Project 06 - transfer learning - cat vs dog .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "08b6a4d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0263e241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "520434de",
   "metadata": {},
   "source": [
    "# instantiate the pre trained model and loading weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6682d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "local_weights_file = 'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "\n",
    "pre_trained_model = InceptionV3(input_shape = (150, 150, 3), \n",
    "                                include_top = False, \n",
    "                                weights = None)\n",
    "\n",
    "pre_trained_model.load_weights(local_weights_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcbcc56",
   "metadata": {},
   "source": [
    "# Freezing the layers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8de5c31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in pre_trained_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f25e6dde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"inception_v3\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 150, 150, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 74, 74, 32)   864         ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 74, 74, 32)  96          ['conv2d[0][0]']                 \n",
      " alization)                                                                                       \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 74, 74, 32)   0           ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 72, 72, 32)   9216        ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 72, 72, 32)  96          ['conv2d_1[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 72, 72, 32)   0           ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 72, 72, 64)   18432       ['activation_1[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 72, 72, 64)  192         ['conv2d_2[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 72, 72, 64)   0           ['batch_normalization_2[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 35, 35, 64)   0           ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 35, 35, 80)   5120        ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 35, 35, 80)  240         ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 35, 35, 80)   0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 33, 33, 192)  138240      ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 33, 33, 192)  576        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 33, 33, 192)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 192)  0          ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 16, 16, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 16, 16, 64)  192         ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 16, 16, 64)   0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 16, 16, 48)   9216        ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 16, 16, 96)   55296       ['activation_8[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 16, 16, 48)  144         ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 16, 16, 96)  288         ['conv2d_9[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 16, 16, 48)   0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 16, 16, 96)   0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, 16, 16, 192)  0          ['max_pooling2d_1[0][0]']        \n",
      " ing2D)                                                                                           \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 16, 16, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 16, 16, 64)   76800       ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 16, 16, 96)   82944       ['activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 16, 16, 32)   6144        ['average_pooling2d[0][0]']      \n",
      "                                                                                                  \n",
      " batch_normalization_5 (BatchNo  (None, 16, 16, 64)  192         ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 16, 16, 64)  192         ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " batch_normalization_10 (BatchN  (None, 16, 16, 96)  288         ['conv2d_10[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 16, 16, 32)  96          ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 16, 16, 64)   0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 16, 16, 64)   0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 16, 16, 32)   0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, 16, 16, 256)  0           ['activation_5[0][0]',           \n",
      "                                                                  'activation_7[0][0]',           \n",
      "                                                                  'activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 16, 16, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 16, 16, 64)  192         ['conv2d_15[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 16, 16, 48)   12288       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 16, 16, 96)   55296       ['activation_15[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 16, 16, 48)  144         ['conv2d_13[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 16, 16, 96)  288         ['conv2d_16[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 16, 16, 48)   0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_16[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_1 (AveragePo  (None, 16, 16, 256)  0          ['mixed0[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 16, 16, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 16, 16, 64)   76800       ['activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 16, 16, 96)   82944       ['activation_16[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 16, 16, 64)   16384       ['average_pooling2d_1[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 16, 16, 64)  192         ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 16, 16, 64)  192         ['conv2d_14[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 16, 16, 96)  288         ['conv2d_17[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 16, 16, 64)  192         ['conv2d_18[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_14[0][0]'] \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_17[0][0]'] \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_18[0][0]'] \n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, 16, 16, 288)  0           ['activation_12[0][0]',          \n",
      "                                                                  'activation_14[0][0]',          \n",
      "                                                                  'activation_17[0][0]',          \n",
      "                                                                  'activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 16, 16, 64)   18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 16, 16, 64)  192         ['conv2d_22[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_22[0][0]'] \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " conv2d_20 (Conv2D)             (None, 16, 16, 48)   13824       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, 16, 16, 96)   55296       ['activation_22[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 16, 16, 48)  144         ['conv2d_20[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 16, 16, 96)  288         ['conv2d_23[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 16, 16, 48)   0           ['batch_normalization_20[0][0]'] \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_23[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_2 (AveragePo  (None, 16, 16, 288)  0          ['mixed1[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 16, 16, 64)   18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 16, 16, 64)   76800       ['activation_20[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, 16, 16, 96)   82944       ['activation_23[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, 16, 16, 64)   18432       ['average_pooling2d_2[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 16, 16, 64)  192         ['conv2d_19[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 16, 16, 64)  192         ['conv2d_21[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 16, 16, 96)  288         ['conv2d_24[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 16, 16, 64)  192         ['conv2d_25[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_19[0][0]'] \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_21[0][0]'] \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_24[0][0]'] \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_25[0][0]'] \n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, 16, 16, 288)  0           ['activation_19[0][0]',          \n",
      "                                                                  'activation_21[0][0]',          \n",
      "                                                                  'activation_24[0][0]',          \n",
      "                                                                  'activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, 16, 16, 64)   18432       ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 16, 16, 64)  192         ['conv2d_27[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 16, 16, 64)   0           ['batch_normalization_27[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, 16, 16, 96)   55296       ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 16, 16, 96)  288         ['conv2d_28[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 16, 16, 96)   0           ['batch_normalization_28[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, 7, 7, 384)    995328      ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, 7, 7, 96)     82944       ['activation_28[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 7, 7, 384)   1152        ['conv2d_26[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 7, 7, 96)    288         ['conv2d_29[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 7, 7, 384)    0           ['batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 7, 7, 96)     0           ['batch_normalization_29[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 288)   0           ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, 7, 7, 768)    0           ['activation_26[0][0]',          \n",
      "                                                                  'activation_29[0][0]',          \n",
      "                                                                  'max_pooling2d_2[0][0]']        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 7, 7, 128)    98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 7, 7, 128)   384         ['conv2d_34[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_34[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_34[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 7, 7, 128)   384         ['conv2d_35[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_35[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, 7, 7, 128)    98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_35[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 7, 7, 128)   384         ['conv2d_31[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, 7, 7, 128)   384         ['conv2d_36[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_31[0][0]'] \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_36[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_31[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, 7, 7, 128)    114688      ['activation_36[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 7, 7, 128)   384         ['conv2d_32[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, 7, 7, 128)   384         ['conv2d_37[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_32[0][0]'] \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, 7, 7, 128)    0           ['batch_normalization_37[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_3 (AveragePo  (None, 7, 7, 768)   0           ['mixed3[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 7, 7, 192)    172032      ['activation_32[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, 7, 7, 192)    172032      ['activation_37[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_3[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 7, 7, 192)   576         ['conv2d_30[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 7, 7, 192)   576         ['conv2d_33[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, 7, 7, 192)   576         ['conv2d_38[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, 7, 7, 192)   576         ['conv2d_39[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_30[0][0]'] \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_33[0][0]'] \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_38[0][0]'] \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_39[0][0]'] \n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, 7, 7, 768)    0           ['activation_30[0][0]',          \n",
      "                                                                  'activation_33[0][0]',          \n",
      "                                                                  'activation_38[0][0]',          \n",
      "                                                                  'activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_44 (BatchN  (None, 7, 7, 160)   480         ['conv2d_44[0][0]']              \n",
      " ormalization)                                                                                    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_44[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_44[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_45 (BatchN  (None, 7, 7, 160)   480         ['conv2d_45[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_45[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_45[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, 7, 7, 160)   480         ['conv2d_41[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_46 (BatchN  (None, 7, 7, 160)   480         ['conv2d_46[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_41[0][0]'] \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_46[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_41[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_46[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_42 (BatchN  (None, 7, 7, 160)   480         ['conv2d_42[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_47 (BatchN  (None, 7, 7, 160)   480         ['conv2d_47[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_42[0][0]'] \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_47[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_4 (AveragePo  (None, 7, 7, 768)   0           ['mixed4[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_42[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_47[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_4[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, 7, 7, 192)   576         ['conv2d_40[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_43 (BatchN  (None, 7, 7, 192)   576         ['conv2d_43[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_48 (BatchN  (None, 7, 7, 192)   576         ['conv2d_48[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_49 (BatchN  (None, 7, 7, 192)   576         ['conv2d_49[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_40[0][0]'] \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_43[0][0]'] \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_48[0][0]'] \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_49[0][0]'] \n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, 7, 7, 768)    0           ['activation_40[0][0]',          \n",
      "                                                                  'activation_43[0][0]',          \n",
      "                                                                  'activation_48[0][0]',          \n",
      "                                                                  'activation_49[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_54 (BatchN  (None, 7, 7, 160)   480         ['conv2d_54[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_54 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_54[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_54[0][0]']          \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " batch_normalization_55 (BatchN  (None, 7, 7, 160)   480         ['conv2d_55[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_55 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_55[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, 7, 7, 160)    122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_55[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_51 (BatchN  (None, 7, 7, 160)   480         ['conv2d_51[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_56 (BatchN  (None, 7, 7, 160)   480         ['conv2d_56[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_51[0][0]'] \n",
      "                                                                                                  \n",
      " activation_56 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_56[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_51[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)             (None, 7, 7, 160)    179200      ['activation_56[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_52 (BatchN  (None, 7, 7, 160)   480         ['conv2d_52[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_57 (BatchN  (None, 7, 7, 160)   480         ['conv2d_57[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_52[0][0]'] \n",
      "                                                                                                  \n",
      " activation_57 (Activation)     (None, 7, 7, 160)    0           ['batch_normalization_57[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_5 (AveragePo  (None, 7, 7, 768)   0           ['mixed5[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_52[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)             (None, 7, 7, 192)    215040      ['activation_57[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_5[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_50 (BatchN  (None, 7, 7, 192)   576         ['conv2d_50[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_53 (BatchN  (None, 7, 7, 192)   576         ['conv2d_53[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_58 (BatchN  (None, 7, 7, 192)   576         ['conv2d_58[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_59 (BatchN  (None, 7, 7, 192)   576         ['conv2d_59[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_50[0][0]'] \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_53[0][0]'] \n",
      "                                                                                                  \n",
      " activation_58 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_58[0][0]'] \n",
      "                                                                                                  \n",
      " activation_59 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_59[0][0]'] \n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, 7, 7, 768)    0           ['activation_50[0][0]',          \n",
      "                                                                  'activation_53[0][0]',          \n",
      "                                                                  'activation_58[0][0]',          \n",
      "                                                                  'activation_59[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_64 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_64 (BatchN  (None, 7, 7, 192)   576         ['conv2d_64[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_64 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_64[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_65 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_64[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_65 (BatchN  (None, 7, 7, 192)   576         ['conv2d_65[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_65 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_65[0][0]'] \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " conv2d_61 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_66 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_65[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_61 (BatchN  (None, 7, 7, 192)   576         ['conv2d_61[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_66 (BatchN  (None, 7, 7, 192)   576         ['conv2d_66[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_61 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_61[0][0]'] \n",
      "                                                                                                  \n",
      " activation_66 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_66[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_62 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_61[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_67 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_66[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_62 (BatchN  (None, 7, 7, 192)   576         ['conv2d_62[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_67 (BatchN  (None, 7, 7, 192)   576         ['conv2d_67[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_62 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_62[0][0]'] \n",
      "                                                                                                  \n",
      " activation_67 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_67[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_6 (AveragePo  (None, 7, 7, 768)   0           ['mixed6[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_63 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_62[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_68 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_67[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_69 (Conv2D)             (None, 7, 7, 192)    147456      ['average_pooling2d_6[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_60 (BatchN  (None, 7, 7, 192)   576         ['conv2d_60[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_63 (BatchN  (None, 7, 7, 192)   576         ['conv2d_63[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_68 (BatchN  (None, 7, 7, 192)   576         ['conv2d_68[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_69 (BatchN  (None, 7, 7, 192)   576         ['conv2d_69[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_60 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_60[0][0]'] \n",
      "                                                                                                  \n",
      " activation_63 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_63[0][0]'] \n",
      "                                                                                                  \n",
      " activation_68 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_68[0][0]'] \n",
      "                                                                                                  \n",
      " activation_69 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_69[0][0]'] \n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, 7, 7, 768)    0           ['activation_60[0][0]',          \n",
      "                                                                  'activation_63[0][0]',          \n",
      "                                                                  'activation_68[0][0]',          \n",
      "                                                                  'activation_69[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_72 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_72 (BatchN  (None, 7, 7, 192)   576         ['conv2d_72[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_72 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_72[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_73 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_72[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_73 (BatchN  (None, 7, 7, 192)   576         ['conv2d_73[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_73 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_73[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_70 (Conv2D)             (None, 7, 7, 192)    147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_74 (Conv2D)             (None, 7, 7, 192)    258048      ['activation_73[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_70 (BatchN  (None, 7, 7, 192)   576         ['conv2d_70[0][0]']              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_74 (BatchN  (None, 7, 7, 192)   576         ['conv2d_74[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_70 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_70[0][0]'] \n",
      "                                                                                                  \n",
      " activation_74 (Activation)     (None, 7, 7, 192)    0           ['batch_normalization_74[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_71 (Conv2D)             (None, 3, 3, 320)    552960      ['activation_70[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_75 (Conv2D)             (None, 3, 3, 192)    331776      ['activation_74[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_71 (BatchN  (None, 3, 3, 320)   960         ['conv2d_71[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_75 (BatchN  (None, 3, 3, 192)   576         ['conv2d_75[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_71 (Activation)     (None, 3, 3, 320)    0           ['batch_normalization_71[0][0]'] \n",
      "                                                                                                  \n",
      " activation_75 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_75[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 3, 3, 768)   0           ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed8 (Concatenate)           (None, 3, 3, 1280)   0           ['activation_71[0][0]',          \n",
      "                                                                  'activation_75[0][0]',          \n",
      "                                                                  'max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_80 (Conv2D)             (None, 3, 3, 448)    573440      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_80 (BatchN  (None, 3, 3, 448)   1344        ['conv2d_80[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_80 (Activation)     (None, 3, 3, 448)    0           ['batch_normalization_80[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_77 (Conv2D)             (None, 3, 3, 384)    491520      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_81 (Conv2D)             (None, 3, 3, 384)    1548288     ['activation_80[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_77 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_77[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_81 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_81[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_77 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_77[0][0]'] \n",
      "                                                                                                  \n",
      " activation_81 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_81[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_78 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_79 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_82 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_83 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_7 (AveragePo  (None, 3, 3, 1280)  0           ['mixed8[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_76 (Conv2D)             (None, 3, 3, 320)    409600      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_78 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_78[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_79 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_79[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_82 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_82[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_83 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_83[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_84 (Conv2D)             (None, 3, 3, 192)    245760      ['average_pooling2d_7[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_76 (BatchN  (None, 3, 3, 320)   960         ['conv2d_76[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_78 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_78[0][0]'] \n",
      "                                                                                                  \n",
      " activation_79 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_79[0][0]'] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " activation_82 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_82[0][0]'] \n",
      "                                                                                                  \n",
      " activation_83 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_83[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_84 (BatchN  (None, 3, 3, 192)   576         ['conv2d_84[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_76 (Activation)     (None, 3, 3, 320)    0           ['batch_normalization_76[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_0 (Concatenate)         (None, 3, 3, 768)    0           ['activation_78[0][0]',          \n",
      "                                                                  'activation_79[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 3, 3, 768)    0           ['activation_82[0][0]',          \n",
      "                                                                  'activation_83[0][0]']          \n",
      "                                                                                                  \n",
      " activation_84 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_84[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9 (Concatenate)           (None, 3, 3, 2048)   0           ['activation_76[0][0]',          \n",
      "                                                                  'mixed9_0[0][0]',               \n",
      "                                                                  'concatenate[0][0]',            \n",
      "                                                                  'activation_84[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_89 (Conv2D)             (None, 3, 3, 448)    917504      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_89 (BatchN  (None, 3, 3, 448)   1344        ['conv2d_89[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_89 (Activation)     (None, 3, 3, 448)    0           ['batch_normalization_89[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_86 (Conv2D)             (None, 3, 3, 384)    786432      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_90 (Conv2D)             (None, 3, 3, 384)    1548288     ['activation_89[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_86 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_86[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_90 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_90[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_86 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_86[0][0]'] \n",
      "                                                                                                  \n",
      " activation_90 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_90[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_87 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_88 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_91 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_92 (Conv2D)             (None, 3, 3, 384)    442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_8 (AveragePo  (None, 3, 3, 2048)  0           ['mixed9[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_85 (Conv2D)             (None, 3, 3, 320)    655360      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_87 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_87[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_88 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_88[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_91 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_91[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_92 (BatchN  (None, 3, 3, 384)   1152        ['conv2d_92[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_93 (Conv2D)             (None, 3, 3, 192)    393216      ['average_pooling2d_8[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_85 (BatchN  (None, 3, 3, 320)   960         ['conv2d_85[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_87 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_87[0][0]'] \n",
      "                                                                                                  \n",
      " activation_88 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_88[0][0]'] \n",
      "                                                                                                  \n",
      " activation_91 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_91[0][0]'] \n",
      "                                                                                                  \n",
      " activation_92 (Activation)     (None, 3, 3, 384)    0           ['batch_normalization_92[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_93 (BatchN  (None, 3, 3, 192)   576         ['conv2d_93[0][0]']              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_85 (Activation)     (None, 3, 3, 320)    0           ['batch_normalization_85[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_1 (Concatenate)         (None, 3, 3, 768)    0           ['activation_87[0][0]',          \n",
      "                                                                  'activation_88[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 3, 3, 768)    0           ['activation_91[0][0]',          \n",
      "                                                                  'activation_92[0][0]']          \n",
      "                                                                                                  \n",
      " activation_93 (Activation)     (None, 3, 3, 192)    0           ['batch_normalization_93[0][0]'] \n",
      "                                                                                                  \n",
      " mixed10 (Concatenate)          (None, 3, 3, 2048)   0           ['activation_85[0][0]',          \n",
      "                                                                  'mixed9_1[0][0]',               \n",
      "                                                                  'concatenate_1[0][0]',          \n",
      "                                                                  'activation_93[0][0]']          \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 21,802,784\n",
      "Trainable params: 0\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "pre_trained_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5bfc96",
   "metadata": {},
   "source": [
    "# taking the layer named 'mixed7' as last output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d6e71c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last layer output shape:  (None, 7, 7, 768)\n"
     ]
    }
   ],
   "source": [
    "last_layer = pre_trained_model.get_layer('mixed7')\n",
    "print('last layer output shape: ', last_layer.output_shape)\n",
    "last_output = last_layer.output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3608e6",
   "metadata": {},
   "source": [
    "# Defining the model (another form of using model API )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2935c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flatten the output layer to 1 dimension\n",
    "x = layers.Flatten()(last_output)\n",
    "\n",
    "# Add a fully connected layer with 1,024 hidden units and ReLU activation\n",
    "x = layers.Dense(1024, activation = 'relu')(x)\n",
    "\n",
    "# Add a dropout rate of 0.2 to prevent overfitting \n",
    "x = layers.Dropout(0.2)(x)   \n",
    "\n",
    "# Add a final sigmoid layer for classification\n",
    "\n",
    "x = layers.Dense  (1, activation='sigmoid')(x)           \n",
    "\n",
    "model = Model( pre_trained_model.input, x)  # defining new model using pretrained model + new defined layers \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed798e6",
   "metadata": {},
   "source": [
    "# Model Compile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "630a708d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import RMSprop\n",
    "\n",
    "model.compile(optimizer = RMSprop(learning_rate=0.0001), \n",
    "              loss = 'binary_crossentropy', \n",
    "              metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e55df6",
   "metadata": {},
   "source": [
    "# Add our data-augmentation parameters to ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc66e97e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255.,\n",
    "                                   rotation_range = 40,\n",
    "                                   width_shift_range = 0.2,\n",
    "                                   height_shift_range = 0.2,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "# Note that the validation data should not be augmented!\n",
    "test_datagen = ImageDataGenerator( rescale = 1.0/255. )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "985c71a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cats_and_dogs_filtered\\\\train'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_dir = 'cats_and_dogs_filtered'\n",
    "train_dir = os.path.join(base_dir, 'train')  # add train to the end of the last address\n",
    "validation_dir = os.path.join(base_dir, 'validation')  \n",
    "train_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ad873c47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# Flow training images in batches of 20 using train_datagen generator\n",
    "\n",
    "\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                    batch_size = 20,\n",
    "                                                    class_mode = 'binary', \n",
    "                                                    target_size = (150, 150))     \n",
    "\n",
    "# Flow validation images in batches of 20 using test_datagen generator\n",
    "validation_generator =  test_datagen.flow_from_directory( validation_dir,\n",
    "                                                          batch_size  = 20,\n",
    "                                                          class_mode  = 'binary', \n",
    "                                                          target_size = (150, 150))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81ec4e54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "100/100 [==============================] - 64s 578ms/step - loss: 0.5544 - accuracy: 0.7832 - val_loss: 0.1255 - val_accuracy: 0.9490\n",
      "Epoch 2/5\n",
      "100/100 [==============================] - 56s 562ms/step - loss: 0.1818 - accuracy: 0.9330 - val_loss: 0.1525 - val_accuracy: 0.9430\n",
      "Epoch 3/5\n",
      "100/100 [==============================] - 56s 563ms/step - loss: 0.2090 - accuracy: 0.9193 - val_loss: 0.0957 - val_accuracy: 0.9670\n",
      "Epoch 4/5\n",
      "100/100 [==============================] - 57s 566ms/step - loss: 0.1805 - accuracy: 0.9366 - val_loss: 0.1074 - val_accuracy: 0.9630\n",
      "Epoch 5/5\n",
      "100/100 [==============================] - 60s 602ms/step - loss: 0.2324 - accuracy: 0.9210 - val_loss: 0.1108 - val_accuracy: 0.9610\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "            train_generator,\n",
    "            validation_data = validation_generator,\n",
    "            steps_per_epoch = 100,\n",
    "            epochs = 5,\n",
    "            validation_steps = 50,\n",
    "            verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d4a8d720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'b', label='Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend(loc=0)\n",
    "plt.figure()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a48dcde",
   "metadata": {},
   "source": [
    "# Results for google colab for 50 epochs "
   ]
  },
  {
   "attachments": {
    "image-4.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "d910117a",
   "metadata": {},
   "source": [
    "![image-4.png](attachment:image-4.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa264930",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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