covid-19_diagnosis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDA.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFMCsf5mmjHn"
      },
      "source": [
        "# Dataset Info:\n",
        "# COVID-19 RADIOGRAPHY\n",
        "A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images.\n",
        "\n",
        "<br>\n",
        "<hr>\n",
        "<br>\n",
        "\n",
        "### Citation:\n",
        "- M.E.H. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M.A. Kadir, Z.B. Mahbub, K.R. Islam, M.S. Khan, A. Iqbal, N. Al-Emadi, M.B.I. Reaz, M. T. Islam, “Can AI help in screening Viral and COVID-19 pneumonia?” IEEE Access, Vol. 8, 2020, pp. 132665 - 132676. [Paper link](https://ieeexplore.ieee.org/document/9144185)\n",
        "\n",
        "- Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, M.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S. and Chowdhury, M.E., 2020. Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-ray Images. [Paper Link](https://doi.org/10.1016/j.compbiomed.2021.104319)\n",
        "\n",
        "<br>\n",
        "<hr>\n",
        "<br>\n",
        "\n",
        "### Acknowledgments:\n",
        "Thanks to the Italian Society of Medical and Interventional Radiology (SIRM) for publicly providing the COVID-19 Chest X-Ray dataset, Valencia Region Image Bank (BIMCV) padchest dataset and would like to thank J. P. Cohen for taking the initiative to gather images from articles and online resources. Finally to the Chest X-Ray Images (pneumonia) database in Kaggle and Radiological Society of North America (RSNA) Kaggle database for making a wonderful X-ray database for normal, lung opacity, viral, and bacterial pneumonia images. Also, a big thanks to our collaborators!\n",
        "\n",
        "<br>\n",
        "<hr>\n",
        "<br>\n",
        "\n",
        "### Dataset Link:\n",
        "[COVID-19 Radiography Database](https://www.kaggle.com/tawsifurrahman/covid19-radiography-database)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-buG8_bctKD6"
      },
      "source": [
        "# Motivation:\n",
        "Thousands of individuals have died as a result of the coronavirus epidemic, which has afflicted millions of people around the world. Any technical device that allows for rapid and accurate COVID-19 infection detection can be extremely beneficial to healthcare providers.\n",
        "\n",
        "X-ray imaging is an easily accessible method used to diagnose COVID-19 patients, according to Chowdhury et al. (2020). Despite the fact that regular Chest X-Ray (XCR) scans can help with early detection of suspected cases, the images of various viral pneumonia patients are identical. As a result, radiologists have a hard time distinguishing COVID-19 from other viral pneumonia patients.\n",
        "\n",
        "The goal of this database and current study is to see how useful artificial intelligence (AI) can be in detecting COVID-19 from chest X-ray pictures quickly and accurately."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyDteVerqa6h"
      },
      "source": [
        "# Contents:\n",
        "<blockquote>\n",
        "1. Reading the Dataset & Selecting 2 classes <br>\n",
        "2. EDA <br>\n",
        "3. Data Augmentation <br>\n",
        "4. Train-Test Split <br>\n",
        "5. Model Training <br>\n",
        "6. Fine Tuning the Model <br>\n",
        "7. Plotting Losses\n",
        "</blockquote>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gq94dgMTos8S"
      },
      "source": [
        "# 1. Reading the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWQvekQit94U"
      },
      "source": [
        "There are a total of 21165 samples, which are classified into four categories:\n",
        "1. Covid-19\n",
        "2. Lung Opacity\n",
        "3. Normal\n",
        "4. Viral Pneumonia\n",
        "\n",
        "The photos are all in the Portable Network Graphics (PNG) file type and are 299x299 pixels in size. The database presently contains 3,616 COVID-19 positive cases, 10,192 Normal, 6,012 Lung Opacity (Non-COVID lung infection), and 1,345 Viral Pneumonia pictures, according to the most recent update.\n",
        "\n",
        "We will only train our model on two of these four classes, namely the 'Normal' and 'COVID' classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kAX2-LOGKJ5L",
        "outputId": "73e9adb1-7d2b-4289-b0a6-d16637862dda"
      },
      "source": [
        "# Unzip the Dataset\n",
        "\n",
        "from zipfile import ZipFile\n",
        "file_name = \"/content/drive/MyDrive/Kaggle/COVID-19 Prediction/COVID-19.zip\"\n",
        "\n",
        "with ZipFile(file_name, 'r') as zip:\n",
        "  zip.extractall()\n",
        "  print('Done')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3qSxxgD4KrvA"
      },
      "source": [
        "# Import the required modules\n",
        "import datetime\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "matplotlib.rcParams['font.size'] = 8\n",
        "from skimage import img_as_float\n",
        "from skimage import exposure\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "import os\n",
        "import glob\n",
        "import random\n",
        "from skimage import io # To preprocess the images\n",
        "from distutils.file_util import copy_file\n",
        "import seaborn as sns\n",
        "import cv2\n",
        "import keras\n",
        "from keras.models import load_model\n",
        "from keras import backend as K\n",
        "import tensorflow as tf\n",
        "\n",
        "from skimage.transform import rescale\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPpXupXEPtOU"
      },
      "source": [
        "# Set dataset path\n",
        "\n",
        "DATASET_PATH = '/content/COVID-19_Radiography_Dataset'\n",
        "\n",
        "# There are two classes of images that we will deal with\n",
        "cls = ['COVID', 'Normal']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ea_7Z33ozkK"
      },
      "source": [
        "# 2. Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "IiI01A-Tnxp9",
        "outputId": "5c654798-46ff-47db-d30d-3d486c0413e9"
      },
      "source": [
        "covid_path = os.path.join(DATASET_PATH, cls[0], '*')\n",
        "covid = glob.glob(covid_path)\n",
        "covid = io.imread(covid[0])\n",
        "\n",
        "normal_path = os.path.join(DATASET_PATH, cls[1], '*')\n",
        "normal = glob.glob(normal_path)\n",
        "normal = io.imread(normal[0])\n",
        "\n",
        "f, axes = plt.subplots(1, 2, sharey=True)\n",
        "f.set_figwidth(10)\n",
        "\n",
        "axes[0].imshow(covid, cmap='gray')\n",
        "axes[1].imshow(normal, cmap='gray')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f8e18b82710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-xGY0s5oFWF",
        "outputId": "c0faaef0-0856-4a79-a1c3-ff5921c0a431"
      },
      "source": [
        "print(f'Image shape for COVID dataset is: {covid.shape}')\n",
        "print(f'Image shape for Normal dataset is: {normal.shape}')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image shape for COVID dataset is: (299, 299)\n",
            "Image shape for Normal dataset is: (299, 299)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8ZRgYJzq577",
        "outputId": "ab848583-c6b1-4d9e-89a6-2cc4246ce855"
      },
      "source": [
        "print(f'Number of COVID Images: {len(os.listdir(covid_path[:-2]))} \\\n",
        "\\nNumber of Non-COVID Images: {len(os.listdir(normal_path[:-2]))}')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of COVID Images: 3616 \n",
            "Number of Non-COVID Images: 10192\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4J5JVIrrn95"
      },
      "source": [
        "> <strong> This is an Imbalanced Dataset!! </strong>\n",
        ">\n",
        "> To deal with this issue, we have applied ***focal loss*** later in the section."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MqevUHKrXfJt"
      },
      "source": [
        "## Preprocessing Images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTZ293zA5ByF"
      },
      "source": [
        "# Histogram Equalization\n",
        "\n",
        "def plot_img_and_hist(image, axes, bins=256):\n",
        "    \"\"\"Plot an image along with its histogram and cumulative histogram.\n",
        "\n",
        "    \"\"\"\n",
        "    image = img_as_float(image)\n",
        "    ax_img, ax_hist = axes\n",
        "    ax_cdf = ax_hist.twinx()\n",
        "\n",
        "    # Display image\n",
        "    ax_img.imshow(image, cmap=plt.cm.gray)\n",
        "    ax_img.set_axis_off()\n",
        "\n",
        "    # Display histogram\n",
        "    ax_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')\n",
        "    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))\n",
        "    ax_hist.set_xlabel('Pixel intensity')\n",
        "    ax_hist.set_xlim(0, 1)\n",
        "    ax_hist.set_yticks([])\n",
        "\n",
        "    # Display cumulative distribution\n",
        "    img_cdf, bins = exposure.cumulative_distribution(image, bins)\n",
        "    ax_cdf.plot(bins, img_cdf, 'r')\n",
        "    ax_cdf.set_yticks([])\n",
        "\n",
        "    return ax_img, ax_hist, ax_cdf"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "KxixsQiq6y9s",
        "outputId": "507cfd30-5b37-4c3c-a765-c70186aac595"
      },
      "source": [
        "# Load a normal image\n",
        "img = normal\n",
        "\n",
        "# Contrast stretching\n",
        "p2, p98 = np.percentile(img, (2, 98))\n",
        "img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))\n",
        "\n",
        "# Equalization\n",
        "img_eq = exposure.equalize_hist(img)\n",
        "\n",
        "# Adaptive Equalization\n",
        "img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)\n",
        "\n",
        "# Display results\n",
        "fig = plt.figure(figsize=(12, 8))\n",
        "axes = np.zeros((2, 4), dtype=np.object)\n",
        "axes[0, 0] = fig.add_subplot(2, 4, 1)\n",
        "for i in range(1, 4):\n",
        "    axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])\n",
        "for i in range(0, 4):\n",
        "    axes[1, i] = fig.add_subplot(2, 4, 5+i)\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])\n",
        "ax_img.set_title('Low contrast image')\n",
        "\n",
        "y_min, y_max = ax_hist.get_ylim()\n",
        "ax_hist.set_ylabel('Number of pixels')\n",
        "ax_hist.set_yticks(np.linspace(0, y_max, 5))\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])\n",
        "ax_img.set_title('Contrast stretching')\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])\n",
        "ax_img.set_title('Histogram equalization')\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])\n",
        "ax_img.set_title('Adaptive equalization')\n",
        "\n",
        "ax_cdf.set_ylabel('Fraction of total intensity')\n",
        "ax_cdf.set_yticks(np.linspace(0, 1, 5))\n",
        "\n",
        "# prevent overlap of y-axis labels\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "-9LHCgNK8use",
        "outputId": "a9cad7b6-0d2b-4446-df81-912225498453"
      },
      "source": [
        "# Load a covid image\n",
        "img = covid\n",
        "\n",
        "# Contrast stretching\n",
        "p2, p98 = np.percentile(img, (2, 98))\n",
        "img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))\n",
        "\n",
        "# Equalization\n",
        "img_eq = exposure.equalize_hist(img)\n",
        "\n",
        "# Adaptive Equalization\n",
        "img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)\n",
        "\n",
        "# Display results\n",
        "fig = plt.figure(figsize=(12, 8))\n",
        "axes = np.zeros((2, 4), dtype=np.object)\n",
        "axes[0, 0] = fig.add_subplot(2, 4, 1)\n",
        "for i in range(1, 4):\n",
        "    axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])\n",
        "for i in range(0, 4):\n",
        "    axes[1, i] = fig.add_subplot(2, 4, 5+i)\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])\n",
        "ax_img.set_title('Low contrast image')\n",
        "\n",
        "y_min, y_max = ax_hist.get_ylim()\n",
        "ax_hist.set_ylabel('Number of pixels')\n",
        "ax_hist.set_yticks(np.linspace(0, y_max, 5))\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])\n",
        "ax_img.set_title('Contrast stretching')\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])\n",
        "ax_img.set_title('Histogram equalization')\n",
        "\n",
        "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])\n",
        "ax_img.set_title('Adaptive equalization')\n",
        "\n",
        "ax_cdf.set_ylabel('Fraction of total intensity')\n",
        "ax_cdf.set_yticks(np.linspace(0, 1, 5))\n",
        "\n",
        "# prevent overlap of y-axis labels\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMsdiYtB9652"
      },
      "source": [
        "# 3. Data Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfPX6-0j1HIr"
      },
      "source": [
        "# Create the list of paths to the images\n",
        "\n",
        "# Lists for access paths\n",
        "listCovidPaths = []\n",
        "listNormalPaths = []\n",
        "\n",
        "# Get covid images files paths\n",
        "for root, directories, files in os.walk(covid_path[:-2]):\n",
        "    for name in files:\n",
        "        listCovidPaths.append(os.path.join(root, name))\n",
        "        \n",
        "# Get normal images files paths\n",
        "for root, directories, files in os.walk(normal_path[:-2]):\n",
        "    for name in files:\n",
        "        listNormalPaths.append(os.path.join(root, name))\n",
        "\n",
        "# Shuffle lists for random train / test\n",
        "\n",
        "random.shuffle(listCovidPaths)\n",
        "random.shuffle(listNormalPaths)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ftFDtw1YivR"
      },
      "source": [
        "# 4. Train-Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxfX1_ef1YXv"
      },
      "source": [
        "# Create new folders for image training\n",
        "\n",
        "# main folder\n",
        "!mkdir ./Data/\n",
        "\n",
        "# Train data folders\n",
        "!mkdir ./Data/Train/\n",
        "!mkdir ./Data/Train/Covid/\n",
        "!mkdir ./Data/Train/Normal/\n",
        "\n",
        "# Test data folders\n",
        "!mkdir ./Data/Test/\n",
        "!mkdir ./Data/Test/Covid/\n",
        "!mkdir ./Data/Test/Normal/\n",
        "\n",
        "# Paths to covid images folders\n",
        "pathCovidTrain = './Data/Train/Covid/'\n",
        "pathCovidTest = './Data/Test/Covid/'\n",
        "\n",
        "# Paths to normal images folders\n",
        "pathNormalTrain = './Data/Train/Normal/'\n",
        "pathNormalTest = './Data/Test/Normal/'"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7JWGISf1GvH"
      },
      "source": [
        "# Move files to new folders in the 80:20 ratio\n",
        "\n",
        "len_covid = len(os.listdir(covid_path[:-2]))\n",
        "len_normal = len(os.listdir(normal_path[:-2]))\n",
        "covid_80 = round(len(os.listdir(covid_path[:-2])) * 0.8)     # 80% of the COVID data\n",
        "normal_80 = round(len(os.listdir(normal_path[:-2])) * 0.8)   # 80% of the Normal data\n",
        "\n",
        "# Move covid images files to new folders\n",
        "for i in range(len_covid):\n",
        "    if i < covid_80:\n",
        "        copy_file(listCovidPaths[i], pathCovidTrain)\n",
        "    else : \n",
        "        copy_file(listCovidPaths[i], pathCovidTest)\n",
        "\n",
        "# Move normal images files to new folders\n",
        "for i in range(len_normal):\n",
        "    if i < normal_80:\n",
        "        copy_file(listNormalPaths[i], pathNormalTrain)\n",
        "    else:\n",
        "        copy_file(listNormalPaths[i], pathNormalTest)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yfApMT381qa"
      },
      "source": [
        "# datagen = ImageDataGenerator(\n",
        "#     featurewise_center = True,\n",
        "#     featurewise_std_normalization = True,\n",
        "#     rotation_range = 10,\n",
        "#     width_shift_range = 0,\n",
        "#     height_shift_range = 0,\n",
        "#     vertical_flip = False\n",
        "# )\n",
        "\n",
        "# def preprocess_img(img, mode):\n",
        "#   # standarising the image\n",
        "#   img = (img - img.min()) / (img.max() - img.min())\n",
        "\n",
        "#   if mode == 'train':\n",
        "#     img = datagen.random_transform(img)\n",
        "#   return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RwRNiGHxl-4"
      },
      "source": [
        "# Definition of data generators\n",
        "\n",
        "# for train data\n",
        "trainGenerator = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range = 20,\n",
        "    zoom_range = 0.2,\n",
        "    shear_range = 0.2,\n",
        "    featurewise_center = True,\n",
        "    featurewise_std_normalization = True,\n",
        "    width_shift_range = 0,\n",
        "    height_shift_range = 0,\n",
        "    vertical_flip = False,\n",
        "    fill_mode = 'nearest'\n",
        ")\n",
        "\n",
        "# for test data\n",
        "testGenerator = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale = 1./255,    \n",
        ")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CE6NsnKx04Pb",
        "outputId": "82c9d6ac-216d-48dd-bbfd-e322bec81262"
      },
      "source": [
        "# Build data generators\n",
        "\n",
        "# Build for train data\n",
        "pathTrainDir = './Data/Train/'\n",
        "\n",
        "trainGeneratorBuild = trainGenerator.flow_from_directory(\n",
        "    pathTrainDir,\n",
        "    target_size = (299, 299),\n",
        "    class_mode = 'binary',\n",
        "    batch_size = 16,\n",
        "    shuffle = True \n",
        ")\n",
        "\n",
        "# Build for test data\n",
        "pathTestDir = './Data/Test/'\n",
        "\n",
        "testGeneratorBuild = testGenerator.flow_from_directory(\n",
        "    pathTestDir,\n",
        "    target_size = (299, 299),\n",
        "    class_mode = 'binary',\n",
        "    batch_size = 16,\n",
        "    shuffle = True\n",
        ")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 11047 images belonging to 2 classes.\n",
            "Found 2761 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EoLhAMkj2yHa"
      },
      "source": [
        "# 5. Model Traning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNJIiU0o1JTT"
      },
      "source": [
        "## Train Model from Scratch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-Cm-zEkIT-e"
      },
      "source": [
        "# COVID and Normal dataset directory\n",
        "BASIS_DIR = '/content/COVID-19_Radiography_Dataset'\n",
        "CLASSES = [\"COVID\",  \"Normal\"]"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybPWieNUIdKV"
      },
      "source": [
        "#Image augmentation process:\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range = 20,\n",
        "    horizontal_flip = True,\n",
        "    vertical_flip = False,\n",
        "    shear_range = 0.2,\n",
        "    zoom_range = 0.2,\n",
        "    width_shift_range = 0.2,\n",
        "    height_shift_range = 0.2,\n",
        "    fill_mode = 'nearest',\n",
        "    \n",
        "    #split dataset to training(80%) and validation(20%):\n",
        "    validation_split = 0.2\n",
        ")"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVMi8GSyIrLx",
        "outputId": "41ccb015-3219-4ccc-e7ea-9569b6c6cb36"
      },
      "source": [
        "# Training dataset and Validation dataset:\n",
        "train_data = train_datagen.flow_from_directory(\n",
        "    directory=BASIS_DIR,\n",
        "    target_size=(299, 299),\n",
        "    batch_size=32,\n",
        "    shuffle=True,\n",
        "    class_mode='binary',\n",
        "    subset='training',\n",
        "    classes=CLASSES\n",
        "    )\n",
        "val_data = train_datagen.flow_from_directory(\n",
        "    directory=BASIS_DIR,\n",
        "    target_size=(299, 299),\n",
        "    batch_size=32,\n",
        "    shuffle=True,\n",
        "    class_mode='binary',\n",
        "    subset='validation',\n",
        "    classes=CLASSES\n",
        "    )"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 11047 images belonging to 2 classes.\n",
            "Found 2761 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPZUDK3OIz4g",
        "outputId": "3c302e99-be89-476a-968a-b0ab18036c23"
      },
      "source": [
        "#Using sequential model:\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(299, 299, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    \n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "    \n",
        "    tf.keras.layers.Conv2D(256, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(256, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 297, 297, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 148, 148, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 146, 146, 32)      9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 73, 73, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 71, 71, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 35, 35, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 33, 33, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 14, 14, 256)       295168    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 12544)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               3211520   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 3,609,441\n",
            "Trainable params: 3,609,441\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3HfTVqJJHQr"
      },
      "source": [
        "# Define our custom loss function\n",
        "\n",
        "def focal_loss(y_true, y_pred):\n",
        "    gamma = 2.0\n",
        "    alpha = 0.25\n",
        "    pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))\n",
        "    pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))\n",
        "    return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1))-K.sum((1-alpha) * K.pow( pt_0, gamma) * K.log(1. - pt_0))\n",
        "\n",
        "\n",
        "# Compile our model\n",
        "adam = tf.keras.optimizers.Adam(lr=0.0001)\n",
        "model.compile(loss=[focal_loss], metrics=[\"accuracy\"], optimizer=adam)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFYlTAHmJOZc",
        "outputId": "f4bdfa13-f31b-4546-e059-6b22b9495f87"
      },
      "source": [
        "# Training process:\n",
        "start = datetime.datetime.now()\n",
        "\n",
        "number_epochs = 25\n",
        "\n",
        "history = model.fit(train_data,\n",
        "                    epochs = number_epochs, \n",
        "                    validation_data = val_data,\n",
        "                    verbose = 1)\n",
        "\n",
        "end = datetime.datetime.now()\n",
        "print(f'Total Training Time: {end - start}')\n",
        "\n",
        "# verbose = 0 => silent,\n",
        "#         = 1 => progress bar,\n",
        "#         = 2 => one line per epoch"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "346/346 [==============================] - 345s 901ms/step - loss: 1.9688 - accuracy: 0.6035 - val_loss: 1.8364 - val_accuracy: 0.5465\n",
            "Epoch 2/25\n",
            "346/346 [==============================] - 312s 901ms/step - loss: 1.7965 - accuracy: 0.6605 - val_loss: 1.5507 - val_accuracy: 0.7715\n",
            "Epoch 3/25\n",
            "346/346 [==============================] - 314s 906ms/step - loss: 1.6745 - accuracy: 0.6930 - val_loss: 1.4869 - val_accuracy: 0.7809\n",
            "Epoch 4/25\n",
            "346/346 [==============================] - 313s 904ms/step - loss: 1.5491 - accuracy: 0.7257 - val_loss: 1.5324 - val_accuracy: 0.6824\n",
            "Epoch 5/25\n",
            "346/346 [==============================] - 307s 889ms/step - loss: 1.4831 - accuracy: 0.7354 - val_loss: 1.2733 - val_accuracy: 0.8041\n",
            "Epoch 6/25\n",
            "346/346 [==============================] - 311s 898ms/step - loss: 1.4494 - accuracy: 0.7472 - val_loss: 1.3099 - val_accuracy: 0.7334\n",
            "Epoch 7/25\n",
            "346/346 [==============================] - 310s 896ms/step - loss: 1.3797 - accuracy: 0.7665 - val_loss: 1.2520 - val_accuracy: 0.7794\n",
            "Epoch 8/25\n",
            "346/346 [==============================] - 314s 908ms/step - loss: 1.3061 - accuracy: 0.7807 - val_loss: 1.2725 - val_accuracy: 0.8700\n",
            "Epoch 9/25\n",
            "346/346 [==============================] - 309s 892ms/step - loss: 1.3161 - accuracy: 0.7845 - val_loss: 1.2129 - val_accuracy: 0.8225\n",
            "Epoch 10/25\n",
            "346/346 [==============================] - 308s 890ms/step - loss: 1.2263 - accuracy: 0.7998 - val_loss: 1.1040 - val_accuracy: 0.8533\n",
            "Epoch 11/25\n",
            "346/346 [==============================] - 313s 904ms/step - loss: 1.1855 - accuracy: 0.8121 - val_loss: 1.0453 - val_accuracy: 0.8537\n",
            "Epoch 12/25\n",
            "346/346 [==============================] - 309s 893ms/step - loss: 1.1458 - accuracy: 0.8181 - val_loss: 1.0167 - val_accuracy: 0.8367\n",
            "Epoch 13/25\n",
            "346/346 [==============================] - 309s 893ms/step - loss: 1.0886 - accuracy: 0.8295 - val_loss: 0.9762 - val_accuracy: 0.8352\n",
            "Epoch 14/25\n",
            "346/346 [==============================] - 310s 896ms/step - loss: 1.0286 - accuracy: 0.8456 - val_loss: 0.9987 - val_accuracy: 0.8482\n",
            "Epoch 15/25\n",
            "346/346 [==============================] - 315s 911ms/step - loss: 1.0023 - accuracy: 0.8496 - val_loss: 0.9211 - val_accuracy: 0.8609\n",
            "Epoch 16/25\n",
            "346/346 [==============================] - 314s 907ms/step - loss: 0.9898 - accuracy: 0.8529 - val_loss: 0.8564 - val_accuracy: 0.8587\n",
            "Epoch 17/25\n",
            "346/346 [==============================] - 313s 904ms/step - loss: 0.9418 - accuracy: 0.8573 - val_loss: 0.8716 - val_accuracy: 0.9008\n",
            "Epoch 18/25\n",
            "346/346 [==============================] - 309s 895ms/step - loss: 0.9322 - accuracy: 0.8647 - val_loss: 0.9581 - val_accuracy: 0.9051\n",
            "Epoch 19/25\n",
            "346/346 [==============================] - 310s 896ms/step - loss: 0.8602 - accuracy: 0.8732 - val_loss: 0.7579 - val_accuracy: 0.9011\n",
            "Epoch 20/25\n",
            "346/346 [==============================] - 312s 901ms/step - loss: 0.8134 - accuracy: 0.8861 - val_loss: 0.8175 - val_accuracy: 0.8613\n",
            "Epoch 21/25\n",
            "346/346 [==============================] - 315s 911ms/step - loss: 0.7891 - accuracy: 0.8891 - val_loss: 0.7223 - val_accuracy: 0.9105\n",
            "Epoch 22/25\n",
            "346/346 [==============================] - 309s 893ms/step - loss: 0.7963 - accuracy: 0.8857 - val_loss: 0.8218 - val_accuracy: 0.8859\n",
            "Epoch 23/25\n",
            "346/346 [==============================] - 311s 899ms/step - loss: 0.7757 - accuracy: 0.8963 - val_loss: 0.7474 - val_accuracy: 0.9040\n",
            "Epoch 24/25\n",
            "346/346 [==============================] - 311s 899ms/step - loss: 0.7477 - accuracy: 0.8951 - val_loss: 0.7299 - val_accuracy: 0.9109\n",
            "Epoch 25/25\n",
            "346/346 [==============================] - 297s 857ms/step - loss: 0.7234 - accuracy: 0.9038 - val_loss: 0.7329 - val_accuracy: 0.9334\n",
            "Total Training Time: 2:10:08.075268\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bj48aOjqGgOd"
      },
      "source": [
        "model.save('my_model_1.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vev95aPfJSXr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "b002518d-77f2-4bb2-ee1c-3a640b776af9"
      },
      "source": [
        "# Plot training accuracy and validation accuracy\n",
        "\n",
        "plt.plot(history.history['accuracy'], 'r', label='Accuracy Training')\n",
        "plt.plot(history.history['val_accuracy'], 'b', label='Accuracy Validation')\n",
        "plt.title('Accuracy Training and Validation')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc=0)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyT26KTCJarr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "48f56f80-b82e-42c7-e72f-c120c799119d"
      },
      "source": [
        "# Plotting training loss and validation loss\n",
        "plt.plot(history.history['loss'], 'r', label='Loss Training')\n",
        "plt.plot(history.history['val_loss'], 'b', label='Loss Validation')\n",
        "plt.title('Loss Training and Validation')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loc=0)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1a4iXva1y4R"
      },
      "source": [
        "## Train Model using Trasfer Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuuoCSw62s8C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9db60a6d-cebf-47f9-ecc5-444a7160eaa8"
      },
      "source": [
        "# Define the Keras model\n",
        "# Use InceptionResNetV2 Keras model\n",
        "\n",
        "engine = tf.keras.applications.InceptionResNetV2(\n",
        "    # Freezing the weights of the top layer in the InceptionResNetV2 pre-traiined model\n",
        "    include_top = False,\n",
        "\n",
        "    # Use Imagenet weights\n",
        "    weights = 'imagenet',\n",
        "\n",
        "    # Define input shape to 224x224x3\n",
        "    input_shape = (224, 224, 3),\n",
        "\n",
        "    # Set classifier activation to sigmoid\n",
        "    classifier_activation = 'sigmoid'\n",
        ")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "219062272/219055592 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8cjIDm923RY"
      },
      "source": [
        "# Define the Keras model outputs\n",
        "\n",
        "x = tf.keras.layers.GlobalAveragePooling2D(name = 'avg_pool')(engine.output)\n",
        "out = tf.keras.layers.Dense(1, activation = 'sigmoid', name = 'dense_output')(x)\n",
        "\n",
        "\n",
        "# Build the Keras model\n",
        "\n",
        "model = tf.keras.models.Model(inputs = engine.input, outputs = out)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1x7JVXRe8Qo3"
      },
      "source": [
        "\n",
        "### (i) Loss: binary_crossentropy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySeOp7EL2-pI"
      },
      "source": [
        "# Compile the model\n",
        "\n",
        "model.compile(\n",
        "    # Set optimizer to Adam(0.001)\n",
        "    optimizer = tf.keras.optimizers.Adam(0.001),\n",
        "\n",
        "    # Set loss to binary crossentropy\n",
        "    loss = 'binary_crossentropy',\n",
        "\n",
        "    # Set metrics to accuracy\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_ag2rfn2_qB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32fe06a3-871b-4d69-bb44-85a16a181c8c"
      },
      "source": [
        "# Fit Keras model\n",
        "start = datetime.datetime.now()\n",
        "\n",
        "history = model.fit_generator(\n",
        "    # Use train generator\n",
        "    trainGeneratorBuild,\n",
        "\n",
        "    # Set epochs to 12\n",
        "    epochs = 12,\n",
        "\n",
        "    # Set steps per epochs to 300\n",
        "    steps_per_epoch = 300,\n",
        "    \n",
        "    # Set verbose to 1\n",
        "    verbose = 1\n",
        ")\n",
        "\n",
        "end = datetime.datetime.now()\n",
        "print(f'Total Training Time: {end - start}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/12\n",
            "300/300 [==============================] - 194s 573ms/step - loss: 0.2276 - accuracy: 0.9162\n",
            "Epoch 2/12\n",
            "300/300 [==============================] - 171s 570ms/step - loss: 0.1465 - accuracy: 0.9500\n",
            "Epoch 3/12\n",
            "300/300 [==============================] - 171s 570ms/step - loss: 0.1132 - accuracy: 0.9571\n",
            "Epoch 4/12\n",
            "300/300 [==============================] - 172s 572ms/step - loss: 0.1154 - accuracy: 0.9571\n",
            "Epoch 5/12\n",
            "300/300 [==============================] - 171s 570ms/step - loss: 0.0980 - accuracy: 0.9633\n",
            "Epoch 6/12\n",
            "300/300 [==============================] - 172s 572ms/step - loss: 0.0939 - accuracy: 0.9677\n",
            "Epoch 7/12\n",
            "300/300 [==============================] - 173s 575ms/step - loss: 0.0845 - accuracy: 0.9692\n",
            "Epoch 8/12\n",
            "300/300 [==============================] - 174s 578ms/step - loss: 0.0858 - accuracy: 0.9716\n",
            "Epoch 9/12\n",
            "300/300 [==============================] - 171s 568ms/step - loss: 0.0887 - accuracy: 0.9700\n",
            "Epoch 10/12\n",
            "300/300 [==============================] - 171s 567ms/step - loss: 0.0833 - accuracy: 0.9708\n",
            "Epoch 11/12\n",
            "300/300 [==============================] - 170s 564ms/step - loss: 0.0752 - accuracy: 0.9747\n",
            "Epoch 12/12\n",
            "300/300 [==============================] - 170s 567ms/step - loss: 0.0626 - accuracy: 0.9808\n",
            "Total Training Time: 0:37:08.318260\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzBFywnsGrcw"
      },
      "source": [
        "model.save('my_model_2.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
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          "height": 542
        },
        "outputId": "dbe6c089-7217-480d-dae1-772c3976fc52"
      },
      "source": [
        "# Create a graph representing the loss\n",
        "\n",
        "# Get loss data\n",
        "lossG = history.history['loss']\n",
        "accuracyG = history.history['accuracy']\n",
        "epochs = [i for i in range(len(lossG))]\n",
        "\n",
        "# Create graph\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x = epochs,\n",
        "        y = lossG,\n",
        "        name = 'Loss',\n",
        "        marker = dict(\n",
        "            color = 'rgba(250,50,50,1)'        \n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x = epochs,\n",
        "        y = accuracyG,\n",
        "        name = 'Accuracy',\n",
        "        marker = dict(\n",
        "            color = 'rgba(50,250,50,1)'        \n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    title = 'Model loss',\n",
        "    template = 'plotly_white'\n",
        ")\n",
        "\n",
        "fig.update_xaxes(\n",
        "    title_text='Epochs'\n",
        ")\n",
        "fig.update_yaxes(\n",
        "    title_text='Loss / Accuracy values'\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "metadata": {
        "id": "84f9IUbn3HjZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a84327fc-7361-4a10-d3c3-b5c98fe5c46a"
      },
      "source": [
        "# Checke the accuracy of the Keras model on the test data\n",
        "\n",
        "testLoss, testAccuracy = model.evaluate(\n",
        "    # Use test generator\n",
        "    testGeneratorBuild,\n",
        "    \n",
        "    # Set verbose to 1\n",
        "    verbose = 1\n",
        ")\n",
        "\n",
        "# Print results\n",
        "print('Accuracy of model : ' + str(round(testAccuracy,4)*100) + ' %')\n",
        "print('Loss of model : ' + str(round(testLoss,4)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "173/173 [==============================] - 35s 182ms/step - loss: 0.3006 - accuracy: 0.8725\n",
            "Accuracy of model : 87.25 %\n",
            "Loss of model : 0.3006\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRcHHJHh2C4h"
      },
      "source": [
        "# 6. Fine Tuning the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oKzgoqQ8U4a"
      },
      "source": [
        "### (ii) Loss: Focal Loss\n",
        "Because our dataset is unbalanced, we can utilise this strategy to balance the weighting of our training instances.\n",
        "Instead of giving all training examples the same weight, focus loss gives the well-classified instances a lower weight. As a result, more training emphasis is placed on data that is difficult to classify!\n",
        "\n",
        "If we have a data imbalance in practise, our majority class will soon become well-classified because we have considerably more data for it. As a result, we may use the focus loss to give those minority class instances more relative weight during training, ensuring that we obtain high accuracy on our minority class as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uyq8OBxT8U4c"
      },
      "source": [
        "# Define our custom loss function\n",
        "\n",
        "def focal_loss(y_true, y_pred):\n",
        "    gamma = 2.0\n",
        "    alpha = 0.25\n",
        "    pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))\n",
        "    pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))\n",
        "    return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1))-K.sum((1-alpha) * K.pow( pt_0, gamma) * K.log(1. - pt_0))\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDmBXZLyzI2o"
      },
      "source": [
        "# Compile our model\n",
        "\n",
        "adam = tf.keras.optimizers.Adam(lr=0.0001)\n",
        "model.compile(loss=[focal_loss], metrics=[\"accuracy\"], optimizer=adam)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTfSewE48U4d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fb14628-7b3a-497d-8533-494198dc2fef"
      },
      "source": [
        "# Fit Keras model\n",
        "start = datetime.datetime.now()\n",
        "\n",
        "history = model.fit_generator(\n",
        "    # Use train generator\n",
        "    trainGeneratorBuild,\n",
        "\n",
        "    # Set epochs to 20\n",
        "    epochs = 20,\n",
        "\n",
        "    # Set steps per epochs to 300\n",
        "    steps_per_epoch = 300,       # batch_size\n",
        "\n",
        "    # Set verbose to 1\n",
        "    verbose = 1\n",
        ")\n",
        "\n",
        "end = datetime.datetime.now()\n",
        "print(f'Total Training Time: {end - start}')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "300/300 [==============================] - 185s 564ms/step - loss: 0.0825 - accuracy: 0.9825\n",
            "Epoch 2/20\n",
            "300/300 [==============================] - 171s 568ms/step - loss: 0.0744 - accuracy: 0.9846\n",
            "Epoch 3/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0610 - accuracy: 0.9858\n",
            "Epoch 4/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0571 - accuracy: 0.9877\n",
            "Epoch 5/20\n",
            "300/300 [==============================] - 170s 565ms/step - loss: 0.0648 - accuracy: 0.9892\n",
            "Epoch 6/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0645 - accuracy: 0.9894\n",
            "Epoch 7/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0694 - accuracy: 0.9867\n",
            "Epoch 8/20\n",
            "300/300 [==============================] - 170s 567ms/step - loss: 0.0498 - accuracy: 0.9904\n",
            "Epoch 9/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0424 - accuracy: 0.9917\n",
            "Epoch 10/20\n",
            "300/300 [==============================] - 171s 568ms/step - loss: 0.0469 - accuracy: 0.9902\n",
            "Epoch 11/20\n",
            "300/300 [==============================] - 170s 565ms/step - loss: 0.0222 - accuracy: 0.9939\n",
            "Epoch 12/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0655 - accuracy: 0.9865\n",
            "Epoch 13/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0288 - accuracy: 0.9933\n",
            "Epoch 14/20\n",
            "300/300 [==============================] - 171s 569ms/step - loss: 0.0632 - accuracy: 0.9885\n",
            "Epoch 15/20\n",
            "300/300 [==============================] - 170s 564ms/step - loss: 0.0472 - accuracy: 0.9906\n",
            "Epoch 16/20\n",
            "300/300 [==============================] - 169s 563ms/step - loss: 0.0439 - accuracy: 0.9919\n",
            "Epoch 17/20\n",
            "300/300 [==============================] - 169s 562ms/step - loss: 0.0555 - accuracy: 0.9875\n",
            "Epoch 18/20\n",
            "300/300 [==============================] - 169s 563ms/step - loss: 0.0302 - accuracy: 0.9935\n",
            "Epoch 19/20\n",
            "300/300 [==============================] - 169s 563ms/step - loss: 0.0460 - accuracy: 0.9898\n",
            "Epoch 20/20\n",
            "300/300 [==============================] - 169s 562ms/step - loss: 0.0304 - accuracy: 0.9906\n",
            "Total Training Time: 1:02:20.600257\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yayQn8zKGx8U"
      },
      "source": [
        "model.save('my_model_3.h5')"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVJxQbE02Sgl"
      },
      "source": [
        "# 7. Plot Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvjhzbIM8U4h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "29601e39-9993-4513-b4e6-66b7916aa164"
      },
      "source": [
        "# Create a graph representing the loss\n",
        "\n",
        "# Get loss data\n",
        "lossG = history.history['loss']\n",
        "accuracyG = history.history['accuracy']\n",
        "epochs = [i for i in range(len(lossG))]\n",
        "\n",
        "# Create graph\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x = epochs,\n",
        "        y = lossG,\n",
        "        name = 'Loss',\n",
        "        marker = dict(\n",
        "            color = 'rgba(250,50,50,1)'        \n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x = epochs,\n",
        "        y = accuracyG,\n",
        "        name = 'Accuracy',\n",
        "        marker = dict(\n",
        "            color = 'rgba(50,250,50,1)'        \n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    title = 'Model loss',\n",
        "    template = 'plotly_white'\n",
        ")\n",
        "\n",
        "fig.update_xaxes(\n",
        "    title_text='Epochs'\n",
        ")\n",
        "fig.update_yaxes(\n",
        "    title_text='Loss / Accuracy values'\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoCOWfZ58U4j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca094ef5-0a23-4d12-b025-54a2f3695b7f"
      },
      "source": [
        "# Check the accuracy of the Keras model on the test data\n",
        "\n",
        "testLoss, testAccuracy = model.evaluate(\n",
        "    # Use test generator\n",
        "    testGeneratorBuild,\n",
        "    \n",
        "    # Set verbose to 1\n",
        "    verbose = 1\n",
        ")\n",
        "\n",
        "# Print results\n",
        "print('Accuracy of model : ' + str(round(testAccuracy,4)*100) + ' %')\n",
        "print('Loss of model : ' + str(round(testLoss,4)))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "173/173 [==============================] - 33s 172ms/step - loss: 0.0300 - accuracy: 0.9935\n",
            "Accuracy of model : 99.35000000000001 %\n",
            "Loss of model : 0.03\n"
          ],
          "name": "stdout"
        }
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      "cell_type": "code",
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        ""
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      "execution_count": null,
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}