Transfer_Learning_ResNet50V2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Transfer_Learning_ResNet50V2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSX2dkDb41-N",
        "outputId": "1564a049-b5b7-4cb0-9307-d68708be8c12"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TR-XjArX45tN",
        "outputId": "532c50fa-b693-4a14-a049-cf1cb92aadb5"
      },
      "source": [
        "#changing the working directory and entering to dataset directory for my google drive\n",
        "%cd drive/MyDrive/kaggle/image_classification/"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/kaggle/image_classification\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPCO53VI45vY",
        "outputId": "a989fbec-1ca2-4748-abf0-1b387d6c1361"
      },
      "source": [
        "%cd flower_classification/"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/kaggle/image_classification/flower_classification\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7YiaeI945xm",
        "outputId": "2f66b528-1ad5-4ab6-e9ca-6d59c76fec8d"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AccVal_acc.png\tLossVal_loss.png  test\ttrain\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3slVL0Rk45zd"
      },
      "source": [
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from keras.models import Model\n",
        "from keras.applications.resnet_v2 import ResNet50V2\n",
        "from keras.applications.resnet_v2 import preprocess_input\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwlxQp6k451Y"
      },
      "source": [
        "# re-size all the images to this\n",
        "IMAGE_SIZE = [224, 224]"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LeZdPNl1453F"
      },
      "source": [
        "train_path = 'train'\n",
        "valid_path = 'test'"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aik039Ox455F",
        "outputId": "bd7dd562-14e3-4a65-9a56-af16b0d59f3b"
      },
      "source": [
        "# add preprocessing layer to the front of ResNet50V2\n",
        "resnet = ResNet50V2(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94674944/94668760 [==============================] - 1s 0us/step\n",
            "94683136/94668760 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zz25aSrv457L"
      },
      "source": [
        "# don't train existing weights\n",
        "for layer in resnet.layers:\n",
        "  layer.trainable = False"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D65J7Bjn458t"
      },
      "source": [
        "# useful for getting number of classes\n",
        "folders = glob('train/*')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UvdvNxS45-0"
      },
      "source": [
        "# our layers - you can add more if you want\n",
        "x = Flatten()(resnet.output)\n",
        "# x = Dense(1000, activation='relu')(x)\n",
        "prediction = Dense(len(folders), activation='softmax')(x)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHixHaaf46Ad"
      },
      "source": [
        "# create a model object\n",
        "model = Model(inputs=resnet.input, outputs=prediction)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBkUymp446Ce",
        "outputId": "e7cd7cf4-5f4e-43e6-b081-617365dbb31a"
      },
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\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",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_preact_bn (BatchNo (None, 56, 56, 64)   256         pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_preact_relu (Activ (None, 56, 56, 64)   0           conv2_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4096        conv2_block1_preact_relu[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_pad (ZeroPadding (None, 58, 58, 64)   0           conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36864       conv2_block1_2_pad[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       conv2_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Add)          (None, 56, 56, 256)  0           conv2_block1_0_conv[0][0]        \n",
            "                                                                 conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_preact_bn (BatchNo (None, 56, 56, 256)  1024        conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_preact_relu (Activ (None, 56, 56, 256)  0           conv2_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16384       conv2_block2_preact_relu[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_pad (ZeroPadding (None, 58, 58, 64)   0           conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36864       conv2_block2_2_pad[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_out (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_preact_bn (BatchNo (None, 56, 56, 256)  1024        conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_preact_relu (Activ (None, 56, 56, 256)  0           conv2_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16384       conv2_block3_preact_relu[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_pad (ZeroPadding (None, 58, 58, 64)   0           conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 28, 28, 64)   36864       conv2_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 28, 28, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 28, 28, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 28, 28, 256)  0           conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 28, 28, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Add)          (None, 28, 28, 256)  0           max_pooling2d[0][0]              \n",
            "                                                                 conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_preact_bn (BatchNo (None, 28, 28, 256)  1024        conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_preact_relu (Activ (None, 28, 28, 256)  0           conv3_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32768       conv3_block1_preact_relu[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_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147456      conv3_block1_2_pad[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      conv3_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Add)          (None, 28, 28, 512)  0           conv3_block1_0_conv[0][0]        \n",
            "                                                                 conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_preact_bn (BatchNo (None, 28, 28, 512)  2048        conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_preact_relu (Activ (None, 28, 28, 512)  0           conv3_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65536       conv3_block2_preact_relu[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_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147456      conv3_block2_2_pad[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_out (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_preact_bn (BatchNo (None, 28, 28, 512)  2048        conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_preact_relu (Activ (None, 28, 28, 512)  0           conv3_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65536       conv3_block3_preact_relu[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_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147456      conv3_block3_2_pad[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_out (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_preact_bn (BatchNo (None, 28, 28, 512)  2048        conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_preact_relu (Activ (None, 28, 28, 512)  0           conv3_block4_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65536       conv3_block4_preact_relu[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_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 14, 14, 128)  147456      conv3_block4_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 14, 14, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 14, 14, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 14, 14, 512)  0           conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 14, 14, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Add)          (None, 14, 14, 512)  0           max_pooling2d_1[0][0]            \n",
            "                                                                 conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_preact_bn (BatchNo (None, 14, 14, 512)  2048        conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_preact_relu (Activ (None, 14, 14, 512)  0           conv4_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131072      conv4_block1_preact_relu[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_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block1_2_pad[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      conv4_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_conv[0][0]        \n",
            "                                                                 conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block2_preact_relu[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_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block2_2_pad[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_out (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block3_preact_relu[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_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block3_2_pad[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_out (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block4_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block4_preact_relu[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_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block4_2_pad[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_out (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block5_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block5_preact_relu[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_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block5_2_pad[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_out (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block6_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block6_preact_relu[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_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 7, 7, 256)    589824      conv4_block6_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 7, 7, 256)    1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 7, 7, 256)    0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 1024)   0           conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 7, 7, 1024)   263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Add)          (None, 7, 7, 1024)   0           max_pooling2d_2[0][0]            \n",
            "                                                                 conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_preact_bn (BatchNo (None, 7, 7, 1024)   4096        conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_preact_relu (Activ (None, 7, 7, 1024)   0           conv5_block1_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524288      conv5_block1_preact_relu[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_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block1_2_pad[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     conv5_block1_preact_relu[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_conv[0][0]        \n",
            "                                                                 conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_preact_bn (BatchNo (None, 7, 7, 2048)   8192        conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_preact_relu (Activ (None, 7, 7, 2048)   0           conv5_block2_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1048576     conv5_block2_preact_relu[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_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block2_2_pad[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_out (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_preact_bn (BatchNo (None, 7, 7, 2048)   8192        conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_preact_relu (Activ (None, 7, 7, 2048)   0           conv5_block3_preact_bn[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1048576     conv5_block3_preact_relu[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_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block3_2_pad[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_out (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "post_bn (BatchNormalization)    (None, 7, 7, 2048)   8192        conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "post_relu (Activation)          (None, 7, 7, 2048)   0           post_bn[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 100352)       0           post_relu[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 5)            501765      flatten[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 24,066,565\n",
            "Trainable params: 501,765\n",
            "Non-trainable params: 23,564,800\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tTw_wbA46Ei"
      },
      "source": [
        "# tell the model what cost and optimization method to use\n",
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EL64fQF_46H6"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1AsDoYQ46Jx",
        "outputId": "7aedf629-974a-466d-b1ed-821febc3d9e2"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory('train',\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 64,\n",
        "                                                 class_mode = 'categorical')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3027 images belonging to 5 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wlhYfIoK46NN",
        "outputId": "dee8465a-e93c-4318-a976-55425661f798"
      },
      "source": [
        "test_set = test_datagen.flow_from_directory('test',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 64,\n",
        "                                            class_mode = 'categorical')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1296 images belonging to 5 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuX6BGSu46Ph",
        "outputId": "daaa3071-59b1-4561-8791-dcf3396a2b7b"
      },
      "source": [
        "# fit the model\n",
        "r = model.fit_generator(\n",
        "  training_set,\n",
        "  validation_data=test_set,\n",
        "  epochs=4,\n",
        "  steps_per_epoch=len(training_set),\n",
        "  validation_steps=len(test_set)\n",
        ")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "48/48 [==============================] - 1071s 22s/step - loss: 2.6748 - accuracy: 0.7595 - val_loss: 3.1885 - val_accuracy: 0.7778\n",
            "Epoch 2/4\n",
            "48/48 [==============================] - 54s 1s/step - loss: 1.1465 - accuracy: 0.8926 - val_loss: 3.2780 - val_accuracy: 0.7809\n",
            "Epoch 3/4\n",
            "48/48 [==============================] - 54s 1s/step - loss: 0.8698 - accuracy: 0.9171 - val_loss: 3.3943 - val_accuracy: 0.7986\n",
            "Epoch 4/4\n",
            "48/48 [==============================] - 53s 1s/step - loss: 0.6659 - accuracy: 0.9352 - val_loss: 4.5967 - val_accuracy: 0.7747\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "g_YsdAUz46U2",
        "outputId": "4cc2ca13-3c0b-46f5-d466-757cd601628b"
      },
      "source": [
        "# loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "OhhY9Umb46XX",
        "outputId": "f463d6f1-b769-469c-b1fe-a142c2d34910"
      },
      "source": [
        "# accuracies\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRM0wZT646Zl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}