4장. 4절. 케라스 모델(ResNet50).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-03T03:18:15.331472Z",
     "start_time": "2021-12-03T03:18:12.779895Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
      "                                                                 conv2_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
      "                                                                 conv2_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
      "                                                                 conv2_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
      "                                                                 conv3_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
      "                                                                 conv3_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
      "                                                                 conv3_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
      "                                                                 conv3_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
      "                                                                 conv4_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
      "                                                                 conv4_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
      "                                                                 conv4_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
      "                                                                 conv4_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
      "                                                                 conv4_block5_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
      "                                                                 conv4_block6_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
      "                                                                 conv5_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
      "                                                                 conv5_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
      "                                                                 conv5_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 25,636,712\n",
      "Trainable params: 25,583,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import ResNet50\n",
    "\n",
    "resnet = ResNet50(weights='imagenet')\n",
    "resnet.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-03T03:18:18.021890Z",
     "start_time": "2021-12-03T03:18:17.979174Z"
    }
   },
   "outputs": [],
   "source": [
    "json_string = resnet.to_json()\n",
    " \n",
    "with open(\"model.json\", \"w\") as f : \n",
    "    f.write(json_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-03T03:51:02.524782Z",
     "start_time": "2021-12-03T03:51:01.286000Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [('n02099712', 'Labrador_retriever', 0.270211), ('n02108089', 'boxer', 0.14952454), ('n02099849', 'Chesapeake_Bay_retriever', 0.11921774)]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import numpy as np\n",
    "\n",
    "model = ResNet50(weights='imagenet')\n",
    "\n",
    "img_path = 'YellowLabradorLooking_new.jpg'\n",
    "img = image.load_img(img_path, target_size=(224, 224))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "\n",
    "preds = model.predict(x)\n",
    "print('Predicted:', decode_predictions(preds, top=3)[0]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-03T03:20:21.211063Z",
     "start_time": "2021-12-03T03:20:21.158745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"class_name\": \"Functional\", \"config\": {\"name\": \"resnet50\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 224, 224, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_2\"}, \"name\": \"input_2\", \"inbound_nodes\": []}, {\"class_name\": \"ZeroPadding2D\", \"config\": {\"name\": \"conv1_pad\", \"trainable\": true, \"dtype\": \"float32\", \"padding\": [[3, 3], [3, 3]], \"data_format\": \"channels_last\"}, \"name\": \"conv1_pad\", \"inbound_nodes\": [[[\"input_2\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv1_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [7, 7], \"strides\": [2, 2], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv1_conv\", \"inbound_nodes\": [[[\"conv1_pad\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv1_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv1_bn\", \"inbound_nodes\": [[[\"conv1_conv\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv1_relu\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv1_relu\", \"inbound_nodes\": [[[\"conv1_bn\", 0, 0, {}]]]}, {\"class_name\": \"ZeroPadding2D\", \"config\": {\"name\": \"pool1_pad\", \"trainable\": true, \"dtype\": \"float32\", \"padding\": [[1, 1], [1, 1]], \"data_format\": \"channels_last\"}, \"name\": \"pool1_pad\", \"inbound_nodes\": [[[\"conv1_relu\", 0, 0, {}]]]}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"pool1_pool\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [3, 3], \"padding\": \"valid\", \"strides\": [2, 2], \"data_format\": \"channels_last\"}, \"name\": \"pool1_pool\", \"inbound_nodes\": [[[\"pool1_pad\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2_block1_1_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv2_block1_1_conv\", \"inbound_nodes\": [[[\"pool1_pool\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv2_block1_1_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv2_block1_1_bn\", \"inbound_nodes\": [[[\"conv2_block1_1_conv\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv2_block1_1_relu\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv2_block1_1_relu\", \"inbound_nodes\": [[[\"conv2_block1_1_bn\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2_block1_2_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv2_block1_2_conv\", \"inbound_nodes\": [[[\"conv2_block1_1_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv2_block1_2_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv2_block1_2_bn\", \"inbound_nodes\": [[[\"conv2_block1_2_conv\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv2_block1_2_relu\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv2_block1_2_relu\", \"inbound_nodes\": [[[\"conv2_block1_2_bn\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2_block1_0_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 256, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv2_block1_0_conv\", \"inbound_nodes\": [[[\"pool1_pool\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2_block1_3_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 256, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv2_block1_3_conv\", \"inbound_nodes\": [[[\"conv2_block1_2_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv2_block1_0_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv2_block1_0_bn\", \"inbound_nodes\": [[[\"conv2_block1_0_conv\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv2_block1_3_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv2_block1_3_bn\", \"inbound_nodes\": [[[\"conv2_block1_3_conv\", 0, 0, {}]]]}, {\"class_name\": \"Add\", \"config\": {\"name\": \"conv2_block1_add\", \"trainable\": true, \"dtype\": \"float32\"}, \"name\": \"conv2_block1_add\", \"inbound_nodes\": [[[\"conv2_block1_0_bn\", 0, 0, {}], [\"conv2_block1_3_bn\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv2_block1_out\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv2_block1_out\", \"inbound_nodes\": [[[\"conv2_block1_add\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2_block2_1_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv2_block2_1_conv\", \"inbound_nodes\": [[[\"conv2_block1_out\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv2_block2_1_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv2_block2_1_bn\", 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\"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv5_block1_2_conv\", \"inbound_nodes\": [[[\"conv5_block1_1_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block1_2_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block1_2_bn\", \"inbound_nodes\": [[[\"conv5_block1_2_conv\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv5_block1_2_relu\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv5_block1_2_relu\", \"inbound_nodes\": [[[\"conv5_block1_2_bn\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv5_block1_0_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 2048, \"kernel_size\": [1, 1], \"strides\": [2, 2], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv5_block1_0_conv\", \"inbound_nodes\": [[[\"conv4_block6_out\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv5_block1_3_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 2048, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv5_block1_3_conv\", \"inbound_nodes\": [[[\"conv5_block1_2_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block1_0_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block1_0_bn\", \"inbound_nodes\": [[[\"conv5_block1_0_conv\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block1_3_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block1_3_bn\", \"inbound_nodes\": [[[\"conv5_block1_3_conv\", 0, 0, {}]]]}, {\"class_name\": \"Add\", \"config\": {\"name\": \"conv5_block1_add\", \"trainable\": true, \"dtype\": \"float32\"}, \"name\": \"conv5_block1_add\", \"inbound_nodes\": [[[\"conv5_block1_0_bn\", 0, 0, {}], [\"conv5_block1_3_bn\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv5_block1_out\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv5_block1_out\", \"inbound_nodes\": [[[\"conv5_block1_add\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv5_block2_1_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv5_block2_1_conv\", \"inbound_nodes\": [[[\"conv5_block1_out\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block2_1_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block2_1_bn\", \"inbound_nodes\": [[[\"conv5_block2_1_conv\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv5_block2_1_relu\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv5_block2_1_relu\", \"inbound_nodes\": [[[\"conv5_block2_1_bn\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv5_block2_2_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv5_block2_2_conv\", \"inbound_nodes\": [[[\"conv5_block2_1_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block2_2_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block2_2_bn\", \"inbound_nodes\": 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[[[\"conv5_block2_2_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block2_3_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block2_3_bn\", \"inbound_nodes\": [[[\"conv5_block2_3_conv\", 0, 0, {}]]]}, {\"class_name\": \"Add\", \"config\": {\"name\": \"conv5_block2_add\", \"trainable\": true, \"dtype\": \"float32\"}, \"name\": \"conv5_block2_add\", \"inbound_nodes\": [[[\"conv5_block1_out\", 0, 0, {}], [\"conv5_block2_3_bn\", 0, 0, 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\"BatchNormalization\", \"config\": {\"name\": \"conv5_block3_1_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block3_1_bn\", \"inbound_nodes\": [[[\"conv5_block3_1_conv\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv5_block3_1_relu\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv5_block3_1_relu\", \"inbound_nodes\": [[[\"conv5_block3_1_bn\", 0, 0, {}]]]}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv5_block3_2_conv\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv5_block3_2_conv\", \"inbound_nodes\": [[[\"conv5_block3_1_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block3_2_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": 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\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"conv5_block3_3_conv\", \"inbound_nodes\": [[[\"conv5_block3_2_relu\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"conv5_block3_3_bn\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 1.001e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"conv5_block3_3_bn\", \"inbound_nodes\": [[[\"conv5_block3_3_conv\", 0, 0, {}]]]}, {\"class_name\": \"Add\", \"config\": {\"name\": \"conv5_block3_add\", \"trainable\": true, \"dtype\": \"float32\"}, \"name\": \"conv5_block3_add\", \"inbound_nodes\": [[[\"conv5_block2_out\", 0, 0, {}], [\"conv5_block3_3_bn\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"conv5_block3_out\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"conv5_block3_out\", \"inbound_nodes\": [[[\"conv5_block3_add\", 0, 0, {}]]]}, {\"class_name\": \"GlobalAveragePooling2D\", \"config\": {\"name\": \"avg_pool\", \"trainable\": true, \"dtype\": \"float32\", \"data_format\": \"channels_last\"}, \"name\": \"avg_pool\", \"inbound_nodes\": [[[\"conv5_block3_out\", 0, 0, {}]]]}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"predictions\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 1000, \"activation\": \"softmax\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}, \"name\": \"predictions\", \"inbound_nodes\": [[[\"avg_pool\", 0, 0, {}]]]}], \"input_layers\": [[\"input_2\", 0, 0]], \"output_layers\": [[\"predictions\", 0, 0]]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-03T03:33:49.929857Z",
     "start_time": "2021-12-03T03:33:49.260936Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import models, layers\n",
    "from tensorflow.keras import Input\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras import optimizers, initializers, regularizers, metrics\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.layers import BatchNormalization, Conv2D, Activation, Dense, GlobalAveragePooling2D, MaxPooling2D, ZeroPadding2D, Add\n",
    " \n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    " \n",
    "\n",
    "# number of classes\n",
    "K = 4\n",
    "\n",
    "\n",
    "input_tensor = Input(shape=(224, 224, 3), dtype='float32', name='input')\n",
    "\n",
    " \n",
    "def conv1_layer(x):    \n",
    "    x = ZeroPadding2D(padding=(3, 3))(x)\n",
    "    x = Conv2D(64, (7, 7), strides=(2, 2))(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "    x = ZeroPadding2D(padding=(1,1))(x)\n",
    " \n",
    "    return x   \n",
    " \n",
    "    \n",
    "\n",
    "def conv2_layer(x):         \n",
    "    x = MaxPooling2D((3, 3), 2)(x)     \n",
    " \n",
    "    shortcut = x\n",
    " \n",
    "    for i in range(3):\n",
    "        if (i == 0):\n",
    "            x = Conv2D(64, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    "            \n",
    "            x = Conv2D(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            x = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            shortcut = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(shortcut)            \n",
    "            x = BatchNormalization()(x)\n",
    "            shortcut = BatchNormalization()(shortcut)\n",
    " \n",
    "            x = Add()([x, shortcut])\n",
    "            x = Activation('relu')(x)\n",
    "            \n",
    "            shortcut = x\n",
    " \n",
    "        else:\n",
    "            x = Conv2D(64, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    "            \n",
    "            x = Conv2D(64, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            x = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)            \n",
    " \n",
    "            x = Add()([x, shortcut])   \n",
    "            x = Activation('relu')(x)  \n",
    " \n",
    "            shortcut = x        \n",
    "    \n",
    "    return x\n",
    " \n",
    " \n",
    " \n",
    "def conv3_layer(x):        \n",
    "    shortcut = x    \n",
    "    \n",
    "    for i in range(4):     \n",
    "        if(i == 0):            \n",
    "            x = Conv2D(128, (1, 1), strides=(2, 2), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)        \n",
    "            \n",
    "            x = Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)  \n",
    " \n",
    "            x = Conv2D(512, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            shortcut = Conv2D(512, (1, 1), strides=(2, 2), padding='valid')(shortcut)\n",
    "            x = BatchNormalization()(x)\n",
    "            shortcut = BatchNormalization()(shortcut)            \n",
    " \n",
    "            x = Add()([x, shortcut])    \n",
    "            x = Activation('relu')(x)    \n",
    " \n",
    "            shortcut = x              \n",
    "        \n",
    "        else:\n",
    "            x = Conv2D(128, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    "            \n",
    "            x = Conv2D(128, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            x = Conv2D(512, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)            \n",
    " \n",
    "            x = Add()([x, shortcut])     \n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            shortcut = x      \n",
    "            \n",
    "    return x\n",
    " \n",
    " \n",
    " \n",
    "def conv4_layer(x):\n",
    "    shortcut = x        \n",
    "  \n",
    "    for i in range(6):     \n",
    "        if(i == 0):            \n",
    "            x = Conv2D(256, (1, 1), strides=(2, 2), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)        \n",
    "            \n",
    "            x = Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)  \n",
    " \n",
    "            x = Conv2D(1024, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            shortcut = Conv2D(1024, (1, 1), strides=(2, 2), padding='valid')(shortcut)\n",
    "            x = BatchNormalization()(x)\n",
    "            shortcut = BatchNormalization()(shortcut)\n",
    " \n",
    "            x = Add()([x, shortcut]) \n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            shortcut = x               \n",
    "        \n",
    "        else:\n",
    "            x = Conv2D(256, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    "            \n",
    "            x = Conv2D(256, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            x = Conv2D(1024, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)            \n",
    " \n",
    "            x = Add()([x, shortcut])    \n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            shortcut = x      \n",
    " \n",
    "    return x\n",
    " \n",
    " \n",
    " \n",
    "def conv5_layer(x):\n",
    "    shortcut = x    \n",
    "  \n",
    "    for i in range(3):     \n",
    "        if(i == 0):            \n",
    "            x = Conv2D(512, (1, 1), strides=(2, 2), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)        \n",
    "            \n",
    "            x = Conv2D(512, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)  \n",
    " \n",
    "            x = Conv2D(2048, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            shortcut = Conv2D(2048, (1, 1), strides=(2, 2), padding='valid')(shortcut)\n",
    "            x = BatchNormalization()(x)\n",
    "            shortcut = BatchNormalization()(shortcut)            \n",
    " \n",
    "            x = Add()([x, shortcut])  \n",
    "            x = Activation('relu')(x)      \n",
    " \n",
    "            shortcut = x               \n",
    "        \n",
    "        else:\n",
    "            x = Conv2D(512, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    "            \n",
    "            x = Conv2D(512, (3, 3), strides=(1, 1), padding='same')(x)\n",
    "            x = BatchNormalization()(x)\n",
    "            x = Activation('relu')(x)\n",
    " \n",
    "            x = Conv2D(2048, (1, 1), strides=(1, 1), padding='valid')(x)\n",
    "            x = BatchNormalization()(x)           \n",
    "            \n",
    "            x = Add()([x, shortcut]) \n",
    "            x = Activation('relu')(x)       \n",
    " \n",
    "            shortcut = x                  \n",
    " \n",
    "    return x\n",
    " \n",
    " \n",
    " \n",
    "x = conv1_layer(input_tensor)\n",
    "x = conv2_layer(x)\n",
    "x = conv3_layer(x)\n",
    "x = conv4_layer(x)\n",
    "x = conv5_layer(x)\n",
    " \n",
    "x = GlobalAveragePooling2D()(x)\n",
    "output_tensor = Dense(K, activation='softmax')(x)\n",
    " \n",
    "resnet50 = Model(input_tensor, output_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-03T03:33:56.415152Z",
     "start_time": "2021-12-03T03:33:56.382478Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input (InputLayer)              [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d (ZeroPadding2D)  (None, 230, 230, 3)  0           input[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 112, 112, 64) 9472        zero_padding2d[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 112, 112, 64) 256         conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 112, 112, 64) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D (None, 114, 114, 64) 0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 56, 56, 64)   0           zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 56, 56, 64)   4160        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 56, 56, 64)   256         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 56, 56, 64)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 56, 56, 64)   36928       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 56, 56, 64)   256         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 64)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 56, 56, 256)  16640       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 56, 56, 256)  16640       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 56, 56, 256)  1024        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 56, 56, 256)  1024        conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 56, 56, 256)  0           batch_normalization_3[0][0]      \n",
      "                                                                 batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 56, 56, 256)  0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 56, 56, 64)   16448       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 56, 56, 64)   256         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 56, 56, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 56, 56, 64)   36928       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 56, 56, 64)   256         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 56, 56, 64)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 56, 56, 256)  16640       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 56, 56, 256)  1024        conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           batch_normalization_7[0][0]      \n",
      "                                                                 activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 56, 56, 64)   16448       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 56, 56, 64)   256         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 56, 56, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 56, 56, 64)   36928       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 56, 56, 64)   256         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 56, 56, 64)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 56, 56, 256)  16640       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 56, 56, 256)  1024        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           batch_normalization_10[0][0]     \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 28, 28, 128)  32896       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 28, 28, 128)  512         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 28, 28, 128)  0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 28, 28, 128)  147584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 28, 28, 128)  512         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 28, 28, 512)  66048       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 28, 28, 512)  131584      activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 28, 28, 512)  2048        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 28, 28, 512)  2048        conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 28, 28, 512)  0           batch_normalization_13[0][0]     \n",
      "                                                                 batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 512)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 28, 28, 128)  65664       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 28, 28, 128)  512         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 128)  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 28, 28, 128)  147584      activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 28, 28, 128)  512         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 28, 28, 512)  66048       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 28, 28, 512)  2048        conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           batch_normalization_17[0][0]     \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 28, 28, 128)  65664       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 28, 28, 128)  512         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 128)  0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 28, 28, 128)  147584      activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 28, 28, 128)  512         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 28, 28, 512)  66048       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 28, 28, 512)  2048        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           batch_normalization_20[0][0]     \n",
      "                                                                 activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 28, 28, 128)  65664       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 28, 28, 128)  512         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 128)  0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 28, 28, 128)  147584      activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 28, 28, 128)  512         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 28, 28, 512)  66048       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 28, 28, 512)  2048        conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           batch_normalization_23[0][0]     \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 14, 14, 256)  131328      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 14, 14, 256)  1024        conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 14, 14, 256)  0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 14, 14, 256)  590080      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 14, 14, 256)  1024        conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 14, 14, 1024) 263168      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 14, 14, 1024) 525312      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 14, 14, 1024) 4096        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 14, 14, 1024) 4096        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 14, 14, 1024) 0           batch_normalization_26[0][0]     \n",
      "                                                                 batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 1024) 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 14, 14, 256)  262400      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 14, 14, 256)  1024        conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 256)  0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 14, 14, 256)  590080      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 14, 14, 256)  1024        conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 14, 14, 1024) 263168      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 14, 14, 1024) 4096        conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           batch_normalization_30[0][0]     \n",
      "                                                                 activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 14, 14, 256)  262400      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 14, 14, 256)  1024        conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 256)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 14, 14, 256)  590080      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 14, 14, 256)  1024        conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 14, 14, 1024) 263168      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 14, 14, 1024) 4096        conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           batch_normalization_33[0][0]     \n",
      "                                                                 activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 14, 14, 256)  262400      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 14, 14, 256)  1024        conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 256)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 14, 14, 256)  590080      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 14, 14, 256)  1024        conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 14, 14, 1024) 263168      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 14, 14, 1024) 4096        conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           batch_normalization_36[0][0]     \n",
      "                                                                 activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 14, 14, 256)  262400      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 14, 14, 256)  1024        conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 256)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 14, 14, 256)  590080      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 14, 14, 256)  1024        conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 14, 14, 1024) 263168      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 14, 14, 1024) 4096        conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           batch_normalization_39[0][0]     \n",
      "                                                                 activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 14, 14, 256)  262400      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 14, 14, 256)  1024        conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 256)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 14, 14, 256)  590080      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 14, 14, 256)  1024        conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 14, 14, 1024) 263168      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 14, 14, 1024) 4096        conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           batch_normalization_42[0][0]     \n",
      "                                                                 activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 7, 7, 512)    524800      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 7, 7, 512)    2048        conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 7, 7, 512)    0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 7, 7, 512)    2359808     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 7, 7, 512)    2048        conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 7, 7, 2048)   1050624     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 7, 7, 2048)   2099200     activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 7, 7, 2048)   8192        conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 7, 7, 2048)   8192        conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 7, 7, 2048)   0           batch_normalization_45[0][0]     \n",
      "                                                                 batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 2048)   0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 7, 7, 512)    1049088     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 7, 7, 512)    2048        conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 512)    0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 7, 7, 512)    2359808     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 7, 7, 512)    2048        conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 7, 7, 2048)   1050624     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 7, 7, 2048)   8192        conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           batch_normalization_49[0][0]     \n",
      "                                                                 activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 7, 7, 512)    1049088     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 7, 7, 512)    2048        conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 512)    0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 7, 7, 512)    2359808     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 7, 7, 512)    2048        conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 7, 7, 2048)   1050624     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 7, 7, 2048)   8192        conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           batch_normalization_52[0][0]     \n",
      "                                                                 activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 2048)         0           activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 4)            8196        global_average_pooling2d[0][0]   \n",
      "==================================================================================================\n",
      "Total params: 23,595,908\n",
      "Trainable params: 23,542,788\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "resnet50.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "\n",
    "train_dir = os.path.join('./dataset/1/images/train')\n",
    "val_dir = os.path.join('./dataset/1/images/val')\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(train_dir, batch_size=16, target_size=(224, 224), color_mode='rgb')\n",
    "val_generator = val_datagen.flow_from_directory(val_dir, batch_size=16, target_size=(224, 224), color_mode='rgb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Input, Conv2D, Dense, MaxPool2D\n",
    "\n",
    "cnn_model = ResNet50(include_top=False)\n",
    "cnn_model.trainable = True\n",
    "model = Sequential()\n",
    "model.add(cnn_model)\n",
    "model.add(MaxPool2D())\n",
    "model.add(Dense(2048, activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " resnet50 (Functional)       (None, None, None, 2048   23587712  \n",
      "                             )                                   \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, None, None, 2048   0         \n",
      " D)                          )                                   \n",
      "                                                                 \n",
      " dense (Dense)               (None, None, None, 2048   4196352   \n",
      "                             )                                   \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 27784064 (105.99 MB)\n",
      "Trainable params: 27730944 (105.79 MB)\n",
      "Non-trainable params: 53120 (207.50 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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