dl_diabetic_retinopathy_mvp.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/slp22/deep-learning-project/blob/main/dl_diabetic_retinopathy_mvp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74975ac3"
      },
      "source": [
        "#### Deep Learning | MVP\n",
        "\n",
        "# Diabetic Retinopathy<a id='top'></a> "
      ],
      "id": "74975ac3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ea017a0f"
      },
      "source": [
        "## **Analysis Goal**  \n",
        "The client, the [National Eye Institute](https://www.nei.nih.gov/), part of the National Institutes of Health, wants a model that can quickly identify [diabetic retinopathy](https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/diabetic-retinopathy) in patients participating in early phase clinical trials.\n",
        "\n",
        "**RQ:** How well can a neural network diagnose diabetic retinopathy from a retinal image?\n",
        "\n",
        "## **Process**\n",
        "**Data source:** [Diabetic Retinopathy](https://www.kaggle.com/datasets/sovitrath/diabetic-retinopathy-2015-data-colored-resized)retinal images at various stages of the disease, modified from the [Diabetic Retinopathy Detection Kaggle Competition](https://www.kaggle.com/c/diabetic-retinopathy-detection); images are courtesy of [EyePACS](http://eyepacs.com/),a free platform for retinopathy screening. The dataset (n=35,126) is classified as:  \n",
        "  0 - No_DR (healthy eye)\n",
        "  1 - Mild\n",
        "  2 - Moderate\n",
        "  3 - Severe\n",
        "  4 - Proliferate_DR\n",
        "\n",
        "**Error Metric:** Accuracy (for MVP), precision and recall (for final model)\n",
        "\n",
        "**Base Model:** \n",
        "  * First iteration deep learning model:\n",
        "    * accuracy: 0.1530\n",
        "    * val_accuracy: 0.1550\n",
        "\n",
        "## **Preliminary Conclusions**\n",
        "The model with 5 epochs does not perform well. \n",
        "\n",
        "The next step for this analysis is to run the model with more epochs; and use transfer learning to improve the performance. "
      ],
      "id": "ea017a0f"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "HYTw2GEqeY6s"
      },
      "id": "HYTw2GEqeY6s"
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64179b94",
        "outputId": "3c81e957-a91e-4cef-e5e5-62ae934fcecb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n",
            "Num GPUs Available:  1\n",
            "Matplotlib version: 3.2.2\n",
            "Numpy version: 1.21.6\n",
            "Pandas version: 1.3.5\n",
            "TensorFlow version: 2.8.2\n",
            "Keras Version: 2.8.0\n",
            "Scikit-learn version: 1.0.2\n"
          ]
        }
      ],
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os, shutil\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import PIL\n",
        "import random\n",
        "import seaborn as sns\n",
        "import shutil\n",
        "import sklearn as sk\n",
        "import tensorflow as tf\n",
        "import warnings\n",
        "\n",
        "from glob import glob\n",
        "from tensorflow import keras\n",
        "from keras import backend as K\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing import image as IMG\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "# from keras.utils import to_categorical\n",
        "\n",
        "from PIL import Image\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "%pylab inline\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_formats = ['retina']  # or svg\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "sns.set(context='notebook', style='whitegrid')\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))\n",
        "print(\"Matplotlib version:\", matplotlib.__version__)\n",
        "print(\"Numpy version:\", np.__version__)\n",
        "print(\"Pandas version:\", pd.__version__)\n",
        "print(\"TensorFlow version:\", tf.__version__)\n",
        "print(\"Keras Version:\", tf.keras.__version__)\n",
        "print(\"Scikit-learn version:\", sk.__version__)\n"
      ],
      "id": "64179b94"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d38894c8"
      },
      "source": [
        "## 1 | Dataset: [Diabetic Retinopathy Retinal Images](https://www.kaggle.com/datasets/sovitrath/diabetic-retinopathy-2015-data-colored-resized)"
      ],
      "id": "d38894c8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Code adapted from [github/PurpleGrace](https://github.com/PurpleGrace/Deep_Learning_Tools_Image_Classification/blob/main/code/1.EDA_Sample_Images.ipynb)\n"
      ],
      "metadata": {
        "id": "8Xw-Rr1OkC8R"
      },
      "id": "8Xw-Rr1OkC8R"
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "status": 200,
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      "id": "3uJyuCs9jkJ7",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"sandralparedes\",\"key\":\"52716e458c895e3e9d7fcfde80b0d59e\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXLwZ2Vnn7ge",
        "outputId": "69030c83-35d5-4c38-bd6e-38948135a41d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "GXLwZ2Vnn7ge"
    },
    {
      "cell_type": "code",
      "source": [
        "! pwd\n",
        "! ls"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "63fdf1eb-4c79-464d-8071-bf8b31e15772"
      },
      "id": "q0Zy3er5mJgR",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "drive  kaggle.json  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "import os\n",
        "\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content\""
      ],
      "metadata": {
        "id": "As3njbbDmjAY"
      },
      "id": "As3njbbDmjAY",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! kaggle datasets download -d sovitrath/diabetic-retinopathy-2015-data-colored-resized"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jD2q1R_8movA",
        "outputId": "ffbfdd19-f7ba-4c44-a5ea-584c080cebdc"
      },
      "id": "jD2q1R_8movA",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /content/kaggle.json'\n",
            "Downloading diabetic-retinopathy-2015-data-colored-resized.zip to /content\n",
            "100% 1.93G/1.94G [00:14<00:00, 164MB/s]\n",
            "100% 1.94G/1.94G [00:14<00:00, 142MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# unzip kaggle file\n",
        "\n",
        "zip_ref = zipfile.ZipFile('diabetic-retinopathy-2015-data-colored-resized.zip', 'r') #Opens the zip file in read mode\n",
        "zip_ref.extractall('/tmp') #Extracts the files into the /tmp folder\n",
        "zip_ref.close()"
      ],
      "metadata": {
        "id": "8bszkBF7ndyj"
      },
      "id": "8bszkBF7ndyj",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# show number of classes and images\n",
        "\n",
        "source_images_path = '/tmp/colored_images/colored_images/'\n",
        "extracted_directories_path = os.listdir(source_images_path)\n",
        "\n",
        "for image_directory in extracted_directories_path:\n",
        "  print(image_directory, len(os.listdir(os.path.join(source_images_path, image_directory))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Wqza8h6nqEY",
        "outputId": "f2023eaa-4179-4f0c-d79c-0dc0e68f85dd"
      },
      "id": "5Wqza8h6nqEY",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Proliferate_DR 708\n",
            "Severe 873\n",
            "Mild 2443\n",
            "Moderate 5292\n",
            "No_DR 25810\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "678fce99"
      },
      "source": [
        "## 2 | Exploratory Data Analysis"
      ],
      "id": "678fce99"
    },
    {
      "cell_type": "code",
      "source": [
        "# read in files and diagnosis label and store as dataframe\n",
        "\n",
        "filepath = '/tmp/colored_images/colored_images'\n",
        "dataframe = {'image_path':[],'diagnosis':[]}\n",
        "\n",
        "for diagnosis in os.listdir(filepath):\n",
        "    print(diagnosis)\n",
        "    if diagnosis != 'colored_images':        \n",
        "        for image in os.listdir(filepath +\"/\"+diagnosis):\n",
        "            if image != 'Dataset':\n",
        "                dataframe['image_path'].append(filepath +\"/\"+diagnosis+\"/\"+ image)\n",
        "                dataframe['diagnosis'].append(diagnosis)\n",
        "dataframe = pd.DataFrame(dataframe)  \n",
        "dataframe.info()\n",
        "dataframe.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "Vs7otXFJke-S",
        "outputId": "80b65a5a-b8aa-4a73-e74f-053d5bb74c11"
      },
      "id": "Vs7otXFJke-S",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Proliferate_DR\n",
            "Severe\n",
            "Mild\n",
            "Moderate\n",
            "No_DR\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 35126 entries, 0 to 35125\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   image_path  35126 non-null  object\n",
            " 1   diagnosis   35126 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 549.0+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                          image_path  \\\n",
              "0  /tmp/colored_images/colored_images/Proliferate_DR/30747_right.png   \n",
              "1   /tmp/colored_images/colored_images/Proliferate_DR/19600_left.png   \n",
              "\n",
              "        diagnosis  \n",
              "0  Proliferate_DR  \n",
              "1  Proliferate_DR  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d9e2cf8-af02-4f93-8b28-b9bcb0ee7661\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_path</th>\n",
              "      <th>diagnosis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/tmp/colored_images/colored_images/Proliferate_DR/30747_right.png</td>\n",
              "      <td>Proliferate_DR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/tmp/colored_images/colored_images/Proliferate_DR/19600_left.png</td>\n",
              "      <td>Proliferate_DR</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d9e2cf8-af02-4f93-8b28-b9bcb0ee7661')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8d9e2cf8-af02-4f93-8b28-b9bcb0ee7661 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8d9e2cf8-af02-4f93-8b28-b9bcb0ee7661');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data set n = 35,126"
      ],
      "metadata": {
        "id": "HRx23uUVzhLD"
      },
      "id": "HRx23uUVzhLD"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "9SYJaUP4H-Cs",
        "outputId": "3a0a38de-1cc7-4e15-bd62-23e82d9de6f4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 912,
              "height": 392
            },
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# distribution of five diagnosis classes in order of severity of disease\n",
        "\n",
        "plt.figure(figsize=(15,6))\n",
        "sns.barplot(dataframe.diagnosis.value_counts().index,\n",
        "            dataframe.diagnosis.value_counts(),\n",
        "            palette=None)\n",
        "plt.title(\"Distribution of Diabetic Retinopahty Diagnosis Classes\")\n",
        "plt.xlabel(\"Diagnosis\")\n",
        "plt.ylabel(\"Frequency\");\n"
      ],
      "id": "9SYJaUP4H-Cs"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Classes are unbalanced"
      ],
      "metadata": {
        "id": "OGGX9W-lzlMS"
      },
      "id": "OGGX9W-lzlMS"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "5wM6uskkH-Cs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "3f6d7879-b5e8-4a68-ca85-cf0a66466160"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No Diabetic Retinopthy\n",
            "Datatype: <class 'PIL.PngImagePlugin.PngImageFile'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7F29F14AF7D0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# view one healthy eye image\n",
        "\n",
        "path = '/tmp/colored_images/colored_images/No_DR/10003_left.png'\n",
        "\n",
        "healthy_image = tf.keras.preprocessing.image.load_img(\n",
        "   path,\n",
        "   grayscale=False, \n",
        "   color_mode=\"rgb\", \n",
        "  #  target_size=(256, 256),\n",
        "   interpolation=\"nearest\")\n",
        "\n",
        "print('No Diabetic Retinopthy')\n",
        "print('Datatype:', type(healthy_image))\n",
        "healthy_image \n"
      ],
      "id": "5wM6uskkH-Cs"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "jdQHTkYEH-Cs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "a4d8874b-11aa-48d4-d26d-878f2d7d0d8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Proliferate Diabetic Retinopthy\n",
            "Datatype: <class 'PIL.PngImagePlugin.PngImageFile'>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=224x224 at 0x7F29F1227150>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# view one proliferate DR image (most advanced stage of disease)\n",
        "\n",
        "path = '/tmp/colored_images/colored_images/Proliferate_DR/10017_left.png'\n",
        "\n",
        "proliferate_image = tf.keras.preprocessing.image.load_img(\n",
        "   path,\n",
        "   grayscale=False, \n",
        "   color_mode=\"rgb\", \n",
        "  #  target_size=(256, 256), \n",
        "   interpolation=\"nearest\")\n",
        "\n",
        "print('Proliferate Diabetic Retinopthy')\n",
        "print('Datatype:', type(proliferate_image))\n",
        "proliferate_image \n"
      ],
      "id": "jdQHTkYEH-Cs"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GwlHbfpL1JAH"
      },
      "source": [
        "## 3 | Deep Learning Model"
      ],
      "id": "GwlHbfpL1JAH"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Validation Split"
      ],
      "metadata": {
        "id": "dGB1VbycEzd-"
      },
      "id": "dGB1VbycEzd-"
    },
    {
      "cell_type": "code",
      "source": [
        "# set up for train/test split\n",
        "\n",
        "temp_path = '/tmp'\n",
        "raw_data_dir_name = 'raw_data'\n",
        "train_data_dir_name = 'train'\n",
        "test_data_dir_name = 'test'\n",
        "raw_data_directories = [train_data_dir_name, test_data_dir_name]\n",
        "\n",
        "images_path =  os.path.join(temp_path, raw_data_dir_name)\n",
        "tmp_directories = os.listdir(temp_path)\n",
        "\n",
        "def build_raw_data_directory(parent, directory):\n",
        "  dir_path = os.path.join(parent, directory)\n",
        "  os.mkdir(dir_path)\n",
        "  for dir in extracted_directories_path:\n",
        "    os.mkdir(os.path.join(dir_path, dir))\n",
        "\n",
        "if raw_data_dir_name not in tmp_directories:\n",
        "  os.mkdir(images_path)\n",
        "  for directory in raw_data_directories:\n",
        "    build_raw_data_directory(images_path, directory)\n",
        "else:\n",
        "  print(raw_data_dir_name, \" already exist\")\n",
        "  images_path_directories = os.listdir(images_path)\n",
        "  for directory in raw_data_directories:\n",
        "    if directory not in images_path_directories:\n",
        "      build_raw_data_directory(images_path, directory)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "kzKM7y_dOIkG"
      },
      "id": "kzKM7y_dOIkG",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split data into train/test folders\n",
        "\n",
        "distribution = 0.2\n",
        "\n",
        "raw_data_path = os.path.join(temp_path, raw_data_dir_name)\n",
        "test_data_path_root = os.path.join(raw_data_path, test_data_dir_name)\n",
        "train_data_path_root = os.path.join(raw_data_path, train_data_dir_name)\n",
        "\n",
        "data_dirs = os.listdir(source_images_path)\n",
        "for dir in data_dirs:\n",
        "  path = os.path.join(source_images_path, dir)\n",
        "  test_data_path = os.path.join(test_data_path_root, dir)\n",
        "  train_data_path = os.path.join(train_data_path_root, dir)\n",
        "  images = os.listdir(path)\n",
        "  print(path, test_data_path)\n",
        "  for image in images:\n",
        "    random_number = random.uniform(0, 1)\n",
        "    is_test_image = random_number < distribution\n",
        "    source_image_path = os.path.join(path, image)\n",
        "    # shutil.copy(src, dst)\n",
        "    if is_test_image:\n",
        "      shutil.copy(source_image_path, test_data_path)\n",
        "      # print(source_image_path, \"goes to test\", test_data_path)\n",
        "    else:\n",
        "      shutil.copy(source_image_path, train_data_path)\n",
        "      # print(source_image_path, \"goes to train\", train_data_path)\n",
        "\n",
        "test_data_path_root = os.path.join(raw_data_path, test_data_dir_name)\n",
        "train_data_path_root = os.path.join(raw_data_path, train_data_dir_name)\n",
        "\n",
        "test_dirs = os.listdir(test_data_path_root)\n",
        "train_dirs = os.listdir(train_data_path_root)\n",
        "\n",
        "for dir in test_dirs:\n",
        "  path = os.path.join(test_data_path_root, dir)\n",
        "  files = os.listdir(path)\n",
        "  print(path, len(files))\n",
        "\n",
        "for dir in train_dirs:\n",
        "  path = os.path.join(train_data_path_root, dir)\n",
        "  files = os.listdir(path)\n",
        "  print(path, len(files))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-530XeBUQXs",
        "outputId": "88494ce7-069d-483d-a5c7-6a5e1819887c"
      },
      "id": "C-530XeBUQXs",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/tmp/colored_images/colored_images/Proliferate_DR /tmp/raw_data/test/Proliferate_DR\n",
            "/tmp/colored_images/colored_images/Severe /tmp/raw_data/test/Severe\n",
            "/tmp/colored_images/colored_images/Mild /tmp/raw_data/test/Mild\n",
            "/tmp/colored_images/colored_images/Moderate /tmp/raw_data/test/Moderate\n",
            "/tmp/colored_images/colored_images/No_DR /tmp/raw_data/test/No_DR\n",
            "/tmp/raw_data/test/Proliferate_DR 128\n",
            "/tmp/raw_data/test/Severe 175\n",
            "/tmp/raw_data/test/Mild 534\n",
            "/tmp/raw_data/test/Moderate 1050\n",
            "/tmp/raw_data/test/No_DR 5144\n",
            "/tmp/raw_data/train/Proliferate_DR 580\n",
            "/tmp/raw_data/train/Severe 698\n",
            "/tmp/raw_data/train/Mild 1909\n",
            "/tmp/raw_data/train/Moderate 4242\n",
            "/tmp/raw_data/train/No_DR 20666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Deep Learning First Model"
      ],
      "metadata": {
        "id": "oh0NfWeMwb4f"
      },
      "id": "oh0NfWeMwb4f"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Code source: https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d "
      ],
      "metadata": {
        "id": "Di9VDJ555WHh"
      },
      "id": "Di9VDJ555WHh"
    },
    {
      "cell_type": "code",
      "source": [
        "img_width, img_height = 150, 150\n",
        "\n",
        "train_data_dir = 'data/train'\n",
        "validation_data_dir = 'data/validation'\n",
        "nb_train_samples = 2000\n",
        "nb_validation_samples = 800\n",
        "epochs = 50\n",
        "batch_size = 16"
      ],
      "metadata": {
        "id": "2YcpKfoF3qwZ"
      },
      "id": "2YcpKfoF3qwZ",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if K.image_data_format() == 'channels_first':\n",
        "    input_shape = (3, img_width, img_height)\n",
        "else:\n",
        "    input_shape = (img_width, img_height, 3)"
      ],
      "metadata": {
        "id": "WBv-VnOP3qzS"
      },
      "id": "WBv-VnOP3qzS",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "FjzZk4kvH-Cu"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), input_shape=input_shape))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1))\n",
        "model.add(Activation('sigmoid'))\n"
      ],
      "id": "FjzZk4kvH-Cu"
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "uxuBD5Zx3q6S"
      },
      "id": "uxuBD5Zx3q6S",
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        shear_range=0.2,\n",
        "        zoom_range=0.2,\n",
        "        horizontal_flip=True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        '/tmp/raw_data/train',  \n",
        "        target_size=(150, 150), \n",
        "        batch_size=batch_size,\n",
        "        class_mode='binary')  \n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        '/tmp/raw_data/test',\n",
        "        target_size=(150, 150),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='binary')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1x2C2Sl5zyW2",
        "outputId": "be8b065d-cbac-41e0-a36b-211d27a3b74f"
      },
      "id": "1x2C2Sl5zyW2",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 28095 images belonging to 5 classes.\n",
            "Found 7031 images belonging to 5 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit_generator(\n",
        "        train_generator,\n",
        "        steps_per_epoch=nb_train_samples // batch_size,\n",
        "        epochs=5,\n",
        "        validation_data=validation_generator,\n",
        "        validation_steps=nb_validation_samples // batch_size\n",
        "        )\n",
        "\n",
        "model.save_weights('first_try.h5')  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4P_8jaBwzyfH",
        "outputId": "bbeda49d-bcf3-4406-c67d-3815f97a94f3"
      },
      "id": "4P_8jaBwzyfH",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "125/125 [==============================] - 15s 118ms/step - loss: -214507824.0000 - accuracy: 0.1370 - val_loss: -578096064.0000 - val_accuracy: 0.1825\n",
            "Epoch 2/5\n",
            "125/125 [==============================] - 14s 114ms/step - loss: -2003340928.0000 - accuracy: 0.1605 - val_loss: -4200994304.0000 - val_accuracy: 0.1275\n",
            "Epoch 3/5\n",
            "125/125 [==============================] - 15s 117ms/step - loss: -8443093504.0000 - accuracy: 0.1550 - val_loss: -14637317120.0000 - val_accuracy: 0.1587\n",
            "Epoch 4/5\n",
            "125/125 [==============================] - 14s 112ms/step - loss: -26486697984.0000 - accuracy: 0.1581 - val_loss: -39455670272.0000 - val_accuracy: 0.1625\n",
            "Epoch 5/5\n",
            "125/125 [==============================] - 14s 111ms/step - loss: -64640176128.0000 - accuracy: 0.1530 - val_loss: -94855258112.0000 - val_accuracy: 0.1550\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "dl-diabetic-retinopathy-mvp.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (deep_learning)",
      "language": "python",
      "name": "deep_learning"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.12"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}