Densenet Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "aGzaYlAiKLnA",
    "outputId": "0b89ae59-a4fd-4201-c67a-a9d3fca2fc02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Lpj0mShhKVAl"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications.mobilenet import preprocess_input\n",
    "import pickle\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras import applications\n",
    "from tensorflow.keras.layers import BatchNormalization,Conv2D, AveragePooling2D,TimeDistributed,Dense, Dropout, Activation, Flatten,GlobalAveragePooling2D,MaxPool2D\n",
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "\n",
    "    if(logs.get('val_accuracy')>0.94):\n",
    "\n",
    "      print(\"\\nReached 98% accuracy so cancelling training!\")\n",
    "\n",
    "      self.model.stop_training = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_SecOB3qKZ1X"
   },
   "outputs": [],
   "source": [
    "pickle_in = open(\"/content/drive/My Drive/Dataset/X1.pickle\",\"rb\")\n",
    "X = pickle.load(pickle_in)\n",
    "\n",
    "\n",
    "pickle_in = open(\"/content/drive/My Drive/Dataset/y1.pickle\",\"rb\")\n",
    "y = pickle.load(pickle_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ognfX9TqJc8B"
   },
   "outputs": [],
   "source": [
    "%load_ext tensorboard\n",
    "# # %reload_ext tensorboard\n",
    "%tensorboard --logdir {logs_base_dir}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "cT0phCVHKvbd",
    "outputId": "ff5be5b7-8d23-47e3-fcb6-365e555aa00a"
   },
   "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, 300, 300, 1) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 306, 306, 1)  0           input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_conv (Conv2D)             (None, 150, 150, 64) 3200        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_bn (BatchNormalization)   (None, 150, 150, 64) 256         conv1_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_relu (Activation)         (None, 150, 150, 64) 0           conv1_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 152, 152, 64) 0           conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pool (MaxPooling2D)       (None, 75, 75, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 75, 75, 64)   4160        pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 75, 75, 64)   0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 75, 75, 64)   36928       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_relu (Activation (None, 75, 75, 64)   0           conv2_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_conv (Conv2D)    (None, 75, 75, 256)  16640       pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_conv (Conv2D)    (None, 75, 75, 256)  16640       conv2_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_add (Add)          (None, 75, 75, 256)  0           conv2_block1_0_bn[0][0]          \n",
      "                                                                 conv2_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_out (Activation)   (None, 75, 75, 256)  0           conv2_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 75, 75, 64)   16448       conv2_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 75, 75, 64)   0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 75, 75, 64)   36928       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_relu (Activation (None, 75, 75, 64)   0           conv2_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_conv (Conv2D)    (None, 75, 75, 256)  16640       conv2_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_add (Add)          (None, 75, 75, 256)  0           conv2_block1_out[0][0]           \n",
      "                                                                 conv2_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_out (Activation)   (None, 75, 75, 256)  0           conv2_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 75, 75, 64)   16448       conv2_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 75, 75, 64)   0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 75, 75, 64)   36928       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_relu (Activation (None, 75, 75, 64)   0           conv2_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_conv (Conv2D)    (None, 75, 75, 256)  16640       conv2_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_add (Add)          (None, 75, 75, 256)  0           conv2_block2_out[0][0]           \n",
      "                                                                 conv2_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_out (Activation)   (None, 75, 75, 256)  0           conv2_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 38, 38, 128)  32896       conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 38, 38, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_relu (Activation (None, 38, 38, 128)  0           conv3_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_conv (Conv2D)    (None, 38, 38, 512)  131584      conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_add (Add)          (None, 38, 38, 512)  0           conv3_block1_0_bn[0][0]          \n",
      "                                                                 conv3_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_out (Activation)   (None, 38, 38, 512)  0           conv3_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 38, 38, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_relu (Activation (None, 38, 38, 128)  0           conv3_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_add (Add)          (None, 38, 38, 512)  0           conv3_block1_out[0][0]           \n",
      "                                                                 conv3_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_out (Activation)   (None, 38, 38, 512)  0           conv3_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 38, 38, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_relu (Activation (None, 38, 38, 128)  0           conv3_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_add (Add)          (None, 38, 38, 512)  0           conv3_block2_out[0][0]           \n",
      "                                                                 conv3_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_out (Activation)   (None, 38, 38, 512)  0           conv3_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 38, 38, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_relu (Activation (None, 38, 38, 128)  0           conv3_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_add (Add)          (None, 38, 38, 512)  0           conv3_block3_out[0][0]           \n",
      "                                                                 conv3_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_out (Activation)   (None, 38, 38, 512)  0           conv3_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 19, 19, 256)  131328      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 19, 19, 256)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_relu (Activation (None, 19, 19, 256)  0           conv4_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_conv (Conv2D)    (None, 19, 19, 1024) 525312      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_add (Add)          (None, 19, 19, 1024) 0           conv4_block1_0_bn[0][0]          \n",
      "                                                                 conv4_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_out (Activation)   (None, 19, 19, 1024) 0           conv4_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 19, 19, 256)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_relu (Activation (None, 19, 19, 256)  0           conv4_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_add (Add)          (None, 19, 19, 1024) 0           conv4_block1_out[0][0]           \n",
      "                                                                 conv4_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_out (Activation)   (None, 19, 19, 1024) 0           conv4_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 19, 19, 256)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_relu (Activation (None, 19, 19, 256)  0           conv4_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_add (Add)          (None, 19, 19, 1024) 0           conv4_block2_out[0][0]           \n",
      "                                                                 conv4_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_out (Activation)   (None, 19, 19, 1024) 0           conv4_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 19, 19, 256)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_relu (Activation (None, 19, 19, 256)  0           conv4_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_add (Add)          (None, 19, 19, 1024) 0           conv4_block3_out[0][0]           \n",
      "                                                                 conv4_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_out (Activation)   (None, 19, 19, 1024) 0           conv4_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 19, 19, 256)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_relu (Activation (None, 19, 19, 256)  0           conv4_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_add (Add)          (None, 19, 19, 1024) 0           conv4_block4_out[0][0]           \n",
      "                                                                 conv4_block5_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_out (Activation)   (None, 19, 19, 1024) 0           conv4_block5_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 19, 19, 256)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_relu (Activation (None, 19, 19, 256)  0           conv4_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_add (Add)          (None, 19, 19, 1024) 0           conv4_block5_out[0][0]           \n",
      "                                                                 conv4_block6_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_out (Activation)   (None, 19, 19, 1024) 0           conv4_block6_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 10, 10, 512)  524800      conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 10, 10, 512)  0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 10, 10, 512)  2359808     conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_relu (Activation (None, 10, 10, 512)  0           conv5_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_conv (Conv2D)    (None, 10, 10, 2048) 2099200     conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_conv (Conv2D)    (None, 10, 10, 2048) 1050624     conv5_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_add (Add)          (None, 10, 10, 2048) 0           conv5_block1_0_bn[0][0]          \n",
      "                                                                 conv5_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_out (Activation)   (None, 10, 10, 2048) 0           conv5_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 10, 10, 512)  1049088     conv5_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 10, 10, 512)  0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 10, 10, 512)  2359808     conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_relu (Activation (None, 10, 10, 512)  0           conv5_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_conv (Conv2D)    (None, 10, 10, 2048) 1050624     conv5_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_add (Add)          (None, 10, 10, 2048) 0           conv5_block1_out[0][0]           \n",
      "                                                                 conv5_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_out (Activation)   (None, 10, 10, 2048) 0           conv5_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 10, 10, 512)  1049088     conv5_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 10, 10, 512)  0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 10, 10, 512)  2359808     conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 10, 10, 512)  0           conv5_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 10, 10, 2048) 1050624     conv5_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_add (Add)          (None, 10, 10, 2048) 0           conv5_block2_out[0][0]           \n",
      "                                                                 conv5_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_out (Activation)   (None, 10, 10, 2048) 0           conv5_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 2048)         0           global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 3)            6147        dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,587\n",
      "Trainable params: 23,534,467\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "X = X/255.0\n",
    "X=np.array(X)\n",
    "y=np.array(y)\n",
    "img_height,img_width=300,300\n",
    "base_model = applications.densenet.DenseNet121(weights= None, include_top=False, input_shape= (img_height,img_width,1))\n",
    "\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "predictions = Dense(3, activation= 'softmax')(x)\n",
    "model = Model(inputs = base_model.input, outputs = predictions)\n",
    "model.compile(optimizer= 'adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HdxKhNJZK7KP",
    "outputId": "ce3399e7-d122-468e-b527-958d8c1d04b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113/113 [==============================] - 146s 1s/step - loss: 0.6213 - accuracy: 0.8086 - val_loss: 3.8903 - val_accuracy: 0.3356\n"
     ]
    }
   ],
   "source": [
    "callbacks = myCallback()\n",
    "history=model.fit(X, y, batch_size=32, epochs=1, validation_split=0.2,callbacks=[callbacks])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "colab_type": "code",
    "id": "UhMWFE22K-LC",
    "outputId": "e5dd27cc-aa4c-4b4c-8222-a55454512e3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.save('/content/drive/My Drive/Model/02densenet.h5')\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y-vCQKTAqjB-"
   },
   "outputs": [],
   "source": [
    "def prepare(ima):\n",
    "    IMG_SIZE = 300  # 50 in txt-based\n",
    "    img_array = cv2.cvtColor(ima,cv2.COLOR_BGR2GRAY)\n",
    "    img_array=img_array/255.0  # filepathread in the image, convert to grayscale\n",
    "    new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # resize image to match model's expected sizing\n",
    "    return new_array.reshape(-1,IMG_SIZE, IMG_SIZE,1)\n",
    "\n",
    "def disease(q):\n",
    "    if(q==0):\n",
    "        q=\"COVID19\"\n",
    "    elif(q==1):\n",
    "        q=\"NORMAL\"\n",
    "    elif(q==2):\n",
    "        q=\"PNEUMONIA\"\n",
    "    else:\n",
    "        q=\"UNKNOWN\"\n",
    "    return str(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 850
    },
    "colab_type": "code",
    "id": "79akoXfKq-lP",
    "outputId": "5ce3ff91-b778-415e-b80a-c61c213553f3"
   },
   "outputs": [],
   "source": [
    "model=tf.keras.models.load_model(\"01densenet.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n",
      "COVID19\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "for i in range(1,50):\n",
    "  img=cv2.imread(\"Dataset/VALIDATION/COVID19/\"+str(i)+\".jpeg\")\n",
    "  a=np.argmax(model.predict(prepare(img)))    \n",
    "  print(disease(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "P_xuC53LF7DP",
    "outputId": "efbbeae2-c4cc-468c-def7-f3b8cf042a0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4500\n",
      "4500\n"
     ]
    }
   ],
   "source": [
    "model=tf.keras.models.load_model(\"/content/drive/My Drive/Model/01densenet.h5\")\n",
    "print(len(X))\n",
    "print(len(y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-_M4rtq_IvDJ"
   },
   "outputs": [],
   "source": [
    "predict=np.empty(len(y),dtype='int')\n",
    "actual=y\n",
    "import cv2\n",
    "for i in range(0,len(y)-1):\n",
    "  img=X[i]\n",
    "  a=np.argmax(model.predict(prepare(img)))    \n",
    "  predict[i]=a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 377
    },
    "colab_type": "code",
    "id": "NKyCw8jIF8kf",
    "outputId": "60073ec9-c3ba-4faf-9ed5-fd07be4e1a17"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix :\n",
      "[[159   0   0   1]\n",
      " [  3  39  41   0]\n",
      " [  1   6 153   0]\n",
      " [  0   0   0   0]]\n",
      "Accuracy Score : 0.8709677419354839\n",
      "Report : \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.98       160\n",
      "           1       0.87      0.47      0.61        83\n",
      "           2       0.79      0.96      0.86       160\n",
      "    10914464       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.87       403\n",
      "   macro avg       0.66      0.60      0.61       403\n",
      "weighted avg       0.88      0.87      0.86       403\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report \n",
    "   \n",
    "results = confusion_matrix(actual, predict) \n",
    "  \n",
    "print ('Confusion Matrix :')\n",
    "print(results) \n",
    "print ('Accuracy Score :',accuracy_score(actual, predict)) \n",
    "print ('Report : ')\n",
    "print (classification_report(actual, predict)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n3mMIaqvI0_y"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EWShfD5Krmng"
   },
   "outputs": [],
   "source": []
  }
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