diff --git a/09_up_and_running_with_tensorflow.ipynb b/09_up_and_running_with_tensorflow.ipynb
index 4b24bba..90e0e27 100644
--- a/09_up_and_running_with_tensorflow.ipynb
+++ b/09_up_and_running_with_tensorflow.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "scrolled": false
    },
@@ -11,19 +11,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPython 3.5.5\n",
-      "IPython 6.3.0\n",
+      "The watermark extension is already loaded. To reload it, use:\n",
+      "  %reload_ext watermark\n",
+      "CPython 3.6.5\n",
+      "IPython 6.4.0\n",
       "\n",
       "numpy 1.14.3\n",
-      "scipy 1.0.1\n",
+      "scipy 1.1.0\n",
       "matplotlib 2.2.2\n",
+      "sklearn 0.19.1\n",
       "tensorflow 1.8.0\n"
      ]
     }
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -v -p numpy,scipy,matplotlib,tensorflow"
+    "%watermark -v -p numpy,scipy,matplotlib,sklearn,tensorflow"
    ]
   },
   {
@@ -56,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -129,7 +132,7 @@
        "<tf.Tensor 'add_1:0' shape=() dtype=int32>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -140,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -161,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +194,7 @@
        "42"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -224,7 +227,7 @@
        "42"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -235,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +285,7 @@
        "42"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -298,29 +301,6 @@
     "# 그래프 다루기"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reset_graph()\n",
-    "\n",
-    "x1 = tf.Variable(1)\n",
-    "x1.graph is tf.get_default_graph()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -337,6 +317,29 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "reset_graph()\n",
+    "\n",
+    "x1 = tf.Variable(1)\n",
+    "x1.graph is tf.get_default_graph()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "graph = tf.Graph()\n",
     "with graph.as_default():\n",
@@ -347,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
@@ -358,7 +361,7 @@
        "False"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -394,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -429,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -470,7 +473,7 @@
        "       [-4.3721911e-01]], dtype=float32)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -488,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -524,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -579,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -612,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -662,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -679,7 +682,7 @@
        "       [-0.6375279 ]], dtype=float32)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -732,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -790,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -803,7 +806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -812,7 +815,7 @@
        "-0.21253923284754914"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -823,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -848,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -876,7 +879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -895,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -905,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -961,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -980,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -990,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1001,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1050,7 +1053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1075,7 +1078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1100,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1110,7 +1113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1122,7 +1125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1138,7 +1141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1147,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1157,7 +1160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1181,7 +1184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1198,7 +1201,7 @@
        "       [-0.8030471 ]], dtype=float32)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1216,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1269,7 +1272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1286,7 +1289,7 @@
        "       [-0.6375278 ]], dtype=float32)"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1297,7 +1300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1316,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1325,7 +1328,7 @@
        "True"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1343,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1359,7 +1362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1384,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1393,7 +1396,7 @@
        "True"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1426,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1435,7 +1438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1443,6 +1446,7 @@
       "text/html": [
        "\n",
        "        <iframe seamless style=\"width:1200px;height:620px;border:0\" srcdoc=\"\n",
+       "        <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/polymer/0.3.3/platform.js&quot;></script>\n",
        "        <script>\n",
        "          function load() {\n",
        "            document.getElementById(&quot;graph0.3745401188473625&quot;).pbtxt = 'node {\\n  name: &quot;save/RestoreV2/shape_and_slices&quot;\\n  op: &quot;Const&quot;\\n  device: &quot;/device:CPU:0&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: &quot;&quot;\\n      }\\n    }\\n  }\\n}\\nnode {\\n  name: &quot;save/RestoreV2/tensor_names&quot;\\n  op: &quot;Const&quot;\\n  device: &quot;/device:CPU:0&quot;\\n  attr {\\n    key: &quot;dtype&quot;\\n    value {\\n      type: DT_STRING\\n    }\\n  }\\n  attr {\\n    key: &quot;value&quot;\\n    value {\\n      tensor {\\n        dtype: DT_STRING\\n        tensor_shape {\\n          dim {\\n            size: 1\\n          }\\n        }\\n        string_val: 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@@ -1909,7 +1913,7 @@
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@@ -1928,7 +1932,7 @@
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@@ -1939,7 +1943,7 @@
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@@ -1952,7 +1956,7 @@
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@@ -1962,7 +1966,7 @@
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@@ -1973,7 +1977,7 @@
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@@ -1998,7 +2002,7 @@
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@@ -2008,7 +2012,7 @@
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@@ -2036,7 +2040,7 @@
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@@ -2061,7 +2065,7 @@
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@@ -2078,7 +2082,7 @@
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     {
@@ -2139,7 +2143,7 @@
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@@ -2232,7 +2236,7 @@
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@@ -2262,7 +2266,7 @@
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@@ -2278,7 +2282,7 @@
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@@ -2291,7 +2295,7 @@
        "       [ 1.        , -0.83343911,  0.53505665]])"
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@@ -2309,7 +2313,7 @@
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@@ -2325,7 +2329,7 @@
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@@ -2346,7 +2350,7 @@
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@@ -2366,7 +2370,7 @@
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@@ -2379,7 +2383,7 @@
        "       [ 1.        , -0.98941204,  0.02473116]])"
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@@ -2391,7 +2395,7 @@
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@@ -2404,7 +2408,7 @@
        "       [0]])"
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@@ -2429,7 +2433,7 @@
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@@ -2445,7 +2449,7 @@
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@@ -2476,7 +2480,7 @@
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@@ -2496,7 +2500,7 @@
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@@ -2516,7 +2520,7 @@
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@@ -2533,7 +2537,7 @@
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@@ -2549,7 +2553,7 @@
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@@ -2567,7 +2571,7 @@
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@@ -2590,7 +2594,7 @@
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@@ -2645,7 +2649,7 @@
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     {
@@ -2658,7 +2662,7 @@
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@@ -2676,7 +2680,7 @@
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     {
@@ -2689,7 +2693,7 @@
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@@ -2715,7 +2719,7 @@
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     {
@@ -2724,7 +2728,7 @@
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@@ -2737,7 +2741,7 @@
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     {
@@ -2746,7 +2750,7 @@
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@@ -2764,7 +2768,7 @@
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     {
@@ -2863,7 +2867,7 @@
        "         1.53179024e-01]])"
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@@ -2955,7 +2959,7 @@
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@@ -2981,7 +2985,7 @@
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     {
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@@ -3067,7 +3071,7 @@
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     {
@@ -3076,7 +3080,7 @@
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     {
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        "0.9797979797979798"
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@@ -3107,7 +3111,7 @@
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     {
@@ -3164,70 +3168,70 @@
      "output_type": "stream",
      "text": [
       "반복 0\n",
-      "  logdir: tf_logs/logreg-run-20180430082412/\n",
+      "  logdir: tf_logs/logreg-run-20180523013852/\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-20180430082554/\n",
+      "  logdir: tf_logs/logreg-run-20180523014034/\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-20180430082952/\n",
+      "  logdir: tf_logs/logreg-run-20180523014434/\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-20180430083111/\n",
+      "  logdir: tf_logs/logreg-run-20180523014552/\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-20180430083254/\n",
+      "  logdir: tf_logs/logreg-run-20180523014731/\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-20180430083430/\n",
+      "  logdir: tf_logs/logreg-run-20180523014904/\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-20180430083531/\n",
+      "  logdir: tf_logs/logreg-run-20180523015007/\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-20180430083649/\n",
+      "  logdir: tf_logs/logreg-run-20180523015125/\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-20180430083828/\n",
+      "  logdir: tf_logs/logreg-run-20180523015304/\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-20180430084000/\n",
+      "  logdir: tf_logs/logreg-run-20180523015437/\n",
       "  batch_size: 92\n",
       "  learning_rate: 0.00010742922968438615\n",
       "  훈련: .....................\n",
@@ -3315,7 +3319,7 @@
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