05_ResNet50_with_augmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TfWEgbPgblp3"
   },
   "source": [
    "## Import relevant libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Io5NCIV7bkl3"
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import os\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.layers import Flatten, Conv2D, BatchNormalization, Dropout, Dense\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cxKEDCgz6z0K"
   },
   "source": [
    "## Apply ResNet50 Transfer Learning with Image Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8vw6FVrd6qjz",
    "outputId": "1d4154f3-cd42-4bde-9ee3-c42151b022c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 66788 images belonging to 4 classes.\n",
      "Found 16696 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# generate batches of tensor image data with real-time data augmentation\n",
    "\n",
    "datagen=ImageDataGenerator(rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,\n",
    "        validation_split=0.2)\n",
    "\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    \"/OCT2017 /train\",\n",
    "    target_size = (224, 224),\n",
    "    batch_size=128,\n",
    "    class_mode = \"categorical\",\n",
    "    subset=\"training\")\n",
    "\n",
    "validation_generator = datagen.flow_from_directory(\n",
    "    \"/OCT2017 /train\",\n",
    "    target_size = (224, 224),\n",
    "    batch_size=128,\n",
    "    class_mode = \"categorical\",\n",
    "    subset=\"validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "zHazHC097SNO"
   },
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2P_-j2ikTAbu",
    "outputId": "0c2dbb86-fcfa-45d1-c0e1-970b966d0343"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94773248/94765736 [==============================] - 1s 0us/step\n",
      "94781440/94765736 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Reference: https://keras.io/api/applications/resnet/\n",
    "resnet50_model = ResNet50(include_top=False,weights=\"imagenet\",pooling=\"max\",input_shape=(224,224,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "LgJ16nGWWjXl"
   },
   "outputs": [],
   "source": [
    "for layer in resnet50_model.layers[:3]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hgZVHvU7TAjg",
    "outputId": "042548da-da41-4fa4-f93e-e341850638c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " max_pool (GlobalMaxPooling2D)  (None, 2048)         0           ['conv5_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 23,525,120\n",
      "Non-trainable params: 62,592\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "resnet50_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "B0cmWrbs7STe"
   },
   "outputs": [],
   "source": [
    "# Reference: https://keras.io/api/layers/regularization_layers/dropout/\n",
    "# Reference: https://keras.io/api/layers/core_layers/dense/\n",
    "# Reference: https://keras.io/api/layers/normalization_layers/batch_normalization/\n",
    "# Reference: https://keras.io/api/layers/regularization_layers/dropout/\n",
    "\n",
    "# Dropout Layer\n",
    "dropout = Dropout(rate=0.6)(resnet50_model.output)\n",
    "\n",
    "# Dense Layer\n",
    "Output_Layer = Dense(4, activation=\"softmax\")(dropout)\n",
    "\n",
    "# Creating a model\n",
    "model4 = Model(inputs=resnet50_model.input,outputs=Output_Layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KTTAwgxS7SWd",
    "outputId": "8071c303-d70a-4408-a54b-9ac665a23c98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " max_pool (GlobalMaxPooling2D)  (None, 2048)         0           ['conv5_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " dropout (Dropout)              (None, 2048)         0           ['max_pool[0][0]']               \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 4)            8196        ['dropout[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,595,908\n",
      "Trainable params: 23,533,316\n",
      "Non-trainable params: 62,592\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cU89aRBWv0zB",
    "outputId": "88253364-e37e-41ef-f005-2d50b5c5c545"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.8653 - accuracy: 0.8329\n",
      "Epoch 1: val_accuracy improved from -inf to 0.44556, saving model to model4_with_augmentation_save/weights-01-0.4456.hdf5\n",
      "521/521 [==============================] - 1196s 2s/step - loss: 0.8653 - accuracy: 0.8329 - val_loss: 1.2896 - val_accuracy: 0.4456\n",
      "Epoch 2/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.5212 - accuracy: 0.8822\n",
      "Epoch 2: val_accuracy did not improve from 0.44556\n",
      "521/521 [==============================] - 1081s 2s/step - loss: 0.5212 - accuracy: 0.8822 - val_loss: 1303.8081 - val_accuracy: 0.4393\n",
      "Epoch 3/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.4226 - accuracy: 0.8758\n",
      "Epoch 3: val_accuracy improved from 0.44556 to 0.81055, saving model to model4_with_augmentation_save/weights-03-0.8106.hdf5\n",
      "521/521 [==============================] - 1088s 2s/step - loss: 0.4226 - accuracy: 0.8758 - val_loss: 3.3090 - val_accuracy: 0.8106\n",
      "Epoch 4/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.2675 - accuracy: 0.9171\n",
      "Epoch 4: val_accuracy improved from 0.81055 to 0.93663, saving model to model4_with_augmentation_save/weights-04-0.9366.hdf5\n",
      "521/521 [==============================] - 1107s 2s/step - loss: 0.2675 - accuracy: 0.9171 - val_loss: 0.2052 - val_accuracy: 0.9366\n",
      "Epoch 5/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.3561 - accuracy: 0.9017\n",
      "Epoch 5: val_accuracy did not improve from 0.93663\n",
      "521/521 [==============================] - 1127s 2s/step - loss: 0.3561 - accuracy: 0.9017 - val_loss: 0.2857 - val_accuracy: 0.9003\n",
      "Epoch 6/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 1.0425 - accuracy: 0.8407\n",
      "Epoch 6: val_accuracy did not improve from 0.93663\n",
      "521/521 [==============================] - 1132s 2s/step - loss: 1.0425 - accuracy: 0.8407 - val_loss: 0.7357 - val_accuracy: 0.8038\n",
      "Epoch 7/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.7064 - accuracy: 0.8900\n",
      "Epoch 7: val_accuracy did not improve from 0.93663\n",
      "521/521 [==============================] - 1125s 2s/step - loss: 0.7064 - accuracy: 0.8900 - val_loss: 0.3495 - val_accuracy: 0.8812\n",
      "Epoch 8/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.2635 - accuracy: 0.9153\n",
      "Epoch 8: val_accuracy did not improve from 0.93663\n",
      "521/521 [==============================] - 1114s 2s/step - loss: 0.2635 - accuracy: 0.9153 - val_loss: 0.2268 - val_accuracy: 0.9317\n",
      "Epoch 9/50\n",
      "521/521 [==============================] - ETA: 0s - loss: 0.2929 - accuracy: 0.9073\n",
      "Epoch 9: val_accuracy did not improve from 0.93663\n",
      "521/521 [==============================] - 1119s 2s/step - loss: 0.2929 - accuracy: 0.9073 - val_loss: 7.4796 - val_accuracy: 0.5195\n",
      "Epoch 9: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f631f3ea4d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earlystop = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=5, verbose=1)\n",
    "\n",
    "# Creating model4_with_augmentation_save folder before running the code.\n",
    "filepath=\"model4_with_augmentation_save/weights-{epoch:02d}-{val_accuracy:.4f}.hdf5\"\n",
    "checkpoint = ModelCheckpoint(filepath=filepath, monitor='val_accuracy',verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "# Callbacks\n",
    "optimizer = tf.keras.optimizers.Adam()\n",
    "model4.compile(optimizer=optimizer,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
    "\n",
    "# Creating a list with all the callbacks we want\n",
    "callback_list = [earlystop, checkpoint]\n",
    "\n",
    "train_samples = 66788\n",
    "validation_samples = 16696\n",
    "batch_size = 128\n",
    "model4.fit(train_generator, validation_data= validation_generator, steps_per_epoch=train_samples//batch_size, callbacks=callback_list, epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 232
    },
    "id": "FD9Bd4bH7ScN",
    "outputId": "30f0d883-fa0f-4461-cdce-4d718015c65b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation acc values\n",
    "plt.figure(figsize=(30, 5))\n",
    "plt.subplot(121)\n",
    "plt.plot(model4.history.history['accuracy'])\n",
    "plt.plot(model4.history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(122)\n",
    "plt.plot(model4.history.history['loss'])\n",
    "plt.plot(model4.history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4b0UPNxUBx66",
    "outputId": "a99f2515-649d-43ec-aedd-157c6a60a1ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 968 images belonging to 4 classes.\n",
      "8/8 [==============================] - 13s 1s/step - loss: 0.1236 - accuracy: 0.9618\n",
      "Accuracy on Test Dataset:96.1776852607727, Loss on Test Dataset: 0.12363702803850174\n"
     ]
    }
   ],
   "source": [
    "# Accuracy on test dataset\n",
    "\n",
    "test_generator = datagen.flow_from_directory(\n",
    "    \"/OCT2017 /test\",\n",
    "    target_size = (224, 224),\n",
    "    batch_size=128,\n",
    "    class_mode = \"categorical\",\n",
    "    shuffle = False)\n",
    "test_samples = 968\n",
    "batch_size = 128\n",
    "\n",
    "result = model4.evaluate(test_generator, steps = test_samples // batch_size+1, verbose = 1)\n",
    "print(\"Accuracy on Test Dataset:{0}, Loss on Test Dataset: {1}\".format(result[1]*100,result[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "VY-mqqNbAhtz",
    "outputId": "fbd01f72-af1a-4210-c4d8-5d1724f7f30b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Confusion Matrix\n",
    "\n",
    "# predicted values\n",
    "y_pred = model4.predict(test_generator, test_samples // batch_size+1)\n",
    "predicted_values = np.argmax(y_pred, axis=1)\n",
    "\n",
    "confusionmatrix = confusion_matrix(test_generator.classes,predicted_values)\n",
    "plt.title(\"ResNet50 Transfer Learning with Image Augmentation\")\n",
    "sns.heatmap(confusionmatrix, annot=True, cmap=\"YlGnBu\")\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('Actual Values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ayh0Db9SKlG"
   },
   "source": [
    "**Observations**\n",
    "\n",
    "We are getting an accuracy of 0.96 on the test dataset using ResNet50 Transfer Learning Model with Image Augmentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "JKg75vtwRu7d"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "05 ResNet50 Transfer Learning with Image Augmentation.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}