covidresearch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "covidresearch",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harshulgupt/Adhoc-Networks-Non-Tech/blob/master/covidresearch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcSTCf3Xp2f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1c0bd22-c19f-4abd-f582-f6968ea8300a"
      },
      "source": [
        "# Dataset : http://cb.lk/covid_19\n",
        "\n",
        "!wget http://cb.lk/covid_19\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-14 19:40:19--  http://cb.lk/covid_19\n",
            "Resolving cb.lk (cb.lk)... 159.89.170.74\n",
            "Connecting to cb.lk (cb.lk)|159.89.170.74|:80... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://cb.lk/covid_19 [following]\n",
            "--2020-11-14 19:40:20--  https://cb.lk/covid_19\n",
            "Connecting to cb.lk (cb.lk)|159.89.170.74|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://www.dropbox.com/s/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip?dl=0 [following]\n",
            "--2020-11-14 19:40:20--  https://www.dropbox.com/s/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.1, 2620:100:6016:1::a27d:101\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip [following]\n",
            "--2020-11-14 19:40:21--  https://www.dropbox.com/s/raw/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com/cd/0/inline/BDPPABzMDKK9WgSyupXUpE8lkJbhfXVM-zVCLNYDvp7whDszCOV6ZG74NLTAaDVMOlapPB-ZsgRsqmexek-umnBbPsmKDrWqjSSk2LvCsDy4lh3DZk7X5uF98G9wnzNwpg0/file# [following]\n",
            "--2020-11-14 19:40:21--  https://uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com/cd/0/inline/BDPPABzMDKK9WgSyupXUpE8lkJbhfXVM-zVCLNYDvp7whDszCOV6ZG74NLTAaDVMOlapPB-ZsgRsqmexek-umnBbPsmKDrWqjSSk2LvCsDy4lh3DZk7X5uF98G9wnzNwpg0/file\n",
            "Resolving uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com (uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com)... 162.125.1.15, 2620:100:6016:15::a27d:10f\n",
            "Connecting to uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com (uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com)|162.125.1.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/BDN81hjEY_qK0PtwlmZXWVrccbDO2sE8Yg40KuixgsF9N1CBYAd-WyWQClRQqB9Ajs2dKmRUAFcRZywE6jOPqaB6tKy_Qzqy6gDzPFKcIDnXzvp8QSbyJw4BRk-EGHK-iL8Nz1VYnTDyEML2utIcVCxrhzlIJOLnZLcM9M-SvYwe4p378Cf2od5l0-s7ftt6Jt3no54D3ciqDKgwe7Tgahi3JKl76A0DG8IHuU0HGVqCdDUUlk0b5hAOQ7B2tyfm1nFTDvdpiVSURDXp3mGiZhgvs_zJZ0uBNHo2pv2u_-SaIN9PyIR7t3uXSRbdr3TtWCky-BICRdW8OOfvhj6zenwUMnW0hYGMPubRKVYmBY3wLw/file [following]\n",
            "--2020-11-14 19:40:22--  https://uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com/cd/0/inline2/BDN81hjEY_qK0PtwlmZXWVrccbDO2sE8Yg40KuixgsF9N1CBYAd-WyWQClRQqB9Ajs2dKmRUAFcRZywE6jOPqaB6tKy_Qzqy6gDzPFKcIDnXzvp8QSbyJw4BRk-EGHK-iL8Nz1VYnTDyEML2utIcVCxrhzlIJOLnZLcM9M-SvYwe4p378Cf2od5l0-s7ftt6Jt3no54D3ciqDKgwe7Tgahi3JKl76A0DG8IHuU0HGVqCdDUUlk0b5hAOQ7B2tyfm1nFTDvdpiVSURDXp3mGiZhgvs_zJZ0uBNHo2pv2u_-SaIN9PyIR7t3uXSRbdr3TtWCky-BICRdW8OOfvhj6zenwUMnW0hYGMPubRKVYmBY3wLw/file\n",
            "Reusing existing connection to uc85b2b2a046aafcbc00441eb382.dl.dropboxusercontent.com:443.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 140830621 (134M) [application/zip]\n",
            "Saving to: ‘covid_19’\n",
            "\n",
            "covid_19            100%[===================>] 134.31M  34.9MB/s    in 4.2s    \n",
            "\n",
            "2020-11-14 19:40:27 (32.0 MB/s) - ‘covid_19’ saved [140830621/140830621]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FPSlY63qFJ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "339c0d37-bc58-406d-cb30-bfee31c1b6e9"
      },
      "source": [
        "!unzip covid_19"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  covid_19\n",
            "  inflating: CovidDataset/Val/Covid/88de9d8c39e946abd495b37cd07d89e5-6531-0.jpg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klZeEzJSqUvQ"
      },
      "source": [
        "TRAIN_PATH = \"CovidDataset/Train\"\n",
        "VAL_PATH = \"CovidDataset/Test\"\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1s_VfacWrA-E"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "from keras.layers import *\n",
        "from keras.models import * \n",
        "from keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vRL0YMtrIsn"
      },
      "source": [
        "# CNN Based Model in Keras\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(224,224,3)))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.compile(loss=keras.losses.binary_crossentropy,optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNxzu23Ouxcp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9944311e-a172-4a2e-f84a-3da93d7a51a1"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 222, 222, 32)      896       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 220, 220, 64)      18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 110, 110, 64)      0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 110, 110, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 108, 108, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 54, 54, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 54, 54, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 52, 52, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 86528)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                5537856   \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 5,668,097\n",
            "Trainable params: 5,668,097\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2nnojiosu2YM"
      },
      "source": [
        "# Train from scratch\n",
        "train_datagen = image.ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    shear_range = 0.2,\n",
        "    zoom_range = 0.2,\n",
        "    horizontal_flip = True,\n",
        ")\n",
        "\n",
        "test_dataset = image.ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iLC3vWiHv2s8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c3dd04a-269b-43dd-a4b8-c59ec55815e6"
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    'CovidDataset/Train',\n",
        "    target_size = (224,224),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'binary')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 224 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_ov9LTgwQSg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7311f83c-d5bd-4855-9b01-c7b7100c884e"
      },
      "source": [
        "train_generator.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Covid': 0, 'Normal': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RQ5LeuQwmxj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ec72d67-c6d7-45d5-c677-d82b5e931a02"
      },
      "source": [
        "validation_generator = test_dataset.flow_from_directory(\n",
        "    'CovidDataset/Val',\n",
        "    target_size = (224,224),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'binary')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 60 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGuVlZF5wZsh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f545427d-32a9-4b2e-b601-a2362782ce8a"
      },
      "source": [
        "hist = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch=8,\n",
        "    epochs = 10,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps=2\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-11-266895c25328>:6: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "Epoch 1/10\n",
            "7/8 [=========================>....] - ETA: 0s - loss: 0.9494 - accuracy: 0.5045WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 80 batches). You may need to use the repeat() function when building your dataset.\n",
            "7/8 [=========================>....] - 9s 1s/step - loss: 0.9494 - accuracy: 0.5045 - val_loss: 0.6872 - val_accuracy: 0.9333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oO31IcR5zIm3"
      },
      "source": [
        "# Class Activation Maps\n",
        "# Grad-CAM "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTpdKm8Lxz6z"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HXg6c3oEzPc2"
      },
      "source": [
        ""
      ]
    }
  ]
}