FINAL PROJECT END.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255,validation_split=0.3,horizontal_flip=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir=os.path.join(r'\\project_dataset\\train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "IMG_HEIGHT = 224\n",
    "IMG_WIDTH = 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3284 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_gen = image_generator.flow_from_directory(directory=str(data_dir),\n",
    "                                                     batch_size=BATCH_SIZE,\n",
    "                                                     shuffle=True,\n",
    "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                     subset='training',class_mode='binary'\n",
    "                                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1407 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_data_gen =image_generator.flow_from_directory(directory=str(data_dir),\n",
    "                                                     batch_size=10,\n",
    "                                                     shuffle=True,\n",
    "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                   subset='validation',class_mode='binary'\n",
    "                                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\keras_applications\\resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\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 (Conv2D)                  (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalization)   (None, 112, 112, 64) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 112, 112, 64) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 56, 56, 64)   4160        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 56, 56, 256)  16640       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 56, 56, 256)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 56, 56, 64)   16448       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 56, 56, 64)   36928       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 56, 56, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 56, 56, 256)  16640       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 56, 56, 256)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 56, 56, 256)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 56, 56, 256)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 28, 28, 128)  32896       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 128)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 28, 28, 512)  131584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 28, 28, 512)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 128)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 128)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 28, 28, 128)  65664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 28, 28, 128)  147584      activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 28, 28, 128)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 128)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 28, 28, 512)  66048       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 28, 28, 512)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 28, 28, 512)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 28, 28, 512)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 14, 14, 256)  131328      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 256)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 14, 14, 1024) 525312      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 14, 14, 1024) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 256)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 256)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 256)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 256)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 14, 14, 256)  262400      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 14, 14, 256)  590080      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 14, 14, 256)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 256)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 14, 14, 1024) 263168      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 14, 14, 1024) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 14, 14, 1024) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 1024) 0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 7, 7, 512)    524800      activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 512)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 7, 7, 2048)   2099200     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 7, 7, 2048)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 512)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 7, 7, 512)    1049088     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 7, 7, 512)    2359808     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 7, 7, 512)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 512)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 7, 7, 2048)   1050624     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 7, 7, 2048)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 7, 7, 2048)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 7, 7, 2048)   0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 100352)       0           activation_49[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 0\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Shubham\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: Update your `Model` call to the Keras 2 API: `Model(Tensor(\"in..., outputs=Tensor(\"fl...)`\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.resnet50 import ResNet50\n",
    "from keras.models import Model\n",
    "import keras\n",
    "restnet = ResNet50(include_top=False, weights='imagenet', input_shape=(IMG_HEIGHT,IMG_WIDTH,3))\n",
    "output = restnet.layers[-1].output\n",
    "output = keras.layers.Flatten()(output)\n",
    "restnet = Model(restnet.input, output=output)\n",
    "for layer in restnet.layers:\n",
    "    layer.trainable = False\n",
    "restnet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, InputLayer\n",
    "from keras.models import Sequential\n",
    "from keras import optimizers\n",
    "model = Sequential()\n",
    "model.add(restnet)\n",
    "model.add(Dense(512, activation='relu', input_dim=(IMG_HEIGHT,IMG_WIDTH,3)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      " - 114s - loss: 17.2763 - accuracy: 0.5195 - val_loss: 5.1552 - val_accuracy: 0.5000\n",
      "Epoch 2/30\n",
      " - 102s - loss: 3.9861 - accuracy: 0.6875 - val_loss: 1.4871 - val_accuracy: 0.4875\n",
      "Epoch 3/30\n",
      " - 104s - loss: 1.9993 - accuracy: 0.7695 - val_loss: 4.2442 - val_accuracy: 0.4750\n",
      "Epoch 4/30\n",
      " - 104s - loss: 0.9470 - accuracy: 0.8633 - val_loss: 1.8139 - val_accuracy: 0.4375\n",
      "Epoch 5/30\n",
      " - 96s - loss: 0.8608 - accuracy: 0.8934 - val_loss: 1.0816 - val_accuracy: 0.5875\n",
      "Epoch 6/30\n",
      " - 106s - loss: 1.3235 - accuracy: 0.8516 - val_loss: 2.0461 - val_accuracy: 0.4625\n",
      "Epoch 7/30\n",
      " - 95s - loss: 0.7556 - accuracy: 0.9023 - val_loss: 5.7999 - val_accuracy: 0.4750\n",
      "Epoch 8/30\n",
      " - 98s - loss: 1.9740 - accuracy: 0.8633 - val_loss: 2.9126 - val_accuracy: 0.3875\n",
      "Epoch 9/30\n",
      " - 106s - loss: 1.5525 - accuracy: 0.8633 - val_loss: 3.1448 - val_accuracy: 0.5750\n",
      "Epoch 10/30\n",
      " - 102s - loss: 1.1838 - accuracy: 0.9102 - val_loss: 4.7609 - val_accuracy: 0.4875\n",
      "Epoch 11/30\n",
      " - 113s - loss: 0.4594 - accuracy: 0.9258 - val_loss: 3.4718 - val_accuracy: 0.6875\n",
      "Epoch 12/30\n",
      " - 107s - loss: 0.6864 - accuracy: 0.9219 - val_loss: 4.9666 - val_accuracy: 0.5125\n",
      "Epoch 13/30\n",
      " - 121s - loss: 0.2811 - accuracy: 0.9414 - val_loss: 3.6160 - val_accuracy: 0.6125\n",
      "Epoch 14/30\n",
      " - 112s - loss: 0.4798 - accuracy: 0.9453 - val_loss: 3.7382 - val_accuracy: 0.5500\n",
      "Epoch 15/30\n",
      " - 107s - loss: 0.4472 - accuracy: 0.9180 - val_loss: 4.2700 - val_accuracy: 0.4500\n",
      "Epoch 16/30\n",
      " - 112s - loss: 0.2469 - accuracy: 0.9570 - val_loss: 4.7953 - val_accuracy: 0.6125\n",
      "Epoch 17/30\n",
      " - 105s - loss: 0.4203 - accuracy: 0.9609 - val_loss: 2.4692 - val_accuracy: 0.6000\n",
      "Epoch 18/30\n",
      " - 93s - loss: 0.2246 - accuracy: 0.9570 - val_loss: 2.6958 - val_accuracy: 0.5714\n",
      "Epoch 19/30\n",
      " - 117s - loss: 0.3996 - accuracy: 0.9492 - val_loss: 3.0135 - val_accuracy: 0.5750\n",
      "Epoch 20/30\n",
      " - 109s - loss: 0.3405 - accuracy: 0.9414 - val_loss: 7.1689 - val_accuracy: 0.4625\n",
      "Epoch 21/30\n",
      " - 100s - loss: 0.7602 - accuracy: 0.9414 - val_loss: 7.2754 - val_accuracy: 0.4750\n",
      "Epoch 22/30\n",
      " - 97s - loss: 0.5042 - accuracy: 0.9531 - val_loss: 4.2799 - val_accuracy: 0.5625\n",
      "Epoch 23/30\n",
      " - 100s - loss: 0.7136 - accuracy: 0.9258 - val_loss: 4.3449 - val_accuracy: 0.5125\n",
      "Epoch 24/30\n",
      " - 99s - loss: 0.3125 - accuracy: 0.9609 - val_loss: 2.8432 - val_accuracy: 0.4875\n",
      "Epoch 25/30\n",
      " - 97s - loss: 0.4996 - accuracy: 0.9375 - val_loss: 6.8808 - val_accuracy: 0.6000\n",
      "Epoch 26/30\n",
      " - 89s - loss: 0.7237 - accuracy: 0.9221 - val_loss: 2.6784 - val_accuracy: 0.6500\n",
      "Epoch 27/30\n",
      " - 94s - loss: 0.5666 - accuracy: 0.9570 - val_loss: 5.3630 - val_accuracy: 0.4875\n",
      "Epoch 28/30\n",
      " - 90s - loss: 0.5715 - accuracy: 0.9492 - val_loss: 3.1214 - val_accuracy: 0.5500\n",
      "Epoch 29/30\n",
      " - 90s - loss: 0.2712 - accuracy: 0.9727 - val_loss: 7.5636 - val_accuracy: 0.5500\n",
      "Epoch 30/30\n",
      " - 91s - loss: 0.5712 - accuracy: 0.9648 - val_loss: 7.0066 - val_accuracy: 0.4500\n"
     ]
    }
   ],
   "source": [
    "history=model.fit_generator(\n",
    "            train_data_gen,\n",
    "            steps_per_epoch=8,\n",
    "            epochs=30,\n",
    "            validation_data=validation_data_gen,\n",
    "            validation_steps=8,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# serialize model structure to JSON\n",
    "model_json = model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('FINALENDMODEL.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]]\n"
     ]
    },
    {
     "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": [
    "img_width, img_height = 224, 224\n",
    "model= tf.keras.models.load_model('FINALENDMODEL.h5')\n",
    "img = image.load_img('img_33.jpg', target_size=(img_width, img_height))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "images = np.vstack([x])\n",
    "classes = model.predict_classes(images, batch_size=1)\n",
    "print(classes)\n",
    "im = mpimg.imread(\"img_33.jpg\")\n",
    "plt.imshow(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#[0]-safe driving\n",
    "#[1]-not safe driving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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