손실: 0.041694\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.067966\t최선의 손실: 0.041694\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.047967\t최선의 손실: 0.041694\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.064991\t최선의 손실: 0.041694\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.191775\t최선의 손실: 0.041694\t정확도: 98.08%\n",
+      "31\t검증 세트 손실: 0.076747\t최선의 손실: 0.041694\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=100, total=  43.4s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.98, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.154336\t최선의 손실: 0.154336\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.072142\t최선의 손실: 0.072142\t정확도: 97.77%\n",
-      "2\t검증 세트 손실: 0.080083\t최선의 손실: 0.072142\t정확도: 97.93%\n",
-      "3\t검증 세트 손실: 0.047295\t최선의 손실: 0.047295\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.052368\t최선의 손실: 0.047295\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.100531\t최선의 손실: 0.047295\t정확도: 97.65%\n",
-      "6\t검증 세트 손실: 0.080323\t최선의 손실: 0.047295\t정확도: 97.81%\n",
-      "7\t검증 세트 손실: 0.073890\t최선의 손실: 0.047295\t정확도: 98.20%\n",
-      "8\t검증 세트 손실: 0.053948\t최선의 손실: 0.047295\t정확도: 98.36%\n",
-      "9\t검증 세트 손실: 0.071159\t최선의 손실: 0.047295\t정확도: 98.32%\n",
-      "10\t검증 세트 손실: 0.075697\t최선의 손실: 0.047295\t정확도: 98.24%\n",
-      "11\t검증 세트 손실: 0.088875\t최선의 손실: 0.047295\t정확도: 98.01%\n",
-      "12\t검증 세트 손실: 0.059208\t최선의 손실: 0.047295\t정확도: 98.44%\n",
-      "13\t검증 세트 손실: 0.086147\t최선의 손실: 0.047295\t정확도: 98.08%\n",
-      "14\t검증 세트 손실: 0.063663\t최선의 손실: 0.047295\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.048047\t최선의 손실: 0.047295\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.055194\t최선의 손실: 0.047295\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.050258\t최선의 손실: 0.047295\t정확도: 98.63%\n",
-      "18\t검증 세트 손실: 0.054256\t최선의 손실: 0.047295\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.045851\t최선의 손실: 0.045851\t정확도: 98.91%\n",
-      "20\t검증 세트 손실: 0.058089\t최선의 손실: 0.045851\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.055378\t최선의 손실: 0.045851\t정확도: 98.75%\n",
-      "22\t검증 세트 손실: 0.060625\t최선의 손실: 0.045851\t정확도: 98.32%\n",
-      "23\t검증 세트 손실: 0.082603\t최선의 손실: 0.045851\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.081643\t최선의 손실: 0.045851\t정확도: 98.40%\n",
-      "25\t검증 세트 손실: 0.065166\t최선의 손실: 0.045851\t정확도: 98.67%\n",
-      "26\t검증 세트 손실: 0.051634\t최선의 손실: 0.045851\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.068264\t최선의 손실: 0.045851\t정확도: 98.87%\n",
-      "28\t검증 세트 손실: 0.053363\t최선의 손실: 0.045851\t정확도: 98.98%\n",
-      "29\t검증 세트 손실: 0.048351\t최선의 손실: 0.045851\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.044410\t최선의 손실: 0.044410\t정확도: 99.10%\n",
-      "31\t검증 세트 손실: 0.040340\t최선의 손실: 0.040340\t정확도: 99.02%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=100, total=  47.1s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.131640\t최선의 손실: 0.131640\t정확도: 97.54%\n",
+      "1\t검증 세트 손실: 0.067777\t최선의 손실: 0.067777\t정확도: 98.32%\n",
+      "2\t검증 세트 손실: 0.058200\t최선의 손실: 0.058200\t정확도: 98.28%\n",
+      "3\t검증 세트 손실: 0.044044\t최선의 손실: 0.044044\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.056763\t최선의 손실: 0.044044\t정확도: 98.55%\n",
+      "5\t검증 세트 손실: 0.087139\t최선의 손실: 0.044044\t정확도: 97.85%\n",
+      "6\t검증 세트 손실: 0.054986\t최선의 손실: 0.044044\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.060833\t최선의 손실: 0.044044\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.048951\t최선의 손실: 0.044044\t정확도: 98.79%\n",
+      "9\t검증 세트 손실: 0.057692\t최선의 손실: 0.044044\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.049042\t최선의 손실: 0.044044\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.084361\t최선의 손실: 0.044044\t정확도: 98.08%\n",
+      "12\t검증 세트 손실: 0.073885\t최선의 손실: 0.044044\t정확도: 98.40%\n",
+      "13\t검증 세트 손실: 0.064831\t최선의 손실: 0.044044\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.063182\t최선의 손실: 0.044044\t정확도: 98.48%\n",
+      "15\t검증 세트 손실: 0.053121\t최선의 손실: 0.044044\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.052870\t최선의 손실: 0.044044\t정확도: 98.87%\n",
+      "17\t검증 세트 손실: 0.058369\t최선의 손실: 0.044044\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.065199\t최선의 손실: 0.044044\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.059242\t최선의 손실: 0.044044\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.069061\t최선의 손실: 0.044044\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.072166\t최선의 손실: 0.044044\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.069980\t최선의 손실: 0.044044\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.060373\t최선의 손실: 0.044044\t정확도: 98.91%\n",
+      "24\t검증 세트 손실: 0.043956\t최선의 손실: 0.043956\t정확도: 98.87%\n",
+      "25\t검증 세트 손실: 0.049550\t최선의 손실: 0.043956\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.039582\t최선의 손실: 0.039582\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.048039\t최선의 손실: 0.039582\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.055581\t최선의 손실: 0.039582\t정확도: 98.91%\n",
+      "29\t검증 세트 손실: 0.051939\t최선의 손실: 0.039582\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.060323\t최선의 손실: 0.039582\t정확도: 98.87%\n",
+      "31\t검증 세트 손실: 0.073143\t최선의 손실: 0.039582\t정확도: 98.55%\n",
+      "32\t검증 세트 손실: 0.060483\t최선의 손실: 0.039582\t정확도: 98.59%\n",
+      "33\t검증 세트 손실: 0.052950\t최선의 손실: 0.039582\t정확도: 98.83%\n",
+      "34\t검증 세트 손실: 0.052075\t최선의 손실: 0.039582\t정확도: 98.87%\n",
+      "35\t검증 세트 손실: 0.044289\t최선의 손실: 0.039582\t정확도: 99.14%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "32\t검증 세트 손실: 0.060340\t최선의 손실: 0.040340\t정확도: 98.71%\n",
-      "33\t검증 세트 손실: 0.053967\t최선의 손실: 0.040340\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.046992\t최선의 손실: 0.040340\t정확도: 98.94%\n",
-      "35\t검증 세트 손실: 0.039491\t최선의 손실: 0.039491\t정확도: 99.22%\n",
-      "36\t검증 세트 손실: 0.040713\t최선의 손실: 0.039491\t정확도: 99.10%\n",
-      "37\t검증 세트 손실: 0.041830\t최선의 손실: 0.039491\t정확도: 99.06%\n",
-      "38\t검증 세트 손실: 0.045313\t최선의 손실: 0.039491\t정확도: 98.98%\n",
-      "39\t검증 세트 손실: 0.064247\t최선의 손실: 0.039491\t정확도: 98.44%\n",
-      "40\t검증 세트 손실: 0.044413\t최선의 손실: 0.039491\t정확도: 99.02%\n",
-      "41\t검증 세트 손실: 0.078167\t최선의 손실: 0.039491\t정확도: 98.59%\n",
-      "42\t검증 세트 손실: 0.065799\t최선의 손실: 0.039491\t정확도: 98.75%\n",
-      "43\t검증 세트 손실: 0.052890\t최선의 손실: 0.039491\t정확도: 98.79%\n",
-      "44\t검증 세트 손실: 0.082235\t최선의 손실: 0.039491\t정확도: 98.32%\n",
-      "45\t검증 세트 손실: 0.072752\t최선의 손실: 0.039491\t정확도: 98.59%\n",
-      "46\t검증 세트 손실: 0.070896\t최선의 손실: 0.039491\t정확도: 98.55%\n",
-      "47\t검증 세트 손실: 0.063396\t최선의 손실: 0.039491\t정확도: 98.87%\n",
-      "48\t검증 세트 손실: 0.060936\t최선의 손실: 0.039491\t정확도: 98.67%\n",
-      "49\t검증 세트 손실: 0.060982\t최선의 손실: 0.039491\t정확도: 98.83%\n",
-      "50\t검증 세트 손실: 0.056628\t최선의 손실: 0.039491\t정확도: 98.91%\n",
-      "51\t검증 세트 손실: 0.064418\t최선의 손실: 0.039491\t정확도: 98.63%\n",
-      "52\t검증 세트 손실: 0.080424\t최선의 손실: 0.039491\t정확도: 98.51%\n",
-      "53\t검증 세트 손실: 0.058229\t최선의 손실: 0.039491\t정확도: 98.83%\n",
-      "54\t검증 세트 손실: 0.077659\t최선의 손실: 0.039491\t정확도: 98.59%\n",
-      "55\t검증 세트 손실: 0.055462\t최선의 손실: 0.039491\t정확도: 98.79%\n",
-      "56\t검증 세트 손실: 0.088383\t최선의 손실: 0.039491\t정확도: 98.83%\n",
+      "36\t검증 세트 손실: 0.062748\t최선의 손실: 0.039582\t정확도: 98.75%\n",
+      "37\t검증 세트 손실: 0.069537\t최선의 손실: 0.039582\t정확도: 98.75%\n",
+      "38\t검증 세트 손실: 0.064353\t최선의 손실: 0.039582\t정확도: 98.51%\n",
+      "39\t검증 세트 손실: 0.058209\t최선의 손실: 0.039582\t정확도: 98.79%\n",
+      "40\t검증 세트 손실: 0.061472\t최선의 손실: 0.039582\t정확도: 98.67%\n",
+      "41\t검증 세트 손실: 0.060261\t최선의 손실: 0.039582\t정확도: 98.75%\n",
+      "42\t검증 세트 손실: 0.058232\t최선의 손실: 0.039582\t정확도: 98.83%\n",
+      "43\t검증 세트 손실: 0.057877\t최선의 손실: 0.039582\t정확도: 98.87%\n",
+      "44\t검증 세트 손실: 0.061736\t최선의 손실: 0.039582\t정확도: 98.83%\n",
+      "45\t검증 세트 손실: 0.059831\t최선의 손실: 0.039582\t정확도: 98.91%\n",
+      "46\t검증 세트 손실: 0.060041\t최선의 손실: 0.039582\t정확도: 98.91%\n",
+      "47\t검증 세트 손실: 0.058600\t최선의 손실: 0.039582\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.98, n_neurons=50, total=  11.1s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.98, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.193910\t최선의 손실: 0.193910\t정확도: 96.48%\n",
-      "1\t검증 세트 손실: 0.075612\t최선의 손실: 0.075612\t정확도: 98.12%\n",
-      "2\t검증 세트 손실: 0.061754\t최선의 손실: 0.061754\t정확도: 98.36%\n",
-      "3\t검증 세트 손실: 0.058554\t최선의 손실: 0.058554\t정확도: 98.32%\n",
-      "4\t검증 세트 손실: 0.050835\t최선의 손실: 0.050835\t정확도: 98.48%\n",
-      "5\t검증 세트 손실: 0.058537\t최선의 손실: 0.050835\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.047585\t최선의 손실: 0.047585\t정확도: 98.63%\n",
-      "7\t검증 세트 손실: 0.056868\t최선의 손실: 0.047585\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.039209\t최선의 손실: 0.039209\t정확도: 98.79%\n",
-      "9\t검증 세트 손실: 0.052378\t최선의 손실: 0.039209\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.065940\t최선의 손실: 0.039209\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.078267\t최선의 손실: 0.039209\t정확도: 98.20%\n",
-      "12\t검증 세트 손실: 0.045151\t최선의 손실: 0.039209\t정확도: 98.98%\n",
-      "13\t검증 세트 손실: 0.055072\t최선의 손실: 0.039209\t정확도: 98.36%\n",
-      "14\t검증 세트 손실: 0.043636\t최선의 손실: 0.039209\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.067308\t최선의 손실: 0.039209\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.044480\t최선의 손실: 0.039209\t정확도: 98.94%\n",
-      "17\t검증 세트 손실: 0.107697\t최선의 손실: 0.039209\t정확도: 97.73%\n",
-      "18\t검증 세트 손실: 0.054082\t최선의 손실: 0.039209\t정확도: 98.75%\n",
-      "19\t검증 세트 손실: 0.051299\t최선의 손실: 0.039209\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.049166\t최선의 손실: 0.039209\t정확도: 99.10%\n",
-      "21\t검증 세트 손실: 0.043658\t최선의 손실: 0.039209\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.058679\t최선의 손실: 0.039209\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.061277\t최선의 손실: 0.039209\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.059789\t최선의 손실: 0.039209\t정확도: 98.63%\n",
-      "25\t검증 세트 손실: 0.068519\t최선의 손실: 0.039209\t정확도: 98.55%\n",
-      "26\t검증 세트 손실: 0.046953\t최선의 손실: 0.039209\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.039646\t최선의 손실: 0.039209\t정확도: 98.98%\n",
-      "28\t검증 세트 손실: 0.056598\t최선의 손실: 0.039209\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.057140\t최선의 손실: 0.039209\t정확도: 98.67%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=50, total=  10.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.310257\t최선의 손실: 0.310257\t정확도: 94.53%\n",
+      "1\t검증 세트 손실: 0.081824\t최선의 손실: 0.081824\t정확도: 97.93%\n",
+      "2\t검증 세트 손실: 0.070580\t최선의 손실: 0.070580\t정확도: 98.12%\n",
+      "3\t검증 세트 손실: 0.048284\t최선의 손실: 0.048284\t정확도: 98.71%\n",
+      "4\t검증 세트 손실: 0.053962\t최선의 손실: 0.048284\t정확도: 98.59%\n",
+      "5\t검증 세트 손실: 0.048556\t최선의 손실: 0.048284\t정확도: 98.59%\n",
+      "6\t검증 세트 손실: 0.045711\t최선의 손실: 0.045711\t정확도: 98.83%\n",
+      "7\t검증 세트 손실: 0.066662\t최선의 손실: 0.045711\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.061468\t최선의 손실: 0.045711\t정확도: 98.32%\n",
+      "9\t검증 세트 손실: 0.056925\t최선의 손실: 0.045711\t정확도: 98.63%\n",
+      "10\t검증 세트 손실: 0.054374\t최선의 손실: 0.045711\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.064617\t최선의 손실: 0.045711\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.054291\t최선의 손실: 0.045711\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.072076\t최선의 손실: 0.045711\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.089676\t최선의 손실: 0.045711\t정확도: 98.16%\n",
+      "15\t검증 세트 손실: 0.106082\t최선의 손실: 0.045711\t정확도: 97.97%\n",
+      "16\t검증 세트 손실: 0.048377\t최선의 손실: 0.045711\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.054448\t최선의 손실: 0.045711\t정확도: 98.67%\n",
+      "18\t검증 세트 손실: 0.043516\t최선의 손실: 0.043516\t정확도: 98.91%\n",
+      "19\t검증 세트 손실: 0.047234\t최선의 손실: 0.043516\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.086866\t최선의 손실: 0.043516\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.054887\t최선의 손실: 0.043516\t정확도: 98.91%\n",
+      "22\t검증 세트 손실: 0.070188\t최선의 손실: 0.043516\t정확도: 98.59%\n",
+      "23\t검증 세트 손실: 0.066819\t최선의 손실: 0.043516\t정확도: 98.63%\n",
+      "24\t검증 세트 손실: 0.047303\t최선의 손실: 0.043516\t정확도: 98.87%\n",
+      "25\t검증 세트 손실: 0.082098\t최선의 손실: 0.043516\t정확도: 98.51%\n",
+      "26\t검증 세트 손실: 0.069090\t최선의 손실: 0.043516\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.069150\t최선의 손실: 0.043516\t정확도: 98.44%\n",
+      "28\t검증 세트 손실: 0.078577\t최선의 손실: 0.043516\t정확도: 98.59%\n",
+      "29\t검증 세트 손실: 0.061226\t최선의 손실: 0.043516\t정확도: 98.83%\n",
+      "30\t검증 세트 손실: 0.068417\t최선의 손실: 0.043516\t정확도: 98.75%\n",
+      "31\t검증 세트 손실: 0.060865\t최선의 손실: 0.043516\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.055494\t최선의 손실: 0.043516\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.068801\t최선의 손실: 0.043516\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.062725\t최선의 손실: 0.043516\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.058445\t최선의 손실: 0.043516\t정확도: 98.71%\n",
+      "36\t검증 세트 손실: 0.057326\t최선의 손실: 0.043516\t정확도: 98.75%\n",
+      "37\t검증 세트 손실: 0.084973\t최선의 손실: 0.043516\t정확도: 98.24%\n",
+      "38\t검증 세트 손실: 0.055963\t최선의 손실: 0.043516\t정확도: 98.83%\n",
+      "39\t검증 세트 손실: 0.056920\t최선의 손실: 0.043516\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.98, n_neurons=50, total=   6.4s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.98, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.132211\t최선의 손실: 0.132211\t정확도: 97.38%\n",
-      "1\t검증 세트 손실: 0.069260\t최선의 손실: 0.069260\t정확도: 98.05%\n",
-      "2\t검증 세트 손실: 0.059102\t최선의 손실: 0.059102\t정확도: 98.48%\n",
-      "3\t검증 세트 손실: 0.051656\t최선의 손실: 0.051656\t정확도: 98.63%\n",
-      "4\t검증 세트 손실: 0.063160\t최선의 손실: 0.051656\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.075790\t최선의 손실: 0.051656\t정확도: 98.05%\n",
-      "6\t검증 세트 손실: 0.057422\t최선의 손실: 0.051656\t정확도: 98.51%\n",
-      "7\t검증 세트 손실: 0.057366\t최선의 손실: 0.051656\t정확도: 98.67%\n",
-      "8\t검증 세트 손실: 0.068182\t최선의 손실: 0.051656\t정확도: 98.40%\n",
-      "9\t검증 세트 손실: 0.045274\t최선의 손실: 0.045274\t정확도: 99.02%\n",
-      "10\t검증 세트 손실: 0.062349\t최선의 손실: 0.045274\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.054358\t최선의 손실: 0.045274\t정확도: 98.63%\n",
-      "12\t검증 세트 손실: 0.065610\t최선의 손실: 0.045274\t정확도: 98.44%\n",
-      "13\t검증 세트 손실: 0.048835\t최선의 손실: 0.045274\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.047278\t최선의 손실: 0.045274\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.047456\t최선의 손실: 0.045274\t정확도: 98.98%\n",
-      "16\t검증 세트 손실: 0.056225\t최선의 손실: 0.045274\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.062497\t최선의 손실: 0.045274\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.059860\t최선의 손실: 0.045274\t정확도: 98.87%\n",
-      "19\t검증 세트 손실: 0.064132\t최선의 손실: 0.045274\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.066890\t최선의 손실: 0.045274\t정확도: 98.59%\n",
-      "21\t검증 세트 손실: 0.052859\t최선의 손실: 0.045274\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.053546\t최선의 손실: 0.045274\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.050271\t최선의 손실: 0.045274\t정확도: 98.75%\n",
-      "24\t검증 세트 손실: 0.057781\t최선의 손실: 0.045274\t정확도: 98.87%\n",
-      "25\t검증 세트 손실: 0.108812\t최선의 손실: 0.045274\t정확도: 97.81%\n",
-      "26\t검증 세트 손실: 0.053651\t최선의 손실: 0.045274\t정확도: 98.71%\n",
-      "27\t검증 세트 손실: 0.059924\t최선의 손실: 0.045274\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.056230\t최선의 손실: 0.045274\t정확도: 98.71%\n",
-      "29\t검증 세트 손실: 0.071388\t최선의 손실: 0.045274\t정확도: 98.59%\n",
-      "30\t검증 세트 손실: 0.050406\t최선의 손실: 0.045274\t정확도: 98.94%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=50, total=   8.6s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.213854\t최선의 손실: 0.213854\t정확도: 95.82%\n",
+      "1\t검증 세트 손실: 0.063771\t최선의 손실: 0.063771\t정확도: 98.20%\n",
+      "2\t검증 세트 손실: 0.058673\t최선의 손실: 0.058673\t정확도: 98.55%\n",
+      "3\t검증 세트 손실: 0.058303\t최선의 손실: 0.058303\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.045434\t최선의 손실: 0.045434\t정확도: 98.71%\n",
+      "5\t검증 세트 손실: 0.055844\t최선의 손실: 0.045434\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.058532\t최선의 손실: 0.045434\t정확도: 98.20%\n",
+      "7\t검증 세트 손실: 0.052838\t최선의 손실: 0.045434\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.063394\t최선의 손실: 0.045434\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.041718\t최선의 손실: 0.041718\t정확도: 99.02%\n",
+      "10\t검증 세트 손실: 0.071004\t최선의 손실: 0.041718\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.057462\t최선의 손실: 0.041718\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.056396\t최선의 손실: 0.041718\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.052997\t최선의 손실: 0.041718\t정확도: 98.79%\n",
+      "14\t검증 세트 손실: 0.051012\t최선의 손실: 0.041718\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.054715\t최선의 손실: 0.041718\t정확도: 98.98%\n",
+      "16\t검증 세트 손실: 0.056088\t최선의 손실: 0.041718\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.051878\t최선의 손실: 0.041718\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.050674\t최선의 손실: 0.041718\t정확도: 98.75%\n",
+      "19\t검증 세트 손실: 0.060026\t최선의 손실: 0.041718\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.056220\t최선의 손실: 0.041718\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.083511\t최선의 손실: 0.041718\t정확도: 98.40%\n",
+      "22\t검증 세트 손실: 0.083593\t최선의 손실: 0.041718\t정확도: 98.32%\n",
+      "23\t검증 세트 손실: 0.056804\t최선의 손실: 0.041718\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.057978\t최선의 손실: 0.041718\t정확도: 99.06%\n",
+      "25\t검증 세트 손실: 0.054040\t최선의 손실: 0.041718\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.048978\t최선의 손실: 0.041718\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.043681\t최선의 손실: 0.041718\t정확도: 99.14%\n",
+      "28\t검증 세트 손실: 0.046134\t최선의 손실: 0.041718\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.055090\t최선의 손실: 0.041718\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.082211\t최선의 손실: 0.041718\t정확도: 98.44%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.98, n_neurons=50, total=   6.7s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.099809\t최선의 손실: 0.099809\t정확도: 97.89%\n",
-      "1\t검증 세트 손실: 0.102557\t최선의 손실: 0.099809\t정확도: 97.65%\n",
-      "2\t검증 세트 손실: 0.100683\t최선의 손실: 0.099809\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.064577\t최선의 손실: 0.064577\t정확도: 98.51%\n",
-      "4\t검증 세트 손실: 0.041646\t최선의 손실: 0.041646\t정확도: 98.87%\n",
-      "5\t검증 세트 손실: 0.043742\t최선의 손실: 0.041646\t정확도: 99.02%\n",
-      "6\t검증 세트 손실: 0.036152\t최선의 손실: 0.036152\t정확도: 99.02%\n",
-      "7\t검증 세트 손실: 0.050014\t최선의 손실: 0.036152\t정확도: 98.94%\n",
-      "8\t검증 세트 손실: 0.055131\t최선의 손실: 0.036152\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.051995\t최선의 손실: 0.036152\t정확도: 98.83%\n",
-      "10\t검증 세트 손실: 0.065939\t최선의 손실: 0.036152\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.048808\t최선의 손실: 0.036152\t정확도: 98.83%\n",
-      "12\t검증 세트 손실: 0.076167\t최선의 손실: 0.036152\t정확도: 98.71%\n",
-      "13\t검증 세트 손실: 0.060907\t최선의 손실: 0.036152\t정확도: 98.51%\n",
-      "14\t검증 세트 손실: 0.058568\t최선의 손실: 0.036152\t정확도: 98.87%\n",
-      "15\t검증 세트 손실: 0.057393\t최선의 손실: 0.036152\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.062035\t최선의 손실: 0.036152\t정확도: 98.87%\n",
-      "17\t검증 세트 손실: 0.053957\t최선의 손실: 0.036152\t정확도: 98.87%\n",
-      "18\t검증 세트 손실: 0.049565\t최선의 손실: 0.036152\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.039060\t최선의 손실: 0.036152\t정확도: 99.10%\n",
-      "20\t검증 세트 손실: 0.071344\t최선의 손실: 0.036152\t정확도: 98.51%\n",
-      "21\t검증 세트 손실: 0.063464\t최선의 손실: 0.036152\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.041385\t최선의 손실: 0.036152\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.039041\t최선의 손실: 0.036152\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.055461\t최선의 손실: 0.036152\t정확도: 98.75%\n",
-      "25\t검증 세트 손실: 0.039824\t최선의 손실: 0.036152\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.069074\t최선의 손실: 0.036152\t정확도: 98.51%\n",
-      "27\t검증 세트 손실: 0.050838\t최선의 손실: 0.036152\t정확도: 98.94%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.02, batch_norm_momentum=0.98, n_neurons=50, total=   7.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.084799\t최선의 손실: 0.084799\t정확도: 98.01%\n",
+      "1\t검증 세트 손실: 0.139311\t최선의 손실: 0.084799\t정확도: 97.58%\n",
+      "2\t검증 세트 손실: 0.064946\t최선의 손실: 0.064946\t정확도: 98.28%\n",
+      "3\t검증 세트 손실: 0.051408\t최선의 손실: 0.051408\t정확도: 98.40%\n",
+      "4\t검증 세트 손실: 0.052306\t최선의 손실: 0.051408\t정확도: 98.67%\n",
+      "5\t검증 세트 손실: 0.044925\t최선의 손실: 0.044925\t정확도: 98.63%\n",
+      "6\t검증 세트 손실: 0.044967\t최선의 손실: 0.044925\t정확도: 98.83%\n",
+      "7\t검증 세트 손실: 0.043314\t최선의 손실: 0.043314\t정확도: 99.14%\n",
+      "8\t검증 세트 손실: 0.053872\t최선의 손실: 0.043314\t정확도: 98.79%\n",
+      "9\t검증 세트 손실: 0.049529\t최선의 손실: 0.043314\t정확도: 99.06%\n",
+      "10\t검증 세트 손실: 0.046229\t최선의 손실: 0.043314\t정확도: 98.91%\n",
+      "11\t검증 세트 손실: 0.060731\t최선의 손실: 0.043314\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.058213\t최선의 손실: 0.043314\t정확도: 98.48%\n",
+      "13\t검증 세트 손실: 0.056809\t최선의 손실: 0.043314\t정확도: 98.48%\n",
+      "14\t검증 세트 손실: 0.047647\t최선의 손실: 0.043314\t정확도: 98.79%\n",
+      "15\t검증 세트 손실: 0.070820\t최선의 손실: 0.043314\t정확도: 98.63%\n",
+      "16\t검증 세트 손실: 0.051094\t최선의 손실: 0.043314\t정확도: 98.91%\n",
+      "17\t검증 세트 손실: 0.054431\t최선의 손실: 0.043314\t정확도: 98.91%\n",
+      "18\t검증 세트 손실: 0.052506\t최선의 손실: 0.043314\t정확도: 99.02%\n",
+      "19\t검증 세트 손실: 0.047031\t최선의 손실: 0.043314\t정확도: 98.98%\n",
+      "20\t검증 세트 손실: 0.052852\t최선의 손실: 0.043314\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.042752\t최선의 손실: 0.042752\t정확도: 99.02%\n",
+      "22\t검증 세트 손실: 0.059583\t최선의 손실: 0.042752\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.038408\t최선의 손실: 0.038408\t정확도: 99.26%\n",
+      "24\t검증 세트 손실: 0.053986\t최선의 손실: 0.038408\t정확도: 99.06%\n",
+      "25\t검증 세트 손실: 0.064286\t최선의 손실: 0.038408\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.059326\t최선의 손실: 0.038408\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.047345\t최선의 손실: 0.038408\t정확도: 99.22%\n",
+      "28\t검증 세트 손실: 0.044879\t최선의 손실: 0.038408\t정확도: 99.14%\n",
+      "29\t검증 세트 손실: 0.051620\t최선의 손실: 0.038408\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.050484\t최선의 손실: 0.038408\t정확도: 99.10%\n",
+      "31\t검증 세트 손실: 0.052213\t최선의 손실: 0.038408\t정확도: 98.83%\n",
+      "32\t검증 세트 손실: 0.094945\t최선의 손실: 0.038408\t정확도: 98.44%\n",
+      "33\t검증 세트 손실: 0.063699\t최선의 손실: 0.038408\t정확도: 98.75%\n",
+      "34\t검증 세트 손실: 0.042543\t최선의 손실: 0.038408\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.053537\t최선의 손실: 0.038408\t정확도: 99.18%\n",
+      "36\t검증 세트 손실: 0.046573\t최선의 손실: 0.038408\t정확도: 99.30%\n",
+      "37\t검증 세트 손실: 0.052535\t최선의 손실: 0.038408\t정확도: 99.14%\n",
+      "38\t검증 세트 손실: 0.063129\t최선의 손실: 0.038408\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.051969\t최선의 손실: 0.038408\t정확도: 99.10%\n",
+      "40\t검증 세트 손실: 0.056391\t최선의 손실: 0.038408\t정확도: 98.83%\n",
+      "41\t검증 세트 손실: 0.051401\t최선의 손실: 0.038408\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.052303\t최선의 손실: 0.038408\t정확도: 98.98%\n",
+      "43\t검증 세트 손실: 0.053917\t최선의 손실: 0.038408\t정확도: 99.02%\n",
+      "44\t검증 세트 손실: 0.069120\t최선의 손실: 0.038408\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=160, total=  21.4s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.121633\t최선의 손실: 0.121633\t정확도: 97.65%\n",
-      "1\t검증 세트 손실: 0.090760\t최선의 손실: 0.090760\t정확도: 98.24%\n",
-      "2\t검증 세트 손실: 0.060906\t최선의 손실: 0.060906\t정확도: 98.36%\n",
-      "3\t검증 세트 손실: 0.075383\t최선의 손실: 0.060906\t정확도: 98.24%\n",
-      "4\t검증 세트 손실: 0.051189\t최선의 손실: 0.051189\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.040895\t최선의 손실: 0.040895\t정확도: 98.94%\n",
-      "6\t검증 세트 손실: 0.042624\t최선의 손실: 0.040895\t정확도: 98.75%\n",
-      "7\t검증 세트 손실: 0.037884\t최선의 손실: 0.037884\t정확도: 98.79%\n",
-      "8\t검증 세트 손실: 0.051395\t최선의 손실: 0.037884\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.043971\t최선의 손실: 0.037884\t정확도: 99.10%\n",
-      "10\t검증 세트 손실: 0.046663\t최선의 손실: 0.037884\t정확도: 99.06%\n",
-      "11\t검증 세트 손실: 0.027325\t최선의 손실: 0.027325\t정확도: 99.18%\n",
-      "12\t검증 세트 손실: 0.068295\t최선의 손실: 0.027325\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.036877\t최선의 손실: 0.027325\t정확도: 99.18%\n",
-      "14\t검증 세트 손실: 0.036956\t최선의 손실: 0.027325\t정확도: 99.02%\n",
-      "15\t검증 세트 손실: 0.041401\t최선의 손실: 0.027325\t정확도: 99.06%\n",
-      "16\t검증 세트 손실: 0.050751\t최선의 손실: 0.027325\t정확도: 98.83%\n",
-      "17\t검증 세트 손실: 0.046844\t최선의 손실: 0.027325\t정확도: 98.83%\n",
-      "18\t검증 세트 손실: 0.041638\t최선의 손실: 0.027325\t정확도: 99.02%\n",
-      "19\t검증 세트 손실: 0.050151\t최선의 손실: 0.027325\t정확도: 98.79%\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=160, total=  35.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.152589\t최선의 손실: 0.152589\t정확도: 96.52%\n",
+      "1\t검증 세트 손실: 0.066641\t최선의 손실: 0.066641\t정확도: 98.16%\n",
+      "2\t검증 세트 손실: 0.048475\t최선의 손실: 0.048475\t정확도: 98.48%\n",
+      "3\t검증 세트 손실: 0.057917\t최선의 손실: 0.048475\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.091609\t최선의 손실: 0.048475\t정확도: 98.20%\n",
+      "5\t검증 세트 손실: 0.048813\t최선의 손실: 0.048475\t정확도: 98.83%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20\t검증 세트 손실: 0.045392\t최선의 손실: 0.027325\t정확도: 98.98%\n",
-      "21\t검증 세트 손실: 0.053537\t최선의 손실: 0.027325\t정확도: 99.10%\n",
-      "22\t검증 세트 손실: 0.047140\t최선의 손실: 0.027325\t정확도: 99.06%\n",
-      "23\t검증 세트 손실: 0.042533\t최선의 손실: 0.027325\t정확도: 99.18%\n",
-      "24\t검증 세트 손실: 0.040182\t최선의 손실: 0.027325\t정확도: 99.02%\n",
-      "25\t검증 세트 손실: 0.040391\t최선의 손실: 0.027325\t정확도: 99.02%\n",
-      "26\t검증 세트 손실: 0.069265\t최선의 손실: 0.027325\t정확도: 98.55%\n",
-      "27\t검증 세트 손실: 0.039987\t최선의 손실: 0.027325\t정확도: 98.98%\n",
-      "28\t검증 세트 손실: 0.062627\t최선의 손실: 0.027325\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.048854\t최선의 손실: 0.027325\t정확도: 98.94%\n",
-      "30\t검증 세트 손실: 0.050926\t최선의 손실: 0.027325\t정확도: 99.22%\n",
-      "31\t검증 세트 손실: 0.066723\t최선의 손실: 0.027325\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.041954\t최선의 손실: 0.027325\t정확도: 98.94%\n",
+      "6\t검증 세트 손실: 0.048777\t최선의 손실: 0.048475\t정확도: 98.91%\n",
+      "7\t검증 세트 손실: 0.037165\t최선의 손실: 0.037165\t정확도: 99.14%\n",
+      "8\t검증 세트 손실: 0.046081\t최선의 손실: 0.037165\t정확도: 99.02%\n",
+      "9\t검증 세트 손실: 0.063288\t최선의 손실: 0.037165\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.056553\t최선의 손실: 0.037165\t정확도: 98.67%\n",
+      "11\t검증 세트 손실: 0.050754\t최선의 손실: 0.037165\t정확도: 98.83%\n",
+      "12\t검증 세트 손실: 0.052732\t최선의 손실: 0.037165\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.048010\t최선의 손실: 0.037165\t정확도: 99.06%\n",
+      "14\t검증 세트 손실: 0.039794\t최선의 손실: 0.037165\t정확도: 99.10%\n",
+      "15\t검증 세트 손실: 0.049692\t최선의 손실: 0.037165\t정확도: 98.98%\n",
+      "16\t검증 세트 손실: 0.042703\t최선의 손실: 0.037165\t정확도: 99.14%\n",
+      "17\t검증 세트 손실: 0.053856\t최선의 손실: 0.037165\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.058080\t최선의 손실: 0.037165\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.079347\t최선의 손실: 0.037165\t정확도: 98.40%\n",
+      "20\t검증 세트 손실: 0.049026\t최선의 손실: 0.037165\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.062239\t최선의 손실: 0.037165\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.060031\t최선의 손실: 0.037165\t정확도: 98.75%\n",
+      "23\t검증 세트 손실: 0.052763\t최선의 손실: 0.037165\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.042598\t최선의 손실: 0.037165\t정확도: 99.22%\n",
+      "25\t검증 세트 손실: 0.050408\t최선의 손실: 0.037165\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.052068\t최선의 손실: 0.037165\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.045806\t최선의 손실: 0.037165\t정확도: 99.26%\n",
+      "28\t검증 세트 손실: 0.031692\t최선의 손실: 0.031692\t정확도: 99.22%\n",
+      "29\t검증 세트 손실: 0.050749\t최선의 손실: 0.031692\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.031666\t최선의 손실: 0.031666\t정확도: 99.14%\n",
+      "31\t검증 세트 손실: 0.031157\t최선의 손실: 0.031157\t정확도: 99.18%\n",
+      "32\t검증 세트 손실: 0.047237\t최선의 손실: 0.031157\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.043049\t최선의 손실: 0.031157\t정확도: 98.91%\n",
+      "34\t검증 세트 손실: 0.050080\t최선의 손실: 0.031157\t정확도: 99.06%\n",
+      "35\t검증 세트 손실: 0.039172\t최선의 손실: 0.031157\t정확도: 99.22%\n",
+      "36\t검증 세트 손실: 0.049115\t최선의 손실: 0.031157\t정확도: 98.83%\n",
+      "37\t검증 세트 손실: 0.052377\t최선의 손실: 0.031157\t정확도: 98.94%\n",
+      "38\t검증 세트 손실: 0.043241\t최선의 손실: 0.031157\t정확도: 99.10%\n",
+      "39\t검증 세트 손실: 0.030910\t최선의 손실: 0.030910\t정확도: 99.18%\n",
+      "40\t검증 세트 손실: 0.034429\t최선의 손실: 0.030910\t정확도: 99.26%\n",
+      "41\t검증 세트 손실: 0.047494\t최선의 손실: 0.030910\t정확도: 99.10%\n",
+      "42\t검증 세트 손실: 0.037417\t최선의 손실: 0.030910\t정확도: 99.26%\n",
+      "43\t검증 세트 손실: 0.037431\t최선의 손실: 0.030910\t정확도: 99.26%\n",
+      "44\t검증 세트 손실: 0.067214\t최선의 손실: 0.030910\t정확도: 98.79%\n",
+      "45\t검증 세트 손실: 0.046348\t최선의 손실: 0.030910\t정확도: 98.98%\n",
+      "46\t검증 세트 손실: 0.049231\t최선의 손실: 0.030910\t정확도: 99.10%\n",
+      "47\t검증 세트 손실: 0.051206\t최선의 손실: 0.030910\t정확도: 99.06%\n",
+      "48\t검증 세트 손실: 0.067522\t최선의 손실: 0.030910\t정확도: 98.55%\n",
+      "49\t검증 세트 손실: 0.055208\t최선의 손실: 0.030910\t정확도: 98.83%\n",
+      "50\t검증 세트 손실: 0.047343\t최선의 손실: 0.030910\t정확도: 99.22%\n",
+      "51\t검증 세트 손실: 0.044135\t최선의 손실: 0.030910\t정확도: 99.26%\n",
+      "52\t검증 세트 손실: 0.047007\t최선의 손실: 0.030910\t정확도: 99.02%\n",
+      "53\t검증 세트 손실: 0.042780\t최선의 손실: 0.030910\t정확도: 99.22%\n",
+      "54\t검증 세트 손실: 0.035180\t최선의 손실: 0.030910\t정확도: 99.37%\n",
+      "55\t검증 세트 손실: 0.049428\t최선의 손실: 0.030910\t정확도: 99.06%\n",
+      "56\t검증 세트 손실: 0.036936\t최선의 손실: 0.030910\t정확도: 99.18%\n",
+      "57\t검증 세트 손실: 0.037086\t최선의 손실: 0.030910\t정확도: 99.22%\n",
+      "58\t검증 세트 손실: 0.051966\t최선의 손실: 0.030910\t정확도: 99.06%\n",
+      "59\t검증 세트 손실: 0.044551\t최선의 손실: 0.030910\t정확도: 99.18%\n",
+      "60\t검증 세트 손실: 0.041249\t최선의 손실: 0.030910\t정확도: 99.34%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=160, total=  25.1s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.143821\t최선의 손실: 0.143821\t정확도: 96.83%\n",
-      "1\t검증 세트 손실: 0.074188\t최선의 손실: 0.074188\t정확도: 98.20%\n",
-      "2\t검증 세트 손실: 0.043997\t최선의 손실: 0.043997\t정확도: 98.71%\n",
-      "3\t검증 세트 손실: 0.045538\t최선의 손실: 0.043997\t정확도: 98.63%\n",
-      "4\t검증 세트 손실: 0.029455\t최선의 손실: 0.029455\t정확도: 99.06%\n",
-      "5\t검증 세트 손실: 0.036175\t최선의 손실: 0.029455\t정확도: 98.79%\n",
-      "6\t검증 세트 손실: 0.045915\t최선의 손실: 0.029455\t정확도: 98.91%\n",
-      "7\t검증 세트 손실: 0.072363\t최선의 손실: 0.029455\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.049782\t최선의 손실: 0.029455\t정확도: 98.87%\n",
-      "9\t검증 세트 손실: 0.047710\t최선의 손실: 0.029455\t정확도: 98.67%\n",
-      "10\t검증 세트 손실: 0.044349\t최선의 손실: 0.029455\t정확도: 98.98%\n",
-      "11\t검증 세트 손실: 0.065938\t최선의 손실: 0.029455\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.040379\t최선의 손실: 0.029455\t정확도: 98.94%\n",
-      "13\t검증 세트 손실: 0.036963\t최선의 손실: 0.029455\t정확도: 98.98%\n",
-      "14\t검증 세트 손실: 0.088642\t최선의 손실: 0.029455\t정확도: 98.28%\n",
-      "15\t검증 세트 손실: 0.053936\t최선의 손실: 0.029455\t정확도: 98.94%\n",
-      "16\t검증 세트 손실: 0.062766\t최선의 손실: 0.029455\t정확도: 98.40%\n",
-      "17\t검증 세트 손실: 0.041744\t최선의 손실: 0.029455\t정확도: 99.02%\n",
-      "18\t검증 세트 손실: 0.047018\t최선의 손실: 0.029455\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.070653\t최선의 손실: 0.029455\t정확도: 98.16%\n",
-      "20\t검증 세트 손실: 0.043268\t최선의 손실: 0.029455\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.057222\t최선의 손실: 0.029455\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.051047\t최선의 손실: 0.029455\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.023522\t최선의 손실: 0.023522\t정확도: 99.34%\n",
-      "24\t검증 세트 손실: 0.032566\t최선의 손실: 0.023522\t정확도: 99.22%\n",
-      "25\t검증 세트 손실: 0.024262\t최선의 손실: 0.023522\t정확도: 99.37%\n",
-      "26\t검증 세트 손실: 0.035253\t최선의 손실: 0.023522\t정확도: 99.14%\n",
-      "27\t검증 세트 손실: 0.026921\t최선의 손실: 0.023522\t정확도: 99.14%\n",
-      "28\t검증 세트 손실: 0.030872\t최선의 손실: 0.023522\t정확도: 99.41%\n",
-      "29\t검증 세트 손실: 0.040542\t최선의 손실: 0.023522\t정확도: 99.10%\n",
-      "30\t검증 세트 손실: 0.031807\t최선의 손실: 0.023522\t정확도: 99.30%\n",
-      "31\t검증 세트 손실: 0.028871\t최선의 손실: 0.023522\t정확도: 99.41%\n",
-      "32\t검증 세트 손실: 0.044266\t최선의 손실: 0.023522\t정확도: 99.06%\n",
-      "33\t검증 세트 손실: 0.038577\t최선의 손실: 0.023522\t정확도: 98.98%\n",
-      "34\t검증 세트 손실: 0.056749\t최선의 손실: 0.023522\t정확도: 98.91%\n",
-      "35\t검증 세트 손실: 0.060264\t최선의 손실: 0.023522\t정확도: 99.06%\n",
-      "36\t검증 세트 손실: 0.053790\t최선의 손실: 0.023522\t정확도: 98.79%\n",
-      "37\t검증 세트 손실: 0.044742\t최선의 손실: 0.023522\t정확도: 99.10%\n",
-      "38\t검증 세트 손실: 0.031090\t최선의 손실: 0.023522\t정확도: 99.30%\n",
-      "39\t검증 세트 손실: 0.036079\t최선의 손실: 0.023522\t정확도: 99.14%\n",
-      "40\t검증 세트 손실: 0.039978\t최선의 손실: 0.023522\t정확도: 99.18%\n",
-      "41\t검증 세트 손실: 0.046408\t최선의 손실: 0.023522\t정확도: 99.06%\n",
-      "42\t검증 세트 손실: 0.036865\t최선의 손실: 0.023522\t정확도: 99.30%\n",
-      "43\t검증 세트 손실: 0.056004\t최선의 손실: 0.023522\t정확도: 99.02%\n",
-      "44\t검증 세트 손실: 0.046881\t최선의 손실: 0.023522\t정확도: 99.06%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=160, total=  46.9s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.099057\t최선의 손실: 0.099057\t정확도: 97.58%\n",
+      "1\t검증 세트 손실: 0.057936\t최선의 손실: 0.057936\t정확도: 98.48%\n",
+      "2\t검증 세트 손실: 0.069850\t최선의 손실: 0.057936\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.054781\t최선의 손실: 0.054781\t정확도: 98.75%\n",
+      "4\t검증 세트 손실: 0.036577\t최선의 손실: 0.036577\t정확도: 99.14%\n",
+      "5\t검증 세트 손실: 0.042539\t최선의 손실: 0.036577\t정확도: 98.83%\n",
+      "6\t검증 세트 손실: 0.052452\t최선의 손실: 0.036577\t정확도: 98.71%\n",
+      "7\t검증 세트 손실: 0.051174\t최선의 손실: 0.036577\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.043023\t최선의 손실: 0.036577\t정확도: 98.91%\n",
+      "9\t검증 세트 손실: 0.058102\t최선의 손실: 0.036577\t정확도: 98.91%\n",
+      "10\t검증 세트 손실: 0.045111\t최선의 손실: 0.036577\t정확도: 98.75%\n",
+      "11\t검증 세트 손실: 0.057733\t최선의 손실: 0.036577\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.045594\t최선의 손실: 0.036577\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.041318\t최선의 손실: 0.036577\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.046061\t최선의 손실: 0.036577\t정확도: 99.18%\n",
+      "15\t검증 세트 손실: 0.055090\t최선의 손실: 0.036577\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.062471\t최선의 손실: 0.036577\t정확도: 98.75%\n",
+      "17\t검증 세트 손실: 0.058418\t최선의 손실: 0.036577\t정확도: 98.94%\n",
+      "18\t검증 세트 손실: 0.056053\t최선의 손실: 0.036577\t정확도: 98.94%\n",
+      "19\t검증 세트 손실: 0.040588\t최선의 손실: 0.036577\t정확도: 99.26%\n",
+      "20\t검증 세트 손실: 0.052022\t최선의 손실: 0.036577\t정확도: 99.22%\n",
+      "21\t검증 세트 손실: 0.050366\t최선의 손실: 0.036577\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.042717\t최선의 손실: 0.036577\t정확도: 99.22%\n",
+      "23\t검증 세트 손실: 0.034326\t최선의 손실: 0.034326\t정확도: 99.14%\n",
+      "24\t검증 세트 손실: 0.029079\t최선의 손실: 0.029079\t정확도: 99.26%\n",
+      "25\t검증 세트 손실: 0.047359\t최선의 손실: 0.029079\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.042310\t최선의 손실: 0.029079\t정확도: 99.18%\n",
+      "27\t검증 세트 손실: 0.045284\t최선의 손실: 0.029079\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.032511\t최선의 손실: 0.029079\t정확도: 99.30%\n",
+      "29\t검증 세트 손실: 0.030979\t최선의 손실: 0.029079\t정확도: 99.10%\n",
+      "30\t검증 세트 손실: 0.047930\t최선의 손실: 0.029079\t정확도: 98.87%\n",
+      "31\t검증 세트 손실: 0.041515\t최선의 손실: 0.029079\t정확도: 99.18%\n",
+      "32\t검증 세트 손실: 0.049346\t최선의 손실: 0.029079\t정확도: 99.10%\n",
+      "33\t검증 세트 손실: 0.037683\t최선의 손실: 0.029079\t정확도: 99.10%\n",
+      "34\t검증 세트 손실: 0.076145\t최선의 손실: 0.029079\t정확도: 98.71%\n",
+      "35\t검증 세트 손실: 0.050864\t최선의 손실: 0.029079\t정확도: 98.87%\n",
+      "36\t검증 세트 손실: 0.033240\t최선의 손실: 0.029079\t정확도: 99.14%\n",
+      "37\t검증 세트 손실: 0.051695\t최선의 손실: 0.029079\t정확도: 98.91%\n",
+      "38\t검증 세트 손실: 0.036021\t최선의 손실: 0.029079\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.045270\t최선의 손실: 0.029079\t정확도: 99.18%\n",
+      "40\t검증 세트 손실: 0.041446\t최선의 손실: 0.029079\t정확도: 99.10%\n",
+      "41\t검증 세트 손실: 0.029420\t최선의 손실: 0.029079\t정확도: 99.30%\n",
+      "42\t검증 세트 손실: 0.031106\t최선의 손실: 0.029079\t정확도: 99.34%\n",
+      "43\t검증 세트 손실: 0.046610\t최선의 손실: 0.029079\t정확도: 99.06%\n",
+      "44\t검증 세트 손실: 0.049976\t최선의 손실: 0.029079\t정확도: 99.18%\n",
+      "45\t검증 세트 손실: 0.047625\t최선의 손실: 0.029079\t정확도: 98.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.99, n_neurons=160, total=  33.5s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.145385\t최선의 손실: 0.145385\t정확도: 95.43%\n",
-      "1\t검증 세트 손실: 0.098029\t최선의 손실: 0.098029\t정확도: 96.68%\n",
-      "2\t검증 세트 손실: 0.092794\t최선의 손실: 0.092794\t정확도: 97.22%\n",
-      "3\t검증 세트 손실: 0.085606\t최선의 손실: 0.085606\t정확도: 97.26%\n",
-      "4\t검증 세트 손실: 0.075756\t최선의 손실: 0.075756\t정확도: 97.54%\n",
-      "5\t검증 세트 손실: 0.127433\t최선의 손실: 0.075756\t정확도: 96.40%\n",
-      "6\t검증 세트 손실: 0.072414\t최선의 손실: 0.072414\t정확도: 97.93%\n",
-      "7\t검증 세트 손실: 0.092096\t최선의 손실: 0.072414\t정확도: 97.77%\n",
-      "8\t검증 세트 손실: 0.069594\t최선의 손실: 0.069594\t정확도: 98.08%\n",
-      "9\t검증 세트 손실: 0.059457\t최선의 손실: 0.059457\t정확도: 98.40%\n",
-      "10\t검증 세트 손실: 0.059573\t최선의 손실: 0.059457\t정확도: 98.48%\n",
-      "11\t검증 세트 손실: 0.066084\t최선의 손실: 0.059457\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.050499\t최선의 손실: 0.050499\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.060358\t최선의 손실: 0.050499\t정확도: 98.55%\n",
-      "14\t검증 세트 손실: 0.077169\t최선의 손실: 0.050499\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.086418\t최선의 손실: 0.050499\t정확도: 98.20%\n",
-      "16\t검증 세트 손실: 0.097558\t최선의 손실: 0.050499\t정확도: 97.81%\n",
-      "17\t검증 세트 손실: 0.058095\t최선의 손실: 0.050499\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.054133\t최선의 손실: 0.050499\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.054473\t최선의 손실: 0.050499\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.058798\t최선의 손실: 0.050499\t정확도: 98.55%\n",
-      "21\t검증 세트 손실: 0.063749\t최선의 손실: 0.050499\t정확도: 98.51%\n",
-      "22\t검증 세트 손실: 0.073280\t최선의 손실: 0.050499\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.075807\t최선의 손실: 0.050499\t정확도: 98.55%\n",
-      "24\t검증 세트 손실: 0.068879\t최선의 손실: 0.050499\t정확도: 98.71%\n",
-      "25\t검증 세트 손실: 0.073654\t최선의 손실: 0.050499\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.072752\t최선의 손실: 0.050499\t정확도: 98.55%\n",
-      "27\t검증 세트 손실: 0.080952\t최선의 손실: 0.050499\t정확도: 98.16%\n",
-      "28\t검증 세트 손실: 0.063769\t최선의 손실: 0.050499\t정확도: 98.59%\n",
-      "29\t검증 세트 손실: 0.074715\t최선의 손실: 0.050499\t정확도: 98.59%\n",
-      "30\t검증 세트 손실: 0.089112\t최선의 손실: 0.050499\t정확도: 98.40%\n",
-      "31\t검증 세트 손실: 0.076401\t최선의 손실: 0.050499\t정확도: 98.44%\n",
-      "32\t검증 세트 손실: 0.066472\t최선의 손실: 0.050499\t정확도: 98.67%\n",
-      "33\t검증 세트 손실: 0.064671\t최선의 손실: 0.050499\t정확도: 98.91%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=160, total=  35.7s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.156844\t최선의 손실: 0.156844\t정확도: 95.62%\n",
+      "1\t검증 세트 손실: 0.091185\t최선의 손실: 0.091185\t정확도: 97.03%\n",
+      "2\t검증 세트 손실: 0.095744\t최선의 손실: 0.091185\t정확도: 97.19%\n",
+      "3\t검증 세트 손실: 0.072913\t최선의 손실: 0.072913\t정확도: 97.65%\n",
+      "4\t검증 세트 손실: 0.072213\t최선의 손실: 0.072213\t정확도: 97.97%\n",
+      "5\t검증 세트 손실: 0.140449\t최선의 손실: 0.072213\t정확도: 96.21%\n",
+      "6\t검증 세트 손실: 0.066621\t최선의 손실: 0.066621\t정확도: 97.93%\n",
+      "7\t검증 세트 손실: 0.082047\t최선의 손실: 0.066621\t정확도: 97.89%\n",
+      "8\t검증 세트 손실: 0.094382\t최선의 손실: 0.066621\t정확도: 97.26%\n",
+      "9\t검증 세트 손실: 0.061278\t최선의 손실: 0.061278\t정확도: 98.40%\n",
+      "10\t검증 세트 손실: 0.071998\t최선의 손실: 0.061278\t정확도: 98.24%\n",
+      "11\t검증 세트 손실: 0.064475\t최선의 손실: 0.061278\t정확도: 97.97%\n",
+      "12\t검증 세트 손실: 0.067975\t최선의 손실: 0.061278\t정확도: 98.01%\n",
+      "13\t검증 세트 손실: 0.076488\t최선의 손실: 0.061278\t정확도: 98.05%\n",
+      "14\t검증 세트 손실: 0.093151\t최선의 손실: 0.061278\t정확도: 97.65%\n",
+      "15\t검증 세트 손실: 0.088873\t최선의 손실: 0.061278\t정확도: 97.54%\n",
+      "16\t검증 세트 손실: 0.079892\t최선의 손실: 0.061278\t정확도: 98.12%\n",
+      "17\t검증 세트 손실: 0.073544\t최선의 손실: 0.061278\t정확도: 98.24%\n",
+      "18\t검증 세트 손실: 0.074617\t최선의 손실: 0.061278\t정확도: 98.01%\n",
+      "19\t검증 세트 손실: 0.073107\t최선의 손실: 0.061278\t정확도: 98.32%\n",
+      "20\t검증 세트 손실: 0.085943\t최선의 손실: 0.061278\t정확도: 97.89%\n",
+      "21\t검증 세트 손실: 0.083374\t최선의 손실: 0.061278\t정확도: 98.24%\n",
+      "22\t검증 세트 손실: 0.083410\t최선의 손실: 0.061278\t정확도: 98.28%\n",
+      "23\t검증 세트 손실: 0.072515\t최선의 손실: 0.061278\t정확도: 98.28%\n",
+      "24\t검증 세트 손실: 0.076070\t최선의 손실: 0.061278\t정확도: 98.24%\n",
+      "25\t검증 세트 손실: 0.080725\t최선의 손실: 0.061278\t정확도: 98.01%\n",
+      "26\t검증 세트 손실: 0.074257\t최선의 손실: 0.061278\t정확도: 98.16%\n",
+      "27\t검증 세트 손실: 0.068803\t최선의 손실: 0.061278\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.080603\t최선의 손실: 0.061278\t정확도: 98.05%\n",
+      "29\t검증 세트 손실: 0.087916\t최선의 손실: 0.061278\t정확도: 97.97%\n",
+      "30\t검증 세트 손실: 0.068466\t최선의 손실: 0.061278\t정확도: 98.36%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=50, total=   6.7s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.139768\t최선의 손실: 0.139768\t정확도: 95.97%\n",
-      "1\t검증 세트 손실: 0.100034\t최선의 손실: 0.100034\t정확도: 97.26%\n",
-      "2\t검증 세트 손실: 0.083780\t최선의 손실: 0.083780\t정확도: 97.26%\n",
-      "3\t검증 세트 손실: 0.076184\t최선의 손실: 0.076184\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.069100\t최선의 손실: 0.069100\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.064878\t최선의 손실: 0.064878\t정확도: 97.65%\n",
-      "6\t검증 세트 손실: 0.059625\t최선의 손실: 0.059625\t정확도: 97.97%\n",
-      "7\t검증 세트 손실: 0.067175\t최선의 손실: 0.059625\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.053954\t최선의 손실: 0.053954\t정확도: 98.48%\n",
-      "9\t검증 세트 손실: 0.066931\t최선의 손실: 0.053954\t정확도: 98.24%\n",
-      "10\t검증 세트 손실: 0.055768\t최선의 손실: 0.053954\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.077122\t최선의 손실: 0.053954\t정확도: 98.20%\n",
-      "12\t검증 세트 손실: 0.068106\t최선의 손실: 0.053954\t정확도: 98.08%\n",
-      "13\t검증 세트 손실: 0.067678\t최선의 손실: 0.053954\t정확도: 98.24%\n",
-      "14\t검증 세트 손실: 0.064386\t최선의 손실: 0.053954\t정확도: 98.48%\n",
-      "15\t검증 세트 손실: 0.090695\t최선의 손실: 0.053954\t정확도: 97.97%\n",
-      "16\t검증 세트 손실: 0.062393\t최선의 손실: 0.053954\t정확도: 98.44%\n",
-      "17\t검증 세트 손실: 0.058573\t최선의 손실: 0.053954\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.054466\t최선의 손실: 0.053954\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.066521\t최선의 손실: 0.053954\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.069521\t최선의 손실: 0.053954\t정확도: 98.55%\n",
-      "21\t검증 세트 손실: 0.086003\t최선의 손실: 0.053954\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.057717\t최선의 손실: 0.053954\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.063686\t최선의 손실: 0.053954\t정확도: 98.48%\n",
-      "24\t검증 세트 손실: 0.058807\t최선의 손실: 0.053954\t정확도: 98.51%\n",
-      "25\t검증 세트 손실: 0.070073\t최선의 손실: 0.053954\t정확도: 98.63%\n",
-      "26\t검증 세트 손실: 0.073257\t최선의 손실: 0.053954\t정확도: 98.55%\n",
-      "27\t검증 세트 손실: 0.069889\t최선의 손실: 0.053954\t정확도: 98.67%\n",
-      "28\t검증 세트 손실: 0.057232\t최선의 손실: 0.053954\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.072309\t최선의 손실: 0.053954\t정확도: 98.51%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=50, total=   6.1s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.165314\t최선의 손실: 0.165314\t정확도: 95.07%\n",
-      "1\t검증 세트 손실: 0.100708\t최선의 손실: 0.100708\t정확도: 96.91%\n",
-      "2\t검증 세트 손실: 0.084099\t최선의 손실: 0.084099\t정확도: 96.95%\n",
-      "3\t검증 세트 손실: 0.069079\t최선의 손실: 0.069079\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.112034\t최선의 손실: 0.069079\t정확도: 96.76%\n",
-      "5\t검증 세트 손실: 0.092555\t최선의 손실: 0.069079\t정확도: 97.30%\n",
-      "6\t검증 세트 손실: 0.058497\t최선의 손실: 0.058497\t정확도: 98.16%\n",
-      "7\t검증 세트 손실: 0.066064\t최선의 손실: 0.058497\t정확도: 98.08%\n",
-      "8\t검증 세트 손실: 0.052122\t최선의 손실: 0.052122\t정확도: 98.40%\n",
-      "9\t검증 세트 손실: 0.047402\t최선의 손실: 0.047402\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.061639\t최선의 손실: 0.047402\t정확도: 98.05%\n",
-      "11\t검증 세트 손실: 0.043929\t최선의 손실: 0.043929\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.051608\t최선의 손실: 0.043929\t정확도: 98.40%\n",
-      "13\t검증 세트 손실: 0.065430\t최선의 손실: 0.043929\t정확도: 98.32%\n",
-      "14\t검증 세트 손실: 0.057128\t최선의 손실: 0.043929\t정확도: 98.16%\n",
-      "15\t검증 세트 손실: 0.058462\t최선의 손실: 0.043929\t정확도: 98.40%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=50, total=   6.5s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.179213\t최선의 손실: 0.179213\t정확도: 94.49%\n",
+      "1\t검증 세트 손실: 0.089666\t최선의 손실: 0.089666\t정확도: 97.15%\n",
+      "2\t검증 세트 손실: 0.079928\t최선의 손실: 0.079928\t정확도: 97.50%\n",
+      "3\t검증 세트 손실: 0.072080\t최선의 손실: 0.072080\t정확도: 97.81%\n",
+      "4\t검증 세트 손실: 0.068001\t최선의 손실: 0.068001\t정확도: 98.01%\n",
+      "5\t검증 세트 손실: 0.066494\t최선의 손실: 0.066494\t정확도: 97.77%\n",
+      "6\t검증 세트 손실: 0.070096\t최선의 손실: 0.066494\t정확도: 97.97%\n",
+      "7\t검증 세트 손실: 0.077662\t최선의 손실: 0.066494\t정확도: 97.85%\n",
+      "8\t검증 세트 손실: 0.088638\t최선의 손실: 0.066494\t정확도: 97.69%\n",
+      "9\t검증 세트 손실: 0.066893\t최선의 손실: 0.066494\t정확도: 98.16%\n",
+      "10\t검증 세트 손실: 0.062447\t최선의 손실: 0.062447\t정확도: 98.36%\n",
+      "11\t검증 세트 손실: 0.067889\t최선의 손실: 0.062447\t정확도: 98.28%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "16\t검증 세트 손실: 0.053314\t최선의 손실: 0.043929\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.055683\t최선의 손실: 0.043929\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.059099\t최선의 손실: 0.043929\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.089699\t최선의 손실: 0.043929\t정확도: 98.24%\n",
-      "20\t검증 세트 손실: 0.069401\t최선의 손실: 0.043929\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.052915\t최선의 손실: 0.043929\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.055911\t최선의 손실: 0.043929\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.056775\t최선의 손실: 0.043929\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.088662\t최선의 손실: 0.043929\t정확도: 98.24%\n",
-      "25\t검증 세트 손실: 0.058958\t최선의 손실: 0.043929\t정확도: 98.63%\n",
-      "26\t검증 세트 손실: 0.052383\t최선의 손실: 0.043929\t정확도: 98.98%\n",
-      "27\t검증 세트 손실: 0.066355\t최선의 손실: 0.043929\t정확도: 98.71%\n",
-      "28\t검증 세트 손실: 0.058125\t최선의 손실: 0.043929\t정확도: 98.87%\n",
-      "29\t검증 세트 손실: 0.059356\t최선의 손실: 0.043929\t정확도: 98.71%\n",
-      "30\t검증 세트 손실: 0.055873\t최선의 손실: 0.043929\t정확도: 98.55%\n",
-      "31\t검증 세트 손실: 0.054903\t최선의 손실: 0.043929\t정확도: 98.67%\n",
-      "32\t검증 세트 손실: 0.093293\t최선의 손실: 0.043929\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.061717\t최선의 손실: 0.061717\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.082593\t최선의 손실: 0.061717\t정확도: 97.77%\n",
+      "14\t검증 세트 손실: 0.057345\t최선의 손실: 0.057345\t정확도: 98.51%\n",
+      "15\t검증 세트 손실: 0.076093\t최선의 손실: 0.057345\t정확도: 98.51%\n",
+      "16\t검증 세트 손실: 0.073575\t최선의 손실: 0.057345\t정확도: 98.40%\n",
+      "17\t검증 세트 손실: 0.073913\t최선의 손실: 0.057345\t정확도: 98.40%\n",
+      "18\t검증 세트 손실: 0.066565\t최선의 손실: 0.057345\t정확도: 98.55%\n",
+      "19\t검증 세트 손실: 0.073315\t최선의 손실: 0.057345\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.082725\t최선의 손실: 0.057345\t정확도: 98.55%\n",
+      "21\t검증 세트 손실: 0.086592\t최선의 손실: 0.057345\t정확도: 98.40%\n",
+      "22\t검증 세트 손실: 0.085665\t최선의 손실: 0.057345\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.072130\t최선의 손실: 0.057345\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.067758\t최선의 손실: 0.057345\t정확도: 98.75%\n",
+      "25\t검증 세트 손실: 0.085129\t최선의 손실: 0.057345\t정확도: 98.44%\n",
+      "26\t검증 세트 손실: 0.074461\t최선의 손실: 0.057345\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.058741\t최선의 손실: 0.057345\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.061242\t최선의 손실: 0.057345\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.058648\t최선의 손실: 0.057345\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.067934\t최선의 손실: 0.057345\t정확도: 98.87%\n",
+      "31\t검증 세트 손실: 0.077883\t최선의 손실: 0.057345\t정확도: 98.79%\n",
+      "32\t검증 세트 손실: 0.069408\t최선의 손실: 0.057345\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.124454\t최선의 손실: 0.057345\t정확도: 98.40%\n",
+      "34\t검증 세트 손실: 0.088713\t최선의 손실: 0.057345\t정확도: 98.55%\n",
+      "35\t검증 세트 손실: 0.078442\t최선의 손실: 0.057345\t정확도: 98.63%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.9, n_neurons=50, total=   6.6s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.162600\t최선의 손실: 0.162600\t정확도: 95.31%\n",
-      "1\t검증 세트 손실: 0.090670\t최선의 손실: 0.090670\t정확도: 97.03%\n",
-      "2\t검증 세트 손실: 0.082959\t최선의 손실: 0.082959\t정확도: 97.50%\n",
-      "3\t검증 세트 손실: 0.074291\t최선의 손실: 0.074291\t정확도: 97.58%\n",
-      "4\t검증 세트 손실: 0.064504\t최선의 손실: 0.064504\t정확도: 97.85%\n",
-      "5\t검증 세트 손실: 0.079311\t최선의 손실: 0.064504\t정확도: 97.77%\n",
-      "6\t검증 세트 손실: 0.067132\t최선의 손실: 0.064504\t정확도: 98.08%\n",
-      "7\t검증 세트 손실: 0.073225\t최선의 손실: 0.064504\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.054826\t최선의 손실: 0.054826\t정확도: 98.40%\n",
-      "9\t검증 세트 손실: 0.054884\t최선의 손실: 0.054826\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.060996\t최선의 손실: 0.054826\t정확도: 98.08%\n",
-      "11\t검증 세트 손실: 0.099645\t최선의 손실: 0.054826\t정확도: 97.89%\n",
-      "12\t검증 세트 손실: 0.060208\t최선의 손실: 0.054826\t정확도: 98.28%\n",
-      "13\t검증 세트 손실: 0.049029\t최선의 손실: 0.049029\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.051747\t최선의 손실: 0.049029\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.059806\t최선의 손실: 0.049029\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.077167\t최선의 손실: 0.049029\t정확도: 98.05%\n",
-      "17\t검증 세트 손실: 0.061388\t최선의 손실: 0.049029\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.079926\t최선의 손실: 0.049029\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.070152\t최선의 손실: 0.049029\t정확도: 98.51%\n",
-      "20\t검증 세트 손실: 0.071026\t최선의 손실: 0.049029\t정확도: 98.40%\n",
-      "21\t검증 세트 손실: 0.057440\t최선의 손실: 0.049029\t정확도: 98.28%\n",
-      "22\t검증 세트 손실: 0.052292\t최선의 손실: 0.049029\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.069895\t최선의 손실: 0.049029\t정확도: 98.55%\n",
-      "24\t검증 세트 손실: 0.056562\t최선의 손실: 0.049029\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.085635\t최선의 손실: 0.049029\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.083296\t최선의 손실: 0.049029\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.060027\t최선의 손실: 0.049029\t정확도: 98.67%\n",
-      "28\t검증 세트 손실: 0.075896\t최선의 손실: 0.049029\t정확도: 98.36%\n",
-      "29\t검증 세트 손실: 0.062594\t최선의 손실: 0.049029\t정확도: 98.75%\n",
-      "30\t검증 세트 손실: 0.081288\t최선의 손실: 0.049029\t정확도: 98.59%\n",
-      "31\t검증 세트 손실: 0.062337\t최선의 손실: 0.049029\t정확도: 98.63%\n",
-      "32\t검증 세트 손실: 0.087182\t최선의 손실: 0.049029\t정확도: 98.12%\n",
-      "33\t검증 세트 손실: 0.050145\t최선의 손실: 0.049029\t정확도: 98.71%\n",
-      "34\t검증 세트 손실: 0.060555\t최선의 손실: 0.049029\t정확도: 98.63%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=50, total=   7.4s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.159377\t최선의 손실: 0.159377\t정확도: 94.49%\n",
+      "1\t검증 세트 손실: 0.094089\t최선의 손실: 0.094089\t정확도: 97.22%\n",
+      "2\t검증 세트 손실: 0.079831\t최선의 손실: 0.079831\t정확도: 97.81%\n",
+      "3\t검증 세트 손실: 0.070357\t최선의 손실: 0.070357\t정확도: 97.89%\n",
+      "4\t검증 세트 손실: 0.079704\t최선의 손실: 0.070357\t정확도: 97.73%\n",
+      "5\t검증 세트 손실: 0.061437\t최선의 손실: 0.061437\t정확도: 97.97%\n",
+      "6\t검증 세트 손실: 0.058507\t최선의 손실: 0.058507\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.050435\t최선의 손실: 0.050435\t정확도: 98.24%\n",
+      "8\t검증 세트 손실: 0.058137\t최선의 손실: 0.050435\t정확도: 98.28%\n",
+      "9\t검증 세트 손실: 0.060758\t최선의 손실: 0.050435\t정확도: 98.28%\n",
+      "10\t검증 세트 손실: 0.071140\t최선의 손실: 0.050435\t정확도: 98.01%\n",
+      "11\t검증 세트 손실: 0.055306\t최선의 손실: 0.050435\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.047806\t최선의 손실: 0.047806\t정확도: 98.79%\n",
+      "13\t검증 세트 손실: 0.063857\t최선의 손실: 0.047806\t정확도: 98.36%\n",
+      "14\t검증 세트 손실: 0.073359\t최선의 손실: 0.047806\t정확도: 98.24%\n",
+      "15\t검증 세트 손실: 0.068553\t최선의 손실: 0.047806\t정확도: 98.44%\n",
+      "16\t검증 세트 손실: 0.044919\t최선의 손실: 0.044919\t정확도: 98.59%\n",
+      "17\t검증 세트 손실: 0.051298\t최선의 손실: 0.044919\t정확도: 98.55%\n",
+      "18\t검증 세트 손실: 0.055715\t최선의 손실: 0.044919\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.070301\t최선의 손실: 0.044919\t정확도: 98.44%\n",
+      "20\t검증 세트 손실: 0.061103\t최선의 손실: 0.044919\t정확도: 98.71%\n",
+      "21\t검증 세트 손실: 0.047031\t최선의 손실: 0.044919\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.087924\t최선의 손실: 0.044919\t정확도: 97.73%\n",
+      "23\t검증 세트 손실: 0.068942\t최선의 손실: 0.044919\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.058679\t최선의 손실: 0.044919\t정확도: 98.55%\n",
+      "25\t검증 세트 손실: 0.073182\t최선의 손실: 0.044919\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.053898\t최선의 손실: 0.044919\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.061242\t최선의 손실: 0.044919\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.087533\t최선의 손실: 0.044919\t정확도: 98.36%\n",
+      "29\t검증 세트 손실: 0.099440\t최선의 손실: 0.044919\t정확도: 98.24%\n",
+      "30\t검증 세트 손실: 0.068251\t최선의 손실: 0.044919\t정확도: 98.51%\n",
+      "31\t검증 세트 손실: 0.053898\t최선의 손실: 0.044919\t정확도: 98.98%\n",
+      "32\t검증 세트 손실: 0.068974\t최선의 손실: 0.044919\t정확도: 98.71%\n",
+      "33\t검증 세트 손실: 0.087507\t최선의 손실: 0.044919\t정확도: 97.89%\n",
+      "34\t검증 세트 손실: 0.088117\t최선의 손실: 0.044919\t정확도: 98.55%\n",
+      "35\t검증 세트 손실: 0.053607\t최선의 손실: 0.044919\t정확도: 98.87%\n",
+      "36\t검증 세트 손실: 0.059511\t최선의 손실: 0.044919\t정확도: 98.91%\n",
+      "37\t검증 세트 손실: 0.054171\t최선의 손실: 0.044919\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=90, total=   7.3s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.147777\t최선의 손실: 0.147777\t정확도: 95.97%\n",
-      "1\t검증 세트 손실: 0.080823\t최선의 손실: 0.080823\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.064257\t최선의 손실: 0.064257\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.061337\t최선의 손실: 0.061337\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.084518\t최선의 손실: 0.061337\t정확도: 97.73%\n",
-      "5\t검증 세트 손실: 0.057816\t최선의 손실: 0.057816\t정확도: 98.24%\n",
-      "6\t검증 세트 손실: 0.054750\t최선의 손실: 0.054750\t정확도: 98.36%\n",
-      "7\t검증 세트 손실: 0.069078\t최선의 손실: 0.054750\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.053544\t최선의 손실: 0.053544\t정확도: 98.59%\n",
-      "9\t검증 세트 손실: 0.066817\t최선의 손실: 0.053544\t정확도: 98.48%\n",
-      "10\t검증 세트 손실: 0.057693\t최선의 손실: 0.053544\t정확도: 98.44%\n",
-      "11\t검증 세트 손실: 0.064269\t최선의 손실: 0.053544\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.045958\t최선의 손실: 0.045958\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.060352\t최선의 손실: 0.045958\t정확도: 98.40%\n",
-      "14\t검증 세트 손실: 0.079964\t최선의 손실: 0.045958\t정확도: 98.20%\n",
-      "15\t검증 세트 손실: 0.060772\t최선의 손실: 0.045958\t정확도: 98.63%\n",
-      "16\t검증 세트 손실: 0.062582\t최선의 손실: 0.045958\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.072012\t최선의 손실: 0.045958\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.062042\t최선의 손실: 0.045958\t정확도: 98.55%\n",
-      "19\t검증 세트 손실: 0.058763\t최선의 손실: 0.045958\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.063524\t최선의 손실: 0.045958\t정확도: 98.40%\n",
-      "21\t검증 세트 손실: 0.066741\t최선의 손실: 0.045958\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.073144\t최선의 손실: 0.045958\t정확도: 98.59%\n",
-      "23\t검증 세트 손실: 0.079976\t최선의 손실: 0.045958\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.074059\t최선의 손실: 0.045958\t정확도: 98.51%\n",
-      "25\t검증 세트 손실: 0.074241\t최선의 손실: 0.045958\t정확도: 98.28%\n",
-      "26\t검증 세트 손실: 0.068694\t최선의 손실: 0.045958\t정확도: 98.48%\n",
-      "27\t검증 세트 손실: 0.079602\t최선의 손실: 0.045958\t정확도: 98.71%\n",
-      "28\t검증 세트 손실: 0.075854\t최선의 손실: 0.045958\t정확도: 98.63%\n",
-      "29\t검증 세트 손실: 0.081585\t최선의 손실: 0.045958\t정확도: 98.55%\n",
-      "30\t검증 세트 손실: 0.063648\t최선의 손실: 0.045958\t정확도: 98.83%\n",
-      "31\t검증 세트 손실: 0.068812\t최선의 손실: 0.045958\t정확도: 98.48%\n",
-      "32\t검증 세트 손실: 0.070808\t최선의 손실: 0.045958\t정확도: 98.55%\n",
-      "33\t검증 세트 손실: 0.081336\t최선의 손실: 0.045958\t정확도: 98.83%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=50, total=   7.8s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.127901\t최선의 손실: 0.127901\t정확도: 96.40%\n",
+      "1\t검증 세트 손실: 0.077788\t최선의 손실: 0.077788\t정확도: 97.62%\n",
+      "2\t검증 세트 손실: 0.074908\t최선의 손실: 0.074908\t정확도: 97.62%\n",
+      "3\t검증 세트 손실: 0.064550\t최선의 손실: 0.064550\t정확도: 98.16%\n",
+      "4\t검증 세트 손실: 0.062282\t최선의 손실: 0.062282\t정확도: 98.05%\n",
+      "5\t검증 세트 손실: 0.068280\t최선의 손실: 0.062282\t정확도: 97.81%\n",
+      "6\t검증 세트 손실: 0.063382\t최선의 손실: 0.062282\t정확도: 98.05%\n",
+      "7\t검증 세트 손실: 0.077420\t최선의 손실: 0.062282\t정확도: 98.01%\n",
+      "8\t검증 세트 손실: 0.052871\t최선의 손실: 0.052871\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.056443\t최선의 손실: 0.052871\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.050707\t최선의 손실: 0.050707\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.077185\t최선의 손실: 0.050707\t정확도: 98.32%\n",
+      "12\t검증 세트 손실: 0.068348\t최선의 손실: 0.050707\t정확도: 98.20%\n",
+      "13\t검증 세트 손실: 0.077914\t최선의 손실: 0.050707\t정확도: 97.93%\n",
+      "14\t검증 세트 손실: 0.058825\t최선의 손실: 0.050707\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.080160\t최선의 손실: 0.050707\t정확도: 98.16%\n",
+      "16\t검증 세트 손실: 0.065129\t최선의 손실: 0.050707\t정확도: 98.55%\n",
+      "17\t검증 세트 손실: 0.092252\t최선의 손실: 0.050707\t정확도: 98.16%\n",
+      "18\t검증 세트 손실: 0.063728\t최선의 손실: 0.050707\t정확도: 98.40%\n",
+      "19\t검증 세트 손실: 0.057678\t최선의 손실: 0.050707\t정확도: 98.59%\n",
+      "20\t검증 세트 손실: 0.057497\t최선의 손실: 0.050707\t정확도: 98.71%\n",
+      "21\t검증 세트 손실: 0.080308\t최선의 손실: 0.050707\t정확도: 97.93%\n",
+      "22\t검증 세트 손실: 0.055157\t최선의 손실: 0.050707\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.067587\t최선의 손실: 0.050707\t정확도: 98.63%\n",
+      "24\t검증 세트 손실: 0.071486\t최선의 손실: 0.050707\t정확도: 98.55%\n",
+      "25\t검증 세트 손실: 0.072537\t최선의 손실: 0.050707\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.064644\t최선의 손실: 0.050707\t정확도: 98.63%\n",
+      "27\t검증 세트 손실: 0.074307\t최선의 손실: 0.050707\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.063573\t최선의 손실: 0.050707\t정확도: 98.59%\n",
+      "29\t검증 세트 손실: 0.095083\t최선의 손실: 0.050707\t정확도: 98.24%\n",
+      "30\t검증 세트 손실: 0.059401\t최선의 손실: 0.050707\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.076137\t최선의 손실: 0.050707\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=90, total=   7.1s\n",
-      "[CV] batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.116946\t최선의 손실: 0.116946\t정확도: 96.79%\n",
-      "1\t검증 세트 손실: 0.072551\t최선의 손실: 0.072551\t정확도: 97.50%\n",
-      "2\t검증 세트 손실: 0.062147\t최선의 손실: 0.062147\t정확도: 98.28%\n",
-      "3\t검증 세트 손실: 0.061100\t최선의 손실: 0.061100\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.063050\t최선의 손실: 0.061100\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.065335\t최선의 손실: 0.061100\t정확도: 98.24%\n",
-      "6\t검증 세트 손실: 0.054822\t최선의 손실: 0.054822\t정확도: 98.55%\n",
-      "7\t검증 세트 손실: 0.052150\t최선의 손실: 0.052150\t정확도: 98.71%\n",
-      "8\t검증 세트 손실: 0.055068\t최선의 손실: 0.052150\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.058907\t최선의 손실: 0.052150\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.068395\t최선의 손실: 0.052150\t정확도: 98.28%\n",
-      "11\t검증 세트 손실: 0.069834\t최선의 손실: 0.052150\t정확도: 98.63%\n",
-      "12\t검증 세트 손실: 0.057206\t최선의 손실: 0.052150\t정확도: 98.48%\n",
-      "13\t검증 세트 손실: 0.058404\t최선의 손실: 0.052150\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.072459\t최선의 손실: 0.052150\t정확도: 97.89%\n",
-      "15\t검증 세트 손실: 0.076392\t최선의 손실: 0.052150\t정확도: 98.40%\n",
-      "16\t검증 세트 손실: 0.045432\t최선의 손실: 0.045432\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.061272\t최선의 손실: 0.045432\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.070484\t최선의 손실: 0.045432\t정확도: 98.48%\n",
-      "19\t검증 세트 손실: 0.071446\t최선의 손실: 0.045432\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.063697\t최선의 손실: 0.045432\t정확도: 98.59%\n",
-      "21\t검증 세트 손실: 0.068260\t최선의 손실: 0.045432\t정확도: 98.71%\n",
-      "22\t검증 세트 손실: 0.066775\t최선의 손실: 0.045432\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.063700\t최선의 손실: 0.045432\t정확도: 98.67%\n",
-      "24\t검증 세트 손실: 0.063139\t최선의 손실: 0.045432\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.068888\t최선의 손실: 0.045432\t정확도: 98.44%\n",
-      "26\t검증 세트 손실: 0.058492\t최선의 손실: 0.045432\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.068402\t최선의 손실: 0.045432\t정확도: 98.63%\n",
-      "28\t검증 세트 손실: 0.059810\t최선의 손실: 0.045432\t정확도: 98.71%\n",
-      "29\t검증 세트 손실: 0.049701\t최선의 손실: 0.045432\t정확도: 98.75%\n",
-      "30\t검증 세트 손실: 0.056529\t최선의 손실: 0.045432\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.052768\t최선의 손실: 0.045432\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.060967\t최선의 손실: 0.045432\t정확도: 98.75%\n",
-      "33\t검증 세트 손실: 0.072694\t최선의 손실: 0.045432\t정확도: 98.75%\n",
-      "34\t검증 세트 손실: 0.081118\t최선의 손실: 0.045432\t정확도: 98.55%\n",
-      "35\t검증 세트 손실: 0.073760\t최선의 손실: 0.045432\t정확도: 98.91%\n",
-      "36\t검증 세트 손실: 0.119214\t최선의 손실: 0.045432\t정확도: 97.81%\n",
-      "37\t검증 세트 손실: 0.064996\t최선의 손실: 0.045432\t정확도: 98.83%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=90, total=   7.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.125106\t최선의 손실: 0.125106\t정확도: 96.29%\n",
+      "1\t검증 세트 손실: 0.077279\t최선의 손실: 0.077279\t정확도: 97.65%\n",
+      "2\t검증 세트 손실: 0.058493\t최선의 손실: 0.058493\t정확도: 98.08%\n",
+      "3\t검증 세트 손실: 0.055457\t최선의 손실: 0.055457\t정확도: 98.16%\n",
+      "4\t검증 세트 손실: 0.073401\t최선의 손실: 0.055457\t정확도: 98.05%\n",
+      "5\t검증 세트 손실: 0.050215\t최선의 손실: 0.050215\t정확도: 98.36%\n",
+      "6\t검증 세트 손실: 0.053312\t최선의 손실: 0.050215\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.059130\t최선의 손실: 0.050215\t정확도: 98.36%\n",
+      "8\t검증 세트 손실: 0.049490\t최선의 손실: 0.049490\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.059782\t최선의 손실: 0.049490\t정확도: 98.24%\n",
+      "10\t검증 세트 손실: 0.071585\t최선의 손실: 0.049490\t정확도: 98.40%\n",
+      "11\t검증 세트 손실: 0.050226\t최선의 손실: 0.049490\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.058605\t최선의 손실: 0.049490\t정확도: 98.51%\n",
+      "13\t검증 세트 손실: 0.068625\t최선의 손실: 0.049490\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.084423\t최선의 손실: 0.049490\t정확도: 97.93%\n",
+      "15\t검증 세트 손실: 0.094974\t최선의 손실: 0.049490\t정확도: 98.01%\n",
+      "16\t검증 세트 손실: 0.056237\t최선의 손실: 0.049490\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.058291\t최선의 손실: 0.049490\t정확도: 98.51%\n",
+      "18\t검증 세트 손실: 0.061583\t최선의 손실: 0.049490\t정확도: 98.63%\n",
+      "19\t검증 세트 손실: 0.054132\t최선의 손실: 0.049490\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.074941\t최선의 손실: 0.049490\t정확도: 98.44%\n",
+      "21\t검증 세트 손실: 0.081716\t최선의 손실: 0.049490\t정확도: 98.44%\n",
+      "22\t검증 세트 손실: 0.058802\t최선의 손실: 0.049490\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.062441\t최선의 손실: 0.049490\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.057612\t최선의 손실: 0.049490\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.049616\t최선의 손실: 0.049490\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.058112\t최선의 손실: 0.049490\t정확도: 98.59%\n",
+      "27\t검증 세트 손실: 0.060132\t최선의 손실: 0.049490\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.077606\t최선의 손실: 0.049490\t정확도: 98.32%\n",
+      "29\t검증 세트 손실: 0.068543\t최선의 손실: 0.049490\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=90, total=   7.7s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.057350\t최선의 손실: 0.057350\t정확도: 98.16%\n",
-      "1\t검증 세트 손실: 0.052210\t최선의 손실: 0.052210\t정확도: 98.44%\n",
-      "2\t검증 세트 손실: 0.041407\t최선의 손실: 0.041407\t정확도: 98.59%\n",
-      "3\t검증 세트 손실: 0.062770\t최선의 손실: 0.041407\t정확도: 98.01%\n",
-      "4\t검증 세트 손실: 0.041731\t최선의 손실: 0.041407\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.066983\t최선의 손실: 0.041407\t정확도: 98.40%\n",
-      "6\t검증 세트 손실: 0.036567\t최선의 손실: 0.036567\t정확도: 98.98%\n",
-      "7\t검증 세트 손실: 0.037629\t최선의 손실: 0.036567\t정확도: 98.67%\n",
-      "8\t검증 세트 손실: 0.033490\t최선의 손실: 0.033490\t정확도: 99.10%\n",
-      "9\t검증 세트 손실: 0.038353\t최선의 손실: 0.033490\t정확도: 99.06%\n",
-      "10\t검증 세트 손실: 0.050078\t최선의 손실: 0.033490\t정확도: 98.75%\n",
-      "11\t검증 세트 손실: 0.064079\t최선의 손실: 0.033490\t정확도: 98.79%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=90, total=   7.3s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.117131\t최선의 손실: 0.117131\t정확도: 96.33%\n",
+      "1\t검증 세트 손실: 0.078611\t최선의 손실: 0.078611\t정확도: 97.22%\n",
+      "2\t검증 세트 손실: 0.061023\t최선의 손실: 0.061023\t정확도: 98.20%\n",
+      "3\t검증 세트 손실: 0.059868\t최선의 손실: 0.059868\t정확도: 98.12%\n",
+      "4\t검증 세트 손실: 0.047879\t최선의 손실: 0.047879\t정확도: 98.59%\n",
+      "5\t검증 세트 손실: 0.057601\t최선의 손실: 0.047879\t정확도: 98.28%\n",
+      "6\t검증 세트 손실: 0.044868\t최선의 손실: 0.044868\t정확도: 98.67%\n",
+      "7\t검증 세트 손실: 0.047893\t최선의 손실: 0.044868\t정확도: 98.55%\n",
+      "8\t검증 세트 손실: 0.047836\t최선의 손실: 0.044868\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.060929\t최선의 손실: 0.044868\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.065200\t최선의 손실: 0.044868\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.052305\t최선의 손실: 0.044868\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.047940\t최선의 손실: 0.044868\t정확도: 98.83%\n",
+      "13\t검증 세트 손실: 0.050482\t최선의 손실: 0.044868\t정확도: 98.71%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 0.044563\t최선의 손실: 0.033490\t정확도: 98.79%\n",
-      "13\t검증 세트 손실: 0.048065\t최선의 손실: 0.033490\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.043103\t최선의 손실: 0.033490\t정확도: 99.10%\n",
-      "15\t검증 세트 손실: 0.058623\t최선의 손실: 0.033490\t정확도: 98.67%\n",
-      "16\t검증 세트 손실: 0.055288\t최선의 손실: 0.033490\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.052883\t최선의 손실: 0.033490\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.044118\t최선의 손실: 0.033490\t정확도: 99.18%\n",
-      "19\t검증 세트 손실: 0.060354\t최선의 손실: 0.033490\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.049443\t최선의 손실: 0.033490\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.052344\t최선의 손실: 0.033490\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.051711\t최선의 손실: 0.033490\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.047097\t최선의 손실: 0.033490\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.044155\t최선의 손실: 0.033490\t정확도: 98.98%\n",
-      "25\t검증 세트 손실: 0.042681\t최선의 손실: 0.033490\t정확도: 99.14%\n",
-      "26\t검증 세트 손실: 0.049689\t최선의 손실: 0.033490\t정확도: 99.10%\n",
-      "27\t검증 세트 손실: 0.047500\t최선의 손실: 0.033490\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.042153\t최선의 손실: 0.033490\t정확도: 99.14%\n",
-      "29\t검증 세트 손실: 0.056237\t최선의 손실: 0.033490\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.065794\t최선의 손실: 0.044868\t정확도: 98.55%\n",
+      "15\t검증 세트 손실: 0.058689\t최선의 손실: 0.044868\t정확도: 98.67%\n",
+      "16\t검증 세트 손실: 0.060284\t최선의 손실: 0.044868\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.055110\t최선의 손실: 0.044868\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.056835\t최선의 손실: 0.044868\t정확도: 98.59%\n",
+      "19\t검증 세트 손실: 0.055508\t최선의 손실: 0.044868\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.085006\t최선의 손실: 0.044868\t정확도: 98.12%\n",
+      "21\t검증 세트 손실: 0.062964\t최선의 손실: 0.044868\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.056669\t최선의 손실: 0.044868\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.054541\t최선의 손실: 0.044868\t정확도: 98.83%\n",
+      "24\t검증 세트 손실: 0.071928\t최선의 손실: 0.044868\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.059803\t최선의 손실: 0.044868\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.058841\t최선의 손실: 0.044868\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.055096\t최선의 손실: 0.044868\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.9, n_neurons=140, total=   6.0s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.071531\t최선의 손실: 0.071531\t정확도: 97.77%\n",
-      "1\t검증 세트 손실: 0.051191\t최선의 손실: 0.051191\t정확도: 98.44%\n",
-      "2\t검증 세트 손실: 0.045037\t최선의 손실: 0.045037\t정확도: 98.48%\n",
-      "3\t검증 세트 손실: 0.042416\t최선의 손실: 0.042416\t정확도: 98.71%\n",
-      "4\t검증 세트 손실: 0.058339\t최선의 손실: 0.042416\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.063301\t최선의 손실: 0.042416\t정확도: 98.28%\n",
-      "6\t검증 세트 손실: 0.044525\t최선의 손실: 0.042416\t정확도: 98.63%\n",
-      "7\t검증 세트 손실: 0.048066\t최선의 손실: 0.042416\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.057444\t최선의 손실: 0.042416\t정확도: 98.55%\n",
-      "9\t검증 세트 손실: 0.046540\t최선의 손실: 0.042416\t정확도: 98.79%\n",
-      "10\t검증 세트 손실: 0.036395\t최선의 손실: 0.036395\t정확도: 98.98%\n",
-      "11\t검증 세트 손실: 0.043674\t최선의 손실: 0.036395\t정확도: 98.94%\n",
-      "12\t검증 세트 손실: 0.041701\t최선의 손실: 0.036395\t정확도: 99.06%\n",
-      "13\t검증 세트 손실: 0.045239\t최선의 손실: 0.036395\t정확도: 98.98%\n",
-      "14\t검증 세트 손실: 0.057276\t최선의 손실: 0.036395\t정확도: 98.83%\n",
-      "15\t검증 세트 손실: 0.070784\t최선의 손실: 0.036395\t정확도: 98.51%\n",
-      "16\t검증 세트 손실: 0.062867\t최선의 손실: 0.036395\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.043773\t최선의 손실: 0.036395\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.044634\t최선의 손실: 0.036395\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.042422\t최선의 손실: 0.036395\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.039209\t최선의 손실: 0.036395\t정확도: 99.14%\n",
-      "21\t검증 세트 손실: 0.046498\t최선의 손실: 0.036395\t정확도: 99.06%\n",
-      "22\t검증 세트 손실: 0.051640\t최선의 손실: 0.036395\t정확도: 98.98%\n",
-      "23\t검증 세트 손실: 0.048751\t최선의 손실: 0.036395\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.048855\t최선의 손실: 0.036395\t정확도: 98.98%\n",
-      "25\t검증 세트 손실: 0.053193\t최선의 손실: 0.036395\t정확도: 98.71%\n",
-      "26\t검증 세트 손실: 0.047260\t최선의 손실: 0.036395\t정확도: 99.06%\n",
-      "27\t검증 세트 손실: 0.052309\t최선의 손실: 0.036395\t정확도: 98.91%\n",
-      "28\t검증 세트 손실: 0.041078\t최선의 손실: 0.036395\t정확도: 99.10%\n",
-      "29\t검증 세트 손실: 0.040839\t최선의 손실: 0.036395\t정확도: 98.98%\n",
-      "30\t검증 세트 손실: 0.044943\t최선의 손실: 0.036395\t정확도: 99.02%\n",
-      "31\t검증 세트 손실: 0.043244\t최선의 손실: 0.036395\t정확도: 98.94%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=90, total=   6.6s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.063335\t최선의 손실: 0.063335\t정확도: 98.32%\n",
+      "1\t검증 세트 손실: 0.038618\t최선의 손실: 0.038618\t정확도: 98.79%\n",
+      "2\t검증 세트 손실: 0.049504\t최선의 손실: 0.038618\t정확도: 98.36%\n",
+      "3\t검증 세트 손실: 0.032138\t최선의 손실: 0.032138\t정확도: 98.94%\n",
+      "4\t검증 세트 손실: 0.049675\t최선의 손실: 0.032138\t정확도: 98.59%\n",
+      "5\t검증 세트 손실: 0.038140\t최선의 손실: 0.032138\t정확도: 98.71%\n",
+      "6\t검증 세트 손실: 0.041621\t최선의 손실: 0.032138\t정확도: 98.91%\n",
+      "7\t검증 세트 손실: 0.035683\t최선의 손실: 0.032138\t정확도: 98.98%\n",
+      "8\t검증 세트 손실: 0.035443\t최선의 손실: 0.032138\t정확도: 98.91%\n",
+      "9\t검증 세트 손실: 0.040408\t최선의 손실: 0.032138\t정확도: 99.10%\n",
+      "10\t검증 세트 손실: 0.035757\t최선의 손실: 0.032138\t정확도: 99.14%\n",
+      "11\t검증 세트 손실: 0.047156\t최선의 손실: 0.032138\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.050210\t최선의 손실: 0.032138\t정확도: 98.67%\n",
+      "13\t검증 세트 손실: 0.042609\t최선의 손실: 0.032138\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.052826\t최선의 손실: 0.032138\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.048722\t최선의 손실: 0.032138\t정확도: 99.02%\n",
+      "16\t검증 세트 손실: 0.053785\t최선의 손실: 0.032138\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.034717\t최선의 손실: 0.032138\t정확도: 99.10%\n",
+      "18\t검증 세트 손실: 0.042915\t최선의 손실: 0.032138\t정확도: 98.87%\n",
+      "19\t검증 세트 손실: 0.031272\t최선의 손실: 0.031272\t정확도: 99.14%\n",
+      "20\t검증 세트 손실: 0.049097\t최선의 손실: 0.031272\t정확도: 99.02%\n",
+      "21\t검증 세트 손실: 0.049258\t최선의 손실: 0.031272\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.032383\t최선의 손실: 0.031272\t정확도: 99.14%\n",
+      "23\t검증 세트 손실: 0.043546\t최선의 손실: 0.031272\t정확도: 99.22%\n",
+      "24\t검증 세트 손실: 0.041894\t최선의 손실: 0.031272\t정확도: 99.14%\n",
+      "25\t검증 세트 손실: 0.064502\t최선의 손실: 0.031272\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.053228\t최선의 손실: 0.031272\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.054926\t최선의 손실: 0.031272\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.052795\t최선의 손실: 0.031272\t정확도: 98.91%\n",
+      "29\t검증 세트 손실: 0.053428\t최선의 손실: 0.031272\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.060980\t최선의 손실: 0.031272\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.047749\t최선의 손실: 0.031272\t정확도: 98.91%\n",
+      "32\t검증 세트 손실: 0.061754\t최선의 손실: 0.031272\t정확도: 98.83%\n",
+      "33\t검증 세트 손실: 0.060219\t최선의 손실: 0.031272\t정확도: 98.75%\n",
+      "34\t검증 세트 손실: 0.046827\t최선의 손실: 0.031272\t정확도: 98.67%\n",
+      "35\t검증 세트 손실: 0.049114\t최선의 손실: 0.031272\t정확도: 99.02%\n",
+      "36\t검증 세트 손실: 0.059799\t최선의 손실: 0.031272\t정확도: 98.94%\n",
+      "37\t검증 세트 손실: 0.062679\t최선의 손실: 0.031272\t정확도: 98.91%\n",
+      "38\t검증 세트 손실: 0.065169\t최선의 손실: 0.031272\t정확도: 98.87%\n",
+      "39\t검증 세트 손실: 0.057588\t최선의 손실: 0.031272\t정확도: 98.87%\n",
+      "40\t검증 세트 손실: 0.054553\t최선의 손실: 0.031272\t정확도: 98.79%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.9, n_neurons=140, total=   6.4s\n",
-      "[CV] batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.048149\t최선의 손실: 0.048149\t정확도: 98.48%\n",
-      "1\t검증 세트 손실: 0.058850\t최선의 손실: 0.048149\t정확도: 98.20%\n",
-      "2\t검증 세트 손실: 0.045019\t최선의 손실: 0.045019\t정확도: 98.55%\n",
-      "3\t검증 세트 손실: 0.040725\t최선의 손실: 0.040725\t정확도: 98.71%\n",
-      "4\t검증 세트 손실: 0.033206\t최선의 손실: 0.033206\t정확도: 98.94%\n",
-      "5\t검증 세트 손실: 0.053123\t최선의 손실: 0.033206\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.041664\t최선의 손실: 0.033206\t정확도: 98.83%\n",
-      "7\t검증 세트 손실: 0.043216\t최선의 손실: 0.033206\t정확도: 98.87%\n",
-      "8\t검증 세트 손실: 0.053148\t최선의 손실: 0.033206\t정확도: 98.67%\n",
-      "9\t검증 세트 손실: 0.046701\t최선의 손실: 0.033206\t정확도: 98.91%\n",
-      "10\t검증 세트 손실: 0.040155\t최선의 손실: 0.033206\t정확도: 98.98%\n",
-      "11\t검증 세트 손실: 0.050935\t최선의 손실: 0.033206\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.041062\t최선의 손실: 0.033206\t정확도: 98.91%\n",
-      "13\t검증 세트 손실: 0.039504\t최선의 손실: 0.033206\t정확도: 99.10%\n",
-      "14\t검증 세트 손실: 0.053432\t최선의 손실: 0.033206\t정확도: 98.87%\n",
-      "15\t검증 세트 손실: 0.031829\t최선의 손실: 0.031829\t정확도: 99.10%\n",
-      "16\t검증 세트 손실: 0.035027\t최선의 손실: 0.031829\t정확도: 98.98%\n",
-      "17\t검증 세트 손실: 0.039191\t최선의 손실: 0.031829\t정확도: 99.02%\n",
-      "18\t검증 세트 손실: 0.036108\t최선의 손실: 0.031829\t정확도: 99.26%\n",
-      "19\t검증 세트 손실: 0.051390\t최선의 손실: 0.031829\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.042165\t최선의 손실: 0.031829\t정확도: 99.02%\n",
-      "21\t검증 세트 손실: 0.038319\t최선의 손실: 0.031829\t정확도: 99.06%\n",
-      "22\t검증 세트 손실: 0.034815\t최선의 손실: 0.031829\t정확도: 99.30%\n",
-      "23\t검증 세트 손실: 0.038580\t최선의 손실: 0.031829\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.037499\t최선의 손실: 0.031829\t정확도: 99.10%\n",
-      "25\t검증 세트 손실: 0.053063\t최선의 손실: 0.031829\t정확도: 98.87%\n",
-      "26\t검증 세트 손실: 0.057284\t최선의 손실: 0.031829\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.045044\t최선의 손실: 0.031829\t정확도: 98.94%\n",
-      "28\t검증 세트 손실: 0.044698\t최선의 손실: 0.031829\t정확도: 99.06%\n",
-      "29\t검증 세트 손실: 0.041292\t최선의 손실: 0.031829\t정확도: 99.10%\n",
-      "30\t검증 세트 손실: 0.046449\t최선의 손실: 0.031829\t정확도: 99.10%\n",
-      "31\t검증 세트 손실: 0.040766\t최선의 손실: 0.031829\t정확도: 99.14%\n",
-      "32\t검증 세트 손실: 0.046359\t최선의 손실: 0.031829\t정확도: 99.10%\n",
-      "33\t검증 세트 손실: 0.055443\t최선의 손실: 0.031829\t정확도: 98.83%\n",
-      "34\t검증 세트 손실: 0.037707\t최선의 손실: 0.031829\t정확도: 99.10%\n",
-      "35\t검증 세트 손실: 0.053828\t최선의 손실: 0.031829\t정확도: 98.94%\n",
-      "36\t검증 세트 손실: 0.052368\t최선의 손실: 0.031829\t정확도: 98.91%\n",
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140, total=   8.4s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.067267\t최선의 손실: 0.067267\t정확도: 98.36%\n",
+      "1\t검증 세트 손실: 0.047964\t최선의 손실: 0.047964\t정확도: 98.48%\n",
+      "2\t검증 세트 손실: 0.040943\t최선의 손실: 0.040943\t정확도: 98.79%\n",
+      "3\t검증 세트 손실: 0.041160\t최선의 손실: 0.040943\t정확도: 98.75%\n",
+      "4\t검증 세트 손실: 0.053311\t최선의 손실: 0.040943\t정확도: 98.83%\n",
+      "5\t검증 세트 손실: 0.083492\t최선의 손실: 0.040943\t정확도: 97.97%\n",
+      "6\t검증 세트 손실: 0.046856\t최선의 손실: 0.040943\t정확도: 98.71%\n",
+      "7\t검증 세트 손실: 0.053999\t최선의 손실: 0.040943\t정확도: 98.67%\n",
+      "8\t검증 세트 손실: 0.046343\t최선의 손실: 0.040943\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.044282\t최선의 손실: 0.040943\t정확도: 99.02%\n",
+      "10\t검증 세트 손실: 0.035600\t최선의 손실: 0.035600\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.040050\t최선의 손실: 0.035600\t정확도: 99.22%\n",
+      "12\t검증 세트 손실: 0.040235\t최선의 손실: 0.035600\t정확도: 99.06%\n",
+      "13\t검증 세트 손실: 0.041725\t최선의 손실: 0.035600\t정확도: 99.18%\n",
+      "14\t검증 세트 손실: 0.047580\t최선의 손실: 0.035600\t정확도: 99.10%\n",
+      "15\t검증 세트 손실: 0.072373\t최선의 손실: 0.035600\t정확도: 98.40%\n",
+      "16\t검증 세트 손실: 0.050393\t최선의 손실: 0.035600\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.049347\t최선의 손실: 0.035600\t정확도: 98.94%\n",
+      "18\t검증 세트 손실: 0.047268\t최선의 손실: 0.035600\t정확도: 98.91%\n",
+      "19\t검증 세트 손실: 0.048110\t최선의 손실: 0.035600\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.047093\t최선의 손실: 0.035600\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.048145\t최선의 손실: 0.035600\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.045099\t최선의 손실: 0.035600\t정확도: 99.02%\n",
+      "23\t검증 세트 손실: 0.045521\t최선의 손실: 0.035600\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.037144\t최선의 손실: 0.035600\t정확도: 99.06%\n",
+      "25\t검증 세트 손실: 0.039588\t최선의 손실: 0.035600\t정확도: 99.26%\n",
+      "26\t검증 세트 손실: 0.046607\t최선의 손실: 0.035600\t정확도: 99.06%\n",
+      "27\t검증 세트 손실: 0.045448\t최선의 손실: 0.035600\t정확도: 99.10%\n",
+      "28\t검증 세트 손실: 0.071973\t최선의 손실: 0.035600\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.048649\t최선의 손실: 0.035600\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.048138\t최선의 손실: 0.035600\t정확도: 99.14%\n",
+      "31\t검증 세트 손실: 0.048046\t최선의 손실: 0.035600\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.9, n_neurons=140, total=   7.3s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.053838\t최선의 손실: 0.053838\t정확도: 98.24%\n",
-      "1\t검증 세트 손실: 0.051422\t최선의 손실: 0.051422\t정확도: 98.40%\n",
-      "2\t검증 세트 손실: 0.045939\t최선의 손실: 0.045939\t정확도: 98.51%\n",
-      "3\t검증 세트 손실: 0.056562\t최선의 손실: 0.045939\t정확도: 98.40%\n",
-      "4\t검증 세트 손실: 0.046373\t최선의 손실: 0.045939\t정확도: 98.83%\n",
-      "5\t검증 세트 손실: 0.059249\t최선의 손실: 0.045939\t정확도: 98.36%\n",
-      "6\t검증 세트 손실: 0.054935\t최선의 손실: 0.045939\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.061278\t최선의 손실: 0.045939\t정확도: 98.24%\n",
-      "8\t검증 세트 손실: 0.040643\t최선의 손실: 0.040643\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.037598\t최선의 손실: 0.037598\t정확도: 99.10%\n",
-      "10\t검증 세트 손실: 0.058536\t최선의 손실: 0.037598\t정확도: 98.55%\n",
-      "11\t검증 세트 손실: 0.046976\t최선의 손실: 0.037598\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.042965\t최선의 손실: 0.037598\t정확도: 98.87%\n",
-      "13\t검증 세트 손실: 0.046831\t최선의 손실: 0.037598\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.060359\t최선의 손실: 0.037598\t정확도: 98.94%\n",
-      "15\t검증 세트 손실: 0.055486\t최선의 손실: 0.037598\t정확도: 98.75%\n",
-      "16\t검증 세트 손실: 0.059776\t최선의 손실: 0.037598\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.054789\t최선의 손실: 0.037598\t정확도: 98.71%\n",
-      "18\t검증 세트 손실: 0.048732\t최선의 손실: 0.037598\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.057818\t최선의 손실: 0.037598\t정확도: 98.32%\n",
-      "20\t검증 세트 손실: 0.056241\t최선의 손실: 0.037598\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.052979\t최선의 손실: 0.037598\t정확도: 98.94%\n",
-      "22\t검증 세트 손실: 0.058871\t최선의 손실: 0.037598\t정확도: 98.83%\n",
-      "23\t검증 세트 손실: 0.061856\t최선의 손실: 0.037598\t정확도: 98.83%\n",
-      "24\t검증 세트 손실: 0.051811\t최선의 손실: 0.037598\t정확도: 98.98%\n",
-      "25\t검증 세트 손실: 0.072354\t최선의 손실: 0.037598\t정확도: 98.48%\n",
-      "26\t검증 세트 손실: 0.044662\t최선의 손실: 0.037598\t정확도: 99.10%\n",
-      "27\t검증 세트 손실: 0.048952\t최선의 손실: 0.037598\t정확도: 99.02%\n",
-      "28\t검증 세트 손실: 0.046777\t최선의 손실: 0.037598\t정확도: 98.87%\n",
-      "29\t검증 세트 손실: 0.043358\t최선의 손실: 0.037598\t정확도: 99.10%\n",
-      "30\t검증 세트 손실: 0.052609\t최선의 손실: 0.037598\t정확도: 98.87%\n",
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140, total=   6.8s\n",
+      "[CV] batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.054568\t최선의 손실: 0.054568\t정확도: 98.36%\n",
+      "1\t검증 세트 손실: 0.041864\t최선의 손실: 0.041864\t정확도: 98.55%\n",
+      "2\t검증 세트 손실: 0.043607\t최선의 손실: 0.041864\t정확도: 98.63%\n",
+      "3\t검증 세트 손실: 0.038952\t최선의 손실: 0.038952\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.044816\t최선의 손실: 0.038952\t정확도: 98.83%\n",
+      "5\t검증 세트 손실: 0.056749\t최선의 손실: 0.038952\t정확도: 98.36%\n",
+      "6\t검증 세트 손실: 0.033830\t최선의 손실: 0.033830\t정확도: 99.10%\n",
+      "7\t검증 세트 손실: 0.045286\t최선의 손실: 0.033830\t정확도: 98.83%\n",
+      "8\t검증 세트 손실: 0.054171\t최선의 손실: 0.033830\t정확도: 98.51%\n",
+      "9\t검증 세트 손실: 0.032349\t최선의 손실: 0.032349\t정확도: 99.10%\n",
+      "10\t검증 세트 손실: 0.039896\t최선의 손실: 0.032349\t정확도: 99.02%\n",
+      "11\t검증 세트 손실: 0.038936\t최선의 손실: 0.032349\t정확도: 99.06%\n",
+      "12\t검증 세트 손실: 0.056325\t최선의 손실: 0.032349\t정확도: 98.94%\n",
+      "13\t검증 세트 손실: 0.046094\t최선의 손실: 0.032349\t정확도: 98.91%\n",
+      "14\t검증 세트 손실: 0.050346\t최선의 손실: 0.032349\t정확도: 98.98%\n",
+      "15\t검증 세트 손실: 0.045519\t최선의 손실: 0.032349\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.039615\t최선의 손실: 0.032349\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.054653\t최선의 손실: 0.032349\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.043707\t최선의 손실: 0.032349\t정확도: 99.06%\n",
+      "19\t검증 세트 손실: 0.041024\t최선의 손실: 0.032349\t정확도: 98.83%\n",
+      "20\t검증 세트 손실: 0.043258\t최선의 손실: 0.032349\t정확도: 99.06%\n",
+      "21\t검증 세트 손실: 0.046458\t최선의 손실: 0.032349\t정확도: 99.10%\n",
+      "22\t검증 세트 손실: 0.059106\t최선의 손실: 0.032349\t정확도: 98.67%\n",
+      "23\t검증 세트 손실: 0.044273\t최선의 손실: 0.032349\t정확도: 99.18%\n",
+      "24\t검증 세트 손실: 0.061021\t최선의 손실: 0.032349\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.048934\t최선의 손실: 0.032349\t정확도: 99.18%\n",
+      "26\t검증 세트 손실: 0.053888\t최선의 손실: 0.032349\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.032923\t최선의 손실: 0.032349\t정확도: 99.37%\n",
+      "28\t검증 세트 손실: 0.046090\t최선의 손실: 0.032349\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.045241\t최선의 손실: 0.032349\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.038401\t최선의 손실: 0.032349\t정확도: 99.18%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=140, total=   6.7s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.055070\t최선의 손실: 0.055070\t정확도: 98.40%\n",
-      "1\t검증 세트 손실: 0.041821\t최선의 손실: 0.041821\t정확도: 98.55%\n",
-      "2\t검증 세트 손실: 0.037292\t최선의 손실: 0.037292\t정확도: 98.67%\n",
-      "3\t검증 세트 손실: 0.039541\t최선의 손실: 0.037292\t정확도: 98.71%\n",
-      "4\t검증 세트 손실: 0.044156\t최선의 손실: 0.037292\t정확도: 98.59%\n",
-      "5\t검증 세트 손실: 0.066392\t최선의 손실: 0.037292\t정확도: 98.24%\n",
-      "6\t검증 세트 손실: 0.045882\t최선의 손실: 0.037292\t정확도: 98.79%\n",
-      "7\t검증 세트 손실: 0.048415\t최선의 손실: 0.037292\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.051855\t최선의 손실: 0.037292\t정확도: 98.67%\n",
-      "9\t검증 세트 손실: 0.043417\t최선의 손실: 0.037292\t정확도: 98.91%\n",
-      "10\t검증 세트 손실: 0.037830\t최선의 손실: 0.037292\t정확도: 99.02%\n",
-      "11\t검증 세트 손실: 0.051323\t최선의 손실: 0.037292\t정확도: 98.87%\n",
-      "12\t검증 세트 손실: 0.043395\t최선의 손실: 0.037292\t정확도: 98.91%\n",
-      "13\t검증 세트 손실: 0.043614\t최선의 손실: 0.037292\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.039238\t최선의 손실: 0.037292\t정확도: 98.98%\n",
-      "15\t검증 세트 손실: 0.047451\t최선의 손실: 0.037292\t정확도: 98.83%\n",
-      "16\t검증 세트 손실: 0.039849\t최선의 손실: 0.037292\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.046119\t최선의 손실: 0.037292\t정확도: 98.98%\n",
-      "18\t검증 세트 손실: 0.038712\t최선의 손실: 0.037292\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.041135\t최선의 손실: 0.037292\t정확도: 98.94%\n"
+      "[CV]  batch_size=500, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.02, batch_norm_momentum=0.9, n_neurons=140, total=   6.7s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.066357\t최선의 손실: 0.066357\t정확도: 98.36%\n",
+      "1\t검증 세트 손실: 0.050730\t최선의 손실: 0.050730\t정확도: 98.51%\n",
+      "2\t검증 세트 손실: 0.041090\t최선의 손실: 0.041090\t정확도: 98.94%\n",
+      "3\t검증 세트 손실: 0.050092\t최선의 손실: 0.041090\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.032200\t최선의 손실: 0.032200\t정확도: 99.06%\n",
+      "5\t검증 세트 손실: 0.062294\t최선의 손실: 0.032200\t정확도: 98.51%\n",
+      "6\t검증 세트 손실: 0.047585\t최선의 손실: 0.032200\t정확도: 98.91%\n",
+      "7\t검증 세트 손실: 0.041306\t최선의 손실: 0.032200\t정확도: 99.02%\n",
+      "8\t검증 세트 손실: 0.035877\t최선의 손실: 0.032200\t정확도: 99.06%\n",
+      "9\t검증 세트 손실: 0.046501\t최선의 손실: 0.032200\t정확도: 98.79%\n",
+      "10\t검증 세트 손실: 0.045153\t최선의 손실: 0.032200\t정확도: 98.87%\n",
+      "11\t검증 세트 손실: 0.077506\t최선의 손실: 0.032200\t정확도: 98.36%\n",
+      "12\t검증 세트 손실: 0.040705\t최선의 손실: 0.032200\t정확도: 99.10%\n",
+      "13\t검증 세트 손실: 0.043831\t최선의 손실: 0.032200\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.053311\t최선의 손실: 0.032200\t정확도: 98.91%\n",
+      "15\t검증 세트 손실: 0.058056\t최선의 손실: 0.032200\t정확도: 99.10%\n",
+      "16\t검증 세트 손실: 0.051227\t최선의 손실: 0.032200\t정확도: 98.94%\n",
+      "17\t검증 세트 손실: 0.066968\t최선의 손실: 0.032200\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.066215\t최선의 손실: 0.032200\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.051190\t최선의 손실: 0.032200\t정확도: 98.94%\n",
+      "20\t검증 세트 손실: 0.056974\t최선의 손실: 0.032200\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.052588\t최선의 손실: 0.032200\t정확도: 98.83%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20\t검증 세트 손실: 0.049616\t최선의 손실: 0.037292\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.063578\t최선의 손실: 0.037292\t정확도: 98.48%\n",
-      "22\t검증 세트 손실: 0.045860\t최선의 손실: 0.037292\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.061486\t최선의 손실: 0.037292\t정확도: 98.59%\n",
+      "22\t검증 세트 손실: 0.048454\t최선의 손실: 0.032200\t정확도: 98.98%\n",
+      "23\t검증 세트 손실: 0.044931\t최선의 손실: 0.032200\t정확도: 99.06%\n",
+      "24\t검증 세트 손실: 0.057418\t최선의 손실: 0.032200\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.050679\t최선의 손실: 0.032200\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=140, total=   5.5s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.050259\t최선의 손실: 0.050259\t정확도: 98.40%\n",
-      "1\t검증 세트 손실: 0.038556\t최선의 손실: 0.038556\t정확도: 98.55%\n",
-      "2\t검증 세트 손실: 0.035868\t최선의 손실: 0.035868\t정확도: 98.91%\n",
-      "3\t검증 세트 손실: 0.043328\t최선의 손실: 0.035868\t정확도: 98.75%\n",
-      "4\t검증 세트 손실: 0.043032\t최선의 손실: 0.035868\t정확도: 98.94%\n",
-      "5\t검증 세트 손실: 0.057617\t최선의 손실: 0.035868\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.050700\t최선의 손실: 0.035868\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.043275\t최선의 손실: 0.035868\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.050644\t최선의 손실: 0.035868\t정확도: 98.75%\n",
-      "9\t검증 세트 손실: 0.036748\t최선의 손실: 0.035868\t정확도: 99.10%\n",
-      "10\t검증 세트 손실: 0.025810\t최선의 손실: 0.025810\t정확도: 98.98%\n",
-      "11\t검증 세트 손실: 0.031798\t최선의 손실: 0.025810\t정확도: 99.10%\n",
-      "12\t검증 세트 손실: 0.044423\t최선의 손실: 0.025810\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.036386\t최선의 손실: 0.025810\t정확도: 98.98%\n",
-      "14\t검증 세트 손실: 0.041481\t최선의 손실: 0.025810\t정확도: 99.06%\n",
-      "15\t검증 세트 손실: 0.039866\t최선의 손실: 0.025810\t정확도: 98.94%\n",
-      "16\t검증 세트 손실: 0.052641\t최선의 손실: 0.025810\t정확도: 98.63%\n",
-      "17\t검증 세트 손실: 0.063098\t최선의 손실: 0.025810\t정확도: 98.40%\n",
-      "18\t검증 세트 손실: 0.034803\t최선의 손실: 0.025810\t정확도: 99.18%\n",
-      "19\t검증 세트 손실: 0.039490\t최선의 손실: 0.025810\t정확도: 99.06%\n",
-      "20\t검증 세트 손실: 0.037150\t최선의 손실: 0.025810\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.036214\t최선의 손실: 0.025810\t정확도: 98.91%\n",
-      "22\t검증 세트 손실: 0.033651\t최선의 손실: 0.025810\t정확도: 99.10%\n",
-      "23\t검증 세트 손실: 0.038579\t최선의 손실: 0.025810\t정확도: 99.10%\n",
-      "24\t검증 세트 손실: 0.048891\t최선의 손실: 0.025810\t정확도: 98.75%\n",
-      "25\t검증 세트 손실: 0.038055\t최선의 손실: 0.025810\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.047810\t최선의 손실: 0.025810\t정확도: 98.83%\n",
-      "27\t검증 세트 손실: 0.034931\t최선의 손실: 0.025810\t정확도: 99.22%\n",
-      "28\t검증 세트 손실: 0.040127\t최선의 손실: 0.025810\t정확도: 99.14%\n",
-      "29\t검증 세트 손실: 0.041582\t최선의 손실: 0.025810\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.038112\t최선의 손실: 0.025810\t정확도: 99.18%\n",
-      "31\t검증 세트 손실: 0.024937\t최선의 손실: 0.024937\t정확도: 99.18%\n",
-      "32\t검증 세트 손실: 0.031900\t최선의 손실: 0.024937\t정확도: 99.14%\n",
-      "33\t검증 세트 손실: 0.037251\t최선의 손실: 0.024937\t정확도: 98.91%\n",
-      "34\t검증 세트 손실: 0.042376\t최선의 손실: 0.024937\t정확도: 99.06%\n",
-      "35\t검증 세트 손실: 0.033925\t최선의 손실: 0.024937\t정확도: 99.14%\n",
-      "36\t검증 세트 손실: 0.047935\t최선의 손실: 0.024937\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.036037\t최선의 손실: 0.024937\t정확도: 99.10%\n",
-      "38\t검증 세트 손실: 0.041063\t최선의 손실: 0.024937\t정확도: 99.14%\n",
-      "39\t검증 세트 손실: 0.036823\t최선의 손실: 0.024937\t정확도: 99.06%\n",
-      "40\t검증 세트 손실: 0.033315\t최선의 손실: 0.024937\t정확도: 99.06%\n",
-      "41\t검증 세트 손실: 0.032978\t최선의 손실: 0.024937\t정확도: 99.22%\n",
-      "42\t검증 세트 손실: 0.032919\t최선의 손실: 0.024937\t정확도: 99.18%\n",
-      "43\t검증 세트 손실: 0.032168\t최선의 손실: 0.024937\t정확도: 99.14%\n",
-      "44\t검증 세트 손실: 0.033843\t최선의 손실: 0.024937\t정확도: 99.18%\n",
-      "45\t검증 세트 손실: 0.033344\t최선의 손실: 0.024937\t정확도: 99.18%\n",
-      "46\t검증 세트 손실: 0.032988\t최선의 손실: 0.024937\t정확도: 99.14%\n",
-      "47\t검증 세트 손실: 0.033527\t최선의 손실: 0.024937\t정확도: 99.18%\n",
-      "48\t검증 세트 손실: 0.033660\t최선의 손실: 0.024937\t정확도: 99.30%\n",
-      "49\t검증 세트 손실: 0.033357\t최선의 손실: 0.024937\t정확도: 99.22%\n",
-      "50\t검증 세트 손실: 0.033987\t최선의 손실: 0.024937\t정확도: 99.30%\n",
-      "51\t검증 세트 손실: 0.034586\t최선의 손실: 0.024937\t정확도: 99.18%\n",
-      "52\t검증 세트 손실: 0.034153\t최선의 손실: 0.024937\t정확도: 99.34%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=140, total=   6.2s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.056921\t최선의 손실: 0.056921\t정확도: 98.08%\n",
+      "1\t검증 세트 손실: 0.046394\t최선의 손실: 0.046394\t정확도: 98.55%\n",
+      "2\t검증 세트 손실: 0.044573\t최선의 손실: 0.044573\t정확도: 98.59%\n",
+      "3\t검증 세트 손실: 0.050558\t최선의 손실: 0.044573\t정확도: 98.55%\n",
+      "4\t검증 세트 손실: 0.046283\t최선의 손실: 0.044573\t정확도: 98.67%\n",
+      "5\t검증 세트 손실: 0.049044\t최선의 손실: 0.044573\t정확도: 98.67%\n",
+      "6\t검증 세트 손실: 0.037831\t최선의 손실: 0.037831\t정확도: 98.94%\n",
+      "7\t검증 세트 손실: 0.055440\t최선의 손실: 0.037831\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.060828\t최선의 손실: 0.037831\t정확도: 98.91%\n",
+      "9\t검증 세트 손실: 0.059158\t최선의 손실: 0.037831\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.040160\t최선의 손실: 0.037831\t정확도: 98.83%\n",
+      "11\t검증 세트 손실: 0.055848\t최선의 손실: 0.037831\t정확도: 98.94%\n",
+      "12\t검증 세트 손실: 0.041807\t최선의 손실: 0.037831\t정확도: 99.02%\n",
+      "13\t검증 세트 손실: 0.036333\t최선의 손실: 0.036333\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.042120\t최선의 손실: 0.036333\t정확도: 99.22%\n",
+      "15\t검증 세트 손실: 0.040308\t최선의 손실: 0.036333\t정확도: 98.98%\n",
+      "16\t검증 세트 손실: 0.037431\t최선의 손실: 0.036333\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.050488\t최선의 손실: 0.036333\t정확도: 98.94%\n",
+      "18\t검증 세트 손실: 0.048771\t최선의 손실: 0.036333\t정확도: 98.67%\n",
+      "19\t검증 세트 손실: 0.042726\t최선의 손실: 0.036333\t정확도: 99.06%\n",
+      "20\t검증 세트 손실: 0.044913\t최선의 손실: 0.036333\t정확도: 99.18%\n",
+      "21\t검증 세트 손실: 0.043223\t최선의 손실: 0.036333\t정확도: 99.26%\n",
+      "22\t검증 세트 손실: 0.048663\t최선의 손실: 0.036333\t정확도: 99.14%\n",
+      "23\t검증 세트 손실: 0.049508\t최선의 손실: 0.036333\t정확도: 99.14%\n",
+      "24\t검증 세트 손실: 0.053865\t최선의 손실: 0.036333\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.050363\t최선의 손실: 0.036333\t정확도: 98.94%\n",
+      "26\t검증 세트 손실: 0.076581\t최선의 손실: 0.036333\t정확도: 98.63%\n",
+      "27\t검증 세트 손실: 0.086406\t최선의 손실: 0.036333\t정확도: 98.32%\n",
+      "28\t검증 세트 손실: 0.063638\t최선의 손실: 0.036333\t정확도: 98.83%\n",
+      "29\t검증 세트 손실: 0.059319\t최선의 손실: 0.036333\t정확도: 98.59%\n",
+      "30\t검증 세트 손실: 0.052663\t최선의 손실: 0.036333\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.052377\t최선의 손실: 0.036333\t정확도: 99.02%\n",
+      "32\t검증 세트 손실: 0.055749\t최선의 손실: 0.036333\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.048183\t최선의 손실: 0.036333\t정확도: 98.83%\n",
+      "34\t검증 세트 손실: 0.048848\t최선의 손실: 0.036333\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.9, n_neurons=140, total=  10.4s\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=160 \n",
-      "0\t검증 세트 손실: 6.452457\t최선의 손실: 6.452457\t정확도: 85.65%\n",
-      "1\t검증 세트 손실: 1.276747\t최선의 손실: 1.276747\t정확도: 92.65%\n",
-      "2\t검증 세트 손실: 0.829677\t최선의 손실: 0.829677\t정확도: 93.55%\n",
-      "3\t검증 세트 손실: 0.334199\t최선의 손실: 0.334199\t정확도: 96.48%\n",
-      "4\t검증 세트 손실: 0.204737\t최선의 손실: 0.204737\t정확도: 97.30%\n",
-      "5\t검증 세트 손실: 0.252250\t최선의 손실: 0.204737\t정확도: 97.62%\n",
-      "6\t검증 세트 손실: 1.513603\t최선의 손실: 0.204737\t정확도: 87.88%\n",
-      "7\t검증 세트 손실: 0.094271\t최선의 손실: 0.094271\t정확도: 98.16%\n",
-      "8\t검증 세트 손실: 0.445378\t최선의 손실: 0.094271\t정확도: 97.19%\n",
-      "9\t검증 세트 손실: 0.162570\t최선의 손실: 0.094271\t정확도: 97.85%\n",
-      "10\t검증 세트 손실: 0.192412\t최선의 손실: 0.094271\t정확도: 97.85%\n",
-      "11\t검증 세트 손실: 0.234943\t최선의 손실: 0.094271\t정확도: 97.34%\n",
-      "12\t검증 세트 손실: 0.253149\t최선의 손실: 0.094271\t정확도: 96.44%\n",
-      "13\t검증 세트 손실: 0.220070\t최선의 손실: 0.094271\t정확도: 97.34%\n",
-      "14\t검증 세트 손실: 0.163260\t최선의 손실: 0.094271\t정확도: 97.46%\n",
-      "15\t검증 세트 손실: 0.182872\t최선의 손실: 0.094271\t정확도: 98.16%\n",
-      "16\t검증 세트 손실: 0.111768\t최선의 손실: 0.094271\t정확도: 98.40%\n",
-      "17\t검증 세트 손실: 0.132373\t최선의 손실: 0.094271\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.280819\t최선의 손실: 0.094271\t정확도: 97.46%\n",
-      "19\t검증 세트 손실: 0.693475\t최선의 손실: 0.094271\t정확도: 95.27%\n",
-      "20\t검증 세트 손실: 0.190022\t최선의 손실: 0.094271\t정확도: 98.59%\n",
-      "21\t검증 세트 손실: 0.254922\t최선의 손실: 0.094271\t정확도: 97.89%\n",
-      "22\t검증 세트 손실: 0.184326\t최선의 손실: 0.094271\t정확도: 98.40%\n",
-      "23\t검증 세트 손실: 0.164022\t최선의 손실: 0.094271\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.256905\t최선의 손실: 0.094271\t정확도: 97.46%\n",
-      "25\t검증 세트 손실: 0.215052\t최선의 손실: 0.094271\t정확도: 98.32%\n",
-      "26\t검증 세트 손실: 0.331683\t최선의 손실: 0.094271\t정확도: 97.46%\n",
-      "27\t검증 세트 손실: 0.284602\t최선의 손실: 0.094271\t정확도: 97.97%\n",
-      "28\t검증 세트 손실: 1.751258\t최선의 손실: 0.094271\t정확도: 94.57%\n",
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=140, total=   7.8s\n",
+      "[CV] batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.054452\t최선의 손실: 0.054452\t정확도: 98.36%\n",
+      "1\t검증 세트 손실: 0.048056\t최선의 손실: 0.048056\t정확도: 98.48%\n",
+      "2\t검증 세트 손실: 0.045235\t최선의 손실: 0.045235\t정확도: 98.87%\n",
+      "3\t검증 세트 손실: 0.042560\t최선의 손실: 0.042560\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.039681\t최선의 손실: 0.039681\t정확도: 98.79%\n",
+      "5\t검증 세트 손실: 0.048611\t최선의 손실: 0.039681\t정확도: 98.67%\n",
+      "6\t검증 세트 손실: 0.040204\t최선의 손실: 0.039681\t정확도: 98.79%\n",
+      "7\t검증 세트 손실: 0.041293\t최선의 손실: 0.039681\t정확도: 98.83%\n",
+      "8\t검증 세트 손실: 0.061015\t최선의 손실: 0.039681\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.044629\t최선의 손실: 0.039681\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.054142\t최선의 손실: 0.039681\t정확도: 98.75%\n",
+      "11\t검증 세트 손실: 0.040387\t최선의 손실: 0.039681\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.052586\t최선의 손실: 0.039681\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.054709\t최선의 손실: 0.039681\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.058777\t최선의 손실: 0.039681\t정확도: 98.51%\n",
+      "15\t검증 세트 손실: 0.040559\t최선의 손실: 0.039681\t정확도: 98.94%\n",
+      "16\t검증 세트 손실: 0.049943\t최선의 손실: 0.039681\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.046184\t최선의 손실: 0.039681\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.041955\t최선의 손실: 0.039681\t정확도: 99.02%\n",
+      "19\t검증 세트 손실: 0.044302\t최선의 손실: 0.039681\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.032009\t최선의 손실: 0.032009\t정확도: 99.06%\n",
+      "21\t검증 세트 손실: 0.044219\t최선의 손실: 0.032009\t정확도: 98.91%\n",
+      "22\t검증 세트 손실: 0.035678\t최선의 손실: 0.032009\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.037836\t최선의 손실: 0.032009\t정확도: 99.10%\n",
+      "24\t검증 세트 손실: 0.043236\t최선의 손실: 0.032009\t정확도: 99.10%\n",
+      "25\t검증 세트 손실: 0.040817\t최선의 손실: 0.032009\t정확도: 99.22%\n",
+      "26\t검증 세트 손실: 0.037308\t최선의 손실: 0.032009\t정확도: 99.18%\n",
+      "27\t검증 세트 손실: 0.039765\t최선의 손실: 0.032009\t정확도: 99.18%\n",
+      "28\t검증 세트 손실: 0.050416\t최선의 손실: 0.032009\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.063658\t최선의 손실: 0.032009\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.052800\t최선의 손실: 0.032009\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.043767\t최선의 손실: 0.032009\t정확도: 99.14%\n",
+      "32\t검증 세트 손실: 0.052215\t최선의 손실: 0.032009\t정확도: 98.71%\n",
+      "33\t검증 세트 손실: 0.055052\t최선의 손실: 0.032009\t정확도: 98.94%\n",
+      "34\t검증 세트 손실: 0.053464\t최선의 손실: 0.032009\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.049468\t최선의 손실: 0.032009\t정확도: 98.67%\n",
+      "36\t검증 세트 손실: 0.031984\t최선의 손실: 0.031984\t정확도: 99.18%\n",
+      "37\t검증 세트 손실: 0.029672\t최선의 손실: 0.029672\t정확도: 99.18%\n",
+      "38\t검증 세트 손실: 0.055158\t최선의 손실: 0.029672\t정확도: 99.02%\n",
+      "39\t검증 세트 손실: 0.046245\t최선의 손실: 0.029672\t정확도: 98.83%\n",
+      "40\t검증 세트 손실: 0.054413\t최선의 손실: 0.029672\t정확도: 98.79%\n",
+      "41\t검증 세트 손실: 0.044089\t최선의 손실: 0.029672\t정확도: 99.02%\n",
+      "42\t검증 세트 손실: 0.040131\t최선의 손실: 0.029672\t정확도: 99.02%\n",
+      "43\t검증 세트 손실: 0.040692\t최선의 손실: 0.029672\t정확도: 99.06%\n",
+      "44\t검증 세트 손실: 0.041724\t최선의 손실: 0.029672\t정확도: 98.94%\n",
+      "45\t검증 세트 손실: 0.067827\t최선의 손실: 0.029672\t정확도: 98.36%\n",
+      "46\t검증 세트 손실: 0.036614\t최선의 손실: 0.029672\t정확도: 98.94%\n",
+      "47\t검증 세트 손실: 0.046851\t최선의 손실: 0.029672\t정확도: 98.83%\n",
+      "48\t검증 세트 손실: 0.047860\t최선의 손실: 0.029672\t정확도: 98.91%\n",
+      "49\t검증 세트 손실: 0.049455\t최선의 손실: 0.029672\t정확도: 98.94%\n",
+      "50\t검증 세트 손실: 0.050313\t최선의 손실: 0.029672\t정확도: 98.98%\n",
+      "51\t검증 세트 손실: 0.048054\t최선의 손실: 0.029672\t정확도: 99.10%\n",
+      "52\t검증 세트 손실: 0.050048\t최선의 손실: 0.029672\t정확도: 99.06%\n",
+      "53\t검증 세트 손실: 0.047867\t최선의 손실: 0.029672\t정확도: 99.18%\n",
+      "54\t검증 세트 손실: 0.046316\t최선의 손실: 0.029672\t정확도: 99.14%\n",
+      "55\t검증 세트 손실: 0.046693\t최선의 손실: 0.029672\t정확도: 99.14%\n",
+      "56\t검증 세트 손실: 0.047013\t최선의 손실: 0.029672\t정확도: 99.10%\n",
+      "57\t검증 세트 손실: 0.047155\t최선의 손실: 0.029672\t정확도: 99.06%\n",
+      "58\t검증 세트 손실: 0.049513\t최선의 손실: 0.029672\t정확도: 99.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=160, total= 3.3min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=160 \n",
-      "0\t검증 세트 손실: 2.983854\t최선의 손실: 2.983854\t정확도: 87.61%\n",
-      "1\t검증 세트 손실: 1.467239\t최선의 손실: 1.467239\t정확도: 94.49%\n",
-      "2\t검증 세트 손실: 0.745703\t최선의 손실: 0.745703\t정확도: 95.39%\n",
-      "3\t검증 세트 손실: 0.260712\t최선의 손실: 0.260712\t정확도: 97.26%\n",
-      "4\t검증 세트 손실: 1.173630\t최선의 손실: 0.260712\t정확도: 95.43%\n",
-      "5\t검증 세트 손실: 0.775070\t최선의 손실: 0.260712\t정확도: 90.62%\n",
-      "6\t검증 세트 손실: 0.453629\t최선의 손실: 0.260712\t정확도: 96.13%\n",
-      "7\t검증 세트 손실: 0.414596\t최선의 손실: 0.260712\t정확도: 97.54%\n",
-      "8\t검증 세트 손실: 0.172884\t최선의 손실: 0.172884\t정확도: 97.73%\n",
-      "9\t검증 세트 손실: 0.214580\t최선의 손실: 0.172884\t정확도: 97.97%\n",
-      "10\t검증 세트 손실: 0.926076\t최선의 손실: 0.172884\t정확도: 91.52%\n",
-      "11\t검증 세트 손실: 0.119338\t최선의 손실: 0.119338\t정확도: 98.59%\n",
-      "12\t검증 세트 손실: 0.213217\t최선의 손실: 0.119338\t정확도: 96.36%\n",
-      "13\t검증 세트 손실: 0.355091\t최선의 손실: 0.119338\t정확도: 97.15%\n",
-      "14\t검증 세트 손실: 0.118773\t최선의 손실: 0.118773\t정확도: 98.05%\n",
-      "15\t검증 세트 손실: 0.270584\t최선의 손실: 0.118773\t정확도: 97.69%\n",
-      "16\t검증 세트 손실: 0.198932\t최선의 손실: 0.118773\t정확도: 98.05%\n",
-      "17\t검증 세트 손실: 0.327635\t최선의 손실: 0.118773\t정확도: 96.76%\n",
-      "18\t검증 세트 손실: 0.259442\t최선의 손실: 0.118773\t정확도: 97.42%\n",
-      "19\t검증 세트 손실: 1.242744\t최선의 손실: 0.118773\t정확도: 90.93%\n",
-      "20\t검증 세트 손실: 0.130557\t최선의 손실: 0.118773\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.152820\t최선의 손실: 0.118773\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.222975\t최선의 손실: 0.118773\t정확도: 97.69%\n",
-      "23\t검증 세트 손실: 0.194649\t최선의 손실: 0.118773\t정확도: 98.28%\n",
-      "24\t검증 세트 손실: 0.184290\t최선의 손실: 0.118773\t정확도: 98.51%\n",
-      "25\t검증 세트 손실: 0.199896\t최선의 손실: 0.118773\t정확도: 98.63%\n",
-      "26\t검증 세트 손실: 0.182387\t최선의 손실: 0.118773\t정확도: 98.40%\n",
-      "27\t검증 세트 손실: 0.217634\t최선의 손실: 0.118773\t정확도: 97.73%\n",
-      "28\t검증 세트 손실: 0.267141\t최선의 손실: 0.118773\t정확도: 97.73%\n",
-      "29\t검증 세트 손실: 0.217463\t최선의 손실: 0.118773\t정확도: 98.48%\n",
-      "30\t검증 세트 손실: 0.187310\t최선의 손실: 0.118773\t정확도: 98.36%\n",
-      "31\t검증 세트 손실: 0.181474\t최선의 손실: 0.118773\t정확도: 98.55%\n",
-      "32\t검증 세트 손실: 0.201517\t최선의 손실: 0.118773\t정확도: 98.67%\n",
-      "33\t검증 세트 손실: 0.197964\t최선의 손실: 0.118773\t정확도: 98.48%\n",
-      "34\t검증 세트 손실: 0.251413\t최선의 손실: 0.118773\t정확도: 98.59%\n",
-      "35\t검증 세트 손실: 0.225692\t최선의 손실: 0.118773\t정확도: 98.20%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=160, total= 4.1min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=160 \n",
-      "0\t검증 세트 손실: 5.732498\t최선의 손실: 5.732498\t정확도: 80.14%\n",
-      "1\t검증 세트 손실: 1.723363\t최선의 손실: 1.723363\t정확도: 88.27%\n",
-      "2\t검증 세트 손실: 3.602542\t최선의 손실: 1.723363\t정확도: 70.45%\n",
-      "3\t검증 세트 손실: 0.754069\t최선의 손실: 0.754069\t정확도: 91.87%\n",
-      "4\t검증 세트 손실: 0.889134\t최선의 손실: 0.754069\t정확도: 93.16%\n",
-      "5\t검증 세트 손실: 0.575134\t최선의 손실: 0.575134\t정확도: 94.84%\n",
-      "6\t검증 세트 손실: 0.314228\t최선의 손실: 0.314228\t정확도: 97.22%\n",
-      "7\t검증 세트 손실: 0.338599\t최선의 손실: 0.314228\t정확도: 95.35%\n",
-      "8\t검증 세트 손실: 0.240730\t최선의 손실: 0.240730\t정확도: 98.12%\n",
-      "9\t검증 세트 손실: 0.646224\t최선의 손실: 0.240730\t정확도: 93.51%\n",
-      "10\t검증 세트 손실: 0.175883\t최선의 손실: 0.175883\t정확도: 97.46%\n",
-      "11\t검증 세트 손실: 0.248859\t최선의 손실: 0.175883\t정확도: 95.70%\n"
+      "[CV]  batch_size=500, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.9, n_neurons=140, total=  12.1s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.999, n_neurons=160 \n",
+      "0\t검증 세트 손실: 12.404781\t최선의 손실: 12.404781\t정확도: 81.55%\n",
+      "1\t검증 세트 손실: 1.165093\t최선의 손실: 1.165093\t정확도: 90.54%\n",
+      "2\t검증 세트 손실: 0.886011\t최선의 손실: 0.886011\t정확도: 95.04%\n",
+      "3\t검증 세트 손실: 0.188608\t최선의 손실: 0.188608\t정확도: 96.64%\n",
+      "4\t검증 세트 손실: 0.334942\t최선의 손실: 0.188608\t정확도: 96.87%\n",
+      "5\t검증 세트 손실: 0.447536\t최선의 손실: 0.188608\t정확도: 94.84%\n",
+      "6\t검증 세트 손실: 0.551342\t최선의 손실: 0.188608\t정확도: 93.43%\n",
+      "7\t검증 세트 손실: 0.275792\t최선의 손실: 0.188608\t정확도: 94.61%\n",
+      "8\t검증 세트 손실: 0.859471\t최선의 손실: 0.188608\t정확도: 95.62%\n",
+      "9\t검증 세트 손실: 0.366413\t최선의 손실: 0.188608\t정확도: 94.96%\n",
+      "10\t검증 세트 손실: 0.358832\t최선의 손실: 0.188608\t정확도: 96.44%\n",
+      "11\t검증 세트 손실: 0.291471\t최선의 손실: 0.188608\t정확도: 96.83%\n",
+      "12\t검증 세트 손실: 0.212431\t최선의 손실: 0.188608\t정확도: 97.34%\n",
+      "13\t검증 세트 손실: 0.283574\t최선의 손실: 0.188608\t정확도: 97.77%\n",
+      "14\t검증 세트 손실: 0.186273\t최선의 손실: 0.186273\t정확도: 98.12%\n",
+      "15\t검증 세트 손실: 0.204032\t최선의 손실: 0.186273\t정확도: 97.89%\n",
+      "16\t검증 세트 손실: 0.306143\t최선의 손실: 0.186273\t정확도: 97.30%\n",
+      "17\t검증 세트 손실: 0.183629\t최선의 손실: 0.183629\t정확도: 98.36%\n",
+      "18\t검증 세트 손실: 0.272847\t최선의 손실: 0.183629\t정확도: 97.85%\n",
+      "19\t검증 세트 손실: 0.213216\t최선의 손실: 0.183629\t정확도: 98.08%\n",
+      "20\t검증 세트 손실: 0.312479\t최선의 손실: 0.183629\t정확도: 97.65%\n",
+      "21\t검증 세트 손실: 0.316772\t최선의 손실: 0.183629\t정확도: 97.26%\n",
+      "22\t검증 세트 손실: 0.411407\t최선의 손실: 0.183629\t정확도: 97.46%\n",
+      "23\t검증 세트 손실: 0.142618\t최선의 손실: 0.142618\t정확도: 98.44%\n",
+      "24\t검증 세트 손실: 0.140662\t최선의 손실: 0.140662\t정확도: 98.83%\n",
+      "25\t검증 세트 손실: 0.380379\t최선의 손실: 0.140662\t정확도: 97.89%\n",
+      "26\t검증 세트 손실: 0.112672\t최선의 손실: 0.112672\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.226894\t최선의 손실: 0.112672\t정확도: 98.71%\n",
+      "28\t검증 세트 손실: 0.309039\t최선의 손실: 0.112672\t정확도: 98.24%\n",
+      "29\t검증 세트 손실: 0.248673\t최선의 손실: 0.112672\t정확도: 98.24%\n",
+      "30\t검증 세트 손실: 0.175645\t최선의 손실: 0.112672\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.419740\t최선의 손실: 0.112672\t정확도: 98.36%\n",
+      "32\t검증 세트 손실: 0.214152\t최선의 손실: 0.112672\t정확도: 98.59%\n",
+      "33\t검증 세트 손실: 0.243632\t최선의 손실: 0.112672\t정확도: 98.40%\n",
+      "34\t검증 세트 손실: 0.303171\t최선의 손실: 0.112672\t정확도: 98.28%\n",
+      "35\t검증 세트 손실: 0.320183\t최선의 손실: 0.112672\t정확도: 98.51%\n",
+      "36\t검증 세트 손실: 0.285973\t최선의 손실: 0.112672\t정확도: 98.59%\n",
+      "37\t검증 세트 손실: 0.266382\t최선의 손실: 0.112672\t정확도: 98.59%\n",
+      "38\t검증 세트 손실: 0.271029\t최선의 손실: 0.112672\t정확도: 98.44%\n",
+      "39\t검증 세트 손실: 0.165325\t최선의 손실: 0.112672\t정확도: 98.91%\n",
+      "40\t검증 세트 손실: 0.280748\t최선의 손실: 0.112672\t정확도: 98.55%\n",
+      "41\t검증 세트 손실: 0.538206\t최선의 손실: 0.112672\t정확도: 96.91%\n",
+      "42\t검증 세트 손실: 0.405633\t최선의 손실: 0.112672\t정확도: 98.16%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 0.370688\t최선의 손실: 0.175883\t정확도: 97.15%\n",
-      "13\t검증 세트 손실: 0.201926\t최선의 손실: 0.175883\t정확도: 96.64%\n",
-      "14\t검증 세트 손실: 0.194749\t최선의 손실: 0.175883\t정확도: 98.12%\n",
-      "15\t검증 세트 손실: 0.153140\t최선의 손실: 0.153140\t정확도: 97.81%\n",
-      "16\t검증 세트 손실: 0.187419\t최선의 손실: 0.153140\t정확도: 97.85%\n",
-      "17\t검증 세트 손실: 0.134893\t최선의 손실: 0.134893\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.186194\t최선의 손실: 0.134893\t정확도: 97.85%\n",
-      "19\t검증 세트 손실: 0.278193\t최선의 손실: 0.134893\t정확도: 98.01%\n",
-      "20\t검증 세트 손실: 0.343353\t최선의 손실: 0.134893\t정확도: 97.85%\n",
-      "21\t검증 세트 손실: 0.122678\t최선의 손실: 0.122678\t정확도: 97.77%\n",
-      "22\t검증 세트 손실: 0.179308\t최선의 손실: 0.122678\t정확도: 98.55%\n",
-      "23\t검증 세트 손실: 0.164772\t최선의 손실: 0.122678\t정확도: 98.63%\n",
-      "24\t검증 세트 손실: 0.280960\t최선의 손실: 0.122678\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.471320\t최선의 손실: 0.122678\t정확도: 96.52%\n",
-      "26\t검증 세트 손실: 0.224359\t최선의 손실: 0.122678\t정확도: 98.83%\n",
-      "27\t검증 세트 손실: 0.392449\t최선의 손실: 0.122678\t정확도: 95.70%\n",
-      "28\t검증 세트 손실: 0.086054\t최선의 손실: 0.086054\t정확도: 98.87%\n",
-      "29\t검증 세트 손실: 0.190444\t최선의 손실: 0.086054\t정확도: 98.75%\n",
-      "30\t검증 세트 손실: 0.241463\t최선의 손실: 0.086054\t정확도: 98.16%\n",
-      "31\t검증 세트 손실: 0.182151\t최선의 손실: 0.086054\t정확도: 98.01%\n",
-      "32\t검증 세트 손실: 0.167417\t최선의 손실: 0.086054\t정확도: 98.83%\n",
-      "33\t검증 세트 손실: 0.261582\t최선의 손실: 0.086054\t정확도: 98.24%\n",
-      "34\t검증 세트 손실: 0.361062\t최선의 손실: 0.086054\t정확도: 98.24%\n",
-      "35\t검증 세트 손실: 0.158913\t최선의 손실: 0.086054\t정확도: 98.63%\n",
-      "36\t검증 세트 손실: 0.324458\t최선의 손실: 0.086054\t정확도: 97.73%\n",
-      "37\t검증 세트 손실: 0.165804\t최선의 손실: 0.086054\t정확도: 98.83%\n",
-      "38\t검증 세트 손실: 0.349725\t최선의 손실: 0.086054\t정확도: 97.34%\n",
-      "39\t검증 세트 손실: 0.109129\t최선의 손실: 0.086054\t정확도: 98.79%\n",
-      "40\t검증 세트 손실: 0.140751\t최선의 손실: 0.086054\t정확도: 98.83%\n",
-      "41\t검증 세트 손실: 0.183348\t최선의 손실: 0.086054\t정확도: 98.55%\n",
-      "42\t검증 세트 손실: 0.283282\t최선의 손실: 0.086054\t정확도: 98.12%\n",
-      "43\t검증 세트 손실: 0.267976\t최선의 손실: 0.086054\t정확도: 98.16%\n",
-      "44\t검증 세트 손실: 0.179662\t최선의 손실: 0.086054\t정확도: 98.63%\n",
-      "45\t검증 세트 손실: 0.211499\t최선의 손실: 0.086054\t정확도: 98.55%\n",
-      "46\t검증 세트 손실: 1.010714\t최선의 손실: 0.086054\t정확도: 90.81%\n",
-      "47\t검증 세트 손실: 0.229027\t최선의 손실: 0.086054\t정확도: 97.77%\n",
-      "48\t검증 세트 손실: 0.327112\t최선의 손실: 0.086054\t정확도: 97.30%\n",
-      "49\t검증 세트 손실: 0.628731\t최선의 손실: 0.086054\t정확도: 96.44%\n",
+      "43\t검증 세트 손실: 0.441366\t최선의 손실: 0.112672\t정확도: 98.44%\n",
+      "44\t검증 세트 손실: 0.353795\t최선의 손실: 0.112672\t정확도: 98.44%\n",
+      "45\t검증 세트 손실: 0.425371\t최선의 손실: 0.112672\t정확도: 98.59%\n",
+      "46\t검증 세트 손실: 0.418789\t최선의 손실: 0.112672\t정확도: 98.28%\n",
+      "47\t검증 세트 손실: 0.643492\t최선의 손실: 0.112672\t정확도: 96.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.999, n_neurons=160, total= 5.6min\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.112731\t최선의 손실: 0.112731\t정확도: 97.11%\n",
-      "1\t검증 세트 손실: 0.076906\t최선의 손실: 0.076906\t정확도: 98.12%\n",
-      "2\t검증 세트 손실: 0.086599\t최선의 손실: 0.076906\t정확도: 98.51%\n",
-      "3\t검증 세트 손실: 0.075112\t최선의 손실: 0.075112\t정확도: 97.58%\n",
-      "4\t검증 세트 손실: 0.057410\t최선의 손실: 0.057410\t정확도: 98.55%\n",
-      "5\t검증 세트 손실: 0.055946\t최선의 손실: 0.055946\t정확도: 98.71%\n",
-      "6\t검증 세트 손실: 0.034106\t최선의 손실: 0.034106\t정확도: 99.10%\n",
-      "7\t검증 세트 손실: 0.047621\t최선의 손실: 0.034106\t정확도: 98.87%\n",
-      "8\t검증 세트 손실: 0.045185\t최선의 손실: 0.034106\t정확도: 98.98%\n",
-      "9\t검증 세트 손실: 0.076075\t최선의 손실: 0.034106\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.058499\t최선의 손실: 0.034106\t정확도: 98.55%\n",
-      "11\t검증 세트 손실: 0.043007\t최선의 손실: 0.034106\t정확도: 98.87%\n",
-      "12\t검증 세트 손실: 0.056895\t최선의 손실: 0.034106\t정확도: 98.71%\n",
-      "13\t검증 세트 손실: 0.062513\t최선의 손실: 0.034106\t정확도: 98.79%\n",
-      "14\t검증 세트 손실: 0.065382\t최선의 손실: 0.034106\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.052309\t최선의 손실: 0.034106\t정확도: 99.02%\n",
-      "16\t검증 세트 손실: 0.055482\t최선의 손실: 0.034106\t정확도: 98.98%\n",
-      "17\t검증 세트 손실: 0.043112\t최선의 손실: 0.034106\t정확도: 98.98%\n",
-      "18\t검증 세트 손실: 0.064859\t최선의 손실: 0.034106\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.070501\t최선의 손실: 0.034106\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.063665\t최선의 손실: 0.034106\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.069652\t최선의 손실: 0.034106\t정확도: 98.63%\n",
-      "22\t검증 세트 손실: 0.049715\t최선의 손실: 0.034106\t정확도: 98.98%\n",
-      "23\t검증 세트 손실: 0.058451\t최선의 손실: 0.034106\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.048909\t최선의 손실: 0.034106\t정확도: 99.10%\n",
-      "25\t검증 세트 손실: 0.097687\t최선의 손실: 0.034106\t정확도: 97.97%\n",
-      "26\t검증 세트 손실: 0.053783\t최선의 손실: 0.034106\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.045311\t최선의 손실: 0.034106\t정확도: 99.06%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.999, n_neurons=160, total= 5.4min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.999, n_neurons=160 \n",
+      "0\t검증 세트 손실: 2.164887\t최선의 손실: 2.164887\t정확도: 90.58%\n",
+      "1\t검증 세트 손실: 4.042850\t최선의 손실: 2.164887\t정확도: 92.46%\n",
+      "2\t검증 세트 손실: 0.485385\t최선의 손실: 0.485385\t정확도: 94.10%\n",
+      "3\t검증 세트 손실: 0.328102\t최선의 손실: 0.328102\t정확도: 96.76%\n",
+      "4\t검증 세트 손실: 0.435461\t최선의 손실: 0.328102\t정확도: 97.15%\n",
+      "5\t검증 세트 손실: 0.488258\t최선의 손실: 0.328102\t정확도: 94.45%\n",
+      "6\t검증 세트 손실: 0.574346\t최선의 손실: 0.328102\t정확도: 95.07%\n",
+      "7\t검증 세트 손실: 0.464429\t최선의 손실: 0.328102\t정확도: 96.56%\n",
+      "8\t검증 세트 손실: 0.304956\t최선의 손실: 0.304956\t정확도: 96.36%\n",
+      "9\t검증 세트 손실: 0.378182\t최선의 손실: 0.304956\t정확도: 97.58%\n",
+      "10\t검증 세트 손실: 0.333229\t최선의 손실: 0.304956\t정확도: 95.27%\n",
+      "11\t검증 세트 손실: 0.122098\t최선의 손실: 0.122098\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.940346\t최선의 손실: 0.122098\t정확도: 92.26%\n",
+      "13\t검증 세트 손실: 0.403638\t최선의 손실: 0.122098\t정확도: 97.97%\n",
+      "14\t검증 세트 손실: 0.141495\t최선의 손실: 0.122098\t정확도: 97.93%\n",
+      "15\t검증 세트 손실: 0.391154\t최선의 손실: 0.122098\t정확도: 95.74%\n",
+      "16\t검증 세트 손실: 0.389622\t최선의 손실: 0.122098\t정확도: 96.91%\n",
+      "17\t검증 세트 손실: 0.467329\t최선의 손실: 0.122098\t정확도: 97.46%\n",
+      "18\t검증 세트 손실: 0.135555\t최선의 손실: 0.122098\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 1.281357\t최선의 손실: 0.122098\t정확도: 93.00%\n",
+      "20\t검증 세트 손실: 0.186977\t최선의 손실: 0.122098\t정확도: 98.55%\n",
+      "21\t검증 세트 손실: 0.737944\t최선의 손실: 0.122098\t정확도: 94.64%\n",
+      "22\t검증 세트 손실: 0.279441\t최선의 손실: 0.122098\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.248304\t최선의 손실: 0.122098\t정확도: 97.77%\n",
+      "24\t검증 세트 손실: 0.179294\t최선의 손실: 0.122098\t정확도: 98.12%\n",
+      "25\t검증 세트 손실: 0.204056\t최선의 손실: 0.122098\t정확도: 98.67%\n",
+      "26\t검증 세트 손실: 0.208904\t최선의 손실: 0.122098\t정확도: 98.05%\n",
+      "27\t검증 세트 손실: 0.268777\t최선의 손실: 0.122098\t정확도: 97.69%\n",
+      "28\t검증 세트 손실: 0.364215\t최선의 손실: 0.122098\t정확도: 98.24%\n",
+      "29\t검증 세트 손실: 0.191645\t최선의 손실: 0.122098\t정확도: 98.36%\n",
+      "30\t검증 세트 손실: 0.700360\t최선의 손실: 0.122098\t정확도: 96.48%\n",
+      "31\t검증 세트 손실: 0.201593\t최선의 손실: 0.122098\t정확도: 98.79%\n",
+      "32\t검증 세트 손실: 0.589171\t최선의 손실: 0.122098\t정확도: 97.11%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100, total=  21.4s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.143948\t최선의 손실: 0.143948\t정확도: 96.33%\n",
-      "1\t검증 세트 손실: 0.065971\t최선의 손실: 0.065971\t정확도: 98.55%\n",
-      "2\t검증 세트 손실: 0.055107\t최선의 손실: 0.055107\t정확도: 98.36%\n",
-      "3\t검증 세트 손실: 0.066070\t최선의 손실: 0.055107\t정확도: 98.28%\n",
-      "4\t검증 세트 손실: 0.062758\t최선의 손실: 0.055107\t정확도: 98.63%\n",
-      "5\t검증 세트 손실: 0.060984\t최선의 손실: 0.055107\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.053280\t최선의 손실: 0.053280\t정확도: 98.67%\n",
-      "7\t검증 세트 손실: 0.068407\t최선의 손실: 0.053280\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.061899\t최선의 손실: 0.053280\t정확도: 98.59%\n",
-      "9\t검증 세트 손실: 0.066192\t최선의 손실: 0.053280\t정확도: 98.48%\n",
-      "10\t검증 세트 손실: 0.079929\t최선의 손실: 0.053280\t정확도: 98.16%\n",
-      "11\t검증 세트 손실: 0.058679\t최선의 손실: 0.053280\t정확도: 98.59%\n",
-      "12\t검증 세트 손실: 0.035358\t최선의 손실: 0.035358\t정확도: 99.06%\n",
-      "13\t검증 세트 손실: 0.056564\t최선의 손실: 0.035358\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.034933\t최선의 손실: 0.034933\t정확도: 99.02%\n",
-      "15\t검증 세트 손실: 0.060008\t최선의 손실: 0.034933\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.037792\t최선의 손실: 0.034933\t정확도: 99.06%\n",
-      "17\t검증 세트 손실: 0.047494\t최선의 손실: 0.034933\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.037732\t최선의 손실: 0.034933\t정확도: 99.10%\n",
-      "19\t검증 세트 손실: 0.051190\t최선의 손실: 0.034933\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.058477\t최선의 손실: 0.034933\t정확도: 98.40%\n",
-      "21\t검증 세트 손실: 0.045688\t최선의 손실: 0.034933\t정확도: 99.02%\n",
-      "22\t검증 세트 손실: 0.040096\t최선의 손실: 0.034933\t정확도: 99.10%\n",
-      "23\t검증 세트 손실: 0.106831\t최선의 손실: 0.034933\t정확도: 98.16%\n",
-      "24\t검증 세트 손실: 0.049712\t최선의 손실: 0.034933\t정확도: 99.14%\n",
-      "25\t검증 세트 손실: 0.057473\t최선의 손실: 0.034933\t정확도: 98.94%\n",
-      "26\t검증 세트 손실: 0.043400\t최선의 손실: 0.034933\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.056506\t최선의 손실: 0.034933\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.045056\t최선의 손실: 0.034933\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.047316\t최선의 손실: 0.034933\t정확도: 99.06%\n",
-      "30\t검증 세트 손실: 0.077122\t최선의 손실: 0.034933\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.063994\t최선의 손실: 0.034933\t정확도: 98.94%\n",
-      "32\t검증 세트 손실: 0.057340\t최선의 손실: 0.034933\t정확도: 98.91%\n",
-      "33\t검증 세트 손실: 0.048950\t최선의 손실: 0.034933\t정확도: 98.79%\n",
-      "34\t검증 세트 손실: 0.066141\t최선의 손실: 0.034933\t정확도: 99.18%\n",
-      "35\t검증 세트 손실: 0.044151\t최선의 손실: 0.034933\t정확도: 99.10%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.999, n_neurons=160, total= 3.7min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.999, n_neurons=160 \n",
+      "0\t검증 세트 손실: 8.353540\t최선의 손실: 8.353540\t정확도: 87.88%\n",
+      "1\t검증 세트 손실: 2.348000\t최선의 손실: 2.348000\t정확도: 92.34%\n",
+      "2\t검증 세트 손실: 1.656776\t최선의 손실: 1.656776\t정확도: 87.88%\n",
+      "3\t검증 세트 손실: 0.366419\t최선의 손실: 0.366419\t정확도: 93.78%\n",
+      "4\t검증 세트 손실: 0.818430\t최선의 손실: 0.366419\t정확도: 97.22%\n",
+      "5\t검증 세트 손실: 0.384452\t최선의 손실: 0.366419\t정확도: 92.18%\n",
+      "6\t검증 세트 손실: 0.321514\t최선의 손실: 0.321514\t정확도: 96.83%\n",
+      "7\t검증 세트 손실: 0.322048\t최선의 손실: 0.321514\t정확도: 94.41%\n",
+      "8\t검증 세트 손실: 0.323052\t최선의 손실: 0.321514\t정확도: 97.50%\n",
+      "9\t검증 세트 손실: 0.385919\t최선의 손실: 0.321514\t정확도: 94.57%\n",
+      "10\t검증 세트 손실: 0.180219\t최선의 손실: 0.180219\t정확도: 97.34%\n",
+      "11\t검증 세트 손실: 0.937840\t최선의 손실: 0.180219\t정확도: 88.35%\n",
+      "12\t검증 세트 손실: 2.571483\t최선의 손실: 0.180219\t정확도: 88.66%\n",
+      "13\t검증 세트 손실: 0.247830\t최선의 손실: 0.180219\t정확도: 97.15%\n",
+      "14\t검증 세트 손실: 0.072703\t최선의 손실: 0.072703\t정확도: 98.98%\n",
+      "15\t검증 세트 손실: 0.150375\t최선의 손실: 0.072703\t정확도: 98.08%\n",
+      "16\t검증 세트 손실: 0.089818\t최선의 손실: 0.072703\t정확도: 98.44%\n",
+      "17\t검증 세트 손실: 0.732057\t최선의 손실: 0.072703\t정확도: 94.29%\n",
+      "18\t검증 세트 손실: 0.116479\t최선의 손실: 0.072703\t정확도: 98.36%\n",
+      "19\t검증 세트 손실: 1.672525\t최선의 손실: 0.072703\t정확도: 84.64%\n",
+      "20\t검증 세트 손실: 0.340295\t최선의 손실: 0.072703\t정확도: 96.99%\n",
+      "21\t검증 세트 손실: 0.391266\t최선의 손실: 0.072703\t정확도: 95.11%\n",
+      "22\t검증 세트 손실: 0.105073\t최선의 손실: 0.072703\t정확도: 98.71%\n",
+      "23\t검증 세트 손실: 0.197473\t최선의 손실: 0.072703\t정확도: 98.08%\n",
+      "24\t검증 세트 손실: 0.826520\t최선의 손실: 0.072703\t정확도: 95.66%\n",
+      "25\t검증 세트 손실: 0.246479\t최선의 손실: 0.072703\t정확도: 97.77%\n",
+      "26\t검증 세트 손실: 0.256992\t최선의 손실: 0.072703\t정확도: 97.65%\n",
+      "27\t검증 세트 손실: 0.157127\t최선의 손실: 0.072703\t정확도: 98.24%\n",
+      "28\t검증 세트 손실: 0.145652\t최선의 손실: 0.072703\t정확도: 98.55%\n",
+      "29\t검증 세트 손실: 0.246523\t최선의 손실: 0.072703\t정확도: 98.24%\n",
+      "30\t검증 세트 손실: 1.031174\t최선의 손실: 0.072703\t정확도: 91.59%\n",
+      "31\t검증 세트 손실: 0.154950\t최선의 손실: 0.072703\t정확도: 98.28%\n",
+      "32\t검증 세트 손실: 0.164489\t최선의 손실: 0.072703\t정확도: 98.32%\n",
+      "33\t검증 세트 손실: 0.158793\t최선의 손실: 0.072703\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.124996\t최선의 손실: 0.072703\t정확도: 98.71%\n",
+      "35\t검증 세트 손실: 0.175614\t최선의 손실: 0.072703\t정확도: 98.51%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100, total=  26.9s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.124401\t최선의 손실: 0.124401\t정확도: 97.22%\n",
-      "1\t검증 세트 손실: 0.077896\t최선의 손실: 0.077896\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.048239\t최선의 손실: 0.048239\t정확도: 98.28%\n",
-      "3\t검증 세트 손실: 0.040404\t최선의 손실: 0.040404\t정확도: 98.91%\n",
-      "4\t검증 세트 손실: 0.041122\t최선의 손실: 0.040404\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.042426\t최선의 손실: 0.040404\t정확도: 98.94%\n",
-      "6\t검증 세트 손실: 0.044458\t최선의 손실: 0.040404\t정확도: 98.79%\n",
-      "7\t검증 세트 손실: 0.062798\t최선의 손실: 0.040404\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.031988\t최선의 손실: 0.031988\t정확도: 98.98%\n",
-      "9\t검증 세트 손실: 0.051877\t최선의 손실: 0.031988\t정확도: 98.87%\n",
-      "10\t검증 세트 손실: 0.061644\t최선의 손실: 0.031988\t정확도: 98.59%\n",
-      "11\t검증 세트 손실: 0.041409\t최선의 손실: 0.031988\t정확도: 98.87%\n",
-      "12\t검증 세트 손실: 0.053232\t최선의 손실: 0.031988\t정확도: 98.91%\n",
-      "13\t검증 세트 손실: 0.045659\t최선의 손실: 0.031988\t정확도: 98.83%\n",
-      "14\t검증 세트 손실: 0.040112\t최선의 손실: 0.031988\t정확도: 98.91%\n",
-      "15\t검증 세트 손실: 0.052066\t최선의 손실: 0.031988\t정확도: 99.02%\n",
-      "16\t검증 세트 손실: 0.049719\t최선의 손실: 0.031988\t정확도: 98.91%\n",
-      "17\t검증 세트 손실: 0.029537\t최선의 손실: 0.029537\t정확도: 99.18%\n",
-      "18\t검증 세트 손실: 0.053247\t최선의 손실: 0.029537\t정확도: 98.79%\n",
-      "19\t검증 세트 손실: 0.051354\t최선의 손실: 0.029537\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.048345\t최선의 손실: 0.029537\t정확도: 99.02%\n",
-      "21\t검증 세트 손실: 0.044090\t최선의 손실: 0.029537\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.035865\t최선의 손실: 0.029537\t정확도: 99.14%\n",
-      "23\t검증 세트 손실: 0.034852\t최선의 손실: 0.029537\t정확도: 99.37%\n",
-      "24\t검증 세트 손실: 0.035928\t최선의 손실: 0.029537\t정확도: 99.22%\n",
-      "25\t검증 세트 손실: 0.055481\t최선의 손실: 0.029537\t정확도: 98.67%\n",
-      "26\t검증 세트 손실: 0.046836\t최선의 손실: 0.029537\t정확도: 98.67%\n",
-      "27\t검증 세트 손실: 0.043219\t최선의 손실: 0.029537\t정확도: 98.94%\n",
-      "28\t검증 세트 손실: 0.033127\t최선의 손실: 0.029537\t정확도: 99.18%\n",
-      "29\t검증 세트 손실: 0.040414\t최선의 손실: 0.029537\t정확도: 99.02%\n",
-      "30\t검증 세트 손실: 0.034912\t최선의 손실: 0.029537\t정확도: 99.18%\n",
-      "31\t검증 세트 손실: 0.037988\t최선의 손실: 0.029537\t정확도: 99.34%\n",
-      "32\t검증 세트 손실: 0.045099\t최선의 손실: 0.029537\t정확도: 99.10%\n",
-      "33\t검증 세트 손실: 0.056525\t최선의 손실: 0.029537\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.050354\t최선의 손실: 0.029537\t정확도: 99.02%\n",
-      "35\t검증 세트 손실: 0.057267\t최선의 손실: 0.029537\t정확도: 98.91%\n",
-      "36\t검증 세트 손실: 0.037336\t최선의 손실: 0.029537\t정확도: 99.34%\n",
-      "37\t검증 세트 손실: 0.044446\t최선의 손실: 0.029537\t정확도: 99.10%\n",
-      "38\t검증 세트 손실: 0.035039\t최선의 손실: 0.029537\t정확도: 99.14%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.999, n_neurons=160, total= 4.0min\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.102314\t최선의 손실: 0.102314\t정확도: 97.97%\n",
+      "1\t검증 세트 손실: 0.129371\t최선의 손실: 0.102314\t정확도: 97.38%\n",
+      "2\t검증 세트 손실: 0.085769\t최선의 손실: 0.085769\t정확도: 98.24%\n",
+      "3\t검증 세트 손실: 0.075509\t최선의 손실: 0.075509\t정확도: 98.05%\n",
+      "4\t검증 세트 손실: 0.079170\t최선의 손실: 0.075509\t정확도: 98.16%\n",
+      "5\t검증 세트 손실: 0.075839\t최선의 손실: 0.075509\t정확도: 98.51%\n",
+      "6\t검증 세트 손실: 0.039396\t최선의 손실: 0.039396\t정확도: 98.83%\n",
+      "7\t검증 세트 손실: 0.051735\t최선의 손실: 0.039396\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.049114\t최선의 손실: 0.039396\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.070319\t최선의 손실: 0.039396\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.049911\t최선의 손실: 0.039396\t정확도: 98.75%\n",
+      "11\t검증 세트 손실: 0.090767\t최선의 손실: 0.039396\t정확도: 98.12%\n",
+      "12\t검증 세트 손실: 0.062656\t최선의 손실: 0.039396\t정확도: 98.40%\n",
+      "13\t검증 세트 손실: 0.046628\t최선의 손실: 0.039396\t정확도: 98.94%\n",
+      "14\t검증 세트 손실: 0.052898\t최선의 손실: 0.039396\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.041819\t최선의 손실: 0.039396\t정확도: 99.02%\n",
+      "16\t검증 세트 손실: 0.079374\t최선의 손실: 0.039396\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.045926\t최선의 손실: 0.039396\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.052596\t최선의 손실: 0.039396\t정확도: 98.98%\n",
+      "19\t검증 세트 손실: 0.076897\t최선의 손실: 0.039396\t정확도: 98.59%\n",
+      "20\t검증 세트 손실: 0.059759\t최선의 손실: 0.039396\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.063086\t최선의 손실: 0.039396\t정확도: 98.71%\n",
+      "22\t검증 세트 손실: 0.046622\t최선의 손실: 0.039396\t정확도: 99.02%\n",
+      "23\t검증 세트 손실: 0.064550\t최선의 손실: 0.039396\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.044008\t최선의 손실: 0.039396\t정확도: 99.02%\n",
+      "25\t검증 세트 손실: 0.069505\t최선의 손실: 0.039396\t정확도: 98.79%\n",
+      "26\t검증 세트 손실: 0.041251\t최선의 손실: 0.039396\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.059744\t최선의 손실: 0.039396\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100, total=  29.1s\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100 \n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=100, total=  22.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.161394\t최선의 손실: 0.161394\t정확도: 96.56%\n",
+      "1\t검증 세트 손실: 0.067289\t최선의 손실: 0.067289\t정확도: 98.36%\n",
+      "2\t검증 세트 손실: 0.056330\t최선의 손실: 0.056330\t정확도: 98.44%\n",
+      "3\t검증 세트 손실: 0.053458\t최선의 손실: 0.053458\t정확도: 98.59%\n",
+      "4\t검증 세트 손실: 0.071447\t최선의 손실: 0.053458\t정확도: 98.48%\n",
+      "5\t검증 세트 손실: 0.044694\t최선의 손실: 0.044694\t정확도: 98.71%\n",
+      "6\t검증 세트 손실: 0.061146\t최선의 손실: 0.044694\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.043055\t최선의 손실: 0.043055\t정확도: 98.79%\n",
+      "8\t검증 세트 손실: 0.052526\t최선의 손실: 0.043055\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.045807\t최선의 손실: 0.043055\t정확도: 98.75%\n",
+      "10\t검증 세트 손실: 0.043742\t최선의 손실: 0.043055\t정확도: 98.87%\n",
+      "11\t검증 세트 손실: 0.048216\t최선의 손실: 0.043055\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.050116\t최선의 손실: 0.043055\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.037879\t최선의 손실: 0.037879\t정확도: 99.02%\n",
+      "14\t검증 세트 손실: 0.041612\t최선의 손실: 0.037879\t정확도: 99.10%\n",
+      "15\t검증 세트 손실: 0.054091\t최선의 손실: 0.037879\t정확도: 98.59%\n",
+      "16\t검증 세트 손실: 0.052866\t최선의 손실: 0.037879\t정확도: 98.87%\n",
+      "17\t검증 세트 손실: 0.116048\t최선의 손실: 0.037879\t정확도: 97.93%\n",
+      "18\t검증 세트 손실: 0.057383\t최선의 손실: 0.037879\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.094535\t최선의 손실: 0.037879\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.046133\t최선의 손실: 0.037879\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.061255\t최선의 손실: 0.037879\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.069009\t최선의 손실: 0.037879\t정확도: 98.40%\n",
+      "23\t검증 세트 손실: 0.055576\t최선의 손실: 0.037879\t정확도: 98.71%\n",
+      "24\t검증 세트 손실: 0.057096\t최선의 손실: 0.037879\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.089235\t최선의 손실: 0.037879\t정확도: 98.44%\n",
+      "26\t검증 세트 손실: 0.048262\t최선의 손실: 0.037879\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.059564\t최선의 손실: 0.037879\t정확도: 98.79%\n",
+      "28\t검증 세트 손실: 0.084816\t최선의 손실: 0.037879\t정확도: 98.28%\n",
+      "29\t검증 세트 손실: 0.062579\t최선의 손실: 0.037879\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.064137\t최선의 손실: 0.037879\t정확도: 99.06%\n",
+      "31\t검증 세트 손실: 0.056499\t최선의 손실: 0.037879\t정확도: 98.71%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.168384\t최선의 손실: 0.168384\t정확도: 95.66%\n",
-      "1\t검증 세트 손실: 0.183874\t최선의 손실: 0.168384\t정확도: 93.82%\n",
-      "2\t검증 세트 손실: 0.125952\t최선의 손실: 0.125952\t정확도: 96.83%\n",
-      "3\t검증 세트 손실: 0.117678\t최선의 손실: 0.117678\t정확도: 96.56%\n",
-      "4\t검증 세트 손실: 0.111444\t최선의 손실: 0.111444\t정확도: 96.83%\n",
-      "5\t검증 세트 손실: 0.084458\t최선의 손실: 0.084458\t정확도: 97.69%\n",
-      "6\t검증 세트 손실: 0.067949\t최선의 손실: 0.067949\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.073644\t최선의 손실: 0.067949\t정확도: 97.62%\n",
-      "8\t검증 세트 손실: 0.072837\t최선의 손실: 0.067949\t정확도: 97.93%\n",
-      "9\t검증 세트 손실: 0.108688\t최선의 손실: 0.067949\t정확도: 97.62%\n",
-      "10\t검증 세트 손실: 0.102751\t최선의 손실: 0.067949\t정확도: 97.73%\n",
-      "11\t검증 세트 손실: 0.129216\t최선의 손실: 0.067949\t정확도: 97.11%\n",
-      "12\t검증 세트 손실: 0.117659\t최선의 손실: 0.067949\t정확도: 96.99%\n",
-      "13\t검증 세트 손실: 0.074464\t최선의 손실: 0.067949\t정확도: 98.05%\n",
-      "14\t검증 세트 손실: 0.079117\t최선의 손실: 0.067949\t정확도: 98.05%\n",
-      "15\t검증 세트 손실: 0.066480\t최선의 손실: 0.066480\t정확도: 98.44%\n",
-      "16\t검증 세트 손실: 0.054830\t최선의 손실: 0.054830\t정확도: 99.06%\n",
-      "17\t검증 세트 손실: 0.093794\t최선의 손실: 0.054830\t정확도: 98.05%\n",
-      "18\t검증 세트 손실: 0.089389\t최선의 손실: 0.054830\t정확도: 98.12%\n",
-      "19\t검증 세트 손실: 0.049833\t최선의 손실: 0.049833\t정확도: 98.79%\n",
-      "20\t검증 세트 손실: 0.068484\t최선의 손실: 0.049833\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.058101\t최선의 손실: 0.049833\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.097697\t최선의 손실: 0.049833\t정확도: 98.32%\n",
-      "23\t검증 세트 손실: 0.064056\t최선의 손실: 0.049833\t정확도: 98.28%\n",
-      "24\t검증 세트 손실: 0.075740\t최선의 손실: 0.049833\t정확도: 98.01%\n",
-      "25\t검증 세트 손실: 0.080390\t최선의 손실: 0.049833\t정확도: 98.51%\n",
-      "26\t검증 세트 손실: 0.063356\t최선의 손실: 0.049833\t정확도: 98.44%\n",
-      "27\t검증 세트 손실: 0.086273\t최선의 손실: 0.049833\t정확도: 98.24%\n",
-      "28\t검증 세트 손실: 0.065255\t최선의 손실: 0.049833\t정확도: 98.40%\n",
-      "29\t검증 세트 손실: 0.077373\t최선의 손실: 0.049833\t정확도: 98.63%\n",
-      "30\t검증 세트 손실: 0.099288\t최선의 손실: 0.049833\t정확도: 98.08%\n",
-      "31\t검증 세트 손실: 0.060742\t최선의 손실: 0.049833\t정확도: 98.59%\n",
-      "32\t검증 세트 손실: 0.057891\t최선의 손실: 0.049833\t정확도: 98.83%\n",
-      "33\t검증 세트 손실: 0.079496\t최선의 손실: 0.049833\t정확도: 98.44%\n",
-      "34\t검증 세트 손실: 0.057960\t최선의 손실: 0.049833\t정확도: 98.63%\n",
-      "35\t검증 세트 손실: 0.152059\t최선의 손실: 0.049833\t정확도: 97.34%\n",
-      "36\t검증 세트 손실: 0.062301\t최선의 손실: 0.049833\t정확도: 98.79%\n",
-      "37\t검증 세트 손실: 0.076158\t최선의 손실: 0.049833\t정확도: 98.51%\n",
-      "38\t검증 세트 손실: 0.102880\t최선의 손실: 0.049833\t정확도: 98.24%\n",
-      "39\t검증 세트 손실: 0.060986\t최선의 손실: 0.049833\t정확도: 98.79%\n",
-      "40\t검증 세트 손실: 0.093893\t최선의 손실: 0.049833\t정확도: 98.01%\n",
+      "32\t검증 세트 손실: 0.049268\t최선의 손실: 0.037879\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.061203\t최선의 손실: 0.037879\t정확도: 98.75%\n",
+      "34\t검증 세트 손실: 0.082191\t최선의 손실: 0.037879\t정확도: 98.28%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100, total= 4.5min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.107615\t최선의 손실: 0.107615\t정확도: 96.64%\n",
-      "1\t검증 세트 손실: 0.110525\t최선의 손실: 0.107615\t정확도: 97.50%\n",
-      "2\t검증 세트 손실: 0.107429\t최선의 손실: 0.107429\t정확도: 96.87%\n",
-      "3\t검증 세트 손실: 0.078045\t최선의 손실: 0.078045\t정확도: 97.54%\n",
-      "4\t검증 세트 손실: 0.077262\t최선의 손실: 0.077262\t정확도: 97.81%\n",
-      "5\t검증 세트 손실: 0.090713\t최선의 손실: 0.077262\t정확도: 97.54%\n",
-      "6\t검증 세트 손실: 0.158661\t최선의 손실: 0.077262\t정확도: 96.13%\n",
-      "7\t검증 세트 손실: 0.071949\t최선의 손실: 0.071949\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.072637\t최선의 손실: 0.071949\t정확도: 98.12%\n",
-      "9\t검증 세트 손실: 0.074508\t최선의 손실: 0.071949\t정확도: 97.65%\n",
-      "10\t검증 세트 손실: 0.120506\t최선의 손실: 0.071949\t정확도: 95.97%\n",
-      "11\t검증 세트 손실: 0.066215\t최선의 손실: 0.066215\t정확도: 98.16%\n",
-      "12\t검증 세트 손실: 0.048824\t최선의 손실: 0.048824\t정확도: 98.48%\n",
-      "13\t검증 세트 손실: 0.065436\t최선의 손실: 0.048824\t정확도: 98.08%\n",
-      "14\t검증 세트 손실: 0.078543\t최선의 손실: 0.048824\t정확도: 97.81%\n",
-      "15\t검증 세트 손실: 0.100961\t최선의 손실: 0.048824\t정확도: 97.07%\n",
-      "16\t검증 세트 손실: 0.136486\t최선의 손실: 0.048824\t정확도: 96.13%\n",
-      "17\t검증 세트 손실: 0.108710\t최선의 손실: 0.048824\t정확도: 97.50%\n",
-      "18\t검증 세트 손실: 0.064774\t최선의 손실: 0.048824\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.100774\t최선의 손실: 0.048824\t정확도: 97.97%\n",
-      "20\t검증 세트 손실: 0.056323\t최선의 손실: 0.048824\t정확도: 98.59%\n",
-      "21\t검증 세트 손실: 0.068761\t최선의 손실: 0.048824\t정확도: 98.44%\n",
-      "22\t검증 세트 손실: 0.070105\t최선의 손실: 0.048824\t정확도: 98.32%\n",
-      "23\t검증 세트 손실: 0.072182\t최선의 손실: 0.048824\t정확도: 97.97%\n",
-      "24\t검증 세트 손실: 0.082768\t최선의 손실: 0.048824\t정확도: 98.24%\n",
-      "25\t검증 세트 손실: 0.068886\t최선의 손실: 0.048824\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.099516\t최선의 손실: 0.048824\t정확도: 97.77%\n",
-      "27\t검증 세트 손실: 0.051563\t최선의 손실: 0.048824\t정확도: 98.67%\n",
-      "28\t검증 세트 손실: 0.109890\t최선의 손실: 0.048824\t정확도: 98.36%\n",
-      "29\t검증 세트 손실: 0.301644\t최선의 손실: 0.048824\t정확도: 94.76%\n",
-      "30\t검증 세트 손실: 0.093325\t최선의 손실: 0.048824\t정확도: 98.05%\n",
-      "31\t검증 세트 손실: 0.081047\t최선의 손실: 0.048824\t정확도: 98.28%\n",
-      "32\t검증 세트 손실: 0.128269\t최선의 손실: 0.048824\t정확도: 98.16%\n",
-      "33\t검증 세트 손실: 0.044747\t최선의 손실: 0.044747\t정확도: 98.55%\n",
-      "34\t검증 세트 손실: 0.041230\t최선의 손실: 0.041230\t정확도: 98.71%\n",
-      "35\t검증 세트 손실: 0.091427\t최선의 손실: 0.041230\t정확도: 97.89%\n",
-      "36\t검증 세트 손실: 0.144034\t최선의 손실: 0.041230\t정확도: 97.11%\n",
-      "37\t검증 세트 손실: 0.068675\t최선의 손실: 0.041230\t정확도: 98.36%\n",
-      "38\t검증 세트 손실: 0.060470\t최선의 손실: 0.041230\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.060573\t최선의 손실: 0.041230\t정확도: 98.20%\n",
-      "40\t검증 세트 손실: 0.076512\t최선의 손실: 0.041230\t정확도: 98.36%\n",
-      "41\t검증 세트 손실: 0.077706\t최선의 손실: 0.041230\t정확도: 98.32%\n",
-      "42\t검증 세트 손실: 0.081979\t최선의 손실: 0.041230\t정확도: 98.71%\n",
-      "43\t검증 세트 손실: 0.081326\t최선의 손실: 0.041230\t정확도: 98.40%\n",
-      "44\t검증 세트 손실: 0.058565\t최선의 손실: 0.041230\t정확도: 98.67%\n",
-      "45\t검증 세트 손실: 0.104117\t최선의 손실: 0.041230\t정확도: 97.15%\n",
-      "46\t검증 세트 손실: 0.265317\t최선의 손실: 0.041230\t정확도: 96.68%\n",
-      "47\t검증 세트 손실: 0.078761\t최선의 손실: 0.041230\t정확도: 98.63%\n",
-      "48\t검증 세트 손실: 0.075423\t최선의 손실: 0.041230\t정확도: 98.16%\n",
-      "49\t검증 세트 손실: 0.090239\t최선의 손실: 0.041230\t정확도: 98.51%\n",
-      "50\t검증 세트 손실: 0.077168\t최선의 손실: 0.041230\t정확도: 98.24%\n",
-      "51\t검증 세트 손실: 0.098744\t최선의 손실: 0.041230\t정확도: 98.40%\n",
-      "52\t검증 세트 손실: 0.074640\t최선의 손실: 0.041230\t정확도: 98.16%\n",
-      "53\t검증 세트 손실: 0.110454\t최선의 손실: 0.041230\t정확도: 97.65%\n",
-      "54\t검증 세트 손실: 0.086504\t최선의 손실: 0.041230\t정확도: 97.62%\n",
-      "55\t검증 세트 손실: 0.066505\t최선의 손실: 0.041230\t정확도: 97.97%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=100, total=  27.3s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.133654\t최선의 손실: 0.133654\t정확도: 97.34%\n",
+      "1\t검증 세트 손실: 0.110461\t최선의 손실: 0.110461\t정확도: 97.30%\n",
+      "2\t검증 세트 손실: 0.072654\t최선의 손실: 0.072654\t정확도: 97.85%\n",
+      "3\t검증 세트 손실: 0.077950\t최선의 손실: 0.072654\t정확도: 98.24%\n",
+      "4\t검증 세트 손실: 0.035914\t최선의 손실: 0.035914\t정확도: 99.06%\n",
+      "5\t검증 세트 손실: 0.038382\t최선의 손실: 0.035914\t정확도: 99.02%\n",
+      "6\t검증 세트 손실: 0.059620\t최선의 손실: 0.035914\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.055095\t최선의 손실: 0.035914\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.054558\t최선의 손실: 0.035914\t정확도: 98.59%\n",
+      "9\t검증 세트 손실: 0.047655\t최선의 손실: 0.035914\t정확도: 98.98%\n",
+      "10\t검증 세트 손실: 0.046808\t최선의 손실: 0.035914\t정확도: 98.75%\n",
+      "11\t검증 세트 손실: 0.051341\t최선의 손실: 0.035914\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.064113\t최선의 손실: 0.035914\t정확도: 98.59%\n",
+      "13\t검증 세트 손실: 0.031824\t최선의 손실: 0.031824\t정확도: 99.18%\n",
+      "14\t검증 세트 손실: 0.050515\t최선의 손실: 0.031824\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.068757\t최선의 손실: 0.031824\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.050589\t최선의 손실: 0.031824\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.044696\t최선의 손실: 0.031824\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.071638\t최선의 손실: 0.031824\t정확도: 98.36%\n",
+      "19\t검증 세트 손실: 0.049810\t최선의 손실: 0.031824\t정확도: 98.94%\n",
+      "20\t검증 세트 손실: 0.048257\t최선의 손실: 0.031824\t정확도: 99.02%\n",
+      "21\t검증 세트 손실: 0.053362\t최선의 손실: 0.031824\t정확도: 99.02%\n",
+      "22\t검증 세트 손실: 0.081526\t최선의 손실: 0.031824\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.049269\t최선의 손실: 0.031824\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.036882\t최선의 손실: 0.031824\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.041976\t최선의 손실: 0.031824\t정확도: 99.22%\n",
+      "26\t검증 세트 손실: 0.075618\t최선의 손실: 0.031824\t정확도: 98.24%\n",
+      "27\t검증 세트 손실: 0.042882\t최선의 손실: 0.031824\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.050440\t최선의 손실: 0.031824\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.052706\t최선의 손실: 0.031824\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.056886\t최선의 손실: 0.031824\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.049100\t최선의 손실: 0.031824\t정확도: 99.06%\n",
+      "32\t검증 세트 손실: 0.050914\t최선의 손실: 0.031824\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.065283\t최선의 손실: 0.031824\t정확도: 98.98%\n",
+      "34\t검증 세트 손실: 0.063661\t최선의 손실: 0.031824\t정확도: 98.67%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100, total= 6.2min\n",
-      "[CV] batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.106830\t최선의 손실: 0.106830\t정확도: 96.95%\n",
-      "1\t검증 세트 손실: 0.087398\t최선의 손실: 0.087398\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.083018\t최선의 손실: 0.083018\t정확도: 97.22%\n",
-      "3\t검증 세트 손실: 0.087518\t최선의 손실: 0.083018\t정확도: 97.38%\n",
-      "4\t검증 세트 손실: 0.114980\t최선의 손실: 0.083018\t정확도: 97.26%\n",
-      "5\t검증 세트 손실: 0.254916\t최선의 손실: 0.083018\t정확도: 84.36%\n",
-      "6\t검증 세트 손실: 0.097893\t최선의 손실: 0.083018\t정확도: 97.26%\n",
-      "7\t검증 세트 손실: 0.086771\t최선의 손실: 0.083018\t정확도: 98.01%\n",
-      "8\t검증 세트 손실: 0.086129\t최선의 손실: 0.083018\t정확도: 97.65%\n",
-      "9\t검증 세트 손실: 0.090176\t최선의 손실: 0.083018\t정확도: 97.38%\n",
-      "10\t검증 세트 손실: 0.095937\t최선의 손실: 0.083018\t정확도: 97.85%\n",
-      "11\t검증 세트 손실: 0.049568\t최선의 손실: 0.049568\t정확도: 98.71%\n",
-      "12\t검증 세트 손실: 0.077188\t최선의 손실: 0.049568\t정확도: 98.16%\n",
-      "13\t검증 세트 손실: 0.075951\t최선의 손실: 0.049568\t정확도: 98.36%\n",
-      "14\t검증 세트 손실: 0.121075\t최선의 손실: 0.049568\t정확도: 97.46%\n",
-      "15\t검증 세트 손실: 0.095755\t최선의 손실: 0.049568\t정확도: 97.46%\n",
-      "16\t검증 세트 손실: 0.062389\t최선의 손실: 0.049568\t정확도: 98.44%\n",
-      "17\t검증 세트 손실: 0.084259\t최선의 손실: 0.049568\t정확도: 98.20%\n",
-      "18\t검증 세트 손실: 0.065424\t최선의 손실: 0.049568\t정확도: 98.63%\n",
-      "19\t검증 세트 손실: 0.107552\t최선의 손실: 0.049568\t정확도: 98.28%\n",
-      "20\t검증 세트 손실: 0.103515\t최선의 손실: 0.049568\t정확도: 97.89%\n",
-      "21\t검증 세트 손실: 0.078146\t최선의 손실: 0.049568\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.078265\t최선의 손실: 0.049568\t정확도: 98.44%\n",
-      "23\t검증 세트 손실: 0.058457\t최선의 손실: 0.049568\t정확도: 98.36%\n",
-      "24\t검증 세트 손실: 0.064768\t최선의 손실: 0.049568\t정확도: 98.20%\n",
-      "25\t검증 세트 손실: 0.088660\t최선의 손실: 0.049568\t정확도: 97.93%\n",
-      "26\t검증 세트 손실: 0.078711\t최선의 손실: 0.049568\t정확도: 98.28%\n",
-      "27\t검증 세트 손실: 0.069621\t최선의 손실: 0.049568\t정확도: 98.40%\n",
-      "28\t검증 세트 손실: 0.104384\t최선의 손실: 0.049568\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.179617\t최선의 손실: 0.049568\t정확도: 97.15%\n",
-      "30\t검증 세트 손실: 0.101080\t최선의 손실: 0.049568\t정확도: 97.97%\n",
-      "31\t검증 세트 손실: 0.059724\t최선의 손실: 0.049568\t정확도: 98.24%\n",
-      "32\t검증 세트 손실: 0.061376\t최선의 손실: 0.049568\t정확도: 97.93%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.01, batch_norm_momentum=0.99, n_neurons=100, total=  27.3s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.144551\t최선의 손실: 0.144551\t정확도: 95.90%\n",
+      "1\t검증 세트 손실: 0.116310\t최선의 손실: 0.116310\t정확도: 95.93%\n",
+      "2\t검증 세트 손실: 0.100381\t최선의 손실: 0.100381\t정확도: 97.26%\n",
+      "3\t검증 세트 손실: 0.119972\t최선의 손실: 0.100381\t정확도: 96.64%\n",
+      "4\t검증 세트 손실: 0.067615\t최선의 손실: 0.067615\t정확도: 98.05%\n",
+      "5\t검증 세트 손실: 0.076297\t최선의 손실: 0.067615\t정확도: 97.54%\n",
+      "6\t검증 세트 손실: 0.176198\t최선의 손실: 0.067615\t정확도: 96.25%\n",
+      "7\t검증 세트 손실: 0.051072\t최선의 손실: 0.051072\t정확도: 98.55%\n",
+      "8\t검증 세트 손실: 0.082424\t최선의 손실: 0.051072\t정확도: 97.22%\n",
+      "9\t검증 세트 손실: 0.158158\t최선의 손실: 0.051072\t정확도: 96.21%\n",
+      "10\t검증 세트 손실: 0.086964\t최선의 손실: 0.051072\t정확도: 97.69%\n",
+      "11\t검증 세트 손실: 0.060028\t최선의 손실: 0.051072\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.072540\t최선의 손실: 0.051072\t정확도: 97.89%\n",
+      "13\t검증 세트 손실: 0.079637\t최선의 손실: 0.051072\t정확도: 97.77%\n",
+      "14\t검증 세트 손실: 0.064637\t최선의 손실: 0.051072\t정확도: 98.36%\n",
+      "15\t검증 세트 손실: 0.068313\t최선의 손실: 0.051072\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.101897\t최선의 손실: 0.051072\t정확도: 97.73%\n",
+      "17\t검증 세트 손실: 0.080606\t최선의 손실: 0.051072\t정확도: 98.12%\n",
+      "18\t검증 세트 손실: 0.078153\t최선의 손실: 0.051072\t정확도: 98.24%\n",
+      "19\t검증 세트 손실: 0.053681\t최선의 손실: 0.051072\t정확도: 98.63%\n",
+      "20\t검증 세트 손실: 0.059021\t최선의 손실: 0.051072\t정확도: 98.83%\n",
+      "21\t검증 세트 손실: 0.060512\t최선의 손실: 0.051072\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.121736\t최선의 손실: 0.051072\t정확도: 98.16%\n",
+      "23\t검증 세트 손실: 0.054560\t최선의 손실: 0.051072\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.058051\t최선의 손실: 0.051072\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.094590\t최선의 손실: 0.051072\t정확도: 98.48%\n",
+      "26\t검증 세트 손실: 0.066408\t최선의 손실: 0.051072\t정확도: 98.59%\n",
+      "27\t검증 세트 손실: 0.082490\t최선의 손실: 0.051072\t정확도: 97.81%\n",
+      "28\t검증 세트 손실: 0.077736\t최선의 손실: 0.051072\t정확도: 98.24%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.9, n_neurons=100, total= 3.7min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.087054\t최선의 손실: 1.087054\t정확도: 91.44%\n",
-      "1\t검증 세트 손실: 1.110997\t최선의 손실: 1.087054\t정확도: 81.90%\n",
-      "2\t검증 세트 손실: 0.654894\t최선의 손실: 0.654894\t정확도: 91.28%\n",
-      "3\t검증 세트 손실: 0.440913\t최선의 손실: 0.440913\t정확도: 91.99%\n",
-      "4\t검증 세트 손실: 0.997604\t최선의 손실: 0.440913\t정확도: 88.70%\n",
-      "5\t검증 세트 손실: 0.259512\t최선의 손실: 0.259512\t정확도: 92.46%\n",
-      "6\t검증 세트 손실: 0.440711\t최선의 손실: 0.259512\t정확도: 94.80%\n",
-      "7\t검증 세트 손실: 0.117466\t최선의 손실: 0.117466\t정확도: 97.07%\n",
-      "8\t검증 세트 손실: 0.417906\t최선의 손실: 0.117466\t정확도: 93.43%\n",
-      "9\t검증 세트 손실: 0.220368\t최선의 손실: 0.117466\t정확도: 96.68%\n",
-      "10\t검증 세트 손실: 0.090086\t최선의 손실: 0.090086\t정확도: 98.05%\n",
-      "11\t검증 세트 손실: 0.259872\t최선의 손실: 0.090086\t정확도: 96.60%\n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=100, total= 3.3min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.158769\t최선의 손실: 0.158769\t정확도: 95.23%\n",
+      "1\t검증 세트 손실: 0.130086\t최선의 손실: 0.130086\t정확도: 96.09%\n",
+      "2\t검증 세트 손실: 0.102388\t최선의 손실: 0.102388\t정확도: 96.99%\n",
+      "3\t검증 세트 손실: 0.103125\t최선의 손실: 0.102388\t정확도: 97.22%\n",
+      "4\t검증 세트 손실: 0.080375\t최선의 손실: 0.080375\t정확도: 97.81%\n",
+      "5\t검증 세트 손실: 0.102045\t최선의 손실: 0.080375\t정확도: 97.85%\n",
+      "6\t검증 세트 손실: 0.083224\t최선의 손실: 0.080375\t정확도: 98.05%\n",
+      "7\t검증 세트 손실: 0.085090\t최선의 손실: 0.080375\t정확도: 97.85%\n",
+      "8\t검증 세트 손실: 0.100082\t최선의 손실: 0.080375\t정확도: 97.85%\n",
+      "9\t검증 세트 손실: 0.079508\t최선의 손실: 0.079508\t정확도: 98.16%\n",
+      "10\t검증 세트 손실: 0.103537\t최선의 손실: 0.079508\t정확도: 97.46%\n",
+      "11\t검증 세트 손실: 0.065633\t최선의 손실: 0.065633\t정확도: 98.87%\n",
+      "12\t검증 세트 손실: 0.070693\t최선의 손실: 0.065633\t정확도: 98.36%\n",
+      "13\t검증 세트 손실: 0.105670\t최선의 손실: 0.065633\t정확도: 97.69%\n",
+      "14\t검증 세트 손실: 0.057834\t최선의 손실: 0.057834\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.070851\t최선의 손실: 0.057834\t정확도: 98.36%\n",
+      "16\t검증 세트 손실: 0.088599\t최선의 손실: 0.057834\t정확도: 98.12%\n",
+      "17\t검증 세트 손실: 0.052759\t최선의 손실: 0.052759\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.097003\t최선의 손실: 0.052759\t정확도: 97.69%\n",
+      "19\t검증 세트 손실: 0.080440\t최선의 손실: 0.052759\t정확도: 98.55%\n",
+      "20\t검증 세트 손실: 0.048717\t최선의 손실: 0.048717\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.082395\t최선의 손실: 0.048717\t정확도: 98.20%\n",
+      "22\t검증 세트 손실: 0.061601\t최선의 손실: 0.048717\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.055394\t최선의 손실: 0.048717\t정확도: 98.75%\n",
+      "24\t검증 세트 손실: 0.089831\t최선의 손실: 0.048717\t정확도: 97.97%\n",
+      "25\t검증 세트 손실: 0.063026\t최선의 손실: 0.048717\t정확도: 98.28%\n",
+      "26\t검증 세트 손실: 0.097280\t최선의 손실: 0.048717\t정확도: 98.40%\n",
+      "27\t검증 세트 손실: 0.065314\t최선의 손실: 0.048717\t정확도: 98.36%\n",
+      "28\t검증 세트 손실: 0.048612\t최선의 손실: 0.048612\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.046045\t최선의 손실: 0.046045\t정확도: 98.83%\n",
+      "30\t검증 세트 손실: 0.066140\t최선의 손실: 0.046045\t정확도: 97.97%\n",
+      "31\t검증 세트 손실: 0.056148\t최선의 손실: 0.046045\t정확도: 98.36%\n",
+      "32\t검증 세트 손실: 0.063132\t최선의 손실: 0.046045\t정확도: 98.24%\n",
+      "33\t검증 세트 손실: 0.081330\t최선의 손실: 0.046045\t정확도: 97.58%\n",
+      "34\t검증 세트 손실: 0.052933\t최선의 손실: 0.046045\t정확도: 98.71%\n",
+      "35\t검증 세트 손실: 0.118788\t최선의 손실: 0.046045\t정확도: 96.76%\n",
+      "36\t검증 세트 손실: 0.064742\t최선의 손실: 0.046045\t정확도: 98.63%\n",
+      "37\t검증 세트 손실: 0.060297\t최선의 손실: 0.046045\t정확도: 98.67%\n",
+      "38\t검증 세트 손실: 0.057539\t최선의 손실: 0.046045\t정확도: 98.63%\n",
+      "39\t검증 세트 손실: 0.055329\t최선의 손실: 0.046045\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.047086\t최선의 손실: 0.046045\t정확도: 98.79%\n",
+      "41\t검증 세트 손실: 0.349151\t최선의 손실: 0.046045\t정확도: 92.73%\n",
+      "42\t검증 세트 손실: 0.067062\t최선의 손실: 0.046045\t정확도: 98.63%\n",
+      "43\t검증 세트 손실: 0.048457\t최선의 손실: 0.046045\t정확도: 98.83%\n",
+      "44\t검증 세트 손실: 0.062351\t최선의 손실: 0.046045\t정확도: 98.67%\n",
+      "45\t검증 세트 손실: 0.087215\t최선의 손실: 0.046045\t정확도: 97.89%\n",
+      "46\t검증 세트 손실: 0.093897\t최선의 손실: 0.046045\t정확도: 98.67%\n",
+      "47\t검증 세트 손실: 0.063740\t최선의 손실: 0.046045\t정확도: 98.67%\n",
+      "48\t검증 세트 손실: 0.058062\t최선의 손실: 0.046045\t정확도: 98.83%\n",
+      "49\t검증 세트 손실: 0.068311\t최선의 손실: 0.046045\t정확도: 98.87%\n",
+      "50\t검증 세트 손실: 0.087333\t최선의 손실: 0.046045\t정확도: 98.59%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=100, total= 5.7min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.129841\t최선의 손실: 0.129841\t정확도: 97.07%\n",
+      "1\t검증 세트 손실: 0.109284\t최선의 손실: 0.109284\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.106268\t최선의 손실: 0.106268\t정확도: 97.22%\n",
+      "3\t검증 세트 손실: 0.091080\t최선의 손실: 0.091080\t정확도: 97.46%\n",
+      "4\t검증 세트 손실: 0.148931\t최선의 손실: 0.091080\t정확도: 95.70%\n",
+      "5\t검증 세트 손실: 0.072530\t최선의 손실: 0.072530\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.082197\t최선의 손실: 0.072530\t정확도: 97.38%\n",
+      "7\t검증 세트 손실: 0.091775\t최선의 손실: 0.072530\t정확도: 98.16%\n",
+      "8\t검증 세트 손실: 0.067102\t최선의 손실: 0.067102\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.072692\t최선의 손실: 0.067102\t정확도: 98.05%\n",
+      "10\t검증 세트 손실: 0.080821\t최선의 손실: 0.067102\t정확도: 97.58%\n",
+      "11\t검증 세트 손실: 0.072944\t최선의 손실: 0.067102\t정확도: 98.24%\n",
+      "12\t검증 세트 손실: 0.062013\t최선의 손실: 0.062013\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.061679\t최선의 손실: 0.061679\t정확도: 98.32%\n",
+      "14\t검증 세트 손실: 0.050635\t최선의 손실: 0.050635\t정확도: 98.36%\n",
+      "15\t검증 세트 손실: 0.067161\t최선의 손실: 0.050635\t정확도: 98.24%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 0.207397\t최선의 손실: 0.090086\t정확도: 97.26%\n",
-      "13\t검증 세트 손실: 0.163452\t최선의 손실: 0.090086\t정확도: 97.89%\n",
-      "14\t검증 세트 손실: 0.095154\t최선의 손실: 0.090086\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.175490\t최선의 손실: 0.090086\t정확도: 97.93%\n",
-      "16\t검증 세트 손실: 0.282783\t최선의 손실: 0.090086\t정확도: 96.13%\n",
-      "17\t검증 세트 손실: 0.119185\t최선의 손실: 0.090086\t정확도: 98.44%\n",
-      "18\t검증 세트 손실: 0.133034\t최선의 손실: 0.090086\t정확도: 98.51%\n",
-      "19\t검증 세트 손실: 0.114499\t최선의 손실: 0.090086\t정확도: 98.40%\n",
-      "20\t검증 세트 손실: 0.146958\t최선의 손실: 0.090086\t정확도: 98.40%\n",
-      "21\t검증 세트 손실: 0.189958\t최선의 손실: 0.090086\t정확도: 97.73%\n",
-      "22\t검증 세트 손실: 0.132703\t최선의 손실: 0.090086\t정확도: 98.28%\n",
-      "23\t검증 세트 손실: 0.144382\t최선의 손실: 0.090086\t정확도: 98.59%\n",
-      "24\t검증 세트 손실: 0.145808\t최선의 손실: 0.090086\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.215773\t최선의 손실: 0.090086\t정확도: 97.42%\n",
-      "26\t검증 세트 손실: 0.153022\t최선의 손실: 0.090086\t정확도: 98.36%\n",
-      "27\t검증 세트 손실: 0.169206\t최선의 손실: 0.090086\t정확도: 98.24%\n",
-      "28\t검증 세트 손실: 0.246328\t최선의 손실: 0.090086\t정확도: 96.68%\n",
-      "29\t검증 세트 손실: 0.271059\t최선의 손실: 0.090086\t정확도: 97.77%\n",
-      "30\t검증 세트 손실: 0.177311\t최선의 손실: 0.090086\t정확도: 98.08%\n",
-      "31\t검증 세트 손실: 0.160220\t최선의 손실: 0.090086\t정확도: 98.55%\n",
+      "16\t검증 세트 손실: 0.044588\t최선의 손실: 0.044588\t정확도: 98.59%\n",
+      "17\t검증 세트 손실: 0.045706\t최선의 손실: 0.044588\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.062400\t최선의 손실: 0.044588\t정확도: 98.24%\n",
+      "19\t검증 세트 손실: 0.055432\t최선의 손실: 0.044588\t정확도: 98.67%\n",
+      "20\t검증 세트 손실: 0.264644\t최선의 손실: 0.044588\t정확도: 94.53%\n",
+      "21\t검증 세트 손실: 0.042788\t최선의 손실: 0.042788\t정확도: 98.91%\n",
+      "22\t검증 세트 손실: 0.068676\t최선의 손실: 0.042788\t정확도: 98.24%\n",
+      "23\t검증 세트 손실: 0.053952\t최선의 손실: 0.042788\t정확도: 98.48%\n",
+      "24\t검증 세트 손실: 0.098559\t최선의 손실: 0.042788\t정확도: 97.73%\n",
+      "25\t검증 세트 손실: 0.061829\t최선의 손실: 0.042788\t정확도: 98.75%\n",
+      "26\t검증 세트 손실: 0.060669\t최선의 손실: 0.042788\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.048550\t최선의 손실: 0.042788\t정확도: 98.51%\n",
+      "28\t검증 세트 손실: 0.044040\t최선의 손실: 0.042788\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.057678\t최선의 손실: 0.042788\t정확도: 98.48%\n",
+      "30\t검증 세트 손실: 0.087282\t최선의 손실: 0.042788\t정확도: 97.93%\n",
+      "31\t검증 세트 손실: 0.100054\t최선의 손실: 0.042788\t정확도: 98.24%\n",
+      "32\t검증 세트 손실: 0.044889\t최선의 손실: 0.042788\t정확도: 98.71%\n",
+      "33\t검증 세트 손실: 0.064980\t최선의 손실: 0.042788\t정확도: 98.63%\n",
+      "34\t검증 세트 손실: 0.053308\t최선의 손실: 0.042788\t정확도: 98.83%\n",
+      "35\t검증 세트 손실: 0.054006\t최선의 손실: 0.042788\t정확도: 98.91%\n",
+      "36\t검증 세트 손실: 0.132288\t최선의 손실: 0.042788\t정확도: 97.15%\n",
+      "37\t검증 세트 손실: 0.057736\t최선의 손실: 0.042788\t정확도: 98.40%\n",
+      "38\t검증 세트 손실: 0.060927\t최선의 손실: 0.042788\t정확도: 98.32%\n",
+      "39\t검증 세트 손실: 0.048779\t최선의 손실: 0.042788\t정확도: 98.75%\n",
+      "40\t검증 세트 손실: 0.049862\t최선의 손실: 0.042788\t정확도: 98.75%\n",
+      "41\t검증 세트 손실: 0.224073\t최선의 손실: 0.042788\t정확도: 98.59%\n",
+      "42\t검증 세트 손실: 0.083405\t최선의 손실: 0.042788\t정확도: 98.55%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140, total= 3.3min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.714522\t최선의 손실: 0.714522\t정확도: 93.82%\n",
-      "1\t검증 세트 손실: 0.180522\t최선의 손실: 0.180522\t정확도: 96.95%\n",
-      "2\t검증 세트 손실: 0.140966\t최선의 손실: 0.140966\t정확도: 97.03%\n",
-      "3\t검증 세트 손실: 0.197282\t최선의 손실: 0.140966\t정확도: 96.72%\n",
-      "4\t검증 세트 손실: 1.738843\t최선의 손실: 0.140966\t정확도: 85.38%\n",
-      "5\t검증 세트 손실: 0.136472\t최선의 손실: 0.136472\t정확도: 96.40%\n",
-      "6\t검증 세트 손실: 0.260628\t최선의 손실: 0.136472\t정확도: 96.44%\n",
-      "7\t검증 세트 손실: 0.052152\t최선의 손실: 0.052152\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.084876\t최선의 손실: 0.052152\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.186840\t최선의 손실: 0.052152\t정확도: 97.38%\n",
-      "10\t검증 세트 손실: 1.016271\t최선의 손실: 0.052152\t정확도: 87.88%\n",
-      "11\t검증 세트 손실: 0.063412\t최선의 손실: 0.052152\t정확도: 98.98%\n",
-      "12\t검증 세트 손실: 0.098231\t최선의 손실: 0.052152\t정확도: 98.12%\n",
-      "13\t검증 세트 손실: 0.142877\t최선의 손실: 0.052152\t정확도: 98.20%\n",
-      "14\t검증 세트 손실: 0.116131\t최선의 손실: 0.052152\t정확도: 97.15%\n",
-      "15\t검증 세트 손실: 0.171068\t최선의 손실: 0.052152\t정확도: 97.54%\n",
-      "16\t검증 세트 손실: 0.189537\t최선의 손실: 0.052152\t정확도: 97.54%\n",
-      "17\t검증 세트 손실: 0.183682\t최선의 손실: 0.052152\t정확도: 98.24%\n",
-      "18\t검증 세트 손실: 0.290977\t최선의 손실: 0.052152\t정확도: 96.36%\n",
-      "19\t검증 세트 손실: 0.234023\t최선의 손실: 0.052152\t정확도: 97.54%\n",
-      "20\t검증 세트 손실: 0.092319\t최선의 손실: 0.052152\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.122579\t최선의 손실: 0.052152\t정확도: 98.63%\n",
-      "22\t검증 세트 손실: 0.103447\t최선의 손실: 0.052152\t정확도: 98.79%\n",
-      "23\t검증 세트 손실: 0.168372\t최선의 손실: 0.052152\t정확도: 98.16%\n",
-      "24\t검증 세트 손실: 0.166951\t최선의 손실: 0.052152\t정확도: 98.32%\n",
-      "25\t검증 세트 손실: 0.116456\t최선의 손실: 0.052152\t정확도: 98.71%\n",
-      "26\t검증 세트 손실: 0.106733\t최선의 손실: 0.052152\t정확도: 98.44%\n",
-      "27\t검증 세트 손실: 0.920902\t최선의 손실: 0.052152\t정확도: 93.16%\n",
-      "28\t검증 세트 손실: 0.354307\t최선의 손실: 0.052152\t정확도: 97.46%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.1, batch_norm_momentum=0.9, n_neurons=100, total= 4.8min\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.509582\t최선의 손실: 0.509582\t정확도: 96.13%\n",
+      "1\t검증 세트 손실: 0.291314\t최선의 손실: 0.291314\t정확도: 96.68%\n",
+      "2\t검증 세트 손실: 0.234906\t최선의 손실: 0.234906\t정확도: 94.96%\n",
+      "3\t검증 세트 손실: 0.128765\t최선의 손실: 0.128765\t정확도: 96.56%\n",
+      "4\t검증 세트 손실: 0.134042\t최선의 손실: 0.128765\t정확도: 96.95%\n",
+      "5\t검증 세트 손실: 0.115241\t최선의 손실: 0.115241\t정확도: 96.95%\n",
+      "6\t검증 세트 손실: 0.235785\t최선의 손실: 0.115241\t정확도: 95.47%\n",
+      "7\t검증 세트 손실: 0.067992\t최선의 손실: 0.067992\t정확도: 98.20%\n",
+      "8\t검증 세트 손실: 0.230837\t최선의 손실: 0.067992\t정확도: 96.72%\n",
+      "9\t검증 세트 손실: 0.189988\t최선의 손실: 0.067992\t정확도: 97.11%\n",
+      "10\t검증 세트 손실: 0.064175\t최선의 손실: 0.064175\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.170783\t최선의 손실: 0.064175\t정확도: 97.69%\n",
+      "12\t검증 세트 손실: 0.201488\t최선의 손실: 0.064175\t정확도: 97.34%\n",
+      "13\t검증 세트 손실: 0.294559\t최선의 손실: 0.064175\t정확도: 96.91%\n",
+      "14\t검증 세트 손실: 0.106344\t최선의 손실: 0.064175\t정확도: 98.12%\n",
+      "15\t검증 세트 손실: 0.299656\t최선의 손실: 0.064175\t정확도: 96.21%\n",
+      "16\t검증 세트 손실: 0.237869\t최선의 손실: 0.064175\t정확도: 97.97%\n",
+      "17\t검증 세트 손실: 0.116589\t최선의 손실: 0.064175\t정확도: 98.71%\n",
+      "18\t검증 세트 손실: 0.176744\t최선의 손실: 0.064175\t정확도: 97.46%\n",
+      "19\t검증 세트 손실: 0.101439\t최선의 손실: 0.064175\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.071610\t최선의 손실: 0.064175\t정확도: 98.79%\n",
+      "21\t검증 세트 손실: 0.126507\t최선의 손실: 0.064175\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.155054\t최선의 손실: 0.064175\t정확도: 98.20%\n",
+      "23\t검증 세트 손실: 0.106519\t최선의 손실: 0.064175\t정확도: 98.71%\n",
+      "24\t검증 세트 손실: 0.149832\t최선의 손실: 0.064175\t정확도: 98.05%\n",
+      "25\t검증 세트 손실: 0.211202\t최선의 손실: 0.064175\t정확도: 98.16%\n",
+      "26\t검증 세트 손실: 0.074377\t최선의 손실: 0.064175\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.165355\t최선의 손실: 0.064175\t정확도: 98.59%\n",
+      "28\t검증 세트 손실: 0.136431\t최선의 손실: 0.064175\t정확도: 98.36%\n",
+      "29\t검증 세트 손실: 0.525734\t최선의 손실: 0.064175\t정확도: 97.07%\n",
+      "30\t검증 세트 손실: 0.162243\t최선의 손실: 0.064175\t정확도: 98.87%\n",
+      "31\t검증 세트 손실: 0.153574\t최선의 손실: 0.064175\t정확도: 98.79%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140, total= 3.4min\n",
-      "[CV] batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.604146\t최선의 손실: 0.604146\t정확도: 95.35%\n",
-      "1\t검증 세트 손실: 1.329873\t최선의 손실: 0.604146\t정확도: 91.95%\n",
-      "2\t검증 세트 손실: 0.238045\t최선의 손실: 0.238045\t정확도: 94.92%\n",
-      "3\t검증 세트 손실: 0.268044\t최선의 손실: 0.238045\t정확도: 95.47%\n",
-      "4\t검증 세트 손실: 0.311353\t최선의 손실: 0.238045\t정확도: 97.46%\n",
-      "5\t검증 세트 손실: 0.153883\t최선의 손실: 0.153883\t정확도: 97.93%\n",
-      "6\t검증 세트 손실: 0.186666\t최선의 손실: 0.153883\t정확도: 97.58%\n",
-      "7\t검증 세트 손실: 0.248684\t최선의 손실: 0.153883\t정확도: 97.93%\n",
-      "8\t검증 세트 손실: 0.177618\t최선의 손실: 0.153883\t정확도: 97.26%\n",
-      "9\t검증 세트 손실: 0.085279\t최선의 손실: 0.085279\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.475930\t최선의 손실: 0.085279\t정확도: 91.44%\n",
-      "11\t검증 세트 손실: 0.126672\t최선의 손실: 0.085279\t정확도: 97.81%\n",
-      "12\t검증 세트 손실: 0.115656\t최선의 손실: 0.085279\t정확도: 98.16%\n",
-      "13\t검증 세트 손실: 0.497583\t최선의 손실: 0.085279\t정확도: 97.34%\n",
-      "14\t검증 세트 손실: 0.128585\t최선의 손실: 0.085279\t정확도: 98.05%\n",
-      "15\t검증 세트 손실: 0.317791\t최선의 손실: 0.085279\t정확도: 95.23%\n",
-      "16\t검증 세트 손실: 0.154731\t최선의 손실: 0.085279\t정확도: 98.24%\n",
-      "17\t검증 세트 손실: 0.098246\t최선의 손실: 0.085279\t정확도: 98.67%\n",
-      "18\t검증 세트 손실: 0.090112\t최선의 손실: 0.085279\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.264705\t최선의 손실: 0.085279\t정확도: 97.34%\n",
-      "20\t검증 세트 손실: 0.195188\t최선의 손실: 0.085279\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.131145\t최선의 손실: 0.085279\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.155213\t최선의 손실: 0.085279\t정확도: 98.12%\n",
-      "23\t검증 세트 손실: 0.098376\t최선의 손실: 0.085279\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.109590\t최선의 손실: 0.085279\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.212864\t최선의 손실: 0.085279\t정확도: 98.48%\n",
-      "26\t검증 세트 손실: 0.248485\t최선의 손실: 0.085279\t정확도: 97.65%\n",
-      "27\t검증 세트 손실: 0.104103\t최선의 손실: 0.085279\t정확도: 98.44%\n",
-      "28\t검증 세트 손실: 0.146708\t최선의 손실: 0.085279\t정확도: 98.24%\n",
-      "29\t검증 세트 손실: 0.124576\t최선의 손실: 0.085279\t정확도: 98.36%\n",
-      "30\t검증 세트 손실: 0.166491\t최선의 손실: 0.085279\t정확도: 98.59%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140, total= 3.3min\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.245778\t최선의 손실: 0.245778\t정확도: 97.03%\n",
+      "1\t검증 세트 손실: 0.898370\t최선의 손실: 0.245778\t정확도: 92.61%\n",
+      "2\t검증 세트 손실: 0.738140\t최선의 손실: 0.245778\t정확도: 89.01%\n",
+      "3\t검증 세트 손실: 0.190951\t최선의 손실: 0.190951\t정확도: 95.74%\n",
+      "4\t검증 세트 손실: 0.247942\t최선의 손실: 0.190951\t정확도: 95.04%\n",
+      "5\t검증 세트 손실: 1.474474\t최선의 손실: 0.190951\t정확도: 91.83%\n",
+      "6\t검증 세트 손실: 0.189150\t최선의 손실: 0.189150\t정확도: 96.91%\n",
+      "7\t검증 세트 손실: 0.889207\t최선의 손실: 0.189150\t정확도: 92.61%\n",
+      "8\t검증 세트 손실: 0.047392\t최선의 손실: 0.047392\t정확도: 98.71%\n",
+      "9\t검증 세트 손실: 0.152904\t최선의 손실: 0.047392\t정확도: 98.24%\n",
+      "10\t검증 세트 손실: 0.305628\t최선의 손실: 0.047392\t정확도: 97.73%\n",
+      "11\t검증 세트 손실: 0.191479\t최선의 손실: 0.047392\t정확도: 96.52%\n",
+      "12\t검증 세트 손실: 0.165395\t최선의 손실: 0.047392\t정확도: 96.87%\n",
+      "13\t검증 세트 손실: 0.260276\t최선의 손실: 0.047392\t정확도: 95.97%\n",
+      "14\t검증 세트 손실: 0.179698\t최선의 손실: 0.047392\t정확도: 97.38%\n",
+      "15\t검증 세트 손실: 0.056757\t최선의 손실: 0.047392\t정확도: 98.59%\n",
+      "16\t검증 세트 손실: 0.260506\t최선의 손실: 0.047392\t정확도: 97.22%\n",
+      "17\t검증 세트 손실: 1.414460\t최선의 손실: 0.047392\t정확도: 88.35%\n",
+      "18\t검증 세트 손실: 0.113564\t최선의 손실: 0.047392\t정확도: 97.81%\n",
+      "19\t검증 세트 손실: 1.243818\t최선의 손실: 0.047392\t정확도: 94.80%\n",
+      "20\t검증 세트 손실: 0.101798\t최선의 손실: 0.047392\t정확도: 98.55%\n",
+      "21\t검증 세트 손실: 0.190385\t최선의 손실: 0.047392\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.099198\t최선의 손실: 0.047392\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.141255\t최선의 손실: 0.047392\t정확도: 98.12%\n",
+      "24\t검증 세트 손실: 0.099300\t최선의 손실: 0.047392\t정확도: 98.51%\n",
+      "25\t검증 세트 손실: 0.117514\t최선의 손실: 0.047392\t정확도: 98.51%\n",
+      "26\t검증 세트 손실: 0.159143\t최선의 손실: 0.047392\t정확도: 98.01%\n",
+      "27\t검증 세트 손실: 0.170380\t최선의 손실: 0.047392\t정확도: 97.89%\n",
+      "28\t검증 세트 손실: 0.156193\t최선의 손실: 0.047392\t정확도: 97.73%\n",
+      "29\t검증 세트 손실: 0.203965\t최선의 손실: 0.047392\t정확도: 98.44%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=140, total= 3.8min\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.180163\t최선의 손실: 0.180163\t정확도: 95.78%\n",
-      "1\t검증 세트 손실: 0.085285\t최선의 손실: 0.085285\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.080321\t최선의 손실: 0.080321\t정확도: 97.73%\n",
-      "3\t검증 세트 손실: 0.079083\t최선의 손실: 0.079083\t정확도: 98.12%\n",
-      "4\t검증 세트 손실: 0.073587\t최선의 손실: 0.073587\t정확도: 98.24%\n",
-      "5\t검증 세트 손실: 0.062245\t최선의 손실: 0.062245\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.078464\t최선의 손실: 0.062245\t정확도: 98.01%\n",
-      "7\t검증 세트 손실: 0.056421\t최선의 손실: 0.056421\t정확도: 98.40%\n",
-      "8\t검증 세트 손실: 0.044914\t최선의 손실: 0.044914\t정확도: 98.94%\n",
-      "9\t검증 세트 손실: 0.115831\t최선의 손실: 0.044914\t정확도: 97.30%\n",
-      "10\t검증 세트 손실: 0.063452\t최선의 손실: 0.044914\t정확도: 98.24%\n",
-      "11\t검증 세트 손실: 0.054732\t최선의 손실: 0.044914\t정확도: 98.55%\n",
-      "12\t검증 세트 손실: 0.067364\t최선의 손실: 0.044914\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.062927\t최선의 손실: 0.044914\t정확도: 98.36%\n",
-      "14\t검증 세트 손실: 0.123963\t최선의 손실: 0.044914\t정확도: 97.85%\n",
-      "15\t검증 세트 손실: 0.093460\t최선의 손실: 0.044914\t정확도: 97.50%\n",
-      "16\t검증 세트 손실: 0.085669\t최선의 손실: 0.044914\t정확도: 97.77%\n",
-      "17\t검증 세트 손실: 0.055481\t최선의 손실: 0.044914\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.065671\t최선의 손실: 0.044914\t정확도: 98.28%\n",
-      "19\t검증 세트 손실: 0.061593\t최선의 손실: 0.044914\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.043911\t최선의 손실: 0.043911\t정확도: 98.87%\n",
-      "21\t검증 세트 손실: 0.054520\t최선의 손실: 0.043911\t정확도: 98.87%\n",
-      "22\t검증 세트 손실: 0.094756\t최선의 손실: 0.043911\t정확도: 97.77%\n",
-      "23\t검증 세트 손실: 0.195377\t최선의 손실: 0.043911\t정확도: 96.91%\n",
-      "24\t검증 세트 손실: 0.074132\t최선의 손실: 0.043911\t정확도: 98.55%\n",
-      "25\t검증 세트 손실: 0.052175\t최선의 손실: 0.043911\t정확도: 98.83%\n",
-      "26\t검증 세트 손실: 0.075529\t최선의 손실: 0.043911\t정확도: 98.51%\n",
-      "27\t검증 세트 손실: 0.062674\t최선의 손실: 0.043911\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.080573\t최선의 손실: 0.043911\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.066840\t최선의 손실: 0.043911\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.072856\t최선의 손실: 0.043911\t정확도: 98.51%\n",
-      "31\t검증 세트 손실: 0.113146\t최선의 손실: 0.043911\t정확도: 97.97%\n",
-      "32\t검증 세트 손실: 0.106511\t최선의 손실: 0.043911\t정확도: 98.63%\n",
-      "33\t검증 세트 손실: 0.100819\t최선의 손실: 0.043911\t정확도: 98.24%\n",
-      "34\t검증 세트 손실: 0.065776\t최선의 손실: 0.043911\t정확도: 98.75%\n",
-      "35\t검증 세트 손실: 0.063220\t최선의 손실: 0.043911\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.069031\t최선의 손실: 0.043911\t정확도: 98.91%\n",
-      "37\t검증 세트 손실: 0.095382\t최선의 손실: 0.043911\t정확도: 98.24%\n",
-      "38\t검증 세트 손실: 0.088495\t최선의 손실: 0.043911\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.060564\t최선의 손실: 0.043911\t정확도: 98.75%\n",
-      "40\t검증 세트 손실: 0.089562\t최선의 손실: 0.043911\t정확도: 98.59%\n",
-      "41\t검증 세트 손실: 0.073790\t최선의 손실: 0.043911\t정확도: 98.79%\n",
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140, total= 3.1min\n",
+      "[CV] batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.493161\t최선의 손실: 0.493161\t정확도: 95.54%\n",
+      "1\t검증 세트 손실: 0.441118\t최선의 손실: 0.441118\t정확도: 94.68%\n",
+      "2\t검증 세트 손실: 0.522968\t최선의 손실: 0.441118\t정확도: 90.34%\n",
+      "3\t검증 세트 손실: 0.290910\t최선의 손실: 0.290910\t정확도: 96.99%\n",
+      "4\t검증 세트 손실: 1.146777\t최선의 손실: 0.290910\t정확도: 96.87%\n",
+      "5\t검증 세트 손실: 0.300799\t최선의 손실: 0.290910\t정확도: 94.29%\n",
+      "6\t검증 세트 손실: 0.184808\t최선의 손실: 0.184808\t정확도: 97.42%\n",
+      "7\t검증 세트 손실: 0.189519\t최선의 손실: 0.184808\t정확도: 96.99%\n",
+      "8\t검증 세트 손실: 0.086486\t최선의 손실: 0.086486\t정확도: 97.93%\n",
+      "9\t검증 세트 손실: 0.142187\t최선의 손실: 0.086486\t정확도: 97.62%\n",
+      "10\t검증 세트 손실: 0.138434\t최선의 손실: 0.086486\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.345438\t최선의 손실: 0.086486\t정확도: 94.02%\n",
+      "12\t검증 세트 손실: 0.105808\t최선의 손실: 0.086486\t정확도: 98.20%\n",
+      "13\t검증 세트 손실: 0.107850\t최선의 손실: 0.086486\t정확도: 98.48%\n",
+      "14\t검증 세트 손실: 0.139177\t최선의 손실: 0.086486\t정확도: 97.97%\n",
+      "15\t검증 세트 손실: 0.500896\t최선의 손실: 0.086486\t정확도: 94.21%\n",
+      "16\t검증 세트 손실: 0.155276\t최선의 손실: 0.086486\t정확도: 98.28%\n",
+      "17\t검증 세트 손실: 0.493875\t최선의 손실: 0.086486\t정확도: 95.31%\n",
+      "18\t검증 세트 손실: 0.064763\t최선의 손실: 0.064763\t정확도: 98.55%\n",
+      "19\t검증 세트 손실: 0.226233\t최선의 손실: 0.064763\t정확도: 97.89%\n",
+      "20\t검증 세트 손실: 0.204016\t최선의 손실: 0.064763\t정확도: 98.05%\n",
+      "21\t검증 세트 손실: 0.129152\t최선의 손실: 0.064763\t정확도: 98.20%\n",
+      "22\t검증 세트 손실: 0.104999\t최선의 손실: 0.064763\t정확도: 98.32%\n",
+      "23\t검증 세트 손실: 0.142954\t최선의 손실: 0.064763\t정확도: 98.59%\n",
+      "24\t검증 세트 손실: 0.080547\t최선의 손실: 0.064763\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.675306\t최선의 손실: 0.064763\t정확도: 94.37%\n",
+      "26\t검증 세트 손실: 0.083711\t최선의 손실: 0.064763\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.118496\t최선의 손실: 0.064763\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.062209\t최선의 손실: 0.062209\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.125865\t최선의 손실: 0.062209\t정확도: 98.87%\n",
+      "30\t검증 세트 손실: 0.236404\t최선의 손실: 0.062209\t정확도: 98.28%\n",
+      "31\t검증 세트 손실: 0.104822\t최선의 손실: 0.062209\t정확도: 99.06%\n",
+      "32\t검증 세트 손실: 0.149137\t최선의 손실: 0.062209\t정확도: 98.91%\n",
+      "33\t검증 세트 손실: 0.171076\t최선의 손실: 0.062209\t정확도: 98.05%\n",
+      "34\t검증 세트 손실: 0.161566\t최선의 손실: 0.062209\t정확도: 98.71%\n",
+      "35\t검증 세트 손실: 0.182610\t최선의 손실: 0.062209\t정확도: 98.55%\n",
+      "36\t검증 세트 손실: 0.224212\t최선의 손실: 0.062209\t정확도: 98.48%\n",
+      "37\t검증 세트 손실: 0.232257\t최선의 손실: 0.062209\t정확도: 98.12%\n",
+      "38\t검증 세트 손실: 0.307822\t최선의 손실: 0.062209\t정확도: 97.97%\n",
+      "39\t검증 세트 손실: 0.185401\t최선의 손실: 0.062209\t정확도: 98.36%\n",
+      "40\t검증 세트 손실: 0.139612\t최선의 손실: 0.062209\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.337792\t최선의 손실: 0.062209\t정확도: 97.58%\n",
+      "42\t검증 세트 손실: 0.162186\t최선의 손실: 0.062209\t정확도: 98.59%\n",
+      "43\t검증 세트 손실: 0.277750\t최선의 손실: 0.062209\t정확도: 98.32%\n",
+      "44\t검증 세트 손실: 0.113696\t최선의 손실: 0.062209\t정확도: 98.94%\n",
+      "45\t검증 세트 손실: 0.151858\t최선의 손실: 0.062209\t정확도: 98.75%\n",
+      "46\t검증 세트 손실: 0.365426\t최선의 손실: 0.062209\t정확도: 98.05%\n",
+      "47\t검증 세트 손실: 0.114940\t최선의 손실: 0.062209\t정확도: 98.83%\n",
+      "48\t검증 세트 손실: 0.366033\t최선의 손실: 0.062209\t정확도: 97.50%\n",
+      "49\t검증 세트 손실: 0.265177\t최선의 손실: 0.062209\t정확도: 98.05%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=70, total= 1.0min\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.137164\t최선의 손실: 0.137164\t정확도: 95.90%\n",
-      "1\t검증 세트 손실: 0.086110\t최선의 손실: 0.086110\t정확도: 97.73%\n",
-      "2\t검증 세트 손실: 0.116161\t최선의 손실: 0.086110\t정확도: 96.95%\n",
-      "3\t검증 세트 손실: 0.075776\t최선의 손실: 0.075776\t정확도: 97.81%\n",
-      "4\t검증 세트 손실: 0.085899\t최선의 손실: 0.075776\t정확도: 97.62%\n",
-      "5\t검증 세트 손실: 0.057008\t최선의 손실: 0.057008\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.122541\t최선의 손실: 0.057008\t정확도: 97.30%\n",
-      "7\t검증 세트 손실: 0.091711\t최선의 손실: 0.057008\t정확도: 97.77%\n",
-      "8\t검증 세트 손실: 0.084821\t최선의 손실: 0.057008\t정확도: 98.05%\n",
-      "9\t검증 세트 손실: 0.066841\t최선의 손실: 0.057008\t정확도: 98.24%\n",
-      "10\t검증 세트 손실: 0.050618\t최선의 손실: 0.050618\t정확도: 98.36%\n",
-      "11\t검증 세트 손실: 0.056530\t최선의 손실: 0.050618\t정확도: 98.48%\n",
-      "12\t검증 세트 손실: 0.075022\t최선의 손실: 0.050618\t정확도: 98.24%\n",
-      "13\t검증 세트 손실: 0.060975\t최선의 손실: 0.050618\t정확도: 98.36%\n",
-      "14\t검증 세트 손실: 0.096987\t최선의 손실: 0.050618\t정확도: 97.69%\n",
-      "15\t검증 세트 손실: 0.080946\t최선의 손실: 0.050618\t정확도: 98.24%\n"
+      "[CV]  batch_size=10, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.999, n_neurons=140, total= 5.2min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=70 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "16\t검증 세트 손실: 0.065426\t최선의 손실: 0.050618\t정확도: 98.55%\n",
-      "17\t검증 세트 손실: 0.053848\t최선의 손실: 0.050618\t정확도: 98.48%\n",
-      "18\t검증 세트 손실: 0.062064\t최선의 손실: 0.050618\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.047488\t최선의 손실: 0.047488\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.207001\t최선의 손실: 0.047488\t정확도: 94.96%\n",
-      "21\t검증 세트 손실: 0.075355\t최선의 손실: 0.047488\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.070765\t최선의 손실: 0.047488\t정확도: 98.08%\n",
-      "23\t검증 세트 손실: 0.073128\t최선의 손실: 0.047488\t정확도: 98.51%\n",
-      "24\t검증 세트 손실: 0.119472\t최선의 손실: 0.047488\t정확도: 98.67%\n",
-      "25\t검증 세트 손실: 0.106690\t최선의 손실: 0.047488\t정확도: 98.67%\n",
-      "26\t검증 세트 손실: 0.068207\t최선의 손실: 0.047488\t정확도: 98.44%\n",
-      "27\t검증 세트 손실: 0.049663\t최선의 손실: 0.047488\t정확도: 98.83%\n",
-      "28\t검증 세트 손실: 0.070290\t최선의 손실: 0.047488\t정확도: 98.79%\n",
-      "29\t검증 세트 손실: 0.065552\t최선의 손실: 0.047488\t정확도: 98.67%\n",
-      "30\t검증 세트 손실: 0.065338\t최선의 손실: 0.047488\t정확도: 98.59%\n",
-      "31\t검증 세트 손실: 0.080602\t최선의 손실: 0.047488\t정확도: 98.40%\n",
-      "32\t검증 세트 손실: 0.078689\t최선의 손실: 0.047488\t정확도: 98.55%\n",
-      "33\t검증 세트 손실: 0.077306\t최선의 손실: 0.047488\t정확도: 98.83%\n",
-      "34\t검증 세트 손실: 0.072220\t최선의 손실: 0.047488\t정확도: 98.63%\n",
-      "35\t검증 세트 손실: 0.137829\t최선의 손실: 0.047488\t정확도: 98.55%\n",
-      "36\t검증 세트 손실: 0.563273\t최선의 손실: 0.047488\t정확도: 96.48%\n",
-      "37\t검증 세트 손실: 0.077476\t최선의 손실: 0.047488\t정확도: 98.44%\n",
-      "38\t검증 세트 손실: 0.195303\t최선의 손실: 0.047488\t정확도: 97.34%\n",
-      "39\t검증 세트 손실: 0.115422\t최선의 손실: 0.047488\t정확도: 98.51%\n",
-      "40\t검증 세트 손실: 0.088552\t최선의 손실: 0.047488\t정확도: 98.51%\n",
+      "0\t검증 세트 손실: 0.159471\t최선의 손실: 0.159471\t정확도: 95.43%\n",
+      "1\t검증 세트 손실: 0.086191\t최선의 손실: 0.086191\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.088526\t최선의 손실: 0.086191\t정확도: 97.73%\n",
+      "3\t검증 세트 손실: 0.079645\t최선의 손실: 0.079645\t정확도: 97.89%\n",
+      "4\t검증 세트 손실: 0.073451\t최선의 손실: 0.073451\t정확도: 97.85%\n",
+      "5\t검증 세트 손실: 0.095928\t최선의 손실: 0.073451\t정확도: 98.32%\n",
+      "6\t검증 세트 손실: 0.067359\t최선의 손실: 0.067359\t정확도: 98.28%\n",
+      "7\t검증 세트 손실: 0.053618\t최선의 손실: 0.053618\t정확도: 98.51%\n",
+      "8\t검증 세트 손실: 0.065295\t최선의 손실: 0.053618\t정확도: 98.91%\n",
+      "9\t검증 세트 손실: 0.104914\t최선의 손실: 0.053618\t정확도: 97.89%\n",
+      "10\t검증 세트 손실: 0.051703\t최선의 손실: 0.051703\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.091118\t최선의 손실: 0.051703\t정확도: 98.36%\n",
+      "12\t검증 세트 손실: 0.099088\t최선의 손실: 0.051703\t정확도: 98.16%\n",
+      "13\t검증 세트 손실: 0.048817\t최선의 손실: 0.048817\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.086798\t최선의 손실: 0.048817\t정확도: 98.44%\n",
+      "15\t검증 세트 손실: 0.096072\t최선의 손실: 0.048817\t정확도: 98.01%\n",
+      "16\t검증 세트 손실: 0.059161\t최선의 손실: 0.048817\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.046486\t최선의 손실: 0.046486\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.085353\t최선의 손실: 0.046486\t정확도: 98.40%\n",
+      "19\t검증 세트 손실: 0.082220\t최선의 손실: 0.046486\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.062105\t최선의 손실: 0.046486\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.079298\t최선의 손실: 0.046486\t정확도: 98.79%\n",
+      "22\t검증 세트 손실: 0.057623\t최선의 손실: 0.046486\t정확도: 98.67%\n",
+      "23\t검증 세트 손실: 0.057712\t최선의 손실: 0.046486\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.069260\t최선의 손실: 0.046486\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.056484\t최선의 손실: 0.046486\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.083098\t최선의 손실: 0.046486\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.081916\t최선의 손실: 0.046486\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.079876\t최선의 손실: 0.046486\t정확도: 98.51%\n",
+      "29\t검증 세트 손실: 0.074126\t최선의 손실: 0.046486\t정확도: 98.83%\n",
+      "30\t검증 세트 손실: 0.092348\t최선의 손실: 0.046486\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.058143\t최선의 손실: 0.046486\t정확도: 99.10%\n",
+      "32\t검증 세트 손실: 0.049243\t최선의 손실: 0.046486\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.050219\t최선의 손실: 0.046486\t정확도: 99.34%\n",
+      "34\t검증 세트 손실: 0.072959\t최선의 손실: 0.046486\t정확도: 98.75%\n",
+      "35\t검증 세트 손실: 0.080203\t최선의 손실: 0.046486\t정확도: 98.87%\n",
+      "36\t검증 세트 손실: 0.067982\t최선의 손실: 0.046486\t정확도: 98.75%\n",
+      "37\t검증 세트 손실: 0.124554\t최선의 손실: 0.046486\t정확도: 98.20%\n",
+      "38\t검증 세트 손실: 0.080788\t최선의 손실: 0.046486\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=70, total=  58.9s\n",
-      "[CV] batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.183091\t최선의 손실: 0.183091\t정확도: 96.25%\n",
-      "1\t검증 세트 손실: 0.112506\t최선의 손실: 0.112506\t정확도: 96.79%\n",
-      "2\t검증 세트 손실: 0.069493\t최선의 손실: 0.069493\t정확도: 98.05%\n",
-      "3\t검증 세트 손실: 0.072479\t최선의 손실: 0.069493\t정확도: 98.12%\n",
-      "4\t검증 세트 손실: 0.088170\t최선의 손실: 0.069493\t정확도: 97.97%\n",
-      "5\t검증 세트 손실: 0.074027\t최선의 손실: 0.069493\t정확도: 98.05%\n",
-      "6\t검증 세트 손실: 0.099514\t최선의 손실: 0.069493\t정확도: 97.11%\n",
-      "7\t검증 세트 손실: 0.081471\t최선의 손실: 0.069493\t정확도: 98.32%\n",
-      "8\t검증 세트 손실: 0.067505\t최선의 손실: 0.067505\t정확도: 98.55%\n",
-      "9\t검증 세트 손실: 0.101258\t최선의 손실: 0.067505\t정확도: 97.93%\n",
-      "10\t검증 세트 손실: 0.058994\t최선의 손실: 0.058994\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.060107\t최선의 손실: 0.058994\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.227467\t최선의 손실: 0.058994\t정확도: 97.15%\n",
-      "13\t검증 세트 손실: 0.055959\t최선의 손실: 0.055959\t정확도: 98.55%\n",
-      "14\t검증 세트 손실: 0.061857\t최선의 손실: 0.055959\t정확도: 98.16%\n",
-      "15\t검증 세트 손실: 0.153160\t최선의 손실: 0.055959\t정확도: 96.56%\n",
-      "16\t검증 세트 손실: 0.049096\t최선의 손실: 0.049096\t정확도: 98.79%\n",
-      "17\t검증 세트 손실: 0.124282\t최선의 손실: 0.049096\t정확도: 97.50%\n",
-      "18\t검증 세트 손실: 0.068127\t최선의 손실: 0.049096\t정확도: 98.55%\n",
-      "19\t검증 세트 손실: 0.074205\t최선의 손실: 0.049096\t정확도: 98.71%\n",
-      "20\t검증 세트 손실: 0.080937\t최선의 손실: 0.049096\t정확도: 98.16%\n",
-      "21\t검증 세트 손실: 0.076139\t최선의 손실: 0.049096\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.053034\t최선의 손실: 0.049096\t정확도: 98.87%\n",
-      "23\t검증 세트 손실: 0.082813\t최선의 손실: 0.049096\t정확도: 98.63%\n",
-      "24\t검증 세트 손실: 0.061569\t최선의 손실: 0.049096\t정확도: 98.83%\n",
-      "25\t검증 세트 손실: 0.050362\t최선의 손실: 0.049096\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.084633\t최선의 손실: 0.049096\t정확도: 98.63%\n",
-      "27\t검증 세트 손실: 0.062067\t최선의 손실: 0.049096\t정확도: 98.71%\n",
-      "28\t검증 세트 손실: 0.045355\t최선의 손실: 0.045355\t정확도: 99.10%\n",
-      "29\t검증 세트 손실: 0.051662\t최선의 손실: 0.045355\t정확도: 98.87%\n",
-      "30\t검증 세트 손실: 0.085180\t최선의 손실: 0.045355\t정확도: 98.67%\n",
-      "31\t검증 세트 손실: 0.091504\t최선의 손실: 0.045355\t정확도: 98.16%\n",
-      "32\t검증 세트 손실: 0.054171\t최선의 손실: 0.045355\t정확도: 99.02%\n",
-      "33\t검증 세트 손실: 0.069914\t최선의 손실: 0.045355\t정확도: 98.83%\n",
-      "34\t검증 세트 손실: 0.074876\t최선의 손실: 0.045355\t정확도: 98.94%\n",
-      "35\t검증 세트 손실: 0.082682\t최선의 손실: 0.045355\t정확도: 98.75%\n",
-      "36\t검증 세트 손실: 0.073767\t최선의 손실: 0.045355\t정확도: 98.79%\n",
-      "37\t검증 세트 손실: 0.072253\t최선의 손실: 0.045355\t정확도: 98.79%\n",
-      "38\t검증 세트 손실: 0.062028\t최선의 손실: 0.045355\t정확도: 98.94%\n",
-      "39\t검증 세트 손실: 0.080085\t최선의 손실: 0.045355\t정확도: 98.48%\n",
-      "40\t검증 세트 손실: 0.046326\t최선의 손실: 0.045355\t정확도: 98.91%\n",
-      "41\t검증 세트 손실: 0.102136\t최선의 손실: 0.045355\t정확도: 98.44%\n",
-      "42\t검증 세트 손실: 0.202637\t최선의 손실: 0.045355\t정확도: 98.79%\n",
-      "43\t검증 세트 손실: 0.061365\t최선의 손실: 0.045355\t정확도: 99.06%\n",
-      "44\t검증 세트 손실: 0.049463\t최선의 손실: 0.045355\t정확도: 99.02%\n",
-      "45\t검증 세트 손실: 0.076358\t최선의 손실: 0.045355\t정확도: 98.91%\n",
-      "46\t검증 세트 손실: 0.092906\t최선의 손실: 0.045355\t정확도: 98.63%\n",
-      "47\t검증 세트 손실: 0.046086\t최선의 손실: 0.045355\t정확도: 99.26%\n",
-      "48\t검증 세트 손실: 0.065314\t최선의 손실: 0.045355\t정확도: 99.02%\n",
-      "49\t검증 세트 손실: 0.105734\t최선의 손실: 0.045355\t정확도: 98.51%\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=70, total=  57.3s\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.170013\t최선의 손실: 0.170013\t정확도: 96.01%\n",
+      "1\t검증 세트 손실: 0.075382\t최선의 손실: 0.075382\t정확도: 98.12%\n",
+      "2\t검증 세트 손실: 0.091120\t최선의 손실: 0.075382\t정확도: 97.73%\n",
+      "3\t검증 세트 손실: 0.101989\t최선의 손실: 0.075382\t정확도: 97.46%\n",
+      "4\t검증 세트 손실: 0.060178\t최선의 손실: 0.060178\t정확도: 98.63%\n",
+      "5\t검증 세트 손실: 0.152828\t최선의 손실: 0.060178\t정확도: 97.19%\n",
+      "6\t검증 세트 손실: 0.079138\t최선의 손실: 0.060178\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.059889\t최선의 손실: 0.059889\t정확도: 98.08%\n",
+      "8\t검증 세트 손실: 0.056250\t최선의 손실: 0.056250\t정확도: 98.32%\n",
+      "9\t검증 세트 손실: 0.074646\t최선의 손실: 0.056250\t정확도: 98.59%\n",
+      "10\t검증 세트 손실: 0.064788\t최선의 손실: 0.056250\t정확도: 98.05%\n",
+      "11\t검증 세트 손실: 0.052458\t최선의 손실: 0.052458\t정확도: 98.71%\n",
+      "12\t검증 세트 손실: 0.058304\t최선의 손실: 0.052458\t정확도: 98.59%\n",
+      "13\t검증 세트 손실: 0.058086\t최선의 손실: 0.052458\t정확도: 98.51%\n",
+      "14\t검증 세트 손실: 0.095570\t최선의 손실: 0.052458\t정확도: 98.16%\n",
+      "15\t검증 세트 손실: 0.069161\t최선의 손실: 0.052458\t정확도: 98.44%\n",
+      "16\t검증 세트 손실: 0.083527\t최선의 손실: 0.052458\t정확도: 98.28%\n",
+      "17\t검증 세트 손실: 0.094122\t최선의 손실: 0.052458\t정확도: 98.32%\n",
+      "18\t검증 세트 손실: 0.054591\t최선의 손실: 0.052458\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.081997\t최선의 손실: 0.052458\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.068243\t최선의 손실: 0.052458\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.090026\t최선의 손실: 0.052458\t정확도: 98.40%\n",
+      "22\t검증 세트 손실: 0.052689\t최선의 손실: 0.052458\t정확도: 99.06%\n",
+      "23\t검증 세트 손실: 0.045850\t최선의 손실: 0.045850\t정확도: 98.87%\n",
+      "24\t검증 세트 손실: 0.077498\t최선의 손실: 0.045850\t정확도: 98.51%\n",
+      "25\t검증 세트 손실: 0.065465\t최선의 손실: 0.045850\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.059927\t최선의 손실: 0.045850\t정확도: 99.02%\n",
+      "27\t검증 세트 손실: 0.053308\t최선의 손실: 0.045850\t정확도: 98.91%\n",
+      "28\t검증 세트 손실: 0.067475\t최선의 손실: 0.045850\t정확도: 98.75%\n",
+      "29\t검증 세트 손실: 0.051927\t최선의 손실: 0.045850\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.137845\t최선의 손실: 0.045850\t정확도: 98.40%\n",
+      "31\t검증 세트 손실: 0.078815\t최선의 손실: 0.045850\t정확도: 98.55%\n",
+      "32\t검증 세트 손실: 0.052088\t최선의 손실: 0.045850\t정확도: 99.14%\n",
+      "33\t검증 세트 손실: 0.111957\t최선의 손실: 0.045850\t정확도: 98.51%\n",
+      "34\t검증 세트 손실: 0.065633\t최선의 손실: 0.045850\t정확도: 98.67%\n",
+      "35\t검증 세트 손실: 0.078841\t최선의 손실: 0.045850\t정확도: 98.40%\n",
+      "36\t검증 세트 손실: 0.064393\t최선의 손실: 0.045850\t정확도: 98.51%\n",
+      "37\t검증 세트 손실: 0.102391\t최선의 손실: 0.045850\t정확도: 98.40%\n",
+      "38\t검증 세트 손실: 0.099589\t최선의 손실: 0.045850\t정확도: 98.36%\n",
+      "39\t검증 세트 손실: 0.086384\t최선의 손실: 0.045850\t정확도: 98.24%\n",
+      "40\t검증 세트 손실: 0.059899\t최선의 손실: 0.045850\t정확도: 98.91%\n",
+      "41\t검증 세트 손실: 0.064106\t최선의 손실: 0.045850\t정확도: 98.75%\n",
+      "42\t검증 세트 손실: 0.056259\t최선의 손실: 0.045850\t정확도: 98.87%\n",
+      "43\t검증 세트 손실: 0.109210\t최선의 손실: 0.045850\t정확도: 98.63%\n",
+      "44\t검증 세트 손실: 0.101052\t최선의 손실: 0.045850\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.95, n_neurons=70, total= 1.2min\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.152807\t최선의 손실: 1.152807\t정확도: 81.67%\n",
-      "1\t검증 세트 손실: 1.317268\t최선의 손실: 1.152807\t정확도: 77.25%\n",
-      "2\t검증 세트 손실: 0.436730\t최선의 손실: 0.436730\t정확도: 92.42%\n",
-      "3\t검증 세트 손실: 0.420289\t최선의 손실: 0.420289\t정확도: 92.10%\n",
-      "4\t검증 세트 손실: 0.501193\t최선의 손실: 0.420289\t정확도: 90.66%\n",
-      "5\t검증 세트 손실: 0.193981\t최선의 손실: 0.193981\t정확도: 97.07%\n",
-      "6\t검증 세트 손실: 0.244930\t최선의 손실: 0.193981\t정확도: 96.13%\n",
-      "7\t검증 세트 손실: 0.291379\t최선의 손실: 0.193981\t정확도: 95.54%\n",
-      "8\t검증 세트 손실: 0.219410\t최선의 손실: 0.193981\t정확도: 96.87%\n",
-      "9\t검증 세트 손실: 0.217194\t최선의 손실: 0.193981\t정확도: 96.99%\n",
-      "10\t검증 세트 손실: 0.494738\t최선의 손실: 0.193981\t정확도: 93.16%\n",
-      "11\t검증 세트 손실: 0.246143\t최선의 손실: 0.193981\t정확도: 97.34%\n",
-      "12\t검증 세트 손실: 0.445982\t최선의 손실: 0.193981\t정확도: 93.35%\n",
-      "13\t검증 세트 손실: 0.240379\t최선의 손실: 0.193981\t정확도: 97.15%\n",
-      "14\t검증 세트 손실: 0.202441\t최선의 손실: 0.193981\t정확도: 97.93%\n",
-      "15\t검증 세트 손실: 0.207170\t최선의 손실: 0.193981\t정확도: 97.65%\n",
-      "16\t검증 세트 손실: 0.234739\t최선의 손실: 0.193981\t정확도: 96.87%\n",
-      "17\t검증 세트 손실: 0.188805\t최선의 손실: 0.188805\t정확도: 97.77%\n",
-      "18\t검증 세트 손실: 0.224284\t최선의 손실: 0.188805\t정확도: 97.58%\n",
-      "19\t검증 세트 손실: 0.176917\t최선의 손실: 0.176917\t정확도: 98.01%\n",
-      "20\t검증 세트 손실: 0.193649\t최선의 손실: 0.176917\t정확도: 98.12%\n",
-      "21\t검증 세트 손실: 0.181334\t최선의 손실: 0.176917\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.141506\t최선의 손실: 0.141506\t정확도: 98.63%\n",
-      "23\t검증 세트 손실: 0.154422\t최선의 손실: 0.141506\t정확도: 98.71%\n",
-      "24\t검증 세트 손실: 0.141245\t최선의 손실: 0.141245\t정확도: 98.67%\n",
-      "25\t검증 세트 손실: 0.135895\t최선의 손실: 0.135895\t정확도: 98.67%\n",
-      "26\t검증 세트 손실: 0.130269\t최선의 손실: 0.130269\t정확도: 98.63%\n",
-      "27\t검증 세트 손실: 0.127965\t최선의 손실: 0.127965\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.117331\t최선의 손실: 0.117331\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.117624\t최선의 손실: 0.117331\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.109017\t최선의 손실: 0.109017\t정확도: 98.79%\n",
-      "31\t검증 세트 손실: 0.102668\t최선의 손실: 0.102668\t정확도: 98.83%\n",
-      "32\t검증 세트 손실: 0.099451\t최선의 손실: 0.099451\t정확도: 98.91%\n",
-      "33\t검증 세트 손실: 0.096105\t최선의 손실: 0.096105\t정확도: 98.83%\n",
-      "34\t검증 세트 손실: 0.093713\t최선의 손실: 0.093713\t정확도: 98.98%\n",
-      "35\t검증 세트 손실: 0.090806\t최선의 손실: 0.090806\t정확도: 98.94%\n",
-      "36\t검증 세트 손실: 0.089392\t최선의 손실: 0.089392\t정확도: 98.98%\n",
-      "37\t검증 세트 손실: 0.087496\t최선의 손실: 0.087496\t정확도: 98.98%\n",
-      "38\t검증 세트 손실: 0.089158\t최선의 손실: 0.087496\t정확도: 98.91%\n",
-      "39\t검증 세트 손실: 0.085555\t최선의 손실: 0.085555\t정확도: 98.98%\n",
-      "40\t검증 세트 손실: 0.083724\t최선의 손실: 0.083724\t정확도: 98.98%\n",
-      "41\t검증 세트 손실: 0.080747\t최선의 손실: 0.080747\t정확도: 98.98%\n",
-      "42\t검증 세트 손실: 0.078930\t최선의 손실: 0.078930\t정확도: 98.91%\n",
-      "43\t검증 세트 손실: 0.077307\t최선의 손실: 0.077307\t정확도: 99.02%\n",
-      "44\t검증 세트 손실: 0.075956\t최선의 손실: 0.075956\t정확도: 98.91%\n",
-      "45\t검증 세트 손실: 0.073317\t최선의 손실: 0.073317\t정확도: 98.94%\n",
-      "46\t검증 세트 손실: 0.072001\t최선의 손실: 0.072001\t정확도: 98.94%\n",
-      "47\t검증 세트 손실: 0.071758\t최선의 손실: 0.071758\t정확도: 98.98%\n",
-      "48\t검증 세트 손실: 0.071492\t최선의 손실: 0.071492\t정확도: 98.94%\n",
-      "49\t검증 세트 손실: 0.072253\t최선의 손실: 0.071492\t정확도: 99.02%\n",
-      "50\t검증 세트 손실: 0.075073\t최선의 손실: 0.071492\t정확도: 98.91%\n",
-      "51\t검증 세트 손실: 0.072123\t최선의 손실: 0.071492\t정확도: 98.91%\n",
-      "52\t검증 세트 손실: 0.070452\t최선의 손실: 0.070452\t정확도: 98.87%\n",
-      "53\t검증 세트 손실: 0.069603\t최선의 손실: 0.069603\t정확도: 98.94%\n",
-      "54\t검증 세트 손실: 0.070613\t최선의 손실: 0.069603\t정확도: 98.91%\n",
-      "55\t검증 세트 손실: 0.072809\t최선의 손실: 0.069603\t정확도: 98.87%\n",
-      "56\t검증 세트 손실: 0.071123\t최선의 손실: 0.069603\t정확도: 98.94%\n",
-      "57\t검증 세트 손실: 0.069997\t최선의 손실: 0.069603\t정확도: 98.94%\n",
-      "58\t검증 세트 손실: 0.068075\t최선의 손실: 0.068075\t정확도: 98.94%\n",
-      "59\t검증 세트 손실: 0.070116\t최선의 손실: 0.068075\t정확도: 98.91%\n",
-      "60\t검증 세트 손실: 0.068700\t최선의 손실: 0.068075\t정확도: 98.87%\n",
-      "61\t검증 세트 손실: 0.068467\t최선의 손실: 0.068075\t정확도: 98.91%\n",
-      "62\t검증 세트 손실: 0.067216\t최선의 손실: 0.067216\t정확도: 98.98%\n",
-      "63\t검증 세트 손실: 0.067196\t최선의 손실: 0.067196\t정확도: 98.94%\n",
-      "64\t검증 세트 손실: 0.066646\t최선의 손실: 0.066646\t정확도: 98.98%\n",
-      "65\t검증 세트 손실: 0.066118\t최선의 손실: 0.066118\t정확도: 99.02%\n",
-      "66\t검증 세트 손실: 0.066986\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "67\t검증 세트 손실: 0.067185\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "68\t검증 세트 손실: 0.067205\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "69\t검증 세트 손실: 0.067706\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "70\t검증 세트 손실: 0.068470\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "71\t검증 세트 손실: 0.067512\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "72\t검증 세트 손실: 0.067463\t최선의 손실: 0.066118\t정확도: 98.91%\n",
-      "73\t검증 세트 손실: 0.066774\t최선의 손실: 0.066118\t정확도: 98.98%\n"
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=70, total= 1.1min\n",
+      "[CV] batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.209219\t최선의 손실: 0.209219\t정확도: 95.66%\n",
+      "1\t검증 세트 손실: 0.076929\t최선의 손실: 0.076929\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.091216\t최선의 손실: 0.076929\t정확도: 97.65%\n",
+      "3\t검증 세트 손실: 0.072728\t최선의 손실: 0.072728\t정확도: 97.93%\n",
+      "4\t검증 세트 손실: 0.078856\t최선의 손실: 0.072728\t정확도: 97.97%\n",
+      "5\t검증 세트 손실: 0.073982\t최선의 손실: 0.072728\t정확도: 98.01%\n",
+      "6\t검증 세트 손실: 0.072736\t최선의 손실: 0.072728\t정확도: 97.89%\n",
+      "7\t검증 세트 손실: 0.151626\t최선의 손실: 0.072728\t정확도: 96.64%\n",
+      "8\t검증 세트 손실: 0.069100\t최선의 손실: 0.069100\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.061906\t최선의 손실: 0.061906\t정확도: 98.12%\n",
+      "10\t검증 세트 손실: 0.094723\t최선의 손실: 0.061906\t정확도: 98.12%\n",
+      "11\t검증 세트 손실: 0.058318\t최선의 손실: 0.058318\t정확도: 98.40%\n",
+      "12\t검증 세트 손실: 0.060118\t최선의 손실: 0.058318\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.059640\t최선의 손실: 0.058318\t정확도: 98.32%\n",
+      "14\t검증 세트 손실: 0.078938\t최선의 손실: 0.058318\t정확도: 98.01%\n",
+      "15\t검증 세트 손실: 0.066538\t최선의 손실: 0.058318\t정확도: 98.40%\n",
+      "16\t검증 세트 손실: 0.067758\t최선의 손실: 0.058318\t정확도: 98.40%\n",
+      "17\t검증 세트 손실: 0.083072\t최선의 손실: 0.058318\t정확도: 97.97%\n",
+      "18\t검증 세트 손실: 0.137469\t최선의 손실: 0.058318\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.060224\t최선의 손실: 0.058318\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.063920\t최선의 손실: 0.058318\t정확도: 98.63%\n",
+      "21\t검증 세트 손실: 0.050515\t최선의 손실: 0.050515\t정확도: 99.02%\n",
+      "22\t검증 세트 손실: 0.059857\t최선의 손실: 0.050515\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.065171\t최선의 손실: 0.050515\t정확도: 98.71%\n",
+      "24\t검증 세트 손실: 0.054030\t최선의 손실: 0.050515\t정확도: 98.94%\n",
+      "25\t검증 세트 손실: 0.062598\t최선의 손실: 0.050515\t정확도: 98.44%\n",
+      "26\t검증 세트 손실: 0.051893\t최선의 손실: 0.050515\t정확도: 98.67%\n",
+      "27\t검증 세트 손실: 0.049937\t최선의 손실: 0.049937\t정확도: 98.91%\n",
+      "28\t검증 세트 손실: 0.056449\t최선의 손실: 0.049937\t정확도: 98.91%\n",
+      "29\t검증 세트 손실: 0.059673\t최선의 손실: 0.049937\t정확도: 98.98%\n",
+      "30\t검증 세트 손실: 0.160729\t최선의 손실: 0.049937\t정확도: 98.28%\n",
+      "31\t검증 세트 손실: 0.065066\t최선의 손실: 0.049937\t정확도: 98.55%\n",
+      "32\t검증 세트 손실: 0.067019\t최선의 손실: 0.049937\t정확도: 98.75%\n",
+      "33\t검증 세트 손실: 0.065709\t최선의 손실: 0.049937\t정확도: 98.51%\n",
+      "34\t검증 세트 손실: 0.071227\t최선의 손실: 0.049937\t정확도: 98.63%\n",
+      "35\t검증 세트 손실: 0.127996\t최선의 손실: 0.049937\t정확도: 98.55%\n",
+      "36\t검증 세트 손실: 0.080193\t최선의 손실: 0.049937\t정확도: 98.67%\n",
+      "37\t검증 세트 손실: 0.069568\t최선의 손실: 0.049937\t정확도: 98.87%\n",
+      "38\t검증 세트 손실: 0.074172\t최선의 손실: 0.049937\t정확도: 98.87%\n",
+      "39\t검증 세트 손실: 0.133766\t최선의 손실: 0.049937\t정확도: 98.40%\n",
+      "40\t검증 세트 손실: 0.080795\t최선의 손실: 0.049937\t정확도: 98.71%\n",
+      "41\t검증 세트 손실: 0.107418\t최선의 손실: 0.049937\t정확도: 98.40%\n",
+      "42\t검증 세트 손실: 0.073153\t최선의 손실: 0.049937\t정확도: 98.51%\n",
+      "43\t검증 세트 손실: 0.078024\t최선의 손실: 0.049937\t정확도: 98.67%\n",
+      "44\t검증 세트 손실: 0.063595\t최선의 손실: 0.049937\t정확도: 98.94%\n",
+      "45\t검증 세트 손실: 0.110486\t최선의 손실: 0.049937\t정확도: 98.87%\n",
+      "46\t검증 세트 손실: 0.109057\t최선의 손실: 0.049937\t정확도: 98.79%\n",
+      "47\t검증 세트 손실: 0.092582\t최선의 손실: 0.049937\t정확도: 98.75%\n",
+      "48\t검증 세트 손실: 0.073971\t최선의 손실: 0.049937\t정확도: 98.91%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.95, n_neurons=70, total= 1.2min\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.807715\t최선의 손실: 0.807715\t정확도: 86.63%\n",
+      "1\t검증 세트 손실: 0.873947\t최선의 손실: 0.807715\t정확도: 85.22%\n",
+      "2\t검증 세트 손실: 0.781258\t최선의 손실: 0.781258\t정확도: 88.00%\n",
+      "3\t검증 세트 손실: 0.380515\t최선의 손실: 0.380515\t정확도: 93.35%\n",
+      "4\t검증 세트 손실: 0.341699\t최선의 손실: 0.341699\t정확도: 93.32%\n",
+      "5\t검증 세트 손실: 0.512823\t최선의 손실: 0.341699\t정확도: 92.06%\n",
+      "6\t검증 세트 손실: 0.192770\t최선의 손실: 0.192770\t정확도: 96.91%\n",
+      "7\t검증 세트 손실: 0.264989\t최선의 손실: 0.192770\t정확도: 96.52%\n",
+      "8\t검증 세트 손실: 0.293473\t최선의 손실: 0.192770\t정확도: 95.82%\n",
+      "9\t검증 세트 손실: 0.264163\t최선의 손실: 0.192770\t정확도: 96.79%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "74\t검증 세트 손실: 0.066629\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "75\t검증 세트 손실: 0.066557\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "76\t검증 세트 손실: 0.066297\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "77\t검증 세트 손실: 0.066373\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "78\t검증 세트 손실: 0.067478\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "79\t검증 세트 손실: 0.067398\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "80\t검증 세트 손실: 0.067123\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "81\t검증 세트 손실: 0.066879\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "82\t검증 세트 손실: 0.066565\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "83\t검증 세트 손실: 0.066180\t최선의 손실: 0.066118\t정확도: 98.98%\n",
-      "84\t검증 세트 손실: 0.066220\t최선의 손실: 0.066118\t정확도: 98.94%\n",
-      "85\t검증 세트 손실: 0.066672\t최선의 손실: 0.066118\t정확도: 98.87%\n",
-      "86\t검증 세트 손실: 0.066572\t최선의 손실: 0.066118\t정확도: 98.94%\n",
+      "10\t검증 세트 손실: 0.319068\t최선의 손실: 0.192770\t정확도: 96.60%\n",
+      "11\t검증 세트 손실: 0.301009\t최선의 손실: 0.192770\t정확도: 96.79%\n",
+      "12\t검증 세트 손실: 0.255210\t최선의 손실: 0.192770\t정확도: 97.34%\n",
+      "13\t검증 세트 손실: 0.411416\t최선의 손실: 0.192770\t정확도: 96.29%\n",
+      "14\t검증 세트 손실: 0.235172\t최선의 손실: 0.192770\t정확도: 97.62%\n",
+      "15\t검증 세트 손실: 0.172069\t최선의 손실: 0.172069\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.219581\t최선의 손실: 0.172069\t정확도: 97.58%\n",
+      "17\t검증 세트 손실: 0.204718\t최선의 손실: 0.172069\t정확도: 97.77%\n",
+      "18\t검증 세트 손실: 0.218686\t최선의 손실: 0.172069\t정확도: 97.73%\n",
+      "19\t검증 세트 손실: 0.192214\t최선의 손실: 0.172069\t정확도: 98.16%\n",
+      "20\t검증 세트 손실: 0.171978\t최선의 손실: 0.171978\t정확도: 98.32%\n",
+      "21\t검증 세트 손실: 0.229062\t최선의 손실: 0.171978\t정확도: 98.16%\n",
+      "22\t검증 세트 손실: 0.243987\t최선의 손실: 0.171978\t정확도: 97.38%\n",
+      "23\t검증 세트 손실: 0.190578\t최선의 손실: 0.171978\t정확도: 98.44%\n",
+      "24\t검증 세트 손실: 0.163932\t최선의 손실: 0.163932\t정확도: 98.55%\n",
+      "25\t검증 세트 손실: 0.188630\t최선의 손실: 0.163932\t정확도: 98.24%\n",
+      "26\t검증 세트 손실: 0.209388\t최선의 손실: 0.163932\t정확도: 98.16%\n",
+      "27\t검증 세트 손실: 0.153010\t최선의 손실: 0.153010\t정확도: 98.55%\n",
+      "28\t검증 세트 손실: 0.137776\t최선의 손실: 0.137776\t정확도: 98.63%\n",
+      "29\t검증 세트 손실: 0.134993\t최선의 손실: 0.134993\t정확도: 98.75%\n",
+      "30\t검증 세트 손실: 0.114397\t최선의 손실: 0.114397\t정확도: 98.48%\n",
+      "31\t검증 세트 손실: 0.108852\t최선의 손실: 0.108852\t정확도: 98.59%\n",
+      "32\t검증 세트 손실: 0.101213\t최선의 손실: 0.101213\t정확도: 98.67%\n",
+      "33\t검증 세트 손실: 0.098962\t최선의 손실: 0.098962\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.092069\t최선의 손실: 0.092069\t정확도: 98.71%\n",
+      "35\t검증 세트 손실: 0.089113\t최선의 손실: 0.089113\t정확도: 98.75%\n",
+      "36\t검증 세트 손실: 0.086567\t최선의 손실: 0.086567\t정확도: 98.79%\n",
+      "37\t검증 세트 손실: 0.086999\t최선의 손실: 0.086567\t정확도: 98.75%\n",
+      "38\t검증 세트 손실: 0.082672\t최선의 손실: 0.082672\t정확도: 98.79%\n",
+      "39\t검증 세트 손실: 0.080718\t최선의 손실: 0.080718\t정확도: 98.79%\n",
+      "40\t검증 세트 손실: 0.079001\t최선의 손실: 0.079001\t정확도: 98.79%\n",
+      "41\t검증 세트 손실: 0.077647\t최선의 손실: 0.077647\t정확도: 98.79%\n",
+      "42\t검증 세트 손실: 0.076493\t최선의 손실: 0.076493\t정확도: 98.75%\n",
+      "43\t검증 세트 손실: 0.074591\t최선의 손실: 0.074591\t정확도: 98.83%\n",
+      "44\t검증 세트 손실: 0.073490\t최선의 손실: 0.073490\t정확도: 98.75%\n",
+      "45\t검증 세트 손실: 0.073193\t최선의 손실: 0.073193\t정확도: 98.75%\n",
+      "46\t검증 세트 손실: 0.072099\t최선의 손실: 0.072099\t정확도: 98.87%\n",
+      "47\t검증 세트 손실: 0.070630\t최선의 손실: 0.070630\t정확도: 98.83%\n",
+      "48\t검증 세트 손실: 0.069077\t최선의 손실: 0.069077\t정확도: 98.87%\n",
+      "49\t검증 세트 손실: 0.069835\t최선의 손실: 0.069077\t정확도: 98.79%\n",
+      "50\t검증 세트 손실: 0.069817\t최선의 손실: 0.069077\t정확도: 98.79%\n",
+      "51\t검증 세트 손실: 0.068487\t최선의 손실: 0.068487\t정확도: 98.87%\n",
+      "52\t검증 세트 손실: 0.066781\t최선의 손실: 0.066781\t정확도: 98.91%\n",
+      "53\t검증 세트 손실: 0.067611\t최선의 손실: 0.066781\t정확도: 98.79%\n",
+      "54\t검증 세트 손실: 0.069005\t최선의 손실: 0.066781\t정확도: 98.83%\n",
+      "55\t검증 세트 손실: 0.068396\t최선의 손실: 0.066781\t정확도: 98.83%\n",
+      "56\t검증 세트 손실: 0.065865\t최선의 손실: 0.065865\t정확도: 98.87%\n",
+      "57\t검증 세트 손실: 0.065482\t최선의 손실: 0.065482\t정확도: 98.83%\n",
+      "58\t검증 세트 손실: 0.065216\t최선의 손실: 0.065216\t정확도: 98.83%\n",
+      "59\t검증 세트 손실: 0.064563\t최선의 손실: 0.064563\t정확도: 98.87%\n",
+      "60\t검증 세트 손실: 0.062341\t최선의 손실: 0.062341\t정확도: 98.91%\n",
+      "61\t검증 세트 손실: 0.063714\t최선의 손실: 0.062341\t정확도: 98.83%\n",
+      "62\t검증 세트 손실: 0.063006\t최선의 손실: 0.062341\t정확도: 98.79%\n",
+      "63\t검증 세트 손실: 0.063267\t최선의 손실: 0.062341\t정확도: 98.87%\n",
+      "64\t검증 세트 손실: 0.062413\t최선의 손실: 0.062341\t정확도: 98.87%\n",
+      "65\t검증 세트 손실: 0.061477\t최선의 손실: 0.061477\t정확도: 98.91%\n",
+      "66\t검증 세트 손실: 0.062323\t최선의 손실: 0.061477\t정확도: 98.83%\n",
+      "67\t검증 세트 손실: 0.063324\t최선의 손실: 0.061477\t정확도: 98.83%\n",
+      "68\t검증 세트 손실: 0.062594\t최선의 손실: 0.061477\t정확도: 98.87%\n",
+      "69\t검증 세트 손실: 0.061164\t최선의 손실: 0.061164\t정확도: 98.87%\n",
+      "70\t검증 세트 손실: 0.062381\t최선의 손실: 0.061164\t정확도: 98.87%\n",
+      "71\t검증 세트 손실: 0.062474\t최선의 손실: 0.061164\t정확도: 98.87%\n",
+      "72\t검증 세트 손실: 0.062384\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "73\t검증 세트 손실: 0.062537\t최선의 손실: 0.061164\t정확도: 98.83%\n",
+      "74\t검증 세트 손실: 0.061997\t최선의 손실: 0.061164\t정확도: 98.87%\n",
+      "75\t검증 세트 손실: 0.062275\t최선의 손실: 0.061164\t정확도: 98.87%\n",
+      "76\t검증 세트 손실: 0.062123\t최선의 손실: 0.061164\t정확도: 98.94%\n",
+      "77\t검증 세트 손실: 0.061821\t최선의 손실: 0.061164\t정확도: 98.94%\n",
+      "78\t검증 세트 손실: 0.063660\t최선의 손실: 0.061164\t정확도: 98.83%\n",
+      "79\t검증 세트 손실: 0.063238\t최선의 손실: 0.061164\t정확도: 98.87%\n",
+      "80\t검증 세트 손실: 0.063775\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "81\t검증 세트 손실: 0.062609\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "82\t검증 세트 손실: 0.062288\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "83\t검증 세트 손실: 0.061726\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "84\t검증 세트 손실: 0.061228\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "85\t검증 세트 손실: 0.062219\t최선의 손실: 0.061164\t정확도: 98.98%\n",
+      "86\t검증 세트 손실: 0.061269\t최선의 손실: 0.061164\t정확도: 98.94%\n",
+      "87\t검증 세트 손실: 0.061755\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "88\t검증 세트 손실: 0.061285\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "89\t검증 세트 손실: 0.061301\t최선의 손실: 0.061164\t정확도: 98.91%\n",
+      "90\t검증 세트 손실: 0.061250\t최선의 손실: 0.061164\t정확도: 98.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=30, total=  15.2s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.272899\t최선의 손실: 1.272899\t정확도: 81.31%\n",
-      "1\t검증 세트 손실: 0.659657\t최선의 손실: 0.659657\t정확도: 88.74%\n",
-      "2\t검증 세트 손실: 0.323906\t최선의 손실: 0.323906\t정확도: 94.49%\n",
-      "3\t검증 세트 손실: 0.755817\t최선의 손실: 0.323906\t정확도: 89.76%\n",
-      "4\t검증 세트 손실: 0.168105\t최선의 손실: 0.168105\t정확도: 97.54%\n",
-      "5\t검증 세트 손실: 0.236237\t최선의 손실: 0.168105\t정확도: 97.07%\n",
-      "6\t검증 세트 손실: 0.133713\t최선의 손실: 0.133713\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.194322\t최선의 손실: 0.133713\t정확도: 96.52%\n",
-      "8\t검증 세트 손실: 0.505750\t최선의 손실: 0.133713\t정확도: 91.75%\n",
-      "9\t검증 세트 손실: 0.415184\t최선의 손실: 0.133713\t정확도: 95.58%\n",
-      "10\t검증 세트 손실: 0.175078\t최선의 손실: 0.133713\t정확도: 97.93%\n",
-      "11\t검증 세트 손실: 0.274872\t최선의 손실: 0.133713\t정확도: 96.79%\n",
-      "12\t검증 세트 손실: 0.190456\t최선의 손실: 0.133713\t정확도: 97.89%\n",
-      "13\t검증 세트 손실: 0.216279\t최선의 손실: 0.133713\t정확도: 97.42%\n",
-      "14\t검증 세트 손실: 0.157608\t최선의 손실: 0.133713\t정확도: 97.85%\n",
-      "15\t검증 세트 손실: 0.155730\t최선의 손실: 0.133713\t정확도: 98.24%\n",
-      "16\t검증 세트 손실: 0.161293\t최선의 손실: 0.133713\t정확도: 98.28%\n",
-      "17\t검증 세트 손실: 0.201939\t최선의 손실: 0.133713\t정확도: 97.97%\n",
-      "18\t검증 세트 손실: 0.218037\t최선의 손실: 0.133713\t정확도: 97.89%\n",
-      "19\t검증 세트 손실: 0.296012\t최선의 손실: 0.133713\t정확도: 97.19%\n",
-      "20\t검증 세트 손실: 0.248938\t최선의 손실: 0.133713\t정확도: 97.89%\n",
-      "21\t검증 세트 손실: 0.286406\t최선의 손실: 0.133713\t정확도: 96.87%\n",
-      "22\t검증 세트 손실: 0.291234\t최선의 손실: 0.133713\t정확도: 96.91%\n",
-      "23\t검증 세트 손실: 0.175471\t최선의 손실: 0.133713\t정확도: 98.32%\n",
-      "24\t검증 세트 손실: 0.221118\t최선의 손실: 0.133713\t정확도: 97.77%\n",
-      "25\t검증 세트 손실: 0.226083\t최선의 손실: 0.133713\t정확도: 98.16%\n",
-      "26\t검증 세트 손실: 0.192460\t최선의 손실: 0.133713\t정확도: 98.28%\n",
-      "27\t검증 세트 손실: 0.154790\t최선의 손실: 0.133713\t정확도: 98.28%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=30, total=   5.8s\n",
-      "[CV] batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.326524\t최선의 손실: 1.326524\t정확도: 80.10%\n",
-      "1\t검증 세트 손실: 1.045817\t최선의 손실: 1.045817\t정확도: 84.44%\n",
-      "2\t검증 세트 손실: 0.255803\t최선의 손실: 0.255803\t정확도: 94.72%\n",
-      "3\t검증 세트 손실: 0.281417\t최선의 손실: 0.255803\t정확도: 94.92%\n",
-      "4\t검증 세트 손실: 0.220463\t최선의 손실: 0.220463\t정확도: 95.43%\n",
-      "5\t검증 세트 손실: 0.141891\t최선의 손실: 0.141891\t정확도: 97.81%\n",
-      "6\t검증 세트 손실: 0.214809\t최선의 손실: 0.141891\t정확도: 97.30%\n",
-      "7\t검증 세트 손실: 0.225963\t최선의 손실: 0.141891\t정확도: 96.83%\n",
-      "8\t검증 세트 손실: 0.270997\t최선의 손실: 0.141891\t정확도: 95.90%\n",
-      "9\t검증 세트 손실: 0.222565\t최선의 손실: 0.141891\t정확도: 97.03%\n",
-      "10\t검증 세트 손실: 0.212250\t최선의 손실: 0.141891\t정확도: 97.38%\n",
-      "11\t검증 세트 손실: 0.160389\t최선의 손실: 0.141891\t정확도: 97.73%\n",
-      "12\t검증 세트 손실: 0.262997\t최선의 손실: 0.141891\t정확도: 96.60%\n",
-      "13\t검증 세트 손실: 0.148184\t최선의 손실: 0.141891\t정확도: 98.40%\n",
-      "14\t검증 세트 손실: 0.216892\t최선의 손실: 0.141891\t정확도: 97.62%\n",
-      "15\t검증 세트 손실: 0.170018\t최선의 손실: 0.141891\t정확도: 98.05%\n",
-      "16\t검증 세트 손실: 0.223192\t최선의 손실: 0.141891\t정확도: 97.65%\n",
-      "17\t검증 세트 손실: 0.226304\t최선의 손실: 0.141891\t정확도: 97.77%\n",
-      "18\t검증 세트 손실: 0.243864\t최선의 손실: 0.141891\t정확도: 97.26%\n",
-      "19\t검증 세트 손실: 0.183611\t최선의 손실: 0.141891\t정확도: 98.36%\n",
-      "20\t검증 세트 손실: 0.138301\t최선의 손실: 0.138301\t정확도: 98.48%\n",
-      "21\t검증 세트 손실: 0.191450\t최선의 손실: 0.138301\t정확도: 97.93%\n",
-      "22\t검증 세트 손실: 0.129871\t최선의 손실: 0.129871\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.143135\t최선의 손실: 0.129871\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.137809\t최선의 손실: 0.129871\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.150924\t최선의 손실: 0.129871\t정확도: 98.36%\n",
-      "26\t검증 세트 손실: 0.132121\t최선의 손실: 0.129871\t정확도: 98.44%\n",
-      "27\t검증 세트 손실: 0.128433\t최선의 손실: 0.128433\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.139678\t최선의 손실: 0.128433\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.121402\t최선의 손실: 0.121402\t정확도: 98.67%\n",
-      "30\t검증 세트 손실: 0.157907\t최선의 손실: 0.121402\t정확도: 98.36%\n",
-      "31\t검증 세트 손실: 0.233990\t최선의 손실: 0.121402\t정확도: 97.42%\n",
-      "32\t검증 세트 손실: 0.141736\t최선의 손실: 0.121402\t정확도: 98.51%\n",
-      "33\t검증 세트 손실: 0.147316\t최선의 손실: 0.121402\t정확도: 98.28%\n",
-      "34\t검증 세트 손실: 0.136803\t최선의 손실: 0.121402\t정확도: 98.55%\n",
-      "35\t검증 세트 손실: 0.127220\t최선의 손실: 0.121402\t정확도: 98.67%\n",
-      "36\t검증 세트 손실: 0.174770\t최선의 손실: 0.121402\t정확도: 98.24%\n",
-      "37\t검증 세트 손실: 0.173465\t최선의 손실: 0.121402\t정확도: 98.28%\n",
-      "38\t검증 세트 손실: 0.146237\t최선의 손실: 0.121402\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.191254\t최선의 손실: 0.121402\t정확도: 98.44%\n",
-      "40\t검증 세트 손실: 0.234648\t최선의 손실: 0.121402\t정확도: 98.12%\n",
-      "41\t검증 세트 손실: 0.112306\t최선의 손실: 0.112306\t정확도: 98.91%\n",
-      "42\t검증 세트 손실: 0.132876\t최선의 손실: 0.112306\t정확도: 98.71%\n",
-      "43\t검증 세트 손실: 0.099174\t최선의 손실: 0.099174\t정확도: 98.79%\n",
-      "44\t검증 세트 손실: 0.085755\t최선의 손실: 0.085755\t정확도: 99.02%\n",
-      "45\t검증 세트 손실: 0.083403\t최선의 손실: 0.083403\t정확도: 99.02%\n",
-      "46\t검증 세트 손실: 0.081131\t최선의 손실: 0.081131\t정확도: 99.14%\n",
-      "47\t검증 세트 손실: 0.078646\t최선의 손실: 0.078646\t정확도: 99.10%\n",
-      "48\t검증 세트 손실: 0.078990\t최선의 손실: 0.078646\t정확도: 99.02%\n",
-      "49\t검증 세트 손실: 0.073468\t최선의 손실: 0.073468\t정확도: 99.06%\n",
-      "50\t검증 세트 손실: 0.069474\t최선의 손실: 0.069474\t정확도: 99.10%\n",
-      "51\t검증 세트 손실: 0.067643\t최선의 손실: 0.067643\t정확도: 99.14%\n",
-      "52\t검증 세트 손실: 0.066302\t최선의 손실: 0.066302\t정확도: 99.02%\n",
-      "53\t검증 세트 손실: 0.063716\t최선의 손실: 0.063716\t정확도: 99.02%\n",
-      "54\t검증 세트 손실: 0.061768\t최선의 손실: 0.061768\t정확도: 99.14%\n",
-      "55\t검증 세트 손실: 0.060066\t최선의 손실: 0.060066\t정확도: 99.06%\n",
-      "56\t검증 세트 손실: 0.058461\t최선의 손실: 0.058461\t정확도: 99.06%\n",
-      "57\t검증 세트 손실: 0.059935\t최선의 손실: 0.058461\t정확도: 99.14%\n",
-      "58\t검증 세트 손실: 0.058835\t최선의 손실: 0.058461\t정확도: 99.10%\n",
-      "59\t검증 세트 손실: 0.058129\t최선의 손실: 0.058129\t정확도: 99.10%\n",
-      "60\t검증 세트 손실: 0.057267\t최선의 손실: 0.057267\t정확도: 99.10%\n",
-      "61\t검증 세트 손실: 0.056332\t최선의 손실: 0.056332\t정확도: 99.18%\n",
-      "62\t검증 세트 손실: 0.055378\t최선의 손실: 0.055378\t정확도: 99.10%\n",
-      "63\t검증 세트 손실: 0.054900\t최선의 손실: 0.054900\t정확도: 99.14%\n",
-      "64\t검증 세트 손실: 0.054814\t최선의 손실: 0.054814\t정확도: 99.14%\n",
-      "65\t검증 세트 손실: 0.054069\t최선의 손실: 0.054069\t정확도: 99.22%\n",
-      "66\t검증 세트 손실: 0.052780\t최선의 손실: 0.052780\t정확도: 99.14%\n",
-      "67\t검증 세트 손실: 0.051259\t최선의 손실: 0.051259\t정확도: 99.14%\n",
-      "68\t검증 세트 손실: 0.050946\t최선의 손실: 0.050946\t정확도: 99.22%\n",
-      "69\t검증 세트 손실: 0.050731\t최선의 손실: 0.050731\t정확도: 99.18%\n",
-      "70\t검증 세트 손실: 0.050311\t최선의 손실: 0.050311\t정확도: 99.18%\n",
-      "71\t검증 세트 손실: 0.049873\t최선의 손실: 0.049873\t정확도: 99.22%\n",
-      "72\t검증 세트 손실: 0.048573\t최선의 손실: 0.048573\t정확도: 99.22%\n",
-      "73\t검증 세트 손실: 0.048725\t최선의 손실: 0.048573\t정확도: 99.18%\n",
-      "74\t검증 세트 손실: 0.048509\t최선의 손실: 0.048509\t정확도: 99.18%\n",
-      "75\t검증 세트 손실: 0.048524\t최선의 손실: 0.048509\t정확도: 99.22%\n",
-      "76\t검증 세트 손실: 0.048702\t최선의 손실: 0.048509\t정확도: 99.18%\n",
-      "77\t검증 세트 손실: 0.049143\t최선의 손실: 0.048509\t정확도: 99.14%\n",
-      "78\t검증 세트 손실: 0.049055\t최선의 손실: 0.048509\t정확도: 99.10%\n",
-      "79\t검증 세트 손실: 0.049061\t최선의 손실: 0.048509\t정확도: 99.06%\n",
-      "80\t검증 세트 손실: 0.048950\t최선의 손실: 0.048509\t정확도: 99.06%\n",
-      "81\t검증 세트 손실: 0.048345\t최선의 손실: 0.048345\t정확도: 99.10%\n",
-      "82\t검증 세트 손실: 0.048301\t최선의 손실: 0.048301\t정확도: 99.06%\n",
-      "83\t검증 세트 손실: 0.047085\t최선의 손실: 0.047085\t정확도: 99.14%\n",
-      "84\t검증 세트 손실: 0.047333\t최선의 손실: 0.047085\t정확도: 99.06%\n",
-      "85\t검증 세트 손실: 0.046798\t최선의 손실: 0.046798\t정확도: 99.10%\n",
-      "86\t검증 세트 손실: 0.047484\t최선의 손실: 0.046798\t정확도: 99.14%\n",
-      "87\t검증 세트 손실: 0.047790\t최선의 손실: 0.046798\t정확도: 99.02%\n",
-      "88\t검증 세트 손실: 0.047588\t최선의 손실: 0.046798\t정확도: 99.06%\n",
-      "89\t검증 세트 손실: 0.046519\t최선의 손실: 0.046519\t정확도: 99.06%\n",
-      "90\t검증 세트 손실: 0.046884\t최선의 손실: 0.046519\t정확도: 99.06%\n",
-      "91\t검증 세트 손실: 0.046849\t최선의 손실: 0.046519\t정확도: 99.06%\n",
-      "92\t검증 세트 손실: 0.046748\t최선의 손실: 0.046519\t정확도: 99.06%\n",
-      "93\t검증 세트 손실: 0.046385\t최선의 손실: 0.046385\t정확도: 99.06%\n",
-      "94\t검증 세트 손실: 0.046341\t최선의 손실: 0.046341\t정확도: 99.10%\n",
-      "95\t검증 세트 손실: 0.046619\t최선의 손실: 0.046341\t정확도: 99.06%\n",
-      "96\t검증 세트 손실: 0.046911\t최선의 손실: 0.046341\t정확도: 99.06%\n",
-      "97\t검증 세트 손실: 0.046788\t최선의 손실: 0.046341\t정확도: 99.06%\n",
-      "98\t검증 세트 손실: 0.046706\t최선의 손실: 0.046341\t정확도: 99.06%\n",
-      "99\t검증 세트 손실: 0.045556\t최선의 손실: 0.045556\t정확도: 99.06%\n",
-      "100\t검증 세트 손실: 0.045544\t최선의 손실: 0.045544\t정확도: 99.06%\n",
-      "101\t검증 세트 손실: 0.046738\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "102\t검증 세트 손실: 0.046982\t최선의 손실: 0.045544\t정확도: 99.06%\n",
-      "103\t검증 세트 손실: 0.046444\t최선의 손실: 0.045544\t정확도: 99.06%\n",
-      "104\t검증 세트 손실: 0.046117\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "105\t검증 세트 손실: 0.047513\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "106\t검증 세트 손실: 0.046802\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "107\t검증 세트 손실: 0.046591\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "108\t검증 세트 손실: 0.047058\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "109\t검증 세트 손실: 0.047018\t최선의 손실: 0.045544\t정확도: 99.02%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=30, total=  17.3s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.155564\t최선의 손실: 1.155564\t정확도: 83.11%\n",
+      "1\t검증 세트 손실: 0.514831\t최선의 손실: 0.514831\t정확도: 91.79%\n",
+      "2\t검증 세트 손실: 0.535744\t최선의 손실: 0.514831\t정확도: 91.05%\n",
+      "3\t검증 세트 손실: 0.297607\t최선의 손실: 0.297607\t정확도: 95.62%\n",
+      "4\t검증 세트 손실: 0.274796\t최선의 손실: 0.274796\t정확도: 96.33%\n",
+      "5\t검증 세트 손실: 0.689869\t최선의 손실: 0.274796\t정확도: 91.24%\n",
+      "6\t검증 세트 손실: 0.250661\t최선의 손실: 0.250661\t정확도: 96.95%\n",
+      "7\t검증 세트 손실: 0.321565\t최선의 손실: 0.250661\t정확도: 96.33%\n",
+      "8\t검증 세트 손실: 0.446385\t최선의 손실: 0.250661\t정확도: 94.72%\n",
+      "9\t검증 세트 손실: 0.262028\t최선의 손실: 0.250661\t정확도: 97.11%\n",
+      "10\t검증 세트 손실: 0.207130\t최선의 손실: 0.207130\t정확도: 97.77%\n",
+      "11\t검증 세트 손실: 0.293213\t최선의 손실: 0.207130\t정확도: 97.11%\n",
+      "12\t검증 세트 손실: 0.247475\t최선의 손실: 0.207130\t정확도: 97.42%\n",
+      "13\t검증 세트 손실: 0.281591\t최선의 손실: 0.207130\t정확도: 97.34%\n",
+      "14\t검증 세트 손실: 0.219772\t최선의 손실: 0.207130\t정확도: 97.81%\n",
+      "15\t검증 세트 손실: 0.358786\t최선의 손실: 0.207130\t정확도: 96.60%\n",
+      "16\t검증 세트 손실: 0.233113\t최선의 손실: 0.207130\t정확도: 97.85%\n",
+      "17\t검증 세트 손실: 0.281210\t최선의 손실: 0.207130\t정확도: 97.85%\n",
+      "18\t검증 세트 손실: 0.230709\t최선의 손실: 0.207130\t정확도: 97.54%\n",
+      "19\t검증 세트 손실: 0.167845\t최선의 손실: 0.167845\t정확도: 98.01%\n",
+      "20\t검증 세트 손실: 0.187677\t최선의 손실: 0.167845\t정확도: 98.28%\n",
+      "21\t검증 세트 손실: 0.186255\t최선의 손실: 0.167845\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.224939\t최선의 손실: 0.167845\t정확도: 97.89%\n",
+      "23\t검증 세트 손실: 0.265500\t최선의 손실: 0.167845\t정확도: 97.81%\n",
+      "24\t검증 세트 손실: 0.283337\t최선의 손실: 0.167845\t정확도: 97.85%\n",
+      "25\t검증 세트 손실: 0.312451\t최선의 손실: 0.167845\t정확도: 96.83%\n",
+      "26\t검증 세트 손실: 0.206318\t최선의 손실: 0.167845\t정확도: 98.20%\n",
+      "27\t검증 세트 손실: 0.251022\t최선의 손실: 0.167845\t정확도: 97.62%\n",
+      "28\t검증 세트 손실: 0.162218\t최선의 손실: 0.162218\t정확도: 98.63%\n",
+      "29\t검증 세트 손실: 0.201514\t최선의 손실: 0.162218\t정확도: 98.12%\n",
+      "30\t검증 세트 손실: 0.166709\t최선의 손실: 0.162218\t정확도: 98.48%\n",
+      "31\t검증 세트 손실: 0.133228\t최선의 손실: 0.133228\t정확도: 98.67%\n",
+      "32\t검증 세트 손실: 0.117905\t최선의 손실: 0.117905\t정확도: 98.71%\n",
+      "33\t검증 세트 손실: 0.159426\t최선의 손실: 0.117905\t정확도: 98.48%\n",
+      "34\t검증 세트 손실: 0.134436\t최선의 손실: 0.117905\t정확도: 98.75%\n",
+      "35\t검증 세트 손실: 0.125179\t최선의 손실: 0.117905\t정확도: 98.67%\n",
+      "36\t검증 세트 손실: 0.122108\t최선의 손실: 0.117905\t정확도: 98.87%\n",
+      "37\t검증 세트 손실: 0.106403\t최선의 손실: 0.106403\t정확도: 98.91%\n",
+      "38\t검증 세트 손실: 0.100628\t최선의 손실: 0.100628\t정확도: 98.87%\n",
+      "39\t검증 세트 손실: 0.103968\t최선의 손실: 0.100628\t정확도: 98.98%\n",
+      "40\t검증 세트 손실: 0.099073\t최선의 손실: 0.099073\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.093878\t최선의 손실: 0.093878\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.091302\t최선의 손실: 0.091302\t정확도: 98.98%\n",
+      "43\t검증 세트 손실: 0.089769\t최선의 손실: 0.089769\t정확도: 98.94%\n",
+      "44\t검증 세트 손실: 0.088655\t최선의 손실: 0.088655\t정확도: 98.94%\n",
+      "45\t검증 세트 손실: 0.086573\t최선의 손실: 0.086573\t정확도: 98.94%\n",
+      "46\t검증 세트 손실: 0.083486\t최선의 손실: 0.083486\t정확도: 98.94%\n",
+      "47\t검증 세트 손실: 0.082863\t최선의 손실: 0.082863\t정확도: 98.98%\n",
+      "48\t검증 세트 손실: 0.080195\t최선의 손실: 0.080195\t정확도: 99.02%\n",
+      "49\t검증 세트 손실: 0.078377\t최선의 손실: 0.078377\t정확도: 99.02%\n",
+      "50\t검증 세트 손실: 0.076919\t최선의 손실: 0.076919\t정확도: 99.02%\n",
+      "51\t검증 세트 손실: 0.076093\t최선의 손실: 0.076093\t정확도: 98.98%\n",
+      "52\t검증 세트 손실: 0.076204\t최선의 손실: 0.076093\t정확도: 98.98%\n",
+      "53\t검증 세트 손실: 0.075865\t최선의 손실: 0.075865\t정확도: 99.06%\n",
+      "54\t검증 세트 손실: 0.077270\t최선의 손실: 0.075865\t정확도: 99.02%\n",
+      "55\t검증 세트 손실: 0.075989\t최선의 손실: 0.075865\t정확도: 98.98%\n",
+      "56\t검증 세트 손실: 0.073566\t최선의 손실: 0.073566\t정확도: 98.98%\n",
+      "57\t검증 세트 손실: 0.072004\t최선의 손실: 0.072004\t정확도: 98.98%\n",
+      "58\t검증 세트 손실: 0.071743\t최선의 손실: 0.071743\t정확도: 98.98%\n",
+      "59\t검증 세트 손실: 0.071845\t최선의 손실: 0.071743\t정확도: 98.98%\n",
+      "60\t검증 세트 손실: 0.071274\t최선의 손실: 0.071274\t정확도: 98.98%\n",
+      "61\t검증 세트 손실: 0.070807\t최선의 손실: 0.070807\t정확도: 98.98%\n",
+      "62\t검증 세트 손실: 0.072289\t최선의 손실: 0.070807\t정확도: 98.94%\n",
+      "63\t검증 세트 손실: 0.067975\t최선의 손실: 0.067975\t정확도: 98.91%\n",
+      "64\t검증 세트 손실: 0.067646\t최선의 손실: 0.067646\t정확도: 98.94%\n",
+      "65\t검증 세트 손실: 0.065065\t최선의 손실: 0.065065\t정확도: 98.94%\n",
+      "66\t검증 세트 손실: 0.064693\t최선의 손실: 0.064693\t정확도: 98.91%\n",
+      "67\t검증 세트 손실: 0.065611\t최선의 손실: 0.064693\t정확도: 98.87%\n",
+      "68\t검증 세트 손실: 0.066022\t최선의 손실: 0.064693\t정확도: 98.83%\n",
+      "69\t검증 세트 손실: 0.066342\t최선의 손실: 0.064693\t정확도: 98.87%\n",
+      "70\t검증 세트 손실: 0.066881\t최선의 손실: 0.064693\t정확도: 98.87%\n",
+      "71\t검증 세트 손실: 0.066504\t최선의 손실: 0.064693\t정확도: 98.91%\n",
+      "72\t검증 세트 손실: 0.066354\t최선의 손실: 0.064693\t정확도: 98.94%\n",
+      "73\t검증 세트 손실: 0.066274\t최선의 손실: 0.064693\t정확도: 98.94%\n",
+      "74\t검증 세트 손실: 0.066034\t최선의 손실: 0.064693\t정확도: 98.94%\n",
+      "75\t검증 세트 손실: 0.065188\t최선의 손실: 0.064693\t정확도: 98.94%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "110\t검증 세트 손실: 0.046950\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "111\t검증 세트 손실: 0.047334\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "112\t검증 세트 손실: 0.047235\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "113\t검증 세트 손실: 0.047549\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "114\t검증 세트 손실: 0.047601\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "115\t검증 세트 손실: 0.047154\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "116\t검증 세트 손실: 0.047645\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "117\t검증 세트 손실: 0.047684\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "118\t검증 세트 손실: 0.047716\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "119\t검증 세트 손실: 0.047987\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "120\t검증 세트 손실: 0.047525\t최선의 손실: 0.045544\t정확도: 99.02%\n",
-      "121\t검증 세트 손실: 0.047933\t최선의 손실: 0.045544\t정확도: 99.02%\n",
+      "76\t검증 세트 손실: 0.064487\t최선의 손실: 0.064487\t정확도: 98.91%\n",
+      "77\t검증 세트 손실: 0.064023\t최선의 손실: 0.064023\t정확도: 98.98%\n",
+      "78\t검증 세트 손실: 0.063898\t최선의 손실: 0.063898\t정확도: 98.94%\n",
+      "79\t검증 세트 손실: 0.064060\t최선의 손실: 0.063898\t정확도: 98.91%\n",
+      "80\t검증 세트 손실: 0.063392\t최선의 손실: 0.063392\t정확도: 98.91%\n",
+      "81\t검증 세트 손실: 0.062782\t최선의 손실: 0.062782\t정확도: 98.91%\n",
+      "82\t검증 세트 손실: 0.062901\t최선의 손실: 0.062782\t정확도: 98.87%\n",
+      "83\t검증 세트 손실: 0.063029\t최선의 손실: 0.062782\t정확도: 98.91%\n",
+      "84\t검증 세트 손실: 0.063280\t최선의 손실: 0.062782\t정확도: 98.87%\n",
+      "85\t검증 세트 손실: 0.063269\t최선의 손실: 0.062782\t정확도: 98.91%\n",
+      "86\t검증 세트 손실: 0.063340\t최선의 손실: 0.062782\t정확도: 98.87%\n",
+      "87\t검증 세트 손실: 0.063215\t최선의 손실: 0.062782\t정확도: 98.91%\n",
+      "88\t검증 세트 손실: 0.062545\t최선의 손실: 0.062545\t정확도: 98.94%\n",
+      "89\t검증 세트 손실: 0.063188\t최선의 손실: 0.062545\t정확도: 98.87%\n",
+      "90\t검증 세트 손실: 0.063373\t최선의 손실: 0.062545\t정확도: 98.83%\n",
+      "91\t검증 세트 손실: 0.061868\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "92\t검증 세트 손실: 0.062247\t최선의 손실: 0.061868\t정확도: 98.94%\n",
+      "93\t검증 세트 손실: 0.062627\t최선의 손실: 0.061868\t정확도: 98.98%\n",
+      "94\t검증 세트 손실: 0.062289\t최선의 손실: 0.061868\t정확도: 98.94%\n",
+      "95\t검증 세트 손실: 0.062109\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "96\t검증 세트 손실: 0.062755\t최선의 손실: 0.061868\t정확도: 98.94%\n",
+      "97\t검증 세트 손실: 0.062883\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "98\t검증 세트 손실: 0.062671\t최선의 손실: 0.061868\t정확도: 98.87%\n",
+      "99\t검증 세트 손실: 0.062705\t최선의 손실: 0.061868\t정확도: 98.87%\n",
+      "100\t검증 세트 손실: 0.062969\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "101\t검증 세트 손실: 0.063227\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "102\t검증 세트 손실: 0.062429\t최선의 손실: 0.061868\t정확도: 98.98%\n",
+      "103\t검증 세트 손실: 0.063412\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "104\t검증 세트 손실: 0.064090\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "105\t검증 세트 손실: 0.063990\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "106\t검증 세트 손실: 0.063286\t최선의 손실: 0.061868\t정확도: 98.87%\n",
+      "107\t검증 세트 손실: 0.062578\t최선의 손실: 0.061868\t정확도: 98.83%\n",
+      "108\t검증 세트 손실: 0.062719\t최선의 손실: 0.061868\t정확도: 98.87%\n",
+      "109\t검증 세트 손실: 0.062232\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "110\t검증 세트 손실: 0.062795\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "111\t검증 세트 손실: 0.063615\t최선의 손실: 0.061868\t정확도: 98.91%\n",
+      "112\t검증 세트 손실: 0.063750\t최선의 손실: 0.061868\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.999, n_neurons=30, total=  20.7s\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.999, n_neurons=90 \n",
-      "0\t검증 세트 손실: 2.199320\t최선의 손실: 2.199320\t정확도: 78.30%\n",
-      "1\t검증 세트 손실: 0.146591\t최선의 손실: 0.146591\t정확도: 97.42%\n",
-      "2\t검증 세트 손실: 0.149003\t최선의 손실: 0.146591\t정확도: 97.58%\n",
-      "3\t검증 세트 손실: 0.212626\t최선의 손실: 0.146591\t정확도: 95.19%\n",
-      "4\t검증 세트 손실: 0.069007\t최선의 손실: 0.069007\t정확도: 97.97%\n",
-      "5\t검증 세트 손실: 0.065863\t최선의 손실: 0.065863\t정확도: 97.97%\n",
-      "6\t검증 세트 손실: 0.081491\t최선의 손실: 0.065863\t정확도: 97.54%\n",
-      "7\t검증 세트 손실: 0.060843\t최선의 손실: 0.060843\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.045310\t최선의 손실: 0.045310\t정확도: 98.71%\n",
-      "9\t검증 세트 손실: 0.052313\t최선의 손실: 0.045310\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.060220\t최선의 손실: 0.045310\t정확도: 98.40%\n",
-      "11\t검증 세트 손실: 0.064255\t최선의 손실: 0.045310\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.076734\t최선의 손실: 0.045310\t정확도: 98.08%\n",
-      "13\t검증 세트 손실: 0.056662\t최선의 손실: 0.045310\t정확도: 98.63%\n",
-      "14\t검증 세트 손실: 0.067125\t최선의 손실: 0.045310\t정확도: 98.44%\n",
-      "15\t검증 세트 손실: 0.072027\t최선의 손실: 0.045310\t정확도: 97.69%\n",
-      "16\t검증 세트 손실: 0.054642\t최선의 손실: 0.045310\t정확도: 98.48%\n",
-      "17\t검증 세트 손실: 0.040408\t최선의 손실: 0.040408\t정확도: 98.79%\n",
-      "18\t검증 세트 손실: 0.045929\t최선의 손실: 0.040408\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.047149\t최선의 손실: 0.040408\t정확도: 98.83%\n",
-      "20\t검증 세트 손실: 0.042773\t최선의 손실: 0.040408\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.049280\t최선의 손실: 0.040408\t정확도: 98.59%\n",
-      "22\t검증 세트 손실: 0.043692\t최선의 손실: 0.040408\t정확도: 98.75%\n",
-      "23\t검증 세트 손실: 0.109020\t최선의 손실: 0.040408\t정확도: 97.26%\n",
-      "24\t검증 세트 손실: 0.052469\t최선의 손실: 0.040408\t정확도: 98.48%\n",
-      "25\t검증 세트 손실: 0.049459\t최선의 손실: 0.040408\t정확도: 98.71%\n",
-      "26\t검증 세트 손실: 0.058406\t최선의 손실: 0.040408\t정확도: 98.79%\n",
-      "27\t검증 세트 손실: 0.057802\t최선의 손실: 0.040408\t정확도: 98.59%\n",
-      "28\t검증 세트 손실: 0.057026\t최선의 손실: 0.040408\t정확도: 98.48%\n",
-      "29\t검증 세트 손실: 0.065962\t최선의 손실: 0.040408\t정확도: 98.01%\n",
-      "30\t검증 세트 손실: 0.050568\t최선의 손실: 0.040408\t정확도: 98.91%\n",
-      "31\t검증 세트 손실: 0.049371\t최선의 손실: 0.040408\t정확도: 98.63%\n",
-      "32\t검증 세트 손실: 0.049459\t최선의 손실: 0.040408\t정확도: 99.06%\n",
-      "33\t검증 세트 손실: 0.039981\t최선의 손실: 0.039981\t정확도: 99.10%\n",
-      "34\t검증 세트 손실: 0.043391\t최선의 손실: 0.039981\t정확도: 98.98%\n",
-      "35\t검증 세트 손실: 0.087836\t최선의 손실: 0.039981\t정확도: 98.05%\n",
-      "36\t검증 세트 손실: 0.045710\t최선의 손실: 0.039981\t정확도: 98.75%\n",
-      "37\t검증 세트 손실: 0.062128\t최선의 손실: 0.039981\t정확도: 98.79%\n",
-      "38\t검증 세트 손실: 0.056402\t최선의 손실: 0.039981\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.034631\t최선의 손실: 0.034631\t정확도: 99.30%\n",
-      "40\t검증 세트 손실: 0.055632\t최선의 손실: 0.034631\t정확도: 98.63%\n",
-      "41\t검증 세트 손실: 0.078689\t최선의 손실: 0.034631\t정확도: 98.16%\n",
-      "42\t검증 세트 손실: 0.034134\t최선의 손실: 0.034134\t정확도: 98.98%\n",
-      "43\t검증 세트 손실: 0.051334\t최선의 손실: 0.034134\t정확도: 98.87%\n",
-      "44\t검증 세트 손실: 0.038759\t최선의 손실: 0.034134\t정확도: 99.26%\n",
-      "45\t검증 세트 손실: 0.060327\t최선의 손실: 0.034134\t정확도: 98.75%\n",
-      "46\t검증 세트 손실: 0.046834\t최선의 손실: 0.034134\t정확도: 98.75%\n",
-      "47\t검증 세트 손실: 0.061680\t최선의 손실: 0.034134\t정확도: 98.55%\n",
-      "48\t검증 세트 손실: 0.054846\t최선의 손실: 0.034134\t정확도: 98.79%\n",
-      "49\t검증 세트 손실: 0.062340\t최선의 손실: 0.034134\t정확도: 98.83%\n",
-      "50\t검증 세트 손실: 0.045520\t최선의 손실: 0.034134\t정확도: 98.94%\n",
-      "51\t검증 세트 손실: 0.052085\t최선의 손실: 0.034134\t정확도: 98.87%\n",
-      "52\t검증 세트 손실: 0.048155\t최선의 손실: 0.034134\t정확도: 98.98%\n",
-      "53\t검증 세트 손실: 0.077346\t최선의 손실: 0.034134\t정확도: 98.63%\n",
-      "54\t검증 세트 손실: 0.046717\t최선의 손실: 0.034134\t정확도: 98.71%\n",
-      "55\t검증 세트 손실: 0.036513\t최선의 손실: 0.034134\t정확도: 98.83%\n",
-      "56\t검증 세트 손실: 0.036108\t최선의 손실: 0.034134\t정확도: 98.98%\n",
-      "57\t검증 세트 손실: 0.085010\t최선의 손실: 0.034134\t정확도: 98.28%\n",
-      "58\t검증 세트 손실: 0.042280\t최선의 손실: 0.034134\t정확도: 99.14%\n",
-      "59\t검증 세트 손실: 0.067106\t최선의 손실: 0.034134\t정확도: 98.79%\n",
-      "60\t검증 세트 손실: 0.067729\t최선의 손실: 0.034134\t정확도: 98.71%\n",
-      "61\t검증 세트 손실: 0.045623\t최선의 손실: 0.034134\t정확도: 98.87%\n",
-      "62\t검증 세트 손실: 0.045058\t최선의 손실: 0.034134\t정확도: 99.02%\n",
-      "63\t검증 세트 손실: 0.050952\t최선의 손실: 0.034134\t정확도: 98.75%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.999, n_neurons=90, total= 7.1min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.999, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.270709\t최선의 손실: 0.270709\t정확도: 96.40%\n",
-      "1\t검증 세트 손실: 0.246091\t최선의 손실: 0.246091\t정확도: 96.05%\n",
-      "2\t검증 세트 손실: 0.116819\t최선의 손실: 0.116819\t정확도: 97.42%\n",
-      "3\t검증 세트 손실: 0.144341\t최선의 손실: 0.116819\t정확도: 96.25%\n",
-      "4\t검증 세트 손실: 0.125447\t최선의 손실: 0.116819\t정확도: 96.91%\n",
-      "5\t검증 세트 손실: 0.062308\t최선의 손실: 0.062308\t정확도: 98.36%\n",
-      "6\t검증 세트 손실: 0.079210\t최선의 손실: 0.062308\t정확도: 98.36%\n",
-      "7\t검증 세트 손실: 0.094363\t최선의 손실: 0.062308\t정확도: 97.69%\n",
-      "8\t검증 세트 손실: 0.060427\t최선의 손실: 0.060427\t정확도: 98.63%\n",
-      "9\t검증 세트 손실: 0.078760\t최선의 손실: 0.060427\t정확도: 97.85%\n",
-      "10\t검증 세트 손실: 0.076046\t최선의 손실: 0.060427\t정확도: 98.16%\n",
-      "11\t검증 세트 손실: 0.056504\t최선의 손실: 0.056504\t정확도: 98.63%\n",
-      "12\t검증 세트 손실: 0.059267\t최선의 손실: 0.056504\t정확도: 98.44%\n",
-      "13\t검증 세트 손실: 0.069710\t최선의 손실: 0.056504\t정확도: 98.01%\n",
-      "14\t검증 세트 손실: 0.051546\t최선의 손실: 0.051546\t정확도: 98.48%\n",
-      "15\t검증 세트 손실: 0.077808\t최선의 손실: 0.051546\t정확도: 97.77%\n",
-      "16\t검증 세트 손실: 0.042670\t최선의 손실: 0.042670\t정확도: 98.59%\n",
-      "17\t검증 세트 손실: 0.063141\t최선의 손실: 0.042670\t정확도: 98.01%\n",
-      "18\t검증 세트 손실: 0.068379\t최선의 손실: 0.042670\t정확도: 98.08%\n",
-      "19\t검증 세트 손실: 0.061058\t최선의 손실: 0.042670\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.041591\t최선의 손실: 0.041591\t정확도: 98.83%\n",
-      "21\t검증 세트 손실: 0.062355\t최선의 손실: 0.041591\t정확도: 98.59%\n",
-      "22\t검증 세트 손실: 0.073613\t최선의 손실: 0.041591\t정확도: 98.48%\n",
-      "23\t검증 세트 손실: 0.069303\t최선의 손실: 0.041591\t정확도: 98.16%\n",
-      "24\t검증 세트 손실: 0.066426\t최선의 손실: 0.041591\t정확도: 98.16%\n",
-      "25\t검증 세트 손실: 0.046575\t최선의 손실: 0.041591\t정확도: 98.75%\n",
-      "26\t검증 세트 손실: 0.045304\t최선의 손실: 0.041591\t정확도: 98.91%\n",
-      "27\t검증 세트 손실: 0.053389\t최선의 손실: 0.041591\t정확도: 98.75%\n",
-      "28\t검증 세트 손실: 0.057884\t최선의 손실: 0.041591\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.073004\t최선의 손실: 0.041591\t정확도: 98.67%\n",
-      "30\t검증 세트 손실: 0.056654\t최선의 손실: 0.041591\t정확도: 98.40%\n",
-      "31\t검증 세트 손실: 0.063945\t최선의 손실: 0.041591\t정확도: 98.51%\n",
-      "32\t검증 세트 손실: 0.056385\t최선의 손실: 0.041591\t정확도: 98.59%\n",
-      "33\t검증 세트 손실: 0.040144\t최선의 손실: 0.040144\t정확도: 98.79%\n",
-      "34\t검증 세트 손실: 0.049929\t최선의 손실: 0.040144\t정확도: 98.87%\n",
-      "35\t검증 세트 손실: 0.070153\t최선의 손실: 0.040144\t정확도: 98.12%\n",
-      "36\t검증 세트 손실: 0.054980\t최선의 손실: 0.040144\t정확도: 98.83%\n",
-      "37\t검증 세트 손실: 0.052148\t최선의 손실: 0.040144\t정확도: 98.87%\n",
-      "38\t검증 세트 손실: 0.062643\t최선의 손실: 0.040144\t정확도: 98.44%\n",
-      "39\t검증 세트 손실: 0.059469\t최선의 손실: 0.040144\t정확도: 98.24%\n",
-      "40\t검증 세트 손실: 0.050956\t최선의 손실: 0.040144\t정확도: 98.91%\n",
-      "41\t검증 세트 손실: 0.035001\t최선의 손실: 0.035001\t정확도: 99.10%\n",
-      "42\t검증 세트 손실: 0.072311\t최선의 손실: 0.035001\t정확도: 98.16%\n",
-      "43\t검증 세트 손실: 0.059109\t최선의 손실: 0.035001\t정확도: 98.59%\n",
-      "44\t검증 세트 손실: 0.047686\t최선의 손실: 0.035001\t정확도: 98.87%\n",
-      "45\t검증 세트 손실: 0.041440\t최선의 손실: 0.035001\t정확도: 99.10%\n",
-      "46\t검증 세트 손실: 0.057682\t최선의 손실: 0.035001\t정확도: 98.94%\n",
-      "47\t검증 세트 손실: 0.058027\t최선의 손실: 0.035001\t정확도: 98.63%\n",
-      "48\t검증 세트 손실: 0.042414\t최선의 손실: 0.035001\t정확도: 98.94%\n",
-      "49\t검증 세트 손실: 0.044752\t최선의 손실: 0.035001\t정확도: 99.14%\n",
-      "50\t검증 세트 손실: 0.048208\t최선의 손실: 0.035001\t정확도: 98.51%\n",
-      "51\t검증 세트 손실: 0.054776\t최선의 손실: 0.035001\t정확도: 98.91%\n",
-      "52\t검증 세트 손실: 0.061333\t최선의 손실: 0.035001\t정확도: 98.98%\n",
-      "53\t검증 세트 손실: 0.037544\t최선의 손실: 0.035001\t정확도: 98.91%\n",
-      "54\t검증 세트 손실: 0.086931\t최선의 손실: 0.035001\t정확도: 97.58%\n",
-      "55\t검증 세트 손실: 0.062753\t최선의 손실: 0.035001\t정확도: 98.59%\n",
-      "56\t검증 세트 손실: 0.084162\t최선의 손실: 0.035001\t정확도: 97.93%\n",
-      "57\t검증 세트 손실: 0.062092\t최선의 손실: 0.035001\t정확도: 98.55%\n",
-      "58\t검증 세트 손실: 0.042129\t최선의 손실: 0.035001\t정확도: 99.22%\n",
-      "59\t검증 세트 손실: 0.037002\t최선의 손실: 0.035001\t정확도: 98.98%\n",
-      "60\t검증 세트 손실: 0.039063\t최선의 손실: 0.035001\t정확도: 99.14%\n",
-      "61\t검증 세트 손실: 0.052359\t최선의 손실: 0.035001\t정확도: 99.10%\n",
-      "62\t검증 세트 손실: 0.063508\t최선의 손실: 0.035001\t정확도: 98.59%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.999, n_neurons=90, total= 7.0min\n",
-      "[CV] batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.999, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.510408\t최선의 손실: 0.510408\t정확도: 92.81%\n",
-      "1\t검증 세트 손실: 0.128629\t최선의 손실: 0.128629\t정확도: 97.73%\n"
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=30, total=  21.1s\n",
+      "[CV] batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=30 \n",
+      "0\t검증 세트 손실: 2.071557\t최선의 손실: 2.071557\t정확도: 75.96%\n",
+      "1\t검증 세트 손실: 0.836531\t최선의 손실: 0.836531\t정확도: 87.45%\n",
+      "2\t검증 세트 손실: 0.690738\t최선의 손실: 0.690738\t정확도: 89.41%\n",
+      "3\t검증 세트 손실: 0.443596\t최선의 손실: 0.443596\t정확도: 92.26%\n",
+      "4\t검증 세트 손실: 0.278550\t최선의 손실: 0.278550\t정확도: 95.58%\n",
+      "5\t검증 세트 손실: 0.343146\t최선의 손실: 0.278550\t정확도: 94.80%\n",
+      "6\t검증 세트 손실: 0.299596\t최선의 손실: 0.278550\t정확도: 95.78%\n",
+      "7\t검증 세트 손실: 0.267364\t최선의 손실: 0.267364\t정확도: 96.21%\n",
+      "8\t검증 세트 손실: 0.228093\t최선의 손실: 0.228093\t정확도: 97.26%\n",
+      "9\t검증 세트 손실: 0.178495\t최선의 손실: 0.178495\t정확도: 97.62%\n",
+      "10\t검증 세트 손실: 0.227205\t최선의 손실: 0.178495\t정확도: 97.07%\n",
+      "11\t검증 세트 손실: 0.244553\t최선의 손실: 0.178495\t정확도: 97.22%\n",
+      "12\t검증 세트 손실: 0.216027\t최선의 손실: 0.178495\t정확도: 97.19%\n",
+      "13\t검증 세트 손실: 0.180679\t최선의 손실: 0.178495\t정확도: 98.08%\n",
+      "14\t검증 세트 손실: 0.174364\t최선의 손실: 0.174364\t정확도: 98.36%\n",
+      "15\t검증 세트 손실: 0.158282\t최선의 손실: 0.158282\t정확도: 98.55%\n",
+      "16\t검증 세트 손실: 0.121757\t최선의 손실: 0.121757\t정확도: 98.71%\n",
+      "17\t검증 세트 손실: 0.164822\t최선의 손실: 0.121757\t정확도: 98.20%\n",
+      "18\t검증 세트 손실: 0.151618\t최선의 손실: 0.121757\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.222920\t최선의 손실: 0.121757\t정확도: 97.73%\n",
+      "20\t검증 세트 손실: 0.165313\t최선의 손실: 0.121757\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.209737\t최선의 손실: 0.121757\t정확도: 98.16%\n",
+      "22\t검증 세트 손실: 0.190188\t최선의 손실: 0.121757\t정확도: 98.55%\n",
+      "23\t검증 세트 손실: 0.174642\t최선의 손실: 0.121757\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.111173\t최선의 손실: 0.111173\t정확도: 98.71%\n",
+      "25\t검증 세트 손실: 0.124024\t최선의 손실: 0.111173\t정확도: 98.63%\n",
+      "26\t검증 세트 손실: 0.127780\t최선의 손실: 0.111173\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.115838\t최선의 손실: 0.111173\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.084719\t최선의 손실: 0.084719\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.104794\t최선의 손실: 0.084719\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.109063\t최선의 손실: 0.084719\t정확도: 98.83%\n",
+      "31\t검증 세트 손실: 0.122138\t최선의 손실: 0.084719\t정확도: 98.75%\n",
+      "32\t검증 세트 손실: 0.107258\t최선의 손실: 0.084719\t정확도: 98.79%\n",
+      "33\t검증 세트 손실: 0.084208\t최선의 손실: 0.084208\t정확도: 98.98%\n",
+      "34\t검증 세트 손실: 0.097947\t최선의 손실: 0.084208\t정확도: 98.79%\n",
+      "35\t검증 세트 손실: 0.115107\t최선의 손실: 0.084208\t정확도: 98.51%\n",
+      "36\t검증 세트 손실: 0.088437\t최선의 손실: 0.084208\t정확도: 98.94%\n",
+      "37\t검증 세트 손실: 0.113297\t최선의 손실: 0.084208\t정확도: 98.91%\n",
+      "38\t검증 세트 손실: 0.101319\t최선의 손실: 0.084208\t정확도: 98.79%\n",
+      "39\t검증 세트 손실: 0.161548\t최선의 손실: 0.084208\t정확도: 98.28%\n",
+      "40\t검증 세트 손실: 0.231355\t최선의 손실: 0.084208\t정확도: 98.08%\n",
+      "41\t검증 세트 손실: 0.118496\t최선의 손실: 0.084208\t정확도: 98.94%\n",
+      "42\t검증 세트 손실: 0.112695\t최선의 손실: 0.084208\t정확도: 98.91%\n",
+      "43\t검증 세트 손실: 0.150932\t최선의 손실: 0.084208\t정확도: 98.55%\n",
+      "44\t검증 세트 손실: 0.090831\t최선의 손실: 0.084208\t정확도: 98.91%\n",
+      "45\t검증 세트 손실: 0.102440\t최선의 손실: 0.084208\t정확도: 98.79%\n",
+      "46\t검증 세트 손실: 0.088000\t최선의 손실: 0.084208\t정확도: 98.98%\n",
+      "47\t검증 세트 손실: 0.088635\t최선의 손실: 0.084208\t정확도: 99.02%\n",
+      "48\t검증 세트 손실: 0.081033\t최선의 손실: 0.081033\t정확도: 98.94%\n",
+      "49\t검증 세트 손실: 0.081429\t최선의 손실: 0.081033\t정확도: 99.02%\n",
+      "50\t검증 세트 손실: 0.073939\t최선의 손실: 0.073939\t정확도: 99.14%\n",
+      "51\t검증 세트 손실: 0.069664\t최선의 손실: 0.069664\t정확도: 99.18%\n",
+      "52\t검증 세트 손실: 0.065464\t최선의 손실: 0.065464\t정확도: 99.14%\n",
+      "53\t검증 세트 손실: 0.061489\t최선의 손실: 0.061489\t정확도: 99.10%\n",
+      "54\t검증 세트 손실: 0.059905\t최선의 손실: 0.059905\t정확도: 99.22%\n",
+      "55\t검증 세트 손실: 0.058369\t최선의 손실: 0.058369\t정확도: 99.14%\n",
+      "56\t검증 세트 손실: 0.057472\t최선의 손실: 0.057472\t정확도: 99.06%\n",
+      "57\t검증 세트 손실: 0.053208\t최선의 손실: 0.053208\t정확도: 99.14%\n",
+      "58\t검증 세트 손실: 0.052357\t최선의 손실: 0.052357\t정확도: 99.18%\n",
+      "59\t검증 세트 손실: 0.051203\t최선의 손실: 0.051203\t정확도: 99.14%\n",
+      "60\t검증 세트 손실: 0.050362\t최선의 손실: 0.050362\t정확도: 99.22%\n",
+      "61\t검증 세트 손실: 0.049733\t최선의 손실: 0.049733\t정확도: 99.18%\n",
+      "62\t검증 세트 손실: 0.048465\t최선의 손실: 0.048465\t정확도: 99.18%\n",
+      "63\t검증 세트 손실: 0.046849\t최선의 손실: 0.046849\t정확도: 99.26%\n",
+      "64\t검증 세트 손실: 0.046633\t최선의 손실: 0.046633\t정확도: 99.26%\n",
+      "65\t검증 세트 손실: 0.047047\t최선의 손실: 0.046633\t정확도: 99.26%\n",
+      "66\t검증 세트 손실: 0.049779\t최선의 손실: 0.046633\t정확도: 99.22%\n",
+      "67\t검증 세트 손실: 0.049278\t최선의 손실: 0.046633\t정확도: 99.30%\n",
+      "68\t검증 세트 손실: 0.049606\t최선의 손실: 0.046633\t정확도: 99.18%\n",
+      "69\t검증 세트 손실: 0.049420\t최선의 손실: 0.046633\t정확도: 99.18%\n",
+      "70\t검증 세트 손실: 0.048281\t최선의 손실: 0.046633\t정확도: 99.18%\n",
+      "71\t검증 세트 손실: 0.045840\t최선의 손실: 0.045840\t정확도: 99.18%\n",
+      "72\t검증 세트 손실: 0.045030\t최선의 손실: 0.045030\t정확도: 99.18%\n",
+      "73\t검증 세트 손실: 0.044936\t최선의 손실: 0.044936\t정확도: 99.26%\n",
+      "74\t검증 세트 손실: 0.043930\t최선의 손실: 0.043930\t정확도: 99.22%\n",
+      "75\t검증 세트 손실: 0.043545\t최선의 손실: 0.043545\t정확도: 99.22%\n",
+      "76\t검증 세트 손실: 0.043012\t최선의 손실: 0.043012\t정확도: 99.26%\n",
+      "77\t검증 세트 손실: 0.042348\t최선의 손실: 0.042348\t정확도: 99.26%\n",
+      "78\t검증 세트 손실: 0.041744\t최선의 손실: 0.041744\t정확도: 99.30%\n",
+      "79\t검증 세트 손실: 0.041890\t최선의 손실: 0.041744\t정확도: 99.22%\n",
+      "80\t검증 세트 손실: 0.042413\t최선의 손실: 0.041744\t정확도: 99.14%\n",
+      "81\t검증 세트 손실: 0.041852\t최선의 손실: 0.041744\t정확도: 99.22%\n",
+      "82\t검증 세트 손실: 0.041593\t최선의 손실: 0.041593\t정확도: 99.22%\n",
+      "83\t검증 세트 손실: 0.041465\t최선의 손실: 0.041465\t정확도: 99.26%\n",
+      "84\t검증 세트 손실: 0.041839\t최선의 손실: 0.041465\t정확도: 99.26%\n",
+      "85\t검증 세트 손실: 0.041735\t최선의 손실: 0.041465\t정확도: 99.26%\n",
+      "86\t검증 세트 손실: 0.041329\t최선의 손실: 0.041329\t정확도: 99.26%\n",
+      "87\t검증 세트 손실: 0.039910\t최선의 손실: 0.039910\t정확도: 99.22%\n",
+      "88\t검증 세트 손실: 0.039779\t최선의 손실: 0.039779\t정확도: 99.22%\n",
+      "89\t검증 세트 손실: 0.039632\t최선의 손실: 0.039632\t정확도: 99.26%\n",
+      "90\t검증 세트 손실: 0.039923\t최선의 손실: 0.039632\t정확도: 99.26%\n",
+      "91\t검증 세트 손실: 0.039724\t최선의 손실: 0.039632\t정확도: 99.26%\n",
+      "92\t검증 세트 손실: 0.039472\t최선의 손실: 0.039472\t정확도: 99.22%\n",
+      "93\t검증 세트 손실: 0.039465\t최선의 손실: 0.039465\t정확도: 99.22%\n",
+      "94\t검증 세트 손실: 0.038355\t최선의 손실: 0.038355\t정확도: 99.18%\n",
+      "95\t검증 세트 손실: 0.038630\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "96\t검증 세트 손실: 0.038579\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "97\t검증 세트 손실: 0.038836\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "98\t검증 세트 손실: 0.039050\t최선의 손실: 0.038355\t정확도: 99.26%\n",
+      "99\t검증 세트 손실: 0.040340\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "100\t검증 세트 손실: 0.040915\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "101\t검증 세트 손실: 0.040603\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "102\t검증 세트 손실: 0.040032\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "103\t검증 세트 손실: 0.039537\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "104\t검증 세트 손실: 0.039892\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "105\t검증 세트 손실: 0.041110\t최선의 손실: 0.038355\t정확도: 99.18%\n",
+      "106\t검증 세트 손실: 0.039834\t최선의 손실: 0.038355\t정확도: 99.26%\n",
+      "107\t검증 세트 손실: 0.039222\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "108\t검증 세트 손실: 0.039439\t최선의 손실: 0.038355\t정확도: 99.22%\n",
+      "109\t검증 세트 손실: 0.040351\t최선의 손실: 0.038355\t정확도: 99.30%\n",
+      "110\t검증 세트 손실: 0.040047\t최선의 손실: 0.038355\t정확도: 99.26%\n",
+      "111\t검증 세트 손실: 0.039982\t최선의 손실: 0.038355\t정확도: 99.26%\n",
+      "112\t검증 세트 손실: 0.038274\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "113\t검증 세트 손실: 0.039569\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "114\t검증 세트 손실: 0.039823\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "115\t검증 세트 손실: 0.039976\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "116\t검증 세트 손실: 0.039691\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "117\t검증 세트 손실: 0.039648\t최선의 손실: 0.038274\t정확도: 99.26%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2\t검증 세트 손실: 0.100878\t최선의 손실: 0.100878\t정확도: 97.65%\n",
-      "3\t검증 세트 손실: 0.075923\t최선의 손실: 0.075923\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.060070\t최선의 손실: 0.060070\t정확도: 98.24%\n",
-      "5\t검증 세트 손실: 0.113278\t최선의 손실: 0.060070\t정확도: 97.26%\n",
-      "6\t검증 세트 손실: 0.123654\t최선의 손실: 0.060070\t정확도: 95.74%\n",
-      "7\t검증 세트 손실: 0.046451\t최선의 손실: 0.046451\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.059245\t최선의 손실: 0.046451\t정확도: 98.48%\n",
-      "9\t검증 세트 손실: 0.043842\t최선의 손실: 0.043842\t정확도: 98.67%\n",
-      "10\t검증 세트 손실: 0.053229\t최선의 손실: 0.043842\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.047755\t최선의 손실: 0.043842\t정확도: 98.51%\n",
-      "12\t검증 세트 손실: 0.038938\t최선의 손실: 0.038938\t정확도: 98.63%\n",
-      "13\t검증 세트 손실: 0.065199\t최선의 손실: 0.038938\t정확도: 98.20%\n",
-      "14\t검증 세트 손실: 0.059056\t최선의 손실: 0.038938\t정확도: 98.44%\n",
-      "15\t검증 세트 손실: 0.062098\t최선의 손실: 0.038938\t정확도: 98.32%\n",
-      "16\t검증 세트 손실: 0.042888\t최선의 손실: 0.038938\t정확도: 98.83%\n",
-      "17\t검증 세트 손실: 0.042650\t최선의 손실: 0.038938\t정확도: 98.67%\n",
-      "18\t검증 세트 손실: 0.049033\t최선의 손실: 0.038938\t정확도: 98.67%\n",
-      "19\t검증 세트 손실: 0.048306\t최선의 손실: 0.038938\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.061602\t최선의 손실: 0.038938\t정확도: 98.44%\n",
-      "21\t검증 세트 손실: 0.039439\t최선의 손실: 0.038938\t정확도: 98.83%\n",
-      "22\t검증 세트 손실: 0.035039\t최선의 손실: 0.035039\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.035587\t최선의 손실: 0.035039\t정확도: 98.91%\n",
-      "24\t검증 세트 손실: 0.118175\t최선의 손실: 0.035039\t정확도: 96.79%\n",
-      "25\t검증 세트 손실: 0.051781\t최선의 손실: 0.035039\t정확도: 98.44%\n",
-      "26\t검증 세트 손실: 0.062159\t최선의 손실: 0.035039\t정확도: 98.40%\n",
-      "27\t검증 세트 손실: 0.099348\t최선의 손실: 0.035039\t정확도: 97.42%\n",
-      "28\t검증 세트 손실: 0.039063\t최선의 손실: 0.035039\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.027372\t최선의 손실: 0.027372\t정확도: 99.14%\n",
-      "30\t검증 세트 손실: 0.085636\t최선의 손실: 0.027372\t정확도: 97.85%\n",
-      "31\t검증 세트 손실: 0.031035\t최선의 손실: 0.027372\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.036227\t최선의 손실: 0.027372\t정확도: 98.83%\n",
-      "33\t검증 세트 손실: 0.041824\t최선의 손실: 0.027372\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.039604\t최선의 손실: 0.027372\t정확도: 98.87%\n",
-      "35\t검증 세트 손실: 0.046249\t최선의 손실: 0.027372\t정확도: 98.75%\n",
-      "36\t검증 세트 손실: 0.039248\t최선의 손실: 0.027372\t정확도: 98.75%\n",
-      "37\t검증 세트 손실: 0.033406\t최선의 손실: 0.027372\t정확도: 99.14%\n",
-      "38\t검증 세트 손실: 0.039623\t최선의 손실: 0.027372\t정확도: 98.83%\n",
-      "39\t검증 세트 손실: 0.027921\t최선의 손실: 0.027372\t정확도: 99.06%\n",
-      "40\t검증 세트 손실: 0.024910\t최선의 손실: 0.024910\t정확도: 99.30%\n",
-      "41\t검증 세트 손실: 0.041719\t최선의 손실: 0.024910\t정확도: 98.79%\n",
-      "42\t검증 세트 손실: 0.082701\t최선의 손실: 0.024910\t정확도: 97.89%\n",
-      "43\t검증 세트 손실: 0.060245\t최선의 손실: 0.024910\t정확도: 98.75%\n",
-      "44\t검증 세트 손실: 0.038733\t최선의 손실: 0.024910\t정확도: 98.94%\n",
-      "45\t검증 세트 손실: 0.040279\t최선의 손실: 0.024910\t정확도: 99.10%\n",
-      "46\t검증 세트 손실: 0.071209\t최선의 손실: 0.024910\t정확도: 98.20%\n",
-      "47\t검증 세트 손실: 0.041079\t최선의 손실: 0.024910\t정확도: 99.02%\n",
-      "48\t검증 세트 손실: 0.038818\t최선의 손실: 0.024910\t정확도: 99.06%\n",
-      "49\t검증 세트 손실: 0.035422\t최선의 손실: 0.024910\t정확도: 99.10%\n",
-      "50\t검증 세트 손실: 0.067541\t최선의 손실: 0.024910\t정확도: 98.94%\n",
-      "51\t검증 세트 손실: 0.037902\t최선의 손실: 0.024910\t정확도: 99.02%\n",
-      "52\t검증 세트 손실: 0.046894\t최선의 손실: 0.024910\t정확도: 98.98%\n",
-      "53\t검증 세트 손실: 0.049109\t최선의 손실: 0.024910\t정확도: 98.75%\n",
-      "54\t검증 세트 손실: 0.032968\t최선의 손실: 0.024910\t정확도: 99.34%\n",
-      "55\t검증 세트 손실: 0.048511\t최선의 손실: 0.024910\t정확도: 98.98%\n",
-      "56\t검증 세트 손실: 0.031742\t최선의 손실: 0.024910\t정확도: 99.18%\n",
-      "57\t검증 세트 손실: 0.033864\t최선의 손실: 0.024910\t정확도: 98.91%\n",
-      "58\t검증 세트 손실: 0.046327\t최선의 손실: 0.024910\t정확도: 98.98%\n",
-      "59\t검증 세트 손실: 0.038524\t최선의 손실: 0.024910\t정확도: 99.06%\n",
-      "60\t검증 세트 손실: 0.061976\t최선의 손실: 0.024910\t정확도: 98.24%\n",
-      "61\t검증 세트 손실: 0.040774\t최선의 손실: 0.024910\t정확도: 99.02%\n",
+      "118\t검증 세트 손실: 0.039581\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "119\t검증 세트 손실: 0.039879\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "120\t검증 세트 손실: 0.040054\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "121\t검증 세트 손실: 0.039658\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "122\t검증 세트 손실: 0.039412\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "123\t검증 세트 손실: 0.039386\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "124\t검증 세트 손실: 0.039033\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "125\t검증 세트 손실: 0.038885\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "126\t검증 세트 손실: 0.039280\t최선의 손실: 0.038274\t정확도: 99.26%\n",
+      "127\t검증 세트 손실: 0.042395\t최선의 손실: 0.038274\t정확도: 99.22%\n",
+      "128\t검증 세트 손실: 0.038072\t최선의 손실: 0.038072\t정확도: 99.14%\n",
+      "129\t검증 세트 손실: 0.038175\t최선의 손실: 0.038072\t정확도: 99.10%\n",
+      "130\t검증 세트 손실: 0.038238\t최선의 손실: 0.038072\t정확도: 99.18%\n",
+      "131\t검증 세트 손실: 0.037953\t최선의 손실: 0.037953\t정확도: 99.18%\n",
+      "132\t검증 세트 손실: 0.037770\t최선의 손실: 0.037770\t정확도: 99.18%\n",
+      "133\t검증 세트 손실: 0.037593\t최선의 손실: 0.037593\t정확도: 99.18%\n",
+      "134\t검증 세트 손실: 0.037741\t최선의 손실: 0.037593\t정확도: 99.18%\n",
+      "135\t검증 세트 손실: 0.037763\t최선의 손실: 0.037593\t정확도: 99.18%\n",
+      "136\t검증 세트 손실: 0.037681\t최선의 손실: 0.037593\t정확도: 99.22%\n",
+      "137\t검증 세트 손실: 0.037547\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "138\t검증 세트 손실: 0.038157\t최선의 손실: 0.037547\t정확도: 99.26%\n",
+      "139\t검증 세트 손실: 0.038203\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "140\t검증 세트 손실: 0.038354\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "141\t검증 세트 손실: 0.038182\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "142\t검증 세트 손실: 0.038327\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "143\t검증 세트 손실: 0.038246\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "144\t검증 세트 손실: 0.038811\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "145\t검증 세트 손실: 0.038574\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "146\t검증 세트 손실: 0.038836\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "147\t검증 세트 손실: 0.039027\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "148\t검증 세트 손실: 0.038707\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "149\t검증 세트 손실: 0.038941\t최선의 손실: 0.037547\t정확도: 99.18%\n",
+      "150\t검증 세트 손실: 0.038582\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "151\t검증 세트 손실: 0.038559\t최선의 손실: 0.037547\t정확도: 99.26%\n",
+      "152\t검증 세트 손실: 0.039587\t최선의 손실: 0.037547\t정확도: 99.26%\n",
+      "153\t검증 세트 손실: 0.040815\t최선의 손실: 0.037547\t정확도: 99.26%\n",
+      "154\t검증 세트 손실: 0.040621\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "155\t검증 세트 손실: 0.040149\t최선의 손실: 0.037547\t정확도: 99.26%\n",
+      "156\t검증 세트 손실: 0.039620\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "157\t검증 세트 손실: 0.040005\t최선의 손실: 0.037547\t정확도: 99.22%\n",
+      "158\t검증 세트 손실: 0.039979\t최선의 손실: 0.037547\t정확도: 99.22%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.999, n_neurons=90, total= 6.9min\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.087257\t최선의 손실: 0.087257\t정확도: 97.38%\n",
-      "1\t검증 세트 손실: 0.073414\t최선의 손실: 0.073414\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.061093\t최선의 손실: 0.061093\t정확도: 98.40%\n",
-      "3\t검증 세트 손실: 0.048418\t최선의 손실: 0.048418\t정확도: 98.63%\n",
-      "4\t검증 세트 손실: 0.047303\t최선의 손실: 0.047303\t정확도: 98.71%\n",
-      "5\t검증 세트 손실: 0.049625\t최선의 손실: 0.047303\t정확도: 98.63%\n",
-      "6\t검증 세트 손실: 0.034039\t최선의 손실: 0.034039\t정확도: 98.94%\n",
-      "7\t검증 세트 손실: 0.040793\t최선의 손실: 0.034039\t정확도: 98.91%\n",
-      "8\t검증 세트 손실: 0.051511\t최선의 손실: 0.034039\t정확도: 98.59%\n",
-      "9\t검증 세트 손실: 0.052937\t최선의 손실: 0.034039\t정확도: 98.63%\n",
-      "10\t검증 세트 손실: 0.045465\t최선의 손실: 0.034039\t정확도: 99.10%\n",
-      "11\t검증 세트 손실: 0.067734\t최선의 손실: 0.034039\t정확도: 98.24%\n",
-      "12\t검증 세트 손실: 0.053066\t최선의 손실: 0.034039\t정확도: 98.75%\n",
-      "13\t검증 세트 손실: 0.043916\t최선의 손실: 0.034039\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.061836\t최선의 손실: 0.034039\t정확도: 98.51%\n",
-      "15\t검증 세트 손실: 0.091925\t최선의 손실: 0.034039\t정확도: 98.51%\n",
-      "16\t검증 세트 손실: 0.052581\t최선의 손실: 0.034039\t정확도: 98.71%\n",
-      "17\t검증 세트 손실: 0.091004\t최선의 손실: 0.034039\t정확도: 98.24%\n",
-      "18\t검증 세트 손실: 0.075917\t최선의 손실: 0.034039\t정확도: 98.71%\n",
-      "19\t검증 세트 손실: 0.054033\t최선의 손실: 0.034039\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.065018\t최선의 손실: 0.034039\t정확도: 98.91%\n",
-      "21\t검증 세트 손실: 0.031348\t최선의 손실: 0.031348\t정확도: 99.10%\n",
-      "22\t검증 세트 손실: 0.048176\t최선의 손실: 0.031348\t정확도: 98.98%\n",
-      "23\t검증 세트 손실: 0.055454\t최선의 손실: 0.031348\t정확도: 98.79%\n",
-      "24\t검증 세트 손실: 0.059354\t최선의 손실: 0.031348\t정확도: 98.91%\n",
-      "25\t검증 세트 손실: 0.052506\t최선의 손실: 0.031348\t정확도: 98.91%\n",
-      "26\t검증 세트 손실: 0.082935\t최선의 손실: 0.031348\t정확도: 98.24%\n",
-      "27\t검증 세트 손실: 0.044255\t최선의 손실: 0.031348\t정확도: 99.10%\n",
-      "28\t검증 세트 손실: 0.053035\t최선의 손실: 0.031348\t정확도: 98.91%\n",
-      "29\t검증 세트 손실: 0.060198\t최선의 손실: 0.031348\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.052040\t최선의 손실: 0.031348\t정확도: 98.94%\n",
-      "31\t검증 세트 손실: 0.067502\t최선의 손실: 0.031348\t정확도: 98.59%\n",
-      "32\t검증 세트 손실: 0.054455\t최선의 손실: 0.031348\t정확도: 98.79%\n",
-      "33\t검증 세트 손실: 0.040369\t최선의 손실: 0.031348\t정확도: 99.14%\n",
-      "34\t검증 세트 손실: 0.059729\t최선의 손실: 0.031348\t정확도: 98.75%\n",
-      "35\t검증 세트 손실: 0.052290\t최선의 손실: 0.031348\t정확도: 98.79%\n",
-      "36\t검증 세트 손실: 0.045790\t최선의 손실: 0.031348\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.052308\t최선의 손실: 0.031348\t정확도: 98.94%\n",
-      "38\t검증 세트 손실: 0.051965\t최선의 손실: 0.031348\t정확도: 98.91%\n",
-      "39\t검증 세트 손실: 0.068879\t최선의 손실: 0.031348\t정확도: 98.63%\n",
-      "40\t검증 세트 손실: 0.049846\t최선의 손실: 0.031348\t정확도: 98.91%\n",
-      "41\t검증 세트 손실: 0.056629\t최선의 손실: 0.031348\t정확도: 99.02%\n",
-      "42\t검증 세트 손실: 0.057567\t최선의 손실: 0.031348\t정확도: 99.06%\n",
+      "[CV]  batch_size=500, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.01, batch_norm_momentum=0.999, n_neurons=30, total=  29.1s\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.999, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.222787\t최선의 손실: 1.222787\t정확도: 89.25%\n",
+      "1\t검증 세트 손실: 0.145905\t최선의 손실: 0.145905\t정확도: 97.77%\n",
+      "2\t검증 세트 손실: 0.121568\t최선의 손실: 0.121568\t정확도: 98.16%\n",
+      "3\t검증 세트 손실: 0.098396\t최선의 손실: 0.098396\t정확도: 97.97%\n",
+      "4\t검증 세트 손실: 0.061845\t최선의 손실: 0.061845\t정확도: 98.32%\n",
+      "5\t검증 세트 손실: 0.089877\t최선의 손실: 0.061845\t정확도: 97.42%\n",
+      "6\t검증 세트 손실: 0.092608\t최선의 손실: 0.061845\t정확도: 97.22%\n",
+      "7\t검증 세트 손실: 0.055512\t최선의 손실: 0.055512\t정확도: 98.32%\n",
+      "8\t검증 세트 손실: 0.082699\t최선의 손실: 0.055512\t정확도: 97.77%\n",
+      "9\t검증 세트 손실: 0.058193\t최선의 손실: 0.055512\t정확도: 98.48%\n",
+      "10\t검증 세트 손실: 0.075556\t최선의 손실: 0.055512\t정확도: 97.89%\n",
+      "11\t검증 세트 손실: 0.044831\t최선의 손실: 0.044831\t정확도: 98.51%\n",
+      "12\t검증 세트 손실: 0.052319\t최선의 손실: 0.044831\t정확도: 98.55%\n",
+      "13\t검증 세트 손실: 0.070776\t최선의 손실: 0.044831\t정확도: 98.12%\n",
+      "14\t검증 세트 손실: 0.081133\t최선의 손실: 0.044831\t정확도: 98.28%\n",
+      "15\t검증 세트 손실: 0.064737\t최선의 손실: 0.044831\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.045890\t최선의 손실: 0.044831\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.055936\t최선의 손실: 0.044831\t정확도: 98.59%\n",
+      "18\t검증 세트 손실: 0.057638\t최선의 손실: 0.044831\t정확도: 98.71%\n",
+      "19\t검증 세트 손실: 0.045185\t최선의 손실: 0.044831\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.046436\t최선의 손실: 0.044831\t정확도: 98.83%\n",
+      "21\t검증 세트 손실: 0.040164\t최선의 손실: 0.040164\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.036420\t최선의 손실: 0.036420\t정확도: 99.06%\n",
+      "23\t검증 세트 손실: 0.054534\t최선의 손실: 0.036420\t정확도: 98.63%\n",
+      "24\t검증 세트 손실: 0.061577\t최선의 손실: 0.036420\t정확도: 98.48%\n",
+      "25\t검증 세트 손실: 0.053326\t최선의 손실: 0.036420\t정확도: 98.83%\n",
+      "26\t검증 세트 손실: 0.046493\t최선의 손실: 0.036420\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.051856\t최선의 손실: 0.036420\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.048684\t최선의 손실: 0.036420\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.051745\t최선의 손실: 0.036420\t정확도: 98.83%\n",
+      "30\t검증 세트 손실: 0.051121\t최선의 손실: 0.036420\t정확도: 98.63%\n",
+      "31\t검증 세트 손실: 0.037121\t최선의 손실: 0.036420\t정확도: 98.98%\n",
+      "32\t검증 세트 손실: 0.042323\t최선의 손실: 0.036420\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.055486\t최선의 손실: 0.036420\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.055202\t최선의 손실: 0.036420\t정확도: 98.94%\n",
+      "35\t검증 세트 손실: 0.064269\t최선의 손실: 0.036420\t정확도: 98.32%\n",
+      "36\t검증 세트 손실: 0.051545\t최선의 손실: 0.036420\t정확도: 98.67%\n",
+      "37\t검증 세트 손실: 0.064434\t최선의 손실: 0.036420\t정확도: 98.51%\n",
+      "38\t검증 세트 손실: 0.037803\t최선의 손실: 0.036420\t정확도: 99.02%\n",
+      "39\t검증 세트 손실: 0.051444\t최선의 손실: 0.036420\t정확도: 98.55%\n",
+      "40\t검증 세트 손실: 0.049253\t최선의 손실: 0.036420\t정확도: 98.79%\n",
+      "41\t검증 세트 손실: 0.053765\t최선의 손실: 0.036420\t정확도: 98.63%\n",
+      "42\t검증 세트 손실: 0.061515\t최선의 손실: 0.036420\t정확도: 98.48%\n",
+      "43\t검증 세트 손실: 0.044544\t최선의 손실: 0.036420\t정확도: 98.75%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=70, total=  31.9s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.071545\t최선의 손실: 0.071545\t정확도: 97.77%\n",
-      "1\t검증 세트 손실: 0.052431\t최선의 손실: 0.052431\t정확도: 98.36%\n",
-      "2\t검증 세트 손실: 0.051440\t최선의 손실: 0.051440\t정확도: 98.51%\n",
-      "3\t검증 세트 손실: 0.076911\t최선의 손실: 0.051440\t정확도: 97.85%\n",
-      "4\t검증 세트 손실: 0.055833\t최선의 손실: 0.051440\t정확도: 98.51%\n",
-      "5\t검증 세트 손실: 0.042488\t최선의 손실: 0.042488\t정확도: 98.59%\n",
-      "6\t검증 세트 손실: 0.040525\t최선의 손실: 0.040525\t정확도: 98.83%\n",
-      "7\t검증 세트 손실: 0.048224\t최선의 손실: 0.040525\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.036363\t최선의 손실: 0.036363\t정확도: 98.94%\n",
-      "9\t검증 세트 손실: 0.049098\t최선의 손실: 0.036363\t정확도: 98.75%\n",
-      "10\t검증 세트 손실: 0.047101\t최선의 손실: 0.036363\t정확도: 98.83%\n",
-      "11\t검증 세트 손실: 0.044279\t최선의 손실: 0.036363\t정확도: 98.91%\n",
-      "12\t검증 세트 손실: 0.036478\t최선의 손실: 0.036363\t정확도: 99.18%\n",
-      "13\t검증 세트 손실: 0.047780\t최선의 손실: 0.036363\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.051915\t최선의 손실: 0.036363\t정확도: 98.71%\n",
-      "15\t검증 세트 손실: 0.036202\t최선의 손실: 0.036202\t정확도: 99.02%\n",
-      "16\t검증 세트 손실: 0.044549\t최선의 손실: 0.036202\t정확도: 98.98%\n",
-      "17\t검증 세트 손실: 0.061378\t최선의 손실: 0.036202\t정확도: 98.59%\n",
-      "18\t검증 세트 손실: 0.038321\t최선의 손실: 0.036202\t정확도: 99.06%\n",
-      "19\t검증 세트 손실: 0.045608\t최선의 손실: 0.036202\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.036121\t최선의 손실: 0.036121\t정확도: 99.06%\n",
-      "21\t검증 세트 손실: 0.033907\t최선의 손실: 0.033907\t정확도: 99.02%\n",
-      "22\t검증 세트 손실: 0.050741\t최선의 손실: 0.033907\t정확도: 98.55%\n",
-      "23\t검증 세트 손실: 0.049860\t최선의 손실: 0.033907\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.035330\t최선의 손실: 0.033907\t정확도: 99.18%\n",
-      "25\t검증 세트 손실: 0.051139\t최선의 손실: 0.033907\t정확도: 99.30%\n",
-      "26\t검증 세트 손실: 0.049479\t최선의 손실: 0.033907\t정확도: 99.18%\n",
-      "27\t검증 세트 손실: 0.044799\t최선의 손실: 0.033907\t정확도: 99.06%\n",
-      "28\t검증 세트 손실: 0.058343\t최선의 손실: 0.033907\t정확도: 98.87%\n",
-      "29\t검증 세트 손실: 0.042745\t최선의 손실: 0.033907\t정확도: 98.91%\n",
-      "30\t검증 세트 손실: 0.041733\t최선의 손실: 0.033907\t정확도: 99.18%\n",
-      "31\t검증 세트 손실: 0.059734\t최선의 손실: 0.033907\t정확도: 98.87%\n",
-      "32\t검증 세트 손실: 0.054659\t최선의 손실: 0.033907\t정확도: 99.18%\n",
-      "33\t검증 세트 손실: 0.043869\t최선의 손실: 0.033907\t정확도: 99.06%\n",
-      "34\t검증 세트 손실: 0.051507\t최선의 손실: 0.033907\t정확도: 98.98%\n",
-      "35\t검증 세트 손실: 0.053013\t최선의 손실: 0.033907\t정확도: 98.79%\n",
-      "36\t검증 세트 손실: 0.042984\t최선의 손실: 0.033907\t정확도: 98.83%\n",
-      "37\t검증 세트 손실: 0.038813\t최선의 손실: 0.033907\t정확도: 99.22%\n",
-      "38\t검증 세트 손실: 0.055721\t최선의 손실: 0.033907\t정확도: 98.98%\n",
-      "39\t검증 세트 손실: 0.048653\t최선의 손실: 0.033907\t정확도: 98.94%\n",
-      "40\t검증 세트 손실: 0.052959\t최선의 손실: 0.033907\t정확도: 99.14%\n",
-      "41\t검증 세트 손실: 0.043788\t최선의 손실: 0.033907\t정확도: 99.06%\n",
-      "42\t검증 세트 손실: 0.064548\t최선의 손실: 0.033907\t정확도: 99.02%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.999, n_neurons=90, total= 4.9min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.999, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.231920\t최선의 손실: 0.231920\t정확도: 96.44%\n",
+      "1\t검증 세트 손실: 0.302893\t최선의 손실: 0.231920\t정확도: 93.39%\n",
+      "2\t검증 세트 손실: 0.088163\t최선의 손실: 0.088163\t정확도: 97.89%\n",
+      "3\t검증 세트 손실: 0.090105\t최선의 손실: 0.088163\t정확도: 98.01%\n",
+      "4\t검증 세트 손실: 0.146557\t최선의 손실: 0.088163\t정확도: 96.76%\n",
+      "5\t검증 세트 손실: 0.116529\t최선의 손실: 0.088163\t정확도: 97.11%\n",
+      "6\t검증 세트 손실: 0.088754\t최선의 손실: 0.088163\t정확도: 97.62%\n",
+      "7\t검증 세트 손실: 0.064790\t최선의 손실: 0.064790\t정확도: 98.24%\n",
+      "8\t검증 세트 손실: 0.057653\t최선의 손실: 0.057653\t정확도: 98.83%\n",
+      "9\t검증 세트 손실: 0.066521\t최선의 손실: 0.057653\t정확도: 98.44%\n",
+      "10\t검증 세트 손실: 0.120275\t최선의 손실: 0.057653\t정확도: 97.15%\n",
+      "11\t검증 세트 손실: 0.057794\t최선의 손실: 0.057653\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.058682\t최선의 손실: 0.057653\t정확도: 98.40%\n",
+      "13\t검증 세트 손실: 0.050780\t최선의 손실: 0.050780\t정확도: 98.51%\n",
+      "14\t검증 세트 손실: 0.050458\t최선의 손실: 0.050458\t정확도: 98.48%\n",
+      "15\t검증 세트 손실: 0.048437\t최선의 손실: 0.048437\t정확도: 98.40%\n",
+      "16\t검증 세트 손실: 0.045325\t최선의 손실: 0.045325\t정확도: 98.63%\n",
+      "17\t검증 세트 손실: 0.045930\t최선의 손실: 0.045325\t정확도: 98.87%\n",
+      "18\t검증 세트 손실: 0.034150\t최선의 손실: 0.034150\t정확도: 98.98%\n",
+      "19\t검증 세트 손실: 0.052354\t최선의 손실: 0.034150\t정확도: 98.98%\n",
+      "20\t검증 세트 손실: 0.045701\t최선의 손실: 0.034150\t정확도: 98.87%\n",
+      "21\t검증 세트 손실: 0.056819\t최선의 손실: 0.034150\t정확도: 98.63%\n",
+      "22\t검증 세트 손실: 0.056642\t최선의 손실: 0.034150\t정확도: 98.48%\n",
+      "23\t검증 세트 손실: 0.073236\t최선의 손실: 0.034150\t정확도: 98.48%\n",
+      "24\t검증 세트 손실: 0.052801\t최선의 손실: 0.034150\t정확도: 98.55%\n",
+      "25\t검증 세트 손실: 0.045939\t최선의 손실: 0.034150\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.043158\t최선의 손실: 0.034150\t정확도: 98.79%\n",
+      "27\t검증 세트 손실: 0.046068\t최선의 손실: 0.034150\t정확도: 98.63%\n",
+      "28\t검증 세트 손실: 0.052544\t최선의 손실: 0.034150\t정확도: 98.71%\n",
+      "29\t검증 세트 손실: 0.066632\t최선의 손실: 0.034150\t정확도: 98.44%\n",
+      "30\t검증 세트 손실: 0.095002\t최선의 손실: 0.034150\t정확도: 97.15%\n",
+      "31\t검증 세트 손실: 0.059935\t최선의 손실: 0.034150\t정확도: 98.55%\n",
+      "32\t검증 세트 손실: 0.038292\t최선의 손실: 0.034150\t정확도: 98.98%\n",
+      "33\t검증 세트 손실: 0.028571\t최선의 손실: 0.028571\t정확도: 99.02%\n",
+      "34\t검증 세트 손실: 0.037646\t최선의 손실: 0.028571\t정확도: 98.87%\n",
+      "35\t검증 세트 손실: 0.047495\t최선의 손실: 0.028571\t정확도: 98.87%\n",
+      "36\t검증 세트 손실: 0.041674\t최선의 손실: 0.028571\t정확도: 98.79%\n",
+      "37\t검증 세트 손실: 0.043881\t최선의 손실: 0.028571\t정확도: 98.98%\n",
+      "38\t검증 세트 손실: 0.038096\t최선의 손실: 0.028571\t정확도: 98.91%\n",
+      "39\t검증 세트 손실: 0.049818\t최선의 손실: 0.028571\t정확도: 98.63%\n",
+      "40\t검증 세트 손실: 0.036491\t최선의 손실: 0.028571\t정확도: 98.79%\n",
+      "41\t검증 세트 손실: 0.049623\t최선의 손실: 0.028571\t정확도: 98.94%\n",
+      "42\t검증 세트 손실: 0.055309\t최선의 손실: 0.028571\t정확도: 98.48%\n",
+      "43\t검증 세트 손실: 0.045092\t최선의 손실: 0.028571\t정확도: 98.98%\n",
+      "44\t검증 세트 손실: 0.061969\t최선의 손실: 0.028571\t정확도: 98.63%\n",
+      "45\t검증 세트 손실: 0.035158\t최선의 손실: 0.028571\t정확도: 99.14%\n",
+      "46\t검증 세트 손실: 0.051832\t최선의 손실: 0.028571\t정확도: 98.59%\n",
+      "47\t검증 세트 손실: 0.065410\t최선의 손실: 0.028571\t정확도: 98.83%\n",
+      "48\t검증 세트 손실: 0.059482\t최선의 손실: 0.028571\t정확도: 98.79%\n",
+      "49\t검증 세트 손실: 0.056751\t최선의 손실: 0.028571\t정확도: 98.67%\n",
+      "50\t검증 세트 손실: 0.066098\t최선의 손실: 0.028571\t정확도: 98.83%\n",
+      "51\t검증 세트 손실: 0.041228\t최선의 손실: 0.028571\t정확도: 98.94%\n",
+      "52\t검증 세트 손실: 0.039405\t최선의 손실: 0.028571\t정확도: 99.06%\n",
+      "53\t검증 세트 손실: 0.049228\t최선의 손실: 0.028571\t정확도: 98.87%\n",
+      "54\t검증 세트 손실: 0.037108\t최선의 손실: 0.028571\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=70, total=  32.1s\n",
-      "[CV] batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=70 \n"
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.999, n_neurons=90, total= 6.2min\n",
+      "[CV] batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.999, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.509211\t최선의 손실: 0.509211\t정확도: 93.16%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.072767\t최선의 손실: 0.072767\t정확도: 98.01%\n",
-      "1\t검증 세트 손실: 0.062928\t최선의 손실: 0.062928\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.062296\t최선의 손실: 0.062296\t정확도: 98.08%\n",
-      "3\t검증 세트 손실: 0.070418\t최선의 손실: 0.062296\t정확도: 98.28%\n",
-      "4\t검증 세트 손실: 0.039939\t최선의 손실: 0.039939\t정확도: 98.87%\n",
-      "5\t검증 세트 손실: 0.045700\t최선의 손실: 0.039939\t정확도: 98.67%\n",
-      "6\t검증 세트 손실: 0.043909\t최선의 손실: 0.039939\t정확도: 98.98%\n",
-      "7\t검증 세트 손실: 0.046372\t최선의 손실: 0.039939\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.048852\t최선의 손실: 0.039939\t정확도: 98.67%\n",
-      "9\t검증 세트 손실: 0.103188\t최선의 손실: 0.039939\t정확도: 97.73%\n",
-      "10\t검증 세트 손실: 0.050162\t최선의 손실: 0.039939\t정확도: 98.79%\n",
-      "11\t검증 세트 손실: 0.044736\t최선의 손실: 0.039939\t정확도: 98.67%\n",
-      "12\t검증 세트 손실: 0.039633\t최선의 손실: 0.039633\t정확도: 99.06%\n",
-      "13\t검증 세트 손실: 0.046220\t최선의 손실: 0.039633\t정확도: 99.18%\n",
-      "14\t검증 세트 손실: 0.045817\t최선의 손실: 0.039633\t정확도: 98.94%\n",
-      "15\t검증 세트 손실: 0.081832\t최선의 손실: 0.039633\t정확도: 98.16%\n",
-      "16\t검증 세트 손실: 0.050451\t최선의 손실: 0.039633\t정확도: 98.83%\n",
-      "17\t검증 세트 손실: 0.044912\t최선의 손실: 0.039633\t정확도: 98.75%\n",
-      "18\t검증 세트 손실: 0.048847\t최선의 손실: 0.039633\t정확도: 98.59%\n",
-      "19\t검증 세트 손실: 0.048063\t최선의 손실: 0.039633\t정확도: 98.98%\n",
-      "20\t검증 세트 손실: 0.050720\t최선의 손실: 0.039633\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.056742\t최선의 손실: 0.039633\t정확도: 98.79%\n",
-      "22\t검증 세트 손실: 0.031526\t최선의 손실: 0.031526\t정확도: 99.18%\n",
-      "23\t검증 세트 손실: 0.056736\t최선의 손실: 0.031526\t정확도: 98.98%\n",
-      "24\t검증 세트 손실: 0.049128\t최선의 손실: 0.031526\t정확도: 98.94%\n",
-      "25\t검증 세트 손실: 0.037351\t최선의 손실: 0.031526\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.034727\t최선의 손실: 0.031526\t정확도: 99.14%\n",
-      "27\t검증 세트 손실: 0.041441\t최선의 손실: 0.031526\t정확도: 99.14%\n",
-      "28\t검증 세트 손실: 0.040562\t최선의 손실: 0.031526\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.055063\t최선의 손실: 0.031526\t정확도: 98.75%\n",
-      "30\t검증 세트 손실: 0.042258\t최선의 손실: 0.031526\t정확도: 99.06%\n",
-      "31\t검증 세트 손실: 0.048207\t최선의 손실: 0.031526\t정확도: 99.10%\n",
-      "32\t검증 세트 손실: 0.051822\t최선의 손실: 0.031526\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.043065\t최선의 손실: 0.031526\t정확도: 99.02%\n",
-      "34\t검증 세트 손실: 0.070941\t최선의 손실: 0.031526\t정확도: 98.44%\n",
-      "35\t검증 세트 손실: 0.056729\t최선의 손실: 0.031526\t정확도: 99.06%\n",
-      "36\t검증 세트 손실: 0.048574\t최선의 손실: 0.031526\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.049758\t최선의 손실: 0.031526\t정확도: 99.14%\n",
-      "38\t검증 세트 손실: 0.041818\t최선의 손실: 0.031526\t정확도: 99.10%\n",
-      "39\t검증 세트 손실: 0.059754\t최선의 손실: 0.031526\t정확도: 99.02%\n",
-      "40\t검증 세트 손실: 0.050901\t최선의 손실: 0.031526\t정확도: 98.94%\n",
-      "41\t검증 세트 손실: 0.042872\t최선의 손실: 0.031526\t정확도: 99.14%\n",
-      "42\t검증 세트 손실: 0.056838\t최선의 손실: 0.031526\t정확도: 99.06%\n",
-      "43\t검증 세트 손실: 0.086393\t최선의 손실: 0.031526\t정확도: 98.51%\n",
+      "1\t검증 세트 손실: 0.154125\t최선의 손실: 0.154125\t정확도: 97.65%\n",
+      "2\t검증 세트 손실: 0.143028\t최선의 손실: 0.143028\t정확도: 96.13%\n",
+      "3\t검증 세트 손실: 0.092420\t최선의 손실: 0.092420\t정확도: 97.93%\n",
+      "4\t검증 세트 손실: 0.106767\t최선의 손실: 0.092420\t정확도: 96.48%\n",
+      "5\t검증 세트 손실: 0.440529\t최선의 손실: 0.092420\t정확도: 89.64%\n",
+      "6\t검증 세트 손실: 0.053277\t최선의 손실: 0.053277\t정확도: 98.59%\n",
+      "7\t검증 세트 손실: 0.102831\t최선의 손실: 0.053277\t정확도: 96.99%\n",
+      "8\t검증 세트 손실: 0.055311\t최선의 손실: 0.053277\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.050859\t최선의 손실: 0.050859\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.052555\t최선의 손실: 0.050859\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.046673\t최선의 손실: 0.046673\t정확도: 98.67%\n",
+      "12\t검증 세트 손실: 0.037888\t최선의 손실: 0.037888\t정확도: 98.75%\n",
+      "13\t검증 세트 손실: 0.055524\t최선의 손실: 0.037888\t정확도: 98.67%\n",
+      "14\t검증 세트 손실: 0.041076\t최선의 손실: 0.037888\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.056087\t최선의 손실: 0.037888\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.042697\t최선의 손실: 0.037888\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.054755\t최선의 손실: 0.037888\t정확도: 98.63%\n",
+      "18\t검증 세트 손실: 0.045557\t최선의 손실: 0.037888\t정확도: 98.87%\n",
+      "19\t검증 세트 손실: 0.041613\t최선의 손실: 0.037888\t정확도: 98.98%\n",
+      "20\t검증 세트 손실: 0.072091\t최선의 손실: 0.037888\t정확도: 98.40%\n",
+      "21\t검증 세트 손실: 0.035773\t최선의 손실: 0.035773\t정확도: 98.94%\n",
+      "22\t검증 세트 손실: 0.051965\t최선의 손실: 0.035773\t정확도: 98.67%\n",
+      "23\t검증 세트 손실: 0.040352\t최선의 손실: 0.035773\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.044118\t최선의 손실: 0.035773\t정확도: 98.98%\n",
+      "25\t검증 세트 손실: 0.087325\t최선의 손실: 0.035773\t정확도: 97.97%\n",
+      "26\t검증 세트 손실: 0.036186\t최선의 손실: 0.035773\t정확도: 98.87%\n",
+      "27\t검증 세트 손실: 0.047193\t최선의 손실: 0.035773\t정확도: 98.79%\n",
+      "28\t검증 세트 손실: 0.048336\t최선의 손실: 0.035773\t정확도: 98.79%\n",
+      "29\t검증 세트 손실: 0.045217\t최선의 손실: 0.035773\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.037545\t최선의 손실: 0.035773\t정확도: 99.02%\n",
+      "31\t검증 세트 손실: 0.062496\t최선의 손실: 0.035773\t정확도: 98.40%\n",
+      "32\t검증 세트 손실: 0.027539\t최선의 손실: 0.027539\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.050007\t최선의 손실: 0.027539\t정확도: 98.75%\n",
+      "34\t검증 세트 손실: 0.028711\t최선의 손실: 0.027539\t정확도: 99.02%\n",
+      "35\t검증 세트 손실: 0.041673\t최선의 손실: 0.027539\t정확도: 98.91%\n",
+      "36\t검증 세트 손실: 0.049211\t최선의 손실: 0.027539\t정확도: 98.79%\n",
+      "37\t검증 세트 손실: 0.038846\t최선의 손실: 0.027539\t정확도: 99.22%\n",
+      "38\t검증 세트 손실: 0.050473\t최선의 손실: 0.027539\t정확도: 98.71%\n",
+      "39\t검증 세트 손실: 0.057025\t최선의 손실: 0.027539\t정확도: 98.67%\n",
+      "40\t검증 세트 손실: 0.046713\t최선의 손실: 0.027539\t정확도: 98.91%\n",
+      "41\t검증 세트 손실: 0.035716\t최선의 손실: 0.027539\t정확도: 99.18%\n",
+      "42\t검증 세트 손실: 0.040153\t최선의 손실: 0.027539\t정확도: 99.06%\n",
+      "43\t검증 세트 손실: 0.043382\t최선의 손실: 0.027539\t정확도: 98.75%\n",
+      "44\t검증 세트 손실: 0.028385\t최선의 손실: 0.027539\t정확도: 99.22%\n",
+      "45\t검증 세트 손실: 0.044109\t최선의 손실: 0.027539\t정확도: 99.22%\n",
+      "46\t검증 세트 손실: 0.056230\t최선의 손실: 0.027539\t정확도: 98.79%\n",
+      "47\t검증 세트 손실: 0.046310\t최선의 손실: 0.027539\t정확도: 98.71%\n",
+      "48\t검증 세트 손실: 0.064205\t최선의 손실: 0.027539\t정확도: 98.28%\n",
+      "49\t검증 세트 손실: 0.041383\t최선의 손실: 0.027539\t정확도: 99.06%\n",
+      "50\t검증 세트 손실: 0.044330\t최선의 손실: 0.027539\t정확도: 98.98%\n",
+      "51\t검증 세트 손실: 0.224173\t최선의 손실: 0.027539\t정확도: 93.86%\n",
+      "52\t검증 세트 손실: 0.035513\t최선의 손실: 0.027539\t정확도: 99.10%\n",
+      "53\t검증 세트 손실: 0.060580\t최선의 손실: 0.027539\t정확도: 98.44%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, batch_norm_momentum=0.98, n_neurons=70, total=  32.7s\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.097802\t최선의 손실: 0.097802\t정확도: 97.03%\n",
-      "1\t검증 세트 손실: 0.077907\t최선의 손실: 0.077907\t정확도: 97.65%\n",
-      "2\t검증 세트 손실: 0.071357\t최선의 손실: 0.071357\t정확도: 98.24%\n",
-      "3\t검증 세트 손실: 0.066758\t최선의 손실: 0.066758\t정확도: 98.12%\n",
-      "4\t검증 세트 손실: 0.041572\t최선의 손실: 0.041572\t정확도: 98.87%\n",
-      "5\t검증 세트 손실: 0.048940\t최선의 손실: 0.041572\t정확도: 98.48%\n",
-      "6\t검증 세트 손실: 0.054889\t최선의 손실: 0.041572\t정확도: 98.24%\n",
-      "7\t검증 세트 손실: 0.045257\t최선의 손실: 0.041572\t정확도: 98.59%\n",
-      "8\t검증 세트 손실: 0.040025\t최선의 손실: 0.040025\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.064849\t최선의 손실: 0.040025\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.041169\t최선의 손실: 0.040025\t정확도: 98.75%\n",
-      "11\t검증 세트 손실: 0.037320\t최선의 손실: 0.037320\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.041255\t최선의 손실: 0.037320\t정확도: 98.83%\n",
-      "13\t검증 세트 손실: 0.044684\t최선의 손실: 0.037320\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.044125\t최선의 손실: 0.037320\t정확도: 98.79%\n",
-      "15\t검증 세트 손실: 0.035448\t최선의 손실: 0.035448\t정확도: 98.94%\n",
-      "16\t검증 세트 손실: 0.055567\t최선의 손실: 0.035448\t정확도: 98.36%\n",
-      "17\t검증 세트 손실: 0.033224\t최선의 손실: 0.033224\t정확도: 99.22%\n",
-      "18\t검증 세트 손실: 0.035858\t최선의 손실: 0.033224\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.042844\t최선의 손실: 0.033224\t정확도: 98.87%\n",
-      "20\t검증 세트 손실: 0.039270\t최선의 손실: 0.033224\t정확도: 98.94%\n",
-      "21\t검증 세트 손실: 0.030535\t최선의 손실: 0.030535\t정확도: 99.14%\n",
-      "22\t검증 세트 손실: 0.046309\t최선의 손실: 0.030535\t정확도: 98.63%\n",
-      "23\t검증 세트 손실: 0.040682\t최선의 손실: 0.030535\t정확도: 98.91%\n",
-      "24\t검증 세트 손실: 0.038869\t최선의 손실: 0.030535\t정확도: 99.06%\n",
-      "25\t검증 세트 손실: 0.032898\t최선의 손실: 0.030535\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.044220\t최선의 손실: 0.030535\t정확도: 98.87%\n",
-      "27\t검증 세트 손실: 0.047230\t최선의 손실: 0.030535\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.040284\t최선의 손실: 0.030535\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.039927\t최선의 손실: 0.030535\t정확도: 98.98%\n",
-      "30\t검증 세트 손실: 0.040719\t최선의 손실: 0.030535\t정확도: 98.83%\n",
-      "31\t검증 세트 손실: 0.041075\t최선의 손실: 0.030535\t정확도: 98.67%\n",
-      "32\t검증 세트 손실: 0.044644\t최선의 손실: 0.030535\t정확도: 98.87%\n",
-      "33\t검증 세트 손실: 0.053182\t최선의 손실: 0.030535\t정확도: 98.59%\n",
-      "34\t검증 세트 손실: 0.036526\t최선의 손실: 0.030535\t정확도: 99.06%\n",
-      "35\t검증 세트 손실: 0.045619\t최선의 손실: 0.030535\t정확도: 98.87%\n",
-      "36\t검증 세트 손실: 0.039261\t최선의 손실: 0.030535\t정확도: 98.98%\n",
-      "37\t검증 세트 손실: 0.048663\t최선의 손실: 0.030535\t정확도: 98.71%\n",
-      "38\t검증 세트 손실: 0.027938\t최선의 손실: 0.027938\t정확도: 99.06%\n",
-      "39\t검증 세트 손실: 0.040556\t최선의 손실: 0.027938\t정확도: 99.02%\n",
-      "40\t검증 세트 손실: 0.036760\t최선의 손실: 0.027938\t정확도: 99.18%\n",
-      "41\t검증 세트 손실: 0.044386\t최선의 손실: 0.027938\t정확도: 98.87%\n",
-      "42\t검증 세트 손실: 0.044708\t최선의 손실: 0.027938\t정확도: 98.71%\n",
-      "43\t검증 세트 손실: 0.045699\t최선의 손실: 0.027938\t정확도: 98.98%\n",
-      "44\t검증 세트 손실: 0.034914\t최선의 손실: 0.027938\t정확도: 98.98%\n",
-      "45\t검증 세트 손실: 0.040455\t최선의 손실: 0.027938\t정확도: 99.06%\n",
-      "46\t검증 세트 손실: 0.041714\t최선의 손실: 0.027938\t정확도: 99.06%\n",
-      "47\t검증 세트 손실: 0.047191\t최선의 손실: 0.027938\t정확도: 98.87%\n",
-      "48\t검증 세트 손실: 0.035919\t최선의 손실: 0.027938\t정확도: 99.06%\n",
-      "49\t검증 세트 손실: 0.041453\t최선의 손실: 0.027938\t정확도: 98.87%\n",
-      "50\t검증 세트 손실: 0.043371\t최선의 손실: 0.027938\t정확도: 99.06%\n",
-      "51\t검증 세트 손실: 0.042362\t최선의 손실: 0.027938\t정확도: 98.94%\n",
-      "52\t검증 세트 손실: 0.044192\t최선의 손실: 0.027938\t정확도: 99.02%\n",
-      "53\t검증 세트 손실: 0.030686\t최선의 손실: 0.027938\t정확도: 99.10%\n",
-      "54\t검증 세트 손실: 0.046715\t최선의 손실: 0.027938\t정확도: 98.98%\n",
-      "55\t검증 세트 손실: 0.037211\t최선의 손실: 0.027938\t정확도: 99.02%\n",
-      "56\t검증 세트 손실: 0.041992\t최선의 손실: 0.027938\t정확도: 99.10%\n",
-      "57\t검증 세트 손실: 0.038866\t최선의 손실: 0.027938\t정확도: 98.87%\n",
-      "58\t검증 세트 손실: 0.033319\t최선의 손실: 0.027938\t정확도: 99.10%\n",
-      "59\t검증 세트 손실: 0.042765\t최선의 손실: 0.027938\t정확도: 99.14%\n",
+      "[CV]  batch_size=10, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.02, batch_norm_momentum=0.999, n_neurons=90, total= 6.0min\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.079689\t최선의 손실: 0.079689\t정확도: 98.05%\n",
+      "1\t검증 세트 손실: 0.087904\t최선의 손실: 0.079689\t정확도: 97.69%\n",
+      "2\t검증 세트 손실: 0.074079\t최선의 손실: 0.074079\t정확도: 97.93%\n",
+      "3\t검증 세트 손실: 0.062604\t최선의 손실: 0.062604\t정확도: 98.05%\n",
+      "4\t검증 세트 손실: 0.042636\t최선의 손실: 0.042636\t정확도: 98.71%\n",
+      "5\t검증 세트 손실: 0.038079\t최선의 손실: 0.038079\t정확도: 98.87%\n",
+      "6\t검증 세트 손실: 0.034619\t최선의 손실: 0.034619\t정확도: 98.94%\n",
+      "7\t검증 세트 손실: 0.042526\t최선의 손실: 0.034619\t정확도: 98.71%\n",
+      "8\t검증 세트 손실: 0.045885\t최선의 손실: 0.034619\t정확도: 98.75%\n",
+      "9\t검증 세트 손실: 0.042420\t최선의 손실: 0.034619\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.050029\t최선의 손실: 0.034619\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.039804\t최선의 손실: 0.034619\t정확도: 98.87%\n",
+      "12\t검증 세트 손실: 0.040726\t최선의 손실: 0.034619\t정확도: 98.79%\n",
+      "13\t검증 세트 손실: 0.063324\t최선의 손실: 0.034619\t정확도: 98.55%\n",
+      "14\t검증 세트 손실: 0.074780\t최선의 손실: 0.034619\t정확도: 98.32%\n",
+      "15\t검증 세트 손실: 0.056216\t최선의 손실: 0.034619\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.052356\t최선의 손실: 0.034619\t정확도: 99.10%\n",
+      "17\t검증 세트 손실: 0.057881\t최선의 손실: 0.034619\t정확도: 98.75%\n",
+      "18\t검증 세트 손실: 0.041021\t최선의 손실: 0.034619\t정확도: 99.06%\n",
+      "19\t검증 세트 손실: 0.054007\t최선의 손실: 0.034619\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.069950\t최선의 손실: 0.034619\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.055257\t최선의 손실: 0.034619\t정확도: 98.71%\n",
+      "22\t검증 세트 손실: 0.046504\t최선의 손실: 0.034619\t정확도: 98.94%\n",
+      "23\t검증 세트 손실: 0.047095\t최선의 손실: 0.034619\t정확도: 98.83%\n",
+      "24\t검증 세트 손실: 0.079192\t최선의 손실: 0.034619\t정확도: 98.36%\n",
+      "25\t검증 세트 손실: 0.052534\t최선의 손실: 0.034619\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.049041\t최선의 손실: 0.034619\t정확도: 98.83%\n",
+      "27\t검증 세트 손실: 0.046368\t최선의 손실: 0.034619\t정확도: 98.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160, total= 6.3min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.091832\t최선의 손실: 0.091832\t정확도: 97.15%\n",
-      "1\t검증 세트 손실: 0.072682\t최선의 손실: 0.072682\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.049114\t최선의 손실: 0.049114\t정확도: 98.44%\n",
-      "3\t검증 세트 손실: 0.049468\t최선의 손실: 0.049114\t정확도: 98.28%\n",
-      "4\t검증 세트 손실: 0.043963\t최선의 손실: 0.043963\t정확도: 98.59%\n",
-      "5\t검증 세트 손실: 0.046763\t최선의 손실: 0.043963\t정확도: 98.67%\n",
-      "6\t검증 세트 손실: 0.047995\t최선의 손실: 0.043963\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.042840\t최선의 손실: 0.042840\t정확도: 98.48%\n",
-      "8\t검증 세트 손실: 0.032022\t최선의 손실: 0.032022\t정확도: 99.10%\n",
-      "9\t검증 세트 손실: 0.042445\t최선의 손실: 0.032022\t정확도: 98.71%\n",
-      "10\t검증 세트 손실: 0.055116\t최선의 손실: 0.032022\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.036240\t최선의 손실: 0.032022\t정확도: 98.79%\n",
-      "12\t검증 세트 손실: 0.035852\t최선의 손실: 0.032022\t정확도: 98.67%\n",
-      "13\t검증 세트 손실: 0.035593\t최선의 손실: 0.032022\t정확도: 99.02%\n",
-      "14\t검증 세트 손실: 0.034144\t최선의 손실: 0.032022\t정확도: 98.94%\n",
-      "15\t검증 세트 손실: 0.039234\t최선의 손실: 0.032022\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.038136\t최선의 손실: 0.032022\t정확도: 99.02%\n",
-      "17\t검증 세트 손실: 0.030603\t최선의 손실: 0.030603\t정확도: 99.18%\n",
-      "18\t검증 세트 손실: 0.036250\t최선의 손실: 0.030603\t정확도: 98.98%\n",
-      "19\t검증 세트 손실: 0.030253\t최선의 손실: 0.030253\t정확도: 99.10%\n",
-      "20\t검증 세트 손실: 0.034847\t최선의 손실: 0.030253\t정확도: 98.91%\n",
-      "21\t검증 세트 손실: 0.034756\t최선의 손실: 0.030253\t정확도: 99.14%\n",
-      "22\t검증 세트 손실: 0.036171\t최선의 손실: 0.030253\t정확도: 98.94%\n",
-      "23\t검증 세트 손실: 0.040418\t최선의 손실: 0.030253\t정확도: 98.94%\n",
-      "24\t검증 세트 손실: 0.035573\t최선의 손실: 0.030253\t정확도: 99.10%\n",
-      "25\t검증 세트 손실: 0.032953\t최선의 손실: 0.030253\t정확도: 99.10%\n",
-      "26\t검증 세트 손실: 0.032015\t최선의 손실: 0.030253\t정확도: 99.14%\n",
-      "27\t검증 세트 손실: 0.031621\t최선의 손실: 0.030253\t정확도: 99.10%\n",
-      "28\t검증 세트 손실: 0.038519\t최선의 손실: 0.030253\t정확도: 99.02%\n",
-      "29\t검증 세트 손실: 0.037222\t최선의 손실: 0.030253\t정확도: 99.10%\n",
-      "30\t검증 세트 손실: 0.035533\t최선의 손실: 0.030253\t정확도: 99.02%\n",
-      "31\t검증 세트 손실: 0.033522\t최선의 손실: 0.030253\t정확도: 99.14%\n",
-      "32\t검증 세트 손실: 0.032537\t최선의 손실: 0.030253\t정확도: 99.18%\n",
-      "33\t검증 세트 손실: 0.034856\t최선의 손실: 0.030253\t정확도: 98.94%\n",
-      "34\t검증 세트 손실: 0.034410\t최선의 손실: 0.030253\t정확도: 99.26%\n",
-      "35\t검증 세트 손실: 0.032579\t최선의 손실: 0.030253\t정확도: 99.06%\n",
-      "36\t검증 세트 손실: 0.036538\t최선의 손실: 0.030253\t정확도: 98.94%\n",
-      "37\t검증 세트 손실: 0.033341\t최선의 손실: 0.030253\t정확도: 98.87%\n",
-      "38\t검증 세트 손실: 0.039523\t최선의 손실: 0.030253\t정확도: 98.98%\n",
-      "39\t검증 세트 손실: 0.030846\t최선의 손실: 0.030253\t정확도: 99.10%\n",
-      "40\t검증 세트 손실: 0.038154\t최선의 손실: 0.030253\t정확도: 98.87%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160, total= 4.3min\n",
-      "[CV] batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160 \n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=70, total=  22.2s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.101712\t최선의 손실: 0.101712\t정확도: 97.34%\n",
+      "1\t검증 세트 손실: 0.062402\t최선의 손실: 0.062402\t정확도: 98.20%\n",
+      "2\t검증 세트 손실: 0.049311\t최선의 손실: 0.049311\t정확도: 98.48%\n",
+      "3\t검증 세트 손실: 0.056752\t최선의 손실: 0.049311\t정확도: 98.36%\n",
+      "4\t검증 세트 손실: 0.052336\t최선의 손실: 0.049311\t정확도: 98.40%\n",
+      "5\t검증 세트 손실: 0.051348\t최선의 손실: 0.049311\t정확도: 98.59%\n",
+      "6\t검증 세트 손실: 0.059225\t최선의 손실: 0.049311\t정확도: 98.12%\n",
+      "7\t검증 세트 손실: 0.046526\t최선의 손실: 0.046526\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.039620\t최선의 손실: 0.039620\t정확도: 98.87%\n",
+      "9\t검증 세트 손실: 0.039678\t최선의 손실: 0.039620\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.048783\t최선의 손실: 0.039620\t정확도: 98.87%\n",
+      "11\t검증 세트 손실: 0.046172\t최선의 손실: 0.039620\t정확도: 98.75%\n",
+      "12\t검증 세트 손실: 0.046344\t최선의 손실: 0.039620\t정확도: 99.10%\n",
+      "13\t검증 세트 손실: 0.045789\t최선의 손실: 0.039620\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.043098\t최선의 손실: 0.039620\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.032391\t최선의 손실: 0.032391\t정확도: 99.30%\n",
+      "16\t검증 세트 손실: 0.046578\t최선의 손실: 0.032391\t정확도: 98.98%\n",
+      "17\t검증 세트 손실: 0.045556\t최선의 손실: 0.032391\t정확도: 98.83%\n",
+      "18\t검증 세트 손실: 0.042524\t최선의 손실: 0.032391\t정확도: 99.10%\n",
+      "19\t검증 세트 손실: 0.032031\t최선의 손실: 0.032031\t정확도: 99.26%\n",
+      "20\t검증 세트 손실: 0.046288\t최선의 손실: 0.032031\t정확도: 98.98%\n",
+      "21\t검증 세트 손실: 0.039022\t최선의 손실: 0.032031\t정확도: 98.91%\n",
+      "22\t검증 세트 손실: 0.038616\t최선의 손실: 0.032031\t정확도: 99.06%\n",
+      "23\t검증 세트 손실: 0.055678\t최선의 손실: 0.032031\t정확도: 98.91%\n",
+      "24\t검증 세트 손실: 0.051878\t최선의 손실: 0.032031\t정확도: 98.91%\n",
+      "25\t검증 세트 손실: 0.053500\t최선의 손실: 0.032031\t정확도: 98.87%\n",
+      "26\t검증 세트 손실: 0.053286\t최선의 손실: 0.032031\t정확도: 98.79%\n",
+      "27\t검증 세트 손실: 0.036357\t최선의 손실: 0.032031\t정확도: 99.18%\n",
+      "28\t검증 세트 손실: 0.039309\t최선의 손실: 0.032031\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.051259\t최선의 손실: 0.032031\t정확도: 98.79%\n",
+      "30\t검증 세트 손실: 0.053425\t최선의 손실: 0.032031\t정확도: 98.63%\n",
+      "31\t검증 세트 손실: 0.039833\t최선의 손실: 0.032031\t정확도: 99.14%\n",
+      "32\t검증 세트 손실: 0.042526\t최선의 손실: 0.032031\t정확도: 99.14%\n",
+      "33\t검증 세트 손실: 0.063431\t최선의 손실: 0.032031\t정확도: 98.63%\n",
+      "34\t검증 세트 손실: 0.045219\t최선의 손실: 0.032031\t정확도: 99.41%\n",
+      "35\t검증 세트 손실: 0.049184\t최선의 손실: 0.032031\t정확도: 99.06%\n",
+      "36\t검증 세트 손실: 0.038502\t최선의 손실: 0.032031\t정확도: 99.18%\n",
+      "37\t검증 세트 손실: 0.031137\t최선의 손실: 0.031137\t정확도: 99.41%\n",
+      "38\t검증 세트 손실: 0.030395\t최선의 손실: 0.030395\t정확도: 99.37%\n",
+      "39\t검증 세트 손실: 0.050383\t최선의 손실: 0.030395\t정확도: 98.71%\n",
+      "40\t검증 세트 손실: 0.034855\t최선의 손실: 0.030395\t정확도: 99.06%\n",
+      "41\t검증 세트 손실: 0.036598\t최선의 손실: 0.030395\t정확도: 99.18%\n",
+      "42\t검증 세트 손실: 0.055314\t최선의 손실: 0.030395\t정확도: 98.94%\n",
+      "43\t검증 세트 손실: 0.057435\t최선의 손실: 0.030395\t정확도: 98.91%\n",
+      "44\t검증 세트 손실: 0.038605\t최선의 손실: 0.030395\t정확도: 98.98%\n",
+      "45\t검증 세트 손실: 0.042323\t최선의 손실: 0.030395\t정확도: 98.94%\n",
+      "46\t검증 세트 손실: 0.047634\t최선의 손실: 0.030395\t정확도: 99.22%\n",
+      "47\t검증 세트 손실: 0.042662\t최선의 손실: 0.030395\t정확도: 99.34%\n",
+      "48\t검증 세트 손실: 0.047816\t최선의 손실: 0.030395\t정확도: 99.18%\n",
+      "49\t검증 세트 손실: 0.048256\t최선의 손실: 0.030395\t정확도: 99.02%\n",
+      "50\t검증 세트 손실: 0.043679\t최선의 손실: 0.030395\t정확도: 99.10%\n",
+      "51\t검증 세트 손실: 0.068706\t최선의 손실: 0.030395\t정확도: 98.83%\n",
+      "52\t검증 세트 손실: 0.065680\t최선의 손실: 0.030395\t정확도: 98.83%\n",
+      "53\t검증 세트 손실: 0.043498\t최선의 손실: 0.030395\t정확도: 98.98%\n",
+      "54\t검증 세트 손실: 0.038306\t최선의 손실: 0.030395\t정확도: 99.06%\n",
+      "55\t검증 세트 손실: 0.035384\t최선의 손실: 0.030395\t정확도: 99.22%\n",
+      "56\t검증 세트 손실: 0.036625\t최선의 손실: 0.030395\t정확도: 99.14%\n",
+      "57\t검증 세트 손실: 0.029292\t최선의 손실: 0.029292\t정확도: 99.26%\n",
+      "58\t검증 세트 손실: 0.033059\t최선의 손실: 0.029292\t정확도: 99.22%\n",
+      "59\t검증 세트 손실: 0.043839\t최선의 손실: 0.029292\t정확도: 99.10%\n",
+      "60\t검증 세트 손실: 0.043441\t최선의 손실: 0.029292\t정확도: 99.18%\n",
+      "61\t검증 세트 손실: 0.034448\t최선의 손실: 0.029292\t정확도: 99.34%\n",
+      "62\t검증 세트 손실: 0.042042\t최선의 손실: 0.029292\t정확도: 99.30%\n",
+      "63\t검증 세트 손실: 0.049148\t최선의 손실: 0.029292\t정확도: 99.14%\n",
+      "64\t검증 세트 손실: 0.052765\t최선의 손실: 0.029292\t정확도: 99.06%\n",
+      "65\t검증 세트 손실: 0.037383\t최선의 손실: 0.029292\t정확도: 99.26%\n",
+      "66\t검증 세트 손실: 0.034276\t최선의 손실: 0.029292\t정확도: 99.41%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.080754\t최선의 손실: 0.080754\t정확도: 97.62%\n",
-      "1\t검증 세트 손실: 0.055718\t최선의 손실: 0.055718\t정확도: 98.20%\n",
-      "2\t검증 세트 손실: 0.065343\t최선의 손실: 0.055718\t정확도: 98.01%\n",
-      "3\t검증 세트 손실: 0.040092\t최선의 손실: 0.040092\t정확도: 98.63%\n",
-      "4\t검증 세트 손실: 0.075982\t최선의 손실: 0.040092\t정확도: 97.38%\n",
-      "5\t검증 세트 손실: 0.045261\t최선의 손실: 0.040092\t정확도: 98.75%\n",
-      "6\t검증 세트 손실: 0.051780\t최선의 손실: 0.040092\t정확도: 98.59%\n",
-      "7\t검증 세트 손실: 0.037308\t최선의 손실: 0.037308\t정확도: 98.67%\n",
-      "8\t검증 세트 손실: 0.040293\t최선의 손실: 0.037308\t정확도: 98.59%\n",
-      "9\t검증 세트 손실: 0.054454\t최선의 손실: 0.037308\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.036806\t최선의 손실: 0.036806\t정확도: 98.75%\n",
-      "11\t검증 세트 손실: 0.032714\t최선의 손실: 0.032714\t정확도: 99.02%\n",
-      "12\t검증 세트 손실: 0.038736\t최선의 손실: 0.032714\t정확도: 98.91%\n",
-      "13\t검증 세트 손실: 0.032773\t최선의 손실: 0.032714\t정확도: 99.18%\n",
-      "14\t검증 세트 손실: 0.035461\t최선의 손실: 0.032714\t정확도: 98.98%\n",
-      "15\t검증 세트 손실: 0.031598\t최선의 손실: 0.031598\t정확도: 99.14%\n",
-      "16\t검증 세트 손실: 0.028548\t최선의 손실: 0.028548\t정확도: 99.26%\n",
-      "17\t검증 세트 손실: 0.036301\t최선의 손실: 0.028548\t정확도: 98.94%\n",
-      "18\t검증 세트 손실: 0.033774\t최선의 손실: 0.028548\t정확도: 98.83%\n",
-      "19\t검증 세트 손실: 0.041756\t최선의 손실: 0.028548\t정확도: 98.94%\n",
-      "20\t검증 세트 손실: 0.041451\t최선의 손실: 0.028548\t정확도: 98.79%\n",
-      "21\t검증 세트 손실: 0.029210\t최선의 손실: 0.028548\t정확도: 99.26%\n",
-      "22\t검증 세트 손실: 0.029229\t최선의 손실: 0.028548\t정확도: 99.14%\n",
-      "23\t검증 세트 손실: 0.026073\t최선의 손실: 0.026073\t정확도: 99.06%\n",
-      "24\t검증 세트 손실: 0.026517\t최선의 손실: 0.026073\t정확도: 99.26%\n",
-      "25\t검증 세트 손실: 0.026904\t최선의 손실: 0.026073\t정확도: 99.37%\n",
-      "26\t검증 세트 손실: 0.036596\t최선의 손실: 0.026073\t정확도: 99.14%\n",
-      "27\t검증 세트 손실: 0.033778\t최선의 손실: 0.026073\t정확도: 99.22%\n",
-      "28\t검증 세트 손실: 0.019503\t최선의 손실: 0.019503\t정확도: 99.34%\n",
-      "29\t검증 세트 손실: 0.035419\t최선의 손실: 0.019503\t정확도: 98.83%\n",
-      "30\t검증 세트 손실: 0.040801\t최선의 손실: 0.019503\t정확도: 98.59%\n",
-      "31\t검증 세트 손실: 0.031176\t최선의 손실: 0.019503\t정확도: 99.26%\n",
-      "32\t검증 세트 손실: 0.028923\t최선의 손실: 0.019503\t정확도: 99.22%\n",
-      "33\t검증 세트 손실: 0.030610\t최선의 손실: 0.019503\t정확도: 99.14%\n",
-      "34\t검증 세트 손실: 0.027937\t최선의 손실: 0.019503\t정확도: 99.06%\n",
-      "35\t검증 세트 손실: 0.038880\t최선의 손실: 0.019503\t정확도: 98.79%\n",
-      "36\t검증 세트 손실: 0.024003\t최선의 손실: 0.019503\t정확도: 99.30%\n",
-      "37\t검증 세트 손실: 0.035171\t최선의 손실: 0.019503\t정확도: 99.18%\n",
-      "38\t검증 세트 손실: 0.037820\t최선의 손실: 0.019503\t정확도: 98.75%\n",
-      "39\t검증 세트 손실: 0.034777\t최선의 손실: 0.019503\t정확도: 98.91%\n",
-      "40\t검증 세트 손실: 0.028007\t최선의 손실: 0.019503\t정확도: 99.14%\n",
-      "41\t검증 세트 손실: 0.032504\t최선의 손실: 0.019503\t정확도: 99.02%\n",
-      "42\t검증 세트 손실: 0.047716\t최선의 손실: 0.019503\t정확도: 98.67%\n",
-      "43\t검증 세트 손실: 0.047912\t최선의 손실: 0.019503\t정확도: 98.67%\n",
-      "44\t검증 세트 손실: 0.031951\t최선의 손실: 0.019503\t정확도: 99.18%\n",
-      "45\t검증 세트 손실: 0.029562\t최선의 손실: 0.019503\t정확도: 99.18%\n",
-      "46\t검증 세트 손실: 0.024902\t최선의 손실: 0.019503\t정확도: 99.26%\n",
-      "47\t검증 세트 손실: 0.037067\t최선의 손실: 0.019503\t정확도: 98.83%\n",
-      "48\t검증 세트 손실: 0.044467\t최선의 손실: 0.019503\t정확도: 98.87%\n",
-      "49\t검증 세트 손실: 0.037325\t최선의 손실: 0.019503\t정확도: 99.30%\n",
+      "67\t검증 세트 손실: 0.038295\t최선의 손실: 0.029292\t정확도: 99.10%\n",
+      "68\t검증 세트 손실: 0.036326\t최선의 손실: 0.029292\t정확도: 99.34%\n",
+      "69\t검증 세트 손실: 0.030817\t최선의 손실: 0.029292\t정확도: 99.30%\n",
+      "70\t검증 세트 손실: 0.042943\t최선의 손실: 0.029292\t정확도: 99.18%\n",
+      "71\t검증 세트 손실: 0.054757\t최선의 손실: 0.029292\t정확도: 98.94%\n",
+      "72\t검증 세트 손실: 0.036897\t최선의 손실: 0.029292\t정확도: 99.34%\n",
+      "73\t검증 세트 손실: 0.048006\t최선의 손실: 0.029292\t정확도: 98.83%\n",
+      "74\t검증 세트 손실: 0.033149\t최선의 손실: 0.029292\t정확도: 99.41%\n",
+      "75\t검증 세트 손실: 0.043783\t최선의 손실: 0.029292\t정확도: 99.30%\n",
+      "76\t검증 세트 손실: 0.042742\t최선의 손실: 0.029292\t정확도: 99.37%\n",
+      "77\t검증 세트 손실: 0.043424\t최선의 손실: 0.029292\t정확도: 99.30%\n",
+      "78\t검증 세트 손실: 0.065959\t최선의 손실: 0.029292\t정확도: 98.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, batch_norm_momentum=0.98, n_neurons=160, total= 5.2min\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.208644\t최선의 손실: 0.208644\t정확도: 96.33%\n",
-      "1\t검증 세트 손실: 0.099864\t최선의 손실: 0.099864\t정확도: 98.12%\n",
-      "2\t검증 세트 손실: 0.264351\t최선의 손실: 0.099864\t정확도: 96.13%\n",
-      "3\t검증 세트 손실: 0.084986\t최선의 손실: 0.084986\t정확도: 98.20%\n",
-      "4\t검증 세트 손실: 0.177509\t최선의 손실: 0.084986\t정확도: 95.93%\n",
-      "5\t검증 세트 손실: 0.095689\t최선의 손실: 0.084986\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.058981\t최선의 손실: 0.058981\t정확도: 98.44%\n",
-      "7\t검증 세트 손실: 0.080439\t최선의 손실: 0.058981\t정확도: 98.51%\n",
-      "8\t검증 세트 손실: 0.110599\t최선의 손실: 0.058981\t정확도: 97.30%\n",
-      "9\t검증 세트 손실: 0.090065\t최선의 손실: 0.058981\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.097315\t최선의 손실: 0.058981\t정확도: 97.89%\n",
-      "11\t검증 세트 손실: 0.080828\t최선의 손실: 0.058981\t정확도: 98.24%\n",
-      "12\t검증 세트 손실: 0.073349\t최선의 손실: 0.058981\t정확도: 98.32%\n",
-      "13\t검증 세트 손실: 0.076959\t최선의 손실: 0.058981\t정확도: 98.48%\n",
-      "14\t검증 세트 손실: 0.094244\t최선의 손실: 0.058981\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.066525\t최선의 손실: 0.058981\t정확도: 98.44%\n",
-      "16\t검증 세트 손실: 0.117505\t최선의 손실: 0.058981\t정확도: 98.32%\n",
-      "17\t검증 세트 손실: 0.079025\t최선의 손실: 0.058981\t정확도: 98.51%\n",
-      "18\t검증 세트 손실: 0.075135\t최선의 손실: 0.058981\t정확도: 98.63%\n",
-      "19\t검증 세트 손실: 0.060104\t최선의 손실: 0.058981\t정확도: 98.59%\n",
-      "20\t검증 세트 손실: 0.074982\t최선의 손실: 0.058981\t정확도: 98.67%\n",
-      "21\t검증 세트 손실: 0.140299\t최선의 손실: 0.058981\t정확도: 98.28%\n",
-      "22\t검증 세트 손실: 0.084353\t최선의 손실: 0.058981\t정확도: 98.63%\n",
-      "23\t검증 세트 손실: 0.116155\t최선의 손실: 0.058981\t정확도: 98.59%\n",
-      "24\t검증 세트 손실: 0.111623\t최선의 손실: 0.058981\t정확도: 98.24%\n",
-      "25\t검증 세트 손실: 0.062263\t최선의 손실: 0.058981\t정확도: 98.79%\n",
-      "26\t검증 세트 손실: 0.064812\t최선의 손실: 0.058981\t정확도: 98.71%\n",
-      "27\t검증 세트 손실: 0.083165\t최선의 손실: 0.058981\t정확도: 99.02%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=70, total=  59.6s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.067144\t최선의 손실: 0.067144\t정확도: 98.01%\n",
+      "1\t검증 세트 손실: 0.054913\t최선의 손실: 0.054913\t정확도: 98.28%\n",
+      "2\t검증 세트 손실: 0.050591\t최선의 손실: 0.050591\t정확도: 98.40%\n",
+      "3\t검증 세트 손실: 0.041975\t최선의 손실: 0.041975\t정확도: 98.71%\n",
+      "4\t검증 세트 손실: 0.042864\t최선의 손실: 0.041975\t정확도: 98.83%\n",
+      "5\t검증 세트 손실: 0.035743\t최선의 손실: 0.035743\t정확도: 98.75%\n",
+      "6\t검증 세트 손실: 0.042842\t최선의 손실: 0.035743\t정확도: 98.75%\n",
+      "7\t검증 세트 손실: 0.052148\t최선의 손실: 0.035743\t정확도: 98.48%\n",
+      "8\t검증 세트 손실: 0.047309\t최선의 손실: 0.035743\t정확도: 99.02%\n",
+      "9\t검증 세트 손실: 0.055008\t최선의 손실: 0.035743\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.046197\t최선의 손실: 0.035743\t정확도: 98.71%\n",
+      "11\t검증 세트 손실: 0.044221\t최선의 손실: 0.035743\t정확도: 98.79%\n",
+      "12\t검증 세트 손실: 0.066396\t최선의 손실: 0.035743\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.049269\t최선의 손실: 0.035743\t정확도: 98.83%\n",
+      "14\t검증 세트 손실: 0.035831\t최선의 손실: 0.035743\t정확도: 98.75%\n",
+      "15\t검증 세트 손실: 0.044986\t최선의 손실: 0.035743\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.033805\t최선의 손실: 0.033805\t정확도: 98.94%\n",
+      "17\t검증 세트 손실: 0.043624\t최선의 손실: 0.033805\t정확도: 98.98%\n",
+      "18\t검증 세트 손실: 0.043181\t최선의 손실: 0.033805\t정확도: 98.98%\n",
+      "19\t검증 세트 손실: 0.042283\t최선의 손실: 0.033805\t정확도: 99.14%\n",
+      "20\t검증 세트 손실: 0.053266\t최선의 손실: 0.033805\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.032630\t최선의 손실: 0.032630\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.030870\t최선의 손실: 0.030870\t정확도: 99.10%\n",
+      "23\t검증 세트 손실: 0.043858\t최선의 손실: 0.030870\t정확도: 98.79%\n",
+      "24\t검증 세트 손실: 0.031849\t최선의 손실: 0.030870\t정확도: 99.34%\n",
+      "25\t검증 세트 손실: 0.037964\t최선의 손실: 0.030870\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.042762\t최선의 손실: 0.030870\t정확도: 98.98%\n",
+      "27\t검증 세트 손실: 0.070489\t최선의 손실: 0.030870\t정확도: 98.20%\n",
+      "28\t검증 세트 손실: 0.102231\t최선의 손실: 0.030870\t정확도: 98.08%\n",
+      "29\t검증 세트 손실: 0.045944\t최선의 손실: 0.030870\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.041106\t최선의 손실: 0.030870\t정확도: 99.02%\n",
+      "31\t검증 세트 손실: 0.057360\t최선의 손실: 0.030870\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.049764\t최선의 손실: 0.030870\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.043030\t최선의 손실: 0.030870\t정확도: 99.18%\n",
+      "34\t검증 세트 손실: 0.039234\t최선의 손실: 0.030870\t정확도: 99.02%\n",
+      "35\t검증 세트 손실: 0.040069\t최선의 손실: 0.030870\t정확도: 99.10%\n",
+      "36\t검증 세트 손실: 0.039902\t최선의 손실: 0.030870\t정확도: 99.22%\n",
+      "37\t검증 세트 손실: 0.044940\t최선의 손실: 0.030870\t정확도: 99.06%\n",
+      "38\t검증 세트 손실: 0.042215\t최선의 손실: 0.030870\t정확도: 99.02%\n",
+      "39\t검증 세트 손실: 0.040002\t최선의 손실: 0.030870\t정확도: 99.18%\n",
+      "40\t검증 세트 손실: 0.043565\t최선의 손실: 0.030870\t정확도: 98.87%\n",
+      "41\t검증 세트 손실: 0.050889\t최선의 손실: 0.030870\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.043313\t최선의 손실: 0.030870\t정확도: 99.06%\n",
+      "43\t검증 세트 손실: 0.043706\t최선의 손실: 0.030870\t정확도: 98.94%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100, total=  21.3s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.249848\t최선의 손실: 0.249848\t정확도: 96.05%\n",
-      "1\t검증 세트 손실: 0.092616\t최선의 손실: 0.092616\t정확도: 97.93%\n",
-      "2\t검증 세트 손실: 0.081294\t최선의 손실: 0.081294\t정확도: 98.28%\n",
-      "3\t검증 세트 손실: 0.082285\t최선의 손실: 0.081294\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.089029\t최선의 손실: 0.081294\t정확도: 98.24%\n",
-      "5\t검증 세트 손실: 0.064244\t최선의 손실: 0.064244\t정확도: 98.32%\n",
-      "6\t검증 세트 손실: 0.080547\t최선의 손실: 0.064244\t정확도: 98.16%\n",
-      "7\t검증 세트 손실: 0.090929\t최선의 손실: 0.064244\t정확도: 97.89%\n",
-      "8\t검증 세트 손실: 0.091089\t최선의 손실: 0.064244\t정확도: 98.48%\n",
-      "9\t검증 세트 손실: 0.077891\t최선의 손실: 0.064244\t정확도: 98.16%\n",
-      "10\t검증 세트 손실: 0.070667\t최선의 손실: 0.064244\t정확도: 98.63%\n",
-      "11\t검증 세트 손실: 0.152904\t최선의 손실: 0.064244\t정확도: 98.08%\n",
-      "12\t검증 세트 손실: 0.069474\t최선의 손실: 0.064244\t정확도: 98.51%\n",
-      "13\t검증 세트 손실: 0.190005\t최선의 손실: 0.064244\t정확도: 97.22%\n",
-      "14\t검증 세트 손실: 0.067653\t최선의 손실: 0.064244\t정확도: 98.48%\n",
-      "15\t검증 세트 손실: 0.080059\t최선의 손실: 0.064244\t정확도: 98.44%\n",
-      "16\t검증 세트 손실: 0.076597\t최선의 손실: 0.064244\t정확도: 98.24%\n",
-      "17\t검증 세트 손실: 0.113024\t최선의 손실: 0.064244\t정확도: 97.85%\n",
-      "18\t검증 세트 손실: 0.102267\t최선의 손실: 0.064244\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.054107\t최선의 손실: 0.054107\t정확도: 98.63%\n",
-      "20\t검증 세트 손실: 0.095285\t최선의 손실: 0.054107\t정확도: 98.44%\n",
-      "21\t검증 세트 손실: 0.120344\t최선의 손실: 0.054107\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.082585\t최선의 손실: 0.054107\t정확도: 98.63%\n",
-      "23\t검증 세트 손실: 0.083891\t최선의 손실: 0.054107\t정확도: 98.36%\n",
-      "24\t검증 세트 손실: 0.190373\t최선의 손실: 0.054107\t정확도: 97.69%\n",
-      "25\t검증 세트 손실: 0.108127\t최선의 손실: 0.054107\t정확도: 98.83%\n",
-      "26\t검증 세트 손실: 0.067375\t최선의 손실: 0.054107\t정확도: 98.71%\n",
-      "27\t검증 세트 손실: 0.079860\t최선의 손실: 0.054107\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.076112\t최선의 손실: 0.054107\t정확도: 98.67%\n",
-      "29\t검증 세트 손실: 0.066305\t최선의 손실: 0.054107\t정확도: 98.83%\n",
-      "30\t검증 세트 손실: 0.089056\t최선의 손실: 0.054107\t정확도: 98.79%\n",
-      "31\t검증 세트 손실: 0.100493\t최선의 손실: 0.054107\t정확도: 98.71%\n",
-      "32\t검증 세트 손실: 0.077236\t최선의 손실: 0.054107\t정확도: 98.83%\n",
-      "33\t검증 세트 손실: 0.140935\t최선의 손실: 0.054107\t정확도: 98.51%\n",
-      "34\t검증 세트 손실: 0.206141\t최선의 손실: 0.054107\t정확도: 97.11%\n",
-      "35\t검증 세트 손실: 0.120588\t최선의 손실: 0.054107\t정확도: 98.44%\n",
-      "36\t검증 세트 손실: 0.096093\t최선의 손실: 0.054107\t정확도: 98.51%\n",
-      "37\t검증 세트 손실: 0.119036\t최선의 손실: 0.054107\t정확도: 98.20%\n",
-      "38\t검증 세트 손실: 0.195898\t최선의 손실: 0.054107\t정확도: 98.44%\n",
-      "39\t검증 세트 손실: 0.110913\t최선의 손실: 0.054107\t정확도: 98.79%\n",
-      "40\t검증 세트 손실: 0.087011\t최선의 손실: 0.054107\t정확도: 98.91%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=70, total=  34.1s\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.096425\t최선의 손실: 0.096425\t정확도: 97.42%\n",
+      "1\t검증 세트 손실: 0.085447\t최선의 손실: 0.085447\t정확도: 97.19%\n",
+      "2\t검증 세트 손실: 0.056368\t최선의 손실: 0.056368\t정확도: 98.59%\n",
+      "3\t검증 세트 손실: 0.062206\t최선의 손실: 0.056368\t정확도: 98.08%\n",
+      "4\t검증 세트 손실: 0.045111\t최선의 손실: 0.045111\t정확도: 98.55%\n",
+      "5\t검증 세트 손실: 0.071518\t최선의 손실: 0.045111\t정확도: 97.69%\n",
+      "6\t검증 세트 손실: 0.045895\t최선의 손실: 0.045111\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.043598\t최선의 손실: 0.043598\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.036469\t최선의 손실: 0.036469\t정확도: 99.06%\n",
+      "9\t검증 세트 손실: 0.034446\t최선의 손실: 0.034446\t정확도: 98.91%\n",
+      "10\t검증 세트 손실: 0.041765\t최선의 손실: 0.034446\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.035290\t최선의 손실: 0.034446\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.055551\t최선의 손실: 0.034446\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.031404\t최선의 손실: 0.031404\t정확도: 98.87%\n",
+      "14\t검증 세트 손실: 0.044144\t최선의 손실: 0.031404\t정확도: 98.40%\n",
+      "15\t검증 세트 손실: 0.044451\t최선의 손실: 0.031404\t정확도: 98.67%\n",
+      "16\t검증 세트 손실: 0.032306\t최선의 손실: 0.031404\t정확도: 98.83%\n",
+      "17\t검증 세트 손실: 0.028589\t최선의 손실: 0.028589\t정확도: 99.02%\n",
+      "18\t검증 세트 손실: 0.030549\t최선의 손실: 0.028589\t정확도: 99.06%\n",
+      "19\t검증 세트 손실: 0.032095\t최선의 손실: 0.028589\t정확도: 99.18%\n",
+      "20\t검증 세트 손실: 0.036157\t최선의 손실: 0.028589\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.036337\t최선의 손실: 0.028589\t정확도: 98.98%\n",
+      "22\t검증 세트 손실: 0.030830\t최선의 손실: 0.028589\t정확도: 99.10%\n",
+      "23\t검증 세트 손실: 0.040532\t최선의 손실: 0.028589\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.034079\t최선의 손실: 0.028589\t정확도: 99.14%\n",
+      "25\t검증 세트 손실: 0.028735\t최선의 손실: 0.028589\t정확도: 99.18%\n",
+      "26\t검증 세트 손실: 0.036802\t최선의 손실: 0.028589\t정확도: 99.10%\n",
+      "27\t검증 세트 손실: 0.034832\t최선의 손실: 0.028589\t정확도: 98.94%\n",
+      "28\t검증 세트 손실: 0.035065\t최선의 손실: 0.028589\t정확도: 98.87%\n",
+      "29\t검증 세트 손실: 0.031655\t최선의 손실: 0.028589\t정확도: 99.02%\n",
+      "30\t검증 세트 손실: 0.037086\t최선의 손실: 0.028589\t정확도: 98.94%\n",
+      "31\t검증 세트 손실: 0.039528\t최선의 손실: 0.028589\t정확도: 98.83%\n",
+      "32\t검증 세트 손실: 0.038741\t최선의 손실: 0.028589\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.045782\t최선의 손실: 0.028589\t정확도: 98.83%\n",
+      "34\t검증 세트 손실: 0.026784\t최선의 손실: 0.026784\t정확도: 99.34%\n",
+      "35\t검증 세트 손실: 0.036696\t최선의 손실: 0.026784\t정확도: 98.91%\n",
+      "36\t검증 세트 손실: 0.036399\t최선의 손실: 0.026784\t정확도: 98.87%\n",
+      "37\t검증 세트 손실: 0.054533\t최선의 손실: 0.026784\t정확도: 98.67%\n",
+      "38\t검증 세트 손실: 0.046361\t최선의 손실: 0.026784\t정확도: 98.94%\n",
+      "39\t검증 세트 손실: 0.038532\t최선의 손실: 0.026784\t정확도: 99.02%\n",
+      "40\t검증 세트 손실: 0.038239\t최선의 손실: 0.026784\t정확도: 98.98%\n",
+      "41\t검증 세트 손실: 0.047633\t최선의 손실: 0.026784\t정확도: 98.87%\n",
+      "42\t검증 세트 손실: 0.031835\t최선의 손실: 0.026784\t정확도: 99.18%\n",
+      "43\t검증 세트 손실: 0.055877\t최선의 손실: 0.026784\t정확도: 98.75%\n",
+      "44\t검증 세트 손실: 0.035196\t최선의 손실: 0.026784\t정확도: 98.98%\n",
+      "45\t검증 세트 손실: 0.066143\t최선의 손실: 0.026784\t정확도: 98.59%\n",
+      "46\t검증 세트 손실: 0.038274\t최선의 손실: 0.026784\t정확도: 98.98%\n",
+      "47\t검증 세트 손실: 0.047818\t최선의 손실: 0.026784\t정확도: 98.91%\n",
+      "48\t검증 세트 손실: 0.030867\t최선의 손실: 0.026784\t정확도: 99.26%\n",
+      "49\t검증 세트 손실: 0.046787\t최선의 손실: 0.026784\t정확도: 98.98%\n",
+      "50\t검증 세트 손실: 0.051540\t최선의 손실: 0.026784\t정확도: 98.79%\n",
+      "51\t검증 세트 손실: 0.044232\t최선의 손실: 0.026784\t정확도: 99.06%\n",
+      "52\t검증 세트 손실: 0.053814\t최선의 손실: 0.026784\t정확도: 98.79%\n",
+      "53\t검증 세트 손실: 0.049278\t최선의 손실: 0.026784\t정확도: 99.10%\n",
+      "54\t검증 세트 손실: 0.042503\t최선의 손실: 0.026784\t정확도: 98.98%\n",
+      "55\t검증 세트 손실: 0.040357\t최선의 손실: 0.026784\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100, total=  30.7s\n",
-      "[CV] batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.255603\t최선의 손실: 0.255603\t정확도: 95.54%\n",
-      "1\t검증 세트 손실: 0.104553\t최선의 손실: 0.104553\t정확도: 97.97%\n",
-      "2\t검증 세트 손실: 0.100638\t최선의 손실: 0.100638\t정확도: 98.05%\n",
-      "3\t검증 세트 손실: 0.093254\t최선의 손실: 0.093254\t정확도: 98.28%\n",
-      "4\t검증 세트 손실: 0.097882\t최선의 손실: 0.093254\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 0.094660\t최선의 손실: 0.093254\t정확도: 97.97%\n",
-      "6\t검증 세트 손실: 0.093660\t최선의 손실: 0.093254\t정확도: 98.16%\n",
-      "7\t검증 세트 손실: 0.141779\t최선의 손실: 0.093254\t정확도: 97.50%\n",
-      "8\t검증 세트 손실: 0.074408\t최선의 손실: 0.074408\t정확도: 98.55%\n",
-      "9\t검증 세트 손실: 0.059619\t최선의 손실: 0.059619\t정확도: 98.59%\n",
-      "10\t검증 세트 손실: 0.079762\t최선의 손실: 0.059619\t정확도: 98.51%\n",
-      "11\t검증 세트 손실: 0.072021\t최선의 손실: 0.059619\t정확도: 98.24%\n",
-      "12\t검증 세트 손실: 0.063043\t최선의 손실: 0.059619\t정확도: 98.71%\n",
-      "13\t검증 세트 손실: 0.144405\t최선의 손실: 0.059619\t정확도: 96.36%\n",
-      "14\t검증 세트 손실: 0.089141\t최선의 손실: 0.059619\t정확도: 98.28%\n",
-      "15\t검증 세트 손실: 0.098598\t최선의 손실: 0.059619\t정확도: 98.48%\n",
-      "16\t검증 세트 손실: 0.559602\t최선의 손실: 0.059619\t정확도: 90.38%\n",
-      "17\t검증 세트 손실: 0.071988\t최선의 손실: 0.059619\t정확도: 98.87%\n",
-      "18\t검증 세트 손실: 0.101138\t최선의 손실: 0.059619\t정확도: 98.36%\n",
-      "19\t검증 세트 손실: 0.064829\t최선의 손실: 0.059619\t정확도: 98.75%\n",
-      "20\t검증 세트 손실: 0.115658\t최선의 손실: 0.059619\t정확도: 98.32%\n",
-      "21\t검증 세트 손실: 0.131462\t최선의 손실: 0.059619\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.072842\t최선의 손실: 0.059619\t정확도: 98.63%\n"
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160, total= 5.8min\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.084538\t최선의 손실: 0.084538\t정확도: 97.42%\n",
+      "1\t검증 세트 손실: 0.073821\t최선의 손실: 0.073821\t정확도: 97.62%\n",
+      "2\t검증 세트 손실: 0.061198\t최선의 손실: 0.061198\t정확도: 98.16%\n",
+      "3\t검증 세트 손실: 0.052916\t최선의 손실: 0.052916\t정확도: 98.40%\n",
+      "4\t검증 세트 손실: 0.049352\t최선의 손실: 0.049352\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.048191\t최선의 손실: 0.048191\t정확도: 98.55%\n",
+      "6\t검증 세트 손실: 0.043616\t최선의 손실: 0.043616\t정확도: 98.71%\n",
+      "7\t검증 세트 손실: 0.043730\t최선의 손실: 0.043616\t정확도: 98.44%\n",
+      "8\t검증 세트 손실: 0.036991\t최선의 손실: 0.036991\t정확도: 98.98%\n",
+      "9\t검증 세트 손실: 0.051148\t최선의 손실: 0.036991\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.042031\t최선의 손실: 0.036991\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.036347\t최선의 손실: 0.036347\t정확도: 98.79%\n",
+      "12\t검증 세트 손실: 0.034439\t최선의 손실: 0.034439\t정확도: 98.91%\n",
+      "13\t검증 세트 손실: 0.036673\t최선의 손실: 0.034439\t정확도: 98.98%\n",
+      "14\t검증 세트 손실: 0.040298\t최선의 손실: 0.034439\t정확도: 98.83%\n",
+      "15\t검증 세트 손실: 0.039716\t최선의 손실: 0.034439\t정확도: 98.83%\n",
+      "16\t검증 세트 손실: 0.038812\t최선의 손실: 0.034439\t정확도: 98.67%\n",
+      "17\t검증 세트 손실: 0.036187\t최선의 손실: 0.034439\t정확도: 99.10%\n",
+      "18\t검증 세트 손실: 0.042548\t최선의 손실: 0.034439\t정확도: 98.94%\n",
+      "19\t검증 세트 손실: 0.038665\t최선의 손실: 0.034439\t정확도: 98.87%\n",
+      "20\t검증 세트 손실: 0.041848\t최선의 손실: 0.034439\t정확도: 98.94%\n",
+      "21\t검증 세트 손실: 0.039294\t최선의 손실: 0.034439\t정확도: 98.91%\n",
+      "22\t검증 세트 손실: 0.049091\t최선의 손실: 0.034439\t정확도: 98.63%\n",
+      "23\t검증 세트 손실: 0.038221\t최선의 손실: 0.034439\t정확도: 98.98%\n",
+      "24\t검증 세트 손실: 0.030232\t최선의 손실: 0.030232\t정확도: 99.30%\n",
+      "25\t검증 세트 손실: 0.039030\t최선의 손실: 0.030232\t정확도: 98.91%\n",
+      "26\t검증 세트 손실: 0.029369\t최선의 손실: 0.029369\t정확도: 99.26%\n",
+      "27\t검증 세트 손실: 0.032020\t최선의 손실: 0.029369\t정확도: 98.98%\n",
+      "28\t검증 세트 손실: 0.029907\t최선의 손실: 0.029369\t정확도: 98.98%\n",
+      "29\t검증 세트 손실: 0.038126\t최선의 손실: 0.029369\t정확도: 99.10%\n",
+      "30\t검증 세트 손실: 0.039118\t최선의 손실: 0.029369\t정확도: 99.14%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "23\t검증 세트 손실: 0.078643\t최선의 손실: 0.059619\t정확도: 98.87%\n",
-      "24\t검증 세트 손실: 0.090300\t최선의 손실: 0.059619\t정확도: 98.59%\n",
-      "25\t검증 세트 손실: 0.072490\t최선의 손실: 0.059619\t정확도: 98.55%\n",
-      "26\t검증 세트 손실: 0.123996\t최선의 손실: 0.059619\t정확도: 98.32%\n",
-      "27\t검증 세트 손실: 0.062685\t최선의 손실: 0.059619\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.176869\t최선의 손실: 0.059619\t정확도: 98.36%\n",
-      "29\t검증 세트 손실: 0.069519\t최선의 손실: 0.059619\t정확도: 98.79%\n",
-      "30\t검증 세트 손실: 0.134855\t최선의 손실: 0.059619\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.035614\t최선의 손실: 0.029369\t정확도: 99.10%\n",
+      "32\t검증 세트 손실: 0.032993\t최선의 손실: 0.029369\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.042674\t최선의 손실: 0.029369\t정확도: 98.94%\n",
+      "34\t검증 세트 손실: 0.036620\t최선의 손실: 0.029369\t정확도: 99.06%\n",
+      "35\t검증 세트 손실: 0.040542\t최선의 손실: 0.029369\t정확도: 98.83%\n",
+      "36\t검증 세트 손실: 0.036386\t최선의 손실: 0.029369\t정확도: 99.18%\n",
+      "37\t검증 세트 손실: 0.044442\t최선의 손실: 0.029369\t정확도: 98.55%\n",
+      "38\t검증 세트 손실: 0.032158\t최선의 손실: 0.029369\t정확도: 99.14%\n",
+      "39\t검증 세트 손실: 0.029448\t최선의 손실: 0.029369\t정확도: 99.26%\n",
+      "40\t검증 세트 손실: 0.035393\t최선의 손실: 0.029369\t정확도: 99.10%\n",
+      "41\t검증 세트 손실: 0.033461\t최선의 손실: 0.029369\t정확도: 99.10%\n",
+      "42\t검증 세트 손실: 0.032154\t최선의 손실: 0.029369\t정확도: 99.10%\n",
+      "43\t검증 세트 손실: 0.043954\t최선의 손실: 0.029369\t정확도: 98.94%\n",
+      "44\t검증 세트 손실: 0.030341\t최선의 손실: 0.029369\t정확도: 99.26%\n",
+      "45\t검증 세트 손실: 0.028665\t최선의 손실: 0.028665\t정확도: 99.22%\n",
+      "46\t검증 세트 손실: 0.031814\t최선의 손실: 0.028665\t정확도: 99.14%\n",
+      "47\t검증 세트 손실: 0.041641\t최선의 손실: 0.028665\t정확도: 99.10%\n",
+      "48\t검증 세트 손실: 0.041181\t최선의 손실: 0.028665\t정확도: 98.91%\n",
+      "49\t검증 세트 손실: 0.040978\t최선의 손실: 0.028665\t정확도: 99.14%\n",
+      "50\t검증 세트 손실: 0.029696\t최선의 손실: 0.028665\t정확도: 99.26%\n",
+      "51\t검증 세트 손실: 0.039678\t최선의 손실: 0.028665\t정확도: 98.98%\n",
+      "52\t검증 세트 손실: 0.029006\t최선의 손실: 0.028665\t정확도: 99.18%\n",
+      "53\t검증 세트 손실: 0.023878\t최선의 손실: 0.023878\t정확도: 99.22%\n",
+      "54\t검증 세트 손실: 0.039372\t최선의 손실: 0.023878\t정확도: 99.22%\n",
+      "55\t검증 세트 손실: 0.035560\t최선의 손실: 0.023878\t정확도: 99.06%\n",
+      "56\t검증 세트 손실: 0.028179\t최선의 손실: 0.023878\t정확도: 99.37%\n",
+      "57\t검증 세트 손실: 0.022981\t최선의 손실: 0.022981\t정확도: 99.37%\n",
+      "58\t검증 세트 손실: 0.036060\t최선의 손실: 0.022981\t정확도: 99.14%\n",
+      "59\t검증 세트 손실: 0.042231\t최선의 손실: 0.022981\t정확도: 98.94%\n",
+      "60\t검증 세트 손실: 0.036020\t최선의 손실: 0.022981\t정확도: 99.14%\n",
+      "61\t검증 세트 손실: 0.034911\t최선의 손실: 0.022981\t정확도: 99.10%\n",
+      "62\t검증 세트 손실: 0.034953\t최선의 손실: 0.022981\t정확도: 98.94%\n",
+      "63\t검증 세트 손실: 0.036893\t최선의 손실: 0.022981\t정확도: 98.94%\n",
+      "64\t검증 세트 손실: 0.031231\t최선의 손실: 0.022981\t정확도: 99.30%\n",
+      "65\t검증 세트 손실: 0.033498\t최선의 손실: 0.022981\t정확도: 99.14%\n",
+      "66\t검증 세트 손실: 0.025869\t최선의 손실: 0.022981\t정확도: 99.14%\n",
+      "67\t검증 세트 손실: 0.028080\t최선의 손실: 0.022981\t정확도: 99.26%\n",
+      "68\t검증 세트 손실: 0.033007\t최선의 손실: 0.022981\t정확도: 99.30%\n",
+      "69\t검증 세트 손실: 0.030608\t최선의 손실: 0.022981\t정확도: 99.22%\n",
+      "70\t검증 세트 손실: 0.057068\t최선의 손실: 0.022981\t정확도: 99.02%\n",
+      "71\t검증 세트 손실: 0.028250\t최선의 손실: 0.022981\t정확도: 99.34%\n",
+      "72\t검증 세트 손실: 0.029925\t최선의 손실: 0.022981\t정확도: 99.37%\n",
+      "73\t검증 세트 손실: 0.035956\t최선의 손실: 0.022981\t정확도: 99.22%\n",
+      "74\t검증 세트 손실: 0.032857\t최선의 손실: 0.022981\t정확도: 99.30%\n",
+      "75\t검증 세트 손실: 0.045135\t최선의 손실: 0.022981\t정확도: 98.91%\n",
+      "76\t검증 세트 손실: 0.036102\t최선의 손실: 0.022981\t정확도: 99.14%\n",
+      "77\t검증 세트 손실: 0.035665\t최선의 손실: 0.022981\t정확도: 99.22%\n",
+      "78\t검증 세트 손실: 0.064432\t최선의 손실: 0.022981\t정확도: 98.36%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>, batch_norm_momentum=0.99, n_neurons=100, total=  23.4s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.116036\t최선의 손실: 0.116036\t정확도: 96.83%\n",
-      "1\t검증 세트 손실: 0.070437\t최선의 손실: 0.070437\t정확도: 98.01%\n",
-      "2\t검증 세트 손실: 0.101129\t최선의 손실: 0.070437\t정확도: 97.69%\n",
-      "3\t검증 세트 손실: 0.075461\t최선의 손실: 0.070437\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.051457\t최선의 손실: 0.051457\t정확도: 98.48%\n",
-      "5\t검증 세트 손실: 0.079957\t최선의 손실: 0.051457\t정확도: 97.89%\n",
-      "6\t검증 세트 손실: 0.083230\t최선의 손실: 0.051457\t정확도: 98.48%\n",
-      "7\t검증 세트 손실: 0.060149\t최선의 손실: 0.051457\t정확도: 98.63%\n",
-      "8\t검증 세트 손실: 0.125293\t최선의 손실: 0.051457\t정확도: 97.93%\n",
-      "9\t검증 세트 손실: 0.056908\t최선의 손실: 0.051457\t정확도: 98.67%\n",
-      "10\t검증 세트 손실: 0.056625\t최선의 손실: 0.051457\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.245855\t최선의 손실: 0.051457\t정확도: 96.09%\n",
-      "12\t검증 세트 손실: 0.087426\t최선의 손실: 0.051457\t정확도: 98.67%\n",
-      "13\t검증 세트 손실: 0.042577\t최선의 손실: 0.042577\t정확도: 98.71%\n",
-      "14\t검증 세트 손실: 0.038710\t최선의 손실: 0.038710\t정확도: 98.98%\n",
-      "15\t검증 세트 손실: 0.044841\t최선의 손실: 0.038710\t정확도: 98.87%\n",
-      "16\t검증 세트 손실: 0.034571\t최선의 손실: 0.034571\t정확도: 99.18%\n",
-      "17\t검증 세트 손실: 0.057882\t최선의 손실: 0.034571\t정확도: 98.91%\n",
-      "18\t검증 세트 손실: 0.111863\t최선의 손실: 0.034571\t정확도: 98.24%\n",
-      "19\t검증 세트 손실: 0.044901\t최선의 손실: 0.034571\t정확도: 99.02%\n",
-      "20\t검증 세트 손실: 0.098768\t최선의 손실: 0.034571\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.172766\t최선의 손실: 0.034571\t정확도: 98.36%\n",
-      "22\t검증 세트 손실: 0.055981\t최선의 손실: 0.034571\t정확도: 99.02%\n",
-      "23\t검증 세트 손실: 0.047185\t최선의 손실: 0.034571\t정확도: 99.18%\n",
-      "24\t검증 세트 손실: 0.042230\t최선의 손실: 0.034571\t정확도: 99.10%\n",
-      "25\t검증 세트 손실: 0.046416\t최선의 손실: 0.034571\t정확도: 99.06%\n",
-      "26\t검증 세트 손실: 0.049834\t최선의 손실: 0.034571\t정확도: 99.06%\n",
-      "27\t검증 세트 손실: 0.065440\t최선의 손실: 0.034571\t정확도: 99.02%\n",
-      "28\t검증 세트 손실: 0.064868\t최선의 손실: 0.034571\t정확도: 98.87%\n",
-      "29\t검증 세트 손실: 0.160526\t최선의 손실: 0.034571\t정확도: 98.01%\n",
-      "30\t검증 세트 손실: 0.118330\t최선의 손실: 0.034571\t정확도: 98.63%\n",
-      "31\t검증 세트 손실: 0.136285\t최선의 손실: 0.034571\t정확도: 98.16%\n",
-      "32\t검증 세트 손실: 0.230307\t최선의 손실: 0.034571\t정확도: 98.28%\n",
-      "33\t검증 세트 손실: 0.086788\t최선의 손실: 0.034571\t정확도: 98.83%\n",
-      "34\t검증 세트 손실: 0.075803\t최선의 손실: 0.034571\t정확도: 99.02%\n",
-      "35\t검증 세트 손실: 0.066648\t최선의 손실: 0.034571\t정확도: 99.02%\n",
-      "36\t검증 세트 손실: 0.065761\t최선의 손실: 0.034571\t정확도: 98.87%\n",
-      "37\t검증 세트 손실: 0.402216\t최선의 손실: 0.034571\t정확도: 97.19%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160, total= 8.2min\n",
+      "[CV] batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.085820\t최선의 손실: 0.085820\t정확도: 97.65%\n",
+      "1\t검증 세트 손실: 0.058153\t최선의 손실: 0.058153\t정확도: 98.20%\n",
+      "2\t검증 세트 손실: 0.065788\t최선의 손실: 0.058153\t정확도: 97.81%\n",
+      "3\t검증 세트 손실: 0.048446\t최선의 손실: 0.048446\t정확도: 98.51%\n",
+      "4\t검증 세트 손실: 0.045191\t최선의 손실: 0.045191\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.055042\t최선의 손실: 0.045191\t정확도: 98.20%\n",
+      "6\t검증 세트 손실: 0.043875\t최선의 손실: 0.043875\t정확도: 98.63%\n",
+      "7\t검증 세트 손실: 0.039239\t최선의 손실: 0.039239\t정확도: 98.87%\n",
+      "8\t검증 세트 손실: 0.047206\t최선의 손실: 0.039239\t정확도: 98.67%\n",
+      "9\t검증 세트 손실: 0.041365\t최선의 손실: 0.039239\t정확도: 98.55%\n",
+      "10\t검증 세트 손실: 0.041298\t최선의 손실: 0.039239\t정확도: 98.87%\n",
+      "11\t검증 세트 손실: 0.040746\t최선의 손실: 0.039239\t정확도: 98.91%\n",
+      "12\t검증 세트 손실: 0.030013\t최선의 손실: 0.030013\t정확도: 99.06%\n",
+      "13\t검증 세트 손실: 0.028235\t최선의 손실: 0.028235\t정확도: 99.02%\n",
+      "14\t검증 세트 손실: 0.036419\t최선의 손실: 0.028235\t정확도: 98.75%\n",
+      "15\t검증 세트 손실: 0.033063\t최선의 손실: 0.028235\t정확도: 98.98%\n",
+      "16\t검증 세트 손실: 0.028039\t최선의 손실: 0.028039\t정확도: 99.18%\n",
+      "17\t검증 세트 손실: 0.041816\t최선의 손실: 0.028039\t정확도: 98.59%\n",
+      "18\t검증 세트 손실: 0.039080\t최선의 손실: 0.028039\t정확도: 98.94%\n",
+      "19\t검증 세트 손실: 0.029332\t최선의 손실: 0.028039\t정확도: 98.94%\n",
+      "20\t검증 세트 손실: 0.031377\t최선의 손실: 0.028039\t정확도: 99.10%\n",
+      "21\t검증 세트 손실: 0.025528\t최선의 손실: 0.025528\t정확도: 99.18%\n",
+      "22\t검증 세트 손실: 0.036906\t최선의 손실: 0.025528\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.030436\t최선의 손실: 0.025528\t정확도: 99.02%\n",
+      "24\t검증 세트 손실: 0.051171\t최선의 손실: 0.025528\t정확도: 98.67%\n",
+      "25\t검증 세트 손실: 0.042641\t최선의 손실: 0.025528\t정확도: 99.06%\n",
+      "26\t검증 세트 손실: 0.029045\t최선의 손실: 0.025528\t정확도: 99.22%\n",
+      "27\t검증 세트 손실: 0.028399\t최선의 손실: 0.025528\t정확도: 99.02%\n",
+      "28\t검증 세트 손실: 0.031169\t최선의 손실: 0.025528\t정확도: 99.06%\n",
+      "29\t검증 세트 손실: 0.034690\t최선의 손실: 0.025528\t정확도: 98.94%\n",
+      "30\t검증 세트 손실: 0.034924\t최선의 손실: 0.025528\t정확도: 98.98%\n",
+      "31\t검증 세트 손실: 0.023999\t최선의 손실: 0.023999\t정확도: 99.22%\n",
+      "32\t검증 세트 손실: 0.035237\t최선의 손실: 0.023999\t정확도: 99.14%\n",
+      "33\t검증 세트 손실: 0.032921\t최선의 손실: 0.023999\t정확도: 99.06%\n",
+      "34\t검증 세트 손실: 0.030156\t최선의 손실: 0.023999\t정확도: 99.18%\n",
+      "35\t검증 세트 손실: 0.049717\t최선의 손실: 0.023999\t정확도: 98.63%\n",
+      "36\t검증 세트 손실: 0.040187\t최선의 손실: 0.023999\t정확도: 98.98%\n",
+      "37\t검증 세트 손실: 0.040080\t최선의 손실: 0.023999\t정확도: 98.79%\n",
+      "38\t검증 세트 손실: 0.040052\t최선의 손실: 0.023999\t정확도: 98.94%\n",
+      "39\t검증 세트 손실: 0.043558\t최선의 손실: 0.023999\t정확도: 98.83%\n",
+      "40\t검증 세트 손실: 0.032510\t최선의 손실: 0.023999\t정확도: 98.94%\n",
+      "41\t검증 세트 손실: 0.039498\t최선의 손실: 0.023999\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.035875\t최선의 손실: 0.023999\t정확도: 98.98%\n",
+      "43\t검증 세트 손실: 0.033222\t최선의 손실: 0.023999\t정확도: 99.10%\n",
+      "44\t검증 세트 손실: 0.027574\t최선의 손실: 0.023999\t정확도: 99.06%\n",
+      "45\t검증 세트 손실: 0.034251\t최선의 손실: 0.023999\t정확도: 98.98%\n",
+      "46\t검증 세트 손실: 0.042008\t최선의 손실: 0.023999\t정확도: 98.94%\n",
+      "47\t검증 세트 손실: 0.032645\t최선의 손실: 0.023999\t정확도: 98.98%\n",
+      "48\t검증 세트 손실: 0.051769\t최선의 손실: 0.023999\t정확도: 98.91%\n",
+      "49\t검증 세트 손실: 0.047539\t최선의 손실: 0.023999\t정확도: 98.67%\n",
+      "50\t검증 세트 손실: 0.033489\t최선의 손실: 0.023999\t정확도: 99.14%\n",
+      "51\t검증 세트 손실: 0.036434\t최선의 손실: 0.023999\t정확도: 99.02%\n",
+      "52\t검증 세트 손실: 0.038560\t최선의 손실: 0.023999\t정확도: 98.98%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=140, total=  51.2s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.175481\t최선의 손실: 0.175481\t정확도: 95.97%\n",
-      "1\t검증 세트 손실: 0.085503\t최선의 손실: 0.085503\t정확도: 97.77%\n",
-      "2\t검증 세트 손실: 0.086079\t최선의 손실: 0.085503\t정확도: 97.69%\n",
-      "3\t검증 세트 손실: 0.069304\t최선의 손실: 0.069304\t정확도: 98.32%\n",
-      "4\t검증 세트 손실: 0.070563\t최선의 손실: 0.069304\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.048847\t최선의 손실: 0.048847\t정확도: 98.75%\n",
-      "6\t검증 세트 손실: 0.112467\t최선의 손실: 0.048847\t정확도: 97.58%\n",
-      "7\t검증 세트 손실: 0.069383\t최선의 손실: 0.048847\t정확도: 98.05%\n",
-      "8\t검증 세트 손실: 0.045842\t최선의 손실: 0.045842\t정확도: 98.83%\n",
-      "9\t검증 세트 손실: 0.099595\t최선의 손실: 0.045842\t정확도: 98.36%\n",
-      "10\t검증 세트 손실: 0.066461\t최선의 손실: 0.045842\t정확도: 98.67%\n",
-      "11\t검증 세트 손실: 0.061404\t최선의 손실: 0.045842\t정확도: 98.44%\n",
-      "12\t검증 세트 손실: 0.095229\t최선의 손실: 0.045842\t정확도: 98.12%\n",
-      "13\t검증 세트 손실: 0.068733\t최선의 손실: 0.045842\t정확도: 98.40%\n",
-      "14\t검증 세트 손실: 0.097289\t최선의 손실: 0.045842\t정확도: 97.93%\n",
-      "15\t검증 세트 손실: 0.102094\t최선의 손실: 0.045842\t정확도: 98.36%\n",
-      "16\t검증 세트 손실: 0.068522\t최선의 손실: 0.045842\t정확도: 98.67%\n",
-      "17\t검증 세트 손실: 0.090797\t최선의 손실: 0.045842\t정확도: 98.16%\n",
-      "18\t검증 세트 손실: 0.069919\t최선의 손실: 0.045842\t정확도: 98.44%\n",
-      "19\t검증 세트 손실: 0.085488\t최선의 손실: 0.045842\t정확도: 98.67%\n",
-      "20\t검증 세트 손실: 0.081356\t최선의 손실: 0.045842\t정확도: 98.75%\n",
-      "21\t검증 세트 손실: 0.061114\t최선의 손실: 0.045842\t정확도: 98.67%\n",
-      "22\t검증 세트 손실: 0.092630\t최선의 손실: 0.045842\t정확도: 98.91%\n",
-      "23\t검증 세트 손실: 0.285033\t최선의 손실: 0.045842\t정확도: 96.76%\n",
-      "24\t검증 세트 손실: 0.060864\t최선의 손실: 0.045842\t정확도: 98.98%\n",
-      "25\t검증 세트 손실: 0.063679\t최선의 손실: 0.045842\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.049038\t최선의 손실: 0.045842\t정확도: 98.94%\n",
-      "27\t검증 세트 손실: 0.085958\t최선의 손실: 0.045842\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.065399\t최선의 손실: 0.045842\t정확도: 98.83%\n",
-      "29\t검증 세트 손실: 0.134746\t최선의 손실: 0.045842\t정확도: 98.05%\n",
+      "[CV]  batch_size=10, activation=<function relu at 0x7f738a2ee510>, learning_rate=0.01, batch_norm_momentum=0.98, n_neurons=160, total= 5.5min\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.148160\t최선의 손실: 0.148160\t정확도: 96.91%\n",
+      "1\t검증 세트 손실: 0.118993\t최선의 손실: 0.118993\t정확도: 97.89%\n",
+      "2\t검증 세트 손실: 0.195716\t최선의 손실: 0.118993\t정확도: 97.46%\n",
+      "3\t검증 세트 손실: 0.111618\t최선의 손실: 0.111618\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.138943\t최선의 손실: 0.111618\t정확도: 97.46%\n",
+      "5\t검증 세트 손실: 0.100274\t최선의 손실: 0.100274\t정확도: 98.40%\n",
+      "6\t검증 세트 손실: 0.077783\t최선의 손실: 0.077783\t정확도: 98.32%\n",
+      "7\t검증 세트 손실: 0.087436\t최선의 손실: 0.077783\t정확도: 98.08%\n",
+      "8\t검증 세트 손실: 0.118321\t최선의 손실: 0.077783\t정확도: 98.08%\n",
+      "9\t검증 세트 손실: 0.090182\t최선의 손실: 0.077783\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.067583\t최선의 손실: 0.067583\t정확도: 98.51%\n",
+      "11\t검증 세트 손실: 0.086251\t최선의 손실: 0.067583\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.081990\t최선의 손실: 0.067583\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.090893\t최선의 손실: 0.067583\t정확도: 98.32%\n",
+      "14\t검증 세트 손실: 0.070392\t최선의 손실: 0.067583\t정확도: 98.71%\n",
+      "15\t검증 세트 손실: 0.085099\t최선의 손실: 0.067583\t정확도: 98.59%\n",
+      "16\t검증 세트 손실: 0.067875\t최선의 손실: 0.067583\t정확도: 98.59%\n",
+      "17\t검증 세트 손실: 0.144479\t최선의 손실: 0.067583\t정확도: 97.73%\n",
+      "18\t검증 세트 손실: 0.077123\t최선의 손실: 0.067583\t정확도: 98.91%\n",
+      "19\t검증 세트 손실: 0.118697\t최선의 손실: 0.067583\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.117552\t최선의 손실: 0.067583\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.084112\t최선의 손실: 0.067583\t정확도: 98.51%\n",
+      "22\t검증 세트 손실: 0.072727\t최선의 손실: 0.067583\t정확도: 98.44%\n",
+      "23\t검증 세트 손실: 0.070832\t최선의 손실: 0.067583\t정확도: 98.67%\n",
+      "24\t검증 세트 손실: 0.130780\t최선의 손실: 0.067583\t정확도: 97.50%\n",
+      "25\t검증 세트 손실: 0.064428\t최선의 손실: 0.064428\t정확도: 98.79%\n",
+      "26\t검증 세트 손실: 0.104403\t최선의 손실: 0.064428\t정확도: 98.59%\n",
+      "27\t검증 세트 손실: 0.057099\t최선의 손실: 0.057099\t정확도: 98.87%\n",
+      "28\t검증 세트 손실: 0.063509\t최선의 손실: 0.057099\t정확도: 98.87%\n",
+      "29\t검증 세트 손실: 0.203346\t최선의 손실: 0.057099\t정확도: 98.40%\n",
+      "30\t검증 세트 손실: 0.083960\t최선의 손실: 0.057099\t정확도: 98.71%\n",
+      "31\t검증 세트 손실: 0.082844\t최선의 손실: 0.057099\t정확도: 98.83%\n",
+      "32\t검증 세트 손실: 0.097079\t최선의 손실: 0.057099\t정확도: 98.87%\n",
+      "33\t검증 세트 손실: 0.084168\t최선의 손실: 0.057099\t정확도: 98.51%\n",
+      "34\t검증 세트 손실: 0.131311\t최선의 손실: 0.057099\t정확도: 97.97%\n",
+      "35\t검증 세트 손실: 0.074069\t최선의 손실: 0.057099\t정확도: 98.91%\n",
+      "36\t검증 세트 손실: 0.076639\t최선의 손실: 0.057099\t정확도: 98.79%\n",
+      "37\t검증 세트 손실: 0.125278\t최선의 손실: 0.057099\t정확도: 98.55%\n",
+      "38\t검증 세트 손실: 0.124819\t최선의 손실: 0.057099\t정확도: 97.93%\n",
+      "39\t검증 세트 손실: 0.056288\t최선의 손실: 0.056288\t정확도: 98.87%\n",
+      "40\t검증 세트 손실: 0.055309\t최선의 손실: 0.055309\t정확도: 99.10%\n",
+      "41\t검증 세트 손실: 0.072681\t최선의 손실: 0.055309\t정확도: 98.98%\n",
+      "42\t검증 세트 손실: 0.058018\t최선의 손실: 0.055309\t정확도: 99.02%\n",
+      "43\t검증 세트 손실: 0.082851\t최선의 손실: 0.055309\t정확도: 98.67%\n",
+      "44\t검증 세트 손실: 0.053856\t최선의 손실: 0.053856\t정확도: 98.98%\n",
+      "45\t검증 세트 손실: 0.076079\t최선의 손실: 0.053856\t정확도: 98.83%\n",
+      "46\t검증 세트 손실: 3.049181\t최선의 손실: 0.053856\t정확도: 88.62%\n",
+      "47\t검증 세트 손실: 0.544072\t최선의 손실: 0.053856\t정확도: 97.77%\n",
+      "48\t검증 세트 손실: 0.111416\t최선의 손실: 0.053856\t정확도: 98.87%\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "49\t검증 세트 손실: 0.112044\t최선의 손실: 0.053856\t정확도: 98.94%\n",
+      "50\t검증 세트 손실: 0.088844\t최선의 손실: 0.053856\t정확도: 98.79%\n",
+      "51\t검증 세트 손실: 0.101536\t최선의 손실: 0.053856\t정확도: 98.75%\n",
+      "52\t검증 세트 손실: 0.074789\t최선의 손실: 0.053856\t정확도: 98.87%\n",
+      "53\t검증 세트 손실: 0.098000\t최선의 손실: 0.053856\t정확도: 98.83%\n",
+      "54\t검증 세트 손실: 0.130868\t최선의 손실: 0.053856\t정확도: 98.55%\n",
+      "55\t검증 세트 손실: 0.092992\t최선의 손실: 0.053856\t정확도: 98.79%\n",
+      "56\t검증 세트 손실: 0.134793\t최선의 손실: 0.053856\t정확도: 99.06%\n",
+      "57\t검증 세트 손실: 0.154402\t최선의 손실: 0.053856\t정확도: 98.83%\n",
+      "58\t검증 세트 손실: 0.085951\t최선의 손실: 0.053856\t정확도: 99.10%\n",
+      "59\t검증 세트 손실: 0.133642\t최선의 손실: 0.053856\t정확도: 98.87%\n",
+      "60\t검증 세트 손실: 0.074166\t최선의 손실: 0.053856\t정확도: 99.26%\n",
+      "61\t검증 세트 손실: 0.134175\t최선의 손실: 0.053856\t정확도: 98.71%\n",
+      "62\t검증 세트 손실: 0.114631\t최선의 손실: 0.053856\t정확도: 98.75%\n",
+      "63\t검증 세트 손실: 0.105282\t최선의 손실: 0.053856\t정확도: 98.71%\n",
+      "64\t검증 세트 손실: 0.102412\t최선의 손실: 0.053856\t정확도: 98.94%\n",
+      "65\t검증 세트 손실: 0.115263\t최선의 손실: 0.053856\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=140, total=  41.1s\n",
-      "[CV] batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.142991\t최선의 손실: 0.142991\t정확도: 96.68%\n",
-      "1\t검증 세트 손실: 0.067033\t최선의 손실: 0.067033\t정확도: 98.16%\n",
-      "2\t검증 세트 손실: 0.053672\t최선의 손실: 0.053672\t정확도: 98.44%\n",
-      "3\t검증 세트 손실: 0.116331\t최선의 손실: 0.053672\t정확도: 97.26%\n",
-      "4\t검증 세트 손실: 0.066192\t최선의 손실: 0.053672\t정확도: 98.48%\n",
-      "5\t검증 세트 손실: 0.079781\t최선의 손실: 0.053672\t정확도: 98.12%\n",
-      "6\t검증 세트 손실: 0.189940\t최선의 손실: 0.053672\t정확도: 93.98%\n",
-      "7\t검증 세트 손실: 0.042523\t최선의 손실: 0.042523\t정확도: 98.75%\n",
-      "8\t검증 세트 손실: 0.046011\t최선의 손실: 0.042523\t정확도: 98.94%\n",
-      "9\t검증 세트 손실: 0.580724\t최선의 손실: 0.042523\t정확도: 88.82%\n",
-      "10\t검증 세트 손실: 0.067409\t최선의 손실: 0.042523\t정확도: 98.91%\n",
-      "11\t검증 세트 손실: 0.061916\t최선의 손실: 0.042523\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.102362\t최선의 손실: 0.042523\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.053858\t최선의 손실: 0.042523\t정확도: 98.75%\n",
-      "14\t검증 세트 손실: 0.033130\t최선의 손실: 0.033130\t정확도: 99.34%\n",
-      "15\t검증 세트 손실: 0.031017\t최선의 손실: 0.031017\t정확도: 99.37%\n",
-      "16\t검증 세트 손실: 0.030011\t최선의 손실: 0.030011\t정확도: 99.30%\n",
-      "17\t검증 세트 손실: 0.109775\t최선의 손실: 0.030011\t정확도: 97.81%\n",
-      "18\t검증 세트 손실: 0.137633\t최선의 손실: 0.030011\t정확도: 97.65%\n",
-      "19\t검증 세트 손실: 0.083081\t최선의 손실: 0.030011\t정확도: 98.48%\n",
-      "20\t검증 세트 손실: 0.055828\t최선의 손실: 0.030011\t정확도: 98.91%\n",
-      "21\t검증 세트 손실: 0.047253\t최선의 손실: 0.030011\t정확도: 99.02%\n",
-      "22\t검증 세트 손실: 0.049892\t최선의 손실: 0.030011\t정확도: 99.06%\n",
-      "23\t검증 세트 손실: 0.046204\t최선의 손실: 0.030011\t정확도: 99.26%\n",
-      "24\t검증 세트 손실: 0.044770\t최선의 손실: 0.030011\t정확도: 99.30%\n",
-      "25\t검증 세트 손실: 0.068779\t최선의 손실: 0.030011\t정확도: 98.63%\n",
-      "26\t검증 세트 손실: 0.117709\t최선의 손실: 0.030011\t정확도: 98.08%\n",
-      "27\t검증 세트 손실: 0.061220\t최선의 손실: 0.030011\t정확도: 98.98%\n",
-      "28\t검증 세트 손실: 0.068608\t최선의 손실: 0.030011\t정확도: 98.94%\n",
-      "29\t검증 세트 손실: 0.042272\t최선의 손실: 0.030011\t정확도: 99.02%\n",
-      "30\t검증 세트 손실: 0.033851\t최선의 손실: 0.030011\t정확도: 99.34%\n",
-      "31\t검증 세트 손실: 0.045473\t최선의 손실: 0.030011\t정확도: 99.02%\n",
-      "32\t검증 세트 손실: 0.055821\t최선의 손실: 0.030011\t정확도: 99.22%\n",
-      "33\t검증 세트 손실: 0.077885\t최선의 손실: 0.030011\t정확도: 98.91%\n",
-      "34\t검증 세트 손실: 0.091153\t최선의 손실: 0.030011\t정확도: 98.98%\n",
-      "35\t검증 세트 손실: 0.046336\t최선의 손실: 0.030011\t정확도: 99.10%\n",
-      "36\t검증 세트 손실: 0.046789\t최선의 손실: 0.030011\t정확도: 99.18%\n",
-      "37\t검증 세트 손실: 0.040459\t최선의 손실: 0.030011\t정확도: 99.10%\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=100, total=  50.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.956434\t최선의 손실: 0.956434\t정확도: 93.86%\n",
+      "1\t검증 세트 손실: 0.119549\t최선의 손실: 0.119549\t정확도: 97.73%\n",
+      "2\t검증 세트 손실: 0.118120\t최선의 손실: 0.118120\t정확도: 97.26%\n",
+      "3\t검증 세트 손실: 0.123112\t최선의 손실: 0.118120\t정확도: 97.15%\n",
+      "4\t검증 세트 손실: 0.126990\t최선의 손실: 0.118120\t정확도: 97.54%\n",
+      "5\t검증 세트 손실: 0.064321\t최선의 손실: 0.064321\t정확도: 98.32%\n",
+      "6\t검증 세트 손실: 0.087609\t최선의 손실: 0.064321\t정확도: 98.16%\n",
+      "7\t검증 세트 손실: 0.102494\t최선의 손실: 0.064321\t정확도: 97.81%\n",
+      "8\t검증 세트 손실: 0.122984\t최선의 손실: 0.064321\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.516196\t최선의 손실: 0.064321\t정확도: 95.15%\n",
+      "10\t검증 세트 손실: 0.094879\t최선의 손실: 0.064321\t정확도: 98.44%\n",
+      "11\t검증 세트 손실: 0.073287\t최선의 손실: 0.064321\t정확도: 98.63%\n",
+      "12\t검증 세트 손실: 0.064735\t최선의 손실: 0.064321\t정확도: 98.94%\n",
+      "13\t검증 세트 손실: 0.060212\t최선의 손실: 0.060212\t정확도: 98.75%\n",
+      "14\t검증 세트 손실: 0.057979\t최선의 손실: 0.057979\t정확도: 98.59%\n",
+      "15\t검증 세트 손실: 0.089249\t최선의 손실: 0.057979\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.093672\t최선의 손실: 0.057979\t정확도: 98.08%\n",
+      "17\t검증 세트 손실: 0.097515\t최선의 손실: 0.057979\t정확도: 98.28%\n",
+      "18\t검증 세트 손실: 0.068477\t최선의 손실: 0.057979\t정확도: 98.91%\n",
+      "19\t검증 세트 손실: 0.058878\t최선의 손실: 0.057979\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.061606\t최선의 손실: 0.057979\t정확도: 98.59%\n",
+      "21\t검증 세트 손실: 0.129801\t최선의 손실: 0.057979\t정확도: 97.85%\n",
+      "22\t검증 세트 손실: 0.172462\t최선의 손실: 0.057979\t정확도: 97.65%\n",
+      "23\t검증 세트 손실: 0.099830\t최선의 손실: 0.057979\t정확도: 98.71%\n",
+      "24\t검증 세트 손실: 0.094421\t최선의 손실: 0.057979\t정확도: 98.59%\n",
+      "25\t검증 세트 손실: 0.044927\t최선의 손실: 0.044927\t정확도: 98.98%\n",
+      "26\t검증 세트 손실: 0.050414\t최선의 손실: 0.044927\t정확도: 98.79%\n",
+      "27\t검증 세트 손실: 0.083735\t최선의 손실: 0.044927\t정확도: 98.71%\n",
+      "28\t검증 세트 손실: 0.131055\t최선의 손실: 0.044927\t정확도: 98.48%\n",
+      "29\t검증 세트 손실: 0.104745\t최선의 손실: 0.044927\t정확도: 98.08%\n",
+      "30\t검증 세트 손실: 0.173490\t최선의 손실: 0.044927\t정확도: 98.12%\n",
+      "31\t검증 세트 손실: 0.161628\t최선의 손실: 0.044927\t정확도: 97.46%\n",
+      "32\t검증 세트 손실: 0.058688\t최선의 손실: 0.044927\t정확도: 98.94%\n",
+      "33\t검증 세트 손실: 0.078395\t최선의 손실: 0.044927\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.082828\t최선의 손실: 0.044927\t정확도: 98.63%\n",
+      "35\t검증 세트 손실: 0.141703\t최선의 손실: 0.044927\t정확도: 98.32%\n",
+      "36\t검증 세트 손실: 0.096862\t최선의 손실: 0.044927\t정확도: 98.48%\n",
+      "37\t검증 세트 손실: 0.083520\t최선의 손실: 0.044927\t정확도: 98.71%\n",
+      "38\t검증 세트 손실: 0.082070\t최선의 손실: 0.044927\t정확도: 98.83%\n",
+      "39\t검증 세트 손실: 0.087943\t최선의 손실: 0.044927\t정확도: 98.59%\n",
+      "40\t검증 세트 손실: 0.053893\t최선의 손실: 0.044927\t정확도: 98.98%\n",
+      "41\t검증 세트 손실: 0.083658\t최선의 손실: 0.044927\t정확도: 98.48%\n",
+      "42\t검증 세트 손실: 0.168787\t최선의 손실: 0.044927\t정확도: 98.01%\n",
+      "43\t검증 세트 손실: 0.098514\t최선의 손실: 0.044927\t정확도: 98.75%\n",
+      "44\t검증 세트 손실: 0.227392\t최선의 손실: 0.044927\t정확도: 98.01%\n",
+      "45\t검증 세트 손실: 0.339799\t최선의 손실: 0.044927\t정확도: 98.12%\n",
+      "46\t검증 세트 손실: 0.079133\t최선의 손실: 0.044927\t정확도: 99.10%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, batch_norm_momentum=0.99, n_neurons=140, total=  51.4s\n"
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=100, total=  36.1s\n",
+      "[CV] batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.260659\t최선의 손실: 0.260659\t정확도: 95.90%\n",
+      "1\t검증 세트 손실: 0.144921\t최선의 손실: 0.144921\t정확도: 97.26%\n",
+      "2\t검증 세트 손실: 0.130803\t최선의 손실: 0.130803\t정확도: 97.34%\n",
+      "3\t검증 세트 손실: 0.102405\t최선의 손실: 0.102405\t정확도: 98.01%\n",
+      "4\t검증 세트 손실: 0.085434\t최선의 손실: 0.085434\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.171491\t최선의 손실: 0.085434\t정확도: 96.60%\n",
+      "6\t검증 세트 손실: 0.081782\t최선의 손실: 0.081782\t정확도: 98.16%\n",
+      "7\t검증 세트 손실: 0.115276\t최선의 손실: 0.081782\t정확도: 97.58%\n",
+      "8\t검증 세트 손실: 0.070404\t최선의 손실: 0.070404\t정확도: 98.16%\n",
+      "9\t검증 세트 손실: 0.113719\t최선의 손실: 0.070404\t정확도: 97.62%\n",
+      "10\t검증 세트 손실: 0.074893\t최선의 손실: 0.070404\t정확도: 98.63%\n",
+      "11\t검증 세트 손실: 0.089308\t최선의 손실: 0.070404\t정확도: 98.24%\n",
+      "12\t검증 세트 손실: 0.081746\t최선의 손실: 0.070404\t정확도: 98.48%\n",
+      "13\t검증 세트 손실: 0.094099\t최선의 손실: 0.070404\t정확도: 98.28%\n",
+      "14\t검증 세트 손실: 0.049303\t최선의 손실: 0.049303\t정확도: 98.94%\n",
+      "15\t검증 세트 손실: 0.060011\t최선의 손실: 0.049303\t정확도: 98.79%\n",
+      "16\t검증 세트 손실: 0.078212\t최선의 손실: 0.049303\t정확도: 98.51%\n",
+      "17\t검증 세트 손실: 0.082789\t최선의 손실: 0.049303\t정확도: 98.67%\n",
+      "18\t검증 세트 손실: 0.084642\t최선의 손실: 0.049303\t정확도: 98.24%\n",
+      "19\t검증 세트 손실: 0.062490\t최선의 손실: 0.049303\t정확도: 98.71%\n",
+      "20\t검증 세트 손실: 0.114636\t최선의 손실: 0.049303\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 1.519289\t최선의 손실: 0.049303\t정확도: 82.76%\n",
+      "22\t검증 세트 손실: 0.071085\t최선의 손실: 0.049303\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.078298\t최선의 손실: 0.049303\t정확도: 98.51%\n",
+      "24\t검증 세트 손실: 0.067901\t최선의 손실: 0.049303\t정확도: 98.87%\n",
+      "25\t검증 세트 손실: 0.081811\t최선의 손실: 0.049303\t정확도: 98.71%\n",
+      "26\t검증 세트 손실: 0.085421\t최선의 손실: 0.049303\t정확도: 98.59%\n",
+      "27\t검증 세트 손실: 0.066546\t최선의 손실: 0.049303\t정확도: 98.75%\n",
+      "28\t검증 세트 손실: 0.049642\t최선의 손실: 0.049303\t정확도: 98.87%\n",
+      "29\t검증 세트 손실: 0.049375\t최선의 손실: 0.049303\t정확도: 99.14%\n",
+      "30\t검증 세트 손실: 0.095365\t최선의 손실: 0.049303\t정확도: 98.75%\n",
+      "31\t검증 세트 손실: 0.084198\t최선의 손실: 0.049303\t정확도: 98.63%\n",
+      "32\t검증 세트 손실: 0.080280\t최선의 손실: 0.049303\t정확도: 99.06%\n",
+      "33\t검증 세트 손실: 0.199324\t최선의 손실: 0.049303\t정확도: 98.24%\n",
+      "34\t검증 세트 손실: 0.127343\t최선의 손실: 0.049303\t정확도: 98.28%\n",
+      "35\t검증 세트 손실: 0.096491\t최선의 손실: 0.049303\t정확도: 98.36%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>, learning_rate=0.1, batch_norm_momentum=0.99, n_neurons=100, total=  28.1s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.168566\t최선의 손실: 0.168566\t정확도: 95.23%\n",
+      "1\t검증 세트 손실: 0.076786\t최선의 손실: 0.076786\t정확도: 98.16%\n",
+      "2\t검증 세트 손실: 0.070977\t최선의 손실: 0.070977\t정확도: 97.97%\n",
+      "3\t검증 세트 손실: 0.120925\t최선의 손실: 0.070977\t정확도: 96.99%\n",
+      "4\t검증 세트 손실: 0.077866\t최선의 손실: 0.070977\t정확도: 98.48%\n",
+      "5\t검증 세트 손실: 0.069627\t최선의 손실: 0.069627\t정확도: 98.48%\n",
+      "6\t검증 세트 손실: 0.277481\t최선의 손실: 0.069627\t정확도: 96.17%\n",
+      "7\t검증 세트 손실: 0.050863\t최선의 손실: 0.050863\t정확도: 98.83%\n",
+      "8\t검증 세트 손실: 0.048399\t최선의 손실: 0.048399\t정확도: 98.63%\n",
+      "9\t검증 세트 손실: 0.059025\t최선의 손실: 0.048399\t정확도: 98.71%\n",
+      "10\t검증 세트 손실: 0.063731\t최선의 손실: 0.048399\t정확도: 98.79%\n",
+      "11\t검증 세트 손실: 0.061433\t최선의 손실: 0.048399\t정확도: 98.91%\n",
+      "12\t검증 세트 손실: 0.086001\t최선의 손실: 0.048399\t정확도: 98.71%\n",
+      "13\t검증 세트 손실: 0.051767\t최선의 손실: 0.048399\t정확도: 98.71%\n",
+      "14\t검증 세트 손실: 0.043881\t최선의 손실: 0.043881\t정확도: 98.87%\n",
+      "15\t검증 세트 손실: 0.057007\t최선의 손실: 0.043881\t정확도: 98.87%\n",
+      "16\t검증 세트 손실: 0.064264\t최선의 손실: 0.043881\t정확도: 98.79%\n",
+      "17\t검증 세트 손실: 0.070962\t최선의 손실: 0.043881\t정확도: 98.67%\n",
+      "18\t검증 세트 손실: 0.100781\t최선의 손실: 0.043881\t정확도: 98.63%\n",
+      "19\t검증 세트 손실: 0.100746\t최선의 손실: 0.043881\t정확도: 98.79%\n",
+      "20\t검증 세트 손실: 0.076990\t최선의 손실: 0.043881\t정확도: 98.75%\n",
+      "21\t검증 세트 손실: 0.105787\t최선의 손실: 0.043881\t정확도: 98.20%\n",
+      "22\t검증 세트 손실: 0.079515\t최선의 손실: 0.043881\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.241563\t최선의 손실: 0.043881\t정확도: 98.40%\n",
+      "24\t검증 세트 손실: 0.037368\t최선의 손실: 0.037368\t정확도: 99.18%\n",
+      "25\t검증 세트 손실: 0.042256\t최선의 손실: 0.037368\t정확도: 99.18%\n",
+      "26\t검증 세트 손실: 0.038282\t최선의 손실: 0.037368\t정확도: 99.18%\n",
+      "27\t검증 세트 손실: 0.052527\t최선의 손실: 0.037368\t정확도: 99.10%\n",
+      "28\t검증 세트 손실: 0.074215\t최선의 손실: 0.037368\t정확도: 98.75%\n",
+      "29\t검증 세트 손실: 0.136831\t최선의 손실: 0.037368\t정확도: 98.28%\n",
+      "30\t검증 세트 손실: 0.178128\t최선의 손실: 0.037368\t정확도: 98.55%\n",
+      "31\t검증 세트 손실: 0.063459\t최선의 손실: 0.037368\t정확도: 98.94%\n",
+      "32\t검증 세트 손실: 0.058044\t최선의 손실: 0.037368\t정확도: 99.34%\n",
+      "33\t검증 세트 손실: 0.070263\t최선의 손실: 0.037368\t정확도: 98.79%\n",
+      "34\t검증 세트 손실: 0.069229\t최선의 손실: 0.037368\t정확도: 99.02%\n",
+      "35\t검증 세트 손실: 0.068915\t최선의 손실: 0.037368\t정확도: 98.98%\n",
+      "36\t검증 세트 손실: 0.112329\t최선의 손실: 0.037368\t정확도: 98.55%\n",
+      "37\t검증 세트 손실: 0.296199\t최선의 손실: 0.037368\t정확도: 98.48%\n",
+      "38\t검증 세트 손실: 0.068715\t최선의 손실: 0.037368\t정확도: 98.98%\n",
+      "39\t검증 세트 손실: 0.089878\t최선의 손실: 0.037368\t정확도: 98.83%\n",
+      "40\t검증 세트 손실: 0.113317\t최선의 손실: 0.037368\t정확도: 98.75%\n",
+      "41\t검증 세트 손실: 0.096049\t최선의 손실: 0.037368\t정확도: 98.91%\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "42\t검증 세트 손실: 0.072405\t최선의 손실: 0.037368\t정확도: 99.14%\n",
+      "43\t검증 세트 손실: 0.093697\t최선의 손실: 0.037368\t정확도: 98.91%\n",
+      "44\t검증 세트 손실: 0.411707\t최선의 손실: 0.037368\t정확도: 97.62%\n",
+      "45\t검증 세트 손실: 0.129432\t최선의 손실: 0.037368\t정확도: 98.75%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=140, total= 1.1min\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.165604\t최선의 손실: 0.165604\t정확도: 96.17%\n",
+      "1\t검증 세트 손실: 0.101739\t최선의 손실: 0.101739\t정확도: 97.26%\n",
+      "2\t검증 세트 손실: 0.077055\t최선의 손실: 0.077055\t정확도: 98.20%\n",
+      "3\t검증 세트 손실: 0.072214\t최선의 손실: 0.072214\t정확도: 98.08%\n",
+      "4\t검증 세트 손실: 0.078594\t최선의 손실: 0.072214\t정확도: 97.85%\n",
+      "5\t검증 세트 손실: 0.117106\t최선의 손실: 0.072214\t정확도: 97.69%\n",
+      "6\t검증 세트 손실: 0.075206\t최선의 손실: 0.072214\t정확도: 98.08%\n",
+      "7\t검증 세트 손실: 0.043270\t최선의 손실: 0.043270\t정확도: 98.79%\n",
+      "8\t검증 세트 손실: 0.068778\t최선의 손실: 0.043270\t정확도: 98.24%\n",
+      "9\t검증 세트 손실: 0.052392\t최선의 손실: 0.043270\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.066052\t최선의 손실: 0.043270\t정확도: 98.24%\n",
+      "11\t검증 세트 손실: 0.270648\t최선의 손실: 0.043270\t정확도: 98.01%\n",
+      "12\t검증 세트 손실: 0.039745\t최선의 손실: 0.039745\t정확도: 98.87%\n",
+      "13\t검증 세트 손실: 0.034715\t최선의 손실: 0.034715\t정확도: 98.94%\n",
+      "14\t검증 세트 손실: 0.054818\t최선의 손실: 0.034715\t정확도: 98.63%\n",
+      "15\t검증 세트 손실: 0.080160\t최선의 손실: 0.034715\t정확도: 98.08%\n",
+      "16\t검증 세트 손실: 0.065784\t최선의 손실: 0.034715\t정확도: 98.48%\n",
+      "17\t검증 세트 손실: 0.088054\t최선의 손실: 0.034715\t정확도: 98.40%\n",
+      "18\t검증 세트 손실: 0.067658\t최선의 손실: 0.034715\t정확도: 98.51%\n",
+      "19\t검증 세트 손실: 0.354436\t최선의 손실: 0.034715\t정확도: 96.40%\n",
+      "20\t검증 세트 손실: 0.067736\t최선의 손실: 0.034715\t정확도: 98.67%\n",
+      "21\t검증 세트 손실: 0.090547\t최선의 손실: 0.034715\t정확도: 98.83%\n",
+      "22\t검증 세트 손실: 0.074185\t최선의 손실: 0.034715\t정확도: 98.79%\n",
+      "23\t검증 세트 손실: 0.141325\t최선의 손실: 0.034715\t정확도: 98.01%\n",
+      "24\t검증 세트 손실: 0.124468\t최선의 손실: 0.034715\t정확도: 98.24%\n",
+      "25\t검증 세트 손실: 0.068752\t최선의 손실: 0.034715\t정확도: 98.71%\n",
+      "26\t검증 세트 손실: 0.056516\t최선의 손실: 0.034715\t정확도: 98.94%\n",
+      "27\t검증 세트 손실: 0.045568\t최선의 손실: 0.034715\t정확도: 99.06%\n",
+      "28\t검증 세트 손실: 0.061457\t최선의 손실: 0.034715\t정확도: 98.94%\n",
+      "29\t검증 세트 손실: 0.055946\t최선의 손실: 0.034715\t정확도: 98.91%\n",
+      "30\t검증 세트 손실: 0.058222\t최선의 손실: 0.034715\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.065218\t최선의 손실: 0.034715\t정확도: 98.83%\n",
+      "32\t검증 세트 손실: 0.139678\t최선의 손실: 0.034715\t정확도: 98.20%\n",
+      "33\t검증 세트 손실: 0.116010\t최선의 손실: 0.034715\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.078088\t최선의 손실: 0.034715\t정확도: 98.71%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=140, total=  48.3s\n",
+      "[CV] batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.182922\t최선의 손실: 0.182922\t정확도: 95.50%\n",
+      "1\t검증 세트 손실: 0.084590\t최선의 손실: 0.084590\t정확도: 98.01%\n",
+      "2\t검증 세트 손실: 0.073171\t최선의 손실: 0.073171\t정확도: 98.16%\n",
+      "3\t검증 세트 손실: 0.112833\t최선의 손실: 0.073171\t정확도: 97.65%\n",
+      "4\t검증 세트 손실: 0.054152\t최선의 손실: 0.054152\t정확도: 98.71%\n",
+      "5\t검증 세트 손실: 0.071914\t최선의 손실: 0.054152\t정확도: 98.24%\n",
+      "6\t검증 세트 손실: 0.165654\t최선의 손실: 0.054152\t정확도: 95.04%\n",
+      "7\t검증 세트 손실: 0.146447\t최선의 손실: 0.054152\t정확도: 97.62%\n",
+      "8\t검증 세트 손실: 0.057221\t최선의 손실: 0.054152\t정확도: 98.55%\n",
+      "9\t검증 세트 손실: 0.036044\t최선의 손실: 0.036044\t정확도: 99.02%\n",
+      "10\t검증 세트 손실: 0.053146\t최선의 손실: 0.036044\t정확도: 98.94%\n",
+      "11\t검증 세트 손실: 0.061673\t최선의 손실: 0.036044\t정확도: 98.28%\n",
+      "12\t검증 세트 손실: 0.105205\t최선의 손실: 0.036044\t정확도: 98.24%\n",
+      "13\t검증 세트 손실: 0.117441\t최선의 손실: 0.036044\t정확도: 98.44%\n",
+      "14\t검증 세트 손실: 0.044596\t최선의 손실: 0.036044\t정확도: 99.02%\n",
+      "15\t검증 세트 손실: 0.029053\t최선의 손실: 0.029053\t정확도: 99.30%\n",
+      "16\t검증 세트 손실: 0.027236\t최선의 손실: 0.027236\t정확도: 99.34%\n",
+      "17\t검증 세트 손실: 0.078262\t최선의 손실: 0.027236\t정확도: 98.08%\n",
+      "18\t검증 세트 손실: 0.050703\t최선의 손실: 0.027236\t정확도: 98.79%\n",
+      "19\t검증 세트 손실: 1.297021\t최선의 손실: 0.027236\t정확도: 96.95%\n",
+      "20\t검증 세트 손실: 0.068767\t최선의 손실: 0.027236\t정확도: 98.91%\n",
+      "21\t검증 세트 손실: 0.038300\t최선의 손실: 0.027236\t정확도: 99.06%\n",
+      "22\t검증 세트 손실: 0.045134\t최선의 손실: 0.027236\t정확도: 99.26%\n",
+      "23\t검증 세트 손실: 0.037370\t최선의 손실: 0.027236\t정확도: 99.18%\n",
+      "24\t검증 세트 손실: 0.047406\t최선의 손실: 0.027236\t정확도: 99.22%\n",
+      "25\t검증 세트 손실: 0.047665\t최선의 손실: 0.027236\t정확도: 99.22%\n",
+      "26\t검증 세트 손실: 0.073990\t최선의 손실: 0.027236\t정확도: 98.71%\n",
+      "27\t검증 세트 손실: 0.097039\t최선의 손실: 0.027236\t정확도: 98.83%\n",
+      "28\t검증 세트 손실: 0.079830\t최선의 손실: 0.027236\t정확도: 98.87%\n",
+      "29\t검증 세트 손실: 0.037542\t최선의 손실: 0.027236\t정확도: 99.14%\n",
+      "30\t검증 세트 손실: 0.042977\t최선의 손실: 0.027236\t정확도: 99.06%\n",
+      "31\t검증 세트 손실: 0.083389\t최선의 손실: 0.027236\t정확도: 98.91%\n",
+      "32\t검증 세트 손실: 2.708771\t최선의 손실: 0.027236\t정확도: 92.26%\n",
+      "33\t검증 세트 손실: 0.112853\t최선의 손실: 0.027236\t정확도: 98.71%\n",
+      "34\t검증 세트 손실: 0.040287\t최선의 손실: 0.027236\t정확도: 99.18%\n",
+      "35\t검증 세트 손실: 0.037146\t최선의 손실: 0.027236\t정확도: 99.26%\n",
+      "36\t검증 세트 손실: 0.038783\t최선의 손실: 0.027236\t정확도: 99.45%\n",
+      "37\t검증 세트 손실: 0.044508\t최선의 손실: 0.027236\t정확도: 99.18%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, activation=<function elu at 0x7f738a2bd1e0>, learning_rate=0.05, batch_norm_momentum=0.99, n_neurons=140, total=  52.4s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 214.4min finished\n"
+      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 217.5min finished\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.086855\t최선의 손실: 0.086855\t정확도: 97.65%\n",
-      "1\t검증 세트 손실: 0.056744\t최선의 손실: 0.056744\t정확도: 98.12%\n",
-      "2\t검증 세트 손실: 0.050067\t최선의 손실: 0.050067\t정확도: 98.55%\n",
-      "3\t검증 세트 손실: 0.039836\t최선의 손실: 0.039836\t정확도: 98.79%\n",
-      "4\t검증 세트 손실: 0.029879\t최선의 손실: 0.029879\t정확도: 98.94%\n",
-      "5\t검증 세트 손실: 0.028733\t최선의 손실: 0.028733\t정확도: 99.10%\n",
-      "6\t검증 세트 손실: 0.034600\t최선의 손실: 0.028733\t정확도: 98.91%\n",
-      "7\t검증 세트 손실: 0.063165\t최선의 손실: 0.028733\t정확도: 97.65%\n",
-      "8\t검증 세트 손실: 0.031315\t최선의 손실: 0.028733\t정확도: 99.02%\n",
-      "9\t검증 세트 손실: 0.028066\t최선의 손실: 0.028066\t정확도: 99.18%\n",
-      "10\t검증 세트 손실: 0.040443\t최선의 손실: 0.028066\t정확도: 98.91%\n",
-      "11\t검증 세트 손실: 0.034488\t최선의 손실: 0.028066\t정확도: 99.06%\n",
-      "12\t검증 세트 손실: 0.029799\t최선의 손실: 0.028066\t정확도: 99.10%\n",
-      "13\t검증 세트 손실: 0.026692\t최선의 손실: 0.026692\t정확도: 99.14%\n",
-      "14\t검증 세트 손실: 0.029288\t최선의 손실: 0.026692\t정확도: 99.10%\n",
-      "15\t검증 세트 손실: 0.042620\t최선의 손실: 0.026692\t정확도: 98.79%\n",
-      "16\t검증 세트 손실: 0.032272\t최선의 손실: 0.026692\t정확도: 98.83%\n",
-      "17\t검증 세트 손실: 0.027286\t최선의 손실: 0.026692\t정확도: 99.26%\n",
-      "18\t검증 세트 손실: 0.027152\t최선의 손실: 0.026692\t정확도: 99.10%\n",
-      "19\t검증 세트 손실: 0.032403\t최선의 손실: 0.026692\t정확도: 99.10%\n",
-      "20\t검증 세트 손실: 0.023381\t최선의 손실: 0.023381\t정확도: 99.26%\n",
-      "21\t검증 세트 손실: 0.027104\t최선의 손실: 0.023381\t정확도: 99.18%\n",
-      "22\t검증 세트 손실: 0.022706\t최선의 손실: 0.022706\t정확도: 99.30%\n",
-      "23\t검증 세트 손실: 0.029342\t최선의 손실: 0.022706\t정확도: 99.22%\n",
-      "24\t검증 세트 손실: 0.037842\t최선의 손실: 0.022706\t정확도: 98.94%\n",
-      "25\t검증 세트 손실: 0.028311\t최선의 손실: 0.022706\t정확도: 99.30%\n",
-      "26\t검증 세트 손실: 0.043610\t최선의 손실: 0.022706\t정확도: 99.02%\n",
-      "27\t검증 세트 손실: 0.031246\t최선의 손실: 0.022706\t정확도: 99.26%\n",
-      "28\t검증 세트 손실: 0.031783\t최선의 손실: 0.022706\t정확도: 99.06%\n",
-      "29\t검증 세트 손실: 0.033919\t최선의 손실: 0.022706\t정확도: 99.02%\n",
-      "30\t검증 세트 손실: 0.026501\t최선의 손실: 0.022706\t정확도: 99.30%\n",
-      "31\t검증 세트 손실: 0.019552\t최선의 손실: 0.019552\t정확도: 99.30%\n",
-      "32\t검증 세트 손실: 0.029951\t최선의 손실: 0.019552\t정확도: 99.22%\n",
-      "33\t검증 세트 손실: 0.026658\t최선의 손실: 0.019552\t정확도: 99.26%\n",
-      "34\t검증 세트 손실: 0.023785\t최선의 손실: 0.019552\t정확도: 99.34%\n",
-      "35\t검증 세트 손실: 0.028242\t최선의 손실: 0.019552\t정확도: 99.26%\n",
-      "36\t검증 세트 손실: 0.024397\t최선의 손실: 0.019552\t정확도: 99.14%\n",
-      "37\t검증 세트 손실: 0.022806\t최선의 손실: 0.019552\t정확도: 99.30%\n",
-      "38\t검증 세트 손실: 0.028638\t최선의 손실: 0.019552\t정확도: 99.30%\n",
-      "39\t검증 세트 손실: 0.020233\t최선의 손실: 0.019552\t정확도: 99.49%\n",
-      "40\t검증 세트 손실: 0.021968\t최선의 손실: 0.019552\t정확도: 99.37%\n",
-      "41\t검증 세트 손실: 0.022132\t최선의 손실: 0.019552\t정확도: 99.34%\n",
-      "42\t검증 세트 손실: 0.026455\t최선의 손실: 0.019552\t정확도: 99.26%\n",
-      "43\t검증 세트 손실: 0.022478\t최선의 손실: 0.019552\t정확도: 99.30%\n",
-      "44\t검증 세트 손실: 0.025199\t최선의 손실: 0.019552\t정확도: 99.45%\n",
-      "45\t검증 세트 손실: 0.021953\t최선의 손실: 0.019552\t정확도: 99.49%\n",
-      "46\t검증 세트 손실: 0.028450\t최선의 손실: 0.019552\t정확도: 99.34%\n",
-      "47\t검증 세트 손실: 0.022184\t최선의 손실: 0.019552\t정확도: 99.45%\n",
-      "48\t검증 세트 손실: 0.024475\t최선의 손실: 0.019552\t정확도: 99.41%\n",
-      "49\t검증 세트 손실: 0.016337\t최선의 손실: 0.016337\t정확도: 99.65%\n",
-      "50\t검증 세트 손실: 0.022768\t최선의 손실: 0.016337\t정확도: 99.41%\n",
-      "51\t검증 세트 손실: 0.030467\t최선의 손실: 0.016337\t정확도: 99.30%\n",
-      "52\t검증 세트 손실: 0.023837\t최선의 손실: 0.016337\t정확도: 99.37%\n",
-      "53\t검증 세트 손실: 0.031694\t최선의 손실: 0.016337\t정확도: 99.14%\n",
-      "54\t검증 세트 손실: 0.029076\t최선의 손실: 0.016337\t정확도: 99.30%\n",
-      "55\t검증 세트 손실: 0.034423\t최선의 손실: 0.016337\t정확도: 99.30%\n",
-      "56\t검증 세트 손실: 0.035011\t최선의 손실: 0.016337\t정확도: 99.26%\n",
-      "57\t검증 세트 손실: 0.029345\t최선의 손실: 0.016337\t정확도: 99.37%\n",
-      "58\t검증 세트 손실: 0.031537\t최선의 손실: 0.016337\t정확도: 99.18%\n",
-      "59\t검증 세트 손실: 0.033169\t최선의 손실: 0.016337\t정확도: 99.26%\n",
-      "60\t검증 세트 손실: 0.030780\t최선의 손실: 0.016337\t정확도: 99.34%\n",
-      "61\t검증 세트 손실: 0.024225\t최선의 손실: 0.016337\t정확도: 99.26%\n",
-      "62\t검증 세트 손실: 0.019898\t최선의 손실: 0.016337\t정확도: 99.37%\n",
-      "63\t검증 세트 손실: 0.033827\t최선의 손실: 0.016337\t정확도: 99.14%\n",
-      "64\t검증 세트 손실: 0.026347\t최선의 손실: 0.016337\t정확도: 99.37%\n",
-      "65\t검증 세트 손실: 0.028053\t최선의 손실: 0.016337\t정확도: 99.22%\n",
-      "66\t검증 세트 손실: 0.029972\t최선의 손실: 0.016337\t정확도: 99.37%\n",
-      "67\t검증 세트 손실: 0.030138\t최선의 손실: 0.016337\t정확도: 99.34%\n",
-      "68\t검증 세트 손실: 0.028297\t최선의 손실: 0.016337\t정확도: 99.30%\n",
-      "69\t검증 세트 손실: 0.024084\t최선의 손실: 0.016337\t정확도: 99.37%\n",
-      "70\t검증 세트 손실: 0.028549\t최선의 손실: 0.016337\t정확도: 99.30%\n",
+      "0\t검증 세트 손실: 0.075115\t최선의 손실: 0.075115\t정확도: 97.65%\n",
+      "1\t검증 세트 손실: 0.055800\t최선의 손실: 0.055800\t정확도: 98.28%\n",
+      "2\t검증 세트 손실: 0.044505\t최선의 손실: 0.044505\t정확도: 98.67%\n",
+      "3\t검증 세트 손실: 0.047578\t최선의 손실: 0.044505\t정확도: 98.63%\n",
+      "4\t검증 세트 손실: 0.035123\t최선의 손실: 0.035123\t정확도: 98.98%\n",
+      "5\t검증 세트 손실: 0.030424\t최선의 손실: 0.030424\t정확도: 99.02%\n",
+      "6\t검증 세트 손실: 0.032113\t최선의 손실: 0.030424\t정확도: 99.06%\n",
+      "7\t검증 세트 손실: 0.032850\t최선의 손실: 0.030424\t정확도: 98.94%\n",
+      "8\t검증 세트 손실: 0.026317\t최선의 손실: 0.026317\t정확도: 99.10%\n",
+      "9\t검증 세트 손실: 0.026240\t최선의 손실: 0.026240\t정확도: 99.14%\n",
+      "10\t검증 세트 손실: 0.024848\t최선의 손실: 0.024848\t정확도: 99.26%\n",
+      "11\t검증 세트 손실: 0.027026\t최선의 손실: 0.024848\t정확도: 99.22%\n",
+      "12\t검증 세트 손실: 0.033376\t최선의 손실: 0.024848\t정확도: 99.14%\n",
+      "13\t검증 세트 손실: 0.028493\t최선의 손실: 0.024848\t정확도: 99.10%\n",
+      "14\t검증 세트 손실: 0.026331\t최선의 손실: 0.024848\t정확도: 99.10%\n",
+      "15\t검증 세트 손실: 0.028913\t최선의 손실: 0.024848\t정확도: 99.18%\n",
+      "16\t검증 세트 손실: 0.026107\t최선의 손실: 0.024848\t정확도: 99.30%\n",
+      "17\t검증 세트 손실: 0.038757\t최선의 손실: 0.024848\t정확도: 98.79%\n",
+      "18\t검증 세트 손실: 0.031658\t최선의 손실: 0.024848\t정확도: 99.18%\n",
+      "19\t검증 세트 손실: 0.030226\t최선의 손실: 0.024848\t정확도: 99.30%\n",
+      "20\t검증 세트 손실: 0.033438\t최선의 손실: 0.024848\t정확도: 99.18%\n",
+      "21\t검증 세트 손실: 0.027085\t최선의 손실: 0.024848\t정확도: 99.22%\n",
+      "22\t검증 세트 손실: 0.029832\t최선의 손실: 0.024848\t정확도: 99.22%\n",
+      "23\t검증 세트 손실: 0.035639\t최선의 손실: 0.024848\t정확도: 99.18%\n",
+      "24\t검증 세트 손실: 0.047529\t최선의 손실: 0.024848\t정확도: 98.79%\n",
+      "25\t검증 세트 손실: 0.025108\t최선의 손실: 0.024848\t정확도: 99.22%\n",
+      "26\t검증 세트 손실: 0.026803\t최선의 손실: 0.024848\t정확도: 99.37%\n",
+      "27\t검증 세트 손실: 0.032334\t최선의 손실: 0.024848\t정확도: 99.14%\n",
+      "28\t검증 세트 손실: 0.023959\t최선의 손실: 0.023959\t정확도: 99.37%\n",
+      "29\t검증 세트 손실: 0.029242\t최선의 손실: 0.023959\t정확도: 99.22%\n",
+      "30\t검증 세트 손실: 0.027949\t최선의 손실: 0.023959\t정확도: 99.30%\n",
+      "31\t검증 세트 손실: 0.027325\t최선의 손실: 0.023959\t정확도: 99.26%\n",
+      "32\t검증 세트 손실: 0.029723\t최선의 손실: 0.023959\t정확도: 99.26%\n",
+      "33\t검증 세트 손실: 0.027126\t최선의 손실: 0.023959\t정확도: 99.34%\n",
+      "34\t검증 세트 손실: 0.028486\t최선의 손실: 0.023959\t정확도: 99.10%\n",
+      "35\t검증 세트 손실: 0.027864\t최선의 손실: 0.023959\t정확도: 99.34%\n",
+      "36\t검증 세트 손실: 0.037534\t최선의 손실: 0.023959\t정확도: 98.98%\n",
+      "37\t검증 세트 손실: 0.024888\t최선의 손실: 0.023959\t정확도: 99.22%\n",
+      "38\t검증 세트 손실: 0.025950\t최선의 손실: 0.023959\t정확도: 99.18%\n",
+      "39\t검증 세트 손실: 0.029853\t최선의 손실: 0.023959\t정확도: 99.06%\n",
+      "40\t검증 세트 손실: 0.024515\t최선의 손실: 0.023959\t정확도: 99.30%\n",
+      "41\t검증 세트 손실: 0.041579\t최선의 손실: 0.023959\t정확도: 99.10%\n",
+      "42\t검증 세트 손실: 0.025913\t최선의 손실: 0.023959\t정확도: 99.22%\n",
+      "43\t검증 세트 손실: 0.038939\t최선의 손실: 0.023959\t정확도: 99.06%\n",
+      "44\t검증 세트 손실: 0.027479\t최선의 손실: 0.023959\t정확도: 99.30%\n",
+      "45\t검증 세트 손실: 0.035361\t최선의 손실: 0.023959\t정확도: 99.10%\n",
+      "46\t검증 세트 손실: 0.041640\t최선의 손실: 0.023959\t정확도: 98.98%\n",
+      "47\t검증 세트 손실: 0.033505\t최선의 손실: 0.023959\t정확도: 99.34%\n",
+      "48\t검증 세트 손실: 0.037782\t최선의 손실: 0.023959\t정확도: 99.02%\n",
+      "49\t검증 세트 손실: 0.032728\t최선의 손실: 0.023959\t정확도: 99.34%\n",
       "조기 종료!\n"
      ]
     },
@@ -13795,19 +14145,19 @@
      "data": {
       "text/plain": [
        "RandomizedSearchCV(cv=None, error_score='raise',\n",
-       "          estimator=DNNClassifier(activation=<function elu at 0x7f99417cf6a8>,\n",
+       "          estimator=DNNClassifier(activation=<function elu at 0x7f738a2bd1e0>,\n",
        "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42),\n",
        "          fit_params=None, iid=True, n_iter=50, n_jobs=1,\n",
-       "          param_distributions={'batch_norm_momentum': [0.9, 0.95, 0.98, 0.99, 0.999], 'batch_size': [10, 50, 100, 500], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x7f994177e9d8>, <function elu at 0x7f99417cf6a8>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56b70>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d569d8>], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160]},\n",
+       "          param_distributions={'batch_size': [10, 50, 100, 500], 'activation': [<function relu at 0x7f738a2ee510>, <function elu at 0x7f738a2bd1e0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033730>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f73540339d8>], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'batch_norm_momentum': [0.9, 0.95, 0.98, 0.99, 0.999], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160]},\n",
        "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
        "          return_train_score='warn', scoring=None, verbose=2)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -13834,7 +14184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -13847,7 +14197,7 @@
        " 'n_neurons': 160}"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -13858,16 +14208,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9937731076084841"
+       "0.9949406499318934"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -13907,16 +14257,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.996469077680291"
+       "0.9990013553035166"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -13935,70 +14285,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.190160\t최선의 손실: 0.190160\t정확도: 95.47%\n",
-      "1\t검증 세트 손실: 0.113355\t최선의 손실: 0.113355\t정확도: 96.95%\n",
-      "2\t검증 세트 손실: 0.110128\t최선의 손실: 0.110128\t정확도: 96.79%\n",
-      "3\t검증 세트 손실: 0.101350\t최선의 손실: 0.101350\t정확도: 97.34%\n",
-      "4\t검증 세트 손실: 0.102447\t최선의 손실: 0.101350\t정확도: 97.46%\n",
-      "5\t검증 세트 손실: 0.085108\t최선의 손실: 0.085108\t정확도: 97.89%\n",
-      "6\t검증 세트 손실: 0.086930\t최선의 손실: 0.085108\t정확도: 97.81%\n",
-      "7\t검증 세트 손실: 0.089716\t최선의 손실: 0.085108\t정확도: 98.01%\n",
-      "8\t검증 세트 손실: 0.077209\t최선의 손실: 0.077209\t정확도: 97.97%\n",
-      "9\t검증 세트 손실: 0.097602\t최선의 손실: 0.077209\t정확도: 97.54%\n",
-      "10\t검증 세트 손실: 0.077463\t최선의 손실: 0.077209\t정확도: 97.69%\n",
-      "11\t검증 세트 손실: 0.073117\t최선의 손실: 0.073117\t정확도: 98.01%\n",
-      "12\t검증 세트 손실: 0.083305\t최선의 손실: 0.073117\t정확도: 97.93%\n",
-      "13\t검증 세트 손실: 0.072572\t최선의 손실: 0.072572\t정확도: 98.20%\n",
-      "14\t검증 세트 손실: 0.076066\t최선의 손실: 0.072572\t정확도: 98.05%\n",
-      "15\t검증 세트 손실: 0.079173\t최선의 손실: 0.072572\t정확도: 97.89%\n",
-      "16\t검증 세트 손실: 0.071619\t최선의 손실: 0.071619\t정확도: 98.12%\n",
-      "17\t검증 세트 손실: 0.076702\t최선의 손실: 0.071619\t정확도: 97.93%\n",
-      "18\t검증 세트 손실: 0.077009\t최선의 손실: 0.071619\t정확도: 98.24%\n",
-      "19\t검증 세트 손실: 0.074993\t최선의 손실: 0.071619\t정확도: 98.24%\n",
-      "20\t검증 세트 손실: 0.067138\t최선의 손실: 0.067138\t정확도: 98.16%\n",
-      "21\t검증 세트 손실: 0.072849\t최선의 손실: 0.067138\t정확도: 98.01%\n",
-      "22\t검증 세트 손실: 0.077277\t최선의 손실: 0.067138\t정확도: 97.97%\n",
-      "23\t검증 세트 손실: 0.071385\t최선의 손실: 0.067138\t정확도: 98.08%\n",
-      "24\t검증 세트 손실: 0.075539\t최선의 손실: 0.067138\t정확도: 98.12%\n",
-      "25\t검증 세트 손실: 0.070027\t최선의 손실: 0.067138\t정확도: 98.24%\n",
-      "26\t검증 세트 손실: 0.105584\t최선의 손실: 0.067138\t정확도: 98.01%\n",
-      "27\t검증 세트 손실: 0.082378\t최선의 손실: 0.067138\t정확도: 98.44%\n",
-      "28\t검증 세트 손실: 0.080284\t최선의 손실: 0.067138\t정확도: 97.77%\n",
-      "29\t검증 세트 손실: 0.107237\t최선의 손실: 0.067138\t정확도: 98.01%\n",
-      "30\t검증 세트 손실: 0.092187\t최선의 손실: 0.067138\t정확도: 97.73%\n",
-      "31\t검증 세트 손실: 0.145398\t최선의 손실: 0.067138\t정확도: 97.15%\n",
-      "32\t검증 세트 손실: 0.125302\t최선의 손실: 0.067138\t정확도: 96.76%\n",
-      "33\t검증 세트 손실: 0.121177\t최선의 손실: 0.067138\t정확도: 95.86%\n",
-      "34\t검증 세트 손실: 0.101141\t최선의 손실: 0.067138\t정확도: 97.22%\n",
-      "35\t검증 세트 손실: 0.101801\t최선의 손실: 0.067138\t정확도: 97.34%\n",
-      "36\t검증 세트 손실: 0.128745\t최선의 손실: 0.067138\t정확도: 96.87%\n",
-      "37\t검증 세트 손실: 0.112541\t최선의 손실: 0.067138\t정확도: 96.76%\n",
-      "38\t검증 세트 손실: 0.111483\t최선의 손실: 0.067138\t정확도: 96.91%\n",
-      "39\t검증 세트 손실: 0.117927\t최선의 손실: 0.067138\t정확도: 96.68%\n",
-      "40\t검증 세트 손실: 0.106274\t최선의 손실: 0.067138\t정확도: 97.26%\n",
-      "41\t검증 세트 손실: 0.098010\t최선의 손실: 0.067138\t정확도: 97.34%\n",
+      "0\t검증 세트 손실: 0.164877\t최선의 손실: 0.164877\t정확도: 95.07%\n",
+      "1\t검증 세트 손실: 0.125693\t최선의 손실: 0.125693\t정확도: 96.60%\n",
+      "2\t검증 세트 손실: 0.112100\t최선의 손실: 0.112100\t정확도: 97.03%\n",
+      "3\t검증 세트 손실: 0.106780\t최선의 손실: 0.106780\t정확도: 97.07%\n",
+      "4\t검증 세트 손실: 0.099562\t최선의 손실: 0.099562\t정확도: 97.58%\n",
+      "5\t검증 세트 손실: 0.081377\t최선의 손실: 0.081377\t정확도: 97.65%\n",
+      "6\t검증 세트 손실: 0.086977\t최선의 손실: 0.081377\t정확도: 97.89%\n",
+      "7\t검증 세트 손실: 0.099665\t최선의 손실: 0.081377\t정확도: 97.69%\n",
+      "8\t검증 세트 손실: 0.076637\t최선의 손실: 0.076637\t정확도: 97.81%\n",
+      "9\t검증 세트 손실: 0.085823\t최선의 손실: 0.076637\t정확도: 97.69%\n",
+      "10\t검증 세트 손실: 0.080536\t최선의 손실: 0.076637\t정확도: 98.12%\n",
+      "11\t검증 세트 손실: 0.075929\t최선의 손실: 0.075929\t정확도: 98.01%\n",
+      "12\t검증 세트 손실: 0.071711\t최선의 손실: 0.071711\t정확도: 98.20%\n",
+      "13\t검증 세트 손실: 0.071252\t최선의 손실: 0.071252\t정확도: 97.93%\n",
+      "14\t검증 세트 손실: 0.072574\t최선의 손실: 0.071252\t정확도: 98.01%\n",
+      "15\t검증 세트 손실: 0.074046\t최선의 손실: 0.071252\t정확도: 98.24%\n",
+      "16\t검증 세트 손실: 0.081158\t최선의 손실: 0.071252\t정확도: 98.08%\n",
+      "17\t검증 세트 손실: 0.063611\t최선의 손실: 0.063611\t정확도: 98.24%\n",
+      "18\t검증 세트 손실: 0.074846\t최선의 손실: 0.063611\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.067766\t최선의 손실: 0.063611\t정확도: 98.48%\n",
+      "20\t검증 세트 손실: 0.067653\t최선의 손실: 0.063611\t정확도: 98.12%\n",
+      "21\t검증 세트 손실: 0.067341\t최선의 손실: 0.063611\t정확도: 98.32%\n",
+      "22\t검증 세트 손실: 0.069543\t최선의 손실: 0.063611\t정확도: 98.28%\n",
+      "23\t검증 세트 손실: 0.068828\t최선의 손실: 0.063611\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.066190\t최선의 손실: 0.063611\t정확도: 98.32%\n",
+      "25\t검증 세트 손실: 0.068393\t최선의 손실: 0.063611\t정확도: 98.20%\n",
+      "26\t검증 세트 손실: 0.065304\t최선의 손실: 0.063611\t정확도: 98.28%\n",
+      "27\t검증 세트 손실: 0.068476\t최선의 손실: 0.063611\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 0.064938\t최선의 손실: 0.063611\t정확도: 98.28%\n",
+      "29\t검증 세트 손실: 0.073792\t최선의 손실: 0.063611\t정확도: 98.44%\n",
+      "30\t검증 세트 손실: 0.057035\t최선의 손실: 0.057035\t정확도: 98.51%\n",
+      "31\t검증 세트 손실: 0.096908\t최선의 손실: 0.057035\t정확도: 97.38%\n",
+      "32\t검증 세트 손실: 0.092149\t최선의 손실: 0.057035\t정확도: 98.01%\n",
+      "33\t검증 세트 손실: 0.072100\t최선의 손실: 0.057035\t정확도: 98.28%\n",
+      "34\t검증 세트 손실: 0.073842\t최선의 손실: 0.057035\t정확도: 98.05%\n",
+      "35\t검증 세트 손실: 0.096235\t최선의 손실: 0.057035\t정확도: 97.38%\n",
+      "36\t검증 세트 손실: 0.116559\t최선의 손실: 0.057035\t정확도: 97.73%\n",
+      "37\t검증 세트 손실: 0.085090\t최선의 손실: 0.057035\t정확도: 98.01%\n",
+      "38\t검증 세트 손실: 0.083896\t최선의 손실: 0.057035\t정확도: 98.05%\n",
+      "39\t검증 세트 손실: 0.082442\t최선의 손실: 0.057035\t정확도: 98.20%\n",
+      "40\t검증 세트 손실: 0.072432\t최선의 손실: 0.057035\t정확도: 98.20%\n",
+      "41\t검증 세트 손실: 0.068573\t최선의 손실: 0.057035\t정확도: 98.05%\n",
+      "42\t검증 세트 손실: 0.066772\t최선의 손실: 0.057035\t정확도: 98.24%\n",
+      "43\t검증 세트 손실: 0.070165\t최선의 손실: 0.057035\t정확도: 98.24%\n",
+      "44\t검증 세트 손실: 0.068059\t최선의 손실: 0.057035\t정확도: 98.20%\n",
+      "45\t검증 세트 손실: 0.073538\t최선의 손실: 0.057035\t정확도: 97.77%\n",
+      "46\t검증 세트 손실: 0.120571\t최선의 손실: 0.057035\t정확도: 97.38%\n",
+      "47\t검증 세트 손실: 0.147346\t최선의 손실: 0.057035\t정확도: 96.56%\n",
+      "48\t검증 세트 손실: 0.221838\t최선의 손실: 0.057035\t정확도: 92.53%\n",
+      "49\t검증 세트 손실: 0.208795\t최선의 손실: 0.057035\t정확도: 92.92%\n",
+      "50\t검증 세트 손실: 0.134298\t최선의 손실: 0.057035\t정확도: 95.97%\n",
+      "51\t검증 세트 손실: 0.140104\t최선의 손실: 0.057035\t정확도: 96.01%\n",
       "조기 종료!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f9906d56488>,\n",
+       "DNNClassifier(activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f7354033f28>,\n",
        "       batch_norm_momentum=None, batch_size=500, dropout_rate=0.5,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=5, n_neurons=90,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14026,16 +14386,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9861840825063242"
+       "0.9881299863786729"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -14054,7 +14414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -14062,4533 +14422,4226 @@
      "output_type": "stream",
      "text": [
       "Fitting 3 folds for each of 50 candidates, totalling 150 fits\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.342959\t최선의 손실: 0.342959\t정확도: 91.67%\n",
-      "1\t검증 세트 손실: 0.238329\t최선의 손실: 0.238329\t정확도: 93.82%\n",
-      "2\t검증 세트 손실: 0.276836\t최선의 손실: 0.238329\t정확도: 94.41%\n",
-      "3\t검증 세트 손실: 0.215423\t최선의 손실: 0.215423\t정확도: 94.72%\n",
-      "4\t검증 세트 손실: 0.228305\t최선의 손실: 0.215423\t정확도: 95.11%\n",
-      "5\t검증 세트 손실: 0.198798\t최선의 손실: 0.198798\t정확도: 95.19%\n",
-      "6\t검증 세트 손실: 0.193895\t최선의 손실: 0.193895\t정확도: 96.01%\n",
-      "7\t검증 세트 손실: 0.179902\t최선의 손실: 0.179902\t정확도: 95.70%\n",
-      "8\t검증 세트 손실: 0.189713\t최선의 손실: 0.179902\t정확도: 95.90%\n",
-      "9\t검증 세트 손실: 0.170023\t최선의 손실: 0.170023\t정확도: 95.78%\n",
-      "10\t검증 세트 손실: 0.208070\t최선의 손실: 0.170023\t정확도: 95.50%\n",
-      "11\t검증 세트 손실: 0.209531\t최선의 손실: 0.170023\t정확도: 94.96%\n",
-      "12\t검증 세트 손실: 0.213311\t최선의 손실: 0.170023\t정확도: 95.43%\n",
-      "13\t검증 세트 손실: 0.216316\t최선의 손실: 0.170023\t정확도: 94.96%\n",
-      "14\t검증 세트 손실: 0.248793\t최선의 손실: 0.170023\t정확도: 95.19%\n",
-      "15\t검증 세트 손실: 0.191311\t최선의 손실: 0.170023\t정확도: 95.19%\n",
-      "16\t검증 세트 손실: 0.200811\t최선의 손실: 0.170023\t정확도: 95.43%\n",
-      "17\t검증 세트 손실: 0.173225\t최선의 손실: 0.170023\t정확도: 96.17%\n",
-      "18\t검증 세트 손실: 0.200601\t최선의 손실: 0.170023\t정확도: 96.25%\n",
-      "19\t검증 세트 손실: 0.160905\t최선의 손실: 0.160905\t정확도: 95.97%\n",
-      "20\t검증 세트 손실: 0.194954\t최선의 손실: 0.160905\t정확도: 95.78%\n",
-      "21\t검증 세트 손실: 0.179598\t최선의 손실: 0.160905\t정확도: 95.27%\n",
-      "22\t검증 세트 손실: 0.190249\t최선의 손실: 0.160905\t정확도: 95.43%\n",
-      "23\t검증 세트 손실: 0.189648\t최선의 손실: 0.160905\t정확도: 95.58%\n",
-      "24\t검증 세트 손실: 0.179043\t최선의 손실: 0.160905\t정확도: 95.78%\n",
-      "25\t검증 세트 손실: 0.198513\t최선의 손실: 0.160905\t정확도: 95.66%\n",
-      "26\t검증 세트 손실: 0.169970\t최선의 손실: 0.160905\t정확도: 96.21%\n",
-      "27\t검증 세트 손실: 0.170456\t최선의 손실: 0.160905\t정확도: 95.70%\n",
-      "28\t검증 세트 손실: 0.207471\t최선의 손실: 0.160905\t정확도: 95.66%\n",
-      "29\t검증 세트 손실: 0.198148\t최선의 손실: 0.160905\t정확도: 95.86%\n",
-      "30\t검증 세트 손실: 0.177192\t최선의 손실: 0.160905\t정확도: 95.78%\n",
-      "31\t검증 세트 손실: 0.195275\t최선의 손실: 0.160905\t정확도: 95.43%\n",
-      "32\t검증 세트 손실: 0.181674\t최선의 손실: 0.160905\t정확도: 95.82%\n",
-      "33\t검증 세트 손실: 0.184905\t최선의 손실: 0.160905\t정확도: 95.86%\n",
-      "34\t검증 세트 손실: 0.182824\t최선의 손실: 0.160905\t정확도: 95.47%\n",
-      "35\t검증 세트 손실: 0.182966\t최선의 손실: 0.160905\t정확도: 95.86%\n",
-      "36\t검증 세트 손실: 0.183181\t최선의 손실: 0.160905\t정확도: 95.78%\n",
-      "37\t검증 세트 손실: 0.175387\t최선의 손실: 0.160905\t정확도: 95.58%\n",
-      "38\t검증 세트 손실: 0.182551\t최선의 손실: 0.160905\t정확도: 95.93%\n",
-      "39\t검증 세트 손실: 0.200725\t최선의 손실: 0.160905\t정확도: 95.62%\n",
-      "40\t검증 세트 손실: 0.174952\t최선의 손실: 0.160905\t정확도: 95.90%\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.642312\t최선의 손실: 0.642312\t정확도: 74.90%\n",
+      "1\t검증 세트 손실: 0.246857\t최선의 손실: 0.246857\t정확도: 94.33%\n",
+      "2\t검증 세트 손실: 0.235708\t최선의 손실: 0.235708\t정확도: 94.88%\n",
+      "3\t검증 세트 손실: 0.193189\t최선의 손실: 0.193189\t정확도: 95.27%\n",
+      "4\t검증 세트 손실: 0.217870\t최선의 손실: 0.193189\t정확도: 95.35%\n",
+      "5\t검증 세트 손실: 0.203067\t최선의 손실: 0.193189\t정확도: 95.47%\n",
+      "6\t검증 세트 손실: 0.182191\t최선의 손실: 0.182191\t정확도: 96.01%\n",
+      "7\t검증 세트 손실: 0.185686\t최선의 손실: 0.182191\t정확도: 95.74%\n",
+      "8\t검증 세트 손실: 0.222046\t최선의 손실: 0.182191\t정확도: 95.50%\n",
+      "9\t검증 세트 손실: 0.196364\t최선의 손실: 0.182191\t정확도: 95.54%\n",
+      "10\t검증 세트 손실: 0.214568\t최선의 손실: 0.182191\t정확도: 94.84%\n",
+      "11\t검증 세트 손실: 0.190757\t최선의 손실: 0.182191\t정확도: 95.66%\n",
+      "12\t검증 세트 손실: 0.265067\t최선의 손실: 0.182191\t정확도: 94.80%\n",
+      "13\t검증 세트 손실: 0.224544\t최선의 손실: 0.182191\t정확도: 95.39%\n",
+      "14\t검증 세트 손실: 0.202190\t최선의 손실: 0.182191\t정확도: 95.70%\n",
+      "15\t검증 세트 손실: 0.176293\t최선의 손실: 0.176293\t정확도: 95.47%\n",
+      "16\t검증 세트 손실: 0.209432\t최선의 손실: 0.176293\t정확도: 95.23%\n",
+      "17\t검증 세트 손실: 0.248119\t최선의 손실: 0.176293\t정확도: 94.76%\n",
+      "18\t검증 세트 손실: 0.198097\t최선의 손실: 0.176293\t정확도: 95.86%\n",
+      "19\t검증 세트 손실: 0.189158\t최선의 손실: 0.176293\t정확도: 95.74%\n",
+      "20\t검증 세트 손실: 0.195897\t최선의 손실: 0.176293\t정확도: 95.39%\n",
+      "21\t검증 세트 손실: 0.217057\t최선의 손실: 0.176293\t정확도: 95.97%\n",
+      "22\t검증 세트 손실: 0.216957\t최선의 손실: 0.176293\t정확도: 95.31%\n",
+      "23\t검증 세트 손실: 0.194688\t최선의 손실: 0.176293\t정확도: 95.58%\n",
+      "24\t검증 세트 손실: 0.241097\t최선의 손실: 0.176293\t정확도: 96.13%\n",
+      "25\t검증 세트 손실: 0.216482\t최선의 손실: 0.176293\t정확도: 95.15%\n",
+      "26\t검증 세트 손실: 0.187766\t최선의 손실: 0.176293\t정확도: 95.43%\n",
+      "27\t검증 세트 손실: 0.160965\t최선의 손실: 0.160965\t정확도: 96.33%\n",
+      "28\t검증 세트 손실: 0.206022\t최선의 손실: 0.160965\t정확도: 95.70%\n",
+      "29\t검증 세트 손실: 0.231958\t최선의 손실: 0.160965\t정확도: 95.23%\n",
+      "30\t검증 세트 손실: 0.202318\t최선의 손실: 0.160965\t정확도: 95.86%\n",
+      "31\t검증 세트 손실: 0.170422\t최선의 손실: 0.160965\t정확도: 96.36%\n",
+      "32\t검증 세트 손실: 0.180134\t최선의 손실: 0.160965\t정확도: 96.13%\n",
+      "33\t검증 세트 손실: 0.187408\t최선의 손실: 0.160965\t정확도: 95.86%\n",
+      "34\t검증 세트 손실: 0.172766\t최선의 손실: 0.160965\t정확도: 96.21%\n",
+      "35\t검증 세트 손실: 0.211479\t최선의 손실: 0.160965\t정확도: 95.70%\n",
+      "36\t검증 세트 손실: 0.187087\t최선의 손실: 0.160965\t정확도: 95.86%\n",
+      "37\t검증 세트 손실: 0.173479\t최선의 손실: 0.160965\t정확도: 95.70%\n",
+      "38\t검증 세트 손실: 0.181360\t최선의 손실: 0.160965\t정확도: 95.97%\n",
+      "39\t검증 세트 손실: 0.221640\t최선의 손실: 0.160965\t정확도: 96.01%\n",
+      "40\t검증 세트 손실: 0.203683\t최선의 손실: 0.160965\t정확도: 96.21%\n",
+      "41\t검증 세트 손실: 0.184147\t최선의 손실: 0.160965\t정확도: 95.19%\n",
+      "42\t검증 세트 손실: 0.189295\t최선의 손실: 0.160965\t정확도: 95.90%\n",
+      "43\t검증 세트 손실: 0.178151\t최선의 손실: 0.160965\t정확도: 96.21%\n",
+      "44\t검증 세트 손실: 0.185048\t최선의 손실: 0.160965\t정확도: 96.33%\n",
+      "45\t검증 세트 손실: 0.192258\t최선의 손실: 0.160965\t정확도: 96.29%\n",
+      "46\t검증 세트 손실: 0.179968\t최선의 손실: 0.160965\t정확도: 96.25%\n",
+      "47\t검증 세트 손실: 0.184349\t최선의 손실: 0.160965\t정확도: 96.25%\n",
+      "48\t검증 세트 손실: 0.170941\t최선의 손실: 0.160965\t정확도: 96.56%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=70, total=  10.1s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=70 \n"
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=70, total=  12.9s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=70 \n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   10.1s remaining:    0.0s\n"
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   13.0s remaining:    0.0s\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.354363\t최선의 손실: 0.354363\t정확도: 89.95%\n",
-      "1\t검증 세트 손실: 0.273731\t최선의 손실: 0.273731\t정확도: 93.12%\n",
-      "2\t검증 세트 손실: 0.219755\t최선의 손실: 0.219755\t정확도: 94.96%\n",
-      "3\t검증 세트 손실: 0.268855\t최선의 손실: 0.219755\t정확도: 94.02%\n",
-      "4\t검증 세트 손실: 0.225182\t최선의 손실: 0.219755\t정확도: 95.47%\n",
-      "5\t검증 세트 손실: 0.224989\t최선의 손실: 0.219755\t정확도: 95.11%\n",
-      "6\t검증 세트 손실: 0.200391\t최선의 손실: 0.200391\t정확도: 96.09%\n",
-      "7\t검증 세트 손실: 0.191552\t최선의 손실: 0.191552\t정확도: 95.50%\n",
-      "8\t검증 세트 손실: 0.186297\t최선의 손실: 0.186297\t정확도: 95.86%\n",
-      "9\t검증 세트 손실: 0.192538\t최선의 손실: 0.186297\t정확도: 95.90%\n",
-      "10\t검증 세트 손실: 0.190730\t최선의 손실: 0.186297\t정확도: 95.74%\n",
-      "11\t검증 세트 손실: 0.205465\t최선의 손실: 0.186297\t정확도: 95.50%\n",
-      "12\t검증 세트 손실: 0.213689\t최선의 손실: 0.186297\t정확도: 95.47%\n",
-      "13\t검증 세트 손실: 0.200818\t최선의 손실: 0.186297\t정확도: 95.97%\n",
-      "14\t검증 세트 손실: 0.178351\t최선의 손실: 0.178351\t정확도: 95.86%\n",
-      "15\t검증 세트 손실: 0.223087\t최선의 손실: 0.178351\t정확도: 95.74%\n",
-      "16\t검증 세트 손실: 0.224053\t최선의 손실: 0.178351\t정확도: 96.05%\n",
-      "17\t검증 세트 손실: 0.284814\t최선의 손실: 0.178351\t정확도: 95.35%\n",
-      "18\t검증 세트 손실: 0.163524\t최선의 손실: 0.163524\t정확도: 95.54%\n",
-      "19\t검증 세트 손실: 0.163809\t최선의 손실: 0.163524\t정확도: 96.13%\n",
-      "20\t검증 세트 손실: 0.172397\t최선의 손실: 0.163524\t정확도: 96.48%\n",
-      "21\t검증 세트 손실: 0.174707\t최선의 손실: 0.163524\t정확도: 96.36%\n",
-      "22\t검증 세트 손실: 0.165645\t최선의 손실: 0.163524\t정확도: 95.93%\n",
-      "23\t검증 세트 손실: 0.241909\t최선의 손실: 0.163524\t정확도: 94.68%\n",
-      "24\t검증 세트 손실: 0.154047\t최선의 손실: 0.154047\t정확도: 96.25%\n",
-      "25\t검증 세트 손실: 0.167390\t최선의 손실: 0.154047\t정확도: 96.21%\n",
-      "26\t검증 세트 손실: 0.171698\t최선의 손실: 0.154047\t정확도: 96.09%\n",
-      "27\t검증 세트 손실: 0.155617\t최선의 손실: 0.154047\t정확도: 96.40%\n",
-      "28\t검증 세트 손실: 0.168931\t최선의 손실: 0.154047\t정확도: 96.01%\n",
-      "29\t검증 세트 손실: 0.179614\t최선의 손실: 0.154047\t정확도: 95.93%\n",
-      "30\t검증 세트 손실: 0.216352\t최선의 손실: 0.154047\t정확도: 96.48%\n",
-      "31\t검증 세트 손실: 0.199618\t최선의 손실: 0.154047\t정확도: 96.25%\n",
-      "32\t검증 세트 손실: 0.177004\t최선의 손실: 0.154047\t정확도: 95.90%\n",
-      "33\t검증 세트 손실: 0.162902\t최선의 손실: 0.154047\t정확도: 96.52%\n",
-      "34\t검증 세트 손실: 0.159733\t최선의 손실: 0.154047\t정확도: 96.56%\n",
-      "35\t검증 세트 손실: 0.165924\t최선의 손실: 0.154047\t정확도: 96.56%\n",
-      "36\t검증 세트 손실: 0.195400\t최선의 손실: 0.154047\t정확도: 96.13%\n",
-      "37\t검증 세트 손실: 0.197096\t최선의 손실: 0.154047\t정확도: 96.25%\n",
-      "38\t검증 세트 손실: 0.149821\t최선의 손실: 0.149821\t정확도: 96.33%\n",
-      "39\t검증 세트 손실: 0.231647\t최선의 손실: 0.149821\t정확도: 96.56%\n",
-      "40\t검증 세트 손실: 0.176854\t최선의 손실: 0.149821\t정확도: 96.01%\n",
-      "41\t검증 세트 손실: 0.192419\t최선의 손실: 0.149821\t정확도: 96.09%\n",
-      "42\t검증 세트 손실: 0.170873\t최선의 손실: 0.149821\t정확도: 96.13%\n",
-      "43\t검증 세트 손실: 0.154569\t최선의 손실: 0.149821\t정확도: 96.40%\n",
-      "44\t검증 세트 손실: 0.161002\t최선의 손실: 0.149821\t정확도: 96.01%\n",
-      "45\t검증 세트 손실: 0.156681\t최선의 손실: 0.149821\t정확도: 96.64%\n",
-      "46\t검증 세트 손실: 0.178007\t최선의 손실: 0.149821\t정확도: 96.13%\n",
-      "47\t검증 세트 손실: 0.170809\t최선의 손실: 0.149821\t정확도: 95.97%\n",
-      "48\t검증 세트 손실: 0.431150\t최선의 손실: 0.149821\t정확도: 80.53%\n",
-      "49\t검증 세트 손실: 0.177418\t최선의 손실: 0.149821\t정확도: 96.33%\n",
-      "50\t검증 세트 손실: 0.181807\t최선의 손실: 0.149821\t정확도: 96.44%\n",
-      "51\t검증 세트 손실: 0.177141\t최선의 손실: 0.149821\t정확도: 96.09%\n",
-      "52\t검증 세트 손실: 0.184722\t최선의 손실: 0.149821\t정확도: 95.93%\n",
-      "53\t검증 세트 손실: 0.210054\t최선의 손실: 0.149821\t정확도: 95.78%\n",
-      "54\t검증 세트 손실: 0.195354\t최선의 손실: 0.149821\t정확도: 95.50%\n",
-      "55\t검증 세트 손실: 0.188747\t최선의 손실: 0.149821\t정확도: 96.05%\n",
-      "56\t검증 세트 손실: 0.216960\t최선의 손실: 0.149821\t정확도: 96.13%\n",
-      "57\t검증 세트 손실: 0.182140\t최선의 손실: 0.149821\t정확도: 95.93%\n",
-      "58\t검증 세트 손실: 0.200259\t최선의 손실: 0.149821\t정확도: 95.58%\n",
-      "59\t검증 세트 손실: 0.195092\t최선의 손실: 0.149821\t정확도: 95.27%\n",
+      "0\t검증 세트 손실: 0.532325\t최선의 손실: 0.532325\t정확도: 80.22%\n",
+      "1\t검증 세트 손실: 0.393442\t최선의 손실: 0.393442\t정확도: 90.46%\n",
+      "2\t검증 세트 손실: 0.259364\t최선의 손실: 0.259364\t정확도: 93.82%\n",
+      "3\t검증 세트 손실: 0.235185\t최선의 손실: 0.235185\t정확도: 93.63%\n",
+      "4\t검증 세트 손실: 0.265762\t최선의 손실: 0.235185\t정확도: 94.64%\n",
+      "5\t검증 세트 손실: 0.240393\t최선의 손실: 0.235185\t정확도: 93.82%\n",
+      "6\t검증 세트 손실: 0.246490\t최선의 손실: 0.235185\t정확도: 94.06%\n",
+      "7\t검증 세트 손실: 0.171823\t최선의 손실: 0.171823\t정확도: 95.35%\n",
+      "8\t검증 세트 손실: 0.178645\t최선의 손실: 0.171823\t정확도: 95.90%\n",
+      "9\t검증 세트 손실: 0.177762\t최선의 손실: 0.171823\t정확도: 95.74%\n",
+      "10\t검증 세트 손실: 0.169223\t최선의 손실: 0.169223\t정확도: 95.93%\n",
+      "11\t검증 세트 손실: 0.151553\t최선의 손실: 0.151553\t정확도: 96.64%\n",
+      "12\t검증 세트 손실: 0.185944\t최선의 손실: 0.151553\t정확도: 95.70%\n",
+      "13\t검증 세트 손실: 0.178171\t최선의 손실: 0.151553\t정확도: 95.70%\n",
+      "14\t검증 세트 손실: 0.179702\t최선의 손실: 0.151553\t정확도: 96.21%\n",
+      "15\t검증 세트 손실: 0.173852\t최선의 손실: 0.151553\t정확도: 95.43%\n",
+      "16\t검증 세트 손실: 0.181027\t최선의 손실: 0.151553\t정확도: 95.82%\n",
+      "17\t검증 세트 손실: 0.190215\t최선의 손실: 0.151553\t정확도: 95.54%\n",
+      "18\t검증 세트 손실: 0.156775\t최선의 손실: 0.151553\t정확도: 95.78%\n",
+      "19\t검증 세트 손실: 0.202137\t최선의 손실: 0.151553\t정확도: 96.56%\n",
+      "20\t검증 세트 손실: 0.204871\t최선의 손실: 0.151553\t정확도: 95.23%\n",
+      "21\t검증 세트 손실: 0.158161\t최선의 손실: 0.151553\t정확도: 96.87%\n",
+      "22\t검증 세트 손실: 0.170110\t최선의 손실: 0.151553\t정확도: 96.56%\n",
+      "23\t검증 세트 손실: 0.178517\t최선의 손실: 0.151553\t정확도: 96.33%\n",
+      "24\t검증 세트 손실: 0.163038\t최선의 손실: 0.151553\t정확도: 96.44%\n",
+      "25\t검증 세트 손실: 0.164890\t최선의 손실: 0.151553\t정확도: 96.56%\n",
+      "26\t검증 세트 손실: 0.157295\t최선의 손실: 0.151553\t정확도: 96.52%\n",
+      "27\t검증 세트 손실: 0.175615\t최선의 손실: 0.151553\t정확도: 96.25%\n",
+      "28\t검증 세트 손실: 0.157094\t최선의 손실: 0.151553\t정확도: 96.21%\n",
+      "29\t검증 세트 손실: 0.155013\t최선의 손실: 0.151553\t정확도: 96.33%\n",
+      "30\t검증 세트 손실: 0.186913\t최선의 손실: 0.151553\t정확도: 95.74%\n",
+      "31\t검증 세트 손실: 0.181908\t최선의 손실: 0.151553\t정확도: 96.25%\n",
+      "32\t검증 세트 손실: 0.155025\t최선의 손실: 0.151553\t정확도: 95.74%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=70, total=  19.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.394946\t최선의 손실: 0.394946\t정확도: 90.27%\n",
-      "1\t검증 세트 손실: 0.259836\t최선의 손실: 0.259836\t정확도: 93.67%\n",
-      "2\t검증 세트 손실: 0.267343\t최선의 손실: 0.259836\t정확도: 94.06%\n",
-      "3\t검증 세트 손실: 0.197862\t최선의 손실: 0.197862\t정확도: 95.62%\n",
-      "4\t검증 세트 손실: 0.205904\t최선의 손실: 0.197862\t정확도: 95.19%\n",
-      "5\t검증 세트 손실: 0.170086\t최선의 손실: 0.170086\t정확도: 95.78%\n",
-      "6\t검증 세트 손실: 0.221753\t최선의 손실: 0.170086\t정확도: 95.11%\n",
-      "7\t검증 세트 손실: 0.196870\t최선의 손실: 0.170086\t정확도: 95.47%\n",
-      "8\t검증 세트 손실: 0.227455\t최선의 손실: 0.170086\t정확도: 95.31%\n",
-      "9\t검증 세트 손실: 0.193996\t최선의 손실: 0.170086\t정확도: 95.82%\n",
-      "10\t검증 세트 손실: 0.191451\t최선의 손실: 0.170086\t정확도: 95.82%\n",
-      "11\t검증 세트 손실: 0.165677\t최선의 손실: 0.165677\t정확도: 96.13%\n",
-      "12\t검증 세트 손실: 0.188638\t최선의 손실: 0.165677\t정확도: 95.70%\n",
-      "13\t검증 세트 손실: 0.169094\t최선의 손실: 0.165677\t정확도: 96.13%\n",
-      "14\t검증 세트 손실: 0.170140\t최선의 손실: 0.165677\t정확도: 96.25%\n",
-      "15\t검증 세트 손실: 0.170142\t최선의 손실: 0.165677\t정확도: 96.52%\n",
-      "16\t검증 세트 손실: 0.165996\t최선의 손실: 0.165677\t정확도: 96.05%\n",
-      "17\t검증 세트 손실: 0.211660\t최선의 손실: 0.165677\t정확도: 96.13%\n",
-      "18\t검증 세트 손실: 0.176962\t최선의 손실: 0.165677\t정확도: 95.66%\n",
-      "19\t검증 세트 손실: 0.180675\t최선의 손실: 0.165677\t정확도: 96.79%\n",
-      "20\t검증 세트 손실: 0.166449\t최선의 손실: 0.165677\t정확도: 96.01%\n",
-      "21\t검증 세트 손실: 0.163017\t최선의 손실: 0.163017\t정확도: 96.21%\n",
-      "22\t검증 세트 손실: 0.195849\t최선의 손실: 0.163017\t정확도: 96.01%\n",
-      "23\t검증 세트 손실: 0.206219\t최선의 손실: 0.163017\t정확도: 96.05%\n",
-      "24\t검증 세트 손실: 0.160974\t최선의 손실: 0.160974\t정확도: 96.79%\n",
-      "25\t검증 세트 손실: 0.170719\t최선의 손실: 0.160974\t정확도: 96.29%\n",
-      "26\t검증 세트 손실: 0.171445\t최선의 손실: 0.160974\t정확도: 95.86%\n",
-      "27\t검증 세트 손실: 0.175804\t최선의 손실: 0.160974\t정확도: 96.40%\n",
-      "28\t검증 세트 손실: 0.165145\t최선의 손실: 0.160974\t정확도: 96.68%\n",
-      "29\t검증 세트 손실: 0.190371\t최선의 손실: 0.160974\t정확도: 96.68%\n",
-      "30\t검증 세트 손실: 0.199892\t최선의 손실: 0.160974\t정확도: 96.44%\n",
-      "31\t검증 세트 손실: 0.170760\t최선의 손실: 0.160974\t정확도: 96.44%\n",
-      "32\t검증 세트 손실: 0.204451\t최선의 손실: 0.160974\t정확도: 95.62%\n",
-      "33\t검증 세트 손실: 0.215915\t최선의 손실: 0.160974\t정확도: 96.05%\n",
-      "34\t검증 세트 손실: 0.185233\t최선의 손실: 0.160974\t정확도: 96.17%\n",
-      "35\t검증 세트 손실: 0.177305\t최선의 손실: 0.160974\t정확도: 96.83%\n",
-      "36\t검증 세트 손실: 0.206489\t최선의 손실: 0.160974\t정확도: 96.01%\n",
-      "37\t검증 세트 손실: 0.209268\t최선의 손실: 0.160974\t정확도: 96.21%\n",
-      "38\t검증 세트 손실: 0.193958\t최선의 손실: 0.160974\t정확도: 96.29%\n",
-      "39\t검증 세트 손실: 0.193979\t최선의 손실: 0.160974\t정확도: 96.01%\n",
-      "40\t검증 세트 손실: 0.180942\t최선의 손실: 0.160974\t정확도: 96.60%\n",
-      "41\t검증 세트 손실: 0.158732\t최선의 손실: 0.158732\t정확도: 96.36%\n",
-      "42\t검증 세트 손실: 0.179712\t최선의 손실: 0.158732\t정확도: 95.70%\n",
-      "43\t검증 세트 손실: 0.177307\t최선의 손실: 0.158732\t정확도: 95.78%\n",
-      "44\t검증 세트 손실: 0.163965\t최선의 손실: 0.158732\t정확도: 96.05%\n",
-      "45\t검증 세트 손실: 0.201136\t최선의 손실: 0.158732\t정확도: 96.09%\n",
-      "46\t검증 세트 손실: 0.210077\t최선의 손실: 0.158732\t정확도: 96.44%\n",
-      "47\t검증 세트 손실: 0.163544\t최선의 손실: 0.158732\t정확도: 96.68%\n",
-      "48\t검증 세트 손실: 0.181422\t최선의 손실: 0.158732\t정확도: 96.33%\n",
-      "49\t검증 세트 손실: 0.154855\t최선의 손실: 0.154855\t정확도: 96.87%\n",
-      "50\t검증 세트 손실: 0.193843\t최선의 손실: 0.154855\t정확도: 96.56%\n",
-      "51\t검증 세트 손실: 0.199003\t최선의 손실: 0.154855\t정확도: 96.48%\n",
-      "52\t검증 세트 손실: 0.181553\t최선의 손실: 0.154855\t정확도: 96.05%\n",
-      "53\t검증 세트 손실: 0.165555\t최선의 손실: 0.154855\t정확도: 96.33%\n",
-      "54\t검증 세트 손실: 0.160786\t최선의 손실: 0.154855\t정확도: 96.13%\n",
-      "55\t검증 세트 손실: 0.197541\t최선의 손실: 0.154855\t정확도: 95.93%\n",
-      "56\t검증 세트 손실: 0.185540\t최선의 손실: 0.154855\t정확도: 96.52%\n",
-      "57\t검증 세트 손실: 0.171623\t최선의 손실: 0.154855\t정확도: 96.40%\n",
-      "58\t검증 세트 손실: 0.187168\t최선의 손실: 0.154855\t정확도: 96.56%\n",
-      "59\t검증 세트 손실: 0.158928\t최선의 손실: 0.154855\t정확도: 96.40%\n",
-      "60\t검증 세트 손실: 0.163011\t최선의 손실: 0.154855\t정확도: 96.40%\n",
-      "61\t검증 세트 손실: 0.159616\t최선의 손실: 0.154855\t정확도: 96.68%\n",
-      "62\t검증 세트 손실: 0.196329\t최선의 손실: 0.154855\t정확도: 96.44%\n",
-      "63\t검증 세트 손실: 0.198748\t최선의 손실: 0.154855\t정확도: 96.36%\n",
-      "64\t검증 세트 손실: 0.167288\t최선의 손실: 0.154855\t정확도: 96.56%\n",
-      "65\t검증 세트 손실: 0.183484\t최선의 손실: 0.154855\t정확도: 96.56%\n",
-      "66\t검증 세트 손실: 0.193981\t최선의 손실: 0.154855\t정확도: 96.36%\n",
-      "67\t검증 세트 손실: 0.141206\t최선의 손실: 0.141206\t정확도: 96.95%\n",
-      "68\t검증 세트 손실: 0.193575\t최선의 손실: 0.141206\t정확도: 96.13%\n",
-      "69\t검증 세트 손실: 0.229965\t최선의 손실: 0.141206\t정확도: 96.87%\n",
-      "70\t검증 세트 손실: 0.163258\t최선의 손실: 0.141206\t정확도: 96.87%\n",
-      "71\t검증 세트 손실: 0.175650\t최선의 손실: 0.141206\t정확도: 96.36%\n",
-      "72\t검증 세트 손실: 0.216418\t최선의 손실: 0.141206\t정확도: 96.48%\n",
-      "73\t검증 세트 손실: 0.172447\t최선의 손실: 0.141206\t정확도: 96.33%\n",
-      "74\t검증 세트 손실: 0.174659\t최선의 손실: 0.141206\t정확도: 96.44%\n",
-      "75\t검증 세트 손실: 0.160520\t최선의 손실: 0.141206\t정확도: 96.72%\n",
-      "76\t검증 세트 손실: 0.195645\t최선의 손실: 0.141206\t정확도: 96.09%\n",
-      "77\t검증 세트 손실: 0.165721\t최선의 손실: 0.141206\t정확도: 96.56%\n",
-      "78\t검증 세트 손실: 0.312155\t최선의 손실: 0.141206\t정확도: 94.10%\n",
-      "79\t검증 세트 손실: 0.185278\t최선의 손실: 0.141206\t정확도: 96.60%\n",
-      "80\t검증 세트 손실: 0.199910\t최선의 손실: 0.141206\t정확도: 96.36%\n",
-      "81\t검증 세트 손실: 0.164721\t최선의 손실: 0.141206\t정확도: 96.13%\n",
-      "82\t검증 세트 손실: 0.183062\t최선의 손실: 0.141206\t정확도: 95.70%\n",
-      "83\t검증 세트 손실: 0.254959\t최선의 손실: 0.141206\t정확도: 95.90%\n",
-      "84\t검증 세트 손실: 0.165530\t최선의 손실: 0.141206\t정확도: 97.03%\n",
-      "85\t검증 세트 손실: 0.139198\t최선의 손실: 0.139198\t정확도: 97.15%\n",
-      "86\t검증 세트 손실: 0.182149\t최선의 손실: 0.139198\t정확도: 96.44%\n",
-      "87\t검증 세트 손실: 0.158678\t최선의 손실: 0.139198\t정확도: 96.95%\n",
-      "88\t검증 세트 손실: 0.154007\t최선의 손실: 0.139198\t정확도: 96.95%\n",
-      "89\t검증 세트 손실: 0.153104\t최선의 손실: 0.139198\t정확도: 97.11%\n",
-      "90\t검증 세트 손실: 0.186303\t최선의 손실: 0.139198\t정확도: 96.83%\n",
-      "91\t검증 세트 손실: 0.173144\t최선의 손실: 0.139198\t정확도: 96.76%\n",
-      "92\t검증 세트 손실: 0.150601\t최선의 손실: 0.139198\t정확도: 96.83%\n",
-      "93\t검증 세트 손실: 0.176512\t최선의 손실: 0.139198\t정확도: 96.99%\n",
-      "94\t검증 세트 손실: 0.157839\t최선의 손실: 0.139198\t정확도: 96.83%\n",
-      "95\t검증 세트 손실: 0.187480\t최선의 손실: 0.139198\t정확도: 97.07%\n"
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=70, total=   8.8s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.441622\t최선의 손실: 0.441622\t정확도: 89.17%\n",
+      "1\t검증 세트 손실: 0.271857\t최선의 손실: 0.271857\t정확도: 93.39%\n",
+      "2\t검증 세트 손실: 0.209448\t최선의 손실: 0.209448\t정확도: 94.92%\n",
+      "3\t검증 세트 손실: 0.232165\t최선의 손실: 0.209448\t정확도: 94.53%\n",
+      "4\t검증 세트 손실: 0.208333\t최선의 손실: 0.208333\t정확도: 95.31%\n",
+      "5\t검증 세트 손실: 0.220057\t최선의 손실: 0.208333\t정확도: 95.78%\n",
+      "6\t검증 세트 손실: 0.205971\t최선의 손실: 0.205971\t정확도: 95.07%\n",
+      "7\t검증 세트 손실: 0.217318\t최선의 손실: 0.205971\t정확도: 95.31%\n",
+      "8\t검증 세트 손실: 0.186803\t최선의 손실: 0.186803\t정확도: 95.50%\n",
+      "9\t검증 세트 손실: 0.194400\t최선의 손실: 0.186803\t정확도: 95.82%\n",
+      "10\t검증 세트 손실: 0.201880\t최선의 손실: 0.186803\t정확도: 95.90%\n",
+      "11\t검증 세트 손실: 0.165139\t최선의 손실: 0.165139\t정확도: 95.90%\n",
+      "12\t검증 세트 손실: 0.190738\t최선의 손실: 0.165139\t정확도: 96.09%\n",
+      "13\t검증 세트 손실: 0.180658\t최선의 손실: 0.165139\t정확도: 96.21%\n",
+      "14\t검증 세트 손실: 0.191751\t최선의 손실: 0.165139\t정확도: 95.66%\n",
+      "15\t검증 세트 손실: 0.168989\t최선의 손실: 0.165139\t정확도: 96.40%\n",
+      "16\t검증 세트 손실: 0.190279\t최선의 손실: 0.165139\t정확도: 95.66%\n",
+      "17\t검증 세트 손실: 0.198651\t최선의 손실: 0.165139\t정확도: 96.09%\n",
+      "18\t검증 세트 손실: 0.224099\t최선의 손실: 0.165139\t정확도: 95.90%\n",
+      "19\t검증 세트 손실: 0.193308\t최선의 손실: 0.165139\t정확도: 95.93%\n",
+      "20\t검증 세트 손실: 0.213549\t최선의 손실: 0.165139\t정확도: 95.66%\n",
+      "21\t검증 세트 손실: 0.179633\t최선의 손실: 0.165139\t정확도: 96.01%\n",
+      "22\t검증 세트 손실: 0.171253\t최선의 손실: 0.165139\t정확도: 96.05%\n",
+      "23\t검증 세트 손실: 0.178211\t최선의 손실: 0.165139\t정확도: 95.86%\n",
+      "24\t검증 세트 손실: 0.169027\t최선의 손실: 0.165139\t정확도: 95.97%\n",
+      "25\t검증 세트 손실: 0.179837\t최선의 손실: 0.165139\t정확도: 95.62%\n",
+      "26\t검증 세트 손실: 0.161571\t최선의 손실: 0.161571\t정확도: 95.54%\n",
+      "27\t검증 세트 손실: 0.158761\t최선의 손실: 0.158761\t정확도: 96.09%\n",
+      "28\t검증 세트 손실: 0.182836\t최선의 손실: 0.158761\t정확도: 96.17%\n",
+      "29\t검증 세트 손실: 0.186746\t최선의 손실: 0.158761\t정확도: 95.90%\n",
+      "30\t검증 세트 손실: 0.176855\t최선의 손실: 0.158761\t정확도: 96.17%\n",
+      "31\t검증 세트 손실: 0.179175\t최선의 손실: 0.158761\t정확도: 95.86%\n",
+      "32\t검증 세트 손실: 0.159778\t최선의 손실: 0.158761\t정확도: 96.40%\n",
+      "33\t검증 세트 손실: 0.199193\t최선의 손실: 0.158761\t정확도: 96.91%\n",
+      "34\t검증 세트 손실: 0.166915\t최선의 손실: 0.158761\t정확도: 96.44%\n",
+      "35\t검증 세트 손실: 0.236168\t최선의 손실: 0.158761\t정확도: 96.29%\n",
+      "36\t검증 세트 손실: 0.207188\t최선의 손실: 0.158761\t정확도: 96.72%\n",
+      "37\t검증 세트 손실: 0.180584\t최선의 손실: 0.158761\t정확도: 96.64%\n",
+      "38\t검증 세트 손실: 0.189679\t최선의 손실: 0.158761\t정확도: 96.79%\n",
+      "39\t검증 세트 손실: 0.201452\t최선의 손실: 0.158761\t정확도: 96.68%\n",
+      "40\t검증 세트 손실: 0.184477\t최선의 손실: 0.158761\t정확도: 96.09%\n",
+      "41\t검증 세트 손실: 0.178517\t최선의 손실: 0.158761\t정확도: 96.56%\n",
+      "42\t검증 세트 손실: 0.220323\t최선의 손실: 0.158761\t정확도: 95.90%\n",
+      "43\t검증 세트 손실: 0.189469\t최선의 손실: 0.158761\t정확도: 95.86%\n",
+      "44\t검증 세트 손실: 0.211570\t최선의 손실: 0.158761\t정확도: 96.60%\n",
+      "45\t검증 세트 손실: 0.159326\t최선의 손실: 0.158761\t정확도: 96.52%\n",
+      "46\t검증 세트 손실: 0.167901\t최선의 손실: 0.158761\t정확도: 96.21%\n",
+      "47\t검증 세트 손실: 0.198988\t최선의 손실: 0.158761\t정확도: 96.29%\n",
+      "48\t검증 세트 손실: 0.161220\t최선의 손실: 0.158761\t정확도: 96.79%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=70, total=  12.9s\n",
+      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.088507\t최선의 손실: 1.088507\t정확도: 52.46%\n",
+      "1\t검증 세트 손실: 0.932183\t최선의 손실: 0.932183\t정확도: 55.86%\n",
+      "2\t검증 세트 손실: 236.534286\t최선의 손실: 0.932183\t정확도: 31.90%\n",
+      "3\t검증 세트 손실: 59.662811\t최선의 손실: 0.932183\t정확도: 22.05%\n",
+      "4\t검증 세트 손실: 28.098295\t최선의 손실: 0.932183\t정확도: 18.30%\n",
+      "5\t검증 세트 손실: 9.953935\t최선의 손실: 0.932183\t정확도: 31.27%\n",
+      "6\t검증 세트 손실: 3.768218\t최선의 손실: 0.932183\t정확도: 39.41%\n",
+      "7\t검증 세트 손실: 5.097012\t최선의 손실: 0.932183\t정확도: 40.70%\n",
+      "8\t검증 세트 손실: 3.368192\t최선의 손실: 0.932183\t정확도: 30.53%\n",
+      "9\t검증 세트 손실: 17.803524\t최선의 손실: 0.932183\t정확도: 29.40%\n",
+      "10\t검증 세트 손실: 4.982166\t최선의 손실: 0.932183\t정확도: 26.19%\n",
+      "11\t검증 세트 손실: 20.342417\t최선의 손실: 0.932183\t정확도: 36.67%\n",
+      "12\t검증 세트 손실: 178.033249\t최선의 손실: 0.932183\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 43.935894\t최선의 손실: 0.932183\t정확도: 25.41%\n",
+      "14\t검증 세트 손실: 27.695789\t최선의 손실: 0.932183\t정확도: 20.99%\n",
+      "15\t검증 세트 손실: 13.761216\t최선의 손실: 0.932183\t정확도: 31.31%\n",
+      "16\t검증 세트 손실: 50.261894\t최선의 손실: 0.932183\t정확도: 22.75%\n",
+      "17\t검증 세트 손실: 14.577541\t최선의 손실: 0.932183\t정확도: 34.01%\n",
+      "18\t검증 세트 손실: 8.299211\t최선의 손실: 0.932183\t정확도: 34.25%\n",
+      "19\t검증 세트 손실: 7.608750\t최선의 손실: 0.932183\t정확도: 30.84%\n",
+      "20\t검증 세트 손실: 41.410618\t최선의 손실: 0.932183\t정확도: 28.30%\n",
+      "21\t검증 세트 손실: 42.684196\t최선의 손실: 0.932183\t정확도: 23.73%\n",
+      "22\t검증 세트 손실: 38.724792\t최선의 손실: 0.932183\t정확도: 27.37%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total= 1.0min\n",
+      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.236978\t최선의 손실: 1.236978\t정확도: 46.36%\n",
+      "1\t검증 세트 손실: 2.502375\t최선의 손실: 1.236978\t정확도: 45.58%\n",
+      "2\t검증 세트 손실: 1.453171\t최선의 손실: 1.236978\t정확도: 49.49%\n",
+      "3\t검증 세트 손실: 2.134201\t최선의 손실: 1.236978\t정확도: 33.89%\n",
+      "4\t검증 세트 손실: 2.673335\t최선의 손실: 1.236978\t정확도: 27.09%\n",
+      "5\t검증 세트 손실: 21.791735\t최선의 손실: 1.236978\t정확도: 19.19%\n",
+      "6\t검증 세트 손실: 43.555122\t최선의 손실: 1.236978\t정확도: 23.49%\n",
+      "7\t검증 세트 손실: 20.566616\t최선의 손실: 1.236978\t정확도: 36.28%\n",
+      "8\t검증 세트 손실: 19.957058\t최선의 손실: 1.236978\t정확도: 28.69%\n",
+      "9\t검증 세트 손실: 40.627052\t최선의 손실: 1.236978\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 18.220385\t최선의 손실: 1.236978\t정확도: 30.88%\n",
+      "11\t검증 세트 손실: 7.287315\t최선의 손실: 1.236978\t정확도: 38.12%\n",
+      "12\t검증 세트 손실: 4.552297\t최선의 손실: 1.236978\t정확도: 36.32%\n",
+      "13\t검증 세트 손실: 5.400605\t최선의 손실: 1.236978\t정확도: 35.18%\n",
+      "14\t검증 세트 손실: 5.387339\t최선의 손실: 1.236978\t정확도: 36.83%\n",
+      "15\t검증 세트 손실: 6.575460\t최선의 손실: 1.236978\t정확도: 34.05%\n",
+      "16\t검증 세트 손실: 10.514853\t최선의 손실: 1.236978\t정확도: 35.11%\n",
+      "17\t검증 세트 손실: 20.215736\t최선의 손실: 1.236978\t정확도: 28.81%\n",
+      "18\t검증 세트 손실: 77.829918\t최선의 손실: 1.236978\t정확도: 28.46%\n",
+      "19\t검증 세트 손실: 30.155993\t최선의 손실: 1.236978\t정확도: 37.18%\n",
+      "20\t검증 세트 손실: 17.781517\t최선의 손실: 1.236978\t정확도: 24.00%\n",
+      "21\t검증 세트 손실: 6.075872\t최선의 손실: 1.236978\t정확도: 35.26%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=  59.1s\n",
+      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.487661\t최선의 손실: 1.487661\t정확도: 34.79%\n",
+      "1\t검증 세트 손실: 4.091078\t최선의 손실: 1.487661\t정확도: 28.93%\n",
+      "2\t검증 세트 손실: 6.067765\t최선의 손실: 1.487661\t정확도: 44.88%\n",
+      "3\t검증 세트 손실: 172.776443\t최선의 손실: 1.487661\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 21.366634\t최선의 손실: 1.487661\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 6.511680\t최선의 손실: 1.487661\t정확도: 38.70%\n",
+      "6\t검증 세트 손실: 28.574207\t최선의 손실: 1.487661\t정확도: 20.48%\n",
+      "7\t검증 세트 손실: 92.575264\t최선의 손실: 1.487661\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 75.915337\t최선의 손실: 1.487661\t정확도: 19.12%\n",
+      "9\t검증 세트 손실: 228.751755\t최선의 손실: 1.487661\t정확도: 19.23%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "96\t검증 세트 손실: 0.174195\t최선의 손실: 0.139198\t정확도: 96.76%\n",
-      "97\t검증 세트 손실: 0.158467\t최선의 손실: 0.139198\t정확도: 96.83%\n",
-      "98\t검증 세트 손실: 0.162821\t최선의 손실: 0.139198\t정확도: 96.87%\n",
-      "99\t검증 세트 손실: 0.194824\t최선의 손실: 0.139198\t정확도: 96.68%\n",
-      "100\t검증 세트 손실: 0.204943\t최선의 손실: 0.139198\t정확도: 96.87%\n",
-      "101\t검증 세트 손실: 0.202168\t최선의 손실: 0.139198\t정확도: 96.79%\n",
-      "102\t검증 세트 손실: 0.164575\t최선의 손실: 0.139198\t정확도: 96.48%\n",
-      "103\t검증 세트 손실: 0.174196\t최선의 손실: 0.139198\t정확도: 96.52%\n",
-      "104\t검증 세트 손실: 0.173975\t최선의 손실: 0.139198\t정확도: 96.87%\n",
-      "105\t검증 세트 손실: 0.167463\t최선의 손실: 0.139198\t정확도: 96.87%\n",
-      "106\t검증 세트 손실: 0.165817\t최선의 손실: 0.139198\t정확도: 97.07%\n",
+      "10\t검증 세트 손실: 18.089661\t최선의 손실: 1.487661\t정확도: 43.82%\n",
+      "11\t검증 세트 손실: 131.517151\t최선의 손실: 1.487661\t정확도: 23.65%\n",
+      "12\t검증 세트 손실: 71.557182\t최선의 손실: 1.487661\t정확도: 23.61%\n",
+      "13\t검증 세트 손실: 35.226643\t최선의 손실: 1.487661\t정확도: 31.94%\n",
+      "14\t검증 세트 손실: 25.314777\t최선의 손실: 1.487661\t정확도: 33.31%\n",
+      "15\t검증 세트 손실: 16.703325\t최선의 손실: 1.487661\t정확도: 39.17%\n",
+      "16\t검증 세트 손실: 113.601883\t최선의 손실: 1.487661\t정확도: 25.80%\n",
+      "17\t검증 세트 손실: 25.405695\t최선의 손실: 1.487661\t정확도: 31.98%\n",
+      "18\t검증 세트 손실: 707.799927\t최선의 손실: 1.487661\t정확도: 30.81%\n",
+      "19\t검증 세트 손실: 106.352486\t최선의 손실: 1.487661\t정확도: 24.16%\n",
+      "20\t검증 세트 손실: 651.329529\t최선의 손실: 1.487661\t정확도: 18.69%\n",
+      "21\t검증 세트 손실: 17.256878\t최선의 손실: 1.487661\t정확도: 39.41%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=70, total=  51.7s\n",
-      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1.430615\t최선의 손실: 1.430615\t정확도: 37.80%\n",
-      "1\t검증 세트 손실: 38.238628\t최선의 손실: 1.430615\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 18.916872\t최선의 손실: 1.430615\t정확도: 20.21%\n",
-      "3\t검증 세트 손실: 17.523785\t최선의 손실: 1.430615\t정확도: 29.20%\n",
-      "4\t검증 세트 손실: 15.027767\t최선의 손실: 1.430615\t정확도: 45.11%\n",
-      "5\t검증 세트 손실: 16.884132\t최선의 손실: 1.430615\t정확도: 30.26%\n",
-      "6\t검증 세트 손실: 7.558291\t최선의 손실: 1.430615\t정확도: 39.60%\n",
-      "7\t검증 세트 손실: 4718.333984\t최선의 손실: 1.430615\t정확도: 30.84%\n",
-      "8\t검증 세트 손실: 36.926899\t최선의 손실: 1.430615\t정확도: 38.86%\n",
-      "9\t검증 세트 손실: 11.110150\t최선의 손실: 1.430615\t정확도: 43.98%\n",
-      "10\t검증 세트 손실: 6.850830\t최선의 손실: 1.430615\t정확도: 37.02%\n",
-      "11\t검증 세트 손실: 62.971138\t최선의 손실: 1.430615\t정확도: 32.21%\n",
-      "12\t검증 세트 손실: 22.289639\t최선의 손실: 1.430615\t정확도: 22.75%\n",
-      "13\t검증 세트 손실: 2771.322021\t최선의 손실: 1.430615\t정확도: 19.19%\n",
-      "14\t검증 세트 손실: 44.013512\t최선의 손실: 1.430615\t정확도: 20.88%\n",
-      "15\t검증 세트 손실: 152.629501\t최선의 손실: 1.430615\t정확도: 18.84%\n",
-      "16\t검증 세트 손실: 53.050674\t최선의 손실: 1.430615\t정확도: 34.79%\n",
-      "17\t검증 세트 손실: 16.329567\t최선의 손실: 1.430615\t정확도: 26.62%\n",
-      "18\t검증 세트 손실: 225.724640\t최선의 손실: 1.430615\t정확도: 22.05%\n",
-      "19\t검증 세트 손실: 114.905930\t최선의 손실: 1.430615\t정확도: 20.76%\n",
-      "20\t검증 세트 손실: 56.632427\t최선의 손실: 1.430615\t정확도: 22.91%\n",
-      "21\t검증 세트 손실: 21.832842\t최선의 손실: 1.430615\t정확도: 41.52%\n",
+      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=  58.8s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.144075\t최선의 손실: 0.144075\t정확도: 95.86%\n",
+      "1\t검증 세트 손실: 0.119366\t최선의 손실: 0.119366\t정확도: 96.68%\n",
+      "2\t검증 세트 손실: 0.109820\t최선의 손실: 0.109820\t정확도: 97.03%\n",
+      "3\t검증 세트 손실: 0.101285\t최선의 손실: 0.101285\t정확도: 97.03%\n",
+      "4\t검증 세트 손실: 0.102659\t최선의 손실: 0.101285\t정확도: 97.11%\n",
+      "5\t검증 세트 손실: 0.104793\t최선의 손실: 0.101285\t정확도: 97.34%\n",
+      "6\t검증 세트 손실: 0.095102\t최선의 손실: 0.095102\t정확도: 97.62%\n",
+      "7\t검증 세트 손실: 0.107752\t최선의 손실: 0.095102\t정확도: 97.11%\n",
+      "8\t검증 세트 손실: 0.108002\t최선의 손실: 0.095102\t정확도: 97.73%\n",
+      "9\t검증 세트 손실: 0.104549\t최선의 손실: 0.095102\t정확도: 97.26%\n",
+      "10\t검증 세트 손실: 0.096523\t최선의 손실: 0.095102\t정확도: 97.65%\n",
+      "11\t검증 세트 손실: 0.113460\t최선의 손실: 0.095102\t정확도: 97.38%\n",
+      "12\t검증 세트 손실: 0.117181\t최선의 손실: 0.095102\t정확도: 96.87%\n",
+      "13\t검증 세트 손실: 0.115592\t최선의 손실: 0.095102\t정확도: 96.79%\n",
+      "14\t검증 세트 손실: 0.101360\t최선의 손실: 0.095102\t정확도: 97.73%\n",
+      "15\t검증 세트 손실: 0.110849\t최선의 손실: 0.095102\t정확도: 97.07%\n",
+      "16\t검증 세트 손실: 0.091438\t최선의 손실: 0.091438\t정확도: 97.65%\n",
+      "17\t검증 세트 손실: 0.099723\t최선의 손실: 0.091438\t정확도: 97.62%\n",
+      "18\t검증 세트 손실: 0.085660\t최선의 손실: 0.085660\t정확도: 98.01%\n",
+      "19\t검증 세트 손실: 0.084668\t최선의 손실: 0.084668\t정확도: 97.97%\n",
+      "20\t검증 세트 손실: 0.083226\t최선의 손실: 0.083226\t정확도: 97.77%\n",
+      "21\t검증 세트 손실: 0.084696\t최선의 손실: 0.083226\t정확도: 98.01%\n",
+      "22\t검증 세트 손실: 0.114255\t최선의 손실: 0.083226\t정확도: 97.11%\n",
+      "23\t검증 세트 손실: 0.122273\t최선의 손실: 0.083226\t정확도: 98.05%\n",
+      "24\t검증 세트 손실: 0.089704\t최선의 손실: 0.083226\t정확도: 97.85%\n",
+      "25\t검증 세트 손실: 0.089409\t최선의 손실: 0.083226\t정확도: 97.69%\n",
+      "26\t검증 세트 손실: 0.100716\t최선의 손실: 0.083226\t정확도: 98.01%\n",
+      "27\t검증 세트 손실: 0.093639\t최선의 손실: 0.083226\t정확도: 97.89%\n",
+      "28\t검증 세트 손실: 0.107580\t최선의 손실: 0.083226\t정확도: 97.58%\n",
+      "29\t검증 세트 손실: 0.103311\t최선의 손실: 0.083226\t정확도: 97.77%\n",
+      "30\t검증 세트 손실: 0.083483\t최선의 손실: 0.083226\t정확도: 97.93%\n",
+      "31\t검증 세트 손실: 0.148828\t최선의 손실: 0.083226\t정확도: 97.50%\n",
+      "32\t검증 세트 손실: 0.089313\t최선의 손실: 0.083226\t정확도: 97.62%\n",
+      "33\t검증 세트 손실: 0.100540\t최선의 손실: 0.083226\t정확도: 97.58%\n",
+      "34\t검증 세트 손실: 0.147756\t최선의 손실: 0.083226\t정확도: 97.15%\n",
+      "35\t검증 세트 손실: 0.142736\t최선의 손실: 0.083226\t정확도: 97.46%\n",
+      "36\t검증 세트 손실: 0.105999\t최선의 손실: 0.083226\t정확도: 97.26%\n",
+      "37\t검증 세트 손실: 0.098263\t최선의 손실: 0.083226\t정확도: 97.93%\n",
+      "38\t검증 세트 손실: 0.087927\t최선의 손실: 0.083226\t정확도: 98.12%\n",
+      "39\t검증 세트 손실: 0.088553\t최선의 손실: 0.083226\t정확도: 97.65%\n",
+      "40\t검증 세트 손실: 0.092312\t최선의 손실: 0.083226\t정확도: 97.50%\n",
+      "41\t검증 세트 손실: 0.091019\t최선의 손실: 0.083226\t정확도: 97.65%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total= 1.8min\n",
-      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1.223082\t최선의 손실: 1.223082\t정확도: 54.38%\n",
-      "1\t검증 세트 손실: 58.416733\t최선의 손실: 1.223082\t정확도: 31.39%\n",
-      "2\t검증 세트 손실: 229060.234375\t최선의 손실: 1.223082\t정확도: 22.87%\n",
-      "3\t검증 세트 손실: 55.743690\t최선의 손실: 1.223082\t정확도: 29.09%\n",
-      "4\t검증 세트 손실: 121.876602\t최선의 손실: 1.223082\t정확도: 21.77%\n",
-      "5\t검증 세트 손실: 46.312550\t최선의 손실: 1.223082\t정확도: 28.07%\n",
-      "6\t검증 세트 손실: 12.212029\t최선의 손실: 1.223082\t정확도: 44.76%\n",
-      "7\t검증 세트 손실: 24.662170\t최선의 손실: 1.223082\t정확도: 35.11%\n",
-      "8\t검증 세트 손실: 11.780501\t최선의 손실: 1.223082\t정확도: 39.21%\n",
-      "9\t검증 세트 손실: 24.701797\t최선의 손실: 1.223082\t정확도: 26.51%\n",
-      "10\t검증 세트 손실: 4.791701\t최선의 손실: 1.223082\t정확도: 38.43%\n",
-      "11\t검증 세트 손실: 8.200578\t최선의 손실: 1.223082\t정확도: 28.73%\n",
-      "12\t검증 세트 손실: 8.703090\t최선의 손실: 1.223082\t정확도: 28.19%\n",
-      "13\t검증 세트 손실: 6.146657\t최선의 손실: 1.223082\t정확도: 32.29%\n",
-      "14\t검증 세트 손실: 8.217604\t최선의 손실: 1.223082\t정확도: 35.07%\n",
-      "15\t검증 세트 손실: 9.211543\t최선의 손실: 1.223082\t정확도: 21.07%\n",
-      "16\t검증 세트 손실: 2.728628\t최선의 손실: 1.223082\t정확도: 33.93%\n",
-      "17\t검증 세트 손실: 6.443649\t최선의 손실: 1.223082\t정확도: 26.27%\n",
-      "18\t검증 세트 손실: 3.804056\t최선의 손실: 1.223082\t정확도: 27.05%\n",
-      "19\t검증 세트 손실: 2.494663\t최선의 손실: 1.223082\t정확도: 39.87%\n",
-      "20\t검증 세트 손실: 2.342536\t최선의 손실: 1.223082\t정확도: 35.57%\n",
-      "21\t검증 세트 손실: 8.871885\t최선의 손실: 1.223082\t정확도: 23.46%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=30, total=  24.3s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.165653\t최선의 손실: 0.165653\t정확도: 95.62%\n",
+      "1\t검증 세트 손실: 0.119382\t최선의 손실: 0.119382\t정확도: 96.13%\n",
+      "2\t검증 세트 손실: 0.107995\t최선의 손실: 0.107995\t정확도: 96.76%\n",
+      "3\t검증 세트 손실: 0.101311\t최선의 손실: 0.101311\t정확도: 97.19%\n",
+      "4\t검증 세트 손실: 0.122335\t최선의 손실: 0.101311\t정확도: 96.72%\n",
+      "5\t검증 세트 손실: 0.105150\t최선의 손실: 0.101311\t정확도: 97.22%\n",
+      "6\t검증 세트 손실: 0.103656\t최선의 손실: 0.101311\t정확도: 97.38%\n",
+      "7\t검증 세트 손실: 0.092865\t최선의 손실: 0.092865\t정확도: 97.38%\n",
+      "8\t검증 세트 손실: 0.102320\t최선의 손실: 0.092865\t정확도: 97.34%\n",
+      "9\t검증 세트 손실: 0.079748\t최선의 손실: 0.079748\t정확도: 97.85%\n",
+      "10\t검증 세트 손실: 0.101558\t최선의 손실: 0.079748\t정확도: 97.69%\n",
+      "11\t검증 세트 손실: 0.087314\t최선의 손실: 0.079748\t정확도: 97.93%\n",
+      "12\t검증 세트 손실: 0.087575\t최선의 손실: 0.079748\t정확도: 97.46%\n",
+      "13\t검증 세트 손실: 0.096313\t최선의 손실: 0.079748\t정확도: 97.62%\n",
+      "14\t검증 세트 손실: 0.106641\t최선의 손실: 0.079748\t정확도: 97.77%\n",
+      "15\t검증 세트 손실: 0.100108\t최선의 손실: 0.079748\t정확도: 97.65%\n",
+      "16\t검증 세트 손실: 0.115019\t최선의 손실: 0.079748\t정확도: 97.65%\n",
+      "17\t검증 세트 손실: 0.097369\t최선의 손실: 0.079748\t정확도: 97.97%\n",
+      "18\t검증 세트 손실: 0.084793\t최선의 손실: 0.079748\t정확도: 97.65%\n",
+      "19\t검증 세트 손실: 0.108200\t최선의 손실: 0.079748\t정확도: 96.60%\n",
+      "20\t검증 세트 손실: 0.110709\t최선의 손실: 0.079748\t정확도: 97.19%\n",
+      "21\t검증 세트 손실: 0.092517\t최선의 손실: 0.079748\t정확도: 97.81%\n",
+      "22\t검증 세트 손실: 0.084952\t최선의 손실: 0.079748\t정확도: 97.62%\n",
+      "23\t검증 세트 손실: 0.081606\t최선의 손실: 0.079748\t정확도: 97.89%\n",
+      "24\t검증 세트 손실: 0.108766\t최선의 손실: 0.079748\t정확도: 97.58%\n",
+      "25\t검증 세트 손실: 0.102780\t최선의 손실: 0.079748\t정확도: 97.42%\n",
+      "26\t검증 세트 손실: 0.086912\t최선의 손실: 0.079748\t정확도: 97.65%\n",
+      "27\t검증 세트 손실: 0.087250\t최선의 손실: 0.079748\t정확도: 97.50%\n",
+      "28\t검증 세트 손실: 0.090876\t최선의 손실: 0.079748\t정확도: 97.81%\n",
+      "29\t검증 세트 손실: 0.092815\t최선의 손실: 0.079748\t정확도: 97.97%\n",
+      "30\t검증 세트 손실: 0.095165\t최선의 손실: 0.079748\t정확도: 97.62%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total= 1.7min\n",
-      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1.221151\t최선의 손실: 1.221151\t정확도: 49.88%\n",
-      "1\t검증 세트 손실: 2.250655\t최선의 손실: 1.221151\t정확도: 23.57%\n",
-      "2\t검증 세트 손실: 5.030942\t최선의 손실: 1.221151\t정확도: 25.53%\n",
-      "3\t검증 세트 손실: 36.976757\t최선의 손실: 1.221151\t정확도: 19.12%\n",
-      "4\t검증 세트 손실: 102.801331\t최선의 손실: 1.221151\t정확도: 19.70%\n",
-      "5\t검증 세트 손실: 234.592194\t최선의 손실: 1.221151\t정확도: 26.82%\n",
-      "6\t검증 세트 손실: 59.531902\t최선의 손실: 1.221151\t정확도: 21.46%\n",
-      "7\t검증 세트 손실: 286.068481\t최선의 손실: 1.221151\t정확도: 33.66%\n",
-      "8\t검증 세트 손실: 229.780441\t최선의 손실: 1.221151\t정확도: 20.45%\n",
-      "9\t검증 세트 손실: 51.636826\t최선의 손실: 1.221151\t정확도: 19.78%\n",
-      "10\t검증 세트 손실: 13.032432\t최선의 손실: 1.221151\t정확도: 39.05%\n",
-      "11\t검증 세트 손실: 55.516926\t최선의 손실: 1.221151\t정확도: 25.68%\n",
-      "12\t검증 세트 손실: 56.728462\t최선의 손실: 1.221151\t정확도: 36.86%\n",
-      "13\t검증 세트 손실: 28.159964\t최선의 손실: 1.221151\t정확도: 28.03%\n",
-      "14\t검증 세트 손실: 42.436760\t최선의 손실: 1.221151\t정확도: 35.85%\n",
-      "15\t검증 세트 손실: 189.664474\t최선의 손실: 1.221151\t정확도: 18.76%\n",
-      "16\t검증 세트 손실: 43.482456\t최선의 손실: 1.221151\t정확도: 37.29%\n",
-      "17\t검증 세트 손실: 39.041611\t최선의 손실: 1.221151\t정확도: 27.60%\n",
-      "18\t검증 세트 손실: 4245.569824\t최선의 손실: 1.221151\t정확도: 34.44%\n",
-      "19\t검증 세트 손실: 2193.746338\t최선의 손실: 1.221151\t정확도: 32.41%\n",
-      "20\t검증 세트 손실: 289.677643\t최선의 손실: 1.221151\t정확도: 19.12%\n",
-      "21\t검증 세트 손실: 21.472403\t최선의 손실: 1.221151\t정확도: 33.42%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total= 1.5min\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.132391\t최선의 손실: 0.132391\t정확도: 95.62%\n",
-      "1\t검증 세트 손실: 0.132175\t최선의 손실: 0.132175\t정확도: 96.68%\n",
-      "2\t검증 세트 손실: 0.133077\t최선의 손실: 0.132175\t정확도: 96.48%\n",
-      "3\t검증 세트 손실: 0.100249\t최선의 손실: 0.100249\t정확도: 97.11%\n",
-      "4\t검증 세트 손실: 0.114785\t최선의 손실: 0.100249\t정확도: 96.72%\n",
-      "5\t검증 세트 손실: 0.102676\t최선의 손실: 0.100249\t정확도: 97.19%\n",
-      "6\t검증 세트 손실: 0.119667\t최선의 손실: 0.100249\t정확도: 97.15%\n",
-      "7\t검증 세트 손실: 0.127255\t최선의 손실: 0.100249\t정확도: 96.83%\n",
-      "8\t검증 세트 손실: 0.101043\t최선의 손실: 0.100249\t정확도: 97.54%\n",
-      "9\t검증 세트 손실: 0.113913\t최선의 손실: 0.100249\t정확도: 97.19%\n",
-      "10\t검증 세트 손실: 0.100283\t최선의 손실: 0.100249\t정확도: 97.50%\n",
-      "11\t검증 세트 손실: 0.099815\t최선의 손실: 0.099815\t정확도: 97.15%\n",
-      "12\t검증 세트 손실: 0.102147\t최선의 손실: 0.099815\t정확도: 97.34%\n",
-      "13\t검증 세트 손실: 0.107474\t최선의 손실: 0.099815\t정확도: 97.58%\n",
-      "14\t검증 세트 손실: 0.095528\t최선의 손실: 0.095528\t정확도: 97.85%\n",
-      "15\t검증 세트 손실: 0.103259\t최선의 손실: 0.095528\t정확도: 97.50%\n",
-      "16\t검증 세트 손실: 0.128233\t최선의 손실: 0.095528\t정확도: 97.73%\n",
-      "17\t검증 세트 손실: 0.100047\t최선의 손실: 0.095528\t정확도: 97.62%\n",
-      "18\t검증 세트 손실: 0.093971\t최선의 손실: 0.093971\t정확도: 97.65%\n",
-      "19\t검증 세트 손실: 0.102750\t최선의 손실: 0.093971\t정확도: 97.65%\n",
-      "20\t검증 세트 손실: 0.111070\t최선의 손실: 0.093971\t정확도: 97.50%\n",
-      "21\t검증 세트 손실: 0.097712\t최선의 손실: 0.093971\t정확도: 97.73%\n",
-      "22\t검증 세트 손실: 0.121166\t최선의 손실: 0.093971\t정확도: 97.89%\n",
-      "23\t검증 세트 손실: 0.092218\t최선의 손실: 0.092218\t정확도: 97.77%\n",
-      "24\t검증 세트 손실: 0.095471\t최선의 손실: 0.092218\t정확도: 98.08%\n",
-      "25\t검증 세트 손실: 0.090748\t최선의 손실: 0.090748\t정확도: 97.81%\n",
-      "26\t검증 세트 손실: 0.090682\t최선의 손실: 0.090682\t정확도: 97.93%\n",
-      "27\t검증 세트 손실: 0.088028\t최선의 손실: 0.088028\t정확도: 97.97%\n",
-      "28\t검증 세트 손실: 0.092906\t최선의 손실: 0.088028\t정확도: 97.81%\n",
-      "29\t검증 세트 손실: 0.135266\t최선의 손실: 0.088028\t정확도: 97.34%\n",
-      "30\t검증 세트 손실: 0.128251\t최선의 손실: 0.088028\t정확도: 97.22%\n",
-      "31\t검증 세트 손실: 0.115481\t최선의 손실: 0.088028\t정확도: 97.46%\n",
-      "32\t검증 세트 손실: 0.131216\t최선의 손실: 0.088028\t정확도: 97.42%\n",
-      "33\t검증 세트 손실: 0.118386\t최선의 손실: 0.088028\t정확도: 97.26%\n",
-      "34\t검증 세트 손실: 0.103642\t최선의 손실: 0.088028\t정확도: 97.77%\n",
-      "35\t검증 세트 손실: 0.106022\t최선의 손실: 0.088028\t정확도: 97.77%\n",
-      "36\t검증 세트 손실: 0.104649\t최선의 손실: 0.088028\t정확도: 98.12%\n",
-      "37\t검증 세트 손실: 0.141114\t최선의 손실: 0.088028\t정확도: 97.62%\n",
-      "38\t검증 세트 손실: 0.101464\t최선의 손실: 0.088028\t정확도: 97.93%\n",
-      "39\t검증 세트 손실: 0.088225\t최선의 손실: 0.088028\t정확도: 97.65%\n",
-      "40\t검증 세트 손실: 0.100614\t최선의 손실: 0.088028\t정확도: 98.24%\n",
-      "41\t검증 세트 손실: 0.096873\t최선의 손실: 0.088028\t정확도: 97.73%\n",
-      "42\t검증 세트 손실: 0.093265\t최선의 손실: 0.088028\t정확도: 98.20%\n",
-      "43\t검증 세트 손실: 0.094836\t최선의 손실: 0.088028\t정확도: 98.08%\n",
-      "44\t검증 세트 손실: 0.104946\t최선의 손실: 0.088028\t정확도: 98.16%\n",
-      "45\t검증 세트 손실: 0.116162\t최선의 손실: 0.088028\t정확도: 97.54%\n",
-      "46\t검증 세트 손실: 0.192283\t최선의 손실: 0.088028\t정확도: 97.19%\n",
-      "47\t검증 세트 손실: 0.099789\t최선의 손실: 0.088028\t정확도: 97.73%\n",
-      "48\t검증 세트 손실: 0.092374\t최선의 손실: 0.088028\t정확도: 98.08%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=30, total=  40.6s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.158504\t최선의 손실: 0.158504\t정확도: 95.39%\n",
-      "1\t검증 세트 손실: 0.114529\t최선의 손실: 0.114529\t정확도: 96.44%\n"
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=30, total=  18.3s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 0.126570\t최선의 손실: 0.126570\t정확도: 96.40%\n",
+      "1\t검증 세트 손실: 0.120405\t최선의 손실: 0.120405\t정확도: 97.26%\n",
+      "2\t검증 세트 손실: 0.103338\t최선의 손실: 0.103338\t정확도: 97.22%\n",
+      "3\t검증 세트 손실: 0.111369\t최선의 손실: 0.103338\t정확도: 97.34%\n",
+      "4\t검증 세트 손실: 0.102394\t최선의 손실: 0.102394\t정확도: 97.46%\n",
+      "5\t검증 세트 손실: 0.110625\t최선의 손실: 0.102394\t정확도: 97.22%\n",
+      "6\t검증 세트 손실: 0.091709\t최선의 손실: 0.091709\t정확도: 97.81%\n",
+      "7\t검증 세트 손실: 0.108797\t최선의 손실: 0.091709\t정확도: 97.38%\n",
+      "8\t검증 세트 손실: 0.100636\t최선의 손실: 0.091709\t정확도: 97.54%\n",
+      "9\t검증 세트 손실: 0.091388\t최선의 손실: 0.091388\t정확도: 97.85%\n",
+      "10\t검증 세트 손실: 0.095018\t최선의 손실: 0.091388\t정확도: 97.46%\n",
+      "11\t검증 세트 손실: 0.088800\t최선의 손실: 0.088800\t정확도: 98.08%\n",
+      "12\t검증 세트 손실: 0.090480\t최선의 손실: 0.088800\t정확도: 98.05%\n",
+      "13\t검증 세트 손실: 0.100280\t최선의 손실: 0.088800\t정확도: 97.54%\n",
+      "14\t검증 세트 손실: 0.105467\t최선의 손실: 0.088800\t정확도: 97.73%\n",
+      "15\t검증 세트 손실: 0.116027\t최선의 손실: 0.088800\t정확도: 97.38%\n",
+      "16\t검증 세트 손실: 0.111713\t최선의 손실: 0.088800\t정확도: 97.62%\n",
+      "17\t검증 세트 손실: 0.108747\t최선의 손실: 0.088800\t정확도: 97.81%\n",
+      "18\t검증 세트 손실: 0.099019\t최선의 손실: 0.088800\t정확도: 97.85%\n",
+      "19\t검증 세트 손실: 0.099676\t최선의 손실: 0.088800\t정확도: 98.12%\n",
+      "20\t검증 세트 손실: 0.113080\t최선의 손실: 0.088800\t정확도: 97.69%\n",
+      "21\t검증 세트 손실: 0.113889\t최선의 손실: 0.088800\t정확도: 97.97%\n",
+      "22\t검증 세트 손실: 0.091186\t최선의 손실: 0.088800\t정확도: 97.93%\n",
+      "23\t검증 세트 손실: 0.105489\t최선의 손실: 0.088800\t정확도: 97.77%\n",
+      "24\t검증 세트 손실: 0.100674\t최선의 손실: 0.088800\t정확도: 97.69%\n",
+      "25\t검증 세트 손실: 0.088566\t최선의 손실: 0.088566\t정확도: 97.62%\n",
+      "26\t검증 세트 손실: 0.087799\t최선의 손실: 0.087799\t정확도: 97.69%\n",
+      "27\t검증 세트 손실: 0.188874\t최선의 손실: 0.087799\t정확도: 97.15%\n",
+      "28\t검증 세트 손실: 0.121997\t최선의 손실: 0.087799\t정확도: 97.50%\n",
+      "29\t검증 세트 손실: 0.110577\t최선의 손실: 0.087799\t정확도: 97.85%\n",
+      "30\t검증 세트 손실: 0.102798\t최선의 손실: 0.087799\t정확도: 97.93%\n",
+      "31\t검증 세트 손실: 0.113757\t최선의 손실: 0.087799\t정확도: 97.50%\n",
+      "32\t검증 세트 손실: 0.094603\t최선의 손실: 0.087799\t정확도: 97.50%\n",
+      "33\t검증 세트 손실: 0.088567\t최선의 손실: 0.087799\t정확도: 98.05%\n",
+      "34\t검증 세트 손실: 0.105904\t최선의 손실: 0.087799\t정확도: 97.77%\n",
+      "35\t검증 세트 손실: 0.082987\t최선의 손실: 0.082987\t정확도: 97.97%\n",
+      "36\t검증 세트 손실: 0.100726\t최선의 손실: 0.082987\t정확도: 97.50%\n",
+      "37\t검증 세트 손실: 0.087161\t최선의 손실: 0.082987\t정확도: 97.69%\n",
+      "38\t검증 세트 손실: 0.090772\t최선의 손실: 0.082987\t정확도: 98.12%\n",
+      "39\t검증 세트 손실: 0.074589\t최선의 손실: 0.074589\t정확도: 98.28%\n",
+      "40\t검증 세트 손실: 0.095341\t최선의 손실: 0.074589\t정확도: 98.08%\n",
+      "41\t검증 세트 손실: 0.094712\t최선의 손실: 0.074589\t정확도: 98.01%\n",
+      "42\t검증 세트 손실: 0.105667\t최선의 손실: 0.074589\t정확도: 97.77%\n",
+      "43\t검증 세트 손실: 0.085853\t최선의 손실: 0.074589\t정확도: 98.05%\n",
+      "44\t검증 세트 손실: 0.097051\t최선의 손실: 0.074589\t정확도: 97.89%\n",
+      "45\t검증 세트 손실: 0.085605\t최선의 손실: 0.074589\t정확도: 98.20%\n",
+      "46\t검증 세트 손실: 0.166473\t최선의 손실: 0.074589\t정확도: 96.48%\n",
+      "47\t검증 세트 손실: 0.122646\t최선의 손실: 0.074589\t정확도: 97.26%\n",
+      "48\t검증 세트 손실: 0.125746\t최선의 손실: 0.074589\t정확도: 97.69%\n",
+      "49\t검증 세트 손실: 0.103058\t최선의 손실: 0.074589\t정확도: 97.50%\n",
+      "50\t검증 세트 손실: 0.106274\t최선의 손실: 0.074589\t정확도: 97.38%\n",
+      "51\t검증 세트 손실: 0.104610\t최선의 손실: 0.074589\t정확도: 97.50%\n",
+      "52\t검증 세트 손실: 0.106887\t최선의 손실: 0.074589\t정확도: 98.05%\n",
+      "53\t검증 세트 손실: 0.101949\t최선의 손실: 0.074589\t정확도: 98.08%\n",
+      "54\t검증 세트 손실: 0.105508\t최선의 손실: 0.074589\t정확도: 98.12%\n",
+      "55\t검증 세트 손실: 0.098834\t최선의 손실: 0.074589\t정확도: 97.65%\n",
+      "56\t검증 세트 손실: 0.108352\t최선의 손실: 0.074589\t정확도: 97.26%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2\t검증 세트 손실: 0.118122\t최선의 손실: 0.114529\t정확도: 96.56%\n",
-      "3\t검증 세트 손실: 0.091519\t최선의 손실: 0.091519\t정확도: 97.19%\n",
-      "4\t검증 세트 손실: 0.090240\t최선의 손실: 0.090240\t정확도: 97.38%\n",
-      "5\t검증 세트 손실: 0.113809\t최선의 손실: 0.090240\t정확도: 96.68%\n",
-      "6\t검증 세트 손실: 0.115768\t최선의 손실: 0.090240\t정확도: 96.99%\n",
-      "7\t검증 세트 손실: 0.120172\t최선의 손실: 0.090240\t정확도: 96.72%\n",
-      "8\t검증 세트 손실: 0.105240\t최선의 손실: 0.090240\t정확도: 97.07%\n",
-      "9\t검증 세트 손실: 0.098640\t최선의 손실: 0.090240\t정확도: 97.38%\n",
-      "10\t검증 세트 손실: 0.087169\t최선의 손실: 0.087169\t정확도: 97.65%\n",
-      "11\t검증 세트 손실: 0.115663\t최선의 손실: 0.087169\t정확도: 97.50%\n",
-      "12\t검증 세트 손실: 0.086942\t최선의 손실: 0.086942\t정확도: 97.62%\n",
-      "13\t검증 세트 손실: 0.097893\t최선의 손실: 0.086942\t정확도: 97.62%\n",
-      "14\t검증 세트 손실: 0.100845\t최선의 손실: 0.086942\t정확도: 97.42%\n",
-      "15\t검증 세트 손실: 0.091493\t최선의 손실: 0.086942\t정확도: 97.62%\n",
-      "16\t검증 세트 손실: 0.089997\t최선의 손실: 0.086942\t정확도: 97.97%\n",
-      "17\t검증 세트 손실: 0.102576\t최선의 손실: 0.086942\t정확도: 97.46%\n",
-      "18\t검증 세트 손실: 0.078403\t최선의 손실: 0.078403\t정확도: 97.89%\n",
-      "19\t검증 세트 손실: 0.098528\t최선의 손실: 0.078403\t정확도: 97.89%\n",
-      "20\t검증 세트 손실: 0.109468\t최선의 손실: 0.078403\t정확도: 97.54%\n",
-      "21\t검증 세트 손실: 0.095016\t최선의 손실: 0.078403\t정확도: 97.73%\n",
-      "22\t검증 세트 손실: 0.106727\t최선의 손실: 0.078403\t정확도: 97.22%\n",
-      "23\t검증 세트 손실: 0.086758\t최선의 손실: 0.078403\t정확도: 98.12%\n",
-      "24\t검증 세트 손실: 0.084644\t최선의 손실: 0.078403\t정확도: 97.77%\n",
-      "25\t검증 세트 손실: 0.091375\t최선의 손실: 0.078403\t정확도: 97.81%\n",
-      "26\t검증 세트 손실: 0.080302\t최선의 손실: 0.078403\t정확도: 97.93%\n",
-      "27\t검증 세트 손실: 0.080536\t최선의 손실: 0.078403\t정확도: 97.65%\n",
-      "28\t검증 세트 손실: 0.073540\t최선의 손실: 0.073540\t정확도: 97.97%\n",
-      "29\t검증 세트 손실: 0.089292\t최선의 손실: 0.073540\t정확도: 97.77%\n",
-      "30\t검증 세트 손실: 0.085095\t최선의 손실: 0.073540\t정확도: 97.69%\n",
-      "31\t검증 세트 손실: 0.092914\t최선의 손실: 0.073540\t정확도: 97.89%\n",
-      "32\t검증 세트 손실: 0.086162\t최선의 손실: 0.073540\t정확도: 98.12%\n",
-      "33\t검증 세트 손실: 0.131395\t최선의 손실: 0.073540\t정확도: 97.73%\n",
-      "34\t검증 세트 손실: 0.101994\t최선의 손실: 0.073540\t정확도: 97.19%\n",
-      "35\t검증 세트 손실: 0.095657\t최선의 손실: 0.073540\t정확도: 97.77%\n",
-      "36\t검증 세트 손실: 0.078067\t최선의 손실: 0.073540\t정확도: 97.81%\n",
-      "37\t검증 세트 손실: 0.082188\t최선의 손실: 0.073540\t정확도: 97.93%\n",
-      "38\t검증 세트 손실: 0.095109\t최선의 손실: 0.073540\t정확도: 97.69%\n",
-      "39\t검증 세트 손실: 0.100478\t최선의 손실: 0.073540\t정확도: 97.34%\n",
-      "40\t검증 세트 손실: 0.108675\t최선의 손실: 0.073540\t정확도: 97.65%\n",
-      "41\t검증 세트 손실: 0.103485\t최선의 손실: 0.073540\t정확도: 96.91%\n",
-      "42\t검증 세트 손실: 0.090606\t최선의 손실: 0.073540\t정확도: 97.38%\n",
-      "43\t검증 세트 손실: 0.093726\t최선의 손실: 0.073540\t정확도: 97.85%\n",
-      "44\t검증 세트 손실: 0.092228\t최선의 손실: 0.073540\t정확도: 97.54%\n",
-      "45\t검증 세트 손실: 0.093253\t최선의 손실: 0.073540\t정확도: 98.05%\n",
-      "46\t검증 세트 손실: 0.088207\t최선의 손실: 0.073540\t정확도: 97.89%\n",
-      "47\t검증 세트 손실: 0.081244\t최선의 손실: 0.073540\t정확도: 97.97%\n",
-      "48\t검증 세트 손실: 0.092446\t최선의 손실: 0.073540\t정확도: 97.97%\n",
-      "49\t검증 세트 손실: 0.097328\t최선의 손실: 0.073540\t정확도: 97.97%\n",
+      "57\t검증 세트 손실: 0.099213\t최선의 손실: 0.074589\t정확도: 97.62%\n",
+      "58\t검증 세트 손실: 0.109328\t최선의 손실: 0.074589\t정확도: 97.03%\n",
+      "59\t검증 세트 손실: 0.089221\t최선의 손실: 0.074589\t정확도: 97.73%\n",
+      "60\t검증 세트 손실: 0.099848\t최선의 손실: 0.074589\t정확도: 97.85%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=30, total=  30.6s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 0.132748\t최선의 손실: 0.132748\t정확도: 95.93%\n",
-      "1\t검증 세트 손실: 0.128601\t최선의 손실: 0.128601\t정확도: 96.52%\n",
-      "2\t검증 세트 손실: 0.101795\t최선의 손실: 0.101795\t정확도: 97.07%\n",
-      "3\t검증 세트 손실: 0.094923\t최선의 손실: 0.094923\t정확도: 97.73%\n",
-      "4\t검증 세트 손실: 0.104574\t최선의 손실: 0.094923\t정확도: 97.46%\n",
-      "5\t검증 세트 손실: 0.098070\t최선의 손실: 0.094923\t정확도: 97.07%\n",
-      "6\t검증 세트 손실: 0.096384\t최선의 손실: 0.094923\t정확도: 97.69%\n",
-      "7\t검증 세트 손실: 0.087591\t최선의 손실: 0.087591\t정확도: 97.77%\n",
-      "8\t검증 세트 손실: 0.096129\t최선의 손실: 0.087591\t정확도: 97.81%\n",
-      "9\t검증 세트 손실: 0.087051\t최선의 손실: 0.087051\t정확도: 97.65%\n",
-      "10\t검증 세트 손실: 0.106823\t최선의 손실: 0.087051\t정확도: 97.46%\n",
-      "11\t검증 세트 손실: 0.100513\t최선의 손실: 0.087051\t정확도: 97.77%\n",
-      "12\t검증 세트 손실: 0.088493\t최선의 손실: 0.087051\t정확도: 97.97%\n",
-      "13\t검증 세트 손실: 0.092744\t최선의 손실: 0.087051\t정확도: 97.69%\n",
-      "14\t검증 세트 손실: 0.132212\t최선의 손실: 0.087051\t정확도: 97.58%\n",
-      "15\t검증 세트 손실: 0.114787\t최선의 손실: 0.087051\t정확도: 97.46%\n",
-      "16\t검증 세트 손실: 0.088200\t최선의 손실: 0.087051\t정확도: 97.93%\n",
-      "17\t검증 세트 손실: 0.098076\t최선의 손실: 0.087051\t정확도: 97.97%\n",
-      "18\t검증 세트 손실: 0.094970\t최선의 손실: 0.087051\t정확도: 97.26%\n",
-      "19\t검증 세트 손실: 0.128509\t최선의 손실: 0.087051\t정확도: 97.54%\n",
-      "20\t검증 세트 손실: 0.089802\t최선의 손실: 0.087051\t정확도: 97.65%\n",
-      "21\t검증 세트 손실: 0.094732\t최선의 손실: 0.087051\t정확도: 97.89%\n",
-      "22\t검증 세트 손실: 0.081078\t최선의 손실: 0.081078\t정확도: 97.85%\n",
-      "23\t검증 세트 손실: 0.122068\t최선의 손실: 0.081078\t정확도: 97.58%\n",
-      "24\t검증 세트 손실: 0.092031\t최선의 손실: 0.081078\t정확도: 97.77%\n",
-      "25\t검증 세트 손실: 0.092457\t최선의 손실: 0.081078\t정확도: 97.89%\n",
-      "26\t검증 세트 손실: 0.088976\t최선의 손실: 0.081078\t정확도: 97.77%\n",
-      "27\t검증 세트 손실: 0.104787\t최선의 손실: 0.081078\t정확도: 96.91%\n",
-      "28\t검증 세트 손실: 0.102031\t최선의 손실: 0.081078\t정확도: 97.34%\n",
-      "29\t검증 세트 손실: 0.119926\t최선의 손실: 0.081078\t정확도: 97.85%\n",
-      "30\t검증 세트 손실: 0.086494\t최선의 손실: 0.081078\t정확도: 97.89%\n",
-      "31\t검증 세트 손실: 0.103406\t최선의 손실: 0.081078\t정확도: 97.93%\n",
-      "32\t검증 세트 손실: 0.082221\t최선의 손실: 0.081078\t정확도: 97.89%\n",
-      "33\t검증 세트 손실: 0.093253\t최선의 손실: 0.081078\t정확도: 98.08%\n",
-      "34\t검증 세트 손실: 0.098109\t최선의 손실: 0.081078\t정확도: 97.54%\n",
-      "35\t검증 세트 손실: 0.089585\t최선의 손실: 0.081078\t정확도: 97.50%\n",
-      "36\t검증 세트 손실: 0.098972\t최선의 손실: 0.081078\t정확도: 97.62%\n",
-      "37\t검증 세트 손실: 0.088048\t최선의 손실: 0.081078\t정확도: 97.65%\n",
-      "38\t검증 세트 손실: 0.104780\t최선의 손실: 0.081078\t정확도: 97.38%\n",
-      "39\t검증 세트 손실: 0.102054\t최선의 손실: 0.081078\t정확도: 97.62%\n",
-      "40\t검증 세트 손실: 0.105873\t최선의 손실: 0.081078\t정확도: 97.73%\n",
-      "41\t검증 세트 손실: 0.099729\t최선의 손실: 0.081078\t정확도: 97.77%\n",
-      "42\t검증 세트 손실: 0.109357\t최선의 손실: 0.081078\t정확도: 97.93%\n",
-      "43\t검증 세트 손실: 0.174498\t최선의 손실: 0.081078\t정확도: 97.22%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=30, total=  34.6s\n",
+      "[CV] batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 44.603619\t최선의 손실: 44.603619\t정확도: 37.57%\n",
+      "1\t검증 세트 손실: 57.076000\t최선의 손실: 44.603619\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 233.921204\t최선의 손실: 44.603619\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 46.991661\t최선의 손실: 44.603619\t정확도: 20.02%\n",
+      "4\t검증 세트 손실: 373.460785\t최선의 손실: 44.603619\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 1130.587769\t최선의 손실: 44.603619\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 271.191620\t최선의 손실: 44.603619\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 745.257324\t최선의 손실: 44.603619\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1009.146301\t최선의 손실: 44.603619\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 865.743652\t최선의 손실: 44.603619\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 436.311462\t최선의 손실: 44.603619\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 646.576355\t최선의 손실: 44.603619\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 2218.319580\t최선의 손실: 44.603619\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 2307.377930\t최선의 손실: 44.603619\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 737.834839\t최선의 손실: 44.603619\t정확도: 18.84%\n",
+      "15\t검증 세트 손실: 956.285156\t최선의 손실: 44.603619\t정확도: 25.10%\n",
+      "16\t검증 세트 손실: 1471.329346\t최선의 손실: 44.603619\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 471.887085\t최선의 손실: 44.603619\t정확도: 22.60%\n",
+      "18\t검증 세트 손실: 764.497681\t최선의 손실: 44.603619\t정확도: 28.03%\n",
+      "19\t검증 세트 손실: 33088.808594\t최선의 손실: 44.603619\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 2415.676270\t최선의 손실: 44.603619\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 1911.266602\t최선의 손실: 44.603619\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=30, total=  44.4s\n",
-      "[CV] batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 3.935736\t최선의 손실: 3.935736\t정확도: 23.46%\n",
-      "1\t검증 세트 손실: 1.428192\t최선의 손실: 1.428192\t정확도: 36.67%\n",
-      "2\t검증 세트 손실: 87.823257\t최선의 손실: 1.428192\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 477.265045\t최선의 손실: 1.428192\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 515.687439\t최선의 손실: 1.428192\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 767.435486\t최선의 손실: 1.428192\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 294.439850\t최선의 손실: 1.428192\t정확도: 24.16%\n",
-      "7\t검증 세트 손실: 1234.120605\t최선의 손실: 1.428192\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 934.004700\t최선의 손실: 1.428192\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 530.612976\t최선의 손실: 1.428192\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 4956.781250\t최선의 손실: 1.428192\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 620.067627\t최선의 손실: 1.428192\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 172.376007\t최선의 손실: 1.428192\t정확도: 21.89%\n",
-      "13\t검증 세트 손실: 358.428162\t최선의 손실: 1.428192\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1189.850464\t최선의 손실: 1.428192\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 1067.881104\t최선의 손실: 1.428192\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 600.604614\t최선의 손실: 1.428192\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 652.531067\t최선의 손실: 1.428192\t정확도: 19.62%\n",
-      "18\t검증 세트 손실: 713.949341\t최선의 손실: 1.428192\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1786.645264\t최선의 손실: 1.428192\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1641.500366\t최선의 손실: 1.428192\t정확도: 18.69%\n",
-      "21\t검증 세트 손실: 2492.240479\t최선의 손실: 1.428192\t정확도: 20.21%\n",
-      "22\t검증 세트 손실: 1342.372437\t최선의 손실: 1.428192\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160, total=  59.9s\n",
+      "[CV] batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 31.417385\t최선의 손실: 31.417385\t정확도: 23.92%\n",
+      "1\t검증 세트 손실: 35.821529\t최선의 손실: 31.417385\t정확도: 25.65%\n",
+      "2\t검증 세트 손실: 305.805878\t최선의 손실: 31.417385\t정확도: 19.04%\n",
+      "3\t검증 세트 손실: 339.897125\t최선의 손실: 31.417385\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 463.556549\t최선의 손실: 31.417385\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 214.016220\t최선의 손실: 31.417385\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 268.307953\t최선의 손실: 31.417385\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 294.589142\t최선의 손실: 31.417385\t정확도: 22.17%\n",
+      "8\t검증 세트 손실: 1902.266602\t최선의 손실: 31.417385\t정확도: 20.91%\n",
+      "9\t검증 세트 손실: 687.182861\t최선의 손실: 31.417385\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 2618.090576\t최선의 손실: 31.417385\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 993.678833\t최선의 손실: 31.417385\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 2589.103516\t최선의 손실: 31.417385\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 488.464325\t최선의 손실: 31.417385\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 2610.805664\t최선의 손실: 31.417385\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 888.818237\t최선의 손실: 31.417385\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 1280.261597\t최선의 손실: 31.417385\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1762.280640\t최선의 손실: 31.417385\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 3155.860352\t최선의 손실: 31.417385\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 3672.597412\t최선의 손실: 31.417385\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 4980.899414\t최선의 손실: 31.417385\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 44855.925781\t최선의 손실: 31.417385\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 1.655930\t최선의 손실: 1.655930\t정확도: 23.10%\n",
-      "1\t검증 세트 손실: 13.059125\t최선의 손실: 1.655930\t정확도: 18.76%\n",
-      "2\t검증 세트 손실: 25.016153\t최선의 손실: 1.655930\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 18.822029\t최선의 손실: 1.655930\t정확도: 19.35%\n",
-      "4\t검증 세트 손실: 108.908012\t최선의 손실: 1.655930\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 93.154060\t최선의 손실: 1.655930\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 320.722687\t최선의 손실: 1.655930\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 565.895874\t최선의 손실: 1.655930\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 183.174759\t최선의 손실: 1.655930\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 490.149048\t최선의 손실: 1.655930\t정확도: 25.18%\n",
-      "10\t검증 세트 손실: 649.212646\t최선의 손실: 1.655930\t정확도: 21.46%\n",
-      "11\t검증 세트 손실: 222.436310\t최선의 손실: 1.655930\t정확도: 30.14%\n",
-      "12\t검증 세트 손실: 340.904266\t최선의 손실: 1.655930\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 81.053017\t최선의 손실: 1.655930\t정확도: 26.66%\n",
-      "14\t검증 세트 손실: 291.171570\t최선의 손실: 1.655930\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 155.551132\t최선의 손실: 1.655930\t정확도: 19.39%\n",
-      "16\t검증 세트 손실: 252.882324\t최선의 손실: 1.655930\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 687.757690\t최선의 손실: 1.655930\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 600.973694\t최선의 손실: 1.655930\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 690.092163\t최선의 손실: 1.655930\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 2785.769287\t최선의 손실: 1.655930\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1472.519409\t최선의 손실: 1.655930\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160, total=  59.9s\n",
+      "[CV] batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 8.962685\t최선의 손실: 8.962685\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 37.688961\t최선의 손실: 8.962685\t정확도: 24.82%\n",
+      "2\t검증 세트 손실: 557.700623\t최선의 손실: 8.962685\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 229.770920\t최선의 손실: 8.962685\t정확도: 20.91%\n",
+      "4\t검증 세트 손실: 901.391113\t최선의 손실: 8.962685\t정확도: 19.94%\n",
+      "5\t검증 세트 손실: 985.784424\t최선의 손실: 8.962685\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 364.175476\t최선의 손실: 8.962685\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1027.166260\t최선의 손실: 8.962685\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1052.942871\t최선의 손실: 8.962685\t정확도: 34.79%\n",
+      "9\t검증 세트 손실: 789.573486\t최선의 손실: 8.962685\t정확도: 19.43%\n",
+      "10\t검증 세트 손실: 522.053772\t최선의 손실: 8.962685\t정확도: 19.27%\n",
+      "11\t검증 세트 손실: 441.496002\t최선의 손실: 8.962685\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 943.491760\t최선의 손실: 8.962685\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 8188.519043\t최선의 손실: 8.962685\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1500.466064\t최선의 손실: 8.962685\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1963.799805\t최선의 손실: 8.962685\t정확도: 21.03%\n",
+      "16\t검증 세트 손실: 1803.571167\t최선의 손실: 8.962685\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 1125.780884\t최선의 손실: 8.962685\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 2938.617188\t최선의 손실: 8.962685\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 3879.210205\t최선의 손실: 8.962685\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 11356.883789\t최선의 손실: 8.962685\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 53268.101562\t최선의 손실: 8.962685\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160 \n"
+      "[CV]  batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160, total= 1.0min\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.118075\t최선의 손실: 0.118075\t정확도: 96.91%\n",
+      "1\t검증 세트 손실: 0.106562\t최선의 손실: 0.106562\t정확도: 97.42%\n",
+      "2\t검증 세트 손실: 0.118371\t최선의 손실: 0.106562\t정확도: 97.38%\n",
+      "3\t검증 세트 손실: 0.172765\t최선의 손실: 0.106562\t정확도: 95.31%\n",
+      "4\t검증 세트 손실: 0.275066\t최선의 손실: 0.106562\t정확도: 93.08%\n",
+      "5\t검증 세트 손실: 1.608755\t최선의 손실: 0.106562\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.653711\t최선의 손실: 0.106562\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.643294\t최선의 손실: 0.106562\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.672089\t최선의 손실: 0.106562\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.793621\t최선의 손실: 0.106562\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 1.673954\t최선의 손실: 0.106562\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.670698\t최선의 손실: 0.106562\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.615902\t최선의 손실: 0.106562\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.614122\t최선의 손실: 0.106562\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.644770\t최선의 손실: 0.106562\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.635924\t최선의 손실: 0.106562\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.656533\t최선의 손실: 0.106562\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.653952\t최선의 손실: 0.106562\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.663719\t최선의 손실: 0.106562\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.642034\t최선의 손실: 0.106562\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.678812\t최선의 손실: 0.106562\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.632905\t최선의 손실: 0.106562\t정확도: 19.08%\n",
+      "22\t검증 세트 손실: 1.633938\t최선의 손실: 0.106562\t정확도: 22.01%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=100, total=   6.3s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.127634\t최선의 손실: 0.127634\t정확도: 95.82%\n",
+      "1\t검증 세트 손실: 0.135552\t최선의 손실: 0.127634\t정확도: 97.03%\n",
+      "2\t검증 세트 손실: 0.243286\t최선의 손실: 0.127634\t정확도: 94.33%\n",
+      "3\t검증 세트 손실: 0.223460\t최선의 손실: 0.127634\t정확도: 91.83%\n",
+      "4\t검증 세트 손실: 0.208069\t최선의 손실: 0.127634\t정확도: 95.97%\n",
+      "5\t검증 세트 손실: 0.137219\t최선의 손실: 0.127634\t정확도: 96.33%\n",
+      "6\t검증 세트 손실: 0.747697\t최선의 손실: 0.127634\t정확도: 76.39%\n",
+      "7\t검증 세트 손실: 1.673099\t최선의 손실: 0.127634\t정확도: 19.55%\n",
+      "8\t검증 세트 손실: 1.646768\t최선의 손실: 0.127634\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.653666\t최선의 손실: 0.127634\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.653279\t최선의 손실: 0.127634\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.613747\t최선의 손실: 0.127634\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.648018\t최선의 손실: 0.127634\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.634821\t최선의 손실: 0.127634\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.674782\t최선의 손실: 0.127634\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.636206\t최선의 손실: 0.127634\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.654451\t최선의 손실: 0.127634\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1.718930\t최선의 손실: 0.127634\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.685642\t최선의 손실: 0.127634\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.626621\t최선의 손실: 0.127634\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.669104\t최선의 손실: 0.127634\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.635254\t최선의 손실: 0.127634\t정확도: 18.73%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=100, total=   6.1s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.102803\t최선의 손실: 0.102803\t정확도: 97.50%\n",
+      "1\t검증 세트 손실: 0.250941\t최선의 손실: 0.102803\t정확도: 94.88%\n",
+      "2\t검증 세트 손실: 0.177041\t최선의 손실: 0.102803\t정확도: 95.74%\n",
+      "3\t검증 세트 손실: 0.100339\t최선의 손실: 0.100339\t정확도: 97.62%\n",
+      "4\t검증 세트 손실: 0.224696\t최선의 손실: 0.100339\t정확도: 97.77%\n",
+      "5\t검증 세트 손실: 0.157299\t최선의 손실: 0.100339\t정확도: 97.07%\n",
+      "6\t검증 세트 손실: 1.349410\t최선의 손실: 0.100339\t정확도: 39.05%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 1.643614\t최선의 손실: 1.643614\t정확도: 22.05%\n",
-      "1\t검증 세트 손실: 1.563460\t최선의 손실: 1.563460\t정확도: 33.31%\n",
-      "2\t검증 세트 손실: 2.099729\t최선의 손실: 1.563460\t정확도: 18.26%\n",
-      "3\t검증 세트 손실: 9.366303\t최선의 손실: 1.563460\t정확도: 19.35%\n",
-      "4\t검증 세트 손실: 8.751148\t최선의 손실: 1.563460\t정확도: 19.35%\n",
-      "5\t검증 세트 손실: 107.167450\t최선의 손실: 1.563460\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 157.047256\t최선의 손실: 1.563460\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 358.907196\t최선의 손실: 1.563460\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 156.436310\t최선의 손실: 1.563460\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 185.187714\t최선의 손실: 1.563460\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 209.566559\t최선의 손실: 1.563460\t정확도: 20.60%\n",
-      "11\t검증 세트 손실: 1332.775757\t최선의 손실: 1.563460\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 465.334747\t최선의 손실: 1.563460\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 477.056091\t최선의 손실: 1.563460\t정확도: 30.38%\n",
-      "14\t검증 세트 손실: 654.905396\t최선의 손실: 1.563460\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1137.558472\t최선의 손실: 1.563460\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 584.289978\t최선의 손실: 1.563460\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 4246.041504\t최선의 손실: 1.563460\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1478.465454\t최선의 손실: 1.563460\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 3624.825195\t최선의 손실: 1.563460\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1890.855713\t최선의 손실: 1.563460\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1532.874878\t최선의 손실: 1.563460\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 4124.817871\t최선의 손실: 1.563460\t정확도: 20.91%\n",
+      "7\t검증 세트 손실: 1.652212\t최선의 손실: 0.100339\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.651821\t최선의 손실: 0.100339\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.666307\t최선의 손실: 0.100339\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.648440\t최선의 손실: 0.100339\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.672705\t최선의 손실: 0.100339\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.630117\t최선의 손실: 0.100339\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.654115\t최선의 손실: 0.100339\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.656743\t최선의 손실: 0.100339\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.623627\t최선의 손실: 0.100339\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.691659\t최선의 손실: 0.100339\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1.663535\t최선의 손실: 0.100339\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.686658\t최선의 손실: 0.100339\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.665798\t최선의 손실: 0.100339\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.646455\t최선의 손실: 0.100339\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 1.653749\t최선의 손실: 0.100339\t정확도: 18.73%\n",
+      "22\t검증 세트 손실: 1.655734\t최선의 손실: 0.100339\t정확도: 19.08%\n",
+      "23\t검증 세트 손실: 1.657946\t최선의 손실: 0.100339\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.766302\t최선의 손실: 0.100339\t정확도: 20.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160, total= 1.6min\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.140844\t최선의 손실: 0.140844\t정확도: 96.05%\n",
-      "1\t검증 세트 손실: 0.107733\t최선의 손실: 0.107733\t정확도: 97.38%\n",
-      "2\t검증 세트 손실: 0.086312\t최선의 손실: 0.086312\t정확도: 97.93%\n",
-      "3\t검증 세트 손실: 0.105199\t최선의 손실: 0.086312\t정확도: 97.42%\n",
-      "4\t검증 세트 손실: 0.100894\t최선의 손실: 0.086312\t정확도: 98.20%\n",
-      "5\t검증 세트 손실: 1.704461\t최선의 손실: 0.086312\t정확도: 18.73%\n",
-      "6\t검증 세트 손실: 1.667866\t최선의 손실: 0.086312\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.640434\t최선의 손실: 0.086312\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.682735\t최선의 손실: 0.086312\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.839640\t최선의 손실: 0.086312\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.632386\t최선의 손실: 0.086312\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.657753\t최선의 손실: 0.086312\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.614745\t최선의 손실: 0.086312\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.608490\t최선의 손실: 0.086312\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.642197\t최선의 손실: 0.086312\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.647208\t최선의 손실: 0.086312\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.663868\t최선의 손실: 0.086312\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.652653\t최선의 손실: 0.086312\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.664953\t최선의 손실: 0.086312\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.636043\t최선의 손실: 0.086312\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.628326\t최선의 손실: 0.086312\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.625710\t최선의 손실: 0.086312\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.655701\t최선의 손실: 0.086312\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.613122\t최선의 손실: 0.086312\t정확도: 19.08%\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=100, total=   6.8s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 2.358086\t최선의 손실: 2.358086\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.625203\t최선의 손실: 1.625203\t정확도: 20.91%\n",
+      "2\t검증 세트 손실: 1.618189\t최선의 손실: 1.618189\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.667678\t최선의 손실: 1.618189\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 2.071137\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.722932\t최선의 손실: 1.618189\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1.711341\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.669814\t최선의 손실: 1.618189\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.696455\t최선의 손실: 1.618189\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.685053\t최선의 손실: 1.618189\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 1.665492\t최선의 손실: 1.618189\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.682343\t최선의 손실: 1.618189\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 1.711671\t최선의 손실: 1.618189\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 1.997025\t최선의 손실: 1.618189\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.833612\t최선의 손실: 1.618189\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.737887\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.830271\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.731167\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 1.992920\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.919410\t최선의 손실: 1.618189\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.622247\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.734315\t최선의 손실: 1.618189\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.621186\t최선의 손실: 1.618189\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.925248\t최선의 손실: 1.618189\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total=  15.8s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.113305\t최선의 손실: 0.113305\t정확도: 96.64%\n",
-      "1\t검증 세트 손실: 0.107335\t최선의 손실: 0.107335\t정확도: 97.38%\n",
-      "2\t검증 세트 손실: 0.084404\t최선의 손실: 0.084404\t정확도: 97.77%\n",
-      "3\t검증 세트 손실: 0.123882\t최선의 손실: 0.084404\t정확도: 97.19%\n",
-      "4\t검증 세트 손실: 0.077747\t최선의 손실: 0.077747\t정확도: 98.12%\n",
-      "5\t검증 세트 손실: 0.110850\t최선의 손실: 0.077747\t정확도: 97.34%\n",
-      "6\t검증 세트 손실: 0.101198\t최선의 손실: 0.077747\t정확도: 97.65%\n",
-      "7\t검증 세트 손실: 1.650525\t최선의 손실: 0.077747\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.633111\t최선의 손실: 0.077747\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.651657\t최선의 손실: 0.077747\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.634949\t최선의 손실: 0.077747\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.632046\t최선의 손실: 0.077747\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.644089\t최선의 손실: 0.077747\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.671789\t최선의 손실: 0.077747\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 1.656988\t최선의 손실: 0.077747\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.634520\t최선의 손실: 0.077747\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.639502\t최선의 손실: 0.077747\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 1.698394\t최선의 손실: 0.077747\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.676228\t최선의 손실: 0.077747\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.625249\t최선의 손실: 0.077747\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.652293\t최선의 손실: 0.077747\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.641266\t최선의 손실: 0.077747\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.639094\t최선의 손실: 0.077747\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.693511\t최선의 손실: 0.077747\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.776209\t최선의 손실: 0.077747\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 1.629433\t최선의 손실: 0.077747\t정확도: 20.91%\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=90, total=   6.5s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.645130\t최선의 손실: 1.645130\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.696734\t최선의 손실: 1.645130\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.773051\t최선의 손실: 1.645130\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.616661\t최선의 손실: 1.616661\t정확도: 20.91%\n",
+      "4\t검증 세트 손실: 1.731495\t최선의 손실: 1.616661\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.746448\t최선의 손실: 1.616661\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.683097\t최선의 손실: 1.616661\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.746264\t최선의 손실: 1.616661\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.883200\t최선의 손실: 1.616661\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.649718\t최선의 손실: 1.616661\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 1.696744\t최선의 손실: 1.616661\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.682731\t최선의 손실: 1.616661\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.683138\t최선의 손실: 1.616661\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 1.670957\t최선의 손실: 1.616661\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.737317\t최선의 손실: 1.616661\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.630379\t최선의 손실: 1.616661\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 1.709789\t최선의 손실: 1.616661\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.768050\t최선의 손실: 1.616661\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.829722\t최선의 손실: 1.616661\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.643881\t최선의 손실: 1.616661\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1.847711\t최선의 손실: 1.616661\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 1.833085\t최선의 손실: 1.616661\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.684882\t최선의 손실: 1.616661\t정확도: 19.08%\n",
+      "23\t검증 세트 손실: 1.832161\t최선의 손실: 1.616661\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.847516\t최선의 손실: 1.616661\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total=   6.7s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.130376\t최선의 손실: 0.130376\t정확도: 95.90%\n",
-      "1\t검증 세트 손실: 0.135665\t최선의 손실: 0.130376\t정확도: 96.91%\n",
-      "2\t검증 세트 손실: 0.087599\t최선의 손실: 0.087599\t정확도: 97.89%\n",
-      "3\t검증 세트 손실: 0.091041\t최선의 손실: 0.087599\t정확도: 97.97%\n",
-      "4\t검증 세트 손실: 0.246927\t최선의 손실: 0.087599\t정확도: 95.35%\n",
-      "5\t검증 세트 손실: 1.644476\t최선의 손실: 0.087599\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.788630\t최선의 손실: 0.087599\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.622952\t최선의 손실: 0.087599\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.650047\t최선의 손실: 0.087599\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.665366\t최선의 손실: 0.087599\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.643437\t최선의 손실: 0.087599\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.663334\t최선의 손실: 0.087599\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.626755\t최선의 손실: 0.087599\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.649446\t최선의 손실: 0.087599\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.657714\t최선의 손실: 0.087599\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.626322\t최선의 손실: 0.087599\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.687102\t최선의 손실: 0.087599\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 1.658533\t최선의 손실: 0.087599\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.683232\t최선의 손실: 0.087599\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.665227\t최선의 손실: 0.087599\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.645235\t최선의 손실: 0.087599\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 1.649010\t최선의 손실: 0.087599\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.657277\t최선의 손실: 0.087599\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.658349\t최선의 손실: 0.087599\t정확도: 22.01%\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=90, total=   6.8s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 1.760989\t최선의 손실: 1.760989\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.762391\t최선의 손실: 1.760989\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.634605\t최선의 손실: 1.634605\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.627629\t최선의 손실: 1.627629\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 1.801025\t최선의 손실: 1.627629\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 1.795902\t최선의 손실: 1.627629\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.761317\t최선의 손실: 1.627629\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.746977\t최선의 손실: 1.627629\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.629144\t최선의 손실: 1.627629\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.679759\t최선의 손실: 1.627629\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 1.614322\t최선의 손실: 1.614322\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.781722\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.744356\t최선의 손실: 1.614322\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.676337\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.957881\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.700733\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.901250\t최선의 손실: 1.614322\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.617781\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 1.627859\t최선의 손실: 1.614322\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.747921\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.767990\t최선의 손실: 1.614322\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 2.035471\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.814874\t최선의 손실: 1.614322\t정확도: 20.91%\n",
+      "23\t검증 세트 손실: 1.685566\t최선의 손실: 1.614322\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 1.814025\t최선의 손실: 1.614322\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 1.699055\t최선의 손실: 1.614322\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.702359\t최선의 손실: 1.614322\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 1.746646\t최선의 손실: 1.614322\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 2.184501\t최선의 손실: 1.614322\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.886002\t최선의 손실: 1.614322\t정확도: 19.08%\n",
+      "30\t검증 세트 손실: 2.034138\t최선의 손실: 1.614322\t정확도: 20.91%\n",
+      "31\t검증 세트 손실: 1.854042\t최선의 손실: 1.614322\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=100, total=   6.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1.812570\t최선의 손실: 1.812570\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 1.635783\t최선의 손실: 1.635783\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.721149\t최선의 손실: 1.635783\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.721517\t최선의 손실: 1.635783\t정확도: 20.91%\n",
-      "4\t검증 세트 손실: 1.765401\t최선의 손실: 1.635783\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.660566\t최선의 손실: 1.635783\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.911692\t최선의 손실: 1.635783\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.691969\t최선의 손실: 1.635783\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 1.692442\t최선의 손실: 1.635783\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 1.723169\t최선의 손실: 1.635783\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.773139\t최선의 손실: 1.635783\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.762647\t최선의 손실: 1.635783\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.648907\t최선의 손실: 1.635783\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.798253\t최선의 손실: 1.635783\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.968955\t최선의 손실: 1.635783\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.741186\t최선의 손실: 1.635783\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.863459\t최선의 손실: 1.635783\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.728052\t최선의 손실: 1.635783\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.740497\t최선의 손실: 1.635783\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.770992\t최선의 손실: 1.635783\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.633493\t최선의 손실: 1.633493\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.739604\t최선의 손실: 1.633493\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.621955\t최선의 손실: 1.621955\t정확도: 20.91%\n",
-      "23\t검증 세트 손실: 1.760491\t최선의 손실: 1.621955\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.621809\t최선의 손실: 1.621809\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.804128\t최선의 손실: 1.621809\t정확도: 19.08%\n",
-      "26\t검증 세트 손실: 1.731149\t최선의 손실: 1.621809\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 1.914132\t최선의 손실: 1.621809\t정확도: 19.27%\n",
-      "28\t검증 세트 손실: 1.787944\t최선의 손실: 1.621809\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.712636\t최선의 손실: 1.621809\t정확도: 18.73%\n",
-      "30\t검증 세트 손실: 2.050191\t최선의 손실: 1.621809\t정확도: 18.73%\n",
-      "31\t검증 세트 손실: 1.623982\t최선의 손실: 1.621809\t정확도: 19.27%\n",
-      "32\t검증 세트 손실: 1.788369\t최선의 손실: 1.621809\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 1.707813\t최선의 손실: 1.621809\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 1.646429\t최선의 손실: 1.621809\t정확도: 19.27%\n",
-      "35\t검증 세트 손실: 1.726512\t최선의 손실: 1.621809\t정확도: 19.08%\n",
-      "36\t검증 세트 손실: 1.662973\t최선의 손실: 1.621809\t정확도: 22.01%\n",
-      "37\t검증 세트 손실: 1.733527\t최선의 손실: 1.621809\t정확도: 18.73%\n",
-      "38\t검증 세트 손실: 1.772076\t최선의 손실: 1.621809\t정확도: 22.01%\n",
-      "39\t검증 세트 손실: 1.686108\t최선의 손실: 1.621809\t정확도: 19.08%\n",
-      "40\t검증 세트 손실: 1.651766\t최선의 손실: 1.621809\t정확도: 22.01%\n",
-      "41\t검증 세트 손실: 1.986846\t최선의 손실: 1.621809\t정확도: 18.73%\n",
-      "42\t검증 세트 손실: 1.691391\t최선의 손실: 1.621809\t정확도: 19.27%\n"
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=90, total=   8.6s\n",
+      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.136570\t최선의 손실: 0.136570\t정확도: 96.44%\n",
+      "1\t검증 세트 손실: 0.113560\t최선의 손실: 0.113560\t정확도: 96.60%\n",
+      "2\t검증 세트 손실: 0.331945\t최선의 손실: 0.113560\t정확도: 85.50%\n",
+      "3\t검증 세트 손실: 0.220078\t최선의 손실: 0.113560\t정확도: 93.35%\n",
+      "4\t검증 세트 손실: 0.155069\t최선의 손실: 0.113560\t정확도: 95.54%\n",
+      "5\t검증 세트 손실: 0.147302\t최선의 손실: 0.113560\t정확도: 94.68%\n",
+      "6\t검증 세트 손실: 0.138980\t최선의 손실: 0.113560\t정확도: 96.33%\n",
+      "7\t검증 세트 손실: 0.456172\t최선의 손실: 0.113560\t정확도: 77.13%\n",
+      "8\t검증 세트 손실: 1.260594\t최선의 손실: 0.113560\t정확도: 43.71%\n",
+      "9\t검증 세트 손실: 1.801050\t최선의 손실: 0.113560\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 1.636597\t최선의 손실: 0.113560\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.634745\t최선의 손실: 0.113560\t정확도: 22.13%\n",
+      "12\t검증 세트 손실: 1.615496\t최선의 손실: 0.113560\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.608788\t최선의 손실: 0.113560\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.643380\t최선의 손실: 0.113560\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.622566\t최선의 손실: 0.113560\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.631819\t최선의 손실: 0.113560\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.630251\t최선의 손실: 0.113560\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.683307\t최선의 손실: 0.113560\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.622398\t최선의 손실: 0.113560\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.655218\t최선의 손실: 0.113560\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.655367\t최선의 손실: 0.113560\t정확도: 19.08%\n",
+      "22\t검증 세트 손실: 1.630242\t최선의 손실: 0.113560\t정확도: 22.01%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=70, total=   6.2s\n",
+      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.141831\t최선의 손실: 0.141831\t정확도: 96.44%\n",
+      "1\t검증 세트 손실: 0.137536\t최선의 손실: 0.137536\t정확도: 96.36%\n",
+      "2\t검증 세트 손실: 0.102113\t최선의 손실: 0.102113\t정확도: 97.30%\n",
+      "3\t검증 세트 손실: 0.181388\t최선의 손실: 0.102113\t정확도: 96.13%\n",
+      "4\t검증 세트 손실: 0.137699\t최선의 손실: 0.102113\t정확도: 97.54%\n",
+      "5\t검증 세트 손실: 0.174479\t최선의 손실: 0.102113\t정확도: 95.97%\n",
+      "6\t검증 세트 손실: 1.691823\t최선의 손실: 0.102113\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 1.624843\t최선의 손실: 0.102113\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.645752\t최선의 손실: 0.102113\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.689136\t최선의 손실: 0.102113\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.615376\t최선의 손실: 0.102113\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.647802\t최선의 손실: 0.102113\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.636864\t최선의 손실: 0.102113\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.655694\t최선의 손실: 0.102113\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.647959\t최선의 손실: 0.102113\t정확도: 22.01%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "43\t검증 세트 손실: 1.662178\t최선의 손실: 1.621809\t정확도: 18.73%\n",
-      "44\t검증 세트 손실: 1.691987\t최선의 손실: 1.621809\t정확도: 19.27%\n",
-      "45\t검증 세트 손실: 1.656098\t최선의 손실: 1.621809\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.626618\t최선의 손실: 0.102113\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.620060\t최선의 손실: 0.102113\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1.648194\t최선의 손실: 0.102113\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.685862\t최선의 손실: 0.102113\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.626079\t최선의 손실: 0.102113\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.634284\t최선의 손실: 0.102113\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.696780\t최선의 손실: 0.102113\t정확도: 18.73%\n",
+      "22\t검증 세트 손실: 1.620134\t최선의 손실: 0.102113\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.706395\t최선의 손실: 0.102113\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=  15.8s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1.831577\t최선의 손실: 1.831577\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.692328\t최선의 손실: 1.692328\t정확도: 20.91%\n",
-      "2\t검증 세트 손실: 1.611309\t최선의 손실: 1.611309\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.647887\t최선의 손실: 1.611309\t정확도: 20.91%\n",
-      "4\t검증 세트 손실: 1.659801\t최선의 손실: 1.611309\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 4.360977\t최선의 손실: 1.611309\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.641649\t최선의 손실: 1.611309\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 1.641531\t최선의 손실: 1.611309\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.694308\t최선의 손실: 1.611309\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.668923\t최선의 손실: 1.611309\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.685468\t최선의 손실: 1.611309\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.660922\t최선의 손실: 1.611309\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.684038\t최선의 손실: 1.611309\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 1.647720\t최선의 손실: 1.611309\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.933240\t최선의 손실: 1.611309\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.685225\t최선의 손실: 1.611309\t정확도: 19.27%\n",
-      "16\t검증 세트 손실: 1.699828\t최선의 손실: 1.611309\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 2.380192\t최선의 손실: 1.611309\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.632355\t최선의 손실: 1.611309\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.673223\t최선의 손실: 1.611309\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.677084\t최선의 손실: 1.611309\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.799912\t최선의 손실: 1.611309\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.771075\t최선의 손실: 1.611309\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.663177\t최선의 손실: 1.611309\t정확도: 22.01%\n",
+      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=70, total=   6.7s\n",
+      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.147154\t최선의 손실: 0.147154\t정확도: 96.33%\n",
+      "1\t검증 세트 손실: 0.142988\t최선의 손실: 0.142988\t정확도: 97.07%\n",
+      "2\t검증 세트 손실: 0.168618\t최선의 손실: 0.142988\t정확도: 96.13%\n",
+      "3\t검증 세트 손실: 0.160115\t최선의 손실: 0.142988\t정확도: 96.29%\n",
+      "4\t검증 세트 손실: 1.655345\t최선의 손실: 0.142988\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.636789\t최선의 손실: 0.142988\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.625242\t최선의 손실: 0.142988\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.653733\t최선의 손실: 0.142988\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.650574\t최선의 손실: 0.142988\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.698096\t최선의 손실: 0.142988\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.636430\t최선의 손실: 0.142988\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.660036\t최선의 손실: 0.142988\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.655874\t최선의 손실: 0.142988\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.618041\t최선의 손실: 0.142988\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.652179\t최선의 손실: 0.142988\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.654369\t최선의 손실: 0.142988\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.687925\t최선의 손실: 0.142988\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.662491\t최선의 손실: 0.142988\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.664543\t최선의 손실: 0.142988\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.624927\t최선의 손실: 0.142988\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.608333\t최선의 손실: 0.142988\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.693674\t최선의 손실: 0.142988\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.619706\t최선의 손실: 0.142988\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=   6.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1.658048\t최선의 손실: 1.658048\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 1.674186\t최선의 손실: 1.658048\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.675104\t최선의 손실: 1.658048\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.617959\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.909110\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.689467\t최선의 손실: 1.617959\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.783185\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.639498\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.674492\t최선의 손실: 1.617959\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.783830\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.673468\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.792371\t최선의 손실: 1.617959\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.755255\t최선의 손실: 1.617959\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.751409\t최선의 손실: 1.617959\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.717926\t최선의 손실: 1.617959\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.738792\t최선의 손실: 1.617959\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 1.723846\t최선의 손실: 1.617959\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.733323\t최선의 손실: 1.617959\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.645533\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 1.652473\t최선의 손실: 1.617959\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.658781\t최선의 손실: 1.617959\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 2.150284\t최선의 손실: 1.617959\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.735653\t최선의 손실: 1.617959\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.679609\t최선의 손실: 1.617959\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.623802\t최선의 손실: 1.617959\t정확도: 18.73%\n",
+      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=70, total=   6.2s\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 374.142914\t최선의 손실: 374.142914\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 48.390709\t최선의 손실: 48.390709\t정확도: 20.95%\n",
+      "2\t검증 세트 손실: 6360.429199\t최선의 손실: 48.390709\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 513.527710\t최선의 손실: 48.390709\t정확도: 22.95%\n",
+      "4\t검증 세트 손실: 1913.034424\t최선의 손실: 48.390709\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 770.760437\t최선의 손실: 48.390709\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 1609.601685\t최선의 손실: 48.390709\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 3114.935303\t최선의 손실: 48.390709\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 723.725159\t최선의 손실: 48.390709\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 14836.805664\t최선의 손실: 48.390709\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 15184.696289\t최선의 손실: 48.390709\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 939.771667\t최선의 손실: 48.390709\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 3369.956299\t최선의 손실: 48.390709\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 14263.865234\t최선의 손실: 48.390709\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1471.974731\t최선의 손실: 48.390709\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 2158.067383\t최선의 손실: 48.390709\t정확도: 36.94%\n",
+      "16\t검증 세트 손실: 99986.835938\t최선의 손실: 48.390709\t정확도: 22.09%\n",
+      "17\t검증 세트 손실: 5382.354980\t최선의 손실: 48.390709\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 2891.717041\t최선의 손실: 48.390709\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 6483.016602\t최선의 손실: 48.390709\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 272.895569\t최선의 손실: 48.390709\t정확도: 22.36%\n",
+      "21\t검증 세트 손실: 17198.021484\t최선의 손실: 48.390709\t정확도: 19.31%\n",
+      "22\t검증 세트 손실: 3746.890137\t최선의 손실: 48.390709\t정확도: 23.53%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=90, total=   6.4s\n",
-      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.143820\t최선의 손실: 0.143820\t정확도: 96.56%\n",
-      "1\t검증 세트 손실: 0.121053\t최선의 손실: 0.121053\t정확도: 96.91%\n",
-      "2\t검증 세트 손실: 0.153223\t최선의 손실: 0.121053\t정확도: 96.40%\n",
-      "3\t검증 세트 손실: 0.646729\t최선의 손실: 0.121053\t정확도: 68.84%\n",
-      "4\t검증 세트 손실: 0.954148\t최선의 손실: 0.121053\t정확도: 56.45%\n",
-      "5\t검증 세트 손실: 1.073974\t최선의 손실: 0.121053\t정확도: 58.91%\n",
-      "6\t검증 세트 손실: 0.986529\t최선의 손실: 0.121053\t정확도: 53.28%\n",
-      "7\t검증 세트 손실: 1.620921\t최선의 손실: 0.121053\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.717676\t최선의 손실: 0.121053\t정확도: 19.39%\n",
-      "9\t검증 세트 손실: 1.674981\t최선의 손실: 0.121053\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.631496\t최선의 손실: 0.121053\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.629994\t최선의 손실: 0.121053\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.620388\t최선의 손실: 0.121053\t정확도: 22.13%\n",
-      "13\t검증 세트 손실: 1.611669\t최선의 손실: 0.121053\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.645818\t최선의 손실: 0.121053\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.622679\t최선의 손실: 0.121053\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.625769\t최선의 손실: 0.121053\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.629560\t최선의 손실: 0.121053\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.686339\t최선의 손실: 0.121053\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.622157\t최선의 손실: 0.121053\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.654976\t최선의 손실: 0.121053\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.653416\t최선의 손실: 0.121053\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 1.631867\t최선의 손실: 0.121053\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total= 1.0min\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 55688.925781\t최선의 손실: 55688.925781\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 4392.565918\t최선의 손실: 4392.565918\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 828.693970\t최선의 손실: 828.693970\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 2576.187744\t최선의 손실: 828.693970\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 745.360291\t최선의 손실: 745.360291\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 10470.461914\t최선의 손실: 745.360291\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 6220.396973\t최선의 손실: 745.360291\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 2581.976074\t최선의 손실: 745.360291\t정확도: 20.56%\n",
+      "8\t검증 세트 손실: 1202.184937\t최선의 손실: 745.360291\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 1574.841919\t최선의 손실: 745.360291\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 2901.752930\t최선의 손실: 745.360291\t정확도: 29.67%\n",
+      "11\t검증 세트 손실: 541.390198\t최선의 손실: 541.390198\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 300.838196\t최선의 손실: 300.838196\t정확도: 34.25%\n",
+      "13\t검증 세트 손실: 1486.160034\t최선의 손실: 300.838196\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 11520.755859\t최선의 손실: 300.838196\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 857.083679\t최선의 손실: 300.838196\t정확도: 18.88%\n",
+      "16\t검증 세트 손실: 1832.955688\t최선의 손실: 300.838196\t정확도: 19.23%\n",
+      "17\t검증 세트 손실: 1246.401733\t최선의 손실: 300.838196\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 279.770508\t최선의 손실: 279.770508\t정확도: 21.38%\n",
+      "19\t검증 세트 손실: 10179.719727\t최선의 손실: 279.770508\t정확도: 19.31%\n",
+      "20\t검증 세트 손실: 17181.216797\t최선의 손실: 279.770508\t정확도: 22.05%\n",
+      "21\t검증 세트 손실: 3282.569092\t최선의 손실: 279.770508\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 12479.372070\t최선의 손실: 279.770508\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 802.816162\t최선의 손실: 279.770508\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 2398.779541\t최선의 손실: 279.770508\t정확도: 19.08%\n",
+      "25\t검증 세트 손실: 4071.771973\t최선의 손실: 279.770508\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 4728.353516\t최선의 손실: 279.770508\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 588.928406\t최선의 손실: 279.770508\t정확도: 25.41%\n",
+      "28\t검증 세트 손실: 7413.416504\t최선의 손실: 279.770508\t정확도: 19.27%\n",
+      "29\t검증 세트 손실: 703.142517\t최선의 손실: 279.770508\t정확도: 20.17%\n",
+      "30\t검증 세트 손실: 10895.582031\t최선의 손실: 279.770508\t정확도: 22.09%\n",
+      "31\t검증 세트 손실: 50191.824219\t최선의 손실: 279.770508\t정확도: 18.73%\n",
+      "32\t검증 세트 손실: 5605.180176\t최선의 손실: 279.770508\t정확도: 18.73%\n",
+      "33\t검증 세트 손실: 8217.391602\t최선의 손실: 279.770508\t정확도: 22.01%\n",
+      "34\t검증 세트 손실: 1585.447021\t최선의 손실: 279.770508\t정확도: 39.95%\n",
+      "35\t검증 세트 손실: 5923.179688\t최선의 손실: 279.770508\t정확도: 21.50%\n",
+      "36\t검증 세트 손실: 34127.644531\t최선의 손실: 279.770508\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 3251.662598\t최선의 손실: 279.770508\t정확도: 20.91%\n",
+      "38\t검증 세트 손실: 5515.391602\t최선의 손실: 279.770508\t정확도: 18.73%\n",
+      "39\t검증 세트 손실: 1658.124878\t최선의 손실: 279.770508\t정확도: 19.23%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=   5.9s\n",
-      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.183666\t최선의 손실: 0.183666\t정확도: 96.09%\n",
-      "1\t검증 세트 손실: 0.115698\t최선의 손실: 0.115698\t정확도: 97.38%\n",
-      "2\t검증 세트 손실: 0.190165\t최선의 손실: 0.115698\t정확도: 95.04%\n",
-      "3\t검증 세트 손실: 0.880207\t최선의 손실: 0.115698\t정확도: 62.08%\n",
-      "4\t검증 세트 손실: 1.359969\t최선의 손실: 0.115698\t정확도: 39.84%\n",
-      "5\t검증 세트 손실: 1.459699\t최선의 손실: 0.115698\t정확도: 32.29%\n",
-      "6\t검증 세트 손실: 1.228725\t최선의 손실: 0.115698\t정확도: 38.82%\n",
-      "7\t검증 세트 손실: 1.227068\t최선의 손실: 0.115698\t정확도: 38.12%\n",
-      "8\t검증 세트 손실: 1.294101\t최선의 손실: 0.115698\t정확도: 36.90%\n",
-      "9\t검증 세트 손실: 1.262501\t최선의 손실: 0.115698\t정확도: 38.98%\n",
-      "10\t검증 세트 손실: 1.213813\t최선의 손실: 0.115698\t정확도: 38.15%\n",
-      "11\t검증 세트 손실: 1.207595\t최선의 손실: 0.115698\t정확도: 39.48%\n",
-      "12\t검증 세트 손실: 1.513624\t최선의 손실: 0.115698\t정확도: 27.91%\n",
-      "13\t검증 세트 손실: 1.650400\t최선의 손실: 0.115698\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 1.634100\t최선의 손실: 0.115698\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.630492\t최선의 손실: 0.115698\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.618201\t최선의 손실: 0.115698\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 1.653457\t최선의 손실: 0.115698\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.685300\t최선의 손실: 0.115698\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.632604\t최선의 손실: 0.115698\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.636881\t최선의 손실: 0.115698\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 1.695750\t최선의 손실: 0.115698\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.623224\t최선의 손실: 0.115698\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total= 1.8min\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 29066.746094\t최선의 손실: 29066.746094\t정확도: 21.58%\n",
+      "1\t검증 세트 손실: 3538.552734\t최선의 손실: 3538.552734\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 3318.723145\t최선의 손실: 3318.723145\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1678.035767\t최선의 손실: 1678.035767\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1048.679321\t최선의 손실: 1048.679321\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1289.888184\t최선의 손실: 1048.679321\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1431.220337\t최선의 손실: 1048.679321\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 3054.009766\t최선의 손실: 1048.679321\t정확도: 19.00%\n",
+      "8\t검증 세트 손실: 1389.281250\t최선의 손실: 1048.679321\t정확도: 35.38%\n",
+      "9\t검증 세트 손실: 8070.395508\t최선의 손실: 1048.679321\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 999.222412\t최선의 손실: 999.222412\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 217.057571\t최선의 손실: 217.057571\t정확도: 22.56%\n",
+      "12\t검증 세트 손실: 17120.628906\t최선의 손실: 217.057571\t정확도: 21.31%\n",
+      "13\t검증 세트 손실: 3380.516113\t최선의 손실: 217.057571\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 696.650818\t최선의 손실: 217.057571\t정확도: 23.53%\n",
+      "15\t검증 세트 손실: 2487.962646\t최선의 손실: 217.057571\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 254.737030\t최선의 손실: 217.057571\t정확도: 22.17%\n",
+      "17\t검증 세트 손실: 10804.982422\t최선의 손실: 217.057571\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 1193.532837\t최선의 손실: 217.057571\t정확도: 19.51%\n",
+      "19\t검증 세트 손실: 30195.392578\t최선의 손실: 217.057571\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 981.705017\t최선의 손실: 217.057571\t정확도: 18.80%\n",
+      "21\t검증 세트 손실: 4985.041016\t최선의 손실: 217.057571\t정확도: 19.23%\n",
+      "22\t검증 세트 손실: 289.108276\t최선의 손실: 217.057571\t정확도: 40.03%\n",
+      "23\t검증 세트 손실: 4222.863281\t최선의 손실: 217.057571\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 10291.775391\t최선의 손실: 217.057571\t정확도: 19.27%\n",
+      "25\t검증 세트 손실: 900.752930\t최선의 손실: 217.057571\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 7897.083496\t최선의 손실: 217.057571\t정확도: 20.91%\n",
+      "27\t검증 세트 손실: 11357.541016\t최선의 손실: 217.057571\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 5441.356445\t최선의 손실: 217.057571\t정확도: 22.01%\n",
+      "29\t검증 세트 손실: 4268.490234\t최선의 손실: 217.057571\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 5862.382812\t최선의 손실: 217.057571\t정확도: 21.31%\n",
+      "31\t검증 세트 손실: 2207.669189\t최선의 손실: 217.057571\t정확도: 21.85%\n",
+      "32\t검증 세트 손실: 7359.359863\t최선의 손실: 217.057571\t정확도: 18.76%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=  12.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.149209\t최선의 손실: 0.149209\t정확도: 96.76%\n",
-      "1\t검증 세트 손실: 0.133343\t최선의 손실: 0.133343\t정확도: 96.95%\n",
-      "2\t검증 세트 손실: 0.112315\t최선의 손실: 0.112315\t정확도: 97.15%\n",
-      "3\t검증 세트 손실: 0.093374\t최선의 손실: 0.093374\t정확도: 97.50%\n",
-      "4\t검증 세트 손실: 0.144200\t최선의 손실: 0.093374\t정확도: 96.95%\n",
-      "5\t검증 세트 손실: 0.564498\t최선의 손실: 0.093374\t정확도: 83.97%\n",
-      "6\t검증 세트 손실: 1.747772\t최선의 손실: 0.093374\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.642076\t최선의 손실: 0.093374\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.653438\t최선의 손실: 0.093374\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.701074\t최선의 손실: 0.093374\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.635780\t최선의 손실: 0.093374\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.663325\t최선의 손실: 0.093374\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.654191\t최선의 손실: 0.093374\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.615602\t최선의 손실: 0.093374\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.649638\t최선의 손실: 0.093374\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.679013\t최선의 손실: 0.093374\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.675822\t최선의 손실: 0.093374\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.651039\t최선의 손실: 0.093374\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.667151\t최선의 손실: 0.093374\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.624195\t최선의 손실: 0.093374\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.607952\t최선의 손실: 0.093374\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.680789\t최선의 손실: 0.093374\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.620222\t최선의 손실: 0.093374\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.700507\t최선의 손실: 0.093374\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.761226\t최선의 손실: 0.093374\t정확도: 20.91%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=70, total=   7.8s\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 444.737091\t최선의 손실: 444.737091\t정확도: 19.04%\n",
-      "1\t검증 세트 손실: 26800.894531\t최선의 손실: 444.737091\t정확도: 19.31%\n",
-      "2\t검증 세트 손실: 3236.734131\t최선의 손실: 444.737091\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 4373.140137\t최선의 손실: 444.737091\t정확도: 19.31%\n",
-      "4\t검증 세트 손실: 616.889648\t최선의 손실: 444.737091\t정확도: 19.04%\n",
-      "5\t검증 세트 손실: 682.468506\t최선의 손실: 444.737091\t정확도: 32.49%\n",
-      "6\t검증 세트 손실: 1305.881714\t최선의 손실: 444.737091\t정확도: 18.69%\n",
-      "7\t검증 세트 손실: 169.501068\t최선의 손실: 169.501068\t정확도: 30.53%\n"
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total= 1.5min\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8\t검증 세트 손실: 23960.976562\t최선의 손실: 169.501068\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1319.437500\t최선의 손실: 169.501068\t정확도: 35.22%\n",
-      "10\t검증 세트 손실: 462.103790\t최선의 손실: 169.501068\t정확도: 33.74%\n",
-      "11\t검증 세트 손실: 42986.078125\t최선의 손실: 169.501068\t정확도: 18.53%\n",
-      "12\t검증 세트 손실: 1569.094238\t최선의 손실: 169.501068\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 953.420227\t최선의 손실: 169.501068\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 2262.987061\t최선의 손실: 169.501068\t정확도: 19.23%\n",
-      "15\t검증 세트 손실: 986.178528\t최선의 손실: 169.501068\t정확도: 19.12%\n",
-      "16\t검증 세트 손실: 1899.428711\t최선의 손실: 169.501068\t정확도: 36.47%\n",
-      "17\t검증 세트 손실: 1127.777222\t최선의 손실: 169.501068\t정확도: 19.31%\n",
-      "18\t검증 세트 손실: 246.908615\t최선의 손실: 169.501068\t정확도: 33.50%\n",
-      "19\t검증 세트 손실: 25861.439453\t최선의 손실: 169.501068\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 7030.061035\t최선의 손실: 169.501068\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 874.330505\t최선의 손실: 169.501068\t정확도: 20.99%\n",
-      "22\t검증 세트 손실: 856.643494\t최선의 손실: 169.501068\t정확도: 20.91%\n",
-      "23\t검증 세트 손실: 1058.735107\t최선의 손실: 169.501068\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 399.943329\t최선의 손실: 169.501068\t정확도: 25.92%\n",
-      "25\t검증 세트 손실: 11686.680664\t최선의 손실: 169.501068\t정확도: 19.27%\n",
-      "26\t검증 세트 손실: 8880.919922\t최선의 손실: 169.501068\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 12072.145508\t최선의 손실: 169.501068\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 2414.730225\t최선의 손실: 169.501068\t정확도: 22.01%\n",
+      "0\t검증 세트 손실: 0.674005\t최선의 손실: 0.674005\t정확도: 59.11%\n",
+      "1\t검증 세트 손실: 0.557908\t최선의 손실: 0.557908\t정확도: 74.35%\n",
+      "2\t검증 세트 손실: 0.534037\t최선의 손실: 0.534037\t정확도: 74.71%\n",
+      "3\t검증 세트 손실: 0.466092\t최선의 손실: 0.466092\t정확도: 76.66%\n",
+      "4\t검증 세트 손실: 0.443895\t최선의 손실: 0.443895\t정확도: 75.37%\n",
+      "5\t검증 세트 손실: 0.443703\t최선의 손실: 0.443703\t정확도: 75.29%\n",
+      "6\t검증 세트 손실: 0.491877\t최선의 손실: 0.443703\t정확도: 75.02%\n",
+      "7\t검증 세트 손실: 0.456598\t최선의 손실: 0.443703\t정확도: 75.61%\n",
+      "8\t검증 세트 손실: 0.460923\t최선의 손실: 0.443703\t정확도: 76.11%\n",
+      "9\t검증 세트 손실: 0.447095\t최선의 손실: 0.443703\t정확도: 76.43%\n",
+      "10\t검증 세트 손실: 0.451221\t최선의 손실: 0.443703\t정확도: 76.97%\n",
+      "11\t검증 세트 손실: 0.445150\t최선의 손실: 0.443703\t정확도: 83.70%\n",
+      "12\t검증 세트 손실: 0.298430\t최선의 손실: 0.298430\t정확도: 95.50%\n",
+      "13\t검증 세트 손실: 0.335397\t최선의 손실: 0.298430\t정확도: 95.47%\n",
+      "14\t검증 세트 손실: 0.326261\t최선의 손실: 0.298430\t정확도: 95.86%\n",
+      "15\t검증 세트 손실: 0.325859\t최선의 손실: 0.298430\t정확도: 94.02%\n",
+      "16\t검증 세트 손실: 0.268863\t최선의 손실: 0.268863\t정확도: 95.82%\n",
+      "17\t검증 세트 손실: 0.311431\t최선의 손실: 0.268863\t정확도: 95.19%\n",
+      "18\t검증 세트 손실: 0.296185\t최선의 손실: 0.268863\t정확도: 95.58%\n",
+      "19\t검증 세트 손실: 0.269534\t최선의 손실: 0.268863\t정확도: 95.39%\n",
+      "20\t검증 세트 손실: 0.294525\t최선의 손실: 0.268863\t정확도: 92.96%\n",
+      "21\t검증 세트 손실: 0.278058\t최선의 손실: 0.268863\t정확도: 95.47%\n",
+      "22\t검증 세트 손실: 0.334635\t최선의 손실: 0.268863\t정확도: 94.76%\n",
+      "23\t검증 세트 손실: 0.364918\t최선의 손실: 0.268863\t정확도: 92.61%\n",
+      "24\t검증 세트 손실: 0.335652\t최선의 손실: 0.268863\t정확도: 95.19%\n",
+      "25\t검증 세트 손실: 0.287977\t최선의 손실: 0.268863\t정확도: 95.27%\n",
+      "26\t검증 세트 손실: 0.343063\t최선의 손실: 0.268863\t정확도: 94.10%\n",
+      "27\t검증 세트 손실: 0.314214\t최선의 손실: 0.268863\t정확도: 95.54%\n",
+      "28\t검증 세트 손실: 0.280781\t최선의 손실: 0.268863\t정확도: 95.07%\n",
+      "29\t검증 세트 손실: 0.280982\t최선의 손실: 0.268863\t정확도: 95.04%\n",
+      "30\t검증 세트 손실: 0.272444\t최선의 손실: 0.268863\t정확도: 95.23%\n",
+      "31\t검증 세트 손실: 0.311316\t최선의 손실: 0.268863\t정확도: 95.54%\n",
+      "32\t검증 세트 손실: 0.350006\t최선의 손실: 0.268863\t정확도: 89.84%\n",
+      "33\t검증 세트 손실: 0.321582\t최선의 손실: 0.268863\t정확도: 94.64%\n",
+      "34\t검증 세트 손실: 0.335901\t최선의 손실: 0.268863\t정확도: 93.24%\n",
+      "35\t검증 세트 손실: 0.298195\t최선의 손실: 0.268863\t정확도: 94.80%\n",
+      "36\t검증 세트 손실: 0.323375\t최선의 손실: 0.268863\t정확도: 92.89%\n",
+      "37\t검증 세트 손실: 0.303235\t최선의 손실: 0.268863\t정확도: 94.61%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total= 2.2min\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 4208.644531\t최선의 손실: 4208.644531\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 474.482605\t최선의 손실: 474.482605\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1141.142822\t최선의 손실: 474.482605\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 603.849792\t최선의 손실: 474.482605\t정확도: 26.47%\n",
-      "4\t검증 세트 손실: 3734.246582\t최선의 손실: 474.482605\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 3105.171143\t최선의 손실: 474.482605\t정확도: 19.23%\n",
-      "6\t검증 세트 손실: 731.516724\t최선의 손실: 474.482605\t정확도: 19.12%\n",
-      "7\t검증 세트 손실: 541.957336\t최선의 손실: 474.482605\t정확도: 19.12%\n",
-      "8\t검증 세트 손실: 46294.085938\t최선의 손실: 474.482605\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 8914.658203\t최선의 손실: 474.482605\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 4019.611084\t최선의 손실: 474.482605\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 469.517822\t최선의 손실: 469.517822\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1070.629395\t최선의 손실: 469.517822\t정확도: 22.71%\n",
-      "13\t검증 세트 손실: 1341.902100\t최선의 손실: 469.517822\t정확도: 34.56%\n",
-      "14\t검증 세트 손실: 5220.530762\t최선의 손실: 469.517822\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 58848.808594\t최선의 손실: 469.517822\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 826.622559\t최선의 손실: 469.517822\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 3797.893311\t최선의 손실: 469.517822\t정확도: 26.08%\n",
-      "18\t검증 세트 손실: 27173.535156\t최선의 손실: 469.517822\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 7439.004883\t최선의 손실: 469.517822\t정확도: 18.88%\n",
-      "20\t검증 세트 손실: 1790.433716\t최선의 손실: 469.517822\t정확도: 19.31%\n",
-      "21\t검증 세트 손실: 472720.468750\t최선의 손실: 469.517822\t정확도: 22.95%\n",
-      "22\t검증 세트 손실: 188030.843750\t최선의 손실: 469.517822\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 29054.470703\t최선의 손실: 469.517822\t정확도: 20.80%\n",
-      "24\t검증 세트 손실: 6909.081543\t최선의 손실: 469.517822\t정확도: 19.08%\n",
-      "25\t검증 세트 손실: 2493.844727\t최선의 손실: 469.517822\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 5861.476562\t최선의 손실: 469.517822\t정확도: 25.02%\n",
-      "27\t검증 세트 손실: 1155.141479\t최선의 손실: 469.517822\t정확도: 33.19%\n",
-      "28\t검증 세트 손실: 5287.132812\t최선의 손실: 469.517822\t정확도: 18.02%\n",
-      "29\t검증 세트 손실: 1426.457397\t최선의 손실: 469.517822\t정확도: 29.12%\n",
-      "30\t검증 세트 손실: 504.510010\t최선의 손실: 469.517822\t정확도: 27.83%\n",
-      "31\t검증 세트 손실: 420.027252\t최선의 손실: 420.027252\t정확도: 29.71%\n",
-      "32\t검증 세트 손실: 8494.986328\t최선의 손실: 420.027252\t정확도: 19.94%\n",
-      "33\t검증 세트 손실: 1381.434570\t최선의 손실: 420.027252\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 594.288940\t최선의 손실: 420.027252\t정확도: 21.34%\n",
-      "35\t검증 세트 손실: 58069.171875\t최선의 손실: 420.027252\t정확도: 22.01%\n",
-      "36\t검증 세트 손실: 28951.474609\t최선의 손실: 420.027252\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 5439.704102\t최선의 손실: 420.027252\t정확도: 20.91%\n",
-      "38\t검증 세트 손실: 3487.920166\t최선의 손실: 420.027252\t정확도: 22.05%\n",
-      "39\t검증 세트 손실: 10616.929688\t최선의 손실: 420.027252\t정확도: 18.73%\n",
-      "40\t검증 세트 손실: 1164.177368\t최선의 손실: 420.027252\t정확도: 20.91%\n",
-      "41\t검증 세트 손실: 37339.175781\t최선의 손실: 420.027252\t정확도: 19.27%\n",
-      "42\t검증 세트 손실: 5576.231445\t최선의 손실: 420.027252\t정확도: 20.91%\n",
-      "43\t검증 세트 손실: 2366.677979\t최선의 손실: 420.027252\t정확도: 24.04%\n",
-      "44\t검증 세트 손실: 3121.158447\t최선의 손실: 420.027252\t정확도: 19.12%\n",
-      "45\t검증 세트 손실: 7210.970215\t최선의 손실: 420.027252\t정확도: 18.76%\n",
-      "46\t검증 세트 손실: 9186.911133\t최선의 손실: 420.027252\t정확도: 18.73%\n",
-      "47\t검증 세트 손실: 1011.517273\t최선의 손실: 420.027252\t정확도: 22.05%\n",
-      "48\t검증 세트 손실: 1055.096802\t최선의 손실: 420.027252\t정확도: 22.01%\n",
-      "49\t검증 세트 손실: 4535.713867\t최선의 손실: 420.027252\t정확도: 19.27%\n",
-      "50\t검증 세트 손실: 24646.960938\t최선의 손실: 420.027252\t정확도: 19.08%\n",
-      "51\t검증 세트 손실: 11978.223633\t최선의 손실: 420.027252\t정확도: 22.05%\n",
-      "52\t검증 세트 손실: 887.411438\t최선의 손실: 420.027252\t정확도: 33.78%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10, total=  18.3s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.405709\t최선의 손실: 0.405709\t정확도: 78.69%\n",
+      "1\t검증 세트 손실: 0.395435\t최선의 손실: 0.395435\t정확도: 79.05%\n",
+      "2\t검증 세트 손실: 0.365068\t최선의 손실: 0.365068\t정확도: 79.95%\n",
+      "3\t검증 세트 손실: 0.386100\t최선의 손실: 0.365068\t정확도: 78.46%\n",
+      "4\t검증 세트 손실: 0.373236\t최선의 손실: 0.365068\t정확도: 78.38%\n",
+      "5\t검증 세트 손실: 0.371245\t최선의 손실: 0.365068\t정확도: 78.62%\n",
+      "6\t검증 세트 손실: 0.371238\t최선의 손실: 0.365068\t정확도: 78.30%\n",
+      "7\t검증 세트 손실: 0.386803\t최선의 손실: 0.365068\t정확도: 78.62%\n",
+      "8\t검증 세트 손실: 0.388551\t최선의 손실: 0.365068\t정확도: 79.20%\n",
+      "9\t검증 세트 손실: 0.360023\t최선의 손실: 0.360023\t정확도: 79.48%\n",
+      "10\t검증 세트 손실: 0.380214\t최선의 손실: 0.360023\t정확도: 79.01%\n",
+      "11\t검증 세트 손실: 0.380110\t최선의 손실: 0.360023\t정확도: 78.54%\n",
+      "12\t검증 세트 손실: 0.381870\t최선의 손실: 0.360023\t정확도: 79.48%\n",
+      "13\t검증 세트 손실: 0.360127\t최선의 손실: 0.360023\t정확도: 78.54%\n",
+      "14\t검증 세트 손실: 0.369027\t최선의 손실: 0.360023\t정확도: 79.71%\n",
+      "15\t검증 세트 손실: 0.378898\t최선의 손실: 0.360023\t정확도: 79.67%\n",
+      "16\t검증 세트 손실: 0.406336\t최선의 손실: 0.360023\t정확도: 79.48%\n",
+      "17\t검증 세트 손실: 0.392075\t최선의 손실: 0.360023\t정확도: 78.26%\n",
+      "18\t검증 세트 손실: 0.383996\t최선의 손실: 0.360023\t정확도: 79.20%\n",
+      "19\t검증 세트 손실: 0.363484\t최선의 손실: 0.360023\t정확도: 80.10%\n",
+      "20\t검증 세트 손실: 0.367012\t최선의 손실: 0.360023\t정확도: 79.79%\n",
+      "21\t검증 세트 손실: 0.372182\t최선의 손실: 0.360023\t정확도: 79.44%\n",
+      "22\t검증 세트 손실: 0.369918\t최선의 손실: 0.360023\t정확도: 79.20%\n",
+      "23\t검증 세트 손실: 0.359489\t최선의 손실: 0.359489\t정확도: 79.48%\n",
+      "24\t검증 세트 손실: 0.371215\t최선의 손실: 0.359489\t정확도: 78.58%\n",
+      "25\t검증 세트 손실: 0.370321\t최선의 손실: 0.359489\t정확도: 79.55%\n",
+      "26\t검증 세트 손실: 0.390599\t최선의 손실: 0.359489\t정확도: 78.34%\n",
+      "27\t검증 세트 손실: 0.377223\t최선의 손실: 0.359489\t정확도: 78.50%\n",
+      "28\t검증 세트 손실: 0.371534\t최선의 손실: 0.359489\t정확도: 78.81%\n",
+      "29\t검증 세트 손실: 0.399552\t최선의 손실: 0.359489\t정확도: 79.01%\n",
+      "30\t검증 세트 손실: 0.375903\t최선의 손실: 0.359489\t정확도: 79.28%\n",
+      "31\t검증 세트 손실: 0.376446\t최선의 손실: 0.359489\t정확도: 82.60%\n",
+      "32\t검증 세트 손실: 0.378590\t최선의 손실: 0.359489\t정확도: 78.81%\n",
+      "33\t검증 세트 손실: 0.393494\t최선의 손실: 0.359489\t정확도: 78.50%\n",
+      "34\t검증 세트 손실: 0.650816\t최선의 손실: 0.359489\t정확도: 58.21%\n",
+      "35\t검증 세트 손실: 0.399453\t최선의 손실: 0.359489\t정확도: 78.50%\n",
+      "36\t검증 세트 손실: 0.396557\t최선의 손실: 0.359489\t정확도: 79.44%\n",
+      "37\t검증 세트 손실: 0.397939\t최선의 손실: 0.359489\t정확도: 79.16%\n",
+      "38\t검증 세트 손실: 0.382273\t최선의 손실: 0.359489\t정확도: 81.16%\n",
+      "39\t검증 세트 손실: 0.366426\t최선의 손실: 0.359489\t정확도: 79.16%\n",
+      "40\t검증 세트 손실: 0.384919\t최선의 손실: 0.359489\t정확도: 79.28%\n",
+      "41\t검증 세트 손실: 0.366524\t최선의 손실: 0.359489\t정확도: 83.23%\n",
+      "42\t검증 세트 손실: 0.383192\t최선의 손실: 0.359489\t정확도: 79.16%\n",
+      "43\t검증 세트 손실: 0.399680\t최선의 손실: 0.359489\t정확도: 78.30%\n",
+      "44\t검증 세트 손실: 0.372708\t최선의 손실: 0.359489\t정확도: 79.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total= 3.3min\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 8143.129883\t최선의 손실: 8143.129883\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 2544.938721\t최선의 손실: 2544.938721\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 4679.025879\t최선의 손실: 2544.938721\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 439.178497\t최선의 손실: 439.178497\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 17534.851562\t최선의 손실: 439.178497\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1163.402466\t최선의 손실: 439.178497\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1928.298340\t최선의 손실: 439.178497\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 24346.152344\t최선의 손실: 439.178497\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1575.763428\t최선의 손실: 439.178497\t정확도: 35.22%\n",
-      "9\t검증 세트 손실: 856.974365\t최선의 손실: 439.178497\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 4000.430908\t최선의 손실: 439.178497\t정확도: 29.63%\n",
-      "11\t검증 세트 손실: 3127.111572\t최선의 손실: 439.178497\t정확도: 21.23%\n",
-      "12\t검증 세트 손실: 4169.075684\t최선의 손실: 439.178497\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 4765.434082\t최선의 손실: 439.178497\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 539.948547\t최선의 손실: 439.178497\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1395.689819\t최선의 손실: 439.178497\t정확도: 18.92%\n",
-      "16\t검증 세트 손실: 285.086975\t최선의 손실: 285.086975\t정확도: 32.37%\n",
-      "17\t검증 세트 손실: 151.160538\t최선의 손실: 151.160538\t정확도: 29.40%\n",
-      "18\t검증 세트 손실: 6738.748047\t최선의 손실: 151.160538\t정확도: 19.23%\n",
-      "19\t검증 세트 손실: 2525.914062\t최선의 손실: 151.160538\t정확도: 19.74%\n",
-      "20\t검증 세트 손실: 1416.074829\t최선의 손실: 151.160538\t정확도: 19.31%\n",
-      "21\t검증 세트 손실: 17502.312500\t최선의 손실: 151.160538\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 2203.310547\t최선의 손실: 151.160538\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 16185.087891\t최선의 손실: 151.160538\t정확도: 20.80%\n",
-      "24\t검증 세트 손실: 20345.431641\t최선의 손실: 151.160538\t정확도: 26.86%\n",
-      "25\t검증 세트 손실: 3097.741699\t최선의 손실: 151.160538\t정확도: 18.73%\n",
-      "26\t검증 세트 손실: 628.295471\t최선의 손실: 151.160538\t정확도: 20.33%\n",
-      "27\t검증 세트 손실: 13167.903320\t최선의 손실: 151.160538\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 39091.539062\t최선의 손실: 151.160538\t정확도: 22.01%\n",
-      "29\t검증 세트 손실: 910.058167\t최선의 손실: 151.160538\t정확도: 19.16%\n",
-      "30\t검증 세트 손실: 3875.266113\t최선의 손실: 151.160538\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 14890.860352\t최선의 손실: 151.160538\t정확도: 18.76%\n",
-      "32\t검증 세트 손실: 4470.686035\t최선의 손실: 151.160538\t정확도: 19.23%\n",
-      "33\t검증 세트 손실: 3267.979492\t최선의 손실: 151.160538\t정확도: 22.05%\n",
-      "34\t검증 세트 손실: 6479.271973\t최선의 손실: 151.160538\t정확도: 19.08%\n",
-      "35\t검증 세트 손실: 2556.612305\t최선의 손실: 151.160538\t정확도: 35.54%\n",
-      "36\t검증 세트 손실: 2117.196289\t최선의 손실: 151.160538\t정확도: 19.08%\n",
-      "37\t검증 세트 손실: 2792.405762\t최선의 손실: 151.160538\t정확도: 36.98%\n",
-      "38\t검증 세트 손실: 8590.684570\t최선의 손실: 151.160538\t정확도: 22.95%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10, total=  21.6s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.411077\t최선의 손실: 0.411077\t정확도: 78.69%\n",
+      "1\t검증 세트 손실: 0.396426\t최선의 손실: 0.396426\t정확도: 78.93%\n",
+      "2\t검증 세트 손실: 0.383113\t최선의 손실: 0.383113\t정확도: 79.44%\n",
+      "3\t검증 세트 손실: 0.417203\t최선의 손실: 0.383113\t정확도: 78.34%\n",
+      "4\t검증 세트 손실: 0.384018\t최선의 손실: 0.383113\t정확도: 78.34%\n",
+      "5\t검증 세트 손실: 0.380794\t최선의 손실: 0.380794\t정확도: 80.81%\n",
+      "6\t검증 세트 손실: 0.371570\t최선의 손실: 0.371570\t정확도: 79.44%\n",
+      "7\t검증 세트 손실: 0.395878\t최선의 손실: 0.371570\t정확도: 77.52%\n",
+      "8\t검증 세트 손실: 0.397290\t최선의 손실: 0.371570\t정확도: 79.05%\n",
+      "9\t검증 세트 손실: 0.389890\t최선의 손실: 0.371570\t정확도: 78.42%\n",
+      "10\t검증 세트 손실: 0.370901\t최선의 손실: 0.370901\t정확도: 79.16%\n",
+      "11\t검증 세트 손실: 0.391938\t최선의 손실: 0.370901\t정확도: 78.85%\n",
+      "12\t검증 세트 손실: 0.371352\t최선의 손실: 0.370901\t정확도: 78.15%\n",
+      "13\t검증 세트 손실: 0.390422\t최선의 손실: 0.370901\t정확도: 77.91%\n",
+      "14\t검증 세트 손실: 0.392411\t최선의 손실: 0.370901\t정확도: 78.26%\n",
+      "15\t검증 세트 손실: 0.386335\t최선의 손실: 0.370901\t정확도: 77.99%\n",
+      "16\t검증 세트 손실: 0.412546\t최선의 손실: 0.370901\t정확도: 78.97%\n",
+      "17\t검증 세트 손실: 0.405955\t최선의 손실: 0.370901\t정확도: 78.26%\n",
+      "18\t검증 세트 손실: 0.382148\t최선의 손실: 0.370901\t정확도: 78.26%\n",
+      "19\t검증 세트 손실: 0.376561\t최선의 손실: 0.370901\t정확도: 79.40%\n",
+      "20\t검증 세트 손실: 0.372523\t최선의 손실: 0.370901\t정확도: 79.63%\n",
+      "21\t검증 세트 손실: 0.418009\t최선의 손실: 0.370901\t정확도: 79.28%\n",
+      "22\t검증 세트 손실: 0.388013\t최선의 손실: 0.370901\t정확도: 77.68%\n",
+      "23\t검증 세트 손실: 0.382658\t최선의 손실: 0.370901\t정확도: 78.23%\n",
+      "24\t검증 세트 손실: 0.382040\t최선의 손실: 0.370901\t정확도: 79.67%\n",
+      "25\t검증 세트 손실: 0.391961\t최선의 손실: 0.370901\t정확도: 79.28%\n",
+      "26\t검증 세트 손실: 0.382281\t최선의 손실: 0.370901\t정확도: 79.52%\n",
+      "27\t검증 세트 손실: 0.378916\t최선의 손실: 0.370901\t정확도: 79.44%\n",
+      "28\t검증 세트 손실: 0.375807\t최선의 손실: 0.370901\t정확도: 78.30%\n",
+      "29\t검증 세트 손실: 0.403290\t최선의 손실: 0.370901\t정확도: 79.36%\n",
+      "30\t검증 세트 손실: 0.379629\t최선의 손실: 0.370901\t정확도: 78.30%\n",
+      "31\t검증 세트 손실: 0.371774\t최선의 손실: 0.370901\t정확도: 78.46%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total= 2.7min\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.227188\t최선의 손실: 0.227188\t정확도: 94.14%\n",
-      "1\t검증 세트 손실: 0.189959\t최선의 손실: 0.189959\t정확도: 95.23%\n",
-      "2\t검증 세트 손실: 0.158893\t최선의 손실: 0.158893\t정확도: 95.93%\n",
-      "3\t검증 세트 손실: 0.186139\t최선의 손실: 0.158893\t정확도: 95.07%\n",
-      "4\t검증 세트 손실: 0.156177\t최선의 손실: 0.156177\t정확도: 95.58%\n",
-      "5\t검증 세트 손실: 0.135021\t최선의 손실: 0.135021\t정확도: 96.76%\n",
-      "6\t검증 세트 손실: 0.152597\t최선의 손실: 0.135021\t정확도: 96.21%\n",
-      "7\t검증 세트 손실: 0.156340\t최선의 손실: 0.135021\t정확도: 95.82%\n",
-      "8\t검증 세트 손실: 0.177257\t최선의 손실: 0.135021\t정확도: 96.05%\n",
-      "9\t검증 세트 손실: 0.145230\t최선의 손실: 0.135021\t정확도: 96.21%\n",
-      "10\t검증 세트 손실: 0.157297\t최선의 손실: 0.135021\t정확도: 96.01%\n",
-      "11\t검증 세트 손실: 0.153907\t최선의 손실: 0.135021\t정확도: 96.29%\n",
-      "12\t검증 세트 손실: 0.186910\t최선의 손실: 0.135021\t정확도: 95.74%\n",
-      "13\t검증 세트 손실: 0.144254\t최선의 손실: 0.135021\t정확도: 96.40%\n",
-      "14\t검증 세트 손실: 0.155659\t최선의 손실: 0.135021\t정확도: 96.13%\n",
-      "15\t검증 세트 손실: 0.150025\t최선의 손실: 0.135021\t정확도: 96.21%\n",
-      "16\t검증 세트 손실: 0.138514\t최선의 손실: 0.135021\t정확도: 96.72%\n",
-      "17\t검증 세트 손실: 0.133131\t최선의 손실: 0.133131\t정확도: 96.79%\n",
-      "18\t검증 세트 손실: 0.146138\t최선의 손실: 0.133131\t정확도: 96.64%\n"
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10, total=  15.7s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.466338\t최선의 손실: 0.466338\t정확도: 86.28%\n",
+      "1\t검증 세트 손실: 0.270657\t최선의 손실: 0.270657\t정확도: 93.43%\n",
+      "2\t검증 세트 손실: 0.215395\t최선의 손실: 0.215395\t정확도: 95.07%\n",
+      "3\t검증 세트 손실: 0.228071\t최선의 손실: 0.215395\t정확도: 94.64%\n",
+      "4\t검증 세트 손실: 0.190408\t최선의 손실: 0.190408\t정확도: 94.57%\n",
+      "5\t검증 세트 손실: 0.188796\t최선의 손실: 0.188796\t정확도: 95.04%\n",
+      "6\t검증 세트 손실: 0.160743\t최선의 손실: 0.160743\t정확도: 95.66%\n",
+      "7\t검증 세트 손실: 0.164495\t최선의 손실: 0.160743\t정확도: 95.27%\n",
+      "8\t검증 세트 손실: 0.162983\t최선의 손실: 0.160743\t정확도: 95.78%\n",
+      "9\t검증 세트 손실: 0.144067\t최선의 손실: 0.144067\t정확도: 96.56%\n",
+      "10\t검증 세트 손실: 0.138442\t최선의 손실: 0.138442\t정확도: 96.48%\n",
+      "11\t검증 세트 손실: 0.136300\t최선의 손실: 0.136300\t정확도: 96.40%\n",
+      "12\t검증 세트 손실: 0.155399\t최선의 손실: 0.136300\t정확도: 95.39%\n",
+      "13\t검증 세트 손실: 0.136001\t최선의 손실: 0.136001\t정확도: 96.52%\n",
+      "14\t검증 세트 손실: 0.135616\t최선의 손실: 0.135616\t정확도: 96.29%\n",
+      "15\t검증 세트 손실: 0.149618\t최선의 손실: 0.135616\t정확도: 96.44%\n",
+      "16\t검증 세트 손실: 0.140708\t최선의 손실: 0.135616\t정확도: 96.21%\n",
+      "17\t검증 세트 손실: 0.133795\t최선의 손실: 0.133795\t정확도: 96.52%\n",
+      "18\t검증 세트 손실: 0.130990\t최선의 손실: 0.130990\t정확도: 97.07%\n",
+      "19\t검증 세트 손실: 0.139325\t최선의 손실: 0.130990\t정확도: 96.76%\n",
+      "20\t검증 세트 손실: 0.127210\t최선의 손실: 0.127210\t정확도: 96.91%\n",
+      "21\t검증 세트 손실: 0.143381\t최선의 손실: 0.127210\t정확도: 96.48%\n",
+      "22\t검증 세트 손실: 0.149938\t최선의 손실: 0.127210\t정확도: 96.40%\n",
+      "23\t검증 세트 손실: 0.137837\t최선의 손실: 0.127210\t정확도: 96.56%\n",
+      "24\t검증 세트 손실: 0.148016\t최선의 손실: 0.127210\t정확도: 95.97%\n",
+      "25\t검증 세트 손실: 0.145332\t최선의 손실: 0.127210\t정확도: 96.21%\n",
+      "26\t검증 세트 손실: 0.139641\t최선의 손실: 0.127210\t정확도: 96.25%\n",
+      "27\t검증 세트 손실: 0.133113\t최선의 손실: 0.127210\t정확도: 96.64%\n",
+      "28\t검증 세트 손실: 0.148711\t최선의 손실: 0.127210\t정확도: 95.90%\n",
+      "29\t검증 세트 손실: 0.126920\t최선의 손실: 0.126920\t정확도: 96.68%\n",
+      "30\t검증 세트 손실: 0.143212\t최선의 손실: 0.126920\t정확도: 96.01%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "19\t검증 세트 손실: 0.146221\t최선의 손실: 0.133131\t정확도: 96.29%\n",
-      "20\t검증 세트 손실: 0.154336\t최선의 손실: 0.133131\t정확도: 96.33%\n",
-      "21\t검증 세트 손실: 0.122147\t최선의 손실: 0.122147\t정확도: 96.99%\n",
-      "22\t검증 세트 손실: 0.138493\t최선의 손실: 0.122147\t정확도: 96.68%\n",
-      "23\t검증 세트 손실: 0.158460\t최선의 손실: 0.122147\t정확도: 96.01%\n",
-      "24\t검증 세트 손실: 0.144235\t최선의 손실: 0.122147\t정확도: 96.44%\n",
-      "25\t검증 세트 손실: 0.147731\t최선의 손실: 0.122147\t정확도: 97.07%\n",
-      "26\t검증 세트 손실: 0.182859\t최선의 손실: 0.122147\t정확도: 95.74%\n",
-      "27\t검증 세트 손실: 0.156119\t최선의 손실: 0.122147\t정확도: 96.09%\n",
-      "28\t검증 세트 손실: 0.141370\t최선의 손실: 0.122147\t정확도: 96.79%\n",
-      "29\t검증 세트 손실: 0.135368\t최선의 손실: 0.122147\t정확도: 97.07%\n",
-      "30\t검증 세트 손실: 0.167841\t최선의 손실: 0.122147\t정확도: 96.40%\n",
-      "31\t검증 세트 손실: 0.167080\t최선의 손실: 0.122147\t정확도: 96.64%\n",
-      "32\t검증 세트 손실: 0.144059\t최선의 손실: 0.122147\t정확도: 96.29%\n",
-      "33\t검증 세트 손실: 0.146619\t최선의 손실: 0.122147\t정확도: 96.44%\n",
-      "34\t검증 세트 손실: 0.146388\t최선의 손실: 0.122147\t정확도: 96.21%\n",
-      "35\t검증 세트 손실: 0.143535\t최선의 손실: 0.122147\t정확도: 96.68%\n",
-      "36\t검증 세트 손실: 0.141846\t최선의 손실: 0.122147\t정확도: 97.07%\n",
-      "37\t검증 세트 손실: 0.151329\t최선의 손실: 0.122147\t정확도: 96.48%\n",
-      "38\t검증 세트 손실: 0.144503\t최선의 손실: 0.122147\t정확도: 96.83%\n",
-      "39\t검증 세트 손실: 0.179310\t최선의 손실: 0.122147\t정확도: 96.48%\n",
-      "40\t검증 세트 손실: 0.150109\t최선의 손실: 0.122147\t정확도: 96.68%\n",
-      "41\t검증 세트 손실: 0.168174\t최선의 손실: 0.122147\t정확도: 96.25%\n",
-      "42\t검증 세트 손실: 0.172743\t최선의 손실: 0.122147\t정확도: 96.36%\n",
+      "31\t검증 세트 손실: 0.129518\t최선의 손실: 0.126920\t정확도: 96.56%\n",
+      "32\t검증 세트 손실: 0.128961\t최선의 손실: 0.126920\t정확도: 96.33%\n",
+      "33\t검증 세트 손실: 0.127866\t최선의 손실: 0.126920\t정확도: 96.44%\n",
+      "34\t검증 세트 손실: 0.126225\t최선의 손실: 0.126225\t정확도: 96.29%\n",
+      "35\t검증 세트 손실: 0.125937\t최선의 손실: 0.125937\t정확도: 96.79%\n",
+      "36\t검증 세트 손실: 0.129023\t최선의 손실: 0.125937\t정확도: 96.99%\n",
+      "37\t검증 세트 손실: 0.130004\t최선의 손실: 0.125937\t정확도: 96.52%\n",
+      "38\t검증 세트 손실: 0.125417\t최선의 손실: 0.125417\t정확도: 96.99%\n",
+      "39\t검증 세트 손실: 0.122350\t최선의 손실: 0.122350\t정확도: 96.56%\n",
+      "40\t검증 세트 손실: 0.120243\t최선의 손실: 0.120243\t정확도: 96.95%\n",
+      "41\t검증 세트 손실: 0.127696\t최선의 손실: 0.120243\t정확도: 96.60%\n",
+      "42\t검증 세트 손실: 0.140390\t최선의 손실: 0.120243\t정확도: 96.48%\n",
+      "43\t검증 세트 손실: 0.138939\t최선의 손실: 0.120243\t정확도: 96.72%\n",
+      "44\t검증 세트 손실: 0.136159\t최선의 손실: 0.120243\t정확도: 96.68%\n",
+      "45\t검증 세트 손실: 0.133427\t최선의 손실: 0.120243\t정확도: 96.48%\n",
+      "46\t검증 세트 손실: 0.122520\t최선의 손실: 0.120243\t정확도: 96.76%\n",
+      "47\t검증 세트 손실: 0.139667\t최선의 손실: 0.120243\t정확도: 96.68%\n",
+      "48\t검증 세트 손실: 0.133790\t최선의 손실: 0.120243\t정확도: 96.40%\n",
+      "49\t검증 세트 손실: 0.129479\t최선의 손실: 0.120243\t정확도: 96.68%\n",
+      "50\t검증 세트 손실: 0.134099\t최선의 손실: 0.120243\t정확도: 96.44%\n",
+      "51\t검증 세트 손실: 0.133670\t최선의 손실: 0.120243\t정확도: 96.60%\n",
+      "52\t검증 세트 손실: 0.134149\t최선의 손실: 0.120243\t정확도: 96.52%\n",
+      "53\t검증 세트 손실: 0.135098\t최선의 손실: 0.120243\t정확도: 96.83%\n",
+      "54\t검증 세트 손실: 0.122660\t최선의 손실: 0.120243\t정확도: 96.64%\n",
+      "55\t검증 세트 손실: 0.141732\t최선의 손실: 0.120243\t정확도: 96.68%\n",
+      "56\t검증 세트 손실: 0.139687\t최선의 손실: 0.120243\t정확도: 96.87%\n",
+      "57\t검증 세트 손실: 0.136196\t최선의 손실: 0.120243\t정확도: 96.83%\n",
+      "58\t검증 세트 손실: 0.124211\t최선의 손실: 0.120243\t정확도: 96.83%\n",
+      "59\t검증 세트 손실: 0.130168\t최선의 손실: 0.120243\t정확도: 97.07%\n",
+      "60\t검증 세트 손실: 0.141927\t최선의 손실: 0.120243\t정확도: 96.21%\n",
+      "61\t검증 세트 손실: 0.127420\t최선의 손실: 0.120243\t정확도: 96.95%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=  30.5s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.513666\t최선의 손실: 0.513666\t정확도: 77.72%\n",
-      "1\t검증 세트 손실: 0.314411\t최선의 손실: 0.314411\t정확도: 93.90%\n",
-      "2\t검증 세트 손실: 0.309262\t최선의 손실: 0.309262\t정확도: 93.63%\n",
-      "3\t검증 세트 손실: 0.337582\t최선의 손실: 0.309262\t정확도: 93.94%\n",
-      "4\t검증 세트 손실: 0.258618\t최선의 손실: 0.258618\t정확도: 94.25%\n",
-      "5\t검증 세트 손실: 0.326205\t최선의 손실: 0.258618\t정확도: 90.89%\n",
-      "6\t검증 세트 손실: 0.236478\t최선의 손실: 0.236478\t정확도: 95.04%\n",
-      "7\t검증 세트 손실: 0.261174\t최선의 손실: 0.236478\t정확도: 94.64%\n",
-      "8\t검증 세트 손실: 0.300449\t최선의 손실: 0.236478\t정확도: 94.80%\n",
-      "9\t검증 세트 손실: 0.253684\t최선의 손실: 0.236478\t정확도: 95.15%\n",
-      "10\t검증 세트 손실: 0.218087\t최선의 손실: 0.218087\t정확도: 95.15%\n",
-      "11\t검증 세트 손실: 0.253749\t최선의 손실: 0.218087\t정확도: 94.61%\n",
-      "12\t검증 세트 손실: 0.256080\t최선의 손실: 0.218087\t정확도: 94.64%\n",
-      "13\t검증 세트 손실: 0.229042\t최선의 손실: 0.218087\t정확도: 95.31%\n",
-      "14\t검증 세트 손실: 0.240963\t최선의 손실: 0.218087\t정확도: 95.19%\n",
-      "15\t검증 세트 손실: 0.247840\t최선의 손실: 0.218087\t정확도: 95.27%\n",
-      "16\t검증 세트 손실: 0.381235\t최선의 손실: 0.218087\t정확도: 94.57%\n",
-      "17\t검증 세트 손실: 0.269904\t최선의 손실: 0.218087\t정확도: 95.04%\n",
-      "18\t검증 세트 손실: 0.257120\t최선의 손실: 0.218087\t정확도: 95.15%\n",
-      "19\t검증 세트 손실: 0.307497\t최선의 손실: 0.218087\t정확도: 95.31%\n",
-      "20\t검증 세트 손실: 0.293286\t최선의 손실: 0.218087\t정확도: 95.78%\n",
-      "21\t검증 세트 손실: 0.303974\t최선의 손실: 0.218087\t정확도: 94.92%\n",
-      "22\t검증 세트 손실: 0.291857\t최선의 손실: 0.218087\t정확도: 94.72%\n",
-      "23\t검증 세트 손실: 0.277860\t최선의 손실: 0.218087\t정확도: 94.80%\n",
-      "24\t검증 세트 손실: 0.262129\t최선의 손실: 0.218087\t정확도: 95.04%\n",
-      "25\t검증 세트 손실: 0.239033\t최선의 손실: 0.218087\t정확도: 95.07%\n",
-      "26\t검증 세트 손실: 0.213058\t최선의 손실: 0.213058\t정확도: 95.70%\n",
-      "27\t검증 세트 손실: 0.210960\t최선의 손실: 0.210960\t정확도: 96.09%\n",
-      "28\t검증 세트 손실: 0.209877\t최선의 손실: 0.209877\t정확도: 95.23%\n",
-      "29\t검증 세트 손실: 0.215901\t최선의 손실: 0.209877\t정확도: 95.86%\n",
-      "30\t검증 세트 손실: 0.247046\t최선의 손실: 0.209877\t정확도: 94.92%\n",
-      "31\t검증 세트 손실: 0.221460\t최선의 손실: 0.209877\t정확도: 96.05%\n",
-      "32\t검증 세트 손실: 0.220511\t최선의 손실: 0.209877\t정확도: 95.35%\n",
-      "33\t검증 세트 손실: 0.214680\t최선의 손실: 0.209877\t정확도: 95.62%\n",
-      "34\t검증 세트 손실: 0.242779\t최선의 손실: 0.209877\t정확도: 95.27%\n",
-      "35\t검증 세트 손실: 0.200400\t최선의 손실: 0.200400\t정확도: 95.90%\n",
-      "36\t검증 세트 손실: 0.269096\t최선의 손실: 0.200400\t정확도: 96.05%\n",
-      "37\t검증 세트 손실: 0.228209\t최선의 손실: 0.200400\t정확도: 96.01%\n",
-      "38\t검증 세트 손실: 0.212252\t최선의 손실: 0.200400\t정확도: 95.97%\n",
-      "39\t검증 세트 손실: 0.222997\t최선의 손실: 0.200400\t정확도: 95.47%\n",
-      "40\t검증 세트 손실: 0.227570\t최선의 손실: 0.200400\t정확도: 96.01%\n",
-      "41\t검증 세트 손실: 0.259980\t최선의 손실: 0.200400\t정확도: 95.58%\n",
-      "42\t검증 세트 손실: 0.216169\t최선의 손실: 0.200400\t정확도: 94.96%\n",
-      "43\t검증 세트 손실: 0.213259\t최선의 손실: 0.200400\t정확도: 95.78%\n",
-      "44\t검증 세트 손실: 0.257932\t최선의 손실: 0.200400\t정확도: 95.62%\n",
-      "45\t검증 세트 손실: 0.270390\t최선의 손실: 0.200400\t정확도: 95.66%\n",
-      "46\t검증 세트 손실: 0.216545\t최선의 손실: 0.200400\t정확도: 96.29%\n",
-      "47\t검증 세트 손실: 0.267854\t최선의 손실: 0.200400\t정확도: 96.44%\n",
-      "48\t검증 세트 손실: 0.240996\t최선의 손실: 0.200400\t정확도: 95.90%\n",
-      "49\t검증 세트 손실: 0.258312\t최선의 손실: 0.200400\t정확도: 96.05%\n",
-      "50\t검증 세트 손실: 0.203823\t최선의 손실: 0.200400\t정확도: 96.05%\n",
-      "51\t검증 세트 손실: 0.201012\t최선의 손실: 0.200400\t정확도: 96.13%\n",
-      "52\t검증 세트 손실: 0.205984\t최선의 손실: 0.200400\t정확도: 95.82%\n",
-      "53\t검증 세트 손실: 0.192743\t최선의 손실: 0.192743\t정확도: 96.29%\n",
-      "54\t검증 세트 손실: 0.204798\t최선의 손실: 0.192743\t정확도: 96.40%\n",
-      "55\t검증 세트 손실: 0.246185\t최선의 손실: 0.192743\t정확도: 96.29%\n",
-      "56\t검증 세트 손실: 0.190996\t최선의 손실: 0.190996\t정확도: 96.09%\n",
-      "57\t검증 세트 손실: 0.206969\t최선의 손실: 0.190996\t정확도: 95.66%\n",
-      "58\t검증 세트 손실: 0.197689\t최선의 손실: 0.190996\t정확도: 95.93%\n",
-      "59\t검증 세트 손실: 0.225352\t최선의 손실: 0.190996\t정확도: 95.70%\n",
-      "60\t검증 세트 손실: 0.214930\t최선의 손실: 0.190996\t정확도: 95.90%\n",
-      "61\t검증 세트 손실: 0.228930\t최선의 손실: 0.190996\t정확도: 95.82%\n",
-      "62\t검증 세트 손실: 0.189455\t최선의 손실: 0.189455\t정확도: 96.29%\n",
-      "63\t검증 세트 손실: 0.200026\t최선의 손실: 0.189455\t정확도: 96.36%\n",
-      "64\t검증 세트 손실: 0.201702\t최선의 손실: 0.189455\t정확도: 96.05%\n",
-      "65\t검증 세트 손실: 0.191120\t최선의 손실: 0.189455\t정확도: 96.29%\n",
-      "66\t검증 세트 손실: 0.302130\t최선의 손실: 0.189455\t정확도: 96.05%\n",
-      "67\t검증 세트 손실: 0.245390\t최선의 손실: 0.189455\t정확도: 96.13%\n",
-      "68\t검증 세트 손실: 0.251377\t최선의 손실: 0.189455\t정확도: 95.19%\n",
-      "69\t검증 세트 손실: 0.210060\t최선의 손실: 0.189455\t정확도: 96.48%\n",
-      "70\t검증 세트 손실: 0.213605\t최선의 손실: 0.189455\t정확도: 96.17%\n",
-      "71\t검증 세트 손실: 0.200379\t최선의 손실: 0.189455\t정확도: 96.29%\n",
-      "72\t검증 세트 손실: 0.249235\t최선의 손실: 0.189455\t정확도: 96.17%\n",
-      "73\t검증 세트 손실: 0.199121\t최선의 손실: 0.189455\t정확도: 95.97%\n",
-      "74\t검증 세트 손실: 0.201487\t최선의 손실: 0.189455\t정확도: 96.17%\n",
-      "75\t검증 세트 손실: 0.222463\t최선의 손실: 0.189455\t정확도: 96.13%\n",
-      "76\t검증 세트 손실: 0.210606\t최선의 손실: 0.189455\t정확도: 96.01%\n",
-      "77\t검증 세트 손실: 0.481773\t최선의 손실: 0.189455\t정확도: 76.54%\n",
-      "78\t검증 세트 손실: 0.259756\t최선의 손실: 0.189455\t정확도: 95.86%\n",
-      "79\t검증 세트 손실: 0.205991\t최선의 손실: 0.189455\t정확도: 96.29%\n",
-      "80\t검증 세트 손실: 0.203178\t최선의 손실: 0.189455\t정확도: 96.17%\n",
-      "81\t검증 세트 손실: 0.185965\t최선의 손실: 0.185965\t정확도: 96.17%\n",
-      "82\t검증 세트 손실: 0.201199\t최선의 손실: 0.185965\t정확도: 96.13%\n",
-      "83\t검증 세트 손실: 0.214113\t최선의 손실: 0.185965\t정확도: 95.27%\n",
-      "84\t검증 세트 손실: 0.208005\t최선의 손실: 0.185965\t정확도: 96.29%\n",
-      "85\t검증 세트 손실: 0.239480\t최선의 손실: 0.185965\t정확도: 95.93%\n",
-      "86\t검증 세트 손실: 0.204432\t최선의 손실: 0.185965\t정확도: 96.05%\n",
-      "87\t검증 세트 손실: 0.228965\t최선의 손실: 0.185965\t정확도: 95.39%\n",
-      "88\t검증 세트 손실: 0.198849\t최선의 손실: 0.185965\t정확도: 96.25%\n",
-      "89\t검증 세트 손실: 0.241057\t최선의 손실: 0.185965\t정확도: 95.90%\n",
-      "90\t검증 세트 손실: 0.238729\t최선의 손실: 0.185965\t정확도: 96.13%\n",
-      "91\t검증 세트 손실: 0.241334\t최선의 손실: 0.185965\t정확도: 95.54%\n",
-      "92\t검증 세트 손실: 0.221736\t최선의 손실: 0.185965\t정확도: 95.50%\n",
-      "93\t검증 세트 손실: 0.208823\t최선의 손실: 0.185965\t정확도: 96.25%\n",
-      "94\t검증 세트 손실: 0.209834\t최선의 손실: 0.185965\t정확도: 96.13%\n",
-      "95\t검증 세트 손실: 0.185916\t최선의 손실: 0.185916\t정확도: 96.40%\n",
-      "96\t검증 세트 손실: 0.224300\t최선의 손실: 0.185916\t정확도: 95.97%\n",
-      "97\t검증 세트 손실: 0.297324\t최선의 손실: 0.185916\t정확도: 95.78%\n",
-      "98\t검증 세트 손실: 0.216162\t최선의 손실: 0.185916\t정확도: 95.90%\n",
-      "99\t검증 세트 손실: 0.261570\t최선의 손실: 0.185916\t정확도: 95.93%\n",
-      "100\t검증 세트 손실: 0.214786\t최선의 손실: 0.185916\t정확도: 95.90%\n",
-      "101\t검증 세트 손실: 0.187934\t최선의 손실: 0.185916\t정확도: 96.40%\n",
-      "102\t검증 세트 손실: 0.189231\t최선의 손실: 0.185916\t정확도: 96.29%\n",
-      "103\t검증 세트 손실: 0.237603\t최선의 손실: 0.185916\t정확도: 95.86%\n",
-      "104\t검증 세트 손실: 0.209197\t최선의 손실: 0.185916\t정확도: 96.33%\n",
-      "105\t검증 세트 손실: 0.194494\t최선의 손실: 0.185916\t정확도: 96.25%\n",
-      "106\t검증 세트 손실: 0.214449\t최선의 손실: 0.185916\t정확도: 96.29%\n",
-      "107\t검증 세트 손실: 0.262923\t최선의 손실: 0.185916\t정확도: 96.05%\n",
-      "108\t검증 세트 손실: 0.483799\t최선의 손실: 0.185916\t정확도: 77.76%\n",
-      "109\t검증 세트 손실: 0.254110\t최선의 손실: 0.185916\t정확도: 96.21%\n",
-      "110\t검증 세트 손실: 0.242413\t최선의 손실: 0.185916\t정확도: 95.86%\n",
-      "111\t검증 세트 손실: 0.231192\t최선의 손실: 0.185916\t정확도: 95.90%\n",
-      "112\t검증 세트 손실: 0.252501\t최선의 손실: 0.185916\t정확도: 95.93%\n",
-      "113\t검증 세트 손실: 0.220193\t최선의 손실: 0.185916\t정확도: 96.09%\n",
-      "114\t검증 세트 손실: 0.222895\t최선의 손실: 0.185916\t정확도: 96.29%\n",
-      "115\t검증 세트 손실: 0.196261\t최선의 손실: 0.185916\t정확도: 96.29%\n",
-      "116\t검증 세트 손실: 0.208593\t최선의 손실: 0.185916\t정확도: 96.09%\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=10, total=  18.9s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.282089\t최선의 손실: 0.282089\t정확도: 92.73%\n",
+      "1\t검증 세트 손실: 0.195310\t최선의 손실: 0.195310\t정확도: 95.31%\n",
+      "2\t검증 세트 손실: 0.161105\t최선의 손실: 0.161105\t정확도: 95.97%\n",
+      "3\t검증 세트 손실: 0.158417\t최선의 손실: 0.158417\t정확도: 95.66%\n",
+      "4\t검증 세트 손실: 0.145863\t최선의 손실: 0.145863\t정확도: 95.97%\n",
+      "5\t검증 세트 손실: 0.151379\t최선의 손실: 0.145863\t정확도: 96.13%\n",
+      "6\t검증 세트 손실: 0.133247\t최선의 손실: 0.133247\t정확도: 96.25%\n",
+      "7\t검증 세트 손실: 0.131486\t최선의 손실: 0.131486\t정확도: 96.40%\n",
+      "8\t검증 세트 손실: 0.123289\t최선의 손실: 0.123289\t정확도: 96.13%\n",
+      "9\t검증 세트 손실: 0.125267\t최선의 손실: 0.123289\t정확도: 96.48%\n",
+      "10\t검증 세트 손실: 0.139049\t최선의 손실: 0.123289\t정확도: 96.40%\n",
+      "11\t검증 세트 손실: 0.126973\t최선의 손실: 0.123289\t정확도: 95.86%\n",
+      "12\t검증 세트 손실: 0.119099\t최선의 손실: 0.119099\t정확도: 96.52%\n",
+      "13\t검증 세트 손실: 0.126471\t최선의 손실: 0.119099\t정확도: 96.40%\n",
+      "14\t검증 세트 손실: 0.129475\t최선의 손실: 0.119099\t정확도: 96.25%\n",
+      "15\t검증 세트 손실: 0.154800\t최선의 손실: 0.119099\t정확도: 96.13%\n",
+      "16\t검증 세트 손실: 0.118546\t최선의 손실: 0.118546\t정확도: 96.56%\n",
+      "17\t검증 세트 손실: 0.137687\t최선의 손실: 0.118546\t정확도: 96.01%\n",
+      "18\t검증 세트 손실: 0.118255\t최선의 손실: 0.118255\t정확도: 96.44%\n",
+      "19\t검증 세트 손실: 0.122315\t최선의 손실: 0.118255\t정확도: 96.60%\n",
+      "20\t검증 세트 손실: 0.122466\t최선의 손실: 0.118255\t정확도: 96.29%\n",
+      "21\t검증 세트 손실: 0.112883\t최선의 손실: 0.112883\t정확도: 96.48%\n",
+      "22\t검증 세트 손실: 0.116544\t최선의 손실: 0.112883\t정확도: 96.48%\n",
+      "23\t검증 세트 손실: 0.120913\t최선의 손실: 0.112883\t정확도: 96.68%\n",
+      "24\t검증 세트 손실: 0.123924\t최선의 손실: 0.112883\t정확도: 96.29%\n",
+      "25\t검증 세트 손실: 0.131000\t최선의 손실: 0.112883\t정확도: 96.09%\n",
+      "26\t검증 세트 손실: 0.132918\t최선의 손실: 0.112883\t정확도: 96.44%\n",
+      "27\t검증 세트 손실: 0.122686\t최선의 손실: 0.112883\t정확도: 96.52%\n",
+      "28\t검증 세트 손실: 0.123274\t최선의 손실: 0.112883\t정확도: 96.52%\n",
+      "29\t검증 세트 손실: 0.117238\t최선의 손실: 0.112883\t정확도: 96.48%\n",
+      "30\t검증 세트 손실: 0.120557\t최선의 손실: 0.112883\t정확도: 96.56%\n",
+      "31\t검증 세트 손실: 0.125022\t최선의 손실: 0.112883\t정확도: 96.44%\n",
+      "32\t검증 세트 손실: 0.117336\t최선의 손실: 0.112883\t정확도: 96.72%\n",
+      "33\t검증 세트 손실: 0.121554\t최선의 손실: 0.112883\t정확도: 96.44%\n",
+      "34\t검증 세트 손실: 0.127943\t최선의 손실: 0.112883\t정확도: 96.52%\n",
+      "35\t검증 세트 손실: 0.124236\t최선의 손실: 0.112883\t정확도: 96.36%\n",
+      "36\t검증 세트 손실: 0.116109\t최선의 손실: 0.112883\t정확도: 96.83%\n",
+      "37\t검증 세트 손실: 0.118434\t최선의 손실: 0.112883\t정확도: 96.33%\n",
+      "38\t검증 세트 손실: 0.121597\t최선의 손실: 0.112883\t정확도: 96.72%\n",
+      "39\t검증 세트 손실: 0.121922\t최선의 손실: 0.112883\t정확도: 96.21%\n",
+      "40\t검증 세트 손실: 0.128061\t최선의 손실: 0.112883\t정확도: 96.68%\n",
+      "41\t검증 세트 손실: 0.106023\t최선의 손실: 0.106023\t정확도: 96.76%\n",
+      "42\t검증 세트 손실: 0.111704\t최선의 손실: 0.106023\t정확도: 97.03%\n",
+      "43\t검증 세트 손실: 0.122189\t최선의 손실: 0.106023\t정확도: 96.87%\n",
+      "44\t검증 세트 손실: 0.107220\t최선의 손실: 0.106023\t정확도: 96.76%\n",
+      "45\t검증 세트 손실: 0.117345\t최선의 손실: 0.106023\t정확도: 96.56%\n",
+      "46\t검증 세트 손실: 0.121089\t최선의 손실: 0.106023\t정확도: 96.76%\n",
+      "47\t검증 세트 손실: 0.117144\t최선의 손실: 0.106023\t정확도: 96.76%\n",
+      "48\t검증 세트 손실: 0.109391\t최선의 손실: 0.106023\t정확도: 96.95%\n",
+      "49\t검증 세트 손실: 0.115517\t최선의 손실: 0.106023\t정확도: 97.19%\n",
+      "50\t검증 세트 손실: 0.111000\t최선의 손실: 0.106023\t정확도: 96.76%\n",
+      "51\t검증 세트 손실: 0.110359\t최선의 손실: 0.106023\t정확도: 97.07%\n",
+      "52\t검증 세트 손실: 0.104058\t최선의 손실: 0.104058\t정확도: 97.03%\n",
+      "53\t검증 세트 손실: 0.116965\t최선의 손실: 0.104058\t정확도: 97.42%\n",
+      "54\t검증 세트 손실: 0.121748\t최선의 손실: 0.104058\t정확도: 97.03%\n",
+      "55\t검증 세트 손실: 0.124104\t최선의 손실: 0.104058\t정확도: 96.91%\n",
+      "56\t검증 세트 손실: 0.112786\t최선의 손실: 0.104058\t정확도: 96.76%\n",
+      "57\t검증 세트 손실: 0.120756\t최선의 손실: 0.104058\t정확도: 96.83%\n",
+      "58\t검증 세트 손실: 0.118169\t최선의 손실: 0.104058\t정확도: 96.83%\n",
+      "59\t검증 세트 손실: 0.109470\t최선의 손실: 0.104058\t정확도: 96.72%\n",
+      "60\t검증 세트 손실: 0.105094\t최선의 손실: 0.104058\t정확도: 96.83%\n",
+      "61\t검증 세트 손실: 0.114695\t최선의 손실: 0.104058\t정확도: 96.72%\n",
+      "62\t검증 세트 손실: 0.114006\t최선의 손실: 0.104058\t정확도: 97.22%\n",
+      "63\t검증 세트 손실: 0.109203\t최선의 손실: 0.104058\t정확도: 97.07%\n",
+      "64\t검증 세트 손실: 0.117893\t최선의 손실: 0.104058\t정확도: 96.99%\n",
+      "65\t검증 세트 손실: 0.116034\t최선의 손실: 0.104058\t정확도: 96.95%\n",
+      "66\t검증 세트 손실: 0.112691\t최선의 손실: 0.104058\t정확도: 96.60%\n",
+      "67\t검증 세트 손실: 0.110516\t최선의 손실: 0.104058\t정확도: 96.99%\n",
+      "68\t검증 세트 손실: 0.114152\t최선의 손실: 0.104058\t정확도: 96.76%\n",
+      "69\t검증 세트 손실: 0.114642\t최선의 손실: 0.104058\t정확도: 96.60%\n",
+      "70\t검증 세트 손실: 0.114978\t최선의 손실: 0.104058\t정확도: 97.19%\n",
+      "71\t검증 세트 손실: 0.121338\t최선의 손실: 0.104058\t정확도: 97.26%\n",
+      "72\t검증 세트 손실: 0.114383\t최선의 손실: 0.104058\t정확도: 97.11%\n",
+      "73\t검증 세트 손실: 0.117415\t최선의 손실: 0.104058\t정확도: 96.99%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=  52.7s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.193212\t최선의 손실: 0.193212\t정확도: 95.39%\n",
-      "1\t검증 세트 손실: 0.172138\t최선의 손실: 0.172138\t정확도: 95.47%\n",
-      "2\t검증 세트 손실: 0.166111\t최선의 손실: 0.166111\t정확도: 95.82%\n",
-      "3\t검증 세트 손실: 0.147652\t최선의 손실: 0.147652\t정확도: 96.09%\n",
-      "4\t검증 세트 손실: 0.186251\t최선의 손실: 0.147652\t정확도: 94.88%\n",
-      "5\t검증 세트 손실: 0.156088\t최선의 손실: 0.147652\t정확도: 96.05%\n",
-      "6\t검증 세트 손실: 0.140994\t최선의 손실: 0.140994\t정확도: 96.60%\n",
-      "7\t검증 세트 손실: 0.181214\t최선의 손실: 0.140994\t정확도: 95.78%\n",
-      "8\t검증 세트 손실: 0.141571\t최선의 손실: 0.140994\t정확도: 96.36%\n",
-      "9\t검증 세트 손실: 0.134045\t최선의 손실: 0.134045\t정확도: 96.64%\n"
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=10, total=  22.4s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.307121\t최선의 손실: 0.307121\t정확도: 92.57%\n",
+      "1\t검증 세트 손실: 0.256140\t최선의 손실: 0.256140\t정확도: 92.96%\n",
+      "2\t검증 세트 손실: 0.180044\t최선의 손실: 0.180044\t정확도: 95.27%\n",
+      "3\t검증 세트 손실: 0.169798\t최선의 손실: 0.169798\t정확도: 95.11%\n",
+      "4\t검증 세트 손실: 0.165698\t최선의 손실: 0.165698\t정확도: 94.68%\n",
+      "5\t검증 세트 손실: 0.158453\t최선의 손실: 0.158453\t정확도: 95.43%\n",
+      "6\t검증 세트 손실: 0.164896\t최선의 손실: 0.158453\t정확도: 94.80%\n",
+      "7\t검증 세트 손실: 0.159754\t최선의 손실: 0.158453\t정확도: 95.04%\n",
+      "8\t검증 세트 손실: 0.164672\t최선의 손실: 0.158453\t정확도: 95.11%\n",
+      "9\t검증 세트 손실: 0.157744\t최선의 손실: 0.157744\t정확도: 95.11%\n",
+      "10\t검증 세트 손실: 0.150684\t최선의 손실: 0.150684\t정확도: 95.27%\n",
+      "11\t검증 세트 손실: 0.145617\t최선의 손실: 0.145617\t정확도: 95.74%\n",
+      "12\t검증 세트 손실: 0.162225\t최선의 손실: 0.145617\t정확도: 95.39%\n",
+      "13\t검증 세트 손실: 0.142985\t최선의 손실: 0.142985\t정확도: 95.54%\n",
+      "14\t검증 세트 손실: 0.167273\t최선의 손실: 0.142985\t정확도: 94.84%\n",
+      "15\t검증 세트 손실: 0.150520\t최선의 손실: 0.142985\t정확도: 96.13%\n",
+      "16\t검증 세트 손실: 0.150800\t최선의 손실: 0.142985\t정확도: 95.78%\n",
+      "17\t검증 세트 손실: 0.133367\t최선의 손실: 0.133367\t정확도: 96.17%\n",
+      "18\t검증 세트 손실: 0.138234\t최선의 손실: 0.133367\t정확도: 96.36%\n",
+      "19\t검증 세트 손실: 0.146268\t최선의 손실: 0.133367\t정확도: 95.93%\n",
+      "20\t검증 세트 손실: 0.137602\t최선의 손실: 0.133367\t정확도: 96.13%\n",
+      "21\t검증 세트 손실: 0.148513\t최선의 손실: 0.133367\t정확도: 95.82%\n",
+      "22\t검증 세트 손실: 0.140234\t최선의 손실: 0.133367\t정확도: 95.90%\n",
+      "23\t검증 세트 손실: 0.144860\t최선의 손실: 0.133367\t정확도: 95.78%\n",
+      "24\t검증 세트 손실: 0.157412\t최선의 손실: 0.133367\t정확도: 95.74%\n",
+      "25\t검증 세트 손실: 0.148520\t최선의 손실: 0.133367\t정확도: 96.05%\n",
+      "26\t검증 세트 손실: 0.151970\t최선의 손실: 0.133367\t정확도: 95.86%\n",
+      "27\t검증 세트 손실: 0.141174\t최선의 손실: 0.133367\t정확도: 96.21%\n",
+      "28\t검증 세트 손실: 0.139865\t최선의 손실: 0.133367\t정확도: 96.09%\n",
+      "29\t검증 세트 손실: 0.137488\t최선의 손실: 0.133367\t정확도: 96.09%\n",
+      "30\t검증 세트 손실: 0.136462\t최선의 손실: 0.133367\t정확도: 96.40%\n",
+      "31\t검증 세트 손실: 0.128136\t최선의 손실: 0.128136\t정확도: 96.44%\n",
+      "32\t검증 세트 손실: 0.143801\t최선의 손실: 0.128136\t정확도: 95.90%\n",
+      "33\t검증 세트 손실: 0.134158\t최선의 손실: 0.128136\t정확도: 96.56%\n",
+      "34\t검증 세트 손실: 0.141175\t최선의 손실: 0.128136\t정확도: 95.90%\n",
+      "35\t검증 세트 손실: 0.143575\t최선의 손실: 0.128136\t정확도: 95.86%\n",
+      "36\t검증 세트 손실: 0.127115\t최선의 손실: 0.127115\t정확도: 96.36%\n",
+      "37\t검증 세트 손실: 0.143306\t최선의 손실: 0.127115\t정확도: 96.09%\n",
+      "38\t검증 세트 손실: 0.126951\t최선의 손실: 0.126951\t정확도: 96.13%\n",
+      "39\t검증 세트 손실: 0.130697\t최선의 손실: 0.126951\t정확도: 96.44%\n",
+      "40\t검증 세트 손실: 0.142860\t최선의 손실: 0.126951\t정확도: 96.05%\n",
+      "41\t검증 세트 손실: 0.136522\t최선의 손실: 0.126951\t정확도: 96.25%\n",
+      "42\t검증 세트 손실: 0.133501\t최선의 손실: 0.126951\t정확도: 96.44%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10\t검증 세트 손실: 0.159752\t최선의 손실: 0.134045\t정확도: 95.66%\n",
-      "11\t검증 세트 손실: 0.149905\t최선의 손실: 0.134045\t정확도: 96.01%\n",
-      "12\t검증 세트 손실: 0.142964\t최선의 손실: 0.134045\t정확도: 96.64%\n",
-      "13\t검증 세트 손실: 0.151092\t최선의 손실: 0.134045\t정확도: 96.36%\n",
-      "14\t검증 세트 손실: 0.160286\t최선의 손실: 0.134045\t정확도: 95.50%\n",
-      "15\t검증 세트 손실: 0.154689\t최선의 손실: 0.134045\t정확도: 96.76%\n",
-      "16\t검증 세트 손실: 0.179863\t최선의 손실: 0.134045\t정확도: 95.74%\n",
-      "17\t검증 세트 손실: 0.157206\t최선의 손실: 0.134045\t정확도: 95.97%\n",
-      "18\t검증 세트 손실: 0.149040\t최선의 손실: 0.134045\t정확도: 95.86%\n",
-      "19\t검증 세트 손실: 0.147151\t최선의 손실: 0.134045\t정확도: 96.29%\n",
-      "20\t검증 세트 손실: 0.149295\t최선의 손실: 0.134045\t정확도: 96.72%\n",
-      "21\t검증 세트 손실: 0.166617\t최선의 손실: 0.134045\t정확도: 95.70%\n",
-      "22\t검증 세트 손실: 0.149504\t최선의 손실: 0.134045\t정확도: 96.52%\n",
-      "23\t검증 세트 손실: 0.145959\t최선의 손실: 0.134045\t정확도: 96.60%\n",
-      "24\t검증 세트 손실: 0.154180\t최선의 손실: 0.134045\t정확도: 96.40%\n",
-      "25\t검증 세트 손실: 0.167248\t최선의 손실: 0.134045\t정확도: 96.17%\n",
-      "26\t검증 세트 손실: 0.155400\t최선의 손실: 0.134045\t정확도: 96.44%\n",
-      "27\t검증 세트 손실: 0.162752\t최선의 손실: 0.134045\t정확도: 96.33%\n",
-      "28\t검증 세트 손실: 0.157683\t최선의 손실: 0.134045\t정확도: 96.87%\n",
-      "29\t검증 세트 손실: 0.210522\t최선의 손실: 0.134045\t정확도: 95.11%\n",
-      "30\t검증 세트 손실: 0.158380\t최선의 손실: 0.134045\t정확도: 96.52%\n",
+      "43\t검증 세트 손실: 0.130090\t최선의 손실: 0.126951\t정확도: 96.09%\n",
+      "44\t검증 세트 손실: 0.143643\t최선의 손실: 0.126951\t정확도: 96.09%\n",
+      "45\t검증 세트 손실: 0.147790\t최선의 손실: 0.126951\t정확도: 95.43%\n",
+      "46\t검증 세트 손실: 0.134437\t최선의 손실: 0.126951\t정확도: 95.78%\n",
+      "47\t검증 세트 손실: 0.134925\t최선의 손실: 0.126951\t정확도: 96.36%\n",
+      "48\t검증 세트 손실: 0.126675\t최선의 손실: 0.126675\t정확도: 96.72%\n",
+      "49\t검증 세트 손실: 0.136615\t최선의 손실: 0.126675\t정확도: 96.44%\n",
+      "50\t검증 세트 손실: 0.123956\t최선의 손실: 0.123956\t정확도: 96.64%\n",
+      "51\t검증 세트 손실: 0.132093\t최선의 손실: 0.123956\t정확도: 96.05%\n",
+      "52\t검증 세트 손실: 0.131750\t최선의 손실: 0.123956\t정확도: 96.25%\n",
+      "53\t검증 세트 손실: 0.129719\t최선의 손실: 0.123956\t정확도: 95.54%\n",
+      "54\t검증 세트 손실: 0.128512\t최선의 손실: 0.123956\t정확도: 96.21%\n",
+      "55\t검증 세트 손실: 0.131989\t최선의 손실: 0.123956\t정확도: 96.52%\n",
+      "56\t검증 세트 손실: 0.147084\t최선의 손실: 0.123956\t정확도: 95.31%\n",
+      "57\t검증 세트 손실: 0.133625\t최선의 손실: 0.123956\t정확도: 96.17%\n",
+      "58\t검증 세트 손실: 0.156154\t최선의 손실: 0.123956\t정확도: 95.70%\n",
+      "59\t검증 세트 손실: 0.137674\t최선의 손실: 0.123956\t정확도: 96.13%\n",
+      "60\t검증 세트 손실: 0.138594\t최선의 손실: 0.123956\t정확도: 96.44%\n",
+      "61\t검증 세트 손실: 0.144520\t최선의 손실: 0.123956\t정확도: 95.90%\n",
+      "62\t검증 세트 손실: 0.132273\t최선의 손실: 0.123956\t정확도: 96.52%\n",
+      "63\t검증 세트 손실: 0.127300\t최선의 손실: 0.123956\t정확도: 96.29%\n",
+      "64\t검증 세트 손실: 0.130862\t최선의 손실: 0.123956\t정확도: 96.64%\n",
+      "65\t검증 세트 손실: 0.131325\t최선의 손실: 0.123956\t정확도: 96.87%\n",
+      "66\t검증 세트 손실: 0.123277\t최선의 손실: 0.123277\t정확도: 96.76%\n",
+      "67\t검증 세트 손실: 0.148008\t최선의 손실: 0.123277\t정확도: 96.60%\n",
+      "68\t검증 세트 손실: 0.122247\t최선의 손실: 0.122247\t정확도: 96.91%\n",
+      "69\t검증 세트 손실: 0.141666\t최선의 손실: 0.122247\t정확도: 96.21%\n",
+      "70\t검증 세트 손실: 0.141379\t최선의 손실: 0.122247\t정확도: 96.29%\n",
+      "71\t검증 세트 손실: 0.134302\t최선의 손실: 0.122247\t정확도: 96.21%\n",
+      "72\t검증 세트 손실: 0.151680\t최선의 손실: 0.122247\t정확도: 96.56%\n",
+      "73\t검증 세트 손실: 0.127665\t최선의 손실: 0.122247\t정확도: 96.48%\n",
+      "74\t검증 세트 손실: 0.124222\t최선의 손실: 0.122247\t정확도: 96.72%\n",
+      "75\t검증 세트 손실: 0.149626\t최선의 손실: 0.122247\t정확도: 96.29%\n",
+      "76\t검증 세트 손실: 0.140207\t최선의 손실: 0.122247\t정확도: 96.52%\n",
+      "77\t검증 세트 손실: 0.139907\t최선의 손실: 0.122247\t정확도: 96.05%\n",
+      "78\t검증 세트 손실: 0.130125\t최선의 손실: 0.122247\t정확도: 96.40%\n",
+      "79\t검증 세트 손실: 0.131447\t최선의 손실: 0.122247\t정확도: 96.52%\n",
+      "80\t검증 세트 손실: 0.145773\t최선의 손실: 0.122247\t정확도: 95.62%\n",
+      "81\t검증 세트 손실: 0.131525\t최선의 손실: 0.122247\t정확도: 96.17%\n",
+      "82\t검증 세트 손실: 0.127507\t최선의 손실: 0.122247\t정확도: 95.93%\n",
+      "83\t검증 세트 손실: 0.133957\t최선의 손실: 0.122247\t정확도: 96.29%\n",
+      "84\t검증 세트 손실: 0.123614\t최선의 손실: 0.122247\t정확도: 96.79%\n",
+      "85\t검증 세트 손실: 0.140912\t최선의 손실: 0.122247\t정확도: 96.36%\n",
+      "86\t검증 세트 손실: 0.124959\t최선의 손실: 0.122247\t정확도: 96.72%\n",
+      "87\t검증 세트 손실: 0.120447\t최선의 손실: 0.120447\t정확도: 96.56%\n",
+      "88\t검증 세트 손실: 0.121706\t최선의 손실: 0.120447\t정확도: 96.56%\n",
+      "89\t검증 세트 손실: 0.130101\t최선의 손실: 0.120447\t정확도: 96.76%\n",
+      "90\t검증 세트 손실: 0.131276\t최선의 손실: 0.120447\t정확도: 96.25%\n",
+      "91\t검증 세트 손실: 0.127770\t최선의 손실: 0.120447\t정확도: 96.33%\n",
+      "92\t검증 세트 손실: 0.129307\t최선의 손실: 0.120447\t정확도: 96.64%\n",
+      "93\t검증 세트 손실: 0.137425\t최선의 손실: 0.120447\t정확도: 96.29%\n",
+      "94\t검증 세트 손실: 0.145790\t최선의 손실: 0.120447\t정확도: 96.21%\n",
+      "95\t검증 세트 손실: 0.128904\t최선의 손실: 0.120447\t정확도: 96.33%\n",
+      "96\t검증 세트 손실: 0.128496\t최선의 손실: 0.120447\t정확도: 95.97%\n",
+      "97\t검증 세트 손실: 0.131516\t최선의 손실: 0.120447\t정확도: 96.40%\n",
+      "98\t검증 세트 손실: 0.127034\t최선의 손실: 0.120447\t정확도: 96.48%\n",
+      "99\t검증 세트 손실: 0.130344\t최선의 손실: 0.120447\t정확도: 96.79%\n",
+      "100\t검증 세트 손실: 0.127251\t최선의 손실: 0.120447\t정확도: 96.44%\n",
+      "101\t검증 세트 손실: 0.134547\t최선의 손실: 0.120447\t정확도: 96.64%\n",
+      "102\t검증 세트 손실: 0.134236\t최선의 손실: 0.120447\t정확도: 96.72%\n",
+      "103\t검증 세트 손실: 0.130010\t최선의 손실: 0.120447\t정확도: 96.64%\n",
+      "104\t검증 세트 손실: 0.123375\t최선의 손실: 0.120447\t정확도: 96.99%\n",
+      "105\t검증 세트 손실: 0.133529\t최선의 손실: 0.120447\t정확도: 96.91%\n",
+      "106\t검증 세트 손실: 0.131350\t최선의 손실: 0.120447\t정확도: 96.79%\n",
+      "107\t검증 세트 손실: 0.136164\t최선의 손실: 0.120447\t정확도: 96.56%\n",
+      "108\t검증 세트 손실: 0.122908\t최선의 손실: 0.120447\t정확도: 96.05%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=  14.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.402808\t최선의 손실: 0.402808\t정확도: 84.48%\n",
-      "1\t검증 세트 손실: 0.360668\t최선의 손실: 0.360668\t정확도: 85.69%\n",
-      "2\t검증 세트 손실: 0.307257\t최선의 손실: 0.307257\t정확도: 91.28%\n",
-      "3\t검증 세트 손실: 0.306811\t최선의 손실: 0.306811\t정확도: 90.54%\n",
-      "4\t검증 세트 손실: 0.265901\t최선의 손실: 0.265901\t정확도: 92.85%\n",
-      "5\t검증 세트 손실: 0.266989\t최선의 손실: 0.265901\t정확도: 92.46%\n",
-      "6\t검증 세트 손실: 0.265475\t최선의 손실: 0.265475\t정확도: 93.08%\n",
-      "7\t검증 세트 손실: 0.243313\t최선의 손실: 0.243313\t정확도: 94.53%\n",
-      "8\t검증 세트 손실: 0.250914\t최선의 손실: 0.243313\t정확도: 93.12%\n",
-      "9\t검증 세트 손실: 0.219830\t최선의 손실: 0.219830\t정확도: 94.61%\n",
-      "10\t검증 세트 손실: 0.231322\t최선의 손실: 0.219830\t정확도: 94.21%\n",
-      "11\t검증 세트 손실: 0.243271\t최선의 손실: 0.219830\t정확도: 93.78%\n",
-      "12\t검증 세트 손실: 0.224065\t최선의 손실: 0.219830\t정확도: 95.15%\n",
-      "13\t검증 세트 손실: 0.230148\t최선의 손실: 0.219830\t정확도: 94.21%\n",
-      "14\t검증 세트 손실: 0.205809\t최선의 손실: 0.205809\t정확도: 94.53%\n",
-      "15\t검증 세트 손실: 0.226727\t최선의 손실: 0.205809\t정확도: 93.82%\n",
-      "16\t검증 세트 손실: 0.212841\t최선의 손실: 0.205809\t정확도: 94.64%\n",
-      "17\t검증 세트 손실: 0.209008\t최선의 손실: 0.205809\t정확도: 94.64%\n",
-      "18\t검증 세트 손실: 0.207796\t최선의 손실: 0.205809\t정확도: 94.33%\n",
-      "19\t검증 세트 손실: 0.198621\t최선의 손실: 0.198621\t정확도: 94.68%\n",
-      "20\t검증 세트 손실: 0.203403\t최선의 손실: 0.198621\t정확도: 95.04%\n",
-      "21\t검증 세트 손실: 0.205336\t최선의 손실: 0.198621\t정확도: 94.61%\n",
-      "22\t검증 세트 손실: 0.205049\t최선의 손실: 0.198621\t정확도: 94.76%\n",
-      "23\t검증 세트 손실: 0.214850\t최선의 손실: 0.198621\t정확도: 93.75%\n",
-      "24\t검증 세트 손실: 0.212452\t최선의 손실: 0.198621\t정확도: 94.76%\n",
-      "25\t검증 세트 손실: 0.220208\t최선의 손실: 0.198621\t정확도: 94.33%\n",
-      "26\t검증 세트 손실: 0.237287\t최선의 손실: 0.198621\t정확도: 93.55%\n",
-      "27\t검증 세트 손실: 0.200802\t최선의 손실: 0.198621\t정확도: 94.37%\n",
-      "28\t검증 세트 손실: 0.197662\t최선의 손실: 0.197662\t정확도: 94.84%\n",
-      "29\t검증 세트 손실: 0.208909\t최선의 손실: 0.197662\t정확도: 94.37%\n",
-      "30\t검증 세트 손실: 0.206267\t최선의 손실: 0.197662\t정확도: 94.37%\n",
-      "31\t검증 세트 손실: 0.189770\t최선의 손실: 0.189770\t정확도: 95.11%\n",
-      "32\t검증 세트 손실: 0.186614\t최선의 손실: 0.186614\t정확도: 94.45%\n",
-      "33\t검증 세트 손실: 0.187920\t최선의 손실: 0.186614\t정확도: 94.64%\n",
-      "34\t검증 세트 손실: 0.229894\t최선의 손실: 0.186614\t정확도: 93.67%\n",
-      "35\t검증 세트 손실: 0.189019\t최선의 손실: 0.186614\t정확도: 94.41%\n",
-      "36\t검증 세트 손실: 0.187919\t최선의 손실: 0.186614\t정확도: 94.76%\n",
-      "37\t검증 세트 손실: 0.189238\t최선의 손실: 0.186614\t정확도: 94.10%\n",
-      "38\t검증 세트 손실: 0.200028\t최선의 손실: 0.186614\t정확도: 94.92%\n",
-      "39\t검증 세트 손실: 0.200463\t최선의 손실: 0.186614\t정확도: 94.72%\n",
-      "40\t검증 세트 손실: 0.198016\t최선의 손실: 0.186614\t정확도: 94.57%\n",
-      "41\t검증 세트 손실: 0.185522\t최선의 손실: 0.185522\t정확도: 94.21%\n",
-      "42\t검증 세트 손실: 0.207557\t최선의 손실: 0.185522\t정확도: 94.37%\n",
-      "43\t검증 세트 손실: 0.198471\t최선의 손실: 0.185522\t정확도: 95.07%\n",
-      "44\t검증 세트 손실: 0.177586\t최선의 손실: 0.177586\t정확도: 95.39%\n",
-      "45\t검증 세트 손실: 0.190700\t최선의 손실: 0.177586\t정확도: 94.21%\n",
-      "46\t검증 세트 손실: 0.175270\t최선의 손실: 0.175270\t정확도: 95.11%\n",
-      "47\t검증 세트 손실: 0.190870\t최선의 손실: 0.175270\t정확도: 95.54%\n",
-      "48\t검증 세트 손실: 0.192011\t최선의 손실: 0.175270\t정확도: 95.35%\n",
-      "49\t검증 세트 손실: 0.203637\t최선의 손실: 0.175270\t정확도: 94.88%\n",
-      "50\t검증 세트 손실: 0.186488\t최선의 손실: 0.175270\t정확도: 95.39%\n",
-      "51\t검증 세트 손실: 0.172848\t최선의 손실: 0.172848\t정확도: 95.19%\n",
-      "52\t검증 세트 손실: 0.204333\t최선의 손실: 0.172848\t정확도: 94.25%\n",
-      "53\t검증 세트 손실: 0.182707\t최선의 손실: 0.172848\t정확도: 95.23%\n",
-      "54\t검증 세트 손실: 0.176608\t최선의 손실: 0.172848\t정확도: 95.27%\n",
-      "55\t검증 세트 손실: 0.189401\t최선의 손실: 0.172848\t정확도: 95.43%\n",
-      "56\t검증 세트 손실: 0.190252\t최선의 손실: 0.172848\t정확도: 95.11%\n",
-      "57\t검증 세트 손실: 0.170602\t최선의 손실: 0.170602\t정확도: 95.19%\n",
-      "58\t검증 세트 손실: 0.180477\t최선의 손실: 0.170602\t정확도: 95.04%\n",
-      "59\t검증 세트 손실: 0.185558\t최선의 손실: 0.170602\t정확도: 95.66%\n",
-      "60\t검증 세트 손실: 0.167079\t최선의 손실: 0.167079\t정확도: 95.78%\n",
-      "61\t검증 세트 손실: 0.176289\t최선의 손실: 0.167079\t정확도: 95.00%\n",
-      "62\t검증 세트 손실: 0.167421\t최선의 손실: 0.167079\t정확도: 95.70%\n",
-      "63\t검증 세트 손실: 0.181682\t최선의 손실: 0.167079\t정확도: 95.74%\n",
-      "64\t검증 세트 손실: 0.176433\t최선의 손실: 0.167079\t정확도: 95.43%\n",
-      "65\t검증 세트 손실: 0.207110\t최선의 손실: 0.167079\t정확도: 94.25%\n",
-      "66\t검증 세트 손실: 0.185648\t최선의 손실: 0.167079\t정확도: 94.88%\n",
-      "67\t검증 세트 손실: 0.211093\t최선의 손실: 0.167079\t정확도: 94.06%\n",
-      "68\t검증 세트 손실: 0.185972\t최선의 손실: 0.167079\t정확도: 94.37%\n",
-      "69\t검증 세트 손실: 0.173467\t최선의 손실: 0.167079\t정확도: 94.76%\n",
-      "70\t검증 세트 손실: 0.160479\t최선의 손실: 0.160479\t정확도: 95.58%\n",
-      "71\t검증 세트 손실: 0.178216\t최선의 손실: 0.160479\t정확도: 95.47%\n",
-      "72\t검증 세트 손실: 0.189568\t최선의 손실: 0.160479\t정확도: 93.90%\n",
-      "73\t검증 세트 손실: 0.164822\t최선의 손실: 0.160479\t정확도: 95.58%\n",
-      "74\t검증 세트 손실: 0.167380\t최선의 손실: 0.160479\t정확도: 95.47%\n",
-      "75\t검증 세트 손실: 0.184531\t최선의 손실: 0.160479\t정확도: 95.00%\n",
-      "76\t검증 세트 손실: 0.180856\t최선의 손실: 0.160479\t정확도: 95.19%\n",
-      "77\t검증 세트 손실: 0.174177\t최선의 손실: 0.160479\t정확도: 95.54%\n",
-      "78\t검증 세트 손실: 0.177798\t최선의 손실: 0.160479\t정확도: 95.00%\n",
-      "79\t검증 세트 손실: 0.163106\t최선의 손실: 0.160479\t정확도: 95.97%\n",
-      "80\t검증 세트 손실: 0.174723\t최선의 손실: 0.160479\t정확도: 95.70%\n",
-      "81\t검증 세트 손실: 0.172790\t최선의 손실: 0.160479\t정확도: 95.07%\n",
-      "82\t검증 세트 손실: 0.176230\t최선의 손실: 0.160479\t정확도: 95.54%\n",
-      "83\t검증 세트 손실: 0.190285\t최선의 손실: 0.160479\t정확도: 94.96%\n",
-      "84\t검증 세트 손실: 0.226974\t최선의 손실: 0.160479\t정확도: 94.21%\n",
-      "85\t검증 세트 손실: 0.173357\t최선의 손실: 0.160479\t정확도: 95.66%\n",
-      "86\t검증 세트 손실: 0.170020\t최선의 손실: 0.160479\t정확도: 95.70%\n",
-      "87\t검증 세트 손실: 0.195018\t최선의 손실: 0.160479\t정확도: 95.23%\n",
-      "88\t검증 세트 손실: 0.184475\t최선의 손실: 0.160479\t정확도: 95.62%\n",
-      "89\t검증 세트 손실: 0.173832\t최선의 손실: 0.160479\t정확도: 94.92%\n",
-      "90\t검증 세트 손실: 0.191391\t최선의 손실: 0.160479\t정확도: 94.06%\n",
-      "91\t검증 세트 손실: 0.181579\t최선의 손실: 0.160479\t정확도: 95.58%\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=10, total=  32.4s\n",
+      "[CV] batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.208418\t최선의 손실: 0.208418\t정확도: 93.00%\n",
+      "1\t검증 세트 손실: 0.131514\t최선의 손실: 0.131514\t정확도: 96.09%\n",
+      "2\t검증 세트 손실: 0.116612\t최선의 손실: 0.116612\t정확도: 96.83%\n",
+      "3\t검증 세트 손실: 0.109485\t최선의 손실: 0.109485\t정확도: 97.15%\n",
+      "4\t검증 세트 손실: 0.107998\t최선의 손실: 0.107998\t정확도: 97.22%\n",
+      "5\t검증 세트 손실: 0.093085\t최선의 손실: 0.093085\t정확도: 97.50%\n",
+      "6\t검증 세트 손실: 0.091669\t최선의 손실: 0.091669\t정확도: 97.54%\n",
+      "7\t검증 세트 손실: 0.092344\t최선의 손실: 0.091669\t정확도: 97.42%\n",
+      "8\t검증 세트 손실: 0.086254\t최선의 손실: 0.086254\t정확도: 97.73%\n",
+      "9\t검증 세트 손실: 0.077014\t최선의 손실: 0.077014\t정확도: 97.81%\n",
+      "10\t검증 세트 손실: 0.080773\t최선의 손실: 0.077014\t정확도: 97.85%\n",
+      "11\t검증 세트 손실: 0.078482\t최선의 손실: 0.077014\t정확도: 97.97%\n",
+      "12\t검증 세트 손실: 0.075378\t최선의 손실: 0.075378\t정확도: 98.05%\n",
+      "13\t검증 세트 손실: 0.073184\t최선의 손실: 0.073184\t정확도: 97.93%\n",
+      "14\t검증 세트 손실: 0.075138\t최선의 손실: 0.073184\t정확도: 97.69%\n",
+      "15\t검증 세트 손실: 0.072208\t최선의 손실: 0.072208\t정확도: 97.97%\n",
+      "16\t검증 세트 손실: 0.067223\t최선의 손실: 0.067223\t정확도: 98.16%\n",
+      "17\t검증 세트 손실: 0.070692\t최선의 손실: 0.067223\t정확도: 97.93%\n",
+      "18\t검증 세트 손실: 0.066938\t최선의 손실: 0.066938\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.063189\t최선의 손실: 0.063189\t정확도: 98.24%\n",
+      "20\t검증 세트 손실: 0.062912\t최선의 손실: 0.062912\t정확도: 98.40%\n",
+      "21\t검증 세트 손실: 0.067825\t최선의 손실: 0.062912\t정확도: 98.12%\n",
+      "22\t검증 세트 손실: 0.070182\t최선의 손실: 0.062912\t정확도: 98.05%\n",
+      "23\t검증 세트 손실: 0.062232\t최선의 손실: 0.062232\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.067423\t최선의 손실: 0.062232\t정확도: 98.36%\n",
+      "25\t검증 세트 손실: 0.070078\t최선의 손실: 0.062232\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.073484\t최선의 손실: 0.062232\t정확도: 98.24%\n",
+      "27\t검증 세트 손실: 0.070767\t최선의 손실: 0.062232\t정확도: 98.28%\n",
+      "28\t검증 세트 손실: 0.077364\t최선의 손실: 0.062232\t정확도: 98.20%\n",
+      "29\t검증 세트 손실: 0.067912\t최선의 손실: 0.062232\t정확도: 98.40%\n",
+      "30\t검증 세트 손실: 0.067111\t최선의 손실: 0.062232\t정확도: 98.20%\n",
+      "31\t검증 세트 손실: 0.064761\t최선의 손실: 0.062232\t정확도: 98.32%\n",
+      "32\t검증 세트 손실: 0.062255\t최선의 손실: 0.062232\t정확도: 98.28%\n",
+      "33\t검증 세트 손실: 0.065829\t최선의 손실: 0.062232\t정확도: 98.20%\n",
+      "34\t검증 세트 손실: 0.063921\t최선의 손실: 0.062232\t정확도: 98.55%\n",
+      "35\t검증 세트 손실: 0.066188\t최선의 손실: 0.062232\t정확도: 98.32%\n",
+      "36\t검증 세트 손실: 0.062165\t최선의 손실: 0.062165\t정확도: 98.44%\n",
+      "37\t검증 세트 손실: 0.063246\t최선의 손실: 0.062165\t정확도: 98.63%\n",
+      "38\t검증 세트 손실: 0.061272\t최선의 손실: 0.061272\t정확도: 98.40%\n",
+      "39\t검증 세트 손실: 0.064451\t최선의 손실: 0.061272\t정확도: 98.44%\n",
+      "40\t검증 세트 손실: 0.066005\t최선의 손실: 0.061272\t정확도: 98.32%\n",
+      "41\t검증 세트 손실: 0.059444\t최선의 손실: 0.059444\t정확도: 98.36%\n",
+      "42\t검증 세트 손실: 0.060318\t최선의 손실: 0.059444\t정확도: 98.51%\n",
+      "43\t검증 세트 손실: 0.061838\t최선의 손실: 0.059444\t정확도: 98.40%\n",
+      "44\t검증 세트 손실: 0.061743\t최선의 손실: 0.059444\t정확도: 98.59%\n",
+      "45\t검증 세트 손실: 0.067709\t최선의 손실: 0.059444\t정확도: 98.24%\n",
+      "46\t검증 세트 손실: 0.064580\t최선의 손실: 0.059444\t정확도: 98.28%\n",
+      "47\t검증 세트 손실: 0.068257\t최선의 손실: 0.059444\t정확도: 98.36%\n",
+      "48\t검증 세트 손실: 0.067751\t최선의 손실: 0.059444\t정확도: 98.20%\n",
+      "49\t검증 세트 손실: 0.066927\t최선의 손실: 0.059444\t정확도: 98.48%\n",
+      "50\t검증 세트 손실: 0.064087\t최선의 손실: 0.059444\t정확도: 98.44%\n",
+      "51\t검증 세트 손실: 0.063537\t최선의 손실: 0.059444\t정확도: 98.40%\n",
+      "52\t검증 세트 손실: 0.063149\t최선의 손실: 0.059444\t정확도: 98.36%\n",
+      "53\t검증 세트 손실: 0.066542\t최선의 손실: 0.059444\t정확도: 98.20%\n",
+      "54\t검증 세트 손실: 0.061939\t최선의 손실: 0.059444\t정확도: 98.55%\n",
+      "55\t검증 세트 손실: 0.062873\t최선의 손실: 0.059444\t정확도: 98.36%\n",
+      "56\t검증 세트 손실: 0.065283\t최선의 손실: 0.059444\t정확도: 98.48%\n",
+      "57\t검증 세트 손실: 0.063883\t최선의 손실: 0.059444\t정확도: 98.36%\n",
+      "58\t검증 세트 손실: 0.065655\t최선의 손실: 0.059444\t정확도: 98.48%\n",
+      "59\t검증 세트 손실: 0.066004\t최선의 손실: 0.059444\t정확도: 98.55%\n",
+      "60\t검증 세트 손실: 0.068678\t최선의 손실: 0.059444\t정확도: 98.12%\n",
+      "61\t검증 세트 손실: 0.072457\t최선의 손실: 0.059444\t정확도: 98.20%\n",
+      "62\t검증 세트 손실: 0.067227\t최선의 손실: 0.059444\t정확도: 98.44%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=10, total=  24.8s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.554896\t최선의 손실: 0.554896\t정확도: 78.69%\n",
-      "1\t검증 세트 손실: 0.217109\t최선의 손실: 0.217109\t정확도: 94.06%\n",
-      "2\t검증 세트 손실: 0.198770\t최선의 손실: 0.198770\t정확도: 94.96%\n",
-      "3\t검증 세트 손실: 0.178994\t최선의 손실: 0.178994\t정확도: 95.31%\n",
-      "4\t검증 세트 손실: 0.174699\t최선의 손실: 0.174699\t정확도: 95.31%\n",
-      "5\t검증 세트 손실: 0.177241\t최선의 손실: 0.174699\t정확도: 95.35%\n",
-      "6\t검증 세트 손실: 0.154030\t최선의 손실: 0.154030\t정확도: 95.31%\n",
-      "7\t검증 세트 손실: 0.166085\t최선의 손실: 0.154030\t정확도: 95.74%\n",
-      "8\t검증 세트 손실: 0.163879\t최선의 손실: 0.154030\t정확도: 95.47%\n",
-      "9\t검증 세트 손실: 0.146752\t최선의 손실: 0.146752\t정확도: 95.66%\n",
-      "10\t검증 세트 손실: 0.147273\t최선의 손실: 0.146752\t정확도: 95.86%\n",
-      "11\t검증 세트 손실: 0.134725\t최선의 손실: 0.134725\t정확도: 96.40%\n",
-      "12\t검증 세트 손실: 0.160580\t최선의 손실: 0.134725\t정확도: 95.74%\n",
-      "13\t검증 세트 손실: 0.155156\t최선의 손실: 0.134725\t정확도: 96.05%\n",
-      "14\t검증 세트 손실: 0.154726\t최선의 손실: 0.134725\t정확도: 96.01%\n",
-      "15\t검증 세트 손실: 0.158891\t최선의 손실: 0.134725\t정확도: 95.74%\n",
-      "16\t검증 세트 손실: 0.136199\t최선의 손실: 0.134725\t정확도: 95.97%\n",
-      "17\t검증 세트 손실: 0.135836\t최선의 손실: 0.134725\t정확도: 96.17%\n",
-      "18\t검증 세트 손실: 0.127366\t최선의 손실: 0.127366\t정확도: 96.13%\n",
-      "19\t검증 세트 손실: 0.137329\t최선의 손실: 0.127366\t정확도: 96.21%\n",
-      "20\t검증 세트 손실: 0.146721\t최선의 손실: 0.127366\t정확도: 96.05%\n",
-      "21\t검증 세트 손실: 0.155392\t최선의 손실: 0.127366\t정확도: 96.05%\n",
-      "22\t검증 세트 손실: 0.137315\t최선의 손실: 0.127366\t정확도: 96.05%\n",
-      "23\t검증 세트 손실: 0.143278\t최선의 손실: 0.127366\t정확도: 96.21%\n",
-      "24\t검증 세트 손실: 0.144866\t최선의 손실: 0.127366\t정확도: 95.66%\n",
-      "25\t검증 세트 손실: 0.162370\t최선의 손실: 0.127366\t정확도: 94.45%\n",
-      "26\t검증 세트 손실: 0.132849\t최선의 손실: 0.127366\t정확도: 96.40%\n",
-      "27\t검증 세트 손실: 0.144698\t최선의 손실: 0.127366\t정확도: 95.47%\n",
-      "28\t검증 세트 손실: 0.142077\t최선의 손실: 0.127366\t정확도: 95.93%\n",
-      "29\t검증 세트 손실: 0.143105\t최선의 손실: 0.127366\t정확도: 96.13%\n",
-      "30\t검증 세트 손실: 0.131948\t최선의 손실: 0.127366\t정확도: 96.25%\n",
-      "31\t검증 세트 손실: 0.130463\t최선의 손실: 0.127366\t정확도: 96.29%\n",
-      "32\t검증 세트 손실: 0.143601\t최선의 손실: 0.127366\t정확도: 96.21%\n",
-      "33\t검증 세트 손실: 0.134871\t최선의 손실: 0.127366\t정확도: 96.29%\n",
-      "34\t검증 세트 손실: 0.131993\t최선의 손실: 0.127366\t정확도: 96.25%\n",
-      "35\t검증 세트 손실: 0.134874\t최선의 손실: 0.127366\t정확도: 96.25%\n"
+      "[CV]  batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total=   6.2s\n",
+      "[CV] batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.231476\t최선의 손실: 0.231476\t정확도: 92.34%\n",
+      "1\t검증 세트 손실: 0.138865\t최선의 손실: 0.138865\t정확도: 95.90%\n",
+      "2\t검증 세트 손실: 0.121507\t최선의 손실: 0.121507\t정확도: 96.72%\n",
+      "3\t검증 세트 손실: 0.095945\t최선의 손실: 0.095945\t정확도: 97.15%\n",
+      "4\t검증 세트 손실: 0.089188\t최선의 손실: 0.089188\t정확도: 97.38%\n",
+      "5\t검증 세트 손실: 0.086400\t최선의 손실: 0.086400\t정확도: 97.54%\n",
+      "6\t검증 세트 손실: 0.086006\t최선의 손실: 0.086006\t정확도: 97.69%\n",
+      "7\t검증 세트 손실: 0.090573\t최선의 손실: 0.086006\t정확도: 97.54%\n",
+      "8\t검증 세트 손실: 0.088316\t최선의 손실: 0.086006\t정확도: 97.77%\n",
+      "9\t검증 세트 손실: 0.076702\t최선의 손실: 0.076702\t정확도: 97.93%\n",
+      "10\t검증 세트 손실: 0.084417\t최선의 손실: 0.076702\t정확도: 97.81%\n",
+      "11\t검증 세트 손실: 0.077797\t최선의 손실: 0.076702\t정확도: 97.85%\n",
+      "12\t검증 세트 손실: 0.078754\t최선의 손실: 0.076702\t정확도: 97.81%\n",
+      "13\t검증 세트 손실: 0.077666\t최선의 손실: 0.076702\t정확도: 97.73%\n",
+      "14\t검증 세트 손실: 0.087722\t최선의 손실: 0.076702\t정확도: 97.85%\n",
+      "15\t검증 세트 손실: 0.072268\t최선의 손실: 0.072268\t정확도: 98.08%\n",
+      "16\t검증 세트 손실: 0.066666\t최선의 손실: 0.066666\t정확도: 98.28%\n",
+      "17\t검증 세트 손실: 0.067972\t최선의 손실: 0.066666\t정확도: 98.16%\n",
+      "18\t검증 세트 손실: 0.071899\t최선의 손실: 0.066666\t정확도: 98.20%\n",
+      "19\t검증 세트 손실: 0.064921\t최선의 손실: 0.064921\t정확도: 98.36%\n",
+      "20\t검증 세트 손실: 0.065867\t최선의 손실: 0.064921\t정확도: 98.20%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "36\t검증 세트 손실: 0.134833\t최선의 손실: 0.127366\t정확도: 96.40%\n",
-      "37\t검증 세트 손실: 0.132781\t최선의 손실: 0.127366\t정확도: 96.68%\n",
-      "38\t검증 세트 손실: 0.123229\t최선의 손실: 0.123229\t정확도: 96.52%\n",
-      "39\t검증 세트 손실: 0.120722\t최선의 손실: 0.120722\t정확도: 96.68%\n",
-      "40\t검증 세트 손실: 0.142990\t최선의 손실: 0.120722\t정확도: 96.05%\n",
-      "41\t검증 세트 손실: 0.141371\t최선의 손실: 0.120722\t정확도: 96.33%\n",
-      "42\t검증 세트 손실: 0.135655\t최선의 손실: 0.120722\t정확도: 96.36%\n",
-      "43\t검증 세트 손실: 0.138542\t최선의 손실: 0.120722\t정확도: 96.36%\n",
-      "44\t검증 세트 손실: 0.128902\t최선의 손실: 0.120722\t정확도: 96.44%\n",
-      "45\t검증 세트 손실: 0.123613\t최선의 손실: 0.120722\t정확도: 96.99%\n",
-      "46\t검증 세트 손실: 0.128487\t최선의 손실: 0.120722\t정확도: 96.87%\n",
-      "47\t검증 세트 손실: 0.135061\t최선의 손실: 0.120722\t정확도: 96.91%\n",
-      "48\t검증 세트 손실: 0.131890\t최선의 손실: 0.120722\t정확도: 96.79%\n",
-      "49\t검증 세트 손실: 0.121828\t최선의 손실: 0.120722\t정확도: 96.76%\n",
-      "50\t검증 세트 손실: 0.112770\t최선의 손실: 0.112770\t정확도: 97.19%\n",
-      "51\t검증 세트 손실: 0.116972\t최선의 손실: 0.112770\t정확도: 97.03%\n",
-      "52\t검증 세트 손실: 0.126746\t최선의 손실: 0.112770\t정확도: 96.60%\n",
-      "53\t검증 세트 손실: 0.122672\t최선의 손실: 0.112770\t정확도: 96.68%\n",
-      "54\t검증 세트 손실: 0.121871\t최선의 손실: 0.112770\t정확도: 96.95%\n",
-      "55\t검증 세트 손실: 0.115953\t최선의 손실: 0.112770\t정확도: 96.87%\n",
-      "56\t검증 세트 손실: 0.132583\t최선의 손실: 0.112770\t정확도: 96.44%\n",
-      "57\t검증 세트 손실: 0.113532\t최선의 손실: 0.112770\t정확도: 96.79%\n",
-      "58\t검증 세트 손실: 0.140040\t최선의 손실: 0.112770\t정확도: 96.36%\n",
-      "59\t검증 세트 손실: 0.121286\t최선의 손실: 0.112770\t정확도: 96.99%\n",
-      "60\t검증 세트 손실: 0.123651\t최선의 손실: 0.112770\t정확도: 96.99%\n",
-      "61\t검증 세트 손실: 0.121658\t최선의 손실: 0.112770\t정확도: 96.72%\n",
-      "62\t검증 세트 손실: 0.125516\t최선의 손실: 0.112770\t정확도: 96.60%\n",
-      "63\t검증 세트 손실: 0.119503\t최선의 손실: 0.112770\t정확도: 96.87%\n",
-      "64\t검증 세트 손실: 0.115322\t최선의 손실: 0.112770\t정확도: 96.91%\n",
-      "65\t검증 세트 손실: 0.117240\t최선의 손실: 0.112770\t정확도: 97.07%\n",
-      "66\t검증 세트 손실: 0.123960\t최선의 손실: 0.112770\t정확도: 96.87%\n",
-      "67\t검증 세트 손실: 0.117090\t최선의 손실: 0.112770\t정확도: 96.68%\n",
-      "68\t검증 세트 손실: 0.121502\t최선의 손실: 0.112770\t정확도: 96.87%\n",
-      "69\t검증 세트 손실: 0.130445\t최선의 손실: 0.112770\t정확도: 96.40%\n",
-      "70\t검증 세트 손실: 0.127514\t최선의 손실: 0.112770\t정확도: 96.48%\n",
-      "71\t검증 세트 손실: 0.122013\t최선의 손실: 0.112770\t정확도: 97.07%\n",
+      "21\t검증 세트 손실: 0.061339\t최선의 손실: 0.061339\t정확도: 98.28%\n",
+      "22\t검증 세트 손실: 0.057686\t최선의 손실: 0.057686\t정확도: 98.40%\n",
+      "23\t검증 세트 손실: 0.063490\t최선의 손실: 0.057686\t정확도: 98.36%\n",
+      "24\t검증 세트 손실: 0.063781\t최선의 손실: 0.057686\t정확도: 98.28%\n",
+      "25\t검증 세트 손실: 0.068521\t최선의 손실: 0.057686\t정확도: 98.20%\n",
+      "26\t검증 세트 손실: 0.061976\t최선의 손실: 0.057686\t정확도: 98.28%\n",
+      "27\t검증 세트 손실: 0.061133\t최선의 손실: 0.057686\t정확도: 98.51%\n",
+      "28\t검증 세트 손실: 0.063577\t최선의 손실: 0.057686\t정확도: 98.36%\n",
+      "29\t검증 세트 손실: 0.061456\t최선의 손실: 0.057686\t정확도: 98.32%\n",
+      "30\t검증 세트 손실: 0.057813\t최선의 손실: 0.057686\t정확도: 98.36%\n",
+      "31\t검증 세트 손실: 0.054821\t최선의 손실: 0.054821\t정확도: 98.55%\n",
+      "32\t검증 세트 손실: 0.054690\t최선의 손실: 0.054690\t정확도: 98.44%\n",
+      "33\t검증 세트 손실: 0.058148\t최선의 손실: 0.054690\t정확도: 98.55%\n",
+      "34\t검증 세트 손실: 0.057676\t최선의 손실: 0.054690\t정확도: 98.55%\n",
+      "35\t검증 세트 손실: 0.052020\t최선의 손실: 0.052020\t정확도: 98.94%\n",
+      "36\t검증 세트 손실: 0.052712\t최선의 손실: 0.052020\t정확도: 98.67%\n",
+      "37\t검증 세트 손실: 0.059990\t최선의 손실: 0.052020\t정확도: 98.59%\n",
+      "38\t검증 세트 손실: 0.062259\t최선의 손실: 0.052020\t정확도: 98.48%\n",
+      "39\t검증 세트 손실: 0.060577\t최선의 손실: 0.052020\t정확도: 98.24%\n",
+      "40\t검증 세트 손실: 0.060859\t최선의 손실: 0.052020\t정확도: 98.59%\n",
+      "41\t검증 세트 손실: 0.065045\t최선의 손실: 0.052020\t정확도: 98.24%\n",
+      "42\t검증 세트 손실: 0.066540\t최선의 손실: 0.052020\t정확도: 98.40%\n",
+      "43\t검증 세트 손실: 0.057385\t최선의 손실: 0.052020\t정확도: 98.44%\n",
+      "44\t검증 세트 손실: 0.055743\t최선의 손실: 0.052020\t정확도: 98.59%\n",
+      "45\t검증 세트 손실: 0.059799\t최선의 손실: 0.052020\t정확도: 98.44%\n",
+      "46\t검증 세트 손실: 0.060326\t최선의 손실: 0.052020\t정확도: 98.48%\n",
+      "47\t검증 세트 손실: 0.058531\t최선의 손실: 0.052020\t정확도: 98.48%\n",
+      "48\t검증 세트 손실: 0.055919\t최선의 손실: 0.052020\t정확도: 98.44%\n",
+      "49\t검증 세트 손실: 0.059682\t최선의 손실: 0.052020\t정확도: 98.48%\n",
+      "50\t검증 세트 손실: 0.055605\t최선의 손실: 0.052020\t정확도: 98.59%\n",
+      "51\t검증 세트 손실: 0.052884\t최선의 손실: 0.052020\t정확도: 98.67%\n",
+      "52\t검증 세트 손실: 0.057476\t최선의 손실: 0.052020\t정확도: 98.44%\n",
+      "53\t검증 세트 손실: 0.055273\t최선의 손실: 0.052020\t정확도: 98.51%\n",
+      "54\t검증 세트 손실: 0.060743\t최선의 손실: 0.052020\t정확도: 98.44%\n",
+      "55\t검증 세트 손실: 0.053315\t최선의 손실: 0.052020\t정확도: 98.40%\n",
+      "56\t검증 세트 손실: 0.053814\t최선의 손실: 0.052020\t정확도: 98.67%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=10, total=  27.6s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.480640\t최선의 손실: 0.480640\t정확도: 78.93%\n",
-      "1\t검증 세트 손실: 0.384957\t최선의 손실: 0.384957\t정확도: 85.54%\n",
-      "2\t검증 세트 손실: 0.342405\t최선의 손실: 0.342405\t정확도: 88.62%\n",
-      "3\t검증 세트 손실: 0.265272\t최선의 손실: 0.265272\t정확도: 93.75%\n",
-      "4\t검증 세트 손실: 0.198060\t최선의 손실: 0.198060\t정확도: 94.88%\n",
-      "5\t검증 세트 손실: 0.192700\t최선의 손실: 0.192700\t정확도: 95.43%\n",
-      "6\t검증 세트 손실: 0.196183\t최선의 손실: 0.192700\t정확도: 95.66%\n",
-      "7\t검증 세트 손실: 0.190556\t최선의 손실: 0.190556\t정확도: 95.31%\n",
-      "8\t검증 세트 손실: 0.185830\t최선의 손실: 0.185830\t정확도: 95.35%\n",
-      "9\t검증 세트 손실: 0.181111\t최선의 손실: 0.181111\t정확도: 94.61%\n",
-      "10\t검증 세트 손실: 0.195451\t최선의 손실: 0.181111\t정확도: 94.80%\n",
-      "11\t검증 세트 손실: 0.190169\t최선의 손실: 0.181111\t정확도: 95.07%\n",
-      "12\t검증 세트 손실: 0.166221\t최선의 손실: 0.166221\t정확도: 95.54%\n",
-      "13\t검증 세트 손실: 0.193721\t최선의 손실: 0.166221\t정확도: 95.23%\n",
-      "14\t검증 세트 손실: 0.209177\t최선의 손실: 0.166221\t정확도: 94.68%\n",
-      "15\t검증 세트 손실: 0.168764\t최선의 손실: 0.166221\t정확도: 95.86%\n",
-      "16\t검증 세트 손실: 0.175054\t최선의 손실: 0.166221\t정확도: 94.88%\n",
-      "17\t검증 세트 손실: 0.165487\t최선의 손실: 0.165487\t정확도: 95.74%\n",
-      "18\t검증 세트 손실: 0.171369\t최선의 손실: 0.165487\t정확도: 95.11%\n",
-      "19\t검증 세트 손실: 0.161732\t최선의 손실: 0.161732\t정확도: 95.97%\n",
-      "20\t검증 세트 손실: 0.168825\t최선의 손실: 0.161732\t정확도: 95.66%\n",
-      "21\t검증 세트 손실: 0.169287\t최선의 손실: 0.161732\t정확도: 96.01%\n",
-      "22\t검증 세트 손실: 0.166509\t최선의 손실: 0.161732\t정확도: 95.97%\n",
-      "23\t검증 세트 손실: 0.177572\t최선의 손실: 0.161732\t정확도: 95.23%\n",
-      "24\t검증 세트 손실: 0.158964\t최선의 손실: 0.158964\t정확도: 96.17%\n",
-      "25\t검증 세트 손실: 0.157915\t최선의 손실: 0.157915\t정확도: 96.56%\n",
-      "26\t검증 세트 손실: 0.145338\t최선의 손실: 0.145338\t정확도: 96.17%\n",
-      "27\t검증 세트 손실: 0.151522\t최선의 손실: 0.145338\t정확도: 95.47%\n",
-      "28\t검증 세트 손실: 0.166189\t최선의 손실: 0.145338\t정확도: 96.21%\n",
-      "29\t검증 세트 손실: 0.153160\t최선의 손실: 0.145338\t정확도: 95.74%\n",
-      "30\t검증 세트 손실: 0.167864\t최선의 손실: 0.145338\t정확도: 96.17%\n",
-      "31\t검증 세트 손실: 0.158313\t최선의 손실: 0.145338\t정확도: 95.93%\n",
-      "32\t검증 세트 손실: 0.165449\t최선의 손실: 0.145338\t정확도: 95.58%\n",
-      "33\t검증 세트 손실: 0.159742\t최선의 손실: 0.145338\t정확도: 95.86%\n",
-      "34\t검증 세트 손실: 0.154050\t최선의 손실: 0.145338\t정확도: 96.01%\n",
-      "35\t검증 세트 손실: 0.164509\t최선의 손실: 0.145338\t정확도: 95.50%\n",
-      "36\t검증 세트 손실: 0.145402\t최선의 손실: 0.145338\t정확도: 96.05%\n",
-      "37\t검증 세트 손실: 0.139301\t최선의 손실: 0.139301\t정확도: 96.52%\n",
-      "38\t검증 세트 손실: 0.143407\t최선의 손실: 0.139301\t정확도: 96.60%\n",
-      "39\t검증 세트 손실: 0.145775\t최선의 손실: 0.139301\t정확도: 96.36%\n",
-      "40\t검증 세트 손실: 0.138808\t최선의 손실: 0.138808\t정확도: 96.36%\n",
-      "41\t검증 세트 손실: 0.147339\t최선의 손실: 0.138808\t정확도: 96.36%\n",
-      "42\t검증 세트 손실: 0.162627\t최선의 손실: 0.138808\t정확도: 96.64%\n",
-      "43\t검증 세트 손실: 0.145658\t최선의 손실: 0.138808\t정확도: 96.44%\n",
-      "44\t검증 세트 손실: 0.149498\t최선의 손실: 0.138808\t정확도: 96.09%\n",
-      "45\t검증 세트 손실: 0.141232\t최선의 손실: 0.138808\t정확도: 96.48%\n",
-      "46\t검증 세트 손실: 0.137831\t최선의 손실: 0.137831\t정확도: 96.40%\n",
-      "47\t검증 세트 손실: 0.163164\t최선의 손실: 0.137831\t정확도: 95.62%\n",
-      "48\t검증 세트 손실: 0.182826\t최선의 손실: 0.137831\t정확도: 94.18%\n",
-      "49\t검증 세트 손실: 0.144931\t최선의 손실: 0.137831\t정확도: 96.01%\n",
-      "50\t검증 세트 손실: 0.130731\t최선의 손실: 0.130731\t정확도: 96.29%\n",
-      "51\t검증 세트 손실: 0.135628\t최선의 손실: 0.130731\t정확도: 96.44%\n",
-      "52\t검증 세트 손실: 0.151832\t최선의 손실: 0.130731\t정확도: 95.78%\n",
-      "53\t검증 세트 손실: 0.135290\t최선의 손실: 0.130731\t정확도: 96.09%\n",
-      "54\t검증 세트 손실: 0.126349\t최선의 손실: 0.126349\t정확도: 96.48%\n",
-      "55\t검증 세트 손실: 0.119827\t최선의 손실: 0.119827\t정확도: 96.25%\n",
-      "56\t검증 세트 손실: 0.155012\t최선의 손실: 0.119827\t정확도: 95.19%\n",
-      "57\t검증 세트 손실: 0.167725\t최선의 손실: 0.119827\t정확도: 94.92%\n",
-      "58\t검증 세트 손실: 0.137306\t최선의 손실: 0.119827\t정확도: 96.60%\n",
-      "59\t검증 세트 손실: 0.129169\t최선의 손실: 0.119827\t정확도: 96.60%\n",
-      "60\t검증 세트 손실: 0.135733\t최선의 손실: 0.119827\t정확도: 96.56%\n",
-      "61\t검증 세트 손실: 0.138789\t최선의 손실: 0.119827\t정확도: 96.17%\n",
-      "62\t검증 세트 손실: 0.133288\t최선의 손실: 0.119827\t정확도: 96.40%\n",
-      "63\t검증 세트 손실: 0.151902\t최선의 손실: 0.119827\t정확도: 95.43%\n",
-      "64\t검증 세트 손실: 0.140613\t최선의 손실: 0.119827\t정확도: 96.33%\n",
-      "65\t검증 세트 손실: 0.131262\t최선의 손실: 0.119827\t정확도: 96.79%\n",
-      "66\t검증 세트 손실: 0.128970\t최선의 손실: 0.119827\t정확도: 96.56%\n",
-      "67\t검증 세트 손실: 0.128970\t최선의 손실: 0.119827\t정확도: 96.44%\n",
-      "68\t검증 세트 손실: 0.135893\t최선의 손실: 0.119827\t정확도: 96.68%\n",
-      "69\t검증 세트 손실: 0.128417\t최선의 손실: 0.119827\t정확도: 96.52%\n",
-      "70\t검증 세트 손실: 0.138552\t최선의 손실: 0.119827\t정확도: 95.86%\n",
-      "71\t검증 세트 손실: 0.120680\t최선의 손실: 0.119827\t정확도: 96.76%\n",
-      "72\t검증 세트 손실: 0.118257\t최선의 손실: 0.118257\t정확도: 96.60%\n",
-      "73\t검증 세트 손실: 0.124932\t최선의 손실: 0.118257\t정확도: 96.48%\n",
-      "74\t검증 세트 손실: 0.120634\t최선의 손실: 0.118257\t정확도: 96.56%\n",
-      "75\t검증 세트 손실: 0.136866\t최선의 손실: 0.118257\t정확도: 96.21%\n",
-      "76\t검증 세트 손실: 0.172791\t최선의 손실: 0.118257\t정확도: 95.70%\n",
-      "77\t검증 세트 손실: 0.126621\t최선의 손실: 0.118257\t정확도: 96.60%\n",
-      "78\t검증 세트 손실: 0.124738\t최선의 손실: 0.118257\t정확도: 96.72%\n",
-      "79\t검증 세트 손실: 0.140077\t최선의 손실: 0.118257\t정확도: 96.36%\n",
-      "80\t검증 세트 손실: 0.129412\t최선의 손실: 0.118257\t정확도: 96.33%\n",
-      "81\t검증 세트 손실: 0.124508\t최선의 손실: 0.118257\t정확도: 96.76%\n",
-      "82\t검증 세트 손실: 0.144490\t최선의 손실: 0.118257\t정확도: 96.52%\n",
-      "83\t검증 세트 손실: 0.126835\t최선의 손실: 0.118257\t정확도: 96.68%\n",
-      "84\t검증 세트 손실: 0.129941\t최선의 손실: 0.118257\t정확도: 96.91%\n",
-      "85\t검증 세트 손실: 0.130352\t최선의 손실: 0.118257\t정확도: 96.25%\n",
-      "86\t검증 세트 손실: 0.147628\t최선의 손실: 0.118257\t정확도: 95.93%\n",
-      "87\t검증 세트 손실: 0.132711\t최선의 손실: 0.118257\t정확도: 96.87%\n",
-      "88\t검증 세트 손실: 0.130108\t최선의 손실: 0.118257\t정확도: 96.33%\n",
-      "89\t검증 세트 손실: 0.122747\t최선의 손실: 0.118257\t정확도: 96.36%\n",
-      "90\t검증 세트 손실: 0.138329\t최선의 손실: 0.118257\t정확도: 95.97%\n",
-      "91\t검증 세트 손실: 0.129694\t최선의 손실: 0.118257\t정확도: 96.05%\n",
-      "92\t검증 세트 손실: 0.132165\t최선의 손실: 0.118257\t정확도: 96.09%\n",
-      "93\t검증 세트 손실: 0.141771\t최선의 손실: 0.118257\t정확도: 95.97%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=10, total=  38.7s\n",
-      "[CV] batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.162545\t최선의 손실: 0.162545\t정확도: 94.92%\n",
-      "1\t검증 세트 손실: 0.129032\t최선의 손실: 0.129032\t정확도: 96.40%\n",
-      "2\t검증 세트 손실: 0.111981\t최선의 손실: 0.111981\t정확도: 96.76%\n",
-      "3\t검증 세트 손실: 0.110049\t최선의 손실: 0.110049\t정확도: 97.15%\n",
-      "4\t검증 세트 손실: 0.096638\t최선의 손실: 0.096638\t정확도: 97.34%\n",
-      "5\t검증 세트 손실: 0.097413\t최선의 손실: 0.096638\t정확도: 97.26%\n",
-      "6\t검증 세트 손실: 0.091725\t최선의 손실: 0.091725\t정확도: 97.58%\n",
-      "7\t검증 세트 손실: 0.089784\t최선의 손실: 0.089784\t정확도: 97.69%\n",
-      "8\t검증 세트 손실: 0.083491\t최선의 손실: 0.083491\t정확도: 98.05%\n",
-      "9\t검증 세트 손실: 0.082162\t최선의 손실: 0.082162\t정확도: 97.81%\n",
-      "10\t검증 세트 손실: 0.076218\t최선의 손실: 0.076218\t정확도: 97.93%\n",
-      "11\t검증 세트 손실: 0.078193\t최선의 손실: 0.076218\t정확도: 97.93%\n",
-      "12\t검증 세트 손실: 0.072070\t최선의 손실: 0.072070\t정확도: 97.97%\n",
-      "13\t검증 세트 손실: 0.077809\t최선의 손실: 0.072070\t정확도: 97.93%\n",
-      "14\t검증 세트 손실: 0.075646\t최선의 손실: 0.072070\t정확도: 98.24%\n",
-      "15\t검증 세트 손실: 0.072039\t최선의 손실: 0.072039\t정확도: 98.01%\n",
-      "16\t검증 세트 손실: 0.075417\t최선의 손실: 0.072039\t정확도: 97.93%\n",
-      "17\t검증 세트 손실: 0.071820\t최선의 손실: 0.071820\t정확도: 98.05%\n"
+      "[CV]  batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total=   5.6s\n",
+      "[CV] batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.353325\t최선의 손실: 0.353325\t정확도: 87.02%\n",
+      "1\t검증 세트 손실: 0.152990\t최선의 손실: 0.152990\t정확도: 95.11%\n",
+      "2\t검증 세트 손실: 0.119134\t최선의 손실: 0.119134\t정확도: 96.36%\n",
+      "3\t검증 세트 손실: 0.114856\t최선의 손실: 0.114856\t정확도: 96.68%\n",
+      "4\t검증 세트 손실: 0.110793\t최선의 손실: 0.110793\t정확도: 96.91%\n",
+      "5\t검증 세트 손실: 0.099682\t최선의 손실: 0.099682\t정확도: 97.30%\n",
+      "6\t검증 세트 손실: 0.087781\t최선의 손실: 0.087781\t정확도: 97.62%\n",
+      "7\t검증 세트 손실: 0.086127\t최선의 손실: 0.086127\t정확도: 97.89%\n",
+      "8\t검증 세트 손실: 0.090781\t최선의 손실: 0.086127\t정확도: 97.46%\n",
+      "9\t검증 세트 손실: 0.081340\t최선의 손실: 0.081340\t정확도: 97.65%\n",
+      "10\t검증 세트 손실: 0.083002\t최선의 손실: 0.081340\t정확도: 97.77%\n",
+      "11\t검증 세트 손실: 0.093984\t최선의 손실: 0.081340\t정확도: 97.26%\n",
+      "12\t검증 세트 손실: 0.076746\t최선의 손실: 0.076746\t정확도: 97.85%\n",
+      "13\t검증 세트 손실: 0.073379\t최선의 손실: 0.073379\t정확도: 98.16%\n",
+      "14\t검증 세트 손실: 0.077340\t최선의 손실: 0.073379\t정확도: 98.01%\n",
+      "15\t검증 세트 손실: 0.069987\t최선의 손실: 0.069987\t정확도: 98.08%\n",
+      "16\t검증 세트 손실: 0.067198\t최선의 손실: 0.067198\t정확도: 98.32%\n",
+      "17\t검증 세트 손실: 0.069973\t최선의 손실: 0.067198\t정확도: 98.05%\n",
+      "18\t검증 세트 손실: 0.069119\t최선의 손실: 0.067198\t정확도: 98.20%\n",
+      "19\t검증 세트 손실: 0.067091\t최선의 손실: 0.067091\t정확도: 98.28%\n",
+      "20\t검증 세트 손실: 0.071033\t최선의 손실: 0.067091\t정확도: 98.01%\n",
+      "21\t검증 세트 손실: 0.077187\t최선의 손실: 0.067091\t정확도: 97.89%\n",
+      "22\t검증 세트 손실: 0.070269\t최선의 손실: 0.067091\t정확도: 98.12%\n",
+      "23\t검증 세트 손실: 0.061673\t최선의 손실: 0.061673\t정확도: 98.32%\n",
+      "24\t검증 세트 손실: 0.070292\t최선의 손실: 0.061673\t정확도: 98.40%\n",
+      "25\t검증 세트 손실: 0.067662\t최선의 손실: 0.061673\t정확도: 98.08%\n",
+      "26\t검증 세트 손실: 0.062105\t최선의 손실: 0.061673\t정확도: 98.36%\n",
+      "27\t검증 세트 손실: 0.063499\t최선의 손실: 0.061673\t정확도: 98.28%\n",
+      "28\t검증 세트 손실: 0.065363\t최선의 손실: 0.061673\t정확도: 98.12%\n",
+      "29\t검증 세트 손실: 0.063166\t최선의 손실: 0.061673\t정확도: 98.48%\n",
+      "30\t검증 세트 손실: 0.066246\t최선의 손실: 0.061673\t정확도: 98.16%\n",
+      "31\t검증 세트 손실: 0.061956\t최선의 손실: 0.061673\t정확도: 98.32%\n",
+      "32\t검증 세트 손실: 0.065886\t최선의 손실: 0.061673\t정확도: 98.40%\n",
+      "33\t검증 세트 손실: 0.065987\t최선의 손실: 0.061673\t정확도: 98.24%\n",
+      "34\t검증 세트 손실: 0.059794\t최선의 손실: 0.059794\t정확도: 98.51%\n",
+      "35\t검증 세트 손실: 0.057049\t최선의 손실: 0.057049\t정확도: 98.51%\n",
+      "36\t검증 세트 손실: 0.064780\t최선의 손실: 0.057049\t정확도: 98.08%\n",
+      "37\t검증 세트 손실: 0.060050\t최선의 손실: 0.057049\t정확도: 98.55%\n",
+      "38\t검증 세트 손실: 0.056687\t최선의 손실: 0.056687\t정확도: 98.63%\n",
+      "39\t검증 세트 손실: 0.061788\t최선의 손실: 0.056687\t정확도: 98.55%\n",
+      "40\t검증 세트 손실: 0.058091\t최선의 손실: 0.056687\t정확도: 98.40%\n",
+      "41\t검증 세트 손실: 0.063428\t최선의 손실: 0.056687\t정확도: 98.44%\n",
+      "42\t검증 세트 손실: 0.064661\t최선의 손실: 0.056687\t정확도: 98.24%\n",
+      "43\t검증 세트 손실: 0.061885\t최선의 손실: 0.056687\t정확도: 98.36%\n",
+      "44\t검증 세트 손실: 0.063682\t최선의 손실: 0.056687\t정확도: 98.44%\n",
+      "45\t검증 세트 손실: 0.062367\t최선의 손실: 0.056687\t정확도: 98.51%\n",
+      "46\t검증 세트 손실: 0.059620\t최선의 손실: 0.056687\t정확도: 98.44%\n",
+      "47\t검증 세트 손실: 0.060376\t최선의 손실: 0.056687\t정확도: 98.63%\n",
+      "48\t검증 세트 손실: 0.059548\t최선의 손실: 0.056687\t정확도: 98.48%\n",
+      "49\t검증 세트 손실: 0.057044\t최선의 손실: 0.056687\t정확도: 98.44%\n",
+      "50\t검증 세트 손실: 0.060560\t최선의 손실: 0.056687\t정확도: 98.40%\n",
+      "51\t검증 세트 손실: 0.061017\t최선의 손실: 0.056687\t정확도: 98.44%\n",
+      "52\t검증 세트 손실: 0.059421\t최선의 손실: 0.056687\t정확도: 98.48%\n",
+      "53\t검증 세트 손실: 0.055321\t최선의 손실: 0.055321\t정확도: 98.67%\n",
+      "54\t검증 세트 손실: 0.057908\t최선의 손실: 0.055321\t정확도: 98.40%\n",
+      "55\t검증 세트 손실: 0.058594\t최선의 손실: 0.055321\t정확도: 98.67%\n",
+      "56\t검증 세트 손실: 0.059056\t최선의 손실: 0.055321\t정확도: 98.51%\n",
+      "57\t검증 세트 손실: 0.057601\t최선의 손실: 0.055321\t정확도: 98.67%\n",
+      "58\t검증 세트 손실: 0.058733\t최선의 손실: 0.055321\t정확도: 98.75%\n",
+      "59\t검증 세트 손실: 0.054183\t최선의 손실: 0.054183\t정확도: 98.55%\n",
+      "60\t검증 세트 손실: 0.057487\t최선의 손실: 0.054183\t정확도: 98.48%\n",
+      "61\t검증 세트 손실: 0.057595\t최선의 손실: 0.054183\t정확도: 98.59%\n",
+      "62\t검증 세트 손실: 0.061595\t최선의 손실: 0.054183\t정확도: 98.55%\n",
+      "63\t검증 세트 손실: 0.055349\t최선의 손실: 0.054183\t정확도: 98.55%\n",
+      "64\t검증 세트 손실: 0.062202\t최선의 손실: 0.054183\t정확도: 98.51%\n",
+      "65\t검증 세트 손실: 0.058272\t최선의 손실: 0.054183\t정확도: 98.51%\n",
+      "66\t검증 세트 손실: 0.053451\t최선의 손실: 0.053451\t정확도: 98.55%\n",
+      "67\t검증 세트 손실: 0.056273\t최선의 손실: 0.053451\t정확도: 98.48%\n",
+      "68\t검증 세트 손실: 0.056162\t최선의 손실: 0.053451\t정확도: 98.40%\n",
+      "69\t검증 세트 손실: 0.056161\t최선의 손실: 0.053451\t정확도: 98.63%\n",
+      "70\t검증 세트 손실: 0.057441\t최선의 손실: 0.053451\t정확도: 98.63%\n",
+      "71\t검증 세트 손실: 0.057356\t최선의 손실: 0.053451\t정확도: 98.75%\n",
+      "72\t검증 세트 손실: 0.060878\t최선의 손실: 0.053451\t정확도: 98.59%\n",
+      "73\t검증 세트 손실: 0.059916\t최선의 손실: 0.053451\t정확도: 98.44%\n",
+      "74\t검증 세트 손실: 0.055751\t최선의 손실: 0.053451\t정확도: 98.67%\n",
+      "75\t검증 세트 손실: 0.063158\t최선의 손실: 0.053451\t정확도: 98.55%\n",
+      "76\t검증 세트 손실: 0.061444\t최선의 손실: 0.053451\t정확도: 98.51%\n",
+      "77\t검증 세트 손실: 0.055258\t최선의 손실: 0.053451\t정확도: 98.75%\n",
+      "78\t검증 세트 손실: 0.060920\t최선의 손실: 0.053451\t정확도: 98.32%\n",
+      "79\t검증 세트 손실: 0.068852\t최선의 손실: 0.053451\t정확도: 98.36%\n",
+      "80\t검증 세트 손실: 0.057749\t최선의 손실: 0.053451\t정확도: 98.44%\n",
+      "81\t검증 세트 손실: 0.061186\t최선의 손실: 0.053451\t정확도: 98.48%\n",
+      "82\t검증 세트 손실: 0.057202\t최선의 손실: 0.053451\t정확도: 98.55%\n",
+      "83\t검증 세트 손실: 0.055033\t최선의 손실: 0.053451\t정확도: 98.48%\n",
+      "84\t검증 세트 손실: 0.055852\t최선의 손실: 0.053451\t정확도: 98.51%\n",
+      "85\t검증 세트 손실: 0.052282\t최선의 손실: 0.052282\t정확도: 98.63%\n",
+      "86\t검증 세트 손실: 0.059017\t최선의 손실: 0.052282\t정확도: 98.48%\n",
+      "87\t검증 세트 손실: 0.059203\t최선의 손실: 0.052282\t정확도: 98.71%\n",
+      "88\t검증 세트 손실: 0.056657\t최선의 손실: 0.052282\t정확도: 98.71%\n",
+      "89\t검증 세트 손실: 0.058050\t최선의 손실: 0.052282\t정확도: 98.48%\n",
+      "90\t검증 세트 손실: 0.057536\t최선의 손실: 0.052282\t정확도: 98.67%\n",
+      "91\t검증 세트 손실: 0.055260\t최선의 손실: 0.052282\t정확도: 98.67%\n",
+      "92\t검증 세트 손실: 0.056099\t최선의 손실: 0.052282\t정확도: 98.59%\n",
+      "93\t검증 세트 손실: 0.058747\t최선의 손실: 0.052282\t정확도: 98.48%\n",
+      "94\t검증 세트 손실: 0.053741\t최선의 손실: 0.052282\t정확도: 98.67%\n",
+      "95\t검증 세트 손실: 0.049477\t최선의 손실: 0.049477\t정확도: 98.51%\n",
+      "96\t검증 세트 손실: 0.052689\t최선의 손실: 0.049477\t정확도: 98.67%\n",
+      "97\t검증 세트 손실: 0.051816\t최선의 손실: 0.049477\t정확도: 98.71%\n",
+      "98\t검증 세트 손실: 0.049020\t최선의 손실: 0.049020\t정확도: 98.59%\n",
+      "99\t검증 세트 손실: 0.055800\t최선의 손실: 0.049020\t정확도: 98.75%\n",
+      "100\t검증 세트 손실: 0.057681\t최선의 손실: 0.049020\t정확도: 98.71%\n",
+      "101\t검증 세트 손실: 0.060016\t최선의 손실: 0.049020\t정확도: 98.71%\n",
+      "102\t검증 세트 손실: 0.052474\t최선의 손실: 0.049020\t정확도: 98.63%\n",
+      "103\t검증 세트 손실: 0.052929\t최선의 손실: 0.049020\t정확도: 98.71%\n",
+      "104\t검증 세트 손실: 0.048876\t최선의 손실: 0.048876\t정확도: 98.63%\n",
+      "105\t검증 세트 손실: 0.055728\t최선의 손실: 0.048876\t정확도: 98.55%\n",
+      "106\t검증 세트 손실: 0.055885\t최선의 손실: 0.048876\t정확도: 98.67%\n",
+      "107\t검증 세트 손실: 0.053050\t최선의 손실: 0.048876\t정확도: 98.79%\n",
+      "108\t검증 세트 손실: 0.057938\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "109\t검증 세트 손실: 0.057340\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "110\t검증 세트 손실: 0.053190\t최선의 손실: 0.048876\t정확도: 98.71%\n",
+      "111\t검증 세트 손실: 0.052010\t최선의 손실: 0.048876\t정확도: 98.79%\n",
+      "112\t검증 세트 손실: 0.053243\t최선의 손실: 0.048876\t정확도: 98.79%\n",
+      "113\t검증 세트 손실: 0.054266\t최선의 손실: 0.048876\t정확도: 98.71%\n",
+      "114\t검증 세트 손실: 0.053766\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "115\t검증 세트 손실: 0.053983\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "116\t검증 세트 손실: 0.052483\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "117\t검증 세트 손실: 0.057985\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "118\t검증 세트 손실: 0.061497\t최선의 손실: 0.048876\t정확도: 98.51%\n",
+      "119\t검증 세트 손실: 0.050972\t최선의 손실: 0.048876\t정확도: 98.59%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "18\t검증 세트 손실: 0.073818\t최선의 손실: 0.071820\t정확도: 98.12%\n",
-      "19\t검증 세트 손실: 0.073971\t최선의 손실: 0.071820\t정확도: 98.08%\n",
-      "20\t검증 세트 손실: 0.070696\t최선의 손실: 0.070696\t정확도: 98.20%\n",
-      "21\t검증 세트 손실: 0.065098\t최선의 손실: 0.065098\t정확도: 98.12%\n",
-      "22\t검증 세트 손실: 0.075769\t최선의 손실: 0.065098\t정확도: 97.93%\n",
-      "23\t검증 세트 손실: 0.068596\t최선의 손실: 0.065098\t정확도: 98.16%\n",
-      "24\t검증 세트 손실: 0.069732\t최선의 손실: 0.065098\t정확도: 98.32%\n",
-      "25\t검증 세트 손실: 0.060926\t최선의 손실: 0.060926\t정확도: 98.28%\n",
-      "26\t검증 세트 손실: 0.070365\t최선의 손실: 0.060926\t정확도: 98.20%\n",
-      "27\t검증 세트 손실: 0.064983\t최선의 손실: 0.060926\t정확도: 98.20%\n",
-      "28\t검증 세트 손실: 0.066468\t최선의 손실: 0.060926\t정확도: 98.32%\n",
-      "29\t검증 세트 손실: 0.063473\t최선의 손실: 0.060926\t정확도: 98.48%\n",
-      "30\t검증 세트 손실: 0.071152\t최선의 손실: 0.060926\t정확도: 98.16%\n",
-      "31\t검증 세트 손실: 0.065429\t최선의 손실: 0.060926\t정확도: 98.36%\n",
-      "32\t검증 세트 손실: 0.069920\t최선의 손실: 0.060926\t정확도: 98.28%\n",
-      "33\t검증 세트 손실: 0.063354\t최선의 손실: 0.060926\t정확도: 98.44%\n",
-      "34\t검증 세트 손실: 0.062202\t최선의 손실: 0.060926\t정확도: 98.40%\n",
-      "35\t검증 세트 손실: 0.059978\t최선의 손실: 0.059978\t정확도: 98.51%\n",
-      "36\t검증 세트 손실: 0.070018\t최선의 손실: 0.059978\t정확도: 98.32%\n",
-      "37\t검증 세트 손실: 0.061524\t최선의 손실: 0.059978\t정확도: 98.36%\n",
-      "38\t검증 세트 손실: 0.058990\t최선의 손실: 0.058990\t정확도: 98.24%\n",
-      "39\t검증 세트 손실: 0.063573\t최선의 손실: 0.058990\t정확도: 98.44%\n",
-      "40\t검증 세트 손실: 0.055520\t최선의 손실: 0.055520\t정확도: 98.59%\n",
-      "41\t검증 세트 손실: 0.058693\t최선의 손실: 0.055520\t정확도: 98.36%\n",
-      "42\t검증 세트 손실: 0.065731\t최선의 손실: 0.055520\t정확도: 98.55%\n",
-      "43\t검증 세트 손실: 0.060361\t최선의 손실: 0.055520\t정확도: 98.28%\n",
-      "44\t검증 세트 손실: 0.060988\t최선의 손실: 0.055520\t정확도: 98.28%\n",
-      "45\t검증 세트 손실: 0.062293\t최선의 손실: 0.055520\t정확도: 98.40%\n",
-      "46\t검증 세트 손실: 0.068295\t최선의 손실: 0.055520\t정확도: 98.40%\n",
-      "47\t검증 세트 손실: 0.065706\t최선의 손실: 0.055520\t정확도: 98.32%\n",
-      "48\t검증 세트 손실: 0.061562\t최선의 손실: 0.055520\t정확도: 98.48%\n",
-      "49\t검증 세트 손실: 0.067117\t최선의 손실: 0.055520\t정확도: 98.24%\n",
-      "50\t검증 세트 손실: 0.060158\t최선의 손실: 0.055520\t정확도: 98.40%\n",
-      "51\t검증 세트 손실: 0.067415\t최선의 손실: 0.055520\t정확도: 98.32%\n",
-      "52\t검증 세트 손실: 0.057533\t최선의 손실: 0.055520\t정확도: 98.48%\n",
-      "53\t검증 세트 손실: 0.062441\t최선의 손실: 0.055520\t정확도: 98.44%\n",
-      "54\t검증 세트 손실: 0.058921\t최선의 손실: 0.055520\t정확도: 98.32%\n",
-      "55\t검증 세트 손실: 0.050560\t최선의 손실: 0.050560\t정확도: 98.59%\n",
-      "56\t검증 세트 손실: 0.061029\t최선의 손실: 0.050560\t정확도: 98.40%\n",
-      "57\t검증 세트 손실: 0.066744\t최선의 손실: 0.050560\t정확도: 98.40%\n",
-      "58\t검증 세트 손실: 0.059081\t최선의 손실: 0.050560\t정확도: 98.55%\n",
-      "59\t검증 세트 손실: 0.061996\t최선의 손실: 0.050560\t정확도: 98.55%\n",
-      "60\t검증 세트 손실: 0.061597\t최선의 손실: 0.050560\t정확도: 98.40%\n",
-      "61\t검증 세트 손실: 0.059177\t최선의 손실: 0.050560\t정확도: 98.20%\n",
-      "62\t검증 세트 손실: 0.060617\t최선의 손실: 0.050560\t정확도: 98.51%\n",
-      "63\t검증 세트 손실: 0.056434\t최선의 손실: 0.050560\t정확도: 98.40%\n",
-      "64\t검증 세트 손실: 0.060190\t최선의 손실: 0.050560\t정확도: 98.32%\n",
-      "65\t검증 세트 손실: 0.062574\t최선의 손실: 0.050560\t정확도: 98.48%\n",
-      "66\t검증 세트 손실: 0.068968\t최선의 손실: 0.050560\t정확도: 98.28%\n",
-      "67\t검증 세트 손실: 0.063430\t최선의 손실: 0.050560\t정확도: 98.20%\n",
-      "68\t검증 세트 손실: 0.060088\t최선의 손실: 0.050560\t정확도: 98.48%\n",
-      "69\t검증 세트 손실: 0.064553\t최선의 손실: 0.050560\t정확도: 98.32%\n",
-      "70\t검증 세트 손실: 0.061296\t최선의 손실: 0.050560\t정확도: 98.44%\n",
-      "71\t검증 세트 손실: 0.059575\t최선의 손실: 0.050560\t정확도: 98.40%\n",
-      "72\t검증 세트 손실: 0.057303\t최선의 손실: 0.050560\t정확도: 98.75%\n",
-      "73\t검증 세트 손실: 0.062071\t최선의 손실: 0.050560\t정확도: 98.51%\n",
-      "74\t검증 세트 손실: 0.059465\t최선의 손실: 0.050560\t정확도: 98.44%\n",
-      "75\t검증 세트 손실: 0.057836\t최선의 손실: 0.050560\t정확도: 98.59%\n",
-      "76\t검증 세트 손실: 0.057002\t최선의 손실: 0.050560\t정확도: 98.48%\n",
+      "120\t검증 세트 손실: 0.055582\t최선의 손실: 0.048876\t정확도: 98.75%\n",
+      "121\t검증 세트 손실: 0.057221\t최선의 손실: 0.048876\t정확도: 98.87%\n",
+      "122\t검증 세트 손실: 0.051209\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "123\t검증 세트 손실: 0.055801\t최선의 손실: 0.048876\t정확도: 98.63%\n",
+      "124\t검증 세트 손실: 0.059603\t최선의 손실: 0.048876\t정확도: 98.59%\n",
+      "125\t검증 세트 손실: 0.058904\t최선의 손실: 0.048876\t정확도: 98.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total=   7.2s\n",
-      "[CV] batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.159684\t최선의 손실: 0.159684\t정확도: 95.00%\n",
-      "1\t검증 세트 손실: 0.118204\t최선의 손실: 0.118204\t정확도: 96.25%\n",
-      "2\t검증 세트 손실: 0.098729\t최선의 손실: 0.098729\t정확도: 96.83%\n",
-      "3\t검증 세트 손실: 0.097129\t최선의 손실: 0.097129\t정확도: 96.91%\n",
-      "4\t검증 세트 손실: 0.101029\t최선의 손실: 0.097129\t정확도: 97.19%\n",
-      "5\t검증 세트 손실: 0.087534\t최선의 손실: 0.087534\t정확도: 97.34%\n",
-      "6\t검증 세트 손실: 0.086966\t최선의 손실: 0.086966\t정확도: 97.58%\n",
-      "7\t검증 세트 손실: 0.081001\t최선의 손실: 0.081001\t정확도: 97.38%\n",
-      "8\t검증 세트 손실: 0.082387\t최선의 손실: 0.081001\t정확도: 97.81%\n",
-      "9\t검증 세트 손실: 0.081494\t최선의 손실: 0.081001\t정확도: 97.73%\n",
-      "10\t검증 세트 손실: 0.080136\t최선의 손실: 0.080136\t정확도: 97.50%\n",
-      "11\t검증 세트 손실: 0.075827\t최선의 손실: 0.075827\t정확도: 97.69%\n",
-      "12\t검증 세트 손실: 0.082511\t최선의 손실: 0.075827\t정확도: 97.50%\n",
-      "13\t검증 세트 손실: 0.080026\t최선의 손실: 0.075827\t정확도: 97.69%\n",
-      "14\t검증 세트 손실: 0.081194\t최선의 손실: 0.075827\t정확도: 98.01%\n",
-      "15\t검증 세트 손실: 0.077603\t최선의 손실: 0.075827\t정확도: 97.81%\n",
-      "16\t검증 세트 손실: 0.076773\t최선의 손실: 0.075827\t정확도: 97.73%\n",
-      "17\t검증 세트 손실: 0.070129\t최선의 손실: 0.070129\t정확도: 98.12%\n",
-      "18\t검증 세트 손실: 0.075729\t최선의 손실: 0.070129\t정확도: 98.05%\n",
-      "19\t검증 세트 손실: 0.070030\t최선의 손실: 0.070030\t정확도: 98.01%\n",
-      "20\t검증 세트 손실: 0.067242\t최선의 손실: 0.067242\t정확도: 98.08%\n",
-      "21\t검증 세트 손실: 0.066646\t최선의 손실: 0.066646\t정확도: 98.20%\n",
-      "22\t검증 세트 손실: 0.066371\t최선의 손실: 0.066371\t정확도: 98.24%\n",
-      "23\t검증 세트 손실: 0.068831\t최선의 손실: 0.066371\t정확도: 98.12%\n",
-      "24\t검증 세트 손실: 0.060865\t최선의 손실: 0.060865\t정확도: 98.32%\n",
-      "25\t검증 세트 손실: 0.072242\t최선의 손실: 0.060865\t정확도: 98.12%\n",
-      "26\t검증 세트 손실: 0.060605\t최선의 손실: 0.060605\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.062072\t최선의 손실: 0.060605\t정확도: 98.20%\n",
-      "28\t검증 세트 손실: 0.061461\t최선의 손실: 0.060605\t정확도: 98.28%\n",
-      "29\t검증 세트 손실: 0.062742\t최선의 손실: 0.060605\t정확도: 98.32%\n",
-      "30\t검증 세트 손실: 0.062703\t최선의 손실: 0.060605\t정확도: 98.36%\n",
-      "31\t검증 세트 손실: 0.060665\t최선의 손실: 0.060605\t정확도: 98.32%\n",
-      "32\t검증 세트 손실: 0.065656\t최선의 손실: 0.060605\t정확도: 98.24%\n",
-      "33\t검증 세트 손실: 0.060915\t최선의 손실: 0.060605\t정확도: 98.36%\n",
-      "34\t검증 세트 손실: 0.060894\t최선의 손실: 0.060605\t정확도: 98.32%\n",
-      "35\t검증 세트 손실: 0.060093\t최선의 손실: 0.060093\t정확도: 98.16%\n",
-      "36\t검증 세트 손실: 0.058842\t최선의 손실: 0.058842\t정확도: 98.24%\n",
-      "37\t검증 세트 손실: 0.070179\t최선의 손실: 0.058842\t정확도: 98.28%\n",
-      "38\t검증 세트 손실: 0.062202\t최선의 손실: 0.058842\t정확도: 98.32%\n",
-      "39\t검증 세트 손실: 0.063839\t최선의 손실: 0.058842\t정확도: 98.32%\n",
-      "40\t검증 세트 손실: 0.066302\t최선의 손실: 0.058842\t정확도: 98.08%\n",
-      "41\t검증 세트 손실: 0.061916\t최선의 손실: 0.058842\t정확도: 98.36%\n",
-      "42\t검증 세트 손실: 0.065884\t최선의 손실: 0.058842\t정확도: 98.36%\n",
-      "43\t검증 세트 손실: 0.059953\t최선의 손실: 0.058842\t정확도: 98.44%\n",
-      "44\t검증 세트 손실: 0.057292\t최선의 손실: 0.057292\t정확도: 98.12%\n",
-      "45\t검증 세트 손실: 0.063893\t최선의 손실: 0.057292\t정확도: 98.20%\n",
-      "46\t검증 세트 손실: 0.063872\t최선의 손실: 0.057292\t정확도: 98.28%\n",
-      "47\t검증 세트 손실: 0.062453\t최선의 손실: 0.057292\t정확도: 98.20%\n",
-      "48\t검증 세트 손실: 0.058270\t최선의 손실: 0.057292\t정확도: 98.51%\n",
-      "49\t검증 세트 손실: 0.063683\t최선의 손실: 0.057292\t정확도: 98.28%\n",
-      "50\t검증 세트 손실: 0.062157\t최선의 손실: 0.057292\t정확도: 98.32%\n",
-      "51\t검증 세트 손실: 0.064571\t최선의 손실: 0.057292\t정확도: 98.16%\n",
-      "52\t검증 세트 손실: 0.063286\t최선의 손실: 0.057292\t정확도: 98.28%\n",
-      "53\t검증 세트 손실: 0.061424\t최선의 손실: 0.057292\t정확도: 98.32%\n",
-      "54\t검증 세트 손실: 0.059746\t최선의 손실: 0.057292\t정확도: 98.32%\n",
-      "55\t검증 세트 손실: 0.063291\t최선의 손실: 0.057292\t정확도: 98.36%\n",
-      "56\t검증 세트 손실: 0.062901\t최선의 손실: 0.057292\t정확도: 98.24%\n",
-      "57\t검증 세트 손실: 0.057815\t최선의 손실: 0.057292\t정확도: 98.44%\n",
-      "58\t검증 세트 손실: 0.065720\t최선의 손실: 0.057292\t정확도: 98.40%\n",
-      "59\t검증 세트 손실: 0.066059\t최선의 손실: 0.057292\t정확도: 98.32%\n",
-      "60\t검증 세트 손실: 0.064006\t최선의 손실: 0.057292\t정확도: 98.24%\n",
-      "61\t검증 세트 손실: 0.059672\t최선의 손실: 0.057292\t정확도: 98.40%\n",
-      "62\t검증 세트 손실: 0.061473\t최선의 손실: 0.057292\t정확도: 98.32%\n",
-      "63\t검증 세트 손실: 0.056101\t최선의 손실: 0.056101\t정확도: 98.55%\n",
-      "64\t검증 세트 손실: 0.058909\t최선의 손실: 0.056101\t정확도: 98.32%\n",
-      "65\t검증 세트 손실: 0.055850\t최선의 손실: 0.055850\t정확도: 98.63%\n",
-      "66\t검증 세트 손실: 0.057042\t최선의 손실: 0.055850\t정확도: 98.36%\n",
-      "67\t검증 세트 손실: 0.055284\t최선의 손실: 0.055284\t정확도: 98.51%\n",
-      "68\t검증 세트 손실: 0.057834\t최선의 손실: 0.055284\t정확도: 98.51%\n",
-      "69\t검증 세트 손실: 0.059890\t최선의 손실: 0.055284\t정확도: 98.44%\n",
-      "70\t검증 세트 손실: 0.063949\t최선의 손실: 0.055284\t정확도: 98.36%\n",
-      "71\t검증 세트 손실: 0.056053\t최선의 손실: 0.055284\t정확도: 98.63%\n",
-      "72\t검증 세트 손실: 0.059162\t최선의 손실: 0.055284\t정확도: 98.44%\n",
-      "73\t검증 세트 손실: 0.058732\t최선의 손실: 0.055284\t정확도: 98.48%\n",
-      "74\t검증 세트 손실: 0.058988\t최선의 손실: 0.055284\t정확도: 98.36%\n",
-      "75\t검증 세트 손실: 0.062297\t최선의 손실: 0.055284\t정확도: 98.40%\n",
-      "76\t검증 세트 손실: 0.056786\t최선의 손실: 0.055284\t정확도: 98.40%\n",
-      "77\t검증 세트 손실: 0.058508\t최선의 손실: 0.055284\t정확도: 98.40%\n",
-      "78\t검증 세트 손실: 0.059450\t최선의 손실: 0.055284\t정확도: 98.48%\n",
-      "79\t검증 세트 손실: 0.058012\t최선의 손실: 0.055284\t정확도: 98.48%\n",
-      "80\t검증 세트 손실: 0.057261\t최선의 손실: 0.055284\t정확도: 98.48%\n",
-      "81\t검증 세트 손실: 0.061077\t최선의 손실: 0.055284\t정확도: 98.36%\n",
-      "82\t검증 세트 손실: 0.061937\t최선의 손실: 0.055284\t정확도: 98.24%\n",
-      "83\t검증 세트 손실: 0.056137\t최선의 손실: 0.055284\t정확도: 98.55%\n",
-      "84\t검증 세트 손실: 0.062981\t최선의 손실: 0.055284\t정확도: 98.36%\n",
-      "85\t검증 세트 손실: 0.058596\t최선의 손실: 0.055284\t정확도: 98.48%\n",
-      "86\t검증 세트 손실: 0.063282\t최선의 손실: 0.055284\t정확도: 98.44%\n",
-      "87\t검증 세트 손실: 0.061874\t최선의 손실: 0.055284\t정확도: 98.55%\n",
-      "88\t검증 세트 손실: 0.053960\t최선의 손실: 0.053960\t정확도: 98.63%\n",
-      "89\t검증 세트 손실: 0.063695\t최선의 손실: 0.053960\t정확도: 98.40%\n",
-      "90\t검증 세트 손실: 0.055487\t최선의 손실: 0.053960\t정확도: 98.51%\n",
-      "91\t검증 세트 손실: 0.055246\t최선의 손실: 0.053960\t정확도: 98.48%\n",
-      "92\t검증 세트 손실: 0.055782\t최선의 손실: 0.053960\t정확도: 98.59%\n",
-      "93\t검증 세트 손실: 0.055728\t최선의 손실: 0.053960\t정확도: 98.48%\n",
-      "94\t검증 세트 손실: 0.058731\t최선의 손실: 0.053960\t정확도: 98.55%\n",
-      "95\t검증 세트 손실: 0.060584\t최선의 손실: 0.053960\t정확도: 98.40%\n"
+      "[CV]  batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total=  11.6s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.512895\t최선의 손실: 0.512895\t정확도: 78.19%\n",
+      "1\t검증 세트 손실: 0.324697\t최선의 손실: 0.324697\t정확도: 88.31%\n",
+      "2\t검증 세트 손실: 0.242306\t최선의 손실: 0.242306\t정확도: 93.16%\n",
+      "3\t검증 세트 손실: 0.400412\t최선의 손실: 0.242306\t정확도: 90.89%\n",
+      "4\t검증 세트 손실: 0.175527\t최선의 손실: 0.175527\t정확도: 95.47%\n",
+      "5\t검증 세트 손실: 0.159877\t최선의 손실: 0.159877\t정확도: 95.50%\n",
+      "6\t검증 세트 손실: 0.144359\t최선의 손실: 0.144359\t정확도: 95.90%\n",
+      "7\t검증 세트 손실: 0.127455\t최선의 손실: 0.127455\t정확도: 96.17%\n",
+      "8\t검증 세트 손실: 0.163201\t최선의 손실: 0.127455\t정확도: 95.90%\n",
+      "9\t검증 세트 손실: 0.183390\t최선의 손실: 0.127455\t정확도: 95.82%\n",
+      "10\t검증 세트 손실: 0.186553\t최선의 손실: 0.127455\t정확도: 95.50%\n",
+      "11\t검증 세트 손실: 0.159863\t최선의 손실: 0.127455\t정확도: 96.21%\n",
+      "12\t검증 세트 손실: 0.137605\t최선의 손실: 0.127455\t정확도: 96.13%\n",
+      "13\t검증 세트 손실: 0.189803\t최선의 손실: 0.127455\t정확도: 96.44%\n",
+      "14\t검증 세트 손실: 0.220794\t최선의 손실: 0.127455\t정확도: 95.74%\n",
+      "15\t검증 세트 손실: 0.659550\t최선의 손실: 0.127455\t정확도: 78.38%\n",
+      "16\t검증 세트 손실: 0.708489\t최선의 손실: 0.127455\t정확도: 68.88%\n",
+      "17\t검증 세트 손실: 0.519953\t최선의 손실: 0.127455\t정확도: 78.58%\n",
+      "18\t검증 세트 손실: 0.442033\t최선의 손실: 0.127455\t정확도: 89.09%\n",
+      "19\t검증 세트 손실: 0.311525\t최선의 손실: 0.127455\t정확도: 92.42%\n",
+      "20\t검증 세트 손실: 0.249738\t최선의 손실: 0.127455\t정확도: 92.65%\n",
+      "21\t검증 세트 손실: 0.240654\t최선의 손실: 0.127455\t정확도: 94.21%\n",
+      "22\t검증 세트 손실: 0.233694\t최선의 손실: 0.127455\t정확도: 93.67%\n",
+      "23\t검증 세트 손실: 0.297756\t최선의 손실: 0.127455\t정확도: 94.10%\n",
+      "24\t검증 세트 손실: 0.250933\t최선의 손실: 0.127455\t정확도: 94.37%\n",
+      "25\t검증 세트 손실: 0.240308\t최선의 손실: 0.127455\t정확도: 94.45%\n",
+      "26\t검증 세트 손실: 0.220690\t최선의 손실: 0.127455\t정확도: 94.92%\n",
+      "27\t검증 세트 손실: 0.208702\t최선의 손실: 0.127455\t정확도: 95.04%\n",
+      "28\t검증 세트 손실: 0.186401\t최선의 손실: 0.127455\t정확도: 95.58%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=10, total=   3.1s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.760454\t최선의 손실: 0.760454\t정확도: 69.23%\n",
+      "1\t검증 세트 손실: 0.355315\t최선의 손실: 0.355315\t정확도: 89.17%\n",
+      "2\t검증 세트 손실: 0.266805\t최선의 손실: 0.266805\t정확도: 93.35%\n",
+      "3\t검증 세트 손실: 0.211765\t최선의 손실: 0.211765\t정확도: 94.88%\n",
+      "4\t검증 세트 손실: 0.205087\t최선의 손실: 0.205087\t정확도: 94.96%\n",
+      "5\t검증 세트 손실: 0.197814\t최선의 손실: 0.197814\t정확도: 94.84%\n",
+      "6\t검증 세트 손실: 0.165538\t최선의 손실: 0.165538\t정확도: 95.31%\n",
+      "7\t검증 세트 손실: 0.172155\t최선의 손실: 0.165538\t정확도: 95.58%\n",
+      "8\t검증 세트 손실: 0.219746\t최선의 손실: 0.165538\t정확도: 95.54%\n",
+      "9\t검증 세트 손실: 0.187405\t최선의 손실: 0.165538\t정확도: 95.23%\n",
+      "10\t검증 세트 손실: 0.188732\t최선의 손실: 0.165538\t정확도: 95.58%\n",
+      "11\t검증 세트 손실: 0.175024\t최선의 손실: 0.165538\t정확도: 95.93%\n",
+      "12\t검증 세트 손실: 0.259967\t최선의 손실: 0.165538\t정확도: 93.32%\n",
+      "13\t검증 세트 손실: 0.246603\t최선의 손실: 0.165538\t정확도: 94.68%\n",
+      "14\t검증 세트 손실: 0.198849\t최선의 손실: 0.165538\t정확도: 94.96%\n",
+      "15\t검증 세트 손실: 0.195960\t최선의 손실: 0.165538\t정확도: 95.74%\n",
+      "16\t검증 세트 손실: 0.198972\t최선의 손실: 0.165538\t정확도: 95.15%\n",
+      "17\t검증 세트 손실: 0.260954\t최선의 손실: 0.165538\t정확도: 93.67%\n",
+      "18\t검증 세트 손실: 0.225204\t최선의 손실: 0.165538\t정확도: 95.50%\n",
+      "19\t검증 세트 손실: 0.207143\t최선의 손실: 0.165538\t정확도: 95.23%\n",
+      "20\t검증 세트 손실: 0.181553\t최선의 손실: 0.165538\t정확도: 95.23%\n",
+      "21\t검증 세트 손실: 0.180151\t최선의 손실: 0.165538\t정확도: 95.62%\n",
+      "22\t검증 세트 손실: 0.169451\t최선의 손실: 0.165538\t정확도: 95.86%\n",
+      "23\t검증 세트 손실: 0.163785\t최선의 손실: 0.163785\t정확도: 95.93%\n",
+      "24\t검증 세트 손실: 0.158938\t최선의 손실: 0.158938\t정확도: 96.21%\n",
+      "25\t검증 세트 손실: 0.176190\t최선의 손실: 0.158938\t정확도: 96.25%\n",
+      "26\t검증 세트 손실: 0.166127\t최선의 손실: 0.158938\t정확도: 96.29%\n",
+      "27\t검증 세트 손실: 0.166227\t최선의 손실: 0.158938\t정확도: 95.93%\n",
+      "28\t검증 세트 손실: 0.199607\t최선의 손실: 0.158938\t정확도: 95.93%\n",
+      "29\t검증 세트 손실: 0.233973\t최선의 손실: 0.158938\t정확도: 93.90%\n",
+      "30\t검증 세트 손실: 0.875996\t최선의 손실: 0.158938\t정확도: 58.80%\n",
+      "31\t검증 세트 손실: 0.490631\t최선의 손실: 0.158938\t정확도: 82.88%\n",
+      "32\t검증 세트 손실: 0.552396\t최선의 손실: 0.158938\t정확도: 75.02%\n",
+      "33\t검증 세트 손실: 0.413278\t최선의 손실: 0.158938\t정확도: 89.09%\n",
+      "34\t검증 세트 손실: 0.339275\t최선의 손실: 0.158938\t정확도: 90.93%\n",
+      "35\t검증 세트 손실: 0.272249\t최선의 손실: 0.158938\t정확도: 93.90%\n",
+      "36\t검증 세트 손실: 0.287680\t최선의 손실: 0.158938\t정확도: 94.53%\n",
+      "37\t검증 세트 손실: 0.281981\t최선의 손실: 0.158938\t정확도: 92.89%\n",
+      "38\t검증 세트 손실: 0.288426\t최선의 손실: 0.158938\t정확도: 92.03%\n",
+      "39\t검증 세트 손실: 0.231711\t최선의 손실: 0.158938\t정확도: 93.59%\n",
+      "40\t검증 세트 손실: 0.271110\t최선의 손실: 0.158938\t정확도: 95.35%\n",
+      "41\t검증 세트 손실: 0.212946\t최선의 손실: 0.158938\t정확도: 95.23%\n",
+      "42\t검증 세트 손실: 0.212500\t최선의 손실: 0.158938\t정확도: 95.62%\n",
+      "43\t검증 세트 손실: 0.207028\t최선의 손실: 0.158938\t정확도: 95.31%\n",
+      "44\t검증 세트 손실: 0.191211\t최선의 손실: 0.158938\t정확도: 94.25%\n",
+      "45\t검증 세트 손실: 0.193397\t최선의 손실: 0.158938\t정확도: 95.90%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=10, total=   4.8s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.385003\t최선의 손실: 0.385003\t정확도: 91.32%\n",
+      "1\t검증 세트 손실: 0.269206\t최선의 손실: 0.269206\t정확도: 93.32%\n",
+      "2\t검증 세트 손실: 0.181884\t최선의 손실: 0.181884\t정확도: 95.39%\n",
+      "3\t검증 세트 손실: 0.183424\t최선의 손실: 0.181884\t정확도: 95.27%\n",
+      "4\t검증 세트 손실: 0.178908\t최선의 손실: 0.178908\t정확도: 95.47%\n",
+      "5\t검증 세트 손실: 0.185586\t최선의 손실: 0.178908\t정확도: 95.70%\n",
+      "6\t검증 세트 손실: 0.169135\t최선의 손실: 0.169135\t정확도: 95.39%\n",
+      "7\t검증 세트 손실: 0.160427\t최선의 손실: 0.160427\t정확도: 96.13%\n",
+      "8\t검증 세트 손실: 0.140617\t최선의 손실: 0.140617\t정확도: 96.21%\n",
+      "9\t검증 세트 손실: 0.145997\t최선의 손실: 0.140617\t정확도: 95.78%\n",
+      "10\t검증 세트 손실: 0.140892\t최선의 손실: 0.140617\t정확도: 96.25%\n",
+      "11\t검증 세트 손실: 0.152716\t최선의 손실: 0.140617\t정확도: 96.68%\n",
+      "12\t검증 세트 손실: 0.157583\t최선의 손실: 0.140617\t정확도: 96.25%\n",
+      "13\t검증 세트 손실: 0.142713\t최선의 손실: 0.140617\t정확도: 96.52%\n",
+      "14\t검증 세트 손실: 0.133937\t최선의 손실: 0.133937\t정확도: 96.48%\n",
+      "15\t검증 세트 손실: 0.159018\t최선의 손실: 0.133937\t정확도: 96.33%\n",
+      "16\t검증 세트 손실: 0.148157\t최선의 손실: 0.133937\t정확도: 96.40%\n",
+      "17\t검증 세트 손실: 0.138812\t최선의 손실: 0.133937\t정확도: 96.79%\n",
+      "18\t검증 세트 손실: 0.152783\t최선의 손실: 0.133937\t정확도: 96.44%\n",
+      "19\t검증 세트 손실: 0.182483\t최선의 손실: 0.133937\t정확도: 95.62%\n",
+      "20\t검증 세트 손실: 0.162936\t최선의 손실: 0.133937\t정확도: 95.47%\n",
+      "21\t검증 세트 손실: 0.207875\t최선의 손실: 0.133937\t정확도: 95.86%\n",
+      "22\t검증 세트 손실: 0.151439\t최선의 손실: 0.133937\t정확도: 95.58%\n",
+      "23\t검증 세트 손실: 0.173215\t최선의 손실: 0.133937\t정확도: 96.01%\n",
+      "24\t검증 세트 손실: 0.172206\t최선의 손실: 0.133937\t정확도: 95.82%\n",
+      "25\t검증 세트 손실: 0.156306\t최선의 손실: 0.133937\t정확도: 96.36%\n",
+      "26\t검증 세트 손실: 0.136449\t최선의 손실: 0.133937\t정확도: 96.64%\n",
+      "27\t검증 세트 손실: 0.148315\t최선의 손실: 0.133937\t정확도: 96.44%\n",
+      "28\t검증 세트 손실: 0.153443\t최선의 손실: 0.133937\t정확도: 96.13%\n",
+      "29\t검증 세트 손실: 0.145014\t최선의 손실: 0.133937\t정확도: 96.09%\n",
+      "30\t검증 세트 손실: 0.134595\t최선의 손실: 0.133937\t정확도: 96.17%\n",
+      "31\t검증 세트 손실: 0.160978\t최선의 손실: 0.133937\t정확도: 96.44%\n",
+      "32\t검증 세트 손실: 0.143302\t최선의 손실: 0.133937\t정확도: 95.58%\n",
+      "33\t검증 세트 손실: 0.158284\t최선의 손실: 0.133937\t정확도: 96.21%\n",
+      "34\t검증 세트 손실: 0.140848\t최선의 손실: 0.133937\t정확도: 96.44%\n",
+      "35\t검증 세트 손실: 0.146795\t최선의 손실: 0.133937\t정확도: 96.83%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=10, total=   3.8s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.093108\t최선의 손실: 0.093108\t정확도: 97.42%\n",
+      "1\t검증 세트 손실: 0.113548\t최선의 손실: 0.093108\t정확도: 97.46%\n",
+      "2\t검증 세트 손실: 0.075953\t최선의 손실: 0.075953\t정확도: 98.16%\n",
+      "3\t검증 세트 손실: 0.092154\t최선의 손실: 0.075953\t정확도: 97.54%\n",
+      "4\t검증 세트 손실: 0.076297\t최선의 손실: 0.075953\t정확도: 98.24%\n",
+      "5\t검증 세트 손실: 0.076057\t최선의 손실: 0.075953\t정확도: 97.93%\n",
+      "6\t검증 세트 손실: 0.087860\t최선의 손실: 0.075953\t정확도: 97.73%\n",
+      "7\t검증 세트 손실: 0.086784\t최선의 손실: 0.075953\t정확도: 97.93%\n",
+      "8\t검증 세트 손실: 0.096522\t최선의 손실: 0.075953\t정확도: 98.01%\n",
+      "9\t검증 세트 손실: 0.084123\t최선의 손실: 0.075953\t정확도: 97.97%\n",
+      "10\t검증 세트 손실: 0.081611\t최선의 손실: 0.075953\t정확도: 97.77%\n",
+      "11\t검증 세트 손실: 0.069748\t최선의 손실: 0.069748\t정확도: 98.16%\n",
+      "12\t검증 세트 손실: 0.073723\t최선의 손실: 0.069748\t정확도: 98.24%\n",
+      "13\t검증 세트 손실: 0.074032\t최선의 손실: 0.069748\t정확도: 97.97%\n",
+      "14\t검증 세트 손실: 0.102054\t최선의 손실: 0.069748\t정확도: 97.93%\n",
+      "15\t검증 세트 손실: 0.072969\t최선의 손실: 0.069748\t정확도: 98.32%\n",
+      "16\t검증 세트 손실: 0.087902\t최선의 손실: 0.069748\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.091786\t최선의 손실: 0.069748\t정확도: 97.97%\n",
+      "18\t검증 세트 손실: 0.074677\t최선의 손실: 0.069748\t정확도: 98.08%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "96\t검증 세트 손실: 0.058796\t최선의 손실: 0.053960\t정확도: 98.36%\n",
-      "97\t검증 세트 손실: 0.055678\t최선의 손실: 0.053960\t정확도: 98.59%\n",
-      "98\t검증 세트 손실: 0.060807\t최선의 손실: 0.053960\t정확도: 98.36%\n",
-      "99\t검증 세트 손실: 0.067119\t최선의 손실: 0.053960\t정확도: 98.28%\n",
-      "100\t검증 세트 손실: 0.056820\t최선의 손실: 0.053960\t정확도: 98.51%\n",
-      "101\t검증 세트 손실: 0.058339\t최선의 손실: 0.053960\t정확도: 98.48%\n",
-      "102\t검증 세트 손실: 0.056250\t최선의 손실: 0.053960\t정확도: 98.59%\n",
-      "103\t검증 세트 손실: 0.056021\t최선의 손실: 0.053960\t정확도: 98.55%\n",
-      "104\t검증 세트 손실: 0.059947\t최선의 손실: 0.053960\t정확도: 98.67%\n",
-      "105\t검증 세트 손실: 0.056121\t최선의 손실: 0.053960\t정확도: 98.44%\n",
-      "106\t검증 세트 손실: 0.053030\t최선의 손실: 0.053030\t정확도: 98.59%\n",
-      "107\t검증 세트 손실: 0.057528\t최선의 손실: 0.053030\t정확도: 98.36%\n",
-      "108\t검증 세트 손실: 0.068699\t최선의 손실: 0.053030\t정확도: 98.40%\n",
-      "109\t검증 세트 손실: 0.059456\t최선의 손실: 0.053030\t정확도: 98.63%\n",
-      "110\t검증 세트 손실: 0.059127\t최선의 손실: 0.053030\t정확도: 98.36%\n",
-      "111\t검증 세트 손실: 0.057102\t최선의 손실: 0.053030\t정확도: 98.51%\n",
-      "112\t검증 세트 손실: 0.061940\t최선의 손실: 0.053030\t정확도: 98.48%\n",
-      "113\t검증 세트 손실: 0.072671\t최선의 손실: 0.053030\t정확도: 98.40%\n",
-      "114\t검증 세트 손실: 0.066382\t최선의 손실: 0.053030\t정확도: 98.55%\n",
-      "115\t검증 세트 손실: 0.063806\t최선의 손실: 0.053030\t정확도: 98.63%\n",
-      "116\t검증 세트 손실: 0.070131\t최선의 손실: 0.053030\t정확도: 98.36%\n",
-      "117\t검증 세트 손실: 0.067598\t최선의 손실: 0.053030\t정확도: 98.44%\n",
-      "118\t검증 세트 손실: 0.064130\t최선의 손실: 0.053030\t정확도: 98.40%\n",
-      "119\t검증 세트 손실: 0.065389\t최선의 손실: 0.053030\t정확도: 98.59%\n",
-      "120\t검증 세트 손실: 0.064222\t최선의 손실: 0.053030\t정확도: 98.40%\n",
-      "121\t검증 세트 손실: 0.069090\t최선의 손실: 0.053030\t정확도: 98.55%\n",
-      "122\t검증 세트 손실: 0.058951\t최선의 손실: 0.053030\t정확도: 98.48%\n",
-      "123\t검증 세트 손실: 0.063054\t최선의 손실: 0.053030\t정확도: 98.36%\n",
-      "124\t검증 세트 손실: 0.065044\t최선의 손실: 0.053030\t정확도: 98.59%\n",
-      "125\t검증 세트 손실: 0.061850\t최선의 손실: 0.053030\t정확도: 98.51%\n",
-      "126\t검증 세트 손실: 0.059398\t최선의 손실: 0.053030\t정확도: 98.63%\n",
-      "127\t검증 세트 손실: 0.062487\t최선의 손실: 0.053030\t정확도: 98.28%\n",
+      "19\t검증 세트 손실: 0.081496\t최선의 손실: 0.069748\t정확도: 98.48%\n",
+      "20\t검증 세트 손실: 0.074081\t최선의 손실: 0.069748\t정확도: 98.12%\n",
+      "21\t검증 세트 손실: 0.087148\t최선의 손실: 0.069748\t정확도: 98.16%\n",
+      "22\t검증 세트 손실: 0.070474\t최선의 손실: 0.069748\t정확도: 98.08%\n",
+      "23\t검증 세트 손실: 0.091942\t최선의 손실: 0.069748\t정확도: 98.55%\n",
+      "24\t검증 세트 손실: 0.085575\t최선의 손실: 0.069748\t정확도: 98.32%\n",
+      "25\t검증 세트 손실: 0.070483\t최선의 손실: 0.069748\t정확도: 98.28%\n",
+      "26\t검증 세트 손실: 0.059415\t최선의 손실: 0.059415\t정확도: 98.51%\n",
+      "27\t검증 세트 손실: 0.067901\t최선의 손실: 0.059415\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.091211\t최선의 손실: 0.059415\t정확도: 98.16%\n",
+      "29\t검증 세트 손실: 0.080229\t최선의 손실: 0.059415\t정확도: 98.08%\n",
+      "30\t검증 세트 손실: 0.120813\t최선의 손실: 0.059415\t정확도: 97.85%\n",
+      "31\t검증 세트 손실: 0.072878\t최선의 손실: 0.059415\t정확도: 98.08%\n",
+      "32\t검증 세트 손실: 0.120774\t최선의 손실: 0.059415\t정확도: 98.24%\n",
+      "33\t검증 세트 손실: 0.171966\t최선의 손실: 0.059415\t정확도: 94.68%\n",
+      "34\t검증 세트 손실: 0.145891\t최선의 손실: 0.059415\t정확도: 98.28%\n",
+      "35\t검증 세트 손실: 0.119707\t최선의 손실: 0.059415\t정확도: 98.28%\n",
+      "36\t검증 세트 손실: 0.074878\t최선의 손실: 0.059415\t정확도: 98.05%\n",
+      "37\t검증 세트 손실: 0.089281\t최선의 손실: 0.059415\t정확도: 98.24%\n",
+      "38\t검증 세트 손실: 0.100903\t최선의 손실: 0.059415\t정확도: 98.28%\n",
+      "39\t검증 세트 손실: 0.088704\t최선의 손실: 0.059415\t정확도: 98.32%\n",
+      "40\t검증 세트 손실: 0.089331\t최선의 손실: 0.059415\t정확도: 98.71%\n",
+      "41\t검증 세트 손실: 0.107774\t최선의 손실: 0.059415\t정확도: 98.55%\n",
+      "42\t검증 세트 손실: 0.102709\t최선의 손실: 0.059415\t정확도: 98.12%\n",
+      "43\t검증 세트 손실: 0.168017\t최선의 손실: 0.059415\t정확도: 98.16%\n",
+      "44\t검증 세트 손실: 0.095553\t최선의 손실: 0.059415\t정확도: 98.12%\n",
+      "45\t검증 세트 손실: 0.098561\t최선의 손실: 0.059415\t정확도: 97.69%\n",
+      "46\t검증 세트 손실: 0.168600\t최선의 손실: 0.059415\t정확도: 97.73%\n",
+      "47\t검증 세트 손실: 0.126357\t최선의 손실: 0.059415\t정확도: 98.28%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total=   9.7s\n",
-      "[CV] batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.149504\t최선의 손실: 0.149504\t정확도: 95.66%\n",
-      "1\t검증 세트 손실: 0.125813\t최선의 손실: 0.125813\t정확도: 96.33%\n",
-      "2\t검증 세트 손실: 0.108501\t최선의 손실: 0.108501\t정확도: 96.76%\n",
-      "3\t검증 세트 손실: 0.097880\t최선의 손실: 0.097880\t정확도: 97.30%\n",
-      "4\t검증 세트 손실: 0.091605\t최선의 손실: 0.091605\t정확도: 97.26%\n",
-      "5\t검증 세트 손실: 0.087990\t최선의 손실: 0.087990\t정확도: 97.73%\n",
-      "6\t검증 세트 손실: 0.085492\t최선의 손실: 0.085492\t정확도: 97.77%\n",
-      "7\t검증 세트 손실: 0.078456\t최선의 손실: 0.078456\t정확도: 97.73%\n",
-      "8\t검증 세트 손실: 0.082907\t최선의 손실: 0.078456\t정확도: 98.01%\n",
-      "9\t검증 세트 손실: 0.078935\t최선의 손실: 0.078456\t정확도: 97.81%\n",
-      "10\t검증 세트 손실: 0.073474\t최선의 손실: 0.073474\t정확도: 98.12%\n",
-      "11\t검증 세트 손실: 0.072648\t최선의 손실: 0.072648\t정확도: 97.85%\n",
-      "12\t검증 세트 손실: 0.073037\t최선의 손실: 0.072648\t정확도: 97.97%\n",
-      "13\t검증 세트 손실: 0.065366\t최선의 손실: 0.065366\t정확도: 98.12%\n",
-      "14\t검증 세트 손실: 0.067183\t최선의 손실: 0.065366\t정확도: 98.36%\n",
-      "15\t검증 세트 손실: 0.068739\t최선의 손실: 0.065366\t정확도: 98.32%\n",
-      "16\t검증 세트 손실: 0.080266\t최선의 손실: 0.065366\t정확도: 97.89%\n",
-      "17\t검증 세트 손실: 0.074812\t최선의 손실: 0.065366\t정확도: 98.12%\n",
-      "18\t검증 세트 손실: 0.062458\t최선의 손실: 0.062458\t정확도: 98.32%\n",
-      "19\t검증 세트 손실: 0.071743\t최선의 손실: 0.062458\t정확도: 98.05%\n",
-      "20\t검증 세트 손실: 0.068277\t최선의 손실: 0.062458\t정확도: 98.40%\n",
-      "21\t검증 세트 손실: 0.066266\t최선의 손실: 0.062458\t정확도: 98.20%\n",
-      "22\t검증 세트 손실: 0.064531\t최선의 손실: 0.062458\t정확도: 98.32%\n",
-      "23\t검증 세트 손실: 0.062780\t최선의 손실: 0.062458\t정확도: 98.28%\n",
-      "24\t검증 세트 손실: 0.070362\t최선의 손실: 0.062458\t정확도: 98.12%\n",
-      "25\t검증 세트 손실: 0.067591\t최선의 손실: 0.062458\t정확도: 98.24%\n",
-      "26\t검증 세트 손실: 0.065205\t최선의 손실: 0.062458\t정확도: 98.44%\n",
-      "27\t검증 세트 손실: 0.062953\t최선의 손실: 0.062458\t정확도: 98.16%\n",
-      "28\t검증 세트 손실: 0.068401\t최선의 손실: 0.062458\t정확도: 98.28%\n",
-      "29\t검증 세트 손실: 0.073488\t최선의 손실: 0.062458\t정확도: 98.08%\n",
-      "30\t검증 세트 손실: 0.065873\t최선의 손실: 0.062458\t정확도: 98.20%\n",
-      "31\t검증 세트 손실: 0.063075\t최선의 손실: 0.062458\t정확도: 98.28%\n",
-      "32\t검증 세트 손실: 0.063984\t최선의 손실: 0.062458\t정확도: 98.20%\n",
-      "33\t검증 세트 손실: 0.068485\t최선의 손실: 0.062458\t정확도: 98.20%\n",
-      "34\t검증 세트 손실: 0.061801\t최선의 손실: 0.061801\t정확도: 98.32%\n",
-      "35\t검증 세트 손실: 0.059059\t최선의 손실: 0.059059\t정확도: 98.40%\n",
-      "36\t검증 세트 손실: 0.062142\t최선의 손실: 0.059059\t정확도: 98.32%\n",
-      "37\t검증 세트 손실: 0.059269\t최선의 손실: 0.059059\t정확도: 98.51%\n",
-      "38\t검증 세트 손실: 0.061659\t최선의 손실: 0.059059\t정확도: 98.48%\n",
-      "39\t검증 세트 손실: 0.057572\t최선의 손실: 0.057572\t정확도: 98.44%\n",
-      "40\t검증 세트 손실: 0.057524\t최선의 손실: 0.057524\t정확도: 98.36%\n",
-      "41\t검증 세트 손실: 0.055452\t최선의 손실: 0.055452\t정확도: 98.51%\n",
-      "42\t검증 세트 손실: 0.058257\t최선의 손실: 0.055452\t정확도: 98.48%\n",
-      "43\t검증 세트 손실: 0.066453\t최선의 손실: 0.055452\t정확도: 98.20%\n",
-      "44\t검증 세트 손실: 0.061141\t최선의 손실: 0.055452\t정확도: 98.48%\n",
-      "45\t검증 세트 손실: 0.060621\t최선의 손실: 0.055452\t정확도: 98.51%\n",
-      "46\t검증 세트 손실: 0.060350\t최선의 손실: 0.055452\t정확도: 98.36%\n",
-      "47\t검증 세트 손실: 0.061903\t최선의 손실: 0.055452\t정확도: 98.44%\n",
-      "48\t검증 세트 손실: 0.055156\t최선의 손실: 0.055156\t정확도: 98.55%\n",
-      "49\t검증 세트 손실: 0.061574\t최선의 손실: 0.055156\t정확도: 98.67%\n",
-      "50\t검증 세트 손실: 0.054134\t최선의 손실: 0.054134\t정확도: 98.55%\n",
-      "51\t검증 세트 손실: 0.062284\t최선의 손실: 0.054134\t정확도: 98.51%\n",
-      "52\t검증 세트 손실: 0.059245\t최선의 손실: 0.054134\t정확도: 98.63%\n",
-      "53\t검증 세트 손실: 0.057354\t최선의 손실: 0.054134\t정확도: 98.40%\n",
-      "54\t검증 세트 손실: 0.058308\t최선의 손실: 0.054134\t정확도: 98.48%\n",
-      "55\t검증 세트 손실: 0.054440\t최선의 손실: 0.054134\t정확도: 98.55%\n",
-      "56\t검증 세트 손실: 0.059197\t최선의 손실: 0.054134\t정확도: 98.36%\n",
-      "57\t검증 세트 손실: 0.058399\t최선의 손실: 0.054134\t정확도: 98.24%\n",
-      "58\t검증 세트 손실: 0.061425\t최선의 손실: 0.054134\t정확도: 98.36%\n",
-      "59\t검증 세트 손실: 0.058482\t최선의 손실: 0.054134\t정확도: 98.51%\n",
-      "60\t검증 세트 손실: 0.060514\t최선의 손실: 0.054134\t정확도: 98.51%\n",
-      "61\t검증 세트 손실: 0.065753\t최선의 손실: 0.054134\t정확도: 98.40%\n",
-      "62\t검증 세트 손실: 0.067745\t최선의 손실: 0.054134\t정확도: 98.32%\n",
-      "63\t검증 세트 손실: 0.055869\t최선의 손실: 0.054134\t정확도: 98.51%\n",
-      "64\t검증 세트 손실: 0.058032\t최선의 손실: 0.054134\t정확도: 98.40%\n",
-      "65\t검증 세트 손실: 0.064120\t최선의 손실: 0.054134\t정확도: 98.51%\n",
-      "66\t검증 세트 손실: 0.059212\t최선의 손실: 0.054134\t정확도: 98.44%\n",
-      "67\t검증 세트 손실: 0.057353\t최선의 손실: 0.054134\t정확도: 98.67%\n",
-      "68\t검증 세트 손실: 0.066214\t최선의 손실: 0.054134\t정확도: 98.36%\n",
-      "69\t검증 세트 손실: 0.056177\t최선의 손실: 0.054134\t정확도: 98.55%\n",
-      "70\t검증 세트 손실: 0.063487\t최선의 손실: 0.054134\t정확도: 98.51%\n",
-      "71\t검증 세트 손실: 0.061097\t최선의 손실: 0.054134\t정확도: 98.59%\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160, total=  12.6s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.107637\t최선의 손실: 0.107637\t정확도: 97.38%\n",
+      "1\t검증 세트 손실: 0.099182\t최선의 손실: 0.099182\t정확도: 97.62%\n",
+      "2\t검증 세트 손실: 0.071819\t최선의 손실: 0.071819\t정확도: 98.28%\n",
+      "3\t검증 세트 손실: 0.085597\t최선의 손실: 0.071819\t정확도: 98.20%\n",
+      "4\t검증 세트 손실: 0.092732\t최선의 손실: 0.071819\t정확도: 98.05%\n",
+      "5\t검증 세트 손실: 0.117763\t최선의 손실: 0.071819\t정확도: 97.46%\n",
+      "6\t검증 세트 손실: 0.103164\t최선의 손실: 0.071819\t정확도: 97.65%\n",
+      "7\t검증 세트 손실: 0.066824\t최선의 손실: 0.066824\t정확도: 98.16%\n",
+      "8\t검증 세트 손실: 0.100920\t최선의 손실: 0.066824\t정확도: 97.73%\n",
+      "9\t검증 세트 손실: 0.084618\t최선의 손실: 0.066824\t정확도: 97.93%\n",
+      "10\t검증 세트 손실: 0.068220\t최선의 손실: 0.066824\t정확도: 98.59%\n",
+      "11\t검증 세트 손실: 0.070286\t최선의 손실: 0.066824\t정확도: 98.55%\n",
+      "12\t검증 세트 손실: 0.074611\t최선의 손실: 0.066824\t정확도: 98.32%\n",
+      "13\t검증 세트 손실: 0.093810\t최선의 손실: 0.066824\t정확도: 98.24%\n",
+      "14\t검증 세트 손실: 0.087801\t최선의 손실: 0.066824\t정확도: 98.28%\n",
+      "15\t검증 세트 손실: 0.104306\t최선의 손실: 0.066824\t정확도: 98.20%\n",
+      "16\t검증 세트 손실: 0.058634\t최선의 손실: 0.058634\t정확도: 98.55%\n",
+      "17\t검증 세트 손실: 0.068186\t최선의 손실: 0.058634\t정확도: 98.16%\n",
+      "18\t검증 세트 손실: 0.070084\t최선의 손실: 0.058634\t정확도: 98.40%\n",
+      "19\t검증 세트 손실: 0.070946\t최선의 손실: 0.058634\t정확도: 98.08%\n",
+      "20\t검증 세트 손실: 0.080420\t최선의 손실: 0.058634\t정확도: 98.44%\n",
+      "21\t검증 세트 손실: 0.125773\t최선의 손실: 0.058634\t정확도: 98.05%\n",
+      "22\t검증 세트 손실: 0.106441\t최선의 손실: 0.058634\t정확도: 98.01%\n",
+      "23\t검증 세트 손실: 0.108412\t최선의 손실: 0.058634\t정확도: 97.81%\n",
+      "24\t검증 세트 손실: 0.096699\t최선의 손실: 0.058634\t정확도: 98.16%\n",
+      "25\t검증 세트 손실: 0.079768\t최선의 손실: 0.058634\t정확도: 98.16%\n",
+      "26\t검증 세트 손실: 0.070376\t최선의 손실: 0.058634\t정확도: 98.16%\n",
+      "27\t검증 세트 손실: 0.084527\t최선의 손실: 0.058634\t정확도: 98.16%\n",
+      "28\t검증 세트 손실: 0.066667\t최선의 손실: 0.058634\t정확도: 98.67%\n",
+      "29\t검증 세트 손실: 0.135974\t최선의 손실: 0.058634\t정확도: 98.20%\n",
+      "30\t검증 세트 손실: 0.068965\t최선의 손실: 0.058634\t정확도: 98.67%\n",
+      "31\t검증 세트 손실: 0.058134\t최선의 손실: 0.058134\t정확도: 98.87%\n",
+      "32\t검증 세트 손실: 0.055563\t최선의 손실: 0.055563\t정확도: 98.75%\n",
+      "33\t검증 세트 손실: 0.076457\t최선의 손실: 0.055563\t정확도: 98.40%\n",
+      "34\t검증 세트 손실: 0.102981\t최선의 손실: 0.055563\t정확도: 97.65%\n",
+      "35\t검증 세트 손실: 0.090950\t최선의 손실: 0.055563\t정확도: 97.73%\n",
+      "36\t검증 세트 손실: 0.068787\t최선의 손실: 0.055563\t정확도: 98.55%\n",
+      "37\t검증 세트 손실: 0.121973\t최선의 손실: 0.055563\t정확도: 98.48%\n",
+      "38\t검증 세트 손실: 0.089979\t최선의 손실: 0.055563\t정확도: 98.51%\n",
+      "39\t검증 세트 손실: 0.075808\t최선의 손실: 0.055563\t정확도: 98.48%\n",
+      "40\t검증 세트 손실: 0.078028\t최선의 손실: 0.055563\t정확도: 98.79%\n",
+      "41\t검증 세트 손실: 0.107824\t최선의 손실: 0.055563\t정확도: 98.40%\n",
+      "42\t검증 세트 손실: 0.133584\t최선의 손실: 0.055563\t정확도: 98.01%\n",
+      "43\t검증 세트 손실: 0.116490\t최선의 손실: 0.055563\t정확도: 98.12%\n",
+      "44\t검증 세트 손실: 0.080683\t최선의 손실: 0.055563\t정확도: 98.20%\n",
+      "45\t검증 세트 손실: 0.074222\t최선의 손실: 0.055563\t정확도: 98.16%\n",
+      "46\t검증 세트 손실: 0.146153\t최선의 손실: 0.055563\t정확도: 98.36%\n",
+      "47\t검증 세트 손실: 0.070070\t최선의 손실: 0.055563\t정확도: 98.16%\n",
+      "48\t검증 세트 손실: 0.069659\t최선의 손실: 0.055563\t정확도: 98.24%\n",
+      "49\t검증 세트 손실: 0.062451\t최선의 손실: 0.055563\t정확도: 98.51%\n",
+      "50\t검증 세트 손실: 0.065752\t최선의 손실: 0.055563\t정확도: 98.12%\n",
+      "51\t검증 세트 손실: 0.105613\t최선의 손실: 0.055563\t정확도: 98.20%\n",
+      "52\t검증 세트 손실: 0.093338\t최선의 손실: 0.055563\t정확도: 98.08%\n",
+      "53\t검증 세트 손실: 0.140303\t최선의 손실: 0.055563\t정확도: 98.24%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total=   5.7s\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.805018\t최선의 손실: 0.805018\t정확도: 59.27%\n",
-      "1\t검증 세트 손실: 0.520516\t최선의 손실: 0.520516\t정확도: 76.78%\n",
-      "2\t검증 세트 손실: 0.386182\t최선의 손실: 0.386182\t정확도: 81.90%\n",
-      "3\t검증 세트 손실: 0.282630\t최선의 손실: 0.282630\t정확도: 93.94%\n",
-      "4\t검증 세트 손실: 0.231169\t최선의 손실: 0.231169\t정확도: 93.28%\n",
-      "5\t검증 세트 손실: 0.195436\t최선의 손실: 0.195436\t정확도: 95.11%\n",
-      "6\t검증 세트 손실: 0.201536\t최선의 손실: 0.195436\t정확도: 95.07%\n",
-      "7\t검증 세트 손실: 0.175609\t최선의 손실: 0.175609\t정확도: 95.58%\n",
-      "8\t검증 세트 손실: 0.201122\t최선의 손실: 0.175609\t정확도: 94.61%\n",
-      "9\t검증 세트 손실: 0.213535\t최선의 손실: 0.175609\t정확도: 94.25%\n",
-      "10\t검증 세트 손실: 0.230067\t최선의 손실: 0.175609\t정확도: 95.66%\n",
-      "11\t검증 세트 손실: 0.322366\t최선의 손실: 0.175609\t정확도: 94.64%\n",
-      "12\t검증 세트 손실: 0.211496\t최선의 손실: 0.175609\t정확도: 95.43%\n",
-      "13\t검증 세트 손실: 0.236593\t최선의 손실: 0.175609\t정확도: 95.62%\n",
-      "14\t검증 세트 손실: 0.196176\t최선의 손실: 0.175609\t정확도: 94.21%\n",
-      "15\t검증 세트 손실: 0.169640\t최선의 손실: 0.169640\t정확도: 95.82%\n",
-      "16\t검증 세트 손실: 0.171296\t최선의 손실: 0.169640\t정확도: 94.68%\n",
-      "17\t검증 세트 손실: 0.168893\t최선의 손실: 0.168893\t정확도: 96.29%\n",
-      "18\t검증 세트 손실: 0.175377\t최선의 손실: 0.168893\t정확도: 94.96%\n",
-      "19\t검증 세트 손실: 0.143879\t최선의 손실: 0.143879\t정확도: 96.64%\n",
-      "20\t검증 세트 손실: 0.141714\t최선의 손실: 0.141714\t정확도: 96.64%\n",
-      "21\t검증 세트 손실: 0.148164\t최선의 손실: 0.141714\t정확도: 95.86%\n",
-      "22\t검증 세트 손실: 0.144181\t최선의 손실: 0.141714\t정확도: 96.72%\n",
-      "23\t검증 세트 손실: 0.149312\t최선의 손실: 0.141714\t정확도: 96.76%\n",
-      "24\t검증 세트 손실: 0.478556\t최선의 손실: 0.141714\t정확도: 83.39%\n",
-      "25\t검증 세트 손실: 1.777941\t최선의 손실: 0.141714\t정확도: 24.67%\n",
-      "26\t검증 세트 손실: 1.590656\t최선의 손실: 0.141714\t정확도: 19.27%\n",
-      "27\t검증 세트 손실: 1.504221\t최선의 손실: 0.141714\t정확도: 36.63%\n",
-      "28\t검증 세트 손실: 1.423730\t최선의 손실: 0.141714\t정확도: 42.34%\n",
-      "29\t검증 세트 손실: 1.260817\t최선의 손실: 0.141714\t정확도: 48.75%\n",
-      "30\t검증 세트 손실: 1.220420\t최선의 손실: 0.141714\t정확도: 49.88%\n",
-      "31\t검증 세트 손실: 1.133780\t최선의 손실: 0.141714\t정확도: 45.04%\n",
-      "32\t검증 세트 손실: 1.014697\t최선의 손실: 0.141714\t정확도: 55.00%\n",
-      "33\t검증 세트 손실: 0.911995\t최선의 손실: 0.141714\t정확도: 54.65%\n",
-      "34\t검증 세트 손실: 0.860866\t최선의 손실: 0.141714\t정확도: 60.79%\n",
-      "35\t검증 세트 손실: 0.799819\t최선의 손실: 0.141714\t정확도: 60.28%\n",
-      "36\t검증 세트 손실: 0.799635\t최선의 손실: 0.141714\t정확도: 59.93%\n",
-      "37\t검증 세트 손실: 0.811219\t최선의 손실: 0.141714\t정확도: 59.62%\n",
-      "38\t검증 세트 손실: 0.777004\t최선의 손실: 0.141714\t정확도: 62.20%\n",
-      "39\t검증 세트 손실: 0.780857\t최선의 손실: 0.141714\t정확도: 58.21%\n",
-      "40\t검증 세트 손실: 0.794269\t최선의 손실: 0.141714\t정확도: 63.06%\n",
-      "41\t검증 세트 손실: 0.773179\t최선의 손실: 0.141714\t정확도: 61.49%\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160, total=  14.1s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.092775\t최선의 손실: 0.092775\t정확도: 97.81%\n",
+      "1\t검증 세트 손실: 0.090164\t최선의 손실: 0.090164\t정확도: 97.89%\n",
+      "2\t검증 세트 손실: 0.082413\t최선의 손실: 0.082413\t정확도: 98.20%\n",
+      "3\t검증 세트 손실: 0.076565\t최선의 손실: 0.076565\t정확도: 98.36%\n",
+      "4\t검증 세트 손실: 0.071170\t최선의 손실: 0.071170\t정확도: 98.44%\n",
+      "5\t검증 세트 손실: 0.066614\t최선의 손실: 0.066614\t정확도: 98.12%\n",
+      "6\t검증 세트 손실: 0.070057\t최선의 손실: 0.066614\t정확도: 98.16%\n",
+      "7\t검증 세트 손실: 0.088508\t최선의 손실: 0.066614\t정확도: 97.97%\n",
+      "8\t검증 세트 손실: 0.077610\t최선의 손실: 0.066614\t정확도: 98.32%\n",
+      "9\t검증 세트 손실: 0.084708\t최선의 손실: 0.066614\t정확도: 97.46%\n",
+      "10\t검증 세트 손실: 0.082429\t최선의 손실: 0.066614\t정확도: 97.97%\n",
+      "11\t검증 세트 손실: 0.080050\t최선의 손실: 0.066614\t정확도: 98.32%\n",
+      "12\t검증 세트 손실: 0.067165\t최선의 손실: 0.066614\t정확도: 98.28%\n",
+      "13\t검증 세트 손실: 0.067363\t최선의 손실: 0.066614\t정확도: 98.28%\n",
+      "14\t검증 세트 손실: 0.065571\t최선의 손실: 0.065571\t정확도: 98.36%\n",
+      "15\t검증 세트 손실: 0.079920\t최선의 손실: 0.065571\t정확도: 98.24%\n",
+      "16\t검증 세트 손실: 0.083870\t최선의 손실: 0.065571\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.119729\t최선의 손실: 0.065571\t정확도: 97.58%\n",
+      "18\t검증 세트 손실: 0.112237\t최선의 손실: 0.065571\t정확도: 97.81%\n",
+      "19\t검증 세트 손실: 0.083094\t최선의 손실: 0.065571\t정확도: 98.12%\n",
+      "20\t검증 세트 손실: 0.105316\t최선의 손실: 0.065571\t정확도: 98.40%\n",
+      "21\t검증 세트 손실: 0.096691\t최선의 손실: 0.065571\t정확도: 97.97%\n",
+      "22\t검증 세트 손실: 0.091569\t최선의 손실: 0.065571\t정확도: 98.28%\n",
+      "23\t검증 세트 손실: 0.075106\t최선의 손실: 0.065571\t정확도: 98.28%\n",
+      "24\t검증 세트 손실: 0.072993\t최선의 손실: 0.065571\t정확도: 98.12%\n",
+      "25\t검증 세트 손실: 0.086236\t최선의 손실: 0.065571\t정확도: 98.48%\n",
+      "26\t검증 세트 손실: 0.096582\t최선의 손실: 0.065571\t정확도: 98.16%\n",
+      "27\t검증 세트 손실: 0.069588\t최선의 손실: 0.065571\t정확도: 98.48%\n",
+      "28\t검증 세트 손실: 0.094651\t최선의 손실: 0.065571\t정확도: 98.16%\n",
+      "29\t검증 세트 손실: 0.078284\t최선의 손실: 0.065571\t정확도: 98.28%\n",
+      "30\t검증 세트 손실: 0.140838\t최선의 손실: 0.065571\t정확도: 98.59%\n",
+      "31\t검증 세트 손실: 0.104368\t최선의 손실: 0.065571\t정확도: 97.89%\n",
+      "32\t검증 세트 손실: 0.126649\t최선의 손실: 0.065571\t정확도: 98.20%\n",
+      "33\t검증 세트 손실: 0.114887\t최선의 손실: 0.065571\t정확도: 98.44%\n",
+      "34\t검증 세트 손실: 0.079874\t최선의 손실: 0.065571\t정확도: 98.24%\n",
+      "35\t검증 세트 손실: 0.086093\t최선의 손실: 0.065571\t정확도: 98.24%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=10, total=   3.7s\n"
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160, total=   9.8s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 5043.290527\t최선의 손실: 5043.290527\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 8360.097656\t최선의 손실: 5043.290527\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 11465.927734\t최선의 손실: 5043.290527\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 79917.242188\t최선의 손실: 5043.290527\t정확도: 20.91%\n",
+      "4\t검증 세트 손실: 106606.335938\t최선의 손실: 5043.290527\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 149272.609375\t최선의 손실: 5043.290527\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 67540.773438\t최선의 손실: 5043.290527\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 40570.312500\t최선의 손실: 5043.290527\t정확도: 18.69%\n",
+      "8\t검증 세트 손실: 20557.707031\t최선의 손실: 5043.290527\t정확도: 20.91%\n",
+      "9\t검증 세트 손실: 346337.406250\t최선의 손실: 5043.290527\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 88110.875000\t최선의 손실: 5043.290527\t정확도: 19.27%\n",
+      "11\t검증 세트 손실: 72824.023438\t최선의 손실: 5043.290527\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 19310.361328\t최선의 손실: 5043.290527\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 29740.146484\t최선의 손실: 5043.290527\t정확도: 19.47%\n",
+      "14\t검증 세트 손실: 1058169.250000\t최선의 손실: 5043.290527\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 186798.828125\t최선의 손실: 5043.290527\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 51251.527344\t최선의 손실: 5043.290527\t정확도: 18.76%\n",
+      "17\t검증 세트 손실: 349513.218750\t최선의 손실: 5043.290527\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 301096.562500\t최선의 손실: 5043.290527\t정확도: 20.88%\n",
+      "19\t검증 세트 손실: 211249.531250\t최선의 손실: 5043.290527\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 194893.468750\t최선의 손실: 5043.290527\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 169908.359375\t최선의 손실: 5043.290527\t정확도: 19.39%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  13.0s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.556211\t최선의 손실: 0.556211\t정확도: 80.69%\n",
-      "1\t검증 세트 손실: 0.279295\t최선의 손실: 0.279295\t정확도: 92.30%\n",
-      "2\t검증 세트 손실: 0.250035\t최선의 손실: 0.250035\t정확도: 93.90%\n",
-      "3\t검증 세트 손실: 0.245639\t최선의 손실: 0.245639\t정확도: 93.08%\n",
-      "4\t검증 세트 손실: 0.232753\t최선의 손실: 0.232753\t정확도: 93.75%\n",
-      "5\t검증 세트 손실: 0.221555\t최선의 손실: 0.221555\t정확도: 94.37%\n",
-      "6\t검증 세트 손실: 0.203735\t최선의 손실: 0.203735\t정확도: 94.37%\n",
-      "7\t검증 세트 손실: 0.293470\t최선의 손실: 0.203735\t정확도: 89.72%\n",
-      "8\t검증 세트 손실: 0.511602\t최선의 손실: 0.203735\t정확도: 80.34%\n",
-      "9\t검증 세트 손실: 0.262656\t최선의 손실: 0.203735\t정확도: 94.06%\n",
-      "10\t검증 세트 손실: 0.241662\t최선의 손실: 0.203735\t정확도: 94.45%\n",
-      "11\t검증 세트 손실: 0.220672\t최선의 손실: 0.203735\t정확도: 93.67%\n",
-      "12\t검증 세트 손실: 0.195666\t최선의 손실: 0.195666\t정확도: 95.15%\n",
-      "13\t검증 세트 손실: 0.205051\t최선의 손실: 0.195666\t정확도: 94.92%\n",
-      "14\t검증 세트 손실: 0.183535\t최선의 손실: 0.183535\t정확도: 95.04%\n",
-      "15\t검증 세트 손실: 0.202334\t최선의 손실: 0.183535\t정확도: 95.11%\n",
-      "16\t검증 세트 손실: 0.181275\t최선의 손실: 0.181275\t정확도: 95.78%\n",
-      "17\t검증 세트 손실: 0.159252\t최선의 손실: 0.159252\t정확도: 95.31%\n",
-      "18\t검증 세트 손실: 0.179151\t최선의 손실: 0.159252\t정확도: 95.86%\n",
-      "19\t검증 세트 손실: 0.193039\t최선의 손실: 0.159252\t정확도: 95.86%\n",
-      "20\t검증 세트 손실: 0.225224\t최선의 손실: 0.159252\t정확도: 95.04%\n",
-      "21\t검증 세트 손실: 0.215026\t최선의 손실: 0.159252\t정확도: 94.92%\n",
-      "22\t검증 세트 손실: 0.193712\t최선의 손실: 0.159252\t정확도: 94.92%\n",
-      "23\t검증 세트 손실: 0.186073\t최선의 손실: 0.159252\t정확도: 96.13%\n",
-      "24\t검증 세트 손실: 0.182418\t최선의 손실: 0.159252\t정확도: 95.31%\n",
-      "25\t검증 세트 손실: 0.191849\t최선의 손실: 0.159252\t정확도: 96.05%\n",
-      "26\t검증 세트 손실: 0.150801\t최선의 손실: 0.150801\t정확도: 96.25%\n",
-      "27\t검증 세트 손실: 0.176126\t최선의 손실: 0.150801\t정확도: 96.01%\n",
-      "28\t검증 세트 손실: 0.206774\t최선의 손실: 0.150801\t정확도: 94.02%\n",
-      "29\t검증 세트 손실: 0.199009\t최선의 손실: 0.150801\t정확도: 94.88%\n",
-      "30\t검증 세트 손실: 0.193593\t최선의 손실: 0.150801\t정확도: 95.78%\n",
-      "31\t검증 세트 손실: 0.171166\t최선의 손실: 0.150801\t정확도: 95.82%\n",
-      "32\t검증 세트 손실: 0.167505\t최선의 손실: 0.150801\t정확도: 96.05%\n",
-      "33\t검증 세트 손실: 0.156135\t최선의 손실: 0.150801\t정확도: 96.13%\n",
-      "34\t검증 세트 손실: 0.155031\t최선의 손실: 0.150801\t정확도: 96.33%\n",
-      "35\t검증 세트 손실: 0.153624\t최선의 손실: 0.150801\t정확도: 96.01%\n",
-      "36\t검증 세트 손실: 0.182813\t최선의 손실: 0.150801\t정확도: 94.88%\n",
-      "37\t검증 세트 손실: 0.177195\t최선의 손실: 0.150801\t정확도: 95.43%\n",
-      "38\t검증 세트 손실: 0.188397\t최선의 손실: 0.150801\t정확도: 95.90%\n",
-      "39\t검증 세트 손실: 0.188528\t최선의 손실: 0.150801\t정확도: 94.92%\n",
-      "40\t검증 세트 손실: 0.257771\t최선의 손실: 0.150801\t정확도: 94.21%\n",
-      "41\t검증 세트 손실: 0.200555\t최선의 손실: 0.150801\t정확도: 95.58%\n",
-      "42\t검증 세트 손실: 0.333770\t최선의 손실: 0.150801\t정확도: 92.57%\n",
-      "43\t검증 세트 손실: 1.888472\t최선의 손실: 0.150801\t정확도: 20.95%\n",
-      "44\t검증 세트 손실: 1.617333\t최선의 손실: 0.150801\t정확도: 22.67%\n",
-      "45\t검증 세트 손실: 1.147536\t최선의 손실: 0.150801\t정확도: 43.47%\n",
-      "46\t검증 세트 손실: 1.075790\t최선의 손실: 0.150801\t정확도: 54.50%\n",
-      "47\t검증 세트 손실: 1.077592\t최선의 손실: 0.150801\t정확도: 52.27%\n",
+      "0\t검증 세트 손실: 34925.375000\t최선의 손실: 34925.375000\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 6177.822754\t최선의 손실: 6177.822754\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 34105.937500\t최선의 손실: 6177.822754\t정확도: 18.88%\n",
+      "3\t검증 세트 손실: 14816.296875\t최선의 손실: 6177.822754\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 5904.153809\t최선의 손실: 5904.153809\t정확도: 21.19%\n",
+      "5\t검증 세트 손실: 4017.696533\t최선의 손실: 4017.696533\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 94050.992188\t최선의 손실: 4017.696533\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 15395.276367\t최선의 손실: 4017.696533\t정확도: 22.20%\n",
+      "8\t검증 세트 손실: 130622.070312\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 320905.218750\t최선의 손실: 4017.696533\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 32908.945312\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 158806.046875\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 778062.812500\t최선의 손실: 4017.696533\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1412091.000000\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 565844.875000\t최선의 손실: 4017.696533\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 534323.625000\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 294496.687500\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 406404.468750\t최선의 손실: 4017.696533\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 307900.406250\t최선의 손실: 4017.696533\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 734542.687500\t최선의 손실: 4017.696533\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 415058.062500\t최선의 손실: 4017.696533\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 129053.773438\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "22\t검증 세트 손실: 2695454.250000\t최선의 손실: 4017.696533\t정확도: 20.91%\n",
+      "23\t검증 세트 손실: 1108193.625000\t최선의 손실: 4017.696533\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1315146.000000\t최선의 손실: 4017.696533\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 846490.625000\t최선의 손실: 4017.696533\t정확도: 16.42%\n",
+      "26\t검증 세트 손실: 1715596.250000\t최선의 손실: 4017.696533\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=10, total=   4.1s\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.516620\t최선의 손실: 0.516620\t정확도: 80.02%\n",
-      "1\t검증 세트 손실: 0.351556\t최선의 손실: 0.351556\t정확도: 89.21%\n",
-      "2\t검증 세트 손실: 0.252829\t최선의 손실: 0.252829\t정확도: 93.78%\n",
-      "3\t검증 세트 손실: 0.199606\t최선의 손실: 0.199606\t정확도: 94.88%\n",
-      "4\t검증 세트 손실: 0.184145\t최선의 손실: 0.184145\t정확도: 94.80%\n",
-      "5\t검증 세트 손실: 0.166711\t최선의 손실: 0.166711\t정확도: 95.23%\n",
-      "6\t검증 세트 손실: 0.185779\t최선의 손실: 0.166711\t정확도: 95.00%\n",
-      "7\t검증 세트 손실: 0.172155\t최선의 손실: 0.166711\t정확도: 95.04%\n",
-      "8\t검증 세트 손실: 0.137502\t최선의 손실: 0.137502\t정확도: 95.93%\n",
-      "9\t검증 세트 손실: 0.140114\t최선의 손실: 0.137502\t정확도: 95.90%\n",
-      "10\t검증 세트 손실: 0.138443\t최선의 손실: 0.137502\t정확도: 96.21%\n",
-      "11\t검증 세트 손실: 0.126694\t최선의 손실: 0.126694\t정확도: 96.48%\n",
-      "12\t검증 세트 손실: 0.142077\t최선의 손실: 0.126694\t정확도: 96.05%\n",
-      "13\t검증 세트 손실: 0.180690\t최선의 손실: 0.126694\t정확도: 94.88%\n",
-      "14\t검증 세트 손실: 0.481468\t최선의 손실: 0.126694\t정확도: 87.37%\n",
-      "15\t검증 세트 손실: 0.898294\t최선의 손실: 0.126694\t정확도: 61.69%\n",
-      "16\t검증 세트 손실: 0.610886\t최선의 손실: 0.126694\t정확도: 76.54%\n",
-      "17\t검증 세트 손실: 0.832022\t최선의 손실: 0.126694\t정확도: 72.40%\n",
-      "18\t검증 세트 손실: 0.807748\t최선의 손실: 0.126694\t정확도: 60.79%\n",
-      "19\t검증 세트 손실: 0.587508\t최선의 손실: 0.126694\t정확도: 72.83%\n",
-      "20\t검증 세트 손실: 0.512434\t최선의 손실: 0.126694\t정확도: 75.68%\n",
-      "21\t검증 세트 손실: 0.463763\t최선의 손실: 0.126694\t정확도: 78.62%\n",
-      "22\t검증 세트 손실: 0.476031\t최선의 손실: 0.126694\t정확도: 76.62%\n",
-      "23\t검증 세트 손실: 0.476789\t최선의 손실: 0.126694\t정확도: 77.99%\n",
-      "24\t검증 세트 손실: 0.751214\t최선의 손실: 0.126694\t정확도: 77.01%\n",
-      "25\t검증 세트 손실: 0.437157\t최선의 손실: 0.126694\t정확도: 78.54%\n",
-      "26\t검증 세트 손실: 0.483307\t최선의 손실: 0.126694\t정확도: 76.27%\n",
-      "27\t검증 세트 손실: 0.647475\t최선의 손실: 0.126694\t정확도: 78.19%\n",
-      "28\t검증 세트 손실: 1.375481\t최선의 손실: 0.126694\t정확도: 45.82%\n",
-      "29\t검증 세트 손실: 0.874884\t최선의 손실: 0.126694\t정확도: 58.60%\n",
-      "30\t검증 세트 손실: 0.849575\t최선의 손실: 0.126694\t정확도: 60.99%\n",
-      "31\t검증 세트 손실: 0.698312\t최선의 손실: 0.126694\t정확도: 70.37%\n",
-      "32\t검증 세트 손실: 0.621967\t최선의 손실: 0.126694\t정확도: 71.07%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  16.5s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 17232.982422\t최선의 손실: 17232.982422\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 29236.134766\t최선의 손실: 17232.982422\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 22981.980469\t최선의 손실: 17232.982422\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 13564.606445\t최선의 손실: 13564.606445\t정확도: 21.97%\n",
+      "4\t검증 세트 손실: 18326.937500\t최선의 손실: 13564.606445\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 61662.148438\t최선의 손실: 13564.606445\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 111795.640625\t최선의 손실: 13564.606445\t정확도: 20.91%\n",
+      "7\t검증 세트 손실: 514071.625000\t최선의 손실: 13564.606445\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 133971.609375\t최선의 손실: 13564.606445\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 323626.718750\t최선의 손실: 13564.606445\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 213183.453125\t최선의 손실: 13564.606445\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 97625.867188\t최선의 손실: 13564.606445\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 45016.164062\t최선의 손실: 13564.606445\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 394898.500000\t최선의 손실: 13564.606445\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 65370.027344\t최선의 손실: 13564.606445\t정확도: 19.04%\n",
+      "15\t검증 세트 손실: 87056.554688\t최선의 손실: 13564.606445\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 125326.273438\t최선의 손실: 13564.606445\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 69701.015625\t최선의 손실: 13564.606445\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 241578.328125\t최선의 손실: 13564.606445\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 334753.812500\t최선의 손실: 13564.606445\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 289600.093750\t최선의 손실: 13564.606445\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 95515.031250\t최선의 손실: 13564.606445\t정확도: 19.12%\n",
+      "22\t검증 세트 손실: 198714.640625\t최선의 손실: 13564.606445\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 210516.437500\t최선의 손실: 13564.606445\t정확도: 19.27%\n",
+      "24\t검증 세트 손실: 476049.156250\t최선의 손실: 13564.606445\t정확도: 20.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=10, total=   4.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.089159\t최선의 손실: 0.089159\t정확도: 97.54%\n",
-      "1\t검증 세트 손실: 0.093306\t최선의 손실: 0.089159\t정확도: 97.77%\n",
-      "2\t검증 세트 손실: 0.098750\t최선의 손실: 0.089159\t정확도: 97.58%\n",
-      "3\t검증 세트 손실: 0.076237\t최선의 손실: 0.076237\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.080439\t최선의 손실: 0.076237\t정확도: 98.48%\n",
-      "5\t검증 세트 손실: 0.071473\t최선의 손실: 0.071473\t정확도: 97.97%\n",
-      "6\t검증 세트 손실: 0.098244\t최선의 손실: 0.071473\t정확도: 98.08%\n",
-      "7\t검증 세트 손실: 0.078965\t최선의 손실: 0.071473\t정확도: 98.12%\n",
-      "8\t검증 세트 손실: 0.080674\t최선의 손실: 0.071473\t정확도: 98.08%\n",
-      "9\t검증 세트 손실: 0.079070\t최선의 손실: 0.071473\t정확도: 98.20%\n",
-      "10\t검증 세트 손실: 0.101863\t최선의 손실: 0.071473\t정확도: 97.26%\n",
-      "11\t검증 세트 손실: 0.078634\t최선의 손실: 0.071473\t정확도: 98.05%\n",
-      "12\t검증 세트 손실: 0.095486\t최선의 손실: 0.071473\t정확도: 97.97%\n",
-      "13\t검증 세트 손실: 0.113200\t최선의 손실: 0.071473\t정확도: 98.01%\n",
-      "14\t검증 세트 손실: 0.080522\t최선의 손실: 0.071473\t정확도: 98.16%\n",
-      "15\t검증 세트 손실: 0.172577\t최선의 손실: 0.071473\t정확도: 97.62%\n",
-      "16\t검증 세트 손실: 0.119898\t최선의 손실: 0.071473\t정확도: 97.15%\n",
-      "17\t검증 세트 손실: 0.108873\t최선의 손실: 0.071473\t정확도: 98.20%\n",
-      "18\t검증 세트 손실: 0.166576\t최선의 손실: 0.071473\t정확도: 97.93%\n",
-      "19\t검증 세트 손실: 0.083806\t최선의 손실: 0.071473\t정확도: 98.12%\n",
-      "20\t검증 세트 손실: 0.066482\t최선의 손실: 0.066482\t정확도: 98.63%\n",
-      "21\t검증 세트 손실: 0.135285\t최선의 손실: 0.066482\t정확도: 97.77%\n",
-      "22\t검증 세트 손실: 0.127557\t최선의 손실: 0.066482\t정확도: 97.65%\n",
-      "23\t검증 세트 손실: 0.115671\t최선의 손실: 0.066482\t정확도: 97.26%\n",
-      "24\t검증 세트 손실: 0.119617\t최선의 손실: 0.066482\t정확도: 98.44%\n",
-      "25\t검증 세트 손실: 0.093805\t최선의 손실: 0.066482\t정확도: 98.12%\n",
-      "26\t검증 세트 손실: 0.198460\t최선의 손실: 0.066482\t정확도: 98.51%\n",
-      "27\t검증 세트 손실: 0.209614\t최선의 손실: 0.066482\t정확도: 98.16%\n",
-      "28\t검증 세트 손실: 0.216969\t최선의 손실: 0.066482\t정확도: 98.16%\n",
-      "29\t검증 세트 손실: 0.157216\t최선의 손실: 0.066482\t정확도: 97.54%\n",
-      "30\t검증 세트 손실: 0.139797\t최선의 손실: 0.066482\t정확도: 97.42%\n",
-      "31\t검증 세트 손실: 0.094247\t최선의 손실: 0.066482\t정확도: 98.08%\n",
-      "32\t검증 세트 손실: 0.120514\t최선의 손실: 0.066482\t정확도: 98.24%\n",
-      "33\t검증 세트 손실: 0.087666\t최선의 손실: 0.066482\t정확도: 98.12%\n",
-      "34\t검증 세트 손실: 0.084453\t최선의 손실: 0.066482\t정확도: 98.05%\n",
-      "35\t검증 세트 손실: 0.094223\t최선의 손실: 0.066482\t정확도: 98.24%\n",
-      "36\t검증 세트 손실: 0.073201\t최선의 손실: 0.066482\t정확도: 98.59%\n",
-      "37\t검증 세트 손실: 0.062397\t최선의 손실: 0.062397\t정확도: 98.63%\n",
-      "38\t검증 세트 손실: 0.061649\t최선의 손실: 0.061649\t정확도: 98.71%\n",
-      "39\t검증 세트 손실: 0.078767\t최선의 손실: 0.061649\t정확도: 98.44%\n",
-      "40\t검증 세트 손실: 0.074800\t최선의 손실: 0.061649\t정확도: 98.87%\n",
-      "41\t검증 세트 손실: 0.081514\t최선의 손실: 0.061649\t정확도: 98.55%\n",
-      "42\t검증 세트 손실: 0.091736\t최선의 손실: 0.061649\t정확도: 98.40%\n",
-      "43\t검증 세트 손실: 0.148783\t최선의 손실: 0.061649\t정확도: 98.16%\n",
-      "44\t검증 세트 손실: 0.181197\t최선의 손실: 0.061649\t정확도: 97.19%\n",
-      "45\t검증 세트 손실: 0.283546\t최선의 손실: 0.061649\t정확도: 98.28%\n",
-      "46\t검증 세트 손실: 0.145033\t최선의 손실: 0.061649\t정확도: 97.54%\n",
-      "47\t검증 세트 손실: 0.135827\t최선의 손실: 0.061649\t정확도: 97.81%\n",
-      "48\t검증 세트 손실: 0.174380\t최선의 손실: 0.061649\t정확도: 97.69%\n",
-      "49\t검증 세트 손실: 0.135276\t최선의 손실: 0.061649\t정확도: 97.46%\n",
-      "50\t검증 세트 손실: 0.106143\t최선의 손실: 0.061649\t정확도: 98.59%\n",
-      "51\t검증 세트 손실: 0.115804\t최선의 손실: 0.061649\t정확도: 98.01%\n",
-      "52\t검증 세트 손실: 0.158548\t최선의 손실: 0.061649\t정확도: 97.85%\n",
-      "53\t검증 세트 손실: 0.435588\t최선의 손실: 0.061649\t정확도: 96.95%\n",
-      "54\t검증 세트 손실: 0.184975\t최선의 손실: 0.061649\t정확도: 97.58%\n",
-      "55\t검증 세트 손실: 0.158674\t최선의 손실: 0.061649\t정확도: 97.50%\n",
-      "56\t검증 세트 손실: 0.174737\t최선의 손실: 0.061649\t정확도: 97.62%\n",
-      "57\t검증 세트 손실: 0.142357\t최선의 손실: 0.061649\t정확도: 97.46%\n",
-      "58\t검증 세트 손실: 0.104387\t최선의 손실: 0.061649\t정확도: 98.01%\n",
-      "59\t검증 세트 손실: 0.139115\t최선의 손실: 0.061649\t정확도: 98.40%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  15.0s\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 2.237760\t최선의 손실: 2.237760\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.859293\t최선의 손실: 1.859293\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 2.374150\t최선의 손실: 1.859293\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 3.220951\t최선의 손실: 1.859293\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.997613\t최선의 손실: 1.859293\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 1.705160\t최선의 손실: 1.705160\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 2.348696\t최선의 손실: 1.705160\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.711352\t최선의 손실: 1.705160\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 3.185021\t최선의 손실: 1.705160\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 2.531055\t최선의 손실: 1.705160\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.699842\t최선의 손실: 1.699842\t정확도: 19.27%\n",
+      "11\t검증 세트 손실: 2.247969\t최선의 손실: 1.699842\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 2.204040\t최선의 손실: 1.699842\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 2.138690\t최선의 손실: 1.699842\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 2.946183\t최선의 손실: 1.699842\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.975804\t최선의 손실: 1.699842\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 2.287097\t최선의 손실: 1.699842\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 2.556262\t최선의 손실: 1.699842\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 3.859180\t최선의 손실: 1.699842\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 3.267152\t최선의 손실: 1.699842\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 3.242235\t최선의 손실: 1.699842\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 2.454847\t최선의 손실: 1.699842\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 2.135019\t최선의 손실: 1.699842\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.878484\t최선의 손실: 1.699842\t정확도: 19.08%\n",
+      "24\t검증 세트 손실: 2.478139\t최선의 손실: 1.699842\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 2.917542\t최선의 손실: 1.699842\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 2.305035\t최선의 손실: 1.699842\t정확도: 19.27%\n",
+      "27\t검증 세트 손실: 1.861966\t최선의 손실: 1.699842\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 2.357648\t최선의 손실: 1.699842\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 2.071940\t최선의 손실: 1.699842\t정확도: 19.08%\n",
+      "30\t검증 세트 손실: 1.761052\t최선의 손실: 1.699842\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 2.451755\t최선의 손실: 1.699842\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160, total=  14.7s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160 \n"
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total= 1.2min\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.814340\t최선의 손실: 1.814340\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 2.261935\t최선의 손실: 1.814340\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 2.854175\t최선의 손실: 1.814340\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 2.152662\t최선의 손실: 1.814340\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.986668\t최선의 손실: 1.814340\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 2.046327\t최선의 손실: 1.814340\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 2.045065\t최선의 손실: 1.814340\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 2.479764\t최선의 손실: 1.814340\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 2.568431\t최선의 손실: 1.814340\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.940594\t최선의 손실: 1.814340\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 2.119608\t최선의 손실: 1.814340\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 2.872692\t최선의 손실: 1.814340\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 2.472661\t최선의 손실: 1.814340\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 3.368681\t최선의 손실: 1.814340\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 2.878073\t최선의 손실: 1.814340\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 2.294891\t최선의 손실: 1.814340\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.866396\t최선의 손실: 1.814340\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 3.254826\t최선의 손실: 1.814340\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.866752\t최선의 손실: 1.814340\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 2.937172\t최선의 손실: 1.814340\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.728158\t최선의 손실: 1.728158\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.731786\t최선의 손실: 1.728158\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 1.675062\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 2.132677\t최선의 손실: 1.675062\t정확도: 19.27%\n",
+      "24\t검증 세트 손실: 2.059569\t최선의 손실: 1.675062\t정확도: 19.27%\n",
+      "25\t검증 세트 손실: 2.065344\t최선의 손실: 1.675062\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 3.684382\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 2.307222\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "28\t검증 세트 손실: 2.223740\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "29\t검증 세트 손실: 1.954040\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 2.425427\t최선의 손실: 1.675062\t정확도: 22.01%\n",
+      "31\t검증 세트 손실: 2.712282\t최선의 손실: 1.675062\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 2.117549\t최선의 손실: 1.675062\t정확도: 19.27%\n",
+      "33\t검증 세트 손실: 2.735460\t최선의 손실: 1.675062\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 2.376840\t최선의 손실: 1.675062\t정확도: 20.91%\n",
+      "35\t검증 세트 손실: 3.073526\t최선의 손실: 1.675062\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 2.708860\t최선의 손실: 1.675062\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 3.303518\t최선의 손실: 1.675062\t정확도: 20.91%\n",
+      "38\t검증 세트 손실: 1.699607\t최선의 손실: 1.675062\t정확도: 19.27%\n",
+      "39\t검증 세트 손실: 3.812864\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "40\t검증 세트 손실: 2.731928\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "41\t검증 세트 손실: 4.363173\t최선의 손실: 1.675062\t정확도: 19.08%\n",
+      "42\t검증 세트 손실: 2.350188\t최선의 손실: 1.675062\t정확도: 20.91%\n",
+      "43\t검증 세트 손실: 2.580198\t최선의 손실: 1.675062\t정확도: 18.73%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total= 1.6min\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.766498\t최선의 손실: 1.766498\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 2.131442\t최선의 손실: 1.766498\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 2.484733\t최선의 손실: 1.766498\t정확도: 19.08%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.130687\t최선의 손실: 0.130687\t정확도: 96.76%\n",
-      "1\t검증 세트 손실: 0.082735\t최선의 손실: 0.082735\t정확도: 97.81%\n",
-      "2\t검증 세트 손실: 0.088424\t최선의 손실: 0.082735\t정확도: 97.77%\n",
-      "3\t검증 세트 손실: 0.071216\t최선의 손실: 0.071216\t정확도: 98.16%\n",
-      "4\t검증 세트 손실: 0.063894\t최선의 손실: 0.063894\t정확도: 98.44%\n",
-      "5\t검증 세트 손실: 0.073357\t최선의 손실: 0.063894\t정확도: 97.81%\n",
-      "6\t검증 세트 손실: 0.060985\t최선의 손실: 0.060985\t정확도: 98.36%\n",
-      "7\t검증 세트 손실: 0.067150\t최선의 손실: 0.060985\t정확도: 98.51%\n",
-      "8\t검증 세트 손실: 0.078438\t최선의 손실: 0.060985\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.082944\t최선의 손실: 0.060985\t정확도: 98.48%\n",
-      "10\t검증 세트 손실: 0.080175\t최선의 손실: 0.060985\t정확도: 98.24%\n",
-      "11\t검증 세트 손실: 0.074773\t최선의 손실: 0.060985\t정확도: 98.48%\n",
-      "12\t검증 세트 손실: 0.066485\t최선의 손실: 0.060985\t정확도: 98.28%\n",
-      "13\t검증 세트 손실: 0.064794\t최선의 손실: 0.060985\t정확도: 98.12%\n",
-      "14\t검증 세트 손실: 0.056476\t최선의 손실: 0.056476\t정확도: 98.59%\n",
-      "15\t검증 세트 손실: 0.084186\t최선의 손실: 0.056476\t정확도: 98.20%\n",
-      "16\t검증 세트 손실: 0.070082\t최선의 손실: 0.056476\t정확도: 98.28%\n",
-      "17\t검증 세트 손실: 0.081743\t최선의 손실: 0.056476\t정확도: 98.28%\n",
-      "18\t검증 세트 손실: 0.071314\t최선의 손실: 0.056476\t정확도: 98.16%\n",
-      "19\t검증 세트 손실: 0.068109\t최선의 손실: 0.056476\t정확도: 98.40%\n",
-      "20\t검증 세트 손실: 0.068129\t최선의 손실: 0.056476\t정확도: 98.32%\n",
-      "21\t검증 세트 손실: 0.063737\t최선의 손실: 0.056476\t정확도: 98.63%\n",
-      "22\t검증 세트 손실: 0.054937\t최선의 손실: 0.054937\t정확도: 98.44%\n",
-      "23\t검증 세트 손실: 0.052159\t최선의 손실: 0.052159\t정확도: 98.40%\n",
-      "24\t검증 세트 손실: 0.204177\t최선의 손실: 0.052159\t정확도: 98.20%\n",
-      "25\t검증 세트 손실: 0.130294\t최선의 손실: 0.052159\t정확도: 97.93%\n",
-      "26\t검증 세트 손실: 0.105233\t최선의 손실: 0.052159\t정확도: 97.50%\n",
-      "27\t검증 세트 손실: 0.107185\t최선의 손실: 0.052159\t정확도: 97.97%\n",
-      "28\t검증 세트 손실: 0.104060\t최선의 손실: 0.052159\t정확도: 97.69%\n",
-      "29\t검증 세트 손실: 0.079473\t최선의 손실: 0.052159\t정확도: 98.36%\n",
-      "30\t검증 세트 손실: 0.091722\t최선의 손실: 0.052159\t정확도: 97.93%\n",
-      "31\t검증 세트 손실: 0.147696\t최선의 손실: 0.052159\t정확도: 98.24%\n",
-      "32\t검증 세트 손실: 0.090977\t최선의 손실: 0.052159\t정확도: 98.16%\n",
-      "33\t검증 세트 손실: 0.122129\t최선의 손실: 0.052159\t정확도: 97.97%\n",
-      "34\t검증 세트 손실: 0.077327\t최선의 손실: 0.052159\t정확도: 98.12%\n",
-      "35\t검증 세트 손실: 0.084122\t최선의 손실: 0.052159\t정확도: 98.08%\n",
-      "36\t검증 세트 손실: 0.108302\t최선의 손실: 0.052159\t정확도: 98.40%\n",
-      "37\t검증 세트 손실: 0.075597\t최선의 손실: 0.052159\t정확도: 98.24%\n",
-      "38\t검증 세트 손실: 0.081819\t최선의 손실: 0.052159\t정확도: 98.28%\n",
-      "39\t검증 세트 손실: 0.091792\t최선의 손실: 0.052159\t정확도: 98.32%\n",
-      "40\t검증 세트 손실: 0.079557\t최선의 손실: 0.052159\t정확도: 98.40%\n",
-      "41\t검증 세트 손실: 0.085266\t최선의 손실: 0.052159\t정확도: 98.32%\n",
-      "42\t검증 세트 손실: 0.098982\t최선의 손실: 0.052159\t정확도: 98.24%\n",
-      "43\t검증 세트 손실: 0.083286\t최선의 손실: 0.052159\t정확도: 98.32%\n",
-      "44\t검증 세트 손실: 0.102170\t최선의 손실: 0.052159\t정확도: 98.32%\n",
+      "3\t검증 세트 손실: 2.354620\t최선의 손실: 1.766498\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 3.989748\t최선의 손실: 1.766498\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 1.734422\t최선의 손실: 1.734422\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.875788\t최선의 손실: 1.734422\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.690576\t최선의 손실: 1.690576\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 1.654367\t최선의 손실: 1.654367\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 2.162101\t최선의 손실: 1.654367\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 2.242494\t최선의 손실: 1.654367\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 2.273765\t최선의 손실: 1.654367\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 2.919270\t최선의 손실: 1.654367\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 4.261509\t최선의 손실: 1.654367\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 2.142127\t최선의 손실: 1.654367\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 2.117087\t최선의 손실: 1.654367\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 1.922424\t최선의 손실: 1.654367\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 2.185005\t최선의 손실: 1.654367\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.894808\t최선의 손실: 1.654367\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 3.395297\t최선의 손실: 1.654367\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.859136\t최선의 손실: 1.654367\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 2.997280\t최선의 손실: 1.654367\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.827122\t최선의 손실: 1.654367\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 3.343231\t최선의 손실: 1.654367\t정확도: 19.08%\n",
+      "24\t검증 세트 손실: 2.284718\t최선의 손실: 1.654367\t정확도: 19.27%\n",
+      "25\t검증 세트 손실: 2.332708\t최선의 손실: 1.654367\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 2.233398\t최선의 손실: 1.654367\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 2.307961\t최선의 손실: 1.654367\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.704519\t최선의 손실: 1.654367\t정확도: 20.91%\n",
+      "29\t검증 세트 손실: 3.979462\t최선의 손실: 1.654367\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160, total=  17.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.083920\t최선의 손실: 0.083920\t정확도: 98.12%\n",
-      "1\t검증 세트 손실: 0.099985\t최선의 손실: 0.083920\t정확도: 97.81%\n",
-      "2\t검증 세트 손실: 0.092849\t최선의 손실: 0.083920\t정확도: 97.69%\n",
-      "3\t검증 세트 손실: 0.078546\t최선의 손실: 0.078546\t정확도: 98.05%\n",
-      "4\t검증 세트 손실: 0.068628\t최선의 손실: 0.068628\t정확도: 98.32%\n",
-      "5\t검증 세트 손실: 0.081211\t최선의 손실: 0.068628\t정확도: 98.01%\n",
-      "6\t검증 세트 손실: 0.083316\t최선의 손실: 0.068628\t정확도: 97.93%\n",
-      "7\t검증 세트 손실: 0.086028\t최선의 손실: 0.068628\t정확도: 97.85%\n",
-      "8\t검증 세트 손실: 0.071232\t최선의 손실: 0.068628\t정확도: 98.20%\n",
-      "9\t검증 세트 손실: 0.059139\t최선의 손실: 0.059139\t정확도: 98.32%\n",
-      "10\t검증 세트 손실: 0.071236\t최선의 손실: 0.059139\t정확도: 98.71%\n",
-      "11\t검증 세트 손실: 0.083311\t최선의 손실: 0.059139\t정확도: 98.48%\n",
-      "12\t검증 세트 손실: 0.097270\t최선의 손실: 0.059139\t정확도: 97.69%\n",
-      "13\t검증 세트 손실: 0.139672\t최선의 손실: 0.059139\t정확도: 98.28%\n",
-      "14\t검증 세트 손실: 0.094122\t최선의 손실: 0.059139\t정확도: 98.55%\n",
-      "15\t검증 세트 손실: 0.120736\t최선의 손실: 0.059139\t정확도: 97.93%\n",
-      "16\t검증 세트 손실: 0.076589\t최선의 손실: 0.059139\t정확도: 98.48%\n",
-      "17\t검증 세트 손실: 0.059330\t최선의 손실: 0.059139\t정확도: 98.63%\n",
-      "18\t검증 세트 손실: 0.085446\t최선의 손실: 0.059139\t정확도: 98.20%\n",
-      "19\t검증 세트 손실: 0.158666\t최선의 손실: 0.059139\t정확도: 98.08%\n",
-      "20\t검증 세트 손실: 0.111251\t최선의 손실: 0.059139\t정확도: 97.97%\n",
-      "21\t검증 세트 손실: 0.165912\t최선의 손실: 0.059139\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.138768\t최선의 손실: 0.059139\t정확도: 98.01%\n",
-      "23\t검증 세트 손실: 0.125085\t최선의 손실: 0.059139\t정확도: 98.16%\n",
-      "24\t검증 세트 손실: 0.080381\t최선의 손실: 0.059139\t정확도: 98.28%\n",
-      "25\t검증 세트 손실: 0.078958\t최선의 손실: 0.059139\t정확도: 98.36%\n",
-      "26\t검증 세트 손실: 0.240430\t최선의 손실: 0.059139\t정확도: 98.55%\n",
-      "27\t검증 세트 손실: 0.118313\t최선의 손실: 0.059139\t정확도: 98.24%\n",
-      "28\t검증 세트 손실: 0.083155\t최선의 손실: 0.059139\t정확도: 98.20%\n",
-      "29\t검증 세트 손실: 0.067190\t최선의 손실: 0.059139\t정확도: 98.12%\n",
-      "30\t검증 세트 손실: 0.112337\t최선의 손실: 0.059139\t정확도: 98.44%\n",
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total= 1.1min\n",
+      "[CV] batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.835342\t최선의 손실: 1.835342\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 2.328015\t최선의 손실: 1.835342\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 2.537128\t최선의 손실: 1.835342\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 3.188519\t최선의 손실: 1.835342\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 2.451356\t최선의 손실: 1.835342\t정확도: 18.73%\n",
+      "5\t검증 세트 손실: 1.754502\t최선의 손실: 1.754502\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.070538\t최선의 손실: 1.754502\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.748136\t최선의 손실: 1.748136\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 2.620905\t최선의 손실: 1.748136\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 2.645242\t최선의 손실: 1.748136\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.781578\t최선의 손실: 1.748136\t정확도: 19.27%\n",
+      "11\t검증 세트 손실: 2.300453\t최선의 손실: 1.748136\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 2.122102\t최선의 손실: 1.748136\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 2.098135\t최선의 손실: 1.748136\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 3.250401\t최선의 손실: 1.748136\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 2.423855\t최선의 손실: 1.748136\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 2.456737\t최선의 손실: 1.748136\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 2.555985\t최선의 손실: 1.748136\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 3.406431\t최선의 손실: 1.748136\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 3.492964\t최선의 손실: 1.748136\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.708956\t최선의 손실: 1.748136\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 2.422107\t최선의 손실: 1.748136\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 2.165944\t최선의 손실: 1.748136\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 2.098628\t최선의 손실: 1.748136\t정확도: 19.08%\n",
+      "24\t검증 세트 손실: 2.474760\t최선의 손실: 1.748136\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 2.090918\t최선의 손실: 1.748136\t정확도: 19.27%\n",
+      "26\t검증 세트 손실: 2.462191\t최선의 손실: 1.748136\t정확도: 20.91%\n",
+      "27\t검증 세트 손실: 1.769079\t최선의 손실: 1.748136\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 2.318099\t최선의 손실: 1.748136\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160, total=   7.9s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 10086.128906\t최선의 손실: 10086.128906\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 34538.246094\t최선의 손실: 10086.128906\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 145842.640625\t최선의 손실: 10086.128906\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 15109.929688\t최선의 손실: 10086.128906\t정확도: 18.84%\n",
-      "4\t검증 세트 손실: 14410.993164\t최선의 손실: 10086.128906\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 127878.007812\t최선의 손실: 10086.128906\t정확도: 18.73%\n",
-      "6\t검증 세트 손실: 80914.382812\t최선의 손실: 10086.128906\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 41731.929688\t최선의 손실: 10086.128906\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 41089.355469\t최선의 손실: 10086.128906\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 26558.214844\t최선의 손실: 10086.128906\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 50685.089844\t최선의 손실: 10086.128906\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 56369.308594\t최선의 손실: 10086.128906\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 24995.796875\t최선의 손실: 10086.128906\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 39666.222656\t최선의 손실: 10086.128906\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 19385.039062\t최선의 손실: 10086.128906\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1123737.875000\t최선의 손실: 10086.128906\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 306556.343750\t최선의 손실: 10086.128906\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 183404.843750\t최선의 손실: 10086.128906\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 282039.562500\t최선의 손실: 10086.128906\t정확도: 18.92%\n",
-      "19\t검증 세트 손실: 893791.687500\t최선의 손실: 10086.128906\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 2130074.500000\t최선의 손실: 10086.128906\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 537616.875000\t최선의 손실: 10086.128906\t정확도: 20.91%\n",
+      "[CV]  batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total= 1.1min\n",
+      "[CV] batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 2.010987\t최선의 손실: 2.010987\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 2.250907\t최선의 손실: 2.010987\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.711940\t최선의 손실: 1.711940\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 2.464531\t최선의 손실: 1.711940\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 1.955620\t최선의 손실: 1.711940\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 2.036624\t최선의 손실: 1.711940\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 2.646753\t최선의 손실: 1.711940\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 2.219077\t최선의 손실: 1.711940\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 3.172143\t최선의 손실: 1.711940\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.971255\t최선의 손실: 1.711940\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 2.093254\t최선의 손실: 1.711940\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 3.037933\t최선의 손실: 1.711940\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 2.059215\t최선의 손실: 1.711940\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 3.246908\t최선의 손실: 1.711940\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 3.004365\t최선의 손실: 1.711940\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 2.133568\t최선의 손실: 1.711940\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.739130\t최선의 손실: 1.711940\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 2.827487\t최선의 손실: 1.711940\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 2.356352\t최선의 손실: 1.711940\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 2.593230\t최선의 손실: 1.711940\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.726289\t최선의 손실: 1.711940\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.741268\t최선의 손실: 1.711940\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 1.684611\t최선의 손실: 1.684611\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 2.366592\t최선의 손실: 1.684611\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 2.155076\t최선의 손실: 1.684611\t정확도: 19.27%\n",
+      "25\t검증 세트 손실: 1.883853\t최선의 손실: 1.684611\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 3.726477\t최선의 손실: 1.684611\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 2.628488\t최선의 손실: 1.684611\t정확도: 20.91%\n",
+      "28\t검증 세트 손실: 2.442934\t최선의 손실: 1.684611\t정확도: 20.91%\n",
+      "29\t검증 세트 손실: 2.111892\t최선의 손실: 1.684611\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 2.479018\t최선의 손실: 1.684611\t정확도: 22.01%\n",
+      "31\t검증 세트 손실: 2.348620\t최선의 손실: 1.684611\t정확도: 19.08%\n",
+      "32\t검증 세트 손실: 1.933364\t최선의 손실: 1.684611\t정확도: 19.27%\n",
+      "33\t검증 세트 손실: 3.568138\t최선의 손실: 1.684611\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 2.262289\t최선의 손실: 1.684611\t정확도: 20.91%\n",
+      "35\t검증 세트 손실: 3.247323\t최선의 손실: 1.684611\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 3.500853\t최선의 손실: 1.684611\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 3.720392\t최선의 손실: 1.684611\t정확도: 20.91%\n",
+      "38\t검증 세트 손실: 1.764349\t최선의 손실: 1.684611\t정확도: 18.73%\n",
+      "39\t검증 세트 손실: 4.277288\t최선의 손실: 1.684611\t정확도: 18.73%\n",
+      "40\t검증 세트 손실: 3.151234\t최선의 손실: 1.684611\t정확도: 18.73%\n",
+      "41\t검증 세트 손실: 4.095318\t최선의 손실: 1.684611\t정확도: 19.27%\n",
+      "42\t검증 세트 손실: 3.240806\t최선의 손실: 1.684611\t정확도: 20.91%\n",
+      "43\t검증 세트 손실: 2.468709\t최선의 손실: 1.684611\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total=  22.2s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 12887.896484\t최선의 손실: 12887.896484\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 6023.818848\t최선의 손실: 6023.818848\t정확도: 20.91%\n",
-      "2\t검증 세트 손실: 850.845459\t최선의 손실: 850.845459\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 73426.343750\t최선의 손실: 850.845459\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 60664.855469\t최선의 손실: 850.845459\t정확도: 19.12%\n",
-      "5\t검증 세트 손실: 22301.222656\t최선의 손실: 850.845459\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 6519.059570\t최선의 손실: 850.845459\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 3553.809326\t최선의 손실: 850.845459\t정확도: 16.03%\n",
-      "8\t검증 세트 손실: 13132.827148\t최선의 손실: 850.845459\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 5443.671875\t최선의 손실: 850.845459\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 5692.432617\t최선의 손실: 850.845459\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 4271.311035\t최선의 손실: 850.845459\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 39640.492188\t최선의 손실: 850.845459\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1370.587402\t최선의 손실: 850.845459\t정확도: 36.59%\n",
-      "14\t검증 세트 손실: 8027.042969\t최선의 손실: 850.845459\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 29139.652344\t최선의 손실: 850.845459\t정확도: 19.27%\n",
-      "16\t검증 세트 손실: 12104.925781\t최선의 손실: 850.845459\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 39151.835938\t최선의 손실: 850.845459\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 50326.074219\t최선의 손실: 850.845459\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 44586.644531\t최선의 손실: 850.845459\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 70168.296875\t최선의 손실: 850.845459\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 379384.812500\t최선의 손실: 850.845459\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 47824.117188\t최선의 손실: 850.845459\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 79076.281250\t최선의 손실: 850.845459\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total= 1.6min\n",
+      "[CV] batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 2.015094\t최선의 손실: 2.015094\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 2.200569\t최선의 손실: 2.015094\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 2.529754\t최선의 손실: 2.015094\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 2.592198\t최선의 손실: 2.015094\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 3.885439\t최선의 손실: 2.015094\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 3.117450\t최선의 손실: 2.015094\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.810380\t최선의 손실: 2.015094\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.888613\t최선의 손실: 1.888613\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 1.755347\t최선의 손실: 1.755347\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 3.195272\t최선의 손실: 1.755347\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 2.316414\t최선의 손실: 1.755347\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 2.363100\t최선의 손실: 1.755347\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 2.344953\t최선의 손실: 1.755347\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 4.575258\t최선의 손실: 1.755347\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.951815\t최선의 손실: 1.755347\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 2.089362\t최선의 손실: 1.755347\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 2.247236\t최선의 손실: 1.755347\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 2.229265\t최선의 손실: 1.755347\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.916000\t최선의 손실: 1.755347\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 2.465827\t최선의 손실: 1.755347\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.709022\t최선의 손실: 1.709022\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 2.210298\t최선의 손실: 1.709022\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.969363\t최선의 손실: 1.709022\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 3.443788\t최선의 손실: 1.709022\t정확도: 19.08%\n",
+      "24\t검증 세트 손실: 2.280291\t최선의 손실: 1.709022\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 1.714924\t최선의 손실: 1.709022\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.897234\t최선의 손실: 1.709022\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 2.323094\t최선의 손실: 1.709022\t정확도: 18.73%\n",
+      "28\t검증 세트 손실: 2.317081\t최선의 손실: 1.709022\t정확도: 20.91%\n",
+      "29\t검증 세트 손실: 4.201362\t최선의 손실: 1.709022\t정확도: 19.08%\n",
+      "30\t검증 세트 손실: 1.938442\t최선의 손실: 1.709022\t정확도: 19.27%\n",
+      "31\t검증 세트 손실: 1.785207\t최선의 손실: 1.709022\t정확도: 18.73%\n",
+      "32\t검증 세트 손실: 2.295576\t최선의 손실: 1.709022\t정확도: 19.27%\n",
+      "33\t검증 세트 손실: 1.721447\t최선의 손실: 1.709022\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 3.300692\t최선의 손실: 1.709022\t정확도: 19.08%\n",
+      "35\t검증 세트 손실: 1.729299\t최선의 손실: 1.709022\t정확도: 19.08%\n",
+      "36\t검증 세트 손실: 2.358642\t최선의 손실: 1.709022\t정확도: 19.27%\n",
+      "37\t검증 세트 손실: 2.395441\t최선의 손실: 1.709022\t정확도: 19.08%\n",
+      "38\t검증 세트 손실: 2.611983\t최선의 손실: 1.709022\t정확도: 20.91%\n",
+      "39\t검증 세트 손실: 3.024051\t최선의 손실: 1.709022\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 2.125805\t최선의 손실: 1.709022\t정확도: 19.27%\n",
+      "41\t검증 세트 손실: 2.462298\t최선의 손실: 1.709022\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total=  13.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 3956.904541\t최선의 손실: 3956.904541\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 6210.605957\t최선의 손실: 3956.904541\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 273651.250000\t최선의 손실: 3956.904541\t정확도: 20.91%\n",
-      "3\t검증 세트 손실: 72399.328125\t최선의 손실: 3956.904541\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 132282.531250\t최선의 손실: 3956.904541\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 182001.390625\t최선의 손실: 3956.904541\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 93736.945312\t최선의 손실: 3956.904541\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 47044.847656\t최선의 손실: 3956.904541\t정확도: 31.70%\n"
+      "[CV]  batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total= 1.6min\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f738a2ee510>, n_neurons=70 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8\t검증 세트 손실: 264054.750000\t최선의 손실: 3956.904541\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 188817.031250\t최선의 손실: 3956.904541\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 177651.578125\t최선의 손실: 3956.904541\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 187931.218750\t최선의 손실: 3956.904541\t정확도: 20.91%\n",
-      "12\t검증 세트 손실: 364593.968750\t최선의 손실: 3956.904541\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 266410.187500\t최선의 손실: 3956.904541\t정확도: 24.32%\n",
-      "14\t검증 세트 손실: 162290.203125\t최선의 손실: 3956.904541\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 121577.218750\t최선의 손실: 3956.904541\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 142760.828125\t최선의 손실: 3956.904541\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 303611.531250\t최선의 손실: 3956.904541\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 92249.101562\t최선의 손실: 3956.904541\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 213589.515625\t최선의 손실: 3956.904541\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 415365.250000\t최선의 손실: 3956.904541\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 700023.125000\t최선의 손실: 3956.904541\t정확도: 20.91%\n",
+      "0\t검증 세트 손실: 1.611484\t최선의 손실: 1.611484\t정확도: 19.08%\n",
+      "1\t검증 세트 손실: 1.613299\t최선의 손실: 1.611484\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.609193\t최선의 손실: 1.609193\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.609864\t최선의 손실: 1.609193\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.618616\t최선의 손실: 1.609193\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.612607\t최선의 손실: 1.609193\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1.609827\t최선의 손실: 1.609193\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.615095\t최선의 손실: 1.609193\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.621949\t최선의 손실: 1.609193\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.610800\t최선의 손실: 1.609193\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.608212\t최선의 손실: 1.608212\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.619424\t최선의 손실: 1.608212\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.608470\t최선의 손실: 1.608212\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.618596\t최선의 손실: 1.608212\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.613479\t최선의 손실: 1.608212\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.611293\t최선의 손실: 1.608212\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.611450\t최선의 손실: 1.608212\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.610988\t최선의 손실: 1.608212\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.614757\t최선의 손실: 1.608212\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.610596\t최선의 손실: 1.608212\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.611319\t최선의 손실: 1.608212\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.614840\t최선의 손실: 1.608212\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.616512\t최선의 손실: 1.608212\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.618043\t최선의 손실: 1.608212\t정확도: 19.27%\n",
+      "24\t검증 세트 손실: 1.614490\t최선의 손실: 1.608212\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.609587\t최선의 손실: 1.608212\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.610508\t최선의 손실: 1.608212\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.612529\t최선의 손실: 1.608212\t정확도: 19.08%\n",
+      "28\t검증 세트 손실: 1.610707\t최선의 손실: 1.608212\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.614641\t최선의 손실: 1.608212\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 1.622687\t최선의 손실: 1.608212\t정확도: 19.27%\n",
+      "31\t검증 세트 손실: 1.608783\t최선의 손실: 1.608212\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total=  11.8s\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 2.361569\t최선의 손실: 2.361569\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 2.076067\t최선의 손실: 2.076067\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 2.404369\t최선의 손실: 2.076067\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 2.822533\t최선의 손실: 2.076067\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 2.500021\t최선의 손실: 2.076067\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.788147\t최선의 손실: 1.788147\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 2.580087\t최선의 손실: 1.788147\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.857263\t최선의 손실: 1.788147\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 2.591894\t최선의 손실: 1.788147\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 2.254758\t최선의 손실: 1.788147\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.736591\t최선의 손실: 1.736591\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 2.664306\t최선의 손실: 1.736591\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2.123237\t최선의 손실: 1.736591\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 2.113195\t최선의 손실: 1.736591\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 3.270177\t최선의 손실: 1.736591\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.687831\t최선의 손실: 1.687831\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 3.002699\t최선의 손실: 1.687831\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 2.600405\t최선의 손실: 1.687831\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 3.903158\t최선의 손실: 1.687831\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 3.050021\t최선의 손실: 1.687831\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 3.012521\t최선의 손실: 1.687831\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 2.083357\t최선의 손실: 1.687831\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 2.141208\t최선의 손실: 1.687831\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2.015631\t최선의 손실: 1.687831\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 2.584775\t최선의 손실: 1.687831\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 2.686496\t최선의 손실: 1.687831\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.719335\t최선의 손실: 1.687831\t정확도: 19.27%\n",
-      "27\t검증 세트 손실: 1.705161\t최선의 손실: 1.687831\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 2.464378\t최선의 손실: 1.687831\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 2.115768\t최선의 손실: 1.687831\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 1.806481\t최선의 손실: 1.687831\t정확도: 19.08%\n",
-      "31\t검증 세트 손실: 2.480118\t최선의 손실: 1.687831\t정확도: 19.08%\n",
-      "32\t검증 세트 손실: 2.015642\t최선의 손실: 1.687831\t정확도: 19.27%\n",
-      "33\t검증 세트 손실: 2.155074\t최선의 손실: 1.687831\t정확도: 20.91%\n",
-      "34\t검증 세트 손실: 1.635633\t최선의 손실: 1.635633\t정확도: 22.01%\n",
-      "35\t검증 세트 손실: 2.876410\t최선의 손실: 1.635633\t정확도: 18.73%\n",
-      "36\t검증 세트 손실: 2.460107\t최선의 손실: 1.635633\t정확도: 22.01%\n",
-      "37\t검증 세트 손실: 2.727285\t최선의 손실: 1.635633\t정확도: 20.91%\n",
-      "38\t검증 세트 손실: 1.884980\t최선의 손실: 1.635633\t정확도: 18.73%\n",
-      "39\t검증 세트 손실: 2.798203\t최선의 손실: 1.635633\t정확도: 18.73%\n",
-      "40\t검증 세트 손실: 2.870692\t최선의 손실: 1.635633\t정확도: 22.01%\n",
-      "41\t검증 세트 손실: 2.400677\t최선의 손실: 1.635633\t정확도: 19.27%\n",
-      "42\t검증 세트 손실: 2.909082\t최선의 손실: 1.635633\t정확도: 19.27%\n",
-      "43\t검증 세트 손실: 2.125144\t최선의 손실: 1.635633\t정확도: 19.27%\n",
-      "44\t검증 세트 손실: 3.998050\t최선의 손실: 1.635633\t정확도: 18.73%\n",
-      "45\t검증 세트 손실: 2.378212\t최선의 손실: 1.635633\t정확도: 18.73%\n",
-      "46\t검증 세트 손실: 2.814745\t최선의 손실: 1.635633\t정확도: 18.73%\n",
-      "47\t검증 세트 손실: 2.580415\t최선의 손실: 1.635633\t정확도: 19.08%\n",
-      "48\t검증 세트 손실: 2.165663\t최선의 손실: 1.635633\t정확도: 18.73%\n",
-      "49\t검증 세트 손실: 1.880654\t최선의 손실: 1.635633\t정확도: 19.27%\n",
-      "50\t검증 세트 손실: 2.567864\t최선의 손실: 1.635633\t정확도: 19.27%\n",
-      "51\t검증 세트 손실: 1.716849\t최선의 손실: 1.635633\t정확도: 22.01%\n",
-      "52\t검증 세트 손실: 2.426128\t최선의 손실: 1.635633\t정확도: 20.91%\n",
-      "53\t검증 세트 손실: 3.342173\t최선의 손실: 1.635633\t정확도: 19.27%\n",
-      "54\t검증 세트 손실: 2.700184\t최선의 손실: 1.635633\t정확도: 19.27%\n",
-      "55\t검증 세트 손실: 2.284999\t최선의 손실: 1.635633\t정확도: 19.27%\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f738a2ee510>, n_neurons=70, total=   8.6s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f738a2ee510>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 1.610780\t최선의 손실: 1.610780\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.610259\t최선의 손실: 1.610259\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.615143\t최선의 손실: 1.610259\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.624554\t최선의 손실: 1.610259\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 1.614303\t최선의 손실: 1.610259\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.614205\t최선의 손실: 1.610259\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1.614520\t최선의 손실: 1.610259\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.612075\t최선의 손실: 1.610259\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.610390\t최선의 손실: 1.610259\t정확도: 20.91%\n",
+      "9\t검증 세트 손실: 1.609672\t최선의 손실: 1.609672\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.618959\t최선의 손실: 1.609672\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.612237\t최선의 손실: 1.609672\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.609190\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.613473\t최선의 손실: 1.609190\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.613057\t최선의 손실: 1.609190\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 1.612377\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.612115\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.618452\t최선의 손실: 1.609190\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.610909\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.610620\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.612257\t최선의 손실: 1.609190\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 1.610892\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.617964\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.616939\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.612181\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.615083\t최선의 손실: 1.609190\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.607682\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.611634\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 1.626024\t최선의 손실: 1.607682\t정확도: 18.73%\n",
+      "29\t검증 세트 손실: 1.609978\t최선의 손실: 1.607682\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 1.611089\t최선의 손실: 1.607682\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 1.611650\t최선의 손실: 1.607682\t정확도: 19.27%\n",
+      "32\t검증 세트 손실: 1.612803\t최선의 손실: 1.607682\t정확도: 18.73%\n",
+      "33\t검증 세트 손실: 1.608830\t최선의 손실: 1.607682\t정확도: 20.91%\n",
+      "34\t검증 세트 손실: 1.613910\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 1.610116\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 1.613157\t최선의 손실: 1.607682\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 1.609832\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "38\t검증 세트 손실: 1.620759\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "39\t검증 세트 손실: 1.623758\t최선의 손실: 1.607682\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 1.609532\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "41\t검증 세트 손실: 1.609141\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "42\t검증 세트 손실: 1.622815\t최선의 손실: 1.607682\t정확도: 19.27%\n",
+      "43\t검증 세트 손실: 1.617255\t최선의 손실: 1.607682\t정확도: 22.01%\n",
+      "44\t검증 세트 손실: 1.616077\t최선의 손실: 1.607682\t정확도: 19.08%\n",
+      "45\t검증 세트 손실: 1.611638\t최선의 손실: 1.607682\t정확도: 19.08%\n",
+      "46\t검증 세트 손실: 1.623886\t최선의 손실: 1.607682\t정확도: 18.73%\n",
+      "47\t검증 세트 손실: 1.611055\t최선의 손실: 1.607682\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total= 3.3min\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.949960\t최선의 손실: 1.949960\t정확도: 19.08%\n",
-      "1\t검증 세트 손실: 2.245592\t최선의 손실: 1.949960\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.818514\t최선의 손실: 1.818514\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 2.133919\t최선의 손실: 1.818514\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 2.039951\t최선의 손실: 1.818514\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.909525\t최선의 손실: 1.818514\t정확도: 18.73%\n",
-      "6\t검증 세트 손실: 2.242582\t최선의 손실: 1.818514\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 2.301777\t최선의 손실: 1.818514\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 2.327382\t최선의 손실: 1.818514\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.947400\t최선의 손실: 1.818514\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 2.147578\t최선의 손실: 1.818514\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 3.136085\t최선의 손실: 1.818514\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2.156249\t최선의 손실: 1.818514\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 3.268481\t최선의 손실: 1.818514\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 3.023477\t최선의 손실: 1.818514\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 2.696004\t최선의 손실: 1.818514\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.618721\t최선의 손실: 1.618721\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 2.910825\t최선의 손실: 1.618721\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 2.331073\t최선의 손실: 1.618721\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 2.791260\t최선의 손실: 1.618721\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.880168\t최선의 손실: 1.618721\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 2.012757\t최선의 손실: 1.618721\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.679420\t최선의 손실: 1.618721\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2.898623\t최선의 손실: 1.618721\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 2.017277\t최선의 손실: 1.618721\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 1.651335\t최선의 손실: 1.618721\t정확도: 18.73%\n",
-      "26\t검증 세트 손실: 3.841341\t최선의 손실: 1.618721\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 2.635069\t최선의 손실: 1.618721\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 2.412288\t최선의 손실: 1.618721\t정확도: 20.91%\n",
-      "29\t검증 세트 손실: 2.112701\t최선의 손실: 1.618721\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 2.599372\t최선의 손실: 1.618721\t정확도: 22.01%\n",
-      "31\t검증 세트 손실: 2.085528\t최선의 손실: 1.618721\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 1.869347\t최선의 손실: 1.618721\t정확도: 19.27%\n",
-      "33\t검증 세트 손실: 3.370644\t최선의 손실: 1.618721\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 2.132514\t최선의 손실: 1.618721\t정확도: 20.91%\n",
-      "35\t검증 세트 손실: 3.163002\t최선의 손실: 1.618721\t정확도: 22.01%\n",
-      "36\t검증 세트 손실: 3.505755\t최선의 손실: 1.618721\t정확도: 19.27%\n",
-      "37\t검증 세트 손실: 3.521058\t최선의 손실: 1.618721\t정확도: 20.91%\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f738a2ee510>, n_neurons=70, total=  12.3s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f738a2ee510>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 1.614589\t최선의 손실: 1.614589\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.610019\t최선의 손실: 1.610019\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.620105\t최선의 손실: 1.610019\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.620693\t최선의 손실: 1.610019\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.609200\t최선의 손실: 1.609200\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.610796\t최선의 손실: 1.609200\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.615220\t최선의 손실: 1.609200\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.616866\t최선의 손실: 1.609200\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.611613\t최선의 손실: 1.609200\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.613918\t최선의 손실: 1.609200\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.612549\t최선의 손실: 1.609200\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.623473\t최선의 손실: 1.609200\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.608303\t최선의 손실: 1.608303\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.611824\t최선의 손실: 1.608303\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.608464\t최선의 손실: 1.608303\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.617294\t최선의 손실: 1.608303\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.618880\t최선의 손실: 1.608303\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.615118\t최선의 손실: 1.608303\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.608076\t최선의 손실: 1.608076\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.608634\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.620248\t최선의 손실: 1.608076\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.611786\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.613175\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.613923\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.613855\t최선의 손실: 1.608076\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.617788\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.613065\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.614675\t최선의 손실: 1.608076\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.630540\t최선의 손실: 1.608076\t정확도: 18.73%\n",
+      "29\t검증 세트 손실: 1.611470\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "30\t검증 세트 손실: 1.611871\t최선의 손실: 1.608076\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 1.611842\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 1.615410\t최선의 손실: 1.608076\t정확도: 18.73%\n",
+      "33\t검증 세트 손실: 1.608509\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "34\t검증 세트 손실: 1.613685\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 1.609653\t최선의 손실: 1.608076\t정확도: 19.27%\n",
+      "36\t검증 세트 손실: 1.609762\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "37\t검증 세트 손실: 1.608676\t최선의 손실: 1.608076\t정확도: 22.01%\n",
+      "38\t검증 세트 손실: 1.617389\t최선의 손실: 1.608076\t정확도: 19.27%\n",
+      "39\t검증 세트 손실: 1.625803\t최선의 손실: 1.608076\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total= 2.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.934365\t최선의 손실: 1.934365\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 2.151906\t최선의 손실: 1.934365\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 2.482390\t최선의 손실: 1.934365\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 2.744643\t최선의 손실: 1.934365\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 3.432846\t최선의 손실: 1.934365\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 3.555560\t최선의 손실: 1.934365\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 2.664465\t최선의 손실: 1.934365\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 2.198073\t최선의 손실: 1.934365\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 1.886915\t최선의 손실: 1.886915\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 2.144152\t최선의 손실: 1.886915\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 2.325416\t최선의 손실: 1.886915\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 2.519683\t최선의 손실: 1.886915\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 2.567047\t최선의 손실: 1.886915\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 3.530001\t최선의 손실: 1.886915\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.836066\t최선의 손실: 1.836066\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.755919\t최선의 손실: 1.755919\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 2.509749\t최선의 손실: 1.755919\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 2.177889\t최선의 손실: 1.755919\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.899279\t최선의 손실: 1.755919\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 3.175292\t최선의 손실: 1.755919\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.232392\t최선의 손실: 1.755919\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 3.301369\t최선의 손실: 1.755919\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 2.005447\t최선의 손실: 1.755919\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 3.744418\t최선의 손실: 1.755919\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 2.221690\t최선의 손실: 1.755919\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 2.308816\t최선의 손실: 1.755919\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 2.347103\t최선의 손실: 1.755919\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 2.398565\t최선의 손실: 1.755919\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 2.330910\t최선의 손실: 1.755919\t정확도: 20.91%\n",
-      "29\t검증 세트 손실: 3.062289\t최선의 손실: 1.755919\t정확도: 20.91%\n",
-      "30\t검증 세트 손실: 2.232899\t최선의 손실: 1.755919\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 1.990172\t최선의 손실: 1.755919\t정확도: 19.08%\n",
-      "32\t검증 세트 손실: 2.438927\t최선의 손실: 1.755919\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 1.955514\t최선의 손실: 1.755919\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 3.061836\t최선의 손실: 1.755919\t정확도: 19.27%\n",
-      "35\t검증 세트 손실: 1.670097\t최선의 손실: 1.670097\t정확도: 20.91%\n"
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f738a2ee510>, n_neurons=70, total=  10.5s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f738a2ee510>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.440318\t최선의 손실: 1.440318\t정확도: 29.79%\n",
+      "1\t검증 세트 손실: 1.612716\t최선의 손실: 1.440318\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.609394\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.608335\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.616192\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.614087\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.609110\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.613848\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.617025\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.608657\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.608034\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.613575\t최선의 손실: 1.440318\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.609030\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.615785\t최선의 손실: 1.440318\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.613140\t최선의 손실: 1.440318\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.611248\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.609467\t최선의 손실: 1.440318\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.610098\t최선의 손실: 1.440318\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.615320\t최선의 손실: 1.440318\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.608987\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.609125\t최선의 손실: 1.440318\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.615656\t최선의 손실: 1.440318\t정확도: 22.01%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f738a2ee510>, n_neurons=100, total=  11.0s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f738a2ee510>, n_neurons=100 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "36\t검증 세트 손실: 3.327657\t최선의 손실: 1.670097\t정확도: 19.27%\n",
-      "37\t검증 세트 손실: 2.734527\t최선의 손실: 1.670097\t정확도: 19.08%\n",
-      "38\t검증 세트 손실: 2.385942\t최선의 손실: 1.670097\t정확도: 20.91%\n",
-      "39\t검증 세트 손실: 3.512375\t최선의 손실: 1.670097\t정확도: 19.27%\n",
-      "40\t검증 세트 손실: 2.373364\t최선의 손실: 1.670097\t정확도: 19.27%\n",
-      "41\t검증 세트 손실: 2.188042\t최선의 손실: 1.670097\t정확도: 22.01%\n",
-      "42\t검증 세트 손실: 2.786279\t최선의 손실: 1.670097\t정확도: 20.91%\n",
-      "43\t검증 세트 손실: 2.747198\t최선의 손실: 1.670097\t정확도: 18.73%\n",
-      "44\t검증 세트 손실: 2.257949\t최선의 손실: 1.670097\t정확도: 18.73%\n",
-      "45\t검증 세트 손실: 2.809482\t최선의 손실: 1.670097\t정확도: 19.27%\n",
-      "46\t검증 세트 손실: 3.845476\t최선의 손실: 1.670097\t정확도: 20.91%\n",
-      "47\t검증 세트 손실: 2.546279\t최선의 손실: 1.670097\t정확도: 19.08%\n",
-      "48\t검증 세트 손실: 1.735817\t최선의 손실: 1.670097\t정확도: 18.73%\n",
-      "49\t검증 세트 손실: 2.338602\t최선의 손실: 1.670097\t정확도: 20.91%\n",
-      "50\t검증 세트 손실: 2.363643\t최선의 손실: 1.670097\t정확도: 20.91%\n",
-      "51\t검증 세트 손실: 1.898391\t최선의 손실: 1.670097\t정확도: 22.01%\n",
-      "52\t검증 세트 손실: 1.898982\t최선의 손실: 1.670097\t정확도: 19.27%\n",
-      "53\t검증 세트 손실: 2.186515\t최선의 손실: 1.670097\t정확도: 20.91%\n",
-      "54\t검증 세트 손실: 2.788328\t최선의 손실: 1.670097\t정확도: 19.08%\n",
-      "55\t검증 세트 손실: 2.149074\t최선의 손실: 1.670097\t정확도: 20.91%\n",
-      "56\t검증 세트 손실: 1.857988\t최선의 손실: 1.670097\t정확도: 22.01%\n",
+      "0\t검증 세트 손실: 2.470155\t최선의 손실: 2.470155\t정확도: 53.17%\n",
+      "1\t검증 세트 손실: 1.038027\t최선의 손실: 1.038027\t정확도: 39.52%\n",
+      "2\t검증 세트 손실: 1.223830\t최선의 손실: 1.038027\t정확도: 40.30%\n",
+      "3\t검증 세트 손실: 1.265478\t최선의 손실: 1.038027\t정확도: 40.46%\n",
+      "4\t검증 세트 손실: 1.612655\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.610910\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.616894\t최선의 손실: 1.038027\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.323521\t최선의 손실: 1.038027\t정확도: 36.12%\n",
+      "8\t검증 세트 손실: 1.287127\t최선의 손실: 1.038027\t정확도: 41.16%\n",
+      "9\t검증 세트 손실: 1.299512\t최선의 손실: 1.038027\t정확도: 38.78%\n",
+      "10\t검증 세트 손실: 1.366036\t최선의 손실: 1.038027\t정확도: 33.82%\n",
+      "11\t검증 세트 손실: 1.361776\t최선의 손실: 1.038027\t정확도: 34.01%\n",
+      "12\t검증 세트 손실: 1.373525\t최선의 손실: 1.038027\t정확도: 34.44%\n",
+      "13\t검증 세트 손실: 1.612909\t최선의 손실: 1.038027\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.609103\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.610241\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.613098\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.615482\t최선의 손실: 1.038027\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.610282\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.609069\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.610653\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.612892\t최선의 손실: 1.038027\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.618638\t최선의 손실: 1.038027\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total= 3.2min\n",
-      "[CV] batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.922198\t최선의 손실: 1.922198\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 2.472990\t최선의 손실: 1.922198\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 2.391239\t최선의 손실: 1.922198\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 3.444110\t최선의 손실: 1.922198\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 2.400486\t최선의 손실: 1.922198\t정확도: 18.73%\n",
-      "5\t검증 세트 손실: 1.924523\t최선의 손실: 1.922198\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 2.058573\t최선의 손실: 1.922198\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 2.145397\t최선의 손실: 1.922198\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.690282\t최선의 손실: 1.690282\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 2.903592\t최선의 손실: 1.690282\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.984954\t최선의 손실: 1.690282\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 2.458250\t최선의 손실: 1.690282\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2.167703\t최선의 손실: 1.690282\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.994214\t최선의 손실: 1.690282\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 3.413045\t최선의 손실: 1.690282\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 2.214465\t최선의 손실: 1.690282\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 2.402373\t최선의 손실: 1.690282\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 2.535804\t최선의 손실: 1.690282\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 3.089533\t최선의 손실: 1.690282\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 2.714175\t최선의 손실: 1.690282\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.915987\t최선의 손실: 1.690282\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 2.053297\t최선의 손실: 1.690282\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 2.169532\t최선의 손실: 1.690282\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2.142084\t최선의 손실: 1.690282\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 2.919422\t최선의 손실: 1.690282\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 2.069153\t최선의 손실: 1.690282\t정확도: 19.27%\n",
-      "26\t검증 세트 손실: 1.977872\t최선의 손실: 1.690282\t정확도: 19.27%\n",
-      "27\t검증 세트 손실: 1.707142\t최선의 손실: 1.690282\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 2.522864\t최선의 손실: 1.690282\t정확도: 22.01%\n",
-      "29\t검증 세트 손실: 2.650342\t최선의 손실: 1.690282\t정확도: 19.08%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f738a2ee510>, n_neurons=100, total=  11.4s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f738a2ee510>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.611868\t최선의 손실: 1.611868\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.533358\t최선의 손실: 1.533358\t정확도: 24.12%\n",
+      "2\t검증 세트 손실: 1.615887\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.622258\t최선의 손실: 1.533358\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.608032\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.610745\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.612949\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.615565\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.609178\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.611432\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.612139\t최선의 손실: 1.533358\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.617146\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.608020\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.614052\t최선의 손실: 1.533358\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.609036\t최선의 손실: 1.533358\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.613213\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.620483\t최선의 손실: 1.533358\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.614961\t최선의 손실: 1.533358\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.608522\t최선의 손실: 1.533358\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.609453\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.617041\t최선의 손실: 1.533358\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.612379\t최선의 손실: 1.533358\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.613450\t최선의 손실: 1.533358\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total= 1.4min\n",
-      "[CV] batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.934777\t최선의 손실: 1.934777\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 2.144690\t최선의 손실: 1.934777\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.638226\t최선의 손실: 1.638226\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 2.315424\t최선의 손실: 1.638226\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.874538\t최선의 손실: 1.638226\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 2.065059\t최선의 손실: 1.638226\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.759183\t최선의 손실: 1.638226\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 2.337949\t최선의 손실: 1.638226\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 2.583961\t최선의 손실: 1.638226\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 2.010082\t최선의 손실: 1.638226\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 2.443654\t최선의 손실: 1.638226\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 3.181373\t최선의 손실: 1.638226\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2.130561\t최선의 손실: 1.638226\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 3.304223\t최선의 손실: 1.638226\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 2.880376\t최선의 손실: 1.638226\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 2.013487\t최선의 손실: 1.638226\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.766268\t최선의 손실: 1.638226\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 2.563006\t최선의 손실: 1.638226\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 2.335246\t최선의 손실: 1.638226\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 3.021446\t최선의 손실: 1.638226\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.742406\t최선의 손실: 1.638226\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 2.062181\t최선의 손실: 1.638226\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.714521\t최선의 손실: 1.638226\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2.855302\t최선의 손실: 1.638226\t정확도: 22.01%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f738a2ee510>, n_neurons=100, total=  11.5s\n",
+      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.673108\t최선의 손실: 0.673108\t정확도: 76.66%\n",
+      "1\t검증 세트 손실: 0.538801\t최선의 손실: 0.538801\t정확도: 85.50%\n",
+      "2\t검증 세트 손실: 0.636005\t최선의 손실: 0.538801\t정확도: 81.55%\n",
+      "3\t검증 세트 손실: 0.510097\t최선의 손실: 0.510097\t정확도: 89.52%\n",
+      "4\t검증 세트 손실: 1.122313\t최선의 손실: 0.510097\t정확도: 50.20%\n",
+      "5\t검증 세트 손실: 0.956857\t최선의 손실: 0.510097\t정확도: 56.45%\n",
+      "6\t검증 세트 손실: 1.065535\t최선의 손실: 0.510097\t정확도: 50.51%\n",
+      "7\t검증 세트 손실: 1.004726\t최선의 손실: 0.510097\t정확도: 53.44%\n",
+      "8\t검증 세트 손실: 1.143914\t최선의 손실: 0.510097\t정확도: 47.26%\n",
+      "9\t검증 세트 손실: 1.037079\t최선의 손실: 0.510097\t정확도: 51.49%\n",
+      "10\t검증 세트 손실: 1.264636\t최선의 손실: 0.510097\t정확도: 37.26%\n",
+      "11\t검증 세트 손실: 1.212556\t최선의 손실: 0.510097\t정확도: 39.91%\n",
+      "12\t검증 세트 손실: 1.180032\t최선의 손실: 0.510097\t정확도: 39.87%\n",
+      "13\t검증 세트 손실: 1.198402\t최선의 손실: 0.510097\t정확도: 39.80%\n",
+      "14\t검증 세트 손실: 1.273570\t최선의 손실: 0.510097\t정확도: 37.14%\n",
+      "15\t검증 세트 손실: 1.283038\t최선의 손실: 0.510097\t정확도: 36.43%\n",
+      "16\t검증 세트 손실: 1.231716\t최선의 손실: 0.510097\t정확도: 38.86%\n",
+      "17\t검증 세트 손실: 1.224527\t최선의 손실: 0.510097\t정확도: 38.23%\n",
+      "18\t검증 세트 손실: 1.255184\t최선의 손실: 0.510097\t정확도: 37.22%\n",
+      "19\t검증 세트 손실: 1.210156\t최선의 손실: 0.510097\t정확도: 39.41%\n",
+      "20\t검증 세트 손실: 1.196908\t최선의 손실: 0.510097\t정확도: 41.56%\n",
+      "21\t검증 세트 손실: 1.184372\t최선의 손실: 0.510097\t정확도: 39.87%\n",
+      "22\t검증 세트 손실: 1.208355\t최선의 손실: 0.510097\t정확도: 39.84%\n",
+      "23\t검증 세트 손실: 1.280525\t최선의 손실: 0.510097\t정확도: 36.79%\n",
+      "24\t검증 세트 손실: 1.194320\t최선의 손실: 0.510097\t정확도: 39.64%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.956877\t최선의 손실: 1.956877\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 1.943745\t최선의 손실: 1.943745\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 2.490138\t최선의 손실: 1.943745\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 2.187531\t최선의 손실: 1.943745\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 3.480005\t최선의 손실: 1.943745\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 2.768101\t최선의 손실: 1.943745\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 2.956459\t최선의 손실: 1.943745\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.976760\t최선의 손실: 1.943745\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.745659\t최선의 손실: 1.745659\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 2.690661\t최선의 손실: 1.745659\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 2.319391\t최선의 손실: 1.745659\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 2.266762\t최선의 손실: 1.745659\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 2.264224\t최선의 손실: 1.745659\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 3.090765\t최선의 손실: 1.745659\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.840949\t최선의 손실: 1.745659\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 2.136446\t최선의 손실: 1.745659\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 2.056860\t최선의 손실: 1.745659\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 2.087981\t최선의 손실: 1.745659\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.683481\t최선의 손실: 1.683481\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 3.301216\t최선의 손실: 1.683481\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.083171\t최선의 손실: 1.683481\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 3.120386\t최선의 손실: 1.683481\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 1.911096\t최선의 손실: 1.683481\t정확도: 20.91%\n",
-      "23\t검증 세트 손실: 3.657172\t최선의 손실: 1.683481\t정확도: 19.08%\n",
-      "24\t검증 세트 손실: 2.123944\t최선의 손실: 1.683481\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 2.462884\t최선의 손실: 1.683481\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.805811\t최선의 손실: 1.683481\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 2.308318\t최선의 손실: 1.683481\t정확도: 19.27%\n",
-      "28\t검증 세트 손실: 2.767476\t최선의 손실: 1.683481\t정확도: 20.91%\n",
-      "29\t검증 세트 손실: 4.274524\t최선의 손실: 1.683481\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 2.881239\t최선의 손실: 1.683481\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 2.021810\t최선의 손실: 1.683481\t정확도: 19.08%\n",
-      "32\t검증 세트 손실: 1.725065\t최선의 손실: 1.683481\t정확도: 19.27%\n",
-      "33\t검증 세트 손실: 2.414566\t최선의 손실: 1.683481\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 3.369987\t최선의 손실: 1.683481\t정확도: 19.27%\n",
-      "35\t검증 세트 손실: 1.769934\t최선의 손실: 1.683481\t정확도: 19.08%\n",
-      "36\t검증 세트 손실: 3.341726\t최선의 손실: 1.683481\t정확도: 19.27%\n",
-      "37\t검증 세트 손실: 2.504773\t최선의 손실: 1.683481\t정확도: 19.08%\n",
-      "38\t검증 세트 손실: 2.025803\t최선의 손실: 1.683481\t정확도: 18.73%\n",
-      "39\t검증 세트 손실: 2.696106\t최선의 손실: 1.683481\t정확도: 19.08%\n",
+      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=140, total=  12.5s\n",
+      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.772444\t최선의 손실: 0.772444\t정확도: 73.65%\n",
+      "1\t검증 세트 손실: 0.555194\t최선의 손실: 0.555194\t정확도: 84.40%\n",
+      "2\t검증 세트 손실: 0.529470\t최선의 손실: 0.529470\t정확도: 88.47%\n",
+      "3\t검증 세트 손실: 0.581944\t최선의 손실: 0.529470\t정확도: 84.44%\n",
+      "4\t검증 세트 손실: 0.542961\t최선의 손실: 0.529470\t정확도: 85.26%\n",
+      "5\t검증 세트 손실: 0.615791\t최선의 손실: 0.529470\t정확도: 72.75%\n",
+      "6\t검증 세트 손실: 0.728394\t최선의 손실: 0.529470\t정확도: 74.51%\n",
+      "7\t검증 세트 손실: 0.736235\t최선의 손실: 0.529470\t정확도: 73.06%\n",
+      "8\t검증 세트 손실: 0.805739\t최선의 손실: 0.529470\t정확도: 70.80%\n",
+      "9\t검증 세트 손실: 0.701817\t최선의 손실: 0.529470\t정확도: 74.08%\n",
+      "10\t검증 세트 손실: 1.084178\t최선의 손실: 0.529470\t정확도: 51.33%\n",
+      "11\t검증 세트 손실: 0.988946\t최선의 손실: 0.529470\t정확도: 55.67%\n",
+      "12\t검증 세트 손실: 1.134560\t최선의 손실: 0.529470\t정확도: 49.88%\n",
+      "13\t검증 세트 손실: 1.079072\t최선의 손실: 0.529470\t정확도: 54.10%\n",
+      "14\t검증 세트 손실: 1.284150\t최선의 손실: 0.529470\t정확도: 39.72%\n",
+      "15\t검증 세트 손실: 1.260742\t최선의 손실: 0.529470\t정확도: 40.11%\n",
+      "16\t검증 세트 손실: 1.292715\t최선의 손실: 0.529470\t정확도: 39.52%\n",
+      "17\t검증 세트 손실: 1.411833\t최선의 손실: 0.529470\t정확도: 33.89%\n",
+      "18\t검증 세트 손실: 1.287164\t최선의 손실: 0.529470\t정확도: 38.86%\n",
+      "19\t검증 세트 손실: 1.287186\t최선의 손실: 0.529470\t정확도: 38.04%\n",
+      "20\t검증 세트 손실: 1.238894\t최선의 손실: 0.529470\t정확도: 39.84%\n",
+      "21\t검증 세트 손실: 1.237102\t최선의 손실: 0.529470\t정확도: 39.80%\n",
+      "22\t검증 세트 손실: 1.249358\t최선의 손실: 0.529470\t정확도: 39.99%\n",
+      "23\t검증 세트 손실: 1.239751\t최선의 손실: 0.529470\t정확도: 39.87%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total= 2.7min\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.610654\t최선의 손실: 1.610654\t정확도: 19.08%\n",
-      "1\t검증 세트 손실: 1.613268\t최선의 손실: 1.610654\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.609692\t최선의 손실: 1.609692\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.610006\t최선의 손실: 1.609692\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.617764\t최선의 손실: 1.609692\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.612465\t최선의 손실: 1.609692\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.609906\t최선의 손실: 1.609692\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.615114\t최선의 손실: 1.609692\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.621897\t최선의 손실: 1.609692\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.611268\t최선의 손실: 1.609692\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.608237\t최선의 손실: 1.608237\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.619371\t최선의 손실: 1.608237\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.608450\t최선의 손실: 1.608237\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.618540\t최선의 손실: 1.608237\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.613317\t최선의 손실: 1.608237\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.611220\t최선의 손실: 1.608237\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.611345\t최선의 손실: 1.608237\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.610974\t최선의 손실: 1.608237\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.614756\t최선의 손실: 1.608237\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.610592\t최선의 손실: 1.608237\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.611278\t최선의 손실: 1.608237\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.614821\t최선의 손실: 1.608237\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.616491\t최선의 손실: 1.608237\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.618027\t최선의 손실: 1.608237\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 1.614462\t최선의 손실: 1.608237\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.609595\t최선의 손실: 1.608237\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.610511\t최선의 손실: 1.608237\t정확도: 22.01%\n"
+      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=140, total=  12.1s\n",
+      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.952892\t최선의 손실: 0.952892\t정확도: 58.37%\n",
+      "1\t검증 세트 손실: 0.650496\t최선의 손실: 0.650496\t정확도: 72.01%\n",
+      "2\t검증 세트 손실: 0.572504\t최선의 손실: 0.572504\t정확도: 74.28%\n",
+      "3\t검증 세트 손실: 0.571712\t최선의 손실: 0.571712\t정확도: 75.57%\n",
+      "4\t검증 세트 손실: 0.974680\t최선의 손실: 0.571712\t정확도: 54.10%\n",
+      "5\t검증 세트 손실: 1.192194\t최선의 손실: 0.571712\t정확도: 43.16%\n",
+      "6\t검증 세트 손실: 1.222247\t최선의 손실: 0.571712\t정확도: 39.13%\n",
+      "7\t검증 세트 손실: 1.514921\t최선의 손실: 0.571712\t정확도: 27.40%\n",
+      "8\t검증 세트 손실: 1.231929\t최선의 손실: 0.571712\t정확도: 39.41%\n",
+      "9\t검증 세트 손실: 1.195793\t최선의 손실: 0.571712\t정확도: 39.33%\n",
+      "10\t검증 세트 손실: 1.194821\t최선의 손실: 0.571712\t정확도: 40.11%\n",
+      "11\t검증 세트 손실: 1.637787\t최선의 손실: 0.571712\t정확도: 23.14%\n",
+      "12\t검증 세트 손실: 1.253844\t최선의 손실: 0.571712\t정확도: 37.69%\n",
+      "13\t검증 세트 손실: 1.189251\t최선의 손실: 0.571712\t정확도: 40.03%\n",
+      "14\t검증 세트 손실: 1.174033\t최선의 손실: 0.571712\t정확도: 41.83%\n",
+      "15\t검증 세트 손실: 1.162280\t최선의 손실: 0.571712\t정확도: 40.38%\n",
+      "16\t검증 세트 손실: 1.185028\t최선의 손실: 0.571712\t정확도: 39.56%\n",
+      "17\t검증 세트 손실: 1.226666\t최선의 손실: 0.571712\t정확도: 38.15%\n",
+      "18\t검증 세트 손실: 1.327712\t최선의 손실: 0.571712\t정확도: 35.11%\n",
+      "19\t검증 세트 손실: 1.191002\t최선의 손실: 0.571712\t정확도: 40.23%\n",
+      "20\t검증 세트 손실: 1.354764\t최선의 손실: 0.571712\t정확도: 34.09%\n",
+      "21\t검증 세트 손실: 1.225404\t최선의 손실: 0.571712\t정확도: 38.43%\n",
+      "22\t검증 세트 손실: 1.232350\t최선의 손실: 0.571712\t정확도: 38.70%\n",
+      "23\t검증 세트 손실: 1.249472\t최선의 손실: 0.571712\t정확도: 37.53%\n",
+      "24\t검증 세트 손실: 1.249523\t최선의 손실: 0.571712\t정확도: 38.00%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=140, total=  12.5s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.611268\t최선의 손실: 1.611268\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 1.693916\t최선의 손실: 1.611268\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.641053\t최선의 손실: 1.611268\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.679520\t최선의 손실: 1.611268\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.650549\t최선의 손실: 1.611268\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.645451\t최선의 손실: 1.611268\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1.637692\t최선의 손실: 1.611268\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.620979\t최선의 손실: 1.611268\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.644779\t최선의 손실: 1.611268\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.651834\t최선의 손실: 1.611268\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.618847\t최선의 손실: 1.611268\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.662808\t최선의 손실: 1.611268\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.660038\t최선의 손실: 1.611268\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.621496\t최선의 손실: 1.611268\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.783087\t최선의 손실: 1.611268\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.656372\t최선의 손실: 1.611268\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.625889\t최선의 손실: 1.611268\t정확도: 18.73%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "27\t검증 세트 손실: 1.612528\t최선의 손실: 1.608237\t정확도: 19.08%\n",
-      "28\t검증 세트 손실: 1.610709\t최선의 손실: 1.608237\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.614632\t최선의 손실: 1.608237\t정확도: 18.73%\n",
-      "30\t검증 세트 손실: 1.622688\t최선의 손실: 1.608237\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 1.608783\t최선의 손실: 1.608237\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.635978\t최선의 손실: 1.611268\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 1.660423\t최선의 손실: 1.611268\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.646394\t최선의 손실: 1.611268\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.642969\t최선의 손실: 1.611268\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.649452\t최선의 손실: 1.611268\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=70, total=  15.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.609632\t최선의 손실: 1.609632\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 1.609750\t최선의 손실: 1.609632\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.615702\t최선의 손실: 1.609632\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.624399\t최선의 손실: 1.609632\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.614294\t최선의 손실: 1.609632\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.614216\t최선의 손실: 1.609632\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.614516\t최선의 손실: 1.609632\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.612042\t최선의 손실: 1.609632\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.609759\t최선의 손실: 1.609632\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 1.609657\t최선의 손실: 1.609632\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.620698\t최선의 손실: 1.609632\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.612236\t최선의 손실: 1.609632\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.609186\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.613460\t최선의 손실: 1.609186\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.613070\t최선의 손실: 1.609186\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 1.612373\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.612072\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.618460\t최선의 손실: 1.609186\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.610899\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.610627\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.612258\t최선의 손실: 1.609186\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 1.610887\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.617964\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.616929\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.612184\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.615082\t최선의 손실: 1.609186\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.607665\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "27\t검증 세트 손실: 1.611634\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 1.626025\t최선의 손실: 1.607665\t정확도: 18.73%\n",
-      "29\t검증 세트 손실: 1.609977\t최선의 손실: 1.607665\t정확도: 18.73%\n",
-      "30\t검증 세트 손실: 1.611089\t최선의 손실: 1.607665\t정확도: 19.08%\n",
-      "31\t검증 세트 손실: 1.611650\t최선의 손실: 1.607665\t정확도: 19.27%\n",
-      "32\t검증 세트 손실: 1.612803\t최선의 손실: 1.607665\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 1.608831\t최선의 손실: 1.607665\t정확도: 20.91%\n",
-      "34\t검증 세트 손실: 1.613911\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "35\t검증 세트 손실: 1.610115\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "36\t검증 세트 손실: 1.613157\t최선의 손실: 1.607665\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 1.609832\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "38\t검증 세트 손실: 1.620759\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "39\t검증 세트 손실: 1.623757\t최선의 손실: 1.607665\t정확도: 19.27%\n",
-      "40\t검증 세트 손실: 1.609532\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "41\t검증 세트 손실: 1.609141\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "42\t검증 세트 손실: 1.622814\t최선의 손실: 1.607665\t정확도: 19.27%\n",
-      "43\t검증 세트 손실: 1.617255\t최선의 손실: 1.607665\t정확도: 22.01%\n",
-      "44\t검증 세트 손실: 1.616077\t최선의 손실: 1.607665\t정확도: 19.08%\n",
-      "45\t검증 세트 손실: 1.611638\t최선의 손실: 1.607665\t정확도: 19.08%\n",
-      "46\t검증 세트 손실: 1.623886\t최선의 손실: 1.607665\t정확도: 18.73%\n",
-      "47\t검증 세트 손실: 1.611055\t최선의 손실: 1.607665\t정확도: 19.08%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30, total=  48.4s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.668068\t최선의 손실: 1.668068\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 1.618436\t최선의 손실: 1.618436\t정확도: 20.91%\n",
+      "2\t검증 세트 손실: 1.681890\t최선의 손실: 1.618436\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.690600\t최선의 손실: 1.618436\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 1.687662\t최선의 손실: 1.618436\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.676370\t최선의 손실: 1.618436\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.677379\t최선의 손실: 1.618436\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.650600\t최선의 손실: 1.618436\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.719023\t최선의 손실: 1.618436\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.648692\t최선의 손실: 1.618436\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.671177\t최선의 손실: 1.618436\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.695474\t최선의 손실: 1.618436\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 1.671332\t최선의 손실: 1.618436\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.651847\t최선의 손실: 1.618436\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.881861\t최선의 손실: 1.618436\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.769463\t최선의 손실: 1.618436\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 1.612641\t최선의 손실: 1.612641\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.649053\t최선의 손실: 1.612641\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.626177\t최선의 손실: 1.612641\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.614903\t최선의 손실: 1.612641\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.617448\t최선의 손실: 1.612641\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.660501\t최선의 손실: 1.612641\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.640868\t최선의 손실: 1.612641\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.696057\t최선의 손실: 1.612641\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 2.890901\t최선의 손실: 1.612641\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.705407\t최선의 손실: 1.612641\t정확도: 18.73%\n",
+      "26\t검증 세트 손실: 1.676645\t최선의 손실: 1.612641\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 1.612305\t최선의 손실: 1.612305\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 1.661815\t최선의 손실: 1.612305\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.698772\t최선의 손실: 1.612305\t정확도: 22.01%\n",
+      "30\t검증 세트 손실: 1.619362\t최선의 손실: 1.612305\t정확도: 18.73%\n",
+      "31\t검증 세트 손실: 1.663035\t최선의 손실: 1.612305\t정확도: 19.08%\n",
+      "32\t검증 세트 손실: 1.788459\t최선의 손실: 1.612305\t정확도: 19.08%\n",
+      "33\t검증 세트 손실: 1.615520\t최선의 손실: 1.612305\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 1.675166\t최선의 손실: 1.612305\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 1.669426\t최선의 손실: 1.612305\t정확도: 20.91%\n",
+      "36\t검증 세트 손실: 1.711804\t최선의 손실: 1.612305\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 1.661949\t최선의 손실: 1.612305\t정확도: 20.91%\n",
+      "38\t검증 세트 손실: 1.668036\t최선의 손실: 1.612305\t정확도: 18.73%\n",
+      "39\t검증 세트 손실: 1.654900\t최선의 손실: 1.612305\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 1.715632\t최선의 손실: 1.612305\t정확도: 18.73%\n",
+      "41\t검증 세트 손실: 1.791241\t최선의 손실: 1.612305\t정확도: 22.01%\n",
+      "42\t검증 세트 손실: 1.648479\t최선의 손실: 1.612305\t정확도: 20.91%\n",
+      "43\t검증 세트 손실: 1.672577\t최선의 손실: 1.612305\t정확도: 18.73%\n",
+      "44\t검증 세트 손실: 1.670219\t최선의 손실: 1.612305\t정확도: 20.91%\n",
+      "45\t검증 세트 손실: 1.638567\t최선의 손실: 1.612305\t정확도: 20.91%\n",
+      "46\t검증 세트 손실: 1.680690\t최선의 손실: 1.612305\t정확도: 19.27%\n",
+      "47\t검증 세트 손실: 1.643035\t최선의 손실: 1.612305\t정확도: 18.73%\n",
+      "48\t검증 세트 손실: 1.680250\t최선의 손실: 1.612305\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=70, total=  20.9s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 1.614372\t최선의 손실: 1.614372\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.609402\t최선의 손실: 1.609402\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.618020\t최선의 손실: 1.609402\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.620628\t최선의 손실: 1.609402\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.609382\t최선의 손실: 1.609382\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.609924\t최선의 손실: 1.609382\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.614879\t최선의 손실: 1.609382\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.616988\t최선의 손실: 1.609382\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.611730\t최선의 손실: 1.609382\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.613925\t최선의 손실: 1.609382\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.612546\t최선의 손실: 1.609382\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 1.623261\t최선의 손실: 1.609382\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.608312\t최선의 손실: 1.608312\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.611798\t최선의 손실: 1.608312\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.608483\t최선의 손실: 1.608312\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.617316\t최선의 손실: 1.608312\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.618908\t최선의 손실: 1.608312\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.615150\t최선의 손실: 1.608312\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.608066\t최선의 손실: 1.608066\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.608519\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.620146\t최선의 손실: 1.608066\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.611800\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.613168\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.613915\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.613857\t최선의 손실: 1.608066\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.617796\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.613061\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "27\t검증 세트 손실: 1.614668\t최선의 손실: 1.608066\t정확도: 19.27%\n",
-      "28\t검증 세트 손실: 1.630538\t최선의 손실: 1.608066\t정확도: 18.73%\n",
-      "29\t검증 세트 손실: 1.611464\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "30\t검증 세트 손실: 1.611856\t최선의 손실: 1.608066\t정확도: 19.08%\n",
-      "31\t검증 세트 손실: 1.611841\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 1.615407\t최선의 손실: 1.608066\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 1.608511\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "34\t검증 세트 손실: 1.613684\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "35\t검증 세트 손실: 1.609654\t최선의 손실: 1.608066\t정확도: 19.27%\n",
-      "36\t검증 세트 손실: 1.609912\t최선의 손실: 1.608066\t정확도: 18.73%\n",
-      "37\t검증 세트 손실: 1.608671\t최선의 손실: 1.608066\t정확도: 22.01%\n",
-      "38\t검증 세트 손실: 1.617390\t최선의 손실: 1.608066\t정확도: 19.27%\n",
-      "39\t검증 세트 손실: 1.625784\t최선의 손실: 1.608066\t정확도: 19.27%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30, total= 1.8min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.625598\t최선의 손실: 1.625598\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 1.644973\t최선의 손실: 1.625598\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.618949\t최선의 손실: 1.618949\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.614267\t최선의 손실: 1.614267\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 1.626944\t최선의 손실: 1.614267\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.677145\t최선의 손실: 1.614267\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.668343\t최선의 손실: 1.614267\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 2.145596\t최선의 손실: 1.614267\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.617523\t최선의 손실: 1.614267\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.623721\t최선의 손실: 1.614267\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.630333\t최선의 손실: 1.614267\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.639285\t최선의 손실: 1.614267\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.666674\t최선의 손실: 1.614267\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.671474\t최선의 손실: 1.614267\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.806768\t최선의 손실: 1.614267\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.632006\t최선의 손실: 1.614267\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 2.012752\t최선의 손실: 1.614267\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1.611605\t최선의 손실: 1.611605\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 4.140465\t최선의 손실: 1.611605\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.616321\t최선의 손실: 1.611605\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1.610011\t최선의 손실: 1.610011\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.678869\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.631338\t최선의 손실: 1.610011\t정확도: 20.91%\n",
+      "23\t검증 세트 손실: 1.630972\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.744445\t최선의 손실: 1.610011\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.612460\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.614693\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.722808\t최선의 손실: 1.610011\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.631027\t최선의 손실: 1.610011\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.681753\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "30\t검증 세트 손실: 1.630410\t최선의 손실: 1.610011\t정확도: 19.27%\n",
+      "31\t검증 세트 손실: 1.685849\t최선의 손실: 1.610011\t정확도: 18.73%\n",
+      "32\t검증 세트 손실: 1.672644\t최선의 손실: 1.610011\t정확도: 19.27%\n",
+      "33\t검증 세트 손실: 1.653108\t최선의 손실: 1.610011\t정확도: 19.27%\n",
+      "34\t검증 세트 손실: 1.647830\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 1.679805\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 1.665610\t최선의 손실: 1.610011\t정확도: 18.73%\n",
+      "37\t검증 세트 손실: 1.708641\t최선의 손실: 1.610011\t정확도: 19.08%\n",
+      "38\t검증 세트 손실: 1.630365\t최선의 손실: 1.610011\t정확도: 22.01%\n",
+      "39\t검증 세트 손실: 1.648704\t최선의 손실: 1.610011\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 1.691851\t최선의 손실: 1.610011\t정확도: 19.08%\n",
+      "41\t검증 세트 손실: 1.721025\t최선의 손실: 1.610011\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function relu at 0x7f994177e9d8>, n_neurons=70, total=  10.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.500266\t최선의 손실: 1.500266\t정확도: 25.88%\n",
-      "1\t검증 세트 손실: 1.527802\t최선의 손실: 1.500266\t정확도: 24.71%\n",
-      "2\t검증 세트 손실: 1.609345\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.608726\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.616084\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.614083\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.609110\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.614274\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.616918\t최선의 손실: 1.500266\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.608655\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.608024\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.613571\t최선의 손실: 1.500266\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.608943\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.615937\t최선의 손실: 1.500266\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.613151\t최선의 손실: 1.500266\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.611236\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.609469\t최선의 손실: 1.500266\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.610097\t최선의 손실: 1.500266\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.615385\t최선의 손실: 1.500266\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.609033\t최선의 손실: 1.500266\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.609120\t최선의 손실: 1.500266\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.615634\t최선의 손실: 1.500266\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30, total= 1.5min\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.184701\t최선의 손실: 0.184701\t정확도: 94.14%\n",
+      "1\t검증 세트 손실: 1.662242\t최선의 손실: 0.184701\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.866325\t최선의 손실: 0.184701\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.774166\t최선의 손실: 0.184701\t정확도: 19.39%\n",
+      "4\t검증 세트 손실: 1.956289\t최선의 손실: 0.184701\t정확도: 18.73%\n",
+      "5\t검증 세트 손실: 1.639598\t최선의 손실: 0.184701\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.705936\t최선의 손실: 0.184701\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.752148\t최선의 손실: 0.184701\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.783030\t최선의 손실: 0.184701\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.890967\t최선의 손실: 0.184701\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.723646\t최선의 손실: 0.184701\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.885967\t최선의 손실: 0.184701\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.646619\t최선의 손실: 0.184701\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.883753\t최선의 손실: 0.184701\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.776162\t최선의 손실: 0.184701\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.676282\t최선의 손실: 0.184701\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.791427\t최선의 손실: 0.184701\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.642619\t최선의 손실: 0.184701\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.684228\t최선의 손실: 0.184701\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 1.689974\t최선의 손실: 0.184701\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.675498\t최선의 손실: 0.184701\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.825737\t최선의 손실: 0.184701\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=100, total=  24.3s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.259622\t최선의 손실: 1.259622\t정확도: 39.95%\n",
-      "1\t검증 세트 손실: 1.620462\t최선의 손실: 1.259622\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.611668\t최선의 손실: 1.259622\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.619763\t최선의 손실: 1.259622\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.612769\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.610873\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.616756\t최선의 손실: 1.259622\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.613199\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.609137\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.608314\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.614510\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.610569\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.609134\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.612339\t최선의 손실: 1.259622\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.609174\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.610180\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.613044\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.615458\t최선의 손실: 1.259622\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.610241\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.609071\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.610654\t최선의 손실: 1.259622\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.612837\t최선의 손실: 1.259622\t정확도: 22.01%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=  11.2s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 0.533132\t최선의 손실: 0.533132\t정확도: 76.00%\n",
+      "1\t검증 세트 손실: 1.639761\t최선의 손실: 0.533132\t정확도: 20.91%\n",
+      "2\t검증 세트 손실: 1.683448\t최선의 손실: 0.533132\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.666609\t최선의 손실: 0.533132\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.677254\t최선의 손실: 0.533132\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 1.752475\t최선의 손실: 0.533132\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.663035\t최선의 손실: 0.533132\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.795001\t최선의 손실: 0.533132\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.839021\t최선의 손실: 0.533132\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.694574\t최선의 손실: 0.533132\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 1.664454\t최선의 손실: 0.533132\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.686914\t최선의 손실: 0.533132\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.879839\t최선의 손실: 0.533132\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.671734\t최선의 손실: 0.533132\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.637147\t최선의 손실: 0.533132\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.852155\t최선의 손실: 0.533132\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.666892\t최선의 손실: 0.533132\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.675425\t최선의 손실: 0.533132\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.659477\t최선의 손실: 0.533132\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 1.695333\t최선의 손실: 0.533132\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.699587\t최선의 손실: 0.533132\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.753036\t최선의 손실: 0.533132\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=100, total=  10.6s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=100 \n"
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=  11.1s\n",
+      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 1.592901\t최선의 손실: 1.592901\t정확도: 22.05%\n",
-      "1\t검증 세트 손실: 1.610557\t최선의 손실: 1.592901\t정확도: 19.31%\n",
-      "2\t검증 세트 손실: 1.613844\t최선의 손실: 1.592901\t정확도: 19.39%\n",
-      "3\t검증 세트 손실: 1.622226\t최선의 손실: 1.592901\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.608062\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.610913\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.612927\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.615320\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.609178\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.611431\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.612139\t최선의 손실: 1.592901\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 1.617146\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.608020\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.614054\t최선의 손실: 1.592901\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.609036\t최선의 손실: 1.592901\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.613212\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.620491\t최선의 손실: 1.592901\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.614969\t최선의 손실: 1.592901\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.608522\t최선의 손실: 1.592901\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.609454\t최선의 손실: 1.592901\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.617041\t최선의 손실: 1.592901\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.612381\t최선의 손실: 1.592901\t정확도: 22.01%\n",
+      "0\t검증 세트 손실: 0.365887\t최선의 손실: 0.365887\t정확도: 90.15%\n",
+      "1\t검증 세트 손실: 1.710880\t최선의 손실: 0.365887\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.814394\t최선의 손실: 0.365887\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.671085\t최선의 손실: 0.365887\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.776044\t최선의 손실: 0.365887\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.803235\t최선의 손실: 0.365887\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.631146\t최선의 손실: 0.365887\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.645681\t최선의 손실: 0.365887\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.716405\t최선의 손실: 0.365887\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.922274\t최선의 손실: 0.365887\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.630333\t최선의 손실: 0.365887\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.788273\t최선의 손실: 0.365887\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.977602\t최선의 손실: 0.365887\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.637210\t최선의 손실: 0.365887\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.732367\t최선의 손실: 0.365887\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.659367\t최선의 손실: 0.365887\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.622509\t최선의 손실: 0.365887\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.663513\t최선의 손실: 0.365887\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.646249\t최선의 손실: 0.365887\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 1.728003\t최선의 손실: 0.365887\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.723647\t최선의 손실: 0.365887\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.828580\t최선의 손실: 0.365887\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.05, activation=<function relu at 0x7f994177e9d8>, n_neurons=100, total=  28.6s\n",
-      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.516703\t최선의 손실: 0.516703\t정확도: 75.14%\n",
-      "1\t검증 세트 손실: 0.497416\t최선의 손실: 0.497416\t정확도: 77.01%\n",
-      "2\t검증 세트 손실: 0.505617\t최선의 손실: 0.497416\t정확도: 76.90%\n",
-      "3\t검증 세트 손실: 0.490938\t최선의 손실: 0.490938\t정확도: 77.72%\n",
-      "4\t검증 세트 손실: 0.536259\t최선의 손실: 0.490938\t정확도: 76.04%\n",
-      "5\t검증 세트 손실: 0.542144\t최선의 손실: 0.490938\t정확도: 75.37%\n",
-      "6\t검증 세트 손실: 0.834592\t최선의 손실: 0.490938\t정확도: 57.15%\n",
-      "7\t검증 세트 손실: 0.792917\t최선의 손실: 0.490938\t정확도: 59.11%\n",
-      "8\t검증 세트 손실: 0.780662\t최선의 손실: 0.490938\t정확도: 60.09%\n",
-      "9\t검증 세트 손실: 0.808566\t최선의 손실: 0.490938\t정확도: 56.33%\n",
-      "10\t검증 세트 손실: 0.778486\t최선의 손실: 0.490938\t정확도: 57.15%\n",
-      "11\t검증 세트 손실: 0.814406\t최선의 손실: 0.490938\t정확도: 57.58%\n",
-      "12\t검증 세트 손실: 0.811831\t최선의 손실: 0.490938\t정확도: 55.04%\n",
-      "13\t검증 세트 손실: 0.795911\t최선의 손실: 0.490938\t정확도: 56.72%\n",
-      "14\t검증 세트 손실: 0.804812\t최선의 손실: 0.490938\t정확도: 55.59%\n",
-      "15\t검증 세트 손실: 0.822891\t최선의 손실: 0.490938\t정확도: 56.33%\n",
-      "16\t검증 세트 손실: 0.827952\t최선의 손실: 0.490938\t정확도: 55.71%\n",
-      "17\t검증 세트 손실: 0.787985\t최선의 손실: 0.490938\t정확도: 58.01%\n",
-      "18\t검증 세트 손실: 0.855343\t최선의 손실: 0.490938\t정확도: 55.63%\n",
-      "19\t검증 세트 손실: 0.798118\t최선의 손실: 0.490938\t정확도: 58.05%\n",
-      "20\t검증 세트 손실: 0.813116\t최선의 손실: 0.490938\t정확도: 56.65%\n",
-      "21\t검증 세트 손실: 0.836301\t최선의 손실: 0.490938\t정확도: 56.88%\n",
-      "22\t검증 세트 손실: 0.809785\t최선의 손실: 0.490938\t정확도: 60.36%\n",
-      "23\t검증 세트 손실: 0.827014\t최선의 손실: 0.490938\t정확도: 59.15%\n",
-      "24\t검증 세트 손실: 0.820456\t최선의 손실: 0.490938\t정확도: 58.41%\n",
+      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=  11.2s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 1.769146\t최선의 손실: 1.769146\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.646795\t최선의 손실: 1.646795\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.634111\t최선의 손실: 1.634111\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1.647388\t최선의 손실: 1.634111\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.763482\t최선의 손실: 1.634111\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.702955\t최선의 손실: 1.634111\t정확도: 19.08%\n",
+      "6\t검증 세트 손실: 1.756540\t최선의 손실: 1.634111\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.722197\t최선의 손실: 1.634111\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.882248\t최선의 손실: 1.634111\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.698307\t최선의 손실: 1.634111\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.719433\t최선의 손실: 1.634111\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.808306\t최선의 손실: 1.634111\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 2.161842\t최선의 손실: 1.634111\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.706246\t최선의 손실: 1.634111\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 1.716986\t최선의 손실: 1.634111\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.872774\t최선의 손실: 1.634111\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 1.830296\t최선의 손실: 1.634111\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.781064\t최선의 손실: 1.634111\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 1.867060\t최선의 손실: 1.634111\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.662108\t최선의 손실: 1.634111\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.828079\t최선의 손실: 1.634111\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.777346\t최선의 손실: 1.634111\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.827889\t최선의 손실: 1.634111\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.911451\t최선의 손실: 1.634111\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total=  19.3s\n",
-      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.624550\t최선의 손실: 0.624550\t정확도: 85.81%\n",
-      "1\t검증 세트 손실: 0.412715\t최선의 손실: 0.412715\t정확도: 90.07%\n",
-      "2\t검증 세트 손실: 0.393601\t최선의 손실: 0.393601\t정확도: 90.38%\n",
-      "3\t검증 세트 손실: 0.509084\t최선의 손실: 0.393601\t정확도: 86.24%\n",
-      "4\t검증 세트 손실: 0.676661\t최선의 손실: 0.393601\t정확도: 78.19%\n",
-      "5\t검증 세트 손실: 0.791478\t최선의 손실: 0.393601\t정확도: 67.59%\n",
-      "6\t검증 세트 손실: 0.893606\t최선의 손실: 0.393601\t정확도: 65.17%\n",
-      "7\t검증 세트 손실: 1.032962\t최선의 손실: 0.393601\t정확도: 50.90%\n",
-      "8\t검증 세트 손실: 1.000089\t최선의 손실: 0.393601\t정확도: 52.50%\n",
-      "9\t검증 세트 손실: 1.221840\t최선의 손실: 0.393601\t정확도: 41.20%\n",
-      "10\t검증 세트 손실: 1.206883\t최선의 손실: 0.393601\t정확도: 40.30%\n",
-      "11\t검증 세트 손실: 1.220463\t최선의 손실: 0.393601\t정확도: 39.99%\n",
-      "12\t검증 세트 손실: 1.199128\t최선의 손실: 0.393601\t정확도: 39.95%\n",
-      "13\t검증 세트 손실: 1.201802\t최선의 손실: 0.393601\t정확도: 40.07%\n",
-      "14\t검증 세트 손실: 1.206960\t최선의 손실: 0.393601\t정확도: 40.11%\n",
-      "15\t검증 세트 손실: 1.568124\t최선의 손실: 0.393601\t정확도: 25.65%\n",
-      "16\t검증 세트 손실: 1.174464\t최선의 손실: 0.393601\t정확도: 40.27%\n",
-      "17\t검증 세트 손실: 1.191236\t최선의 손실: 0.393601\t정확도: 39.95%\n",
-      "18\t검증 세트 손실: 1.207579\t최선의 손실: 0.393601\t정확도: 39.60%\n",
-      "19\t검증 세트 손실: 1.248702\t최선의 손실: 0.393601\t정확도: 37.65%\n",
-      "20\t검증 세트 손실: 1.243219\t최선의 손실: 0.393601\t정확도: 37.88%\n",
-      "21\t검증 세트 손실: 1.205545\t최선의 손실: 0.393601\t정확도: 39.33%\n",
-      "22\t검증 세트 손실: 1.192137\t최선의 손실: 0.393601\t정확도: 39.68%\n",
-      "23\t검증 세트 손실: 1.264035\t최선의 손실: 0.393601\t정확도: 37.33%\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=120, total=   6.5s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 1.891222\t최선의 손실: 1.891222\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 1.846709\t최선의 손실: 1.846709\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.777825\t최선의 손실: 1.777825\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.839988\t최선의 손실: 1.777825\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.658626\t최선의 손실: 1.658626\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.680114\t최선의 손실: 1.658626\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.688877\t최선의 손실: 1.658626\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 2.017681\t최선의 손실: 1.658626\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.711785\t최선의 손실: 1.658626\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.956217\t최선의 손실: 1.658626\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.764643\t최선의 손실: 1.658626\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.953496\t최선의 손실: 1.658626\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 2.190019\t최선의 손실: 1.658626\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.741336\t최선의 손실: 1.658626\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.754822\t최선의 손실: 1.658626\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 1.772567\t최선의 손실: 1.658626\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 2.013352\t최선의 손실: 1.658626\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.652127\t최선의 손실: 1.652127\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.971378\t최선의 손실: 1.652127\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 2.023587\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.273651\t최선의 손실: 1.652127\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.793894\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 2.016310\t최선의 손실: 1.652127\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.770661\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 2.176994\t최선의 손실: 1.652127\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 2.408891\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.931970\t최선의 손실: 1.652127\t정확도: 20.91%\n",
+      "27\t검증 세트 손실: 2.241311\t최선의 손실: 1.652127\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.867962\t최선의 손실: 1.652127\t정확도: 20.91%\n",
+      "29\t검증 세트 손실: 1.717847\t최선의 손실: 1.652127\t정확도: 20.91%\n",
+      "30\t검증 세트 손실: 1.837496\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "31\t검증 세트 손실: 2.561028\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 2.228803\t최선의 손실: 1.652127\t정확도: 19.08%\n",
+      "33\t검증 세트 손실: 1.717016\t최선의 손실: 1.652127\t정확도: 18.73%\n",
+      "34\t검증 세트 손실: 1.663389\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 2.010629\t최선의 손실: 1.652127\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 1.992832\t최선의 손실: 1.652127\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 1.824501\t최선의 손실: 1.652127\t정확도: 19.27%\n",
+      "38\t검증 세트 손실: 1.917272\t최선의 손실: 1.652127\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total=  15.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.753948\t최선의 손실: 0.753948\t정확도: 66.61%\n",
-      "1\t검증 세트 손실: 0.639230\t최선의 손실: 0.639230\t정확도: 71.85%\n",
-      "2\t검증 세트 손실: 0.587547\t최선의 손실: 0.587547\t정확도: 74.59%\n",
-      "3\t검증 세트 손실: 0.710908\t최선의 손실: 0.587547\t정확도: 71.54%\n",
-      "4\t검증 세트 손실: 0.699938\t최선의 손실: 0.587547\t정확도: 72.36%\n",
-      "5\t검증 세트 손실: 1.269490\t최선의 손실: 0.587547\t정확도: 38.00%\n",
-      "6\t검증 세트 손실: 1.294025\t최선의 손실: 0.587547\t정확도: 37.18%\n",
-      "7\t검증 세트 손실: 1.210774\t최선의 손실: 0.587547\t정확도: 39.87%\n",
-      "8\t검증 세트 손실: 1.209148\t최선의 손실: 0.587547\t정확도: 39.56%\n",
-      "9\t검증 세트 손실: 1.209598\t최선의 손실: 0.587547\t정확도: 40.07%\n",
-      "10\t검증 세트 손실: 1.221755\t최선의 손실: 0.587547\t정확도: 39.29%\n",
-      "11\t검증 세트 손실: 1.245294\t최선의 손실: 0.587547\t정확도: 37.57%\n",
-      "12\t검증 세트 손실: 1.207530\t최선의 손실: 0.587547\t정확도: 39.56%\n",
-      "13\t검증 세트 손실: 1.199839\t최선의 손실: 0.587547\t정확도: 39.91%\n",
-      "14\t검증 세트 손실: 1.316861\t최선의 손실: 0.587547\t정확도: 37.06%\n",
-      "15\t검증 세트 손실: 1.210765\t최선의 손실: 0.587547\t정확도: 39.56%\n",
-      "16\t검증 세트 손실: 1.221950\t최선의 손실: 0.587547\t정확도: 38.82%\n",
-      "17\t검증 세트 손실: 1.208919\t최선의 손실: 0.587547\t정확도: 39.76%\n",
-      "18\t검증 세트 손실: 1.195266\t최선의 손실: 0.587547\t정확도: 40.15%\n",
-      "19\t검증 세트 손실: 1.188014\t최선의 손실: 0.587547\t정확도: 39.80%\n",
-      "20\t검증 세트 손실: 1.206782\t최선의 손실: 0.587547\t정확도: 39.48%\n",
-      "21\t검증 세트 손실: 1.184452\t최선의 손실: 0.587547\t정확도: 39.41%\n",
-      "22\t검증 세트 손실: 1.190137\t최선의 손실: 0.587547\t정확도: 40.19%\n",
-      "23\t검증 세트 손실: 1.190504\t최선의 손실: 0.587547\t정확도: 39.91%\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=120, total=  10.3s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 1.788741\t최선의 손실: 1.788741\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.668635\t최선의 손실: 1.668635\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 1.657613\t최선의 손실: 1.657613\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1.750493\t최선의 손실: 1.657613\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.886503\t최선의 손실: 1.657613\t정확도: 18.73%\n",
+      "5\t검증 세트 손실: 1.922329\t최선의 손실: 1.657613\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.081256\t최선의 손실: 1.657613\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 2.096522\t최선의 손실: 1.657613\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.818816\t최선의 손실: 1.657613\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.867076\t최선의 손실: 1.657613\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 2.069985\t최선의 손실: 1.657613\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.966934\t최선의 손실: 1.657613\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 1.975346\t최선의 손실: 1.657613\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.733311\t최선의 손실: 1.657613\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 2.282833\t최선의 손실: 1.657613\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.642215\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.942255\t최선의 손실: 1.642215\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.738264\t최선의 손실: 1.642215\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1.786659\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 2.161594\t최선의 손실: 1.642215\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1.714582\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 2.440338\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 2.482248\t최선의 손실: 1.642215\t정확도: 20.91%\n",
+      "23\t검증 세트 손실: 1.740083\t최선의 손실: 1.642215\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 1.726208\t최선의 손실: 1.642215\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.855952\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.771278\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.882311\t최선의 손실: 1.642215\t정확도: 20.91%\n",
+      "28\t검증 세트 손실: 1.673043\t최선의 손실: 1.642215\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.738810\t최선의 손실: 1.642215\t정확도: 20.91%\n",
+      "30\t검증 세트 손실: 1.881803\t최선의 손실: 1.642215\t정확도: 20.91%\n",
+      "31\t검증 세트 손실: 2.991334\t최선의 손실: 1.642215\t정확도: 18.73%\n",
+      "32\t검증 세트 손실: 1.779339\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "33\t검증 세트 손실: 1.815410\t최선의 손실: 1.642215\t정확도: 18.73%\n",
+      "34\t검증 세트 손실: 2.058384\t최선의 손실: 1.642215\t정확도: 18.73%\n",
+      "35\t검증 세트 손실: 1.710097\t최선의 손실: 1.642215\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 2.216438\t최선의 손실: 1.642215\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total=  11.4s\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.609574\t최선의 손실: 1.609574\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 1.702361\t최선의 손실: 1.609574\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.637715\t최선의 손실: 1.609574\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.759847\t최선의 손실: 1.609574\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.663132\t최선의 손실: 1.609574\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.622919\t최선의 손실: 1.609574\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.659665\t최선의 손실: 1.609574\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 1.631863\t최선의 손실: 1.609574\t정확도: 20.91%\n",
-      "8\t검증 세트 손실: 1.706360\t최선의 손실: 1.609574\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.654642\t최선의 손실: 1.609574\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 1.642376\t최선의 손실: 1.609574\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.688204\t최선의 손실: 1.609574\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.675265\t최선의 손실: 1.609574\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.677985\t최선의 손실: 1.609574\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.692012\t최선의 손실: 1.609574\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.652004\t최선의 손실: 1.609574\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 1.684764\t최선의 손실: 1.609574\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.689952\t최선의 손실: 1.609574\t정확도: 22.01%\n",
-      "18\t검증 세트 손실: 1.696149\t최선의 손실: 1.609574\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.654715\t최선의 손실: 1.609574\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.626548\t최선의 손실: 1.609574\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.641653\t최선의 손실: 1.609574\t정확도: 19.27%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.666112\t최선의 손실: 1.666112\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 1.615008\t최선의 손실: 1.615008\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 1.666205\t최선의 손실: 1.615008\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.637342\t최선의 손실: 1.615008\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 1.657136\t최선의 손실: 1.615008\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.660546\t최선의 손실: 1.615008\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.642290\t최선의 손실: 1.615008\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.630618\t최선의 손실: 1.615008\t정확도: 20.91%\n",
-      "8\t검증 세트 손실: 1.652610\t최선의 손실: 1.615008\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.632720\t최선의 손실: 1.615008\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.679114\t최선의 손실: 1.615008\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.628161\t최선의 손실: 1.615008\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 3.048755\t최선의 손실: 1.615008\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.610970\t최선의 손실: 1.610970\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.736153\t최선의 손실: 1.610970\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.638696\t최선의 손실: 1.610970\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 1.658394\t최선의 손실: 1.610970\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.705157\t최선의 손실: 1.610970\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.668403\t최선의 손실: 1.610970\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.782320\t최선의 손실: 1.610970\t정확도: 20.91%\n"
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=120, total=   9.7s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.551056\t최선의 손실: 0.551056\t정확도: 79.87%\n",
+      "1\t검증 세트 손실: 0.327082\t최선의 손실: 0.327082\t정확도: 90.15%\n",
+      "2\t검증 세트 손실: 0.213086\t최선의 손실: 0.213086\t정확도: 93.86%\n",
+      "3\t검증 세트 손실: 0.159970\t최선의 손실: 0.159970\t정확도: 95.35%\n",
+      "4\t검증 세트 손실: 0.129179\t최선의 손실: 0.129179\t정확도: 96.40%\n",
+      "5\t검증 세트 손실: 0.134528\t최선의 손실: 0.129179\t정확도: 96.17%\n",
+      "6\t검증 세트 손실: 0.123792\t최선의 손실: 0.123792\t정확도: 96.21%\n",
+      "7\t검증 세트 손실: 0.134896\t최선의 손실: 0.123792\t정확도: 96.44%\n",
+      "8\t검증 세트 손실: 0.124244\t최선의 손실: 0.123792\t정확도: 96.52%\n",
+      "9\t검증 세트 손실: 0.113848\t최선의 손실: 0.113848\t정확도: 96.87%\n",
+      "10\t검증 세트 손실: 0.115933\t최선의 손실: 0.113848\t정확도: 96.99%\n",
+      "11\t검증 세트 손실: 0.125451\t최선의 손실: 0.113848\t정확도: 96.29%\n",
+      "12\t검증 세트 손실: 0.106716\t최선의 손실: 0.106716\t정확도: 96.99%\n",
+      "13\t검증 세트 손실: 0.119229\t최선의 손실: 0.106716\t정확도: 96.83%\n",
+      "14\t검증 세트 손실: 0.116319\t최선의 손실: 0.106716\t정확도: 97.07%\n",
+      "15\t검증 세트 손실: 0.114098\t최선의 손실: 0.106716\t정확도: 96.91%\n",
+      "16\t검증 세트 손실: 0.103023\t최선의 손실: 0.103023\t정확도: 97.19%\n",
+      "17\t검증 세트 손실: 0.114954\t최선의 손실: 0.103023\t정확도: 96.83%\n",
+      "18\t검증 세트 손실: 0.136868\t최선의 손실: 0.103023\t정확도: 96.76%\n",
+      "19\t검증 세트 손실: 0.106723\t최선의 손실: 0.103023\t정확도: 97.15%\n",
+      "20\t검증 세트 손실: 0.108508\t최선의 손실: 0.103023\t정확도: 97.19%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20\t검증 세트 손실: 1.629482\t최선의 손실: 1.610970\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.654980\t최선의 손실: 1.610970\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.615061\t최선의 손실: 1.610970\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.665033\t최선의 손실: 1.610970\t정확도: 18.73%\n",
-      "24\t검증 세트 손실: 1.660217\t최선의 손실: 1.610970\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.707501\t최선의 손실: 1.610970\t정확도: 18.73%\n",
-      "26\t검증 세트 손실: 1.686249\t최선의 손실: 1.610970\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 1.651866\t최선의 손실: 1.610970\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 1.719947\t최선의 손실: 1.610970\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.697745\t최선의 손실: 1.610970\t정확도: 20.91%\n",
-      "30\t검증 세트 손실: 1.662003\t최선의 손실: 1.610970\t정확도: 18.73%\n",
-      "31\t검증 세트 손실: 1.668004\t최선의 손실: 1.610970\t정확도: 19.08%\n",
-      "32\t검증 세트 손실: 1.808075\t최선의 손실: 1.610970\t정확도: 19.08%\n",
-      "33\t검증 세트 손실: 1.616404\t최선의 손실: 1.610970\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 1.671907\t최선의 손실: 1.610970\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 0.108247\t최선의 손실: 0.103023\t정확도: 96.87%\n",
+      "22\t검증 세트 손실: 0.116014\t최선의 손실: 0.103023\t정확도: 96.76%\n",
+      "23\t검증 세트 손실: 0.120019\t최선의 손실: 0.103023\t정확도: 96.95%\n",
+      "24\t검증 세트 손실: 0.103046\t최선의 손실: 0.103023\t정확도: 97.11%\n",
+      "25\t검증 세트 손실: 0.107387\t최선의 손실: 0.103023\t정확도: 96.99%\n",
+      "26\t검증 세트 손실: 0.108353\t최선의 손실: 0.103023\t정확도: 97.26%\n",
+      "27\t검증 세트 손실: 0.113618\t최선의 손실: 0.103023\t정확도: 97.19%\n",
+      "28\t검증 세트 손실: 0.107115\t최선의 손실: 0.103023\t정확도: 97.07%\n",
+      "29\t검증 세트 손실: 0.118462\t최선의 손실: 0.103023\t정확도: 96.79%\n",
+      "30\t검증 세트 손실: 0.103894\t최선의 손실: 0.103023\t정확도: 96.87%\n",
+      "31\t검증 세트 손실: 0.120285\t최선의 손실: 0.103023\t정확도: 96.56%\n",
+      "32\t검증 세트 손실: 0.123786\t최선의 손실: 0.103023\t정확도: 96.68%\n",
+      "33\t검증 세트 손실: 0.112763\t최선의 손실: 0.103023\t정확도: 96.91%\n",
+      "34\t검증 세트 손실: 0.106139\t최선의 손실: 0.103023\t정확도: 97.11%\n",
+      "35\t검증 세트 손실: 0.112444\t최선의 손실: 0.103023\t정확도: 96.91%\n",
+      "36\t검증 세트 손실: 0.117079\t최선의 손실: 0.103023\t정확도: 96.68%\n",
+      "37\t검증 세트 손실: 0.112151\t최선의 손실: 0.103023\t정확도: 96.99%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total= 2.0min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.610494\t최선의 손실: 1.610494\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 1.658772\t최선의 손실: 1.610494\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 5.136837\t최선의 손실: 1.610494\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.618572\t최선의 손실: 1.610494\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 2.965146\t최선의 손실: 1.610494\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 1.635646\t최선의 손실: 1.610494\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.667596\t최선의 손실: 1.610494\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.630374\t최선의 손실: 1.610494\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.703880\t최선의 손실: 1.610494\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 8.169735\t최선의 손실: 1.610494\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.624346\t최선의 손실: 1.610494\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.625754\t최선의 손실: 1.610494\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.622810\t최선의 손실: 1.610494\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.632969\t최선의 손실: 1.610494\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.623982\t최선의 손실: 1.610494\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.628851\t최선의 손실: 1.610494\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.748022\t최선의 손실: 1.610494\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.642322\t최선의 손실: 1.610494\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.684858\t최선의 손실: 1.610494\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 2.238973\t최선의 손실: 1.610494\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.638650\t최선의 손실: 1.610494\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.654966\t최선의 손실: 1.610494\t정확도: 22.01%\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10, total=   3.4s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.579217\t최선의 손실: 0.579217\t정확도: 88.90%\n",
+      "1\t검증 세트 손실: 0.174530\t최선의 손실: 0.174530\t정확도: 95.74%\n",
+      "2\t검증 세트 손실: 0.138306\t최선의 손실: 0.138306\t정확도: 96.25%\n",
+      "3\t검증 세트 손실: 0.128196\t최선의 손실: 0.128196\t정확도: 96.60%\n",
+      "4\t검증 세트 손실: 0.129190\t최선의 손실: 0.128196\t정확도: 96.25%\n",
+      "5\t검증 세트 손실: 0.123000\t최선의 손실: 0.123000\t정확도: 96.21%\n",
+      "6\t검증 세트 손실: 0.118804\t최선의 손실: 0.118804\t정확도: 96.72%\n",
+      "7\t검증 세트 손실: 0.121653\t최선의 손실: 0.118804\t정확도: 96.79%\n",
+      "8\t검증 세트 손실: 0.113871\t최선의 손실: 0.113871\t정확도: 96.72%\n",
+      "9\t검증 세트 손실: 0.114695\t최선의 손실: 0.113871\t정확도: 96.95%\n",
+      "10\t검증 세트 손실: 0.123700\t최선의 손실: 0.113871\t정확도: 96.72%\n",
+      "11\t검증 세트 손실: 0.123084\t최선의 손실: 0.113871\t정확도: 96.72%\n",
+      "12\t검증 세트 손실: 0.130354\t최선의 손실: 0.113871\t정확도: 96.36%\n",
+      "13\t검증 세트 손실: 0.116287\t최선의 손실: 0.113871\t정확도: 96.79%\n",
+      "14\t검증 세트 손실: 0.110998\t최선의 손실: 0.110998\t정확도: 97.07%\n",
+      "15\t검증 세트 손실: 0.126793\t최선의 손실: 0.110998\t정확도: 96.60%\n",
+      "16\t검증 세트 손실: 0.105460\t최선의 손실: 0.105460\t정확도: 97.15%\n",
+      "17\t검증 세트 손실: 0.106005\t최선의 손실: 0.105460\t정확도: 96.91%\n",
+      "18\t검증 세트 손실: 0.110898\t최선의 손실: 0.105460\t정확도: 97.11%\n",
+      "19\t검증 세트 손실: 0.114546\t최선의 손실: 0.105460\t정확도: 96.99%\n",
+      "20\t검증 세트 손실: 0.106591\t최선의 손실: 0.105460\t정확도: 97.19%\n",
+      "21\t검증 세트 손실: 0.107836\t최선의 손실: 0.105460\t정확도: 96.91%\n",
+      "22\t검증 세트 손실: 0.125895\t최선의 손실: 0.105460\t정확도: 96.36%\n",
+      "23\t검증 세트 손실: 0.110552\t최선의 손실: 0.105460\t정확도: 96.95%\n",
+      "24\t검증 세트 손실: 0.120389\t최선의 손실: 0.105460\t정확도: 96.76%\n",
+      "25\t검증 세트 손실: 0.119281\t최선의 손실: 0.105460\t정확도: 97.03%\n",
+      "26\t검증 세트 손실: 0.109635\t최선의 손실: 0.105460\t정확도: 96.87%\n",
+      "27\t검증 세트 손실: 0.123115\t최선의 손실: 0.105460\t정확도: 96.79%\n",
+      "28\t검증 세트 손실: 0.113873\t최선의 손실: 0.105460\t정확도: 96.68%\n",
+      "29\t검증 세트 손실: 0.113347\t최선의 손실: 0.105460\t정확도: 96.79%\n",
+      "30\t검증 세트 손실: 0.120183\t최선의 손실: 0.105460\t정확도: 96.52%\n",
+      "31\t검증 세트 손실: 0.114008\t최선의 손실: 0.105460\t정확도: 96.76%\n",
+      "32\t검증 세트 손실: 0.122729\t최선의 손실: 0.105460\t정확도: 96.60%\n",
+      "33\t검증 세트 손실: 0.118405\t최선의 손실: 0.105460\t정확도: 96.64%\n",
+      "34\t검증 세트 손실: 0.125842\t최선의 손실: 0.105460\t정확도: 96.68%\n",
+      "35\t검증 세트 손실: 0.111003\t최선의 손실: 0.105460\t정확도: 96.79%\n",
+      "36\t검증 세트 손실: 0.122313\t최선의 손실: 0.105460\t정확도: 96.76%\n",
+      "37\t검증 세트 손실: 0.111034\t최선의 손실: 0.105460\t정확도: 96.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total= 1.1min\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.138140\t최선의 손실: 0.138140\t정확도: 96.44%\n",
-      "1\t검증 세트 손실: 1.666057\t최선의 손실: 0.138140\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.855067\t최선의 손실: 0.138140\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.755888\t최선의 손실: 0.138140\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.962044\t최선의 손실: 0.138140\t정확도: 18.73%\n",
-      "5\t검증 세트 손실: 1.663815\t최선의 손실: 0.138140\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.702539\t최선의 손실: 0.138140\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 1.747968\t최선의 손실: 0.138140\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 1.782968\t최선의 손실: 0.138140\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.899366\t최선의 손실: 0.138140\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.725643\t최선의 손실: 0.138140\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.883205\t최선의 손실: 0.138140\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.646193\t최선의 손실: 0.138140\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.881400\t최선의 손실: 0.138140\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.772874\t최선의 손실: 0.138140\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.669567\t최선의 손실: 0.138140\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.799801\t최선의 손실: 0.138140\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.643516\t최선의 손실: 0.138140\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 1.683375\t최선의 손실: 0.138140\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 1.688945\t최선의 손실: 0.138140\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.675129\t최선의 손실: 0.138140\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.828153\t최선의 손실: 0.138140\t정확도: 19.27%\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10, total=   3.4s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10 \n",
+      "0\t검증 세트 손실: 0.700632\t최선의 손실: 0.700632\t정확도: 66.42%\n",
+      "1\t검증 세트 손실: 0.436549\t최선의 손실: 0.436549\t정확도: 82.29%\n",
+      "2\t검증 세트 손실: 0.219477\t최선의 손실: 0.219477\t정확도: 94.10%\n",
+      "3\t검증 세트 손실: 0.163011\t최선의 손실: 0.163011\t정확도: 95.78%\n",
+      "4\t검증 세트 손실: 0.144337\t최선의 손실: 0.144337\t정확도: 96.05%\n",
+      "5\t검증 세트 손실: 0.141510\t최선의 손실: 0.141510\t정확도: 96.40%\n",
+      "6\t검증 세트 손실: 0.129480\t최선의 손실: 0.129480\t정확도: 96.48%\n",
+      "7\t검증 세트 손실: 0.132851\t최선의 손실: 0.129480\t정확도: 96.68%\n",
+      "8\t검증 세트 손실: 0.129688\t최선의 손실: 0.129480\t정확도: 96.72%\n",
+      "9\t검증 세트 손실: 0.128311\t최선의 손실: 0.128311\t정확도: 96.64%\n",
+      "10\t검증 세트 손실: 0.116985\t최선의 손실: 0.116985\t정확도: 96.99%\n",
+      "11\t검증 세트 손실: 0.118963\t최선의 손실: 0.116985\t정확도: 96.83%\n",
+      "12\t검증 세트 손실: 0.124050\t최선의 손실: 0.116985\t정확도: 96.79%\n",
+      "13\t검증 세트 손실: 0.114596\t최선의 손실: 0.114596\t정확도: 96.76%\n",
+      "14\t검증 세트 손실: 0.118657\t최선의 손실: 0.114596\t정확도: 96.87%\n",
+      "15\t검증 세트 손실: 0.118011\t최선의 손실: 0.114596\t정확도: 96.83%\n",
+      "16\t검증 세트 손실: 0.120938\t최선의 손실: 0.114596\t정확도: 96.64%\n",
+      "17\t검증 세트 손실: 0.111373\t최선의 손실: 0.111373\t정확도: 96.83%\n",
+      "18\t검증 세트 손실: 0.112229\t최선의 손실: 0.111373\t정확도: 97.07%\n",
+      "19\t검증 세트 손실: 0.116359\t최선의 손실: 0.111373\t정확도: 96.83%\n",
+      "20\t검증 세트 손실: 0.116279\t최선의 손실: 0.111373\t정확도: 97.11%\n",
+      "21\t검증 세트 손실: 0.116585\t최선의 손실: 0.111373\t정확도: 97.11%\n",
+      "22\t검증 세트 손실: 0.101668\t최선의 손실: 0.101668\t정확도: 97.19%\n",
+      "23\t검증 세트 손실: 0.111051\t최선의 손실: 0.101668\t정확도: 97.03%\n",
+      "24\t검증 세트 손실: 0.116660\t최선의 손실: 0.101668\t정확도: 96.95%\n",
+      "25\t검증 세트 손실: 0.121874\t최선의 손실: 0.101668\t정확도: 96.87%\n",
+      "26\t검증 세트 손실: 0.105303\t최선의 손실: 0.101668\t정확도: 97.19%\n",
+      "27\t검증 세트 손실: 0.111238\t최선의 손실: 0.101668\t정확도: 96.87%\n",
+      "28\t검증 세트 손실: 0.109195\t최선의 손실: 0.101668\t정확도: 97.15%\n",
+      "29\t검증 세트 손실: 0.103903\t최선의 손실: 0.101668\t정확도: 97.15%\n",
+      "30\t검증 세트 손실: 0.109680\t최선의 손실: 0.101668\t정확도: 97.07%\n",
+      "31\t검증 세트 손실: 0.100301\t최선의 손실: 0.100301\t정확도: 97.19%\n",
+      "32\t검증 세트 손실: 0.105629\t최선의 손실: 0.100301\t정확도: 97.07%\n",
+      "33\t검증 세트 손실: 0.111245\t최선의 손실: 0.100301\t정확도: 97.11%\n",
+      "34\t검증 세트 손실: 0.122733\t최선의 손실: 0.100301\t정확도: 96.76%\n",
+      "35\t검증 세트 손실: 0.108336\t최선의 손실: 0.100301\t정확도: 97.03%\n",
+      "36\t검증 세트 손실: 0.103276\t최선의 손실: 0.100301\t정확도: 97.22%\n",
+      "37\t검증 세트 손실: 0.104695\t최선의 손실: 0.100301\t정확도: 96.91%\n",
+      "38\t검증 세트 손실: 0.106578\t최선의 손실: 0.100301\t정확도: 97.34%\n",
+      "39\t검증 세트 손실: 0.105636\t최선의 손실: 0.100301\t정확도: 97.03%\n",
+      "40\t검증 세트 손실: 0.110068\t최선의 손실: 0.100301\t정확도: 96.87%\n",
+      "41\t검증 세트 손실: 0.108185\t최선의 손실: 0.100301\t정확도: 96.99%\n",
+      "42\t검증 세트 손실: 0.103709\t최선의 손실: 0.100301\t정확도: 97.42%\n",
+      "43\t검증 세트 손실: 0.124882\t최선의 손실: 0.100301\t정확도: 96.83%\n",
+      "44\t검증 세트 손실: 0.110405\t최선의 손실: 0.100301\t정확도: 97.11%\n",
+      "45\t검증 세트 손실: 0.116224\t최선의 손실: 0.100301\t정확도: 96.91%\n",
+      "46\t검증 세트 손실: 0.106366\t최선의 손실: 0.100301\t정확도: 97.15%\n",
+      "47\t검증 세트 손실: 0.119872\t최선의 손실: 0.100301\t정확도: 97.03%\n",
+      "48\t검증 세트 손실: 0.119696\t최선의 손실: 0.100301\t정확도: 96.83%\n",
+      "49\t검증 세트 손실: 0.112275\t최선의 손실: 0.100301\t정확도: 96.87%\n",
+      "50\t검증 세트 손실: 0.117290\t최선의 손실: 0.100301\t정확도: 96.83%\n",
+      "51\t검증 세트 손실: 0.105903\t최선의 손실: 0.100301\t정확도: 97.22%\n",
+      "52\t검증 세트 손실: 0.111222\t최선의 손실: 0.100301\t정확도: 97.26%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=  10.5s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 0.247864\t최선의 손실: 0.247864\t정확도: 95.50%\n",
-      "1\t검증 세트 손실: 1.661445\t최선의 손실: 0.247864\t정확도: 20.91%\n",
-      "2\t검증 세트 손실: 1.654583\t최선의 손실: 0.247864\t정확도: 20.91%\n",
-      "3\t검증 세트 손실: 1.680832\t최선의 손실: 0.247864\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.680491\t최선의 손실: 0.247864\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 1.760597\t최선의 손실: 0.247864\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.646310\t최선의 손실: 0.247864\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.799761\t최선의 손실: 0.247864\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.824950\t최선의 손실: 0.247864\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.701816\t최선의 손실: 0.247864\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 1.692645\t최선의 손실: 0.247864\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.689589\t최선의 손실: 0.247864\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.878210\t최선의 손실: 0.247864\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.671834\t최선의 손실: 0.247864\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.638254\t최선의 손실: 0.247864\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.819142\t최선의 손실: 0.247864\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.663772\t최선의 손실: 0.247864\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.666233\t최선의 손실: 0.247864\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.648232\t최선의 손실: 0.247864\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 1.702542\t최선의 손실: 0.247864\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.690735\t최선의 손실: 0.247864\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.748788\t최선의 손실: 0.247864\t정확도: 22.01%\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=10, total=   4.6s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 6.788807\t최선의 손실: 6.788807\t정확도: 93.51%\n",
+      "1\t검증 세트 손실: 1.837317\t최선의 손실: 1.837317\t정확도: 95.31%\n",
+      "2\t검증 세트 손실: 1.676131\t최선의 손실: 1.676131\t정확도: 96.01%\n",
+      "3\t검증 세트 손실: 7798271.000000\t최선의 손실: 1.676131\t정확도: 62.94%\n",
+      "4\t검증 세트 손실: 1850610.000000\t최선의 손실: 1.676131\t정확도: 72.05%\n",
+      "5\t검증 세트 손실: 970665.562500\t최선의 손실: 1.676131\t정확도: 73.81%\n",
+      "6\t검증 세트 손실: 1128778.000000\t최선의 손실: 1.676131\t정확도: 68.88%\n",
+      "7\t검증 세트 손실: 271715.187500\t최선의 손실: 1.676131\t정확도: 91.87%\n",
+      "8\t검증 세트 손실: 187177.859375\t최선의 손실: 1.676131\t정확도: 87.80%\n",
+      "9\t검증 세트 손실: 416332.187500\t최선의 손실: 1.676131\t정확도: 88.04%\n",
+      "10\t검증 세트 손실: 497990.468750\t최선의 손실: 1.676131\t정확도: 93.63%\n",
+      "11\t검증 세트 손실: 140884.921875\t최선의 손실: 1.676131\t정확도: 94.88%\n",
+      "12\t검증 세트 손실: 147681.531250\t최선의 손실: 1.676131\t정확도: 93.82%\n",
+      "13\t검증 세트 손실: 192947.906250\t최선의 손실: 1.676131\t정확도: 92.92%\n",
+      "14\t검증 세트 손실: 194513.609375\t최선의 손실: 1.676131\t정확도: 91.63%\n",
+      "15\t검증 세트 손실: 1624771.000000\t최선의 손실: 1.676131\t정확도: 90.50%\n",
+      "16\t검증 세트 손실: 448674.125000\t최선의 손실: 1.676131\t정확도: 89.80%\n",
+      "17\t검증 세트 손실: 76034.273438\t최선의 손실: 1.676131\t정확도: 95.70%\n",
+      "18\t검증 세트 손실: 139171.796875\t최선의 손실: 1.676131\t정확도: 96.48%\n",
+      "19\t검증 세트 손실: 95697.281250\t최선의 손실: 1.676131\t정확도: 96.25%\n",
+      "20\t검증 세트 손실: 46436.183594\t최선의 손실: 1.676131\t정확도: 96.17%\n",
+      "21\t검증 세트 손실: 100937.382812\t최선의 손실: 1.676131\t정확도: 94.57%\n",
+      "22\t검증 세트 손실: 902852416.000000\t최선의 손실: 1.676131\t정확도: 91.56%\n",
+      "23\t검증 세트 손실: 10690446.000000\t최선의 손실: 1.676131\t정확도: 96.44%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=  10.5s\n",
-      "[CV] batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.064624\t최선의 손실: 1.064624\t정확도: 61.26%\n",
-      "1\t검증 세트 손실: 1.692891\t최선의 손실: 1.064624\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.804587\t최선의 손실: 1.064624\t정확도: 20.91%\n",
-      "3\t검증 세트 손실: 1.685211\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.766681\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.798032\t최선의 손실: 1.064624\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.629639\t최선의 손실: 1.064624\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.633253\t최선의 손실: 1.064624\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.706382\t최선의 손실: 1.064624\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.901958\t최선의 손실: 1.064624\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.616585\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.739565\t최선의 손실: 1.064624\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.993763\t최선의 손실: 1.064624\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.630170\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.737846\t최선의 손실: 1.064624\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.655873\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.623616\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.672467\t최선의 손실: 1.064624\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.646443\t최선의 손실: 1.064624\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.720648\t최선의 손실: 1.064624\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 1.720178\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.817584\t최선의 손실: 1.064624\t정확도: 22.01%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.3, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=  12.7s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 1.995395\t최선의 손실: 1.995395\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.649708\t최선의 손실: 1.649708\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.703337\t최선의 손실: 1.649708\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 1.757775\t최선의 손실: 1.649708\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.695923\t최선의 손실: 1.649708\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.649535\t최선의 손실: 1.649535\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 1.810525\t최선의 손실: 1.649535\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.685136\t최선의 손실: 1.649535\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.833565\t최선의 손실: 1.649535\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.744273\t최선의 손실: 1.649535\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.705477\t최선의 손실: 1.649535\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.770563\t최선의 손실: 1.649535\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2.183831\t최선의 손실: 1.649535\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.685543\t최선의 손실: 1.649535\t정확도: 20.91%\n",
-      "14\t검증 세트 손실: 1.699620\t최선의 손실: 1.649535\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.885617\t최선의 손실: 1.649535\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.704303\t최선의 손실: 1.649535\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.750029\t최선의 손실: 1.649535\t정확도: 22.01%\n",
-      "18\t검증 세트 손실: 2.100176\t최선의 손실: 1.649535\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.626769\t최선의 손실: 1.626769\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.768842\t최선의 손실: 1.626769\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.705132\t최선의 손실: 1.626769\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 1.800928\t최선의 손실: 1.626769\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.980042\t최선의 손실: 1.626769\t정확도: 20.91%\n",
-      "24\t검증 세트 손실: 1.650598\t최선의 손실: 1.626769\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.829817\t최선의 손실: 1.626769\t정확도: 19.27%\n",
-      "26\t검증 세트 손실: 1.629543\t최선의 손실: 1.626769\t정확도: 22.01%\n",
-      "27\t검증 세트 손실: 2.592047\t최선의 손실: 1.626769\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 1.983948\t최선의 손실: 1.626769\t정확도: 22.01%\n",
-      "29\t검증 세트 손실: 1.742049\t최선의 손실: 1.626769\t정확도: 19.27%\n",
-      "30\t검증 세트 손실: 1.715357\t최선의 손실: 1.626769\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 1.915367\t최선의 손실: 1.626769\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 1.728417\t최선의 손실: 1.626769\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 2.674062\t최선의 손실: 1.626769\t정확도: 18.73%\n"
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160, total=  14.4s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 4.809076\t최선의 손실: 4.809076\t정확도: 92.81%\n",
+      "1\t검증 세트 손실: 294262.281250\t최선의 손실: 4.809076\t정확도: 67.83%\n",
+      "2\t검증 세트 손실: 76103.734375\t최선의 손실: 4.809076\t정확도: 70.84%\n",
+      "3\t검증 세트 손실: 39116.460938\t최선의 손실: 4.809076\t정확도: 76.66%\n",
+      "4\t검증 세트 손실: 29329.009766\t최선의 손실: 4.809076\t정확도: 77.91%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "34\t검증 세트 손실: 1.727295\t최선의 손실: 1.626769\t정확도: 22.01%\n",
-      "35\t검증 세트 손실: 2.218793\t최선의 손실: 1.626769\t정확도: 20.91%\n",
-      "36\t검증 세트 손실: 1.781055\t최선의 손실: 1.626769\t정확도: 19.08%\n",
-      "37\t검증 세트 손실: 1.954082\t최선의 손실: 1.626769\t정확도: 18.73%\n",
-      "38\t검증 세트 손실: 2.006041\t최선의 손실: 1.626769\t정확도: 22.01%\n",
-      "39\t검증 세트 손실: 1.709622\t최선의 손실: 1.626769\t정확도: 22.01%\n",
-      "40\t검증 세트 손실: 1.721028\t최선의 손실: 1.626769\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 50808.320312\t최선의 손실: 4.809076\t정확도: 77.68%\n",
+      "6\t검증 세트 손실: 117043.687500\t최선의 손실: 4.809076\t정확도: 75.18%\n",
+      "7\t검증 세트 손실: 9752.245117\t최선의 손실: 4.809076\t정확도: 87.26%\n",
+      "8\t검증 세트 손실: 8739.731445\t최선의 손실: 4.809076\t정확도: 86.40%\n",
+      "9\t검증 세트 손실: 8099.626953\t최선의 손실: 4.809076\t정확도: 78.07%\n",
+      "10\t검증 세트 손실: 31326.693359\t최선의 손실: 4.809076\t정확도: 76.82%\n",
+      "11\t검증 세트 손실: 4223.566895\t최선의 손실: 4.809076\t정확도: 94.29%\n",
+      "12\t검증 세트 손실: 3320.403809\t최선의 손실: 4.809076\t정확도: 92.22%\n",
+      "13\t검증 세트 손실: 16068.195312\t최선의 손실: 4.809076\t정확도: 78.50%\n",
+      "14\t검증 세트 손실: 6137.160645\t최선의 손실: 4.809076\t정확도: 92.18%\n",
+      "15\t검증 세트 손실: 2195.675293\t최선의 손실: 4.809076\t정확도: 95.19%\n",
+      "16\t검증 세트 손실: 2963.812500\t최선의 손실: 4.809076\t정확도: 93.63%\n",
+      "17\t검증 세트 손실: 130073488.000000\t최선의 손실: 4.809076\t정확도: 36.16%\n",
+      "18\t검증 세트 손실: 2311880.250000\t최선의 손실: 4.809076\t정확도: 93.00%\n",
+      "19\t검증 세트 손실: 999952.000000\t최선의 손실: 4.809076\t정확도: 94.84%\n",
+      "20\t검증 세트 손실: 1011524.875000\t최선의 손실: 4.809076\t정확도: 94.41%\n",
+      "21\t검증 세트 손실: 444428.562500\t최선의 손실: 4.809076\t정확도: 95.58%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120, total=  10.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 1.668100\t최선의 손실: 1.668100\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.813105\t최선의 손실: 1.668100\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.676583\t최선의 손실: 1.668100\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 1.803531\t최선의 손실: 1.668100\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.641311\t최선의 손실: 1.641311\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 1.719616\t최선의 손실: 1.641311\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 1.716042\t최선의 손실: 1.641311\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.859000\t최선의 손실: 1.641311\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.719864\t최선의 손실: 1.641311\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.927082\t최선의 손실: 1.641311\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.720778\t최선의 손실: 1.641311\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.691856\t최선의 손실: 1.641311\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 1.688626\t최선의 손실: 1.641311\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.684769\t최선의 손실: 1.641311\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.898281\t최선의 손실: 1.641311\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.774729\t최선의 손실: 1.641311\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.709130\t최선의 손실: 1.641311\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.681220\t최선의 손실: 1.641311\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.638955\t최선의 손실: 1.638955\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.780352\t최선의 손실: 1.638955\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.838553\t최선의 손실: 1.638955\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.910122\t최선의 손실: 1.638955\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.944485\t최선의 손실: 1.638955\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.684305\t최선의 손실: 1.638955\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 2.091593\t최선의 손실: 1.638955\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.790948\t최선의 손실: 1.638955\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.724139\t최선의 손실: 1.638955\t정확도: 20.91%\n",
-      "27\t검증 세트 손실: 1.782733\t최선의 손실: 1.638955\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 2.027250\t최선의 손실: 1.638955\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.975188\t최선의 손실: 1.638955\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 4.830951\t최선의 손실: 1.638955\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 1.715645\t최선의 손실: 1.638955\t정확도: 18.73%\n",
-      "32\t검증 세트 손실: 1.693429\t최선의 손실: 1.638955\t정확도: 19.08%\n",
-      "33\t검증 세트 손실: 1.759462\t최선의 손실: 1.638955\t정확도: 18.73%\n",
-      "34\t검증 세트 손실: 1.746794\t최선의 손실: 1.638955\t정확도: 19.27%\n",
-      "35\t검증 세트 손실: 2.005632\t최선의 손실: 1.638955\t정확도: 20.91%\n",
-      "36\t검증 세트 손실: 1.718128\t최선의 손실: 1.638955\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 1.716231\t최선의 손실: 1.638955\t정확도: 22.01%\n",
-      "38\t검증 세트 손실: 2.000070\t최선의 손실: 1.638955\t정확도: 20.91%\n",
-      "39\t검증 세트 손실: 1.743457\t최선의 손실: 1.638955\t정확도: 18.73%\n",
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160, total=  13.5s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 187.042633\t최선의 손실: 187.042633\t정확도: 72.05%\n",
+      "1\t검증 세트 손실: 43.710331\t최선의 손실: 43.710331\t정확도: 89.17%\n",
+      "2\t검증 세트 손실: 27.601871\t최선의 손실: 27.601871\t정확도: 91.56%\n",
+      "3\t검증 세트 손실: 14.605426\t최선의 손실: 14.605426\t정확도: 94.41%\n",
+      "4\t검증 세트 손실: 8.431691\t최선의 손실: 8.431691\t정확도: 94.84%\n",
+      "5\t검증 세트 손실: 43.751019\t최선의 손실: 8.431691\t정확도: 88.43%\n",
+      "6\t검증 세트 손실: 16.902348\t최선의 손실: 8.431691\t정확도: 94.80%\n",
+      "7\t검증 세트 손실: 11.636637\t최선의 손실: 8.431691\t정확도: 94.64%\n",
+      "8\t검증 세트 손실: 12.522332\t최선의 손실: 8.431691\t정확도: 89.37%\n",
+      "9\t검증 세트 손실: 6.499488\t최선의 손실: 6.499488\t정확도: 93.78%\n",
+      "10\t검증 세트 손실: 691544.937500\t최선의 손실: 6.499488\t정확도: 80.69%\n",
+      "11\t검증 세트 손실: 273113.406250\t최선의 손실: 6.499488\t정확도: 90.23%\n",
+      "12\t검증 세트 손실: 681276.187500\t최선의 손실: 6.499488\t정확도: 76.31%\n",
+      "13\t검증 세트 손실: 128063.937500\t최선의 손실: 6.499488\t정확도: 93.12%\n",
+      "14\t검증 세트 손실: 136832.843750\t최선의 손실: 6.499488\t정확도: 89.44%\n",
+      "15\t검증 세트 손실: 6293546.000000\t최선의 손실: 6.499488\t정확도: 62.28%\n",
+      "16\t검증 세트 손실: 418647.156250\t최선의 손실: 6.499488\t정확도: 90.77%\n",
+      "17\t검증 세트 손실: 159149.000000\t최선의 손실: 6.499488\t정확도: 94.53%\n",
+      "18\t검증 세트 손실: 228270.531250\t최선의 손실: 6.499488\t정확도: 93.04%\n",
+      "19\t검증 세트 손실: 242883.421875\t최선의 손실: 6.499488\t정확도: 94.41%\n",
+      "20\t검증 세트 손실: 459259.906250\t최선의 손실: 6.499488\t정확도: 95.04%\n",
+      "21\t검증 세트 손실: 213868.140625\t최선의 손실: 6.499488\t정확도: 94.96%\n",
+      "22\t검증 세트 손실: 358031.375000\t최선의 손실: 6.499488\t정확도: 82.17%\n",
+      "23\t검증 세트 손실: 129490.179688\t최선의 손실: 6.499488\t정확도: 94.14%\n",
+      "24\t검증 세트 손실: 111819.648438\t최선의 손실: 6.499488\t정확도: 95.11%\n",
+      "25\t검증 세트 손실: 62483.445312\t최선의 손실: 6.499488\t정확도: 94.68%\n",
+      "26\t검증 세트 손실: 135698.062500\t최선의 손실: 6.499488\t정확도: 94.57%\n",
+      "27\t검증 세트 손실: 141749.312500\t최선의 손실: 6.499488\t정확도: 94.49%\n",
+      "28\t검증 세트 손실: 275729.000000\t최선의 손실: 6.499488\t정확도: 76.51%\n",
+      "29\t검증 세트 손실: 82099920.000000\t최선의 손실: 6.499488\t정확도: 92.30%\n",
+      "30\t검증 세트 손실: 7146740.000000\t최선의 손실: 6.499488\t정확도: 95.82%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120, total=  19.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 1.836814\t최선의 손실: 1.836814\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.624650\t최선의 손실: 1.624650\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.677520\t최선의 손실: 1.624650\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 1.750158\t최선의 손실: 1.624650\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.833866\t최선의 손실: 1.624650\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 1.837166\t최선의 손실: 1.624650\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.820451\t최선의 손실: 1.624650\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.795744\t최선의 손실: 1.624650\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.804383\t최선의 손실: 1.624650\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.725124\t최선의 손실: 1.624650\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.737414\t최선의 손실: 1.624650\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.994741\t최선의 손실: 1.624650\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.794573\t최선의 손실: 1.624650\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.734451\t최선의 손실: 1.624650\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.825264\t최선의 손실: 1.624650\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 1.644895\t최선의 손실: 1.624650\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.823519\t최선의 손실: 1.624650\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 2.017848\t최선의 손실: 1.624650\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.847622\t최선의 손실: 1.624650\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.905455\t최선의 손실: 1.624650\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.848299\t최선의 손실: 1.624650\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.987886\t최선의 손실: 1.624650\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 2.320787\t최선의 손실: 1.624650\t정확도: 20.91%\n",
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160, total=  18.6s\n",
+      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.209535\t최선의 손실: 1.209535\t정확도: 38.31%\n",
+      "1\t검증 세트 손실: 1.089813\t최선의 손실: 1.089813\t정확도: 51.02%\n",
+      "2\t검증 세트 손실: 1.142773\t최선의 손실: 1.089813\t정확도: 46.56%\n",
+      "3\t검증 세트 손실: 1.354485\t최선의 손실: 1.089813\t정확도: 31.12%\n",
+      "4\t검증 세트 손실: 1.594454\t최선의 손실: 1.089813\t정확도: 24.86%\n",
+      "5\t검증 세트 손실: 1.568830\t최선의 손실: 1.089813\t정확도: 25.29%\n",
+      "6\t검증 세트 손실: 1.599061\t최선의 손실: 1.089813\t정확도: 22.71%\n",
+      "7\t검증 세트 손실: 1.615541\t최선의 손실: 1.089813\t정확도: 22.13%\n",
+      "8\t검증 세트 손실: 1.529886\t최선의 손실: 1.089813\t정확도: 28.62%\n",
+      "9\t검증 세트 손실: 1.551762\t최선의 손실: 1.089813\t정확도: 24.63%\n",
+      "10\t검증 세트 손실: 1.486638\t최선의 손실: 1.089813\t정확도: 35.54%\n",
+      "11\t검증 세트 손실: 1.480512\t최선의 손실: 1.089813\t정확도: 31.27%\n",
+      "12\t검증 세트 손실: 1.764749\t최선의 손실: 1.089813\t정확도: 26.00%\n",
+      "13\t검증 세트 손실: 4.180819\t최선의 손실: 1.089813\t정확도: 25.92%\n",
+      "14\t검증 세트 손실: 1.677966\t최선의 손실: 1.089813\t정확도: 32.53%\n",
+      "15\t검증 세트 손실: 2.198900\t최선의 손실: 1.089813\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 2.062979\t최선의 손실: 1.089813\t정확도: 35.77%\n",
+      "17\t검증 세트 손실: 2.983961\t최선의 손실: 1.089813\t정확도: 22.17%\n",
+      "18\t검증 세트 손실: 2.974869\t최선의 손실: 1.089813\t정확도: 30.65%\n",
+      "19\t검증 세트 손실: 1.663492\t최선의 손실: 1.089813\t정확도: 26.74%\n",
+      "20\t검증 세트 손실: 6.202923\t최선의 손실: 1.089813\t정확도: 30.34%\n",
+      "21\t검증 세트 손실: 3.923892\t최선의 손실: 1.089813\t정확도: 21.74%\n",
+      "22\t검증 세트 손실: 2.128598\t최선의 손실: 1.089813\t정확도: 22.99%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=120, total=  12.0s\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.422905\t최선의 손실: 0.422905\t정확도: 87.41%\n",
-      "1\t검증 세트 손실: 0.173459\t최선의 손실: 0.173459\t정확도: 95.15%\n",
-      "2\t검증 세트 손실: 0.133290\t최선의 손실: 0.133290\t정확도: 96.25%\n",
-      "3\t검증 세트 손실: 0.138228\t최선의 손실: 0.133290\t정확도: 96.33%\n",
-      "4\t검증 세트 손실: 0.118134\t최선의 손실: 0.118134\t정확도: 96.17%\n",
-      "5\t검증 세트 손실: 0.136573\t최선의 손실: 0.118134\t정확도: 96.72%\n",
-      "6\t검증 세트 손실: 0.117906\t최선의 손실: 0.117906\t정확도: 96.87%\n",
-      "7\t검증 세트 손실: 0.136400\t최선의 손실: 0.117906\t정확도: 96.29%\n",
-      "8\t검증 세트 손실: 0.120305\t최선의 손실: 0.117906\t정확도: 96.60%\n",
-      "9\t검증 세트 손실: 0.123431\t최선의 손실: 0.117906\t정확도: 96.64%\n",
-      "10\t검증 세트 손실: 0.128550\t최선의 손실: 0.117906\t정확도: 96.48%\n",
-      "11\t검증 세트 손실: 0.121921\t최선의 손실: 0.117906\t정확도: 96.99%\n",
-      "12\t검증 세트 손실: 0.119068\t최선의 손실: 0.117906\t정확도: 96.83%\n",
-      "13\t검증 세트 손실: 0.126089\t최선의 손실: 0.117906\t정확도: 96.76%\n",
-      "14\t검증 세트 손실: 0.129725\t최선의 손실: 0.117906\t정확도: 96.52%\n",
-      "15\t검증 세트 손실: 0.120177\t최선의 손실: 0.117906\t정확도: 96.68%\n",
-      "16\t검증 세트 손실: 0.128881\t최선의 손실: 0.117906\t정확도: 96.64%\n",
-      "17\t검증 세트 손실: 0.131884\t최선의 손실: 0.117906\t정확도: 96.60%\n",
-      "18\t검증 세트 손실: 0.127143\t최선의 손실: 0.117906\t정확도: 96.83%\n",
-      "19\t검증 세트 손실: 0.117979\t최선의 손실: 0.117906\t정확도: 96.52%\n",
-      "20\t검증 세트 손실: 0.122384\t최선의 손실: 0.117906\t정확도: 96.48%\n",
-      "21\t검증 세트 손실: 0.114651\t최선의 손실: 0.114651\t정확도: 97.03%\n",
-      "22\t검증 세트 손실: 0.132087\t최선의 손실: 0.114651\t정확도: 96.68%\n",
-      "23\t검증 세트 손실: 0.129007\t최선의 손실: 0.114651\t정확도: 96.76%\n",
-      "24\t검증 세트 손실: 0.127824\t최선의 손실: 0.114651\t정확도: 96.68%\n",
-      "25\t검증 세트 손실: 0.127337\t최선의 손실: 0.114651\t정확도: 96.56%\n",
-      "26\t검증 세트 손실: 0.126687\t최선의 손실: 0.114651\t정확도: 96.56%\n",
-      "27\t검증 세트 손실: 0.122938\t최선의 손실: 0.114651\t정확도: 96.72%\n",
-      "28\t검증 세트 손실: 0.119828\t최선의 손실: 0.114651\t정확도: 96.91%\n",
-      "29\t검증 세트 손실: 0.113775\t최선의 손실: 0.113775\t정확도: 97.11%\n",
-      "30\t검증 세트 손실: 0.122508\t최선의 손실: 0.113775\t정확도: 97.11%\n",
-      "31\t검증 세트 손실: 0.112205\t최선의 손실: 0.112205\t정확도: 97.15%\n",
-      "32\t검증 세트 손실: 0.124002\t최선의 손실: 0.112205\t정확도: 96.79%\n",
-      "33\t검증 세트 손실: 0.124088\t최선의 손실: 0.112205\t정확도: 96.48%\n",
-      "34\t검증 세트 손실: 0.123358\t최선의 손실: 0.112205\t정확도: 96.56%\n",
-      "35\t검증 세트 손실: 0.116985\t최선의 손실: 0.112205\t정확도: 96.83%\n",
-      "36\t검증 세트 손실: 0.128352\t최선의 손실: 0.112205\t정확도: 96.68%\n",
-      "37\t검증 세트 손실: 0.127912\t최선의 손실: 0.112205\t정확도: 96.99%\n",
-      "38\t검증 세트 손실: 0.118355\t최선의 손실: 0.112205\t정확도: 96.83%\n",
-      "39\t검증 세트 손실: 0.130296\t최선의 손실: 0.112205\t정확도: 96.52%\n",
-      "40\t검증 세트 손실: 0.119632\t최선의 손실: 0.112205\t정확도: 96.91%\n",
-      "41\t검증 세트 손실: 0.128383\t최선의 손실: 0.112205\t정확도: 96.60%\n",
-      "42\t검증 세트 손실: 0.135854\t최선의 손실: 0.112205\t정확도: 96.60%\n",
-      "43\t검증 세트 손실: 0.133828\t최선의 손실: 0.112205\t정확도: 96.68%\n",
-      "44\t검증 세트 손실: 0.138171\t최선의 손실: 0.112205\t정확도: 96.60%\n",
-      "45\t검증 세트 손실: 0.130548\t최선의 손실: 0.112205\t정확도: 96.87%\n",
-      "46\t검증 세트 손실: 0.122018\t최선의 손실: 0.112205\t정확도: 96.72%\n",
-      "47\t검증 세트 손실: 0.141098\t최선의 손실: 0.112205\t정확도: 96.64%\n",
-      "48\t검증 세트 손실: 0.136832\t최선의 손실: 0.112205\t정확도: 96.60%\n",
-      "49\t검증 세트 손실: 0.124382\t최선의 손실: 0.112205\t정확도: 96.87%\n",
-      "50\t검증 세트 손실: 0.130666\t최선의 손실: 0.112205\t정확도: 96.40%\n",
-      "51\t검증 세트 손실: 0.113349\t최선의 손실: 0.112205\t정확도: 97.15%\n",
-      "52\t검증 세트 손실: 0.120764\t최선의 손실: 0.112205\t정확도: 96.79%\n",
+      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  13.6s\n",
+      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.357058\t최선의 손실: 1.357058\t정확도: 38.51%\n",
+      "1\t검증 세트 손실: 1.232482\t최선의 손실: 1.232482\t정확도: 48.12%\n",
+      "2\t검증 세트 손실: 1.322143\t최선의 손실: 1.232482\t정확도: 36.98%\n",
+      "3\t검증 세트 손실: 1.616197\t최선의 손실: 1.232482\t정확도: 24.94%\n",
+      "4\t검증 세트 손실: 1.461936\t최선의 손실: 1.232482\t정확도: 33.03%\n",
+      "5\t검증 세트 손실: 1.536854\t최선의 손실: 1.232482\t정확도: 31.90%\n",
+      "6\t검증 세트 손실: 1.472599\t최선의 손실: 1.232482\t정확도: 33.85%\n",
+      "7\t검증 세트 손실: 1.547379\t최선의 손실: 1.232482\t정확도: 37.33%\n",
+      "8\t검증 세트 손실: 1.729134\t최선의 손실: 1.232482\t정확도: 24.28%\n",
+      "9\t검증 세트 손실: 2.610099\t최선의 손실: 1.232482\t정확도: 38.58%\n",
+      "10\t검증 세트 손실: 1.991397\t최선의 손실: 1.232482\t정확도: 27.48%\n",
+      "11\t검증 세트 손실: 4.220920\t최선의 손실: 1.232482\t정확도: 29.91%\n",
+      "12\t검증 세트 손실: 4.835096\t최선의 손실: 1.232482\t정확도: 21.81%\n",
+      "13\t검증 세트 손실: 9.269316\t최선의 손실: 1.232482\t정확도: 19.04%\n",
+      "14\t검증 세트 손실: 5.863304\t최선의 손실: 1.232482\t정확도: 26.23%\n",
+      "15\t검증 세트 손실: 3.191941\t최선의 손실: 1.232482\t정확도: 29.87%\n",
+      "16\t검증 세트 손실: 3.221937\t최선의 손실: 1.232482\t정확도: 23.81%\n",
+      "17\t검증 세트 손실: 5.110563\t최선의 손실: 1.232482\t정확도: 35.18%\n",
+      "18\t검증 세트 손실: 10.009648\t최선의 손실: 1.232482\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 11.104661\t최선의 손실: 1.232482\t정확도: 21.19%\n",
+      "20\t검증 세트 손실: 7.998678\t최선의 손실: 1.232482\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 6.328785\t최선의 손실: 1.232482\t정확도: 35.22%\n",
+      "22\t검증 세트 손실: 21.491182\t최선의 손실: 1.232482\t정확도: 20.21%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=   4.6s\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.307781\t최선의 손실: 0.307781\t정확도: 91.91%\n",
-      "1\t검증 세트 손실: 0.159870\t최선의 손실: 0.159870\t정확도: 95.74%\n",
-      "2\t검증 세트 손실: 0.164285\t최선의 손실: 0.159870\t정확도: 95.97%\n",
-      "3\t검증 세트 손실: 0.142598\t최선의 손실: 0.142598\t정확도: 95.93%\n",
-      "4\t검증 세트 손실: 0.134623\t최선의 손실: 0.134623\t정확도: 96.36%\n",
-      "5\t검증 세트 손실: 0.136521\t최선의 손실: 0.134623\t정확도: 96.29%\n",
-      "6\t검증 세트 손실: 0.122497\t최선의 손실: 0.122497\t정확도: 96.44%\n",
-      "7\t검증 세트 손실: 0.138163\t최선의 손실: 0.122497\t정확도: 96.40%\n",
-      "8\t검증 세트 손실: 0.124782\t최선의 손실: 0.122497\t정확도: 96.60%\n",
-      "9\t검증 세트 손실: 0.121735\t최선의 손실: 0.121735\t정확도: 96.68%\n",
-      "10\t검증 세트 손실: 0.119491\t최선의 손실: 0.119491\t정확도: 96.95%\n",
-      "11\t검증 세트 손실: 0.127076\t최선의 손실: 0.119491\t정확도: 96.64%\n",
-      "12\t검증 세트 손실: 0.112033\t최선의 손실: 0.112033\t정확도: 96.91%\n",
-      "13\t검증 세트 손실: 0.104392\t최선의 손실: 0.104392\t정확도: 97.03%\n",
-      "14\t검증 세트 손실: 0.126658\t최선의 손실: 0.104392\t정확도: 96.52%\n",
-      "15\t검증 세트 손실: 0.114454\t최선의 손실: 0.104392\t정확도: 97.11%\n",
-      "16\t검증 세트 손실: 0.110788\t최선의 손실: 0.104392\t정확도: 96.87%\n",
-      "17\t검증 세트 손실: 0.107835\t최선의 손실: 0.104392\t정확도: 97.11%\n",
-      "18\t검증 세트 손실: 0.118062\t최선의 손실: 0.104392\t정확도: 96.83%\n",
-      "19\t검증 세트 손실: 0.111669\t최선의 손실: 0.104392\t정확도: 96.83%\n"
+      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  13.7s\n",
+      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.209679\t최선의 손실: 1.209679\t정확도: 48.55%\n",
+      "1\t검증 세트 손실: 1.147614\t최선의 손실: 1.147614\t정확도: 44.29%\n",
+      "2\t검증 세트 손실: 1.466882\t최선의 손실: 1.147614\t정확도: 34.48%\n",
+      "3\t검증 세트 손실: 1.568259\t최선의 손실: 1.147614\t정확도: 26.90%\n",
+      "4\t검증 세트 손실: 1.651907\t최선의 손실: 1.147614\t정확도: 23.92%\n",
+      "5\t검증 세트 손실: 1.609950\t최선의 손실: 1.147614\t정확도: 34.56%\n",
+      "6\t검증 세트 손실: 1.536374\t최선의 손실: 1.147614\t정확도: 32.45%\n",
+      "7\t검증 세트 손실: 1.492543\t최선의 손실: 1.147614\t정확도: 30.73%\n",
+      "8\t검증 세트 손실: 1.925661\t최선의 손실: 1.147614\t정확도: 36.04%\n",
+      "9\t검증 세트 손실: 1.530353\t최선의 손실: 1.147614\t정확도: 34.79%\n",
+      "10\t검증 세트 손실: 1.729318\t최선의 손실: 1.147614\t정확도: 35.57%\n",
+      "11\t검증 세트 손실: 2.307492\t최선의 손실: 1.147614\t정확도: 34.68%\n",
+      "12\t검증 세트 손실: 3.608251\t최선의 손실: 1.147614\t정확도: 14.97%\n",
+      "13\t검증 세트 손실: 10.293470\t최선의 손실: 1.147614\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 5.912032\t최선의 손실: 1.147614\t정확도: 25.57%\n",
+      "15\t검증 세트 손실: 89.763275\t최선의 손실: 1.147614\t정확도: 29.71%\n",
+      "16\t검증 세트 손실: 8.580408\t최선의 손실: 1.147614\t정확도: 20.05%\n",
+      "17\t검증 세트 손실: 13.986801\t최선의 손실: 1.147614\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 20.706451\t최선의 손실: 1.147614\t정확도: 21.66%\n",
+      "19\t검증 세트 손실: 7.429577\t최선의 손실: 1.147614\t정확도: 33.85%\n",
+      "20\t검증 세트 손실: 4.176493\t최선의 손실: 1.147614\t정확도: 35.65%\n",
+      "21\t검증 세트 손실: 9.342262\t최선의 손실: 1.147614\t정확도: 36.40%\n",
+      "22\t검증 세트 손실: 7.226376\t최선의 손실: 1.147614\t정확도: 29.79%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  13.7s\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.349935\t최선의 손실: 0.349935\t정확도: 91.20%\n",
+      "1\t검증 세트 손실: 0.834724\t최선의 손실: 0.349935\t정확도: 56.49%\n",
+      "2\t검증 세트 손실: 0.906617\t최선의 손실: 0.349935\t정확도: 76.58%\n",
+      "3\t검증 세트 손실: 0.799141\t최선의 손실: 0.349935\t정확도: 81.51%\n",
+      "4\t검증 세트 손실: 0.910615\t최선의 손실: 0.349935\t정확도: 61.92%\n",
+      "5\t검증 세트 손실: 0.770598\t최선의 손실: 0.349935\t정확도: 70.91%\n",
+      "6\t검증 세트 손실: 0.747538\t최선의 손실: 0.349935\t정확도: 68.80%\n",
+      "7\t검증 세트 손실: 0.916746\t최선의 손실: 0.349935\t정확도: 61.26%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20\t검증 세트 손실: 0.118065\t최선의 손실: 0.104392\t정확도: 96.99%\n",
-      "21\t검증 세트 손실: 0.119449\t최선의 손실: 0.104392\t정확도: 96.87%\n",
-      "22\t검증 세트 손실: 0.116556\t최선의 손실: 0.104392\t정확도: 96.87%\n",
-      "23\t검증 세트 손실: 0.112889\t최선의 손실: 0.104392\t정확도: 96.91%\n",
-      "24\t검증 세트 손실: 0.116955\t최선의 손실: 0.104392\t정확도: 96.48%\n",
-      "25\t검증 세트 손실: 0.123875\t최선의 손실: 0.104392\t정확도: 96.64%\n",
-      "26\t검증 세트 손실: 0.114690\t최선의 손실: 0.104392\t정확도: 97.07%\n",
-      "27\t검증 세트 손실: 0.117543\t최선의 손실: 0.104392\t정확도: 97.22%\n",
-      "28\t검증 세트 손실: 0.109348\t최선의 손실: 0.104392\t정확도: 97.46%\n",
-      "29\t검증 세트 손실: 0.108882\t최선의 손실: 0.104392\t정확도: 97.15%\n",
-      "30\t검증 세트 손실: 0.110396\t최선의 손실: 0.104392\t정확도: 97.19%\n",
-      "31\t검증 세트 손실: 0.113390\t최선의 손실: 0.104392\t정확도: 96.68%\n",
-      "32\t검증 세트 손실: 0.108612\t최선의 손실: 0.104392\t정확도: 97.15%\n",
-      "33\t검증 세트 손실: 0.120423\t최선의 손실: 0.104392\t정확도: 96.91%\n",
-      "34\t검증 세트 손실: 0.112346\t최선의 손실: 0.104392\t정확도: 97.03%\n",
+      "8\t검증 세트 손실: 0.664030\t최선의 손실: 0.349935\t정확도: 70.99%\n",
+      "9\t검증 세트 손실: 1.229623\t최선의 손실: 0.349935\t정확도: 48.91%\n",
+      "10\t검증 세트 손실: 0.787489\t최선의 손실: 0.349935\t정확도: 67.63%\n",
+      "11\t검증 세트 손실: 0.959435\t최선의 손실: 0.349935\t정확도: 61.53%\n",
+      "12\t검증 세트 손실: 1.168812\t최선의 손실: 0.349935\t정확도: 51.80%\n",
+      "13\t검증 세트 손실: 0.981016\t최선의 손실: 0.349935\t정확도: 53.95%\n",
+      "14\t검증 세트 손실: 1.112092\t최선의 손실: 0.349935\t정확도: 50.70%\n",
+      "15\t검증 세트 손실: 0.995639\t최선의 손실: 0.349935\t정확도: 57.82%\n",
+      "16\t검증 세트 손실: 0.967494\t최선의 손실: 0.349935\t정확도: 54.34%\n",
+      "17\t검증 세트 손실: 1.039945\t최선의 손실: 0.349935\t정확도: 59.27%\n",
+      "18\t검증 세트 손실: 2.196686\t최선의 손실: 0.349935\t정확도: 28.66%\n",
+      "19\t검증 세트 손실: 1.741438\t최선의 손실: 0.349935\t정확도: 35.38%\n",
+      "20\t검증 세트 손실: 1.735731\t최선의 손실: 0.349935\t정확도: 40.81%\n",
+      "21\t검증 세트 손실: 1.326596\t최선의 손실: 0.349935\t정확도: 41.95%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=   2.8s\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10 \n",
-      "0\t검증 세트 손실: 0.454206\t최선의 손실: 0.454206\t정확도: 86.16%\n",
-      "1\t검증 세트 손실: 0.170733\t최선의 손실: 0.170733\t정확도: 95.31%\n",
-      "2\t검증 세트 손실: 0.138649\t최선의 손실: 0.138649\t정확도: 96.40%\n",
-      "3\t검증 세트 손실: 0.123493\t최선의 손실: 0.123493\t정확도: 96.60%\n",
-      "4\t검증 세트 손실: 0.123961\t최선의 손실: 0.123493\t정확도: 96.64%\n",
-      "5\t검증 세트 손실: 0.129772\t최선의 손실: 0.123493\t정확도: 96.52%\n",
-      "6\t검증 세트 손실: 0.122293\t최선의 손실: 0.122293\t정확도: 96.48%\n",
-      "7\t검증 세트 손실: 0.131308\t최선의 손실: 0.122293\t정확도: 96.25%\n",
-      "8\t검증 세트 손실: 0.122047\t최선의 손실: 0.122047\t정확도: 96.68%\n",
-      "9\t검증 세트 손실: 0.119257\t최선의 손실: 0.119257\t정확도: 96.79%\n",
-      "10\t검증 세트 손실: 0.131209\t최선의 손실: 0.119257\t정확도: 96.44%\n",
-      "11\t검증 세트 손실: 0.125291\t최선의 손실: 0.119257\t정확도: 96.72%\n",
-      "12\t검증 세트 손실: 0.130191\t최선의 손실: 0.119257\t정확도: 96.68%\n",
-      "13\t검증 세트 손실: 0.122911\t최선의 손실: 0.119257\t정확도: 96.60%\n",
-      "14\t검증 세트 손실: 0.113113\t최선의 손실: 0.113113\t정확도: 96.72%\n",
-      "15\t검증 세트 손실: 0.115949\t최선의 손실: 0.113113\t정확도: 96.76%\n",
-      "16\t검증 세트 손실: 0.125183\t최선의 손실: 0.113113\t정확도: 96.72%\n",
-      "17\t검증 세트 손실: 0.115524\t최선의 손실: 0.113113\t정확도: 96.76%\n",
-      "18\t검증 세트 손실: 0.111095\t최선의 손실: 0.111095\t정확도: 96.87%\n",
-      "19\t검증 세트 손실: 0.119569\t최선의 손실: 0.111095\t정확도: 96.79%\n",
-      "20\t검증 세트 손실: 0.124928\t최선의 손실: 0.111095\t정확도: 96.83%\n",
-      "21\t검증 세트 손실: 0.118234\t최선의 손실: 0.111095\t정확도: 96.56%\n",
-      "22\t검증 세트 손실: 0.113702\t최선의 손실: 0.111095\t정확도: 96.91%\n",
-      "23\t검증 세트 손실: 0.127579\t최선의 손실: 0.111095\t정확도: 96.68%\n",
-      "24\t검증 세트 손실: 0.118937\t최선의 손실: 0.111095\t정확도: 96.99%\n",
-      "25\t검증 세트 손실: 0.132067\t최선의 손실: 0.111095\t정확도: 96.33%\n",
-      "26\t검증 세트 손실: 0.120628\t최선의 손실: 0.111095\t정확도: 96.72%\n",
-      "27\t검증 세트 손실: 0.135204\t최선의 손실: 0.111095\t정확도: 96.40%\n",
-      "28\t검증 세트 손실: 0.112055\t최선의 손실: 0.111095\t정확도: 96.95%\n",
-      "29\t검증 세트 손실: 0.121511\t최선의 손실: 0.111095\t정확도: 96.52%\n",
-      "30\t검증 세트 손실: 0.115169\t최선의 손실: 0.111095\t정확도: 96.95%\n",
-      "31\t검증 세트 손실: 0.127937\t최선의 손실: 0.111095\t정확도: 96.79%\n",
-      "32\t검증 세트 손실: 0.112725\t최선의 손실: 0.111095\t정확도: 97.03%\n",
-      "33\t검증 세트 손실: 0.121546\t최선의 손실: 0.111095\t정확도: 96.60%\n",
-      "34\t검증 세트 손실: 0.119500\t최선의 손실: 0.111095\t정확도: 96.83%\n",
-      "35\t검증 세트 손실: 0.121457\t최선의 손실: 0.111095\t정확도: 96.64%\n",
-      "36\t검증 세트 손실: 0.106066\t최선의 손실: 0.106066\t정확도: 97.11%\n",
-      "37\t검증 세트 손실: 0.128607\t최선의 손실: 0.106066\t정확도: 96.36%\n",
-      "38\t검증 세트 손실: 0.116072\t최선의 손실: 0.106066\t정확도: 97.03%\n",
-      "39\t검증 세트 손실: 0.114607\t최선의 손실: 0.106066\t정확도: 96.60%\n",
-      "40\t검증 세트 손실: 0.120912\t최선의 손실: 0.106066\t정확도: 96.79%\n",
-      "41\t검증 세트 손실: 0.119728\t최선의 손실: 0.106066\t정확도: 96.72%\n",
-      "42\t검증 세트 손실: 0.117991\t최선의 손실: 0.106066\t정확도: 97.03%\n",
-      "43\t검증 세트 손실: 0.116187\t최선의 손실: 0.106066\t정확도: 96.83%\n",
-      "44\t검증 세트 손실: 0.107496\t최선의 손실: 0.106066\t정확도: 96.99%\n",
-      "45\t검증 세트 손실: 0.116356\t최선의 손실: 0.106066\t정확도: 96.99%\n",
-      "46\t검증 세트 손실: 0.114167\t최선의 손실: 0.106066\t정확도: 97.11%\n",
-      "47\t검증 세트 손실: 0.113874\t최선의 손실: 0.106066\t정확도: 96.72%\n",
-      "48\t검증 세트 손실: 0.114606\t최선의 손실: 0.106066\t정확도: 96.79%\n",
-      "49\t검증 세트 손실: 0.109872\t최선의 손실: 0.106066\t정확도: 97.11%\n",
-      "50\t검증 세트 손실: 0.108347\t최선의 손실: 0.106066\t정확도: 97.07%\n",
-      "51\t검증 세트 손실: 0.116553\t최선의 손실: 0.106066\t정확도: 96.68%\n",
-      "52\t검증 세트 손실: 0.127271\t최선의 손실: 0.106066\t정확도: 96.56%\n",
-      "53\t검증 세트 손실: 0.113227\t최선의 손실: 0.106066\t정확도: 96.87%\n",
-      "54\t검증 세트 손실: 0.103372\t최선의 손실: 0.103372\t정확도: 97.30%\n",
-      "55\t검증 세트 손실: 0.129446\t최선의 손실: 0.103372\t정확도: 96.64%\n",
-      "56\t검증 세트 손실: 0.117258\t최선의 손실: 0.103372\t정확도: 96.76%\n",
-      "57\t검증 세트 손실: 0.127109\t최선의 손실: 0.103372\t정확도: 96.52%\n",
-      "58\t검증 세트 손실: 0.111575\t최선의 손실: 0.103372\t정확도: 96.83%\n",
-      "59\t검증 세트 손실: 0.109509\t최선의 손실: 0.103372\t정확도: 97.22%\n",
-      "60\t검증 세트 손실: 0.117796\t최선의 손실: 0.103372\t정확도: 97.30%\n",
-      "61\t검증 세트 손실: 0.123727\t최선의 손실: 0.103372\t정확도: 96.79%\n",
-      "62\t검증 세트 손실: 0.110331\t최선의 손실: 0.103372\t정확도: 97.15%\n",
-      "63\t검증 세트 손실: 0.110415\t최선의 손실: 0.103372\t정확도: 97.22%\n",
-      "64\t검증 세트 손실: 0.113474\t최선의 손실: 0.103372\t정확도: 97.07%\n",
-      "65\t검증 세트 손실: 0.113132\t최선의 손실: 0.103372\t정확도: 96.83%\n",
-      "66\t검증 세트 손실: 0.099665\t최선의 손실: 0.099665\t정확도: 97.54%\n",
-      "67\t검증 세트 손실: 0.115046\t최선의 손실: 0.099665\t정확도: 96.99%\n",
-      "68\t검증 세트 손실: 0.112284\t최선의 손실: 0.099665\t정확도: 96.95%\n",
-      "69\t검증 세트 손실: 0.103937\t최선의 손실: 0.099665\t정확도: 97.26%\n",
-      "70\t검증 세트 손실: 0.120130\t최선의 손실: 0.099665\t정확도: 96.87%\n",
-      "71\t검증 세트 손실: 0.124523\t최선의 손실: 0.099665\t정확도: 96.52%\n",
-      "72\t검증 세트 손실: 0.109828\t최선의 손실: 0.099665\t정확도: 97.19%\n",
-      "73\t검증 세트 손실: 0.116557\t최선의 손실: 0.099665\t정확도: 96.99%\n",
-      "74\t검증 세트 손실: 0.107929\t최선의 손실: 0.099665\t정확도: 97.15%\n",
-      "75\t검증 세트 손실: 0.127045\t최선의 손실: 0.099665\t정확도: 96.44%\n",
-      "76\t검증 세트 손실: 0.108802\t최선의 손실: 0.099665\t정확도: 96.76%\n",
-      "77\t검증 세트 손실: 0.124403\t최선의 손실: 0.099665\t정확도: 96.83%\n",
-      "78\t검증 세트 손실: 0.104627\t최선의 손실: 0.099665\t정확도: 97.15%\n",
-      "79\t검증 세트 손실: 0.105662\t최선의 손실: 0.099665\t정확도: 97.26%\n",
-      "80\t검증 세트 손실: 0.101793\t최선의 손실: 0.099665\t정확도: 97.62%\n",
-      "81\t검증 세트 손실: 0.101160\t최선의 손실: 0.099665\t정확도: 97.42%\n",
-      "82\t검증 세트 손실: 0.104181\t최선의 손실: 0.099665\t정확도: 97.26%\n",
-      "83\t검증 세트 손실: 0.110081\t최선의 손실: 0.099665\t정확도: 97.22%\n",
-      "84\t검증 세트 손실: 0.104304\t최선의 손실: 0.099665\t정확도: 97.42%\n",
-      "85\t검증 세트 손실: 0.103671\t최선의 손실: 0.099665\t정확도: 97.34%\n",
-      "86\t검증 세트 손실: 0.109573\t최선의 손실: 0.099665\t정확도: 97.22%\n",
-      "87\t검증 세트 손실: 0.099142\t최선의 손실: 0.099142\t정확도: 97.38%\n",
-      "88\t검증 세트 손실: 0.112888\t최선의 손실: 0.099142\t정확도: 97.26%\n",
-      "89\t검증 세트 손실: 0.110209\t최선의 손실: 0.099142\t정확도: 97.19%\n",
-      "90\t검증 세트 손실: 0.112219\t최선의 손실: 0.099142\t정확도: 97.19%\n",
-      "91\t검증 세트 손실: 0.121014\t최선의 손실: 0.099142\t정확도: 96.72%\n",
-      "92\t검증 세트 손실: 0.118263\t최선의 손실: 0.099142\t정확도: 96.95%\n",
-      "93\t검증 세트 손실: 0.106116\t최선의 손실: 0.099142\t정확도: 97.50%\n",
-      "94\t검증 세트 손실: 0.112601\t최선의 손실: 0.099142\t정확도: 96.87%\n",
-      "95\t검증 세트 손실: 0.109344\t최선의 손실: 0.099142\t정확도: 96.99%\n",
-      "96\t검증 세트 손실: 0.110498\t최선의 손실: 0.099142\t정확도: 97.26%\n",
-      "97\t검증 세트 손실: 0.110634\t최선의 손실: 0.099142\t정확도: 97.30%\n",
-      "98\t검증 세트 손실: 0.107062\t최선의 손실: 0.099142\t정확도: 97.34%\n",
-      "99\t검증 세트 손실: 0.116838\t최선의 손실: 0.099142\t정확도: 97.34%\n",
-      "100\t검증 세트 손실: 0.104029\t최선의 손실: 0.099142\t정확도: 97.26%\n",
-      "101\t검증 세트 손실: 0.107151\t최선의 손실: 0.099142\t정확도: 97.34%\n",
-      "102\t검증 세트 손실: 0.113644\t최선의 손실: 0.099142\t정확도: 97.15%\n",
-      "103\t검증 세트 손실: 0.102802\t최선의 손실: 0.099142\t정확도: 97.46%\n",
-      "104\t검증 세트 손실: 0.100351\t최선의 손실: 0.099142\t정확도: 97.65%\n",
-      "105\t검증 세트 손실: 0.113797\t최선의 손실: 0.099142\t정확도: 97.19%\n",
-      "106\t검증 세트 손실: 0.106860\t최선의 손실: 0.099142\t정확도: 97.54%\n",
-      "107\t검증 세트 손실: 0.103664\t최선의 손실: 0.099142\t정확도: 97.38%\n",
-      "108\t검증 세트 손실: 0.107537\t최선의 손실: 0.099142\t정확도: 97.19%\n",
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  59.1s\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.431303\t최선의 손실: 0.431303\t정확도: 85.81%\n",
+      "1\t검증 세트 손실: 0.705201\t최선의 손실: 0.431303\t정확도: 77.13%\n",
+      "2\t검증 세트 손실: 0.575120\t최선의 손실: 0.431303\t정확도: 75.92%\n",
+      "3\t검증 세트 손실: 0.673671\t최선의 손실: 0.431303\t정확도: 72.17%\n",
+      "4\t검증 세트 손실: 1.085364\t최선의 손실: 0.431303\t정확도: 62.59%\n",
+      "5\t검증 세트 손실: 1.119733\t최선의 손실: 0.431303\t정확도: 47.62%\n",
+      "6\t검증 세트 손실: 0.999127\t최선의 손실: 0.431303\t정확도: 63.92%\n",
+      "7\t검증 세트 손실: 1.058674\t최선의 손실: 0.431303\t정확도: 54.81%\n",
+      "8\t검증 세트 손실: 1.792455\t최선의 손실: 0.431303\t정확도: 37.22%\n",
+      "9\t검증 세트 손실: 1.125553\t최선의 손실: 0.431303\t정확도: 51.29%\n",
+      "10\t검증 세트 손실: 1.019437\t최선의 손실: 0.431303\t정확도: 55.28%\n",
+      "11\t검증 세트 손실: 1.105839\t최선의 손실: 0.431303\t정확도: 54.46%\n",
+      "12\t검증 세트 손실: 1.316742\t최선의 손실: 0.431303\t정확도: 34.87%\n",
+      "13\t검증 세트 손실: 1.283758\t최선의 손실: 0.431303\t정확도: 44.14%\n",
+      "14\t검증 세트 손실: 1.589319\t최선의 손실: 0.431303\t정확도: 52.50%\n",
+      "15\t검증 세트 손실: 1.993692\t최선의 손실: 0.431303\t정확도: 38.74%\n",
+      "16\t검증 세트 손실: 1.442496\t최선의 손실: 0.431303\t정확도: 40.27%\n",
+      "17\t검증 세트 손실: 1.312326\t최선의 손실: 0.431303\t정확도: 41.40%\n",
+      "18\t검증 세트 손실: 1.585425\t최선의 손실: 0.431303\t정확도: 31.63%\n",
+      "19\t검증 세트 손실: 1.350260\t최선의 손실: 0.431303\t정확도: 38.00%\n",
+      "20\t검증 세트 손실: 1.481212\t최선의 손실: 0.431303\t정확도: 39.09%\n",
+      "21\t검증 세트 손실: 2.441567\t최선의 손실: 0.431303\t정확도: 35.93%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.02, activation=<function elu at 0x7f99417cf6a8>, n_neurons=10, total=  15.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 13.448126\t최선의 손실: 13.448126\t정확도: 93.43%\n",
-      "1\t검증 세트 손실: 4.158259\t최선의 손실: 4.158259\t정확도: 94.06%\n",
-      "2\t검증 세트 손실: 5.429367\t최선의 손실: 4.158259\t정확도: 94.33%\n",
-      "3\t검증 세트 손실: 5.908160\t최선의 손실: 4.158259\t정확도: 92.10%\n",
-      "4\t검증 세트 손실: 2.025134\t최선의 손실: 2.025134\t정확도: 95.00%\n",
-      "5\t검증 세트 손실: 1280241.250000\t최선의 손실: 2.025134\t정확도: 19.23%\n",
-      "6\t검증 세트 손실: 797303.000000\t최선의 손실: 2.025134\t정확도: 40.70%\n",
-      "7\t검증 세트 손실: 292435.125000\t최선의 손실: 2.025134\t정확도: 52.46%\n",
-      "8\t검증 세트 손실: 139389.312500\t최선의 손실: 2.025134\t정확도: 55.51%\n",
-      "9\t검증 세트 손실: 172714.515625\t최선의 손실: 2.025134\t정확도: 46.72%\n",
-      "10\t검증 세트 손실: 73491.625000\t최선의 손실: 2.025134\t정확도: 71.85%\n",
-      "11\t검증 세트 손실: 91317.132812\t최선의 손실: 2.025134\t정확도: 53.36%\n",
-      "12\t검증 세트 손실: 42041.832031\t최선의 손실: 2.025134\t정확도: 57.27%\n",
-      "13\t검증 세트 손실: 150320.859375\t최선의 손실: 2.025134\t정확도: 49.34%\n",
-      "14\t검증 세트 손실: 115371.062500\t최선의 손실: 2.025134\t정확도: 56.68%\n",
-      "15\t검증 세트 손실: 62226.152344\t최선의 손실: 2.025134\t정확도: 48.75%\n",
-      "16\t검증 세트 손실: 69701.648438\t최선의 손실: 2.025134\t정확도: 74.67%\n",
-      "17\t검증 세트 손실: 53071.804688\t최선의 손실: 2.025134\t정확도: 58.91%\n",
-      "18\t검증 세트 손실: 26281.070312\t최선의 손실: 2.025134\t정확도: 74.20%\n",
-      "19\t검증 세트 손실: 21564.775391\t최선의 손실: 2.025134\t정확도: 88.35%\n",
-      "20\t검증 세트 손실: 17585.052734\t최선의 손실: 2.025134\t정확도: 87.41%\n",
-      "21\t검증 세트 손실: 16134.041992\t최선의 손실: 2.025134\t정확도: 89.33%\n",
-      "22\t검증 세트 손실: 22190.298828\t최선의 손실: 2.025134\t정확도: 82.68%\n",
-      "23\t검증 세트 손실: 186121168.000000\t최선의 손실: 2.025134\t정확도: 83.89%\n",
-      "24\t검증 세트 손실: 7589396.500000\t최선의 손실: 2.025134\t정확도: 94.84%\n"
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  59.3s\n",
+      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.509151\t최선의 손실: 0.509151\t정확도: 88.12%\n",
+      "1\t검증 세트 손실: 0.545436\t최선의 손실: 0.509151\t정확도: 79.59%\n",
+      "2\t검증 세트 손실: 0.712667\t최선의 손실: 0.509151\t정확도: 69.74%\n",
+      "3\t검증 세트 손실: 1.010594\t최선의 손실: 0.509151\t정확도: 61.49%\n",
+      "4\t검증 세트 손실: 1.263853\t최선의 손실: 0.509151\t정확도: 48.16%\n",
+      "5\t검증 세트 손실: 1.408302\t최선의 손실: 0.509151\t정확도: 31.98%\n",
+      "6\t검증 세트 손실: 1.191644\t최선의 손실: 0.509151\t정확도: 45.74%\n",
+      "7\t검증 세트 손실: 0.986051\t최선의 손실: 0.509151\t정확도: 52.07%\n",
+      "8\t검증 세트 손실: 0.938623\t최선의 손실: 0.509151\t정확도: 55.86%\n",
+      "9\t검증 세트 손실: 1.101210\t최선의 손실: 0.509151\t정확도: 48.79%\n",
+      "10\t검증 세트 손실: 0.991213\t최선의 손실: 0.509151\t정확도: 57.00%\n",
+      "11\t검증 세트 손실: 1.217354\t최선의 손실: 0.509151\t정확도: 48.75%\n",
+      "12\t검증 세트 손실: 1.164959\t최선의 손실: 0.509151\t정확도: 47.03%\n",
+      "13\t검증 세트 손실: 0.988234\t최선의 손실: 0.509151\t정확도: 56.06%\n",
+      "14\t검증 세트 손실: 1.437760\t최선의 손실: 0.509151\t정확도: 36.47%\n",
+      "15\t검증 세트 손실: 1.355858\t최선의 손실: 0.509151\t정확도: 50.82%\n",
+      "16\t검증 세트 손실: 1.080392\t최선의 손실: 0.509151\t정확도: 51.64%\n",
+      "17\t검증 세트 손실: 1.087990\t최선의 손실: 0.509151\t정확도: 55.28%\n",
+      "18\t검증 세트 손실: 1.555433\t최선의 손실: 0.509151\t정확도: 43.16%\n",
+      "19\t검증 세트 손실: 1.473558\t최선의 손실: 0.509151\t정확도: 41.59%\n",
+      "20\t검증 세트 손실: 1.099342\t최선의 손실: 0.509151\t정확도: 52.03%\n",
+      "21\t검증 세트 손실: 1.146670\t최선의 손실: 0.509151\t정확도: 49.73%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  59.3s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.484699\t최선의 손실: 0.484699\t정확도: 88.19%\n",
+      "1\t검증 세트 손실: 0.230160\t최선의 손실: 0.230160\t정확도: 93.51%\n",
+      "2\t검증 세트 손실: 0.204482\t최선의 손실: 0.204482\t정확도: 94.84%\n",
+      "3\t검증 세트 손실: 0.273462\t최선의 손실: 0.204482\t정확도: 92.06%\n",
+      "4\t검증 세트 손실: 0.247351\t최선의 손실: 0.204482\t정확도: 94.29%\n",
+      "5\t검증 세트 손실: 0.288252\t최선의 손실: 0.204482\t정확도: 91.91%\n",
+      "6\t검증 세트 손실: 0.170305\t최선의 손실: 0.170305\t정확도: 95.35%\n",
+      "7\t검증 세트 손실: 0.219966\t최선의 손실: 0.170305\t정확도: 92.92%\n",
+      "8\t검증 세트 손실: 0.197702\t최선의 손실: 0.170305\t정확도: 94.76%\n",
+      "9\t검증 세트 손실: 0.186956\t최선의 손실: 0.170305\t정확도: 95.31%\n",
+      "10\t검증 세트 손실: 0.231695\t최선의 손실: 0.170305\t정확도: 94.84%\n",
+      "11\t검증 세트 손실: 0.276851\t최선의 손실: 0.170305\t정확도: 94.72%\n",
+      "12\t검증 세트 손실: 0.236466\t최선의 손실: 0.170305\t정확도: 94.29%\n",
+      "13\t검증 세트 손실: 0.200789\t최선의 손실: 0.170305\t정확도: 94.41%\n",
+      "14\t검증 세트 손실: 0.201943\t최선의 손실: 0.170305\t정확도: 95.07%\n",
+      "15\t검증 세트 손실: 8.309652\t최선의 손실: 0.170305\t정확도: 22.95%\n",
+      "16\t검증 세트 손실: 2.382958\t최선의 손실: 0.170305\t정확도: 37.10%\n",
+      "17\t검증 세트 손실: 1.311382\t최선의 손실: 0.170305\t정확도: 47.77%\n",
+      "18\t검증 세트 손실: 1.260901\t최선의 손실: 0.170305\t정확도: 42.96%\n",
+      "19\t검증 세트 손실: 1.185121\t최선의 손실: 0.170305\t정확도: 48.79%\n",
+      "20\t검증 세트 손실: 1.146757\t최선의 손실: 0.170305\t정확도: 52.66%\n",
+      "21\t검증 세트 손실: 1.139347\t최선의 손실: 0.170305\t정확도: 48.71%\n",
+      "22\t검증 세트 손실: 1.072477\t최선의 손실: 0.170305\t정확도: 51.37%\n",
+      "23\t검증 세트 손실: 1.656010\t최선의 손실: 0.170305\t정확도: 38.00%\n",
+      "24\t검증 세트 손실: 0.996243\t최선의 손실: 0.170305\t정확도: 58.84%\n",
+      "25\t검증 세트 손실: 1.057581\t최선의 손실: 0.170305\t정확도: 51.13%\n",
+      "26\t검증 세트 손실: 0.847189\t최선의 손실: 0.170305\t정확도: 65.44%\n",
+      "27\t검증 세트 손실: 0.777239\t최선의 손실: 0.170305\t정확도: 63.88%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=   3.1s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.292873\t최선의 손실: 0.292873\t정확도: 92.96%\n",
+      "1\t검증 세트 손실: 0.227223\t최선의 손실: 0.227223\t정확도: 94.64%\n",
+      "2\t검증 세트 손실: 0.225251\t최선의 손실: 0.225251\t정확도: 94.53%\n",
+      "3\t검증 세트 손실: 0.169476\t최선의 손실: 0.169476\t정확도: 95.27%\n",
+      "4\t검증 세트 손실: 0.183045\t최선의 손실: 0.169476\t정확도: 95.31%\n",
+      "5\t검증 세트 손실: 0.162503\t최선의 손실: 0.162503\t정확도: 95.19%\n",
+      "6\t검증 세트 손실: 0.174221\t최선의 손실: 0.162503\t정확도: 95.31%\n",
+      "7\t검증 세트 손실: 0.169058\t최선의 손실: 0.162503\t정확도: 94.53%\n",
+      "8\t검증 세트 손실: 0.156675\t최선의 손실: 0.156675\t정확도: 95.78%\n",
+      "9\t검증 세트 손실: 1.133917\t최선의 손실: 0.156675\t정확도: 94.06%\n",
+      "10\t검증 세트 손실: 3.280568\t최선의 손실: 0.156675\t정확도: 35.65%\n",
+      "11\t검증 세트 손실: 2.580117\t최선의 손실: 0.156675\t정확도: 35.89%\n",
+      "12\t검증 세트 손실: 1.259834\t최선의 손실: 0.156675\t정확도: 45.00%\n",
+      "13\t검증 세트 손실: 1.183373\t최선의 손실: 0.156675\t정확도: 47.62%\n",
+      "14\t검증 세트 손실: 0.904887\t최선의 손실: 0.156675\t정확도: 59.50%\n",
+      "15\t검증 세트 손실: 1.086861\t최선의 손실: 0.156675\t정확도: 51.80%\n",
+      "16\t검증 세트 손실: 1.129993\t최선의 손실: 0.156675\t정확도: 45.86%\n",
+      "17\t검증 세트 손실: 1.081267\t최선의 손실: 0.156675\t정확도: 51.76%\n",
+      "18\t검증 세트 손실: 1.104960\t최선의 손실: 0.156675\t정확도: 52.42%\n",
+      "19\t검증 세트 손실: 1.018421\t최선의 손실: 0.156675\t정확도: 52.38%\n",
+      "20\t검증 세트 손실: 1.008448\t최선의 손실: 0.156675\t정확도: 53.17%\n",
+      "21\t검증 세트 손실: 1.024783\t최선의 손실: 0.156675\t정확도: 55.51%\n",
+      "22\t검증 세트 손실: 0.965816\t최선의 손실: 0.156675\t정확도: 54.81%\n",
+      "23\t검증 세트 손실: 0.920508\t최선의 손실: 0.156675\t정확도: 62.35%\n",
+      "24\t검증 세트 손실: 0.942411\t최선의 손실: 0.156675\t정확도: 60.24%\n",
+      "25\t검증 세트 손실: 0.936668\t최선의 손실: 0.156675\t정확도: 54.81%\n",
+      "26\t검증 세트 손실: 1.064109\t최선의 손실: 0.156675\t정확도: 56.84%\n",
+      "27\t검증 세트 손실: 0.994956\t최선의 손실: 0.156675\t정확도: 58.17%\n",
+      "28\t검증 세트 손실: 0.959821\t최선의 손실: 0.156675\t정확도: 56.96%\n",
+      "29\t검증 세트 손실: 0.933764\t최선의 손실: 0.156675\t정확도: 60.13%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=   3.2s\n",
+      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.319014\t최선의 손실: 0.319014\t정확도: 91.67%\n",
+      "1\t검증 세트 손실: 0.224763\t최선의 손실: 0.224763\t정확도: 94.49%\n",
+      "2\t검증 세트 손실: 0.184623\t최선의 손실: 0.184623\t정확도: 95.70%\n",
+      "3\t검증 세트 손실: 0.156419\t최선의 손실: 0.156419\t정확도: 95.50%\n",
+      "4\t검증 세트 손실: 0.148546\t최선의 손실: 0.148546\t정확도: 95.90%\n",
+      "5\t검증 세트 손실: 0.140332\t최선의 손실: 0.140332\t정확도: 96.52%\n",
+      "6\t검증 세트 손실: 0.135099\t최선의 손실: 0.135099\t정확도: 96.25%\n",
+      "7\t검증 세트 손실: 0.198008\t최선의 손실: 0.135099\t정확도: 94.92%\n",
+      "8\t검증 세트 손실: 0.216186\t최선의 손실: 0.135099\t정확도: 93.43%\n",
+      "9\t검증 세트 손실: 0.362736\t최선의 손실: 0.135099\t정확도: 87.57%\n",
+      "10\t검증 세트 손실: 0.281175\t최선의 손실: 0.135099\t정확도: 92.34%\n",
+      "11\t검증 세트 손실: 0.200450\t최선의 손실: 0.135099\t정확도: 95.27%\n",
+      "12\t검증 세트 손실: 0.168708\t최선의 손실: 0.135099\t정확도: 95.19%\n",
+      "13\t검증 세트 손실: 0.219186\t최선의 손실: 0.135099\t정확도: 95.62%\n",
+      "14\t검증 세트 손실: 1.280355\t최선의 손실: 0.135099\t정확도: 42.96%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25\t검증 세트 손실: 339864832.000000\t최선의 손실: 2.025134\t정확도: 93.82%\n",
+      "15\t검증 세트 손실: 1.486041\t최선의 손실: 0.135099\t정확도: 34.36%\n",
+      "16\t검증 세트 손실: 1.164109\t최선의 손실: 0.135099\t정확도: 51.52%\n",
+      "17\t검증 세트 손실: 1.134771\t최선의 손실: 0.135099\t정확도: 52.35%\n",
+      "18\t검증 세트 손실: 0.872367\t최선의 손실: 0.135099\t정확도: 64.23%\n",
+      "19\t검증 세트 손실: 0.917716\t최선의 손실: 0.135099\t정확도: 62.90%\n",
+      "20\t검증 세트 손실: 0.774213\t최선의 손실: 0.135099\t정확도: 66.30%\n",
+      "21\t검증 세트 손실: 0.931378\t최선의 손실: 0.135099\t정확도: 74.28%\n",
+      "22\t검증 세트 손실: 0.951106\t최선의 손실: 0.135099\t정확도: 58.25%\n",
+      "23\t검증 세트 손실: 0.783629\t최선의 손실: 0.135099\t정확도: 67.40%\n",
+      "24\t검증 세트 손실: 2.489950\t최선의 손실: 0.135099\t정확도: 32.13%\n",
+      "25\t검증 세트 손실: 61.044945\t최선의 손실: 0.135099\t정확도: 18.61%\n",
+      "26\t검증 세트 손실: 165.494308\t최선의 손실: 0.135099\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 185.986954\t최선의 손실: 0.135099\t정확도: 19.23%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160, total=  25.6s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 8.362897\t최선의 손실: 8.362897\t정확도: 92.06%\n",
-      "1\t검증 세트 손실: 3.744946\t최선의 손실: 3.744946\t정확도: 94.37%\n",
-      "2\t검증 세트 손실: 3.494984\t최선의 손실: 3.494984\t정확도: 93.59%\n",
-      "3\t검증 세트 손실: 411966.968750\t최선의 손실: 3.494984\t정확도: 76.78%\n",
-      "4\t검증 세트 손실: 113234.148438\t최선의 손실: 3.494984\t정확도: 79.36%\n",
-      "5\t검증 세트 손실: 47591.761719\t최선의 손실: 3.494984\t정확도: 92.14%\n",
-      "6\t검증 세트 손실: 77911.437500\t최선의 손실: 3.494984\t정확도: 92.89%\n",
-      "7\t검증 세트 손실: 88909.070312\t최선의 손실: 3.494984\t정확도: 90.81%\n",
-      "8\t검증 세트 손실: 44924.132812\t최선의 손실: 3.494984\t정확도: 94.29%\n",
-      "9\t검증 세트 손실: 41308.375000\t최선의 손실: 3.494984\t정확도: 94.10%\n",
-      "10\t검증 세트 손실: 39173.074219\t최선의 손실: 3.494984\t정확도: 92.49%\n",
-      "11\t검증 세트 손실: 16017.524414\t최선의 손실: 3.494984\t정확도: 95.66%\n",
-      "12\t검증 세트 손실: 18217.089844\t최선의 손실: 3.494984\t정확도: 95.43%\n",
-      "13\t검증 세트 손실: 24980.619141\t최선의 손실: 3.494984\t정확도: 95.35%\n",
-      "14\t검증 세트 손실: 26938.216797\t최선의 손실: 3.494984\t정확도: 92.03%\n",
-      "15\t검증 세트 손실: 119519232.000000\t최선의 손실: 3.494984\t정확도: 73.03%\n",
-      "16\t검증 세트 손실: 1682146.125000\t최선의 손실: 3.494984\t정확도: 94.80%\n",
-      "17\t검증 세트 손실: 3107856.000000\t최선의 손실: 3.494984\t정확도: 94.92%\n",
-      "18\t검증 세트 손실: 1254663.375000\t최선의 손실: 3.494984\t정확도: 96.29%\n",
-      "19\t검증 세트 손실: 1665115.125000\t최선의 손실: 3.494984\t정확도: 93.32%\n",
-      "20\t검증 세트 손실: 641506.812500\t최선의 손실: 3.494984\t정확도: 96.56%\n",
-      "21\t검증 세트 손실: 720731.062500\t최선의 손실: 3.494984\t정확도: 96.25%\n",
-      "22\t검증 세트 손실: 843758.000000\t최선의 손실: 3.494984\t정확도: 94.96%\n",
-      "23\t검증 세트 손실: 815632.687500\t최선의 손실: 3.494984\t정확도: 95.07%\n",
+      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=   3.2s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.168278\t최선의 손실: 0.168278\t정확도: 95.74%\n",
+      "1\t검증 세트 손실: 0.141930\t최선의 손실: 0.141930\t정확도: 96.36%\n",
+      "2\t검증 세트 손실: 0.177505\t최선의 손실: 0.141930\t정확도: 95.90%\n",
+      "3\t검증 세트 손실: 0.126351\t최선의 손실: 0.126351\t정확도: 96.48%\n",
+      "4\t검증 세트 손실: 0.172704\t최선의 손실: 0.126351\t정확도: 95.54%\n",
+      "5\t검증 세트 손실: 0.138664\t최선의 손실: 0.126351\t정확도: 96.52%\n",
+      "6\t검증 세트 손실: 0.149303\t최선의 손실: 0.126351\t정확도: 96.29%\n",
+      "7\t검증 세트 손실: 0.133798\t최선의 손실: 0.126351\t정확도: 96.60%\n",
+      "8\t검증 세트 손실: 0.141214\t최선의 손실: 0.126351\t정확도: 96.68%\n",
+      "9\t검증 세트 손실: 0.124726\t최선의 손실: 0.124726\t정확도: 97.07%\n",
+      "10\t검증 세트 손실: 0.167132\t최선의 손실: 0.124726\t정확도: 96.72%\n",
+      "11\t검증 세트 손실: 0.150123\t최선의 손실: 0.124726\t정확도: 96.72%\n",
+      "12\t검증 세트 손실: 0.152715\t최선의 손실: 0.124726\t정확도: 95.58%\n",
+      "13\t검증 세트 손실: 0.143245\t최선의 손실: 0.124726\t정확도: 96.25%\n",
+      "14\t검증 세트 손실: 0.144990\t최선의 손실: 0.124726\t정확도: 96.76%\n",
+      "15\t검증 세트 손실: 0.133805\t최선의 손실: 0.124726\t정확도: 96.36%\n",
+      "16\t검증 세트 손실: 0.188992\t최선의 손실: 0.124726\t정확도: 94.76%\n",
+      "17\t검증 세트 손실: 0.180727\t최선의 손실: 0.124726\t정확도: 93.63%\n",
+      "18\t검증 세트 손실: 0.134569\t최선의 손실: 0.124726\t정확도: 96.13%\n",
+      "19\t검증 세트 손실: 0.174166\t최선의 손실: 0.124726\t정확도: 95.23%\n",
+      "20\t검증 세트 손실: 0.151174\t최선의 손실: 0.124726\t정확도: 96.60%\n",
+      "21\t검증 세트 손실: 0.143085\t최선의 손실: 0.124726\t정확도: 96.21%\n",
+      "22\t검증 세트 손실: 0.226548\t최선의 손실: 0.124726\t정확도: 96.83%\n",
+      "23\t검증 세트 손실: 0.175530\t최선의 손실: 0.124726\t정확도: 95.62%\n",
+      "24\t검증 세트 손실: 0.151109\t최선의 손실: 0.124726\t정확도: 96.52%\n",
+      "25\t검증 세트 손실: 0.175640\t최선의 손실: 0.124726\t정확도: 96.17%\n",
+      "26\t검증 세트 손실: 0.168065\t최선의 손실: 0.124726\t정확도: 96.79%\n",
+      "27\t검증 세트 손실: 0.206272\t최선의 손실: 0.124726\t정확도: 96.33%\n",
+      "28\t검증 세트 손실: 0.184488\t최선의 손실: 0.124726\t정확도: 96.21%\n",
+      "29\t검증 세트 손실: 0.146942\t최선의 손실: 0.124726\t정확도: 96.79%\n",
+      "30\t검증 세트 손실: 0.134227\t최선의 손실: 0.124726\t정확도: 96.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160, total=  13.4s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 20098.628906\t최선의 손실: 20098.628906\t정확도: 91.24%\n",
-      "1\t검증 세트 손실: 10222.458984\t최선의 손실: 10222.458984\t정확도: 95.39%\n",
-      "2\t검증 세트 손실: 5525.655762\t최선의 손실: 5525.655762\t정확도: 95.31%\n",
-      "3\t검증 세트 손실: 2774.841064\t최선의 손실: 2774.841064\t정확도: 95.58%\n",
-      "4\t검증 세트 손실: 4376.530273\t최선의 손실: 2774.841064\t정확도: 95.93%\n",
-      "5\t검증 세트 손실: 2301.390869\t최선의 손실: 2301.390869\t정확도: 94.33%\n",
-      "6\t검증 세트 손실: 5999041.000000\t최선의 손실: 2301.390869\t정확도: 87.92%\n",
-      "7\t검증 세트 손실: 1919839.125000\t최선의 손실: 2301.390869\t정확도: 87.10%\n",
-      "8\t검증 세트 손실: 895884.187500\t최선의 손실: 2301.390869\t정확도: 94.45%\n",
-      "9\t검증 세트 손실: 856611.812500\t최선의 손실: 2301.390869\t정확도: 94.21%\n",
-      "10\t검증 세트 손실: 386182.906250\t최선의 손실: 2301.390869\t정확도: 95.27%\n",
-      "11\t검증 세트 손실: 374873.031250\t최선의 손실: 2301.390869\t정확도: 95.07%\n",
-      "12\t검증 세트 손실: 433697.281250\t최선의 손실: 2301.390869\t정확도: 95.31%\n",
-      "13\t검증 세트 손실: 597201.750000\t최선의 손실: 2301.390869\t정확도: 92.96%\n",
-      "14\t검증 세트 손실: 1081174.250000\t최선의 손실: 2301.390869\t정확도: 94.45%\n",
-      "15\t검증 세트 손실: 333547.187500\t최선의 손실: 2301.390869\t정확도: 95.70%\n",
-      "16\t검증 세트 손실: 360073.781250\t최선의 손실: 2301.390869\t정확도: 95.11%\n",
-      "17\t검증 세트 손실: 842524.812500\t최선의 손실: 2301.390869\t정확도: 80.26%\n",
-      "18\t검증 세트 손실: 216820.562500\t최선의 손실: 2301.390869\t정확도: 96.21%\n",
-      "19\t검증 세트 손실: 499794.500000\t최선의 손실: 2301.390869\t정확도: 95.82%\n",
-      "20\t검증 세트 손실: 151370816.000000\t최선의 손실: 2301.390869\t정확도: 91.44%\n",
-      "21\t검증 세트 손실: 8260886.500000\t최선의 손실: 2301.390869\t정확도: 92.73%\n",
-      "22\t검증 세트 손실: 3050938.000000\t최선의 손실: 2301.390869\t정확도: 96.01%\n",
-      "23\t검증 세트 손실: 2016950.500000\t최선의 손실: 2301.390869\t정확도: 96.60%\n",
-      "24\t검증 세트 손실: 108181168.000000\t최선의 손실: 2301.390869\t정확도: 92.10%\n",
-      "25\t검증 세트 손실: 9810811.000000\t최선의 손실: 2301.390869\t정확도: 96.05%\n",
-      "26\t검증 세트 손실: 6203740.000000\t최선의 손실: 2301.390869\t정확도: 95.07%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50, total=  15.0s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.193544\t최선의 손실: 0.193544\t정확도: 95.47%\n",
+      "1\t검증 세트 손실: 0.163228\t최선의 손실: 0.163228\t정확도: 95.93%\n",
+      "2\t검증 세트 손실: 0.161448\t최선의 손실: 0.161448\t정확도: 96.09%\n",
+      "3\t검증 세트 손실: 0.158470\t최선의 손실: 0.158470\t정확도: 96.68%\n",
+      "4\t검증 세트 손실: 0.151045\t최선의 손실: 0.151045\t정확도: 96.25%\n",
+      "5\t검증 세트 손실: 0.211585\t최선의 손실: 0.151045\t정확도: 96.60%\n",
+      "6\t검증 세트 손실: 0.118909\t최선의 손실: 0.118909\t정확도: 97.22%\n",
+      "7\t검증 세트 손실: 0.152597\t최선의 손실: 0.118909\t정확도: 96.25%\n",
+      "8\t검증 세트 손실: 0.147519\t최선의 손실: 0.118909\t정확도: 97.15%\n",
+      "9\t검증 세트 손실: 0.108436\t최선의 손실: 0.108436\t정확도: 97.50%\n",
+      "10\t검증 세트 손실: 0.161720\t최선의 손실: 0.108436\t정확도: 97.34%\n",
+      "11\t검증 세트 손실: 0.147460\t최선의 손실: 0.108436\t정확도: 96.83%\n",
+      "12\t검증 세트 손실: 0.129492\t최선의 손실: 0.108436\t정확도: 96.83%\n",
+      "13\t검증 세트 손실: 0.118874\t최선의 손실: 0.108436\t정확도: 96.79%\n",
+      "14\t검증 세트 손실: 0.120794\t최선의 손실: 0.108436\t정확도: 96.99%\n",
+      "15\t검증 세트 손실: 0.145897\t최선의 손실: 0.108436\t정확도: 96.83%\n",
+      "16\t검증 세트 손실: 0.176342\t최선의 손실: 0.108436\t정확도: 96.72%\n",
+      "17\t검증 세트 손실: 0.142715\t최선의 손실: 0.108436\t정확도: 96.40%\n",
+      "18\t검증 세트 손실: 0.153086\t최선의 손실: 0.108436\t정확도: 96.68%\n",
+      "19\t검증 세트 손실: 0.153652\t최선의 손실: 0.108436\t정확도: 96.64%\n",
+      "20\t검증 세트 손실: 0.169133\t최선의 손실: 0.108436\t정확도: 96.17%\n",
+      "21\t검증 세트 손실: 0.170816\t최선의 손실: 0.108436\t정확도: 96.91%\n",
+      "22\t검증 세트 손실: 0.194123\t최선의 손실: 0.108436\t정확도: 96.56%\n",
+      "23\t검증 세트 손실: 0.165200\t최선의 손실: 0.108436\t정확도: 97.03%\n",
+      "24\t검증 세트 손실: 0.202766\t최선의 손실: 0.108436\t정확도: 96.56%\n",
+      "25\t검증 세트 손실: 0.189068\t최선의 손실: 0.108436\t정확도: 95.93%\n",
+      "26\t검증 세트 손실: 0.161119\t최선의 손실: 0.108436\t정확도: 97.11%\n",
+      "27\t검증 세트 손실: 0.192462\t최선의 손실: 0.108436\t정확도: 96.09%\n",
+      "28\t검증 세트 손실: 0.182729\t최선의 손실: 0.108436\t정확도: 96.21%\n",
+      "29\t검증 세트 손실: 0.150162\t최선의 손실: 0.108436\t정확도: 96.99%\n",
+      "30\t검증 세트 손실: 0.167328\t최선의 손실: 0.108436\t정확도: 96.76%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160, total=  38.3s\n",
-      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.163021\t최선의 손실: 1.163021\t정확도: 43.43%\n",
-      "1\t검증 세트 손실: 1.410383\t최선의 손실: 1.163021\t정확도: 32.64%\n",
-      "2\t검증 세트 손실: 1.679752\t최선의 손실: 1.163021\t정확도: 22.09%\n",
-      "3\t검증 세트 손실: 1.651680\t최선의 손실: 1.163021\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.511703\t최선의 손실: 1.163021\t정확도: 25.76%\n",
-      "5\t검증 세트 손실: 1.454087\t최선의 손실: 1.163021\t정확도: 33.58%\n",
-      "6\t검증 세트 손실: 1.515604\t최선의 손실: 1.163021\t정확도: 27.83%\n",
-      "7\t검증 세트 손실: 1.862073\t최선의 손실: 1.163021\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.559443\t최선의 손실: 1.163021\t정확도: 32.13%\n",
-      "9\t검증 세트 손실: 1.461643\t최선의 손실: 1.163021\t정확도: 29.63%\n",
-      "10\t검증 세트 손실: 1.467498\t최선의 손실: 1.163021\t정확도: 33.78%\n",
-      "11\t검증 세트 손실: 1.733190\t최선의 손실: 1.163021\t정확도: 33.27%\n",
-      "12\t검증 세트 손실: 1.496097\t최선의 손실: 1.163021\t정확도: 27.21%\n",
-      "13\t검증 세트 손실: 1.814573\t최선의 손실: 1.163021\t정확도: 23.53%\n",
-      "14\t검증 세트 손실: 1.579706\t최선의 손실: 1.163021\t정확도: 32.10%\n",
-      "15\t검증 세트 손실: 1.508405\t최선의 손실: 1.163021\t정확도: 37.41%\n",
-      "16\t검증 세트 손실: 1.406337\t최선의 손실: 1.163021\t정확도: 36.24%\n",
-      "17\t검증 세트 손실: 1.472331\t최선의 손실: 1.163021\t정확도: 35.81%\n",
-      "18\t검증 세트 손실: 1.634576\t최선의 손실: 1.163021\t정확도: 29.83%\n",
-      "19\t검증 세트 손실: 1.821928\t최선의 손실: 1.163021\t정확도: 22.79%\n",
-      "20\t검증 세트 손실: 1.996842\t최선의 손실: 1.163021\t정확도: 29.24%\n",
-      "21\t검증 세트 손실: 3.003345\t최선의 손실: 1.163021\t정확도: 27.52%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50, total=  15.2s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.183815\t최선의 손실: 0.183815\t정확도: 95.35%\n",
+      "1\t검증 세트 손실: 0.159383\t최선의 손실: 0.159383\t정확도: 96.52%\n",
+      "2\t검증 세트 손실: 0.131879\t최선의 손실: 0.131879\t정확도: 96.36%\n",
+      "3\t검증 세트 손실: 0.143183\t최선의 손실: 0.131879\t정확도: 96.79%\n",
+      "4\t검증 세트 손실: 0.121886\t최선의 손실: 0.121886\t정확도: 96.99%\n",
+      "5\t검증 세트 손실: 0.131564\t최선의 손실: 0.121886\t정확도: 96.68%\n",
+      "6\t검증 세트 손실: 0.157119\t최선의 손실: 0.121886\t정확도: 96.64%\n",
+      "7\t검증 세트 손실: 0.128800\t최선의 손실: 0.121886\t정확도: 96.91%\n",
+      "8\t검증 세트 손실: 0.117811\t최선의 손실: 0.117811\t정확도: 96.91%\n",
+      "9\t검증 세트 손실: 0.134096\t최선의 손실: 0.117811\t정확도: 96.91%\n",
+      "10\t검증 세트 손실: 0.135361\t최선의 손실: 0.117811\t정확도: 97.03%\n",
+      "11\t검증 세트 손실: 0.150821\t최선의 손실: 0.117811\t정확도: 96.79%\n",
+      "12\t검증 세트 손실: 0.145061\t최선의 손실: 0.117811\t정확도: 96.40%\n",
+      "13\t검증 세트 손실: 0.123751\t최선의 손실: 0.117811\t정확도: 96.33%\n",
+      "14\t검증 세트 손실: 0.123539\t최선의 손실: 0.117811\t정확도: 96.79%\n",
+      "15\t검증 세트 손실: 0.137988\t최선의 손실: 0.117811\t정확도: 96.60%\n",
+      "16\t검증 세트 손실: 0.151725\t최선의 손실: 0.117811\t정확도: 96.05%\n",
+      "17\t검증 세트 손실: 0.245156\t최선의 손실: 0.117811\t정확도: 94.02%\n",
+      "18\t검증 세트 손실: 0.164687\t최선의 손실: 0.117811\t정확도: 96.60%\n",
+      "19\t검증 세트 손실: 0.149489\t최선의 손실: 0.117811\t정확도: 96.76%\n",
+      "20\t검증 세트 손실: 0.182208\t최선의 손실: 0.117811\t정확도: 96.21%\n",
+      "21\t검증 세트 손실: 0.217708\t최선의 손실: 0.117811\t정확도: 96.05%\n",
+      "22\t검증 세트 손실: 0.166471\t최선의 손실: 0.117811\t정확도: 96.09%\n",
+      "23\t검증 세트 손실: 0.182633\t최선의 손실: 0.117811\t정확도: 96.01%\n",
+      "24\t검증 세트 손실: 0.223510\t최선의 손실: 0.117811\t정확도: 95.70%\n",
+      "25\t검증 세트 손실: 0.146555\t최선의 손실: 0.117811\t정확도: 96.64%\n",
+      "26\t검증 세트 손실: 0.142681\t최선의 손실: 0.117811\t정확도: 96.76%\n",
+      "27\t검증 세트 손실: 0.150704\t최선의 손실: 0.117811\t정확도: 96.13%\n",
+      "28\t검증 세트 손실: 0.184985\t최선의 손실: 0.117811\t정확도: 96.79%\n",
+      "29\t검증 세트 손실: 0.146062\t최선의 손실: 0.117811\t정확도: 96.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total=  12.1s\n",
-      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.361059\t최선의 손실: 1.361059\t정확도: 35.81%\n",
-      "1\t검증 세트 손실: 1.489703\t최선의 손실: 1.361059\t정확도: 39.01%\n",
-      "2\t검증 세트 손실: 1.341883\t최선의 손실: 1.341883\t정확도: 38.78%\n",
-      "3\t검증 세트 손실: 1.428619\t최선의 손실: 1.341883\t정확도: 40.62%\n",
-      "4\t검증 세트 손실: 1.490795\t최선의 손실: 1.341883\t정확도: 29.40%\n",
-      "5\t검증 세트 손실: 1.580289\t최선의 손실: 1.341883\t정확도: 24.28%\n",
-      "6\t검증 세트 손실: 1.443324\t최선의 손실: 1.341883\t정확도: 32.96%\n",
-      "7\t검증 세트 손실: 1.460093\t최선의 손실: 1.341883\t정확도: 23.73%\n",
-      "8\t검증 세트 손실: 2.118560\t최선의 손실: 1.341883\t정확도: 34.40%\n",
-      "9\t검증 세트 손실: 1.707005\t최선의 손실: 1.341883\t정확도: 24.12%\n",
-      "10\t검증 세트 손실: 1.920897\t최선의 손실: 1.341883\t정확도: 36.08%\n",
-      "11\t검증 세트 손실: 1.840745\t최선의 손실: 1.341883\t정확도: 35.11%\n",
-      "12\t검증 세트 손실: 1.506070\t최선의 손실: 1.341883\t정확도: 32.96%\n",
-      "13\t검증 세트 손실: 1.636718\t최선의 손실: 1.341883\t정확도: 24.12%\n",
-      "14\t검증 세트 손실: 1.549145\t최선의 손실: 1.341883\t정확도: 32.06%\n",
-      "15\t검증 세트 손실: 1.443823\t최선의 손실: 1.341883\t정확도: 32.88%\n",
-      "16\t검증 세트 손실: 1.901719\t최선의 손실: 1.341883\t정확도: 32.21%\n",
-      "17\t검증 세트 손실: 3.513367\t최선의 손실: 1.341883\t정확도: 22.71%\n",
-      "18\t검증 세트 손실: 1.826780\t최선의 손실: 1.341883\t정확도: 31.98%\n",
-      "19\t검증 세트 손실: 2.996827\t최선의 손실: 1.341883\t정확도: 37.41%\n",
-      "20\t검증 세트 손실: 7.528494\t최선의 손실: 1.341883\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 32.788605\t최선의 손실: 1.341883\t정확도: 23.73%\n",
-      "22\t검증 세트 손실: 8.037741\t최선의 손실: 1.341883\t정확도: 25.68%\n",
-      "23\t검증 세트 손실: 9.224151\t최선의 손실: 1.341883\t정확도: 19.39%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50, total=  14.7s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 480.899139\t최선의 손실: 480.899139\t정확도: 19.08%\n",
+      "1\t검증 세트 손실: 191.116882\t최선의 손실: 191.116882\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 3837.997559\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 761.747681\t최선의 손실: 191.116882\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 3042.807129\t최선의 손실: 191.116882\t정확도: 17.87%\n",
+      "5\t검증 세트 손실: 2745.245605\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 1904.212036\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 554.267212\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1898.369629\t최선의 손실: 191.116882\t정확도: 26.31%\n",
+      "9\t검증 세트 손실: 8683.819336\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 6195.576172\t최선의 손실: 191.116882\t정확도: 19.27%\n",
+      "11\t검증 세트 손실: 1314.811401\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 4440.335449\t최선의 손실: 191.116882\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 39503.914062\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 30892.531250\t최선의 손실: 191.116882\t정확도: 18.88%\n",
+      "15\t검증 세트 손실: 10467.781250\t최선의 손실: 191.116882\t정확도: 20.80%\n",
+      "16\t검증 세트 손실: 22365.697266\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 9395.952148\t최선의 손실: 191.116882\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 12288.156250\t최선의 손실: 191.116882\t정확도: 18.76%\n",
+      "19\t검증 세트 손실: 14969.916016\t최선의 손실: 191.116882\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 7057.377441\t최선의 손실: 191.116882\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 7359.060059\t최선의 손실: 191.116882\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 10104.849609\t최선의 손실: 191.116882\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total=  17.5s\n",
-      "[CV] batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 1.028983\t최선의 손실: 1.028983\t정확도: 53.13%\n",
-      "1\t검증 세트 손실: 1.274928\t최선의 손실: 1.028983\t정확도: 35.77%\n",
-      "2\t검증 세트 손실: 1.279027\t최선의 손실: 1.028983\t정확도: 31.67%\n",
-      "3\t검증 세트 손실: 1.288539\t최선의 손실: 1.028983\t정확도: 36.08%\n",
-      "4\t검증 세트 손실: 1.613855\t최선의 손실: 1.028983\t정확도: 23.49%\n",
-      "5\t검증 세트 손실: 1.662246\t최선의 손실: 1.028983\t정확도: 32.56%\n",
-      "6\t검증 세트 손실: 1.648927\t최선의 손실: 1.028983\t정확도: 21.97%\n",
-      "7\t검증 세트 손실: 1.574270\t최선의 손실: 1.028983\t정확도: 24.63%\n",
-      "8\t검증 세트 손실: 1.843898\t최선의 손실: 1.028983\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.582495\t최선의 손실: 1.028983\t정확도: 29.52%\n",
-      "10\t검증 세트 손실: 1.489683\t최선의 손실: 1.028983\t정확도: 26.51%\n",
-      "11\t검증 세트 손실: 2.148149\t최선의 손실: 1.028983\t정확도: 34.60%\n",
-      "12\t검증 세트 손실: 1.925074\t최선의 손실: 1.028983\t정확도: 24.43%\n",
-      "13\t검증 세트 손실: 1.719546\t최선의 손실: 1.028983\t정확도: 29.55%\n",
-      "14\t검증 세트 손실: 7.541655\t최선의 손실: 1.028983\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 6.638692\t최선의 손실: 1.028983\t정확도: 20.76%\n",
-      "16\t검증 세트 손실: 1.551471\t최선의 손실: 1.028983\t정확도: 27.09%\n",
-      "17\t검증 세트 손실: 1.683743\t최선의 손실: 1.028983\t정확도: 29.95%\n",
-      "18\t검증 세트 손실: 1.620499\t최선의 손실: 1.028983\t정확도: 26.74%\n",
-      "19\t검증 세트 손실: 1.573371\t최선의 손실: 1.028983\t정확도: 22.99%\n",
-      "20\t검증 세트 손실: 1.609987\t최선의 손실: 1.028983\t정확도: 28.34%\n",
-      "21\t검증 세트 손실: 1.514449\t최선의 손실: 1.028983\t정확도: 35.03%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.6, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total=  18.6s\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n"
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120, total=  13.9s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 96.439453\t최선의 손실: 96.439453\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 76.724884\t최선의 손실: 76.724884\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 135.622879\t최선의 손실: 76.724884\t정확도: 19.08%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.722104\t최선의 손실: 0.722104\t정확도: 69.94%\n",
-      "1\t검증 세트 손실: 0.958148\t최선의 손실: 0.722104\t정확도: 62.59%\n",
-      "2\t검증 세트 손실: 0.813166\t최선의 손실: 0.722104\t정확도: 69.43%\n",
-      "3\t검증 세트 손실: 1.151313\t최선의 손실: 0.722104\t정확도: 46.60%\n",
-      "4\t검증 세트 손실: 1.232958\t최선의 손실: 0.722104\t정확도: 43.35%\n",
-      "5\t검증 세트 손실: 1.165480\t최선의 손실: 0.722104\t정확도: 49.73%\n",
-      "6\t검증 세트 손실: 1.142016\t최선의 손실: 0.722104\t정확도: 46.68%\n",
-      "7\t검증 세트 손실: 1.056420\t최선의 손실: 0.722104\t정확도: 51.02%\n",
-      "8\t검증 세트 손실: 1.124922\t최선의 손실: 0.722104\t정확도: 49.84%\n",
-      "9\t검증 세트 손실: 1.011565\t최선의 손실: 0.722104\t정확도: 53.24%\n",
-      "10\t검증 세트 손실: 0.917914\t최선의 손실: 0.722104\t정확도: 57.97%\n",
-      "11\t검증 세트 손실: 1.057120\t최선의 손실: 0.722104\t정확도: 52.31%\n",
-      "12\t검증 세트 손실: 1.081920\t최선의 손실: 0.722104\t정확도: 50.23%\n",
-      "13\t검증 세트 손실: 1.122608\t최선의 손실: 0.722104\t정확도: 49.06%\n",
-      "14\t검증 세트 손실: 1.070520\t최선의 손실: 0.722104\t정확도: 51.09%\n",
-      "15\t검증 세트 손실: 0.964486\t최선의 손실: 0.722104\t정확도: 55.67%\n",
-      "16\t검증 세트 손실: 1.096936\t최선의 손실: 0.722104\t정확도: 53.17%\n",
-      "17\t검증 세트 손실: 1.508743\t최선의 손실: 0.722104\t정확도: 44.41%\n",
-      "18\t검증 세트 손실: 1.881454\t최선의 손실: 0.722104\t정확도: 36.20%\n",
-      "19\t검증 세트 손실: 2.873614\t최선의 손실: 0.722104\t정확도: 24.12%\n",
-      "20\t검증 세트 손실: 2.263043\t최선의 손실: 0.722104\t정확도: 29.01%\n",
-      "21\t검증 세트 손실: 1.681912\t최선의 손실: 0.722104\t정확도: 32.84%\n",
+      "3\t검증 세트 손실: 22274.107422\t최선의 손실: 76.724884\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1417.975708\t최선의 손실: 76.724884\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 259.689758\t최선의 손실: 76.724884\t정확도: 19.43%\n",
+      "6\t검증 세트 손실: 2604.279053\t최선의 손실: 76.724884\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 237.676132\t최선의 손실: 76.724884\t정확도: 28.89%\n",
+      "8\t검증 세트 손실: 298.076141\t최선의 손실: 76.724884\t정확도: 21.19%\n",
+      "9\t검증 세트 손실: 479.706299\t최선의 손실: 76.724884\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 161.299408\t최선의 손실: 76.724884\t정확도: 19.66%\n",
+      "11\t검증 세트 손실: 857.283630\t최선의 손실: 76.724884\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 208.134872\t최선의 손실: 76.724884\t정확도: 28.73%\n",
+      "13\t검증 세트 손실: 381.715363\t최선의 손실: 76.724884\t정확도: 18.84%\n",
+      "14\t검증 세트 손실: 902.597534\t최선의 손실: 76.724884\t정확도: 19.47%\n",
+      "15\t검증 세트 손실: 415.410583\t최선의 손실: 76.724884\t정확도: 38.74%\n",
+      "16\t검증 세트 손실: 836.833435\t최선의 손실: 76.724884\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 972.676086\t최선의 손실: 76.724884\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 2688.166016\t최선의 손실: 76.724884\t정확도: 20.88%\n",
+      "19\t검증 세트 손실: 2408.628662\t최선의 손실: 76.724884\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1048.713989\t최선의 손실: 76.724884\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 546.276733\t최선의 손실: 76.724884\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 488.202362\t최선의 손실: 76.724884\t정확도: 19.04%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.306479\t최선의 손실: 0.306479\t정확도: 88.12%\n",
-      "1\t검증 세트 손실: 0.415917\t최선의 손실: 0.306479\t정확도: 90.58%\n",
-      "2\t검증 세트 손실: 0.477975\t최선의 손실: 0.306479\t정확도: 88.86%\n",
-      "3\t검증 세트 손실: 0.759196\t최선의 손실: 0.306479\t정확도: 65.64%\n",
-      "4\t검증 세트 손실: 1.085561\t최선의 손실: 0.306479\t정확도: 48.63%\n",
-      "5\t검증 세트 손실: 0.970256\t최선의 손실: 0.306479\t정확도: 53.56%\n",
-      "6\t검증 세트 손실: 0.873569\t최선의 손실: 0.306479\t정확도: 59.73%\n",
-      "7\t검증 세트 손실: 0.876732\t최선의 손실: 0.306479\t정확도: 58.56%\n",
-      "8\t검증 세트 손실: 1.066333\t최선의 손실: 0.306479\t정확도: 54.22%\n",
-      "9\t검증 세트 손실: 1.115130\t최선의 손실: 0.306479\t정확도: 60.20%\n",
-      "10\t검증 세트 손실: 2.445228\t최선의 손실: 0.306479\t정확도: 50.12%\n",
-      "11\t검증 세트 손실: 1.552302\t최선의 손실: 0.306479\t정확도: 37.26%\n",
-      "12\t검증 세트 손실: 1.205919\t최선의 손실: 0.306479\t정확도: 52.35%\n",
-      "13\t검증 세트 손실: 1.437054\t최선의 손실: 0.306479\t정확도: 41.71%\n",
-      "14\t검증 세트 손실: 1.758335\t최선의 손실: 0.306479\t정확도: 33.39%\n",
-      "15\t검증 세트 손실: 2.727355\t최선의 손실: 0.306479\t정확도: 27.05%\n",
-      "16\t검증 세트 손실: 1.466493\t최선의 손실: 0.306479\t정확도: 31.78%\n",
-      "17\t검증 세트 손실: 4.152368\t최선의 손실: 0.306479\t정확도: 27.40%\n",
-      "18\t검증 세트 손실: 2.045069\t최선의 손실: 0.306479\t정확도: 42.46%\n",
-      "19\t검증 세트 손실: 1.294320\t최선의 손실: 0.306479\t정확도: 44.18%\n",
-      "20\t검증 세트 손실: 1.455276\t최선의 손실: 0.306479\t정확도: 43.39%\n",
-      "21\t검증 세트 손실: 2.896481\t최선의 손실: 0.306479\t정확도: 38.12%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120, total=  13.9s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 236.495773\t최선의 손실: 236.495773\t정확도: 18.96%\n",
+      "1\t검증 세트 손실: 452.827148\t최선의 손실: 236.495773\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 221.333755\t최선의 손실: 221.333755\t정확도: 22.63%\n",
+      "3\t검증 세트 손실: 104.592972\t최선의 손실: 104.592972\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 366.527222\t최선의 손실: 104.592972\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 289.558624\t최선의 손실: 104.592972\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 634.822632\t최선의 손실: 104.592972\t정확도: 20.91%\n",
+      "7\t검증 세트 손실: 778.196533\t최선의 손실: 104.592972\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 690.293579\t최선의 손실: 104.592972\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 2125.658936\t최선의 손실: 104.592972\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 396.446259\t최선의 손실: 104.592972\t정확도: 22.67%\n",
+      "11\t검증 세트 손실: 1110.420044\t최선의 손실: 104.592972\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 3973.747559\t최선의 손실: 104.592972\t정확도: 19.78%\n",
+      "13\t검증 세트 손실: 3272.913330\t최선의 손실: 104.592972\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1735.809814\t최선의 손실: 104.592972\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 7911.509766\t최선의 손실: 104.592972\t정확도: 18.73%\n",
+      "16\t검증 세트 손실: 4074.907715\t최선의 손실: 104.592972\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1289.593506\t최선의 손실: 104.592972\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 3048.917969\t최선의 손실: 104.592972\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 3356.640381\t최선의 손실: 104.592972\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 2593.096680\t최선의 손실: 104.592972\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 68250.445312\t최선의 손실: 104.592972\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 14247.900391\t최선의 손실: 104.592972\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 36868.738281\t최선의 손실: 104.592972\t정확도: 18.73%\n",
+      "24\t검증 세트 손실: 4808.282715\t최선의 손실: 104.592972\t정확도: 18.76%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.614316\t최선의 손실: 0.614316\t정확도: 79.36%\n",
-      "1\t검증 세트 손실: 0.446121\t최선의 손실: 0.446121\t정확도: 92.73%\n",
-      "2\t검증 세트 손실: 0.443425\t최선의 손실: 0.443425\t정확도: 87.65%\n",
-      "3\t검증 세트 손실: 0.857565\t최선의 손실: 0.443425\t정확도: 65.79%\n",
-      "4\t검증 세트 손실: 0.670472\t최선의 손실: 0.443425\t정확도: 77.33%\n",
-      "5\t검증 세트 손실: 0.818967\t최선의 손실: 0.443425\t정확도: 77.80%\n",
-      "6\t검증 세트 손실: 0.963877\t최선의 손실: 0.443425\t정확도: 58.37%\n",
-      "7\t검증 세트 손실: 0.823777\t최선의 손실: 0.443425\t정확도: 59.73%\n",
-      "8\t검증 세트 손실: 0.926835\t최선의 손실: 0.443425\t정확도: 58.72%\n",
-      "9\t검증 세트 손실: 0.738570\t최선의 손실: 0.443425\t정확도: 69.94%\n",
-      "10\t검증 세트 손실: 1.098807\t최선의 손실: 0.443425\t정확도: 49.37%\n",
-      "11\t검증 세트 손실: 1.010552\t최선의 손실: 0.443425\t정확도: 51.52%\n",
-      "12\t검증 세트 손실: 1.191027\t최선의 손실: 0.443425\t정확도: 44.57%\n",
-      "13\t검증 세트 손실: 0.878434\t최선의 손실: 0.443425\t정확도: 70.64%\n",
-      "14\t검증 세트 손실: 1.087718\t최선의 손실: 0.443425\t정확도: 52.07%\n",
-      "15\t검증 세트 손실: 8.741892\t최선의 손실: 0.443425\t정확도: 51.84%\n",
-      "16\t검증 세트 손실: 1.490540\t최선의 손실: 0.443425\t정확도: 40.62%\n",
-      "17\t검증 세트 손실: 1.122944\t최선의 손실: 0.443425\t정확도: 59.54%\n",
-      "18\t검증 세트 손실: 1.403843\t최선의 손실: 0.443425\t정확도: 50.78%\n",
-      "19\t검증 세트 손실: 2.111366\t최선의 손실: 0.443425\t정확도: 39.01%\n",
-      "20\t검증 세트 손실: 1.792004\t최선의 손실: 0.443425\t정확도: 38.12%\n",
-      "21\t검증 세트 손실: 1.583805\t최선의 손실: 0.443425\t정확도: 38.23%\n",
-      "22\t검증 세트 손실: 0.987952\t최선의 손실: 0.443425\t정확도: 56.88%\n",
-      "23\t검증 세트 손실: 1.269124\t최선의 손실: 0.443425\t정확도: 42.73%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120, total=  14.9s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.219602\t최선의 손실: 0.219602\t정확도: 93.59%\n",
+      "1\t검증 세트 손실: 0.163874\t최선의 손실: 0.163874\t정확도: 95.86%\n",
+      "2\t검증 세트 손실: 0.156178\t최선의 손실: 0.156178\t정확도: 95.82%\n",
+      "3\t검증 세트 손실: 0.145441\t최선의 손실: 0.145441\t정확도: 95.66%\n",
+      "4\t검증 세트 손실: 0.154373\t최선의 손실: 0.145441\t정확도: 96.01%\n",
+      "5\t검증 세트 손실: 0.131792\t최선의 손실: 0.131792\t정확도: 95.66%\n",
+      "6\t검증 세트 손실: 0.133930\t최선의 손실: 0.131792\t정확도: 96.87%\n",
+      "7\t검증 세트 손실: 0.135412\t최선의 손실: 0.131792\t정확도: 96.29%\n",
+      "8\t검증 세트 손실: 0.155462\t최선의 손실: 0.131792\t정확도: 95.04%\n",
+      "9\t검증 세트 손실: 0.157770\t최선의 손실: 0.131792\t정확도: 97.22%\n",
+      "10\t검증 세트 손실: 0.147512\t최선의 손실: 0.131792\t정확도: 96.17%\n",
+      "11\t검증 세트 손실: 0.134663\t최선의 손실: 0.131792\t정확도: 96.21%\n",
+      "12\t검증 세트 손실: 0.161034\t최선의 손실: 0.131792\t정확도: 95.39%\n",
+      "13\t검증 세트 손실: 0.150870\t최선의 손실: 0.131792\t정확도: 95.50%\n",
+      "14\t검증 세트 손실: 0.183596\t최선의 손실: 0.131792\t정확도: 94.37%\n",
+      "15\t검증 세트 손실: 0.135103\t최선의 손실: 0.131792\t정확도: 95.70%\n",
+      "16\t검증 세트 손실: 0.169271\t최선의 손실: 0.131792\t정확도: 96.25%\n",
+      "17\t검증 세트 손실: 0.187095\t최선의 손실: 0.131792\t정확도: 94.88%\n",
+      "18\t검증 세트 손실: 0.153598\t최선의 손실: 0.131792\t정확도: 94.80%\n",
+      "19\t검증 세트 손실: 0.152879\t최선의 손실: 0.131792\t정확도: 95.78%\n",
+      "20\t검증 세트 손실: 0.156756\t최선의 손실: 0.131792\t정확도: 95.35%\n",
+      "21\t검증 세트 손실: 0.167825\t최선의 손실: 0.131792\t정확도: 95.19%\n",
+      "22\t검증 세트 손실: 0.160696\t최선의 손실: 0.131792\t정확도: 95.97%\n",
+      "23\t검증 세트 손실: 0.138762\t최선의 손실: 0.131792\t정확도: 95.82%\n",
+      "24\t검증 세트 손실: 0.129928\t최선의 손실: 0.129928\t정확도: 96.64%\n",
+      "25\t검증 세트 손실: 0.144513\t최선의 손실: 0.129928\t정확도: 96.52%\n",
+      "26\t검증 세트 손실: 0.129400\t최선의 손실: 0.129400\t정확도: 96.83%\n",
+      "27\t검증 세트 손실: 0.163027\t최선의 손실: 0.129400\t정확도: 95.74%\n",
+      "28\t검증 세트 손실: 0.120040\t최선의 손실: 0.120040\t정확도: 96.52%\n",
+      "29\t검증 세트 손실: 0.146389\t최선의 손실: 0.120040\t정확도: 95.82%\n",
+      "30\t검증 세트 손실: 0.133784\t최선의 손실: 0.120040\t정확도: 96.33%\n",
+      "31\t검증 세트 손실: 0.121297\t최선의 손실: 0.120040\t정확도: 96.79%\n",
+      "32\t검증 세트 손실: 0.162533\t최선의 손실: 0.120040\t정확도: 96.25%\n",
+      "33\t검증 세트 손실: 0.247806\t최선의 손실: 0.120040\t정확도: 90.38%\n",
+      "34\t검증 세트 손실: 0.207839\t최선의 손실: 0.120040\t정확도: 93.28%\n",
+      "35\t검증 세트 손실: 0.185927\t최선의 손실: 0.120040\t정확도: 93.51%\n",
+      "36\t검증 세트 손실: 0.211933\t최선의 손실: 0.120040\t정확도: 92.06%\n",
+      "37\t검증 세트 손실: 0.222071\t최선의 손실: 0.120040\t정확도: 92.73%\n",
+      "38\t검증 세트 손실: 0.207732\t최선의 손실: 0.120040\t정확도: 93.35%\n",
+      "39\t검증 세트 손실: 0.229559\t최선의 손실: 0.120040\t정확도: 93.78%\n",
+      "40\t검증 세트 손실: 0.307017\t최선의 손실: 0.120040\t정확도: 88.35%\n",
+      "41\t검증 세트 손실: 0.337248\t최선의 손실: 0.120040\t정확도: 91.83%\n",
+      "42\t검증 세트 손실: 0.280765\t최선의 손실: 0.120040\t정확도: 93.43%\n",
+      "43\t검증 세트 손실: 0.223917\t최선의 손실: 0.120040\t정확도: 92.22%\n",
+      "44\t검증 세트 손실: 0.196411\t최선의 손실: 0.120040\t정확도: 94.41%\n",
+      "45\t검증 세트 손실: 0.206316\t최선의 손실: 0.120040\t정확도: 94.14%\n",
+      "46\t검증 세트 손실: 0.174980\t최선의 손실: 0.120040\t정확도: 95.74%\n",
+      "47\t검증 세트 손실: 0.167657\t최선의 손실: 0.120040\t정확도: 95.90%\n",
+      "48\t검증 세트 손실: 0.167782\t최선의 손실: 0.120040\t정확도: 95.74%\n",
+      "49\t검증 세트 손실: 0.148669\t최선의 손실: 0.120040\t정확도: 96.29%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.4, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total= 1.4min\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.532712\t최선의 손실: 0.532712\t정확도: 86.20%\n",
-      "1\t검증 세트 손실: 0.153138\t최선의 손실: 0.153138\t정확도: 96.29%\n",
-      "2\t검증 세트 손실: 0.162162\t최선의 손실: 0.153138\t정확도: 95.82%\n",
-      "3\t검증 세트 손실: 0.172761\t최선의 손실: 0.153138\t정확도: 94.57%\n",
-      "4\t검증 세트 손실: 0.154627\t최선의 손실: 0.153138\t정확도: 95.35%\n",
-      "5\t검증 세트 손실: 0.161452\t최선의 손실: 0.153138\t정확도: 96.01%\n",
-      "6\t검증 세트 손실: 0.169296\t최선의 손실: 0.153138\t정확도: 96.40%\n",
-      "7\t검증 세트 손실: 0.292280\t최선의 손실: 0.153138\t정확도: 90.54%\n",
-      "8\t검증 세트 손실: 4.211092\t최선의 손실: 0.153138\t정확도: 27.95%\n",
-      "9\t검증 세트 손실: 2.581179\t최선의 손실: 0.153138\t정확도: 23.57%\n",
-      "10\t검증 세트 손실: 1.678100\t최선의 손실: 0.153138\t정확도: 31.04%\n",
-      "11\t검증 세트 손실: 1.411630\t최선의 손실: 0.153138\t정확도: 37.69%\n",
-      "12\t검증 세트 손실: 1.444110\t최선의 손실: 0.153138\t정확도: 38.78%\n",
-      "13\t검증 세트 손실: 1.358886\t최선의 손실: 0.153138\t정확도: 37.72%\n",
-      "14\t검증 세트 손실: 1.315644\t최선의 손실: 0.153138\t정확도: 37.10%\n",
-      "15\t검증 세트 손실: 1.307466\t최선의 손실: 0.153138\t정확도: 43.12%\n",
-      "16\t검증 세트 손실: 1.275171\t최선의 손실: 0.153138\t정확도: 42.69%\n",
-      "17\t검증 세트 손실: 1.312477\t최선의 손실: 0.153138\t정확도: 42.38%\n",
-      "18\t검증 세트 손실: 1.216547\t최선의 손실: 0.153138\t정확도: 45.97%\n",
-      "19\t검증 세트 손실: 1.194877\t최선의 손실: 0.153138\t정확도: 46.25%\n",
-      "20\t검증 세트 손실: 1.208991\t최선의 손실: 0.153138\t정확도: 45.86%\n",
-      "21\t검증 세트 손실: 1.261864\t최선의 손실: 0.153138\t정확도: 39.72%\n",
-      "22\t검증 세트 손실: 2.232405\t최선의 손실: 0.153138\t정확도: 36.20%\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120, total=  15.4s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120 \n",
+      "0\t검증 세트 손실: 0.212121\t최선의 손실: 0.212121\t정확도: 93.55%\n",
+      "1\t검증 세트 손실: 0.167847\t최선의 손실: 0.167847\t정확도: 95.39%\n",
+      "2\t검증 세트 손실: 0.132540\t최선의 손실: 0.132540\t정확도: 96.29%\n",
+      "3\t검증 세트 손실: 0.143879\t최선의 손실: 0.132540\t정확도: 96.33%\n",
+      "4\t검증 세트 손실: 0.136005\t최선의 손실: 0.132540\t정확도: 96.25%\n",
+      "5\t검증 세트 손실: 0.130671\t최선의 손실: 0.130671\t정확도: 96.44%\n",
+      "6\t검증 세트 손실: 0.156973\t최선의 손실: 0.130671\t정확도: 96.05%\n",
+      "7\t검증 세트 손실: 0.130925\t최선의 손실: 0.130671\t정확도: 96.64%\n",
+      "8\t검증 세트 손실: 0.135910\t최선의 손실: 0.130671\t정확도: 96.48%\n",
+      "9\t검증 세트 손실: 0.127844\t최선의 손실: 0.127844\t정확도: 96.87%\n",
+      "10\t검증 세트 손실: 0.128402\t최선의 손실: 0.127844\t정확도: 96.52%\n",
+      "11\t검증 세트 손실: 0.130597\t최선의 손실: 0.127844\t정확도: 96.56%\n",
+      "12\t검증 세트 손실: 0.144882\t최선의 손실: 0.127844\t정확도: 96.25%\n",
+      "13\t검증 세트 손실: 0.157982\t최선의 손실: 0.127844\t정확도: 95.93%\n",
+      "14\t검증 세트 손실: 0.144713\t최선의 손실: 0.127844\t정확도: 96.83%\n",
+      "15\t검증 세트 손실: 0.127050\t최선의 손실: 0.127050\t정확도: 96.01%\n",
+      "16\t검증 세트 손실: 0.114857\t최선의 손실: 0.114857\t정확도: 96.40%\n",
+      "17\t검증 세트 손실: 0.128707\t최선의 손실: 0.114857\t정확도: 96.21%\n",
+      "18\t검증 세트 손실: 0.144426\t최선의 손실: 0.114857\t정확도: 96.48%\n",
+      "19\t검증 세트 손실: 0.116324\t최선의 손실: 0.114857\t정확도: 96.25%\n",
+      "20\t검증 세트 손실: 0.142865\t최선의 손실: 0.114857\t정확도: 96.29%\n",
+      "21\t검증 세트 손실: 0.147699\t최선의 손실: 0.114857\t정확도: 94.64%\n",
+      "22\t검증 세트 손실: 0.187150\t최선의 손실: 0.114857\t정확도: 94.10%\n",
+      "23\t검증 세트 손실: 0.118989\t최선의 손실: 0.114857\t정확도: 96.64%\n",
+      "24\t검증 세트 손실: 0.154897\t최선의 손실: 0.114857\t정확도: 95.93%\n",
+      "25\t검증 세트 손실: 0.139647\t최선의 손실: 0.114857\t정확도: 95.70%\n",
+      "26\t검증 세트 손실: 0.132637\t최선의 손실: 0.114857\t정확도: 95.58%\n",
+      "27\t검증 세트 손실: 0.146618\t최선의 손실: 0.114857\t정확도: 96.52%\n",
+      "28\t검증 세트 손실: 0.140587\t최선의 손실: 0.114857\t정확도: 96.01%\n",
+      "29\t검증 세트 손실: 0.137280\t최선의 손실: 0.114857\t정확도: 95.86%\n",
+      "30\t검증 세트 손실: 0.276961\t최선의 손실: 0.114857\t정확도: 93.00%\n",
+      "31\t검증 세트 손실: 0.312309\t최선의 손실: 0.114857\t정확도: 92.46%\n",
+      "32\t검증 세트 손실: 0.319184\t최선의 손실: 0.114857\t정확도: 92.22%\n",
+      "33\t검증 세트 손실: 0.239355\t최선의 손실: 0.114857\t정확도: 93.24%\n",
+      "34\t검증 세트 손실: 0.226688\t최선의 손실: 0.114857\t정확도: 94.64%\n",
+      "35\t검증 세트 손실: 0.226590\t최선의 손실: 0.114857\t정확도: 91.40%\n",
+      "36\t검증 세트 손실: 0.188663\t최선의 손실: 0.114857\t정확도: 94.57%\n",
+      "37\t검증 세트 손실: 0.233461\t최선의 손실: 0.114857\t정확도: 92.92%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total=   2.3s\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.316261\t최선의 손실: 0.316261\t정확도: 90.07%\n",
-      "1\t검증 세트 손실: 0.177308\t최선의 손실: 0.177308\t정확도: 95.35%\n",
-      "2\t검증 세트 손실: 0.191809\t최선의 손실: 0.177308\t정확도: 96.05%\n",
-      "3\t검증 세트 손실: 0.169685\t최선의 손실: 0.169685\t정확도: 95.58%\n",
-      "4\t검증 세트 손실: 0.223481\t최선의 손실: 0.169685\t정확도: 94.68%\n",
-      "5\t검증 세트 손실: 0.403119\t최선의 손실: 0.169685\t정확도: 88.43%\n",
-      "6\t검증 세트 손실: 0.513855\t최선의 손실: 0.169685\t정확도: 82.92%\n",
-      "7\t검증 세트 손실: 0.413561\t최선의 손실: 0.169685\t정확도: 88.19%\n",
-      "8\t검증 세트 손실: 0.419906\t최선의 손실: 0.169685\t정확도: 83.78%\n",
-      "9\t검증 세트 손실: 1.160479\t최선의 손실: 0.169685\t정확도: 74.90%\n",
-      "10\t검증 세트 손실: 1.098887\t최선의 손실: 0.169685\t정확도: 68.14%\n",
-      "11\t검증 세트 손실: 2.070539\t최선의 손실: 0.169685\t정확도: 41.75%\n",
-      "12\t검증 세트 손실: 3.039247\t최선의 손실: 0.169685\t정확도: 20.05%\n",
-      "13\t검증 세트 손실: 4.619025\t최선의 손실: 0.169685\t정확도: 31.74%\n",
-      "14\t검증 세트 손실: 1.894827\t최선의 손실: 0.169685\t정확도: 24.71%\n",
-      "15\t검증 세트 손실: 1.276753\t최선의 손실: 0.169685\t정확도: 41.24%\n",
-      "16\t검증 세트 손실: 1.228308\t최선의 손실: 0.169685\t정확도: 55.32%\n",
-      "17\t검증 세트 손실: 1.330150\t최선의 손실: 0.169685\t정확도: 43.20%\n",
-      "18\t검증 세트 손실: 1.371027\t최선의 손실: 0.169685\t정확도: 39.99%\n",
-      "19\t검증 세트 손실: 1.303328\t최선의 손실: 0.169685\t정확도: 46.76%\n",
-      "20\t검증 세트 손실: 1.287595\t최선의 손실: 0.169685\t정확도: 42.06%\n",
-      "21\t검증 세트 손실: 1.189828\t최선의 손실: 0.169685\t정확도: 51.13%\n",
-      "22\t검증 세트 손실: 1.272159\t최선의 손실: 0.169685\t정확도: 43.71%\n",
-      "23\t검증 세트 손실: 1.157792\t최선의 손실: 0.169685\t정확도: 51.49%\n",
-      "24\t검증 세트 손실: 2.706865\t최선의 손실: 0.169685\t정확도: 26.27%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total=   2.5s\n",
-      "[CV] batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.404828\t최선의 손실: 0.404828\t정확도: 90.46%\n",
-      "1\t검증 세트 손실: 0.223304\t최선의 손실: 0.223304\t정확도: 94.18%\n",
-      "2\t검증 세트 손실: 0.152896\t최선의 손실: 0.152896\t정확도: 95.82%\n",
-      "3\t검증 세트 손실: 0.198507\t최선의 손실: 0.152896\t정확도: 95.47%\n",
-      "4\t검증 세트 손실: 0.169327\t최선의 손실: 0.152896\t정확도: 95.50%\n",
-      "5\t검증 세트 손실: 0.241992\t최선의 손실: 0.152896\t정확도: 94.84%\n",
-      "6\t검증 세트 손실: 0.451883\t최선의 손실: 0.152896\t정확도: 92.61%\n",
-      "7\t검증 세트 손실: 0.228573\t최선의 손실: 0.152896\t정확도: 93.39%\n",
-      "8\t검증 세트 손실: 0.361053\t최선의 손실: 0.152896\t정확도: 89.72%\n",
-      "9\t검증 세트 손실: 0.475905\t최선의 손실: 0.152896\t정확도: 78.11%\n",
-      "10\t검증 세트 손실: 4.396652\t최선의 손실: 0.152896\t정확도: 48.59%\n",
-      "11\t검증 세트 손실: 1.205952\t최선의 손실: 0.152896\t정확도: 59.54%\n",
-      "12\t검증 세트 손실: 7.057359\t최선의 손실: 0.152896\t정확도: 36.12%\n",
-      "13\t검증 세트 손실: 25.022024\t최선의 손실: 0.152896\t정확도: 27.44%\n",
-      "14\t검증 세트 손실: 76.078171\t최선의 손실: 0.152896\t정확도: 18.73%\n"
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120, total=  12.0s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15\t검증 세트 손실: 4.304897\t최선의 손실: 0.152896\t정확도: 23.96%\n",
-      "16\t검증 세트 손실: 2.669651\t최선의 손실: 0.152896\t정확도: 30.02%\n",
-      "17\t검증 세트 손실: 1.737176\t최선의 손실: 0.152896\t정확도: 33.89%\n",
-      "18\t검증 세트 손실: 5.112562\t최선의 손실: 0.152896\t정확도: 31.63%\n",
-      "19\t검증 세트 손실: 2.622483\t최선의 손실: 0.152896\t정확도: 35.57%\n",
-      "20\t검증 세트 손실: 2.089724\t최선의 손실: 0.152896\t정확도: 36.67%\n",
-      "21\t검증 세트 손실: 1.597708\t최선의 손실: 0.152896\t정확도: 37.37%\n",
-      "22\t검증 세트 손실: 1.948242\t최선의 손실: 0.152896\t정확도: 37.41%\n",
-      "23\t검증 세트 손실: 1.575592\t최선의 손실: 0.152896\t정확도: 35.22%\n",
+      "0\t검증 세트 손실: 0.286276\t최선의 손실: 0.286276\t정확도: 93.04%\n",
+      "1\t검증 세트 손실: 0.223933\t최선의 손실: 0.223933\t정확도: 93.78%\n",
+      "2\t검증 세트 손실: 0.157159\t최선의 손실: 0.157159\t정확도: 95.39%\n",
+      "3\t검증 세트 손실: 0.195964\t최선의 손실: 0.157159\t정확도: 95.82%\n",
+      "4\t검증 세트 손실: 0.145649\t최선의 손실: 0.145649\t정확도: 96.36%\n",
+      "5\t검증 세트 손실: 0.133912\t최선의 손실: 0.133912\t정확도: 96.33%\n",
+      "6\t검증 세트 손실: 0.142517\t최선의 손실: 0.133912\t정확도: 96.76%\n",
+      "7\t검증 세트 손실: 0.143957\t최선의 손실: 0.133912\t정확도: 96.56%\n",
+      "8\t검증 세트 손실: 0.131732\t최선의 손실: 0.131732\t정확도: 96.99%\n",
+      "9\t검증 세트 손실: 0.120569\t최선의 손실: 0.120569\t정확도: 96.56%\n",
+      "10\t검증 세트 손실: 0.136236\t최선의 손실: 0.120569\t정확도: 96.52%\n",
+      "11\t검증 세트 손실: 0.161449\t최선의 손실: 0.120569\t정확도: 95.54%\n",
+      "12\t검증 세트 손실: 0.156425\t최선의 손실: 0.120569\t정확도: 95.43%\n",
+      "13\t검증 세트 손실: 0.121661\t최선의 손실: 0.120569\t정확도: 96.68%\n",
+      "14\t검증 세트 손실: 0.155285\t최선의 손실: 0.120569\t정확도: 96.09%\n",
+      "15\t검증 세트 손실: 0.133294\t최선의 손실: 0.120569\t정확도: 96.13%\n",
+      "16\t검증 세트 손실: 0.140850\t최선의 손실: 0.120569\t정확도: 95.39%\n",
+      "17\t검증 세트 손실: 0.137561\t최선의 손실: 0.120569\t정확도: 95.90%\n",
+      "18\t검증 세트 손실: 0.153786\t최선의 손실: 0.120569\t정확도: 95.93%\n",
+      "19\t검증 세트 손실: 0.159079\t최선의 손실: 0.120569\t정확도: 95.04%\n",
+      "20\t검증 세트 손실: 0.142187\t최선의 손실: 0.120569\t정확도: 95.62%\n",
+      "21\t검증 세트 손실: 0.150501\t최선의 손실: 0.120569\t정확도: 96.36%\n",
+      "22\t검증 세트 손실: 0.150134\t최선의 손실: 0.120569\t정확도: 95.62%\n",
+      "23\t검증 세트 손실: 0.155657\t최선의 손실: 0.120569\t정확도: 96.29%\n",
+      "24\t검증 세트 손실: 0.129590\t최선의 손실: 0.120569\t정확도: 96.25%\n",
+      "25\t검증 세트 손실: 0.152604\t최선의 손실: 0.120569\t정확도: 96.09%\n",
+      "26\t검증 세트 손실: 0.152396\t최선의 손실: 0.120569\t정확도: 96.09%\n",
+      "27\t검증 세트 손실: 0.128291\t최선의 손실: 0.120569\t정확도: 96.29%\n",
+      "28\t검증 세트 손실: 0.176841\t최선의 손실: 0.120569\t정확도: 94.18%\n",
+      "29\t검증 세트 손실: 0.129195\t최선의 손실: 0.120569\t정확도: 96.05%\n",
+      "30\t검증 세트 손실: 0.158244\t최선의 손실: 0.120569\t정확도: 95.70%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.2, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total=   2.3s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.148385\t최선의 손실: 0.148385\t정확도: 95.93%\n",
-      "1\t검증 세트 손실: 0.144124\t최선의 손실: 0.144124\t정확도: 96.44%\n",
-      "2\t검증 세트 손실: 0.145150\t최선의 손실: 0.144124\t정확도: 96.33%\n",
-      "3\t검증 세트 손실: 0.143296\t최선의 손실: 0.143296\t정확도: 96.09%\n",
-      "4\t검증 세트 손실: 0.128732\t최선의 손실: 0.128732\t정확도: 96.33%\n",
-      "5\t검증 세트 손실: 0.148984\t최선의 손실: 0.128732\t정확도: 96.95%\n",
-      "6\t검증 세트 손실: 0.128206\t최선의 손실: 0.128206\t정확도: 96.91%\n",
-      "7\t검증 세트 손실: 0.150594\t최선의 손실: 0.128206\t정확도: 96.76%\n",
-      "8\t검증 세트 손실: 0.128195\t최선의 손실: 0.128195\t정확도: 96.76%\n",
-      "9\t검증 세트 손실: 0.175098\t최선의 손실: 0.128195\t정확도: 95.82%\n",
-      "10\t검증 세트 손실: 0.120476\t최선의 손실: 0.120476\t정확도: 97.11%\n",
-      "11\t검증 세트 손실: 0.147656\t최선의 손실: 0.120476\t정확도: 96.29%\n",
-      "12\t검증 세트 손실: 0.156398\t최선의 손실: 0.120476\t정확도: 96.44%\n",
-      "13\t검증 세트 손실: 0.128970\t최선의 손실: 0.120476\t정확도: 96.72%\n",
-      "14\t검증 세트 손실: 0.116647\t최선의 손실: 0.116647\t정확도: 97.26%\n",
-      "15\t검증 세트 손실: 0.148075\t최선의 손실: 0.116647\t정확도: 96.29%\n",
-      "16\t검증 세트 손실: 0.199473\t최선의 손실: 0.116647\t정확도: 96.48%\n",
-      "17\t검증 세트 손실: 0.157639\t최선의 손실: 0.116647\t정확도: 95.70%\n",
-      "18\t검증 세트 손실: 0.162980\t최선의 손실: 0.116647\t정확도: 95.66%\n",
-      "19\t검증 세트 손실: 0.120836\t최선의 손실: 0.116647\t정확도: 96.21%\n",
-      "20\t검증 세트 손실: 0.136849\t최선의 손실: 0.116647\t정확도: 96.95%\n",
-      "21\t검증 세트 손실: 0.120027\t최선의 손실: 0.116647\t정확도: 96.68%\n",
-      "22\t검증 세트 손실: 0.174873\t최선의 손실: 0.116647\t정확도: 97.22%\n",
-      "23\t검증 세트 손실: 0.126809\t최선의 손실: 0.116647\t정확도: 96.79%\n",
-      "24\t검증 세트 손실: 0.136520\t최선의 손실: 0.116647\t정확도: 96.40%\n",
-      "25\t검증 세트 손실: 0.164416\t최선의 손실: 0.116647\t정확도: 96.17%\n",
-      "26\t검증 세트 손실: 0.197923\t최선의 손실: 0.116647\t정확도: 96.01%\n",
-      "27\t검증 세트 손실: 0.145946\t최선의 손실: 0.116647\t정확도: 96.68%\n",
-      "28\t검증 세트 손실: 0.139622\t최선의 손실: 0.116647\t정확도: 96.91%\n",
-      "29\t검증 세트 손실: 0.221763\t최선의 손실: 0.116647\t정확도: 96.95%\n",
-      "30\t검증 세트 손실: 0.172490\t최선의 손실: 0.116647\t정확도: 96.68%\n",
-      "31\t검증 세트 손실: 0.196417\t최선의 손실: 0.116647\t정확도: 96.25%\n",
-      "32\t검증 세트 손실: 0.219635\t최선의 손실: 0.116647\t정확도: 96.60%\n",
-      "33\t검증 세트 손실: 0.200039\t최선의 손실: 0.116647\t정확도: 96.25%\n",
-      "34\t검증 세트 손실: 0.219690\t최선의 손실: 0.116647\t정확도: 95.50%\n",
-      "35\t검증 세트 손실: 0.192876\t최선의 손실: 0.116647\t정확도: 96.68%\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=120, total=   9.9s\n",
+      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.638058\t최선의 손실: 1.638058\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 1.616069\t최선의 손실: 1.616069\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.610213\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.611997\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.644407\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.712320\t최선의 손실: 1.610213\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 1.710432\t최선의 손실: 1.610213\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.618621\t최선의 손실: 1.610213\t정확도: 20.91%\n",
+      "8\t검증 세트 손실: 1.653783\t최선의 손실: 1.610213\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.742126\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.622705\t최선의 손실: 1.610213\t정확도: 19.27%\n",
+      "11\t검증 세트 손실: 1.637253\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.650787\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.648275\t최선의 손실: 1.610213\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.653459\t최선의 손실: 1.610213\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.684515\t최선의 손실: 1.610213\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.655625\t최선의 손실: 1.610213\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1.646176\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 1.692654\t최선의 손실: 1.610213\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.673512\t최선의 손실: 1.610213\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.667650\t최선의 손실: 1.610213\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.617086\t최선의 손실: 1.610213\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.743621\t최선의 손실: 1.610213\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.686756\t최선의 손실: 1.610213\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=  20.7s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.185894\t최선의 손실: 0.185894\t정확도: 95.43%\n",
-      "1\t검증 세트 손실: 0.184360\t최선의 손실: 0.184360\t정확도: 95.78%\n",
-      "2\t검증 세트 손실: 0.144953\t최선의 손실: 0.144953\t정확도: 95.82%\n",
-      "3\t검증 세트 손실: 0.160652\t최선의 손실: 0.144953\t정확도: 96.52%\n",
-      "4\t검증 세트 손실: 0.151320\t최선의 손실: 0.144953\t정확도: 96.13%\n",
-      "5\t검증 세트 손실: 0.152595\t최선의 손실: 0.144953\t정확도: 96.25%\n",
-      "6\t검증 세트 손실: 0.153745\t최선의 손실: 0.144953\t정확도: 95.43%\n",
-      "7\t검증 세트 손실: 0.157682\t최선의 손실: 0.144953\t정확도: 95.86%\n",
-      "8\t검증 세트 손실: 0.159756\t최선의 손실: 0.144953\t정확도: 96.05%\n",
-      "9\t검증 세트 손실: 0.145526\t최선의 손실: 0.144953\t정확도: 96.68%\n",
-      "10\t검증 세트 손실: 0.155224\t최선의 손실: 0.144953\t정확도: 94.80%\n",
-      "11\t검증 세트 손실: 0.155299\t최선의 손실: 0.144953\t정확도: 95.43%\n",
-      "12\t검증 세트 손실: 0.145989\t최선의 손실: 0.144953\t정확도: 96.05%\n",
-      "13\t검증 세트 손실: 0.146776\t최선의 손실: 0.144953\t정확도: 96.68%\n",
-      "14\t검증 세트 손실: 0.175666\t최선의 손실: 0.144953\t정확도: 96.56%\n",
-      "15\t검증 세트 손실: 0.128701\t최선의 손실: 0.128701\t정확도: 96.52%\n",
-      "16\t검증 세트 손실: 0.134847\t최선의 손실: 0.128701\t정확도: 96.48%\n",
-      "17\t검증 세트 손실: 0.124224\t최선의 손실: 0.124224\t정확도: 96.33%\n",
-      "18\t검증 세트 손실: 0.159132\t최선의 손실: 0.124224\t정확도: 96.01%\n",
-      "19\t검증 세트 손실: 0.163596\t최선의 손실: 0.124224\t정확도: 96.29%\n",
-      "20\t검증 세트 손실: 0.227367\t최선의 손실: 0.124224\t정확도: 96.33%\n",
-      "21\t검증 세트 손실: 0.188225\t최선의 손실: 0.124224\t정확도: 96.05%\n",
-      "22\t검증 세트 손실: 0.135135\t최선의 손실: 0.124224\t정확도: 96.09%\n",
-      "23\t검증 세트 손실: 0.237569\t최선의 손실: 0.124224\t정확도: 95.74%\n",
-      "24\t검증 세트 손실: 0.199824\t최선의 손실: 0.124224\t정확도: 95.62%\n",
-      "25\t검증 세트 손실: 0.188029\t최선의 손실: 0.124224\t정확도: 96.29%\n",
-      "26\t검증 세트 손실: 0.194671\t최선의 손실: 0.124224\t정확도: 96.01%\n",
-      "27\t검증 세트 손실: 0.164664\t최선의 손실: 0.124224\t정확도: 96.52%\n",
-      "28\t검증 세트 손실: 0.164055\t최선의 손실: 0.124224\t정확도: 96.60%\n",
-      "29\t검증 세트 손실: 0.160826\t최선의 손실: 0.124224\t정확도: 96.36%\n",
-      "30\t검증 세트 손실: 0.185385\t최선의 손실: 0.124224\t정확도: 96.64%\n",
-      "31\t검증 세트 손실: 0.172114\t최선의 손실: 0.124224\t정확도: 96.48%\n",
-      "32\t검증 세트 손실: 0.163394\t최선의 손실: 0.124224\t정확도: 96.44%\n",
-      "33\t검증 세트 손실: 0.145569\t최선의 손실: 0.124224\t정확도: 96.99%\n",
-      "34\t검증 세트 손실: 0.146057\t최선의 손실: 0.124224\t정확도: 97.15%\n",
-      "35\t검증 세트 손실: 0.173992\t최선의 손실: 0.124224\t정확도: 96.64%\n",
-      "36\t검증 세트 손실: 0.209914\t최선의 손실: 0.124224\t정확도: 96.44%\n",
-      "37\t검증 세트 손실: 0.262440\t최선의 손실: 0.124224\t정확도: 94.57%\n",
-      "38\t검증 세트 손실: 0.177283\t최선의 손실: 0.124224\t정확도: 95.43%\n",
+      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=   2.6s\n",
+      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.709730\t최선의 손실: 1.709730\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.637004\t최선의 손실: 1.637004\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.613780\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.627408\t최선의 손실: 1.613780\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.614217\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.627277\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.738308\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.661599\t최선의 손실: 1.613780\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.696167\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.703317\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.615875\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.630069\t최선의 손실: 1.613780\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 1.647282\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.736236\t최선의 손실: 1.613780\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.670209\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.623162\t최선의 손실: 1.613780\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 1.641104\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.629057\t최선의 손실: 1.613780\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.684787\t최선의 손실: 1.613780\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.708001\t최선의 손실: 1.613780\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.638457\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.758587\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.713553\t최선의 손실: 1.613780\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.656348\t최선의 손실: 1.613780\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=  18.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.178491\t최선의 손실: 0.178491\t정확도: 95.54%\n",
-      "1\t검증 세트 손실: 0.205413\t최선의 손실: 0.178491\t정확도: 95.50%\n",
-      "2\t검증 세트 손실: 0.167836\t최선의 손실: 0.167836\t정확도: 95.70%\n",
-      "3\t검증 세트 손실: 0.134086\t최선의 손실: 0.134086\t정확도: 96.40%\n",
-      "4\t검증 세트 손실: 0.147562\t최선의 손실: 0.134086\t정확도: 96.76%\n",
-      "5\t검증 세트 손실: 0.128901\t최선의 손실: 0.128901\t정확도: 96.76%\n",
-      "6\t검증 세트 손실: 0.157503\t최선의 손실: 0.128901\t정확도: 95.50%\n",
-      "7\t검증 세트 손실: 0.132393\t최선의 손실: 0.128901\t정확도: 96.76%\n",
-      "8\t검증 세트 손실: 0.136766\t최선의 손실: 0.128901\t정확도: 96.48%\n",
-      "9\t검증 세트 손실: 0.167302\t최선의 손실: 0.128901\t정확도: 95.62%\n",
-      "10\t검증 세트 손실: 0.129052\t최선의 손실: 0.128901\t정확도: 97.11%\n",
-      "11\t검증 세트 손실: 0.152485\t최선의 손실: 0.128901\t정확도: 96.01%\n",
-      "12\t검증 세트 손실: 0.129247\t최선의 손실: 0.128901\t정확도: 96.56%\n",
-      "13\t검증 세트 손실: 0.140813\t최선의 손실: 0.128901\t정확도: 96.40%\n",
-      "14\t검증 세트 손실: 0.146568\t최선의 손실: 0.128901\t정확도: 95.35%\n",
-      "15\t검증 세트 손실: 0.307157\t최선의 손실: 0.128901\t정확도: 95.78%\n",
-      "16\t검증 세트 손실: 0.174875\t최선의 손실: 0.128901\t정확도: 96.25%\n",
-      "17\t검증 세트 손실: 0.158979\t최선의 손실: 0.128901\t정확도: 95.74%\n",
-      "18\t검증 세트 손실: 0.141755\t최선의 손실: 0.128901\t정확도: 96.01%\n",
-      "19\t검증 세트 손실: 0.137217\t최선의 손실: 0.128901\t정확도: 96.29%\n",
-      "20\t검증 세트 손실: 0.175926\t최선의 손실: 0.128901\t정확도: 96.52%\n",
-      "21\t검증 세트 손실: 0.160282\t최선의 손실: 0.128901\t정확도: 96.64%\n",
-      "22\t검증 세트 손실: 0.147556\t최선의 손실: 0.128901\t정확도: 96.36%\n",
-      "23\t검증 세트 손실: 0.139084\t최선의 손실: 0.128901\t정확도: 96.09%\n",
-      "24\t검증 세트 손실: 0.133229\t최선의 손실: 0.128901\t정확도: 96.68%\n",
-      "25\t검증 세트 손실: 0.206610\t최선의 손실: 0.128901\t정확도: 94.76%\n",
-      "26\t검증 세트 손실: 0.226123\t최선의 손실: 0.128901\t정확도: 95.31%\n",
+      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=   2.6s\n",
+      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.760716\t최선의 손실: 1.760716\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 1.638659\t최선의 손실: 1.638659\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.675497\t최선의 손실: 1.638659\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.647808\t최선의 손실: 1.638659\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.618451\t최선의 손실: 1.618451\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.614304\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.626277\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 1.668249\t최선의 손실: 1.614304\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.636315\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.644322\t최선의 손실: 1.614304\t정확도: 19.27%\n",
+      "10\t검증 세트 손실: 1.643083\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.643694\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.624024\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.761823\t최선의 손실: 1.614304\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.680997\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.732960\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.675447\t최선의 손실: 1.614304\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.667928\t최선의 손실: 1.614304\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.679304\t최선의 손실: 1.614304\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.667958\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.661430\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.784949\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.670463\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.666315\t최선의 손실: 1.614304\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.679783\t최선의 손실: 1.614304\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 1.737622\t최선의 손실: 1.614304\t정확도: 18.73%\n",
+      "26\t검증 세트 손실: 1.722384\t최선의 손실: 1.614304\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total=  12.8s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 65.873177\t최선의 손실: 65.873177\t정확도: 24.63%\n",
-      "1\t검증 세트 손실: 771.813721\t최선의 손실: 65.873177\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 1144.130615\t최선의 손실: 65.873177\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1579.757080\t최선의 손실: 65.873177\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 10328.280273\t최선의 손실: 65.873177\t정확도: 18.73%\n",
-      "5\t검증 세트 손실: 5308.062500\t최선의 손실: 65.873177\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 2905.466309\t최선의 손실: 65.873177\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 29831.062500\t최선의 손실: 65.873177\t정확도: 19.16%\n",
-      "8\t검증 세트 손실: 7844.668457\t최선의 손실: 65.873177\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 2624.528564\t최선의 손실: 65.873177\t정확도: 23.69%\n",
-      "10\t검증 세트 손실: 11877.867188\t최선의 손실: 65.873177\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 8068.834961\t최선의 손실: 65.873177\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 10579.527344\t최선의 손실: 65.873177\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 4720.257324\t최선의 손실: 65.873177\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 939.121399\t최선의 손실: 65.873177\t정확도: 23.14%\n",
-      "15\t검증 세트 손실: 11445.502930\t최선의 손실: 65.873177\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 6426.256348\t최선의 손실: 65.873177\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 10879.876953\t최선의 손실: 65.873177\t정확도: 22.01%\n",
-      "18\t검증 세트 손실: 12770.194336\t최선의 손실: 65.873177\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 8290.904297\t최선의 손실: 65.873177\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 22306.968750\t최선의 손실: 65.873177\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 4588.500488\t최선의 손실: 65.873177\t정확도: 19.27%\n",
+      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=   2.8s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 5.790218\t최선의 손실: 5.790218\t정확도: 24.43%\n",
+      "1\t검증 세트 손실: 1.726711\t최선의 손실: 1.726711\t정확도: 28.77%\n",
+      "2\t검증 세트 손실: 1.467448\t최선의 손실: 1.467448\t정확도: 34.99%\n",
+      "3\t검증 세트 손실: 7.536595\t최선의 손실: 1.467448\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 3.011818\t최선의 손실: 1.467448\t정확도: 24.00%\n",
+      "5\t검증 세트 손실: 92.539970\t최선의 손실: 1.467448\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 131.181580\t최선의 손실: 1.467448\t정확도: 19.16%\n",
+      "7\t검증 세트 손실: 92.468811\t최선의 손실: 1.467448\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 74.182724\t최선의 손실: 1.467448\t정확도: 22.20%\n",
+      "9\t검증 세트 손실: 105.742645\t최선의 손실: 1.467448\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 342.877625\t최선의 손실: 1.467448\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 441.912445\t최선의 손실: 1.467448\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 931.293579\t최선의 손실: 1.467448\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 466.465118\t최선의 손실: 1.467448\t정확도: 20.33%\n",
+      "14\t검증 세트 손실: 1136.344849\t최선의 손실: 1.467448\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 403.074524\t최선의 손실: 1.467448\t정확도: 22.05%\n",
+      "16\t검증 세트 손실: 740.999634\t최선의 손실: 1.467448\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 315.896271\t최선의 손실: 1.467448\t정확도: 35.65%\n",
+      "18\t검증 세트 손실: 304.169769\t최선의 손실: 1.467448\t정확도: 24.43%\n",
+      "19\t검증 세트 손실: 768.090576\t최선의 손실: 1.467448\t정확도: 20.68%\n",
+      "20\t검증 세트 손실: 752.299133\t최선의 손실: 1.467448\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 337.620239\t최선의 손실: 1.467448\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 305.653015\t최선의 손실: 1.467448\t정확도: 20.09%\n",
+      "23\t검증 세트 손실: 476.849976\t최선의 손실: 1.467448\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120, total=  24.6s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120 \n"
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=140, total= 1.1min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 22.208923\t최선의 손실: 22.208923\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 596.164368\t최선의 손실: 22.208923\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 170.018143\t최선의 손실: 22.208923\t정확도: 19.08%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 130.471558\t최선의 손실: 130.471558\t정확도: 18.84%\n",
-      "1\t검증 세트 손실: 57.935226\t최선의 손실: 57.935226\t정확도: 37.29%\n",
-      "2\t검증 세트 손실: 8202.068359\t최선의 손실: 57.935226\t정확도: 20.91%\n",
-      "3\t검증 세트 손실: 5043.194824\t최선의 손실: 57.935226\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 2990.685791\t최선의 손실: 57.935226\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 3097.613037\t최선의 손실: 57.935226\t정확도: 20.88%\n",
-      "6\t검증 세트 손실: 11080.288086\t최선의 손실: 57.935226\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 4665.386230\t최선의 손실: 57.935226\t정확도: 25.37%\n",
-      "8\t검증 세트 손실: 4243.689453\t최선의 손실: 57.935226\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 6887.295410\t최선의 손실: 57.935226\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 4100.822266\t최선의 손실: 57.935226\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 2119.156250\t최선의 손실: 57.935226\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 12947.197266\t최선의 손실: 57.935226\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 9762.989258\t최선의 손실: 57.935226\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 7557.863770\t최선의 손실: 57.935226\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 11830.940430\t최선의 손실: 57.935226\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 3915.054688\t최선의 손실: 57.935226\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 6598.446289\t최선의 손실: 57.935226\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 4039.540771\t최선의 손실: 57.935226\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 31394.882812\t최선의 손실: 57.935226\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 2757.469482\t최선의 손실: 57.935226\t정확도: 18.84%\n",
-      "21\t검증 세트 손실: 21651.039062\t최선의 손실: 57.935226\t정확도: 18.84%\n",
-      "22\t검증 세트 손실: 18452.556641\t최선의 손실: 57.935226\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 221.703629\t최선의 손실: 22.208923\t정확도: 36.59%\n",
+      "4\t검증 세트 손실: 303.633759\t최선의 손실: 22.208923\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 169.892075\t최선의 손실: 22.208923\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 158.402420\t최선의 손실: 22.208923\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 95.319305\t최선의 손실: 22.208923\t정확도: 19.62%\n",
+      "8\t검증 세트 손실: 3190.629639\t최선의 손실: 22.208923\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 527.821472\t최선의 손실: 22.208923\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 2729.309326\t최선의 손실: 22.208923\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 179.657486\t최선의 손실: 22.208923\t정확도: 22.20%\n",
+      "12\t검증 세트 손실: 713.773132\t최선의 손실: 22.208923\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 297.679688\t최선의 손실: 22.208923\t정확도: 35.18%\n",
+      "14\t검증 세트 손실: 1340.170288\t최선의 손실: 22.208923\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 336.884613\t최선의 손실: 22.208923\t정확도: 19.51%\n",
+      "16\t검증 세트 손실: 431.821777\t최선의 손실: 22.208923\t정확도: 19.31%\n",
+      "17\t검증 세트 손실: 2744.095215\t최선의 손실: 22.208923\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 890.044250\t최선의 손실: 22.208923\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 2070.123535\t최선의 손실: 22.208923\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1980.412842\t최선의 손실: 22.208923\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1088.650757\t최선의 손실: 22.208923\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120, total=  25.1s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 288.901886\t최선의 손실: 288.901886\t정확도: 19.78%\n",
-      "1\t검증 세트 손실: 797.027954\t최선의 손실: 288.901886\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 14776.068359\t최선의 손실: 288.901886\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 2880.522705\t최선의 손실: 288.901886\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1678.668091\t최선의 손실: 288.901886\t정확도: 18.73%\n",
-      "5\t검증 세트 손실: 27114.361328\t최선의 손실: 288.901886\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 5661.112793\t최선의 손실: 288.901886\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 5531.173340\t최선의 손실: 288.901886\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 9094.246094\t최선의 손실: 288.901886\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 2760.871582\t최선의 손실: 288.901886\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 3738.379639\t최선의 손실: 288.901886\t정확도: 30.38%\n",
-      "11\t검증 세트 손실: 1187.253662\t최선의 손실: 288.901886\t정확도: 23.69%\n",
-      "12\t검증 세트 손실: 569.811646\t최선의 손실: 288.901886\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1376.666504\t최선의 손실: 288.901886\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 7297.987305\t최선의 손실: 288.901886\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1173.488525\t최선의 손실: 288.901886\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 772.883911\t최선의 손실: 288.901886\t정확도: 18.76%\n",
-      "17\t검증 세트 손실: 751.802673\t최선의 손실: 288.901886\t정확도: 37.41%\n",
-      "18\t검증 세트 손실: 857.967957\t최선의 손실: 288.901886\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 14837.812500\t최선의 손실: 288.901886\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 12821.346680\t최선의 손실: 288.901886\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1010.343933\t최선의 손실: 288.901886\t정확도: 30.61%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=140, total=  59.4s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 4.738139\t최선의 손실: 4.738139\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 148.714508\t최선의 손실: 4.738139\t정확도: 19.62%\n",
+      "2\t검증 세트 손실: 39.958725\t최선의 손실: 4.738139\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1141.344360\t최선의 손실: 4.738139\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 108.746101\t최선의 손실: 4.738139\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 122.370384\t최선의 손실: 4.738139\t정확도: 37.92%\n",
+      "6\t검증 세트 손실: 50.877415\t최선의 손실: 4.738139\t정확도: 19.55%\n",
+      "7\t검증 세트 손실: 245.592926\t최선의 손실: 4.738139\t정확도: 18.88%\n",
+      "8\t검증 세트 손실: 823.131165\t최선의 손실: 4.738139\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 161.582718\t최선의 손실: 4.738139\t정확도: 28.50%\n",
+      "10\t검증 세트 손실: 2410.691406\t최선의 손실: 4.738139\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 230.574860\t최선의 손실: 4.738139\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 97.127754\t최선의 손실: 4.738139\t정확도: 19.74%\n",
+      "13\t검증 세트 손실: 250.503860\t최선의 손실: 4.738139\t정확도: 34.83%\n",
+      "14\t검증 세트 손실: 322.916656\t최선의 손실: 4.738139\t정확도: 24.63%\n",
+      "15\t검증 세트 손실: 158.949890\t최선의 손실: 4.738139\t정확도: 29.71%\n",
+      "16\t검증 세트 손실: 413.943115\t최선의 손실: 4.738139\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 1044.913452\t최선의 손실: 4.738139\t정확도: 19.55%\n",
+      "18\t검증 세트 손실: 286.121826\t최선의 손실: 4.738139\t정확도: 19.70%\n",
+      "19\t검증 세트 손실: 416.408997\t최선의 손실: 4.738139\t정확도: 19.31%\n",
+      "20\t검증 세트 손실: 459.376404\t최선의 손실: 4.738139\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 251.696518\t최선의 손실: 4.738139\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.05, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120, total=  16.4s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.202431\t최선의 손실: 0.202431\t정확도: 95.11%\n",
-      "1\t검증 세트 손실: 0.141437\t최선의 손실: 0.141437\t정확도: 96.48%\n",
-      "2\t검증 세트 손실: 0.151016\t최선의 손실: 0.141437\t정확도: 95.97%\n",
-      "3\t검증 세트 손실: 0.152287\t최선의 손실: 0.141437\t정확도: 96.17%\n",
-      "4\t검증 세트 손실: 0.138770\t최선의 손실: 0.138770\t정확도: 96.33%\n",
-      "5\t검증 세트 손실: 0.131829\t최선의 손실: 0.131829\t정확도: 96.83%\n",
-      "6\t검증 세트 손실: 0.131031\t최선의 손실: 0.131031\t정확도: 96.52%\n",
-      "7\t검증 세트 손실: 0.131272\t최선의 손실: 0.131031\t정확도: 96.29%\n",
-      "8\t검증 세트 손실: 0.130887\t최선의 손실: 0.130887\t정확도: 96.36%\n",
-      "9\t검증 세트 손실: 0.145749\t최선의 손실: 0.130887\t정확도: 96.05%\n",
-      "10\t검증 세트 손실: 0.140228\t최선의 손실: 0.130887\t정확도: 95.93%\n",
-      "11\t검증 세트 손실: 0.145537\t최선의 손실: 0.130887\t정확도: 95.86%\n",
-      "12\t검증 세트 손실: 0.146062\t최선의 손실: 0.130887\t정확도: 96.17%\n",
-      "13\t검증 세트 손실: 0.150953\t최선의 손실: 0.130887\t정확도: 95.62%\n",
-      "14\t검증 세트 손실: 0.131277\t최선의 손실: 0.130887\t정확도: 95.78%\n",
-      "15\t검증 세트 손실: 0.163375\t최선의 손실: 0.130887\t정확도: 96.05%\n",
-      "16\t검증 세트 손실: 0.145629\t최선의 손실: 0.130887\t정확도: 94.53%\n",
-      "17\t검증 세트 손실: 0.128230\t최선의 손실: 0.128230\t정확도: 96.29%\n",
-      "18\t검증 세트 손실: 0.153992\t최선의 손실: 0.128230\t정확도: 96.40%\n",
-      "19\t검증 세트 손실: 0.141993\t최선의 손실: 0.128230\t정확도: 95.39%\n",
-      "20\t검증 세트 손실: 0.145839\t최선의 손실: 0.128230\t정확도: 95.11%\n",
-      "21\t검증 세트 손실: 0.135163\t최선의 손실: 0.128230\t정확도: 95.58%\n",
-      "22\t검증 세트 손실: 0.143552\t최선의 손실: 0.128230\t정확도: 95.93%\n",
-      "23\t검증 세트 손실: 0.138769\t최선의 손실: 0.128230\t정확도: 96.68%\n",
-      "24\t검증 세트 손실: 0.119759\t최선의 손실: 0.119759\t정확도: 96.36%\n",
-      "25\t검증 세트 손실: 0.131191\t최선의 손실: 0.119759\t정확도: 96.21%\n",
-      "26\t검증 세트 손실: 0.124954\t최선의 손실: 0.119759\t정확도: 96.44%\n",
-      "27\t검증 세트 손실: 0.125380\t최선의 손실: 0.119759\t정확도: 96.09%\n",
-      "28\t검증 세트 손실: 0.138290\t최선의 손실: 0.119759\t정확도: 96.76%\n",
-      "29\t검증 세트 손실: 0.134273\t최선의 손실: 0.119759\t정확도: 96.64%\n",
-      "30\t검증 세트 손실: 0.130521\t최선의 손실: 0.119759\t정확도: 97.15%\n",
-      "31\t검증 세트 손실: 0.134906\t최선의 손실: 0.119759\t정확도: 96.40%\n",
-      "32\t검증 세트 손실: 0.155228\t최선의 손실: 0.119759\t정확도: 94.84%\n",
-      "33\t검증 세트 손실: 0.149926\t최선의 손실: 0.119759\t정확도: 95.70%\n",
-      "34\t검증 세트 손실: 0.130376\t최선의 손실: 0.119759\t정확도: 96.40%\n",
-      "35\t검증 세트 손실: 0.153748\t최선의 손실: 0.119759\t정확도: 95.70%\n",
-      "36\t검증 세트 손실: 0.173342\t최선의 손실: 0.119759\t정확도: 95.62%\n",
-      "37\t검증 세트 손실: 0.169608\t최선의 손실: 0.119759\t정확도: 95.70%\n",
-      "38\t검증 세트 손실: 0.157538\t최선의 손실: 0.119759\t정확도: 95.90%\n",
-      "39\t검증 세트 손실: 0.154153\t최선의 손실: 0.119759\t정확도: 95.47%\n",
-      "40\t검증 세트 손실: 0.162262\t최선의 손실: 0.119759\t정확도: 95.23%\n",
-      "41\t검증 세트 손실: 0.159547\t최선의 손실: 0.119759\t정확도: 94.53%\n",
-      "42\t검증 세트 손실: 0.174492\t최선의 손실: 0.119759\t정확도: 96.29%\n",
-      "43\t검증 세트 손실: 0.174607\t최선의 손실: 0.119759\t정확도: 94.64%\n",
-      "44\t검증 세트 손실: 0.166212\t최선의 손실: 0.119759\t정확도: 95.19%\n",
-      "45\t검증 세트 손실: 0.148777\t최선의 손실: 0.119759\t정확도: 96.56%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=140, total=  60.0s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1.306417\t최선의 손실: 1.306417\t정확도: 73.81%\n",
+      "1\t검증 세트 손실: 1.023207\t최선의 손실: 1.023207\t정확도: 88.51%\n",
+      "2\t검증 세트 손실: 114653.625000\t최선의 손실: 1.023207\t정확도: 60.91%\n",
+      "3\t검증 세트 손실: 10631.974609\t최선의 손실: 1.023207\t정확도: 75.41%\n",
+      "4\t검증 세트 손실: 5486.460449\t최선의 손실: 1.023207\t정확도: 77.52%\n",
+      "5\t검증 세트 손실: 10966.592773\t최선의 손실: 1.023207\t정확도: 72.09%\n",
+      "6\t검증 세트 손실: 4804.203125\t최선의 손실: 1.023207\t정확도: 76.74%\n",
+      "7\t검증 세트 손실: 4701.883301\t최선의 손실: 1.023207\t정확도: 77.60%\n",
+      "8\t검증 세트 손실: 3196.662842\t최선의 손실: 1.023207\t정확도: 77.40%\n",
+      "9\t검증 세트 손실: 1773.644653\t최선의 손실: 1.023207\t정확도: 79.24%\n",
+      "10\t검증 세트 손실: 178065648.000000\t최선의 손실: 1.023207\t정확도: 44.45%\n",
+      "11\t검증 세트 손실: 4770582.500000\t최선의 손실: 1.023207\t정확도: 60.79%\n",
+      "12\t검증 세트 손실: 3518497.750000\t최선의 손실: 1.023207\t정확도: 56.22%\n",
+      "13\t검증 세트 손실: 2507541.500000\t최선의 손실: 1.023207\t정확도: 55.04%\n",
+      "14\t검증 세트 손실: 408813.375000\t최선의 손실: 1.023207\t정확도: 72.36%\n",
+      "15\t검증 세트 손실: 649862.312500\t최선의 손실: 1.023207\t정확도: 76.31%\n",
+      "16\t검증 세트 손실: 406566.437500\t최선의 손실: 1.023207\t정확도: 75.29%\n",
+      "17\t검증 세트 손실: 163242.265625\t최선의 손실: 1.023207\t정확도: 78.69%\n",
+      "18\t검증 세트 손실: 247173.406250\t최선의 손실: 1.023207\t정확도: 85.30%\n",
+      "19\t검증 세트 손실: 442521.218750\t최선의 손실: 1.023207\t정확도: 77.21%\n",
+      "20\t검증 세트 손실: 377406.406250\t최선의 손실: 1.023207\t정확도: 86.94%\n",
+      "21\t검증 세트 손실: 339246.218750\t최선의 손실: 1.023207\t정확도: 77.01%\n",
+      "22\t검증 세트 손실: 158428.703125\t최선의 손실: 1.023207\t정확도: 91.40%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120, total=  25.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.293470\t최선의 손실: 0.293470\t정확도: 91.44%\n",
-      "1\t검증 세트 손실: 0.206673\t최선의 손실: 0.206673\t정확도: 95.58%\n",
-      "2\t검증 세트 손실: 0.166761\t최선의 손실: 0.166761\t정확도: 95.31%\n",
-      "3\t검증 세트 손실: 0.143029\t최선의 손실: 0.143029\t정확도: 96.40%\n",
-      "4\t검증 세트 손실: 0.149099\t최선의 손실: 0.143029\t정확도: 95.78%\n",
-      "5\t검증 세트 손실: 0.133019\t최선의 손실: 0.133019\t정확도: 96.79%\n",
-      "6\t검증 세트 손실: 0.128723\t최선의 손실: 0.128723\t정확도: 96.60%\n",
-      "7\t검증 세트 손실: 0.127944\t최선의 손실: 0.127944\t정확도: 96.48%\n",
-      "8\t검증 세트 손실: 0.115233\t최선의 손실: 0.115233\t정확도: 96.68%\n",
-      "9\t검증 세트 손실: 0.110450\t최선의 손실: 0.110450\t정확도: 96.83%\n",
-      "10\t검증 세트 손실: 0.168930\t최선의 손실: 0.110450\t정확도: 95.74%\n",
-      "11\t검증 세트 손실: 0.123130\t최선의 손실: 0.110450\t정확도: 95.86%\n",
-      "12\t검증 세트 손실: 0.127164\t최선의 손실: 0.110450\t정확도: 96.33%\n",
-      "13\t검증 세트 손실: 0.137040\t최선의 손실: 0.110450\t정확도: 96.44%\n",
-      "14\t검증 세트 손실: 0.139079\t최선의 손실: 0.110450\t정확도: 95.97%\n",
-      "15\t검증 세트 손실: 0.123861\t최선의 손실: 0.110450\t정확도: 96.21%\n",
-      "16\t검증 세트 손실: 0.128830\t최선의 손실: 0.110450\t정확도: 96.95%\n",
-      "17\t검증 세트 손실: 0.121442\t최선의 손실: 0.110450\t정확도: 96.99%\n",
-      "18\t검증 세트 손실: 0.133544\t최선의 손실: 0.110450\t정확도: 96.36%\n",
-      "19\t검증 세트 손실: 0.142443\t최선의 손실: 0.110450\t정확도: 95.27%\n",
-      "20\t검증 세트 손실: 0.133338\t최선의 손실: 0.110450\t정확도: 96.05%\n",
-      "21\t검증 세트 손실: 0.172909\t최선의 손실: 0.110450\t정확도: 94.06%\n",
-      "22\t검증 세트 손실: 0.160216\t최선의 손실: 0.110450\t정확도: 94.88%\n",
-      "23\t검증 세트 손실: 0.147202\t최선의 손실: 0.110450\t정확도: 96.36%\n",
-      "24\t검증 세트 손실: 0.154151\t최선의 손실: 0.110450\t정확도: 95.74%\n",
-      "25\t검증 세트 손실: 0.139145\t최선의 손실: 0.110450\t정확도: 95.74%\n",
-      "26\t검증 세트 손실: 0.174308\t최선의 손실: 0.110450\t정확도: 93.82%\n",
-      "27\t검증 세트 손실: 0.197549\t최선의 손실: 0.110450\t정확도: 95.47%\n",
-      "28\t검증 세트 손실: 0.161739\t최선의 손실: 0.110450\t정확도: 95.70%\n",
-      "29\t검증 세트 손실: 0.162381\t최선의 손실: 0.110450\t정확도: 96.09%\n",
-      "30\t검증 세트 손실: 0.133883\t최선의 손실: 0.110450\t정확도: 96.33%\n",
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=  13.6s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 4644.509277\t최선의 손실: 4644.509277\t정확도: 76.08%\n",
+      "1\t검증 세트 손실: 929.429932\t최선의 손실: 929.429932\t정확도: 70.05%\n",
+      "2\t검증 세트 손실: 995.344421\t최선의 손실: 929.429932\t정확도: 75.06%\n",
+      "3\t검증 세트 손실: 982.972412\t최선의 손실: 929.429932\t정확도: 74.71%\n",
+      "4\t검증 세트 손실: 1500.484009\t최선의 손실: 929.429932\t정확도: 56.53%\n",
+      "5\t검증 세트 손실: 769.882080\t최선의 손실: 769.882080\t정확도: 65.36%\n",
+      "6\t검증 세트 손실: 314.564972\t최선의 손실: 314.564972\t정확도: 77.83%\n",
+      "7\t검증 세트 손실: 401.760040\t최선의 손실: 314.564972\t정확도: 78.03%\n",
+      "8\t검증 세트 손실: 307.981049\t최선의 손실: 307.981049\t정확도: 74.67%\n",
+      "9\t검증 세트 손실: 169.467941\t최선의 손실: 169.467941\t정확도: 89.25%\n",
+      "10\t검증 세트 손실: 221.338867\t최선의 손실: 169.467941\t정확도: 77.13%\n",
+      "11\t검증 세트 손실: 223.256409\t최선의 손실: 169.467941\t정확도: 76.00%\n",
+      "12\t검증 세트 손실: 318.131897\t최선의 손실: 169.467941\t정확도: 72.24%\n",
+      "13\t검증 세트 손실: 11227327.000000\t최선의 손실: 169.467941\t정확도: 41.83%\n",
+      "14\t검증 세트 손실: 138772.109375\t최선의 손실: 169.467941\t정확도: 75.49%\n",
+      "15\t검증 세트 손실: 210947.125000\t최선의 손실: 169.467941\t정확도: 70.88%\n",
+      "16\t검증 세트 손실: 51479.527344\t최선의 손실: 169.467941\t정확도: 77.33%\n",
+      "17\t검증 세트 손실: 89317.789062\t최선의 손실: 169.467941\t정확도: 75.49%\n",
+      "18\t검증 세트 손실: 57589.671875\t최선의 손실: 169.467941\t정확도: 76.31%\n",
+      "19\t검증 세트 손실: 31422.216797\t최선의 손실: 169.467941\t정확도: 77.33%\n",
+      "20\t검증 세트 손실: 34571.058594\t최선의 손실: 169.467941\t정확도: 89.52%\n",
+      "21\t검증 세트 손실: 33154.218750\t최선의 손실: 169.467941\t정확도: 77.64%\n",
+      "22\t검증 세트 손실: 31447.085938\t최선의 손실: 169.467941\t정확도: 77.25%\n",
+      "23\t검증 세트 손실: 37106.273438\t최선의 손실: 169.467941\t정확도: 81.12%\n",
+      "24\t검증 세트 손실: 45620.195312\t최선의 손실: 169.467941\t정확도: 78.85%\n",
+      "25\t검증 세트 손실: 28120.625000\t최선의 손실: 169.467941\t정확도: 76.00%\n",
+      "26\t검증 세트 손실: 10158.872070\t최선의 손실: 169.467941\t정확도: 88.78%\n",
+      "27\t검증 세트 손실: 9493.131836\t최선의 손실: 169.467941\t정확도: 93.82%\n",
+      "28\t검증 세트 손실: 58457.320312\t최선의 손실: 169.467941\t정확도: 83.42%\n",
+      "29\t검증 세트 손실: 26683.527344\t최선의 손실: 169.467941\t정확도: 88.39%\n",
+      "30\t검증 세트 손실: 67615432.000000\t최선의 손실: 169.467941\t정확도: 83.50%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120, total=  11.7s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120 \n",
-      "0\t검증 세트 손실: 0.195595\t최선의 손실: 0.195595\t정확도: 94.37%\n",
-      "1\t검증 세트 손실: 0.162973\t최선의 손실: 0.162973\t정확도: 95.86%\n",
-      "2\t검증 세트 손실: 0.201189\t최선의 손실: 0.162973\t정확도: 96.09%\n",
-      "3\t검증 세트 손실: 0.167429\t최선의 손실: 0.162973\t정확도: 95.35%\n",
-      "4\t검증 세트 손실: 0.157048\t최선의 손실: 0.157048\t정확도: 96.40%\n",
-      "5\t검증 세트 손실: 0.161352\t최선의 손실: 0.157048\t정확도: 96.17%\n",
-      "6\t검증 세트 손실: 0.156032\t최선의 손실: 0.156032\t정확도: 95.86%\n",
-      "7\t검증 세트 손실: 0.138561\t최선의 손실: 0.138561\t정확도: 96.29%\n",
-      "8\t검증 세트 손실: 0.148500\t최선의 손실: 0.138561\t정확도: 95.86%\n"
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=  18.3s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1017.429138\t최선의 손실: 1017.429138\t정확도: 70.95%\n",
+      "1\t검증 세트 손실: 247.376221\t최선의 손실: 247.376221\t정확도: 72.13%\n",
+      "2\t검증 세트 손실: 148.402771\t최선의 손실: 148.402771\t정확도: 74.78%\n",
+      "3\t검증 세트 손실: 365.882477\t최선의 손실: 148.402771\t정확도: 70.37%\n",
+      "4\t검증 세트 손실: 78.237846\t최선의 손실: 78.237846\t정확도: 77.52%\n",
+      "5\t검증 세트 손실: 104.634857\t최선의 손실: 78.237846\t정확도: 75.57%\n",
+      "6\t검증 세트 손실: 870604.187500\t최선의 손실: 78.237846\t정확도: 55.55%\n",
+      "7\t검증 세트 손실: 51430.660156\t최선의 손실: 78.237846\t정확도: 64.89%\n",
+      "8\t검증 세트 손실: 20118.234375\t최선의 손실: 78.237846\t정확도: 82.64%\n",
+      "9\t검증 세트 손실: 36177.714844\t최선의 손실: 78.237846\t정확도: 69.43%\n",
+      "10\t검증 세트 손실: 12205.696289\t최선의 손실: 78.237846\t정확도: 71.07%\n",
+      "11\t검증 세트 손실: 30340.214844\t최선의 손실: 78.237846\t정확도: 72.95%\n",
+      "12\t검증 세트 손실: 34918.398438\t최선의 손실: 78.237846\t정확도: 64.19%\n",
+      "13\t검증 세트 손실: 7181.303223\t최선의 손실: 78.237846\t정확도: 81.47%\n",
+      "14\t검증 세트 손실: 11756.094727\t최선의 손실: 78.237846\t정확도: 71.31%\n",
+      "15\t검증 세트 손실: 5886.253906\t최선의 손실: 78.237846\t정확도: 90.30%\n",
+      "16\t검증 세트 손실: 4551564.500000\t최선의 손실: 78.237846\t정확도: 66.93%\n",
+      "17\t검증 세트 손실: 500786.187500\t최선의 손실: 78.237846\t정확도: 85.69%\n",
+      "18\t검증 세트 손실: 51679488.000000\t최선의 손실: 78.237846\t정확도: 90.97%\n",
+      "19\t검증 세트 손실: 3238294.000000\t최선의 손실: 78.237846\t정확도: 95.39%\n",
+      "20\t검증 세트 손실: 1682699.375000\t최선의 손실: 78.237846\t정확도: 96.01%\n",
+      "21\t검증 세트 손실: 732193.000000\t최선의 손실: 78.237846\t정확도: 95.15%\n",
+      "22\t검증 세트 손실: 859688.125000\t최선의 손실: 78.237846\t정확도: 92.38%\n",
+      "23\t검증 세트 손실: 781092.562500\t최선의 손실: 78.237846\t정확도: 92.85%\n",
+      "24\t검증 세트 손실: 2086726.625000\t최선의 손실: 78.237846\t정확도: 90.42%\n",
+      "25\t검증 세트 손실: 513874.312500\t최선의 손실: 78.237846\t정확도: 95.04%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=  15.3s\n",
+      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9\t검증 세트 손실: 0.159945\t최선의 손실: 0.138561\t정확도: 95.62%\n",
-      "10\t검증 세트 손실: 0.136892\t최선의 손실: 0.136892\t정확도: 95.74%\n",
-      "11\t검증 세트 손실: 0.140086\t최선의 손실: 0.136892\t정확도: 95.86%\n",
-      "12\t검증 세트 손실: 0.169616\t최선의 손실: 0.136892\t정확도: 95.43%\n",
-      "13\t검증 세트 손실: 0.145893\t최선의 손실: 0.136892\t정확도: 95.97%\n",
-      "14\t검증 세트 손실: 0.129599\t최선의 손실: 0.129599\t정확도: 96.36%\n",
-      "15\t검증 세트 손실: 0.150512\t최선의 손실: 0.129599\t정확도: 95.66%\n",
-      "16\t검증 세트 손실: 0.161130\t최선의 손실: 0.129599\t정확도: 96.56%\n",
-      "17\t검증 세트 손실: 0.143289\t최선의 손실: 0.129599\t정확도: 96.05%\n",
-      "18\t검증 세트 손실: 0.160791\t최선의 손실: 0.129599\t정확도: 96.36%\n",
-      "19\t검증 세트 손실: 0.141876\t최선의 손실: 0.129599\t정확도: 95.66%\n",
-      "20\t검증 세트 손실: 0.137846\t최선의 손실: 0.129599\t정확도: 96.68%\n",
-      "21\t검증 세트 손실: 0.132839\t최선의 손실: 0.129599\t정확도: 96.76%\n",
-      "22\t검증 세트 손실: 0.187605\t최선의 손실: 0.129599\t정확도: 95.23%\n",
-      "23\t검증 세트 손실: 0.160721\t최선의 손실: 0.129599\t정확도: 95.47%\n",
-      "24\t검증 세트 손실: 0.163083\t최선의 손실: 0.129599\t정확도: 95.47%\n",
-      "25\t검증 세트 손실: 0.153804\t최선의 손실: 0.129599\t정확도: 96.52%\n",
-      "26\t검증 세트 손실: 0.137927\t최선의 손실: 0.129599\t정확도: 96.40%\n",
-      "27\t검증 세트 손실: 0.139493\t최선의 손실: 0.129599\t정확도: 96.13%\n",
-      "28\t검증 세트 손실: 0.147951\t최선의 손실: 0.129599\t정확도: 96.25%\n",
-      "29\t검증 세트 손실: 0.160328\t최선의 손실: 0.129599\t정확도: 95.90%\n",
-      "30\t검증 세트 손실: 0.150383\t최선의 손실: 0.129599\t정확도: 96.52%\n",
-      "31\t검증 세트 손실: 0.125123\t최선의 손실: 0.125123\t정확도: 96.68%\n",
-      "32\t검증 세트 손실: 0.134713\t최선의 손실: 0.125123\t정확도: 96.29%\n",
-      "33\t검증 세트 손실: 0.134579\t최선의 손실: 0.125123\t정확도: 96.52%\n",
-      "34\t검증 세트 손실: 0.136462\t최선의 손실: 0.125123\t정확도: 95.58%\n",
-      "35\t검증 세트 손실: 0.126426\t최선의 손실: 0.125123\t정확도: 96.48%\n",
-      "36\t검증 세트 손실: 0.179300\t최선의 손실: 0.125123\t정확도: 93.90%\n",
-      "37\t검증 세트 손실: 0.143781\t최선의 손실: 0.125123\t정확도: 95.62%\n",
-      "38\t검증 세트 손실: 0.175808\t최선의 손실: 0.125123\t정확도: 95.15%\n",
-      "39\t검증 세트 손실: 0.216203\t최선의 손실: 0.125123\t정확도: 93.00%\n",
-      "40\t검증 세트 손실: 0.285285\t최선의 손실: 0.125123\t정확도: 92.06%\n",
-      "41\t검증 세트 손실: 0.216944\t최선의 손실: 0.125123\t정확도: 94.53%\n",
-      "42\t검증 세트 손실: 0.232225\t최선의 손실: 0.125123\t정확도: 94.68%\n",
-      "43\t검증 세트 손실: 0.186702\t최선의 손실: 0.125123\t정확도: 95.07%\n",
-      "44\t검증 세트 손실: 0.199666\t최선의 손실: 0.125123\t정확도: 94.72%\n",
-      "45\t검증 세트 손실: 0.205024\t최선의 손실: 0.125123\t정확도: 94.57%\n",
-      "46\t검증 세트 손실: 0.187103\t최선의 손실: 0.125123\t정확도: 95.74%\n",
-      "47\t검증 세트 손실: 0.157072\t최선의 손실: 0.125123\t정확도: 95.86%\n",
-      "48\t검증 세트 손실: 0.145429\t최선의 손실: 0.125123\t정확도: 96.01%\n",
-      "49\t검증 세트 손실: 0.203426\t최선의 손실: 0.125123\t정확도: 93.75%\n",
-      "50\t검증 세트 손실: 0.180122\t최선의 손실: 0.125123\t정확도: 94.88%\n",
-      "51\t검증 세트 손실: 0.206934\t최선의 손실: 0.125123\t정확도: 93.82%\n",
-      "52\t검증 세트 손실: 0.236937\t최선의 손실: 0.125123\t정확도: 93.08%\n",
+      "0\t검증 세트 손실: 0.155950\t최선의 손실: 0.155950\t정확도: 95.70%\n",
+      "1\t검증 세트 손실: 0.179647\t최선의 손실: 0.155950\t정확도: 96.29%\n",
+      "2\t검증 세트 손실: 0.277467\t최선의 손실: 0.155950\t정확도: 93.59%\n",
+      "3\t검증 세트 손실: 0.289330\t최선의 손실: 0.155950\t정확도: 90.07%\n",
+      "4\t검증 세트 손실: 0.391699\t최선의 손실: 0.155950\t정확도: 87.65%\n",
+      "5\t검증 세트 손실: 0.452586\t최선의 손실: 0.155950\t정확도: 83.50%\n",
+      "6\t검증 세트 손실: 0.263413\t최선의 손실: 0.155950\t정확도: 91.05%\n",
+      "7\t검증 세트 손실: 0.243387\t최선의 손실: 0.155950\t정확도: 91.32%\n",
+      "8\t검증 세트 손실: 0.174622\t최선의 손실: 0.155950\t정확도: 94.96%\n",
+      "9\t검증 세트 손실: 0.158461\t최선의 손실: 0.155950\t정확도: 95.70%\n",
+      "10\t검증 세트 손실: 0.682105\t최선의 손실: 0.155950\t정확도: 85.22%\n",
+      "11\t검증 세트 손실: 0.374191\t최선의 손실: 0.155950\t정확도: 90.97%\n",
+      "12\t검증 세트 손실: 0.250340\t최선의 손실: 0.155950\t정확도: 93.08%\n",
+      "13\t검증 세트 손실: 0.253701\t최선의 손실: 0.155950\t정확도: 93.94%\n",
+      "14\t검증 세트 손실: 0.224702\t최선의 손실: 0.155950\t정확도: 94.53%\n",
+      "15\t검증 세트 손실: 0.288143\t최선의 손실: 0.155950\t정확도: 94.45%\n",
+      "16\t검증 세트 손실: 0.492257\t최선의 손실: 0.155950\t정확도: 85.11%\n",
+      "17\t검증 세트 손실: 7.346236\t최선의 손실: 0.155950\t정확도: 56.06%\n",
+      "18\t검증 세트 손실: 2.019565\t최선의 손실: 0.155950\t정확도: 70.05%\n",
+      "19\t검증 세트 손실: 1.439878\t최선의 손실: 0.155950\t정확도: 72.36%\n",
+      "20\t검증 세트 손실: 3.901166\t최선의 손실: 0.155950\t정확도: 72.67%\n",
+      "21\t검증 세트 손실: 2.795127\t최선의 손실: 0.155950\t정확도: 62.71%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=120, total=  18.8s\n",
-      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.645260\t최선의 손실: 1.645260\t정확도: 19.08%\n",
-      "1\t검증 세트 손실: 1.641245\t최선의 손실: 1.641245\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.620744\t최선의 손실: 1.620744\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.614738\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.631677\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.642406\t최선의 손실: 1.614738\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.623733\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.618520\t최선의 손실: 1.614738\t정확도: 20.91%\n",
-      "8\t검증 세트 손실: 1.640181\t최선의 손실: 1.614738\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.699886\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.619412\t최선의 손실: 1.614738\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.621415\t최선의 손실: 1.614738\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.664530\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.641558\t최선의 손실: 1.614738\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.641632\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.641039\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.647630\t최선의 손실: 1.614738\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.626766\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "18\t검증 세트 손실: 1.680628\t최선의 손실: 1.614738\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.681161\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.616044\t최선의 손실: 1.614738\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.622027\t최선의 손실: 1.614738\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.652667\t최선의 손실: 1.614738\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.637201\t최선의 손실: 1.614738\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 1.728613\t최선의 손실: 1.614738\t정확도: 22.01%\n",
+      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total=   7.1s\n",
+      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.165145\t최선의 손실: 0.165145\t정확도: 95.23%\n",
+      "1\t검증 세트 손실: 0.150471\t최선의 손실: 0.150471\t정확도: 96.13%\n",
+      "2\t검증 세트 손실: 0.984010\t최선의 손실: 0.150471\t정확도: 76.35%\n",
+      "3\t검증 세트 손실: 0.677235\t최선의 손실: 0.150471\t정확도: 75.10%\n",
+      "4\t검증 세트 손실: 0.539966\t최선의 손실: 0.150471\t정확도: 80.10%\n",
+      "5\t검증 세트 손실: 0.354333\t최선의 손실: 0.150471\t정확도: 89.05%\n",
+      "6\t검증 세트 손실: 0.356762\t최선의 손실: 0.150471\t정확도: 90.70%\n",
+      "7\t검증 세트 손실: 0.590422\t최선의 손실: 0.150471\t정확도: 80.18%\n",
+      "8\t검증 세트 손실: 2.835197\t최선의 손실: 0.150471\t정확도: 59.38%\n",
+      "9\t검증 세트 손실: 0.546000\t최선의 손실: 0.150471\t정확도: 79.63%\n",
+      "10\t검증 세트 손실: 0.524966\t최선의 손실: 0.150471\t정확도: 79.28%\n",
+      "11\t검증 세트 손실: 0.452351\t최선의 손실: 0.150471\t정확도: 86.75%\n",
+      "12\t검증 세트 손실: 2.810682\t최선의 손실: 0.150471\t정확도: 62.00%\n",
+      "13\t검증 세트 손실: 1.484754\t최선의 손실: 0.150471\t정확도: 67.67%\n",
+      "14\t검증 세트 손실: 0.533682\t최선의 손실: 0.150471\t정확도: 82.88%\n",
+      "15\t검증 세트 손실: 0.396502\t최선의 손실: 0.150471\t정확도: 89.72%\n",
+      "16\t검증 세트 손실: 0.390571\t최선의 손실: 0.150471\t정확도: 87.80%\n",
+      "17\t검증 세트 손실: 0.420651\t최선의 손실: 0.150471\t정확도: 87.29%\n",
+      "18\t검증 세트 손실: 4.188336\t최선의 손실: 0.150471\t정확도: 54.07%\n",
+      "19\t검증 세트 손실: 0.594147\t최선의 손실: 0.150471\t정확도: 76.51%\n",
+      "20\t검증 세트 손실: 0.567035\t최선의 손실: 0.150471\t정확도: 76.47%\n",
+      "21\t검증 세트 손실: 0.440268\t최선의 손실: 0.150471\t정확도: 84.60%\n",
+      "22\t검증 세트 손실: 0.399165\t최선의 손실: 0.150471\t정확도: 86.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.3s\n",
-      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.705137\t최선의 손실: 1.705137\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.675592\t최선의 손실: 1.675592\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.631508\t최선의 손실: 1.631508\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 1.624209\t최선의 손실: 1.624209\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.609474\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.630936\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.640435\t최선의 손실: 1.609474\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.626939\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.794703\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.854073\t최선의 손실: 1.609474\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 1.636963\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.739263\t최선의 손실: 1.609474\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 2.732356\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 2.094511\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.652883\t최선의 손실: 1.609474\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 1.613896\t최선의 손실: 1.609474\t정확도: 19.27%\n",
-      "16\t검증 세트 손실: 1.619857\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.628043\t최선의 손실: 1.609474\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.622349\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.621058\t최선의 손실: 1.609474\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.630999\t최선의 손실: 1.609474\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 1.628810\t최선의 손실: 1.609474\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 1.814447\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.620049\t최선의 손실: 1.609474\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.623322\t최선의 손실: 1.609474\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 1.618574\t최선의 손실: 1.609474\t정확도: 22.01%\n",
+      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total=   7.4s\n",
+      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 0.135781\t최선의 손실: 0.135781\t정확도: 96.91%\n",
+      "1\t검증 세트 손실: 0.142652\t최선의 손실: 0.135781\t정확도: 96.44%\n",
+      "2\t검증 세트 손실: 0.196436\t최선의 손실: 0.135781\t정확도: 92.53%\n",
+      "3\t검증 세트 손실: 0.251614\t최선의 손실: 0.135781\t정확도: 91.36%\n",
+      "4\t검증 세트 손실: 1.730556\t최선의 손실: 0.135781\t정확도: 34.09%\n",
+      "5\t검증 세트 손실: 1.141178\t최선의 손실: 0.135781\t정확도: 54.73%\n",
+      "6\t검증 세트 손실: 2.013357\t최선의 손실: 0.135781\t정확도: 57.39%\n",
+      "7\t검증 세트 손실: 0.576895\t최선의 손실: 0.135781\t정확도: 77.91%\n",
+      "8\t검증 세트 손실: 0.363333\t최선의 손실: 0.135781\t정확도: 88.51%\n",
+      "9\t검증 세트 손실: 0.363505\t최선의 손실: 0.135781\t정확도: 89.91%\n",
+      "10\t검증 세트 손실: 0.294834\t최선의 손실: 0.135781\t정확도: 90.73%\n",
+      "11\t검증 세트 손실: 0.261432\t최선의 손실: 0.135781\t정확도: 93.86%\n",
+      "12\t검증 세트 손실: 0.248895\t최선의 손실: 0.135781\t정확도: 92.77%\n",
+      "13\t검증 세트 손실: 0.207500\t최선의 손실: 0.135781\t정확도: 94.18%\n",
+      "14\t검증 세트 손실: 0.221315\t최선의 손실: 0.135781\t정확도: 94.02%\n",
+      "15\t검증 세트 손실: 0.212709\t최선의 손실: 0.135781\t정확도: 93.82%\n",
+      "16\t검증 세트 손실: 0.185814\t최선의 손실: 0.135781\t정확도: 95.74%\n",
+      "17\t검증 세트 손실: 0.330752\t최선의 손실: 0.135781\t정확도: 94.25%\n",
+      "18\t검증 세트 손실: 3.131548\t최선의 손실: 0.135781\t정확도: 60.52%\n",
+      "19\t검증 세트 손실: 0.923826\t최선의 손실: 0.135781\t정확도: 73.57%\n",
+      "20\t검증 세트 손실: 1.946067\t최선의 손실: 0.135781\t정확도: 69.12%\n",
+      "21\t검증 세트 손실: 1.742298\t최선의 손실: 0.135781\t정확도: 69.78%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.5s\n",
-      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.720449\t최선의 손실: 1.720449\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.616435\t최선의 손실: 1.616435\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 1.640218\t최선의 손실: 1.616435\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 1.616053\t최선의 손실: 1.616053\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.622268\t최선의 손실: 1.616053\t정확도: 18.73%\n",
-      "5\t검증 세트 손실: 1.627531\t최선의 손실: 1.616053\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.635310\t최선의 손실: 1.616053\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.613282\t최선의 손실: 1.613282\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.614207\t최선의 손실: 1.613282\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 1.645051\t최선의 손실: 1.613282\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 1.646489\t최선의 손실: 1.613282\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.676889\t최선의 손실: 1.613282\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.652041\t최선의 손실: 1.613282\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.683775\t최선의 손실: 1.613282\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.656084\t최선의 손실: 1.613282\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.646566\t최선의 손실: 1.613282\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.695141\t최선의 손실: 1.613282\t정확도: 19.27%\n",
-      "17\t검증 세트 손실: 1.672291\t최선의 손실: 1.613282\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.626548\t최선의 손실: 1.613282\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 1.660623\t최선의 손실: 1.613282\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.672235\t최선의 손실: 1.613282\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 1.686574\t최선의 손실: 1.613282\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.732442\t최선의 손실: 1.613282\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.646048\t최선의 손실: 1.613282\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.680721\t최선의 손실: 1.613282\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.668000\t최선의 손실: 1.613282\t정확도: 18.73%\n",
-      "26\t검증 세트 손실: 1.663774\t최선의 손실: 1.613282\t정확도: 20.91%\n",
-      "27\t검증 세트 손실: 1.642905\t최선의 손실: 1.613282\t정확도: 19.27%\n",
-      "28\t검증 세트 손실: 1.655557\t최선의 손실: 1.613282\t정확도: 19.27%\n",
+      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=50, total=   7.4s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.405026\t최선의 손실: 0.405026\t정확도: 88.15%\n",
+      "1\t검증 세트 손실: 0.284918\t최선의 손실: 0.284918\t정확도: 93.75%\n",
+      "2\t검증 세트 손실: 0.257730\t최선의 손실: 0.257730\t정확도: 93.16%\n",
+      "3\t검증 세트 손실: 0.202777\t최선의 손실: 0.202777\t정확도: 93.90%\n",
+      "4\t검증 세트 손실: 0.188762\t최선의 손실: 0.188762\t정확도: 94.14%\n",
+      "5\t검증 세트 손실: 0.203228\t최선의 손실: 0.188762\t정확도: 93.63%\n",
+      "6\t검증 세트 손실: 0.220723\t최선의 손실: 0.188762\t정확도: 93.63%\n",
+      "7\t검증 세트 손실: 0.225919\t최선의 손실: 0.188762\t정확도: 93.20%\n",
+      "8\t검증 세트 손실: 0.332981\t최선의 손실: 0.188762\t정확도: 91.24%\n",
+      "9\t검증 세트 손실: 0.245300\t최선의 손실: 0.188762\t정확도: 93.20%\n",
+      "10\t검증 세트 손실: 0.267262\t최선의 손실: 0.188762\t정확도: 89.72%\n",
+      "11\t검증 세트 손실: 0.268393\t최선의 손실: 0.188762\t정확도: 91.01%\n",
+      "12\t검증 세트 손실: 0.229128\t최선의 손실: 0.188762\t정확도: 92.73%\n",
+      "13\t검증 세트 손실: 0.260318\t최선의 손실: 0.188762\t정확도: 89.84%\n",
+      "14\t검증 세트 손실: 0.265664\t최선의 손실: 0.188762\t정확도: 91.59%\n",
+      "15\t검증 세트 손실: 0.259314\t최선의 손실: 0.188762\t정확도: 91.28%\n",
+      "16\t검증 세트 손실: 0.267329\t최선의 손실: 0.188762\t정확도: 89.76%\n",
+      "17\t검증 세트 손실: 0.262471\t최선의 손실: 0.188762\t정확도: 91.32%\n",
+      "18\t검증 세트 손실: 0.258627\t최선의 손실: 0.188762\t정확도: 92.89%\n",
+      "19\t검증 세트 손실: 0.273655\t최선의 손실: 0.188762\t정확도: 92.38%\n",
+      "20\t검증 세트 손실: 0.285399\t최선의 손실: 0.188762\t정확도: 92.89%\n",
+      "21\t검증 세트 손실: 0.312102\t최선의 손실: 0.188762\t정확도: 91.13%\n",
+      "22\t검증 세트 손실: 0.254362\t최선의 손실: 0.188762\t정확도: 92.65%\n",
+      "23\t검증 세트 손실: 0.268191\t최선의 손실: 0.188762\t정확도: 92.77%\n",
+      "24\t검증 세트 손실: 0.461226\t최선의 손실: 0.188762\t정확도: 79.28%\n",
+      "25\t검증 세트 손실: 0.350336\t최선의 손실: 0.188762\t정확도: 91.83%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.5s\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.449893\t최선의 손실: 1.449893\t정확도: 32.10%\n",
-      "1\t검증 세트 손실: 1.467582\t최선의 손실: 1.449893\t정확도: 36.43%\n",
-      "2\t검증 세트 손실: 1.496460\t최선의 손실: 1.449893\t정확도: 34.91%\n",
-      "3\t검증 세트 손실: 84.444115\t최선의 손실: 1.449893\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 55.259903\t최선의 손실: 1.449893\t정확도: 29.05%\n",
-      "5\t검증 세트 손실: 1535.390137\t최선의 손실: 1.449893\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 343.969727\t최선의 손실: 1.449893\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 2678.328857\t최선의 손실: 1.449893\t정확도: 20.91%\n",
-      "8\t검증 세트 손실: 1170.545532\t최선의 손실: 1.449893\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 280.070557\t최선의 손실: 1.449893\t정확도: 36.28%\n",
-      "10\t검증 세트 손실: 1003.177063\t최선의 손실: 1.449893\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 669.419739\t최선의 손실: 1.449893\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 696.044739\t최선의 손실: 1.449893\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 406.620026\t최선의 손실: 1.449893\t정확도: 23.77%\n",
-      "14\t검증 세트 손실: 245.520432\t최선의 손실: 1.449893\t정확도: 28.11%\n"
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160, total=   8.5s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.446197\t최선의 손실: 0.446197\t정확도: 77.37%\n",
+      "1\t검증 세트 손실: 0.437708\t최선의 손실: 0.437708\t정확도: 77.83%\n",
+      "2\t검증 세트 손실: 0.425767\t최선의 손실: 0.425767\t정확도: 77.95%\n",
+      "3\t검증 세트 손실: 0.438634\t최선의 손실: 0.425767\t정확도: 77.37%\n",
+      "4\t검증 세트 손실: 0.416666\t최선의 손실: 0.416666\t정확도: 77.64%\n",
+      "5\t검증 세트 손실: 0.526796\t최선의 손실: 0.416666\t정확도: 73.34%\n",
+      "6\t검증 세트 손실: 0.446064\t최선의 손실: 0.416666\t정확도: 76.51%\n",
+      "7\t검증 세트 손실: 0.471915\t최선의 손실: 0.416666\t정확도: 74.82%\n",
+      "8\t검증 세트 손실: 0.483497\t최선의 손실: 0.416666\t정확도: 74.24%\n",
+      "9\t검증 세트 손실: 0.508732\t최선의 손실: 0.416666\t정확도: 71.07%\n",
+      "10\t검증 세트 손실: 0.514621\t최선의 손실: 0.416666\t정확도: 77.64%\n",
+      "11\t검증 세트 손실: 0.475588\t최선의 손실: 0.416666\t정확도: 78.97%\n",
+      "12\t검증 세트 손실: 0.492435\t최선의 손실: 0.416666\t정확도: 74.78%\n",
+      "13\t검증 세트 손실: 0.453399\t최선의 손실: 0.416666\t정확도: 75.14%\n",
+      "14\t검증 세트 손실: 0.471350\t최선의 손실: 0.416666\t정확도: 79.28%\n",
+      "15\t검증 세트 손실: 0.475738\t최선의 손실: 0.416666\t정확도: 78.58%\n",
+      "16\t검증 세트 손실: 0.529945\t최선의 손실: 0.416666\t정확도: 76.82%\n",
+      "17\t검증 세트 손실: 0.566036\t최선의 손실: 0.416666\t정확도: 70.48%\n",
+      "18\t검증 세트 손실: 0.569052\t최선의 손실: 0.416666\t정확도: 71.50%\n",
+      "19\t검증 세트 손실: 0.582634\t최선의 손실: 0.416666\t정확도: 75.96%\n",
+      "20\t검증 세트 손실: 0.588827\t최선의 손실: 0.416666\t정확도: 77.44%\n",
+      "21\t검증 세트 손실: 0.651317\t최선의 손실: 0.416666\t정확도: 73.77%\n",
+      "22\t검증 세트 손실: 0.559376\t최선의 손실: 0.416666\t정확도: 74.59%\n",
+      "23\t검증 세트 손실: 0.606415\t최선의 손실: 0.416666\t정확도: 70.68%\n",
+      "24\t검증 세트 손실: 0.512846\t최선의 손실: 0.416666\t정확도: 76.11%\n",
+      "25\t검증 세트 손실: 0.523064\t최선의 손실: 0.416666\t정확도: 72.79%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160, total=   8.5s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.705513\t최선의 손실: 0.705513\t정확도: 74.39%\n",
+      "1\t검증 세트 손실: 0.485401\t최선의 손실: 0.485401\t정확도: 84.68%\n",
+      "2\t검증 세트 손실: 0.392114\t최선의 손실: 0.392114\t정확도: 88.86%\n",
+      "3\t검증 세트 손실: 0.275320\t최선의 손실: 0.275320\t정확도: 91.48%\n",
+      "4\t검증 세트 손실: 0.229472\t최선의 손실: 0.229472\t정확도: 92.81%\n",
+      "5\t검증 세트 손실: 0.210056\t최선의 손실: 0.210056\t정확도: 94.57%\n",
+      "6\t검증 세트 손실: 0.242039\t최선의 손실: 0.210056\t정확도: 93.16%\n",
+      "7\t검증 세트 손실: 0.238860\t최선의 손실: 0.210056\t정확도: 92.61%\n",
+      "8\t검증 세트 손실: 0.269130\t최선의 손실: 0.210056\t정확도: 93.12%\n",
+      "9\t검증 세트 손실: 0.251462\t최선의 손실: 0.210056\t정확도: 91.13%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15\t검증 세트 손실: 701.844238\t최선의 손실: 1.449893\t정확도: 19.27%\n",
-      "16\t검증 세트 손실: 338.890869\t최선의 손실: 1.449893\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 100.992683\t최선의 손실: 1.449893\t정확도: 29.32%\n",
-      "18\t검증 세트 손실: 403.847229\t최선의 손실: 1.449893\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1675.238525\t최선의 손실: 1.449893\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 2061.791748\t최선의 손실: 1.449893\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 955.337402\t최선의 손실: 1.449893\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 0.234608\t최선의 손실: 0.210056\t정확도: 93.67%\n",
+      "11\t검증 세트 손실: 0.260320\t최선의 손실: 0.210056\t정확도: 91.01%\n",
+      "12\t검증 세트 손실: 0.277057\t최선의 손실: 0.210056\t정확도: 91.40%\n",
+      "13\t검증 세트 손실: 0.226658\t최선의 손실: 0.210056\t정확도: 93.00%\n",
+      "14\t검증 세트 손실: 0.241660\t최선의 손실: 0.210056\t정확도: 91.71%\n",
+      "15\t검증 세트 손실: 0.223258\t최선의 손실: 0.210056\t정확도: 93.20%\n",
+      "16\t검증 세트 손실: 0.240950\t최선의 손실: 0.210056\t정확도: 92.30%\n",
+      "17\t검증 세트 손실: 0.218112\t최선의 손실: 0.210056\t정확도: 93.35%\n",
+      "18\t검증 세트 손실: 0.266658\t최선의 손실: 0.210056\t정확도: 92.77%\n",
+      "19\t검증 세트 손실: 0.234930\t최선의 손실: 0.210056\t정확도: 91.79%\n",
+      "20\t검증 세트 손실: 0.259718\t최선의 손실: 0.210056\t정확도: 92.22%\n",
+      "21\t검증 세트 손실: 0.331007\t최선의 손실: 0.210056\t정확도: 88.04%\n",
+      "22\t검증 세트 손실: 0.356495\t최선의 손실: 0.210056\t정확도: 89.37%\n",
+      "23\t검증 세트 손실: 0.359408\t최선의 손실: 0.210056\t정확도: 89.80%\n",
+      "24\t검증 세트 손실: 0.332600\t최선의 손실: 0.210056\t정확도: 86.55%\n",
+      "25\t검증 세트 손실: 0.331460\t최선의 손실: 0.210056\t정확도: 88.90%\n",
+      "26\t검증 세트 손실: 0.344021\t최선의 손실: 0.210056\t정확도: 89.76%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=140, total= 1.6min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 257.846741\t최선의 손실: 257.846741\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 26.089279\t최선의 손실: 26.089279\t정확도: 23.73%\n",
-      "2\t검증 세트 손실: 243.032684\t최선의 손실: 26.089279\t정확도: 19.12%\n",
-      "3\t검증 세트 손실: 421.326477\t최선의 손실: 26.089279\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 376.245270\t최선의 손실: 26.089279\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 262.380798\t최선의 손실: 26.089279\t정확도: 18.73%\n",
-      "6\t검증 세트 손실: 547.171082\t최선의 손실: 26.089279\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 196.756714\t최선의 손실: 26.089279\t정확도: 23.61%\n",
-      "8\t검증 세트 손실: 313.547424\t최선의 손실: 26.089279\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 319.158081\t최선의 손실: 26.089279\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 421.533325\t최선의 손실: 26.089279\t정확도: 23.14%\n",
-      "11\t검증 세트 손실: 516.648926\t최선의 손실: 26.089279\t정확도: 21.42%\n",
-      "12\t검증 세트 손실: 1367.873169\t최선의 손실: 26.089279\t정확도: 22.36%\n",
-      "13\t검증 세트 손실: 660.229187\t최선의 손실: 26.089279\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 479.488464\t최선의 손실: 26.089279\t정확도: 19.78%\n",
-      "15\t검증 세트 손실: 1148.831787\t최선의 손실: 26.089279\t정확도: 19.82%\n",
-      "16\t검증 세트 손실: 3457.371338\t최선의 손실: 26.089279\t정확도: 18.65%\n",
-      "17\t검증 세트 손실: 838.638855\t최선의 손실: 26.089279\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 670.969360\t최선의 손실: 26.089279\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 295.753387\t최선의 손실: 26.089279\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1997.905396\t최선의 손실: 26.089279\t정확도: 19.94%\n",
-      "21\t검증 세트 손실: 1533.532227\t최선의 손실: 26.089279\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1186.438477\t최선의 손실: 26.089279\t정확도: 18.73%\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=160, total=   8.7s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 1.857400\t최선의 손실: 1.857400\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 1.727373\t최선의 손실: 1.727373\t정확도: 22.01%\n",
+      "2\t검증 세트 손실: 1.824529\t최선의 손실: 1.727373\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 1.836183\t최선의 손실: 1.727373\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 1.713821\t최선의 손실: 1.713821\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 1.640345\t최선의 손실: 1.640345\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 2.200105\t최선의 손실: 1.640345\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.870601\t최선의 손실: 1.640345\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 2.017379\t최선의 손실: 1.640345\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 1.962065\t최선의 손실: 1.640345\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.747221\t최선의 손실: 1.640345\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.768023\t최선의 손실: 1.640345\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 1.739300\t최선의 손실: 1.640345\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 1.989933\t최선의 손실: 1.640345\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 2.714126\t최선의 손실: 1.640345\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.644428\t최선의 손실: 1.640345\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 2.567334\t최선의 손실: 1.640345\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.931920\t최선의 손실: 1.640345\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 2.504223\t최선의 손실: 1.640345\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 2.863551\t최선의 손실: 1.640345\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.512117\t최선의 손실: 1.640345\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 2.112478\t최선의 손실: 1.640345\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 2.065133\t최선의 손실: 1.640345\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 2.272592\t최선의 손실: 1.640345\t정확도: 19.27%\n",
+      "24\t검증 세트 손실: 2.165634\t최선의 손실: 1.640345\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.999865\t최선의 손실: 1.640345\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.751678\t최선의 손실: 1.640345\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=140, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.610821\t최선의 손실: 1.610821\t정확도: 23.42%\n",
-      "1\t검증 세트 손실: 9.225888\t최선의 손실: 1.610821\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 30.975298\t최선의 손실: 1.610821\t정확도: 26.62%\n",
-      "3\t검증 세트 손실: 55.295086\t최선의 손실: 1.610821\t정확도: 27.21%\n",
-      "4\t검증 세트 손실: 115.515091\t최선의 손실: 1.610821\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 74.058167\t최선의 손실: 1.610821\t정확도: 21.03%\n",
-      "6\t검증 세트 손실: 83.530609\t최선의 손실: 1.610821\t정확도: 19.31%\n",
-      "7\t검증 세트 손실: 220.141815\t최선의 손실: 1.610821\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 24.329630\t최선의 손실: 1.610821\t정확도: 21.97%\n",
-      "9\t검증 세트 손실: 185.490860\t최선의 손실: 1.610821\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1548.838745\t최선의 손실: 1.610821\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 137.471817\t최선의 손실: 1.610821\t정확도: 24.86%\n",
-      "12\t검증 세트 손실: 203.110931\t최선의 손실: 1.610821\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 676.347595\t최선의 손실: 1.610821\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 211.011307\t최선의 손실: 1.610821\t정확도: 22.48%\n",
-      "15\t검증 세트 손실: 1968.301758\t최선의 손실: 1.610821\t정확도: 23.06%\n",
-      "16\t검증 세트 손실: 528.066956\t최선의 손실: 1.610821\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 211.465652\t최선의 손실: 1.610821\t정확도: 34.83%\n",
-      "18\t검증 세트 손실: 191.715088\t최선의 손실: 1.610821\t정확도: 26.94%\n",
-      "19\t검증 세트 손실: 253.569733\t최선의 손실: 1.610821\t정확도: 34.13%\n",
-      "20\t검증 세트 손실: 284.627258\t최선의 손실: 1.610821\t정확도: 21.38%\n",
-      "21\t검증 세트 손실: 317.804352\t최선의 손실: 1.610821\t정확도: 18.88%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50, total=  59.3s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 1.653266\t최선의 손실: 1.653266\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.660325\t최선의 손실: 1.653266\t정확도: 20.91%\n",
+      "2\t검증 세트 손실: 1.985496\t최선의 손실: 1.653266\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1.694613\t최선의 손실: 1.653266\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.925805\t최선의 손실: 1.653266\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1.657725\t최선의 손실: 1.653266\t정확도: 19.08%\n",
+      "6\t검증 세트 손실: 1.903679\t최선의 손실: 1.653266\t정확도: 20.91%\n",
+      "7\t검증 세트 손실: 2.116689\t최선의 손실: 1.653266\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.811122\t최선의 손실: 1.653266\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 1.754584\t최선의 손실: 1.653266\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 2.003980\t최선의 손실: 1.653266\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.644818\t최선의 손실: 1.644818\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 1.985195\t최선의 손실: 1.644818\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.757740\t최선의 손실: 1.644818\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.719246\t최선의 손실: 1.644818\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 2.188099\t최선의 손실: 1.644818\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.751401\t최선의 손실: 1.644818\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 1.935971\t최선의 손실: 1.644818\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.852175\t최선의 손실: 1.644818\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 1.752338\t최선의 손실: 1.644818\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 2.139223\t최선의 손실: 1.644818\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.840810\t최선의 손실: 1.644818\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.956114\t최선의 손실: 1.644818\t정확도: 19.08%\n",
+      "23\t검증 세트 손실: 1.705414\t최선의 손실: 1.644818\t정확도: 18.73%\n",
+      "24\t검증 세트 손실: 1.895046\t최선의 손실: 1.644818\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.982664\t최선의 손실: 1.644818\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 2.337163\t최선의 손실: 1.644818\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 1.886560\t최선의 손실: 1.644818\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 2.024340\t최선의 손실: 1.644818\t정확도: 18.73%\n",
+      "29\t검증 세트 손실: 2.047047\t최선의 손실: 1.644818\t정확도: 20.91%\n",
+      "30\t검증 세트 손실: 1.643744\t최선의 손실: 1.643744\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 1.753883\t최선의 손실: 1.643744\t정확도: 18.73%\n",
+      "32\t검증 세트 손실: 1.808307\t최선의 손실: 1.643744\t정확도: 19.08%\n",
+      "33\t검증 세트 손실: 2.094042\t최선의 손실: 1.643744\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 2.147145\t최선의 손실: 1.643744\t정확도: 19.27%\n",
+      "35\t검증 세트 손실: 2.074813\t최선의 손실: 1.643744\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 2.406622\t최선의 손실: 1.643744\t정확도: 19.27%\n",
+      "37\t검증 세트 손실: 2.934965\t최선의 손실: 1.643744\t정확도: 20.91%\n",
+      "38\t검증 세트 손실: 1.989229\t최선의 손실: 1.643744\t정확도: 19.27%\n",
+      "39\t검증 세트 손실: 2.684046\t최선의 손실: 1.643744\t정확도: 18.73%\n",
+      "40\t검증 세트 손실: 1.750712\t최선의 손실: 1.643744\t정확도: 18.73%\n",
+      "41\t검증 세트 손실: 3.332995\t최선의 손실: 1.643744\t정확도: 22.01%\n",
+      "42\t검증 세트 손실: 2.041932\t최선의 손실: 1.643744\t정확도: 20.91%\n",
+      "43\t검증 세트 손실: 2.319535\t최선의 손실: 1.643744\t정확도: 18.73%\n",
+      "44\t검증 세트 손실: 2.060653\t최선의 손실: 1.643744\t정확도: 19.08%\n",
+      "45\t검증 세트 손실: 2.249430\t최선의 손실: 1.643744\t정확도: 19.08%\n",
+      "46\t검증 세트 손실: 1.896014\t최선의 손실: 1.643744\t정확도: 20.91%\n",
+      "47\t검증 세트 손실: 2.481059\t최선의 손실: 1.643744\t정확도: 20.91%\n",
+      "48\t검증 세트 손실: 2.207526\t최선의 손실: 1.643744\t정확도: 22.01%\n",
+      "49\t검증 세트 손실: 1.904261\t최선의 손실: 1.643744\t정확도: 22.01%\n",
+      "50\t검증 세트 손실: 1.931312\t최선의 손실: 1.643744\t정확도: 19.27%\n",
+      "51\t검증 세트 손실: 2.396808\t최선의 손실: 1.643744\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=140, total= 1.6min\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 350030.531250\t최선의 손실: 350030.531250\t정확도: 15.17%\n",
-      "1\t검증 세트 손실: 4172.289551\t최선의 손실: 4172.289551\t정확도: 54.03%\n",
-      "2\t검증 세트 손실: 211.056839\t최선의 손실: 211.056839\t정확도: 81.43%\n",
-      "3\t검증 세트 손실: 822.723877\t최선의 손실: 211.056839\t정확도: 76.66%\n",
-      "4\t검증 세트 손실: 4355168.500000\t최선의 손실: 211.056839\t정확도: 67.94%\n",
-      "5\t검증 세트 손실: 68953.578125\t최선의 손실: 211.056839\t정확도: 84.48%\n",
-      "6\t검증 세트 손실: 60395.933594\t최선의 손실: 211.056839\t정확도: 85.38%\n",
-      "7\t검증 세트 손실: 214867.093750\t최선의 손실: 211.056839\t정확도: 77.13%\n",
-      "8\t검증 세트 손실: 34637.175781\t최선의 손실: 211.056839\t정확도: 89.01%\n",
-      "9\t검증 세트 손실: 45883.191406\t최선의 손실: 211.056839\t정확도: 89.25%\n",
-      "10\t검증 세트 손실: 36554.707031\t최선의 손실: 211.056839\t정확도: 90.46%\n",
-      "11\t검증 세트 손실: 16550.759766\t최선의 손실: 211.056839\t정확도: 92.92%\n",
-      "12\t검증 세트 손실: 15358.673828\t최선의 손실: 211.056839\t정확도: 94.06%\n",
-      "13\t검증 세트 손실: 12661.382812\t최선의 손실: 211.056839\t정확도: 85.54%\n",
-      "14\t검증 세트 손실: 15618.947266\t최선의 손실: 211.056839\t정확도: 92.85%\n",
-      "15\t검증 세트 손실: 7048.694336\t최선의 손실: 211.056839\t정확도: 95.39%\n",
-      "16\t검증 세트 손실: 1338735.125000\t최선의 손실: 211.056839\t정확도: 89.29%\n",
-      "17\t검증 세트 손실: 227477.062500\t최선의 손실: 211.056839\t정확도: 93.55%\n",
-      "18\t검증 세트 손실: 2347312.750000\t최선의 손실: 211.056839\t정확도: 94.14%\n",
-      "19\t검증 세트 손실: 1705959.625000\t최선의 손실: 211.056839\t정확도: 94.18%\n",
-      "20\t검증 세트 손실: 1030392.625000\t최선의 손실: 211.056839\t정확도: 95.00%\n",
-      "21\t검증 세트 손실: 829782.937500\t최선의 손실: 211.056839\t정확도: 93.43%\n",
-      "22\t검증 세트 손실: 315174.656250\t최선의 손실: 211.056839\t정확도: 95.47%\n",
-      "23\t검증 세트 손실: 195435.984375\t최선의 손실: 211.056839\t정확도: 94.33%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50, total= 1.9min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50 \n",
+      "0\t검증 세트 손실: 1.754268\t최선의 손실: 1.754268\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.776973\t최선의 손실: 1.754268\t정확도: 20.91%\n",
+      "2\t검증 세트 손실: 1.642456\t최선의 손실: 1.642456\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.927241\t최선의 손실: 1.642456\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.959350\t최선의 손실: 1.642456\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 67.319656\t최선의 손실: 1.642456\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 1.655945\t최선의 손실: 1.642456\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 4.231987\t최선의 손실: 1.642456\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.644245\t최선의 손실: 1.642456\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 1.661494\t최선의 손실: 1.642456\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.615382\t최선의 손실: 1.615382\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.640694\t최선의 손실: 1.615382\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.680492\t최선의 손실: 1.615382\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.670714\t최선의 손실: 1.615382\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.690284\t최선의 손실: 1.615382\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.624795\t최선의 손실: 1.615382\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.803744\t최선의 손실: 1.615382\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 1.841313\t최선의 손실: 1.615382\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 2.085678\t최선의 손실: 1.615382\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 1.733125\t최선의 손실: 1.615382\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.664866\t최선의 손실: 1.615382\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 1.696486\t최선의 손실: 1.615382\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.687026\t최선의 손실: 1.615382\t정확도: 20.91%\n",
+      "23\t검증 세트 손실: 1.641297\t최선의 손실: 1.615382\t정확도: 18.73%\n",
+      "24\t검증 세트 손실: 1.806355\t최선의 손실: 1.615382\t정확도: 22.01%\n",
+      "25\t검증 세트 손실: 1.659745\t최선의 손실: 1.615382\t정확도: 19.27%\n",
+      "26\t검증 세트 손실: 2.071129\t최선의 손실: 1.615382\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 1.678698\t최선의 손실: 1.615382\t정확도: 18.73%\n",
+      "28\t검증 세트 손실: 1.637295\t최선의 손실: 1.615382\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 10.719910\t최선의 손실: 1.615382\t정확도: 22.01%\n",
+      "30\t검증 세트 손실: 32.482723\t최선의 손실: 1.615382\t정확도: 19.08%\n",
+      "31\t검증 세트 손실: 1.675968\t최선의 손실: 1.615382\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  22.3s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.694167\t최선의 손실: 0.694167\t정확도: 81.35%\n",
-      "1\t검증 세트 손실: 0.482977\t최선의 손실: 0.482977\t정확도: 86.98%\n",
-      "2\t검증 세트 손실: 24746.662109\t최선의 손실: 0.482977\t정확도: 62.55%\n",
-      "3\t검증 세트 손실: 14316.905273\t최선의 손실: 0.482977\t정확도: 51.95%\n",
-      "4\t검증 세트 손실: 14394.912109\t최선의 손실: 0.482977\t정확도: 49.14%\n",
-      "5\t검증 세트 손실: 3845.234863\t최선의 손실: 0.482977\t정확도: 68.65%\n",
-      "6\t검증 세트 손실: 1911.332642\t최선의 손실: 0.482977\t정확도: 68.84%\n",
-      "7\t검증 세트 손실: 1571.129150\t최선의 손실: 0.482977\t정확도: 76.39%\n",
-      "8\t검증 세트 손실: 3286.388916\t최선의 손실: 0.482977\t정확도: 64.82%\n",
-      "9\t검증 세트 손실: 3988.700928\t최선의 손실: 0.482977\t정확도: 66.69%\n",
-      "10\t검증 세트 손실: 68260.343750\t최선의 손실: 0.482977\t정확도: 55.55%\n",
-      "11\t검증 세트 손실: 1472.738403\t최선의 손실: 0.482977\t정확도: 89.56%\n",
-      "12\t검증 세트 손실: 1381.288696\t최선의 손실: 0.482977\t정확도: 77.33%\n",
-      "13\t검증 세트 손실: 808.351074\t최선의 손실: 0.482977\t정확도: 81.39%\n",
-      "14\t검증 세트 손실: 440.748779\t최선의 손실: 0.482977\t정확도: 91.20%\n",
-      "15\t검증 세트 손실: 1141201.250000\t최선의 손실: 0.482977\t정확도: 74.16%\n",
-      "16\t검증 세트 손실: 225204.890625\t최선의 손실: 0.482977\t정확도: 69.66%\n",
-      "17\t검증 세트 손실: 110695.921875\t최선의 손실: 0.482977\t정확도: 80.41%\n",
-      "18\t검증 세트 손실: 82812.750000\t최선의 손실: 0.482977\t정확도: 90.27%\n",
-      "19\t검증 세트 손실: 34992.222656\t최선의 손실: 0.482977\t정확도: 92.69%\n",
-      "20\t검증 세트 손실: 63587.488281\t최선의 손실: 0.482977\t정확도: 87.84%\n",
-      "21\t검증 세트 손실: 23204.480469\t최선의 손실: 0.482977\t정확도: 94.96%\n",
-      "22\t검증 세트 손실: 36635.835938\t최선의 손실: 0.482977\t정확도: 92.49%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  19.9s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 243.062698\t최선의 손실: 243.062698\t정확도: 56.02%\n",
-      "1\t검증 세트 손실: 29.338945\t최선의 손실: 29.338945\t정확도: 69.00%\n",
-      "2\t검증 세트 손실: 36.185749\t최선의 손실: 29.338945\t정확도: 76.82%\n",
-      "3\t검증 세트 손실: 1210.892456\t최선의 손실: 29.338945\t정확도: 86.51%\n",
-      "4\t검증 세트 손실: 44816.988281\t최선의 손실: 29.338945\t정확도: 58.80%\n",
-      "5\t검증 세트 손실: 54812.511719\t최선의 손실: 29.338945\t정확도: 55.04%\n",
-      "6\t검증 세트 손실: 158778.078125\t최선의 손실: 29.338945\t정확도: 61.81%\n",
-      "7\t검증 세트 손실: 17785.111328\t최선의 손실: 29.338945\t정확도: 82.17%\n",
-      "8\t검증 세트 손실: 8792.485352\t최선의 손실: 29.338945\t정확도: 80.38%\n",
-      "9\t검증 세트 손실: 5443.742188\t최선의 손실: 29.338945\t정확도: 86.28%\n",
-      "10\t검증 세트 손실: 5936.511230\t최선의 손실: 29.338945\t정확도: 88.47%\n",
-      "11\t검증 세트 손실: 5966.646484\t최선의 손실: 29.338945\t정확도: 84.99%\n",
-      "12\t검증 세트 손실: 4055.557129\t최선의 손실: 29.338945\t정확도: 89.64%\n",
-      "13\t검증 세트 손실: 3759.946777\t최선의 손실: 29.338945\t정확도: 90.23%\n",
-      "14\t검증 세트 손실: 2534.947998\t최선의 손실: 29.338945\t정확도: 90.73%\n",
-      "15\t검증 세트 손실: 17937990.000000\t최선의 손실: 29.338945\t정확도: 40.19%\n",
-      "16\t검증 세트 손실: 73891.500000\t최선의 손실: 29.338945\t정확도: 91.87%\n",
-      "17\t검증 세트 손실: 105279.726562\t최선의 손실: 29.338945\t정확도: 79.83%\n",
-      "18\t검증 세트 손실: 33395.109375\t최선의 손실: 29.338945\t정확도: 91.63%\n",
-      "19\t검증 세트 손실: 60366.960938\t최선의 손실: 29.338945\t정확도: 90.11%\n",
-      "20\t검증 세트 손실: 33171.683594\t최선의 손실: 29.338945\t정확도: 92.38%\n"
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=50, total= 1.2min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 965218.562500\t최선의 손실: 965218.562500\t정확도: 22.60%\n",
+      "1\t검증 세트 손실: 56599.425781\t최선의 손실: 56599.425781\t정확도: 19.16%\n",
+      "2\t검증 세트 손실: 4269866.500000\t최선의 손실: 56599.425781\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 456707.656250\t최선의 손실: 56599.425781\t정확도: 19.08%\n",
+      "4\t검증 세트 손실: 11373196.000000\t최선의 손실: 56599.425781\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 8885152.000000\t최선의 손실: 56599.425781\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 6622211.000000\t최선의 손실: 56599.425781\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 5774050.000000\t최선의 손실: 56599.425781\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 948902.437500\t최선의 손실: 56599.425781\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 2741866.500000\t최선의 손실: 56599.425781\t정확도: 19.27%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "21\t검증 세트 손실: 24777.318359\t최선의 손실: 29.338945\t정확도: 89.99%\n",
-      "22\t검증 세트 손실: 77232.882812\t최선의 손실: 29.338945\t정확도: 84.40%\n",
+      "10\t검증 세트 손실: 5504925.000000\t최선의 손실: 56599.425781\t정확도: 19.27%\n",
+      "11\t검증 세트 손실: 1148592.375000\t최선의 손실: 56599.425781\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 20047214.000000\t최선의 손실: 56599.425781\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 8653484.000000\t최선의 손실: 56599.425781\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 10249975.000000\t최선의 손실: 56599.425781\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 8789765.000000\t최선의 손실: 56599.425781\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 6339575.500000\t최선의 손실: 56599.425781\t정확도: 20.91%\n",
+      "17\t검증 세트 손실: 8611611.000000\t최선의 손실: 56599.425781\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 13698656.000000\t최선의 손실: 56599.425781\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 7502174.500000\t최선의 손실: 56599.425781\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 34609560.000000\t최선의 손실: 56599.425781\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 41107376.000000\t최선의 손실: 56599.425781\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 6450131.500000\t최선의 손실: 56599.425781\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  12.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.128894\t최선의 손실: 0.128894\t정확도: 96.09%\n",
-      "1\t검증 세트 손실: 0.155758\t최선의 손실: 0.128894\t정확도: 95.93%\n",
-      "2\t검증 세트 손실: 0.263312\t최선의 손실: 0.128894\t정확도: 90.93%\n",
-      "3\t검증 세트 손실: 0.691019\t최선의 손실: 0.128894\t정확도: 86.59%\n",
-      "4\t검증 세트 손실: 0.512952\t최선의 손실: 0.128894\t정확도: 85.73%\n",
-      "5\t검증 세트 손실: 0.497637\t최선의 손실: 0.128894\t정확도: 87.41%\n",
-      "6\t검증 세트 손실: 0.382857\t최선의 손실: 0.128894\t정확도: 86.28%\n",
-      "7\t검증 세트 손실: 0.415365\t최선의 손실: 0.128894\t정확도: 89.05%\n",
-      "8\t검증 세트 손실: 0.495443\t최선의 손실: 0.128894\t정확도: 85.34%\n",
-      "9\t검증 세트 손실: 6.894405\t최선의 손실: 0.128894\t정확도: 47.26%\n",
-      "10\t검증 세트 손실: 0.464705\t최선의 손실: 0.128894\t정확도: 85.07%\n",
-      "11\t검증 세트 손실: 0.518020\t최선의 손실: 0.128894\t정확도: 79.12%\n",
-      "12\t검증 세트 손실: 0.399953\t최선의 손실: 0.128894\t정확도: 87.10%\n",
-      "13\t검증 세트 손실: 0.364391\t최선의 손실: 0.128894\t정확도: 86.32%\n",
-      "14\t검증 세트 손실: 0.422875\t최선의 손실: 0.128894\t정확도: 83.58%\n",
-      "15\t검증 세트 손실: 0.256544\t최선의 손실: 0.128894\t정확도: 92.42%\n",
-      "16\t검증 세트 손실: 0.315751\t최선의 손실: 0.128894\t정확도: 89.87%\n",
-      "17\t검증 세트 손실: 0.311328\t최선의 손실: 0.128894\t정확도: 90.27%\n",
-      "18\t검증 세트 손실: 0.258365\t최선의 손실: 0.128894\t정확도: 93.55%\n",
-      "19\t검증 세트 손실: 0.579797\t최선의 손실: 0.128894\t정확도: 82.68%\n",
-      "20\t검증 세트 손실: 0.209175\t최선의 손실: 0.128894\t정확도: 93.98%\n",
-      "21\t검증 세트 손실: 0.287848\t최선의 손실: 0.128894\t정확도: 90.97%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=90, total= 1.0min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 263329.375000\t최선의 손실: 263329.375000\t정확도: 21.50%\n",
+      "1\t검증 세트 손실: 17502862.000000\t최선의 손실: 263329.375000\t정확도: 20.99%\n",
+      "2\t검증 세트 손실: 3453869.500000\t최선의 손실: 263329.375000\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 2533816.500000\t최선의 손실: 263329.375000\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 6009188.000000\t최선의 손실: 263329.375000\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 1077042.000000\t최선의 손실: 263329.375000\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 16450317.000000\t최선의 손실: 263329.375000\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 4846658.500000\t최선의 손실: 263329.375000\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 5077098.500000\t최선의 손실: 263329.375000\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 25232284.000000\t최선의 손실: 263329.375000\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 3365243.500000\t최선의 손실: 263329.375000\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 5734773.000000\t최선의 손실: 263329.375000\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 3018594.500000\t최선의 손실: 263329.375000\t정확도: 21.19%\n",
+      "13\t검증 세트 손실: 2205834.500000\t최선의 손실: 263329.375000\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 11152755.000000\t최선의 손실: 263329.375000\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 32519874.000000\t최선의 손실: 263329.375000\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 2243266.000000\t최선의 손실: 263329.375000\t정확도: 19.27%\n",
+      "17\t검증 세트 손실: 9495048.000000\t최선의 손실: 263329.375000\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 35441848.000000\t최선의 손실: 263329.375000\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 3455419.250000\t최선의 손실: 263329.375000\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 23669290.000000\t최선의 손실: 263329.375000\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 40140208.000000\t최선의 손실: 263329.375000\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total=   6.4s\n",
-      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.191132\t최선의 손실: 0.191132\t정확도: 95.93%\n",
-      "1\t검증 세트 손실: 0.135573\t최선의 손실: 0.135573\t정확도: 96.60%\n",
-      "2\t검증 세트 손실: 0.121691\t최선의 손실: 0.121691\t정확도: 96.76%\n",
-      "3\t검증 세트 손실: 0.231191\t최선의 손실: 0.121691\t정확도: 94.84%\n",
-      "4\t검증 세트 손실: 0.521270\t최선의 손실: 0.121691\t정확도: 80.26%\n",
-      "5\t검증 세트 손실: 0.281947\t최선의 손실: 0.121691\t정확도: 89.56%\n",
-      "6\t검증 세트 손실: 0.266219\t최선의 손실: 0.121691\t정확도: 90.97%\n",
-      "7\t검증 세트 손실: 0.322243\t최선의 손실: 0.121691\t정확도: 88.12%\n",
-      "8\t검증 세트 손실: 0.364990\t최선의 손실: 0.121691\t정확도: 87.41%\n",
-      "9\t검증 세트 손실: 0.235971\t최선의 손실: 0.121691\t정확도: 91.95%\n",
-      "10\t검증 세트 손실: 0.220981\t최선의 손실: 0.121691\t정확도: 92.61%\n",
-      "11\t검증 세트 손실: 0.251226\t최선의 손실: 0.121691\t정확도: 91.32%\n",
-      "12\t검증 세트 손실: 0.193491\t최선의 손실: 0.121691\t정확도: 93.75%\n",
-      "13\t검증 세트 손실: 0.620665\t최선의 손실: 0.121691\t정확도: 76.62%\n",
-      "14\t검증 세트 손실: 23.157345\t최선의 손실: 0.121691\t정확도: 60.32%\n",
-      "15\t검증 세트 손실: 1.524352\t최선의 손실: 0.121691\t정확도: 67.79%\n",
-      "16\t검증 세트 손실: 1.084331\t최선의 손실: 0.121691\t정확도: 71.58%\n",
-      "17\t검증 세트 손실: 2.524638\t최선의 손실: 0.121691\t정확도: 67.83%\n",
-      "18\t검증 세트 손실: 1.445222\t최선의 손실: 0.121691\t정확도: 69.70%\n",
-      "19\t검증 세트 손실: 0.659347\t최선의 손실: 0.121691\t정확도: 78.73%\n",
-      "20\t검증 세트 손실: 0.651086\t최선의 손실: 0.121691\t정확도: 75.68%\n",
-      "21\t검증 세트 손실: 0.816527\t최선의 손실: 0.121691\t정확도: 71.54%\n",
-      "22\t검증 세트 손실: 0.782246\t최선의 손실: 0.121691\t정확도: 73.65%\n",
-      "23\t검증 세트 손실: 0.713955\t최선의 손실: 0.121691\t정확도: 75.61%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=90, total=  59.8s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 892083.812500\t최선의 손실: 892083.812500\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 2170442.750000\t최선의 손실: 892083.812500\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 7379286.000000\t최선의 손실: 892083.812500\t정확도: 18.73%\n",
+      "3\t검증 세트 손실: 14234481.000000\t최선의 손실: 892083.812500\t정확도: 20.95%\n",
+      "4\t검증 세트 손실: 26182100.000000\t최선의 손실: 892083.812500\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 5189057.500000\t최선의 손실: 892083.812500\t정확도: 19.27%\n",
+      "6\t검증 세트 손실: 6334126.500000\t최선의 손실: 892083.812500\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 14936661.000000\t최선의 손실: 892083.812500\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 40908840.000000\t최선의 손실: 892083.812500\t정확도: 20.13%\n",
+      "9\t검증 세트 손실: 8078609.000000\t최선의 손실: 892083.812500\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 22683504.000000\t최선의 손실: 892083.812500\t정확도: 18.10%\n",
+      "11\t검증 세트 손실: 191694688.000000\t최선의 손실: 892083.812500\t정확도: 19.27%\n",
+      "12\t검증 세트 손실: 2659511.000000\t최선의 손실: 892083.812500\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 2680929.500000\t최선의 손실: 892083.812500\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 14335837.000000\t최선의 손실: 892083.812500\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 4791041.500000\t최선의 손실: 892083.812500\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 24955424.000000\t최선의 손실: 892083.812500\t정확도: 19.08%\n",
+      "17\t검증 세트 손실: 2327009.500000\t최선의 손실: 892083.812500\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 162883072.000000\t최선의 손실: 892083.812500\t정확도: 19.08%\n",
+      "19\t검증 세트 손실: 34261676.000000\t최선의 손실: 892083.812500\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 5712796.000000\t최선의 손실: 892083.812500\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 13864906.000000\t최선의 손실: 892083.812500\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total=   8.9s\n",
-      "[CV] batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 0.125989\t최선의 손실: 0.125989\t정확도: 96.40%\n",
-      "1\t검증 세트 손실: 0.130359\t최선의 손실: 0.125989\t정확도: 96.60%\n",
-      "2\t검증 세트 손실: 0.182689\t최선의 손실: 0.125989\t정확도: 94.88%\n",
-      "3\t검증 세트 손실: 0.607608\t최선의 손실: 0.125989\t정확도: 77.80%\n",
-      "4\t검증 세트 손실: 1.198779\t최선의 손실: 0.125989\t정확도: 71.31%\n",
-      "5\t검증 세트 손실: 0.630591\t최선의 손실: 0.125989\t정확도: 75.25%\n",
-      "6\t검증 세트 손실: 0.517732\t최선의 손실: 0.125989\t정확도: 84.87%\n",
-      "7\t검증 세트 손실: 0.772349\t최선의 손실: 0.125989\t정확도: 72.28%\n",
-      "8\t검증 세트 손실: 0.644740\t최선의 손실: 0.125989\t정확도: 76.08%\n",
-      "9\t검증 세트 손실: 0.360410\t최선의 손실: 0.125989\t정확도: 86.98%\n",
-      "10\t검증 세트 손실: 0.445111\t최선의 손실: 0.125989\t정확도: 81.51%\n",
-      "11\t검증 세트 손실: 3.924303\t최선의 손실: 0.125989\t정확도: 73.38%\n",
-      "12\t검증 세트 손실: 0.700974\t최선의 손실: 0.125989\t정확도: 81.08%\n",
-      "13\t검증 세트 손실: 0.484035\t최선의 손실: 0.125989\t정확도: 86.28%\n",
-      "14\t검증 세트 손실: 0.438768\t최선의 손실: 0.125989\t정확도: 86.86%\n",
-      "15\t검증 세트 손실: 0.559843\t최선의 손실: 0.125989\t정확도: 75.96%\n",
-      "16\t검증 세트 손실: 0.572802\t최선의 손실: 0.125989\t정확도: 76.31%\n",
-      "17\t검증 세트 손실: 0.343686\t최선의 손실: 0.125989\t정확도: 88.98%\n",
-      "18\t검증 세트 손실: 0.343623\t최선의 손실: 0.125989\t정확도: 89.21%\n",
-      "19\t검증 세트 손실: 0.333837\t최선의 손실: 0.125989\t정확도: 90.58%\n",
-      "20\t검증 세트 손실: 0.370456\t최선의 손실: 0.125989\t정확도: 84.52%\n",
-      "21\t검증 세트 손실: 2.429919\t최선의 손실: 0.125989\t정확도: 71.89%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=90, total=  59.2s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f738a2ee510>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.610378\t최선의 손실: 1.610378\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.613590\t최선의 손실: 1.610378\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.610069\t최선의 손실: 1.610069\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.607944\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.619746\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.616312\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.613034\t최선의 손실: 1.607944\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.615299\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.621617\t최선의 손실: 1.607944\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.611579\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.608235\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.609513\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.608588\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.612131\t최선의 손실: 1.607944\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 1.610387\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.609271\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.608219\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.611067\t최선의 손실: 1.607944\t정확도: 19.27%\n",
+      "18\t검증 세트 손실: 1.614792\t최선의 손실: 1.607944\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.610238\t최선의 손실: 1.607944\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1.610641\t최선의 손실: 1.607944\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 1.617286\t최선의 손실: 1.607944\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.612227\t최선의 손실: 1.607944\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 1.611052\t최선의 손실: 1.607944\t정확도: 19.27%\n",
+      "24\t검증 세트 손실: 1.614574\t최선의 손실: 1.607944\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=50, total=  12.8s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.569203\t최선의 손실: 0.569203\t정확도: 78.26%\n",
-      "1\t검증 세트 손실: 0.273725\t최선의 손실: 0.273725\t정확도: 91.32%\n",
-      "2\t검증 세트 손실: 0.224032\t최선의 손실: 0.224032\t정확도: 93.75%\n",
-      "3\t검증 세트 손실: 0.201749\t최선의 손실: 0.201749\t정확도: 94.76%\n",
-      "4\t검증 세트 손실: 0.200766\t최선의 손실: 0.200766\t정확도: 94.10%\n",
-      "5\t검증 세트 손실: 0.174179\t최선의 손실: 0.174179\t정확도: 95.23%\n",
-      "6\t검증 세트 손실: 0.170910\t최선의 손실: 0.170910\t정확도: 95.78%\n",
-      "7\t검증 세트 손실: 0.235200\t최선의 손실: 0.170910\t정확도: 92.57%\n",
-      "8\t검증 세트 손실: 0.245730\t최선의 손실: 0.170910\t정확도: 93.35%\n",
-      "9\t검증 세트 손실: 0.216413\t최선의 손실: 0.170910\t정확도: 92.03%\n",
-      "10\t검증 세트 손실: 0.274167\t최선의 손실: 0.170910\t정확도: 89.29%\n",
-      "11\t검증 세트 손실: 0.265062\t최선의 손실: 0.170910\t정확도: 91.99%\n",
-      "12\t검증 세트 손실: 0.210214\t최선의 손실: 0.170910\t정확도: 93.78%\n",
-      "13\t검증 세트 손실: 0.227329\t최선의 손실: 0.170910\t정확도: 92.77%\n",
-      "14\t검증 세트 손실: 0.227050\t최선의 손실: 0.170910\t정확도: 92.92%\n",
-      "15\t검증 세트 손실: 0.247597\t최선의 손실: 0.170910\t정확도: 93.00%\n",
-      "16\t검증 세트 손실: 0.198956\t최선의 손실: 0.170910\t정확도: 94.45%\n",
-      "17\t검증 세트 손실: 0.307206\t최선의 손실: 0.170910\t정확도: 90.42%\n",
-      "18\t검증 세트 손실: 0.259538\t최선의 손실: 0.170910\t정확도: 94.53%\n",
-      "19\t검증 세트 손실: 0.278107\t최선의 손실: 0.170910\t정확도: 90.34%\n",
-      "20\t검증 세트 손실: 0.323945\t최선의 손실: 0.170910\t정확도: 88.98%\n",
-      "21\t검증 세트 손실: 0.250287\t최선의 손실: 0.170910\t정확도: 90.77%\n",
-      "22\t검증 세트 손실: 0.318459\t최선의 손실: 0.170910\t정확도: 91.28%\n",
-      "23\t검증 세트 손실: 0.412615\t최선의 손실: 0.170910\t정확도: 89.37%\n",
-      "24\t검증 세트 손실: 0.309504\t최선의 손실: 0.170910\t정확도: 89.68%\n",
-      "25\t검증 세트 손실: 0.356020\t최선의 손실: 0.170910\t정확도: 88.12%\n",
-      "26\t검증 세트 손실: 0.576901\t최선의 손실: 0.170910\t정확도: 86.32%\n",
-      "27\t검증 세트 손실: 0.444571\t최선의 손실: 0.170910\t정확도: 92.06%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160, total=  16.9s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.607299\t최선의 손실: 0.607299\t정확도: 79.20%\n",
-      "1\t검증 세트 손실: 0.375283\t최선의 손실: 0.375283\t정확도: 88.35%\n",
-      "2\t검증 세트 손실: 0.220219\t최선의 손실: 0.220219\t정확도: 92.85%\n",
-      "3\t검증 세트 손실: 0.237825\t최선의 손실: 0.220219\t정확도: 93.55%\n",
-      "4\t검증 세트 손실: 0.180138\t최선의 손실: 0.180138\t정확도: 95.00%\n",
-      "5\t검증 세트 손실: 0.223090\t최선의 손실: 0.180138\t정확도: 94.45%\n",
-      "6\t검증 세트 손실: 0.187023\t최선의 손실: 0.180138\t정확도: 95.43%\n",
-      "7\t검증 세트 손실: 0.225550\t최선의 손실: 0.180138\t정확도: 93.51%\n",
-      "8\t검증 세트 손실: 0.255854\t최선의 손실: 0.180138\t정확도: 91.24%\n",
-      "9\t검증 세트 손실: 0.253958\t최선의 손실: 0.180138\t정확도: 90.66%\n",
-      "10\t검증 세트 손실: 0.245377\t최선의 손실: 0.180138\t정확도: 92.85%\n",
-      "11\t검증 세트 손실: 0.243606\t최선의 손실: 0.180138\t정확도: 92.81%\n",
-      "12\t검증 세트 손실: 0.241317\t최선의 손실: 0.180138\t정확도: 94.06%\n",
-      "13\t검증 세트 손실: 0.243510\t최선의 손실: 0.180138\t정확도: 91.63%\n",
-      "14\t검증 세트 손실: 0.227843\t최선의 손실: 0.180138\t정확도: 93.43%\n",
-      "15\t검증 세트 손실: 0.225555\t최선의 손실: 0.180138\t정확도: 93.63%\n",
-      "16\t검증 세트 손실: 0.185324\t최선의 손실: 0.180138\t정확도: 93.94%\n",
-      "17\t검증 세트 손실: 0.279578\t최선의 손실: 0.180138\t정확도: 92.03%\n",
-      "18\t검증 세트 손실: 0.268045\t최선의 손실: 0.180138\t정확도: 91.44%\n",
-      "19\t검증 세트 손실: 0.346880\t최선의 손실: 0.180138\t정확도: 90.23%\n",
-      "20\t검증 세트 손실: 0.293778\t최선의 손실: 0.180138\t정확도: 90.50%\n",
-      "21\t검증 세트 손실: 0.542895\t최선의 손실: 0.180138\t정확도: 79.52%\n",
-      "22\t검증 세트 손실: 0.436474\t최선의 손실: 0.180138\t정확도: 88.55%\n",
-      "23\t검증 세트 손실: 0.304843\t최선의 손실: 0.180138\t정확도: 94.57%\n",
-      "24\t검증 세트 손실: 0.319700\t최선의 손실: 0.180138\t정확도: 92.49%\n",
-      "25\t검증 세트 손실: 0.338143\t최선의 손실: 0.180138\t정확도: 92.92%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160, total=   7.6s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160 \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\t검증 세트 손실: 0.686274\t최선의 손실: 0.686274\t정확도: 73.10%\n",
-      "1\t검증 세트 손실: 0.398590\t최선의 손실: 0.398590\t정확도: 88.94%\n",
-      "2\t검증 세트 손실: 0.277414\t최선의 손실: 0.277414\t정확도: 91.99%\n",
-      "3\t검증 세트 손실: 0.365316\t최선의 손실: 0.277414\t정확도: 90.81%\n",
-      "4\t검증 세트 손실: 0.213514\t최선의 손실: 0.213514\t정확도: 93.16%\n",
-      "5\t검증 세트 손실: 0.214142\t최선의 손실: 0.213514\t정확도: 94.49%\n",
-      "6\t검증 세트 손실: 0.296892\t최선의 손실: 0.213514\t정확도: 92.46%\n",
-      "7\t검증 세트 손실: 0.221060\t최선의 손실: 0.213514\t정확도: 93.71%\n",
-      "8\t검증 세트 손실: 0.253386\t최선의 손실: 0.213514\t정확도: 93.98%\n",
-      "9\t검증 세트 손실: 0.249571\t최선의 손실: 0.213514\t정확도: 92.46%\n",
-      "10\t검증 세트 손실: 0.240712\t최선의 손실: 0.213514\t정확도: 93.82%\n",
-      "11\t검증 세트 손실: 0.253325\t최선의 손실: 0.213514\t정확도: 90.97%\n",
-      "12\t검증 세트 손실: 0.220129\t최선의 손실: 0.213514\t정확도: 94.02%\n",
-      "13\t검증 세트 손실: 0.251589\t최선의 손실: 0.213514\t정확도: 93.51%\n",
-      "14\t검증 세트 손실: 0.261745\t최선의 손실: 0.213514\t정확도: 91.44%\n",
-      "15\t검증 세트 손실: 0.234767\t최선의 손실: 0.213514\t정확도: 92.22%\n",
-      "16\t검증 세트 손실: 0.255620\t최선의 손실: 0.213514\t정확도: 91.99%\n",
-      "17\t검증 세트 손실: 0.268996\t최선의 손실: 0.213514\t정확도: 92.73%\n",
-      "18\t검증 세트 손실: 0.279750\t최선의 손실: 0.213514\t정확도: 90.30%\n",
-      "19\t검증 세트 손실: 0.253983\t최선의 손실: 0.213514\t정확도: 91.32%\n",
-      "20\t검증 세트 손실: 0.327516\t최선의 손실: 0.213514\t정확도: 91.71%\n",
-      "21\t검증 세트 손실: 0.311860\t최선의 손실: 0.213514\t정확도: 88.55%\n",
-      "22\t검증 세트 손실: 0.310387\t최선의 손실: 0.213514\t정확도: 91.40%\n",
-      "23\t검증 세트 손실: 0.399062\t최선의 손실: 0.213514\t정확도: 86.63%\n",
-      "24\t검증 세트 손실: 0.468513\t최선의 손실: 0.213514\t정확도: 84.95%\n",
-      "25\t검증 세트 손실: 0.316382\t최선의 손실: 0.213514\t정확도: 90.42%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=160, total=   7.5s\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.946902\t최선의 손실: 1.946902\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 1.802016\t최선의 손실: 1.802016\t정확도: 22.01%\n",
-      "2\t검증 세트 손실: 1.692735\t최선의 손실: 1.692735\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 1.804460\t최선의 손실: 1.692735\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 1.691344\t최선의 손실: 1.691344\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.724321\t최선의 손실: 1.691344\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 2.219584\t최선의 손실: 1.691344\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.943912\t최선의 손실: 1.691344\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 2.052869\t최선의 손실: 1.691344\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 2.113523\t최선의 손실: 1.691344\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.788623\t최선의 손실: 1.691344\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 1.858166\t최선의 손실: 1.691344\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.778966\t최선의 손실: 1.691344\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 2.072271\t최선의 손실: 1.691344\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 2.768911\t최선의 손실: 1.691344\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.653100\t최선의 손실: 1.653100\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 2.269323\t최선의 손실: 1.653100\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.752281\t최선의 손실: 1.653100\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 2.395668\t최선의 손실: 1.653100\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 2.790110\t최선의 손실: 1.653100\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 2.413051\t최선의 손실: 1.653100\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 2.232184\t최선의 손실: 1.653100\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 2.076371\t최선의 손실: 1.653100\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2.118172\t최선의 손실: 1.653100\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 2.023296\t최선의 손실: 1.653100\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.947237\t최선의 손실: 1.653100\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.932240\t최선의 손실: 1.653100\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 1.934936\t최선의 손실: 1.653100\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 2.609958\t최선의 손실: 1.653100\t정확도: 18.73%\n",
-      "29\t검증 세트 손실: 2.038081\t최선의 손실: 1.653100\t정확도: 20.91%\n",
-      "30\t검증 세트 손실: 1.742381\t최선의 손실: 1.653100\t정확도: 18.73%\n",
-      "31\t검증 세트 손실: 1.791282\t최선의 손실: 1.653100\t정확도: 20.91%\n",
-      "32\t검증 세트 손실: 1.987213\t최선의 손실: 1.653100\t정확도: 19.27%\n",
-      "33\t검증 세트 손실: 1.872244\t최선의 손실: 1.653100\t정확도: 19.08%\n",
-      "34\t검증 세트 손실: 2.038513\t최선의 손실: 1.653100\t정확도: 19.08%\n",
-      "35\t검증 세트 손실: 2.442637\t최선의 손실: 1.653100\t정확도: 19.27%\n",
-      "36\t검증 세트 손실: 1.715733\t최선의 손실: 1.653100\t정확도: 18.73%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total= 2.4min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.646612\t최선의 손실: 1.646612\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 1.854965\t최선의 손실: 1.646612\t정확도: 22.01%\n",
-      "2\t검증 세트 손실: 2.043446\t최선의 손실: 1.646612\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 2.167894\t최선의 손실: 1.646612\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 2.041461\t최선의 손실: 1.646612\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 2.161192\t최선의 손실: 1.646612\t정확도: 19.27%\n",
-      "6\t검증 세트 손실: 1.872059\t최선의 손실: 1.646612\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 2.132865\t최선의 손실: 1.646612\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 2.079443\t최선의 손실: 1.646612\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1.920799\t최선의 손실: 1.646612\t정확도: 20.91%\n",
-      "10\t검증 세트 손실: 1.987314\t최선의 손실: 1.646612\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 2.400873\t최선의 손실: 1.646612\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.827853\t최선의 손실: 1.646612\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 2.436979\t최선의 손실: 1.646612\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 2.022706\t최선의 손실: 1.646612\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 2.009875\t최선의 손실: 1.646612\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 1.705228\t최선의 손실: 1.646612\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 1.852769\t최선의 손실: 1.646612\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 2.018070\t최선의 손실: 1.646612\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 1.905105\t최선의 손실: 1.646612\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 2.185684\t최선의 손실: 1.646612\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.924763\t최선의 손실: 1.646612\t정확도: 22.01%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50 \n",
-      "0\t검증 세트 손실: 1.636406\t최선의 손실: 1.636406\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.691020\t최선의 손실: 1.636406\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.892065\t최선의 손실: 1.636406\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 1.904582\t최선의 손실: 1.636406\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 2.256224\t최선의 손실: 1.636406\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 2.033506\t최선의 손실: 1.636406\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 2.025888\t최선의 손실: 1.636406\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.657274\t최선의 손실: 1.636406\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.892316\t최선의 손실: 1.636406\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 2.038878\t최선의 손실: 1.636406\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.863005\t최선의 손실: 1.636406\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 2.213011\t최선의 손실: 1.636406\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 1.921291\t최선의 손실: 1.636406\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 3.036128\t최선의 손실: 1.636406\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.870716\t최선의 손실: 1.636406\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.892565\t최선의 손실: 1.636406\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 2.246214\t최선의 손실: 1.636406\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 2.055595\t최선의 손실: 1.636406\t정확도: 22.01%\n",
-      "18\t검증 세트 손실: 1.819331\t최선의 손실: 1.636406\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 1.918182\t최선의 손실: 1.636406\t정확도: 18.73%\n",
-      "20\t검증 세트 손실: 1.952760\t최선의 손실: 1.636406\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 2.143510\t최선의 손실: 1.636406\t정확도: 22.01%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function elu at 0x7f99417cf6a8>, n_neurons=50, total= 1.3min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 323806.593750\t최선의 손실: 323806.593750\t정확도: 19.23%\n",
-      "1\t검증 세트 손실: 50536632.000000\t최선의 손실: 323806.593750\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 74372856.000000\t최선의 손실: 323806.593750\t정확도: 18.69%\n",
-      "3\t검증 세트 손실: 26458458.000000\t최선의 손실: 323806.593750\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 14313725.000000\t최선의 손실: 323806.593750\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 7835910.500000\t최선의 손실: 323806.593750\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 5949717.000000\t최선의 손실: 323806.593750\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 4853799.000000\t최선의 손실: 323806.593750\t정확도: 27.87%\n",
-      "8\t검증 세트 손실: 8769690.000000\t최선의 손실: 323806.593750\t정확도: 19.08%\n",
-      "9\t검증 세트 손실: 56097060.000000\t최선의 손실: 323806.593750\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 7046591.500000\t최선의 손실: 323806.593750\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 11823831.000000\t최선의 손실: 323806.593750\t정확도: 18.73%\n",
-      "12\t검증 세트 손실: 17796316.000000\t최선의 손실: 323806.593750\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 3469392.000000\t최선의 손실: 323806.593750\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1565935.125000\t최선의 손실: 323806.593750\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 46053492.000000\t최선의 손실: 323806.593750\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 13986399.000000\t최선의 손실: 323806.593750\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 20306438.000000\t최선의 손실: 323806.593750\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 7624146.000000\t최선의 손실: 323806.593750\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 924289.312500\t최선의 손실: 323806.593750\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1011072.500000\t최선의 손실: 323806.593750\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 12369528.000000\t최선의 손실: 323806.593750\t정확도: 18.73%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=90, total= 1.2min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=90 \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\t검증 세트 손실: 23541950.000000\t최선의 손실: 23541950.000000\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 14764677.000000\t최선의 손실: 14764677.000000\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 3862043.250000\t최선의 손실: 3862043.250000\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 20478408.000000\t최선의 손실: 3862043.250000\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 5864571.000000\t최선의 손실: 3862043.250000\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 12813158.000000\t최선의 손실: 3862043.250000\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 6364050.000000\t최선의 손실: 3862043.250000\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 3334163.500000\t최선의 손실: 3334163.500000\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 978781.375000\t최선의 손실: 978781.375000\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 4928455.000000\t최선의 손실: 978781.375000\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 4500257.000000\t최선의 손실: 978781.375000\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 3460807.750000\t최선의 손실: 978781.375000\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 5713997.000000\t최선의 손실: 978781.375000\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 35432584.000000\t최선의 손실: 978781.375000\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 4361265.000000\t최선의 손실: 978781.375000\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 2260514.500000\t최선의 손실: 978781.375000\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 13567952.000000\t최선의 손실: 978781.375000\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 90561352.000000\t최선의 손실: 978781.375000\t정확도: 20.91%\n",
-      "18\t검증 세트 손실: 5409232.500000\t최선의 손실: 978781.375000\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 1846236.000000\t최선의 손실: 978781.375000\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 5977758.500000\t최선의 손실: 978781.375000\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 10774743.000000\t최선의 손실: 978781.375000\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 2046313.875000\t최선의 손실: 978781.375000\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 42907260.000000\t최선의 손실: 978781.375000\t정확도: 20.91%\n",
-      "24\t검증 세트 손실: 2713179.750000\t최선의 손실: 978781.375000\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 6937375.000000\t최선의 손실: 978781.375000\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 15896882.000000\t최선의 손실: 978781.375000\t정확도: 20.91%\n",
-      "27\t검증 세트 손실: 5346084.000000\t최선의 손실: 978781.375000\t정확도: 17.79%\n",
-      "28\t검증 세트 손실: 11702434.000000\t최선의 손실: 978781.375000\t정확도: 18.73%\n",
-      "29\t검증 세트 손실: 6314020.500000\t최선의 손실: 978781.375000\t정확도: 19.31%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=90, total= 1.9min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 1780743.000000\t최선의 손실: 1780743.000000\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 2604351.250000\t최선의 손실: 1780743.000000\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 11764774.000000\t최선의 손실: 1780743.000000\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1911205.750000\t최선의 손실: 1780743.000000\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 2517502.500000\t최선의 손실: 1780743.000000\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 10166105.000000\t최선의 손실: 1780743.000000\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 4049037.750000\t최선의 손실: 1780743.000000\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 1325125.125000\t최선의 손실: 1325125.125000\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 3367962.500000\t최선의 손실: 1325125.125000\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 12410404.000000\t최선의 손실: 1325125.125000\t정확도: 19.66%\n",
-      "10\t검증 세트 손실: 31404144.000000\t최선의 손실: 1325125.125000\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 3781607.250000\t최선의 손실: 1325125.125000\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 7656356.000000\t최선의 손실: 1325125.125000\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 3626051.750000\t최선의 손실: 1325125.125000\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 2864274.000000\t최선의 손실: 1325125.125000\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 11344615.000000\t최선의 손실: 1325125.125000\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 15838800.000000\t최선의 손실: 1325125.125000\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 3517533.250000\t최선의 손실: 1325125.125000\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 15146026.000000\t최선의 손실: 1325125.125000\t정확도: 19.08%\n",
-      "19\t검증 세트 손실: 2774928.750000\t최선의 손실: 1325125.125000\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 4117499.500000\t최선의 손실: 1325125.125000\t정확도: 23.77%\n",
-      "21\t검증 세트 손실: 152799376.000000\t최선의 손실: 1325125.125000\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 7264745.000000\t최선의 손실: 1325125.125000\t정확도: 20.80%\n",
-      "23\t검증 세트 손실: 26607850.000000\t최선의 손실: 1325125.125000\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 24244680.000000\t최선의 손실: 1325125.125000\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 33156972.000000\t최선의 손실: 1325125.125000\t정확도: 19.27%\n",
-      "26\t검증 세트 손실: 18961064.000000\t최선의 손실: 1325125.125000\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 7335350.500000\t최선의 손실: 1325125.125000\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 9385496.000000\t최선의 손실: 1325125.125000\t정확도: 19.08%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=90, total= 1.9min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.577227\t최선의 손실: 1.577227\t정확도: 20.95%\n",
-      "1\t검증 세트 손실: 1.613296\t최선의 손실: 1.577227\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.609822\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.608031\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.618828\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.616220\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.613030\t최선의 손실: 1.577227\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.615293\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.621616\t최선의 손실: 1.577227\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.611578\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.608235\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.609513\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.608589\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 1.612131\t최선의 손실: 1.577227\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 1.610412\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 1.609290\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.608219\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.611068\t최선의 손실: 1.577227\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.614792\t최선의 손실: 1.577227\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.610237\t최선의 손실: 1.577227\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.610641\t최선의 손실: 1.577227\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.617286\t최선의 손실: 1.577227\t정확도: 19.27%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total= 1.3min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.613325\t최선의 손실: 1.613325\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 1.615549\t최선의 손실: 1.613325\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.609284\t최선의 손실: 1.609284\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.613905\t최선의 손실: 1.609284\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 1.612054\t최선의 손실: 1.609284\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.610149\t최선의 손실: 1.609284\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.621746\t최선의 손실: 1.609284\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 1.612061\t최선의 손실: 1.609284\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.611337\t최선의 손실: 1.609284\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.608813\t최선의 손실: 1.608813\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.610081\t최선의 손실: 1.608813\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.610630\t최선의 손실: 1.608813\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.610733\t최선의 손실: 1.608813\t정확도: 19.27%\n",
-      "13\t검증 세트 손실: 1.611636\t최선의 손실: 1.608813\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.608831\t최선의 손실: 1.608813\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f738a2ee510>, n_neurons=140, total=  55.9s\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f738a2ee510>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.613139\t최선의 손실: 1.613139\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.615409\t최선의 손실: 1.613139\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.609278\t최선의 손실: 1.609278\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.613840\t최선의 손실: 1.609278\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 1.612036\t최선의 손실: 1.609278\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.610146\t최선의 손실: 1.609278\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.621748\t최선의 손실: 1.609278\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 1.612059\t최선의 손실: 1.609278\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.611334\t최선의 손실: 1.609278\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.608811\t최선의 손실: 1.608811\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.610081\t최선의 손실: 1.608811\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.610639\t최선의 손실: 1.608811\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.610745\t최선의 손실: 1.608811\t정확도: 19.27%\n",
+      "13\t검증 세트 손실: 1.611638\t최선의 손실: 1.608811\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.608832\t최선의 손실: 1.608811\t정확도: 22.01%\n",
       "15\t검증 세트 손실: 1.608106\t최선의 손실: 1.608106\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.613444\t최선의 손실: 1.608106\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.611919\t최선의 손실: 1.608106\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.614609\t최선의 손실: 1.608106\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.608721\t최선의 손실: 1.608106\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.613541\t최선의 손실: 1.608106\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.611918\t최선의 손실: 1.608106\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 1.614608\t최선의 손실: 1.608106\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.608659\t최선의 손실: 1.608106\t정확도: 22.01%\n",
       "20\t검증 세트 손실: 1.609367\t최선의 손실: 1.608106\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.619997\t최선의 손실: 1.608106\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.620807\t최선의 손실: 1.608106\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.619998\t최선의 손실: 1.608106\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.620808\t최선의 손실: 1.608106\t정확도: 19.27%\n",
       "23\t검증 세트 손실: 1.618630\t최선의 손실: 1.608106\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.613194\t최선의 손실: 1.608106\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.613193\t최선의 손실: 1.608106\t정확도: 22.01%\n",
       "25\t검증 세트 손실: 1.608846\t최선의 손실: 1.608106\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.608840\t최선의 손실: 1.608106\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.608852\t최선의 손실: 1.608106\t정확도: 22.01%\n",
       "27\t검증 세트 손실: 1.612873\t최선의 손실: 1.608106\t정확도: 22.01%\n",
       "28\t검증 세트 손실: 1.622144\t최선의 손실: 1.608106\t정확도: 19.08%\n",
       "29\t검증 세트 손실: 1.609206\t최선의 손실: 1.608106\t정확도: 22.01%\n",
@@ -18601,60 +18654,60 @@
       "36\t검증 세트 손실: 1.610132\t최선의 손실: 1.607765\t정확도: 19.27%\n",
       "37\t검증 세트 손실: 1.612202\t최선의 손실: 1.607765\t정확도: 22.01%\n",
       "38\t검증 세트 손실: 1.612038\t최선의 손실: 1.607765\t정확도: 22.01%\n",
-      "39\t검증 세트 손실: 1.614090\t최선의 손실: 1.607765\t정확도: 19.27%\n",
-      "40\t검증 세트 손실: 1.608473\t최선의 손실: 1.607765\t정확도: 22.01%\n",
+      "39\t검증 세트 손실: 1.614102\t최선의 손실: 1.607765\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 1.608471\t최선의 손실: 1.607765\t정확도: 22.01%\n",
       "41\t검증 세트 손실: 1.609536\t최선의 손실: 1.607765\t정확도: 22.01%\n",
-      "42\t검증 세트 손실: 1.610706\t최선의 손실: 1.607765\t정확도: 19.27%\n",
-      "43\t검증 세트 손실: 1.614007\t최선의 손실: 1.607765\t정확도: 22.01%\n",
-      "44\t검증 세트 손실: 1.616754\t최선의 손실: 1.607765\t정확도: 22.01%\n",
-      "45\t검증 세트 손실: 1.610697\t최선의 손실: 1.607765\t정확도: 22.01%\n",
-      "46\t검증 세트 손실: 1.612059\t최선의 손실: 1.607765\t정확도: 18.73%\n",
-      "47\t검증 세트 손실: 1.608928\t최선의 손실: 1.607765\t정확도: 22.01%\n",
-      "48\t검증 세트 손실: 1.608292\t최선의 손실: 1.607765\t정확도: 22.01%\n",
-      "49\t검증 세트 손실: 1.617473\t최선의 손실: 1.607765\t정확도: 19.27%\n"
+      "42\t검증 세트 손실: 1.610706\t최선의 손실: 1.607765\t정확도: 19.27%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "43\t검증 세트 손실: 1.614245\t최선의 손실: 1.607765\t정확도: 22.01%\n",
+      "44\t검증 세트 손실: 1.616751\t최선의 손실: 1.607765\t정확도: 22.01%\n",
+      "45\t검증 세트 손실: 1.610695\t최선의 손실: 1.607765\t정확도: 22.01%\n",
+      "46\t검증 세트 손실: 1.612055\t최선의 손실: 1.607765\t정확도: 18.73%\n",
+      "47\t검증 세트 손실: 1.608927\t최선의 손실: 1.607765\t정확도: 22.01%\n",
+      "48\t검증 세트 손실: 1.608293\t최선의 손실: 1.607765\t정확도: 22.01%\n",
+      "49\t검증 세트 손실: 1.617473\t최선의 손실: 1.607765\t정확도: 19.27%\n",
       "50\t검증 세트 손실: 1.612355\t최선의 손실: 1.607765\t정확도: 19.27%\n",
       "51\t검증 세트 손실: 1.610517\t최선의 손실: 1.607765\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total= 3.3min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.618137\t최선의 손실: 1.618137\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 1.613489\t최선의 손실: 1.613489\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.613230\t최선의 손실: 1.613230\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.618712\t최선의 손실: 1.613230\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.608355\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "5\t검증 세트 손실: 1.612527\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.614007\t최선의 손실: 1.608355\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.612977\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.611469\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.610273\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 1.610118\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.615072\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.609143\t최선의 손실: 1.608355\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.612667\t최선의 손실: 1.608355\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.610130\t최선의 손실: 1.608355\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.611327\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.620625\t최선의 손실: 1.608355\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.614154\t최선의 손실: 1.608355\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.607983\t최선의 손실: 1.607983\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.610182\t최선의 손실: 1.607983\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.612591\t최선의 손실: 1.607983\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 1.620057\t최선의 손실: 1.607983\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.611555\t최선의 손실: 1.607983\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.614217\t최선의 손실: 1.607983\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.612510\t최선의 손실: 1.607983\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.609939\t최선의 손실: 1.607983\t정확도: 22.01%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f738a2ee510>, n_neurons=140, total= 1.9min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f738a2ee510>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.615186\t최선의 손실: 1.615186\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 1.613460\t최선의 손실: 1.613460\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.613229\t최선의 손실: 1.613229\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1.618722\t최선의 손실: 1.613229\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.608723\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.612530\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 1.613943\t최선의 손실: 1.608723\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.612977\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.611470\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.610273\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 1.610119\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.615093\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.609222\t최선의 손실: 1.608723\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.612667\t최선의 손실: 1.608723\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.610130\t최선의 손실: 1.608723\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 1.611326\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.620624\t최선의 손실: 1.608723\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.614153\t최선의 손실: 1.608723\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.607982\t최선의 손실: 1.607982\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.610182\t최선의 손실: 1.607982\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1.612591\t최선의 손실: 1.607982\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.620052\t최선의 손실: 1.607982\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.611688\t최선의 손실: 1.607982\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.614217\t최선의 손실: 1.607982\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.612511\t최선의 손실: 1.607982\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.609959\t최선의 손실: 1.607982\t정확도: 22.01%\n",
       "26\t검증 세트 손실: 1.607882\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "27\t검증 세트 손실: 1.610300\t최선의 손실: 1.607882\t정확도: 22.01%\n",
-      "28\t검증 세트 손실: 1.617164\t최선의 손실: 1.607882\t정확도: 19.08%\n",
+      "28\t검증 세트 손실: 1.617167\t최선의 손실: 1.607882\t정확도: 19.08%\n",
       "29\t검증 세트 손실: 1.610925\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "30\t검증 세트 손실: 1.609472\t최선의 손실: 1.607882\t정확도: 19.08%\n",
-      "31\t검증 세트 손실: 1.609233\t최선의 손실: 1.607882\t정확도: 22.01%\n",
+      "31\t검증 세트 손실: 1.609234\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "32\t검증 세트 손실: 1.609479\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "33\t검증 세트 손실: 1.609797\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "34\t검증 세트 손실: 1.609309\t최선의 손실: 1.607882\t정확도: 22.01%\n",
@@ -18664,1833 +18717,1614 @@
       "38\t검증 세트 손실: 1.612431\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "39\t검증 세트 손실: 1.616542\t최선의 손실: 1.607882\t정확도: 19.27%\n",
       "40\t검증 세트 손실: 1.610357\t최선의 손실: 1.607882\t정확도: 19.08%\n",
-      "41\t검증 세트 손실: 1.613422\t최선의 손실: 1.607882\t정확도: 19.08%\n",
+      "41\t검증 세트 손실: 1.613421\t최선의 손실: 1.607882\t정확도: 19.08%\n",
       "42\t검증 세트 손실: 1.610970\t최선의 손실: 1.607882\t정확도: 22.01%\n",
-      "43\t검증 세트 손실: 1.609564\t최선의 손실: 1.607882\t정확도: 20.91%\n",
-      "44\t검증 세트 손실: 1.612211\t최선의 손실: 1.607882\t정확도: 22.01%\n",
+      "43\t검증 세트 손실: 1.609565\t최선의 손실: 1.607882\t정확도: 20.91%\n",
+      "44\t검증 세트 손실: 1.612212\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "45\t검증 세트 손실: 1.609153\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "46\t검증 세트 손실: 1.608850\t최선의 손실: 1.607882\t정확도: 22.01%\n",
       "47\t검증 세트 손실: 1.625009\t최선의 손실: 1.607882\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f994177e9d8>, n_neurons=140, total= 3.2min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 28.967146\t최선의 손실: 28.967146\t정확도: 44.02%\n",
-      "1\t검증 세트 손실: 141.402939\t최선의 손실: 28.967146\t정확도: 47.34%\n",
-      "2\t검증 세트 손실: 37.040695\t최선의 손실: 28.967146\t정확도: 57.62%\n",
-      "3\t검증 세트 손실: 152.977783\t최선의 손실: 28.967146\t정확도: 41.32%\n",
-      "4\t검증 세트 손실: 75.873428\t최선의 손실: 28.967146\t정확도: 42.96%\n",
-      "5\t검증 세트 손실: 62.372459\t최선의 손실: 28.967146\t정확도: 45.93%\n",
-      "6\t검증 세트 손실: 88.379364\t최선의 손실: 28.967146\t정확도: 50.43%\n",
-      "7\t검증 세트 손실: 105.082489\t최선의 손실: 28.967146\t정확도: 35.89%\n",
-      "8\t검증 세트 손실: 412.792328\t최선의 손실: 28.967146\t정확도: 54.81%\n",
-      "9\t검증 세트 손실: 352.059448\t최선의 손실: 28.967146\t정확도: 49.22%\n",
-      "10\t검증 세트 손실: 109.914017\t최선의 손실: 28.967146\t정확도: 52.54%\n",
-      "11\t검증 세트 손실: 214.950104\t최선의 손실: 28.967146\t정확도: 55.39%\n",
-      "12\t검증 세트 손실: 133.209229\t최선의 손실: 28.967146\t정확도: 51.92%\n",
-      "13\t검증 세트 손실: 193.776108\t최선의 손실: 28.967146\t정확도: 55.20%\n",
-      "14\t검증 세트 손실: 356.427582\t최선의 손실: 28.967146\t정확도: 55.20%\n",
-      "15\t검증 세트 손실: 519.706482\t최선의 손실: 28.967146\t정확도: 54.77%\n",
-      "16\t검증 세트 손실: 425.701965\t최선의 손실: 28.967146\t정확도: 53.05%\n",
-      "17\t검증 세트 손실: 220.479141\t최선의 손실: 28.967146\t정확도: 55.00%\n",
-      "18\t검증 세트 손실: 184.594727\t최선의 손실: 28.967146\t정확도: 61.65%\n",
-      "19\t검증 세트 손실: 1355.218384\t최선의 손실: 28.967146\t정확도: 51.17%\n",
-      "20\t검증 세트 손실: 138.916946\t최선의 손실: 28.967146\t정확도: 53.32%\n",
-      "21\t검증 세트 손실: 130.000702\t최선의 손실: 28.967146\t정확도: 54.34%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.02, activation=<function relu at 0x7f738a2ee510>, n_neurons=140, total= 1.8min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 33.017578\t최선의 손실: 33.017578\t정확도: 18.80%\n",
+      "1\t검증 세트 손실: 25.312876\t최선의 손실: 25.312876\t정확도: 30.02%\n",
+      "2\t검증 세트 손실: 977.680603\t최선의 손실: 25.312876\t정확도: 19.23%\n",
+      "3\t검증 세트 손실: 115.906845\t최선의 손실: 25.312876\t정확도: 20.64%\n",
+      "4\t검증 세트 손실: 43.372986\t최선의 손실: 25.312876\t정확도: 35.46%\n",
+      "5\t검증 세트 손실: 9.768612\t최선의 손실: 9.768612\t정확도: 51.09%\n",
+      "6\t검증 세트 손실: 129.459000\t최선의 손실: 9.768612\t정확도: 38.00%\n",
+      "7\t검증 세트 손실: 364.953064\t최선의 손실: 9.768612\t정확도: 34.32%\n",
+      "8\t검증 세트 손실: 37.338482\t최선의 손실: 9.768612\t정확도: 30.81%\n",
+      "9\t검증 세트 손실: 134.444717\t최선의 손실: 9.768612\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 14.949549\t최선의 손실: 9.768612\t정확도: 30.81%\n",
+      "11\t검증 세트 손실: 234.325150\t최선의 손실: 9.768612\t정확도: 26.00%\n",
+      "12\t검증 세트 손실: 23.873732\t최선의 손실: 9.768612\t정확도: 33.27%\n",
+      "13\t검증 세트 손실: 14.461299\t최선의 손실: 9.768612\t정확도: 32.33%\n",
+      "14\t검증 세트 손실: 46.124794\t최선의 손실: 9.768612\t정확도: 37.65%\n",
+      "15\t검증 세트 손실: 442.806976\t최선의 손실: 9.768612\t정확도: 30.53%\n",
+      "16\t검증 세트 손실: 289.401001\t최선의 손실: 9.768612\t정확도: 32.33%\n",
+      "17\t검증 세트 손실: 444.409210\t최선의 손실: 9.768612\t정확도: 21.03%\n",
+      "18\t검증 세트 손실: 56.471684\t최선의 손실: 9.768612\t정확도: 51.56%\n",
+      "19\t검증 세트 손실: 1958.138184\t최선의 손실: 9.768612\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 47.746460\t최선의 손실: 9.768612\t정확도: 45.54%\n",
+      "21\t검증 세트 손실: 153.412704\t최선의 손실: 9.768612\t정확도: 29.67%\n",
+      "22\t검증 세트 손실: 90.472778\t최선의 손실: 9.768612\t정확도: 32.84%\n",
+      "23\t검증 세트 손실: 136.474243\t최선의 손실: 9.768612\t정확도: 32.92%\n",
+      "24\t검증 세트 손실: 113.965210\t최선의 손실: 9.768612\t정확도: 42.49%\n",
+      "25\t검증 세트 손실: 104.146317\t최선의 손실: 9.768612\t정확도: 35.14%\n",
+      "26\t검증 세트 손실: 64.506912\t최선의 손실: 9.768612\t정확도: 36.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=140, total= 1.4min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 73.850151\t최선의 손실: 73.850151\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 15.307694\t최선의 손실: 15.307694\t정확도: 19.70%\n",
-      "2\t검증 세트 손실: 11.982148\t최선의 손실: 11.982148\t정확도: 22.17%\n",
-      "3\t검증 세트 손실: 221.782181\t최선의 손실: 11.982148\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 54.573555\t최선의 손실: 11.982148\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 93.505394\t최선의 손실: 11.982148\t정확도: 21.62%\n",
-      "6\t검증 세트 손실: 47.400318\t최선의 손실: 11.982148\t정확도: 23.89%\n",
-      "7\t검증 세트 손실: 38.957600\t최선의 손실: 11.982148\t정확도: 30.96%\n",
-      "8\t검증 세트 손실: 66.158623\t최선의 손실: 11.982148\t정확도: 20.91%\n",
-      "9\t검증 세트 손실: 20.026136\t최선의 손실: 11.982148\t정확도: 31.16%\n",
-      "10\t검증 세트 손실: 450.416046\t최선의 손실: 11.982148\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 16.953480\t최선의 손실: 11.982148\t정확도: 36.94%\n",
-      "12\t검증 세트 손실: 23.232096\t최선의 손실: 11.982148\t정확도: 35.42%\n",
-      "13\t검증 세트 손실: 17.915859\t최선의 손실: 11.982148\t정확도: 33.97%\n",
-      "14\t검증 세트 손실: 13.488964\t최선의 손실: 11.982148\t정확도: 35.34%\n",
-      "15\t검증 세트 손실: 162.285263\t최선의 손실: 11.982148\t정확도: 18.76%\n",
-      "16\t검증 세트 손실: 78.534683\t최선의 손실: 11.982148\t정확도: 36.86%\n",
-      "17\t검증 세트 손실: 65.435844\t최선의 손실: 11.982148\t정확도: 28.66%\n",
-      "18\t검증 세트 손실: 30.994173\t최선의 손실: 11.982148\t정확도: 30.18%\n",
-      "19\t검증 세트 손실: 201.882385\t최선의 손실: 11.982148\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 703.020203\t최선의 손실: 11.982148\t정확도: 18.84%\n",
-      "21\t검증 세트 손실: 262.573090\t최선의 손실: 11.982148\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 239.977646\t최선의 손실: 11.982148\t정확도: 21.15%\n",
-      "23\t검증 세트 손실: 279.684753\t최선의 손실: 11.982148\t정확도: 19.08%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=140, total= 1.2min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 13.799908\t최선의 손실: 13.799908\t정확도: 35.07%\n",
+      "1\t검증 세트 손실: 4.266835\t최선의 손실: 4.266835\t정확도: 52.62%\n",
+      "2\t검증 세트 손실: 37.418324\t최선의 손실: 4.266835\t정확도: 48.32%\n",
+      "3\t검증 세트 손실: 150.776382\t최선의 손실: 4.266835\t정확도: 52.66%\n",
+      "4\t검증 세트 손실: 41.786007\t최선의 손실: 4.266835\t정확도: 48.94%\n",
+      "5\t검증 세트 손실: 40.470741\t최선의 손실: 4.266835\t정확도: 50.51%\n",
+      "6\t검증 세트 손실: 158.662628\t최선의 손실: 4.266835\t정확도: 52.38%\n",
+      "7\t검증 세트 손실: 77.308456\t최선의 손실: 4.266835\t정확도: 52.42%\n",
+      "8\t검증 세트 손실: 152.990494\t최선의 손실: 4.266835\t정확도: 49.41%\n",
+      "9\t검증 세트 손실: 100.214790\t최선의 손실: 4.266835\t정확도: 45.54%\n",
+      "10\t검증 세트 손실: 175.914551\t최선의 손실: 4.266835\t정확도: 38.86%\n",
+      "11\t검증 세트 손실: 613.899658\t최선의 손실: 4.266835\t정확도: 48.44%\n",
+      "12\t검증 세트 손실: 1095.309082\t최선의 손실: 4.266835\t정확도: 55.82%\n",
+      "13\t검증 세트 손실: 226.042053\t최선의 손실: 4.266835\t정확도: 51.76%\n",
+      "14\t검증 세트 손실: 98.258713\t최선의 손실: 4.266835\t정확도: 53.99%\n",
+      "15\t검증 세트 손실: 185.943512\t최선의 손실: 4.266835\t정확도: 53.32%\n",
+      "16\t검증 세트 손실: 436.844940\t최선의 손실: 4.266835\t정확도: 53.75%\n",
+      "17\t검증 세트 손실: 108.171951\t최선의 손실: 4.266835\t정확도: 56.65%\n",
+      "18\t검증 세트 손실: 186.550049\t최선의 손실: 4.266835\t정확도: 53.83%\n",
+      "19\t검증 세트 손실: 1381.226929\t최선의 손실: 4.266835\t정확도: 53.01%\n",
+      "20\t검증 세트 손실: 2006.647583\t최선의 손실: 4.266835\t정확도: 45.43%\n",
+      "21\t검증 세트 손실: 316.517517\t최선의 손실: 4.266835\t정확도: 54.69%\n",
+      "22\t검증 세트 손실: 425.640991\t최선의 손실: 4.266835\t정확도: 51.60%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=140, total= 1.5min\n",
-      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 6.507739\t최선의 손실: 6.507739\t정확도: 41.48%\n",
-      "1\t검증 세트 손실: 9.516665\t최선의 손실: 6.507739\t정확도: 28.11%\n",
-      "2\t검증 세트 손실: 2.979504\t최선의 손실: 2.979504\t정확도: 41.59%\n",
-      "3\t검증 세트 손실: 25.107645\t최선의 손실: 2.979504\t정확도: 35.07%\n",
-      "4\t검증 세트 손실: 43.044624\t최선의 손실: 2.979504\t정확도: 22.09%\n",
-      "5\t검증 세트 손실: 278.044800\t최선의 손실: 2.979504\t정확도: 37.84%\n",
-      "6\t검증 세트 손실: 77.486549\t최선의 손실: 2.979504\t정확도: 20.99%\n",
-      "7\t검증 세트 손실: 79.567513\t최선의 손실: 2.979504\t정확도: 20.91%\n",
-      "8\t검증 세트 손실: 29.228477\t최선의 손실: 2.979504\t정확도: 40.70%\n",
-      "9\t검증 세트 손실: 33.374454\t최선의 손실: 2.979504\t정확도: 32.96%\n",
-      "10\t검증 세트 손실: 404.841888\t최선의 손실: 2.979504\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 77.249611\t최선의 손실: 2.979504\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 36.850586\t최선의 손실: 2.979504\t정확도: 21.07%\n",
-      "13\t검증 세트 손실: 19.463078\t최선의 손실: 2.979504\t정확도: 31.82%\n",
-      "14\t검증 세트 손실: 59.824631\t최선의 손실: 2.979504\t정확도: 21.27%\n",
-      "15\t검증 세트 손실: 24.584972\t최선의 손실: 2.979504\t정확도: 27.09%\n",
-      "16\t검증 세트 손실: 119.164825\t최선의 손실: 2.979504\t정확도: 19.04%\n",
-      "17\t검증 세트 손실: 45.092686\t최선의 손실: 2.979504\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 37.861652\t최선의 손실: 2.979504\t정확도: 24.35%\n",
-      "19\t검증 세트 손실: 48.592003\t최선의 손실: 2.979504\t정확도: 34.01%\n",
-      "20\t검증 세트 손실: 75.127556\t최선의 손실: 2.979504\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 16.738232\t최선의 손실: 2.979504\t정확도: 35.61%\n",
-      "22\t검증 세트 손실: 345.296844\t최선의 손실: 2.979504\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 127.360275\t최선의 손실: 2.979504\t정확도: 22.05%\n",
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=140, total= 1.0min\n",
+      "[CV] batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 86.337845\t최선의 손실: 86.337845\t정확도: 22.05%\n",
+      "1\t검증 세트 손실: 340.069611\t최선의 손실: 86.337845\t정확도: 20.91%\n",
+      "2\t검증 세트 손실: 6.016418\t최선의 손실: 6.016418\t정확도: 20.72%\n",
+      "3\t검증 세트 손실: 27.874828\t최선의 손실: 6.016418\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 64.147842\t최선의 손실: 6.016418\t정확도: 19.27%\n",
+      "5\t검증 세트 손실: 17.785933\t최선의 손실: 6.016418\t정확도: 36.20%\n",
+      "6\t검증 세트 손실: 44.337994\t최선의 손실: 6.016418\t정확도: 25.65%\n",
+      "7\t검증 세트 손실: 25.842176\t최선의 손실: 6.016418\t정확도: 37.72%\n",
+      "8\t검증 세트 손실: 629.289673\t최선의 손실: 6.016418\t정확도: 19.39%\n",
+      "9\t검증 세트 손실: 27.333958\t최선의 손실: 6.016418\t정확도: 24.86%\n",
+      "10\t검증 세트 손실: 19.060112\t최선의 손실: 6.016418\t정확도: 36.16%\n",
+      "11\t검증 세트 손실: 28.211420\t최선의 손실: 6.016418\t정확도: 47.30%\n",
+      "12\t검증 세트 손실: 32.167881\t최선의 손실: 6.016418\t정확도: 36.51%\n",
+      "13\t검증 세트 손실: 48.880829\t최선의 손실: 6.016418\t정확도: 20.91%\n",
+      "14\t검증 세트 손실: 24.576387\t최선의 손실: 6.016418\t정확도: 46.21%\n",
+      "15\t검증 세트 손실: 320.427246\t최선의 손실: 6.016418\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 47.566147\t최선의 손실: 6.016418\t정확도: 21.03%\n",
+      "17\t검증 세트 손실: 61.903629\t최선의 손실: 6.016418\t정확도: 38.12%\n",
+      "18\t검증 세트 손실: 138.987411\t최선의 손실: 6.016418\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 574.825256\t최선의 손실: 6.016418\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 377.905487\t최선의 손실: 6.016418\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 58.845943\t최선의 손실: 6.016418\t정확도: 37.53%\n",
+      "22\t검증 세트 손실: 152.765198\t최선의 손실: 6.016418\t정확도: 19.12%\n",
+      "23\t검증 세트 손실: 53.393864\t최선의 손실: 6.016418\t정확도: 21.50%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=140, total= 1.5min\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 691.427368\t최선의 손실: 691.427368\t정확도: 38.27%\n",
-      "1\t검증 세트 손실: 28.305180\t최선의 손실: 28.305180\t정확도: 58.09%\n",
-      "2\t검증 세트 손실: 23.904905\t최선의 손실: 23.904905\t정확도: 57.97%\n",
-      "3\t검증 세트 손실: 10.716293\t최선의 손실: 10.716293\t정확도: 64.93%\n",
-      "4\t검증 세트 손실: 5.038942\t최선의 손실: 5.038942\t정확도: 76.00%\n",
-      "5\t검증 세트 손실: 7.889843\t최선의 손실: 5.038942\t정확도: 75.37%\n",
-      "6\t검증 세트 손실: 3.044888\t최선의 손실: 3.044888\t정확도: 81.74%\n",
-      "7\t검증 세트 손실: 3.447294\t최선의 손실: 3.044888\t정확도: 77.95%\n",
-      "8\t검증 세트 손실: 2.826966\t최선의 손실: 2.826966\t정확도: 85.18%\n"
+      "[CV]  batch_size=10, dropout_rate=0.5, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=140, total= 1.1min\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9\t검증 세트 손실: 6.151031\t최선의 손실: 2.826966\t정확도: 75.45%\n",
-      "10\t검증 세트 손실: 3.303672\t최선의 손실: 2.826966\t정확도: 78.30%\n",
-      "11\t검증 세트 손실: 1.552268\t최선의 손실: 1.552268\t정확도: 88.00%\n",
-      "12\t검증 세트 손실: 2.767506\t최선의 손실: 1.552268\t정확도: 82.56%\n",
-      "13\t검증 세트 손실: 3.167135\t최선의 손실: 1.552268\t정확도: 81.00%\n",
-      "14\t검증 세트 손실: 1.664353\t최선의 손실: 1.552268\t정확도: 89.48%\n",
-      "15\t검증 세트 손실: 3.174450\t최선의 손실: 1.552268\t정확도: 78.46%\n",
-      "16\t검증 세트 손실: 6.582459\t최선의 손실: 1.552268\t정확도: 74.00%\n",
-      "17\t검증 세트 손실: 1.775612\t최선의 손실: 1.552268\t정확도: 85.73%\n",
-      "18\t검증 세트 손실: 1.166004\t최선의 손실: 1.166004\t정확도: 90.62%\n",
-      "19\t검증 세트 손실: 1.052997\t최선의 손실: 1.052997\t정확도: 91.48%\n",
-      "20\t검증 세트 손실: 1.907110\t최선의 손실: 1.052997\t정확도: 88.51%\n",
-      "21\t검증 세트 손실: 3.780585\t최선의 손실: 1.052997\t정확도: 80.57%\n",
-      "22\t검증 세트 손실: 1.312150\t최선의 손실: 1.052997\t정확도: 90.54%\n",
-      "23\t검증 세트 손실: 3.033271\t최선의 손실: 1.052997\t정확도: 76.27%\n",
-      "24\t검증 세트 손실: 1.152388\t최선의 손실: 1.052997\t정확도: 87.22%\n",
-      "25\t검증 세트 손실: 2.762390\t최선의 손실: 1.052997\t정확도: 87.45%\n",
-      "26\t검증 세트 손실: 2.125766\t최선의 손실: 1.052997\t정확도: 90.62%\n",
-      "27\t검증 세트 손실: 1.805342\t최선의 손실: 1.052997\t정확도: 82.72%\n",
-      "28\t검증 세트 손실: 2.280300\t최선의 손실: 1.052997\t정확도: 88.00%\n",
-      "29\t검증 세트 손실: 1.888125\t최선의 손실: 1.052997\t정확도: 84.52%\n",
-      "30\t검증 세트 손실: 1.335691\t최선의 손실: 1.052997\t정확도: 87.92%\n",
-      "31\t검증 세트 손실: 1.479731\t최선의 손실: 1.052997\t정확도: 85.07%\n",
-      "32\t검증 세트 손실: 1.179318\t최선의 손실: 1.052997\t정확도: 91.67%\n",
-      "33\t검증 세트 손실: 0.750418\t최선의 손실: 0.750418\t정확도: 92.14%\n",
-      "34\t검증 세트 손실: 1.380025\t최선의 손실: 0.750418\t정확도: 87.06%\n",
-      "35\t검증 세트 손실: 1.216913\t최선의 손실: 0.750418\t정확도: 88.19%\n",
-      "36\t검증 세트 손실: 0.720563\t최선의 손실: 0.720563\t정확도: 93.32%\n",
-      "37\t검증 세트 손실: 1.174745\t최선의 손실: 0.720563\t정확도: 93.32%\n",
-      "38\t검증 세트 손실: 0.886748\t최선의 손실: 0.720563\t정확도: 93.43%\n",
-      "39\t검증 세트 손실: 6.624228\t최선의 손실: 0.720563\t정확도: 91.05%\n",
-      "40\t검증 세트 손실: 3.542934\t최선의 손실: 0.720563\t정확도: 91.44%\n",
-      "41\t검증 세트 손실: 2.383058\t최선의 손실: 0.720563\t정확도: 90.81%\n",
-      "42\t검증 세트 손실: 2.538530\t최선의 손실: 0.720563\t정확도: 88.27%\n",
-      "43\t검증 세트 손실: 0.978160\t최선의 손실: 0.720563\t정확도: 91.83%\n",
-      "44\t검증 세트 손실: 1.216315\t최선의 손실: 0.720563\t정확도: 84.56%\n",
-      "45\t검증 세트 손실: 0.840921\t최선의 손실: 0.720563\t정확도: 91.05%\n",
-      "46\t검증 세트 손실: 1.205765\t최선의 손실: 0.720563\t정확도: 92.89%\n",
-      "47\t검증 세트 손실: 0.706110\t최선의 손실: 0.706110\t정확도: 93.47%\n",
-      "48\t검증 세트 손실: 0.617681\t최선의 손실: 0.617681\t정확도: 93.28%\n",
-      "49\t검증 세트 손실: 0.691409\t최선의 손실: 0.617681\t정확도: 93.82%\n",
-      "50\t검증 세트 손실: 1.198956\t최선의 손실: 0.617681\t정확도: 88.74%\n",
-      "51\t검증 세트 손실: 0.769661\t최선의 손실: 0.617681\t정확도: 92.38%\n",
-      "52\t검증 세트 손실: 2.719389\t최선의 손실: 0.617681\t정확도: 89.76%\n",
-      "53\t검증 세트 손실: 1.250960\t최선의 손실: 0.617681\t정확도: 94.06%\n",
-      "54\t검증 세트 손실: 0.643451\t최선의 손실: 0.617681\t정확도: 93.28%\n",
-      "55\t검증 세트 손실: 0.648816\t최선의 손실: 0.617681\t정확도: 91.63%\n",
-      "56\t검증 세트 손실: 0.529958\t최선의 손실: 0.529958\t정확도: 93.47%\n",
-      "57\t검증 세트 손실: 1.500815\t최선의 손실: 0.529958\t정확도: 94.61%\n",
-      "58\t검증 세트 손실: 1.826529\t최선의 손실: 0.529958\t정확도: 94.29%\n",
-      "59\t검증 세트 손실: 1.180102\t최선의 손실: 0.529958\t정확도: 94.45%\n",
-      "60\t검증 세트 손실: 0.860788\t최선의 손실: 0.529958\t정확도: 92.30%\n",
-      "61\t검증 세트 손실: 0.739807\t최선의 손실: 0.529958\t정확도: 94.02%\n",
-      "62\t검증 세트 손실: 0.921214\t최선의 손실: 0.529958\t정확도: 90.15%\n",
-      "63\t검증 세트 손실: 0.837950\t최선의 손실: 0.529958\t정확도: 92.81%\n",
-      "64\t검증 세트 손실: 2.005162\t최선의 손실: 0.529958\t정확도: 93.08%\n",
-      "65\t검증 세트 손실: 1.135535\t최선의 손실: 0.529958\t정확도: 94.72%\n",
-      "66\t검증 세트 손실: 0.830606\t최선의 손실: 0.529958\t정확도: 94.72%\n",
-      "67\t검증 세트 손실: 0.704966\t최선의 손실: 0.529958\t정확도: 93.82%\n",
-      "68\t검증 세트 손실: 0.777731\t최선의 손실: 0.529958\t정확도: 94.02%\n",
-      "69\t검증 세트 손실: 0.664703\t최선의 손실: 0.529958\t정확도: 93.35%\n",
-      "70\t검증 세트 손실: 0.532360\t최선의 손실: 0.529958\t정확도: 94.57%\n",
-      "71\t검증 세트 손실: 0.590609\t최선의 손실: 0.529958\t정확도: 94.76%\n",
-      "72\t검증 세트 손실: 0.490776\t최선의 손실: 0.490776\t정확도: 94.18%\n",
-      "73\t검증 세트 손실: 0.416942\t최선의 손실: 0.416942\t정확도: 94.84%\n",
-      "74\t검증 세트 손실: 0.446678\t최선의 손실: 0.416942\t정확도: 94.29%\n",
-      "75\t검증 세트 손실: 0.377018\t최선의 손실: 0.377018\t정확도: 94.33%\n",
-      "76\t검증 세트 손실: 0.732932\t최선의 손실: 0.377018\t정확도: 94.57%\n",
-      "77\t검증 세트 손실: 0.742027\t최선의 손실: 0.377018\t정확도: 92.22%\n",
-      "78\t검증 세트 손실: 0.435736\t최선의 손실: 0.377018\t정확도: 94.72%\n",
-      "79\t검증 세트 손실: 0.600498\t최선의 손실: 0.377018\t정확도: 93.78%\n",
-      "80\t검증 세트 손실: 0.990675\t최선의 손실: 0.377018\t정확도: 94.72%\n",
-      "81\t검증 세트 손실: 0.765115\t최선의 손실: 0.377018\t정확도: 95.54%\n",
-      "82\t검증 세트 손실: 0.420522\t최선의 손실: 0.377018\t정확도: 94.84%\n",
-      "83\t검증 세트 손실: 0.383646\t최선의 손실: 0.377018\t정확도: 94.68%\n",
-      "84\t검증 세트 손실: 0.682955\t최선의 손실: 0.377018\t정확도: 91.59%\n",
-      "85\t검증 세트 손실: 0.383959\t최선의 손실: 0.377018\t정확도: 94.92%\n",
-      "86\t검증 세트 손실: 0.479163\t최선의 손실: 0.377018\t정확도: 95.00%\n",
-      "87\t검증 세트 손실: 0.576265\t최선의 손실: 0.377018\t정확도: 95.15%\n",
-      "88\t검증 세트 손실: 0.549962\t최선의 손실: 0.377018\t정확도: 93.24%\n",
-      "89\t검증 세트 손실: 0.481838\t최선의 손실: 0.377018\t정확도: 94.84%\n",
-      "90\t검증 세트 손실: 0.386126\t최선의 손실: 0.377018\t정확도: 94.45%\n",
-      "91\t검증 세트 손실: 0.310413\t최선의 손실: 0.310413\t정확도: 95.47%\n",
-      "92\t검증 세트 손실: 0.382885\t최선의 손실: 0.310413\t정확도: 94.92%\n",
-      "93\t검증 세트 손실: 0.657152\t최선의 손실: 0.310413\t정확도: 95.11%\n",
-      "94\t검증 세트 손실: 0.524443\t최선의 손실: 0.310413\t정확도: 92.81%\n",
-      "95\t검증 세트 손실: 0.573741\t최선의 손실: 0.310413\t정확도: 89.44%\n",
-      "96\t검증 세트 손실: 0.466877\t최선의 손실: 0.310413\t정확도: 93.63%\n",
-      "97\t검증 세트 손실: 0.486709\t최선의 손실: 0.310413\t정확도: 92.03%\n",
-      "98\t검증 세트 손실: 0.357258\t최선의 손실: 0.310413\t정확도: 95.15%\n",
-      "99\t검증 세트 손실: 0.394334\t최선의 손실: 0.310413\t정확도: 95.90%\n",
-      "100\t검증 세트 손실: 0.490913\t최선의 손실: 0.310413\t정확도: 95.47%\n",
-      "101\t검증 세트 손실: 0.429613\t최선의 손실: 0.310413\t정확도: 94.68%\n",
-      "102\t검증 세트 손실: 6.449607\t최선의 손실: 0.310413\t정확도: 91.48%\n",
-      "103\t검증 세트 손실: 0.777089\t최선의 손실: 0.310413\t정확도: 95.58%\n",
-      "104\t검증 세트 손실: 0.951996\t최선의 손실: 0.310413\t정확도: 94.02%\n",
-      "105\t검증 세트 손실: 0.715331\t최선의 손실: 0.310413\t정확도: 94.64%\n",
-      "106\t검증 세트 손실: 0.532774\t최선의 손실: 0.310413\t정확도: 92.57%\n",
-      "107\t검증 세트 손실: 0.345484\t최선의 손실: 0.310413\t정확도: 94.88%\n",
-      "108\t검증 세트 손실: 0.771925\t최선의 손실: 0.310413\t정확도: 91.67%\n",
-      "109\t검증 세트 손실: 0.589852\t최선의 손실: 0.310413\t정확도: 96.17%\n",
-      "110\t검증 세트 손실: 4.330959\t최선의 손실: 0.310413\t정확도: 92.65%\n",
-      "111\t검증 세트 손실: 1.847241\t최선의 손실: 0.310413\t정확도: 93.35%\n",
-      "112\t검증 세트 손실: 1.604251\t최선의 손실: 0.310413\t정확도: 93.98%\n",
+      "0\t검증 세트 손실: 176.327576\t최선의 손실: 176.327576\t정확도: 65.32%\n",
+      "1\t검증 세트 손실: 71.843132\t최선의 손실: 71.843132\t정확도: 74.78%\n",
+      "2\t검증 세트 손실: 21.649515\t최선의 손실: 21.649515\t정확도: 83.66%\n",
+      "3\t검증 세트 손실: 3.153890\t최선의 손실: 3.153890\t정확도: 91.28%\n",
+      "4\t검증 세트 손실: 3.321537\t최선의 손실: 3.153890\t정확도: 93.94%\n",
+      "5\t검증 세트 손실: 2.597079\t최선의 손실: 2.597079\t정확도: 85.18%\n",
+      "6\t검증 세트 손실: 3.561383\t최선의 손실: 2.597079\t정확도: 90.66%\n",
+      "7\t검증 세트 손실: 3.152815\t최선의 손실: 2.597079\t정확도: 78.62%\n",
+      "8\t검증 세트 손실: 1.887894\t최선의 손실: 1.887894\t정확도: 91.87%\n",
+      "9\t검증 세트 손실: 3.460547\t최선의 손실: 1.887894\t정확도: 77.76%\n",
+      "10\t검증 세트 손실: 8.308510\t최선의 손실: 1.887894\t정확도: 90.70%\n",
+      "11\t검증 세트 손실: 3.224822\t최선의 손실: 1.887894\t정확도: 80.57%\n",
+      "12\t검증 세트 손실: 1.897383\t최선의 손실: 1.887894\t정확도: 94.29%\n",
+      "13\t검증 세트 손실: 2.421635\t최선의 손실: 1.887894\t정확도: 93.00%\n",
+      "14\t검증 세트 손실: 14.420766\t최선의 손실: 1.887894\t정확도: 93.16%\n",
+      "15\t검증 세트 손실: 14.353847\t최선의 손실: 1.887894\t정확도: 91.01%\n",
+      "16\t검증 세트 손실: 7.986080\t최선의 손실: 1.887894\t정확도: 92.03%\n",
+      "17\t검증 세트 손실: 3.427059\t최선의 손실: 1.887894\t정확도: 95.74%\n",
+      "18\t검증 세트 손실: 1.549557\t최선의 손실: 1.549557\t정확도: 94.18%\n",
+      "19\t검증 세트 손실: 2.511651\t최선의 손실: 1.549557\t정확도: 95.86%\n",
+      "20\t검증 세트 손실: 1.812657\t최선의 손실: 1.549557\t정확도: 94.29%\n",
+      "21\t검증 세트 손실: 1.876656\t최선의 손실: 1.549557\t정확도: 95.04%\n",
+      "22\t검증 세트 손실: 2.653104\t최선의 손실: 1.549557\t정확도: 92.03%\n",
+      "23\t검증 세트 손실: 0.974393\t최선의 손실: 0.974393\t정확도: 95.50%\n",
+      "24\t검증 세트 손실: 35.773689\t최선의 손실: 0.974393\t정확도: 88.00%\n",
+      "25\t검증 세트 손실: 11.836556\t최선의 손실: 0.974393\t정확도: 93.24%\n",
+      "26\t검증 세트 손실: 75.913506\t최선의 손실: 0.974393\t정확도: 90.15%\n",
+      "27\t검증 세트 손실: 103569440.000000\t최선의 손실: 0.974393\t정확도: 23.46%\n",
+      "28\t검증 세트 손실: 1477319.375000\t최선의 손실: 0.974393\t정확도: 49.18%\n",
+      "29\t검증 세트 손실: 97104.210938\t최선의 손실: 0.974393\t정확도: 46.48%\n",
+      "30\t검증 세트 손실: 486475.656250\t최선의 손실: 0.974393\t정확도: 30.06%\n",
+      "31\t검증 세트 손실: 549393.812500\t최선의 손실: 0.974393\t정확도: 34.99%\n",
+      "32\t검증 세트 손실: 147703.484375\t최선의 손실: 0.974393\t정확도: 54.34%\n",
+      "33\t검증 세트 손실: 129593.156250\t최선의 손실: 0.974393\t정확도: 54.26%\n",
+      "34\t검증 세트 손실: 276274.625000\t최선의 손실: 0.974393\t정확도: 37.02%\n",
+      "35\t검증 세트 손실: 330295.593750\t최선의 손실: 0.974393\t정확도: 35.07%\n",
+      "36\t검증 세트 손실: 148890.562500\t최선의 손실: 0.974393\t정확도: 54.42%\n",
+      "37\t검증 세트 손실: 80953.992188\t최선의 손실: 0.974393\t정확도: 70.48%\n",
+      "38\t검증 세트 손실: 117496.015625\t최선의 손실: 0.974393\t정확도: 60.52%\n",
+      "39\t검증 세트 손실: 22277.861328\t최선의 손실: 0.974393\t정확도: 64.19%\n",
+      "40\t검증 세트 손실: 38241.214844\t최선의 손실: 0.974393\t정확도: 68.49%\n",
+      "41\t검증 세트 손실: 54104.230469\t최선의 손실: 0.974393\t정확도: 61.38%\n",
+      "42\t검증 세트 손실: 77786.648438\t최선의 손실: 0.974393\t정확도: 56.80%\n",
+      "43\t검증 세트 손실: 121372.570312\t최선의 손실: 0.974393\t정확도: 45.19%\n",
+      "44\t검증 세트 손실: 74714.835938\t최선의 손실: 0.974393\t정확도: 50.86%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160, total=  13.5s\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 448.842102\t최선의 손실: 448.842102\t정확도: 34.64%\n",
-      "1\t검증 세트 손실: 30.773371\t최선의 손실: 30.773371\t정확도: 45.93%\n",
-      "2\t검증 세트 손실: 17.229973\t최선의 손실: 17.229973\t정확도: 35.65%\n",
-      "3\t검증 세트 손실: 22.327658\t최선의 손실: 17.229973\t정확도: 36.86%\n",
-      "4\t검증 세트 손실: 8.381601\t최선의 손실: 8.381601\t정확도: 44.02%\n",
-      "5\t검증 세트 손실: 7.939511\t최선의 손실: 7.939511\t정확도: 43.98%\n",
-      "6\t검증 세트 손실: 2.926511\t최선의 손실: 2.926511\t정확도: 57.23%\n",
-      "7\t검증 세트 손실: 6.245226\t최선의 손실: 2.926511\t정확도: 58.41%\n",
-      "8\t검증 세트 손실: 6.265105\t최선의 손실: 2.926511\t정확도: 56.88%\n",
-      "9\t검증 세트 손실: 2.069417\t최선의 손실: 2.069417\t정확도: 65.44%\n",
-      "10\t검증 세트 손실: 21030886.000000\t최선의 손실: 2.069417\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 642093.187500\t최선의 손실: 2.069417\t정확도: 49.26%\n",
-      "12\t검증 세트 손실: 178834.609375\t최선의 손실: 2.069417\t정확도: 54.14%\n",
-      "13\t검증 세트 손실: 89051.179688\t최선의 손실: 2.069417\t정확도: 42.61%\n",
-      "14\t검증 세트 손실: 130078.195312\t최선의 손실: 2.069417\t정확도: 51.56%\n",
-      "15\t검증 세트 손실: 71749.414062\t최선의 손실: 2.069417\t정확도: 40.54%\n",
-      "16\t검증 세트 손실: 58555.660156\t최선의 손실: 2.069417\t정확도: 41.16%\n",
-      "17\t검증 세트 손실: 107874.937500\t최선의 손실: 2.069417\t정확도: 39.41%\n",
-      "18\t검증 세트 손실: 166716.500000\t최선의 손실: 2.069417\t정확도: 29.05%\n",
-      "19\t검증 세트 손실: 51785.097656\t최선의 손실: 2.069417\t정확도: 48.55%\n",
-      "20\t검증 세트 손실: 94037.531250\t최선의 손실: 2.069417\t정확도: 48.05%\n",
-      "21\t검증 세트 손실: 25026.984375\t최선의 손실: 2.069417\t정확도: 44.37%\n",
-      "22\t검증 세트 손실: 23929.941406\t최선의 손실: 2.069417\t정확도: 47.22%\n",
-      "23\t검증 세트 손실: 49039.253906\t최선의 손실: 2.069417\t정확도: 53.64%\n",
-      "24\t검증 세트 손실: 32074.365234\t최선의 손실: 2.069417\t정확도: 37.53%\n",
-      "25\t검증 세트 손실: 81830.234375\t최선의 손실: 2.069417\t정확도: 52.03%\n",
-      "26\t검증 세트 손실: 76438.187500\t최선의 손실: 2.069417\t정확도: 37.14%\n",
-      "27\t검증 세트 손실: 19239.142578\t최선의 손실: 2.069417\t정확도: 50.98%\n",
-      "28\t검증 세트 손실: 24116.714844\t최선의 손실: 2.069417\t정확도: 49.92%\n",
-      "29\t검증 세트 손실: 31514.435547\t최선의 손실: 2.069417\t정확도: 49.02%\n",
-      "30\t검증 세트 손실: 12610.793945\t최선의 손실: 2.069417\t정확도: 57.54%\n",
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160, total=   5.0s\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 330.006439\t최선의 손실: 330.006439\t정확도: 58.13%\n",
+      "1\t검증 세트 손실: 16.090376\t최선의 손실: 16.090376\t정확도: 75.33%\n",
+      "2\t검증 세트 손실: 4.055606\t최선의 손실: 4.055606\t정확도: 81.00%\n",
+      "3\t검증 세트 손실: 5.508632\t최선의 손실: 4.055606\t정확도: 80.65%\n",
+      "4\t검증 세트 손실: 3.421844\t최선의 손실: 3.421844\t정확도: 84.05%\n",
+      "5\t검증 세트 손실: 8.045994\t최선의 손실: 3.421844\t정확도: 76.82%\n",
+      "6\t검증 세트 손실: 6.168200\t최선의 손실: 3.421844\t정확도: 64.00%\n",
+      "7\t검증 세트 손실: 2.645014\t최선의 손실: 2.645014\t정확도: 82.49%\n",
+      "8\t검증 세트 손실: 0.982753\t최선의 손실: 0.982753\t정확도: 92.22%\n",
+      "9\t검증 세트 손실: 1.234802\t최선의 손실: 0.982753\t정확도: 83.89%\n",
+      "10\t검증 세트 손실: 2.803602\t최선의 손실: 0.982753\t정확도: 89.48%\n",
+      "11\t검증 세트 손실: 0.927578\t최선의 손실: 0.927578\t정확도: 93.32%\n",
+      "12\t검증 세트 손실: 1.208655\t최선의 손실: 0.927578\t정확도: 85.46%\n",
+      "13\t검증 세트 손실: 2.257218\t최선의 손실: 0.927578\t정확도: 77.29%\n",
+      "14\t검증 세트 손실: 1.847761\t최선의 손실: 0.927578\t정확도: 84.83%\n",
+      "15\t검증 세트 손실: 1.096406\t최선의 손실: 0.927578\t정확도: 91.52%\n",
+      "16\t검증 세트 손실: 1.093610\t최선의 손실: 0.927578\t정확도: 90.89%\n",
+      "17\t검증 세트 손실: 0.477180\t최선의 손실: 0.477180\t정확도: 94.37%\n",
+      "18\t검증 세트 손실: 5.944943\t최선의 손실: 0.477180\t정확도: 69.31%\n",
+      "19\t검증 세트 손실: 1.375362\t최선의 손실: 0.477180\t정확도: 93.98%\n",
+      "20\t검증 세트 손실: 0.519850\t최선의 손실: 0.477180\t정확도: 95.27%\n",
+      "21\t검증 세트 손실: 0.647540\t최선의 손실: 0.477180\t정확도: 94.88%\n",
+      "22\t검증 세트 손실: 0.484415\t최선의 손실: 0.477180\t정확도: 94.96%\n",
+      "23\t검증 세트 손실: 0.468843\t최선의 손실: 0.468843\t정확도: 94.45%\n",
+      "24\t검증 세트 손실: 0.374258\t최선의 손실: 0.374258\t정확도: 94.64%\n",
+      "25\t검증 세트 손실: 0.513686\t최선의 손실: 0.374258\t정확도: 93.78%\n",
+      "26\t검증 세트 손실: 0.355001\t최선의 손실: 0.355001\t정확도: 94.84%\n",
+      "27\t검증 세트 손실: 0.413143\t최선의 손실: 0.355001\t정확도: 95.15%\n",
+      "28\t검증 세트 손실: 0.273239\t최선의 손실: 0.273239\t정확도: 95.70%\n",
+      "29\t검증 세트 손실: 0.248423\t최선의 손실: 0.248423\t정확도: 95.54%\n",
+      "30\t검증 세트 손실: 0.714267\t최선의 손실: 0.248423\t정확도: 95.50%\n",
+      "31\t검증 세트 손실: 0.486230\t최선의 손실: 0.248423\t정확도: 93.82%\n",
+      "32\t검증 세트 손실: 1.210809\t최선의 손실: 0.248423\t정확도: 88.39%\n",
+      "33\t검증 세트 손실: 0.655875\t최선의 손실: 0.248423\t정확도: 92.57%\n",
+      "34\t검증 세트 손실: 0.400567\t최선의 손실: 0.248423\t정확도: 94.29%\n",
+      "35\t검증 세트 손실: 0.256197\t최선의 손실: 0.248423\t정확도: 95.62%\n",
+      "36\t검증 세트 손실: 0.261742\t최선의 손실: 0.248423\t정확도: 95.47%\n",
+      "37\t검증 세트 손실: 0.325446\t최선의 손실: 0.248423\t정확도: 94.45%\n",
+      "38\t검증 세트 손실: 0.201657\t최선의 손실: 0.201657\t정확도: 96.21%\n",
+      "39\t검증 세트 손실: 0.233513\t최선의 손실: 0.201657\t정확도: 95.86%\n",
+      "40\t검증 세트 손실: 0.510566\t최선의 손실: 0.201657\t정확도: 90.30%\n",
+      "41\t검증 세트 손실: 0.373796\t최선의 손실: 0.201657\t정확도: 94.37%\n",
+      "42\t검증 세트 손실: 0.287483\t최선의 손실: 0.201657\t정확도: 94.96%\n",
+      "43\t검증 세트 손실: 0.333200\t최선의 손실: 0.201657\t정확도: 94.92%\n",
+      "44\t검증 세트 손실: 0.476463\t최선의 손실: 0.201657\t정확도: 92.69%\n",
+      "45\t검증 세트 손실: 81004536.000000\t최선의 손실: 0.201657\t정확도: 28.89%\n",
+      "46\t검증 세트 손실: 4395341.000000\t최선의 손실: 0.201657\t정확도: 34.21%\n",
+      "47\t검증 세트 손실: 1094999.625000\t최선의 손실: 0.201657\t정확도: 37.61%\n",
+      "48\t검증 세트 손실: 537411.125000\t최선의 손실: 0.201657\t정확도: 32.45%\n",
+      "49\t검증 세트 손실: 562496.937500\t최선의 손실: 0.201657\t정확도: 31.12%\n",
+      "50\t검증 세트 손실: 462362.531250\t최선의 손실: 0.201657\t정확도: 35.85%\n",
+      "51\t검증 세트 손실: 247615.546875\t최선의 손실: 0.201657\t정확도: 38.27%\n",
+      "52\t검증 세트 손실: 250383.984375\t최선의 손실: 0.201657\t정확도: 49.22%\n",
+      "53\t검증 세트 손실: 209179.046875\t최선의 손실: 0.201657\t정확도: 37.41%\n",
+      "54\t검증 세트 손실: 193484.265625\t최선의 손실: 0.201657\t정확도: 53.36%\n",
+      "55\t검증 세트 손실: 299784.125000\t최선의 손실: 0.201657\t정확도: 37.18%\n",
+      "56\t검증 세트 손실: 291498.375000\t최선의 손실: 0.201657\t정확도: 36.32%\n",
+      "57\t검증 세트 손실: 195062.515625\t최선의 손실: 0.201657\t정확도: 54.34%\n",
+      "58\t검증 세트 손실: 228970.609375\t최선의 손실: 0.201657\t정확도: 38.12%\n",
+      "59\t검증 세트 손실: 87465.289062\t최선의 손실: 0.201657\t정확도: 57.47%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160, total=   2.9s\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 220.561203\t최선의 손실: 220.561203\t정확도: 44.10%\n",
-      "1\t검증 세트 손실: 210.523361\t최선의 손실: 210.523361\t정확도: 37.26%\n",
-      "2\t검증 세트 손실: 21.556311\t최선의 손실: 21.556311\t정확도: 70.37%\n",
-      "3\t검증 세트 손실: 14.538105\t최선의 손실: 14.538105\t정확도: 50.23%\n",
-      "4\t검증 세트 손실: 9.481858\t최선의 손실: 9.481858\t정확도: 63.41%\n",
-      "5\t검증 세트 손실: 3.277360\t최선의 손실: 3.277360\t정확도: 66.77%\n",
-      "6\t검증 세트 손실: 2.850294\t최선의 손실: 2.850294\t정확도: 76.54%\n",
-      "7\t검증 세트 손실: 4.196420\t최선의 손실: 2.850294\t정확도: 74.94%\n",
-      "8\t검증 세트 손실: 6.775042\t최선의 손실: 2.850294\t정확도: 73.77%\n",
-      "9\t검증 세트 손실: 4.392029\t최선의 손실: 2.850294\t정확도: 76.11%\n",
-      "10\t검증 세트 손실: 1.766303\t최선의 손실: 1.766303\t정확도: 85.46%\n",
-      "11\t검증 세트 손실: 2.902469\t최선의 손실: 1.766303\t정확도: 75.29%\n"
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160, total=   6.1s\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 297.714813\t최선의 손실: 297.714813\t정확도: 47.85%\n",
+      "1\t검증 세트 손실: 15.103884\t최선의 손실: 15.103884\t정확도: 76.43%\n",
+      "2\t검증 세트 손실: 7.641291\t최선의 손실: 7.641291\t정확도: 78.89%\n",
+      "3\t검증 세트 손실: 3.352600\t최선의 손실: 3.352600\t정확도: 86.63%\n",
+      "4\t검증 세트 손실: 2.153786\t최선의 손실: 2.153786\t정확도: 93.51%\n",
+      "5\t검증 세트 손실: 1.708294\t최선의 손실: 1.708294\t정확도: 90.93%\n",
+      "6\t검증 세트 손실: 1.804965\t최선의 손실: 1.708294\t정확도: 90.70%\n",
+      "7\t검증 세트 손실: 1.646180\t최선의 손실: 1.646180\t정확도: 93.86%\n",
+      "8\t검증 세트 손실: 1.433139\t최선의 손실: 1.433139\t정확도: 92.81%\n",
+      "9\t검증 세트 손실: 1.301722\t최선의 손실: 1.301722\t정확도: 92.49%\n",
+      "10\t검증 세트 손실: 0.768034\t최선의 손실: 0.768034\t정확도: 94.37%\n",
+      "11\t검증 세트 손실: 0.700226\t최선의 손실: 0.700226\t정확도: 95.47%\n",
+      "12\t검증 세트 손실: 2.157740\t최선의 손실: 0.700226\t정확도: 94.61%\n",
+      "13\t검증 세트 손실: 1.267197\t최선의 손실: 0.700226\t정확도: 95.35%\n",
+      "14\t검증 세트 손실: 0.820139\t최선의 손실: 0.700226\t정확도: 95.39%\n",
+      "15\t검증 세트 손실: 0.864113\t최선의 손실: 0.700226\t정확도: 95.50%\n",
+      "16\t검증 세트 손실: 2.671099\t최선의 손실: 0.700226\t정확도: 80.34%\n",
+      "17\t검증 세트 손실: 0.544908\t최선의 손실: 0.544908\t정확도: 95.35%\n",
+      "18\t검증 세트 손실: 0.415430\t최선의 손실: 0.415430\t정확도: 95.50%\n",
+      "19\t검증 세트 손실: 0.563941\t최선의 손실: 0.415430\t정확도: 95.23%\n",
+      "20\t검증 세트 손실: 0.707338\t최선의 손실: 0.415430\t정확도: 90.38%\n",
+      "21\t검증 세트 손실: 0.635402\t최선의 손실: 0.415430\t정확도: 94.88%\n",
+      "22\t검증 세트 손실: 0.699416\t최선의 손실: 0.415430\t정확도: 95.19%\n",
+      "23\t검증 세트 손실: 1.046321\t최선의 손실: 0.415430\t정확도: 93.28%\n",
+      "24\t검증 세트 손실: 1.297419\t최선의 손실: 0.415430\t정확도: 91.71%\n",
+      "25\t검증 세트 손실: 0.314797\t최선의 손실: 0.314797\t정확도: 96.25%\n",
+      "26\t검증 세트 손실: 0.276362\t최선의 손실: 0.276362\t정확도: 96.40%\n",
+      "27\t검증 세트 손실: 0.308734\t최선의 손실: 0.276362\t정확도: 95.97%\n",
+      "28\t검증 세트 손실: 0.639498\t최선의 손실: 0.276362\t정확도: 95.19%\n",
+      "29\t검증 세트 손실: 0.490015\t최선의 손실: 0.276362\t정확도: 94.76%\n",
+      "30\t검증 세트 손실: 0.603001\t최선의 손실: 0.276362\t정확도: 96.60%\n",
+      "31\t검증 세트 손실: 0.854220\t최선의 손실: 0.276362\t정확도: 91.05%\n",
+      "32\t검증 세트 손실: 0.482940\t최선의 손실: 0.276362\t정확도: 95.07%\n",
+      "33\t검증 세트 손실: 0.360172\t최선의 손실: 0.276362\t정확도: 95.78%\n",
+      "34\t검증 세트 손실: 0.426841\t최선의 손실: 0.276362\t정확도: 94.68%\n",
+      "35\t검증 세트 손실: 0.534144\t최선의 손실: 0.276362\t정확도: 95.62%\n",
+      "36\t검증 세트 손실: 0.357398\t최선의 손실: 0.276362\t정확도: 94.88%\n",
+      "37\t검증 세트 손실: 0.292843\t최선의 손실: 0.276362\t정확도: 95.70%\n",
+      "38\t검증 세트 손실: 0.275793\t최선의 손실: 0.275793\t정확도: 95.27%\n",
+      "39\t검증 세트 손실: 0.337423\t최선의 손실: 0.275793\t정확도: 94.37%\n",
+      "40\t검증 세트 손실: 1.331597\t최선의 손실: 0.275793\t정확도: 96.56%\n",
+      "41\t검증 세트 손실: 0.471213\t최선의 손실: 0.275793\t정확도: 96.72%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 3.119392\t최선의 손실: 1.766303\t정확도: 73.46%\n",
-      "13\t검증 세트 손실: 2.972745\t최선의 손실: 1.766303\t정확도: 74.59%\n",
-      "14\t검증 세트 손실: 3.366093\t최선의 손실: 1.766303\t정확도: 73.65%\n",
-      "15\t검증 세트 손실: 3.381401\t최선의 손실: 1.766303\t정확도: 73.61%\n",
-      "16\t검증 세트 손실: 4.072013\t최선의 손실: 1.766303\t정확도: 76.39%\n",
-      "17\t검증 세트 손실: 2.023656\t최선의 손실: 1.766303\t정확도: 92.14%\n",
-      "18\t검증 세트 손실: 1.788443\t최선의 손실: 1.766303\t정확도: 93.28%\n",
-      "19\t검증 세트 손실: 1.060063\t최선의 손실: 1.060063\t정확도: 94.84%\n",
-      "20\t검증 세트 손실: 1.402746\t최선의 손실: 1.060063\t정확도: 94.21%\n",
-      "21\t검증 세트 손실: 1.441945\t최선의 손실: 1.060063\t정확도: 93.20%\n",
-      "22\t검증 세트 손실: 1.858546\t최선의 손실: 1.060063\t정확도: 91.48%\n",
-      "23\t검증 세트 손실: 1.219669\t최선의 손실: 1.060063\t정확도: 91.63%\n",
-      "24\t검증 세트 손실: 1.607710\t최선의 손실: 1.060063\t정확도: 94.02%\n",
-      "25\t검증 세트 손실: 1.023925\t최선의 손실: 1.023925\t정확도: 93.47%\n",
-      "26\t검증 세트 손실: 0.761425\t최선의 손실: 0.761425\t정확도: 93.59%\n",
-      "27\t검증 세트 손실: 2.324381\t최선의 손실: 0.761425\t정확도: 88.04%\n",
-      "28\t검증 세트 손실: 0.752010\t최선의 손실: 0.752010\t정확도: 94.10%\n",
-      "29\t검증 세트 손실: 0.635010\t최선의 손실: 0.635010\t정확도: 95.43%\n",
-      "30\t검증 세트 손실: 0.564995\t최선의 손실: 0.564995\t정확도: 95.86%\n",
-      "31\t검증 세트 손실: 0.775130\t최선의 손실: 0.564995\t정확도: 95.74%\n",
-      "32\t검증 세트 손실: 0.833601\t최선의 손실: 0.564995\t정확도: 94.64%\n",
-      "33\t검증 세트 손실: 0.660422\t최선의 손실: 0.564995\t정확도: 95.23%\n",
-      "34\t검증 세트 손실: 1.304935\t최선의 손실: 0.564995\t정확도: 96.17%\n",
-      "35\t검증 세트 손실: 0.796914\t최선의 손실: 0.564995\t정확도: 94.49%\n",
-      "36\t검증 세트 손실: 0.766498\t최선의 손실: 0.564995\t정확도: 95.23%\n",
-      "37\t검증 세트 손실: 1.161751\t최선의 손실: 0.564995\t정확도: 94.14%\n",
-      "38\t검증 세트 손실: 0.674686\t최선의 손실: 0.564995\t정확도: 95.27%\n",
-      "39\t검증 세트 손실: 0.931044\t최선의 손실: 0.564995\t정확도: 93.71%\n",
-      "40\t검증 세트 손실: 0.560063\t최선의 손실: 0.560063\t정확도: 95.82%\n",
-      "41\t검증 세트 손실: 2.221519\t최선의 손실: 0.560063\t정확도: 79.16%\n",
-      "42\t검증 세트 손실: 0.633004\t최선의 손실: 0.560063\t정확도: 96.76%\n",
-      "43\t검증 세트 손실: 3.281219\t최선의 손실: 0.560063\t정확도: 96.09%\n",
-      "44\t검증 세트 손실: 1.159059\t최선의 손실: 0.560063\t정확도: 94.29%\n",
-      "45\t검증 세트 손실: 1.126738\t최선의 손실: 0.560063\t정확도: 94.64%\n",
-      "46\t검증 세트 손실: 0.620803\t최선의 손실: 0.560063\t정확도: 94.53%\n",
-      "47\t검증 세트 손실: 0.659082\t최선의 손실: 0.560063\t정확도: 95.47%\n",
-      "48\t검증 세트 손실: 0.662469\t최선의 손실: 0.560063\t정확도: 95.78%\n",
-      "49\t검증 세트 손실: 0.988380\t최선의 손실: 0.560063\t정확도: 95.90%\n",
-      "50\t검증 세트 손실: 0.832599\t최선의 손실: 0.560063\t정확도: 94.06%\n",
-      "51\t검증 세트 손실: 0.454029\t최선의 손실: 0.454029\t정확도: 95.47%\n",
-      "52\t검증 세트 손실: 0.635363\t최선의 손실: 0.454029\t정확도: 95.07%\n",
-      "53\t검증 세트 손실: 0.539044\t최선의 손실: 0.454029\t정확도: 96.40%\n",
-      "54\t검증 세트 손실: 0.437891\t최선의 손실: 0.437891\t정확도: 94.96%\n",
-      "55\t검증 세트 손실: 0.402713\t최선의 손실: 0.402713\t정확도: 96.60%\n",
-      "56\t검증 세트 손실: 1.063624\t최선의 손실: 0.402713\t정확도: 94.53%\n",
-      "57\t검증 세트 손실: 0.678718\t최선의 손실: 0.402713\t정확도: 96.48%\n",
-      "58\t검증 세트 손실: 1.436533\t최선의 손실: 0.402713\t정확도: 93.08%\n",
-      "59\t검증 세트 손실: 0.837463\t최선의 손실: 0.402713\t정확도: 95.19%\n",
-      "60\t검증 세트 손실: 0.936024\t최선의 손실: 0.402713\t정확도: 94.33%\n",
-      "61\t검증 세트 손실: 1.487650\t최선의 손실: 0.402713\t정확도: 95.62%\n",
-      "62\t검증 세트 손실: 0.690896\t최선의 손실: 0.402713\t정확도: 96.21%\n",
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-      "64\t검증 세트 손실: 0.486320\t최선의 손실: 0.402713\t정확도: 97.03%\n",
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-      "66\t검증 세트 손실: 0.383132\t최선의 손실: 0.383132\t정확도: 97.19%\n",
-      "67\t검증 세트 손실: 0.429056\t최선의 손실: 0.383132\t정확도: 95.23%\n",
-      "68\t검증 세트 손실: 1.340133\t최선의 손실: 0.383132\t정확도: 96.95%\n",
-      "69\t검증 세트 손실: 0.579515\t최선의 손실: 0.383132\t정확도: 96.52%\n",
-      "70\t검증 세트 손실: 0.530260\t최선의 손실: 0.383132\t정확도: 96.60%\n",
-      "71\t검증 세트 손실: 0.798639\t최선의 손실: 0.383132\t정확도: 96.72%\n",
-      "72\t검증 세트 손실: 0.482889\t최선의 손실: 0.383132\t정확도: 96.33%\n",
-      "73\t검증 세트 손실: 0.539486\t최선의 손실: 0.383132\t정확도: 96.40%\n",
-      "74\t검증 세트 손실: 0.440067\t최선의 손실: 0.383132\t정확도: 96.09%\n",
-      "75\t검증 세트 손실: 0.429060\t최선의 손실: 0.383132\t정확도: 94.49%\n",
-      "76\t검증 세트 손실: 2.827537\t최선의 손실: 0.383132\t정확도: 95.86%\n",
-      "77\t검증 세트 손실: 1.287889\t최선의 손실: 0.383132\t정확도: 95.74%\n",
-      "78\t검증 세트 손실: 0.876104\t최선의 손실: 0.383132\t정확도: 95.90%\n",
-      "79\t검증 세트 손실: 2.351740\t최선의 손실: 0.383132\t정확도: 94.45%\n",
-      "80\t검증 세트 손실: 0.789180\t최선의 손실: 0.383132\t정확도: 95.47%\n",
-      "81\t검증 세트 손실: 15.026641\t최선의 손실: 0.383132\t정확도: 86.67%\n",
-      "82\t검증 세트 손실: 105659840.000000\t최선의 손실: 0.383132\t정확도: 22.01%\n",
-      "83\t검증 세트 손실: 1210923.750000\t최선의 손실: 0.383132\t정확도: 28.69%\n",
-      "84\t검증 세트 손실: 914577.625000\t최선의 손실: 0.383132\t정확도: 30.18%\n",
-      "85\t검증 세트 손실: 154637.828125\t최선의 손실: 0.383132\t정확도: 48.24%\n",
-      "86\t검증 세트 손실: 191644.328125\t최선의 손실: 0.383132\t정확도: 54.85%\n",
-      "87\t검증 세트 손실: 89010.023438\t최선의 손실: 0.383132\t정확도: 66.18%\n",
+      "42\t검증 세트 손실: 1.884585\t최선의 손실: 0.275793\t정확도: 95.19%\n",
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+      "47\t검증 세트 손실: 8976683.000000\t최선의 손실: 0.275793\t정확도: 19.27%\n",
+      "48\t검증 세트 손실: 3912144.250000\t최선의 손실: 0.275793\t정확도: 25.22%\n",
+      "49\t검증 세트 손실: 343869.781250\t최선의 손실: 0.275793\t정확도: 58.72%\n",
+      "50\t검증 세트 손실: 184405.359375\t최선의 손실: 0.275793\t정확도: 60.20%\n",
+      "51\t검증 세트 손실: 95154.617188\t최선의 손실: 0.275793\t정확도: 66.77%\n",
+      "52\t검증 세트 손실: 73934.328125\t최선의 손실: 0.275793\t정확도: 73.73%\n",
+      "53\t검증 세트 손실: 41615.125000\t최선의 손실: 0.275793\t정확도: 85.14%\n",
+      "54\t검증 세트 손실: 56549.464844\t최선의 손실: 0.275793\t정확도: 64.93%\n",
+      "55\t검증 세트 손실: 43981.636719\t최선의 손실: 0.275793\t정확도: 70.76%\n",
+      "56\t검증 세트 손실: 30328.919922\t최선의 손실: 0.275793\t정확도: 88.19%\n",
+      "57\t검증 세트 손실: 72159.523438\t최선의 손실: 0.275793\t정확도: 59.66%\n",
+      "58\t검증 세트 손실: 129199.937500\t최선의 손실: 0.275793\t정확도: 58.68%\n",
+      "59\t검증 세트 손실: 125364.562500\t최선의 손실: 0.275793\t정확도: 56.18%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=160, total=   8.0s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.265360\t최선의 손실: 0.265360\t정확도: 94.10%\n",
-      "1\t검증 세트 손실: 0.176672\t최선의 손실: 0.176672\t정확도: 95.04%\n",
-      "2\t검증 세트 손실: 0.171119\t최선의 손실: 0.171119\t정확도: 94.45%\n",
-      "3\t검증 세트 손실: 0.195707\t최선의 손실: 0.171119\t정확도: 94.80%\n",
-      "4\t검증 세트 손실: 0.328562\t최선의 손실: 0.171119\t정확도: 87.92%\n",
-      "5\t검증 세트 손실: 0.223957\t최선의 손실: 0.171119\t정확도: 93.75%\n",
-      "6\t검증 세트 손실: 0.290333\t최선의 손실: 0.171119\t정확도: 91.44%\n",
-      "7\t검증 세트 손실: 0.493569\t최선의 손실: 0.171119\t정확도: 76.90%\n",
-      "8\t검증 세트 손실: 0.835535\t최선의 손실: 0.171119\t정확도: 70.84%\n",
-      "9\t검증 세트 손실: 0.627481\t최선의 손실: 0.171119\t정확도: 81.70%\n",
-      "10\t검증 세트 손실: 0.579524\t최선의 손실: 0.171119\t정확도: 77.05%\n",
-      "11\t검증 세트 손실: 0.522164\t최선의 손실: 0.171119\t정확도: 77.56%\n",
-      "12\t검증 세트 손실: 0.377269\t최선의 손실: 0.171119\t정확도: 86.47%\n",
-      "13\t검증 세트 손실: 0.492633\t최선의 손실: 0.171119\t정확도: 80.69%\n",
-      "14\t검증 세트 손실: 0.426616\t최선의 손실: 0.171119\t정확도: 87.76%\n",
-      "15\t검증 세트 손실: 0.414818\t최선의 손실: 0.171119\t정확도: 86.08%\n",
-      "16\t검증 세트 손실: 0.559070\t최선의 손실: 0.171119\t정확도: 78.07%\n",
-      "17\t검증 세트 손실: 0.803195\t최선의 손실: 0.171119\t정확도: 62.78%\n",
-      "18\t검증 세트 손실: 0.618331\t최선의 손실: 0.171119\t정확도: 73.77%\n",
-      "19\t검증 세트 손실: 0.465678\t최선의 손실: 0.171119\t정확도: 80.69%\n",
-      "20\t검증 세트 손실: 0.403313\t최선의 손실: 0.171119\t정확도: 84.60%\n",
-      "21\t검증 세트 손실: 0.723272\t최선의 손실: 0.171119\t정확도: 77.17%\n",
-      "22\t검증 세트 손실: 1.726784\t최선의 손실: 0.171119\t정확도: 62.82%\n",
-      "23\t검증 세트 손실: 0.615756\t최선의 손실: 0.171119\t정확도: 76.62%\n",
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=160, total=   6.3s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.271899\t최선의 손실: 0.271899\t정확도: 91.83%\n",
+      "1\t검증 세트 손실: 0.176052\t최선의 손실: 0.176052\t정확도: 95.15%\n",
+      "2\t검증 세트 손실: 0.164719\t최선의 손실: 0.164719\t정확도: 95.66%\n",
+      "3\t검증 세트 손실: 0.160637\t최선의 손실: 0.160637\t정확도: 96.13%\n",
+      "4\t검증 세트 손실: 0.217903\t최선의 손실: 0.160637\t정확도: 93.35%\n",
+      "5\t검증 세트 손실: 0.371049\t최선의 손실: 0.160637\t정확도: 91.83%\n",
+      "6\t검증 세트 손실: 0.342398\t최선의 손실: 0.160637\t정확도: 84.99%\n",
+      "7\t검증 세트 손실: 0.415715\t최선의 손실: 0.160637\t정확도: 89.60%\n",
+      "8\t검증 세트 손실: 0.594841\t최선의 손실: 0.160637\t정확도: 89.76%\n",
+      "9\t검증 세트 손실: 0.717713\t최선의 손실: 0.160637\t정확도: 68.80%\n",
+      "10\t검증 세트 손실: 0.533684\t최선의 손실: 0.160637\t정확도: 79.98%\n",
+      "11\t검증 세트 손실: 0.708021\t최선의 손실: 0.160637\t정확도: 68.61%\n",
+      "12\t검증 세트 손실: 0.614200\t최선의 손실: 0.160637\t정확도: 70.88%\n",
+      "13\t검증 세트 손실: 0.616901\t최선의 손실: 0.160637\t정확도: 77.83%\n",
+      "14\t검증 세트 손실: 0.761278\t최선의 손실: 0.160637\t정확도: 70.41%\n",
+      "15\t검증 세트 손실: 1.262339\t최선의 손실: 0.160637\t정확도: 53.09%\n",
+      "16\t검증 세트 손실: 0.561974\t최선의 손실: 0.160637\t정확도: 77.68%\n",
+      "17\t검증 세트 손실: 0.422010\t최선의 손실: 0.160637\t정확도: 87.80%\n",
+      "18\t검증 세트 손실: 0.353856\t최선의 손실: 0.160637\t정확도: 87.92%\n",
+      "19\t검증 세트 손실: 0.396002\t최선의 손실: 0.160637\t정확도: 85.93%\n",
+      "20\t검증 세트 손실: 0.420937\t최선의 손실: 0.160637\t정확도: 80.06%\n",
+      "21\t검증 세트 손실: 0.424796\t최선의 손실: 0.160637\t정확도: 80.61%\n",
+      "22\t검증 세트 손실: 1.033505\t최선의 손실: 0.160637\t정확도: 49.92%\n",
+      "23\t검증 세트 손실: 2.014473\t최선의 손실: 0.160637\t정확도: 49.14%\n",
+      "24\t검증 세트 손실: 1.070690\t최선의 손실: 0.160637\t정확도: 50.12%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  11.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.378165\t최선의 손실: 0.378165\t정확도: 88.04%\n",
-      "1\t검증 세트 손실: 0.207037\t최선의 손실: 0.207037\t정확도: 94.10%\n",
-      "2\t검증 세트 손실: 0.219509\t최선의 손실: 0.207037\t정확도: 93.90%\n",
-      "3\t검증 세트 손실: 0.228661\t최선의 손실: 0.207037\t정확도: 93.82%\n",
-      "4\t검증 세트 손실: 0.203050\t최선의 손실: 0.203050\t정확도: 93.82%\n",
-      "5\t검증 세트 손실: 0.463003\t최선의 손실: 0.203050\t정확도: 88.55%\n",
-      "6\t검증 세트 손실: 0.529561\t최선의 손실: 0.203050\t정확도: 81.12%\n",
-      "7\t검증 세트 손실: 0.386396\t최선의 손실: 0.203050\t정확도: 87.14%\n",
-      "8\t검증 세트 손실: 1.277516\t최선의 손실: 0.203050\t정확도: 38.90%\n",
-      "9\t검증 세트 손실: 0.804405\t최선의 손실: 0.203050\t정확도: 64.74%\n",
-      "10\t검증 세트 손실: 0.546310\t최선의 손실: 0.203050\t정확도: 85.22%\n",
-      "11\t검증 세트 손실: 0.533614\t최선의 손실: 0.203050\t정확도: 80.26%\n",
-      "12\t검증 세트 손실: 0.505934\t최선의 손실: 0.203050\t정확도: 84.71%\n",
-      "13\t검증 세트 손실: 0.767130\t최선의 손실: 0.203050\t정확도: 69.55%\n",
-      "14\t검증 세트 손실: 0.711746\t최선의 손실: 0.203050\t정확도: 65.21%\n",
-      "15\t검증 세트 손실: 0.946582\t최선의 손실: 0.203050\t정확도: 62.39%\n",
-      "16\t검증 세트 손실: 0.798799\t최선의 손실: 0.203050\t정확도: 69.43%\n",
-      "17\t검증 세트 손실: 0.953968\t최선의 손실: 0.203050\t정확도: 65.25%\n",
-      "18\t검증 세트 손실: 0.782754\t최선의 손실: 0.203050\t정확도: 62.98%\n",
-      "19\t검증 세트 손실: 0.730975\t최선의 손실: 0.203050\t정확도: 77.40%\n",
-      "20\t검증 세트 손실: 0.965962\t최선의 손실: 0.203050\t정확도: 58.44%\n",
-      "21\t검증 세트 손실: 0.639428\t최선의 손실: 0.203050\t정확도: 71.74%\n",
-      "22\t검증 세트 손실: 0.597680\t최선의 손실: 0.203050\t정확도: 82.80%\n",
-      "23\t검증 세트 손실: 0.822055\t최선의 손실: 0.203050\t정확도: 62.82%\n",
-      "24\t검증 세트 손실: 1.845827\t최선의 손실: 0.203050\t정확도: 31.90%\n",
-      "25\t검증 세트 손실: 2.279753\t최선의 손실: 0.203050\t정확도: 30.45%\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=   8.0s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.278217\t최선의 손실: 0.278217\t정확도: 93.90%\n",
+      "1\t검증 세트 손실: 0.186034\t최선의 손실: 0.186034\t정확도: 95.35%\n",
+      "2\t검증 세트 손실: 0.190940\t최선의 손실: 0.186034\t정확도: 94.92%\n",
+      "3\t검증 세트 손실: 0.172201\t최선의 손실: 0.172201\t정확도: 95.35%\n",
+      "4\t검증 세트 손실: 0.277698\t최선의 손실: 0.172201\t정확도: 90.81%\n",
+      "5\t검증 세트 손실: 0.521482\t최선의 손실: 0.172201\t정확도: 83.39%\n",
+      "6\t검증 세트 손실: 0.403302\t최선의 손실: 0.172201\t정확도: 82.80%\n",
+      "7\t검증 세트 손실: 0.375264\t최선의 손실: 0.172201\t정확도: 88.00%\n",
+      "8\t검증 세트 손실: 0.436595\t최선의 손실: 0.172201\t정확도: 83.82%\n",
+      "9\t검증 세트 손실: 0.458604\t최선의 손실: 0.172201\t정확도: 88.31%\n",
+      "10\t검증 세트 손실: 0.477994\t최선의 손실: 0.172201\t정확도: 83.15%\n",
+      "11\t검증 세트 손실: 0.605498\t최선의 손실: 0.172201\t정확도: 75.53%\n",
+      "12\t검증 세트 손실: 0.433191\t최선의 손실: 0.172201\t정확도: 81.63%\n",
+      "13\t검증 세트 손실: 0.462298\t최선의 손실: 0.172201\t정확도: 86.55%\n",
+      "14\t검증 세트 손실: 0.363577\t최선의 손실: 0.172201\t정확도: 85.03%\n",
+      "15\t검증 세트 손실: 0.478370\t최선의 손실: 0.172201\t정확도: 85.14%\n",
+      "16\t검증 세트 손실: 0.815238\t최선의 손실: 0.172201\t정확도: 58.56%\n",
+      "17\t검증 세트 손실: 1.077014\t최선의 손실: 0.172201\t정확도: 54.30%\n",
+      "18\t검증 세트 손실: 0.819509\t최선의 손실: 0.172201\t정확도: 67.12%\n",
+      "19\t검증 세트 손실: 0.715490\t최선의 손실: 0.172201\t정확도: 73.30%\n",
+      "20\t검증 세트 손실: 0.560053\t최선의 손실: 0.172201\t정확도: 82.68%\n",
+      "21\t검증 세트 손실: 0.565566\t최선의 손실: 0.172201\t정확도: 83.70%\n",
+      "22\t검증 세트 손실: 2.730642\t최선의 손실: 0.172201\t정확도: 34.32%\n",
+      "23\t검증 세트 손실: 2.050656\t최선의 손실: 0.172201\t정확도: 34.52%\n",
+      "24\t검증 세트 손실: 2.403146\t최선의 손실: 0.172201\t정확도: 43.78%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  11.0s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 0.259868\t최선의 손실: 0.259868\t정확도: 93.32%\n",
-      "1\t검증 세트 손실: 0.226663\t최선의 손실: 0.226663\t정확도: 95.31%\n",
-      "2\t검증 세트 손실: 0.233845\t최선의 손실: 0.226663\t정확도: 92.18%\n",
-      "3\t검증 세트 손실: 0.238497\t최선의 손실: 0.226663\t정확도: 93.43%\n",
-      "4\t검증 세트 손실: 0.239796\t최선의 손실: 0.226663\t정확도: 94.29%\n",
-      "5\t검증 세트 손실: 0.286351\t최선의 손실: 0.226663\t정확도: 93.67%\n",
-      "6\t검증 세트 손실: 0.426975\t최선의 손실: 0.226663\t정확도: 86.08%\n",
-      "7\t검증 세트 손실: 0.556547\t최선의 손실: 0.226663\t정확도: 78.15%\n",
-      "8\t검증 세트 손실: 0.428951\t최선의 손실: 0.226663\t정확도: 83.82%\n",
-      "9\t검증 세트 손실: 0.795738\t최선의 손실: 0.226663\t정확도: 68.10%\n",
-      "10\t검증 세트 손실: 0.784568\t최선의 손실: 0.226663\t정확도: 65.29%\n",
-      "11\t검증 세트 손실: 1.349978\t최선의 손실: 0.226663\t정확도: 48.16%\n",
-      "12\t검증 세트 손실: 0.703695\t최선의 손실: 0.226663\t정확도: 70.41%\n",
-      "13\t검증 세트 손실: 0.657616\t최선의 손실: 0.226663\t정확도: 71.19%\n",
-      "14\t검증 세트 손실: 1.350230\t최선의 손실: 0.226663\t정확도: 50.08%\n"
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=   8.1s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 0.289664\t최선의 손실: 0.289664\t정확도: 91.91%\n",
+      "1\t검증 세트 손실: 0.200245\t최선의 손실: 0.200245\t정확도: 94.21%\n",
+      "2\t검증 세트 손실: 0.209176\t최선의 손실: 0.200245\t정확도: 94.41%\n",
+      "3\t검증 세트 손실: 0.170197\t최선의 손실: 0.170197\t정확도: 95.00%\n",
+      "4\t검증 세트 손실: 0.230342\t최선의 손실: 0.170197\t정확도: 91.83%\n",
+      "5\t검증 세트 손실: 0.316439\t최선의 손실: 0.170197\t정확도: 92.03%\n",
+      "6\t검증 세트 손실: 0.819766\t최선의 손실: 0.170197\t정확도: 76.47%\n",
+      "7\t검증 세트 손실: 1.252232\t최선의 손실: 0.170197\t정확도: 53.83%\n",
+      "8\t검증 세트 손실: 0.897620\t최선의 손실: 0.170197\t정확도: 61.49%\n",
+      "9\t검증 세트 손실: 0.807157\t최선의 손실: 0.170197\t정확도: 66.97%\n",
+      "10\t검증 세트 손실: 0.990413\t최선의 손실: 0.170197\t정확도: 52.46%\n",
+      "11\t검증 세트 손실: 0.802099\t최선의 손실: 0.170197\t정확도: 65.32%\n",
+      "12\t검증 세트 손실: 0.839288\t최선의 손실: 0.170197\t정확도: 65.87%\n",
+      "13\t검증 세트 손실: 1.038098\t최선의 손실: 0.170197\t정확도: 60.56%\n",
+      "14\t검증 세트 손실: 1.021767\t최선의 손실: 0.170197\t정확도: 61.92%\n",
+      "15\t검증 세트 손실: 1.275287\t최선의 손실: 0.170197\t정확도: 51.52%\n",
+      "16\t검증 세트 손실: 1.046298\t최선의 손실: 0.170197\t정확도: 60.67%\n",
+      "17\t검증 세트 손실: 0.857438\t최선의 손실: 0.170197\t정확도: 68.06%\n",
+      "18\t검증 세트 손실: 1.001685\t최선의 손실: 0.170197\t정확도: 52.31%\n",
+      "19\t검증 세트 손실: 0.833021\t최선의 손실: 0.170197\t정확도: 65.95%\n",
+      "20\t검증 세트 손실: 1.060698\t최선의 손실: 0.170197\t정확도: 48.16%\n",
+      "21\t검증 세트 손실: 1.100361\t최선의 손실: 0.170197\t정확도: 50.86%\n",
+      "22\t검증 세트 손실: 0.852675\t최선의 손실: 0.170197\t정확도: 56.45%\n",
+      "23\t검증 세트 손실: 0.866461\t최선의 손실: 0.170197\t정확도: 66.22%\n",
+      "24\t검증 세트 손실: 0.992497\t최선의 손실: 0.170197\t정확도: 51.92%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=   8.0s\n",
+      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 697759.312500\t최선의 손실: 697759.312500\t정확도: 22.67%\n",
+      "1\t검증 세트 손실: 169087.343750\t최선의 손실: 169087.343750\t정확도: 19.19%\n",
+      "2\t검증 세트 손실: 514256.156250\t최선의 손실: 169087.343750\t정확도: 29.83%\n",
+      "3\t검증 세트 손실: 568136.187500\t최선의 손실: 169087.343750\t정확도: 22.05%\n",
+      "4\t검증 세트 손실: 69350.031250\t최선의 손실: 69350.031250\t정확도: 32.10%\n",
+      "5\t검증 세트 손실: 134507.578125\t최선의 손실: 69350.031250\t정확도: 19.35%\n",
+      "6\t검증 세트 손실: 58350.792969\t최선의 손실: 58350.792969\t정확도: 22.01%\n",
+      "7\t검증 세트 손실: 96848.078125\t최선의 손실: 58350.792969\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 5045.530273\t최선의 손실: 5045.530273\t정확도: 27.83%\n",
+      "9\t검증 세트 손실: 8396227.000000\t최선의 손실: 5045.530273\t정확도: 20.60%\n",
+      "10\t검증 세트 손실: 547179.375000\t최선의 손실: 5045.530273\t정확도: 23.38%\n",
+      "11\t검증 세트 손실: 1672830.000000\t최선의 손실: 5045.530273\t정확도: 19.00%\n",
+      "12\t검증 세트 손실: 520725.062500\t최선의 손실: 5045.530273\t정확도: 22.79%\n",
+      "13\t검증 세트 손실: 1002350.375000\t최선의 손실: 5045.530273\t정확도: 28.34%\n",
+      "14\t검증 세트 손실: 682120.625000\t최선의 손실: 5045.530273\t정확도: 22.05%\n",
+      "15\t검증 세트 손실: 1059118.375000\t최선의 손실: 5045.530273\t정확도: 41.44%\n",
+      "16\t검증 세트 손실: 208635.921875\t최선의 손실: 5045.530273\t정확도: 23.96%\n",
+      "17\t검증 세트 손실: 104550.398438\t최선의 손실: 5045.530273\t정확도: 18.76%\n",
+      "18\t검증 세트 손실: 98937.226562\t최선의 손실: 5045.530273\t정확도: 15.83%\n",
+      "19\t검증 세트 손실: 30816.490234\t최선의 손실: 5045.530273\t정확도: 22.05%\n",
+      "20\t검증 세트 손실: 22796148.000000\t최선의 손실: 5045.530273\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 701226.937500\t최선의 손실: 5045.530273\t정확도: 24.67%\n",
+      "22\t검증 세트 손실: 846334.625000\t최선의 손실: 5045.530273\t정확도: 22.13%\n",
+      "23\t검증 세트 손실: 1382966.500000\t최선의 손실: 5045.530273\t정확도: 24.55%\n",
+      "24\t검증 세트 손실: 964862.687500\t최선의 손실: 5045.530273\t정확도: 23.18%\n",
+      "25\t검증 세트 손실: 891962.125000\t최선의 손실: 5045.530273\t정확도: 19.23%\n",
+      "26\t검증 세트 손실: 647809.250000\t최선의 손실: 5045.530273\t정확도: 19.90%\n",
+      "27\t검증 세트 손실: 347656.968750\t최선의 손실: 5045.530273\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1602671.125000\t최선의 손실: 5045.530273\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 440695.281250\t최선의 손실: 5045.530273\t정확도: 20.37%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total= 1.3min\n",
+      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15\t검증 세트 손실: 1.317075\t최선의 손실: 0.226663\t정확도: 48.79%\n",
-      "16\t검증 세트 손실: 1.236994\t최선의 손실: 0.226663\t정확도: 41.40%\n",
-      "17\t검증 세트 손실: 0.956980\t최선의 손실: 0.226663\t정확도: 56.61%\n",
-      "18\t검증 세트 손실: 0.763762\t최선의 손실: 0.226663\t정확도: 68.06%\n",
-      "19\t검증 세트 손실: 0.880856\t최선의 손실: 0.226663\t정확도: 61.73%\n",
-      "20\t검증 세트 손실: 0.884210\t최선의 손실: 0.226663\t정확도: 55.12%\n",
-      "21\t검증 세트 손실: 0.973987\t최선의 손실: 0.226663\t정확도: 53.56%\n",
-      "22\t검증 세트 손실: 1.058304\t최선의 손실: 0.226663\t정확도: 52.31%\n",
+      "0\t검증 세트 손실: 1052.432373\t최선의 손실: 1052.432373\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 3211.270020\t최선의 손실: 1052.432373\t정확도: 20.91%\n",
+      "2\t검증 세트 손실: 5823986.000000\t최선의 손실: 1052.432373\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 790791.375000\t최선의 손실: 1052.432373\t정확도: 22.05%\n",
+      "4\t검증 세트 손실: 2834193.500000\t최선의 손실: 1052.432373\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 339417.250000\t최선의 손실: 1052.432373\t정확도: 18.88%\n",
+      "6\t검증 세트 손실: 653892.687500\t최선의 손실: 1052.432373\t정확도: 22.75%\n",
+      "7\t검증 세트 손실: 584327.750000\t최선의 손실: 1052.432373\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1043625.812500\t최선의 손실: 1052.432373\t정확도: 22.36%\n",
+      "9\t검증 세트 손실: 160854.687500\t최선의 손실: 1052.432373\t정확도: 22.05%\n",
+      "10\t검증 세트 손실: 224524.609375\t최선의 손실: 1052.432373\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 73235.843750\t최선의 손실: 1052.432373\t정확도: 22.63%\n",
+      "12\t검증 세트 손실: 333129.343750\t최선의 손실: 1052.432373\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 101869.757812\t최선의 손실: 1052.432373\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 82989.398438\t최선의 손실: 1052.432373\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 236702.343750\t최선의 손실: 1052.432373\t정확도: 18.76%\n",
+      "16\t검증 세트 손실: 114462.046875\t최선의 손실: 1052.432373\t정확도: 19.23%\n",
+      "17\t검증 세트 손실: 268354720.000000\t최선의 손실: 1052.432373\t정확도: 18.76%\n",
+      "18\t검증 세트 손실: 2366265.500000\t최선의 손실: 1052.432373\t정확도: 20.68%\n",
+      "19\t검증 세트 손실: 1016111.062500\t최선의 손실: 1052.432373\t정확도: 18.73%\n",
+      "20\t검증 세트 손실: 1897024.875000\t최선의 손실: 1052.432373\t정확도: 18.73%\n",
+      "21\t검증 세트 손실: 970793.812500\t최선의 손실: 1052.432373\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=   6.7s\n",
-      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 124637.007812\t최선의 손실: 124637.007812\t정확도: 13.14%\n",
-      "1\t검증 세트 손실: 11636.064453\t최선의 손실: 11636.064453\t정확도: 20.45%\n",
-      "2\t검증 세트 손실: 311575.156250\t최선의 손실: 11636.064453\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 55521.675781\t최선의 손실: 11636.064453\t정확도: 18.73%\n",
-      "4\t검증 세트 손실: 555433.250000\t최선의 손실: 11636.064453\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 105387.460938\t최선의 손실: 11636.064453\t정확도: 14.86%\n",
-      "6\t검증 세트 손실: 210633.000000\t최선의 손실: 11636.064453\t정확도: 22.05%\n",
-      "7\t검증 세트 손실: 246126.640625\t최선의 손실: 11636.064453\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 125780.742188\t최선의 손실: 11636.064453\t정확도: 25.61%\n",
-      "9\t검증 세트 손실: 170716.953125\t최선의 손실: 11636.064453\t정확도: 19.31%\n",
-      "10\t검증 세트 손실: 204697.437500\t최선의 손실: 11636.064453\t정확도: 19.27%\n",
-      "11\t검증 세트 손실: 617012.687500\t최선의 손실: 11636.064453\t정확도: 22.05%\n",
-      "12\t검증 세트 손실: 547193.500000\t최선의 손실: 11636.064453\t정확도: 28.30%\n",
-      "13\t검증 세트 손실: 620506.000000\t최선의 손실: 11636.064453\t정확도: 22.95%\n",
-      "14\t검증 세트 손실: 430678.562500\t최선의 손실: 11636.064453\t정확도: 19.62%\n",
-      "15\t검증 세트 손실: 292878.875000\t최선의 손실: 11636.064453\t정확도: 33.93%\n",
-      "16\t검증 세트 손실: 387072.187500\t최선의 손실: 11636.064453\t정확도: 25.10%\n",
-      "17\t검증 세트 손실: 685639.125000\t최선의 손실: 11636.064453\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 357714.250000\t최선의 손실: 11636.064453\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 6326338.500000\t최선의 손실: 11636.064453\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 2810412.250000\t최선의 손실: 11636.064453\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 561812.125000\t최선의 손실: 11636.064453\t정확도: 39.68%\n",
-      "22\t검증 세트 손실: 1703201.000000\t최선의 손실: 11636.064453\t정확도: 31.16%\n",
+      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total=  59.0s\n",
+      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1660532.500000\t최선의 손실: 1660532.500000\t정확도: 21.23%\n",
+      "1\t검증 세트 손실: 148759.953125\t최선의 손실: 148759.953125\t정확도: 44.18%\n",
+      "2\t검증 세트 손실: 108313.796875\t최선의 손실: 108313.796875\t정확도: 36.32%\n",
+      "3\t검증 세트 손실: 82903.367188\t최선의 손실: 82903.367188\t정확도: 26.78%\n",
+      "4\t검증 세트 손실: 19195.408203\t최선의 손실: 19195.408203\t정확도: 28.97%\n",
+      "5\t검증 세트 손실: 135281.859375\t최선의 손실: 19195.408203\t정확도: 19.43%\n",
+      "6\t검증 세트 손실: 44092.859375\t최선의 손실: 19195.408203\t정확도: 19.00%\n",
+      "7\t검증 세트 손실: 55457.671875\t최선의 손실: 19195.408203\t정확도: 19.78%\n",
+      "8\t검증 세트 손실: 4389.279297\t최선의 손실: 4389.279297\t정확도: 33.58%\n",
+      "9\t검증 세트 손실: 39505.894531\t최선의 손실: 4389.279297\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 5588557.500000\t최선의 손실: 4389.279297\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 442717.125000\t최선의 손실: 4389.279297\t정확도: 20.02%\n",
+      "12\t검증 세트 손실: 950357.437500\t최선의 손실: 4389.279297\t정확도: 18.88%\n",
+      "13\t검증 세트 손실: 555490.500000\t최선의 손실: 4389.279297\t정확도: 35.93%\n",
+      "14\t검증 세트 손실: 437504.187500\t최선의 손실: 4389.279297\t정확도: 39.99%\n",
+      "15\t검증 세트 손실: 11955893.000000\t최선의 손실: 4389.279297\t정확도: 19.27%\n",
+      "16\t검증 세트 손실: 4195867.000000\t최선의 손실: 4389.279297\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1197060352.000000\t최선의 손실: 4389.279297\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 1149824.000000\t최선의 손실: 4389.279297\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 804453.625000\t최선의 손실: 4389.279297\t정확도: 18.76%\n",
+      "20\t검증 세트 손실: 1330099.000000\t최선의 손실: 4389.279297\t정확도: 20.95%\n",
+      "21\t검증 세트 손실: 725774.750000\t최선의 손실: 4389.279297\t정확도: 22.05%\n",
+      "22\t검증 세트 손실: 929944256.000000\t최선의 손실: 4389.279297\t정확도: 22.05%\n",
+      "23\t검증 세트 손실: 33682300.000000\t최선의 손실: 4389.279297\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 626012096.000000\t최선의 손실: 4389.279297\t정확도: 24.47%\n",
+      "25\t검증 세트 손실: 708340288.000000\t최선의 손실: 4389.279297\t정확도: 18.10%\n",
+      "26\t검증 세트 손실: 700476032.000000\t최선의 손실: 4389.279297\t정확도: 19.35%\n",
+      "27\t검증 세트 손실: 91523848.000000\t최선의 손실: 4389.279297\t정확도: 28.97%\n",
+      "28\t검증 세트 손실: 290203.218750\t최선의 손실: 4389.279297\t정확도: 26.51%\n",
+      "29\t검증 세트 손실: 459605920.000000\t최선의 손실: 4389.279297\t정확도: 20.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total=  58.7s\n",
-      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 28570378.000000\t최선의 손실: 28570378.000000\t정확도: 25.80%\n",
-      "1\t검증 세트 손실: 184186.171875\t최선의 손실: 184186.171875\t정확도: 22.48%\n",
-      "2\t검증 세트 손실: 107288.234375\t최선의 손실: 107288.234375\t정확도: 19.43%\n",
-      "3\t검증 세트 손실: 123296.421875\t최선의 손실: 107288.234375\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 59069.136719\t최선의 손실: 59069.136719\t정확도: 22.05%\n",
-      "5\t검증 세트 손실: 100483.367188\t최선의 손실: 59069.136719\t정확도: 18.69%\n",
-      "6\t검증 세트 손실: 2311062.000000\t최선의 손실: 59069.136719\t정확도: 24.86%\n",
-      "7\t검증 세트 손실: 715819.500000\t최선의 손실: 59069.136719\t정확도: 21.31%\n",
-      "8\t검증 세트 손실: 249858.078125\t최선의 손실: 59069.136719\t정확도: 26.35%\n",
-      "9\t검증 세트 손실: 225394.093750\t최선의 손실: 59069.136719\t정확도: 23.06%\n",
-      "10\t검증 세트 손실: 188618.875000\t최선의 손실: 59069.136719\t정확도: 26.86%\n",
-      "11\t검증 세트 손실: 540066.562500\t최선의 손실: 59069.136719\t정확도: 28.77%\n",
-      "12\t검증 세트 손실: 630644.937500\t최선의 손실: 59069.136719\t정확도: 20.99%\n",
-      "13\t검증 세트 손실: 7613484.000000\t최선의 손실: 59069.136719\t정확도: 20.29%\n",
-      "14\t검증 세트 손실: 212151.015625\t최선의 손실: 59069.136719\t정확도: 20.64%\n",
-      "15\t검증 세트 손실: 336368.468750\t최선의 손실: 59069.136719\t정확도: 30.96%\n",
-      "16\t검증 세트 손실: 225208.890625\t최선의 손실: 59069.136719\t정확도: 25.25%\n",
-      "17\t검증 세트 손실: 242767.359375\t최선의 손실: 59069.136719\t정확도: 22.01%\n",
-      "18\t검증 세트 손실: 154945.953125\t최선의 손실: 59069.136719\t정확도: 34.13%\n",
-      "19\t검증 세트 손실: 422551.031250\t최선의 손실: 59069.136719\t정확도: 20.02%\n",
-      "20\t검증 세트 손실: 3956223.250000\t최선의 손실: 59069.136719\t정확도: 27.25%\n",
-      "21\t검증 세트 손실: 213391.093750\t최선의 손실: 59069.136719\t정확도: 20.91%\n",
-      "22\t검증 세트 손실: 294419.500000\t최선의 손실: 59069.136719\t정확도: 18.65%\n",
-      "23\t검증 세트 손실: 901363.875000\t최선의 손실: 59069.136719\t정확도: 26.94%\n",
-      "24\t검증 세트 손실: 332960.343750\t최선의 손실: 59069.136719\t정확도: 33.11%\n",
-      "25\t검증 세트 손실: 543843.000000\t최선의 손실: 59069.136719\t정확도: 31.39%\n",
+      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=100, total= 1.3min\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.442687\t최선의 손실: 1.442687\t정확도: 43.16%\n",
+      "1\t검증 세트 손실: 1.136909\t최선의 손실: 1.136909\t정확도: 45.00%\n",
+      "2\t검증 세트 손실: 0.938827\t최선의 손실: 0.938827\t정확도: 61.57%\n",
+      "3\t검증 세트 손실: 0.840693\t최선의 손실: 0.840693\t정확도: 63.76%\n",
+      "4\t검증 세트 손실: 0.787588\t최선의 손실: 0.787588\t정확도: 67.20%\n",
+      "5\t검증 세트 손실: 0.609222\t최선의 손실: 0.609222\t정확도: 74.43%\n",
+      "6\t검증 세트 손실: 0.468993\t최선의 손실: 0.468993\t정확도: 87.88%\n",
+      "7\t검증 세트 손실: 0.435015\t최선의 손실: 0.435015\t정확도: 90.07%\n",
+      "8\t검증 세트 손실: 0.364631\t최선의 손실: 0.364631\t정확도: 91.05%\n",
+      "9\t검증 세트 손실: 0.290403\t최선의 손실: 0.290403\t정확도: 92.65%\n",
+      "10\t검증 세트 손실: 0.260335\t최선의 손실: 0.260335\t정확도: 93.82%\n",
+      "11\t검증 세트 손실: 0.373263\t최선의 손실: 0.260335\t정확도: 85.89%\n",
+      "12\t검증 세트 손실: 0.268471\t최선의 손실: 0.260335\t정확도: 93.94%\n",
+      "13\t검증 세트 손실: 0.254349\t최선의 손실: 0.254349\t정확도: 93.82%\n",
+      "14\t검증 세트 손실: 0.371291\t최선의 손실: 0.254349\t정확도: 88.82%\n",
+      "15\t검증 세트 손실: 0.231508\t최선의 손실: 0.231508\t정확도: 94.80%\n",
+      "16\t검증 세트 손실: 0.244010\t최선의 손실: 0.231508\t정확도: 95.70%\n",
+      "17\t검증 세트 손실: 0.182807\t최선의 손실: 0.182807\t정확도: 96.21%\n",
+      "18\t검증 세트 손실: 0.178986\t최선의 손실: 0.178986\t정확도: 96.48%\n",
+      "19\t검증 세트 손실: 0.162436\t최선의 손실: 0.162436\t정확도: 96.01%\n",
+      "20\t검증 세트 손실: 0.178157\t최선의 손실: 0.162436\t정확도: 96.33%\n",
+      "21\t검증 세트 손실: 0.146825\t최선의 손실: 0.146825\t정확도: 96.72%\n",
+      "22\t검증 세트 손실: 0.235295\t최선의 손실: 0.146825\t정확도: 96.05%\n",
+      "23\t검증 세트 손실: 0.810296\t최선의 손실: 0.146825\t정확도: 89.95%\n",
+      "24\t검증 세트 손실: 1.644811\t최선의 손실: 0.146825\t정확도: 26.31%\n",
+      "25\t검증 세트 손실: 1.629160\t최선의 손실: 0.146825\t정확도: 20.91%\n",
+      "26\t검증 세트 손실: 1.631488\t최선의 손실: 0.146825\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 1.616873\t최선의 손실: 0.146825\t정확도: 22.01%\n",
+      "28\t검증 세트 손실: 1.609523\t최선의 손실: 0.146825\t정확도: 20.91%\n",
+      "29\t검증 세트 손실: 1.626242\t최선의 손실: 0.146825\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 1.721736\t최선의 손실: 0.146825\t정확도: 19.27%\n",
+      "31\t검증 세트 손실: 1.656534\t최선의 손실: 0.146825\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 2.311941\t최선의 손실: 0.146825\t정확도: 19.27%\n",
+      "33\t검증 세트 손실: 1.642627\t최선의 손실: 0.146825\t정확도: 19.08%\n",
+      "34\t검증 세트 손실: 1.666814\t최선의 손실: 0.146825\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 1.669406\t최선의 손실: 0.146825\t정확도: 19.27%\n",
+      "36\t검증 세트 손실: 1.638574\t최선의 손실: 0.146825\t정확도: 18.73%\n",
+      "37\t검증 세트 손실: 1.631664\t최선의 손실: 0.146825\t정확도: 19.27%\n",
+      "38\t검증 세트 손실: 1.662116\t최선의 손실: 0.146825\t정확도: 22.01%\n",
+      "39\t검증 세트 손실: 1.627693\t최선의 손실: 0.146825\t정확도: 19.27%\n",
+      "40\t검증 세트 손실: 1.658662\t최선의 손실: 0.146825\t정확도: 22.01%\n",
+      "41\t검증 세트 손실: 1.675744\t최선의 손실: 0.146825\t정확도: 18.73%\n",
+      "42\t검증 세트 손실: 1.660450\t최선의 손실: 0.146825\t정확도: 22.01%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total= 1.8min\n",
-      "[CV] batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 200303.453125\t최선의 손실: 200303.453125\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 11488.855469\t최선의 손실: 11488.855469\t정확도: 18.73%\n",
-      "2\t검증 세트 손실: 13107.774414\t최선의 손실: 11488.855469\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 11595.158203\t최선의 손실: 11488.855469\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 360871.406250\t최선의 손실: 11488.855469\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 278704.093750\t최선의 손실: 11488.855469\t정확도: 22.56%\n",
-      "6\t검증 세트 손실: 1943266.375000\t최선의 손실: 11488.855469\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 326045.937500\t최선의 손실: 11488.855469\t정확도: 19.08%\n",
-      "8\t검증 세트 손실: 802886.875000\t최선의 손실: 11488.855469\t정확도: 18.73%\n",
-      "9\t검증 세트 손실: 1750131.750000\t최선의 손실: 11488.855469\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 75826.937500\t최선의 손실: 11488.855469\t정확도: 21.62%\n",
-      "11\t검증 세트 손실: 482536.875000\t최선의 손실: 11488.855469\t정확도: 19.27%\n",
-      "12\t검증 세트 손실: 827334.250000\t최선의 손실: 11488.855469\t정확도: 20.95%\n",
-      "13\t검증 세트 손실: 1465966.875000\t최선의 손실: 11488.855469\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 347107.750000\t최선의 손실: 11488.855469\t정확도: 19.39%\n",
-      "15\t검증 세트 손실: 662821.750000\t최선의 손실: 11488.855469\t정확도: 20.91%\n",
-      "16\t검증 세트 손실: 433960.718750\t최선의 손실: 11488.855469\t정확도: 24.08%\n",
-      "17\t검증 세트 손실: 408613.437500\t최선의 손실: 11488.855469\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 816670.250000\t최선의 손실: 11488.855469\t정확도: 19.12%\n",
-      "19\t검증 세트 손실: 367550.593750\t최선의 손실: 11488.855469\t정확도: 21.97%\n",
-      "20\t검증 세트 손실: 2559828.000000\t최선의 손실: 11488.855469\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 5009372.500000\t최선의 손실: 11488.855469\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 495828.625000\t최선의 손실: 11488.855469\t정확도: 19.08%\n",
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=   4.1s\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.953720\t최선의 손실: 1.953720\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.617492\t최선의 손실: 1.617492\t정확도: 19.27%\n",
+      "2\t검증 세트 손실: 1.613540\t최선의 손실: 1.613540\t정확도: 22.01%\n",
+      "3\t검증 세트 손실: 1.623574\t최선의 손실: 1.613540\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.616062\t최선의 손실: 1.613540\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.612414\t최선의 손실: 1.612414\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 1.626306\t최선의 손실: 1.612414\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 1.610147\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 1.616916\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.629183\t최선의 손실: 1.610147\t정확도: 20.91%\n",
+      "10\t검증 세트 손실: 1.631472\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "11\t검증 세트 손실: 1.612134\t최선의 손실: 1.610147\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.619606\t최선의 손실: 1.610147\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.637333\t최선의 손실: 1.610147\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.616308\t최선의 손실: 1.610147\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 1.616049\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.637738\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.655370\t최선의 손실: 1.610147\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.621179\t최선의 손실: 1.610147\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 1.615914\t최선의 손실: 1.610147\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 1.612467\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.676398\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.624750\t최선의 손실: 1.610147\t정확도: 18.73%\n",
+      "23\t검증 세트 손실: 1.668611\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.613451\t최선의 손실: 1.610147\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 1.651021\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.612896\t최선의 손실: 1.610147\t정확도: 22.01%\n",
+      "27\t검증 세트 손실: 1.625595\t최선의 손실: 1.610147\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.631319\t최선의 손실: 1.610147\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=10, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=100, total= 1.3min\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.648431\t최선의 손실: 1.648431\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 1.617979\t최선의 손실: 1.617979\t정확도: 20.91%\n",
-      "2\t검증 세트 손실: 1.612879\t최선의 손실: 1.612879\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 1.377626\t최선의 손실: 1.377626\t정확도: 39.29%\n",
-      "4\t검증 세트 손실: 1.369135\t최선의 손실: 1.369135\t정확도: 32.68%\n",
-      "5\t검증 세트 손실: 1.620438\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.646384\t최선의 손실: 1.369135\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 1.609628\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 1.678384\t최선의 손실: 1.369135\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.637218\t최선의 손실: 1.369135\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.610884\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.635125\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.634281\t최선의 손실: 1.369135\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.651656\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.670715\t최선의 손실: 1.369135\t정확도: 19.08%\n",
-      "15\t검증 세트 손실: 1.563862\t최선의 손실: 1.369135\t정확도: 22.99%\n",
-      "16\t검증 세트 손실: 1.639423\t최선의 손실: 1.369135\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 1.631838\t최선의 손실: 1.369135\t정확도: 19.27%\n",
-      "18\t검증 세트 손실: 1.641140\t최선의 손실: 1.369135\t정확도: 18.73%\n",
-      "19\t검증 세트 손실: 1.645698\t최선의 손실: 1.369135\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.640184\t최선의 손실: 1.369135\t정확도: 20.91%\n",
-      "21\t검증 세트 손실: 1.657158\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.643984\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.734209\t최선의 손실: 1.369135\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 1.708198\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.642398\t최선의 손실: 1.369135\t정확도: 22.01%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.4s\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.284317\t최선의 손실: 1.284317\t정확도: 39.13%\n",
-      "1\t검증 세트 손실: 1.206029\t최선의 손실: 1.206029\t정확도: 39.68%\n",
-      "2\t검증 세트 손실: 1.208414\t최선의 손실: 1.206029\t정확도: 38.90%\n",
-      "3\t검증 세트 손실: 1.177934\t최선의 손실: 1.177934\t정확도: 39.80%\n",
-      "4\t검증 세트 손실: 1.318878\t최선의 손실: 1.177934\t정확도: 36.63%\n",
-      "5\t검증 세트 손실: 1.153589\t최선의 손실: 1.153589\t정확도: 41.95%\n",
-      "6\t검증 세트 손실: 1.187084\t최선의 손실: 1.153589\t정확도: 40.03%\n",
-      "7\t검증 세트 손실: 1.162037\t최선의 손실: 1.153589\t정확도: 40.34%\n",
-      "8\t검증 세트 손실: 1.156580\t최선의 손실: 1.153589\t정확도: 42.06%\n",
-      "9\t검증 세트 손실: 1.161076\t최선의 손실: 1.153589\t정확도: 40.42%\n",
-      "10\t검증 세트 손실: 1.155972\t최선의 손실: 1.153589\t정확도: 40.23%\n",
-      "11\t검증 세트 손실: 1.198637\t최선의 손실: 1.153589\t정확도: 40.07%\n"
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=   3.0s\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.287118\t최선의 손실: 1.287118\t정확도: 39.37%\n",
+      "1\t검증 세트 손실: 1.222458\t최선의 손실: 1.222458\t정확도: 40.15%\n",
+      "2\t검증 세트 손실: 1.141452\t최선의 손실: 1.141452\t정확도: 40.27%\n",
+      "3\t검증 세트 손실: 1.165868\t최선의 손실: 1.141452\t정확도: 40.42%\n",
+      "4\t검증 세트 손실: 0.864673\t최선의 손실: 0.864673\t정확도: 66.34%\n",
+      "5\t검증 세트 손실: 0.701590\t최선의 손실: 0.701590\t정확도: 62.43%\n",
+      "6\t검증 세트 손실: 0.712377\t최선의 손실: 0.701590\t정확도: 59.11%\n",
+      "7\t검증 세트 손실: 0.648790\t최선의 손실: 0.648790\t정확도: 68.53%\n",
+      "8\t검증 세트 손실: 0.632892\t최선의 손실: 0.632892\t정확도: 62.35%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 1.147177\t최선의 손실: 1.147177\t정확도: 40.58%\n",
-      "13\t검증 세트 손실: 1.152905\t최선의 손실: 1.147177\t정확도: 40.46%\n",
-      "14\t검증 세트 손실: 1.214791\t최선의 손실: 1.147177\t정확도: 42.18%\n",
-      "15\t검증 세트 손실: 1.202868\t최선의 손실: 1.147177\t정확도: 40.07%\n",
-      "16\t검증 세트 손실: 1.266143\t최선의 손실: 1.147177\t정확도: 40.38%\n",
-      "17\t검증 세트 손실: 1.717943\t최선의 손실: 1.147177\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.644508\t최선의 손실: 1.147177\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.636021\t최선의 손실: 1.147177\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.610955\t최선의 손실: 1.147177\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.654631\t최선의 손실: 1.147177\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.680775\t최선의 손실: 1.147177\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1.691005\t최선의 손실: 1.147177\t정확도: 19.27%\n",
-      "24\t검증 세트 손실: 1.645131\t최선의 손실: 1.147177\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 1.636657\t최선의 손실: 1.147177\t정확도: 20.91%\n",
-      "26\t검증 세트 손실: 1.652650\t최선의 손실: 1.147177\t정확도: 19.27%\n",
-      "27\t검증 세트 손실: 1.642323\t최선의 손실: 1.147177\t정확도: 19.27%\n",
-      "28\t검증 세트 손실: 1.625659\t최선의 손실: 1.147177\t정확도: 18.73%\n",
-      "29\t검증 세트 손실: 1.716538\t최선의 손실: 1.147177\t정확도: 18.73%\n",
-      "30\t검증 세트 손실: 1.647527\t최선의 손실: 1.147177\t정확도: 20.91%\n",
-      "31\t검증 세트 손실: 1.630986\t최선의 손실: 1.147177\t정확도: 19.27%\n",
-      "32\t검증 세트 손실: 1.634047\t최선의 손실: 1.147177\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 1.666207\t최선의 손실: 1.147177\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 0.679165\t최선의 손실: 0.632892\t정확도: 61.85%\n",
+      "10\t검증 세트 손실: 1.748824\t최선의 손실: 0.632892\t정확도: 35.77%\n",
+      "11\t검증 세트 손실: 1.648183\t최선의 손실: 0.632892\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 1.613602\t최선의 손실: 0.632892\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 1.626628\t최선의 손실: 0.632892\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 1.613184\t최선의 손실: 0.632892\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.625922\t최선의 손실: 0.632892\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.655349\t최선의 손실: 0.632892\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 1.633805\t최선의 손실: 0.632892\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 1.619515\t최선의 손실: 0.632892\t정확도: 18.73%\n",
+      "19\t검증 세트 손실: 1.623284\t최선의 손실: 0.632892\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.640083\t최선의 손실: 0.632892\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.694457\t최선의 손실: 0.632892\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.653092\t최선의 손실: 0.632892\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.629328\t최선의 손실: 0.632892\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.667496\t최선의 손실: 0.632892\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.682033\t최선의 손실: 0.632892\t정확도: 18.73%\n",
+      "26\t검증 세트 손실: 1.652347\t최선의 손실: 0.632892\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 1.636821\t최선의 손실: 0.632892\t정확도: 20.91%\n",
+      "28\t검증 세트 손실: 1.672009\t최선의 손실: 0.632892\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.691723\t최선의 손실: 0.632892\t정확도: 19.27%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.9s\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.383981\t최선의 손실: 1.383981\t정확도: 34.13%\n",
-      "1\t검증 세트 손실: 1.504953\t최선의 손실: 1.383981\t정확도: 26.39%\n",
-      "2\t검증 세트 손실: 1.291709\t최선의 손실: 1.291709\t정확도: 35.93%\n",
-      "3\t검증 세트 손실: 1.640803\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.622455\t최선의 손실: 1.291709\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 1.615713\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.640023\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.620738\t최선의 손실: 1.291709\t정확도: 20.91%\n",
-      "8\t검증 세트 손실: 1.629013\t최선의 손실: 1.291709\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.659545\t최선의 손실: 1.291709\t정확도: 18.73%\n",
-      "10\t검증 세트 손실: 1.639933\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.646373\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.611902\t최선의 손실: 1.291709\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.630163\t최선의 손실: 1.291709\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 1.665317\t최선의 손실: 1.291709\t정확도: 20.91%\n",
-      "15\t검증 세트 손실: 1.630827\t최선의 손실: 1.291709\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 1.634315\t최선의 손실: 1.291709\t정확도: 18.73%\n",
-      "17\t검증 세트 손실: 1.640444\t최선의 손실: 1.291709\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.613193\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.615192\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.648743\t최선의 손실: 1.291709\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 1.660905\t최선의 손실: 1.291709\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 1.682711\t최선의 손실: 1.291709\t정확도: 22.01%\n",
-      "23\t검증 세트 손실: 1.658316\t최선의 손실: 1.291709\t정확도: 22.01%\n",
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=   3.3s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 42.804905\t최선의 손실: 42.804905\t정확도: 87.37%\n",
+      "1\t검증 세트 손실: 21.015423\t최선의 손실: 21.015423\t정확도: 75.53%\n",
+      "2\t검증 세트 손실: 23.915388\t최선의 손실: 21.015423\t정확도: 83.46%\n",
+      "3\t검증 세트 손실: 15.508780\t최선의 손실: 15.508780\t정확도: 87.06%\n",
+      "4\t검증 세트 손실: 11.973853\t최선의 손실: 11.973853\t정확도: 84.21%\n",
+      "5\t검증 세트 손실: 28.786091\t최선의 손실: 11.973853\t정확도: 88.90%\n",
+      "6\t검증 세트 손실: 5216881.500000\t최선의 손실: 11.973853\t정확도: 25.37%\n",
+      "7\t검증 세트 손실: 94772.781250\t최선의 손실: 11.973853\t정확도: 35.54%\n",
+      "8\t검증 세트 손실: 12892.193359\t최선의 손실: 11.973853\t정확도: 77.76%\n",
+      "9\t검증 세트 손실: 45876.082031\t최선의 손실: 11.973853\t정확도: 45.50%\n",
+      "10\t검증 세트 손실: 49796.812500\t최선의 손실: 11.973853\t정확도: 59.23%\n",
+      "11\t검증 세트 손실: 8488.335938\t최선의 손실: 11.973853\t정확도: 69.31%\n",
+      "12\t검증 세트 손실: 9936.710938\t최선의 손실: 11.973853\t정확도: 51.64%\n",
+      "13\t검증 세트 손실: 123113.992188\t최선의 손실: 11.973853\t정확도: 66.42%\n",
+      "14\t검증 세트 손실: 14782.869141\t최선의 손실: 11.973853\t정확도: 80.84%\n",
+      "15\t검증 세트 손실: 2419.840332\t최선의 손실: 11.973853\t정확도: 90.23%\n",
+      "16\t검증 세트 손실: 3974.697510\t최선의 손실: 11.973853\t정확도: 77.56%\n",
+      "17\t검증 세트 손실: 2385.449463\t최선의 손실: 11.973853\t정확도: 89.68%\n",
+      "18\t검증 세트 손실: 1019.522278\t최선의 손실: 11.973853\t정확도: 89.91%\n",
+      "19\t검증 세트 손실: 1918.482056\t최선의 손실: 11.973853\t정확도: 84.75%\n",
+      "20\t검증 세트 손실: 848.202881\t최선의 손실: 11.973853\t정확도: 92.03%\n",
+      "21\t검증 세트 손실: 173324.078125\t최선의 손실: 11.973853\t정확도: 76.97%\n",
+      "22\t검증 세트 손실: 46487.402344\t최선의 손실: 11.973853\t정확도: 92.03%\n",
+      "23\t검증 세트 손실: 65489.820312\t최선의 손실: 11.973853\t정확도: 88.62%\n",
+      "24\t검증 세트 손실: 73682.156250\t최선의 손실: 11.973853\t정확도: 80.88%\n",
+      "25\t검증 세트 손실: 46883.773438\t최선의 손실: 11.973853\t정확도: 77.25%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   2.3s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 19.342131\t최선의 손실: 19.342131\t정확도: 53.09%\n",
-      "1\t검증 세트 손실: 162570.671875\t최선의 손실: 19.342131\t정확도: 58.60%\n",
-      "2\t검증 세트 손실: 55675.160156\t최선의 손실: 19.342131\t정확도: 72.32%\n",
-      "3\t검증 세트 손실: 8520.621094\t최선의 손실: 19.342131\t정확도: 78.46%\n",
-      "4\t검증 세트 손실: 7543.304199\t최선의 손실: 19.342131\t정확도: 89.56%\n",
-      "5\t검증 세트 손실: 63751.292969\t최선의 손실: 19.342131\t정확도: 68.37%\n",
-      "6\t검증 세트 손실: 50114.390625\t최선의 손실: 19.342131\t정확도: 85.89%\n",
-      "7\t검증 세트 손실: 8423.389648\t최선의 손실: 19.342131\t정확도: 85.50%\n",
-      "8\t검증 세트 손실: 3027.999512\t최선의 손실: 19.342131\t정확도: 88.51%\n",
-      "9\t검증 세트 손실: 3194.748779\t최선의 손실: 19.342131\t정확도: 89.80%\n",
-      "10\t검증 세트 손실: 4545.336426\t최선의 손실: 19.342131\t정확도: 89.68%\n",
-      "11\t검증 세트 손실: 7094.003906\t최선의 손실: 19.342131\t정확도: 92.42%\n",
-      "12\t검증 세트 손실: 2705.718262\t최선의 손실: 19.342131\t정확도: 90.27%\n",
-      "13\t검증 세트 손실: 3599.477783\t최선의 손실: 19.342131\t정확도: 86.79%\n",
-      "14\t검증 세트 손실: 1758.897949\t최선의 손실: 19.342131\t정확도: 93.82%\n",
-      "15\t검증 세트 손실: 4491.721680\t최선의 손실: 19.342131\t정확도: 77.37%\n",
-      "16\t검증 세트 손실: 190536.703125\t최선의 손실: 19.342131\t정확도: 78.69%\n",
-      "17\t검증 세트 손실: 65329.195312\t최선의 손실: 19.342131\t정확도: 92.30%\n",
-      "18\t검증 세트 손실: 40160.074219\t최선의 손실: 19.342131\t정확도: 88.74%\n",
-      "19\t검증 세트 손실: 44793.328125\t최선의 손실: 19.342131\t정확도: 88.94%\n",
-      "20\t검증 세트 손실: 20570.017578\t최선의 손실: 19.342131\t정확도: 93.75%\n",
-      "21\t검증 세트 손실: 19556.429688\t최선의 손실: 19.342131\t정확도: 94.18%\n",
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=70, total=  15.3s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 154.754562\t최선의 손실: 154.754562\t정확도: 52.93%\n",
+      "1\t검증 세트 손실: 352739.000000\t최선의 손실: 154.754562\t정확도: 42.34%\n",
+      "2\t검증 세트 손실: 36262.281250\t최선의 손실: 154.754562\t정확도: 58.33%\n",
+      "3\t검증 세트 손실: 10734.582031\t최선의 손실: 154.754562\t정확도: 65.09%\n",
+      "4\t검증 세트 손실: 1561.189941\t최선의 손실: 154.754562\t정확도: 68.18%\n",
+      "5\t검증 세트 손실: 1209.182373\t최선의 손실: 154.754562\t정확도: 75.29%\n",
+      "6\t검증 세트 손실: 887.452881\t최선의 손실: 154.754562\t정확도: 87.84%\n",
+      "7\t검증 세트 손실: 1545.103882\t최선의 손실: 154.754562\t정확도: 69.43%\n",
+      "8\t검증 세트 손실: 2442.301270\t최선의 손실: 154.754562\t정확도: 71.03%\n",
+      "9\t검증 세트 손실: 2405.695312\t최선의 손실: 154.754562\t정확도: 57.74%\n",
+      "10\t검증 세트 손실: 15200.933594\t최선의 손실: 154.754562\t정확도: 65.64%\n",
+      "11\t검증 세트 손실: 41357.570312\t최선의 손실: 154.754562\t정확도: 64.78%\n",
+      "12\t검증 세트 손실: 26145.447266\t최선의 손실: 154.754562\t정확도: 74.59%\n",
+      "13\t검증 세트 손실: 27661.941406\t최선의 손실: 154.754562\t정확도: 65.36%\n",
+      "14\t검증 세트 손실: 28442.843750\t최선의 손실: 154.754562\t정확도: 69.00%\n",
+      "15\t검증 세트 손실: 16544.302734\t최선의 손실: 154.754562\t정확도: 65.21%\n",
+      "16\t검증 세트 손실: 12494.307617\t최선의 손실: 154.754562\t정확도: 70.80%\n",
+      "17\t검증 세트 손실: 18613.650391\t최선의 손실: 154.754562\t정확도: 61.81%\n",
+      "18\t검증 세트 손실: 11672.684570\t최선의 손실: 154.754562\t정확도: 69.74%\n",
+      "19\t검증 세트 손실: 2412.418701\t최선의 손실: 154.754562\t정확도: 74.59%\n",
+      "20\t검증 세트 손실: 4003.198975\t최선의 손실: 154.754562\t정확도: 75.76%\n",
+      "21\t검증 세트 손실: 4533.252441\t최선의 손실: 154.754562\t정확도: 72.36%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=70, total=  27.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 25221.062500\t최선의 손실: 25221.062500\t정확도: 55.16%\n",
-      "1\t검증 세트 손실: 3230.293213\t최선의 손실: 3230.293213\t정확도: 71.89%\n",
-      "2\t검증 세트 손실: 1467.547607\t최선의 손실: 1467.547607\t정확도: 71.03%\n",
-      "3\t검증 세트 손실: 2505.243164\t최선의 손실: 1467.547607\t정확도: 68.10%\n",
-      "4\t검증 세트 손실: 1242.916992\t최선의 손실: 1242.916992\t정확도: 75.76%\n",
-      "5\t검증 세트 손실: 117.197929\t최선의 손실: 117.197929\t정확도: 80.57%\n",
-      "6\t검증 세트 손실: 1208.366455\t최선의 손실: 117.197929\t정확도: 72.44%\n",
-      "7\t검증 세트 손실: 313.917206\t최선의 손실: 117.197929\t정확도: 77.56%\n",
-      "8\t검증 세트 손실: 701.470032\t최선의 손실: 117.197929\t정확도: 60.05%\n",
-      "9\t검증 세트 손실: 129.471024\t최선의 손실: 117.197929\t정확도: 70.84%\n",
-      "10\t검증 세트 손실: 108.320427\t최선의 손실: 108.320427\t정확도: 75.18%\n",
-      "11\t검증 세트 손실: 1553673.750000\t최선의 손실: 108.320427\t정확도: 28.42%\n",
-      "12\t검증 세트 손실: 165966.515625\t최선의 손실: 108.320427\t정확도: 28.58%\n",
-      "13\t검증 세트 손실: 75434.437500\t최선의 손실: 108.320427\t정확도: 36.94%\n",
-      "14\t검증 세트 손실: 17465.595703\t최선의 손실: 108.320427\t정확도: 53.05%\n",
-      "15\t검증 세트 손실: 20878.027344\t최선의 손실: 108.320427\t정확도: 51.13%\n",
-      "16\t검증 세트 손실: 12027.186523\t최선의 손실: 108.320427\t정확도: 52.58%\n",
-      "17\t검증 세트 손실: 72324.046875\t최선의 손실: 108.320427\t정확도: 32.53%\n",
-      "18\t검증 세트 손실: 53380.515625\t최선의 손실: 108.320427\t정확도: 40.11%\n",
-      "19\t검증 세트 손실: 21414.826172\t최선의 손실: 108.320427\t정확도: 46.25%\n",
-      "20\t검증 세트 손실: 7485.926270\t최선의 손실: 108.320427\t정확도: 57.43%\n",
-      "21\t검증 세트 손실: 7189.901367\t최선의 손실: 108.320427\t정확도: 60.28%\n",
-      "22\t검증 세트 손실: 9095.398438\t최선의 손실: 108.320427\t정확도: 59.03%\n",
-      "23\t검증 세트 손실: 2068.782471\t최선의 손실: 108.320427\t정확도: 58.37%\n",
-      "24\t검증 세트 손실: 6164.130371\t최선의 손실: 108.320427\t정확도: 58.33%\n",
-      "25\t검증 세트 손실: 2182.487793\t최선의 손실: 108.320427\t정확도: 63.41%\n",
-      "26\t검증 세트 손실: 3167.906982\t최선의 손실: 108.320427\t정확도: 56.80%\n",
-      "27\t검증 세트 손실: 13063.563477\t최선의 손실: 108.320427\t정확도: 55.36%\n",
-      "28\t검증 세트 손실: 5930.875488\t최선의 손실: 108.320427\t정확도: 54.46%\n",
-      "29\t검증 세트 손실: 1677.265381\t최선의 손실: 108.320427\t정확도: 58.13%\n",
-      "30\t검증 세트 손실: 353.172577\t최선의 손실: 108.320427\t정확도: 63.57%\n",
-      "31\t검증 세트 손실: 567.828491\t최선의 손실: 108.320427\t정확도: 56.88%\n",
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=70, total=  13.2s\n",
+      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 152359.546875\t최선의 손실: 152359.546875\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 1773.402466\t최선의 손실: 1773.402466\t정확도: 34.60%\n",
+      "2\t검증 세트 손실: 1393.369263\t최선의 손실: 1393.369263\t정확도: 40.19%\n",
+      "3\t검증 세트 손실: 143993.796875\t최선의 손실: 1393.369263\t정확도: 23.22%\n",
+      "4\t검증 세트 손실: 69043.796875\t최선의 손실: 1393.369263\t정확도: 36.08%\n",
+      "5\t검증 세트 손실: 25586.814453\t최선의 손실: 1393.369263\t정확도: 46.40%\n",
+      "6\t검증 세트 손실: 13573.019531\t최선의 손실: 1393.369263\t정확도: 53.79%\n",
+      "7\t검증 세트 손실: 12827.626953\t최선의 손실: 1393.369263\t정확도: 51.99%\n",
+      "8\t검증 세트 손실: 13322.582031\t최선의 손실: 1393.369263\t정확도: 55.86%\n",
+      "9\t검증 세트 손실: 7260.292480\t최선의 손실: 1393.369263\t정확도: 54.10%\n",
+      "10\t검증 세트 손실: 7884.419922\t최선의 손실: 1393.369263\t정확도: 56.41%\n",
+      "11\t검증 세트 손실: 20506.361328\t최선의 손실: 1393.369263\t정확도: 40.03%\n",
+      "12\t검증 세트 손실: 1589.922363\t최선의 손실: 1393.369263\t정확도: 58.01%\n",
+      "13\t검증 세트 손실: 4866.111816\t최선의 손실: 1393.369263\t정확도: 54.50%\n",
+      "14\t검증 세트 손실: 3763.677490\t최선의 손실: 1393.369263\t정확도: 58.29%\n",
+      "15\t검증 세트 손실: 1715.946411\t최선의 손실: 1393.369263\t정확도: 57.23%\n",
+      "16\t검증 세트 손실: 3660.599365\t최선의 손실: 1393.369263\t정확도: 57.47%\n",
+      "17\t검증 세트 손실: 8011.971191\t최선의 손실: 1393.369263\t정확도: 56.61%\n",
+      "18\t검증 세트 손실: 2068.136230\t최선의 손실: 1393.369263\t정확도: 56.18%\n",
+      "19\t검증 세트 손실: 4501.954102\t최선의 손실: 1393.369263\t정확도: 55.59%\n",
+      "20\t검증 세트 손실: 1214.716064\t최선의 손실: 1214.716064\t정확도: 58.44%\n",
+      "21\t검증 세트 손실: 1736.499756\t최선의 손실: 1214.716064\t정확도: 50.04%\n",
+      "22\t검증 세트 손실: 2603691.500000\t최선의 손실: 1214.716064\t정확도: 50.74%\n",
+      "23\t검증 세트 손실: 105903.500000\t최선의 손실: 1214.716064\t정확도: 59.07%\n",
+      "24\t검증 세트 손실: 79429.789062\t최선의 손실: 1214.716064\t정확도: 57.31%\n",
+      "25\t검증 세트 손실: 70508.343750\t최선의 손실: 1214.716064\t정확도: 52.89%\n",
+      "26\t검증 세트 손실: 40551.070312\t최선의 손실: 1214.716064\t정확도: 51.95%\n",
+      "27\t검증 세트 손실: 150910.656250\t최선의 손실: 1214.716064\t정확도: 55.75%\n",
+      "28\t검증 세트 손실: 15901.721680\t최선의 손실: 1214.716064\t정확도: 54.85%\n",
+      "29\t검증 세트 손실: 15324.173828\t최선의 손실: 1214.716064\t정확도: 56.76%\n",
+      "30\t검증 세트 손실: 12045.869141\t최선의 손실: 1214.716064\t정확도: 57.74%\n",
+      "31\t검증 세트 손실: 94424.515625\t최선의 손실: 1214.716064\t정확도: 68.30%\n",
+      "32\t검증 세트 손실: 19264.191406\t최선의 손실: 1214.716064\t정확도: 61.81%\n",
+      "33\t검증 세트 손실: 59024.976562\t최선의 손실: 1214.716064\t정확도: 52.27%\n",
+      "34\t검증 세트 손실: 16083.344727\t최선의 손실: 1214.716064\t정확도: 56.02%\n",
+      "35\t검증 세트 손실: 172157.500000\t최선의 손실: 1214.716064\t정확도: 56.72%\n",
+      "36\t검증 세트 손실: 287693.156250\t최선의 손실: 1214.716064\t정확도: 57.66%\n",
+      "37\t검증 세트 손실: 212830.093750\t최선의 손실: 1214.716064\t정확도: 55.90%\n",
+      "38\t검증 세트 손실: 163920.234375\t최선의 손실: 1214.716064\t정확도: 61.65%\n",
+      "39\t검증 세트 손실: 188916.546875\t최선의 손실: 1214.716064\t정확도: 56.25%\n",
+      "40\t검증 세트 손실: 122132.718750\t최선의 손실: 1214.716064\t정확도: 59.34%\n",
+      "41\t검증 세트 손실: 114728.343750\t최선의 손실: 1214.716064\t정확도: 56.06%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=70, total=  22.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 180.216064\t최선의 손실: 180.216064\t정확도: 30.34%\n",
-      "1\t검증 세트 손실: 17387.046875\t최선의 손실: 180.216064\t정확도: 45.50%\n",
-      "2\t검증 세트 손실: 5626.637695\t최선의 손실: 180.216064\t정확도: 70.05%\n",
-      "3\t검증 세트 손실: 1728.827393\t최선의 손실: 180.216064\t정확도: 67.36%\n",
-      "4\t검증 세트 손실: 2629.006348\t최선의 손실: 180.216064\t정확도: 57.31%\n",
-      "5\t검증 세트 손실: 1178.931030\t최선의 손실: 180.216064\t정확도: 64.46%\n",
-      "6\t검증 세트 손실: 2618.914062\t최선의 손실: 180.216064\t정확도: 58.95%\n",
-      "7\t검증 세트 손실: 1063.259521\t최선의 손실: 180.216064\t정확도: 74.90%\n",
-      "8\t검증 세트 손실: 632.411804\t최선의 손실: 180.216064\t정확도: 75.10%\n",
-      "9\t검증 세트 손실: 976840576.000000\t최선의 손실: 180.216064\t정확도: 48.08%\n",
-      "10\t검증 세트 손실: 652366.812500\t최선의 손실: 180.216064\t정확도: 61.81%\n",
-      "11\t검증 세트 손실: 206318.921875\t최선의 손실: 180.216064\t정확도: 81.47%\n",
-      "12\t검증 세트 손실: 265982.625000\t최선의 손실: 180.216064\t정확도: 75.41%\n",
-      "13\t검증 세트 손실: 161501.312500\t최선의 손실: 180.216064\t정확도: 77.21%\n",
-      "14\t검증 세트 손실: 108397.007812\t최선의 손실: 180.216064\t정확도: 81.16%\n",
-      "15\t검증 세트 손실: 221380.109375\t최선의 손실: 180.216064\t정확도: 75.06%\n",
-      "16\t검증 세트 손실: 79695.679688\t최선의 손실: 180.216064\t정확도: 89.68%\n",
-      "17\t검증 세트 손실: 183351.562500\t최선의 손실: 180.216064\t정확도: 71.38%\n",
-      "18\t검증 세트 손실: 104588.851562\t최선의 손실: 180.216064\t정확도: 84.71%\n",
-      "19\t검증 세트 손실: 252534.937500\t최선의 손실: 180.216064\t정확도: 70.68%\n",
-      "20\t검증 세트 손실: 50687.894531\t최선의 손실: 180.216064\t정확도: 89.87%\n",
-      "21\t검증 세트 손실: 381724.437500\t최선의 손실: 180.216064\t정확도: 71.97%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=70, total=  11.7s\n",
-      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.178901\t최선의 손실: 1.178901\t정확도: 45.78%\n",
-      "1\t검증 세트 손실: 1.001651\t최선의 손실: 1.001651\t정확도: 56.88%\n",
-      "2\t검증 세트 손실: 0.736240\t최선의 손실: 0.736240\t정확도: 67.87%\n",
-      "3\t검증 세트 손실: 0.440527\t최선의 손실: 0.440527\t정확도: 77.37%\n"
+      "[CV]  batch_size=50, dropout_rate=0.2, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=70, total=  24.7s\n",
+      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.508427\t최선의 손실: 1.508427\t정확도: 43.20%\n",
+      "1\t검증 세트 손실: 1.038539\t최선의 손실: 1.038539\t정확도: 53.95%\n",
+      "2\t검증 세트 손실: 0.813117\t최선의 손실: 0.813117\t정확도: 65.68%\n",
+      "3\t검증 세트 손실: 0.754268\t최선의 손실: 0.754268\t정확도: 67.40%\n",
+      "4\t검증 세트 손실: 0.690137\t최선의 손실: 0.690137\t정확도: 66.46%\n",
+      "5\t검증 세트 손실: 0.565777\t최선의 손실: 0.565777\t정확도: 72.71%\n",
+      "6\t검증 세트 손실: 0.478596\t최선의 손실: 0.478596\t정확도: 76.97%\n",
+      "7\t검증 세트 손실: 0.442106\t최선의 손실: 0.442106\t정확도: 77.76%\n",
+      "8\t검증 세트 손실: 0.403108\t최선의 손실: 0.403108\t정확도: 78.85%\n",
+      "9\t검증 세트 손실: 0.391004\t최선의 손실: 0.391004\t정확도: 79.12%\n",
+      "10\t검증 세트 손실: 0.378215\t최선의 손실: 0.378215\t정확도: 79.44%\n",
+      "11\t검증 세트 손실: 0.369526\t최선의 손실: 0.369526\t정확도: 79.55%\n",
+      "12\t검증 세트 손실: 0.375639\t최선의 손실: 0.369526\t정확도: 79.44%\n",
+      "13\t검증 세트 손실: 0.376935\t최선의 손실: 0.369526\t정확도: 79.55%\n",
+      "14\t검증 세트 손실: 0.370341\t최선의 손실: 0.369526\t정확도: 78.54%\n",
+      "15\t검증 세트 손실: 0.384165\t최선의 손실: 0.369526\t정확도: 79.48%\n",
+      "16\t검증 세트 손실: 0.385230\t최선의 손실: 0.369526\t정확도: 79.63%\n",
+      "17\t검증 세트 손실: 0.392325\t최선의 손실: 0.369526\t정확도: 79.55%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4\t검증 세트 손실: 0.395033\t최선의 손실: 0.395033\t정확도: 80.22%\n",
-      "5\t검증 세트 손실: 0.428712\t최선의 손실: 0.395033\t정확도: 77.80%\n",
-      "6\t검증 세트 손실: 0.398853\t최선의 손실: 0.395033\t정확도: 79.44%\n",
-      "7\t검증 세트 손실: 0.394693\t최선의 손실: 0.394693\t정확도: 79.48%\n",
-      "8\t검증 세트 손실: 0.393323\t최선의 손실: 0.393323\t정확도: 79.40%\n",
-      "9\t검증 세트 손실: 0.378408\t최선의 손실: 0.378408\t정확도: 79.44%\n",
-      "10\t검증 세트 손실: 0.374772\t최선의 손실: 0.374772\t정확도: 79.52%\n",
-      "11\t검증 세트 손실: 0.374148\t최선의 손실: 0.374148\t정확도: 79.40%\n",
-      "12\t검증 세트 손실: 0.372222\t최선의 손실: 0.372222\t정확도: 79.87%\n",
-      "13\t검증 세트 손실: 0.396489\t최선의 손실: 0.372222\t정확도: 79.44%\n",
-      "14\t검증 세트 손실: 0.378302\t최선의 손실: 0.372222\t정확도: 79.48%\n",
-      "15\t검증 세트 손실: 0.484747\t최선의 손실: 0.372222\t정확도: 79.52%\n",
-      "16\t검증 세트 손실: 0.372197\t최선의 손실: 0.372197\t정확도: 79.75%\n",
-      "17\t검증 세트 손실: 0.373004\t최선의 손실: 0.372197\t정확도: 77.52%\n",
-      "18\t검증 세트 손실: 0.375977\t최선의 손실: 0.372197\t정확도: 79.44%\n",
-      "19\t검증 세트 손실: 0.362914\t최선의 손실: 0.362914\t정확도: 79.59%\n",
-      "20\t검증 세트 손실: 0.393189\t최선의 손실: 0.362914\t정확도: 79.44%\n",
-      "21\t검증 세트 손실: 0.374228\t최선의 손실: 0.362914\t정확도: 79.63%\n",
-      "22\t검증 세트 손실: 0.366910\t최선의 손실: 0.362914\t정확도: 79.59%\n",
-      "23\t검증 세트 손실: 0.369532\t최선의 손실: 0.362914\t정확도: 79.44%\n",
-      "24\t검증 세트 손실: 0.364953\t최선의 손실: 0.362914\t정확도: 79.52%\n",
-      "25\t검증 세트 손실: 0.361960\t최선의 손실: 0.361960\t정확도: 78.73%\n",
-      "26\t검증 세트 손실: 0.364831\t최선의 손실: 0.361960\t정확도: 79.55%\n",
-      "27\t검증 세트 손실: 0.365094\t최선의 손실: 0.361960\t정확도: 78.30%\n",
-      "28\t검증 세트 손실: 0.359082\t최선의 손실: 0.359082\t정확도: 78.42%\n",
-      "29\t검증 세트 손실: 0.385749\t최선의 손실: 0.359082\t정확도: 79.55%\n",
-      "30\t검증 세트 손실: 0.373813\t최선의 손실: 0.359082\t정확도: 78.66%\n",
-      "31\t검증 세트 손실: 0.368402\t최선의 손실: 0.359082\t정확도: 79.55%\n",
-      "32\t검증 세트 손실: 0.361359\t최선의 손실: 0.359082\t정확도: 78.34%\n",
-      "33\t검증 세트 손실: 0.365138\t최선의 손실: 0.359082\t정확도: 79.55%\n",
-      "34\t검증 세트 손실: 0.363073\t최선의 손실: 0.359082\t정확도: 79.44%\n",
-      "35\t검증 세트 손실: 0.361964\t최선의 손실: 0.359082\t정확도: 79.55%\n",
-      "36\t검증 세트 손실: 0.353545\t최선의 손실: 0.353545\t정확도: 78.54%\n",
-      "37\t검증 세트 손실: 0.362395\t최선의 손실: 0.353545\t정확도: 79.59%\n",
-      "38\t검증 세트 손실: 0.357563\t최선의 손실: 0.353545\t정확도: 78.81%\n",
-      "39\t검증 세트 손실: 0.367592\t최선의 손실: 0.353545\t정확도: 78.69%\n",
-      "40\t검증 세트 손실: 0.358783\t최선의 손실: 0.353545\t정확도: 78.89%\n",
-      "41\t검증 세트 손실: 0.362283\t최선의 손실: 0.353545\t정확도: 79.59%\n",
-      "42\t검증 세트 손실: 0.347681\t최선의 손실: 0.347681\t정확도: 78.93%\n",
-      "43\t검증 세트 손실: 0.349734\t최선의 손실: 0.347681\t정확도: 79.87%\n",
-      "44\t검증 세트 손실: 0.355473\t최선의 손실: 0.347681\t정확도: 79.71%\n",
-      "45\t검증 세트 손실: 0.358579\t최선의 손실: 0.347681\t정확도: 78.58%\n",
-      "46\t검증 세트 손실: 0.353037\t최선의 손실: 0.347681\t정확도: 78.81%\n",
-      "47\t검증 세트 손실: 0.356838\t최선의 손실: 0.347681\t정확도: 80.45%\n",
-      "48\t검증 세트 손실: 0.355151\t최선의 손실: 0.347681\t정확도: 79.79%\n",
-      "49\t검증 세트 손실: 0.363091\t최선의 손실: 0.347681\t정확도: 79.95%\n",
-      "50\t검증 세트 손실: 0.360558\t최선의 손실: 0.347681\t정확도: 79.63%\n",
-      "51\t검증 세트 손실: 0.361935\t최선의 손실: 0.347681\t정확도: 78.89%\n",
-      "52\t검증 세트 손실: 0.363499\t최선의 손실: 0.347681\t정확도: 79.55%\n",
-      "53\t검증 세트 손실: 0.352454\t최선의 손실: 0.347681\t정확도: 80.45%\n",
-      "54\t검증 세트 손실: 0.358191\t최선의 손실: 0.347681\t정확도: 78.73%\n",
-      "55\t검증 세트 손실: 0.360641\t최선의 손실: 0.347681\t정확도: 79.79%\n",
-      "56\t검증 세트 손실: 0.356691\t최선의 손실: 0.347681\t정확도: 78.93%\n",
-      "57\t검증 세트 손실: 0.347572\t최선의 손실: 0.347572\t정확도: 79.01%\n",
-      "58\t검증 세트 손실: 0.354915\t최선의 손실: 0.347572\t정확도: 79.20%\n",
-      "59\t검증 세트 손실: 0.355932\t최선의 손실: 0.347572\t정확도: 79.79%\n",
-      "60\t검증 세트 손실: 0.354060\t최선의 손실: 0.347572\t정확도: 78.66%\n",
-      "61\t검증 세트 손실: 0.362438\t최선의 손실: 0.347572\t정확도: 79.71%\n",
-      "62\t검증 세트 손실: 0.356195\t최선의 손실: 0.347572\t정확도: 79.71%\n",
-      "63\t검증 세트 손실: 0.370034\t최선의 손실: 0.347572\t정확도: 79.55%\n",
-      "64\t검증 세트 손실: 0.351429\t최선의 손실: 0.347572\t정확도: 79.67%\n",
-      "65\t검증 세트 손실: 0.353137\t최선의 손실: 0.347572\t정확도: 79.67%\n",
-      "66\t검증 세트 손실: 0.366283\t최선의 손실: 0.347572\t정확도: 79.59%\n",
-      "67\t검증 세트 손실: 0.363983\t최선의 손실: 0.347572\t정확도: 78.07%\n",
-      "68\t검증 세트 손실: 0.369467\t최선의 손실: 0.347572\t정확도: 78.93%\n",
-      "69\t검증 세트 손실: 0.356090\t최선의 손실: 0.347572\t정확도: 79.91%\n",
-      "70\t검증 세트 손실: 0.356684\t최선의 손실: 0.347572\t정확도: 79.75%\n",
-      "71\t검증 세트 손실: 0.351219\t최선의 손실: 0.347572\t정확도: 79.09%\n",
-      "72\t검증 세트 손실: 0.352072\t최선의 손실: 0.347572\t정확도: 79.87%\n",
-      "73\t검증 세트 손실: 0.354139\t최선의 손실: 0.347572\t정확도: 79.79%\n",
-      "74\t검증 세트 손실: 0.354409\t최선의 손실: 0.347572\t정확도: 79.55%\n",
-      "75\t검증 세트 손실: 0.354363\t최선의 손실: 0.347572\t정확도: 79.59%\n",
-      "76\t검증 세트 손실: 0.372204\t최선의 손실: 0.347572\t정확도: 79.71%\n",
-      "77\t검증 세트 손실: 0.362740\t최선의 손실: 0.347572\t정확도: 79.83%\n",
-      "78\t검증 세트 손실: 0.351168\t최선의 손실: 0.347572\t정확도: 78.26%\n",
+      "18\t검증 세트 손실: 0.382668\t최선의 손실: 0.369526\t정확도: 79.48%\n",
+      "19\t검증 세트 손실: 0.362519\t최선의 손실: 0.362519\t정확도: 79.67%\n",
+      "20\t검증 세트 손실: 0.378193\t최선의 손실: 0.362519\t정확도: 79.55%\n",
+      "21\t검증 세트 손실: 0.366864\t최선의 손실: 0.362519\t정확도: 79.95%\n",
+      "22\t검증 세트 손실: 0.368651\t최선의 손실: 0.362519\t정확도: 79.95%\n",
+      "23\t검증 세트 손실: 0.362318\t최선의 손실: 0.362318\t정확도: 78.69%\n",
+      "24\t검증 세트 손실: 0.368190\t최선의 손실: 0.362318\t정확도: 79.59%\n",
+      "25\t검증 세트 손실: 0.371508\t최선의 손실: 0.362318\t정확도: 78.26%\n",
+      "26\t검증 세트 손실: 0.372462\t최선의 손실: 0.362318\t정확도: 79.71%\n",
+      "27\t검증 세트 손실: 0.377449\t최선의 손실: 0.362318\t정확도: 78.34%\n",
+      "28\t검증 세트 손실: 0.362222\t최선의 손실: 0.362222\t정확도: 78.62%\n",
+      "29\t검증 세트 손실: 0.378028\t최선의 손실: 0.362222\t정확도: 79.55%\n",
+      "30\t검증 세트 손실: 0.369941\t최선의 손실: 0.362222\t정확도: 79.44%\n",
+      "31\t검증 세트 손실: 0.364783\t최선의 손실: 0.362222\t정확도: 79.67%\n",
+      "32\t검증 세트 손실: 0.357682\t최선의 손실: 0.357682\t정확도: 79.55%\n",
+      "33\t검증 세트 손실: 0.364925\t최선의 손실: 0.357682\t정확도: 79.44%\n",
+      "34\t검증 세트 손실: 0.358505\t최선의 손실: 0.357682\t정확도: 79.98%\n",
+      "35\t검증 세트 손실: 0.353237\t최선의 손실: 0.353237\t정확도: 79.95%\n",
+      "36\t검증 세트 손실: 0.359884\t최선의 손실: 0.353237\t정확도: 76.74%\n",
+      "37\t검증 세트 손실: 0.367772\t최선의 손실: 0.353237\t정확도: 79.55%\n",
+      "38\t검증 세트 손실: 0.366716\t최선의 손실: 0.353237\t정확도: 79.75%\n",
+      "39\t검증 세트 손실: 0.364067\t최선의 손실: 0.353237\t정확도: 78.58%\n",
+      "40\t검증 세트 손실: 0.354377\t최선의 손실: 0.353237\t정확도: 78.69%\n",
+      "41\t검증 세트 손실: 0.357674\t최선의 손실: 0.353237\t정확도: 79.67%\n",
+      "42\t검증 세트 손실: 0.374142\t최선의 손실: 0.353237\t정확도: 79.67%\n",
+      "43\t검증 세트 손실: 0.366323\t최선의 손실: 0.353237\t정확도: 79.75%\n",
+      "44\t검증 세트 손실: 0.363537\t최선의 손실: 0.353237\t정확도: 79.55%\n",
+      "45\t검증 세트 손실: 0.357720\t최선의 손실: 0.353237\t정확도: 78.54%\n",
+      "46\t검증 세트 손실: 0.356259\t최선의 손실: 0.353237\t정확도: 78.81%\n",
+      "47\t검증 세트 손실: 0.359460\t최선의 손실: 0.353237\t정확도: 77.83%\n",
+      "48\t검증 세트 손실: 0.354157\t최선의 손실: 0.353237\t정확도: 79.67%\n",
+      "49\t검증 세트 손실: 0.353567\t최선의 손실: 0.353237\t정확도: 80.02%\n",
+      "50\t검증 세트 손실: 0.358960\t최선의 손실: 0.353237\t정확도: 79.55%\n",
+      "51\t검증 세트 손실: 0.363446\t최선의 손실: 0.353237\t정확도: 79.83%\n",
+      "52\t검증 세트 손실: 0.357550\t최선의 손실: 0.353237\t정확도: 79.83%\n",
+      "53\t검증 세트 손실: 0.346685\t최선의 손실: 0.346685\t정확도: 78.85%\n",
+      "54\t검증 세트 손실: 0.349615\t최선의 손실: 0.346685\t정확도: 79.67%\n",
+      "55\t검증 세트 손실: 0.358104\t최선의 손실: 0.346685\t정확도: 79.87%\n",
+      "56\t검증 세트 손실: 0.356895\t최선의 손실: 0.346685\t정확도: 78.85%\n",
+      "57\t검증 세트 손실: 0.351928\t최선의 손실: 0.346685\t정확도: 80.02%\n",
+      "58\t검증 세트 손실: 0.357900\t최선의 손실: 0.346685\t정확도: 79.75%\n",
+      "59\t검증 세트 손실: 0.352730\t최선의 손실: 0.346685\t정확도: 79.95%\n",
+      "60\t검증 세트 손실: 0.352533\t최선의 손실: 0.346685\t정확도: 79.01%\n",
+      "61\t검증 세트 손실: 0.359196\t최선의 손실: 0.346685\t정확도: 80.02%\n",
+      "62\t검증 세트 손실: 0.354209\t최선의 손실: 0.346685\t정확도: 79.79%\n",
+      "63\t검증 세트 손실: 0.362590\t최선의 손실: 0.346685\t정확도: 78.77%\n",
+      "64\t검증 세트 손실: 0.350940\t최선의 손실: 0.346685\t정확도: 79.75%\n",
+      "65\t검증 세트 손실: 0.356316\t최선의 손실: 0.346685\t정확도: 78.81%\n",
+      "66\t검증 세트 손실: 0.348089\t최선의 손실: 0.346685\t정확도: 79.87%\n",
+      "67\t검증 세트 손실: 0.362302\t최선의 손실: 0.346685\t정확도: 78.85%\n",
+      "68\t검증 세트 손실: 0.352164\t최선의 손실: 0.346685\t정확도: 78.85%\n",
+      "69\t검증 세트 손실: 0.359001\t최선의 손실: 0.346685\t정확도: 79.63%\n",
+      "70\t검증 세트 손실: 0.350099\t최선의 손실: 0.346685\t정확도: 79.63%\n",
+      "71\t검증 세트 손실: 0.350365\t최선의 손실: 0.346685\t정확도: 79.48%\n",
+      "72\t검증 세트 손실: 0.355264\t최선의 손실: 0.346685\t정확도: 79.63%\n",
+      "73\t검증 세트 손실: 0.357646\t최선의 손실: 0.346685\t정확도: 79.48%\n",
+      "74\t검증 세트 손실: 0.355809\t최선의 손실: 0.346685\t정확도: 79.91%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total=   5.7s\n",
-      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.425262\t최선의 손실: 1.425262\t정확도: 47.73%\n",
-      "1\t검증 세트 손실: 1.028062\t최선의 손실: 1.028062\t정확도: 58.91%\n",
-      "2\t검증 세트 손실: 0.828242\t최선의 손실: 0.828242\t정확도: 70.02%\n",
-      "3\t검증 세트 손실: 0.624867\t최선의 손실: 0.624867\t정확도: 77.64%\n",
-      "4\t검증 세트 손실: 0.475274\t최선의 손실: 0.475274\t정확도: 77.44%\n",
-      "5\t검증 세트 손실: 0.441202\t최선의 손실: 0.441202\t정확도: 77.48%\n",
-      "6\t검증 세트 손실: 0.401078\t최선의 손실: 0.401078\t정확도: 79.01%\n",
-      "7\t검증 세트 손실: 0.395326\t최선의 손실: 0.395326\t정확도: 79.20%\n",
-      "8\t검증 세트 손실: 0.391127\t최선의 손실: 0.391127\t정확도: 79.01%\n",
-      "9\t검증 세트 손실: 0.396436\t최선의 손실: 0.391127\t정확도: 78.54%\n",
-      "10\t검증 세트 손실: 0.379509\t최선의 손실: 0.379509\t정확도: 78.42%\n",
-      "11\t검증 세트 손실: 0.380876\t최선의 손실: 0.379509\t정확도: 79.12%\n",
-      "12\t검증 세트 손실: 0.379366\t최선의 손실: 0.379366\t정확도: 79.20%\n",
-      "13\t검증 세트 손실: 0.391687\t최선의 손실: 0.379366\t정확도: 79.16%\n",
-      "14\t검증 세트 손실: 0.406735\t최선의 손실: 0.379366\t정확도: 79.09%\n",
-      "15\t검증 세트 손실: 0.395464\t최선의 손실: 0.379366\t정확도: 79.01%\n",
-      "16\t검증 세트 손실: 0.388438\t최선의 손실: 0.379366\t정확도: 78.46%\n",
-      "17\t검증 세트 손실: 0.378098\t최선의 손실: 0.378098\t정확도: 79.52%\n",
-      "18\t검증 세트 손실: 0.370772\t최선의 손실: 0.370772\t정확도: 79.32%\n",
-      "19\t검증 세트 손실: 0.372736\t최선의 손실: 0.370772\t정확도: 79.36%\n",
-      "20\t검증 세트 손실: 0.377288\t최선의 손실: 0.370772\t정확도: 78.62%\n",
-      "21\t검증 세트 손실: 0.374792\t최선의 손실: 0.370772\t정확도: 79.67%\n",
-      "22\t검증 세트 손실: 0.364599\t최선의 손실: 0.364599\t정확도: 79.63%\n",
-      "23\t검증 세트 손실: 0.371769\t최선의 손실: 0.364599\t정확도: 78.85%\n",
-      "24\t검증 세트 손실: 0.368340\t최선의 손실: 0.364599\t정확도: 79.67%\n",
-      "25\t검증 세트 손실: 0.360244\t최선의 손실: 0.360244\t정확도: 79.59%\n",
-      "26\t검증 세트 손실: 0.384983\t최선의 손실: 0.360244\t정확도: 78.66%\n",
-      "27\t검증 세트 손실: 0.362843\t최선의 손실: 0.360244\t정확도: 78.66%\n",
-      "28\t검증 세트 손실: 0.367835\t최선의 손실: 0.360244\t정확도: 79.36%\n",
-      "29\t검증 세트 손실: 0.364830\t최선의 손실: 0.360244\t정확도: 79.01%\n",
-      "30\t검증 세트 손실: 0.359611\t최선의 손실: 0.359611\t정확도: 79.75%\n",
-      "31\t검증 세트 손실: 0.353867\t최선의 손실: 0.353867\t정확도: 79.71%\n",
-      "32\t검증 세트 손실: 0.363048\t최선의 손실: 0.353867\t정확도: 79.83%\n",
-      "33\t검증 세트 손실: 0.364946\t최선의 손실: 0.353867\t정확도: 78.62%\n",
-      "34\t검증 세트 손실: 0.355569\t최선의 손실: 0.353867\t정확도: 78.93%\n",
-      "35\t검증 세트 손실: 0.359688\t최선의 손실: 0.353867\t정확도: 78.77%\n",
-      "36\t검증 세트 손실: 0.361493\t최선의 손실: 0.353867\t정확도: 79.67%\n",
-      "37\t검증 세트 손실: 0.354262\t최선의 손실: 0.353867\t정확도: 79.75%\n",
-      "38\t검증 세트 손실: 0.346670\t최선의 손실: 0.346670\t정확도: 80.14%\n",
-      "39\t검증 세트 손실: 0.358070\t최선의 손실: 0.346670\t정확도: 78.81%\n",
-      "40\t검증 세트 손실: 0.351942\t최선의 손실: 0.346670\t정확도: 80.06%\n",
-      "41\t검증 세트 손실: 0.358275\t최선의 손실: 0.346670\t정확도: 79.87%\n",
-      "42\t검증 세트 손실: 0.352995\t최선의 손실: 0.346670\t정확도: 79.01%\n",
-      "43\t검증 세트 손실: 0.346424\t최선의 손실: 0.346424\t정확도: 78.69%\n",
-      "44\t검증 세트 손실: 0.355614\t최선의 손실: 0.346424\t정확도: 79.05%\n",
-      "45\t검증 세트 손실: 0.346032\t최선의 손실: 0.346032\t정확도: 79.98%\n",
-      "46\t검증 세트 손실: 0.348767\t최선의 손실: 0.346032\t정확도: 79.98%\n",
-      "47\t검증 세트 손실: 0.357684\t최선의 손실: 0.346032\t정확도: 79.79%\n",
-      "48\t검증 세트 손실: 0.351588\t최선의 손실: 0.346032\t정확도: 79.87%\n",
-      "49\t검증 세트 손실: 0.356765\t최선의 손실: 0.346032\t정확도: 79.59%\n",
-      "50\t검증 세트 손실: 0.348141\t최선의 손실: 0.346032\t정확도: 79.01%\n",
-      "51\t검증 세트 손실: 0.350947\t최선의 손실: 0.346032\t정확도: 79.87%\n",
-      "52\t검증 세트 손실: 0.356072\t최선의 손실: 0.346032\t정확도: 80.06%\n",
-      "53\t검증 세트 손실: 0.351041\t최선의 손실: 0.346032\t정확도: 79.83%\n",
-      "54\t검증 세트 손실: 0.357770\t최선의 손실: 0.346032\t정확도: 79.71%\n",
-      "55\t검증 세트 손실: 0.360027\t최선의 손실: 0.346032\t정확도: 79.01%\n",
-      "56\t검증 세트 손실: 0.354607\t최선의 손실: 0.346032\t정확도: 79.52%\n",
-      "57\t검증 세트 손실: 0.354505\t최선의 손실: 0.346032\t정확도: 79.87%\n",
-      "58\t검증 세트 손실: 0.350130\t최선의 손실: 0.346032\t정확도: 78.58%\n",
-      "59\t검증 세트 손실: 0.350471\t최선의 손실: 0.346032\t정확도: 79.87%\n",
-      "60\t검증 세트 손실: 0.353551\t최선의 손실: 0.346032\t정확도: 79.75%\n",
-      "61\t검증 세트 손실: 0.356919\t최선의 손실: 0.346032\t정확도: 78.85%\n",
-      "62\t검증 세트 손실: 0.347433\t최선의 손실: 0.346032\t정확도: 79.71%\n",
-      "63\t검증 세트 손실: 0.357032\t최선의 손실: 0.346032\t정확도: 79.83%\n",
-      "64\t검증 세트 손실: 0.354738\t최선의 손실: 0.346032\t정확도: 78.93%\n",
-      "65\t검증 세트 손실: 0.349637\t최선의 손실: 0.346032\t정확도: 80.02%\n",
-      "66\t검증 세트 손실: 0.361553\t최선의 손실: 0.346032\t정확도: 79.55%\n",
+      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30, total=   6.1s\n",
+      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.413666\t최선의 손실: 1.413666\t정확도: 46.44%\n",
+      "1\t검증 세트 손실: 1.122041\t최선의 손실: 1.122041\t정확도: 44.21%\n",
+      "2\t검증 세트 손실: 1.080487\t최선의 손실: 1.080487\t정확도: 40.54%\n",
+      "3\t검증 세트 손실: 0.859669\t최선의 손실: 0.859669\t정확도: 60.48%\n",
+      "4\t검증 세트 손실: 0.699817\t최선의 손실: 0.699817\t정확도: 72.28%\n",
+      "5\t검증 세트 손실: 0.721031\t최선의 손실: 0.699817\t정확도: 60.01%\n",
+      "6\t검증 세트 손실: 0.666032\t최선의 손실: 0.666032\t정확도: 60.87%\n",
+      "7\t검증 세트 손실: 0.604923\t최선의 손실: 0.604923\t정확도: 72.99%\n",
+      "8\t검증 세트 손실: 0.481922\t최선의 손실: 0.481922\t정확도: 77.29%\n",
+      "9\t검증 세트 손실: 0.437629\t최선의 손실: 0.437629\t정확도: 77.56%\n",
+      "10\t검증 세트 손실: 0.381297\t최선의 손실: 0.381297\t정확도: 79.16%\n",
+      "11\t검증 세트 손실: 0.401896\t최선의 손실: 0.381297\t정확도: 78.50%\n",
+      "12\t검증 세트 손실: 0.383612\t최선의 손실: 0.381297\t정확도: 79.55%\n",
+      "13\t검증 세트 손실: 0.387310\t최선의 손실: 0.381297\t정확도: 79.32%\n",
+      "14\t검증 세트 손실: 0.394821\t최선의 손실: 0.381297\t정확도: 79.44%\n",
+      "15\t검증 세트 손실: 0.398791\t최선의 손실: 0.381297\t정확도: 79.52%\n",
+      "16\t검증 세트 손실: 0.369684\t최선의 손실: 0.369684\t정확도: 79.63%\n",
+      "17\t검증 세트 손실: 0.382080\t최선의 손실: 0.369684\t정확도: 79.75%\n",
+      "18\t검증 세트 손실: 0.373074\t최선의 손실: 0.369684\t정확도: 79.48%\n",
+      "19\t검증 세트 손실: 0.379729\t최선의 손실: 0.369684\t정확도: 79.44%\n",
+      "20\t검증 세트 손실: 0.359622\t최선의 손실: 0.359622\t정확도: 79.83%\n",
+      "21\t검증 세트 손실: 0.373740\t최선의 손실: 0.359622\t정확도: 79.36%\n",
+      "22\t검증 세트 손실: 0.374912\t최선의 손실: 0.359622\t정확도: 79.59%\n",
+      "23\t검증 세트 손실: 0.359336\t최선의 손실: 0.359336\t정확도: 78.15%\n",
+      "24\t검증 세트 손실: 0.356343\t최선의 손실: 0.356343\t정확도: 79.55%\n",
+      "25\t검증 세트 손실: 0.356291\t최선의 손실: 0.356291\t정확도: 80.45%\n",
+      "26\t검증 세트 손실: 0.372852\t최선의 손실: 0.356291\t정확도: 78.73%\n",
+      "27\t검증 세트 손실: 0.376793\t최선의 손실: 0.356291\t정확도: 79.55%\n",
+      "28\t검증 세트 손실: 0.368889\t최선의 손실: 0.356291\t정확도: 79.52%\n",
+      "29\t검증 세트 손실: 0.382368\t최선의 손실: 0.356291\t정확도: 78.66%\n",
+      "30\t검증 세트 손실: 0.363004\t최선의 손실: 0.356291\t정확도: 79.63%\n",
+      "31\t검증 세트 손실: 0.354401\t최선의 손실: 0.354401\t정확도: 79.71%\n",
+      "32\t검증 세트 손실: 0.361428\t최선의 손실: 0.354401\t정확도: 79.87%\n",
+      "33\t검증 세트 손실: 0.357523\t최선의 손실: 0.354401\t정확도: 78.66%\n",
+      "34\t검증 세트 손실: 0.375065\t최선의 손실: 0.354401\t정확도: 78.85%\n",
+      "35\t검증 세트 손실: 0.353155\t최선의 손실: 0.353155\t정확도: 79.75%\n",
+      "36\t검증 세트 손실: 0.357860\t최선의 손실: 0.353155\t정확도: 79.55%\n",
+      "37\t검증 세트 손실: 0.357498\t최선의 손실: 0.353155\t정확도: 79.67%\n",
+      "38\t검증 세트 손실: 0.359133\t최선의 손실: 0.353155\t정확도: 78.58%\n",
+      "39\t검증 세트 손실: 0.377964\t최선의 손실: 0.353155\t정확도: 79.40%\n",
+      "40\t검증 세트 손실: 0.364331\t최선의 손실: 0.353155\t정확도: 78.58%\n",
+      "41\t검증 세트 손실: 0.357534\t최선의 손실: 0.353155\t정확도: 79.71%\n",
+      "42\t검증 세트 손실: 0.359216\t최선의 손실: 0.353155\t정확도: 78.58%\n",
+      "43\t검증 세트 손실: 0.349811\t최선의 손실: 0.349811\t정확도: 79.71%\n",
+      "44\t검증 세트 손실: 0.357569\t최선의 손실: 0.349811\t정확도: 78.66%\n",
+      "45\t검증 세트 손실: 0.355176\t최선의 손실: 0.349811\t정확도: 79.71%\n",
+      "46\t검증 세트 손실: 0.354779\t최선의 손실: 0.349811\t정확도: 79.75%\n",
+      "47\t검증 세트 손실: 0.370114\t최선의 손실: 0.349811\t정확도: 78.58%\n",
+      "48\t검증 세트 손실: 0.351141\t최선의 손실: 0.349811\t정확도: 79.71%\n",
+      "49\t검증 세트 손실: 0.355364\t최선의 손실: 0.349811\t정확도: 80.65%\n",
+      "50\t검증 세트 손실: 0.367269\t최선의 손실: 0.349811\t정확도: 78.77%\n",
+      "51\t검증 세트 손실: 0.361093\t최선의 손실: 0.349811\t정확도: 79.63%\n",
+      "52\t검증 세트 손실: 0.364370\t최선의 손실: 0.349811\t정확도: 79.83%\n",
+      "53\t검증 세트 손실: 0.349451\t최선의 손실: 0.349451\t정확도: 79.87%\n",
+      "54\t검증 세트 손실: 0.359972\t최선의 손실: 0.349451\t정확도: 78.54%\n",
+      "55\t검증 세트 손실: 0.354439\t최선의 손실: 0.349451\t정확도: 79.75%\n",
+      "56\t검증 세트 손실: 0.369812\t최선의 손실: 0.349451\t정확도: 78.34%\n",
+      "57\t검증 세트 손실: 0.351944\t최선의 손실: 0.349451\t정확도: 79.91%\n",
+      "58\t검증 세트 손실: 0.351904\t최선의 손실: 0.349451\t정확도: 79.67%\n",
+      "59\t검증 세트 손실: 0.350284\t최선의 손실: 0.349451\t정확도: 79.75%\n",
+      "60\t검증 세트 손실: 0.357565\t최선의 손실: 0.349451\t정확도: 79.71%\n",
+      "61\t검증 세트 손실: 0.350558\t최선의 손실: 0.349451\t정확도: 79.87%\n",
+      "62\t검증 세트 손실: 0.351537\t최선의 손실: 0.349451\t정확도: 79.91%\n",
+      "63\t검증 세트 손실: 0.355300\t최선의 손실: 0.349451\t정확도: 79.71%\n",
+      "64\t검증 세트 손실: 0.358074\t최선의 손실: 0.349451\t정확도: 78.85%\n",
+      "65\t검증 세트 손실: 0.354063\t최선의 손실: 0.349451\t정확도: 78.77%\n",
+      "66\t검증 세트 손실: 0.358219\t최선의 손실: 0.349451\t정확도: 79.71%\n",
+      "67\t검증 세트 손실: 0.367075\t최선의 손실: 0.349451\t정확도: 78.30%\n",
+      "68\t검증 세트 손실: 0.355692\t최선의 손실: 0.349451\t정확도: 79.71%\n",
+      "69\t검증 세트 손실: 0.363143\t최선의 손실: 0.349451\t정확도: 79.55%\n",
+      "70\t검증 세트 손실: 0.354010\t최선의 손실: 0.349451\t정확도: 78.66%\n",
+      "71\t검증 세트 손실: 0.352772\t최선의 손실: 0.349451\t정확도: 80.34%\n",
+      "72\t검증 세트 손실: 0.351087\t최선의 손실: 0.349451\t정확도: 79.79%\n",
+      "73\t검증 세트 손실: 0.352235\t최선의 손실: 0.349451\t정확도: 78.85%\n",
+      "74\t검증 세트 손실: 0.354515\t최선의 손실: 0.349451\t정확도: 78.85%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total=   4.8s\n",
-      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30 \n",
-      "0\t검증 세트 손실: 1.500638\t최선의 손실: 1.500638\t정확도: 22.01%\n",
-      "1\t검증 세트 손실: 0.922764\t최선의 손실: 0.922764\t정확도: 60.79%\n",
-      "2\t검증 세트 손실: 0.737152\t최선의 손실: 0.737152\t정확도: 61.30%\n",
-      "3\t검증 세트 손실: 0.506544\t최선의 손실: 0.506544\t정확도: 80.22%\n",
-      "4\t검증 세트 손실: 0.395410\t최선의 손실: 0.395410\t정확도: 81.08%\n",
-      "5\t검증 세트 손실: 0.391939\t최선의 손실: 0.391939\t정확도: 79.36%\n",
-      "6\t검증 세트 손실: 0.389390\t최선의 손실: 0.389390\t정확도: 78.30%\n",
-      "7\t검증 세트 손실: 0.408950\t최선의 손실: 0.389390\t정확도: 79.12%\n",
-      "8\t검증 세트 손실: 0.379165\t최선의 손실: 0.379165\t정확도: 79.63%\n",
-      "9\t검증 세트 손실: 0.380412\t최선의 손실: 0.379165\t정확도: 78.26%\n",
-      "10\t검증 세트 손실: 0.371042\t최선의 손실: 0.371042\t정확도: 79.83%\n",
-      "11\t검증 세트 손실: 0.371450\t최선의 손실: 0.371042\t정확도: 78.46%\n"
+      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30, total=   6.2s\n",
+      "[CV] batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30 \n",
+      "0\t검증 세트 손실: 1.374480\t최선의 손실: 1.374480\t정확도: 48.63%\n",
+      "1\t검증 세트 손실: 1.119446\t최선의 손실: 1.119446\t정확도: 52.70%\n",
+      "2\t검증 세트 손실: 1.083313\t최선의 손실: 1.083313\t정확도: 45.39%\n",
+      "3\t검증 세트 손실: 0.844371\t최선의 손실: 0.844371\t정확도: 58.17%\n",
+      "4\t검증 세트 손실: 0.727627\t최선의 손실: 0.727627\t정확도: 68.26%\n",
+      "5\t검증 세트 손실: 0.597883\t최선의 손실: 0.597883\t정확도: 74.55%\n",
+      "6\t검증 세트 손실: 0.532007\t최선의 손실: 0.532007\t정확도: 74.82%\n",
+      "7\t검증 세트 손실: 0.435315\t최선의 손실: 0.435315\t정확도: 78.50%\n",
+      "8\t검증 세트 손실: 0.390704\t최선의 손실: 0.390704\t정확도: 79.12%\n",
+      "9\t검증 세트 손실: 0.380262\t최선의 손실: 0.380262\t정확도: 78.15%\n",
+      "10\t검증 세트 손실: 0.379386\t최선의 손실: 0.379386\t정확도: 79.12%\n",
+      "11\t검증 세트 손실: 0.378397\t최선의 손실: 0.378397\t정확도: 78.15%\n",
+      "12\t검증 세트 손실: 0.374279\t최선의 손실: 0.374279\t정확도: 78.50%\n",
+      "13\t검증 세트 손실: 0.378378\t최선의 손실: 0.374279\t정확도: 79.40%\n",
+      "14\t검증 세트 손실: 0.385741\t최선의 손실: 0.374279\t정확도: 78.42%\n",
+      "15\t검증 세트 손실: 0.380809\t최선의 손실: 0.374279\t정확도: 79.52%\n",
+      "16\t검증 세트 손실: 0.371720\t최선의 손실: 0.371720\t정확도: 78.77%\n",
+      "17\t검증 세트 손실: 0.386045\t최선의 손실: 0.371720\t정확도: 79.52%\n",
+      "18\t검증 세트 손실: 0.378336\t최선의 손실: 0.371720\t정확도: 79.71%\n",
+      "19\t검증 세트 손실: 0.368457\t최선의 손실: 0.368457\t정확도: 78.50%\n",
+      "20\t검증 세트 손실: 0.376790\t최선의 손실: 0.368457\t정확도: 79.59%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12\t검증 세트 손실: 0.381057\t최선의 손실: 0.371042\t정확도: 79.67%\n",
-      "13\t검증 세트 손실: 0.386360\t최선의 손실: 0.371042\t정확도: 79.83%\n",
-      "14\t검증 세트 손실: 0.371486\t최선의 손실: 0.371042\t정확도: 79.44%\n",
-      "15\t검증 세트 손실: 0.381716\t최선의 손실: 0.371042\t정확도: 79.63%\n",
-      "16\t검증 세트 손실: 0.363186\t최선의 손실: 0.363186\t정확도: 79.83%\n",
-      "17\t검증 세트 손실: 0.360863\t최선의 손실: 0.360863\t정확도: 79.67%\n",
-      "18\t검증 세트 손실: 0.366602\t최선의 손실: 0.360863\t정확도: 79.83%\n",
-      "19\t검증 세트 손실: 0.368058\t최선의 손실: 0.360863\t정확도: 79.59%\n",
-      "20\t검증 세트 손실: 0.367703\t최선의 손실: 0.360863\t정확도: 78.73%\n",
-      "21\t검증 세트 손실: 0.363763\t최선의 손실: 0.360863\t정확도: 78.73%\n",
-      "22\t검증 세트 손실: 0.372231\t최선의 손실: 0.360863\t정확도: 79.59%\n",
-      "23\t검증 세트 손실: 0.362155\t최선의 손실: 0.360863\t정확도: 80.38%\n",
-      "24\t검증 세트 손실: 0.372964\t최선의 손실: 0.360863\t정확도: 79.79%\n",
-      "25\t검증 세트 손실: 0.363732\t최선의 손실: 0.360863\t정확도: 78.62%\n",
-      "26\t검증 세트 손실: 0.381470\t최선의 손실: 0.360863\t정확도: 78.73%\n",
-      "27\t검증 세트 손실: 0.365492\t최선의 손실: 0.360863\t정확도: 80.02%\n",
-      "28\t검증 세트 손실: 0.380962\t최선의 손실: 0.360863\t정확도: 78.58%\n",
-      "29\t검증 세트 손실: 0.371082\t최선의 손실: 0.360863\t정확도: 79.95%\n",
-      "30\t검증 세트 손실: 0.362036\t최선의 손실: 0.360863\t정확도: 79.71%\n",
-      "31\t검증 세트 손실: 0.376999\t최선의 손실: 0.360863\t정확도: 78.66%\n",
-      "32\t검증 세트 손실: 0.362987\t최선의 손실: 0.360863\t정확도: 80.14%\n",
-      "33\t검증 세트 손실: 0.354648\t최선의 손실: 0.354648\t정확도: 84.56%\n",
-      "34\t검증 세트 손실: 0.398056\t최선의 손실: 0.354648\t정확도: 78.81%\n",
-      "35\t검증 세트 손실: 0.357387\t최선의 손실: 0.354648\t정확도: 83.89%\n",
-      "36\t검증 세트 손실: 0.350735\t최선의 손실: 0.350735\t정확도: 84.09%\n",
-      "37\t검증 세트 손실: 0.336360\t최선의 손실: 0.336360\t정확도: 85.14%\n",
-      "38\t검증 세트 손실: 0.320352\t최선의 손실: 0.320352\t정확도: 93.28%\n",
-      "39\t검증 세트 손실: 0.281542\t최선의 손실: 0.281542\t정확도: 95.23%\n",
-      "40\t검증 세트 손실: 0.246431\t최선의 손실: 0.246431\t정확도: 93.75%\n",
-      "41\t검증 세트 손실: 0.228943\t최선의 손실: 0.228943\t정확도: 95.43%\n",
-      "42\t검증 세트 손실: 0.183853\t최선의 손실: 0.183853\t정확도: 95.70%\n",
-      "43\t검증 세트 손실: 0.175717\t최선의 손실: 0.175717\t정확도: 95.82%\n",
-      "44\t검증 세트 손실: 0.175519\t최선의 손실: 0.175519\t정확도: 96.09%\n",
-      "45\t검증 세트 손실: 0.169779\t최선의 손실: 0.169779\t정확도: 95.82%\n",
-      "46\t검증 세트 손실: 0.167176\t최선의 손실: 0.167176\t정확도: 95.97%\n",
-      "47\t검증 세트 손실: 0.163000\t최선의 손실: 0.163000\t정확도: 96.25%\n",
-      "48\t검증 세트 손실: 0.170360\t최선의 손실: 0.163000\t정확도: 96.13%\n",
-      "49\t검증 세트 손실: 0.152776\t최선의 손실: 0.152776\t정확도: 96.33%\n",
-      "50\t검증 세트 손실: 0.155234\t최선의 손실: 0.152776\t정확도: 96.29%\n",
-      "51\t검증 세트 손실: 0.156351\t최선의 손실: 0.152776\t정확도: 96.21%\n",
-      "52\t검증 세트 손실: 0.152117\t최선의 손실: 0.152117\t정확도: 96.05%\n",
-      "53\t검증 세트 손실: 0.157621\t최선의 손실: 0.152117\t정확도: 96.13%\n",
-      "54\t검증 세트 손실: 0.149596\t최선의 손실: 0.149596\t정확도: 96.36%\n",
-      "55\t검증 세트 손실: 0.149323\t최선의 손실: 0.149323\t정확도: 96.33%\n",
-      "56\t검증 세트 손실: 0.145526\t최선의 손실: 0.145526\t정확도: 96.36%\n",
-      "57\t검증 세트 손실: 0.145157\t최선의 손실: 0.145157\t정확도: 96.48%\n",
-      "58\t검증 세트 손실: 0.151915\t최선의 손실: 0.145157\t정확도: 96.36%\n",
-      "59\t검증 세트 손실: 0.160018\t최선의 손실: 0.145157\t정확도: 96.56%\n",
-      "60\t검증 세트 손실: 0.160999\t최선의 손실: 0.145157\t정확도: 96.40%\n",
-      "61\t검증 세트 손실: 0.147861\t최선의 손실: 0.145157\t정확도: 96.72%\n",
-      "62\t검증 세트 손실: 0.154797\t최선의 손실: 0.145157\t정확도: 96.40%\n",
-      "63\t검증 세트 손실: 0.159026\t최선의 손실: 0.145157\t정확도: 96.33%\n",
-      "64\t검증 세트 손실: 0.141638\t최선의 손실: 0.141638\t정확도: 96.68%\n",
-      "65\t검증 세트 손실: 0.149627\t최선의 손실: 0.141638\t정확도: 96.25%\n",
-      "66\t검증 세트 손실: 0.140652\t최선의 손실: 0.140652\t정확도: 96.79%\n",
-      "67\t검증 세트 손실: 0.142834\t최선의 손실: 0.140652\t정확도: 96.64%\n",
-      "68\t검증 세트 손실: 0.141337\t최선의 손실: 0.140652\t정확도: 96.76%\n",
-      "69\t검증 세트 손실: 0.146739\t최선의 손실: 0.140652\t정확도: 96.52%\n",
-      "70\t검증 세트 손실: 0.142923\t최선의 손실: 0.140652\t정확도: 96.68%\n",
-      "71\t검증 세트 손실: 0.160596\t최선의 손실: 0.140652\t정확도: 96.36%\n",
-      "72\t검증 세트 손실: 0.157331\t최선의 손실: 0.140652\t정확도: 96.56%\n",
-      "73\t검증 세트 손실: 0.150617\t최선의 손실: 0.140652\t정확도: 96.36%\n",
-      "74\t검증 세트 손실: 0.149195\t최선의 손실: 0.140652\t정확도: 96.48%\n",
-      "75\t검증 세트 손실: 0.147025\t최선의 손실: 0.140652\t정확도: 96.40%\n",
-      "76\t검증 세트 손실: 0.154472\t최선의 손실: 0.140652\t정확도: 96.64%\n",
-      "77\t검증 세트 손실: 0.149501\t최선의 손실: 0.140652\t정확도: 96.60%\n",
-      "78\t검증 세트 손실: 0.151003\t최선의 손실: 0.140652\t정확도: 96.52%\n",
-      "79\t검증 세트 손실: 0.141722\t최선의 손실: 0.140652\t정확도: 96.79%\n",
-      "80\t검증 세트 손실: 0.157695\t최선의 손실: 0.140652\t정확도: 96.33%\n",
-      "81\t검증 세트 손실: 0.155156\t최선의 손실: 0.140652\t정확도: 96.52%\n",
-      "82\t검증 세트 손실: 0.158451\t최선의 손실: 0.140652\t정확도: 96.56%\n",
-      "83\t검증 세트 손실: 0.149746\t최선의 손실: 0.140652\t정확도: 96.48%\n",
-      "84\t검증 세트 손실: 0.139676\t최선의 손실: 0.139676\t정확도: 96.95%\n",
-      "85\t검증 세트 손실: 0.153739\t최선의 손실: 0.139676\t정확도: 96.21%\n",
-      "86\t검증 세트 손실: 0.145301\t최선의 손실: 0.139676\t정확도: 96.64%\n",
-      "87\t검증 세트 손실: 0.146683\t최선의 손실: 0.139676\t정확도: 96.72%\n",
-      "88\t검증 세트 손실: 0.153054\t최선의 손실: 0.139676\t정확도: 96.40%\n",
-      "89\t검증 세트 손실: 0.152501\t최선의 손실: 0.139676\t정확도: 96.48%\n",
-      "90\t검증 세트 손실: 0.140986\t최선의 손실: 0.139676\t정확도: 96.87%\n",
-      "91\t검증 세트 손실: 0.149645\t최선의 손실: 0.139676\t정확도: 96.36%\n",
-      "92\t검증 세트 손실: 0.153778\t최선의 손실: 0.139676\t정확도: 96.56%\n",
-      "93\t검증 세트 손실: 0.144965\t최선의 손실: 0.139676\t정확도: 96.52%\n",
-      "94\t검증 세트 손실: 0.151322\t최선의 손실: 0.139676\t정확도: 96.36%\n",
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-      "96\t검증 세트 손실: 0.134842\t최선의 손실: 0.134842\t정확도: 96.72%\n",
-      "97\t검증 세트 손실: 0.142141\t최선의 손실: 0.134842\t정확도: 96.44%\n",
-      "98\t검증 세트 손실: 0.145718\t최선의 손실: 0.134842\t정확도: 96.56%\n",
-      "99\t검증 세트 손실: 0.145243\t최선의 손실: 0.134842\t정확도: 96.36%\n",
-      "100\t검증 세트 손실: 0.151622\t최선의 손실: 0.134842\t정확도: 96.40%\n",
-      "101\t검증 세트 손실: 0.152021\t최선의 손실: 0.134842\t정확도: 96.44%\n",
-      "102\t검증 세트 손실: 0.141384\t최선의 손실: 0.134842\t정확도: 96.48%\n",
-      "103\t검증 세트 손실: 0.139911\t최선의 손실: 0.134842\t정확도: 96.76%\n",
-      "104\t검증 세트 손실: 0.147471\t최선의 손실: 0.134842\t정확도: 96.52%\n",
-      "105\t검증 세트 손실: 0.141017\t최선의 손실: 0.134842\t정확도: 96.68%\n",
-      "106\t검증 세트 손실: 0.138332\t최선의 손실: 0.134842\t정확도: 96.48%\n",
-      "107\t검증 세트 손실: 0.141557\t최선의 손실: 0.134842\t정확도: 96.40%\n",
-      "108\t검증 세트 손실: 0.141405\t최선의 손실: 0.134842\t정확도: 96.52%\n",
-      "109\t검증 세트 손실: 0.139415\t최선의 손실: 0.134842\t정확도: 96.64%\n",
-      "110\t검증 세트 손실: 0.144213\t최선의 손실: 0.134842\t정확도: 96.44%\n",
-      "111\t검증 세트 손실: 0.134736\t최선의 손실: 0.134736\t정확도: 96.52%\n",
-      "112\t검증 세트 손실: 0.137769\t최선의 손실: 0.134736\t정확도: 96.83%\n",
-      "113\t검증 세트 손실: 0.134689\t최선의 손실: 0.134689\t정확도: 96.44%\n",
-      "114\t검증 세트 손실: 0.145034\t최선의 손실: 0.134689\t정확도: 96.52%\n",
-      "115\t검증 세트 손실: 0.132128\t최선의 손실: 0.132128\t정확도: 97.03%\n",
-      "116\t검증 세트 손실: 0.139968\t최선의 손실: 0.132128\t정확도: 96.64%\n",
-      "117\t검증 세트 손실: 0.149064\t최선의 손실: 0.132128\t정확도: 96.25%\n",
-      "118\t검증 세트 손실: 0.138919\t최선의 손실: 0.132128\t정확도: 96.72%\n",
-      "119\t검증 세트 손실: 0.142011\t최선의 손실: 0.132128\t정확도: 96.83%\n",
-      "120\t검증 세트 손실: 0.147373\t최선의 손실: 0.132128\t정확도: 96.52%\n",
-      "121\t검증 세트 손실: 0.141375\t최선의 손실: 0.132128\t정확도: 96.68%\n",
-      "122\t검증 세트 손실: 0.126485\t최선의 손실: 0.126485\t정확도: 97.11%\n",
-      "123\t검증 세트 손실: 0.139500\t최선의 손실: 0.126485\t정확도: 96.91%\n",
-      "124\t검증 세트 손실: 0.135075\t최선의 손실: 0.126485\t정확도: 96.79%\n",
-      "125\t검증 세트 손실: 0.128765\t최선의 손실: 0.126485\t정확도: 96.91%\n",
-      "126\t검증 세트 손실: 0.125633\t최선의 손실: 0.125633\t정확도: 97.03%\n",
-      "127\t검증 세트 손실: 0.138868\t최선의 손실: 0.125633\t정확도: 96.79%\n",
-      "128\t검증 세트 손실: 0.129789\t최선의 손실: 0.125633\t정확도: 97.11%\n",
-      "129\t검증 세트 손실: 0.128632\t최선의 손실: 0.125633\t정확도: 96.95%\n",
-      "130\t검증 세트 손실: 0.125586\t최선의 손실: 0.125586\t정확도: 97.22%\n",
-      "131\t검증 세트 손실: 0.129777\t최선의 손실: 0.125586\t정확도: 97.15%\n",
-      "132\t검증 세트 손실: 0.144333\t최선의 손실: 0.125586\t정확도: 96.60%\n",
-      "133\t검증 세트 손실: 0.139345\t최선의 손실: 0.125586\t정확도: 96.99%\n",
-      "134\t검증 세트 손실: 0.130574\t최선의 손실: 0.125586\t정확도: 96.76%\n",
-      "135\t검증 세트 손실: 0.143318\t최선의 손실: 0.125586\t정확도: 96.52%\n",
-      "136\t검증 세트 손실: 0.126800\t최선의 손실: 0.125586\t정확도: 97.26%\n",
-      "137\t검증 세트 손실: 0.134712\t최선의 손실: 0.125586\t정확도: 97.22%\n",
-      "138\t검증 세트 손실: 0.142025\t최선의 손실: 0.125586\t정확도: 96.76%\n",
-      "139\t검증 세트 손실: 0.132212\t최선의 손실: 0.125586\t정확도: 97.26%\n",
-      "140\t검증 세트 손실: 0.129823\t최선의 손실: 0.125586\t정확도: 96.83%\n",
-      "141\t검증 세트 손실: 0.124015\t최선의 손실: 0.124015\t정확도: 97.22%\n",
-      "142\t검증 세트 손실: 0.139792\t최선의 손실: 0.124015\t정확도: 96.99%\n",
-      "143\t검증 세트 손실: 0.139503\t최선의 손실: 0.124015\t정확도: 97.11%\n",
-      "144\t검증 세트 손실: 0.125915\t최선의 손실: 0.124015\t정확도: 97.38%\n",
-      "145\t검증 세트 손실: 0.138690\t최선의 손실: 0.124015\t정확도: 97.11%\n",
-      "146\t검증 세트 손실: 0.138640\t최선의 손실: 0.124015\t정확도: 96.48%\n",
-      "147\t검증 세트 손실: 0.143709\t최선의 손실: 0.124015\t정확도: 96.68%\n",
-      "148\t검증 세트 손실: 0.127863\t최선의 손실: 0.124015\t정확도: 96.91%\n",
-      "149\t검증 세트 손실: 0.132570\t최선의 손실: 0.124015\t정확도: 96.83%\n",
-      "150\t검증 세트 손실: 0.126504\t최선의 손실: 0.124015\t정확도: 97.30%\n",
-      "151\t검증 세트 손실: 0.136765\t최선의 손실: 0.124015\t정확도: 96.79%\n",
-      "152\t검증 세트 손실: 0.118058\t최선의 손실: 0.118058\t정확도: 97.22%\n",
-      "153\t검증 세트 손실: 0.138194\t최선의 손실: 0.118058\t정확도: 96.64%\n",
-      "154\t검증 세트 손실: 0.122838\t최선의 손실: 0.118058\t정확도: 97.26%\n",
-      "155\t검증 세트 손실: 0.132569\t최선의 손실: 0.118058\t정확도: 96.91%\n",
-      "156\t검증 세트 손실: 0.128984\t최선의 손실: 0.118058\t정확도: 97.30%\n",
-      "157\t검증 세트 손실: 0.142777\t최선의 손실: 0.118058\t정확도: 96.83%\n",
-      "158\t검증 세트 손실: 0.138611\t최선의 손실: 0.118058\t정확도: 97.11%\n",
-      "159\t검증 세트 손실: 0.133413\t최선의 손실: 0.118058\t정확도: 97.11%\n",
-      "160\t검증 세트 손실: 0.138622\t최선의 손실: 0.118058\t정확도: 96.64%\n",
-      "161\t검증 세트 손실: 0.121161\t최선의 손실: 0.118058\t정확도: 97.34%\n",
-      "162\t검증 세트 손실: 0.123845\t최선의 손실: 0.118058\t정확도: 97.19%\n",
-      "163\t검증 세트 손실: 0.124078\t최선의 손실: 0.118058\t정확도: 97.11%\n",
-      "164\t검증 세트 손실: 0.128020\t최선의 손실: 0.118058\t정확도: 96.87%\n",
-      "165\t검증 세트 손실: 0.137618\t최선의 손실: 0.118058\t정확도: 96.95%\n",
-      "166\t검증 세트 손실: 0.134063\t최선의 손실: 0.118058\t정확도: 97.19%\n",
-      "167\t검증 세트 손실: 0.120311\t최선의 손실: 0.118058\t정확도: 97.30%\n",
-      "168\t검증 세트 손실: 0.131416\t최선의 손실: 0.118058\t정확도: 97.11%\n",
-      "169\t검증 세트 손실: 0.133187\t최선의 손실: 0.118058\t정확도: 97.15%\n",
-      "170\t검증 세트 손실: 0.126788\t최선의 손실: 0.118058\t정확도: 97.22%\n",
-      "171\t검증 세트 손실: 0.132936\t최선의 손실: 0.118058\t정확도: 96.99%\n",
-      "172\t검증 세트 손실: 0.130082\t최선의 손실: 0.118058\t정확도: 96.99%\n",
-      "173\t검증 세트 손실: 0.130712\t최선의 손실: 0.118058\t정확도: 97.07%\n",
-      "조기 종료!\n"
+      "21\t검증 세트 손실: 0.361580\t최선의 손실: 0.361580\t정확도: 79.59%\n",
+      "22\t검증 세트 손실: 0.370696\t최선의 손실: 0.361580\t정확도: 79.79%\n",
+      "23\t검증 세트 손실: 0.375503\t최선의 손실: 0.361580\t정확도: 79.83%\n",
+      "24\t검증 세트 손실: 0.367775\t최선의 손실: 0.361580\t정확도: 79.48%\n",
+      "25\t검증 세트 손실: 0.372478\t최선의 손실: 0.361580\t정확도: 78.50%\n",
+      "26\t검증 세트 손실: 0.363384\t최선의 손실: 0.361580\t정확도: 78.66%\n",
+      "27\t검증 세트 손실: 0.361154\t최선의 손실: 0.361154\t정확도: 78.54%\n",
+      "28\t검증 세트 손실: 0.381992\t최선의 손실: 0.361154\t정확도: 79.48%\n",
+      "29\t검증 세트 손실: 0.368399\t최선의 손실: 0.361154\t정확도: 79.44%\n",
+      "30\t검증 세트 손실: 0.362182\t최선의 손실: 0.361154\t정확도: 79.52%\n",
+      "31\t검증 세트 손실: 0.375691\t최선의 손실: 0.361154\t정확도: 78.69%\n",
+      "32\t검증 세트 손실: 0.372563\t최선의 손실: 0.361154\t정확도: 79.48%\n",
+      "33\t검증 세트 손실: 0.364225\t최선의 손실: 0.361154\t정확도: 78.69%\n",
+      "34\t검증 세트 손실: 0.380435\t최선의 손실: 0.361154\t정확도: 78.62%\n",
+      "35\t검증 세트 손실: 0.366205\t최선의 손실: 0.361154\t정확도: 78.69%\n",
+      "36\t검증 세트 손실: 0.358919\t최선의 손실: 0.358919\t정확도: 79.71%\n",
+      "37\t검증 세트 손실: 0.372094\t최선의 손실: 0.358919\t정확도: 79.55%\n",
+      "38\t검증 세트 손실: 0.371138\t최선의 손실: 0.358919\t정확도: 79.40%\n",
+      "39\t검증 세트 손실: 0.388013\t최선의 손실: 0.358919\t정확도: 79.40%\n",
+      "40\t검증 세트 손실: 0.376647\t최선의 손실: 0.358919\t정확도: 78.38%\n",
+      "41\t검증 세트 손실: 0.371309\t최선의 손실: 0.358919\t정확도: 78.62%\n",
+      "42\t검증 세트 손실: 0.364993\t최선의 손실: 0.358919\t정확도: 79.63%\n",
+      "43\t검증 세트 손실: 0.360353\t최선의 손실: 0.358919\t정확도: 78.62%\n",
+      "44\t검증 세트 손실: 0.358623\t최선의 손실: 0.358623\t정확도: 79.67%\n",
+      "45\t검증 세트 손실: 0.352394\t최선의 손실: 0.352394\t정확도: 78.50%\n",
+      "46\t검증 세트 손실: 0.356378\t최선의 손실: 0.352394\t정확도: 78.81%\n",
+      "47\t검증 세트 손실: 0.367483\t최선의 손실: 0.352394\t정확도: 79.83%\n",
+      "48\t검증 세트 손실: 0.364350\t최선의 손실: 0.352394\t정확도: 78.81%\n",
+      "49\t검증 세트 손실: 0.367400\t최선의 손실: 0.352394\t정확도: 79.67%\n",
+      "50\t검증 세트 손실: 0.365146\t최선의 손실: 0.352394\t정확도: 78.58%\n",
+      "51\t검증 세트 손실: 0.355932\t최선의 손실: 0.352394\t정확도: 79.71%\n",
+      "52\t검증 세트 손실: 0.361619\t최선의 손실: 0.352394\t정확도: 79.59%\n",
+      "53\t검증 세트 손실: 0.370934\t최선의 손실: 0.352394\t정확도: 79.40%\n",
+      "54\t검증 세트 손실: 0.357847\t최선의 손실: 0.352394\t정확도: 78.58%\n",
+      "55\t검증 세트 손실: 0.362401\t최선의 손실: 0.352394\t정확도: 78.50%\n",
+      "56\t검증 세트 손실: 0.366463\t최선의 손실: 0.352394\t정확도: 79.71%\n",
+      "57\t검증 세트 손실: 0.363646\t최선의 손실: 0.352394\t정확도: 78.66%\n",
+      "58\t검증 세트 손실: 0.348260\t최선의 손실: 0.348260\t정확도: 79.79%\n",
+      "59\t검증 세트 손실: 0.357522\t최선의 손실: 0.348260\t정확도: 79.63%\n",
+      "60\t검증 세트 손실: 0.350266\t최선의 손실: 0.348260\t정확도: 79.59%\n",
+      "61\t검증 세트 손실: 0.346201\t최선의 손실: 0.346201\t정확도: 79.87%\n",
+      "62\t검증 세트 손실: 0.356755\t최선의 손실: 0.346201\t정확도: 79.75%\n",
+      "63\t검증 세트 손실: 0.358153\t최선의 손실: 0.346201\t정확도: 79.83%\n",
+      "64\t검증 세트 손실: 0.363448\t최선의 손실: 0.346201\t정확도: 78.50%\n",
+      "65\t검증 세트 손실: 0.356743\t최선의 손실: 0.346201\t정확도: 78.81%\n",
+      "66\t검증 세트 손실: 0.350304\t최선의 손실: 0.346201\t정확도: 79.95%\n",
+      "67\t검증 세트 손실: 0.353843\t최선의 손실: 0.346201\t정확도: 78.54%\n",
+      "68\t검증 세트 손실: 0.359308\t최선의 손실: 0.346201\t정확도: 79.59%\n",
+      "69\t검증 세트 손실: 0.345122\t최선의 손실: 0.345122\t정확도: 78.97%\n",
+      "70\t검증 세트 손실: 0.343481\t최선의 손실: 0.343481\t정확도: 79.79%\n",
+      "71\t검증 세트 손실: 0.345465\t최선의 손실: 0.343481\t정확도: 79.91%\n",
+      "72\t검증 세트 손실: 0.349714\t최선의 손실: 0.343481\t정확도: 79.44%\n",
+      "73\t검증 세트 손실: 0.340736\t최선의 손실: 0.340736\t정확도: 79.95%\n",
+      "74\t검증 세트 손실: 0.350472\t최선의 손실: 0.340736\t정확도: 79.28%\n",
+      "75\t검증 세트 손실: 0.344571\t최선의 손실: 0.340736\t정확도: 79.16%\n",
+      "76\t검증 세트 손실: 0.375671\t최선의 손실: 0.340736\t정확도: 79.20%\n",
+      "77\t검증 세트 손실: 0.346682\t최선의 손실: 0.340736\t정확도: 79.91%\n",
+      "78\t검증 세트 손실: 0.345636\t최선의 손실: 0.340736\t정확도: 79.91%\n",
+      "79\t검증 세트 손실: 0.340211\t최선의 손실: 0.340211\t정확도: 79.59%\n",
+      "80\t검증 세트 손실: 0.347218\t최선의 손실: 0.340211\t정확도: 79.05%\n",
+      "81\t검증 세트 손실: 0.367157\t최선의 손실: 0.340211\t정확도: 79.79%\n",
+      "82\t검증 세트 손실: 0.349629\t최선의 손실: 0.340211\t정확도: 79.83%\n",
+      "83\t검증 세트 손실: 0.349602\t최선의 손실: 0.340211\t정확도: 78.89%\n",
+      "84\t검증 세트 손실: 0.346377\t최선의 손실: 0.340211\t정확도: 79.95%\n",
+      "85\t검증 세트 손실: 0.362088\t최선의 손실: 0.340211\t정확도: 79.05%\n",
+      "86\t검증 세트 손실: 0.349904\t최선의 손실: 0.340211\t정확도: 79.20%\n",
+      "87\t검증 세트 손실: 0.344509\t최선의 손실: 0.340211\t정확도: 79.91%\n",
+      "88\t검증 세트 손실: 0.348322\t최선의 손실: 0.340211\t정확도: 79.16%\n",
+      "89\t검증 세트 손실: 0.345854\t최선의 손실: 0.340211\t정확도: 79.75%\n",
+      "90\t검증 세트 손실: 0.367974\t최선의 손실: 0.340211\t정확도: 79.67%\n",
+      "91\t검증 세트 손실: 0.346595\t최선의 손실: 0.340211\t정확도: 78.97%\n",
+      "92\t검증 세트 손실: 0.350459\t최선의 손실: 0.340211\t정확도: 78.93%\n",
+      "93\t검증 세트 손실: 0.362182\t최선의 손실: 0.340211\t정확도: 79.16%\n",
+      "94\t검증 세트 손실: 0.350158\t최선의 손실: 0.340211\t정확도: 80.14%\n",
+      "95\t검증 세트 손실: 0.345832\t최선의 손실: 0.340211\t정확도: 79.36%\n",
+      "96\t검증 세트 손실: 0.343908\t최선의 손실: 0.340211\t정확도: 79.91%\n",
+      "97\t검증 세트 손실: 0.346297\t최선의 손실: 0.340211\t정확도: 80.02%\n",
+      "98\t검증 세트 손실: 0.342548\t최선의 손실: 0.340211\t정확도: 80.06%\n",
+      "99\t검증 세트 손실: 0.341764\t최선의 손실: 0.340211\t정확도: 79.98%\n",
+      "100\t검증 세트 손실: 0.348114\t최선의 손실: 0.340211\t정확도: 80.06%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=30, total=   8.3s\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.170137\t최선의 손실: 0.170137\t정확도: 95.82%\n",
+      "1\t검증 세트 손실: 0.112223\t최선의 손실: 0.112223\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.107462\t최선의 손실: 0.107462\t정확도: 97.26%\n",
+      "3\t검증 세트 손실: 0.097415\t최선의 손실: 0.097415\t정확도: 97.69%\n",
+      "4\t검증 세트 손실: 0.086659\t최선의 손실: 0.086659\t정확도: 97.93%\n",
+      "5\t검증 세트 손실: 0.093971\t최선의 손실: 0.086659\t정확도: 97.73%\n",
+      "6\t검증 세트 손실: 0.089811\t최선의 손실: 0.086659\t정확도: 97.58%\n",
+      "7\t검증 세트 손실: 0.091021\t최선의 손실: 0.086659\t정확도: 97.38%\n",
+      "8\t검증 세트 손실: 0.090911\t최선의 손실: 0.086659\t정확도: 97.97%\n",
+      "9\t검증 세트 손실: 0.091018\t최선의 손실: 0.086659\t정확도: 97.73%\n",
+      "10\t검증 세트 손실: 0.081969\t최선의 손실: 0.081969\t정확도: 97.65%\n",
+      "11\t검증 세트 손실: 0.086499\t최선의 손실: 0.081969\t정확도: 97.81%\n",
+      "12\t검증 세트 손실: 0.080836\t최선의 손실: 0.080836\t정확도: 97.89%\n",
+      "13\t검증 세트 손실: 0.080369\t최선의 손실: 0.080369\t정확도: 98.08%\n",
+      "14\t검증 세트 손실: 0.082626\t최선의 손실: 0.080369\t정확도: 97.89%\n",
+      "15\t검증 세트 손실: 0.082561\t최선의 손실: 0.080369\t정확도: 97.97%\n",
+      "16\t검증 세트 손실: 0.089769\t최선의 손실: 0.080369\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.079732\t최선의 손실: 0.079732\t정확도: 97.85%\n",
+      "18\t검증 세트 손실: 0.090051\t최선의 손실: 0.079732\t정확도: 97.73%\n",
+      "19\t검증 세트 손실: 0.096336\t최선의 손실: 0.079732\t정확도: 97.65%\n",
+      "20\t검증 세트 손실: 0.089754\t최선의 손실: 0.079732\t정확도: 98.08%\n",
+      "21\t검증 세트 손실: 0.100738\t최선의 손실: 0.079732\t정확도: 97.81%\n",
+      "22\t검증 세트 손실: 0.100543\t최선의 손실: 0.079732\t정확도: 97.69%\n",
+      "23\t검증 세트 손실: 0.115126\t최선의 손실: 0.079732\t정확도: 97.30%\n",
+      "24\t검증 세트 손실: 0.090235\t최선의 손실: 0.079732\t정확도: 97.65%\n",
+      "25\t검증 세트 손실: 0.091567\t최선의 손실: 0.079732\t정확도: 97.81%\n",
+      "26\t검증 세트 손실: 0.098280\t최선의 손실: 0.079732\t정확도: 97.62%\n",
+      "27\t검증 세트 손실: 0.094038\t최선의 손실: 0.079732\t정확도: 97.50%\n",
+      "28\t검증 세트 손실: 0.109118\t최선의 손실: 0.079732\t정확도: 97.69%\n",
+      "29\t검증 세트 손실: 0.102614\t최선의 손실: 0.079732\t정확도: 97.07%\n",
+      "30\t검증 세트 손실: 0.088996\t최선의 손실: 0.079732\t정확도: 97.42%\n",
+      "31\t검증 세트 손실: 0.092505\t최선의 손실: 0.079732\t정확도: 97.42%\n",
+      "32\t검증 세트 손실: 0.106500\t최선의 손실: 0.079732\t정확도: 97.50%\n",
+      "33\t검증 세트 손실: 0.100540\t최선의 손실: 0.079732\t정확도: 97.77%\n",
+      "34\t검증 세트 손실: 0.163736\t최선의 손실: 0.079732\t정확도: 97.19%\n",
+      "35\t검증 세트 손실: 0.216090\t최선의 손실: 0.079732\t정확도: 93.75%\n",
+      "36\t검증 세트 손실: 0.240294\t최선의 손실: 0.079732\t정확도: 93.94%\n",
+      "37\t검증 세트 손실: 0.294572\t최선의 손실: 0.079732\t정확도: 95.35%\n",
+      "38\t검증 세트 손실: 0.190992\t최선의 손실: 0.079732\t정확도: 95.43%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=   4.2s\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.146101\t최선의 손실: 0.146101\t정확도: 95.74%\n",
+      "1\t검증 세트 손실: 0.111128\t최선의 손실: 0.111128\t정확도: 96.99%\n",
+      "2\t검증 세트 손실: 0.093952\t최선의 손실: 0.093952\t정확도: 97.22%\n",
+      "3\t검증 세트 손실: 0.094825\t최선의 손실: 0.093952\t정확도: 97.46%\n",
+      "4\t검증 세트 손실: 0.104640\t최선의 손실: 0.093952\t정확도: 97.50%\n",
+      "5\t검증 세트 손실: 0.092439\t최선의 손실: 0.092439\t정확도: 97.65%\n",
+      "6\t검증 세트 손실: 0.089593\t최선의 손실: 0.089593\t정확도: 97.65%\n",
+      "7\t검증 세트 손실: 0.095892\t최선의 손실: 0.089593\t정확도: 97.69%\n",
+      "8\t검증 세트 손실: 0.082794\t최선의 손실: 0.082794\t정확도: 97.93%\n",
+      "9\t검증 세트 손실: 0.084162\t최선의 손실: 0.082794\t정확도: 97.93%\n",
+      "10\t검증 세트 손실: 0.111642\t최선의 손실: 0.082794\t정확도: 97.58%\n",
+      "11\t검증 세트 손실: 0.090441\t최선의 손실: 0.082794\t정확도: 97.62%\n",
+      "12\t검증 세트 손실: 0.093313\t최선의 손실: 0.082794\t정확도: 97.50%\n",
+      "13\t검증 세트 손실: 0.083073\t최선의 손실: 0.082794\t정확도: 98.05%\n",
+      "14\t검증 세트 손실: 0.085389\t최선의 손실: 0.082794\t정확도: 98.01%\n",
+      "15\t검증 세트 손실: 0.093172\t최선의 손실: 0.082794\t정확도: 97.54%\n",
+      "16\t검증 세트 손실: 0.085850\t최선의 손실: 0.082794\t정확도: 98.01%\n",
+      "17\t검증 세트 손실: 0.090656\t최선의 손실: 0.082794\t정확도: 97.97%\n",
+      "18\t검증 세트 손실: 0.101822\t최선의 손실: 0.082794\t정확도: 97.73%\n",
+      "19\t검증 세트 손실: 0.076227\t최선의 손실: 0.076227\t정확도: 98.12%\n",
+      "20\t검증 세트 손실: 0.118536\t최선의 손실: 0.076227\t정확도: 97.89%\n",
+      "21\t검증 세트 손실: 0.100835\t최선의 손실: 0.076227\t정확도: 97.81%\n",
+      "22\t검증 세트 손실: 0.091804\t최선의 손실: 0.076227\t정확도: 98.05%\n",
+      "23\t검증 세트 손실: 0.091698\t최선의 손실: 0.076227\t정확도: 97.69%\n",
+      "24\t검증 세트 손실: 0.110993\t최선의 손실: 0.076227\t정확도: 97.73%\n",
+      "25\t검증 세트 손실: 0.182825\t최선의 손실: 0.076227\t정확도: 97.54%\n",
+      "26\t검증 세트 손실: 0.220435\t최선의 손실: 0.076227\t정확도: 95.58%\n",
+      "27\t검증 세트 손실: 0.188933\t최선의 손실: 0.076227\t정확도: 93.86%\n",
+      "28\t검증 세트 손실: 0.230112\t최선의 손실: 0.076227\t정확도: 94.92%\n",
+      "29\t검증 세트 손실: 0.166694\t최선의 손실: 0.076227\t정확도: 95.58%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[CV]  batch_size=500, dropout_rate=0.6, learning_rate=0.01, activation=<function elu at 0x7f99417cf6a8>, n_neurons=30, total=  17.9s\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.191084\t최선의 손실: 0.191084\t정확도: 94.68%\n",
-      "1\t검증 세트 손실: 0.136013\t최선의 손실: 0.136013\t정확도: 96.01%\n",
-      "2\t검증 세트 손실: 0.135406\t최선의 손실: 0.135406\t정확도: 96.33%\n",
-      "3\t검증 세트 손실: 0.122826\t최선의 손실: 0.122826\t정확도: 96.76%\n",
-      "4\t검증 세트 손실: 0.100447\t최선의 손실: 0.100447\t정확도: 97.15%\n",
-      "5\t검증 세트 손실: 0.099960\t최선의 손실: 0.099960\t정확도: 97.26%\n",
-      "6\t검증 세트 손실: 0.108354\t최선의 손실: 0.099960\t정확도: 97.26%\n",
-      "7\t검증 세트 손실: 0.088175\t최선의 손실: 0.088175\t정확도: 97.50%\n",
-      "8\t검증 세트 손실: 0.096111\t최선의 손실: 0.088175\t정확도: 97.58%\n",
-      "9\t검증 세트 손실: 0.081792\t최선의 손실: 0.081792\t정확도: 97.58%\n",
-      "10\t검증 세트 손실: 0.074770\t최선의 손실: 0.074770\t정확도: 97.85%\n",
-      "11\t검증 세트 손실: 0.090037\t최선의 손실: 0.074770\t정확도: 97.89%\n",
-      "12\t검증 세트 손실: 0.091457\t최선의 손실: 0.074770\t정확도: 97.73%\n",
-      "13\t검증 세트 손실: 0.092750\t최선의 손실: 0.074770\t정확도: 97.85%\n",
-      "14\t검증 세트 손실: 0.099664\t최선의 손실: 0.074770\t정확도: 98.01%\n",
-      "15\t검증 세트 손실: 0.078906\t최선의 손실: 0.074770\t정확도: 98.24%\n",
-      "16\t검증 세트 손실: 0.084347\t최선의 손실: 0.074770\t정확도: 97.93%\n",
-      "17\t검증 세트 손실: 0.080193\t최선의 손실: 0.074770\t정확도: 98.01%\n",
-      "18\t검증 세트 손실: 0.087590\t최선의 손실: 0.074770\t정확도: 97.89%\n",
-      "19\t검증 세트 손실: 0.086864\t최선의 손실: 0.074770\t정확도: 97.97%\n",
-      "20\t검증 세트 손실: 0.118273\t최선의 손실: 0.074770\t정확도: 97.73%\n",
-      "21\t검증 세트 손실: 0.115028\t최선의 손실: 0.074770\t정확도: 97.26%\n",
-      "22\t검증 세트 손실: 0.095475\t최선의 손실: 0.074770\t정확도: 98.01%\n",
-      "23\t검증 세트 손실: 0.109921\t최선의 손실: 0.074770\t정확도: 97.38%\n",
-      "24\t검증 세트 손실: 0.117437\t최선의 손실: 0.074770\t정확도: 97.22%\n",
-      "25\t검증 세트 손실: 0.116161\t최선의 손실: 0.074770\t정확도: 97.50%\n",
-      "26\t검증 세트 손실: 0.107948\t최선의 손실: 0.074770\t정확도: 97.89%\n",
-      "27\t검증 세트 손실: 0.166962\t최선의 손실: 0.074770\t정확도: 97.30%\n",
-      "28\t검증 세트 손실: 0.109546\t최선의 손실: 0.074770\t정확도: 97.34%\n",
-      "29\t검증 세트 손실: 0.115920\t최선의 손실: 0.074770\t정확도: 97.46%\n",
-      "30\t검증 세트 손실: 0.121983\t최선의 손실: 0.074770\t정확도: 97.07%\n",
-      "31\t검증 세트 손실: 0.106915\t최선의 손실: 0.074770\t정확도: 97.65%\n",
+      "30\t검증 세트 손실: 0.165243\t최선의 손실: 0.076227\t정확도: 95.07%\n",
+      "31\t검증 세트 손실: 0.182779\t최선의 손실: 0.076227\t정확도: 96.21%\n",
+      "32\t검증 세트 손실: 0.277647\t최선의 손실: 0.076227\t정확도: 91.75%\n",
+      "33\t검증 세트 손실: 0.232093\t최선의 손실: 0.076227\t정확도: 92.26%\n",
+      "34\t검증 세트 손실: 0.209724\t최선의 손실: 0.076227\t정확도: 95.15%\n",
+      "35\t검증 세트 손실: 0.218149\t최선의 손실: 0.076227\t정확도: 93.47%\n",
+      "36\t검증 세트 손실: 0.174842\t최선의 손실: 0.076227\t정확도: 96.05%\n",
+      "37\t검증 세트 손실: 0.127705\t최선의 손실: 0.076227\t정확도: 96.44%\n",
+      "38\t검증 세트 손실: 0.139084\t최선의 손실: 0.076227\t정확도: 96.01%\n",
+      "39\t검증 세트 손실: 0.125859\t최선의 손실: 0.076227\t정확도: 96.83%\n",
+      "40\t검증 세트 손실: 0.131933\t최선의 손실: 0.076227\t정확도: 96.36%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total=   2.9s\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.492321\t최선의 손실: 0.492321\t정확도: 84.68%\n",
-      "1\t검증 세트 손실: 0.166405\t최선의 손실: 0.166405\t정확도: 95.00%\n",
-      "2\t검증 세트 손실: 0.135796\t최선의 손실: 0.135796\t정확도: 96.40%\n",
-      "3\t검증 세트 손실: 0.118406\t최선의 손실: 0.118406\t정확도: 96.95%\n",
-      "4\t검증 세트 손실: 0.106452\t최선의 손실: 0.106452\t정확도: 97.07%\n",
-      "5\t검증 세트 손실: 0.106450\t최선의 손실: 0.106450\t정확도: 97.42%\n",
-      "6\t검증 세트 손실: 0.097986\t최선의 손실: 0.097986\t정확도: 97.58%\n",
-      "7\t검증 세트 손실: 0.095318\t최선의 손실: 0.095318\t정확도: 97.73%\n",
-      "8\t검증 세트 손실: 0.089209\t최선의 손실: 0.089209\t정확도: 97.81%\n",
-      "9\t검증 세트 손실: 0.104422\t최선의 손실: 0.089209\t정확도: 97.77%\n",
-      "10\t검증 세트 손실: 0.092125\t최선의 손실: 0.089209\t정확도: 97.73%\n",
-      "11\t검증 세트 손실: 0.084480\t최선의 손실: 0.084480\t정확도: 97.93%\n",
-      "12\t검증 세트 손실: 0.095034\t최선의 손실: 0.084480\t정확도: 98.05%\n",
-      "13\t검증 세트 손실: 0.089886\t최선의 손실: 0.084480\t정확도: 97.97%\n",
-      "14\t검증 세트 손실: 0.080781\t최선의 손실: 0.080781\t정확도: 98.01%\n",
-      "15\t검증 세트 손실: 0.087987\t최선의 손실: 0.080781\t정확도: 97.81%\n",
-      "16\t검증 세트 손실: 0.101333\t최선의 손실: 0.080781\t정확도: 97.54%\n",
-      "17\t검증 세트 손실: 0.102983\t최선의 손실: 0.080781\t정확도: 97.81%\n",
-      "18\t검증 세트 손실: 0.115835\t최선의 손실: 0.080781\t정확도: 97.50%\n",
-      "19\t검증 세트 손실: 0.119494\t최선의 손실: 0.080781\t정확도: 97.50%\n",
-      "20\t검증 세트 손실: 0.127698\t최선의 손실: 0.080781\t정확도: 97.81%\n",
-      "21\t검증 세트 손실: 0.109181\t최선의 손실: 0.080781\t정확도: 97.50%\n",
-      "22\t검증 세트 손실: 0.098436\t최선의 손실: 0.080781\t정확도: 98.08%\n",
-      "23\t검증 세트 손실: 0.099329\t최선의 손실: 0.080781\t정확도: 97.69%\n",
-      "24\t검증 세트 손실: 0.105300\t최선의 손실: 0.080781\t정확도: 97.58%\n",
-      "25\t검증 세트 손실: 0.114285\t최선의 손실: 0.080781\t정확도: 96.83%\n",
-      "26\t검증 세트 손실: 0.097566\t최선의 손실: 0.080781\t정확도: 97.69%\n",
-      "27\t검증 세트 손실: 0.091330\t최선의 손실: 0.080781\t정확도: 97.62%\n",
-      "28\t검증 세트 손실: 0.092718\t최선의 손실: 0.080781\t정확도: 97.69%\n",
-      "29\t검증 세트 손실: 0.111793\t최선의 손실: 0.080781\t정확도: 97.93%\n",
-      "30\t검증 세트 손실: 0.104230\t최선의 손실: 0.080781\t정확도: 97.34%\n",
-      "31\t검증 세트 손실: 0.094942\t최선의 손실: 0.080781\t정확도: 97.38%\n",
-      "32\t검증 세트 손실: 0.082988\t최선의 손실: 0.080781\t정확도: 97.89%\n",
-      "33\t검증 세트 손실: 0.078233\t최선의 손실: 0.078233\t정확도: 97.97%\n",
-      "34\t검증 세트 손실: 0.076402\t최선의 손실: 0.076402\t정확도: 98.44%\n",
-      "35\t검증 세트 손실: 0.076052\t최선의 손실: 0.076052\t정확도: 97.97%\n",
-      "36\t검증 세트 손실: 0.078814\t최선의 손실: 0.076052\t정확도: 98.12%\n",
-      "37\t검증 세트 손실: 0.072975\t최선의 손실: 0.072975\t정확도: 98.08%\n",
-      "38\t검증 세트 손실: 0.778444\t최선의 손실: 0.072975\t정확도: 92.34%\n",
-      "39\t검증 세트 손실: 1.017943\t최선의 손실: 0.072975\t정확도: 71.54%\n",
-      "40\t검증 세트 손실: 0.473978\t최선의 손실: 0.072975\t정확도: 85.14%\n",
-      "41\t검증 세트 손실: 0.630591\t최선의 손실: 0.072975\t정확도: 79.24%\n",
-      "42\t검증 세트 손실: 0.418219\t최선의 손실: 0.072975\t정확도: 88.23%\n",
-      "43\t검증 세트 손실: 0.354263\t최선의 손실: 0.072975\t정확도: 89.84%\n",
-      "44\t검증 세트 손실: 0.355846\t최선의 손실: 0.072975\t정확도: 90.38%\n",
-      "45\t검증 세트 손실: 0.664963\t최선의 손실: 0.072975\t정확도: 76.86%\n",
-      "46\t검증 세트 손실: 0.476952\t최선의 손실: 0.072975\t정확도: 87.49%\n",
-      "47\t검증 세트 손실: 0.396491\t최선의 손실: 0.072975\t정확도: 89.48%\n",
-      "48\t검증 세트 손실: 0.394380\t최선의 손실: 0.072975\t정확도: 90.19%\n",
-      "49\t검증 세트 손실: 0.408575\t최선의 손실: 0.072975\t정확도: 88.19%\n",
-      "50\t검증 세트 손실: 0.344465\t최선의 손실: 0.072975\t정확도: 90.46%\n",
-      "51\t검증 세트 손실: 0.343996\t최선의 손실: 0.072975\t정확도: 90.30%\n",
-      "52\t검증 세트 손실: 0.307568\t최선의 손실: 0.072975\t정확도: 91.01%\n",
-      "53\t검증 세트 손실: 0.302498\t최선의 손실: 0.072975\t정확도: 92.57%\n",
-      "54\t검증 세트 손실: 0.284431\t최선의 손실: 0.072975\t정확도: 92.65%\n",
-      "55\t검증 세트 손실: 0.289912\t최선의 손실: 0.072975\t정확도: 92.14%\n",
-      "56\t검증 세트 손실: 0.267816\t최선의 손실: 0.072975\t정확도: 91.91%\n",
-      "57\t검증 세트 손실: 0.244697\t최선의 손실: 0.072975\t정확도: 92.34%\n",
-      "58\t검증 세트 손실: 0.244384\t최선의 손실: 0.072975\t정확도: 92.69%\n",
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=   4.3s\n",
+      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90 \n",
+      "0\t검증 세트 손실: 0.133605\t최선의 손실: 0.133605\t정확도: 96.60%\n",
+      "1\t검증 세트 손실: 0.125731\t최선의 손실: 0.125731\t정확도: 97.11%\n",
+      "2\t검증 세트 손실: 0.098369\t최선의 손실: 0.098369\t정확도: 97.22%\n",
+      "3\t검증 세트 손실: 0.094542\t최선의 손실: 0.094542\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.094899\t최선의 손실: 0.094542\t정확도: 97.58%\n",
+      "5\t검증 세트 손실: 0.086981\t최선의 손실: 0.086981\t정확도: 97.65%\n",
+      "6\t검증 세트 손실: 0.093822\t최선의 손실: 0.086981\t정확도: 97.65%\n",
+      "7\t검증 세트 손실: 0.083132\t최선의 손실: 0.083132\t정확도: 97.89%\n",
+      "8\t검증 세트 손실: 0.077384\t최선의 손실: 0.077384\t정확도: 98.16%\n",
+      "9\t검증 세트 손실: 0.079742\t최선의 손실: 0.077384\t정확도: 97.89%\n",
+      "10\t검증 세트 손실: 0.078630\t최선의 손실: 0.077384\t정확도: 98.05%\n",
+      "11\t검증 세트 손실: 0.079036\t최선의 손실: 0.077384\t정확도: 98.01%\n",
+      "12\t검증 세트 손실: 0.073680\t최선의 손실: 0.073680\t정확도: 98.24%\n",
+      "13\t검증 세트 손실: 0.081129\t최선의 손실: 0.073680\t정확도: 98.28%\n",
+      "14\t검증 세트 손실: 0.078268\t최선의 손실: 0.073680\t정확도: 98.20%\n",
+      "15\t검증 세트 손실: 0.069424\t최선의 손실: 0.069424\t정확도: 98.36%\n",
+      "16\t검증 세트 손실: 0.073173\t최선의 손실: 0.069424\t정확도: 98.32%\n",
+      "17\t검증 세트 손실: 0.078285\t최선의 손실: 0.069424\t정확도: 98.08%\n",
+      "18\t검증 세트 손실: 0.081530\t최선의 손실: 0.069424\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.071363\t최선의 손실: 0.069424\t정확도: 98.16%\n",
+      "20\t검증 세트 손실: 0.073508\t최선의 손실: 0.069424\t정확도: 98.24%\n",
+      "21\t검증 세트 손실: 0.068464\t최선의 손실: 0.068464\t정확도: 98.20%\n",
+      "22\t검증 세트 손실: 0.085656\t최선의 손실: 0.068464\t정확도: 98.40%\n",
+      "23\t검증 세트 손실: 0.079807\t최선의 손실: 0.068464\t정확도: 98.32%\n",
+      "24\t검증 세트 손실: 0.126331\t최선의 손실: 0.068464\t정확도: 97.50%\n",
+      "25\t검증 세트 손실: 0.109763\t최선의 손실: 0.068464\t정확도: 97.77%\n",
+      "26\t검증 세트 손실: 0.099613\t최선의 손실: 0.068464\t정확도: 97.73%\n",
+      "27\t검증 세트 손실: 0.096334\t최선의 손실: 0.068464\t정확도: 97.30%\n",
+      "28\t검증 세트 손실: 0.129122\t최선의 손실: 0.068464\t정확도: 97.93%\n",
+      "29\t검증 세트 손실: 0.168285\t최선의 손실: 0.068464\t정확도: 97.69%\n",
+      "30\t검증 세트 손실: 0.281631\t최선의 손실: 0.068464\t정확도: 95.78%\n",
+      "31\t검증 세트 손실: 0.261534\t최선의 손실: 0.068464\t정확도: 94.45%\n",
+      "32\t검증 세트 손실: 0.197331\t최선의 손실: 0.068464\t정확도: 95.82%\n",
+      "33\t검증 세트 손실: 0.438253\t최선의 손실: 0.068464\t정확도: 93.59%\n",
+      "34\t검증 세트 손실: 0.178324\t최선의 손실: 0.068464\t정확도: 96.21%\n",
+      "35\t검증 세트 손실: 0.151387\t최선의 손실: 0.068464\t정확도: 96.17%\n",
+      "36\t검증 세트 손실: 0.147774\t최선의 손실: 0.068464\t정확도: 97.15%\n",
+      "37\t검증 세트 손실: 0.146523\t최선의 손실: 0.068464\t정확도: 96.99%\n",
+      "38\t검증 세트 손실: 0.153242\t최선의 손실: 0.068464\t정확도: 97.89%\n",
+      "39\t검증 세트 손실: 0.206014\t최선의 손실: 0.068464\t정확도: 97.30%\n",
+      "40\t검증 세트 손실: 0.245907\t최선의 손실: 0.068464\t정확도: 97.26%\n",
+      "41\t검증 세트 손실: 0.312969\t최선의 손실: 0.068464\t정확도: 92.26%\n",
+      "42\t검증 세트 손실: 0.170808\t최선의 손실: 0.068464\t정확도: 96.36%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total=   4.9s\n",
-      "[CV] batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90 \n",
-      "0\t검증 세트 손실: 0.207138\t최선의 손실: 0.207138\t정확도: 93.78%\n",
-      "1\t검증 세트 손실: 0.146590\t최선의 손실: 0.146590\t정확도: 96.09%\n",
-      "2\t검증 세트 손실: 0.105631\t최선의 손실: 0.105631\t정확도: 97.07%\n",
-      "3\t검증 세트 손실: 0.103299\t최선의 손실: 0.103299\t정확도: 97.07%\n",
-      "4\t검증 세트 손실: 0.119655\t최선의 손실: 0.103299\t정확도: 96.68%\n",
-      "5\t검증 세트 손실: 0.093503\t최선의 손실: 0.093503\t정확도: 97.62%\n",
-      "6\t검증 세트 손실: 0.092546\t최선의 손실: 0.092546\t정확도: 97.34%\n",
-      "7\t검증 세트 손실: 0.089102\t최선의 손실: 0.089102\t정확도: 97.58%\n",
-      "8\t검증 세트 손실: 0.090392\t최선의 손실: 0.089102\t정확도: 97.81%\n",
-      "9\t검증 세트 손실: 0.099067\t최선의 손실: 0.089102\t정확도: 97.58%\n",
-      "10\t검증 세트 손실: 0.079142\t최선의 손실: 0.079142\t정확도: 98.08%\n",
-      "11\t검증 세트 손실: 0.088445\t최선의 손실: 0.079142\t정확도: 97.81%\n",
-      "12\t검증 세트 손실: 0.081586\t최선의 손실: 0.079142\t정확도: 98.12%\n",
-      "13\t검증 세트 손실: 0.075511\t최선의 손실: 0.075511\t정확도: 98.16%\n",
-      "14\t검증 세트 손실: 0.079655\t최선의 손실: 0.075511\t정확도: 97.97%\n",
-      "15\t검증 세트 손실: 0.082141\t최선의 손실: 0.075511\t정확도: 97.81%\n",
-      "16\t검증 세트 손실: 0.077626\t최선의 손실: 0.075511\t정확도: 97.93%\n",
-      "17\t검증 세트 손실: 0.086832\t최선의 손실: 0.075511\t정확도: 97.97%\n",
-      "18\t검증 세트 손실: 0.079913\t최선의 손실: 0.075511\t정확도: 98.20%\n",
-      "19\t검증 세트 손실: 0.074329\t최선의 손실: 0.074329\t정확도: 98.44%\n",
-      "20\t검증 세트 손실: 0.084769\t최선의 손실: 0.074329\t정확도: 98.05%\n",
-      "21\t검증 세트 손실: 0.078382\t최선의 손실: 0.074329\t정확도: 98.12%\n",
-      "22\t검증 세트 손실: 0.083292\t최선의 손실: 0.074329\t정확도: 98.16%\n",
-      "23\t검증 세트 손실: 0.092765\t최선의 손실: 0.074329\t정확도: 97.58%\n",
-      "24\t검증 세트 손실: 0.107186\t최선의 손실: 0.074329\t정확도: 97.22%\n",
-      "25\t검증 세트 손실: 0.326740\t최선의 손실: 0.074329\t정확도: 97.11%\n",
-      "26\t검증 세트 손실: 0.199246\t최선의 손실: 0.074329\t정확도: 96.05%\n",
-      "27\t검증 세트 손실: 0.212570\t최선의 손실: 0.074329\t정확도: 94.49%\n",
-      "28\t검증 세트 손실: 0.355432\t최선의 손실: 0.074329\t정확도: 93.98%\n",
-      "29\t검증 세트 손실: 0.297347\t최선의 손실: 0.074329\t정확도: 94.18%\n",
-      "30\t검증 세트 손실: 0.175866\t최선의 손실: 0.074329\t정확도: 96.01%\n",
-      "31\t검증 세트 손실: 0.148378\t최선의 손실: 0.074329\t정확도: 96.21%\n",
-      "32\t검증 세트 손실: 0.149920\t최선의 손실: 0.074329\t정확도: 95.97%\n",
-      "33\t검증 세트 손실: 0.125857\t최선의 손실: 0.074329\t정확도: 96.68%\n",
-      "34\t검증 세트 손실: 0.134249\t최선의 손실: 0.074329\t정확도: 95.97%\n",
-      "35\t검증 세트 손실: 0.135374\t최선의 손실: 0.074329\t정확도: 95.70%\n",
-      "36\t검증 세트 손실: 0.138818\t최선의 손실: 0.074329\t정확도: 96.09%\n",
-      "37\t검증 세트 손실: 0.120750\t최선의 손실: 0.074329\t정확도: 96.83%\n",
-      "38\t검증 세트 손실: 0.120580\t최선의 손실: 0.074329\t정확도: 96.79%\n",
-      "39\t검증 세트 손실: 0.124933\t최선의 손실: 0.074329\t정확도: 96.60%\n",
-      "40\t검증 세트 손실: 0.119356\t최선의 손실: 0.074329\t정확도: 96.52%\n",
+      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=90, total=   4.5s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.096926\t최선의 손실: 0.096926\t정확도: 97.19%\n",
+      "1\t검증 세트 손실: 0.097701\t최선의 손실: 0.096926\t정확도: 97.54%\n",
+      "2\t검증 세트 손실: 0.077784\t최선의 손실: 0.077784\t정확도: 97.69%\n",
+      "3\t검증 세트 손실: 0.073908\t최선의 손실: 0.073908\t정확도: 98.08%\n",
+      "4\t검증 세트 손실: 0.064718\t최선의 손실: 0.064718\t정확도: 98.20%\n",
+      "5\t검증 세트 손실: 0.075204\t최선의 손실: 0.064718\t정확도: 98.16%\n",
+      "6\t검증 세트 손실: 0.070957\t최선의 손실: 0.064718\t정확도: 98.40%\n",
+      "7\t검증 세트 손실: 0.085490\t최선의 손실: 0.064718\t정확도: 98.20%\n",
+      "8\t검증 세트 손실: 0.073187\t최선의 손실: 0.064718\t정확도: 98.36%\n",
+      "9\t검증 세트 손실: 0.085674\t최선의 손실: 0.064718\t정확도: 97.65%\n",
+      "10\t검증 세트 손실: 0.114301\t최선의 손실: 0.064718\t정확도: 97.50%\n",
+      "11\t검증 세트 손실: 0.081393\t최선의 손실: 0.064718\t정확도: 97.69%\n",
+      "12\t검증 세트 손실: 0.071688\t최선의 손실: 0.064718\t정확도: 98.01%\n",
+      "13\t검증 세트 손실: 0.085788\t최선의 손실: 0.064718\t정확도: 97.77%\n",
+      "14\t검증 세트 손실: 0.097490\t최선의 손실: 0.064718\t정확도: 98.08%\n",
+      "15\t검증 세트 손실: 0.077796\t최선의 손실: 0.064718\t정확도: 98.24%\n",
+      "16\t검증 세트 손실: 0.091511\t최선의 손실: 0.064718\t정확도: 98.05%\n",
+      "17\t검증 세트 손실: 0.092200\t최선의 손실: 0.064718\t정확도: 97.89%\n",
+      "18\t검증 세트 손실: 0.166900\t최선의 손실: 0.064718\t정확도: 98.20%\n",
+      "19\t검증 세트 손실: 0.085495\t최선의 손실: 0.064718\t정확도: 98.51%\n",
+      "20\t검증 세트 손실: 0.075939\t최선의 손실: 0.064718\t정확도: 98.44%\n",
+      "21\t검증 세트 손실: 0.064535\t최선의 손실: 0.064535\t정확도: 98.28%\n",
+      "22\t검증 세트 손실: 0.065482\t최선의 손실: 0.064535\t정확도: 98.36%\n",
+      "23\t검증 세트 손실: 0.074349\t최선의 손실: 0.064535\t정확도: 98.12%\n",
+      "24\t검증 세트 손실: 0.069642\t최선의 손실: 0.064535\t정확도: 98.32%\n",
+      "25\t검증 세트 손실: 0.076925\t최선의 손실: 0.064535\t정확도: 98.40%\n",
+      "26\t검증 세트 손실: 0.066892\t최선의 손실: 0.064535\t정확도: 98.48%\n",
+      "27\t검증 세트 손실: 0.076533\t최선의 손실: 0.064535\t정확도: 98.67%\n",
+      "28\t검증 세트 손실: 0.069200\t최선의 손실: 0.064535\t정확도: 98.48%\n",
+      "29\t검증 세트 손실: 0.055739\t최선의 손실: 0.055739\t정확도: 98.67%\n",
+      "30\t검증 세트 손실: 0.058449\t최선의 손실: 0.055739\t정확도: 98.79%\n",
+      "31\t검증 세트 손실: 0.060547\t최선의 손실: 0.055739\t정확도: 98.67%\n",
+      "32\t검증 세트 손실: 0.059313\t최선의 손실: 0.055739\t정확도: 98.63%\n",
+      "33\t검증 세트 손실: 0.057273\t최선의 손실: 0.055739\t정확도: 98.63%\n",
+      "34\t검증 세트 손실: 0.061346\t최선의 손실: 0.055739\t정확도: 98.32%\n",
+      "35\t검증 세트 손실: 0.063653\t최선의 손실: 0.055739\t정확도: 98.44%\n",
+      "36\t검증 세트 손실: 0.101109\t최선의 손실: 0.055739\t정확도: 96.33%\n",
+      "37\t검증 세트 손실: 0.111950\t최선의 손실: 0.055739\t정확도: 97.81%\n",
+      "38\t검증 세트 손실: 0.080212\t최선의 손실: 0.055739\t정확도: 98.55%\n",
+      "39\t검증 세트 손실: 0.072855\t최선의 손실: 0.055739\t정확도: 98.55%\n",
+      "40\t검증 세트 손실: 0.062705\t최선의 손실: 0.055739\t정확도: 98.83%\n",
+      "41\t검증 세트 손실: 0.097229\t최선의 손실: 0.055739\t정확도: 97.93%\n",
+      "42\t검증 세트 손실: 0.081659\t최선의 손실: 0.055739\t정확도: 98.36%\n",
+      "43\t검증 세트 손실: 0.092996\t최선의 손실: 0.055739\t정확도: 98.32%\n",
+      "44\t검증 세트 손실: 0.115519\t최선의 손실: 0.055739\t정확도: 98.16%\n",
+      "45\t검증 세트 손실: 0.098951\t최선의 손실: 0.055739\t정확도: 97.85%\n",
+      "46\t검증 세트 손실: 0.074064\t최선의 손실: 0.055739\t정확도: 98.36%\n",
+      "47\t검증 세트 손실: 0.073955\t최선의 손실: 0.055739\t정확도: 98.63%\n",
+      "48\t검증 세트 손실: 0.090856\t최선의 손실: 0.055739\t정확도: 98.59%\n",
+      "49\t검증 세트 손실: 0.066121\t최선의 손실: 0.055739\t정확도: 98.36%\n",
+      "50\t검증 세트 손실: 0.070816\t최선의 손실: 0.055739\t정확도: 98.55%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=500, dropout_rate=0.3, learning_rate=0.02, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=90, total=   3.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.106289\t최선의 손실: 0.106289\t정확도: 97.03%\n",
-      "1\t검증 세트 손실: 0.092890\t최선의 손실: 0.092890\t정확도: 97.54%\n",
-      "2\t검증 세트 손실: 0.082650\t최선의 손실: 0.082650\t정확도: 97.85%\n",
-      "3\t검증 세트 손실: 0.080805\t최선의 손실: 0.080805\t정확도: 97.97%\n"
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=70, total=  15.7s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.117511\t최선의 손실: 0.117511\t정확도: 96.99%\n",
+      "1\t검증 세트 손실: 0.092859\t최선의 손실: 0.092859\t정확도: 97.97%\n",
+      "2\t검증 세트 손실: 0.078944\t최선의 손실: 0.078944\t정확도: 98.01%\n",
+      "3\t검증 세트 손실: 0.079049\t최선의 손실: 0.078944\t정확도: 98.01%\n",
+      "4\t검증 세트 손실: 0.106459\t최선의 손실: 0.078944\t정확도: 98.01%\n",
+      "5\t검증 세트 손실: 0.074311\t최선의 손실: 0.074311\t정확도: 97.89%\n",
+      "6\t검증 세트 손실: 0.077078\t최선의 손실: 0.074311\t정확도: 98.12%\n",
+      "7\t검증 세트 손실: 0.074356\t최선의 손실: 0.074311\t정확도: 98.16%\n",
+      "8\t검증 세트 손실: 0.073231\t최선의 손실: 0.073231\t정확도: 98.40%\n",
+      "9\t검증 세트 손실: 0.076829\t최선의 손실: 0.073231\t정확도: 98.20%\n",
+      "10\t검증 세트 손실: 0.080532\t최선의 손실: 0.073231\t정확도: 98.40%\n",
+      "11\t검증 세트 손실: 0.061409\t최선의 손실: 0.061409\t정확도: 98.44%\n",
+      "12\t검증 세트 손실: 0.083650\t최선의 손실: 0.061409\t정확도: 97.85%\n",
+      "13\t검증 세트 손실: 0.059578\t최선의 손실: 0.059578\t정확도: 98.63%\n",
+      "14\t검증 세트 손실: 0.097606\t최선의 손실: 0.059578\t정확도: 97.85%\n",
+      "15\t검증 세트 손실: 0.068672\t최선의 손실: 0.059578\t정확도: 97.93%\n",
+      "16\t검증 세트 손실: 0.085994\t최선의 손실: 0.059578\t정확도: 98.36%\n",
+      "17\t검증 세트 손실: 0.083038\t최선의 손실: 0.059578\t정확도: 98.08%\n",
+      "18\t검증 세트 손실: 0.100241\t최선의 손실: 0.059578\t정확도: 98.12%\n",
+      "19\t검증 세트 손실: 0.078024\t최선의 손실: 0.059578\t정확도: 98.63%\n",
+      "20\t검증 세트 손실: 0.074188\t최선의 손실: 0.059578\t정확도: 98.48%\n",
+      "21\t검증 세트 손실: 0.072548\t최선의 손실: 0.059578\t정확도: 98.32%\n",
+      "22\t검증 세트 손실: 0.082939\t최선의 손실: 0.059578\t정확도: 98.28%\n",
+      "23\t검증 세트 손실: 0.069388\t최선의 손실: 0.059578\t정확도: 98.63%\n",
+      "24\t검증 세트 손실: 0.081744\t최선의 손실: 0.059578\t정확도: 98.40%\n",
+      "25\t검증 세트 손실: 0.086303\t최선의 손실: 0.059578\t정확도: 98.28%\n",
+      "26\t검증 세트 손실: 0.058419\t최선의 손실: 0.058419\t정확도: 98.40%\n",
+      "27\t검증 세트 손실: 0.105198\t최선의 손실: 0.058419\t정확도: 98.32%\n",
+      "28\t검증 세트 손실: 0.105461\t최선의 손실: 0.058419\t정확도: 98.16%\n",
+      "29\t검증 세트 손실: 0.085234\t최선의 손실: 0.058419\t정확도: 97.65%\n",
+      "30\t검증 세트 손실: 0.078402\t최선의 손실: 0.058419\t정확도: 98.08%\n",
+      "31\t검증 세트 손실: 0.080891\t최선의 손실: 0.058419\t정확도: 98.01%\n",
+      "32\t검증 세트 손실: 0.076680\t최선의 손실: 0.058419\t정확도: 98.36%\n",
+      "33\t검증 세트 손실: 0.076640\t최선의 손실: 0.058419\t정확도: 98.40%\n",
+      "34\t검증 세트 손실: 0.154137\t최선의 손실: 0.058419\t정확도: 98.16%\n",
+      "35\t검증 세트 손실: 0.077530\t최선의 손실: 0.058419\t정확도: 98.16%\n",
+      "36\t검증 세트 손실: 0.071406\t최선의 손실: 0.058419\t정확도: 98.44%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4\t검증 세트 손실: 0.074079\t최선의 손실: 0.074079\t정확도: 97.93%\n",
-      "5\t검증 세트 손실: 0.076733\t최선의 손실: 0.074079\t정확도: 98.48%\n",
-      "6\t검증 세트 손실: 0.076256\t최선의 손실: 0.074079\t정확도: 98.20%\n",
-      "7\t검증 세트 손실: 0.081397\t최선의 손실: 0.074079\t정확도: 98.08%\n",
-      "8\t검증 세트 손실: 0.071259\t최선의 손실: 0.071259\t정확도: 98.32%\n",
-      "9\t검증 세트 손실: 0.074244\t최선의 손실: 0.071259\t정확도: 98.28%\n",
-      "10\t검증 세트 손실: 0.076722\t최선의 손실: 0.071259\t정확도: 98.16%\n",
-      "11\t검증 세트 손실: 0.082285\t최선의 손실: 0.071259\t정확도: 97.97%\n",
-      "12\t검증 세트 손실: 0.078776\t최선의 손실: 0.071259\t정확도: 97.97%\n",
-      "13\t검증 세트 손실: 0.108513\t최선의 손실: 0.071259\t정확도: 97.97%\n",
-      "14\t검증 세트 손실: 0.078267\t최선의 손실: 0.071259\t정확도: 98.16%\n",
-      "15\t검증 세트 손실: 0.094183\t최선의 손실: 0.071259\t정확도: 98.12%\n",
-      "16\t검증 세트 손실: 0.057069\t최선의 손실: 0.057069\t정확도: 98.51%\n",
-      "17\t검증 세트 손실: 0.075520\t최선의 손실: 0.057069\t정확도: 98.20%\n",
-      "18\t검증 세트 손실: 0.075037\t최선의 손실: 0.057069\t정확도: 98.28%\n",
-      "19\t검증 세트 손실: 0.138401\t최선의 손실: 0.057069\t정확도: 98.08%\n",
-      "20\t검증 세트 손실: 0.094339\t최선의 손실: 0.057069\t정확도: 97.69%\n",
-      "21\t검증 세트 손실: 0.262322\t최선의 손실: 0.057069\t정확도: 97.77%\n",
-      "22\t검증 세트 손실: 0.129006\t최선의 손실: 0.057069\t정확도: 98.01%\n",
-      "23\t검증 세트 손실: 0.081130\t최선의 손실: 0.057069\t정확도: 98.20%\n",
-      "24\t검증 세트 손실: 0.100516\t최선의 손실: 0.057069\t정확도: 98.05%\n",
-      "25\t검증 세트 손실: 0.087830\t최선의 손실: 0.057069\t정확도: 97.77%\n",
-      "26\t검증 세트 손실: 0.077462\t최선의 손실: 0.057069\t정확도: 98.16%\n",
-      "27\t검증 세트 손실: 0.086071\t최선의 손실: 0.057069\t정확도: 98.24%\n",
-      "28\t검증 세트 손실: 0.070202\t최선의 손실: 0.057069\t정확도: 98.36%\n",
-      "29\t검증 세트 손실: 0.076283\t최선의 손실: 0.057069\t정확도: 98.32%\n",
-      "30\t검증 세트 손실: 0.068712\t최선의 손실: 0.057069\t정확도: 98.24%\n",
-      "31\t검증 세트 손실: 0.068926\t최선의 손실: 0.057069\t정확도: 98.32%\n",
-      "32\t검증 세트 손실: 0.135527\t최선의 손실: 0.057069\t정확도: 98.40%\n",
-      "33\t검증 세트 손실: 0.106256\t최선의 손실: 0.057069\t정확도: 98.08%\n",
-      "34\t검증 세트 손실: 0.078194\t최선의 손실: 0.057069\t정확도: 98.28%\n",
-      "35\t검증 세트 손실: 0.082881\t최선의 손실: 0.057069\t정확도: 98.01%\n",
-      "36\t검증 세트 손실: 0.076168\t최선의 손실: 0.057069\t정확도: 98.05%\n",
-      "37\t검증 세트 손실: 0.077554\t최선의 손실: 0.057069\t정확도: 98.12%\n",
+      "37\t검증 세트 손실: 0.063188\t최선의 손실: 0.058419\t정확도: 98.32%\n",
+      "38\t검증 세트 손실: 0.086095\t최선의 손실: 0.058419\t정확도: 98.40%\n",
+      "39\t검증 세트 손실: 0.063817\t최선의 손실: 0.058419\t정확도: 98.63%\n",
+      "40\t검증 세트 손실: 0.098943\t최선의 손실: 0.058419\t정확도: 97.26%\n",
+      "41\t검증 세트 손실: 0.197769\t최선의 손실: 0.058419\t정확도: 97.73%\n",
+      "42\t검증 세트 손실: 0.095173\t최선의 손실: 0.058419\t정확도: 97.89%\n",
+      "43\t검증 세트 손실: 0.081370\t최선의 손실: 0.058419\t정확도: 98.44%\n",
+      "44\t검증 세트 손실: 0.084116\t최선의 손실: 0.058419\t정확도: 97.85%\n",
+      "45\t검증 세트 손실: 0.081747\t최선의 손실: 0.058419\t정확도: 98.12%\n",
+      "46\t검증 세트 손실: 0.072186\t최선의 손실: 0.058419\t정확도: 98.48%\n",
+      "47\t검증 세트 손실: 0.148782\t최선의 손실: 0.058419\t정확도: 97.46%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=70, total=  26.8s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.123327\t최선의 손실: 0.123327\t정확도: 96.79%\n",
-      "1\t검증 세트 손실: 0.095135\t최선의 손실: 0.095135\t정확도: 97.85%\n",
-      "2\t검증 세트 손실: 0.082829\t최선의 손실: 0.082829\t정확도: 97.77%\n",
-      "3\t검증 세트 손실: 0.077664\t최선의 손실: 0.077664\t정확도: 97.62%\n",
-      "4\t검증 세트 손실: 0.076761\t최선의 손실: 0.076761\t정확도: 98.40%\n",
-      "5\t검증 세트 손실: 0.062925\t최선의 손실: 0.062925\t정확도: 98.44%\n",
-      "6\t검증 세트 손실: 0.077227\t최선의 손실: 0.062925\t정확도: 97.65%\n",
-      "7\t검증 세트 손실: 0.075091\t최선의 손실: 0.062925\t정확도: 98.08%\n",
-      "8\t검증 세트 손실: 0.089643\t최선의 손실: 0.062925\t정확도: 98.24%\n",
-      "9\t검증 세트 손실: 0.071016\t최선의 손실: 0.062925\t정확도: 98.05%\n",
-      "10\t검증 세트 손실: 0.062719\t최선의 손실: 0.062719\t정확도: 98.40%\n",
-      "11\t검증 세트 손실: 0.059664\t최선의 손실: 0.059664\t정확도: 98.36%\n",
-      "12\t검증 세트 손실: 0.065897\t최선의 손실: 0.059664\t정확도: 98.16%\n",
-      "13\t검증 세트 손실: 0.074151\t최선의 손실: 0.059664\t정확도: 98.32%\n",
-      "14\t검증 세트 손실: 0.055974\t최선의 손실: 0.055974\t정확도: 98.63%\n",
-      "15\t검증 세트 손실: 0.073046\t최선의 손실: 0.055974\t정확도: 98.16%\n",
-      "16\t검증 세트 손실: 0.060502\t최선의 손실: 0.055974\t정확도: 98.28%\n",
-      "17\t검증 세트 손실: 0.068608\t최선의 손실: 0.055974\t정확도: 98.40%\n",
-      "18\t검증 세트 손실: 0.062682\t최선의 손실: 0.055974\t정확도: 98.51%\n",
-      "19\t검증 세트 손실: 0.065106\t최선의 손실: 0.055974\t정확도: 98.12%\n",
-      "20\t검증 세트 손실: 0.069474\t최선의 손실: 0.055974\t정확도: 98.12%\n",
-      "21\t검증 세트 손실: 0.064709\t최선의 손실: 0.055974\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.062711\t최선의 손실: 0.055974\t정확도: 98.55%\n",
-      "23\t검증 세트 손실: 0.068553\t최선의 손실: 0.055974\t정확도: 98.24%\n",
-      "24\t검증 세트 손실: 0.074633\t최선의 손실: 0.055974\t정확도: 98.12%\n",
-      "25\t검증 세트 손실: 0.069741\t최선의 손실: 0.055974\t정확도: 98.05%\n",
-      "26\t검증 세트 손실: 0.084389\t최선의 손실: 0.055974\t정확도: 98.08%\n",
-      "27\t검증 세트 손실: 0.058436\t최선의 손실: 0.055974\t정확도: 98.40%\n",
-      "28\t검증 세트 손실: 0.076696\t최선의 손실: 0.055974\t정확도: 97.85%\n",
-      "29\t검증 세트 손실: 0.063632\t최선의 손실: 0.055974\t정확도: 98.20%\n",
-      "30\t검증 세트 손실: 0.057045\t최선의 손실: 0.055974\t정확도: 98.59%\n",
-      "31\t검증 세트 손실: 0.059887\t최선의 손실: 0.055974\t정확도: 98.51%\n",
-      "32\t검증 세트 손실: 0.062692\t최선의 손실: 0.055974\t정확도: 98.75%\n",
-      "33\t검증 세트 손실: 0.123813\t최선의 손실: 0.055974\t정확도: 98.71%\n",
-      "34\t검증 세트 손실: 0.116617\t최선의 손실: 0.055974\t정확도: 97.77%\n",
-      "35\t검증 세트 손실: 0.078310\t최선의 손실: 0.055974\t정확도: 98.12%\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=70, total=  14.8s\n",
+      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=70 \n",
+      "0\t검증 세트 손실: 0.107671\t최선의 손실: 0.107671\t정확도: 97.34%\n",
+      "1\t검증 세트 손실: 0.108100\t최선의 손실: 0.107671\t정확도: 97.54%\n",
+      "2\t검증 세트 손실: 0.077520\t최선의 손실: 0.077520\t정확도: 97.69%\n",
+      "3\t검증 세트 손실: 0.075986\t최선의 손실: 0.075986\t정확도: 97.85%\n",
+      "4\t검증 세트 손실: 0.066901\t최선의 손실: 0.066901\t정확도: 98.28%\n",
+      "5\t검증 세트 손실: 0.061182\t최선의 손실: 0.061182\t정확도: 98.08%\n",
+      "6\t검증 세트 손실: 0.069804\t최선의 손실: 0.061182\t정확도: 98.48%\n",
+      "7\t검증 세트 손실: 0.059013\t최선의 손실: 0.059013\t정확도: 98.59%\n",
+      "8\t검증 세트 손실: 0.076534\t최선의 손실: 0.059013\t정확도: 98.05%\n",
+      "9\t검증 세트 손실: 0.061781\t최선의 손실: 0.059013\t정확도: 98.51%\n",
+      "10\t검증 세트 손실: 0.070417\t최선의 손실: 0.059013\t정확도: 97.77%\n",
+      "11\t검증 세트 손실: 0.073931\t최선의 손실: 0.059013\t정확도: 98.36%\n",
+      "12\t검증 세트 손실: 0.063056\t최선의 손실: 0.059013\t정확도: 98.44%\n",
+      "13\t검증 세트 손실: 0.063985\t최선의 손실: 0.059013\t정확도: 98.48%\n",
+      "14\t검증 세트 손실: 0.059387\t최선의 손실: 0.059013\t정확도: 98.36%\n",
+      "15\t검증 세트 손실: 0.072200\t최선의 손실: 0.059013\t정확도: 98.71%\n",
+      "16\t검증 세트 손실: 0.064265\t최선의 손실: 0.059013\t정확도: 98.59%\n",
+      "17\t검증 세트 손실: 0.101285\t최선의 손실: 0.059013\t정확도: 98.08%\n",
+      "18\t검증 세트 손실: 0.138975\t최선의 손실: 0.059013\t정확도: 98.28%\n",
+      "19\t검증 세트 손실: 0.086943\t최선의 손실: 0.059013\t정확도: 98.55%\n",
+      "20\t검증 세트 손실: 0.081119\t최선의 손실: 0.059013\t정확도: 98.20%\n",
+      "21\t검증 세트 손실: 0.077881\t최선의 손실: 0.059013\t정확도: 98.28%\n",
+      "22\t검증 세트 손실: 0.063355\t최선의 손실: 0.059013\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.093029\t최선의 손실: 0.059013\t정확도: 98.01%\n",
+      "24\t검증 세트 손실: 0.072233\t최선의 손실: 0.059013\t정확도: 98.36%\n",
+      "25\t검증 세트 손실: 0.070435\t최선의 손실: 0.059013\t정확도: 98.59%\n",
+      "26\t검증 세트 손실: 0.087029\t최선의 손실: 0.059013\t정확도: 97.97%\n",
+      "27\t검증 세트 손실: 0.070104\t최선의 손실: 0.059013\t정확도: 98.28%\n",
+      "28\t검증 세트 손실: 0.071336\t최선의 손실: 0.059013\t정확도: 98.59%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=70, total=  10.2s\n",
-      "[CV] batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=70 \n",
-      "0\t검증 세트 손실: 0.101183\t최선의 손실: 0.101183\t정확도: 97.19%\n",
-      "1\t검증 세트 손실: 0.094074\t최선의 손실: 0.094074\t정확도: 97.65%\n",
-      "2\t검증 세트 손실: 0.103332\t최선의 손실: 0.094074\t정확도: 96.95%\n",
-      "3\t검증 세트 손실: 0.080913\t최선의 손실: 0.080913\t정확도: 97.89%\n",
-      "4\t검증 세트 손실: 0.078805\t최선의 손실: 0.078805\t정확도: 98.36%\n",
-      "5\t검증 세트 손실: 0.084255\t최선의 손실: 0.078805\t정확도: 97.42%\n",
-      "6\t검증 세트 손실: 0.068398\t최선의 손실: 0.068398\t정확도: 98.28%\n",
-      "7\t검증 세트 손실: 0.072710\t최선의 손실: 0.068398\t정확도: 98.36%\n",
-      "8\t검증 세트 손실: 0.108462\t최선의 손실: 0.068398\t정확도: 98.44%\n",
-      "9\t검증 세트 손실: 0.068573\t최선의 손실: 0.068398\t정확도: 98.44%\n",
-      "10\t검증 세트 손실: 0.079144\t최선의 손실: 0.068398\t정확도: 98.12%\n",
-      "11\t검증 세트 손실: 0.066125\t최선의 손실: 0.066125\t정확도: 98.32%\n",
-      "12\t검증 세트 손실: 0.077547\t최선의 손실: 0.066125\t정확도: 98.36%\n",
-      "13\t검증 세트 손실: 0.061116\t최선의 손실: 0.061116\t정확도: 98.67%\n",
-      "14\t검증 세트 손실: 0.093685\t최선의 손실: 0.061116\t정확도: 98.08%\n",
-      "15\t검증 세트 손실: 0.095388\t최선의 손실: 0.061116\t정확도: 98.55%\n",
-      "16\t검증 세트 손실: 0.077489\t최선의 손실: 0.061116\t정확도: 98.16%\n",
-      "17\t검증 세트 손실: 0.098649\t최선의 손실: 0.061116\t정확도: 97.54%\n",
-      "18\t검증 세트 손실: 0.312874\t최선의 손실: 0.061116\t정확도: 97.65%\n",
-      "19\t검증 세트 손실: 0.127493\t최선의 손실: 0.061116\t정확도: 97.07%\n",
-      "20\t검증 세트 손실: 0.082677\t최선의 손실: 0.061116\t정확도: 98.08%\n",
-      "21\t검증 세트 손실: 0.069928\t최선의 손실: 0.061116\t정확도: 98.32%\n",
-      "22\t검증 세트 손실: 0.075239\t최선의 손실: 0.061116\t정확도: 98.36%\n",
-      "23\t검증 세트 손실: 0.084118\t최선의 손실: 0.061116\t정확도: 98.32%\n",
-      "24\t검증 세트 손실: 0.064181\t최선의 손실: 0.061116\t정확도: 98.36%\n",
-      "25\t검증 세트 손실: 0.064043\t최선의 손실: 0.061116\t정확도: 98.59%\n",
-      "26\t검증 세트 손실: 0.058059\t최선의 손실: 0.058059\t정확도: 98.63%\n",
-      "27\t검증 세트 손실: 0.051134\t최선의 손실: 0.051134\t정확도: 98.79%\n",
-      "28\t검증 세트 손실: 0.049394\t최선의 손실: 0.049394\t정확도: 98.51%\n",
-      "29\t검증 세트 손실: 0.060160\t최선의 손실: 0.049394\t정확도: 98.63%\n",
-      "30\t검증 세트 손실: 0.064548\t최선의 손실: 0.049394\t정확도: 98.55%\n",
-      "31\t검증 세트 손실: 0.057903\t최선의 손실: 0.049394\t정확도: 98.71%\n",
-      "32\t검증 세트 손실: 0.068230\t최선의 손실: 0.049394\t정확도: 98.36%\n",
-      "33\t검증 세트 손실: 0.078121\t최선의 손실: 0.049394\t정확도: 98.67%\n",
-      "34\t검증 세트 손실: 0.072947\t최선의 손실: 0.049394\t정확도: 98.44%\n",
-      "35\t검증 세트 손실: 0.061971\t최선의 손실: 0.049394\t정확도: 98.51%\n",
-      "36\t검증 세트 손실: 0.067431\t최선의 손실: 0.049394\t정확도: 98.44%\n",
-      "37\t검증 세트 손실: 0.074281\t최선의 손실: 0.049394\t정확도: 98.51%\n",
-      "38\t검증 세트 손실: 0.087833\t최선의 손실: 0.049394\t정확도: 98.44%\n",
-      "39\t검증 세트 손실: 0.068649\t최선의 손실: 0.049394\t정확도: 98.59%\n",
-      "40\t검증 세트 손실: 0.176200\t최선의 손실: 0.049394\t정확도: 98.67%\n",
-      "41\t검증 세트 손실: 0.118590\t최선의 손실: 0.049394\t정확도: 97.77%\n",
-      "42\t검증 세트 손실: 0.092525\t최선의 손실: 0.049394\t정확도: 97.69%\n",
-      "43\t검증 세트 손실: 0.082713\t최선의 손실: 0.049394\t정확도: 98.01%\n",
-      "44\t검증 세트 손실: 0.070730\t최선의 손실: 0.049394\t정확도: 98.05%\n",
-      "45\t검증 세트 손실: 0.071763\t최선의 손실: 0.049394\t정확도: 98.36%\n",
-      "46\t검증 세트 손실: 0.073068\t최선의 손실: 0.049394\t정확도: 98.32%\n",
-      "47\t검증 세트 손실: 0.084103\t최선의 손실: 0.049394\t정확도: 98.36%\n",
-      "48\t검증 세트 손실: 0.301437\t최선의 손실: 0.049394\t정확도: 98.05%\n",
-      "49\t검증 세트 손실: 0.086563\t최선의 손실: 0.049394\t정확도: 98.12%\n",
+      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, n_neurons=70, total=   9.3s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.288140\t최선의 손실: 0.288140\t정확도: 93.63%\n",
+      "1\t검증 세트 손실: 0.259345\t최선의 손실: 0.259345\t정확도: 93.82%\n",
+      "2\t검증 세트 손실: 0.267642\t최선의 손실: 0.259345\t정확도: 94.76%\n",
+      "3\t검증 세트 손실: 0.356388\t최선의 손실: 0.259345\t정확도: 92.89%\n",
+      "4\t검증 세트 손실: 0.202708\t최선의 손실: 0.202708\t정확도: 95.70%\n",
+      "5\t검증 세트 손실: 0.423325\t최선의 손실: 0.202708\t정확도: 76.94%\n",
+      "6\t검증 세트 손실: 0.412105\t최선의 손실: 0.202708\t정확도: 92.69%\n",
+      "7\t검증 세트 손실: 0.418036\t최선의 손실: 0.202708\t정확도: 89.21%\n",
+      "8\t검증 세트 손실: 0.665252\t최선의 손실: 0.202708\t정확도: 71.15%\n",
+      "9\t검증 세트 손실: 1.195449\t최선의 손실: 0.202708\t정확도: 43.67%\n",
+      "10\t검증 세트 손실: 1.182877\t최선의 손실: 0.202708\t정확도: 41.99%\n",
+      "11\t검증 세트 손실: 1.206438\t최선의 손실: 0.202708\t정확도: 40.42%\n",
+      "12\t검증 세트 손실: 1.196858\t최선의 손실: 0.202708\t정확도: 39.56%\n",
+      "13\t검증 세트 손실: 1.245581\t최선의 손실: 0.202708\t정확도: 37.84%\n",
+      "14\t검증 세트 손실: 1.197442\t최선의 손실: 0.202708\t정확도: 39.21%\n",
+      "15\t검증 세트 손실: 1.186861\t최선의 손실: 0.202708\t정확도: 40.03%\n",
+      "16\t검증 세트 손실: 1.304581\t최선의 손실: 0.202708\t정확도: 36.43%\n",
+      "17\t검증 세트 손실: 1.178560\t최선의 손실: 0.202708\t정확도: 40.11%\n",
+      "18\t검증 세트 손실: 1.199150\t최선의 손실: 0.202708\t정확도: 39.48%\n",
+      "19\t검증 세트 손실: 1.295731\t최선의 손실: 0.202708\t정확도: 36.08%\n",
+      "20\t검증 세트 손실: 1.507510\t최선의 손실: 0.202708\t정확도: 27.37%\n",
+      "21\t검증 세트 손실: 1.195219\t최선의 손실: 0.202708\t정확도: 38.94%\n",
+      "22\t검증 세트 손실: 1.173150\t최선의 손실: 0.202708\t정확도: 39.87%\n",
+      "23\t검증 세트 손실: 1.167641\t최선의 손실: 0.202708\t정확도: 39.99%\n",
+      "24\t검증 세트 손실: 1.168985\t최선의 손실: 0.202708\t정확도: 39.72%\n",
+      "25\t검증 세트 손실: 1.174510\t최선의 손실: 0.202708\t정확도: 39.84%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.2, learning_rate=0.01, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, n_neurons=70, total=  21.2s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.165304\t최선의 손실: 0.165304\t정확도: 95.47%\n",
-      "1\t검증 세트 손실: 0.171209\t최선의 손실: 0.165304\t정확도: 95.15%\n",
-      "2\t검증 세트 손실: 0.203003\t최선의 손실: 0.165304\t정확도: 93.20%\n",
-      "3\t검증 세트 손실: 0.202830\t최선의 손실: 0.165304\t정확도: 95.58%\n",
-      "4\t검증 세트 손실: 0.177507\t최선의 손실: 0.165304\t정확도: 94.06%\n",
-      "5\t검증 세트 손실: 0.193572\t최선의 손실: 0.165304\t정확도: 94.41%\n",
-      "6\t검증 세트 손실: 0.225930\t최선의 손실: 0.165304\t정확도: 94.10%\n",
-      "7\t검증 세트 손실: 0.184633\t최선의 손실: 0.165304\t정확도: 94.53%\n",
-      "8\t검증 세트 손실: 0.214821\t최선의 손실: 0.165304\t정확도: 93.16%\n",
-      "9\t검증 세트 손실: 0.205462\t최선의 손실: 0.165304\t정확도: 94.02%\n",
-      "10\t검증 세트 손실: 0.312421\t최선의 손실: 0.165304\t정확도: 92.03%\n",
-      "11\t검증 세트 손실: 0.290350\t최선의 손실: 0.165304\t정확도: 95.11%\n",
-      "12\t검증 세트 손실: 0.428987\t최선의 손실: 0.165304\t정확도: 90.34%\n",
-      "13\t검증 세트 손실: 0.415783\t최선의 손실: 0.165304\t정확도: 88.12%\n",
-      "14\t검증 세트 손실: 0.371096\t최선의 손실: 0.165304\t정확도: 89.84%\n",
-      "15\t검증 세트 손실: 0.333716\t최선의 손실: 0.165304\t정확도: 91.01%\n",
-      "16\t검증 세트 손실: 0.350004\t최선의 손실: 0.165304\t정확도: 91.95%\n",
-      "17\t검증 세트 손실: 0.449465\t최선의 손실: 0.165304\t정확도: 87.37%\n",
-      "18\t검증 세트 손실: 0.404958\t최선의 손실: 0.165304\t정확도: 90.19%\n",
-      "19\t검증 세트 손실: 0.436489\t최선의 손실: 0.165304\t정확도: 88.47%\n",
-      "20\t검증 세트 손실: 0.346409\t최선의 손실: 0.165304\t정확도: 92.65%\n",
-      "21\t검증 세트 손실: 0.435993\t최선의 손실: 0.165304\t정확도: 87.10%\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160, total=  13.0s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.293087\t최선의 손실: 0.293087\t정확도: 92.30%\n",
+      "1\t검증 세트 손실: 0.367762\t최선의 손실: 0.293087\t정확도: 91.99%\n",
+      "2\t검증 세트 손실: 0.244094\t최선의 손실: 0.244094\t정확도: 94.45%\n",
+      "3\t검증 세트 손실: 0.269242\t최선의 손실: 0.244094\t정확도: 94.37%\n",
+      "4\t검증 세트 손실: 0.334240\t최선의 손실: 0.244094\t정확도: 93.43%\n",
+      "5\t검증 세트 손실: 0.675773\t최선의 손실: 0.244094\t정확도: 75.14%\n",
+      "6\t검증 세트 손실: 0.397755\t최선의 손실: 0.244094\t정확도: 91.99%\n",
+      "7\t검증 세트 손실: 0.346652\t최선의 손실: 0.244094\t정확도: 92.65%\n",
+      "8\t검증 세트 손실: 0.361607\t최선의 손실: 0.244094\t정확도: 91.79%\n",
+      "9\t검증 세트 손실: 0.661234\t최선의 손실: 0.244094\t정확도: 79.01%\n",
+      "10\t검증 세트 손실: 0.456520\t최선의 손실: 0.244094\t정확도: 89.37%\n",
+      "11\t검증 세트 손실: 0.555468\t최선의 손실: 0.244094\t정확도: 85.38%\n",
+      "12\t검증 세트 손실: 0.785792\t최선의 손실: 0.244094\t정확도: 68.33%\n",
+      "13\t검증 세트 손실: 0.670652\t최선의 손실: 0.244094\t정확도: 74.82%\n",
+      "14\t검증 세트 손실: 0.969319\t최선의 손실: 0.244094\t정확도: 54.81%\n",
+      "15\t검증 세트 손실: 1.036378\t최선의 손실: 0.244094\t정확도: 51.80%\n",
+      "16\t검증 세트 손실: 0.827170\t최선의 손실: 0.244094\t정확도: 71.07%\n",
+      "17\t검증 세트 손실: 0.996155\t최선의 손실: 0.244094\t정확도: 54.69%\n",
+      "18\t검증 세트 손실: 1.163687\t최선의 손실: 0.244094\t정확도: 45.23%\n",
+      "19\t검증 세트 손실: 1.238955\t최선의 손실: 0.244094\t정확도: 39.01%\n",
+      "20\t검증 세트 손실: 1.234000\t최선의 손실: 0.244094\t정확도: 38.90%\n",
+      "21\t검증 세트 손실: 1.225394\t최선의 손실: 0.244094\t정확도: 39.05%\n",
+      "22\t검증 세트 손실: 1.223978\t최선의 손실: 0.244094\t정확도: 39.25%\n",
+      "23\t검증 세트 손실: 1.202560\t최선의 손실: 0.244094\t정확도: 39.52%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160, total=  10.6s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160 \n"
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160, total=  12.1s\n",
+      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160 \n",
+      "0\t검증 세트 손실: 0.240915\t최선의 손실: 0.240915\t정확도: 93.67%\n",
+      "1\t검증 세트 손실: 0.210069\t최선의 손실: 0.210069\t정확도: 95.04%\n",
+      "2\t검증 세트 손실: 0.196012\t최선의 손실: 0.196012\t정확도: 95.04%\n",
+      "3\t검증 세트 손실: 0.259348\t최선의 손실: 0.196012\t정확도: 94.84%\n",
+      "4\t검증 세트 손실: 0.223396\t최선의 손실: 0.196012\t정확도: 94.45%\n",
+      "5\t검증 세트 손실: 0.209319\t최선의 손실: 0.196012\t정확도: 93.98%\n",
+      "6\t검증 세트 손실: 0.257283\t최선의 손실: 0.196012\t정확도: 93.51%\n",
+      "7\t검증 세트 손실: 0.254369\t최선의 손실: 0.196012\t정확도: 93.98%\n",
+      "8\t검증 세트 손실: 0.258320\t최선의 손실: 0.196012\t정확도: 94.96%\n",
+      "9\t검증 세트 손실: 0.262227\t최선의 손실: 0.196012\t정확도: 93.00%\n",
+      "10\t검증 세트 손실: 0.244314\t최선의 손실: 0.196012\t정확도: 93.20%\n",
+      "11\t검증 세트 손실: 0.263238\t최선의 손실: 0.196012\t정확도: 93.24%\n",
+      "12\t검증 세트 손실: 0.403091\t최선의 손실: 0.196012\t정확도: 88.51%\n",
+      "13\t검증 세트 손실: 0.298446\t최선의 손실: 0.196012\t정확도: 91.79%\n",
+      "14\t검증 세트 손실: 0.281315\t최선의 손실: 0.196012\t정확도: 93.98%\n",
+      "15\t검증 세트 손실: 0.320332\t최선의 손실: 0.196012\t정확도: 91.63%\n",
+      "16\t검증 세트 손실: 0.271860\t최선의 손실: 0.196012\t정확도: 92.34%\n",
+      "17\t검증 세트 손실: 0.349187\t최선의 손실: 0.196012\t정확도: 91.71%\n",
+      "18\t검증 세트 손실: 0.304742\t최선의 손실: 0.196012\t정확도: 91.63%\n",
+      "19\t검증 세트 손실: 0.293110\t최선의 손실: 0.196012\t정확도: 92.73%\n",
+      "20\t검증 세트 손실: 0.314301\t최선의 손실: 0.196012\t정확도: 91.48%\n",
+      "21\t검증 세트 손실: 0.581377\t최선의 손실: 0.196012\t정확도: 85.26%\n",
+      "22\t검증 세트 손실: 0.483249\t최선의 손실: 0.196012\t정확도: 85.50%\n",
+      "23\t검증 세트 손실: 0.551061\t최선의 손실: 0.196012\t정확도: 81.82%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f738a2ee510>, n_neurons=160, total=  12.1s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 148336.468750\t최선의 손실: 148336.468750\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 14923.139648\t최선의 손실: 14923.139648\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 1510.096802\t최선의 손실: 1510.096802\t정확도: 19.27%\n",
+      "3\t검증 세트 손실: 1995.338989\t최선의 손실: 1510.096802\t정확도: 18.73%\n",
+      "4\t검증 세트 손실: 131054.164062\t최선의 손실: 1510.096802\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 5229452.500000\t최선의 손실: 1510.096802\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 699802.812500\t최선의 손실: 1510.096802\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 355929.125000\t최선의 손실: 1510.096802\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 13249916.000000\t최선의 손실: 1510.096802\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1371234.875000\t최선의 손실: 1510.096802\t정확도: 22.01%\n",
+      "10\t검증 세트 손실: 752486.312500\t최선의 손실: 1510.096802\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 2587733.750000\t최선의 손실: 1510.096802\t정확도: 18.73%\n",
+      "12\t검증 세트 손실: 271456.062500\t최선의 손실: 1510.096802\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 73412.414062\t최선의 손실: 1510.096802\t정확도: 18.73%\n",
+      "14\t검증 세트 손실: 266723.187500\t최선의 손실: 1510.096802\t정확도: 19.27%\n",
+      "15\t검증 세트 손실: 126303.265625\t최선의 손실: 1510.096802\t정확도: 18.80%\n",
+      "16\t검증 세트 손실: 1080778.625000\t최선의 손실: 1510.096802\t정확도: 20.91%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.207649\t최선의 손실: 0.207649\t정확도: 94.61%\n",
-      "1\t검증 세트 손실: 0.216792\t최선의 손실: 0.207649\t정확도: 94.29%\n",
-      "2\t검증 세트 손실: 0.184361\t최선의 손실: 0.184361\t정확도: 95.62%\n",
-      "3\t검증 세트 손실: 0.180711\t최선의 손실: 0.180711\t정확도: 95.97%\n",
-      "4\t검증 세트 손실: 0.286636\t최선의 손실: 0.180711\t정확도: 92.53%\n",
-      "5\t검증 세트 손실: 0.275509\t최선의 손실: 0.180711\t정확도: 93.63%\n",
-      "6\t검증 세트 손실: 0.226273\t최선의 손실: 0.180711\t정확도: 95.47%\n",
-      "7\t검증 세트 손실: 0.293287\t최선의 손실: 0.180711\t정확도: 94.61%\n",
-      "8\t검증 세트 손실: 0.386845\t최선의 손실: 0.180711\t정확도: 94.61%\n",
-      "9\t검증 세트 손실: 0.303445\t최선의 손실: 0.180711\t정확도: 93.04%\n",
-      "10\t검증 세트 손실: 0.261976\t최선의 손실: 0.180711\t정확도: 93.71%\n",
-      "11\t검증 세트 손실: 0.346238\t최선의 손실: 0.180711\t정확도: 92.42%\n",
-      "12\t검증 세트 손실: 0.346967\t최선의 손실: 0.180711\t정확도: 90.27%\n",
-      "13\t검증 세트 손실: 0.382771\t최선의 손실: 0.180711\t정확도: 91.20%\n",
-      "14\t검증 세트 손실: 0.382967\t최선의 손실: 0.180711\t정확도: 90.30%\n",
-      "15\t검증 세트 손실: 0.391740\t최선의 손실: 0.180711\t정확도: 89.44%\n",
-      "16\t검증 세트 손실: 0.353336\t최선의 손실: 0.180711\t정확도: 91.75%\n",
-      "17\t검증 세트 손실: 0.415519\t최선의 손실: 0.180711\t정확도: 89.37%\n",
-      "18\t검증 세트 손실: 0.398328\t최선의 손실: 0.180711\t정확도: 89.84%\n",
-      "19\t검증 세트 손실: 0.353290\t최선의 손실: 0.180711\t정확도: 90.54%\n",
-      "20\t검증 세트 손실: 0.384820\t최선의 손실: 0.180711\t정확도: 90.73%\n",
-      "21\t검증 세트 손실: 0.405372\t최선의 손실: 0.180711\t정확도: 91.13%\n",
-      "22\t검증 세트 손실: 0.545557\t최선의 손실: 0.180711\t정확도: 84.01%\n",
-      "23\t검증 세트 손실: 0.476285\t최선의 손실: 0.180711\t정확도: 87.18%\n",
-      "24\t검증 세트 손실: 0.454314\t최선의 손실: 0.180711\t정확도: 84.87%\n",
+      "17\t검증 세트 손실: 79076.789062\t최선의 손실: 1510.096802\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 59026.332031\t최선의 손실: 1510.096802\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 63334.492188\t최선의 손실: 1510.096802\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 111498.335938\t최선의 손실: 1510.096802\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 29015.423828\t최선의 손실: 1510.096802\t정확도: 18.73%\n",
+      "22\t검증 세트 손실: 453222.468750\t최선의 손실: 1510.096802\t정확도: 19.27%\n",
+      "23\t검증 세트 손실: 79364.593750\t최선의 손실: 1510.096802\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160, total=  12.0s\n",
-      "[CV] batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160 \n",
-      "0\t검증 세트 손실: 0.240395\t최선의 손실: 0.240395\t정확도: 94.29%\n",
-      "1\t검증 세트 손실: 0.221615\t최선의 손실: 0.221615\t정확도: 95.62%\n",
-      "2\t검증 세트 손실: 0.239356\t최선의 손실: 0.221615\t정확도: 94.37%\n",
-      "3\t검증 세트 손실: 0.225337\t최선의 손실: 0.221615\t정확도: 95.78%\n",
-      "4\t검증 세트 손실: 0.231227\t최선의 손실: 0.221615\t정확도: 95.35%\n",
-      "5\t검증 세트 손실: 0.211466\t최선의 손실: 0.211466\t정확도: 95.31%\n",
-      "6\t검증 세트 손실: 0.339003\t최선의 손실: 0.211466\t정확도: 94.21%\n",
-      "7\t검증 세트 손실: 0.240781\t최선의 손실: 0.211466\t정확도: 94.68%\n",
-      "8\t검증 세트 손실: 0.248512\t최선의 손실: 0.211466\t정확도: 94.29%\n",
-      "9\t검증 세트 손실: 0.233955\t최선의 손실: 0.211466\t정확도: 94.96%\n",
-      "10\t검증 세트 손실: 0.346503\t최선의 손실: 0.211466\t정확도: 90.85%\n",
-      "11\t검증 세트 손실: 0.347305\t최선의 손실: 0.211466\t정확도: 91.59%\n",
-      "12\t검증 세트 손실: 0.491997\t최선의 손실: 0.211466\t정확도: 87.10%\n",
-      "13\t검증 세트 손실: 0.466453\t최선의 손실: 0.211466\t정확도: 90.07%\n",
-      "14\t검증 세트 손실: 0.426381\t최선의 손실: 0.211466\t정확도: 91.13%\n",
-      "15\t검증 세트 손실: 0.356369\t최선의 손실: 0.211466\t정확도: 91.20%\n",
-      "16\t검증 세트 손실: 0.359905\t최선의 손실: 0.211466\t정확도: 92.42%\n",
-      "17\t검증 세트 손실: 0.383723\t최선의 손실: 0.211466\t정확도: 90.30%\n",
-      "18\t검증 세트 손실: 0.286911\t최선의 손실: 0.211466\t정확도: 93.86%\n",
-      "19\t검증 세트 손실: 0.406630\t최선의 손실: 0.211466\t정확도: 93.28%\n",
-      "20\t검증 세트 손실: 0.344793\t최선의 손실: 0.211466\t정확도: 92.81%\n",
-      "21\t검증 세트 손실: 0.299356\t최선의 손실: 0.211466\t정확도: 92.92%\n",
-      "22\t검증 세트 손실: 0.631608\t최선의 손실: 0.211466\t정확도: 83.89%\n",
-      "23\t검증 세트 손실: 0.360155\t최선의 손실: 0.211466\t정확도: 91.13%\n",
-      "24\t검증 세트 손실: 0.461017\t최선의 손실: 0.211466\t정확도: 88.23%\n",
-      "25\t검증 세트 손실: 0.531750\t최선의 손실: 0.211466\t정확도: 87.92%\n",
-      "26\t검증 세트 손실: 0.445410\t최선의 손실: 0.211466\t정확도: 87.26%\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=   7.8s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 1634.575806\t최선의 손실: 1634.575806\t정확도: 22.01%\n",
+      "1\t검증 세트 손실: 993.710632\t최선의 손실: 993.710632\t정확도: 22.36%\n",
+      "2\t검증 세트 손실: 62547.664062\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1548086.500000\t최선의 손실: 993.710632\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 2377654.000000\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "5\t검증 세트 손실: 361640.562500\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "6\t검증 세트 손실: 2904230.250000\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "7\t검증 세트 손실: 164768.718750\t최선의 손실: 993.710632\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 202717.125000\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "9\t검증 세트 손실: 55561.042969\t최선의 손실: 993.710632\t정확도: 18.73%\n",
+      "10\t검증 세트 손실: 65999.640625\t최선의 손실: 993.710632\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1350428.000000\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 272320.437500\t최선의 손실: 993.710632\t정확도: 22.01%\n",
+      "13\t검증 세트 손실: 547282.187500\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "14\t검증 세트 손실: 64083.503906\t최선의 손실: 993.710632\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 76598.945312\t최선의 손실: 993.710632\t정확도: 20.91%\n",
+      "16\t검증 세트 손실: 56487.003906\t최선의 손실: 993.710632\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 34009.578125\t최선의 손실: 993.710632\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 111138.179688\t최선의 손실: 993.710632\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 72397.695312\t최선의 손실: 993.710632\t정확도: 19.27%\n",
+      "20\t검증 세트 손실: 65485.660156\t최선의 손실: 993.710632\t정확도: 20.91%\n",
+      "21\t검증 세트 손실: 47633.843750\t최선의 손실: 993.710632\t정확도: 20.91%\n",
+      "22\t검증 세트 손실: 15381.926758\t최선의 손실: 993.710632\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=50, dropout_rate=0.5, learning_rate=0.01, activation=<function relu at 0x7f994177e9d8>, n_neurons=160, total=  12.8s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 46235.156250\t최선의 손실: 46235.156250\t정확도: 19.08%\n",
-      "1\t검증 세트 손실: 74234.382812\t최선의 손실: 46235.156250\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 36094.988281\t최선의 손실: 36094.988281\t정확도: 19.08%\n",
-      "3\t검증 세트 손실: 55122.902344\t최선의 손실: 36094.988281\t정확도: 19.08%\n",
-      "4\t검증 세트 손실: 167533.578125\t최선의 손실: 36094.988281\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 27292.984375\t최선의 손실: 27292.984375\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 25285.007812\t최선의 손실: 25285.007812\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 56098.632812\t최선의 손실: 25285.007812\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 20089.326172\t최선의 손실: 20089.326172\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 4456.062988\t최선의 손실: 4456.062988\t정확도: 22.01%\n",
-      "10\t검증 세트 손실: 5678.328613\t최선의 손실: 4456.062988\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 2393.367920\t최선의 손실: 2393.367920\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 2624.170410\t최선의 손실: 2393.367920\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 4982.238281\t최선의 손실: 2393.367920\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 2247.558350\t최선의 손실: 2247.558350\t정확도: 22.01%\n",
-      "15\t검증 세트 손실: 17707.851562\t최선의 손실: 2247.558350\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 3625.423096\t최선의 손실: 2247.558350\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 11393.435547\t최선의 손실: 2247.558350\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 8161.443359\t최선의 손실: 2247.558350\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 5539.327637\t최선의 손실: 2247.558350\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1609.906982\t최선의 손실: 1609.906982\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 6811.562012\t최선의 손실: 1609.906982\t정확도: 18.73%\n",
-      "22\t검증 세트 손실: 15962.880859\t최선의 손실: 1609.906982\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 1370.522827\t최선의 손실: 1370.522827\t정확도: 18.73%\n",
-      "24\t검증 세트 손실: 681.413513\t최선의 손실: 681.413513\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 4430.493164\t최선의 손실: 681.413513\t정확도: 19.08%\n",
-      "26\t검증 세트 손실: 6567.683105\t최선의 손실: 681.413513\t정확도: 20.91%\n",
-      "27\t검증 세트 손실: 1497.995972\t최선의 손실: 681.413513\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 2632.444092\t최선의 손실: 681.413513\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 455.309387\t최선의 손실: 455.309387\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 8092.895996\t최선의 손실: 455.309387\t정확도: 20.91%\n",
-      "31\t검증 세트 손실: 630.629150\t최선의 손실: 455.309387\t정확도: 19.27%\n",
-      "32\t검증 세트 손실: 602.147888\t최선의 손실: 455.309387\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 1345.247559\t최선의 손실: 455.309387\t정확도: 18.73%\n",
-      "34\t검증 세트 손실: 2577.809082\t최선의 손실: 455.309387\t정확도: 35.11%\n",
-      "35\t검증 세트 손실: 167.473938\t최선의 손실: 167.473938\t정확도: 25.84%\n",
-      "36\t검증 세트 손실: 839.328064\t최선의 손실: 167.473938\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 148.613968\t최선의 손실: 148.613968\t정확도: 21.81%\n",
-      "38\t검증 세트 손실: 68.536568\t최선의 손실: 68.536568\t정확도: 33.03%\n",
-      "39\t검증 세트 손실: 246.250046\t최선의 손실: 68.536568\t정확도: 20.91%\n",
-      "40\t검증 세트 손실: 185.493713\t최선의 손실: 68.536568\t정확도: 21.15%\n",
-      "41\t검증 세트 손실: 112.209305\t최선의 손실: 68.536568\t정확도: 34.40%\n",
-      "42\t검증 세트 손실: 49.415756\t최선의 손실: 49.415756\t정확도: 33.19%\n",
-      "43\t검증 세트 손실: 89.384384\t최선의 손실: 49.415756\t정확도: 27.44%\n",
-      "44\t검증 세트 손실: 82.785057\t최선의 손실: 49.415756\t정확도: 34.05%\n",
-      "45\t검증 세트 손실: 85.873146\t최선의 손실: 49.415756\t정확도: 26.43%\n",
-      "46\t검증 세트 손실: 66.344376\t최선의 손실: 49.415756\t정확도: 23.81%\n",
-      "47\t검증 세트 손실: 88.047729\t최선의 손실: 49.415756\t정확도: 34.36%\n",
-      "48\t검증 세트 손실: 69.621750\t최선의 손실: 49.415756\t정확도: 23.81%\n",
-      "49\t검증 세트 손실: 57.096390\t최선의 손실: 49.415756\t정확도: 28.66%\n",
-      "50\t검증 세트 손실: 107.224808\t최선의 손실: 49.415756\t정확도: 20.91%\n",
-      "51\t검증 세트 손실: 45.242409\t최선의 손실: 45.242409\t정확도: 34.87%\n",
-      "52\t검증 세트 손실: 109.279465\t최선의 손실: 45.242409\t정확도: 22.01%\n",
-      "53\t검증 세트 손실: 19.245892\t최선의 손실: 19.245892\t정확도: 34.91%\n",
-      "54\t검증 세트 손실: 22.671339\t최선의 손실: 19.245892\t정확도: 37.29%\n",
-      "55\t검증 세트 손실: 46.878353\t최선의 손실: 19.245892\t정확도: 32.06%\n",
-      "56\t검증 세트 손실: 39.040413\t최선의 손실: 19.245892\t정확도: 30.65%\n",
-      "57\t검증 세트 손실: 75.471062\t최선의 손실: 19.245892\t정확도: 27.52%\n",
-      "58\t검증 세트 손실: 20.641830\t최선의 손실: 19.245892\t정확도: 38.66%\n",
-      "59\t검증 세트 손실: 16401255.000000\t최선의 손실: 19.245892\t정확도: 19.08%\n",
-      "60\t검증 세트 손실: 3157283.750000\t최선의 손실: 19.245892\t정확도: 20.91%\n",
-      "61\t검증 세트 손실: 2029512.500000\t최선의 손실: 19.245892\t정확도: 18.73%\n",
-      "62\t검증 세트 손실: 1929005.875000\t최선의 손실: 19.245892\t정확도: 19.27%\n",
-      "63\t검증 세트 손실: 618366.812500\t최선의 손실: 19.245892\t정확도: 18.73%\n",
-      "64\t검증 세트 손실: 575983.687500\t최선의 손실: 19.245892\t정확도: 20.91%\n",
-      "65\t검증 세트 손실: 645195.750000\t최선의 손실: 19.245892\t정확도: 22.01%\n",
-      "66\t검증 세트 손실: 865476.750000\t최선의 손실: 19.245892\t정확도: 22.01%\n",
-      "67\t검증 세트 손실: 615907.187500\t최선의 손실: 19.245892\t정확도: 19.08%\n",
-      "68\t검증 세트 손실: 308236.468750\t최선의 손실: 19.245892\t정확도: 18.73%\n",
-      "69\t검증 세트 손실: 238883.046875\t최선의 손실: 19.245892\t정확도: 19.27%\n",
-      "70\t검증 세트 손실: 226403.375000\t최선의 손실: 19.245892\t정확도: 18.84%\n",
-      "71\t검증 세트 손실: 62589.730469\t최선의 손실: 19.245892\t정확도: 22.01%\n",
-      "72\t검증 세트 손실: 80300.156250\t최선의 손실: 19.245892\t정확도: 18.73%\n",
-      "73\t검증 세트 손실: 109177.906250\t최선의 손실: 19.245892\t정확도: 22.01%\n",
-      "74\t검증 세트 손실: 195031.156250\t최선의 손실: 19.245892\t정확도: 18.73%\n",
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=   7.4s\n",
+      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100 \n",
+      "0\t검증 세트 손실: 62633.558594\t최선의 손실: 62633.558594\t정확도: 18.73%\n",
+      "1\t검증 세트 손실: 149185.875000\t최선의 손실: 62633.558594\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 301049.000000\t최선의 손실: 62633.558594\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 25600.945312\t최선의 손실: 25600.945312\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 64718.929688\t최선의 손실: 25600.945312\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 93476.343750\t최선의 손실: 25600.945312\t정확도: 18.73%\n",
+      "6\t검증 세트 손실: 17584.511719\t최선의 손실: 17584.511719\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 453031.468750\t최선의 손실: 17584.511719\t정확도: 22.01%\n",
+      "8\t검증 세트 손실: 15926.215820\t최선의 손실: 15926.215820\t정확도: 18.73%\n",
+      "9\t검증 세트 손실: 36573.203125\t최선의 손실: 15926.215820\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 19708.011719\t최선의 손실: 15926.215820\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 23944.326172\t최선의 손실: 15926.215820\t정확도: 20.91%\n",
+      "12\t검증 세트 손실: 203592.781250\t최선의 손실: 15926.215820\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 27150.943359\t최선의 손실: 15926.215820\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 6073.775391\t최선의 손실: 6073.775391\t정확도: 20.91%\n",
+      "15\t검증 세트 손실: 8380.060547\t최선의 손실: 6073.775391\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1788.519531\t최선의 손실: 1788.519531\t정확도: 18.73%\n",
+      "17\t검증 세트 손실: 3313.307373\t최선의 손실: 1788.519531\t정확도: 20.91%\n",
+      "18\t검증 세트 손실: 8580.680664\t최선의 손실: 1788.519531\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 4109.913574\t최선의 손실: 1788.519531\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1494.192017\t최선의 손실: 1494.192017\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 880.820984\t최선의 손실: 880.820984\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 48032.054688\t최선의 손실: 880.820984\t정확도: 19.08%\n",
+      "23\t검증 세트 손실: 30884.037109\t최선의 손실: 880.820984\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 94710.789062\t최선의 손실: 880.820984\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 8881772.000000\t최선의 손실: 880.820984\t정확도: 18.73%\n",
+      "26\t검증 세트 손실: 3622079.000000\t최선의 손실: 880.820984\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 331837.000000\t최선의 손실: 880.820984\t정확도: 19.08%\n",
+      "28\t검증 세트 손실: 462966.687500\t최선의 손실: 880.820984\t정확도: 19.27%\n",
+      "29\t검증 세트 손실: 3674574.250000\t최선의 손실: 880.820984\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 496754.343750\t최선의 손실: 880.820984\t정확도: 19.27%\n",
+      "31\t검증 세트 손실: 212056.140625\t최선의 손실: 880.820984\t정확도: 19.27%\n",
+      "32\t검증 세트 손실: 141196.046875\t최선의 손실: 880.820984\t정확도: 19.08%\n",
+      "33\t검증 세트 손실: 30411.705078\t최선의 손실: 880.820984\t정확도: 22.36%\n",
+      "34\t검증 세트 손실: 73475.976562\t최선의 손실: 880.820984\t정확도: 20.91%\n",
+      "35\t검증 세트 손실: 163519.593750\t최선의 손실: 880.820984\t정확도: 22.01%\n",
+      "36\t검증 세트 손실: 162622.750000\t최선의 손실: 880.820984\t정확도: 18.73%\n",
+      "37\t검증 세트 손실: 318713.906250\t최선의 손실: 880.820984\t정확도: 19.27%\n",
+      "38\t검증 세트 손실: 95623.031250\t최선의 손실: 880.820984\t정확도: 18.73%\n",
+      "39\t검증 세트 손실: 268601.812500\t최선의 손실: 880.820984\t정확도: 20.91%\n",
+      "40\t검증 세트 손실: 179693.203125\t최선의 손실: 880.820984\t정확도: 18.73%\n",
+      "41\t검증 세트 손실: 215762.343750\t최선의 손실: 880.820984\t정확도: 18.73%\n",
+      "42\t검증 세트 손실: 76865.101562\t최선의 손실: 880.820984\t정확도: 19.08%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  33.5s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 102365.679688\t최선의 손실: 102365.679688\t정확도: 18.73%\n",
-      "1\t검증 세트 손실: 67670.164062\t최선의 손실: 67670.164062\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 441309.125000\t최선의 손실: 67670.164062\t정확도: 18.73%\n",
-      "3\t검증 세트 손실: 163683.078125\t최선의 손실: 67670.164062\t정확도: 23.42%\n",
-      "4\t검증 세트 손실: 34583.367188\t최선의 손실: 34583.367188\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 10367.556641\t최선의 손실: 10367.556641\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 3736.285889\t최선의 손실: 3736.285889\t정확도: 18.73%\n",
-      "7\t검증 세트 손실: 29690.960938\t최선의 손실: 3736.285889\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 9132.137695\t최선의 손실: 3736.285889\t정확도: 20.91%\n"
+      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>, n_neurons=100, total=  13.5s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.738489\t최선의 손실: 1.738489\t정확도: 19.27%\n",
+      "1\t검증 세트 손실: 1.886513\t최선의 손실: 1.738489\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.740264\t최선의 손실: 1.738489\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1.664359\t최선의 손실: 1.664359\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.706610\t최선의 손실: 1.664359\t정확도: 22.01%\n",
+      "5\t검증 세트 손실: 1.775695\t최선의 손실: 1.664359\t정확도: 22.01%\n",
+      "6\t검증 세트 손실: 2.010352\t최선의 손실: 1.664359\t정확도: 18.73%\n",
+      "7\t검증 세트 손실: 1.718218\t최선의 손실: 1.664359\t정확도: 19.27%\n",
+      "8\t검증 세트 손실: 1.717083\t최선의 손실: 1.664359\t정확도: 19.27%\n",
+      "9\t검증 세트 손실: 1.722362\t최선의 손실: 1.664359\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 1.648281\t최선의 손실: 1.648281\t정확도: 20.91%\n",
+      "11\t검증 세트 손실: 1.672289\t최선의 손실: 1.648281\t정확도: 19.08%\n",
+      "12\t검증 세트 손실: 1.755730\t최선의 손실: 1.648281\t정확도: 18.73%\n",
+      "13\t검증 세트 손실: 1.873558\t최선의 손실: 1.648281\t정확도: 19.27%\n",
+      "14\t검증 세트 손실: 1.799527\t최선의 손실: 1.648281\t정확도: 18.73%\n",
+      "15\t검증 세트 손실: 1.818024\t최선의 손실: 1.648281\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.859969\t최선의 손실: 1.648281\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.640647\t최선의 손실: 1.640647\t정확도: 22.01%\n",
+      "18\t검증 세트 손실: 2.133803\t최선의 손실: 1.640647\t정확도: 19.27%\n",
+      "19\t검증 세트 손실: 1.917773\t최선의 손실: 1.640647\t정확도: 20.91%\n",
+      "20\t검증 세트 손실: 1.629715\t최선의 손실: 1.629715\t정확도: 22.01%\n",
+      "21\t검증 세트 손실: 1.719257\t최선의 손실: 1.629715\t정확도: 19.27%\n",
+      "22\t검증 세트 손실: 1.626234\t최선의 손실: 1.626234\t정확도: 22.01%\n",
+      "23\t검증 세트 손실: 1.855303\t최선의 손실: 1.626234\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 1.674440\t최선의 손실: 1.626234\t정확도: 20.91%\n",
+      "25\t검증 세트 손실: 1.844959\t최선의 손실: 1.626234\t정확도: 19.27%\n",
+      "26\t검증 세트 손실: 1.717187\t최선의 손실: 1.626234\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 1.754613\t최선의 손실: 1.626234\t정확도: 19.27%\n",
+      "28\t검증 세트 손실: 1.867256\t최선의 손실: 1.626234\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.730222\t최선의 손실: 1.626234\t정확도: 18.73%\n",
+      "30\t검증 세트 손실: 1.732871\t최선의 손실: 1.626234\t정확도: 18.73%\n",
+      "31\t검증 세트 손실: 1.664956\t최선의 손실: 1.626234\t정확도: 19.27%\n",
+      "32\t검증 세트 손실: 1.915380\t최선의 손실: 1.626234\t정확도: 18.73%\n",
+      "33\t검증 세트 손실: 1.714958\t최선의 손실: 1.626234\t정확도: 22.01%\n",
+      "34\t검증 세트 손실: 1.672569\t최선의 손실: 1.626234\t정확도: 22.01%\n",
+      "35\t검증 세트 손실: 1.803263\t최선의 손실: 1.626234\t정확도: 19.08%\n",
+      "36\t검증 세트 손실: 1.877122\t최선의 손실: 1.626234\t정확도: 22.01%\n",
+      "37\t검증 세트 손실: 1.873342\t최선의 손실: 1.626234\t정확도: 19.27%\n",
+      "38\t검증 세트 손실: 1.798923\t최선의 손실: 1.626234\t정확도: 19.08%\n",
+      "39\t검증 세트 손실: 1.683498\t최선의 손실: 1.626234\t정확도: 20.91%\n",
+      "40\t검증 세트 손실: 1.685015\t최선의 손실: 1.626234\t정확도: 19.27%\n",
+      "41\t검증 세트 손실: 1.896602\t최선의 손실: 1.626234\t정확도: 20.91%\n",
+      "42\t검증 세트 손실: 1.791105\t최선의 손실: 1.626234\t정확도: 18.73%\n",
+      "43\t검증 세트 손실: 1.759725\t최선의 손실: 1.626234\t정확도: 20.91%\n",
+      "조기 종료!\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=  11.6s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.777285\t최선의 손실: 1.777285\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 1.877205\t최선의 손실: 1.777285\t정확도: 19.08%\n",
+      "2\t검증 세트 손실: 1.655244\t최선의 손실: 1.655244\t정확도: 20.91%\n",
+      "3\t검증 세트 손실: 1.656599\t최선의 손실: 1.655244\t정확도: 22.01%\n",
+      "4\t검증 세트 손실: 1.784726\t최선의 손실: 1.655244\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 1.647859\t최선의 손실: 1.647859\t정확도: 19.08%\n",
+      "6\t검증 세트 손실: 1.735571\t최선의 손실: 1.647859\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.765946\t최선의 손실: 1.647859\t정확도: 19.08%\n",
+      "8\t검증 세트 손실: 1.792578\t최선의 손실: 1.647859\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 2.019326\t최선의 손실: 1.647859\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 1.733482\t최선의 손실: 1.647859\t정확도: 22.01%\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9\t검증 세트 손실: 18598.861328\t최선의 손실: 3736.285889\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 4483.936035\t최선의 손실: 3736.285889\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 2744.088135\t최선의 손실: 2744.088135\t정확도: 20.91%\n",
-      "12\t검증 세트 손실: 137410.843750\t최선의 손실: 2744.088135\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 61864.625000\t최선의 손실: 2744.088135\t정확도: 19.08%\n",
-      "14\t검증 세트 손실: 88852.843750\t최선의 손실: 2744.088135\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 6767.593262\t최선의 손실: 2744.088135\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 69413.062500\t최선의 손실: 2744.088135\t정확도: 19.08%\n",
-      "17\t검증 세트 손실: 7480.527832\t최선의 손실: 2744.088135\t정확도: 19.12%\n",
-      "18\t검증 세트 손실: 5384.528320\t최선의 손실: 2744.088135\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 4154.074219\t최선의 손실: 2744.088135\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 11245.954102\t최선의 손실: 2744.088135\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 4298.452637\t최선의 손실: 2744.088135\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 1477.544067\t최선의 손실: 1477.544067\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 2203.498047\t최선의 손실: 1477.544067\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 6862.663574\t최선의 손실: 1477.544067\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 51704.949219\t최선의 손실: 1477.544067\t정확도: 19.08%\n",
-      "26\t검증 세트 손실: 2213.993896\t최선의 손실: 1477.544067\t정확도: 19.27%\n",
-      "27\t검증 세트 손실: 1827.549316\t최선의 손실: 1477.544067\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 1319.111816\t최선의 손실: 1319.111816\t정확도: 18.73%\n",
-      "29\t검증 세트 손실: 1312.537476\t최선의 손실: 1312.537476\t정확도: 18.73%\n",
-      "30\t검증 세트 손실: 3076.582520\t최선의 손실: 1312.537476\t정확도: 19.27%\n",
-      "31\t검증 세트 손실: 277.560852\t최선의 손실: 277.560852\t정확도: 28.26%\n",
-      "32\t검증 세트 손실: 2644.470215\t최선의 손실: 277.560852\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 154.391724\t최선의 손실: 154.391724\t정확도: 37.06%\n",
-      "34\t검증 세트 손실: 57349.042969\t최선의 손실: 154.391724\t정확도: 22.01%\n",
-      "35\t검증 세트 손실: 13038739.000000\t최선의 손실: 154.391724\t정확도: 19.27%\n",
-      "36\t검증 세트 손실: 10841518.000000\t최선의 손실: 154.391724\t정확도: 19.27%\n",
-      "37\t검증 세트 손실: 8120049.500000\t최선의 손실: 154.391724\t정확도: 20.91%\n",
-      "38\t검증 세트 손실: 3431625.000000\t최선의 손실: 154.391724\t정확도: 19.27%\n",
-      "39\t검증 세트 손실: 4344158.500000\t최선의 손실: 154.391724\t정확도: 19.27%\n",
-      "40\t검증 세트 손실: 3295732.250000\t최선의 손실: 154.391724\t정확도: 19.27%\n",
-      "41\t검증 세트 손실: 2027693.750000\t최선의 손실: 154.391724\t정확도: 22.01%\n",
-      "42\t검증 세트 손실: 1193151.000000\t최선의 손실: 154.391724\t정확도: 19.08%\n",
-      "43\t검증 세트 손실: 2583173.000000\t최선의 손실: 154.391724\t정확도: 19.08%\n",
-      "44\t검증 세트 손실: 598596.750000\t최선의 손실: 154.391724\t정확도: 18.84%\n",
-      "45\t검증 세트 손실: 461199.812500\t최선의 손실: 154.391724\t정확도: 18.73%\n",
-      "46\t검증 세트 손실: 658982.437500\t최선의 손실: 154.391724\t정확도: 18.73%\n",
-      "47\t검증 세트 손실: 240286.046875\t최선의 손실: 154.391724\t정확도: 19.08%\n",
-      "48\t검증 세트 손실: 662162.000000\t최선의 손실: 154.391724\t정확도: 20.91%\n",
-      "49\t검증 세트 손실: 2699251.750000\t최선의 손실: 154.391724\t정확도: 18.73%\n",
-      "50\t검증 세트 손실: 621264.875000\t최선의 손실: 154.391724\t정확도: 18.73%\n",
-      "51\t검증 세트 손실: 348285.593750\t최선의 손실: 154.391724\t정확도: 19.27%\n",
-      "52\t검증 세트 손실: 665754.312500\t최선의 손실: 154.391724\t정확도: 22.01%\n",
-      "53\t검증 세트 손실: 104589.164062\t최선의 손실: 154.391724\t정확도: 19.27%\n",
-      "54\t검증 세트 손실: 103896.890625\t최선의 손실: 154.391724\t정확도: 18.73%\n",
+      "11\t검증 세트 손실: 1.740877\t최선의 손실: 1.647859\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.627548\t최선의 손실: 1.627548\t정확도: 20.91%\n",
+      "13\t검증 세트 손실: 1.635174\t최선의 손실: 1.627548\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 1.826577\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "15\t검증 세트 손실: 1.642616\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "16\t검증 세트 손실: 1.749894\t최선의 손실: 1.627548\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.757566\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "18\t검증 세트 손실: 1.765425\t최선의 손실: 1.627548\t정확도: 22.01%\n",
+      "19\t검증 세트 손실: 1.670449\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "20\t검증 세트 손실: 1.741396\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "21\t검증 세트 손실: 1.758625\t최선의 손실: 1.627548\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.925994\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "23\t검증 세트 손실: 1.695911\t최선의 손실: 1.627548\t정확도: 22.01%\n",
+      "24\t검증 세트 손실: 2.082557\t최선의 손실: 1.627548\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 1.833698\t최선의 손실: 1.627548\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.727754\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "27\t검증 세트 손실: 1.806315\t최선의 손실: 1.627548\t정확도: 20.91%\n",
+      "28\t검증 세트 손실: 2.157050\t최선의 손실: 1.627548\t정확도: 20.91%\n",
+      "29\t검증 세트 손실: 1.776897\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "30\t검증 세트 손실: 1.756456\t최선의 손실: 1.627548\t정확도: 18.73%\n",
+      "31\t검증 세트 손실: 1.987076\t최선의 손실: 1.627548\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 1.830609\t최선의 손실: 1.627548\t정확도: 19.08%\n",
+      "33\t검증 세트 손실: 1.762762\t최선의 손실: 1.627548\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  31.2s\n",
-      "[CV] batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100 \n",
-      "0\t검증 세트 손실: 16606.205078\t최선의 손실: 16606.205078\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 36198.437500\t최선의 손실: 16606.205078\t정확도: 20.91%\n",
-      "2\t검증 세트 손실: 179447.203125\t최선의 손실: 16606.205078\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 98672.171875\t최선의 손실: 16606.205078\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 51731.277344\t최선의 손실: 16606.205078\t정확도: 19.08%\n",
-      "5\t검증 세트 손실: 36864.878906\t최선의 손실: 16606.205078\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 5378.734375\t최선의 손실: 5378.734375\t정확도: 19.08%\n",
-      "7\t검증 세트 손실: 15937.734375\t최선의 손실: 5378.734375\t정확도: 22.01%\n",
-      "8\t검증 세트 손실: 15298.714844\t최선의 손실: 5378.734375\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 8870.941406\t최선의 손실: 5378.734375\t정확도: 19.27%\n",
-      "10\t검증 세트 손실: 16292.291992\t최선의 손실: 5378.734375\t정확도: 18.73%\n",
-      "11\t검증 세트 손실: 139342.187500\t최선의 손실: 5378.734375\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 57579.347656\t최선의 손실: 5378.734375\t정확도: 22.01%\n",
-      "13\t검증 세트 손실: 239306.687500\t최선의 손실: 5378.734375\t정확도: 18.73%\n",
-      "14\t검증 세트 손실: 102040.468750\t최선의 손실: 5378.734375\t정확도: 19.27%\n",
-      "15\t검증 세트 손실: 25557.056641\t최선의 손실: 5378.734375\t정확도: 19.08%\n",
-      "16\t검증 세트 손실: 2502216.500000\t최선의 손실: 5378.734375\t정확도: 20.91%\n",
-      "17\t검증 세트 손실: 249738.906250\t최선의 손실: 5378.734375\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 202130.859375\t최선의 손실: 5378.734375\t정확도: 20.91%\n",
-      "19\t검증 세트 손실: 340861.187500\t최선의 손실: 5378.734375\t정확도: 19.27%\n",
-      "20\t검증 세트 손실: 25316.390625\t최선의 손실: 5378.734375\t정확도: 18.73%\n",
-      "21\t검증 세트 손실: 4914.886719\t최선의 손실: 4914.886719\t정확도: 19.08%\n",
-      "22\t검증 세트 손실: 8126.057617\t최선의 손실: 4914.886719\t정확도: 19.27%\n",
-      "23\t검증 세트 손실: 47516.871094\t최선의 손실: 4914.886719\t정확도: 20.91%\n",
-      "24\t검증 세트 손실: 5135.030273\t최선의 손실: 4914.886719\t정확도: 22.01%\n",
-      "25\t검증 세트 손실: 9061.321289\t최선의 손실: 4914.886719\t정확도: 19.27%\n",
-      "26\t검증 세트 손실: 3626.130615\t최선의 손실: 3626.130615\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 3790.055908\t최선의 손실: 3626.130615\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 3461.950439\t최선의 손실: 3461.950439\t정확도: 20.91%\n",
-      "29\t검증 세트 손실: 2868.234863\t최선의 손실: 2868.234863\t정확도: 19.27%\n",
-      "30\t검증 세트 손실: 3938.862061\t최선의 손실: 2868.234863\t정확도: 20.91%\n",
-      "31\t검증 세트 손실: 20116.503906\t최선의 손실: 2868.234863\t정확도: 20.91%\n",
-      "32\t검증 세트 손실: 57291.378906\t최선의 손실: 2868.234863\t정확도: 22.01%\n",
-      "33\t검증 세트 손실: 5037.669434\t최선의 손실: 2868.234863\t정확도: 19.27%\n",
-      "34\t검증 세트 손실: 2463.037842\t최선의 손실: 2463.037842\t정확도: 18.73%\n",
-      "35\t검증 세트 손실: 875.109070\t최선의 손실: 875.109070\t정확도: 33.70%\n",
-      "36\t검증 세트 손실: 1766.489258\t최선의 손실: 875.109070\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 30582.429688\t최선의 손실: 875.109070\t정확도: 19.08%\n",
-      "38\t검증 세트 손실: 1470.815186\t최선의 손실: 875.109070\t정확도: 25.02%\n",
-      "39\t검증 세트 손실: 5932.288574\t최선의 손실: 875.109070\t정확도: 20.91%\n",
-      "40\t검증 세트 손실: 34803.503906\t최선의 손실: 875.109070\t정확도: 18.73%\n",
-      "41\t검증 세트 손실: 34166248.000000\t최선의 손실: 875.109070\t정확도: 22.01%\n",
-      "42\t검증 세트 손실: 10764979.000000\t최선의 손실: 875.109070\t정확도: 19.08%\n",
-      "43\t검증 세트 손실: 10878757.000000\t최선의 손실: 875.109070\t정확도: 18.73%\n",
-      "44\t검증 세트 손실: 10434073.000000\t최선의 손실: 875.109070\t정확도: 19.27%\n",
-      "45\t검증 세트 손실: 10765347.000000\t최선의 손실: 875.109070\t정확도: 19.08%\n",
-      "46\t검증 세트 손실: 2736914.000000\t최선의 손실: 875.109070\t정확도: 22.01%\n",
-      "47\t검증 세트 손실: 1787282.500000\t최선의 손실: 875.109070\t정확도: 22.01%\n",
-      "48\t검증 세트 손실: 906424.750000\t최선의 손실: 875.109070\t정확도: 20.91%\n",
-      "49\t검증 세트 손실: 337993.281250\t최선의 손실: 875.109070\t정확도: 19.08%\n",
-      "50\t검증 세트 손실: 396387.375000\t최선의 손실: 875.109070\t정확도: 19.27%\n",
-      "51\t검증 세트 손실: 555130.687500\t최선의 손실: 875.109070\t정확도: 22.01%\n",
-      "52\t검증 세트 손실: 556829.437500\t최선의 손실: 875.109070\t정확도: 19.08%\n",
-      "53\t검증 세트 손실: 1159261.125000\t최선의 손실: 875.109070\t정확도: 20.91%\n",
-      "54\t검증 세트 손실: 359081.093750\t최선의 손실: 875.109070\t정확도: 19.08%\n",
-      "55\t검증 세트 손실: 272729.156250\t최선의 손실: 875.109070\t정확도: 20.91%\n",
-      "56\t검증 세트 손실: 1101329.625000\t최선의 손실: 875.109070\t정확도: 19.27%\n",
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=   9.2s\n",
+      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140 \n",
+      "0\t검증 세트 손실: 1.899494\t최선의 손실: 1.899494\t정확도: 20.91%\n",
+      "1\t검증 세트 손실: 1.684400\t최선의 손실: 1.684400\t정확도: 18.73%\n",
+      "2\t검증 세트 손실: 1.717962\t최선의 손실: 1.684400\t정확도: 19.08%\n",
+      "3\t검증 세트 손실: 1.710994\t최선의 손실: 1.684400\t정확도: 19.27%\n",
+      "4\t검증 세트 손실: 1.690144\t최선의 손실: 1.684400\t정확도: 20.91%\n",
+      "5\t검증 세트 손실: 1.880843\t최선의 손실: 1.684400\t정확도: 20.91%\n",
+      "6\t검증 세트 손실: 2.032708\t최선의 손실: 1.684400\t정확도: 19.27%\n",
+      "7\t검증 세트 손실: 1.885430\t최선의 손실: 1.684400\t정확도: 18.73%\n",
+      "8\t검증 세트 손실: 1.670620\t최선의 손실: 1.670620\t정확도: 22.01%\n",
+      "9\t검증 세트 손실: 1.742793\t최선의 손실: 1.670620\t정확도: 19.08%\n",
+      "10\t검증 세트 손실: 1.685296\t최선의 손실: 1.670620\t정확도: 19.08%\n",
+      "11\t검증 세트 손실: 1.829634\t최선의 손실: 1.670620\t정확도: 22.01%\n",
+      "12\t검증 세트 손실: 1.756803\t최선의 손실: 1.670620\t정확도: 19.08%\n",
+      "13\t검증 세트 손실: 1.658075\t최선의 손실: 1.658075\t정확도: 22.01%\n",
+      "14\t검증 세트 손실: 2.057945\t최선의 손실: 1.658075\t정확도: 22.01%\n",
+      "15\t검증 세트 손실: 1.689310\t최선의 손실: 1.658075\t정확도: 22.01%\n",
+      "16\t검증 세트 손실: 1.868093\t최선의 손실: 1.658075\t정확도: 22.01%\n",
+      "17\t검증 세트 손실: 1.635349\t최선의 손실: 1.635349\t정확도: 18.73%\n",
+      "18\t검증 세트 손실: 1.609725\t최선의 손실: 1.609725\t정확도: 20.91%\n",
+      "19\t검증 세트 손실: 1.755853\t최선의 손실: 1.609725\t정확도: 22.01%\n",
+      "20\t검증 세트 손실: 1.770261\t최선의 손실: 1.609725\t정확도: 19.27%\n",
+      "21\t검증 세트 손실: 1.723042\t최선의 손실: 1.609725\t정확도: 22.01%\n",
+      "22\t검증 세트 손실: 1.806255\t최선의 손실: 1.609725\t정확도: 20.91%\n",
+      "23\t검증 세트 손실: 1.663087\t최선의 손실: 1.609725\t정확도: 20.91%\n",
+      "24\t검증 세트 손실: 1.815744\t최선의 손실: 1.609725\t정확도: 18.73%\n",
+      "25\t검증 세트 손실: 1.701659\t최선의 손실: 1.609725\t정확도: 22.01%\n",
+      "26\t검증 세트 손실: 1.868236\t최선의 손실: 1.609725\t정확도: 18.73%\n",
+      "27\t검증 세트 손실: 1.662011\t최선의 손실: 1.609725\t정확도: 20.91%\n",
+      "28\t검증 세트 손실: 1.966487\t최선의 손실: 1.609725\t정확도: 19.08%\n",
+      "29\t검증 세트 손실: 1.959482\t최선의 손실: 1.609725\t정확도: 19.08%\n",
+      "30\t검증 세트 손실: 1.924305\t최선의 손실: 1.609725\t정확도: 20.91%\n",
+      "31\t검증 세트 손실: 1.864602\t최선의 손실: 1.609725\t정확도: 22.01%\n",
+      "32\t검증 세트 손실: 1.724891\t최선의 손실: 1.609725\t정확도: 19.08%\n",
+      "33\t검증 세트 손실: 1.641151\t최선의 손실: 1.609725\t정확도: 18.73%\n",
+      "34\t검증 세트 손실: 1.777585\t최선의 손실: 1.609725\t정확도: 20.91%\n",
+      "35\t검증 세트 손실: 1.657573\t최선의 손실: 1.609725\t정확도: 19.27%\n",
+      "36\t검증 세트 손실: 1.645926\t최선의 손실: 1.609725\t정확도: 20.91%\n",
+      "37\t검증 세트 손실: 1.715135\t최선의 손실: 1.609725\t정확도: 19.08%\n",
+      "38\t검증 세트 손실: 1.829035\t최선의 손실: 1.609725\t정확도: 18.73%\n",
+      "39\t검증 세트 손실: 1.640036\t최선의 손실: 1.609725\t정확도: 18.73%\n",
       "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.6, learning_rate=0.1, activation=<function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>, n_neurons=100, total=  21.8s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.644320\t최선의 손실: 1.644320\t정확도: 19.27%\n",
-      "1\t검증 세트 손실: 1.653483\t최선의 손실: 1.644320\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.719215\t최선의 손실: 1.644320\t정확도: 22.01%\n",
-      "3\t검증 세트 손실: 1.696974\t최선의 손실: 1.644320\t정확도: 22.01%\n",
-      "4\t검증 세트 손실: 1.651405\t최선의 손실: 1.644320\t정확도: 19.27%\n",
-      "5\t검증 세트 손실: 1.707898\t최선의 손실: 1.644320\t정확도: 22.01%\n",
-      "6\t검증 세트 손실: 1.851454\t최선의 손실: 1.644320\t정확도: 22.01%\n",
-      "7\t검증 세트 손실: 1.743905\t최선의 손실: 1.644320\t정확도: 19.27%\n",
-      "8\t검증 세트 손실: 1.710552\t최선의 손실: 1.644320\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.743414\t최선의 손실: 1.644320\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.681003\t최선의 손실: 1.644320\t정확도: 20.91%\n",
-      "11\t검증 세트 손실: 1.659888\t최선의 손실: 1.644320\t정확도: 19.08%\n",
-      "12\t검증 세트 손실: 1.719396\t최선의 손실: 1.644320\t정확도: 18.73%\n",
-      "13\t검증 세트 손실: 1.937718\t최선의 손실: 1.644320\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.831242\t최선의 손실: 1.644320\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.826040\t최선의 손실: 1.644320\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.846875\t최선의 손실: 1.644320\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.637858\t최선의 손실: 1.637858\t정확도: 22.01%\n",
-      "18\t검증 세트 손실: 2.186293\t최선의 손실: 1.637858\t정확도: 19.27%\n",
-      "19\t검증 세트 손실: 1.792092\t최선의 손실: 1.637858\t정확도: 20.91%\n",
-      "20\t검증 세트 손실: 1.627514\t최선의 손실: 1.627514\t정확도: 22.01%\n",
-      "21\t검증 세트 손실: 1.724122\t최선의 손실: 1.627514\t정확도: 19.27%\n",
-      "22\t검증 세트 손실: 1.639088\t최선의 손실: 1.627514\t정확도: 19.08%\n",
-      "23\t검증 세트 손실: 1.892438\t최선의 손실: 1.627514\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 1.671759\t최선의 손실: 1.627514\t정확도: 20.91%\n",
-      "25\t검증 세트 손실: 1.870734\t최선의 손실: 1.627514\t정확도: 19.27%\n",
-      "26\t검증 세트 손실: 1.718385\t최선의 손실: 1.627514\t정확도: 19.08%\n",
-      "27\t검증 세트 손실: 1.780656\t최선의 손실: 1.627514\t정확도: 18.73%\n",
-      "28\t검증 세트 손실: 1.772281\t최선의 손실: 1.627514\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.691118\t최선의 손실: 1.627514\t정확도: 18.73%\n",
-      "30\t검증 세트 손실: 1.728671\t최선의 손실: 1.627514\t정확도: 18.73%\n",
-      "31\t검증 세트 손실: 1.703331\t최선의 손실: 1.627514\t정확도: 22.01%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "32\t검증 세트 손실: 1.780508\t최선의 손실: 1.627514\t정확도: 18.73%\n",
-      "33\t검증 세트 손실: 1.791418\t최선의 손실: 1.627514\t정확도: 22.01%\n",
-      "34\t검증 세트 손실: 1.691227\t최선의 손실: 1.627514\t정확도: 22.01%\n",
-      "35\t검증 세트 손실: 1.911774\t최선의 손실: 1.627514\t정확도: 19.08%\n",
-      "36\t검증 세트 손실: 1.853387\t최선의 손실: 1.627514\t정확도: 22.01%\n",
-      "37\t검증 세트 손실: 1.910979\t최선의 손실: 1.627514\t정확도: 18.73%\n",
-      "38\t검증 세트 손실: 1.855113\t최선의 손실: 1.627514\t정확도: 19.08%\n",
-      "39\t검증 세트 손실: 1.672579\t최선의 손실: 1.627514\t정확도: 19.08%\n",
-      "40\t검증 세트 손실: 1.710523\t최선의 손실: 1.627514\t정확도: 19.27%\n",
-      "41\t검증 세트 손실: 1.845725\t최선의 손실: 1.627514\t정확도: 20.91%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=  19.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.804516\t최선의 손실: 1.804516\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 1.831324\t최선의 손실: 1.804516\t정확도: 19.08%\n",
-      "2\t검증 세트 손실: 1.732017\t최선의 손실: 1.732017\t정확도: 19.27%\n",
-      "3\t검증 세트 손실: 1.645857\t최선의 손실: 1.645857\t정확도: 19.27%\n",
-      "4\t검증 세트 손실: 1.816252\t최선의 손실: 1.645857\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 1.655361\t최선의 손실: 1.645857\t정확도: 19.08%\n",
-      "6\t검증 세트 손실: 1.715950\t최선의 손실: 1.645857\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.711245\t최선의 손실: 1.645857\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 2.156768\t최선의 손실: 1.645857\t정확도: 22.01%\n",
-      "9\t검증 세트 손실: 1.805618\t최선의 손실: 1.645857\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.774984\t최선의 손실: 1.645857\t정확도: 22.01%\n",
-      "11\t검증 세트 손실: 1.752678\t최선의 손실: 1.645857\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.700524\t최선의 손실: 1.645857\t정확도: 20.91%\n",
-      "13\t검증 세트 손실: 1.666700\t최선의 손실: 1.645857\t정확도: 22.01%\n",
-      "14\t검증 세트 손실: 1.780499\t최선의 손실: 1.645857\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.639649\t최선의 손실: 1.639649\t정확도: 18.73%\n",
-      "16\t검증 세트 손실: 1.705021\t최선의 손실: 1.639649\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.757832\t최선의 손실: 1.639649\t정확도: 19.08%\n",
-      "18\t검증 세트 손실: 1.754216\t최선의 손실: 1.639649\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.655147\t최선의 손실: 1.639649\t정확도: 19.08%\n",
-      "20\t검증 세트 손실: 1.742277\t최선의 손실: 1.639649\t정확도: 19.08%\n",
-      "21\t검증 세트 손실: 1.693142\t최선의 손실: 1.639649\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.913250\t최선의 손실: 1.639649\t정확도: 18.73%\n",
-      "23\t검증 세트 손실: 1.702979\t최선의 손실: 1.639649\t정확도: 22.01%\n",
-      "24\t검증 세트 손실: 2.037127\t최선의 손실: 1.639649\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 1.835981\t최선의 손실: 1.639649\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.723547\t최선의 손실: 1.639649\t정확도: 20.91%\n",
-      "27\t검증 세트 손실: 1.809554\t최선의 손실: 1.639649\t정확도: 20.91%\n",
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-      "29\t검증 세트 손실: 1.759387\t최선의 손실: 1.639649\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 1.741954\t최선의 손실: 1.639649\t정확도: 18.73%\n",
-      "31\t검증 세트 손실: 1.990940\t최선의 손실: 1.639649\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 1.831525\t최선의 손실: 1.639649\t정확도: 19.08%\n",
-      "33\t검증 세트 손실: 1.753433\t최선의 손실: 1.639649\t정확도: 18.73%\n",
-      "34\t검증 세트 손실: 1.748428\t최선의 손실: 1.639649\t정확도: 19.27%\n",
-      "35\t검증 세트 손실: 2.184103\t최선의 손실: 1.639649\t정확도: 20.91%\n",
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-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=   9.3s\n",
-      "[CV] batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140 \n",
-      "0\t검증 세트 손실: 1.714286\t최선의 손실: 1.714286\t정확도: 20.91%\n",
-      "1\t검증 세트 손실: 1.673020\t최선의 손실: 1.673020\t정확도: 19.27%\n",
-      "2\t검증 세트 손실: 1.674802\t최선의 손실: 1.673020\t정확도: 19.08%\n",
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-      "4\t검증 세트 손실: 1.721168\t최선의 손실: 1.673020\t정확도: 20.91%\n",
-      "5\t검증 세트 손실: 1.821952\t최선의 손실: 1.673020\t정확도: 20.91%\n",
-      "6\t검증 세트 손실: 1.986490\t최선의 손실: 1.673020\t정확도: 19.27%\n",
-      "7\t검증 세트 손실: 1.818145\t최선의 손실: 1.673020\t정확도: 18.73%\n",
-      "8\t검증 세트 손실: 1.672773\t최선의 손실: 1.672773\t정확도: 19.27%\n",
-      "9\t검증 세트 손실: 1.666432\t최선의 손실: 1.666432\t정확도: 19.08%\n",
-      "10\t검증 세트 손실: 1.685649\t최선의 손실: 1.666432\t정확도: 19.08%\n",
-      "11\t검증 세트 손실: 1.751844\t최선의 손실: 1.666432\t정확도: 22.01%\n",
-      "12\t검증 세트 손실: 1.718766\t최선의 손실: 1.666432\t정확도: 19.08%\n",
-      "13\t검증 세트 손실: 1.660089\t최선의 손실: 1.660089\t정확도: 19.27%\n",
-      "14\t검증 세트 손실: 1.965366\t최선의 손실: 1.660089\t정확도: 18.73%\n",
-      "15\t검증 세트 손실: 1.764700\t최선의 손실: 1.660089\t정확도: 22.01%\n",
-      "16\t검증 세트 손실: 1.893243\t최선의 손실: 1.660089\t정확도: 22.01%\n",
-      "17\t검증 세트 손실: 1.687203\t최선의 손실: 1.660089\t정확도: 18.73%\n",
-      "18\t검증 세트 손실: 1.611146\t최선의 손실: 1.611146\t정확도: 22.01%\n",
-      "19\t검증 세트 손실: 1.736961\t최선의 손실: 1.611146\t정확도: 22.01%\n",
-      "20\t검증 세트 손실: 1.790612\t최선의 손실: 1.611146\t정확도: 19.27%\n",
-      "21\t검증 세트 손실: 2.111502\t최선의 손실: 1.611146\t정확도: 22.01%\n",
-      "22\t검증 세트 손실: 1.751820\t최선의 손실: 1.611146\t정확도: 20.91%\n",
-      "23\t검증 세트 손실: 1.660002\t최선의 손실: 1.611146\t정확도: 20.91%\n",
-      "24\t검증 세트 손실: 1.825585\t최선의 손실: 1.611146\t정확도: 18.73%\n",
-      "25\t검증 세트 손실: 1.678729\t최선의 손실: 1.611146\t정확도: 22.01%\n",
-      "26\t검증 세트 손실: 1.801777\t최선의 손실: 1.611146\t정확도: 18.73%\n",
-      "27\t검증 세트 손실: 1.721965\t최선의 손실: 1.611146\t정확도: 20.91%\n",
-      "28\t검증 세트 손실: 1.675931\t최선의 손실: 1.611146\t정확도: 19.08%\n",
-      "29\t검증 세트 손실: 1.884228\t최선의 손실: 1.611146\t정확도: 19.08%\n",
-      "30\t검증 세트 손실: 1.931579\t최선의 손실: 1.611146\t정확도: 20.91%\n",
-      "31\t검증 세트 손실: 1.797259\t최선의 손실: 1.611146\t정확도: 22.01%\n",
-      "32\t검증 세트 손실: 1.701447\t최선의 손실: 1.611146\t정확도: 19.08%\n",
-      "33\t검증 세트 손실: 1.734368\t최선의 손실: 1.611146\t정확도: 22.01%\n",
-      "34\t검증 세트 손실: 1.740996\t최선의 손실: 1.611146\t정확도: 18.73%\n",
-      "35\t검증 세트 손실: 1.757417\t최선의 손실: 1.611146\t정확도: 19.27%\n",
-      "36\t검증 세트 손실: 1.631478\t최선의 손실: 1.611146\t정확도: 20.91%\n",
-      "37\t검증 세트 손실: 1.817790\t최선의 손실: 1.611146\t정확도: 19.08%\n",
-      "38\t검증 세트 손실: 1.825160\t최선의 손실: 1.611146\t정확도: 18.73%\n",
-      "39\t검증 세트 손실: 1.658551\t최선의 손실: 1.611146\t정확도: 18.73%\n",
-      "조기 종료!\n",
-      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f99417cf6a8>, n_neurons=140, total=  10.0s\n"
+      "[CV]  batch_size=100, dropout_rate=0.5, learning_rate=0.05, activation=<function elu at 0x7f738a2bd1e0>, n_neurons=140, total=  10.6s\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 100.2min finished\n"
+      "[Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 67.3min finished\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.131360\t최선의 손실: 0.131360\t정확도: 95.86%\n",
-      "1\t검증 세트 손실: 0.119933\t최선의 손실: 0.119933\t정확도: 96.36%\n",
-      "2\t검증 세트 손실: 0.122168\t최선의 손실: 0.119933\t정확도: 97.03%\n",
-      "3\t검증 세트 손실: 0.095872\t최선의 손실: 0.095872\t정확도: 97.50%\n",
-      "4\t검증 세트 손실: 0.081462\t최선의 손실: 0.081462\t정확도: 97.77%\n",
-      "5\t검증 세트 손실: 0.080509\t최선의 손실: 0.080509\t정확도: 97.77%\n",
-      "6\t검증 세트 손실: 0.078339\t최선의 손실: 0.078339\t정확도: 97.69%\n",
-      "7\t검증 세트 손실: 0.077064\t최선의 손실: 0.077064\t정확도: 98.05%\n",
-      "8\t검증 세트 손실: 0.075848\t최선의 손실: 0.075848\t정확도: 97.85%\n",
-      "9\t검증 세트 손실: 0.082536\t최선의 손실: 0.075848\t정확도: 97.81%\n",
-      "10\t검증 세트 손실: 0.068357\t최선의 손실: 0.068357\t정확도: 98.20%\n",
-      "11\t검증 세트 손실: 0.067985\t최선의 손실: 0.067985\t정확도: 98.24%\n",
-      "12\t검증 세트 손실: 0.067976\t최선의 손실: 0.067976\t정확도: 98.01%\n",
-      "13\t검증 세트 손실: 0.061581\t최선의 손실: 0.061581\t정확도: 98.24%\n",
-      "14\t검증 세트 손실: 0.065799\t최선의 손실: 0.061581\t정확도: 98.16%\n",
-      "15\t검증 세트 손실: 0.061942\t최선의 손실: 0.061581\t정확도: 98.05%\n",
-      "16\t검증 세트 손실: 0.063398\t최선의 손실: 0.061581\t정확도: 98.20%\n",
-      "17\t검증 세트 손실: 0.062658\t최선의 손실: 0.061581\t정확도: 98.48%\n",
-      "18\t검증 세트 손실: 0.060921\t최선의 손실: 0.060921\t정확도: 98.20%\n",
-      "19\t검증 세트 손실: 0.060935\t최선의 손실: 0.060921\t정확도: 98.32%\n",
-      "20\t검증 세트 손실: 0.061994\t최선의 손실: 0.060921\t정확도: 98.12%\n",
-      "21\t검증 세트 손실: 0.056391\t최선의 손실: 0.056391\t정확도: 98.40%\n",
-      "22\t검증 세트 손실: 0.059623\t최선의 손실: 0.056391\t정확도: 98.28%\n",
-      "23\t검증 세트 손실: 0.061316\t최선의 손실: 0.056391\t정확도: 98.44%\n",
-      "24\t검증 세트 손실: 0.064249\t최선의 손실: 0.056391\t정확도: 98.16%\n",
-      "25\t검증 세트 손실: 0.058985\t최선의 손실: 0.056391\t정확도: 98.40%\n",
-      "26\t검증 세트 손실: 0.058578\t최선의 손실: 0.056391\t정확도: 98.40%\n",
-      "27\t검증 세트 손실: 0.057499\t최선의 손실: 0.056391\t정확도: 98.55%\n",
-      "28\t검증 세트 손실: 0.058286\t최선의 손실: 0.056391\t정확도: 98.32%\n",
-      "29\t검증 세트 손실: 0.057116\t최선의 손실: 0.056391\t정확도: 98.36%\n",
-      "30\t검증 세트 손실: 0.055941\t최선의 손실: 0.055941\t정확도: 98.48%\n",
-      "31\t검증 세트 손실: 0.065117\t최선의 손실: 0.055941\t정확도: 98.44%\n",
-      "32\t검증 세트 손실: 0.061243\t최선의 손실: 0.055941\t정확도: 98.16%\n",
-      "33\t검증 세트 손실: 0.062351\t최선의 손실: 0.055941\t정확도: 98.32%\n",
-      "34\t검증 세트 손실: 0.059844\t최선의 손실: 0.055941\t정확도: 98.48%\n",
-      "35\t검증 세트 손실: 0.060938\t최선의 손실: 0.055941\t정확도: 98.48%\n",
-      "36\t검증 세트 손실: 0.056217\t최선의 손실: 0.055941\t정확도: 98.51%\n",
-      "37\t검증 세트 손실: 0.057297\t최선의 손실: 0.055941\t정확도: 98.48%\n",
-      "38\t검증 세트 손실: 0.059047\t최선의 손실: 0.055941\t정확도: 98.59%\n",
-      "39\t검증 세트 손실: 0.053099\t최선의 손실: 0.053099\t정확도: 98.59%\n",
-      "40\t검증 세트 손실: 0.054116\t최선의 손실: 0.053099\t정확도: 98.51%\n",
-      "41\t검증 세트 손실: 0.055040\t최선의 손실: 0.053099\t정확도: 98.51%\n",
-      "42\t검증 세트 손실: 0.056496\t최선의 손실: 0.053099\t정확도: 98.51%\n",
-      "43\t검증 세트 손실: 0.055101\t최선의 손실: 0.053099\t정확도: 98.71%\n",
-      "44\t검증 세트 손실: 0.052763\t최선의 손실: 0.052763\t정확도: 98.59%\n",
-      "45\t검증 세트 손실: 0.054311\t최선의 손실: 0.052763\t정확도: 98.59%\n",
-      "46\t검증 세트 손실: 0.048983\t최선의 손실: 0.048983\t정확도: 98.67%\n",
-      "47\t검증 세트 손실: 0.055365\t최선의 손실: 0.048983\t정확도: 98.75%\n",
-      "48\t검증 세트 손실: 0.059711\t최선의 손실: 0.048983\t정확도: 98.51%\n",
-      "49\t검증 세트 손실: 0.058687\t최선의 손실: 0.048983\t정확도: 98.51%\n",
-      "50\t검증 세트 손실: 0.051804\t최선의 손실: 0.048983\t정확도: 98.63%\n",
-      "51\t검증 세트 손실: 0.058217\t최선의 손실: 0.048983\t정확도: 98.51%\n",
-      "52\t검증 세트 손실: 0.055234\t최선의 손실: 0.048983\t정확도: 98.67%\n",
-      "53\t검증 세트 손실: 0.059891\t최선의 손실: 0.048983\t정확도: 98.48%\n",
-      "54\t검증 세트 손실: 0.062794\t최선의 손실: 0.048983\t정확도: 98.51%\n",
-      "55\t검증 세트 손실: 0.053535\t최선의 손실: 0.048983\t정확도: 98.63%\n",
-      "56\t검증 세트 손실: 0.058334\t최선의 손실: 0.048983\t정확도: 98.55%\n",
-      "57\t검증 세트 손실: 0.056358\t최선의 손실: 0.048983\t정확도: 98.55%\n",
-      "58\t검증 세트 손실: 0.054802\t최선의 손실: 0.048983\t정확도: 98.79%\n",
-      "59\t검증 세트 손실: 0.054305\t최선의 손실: 0.048983\t정확도: 98.63%\n",
-      "60\t검증 세트 손실: 0.054506\t최선의 손실: 0.048983\t정확도: 98.55%\n",
-      "61\t검증 세트 손실: 0.055785\t최선의 손실: 0.048983\t정확도: 98.63%\n",
-      "62\t검증 세트 손실: 0.050867\t최선의 손실: 0.048983\t정확도: 98.83%\n",
-      "63\t검증 세트 손실: 0.056156\t최선의 손실: 0.048983\t정확도: 98.71%\n",
-      "64\t검증 세트 손실: 0.054518\t최선의 손실: 0.048983\t정확도: 98.67%\n",
-      "65\t검증 세트 손실: 0.057605\t최선의 손실: 0.048983\t정확도: 98.75%\n",
-      "66\t검증 세트 손실: 0.050115\t최선의 손실: 0.048983\t정확도: 98.59%\n",
-      "67\t검증 세트 손실: 0.055503\t최선의 손실: 0.048983\t정확도: 98.51%\n",
+      "0\t검증 세트 손실: 0.118236\t최선의 손실: 0.118236\t정확도: 96.99%\n",
+      "1\t검증 세트 손실: 0.093091\t최선의 손실: 0.093091\t정확도: 97.73%\n",
+      "2\t검증 세트 손실: 0.066741\t최선의 손실: 0.066741\t정확도: 98.24%\n",
+      "3\t검증 세트 손실: 0.068239\t최선의 손실: 0.066741\t정확도: 97.93%\n",
+      "4\t검증 세트 손실: 0.084212\t최선의 손실: 0.066741\t정확도: 98.40%\n",
+      "5\t검증 세트 손실: 0.061941\t최선의 손실: 0.061941\t정확도: 98.51%\n",
+      "6\t검증 세트 손실: 0.066682\t최선의 손실: 0.061941\t정확도: 98.44%\n",
+      "7\t검증 세트 손실: 0.075863\t최선의 손실: 0.061941\t정확도: 98.28%\n",
+      "8\t검증 세트 손실: 0.060383\t최선의 손실: 0.060383\t정확도: 98.44%\n",
+      "9\t검증 세트 손실: 0.060800\t최선의 손실: 0.060383\t정확도: 98.67%\n",
+      "10\t검증 세트 손실: 0.060890\t최선의 손실: 0.060383\t정확도: 98.55%\n",
+      "11\t검증 세트 손실: 0.063054\t최선의 손실: 0.060383\t정확도: 98.24%\n",
+      "12\t검증 세트 손실: 0.056856\t최선의 손실: 0.056856\t정확도: 98.59%\n",
+      "13\t검증 세트 손실: 0.071873\t최선의 손실: 0.056856\t정확도: 98.05%\n",
+      "14\t검증 세트 손실: 0.075582\t최선의 손실: 0.056856\t정확도: 98.20%\n",
+      "15\t검증 세트 손실: 0.058859\t최선의 손실: 0.056856\t정확도: 98.67%\n",
+      "16\t검증 세트 손실: 0.096720\t최선의 손실: 0.056856\t정확도: 98.32%\n",
+      "17\t검증 세트 손실: 0.077195\t최선의 손실: 0.056856\t정확도: 98.24%\n",
+      "18\t검증 세트 손실: 0.064093\t최선의 손실: 0.056856\t정확도: 98.55%\n",
+      "19\t검증 세트 손실: 0.066197\t최선의 손실: 0.056856\t정확도: 98.40%\n",
+      "20\t검증 세트 손실: 0.055566\t최선의 손실: 0.055566\t정확도: 98.51%\n",
+      "21\t검증 세트 손실: 0.059392\t최선의 손실: 0.055566\t정확도: 98.44%\n",
+      "22\t검증 세트 손실: 0.050597\t최선의 손실: 0.050597\t정확도: 98.51%\n",
+      "23\t검증 세트 손실: 0.053956\t최선의 손실: 0.050597\t정확도: 98.48%\n",
+      "24\t검증 세트 손실: 0.047114\t최선의 손실: 0.047114\t정확도: 98.87%\n",
+      "25\t검증 세트 손실: 0.058926\t최선의 손실: 0.047114\t정확도: 98.71%\n",
+      "26\t검증 세트 손실: 0.052567\t최선의 손실: 0.047114\t정확도: 98.48%\n",
+      "27\t검증 세트 손실: 0.084408\t최선의 손실: 0.047114\t정확도: 98.40%\n",
+      "28\t검증 세트 손실: 0.095611\t최선의 손실: 0.047114\t정확도: 98.48%\n",
+      "29\t검증 세트 손실: 0.077153\t최선의 손실: 0.047114\t정확도: 98.16%\n",
+      "30\t검증 세트 손실: 0.094049\t최선의 손실: 0.047114\t정확도: 98.05%\n",
+      "31\t검증 세트 손실: 0.078699\t최선의 손실: 0.047114\t정확도: 98.59%\n",
+      "32\t검증 세트 손실: 0.094053\t최선의 손실: 0.047114\t정확도: 97.50%\n",
+      "33\t검증 세트 손실: 0.078904\t최선의 손실: 0.047114\t정확도: 98.20%\n",
+      "34\t검증 세트 손실: 0.073573\t최선의 손실: 0.047114\t정확도: 98.20%\n",
+      "35\t검증 세트 손실: 0.071339\t최선의 손실: 0.047114\t정확도: 98.48%\n",
+      "36\t검증 세트 손실: 0.068395\t최선의 손실: 0.047114\t정확도: 98.55%\n",
+      "37\t검증 세트 손실: 0.068394\t최선의 손실: 0.047114\t정확도: 98.36%\n",
+      "38\t검증 세트 손실: 0.063651\t최선의 손실: 0.047114\t정확도: 98.63%\n",
+      "39\t검증 세트 손실: 0.065632\t최선의 손실: 0.047114\t정확도: 98.36%\n",
+      "40\t검증 세트 손실: 0.065360\t최선의 손실: 0.047114\t정확도: 98.63%\n",
+      "41\t검증 세트 손실: 0.062944\t최선의 손실: 0.047114\t정확도: 98.59%\n",
+      "42\t검증 세트 손실: 0.070905\t최선의 손실: 0.047114\t정확도: 98.51%\n",
+      "43\t검증 세트 손실: 0.061176\t최선의 손실: 0.047114\t정확도: 98.55%\n",
+      "44\t검증 세트 손실: 0.067180\t최선의 손실: 0.047114\t정확도: 98.71%\n",
+      "45\t검증 세트 손실: 0.054086\t최선의 손실: 0.047114\t정확도: 98.59%\n",
       "조기 종료!\n"
      ]
     },
@@ -20498,19 +20332,19 @@
      "data": {
       "text/plain": [
        "RandomizedSearchCV(cv=None, error_score='raise',\n",
-       "          estimator=DNNClassifier(activation=<function elu at 0x7f99417cf6a8>,\n",
+       "          estimator=DNNClassifier(activation=<function elu at 0x7f738a2bd1e0>,\n",
        "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=5, n_neurons=100,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42),\n",
        "          fit_params=None, iid=True, n_iter=50, n_jobs=1,\n",
-       "          param_distributions={'dropout_rate': [0.2, 0.3, 0.4, 0.5, 0.6], 'batch_size': [10, 50, 100, 500], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'activation': [<function relu at 0x7f994177e9d8>, <function elu at 0x7f99417cf6a8>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0c80>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f974f6c0ae8>], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160]},\n",
+       "          param_distributions={'batch_size': [10, 50, 100, 500], 'activation': [<function relu at 0x7f738a2ee510>, <function elu at 0x7f738a2bd1e0>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19840>, <function leaky_relu.<locals>.parametrized_leaky_relu at 0x7f71a6b19a60>], 'learning_rate': [0.01, 0.02, 0.05, 0.1], 'dropout_rate': [0.2, 0.3, 0.4, 0.5, 0.6], 'n_neurons': [10, 30, 50, 70, 90, 100, 120, 140, 160]},\n",
        "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
        "          return_train_score='warn', scoring=None, verbose=2)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -20537,20 +20371,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'activation': <function __main__.leaky_relu.<locals>.parametrized_leaky_relu(z, name=None)>,\n",
-       " 'batch_size': 500,\n",
-       " 'dropout_rate': 0.4,\n",
+       " 'batch_size': 100,\n",
+       " 'dropout_rate': 0.2,\n",
        " 'learning_rate': 0.01,\n",
-       " 'n_neurons': 50}"
+       " 'n_neurons': 70}"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -20561,16 +20395,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9883245767659078"
+       "0.9889083479276124"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -20626,7 +20460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20651,7 +20485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20664,7 +20498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20698,7 +20532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20719,7 +20553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20736,7 +20570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20753,7 +20587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -20761,48 +20595,39 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\n",
-      "0\t검증 세트 손실: 1.033727\t최선의 손실: 1.033727\t정확도: 60.00%\n",
-      "1\t검증 세트 손실: 0.955005\t최선의 손실: 0.955005\t정확도: 63.33%\n",
-      "2\t검증 세트 손실: 0.937349\t최선의 손실: 0.937349\t정확도: 62.67%\n",
-      "3\t검증 세트 손실: 0.904641\t최선의 손실: 0.904641\t정확도: 65.33%\n",
-      "4\t검증 세트 손실: 0.899408\t최선의 손실: 0.899408\t정확도: 62.67%\n",
-      "5\t검증 세트 손실: 0.828898\t최선의 손실: 0.828898\t정확도: 72.00%\n",
-      "6\t검증 세트 손실: 0.867087\t최선의 손실: 0.828898\t정확도: 70.67%\n",
-      "7\t검증 세트 손실: 0.848966\t최선의 손실: 0.828898\t정확도: 71.33%\n",
-      "8\t검증 세트 손실: 0.825576\t최선의 손실: 0.825576\t정확도: 70.00%\n",
-      "9\t검증 세트 손실: 0.826259\t최선의 손실: 0.825576\t정확도: 72.67%\n",
-      "10\t검증 세트 손실: 0.864259\t최선의 손실: 0.825576\t정확도: 71.33%\n",
-      "11\t검증 세트 손실: 0.859593\t최선의 손실: 0.825576\t정확도: 71.33%\n",
-      "12\t검증 세트 손실: 0.837671\t최선의 손실: 0.825576\t정확도: 73.33%\n",
-      "13\t검증 세트 손실: 0.814570\t최선의 손실: 0.814570\t정확도: 73.33%\n",
-      "14\t검증 세트 손실: 0.878083\t최선의 손실: 0.814570\t정확도: 70.00%\n",
-      "15\t검증 세트 손실: 0.880612\t최선의 손실: 0.814570\t정확도: 70.67%\n",
-      "16\t검증 세트 손실: 0.881547\t최선의 손실: 0.814570\t정확도: 72.00%\n",
-      "17\t검증 세트 손실: 0.782665\t최선의 손실: 0.782665\t정확도: 76.67%\n",
-      "18\t검증 세트 손실: 0.819389\t최선의 손실: 0.782665\t정확도: 73.33%\n",
-      "19\t검증 세트 손실: 0.827952\t최선의 손실: 0.782665\t정확도: 72.00%\n",
-      "20\t검증 세트 손실: 0.821545\t최선의 손실: 0.782665\t정확도: 72.67%\n",
-      "21\t검증 세트 손실: 0.855989\t최선의 손실: 0.782665\t정확도: 72.00%\n",
-      "22\t검증 세트 손실: 0.806475\t최선의 손실: 0.782665\t정확도: 75.33%\n",
-      "23\t검증 세트 손실: 0.802232\t최선의 손실: 0.782665\t정확도: 73.33%\n",
-      "24\t검증 세트 손실: 0.839092\t최선의 손실: 0.782665\t정확도: 75.33%\n",
-      "25\t검증 세트 손실: 0.829860\t최선의 손실: 0.782665\t정확도: 73.33%\n",
-      "26\t검증 세트 손실: 0.839752\t최선의 손실: 0.782665\t정확도: 74.00%\n",
-      "27\t검증 세트 손실: 0.891025\t최선의 손실: 0.782665\t정확도: 71.33%\n",
-      "28\t검증 세트 손실: 0.833040\t최선의 손실: 0.782665\t정확도: 72.67%\n",
-      "29\t검증 세트 손실: 0.824907\t최선의 손실: 0.782665\t정확도: 72.67%\n",
-      "30\t검증 세트 손실: 0.838565\t최선의 손실: 0.782665\t정확도: 72.00%\n",
-      "31\t검증 세트 손실: 0.853131\t최선의 손실: 0.782665\t정확도: 73.33%\n",
-      "32\t검증 세트 손실: 0.842774\t최선의 손실: 0.782665\t정확도: 70.00%\n",
-      "33\t검증 세트 손실: 0.864202\t최선의 손실: 0.782665\t정확도: 70.00%\n",
-      "34\t검증 세트 손실: 0.865824\t최선의 손실: 0.782665\t정확도: 74.67%\n",
-      "35\t검증 세트 손실: 0.900983\t최선의 손실: 0.782665\t정확도: 67.33%\n",
-      "36\t검증 세트 손실: 0.871366\t최선의 손실: 0.782665\t정확도: 69.33%\n",
-      "37\t검증 세트 손실: 0.859163\t최선의 손실: 0.782665\t정확도: 72.67%\n",
+      "0\t검증 세트 손실: 1.262913\t최선의 손실: 1.262913\t정확도: 64.67%\n",
+      "1\t검증 세트 손실: 0.907002\t최선의 손실: 0.907002\t정확도: 71.33%\n",
+      "2\t검증 세트 손실: 0.886537\t최선의 손실: 0.886537\t정확도: 69.33%\n",
+      "3\t검증 세트 손실: 0.962119\t최선의 손실: 0.886537\t정확도: 72.00%\n",
+      "4\t검증 세트 손실: 0.932307\t최선의 손실: 0.886537\t정확도: 70.67%\n",
+      "5\t검증 세트 손실: 0.843027\t최선의 손실: 0.843027\t정확도: 75.33%\n",
+      "6\t검증 세트 손실: 0.904979\t최선의 손실: 0.843027\t정확도: 75.33%\n",
+      "7\t검증 세트 손실: 0.831989\t최선의 손실: 0.831989\t정확도: 73.33%\n",
+      "8\t검증 세트 손실: 0.791916\t최선의 손실: 0.791916\t정확도: 78.67%\n",
+      "9\t검증 세트 손실: 0.871919\t최선의 손실: 0.791916\t정확도: 76.00%\n",
+      "10\t검증 세트 손실: 0.890289\t최선의 손실: 0.791916\t정확도: 74.67%\n",
+      "11\t검증 세트 손실: 0.869197\t최선의 손실: 0.791916\t정확도: 74.67%\n",
+      "12\t검증 세트 손실: 0.883039\t최선의 손실: 0.791916\t정확도: 75.33%\n",
+      "13\t검증 세트 손실: 0.901014\t최선의 손실: 0.791916\t정확도: 76.67%\n",
+      "14\t검증 세트 손실: 0.958109\t최선의 손실: 0.791916\t정확도: 75.33%\n",
+      "15\t검증 세트 손실: 0.960675\t최선의 손실: 0.791916\t정확도: 72.67%\n",
+      "16\t검증 세트 손실: 0.894816\t최선의 손실: 0.791916\t정확도: 76.67%\n",
+      "17\t검증 세트 손실: 0.886800\t최선의 손실: 0.791916\t정확도: 78.67%\n",
+      "18\t검증 세트 손실: 0.925755\t최선의 손실: 0.791916\t정확도: 77.33%\n",
+      "19\t검증 세트 손실: 0.894577\t최선의 손실: 0.791916\t정확도: 76.00%\n",
+      "20\t검증 세트 손실: 0.976480\t최선의 손실: 0.791916\t정확도: 76.00%\n",
+      "21\t검증 세트 손실: 1.023342\t최선의 손실: 0.791916\t정확도: 76.67%\n",
+      "22\t검증 세트 손실: 1.009959\t최선의 손실: 0.791916\t정확도: 73.33%\n",
+      "23\t검증 세트 손실: 0.935665\t최선의 손실: 0.791916\t정확도: 76.67%\n",
+      "24\t검증 세트 손실: 1.023280\t최선의 손실: 0.791916\t정확도: 74.00%\n",
+      "25\t검증 세트 손실: 0.962913\t최선의 손실: 0.791916\t정확도: 78.67%\n",
+      "26\t검증 세트 손실: 0.991648\t최선의 손실: 0.791916\t정확도: 78.00%\n",
+      "27\t검증 세트 손실: 1.018240\t최선의 손실: 0.791916\t정확도: 76.00%\n",
+      "28\t검증 세트 손실: 1.026909\t최선의 손실: 0.791916\t정확도: 76.00%\n",
       "조기 종료!\n",
-      "전체 훈련 시간: 1.3s\n",
+      "전체 훈련 시간: 0.7s\n",
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_five_frozen\n",
-      "최종 테스트 정확도: 70.64%\n"
+      "최종 테스트 정확도: 74.02%\n"
      ]
     }
    ],
@@ -20881,7 +20706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20897,7 +20722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -20905,53 +20730,39 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\n",
-      "0\t검증 세트 손실: 1.049432\t최선의 손실: 1.049432\t정확도: 58.67%\n",
-      "1\t검증 세트 손실: 0.940628\t최선의 손실: 0.940628\t정확도: 68.00%\n",
-      "2\t검증 세트 손실: 0.916611\t최선의 손실: 0.916611\t정확도: 68.00%\n",
-      "3\t검증 세트 손실: 0.879467\t최선의 손실: 0.879467\t정확도: 70.00%\n",
-      "4\t검증 세트 손실: 0.895895\t최선의 손실: 0.879467\t정확도: 67.33%\n",
-      "5\t검증 세트 손실: 0.856078\t최선의 손실: 0.856078\t정확도: 72.67%\n",
-      "6\t검증 세트 손실: 0.875888\t최선의 손실: 0.856078\t정확도: 70.67%\n",
-      "7\t검증 세트 손실: 0.801806\t최선의 손실: 0.801806\t정확도: 73.33%\n",
-      "8\t검증 세트 손실: 0.863690\t최선의 손실: 0.801806\t정확도: 70.00%\n",
-      "9\t검증 세트 손실: 0.908275\t최선의 손실: 0.801806\t정확도: 66.67%\n",
-      "10\t검증 세트 손실: 0.831289\t최선의 손실: 0.801806\t정확도: 69.33%\n",
-      "11\t검증 세트 손실: 0.825551\t최선의 손실: 0.801806\t정확도: 73.33%\n",
-      "12\t검증 세트 손실: 0.866857\t최선의 손실: 0.801806\t정확도: 66.67%\n",
-      "13\t검증 세트 손실: 0.840445\t최선의 손실: 0.801806\t정확도: 71.33%\n",
-      "14\t검증 세트 손실: 0.858019\t최선의 손실: 0.801806\t정확도: 72.00%\n",
-      "15\t검증 세트 손실: 0.854924\t최선의 손실: 0.801806\t정확도: 69.33%\n",
-      "16\t검증 세트 손실: 0.829445\t최선의 손실: 0.801806\t정확도: 73.33%\n",
-      "17\t검증 세트 손실: 0.802240\t최선의 손실: 0.801806\t정확도: 74.67%\n",
-      "18\t검증 세트 손실: 0.917180\t최선의 손실: 0.801806\t정확도: 70.00%\n",
-      "19\t검증 세트 손실: 0.910294\t최선의 손실: 0.801806\t정확도: 70.00%\n",
-      "20\t검증 세트 손실: 0.898889\t최선의 손실: 0.801806\t정확도: 68.67%\n",
-      "21\t검증 세트 손실: 0.906065\t최선의 손실: 0.801806\t정확도: 70.67%\n",
-      "22\t검증 세트 손실: 0.790730\t최선의 손실: 0.790730\t정확도: 73.33%\n",
-      "23\t검증 세트 손실: 0.905560\t최선의 손실: 0.790730\t정확도: 67.33%\n",
-      "24\t검증 세트 손실: 0.812477\t최선의 손실: 0.790730\t정확도: 74.00%\n",
-      "25\t검증 세트 손실: 0.816068\t최선의 손실: 0.790730\t정확도: 72.67%\n",
-      "26\t검증 세트 손실: 0.817256\t최선의 손실: 0.790730\t정확도: 74.00%\n",
-      "27\t검증 세트 손실: 0.869530\t최선의 손실: 0.790730\t정확도: 70.67%\n",
-      "28\t검증 세트 손실: 0.846872\t최선의 손실: 0.790730\t정확도: 72.67%\n",
-      "29\t검증 세트 손실: 0.813477\t최선의 손실: 0.790730\t정확도: 74.00%\n",
-      "30\t검증 세트 손실: 0.903572\t최선의 손실: 0.790730\t정확도: 69.33%\n",
-      "31\t검증 세트 손실: 0.835853\t최선의 손실: 0.790730\t정확도: 72.00%\n",
-      "32\t검증 세트 손실: 0.818891\t최선의 손실: 0.790730\t정확도: 74.00%\n",
-      "33\t검증 세트 손실: 0.839385\t최선의 손실: 0.790730\t정확도: 74.00%\n",
-      "34\t검증 세트 손실: 0.816110\t최선의 손실: 0.790730\t정확도: 74.00%\n",
-      "35\t검증 세트 손실: 0.853949\t최선의 손실: 0.790730\t정확도: 75.33%\n",
-      "36\t검증 세트 손실: 0.868162\t최선의 손실: 0.790730\t정확도: 72.00%\n",
-      "37\t검증 세트 손실: 0.798322\t최선의 손실: 0.790730\t정확도: 76.00%\n",
-      "38\t검증 세트 손실: 0.906934\t최선의 손실: 0.790730\t정확도: 68.67%\n",
-      "39\t검증 세트 손실: 0.870616\t최선의 손실: 0.790730\t정확도: 70.67%\n",
-      "40\t검증 세트 손실: 0.859143\t최선의 손실: 0.790730\t정확도: 70.67%\n",
-      "41\t검증 세트 손실: 0.851021\t최선의 손실: 0.790730\t정확도: 71.33%\n",
-      "42\t검증 세트 손실: 0.847611\t최선의 손실: 0.790730\t정확도: 71.33%\n",
+      "0\t검증 세트 손실: 1.202295\t최선의 손실: 1.202295\t정확도: 65.33%\n",
+      "1\t검증 세트 손실: 0.933995\t최선의 손실: 0.933995\t정확도: 68.67%\n",
+      "2\t검증 세트 손실: 0.814186\t최선의 손실: 0.814186\t정확도: 70.00%\n",
+      "3\t검증 세트 손실: 0.781698\t최선의 손실: 0.781698\t정확도: 78.00%\n",
+      "4\t검증 세트 손실: 0.921291\t최선의 손실: 0.781698\t정확도: 67.33%\n",
+      "5\t검증 세트 손실: 0.994272\t최선의 손실: 0.781698\t정확도: 72.67%\n",
+      "6\t검증 세트 손실: 0.882180\t최선의 손실: 0.781698\t정확도: 72.00%\n",
+      "7\t검증 세트 손실: 0.910248\t최선의 손실: 0.781698\t정확도: 75.33%\n",
+      "8\t검증 세트 손실: 0.774129\t최선의 손실: 0.774129\t정확도: 78.00%\n",
+      "9\t검증 세트 손실: 0.906837\t최선의 손실: 0.774129\t정확도: 73.33%\n",
+      "10\t검증 세트 손실: 0.952088\t최선의 손실: 0.774129\t정확도: 73.33%\n",
+      "11\t검증 세트 손실: 0.873378\t최선의 손실: 0.774129\t정확도: 78.00%\n",
+      "12\t검증 세트 손실: 0.791198\t최선의 손실: 0.774129\t정확도: 78.67%\n",
+      "13\t검증 세트 손실: 0.890449\t최선의 손실: 0.774129\t정확도: 79.33%\n",
+      "14\t검증 세트 손실: 0.851787\t최선의 손실: 0.774129\t정확도: 77.33%\n",
+      "15\t검증 세트 손실: 1.054000\t최선의 손실: 0.774129\t정확도: 76.00%\n",
+      "16\t검증 세트 손실: 0.786068\t최선의 손실: 0.774129\t정확도: 78.00%\n",
+      "17\t검증 세트 손실: 0.916792\t최선의 손실: 0.774129\t정확도: 76.67%\n",
+      "18\t검증 세트 손실: 0.884020\t최선의 손실: 0.774129\t정확도: 76.67%\n",
+      "19\t검증 세트 손실: 1.136296\t최선의 손실: 0.774129\t정확도: 75.33%\n",
+      "20\t검증 세트 손실: 1.066115\t최선의 손실: 0.774129\t정확도: 74.00%\n",
+      "21\t검증 세트 손실: 0.982818\t최선의 손실: 0.774129\t정확도: 75.33%\n",
+      "22\t검증 세트 손실: 0.961077\t최선의 손실: 0.774129\t정확도: 77.33%\n",
+      "23\t검증 세트 손실: 0.991658\t최선의 손실: 0.774129\t정확도: 78.00%\n",
+      "24\t검증 세트 손실: 0.941197\t최선의 손실: 0.774129\t정확도: 76.00%\n",
+      "25\t검증 세트 손실: 1.032557\t최선의 손실: 0.774129\t정확도: 76.00%\n",
+      "26\t검증 세트 손실: 1.006461\t최선의 손실: 0.774129\t정확도: 78.00%\n",
+      "27\t검증 세트 손실: 1.063901\t최선의 손실: 0.774129\t정확도: 74.67%\n",
+      "28\t검증 세트 손실: 0.962244\t최선의 손실: 0.774129\t정확도: 79.33%\n",
       "조기 종료!\n",
-      "전체 훈련 시간: 0.8s\n",
+      "전체 훈련 시간: 0.6s\n",
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_five_frozen\n",
-      "최종 테스트 정확도: 70.54%\n"
+      "최종 테스트 정확도: 74.90%\n"
      ]
     }
    ],
@@ -21026,7 +20837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21057,7 +20868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21080,7 +20891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -21088,52 +20899,39 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_best_mnist_model_0_to_4\n",
-      "0\t검증 세트 손실: 0.935025\t최선의 손실: 0.935025\t정확도: 66.67%\n",
-      "1\t검증 세트 손실: 0.850245\t최선의 손실: 0.850245\t정확도: 70.00%\n",
-      "2\t검증 세트 손실: 0.842121\t최선의 손실: 0.842121\t정확도: 68.00%\n",
-      "3\t검증 세트 손실: 0.828718\t최선의 손실: 0.828718\t정확도: 64.00%\n",
-      "4\t검증 세트 손실: 0.795188\t최선의 손실: 0.795188\t정확도: 67.33%\n",
-      "5\t검증 세트 손실: 0.774223\t최선의 손실: 0.774223\t정확도: 70.67%\n",
-      "6\t검증 세트 손실: 0.799743\t최선의 손실: 0.774223\t정확도: 71.33%\n",
-      "7\t검증 세트 손실: 0.722909\t최선의 손실: 0.722909\t정확도: 75.33%\n",
-      "8\t검증 세트 손실: 0.721824\t최선의 손실: 0.721824\t정확도: 73.33%\n",
-      "9\t검증 세트 손실: 0.713133\t최선의 손실: 0.713133\t정확도: 74.67%\n",
-      "10\t검증 세트 손실: 0.719689\t최선의 손실: 0.713133\t정확도: 75.33%\n",
-      "11\t검증 세트 손실: 0.713757\t최선의 손실: 0.713133\t정확도: 75.33%\n",
-      "12\t검증 세트 손실: 0.712615\t최선의 손실: 0.712615\t정확도: 78.00%\n",
-      "13\t검증 세트 손실: 0.693470\t최선의 손실: 0.693470\t정확도: 76.00%\n",
-      "14\t검증 세트 손실: 0.705978\t최선의 손실: 0.693470\t정확도: 74.67%\n",
-      "15\t검증 세트 손실: 0.712512\t최선의 손실: 0.693470\t정확도: 74.67%\n",
-      "16\t검증 세트 손실: 0.719814\t최선의 손실: 0.693470\t정확도: 74.67%\n",
-      "17\t검증 세트 손실: 0.677740\t최선의 손실: 0.677740\t정확도: 75.33%\n",
-      "18\t검증 세트 손실: 0.692203\t최선의 손실: 0.677740\t정확도: 76.67%\n",
-      "19\t검증 세트 손실: 0.694647\t최선의 손실: 0.677740\t정확도: 76.67%\n",
-      "20\t검증 세트 손실: 0.682613\t최선의 손실: 0.677740\t정확도: 76.67%\n",
-      "21\t검증 세트 손실: 0.715474\t최선의 손실: 0.677740\t정확도: 74.00%\n",
-      "22\t검증 세트 손실: 0.664829\t최선의 손실: 0.664829\t정확도: 76.67%\n",
-      "23\t검증 세트 손실: 0.676336\t최선의 손실: 0.664829\t정확도: 77.33%\n",
-      "24\t검증 세트 손실: 0.710294\t최선의 손실: 0.664829\t정확도: 76.00%\n",
-      "25\t검증 세트 손실: 0.678950\t최선의 손실: 0.664829\t정확도: 80.00%\n",
-      "26\t검증 세트 손실: 0.699065\t최선의 손실: 0.664829\t정확도: 76.67%\n",
-      "27\t검증 세트 손실: 0.730067\t최선의 손실: 0.664829\t정확도: 72.00%\n",
-      "28\t검증 세트 손실: 0.669669\t최선의 손실: 0.664829\t정확도: 80.00%\n",
-      "29\t검증 세트 손실: 0.665041\t최선의 손실: 0.664829\t정확도: 76.67%\n",
-      "30\t검증 세트 손실: 0.681953\t최선의 손실: 0.664829\t정확도: 75.33%\n",
-      "31\t검증 세트 손실: 0.714381\t최선의 손실: 0.664829\t정확도: 77.33%\n",
-      "32\t검증 세트 손실: 0.687661\t최선의 손실: 0.664829\t정확도: 77.33%\n",
-      "33\t검증 세트 손실: 0.695228\t최선의 손실: 0.664829\t정확도: 76.00%\n",
-      "34\t검증 세트 손실: 0.703653\t최선의 손실: 0.664829\t정확도: 78.00%\n",
-      "35\t검증 세트 손실: 0.738633\t최선의 손실: 0.664829\t정확도: 75.33%\n",
-      "36\t검증 세트 손실: 0.687415\t최선의 손실: 0.664829\t정확도: 76.00%\n",
-      "37\t검증 세트 손실: 0.693483\t최선의 손실: 0.664829\t정확도: 78.00%\n",
-      "38\t검증 세트 손실: 0.686928\t최선의 손실: 0.664829\t정확도: 78.67%\n",
-      "39\t검증 세트 손실: 0.727356\t최선의 손실: 0.664829\t정확도: 76.00%\n",
-      "40\t검증 세트 손실: 0.692756\t최선의 손실: 0.664829\t정확도: 79.33%\n",
-      "41\t검증 세트 손실: 0.691602\t최선의 손실: 0.664829\t정확도: 76.00%\n",
-      "42\t검증 세트 손실: 0.673264\t최선의 손실: 0.664829\t정확도: 81.33%\n",
+      "0\t검증 세트 손실: 1.161074\t최선의 손실: 1.161074\t정확도: 68.67%\n",
+      "1\t검증 세트 손실: 0.825706\t최선의 손실: 0.825706\t정확도: 76.00%\n",
+      "2\t검증 세트 손실: 0.849399\t최선의 손실: 0.825706\t정확도: 68.00%\n",
+      "3\t검증 세트 손실: 0.799384\t최선의 손실: 0.799384\t정확도: 77.33%\n",
+      "4\t검증 세트 손실: 0.702611\t최선의 손실: 0.702611\t정확도: 78.67%\n",
+      "5\t검증 세트 손실: 0.735079\t최선의 손실: 0.702611\t정확도: 76.67%\n",
+      "6\t검증 세트 손실: 0.882863\t최선의 손실: 0.702611\t정확도: 75.33%\n",
+      "7\t검증 세트 손실: 0.683173\t최선의 손실: 0.683173\t정확도: 80.67%\n",
+      "8\t검증 세트 손실: 0.674991\t최선의 손실: 0.674991\t정확도: 81.33%\n",
+      "9\t검증 세트 손실: 0.635016\t최선의 손실: 0.635016\t정확도: 82.67%\n",
+      "10\t검증 세트 손실: 0.741493\t최선의 손실: 0.635016\t정확도: 77.33%\n",
+      "11\t검증 세트 손실: 0.689587\t최선의 손실: 0.635016\t정확도: 78.67%\n",
+      "12\t검증 세트 손실: 0.741480\t최선의 손실: 0.635016\t정확도: 78.00%\n",
+      "13\t검증 세트 손실: 0.710071\t최선의 손실: 0.635016\t정확도: 79.33%\n",
+      "14\t검증 세트 손실: 0.785275\t최선의 손실: 0.635016\t정확도: 77.33%\n",
+      "15\t검증 세트 손실: 0.890523\t최선의 손실: 0.635016\t정확도: 76.67%\n",
+      "16\t검증 세트 손실: 0.776709\t최선의 손실: 0.635016\t정확도: 80.00%\n",
+      "17\t검증 세트 손실: 0.672425\t최선의 손실: 0.635016\t정확도: 82.00%\n",
+      "18\t검증 세트 손실: 0.682453\t최선의 손실: 0.635016\t정확도: 80.67%\n",
+      "19\t검증 세트 손실: 0.753956\t최선의 손실: 0.635016\t정확도: 80.67%\n",
+      "20\t검증 세트 손실: 0.707569\t최선의 손실: 0.635016\t정확도: 82.67%\n",
+      "21\t검증 세트 손실: 0.730020\t최선의 손실: 0.635016\t정확도: 80.67%\n",
+      "22\t검증 세트 손실: 0.738284\t최선의 손실: 0.635016\t정확도: 78.67%\n",
+      "23\t검증 세트 손실: 0.821101\t최선의 손실: 0.635016\t정확도: 77.33%\n",
+      "24\t검증 세트 손실: 0.807012\t최선의 손실: 0.635016\t정확도: 80.00%\n",
+      "25\t검증 세트 손실: 0.714766\t최선의 손실: 0.635016\t정확도: 81.33%\n",
+      "26\t검증 세트 손실: 0.733857\t최선의 손실: 0.635016\t정확도: 81.33%\n",
+      "27\t검증 세트 손실: 0.827937\t최선의 손실: 0.635016\t정확도: 80.00%\n",
+      "28\t검증 세트 손실: 0.845062\t최선의 손실: 0.635016\t정확도: 79.33%\n",
+      "29\t검증 세트 손실: 0.836772\t최선의 손실: 0.635016\t정확도: 80.00%\n",
       "조기 종료!\n",
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_four_frozen\n",
-      "최종 테스트 정확도: 74.55%\n"
+      "최종 테스트 정확도: 79.53%\n"
      ]
     }
    ],
@@ -21196,7 +20994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21212,7 +21010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -21220,32 +21018,43 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_four_frozen\n",
-      "0\t검증 세트 손실: 0.924286\t최선의 손실: 0.924286\t정확도: 76.67%\n",
-      "1\t검증 세트 손실: 0.608876\t최선의 손실: 0.608876\t정확도: 80.67%\n",
-      "2\t검증 세트 손실: 0.549349\t최선의 손실: 0.549349\t정확도: 83.33%\n",
-      "3\t검증 세트 손실: 0.614029\t최선의 손실: 0.549349\t정확도: 77.33%\n",
-      "4\t검증 세트 손실: 0.698438\t최선의 손실: 0.549349\t정확도: 82.00%\n",
-      "5\t검증 세트 손실: 0.651578\t최선의 손실: 0.549349\t정확도: 82.00%\n",
-      "6\t검증 세트 손실: 0.762373\t최선의 손실: 0.549349\t정확도: 80.00%\n",
-      "7\t검증 세트 손실: 0.716222\t최선의 손실: 0.549349\t정확도: 82.67%\n",
-      "8\t검증 세트 손실: 0.719121\t최선의 손실: 0.549349\t정확도: 82.67%\n",
-      "9\t검증 세트 손실: 0.737023\t최선의 손실: 0.549349\t정확도: 81.33%\n",
-      "10\t검증 세트 손실: 0.852182\t최선의 손실: 0.549349\t정확도: 85.33%\n",
-      "11\t검증 세트 손실: 0.666508\t최선의 손실: 0.549349\t정확도: 85.33%\n",
-      "12\t검증 세트 손실: 0.662619\t최선의 손실: 0.549349\t정확도: 86.67%\n",
-      "13\t검증 세트 손실: 1.786662\t최선의 손실: 0.549349\t정확도: 84.00%\n",
-      "14\t검증 세트 손실: 0.933122\t최선의 손실: 0.549349\t정확도: 82.67%\n",
-      "15\t검증 세트 손실: 0.904570\t최선의 손실: 0.549349\t정확도: 80.67%\n",
-      "16\t검증 세트 손실: 0.687664\t최선의 손실: 0.549349\t정확도: 88.67%\n",
-      "17\t검증 세트 손실: 0.607057\t최선의 손실: 0.549349\t정확도: 86.00%\n",
-      "18\t검증 세트 손실: 0.912956\t최선의 손실: 0.549349\t정확도: 78.67%\n",
-      "19\t검증 세트 손실: 0.686890\t최선의 손실: 0.549349\t정확도: 84.00%\n",
-      "20\t검증 세트 손실: 0.698205\t최선의 손실: 0.549349\t정확도: 82.67%\n",
-      "21\t검증 세트 손실: 0.715697\t최선의 손실: 0.549349\t정확도: 84.67%\n",
-      "22\t검증 세트 손실: 0.812763\t최선의 손실: 0.549349\t정확도: 82.67%\n",
+      "0\t검증 세트 손실: 0.953523\t최선의 손실: 0.953523\t정확도: 70.67%\n",
+      "1\t검증 세트 손실: 0.638675\t최선의 손실: 0.638675\t정확도: 82.67%\n",
+      "2\t검증 세트 손실: 0.691755\t최선의 손실: 0.638675\t정확도: 85.33%\n",
+      "3\t검증 세트 손실: 0.575561\t최선의 손실: 0.575561\t정확도: 80.67%\n",
+      "4\t검증 세트 손실: 0.647800\t최선의 손실: 0.575561\t정확도: 84.00%\n",
+      "5\t검증 세트 손실: 0.710496\t최선의 손실: 0.575561\t정확도: 86.67%\n",
+      "6\t검증 세트 손실: 0.541074\t최선의 손실: 0.541074\t정확도: 88.67%\n",
+      "7\t검증 세트 손실: 0.734961\t최선의 손실: 0.541074\t정확도: 83.33%\n",
+      "8\t검증 세트 손실: 0.712958\t최선의 손실: 0.541074\t정확도: 84.67%\n",
+      "9\t검증 세트 손실: 0.964374\t최선의 손실: 0.541074\t정확도: 82.00%\n",
+      "10\t검증 세트 손실: 0.768984\t최선의 손실: 0.541074\t정확도: 83.33%\n",
+      "11\t검증 세트 손실: 0.677497\t최선의 손실: 0.541074\t정확도: 86.67%\n",
+      "12\t검증 세트 손실: 1.016240\t최선의 손실: 0.541074\t정확도: 83.33%\n",
+      "13\t검증 세트 손실: 0.494356\t최선의 손실: 0.494356\t정확도: 91.33%\n",
+      "14\t검증 세트 손실: 0.667182\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "15\t검증 세트 손실: 1.004836\t최선의 손실: 0.494356\t정확도: 84.67%\n",
+      "16\t검증 세트 손실: 0.716796\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "17\t검증 세트 손실: 0.763375\t최선의 손실: 0.494356\t정확도: 87.33%\n",
+      "18\t검증 세트 손실: 0.865802\t최선의 손실: 0.494356\t정확도: 84.67%\n",
+      "19\t검증 세트 손실: 0.653018\t최선의 손실: 0.494356\t정확도: 88.00%\n",
+      "20\t검증 세트 손실: 0.717652\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "21\t검증 세트 손실: 0.667797\t최선의 손실: 0.494356\t정확도: 87.33%\n",
+      "22\t검증 세트 손실: 0.599092\t최선의 손실: 0.494356\t정확도: 89.33%\n",
+      "23\t검증 세트 손실: 0.577316\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "24\t검증 세트 손실: 0.598384\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "25\t검증 세트 손실: 0.612967\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "26\t검증 세트 손실: 0.624086\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "27\t검증 세트 손실: 0.633187\t최선의 손실: 0.494356\t정확도: 88.67%\n",
+      "28\t검증 세트 손실: 0.640782\t최선의 손실: 0.494356\t정확도: 89.33%\n",
+      "29\t검증 세트 손실: 0.646566\t최선의 손실: 0.494356\t정확도: 89.33%\n",
+      "30\t검증 세트 손실: 0.651718\t최선의 손실: 0.494356\t정확도: 89.33%\n",
+      "31\t검증 세트 손실: 0.657348\t최선의 손실: 0.494356\t정확도: 89.33%\n",
+      "32\t검증 세트 손실: 0.662439\t최선의 손실: 0.494356\t정확도: 89.33%\n",
+      "33\t검증 세트 손실: 0.665647\t최선의 손실: 0.494356\t정확도: 89.33%\n",
       "조기 종료!\n",
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_two_frozen\n",
-      "최종 테스트 정확도: 79.92%\n"
+      "최종 테스트 정확도: 86.09%\n"
      ]
     }
    ],
@@ -21294,7 +21103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21309,7 +21118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -21317,31 +21126,31 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_two_frozen\n",
-      "0\t검증 세트 손실: 0.640913\t최선의 손실: 0.640913\t정확도: 82.00%\n",
-      "1\t검증 세트 손실: 0.478888\t최선의 손실: 0.478888\t정확도: 84.00%\n",
-      "2\t검증 세트 손실: 0.856660\t최선의 손실: 0.478888\t정확도: 85.33%\n",
-      "3\t검증 세트 손실: 0.580011\t최선의 손실: 0.478888\t정확도: 89.33%\n",
-      "4\t검증 세트 손실: 1.005241\t최선의 손실: 0.478888\t정확도: 91.33%\n",
-      "5\t검증 세트 손실: 0.901711\t최선의 손실: 0.478888\t정확도: 84.00%\n",
-      "6\t검증 세트 손실: 0.928776\t최선의 손실: 0.478888\t정확도: 84.67%\n",
-      "7\t검증 세트 손실: 1.411550\t최선의 손실: 0.478888\t정확도: 89.33%\n",
-      "8\t검증 세트 손실: 1.699672\t최선의 손실: 0.478888\t정확도: 84.00%\n",
-      "9\t검증 세트 손실: 1.818472\t최선의 손실: 0.478888\t정확도: 86.00%\n",
-      "10\t검증 세트 손실: 1.605617\t최선의 손실: 0.478888\t정확도: 89.33%\n",
-      "11\t검증 세트 손실: 1.155525\t최선의 손실: 0.478888\t정확도: 90.67%\n",
-      "12\t검증 세트 손실: 1.607752\t최선의 손실: 0.478888\t정확도: 90.67%\n",
-      "13\t검증 세트 손실: 1.390211\t최선의 손실: 0.478888\t정확도: 90.67%\n",
-      "14\t검증 세트 손실: 1.998731\t최선의 손실: 0.478888\t정확도: 89.33%\n",
-      "15\t검증 세트 손실: 1.863589\t최선의 손실: 0.478888\t정확도: 88.00%\n",
-      "16\t검증 세트 손실: 1.329284\t최선의 손실: 0.478888\t정확도: 88.00%\n",
-      "17\t검증 세트 손실: 2.509893\t최선의 손실: 0.478888\t정확도: 92.00%\n",
-      "18\t검증 세트 손실: 3.215564\t최선의 손실: 0.478888\t정확도: 89.33%\n",
-      "19\t검증 세트 손실: 0.986911\t최선의 손실: 0.478888\t정확도: 87.33%\n",
-      "20\t검증 세트 손실: 1.042441\t최선의 손실: 0.478888\t정확도: 88.00%\n",
-      "21\t검증 세트 손실: 4.069948\t최선의 손실: 0.478888\t정확도: 86.00%\n",
+      "0\t검증 세트 손실: 0.504682\t최선의 손실: 0.504682\t정확도: 84.00%\n",
+      "1\t검증 세트 손실: 0.284745\t최선의 손실: 0.284745\t정확도: 90.00%\n",
+      "2\t검증 세트 손실: 0.759620\t최선의 손실: 0.284745\t정확도: 86.67%\n",
+      "3\t검증 세트 손실: 0.656060\t최선의 손실: 0.284745\t정확도: 90.00%\n",
+      "4\t검증 세트 손실: 0.565989\t최선의 손실: 0.284745\t정확도: 93.33%\n",
+      "5\t검증 세트 손실: 0.577863\t최선의 손실: 0.284745\t정확도: 89.33%\n",
+      "6\t검증 세트 손실: 0.464893\t최선의 손실: 0.284745\t정확도: 93.33%\n",
+      "7\t검증 세트 손실: 1.100843\t최선의 손실: 0.284745\t정확도: 86.00%\n",
+      "8\t검증 세트 손실: 0.726544\t최선의 손실: 0.284745\t정확도: 90.00%\n",
+      "9\t검증 세트 손실: 0.344886\t최선의 손실: 0.284745\t정확도: 88.67%\n",
+      "10\t검증 세트 손실: 1.287486\t최선의 손실: 0.284745\t정확도: 83.33%\n",
+      "11\t검증 세트 손실: 1.087181\t최선의 손실: 0.284745\t정확도: 92.67%\n",
+      "12\t검증 세트 손실: 1.025764\t최선의 손실: 0.284745\t정확도: 93.33%\n",
+      "13\t검증 세트 손실: 1.098690\t최선의 손실: 0.284745\t정확도: 94.67%\n",
+      "14\t검증 세트 손실: 1.095670\t최선의 손실: 0.284745\t정확도: 92.67%\n",
+      "15\t검증 세트 손실: 0.986212\t최선의 손실: 0.284745\t정확도: 94.67%\n",
+      "16\t검증 세트 손실: 1.058280\t최선의 손실: 0.284745\t정확도: 92.67%\n",
+      "17\t검증 세트 손실: 1.054618\t최선의 손실: 0.284745\t정확도: 92.00%\n",
+      "18\t검증 세트 손실: 1.820109\t최선의 손실: 0.284745\t정확도: 74.67%\n",
+      "19\t검증 세트 손실: 3.740706\t최선의 손실: 0.284745\t정확도: 34.00%\n",
+      "20\t검증 세트 손실: 1.123489\t최선의 손실: 0.284745\t정확도: 62.67%\n",
+      "21\t검증 세트 손실: 0.711121\t최선의 손실: 0.284745\t정확도: 88.00%\n",
       "조기 종료!\n",
       "INFO:tensorflow:Restoring parameters from ./my_mnist_model_5_to_9_no_frozen\n",
-      "최종 테스트 정확도: 79.55%\n"
+      "최종 테스트 정확도: 84.98%\n"
      ]
     }
    ],
@@ -21390,52 +21199,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0\t검증 세트 손실: 0.547345\t최선의 손실: 0.547345\t정확도: 82.00%\n",
-      "1\t검증 세트 손실: 0.530747\t최선의 손실: 0.530747\t정확도: 88.00%\n",
-      "2\t검증 세트 손실: 0.419914\t최선의 손실: 0.419914\t정확도: 89.33%\n",
-      "3\t검증 세트 손실: 0.422450\t최선의 손실: 0.419914\t정확도: 90.67%\n",
-      "4\t검증 세트 손실: 0.582988\t최선의 손실: 0.419914\t정확도: 87.33%\n",
-      "5\t검증 세트 손실: 0.497890\t최선의 손실: 0.419914\t정확도: 90.67%\n",
-      "6\t검증 세트 손실: 0.550158\t최선의 손실: 0.419914\t정확도: 87.33%\n",
-      "7\t검증 세트 손실: 0.511480\t최선의 손실: 0.419914\t정확도: 89.33%\n",
-      "8\t검증 세트 손실: 0.564534\t최선의 손실: 0.419914\t정확도: 89.33%\n",
-      "9\t검증 세트 손실: 0.596314\t최선의 손실: 0.419914\t정확도: 90.00%\n",
-      "10\t검증 세트 손실: 0.683051\t최선의 손실: 0.419914\t정확도: 88.00%\n",
-      "11\t검증 세트 손실: 0.561992\t최선의 손실: 0.419914\t정확도: 88.67%\n",
-      "12\t검증 세트 손실: 0.560386\t최선의 손실: 0.419914\t정확도: 87.33%\n",
-      "13\t검증 세트 손실: 0.753223\t최선의 손실: 0.419914\t정확도: 88.00%\n",
-      "14\t검증 세트 손실: 1.214661\t최선의 손실: 0.419914\t정확도: 82.00%\n",
-      "15\t검증 세트 손실: 1.414867\t최선의 손실: 0.419914\t정확도: 78.00%\n",
-      "16\t검증 세트 손실: 1.146828\t최선의 손실: 0.419914\t정확도: 80.67%\n",
-      "17\t검증 세트 손실: 1.581310\t최선의 손실: 0.419914\t정확도: 84.00%\n",
-      "18\t검증 세트 손실: 1.626863\t최선의 손실: 0.419914\t정확도: 88.67%\n",
-      "19\t검증 세트 손실: 2.267840\t최선의 손실: 0.419914\t정확도: 90.67%\n",
-      "20\t검증 세트 손실: 2.231659\t최선의 손실: 0.419914\t정확도: 92.00%\n",
-      "21\t검증 세트 손실: 2.078954\t최선의 손실: 0.419914\t정확도: 90.00%\n",
-      "22\t검증 세트 손실: 2.574847\t최선의 손실: 0.419914\t정확도: 87.33%\n",
-      "23\t검증 세트 손실: 2.090153\t최선의 손실: 0.419914\t정확도: 90.00%\n",
+      "0\t검증 세트 손실: 0.803557\t최선의 손실: 0.803557\t정확도: 71.33%\n",
+      "1\t검증 세트 손실: 0.966743\t최선의 손실: 0.803557\t정확도: 85.33%\n",
+      "2\t검증 세트 손실: 1.158968\t최선의 손실: 0.803557\t정확도: 78.00%\n",
+      "3\t검증 세트 손실: 0.615957\t최선의 손실: 0.615957\t정확도: 88.00%\n",
+      "4\t검증 세트 손실: 0.612616\t최선의 손실: 0.612616\t정확도: 92.00%\n",
+      "5\t검증 세트 손실: 0.686550\t최선의 손실: 0.612616\t정확도: 89.33%\n",
+      "6\t검증 세트 손실: 0.804601\t최선의 손실: 0.612616\t정확도: 89.33%\n",
+      "7\t검증 세트 손실: 0.748599\t최선의 손실: 0.612616\t정확도: 88.00%\n",
+      "8\t검증 세트 손실: 0.926196\t최선의 손실: 0.612616\t정확도: 84.67%\n",
+      "9\t검증 세트 손실: 3.339839\t최선의 손실: 0.612616\t정확도: 86.67%\n",
+      "10\t검증 세트 손실: 1.365345\t최선의 손실: 0.612616\t정확도: 88.00%\n",
+      "11\t검증 세트 손실: 0.812454\t최선의 손실: 0.612616\t정확도: 90.00%\n",
+      "12\t검증 세트 손실: 1.740148\t최선의 손실: 0.612616\t정확도: 94.00%\n",
+      "13\t검증 세트 손실: 1.940180\t최선의 손실: 0.612616\t정확도: 86.67%\n",
+      "14\t검증 세트 손실: 1.698517\t최선의 손실: 0.612616\t정확도: 90.67%\n",
+      "15\t검증 세트 손실: 0.935869\t최선의 손실: 0.612616\t정확도: 90.67%\n",
+      "16\t검증 세트 손실: 1.254556\t최선의 손실: 0.612616\t정확도: 93.33%\n",
+      "17\t검증 세트 손실: 1.633356\t최선의 손실: 0.612616\t정확도: 90.00%\n",
+      "18\t검증 세트 손실: 1.094478\t최선의 손실: 0.612616\t정확도: 89.33%\n",
+      "19\t검증 세트 손실: 1.890037\t최선의 손실: 0.612616\t정확도: 86.67%\n",
+      "20\t검증 세트 손실: 1.789462\t최선의 손실: 0.612616\t정확도: 91.33%\n",
+      "21\t검증 세트 손실: 1.306313\t최선의 손실: 0.612616\t정확도: 86.67%\n",
+      "22\t검증 세트 손실: 2.218063\t최선의 손실: 0.612616\t정확도: 88.67%\n",
+      "23\t검증 세트 손실: 2.017678\t최선의 손실: 0.612616\t정확도: 92.00%\n",
+      "24\t검증 세트 손실: 2.062834\t최선의 손실: 0.612616\t정확도: 92.00%\n",
+      "25\t검증 세트 손실: 2.049384\t최선의 손실: 0.612616\t정확도: 92.00%\n",
       "조기 종료!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "DNNClassifier(activation=<function elu at 0x7f99417cf6a8>,\n",
+       "DNNClassifier(activation=<function elu at 0x7f738a2bd1e0>,\n",
        "       batch_norm_momentum=None, batch_size=20, dropout_rate=None,\n",
-       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f99318589d8>,\n",
+       "       initializer=<function variance_scaling_initializer.<locals>._initializer at 0x7f737fbd3d90>,\n",
        "       learning_rate=0.01, n_hidden_layers=4, n_neurons=100,\n",
        "       optimizer_class=<class 'tensorflow.python.training.adam.AdamOptimizer'>,\n",
        "       random_state=42)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -21447,16 +21258,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8778029212096277"
+       "0.8996091339230611"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -21504,7 +21315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21525,7 +21336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21541,7 +21352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21558,7 +21369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21572,6 +21383,26 @@
     "각 DNN은 (샘플마다) 100개의 활성화 함수 출력을 만드므로 크기는 `[None, 100]`이 됩니다:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TensorShape([Dimension(None), Dimension(100)])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dnn1.shape"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 53,
@@ -21588,26 +21419,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "dnn1.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "TensorShape([Dimension(None), Dimension(100)])"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "dnn2.shape"
    ]
@@ -21621,7 +21432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -21630,7 +21441,7 @@
        "TensorShape([Dimension(None), Dimension(200)])"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -21648,7 +21459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21666,7 +21477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21682,7 +21493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21700,7 +21511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21720,7 +21531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21737,7 +21548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21762,7 +21573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21785,7 +21596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21819,7 +21630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21836,7 +21647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -21845,7 +21656,7 @@
        "((5, 2, 784), dtype('float32'))"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -21863,7 +21674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -21897,7 +21708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -21910,7 +21721,7 @@
        "       [0]])"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -21943,7 +21754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21959,133 +21770,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 손실: 0.5908809\n",
-      "0 테스트 정확도: 0.6774\n",
-      "1 훈련 손실: 0.3974472\n",
-      "2 훈련 손실: 0.3337296\n",
-      "3 훈련 손실: 0.28122666\n",
-      "4 훈련 손실: 0.22434346\n",
-      "5 훈련 손실: 0.16687244\n",
-      "5 테스트 정확도: 0.9156\n",
-      "6 훈련 손실: 0.2250218\n",
-      "7 훈련 손실: 0.16899718\n",
-      "8 훈련 손실: 0.15540238\n",
-      "9 훈련 손실: 0.17644541\n",
-      "10 훈련 손실: 0.11150016\n",
-      "10 테스트 정확도: 0.9416\n",
-      "11 훈련 손실: 0.16785611\n",
-      "12 훈련 손실: 0.12579024\n",
-      "13 훈련 손실: 0.121470354\n",
-      "14 훈련 손실: 0.10476015\n",
-      "15 훈련 손실: 0.08949116\n",
-      "15 테스트 정확도: 0.9523\n",
-      "16 훈련 손실: 0.12330383\n",
-      "17 훈련 손실: 0.1032713\n",
-      "18 훈련 손실: 0.12114446\n",
-      "19 훈련 손실: 0.121041924\n",
-      "20 훈련 손실: 0.113421455\n",
-      "20 테스트 정확도: 0.9593\n",
-      "21 훈련 손실: 0.089824066\n",
-      "22 훈련 손실: 0.09780993\n",
-      "23 훈련 손실: 0.09179806\n",
-      "24 훈련 손실: 0.088439725\n",
-      "25 훈련 손실: 0.08158132\n",
-      "25 테스트 정확도: 0.962\n",
-      "26 훈련 손실: 0.10237064\n",
-      "27 훈련 손실: 0.07873487\n",
-      "28 훈련 손실: 0.0986035\n",
-      "29 훈련 손실: 0.059484202\n",
-      "30 훈련 손실: 0.06356913\n",
-      "30 테스트 정확도: 0.966\n",
-      "31 훈련 손실: 0.08107378\n",
-      "32 훈련 손실: 0.056234695\n",
-      "33 훈련 손실: 0.031837173\n",
-      "34 훈련 손실: 0.05902724\n",
-      "35 훈련 손실: 0.034870278\n",
-      "35 테스트 정확도: 0.9707\n",
-      "36 훈련 손실: 0.05217493\n",
-      "37 훈련 손실: 0.030188724\n",
-      "38 훈련 손실: 0.030357907\n",
-      "39 훈련 손실: 0.044530723\n",
-      "40 훈련 손실: 0.04856783\n",
-      "40 테스트 정확도: 0.9707\n",
-      "41 훈련 손실: 0.05003133\n",
-      "42 훈련 손실: 0.04772441\n",
-      "43 훈련 손실: 0.043759055\n",
-      "44 훈련 손실: 0.06536337\n",
-      "45 훈련 손실: 0.102956876\n",
+      "0 훈련 손실: 0.4924257\n",
+      "0 테스트 정확도: 0.7861\n",
+      "1 훈련 손실: 0.33481342\n",
+      "2 훈련 손실: 0.29043424\n",
+      "3 훈련 손실: 0.25343397\n",
+      "4 훈련 손실: 0.21784273\n",
+      "5 훈련 손실: 0.17126976\n",
+      "5 테스트 정확도: 0.9185\n",
+      "6 훈련 손실: 0.20712842\n",
+      "7 훈련 손실: 0.17227527\n",
+      "8 훈련 손실: 0.16678247\n",
+      "9 훈련 손실: 0.16109405\n",
+      "10 훈련 손실: 0.12513137\n",
+      "10 테스트 정확도: 0.9425\n",
+      "11 훈련 손실: 0.1598237\n",
+      "12 훈련 손실: 0.124751955\n",
+      "13 훈련 손실: 0.11223439\n",
+      "14 훈련 손실: 0.11450212\n",
+      "15 훈련 손실: 0.09500929\n",
+      "15 테스트 정확도: 0.9532\n",
+      "16 훈련 손실: 0.11929634\n",
+      "17 훈련 손실: 0.0754429\n",
+      "18 훈련 손실: 0.11229495\n",
+      "19 훈련 손실: 0.13370793\n",
+      "20 훈련 손실: 0.11354699\n",
+      "20 테스트 정확도: 0.9596\n",
+      "21 훈련 손실: 0.06740806\n",
+      "22 훈련 손실: 0.0936298\n",
+      "23 훈련 손실: 0.09864689\n",
+      "24 훈련 손실: 0.111874945\n",
+      "25 훈련 손실: 0.073562324\n",
+      "25 테스트 정확도: 0.9675\n",
+      "26 훈련 손실: 0.07903241\n",
+      "27 훈련 손실: 0.04876441\n",
+      "28 훈련 손실: 0.086907074\n",
+      "29 훈련 손실: 0.06944222\n",
+      "30 훈련 손실: 0.06008898\n",
+      "30 테스트 정확도: 0.9663\n",
+      "31 훈련 손실: 0.10390249\n",
+      "32 훈련 손실: 0.053595282\n",
+      "33 훈련 손실: 0.031067856\n",
+      "34 훈련 손실: 0.05362943\n",
+      "35 훈련 손실: 0.046265054\n",
+      "35 테스트 정확도: 0.9701\n",
+      "36 훈련 손실: 0.067982085\n",
+      "37 훈련 손실: 0.032665566\n",
+      "38 훈련 손실: 0.035747916\n",
+      "39 훈련 손실: 0.0333373\n",
+      "40 훈련 손실: 0.041511502\n",
+      "40 테스트 정확도: 0.9719\n",
+      "41 훈련 손실: 0.057797644\n",
+      "42 훈련 손실: 0.034278143\n",
+      "43 훈련 손실: 0.043965083\n",
+      "44 훈련 손실: 0.059725434\n",
+      "45 훈련 손실: 0.05886952\n",
       "45 테스트 정확도: 0.9721\n",
-      "46 훈련 손실: 0.03353312\n",
-      "47 훈련 손실: 0.082119904\n",
-      "48 훈련 손실: 0.05256475\n",
-      "49 훈련 손실: 0.034444783\n",
-      "50 훈련 손실: 0.029952224\n",
-      "50 테스트 정확도: 0.9723\n",
-      "51 훈련 손실: 0.026392337\n",
-      "52 훈련 손실: 0.02470571\n",
-      "53 훈련 손실: 0.079164445\n",
-      "54 훈련 손실: 0.044356003\n",
-      "55 훈련 손실: 0.028077709\n",
-      "55 테스트 정확도: 0.9732\n",
-      "56 훈련 손실: 0.030815143\n",
-      "57 훈련 손실: 0.023585403\n",
-      "58 훈련 손실: 0.03575724\n",
-      "59 훈련 손실: 0.03874363\n",
-      "60 훈련 손실: 0.047195707\n",
-      "60 테스트 정확도: 0.9741\n",
-      "61 훈련 손실: 0.012684017\n",
-      "62 훈련 손실: 0.04770386\n",
-      "63 훈련 손실: 0.020042645\n",
-      "64 훈련 손실: 0.032840557\n",
-      "65 훈련 손실: 0.025750607\n",
-      "65 테스트 정확도: 0.9746\n",
-      "66 훈련 손실: 0.032115106\n",
-      "67 훈련 손실: 0.02492068\n",
-      "68 훈련 손실: 0.023986526\n",
-      "69 훈련 손실: 0.029889498\n",
-      "70 훈련 손실: 0.041236106\n",
-      "70 테스트 정확도: 0.9737\n",
-      "71 훈련 손실: 0.026849864\n",
-      "72 훈련 손실: 0.025195658\n",
-      "73 훈련 손실: 0.03281086\n",
-      "74 훈련 손실: 0.030354017\n",
-      "75 훈련 손실: 0.039077085\n",
-      "75 테스트 정확도: 0.9747\n",
-      "76 훈련 손실: 0.013402511\n",
-      "77 훈련 손실: 0.013274606\n",
-      "78 훈련 손실: 0.023054333\n",
-      "79 훈련 손실: 0.024835058\n",
-      "80 훈련 손실: 0.016235076\n",
-      "80 테스트 정확도: 0.9754\n",
-      "81 훈련 손실: 0.028970877\n",
-      "82 훈련 손실: 0.016398685\n",
-      "83 훈련 손실: 0.03992552\n",
-      "84 훈련 손실: 0.01402591\n",
-      "85 훈련 손실: 0.033467222\n",
-      "85 테스트 정확도: 0.9754\n",
-      "86 훈련 손실: 0.007140022\n",
-      "87 훈련 손실: 0.030798174\n",
-      "88 훈련 손실: 0.018083235\n",
-      "89 훈련 손실: 0.029738668\n",
-      "90 훈련 손실: 0.016270772\n",
-      "90 테스트 정확도: 0.9749\n",
-      "91 훈련 손실: 0.028249012\n",
-      "92 훈련 손실: 0.019473169\n",
-      "93 훈련 손실: 0.01780416\n",
-      "94 훈련 손실: 0.0051757586\n",
-      "95 훈련 손실: 0.013085068\n",
-      "95 테스트 정확도: 0.9776\n",
-      "96 훈련 손실: 0.010076451\n",
-      "97 훈련 손실: 0.022442395\n",
-      "98 훈련 손실: 0.015335123\n",
-      "99 훈련 손실: 0.009534181\n"
+      "46 훈련 손실: 0.055682115\n",
+      "47 훈련 손실: 0.06395601\n",
+      "48 훈련 손실: 0.030128464\n",
+      "49 훈련 손실: 0.040267788\n",
+      "50 훈련 손실: 0.04891246\n",
+      "50 테스트 정확도: 0.9751\n",
+      "51 훈련 손실: 0.03945277\n",
+      "52 훈련 손실: 0.023304101\n",
+      "53 훈련 손실: 0.06487805\n",
+      "54 훈련 손실: 0.051019\n",
+      "55 훈련 손실: 0.031261932\n",
+      "55 테스트 정확도: 0.9742\n",
+      "56 훈련 손실: 0.02441569\n",
+      "57 훈련 손실: 0.040908184\n",
+      "58 훈련 손실: 0.03468958\n",
+      "59 훈련 손실: 0.045572672\n",
+      "60 훈련 손실: 0.04882686\n",
+      "60 테스트 정확도: 0.9751\n",
+      "61 훈련 손실: 0.015425295\n",
+      "62 훈련 손실: 0.035887476\n",
+      "63 훈련 손실: 0.0290556\n",
+      "64 훈련 손실: 0.017214358\n",
+      "65 훈련 손실: 0.037799176\n",
+      "65 테스트 정확도: 0.9751\n",
+      "66 훈련 손실: 0.03607863\n",
+      "67 훈련 손실: 0.024027776\n",
+      "68 훈련 손실: 0.031424373\n",
+      "69 훈련 손실: 0.041208133\n",
+      "70 훈련 손실: 0.043910515\n",
+      "70 테스트 정확도: 0.9763\n",
+      "71 훈련 손실: 0.0169656\n",
+      "72 훈련 손실: 0.018130578\n",
+      "73 훈련 손실: 0.021422774\n",
+      "74 훈련 손실: 0.041830193\n",
+      "75 훈련 손실: 0.037862215\n",
+      "75 테스트 정확도: 0.9759\n",
+      "76 훈련 손실: 0.019981772\n",
+      "77 훈련 손실: 0.014583788\n",
+      "78 훈련 손실: 0.01991766\n",
+      "79 훈련 손실: 0.022659792\n",
+      "80 훈련 손실: 0.011981454\n",
+      "80 테스트 정확도: 0.9779\n",
+      "81 훈련 손실: 0.017783236\n",
+      "82 훈련 손실: 0.009815705\n",
+      "83 훈련 손실: 0.027909378\n",
+      "84 훈련 손실: 0.023781892\n",
+      "85 훈련 손실: 0.015777819\n",
+      "85 테스트 정확도: 0.978\n",
+      "86 훈련 손실: 0.00950592\n",
+      "87 훈련 손실: 0.022622295\n",
+      "88 훈련 손실: 0.022659939\n",
+      "89 훈련 손실: 0.018500542\n",
+      "90 훈련 손실: 0.011896701\n",
+      "90 테스트 정확도: 0.976\n",
+      "91 훈련 손실: 0.020905944\n",
+      "92 훈련 손실: 0.018115235\n",
+      "93 훈련 손실: 0.0131696435\n",
+      "94 훈련 손실: 0.017605018\n",
+      "95 훈련 손실: 0.019385973\n",
+      "95 테스트 정확도: 0.976\n",
+      "96 훈련 손실: 0.015653247\n",
+      "97 훈련 손실: 0.013604076\n",
+      "98 훈련 손실: 0.0074303597\n",
+      "99 훈련 손실: 0.02671894\n"
      ]
     }
    ],
@@ -22133,7 +21944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22176,7 +21987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -22184,16 +21995,16 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_digit_comparison_model.ckpt\n",
-      "0 테스트 정확도: 0.9414\n",
-      "10 테스트 정확도: 0.9652\n",
-      "20 테스트 정확도: 0.9656\n",
-      "30 테스트 정확도: 0.966\n",
-      "40 테스트 정확도: 0.966\n",
-      "50 테스트 정확도: 0.9662\n",
-      "60 테스트 정확도: 0.9663\n",
-      "70 테스트 정확도: 0.9663\n",
-      "80 테스트 정확도: 0.9665\n",
-      "90 테스트 정확도: 0.9667\n"
+      "0 테스트 정확도: 0.9269\n",
+      "10 테스트 정확도: 0.9676\n",
+      "20 테스트 정확도: 0.9675\n",
+      "30 테스트 정확도: 0.9673\n",
+      "40 테스트 정확도: 0.9672\n",
+      "50 테스트 정확도: 0.9674\n",
+      "60 테스트 정확도: 0.9674\n",
+      "70 테스트 정확도: 0.9673\n",
+      "80 테스트 정확도: 0.9673\n",
+      "90 테스트 정확도: 0.9673\n"
      ]
     }
    ],
@@ -22226,7 +22037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22261,28 +22072,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 테스트 정확도: 0.8926\n",
-      "10 테스트 정확도: 0.9383\n",
-      "20 테스트 정확도: 0.9439\n",
-      "30 테스트 정확도: 0.9433\n",
-      "40 테스트 정확도: 0.9438\n",
-      "50 테스트 정확도: 0.9437\n",
-      "60 테스트 정확도: 0.944\n",
-      "70 테스트 정확도: 0.9441\n",
-      "80 테스트 정확도: 0.9439\n",
-      "90 테스트 정확도: 0.944\n",
-      "100 테스트 정확도: 0.9439\n",
-      "110 테스트 정확도: 0.9441\n",
-      "120 테스트 정확도: 0.9441\n",
-      "130 테스트 정확도: 0.944\n",
-      "140 테스트 정확도: 0.9439\n"
+      "0 테스트 정확도: 0.8893\n",
+      "10 테스트 정확도: 0.9402\n",
+      "20 테스트 정확도: 0.9481\n",
+      "30 테스트 정확도: 0.9483\n",
+      "40 테스트 정확도: 0.9484\n",
+      "50 테스트 정확도: 0.9482\n",
+      "60 테스트 정확도: 0.9483\n",
+      "70 테스트 정확도: 0.9485\n",
+      "80 테스트 정확도: 0.9481\n",
+      "90 테스트 정확도: 0.9481\n",
+      "100 테스트 정확도: 0.9479\n",
+      "110 테스트 정확도: 0.9478\n",
+      "120 테스트 정확도: 0.9478\n",
+      "130 테스트 정확도: 0.9477\n",
+      "140 테스트 정확도: 0.9479\n"
      ]
     }
    ],
diff --git a/12_distributed_tensorflow.ipynb b/12_distributed_tensorflow.ipynb
index f1d5702..b2a3552 100644
--- a/12_distributed_tensorflow.ipynb
+++ b/12_distributed_tensorflow.ipynb
@@ -12,11 +12,11 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -261,11 +261,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No more files to read\n",
-      "[array([[ 4.0000000e+00,  5.0000000e+00],\n",
-      "       [ 1.0000000e+00, -3.9627734e+03]], dtype=float32), array([1, 0], dtype=int32)]\n",
+      "더 이상 읽을 파일이 없습니다\n",
+      "[array([[4.00000000e+00, 5.00000000e+00],\n",
+      "       [1.00000000e+00, 1.27106565e+36]], dtype=float32), array([1, 0], dtype=int32)]\n",
       "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
-      "No more training instances\n"
+      "더 이상 훈련 샘플이 없습니다\n"
      ]
     }
    ],
@@ -344,8 +344,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[array([[ 4.0000000e+00,  5.0000000e+00],\n",
-      "       [ 1.0000000e+00, -3.9627734e+03]], dtype=float32), array([1, 0], dtype=int32)]\n",
+      "[array([[4.00000000e+00, 5.00000000e+00],\n",
+      "       [1.00000000e+00, 1.27106565e+36]], dtype=float32), array([1, 0], dtype=int32)]\n",
       "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
       "더 이상 훈련 샘플이 없습니다\n"
      ]
@@ -398,8 +398,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[array([[ 4.0000000e+00,  5.0000000e+00],\n",
-      "       [ 1.0000000e+00, -3.9627734e+03]], dtype=float32), array([1, 0], dtype=int32)]\n",
+      "[array([[4.00000000e+00, 5.00000000e+00],\n",
+      "       [1.00000000e+00, 1.27106565e+36]], dtype=float32), array([1, 0], dtype=int32)]\n",
       "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
       "더 이상 훈련 샘플이 없습니다\n"
      ]
diff --git a/13_convolutional_neural_networks.ipynb b/13_convolutional_neural_networks.ipynb
index ea8cf9c..126496c 100644
--- a/13_convolutional_neural_networks.ipynb
+++ b/13_convolutional_neural_networks.ipynb
@@ -12,11 +12,11 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -726,9 +726,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use the retry module or similar alternatives.\n",
       "Extracting /tmp/data/train-images-idx3-ubyte.gz\n",
       "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n",
       "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n",
@@ -754,17 +751,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 정확도: 0.98 검증 세트 정확도: 0.976\n",
-      "1 훈련 정확도: 0.98 검증 세트 정확도: 0.9846\n",
-      "2 훈련 정확도: 0.99 검증 세트 정확도: 0.9826\n",
-      "3 훈련 정확도: 1.0 검증 세트 정확도: 0.9858\n",
-      "4 훈련 정확도: 1.0 검증 세트 정확도: 0.9874\n",
-      "5 훈련 정확도: 0.99 검증 세트 정확도: 0.9862\n",
-      "6 훈련 정확도: 1.0 검증 세트 정확도: 0.9884\n",
-      "7 훈련 정확도: 0.99 검증 세트 정확도: 0.9858\n",
-      "8 훈련 정확도: 1.0 검증 세트 정확도: 0.9886\n",
-      "9 훈련 정확도: 1.0 검증 세트 정확도: 0.9864\n",
-      "테스트 세트에서 최종 정확도: 0.9868\n"
+      "0 훈련 정확도: 0.99 검증 세트 정확도: 0.9784\n",
+      "1 훈련 정확도: 0.96 검증 세트 정확도: 0.9812\n",
+      "2 훈련 정확도: 0.99 검증 세트 정확도: 0.9856\n",
+      "3 훈련 정확도: 1.0 검증 세트 정확도: 0.9864\n",
+      "4 훈련 정확도: 1.0 검증 세트 정확도: 0.9868\n",
+      "5 훈련 정확도: 0.99 검증 세트 정확도: 0.9884\n",
+      "6 훈련 정확도: 1.0 검증 세트 정확도: 0.9878\n",
+      "7 훈련 정확도: 0.99 검증 세트 정확도: 0.9898\n",
+      "8 훈련 정확도: 0.99 검증 세트 정확도: 0.989\n",
+      "9 훈련 정확도: 1.0 검증 세트 정확도: 0.9886\n",
+      "테스트 세트에서 최종 정확도: 0.9875\n"
      ]
     }
    ],
@@ -975,20 +972,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "에포크 0, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.4400%, 검증 세트에서 최선의 손실: 0.059448\n",
-      "에포크 1, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.6000%, 검증 세트에서 최선의 손실: 0.043736\n",
-      "에포크 2, 훈련 정확도: 98.0000%, 검증 세트 정확도: 98.7600%, 검증 세트에서 최선의 손실: 0.039157\n",
-      "에포크 3, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.6600%, 검증 세트에서 최선의 손실: 0.039157\n",
-      "에포크 4, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.1600%, 검증 세트에서 최선의 손실: 0.032197\n",
-      "에포크 5, 훈련 정확도: 98.0000%, 검증 세트 정확도: 99.1000%, 검증 세트에서 최선의 손실: 0.032197\n",
-      "에포크 6, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.1000%, 검증 세트에서 최선의 손실: 0.032197\n",
-      "에포크 7, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.1000%, 검증 세트에서 최선의 손실: 0.032197\n",
-      "에포크 8, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.0000%, 검증 세트에서 최선의 손실: 0.032197\n",
-      "에포크 9, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.2000%, 검증 세트에서 최선의 손실: 0.032197\n",
-      "에포크 10, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.1000%, 검증 세트에서 최선의 손실: 0.032197\n",
-      "에포크 11, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.9600%, 검증 세트에서 최선의 손실: 0.032197\n",
+      "에포크 0, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.5600%, 검증 세트에서 최선의 손실: 0.061169\n",
+      "에포크 1, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.6200%, 검증 세트에서 최선의 손실: 0.041548\n",
+      "에포크 2, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.8600%, 검증 세트에서 최선의 손실: 0.041548\n",
+      "에포크 3, 훈련 정확도: 98.0000%, 검증 세트 정확도: 98.8600%, 검증 세트에서 최선의 손실: 0.037626\n",
+      "에포크 4, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.8600%, 검증 세트에서 최선의 손실: 0.037626\n",
+      "에포크 5, 훈련 정확도: 98.0000%, 검증 세트 정확도: 99.1400%, 검증 세트에서 최선의 손실: 0.037626\n",
+      "에포크 6, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.1400%, 검증 세트에서 최선의 손실: 0.036044\n",
+      "에포크 7, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.0200%, 검증 세트에서 최선의 손실: 0.034466\n",
+      "에포크 8, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.1600%, 검증 세트에서 최선의 손실: 0.034466\n",
+      "에포크 9, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.0200%, 검증 세트에서 최선의 손실: 0.034466\n",
+      "에포크 10, 훈련 정확도: 100.0000%, 검증 세트 정확도: 99.1400%, 검증 세트에서 최선의 손실: 0.034466\n",
+      "에포크 11, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.8000%, 검증 세트에서 최선의 손실: 0.034466\n",
+      "에포크 12, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.9400%, 검증 세트에서 최선의 손실: 0.034466\n",
+      "에포크 13, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.9800%, 검증 세트에서 최선의 손실: 0.034466\n",
+      "에포크 14, 훈련 정확도: 100.0000%, 검증 세트 정확도: 98.9200%, 검증 세트에서 최선의 손실: 0.034466\n",
       "조기 종료!\n",
-      "테스트 세트에서 최종 정확도: 0.9852\n"
+      "테스트 세트에서 최종 정확도: 0.9892\n"
      ]
     }
    ],
@@ -1718,7 +1718,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:35: DeprecationWarning: `imresize` is deprecated!\n",
+      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:34: DeprecationWarning: `imresize` is deprecated!\n",
       "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
       "Use ``skimage.transform.resize`` instead.\n"
      ]
@@ -1760,7 +1760,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:35: DeprecationWarning: `imresize` is deprecated!\n",
+      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:34: DeprecationWarning: `imresize` is deprecated!\n",
       "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
       "Use ``skimage.transform.resize`` instead.\n"
      ]
@@ -2278,9 +2278,9 @@
     {
      "data": {
       "text/plain": [
-       "[('datasets/flowers/flower_photos/daisy/19834392829_7d697871f6.jpg', 0),\n",
-       " ('datasets/flowers/flower_photos/roses/7345657862_689366e79a.jpg', 2),\n",
-       " ('datasets/flowers/flower_photos/dandelion/7719263062_3c8a307a5d.jpg', 1)]"
+       "[('datasets/flowers/flower_photos/dandelion/16656127943_2f70926b6c.jpg', 1),\n",
+       " ('datasets/flowers/flower_photos/tulips/3601085193_de1195d3d7_n.jpg', 4),\n",
+       " ('datasets/flowers/flower_photos/daisy/5944315415_2be8abeb2f_m.jpg', 0)]"
       ]
      },
      "execution_count": 68,
@@ -2325,7 +2325,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:35: DeprecationWarning: `imresize` is deprecated!\n",
+      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:34: DeprecationWarning: `imresize` is deprecated!\n",
       "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
       "Use ``skimage.transform.resize`` instead.\n"
      ]
@@ -2431,7 +2431,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:35: DeprecationWarning: `imresize` is deprecated!\n",
+      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:34: DeprecationWarning: `imresize` is deprecated!\n",
       "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
       "Use ``skimage.transform.resize`` instead.\n"
      ]
@@ -2477,7 +2477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -2492,7 +2492,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:35: DeprecationWarning: `imresize` is deprecated!\n",
+      "/home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/ipykernel_launcher.py:34: DeprecationWarning: `imresize` is deprecated!\n",
       "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
       "Use ``skimage.transform.resize`` instead.\n"
      ]
@@ -2501,16 +2501,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "........................................................................  훈련 정확도: 0.575\n",
-      "에포크 1.........................................................................  훈련 정확도: 0.525\n",
-      "에포크 2.........................................................................  훈련 정확도: 0.65\n",
+      "........................................................................  훈련 정확도: 0.7\n",
+      "에포크 1.........................................................................  훈련 정확도: 0.5\n",
+      "에포크 2.........................................................................  훈련 정확도: 0.55\n",
       "에포크 3.........................................................................  훈련 정확도: 0.675\n",
-      "에포크 4.........................................................................  훈련 정확도: 0.55\n",
-      "에포크 5.........................................................................  훈련 정확도: 0.7\n",
-      "에포크 6.........................................................................  훈련 정확도: 0.575\n",
-      "에포크 7.........................................................................  훈련 정확도: 0.775\n",
-      "에포크 8.........................................................................  훈련 정확도: 0.6\n",
-      "에포크 9.........................................................................  훈련 정확도: 0.875\n"
+      "에포크 4.........................................................................  훈련 정확도: 0.7\n",
+      "에포크 5.........................................................................  훈련 정확도: 0.8\n",
+      "에포크 6.........................................................................  훈련 정확도: 0.75\n",
+      "에포크 7.........................................................................  훈련 정확도: 0.625\n",
+      "에포크 8.........................................................................  훈련 정확도: 0.725\n",
+      "에포크 9.........................................................................  훈련 정확도: 0.65\n"
      ]
     }
    ],
@@ -2538,7 +2538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -2547,7 +2547,7 @@
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_flowers_model\n",
       "테스트 세트에서 최종 정확도를 계산합니다 (시간이 좀 걸립니다)...\n",
-      "테스트 정확도: 0.7084228\n"
+      "테스트 정확도: 0.688023\n"
      ]
     }
    ],
@@ -2570,7 +2570,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "네 70.8% 정확도는 좋지 않습니다(사실 이는 나쁜 편입니다). 하지만 이는 출력층을 제외하고 모든 층을 동결하여 10번의 에포크만 실행한 것입니다. GPU를 가지고 있다면 더 오랫동안 훈련을 시켜보세요(가령, 조기 종료 기법을 사용해). 이미지 전처리 함수를 개선하여 이미지를 더 다양하게 변형시킬 수도 있습니다(가령, 명암이나 색조를 바꾸거나 이미지를 조금 회전시킵니다). 이렇게 하면 95% 정확도 이상을 달성할 수 있습니다. 조금 더 심각하게 파고들고 싶다면 96% 정확도를 달성한 [훌륭한 이 포스트](https://kwotsin.github.io/tech/2017/02/11/transfer-learning.html)에서 자세한 내용을 참고하세요."
+    "네 68.8% 정확도는 좋지 않습니다(사실 이는 나쁜 편입니다). 하지만 이는 출력층을 제외하고 모든 층을 동결하여 10번의 에포크만 실행한 것입니다. GPU를 가지고 있다면 더 오랫동안 훈련을 시켜보세요(가령, 조기 종료 기법을 사용해). 이미지 전처리 함수를 개선하여 이미지를 더 다양하게 변형시킬 수도 있습니다(가령, 명암이나 색조를 바꾸거나 이미지를 조금 회전시킵니다). 이렇게 하면 95% 정확도 이상을 달성할 수 있습니다. 조금 더 심각하게 파고들고 싶다면 96% 정확도를 달성한 [훌륭한 이 포스트](https://kwotsin.github.io/tech/2017/02/11/transfer-learning.html)에서 자세한 내용을 참고하세요."
    ]
   },
   {
diff --git a/14_recurrent_neural_networks.ipynb b/14_recurrent_neural_networks.ipynb
index 0c88d05..3e08016 100644
--- a/14_recurrent_neural_networks.ipynb
+++ b/14_recurrent_neural_networks.ipynb
@@ -12,11 +12,11 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -226,17 +226,7 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use the retry module or similar alternatives.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "reset_graph()\n",
     "\n",
@@ -376,7 +366,7 @@
        "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n",
        "        <script>\n",
        "          function load() {\n",
-       "            document.getElementById(&quot;graph0.3745401188473625&quot;).pbtxt = 'node {\\n  name: &quot;Placeholder&quot;\\n  op: &quot;Placeholder&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;shape&quot;\\n    value {\\n      shape {\\n        dim {\\n          size: -1\\n        }\\n        dim {\\n          size: 3\\n        }\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;Placeholder_1&quot;\\n  op: &quot;Placeholder&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  }\\n  attr {\\n    key: &quot;shape&quot;\\n    value {\\n      shape {\\n        dim {\\n          size: -1\\n        }\\n        dim {\\n          size: 3\\n        }\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;rnn/Shape&quot;\\n  op: &quot;Shape&quot;\\n  input: &quot;Placeholder&quot;\\n  attr {\\n    key: &quot;T&quot;\\n    value {\\n      type: DT_FLOAT\\n    }\\n  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@@ -833,106 +823,106 @@
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-      "1 훈련 정확도: 0.97333336 테스트 정확도: 0.9524\n",
-      "2 훈련 정확도: 0.9266667 테스트 정확도: 0.9593\n",
-      "3 훈련 정확도: 0.97333336 테스트 정확도: 0.9604\n",
-      "4 훈련 정확도: 0.96666664 테스트 정확도: 0.9627\n",
-      "5 훈련 정확도: 0.94666666 테스트 정확도: 0.9626\n",
-      "6 훈련 정확도: 0.97333336 테스트 정확도: 0.9698\n",
-      "7 훈련 정확도: 0.96666664 테스트 정확도: 0.9719\n",
-      "8 훈련 정확도: 0.9533333 테스트 정확도: 0.9696\n",
-      "9 훈련 정확도: 0.96666664 테스트 정확도: 0.9725\n",
-      "10 훈련 정확도: 0.97333336 테스트 정확도: 0.9561\n",
-      "11 훈련 정확도: 0.94666666 테스트 정확도: 0.9703\n",
-      "12 훈련 정확도: 0.9866667 테스트 정확도: 0.9712\n",
-      "13 훈련 정확도: 0.9866667 테스트 정확도: 0.974\n",
-      "14 훈련 정확도: 0.96 테스트 정확도: 0.9664\n",
-      "15 훈련 정확도: 0.99333334 테스트 정확도: 0.9743\n",
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-      "17 훈련 정확도: 0.98 테스트 정확도: 0.9674\n",
-      "18 훈련 정확도: 0.96666664 테스트 정확도: 0.974\n",
-      "19 훈련 정확도: 0.97333336 테스트 정확도: 0.9766\n",
-      "20 훈련 정확도: 0.98 테스트 정확도: 0.9719\n",
-      "21 훈련 정확도: 0.9866667 테스트 정확도: 0.9701\n",
-      "22 훈련 정확도: 0.97333336 테스트 정확도: 0.9736\n",
-      "23 훈련 정확도: 0.97333336 테스트 정확도: 0.976\n",
-      "24 훈련 정확도: 0.9866667 테스트 정확도: 0.9773\n",
-      "25 훈련 정확도: 1.0 테스트 정확도: 0.9733\n",
-      "26 훈련 정확도: 0.96666664 테스트 정확도: 0.9741\n",
-      "27 훈련 정확도: 0.99333334 테스트 정확도: 0.9796\n",
-      "28 훈련 정확도: 0.96666664 테스트 정확도: 0.9712\n",
-      "29 훈련 정확도: 0.99333334 테스트 정확도: 0.9756\n",
-      "30 훈련 정확도: 0.98 테스트 정확도: 0.9767\n",
-      "31 훈련 정확도: 0.99333334 테스트 정확도: 0.9756\n",
-      "32 훈련 정확도: 0.9866667 테스트 정확도: 0.9754\n",
-      "33 훈련 정확도: 0.99333334 테스트 정확도: 0.9769\n",
-      "34 훈련 정확도: 0.99333334 테스트 정확도: 0.9787\n",
-      "35 훈련 정확도: 1.0 테스트 정확도: 0.9738\n",
-      "36 훈련 정확도: 1.0 테스트 정확도: 0.9781\n",
-      "37 훈련 정확도: 1.0 테스트 정확도: 0.9743\n",
-      "38 훈련 정확도: 0.9866667 테스트 정확도: 0.9763\n",
-      "39 훈련 정확도: 1.0 테스트 정확도: 0.9745\n",
-      "40 훈련 정확도: 1.0 테스트 정확도: 0.976\n",
-      "41 훈련 정확도: 0.9866667 테스트 정확도: 0.9739\n",
-      "42 훈련 정확도: 0.99333334 테스트 정확도: 0.9743\n",
-      "43 훈련 정확도: 0.99333334 테스트 정확도: 0.9782\n",
-      "44 훈련 정확도: 0.97333336 테스트 정확도: 0.9773\n",
-      "45 훈련 정확도: 0.99333334 테스트 정확도: 0.9779\n",
-      "46 훈련 정확도: 0.99333334 테스트 정확도: 0.9733\n",
-      "47 훈련 정확도: 1.0 테스트 정확도: 0.9797\n",
-      "48 훈련 정확도: 0.9866667 테스트 정확도: 0.9776\n",
-      "49 훈련 정확도: 0.99333334 테스트 정확도: 0.9785\n",
-      "50 훈련 정확도: 0.99333334 테스트 정확도: 0.9779\n",
-      "51 훈련 정확도: 0.99333334 테스트 정확도: 0.9802\n",
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-      "55 훈련 정확도: 0.98 테스트 정확도: 0.979\n",
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-      "62 훈련 정확도: 0.99333334 테스트 정확도: 0.98\n",
-      "63 훈련 정확도: 0.99333334 테스트 정확도: 0.9723\n",
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-      "76 훈련 정확도: 0.99333334 테스트 정확도: 0.9757\n",
-      "77 훈련 정확도: 1.0 테스트 정확도: 0.9795\n",
-      "78 훈련 정확도: 0.9866667 테스트 정확도: 0.9741\n",
-      "79 훈련 정확도: 0.98 테스트 정확도: 0.9747\n",
-      "80 훈련 정확도: 1.0 테스트 정확도: 0.9783\n",
-      "81 훈련 정확도: 0.98 테스트 정확도: 0.9773\n",
-      "82 훈련 정확도: 0.98 테스트 정확도: 0.9764\n",
-      "83 훈련 정확도: 1.0 테스트 정확도: 0.9763\n",
-      "84 훈련 정확도: 1.0 테스트 정확도: 0.9801\n",
-      "85 훈련 정확도: 1.0 테스트 정확도: 0.9744\n",
-      "86 훈련 정확도: 0.99333334 테스트 정확도: 0.9785\n",
-      "87 훈련 정확도: 0.98 테스트 정확도: 0.9765\n",
-      "88 훈련 정확도: 0.99333334 테스트 정확도: 0.9731\n",
-      "89 훈련 정확도: 0.99333334 테스트 정확도: 0.9707\n",
-      "90 훈련 정확도: 1.0 테스트 정확도: 0.9766\n",
-      "91 훈련 정확도: 0.99333334 테스트 정확도: 0.9765\n",
-      "92 훈련 정확도: 0.99333334 테스트 정확도: 0.9737\n",
-      "93 훈련 정확도: 1.0 테스트 정확도: 0.9805\n",
-      "94 훈련 정확도: 1.0 테스트 정확도: 0.9753\n",
-      "95 훈련 정확도: 1.0 테스트 정확도: 0.977\n",
-      "96 훈련 정확도: 0.99333334 테스트 정확도: 0.9754\n",
-      "97 훈련 정확도: 1.0 테스트 정확도: 0.9759\n",
-      "98 훈련 정확도: 0.9866667 테스트 정확도: 0.9771\n",
-      "99 훈련 정확도: 0.99333334 테스트 정확도: 0.9797\n"
+      "0 훈련 정확도: 0.93333334 테스트 정확도: 0.9311\n",
+      "1 훈련 정확도: 0.96666664 테스트 정확도: 0.9522\n",
+      "2 훈련 정확도: 0.97333336 테스트 정확도: 0.9584\n",
+      "3 훈련 정확도: 0.96 테스트 정확도: 0.966\n",
+      "4 훈련 정확도: 0.97333336 테스트 정확도: 0.9685\n",
+      "5 훈련 정확도: 0.98 테스트 정확도: 0.9711\n",
+      "6 훈련 정확도: 0.96666664 테스트 정확도: 0.9687\n",
+      "7 훈련 정확도: 0.97333336 테스트 정확도: 0.9687\n",
+      "8 훈련 정확도: 0.96666664 테스트 정확도: 0.9718\n",
+      "9 훈련 정확도: 0.98 테스트 정확도: 0.9694\n",
+      "10 훈련 정확도: 0.9866667 테스트 정확도: 0.9736\n",
+      "11 훈련 정확도: 0.96 테스트 정확도: 0.9702\n",
+      "12 훈련 정확도: 0.9866667 테스트 정확도: 0.9771\n",
+      "13 훈련 정확도: 0.96666664 테스트 정확도: 0.9695\n",
+      "14 훈련 정확도: 0.97333336 테스트 정확도: 0.9706\n",
+      "15 훈련 정확도: 0.99333334 테스트 정확도: 0.9763\n",
+      "16 훈련 정확도: 0.9866667 테스트 정확도: 0.9778\n",
+      "17 훈련 정확도: 0.9866667 테스트 정확도: 0.9752\n",
+      "18 훈련 정확도: 0.9866667 테스트 정확도: 0.975\n",
+      "19 훈련 정확도: 0.97333336 테스트 정확도: 0.9745\n",
+      "20 훈련 정확도: 0.99333334 테스트 정확도: 0.9773\n",
+      "21 훈련 정확도: 0.98 테스트 정확도: 0.98\n",
+      "22 훈련 정확도: 0.98 테스트 정확도: 0.9794\n",
+      "23 훈련 정확도: 0.98 테스트 정확도: 0.9739\n",
+      "24 훈련 정확도: 0.98 테스트 정확도: 0.9757\n",
+      "25 훈련 정확도: 1.0 테스트 정확도: 0.9719\n",
+      "26 훈련 정확도: 0.97333336 테스트 정확도: 0.9803\n",
+      "27 훈련 정확도: 1.0 테스트 정확도: 0.9796\n",
+      "28 훈련 정확도: 0.96666664 테스트 정확도: 0.977\n",
+      "29 훈련 정확도: 0.99333334 테스트 정확도: 0.9799\n",
+      "30 훈련 정확도: 0.9866667 테스트 정확도: 0.9754\n",
+      "31 훈련 정확도: 0.99333334 테스트 정확도: 0.9791\n",
+      "32 훈련 정확도: 0.98 테스트 정확도: 0.9775\n",
+      "33 훈련 정확도: 0.99333334 테스트 정확도: 0.9755\n",
+      "34 훈련 정확도: 0.9866667 테스트 정확도: 0.9723\n",
+      "35 훈련 정확도: 1.0 테스트 정확도: 0.9797\n",
+      "36 훈련 정확도: 1.0 테스트 정확도: 0.9798\n",
+      "37 훈련 정확도: 0.99333334 테스트 정확도: 0.9795\n",
+      "38 훈련 정확도: 0.99333334 테스트 정확도: 0.9766\n",
+      "39 훈련 정확도: 0.99333334 테스트 정확도: 0.9792\n",
+      "40 훈련 정확도: 1.0 테스트 정확도: 0.9811\n",
+      "41 훈련 정확도: 0.97333336 테스트 정확도: 0.9728\n",
+      "42 훈련 정확도: 0.98 테스트 정확도: 0.981\n",
+      "43 훈련 정확도: 0.99333334 테스트 정확도: 0.9735\n",
+      "44 훈련 정확도: 0.9866667 테스트 정확도: 0.9754\n",
+      "45 훈련 정확도: 0.9866667 테스트 정확도: 0.9787\n",
+      "46 훈련 정확도: 0.9866667 테스트 정확도: 0.9822\n",
+      "47 훈련 정확도: 1.0 테스트 정확도: 0.9777\n",
+      "48 훈련 정확도: 0.9866667 테스트 정확도: 0.9762\n",
+      "49 훈련 정확도: 1.0 테스트 정확도: 0.976\n",
+      "50 훈련 정확도: 0.99333334 테스트 정확도: 0.9819\n",
+      "51 훈련 정확도: 1.0 테스트 정확도: 0.9787\n",
+      "52 훈련 정확도: 0.9866667 테스트 정확도: 0.9782\n",
+      "53 훈련 정확도: 0.9866667 테스트 정확도: 0.9766\n",
+      "54 훈련 정확도: 0.99333334 테스트 정확도: 0.981\n",
+      "55 훈련 정확도: 0.99333334 테스트 정확도: 0.9789\n",
+      "56 훈련 정확도: 0.99333334 테스트 정확도: 0.9806\n",
+      "57 훈련 정확도: 1.0 테스트 정확도: 0.9835\n",
+      "58 훈련 정확도: 0.9866667 테스트 정확도: 0.9789\n",
+      "59 훈련 정확도: 1.0 테스트 정확도: 0.9738\n",
+      "60 훈련 정확도: 0.99333334 테스트 정확도: 0.9702\n",
+      "61 훈련 정확도: 1.0 테스트 정확도: 0.9803\n",
+      "62 훈련 정확도: 1.0 테스트 정확도: 0.9766\n",
+      "63 훈련 정확도: 0.9866667 테스트 정확도: 0.9797\n",
+      "64 훈련 정확도: 0.9866667 테스트 정확도: 0.9758\n",
+      "65 훈련 정확도: 0.9866667 테스트 정확도: 0.9764\n",
+      "66 훈련 정확도: 0.9866667 테스트 정확도: 0.9773\n",
+      "67 훈련 정확도: 0.99333334 테스트 정확도: 0.9811\n",
+      "68 훈련 정확도: 0.99333334 테스트 정확도: 0.9721\n",
+      "69 훈련 정확도: 0.9866667 테스트 정확도: 0.9795\n",
+      "70 훈련 정확도: 0.98 테스트 정확도: 0.9754\n",
+      "71 훈련 정확도: 1.0 테스트 정확도: 0.9786\n",
+      "72 훈련 정확도: 0.9866667 테스트 정확도: 0.9771\n",
+      "73 훈련 정확도: 1.0 테스트 정확도: 0.9795\n",
+      "74 훈련 정확도: 0.99333334 테스트 정확도: 0.9805\n",
+      "75 훈련 정확도: 0.97333336 테스트 정확도: 0.9807\n",
+      "76 훈련 정확도: 0.97333336 테스트 정확도: 0.9752\n",
+      "77 훈련 정확도: 1.0 테스트 정확도: 0.9808\n",
+      "78 훈련 정확도: 0.99333334 테스트 정확도: 0.9732\n",
+      "79 훈련 정확도: 0.99333334 테스트 정확도: 0.9805\n",
+      "80 훈련 정확도: 0.9866667 테스트 정확도: 0.9779\n",
+      "81 훈련 정확도: 0.99333334 테스트 정확도: 0.9791\n",
+      "82 훈련 정확도: 0.99333334 테스트 정확도: 0.9747\n",
+      "83 훈련 정확도: 0.99333334 테스트 정확도: 0.9784\n",
+      "84 훈련 정확도: 0.99333334 테스트 정확도: 0.9768\n",
+      "85 훈련 정확도: 0.97333336 테스트 정확도: 0.9733\n",
+      "86 훈련 정확도: 0.99333334 테스트 정확도: 0.9801\n",
+      "87 훈련 정확도: 0.99333334 테스트 정확도: 0.9794\n",
+      "88 훈련 정확도: 0.99333334 테스트 정확도: 0.9772\n",
+      "89 훈련 정확도: 0.99333334 테스트 정확도: 0.9773\n",
+      "90 훈련 정확도: 1.0 테스트 정확도: 0.9779\n",
+      "91 훈련 정확도: 0.99333334 테스트 정확도: 0.9761\n",
+      "92 훈련 정확도: 0.97333336 테스트 정확도: 0.9753\n",
+      "93 훈련 정확도: 1.0 테스트 정확도: 0.975\n",
+      "94 훈련 정확도: 1.0 테스트 정확도: 0.9802\n",
+      "95 훈련 정확도: 0.99333334 테스트 정확도: 0.9797\n",
+      "96 훈련 정확도: 0.9866667 테스트 정확도: 0.9796\n",
+      "97 훈련 정확도: 1.0 테스트 정확도: 0.9783\n",
+      "98 훈련 정확도: 0.9866667 테스트 정확도: 0.9772\n",
+      "99 훈련 정확도: 1.0 테스트 정확도: 0.9793\n"
      ]
     }
    ],
@@ -1020,16 +1010,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 정확도: 0.9533333 테스트 정확도: 0.9376\n",
-      "1 훈련 정확도: 0.97333336 테스트 정확도: 0.9602\n",
-      "2 훈련 정확도: 0.96666664 테스트 정확도: 0.9594\n",
-      "3 훈련 정확도: 0.97333336 테스트 정확도: 0.9688\n",
-      "4 훈련 정확도: 0.99333334 테스트 정확도: 0.9763\n",
-      "5 훈련 정확도: 0.98 테스트 정확도: 0.9742\n",
-      "6 훈련 정확도: 0.99333334 테스트 정확도: 0.9779\n",
-      "7 훈련 정확도: 0.97333336 테스트 정확도: 0.9773\n",
-      "8 훈련 정확도: 0.9866667 테스트 정확도: 0.9729\n",
-      "9 훈련 정확도: 0.9866667 테스트 정확도: 0.9822\n"
+      "0 훈련 정확도: 0.93333334 테스트 정확도: 0.939\n",
+      "1 훈련 정확도: 0.98 테스트 정확도: 0.9589\n",
+      "2 훈련 정확도: 0.94666666 테스트 정확도: 0.9677\n",
+      "3 훈련 정확도: 0.98 테스트 정확도: 0.9756\n",
+      "4 훈련 정확도: 0.98 테스트 정확도: 0.9765\n",
+      "5 훈련 정확도: 0.9866667 테스트 정확도: 0.9741\n",
+      "6 훈련 정확도: 0.97333336 테스트 정확도: 0.9783\n",
+      "7 훈련 정확도: 1.0 테스트 정확도: 0.9804\n",
+      "8 훈련 정확도: 0.99333334 테스트 정확도: 0.979\n",
+      "9 훈련 정확도: 0.9866667 테스트 정확도: 0.9815\n"
      ]
     }
    ],
@@ -1280,21 +1270,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 \tMSE: 11.234554\n",
-      "100 \tMSE: 0.38021186\n",
-      "200 \tMSE: 0.12111329\n",
-      "300 \tMSE: 0.06443737\n",
-      "400 \tMSE: 0.05846016\n",
-      "500 \tMSE: 0.056370605\n",
-      "600 \tMSE: 0.05040648\n",
-      "700 \tMSE: 0.045476887\n",
-      "800 \tMSE: 0.04885428\n",
-      "900 \tMSE: 0.046516392\n",
-      "1000 \tMSE: 0.046919473\n",
-      "1100 \tMSE: 0.04594408\n",
-      "1200 \tMSE: 0.03982782\n",
-      "1300 \tMSE: 0.046534885\n",
-      "1400 \tMSE: 0.04091823\n"
+      "0 \tMSE: 11.967253\n",
+      "100 \tMSE: 0.5267912\n",
+      "200 \tMSE: 0.1495199\n",
+      "300 \tMSE: 0.07283236\n",
+      "400 \tMSE: 0.06168884\n",
+      "500 \tMSE: 0.05886846\n",
+      "600 \tMSE: 0.05303419\n",
+      "700 \tMSE: 0.048641633\n",
+      "800 \tMSE: 0.04998502\n",
+      "900 \tMSE: 0.047834214\n",
+      "1000 \tMSE: 0.04796028\n",
+      "1100 \tMSE: 0.04780812\n",
+      "1200 \tMSE: 0.0415957\n",
+      "1300 \tMSE: 0.047843512\n",
+      "1400 \tMSE: 0.04253944\n"
      ]
     }
    ],
@@ -1345,26 +1335,26 @@
     {
      "data": {
       "text/plain": [
-       "array([[[-3.3843877 ],\n",
-       "        [-2.442578  ],\n",
-       "        [-1.1406771 ],\n",
-       "        [ 0.70999134],\n",
-       "        [ 2.169664  ],\n",
-       "        [ 3.0662253 ],\n",
-       "        [ 3.4722242 ],\n",
-       "        [ 3.3540928 ],\n",
-       "        [ 2.83155   ],\n",
-       "        [ 2.201286  ],\n",
-       "        [ 1.7171311 ],\n",
-       "        [ 1.5864581 ],\n",
-       "        [ 1.9358639 ],\n",
-       "        [ 2.733486  ],\n",
-       "        [ 3.8900287 ],\n",
-       "        [ 5.1092954 ],\n",
-       "        [ 6.122177  ],\n",
-       "        [ 6.665222  ],\n",
-       "        [ 6.6429915 ],\n",
-       "        [ 6.080037  ]]], dtype=float32)"
+       "array([[[-3.409996 ],\n",
+       "        [-2.4543931],\n",
+       "        [-1.1121919],\n",
+       "        [ 0.7361519],\n",
+       "        [ 2.16957  ],\n",
+       "        [ 3.024978 ],\n",
+       "        [ 3.4844422],\n",
+       "        [ 3.366301 ],\n",
+       "        [ 2.8970137],\n",
+       "        [ 2.213668 ],\n",
+       "        [ 1.6406606],\n",
+       "        [ 1.5506338],\n",
+       "        [ 1.9295691],\n",
+       "        [ 2.7553375],\n",
+       "        [ 3.8671415],\n",
+       "        [ 5.0822144],\n",
+       "        [ 6.0878325],\n",
+       "        [ 6.6626954],\n",
+       "        [ 6.645318 ],\n",
+       "        [ 6.0674458]]], dtype=float32)"
       ]
      },
      "execution_count": 54,
@@ -1383,7 +1373,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1481,21 +1471,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 \tMSE: 13.798526\n",
-      "100 \tMSE: 0.51873666\n",
-      "200 \tMSE: 0.18096465\n",
-      "300 \tMSE: 0.08363678\n",
-      "400 \tMSE: 0.06251121\n",
-      "500 \tMSE: 0.060582373\n",
-      "600 \tMSE: 0.055472486\n",
-      "700 \tMSE: 0.051638845\n",
-      "800 \tMSE: 0.05216893\n",
-      "900 \tMSE: 0.04946163\n",
-      "1000 \tMSE: 0.049049225\n",
-      "1100 \tMSE: 0.051213406\n",
-      "1200 \tMSE: 0.04204171\n",
-      "1300 \tMSE: 0.049295332\n",
-      "1400 \tMSE: 0.043647002\n"
+      "0 \tMSE: 13.907028\n",
+      "100 \tMSE: 0.5062197\n",
+      "200 \tMSE: 0.20180194\n",
+      "300 \tMSE: 0.101789564\n",
+      "400 \tMSE: 0.06869375\n",
+      "500 \tMSE: 0.0650903\n",
+      "600 \tMSE: 0.056519922\n",
+      "700 \tMSE: 0.048897255\n",
+      "800 \tMSE: 0.050609533\n",
+      "900 \tMSE: 0.04794232\n",
+      "1000 \tMSE: 0.047962066\n",
+      "1100 \tMSE: 0.04990514\n",
+      "1200 \tMSE: 0.041599542\n",
+      "1300 \tMSE: 0.048676066\n",
+      "1400 \tMSE: 0.043152742\n"
      ]
     }
    ],
@@ -1526,26 +1516,26 @@
     {
      "data": {
       "text/plain": [
-       "array([[[-3.4256656],\n",
-       "        [-2.4964879],\n",
-       "        [-1.1188097],\n",
-       "        [ 0.6010748],\n",
-       "        [ 2.0974448],\n",
-       "        [ 3.2044346],\n",
-       "        [ 3.585753 ],\n",
-       "        [ 3.3138356],\n",
-       "        [ 2.8268244],\n",
-       "        [ 2.2785282],\n",
-       "        [ 1.7047077],\n",
-       "        [ 1.5082613],\n",
-       "        [ 1.8684278],\n",
-       "        [ 2.742042 ],\n",
-       "        [ 3.9028168],\n",
-       "        [ 5.073093 ],\n",
-       "        [ 6.147061 ],\n",
-       "        [ 6.7127986],\n",
-       "        [ 6.645899 ],\n",
-       "        [ 6.041297 ]]], dtype=float32)"
+       "array([[[-3.433237  ],\n",
+       "        [-2.454809  ],\n",
+       "        [-1.0924242 ],\n",
+       "        [ 0.70101255],\n",
+       "        [ 2.1101534 ],\n",
+       "        [ 3.0261526 ],\n",
+       "        [ 3.4682128 ],\n",
+       "        [ 3.321571  ],\n",
+       "        [ 2.8254244 ],\n",
+       "        [ 2.2395434 ],\n",
+       "        [ 1.7126881 ],\n",
+       "        [ 1.5265634 ],\n",
+       "        [ 1.9084352 ],\n",
+       "        [ 2.750864  ],\n",
+       "        [ 3.8967836 ],\n",
+       "        [ 5.1164637 ],\n",
+       "        [ 6.1152263 ],\n",
+       "        [ 6.6678505 ],\n",
+       "        [ 6.6391854 ],\n",
+       "        [ 6.045873  ]]], dtype=float32)"
       ]
      },
      "execution_count": 62,
@@ -1564,7 +1554,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1622,7 +1612,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x288 with 1 Axes>"
       ]
@@ -1656,7 +1646,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 792x288 with 2 Axes>"
       ]
@@ -1901,33 +1891,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[[-0.033671    0.00615814 -0.0224805  ... -0.08477943  0.07812577\n",
-      "    0.0409406 ]\n",
-      "  [ 0.2202788   0.06180531  0.22256151 ... -0.23308094  0.06236795\n",
-      "   -0.01974874]\n",
-      "  [-0.13161832  0.00174079  0.15934998 ... -0.14575946 -0.29136425\n",
-      "   -0.22982824]\n",
+      "[[[-3.29020061e-02  1.17019236e-01 -4.58992608e-02 ...  3.84477153e-02\n",
+      "   -5.24900258e-02  5.80436960e-02]\n",
+      "  [ 3.54288332e-02 -7.42610693e-02 -6.42375052e-02 ... -2.45757148e-01\n",
+      "   -1.77626804e-01 -3.26535292e-03]\n",
+      "  [ 2.59726584e-01  1.44605249e-01  3.29312116e-01 ...  3.38821977e-01\n",
+      "    5.98853417e-02 -8.31450596e-02]\n",
       "  ...\n",
-      "  [ 0.40135226 -0.0683148   0.19030328 ... -0.07745527  0.35425738\n",
-      "    0.15830204]\n",
-      "  [ 0.7006527  -0.0154526  -0.06332135 ... -0.20901318 -0.0539793\n",
-      "    0.12965451]\n",
-      "  [ 0.3396977  -0.3388407  -0.1747916  ... -0.31547976 -0.10045247\n",
-      "   -0.00143633]]\n",
+      "  [ 1.18043743e-01 -2.41663873e-01 -8.27798247e-01 ... -1.85782462e-01\n",
+      "    1.89682379e-01 -1.35261893e-01]\n",
+      "  [ 2.87830859e-01  3.92953992e-01 -3.15236300e-01 ... -7.85605535e-02\n",
+      "    4.32724983e-01 -6.61824420e-02]\n",
+      "  [ 7.49779761e-01  6.11052871e-01 -3.01685542e-01 ...  1.95649698e-01\n",
+      "   -1.62457466e-01 -5.23968339e-02]]\n",
       "\n",
-      " [[ 0.04636343  0.05075924 -0.01460598 ... -0.01488171  0.12059579\n",
-      "    0.03546331]\n",
-      "  [ 0.10273134  0.02954917  0.14052095 ... -0.09312268 -0.1330954\n",
-      "   -0.07913667]\n",
-      "  [-0.03998944 -0.23300202  0.10142665 ... -0.27028072  0.0087689\n",
-      "    0.07616787]\n",
+      " [[-6.68027624e-02  7.35576972e-02 -2.90275402e-02 ... -7.79968277e-02\n",
+      "   -3.65773253e-02 -4.66752201e-02]\n",
+      "  [ 1.71546549e-01  4.28418722e-03  2.18812120e-03 ...  3.68538052e-02\n",
+      "   -8.13241452e-02  9.01001915e-02]\n",
+      "  [ 3.45635489e-02 -9.57525298e-02  1.59450233e-01 ...  1.63022086e-01\n",
+      "   -6.83046281e-02  1.07990295e-01]\n",
       "  ...\n",
-      "  [ 0.3201678   0.17390148  0.06568783 ... -0.32528323  0.00921553\n",
-      "    0.37827423]\n",
-      "  [ 0.6501674  -0.10477125 -0.183512   ... -0.5114695   0.00990305\n",
-      "    0.2610496 ]\n",
-      "  [ 0.50272596 -0.12907378  0.07752537 ... -0.2155963   0.02334055\n",
-      "   -0.1325899 ]]]\n"
+      "  [ 1.96414590e-01 -2.15230390e-01 -5.82537949e-01 ...  3.02532643e-01\n",
+      "    5.47862291e-01 -1.06282756e-02]\n",
+      "  [ 2.01206997e-01  2.52655987e-02 -5.02945222e-02 ...  9.56110135e-02\n",
+      "   -2.03766495e-01 -1.52696133e-01]\n",
+      "  [ 4.77758050e-01  1.66605622e-01 -1.56402126e-01 ...  1.67080894e-01\n",
+      "   -2.03233853e-01  7.59940522e-05]]]\n"
      ]
     }
    ],
@@ -2020,21 +2010,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 MSE: 14.73364\n",
-      "100 훈련 MSE: 4.686095\n",
-      "200 훈련 MSE: 3.6556115\n",
-      "300 훈련 MSE: 3.8748214\n",
-      "400 훈련 MSE: 2.3531404\n",
-      "500 훈련 MSE: 3.7559667\n",
-      "600 훈련 MSE: 4.06035\n",
-      "700 훈련 MSE: 4.9739947\n",
-      "800 훈련 MSE: 4.0471015\n",
-      "900 훈련 MSE: 3.8106322\n",
-      "1000 훈련 MSE: 4.920709\n",
-      "1100 훈련 MSE: 3.858466\n",
-      "1200 훈련 MSE: 3.3147843\n",
-      "1300 훈련 MSE: 3.8227804\n",
-      "1400 훈련 MSE: 4.623097\n"
+      "0 훈련 MSE: 16.386942\n",
+      "100 훈련 MSE: 4.25324\n",
+      "200 훈련 MSE: 3.2349327\n",
+      "300 훈련 MSE: 3.5502756\n",
+      "400 훈련 MSE: 2.6301303\n",
+      "500 훈련 MSE: 4.105153\n",
+      "600 훈련 MSE: 3.5806417\n",
+      "700 훈련 MSE: 3.3154318\n",
+      "800 훈련 MSE: 3.5841472\n",
+      "900 훈련 MSE: 3.4554102\n",
+      "1000 훈련 MSE: 3.165893\n",
+      "1100 훈련 MSE: 3.691963\n",
+      "1200 훈련 MSE: 3.7539616\n",
+      "1300 훈련 MSE: 2.4574566\n",
+      "1400 훈련 MSE: 3.9499161\n"
      ]
     }
    ],
@@ -2084,7 +2074,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2215,16 +2205,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "에포크 0 훈련 정확도 = 0.9533333 테스트 정확도 = 0.9506\n",
-      "에포크 1 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9661\n",
-      "에포크 2 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9796\n",
-      "에포크 3 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9777\n",
-      "에포크 4 훈련 정확도 = 0.9866667 테스트 정확도 = 0.9833\n",
-      "에포크 5 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9857\n",
-      "에포크 6 훈련 정확도 = 1.0 테스트 정확도 = 0.9863\n",
-      "에포크 7 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9875\n",
-      "에포크 8 훈련 정확도 = 1.0 테스트 정확도 = 0.9879\n",
-      "에포크 9 훈련 정확도 = 1.0 테스트 정확도 = 0.9868\n"
+      "에포크 0 훈련 정확도 = 0.9533333 테스트 정확도 = 0.9549\n",
+      "에포크 1 훈련 정확도 = 0.9866667 테스트 정확도 = 0.9733\n",
+      "에포크 2 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9769\n",
+      "에포크 3 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9807\n",
+      "에포크 4 훈련 정확도 = 0.98 테스트 정확도 = 0.9845\n",
+      "에포크 5 훈련 정확도 = 0.9866667 테스트 정확도 = 0.9815\n",
+      "에포크 6 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9845\n",
+      "에포크 7 훈련 정확도 = 1.0 테스트 정확도 = 0.9876\n",
+      "에포크 8 훈련 정확도 = 1.0 테스트 정확도 = 0.9877\n",
+      "에포크 9 훈련 정확도 = 0.99333334 테스트 정확도 = 0.9881\n"
      ]
     }
    ],
@@ -2384,7 +2374,7 @@
      "data": {
       "text/plain": [
        "('anarchism originated as a term of abuse first used',\n",
-       " array([5243, 3084,   12,    6,  195,    2, 3135,   46,   59]))"
+       " array([5244, 3083,   12,    6,  195,    2, 3135,   46,   59]))"
       ]
      },
      "execution_count": 97,
@@ -2404,7 +2394,7 @@
     {
      "data": {
       "text/plain": [
-       "'guided matrix as a term of recipient first used'"
+       "'belonging didn as a term of extinct first used'"
       ]
      },
      "execution_count": 98,
@@ -2495,7 +2485,7 @@
     {
      "data": {
       "text/plain": [
-       "(array([3084, 3084,   12,   12,    6,    6,  195,  195], dtype=int32),\n",
+       "(array([3083, 3083,   12,   12,    6,    6,  195,  195], dtype=int32),\n",
        " ['originated', 'originated', 'as', 'as', 'a', 'a', 'term', 'term'])"
       ]
      },
@@ -2517,14 +2507,14 @@
      "data": {
       "text/plain": [
        "(array([[  12],\n",
-       "        [5243],\n",
-       "        [3084],\n",
+       "        [5244],\n",
        "        [   6],\n",
+       "        [3083],\n",
        "        [ 195],\n",
        "        [  12],\n",
-       "        [   6],\n",
-       "        [   2]], dtype=int32),\n",
-       " ['as', 'anarchism', 'originated', 'a', 'term', 'as', 'a', 'of'])"
+       "        [   2],\n",
+       "        [   6]], dtype=int32),\n",
+       " ['as', 'anarchism', 'a', 'originated', 'term', 'as', 'of', 'a'])"
       ]
      },
      "execution_count": 103,
@@ -2659,44 +2649,44 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "반복: 0\t스텝  0 에서의 평균 손실:  293.06048583984375\n",
-      "would에 가장 가까운 단어: factories, daemon, mayan, webern, leveling, swabia, carter, medicinal,\n",
-      "on에 가장 가까운 단어: roofed, assent, freenet, gilles, dagmar, bland, wagers, redevelopment,\n",
-      "four에 가장 가까운 단어: signifies, overlooked, tank, carboxylic, hustler, tina, disastrously, mammuthus,\n",
-      "his에 가장 가까운 단어: penn, suzuki, switzerland, sector, discharge, capacitive, value, backlight,\n",
-      "often에 가장 가까운 단어: mentor, contrasts, bootlegged, ling, observatories, ubangi, robber, implemented,\n",
-      "in에 가장 가까운 단어: intercommunal, congestion, needlessly, tobacco, necronomicon, displays, pottery, skeptics,\n",
-      "an에 가장 가까운 단어: guzm, pickett, validating, practiced, sandinista, vitis, andries, expanses,\n",
-      "eight에 가장 가까운 단어: rig, nagorno, creasy, paschal, disjointed, processing, seconded, retroflex,\n",
-      "these에 가장 가까운 단어: calculating, vaporized, angel, kandahar, relinquish, fluids, bedtime, puzzling,\n",
-      "nine에 가장 가까운 단어: picchi, luke, atheists, signifying, awt, stables, develop, relating,\n",
-      "called에 가장 가까운 단어: callisto, tends, intends, veterinarians, watkins, matador, phenomenal, chilperic,\n",
-      "about에 가장 가까운 단어: nominees, sorcerer, southey, curtin, filters, lightly, davao, indecisive,\n",
-      "up에 가장 가까운 단어: lyricist, hidden, biggs, staged, declining, pilsener, march, aboriginal,\n",
-      "one에 가장 가까운 단어: inclinations, kolkata, foxx, rodan, lalor, lords, edith, collectivism,\n",
-      "and에 가장 가까운 단어: attached, singly, omnivores, tabular, suppliers, silhouette, maradona, metaphase,\n",
-      "been에 가장 가까운 단어: absinthe, entice, nde, hive, linear, unscr, indicted, determinative,\n",
-      "반복: 2000\t스텝  2000 에서의 평균 손실:  131.21407338047027\n",
-      "반복: 4000\t스텝  4000 에서의 평균 손실:  62.32993085551262\n",
-      "반복: 6000\t스텝  6000 에서의 평균 손실:  41.999602062940596\n",
-      "반복: 8000\t스텝  8000 에서의 평균 손실:  31.167041951179506\n",
-      "반복: 10000\t스텝  10000 에서의 평균 손실:  25.85122103357315\n",
-      "would에 가장 가까운 단어: to, shelter, had, could, cannot, niche, should, topalov,\n",
-      "on에 가장 가까운 단어: cooperstown, in, predominant, anand, var, antidepressants, from, ataxia,\n",
-      "four에 가장 가까운 단어: three, five, nine, two, one, eight, seven, zero,\n",
-      "his에 가장 가까운 단어: ampere, actinium, straczynski, s, staggered, celebrates, the, ataxia,\n",
-      "often에 가장 가까운 단어: heuristics, wanting, hochschule, altaic, hoxha, valley, rationale, commonly,\n",
-      "in에 가장 가까운 단어: and, branched, of, for, operates, the, achill, kickoff,\n",
-      "an에 가장 가까운 단어: spectral, preoccupied, atemi, ankh, a, is, columbus, chancellorsville,\n",
-      "eight에 가장 가까운 단어: nine, seven, one, six, zero, three, five, four,\n",
-      "these에 가장 가까운 단어: joy, provable, preservation, amphibian, fame, vibrating, fallacy, outcome,\n",
-      "nine에 가장 가까운 단어: eight, six, five, zero, three, one, four, seven,\n",
-      "called에 가장 가까운 단어: steppe, foot, switch, satire, told, suppose, researches, leech,\n",
-      "about에 가장 가까운 단어: obscure, parasite, horch, garrett, transcriptase, seo, dollfuss, provable,\n",
-      "up에 가장 가까운 단어: actinium, diacritic, the, kournikova, save, and, starvation, appointing,\n",
-      "one에 가장 가까운 단어: three, nine, eight, six, seven, two, four, five,\n",
-      "and에 가장 가까운 단어: in, the, of, asparagales, amhr, chun, operates, arches,\n",
-      "been에 가장 가까운 단어: has, had, omotic, unofficial, have, chadic, by, investigates,\n"
+      "반복: 0\t스텝  0 에서의 평균 손실:  294.72406005859375\n",
+      "would에 가장 가까운 단어: lindy, humorously, engagements, bek, phoenicia, showcase, losing, nf,\n",
+      "on에 가장 가까운 단어: imprints, arbitrarily, cis, prefect, saccharomyces, jj, classmate, ave,\n",
+      "four에 가장 가까운 단어: citations, insist, tank, macarthur, uneducated, plissken, riordan, vasili,\n",
+      "his에 가장 가까운 단어: generous, archie, themes, achieved, somehow, bartle, value, lexis,\n",
+      "often에 가장 가까운 단어: deception, sticks, hitomaro, subtract, calculates, darrow, lavender, anthony,\n",
+      "in에 가장 가까운 단어: termini, bubonic, elaborating, commence, tobacco, subdivided, competing, puppets,\n",
+      "an에 가장 가까운 단어: queene, lavon, chestnuts, solomon, clayton, voce, abolishes, patched,\n",
+      "eight에 가장 가까운 단어: rig, retaliated, spr, bump, poinsot, wild, sportspeople, bison,\n",
+      "these에 가장 가까운 단어: conferences, ypres, neck, refrain, bessarabia, cord, gravely, reconciling,\n",
+      "nine에 가장 가까운 단어: pittsburg, trivia, carol, menorah, overwriting, audition, develop, ranks,\n",
+      "called에 가장 가까운 단어: bon, dealing, stallion, hata, ise, vadim, sizeable, forgetfulness,\n",
+      "about에 가장 가까운 단어: euphoria, hinayana, vikernes, centralization, lt, washing, promontory, mop,\n",
+      "up에 가장 가까운 단어: abet, drives, addictions, piercing, constants, quipped, misr, rainy,\n",
+      "one에 가장 가까운 단어: syntactically, broughton, kuru, radiodiffusion, chute, alan, announcing, pashtuns,\n",
+      "and에 가장 가까운 단어: soft, visas, fasts, lillard, drowsiness, coffin, komondor, vulnerability,\n",
+      "been에 가장 가까운 단어: sang, expositor, reloading, otis, linear, widen, ephraem, astros,\n",
+      "반복: 2000\t스텝  2000 에서의 평균 손실:  131.0253740901947\n",
+      "반복: 4000\t스텝  4000 에서의 평균 손실:  62.29951333236694\n",
+      "반복: 6000\t스텝  6000 에서의 평균 손실:  41.85774783444405\n",
+      "반복: 8000\t스텝  8000 에서의 평균 손실:  30.861831660270692\n",
+      "반복: 10000\t스텝  10000 에서의 평균 손실:  26.37321024131775\n",
+      "would에 가장 가까운 단어: can, mk, to, mari, may, should, doubleday, outdoors,\n",
+      "on에 가장 가까운 단어: cin, bambaataa, and, of, in, halfback, cca, dh,\n",
+      "four에 가장 가까운 단어: three, five, one, six, seven, two, nine, zero,\n",
+      "his에 가장 가까운 단어: the, propositional, achill, livelihood, merchandising, intercollegiate, bukhara, bicycle,\n",
+      "often에 가장 가까운 단어: dubbing, commonly, utf, altaic, also, signed, and, which,\n",
+      "in에 가장 가까운 단어: and, of, from, trench, for, the, altaic, coulomb,\n",
+      "an에 가장 가까운 단어: nen, anointed, rat, chi, antigua, abandonment, abscess, barbara,\n",
+      "eight에 가장 가까운 단어: one, six, nine, seven, five, three, zero, four,\n",
+      "these에 가장 가까운 단어: bremer, notice, bambaataa, nage, automorphism, olympias, psalms, mannerisms,\n",
+      "nine에 가장 가까운 단어: one, six, eight, zero, seven, five, four, two,\n",
+      "called에 가장 가까운 단어: also, is, loser, cma, amalgamated, hat, difficulty, viva,\n",
+      "about에 가장 가까운 단어: altaic, donation, selenium, numidia, preston, paramount, seven, bragg,\n",
+      "up에 가장 가까운 단어: whittier, maguey, windmill, bartholomew, dry, qualities, aardwolf, coffee,\n",
+      "one에 가장 가까운 단어: nine, eight, six, seven, four, two, three, zero,\n",
+      "and에 가장 가까운 단어: in, the, UNK, of, altaic, one, for, a,\n",
+      "been에 가장 가까운 단어: have, cma, enver, was, had, diodorus, pseudonym, argued,\n"
      ]
     }
    ],
@@ -2789,7 +2779,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1296x1296 with 1 Axes>"
       ]
@@ -3045,7 +3035,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "BPBPVPEEPE BPBSXSEPE BPBPTVVXPE BTBPPPSETE BTBPVVSTE BPBTSSXXTSTVVEPE BPETSXXTVPSEPE BPBTXSTPE BTBPTTTVPSXTE BPBTSXXTTTBTTVVEPE BPBVXXVPXTVPXTTVVEPE BPBPTTVBEPE BPBPVVEBE BPXPTTVVEPE BPBPVBSEPE BPBTVXXVVEPE BEBPTTTVPXVVETE BPBTSSXTEPE BPBPVBEPE BEBTXSEPE BTBPTVPXVPXVVETS PPBTSXXTVPXVPSEPE BPBTSXXTTTVVSEPE BPBPVPXVVTPE BTBTSVSETE "
+      "BPBPVPPEPE BPBSXSEPE BPBPTVVTPE BTBPEPSETE BTBPVVVTE BPBTSSXXTSTVVEPE BPVTSXXTVPSEPE BPBTXSPPE BTBPTTTVPSTTE BPBTSXXTTTXTTVVEPE BPBBXXVPXTVPXTTVVEPE BPBPTTVXEPE BPBPVVEEE BPPPTTVVEPE BPBPVESEPE BPBTBXXVVEPE BVBPTTTVPXVVETE BPBTSSXVEPE BPBPVXEPE BVBTXSEPE BTBPTVPXVPXVVETV EPBTSXXTVPXVPSEPE BPBTSXXTTTVBSEPE BPBPVPXVVPPE BTBTSBSETE "
      ]
     }
    ],
@@ -3174,7 +3164,7 @@
        "       [0, 0, 0, 0, 0, 1, 0],\n",
        "       [0, 0, 1, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 1],\n",
-       "       [0, 0, 0, 0, 1, 0, 0],\n",
+       "       [0, 0, 1, 0, 0, 0, 0],\n",
        "       [0, 0, 1, 0, 0, 0, 0],\n",
        "       [0, 0, 0, 0, 0, 0, 1],\n",
        "       [0, 0, 0, 0, 0, 1, 0],\n",
@@ -3336,32 +3326,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
+   "execution_count": 129,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "   0  훈련 손실: 0.6798, 정확도: 58.00%  검증 세트 정확도: 58.60%\n",
-      "   1  훈련 손실: 0.6454, 정확도: 60.00%  검증 세트 정확도: 61.82%\n",
-      "   2  훈련 손실: 0.5676, 정확도: 80.00%  검증 세트 정확도: 69.98%\n",
-      "   3  훈련 손실: 0.4996, 정확도: 78.00%  검증 세트 정확도: 79.56%\n",
-      "   4  훈련 손실: 0.4083, 정확도: 86.00%  검증 세트 정확도: 86.26%\n",
-      "   5  훈련 손실: 0.3434, 정확도: 84.00%  검증 세트 정확도: 85.70%\n",
-      "   6  훈련 손실: 0.2667, 정확도: 90.00%  검증 세트 정확도: 89.78%\n",
-      "   7  훈련 손실: 0.2397, 정확도: 90.00%  검증 세트 정확도: 90.44%\n",
-      "   8  훈련 손실: 0.0917, 정확도: 98.00%  검증 세트 정확도: 95.74%\n",
-      "   9  훈련 손실: 0.1826, 정확도: 96.00%  검증 세트 정확도: 94.10%\n",
-      "  10  훈련 손실: 0.0136, 정확도: 100.00%  검증 세트 정확도: 98.52%\n",
-      "  11  훈련 손실: 0.0161, 정확도: 100.00%  검증 세트 정확도: 98.14%\n",
-      "  12  훈련 손실: 0.0113, 정확도: 100.00%  검증 세트 정확도: 98.62%\n",
-      "  13  훈련 손실: 0.0155, 정확도: 100.00%  검증 세트 정확도: 99.26%\n",
-      "  14  훈련 손실: 0.0089, 정확도: 100.00%  검증 세트 정확도: 99.26%\n",
-      "  15  훈련 손실: 0.0072, 정확도: 100.00%  검증 세트 정확도: 99.00%\n",
-      "  16  훈련 손실: 0.0019, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
-      "  17  훈련 손실: 0.0009, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
-      "  18  훈련 손실: 0.0007, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "   0  훈련 손실: 0.6829, 정확도: 54.00%  검증 세트 정확도: 58.80%\n",
+      "   1  훈련 손실: 0.6518, 정확도: 64.00%  검증 세트 정확도: 62.38%\n",
+      "   2  훈련 손실: 0.6082, 정확도: 72.00%  검증 세트 정확도: 67.86%\n",
+      "   3  훈련 손실: 0.6268, 정확도: 64.00%  검증 세트 정확도: 66.56%\n",
+      "   4  훈련 손실: 0.4726, 정확도: 74.00%  검증 세트 정확도: 77.60%\n",
+      "   5  훈련 손실: 0.3666, 정확도: 84.00%  검증 세트 정확도: 84.02%\n",
+      "   6  훈련 손실: 0.3469, 정확도: 90.00%  검증 세트 정확도: 81.58%\n",
+      "   7  훈련 손실: 0.2350, 정확도: 76.00%  검증 세트 정확도: 71.58%\n",
+      "   8  훈련 손실: 0.2000, 정확도: 94.00%  검증 세트 정확도: 95.86%\n",
+      "   9  훈련 손실: 0.4007, 정확도: 74.00%  검증 세트 정확도: 76.34%\n",
+      "  10  훈련 손실: 0.3353, 정확도: 88.00%  검증 세트 정확도: 90.50%\n",
+      "  11  훈련 손실: 0.0116, 정확도: 100.00%  검증 세트 정확도: 99.44%\n",
+      "  12  훈련 손실: 0.0029, 정확도: 100.00%  검증 세트 정확도: 99.90%\n",
+      "  13  훈련 손실: 0.0016, 정확도: 100.00%  검증 세트 정확도: 99.96%\n",
+      "  14  훈련 손실: 0.0011, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  15  훈련 손실: 0.0010, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  16  훈련 손실: 0.0009, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  17  훈련 손실: 0.0007, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
+      "  18  훈련 손실: 0.0006, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
       "  19  훈련 손실: 0.0006, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
       "  20  훈련 손실: 0.0005, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
       "  21  훈련 손실: 0.0004, 정확도: 100.00%  검증 세트 정확도: 100.00%\n",
@@ -3426,7 +3418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
diff --git a/15_autoencoders.ipynb b/15_autoencoders.ipynb
index 2e6f8da..330a960 100644
--- a/15_autoencoders.ipynb
+++ b/15_autoencoders.ipynb
@@ -12,11 +12,11 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n"
      ]
     }
    ],
@@ -242,7 +242,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 288x216 with 1 Axes>"
       ]
@@ -374,11 +374,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 MSE: 0.022107124\n",
-      "1 훈련 MSE: 0.012154639\n",
-      "2 훈련 MSE: 0.010653543\n",
-      "3 훈련 MSE: 0.010217447\n",
-      "4 훈련 MSE: 0.010719749\n"
+      "0 훈련 MSE: 0.020550018\n",
+      "1 훈련 MSE: 0.01137075\n",
+      "2 훈련 MSE: 0.0102255605\n",
+      "3 훈련 MSE: 0.009902374\n",
+      "4 훈련 MSE: 0.010377134\n"
      ]
     }
    ],
@@ -444,7 +444,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAGoCAYAAAB16I2XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHMJJREFUeJzt3VmMX3X5P/DTbZZOhyktDANUlkKkVSTsraUawVhJDBI1GJULDZhITCSRRA0kJuqNl8qFBo0GEI1LjGhARFFEkVBCGylbtGUHobvQZfaW340X/PP/PJ9yZqbzzLSv1+Vzep5z5jud7zsneT7nM+fNN99sAIA8c7NvAACOdsIYAJIJYwBIJowBIJkwBoBkwhgAkgljAEgmjAEgmTAGgGTzE67plV8cCeZk38DRYOvWrb4vmPUGBgYO+X3hyRgAkgljAEgmjAEgmTAGgGQZA1wATLM5c8ozRLbRnRk8GQNAMmEMAMmEMQAkE8YAkEwYA0AyYQwAySxtAphlpmOZ0tjYWHhswYIFra4f3W9N1Gvu3PgZcjYv0/JkDADJhDEAJBPGAJBMGANAMmEMAMlMUwMEoingqZwOrvVqOzVdmzSORFPTtcnkkZGRYr2zs7P1fY2Ojhbr8+bNK9YPHjwY9prI72Wm8GQMAMmEMQAkE8YAkEwYA0AyYQwAyUxTAwSmcpo6miieymnq8fHxsFdXV1exPjw83Kpec+DAgWI9moxumvhnnD+/HE+1XtH1a6byfdqT4ckYAJIJYwBIJowBIJkwBoBkwhgAkgljAEhmaRNAIFr2EtVrS2uiDQ5qy5GiZTw9PT3Fend3d+vrL1q0qNW1m6ZphoaGWp2zcOHCsFe0uURtQ4hIdP1oM4qmab/kbCL39XZ4MgaAZMIYAJIJYwBIJowBIJkwBoBkpqmBo0Lbyeimiaejo2nisbGxsNeePXuK9ZGRkfCc448/PjxWEk1ZN0080RxtILF48eKwVzSdHE2G1zZdmMjmElPJRhEAQNM0whgA0gljAEgmjAEgmTAGgGSmqf9n/fr1xfrNN98cnnPyyScX67X3w372s58t1pcsWdKqDpS1nZoeHh4Oe0VT06+88kqx/tJLL4W9omO19yYvW7asWI+mrOfPj7/STznllGI9mpquvWd77969xXr0s9TuK5pAj6a8axPmvb29xXpHR0d4TvQ7jtSm7yczge3JGACSCWMASCaMASCZMAaAZMIYAJIJYwBINqc2pn2YTPsF346zzjqrWN+yZcu0XL+vr69YX7169bRcfzqcdtppxfqNN94YnhMtx5gBpvct8keprVu3tv6+iDYriDZkqG3UEC1HeuGFF4r1xx9/POz16quvFuu1DRGOPfbYYj265+eeey7sNTAwUKxHn1e0fKlp4uVAJ510UrG+aNGisFd0/ehnv/zyy8Ne5557brFeW24afZZz507ds+rAwMAhvy88GQNAMmEMAMmEMQAkE8YAkEwYA0AyG0X8z29/+9ti/bHHHgvPefe7312sP/XUU+E5jzzySLH+u9/9rlj/4x//GPY6/fTTi/Xnn38+PKet6AXvJ554YnjOyy+/3Ooa0ZR10zTN1772tVa9IBK9xL+2iUE0BdzT01OsL126NOy1YMGCYr02tRttfPDiiy8W67WNCgYHB1ud89prr4W9os01zjjjjGJ927ZtYa/NmzcX66eeemqxvmLFirDXmWeeWawvXLgwPGcqp6YnY2bcBQAcxYQxACQTxgCQTBgDQDJhDADJhDEAJLNRxAwRLRWIXkjfNPHSptrL4tvq6Ogo1mtLm6L72rFjR7F+5513hr2uvPLKyt2lslHENIg2iqgt4Ym+0w4cOFCsHzx4MOw1OjparEebKLz++uthr+j6xxxzTHhO2w0hxsbGwl7Rpiu7d+9udY2miZcdRd8Ld999d9jrF7/4RbEebTpx7bXXhr2iTSRqn3H0e4nqE2GjCACYBYQxACQTxgCQTBgDQDJhDADJbBQxQ3R1dRXrtZeiR1auXDnZ2zmkaMOLpmmanTt3FuurVq0q1tetWzcl98TRo7YKJJq0jlYG1Kapo80dounc448/Puw1b968VvfVNE2zf//+Yv2EE04o1qMNLJomvuddu3YV6xdeeGHYK9oQY9++fcV6bTOG6POPNumIpsKbJt4Qovb/ZSqnpifDkzEAJBPGAJBMGANAMmEMAMmEMQAkM01NVTTN+bGPfSw8J5qO/O53v1usd3d3t78xmCLRlHPTxFPA0f/x2mR0NJldmzSOpqOjeu360T13dnYW64sXL259X88++2yxvnnz5rBXb29vsb5mzZpivTZNHf0ua+8yjz7/aAL7cO3n4MkYAJIJYwBIJowBIJkwBoBkwhgAkgljAEhmaRNVt912W7G+devW8JzoJfKnnnrqVNwSVEVLT6INAWpLm8bHx4v1aDnQ6Oho2GtoaKj19aNzItGGM00Tfy7Rz1hbcvXCCy8U67/61a+K9cceeyzsFW0g88EPfrBYP+mkk8Je0WdZ2wwiWvZU20DkcPBkDADJhDEAJBPGAJBMGANAMmEMAMlMU9M0TfyC9xtuuKF1r4cffrhYHxgYaN0LMkWTtsPDw8X6yMhI2CuajI42kGiaeGp7/vzyV3dtmjuaDu7r6yvWx8bGwl5/+9vfivV77rmnWK9t1LB69epi/R3veEexHv3sTRP/jLXJ6IlMYB8OnowBIJkwBoBkwhgAkgljAEgmjAEgmWlqmqZpmrvuuqtYjyYqr7rqqrDX8uXLp+SeIFv03uZoOrc2TR1NOtemqbu7u4v1aAI4mvJumngyO3oH9eDgYNjriSeeKNZfe+21Yv3yyy8Pe61Zs6ZYP+aYY4r12pRz9HupTXPX3sE9nWbGXQDAUUwYA0AyYQwAyYQxACQTxgCQTBgDQDJLm44itRe/33nnncV6tBzi29/+dtgrWnYBmd58881W9aaJl8pE53R0dIS9ent7i/WJbHywd+/eYr22tGnhwoXFevS9EC1fapqmefzxx4v1FStWFOuXXHJJ2GvZsmXFerTkq/YzRp9l7Xccic6pLZOaDE/GAJBMGANAMmEMAMmEMQAkE8YAkMw09VHkxz/+cXjswQcfLNY/85nPFOs2g2C2iaZgo4nlmuic2jR1V1dXsR5tRtE0TTM0NFSsR5slRBPbTRNPU7/66qvF+u9///uw1zPPPFOsr1u3rlhftWpV2Kuvr69YjzbdqH1e0QR0bTOOaJrcNDUAHGWEMQAkE8YAkEwYA0AyYQwAyUxTH4Eee+yxYv1LX/pSeM7ixYuL9W9961tTck8wU0WTyTXRO5Cjd7nXzpnINHdPT0+retPEU8j33ntvsf6b3/ym9fUvvfTSYn1gYCDsFb3LPpqmrokmnWvvpj5c09FteTIGgGTCGACSCWMASCaMASCZMAaAZMIYAJJZ2jSLRS+R//SnP12s15ZwXH311cW6DSE4UkTLW2p/F9FypEhtmdL+/fuL9drGB5FoQ4raMp1NmzYV67fffnuxvmPHjrDXFVdcUayfc845xXptA4vo84+WPNV+X9E5td/j8PBweGw6eTIGgGTCGACSCWMASCaMASCZMAaAZKapZ7jadOZHPvKRYv3f//53sb5y5cqw1ze/+c12NwZHsblzy88x0QqHpomngGt/411dXcX6ggULivUXX3wx7PW9732vWN+4cWOx/r73vS/s9clPfrJYP+WUU4r12kYN0Wc2NjZWrEeT5E0TT03Xrh+JfseHiydjAEgmjAEgmTAGgGTCGACSCWMASGaaeobbvXt3eOyBBx5o1euOO+4Ijy1ZsqRVL5htoona2vucownoiUznRlPAtfcmd3d3F+vRpPHdd98d9nrkkUeK9fPOO69Yv/7668NeF110UbEeTYbv27cv7BWJfvZokrxp4s+l9vuq/f6nkydjAEgmjAEgmTAGgGTCGACSCWMASCaMASCZpU0zxBtvvFGsr169unWvn/70p8V6tIQBjmbz5s0Lj0VLZaIlT7VlN9ESpto50ZKcJ554oli/7777wl7btm0r1q+44opi/cILLwx7td0oI/ocm6ZpOjs7W12j1iv6vGqbcURLmyayFG4yPBkDQDJhDADJhDEAJBPGAJBMGANAMtPUM8Stt95arD/33HOte61du7ZYnykvRIcM0XRuTTTpPD4+XqzXNn2IjtX+LqMJ6GhDiPXr14e9oo0Xli9fXqz39vaGvaJp8mjSufbZR5/xRKaZJ7KBR2S6vy89GQNAMmEMAMmEMQAkE8YAkEwYA0AyYQwAySxtmmZbtmwp1r/xjW9M740AhxQtb+no6CjWa0t4ol579+4Nz/n73/9erD/00EPFem3Ti1WrVhXrp59+erEeLV9qmvpmDSW1JV+1TRza/vvo2GxY1unJGACSCWMASCaMASCZMAaAZMIYAJKZpp5mDz74YLG+Z8+e1r1WrlxZrEcvhAfaiaZwo6nl2jR1tLlE7W9/eHi4WD/11FOL9WXLloW9VqxYUawvWbKk1bWbJt7cIZrArk15R+dEn/1UbgYxk3gyBoBkwhgAkgljAEgmjAEgmTAGgGSmqWe4NWvWhMfuu+++Yt00NRxe0TuQa+9zHh0dLdZHRkbCc6Kp6Whqu3b9U045pVjv7+8v1qOJ6aZp/z7p2gT0VE5Hz4Z3UEc8GQNAMmEMAMmEMQAkE8YAkEwYA0AyYQwAyeYkvHT7yHzLN0eb2buGYhbZunXrEf99UVsmFB2LvrcnsoSoo6OjWJ8/P175Gi2hiq4xm5ccTYWBgYFDfgCejAEgmTAGgGTCGACSCWMASCaMASBZxjQ1APAWnowBIJkwBoBkwhgAkgljAEgmjAEgmTAGgGTCGACSCWMASCaMASCZMAaAZMIYAJIJYwBIJowBIJkwBoBkwhgAkgljAEgmjAEgmTAGgGTCGACSCWMASCaMASCZMAaAZMIYAJIJYwBIJowBIJkwBoBkwhgAkgljAEgmjAEgmTAGgGTCGACSCWMASDY/4ZpvJlwTptqc7Bs4Gmzfvt33xVFqzpzyn9ibb86+/xL9/f2H/L7wZAwAyYQxACQTxgCQTBgDQDJhDADJMqapAfifaGq4aeLJ4do5bU3ldPJEes2d65mwaTwZA0A6YQwAyYQxACQTxgCQTBgDQDJhDADJLG0CmAYT2fggOhbVDxw4EPaKlhDNn1+OgVqv6GeJzqktX2p7zsGDB8NekdmwfGrm3yEAHOGEMQAkE8YAkEwYA0AyYQwAyUxTT7Of/exnxfr+/fuL9Y0bN4a9fvjDH7a69te//vXw2GWXXVasf+ADH2h1DTiSTOU08/j4eLE+OjoanjM0NFSs79mzJzwnEk0Uj4yMFOvz5s0Ley1YsKBY7+npKda7u7vDXl1dXeGxtqLJ8IlsYDGVm3G8HZ6MASCZMAaAZMIYAJIJYwBIJowBINmciUyZTdK0X3C6ffGLXwyP/eAHP5jGO3n73vWudxXr//jHP4r1vr6+w3k7s8H0jloepbZv337Yvy9q34HRdPTw8HCxHk0/N03TbN++vVj/17/+FZ6zYcOGVuds3bo17NXf31+sRz//woULw14rV64s1qNVGRdccEHY67jjjivWo3dQ135f0WR4bTI66jeRd4lH+vv7D/l94ckYAJIJYwBIJowBIJkwBoBkwhgAkgljAEhmo4hJiJYwTeXypfPOOy889olPfKJY37JlS7F+++23h72efvrpYv3Xv/51sX7ttdeGvWAmipbKRPWmiZcwRfUdO3aEvdavX1+s//Wvfw3P2bRpU7E+NjZWrJ9xxhlhr2OPPbZY7+3tLdY3b94c9tq2bVuxvmvXrmK9trSo7eYO0fKl2jm1zTg6OjrCY22u0TST21zCkzEAJBPGAJBMGANAMmEMAMmEMQAkM019CC+99FJ47Ec/+lHrfhdddFGxfu+99xbrtZe1R1OA0cvtn3nmmbDXQw89VKzv3LkzPAdmk4lMui5YsKBY37t3b7E+d278fBNNAc+bNy88Z926dcX6qlWrivW1a9eGvZYsWVKsRxPbP//5z8Ne//3vf1vVa9PM0WcZqU2/164TiTb36O7ubt1rMjwZA0AyYQwAyYQxACQTxgCQTBgDQDLT1IdQmyaO3lEaTUw3TdP8+c9/LtYXLVrU7sYqbrvttmL90Ucfbd3ryiuvnOTdwPSKpqajeu1dw22vUXtv8sDAQLG+Zs2a8JzLLrusWI/eWV9bfRFNLb/yyivF+rPPPhv22rdvX7EeTTrX3v8cnRNNRtc+487OzmI9moqv9Yve/12bfvduagCYxYQxACQTxgCQTBgDQDJhDADJhDEAJLO06RDOP//88Fi07Kk2xj8dLx+PNrCYyEvU4UgRLW+pLVWJlreccMIJxXpPT0/YK9pE4j3veU94zjnnnFOsR/dc23ThySefLNbvueeeYv2pp54Ke0XLsU477bRifenSpWGvaDnWrl27wnMi0fdrbcnR+Ph4q2tMZvlSjSdjAEgmjAEgmTAGgGTCGACSCWMASGaaehL6+vpSr3/HHXcU65s2bWrda926dcX6GWec0boXZIqmlqMp2NomArVjJV1dXa17RffbNPEmCoODg8X6tm3bwl7RRjEvvvhisR5tbNE0TXPuuecW62eddVaxXptYHxoaKtaHh4eL9dr0c3SstoFGtFHERDYQmQxPxgCQTBgDQDJhDADJhDEAJBPGAJBMGANAMkubZrh//vOf4bEvfOELxXo0qn/iiSeGvW6++eZive3SDpgOtZf1t12SUusVLck5cOBA617z55e/bqNetXP27NlTrG/dujXs9eyzz4bHSpYvXx4eu+iii4r1aAnRa6+91uraTRNv+hAt92qa+PcVLZOqnRMtk6r9/5rMJhKejAEgmTAGgGTCGACSCWMASCaMASCZaeoZ7uGHHw6PRVPTkeuuuy489s53vrNVL5ipomnXiUxAR5s4RBO9tU0Moqnd2iYGUb9oQ4j7778/7PWHP/yhWD/mmGOK9YsvvjjsdfbZZxfr0eqL0dHRsFc0NR1NktemqSMT+R3bKAIAjjLCGACSCWMASCaMASCZMAaAZKapZ4hrrrmmWP/lL3/ZuteXv/zlYv2rX/1q614wE03l+4FrE9DRdaKJ3tp7pqOp4dqqiO3btxfrf/rTn4r1e+65J+wVvbd69erVxfratWvDXsuWLSvWBwcHi/W9e/eGvfr6+or16LOsTVNHx2r/X7q6usJjbXtNZgLbkzEAJBPGAJBMGANAMmEMAMmEMQAkE8YAkMzSpmm2b9++Yj16ifvw8HDY64QTTijWb7rppmK9o6PjEHcHs1/bjSJqy1GiDQ6iv6VoM4ia/fv3h8c2bNhQrEcbQkTLl5qmaS699NJi/corryzWzzvvvLBX9JlF9c7OzrBXZGxsrFiPNpCoXb/23dd204/a0rm2y+reypMxACQTxgCQTBgDQDJhDADJhDEAJDNNPc2uuuqqYj16IXzN9ddfX6wvWbKkdS840kVTs3Pnxs8k0UTvggULWveKJrP37NkTnvP8888X608//XSxfuKJJ4a91q1bV6yff/75xfqiRYvCXtE9T2Rive3mEr29vWGvSG0Dj7amcpOSt/JkDADJhDEAJBPGAJBMGANAMmEMAMlMUx8GGzduDI898MADrXp9/OMfD4/dcMMNrXrB0Syago0mppsmftd0VK+9N3nnzp3F+vr168Nz7rvvvmJ9x44dxfratWvDXmeffXaxHn0u0f02TTxNHb0DOpokb5p4Arn2WbbtVRNNWkf1idzX2+HJGACSCWMASCaMASCZMAaAZMIYAJIJYwBIZmnTJAwNDRXrN954Y3hObcS/5IILLgiPRcsIgP9ftIlDbXOHaEOIrq6uYr329x0tB3r00UfDczZs2FCsRxtCvP/97w979ff3F+vRMq3x8fGw17HHHlusR5/X66+/HvaKlhAtXry4WI/ut2nizUDeeOON8Jzo9x/9LIeLJ2MASCaMASCZMAaAZMIYAJIJYwBIZpp6Em655ZZi/S9/+UvrXtdcc02xbjMIaKftZgG1fx9N7kYTwLWp3SeffLJVvWma5rjjjivWP/WpTxXrH/7wh8NeAwMDxXo0AT44OBj26uzsLNajCeTaxHp0/d7e3tb3FW2gMTw8HJ7T09NTrEerVWo/S7TpxtvhyRgAkgljAEgmjAEgmTAGgGTCGACSmaaehJtuumnKen3nO98p1r1/GtqJJlqjqenaNHV0LJro3bp1a9hr48aNxfquXbvCc5YuXVqsr1mzptW/b5p4CjiaZt67d2/YK3oHdDR9Xptmjs6JJqN37twZ9nrhhReK9ehd2k3TNH19fcV69LufzMR0jSdjAEgmjAEgmTAGgGTCGACSCWMASCaMASCZpU0zxL59+4r12kvJp1L04ve2L8pvmqYZGRlpde2hoaHw2M0339yqV030s9SWqEUvvmf2mcjSprb//6PlOE3TNJs3by7Wt2zZEp4Tbe6wYcOGYr22tClaXrR///5i/dVXXw17RZs4RPVFixaFvaLvuKeeeqpYf/rpp1vf14c+9KHwnOh3GS1hqi1tartJyVt5MgaAZMIYAJIJYwBIJowBIJkwBoBkpqlniJNPPjn1+tddd12xftJJJxXrtRfif//735+Se5outc/+85///DTeCYdTtLlBtJKgJpqyrk3TRpP5tU0MopUJd911V7F+//33h72iKeD+/v5ifffu3WGvaMq7p6enWI82Y2iapnn55ZeL9eeee65Yr31eF198cbEerVapiaa8TVMDwBFKGANAMmEMAMmEMQAkE8YAkMw09SRcffXVxfqtt946zXcyebfccsthv8b8+eX/btFkas3nPve58Nh73/veVr0uueSS1tdn9okmXaMp65rofcZLliwJzzn77LOL9TfeeCM8Z3BwsFjfu3dvsR69/7ppmmZ8fLxYX7x4cbG+cOHCsFe0miJamVD7G48mxru7u4v1FStWhL3OPPPMYj2aGG+a+HspMpmJ6RpPxgCQTBgDQDJhDADJhDEAJBPGAJBMGANAsjm1l14fJtN+wen2k5/8JDw2Ojo6ZdfZtGlTsT6VGzV85StfKdajJQQ1H/3oR4v12rKDGezwrG/g/7F9+/bW3xfR0pOoHm0I0DRN09HRUawPDw8X67VlSv/5z3+K9WhDhKaJlzZFS5i2b98e9ors3Lmz1bWbJl4OtXz58mK9t7e39X1Fm0vUvnuiDSyWLl0antPV1VWsRxt7tF0K1TRN09/ff8jvC0/GAJBMGANAMmEMAMmEMQAkE8YAkMw0NUyMaeppMJFp6kg0TR1NTNfOib43a5sIRBs1RJtONE08uRuds3v37rDX/v37i/VoyntoaCjsddxxxxXr0TRz7Wds+xnXppk7OzuL9dqmF9G9TWRqOmKaGgBmAWEMAMmEMQAkE8YAkEwYA0AyYQwAySxtgomxtGkaTMfSpnnz5oXnRJtI1JYwtb1+bdlPdG/RJgbR0p6aaMlVVG+a9j9LrdfBgwdb9RobG2vdq/Y7nsgGIm1Z2gQAs4AwBoBkwhgAkgljAEgmjAEg2dS9CRtgBotWjtSmmaNjUa/a6pSJbC4RTU1H54yMjIS92k4HDw4OhseiTRTabmwxEbWfI/pcap/xVE5NT8bMuAsAOIoJYwBIJowBIJkwBoBkwhgAkpmmBo5qE3k//0Qmoycy6Ru9hzm6/kTeAR1dYyLv32577dp1onNmyvTzVDsyfyoAmEWEMQAkE8YAkEwYA0AyYQwAyYQxACSztAmgpWg5Tm2Z1FSeM5XLfjo6Olpd+1DHpuLfT/Sc2cyTMQAkE8YAkEwYA0AyYQwAyYQxACSbM5GXpAMAU8eTMQAkE8YAkEwYA0AyYQwAyYQxACQTxgCQTBgDQDJhDADJhDEAJBPGAJBMGANAMmEMAMmEMQAkE8YAkEwYA0AyYQwAyYQxACQTxgCQTBgDQDJhDADJhDEAJBPGAJBMGANAMmEMAMmEMQAk+z+RH/MACdcoFAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -695,14 +695,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 MSE: 0.018564083\n",
-      "1 훈련 MSE: 0.018759882\n",
-      "2 훈련 MSE: 0.018457456\n",
-      "3 훈련 MSE: 0.019239973\n",
-      "0 훈련 MSE: 0.0042280294\n",
-      "1 훈련 MSE: 0.004805386\n",
-      "2 훈련 MSE: 0.004675172\n",
-      "3 훈련 MSE: 0.004376106\n"
+      "0 훈련 MSE: 0.01851794\n",
+      "1 훈련 MSE: 0.01868272\n",
+      "2 훈련 MSE: 0.018467719\n",
+      "3 훈련 MSE: 0.019231746\n",
+      "0 훈련 MSE: 0.004236093\n",
+      "1 훈련 MSE: 0.0048326333\n",
+      "2 훈련 MSE: 0.004668708\n",
+      "3 훈련 MSE: 0.0044038747\n"
      ]
     }
    ],
@@ -743,7 +743,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -864,7 +864,7 @@
       "2 훈련 MSE: 0.007728097\n",
       "3 훈련 MSE: 0.0074090064\n",
       "훈련 단계 #2\n",
-      "0 훈련 MSE: 0.35758996\n",
+      "09% 훈련 MSE: 0.35758996\n",
       "1 훈련 MSE: 0.00593613\n",
       "2 훈련 MSE: 0.0030089526\n",
       "3 훈련 MSE: 0.0024365915\n",
@@ -1289,16 +1289,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 MSE: 0.046190277\n",
-      "1 훈련 MSE: 0.044343986\n",
-      "2 훈련 MSE: 0.04200526\n",
-      "3 훈련 MSE: 0.040973965\n",
-      "4 훈련 MSE: 0.040393338\n",
-      "5 훈련 MSE: 0.038924493\n",
-      "6 훈련 MSE: 0.0394448\n",
-      "7 훈련 MSE: 0.042411502\n",
-      "8 훈련 MSE: 0.039848443\n",
-      "9 훈련 MSE: 0.04152445\n"
+      "0 훈련 MSE: 0.044004317\n",
+      "1 훈련 MSE: 0.04299514\n",
+      "2 훈련 MSE: 0.042179663\n",
+      "3 훈련 MSE: 0.040822495\n",
+      "4 훈련 MSE: 0.040334057\n",
+      "5 훈련 MSE: 0.038522195\n",
+      "6 훈련 MSE: 0.039496247\n",
+      "7 훈련 MSE: 0.04211809\n",
+      "8 훈련 MSE: 0.03993781\n",
+      "9 훈련 MSE: 0.04112029\n"
      ]
     }
    ],
@@ -1390,16 +1390,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 MSE: 0.027500158\n",
-      "1 훈련 MSE: 0.02639269\n",
-      "2 훈련 MSE: 0.022876455\n",
-      "3 훈련 MSE: 0.0229337\n",
-      "4 훈련 MSE: 0.022025634\n",
-      "5 훈련 MSE: 0.021996608\n",
-      "6 훈련 MSE: 0.021385597\n",
-      "7 훈련 MSE: 0.021463407\n",
-      "8 훈련 MSE: 0.021037336\n",
-      "9 훈련 MSE: 0.02082274\n"
+      "0 훈련 MSE: 0.02953044\n",
+      "1 훈련 MSE: 0.027892366\n",
+      "2 훈련 MSE: 0.024280708\n",
+      "3 훈련 MSE: 0.024674317\n",
+      "4 훈련 MSE: 0.024266083\n",
+      "5 훈련 MSE: 0.024863964\n",
+      "6 훈련 MSE: 0.024136161\n",
+      "7 훈련 MSE: 0.025188059\n",
+      "8 훈련 MSE: 0.024125345\n",
+      "9 훈련 MSE: 0.024291817\n"
      ]
     }
    ],
@@ -1435,7 +1435,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -1547,106 +1547,106 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 MSE: 0.13819474 \t희소 손실: 0.41844434 \t전체 손실: 0.22188361\n",
-      "1 훈련 MSE: 0.059185907 \t희소 손실: 0.010747161 \t전체 손실: 0.06133534\n",
-      "2 훈련 MSE: 0.053931113 \t희소 손실: 0.019957827 \t전체 손실: 0.05792268\n",
-      "3 훈련 MSE: 0.04771372 \t희소 손실: 0.039470375 \t전체 손실: 0.055607796\n",
-      "4 훈련 MSE: 0.044841796 \t희소 손실: 0.011593379 \t전체 손실: 0.047160473\n",
-      "5 훈련 MSE: 0.04044193 \t희소 손실: 0.09246979 \t전체 손실: 0.058935888\n",
-      "6 훈련 MSE: 0.03886659 \t희소 손실: 0.045811094 \t전체 손실: 0.04802881\n",
-      "7 훈련 MSE: 0.037868652 \t희소 손실: 0.07498116 \t전체 손실: 0.052864887\n",
-      "8 훈련 MSE: 0.033122517 \t희소 손실: 0.020321798 \t전체 손실: 0.037186876\n",
-      "9 훈련 MSE: 0.031449173 \t희소 손실: 0.095670365 \t전체 손실: 0.050583243\n",
-      "10 훈련 MSE: 0.027398113 \t희소 손실: 0.065706335 \t전체 손실: 0.04053938\n",
-      "11 훈련 MSE: 0.024734963 \t희소 손실: 0.08889374 \t전체 손실: 0.042513713\n",
-      "12 훈련 MSE: 0.023367295 \t희소 손실: 0.0576614 \t전체 손실: 0.034899574\n",
-      "13 훈련 MSE: 0.023007901 \t희소 손실: 0.061935436 \t전체 손실: 0.03539499\n",
-      "14 훈련 MSE: 0.02123353 \t희소 손실: 0.025881436 \t전체 손실: 0.026409818\n",
-      "15 훈련 MSE: 0.02206469 \t희소 손실: 0.48820436 \t전체 손실: 0.11970556\n",
-      "16 훈련 MSE: 0.01910253 \t희소 손실: 0.032202266 \t전체 손실: 0.025542984\n",
-      "17 훈련 MSE: 0.01898219 \t희소 손실: 0.12995036 \t전체 손실: 0.044972263\n",
-      "18 훈련 MSE: 0.017487913 \t희소 손실: 0.035002217 \t전체 손실: 0.024488356\n",
-      "19 훈련 MSE: 0.017776337 \t희소 손실: 0.11024549 \t전체 손실: 0.039825432\n",
-      "20 훈련 MSE: 0.016910141 \t희소 손실: 0.030320741 \t전체 손실: 0.02297429\n",
-      "21 훈련 MSE: 0.018679725 \t희소 손실: 0.36376768 \t전체 손실: 0.091433264\n",
-      "22 훈련 MSE: 0.016285753 \t희소 손실: 0.055482324 \t전체 손실: 0.027382217\n",
-      "23 훈련 MSE: 0.015950203 \t희소 손실: 0.079365596 \t전체 손실: 0.031823322\n",
-      "24 훈련 MSE: 0.015672017 \t희소 손실: 0.14289412 \t전체 손실: 0.04425084\n",
-      "25 훈련 MSE: 0.014612305 \t희소 손실: 0.13330609 \t전체 손실: 0.041273523\n",
-      "26 훈련 MSE: 0.014524785 \t희소 손실: 0.10948229 \t전체 손실: 0.036421243\n",
-      "27 훈련 MSE: 0.013598451 \t희소 손실: 0.07208246 \t전체 손실: 0.028014943\n",
-      "28 훈련 MSE: 0.014069845 \t희소 손실: 0.15789555 \t전체 손실: 0.045648955\n",
-      "29 훈련 MSE: 0.013441612 \t희소 손실: 0.14449221 \t전체 손실: 0.042340055\n",
-      "30 훈련 MSE: 0.013608929 \t희소 손실: 0.18507166 \t전체 손실: 0.05062326\n",
-      "31 훈련 MSE: 0.014197283 \t희소 손실: 0.06788807 \t전체 손실: 0.027774898\n",
-      "32 훈련 MSE: 0.013759435 \t희소 손실: 0.057576656 \t전체 손실: 0.025274767\n",
-      "33 훈련 MSE: 0.013667367 \t희소 손실: 0.11363616 \t전체 손실: 0.0363946\n",
-      "34 훈련 MSE: 0.0126469685 \t희소 손실: 0.13469556 \t전체 손실: 0.039586082\n",
-      "35 훈련 MSE: 0.012872889 \t희소 손실: 0.16850437 \t전체 손실: 0.046573766\n",
-      "36 훈련 MSE: 0.012669464 \t희소 손실: 0.09313129 \t전체 손실: 0.03129572\n",
-      "37 훈련 MSE: 0.012427666 \t희소 손실: 0.105387196 \t전체 손실: 0.033505104\n",
-      "38 훈련 MSE: 0.01233604 \t희소 손실: 0.19235855 \t전체 손실: 0.05080775\n",
-      "39 훈련 MSE: 0.012337869 \t희소 손실: 0.14231703 \t전체 손실: 0.040801276\n",
-      "40 훈련 MSE: 0.012626693 \t희소 손실: 0.23267308 \t전체 손실: 0.05916131\n",
-      "41 훈련 MSE: 0.012383292 \t희소 손실: 0.104892224 \t전체 손실: 0.033361737\n",
-      "42 훈련 MSE: 0.011781142 \t희소 손실: 0.10161172 \t전체 손실: 0.032103486\n",
-      "43 훈련 MSE: 0.012044032 \t희소 손실: 0.1359405 \t전체 손실: 0.039232135\n",
-      "44 훈련 MSE: 0.011620717 \t희소 손실: 0.11472766 \t전체 손실: 0.03456625\n",
-      "45 훈련 MSE: 0.011669101 \t희소 손실: 0.15798101 \t전체 손실: 0.043265305\n",
-      "46 훈련 MSE: 0.01167006 \t희소 손실: 0.13549636 \t전체 손실: 0.038769335\n",
-      "47 훈련 MSE: 0.011549011 \t희소 손실: 0.15662336 \t전체 손실: 0.042873684\n",
-      "48 훈련 MSE: 0.011729614 \t희소 손실: 0.4430182 \t전체 손실: 0.10033325\n",
-      "49 훈련 MSE: 0.011562036 \t희소 손실: 0.17581894 \t전체 손실: 0.046725824\n",
-      "50 훈련 MSE: 0.01135992 \t희소 손실: 0.18881696 \t전체 손실: 0.049123313\n",
-      "51 훈련 MSE: 0.0115385335 \t희소 손실: 0.26335293 \t전체 손실: 0.06420912\n",
-      "52 훈련 MSE: 0.0120425625 \t희소 손실: 0.3675837 \t전체 손실: 0.0855593\n",
-      "53 훈련 MSE: 0.011238733 \t희소 손실: 0.12488687 \t전체 손실: 0.03621611\n",
-      "54 훈련 MSE: 0.013967819 \t희소 손실: 0.43314826 \t전체 손실: 0.10059747\n",
-      "55 훈련 MSE: 0.012766779 \t희소 손실: 0.07719517 \t전체 손실: 0.028205812\n",
-      "56 훈련 MSE: 0.012239235 \t희소 손실: 0.30152494 \t전체 손실: 0.072544225\n",
-      "57 훈련 MSE: 0.012724373 \t희소 손실: 0.24817757 \t전체 손실: 0.062359888\n",
-      "58 훈련 MSE: 0.023815982 \t희소 손실: 0.2323372 \t전체 손실: 0.07028343\n",
-      "59 훈련 MSE: 0.013152219 \t희소 손실: 0.19813177 \t전체 손실: 0.052778576\n",
-      "60 훈련 MSE: 0.019204749 \t희소 손실: 0.19425029 \t전체 손실: 0.058054805\n",
-      "61 훈련 MSE: 0.01287946 \t희소 손실: 0.22912267 \t전체 손실: 0.058703996\n",
-      "62 훈련 MSE: 0.014201862 \t희소 손실: 0.47936517 \t전체 손실: 0.1100749\n",
-      "63 훈련 MSE: 0.014247858 \t희소 손실: 0.3340563 \t전체 손실: 0.08105911\n",
-      "64 훈련 MSE: 0.01188848 \t희소 손실: 0.17818896 \t전체 손실: 0.04752627\n",
-      "65 훈련 MSE: 0.016912188 \t희소 손실: 0.18625271 \t전체 손실: 0.054162733\n",
-      "66 훈련 MSE: 0.015350101 \t희소 손실: 0.984547 \t전체 손실: 0.21225952\n",
-      "67 훈련 MSE: 0.037533756 \t희소 손실: 0.31189525 \t전체 손실: 0.09991281\n",
-      "68 훈련 MSE: 0.015359703 \t희소 손실: 0.21824294 \t전체 손실: 0.059008293\n",
-      "69 훈련 MSE: 0.0130188465 \t희소 손실: 0.43986538 \t전체 손실: 0.10099193\n",
-      "70 훈련 MSE: 0.04249404 \t희소 손실: 0.7454435 \t전체 손실: 0.19158275\n",
-      "71 훈련 MSE: 0.014368004 \t희소 손실: 0.24009731 \t전체 손실: 0.06238747\n",
-      "72 훈련 MSE: 0.015802076 \t희소 손실: 0.7972901 \t전체 손실: 0.1752601\n",
-      "73 훈련 MSE: 0.014027514 \t희소 손실: 0.31637588 \t전체 손실: 0.077302694\n",
-      "74 훈련 MSE: 0.033579413 \t희소 손실: 0.32424852 \t전체 손실: 0.09842911\n",
-      "75 훈련 MSE: 0.037583392 \t희소 손실: 0.51026374 \t전체 손실: 0.13963614\n",
-      "76 훈련 MSE: 0.018015897 \t희소 손실: 0.19957174 \t전체 손실: 0.057930246\n",
-      "77 훈련 MSE: 0.019651866 \t희소 손실: 0.30766442 \t전체 손실: 0.08118475\n",
-      "78 훈련 MSE: 0.018113064 \t희소 손실: 0.5368116 \t전체 손실: 0.12547539\n",
-      "79 훈련 MSE: 0.019586327 \t희소 손실: 0.4225994 \t전체 손실: 0.10410621\n",
-      "80 훈련 MSE: 0.016286958 \t희소 손실: 0.116765514 \t전체 손실: 0.03964006\n",
-      "81 훈련 MSE: 0.012678387 \t희소 손실: 0.8773603 \t전체 손실: 0.18815045\n",
-      "82 훈련 MSE: 0.015514646 \t희소 손실: 2.136949 \t전체 손실: 0.44290447\n",
-      "83 훈련 MSE: 0.014462668 \t희소 손실: 0.6624632 \t전체 손실: 0.14695531\n",
-      "84 훈련 MSE: 0.0139609 \t희소 손실: 0.2704364 \t전체 손실: 0.06804818\n",
-      "85 훈련 MSE: 0.02716384 \t희소 손실: 0.93438864 \t전체 손실: 0.21404156\n",
-      "86 훈련 MSE: 0.014590265 \t희소 손실: 0.6826812 \t전체 손실: 0.1511265\n",
-      "87 훈련 MSE: 0.012223512 \t희소 손실: 1.41284 \t전체 손실: 0.29479152\n",
-      "88 훈련 MSE: 0.01925102 \t희소 손실: 0.8619595 \t전체 손실: 0.19164293\n",
-      "89 훈련 MSE: 0.029023234 \t희소 손실: 0.2942158 \t전체 손실: 0.087866396\n",
-      "90 훈련 MSE: 0.03109355 \t희소 손실: 0.45987695 \t전체 손실: 0.12306894\n",
-      "91 훈련 MSE: 0.014538759 \t희소 손실: 0.09905543 \t전체 손실: 0.034349844\n",
-      "92 훈련 MSE: 0.015151837 \t희소 손실: 0.38795644 \t전체 손실: 0.09274313\n",
-      "93 훈련 MSE: 0.01682431 \t희소 손실: 0.1613949 \t전체 손실: 0.04910329\n",
-      "94 훈련 MSE: 0.011993256 \t희소 손실: 0.11752173 \t전체 손실: 0.035497602\n",
-      "95 훈련 MSE: 0.0139113525 \t희소 손실: 0.10376862 \t전체 손실: 0.034665078\n",
-      "96 훈련 MSE: 0.018343525 \t희소 손실: 0.23694368 \t전체 손실: 0.06573226\n",
-      "97 훈련 MSE: 0.013478864 \t희소 손실: 0.10932048 \t전체 손실: 0.03534296\n",
-      "98 훈련 MSE: 0.013734443 \t희소 손실: 0.089936554 \t전체 손실: 0.031721756\n",
-      "99 훈련 MSE: 0.0233883 \t희소 손실: 0.33332965 \t전체 손실: 0.09005423\n"
+      "0 훈련 MSE: 0.13819474 \t희소 손실: 0.4184438 \t전체 손실: 0.2218835\n",
+      "1 훈련 MSE: 0.059185907 \t희소 손실: 0.010747713 \t전체 손실: 0.06133545\n",
+      "2 훈련 MSE: 0.053931113 \t희소 손실: 0.01995793 \t전체 손실: 0.0579227\n",
+      "3 훈련 MSE: 0.04771372 \t희소 손실: 0.039470345 \t전체 손실: 0.05560779\n",
+      "4 훈련 MSE: 0.044841796 \t희소 손실: 0.011593144 \t전체 손실: 0.047160424\n",
+      "5 훈련 MSE: 0.04044193 \t희소 손실: 0.092471 \t전체 손실: 0.058936134\n",
+      "6 훈련 MSE: 0.03886659 \t희소 손실: 0.04581263 \t전체 손실: 0.048029117\n",
+      "7 훈련 MSE: 0.037868652 \t희소 손실: 0.07498071 \t전체 손실: 0.052864797\n",
+      "8 훈련 MSE: 0.033122517 \t희소 손실: 0.020321768 \t전체 손실: 0.037186872\n",
+      "9 훈련 MSE: 0.031449173 \t희소 손실: 0.095670156 \t전체 손실: 0.050583206\n",
+      "10 훈련 MSE: 0.027398113 \t희소 손실: 0.06570565 \t전체 손실: 0.040539242\n",
+      "11 훈련 MSE: 0.024734963 \t희소 손실: 0.08889264 \t전체 손실: 0.04251349\n",
+      "12 훈련 MSE: 0.023367295 \t희소 손실: 0.05766213 \t전체 손실: 0.03489972\n",
+      "13 훈련 MSE: 0.023007901 \t희소 손실: 0.06193536 \t전체 손실: 0.035394974\n",
+      "14 훈련 MSE: 0.02123353 \t희소 손실: 0.025881292 \t전체 손실: 0.02640979\n",
+      "15 훈련 MSE: 0.022064691 \t희소 손실: 0.48820361 \t전체 손실: 0.119705416\n",
+      "16 훈련 MSE: 0.01910253 \t희소 손실: 0.03220318 \t전체 손실: 0.025543166\n",
+      "17 훈련 MSE: 0.018982193 \t희소 손실: 0.1299511 \t전체 손실: 0.044972412\n",
+      "18 훈련 MSE: 0.017487913 \t희소 손실: 0.035001524 \t전체 손실: 0.024488218\n",
+      "19 훈련 MSE: 0.017776337 \t희소 손실: 0.11024489 \t전체 손실: 0.039825313\n",
+      "20 훈련 MSE: 0.01691014 \t희소 손실: 0.030320974 \t전체 손실: 0.022974335\n",
+      "21 훈련 MSE: 0.018679725 \t희소 손실: 0.36376736 \t전체 손실: 0.0914332\n",
+      "22 훈련 MSE: 0.016285753 \t희소 손실: 0.055482175 \t전체 손실: 0.027382188\n",
+      "23 훈련 MSE: 0.015950203 \t희소 손실: 0.07936562 \t전체 손실: 0.03182333\n",
+      "24 훈련 MSE: 0.015672015 \t희소 손실: 0.14289382 \t전체 손실: 0.04425078\n",
+      "25 훈련 MSE: 0.014612303 \t희소 손실: 0.13330582 \t전체 손실: 0.041273467\n",
+      "26 훈련 MSE: 0.014524783 \t희소 손실: 0.10948151 \t전체 손실: 0.036421087\n",
+      "27 훈련 MSE: 0.013598451 \t희소 손실: 0.07208073 \t전체 손실: 0.028014597\n",
+      "28 훈련 MSE: 0.014069849 \t희소 손실: 0.1578956 \t전체 손실: 0.045648966\n",
+      "29 훈련 MSE: 0.01344161 \t희소 손실: 0.14449331 \t전체 손실: 0.04234027\n",
+      "30 훈련 MSE: 0.013608929 \t희소 손실: 0.18506998 \t전체 손실: 0.050622925\n",
+      "31 훈련 MSE: 0.014197284 \t희소 손실: 0.06788698 \t전체 손실: 0.02777468\n",
+      "32 훈련 MSE: 0.013759439 \t희소 손실: 0.057576388 \t전체 손실: 0.025274716\n",
+      "33 훈련 MSE: 0.013667366 \t희소 손실: 0.11363433 \t전체 손실: 0.03639423\n",
+      "34 훈련 MSE: 0.01264697 \t희소 손실: 0.13469386 \t전체 손실: 0.039585743\n",
+      "35 훈련 MSE: 0.012872911 \t희소 손실: 0.1685048 \t전체 손실: 0.046573874\n",
+      "36 훈련 MSE: 0.012669464 \t희소 손실: 0.09312945 \t전체 손실: 0.031295355\n",
+      "37 훈련 MSE: 0.012427666 \t희소 손실: 0.10538912 \t전체 손실: 0.033505492\n",
+      "38 훈련 MSE: 0.012336045 \t희소 손실: 0.19236036 \t전체 손실: 0.050808117\n",
+      "39 훈련 MSE: 0.0123378765 \t희소 손실: 0.1423154 \t전체 손실: 0.04080096\n",
+      "40 훈련 MSE: 0.012627057 \t희소 손실: 0.23267198 \t전체 손실: 0.059161454\n",
+      "41 훈련 MSE: 0.0123833865 \t희소 손실: 0.10489211 \t전체 손실: 0.033361807\n",
+      "42 훈련 MSE: 0.011781143 \t희소 손실: 0.10161278 \t전체 손실: 0.0321037\n",
+      "43 훈련 MSE: 0.012043943 \t희소 손실: 0.13593857 \t전체 손실: 0.039231658\n",
+      "44 훈련 MSE: 0.011620727 \t희소 손실: 0.114727125 \t전체 손실: 0.034566153\n",
+      "45 훈련 MSE: 0.011668878 \t희소 손실: 0.15798113 \t전체 손실: 0.043265104\n",
+      "46 훈련 MSE: 0.011761186 \t희소 손실: 0.13549805 \t전체 손실: 0.038860794\n",
+      "47 훈련 MSE: 0.011549666 \t희소 손실: 0.15659894 \t전체 손실: 0.042869456\n",
+      "48 훈련 MSE: 0.011674238 \t희소 손실: 0.44305113 \t전체 손실: 0.100284465\n",
+      "49 훈련 MSE: 0.011562435 \t희소 손실: 0.1758345 \t전체 손실: 0.046729334\n",
+      "50 훈련 MSE: 0.01136023 \t희소 손실: 0.18884605 \t전체 손실: 0.04912944\n",
+      "51 훈련 MSE: 0.01153872 \t희소 손실: 0.2633665 \t전체 손실: 0.06421202\n",
+      "52 훈련 MSE: 0.012044075 \t희소 손실: 0.36758316 \t전체 손실: 0.0855607\n",
+      "53 훈련 MSE: 0.011250994 \t희소 손실: 0.12487886 \t전체 손실: 0.036226768\n",
+      "54 훈련 MSE: 0.013878834 \t희소 손실: 0.43317184 \t전체 손실: 0.100513205\n",
+      "55 훈련 MSE: 0.012782411 \t희소 손실: 0.07718266 \t전체 손실: 0.028218944\n",
+      "56 훈련 MSE: 0.012197349 \t희소 손실: 0.301488 \t전체 손실: 0.072494954\n",
+      "57 훈련 MSE: 0.012758316 \t희소 손실: 0.24829106 \t전체 손실: 0.062416527\n",
+      "58 훈련 MSE: 0.023019526 \t희소 손실: 0.23253448 \t전체 손실: 0.06952642\n",
+      "59 훈련 MSE: 0.0134694 \t희소 손실: 0.1981337 \t전체 손실: 0.053096145\n",
+      "60 훈련 MSE: 0.019005049 \t희소 손실: 0.19429345 \t전체 손실: 0.057863742\n",
+      "61 훈련 MSE: 0.012830879 \t희소 손실: 0.22918573 \t전체 손실: 0.058668025\n",
+      "62 훈련 MSE: 0.013951195 \t희소 손실: 0.47963494 \t전체 손실: 0.10987819\n",
+      "63 훈련 MSE: 0.014569022 \t희소 손실: 0.33389828 \t전체 손실: 0.08134868\n",
+      "64 훈련 MSE: 0.011955362 \t희소 손실: 0.17778753 \t전체 손실: 0.047512867\n",
+      "65 훈련 MSE: 0.016137443 \t희소 손실: 0.18549094 \t전체 손실: 0.05323563\n",
+      "66 훈련 MSE: 0.014922719 \t희소 손실: 0.9833389 \t전체 손실: 0.2115905\n",
+      "67 훈련 MSE: 0.037293304 \t희소 손실: 0.31203473 \t전체 손실: 0.09970025\n",
+      "68 훈련 MSE: 0.015952839 \t희소 손실: 0.21828063 \t전체 손실: 0.059608966\n",
+      "69 훈련 MSE: 0.012261292 \t희소 손실: 0.43984902 \t전체 손실: 0.100231096\n",
+      "70 훈련 MSE: 0.037013255 \t희소 손실: 0.7457465 \t전체 손실: 0.18616256\n",
+      "71 훈련 MSE: 0.014764387 \t희소 손실: 0.24276587 \t전체 손실: 0.06331757\n",
+      "72 훈련 MSE: 0.016606566 \t희소 손실: 0.7994744 \t전체 손실: 0.17650145\n",
+      "73 훈련 MSE: 0.0144267725 \t희소 손실: 0.31668615 \t전체 손실: 0.077764004\n",
+      "74 훈련 MSE: 0.036295358 \t희소 손실: 0.32408017 \t전체 손실: 0.1011114\n",
+      "75 훈련 MSE: 0.038099732 \t희소 손실: 0.5090959 \t전체 손실: 0.13991891\n",
+      "76 훈련 MSE: 0.019446107 \t희소 손실: 0.20034213 \t전체 손실: 0.059514537\n",
+      "77 훈련 MSE: 0.022708207 \t희소 손실: 0.3122869 \t전체 손실: 0.08516559\n",
+      "78 훈련 MSE: 0.016776377 \t희소 손실: 0.5347481 \t전체 손실: 0.123725995\n",
+      "79 훈련 MSE: 0.02007503 \t희소 손실: 0.42173243 \t전체 손실: 0.10442152\n",
+      "80 훈련 MSE: 0.015990663 \t희소 손실: 0.11620571 \t전체 손실: 0.039231807\n",
+      "81 훈련 MSE: 0.012873366 \t희소 손실: 0.8802524 \t전체 손실: 0.18892385\n",
+      "82 훈련 MSE: 0.015848754 \t희소 손실: 2.1350327 \t전체 손실: 0.4428553\n",
+      "83 훈련 MSE: 0.014213436 \t희소 손실: 0.6619952 \t전체 손실: 0.14661248\n",
+      "84 훈련 MSE: 0.0148504 \t희소 손실: 0.27111006 \t전체 손실: 0.06907241\n",
+      "85 훈련 MSE: 0.028653098 \t희소 손실: 0.9375648 \t전체 손실: 0.21616606\n",
+      "86 훈련 MSE: 0.014789838 \t희소 손실: 0.68491566 \t전체 손실: 0.15177298\n",
+      "87 훈련 MSE: 0.012122109 \t희소 손실: 1.4151382 \t전체 손실: 0.29514974\n",
+      "88 훈련 MSE: 0.019227153 \t희소 손실: 0.85921484 \t전체 손실: 0.19107012\n",
+      "89 훈련 MSE: 0.028509108 \t희소 손실: 0.29490086 \t전체 손실: 0.087489285\n",
+      "90 훈련 MSE: 0.028761363 \t희소 손실: 0.45986366 \t전체 손실: 0.120734096\n",
+      "91 훈련 MSE: 0.014937726 \t희소 손실: 0.098271474 \t전체 손실: 0.03459202\n",
+      "92 훈련 MSE: 0.014802266 \t희소 손실: 0.3885908 \t전체 손실: 0.09252043\n",
+      "93 훈련 MSE: 0.015891917 \t희소 손실: 0.1618818 \t전체 손실: 0.048268277\n",
+      "94 훈련 MSE: 0.012146741 \t희소 손실: 0.115840726 \t전체 손실: 0.035314888\n",
+      "95 훈련 MSE: 0.013941603 \t희소 손실: 0.10216563 \t전체 손실: 0.03437473\n",
+      "96 훈련 MSE: 0.019508341 \t희소 손실: 0.23841716 \t전체 손실: 0.06719177\n",
+      "97 훈련 MSE: 0.013997071 \t희소 손실: 0.108866125 \t전체 손실: 0.035770297\n",
+      "98 훈련 MSE: 0.013801815 \t희소 손실: 0.09045001 \t전체 손실: 0.031891815\n",
+      "99 훈련 MSE: 0.02559923 \t희소 손실: 0.33294424 \t전체 손실: 0.092188075\n"
      ]
     }
    ],
@@ -1682,7 +1682,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -1816,56 +1816,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 전체 손실: 33711.305 \t재구성 손실: 24141.234 \t잠재 손실: 9570.07\n",
-      "1 훈련 전체 손실: 27055.078 \t재구성 손실: 22278.68 \t잠재 손실: 4776.3984\n",
-      "2 훈련 전체 손실: 28769.135 \t재구성 손실: 23155.71 \t잠재 손실: 5613.4233\n",
-      "3 훈련 전체 손실: 26304.252 \t재구성 손실: 21337.469 \t잠재 손실: 4966.783\n",
-      "4 훈련 전체 손실: 31762.32 \t재구성 손실: 24771.611 \t잠재 손실: 6990.71\n",
-      "5 훈련 전체 손실: 25975.902 \t재구성 손실: 21666.244 \t잠재 손실: 4309.659\n",
-      "6 훈련 전체 손실: 21084.105 \t재구성 손실: 17912.25 \t잠재 손실: 3171.8557\n",
-      "7 훈련 전체 손실: 20691.05 \t재구성 손실: 17767.701 \t잠재 손실: 2923.3506\n",
-      "8 훈련 전체 손실: 18301.613 \t재구성 손실: 15335.816 \t잠재 손실: 2965.7979\n",
-      "9 훈련 전체 손실: 18003.305 \t재구성 손실: 14857.625 \t잠재 손실: 3145.6807\n",
-      "10 훈련 전체 손실: 16868.87 \t재구성 손실: 13742.127 \t잠재 손실: 3126.7417\n",
-      "11 훈련 전체 손실: 16322.584 \t재구성 손실: 13176.514 \t잠재 손실: 3146.0708\n",
-      "12 훈련 전체 손실: 16848.775 \t재구성 손실: 13641.574 \t잠재 손실: 3207.201\n",
-      "13 훈련 전체 손실: 16411.53 \t재구성 손실: 13193.485 \t잠재 손실: 3218.0444\n",
-      "14 훈련 전체 손실: 16841.875 \t재구성 손실: 13629.753 \t잠재 손실: 3212.122\n",
-      "15 훈련 전체 손실: 18734.246 \t재구성 손실: 15469.811 \t잠재 손실: 3264.4355\n",
-      "16 훈련 전체 손실: 15549.877 \t재구성 손실: 12475.354 \t잠재 손실: 3074.523\n",
-      "17 훈련 전체 손실: 16549.727 \t재구성 손실: 13114.041 \t잠재 손실: 3435.6865\n",
-      "18 훈련 전체 손실: 16204.832 \t재구성 손실: 12911.634 \t잠재 손실: 3293.198\n",
-      "19 훈련 전체 손실: 19072.146 \t재구성 손실: 15676.701 \t잠재 손실: 3395.4456\n",
-      "20 훈련 전체 손실: 18689.686 \t재구성 손실: 14211.309 \t잠재 손실: 4478.3774\n",
-      "21 훈련 전체 손실: 16398.697 \t재구성 손실: 13045.562 \t잠재 손실: 3353.135\n",
-      "22 훈련 전체 손실: 16106.504 \t재구성 손실: 12735.91 \t잠재 손실: 3370.5942\n",
-      "23 훈련 전체 손실: 20430.426 \t재구성 손실: 15874.012 \t잠재 손실: 4556.413\n",
-      "24 훈련 전체 손실: 15652.191 \t재구성 손실: 12415.114 \t잠재 손실: 3237.077\n",
-      "25 훈련 전체 손실: 15460.601 \t재구성 손실: 12243.658 \t잠재 손실: 3216.9421\n",
-      "26 훈련 전체 손실: 15832.671 \t재구성 손실: 12465.408 \t잠재 손실: 3367.2627\n",
-      "27 훈련 전체 손실: 37082.61 \t재구성 손실: 25035.375 \t잠재 손실: 12047.233\n",
-      "28 훈련 전체 손실: 26629.25 \t재구성 손실: 21904.469 \t잠재 손실: 4724.7803\n",
-      "29 훈련 전체 손실: 23798.465 \t재구성 손실: 19210.568 \t잠재 손실: 4587.8955\n",
-      "30 훈련 전체 손실: 20755.977 \t재구성 손실: 17576.453 \t잠재 손실: 3179.5234\n",
-      "31 훈련 전체 손실: 17813.498 \t재구성 손실: 14805.74 \t잠재 손실: 3007.7578\n",
-      "32 훈련 전체 손실: 17064.127 \t재구성 손실: 13854.3125 \t잠재 손실: 3209.8142\n",
-      "33 훈련 전체 손실: 16516.027 \t재구성 손실: 13270.247 \t잠재 손실: 3245.781\n",
-      "34 훈련 전체 손실: 16060.129 \t재구성 손실: 12752.525 \t잠재 손실: 3307.6035\n",
-      "35 훈련 전체 손실: 16070.643 \t재구성 손실: 12676.658 \t잠재 손실: 3393.9841\n",
-      "36 훈련 전체 손실: 16754.852 \t재구성 손실: 13328.36 \t잠재 손실: 3426.492\n",
-      "37 훈련 전체 손실: 16162.049 \t재구성 손실: 12813.662 \t잠재 손실: 3348.3872\n",
-      "38 훈련 전체 손실: 16215.911 \t재구성 손실: 12828.523 \t잠재 손실: 3387.388\n",
-      "39 훈련 전체 손실: 15912.985 \t재구성 손실: 12628.198 \t잠재 손실: 3284.787\n",
-      "40 훈련 전체 손실: 15275.752 \t재구성 손실: 11982.837 \t잠재 손실: 3292.9146\n",
-      "41 훈련 전체 손실: 16066.625 \t재구성 손실: 12757.855 \t잠재 손실: 3308.7693\n",
-      "42 훈련 전체 손실: 17823.701 \t재구성 손실: 14552.526 \t잠재 손실: 3271.1743\n",
-      "43 훈련 전체 손실: 24408.918 \t재구성 손실: 19846.832 \t잠재 손실: 4562.0864\n",
-      "44 훈련 전체 손실: 24694.812 \t재구성 손실: 19852.523 \t잠재 손실: 4842.288\n",
-      "45 훈련 전체 손실: 30566.844 \t재구성 손실: 22793.793 \t잠재 손실: 7773.05\n",
-      "46 훈련 전체 손실: 24413.105 \t재구성 손실: 19724.02 \t잠재 손실: 4689.086\n",
-      "47 훈련 전체 손실: 23234.568 \t재구성 손실: 18196.725 \t잠재 손실: 5037.8438\n",
-      "48 훈련 전체 손실: 17091.879 \t재구성 손실: 13951.564 \t잠재 손실: 3140.3154\n",
-      "49 훈련 전체 손실: 15979.056 \t재구성 손실: 12713.727 \t잠재 손실: 3265.3289\n"
+      "0 훈련 전체 손실: 38016.934 \t재구성 손실: 26717.334 \t잠재 손실: 11299.599\n",
+      "1 훈련 전체 손실: 30065.5 \t재구성 손실: 24319.137 \t잠재 손실: 5746.3633\n",
+      "2 훈련 전체 손실: 32188.535 \t재구성 손실: 24696.451 \t잠재 손실: 7492.085\n",
+      "3 훈련 전체 손실: 29995.074 \t재구성 손실: 24591.836 \t잠재 손실: 5403.2393\n",
+      "4 훈련 전체 손실: 22977.074 \t재구성 손실: 19545.846 \t잠재 손실: 3431.228\n",
+      "5 훈련 전체 손실: 21461.336 \t재구성 손실: 18221.758 \t잠재 손실: 3239.5786\n",
+      "6 훈련 전체 손실: 17799.531 \t재구성 손실: 14987.603 \t잠재 손실: 2811.9297\n",
+      "7 훈련 전체 손실: 17872.271 \t재구성 손실: 14900.03 \t잠재 손실: 2972.2415\n",
+      "8 훈련 전체 손실: 16473.062 \t재구성 손실: 13437.503 \t잠재 손실: 3035.5598\n",
+      "9 훈련 전체 손실: 18285.943 \t재구성 손실: 15230.208 \t잠재 손실: 3055.7349\n",
+      "10 훈련 전체 손실: 17281.297 \t재구성 손실: 14238.846 \t잠재 손실: 3042.4512\n",
+      "11 훈련 전체 손실: 16178.416 \t재구성 손실: 13091.535 \t잠재 손실: 3086.881\n",
+      "12 훈련 전체 손실: 16544.488 \t재구성 손실: 13217.236 \t잠재 손실: 3327.253\n",
+      "13 훈련 전체 손실: 16427.021 \t재구성 손실: 13077.631 \t잠재 손실: 3349.391\n",
+      "14 훈련 전체 손실: 16616.738 \t재구성 손실: 13293.669 \t잠재 손실: 3323.07\n",
+      "15 훈련 전체 손실: 16271.476 \t재구성 손실: 13142.443 \t잠재 손실: 3129.0325\n",
+      "16 훈련 전체 손실: 16567.523 \t재구성 손실: 13333.615 \t잠재 손실: 3233.9075\n",
+      "17 훈련 전체 손실: 29101.3 \t재구성 손실: 24271.592 \t잠재 손실: 4829.709\n",
+      "18 훈련 전체 손실: 24227.15 \t재구성 손실: 20683.895 \t잠재 손실: 3543.2554\n",
+      "19 훈련 전체 손실: 20956.738 \t재구성 손실: 17900.092 \t잠재 손실: 3056.6475\n",
+      "20 훈련 전체 손실: 20429.688 \t재구성 손실: 16620.959 \t잠재 손실: 3808.7285\n",
+      "21 훈련 전체 손실: 17099.916 \t재구성 손실: 13952.262 \t잠재 손실: 3147.6538\n",
+      "22 훈련 전체 손실: 16274.533 \t재구성 손실: 12991.28 \t잠재 손실: 3283.2524\n",
+      "23 훈련 전체 손실: 16308.104 \t재구성 손실: 12951.634 \t잠재 손실: 3356.4697\n",
+      "24 훈련 전체 손실: 15702.99 \t재구성 손실: 12309.805 \t잠재 손실: 3393.1858\n",
+      "25 훈련 전체 손실: 15554.395 \t재구성 손실: 12204.829 \t잠재 손실: 3349.565\n",
+      "26 훈련 전체 손실: 16080.304 \t재구성 손실: 12602.002 \t잠재 손실: 3478.3018\n",
+      "27 훈련 전체 손실: 16153.012 \t재구성 손실: 12719.799 \t잠재 손실: 3433.213\n",
+      "28 훈련 전체 손실: 16836.662 \t재구성 손실: 13554.496 \t잠재 손실: 3282.1663\n",
+      "29 훈련 전체 손실: 16081.364 \t재구성 손실: 12673.35 \t잠재 손실: 3408.0144\n",
+      "30 훈련 전체 손실: 18377.371 \t재구성 손실: 14856.055 \t잠재 손실: 3521.316\n",
+      "31 훈련 전체 손실: 15566.567 \t재구성 손실: 12113.064 \t잠재 손실: 3453.503\n",
+      "32 훈련 전체 손실: 15363.791 \t재구성 손실: 11939.522 \t잠재 손실: 3424.268\n",
+      "33 훈련 전체 손실: 15380.105 \t재구성 손실: 11933.108 \t잠재 손실: 3446.9973\n",
+      "34 훈련 전체 손실: 15197.85 \t재구성 손실: 11855.855 \t잠재 손실: 3341.9941\n",
+      "35 훈련 전체 손실: 15376.002 \t재구성 손실: 11782.05 \t잠재 손실: 3593.952\n",
+      "36 훈련 전체 손실: 15606.907 \t재구성 손실: 12056.068 \t잠재 손실: 3550.839\n",
+      "37 훈련 전체 손실: 18351.05 \t재구성 손실: 14509.541 \t잠재 손실: 3841.5088\n",
+      "38 훈련 전체 손실: 16268.131 \t재구성 손실: 12896.816 \t잠재 손실: 3371.3145\n",
+      "39 훈련 전체 손실: 17762.117 \t재구성 손실: 14202.475 \t잠재 손실: 3559.6433\n",
+      "40 훈련 전체 손실: 23485.436 \t재구성 손실: 19128.334 \t잠재 손실: 4357.101\n",
+      "41 훈련 전체 손실: 36509.016 \t재구성 손실: 26140.258 \t잠재 손실: 10368.758\n",
+      "42 훈련 전체 손실: 25619.293 \t재구성 손실: 20840.367 \t잠재 손실: 4778.926\n",
+      "43 훈련 전체 손실: 28378.17 \t재구성 손실: 22257.338 \t잠재 손실: 6120.8325\n",
+      "44 훈련 전체 손실: 20361.963 \t재구성 손실: 16991.43 \t잠재 손실: 3370.5337\n",
+      "45 훈련 전체 손실: 18422.102 \t재구성 손실: 14912.807 \t잠재 손실: 3509.295\n",
+      "46 훈련 전체 손실: 16764.645 \t재구성 손실: 13529.869 \t잠재 손실: 3234.7764\n",
+      "47 훈련 전체 손실: 15870.282 \t재구성 손실: 12479.645 \t잠재 손실: 3390.6377\n",
+      "48 훈련 전체 손실: 15523.098 \t재구성 손실: 12218.592 \t잠재 손실: 3304.506\n",
+      "49 훈련 전체 손실: 15175.317 \t재구성 손실: 11773.2705 \t잠재 손실: 3402.0469\n"
      ]
     }
    ],
@@ -1960,56 +1960,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 전체 손실: 17845.31 \t재구성 손실: 14199.457 \t잠재 손실: 3645.853\n",
-      "1 훈련 전체 손실: 17223.674 \t재구성 손실: 13457.104 \t잠재 손실: 3766.5708\n",
-      "2 훈련 전체 손실: 16330.138 \t재구성 손실: 12578.04 \t잠재 손실: 3752.098\n",
-      "3 훈련 전체 손실: 16323.255 \t재구성 손실: 12578.617 \t잠재 손실: 3744.638\n",
-      "4 훈련 전체 손실: 16297.912 \t재구성 손실: 12533.176 \t잠재 손실: 3764.7368\n",
-      "5 훈련 전체 손실: 15681.15 \t재구성 손실: 11960.288 \t잠재 손실: 3720.862\n",
-      "6 훈련 전체 손실: 16120.67 \t재구성 손실: 12325.412 \t잠재 손실: 3795.258\n",
-      "7 훈련 전체 손실: 15783.96 \t재구성 손실: 12021.791 \t잠재 손실: 3762.169\n",
-      "8 훈련 전체 손실: 16208.828 \t재구성 손실: 12365.744 \t잠재 손실: 3843.0837\n",
-      "9 훈련 전체 손실: 15524.169 \t재구성 손실: 11801.077 \t잠재 손실: 3723.092\n",
-      "10 훈련 전체 손실: 16092.582 \t재구성 손실: 12279.52 \t잠재 손실: 3813.0625\n",
-      "11 훈련 전체 손실: 16111.75 \t재구성 손실: 12282.848 \t잠재 손실: 3828.9019\n",
-      "12 훈련 전체 손실: 15926.159 \t재구성 손실: 12180.764 \t잠재 손실: 3745.3953\n",
-      "13 훈련 전체 손실: 15271.382 \t재구성 손실: 11631.805 \t잠재 손실: 3639.5774\n",
-      "14 훈련 전체 손실: 16211.1875 \t재구성 손실: 12341.906 \t잠재 손실: 3869.2817\n",
-      "15 훈련 전체 손실: 15781.817 \t재구성 손실: 11920.02 \t잠재 손실: 3861.798\n",
-      "16 훈련 전체 손실: 15513.055 \t재구성 손실: 11780.989 \t잠재 손실: 3732.0657\n",
-      "17 훈련 전체 손실: 15282.611 \t재구성 손실: 11568.492 \t잠재 손실: 3714.1196\n",
-      "18 훈련 전체 손실: 15077.107 \t재구성 손실: 11422.83 \t잠재 손실: 3654.277\n",
-      "19 훈련 전체 손실: 15184.711 \t재구성 손실: 11399.879 \t잠재 손실: 3784.8323\n",
-      "20 훈련 전체 손실: 15317.0625 \t재구성 손실: 11541.074 \t잠재 손실: 3775.988\n",
-      "21 훈련 전체 손실: 15375.975 \t재구성 손실: 11662.023 \t잠재 손실: 3713.9507\n",
-      "22 훈련 전체 손실: 15253.931 \t재구성 손실: 11569.6 \t잠재 손실: 3684.3308\n",
-      "23 훈련 전체 손실: 14920.597 \t재구성 손실: 11226.293 \t잠재 손실: 3694.304\n",
-      "24 훈련 전체 손실: 15152.121 \t재구성 손실: 11461.097 \t잠재 손실: 3691.0242\n",
-      "25 훈련 전체 손실: 15161.9795 \t재구성 손실: 11527.926 \t잠재 손실: 3634.054\n",
-      "26 훈련 전체 손실: 15351.327 \t재구성 손실: 11591.437 \t잠재 손실: 3759.8906\n",
-      "27 훈련 전체 손실: 15188.266 \t재구성 손실: 11508.098 \t잠재 손실: 3680.168\n",
-      "28 훈련 전체 손실: 14944.834 \t재구성 손실: 11275.673 \t잠재 손실: 3669.1616\n",
-      "29 훈련 전체 손실: 14949.296 \t재구성 손실: 11173.085 \t잠재 손실: 3776.211\n",
-      "30 훈련 전체 손실: 14824.383 \t재구성 손실: 11233.498 \t잠재 손실: 3590.8848\n",
-      "31 훈련 전체 손실: 15053.771 \t재구성 손실: 11336.643 \t잠재 손실: 3717.1284\n",
-      "32 훈련 전체 손실: 15209.582 \t재구성 손실: 11480.851 \t잠재 손실: 3728.731\n",
-      "33 훈련 전체 손실: 15029.901 \t재구성 손실: 11239.151 \t잠재 손실: 3790.75\n",
-      "34 훈련 전체 손실: 15018.421 \t재구성 손실: 11247.266 \t잠재 손실: 3771.1555\n",
-      "35 훈련 전체 손실: 15295.654 \t재구성 손실: 11592.32 \t잠재 손실: 3703.334\n",
-      "36 훈련 전체 손실: 15067.271 \t재구성 손실: 11287.572 \t잠재 손실: 3779.6997\n",
-      "37 훈련 전체 손실: 14845.694 \t재구성 손실: 11252.967 \t잠재 손실: 3592.7273\n",
-      "38 훈련 전체 손실: 14872.638 \t재구성 손실: 11183.228 \t잠재 손실: 3689.4102\n",
-      "39 훈련 전체 손실: 14961.719 \t재구성 손실: 11297.793 \t잠재 손실: 3663.9263\n",
-      "40 훈련 전체 손실: 14667.465 \t재구성 손실: 10977.892 \t잠재 손실: 3689.5732\n",
-      "41 훈련 전체 손실: 15164.336 \t재구성 손실: 11456.284 \t잠재 손실: 3708.0515\n",
-      "42 훈련 전체 손실: 14535.691 \t재구성 손실: 10862.055 \t잠재 손실: 3673.6365\n",
-      "43 훈련 전체 손실: 14729.365 \t재구성 손실: 11080.487 \t잠재 손실: 3648.8777\n",
-      "44 훈련 전체 손실: 15044.16 \t재구성 손실: 11426.916 \t잠재 손실: 3617.2446\n",
-      "45 훈련 전체 손실: 14839.276 \t재구성 손실: 11058.229 \t잠재 손실: 3781.0476\n",
-      "46 훈련 전체 손실: 14890.201 \t재구성 손실: 11144.563 \t잠재 손실: 3745.6377\n",
-      "47 훈련 전체 손실: 14605.932 \t재구성 손실: 10951.763 \t잠재 손실: 3654.1694\n",
-      "48 훈련 전체 손실: 14460.681 \t재구성 손실: 10878.129 \t잠재 손실: 3582.552\n",
-      "49 훈련 전체 손실: 14584.321 \t재구성 손실: 10932.154 \t잠재 손실: 3652.1667\n"
+      "0 훈련 전체 손실: 17792.723 \t재구성 손실: 14123.082 \t잠재 손실: 3669.6401\n",
+      "1 훈련 전체 손실: 17332.346 \t재구성 손실: 13560.099 \t잠재 손실: 3772.2463\n",
+      "2 훈련 전체 손실: 16350.873 \t재구성 손실: 12579.393 \t잠재 손실: 3771.4807\n",
+      "3 훈련 전체 손실: 16581.45 \t재구성 손실: 12810.662 \t잠재 손실: 3770.7861\n",
+      "4 훈련 전체 손실: 16224.016 \t재구성 손실: 12450.172 \t잠재 손실: 3773.8433\n",
+      "5 훈련 전체 손실: 15628.262 \t재구성 손실: 11819.755 \t잠재 손실: 3808.5068\n",
+      "6 훈련 전체 손실: 16081.008 \t재구성 손실: 12179.777 \t잠재 손실: 3901.231\n",
+      "7 훈련 전체 손실: 15772.933 \t재구성 손실: 12021.411 \t잠재 손실: 3751.5212\n",
+      "8 훈련 전체 손실: 16276.584 \t재구성 손실: 12404.785 \t잠재 손실: 3871.7988\n",
+      "9 훈련 전체 손실: 15589.723 \t재구성 손실: 11740.717 \t잠재 손실: 3849.0063\n",
+      "10 훈련 전체 손실: 15931.363 \t재구성 손실: 12031.385 \t잠재 손실: 3899.9783\n",
+      "11 훈련 전체 손실: 16112.865 \t재구성 손실: 12238.496 \t잠재 손실: 3874.369\n",
+      "12 훈련 전체 손실: 16002.043 \t재구성 손실: 12185.183 \t잠재 손실: 3816.8608\n",
+      "13 훈련 전체 손실: 15357.919 \t재구성 손실: 11667.58 \t잠재 손실: 3690.3389\n",
+      "14 훈련 전체 손실: 16208.517 \t재구성 손실: 12264.615 \t잠재 손실: 3943.9011\n",
+      "15 훈련 전체 손실: 15970.236 \t재구성 손실: 12158.714 \t잠재 손실: 3811.522\n",
+      "16 훈련 전체 손실: 15551.721 \t재구성 손실: 11783.23 \t잠재 손실: 3768.4897\n",
+      "17 훈련 전체 손실: 15330.088 \t재구성 손실: 11555.771 \t잠재 손실: 3774.3167\n",
+      "18 훈련 전체 손실: 15251.499 \t재구성 손실: 11584.66 \t잠재 손실: 3666.8386\n",
+      "19 훈련 전체 손실: 15196.1455 \t재구성 손실: 11516.703 \t잠재 손실: 3679.4424\n",
+      "20 훈련 전체 손실: 15324.054 \t재구성 손실: 11526.015 \t잠재 손실: 3798.0388\n",
+      "21 훈련 전체 손실: 15358.613 \t재구성 손실: 11515.41 \t잠재 손실: 3843.2036\n",
+      "22 훈련 전체 손실: 15298.06 \t재구성 손실: 11582.691 \t잠재 손실: 3715.3684\n",
+      "23 훈련 전체 손실: 14673.169 \t재구성 손실: 10940.84 \t잠재 손실: 3732.329\n",
+      "24 훈련 전체 손실: 15293.574 \t재구성 손실: 11561.805 \t잠재 손실: 3731.7695\n",
+      "25 훈련 전체 손실: 15256.209 \t재구성 손실: 11540.664 \t잠재 손실: 3715.545\n",
+      "26 훈련 전체 손실: 15305.479 \t재구성 손실: 11475.454 \t잠재 손실: 3830.025\n",
+      "27 훈련 전체 손실: 15276.918 \t재구성 손실: 11449.658 \t잠재 손실: 3827.2593\n",
+      "28 훈련 전체 손실: 14980.705 \t재구성 손실: 11318.064 \t잠재 손실: 3662.6406\n",
+      "29 훈련 전체 손실: 15232.898 \t재구성 손실: 11520.201 \t잠재 손실: 3712.6968\n",
+      "30 훈련 전체 손실: 14872.446 \t재구성 손실: 11173.004 \t잠재 손실: 3699.4421\n",
+      "31 훈련 전체 손실: 14890.485 \t재구성 손실: 11144.245 \t잠재 손실: 3746.2402\n",
+      "32 훈련 전체 손실: 15246.843 \t재구성 손실: 11439.51 \t잠재 손실: 3807.333\n",
+      "33 훈련 전체 손실: 15063.674 \t재구성 손실: 11282.296 \t잠재 손실: 3781.3784\n",
+      "34 훈련 전체 손실: 15046.693 \t재구성 손실: 11310.135 \t잠재 손실: 3736.5586\n",
+      "35 훈련 전체 손실: 15293.908 \t재구성 손실: 11599.437 \t잠재 손실: 3694.4717\n",
+      "36 훈련 전체 손실: 15134.561 \t재구성 손실: 11362.827 \t잠재 손실: 3771.734\n",
+      "37 훈련 전체 손실: 14705.802 \t재구성 손실: 11054.809 \t잠재 손실: 3650.993\n",
+      "38 훈련 전체 손실: 14914.01 \t재구성 손실: 11077.051 \t잠재 손실: 3836.9585\n",
+      "39 훈련 전체 손실: 14848.272 \t재구성 손실: 11198.791 \t잠재 손실: 3649.4814\n",
+      "40 훈련 전체 손실: 14694.52 \t재구성 손실: 10991.713 \t잠재 손실: 3702.807\n",
+      "41 훈련 전체 손실: 15223.752 \t재구성 손실: 11464.886 \t잠재 손실: 3758.8667\n",
+      "42 훈련 전체 손실: 14585.404 \t재구성 손실: 11019.355 \t잠재 손실: 3566.0483\n",
+      "43 훈련 전체 손실: 14579.119 \t재구성 손실: 10931.234 \t잠재 손실: 3647.8848\n",
+      "44 훈련 전체 손실: 15049.283 \t재구성 손실: 11381.96 \t잠재 손실: 3667.3232\n",
+      "45 훈련 전체 손실: 14855.669 \t재구성 손실: 11125.52 \t잠재 손실: 3730.1494\n",
+      "46 훈련 전체 손실: 14777.61 \t재구성 손실: 11093.045 \t잠재 손실: 3684.5652\n",
+      "47 훈련 전체 손실: 14408.994 \t재구성 손실: 10788.754 \t잠재 손실: 3620.24\n",
+      "48 훈련 전체 손실: 14479.196 \t재구성 손실: 10864.229 \t잠재 손실: 3614.9678\n",
+      "49 훈련 전체 손실: 14638.092 \t재구성 손실: 10926.594 \t잠재 손실: 3711.4983\n"
      ]
     }
    ],
@@ -2044,7 +2044,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x3600 with 60 Axes>"
       ]
@@ -2067,7 +2067,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2178,7 +2178,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x540 with 6 Axes>"
       ]
@@ -2217,7 +2217,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
@@ -2227,7 +2227,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
@@ -2237,7 +2237,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
@@ -2247,7 +2247,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
diff --git a/16_reinforcement_learning.ipynb b/16_reinforcement_learning.ipynb
index 08641ac..79a23f3 100644
--- a/16_reinforcement_learning.ipynb
+++ b/16_reinforcement_learning.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -12,17 +14,18 @@
       "CPython 3.5.5\n",
       "IPython 6.3.0\n",
       "\n",
-      "numpy 1.14.2\n",
+      "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
       "scipy 1.0.1\n",
       "matplotlib 2.2.2\n",
-      "tensorflow 1.7.0\n"
+      "tensorflow 1.8.0\n",
+      "gym 0.10.5\n"
      ]
     }
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -v -p numpy,sklearn,scipy,matplotlib,tensorflow"
+    "%watermark -v -p numpy,sklearn,scipy,matplotlib,tensorflow,gym"
    ]
   },
   {
@@ -74,7 +77,7 @@
     "    np.random.seed(seed)\n",
     "\n",
     "# 맷플롯립 설정\n",
-    "%matplotlib nbagg\n",
+    "from IPython.display import HTML\n",
     "import matplotlib\n",
     "import matplotlib.animation as animation\n",
     "import matplotlib.pyplot as plt\n",
@@ -209,791 +212,9 @@
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
+      "image/png": "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\n",
       "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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\" width=\"500\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 360x432 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1001,7 +222,7 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(5,4))\n",
+    "plt.figure(figsize=(5,6))\n",
     "plt.imshow(img)\n",
     "plt.axis(\"off\")\n",
     "save_fig(\"MsPacman\")\n",
@@ -1055,8 +276,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_environment(env, figsize=(5,4)):\n",
-    "    plt.close()  # 이렇게 하지 않으면 nbagg 백엔드가 이전 그래프를 그릴 때가 있습니다\n",
+    "def plot_environment(env, figsize=(5,6)):\n",
     "    plt.figure(figsize=figsize)\n",
     "    img = env.render(mode=\"rgb_array\")\n",
     "    plt.imshow(img)\n",
@@ -1132,791 +352,9 @@
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
+      "image/png": "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\n",
       "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"500\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 360x432 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -2084,7 +522,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "이제 애니메이션으로 한번 보죠(주피터에서는 조금 지글거립니다):"
+    "이제 애니메이션으로 한번 보죠:"
    ]
   },
   {
@@ -2094,15 +532,16 @@
    "outputs": [],
    "source": [
     "def update_scene(num, frames, patch):\n",
+    "    plt.close()  # 이전 그래프를 닫지 않으면 두 개의 그래프가 출력되는 matplotlib의 버그로 보입니다.\n",
     "    patch.set_data(frames[num])\n",
     "    return patch,\n",
     "\n",
-    "def plot_animation(frames, repeat=False, interval=40):\n",
-    "    plt.close()  # 이렇게 하지 않으면 nbagg 백엔드가 이전 그래프를 그릴 때가 있습니다\n",
-    "    fig = plt.figure()\n",
+    "def plot_animation(frames, figsize=(5,6), repeat=False, interval=40):\n",
+    "    fig = plt.figure(figsize=figsize)\n",
     "    patch = plt.imshow(frames[0])\n",
     "    plt.axis('off')\n",
-    "    return animation.FuncAnimation(fig, update_scene, fargs=(frames, patch), frames=len(frames), repeat=repeat, interval=interval)"
+    "    return animation.FuncAnimation(fig, update_scene, fargs=(frames, patch), \n",
+    "                                   frames=len(frames), repeat=repeat, interval=interval)"
    ]
   },
   {
@@ -2110,802 +549,4378 @@
    "execution_count": 21,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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      "data": {
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+       "AAABJAAAANsAAAFqAAACIgAAAXoAAAF/AAABNQAAAiQAAAFrAAABDAAAAQUAAAJXAAABuQAAAPoA\n",
+       "AADaAAACFQAAAQ0AAAFHAAAA5gAAAnYAAAG1AAABPwAAAYkAAAFaAAACgQAAAQcAAADhAAAAPgAA\n",
+       "Ab8AAABeAAAAgwAAADkAAACuAAAAMgAAAGoAAAAdAAAAZgAAADsAAAAmAAAAGgAAAV8AAABxAAAA\n",
+       "KwAAACgAAACVAAAAlwAAACsAAAAoAAAAiAAAACgAAAAqAAAAFHN0Y28AAAAAAAAAAQAAACwAAABi\n",
+       "dWR0YQAAAFptZXRhAAAAAAAAACFoZGxyAAAAAAAAAABtZGlyYXBwbAAAAAAAAAAAAAAAAC1pbHN0\n",
+       "AAAAJal0b28AAAAdZGF0YQAAAAEAAAAATGF2ZjU2LjQwLjEwMQ==\n",
+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
+     "execution_count": 21,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "video = plot_animation(frames)\n",
-    "plt.show()"
+    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
    ]
   },
   {
@@ -2947,7 +4962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -2964,7 +4979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2973,16 +4988,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.02573366, -0.02697052,  0.03177656,  0.02587927])"
+       "array([-0.00326381, -0.00892555,  0.02935513, -0.00128701])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3018,7 +5033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3061,7 +5076,6 @@
     "        return np.array(img)\n",
     "\n",
     "def plot_cart_pole(env, obs):\n",
-    "    plt.close()  # 이렇게 하지 않으면 nbagg 백엔드가 이전 그래프를 그릴 때가 있습니다\n",
     "    img = render_cart_pole(env, obs)\n",
     "    plt.imshow(img)\n",
     "    plt.axis(\"off\")\n",
@@ -3070,805 +5084,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openai_cart_pole_rendering = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
+      "image/png": "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\n",
       "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img 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\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -3888,7 +5111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -3897,7 +5120,7 @@
        "Discrete(2)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3915,7 +5138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3928,796 +5151,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEYCAYAAAAeWvJ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAABS9JREFUeJzt3cFx2lAUQFHIUEXaiNtIG6YOl2HaSBtxG2mDLLKxjbGRkHQhOmfGC3vB/IWZy5feF9vj8bgBgMq3egEArJsQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkNrVC3jH84YA7sd2ihexIwIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkBIiAFJCBEBKiABICREAKSECICVEAKSECICUEAGQEiIAUkIEQEqIAEgJEQApIQIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkBIiAFJCBEBKiABICREAKSECICVEAKSECICUEAGQEiIAUkIEQEqIAEgJEQApIQIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkBIiAFJCBEBKiABICREAKSECICVEAKSECICUEAGQEiIAUkIEQEqIAEgJEQApIQIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkBIiAFJCBEBKiABICREAKSECICVEAKSECICUEAGQEiIAUkIEQEqIAEgJEQApIQIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiC1qxcAa/Ry2L/5/cfjc7QS6NkRwQ14OexP4gRrIUQApIQIgJQQAZASIljYuXtBBhZYKyECICVEAKSECICUEAGQEiJYkEEFOCVEAKSECBbiET7wMSGCmMtyrJ0QAZASIgBSQgQLcH8IzhMiCLk/BEIEQEyIYGYuy8HnhAiAlBABkBIiiBhUgH+ECICUEMGMPG0bviZEAKSECICUEAGQEiKYiYOscBkhgoUZVIC3hAiAlBDBDFyWg8sJEQApIQIgJUSwIIMKcEqIAEgJEUzM8+VgGCECICVEAKSECICUEMGEHGSF4YQIFmBQAc4TIpiI3RCMI0QApIQIZuayHHxOiABICREAKSGCCRhUgPGECGbk/hB8TYgASAkRACkhAiAlRHAlgwpwHSGCmRhUgMsIEQApIYIr+FpwuJ4QAZASIgBSQgRASogASAkRjGRQAaYhRACkhAiAlBABkBIiGMHz5WA6QgQTMqgAwwkRDGQ3BNMSIlZvu90O+jnnYX+Y5HVgbYQIgNSuXgDcm19/Hk/+9vP7IVgJ/B/siABICREM8Pv5dDe02Xy8SwIuI0Qwgaenh3oJcLeECIDU9ng81mt47aYWwzqMHaV+fZnuYT98WOHG3nswxiTnEISI1avO9NzYew/GmOTNc1Pj2w75sSb+37l3U32YuqkQ+YRIwY4IWoYVAEgJEQApIQIgJUQApIQIgJQQAZASIgBSN3WOCArO80DLjgiAlBABkBIiAFJCBEBKiABICREAKSECICVEAKSECICUEAGQEiIAUkIEQEqIAEgJEQApIQIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkBIiAFJCBEBKiABICREAKSECICVEAKSECICUEAGQEiIAUkIEQEqIAEgJEQApIQIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkBIiAFK7egHvbOsFALAsOyIAUkIEQEqIAEgJEQApIQIgJUQApIQIgJQQAZASIgBSQgRASogASAkRACkhAiAlRACkhAiAlBABkBIiAFJCBEBKiABICREAKSECICVEAKSECICUEAGQEiIAUn8Bm29lFSLelIwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -4725,7 +5166,6 @@
     }
    ],
    "source": [
-    "plt.close()  # 이렇게 하지 않으면 nbagg 백엔드가 이전 그래프를 그릴 때가 있습니다\n",
     "img = render_cart_pole(env, obs)\n",
     "plt.imshow(img)\n",
     "plt.axis(\"off\")\n",
@@ -4734,7 +5174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -4743,7 +5183,7 @@
        "(400, 600, 3)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4761,7 +5201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4774,796 +5214,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
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       "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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oGnb6QGc7m89gZMFqj//af3p+WN/ASQ5P1bff0ntM9YKIDMUXYNAUYH0BzRtOz2A0/2tJ3CyQPXnP60/PX/sL4Ak58fq+y+pfcZaAWSGomsQKCqQ/oCmbacHcLq/9QROFqj+/Kf1p+eP/QWQ5PxYff8ltc9YK4DMUHQNAkUF0h/QtO30AE73t57AyQLVn/+0/vT8sb8Akpwfq++/pPYZawWQGYquQaCoQPoDmradHsDp/tYTOFmg+vOf1p+eP/YXQJLzY/X9l9Q+Y60AMkPRNQgUFUh/QNO20wM43d96AicLVH/+0/rT88f+Akhyfqy+/5LaZ6wVQGYougaBogLpD2jadnoAp/tbT+BkgerPf1p/ev7YXwBJzo/V919S+4y1AsgMRdcgUFQg/QFN204P4HR/6wmcLFD9+U/rT88f+wsgyfmx+v5Lap+xVgCZoegaBIoKpD+gadvpAZzubz2BkwWqP/9p/en5Y38BJDk/Vt9/Se0z1gogMxRdg0BRgfQHNG07PYDT/a0ncLJA9ec/rT89f+wvgCTnx+r7L6l9xloBZIaiaxAoKpD+gKZtpwdwur/1BE4WqP78p/Wn54/9BZDk/Fh9/yW1z1grgMxQdA0CRQXSH9C07fQATve3nsDJAtWf/7T+9PyxvwCSnB+r77+k9hlrBZAZiq5BoKhA+gOatp0ewOn+1hM4WaD685/Wn54/9hdAkvNj9f2X1D5j7fIAMqMJ1yBAgAABAgQIECBAoIaAAFJjTqokQIAAAQIECBAg0EJAAGkxRk0QIECAAAECBAgQqCEggNSYkyoJECBAgAABAgQItBAQQFqMURMECBAgQIAAAQIEaggIIDXmpEoCBAgQIECAAAECLQQEkBZj1AQBAgQIECBAgACBGgICSI05qZIAAQIECBAgQIBACwEBpMUYNUGAAAECBAgQIECghoAAUmNOqiRAgAABAgQIECDQQkAAaTFGTRAgQIAAAQIECBCoISCA1JiTKgkQIECAAAECBAi0EBBAWoxREwQIECBAgAABAgRqCAggNeakSgIECBAgQIAAAQItBASQFmPUBAECBAgQIECAAIEaAgJIjTmpkgABAgQIECBAgEALAQGkxRg1QYAAAQIECBAgQKCGgABSY06qJECAAAECBAgQINBCQABpMUZNECBAgAABAgQIEKghIIDUmJMqCRAgQIAAAQIECLQQEEBajFETBAgQIECAAAECBGoICCA15qRKAgQIECBAgAABAi0EBJAWY9QEAQIECBAgQIAAgRoCAkiNOamSAAECBAgQIECAQAsBAaTFGDVBgAABAgQIECBAoIaAAFJjTqokQIAAAQIECBAg0EJAAGkxRk0QIECAAAECBAgQqCEggNSYkyoJECBAgAABAgQItBAQQFqMURMECBAgQIAAAQIEaggIIDXmpEoCBAgQIECAAAECLQQEkBZj1AQBAgQIECBAgACBGgICSI05qZIAAQIECBAgQIBACwEBpMUYNUGAAAECBAgQIECghoAAUmNOqiRAgAABAgQIECDQQkAAaTFGTRAgQIAAAQIECBCoISCA1JiTKgkQIECAAAECBAi0EBBAWoxREwQIECBAgAABAgRqCAggNeakSgIECBAgQIAAAQItBASQFmPUBAECBAgQIECAAIEaAgJIjTmpkgABAgQIECBAgEALAQGkxRg1QYAAAQIECBAgQKCGgABSY06qJECAAAECBAgQINBCQABpMUZNECBAgAABAgQIEKghIIDUmJMqCRAgQIAAAQIECLQQEEBajFETBAgQIECAAAECBGoICCA15qRKAgQIECBAgAABAi0EBJAWY9QEAQIECBAgQIAAgRoCAkiNOamSAAECBAgQIECAQAsBAaTFGDVBgAABAgQIECBAoIaAAFJjTqokQIAAAQIECBAg0EJAAGkxRk0QIECAAAECBAgQqCEggNSYkyoJECBAgAABAgQItBAQQFqMURMECBAgQIAAAQIEaggIIDXmpEoCBAgQIECAAAECLQQEkBZj1AQBAgQIECBAgACBGgICSI05qZIAAQIECBAgQIBACwEBpMUYNUGAAAECBAgQIECghoAAUmNOqiRAgAABAgQIECDQQkAAaTFGTRAgQIAAAQIECBCoISCA1JiTKgkQIECAAAECBAi0EBBAWoxREwQIECBAgAABAgRqCAggNeakSgIECBAgQIAAAQItBASQFmPUBAECBAgQIECAAIEaAgJIjTmpkgABAgQIECBAgEALAQGkxRg1QYAAAQIECBAgQKCGgABSY06qJECAAAECBAgQINBCQABpMUZNECBAgAABAgQIEKghIIDUmJMqCRAgQIAAAQIECLQQEEBajFETBAgQIECAAAECBGoICCA15qRKAgQIECBAgAABAi0EBJAWY9QEAQIECBAgQIAAgRoCAkiNOamSAAECBAgQIECAQAsBAaTFGDVBgAABAgQIECBAoIaAAFJjTqokQIAAAQIECBAg0EJAAGkxRk0QIECAAAECBAgQqCEggNSYkyoJECBAgAABAgQItBAQQFqMURMECBAgQIAAAQIEaggIIDXmpEoCBAgQIECAAAECLQQEkBZj1AQBAgQIECBAgACBGgICSI05qZIAAQIECBAgQIBACwEBpMUYNUGAAAECBAgQIECghoAAUmNOqiRAgAABAgQIECDQQkAAaTFGTRAgQIAAAQIECBCoISCA1JiTKgkQIECAAAECBAi0EBBAWoxREwQIECBAgAABAgRqCAggNeakSgIECBAgQIAAAQItBASQFmPUBAECBAgQIECAAIEaAgJIjTmpkgABAgQIECBAgEALAQGkxRg1QYAAAQIECBAgQKCGgABSY06qJECAAAECBAgQINBCQABpMUZNECBAgAABAgQIEKghIIDUmJMqCRAgQIAAAQIECLQQEEBajFETBAgQIECAAAECBGoICCA15qRKAgQIECBAgAABAi0EBJAWY9QEAQIECBAgQIAAgRoCAkiNOamSAAECBAgQIECAQAsBAaTFGDVBgAABAgQIECBAoIaAAFJjTqokQIAAAQIECBAg0EJAAGkxRk0QIECAAAECBAgQqCEggNSYkyoJECBAgAABAgQItBAQQFqMURMECBAgQIAAAQIEaggIIDXmpEoCBAgQIECAAAECLQQEkBZj1AQBAgQIECBAgACBGgICSI05qZIAAQIECBAgQIBACwEBpMUYNUGAAAECBAgQIECghoAAUmNOqiRAgAABAgQIECDQQkAAaTFGTRAgQIAAAQIECBCoISCA1JiTKgkQIECAAAECBAi0EBBAWoxREwQIECBAgAABAgRqCAggNeakSgIECBAgQIAAAQItBASQFmPUBAECBAgQIECAAIEaAgJIjTmpkgABAgQIECBAgEALAQGkxRg1QYAAAQIECBAgQKCGgABSY06qJECAAAECBAgQINBCQABpMUZNECBAgAABAgQIEKghIIDUmJMqCRAgQIAAAQIECLQQEEBajFETBAgQIECAAAECBGoICCA15qRKAgQIECBAgAABAi0EBJAWY9QEAQIECBAgQIAAgRoCAkiNOamSAAECBAgQIECAQAsBAaTFGDVBgAABAgQIECBAoIaAAFJjTqokQIAAAQIECBAg0EJAAGkxRk0QIECAAAECBAgQqCEggNSYkyoJECBAgAABAgQItBAQQFqMURMECBAgQIAAAQIEaggIIDXmpEoCBAiUFfj7337+j9r/+tMvZftROAECBAhkAgJI5mc1AQIECPxB4M8Cxx+RBBC3DQECBM4VEEDOnb3OCRAgMF3gSvj4bVMhZDq/CxIgQKCEgABSYkyKJECAQB2BqyFEAKkzU5USIEBgpoAAMlPTtQgQIEDgLwKIm4AAAQIEfiQggLg/CBAgQGCqgAAyldPFCBAg0E5AAGk3Ug0RIEBgrcDVAPJVpT/DWjsruxMgQGCFgACyQt2eBAgQaCwggDQertYIECAwQUAAmYDoEgQIECDwe4GrIcS/gLhzCBAgcJ6AAHLezHVMgACB2wUEkNuJbUCAAIGyAgJI2dEpnAABAvsKCCD7zkZlBAgQWC0ggKyegP0JECDQUEAAaThULREgQGCSgAAyCdJlCBAgQOBfAlcDyNcK/x2IO4cAAQJnCQggZ81btwQIEHhM4GoIEUAeG4mNCBAgsIWAALLFGBRBgACBfgICSL+Z6ogAAQIzBASQGYquQYAAAQL/ISCAuCkIECBA4M8EBBD3BQECBAjcInA1gHxt7s+wbhmBixIgQGBLAQFky7EoigABAj0EroYQAaTHvHVBgACBKwICyBUl3yFAgACBbwkIIN9is4gAAQKtBQSQ1uPVHAECBNYKCCBr/e1OgACBHQUEkB2noiYCBAg0EbgaQL7a9WdYTYauDQIECAwEBBC3CAECBAjcJiCA3EbrwgQIECgrIICUHZ3CCRAgUEPgagjxLyA15qlKAgQIpAICSCpoPQECBAj8UEAAcYMQIECAwL8LCCDuBwIECBC4VUAAuZXXxQkQIFBOQAApNzIFEyBAoJbA1QDy1ZU/w6o1W9USIEDgOwICyHfUrCFAgACBjwSuhhAB5CNWXyZAgEBJAQGk5NgUTYAAgVoCAkiteamWAAECdwoIIHfqujYBAgQI/CoggLgRCBAgQOA3AQHEvUCAAAECtwtcDSBfhfgzrNvHYQMCBAgsFRBAlvLbnAABAucIXA0hAsg594ROCRA4U0AAOXPuuiZAgMDjAgLI4+Q2JECAwJYCAsiWY1EUAQIE+gkIIP1mqiMCBAh8R0AA+Y6aNQQIECDwscDVAPJ1YX+G9TGvBQQIECgjIICUGZVCCRAgUFtAAKk9P9UTIEBgloAAMkvSdQgQIEBgKHA1hPgXkCGlLxAgQKCsgABSdnQKJ0CAQD0BAaTezFRMgACB2QICyGxR1yNAgACB/yoggLg5CBAgQEAAcQ8QIECAwGMCVwPIV0H+DOuxsdiIAAECjwoIII9y24wAAQIEroYQAcS9QoAAgZ4CAkjPueqKAAEC2woIINuORmEECBB4REAAeYTZJgQIECDwm4AA4l4gQIDA2QICyNnz1z0BAgQeFxBAHie3IQECBLYSEEC2GodiCBAg0F/gagD5kvDfgfS/H3RIgMB5AgLIeTPXMQECBJYLXA0hAsjyUSmAAAEC0wUEkOmkLkiAAAECIwEBZCTkcwIECPQVEED6zlZnBAgQ2FZAANl2NAojQIDA7QICyO3ENiBAgACBPwpcDSC/rfOnWO4hAgQI9BEQQPrMUicECBAoIyCAlBmVQgkQIDBdQACZTuqCBAgQIHBF4JMQ4l9Aroj6DgECBGoICCA15qRKAgQItBMQQNqNVEMECBC4JCCAXGLyJQIECBCYLSCAzBZ1PQIECNQQEEBqzEmVBAgQaCfwSQD5at6fYbW7BTREgMChAgLIoYPXNgECBHYQ+CSECCA7TEwNBAgQyAUEkNzQFQgQIEDgmwICyDfhLCNAgEBhAQGk8PCUToAAgeoCAkj1Ce5b/+v1ev2ouvf7/d63epUR6C0ggPSer+4IECCwtcAnAeSrEX+GtfU4typOANlqHIoh8DsBAcQNQYAAAQJLBT4JIQLI0lGV2lwAKTUuxR4mIIAcNnDtEiBAYDcBAWS3ifSoRwDpMUdd9BQQQHrOVVcECBAoIyCAlBnV7/+EYvF/YyFg/Pi+We1z9/6j64+eqvS/AVq9/6i/0eej+kc+8frRBqMGfE6AAAECBBKBTwLI1z7+DCvRnrc2fQFJK1m9f1r/3etX+9y9/+j6I9/0/Xf1/qP+Rp+P6h/5xOtHG4wa8DkBAgQIEEgEBJBEb93a9AUkrXz1/mn9d69f7XP3/qPrj3zT99/V+4/6G30+qn/kE68fbTBqwOcECBAgQCAV+CSE+BeQVHvO+vQFJK1i9f5p/XevX+1z9/6j64980/ff1fuP+ht9Pqp/5BOvH20wasDnBAgQIEAgFRBAUsHn149eQEYVpe8fo/3T64/qX73/qL7Vnx8qfjEAABgOSURBVK/2uXv/u68/ml+6/2j9aP/0c/8ReipoPQECBAjEAgJITPj4BdIXmDQgjPZPrz8CXb3/qL7Vn6/2uXv/u68/ml+6/2j9aP/0cwEkFbSeAAECBGKBTwLI12b+DCsmjy+QvsCkAWG0f3r9EdDq/Uf1rf58tc/d+999/dH80v1H60f7p58LIKmg9QQIECAwReCTECKATCGPLpK+wKQBYbR/ev0Rzur9R/Wt/ny1z93733390fzS/UfrR/unnwsgqaD1BAgQIDBFQACZwugiBAgQ2F5AANl+RAokQIDAGQICyBlz1iUBAgQEEPcAAQIECGwh8EkA+SrYn2FtMTZFECBA4GMBAeRjMgsIECBA4A4BAeQOVdckQIDAfgICyH4zUREBAgSOFfgkhPgXkGNvE40TIFBcQAApPkDlEyBAoJOAANJpmnohQIDAnwsIIO4MAgQIENhGQADZZhQKIUCAwG0CAshttC5MgAABAp8KfBJAvq7tz7A+FfZ9AgQIrBcQQNbPQAUECBAg8A8BAcStQIAAgf4CAkj/GeuQAAECpQQ+CSH+BaTUaBVLgACBXwUEEDcCAQIECGwlIIBsNQ7FECBAYLqAADKd1AUJECBAIBEQQBI9awkQILC/gACy/4xUSIAAgaMEPgkgXzD+DOuo20OzBAg0EBBAGgxRCwQIEOgm8EkIEUC6TV8/BAh0FxBAuk9YfwQIECgoIIAUHJqSCRAgcFFAALkI5WsECBAg8JyAAPKctZ0IECDwtMDyAPJ6vV5PN20/AgT+X+D9fr9XWnj+V+rb+3SB6s9/Wn96/tg/+/3gn73/pvff6vNPAFk9AfsTWCiw+gBLf4AW0tmaQHmB6s9/Wn96/thfAEkOgdX3X1L7jLUCyAxF1yBQVCD9AU3bTg/gdH/rCZwsUP35T+tPzx/7CyDJ+bH6/ktqn7FWAJmh6BoEigqkP6Bp2+kBnO5vPYGTBao//2n96fljfwEkOT9W339J7TPWCiAzFF2DQFGB9Ac0bTs9gNP9rSdwskD15z+tPz1/7C+AJOfH6vsvqX3GWgFkhqJrECgqkP6Apm2nB3C6v/UEThao/vyn9afnj/0FkOT8WH3/JbXPWCuAzFB0DQJFBdIf0LTt9ABO97eewMkC1Z//tP70/LG/AJKcH6vvv6T2GWsFkBmKrkGgqED6A5q2nR7A6f7WEzhZoPrzn9afnj/2F0CS82P1/ZfUPmOtADJD0TUIFBVIf0DTttMDON3fegInC1R//tP60/PH/gJIcn6svv+S2mesFUBmKLoGgaIC6Q9o2nZ6AKf7W0/gZIHqz39af3r+2F8ASc6P1fdfUvuMtQLIDEXXIFBUIP0BTdtOD+B0f+sJnCxQ/flP60/PH/sLIMn5sfr+S2qfsVYAmaHoGgSKCqQ/oGnb6QGc7m89gZMFqj//af3p+WN/ASQ5P1bff0ntM9YKIDMUXYNAUYH0BzRtOz2A0/2tJ3CyQPXnP60/PX/sL4Ak58fq+y+pfcZaAWSGomsQKCqQ/oCmbacHcLq/9QROFqj+/Kf1p+eP/QWQ5PxYff8ltc9YK4DMUHQNAkUF0h/QtO30AE73t57AyQLVn/+0/vT8sb8Akpwfq++/pPYZawWQGYquQaCoQPoDmradHsDp/tYTOFmg+vOf1p+eP/YXQJLzY/X9l9Q+Y+3yADKjCdcgQIAAgf4Cf//bz5ea/OtPv1z6ni8RIECAwBoBAWSNu10JECBA4EMBAeRDMF8nQIDApgICyKaDURYBAgQI/F7gagD5WuVfQdw9BAgQ2FdAANl3NiojQIAAgX8TEEDcDgQIEOghIID0mKMuCBAgcITA1RDiX0COuB00SYBAUQEBpOjglE2AAIETBQSQE6euZwIEugkIIN0mqh8CBAg0FhBAGg9XawQIHCMggBwzao0SIECgvsDVAPLVqT/Dqj9vHRAg0FNAAOk5V10RIECgrcDVECKAtL0FNEaAQHEBAaT4AJVPgACB0wQEkNMmrl8CBLoJCCDdJqofAgQINBcQQJoPWHsECLQXEEDaj1iDBAgQ6CVwNYB8de3PsHrNXjcECPQQEEB6zFEXBAgQOErgaggRQI66LTRLgEARAQGkyKCUSYAAAQL/EhBA3A0ECBCoKyCA1J2dygkQIHCsgABy7Og1ToBAAwEBpMEQtUCAAIEKAq/Xa1qZ//vLT5ev9T8//+3ydz/54vv9/uTrvkuAAAEC/xBYHkBeM3+RjJUAgY8E3ovfoDz/H43LlwlMFaj+/Kf1p+eP/bMEzj/7f2TS+2/qYfKNiwkg30CzhEAXgdUHWPoD1GUO+iCwQqD685/Wn54/9hdAkud29f2X1D5jrQAyQ9E1CBQVSH9A07bTAzjd33oCJwtUf/7T+tPzx/4CSHJ+rL7/ktpnrBVAZii6BoGiAukPaNp2egCn+1tP4GSB6s9/Wn96/thfAEnOj9X3X1L7jLUCyAxF1yBQVCD9AU3bTg/gdH/rCZwsUP35T+tPzx/7CyDJ+bH6/ktqn7FWAJmh6BoEigqkP6Bp2+kBnO5vPYGTBao//2n96fljfwEkOT9W339J7TPWCiAzFF2DQFGB9Ac0bTs9gNP9rSdwskD15z+tPz1/7C+AJOfH6vsvqX3GWgFkhqJrECgqkP6Apm2nB3C6v/UEThao/vyn9afnj/0FkOT8WH3/JbXPWCuAzFB0DQJFBdIf0LTt9ABO97eewMkC1Z//tP70/LG/AJKcH6vvv6T2GWsFkBmKrkGgqED6A5q2nR7A6f7WEzhZoPrzn9afnj/2F0CS82P1/ZfUPmOtADJD0TUIFBVIf0DTttMDON3fegInC1R//tP60/PH/gJIcn6svv+S2mesFUBmKLoGgaIC6Q9o2nZ6AKf7W0/gZIHqz39af3r+2F8ASc6P1fdfUvuMtQLIDEXXIFBUIP0BTdtOD+B0f+sJnCxQ/flP60/PH/sLIMn5sfr+S2qfsVYAmaHoGgSKCqQ/oGnb6QGc7m89gZMFqj//af3p+WN/ASQ5P1bff0ntM9YKIDMUXYNAXYH34tJfi/e3PYGTBao//2n96flj/+zpKe1fPYBmo8tXCyC5oSsQqCyQ/oCmvac/QOn+1hM4WaD685/Wn54/9s+entL+Akg4/BQw295qAgQIEDhF4PVK3zf2knpnf4GyVzOqIUCAwIMCy/8F5MFebUWAAAECCwUEkIX4tiZAgMBGAgLIRsNQCgECBDoLCCCdp6s3AgQIXBcQQK5b+SYBAgQIBAICSIBnKQECBBoJCCCNhqkVAgQI7CwggOw8HbURIEDgOQEB5DlrOxEgQOBoAQHk6PFrngABAv8UEEDcDAQIECDwiIAA8gizTQgQILC9gACy/YgUSIAAgR4CAkiPOeqCAAECqYAAkgpaT4AAAQKXBASQS0y+RIAAgfYCAkj7EWuQAAECewgIIHvMQRUECBBYLSCArJ6A/QkQIHCIgAByyKC1SYAAgYGAAOIWIUCAAIFHBASQR5htQoAAge0FBJDtR6RAAgQI9BAQQHrMURcECBBIBQSQVNB6AgQIELgkIIBcYvIlAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQIAAAQIE9hEQQPaZhUoIECBAgAABAgQItBcQQNqPWIMECBAgQOA8gdfr9fpR1+/3+32eio4J7CEggOwxB1UQIECAAAECEwUEkImYLkVgsoAAMhnU5QgQIECAAIH1AgLI+hmogMB/ExBA3BsECBAgQIBAOwEBpN1INdRIQABpNEytECBAgACBpwRWv+Cv3v8p5+/us9rn7v1H1x+5pf8N0Or9R/2NPh/VP/KJ1482GDXgcwIECBAgQOA8gfQFJBVbvX9a/93rV/vcvf/o+iPf9P139f6j/kafj+of+cTrRxuMGvA5AQIECBAgcJ5A+gKSiq3eP63/7vWrfe7ef3T9kW/6/rt6/1F/o89H9Y984vWjDUYN+JwAAQIECBA4TyB9AUnFVu+f1n/3+tU+d+8/uv7IN33/Xb3/qL/R56P6Rz7x+tEGowZ8ToAAAQIECJwnMHoBGYmk7x+j/dPrj+pfvf+ovtWfr/a5e/+7rz+aX7r/aP1o//Rz/xF6Kmg9AQIECBA4UCB9gUkDwmj/9Pqjka7ef1Tf6s9X+9y9/93XH80v3X+0frR/+rkAkgpaT4AAAQIEDhRIX2DSgDDaP73+aKSr9x/Vt/rz1T5373/39UfzS/cfrR/tn34ugKSC1hMgQIAAgQMF0heYNCCM9k+vPxrp6v1H9a3+fLXP3fvfff3R/NL9R+tH+6ef/x/S9oavgXRH2AAAAABJRU5ErkJggg==\" width=\"640\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -5597,7 +5257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5625,805 +5285,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
+     "execution_count": 35,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "video = plot_animation(frames)\n",
-    "plt.show()"
+    "video = plot_animation(frames, figsize=(6,4))\n",
+    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
    ]
   },
   {
@@ -6449,19 +5436,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/haesun/anaconda3/envs/handson-ml/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use the retry module or similar alternatives.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "\n",
@@ -6508,7 +5485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6538,805 +5515,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
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+       "AAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACAAAD\n",
+       "2HRyYWsAAABcdGtoZAAAAAMAAAAAAAAAAAAAAAEAAAAAAAAFAAAAAAAAAAAAAAAAAAAAAAAAAQAA\n",
+       "AAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAEAAAAABsAAAASAAAAAAACRlZHRzAAAAHGVsc3QA\n",
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+       "ABR2bWhkAAAAAQAAAAAAAAAAAAAAJGRpbmYAAAAcZHJlZgAAAAAAAAABAAAADHVybCAAAAABAAAC\n",
+       "u3N0YmwAAACzc3RzZAAAAAAAAAABAAAAo2F2YzEAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAABsAEg\n",
+       "AEgAAABIAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAY//8AAAAxYXZj\n",
+       "QwFkABX/4QAYZ2QAFazZQbCWhAAAAwAEAAADAMg8WLZYAQAGaOvjyyLAAAAAHHV1aWRraEDyXyRP\n",
+       "xbo5pRvPAyPzAAAAAAAAABhzdHRzAAAAAAAAAAEAAAAgAAACAAAAABRzdHNzAAAAAAAAAAEAAAAB\n",
+       "AAABEGN0dHMAAAAAAAAAIAAAAAEAAAQAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAA\n",
+       "AAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAA\n",
+       "AAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAA\n",
+       "AQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAAB\n",
+       "AAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAIAAAAAAEAAAQAAAAAAQAAAAAAAAAcc3RzYwAAAAAA\n",
+       "AAABAAAAAQAAACAAAAABAAAAlHN0c3oAAAAAAAAAAAAAACAAAAQOAAAAdQAAADUAAAAdAAAAHAAA\n",
+       "AEMAAAA7AAAAJgAAACIAAAB3AAAAMgAAACMAAAArAAAAeQAAAEcAAAAhAAAAMwAAAHgAAAA4AAAA\n",
+       "OAAAADAAAAB/AAAASgAAAC8AAAA7AAAAhgAAAEMAAAA4AAAAQQAAAEgAAABJAAAALgAAABRzdGNv\n",
+       "AAAAAAAAAAEAAAAsAAAAYnVkdGEAAABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwA\n",
+       "AAAAAAAAAAAAAAAtaWxzdAAAACWpdG9vAAAAHWRhdGEAAAABAAAAAExhdmY1Ni40MC4xMDE=\n",
+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
+     "execution_count": 38,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "video = plot_animation(frames)\n",
-    "plt.show()"
+    "video = plot_animation(frames, figsize=(6,4))\n",
+    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
    ]
   },
   {
@@ -7348,7 +5627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7390,8 +5669,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -7433,7 +5714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7456,8 +5737,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
+   "execution_count": 42,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -7467,803 +5750,116 @@
       "INFO:tensorflow:Restoring parameters from ./my_policy_net_basic.ckpt\n"
      ]
     },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
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+       "AQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAAB\n",
+       "AAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEA\n",
+       "AAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAA\n",
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+       "AAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAAcc3RzYwAAAAAAAAAB\n",
+       "AAAAAQAAACUAAAABAAAAqHN0c3oAAAAAAAAAAAAAACUAAAQLAAAAWQAAACQAAAAcAAAAGwAAAHsA\n",
+       "AAA2AAAAKQAAACYAAABYAAAAQQAAACAAAAAxAAAAiQAAAC4AAAA1AAAAPQAAALwAAABfAAAALAAA\n",
+       "AEUAAABwAAAAQwAAAC4AAAAoAAAAcAAAAE8AAAA+AAAAOQAAAIkAAABRAAAAQgAAADIAAACgAAAA\n",
+       "cAAAAEoAAABKAAAAFHN0Y28AAAAAAAAAAQAAACwAAABidWR0YQAAAFptZXRhAAAAAAAAACFoZGxy\n",
+       "AAAAAAAAAABtZGlyYXBwbAAAAAAAAAAAAAAAAC1pbHN0AAAAJal0b28AAAAdZGF0YQAAAAEAAAAA\n",
+       "TGF2ZjU2LjQwLjEwMQ==\n",
+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
+     "execution_count": 42,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "frames = render_policy_net(\"./my_policy_net_basic.ckpt\", action, X)\n",
-    "video = plot_animation(frames)\n",
-    "plt.show()"
+    "video = plot_animation(frames, figsize=(6,4))\n",
+    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
    ]
   },
   {
@@ -8291,7 +5887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8334,7 +5930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8356,7 +5952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -8365,7 +5961,7 @@
        "array([-22., -40., -50.])"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8376,7 +5972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -8386,7 +5982,7 @@
        " array([1.26665318, 1.0727777 ])]"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8397,7 +5993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -8452,7 +6048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8461,8 +6057,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
+   "execution_count": 49,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -8472,803 +6070,280 @@
       "INFO:tensorflow:Restoring parameters from ./my_policy_net_pg.ckpt\n"
      ]
     },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
       "text/html": [
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+       "FSwv/wADcuaQk48eXddx9QfvqsS/xmKxAAAAMQGeoXRCvwAEuD4XxHnClyYiYcygAtUU3xWrY13F\n",
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+       "//6plgAC7wZYwmgBbSGKHCv/9b6R936ighV3ix4ScsaFtARHWEugfnvxTOhJI1vU1YseHUv7qFRb\n",
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+       "AAEAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAAAAAAAAAAAAAAIAAAuwdHJhawAAAFx0a2hkAAAAAwAAAAAAAAAAAAAAAQAAAAAA\n",
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+       "ZAAAAAAAAAAAAAAAAAAAMgAAAZAAVcQAAAAAAC1oZGxyAAAAAAAAAAB2aWRlAAAAAAAAAAAAAAAA\n",
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+       "AAAAAAAAAAEAAAAMdXJsIAAAAAEAAAqTc3RibAAAALNzdHNkAAAAAAAAAAEAAACjYXZjMQAAAAAA\n",
+       "AAABAAAAAAAAAAAAAAAAAAAAAAGwASAASAAAAEgAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAAAAABj//wAAADFhdmNDAWQAFf/hABhnZAAVrNlBsJaEAAADAAQAAAMAyDxYtlgB\n",
+       "AAZo6+PLIsAAAAAcdXVpZGtoQPJfJE/FujmlG88DI/MAAAAAAAAAGHN0dHMAAAAAAAAAAQAAAMgA\n",
+       "AAIAAAAAFHN0c3MAAAAAAAAAAQAAAAEAAAZIY3R0cwAAAAAAAADHAAAAAQAABAAAAAABAAAKAAAA\n",
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+       "AQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAAB\n",
+       "AAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEA\n",
+       "AAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAA\n",
+       "BAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAK\n",
+       "AAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIA\n",
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+       "AAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAABAAAAAABAAAAAAAAAAEAAAIAAAAA\n",
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+       "AAAAAAAAAQAAAgAAAAABAAAKAAAAAAEAAAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAoAAAAAAQAA\n",
+       "BAAAAAABAAAAAAAAAAEAAAIAAAAAAQAACgAAAAABAAAEAAAAAAEAAAAAAAAAAQAAAgAAAAABAAAK\n",
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+       "AAQAAAAAAQAAAAAAAAABAAACAAAAAAEAAAYAAAAAAQAAAgAAAAAcc3RzYwAAAAAAAAABAAAAAQAA\n",
+       "AMgAAAABAAADNHN0c3oAAAAAAAAAAAAAAMgAAAQaAAAAYgAAACsAAAAYAAAAFAAAAEIAAAAbAAAA\n",
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+       "AAAnAAAAbwAAAB0AAAAVAAAAFwAAAGUAAAAiAAAAHgAAACgAAABqAAAAJQAAAB8AAAAjAAAAPgAA\n",
+       "ACsAAAAmAAAAJAAAAF8AAAA5AAAALwAAAB8AAAApAAAATgAAADwAAAAaAAAAKwAAAFAAAAA6AAAA\n",
+       "GwAAACEAAABcAAAALAAAACYAAAAtAAAAVAAAAD4AAAAfAAAAIAAAAFcAAAAlAAAAMgAAABsAAAB1\n",
+       "AAAANwAAACQAAAAfAAAAZAAAAEcAAAApAAAAMwAAAGMAAAA7AAAAJgAAADEAAABZAAAAKQAAAD8A\n",
+       "AAA/AAAATgAAAEcAAAA+AAAAHwAAAEMAAAAqAAAAHAAAABwAAAA7AAAAIwAAAB4AAAAtAAAAfAAA\n",
+       "ACwAAABCAAAAUAAAAG8AAABMAAAAPQAAAEIAAAAmAAAAXQAAAD8AAAA7AAAASgAAAFMAAABEAAAA\n",
+       "PQAAACcAAAA/AAAAMQAAACAAAAApAAAATwAAACoAAAAkAAAAGAAAADcAAABCAAAAXwAAADkAAAA8\n",
+       "AAAAMAAAAHcAAAA9AAAALAAAADMAAAB+AAAAOwAAADEAAAAuAAAAUwAAADQAAAAgAAAAHQAAAEMA\n",
+       "AAAlAAAAHQAAABsAAABMAAAAIQAAACEAAAAeAAAAQQAAAB8AAAAcAAAAFwAAAE8AAAAgAAAANQAA\n",
+       "ABYAAABWAAAAJwAAABgAAAAbAAAAQAAAACUAAAAnAAAAFwAAADAAAAAmAAAAFgAAABgAAAAdAAAA\n",
+       "IgAAAB0AAAAUAAAAFwAAADgAAAAlAAAAIQAAABIAAAAkAAAAIAAAABYAAAAWAAAAcgAAACsAAAAm\n",
+       "AAAAKwAAAFwAAAA2AAAAHQAAAB4AAAA/AAAAIQAAABRzdGNvAAAAAAAAAAEAAAAsAAAAYnVkdGEA\n",
+       "AABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAAACWp\n",
+       "dG9vAAAAHWRhdGEAAAABAAAAAExhdmY1Ni40MC4xMDE=\n",
+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
+     "execution_count": 49,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "frames = render_policy_net(\"./my_policy_net_pg.ckpt\", action, X, n_max_steps=1000)\n",
-    "video = plot_animation(frames)\n",
-    "plt.show()"
+    "video = plot_animation(frames, figsize=(6,4))\n",
+    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
    ]
   },
   {
@@ -9280,7 +6355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -9335,7 +6410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -9451,7 +6526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9476,7 +6551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9486,7 +6561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -9497,7 +6572,7 @@
        "       [       -inf, 72.53192111,        -inf]])"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9508,7 +6583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -9549,7 +6624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -9558,7 +6633,7 @@
        "(210, 160, 3)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9571,7 +6646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -9580,7 +6655,7 @@
        "Discrete(9)"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9605,7 +6680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -9630,796 +6705,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
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\" width=\"1000\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 720x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -10449,7 +6742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10494,7 +6787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10510,8 +6803,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
+   "execution_count": 62,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -10528,7 +6823,7 @@
        " '/dense_1/kernel:0': <tf.Variable 'q_networks/online/dense_1/kernel:0' shape=(512, 9) dtype=float32_ref>}"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10539,19 +6834,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From <ipython-input-70-86b805f1efd9>:8: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "learning_rate = 0.001\n",
     "momentum = 0.95\n",
@@ -10576,7 +6861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10598,7 +6883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10616,7 +6901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10642,7 +6927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10661,27 +6946,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
+   "execution_count": 68,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_dqn.ckpt\n",
-      "반복 2427324\t훈련 스텝 603778/4000000 (15.1)%\t손실 4.245887\t평균 최대-Q 69.582249    "
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-78-2a4d092791d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;31m# 메모리에서 샘플링하여 타깃 Q-가치를 얻기 위해 타깃 DQN을 사용합니다\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m         X_state_val, X_action_val, rewards, X_next_state_val, continues = (\n\u001b[0;32m---> 45\u001b[0;31m             sample_memories(batch_size))\n\u001b[0m\u001b[1;32m     46\u001b[0m         next_q_values = target_q_values.eval(\n\u001b[1;32m     47\u001b[0m             feed_dict={X_state: X_next_state_val})\n",
-      "\u001b[0;32m<ipython-input-71-909b0e07a829>\u001b[0m in \u001b[0;36msample_memories\u001b[0;34m(batch_size)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msample_memories\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermutation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreplay_memory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# 상태, 행동, 보상, 다음 상태, 종료 여부\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+      "반복 13269992\t훈련 스텝 3999999/4000000 (100.0)%\t손실 1.899633\t평균 최대-Q 220.667374    "
      ]
     }
    ],
@@ -10758,7 +7033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -10796,814 +7071,3812 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "data": {
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+       "AAFNAAAA8gAAAPUAAAJPAAABFwAAAQIAAAEEAAABjAAAAqUAAAGCAAAA4wAAAToAAAJYAAABOwAA\n",
+       "AO4AAADsAAACwAAAAWIAAAE4AAAA7wAAAtMAAAGTAAABMAAAANsAAAHuAAABqAAAAQcAAADcAAAC\n",
+       "iwAAAXEAAAEuAAAA1gAAAboAAAFlAAAA/gAAAMwAAAKAAAABcQAAANsAAADSAAACaAAAAX0AAACx\n",
+       "AAABKwAAAgsAAAFKAAAA7wAAARoAAAHhAAABRwAAAHcAAACwAAACIwAAARQAAAD6AAABEwAAAi4A\n",
+       "AAE4AAAAuwAAANoAAAIyAAABPgAAAPMAAADIAAAC/AAAAe8AAAEBAAABHQAAAnsAAAKzAAABdgAA\n",
+       "APUAAAEAAAADJwAAAZsAAAErAAABDQAAAj0AAAEBAAAARgAAACUAAABGAAAAIwAAAB8AAAA6AAAB\n",
+       "cAAAAEQAAAAmAAAAIgAAAYgAAAA3AAAAFgAAACcAAAClAAAAKQAAAB8AAAAdAAAAvAAAAJIAAAAV\n",
+       "AAAAHQAAAHoAAAAyAAAAHQAAABwAAAAUc3RjbwAAAAAAAAABAAAALAAAAGJ1ZHRhAAAAWm1ldGEA\n",
+       "AAAAAAAAIWhkbHIAAAAAAAAAAG1kaXJhcHBsAAAAAAAAAAAAAAAALWlsc3QAAAAlqXRvbwAAAB1k\n",
+       "YXRhAAAAAQAAAABMYXZmNTYuNDAuMTAx\n",
+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.animation.FuncAnimation at 0x7fcca59262e8>"
-      ]
-     },
-     "execution_count": 80,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "plot_animation(frames)"
+    "video = plot_animation(frames, figsize=(5,6))\n",
+    "HTML(video.to_html5_video())  # HTML5 동영상으로 만들어 줍니다"
    ]
   },
   {
@@ -11629,7 +10902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11640,7 +10913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11654,796 +10927,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "<IPython.core.display.Javascript object>"
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-     },
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\" width=\"1000\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 720x432 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -12472,7 +10963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12495,796 +10986,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
+      "image/png": "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\n",
       "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"1000\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "<Figure size 720x432 with 2 Axes>"
       ]
      },
      "metadata": {},
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diff --git a/images/rl/MsPacman.png b/images/rl/MsPacman.png
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diff --git a/images/rl/cart_pole_plot.png b/images/rl/cart_pole_plot.png
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diff --git a/images/rl/preprocessing_plot.png b/images/rl/preprocessing_plot.png
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diff --git a/images/rnn/creative_sequence_plot.png b/images/rnn/creative_sequence_plot.png
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diff --git a/images/rnn/time_series_pred_plot.png b/images/rnn/time_series_pred_plot.png
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