Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BMET5933 Assignment 2 Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from skimage.exposure import equalize_hist\n",
    "\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\qjy\\anaconda3\\lib\\site-packages\\skimage\\exposure\\exposure.py:181: UserWarning: This might be a color image. The histogram will be computed on the flattened image. You can instead apply this function to each color channel.\n",
      "  hist, bin_centers = histogram(image, nbins)\n"
     ]
    }
   ],
   "source": [
    "train_img_folder_n = 'chest_xray/train/NORMAL'\n",
    "train_img_list_n = os.listdir(train_img_folder_n)\n",
    "for i in range(len(train_img_list_n)):\n",
    "    img = cv2.imread(train_img_folder_n + '/' + train_img_list_n[i])\n",
    "    img_resized = cv2.resize(img, (224, 224)) #Resize to 224*224\n",
    "    img_equalized = equalize_hist(img_resized) #Enhance contrast by using histogram equalisation\n",
    "    curr_x = np.expand_dims(img_equalized, axis=0)    \n",
    "    if i == 0:\n",
    "        x_train_n = curr_x\n",
    "    else:\n",
    "        x_train_n = np.concatenate((x_train_n, curr_x), axis=0)\n",
    "        \n",
    "train_img_folder_p = 'chest_xray/train/PNEUMONIA'\n",
    "train_img_list_p = os.listdir(train_img_folder_p)\n",
    "for i in range(len(train_img_list_p)):\n",
    "    img = cv2.imread(train_img_folder_p + '/' + train_img_list_p[i])\n",
    "    img_resized = cv2.resize(img, (224, 224)) #Resize to 224*224\n",
    "    img_equalized = equalize_hist(img_resized) #Enhance contrast by using histogram equalisation\n",
    "    curr_x = np.expand_dims(img_equalized, axis=0)\n",
    "    if i == 0:\n",
    "        x_train_p = curr_x\n",
    "    else:\n",
    "        x_train_p = np.concatenate((x_train_p, curr_x), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_img_folder_n = 'chest_xray/test/NORMAL'\n",
    "test_img_list_n = os.listdir(test_img_folder_n)\n",
    "for i in range(len(test_img_list_n)):\n",
    "    img = cv2.imread(test_img_folder_n + '/' + test_img_list_n[i])\n",
    "    img_resized = cv2.resize(img, (224, 224)) #Resize to 224*224\n",
    "    img_equalized = equalize_hist(img_resized) #Enhance contrast by using histogram equalisation\n",
    "    curr_x = np.expand_dims(img_equalized, axis=0)    \n",
    "    if i == 0:\n",
    "        x_test_n = curr_x\n",
    "    else:\n",
    "        x_test_n = np.concatenate((x_test_n, curr_x), axis=0)\n",
    "        \n",
    "test_img_folder_p = 'chest_xray/test/PNEUMONIA'\n",
    "test_img_list_p = os.listdir(test_img_folder_p)\n",
    "for i in range(len(test_img_list_p)):\n",
    "    img = cv2.imread(test_img_folder_p + '/' + test_img_list_p[i])\n",
    "    img_resized = cv2.resize(img, (224, 224)) #Resize to 224*224\n",
    "    img_equalized = equalize_hist(img_resized) #Enhance contrast by using histogram equalisation\n",
    "    curr_x = np.expand_dims(img_equalized, axis=0)\n",
    "    if i == 0:\n",
    "        x_test_p = curr_x\n",
    "    else:\n",
    "        x_test_p = np.concatenate((x_test_p, curr_x), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Putting Data Together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_n = np.zeros(len(train_img_list_n))\n",
    "y_train_p = np.ones(len(train_img_list_p))\n",
    "y_test_n = np.zeros(len(test_img_list_n))\n",
    "y_test_p = np.ones(len(test_img_list_p))\n",
    "\n",
    "x_train = np.concatenate((x_train_n, x_train_p), axis=0)\n",
    "y_train = np.concatenate((y_train_n, y_train_p), axis=0)\n",
    "x_test = np.concatenate((x_test_n, x_test_p), axis=0)\n",
    "y_test = np.concatenate((y_test_n, y_test_p), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shuffling Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "np.random.shuffle(x_train)\n",
    "np.random.seed(1)\n",
    "np.random.shuffle(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x217eed2e608>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[66,:,:,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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",
      "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",
      "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \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 (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \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 (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \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 (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \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 (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \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 (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \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 (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \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 (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \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 (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \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 (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \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 (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \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",
      "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \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",
      "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \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",
      "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \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",
      "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \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",
      "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \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",
      "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \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 (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \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 (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \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 (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \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",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 1)            2049        avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 23,589,761\n",
      "Trainable params: 23,536,641\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = ResNet50(classes=1, weights=None, classifier_activation=\"sigmoid\")\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model = Model(inputs=base_model.input, outputs=base_model.output)\n",
    "my_model.compile(optimizer=RMSprop(lr=0.0001), loss='binary_crossentropy', metrics='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "332/332 [==============================] - 1096s 3s/step - loss: 0.3606 - accuracy: 0.8475 - val_loss: 8.0307 - val_accuracy: 0.6250\n",
      "Epoch 2/20\n",
      "332/332 [==============================] - 1120s 3s/step - loss: 0.2164 - accuracy: 0.9111 - val_loss: 2.3602 - val_accuracy: 0.6250\n",
      "Epoch 3/20\n",
      "332/332 [==============================] - 1149s 3s/step - loss: 0.1727 - accuracy: 0.9386 - val_loss: 0.8900 - val_accuracy: 0.6715\n",
      "Epoch 4/20\n",
      "332/332 [==============================] - 1264s 4s/step - loss: 0.1367 - accuracy: 0.9495 - val_loss: 0.5287 - val_accuracy: 0.8093\n",
      "Epoch 5/20\n",
      "332/332 [==============================] - 1231s 4s/step - loss: 0.1072 - accuracy: 0.9589 - val_loss: 0.5034 - val_accuracy: 0.8365\n",
      "Epoch 6/20\n",
      "332/332 [==============================] - 1047s 3s/step - loss: 0.0898 - accuracy: 0.9646 - val_loss: 0.4472 - val_accuracy: 0.8590\n",
      "Epoch 7/20\n",
      "332/332 [==============================] - 1045s 3s/step - loss: 0.0821 - accuracy: 0.9653 - val_loss: 0.8203 - val_accuracy: 0.7196\n",
      "Epoch 8/20\n",
      "332/332 [==============================] - 1036s 3s/step - loss: 0.0619 - accuracy: 0.9774 - val_loss: 0.7404 - val_accuracy: 0.8109\n",
      "Epoch 9/20\n",
      "332/332 [==============================] - 1230s 4s/step - loss: 0.0486 - accuracy: 0.9804 - val_loss: 0.6297 - val_accuracy: 0.8205\n",
      "Epoch 10/20\n",
      "332/332 [==============================] - 1229s 4s/step - loss: 0.0288 - accuracy: 0.9895 - val_loss: 1.2430 - val_accuracy: 0.8029\n",
      "Epoch 11/20\n",
      "332/332 [==============================] - 1080s 3s/step - loss: 0.0327 - accuracy: 0.9876 - val_loss: 1.4167 - val_accuracy: 0.7468\n",
      "Epoch 12/20\n",
      "332/332 [==============================] - 1092s 3s/step - loss: 0.0250 - accuracy: 0.9917 - val_loss: 0.8778 - val_accuracy: 0.8349\n",
      "Epoch 13/20\n",
      "332/332 [==============================] - 1086s 3s/step - loss: 0.0293 - accuracy: 0.9898 - val_loss: 2.7156 - val_accuracy: 0.6314\n",
      "Epoch 14/20\n",
      "332/332 [==============================] - 1136s 3s/step - loss: 0.0145 - accuracy: 0.9955 - val_loss: 1.9587 - val_accuracy: 0.7372\n",
      "Epoch 15/20\n",
      "332/332 [==============================] - 1099s 3s/step - loss: 0.0191 - accuracy: 0.9925 - val_loss: 1.6039 - val_accuracy: 0.8029\n",
      "Epoch 16/20\n",
      "332/332 [==============================] - 1106s 3s/step - loss: 0.0129 - accuracy: 0.9959 - val_loss: 1.4586 - val_accuracy: 0.8173\n",
      "Epoch 17/20\n",
      "332/332 [==============================] - 1154s 3s/step - loss: 0.0233 - accuracy: 0.9932 - val_loss: 2.2715 - val_accuracy: 0.7548\n",
      "Epoch 18/20\n",
      "332/332 [==============================] - 1073s 3s/step - loss: 0.0156 - accuracy: 0.9962 - val_loss: 2.5960 - val_accuracy: 0.7051\n",
      "Epoch 19/20\n",
      "332/332 [==============================] - 1143s 3s/step - loss: 0.0119 - accuracy: 0.9966 - val_loss: 2.4987 - val_accuracy: 0.7532\n",
      "Epoch 20/20\n",
      "332/332 [==============================] - 1265s 4s/step - loss: 0.0139 - accuracy: 0.9959 - val_loss: 1.1750 - val_accuracy: 0.8253\n"
     ]
    }
   ],
   "source": [
    "history = my_model.fit(x=x_train, y=y_train, validation_data=(x_test, y_test), batch_size=8, epochs=20, verbose=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_model.save('m4.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'],color='r')\n",
    "plt.plot(history.history['val_loss'],color='g')\n",
    "plt.title('Model 4 Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train_loss', 'test_loss'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'],color='b')\n",
    "plt.plot(history.history['val_accuracy'],color='k')\n",
    "plt.title('Model 4 Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train_acc', 'test_acc'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}