Transfer Learning Resnet 50.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning VGG 16 and VGG 19 using Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please download the dataset from the below url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "\n",
    "config = ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the libraries as shown below\n",
    "\n",
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "#from keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "from tensorflow.keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "#import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-size all the images to this\n",
    "IMAGE_SIZE = [224, 224]\n",
    "\n",
    "train_path = 'Datasets/train'\n",
    "valid_path = 'Datasets/test'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Vgg 16 library as shown below and add preprocessing layer to the front of VGG\n",
    "# Here we will be using imagenet weights\n",
    "\n",
    "resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# don't train existing weights\n",
    "for layer in resnet.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "  # useful for getting number of output classes\n",
    "folders = glob('Datasets/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# our layers - you can add more if you want\n",
    "x = Flatten()(resnet.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = Dense(len(folders), activation='softmax')(x)\n",
    "\n",
    "# create a model object\n",
    "model = Model(inputs=resnet.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_2[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",
      "flatten_1 (Flatten)             (None, 100352)       0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 4)            401412      flatten_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 23,989,124\n",
      "Trainable params: 401,412\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# view the structure of the model\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tell the model what cost and optimization method to use\n",
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  metrics=['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the Image Data Generator to import the images from the dataset\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1951 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# Make sure you provide the same target size as initialied for the image size\n",
    "training_set = train_datagen.flow_from_directory('Datasets/train',\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 18 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = test_datagen.flow_from_directory('Datasets/test',\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "61/61 [==============================] - 31s 503ms/step - loss: 1.2868 - accuracy: 0.4936 - val_loss: 1.1249 - val_accuracy: 0.5556\n",
      "Epoch 2/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.9862 - accuracy: 0.6053 - val_loss: 1.1933 - val_accuracy: 0.6111\n",
      "Epoch 3/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.9024 - accuracy: 0.6412 - val_loss: 0.7966 - val_accuracy: 0.7778\n",
      "Epoch 4/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.8691 - accuracy: 0.6627 - val_loss: 0.8158 - val_accuracy: 0.6667\n",
      "Epoch 5/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.9322 - accuracy: 0.6361 - val_loss: 1.0051 - val_accuracy: 0.6667\n",
      "Epoch 6/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.8368 - accuracy: 0.6730 - val_loss: 0.6658 - val_accuracy: 0.7778\n",
      "Epoch 7/20\n",
      "61/61 [==============================] - 19s 305ms/step - loss: 0.8500 - accuracy: 0.6694 - val_loss: 0.8086 - val_accuracy: 0.7222\n",
      "Epoch 8/20\n",
      "61/61 [==============================] - 18s 303ms/step - loss: 0.7391 - accuracy: 0.7176 - val_loss: 0.6687 - val_accuracy: 0.7778\n",
      "Epoch 9/20\n",
      "61/61 [==============================] - 18s 300ms/step - loss: 0.7539 - accuracy: 0.6914 - val_loss: 0.5906 - val_accuracy: 0.7778\n",
      "Epoch 10/20\n",
      "61/61 [==============================] - 18s 296ms/step - loss: 0.6641 - accuracy: 0.7381 - val_loss: 1.0657 - val_accuracy: 0.7222\n",
      "Epoch 11/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.9751 - accuracy: 0.6674 - val_loss: 0.8098 - val_accuracy: 0.7778\n",
      "Epoch 12/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.7192 - accuracy: 0.7258 - val_loss: 0.7715 - val_accuracy: 0.7778\n",
      "Epoch 13/20\n",
      "61/61 [==============================] - 19s 307ms/step - loss: 0.7212 - accuracy: 0.7283 - val_loss: 1.2519 - val_accuracy: 0.7222\n",
      "Epoch 14/20\n",
      "61/61 [==============================] - 23s 369ms/step - loss: 0.6548 - accuracy: 0.7509 - val_loss: 0.7284 - val_accuracy: 0.7778\n",
      "Epoch 15/20\n",
      "61/61 [==============================] - 18s 295ms/step - loss: 0.6957 - accuracy: 0.7278 - val_loss: 0.5299 - val_accuracy: 0.8333\n",
      "Epoch 16/20\n",
      "61/61 [==============================] - 18s 296ms/step - loss: 0.7172 - accuracy: 0.7191 - val_loss: 0.7328 - val_accuracy: 0.7778\n",
      "Epoch 17/20\n",
      "61/61 [==============================] - 18s 294ms/step - loss: 0.6455 - accuracy: 0.7509 - val_loss: 0.6202 - val_accuracy: 0.7778\n",
      "Epoch 18/20\n",
      "61/61 [==============================] - 18s 294ms/step - loss: 0.6164 - accuracy: 0.7565 - val_loss: 0.4560 - val_accuracy: 0.8333\n",
      "Epoch 19/20\n",
      "61/61 [==============================] - 18s 295ms/step - loss: 0.6614 - accuracy: 0.7576 - val_loss: 0.9876 - val_accuracy: 0.7222\n",
      "Epoch 20/20\n",
      "61/61 [==============================] - 18s 294ms/step - loss: 0.7661 - accuracy: 0.7355 - val_loss: 0.5443 - val_accuracy: 0.7778\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "# Run the cell. It will take some time to execute\n",
    "r = model.fit_generator(\n",
    "  training_set,\n",
    "  validation_data=test_set,\n",
    "  epochs=20,\n",
    "  steps_per_epoch=len(training_set),\n",
    "  validation_steps=len(test_set)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n",
    "\n",
    "# plot the accuracy\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save it as a h5 file\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model.save('model_resnet50.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred = model.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.08071638e-03, 3.28080803e-02, 1.40938358e-02, 9.52017426e-01],\n",
       "       [6.48212081e-05, 3.76272453e-07, 9.98108983e-01, 1.82576303e-03],\n",
       "       [9.98069108e-01, 4.38354881e-08, 2.79694970e-04, 1.65126985e-03],\n",
       "       [1.90574420e-03, 6.20834953e-07, 9.98076916e-01, 1.66221180e-05],\n",
       "       [2.93544168e-03, 2.18342058e-03, 1.03987521e-02, 9.84482408e-01],\n",
       "       [1.23585598e-03, 1.37195078e-04, 9.93926585e-01, 4.70046466e-03],\n",
       "       [5.18485345e-03, 9.52562571e-01, 1.32831134e-04, 4.21197526e-02],\n",
       "       [4.34744754e-04, 1.28285792e-02, 3.98747995e-03, 9.82749104e-01],\n",
       "       [8.25350955e-02, 3.11684459e-01, 3.73537302e-01, 2.32243121e-01],\n",
       "       [7.13894069e-01, 1.14700936e-01, 1.23989824e-02, 1.59005970e-01],\n",
       "       [1.23955928e-01, 3.05530787e-01, 3.02462336e-02, 5.40267050e-01],\n",
       "       [2.72632996e-03, 2.09897235e-02, 2.10788921e-02, 9.55205083e-01],\n",
       "       [5.82802715e-03, 4.64445323e-01, 3.76655348e-02, 4.92061198e-01],\n",
       "       [2.54945271e-03, 2.06118330e-06, 3.14365199e-04, 9.97134089e-01],\n",
       "       [1.11564873e-02, 2.96992337e-04, 9.86988664e-01, 1.55796099e-03],\n",
       "       [4.65637725e-03, 8.08736682e-01, 4.15630117e-02, 1.45043999e-01],\n",
       "       [3.95378843e-03, 7.04863906e-01, 1.88800885e-04, 2.90993541e-01],\n",
       "       [1.24013796e-02, 8.02336668e-04, 9.60396826e-01, 2.63994653e-02]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y_pred = np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2, 0, 2, 3, 2, 1, 3, 2, 0, 3, 3, 3, 3, 2, 1, 1, 2], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=load_model('model_resnet50.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 6.7060997e+01,  5.4221001e+01,  4.7320000e+01],\n",
       "         [ 6.9060997e+01,  5.6221001e+01,  4.9320000e+01],\n",
       "         [ 7.3060997e+01,  6.0221001e+01,  5.3320000e+01],\n",
       "         ...,\n",
       "         [ 7.4060997e+01,  5.6221001e+01,  4.6320000e+01],\n",
       "         [ 5.5060997e+01,  3.7221001e+01,  2.7320000e+01],\n",
       "         [ 4.1060997e+01,  2.3221001e+01,  1.3320000e+01]],\n",
       "\n",
       "        [[ 7.5060997e+01,  6.2221001e+01,  5.5320000e+01],\n",
       "         [ 7.8060997e+01,  6.5221001e+01,  5.8320000e+01],\n",
       "         [ 8.1060997e+01,  6.8221001e+01,  6.1320000e+01],\n",
       "         ...,\n",
       "         [ 9.7060997e+01,  7.9221001e+01,  6.9320000e+01],\n",
       "         [ 7.3060997e+01,  5.5221001e+01,  4.5320000e+01],\n",
       "         [ 4.9060997e+01,  3.1221001e+01,  2.1320000e+01]],\n",
       "\n",
       "        [[ 8.7060997e+01,  7.4221001e+01,  6.7320000e+01],\n",
       "         [ 9.0060997e+01,  7.7221001e+01,  7.0320000e+01],\n",
       "         [ 9.3060997e+01,  8.0221001e+01,  7.3320000e+01],\n",
       "         ...,\n",
       "         [ 1.0106100e+02,  8.3221001e+01,  7.3320000e+01],\n",
       "         [ 7.5060997e+01,  5.7221001e+01,  4.7320000e+01],\n",
       "         [ 5.0060997e+01,  3.2221001e+01,  2.2320000e+01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 1.0406100e+02,  8.9221001e+01,  9.4320000e+01],\n",
       "         [ 1.0206100e+02,  8.7221001e+01,  9.2320000e+01],\n",
       "         [ 9.9060997e+01,  8.4221001e+01,  8.9320000e+01],\n",
       "         ...,\n",
       "         [-1.0939003e+01, -1.6778999e+01, -1.4680000e+01],\n",
       "         [-1.0939003e+01, -1.6778999e+01, -1.4680000e+01],\n",
       "         [-1.0939003e+01, -1.6778999e+01, -1.4680000e+01]],\n",
       "\n",
       "        [[ 1.0606100e+02,  9.1221001e+01,  9.6320000e+01],\n",
       "         [ 1.0406100e+02,  8.9221001e+01,  9.4320000e+01],\n",
       "         [ 1.0006100e+02,  8.5221001e+01,  9.0320000e+01],\n",
       "         ...,\n",
       "         [-5.9390030e+00, -1.1778999e+01, -9.6800003e+00],\n",
       "         [-5.9390030e+00, -1.1778999e+01, -9.6800003e+00],\n",
       "         [-5.9390030e+00, -1.1778999e+01, -9.6800003e+00]],\n",
       "\n",
       "        [[ 1.0806100e+02,  9.4221001e+01,  9.6320000e+01],\n",
       "         [ 1.0606100e+02,  9.2221001e+01,  9.4320000e+01],\n",
       "         [ 1.0206100e+02,  8.8221001e+01,  9.0320000e+01],\n",
       "         ...,\n",
       "         [ 6.0997009e-02, -5.7789993e+00, -3.6800003e+00],\n",
       "         [ 6.0997009e-02, -5.7789993e+00, -3.6800003e+00],\n",
       "         [ 6.0997009e-02, -5.7789993e+00, -3.6800003e+00]]]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "img=image.load_img('Datasets/Test/Coffee/download (2).jpg',target_size=(224,224))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        ...,\n",
       "        [254., 254., 254.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        ...,\n",
       "        [254., 254., 254.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        ...,\n",
       "        [254., 254., 254.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        ...,\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        ...,\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        ...,\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=image.img_to_array(img)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.expand_dims(x,axis=0)\n",
    "img_data=preprocess_input(x)\n",
    "img_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9745471, 0.0254529]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(img_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.argmax(model.predict(img_data), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.0'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
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}