Transfer_Learning_MobileNetV2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Transfer_Learning_MobileNetV2.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": "C_zK7H2vhkpi",
        "outputId": "ad545571-5b7b-4cc2-98f2-df3ad967db91"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n73BSgXFhsGz",
        "outputId": "c3ae33d2-ec75-4c8a-cd83-c410d6c1e6bb"
      },
      "source": [
        "#changing the working directory and entering to dataset directory for my google drive\n",
        "%cd drive/MyDrive/kaggle/image_classification/"
      ],
      "execution_count": 2,
      "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": "R40_DPCdhsJZ",
        "outputId": "c30a948d-272d-47b4-e429-140e5980d2b8"
      },
      "source": [
        "%cd flower_classification/"
      ],
      "execution_count": 3,
      "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": "l6IUV_XphsO9",
        "outputId": "130658e8-466f-4885-952e-d287ac154e7d"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AccVal_acc.png\tLossVal_loss.png  test\ttrain\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKrkAzjlhsQ6"
      },
      "source": [
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from keras.models import Model\n",
        "from keras.applications.mobilenet_v2 import MobileNetV2\n",
        "from keras.applications.mobilenet_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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wsf_PYzXhsTK"
      },
      "source": [
        "# re-size all the images to this\n",
        "IMAGE_SIZE = [224, 224]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArnbnhEuhsZV"
      },
      "source": [
        "train_path = 'train'\n",
        "valid_path = 'test'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9dk_7lQhsbd",
        "outputId": "077afcdb-a7f5-4a77-ecbd-13a22720bf7e"
      },
      "source": [
        "# add preprocessing layer to the front of MobileNetV2\n",
        "mobilenet = MobileNetV2(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n",
            "9420800/9406464 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tNM_DtJhsdu"
      },
      "source": [
        "# don't train existing weights\n",
        "for layer in mobilenet.layers:\n",
        "  layer.trainable = False"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQA6Ik22hsf7"
      },
      "source": [
        "# useful for getting number of classes\n",
        "folders = glob('train/*')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4R9mlyKhsiE"
      },
      "source": [
        "# our layers - you can add more if you want\n",
        "x = Flatten()(mobilenet.output)\n",
        "# x = Dense(1000, activation='relu')(x)\n",
        "prediction = Dense(len(folders), activation='softmax')(x)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnAwo5rWhskl"
      },
      "source": [
        "# create a model object\n",
        "model = Model(inputs=mobilenet.input, outputs=prediction)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDAEsK0PhsmH",
        "outputId": "69ed277c-36c1-49d9-d819-e47cde1be6e0"
      },
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ],
      "execution_count": 13,
      "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 (Conv2D)                  (None, 112, 112, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 62720)        0           out_relu[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 5)            313605      flatten[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 2,571,589\n",
            "Trainable params: 313,605\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3KKHWp2hsoh"
      },
      "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": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FY0M1Lhkhsq7"
      },
      "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": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q4NRMfS5hstN",
        "outputId": "6de7404a-23bd-495b-f0e0-22d959655d96"
      },
      "source": [
        "training_set = train_datagen.flow_from_directory('train',\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 64,\n",
        "                                                 class_mode = 'categorical')"
      ],
      "execution_count": 16,
      "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": "HJaU5uO4hsvg",
        "outputId": "10402a11-966d-45f9-adf5-6c54f6ae7614"
      },
      "source": [
        "test_set = test_datagen.flow_from_directory('test',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 64,\n",
        "                                            class_mode = 'categorical')"
      ],
      "execution_count": 17,
      "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": "tZUDWq6nhsyP",
        "outputId": "c616c08c-7d6a-4529-800d-e115cc0242aa"
      },
      "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": 18,
      "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 [==============================] - 1039s 21s/step - loss: 2.0670 - accuracy: 0.7605 - val_loss: 3.3076 - val_accuracy: 0.7708\n",
            "Epoch 2/4\n",
            "48/48 [==============================] - 52s 1s/step - loss: 0.9843 - accuracy: 0.8764 - val_loss: 2.2121 - val_accuracy: 0.7940\n",
            "Epoch 3/4\n",
            "48/48 [==============================] - 51s 1s/step - loss: 0.7420 - accuracy: 0.9065 - val_loss: 2.6072 - val_accuracy: 0.7986\n",
            "Epoch 4/4\n",
            "48/48 [==============================] - 52s 1s/step - loss: 0.5736 - accuracy: 0.9220 - val_loss: 2.2296 - val_accuracy: 0.8202\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "GsZi73Z_hs0j",
        "outputId": "7d362028-c092-4da8-a024-8e09737c5fa3"
      },
      "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": 19,
      "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": "3cbIUfevhs2s",
        "outputId": "52b2b692-b1a4-4bcd-8cd9-c8cc8959a9cc"
      },
      "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": 20,
      "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": "89U3tC-KhtID"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}