diff --git a/12_distributed_tensorflow.ipynb b/12_distributed_tensorflow.ipynb
index 073fc39..84ad74c 100644
--- a/12_distributed_tensorflow.ipynb
+++ b/12_distributed_tensorflow.ipynb
@@ -9,12 +9,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPython 3.5.5\n",
-      "IPython 6.3.0\n",
+      "CPython 3.6.5\n",
+      "IPython 6.4.0\n",
       "\n",
       "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
-      "scipy 1.0.1\n",
+      "scipy 1.1.0\n",
       "matplotlib 2.2.2\n",
       "tensorflow 1.8.0\n"
      ]
@@ -254,9 +254,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.0, 6, 44]\n"
+     ]
+    }
+   ],
    "source": [
     "reset_graph()\n",
     "\n",
@@ -271,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -279,8 +287,8 @@
      "output_type": "stream",
      "text": [
       "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([[ 4.,  5.],\n",
+      "       [ 1., -1.]], dtype=float32), array([1, 0], dtype=int32)]\n",
       "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
       "더 이상 훈련 샘플이 없습니다\n"
      ]
@@ -334,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -354,16 +362,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[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",
+      "[array([[ 7.,  8.],\n",
+      "       [ 1., -1.]], dtype=float32), array([0, 0], dtype=int32)]\n",
+      "[array([[4., 5.]], dtype=float32), array([1], dtype=int32)]\n",
       "더 이상 훈련 샘플이 없습니다\n"
      ]
     }
@@ -408,15 +416,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[array([[4.00000000e+00, 5.00000000e+00],\n",
-      "       [1.00000000e+00, 1.27106565e+36]], dtype=float32), array([1, 0], dtype=int32)]\n",
+      "[array([[ 4.,  5.],\n",
+      "       [ 1., -1.]], dtype=float32), array([1, 0], dtype=int32)]\n",
       "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
       "더 이상 훈련 샘플이 없습니다\n"
      ]
@@ -469,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -526,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -542,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -566,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,9 +591,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 6]\n",
+      "[7 8 9 0 1 2 3]\n",
+      "[4 5 6 7 8 9 0]\n",
+      "[1 2 3 4 5 6 7]\n",
+      "[8 9]\n",
+      "완료\n"
+     ]
+    }
+   ],
    "source": [
     "with tf.Session() as sess:\n",
     "    try:\n",
@@ -611,9 +632,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([0, 1, 2, 3, 4, 5, 6]), array([0, 1, 2, 3, 4, 5, 6])]\n",
+      "[array([7, 8, 9, 0, 1, 2, 3]), array([7, 8, 9, 0, 1, 2, 3])]\n",
+      "[array([4, 5, 6, 7, 8, 9, 0]), array([4, 5, 6, 7, 8, 9, 0])]\n",
+      "[array([1, 2, 3, 4, 5, 6, 7]), array([1, 2, 3, 4, 5, 6, 7])]\n",
+      "[array([8, 9]), array([8, 9])]\n",
+      "완료\n"
+     ]
+    }
+   ],
    "source": [
     "with tf.Session() as sess:\n",
     "    try:\n",
@@ -632,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -641,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -657,9 +691,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0,1,7,8,4,5,2,3,9,0,6,7,4,5,1,2,8,9,6,3,0,1,2,8,9,3,4,5,6,7,완료\n"
+     ]
+    }
+   ],
    "source": [
     "with tf.Session() as sess:\n",
     "    try:\n",
@@ -692,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -701,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -710,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -726,7 +768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -762,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -772,9 +814,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1. -1.] 0.0\n",
+      "[4. 5.] 1.0\n",
+      "[7. 8.] 0.0\n",
+      "완료\n"
+     ]
+    }
+   ],
    "source": [
     "with tf.Session() as sess:\n",
     "    try:\n",
@@ -818,7 +871,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.5"
+   "version": "3.6.5"
   },
   "nav_menu": {},
   "toc": {
diff --git a/15_autoencoders.ipynb b/15_autoencoders.ipynb
index c62c07b..d52f535 100644
--- a/15_autoencoders.ipynb
+++ b/15_autoencoders.ipynb
@@ -9,12 +9,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPython 3.5.5\n",
-      "IPython 6.3.0\n",
+      "CPython 3.6.5\n",
+      "IPython 6.4.0\n",
       "\n",
       "numpy 1.14.3\n",
       "sklearn 0.19.1\n",
-      "scipy 1.0.1\n",
+      "scipy 1.1.0\n",
       "matplotlib 2.2.2\n",
       "tensorflow 1.8.0\n"
      ]
@@ -242,7 +242,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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bqVPul13mPjkZvt99d/h3smbYLGw/dSp8HTwYvnd7nl63DXO/bm1rf0yv2w4eXG//5GT4ORZgxVNkQZQKxsMF7j/V7z5m9iBwFbAjRhtEBuns3sB6NdJ+ygjoXRlkWZyWplJInu/Mmd5dr17dmipeX6qUWSQzewS4BtjRCiORwnXukDt3bjwOCkI3Je2alFHOxdL+fFNTIeSge1h0C6sqntK06EHeCeAwcDlwm7tfHPAQkWh67ZBpFsJBupNBddvZewVT+/MB7NoFs7PZwqJqpzQtuoK5CdgDrADPW3IMPtzv7s8W3BaRVDvkMF2SftVNr2DqVlFVKSyGUfQs0g8AG3hHkYrJ2iXpV930CqYqdnFGpZW8IiPoVQH1q276BUnVujijUsCIRDCoGqlbkPSigBGJpCkh0o8uHStSI8vLYVp9ebnslgSqYERqoorXj1IFI1ITvc6MVyYFjEhNJDNXk5MNP1RARPJXxXU0ChiRGqnazJW6SCISjQJGRKJRwIhINObJuQErzMzOAq+W3Y4urgDeKLsRJdD7bpZu7/vD7n7loAeORcBUlZmtuPtc2e0omt53s4zyvtVFEpFoFDAiEo0CZjRHy25ASfS+m2Xo960xGBGJRhWMiESjgBGRaBQwIhKNAmZEZrbHzP7TzFbM7CUzu6fsNsViZjeY2enW+1wxs75X76yLJv2NuzGzPzGz/zWzA1kfq6OpR2Bmk8C1wHXu/raZXQW8bGYn3f03JTcvV2b2IeBp4GZ3XzazBeCkmV3t7r8rt3XxNOlv3E3r7/4PwLeHebwqmBG4+6q773P3t1ubzgEXgMkSmxXL54BfuvsygLsvAa8B28psVGwN+xtfonUl1hPA14CzwzyHKpgUzGwa+HmXm67vuLb2IeBJdz9TTMsK9RHglY5tr7S2N0md/8adHgR+4u6nzOyzwzyBAiaFVoj0HW8wsweBq4AdhTSqeAasdmy7SIOq4Ab8jd9nZjuAWXf/2ijPo4DJgZk9AlwD7OioaOrk18D2jm2zwHdLaEvhGvI3bncj8Mdm9kLr5z+AMODr7relfRKt5B1Bq496GLgc+Ct3v1hyk6Ixs98ndIk+7e7/ZWafBH4MXO3ub5bbunia9DfuJ5lBcvcDWR6nCmY0NwF7gBXgeTNLtt/v7s+W1qoI3P0tM7sNOG5mTuge3VTncGlpzN84BlUwIhJNYwboRKR4ChgRiUYBIyLRKGBEJBoFjIhEo4ARkWgUMCISjQJGRKJRwIhINAoYEYlGASMi0ShgJHdmtsnMLpiZ9/h6uuw2SjF0NLXEMA3c1WX7V4CPA88U2xwpi46mlkKY2cPA3wL73P3RstsjxVAFI1FZOIHKY8BeYK+7Hy65SVIgjcFINK2zwR0F7gG+1B4uZrbXzP7dzN41s6Wy2ihxqYKRKFrXE/oWcDtwh7t3XlfnNeDvgU8A88W2ToqigJHcmdkm4J+AvwA+7+4bZo2SbWY2W3DzpEAKGMmVmW0G/hn4c+BWd/9hyU2SEilgJG+LwC2E7tHlZnZHx+3fd/ffFt4qKYUCRnLTmjG6sfXjF1pf7daADxTYJCmZAkZy42FR1QfLbodUhwJGSmFmU4T/f1PAhJn9HrDWkKsmNoYCRspyP/B3bT//H/AcsFBKayQKHSogItFoJa+IRKOAEZFoFDAiEo0CRkSiUcCISDQKGBGJRgEjItH8P9PsaVAKIS2AAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARgAAADQCAYAAADcQn7hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEwlJREFUeJzt3V+MHeV5x/Hvs7u2y0XSoIUrqo0JQmpVtYpiJ8qKCra1mwgEvcCiQSo1CYptgoVKJDeSI1AtBZkKAbJQamFbMvGqaoJaaBySSFFIuqTIi6qFtOQqKqTCSgSScQURpNh49+nFeyZ7PD5/Zs6Zd2bOzO8jrdY75997dj2/87x/ZsbcHRGRGKaqboCINJcCRkSiUcCISDQKGBGJRgEjItEoYEQkGgWMiESjgBGRaBQwIhLNTNUNyOKKK67wzZs3V90MEel46aWX3nL3K4fdbyICZvPmzaysrFTdDBHpMLPXs9xPXSQRiUYBIyLRKGBEJBoFzIRaXoaHHgrfRepqIgZ55WLLy7BtG5w/Dxs3wo9+BPPzVbdK5FKqYGpqUIWytBTCZXU1fF9aKrt1ItmoginB8nIIgYWFSyuNXrcNq1AWFsL25PaFhTLehUh+Cpgug4JgnOfsFxbdt83MwBe+ADt39q5QutszPx+ep+i2ihRNAdMRa1xjUFh037a6CkeOwIkTcOjQ8Aplfl7BIvWnMZiOWOMaSXdmevrSsEhuMws/u4fXPns2BNzXvlbsAK5mnqRsqmA6Yo1rDOrOJLctLsLx4yHcktcuukLRzJNUQQHTEXNcY1BYJLclYy+xxlSGjeuIxKCA6TJK1VDUwHDsMRXNPEkVWhkwRYXCJHU7NPMkVagkYMxsD3AP8AGwEXjC3Q+X8dpFhsKkdTs08yRlK30WycymgWuB69x9K3Aj8KiZXVXG6xc5WzRohmiYKmZ0sr6mZpukKKVXMO6+Cuzr2nQWOA9Ml/H6RY5FjNrt6LfALmZ1kbVyG7YwUF0syaMO62AOAU+5++nujWa228xWzGzlzJkzhb1YEgpFrTGZn4f9+8O/s37qd1dR586FBXbbtsWtGLJWbv3ulwTPAw+EgPnSl1ThSAbuXtkX8CDwLLBx0P22bNnidXbqlPtll7lPT4fvp05lu7+Ze1heFx578GD1bex3v4MHw7akvWbZ3mv6uQ8ezPcYqSdgxTPs45XNIpnZI8A1wA53P19FG9Il/6hdgLyDvYMW2MWStTvX735J1/L995OIyTewPUkzblKgLClU5BehW/YE8BQwk+UxMSqY9Cf1kSP5qpBBz9XUT/VTp9zvvtt906b877W7AopdrUl81LiCuQnYA6wAL1hyIA7c7+7PldWIdNXx9NOjTzmPs8akrKnjIgZox1l1rIV+7VTFLNJ3ARt6x8jS/+E//nH48Y9hamq0HaDOa0zyzCBlCY5R3+udd4bvsWfMpD5auZIXLq46ZmfhvvtgbS2sabn33vXZkybsCFnGiGKOkSwvh9D64INw5Pibb8JXvtKM360MVodp6sokU8xnz4Yda20t7ISPPRamY2NPHZcly4LAmKfhXFwMz+kefsff/jZcf70W/LVBayuYbgsLYcHb2lr4hF1bC1+TsPw/q2Hdk7LHSC5cCMHT73erWadmaFQFM84nXpjgCmMwMzOjLf+vo2RHPXYsnC2vn6IXIHbbuRM2bMj3GJ3YvBkaU8GM84m3tBT+Iycl/K5dMDfXjCXxedboxBqonp+H55+Hhx+GkyfD73nDhhA8/WjWqRkaEzDjHNmc/s/cpFmOuuyo8/NhYPf7318f7B12f51eYvI1JmDG2ZH6/WduwsF9ddpRuyvF1dVsK54n9fcuQWMCJuuO1C800v+ZmzTIWJcdtS7VlJSnMQED/XekJFSS9S5ZFpwdOBCOdM46m9SEaie2OlVTUo5GBUwv3ZXI1FQozQeFRnL/JFyyrOxtUrUTW12qKSlHo6ape0lf3GxqKtuCsyRctm8fHhiaUhXprfEVTLrff+hQWLnbr0RP3//AgeGfuBpbEOmt8QGTt98/yjiBxhZEejNPlrDW2NatW31lZaXqZgAazBUBMLOXPJy0f6DGVzBF0mCuSD6NH+QtkgZzRfJRwOQwznWQRNqo1V2kvOMpGswVyae1ATPqeIoWiolkV0kXycw2mNk+M/vAzG6vog1FjKfojGsig1VVwewCHHixotcfe3GcZpREhqskYNz9MICZ3VLF68P44ynjnH9GpC1qOwZjZruB3QBzc3NRXmOc8RQdHiAyXG0Dxt2PAkchrOStuDmXyFIBDZql0opgaYPaBswkGFQBDRqj0fiNtIUW2uWUdeZo0CyVVgRLW6iCySFP5TFojEbjN9IWlQaMuy9U+fp55Z056nexM60IlrZQBZND1sojXen0uv6PVgRLGyhgcshaeWiNjEiggMkpS+WhMRaRQAETQXelMzu7PkukKkbaRgETSRImWu8ibaZ1MBFpvYu0nQImIp0BT9ouUxfJzDYA7wEb+tzlX9391sJa1RBa7yJtl3UMZiNwV4/tXwY+ATxbWIsaRutdpM0yBYy7vwf8Y/c2M3uYEC773P3JCG0TkQmXexbJzAx4HNgL7E1OHiUikpZrkNfMpgjnaLkH+GLXmek2mdkxM/uFmb1rZv9tZvdFaK+ITJDMFYyZTQPfAG4H7nD3b6ae503gM8AvgD8GfmBmb7j7U8U1V0QmSaYKpjOL9C3gL4HPpcIFd3/P3R9w91fdfc3d/xP4HnBd4S0WkYkxNGDMbBPwNHAzcKu7P5PhMTPAnwCvjN1CEZlYWbpIi8AthO7R5WZ2R+r277j7r1PbHgfe6TxWRFpqYMB0Zoxu7Pz4+c5XtzXgQ6nHPEqoXv7M3c8X0koRmUgDA8bdHfhw1iczs0PANkK4vDVm20RkwhV2LJKZPQ5sJ4TLmSH3vcHMXjazV8xsxcw+XVQ7RKQ+Cjldg5l9FLgXOAf8T+hZAfDv7n5j6r4fAZ4Bbnb3ZTNbAE6a2dXu/psi2iMi9VBIwLj764ANvWPwWeDn7r7ceeySmb1B6FrpmCaRBqnidA0fA15LbXuts/23zGx3p/u0cubMwB6XiNRUFQFjwGpq24V0W9z9qLtvdfetV155ZWmNE5HiVBEwvwTSV7Of62yXjqxXkBSpsyrOyXsSeMzM/sjdf2ZmnwJ+H/hhBW2ppVGvXb28rJNbSb2UHjDu/o6Z3QYcNzMndI9ucve3y25LXY1yXaVRQ0kkpkquKuDu/wZ8sorXngSjXFdJF3uTOtJlS2polHP56mJvUkcKmJrqPpdvlrEVnWBc6kgBU3N5xlaS7bqSpNSFAqbm8oytaKBX6kYXXqu5PBdvy3MlSa2zkTKogqm5PGMrWQd6VelIWRQwEyDrxduyhpGmtKUsCpiGyRJGmtKWsihgWkhT2lIWBUxL6ZrZUgbNIolINAqYltH0tJRJXaQW0fS0lE0VTIvkWYgnUgQFTIOlu0N5VgWLFEFdpIbq1x3S9LSUSQHTUOnu0OLierDs319x46Q1FDAN1b1ad2YGjh8PYaPBXSmTAqahurtDp0/DsWPZT/mQVDqg7pSMp/SAMbMNwN8ADwF/7e7fKrsNbZGs1l1ehhMneh97lA6UZNxmehrM4MIFVT0yuioqmF2AAy9W8Nqt1G9wNz0QfOed6+M2a2vhPu464lpGV8VlSw4DmNktZb92m/U69ig9EAzr4zbpCkZT2jKKKAFjZhuBn/S46Xp3P5/xOXYDuwHm5tIXgpQipE/bsHNn+NIYjBTF3L2aFzZbAp7IMgazdetWX1lZid+oFtLVIGUUZvaSu28ddj/NIrVc3tM2xAokBV0zKWAks1gHS+ogzObSsUiSWayDJXUQZnNVVsG4+0JVry2jiXUuX50juLnURZLMYh0sqYMwm0sBI7WgcwQ3kwJGMtNgrOSlQV7JTIOxkpcCRjLTGfEkL3WRJDMNxkpeChjJRYOx5ZvkVc4KGKmNQTvSJO9k48gysF7n340CRmqhe0eanoa77gpHdicnzGrr7FWvgfXu9z7O76aMYFLASC1070irq3DkSDgLXzLm0+8E5k0PmmGrnIcFUD9lhbYCRmoh2ZHefz+cRa/7THoLC6GqWVuDqal2ncC818B6d+Ux6mEWowZTXgoYqYVkR1pchCefvPRMembh+9paCJ+1tYvX4pRd0ZQ57tE9sN6r8kh+b3mUdfyXAkZqI9mRus+qNz8frk554cJ6ZTM1FQJn40aYnS1/fKbKMaFelcfCwvpJ3ZNu5bD2lLXkQAEjtZOeCk9/2h46BGfPhu1llfrdYrxm1oqoV+UxanvKWHKggJHaG/ZpW/apHvJ2L4aFR7+KqNfj+v0uZmZCt3FmZvTfQYxunwJGJkK/T9vYpX6enbzf44d1p/od45WnG7a6GrqPq6ujv88Y3T4FjEy8Ikr9JEhmZ9e7X9B/p8v6mlm6L3m6Pb2CYHExjFFB+L64mP88ywcOwLlzFw+eK2BECpDstMkONjUFmzZdfCG6UXe6dHjMzoZB634V0ezs+vde3bBea4JefrnY915kV1MBI62X7LTJ1SyTT3HINtYyaOyiOzzefhv27g3Pv2nTpRURXFyddA9mJ7d3B9bMTFgTlFQvEJ53587R3vvUFGzfHqqZiR2DMbM9wD3AB8BGwrWRDpfdDpFEstN2f4rPdPaMXjt5tyxjF8nPN9ywHgbnzl1aEaWrk7NnYf/+S58r6Ra9/DKsrIwXDukKq8hwgZIDxsymgWuB69z9XTO7CnjVzE66+6/KbItIIl1lLC3BT38Kx44NH/DMOkW8tHTxAOzU1KUVUZ7ZqRMnLu3SHTgQbkt3wbK+9xiD5KUGjLuvAvu6Np0FzgPT6fvq0rFShvSy+23b1g9XgOFjL1lDYWEhhMC5c+Gwh69/fXB3KnmeXmHRr1sDo80ERV0P4+6FfxG6Pi/2+NqYut8TwNFhz7dlyxYXKdqpU+6XXeY+PR2+3313+HeyZtgsbD91KnwdPBi+93qefreNcr9ebet+TL/bDh5cb//0dPg5FmDFM2RBlArGwwXuPz3oPmb2IHAVsCNGG0SGSXdvYL0a6T5lBPSvDPIsTstSKSTPd/p0/65Xv25NHa8vVckskpk9AlwD7OiEkUjp0jvkzp2XHgcFoZuSdU3KOOdi6X6+mZkQctA7LHqFVR1PaVr2IO8UcBi4HLjN3S8MeYhINP12yCwL4SDbyaB67ez9gqn7+QB27YK5uXxhUbdTmpZdwdwE7AFWgBcsOQYf7nf350pui0imHXKULsmg6qZfMPWqqOoUFqMoexbpu4ANvaNIzeTtkgyqbvoFUx27OOPSSl6RMfSrgAZVN4OCpG5dnHEpYEQiGFaNNC1I+lHAiETSlhAZRJeOFWmQ5eUwrb68XHVLAlUwIg1Rx+tHqYIRaYh+Z8arkgJGpCGSmavp6ZYfKiAixavjOhoFjEiD1G3mSl0kEYlGASMi0ShgRCQa8+TcgDVmZmeA16tuRw9XAG9V3YgK6H23S6/3/VF3v3LYAyciYOrKzFbcfWvV7Sib3ne7jPO+1UUSkWgUMCISjQJmPEerbkBF9L7bZeT3rTEYEYlGFYyIRKOAEZFoFDAiEo0CZkxmtsfM/svMVszsFTO7p+o2xWJmN5jZy533uWJmA6/e2RRt+hv3YmZ/aGb/a2YH8j5WR1OPwcymgWuB69z9XTO7CnjVzE66+68qbl6hzOwjwDPAze6+bGYLwEkzu9rdf1Nt6+Jp09+4l87f/R+Ab47yeFUwY3D3VXff5+7vdjadBc4D0xU2K5bPAj9392UAd18C3gC2Vdmo2Fr2N75I50qsJ4CvAmdGeQ5VMBmY2UbgJz1uuj51be1DwFPufrqclpXqY8BrqW2vdba3SZP/xmkPAj9091Nm9plRnkABk0EnRAaON5jZg8BVwI5SGlU+A1ZT2y7Qoiq4BX/j3zKzHcCcu391nOdRwBTAzB4BrgF2pCqaJvklsD21bQ74lwraUrqW/I273Qj8gZm92Pn59yAM+Lr7bVmfRCt5x9Dpox4GLgf+yt0vVNykaMzsdwldoj9195+Z2aeAHwBXu/vb1bYunjb9jQdJZpDc/UCex6mCGc9NwB5gBXjBzJLt97v7c5W1KgJ3f8fMbgOOm5kTukc3NTlcOlrzN45BFYyIRNOaAToRKZ8CRkSiUcCISDQKGBGJRgEjItEoYEQkGgWMiESjgBGRaBQwIhKNAkZEolHAiEg0ChgpnJltMLPzZuZ9vp6puo1SDh1NLTFsBO7qsf3LwCeAZ8ttjlRFR1NLKczsYeBvgX3u/mjV7ZFyqIKRqCycQOVxYC+w190PV9wkKZHGYCSaztngjgL3AF/sDhcz22tm/2Fm75vZUlVtlLhUwUgUnesJfQO4HbjD3dPX1XkD+Hvgk8B8ua2TsihgpHBmtgH4J+AvgM+5+yWzRsk2M5sruXlSIgWMFMrMNgH/DPw5cKu7f6/iJkmFFDBStEXgFkL36HIzuyN1+3fc/delt0oqoYCRwnRmjG7s/Pj5zle3NeBDJTZJKqaAkcJ4WFT14arbIfWhgJFKmNkM4f/fDDBlZr8DrLXkqomtoYCRqtwP/F3Xz/8HPA8sVNIaiUKHCohINFrJKyLRKGBEJBoFjIhEo4ARkWgUMCISjQJGRKJRwIhINP8Pf0dx1rLRS0sAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 288x216 with 1 Axes>"
       ]
@@ -283,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,20 +312,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "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",
-      "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# from tensorflow.examples.tutorials.mnist import input_data\n",
     "# mnist = input_data.read_data_sets(\"/tmp/data/\")"
@@ -347,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -401,18 +390,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0 훈련 MSE: 0.024246655\n",
+      "1 훈련 MSE: 0.015581552\n",
+      "2 훈련 MSE: 0.014903595\n",
+      "3 훈련 MSE: 0.014060436\n",
+      "4 훈련 MSE: 0.013958205\n"
      ]
     }
    ],
@@ -427,7 +416,7 @@
     "        for iteration in range(n_batches):\n",
     "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # 책에는 없음\n",
     "            sys.stdout.flush()                                          # 책에는 없음\n",
-    "            X_batch, y_batch = shuffle_batch(X_train, y_train, batch_size):\n",
+    "            X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_size))\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})   # 책에는 없음\n",
     "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)             # 책에는 없음\n",
@@ -443,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "scrolled": false
    },
@@ -478,7 +467,7 @@
     },
     {
      "data": {
-      "image/png": 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gmTAGgGTCGACSCWMASDY/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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -508,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -566,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -575,18 +564,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 MSE: 0.01506695\n",
-      "1 훈련 MSE: 0.016488748\n",
-      "2 훈련 MSE: 0.017375939\n",
-      "3 훈련 MSE: 0.016878333\n",
-      "4 훈련 MSE: 0.015587721\n"
+      "0 훈련 MSE: 0.021188682\n",
+      "1 훈련 MSE: 0.018240245\n",
+      "2 훈련 MSE: 0.020062765\n",
+      "3 훈련 MSE: 0.020047668\n",
+      "4 훈련 MSE: 0.019466272\n"
      ]
     }
    ],
@@ -601,7 +590,7 @@
     "        for iteration in range(n_batches):\n",
     "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
     "            sys.stdout.flush()\n",
-    "            X_batch, y_batch = shuffle_batch(X_train, y_train, batch_size):s\n",
+    "            X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_size))\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
     "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
@@ -610,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -622,7 +611,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -658,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,21 +711,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0 훈련 MSE: 0.01838611\n",
+      "1 훈련 MSE: 0.017672632\n",
+      "2 훈련 MSE: 0.019575823\n",
+      "3 훈련 MSE: 0.019316638\n",
+      "0 훈련 MSE: 0.0046300227\n",
+      "1 훈련 MSE: 0.004749075\n",
+      "2 훈련 MSE: 0.004754316\n",
+      "3 훈련 MSE: 0.0044352757\n"
      ]
     }
    ],
@@ -755,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -772,12 +761,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -806,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -852,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -875,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -893,16 +882,16 @@
      "output_type": "stream",
      "text": [
       "훈련 단계 #1\n",
-      "0 훈련 MSE: 0.007406866\n",
-      "1 훈련 MSE: 0.0078288205\n",
-      "2 훈련 MSE: 0.007728097\n",
-      "3 훈련 MSE: 0.0074090064\n",
+      "0 훈련 MSE: 0.008080141\n",
+      "1 훈련 MSE: 0.007390184\n",
+      "2 훈련 MSE: 0.007787427\n",
+      "3 훈련 MSE: 0.0077560465\n",
       "훈련 단계 #2\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",
-      "테스트 MSE: 0.009815396\n"
+      "0 훈련 MSE: 0.1520265\n",
+      "1 훈련 MSE: 0.021897664\n",
+      "2 훈련 MSE: 0.005637195\n",
+      "3 훈련 MSE: 0.003436035\n",
+      "테스트 MSE: 0.010430704\n"
      ]
     }
    ],
@@ -921,7 +910,7 @@
     "            for iteration in range(n_batches):\n",
     "                print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
     "                sys.stdout.flush()\n",
-    "                X_batch, y_batch = shuffle_batch(X_train, y_train, batch_sizes[phase]):\n",
+    "                X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_sizes[phase]))\n",
     "                sess.run(training_ops[phase], feed_dict={X: X_batch})\n",
     "            loss_train = reconstruction_losses[phase].eval(feed_dict={X: X_batch})\n",
     "            print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
@@ -939,7 +928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {
     "scrolled": true
    },
@@ -949,16 +938,16 @@
      "output_type": "stream",
      "text": [
       "훈련 단계 #1\n",
-      "0 훈련 MSE: 0.0075382586\n",
-      "1 훈련 MSE: 0.007754701\n",
-      "2 훈련 MSE: 0.007343679\n",
-      "3 훈련 MSE: 0.007837775\n",
+      "0 훈련 MSE: 0.008428962\n",
+      "1 훈련 MSE: 0.007754741\n",
+      "2 훈련 MSE: 0.007211907\n",
+      "3 훈련 MSE: 0.007919097\n",
       "훈련 단계 #2\n",
-      "0 훈련 MSE: 0.18597357\n",
-      "1 훈련 MSE: 0.0044647465\n",
-      "2 훈련 MSE: 0.002462252\n",
-      "3 훈련 MSE: 0.0020415403\n",
-      "테스트 MSE: 0.009790834\n"
+      "0 훈련 MSE: 0.39475387\n",
+      "1 훈련 MSE: 0.013140139\n",
+      "2 훈련 MSE: 0.0064841826\n",
+      "3 훈련 MSE: 0.0036247855\n",
+      "테스트 MSE: 0.010436372\n"
      ]
     }
    ],
@@ -985,7 +974,7 @@
     "                    feed_dict = {hidden1: hidden1_batch}\n",
     "                    sess.run(training_ops[phase], feed_dict=feed_dict)\n",
     "                else:\n",
-    "                    X_batch, y_batch = shuffle_batch(X_train, y_train, batch_sizes[phase]):\n",
+    "                    X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_sizes[phase]))\n",
     "                    feed_dict = {X: X_batch}\n",
     "                    sess.run(training_ops[phase], feed_dict=feed_dict)\n",
     "            loss_train = reconstruction_losses[phase].eval(feed_dict=feed_dict)\n",
@@ -1004,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1016,7 +1005,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 4 Axes>"
       ]
@@ -1053,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1065,7 +1054,7 @@
     },
     {
      "data": {
-      "image/png": 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8z8JSfKTVapnaIX0eu46Ojto8M6onXBwN+w93jJU6Kg37kWYezmWj4sLjpY4yiSvHpxHhLuHxXm2VSsXaJjUHiZIMDw93RU+kJJms0Wj09AG01UqlYgk9pJTz2YULFyzxAhujjj/MjhCuoBwOh8ORStxTBcXozygcFhUlFg+r4th2u23Zd3Nzc5LUVaSQWCfs4e9//7ukjhL46le/KilRbLCIGzduGBuFOaGkwphp2veDymQyxixhk2SDra6u2r2hPJ9++mlJHYYD43/iiSe6zvnmm2+aLbE7806NRsOeE6weFtVoNIzxkxkYFucldh4vSE0TwnmR7fYZi/cvYv7z2LFjlk1FPJ8Cu4VCwVRAPL81MjJic1b8Dq/ZbNZ+B8YP663VasZQ075/ESoFu2KLQqFgqpE2TPt++umnTVXFyxOmpqasb2COhf6gWq0amw+LAEhJin94TeE+Z7B/bJ5GhOXiAPa5ffu2Lba/evWqpOQ+x8bGzC5k4aGIdu7caenpVDhnycOtW7dsno9nhdo6evSo9TXxvlzT09N2XURq3itSkT+NgzFwLC8v203yAMJJfBo7DsmDGBoasnPgpH/6058kSd/5zncspMfxdJyVSsUmpWng4XbwcaJGmhGnxIfhJAb7xx9/XFJSETocJLAfnV+5XLYQVFgpWeo0XhowHQhkY2lpqSuFWEps2mq1rANPcwJKGOKLkzmy2azZk0GEAfzEiRN2HIMWa0qGhobMx/DRsGZhvF6E55DJZCzkRVuAAKytrdn1pdmeUtKGwvVyUsd/eI/2ht/Mz89biC7emLFer5sdsRW+ePHiRUtcYY3Uo48+aufEj+OKNoODg9b/pHkd1HbgupeWlqyfo92GKegMMNiFtrm5udmTqIOfk2IuJcQXkr937147Zzwtk8lk7Lp8w0KHw+Fw3BdIhYJidA23D2ZkDkd2qcNo/vnPf0pK9jWCPX7lK18xxkNKNaGnqakpY2gcQ7WES5cuWdokbDhUUFxfmlOiAewF5s7/zWbTQmzcG+GOdrvdU/0dFjUwMGBhJ5gUiuvmzZtmG5gYoZszZ87YgkuOCRerxpvSpRVxGC5cJIs9AP6xvLxsduSVxJwwQYTFkjDdiYmJnsoVqIJqtWpJPigpwrjFYrFv9i/CP+I9mCYnJ639w86539dee818kONprzdu3DBf/+IXvygpCVO/++67to8Udg3rQsY7H3DOXC5n/U3aoyZx+0GRhqo63l+r3W5bFIVUcD67cuWKtVv88+tf/7qkztQA/enPf/5zSUkYtt1uW2Qgrg8YXqNXM3c4HA7HfYF7qqAYtePaTaVSySbiSYkmdvzggw/2VNCGZR48eNBUEjFrWNHCwoKxp5gVhVWBURewsnq9nurFjzHiNF7Y09jYmM0XEdsndp/JZHpsgt0KhYJNmoYL76QO82cekErxlKI5ffq0LXqGWfF8w/2L+J20Ah/l2mGomUymZ+Ie1f7DH/7QvkdaLgoqrO2Ij37qU5+y30PVx8kAV69etQQUnh82D2udpR0oKPyT9pbL5bp2fA1fb968aVEPJvO59yeffNLaKrZCNY2Pj+tLX/qSpGRSH7u2Wq2ePbS4tjRXMI8R1ywNk0FIv2eukqUg4f53tL/QLtgKZR+m36OqSKBACY+Pj1s7JwoVljoCvqOuw+FwOO4LpCLNnFEVFjA2NmbzJTDQsEovbBYlFVYnh/2wYJf5klOnTllK5Re+8IWuc4e7a8bzCvV6vYelpBnMQYTZUVKHeXK/MBpY1NbWVlf2j5Sw9LGxMWP6MCleJycnTSXFhWGXlpZsIStzTzzDSqViSjftwEfxNRjnnTt3bAEkjDHcPZf3UJ9hOR1KHZFyi4/euXPH5l/4Prh8+bKxV5gp87RhtmG/qP1Y8VUqFbM1ipIs08XFRTueTElUU6PRMLv84Ac/kCQrdvq1r33NjiejFPuG86DMt4bPo1/sGBfaDueBiD6hRPHPa9eu2fw9qpzoyOzsrNmdNk3xXSlRp/QJ+PXGxob1ofgn/ebGxoZdF8r5vSJV222QBrmysmLyEufDwS5evGiSnBAfxwwPD5vjxmnP58+fNxmLkXhga2trNiBiSH5vc3Ozb5xVSmwab7vx0EMP2QCFbcLV8txvvE37jh07rHOgYyVUWCgU7Huk8zOIl8tl+z3CfiHJYHBMO+KKEPjlwsKC3Tv3x0T+s88+a1U6IDwMVK1Wq2dTSMKkV69etffoJOhgS6WSdTj4dJgk0W/blxB65v9yuWx/0xax2erqqvke4LksLi4aSfrxj38sKRlovvGNb1jfgL/hkzMzMz1kjv/Tnqof1l2kL6NN4huDg4NmI7YlCuuSkn7PYEI4UEr6Ps7JQH7t2jU756c//WlJSZ9dLpe7+gwp6UO2trasz36/oWgP8TkcDocjlbhnCipcBQ1jgWEvLS3Z6mU2emPkHhgY2HbvIanDEFAHsFTCS0eOHLH3SBVmhbWUMCvkPqopm832RWhP6rAnmEos+ffv39+zSA77l0ol+xv2Q/hodnbWmDpSP9yX6NVXX5WUVOwI66mRek4qKmGrzc3Nvqi+ncvlzI7YALZ9+fJlU+Awb47ZsWOHTUjHSyhWV1ctfZowKra4du2a+SYhFzA1NWV+Hu8jVavV7LfTjGw2a3bArryWy2VTj/QHx44dk9SJdBBOhd3D6ufm5qw/+OY3vykpUQqHDx+2UBbh5uPHj0vqPBfCtTyHsI9Js19ybY1Gw8Lt8ULYarVqqjHegn11ddX6ubh6x5UrVywBgmdD6C6svckzIooV7uHHNYU1VuPpnPcKV1AOh8PhSCXumYJqNps9C19hBq1WSy+99JKkhN2wCO/555+3FFOSJWADO3futBEeNoSCyuVyxmpRZ2B6etpGe9hGmGoaVwxOK7a2tnp2CYVZl0olY1nE88P9tMLt36VESR08eNDmC2C4JJtcvHjRtozGbqilmZkZU7hcQ1gZPM0MFbRara70W0ldpZ2Y72TuCXV15syZnuQKbH7r1i1j83yPhJ6rV6+aukLBwmz37dtn6gFmHCqweC+lNKLdbps943m04eFhi2hwf2HSBH4Ju2ee6uTJk9bWqS3J9xYWFqwsGuode25ubtqzAZwnm83+1yr2aUIul/uvCVy1Ws3m5mi34ZIFqryfPHlSUmKXhYWFrn3GOJfU6W/D2p5Skqyyd+9es3G8Z9fKysoHTuJxBeVwOByOVOKeKahsNmsjdMz+pqenbe4EtcMoHqYnU5KDBaezs7PGYsM9oqQOg2WeBNYBcw13fyW2ClsdHR1N9Z4wIVqtlikhrhkV02q17N6Y5yAGPzQ0ZBlT2AaGumfPnq65DikpQbOysmKslbRTstdyuVzXbqnhuUOknfGjOrEB93fixAlj9WTxoa7q9bopII4hm7JWq9ncHMU3Ofb11183/43Tcre2tuz5hUyf1zTbMUQcNeH/hx9+2DLu8C/mjOfn581GZJtRzmhqasqUJKyeY7e2tsyPYfdEW9bW1kwpxBXgG41G3yx85jrj3QEymYz1i2TsEUGanJzUCy+8IClRsKdPn5bUWY7DOYki4bvFYtHUP207nHMO57Sl5Hl8mMhTKipJhOmIUiecQeVxQkZ0oBsbGxYKICc/XOnMQ6AzDTsIJlMJEYZprDg5DyVOL5bSH+KTejdYC0MA4aaPUveW4bzHM8B+4ZYFbP2OTZ577jkb5BnQqYRw+/Ztc3SICB1HtVrddpPKNAMb4GtTU1M2eUy4hMYarqrHZ+gs5ubmzCexCw253W7bOcM0aqnToYbV/sNjisVi6lOjpe62RJvnuvfv32/bjzOocL/nz5+3TpLj8btz585ZmBl/Y53jI488YqSUMBd44IEHzP48j7CqeT8k8YQbjYY1BvmfQZl2Th83OjpqfScC4JVXXpHUSRZjOUO4PQefQdB4fuF2SIT7QhLAdQJPknA4HA7HfYF7qqAYTeMFu8PDwzp69KikRO2Ead+wURgCixqLxaJNosZ1tWZmZkyWwjZiJREeHyqPOPEgrQgX7sWLkkdGRnoW8cLANzc3TbrHdfra7badg0l99js6fPiwKS1WpIeqDBtyDJWQ5+fn+2ZhKT4JcyQEl8/njSlSXZ+EnEOHDlkoChtg69HR0Z6qHfj27t27zS5xCnqr1TLbxunm/aCeAG2Ie+aems2m1d/EdoSgl5aWTGWirlA/i4uLpiKo0M2SkXK5bNGWWHVms1k7jnPF/ZCUfv+kbcaVY0ZHRy1C9Mwzz0hKUuwnJiasT/vb3/7WdZ7HH3/c+lNsTj87MTFh9oirb5RKJVt2gRKNl/xIniThcDgcjvsE91RBxfMeKJutrS2L4fPKMeVy2eLyMCBipbdu3TK2zoQ13x8YGDC2Bttg9A8ZU7wldb8kSID4+rFHqVSyz2CR2KZWq5ktYPww3Y2NDbMzFeVhnJVKxWL7ceVj5vs4h5Q8r0ajkerY/naAFTJfUa1WzbYwRCaa9+zZY3F8FkKGi0CxLbYOz0kbiOdEw6r6cU1EqX/8NK5mjl2r1WpP2Z5QZTPnREIKyT/lcrkrkURKFpSfOnXK5kHivZGGhoYsksD3aA9hWnTasV1NQ6kTMUHR41Moqt27d/fMqeKnR44cMZvFNSMLhYJ9xnv48tramvkzr6isD7OsxBWUw+FwOFKJVCgoRmEY6cbGhjFHGBdZYu12u4uNSkks/+bNm/a9OE23VqvZ+WP2VigUen4H1pH2GHQM7olXMucymUxPiRzsNzg4aHMsMKlwDo4yM7Au2NDg4KD9TZwa5cSCPimZ84IZ90M2JIjTorFLqVSy+4GZgsHBQWPl2CFMZcb+8eJoKWGd8V5pjUajK8IQfi/cb6dfEO9b1m63exaG4p87d+40v2Reg+9vbGzYcfG83cDAgM1lc06+n8/ne9TVdllnaQfXip+EERB8CbWJfavVqql+fIj232g07HsAe66urtq54r20ms2m9ePx4vYPY89UJfvjKOvr6zZQcJNhWXk6wzAMBeLqBeGkNkYNK1ZIHceMO3YSKvodoZPEYUtk+naDPvYO1wLF4YRMJmP2hiTwLMLK8rHD9kslie0QJsrgr3FduUwmYzaLtyhvNBp2HLYO7RtvR0DHunv3bjtHTOjSjrhe3HafhfUZaXvcZz6ft/6A5B3stLa2Zt/Dn0Emk7GEizCFnP/j+nD9RkZDhElkUsfW3A/tL9ycEN/B/kybNJtNa6dxey+VSvYcGNj4vVwu11On725UjvEQn8PhcDhSiVQpqDD0FqdLb1cbL17sVywWLQ01ZkONRsOOgyHA6JC+9yNC5hKn+KJEl5eXe5IqtqtFBmsKa+vBaPkMG4+NjfUwqe2uqd8QstI4nBz6VRxOxccKhUKPkkAx1Ov1nqrU4WLeftqKPMR2ygmEi/WJdsTtW0pCn6iAMDKAiud3CPWF9RI5dxgCe7+b5/UDwmQZfBW7oDDDTVjjyjPLy8sW6oyjKZubmz3HE76v1+sfSXt3BeVwOByOVCLTz3FXh8PhcNy/cAXlcDgcjlTCByiHw+FwpBI+QDkcDocjlfAByuFwOByphA9QDofD4UglfIByOBwORyrhA5TD4XA4UgkfoBwOh8ORSvgA5XA4HI5Uwgcoh8PhcKQSPkA5HA6HI5XwAcrhcDgcqYQPUA6Hw+FIJXyAcjgcDkcq4QOUw+FwOFIJH6AcDofDkUr4AOVwOByOVMIHKIfD4XCkEj5AORwOhyOV8AHK4XA4HKmED1AOh8PhSCV8gHI4HA5HKuEDlMPhcDhSCR+gHA6Hw5FK+ADlcDgcjlTi/wCbu7sBiRIjIwAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 5 Axes>"
       ]
@@ -1103,7 +1092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1163,17 +1152,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 훈련 정확도: 0.97333336 테스트 정확도: 0.9334\n",
-      "1 훈련 정확도: 0.98 테스트 정확도: 0.936\n",
-      "2 훈련 정확도: 0.97333336 테스트 정확도: 0.9382\n",
-      "3 훈련 정확도: 0.9866667 테스트 정확도: 0.9489\n"
+      "0 검증 세트 정확도: 0.93333334 테스트 정확도: 0.9143\n",
+      "1 검증 세트 정확도: 0.97333336 테스트 정확도: 0.9413\n",
+      "2 검증 세트 정확도: 0.96 테스트 정확도: 0.9265\n",
+      "3 검증 세트 정확도: 0.97333336 테스트 정확도: 0.9392\n"
      ]
     }
    ],
@@ -1208,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1216,10 +1205,10 @@
      "output_type": "stream",
      "text": [
       "INFO:tensorflow:Restoring parameters from ./my_model_cache_frozen.ckpt\n",
-      "0 훈련 정확도: 0.9533333\t테스트 정확도: 0.9294\n",
-      "1 훈련 정확도: 0.96666664\t테스트 정확도: 0.9342\n",
-      "2 훈련 정확도: 0.98\t테스트 정확도: 0.9449\n",
-      "3 훈련 정확도: 0.9866667\t테스트 정확도: 0.9462\n"
+      "0 훈련 정확도: 0.96\t테스트 정확도: 0.9113\n",
+      "1 훈련 정확도: 0.9266667\t테스트 정확도: 0.9386\n",
+      "2 훈련 정확도: 0.9533333\t테스트 정확도: 0.9356\n",
+      "3 훈련 정확도: 0.96666664\t테스트 정확도: 0.9433\n"
      ]
     }
    ],
@@ -1264,7 +1253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1281,7 +1270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1303,7 +1292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1316,23 +1305,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0 훈련 MSE: 0.0442446\n",
+      "1 훈련 MSE: 0.03856391\n",
+      "2 훈련 MSE: 0.043473296\n",
+      "3 훈련 MSE: 0.041986465\n",
+      "4 훈련 MSE: 0.04145979\n",
+      "5 훈련 MSE: 0.040962033\n",
+      "6 훈련 MSE: 0.042370714\n",
+      "7 훈련 MSE: 0.040187325\n",
+      "8 훈련 MSE: 0.043344878\n",
+      "9 훈련 MSE: 0.043597136\n"
      ]
     }
    ],
@@ -1347,7 +1336,7 @@
     "        for iteration in range(n_batches):\n",
     "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
     "            sys.stdout.flush()\n",
-    "            X_batch, y_batch = shuffle_batch(X_train, y_train, batch_size):\n",
+    "            X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_size))\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
     "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
@@ -1363,7 +1352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1380,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1404,7 +1393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1417,23 +1406,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0 훈련 MSE: 0.035001453\n",
+      "1 훈련 MSE: 0.027627887\n",
+      "2 훈련 MSE: 0.030352144\n",
+      "3 훈련 MSE: 0.027474204\n",
+      "4 훈련 MSE: 0.026787175\n",
+      "5 훈련 MSE: 0.026213126\n",
+      "6 훈련 MSE: 0.027075535\n",
+      "7 훈련 MSE: 0.025293095\n",
+      "8 훈련 MSE: 0.027090674\n",
+      "9 훈련 MSE: 0.027737001\n"
      ]
     }
    ],
@@ -1448,7 +1437,7 @@
     "        for iteration in range(n_batches):\n",
     "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
     "            sys.stdout.flush()\n",
-    "            X_batch, y_batch = shuffle_batch(X_train, y_train, batch_size):\n",
+    "            X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_size))\n",
     "            sess.run(training_op, feed_dict={X: X_batch, training: True})\n",
     "        loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n",
     "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", loss_train)\n",
@@ -1457,7 +1446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1469,7 +1458,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -1491,7 +1480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1523,7 +1512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1536,7 +1525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1564,7 +1553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1574,113 +1563,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0 훈련 MSE: 0.13764462 \t희소 손실: 0.90867597 \t전체 손실: 0.3193798\n",
+      "1 훈련 MSE: 0.059780527 \t희소 손실: 0.023853423 \t전체 손실: 0.06455121\n",
+      "2 훈련 MSE: 0.052597478 \t희소 손실: 0.028876154 \t전체 손실: 0.05837271\n",
+      "3 훈련 MSE: 0.05017224 \t희소 손실: 0.19674344 \t전체 손실: 0.08952093\n",
+      "4 훈련 MSE: 0.04457513 \t희소 손실: 0.0070949676 \t전체 손실: 0.04599412\n",
+      "5 훈련 MSE: 0.03982869 \t희소 손실: 0.35642004 \t전체 손실: 0.1111127\n",
+      "6 훈련 MSE: 0.03850078 \t희소 손실: 0.04892056 \t전체 손실: 0.048284892\n",
+      "7 훈련 MSE: 0.036211833 \t희소 손실: 0.023475863 \t전체 손실: 0.040907007\n",
+      "8 훈련 MSE: 0.033634793 \t희소 손실: 0.05728952 \t전체 손실: 0.0450927\n",
+      "9 훈련 MSE: 0.030880695 \t희소 손실: 0.042791054 \t전체 손실: 0.039438907\n",
+      "10 훈련 MSE: 0.028282987 \t희소 손실: 0.32261807 \t전체 손실: 0.0928066\n",
+      "11 훈련 MSE: 0.02501558 \t희소 손실: 0.030906674 \t전체 손실: 0.031196915\n",
+      "12 훈련 MSE: 0.023248762 \t희소 손실: 0.08872005 \t전체 손실: 0.040992774\n",
+      "13 훈련 MSE: 0.021769173 \t희소 손실: 0.06630076 \t전체 손실: 0.035029326\n",
+      "14 훈련 MSE: 0.02200607 \t희소 손실: 0.11473313 \t전체 손실: 0.044952698\n",
+      "15 훈련 MSE: 0.020363854 \t희소 손실: 0.047968887 \t전체 손실: 0.029957632\n",
+      "16 훈련 MSE: 0.017985545 \t희소 손실: 0.18309356 \t전체 손실: 0.05460426\n",
+      "17 훈련 MSE: 0.018661918 \t희소 손실: 0.029090682 \t전체 손실: 0.024480054\n",
+      "18 훈련 MSE: 0.017803263 \t희소 손실: 0.23516989 \t전체 손실: 0.06483724\n",
+      "19 훈련 MSE: 0.017381176 \t희소 손실: 0.024946319 \t전체 손실: 0.02237044\n",
+      "20 훈련 MSE: 0.017425464 \t희소 손실: 0.0866967 \t전체 손실: 0.034764804\n",
+      "21 훈련 MSE: 0.016189657 \t희소 손실: 0.041223954 \t전체 손실: 0.024434447\n",
+      "22 훈련 MSE: 0.016698195 \t희소 손실: 0.04975501 \t전체 손실: 0.026649196\n",
+      "23 훈련 MSE: 0.015898876 \t희소 손실: 0.05508917 \t전체 손실: 0.026916709\n",
+      "24 훈련 MSE: 0.01725482 \t희소 손실: 0.07781921 \t전체 손실: 0.032818664\n",
+      "25 훈련 MSE: 0.015573322 \t희소 손실: 0.08851827 \t전체 손실: 0.033276975\n",
+      "26 훈련 MSE: 0.014312166 \t희소 손실: 0.071160555 \t전체 손실: 0.028544277\n",
+      "27 훈련 MSE: 0.013452826 \t희소 손실: 0.039495397 \t전체 손실: 0.021351906\n",
+      "28 훈련 MSE: 0.0141818 \t희소 손실: 0.034272946 \t전체 손실: 0.02103639\n",
+      "29 훈련 MSE: 0.0142338965 \t희소 손실: 0.0917896 \t전체 손실: 0.03259182\n",
+      "30 훈련 MSE: 0.015912354 \t희소 손실: 0.08804717 \t전체 손실: 0.033521786\n",
+      "31 훈련 MSE: 0.014909098 \t희소 손실: 0.09869958 \t전체 손실: 0.034649014\n",
+      "32 훈련 MSE: 0.012845991 \t희소 손실: 0.049286373 \t전체 손실: 0.022703266\n",
+      "33 훈련 MSE: 0.013661297 \t희소 손실: 0.09084565 \t전체 손실: 0.03183043\n",
+      "34 훈련 MSE: 0.013174699 \t희소 손실: 0.041957993 \t전체 손실: 0.021566298\n",
+      "35 훈련 MSE: 0.013076462 \t희소 손실: 0.038095992 \t전체 손실: 0.02069566\n",
+      "36 훈련 MSE: 0.013874385 \t희소 손실: 0.05388694 \t전체 손실: 0.024651773\n",
+      "37 훈련 MSE: 0.014134639 \t희소 손실: 0.087803714 \t전체 손실: 0.03169538\n",
+      "38 훈련 MSE: 0.012819664 \t희소 손실: 0.23281287 \t전체 손실: 0.059382237\n",
+      "39 훈련 MSE: 0.012348052 \t희소 손실: 0.0440435 \t전체 손실: 0.021156752\n",
+      "40 훈련 MSE: 0.012252705 \t희소 손실: 0.13875237 \t전체 손실: 0.04000318\n",
+      "41 훈련 MSE: 0.012996037 \t희소 손실: 0.124482624 \t전체 손실: 0.03789256\n",
+      "42 훈련 MSE: 0.01273182 \t희소 손실: 0.05220629 \t전체 손실: 0.023173079\n",
+      "43 훈련 MSE: 0.012437585 \t희소 손실: 0.15004826 \t전체 손실: 0.042447235\n",
+      "44 훈련 MSE: 0.013994326 \t희소 손실: 0.08615906 \t전체 손실: 0.03122614\n",
+      "45 훈련 MSE: 0.013336399 \t희소 손실: 0.18166158 \t전체 손실: 0.049668714\n",
+      "46 훈련 MSE: 0.012504962 \t희소 손실: 0.17852002 \t전체 손실: 0.048208967\n",
+      "47 훈련 MSE: 0.013035357 \t희소 손실: 0.13480854 \t전체 손실: 0.039997064\n",
+      "48 훈련 MSE: 0.012266207 \t희소 손실: 0.07757069 \t전체 손실: 0.027780345\n",
+      "49 훈련 MSE: 0.013177205 \t희소 손실: 0.0943829 \t전체 손실: 0.032053784\n",
+      "50 훈련 MSE: 0.011335693 \t희소 손실: 0.055620328 \t전체 손실: 0.02245976\n",
+      "51 훈련 MSE: 0.011653456 \t희소 손실: 0.18031868 \t전체 손실: 0.047717195\n",
+      "52 훈련 MSE: 0.011956116 \t희소 손실: 0.05638638 \t전체 손실: 0.023233391\n",
+      "53 훈련 MSE: 0.011858154 \t희소 손실: 0.09703225 \t전체 손실: 0.031264603\n",
+      "54 훈련 MSE: 0.011784152 \t희소 손실: 0.083307154 \t전체 손실: 0.028445583\n",
+      "55 훈련 MSE: 0.0119370725 \t희소 손실: 0.15825328 \t전체 손실: 0.04358773\n",
+      "56 훈련 MSE: 0.012403161 \t희소 손실: 0.18447791 \t전체 손실: 0.049298745\n",
+      "57 훈련 MSE: 0.0120612765 \t희소 손실: 0.14881416 \t전체 손실: 0.04182411\n",
+      "58 훈련 MSE: 0.013030441 \t희소 손실: 0.2036531 \t전체 손실: 0.05376106\n",
+      "59 훈련 MSE: 0.015655164 \t희소 손실: 0.1561568 \t전체 손실: 0.046886522\n",
+      "60 훈련 MSE: 0.012486809 \t희소 손실: 0.25863346 \t전체 손실: 0.06421351\n",
+      "61 훈련 MSE: 0.015972901 \t희소 손실: 0.9596205 \t전체 손실: 0.207897\n",
+      "62 훈련 MSE: 0.017744554 \t희소 손실: 0.2004545 \t전체 손실: 0.057835452\n",
+      "63 훈련 MSE: 0.016123325 \t희소 손실: 0.55819446 \t전체 손실: 0.12776223\n",
+      "64 훈련 MSE: 0.013705003 \t희소 손실: 0.8484207 \t전체 손실: 0.18338914\n",
+      "65 훈련 MSE: 0.013311629 \t희소 손실: 0.14226675 \t전체 손실: 0.04176498\n",
+      "66 훈련 MSE: 0.015507304 \t희소 손실: 1.1463888 \t전체 손실: 0.24478507\n",
+      "67 훈련 MSE: 0.011973931 \t희소 손실: 0.26371512 \t전체 손실: 0.06471696\n",
+      "68 훈련 MSE: 0.015678437 \t희소 손실: 0.16926403 \t전체 손실: 0.049531244\n",
+      "69 훈련 MSE: 0.03865941 \t희소 손실: 0.19129409 \t전체 손실: 0.07691823\n",
+      "70 훈련 MSE: 0.013939619 \t희소 손실: 0.57569885 \t전체 손실: 0.1290794\n",
+      "71 훈련 MSE: 0.016075313 \t희소 손실: 0.17884825 \t전체 손실: 0.051844966\n",
+      "72 훈련 MSE: 0.014636907 \t희소 손실: 0.18175915 \t전체 손실: 0.050988737\n",
+      "73 훈련 MSE: 0.028090823 \t희소 손실: 0.37423268 \t전체 손실: 0.102937356\n",
+      "74 훈련 MSE: 0.022609375 \t희소 손실: 0.1392368 \t전체 손실: 0.050456733\n",
+      "75 훈련 MSE: 0.07680417 \t희소 손실: 0.7973846 \t전체 손실: 0.2362811\n",
+      "76 훈련 MSE: 0.025242347 \t희소 손실: 0.19677864 \t전체 손실: 0.064598076\n",
+      "77 훈련 MSE: 0.015000151 \t희소 손실: 0.7557995 \t전체 손실: 0.16616005\n",
+      "78 훈련 MSE: 0.016652029 \t희소 손실: 0.119504154 \t전체 손실: 0.040552862\n",
+      "79 훈련 MSE: 0.014573195 \t희소 손실: 0.2708728 \t전체 손실: 0.06874776\n",
+      "80 훈련 MSE: 0.0149989035 \t희소 손실: 0.2499745 \t전체 손실: 0.064993806\n",
+      "81 훈련 MSE: 0.012900565 \t희소 손실: 0.6505898 \t전체 손실: 0.14301853\n",
+      "82 훈련 MSE: 0.017544089 \t희소 손실: 0.6239798 \t전체 손실: 0.14234005\n",
+      "83 훈련 MSE: 0.015405096 \t희소 손실: 0.19303808 \t전체 손실: 0.054012712\n",
+      "84 훈련 MSE: 0.029969472 \t희소 손실: 0.18823238 \t전체 손실: 0.06761595\n",
+      "85 훈련 MSE: 0.014427009 \t희소 손실: 0.6550485 \t전체 손실: 0.1454367\n",
+      "86 훈련 MSE: 0.015298896 \t희소 손실: 0.13435858 \t전체 손실: 0.042170614\n",
+      "87 훈련 MSE: 0.015689839 \t희소 손실: 0.25018495 \t전체 손실: 0.06572683\n",
+      "88 훈련 MSE: 0.028420692 \t희소 손실: 0.29241624 \t전체 손실: 0.086903945\n",
+      "89 훈련 MSE: 0.016309444 \t희소 손실: 0.43265104 \t전체 손실: 0.10283965\n",
+      "90 훈련 MSE: 0.018916653 \t희소 손실: 0.17150912 \t전체 손실: 0.053218476\n",
+      "91 훈련 MSE: 0.036028225 \t희소 손실: 0.21815218 \t전체 손실: 0.07965866\n",
+      "92 훈련 MSE: 0.024213655 \t희소 손실: 0.90460145 \t전체 손실: 0.20513394\n",
+      "93 훈련 MSE: 0.015083556 \t희소 손실: 0.07066245 \t전체 손실: 0.029216046\n",
+      "94 훈련 MSE: 0.014100681 \t희소 손실: 0.4641513 \t전체 손실: 0.10693094\n",
+      "95 훈련 MSE: 0.014648245 \t희소 손실: 0.08357882 \t전체 손실: 0.03136401\n",
+      "96 훈련 MSE: 0.01886718 \t희소 손실: 0.95776266 \t전체 손실: 0.21041971\n",
+      "97 훈련 MSE: 0.017021567 \t희소 손실: 1.006425 \t전체 손실: 0.21830657\n",
+      "98 훈련 MSE: 0.019905027 \t희소 손실: 0.11643936 \t전체 손실: 0.0431929\n",
+      "99 훈련 MSE: 0.0144011825 \t희소 손실: 0.28757378 \t전체 손실: 0.07191594\n"
      ]
     }
    ],
@@ -1695,7 +1684,7 @@
     "        for iteration in range(n_batches):\n",
     "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
     "            sys.stdout.flush()\n",
-    "            X_batch, y_batch = shuffle_batch(X_train, y_train, batch_size):\n",
+    "            X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_size))\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        reconstruction_loss_val, sparsity_loss_val, loss_val = sess.run([reconstruction_loss, sparsity_loss, loss], feed_dict={X: X_batch})\n",
     "        print(\"\\r{}\".format(epoch), \"훈련 MSE:\", reconstruction_loss_val, \"\\t희소 손실:\", sparsity_loss_val, \"\\t전체 손실:\", loss_val)\n",
@@ -1704,7 +1693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1716,7 +1705,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 576x432 with 4 Axes>"
       ]
@@ -1738,7 +1727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1754,7 +1743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1774,7 +1763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1816,7 +1805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1828,7 +1817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1843,63 +1832,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0 훈련 전체 손실: 30969.459 \t재구성 손실: 24516.871 \t잠재 손실: 6452.588\n",
+      "1 훈련 전체 손실: 28966.72 \t재구성 손실: 22678.564 \t잠재 손실: 6288.1567\n",
+      "2 훈련 전체 손실: 26492.55 \t재구성 손실: 21714.719 \t잠재 손실: 4777.832\n",
+      "3 훈련 전체 손실: 27487.383 \t재구성 손실: 21461.723 \t잠재 손실: 6025.66\n",
+      "4 훈련 전체 손실: 25391.826 \t재구성 손실: 19977.314 \t잠재 손실: 5414.5117\n",
+      "5 훈련 전체 손실: 22596.055 \t재구성 손실: 18616.91 \t잠재 손실: 3979.1448\n",
+      "6 훈련 전체 손실: 21599.162 \t재구성 손실: 17780.654 \t잠재 손실: 3818.5076\n",
+      "7 훈련 전체 손실: 19658.17 \t재구성 손실: 16267.5625 \t잠재 손실: 3390.6067\n",
+      "8 훈련 전체 손실: 21420.445 \t재구성 손실: 17632.562 \t잠재 손실: 3787.8838\n",
+      "9 훈련 전체 손실: 18866.047 \t재구성 손실: 15429.076 \t잠재 손실: 3436.9712\n",
+      "10 훈련 전체 손실: 16396.531 \t재구성 손실: 13344.035 \t잠재 손실: 3052.4954\n",
+      "11 훈련 전체 손실: 16818.389 \t재구성 손실: 13674.762 \t잠재 손실: 3143.6274\n",
+      "12 훈련 전체 손실: 16475.625 \t재구성 손실: 13151.922 \t잠재 손실: 3323.7021\n",
+      "13 훈련 전체 손실: 16441.584 \t재구성 손실: 13211.978 \t잠재 손실: 3229.606\n",
+      "14 훈련 전체 손실: 15809.6455 \t재구성 손실: 12506.787 \t잠재 손실: 3302.8582\n",
+      "15 훈련 전체 손실: 16582.064 \t재구성 손실: 12987.818 \t잠재 손실: 3594.2454\n",
+      "16 훈련 전체 손실: 17112.182 \t재구성 손실: 13749.431 \t잠재 손실: 3362.7515\n",
+      "17 훈련 전체 손실: 16962.52 \t재구성 손실: 13360.187 \t잠재 손실: 3602.3337\n",
+      "18 훈련 전체 손실: 15885.106 \t재구성 손실: 12414.121 \t잠재 손실: 3470.985\n",
+      "19 훈련 전체 손실: 15645.737 \t재구성 손실: 12275.276 \t잠재 손실: 3370.4607\n",
+      "20 훈련 전체 손실: 21472.46 \t재구성 손실: 17321.988 \t잠재 손실: 4150.472\n",
+      "21 훈련 전체 손실: 30887.754 \t재구성 손실: 23065.543 \t잠재 손실: 7822.21\n",
+      "22 훈련 전체 손실: 34106.145 \t재구성 손실: 23087.611 \t잠재 손실: 11018.534\n",
+      "23 훈련 전체 손실: 28455.574 \t재구성 손실: 23078.035 \t잠재 손실: 5377.539\n",
+      "24 훈련 전체 손실: 29275.6 \t재구성 손실: 23641.562 \t잠재 손실: 5634.0366\n",
+      "25 훈련 전체 손실: 27637.0 \t재구성 손실: 22232.246 \t잠재 손실: 5404.7534\n",
+      "26 훈련 전체 손실: 32224.32 \t재구성 손실: 22636.662 \t잠재 손실: 9587.659\n",
+      "27 훈련 전체 손실: 21313.816 \t재구성 손실: 17978.727 \t잠재 손실: 3335.0898\n",
+      "28 훈련 전체 손실: 22745.164 \t재구성 손실: 18077.477 \t잠재 손실: 4667.6875\n",
+      "29 훈련 전체 손실: 24317.865 \t재구성 손실: 19554.348 \t잠재 손실: 4763.518\n",
+      "30 훈련 전체 손실: 22403.871 \t재구성 손실: 18023.01 \t잠재 손실: 4380.8604\n",
+      "31 훈련 전체 손실: 27779.889 \t재구성 손실: 19985.334 \t잠재 손실: 7794.5547\n",
+      "32 훈련 전체 손실: 21062.633 \t재구성 손실: 17759.936 \t잠재 손실: 3302.6963\n",
+      "33 훈련 전체 손실: 27471.139 \t재구성 손실: 21755.688 \t잠재 손실: 5715.451\n",
+      "34 훈련 전체 손실: 21868.11 \t재구성 손실: 17975.395 \t잠재 손실: 3892.7153\n",
+      "35 훈련 전체 손실: 23293.21 \t재구성 손실: 18515.408 \t잠재 손실: 4777.802\n",
+      "36 훈련 전체 손실: 24222.096 \t재구성 손실: 17747.406 \t잠재 손실: 6474.6895\n",
+      "37 훈련 전체 손실: 27664.906 \t재구성 손실: 20372.93 \t잠재 손실: 7291.976\n",
+      "38 훈련 전체 손실: 23926.232 \t재구성 손실: 19888.672 \t잠재 손실: 4037.5613\n",
+      "39 훈련 전체 손실: 21193.496 \t재구성 손실: 16443.598 \t잠재 손실: 4749.8975\n",
+      "40 훈련 전체 손실: 25521.975 \t재구성 손실: 20088.113 \t잠재 손실: 5433.8613\n",
+      "41 훈련 전체 손실: 39825.066 \t재구성 손실: 23289.588 \t잠재 손실: 16535.479\n",
+      "42 훈련 전체 손실: 24308.13 \t재구성 손실: 19179.758 \t잠재 손실: 5128.373\n",
+      "43 훈련 전체 손실: 25884.607 \t재구성 손실: 20003.258 \t잠재 손실: 5881.3496\n",
+      "44 훈련 전체 손실: 29764.18 \t재구성 손실: 22110.586 \t잠재 손실: 7653.594\n",
+      "45 훈련 전체 손실: 21576.438 \t재구성 손실: 16846.219 \t잠재 손실: 4730.2197\n",
+      "46 훈련 전체 손실: 19421.508 \t재구성 손실: 15778.951 \t잠재 손실: 3642.557\n",
+      "47 훈련 전체 손실: 17019.273 \t재구성 손실: 13826.726 \t잠재 손실: 3192.547\n",
+      "48 훈련 전체 손실: 16891.82 \t재구성 손실: 13830.799 \t잠재 손실: 3061.022\n",
+      "49 훈련 전체 손실: 17236.168 \t재구성 손실: 13861.413 \t잠재 손실: 3374.755\n"
      ]
     }
    ],
@@ -1914,7 +1903,7 @@
     "        for iteration in range(n_batches):\n",
     "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n",
     "            sys.stdout.flush()\n",
-    "            X_batch, y_batch = shuffle_batch(X_train, y_train, batch_size):\n",
+    "            X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_size))\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch})\n",
     "        print(\"\\r{}\".format(epoch), \"훈련 전체 손실:\", loss_val, \"\\t재구성 손실:\", reconstruction_loss_val, \"\\t잠재 손실:\", latent_loss_val)\n",
@@ -1923,7 +1912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1987,63 +1976,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "0 훈련 전체 손실: 18218.76 \t재구성 손실: 14676.055 \t잠재 손실: 3542.7048\n",
+      "1 훈련 전체 손실: 16157.965 \t재구성 손실: 12554.926 \t잠재 손실: 3603.039\n",
+      "2 훈련 전체 손실: 16807.945 \t재구성 손실: 13107.26 \t잠재 손실: 3700.6846\n",
+      "3 훈련 전체 손실: 16130.707 \t재구성 손실: 12394.524 \t잠재 손실: 3736.1821\n",
+      "4 훈련 전체 손실: 16167.954 \t재구성 손실: 12363.075 \t잠재 손실: 3804.8787\n",
+      "5 훈련 전체 손실: 15852.156 \t재구성 손실: 12208.578 \t잠재 손실: 3643.5786\n",
+      "6 훈련 전체 손실: 15705.334 \t재구성 손실: 12007.691 \t잠재 손실: 3697.642\n",
+      "7 훈련 전체 손실: 15155.031 \t재구성 손실: 11477.658 \t잠재 손실: 3677.3735\n",
+      "8 훈련 전체 손실: 15498.601 \t재구성 손실: 11717.781 \t잠재 손실: 3780.8193\n",
+      "9 훈련 전체 손실: 15686.549 \t재구성 손실: 11837.156 \t잠재 손실: 3849.3926\n",
+      "10 훈련 전체 손실: 14910.417 \t재구성 손실: 11226.355 \t잠재 손실: 3684.0615\n",
+      "11 훈련 전체 손실: 15763.072 \t재구성 손실: 11938.736 \t잠재 손실: 3824.3362\n",
+      "12 훈련 전체 손실: 15509.399 \t재구성 손실: 11676.887 \t잠재 손실: 3832.513\n",
+      "13 훈련 전체 손실: 15698.921 \t재구성 손실: 11909.219 \t잠재 손실: 3789.7024\n",
+      "14 훈련 전체 손실: 15117.068 \t재구성 손실: 11384.899 \t잠재 손실: 3732.169\n",
+      "15 훈련 전체 손실: 15423.752 \t재구성 손실: 11687.385 \t잠재 손실: 3736.3667\n",
+      "16 훈련 전체 손실: 15189.444 \t재구성 손실: 11464.85 \t잠재 손실: 3724.5947\n",
+      "17 훈련 전체 손실: 15577.088 \t재구성 손실: 11821.83 \t잠재 손실: 3755.258\n",
+      "18 훈련 전체 손실: 14807.34 \t재구성 손실: 11136.354 \t잠재 손실: 3670.9858\n",
+      "19 훈련 전체 손실: 14838.291 \t재구성 손실: 11174.918 \t잠재 손실: 3663.3726\n",
+      "20 훈련 전체 손실: 15333.88 \t재구성 손실: 11513.043 \t잠재 손실: 3820.837\n",
+      "21 훈련 전체 손실: 15382.906 \t재구성 손실: 11651.821 \t잠재 손실: 3731.0847\n",
+      "22 훈련 전체 손실: 15237.957 \t재구성 손실: 11471.176 \t잠재 손실: 3766.7817\n",
+      "23 훈련 전체 손실: 15370.038 \t재구성 손실: 11551.192 \t잠재 손실: 3818.8457\n",
+      "24 훈련 전체 손실: 15608.235 \t재구성 손실: 11782.35 \t잠재 손실: 3825.8857\n",
+      "25 훈련 전체 손실: 15079.079 \t재구성 손실: 11408.744 \t잠재 손실: 3670.335\n",
+      "26 훈련 전체 손실: 15034.0 \t재구성 손실: 11326.258 \t잠재 손실: 3707.7424\n",
+      "27 훈련 전체 손실: 15015.447 \t재구성 손실: 11293.295 \t잠재 손실: 3722.1528\n",
+      "28 훈련 전체 손실: 15138.809 \t재구성 손실: 11427.032 \t잠재 손실: 3711.7769\n",
+      "29 훈련 전체 손실: 15141.48 \t재구성 손실: 11504.628 \t잠재 손실: 3636.8525\n",
+      "30 훈련 전체 손실: 14788.049 \t재구성 손실: 11041.8955 \t잠재 손실: 3746.1528\n",
+      "31 훈련 전체 손실: 14709.729 \t재구성 손실: 11039.059 \t잠재 손실: 3670.6694\n",
+      "32 훈련 전체 손실: 14682.857 \t재구성 손실: 10992.444 \t잠재 손실: 3690.4136\n",
+      "33 훈련 전체 손실: 15009.77 \t재구성 손실: 11297.744 \t잠재 손실: 3712.0254\n",
+      "34 훈련 전체 손실: 14526.937 \t재구성 손실: 10948.571 \t잠재 손실: 3578.3652\n",
+      "35 훈련 전체 손실: 14206.965 \t재구성 손실: 10654.1875 \t잠재 손실: 3552.777\n",
+      "36 훈련 전체 손실: 14849.254 \t재구성 손실: 11131.516 \t잠재 손실: 3717.7388\n",
+      "37 훈련 전체 손실: 14679.506 \t재구성 손실: 10997.404 \t잠재 손실: 3682.1018\n",
+      "38 훈련 전체 손실: 14744.93 \t재구성 손실: 11116.8545 \t잠재 손실: 3628.075\n",
+      "39 훈련 전체 손실: 15050.721 \t재구성 손실: 11315.764 \t잠재 손실: 3734.9575\n",
+      "40 훈련 전체 손실: 14831.108 \t재구성 손실: 11115.693 \t잠재 손실: 3715.4148\n",
+      "41 훈련 전체 손실: 14920.627 \t재구성 손실: 11216.011 \t잠재 손실: 3704.6162\n",
+      "42 훈련 전체 손실: 14811.677 \t재구성 손실: 11154.946 \t잠재 손실: 3656.7305\n",
+      "43 훈련 전체 손실: 14609.848 \t재구성 손실: 10981.436 \t잠재 손실: 3628.412\n",
+      "44 훈련 전체 손실: 14663.687 \t재구성 손실: 11052.582 \t잠재 손실: 3611.1047\n",
+      "45 훈련 전체 손실: 14979.221 \t재구성 손실: 11217.773 \t잠재 손실: 3761.4468\n",
+      "46 훈련 전체 손실: 15053.541 \t재구성 손실: 11416.833 \t잠재 손실: 3636.7075\n",
+      "47 훈련 전체 손실: 14378.854 \t재구성 손실: 10826.66 \t잠재 손실: 3552.193\n",
+      "48 훈련 전체 손실: 14707.507 \t재구성 손실: 11052.38 \t잠재 손실: 3655.1267\n",
+      "49 훈련 전체 손실: 15307.982 \t재구성 손실: 11553.088 \t잠재 손실: 3754.894\n"
      ]
     }
    ],
@@ -2061,7 +2050,7 @@
     "        for iteration in range(n_batches):\n",
     "            print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\") # not shown in the book\n",
     "            sys.stdout.flush()                                          # not shown\n",
-    "            X_batch, y_batch = shuffle_batch(X_train, y_train, batch_size):\n",
+    "            X_batch, y_batch = next(shuffle_batch(X_train, y_train, batch_size))\n",
     "            sess.run(training_op, feed_dict={X: X_batch})\n",
     "        loss_val, reconstruction_loss_val, latent_loss_val = sess.run([loss, reconstruction_loss, latent_loss], feed_dict={X: X_batch}) # not shown\n",
     "        print(\"\\r{}\".format(epoch), \"훈련 전체 손실:\", loss_val, \"\\t재구성 손실:\", reconstruction_loss_val, \"\\t잠재 손실:\", latent_loss_val)  # not shown\n",
@@ -2073,12 +2062,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x3600 with 60 Axes>"
       ]
@@ -2096,12 +2085,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2127,7 +2116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2151,7 +2140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -2165,7 +2154,7 @@
    "source": [
     "n_digits = 3\n",
     "# X_test, y_test = mnist.test.next_batch(batch_size)\n",
-    "X_test_batch, y_test_batch = shuffle_batch(X_test, y_test, batch_size):\n",
+    "X_test_batch, y_test_batch = next(shuffle_batch(X_test, y_test, batch_size))\n",
     "codings = hidden3\n",
     "\n",
     "with tf.Session() as sess:\n",
@@ -2182,7 +2171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -2208,12 +2197,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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2f4FexG3ixIki6wlNd911l3eO+vp6kXv37h32uk899ZTI7733nsgzZswQuWPHjmHPCaQ7PTFQN7n14opjx471znHppZeKrP/wpaGhwRujF2nU15kyZYrIbdq0/v+eb/0/AQAgoSgkAAATCgkAwIQeyRc8/fTTIu/Zs0fk4uJikcvKyrxz5OXlhbyGXgjSOefmzZsXcsyYMWNC/u9AutO9Sef856ampkZkvQCj7l0451x2drbIuieif0c459wzzzwjcvv27UW+4IILRG5tCzQG4Y0EAGBCIQEAmFBIAAAmadsjCepVTJo0SWQ9b2TatGkiNzU1eef4/PPPQ153+fLl3mf6O1X9vWyXLl1CnhNIN/rZfPPNN71jXnzxRZGzsuSvOz2vJDc31zuH/j2h55o98sgj3hjdrxk6dKjIkWxu19rwRgIAMKGQAABMKCQAABMKCQDAJG2b7StXrvQ+0xOW9EQhPfkoaLe0AwcOhDyHbhIGHVNaWipyKuygBkSTbmivWrXKO0Y3z/Vipz179hT54MGD3jn0H77o3xFbt271xuhJyd/85jdFToUJiBpvJAAAEwoJAMCEQgIAMEmbHomeWBS0KVVQ/+KLHnzwQZFvvPFG75ibbrop5DmGDRsW9rpHjhwRedeuXSJ/+ctfDnkNINXV1dWJ/M9//tM7pqCgQGS9WKKeKFhUVOSdQ286VV1dLXJQj0RPIB40aJB3zBcFTWxubZtdta67BQAkHQoJAMCEQgIAMEmbHonekObYsWPeMRMnThRZ90DOP/98kSOZ36H7G0H0dWfNmiVyfn5+2HMA6UQvfFhRUeEdM3DgQJFHjRolcteuXUUOmt+hF2lcsWKFyLpX45xzV199tcjnnXeeyJmZmWGv29rwRgIAMKGQAABMKCQAAJO06ZHoPsNnn30Wk+vo+Sp/+MMfwo4ZPny4yPREgND0GliXXHKJd8xFF10kcrt27UTWvYqgeWSHDh0SWW9Cp9fvcs65KVOmiJyKG1lpvJEAAEwoJAAAEwoJAMCEQgIAMEmbZnu86ImPy5cvF/nCCy/0xlxxxRUxvScg1ehFDXUj3Tl/McRwCyEGNdsrKytF/uSTT0QePXq0N+aMM84IeZ1UxBsJAMCEQgIAMKGQAABM6JFE2RNPPCHygQMHRA7aUKtDhw4xvScg1QUtfNjcnsinn37qHTN37tyQ5/zlL3/Z7OumovT7iQEAUUUhAQCYUEgAACYZQX87HWNxv2A86X5HQUGByB988IE3JsUWdWv9u/SgOZL2eda/2/S8khMnToh8zz33eOdYunSpyJdffrnIixYt8sboxSBbuYieZ95IAAAmFBIAgAmFBABgQiEBAJgwIdFg2bJl3md6h8R169aJnGKNdSBp6UmK9fX1Ir/xxhsib9q0yTtHaWmpyL/+9a9FTsfJh0H4VwAAmFBIAAAmFBIAgAk9EoMNGzZ4n+kJiSUlJfG6HQAh6H6Gzpdeeqk3ZsiQISJ369ZN5KDFItMRbyQAABMKCQDAhEICADBh0UZEG18apxee59TGoo0AgNijkAAATCgkAAATCgkAwIRCAgAwoZAAAEwoJAAAEwoJAMCEQgIAMKGQAABMKCQAABMKCQDAJBEbW7GoH5A6eJ7BGwkAwIZCAgAwoZAAAEwoJAAAEwoJAMCEQgIAMKGQAABMKCQAABMKCQDAhEICADChkAAATCgkAAATCgkAwIRCAgAwoZAAAEwoJAAAEwoJAMCEQgIAMKGQAABMKCQAABMKCQDAhEICADChkAAATLIScM1TCbgm4icj0TeAuOJ5Tm0RPc+8kQAATCgkAAATCgkAwIRCAgAwoZAAAEwoJAAAEwoJAMCEQgIAMKGQAABMKCQAABMKCQDAhEICADChkAAATCgkAAATCgkAwIRCAgAwScTGVgDQKpw6dSpkbmpq8sa0adMmZE5Fqf8TAgBiikICADChkAAATOiRRNmuXbtEfuutt5p9jm9961si5+TkmO4JQHiNjY3eZ8eOHRP5k08+Efmjjz7yxnTq1Enk3r17i1xQUCByKvRQWv9PAABIKAoJAMCEQgIAMKGQAABMaLaHUFlZKfL3v/99ka+77jpvzCOPPCLy0aNHRdYTmpxzLiMjQ+Tu3buLPGjQIJEXLlzonSM/P9/7DMB/6MmDx48fF/mBBx7wxqxfv17kqqqqkOd0zrns7GyRCwsLRb7hhhtEnjRpkneOzp07i5zsDfnkvjsAQNKjkAAATCgkAACTjKDv7GMs7heM1O233y7y0qVLRdbfqbZEJD2ScM455xzvs7vvvlvk73znOyJ36NChWdcwaN4Pg9YuKZ7noOeqoaFB5L1794o8ceJEkV9++eWw54jkurqfoY9p27atyHrConPOTZ06VeRrrrlG5Nzc3JD3FUURPc+8kQAATCgkAAATCgkAwCRteiR68bVnnnnGO6aiokJkPQckGoL+vc866yyRTzvtNJFff/31Zl9n5MiRIj/++OPeMe3bt2/2eSNAjyS9JOR51vM3/v3vf3vHzJ07V+Tf//73IusFFyPpd7Rr105kvQCjc34v9eTJkyLX19eHva5+NocOHSry4sWLvTG6D9rc3ut/QY8EABB7FBIAgAmFBABgQiEBAJikTbN948aNIg8fPjwm19GTBevq6kQO+vd+9dVXRdZNs2effVbk8ePHe+cI94cBV155pffZihUrRNaNxBai2Z5e4vI86+fm4MGDIv/sZz/zxug/MAlqyH9RUHO6T58+Iv/0pz8V+eKLL/bG1NbWivz222+LPG/ePJGDdlHVDfrMzMyQ9+Wcc3/+859F1r+LWth8p9kOAIg9CgkAwIRCAgAwSZseyXe/+12RdX+gJe6//37vs9tuu01k/V1nkKKiomZdV/dMnHNu7NixIkeywOSwYcNEXr16tch6cbkI0SNJL1F/noN+J+m+w5QpU0TWG8oFjdH0ZMP+/ft7xyxZskRk3XfQvYsg+uc5ceKEyLq34Zxzs2fPFnn37t0iNzY2emP0vb3wwgsiFxcXh73XAPRIAACxRyEBAJhQSAAAJinbI/n73/8usp43or+njIT+DrWystI7plOnTs0+bzRcf/31Iq9cubLZ59ALw+m5NxGiR5Jeov48B20mtWrVKpFvvfVWkfWirEF0P+Pss88WOahvWlZWFvIcLZmboX/nBs1vWbBggci6JxQ0JisrS+Tp06eLfOedd4oc4b3TIwEAxB6FBABgQiEBAJhkhT8k+QV9p6rXlmpJT0T/XbbuiSSqHxIrmzZtSvQtAIHf/8+aNSvsMVq49al+85vfiFxaWhr2HNHYLEqfIzs72zvmoosuErljx44iB/38erOvNWvWiDx58mSRI5kDEyneSAAAJhQSAIAJhQQAYEIhAQCYpESz/bHHHvM+C7fRUyR+/OMfi5xqzXUgGf3jH//wPtuzZ4/IelJfUOO4R48eIv/iF78QedCgQSLn5OR454hGc13T9643v3POuQ8++EDkvLw8kfWCk0Hn1RtqxXLyOW8kAAATCgkAwIRCAgAwSYkeSdB3fy35PlBPQNSbYSXKp59+KvLMmTO9Y/SCcwlYjBNoEf3/1aeffto7JtyijEGT+vQipOXl5SK3b99e5KC+QzSE64ns2LHDG6MXndU936CekL6O/jeJRb/n//FGAgAwoZAAAEwoJAAAk5Tokfzwhz/0PmvJ94Ff/epXRe7cuXOL7ymaFi1aJHJQj0T/vJH8/Lm5uSK/9NJLLbg7ILqefPJJ77OTJ0+KrPsBelMn55z7yle+InK4nkhQXzHccxQ0Ri+eePz4cZF37twp8oMPPuidQz+LetHZoJ5Q27ZtRR47dqzI0VykUeONBABgQiEBAJhQSAAAJhQSAIBJSjTbgxY9a0mz/Wtf+1o0bsfsgQceEHnq1Kkxuc6cOXNE7t+/f0yuAzSH/iOQSLRr1877bMiQISK3pNmsm+m6kR70u6e6ulrkjRs3iqwnDwctUnn48OGQ91FYWOiNmTJlish33HGHd0ys8EYCADChkAAATCgkAACTlOiRXHjhhd5nW7ZsafZ5xowZE43bCUkvwOicP+FQ90QaGhrM1x09erT3WbIsSon0pvuZxcXF3jG7d+8WWT8TQb2K/fv3i6wnKOq+Q1BfVV9HL564efNmb8xDDz0k8tatW0XWC1BG8nzn5+eLfM0113jHVFRUiBy0UVes8EYCADChkAAATCgkAACTlOiRzJ492/ts2LBhIjc2NoY9z7333ivyww8/LLJeFC3ou81du3aJrDfp0XM3nHNu3759Ye8tnI4dO4p84403ijxjxgxvTF5envm6QLRdeeWV3md6roXuM+iFEZ1zbv78+SJ36dJF5L59+4octLHVe++9J7Luf2zYsMEbo/ug4X73BPVm9DyRe+65R+QJEyZ4Y/TzHMuNrDTeSAAAJhQSAIAJhQQAYJIRtDFLjMXlgp06dRL5yJEjzT7H0KFDRT7jjDNErq2t9casXbs25DlbsnlOJPR1R44caT5nC8Xvi1kkg6g/z3qdKef8nt/69etFrq+v98bozZ/086ufRb15lHP+hlo6B/U/wv1O1fM7LrvsMu8Y3d/p1q2byLHcpEqJ6HnmjQQAYEIhAQCYUEgAACYUEgCASco22998802Rx48fL/K2bdvicRsevTGOc8ETob5o3LhxIt9+++3eMWVlZSLrRd7iiGZ7eon68xzUwH799ddFvu2220TesWNHROex0n8Yk5Xlz+nWEwNLS0tFnjVrlshBG+olcnKhQrMdABB7FBIAgAmFBABgkrI9Eu3AgQMiB20MoxeG05OPoiGoR6IXaBs1apTIc+fOFTnJF1ukR5Je4vI86wVSN27cKLJecNU557Zv3y6ynkAcSQ9FT/zTCz/279/fG6Of3+uuu07k9u3bh7xGkqFHAgCIPQoJAMCEQgIAMEmbHkkkXnnlFZFramricl09B6SkpCQu140ReiTpJSHPs+5v1NXVece8//77Iu/fv1/kqqoqkYMWiywoKBC5X79+Ip911lnemKKiIpH14pEJnBPSEvRIAACxRyEBAJhQSAAAJhQSAIAJzXZEW6vqJMIsaZ9n/bstXI7kHLpRHrTgaitrpodDsx0AEHsUEgCACYUEAGDi78oCAClA9ypSrHeRVHgjAQCYUEgAACYUEgCACYUEAGBCIQEAmFBIAAAmFBIAgAmFBABgQiEBAJhQSAAAJhQSAIAJhQQAYEIhAQCYUEgAACYUEgCACYUEAGCSiI2t2F0GSB08z+CNBABgQyEBAJhQSAAAJhQSAIAJhQQAYEIhAQCYUEgAACYUEgCACYUEAGBCIQEAmFBIAAAmFBIAgAmFBABgQiEBAJhQSAAAJhQSAIAJhQQAYEIhAQCYUEgAACYUEgCACYUEAGBCIQEAmFBIAAAmFBIAgAmFBABg8j/mnnog9TLZ8wAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 576x540 with 6 Axes>"
       ]
@@ -2240,7 +2229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -2252,7 +2241,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
@@ -2262,7 +2251,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAowAAAB8CAYAAADjPuHmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGBpJREFUeJzt3XnwXfP9x/FPtLba1xJELUkQEhKCsY/BWGLE0Kl1LDWWtjPoFGPfBdGZWtpaZpjWvoyqttYREUtC1J5IGhUhtCLEEiFp+f3z8/Y8p/d83OS73O35+OtFvvd87/d87jn3M+f9Wfp8/fXXSZIkSaqyWKPfgCRJkpqbHUZJkiRl2WGUJElSlh1GSZIkZdlhlCRJUpYdRkmSJGXZYZQkSVKWHUZJkiRl2WGUJElS1vcb8DvdWqZn9emm49hOPas72sk26lm2UfOzjZqfbdT86mojnzBKkiQpyw6jJEmSsuwwSpIkKcsOoyRJkrLsMEqSJCmrEbOkJbWZr776KnKfPn1qZjXW11/XnmjK/z9v3rzISy+9dGTbVJJPGCVJkpRlh1GSJElZlqTVUlg+szTWu1h2/vLLLwv/9tlnn0VecsklI//gBz+I/P3ve7vpTWyvlFJasGBB5NmzZ0eeOHFiZLbj5MmTI++zzz6Rhw8fXjiu12Hj/Oc//4k8f/78yOXrk9fh4osvHnmxxXxmpPr5aZEkSVKWHUZJkiRl2WGUJElSVp+qpRZ6kJuI96zuGlDUsHYqfyY/+eSTyC+99FLkFVZYITLH5ay99tqRl1122chNNl6nO9qpV9uIS648/vjjhX+79dZbI/ft2zfyqaeeGpltwfGMjW6XzLjYlmujHI5pZFt+8cUXkf/2t79FvuaaayLzvNx+++2F46677rrd+j4XUlu1EfFzWbX00S233BL56quvjvzGG28UjrXDDjtEHjVqVORNNtkk8hJLLNHFd1ypbduojdTVRk31DSpJkqTmY4dRkiRJWW25zgUf3//3v/+NzNJLSil9/vnnkVly4bIgfEzPMtr3vve9mq/VomGbffjhh4V/O+eccyJzCRC2Acttu+66a+RjjjkmMkvV5derNp7Xjz/+OPKNN95Y+Lmnnnoq8kYbbRT55Zdfjjx06NDI3EWEbV8uT/fGtdUp1y//Tp5/3tfWXHPNyO+//37kOXPmRL7yyisLxx09enTN36GFx2uBS+Z8+umnke++++7IF110UeS333675nFSSun555+P/Morr0TeeOONa77GdlQtPmGUJElSlh1GSZIkZbV0Sbqq9PzRRx9F/u1vfxuZMwBTKq6Gz1IYy3DLLLNMZM40O+WUUyKvtNJKheP6OH/hcZeCcePGFf7thRdeiPzWW29FZvuznThD8JFHHom83377FY67yiqrRLbNauM5/uc//xl52rRphZ9jeZ8l6eWWW67mcXmNMZdL0iyX2kZdw/PHzBUG1l9//cg/+tGPIvO622mnnXroHXaecumY90EO57jjjjsijx8/PjK/93itlI/L++NSSy1V8zVeXwuvaiY7hxNw9yS2F9shpeJuPLwPNlO7+IRRkiRJWXYYJUmSlNXSJekFCxZE/s1vfhP5qquuijxr1qzI5VmxK6+8cs3MhaKnT58eefLkyZGnTJkSmYunplR8tKxqfDw/c+bMyM8880zh59g2nMHO0me/fv0iz549O/KYMWMicxZhSin96le/isySTTOVABqNpZUnn3wyModzpJTSoEGDIm+66aaRl19++ZrH5YoFc+fOjcyVC1JKabXVVovMNurOVQqcHfotlsI4M5eL5A8ZMqTwmk4/Z11RLh3zO+3hhx+OzA0LVl111chrrLFG5LFjx0bmdZtS8V7J9mv0ovmNxvPPoTFcFWDChAmF13Bo24svvhiZqwrwPsZj8TuPGxmklNKIESMiX3jhhZFXX331yBxC0Aid/WmRJEnSd7LDKEmSpKyWK0nzkS5Lz5dccklklrtYxirPkj366KMjs4zMxaF53BkzZkR+8MEHI3Mh1ZRSOuywwyJbrqnGEsCbb74ZubxwN2ducqb6zjvvHJllzfvuuy/yzTffHPmBBx4oHJefJS4O7szBb1UtfP/DH/6w8HMDBgyIvMUWW0Rea621IvNcstx53XXXRS7vgcvj/uQnP4nMRdg5y7eqxFYu/fG98G/ksTrRv//978gcjsNrtVzu1KIr31841IL3Om4gwWEaTzzxRGTeA8v7Qg8bNiwyy9ideH9j2X/SpEmRuSD9n//858gsKadUvF9QPd8bVau5pFTco/1f//pXZC7OPnjw4MiN2HjCJ4ySJEnKssMoSZKkrJYoSbOcxD1r+aiWj5k5q4gLd3OP4ZSK5Sf+jv79+0dmeZRly3nz5kXmIt4ppTRy5MjIVQsXq3jO2X7czzallAYOHBh52223jczhBpwNz1nrLLGVywSPPvpo5OOOOy5y3759K1/TadhGnPnHRZ1TKs6S3nDDDSPzGmM55rHHHot8zTXXRGZZLaWUNthgg8gsie+7776Reb2yFMfydHl2IRfT5bXMEnqnYBtPnTo1clWJk+1Qfn2nXy8Lq3y+eL3wc83vNC6gzxm8/ByXv3f4ndSJq3jw3sV+A8vQ/Bl+psvDVPi9w7242b9YccUVI7O8zE0p/vGPfxSOy+uN7XruuedG5ndbI1b28AmjJEmSsuwwSpIkKavlStKcUcmFL1lKOuOMMyLvsccekeudVbT00ktHPuSQQyJfffXVkTlj+oMPPii8nouIn3766ZE7fZHUMrYrS48sI6dUXKyb5TDub3vBBRdE5t7T/B08Tkopbb/99pG5GK5ltW/x/HFmNGc5p1Qsj1TNNOawgRNPPDEyr5/y7M7hw4dHPvjggyNzQXCW4jibl9cxhzyklNJ7770X+Y9//GPkUaNG1Xzv7Yxt/Nxzz0XmbGheH5ylm5LXS3fidwQXS+e9i6s9sKzJ1+65556F43Ix/UYv/txbOATmrLPOinzTTTdFZhma55grcPzyl78sHHfLLbeMXE9ZmPeed955J/Lvfve7ws/df//9kbkIONuYQ3l23333mu+9J69HezCSJEnKssMoSZKkLDuMkiRJymqJwQysyXNcDccucfV6Lr3S1XGDHLtzxBFHROYOMOWdD/7whz9EPvnkkyN34nIGZRwvxTEmHMfG8WUpFceZcAzHzJkzI48ZM6bm7+M4tuOPP77wb+eff37k3hoD0mp4LpZaaqnIPPcpFce+DR06NDLP67HHHhuZ4xl5jZ500kmF45599tk1j1W1JBOPxfde3umF4zFXXnnl1Mk4Lvgvf/lLZI4H5TJKK620Uq+8r07Ez+mXX34ZmePYrr/++sj87uGYx9NOO61w3PL47U7Az/Vtt90W+ZNPPonMMdOce8Dv+kXZ/alq7Dx3qNpnn30Krxk7dmzkt99+OzLH9LNvweuQ4yp5n06pe7/PfMIoSZKkLDuMkiRJymqJkjRxxXuWNLmyfVcfwfL1fBzNpUDuvPPOyNOmTSu8nuVVLvGyzTbbRG7ExuHNjKXE8qbsPL8shbK0wPbnMIKjjz46MocHpFR8dN+Vz0y53NlOJW0uwcHyF5eHSKm4mwE//+PHj4/MoQYcnnHDDTdEPuCAAwrHrec6qfqZcrsQl8M48sgjv/N3tBuWm5955pnI3NmK9z6WvLx3dZ/ycCYup8JhHtzhg/dHLi03YsSIyFymLKX2uifVi/cknjN+9nfaaafIvA90demhquEwbG/u2JNSStOnT6/5czwWh2dNnDgxMnecKQ8/sCQtSZKkXmOHUZIkSVktV5Jef/31I/NR6yuvvBJ58uTJkTljuquPZllG43H5KDmllObPnx/5+eefj7z11ltH5mPqqvfVjqXOqnJ/v379InMHlpRSGjBgQGSWO1nGrtppZNiwYZHLZYZ2OJ89jedss802i8ydVlIqDhXgTNvPPvssMtvlzDPPjDxy5MjIi1LurGrH3Cxp/l2d8jlgKY7DOTgkgCUvljt32WWXyJ1yvnoKZ+/+9a9/Lfzb448/XvM1vL/1798/MtuIqxB0ym4uOU8//XRkrqTAz+8OO+wQuTt3Yqua7c4yMmdlp1S8JolDp7gaDD8TLLlztnxK3ft3+YRRkiRJWXYYJUmSlNUSz635CPnAAw+MfNlll0XmQpeXX355ZC5yuuKKKxaOu7DlL5aa+Qi4XKLh7G0uulmP3MzOdsOyyWqrrRaZm6qnVDwnLOdw4XbOpH711VcjV80kS2nRFmTtNCxnDBo0KDJLlCkVF1R/8803I7MMyvIZy9CNKJ/xd7brosble0nVDM2pU6fWfD1LW1tttVVkS9ILj+f+2WefjXzPPfcUfm7SpEmReX8aPHhwZM6G3nzzzSNz+E1PtVErDZPq27dvZK6iwe9xXgdz5syJzM9+rqTL+xu/m2bPnh2ZC3L/+te/jszF2FMqls1ZhuZQIG6KsPfee0dm36Yn28QnjJIkScqywyhJkqSslihJE/eAPeqooyJzpt+LL74YmeXp/fffv3CsddddN3LVwt+5hTa/wRJ0SsVH2F3Zp7aZH/d3B/59LBGWhwqwDMJzy8zZuOPGjYvM/TZ//OMfF47blYW7O2nowDfYRrwOUyqWYIhtyX29q36+p7B0lFLxmi3vvdrK+Lks/80seXEGKRc45us5G7c8nEffjef7jTfeiHzzzTdH5vCNlIpDmPjdU7VnNBe6577IPaV836u6NzcDzoA+7LDDIt94442RH3rooci8P3FB7w033LBwXA6tYVn55Zdfjvzxxx9HnjBhQmTe98qLtvM7iLOhufnEHnvsEZmrtvDcW5KWJElSw9hhlCRJUlbLlaRZ4jr88MNr/n/ujcpH/vfee2/hWHvuuWdkPtLla/jYd9asWZG552duUeAhQ4ZErudRcbuXoeuROwdsZ7YZ24ALoLLE89prrxWOxRlnnCnb1fJ01YLRrdi2fP8s+5f3kuZC3tzvnYvW8u+/4447InMGKMs95dcs7PtlLg8bYblw1KhRkc8///xF/t2NUlWGLv/NbAtuKMDyGQ0fPjyy+0fXh2XGGTNmRH7iiScic0WIgQMHFl7PmbocKsB7F6+9/fbbLzKHWHWn3GeqmRcIZ4n+4osvjswFr9ku/K7gAuqPPPJI4birrLJKZN5HOGyD/YYtttgiMjcVKeM99Oc//3lkrhrC9877U2/dq3zCKEmSpCw7jJIkScpq3ufJFfjolftK/+xnP4vMkhgf65dnLHPfVO5F/eCDD0Zm2ZMLqVbNmE6pWCKaMmVK5B133DFyPSWeVlokNaeqTNjVY7FtOHOQC3q/9957kUePHl04FmdNc3gCSxlVs8/4WSjPRm31kjTf89y5cyPfcsstkd99993Ca1ga22ijjSI/9dRTkVnmGTNmTOTbbrst8sEHH1w4Lks79ey5zhIR2648I5HXX9UKCa2i6voq/y0sPfO6YJmR56KnFjRvxWsip2pjgb///e+ReY4567W87y+vEQ6V4MofHHJz5ZVXRuYQgkWZ+V/PBgnl4QssgzdzW7KUy2EnnLXMzT84Y7p8f+d/syTPeyXPP4d/cFhP+bgsSe+6664133sjytDkE0ZJkiRl2WGUJElSlh1GSZIkZbXcGEbi+IFVV1018iGHHBKZSxCUp7Tz33isfv36ReZ4Ni6rw7FVH374YeG4HJvAcREcR8UxcFVjEZp5TMjC4Pgdjr/hOef5KI/vrNpBgOeH42w++uijyNxhYfr06YXXv/XWW5E5FodjY/key0u+VL2/Ro8z6Sq215/+9KfI99xzT+Ty7gcHHXRQZC4bwh0TeM38/ve/j3zJJZdELl+jJ510UmTuLsPPSNXSPczlXTD4byeeeGJqZRx7VrU7SErFc8sx2Lxfcdwix3+Xx1vpWzw3M2fOjMzxcRy/3rdv38jlewd38uAuVRx/zWXjOLZx6tSpkTfZZJPCcavGzPMzwvvm66+/HpnjLNdbb73C61vl/lY1/pz3FN632Ka5XaKq8Lzed999kXmvKo/n33bbbSNzHHhv7eJSD58wSpIkKcsOoyRJkrJauiRNfGzLKfEssfARf0rFx74sN44YMaLm66dNmxb51ltvjcxSXUopzZ8/PzJLFFzGh++xHfFxO0vB3Ph98803j7zLLrtELpcPWULgueVm77/4xS8ic0N4lg+4PEFKKe22226RBwwYEJklGJZyWr3UXC+W0rjEFJeEKC+rw+VseM65PAd3Vfjggw8is+z/9NNPF4571FFHRWb5iOef11LVki1VwxpS+t/SbSuoWgKFZeTy0l/cXYf/xvPEc1G1RFFXtdu1w/N0++23R2aJmN89a665ZmQO0yjbYIMNIh9wwAGRuVwPl0ficjHnnHNO4VhVw2z43vmZ6t+/f833Xl5qqdXbku+/ahexnHqWjHv00UcjVw1LSymln/70p5G7svNYT/IJoyRJkrLsMEqSJCmrbUrSxEfL3BC8PFOMq7GzDMlH9nw0zU3H+Th53LhxheOyXMeZ2JwhzF1nmumRc3dhqeOyyy6L/PDDD0d+8sknI7MMvc466xSOxTLbAw88EPnee++N/P7770dmaYCz588+++zCcY899tjI9cxab2c8Zyx5cUcWlqpnzJhReP2hhx4amaXjl156KTKHJrBszfLLMcccUzguh43wuuzONuJseQ5NaGZVu9vcddddkSdOnFh4DUtjPP8s6bNcyhInz72qscTMFRq4mxG/BzbddNPC69mW/O4YP358zddz1i3bl9dgSsXVBrgTGu973GmE/78T277e+wt/jrOpORyBQ6f48+Xvua233nqhf39v8wmjJEmSsuwwSpIkKastnzWz9MwZXixPp1Q9o7Lq/7N0xoWeBw4cWDguZy1ys3aWGPg4uh1n37LkxfIlZ8fy3Jx77rmRuYBqSsUFZTlTneUYzoDefvvtI7McXm6n7pz52U6GDRsWeauttorMkhc/4yml9MILL0TmTPaqmYNrrLFG5AsuuCDyEUccUfi53iiHcUZoq6halJwl0SlTphRewxmaLEMfeOCBkS+66KLILF22y32pJ/DccNMH/v8JEyZEZomyXDrmEBqWnnnfrPpO4j0wd1wOxaoa5mF7LzzeEy+99NLIc+fOjcy+CTcYSel/V/FoRn5jSpIkKcsOoyRJkrLasiRdtRhnDmc4Ve0VyeOyvM2yXfn1nL3LGadDhw6NzLJAVQmv/PubHWfZcabrq6++GnnOnDmROauMs3FTKp4f7sPKhbePO+64yFykttNnP9eL54blq2uvvTYy93weO3Zs4fUsQ3NIwdprrx15yJAhkVl65u9rxDCBqiEozYzvkyXGkSNHRn722WcLr2Eb7bXXXpHPO++8yNzAwCEb9eGQAO4Fze+IK664IvKkSZMil/e3Z1mTM9Y5tGrjjTeOzHvdzjvvHLk8+5rtWrUZgRYe+w33339/zcyf4eoo3CAkpda43pr/HUqSJKmh7DBKkiQpq0+uBNpDev0XVqnaB7KqRFVVtubC0ikV9xN97rnnIrN0wfLcCSecEJkzpcrlgjrLB91VY+hSO/Eccg9izhDkzEHusz148ODCsQYNGhSZJemq/TZbpMzSHW+yx68ltiM/8+VhGyynVLVFvXs7N5GWaKPCL0N7cRWBlIrlTl47Lb5Ic9O2UdX3Bdth1qxZhdewXMyhBsxsu6r9j8vXV4PviU3bRouiah93bl7AkjR/nsOo2E9IKaXlllsucgPaq65f2BJ3bUmSJDWOHUZJkiRltVz9oTvV89i3qozGzDJpSsU9ijkr6qGHHorMfZRZEtp7770jt8q+trXwvPFR+3bbbVczqzmxHVmubMHSZUeomj1d67/Vs6q+L3i/571RrYeL4XNjDuIws9133z0yF9lPqeHDBuriE0ZJkiRl2WGUJElSlh1GSZIkZTkQ6f/VsyxL1f8vL2GwwgorRD7ooIMiL7/88pHnzZsXmbu+cNeL8pJHrTDGQZKkTsDxqFz+bcGCBZHXWmutyPvuu2/kri4tVrUUYE8uWeYTRkmSJGXZYZQkSVJWR+/00qaaYqcXfae22v2gTdlGzc82an5t20bczYc7K5V3WfoGh6U12W5X7vQiSZKkrrPDKEmSpCxL0u3HknRraNsyTRuxjZqfbdT8bKPmZ0lakiRJXWeHUZIkSVmNKElLkiSphfiEUZIkSVl2GCVJkpRlh1GSJElZdhglSZKUZYdRkiRJWXYYJUmSlGWHUZIkSVl2GCVJkpRlh1GSJElZdhglSZKUZYdRkiRJWXYYJUmSlGWHUZIkSVl2GCVJkpRlh1GSJElZdhglSZKUZYdRkiRJWXYYJUmSlGWHUZIkSVl2GCVJkpRlh1GSJElZdhglSZKUZYdRkiRJWXYYJUmSlGWHUZIkSVn/BzmS+jptjB1uAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
@@ -2272,7 +2261,7 @@
     },
     {
      "data": {
-      "image/png": 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a+XqO/+Fx484tp556amTuUMHPThk/JxwL5BjGbcPxwJs3b47MJVs4Loht5PFuPuXrCo85l3nhMiM8V3ntcgxj8+HYxMsuu6zwPZ4LzAcccEBk7vTCnUZ4PyuPYeTSMFVj8Nr7fegvnHPA/gR3WuEOOuV7CK9RjTlmVeNEly9fXvg5Lu3G8fbXX399ZM6JaAteBSRJkpRlh1GSJElZNVmSLuOuBDNmzIi8ePHiyFwVf8CAAZFZXuQSK+XvceeW/v37R2apmyuxc+V9LktRfq8rr7wyMned2V5Lb3w8X34cX7WK/ZYtWyJPmTIl8vTp0xt83913373wvmyn/fffPzKXTOKj+qoy9Pr16wv/5vI9AwcOjFy1BIUah+crMz8fXG7H3XRaRq5cxt2lWJYj7nik5sN2KS/1VbXsSp8+fSJzKA7biMvGlZeC4evZ9vVShuZ1/KSTTop8++23R+Yyb1y6iPfzlIr37qqliPh13o94D+KOP+XfP2bMmMijR49u8He0Be+GkiRJyrLDKEmSpKyaLEmXV0B/6aWXIk+dOjUyZyPz0T7L03379o3M0mhKxTIkcSV4Pr5nOZS7tnA3mJSKu1iwVFq1Cfn2hI/aOWs8peKjeu7q8eCDD0Z+9NFHI3NWGsvLnBGYUkrDhw+PzDbv0KFDZB5nzsbl38QyaErFIQp8r+11uEBb4mf7k08+icxzmZ/5rl27NvhatZzG7ErBUmbv3r3/8bXaNo09rrx28XrI1/P+xPtWStXnWz22K8v43Dll3rx5ke+5557IXHUjpeKuYOXj/JetW7dG5mzoK664InJ5hzjejzh0i/fGtuaVWpIkSVl2GCVJkpRVkyVplhRTKs6G5sxWzsBkuZllMM64Pfjggwvv27Nnz8icGT1s2LDIfMzPsmeuhNmeH/Pz/8bH7ikVN0x/6KGHIj/++OOROVyAODOZs6JTKs4y44K0nHXOr3NR4twiq1Wl0Pbcfi2FQxXYflUz1jkcwePd+thePA9YuqylUli94PnCc4Rf5yYDnFnLn+G9LaXiUIN6PN/4f+Z9/KabborMzR9Wr14dmRt5pFRc0J7D2tg34evnz58f+dtvv43MmesppfTII49EZtm8ltrLJ4ySJEnKssMoSZKkrJosSZfxUfH7778fee3atZG5PydnNU2cODFyeVY0S5dVj+zrfd/Nstz/m8MCli1bFpmlapaIWdbnzPLu3bsX3pcz3TlDcOTIkZG5V2q9l1/aAo8z9/tmmYzls5UrV0aumtWulsOSNFeY4PFnia1Hjx6RPadaDlee4N7QH374YWQOv+H5VV4EXA3jEAwOP5szZ07kBx54IPKiRYsKr1+wYEHkpUuXRt6wYUNkXtPYLuedd17k8j7iHBZXqytH1OZfJUmSpJphh1GSJElZNVmS5iymlFIaNWpU5MceeywySyNcQLNqYVJLKdsuN0uae2pzJhkX8e7Vq1dkDgk49thjI59xxhmF9+WitVWLotu2bYvHn4vas5TJMihnetp2rY9lMi5ezCE/P//8c+SqPXPVvHic161bF5mL4bPtOBSH5xSHAaVUuyXOtlZVnq7aYzqlYltwSAAzh8hx1Qh+vbzf9/ZwXvkpkiRJUpYdRkmSJGXtwEfgraTVf2Gdaa7n2v/YTuW9pKsez/ORPksAXGCdCwZz9nT5NdvDY/tGao7/SE2eS7ymrFq1KjKHJgwdOjQyhxnUWPu22zbiLM5bb701MsugF198cWRuelBj5c121UZcrPuDDz6IzIWg+TNc4JmbH5Tv6/y5cim0FbSrNmqnGtVGNXXmS5IkqfbYYZQkSVKWHUZJkiRlOYax/Wm1MYzaJo7rqX110UYcE8exx9vJslXtto14b2a7VC0Vl7uXt/Hycu22jdoRxzBKkiRp29lhlCRJUpYl6fbHkvT2wTJN7bONap9tVPtso9pnSVqSJEnbzg6jJEmSsuwwSpIkKcsOoyRJkrLsMEqSJCmrLWZJS5IkaTviE0ZJkiRl2WGUJElSlh1GSZIkZdlhlCRJUpYdRkmSJGXZYZQkSVKWHUZJkiRl2WGUJElSlh1GSZIkZdlhlCRJUpYdRkmSJGXZYZQkSVKWHUZJkiRl2WGUJElSlh1GSZIkZdlhlCRJUpYdRkmSJGXZYZQkSVKWHUZJkiRl2WGUJElSlh1GSZIkZdlhlCRJUpYdRkmSJGXZYZQkSVKWHUZJkiRl/Rfbr5qvv9WsdwAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
@@ -2282,7 +2271,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 792x324 with 6 Axes>"
       ]
diff --git a/images/autoencoders/extracted_features_plot.png b/images/autoencoders/extracted_features_plot.png
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diff --git a/images/autoencoders/generated_digits_plot.png b/images/autoencoders/generated_digits_plot.png
index eb9fa7a..3ba6e3b 100644
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diff --git a/images/autoencoders/linear_autoencoder_pca_plot.png b/images/autoencoders/linear_autoencoder_pca_plot.png
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diff --git a/images/autoencoders/reconstruction_plot.png b/images/autoencoders/reconstruction_plot.png
index 0a92b3c..faaa3b3 100644
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