cats_vs_noncats_using_transfer_learning_515.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import tensorflow\n",
    "\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, GlobalAveragePooling2D\n",
    "from tensorflow.keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tensorflow.random.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File format of train_dataset: <HDF5 file \"train_catvnoncat.h5\" (mode r)>\n",
      "File format of test_dataset: <HDF5 file \"test_catvnoncat.h5\" (mode r)>\n"
     ]
    }
   ],
   "source": [
    "train_dataset = h5py.File('/cxldata/datasets/project/cat-non-cat/train_catvnoncat.h5', \"r\")\n",
    "test_dataset = h5py.File('/cxldata/datasets/project/cat-non-cat/test_catvnoncat.h5', \"r\")\n",
    "\n",
    "print(\"File format of train_dataset:\", train_dataset)\n",
    "print(\"File format of test_dataset:\", test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # train set features\n",
    "train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # train set labels\n",
    "\n",
    "test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # test set features\n",
    "test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # test set labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes are:  [b'non-cat' b'cat']\n",
      "Groundtruth stored as:  1\n",
      "cat\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f26384852b0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = np.array(test_dataset[\"list_classes\"][:])\n",
    "\n",
    "print(\"Classes are: \",classes)\n",
    "print(\"Groundtruth stored as: \",train_set_y_orig[2])\n",
    "print(classes[train_set_y_orig[2]].decode('utf-8'))\n",
    "\n",
    "plt.imshow(train_set_x_orig[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_x = test_set_x_orig[:25]\n",
    "validation_y = test_set_y_orig[:25]\n",
    "\n",
    "test_set_x = test_set_x_orig[25:]\n",
    "test_set_y = test_set_y_orig[25:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_set_x shape:  (209, 64, 64, 3)\n",
      "train_set_y shape:  (209,)\n"
     ]
    }
   ],
   "source": [
    "print(\"train_set_x shape: \", train_set_x_orig.shape)\n",
    "print(\"train_set_y shape: \", train_set_y_orig.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation data size:  [[[[158 104  83]\n",
      "   [161 106  85]\n",
      "   [162 107  84]\n",
      "   ...\n",
      "   [170 120 100]\n",
      "   [167 121 103]\n",
      "   [172 127 109]]\n",
      "\n",
      "  [[158 103  82]\n",
      "   [160 104  82]\n",
      "   [162 105  83]\n",
      "   ...\n",
      "   [169 118  99]\n",
      "   [164 117  98]\n",
      "   [168 121 104]]\n",
      "\n",
      "  [[158 104  82]\n",
      "   [161 105  82]\n",
      "   [162 105  83]\n",
      "   ...\n",
      "   [173 123 102]\n",
      "   [169 122 104]\n",
      "   [168 122 104]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[102  68  52]\n",
      "   [ 44  31  23]\n",
      "   [ 28  23  20]\n",
      "   ...\n",
      "   [163 118 102]\n",
      "   [163 120 103]\n",
      "   [166 121 105]]\n",
      "\n",
      "  [[ 99  67  51]\n",
      "   [ 38  28  22]\n",
      "   [ 30  26  23]\n",
      "   ...\n",
      "   [161 117 100]\n",
      "   [164 121 104]\n",
      "   [168 123 106]]\n",
      "\n",
      "  [[127  95  72]\n",
      "   [ 39  29  22]\n",
      "   [ 30  25  22]\n",
      "   ...\n",
      "   [165 122 105]\n",
      "   [169 126 109]\n",
      "   [173 128 110]]]\n",
      "\n",
      "\n",
      " [[[115 110 111]\n",
      "   [137 129 129]\n",
      "   [155 146 145]\n",
      "   ...\n",
      "   [159 156 157]\n",
      "   [141 141 145]\n",
      "   [121 122 127]]\n",
      "\n",
      "  [[123 118 120]\n",
      "   [143 136 136]\n",
      "   [159 153 150]\n",
      "   ...\n",
      "   [167 164 165]\n",
      "   [151 151 154]\n",
      "   [130 133 137]]\n",
      "\n",
      "  [[135 130 130]\n",
      "   [150 145 141]\n",
      "   [164 159 153]\n",
      "   ...\n",
      "   [173 174 172]\n",
      "   [160 162 162]\n",
      "   [141 144 148]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[197 196 205]\n",
      "   [208 209 214]\n",
      "   [215 216 220]\n",
      "   ...\n",
      "   [222 224 229]\n",
      "   [205 208 215]\n",
      "   [185 189 197]]\n",
      "\n",
      "  [[190 192 199]\n",
      "   [203 205 210]\n",
      "   [213 214 218]\n",
      "   ...\n",
      "   [217 220 225]\n",
      "   [198 202 209]\n",
      "   [178 182 191]]\n",
      "\n",
      "  [[183 186 193]\n",
      "   [197 199 205]\n",
      "   [208 210 214]\n",
      "   ...\n",
      "   [212 215 220]\n",
      "   [192 196 203]\n",
      "   [171 176 186]]]\n",
      "\n",
      "\n",
      " [[[255 253 254]\n",
      "   [255 253 254]\n",
      "   [255 253 254]\n",
      "   ...\n",
      "   [197 178 118]\n",
      "   [195 177 116]\n",
      "   [192 176 115]]\n",
      "\n",
      "  [[255 253 254]\n",
      "   [255 253 254]\n",
      "   [255 253 254]\n",
      "   ...\n",
      "   [197 178 120]\n",
      "   [195 176 118]\n",
      "   [193 174 118]]\n",
      "\n",
      "  [[255 253 254]\n",
      "   [255 253 254]\n",
      "   [255 253 254]\n",
      "   ...\n",
      "   [197 177 121]\n",
      "   [194 174 121]\n",
      "   [190 171 122]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[ 94  95  97]\n",
      "   [102 104 108]\n",
      "   [115 113 115]\n",
      "   ...\n",
      "   [ 68  67  91]\n",
      "   [ 71  55  80]\n",
      "   [ 64  51  82]]\n",
      "\n",
      "  [[ 68  71  69]\n",
      "   [ 75  79  80]\n",
      "   [ 82  91  91]\n",
      "   ...\n",
      "   [122 103 109]\n",
      "   [114  84  99]\n",
      "   [ 97  72 100]]\n",
      "\n",
      "  [[ 67  86  89]\n",
      "   [ 62  83  90]\n",
      "   [ 47  73  82]\n",
      "   ...\n",
      "   [147 120 125]\n",
      "   [142 110 118]\n",
      "   [133 101 121]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[ 64 165  72]\n",
      "   [ 62 156  80]\n",
      "   [ 63 148  66]\n",
      "   ...\n",
      "   [ 26  65  26]\n",
      "   [ 23  64  27]\n",
      "   [ 23  65  28]]\n",
      "\n",
      "  [[ 56 162  54]\n",
      "   [ 56 155  69]\n",
      "   [ 52 141  43]\n",
      "   ...\n",
      "   [ 54  98  68]\n",
      "   [ 50  95  62]\n",
      "   [ 31  83  40]]\n",
      "\n",
      "  [[ 57 147  50]\n",
      "   [ 51 142  58]\n",
      "   [ 44 124  31]\n",
      "   ...\n",
      "   [ 57 106  60]\n",
      "   [ 62 132  63]\n",
      "   [ 68 153  65]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[114 192   8]\n",
      "   [124 197  19]\n",
      "   [101 169  26]\n",
      "   ...\n",
      "   [ 25  74  16]\n",
      "   [ 24  65  20]\n",
      "   [ 20  83  21]]\n",
      "\n",
      "  [[109 178  12]\n",
      "   [ 78 142  21]\n",
      "   [ 42 108  23]\n",
      "   ...\n",
      "   [ 48 124  31]\n",
      "   [ 32  78  19]\n",
      "   [ 15  75  17]]\n",
      "\n",
      "  [[ 53 130  11]\n",
      "   [ 22  96   8]\n",
      "   [ 21  89   8]\n",
      "   ...\n",
      "   [ 54 139  33]\n",
      "   [ 48 124  17]\n",
      "   [ 16  71  11]]]\n",
      "\n",
      "\n",
      " [[[255 255 255]\n",
      "   [255 255 255]\n",
      "   [255 255 255]\n",
      "   ...\n",
      "   [255 255 255]\n",
      "   [255 255 255]\n",
      "   [255 255 255]]\n",
      "\n",
      "  [[255 255 255]\n",
      "   [255 255 255]\n",
      "   [255 255 255]\n",
      "   ...\n",
      "   [255 255 255]\n",
      "   [255 255 255]\n",
      "   [255 255 255]]\n",
      "\n",
      "  [[255 255 255]\n",
      "   [255 255 255]\n",
      "   [255 255 255]\n",
      "   ...\n",
      "   [255 255 255]\n",
      "   [255 255 255]\n",
      "   [255 255 255]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[111 111 111]\n",
      "   [123 123 123]\n",
      "   [139 139 139]\n",
      "   ...\n",
      "   [ 57  57  57]\n",
      "   [ 75  75  75]\n",
      "   [ 85  85  85]]\n",
      "\n",
      "  [[ 97  97  97]\n",
      "   [114 114 114]\n",
      "   [144 144 144]\n",
      "   ...\n",
      "   [ 72  72  72]\n",
      "   [ 77  77  77]\n",
      "   [ 84  84  84]]\n",
      "\n",
      "  [[ 97  97  97]\n",
      "   [115 115 115]\n",
      "   [142 142 142]\n",
      "   ...\n",
      "   [ 89  89  89]\n",
      "   [ 84  84  84]\n",
      "   [ 71  71  71]]]\n",
      "\n",
      "\n",
      " [[[ 71  72  56]\n",
      "   [ 61  63  49]\n",
      "   [ 94  95  89]\n",
      "   ...\n",
      "   [  1   1   1]\n",
      "   [  1   1   1]\n",
      "   [  1   1   1]]\n",
      "\n",
      "  [[ 64  64  45]\n",
      "   [ 56  58  43]\n",
      "   [ 95  95  90]\n",
      "   ...\n",
      "   [  2   2   1]\n",
      "   [  2   2   2]\n",
      "   [  2   2   1]]\n",
      "\n",
      "  [[ 59  59  41]\n",
      "   [ 53  56  42]\n",
      "   [ 96  96  90]\n",
      "   ...\n",
      "   [  2   2   1]\n",
      "   [  2   2   2]\n",
      "   [  3   3   1]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[ 70  53  40]\n",
      "   [ 71  55  41]\n",
      "   [ 71  55  41]\n",
      "   ...\n",
      "   [ 85  63  44]\n",
      "   [ 83  64  48]\n",
      "   [ 69  55  40]]\n",
      "\n",
      "  [[ 68  52  38]\n",
      "   [ 68  52  39]\n",
      "   [ 68  52  39]\n",
      "   ...\n",
      "   [ 69  47  29]\n",
      "   [ 70  52  35]\n",
      "   [ 71  56  38]]\n",
      "\n",
      "  [[ 67  53  39]\n",
      "   [ 66  52  38]\n",
      "   [ 66  51  38]\n",
      "   ...\n",
      "   [ 63  37  18]\n",
      "   [ 65  39  20]\n",
      "   [ 62  40  23]]]]\n",
      "Validation data size:  [1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Validation data size: \", validation_x)\n",
    "print(\"Validation data size: \", validation_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_set_x shape:  [[[[209 133  53]\n",
      "   [207 130  51]\n",
      "   [181 107  38]\n",
      "   ...\n",
      "   [ 28  16   2]\n",
      "   [ 24  15   2]\n",
      "   [ 20  14   2]]\n",
      "\n",
      "  [[218 142  59]\n",
      "   [216 140  58]\n",
      "   [202 126  50]\n",
      "   ...\n",
      "   [ 29  17   3]\n",
      "   [ 25  15   3]\n",
      "   [ 22  15   3]]\n",
      "\n",
      "  [[227 151  66]\n",
      "   [226 149  64]\n",
      "   [217 139  59]\n",
      "   ...\n",
      "   [ 29  17   4]\n",
      "   [ 25  15   4]\n",
      "   [ 21  14   3]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[ 21  11   2]\n",
      "   [ 23  13   3]\n",
      "   [ 26  14   3]\n",
      "   ...\n",
      "   [109  98 103]\n",
      "   [104  94  99]\n",
      "   [102  91  97]]\n",
      "\n",
      "  [[ 21  11   2]\n",
      "   [ 23  13   3]\n",
      "   [ 25  13   4]\n",
      "   ...\n",
      "   [109 100 105]\n",
      "   [103  94  99]\n",
      "   [ 98  90  94]]\n",
      "\n",
      "  [[ 20  11   2]\n",
      "   [ 22  12   3]\n",
      "   [ 23  13   3]\n",
      "   ...\n",
      "   [102  90  95]\n",
      "   [ 97  86  90]\n",
      "   [ 94  84  89]]]\n",
      "\n",
      "\n",
      " [[[247 205 148]\n",
      "   [248 207 149]\n",
      "   [249 209 148]\n",
      "   ...\n",
      "   [116  46   5]\n",
      "   [128  58  10]\n",
      "   [137  66  11]]\n",
      "\n",
      "  [[244 200 143]\n",
      "   [242 199 144]\n",
      "   [234 190 134]\n",
      "   ...\n",
      "   [117  46   5]\n",
      "   [119  50   6]\n",
      "   [124  53   7]]\n",
      "\n",
      "  [[240 193 137]\n",
      "   [239 193 138]\n",
      "   [208 154 100]\n",
      "   ...\n",
      "   [115  45   4]\n",
      "   [118  48   4]\n",
      "   [126  56   5]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[197 142  91]\n",
      "   [196 144  92]\n",
      "   [202 153  96]\n",
      "   ...\n",
      "   [111  69  17]\n",
      "   [113  69  15]\n",
      "   [123  77  27]]\n",
      "\n",
      "  [[195 139  89]\n",
      "   [194 141  91]\n",
      "   [201 151  95]\n",
      "   ...\n",
      "   [114  71  24]\n",
      "   [116  71  20]\n",
      "   [121  76  28]]\n",
      "\n",
      "  [[188 134  85]\n",
      "   [194 140  91]\n",
      "   [200 150  95]\n",
      "   ...\n",
      "   [120  75  29]\n",
      "   [116  71  22]\n",
      "   [120  75  29]]]\n",
      "\n",
      "\n",
      " [[[ 44  71  41]\n",
      "   [ 78  99  74]\n",
      "   [104 125 102]\n",
      "   ...\n",
      "   [135 186 106]\n",
      "   [ 80 136  58]\n",
      "   [112 157  79]]\n",
      "\n",
      "  [[ 76  98  76]\n",
      "   [105 125 100]\n",
      "   [123 143 107]\n",
      "   ...\n",
      "   [112 170  83]\n",
      "   [113 165  91]\n",
      "   [112 158  88]]\n",
      "\n",
      "  [[108 129 102]\n",
      "   [108 132  95]\n",
      "   [114 140 102]\n",
      "   ...\n",
      "   [ 97 156  71]\n",
      "   [161 198 132]\n",
      "   [118 164 104]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[163 188 147]\n",
      "   [167 193 153]\n",
      "   [152 183 135]\n",
      "   ...\n",
      "   [ 99 135  78]\n",
      "   [137 168 122]\n",
      "   [102 138  88]]\n",
      "\n",
      "  [[153 178 140]\n",
      "   [156 182 140]\n",
      "   [151 180 132]\n",
      "   ...\n",
      "   [105 137  82]\n",
      "   [135 163 130]\n",
      "   [ 83 120  75]]\n",
      "\n",
      "  [[144 168 130]\n",
      "   [148 174 131]\n",
      "   [161 187 143]\n",
      "   ...\n",
      "   [116 146  93]\n",
      "   [ 98 131  92]\n",
      "   [ 59 100  45]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[ 41  47  84]\n",
      "   [ 72  78 112]\n",
      "   [103 102 124]\n",
      "   ...\n",
      "   [125 112 119]\n",
      "   [107 101 117]\n",
      "   [ 76  79 109]]\n",
      "\n",
      "  [[ 93  96 119]\n",
      "   [112 109 124]\n",
      "   [130 122 136]\n",
      "   ...\n",
      "   [141 121 118]\n",
      "   [130 112 114]\n",
      "   [122 109 117]]\n",
      "\n",
      "  [[119 114 123]\n",
      "   [133 124 128]\n",
      "   [164 149 145]\n",
      "   ...\n",
      "   [187 160 135]\n",
      "   [157 132 120]\n",
      "   [136 115 109]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[233 223 207]\n",
      "   [235 226 218]\n",
      "   [242 236 235]\n",
      "   ...\n",
      "   [164 170 196]\n",
      "   [189 185 196]\n",
      "   [190 182 188]]\n",
      "\n",
      "  [[247 250 248]\n",
      "   [236 238 232]\n",
      "   [219 215 219]\n",
      "   ...\n",
      "   [118 101 117]\n",
      "   [153 134 154]\n",
      "   [174 146 156]]\n",
      "\n",
      "  [[253 254 254]\n",
      "   [246 250 249]\n",
      "   [226 233 234]\n",
      "   ...\n",
      "   [154 111  93]\n",
      "   [176 132 104]\n",
      "   [183 141 116]]]\n",
      "\n",
      "\n",
      " [[[ 18  18  16]\n",
      "   [ 35  36  31]\n",
      "   [ 54  57  52]\n",
      "   ...\n",
      "   [138 140 118]\n",
      "   [139 141 124]\n",
      "   [130 129 114]]\n",
      "\n",
      "  [[ 21  21  18]\n",
      "   [ 44  45  39]\n",
      "   [ 68  74  66]\n",
      "   ...\n",
      "   [128 131 109]\n",
      "   [116 117  98]\n",
      "   [ 95  93  76]]\n",
      "\n",
      "  [[ 30  31  26]\n",
      "   [ 54  58  52]\n",
      "   [ 99 110 104]\n",
      "   ...\n",
      "   [127 129 108]\n",
      "   [113 114  94]\n",
      "   [ 99  99  82]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[178 178 163]\n",
      "   [161 163 146]\n",
      "   [170 174 163]\n",
      "   ...\n",
      "   [112 108  85]\n",
      "   [149 150 131]\n",
      "   [180 185 172]]\n",
      "\n",
      "  [[182 183 167]\n",
      "   [172 175 162]\n",
      "   [170 173 162]\n",
      "   ...\n",
      "   [ 88  90  77]\n",
      "   [122 115  89]\n",
      "   [157 154 131]]\n",
      "\n",
      "  [[188 190 179]\n",
      "   [173 175 163]\n",
      "   [160 162 146]\n",
      "   ...\n",
      "   [ 48  50  44]\n",
      "   [103 103  89]\n",
      "   [144 137 108]]]\n",
      "\n",
      "\n",
      " [[[133 163  75]\n",
      "   [ 98 120  44]\n",
      "   [108 132  47]\n",
      "   ...\n",
      "   [ 96 138  97]\n",
      "   [ 96 146 109]\n",
      "   [ 81 132 101]]\n",
      "\n",
      "  [[ 79 102  39]\n",
      "   [ 83 112  33]\n",
      "   [ 76 106  26]\n",
      "   ...\n",
      "   [ 99 149  76]\n",
      "   [ 71 119  62]\n",
      "   [ 58 106  72]]\n",
      "\n",
      "  [[ 35  53  27]\n",
      "   [ 54  74  46]\n",
      "   [ 35  55  17]\n",
      "   ...\n",
      "   [110 157 122]\n",
      "   [ 72 119  89]\n",
      "   [ 81 131 108]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[ 76 108  79]\n",
      "   [ 70 110  83]\n",
      "   [ 70 105  84]\n",
      "   ...\n",
      "   [ 25  49  21]\n",
      "   [ 26  53  24]\n",
      "   [ 27  54  28]]\n",
      "\n",
      "  [[109 120  97]\n",
      "   [ 86 116  86]\n",
      "   [ 71 105  79]\n",
      "   ...\n",
      "   [ 17  46  20]\n",
      "   [ 23  49  28]\n",
      "   [ 19  33  18]]\n",
      "\n",
      "  [[ 97 117 100]\n",
      "   [ 70 108  75]\n",
      "   [ 75  99  69]\n",
      "   ...\n",
      "   [  8  33  12]\n",
      "   [ 13  35  18]\n",
      "   [  5  22   5]]]]\n",
      "test_set_y shape:  [1 1 0 1 0 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(\"test_set_x shape: \", test_set_x)\n",
    "print(\"test_set_y shape: \", test_set_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 1s 0us/step\n",
      "58900480/58889256 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "vgg_base = VGG16(weights='imagenet', include_top=False)\n",
    "vgg_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = Input(shape=(64, 64, 3), name='image_input')\n",
    "\n",
    "#initiate a model\n",
    "vgg_model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add the VGG base model\n",
    "vgg_model.add(vgg_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg_model.add(GlobalAveragePooling2D())\n",
    "\n",
    "vgg_model.add(Dense(1024,activation='relu'))\n",
    "vgg_model.add(Dropout(0.6))\n",
    "\n",
    "vgg_model.add(Dense(512,activation='relu'))\n",
    "vgg_model.add(Dropout(0.5))\n",
    "\n",
    "vgg_model.add(Dense(1024,activation='relu'))\n",
    "vgg_model.add(Dropout(0.4))\n",
    "\n",
    "vgg_model.add(Dense(1024,activation='relu'))\n",
    "vgg_model.add(Dropout(0.3))\n",
    "\n",
    "vgg_model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/envs/tensorflow2/lib/python3.6/site-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n"
     ]
    }
   ],
   "source": [
    "sgd = SGD(lr=0.025)\n",
    "vgg_model.compile(loss= 'binary_crossentropy', optimizer=sgd, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "7/7 [==============================] - 6s 722ms/step - loss: 58.7498 - accuracy: 0.5120 - val_loss: 0.9869 - val_accuracy: 0.2800\n",
      "Epoch 2/10\n",
      "7/7 [==============================] - 5s 644ms/step - loss: 1.0473 - accuracy: 0.5837 - val_loss: 0.3365 - val_accuracy: 0.8400\n",
      "Epoch 3/10\n",
      "7/7 [==============================] - 4s 545ms/step - loss: 0.6690 - accuracy: 0.7273 - val_loss: 0.3600 - val_accuracy: 0.7600\n",
      "Epoch 4/10\n",
      "7/7 [==============================] - 4s 634ms/step - loss: 0.5443 - accuracy: 0.7608 - val_loss: 0.4032 - val_accuracy: 0.7600\n",
      "Epoch 5/10\n",
      "7/7 [==============================] - 4s 548ms/step - loss: 0.4485 - accuracy: 0.8086 - val_loss: 0.2385 - val_accuracy: 0.8800\n",
      "Epoch 6/10\n",
      "7/7 [==============================] - 4s 546ms/step - loss: 0.3949 - accuracy: 0.8565 - val_loss: 0.2640 - val_accuracy: 0.8000\n",
      "Epoch 7/10\n",
      "7/7 [==============================] - 4s 540ms/step - loss: 0.3853 - accuracy: 0.8469 - val_loss: 0.2344 - val_accuracy: 0.9600\n",
      "Epoch 8/10\n",
      "7/7 [==============================] - 4s 596ms/step - loss: 0.3316 - accuracy: 0.8660 - val_loss: 0.2084 - val_accuracy: 0.9600\n",
      "Epoch 9/10\n",
      "7/7 [==============================] - 4s 545ms/step - loss: 0.2450 - accuracy: 0.9187 - val_loss: 0.2252 - val_accuracy: 0.8800\n",
      "Epoch 10/10\n",
      "7/7 [==============================] - 4s 544ms/step - loss: 0.2646 - accuracy: 0.8947 - val_loss: 0.2362 - val_accuracy: 0.8000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f25f64eca90>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vgg_model.fit(train_set_x_orig, train_set_y_orig, epochs=10, verbose=1, validation_data=(validation_x, validation_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 301ms/step - loss: 4.6343 - accuracy: 0.3600\n"
     ]
    }
   ],
   "source": [
    "vgg_model_loss, vgg_model_acc = vgg_model.evaluate(test_set_x_orig,test_set_y_orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy using VGG16 model as the base: 0.36000001430511475\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy using VGG16 model as the base:', vgg_model_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, None, None, 3)]   0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, None, None, 64)    1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, None, None, 64)    36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, None, None, 128)   73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, None, None, 128)   147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, None, None, 256)   295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, None, None, 256)   590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, None, None, 512)   0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vgg_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(vgg_base, show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow2",
   "language": "python",
   "name": "tensorflow2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}