Gen7Net.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Gen7Net.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1xMBoa-mBSONO_Uu1gDfzgOTECkrsn3Xo",
      "authorship_tag": "ABX9TyOb1qG39PLWJKZXIh4GDdQL",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/thefenriswolf/ML/blob/master/Gen7Net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CP0ixRYbdjdx",
        "colab_type": "code",
        "outputId": "47d11b82-5d66-4af5-8194-5b6e3979d3d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "%tensorflow_version 2.x \n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# check for active GPU\n",
        "tf.test.gpu_device_name()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BNVVy8nm7XU",
        "colab_type": "code",
        "outputId": "a7a02dc9-a1e5-4e38-e4eb-00cc82596c38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148
        }
      },
      "source": [
        "train_dir = 'drive/My Drive/PROSTATE-DIAGNOSIS/train/'\n",
        "test_dir = 'drive/My Drive/PROSTATE-DIAGNOSIS/test/'\n",
        "backup_dir = 'drive/My Drive/models/'\n",
        "\n",
        "pos_train_dir = os.path.join(train_dir, 'pos')\n",
        "neg_train_dir = os.path.join(train_dir, 'neg')\n",
        "pos_test_dir = os.path.join(test_dir, 'pos')\n",
        "neg_test_dir = os.path.join(test_dir, 'neg')\n",
        "\n",
        "num_pos_tr = len(os.listdir(pos_train_dir))\n",
        "num_neg_tr = len(os.listdir(neg_train_dir))\n",
        "num_pos_val = len(os.listdir(pos_test_dir))\n",
        "num_neg_val = len(os.listdir(neg_test_dir))\n",
        "\n",
        "total_train = num_pos_tr + num_neg_tr\n",
        "total_val = num_pos_val + num_neg_val\n",
        "\n",
        "print('total positive training images:', num_pos_tr)\n",
        "print('total negative training images:', num_neg_tr)\n",
        "print('total positive test images:', num_pos_val)\n",
        "print('total negative test images:', num_neg_val)\n",
        "print(\"--------------------------------------\")\n",
        "print(\"Total training images:\", total_train)\n",
        "print(\"Total test images:\", total_val)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total positive training images: 11210\n",
            "total negative training images: 1840\n",
            "total positive test images: 3108\n",
            "total negative test images: 226\n",
            "-+-+-+-+-\n",
            "Total training images: 13050\n",
            "Total test images: 3334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSq4f4aRoGna",
        "colab_type": "code",
        "outputId": "9c648041-9e46-468f-89b6-960a5de78d43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "batch_size = 128\n",
        "epochs = 15\n",
        "IMG_HEIGHT = 160\n",
        "IMG_WIDTH = 160\n",
        "IMG_SHAPE = (IMG_HEIGHT, IMG_WIDTH, 3)\n",
        "\n",
        "\n",
        "train_image_generator = ImageDataGenerator(rescale=1./255, \n",
        "                                           rotation_range=6,\n",
        "                                           #horizontal_flip=True,\n",
        "                                           vertical_flip=True,\n",
        "                                           zoom_range=0.3)\n",
        "\n",
        "test_image_generator = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "print(\"Train:\")\n",
        "train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
        "                                                           directory=train_dir,\n",
        "                                                           shuffle=True,\n",
        "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "                                                           class_mode='binary',\n",
        "                                                           interpolation='nearest')\n",
        "print(\"Test\")\n",
        "test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,\n",
        "                                                              directory=test_dir,\n",
        "                                                              shuffle=True,\n",
        "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "                                                              class_mode='binary',\n",
        "                                                              interpolation='nearest')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train:\n",
            "Found 13050 images belonging to 2 classes.\n",
            "Test\n",
            "Found 3334 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJdlyciFH9ao",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#restore previously trained model\n",
        "\n",
        "restored_model = tf.keras.models.load_model('drive/My Drive/models/cnn_revX.h5')\n",
        "restored_model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWR8lpPnqljY",
        "colab_type": "code",
        "outputId": "94e2e92a-0fd5-4d3c-db84-291ec5280bb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Create the base model from MobileNetV2\n",
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')\n",
        "\n",
        "#base_model.trainable = False # dont train base model\n",
        "base_model.trainable = True # make base model trainable\n",
        "\n",
        "# Print Number of Layers in the base model\n",
        "print(\"Number of layers in the base model: \", len(base_model.layers))\n",
        "\n",
        "# Fine-tune from this layer in the base model onwards\n",
        "fine_tune_at = 100\n",
        "\n",
        "# Freeze all the layers before `fine_tune_at`\n",
        "for layer in base_model.layers[:fine_tune_at]:\n",
        "  layer.trainable =  False\n",
        "\n",
        "base_model.summary()\n",
        "\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "prediction_layer = tf.keras.layers.Dense(1)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of layers in the base model:  155\n",
            "Model: \"mobilenetv2_1.00_160\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 160, 160, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 161, 161, 3)  0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 80, 80, 32)   864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 80, 80, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 80, 80, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 80, 80, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 80, 80, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 80, 80, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 80, 80, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 80, 80, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 80, 80, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 80, 80, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 80, 80, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 81, 81, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 40, 40, 96)   864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 40, 40, 96)   384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 40, 40, 96)   0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 40, 40, 24)   2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 40, 40, 24)   96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 40, 40, 144)  3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 40, 40, 144)  1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 40, 40, 144)  576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 40, 40, 144)  0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 40, 40, 24)   3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 40, 40, 24)   96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 40, 40, 24)   0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 40, 40, 144)  3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 41, 41, 144)  0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 20, 20, 144)  1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 20, 20, 144)  576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 20, 20, 144)  0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 20, 20, 32)   4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 20, 20, 32)   128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 20, 20, 192)  6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 20, 20, 32)   6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 20, 20, 32)   128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 20, 20, 32)   0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 20, 20, 192)  6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 20, 20, 32)   6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 20, 20, 32)   128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 20, 20, 32)   0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 20, 20, 192)  6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 21, 21, 192)  0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 10, 10, 192)  1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 10, 10, 192)  768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 10, 10, 192)  0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 10, 10, 64)   12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 10, 10, 64)   256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 10, 10, 384)  24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 10, 10, 64)   24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 10, 10, 64)   256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 10, 10, 64)   0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 10, 10, 384)  24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 10, 10, 64)   24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 10, 10, 64)   256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 10, 10, 64)   0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 10, 10, 384)  24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 10, 10, 64)   24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 10, 10, 64)   256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 10, 10, 64)   0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 10, 10, 384)  24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 10, 10, 384)  1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 10, 10, 384)  0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 10, 10, 384)  3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 10, 10, 384)  1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 10, 10, 96)   36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 10, 10, 96)   384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 10, 10, 576)  55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 10, 10, 96)   55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 10, 10, 96)   384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 10, 10, 96)   0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 10, 10, 576)  55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 10, 10, 96)   55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 10, 10, 96)   384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 10, 10, 96)   0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 10, 10, 576)  55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 11, 11, 576)  0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 5, 5, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 5, 5, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 5, 5, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 5, 5, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 5, 5, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 5, 5, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 5, 5, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 5, 5, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 5, 5, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 5, 5, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 5, 5, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 5, 5, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 5, 5, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 5, 5, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 5, 5, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 5, 5, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 5, 5, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 5, 5, 1280)   0           Conv_1_bn[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 1,862,592\n",
            "Non-trainable params: 395,392\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvhl7HM_r0rF",
        "colab_type": "code",
        "outputId": "d8d8c56c-5b9b-495d-99bc-71f5e92710a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "  base_model,\n",
        "  global_average_layer,\n",
        "  prediction_layer\n",
        "])\n",
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy', 'mse'])\n",
        "model.summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 1281      \n",
            "=================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,863,873\n",
            "Non-trainable params: 395,392\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNuUiAu0s0S4",
        "colab_type": "code",
        "outputId": "fbd24ec7-d88a-4b7d-8f09-4b67d17b7cfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        }
      },
      "source": [
        "history = model.fit(train_data_gen,\n",
        "                    epochs=epochs,\n",
        "                    steps_per_epoch=total_train//batch_size,\n",
        "                    validation_data=test_data_gen,\n",
        "                     validation_steps=total_val//batch_size,\n",
        "                    verbose=1)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "101/101 [==============================] - 123s 1s/step - loss: 0.3241 - accuracy: 0.8537 - mse: 4.0652 - val_loss: 0.9929 - val_accuracy: 0.4712 - val_mse: 5.3331\n",
            "Epoch 2/15\n",
            "101/101 [==============================] - 122s 1s/step - loss: 0.1598 - accuracy: 0.9406 - mse: 13.7677 - val_loss: 0.8200 - val_accuracy: 0.6869 - val_mse: 17.4014\n",
            "Epoch 3/15\n",
            "101/101 [==============================] - 120s 1s/step - loss: 0.1055 - accuracy: 0.9584 - mse: 26.3522 - val_loss: 1.0217 - val_accuracy: 0.6821 - val_mse: 24.9539\n",
            "Epoch 4/15\n",
            "101/101 [==============================] - 119s 1s/step - loss: 0.0755 - accuracy: 0.9725 - mse: 36.9535 - val_loss: 0.9339 - val_accuracy: 0.7049 - val_mse: 25.7845\n",
            "Epoch 5/15\n",
            "101/101 [==============================] - 118s 1s/step - loss: 0.0575 - accuracy: 0.9779 - mse: 46.5099 - val_loss: 0.8767 - val_accuracy: 0.7488 - val_mse: 47.0862\n",
            "Epoch 6/15\n",
            "101/101 [==============================] - 116s 1s/step - loss: 0.0483 - accuracy: 0.9818 - mse: 53.7310 - val_loss: 1.0390 - val_accuracy: 0.7034 - val_mse: 33.2544\n",
            "Epoch 7/15\n",
            "101/101 [==============================] - 115s 1s/step - loss: 0.0402 - accuracy: 0.9859 - mse: 60.9976 - val_loss: 1.3010 - val_accuracy: 0.6214 - val_mse: 26.7829\n",
            "Epoch 8/15\n",
            "101/101 [==============================] - 114s 1s/step - loss: 0.0338 - accuracy: 0.9878 - mse: 65.8220 - val_loss: 0.7423 - val_accuracy: 0.7491 - val_mse: 35.8177\n",
            "Epoch 9/15\n",
            "101/101 [==============================] - 114s 1s/step - loss: 0.0293 - accuracy: 0.9884 - mse: 73.4429 - val_loss: 1.1218 - val_accuracy: 0.6764 - val_mse: 32.0567\n",
            "Epoch 10/15\n",
            "101/101 [==============================] - 117s 1s/step - loss: 0.0224 - accuracy: 0.9921 - mse: 77.9484 - val_loss: 0.7978 - val_accuracy: 0.7602 - val_mse: 37.3223\n",
            "Epoch 11/15\n",
            "101/101 [==============================] - 114s 1s/step - loss: 0.0229 - accuracy: 0.9913 - mse: 83.4105 - val_loss: 0.6688 - val_accuracy: 0.8438 - val_mse: 72.6674\n",
            "Epoch 12/15\n",
            "101/101 [==============================] - 114s 1s/step - loss: 0.0195 - accuracy: 0.9933 - mse: 88.6772 - val_loss: 0.9609 - val_accuracy: 0.7311 - val_mse: 37.3535\n",
            "Epoch 13/15\n",
            "101/101 [==============================] - 113s 1s/step - loss: 0.0179 - accuracy: 0.9926 - mse: 98.2935 - val_loss: 0.6705 - val_accuracy: 0.7825 - val_mse: 43.9164\n",
            "Epoch 14/15\n",
            "101/101 [==============================] - 112s 1s/step - loss: 0.0164 - accuracy: 0.9944 - mse: 101.0894 - val_loss: 0.4327 - val_accuracy: 0.8732 - val_mse: 60.7007\n",
            "Epoch 15/15\n",
            "101/101 [==============================] - 114s 1s/step - loss: 0.0121 - accuracy: 0.9960 - mse: 105.2555 - val_loss: 0.4877 - val_accuracy: 0.8726 - val_mse: 65.8055\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ksqs-4WdIDuM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save model as HDF5\n",
        "model.save('drive/My Drive/models/cnn_rev7-2.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqGXxF9JePKg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "57aaf065-c110-4360-dd5f-3e88b3ce9e6b"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(acc, label='Training Accuracy')\n",
        "plt.plot(val_acc, label='Validation Accuracy')\n",
        "plt.plot(mse, label='Mean squared error')\n",
        "plt.legend(loc='lower right')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.ylim([min(plt.ylim()),1])\n",
        "plt.title('Accuracy and MSE')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(loss, label='Training Loss')\n",
        "plt.plot(val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.ylabel('Cross Entropy')\n",
        "plt.ylim([0,1.0])\n",
        "plt.title('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.show()\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAJcCAYAAACxEXM4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wayasNQgR3aJ",
        "colab_type": "text"
      },
      "source": [
        "MIT License\n",
        "\n",
        "Copyright (c) 2017 François Chollet\n",
        "\n",
        "Permission is hereby granted, free of charge, to any person obtaining a copy\n",
        "of this software and associated documentation files (the \"Software\"), to deal\n",
        "in the Software without restriction, including without limitation the rights\n",
        "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
        "copies of the Software, and to permit persons to whom the Software is\n",
        "furnished to do so, subject to the following conditions:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all\n",
        "copies or substantial portions of the Software.\n",
        "\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
        "AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
        "OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
        "SOFTWARE."
      ]
    }
  ]
}