COVID_19_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "COVID-19 Detection",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "z16PCpJNNQ8w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 902
        },
        "outputId": "172e1130-70c9-4c54-a457-aea6ad2ca5bd"
      },
      "source": [
        "!wget http://cb.lk/covid_19"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-08-07 20:25:08--  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",
            "--2020-08-07 20:25:09--  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",
            "--2020-08-07 20:25:10--  https://www.dropbox.com/s/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip?dl=0\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.65.1, 2620:100:6021:1::a27d:4101\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.65.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/7rjw6oet4za01op/CovidDataset-20200427T133042Z-001.zip [following]\n",
            "--2020-08-07 20:25:10--  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://uceb24e33a473bd44e76e159022b.dl.dropboxusercontent.com/cd/0/inline/A9DuUAH6f_rgKEl2-Jk6yJTDPXK3dUrVDowDuWI4FUJHLlt2ns6DtTIpe2wonxaQGUuTO3ErVxcyru5rg_1DD5yb_sWZ_TVEUSfgkYZtNKyQXdvXkmSoh9NsF-9kbyIFiX0/file# [following]\n",
            "--2020-08-07 20:25:11--  https://uceb24e33a473bd44e76e159022b.dl.dropboxusercontent.com/cd/0/inline/A9DuUAH6f_rgKEl2-Jk6yJTDPXK3dUrVDowDuWI4FUJHLlt2ns6DtTIpe2wonxaQGUuTO3ErVxcyru5rg_1DD5yb_sWZ_TVEUSfgkYZtNKyQXdvXkmSoh9NsF-9kbyIFiX0/file\n",
            "Resolving uceb24e33a473bd44e76e159022b.dl.dropboxusercontent.com (uceb24e33a473bd44e76e159022b.dl.dropboxusercontent.com)... 162.125.65.15, 2620:100:6021:15::a27d:410f\n",
            "Connecting to uceb24e33a473bd44e76e159022b.dl.dropboxusercontent.com (uceb24e33a473bd44e76e159022b.dl.dropboxusercontent.com)|162.125.65.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: /cd/0/inline2/A9DGLLYc242zS7-F5f7V33tdspn35wMsQ93al7E0ykfltkmQouhhkNS9IKjEXgHF3gTi9VFhLRJFWC8zF1v6_P4Fcqgyog01F5WP51gfhE4H06QiLxkFowlzLKm9Cp4JEqEbaQIqVnn_TleKUh5yVLw5Nh8SZ2L5TBEGdWMXlREw2NzcKQE8xjcQTOOhJ8e2M-TmcKrUBCDSFSTRUqvLOKtx1dwT4aXlYlxqfA7OEtmau1RCtKUxyBi4J_5LYot2-MPetoWI1ppNCbzRq8Vv7s_Bk786-NIys_JZswqW8GR_3-kvw88Sig0Rm-00msV6O3bQ_TrN_SjfoNTep80Onjwhl4PszHhVYdGMTNEPIuO8xw/file [following]\n",
            "--2020-08-07 20:25:11--  https://uceb24e33a473bd44e76e159022b.dl.dropboxusercontent.com/cd/0/inline2/A9DGLLYc242zS7-F5f7V33tdspn35wMsQ93al7E0ykfltkmQouhhkNS9IKjEXgHF3gTi9VFhLRJFWC8zF1v6_P4Fcqgyog01F5WP51gfhE4H06QiLxkFowlzLKm9Cp4JEqEbaQIqVnn_TleKUh5yVLw5Nh8SZ2L5TBEGdWMXlREw2NzcKQE8xjcQTOOhJ8e2M-TmcKrUBCDSFSTRUqvLOKtx1dwT4aXlYlxqfA7OEtmau1RCtKUxyBi4J_5LYot2-MPetoWI1ppNCbzRq8Vv7s_Bk786-NIys_JZswqW8GR_3-kvw88Sig0Rm-00msV6O3bQ_TrN_SjfoNTep80Onjwhl4PszHhVYdGMTNEPIuO8xw/file\n",
            "Reusing existing connection to uceb24e33a473bd44e76e159022b.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  16.6MB/s    in 8.1s    \n",
            "\n",
            "2020-08-07 20:25:20 (16.7 MB/s) - ‘covid_19’ saved [140830621/140830621]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-3e62b6c1444f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'wget http://cb.lk/covid_19'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_shell.py\u001b[0m in \u001b[0;36msystem\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    100\u001b[0m       \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'also_return_output'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_system_commands\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_system_compat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint:disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpip_warn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_system_compat\u001b[0;34m(shell, cmd, also_return_output)\u001b[0m\n\u001b[1;32m    436\u001b[0m   \u001b[0;31m# stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m   result = _run_command(\n\u001b[0;32m--> 438\u001b[0;31m       shell.var_expand(cmd, depth=2), clear_streamed_output=False)\n\u001b[0m\u001b[1;32m    439\u001b[0m   \u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_ns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'_exit_code'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_INTERRUPTED_SIGNALS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_run_command\u001b[0;34m(cmd, clear_streamed_output)\u001b[0m\n\u001b[1;32m    193\u001b[0m       \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild_pty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0m_monitor_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_pty\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate_stdin_widget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m     \u001b[0mepoll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type, value, traceback)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m                 \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_display_stdin_widget\u001b[0;34m(delay_millis)\u001b[0m\n\u001b[1;32m    353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m   \u001b[0mhide_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'cell_remove_stdin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m   \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocking_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mhide_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent_header\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    169\u001b[0m   \u001b[0;31m# unique.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m   \u001b[0mrequest_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msend_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ortru87DNeyY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "903c4fd8-fe21-43c9-de63-ab026dfe2664"
      },
      "source": [
        "!unzip covid_19"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  covid_19\n",
            "  inflating: CovidDataset/Val/Covid/88de9d8c39e946abd495b37cd07d89e5-6531-0.jpg  \n",
            "  inflating: CovidDataset/Val/Covid/covid-19-pneumonia-8.jpg  \n",
            "  inflating: CovidDataset/Val/Covid/85E52EB3-56E9-4D67-82DA-DEA247C82886.jpeg  \n",
            "  inflating: CovidDataset/Val/Covid/4-x-day1.jpg  \n",
            "  inflating: CovidDataset/Val/Covid/covid-19-pneumonia-53.jpg  \n",
            "  inflating: CovidDataset/Val/Covid/4e43e48d52c9e2d4c6c1fb9bc1544f_jumbo.jpeg  \n",
            "  inflating: CovidDataset/Val/Covid/covid-19-pneumonia-35-1.jpg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IucFlDSNhhk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TRAIN_PATH = \"CovidDataset/Train\"\n",
        "VAL_PATH = \"CovidDataset/Test\""
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAIVfNj3PQ8W",
        "colab_type": "code",
        "colab": {}
      },
      "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\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NveBUA5PPozH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "ffd8a0ad-5954-4f4d-de88-df2029b80a62"
      },
      "source": [
        "#cnn model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size = (3,3) , activation = 'relu' , input_shape = (224,224,3)))\n",
        "model.add(Conv2D(64, kernel_size = (3,3) , activation = 'relu' ))\n",
        "model.add(MaxPooling2D( pool_size = (2,2) ))\n",
        "model.add(Dropout( 0.25 ))\n",
        "\n",
        "model.add(Conv2D(64, kernel_size = (3,3) , activation = 'relu' ))\n",
        "model.add(MaxPooling2D( pool_size = (2,2) ))\n",
        "model.add(Dropout( 0.25 ))\n",
        "\n",
        "model.add(Conv2D(128, kernel_size = (3,3) , activation = 'relu' ))\n",
        "model.add(MaxPooling2D( pool_size = (2,2) ))\n",
        "model.add(Dropout( 0.25 ))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation = 'relu' ))\n",
        "model.add(Dropout( 0.25 ))\n",
        "model.add(Dense(1, activation = 'sigmoid'))\n",
        "\n",
        "\n",
        "model.compile(loss = keras.losses.binary_crossentropy , optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "\n",
        "model.summary()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 222, 222, 32)      896       \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 220, 220, 64)      18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 110, 110, 64)      0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 110, 110, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 108, 108, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 54, 54, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 54, 54, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 52, 52, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 26, 26, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 86528)             0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                5537856   \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (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": "Wy7-xLPZ4kHI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "ec750821-384e-4c89-8f1a-36538882d366"
      },
      "source": [
        "#train \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)\n",
        "\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    'CovidDataset/Train', \n",
        "    target_size = (224,224),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'binary'\n",
        ")\n",
        "\n",
        "validation_generator = test_dataset.flow_from_directory(\n",
        "    'CovidDataset/Val', \n",
        "    target_size = (224,224),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'binary'\n",
        ")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 224 images belonging to 2 classes.\n",
            "Found 60 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAjjgZqL5Oya",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "b0864cb3-74ba-45cf-adc7-c67d327a8d29"
      },
      "source": [
        "hist = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch = 7,\n",
        "    epochs = 10,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps = 2\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-13-45b394b22f2d>: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",
            "7/7 [==============================] - 55s 8s/step - loss: 1.0991 - accuracy: 0.5491 - val_loss: 0.6945 - val_accuracy: 0.5000\n",
            "Epoch 2/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.6862 - accuracy: 0.5938 - val_loss: 0.6435 - val_accuracy: 0.8500\n",
            "Epoch 3/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.5182 - accuracy: 0.7902 - val_loss: 0.3257 - val_accuracy: 0.9333\n",
            "Epoch 4/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.3199 - accuracy: 0.8661 - val_loss: 0.2305 - val_accuracy: 0.9500\n",
            "Epoch 5/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.2909 - accuracy: 0.8750 - val_loss: 0.1955 - val_accuracy: 0.9833\n",
            "Epoch 6/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.2483 - accuracy: 0.9107 - val_loss: 0.1425 - val_accuracy: 0.9167\n",
            "Epoch 7/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.2069 - accuracy: 0.9152 - val_loss: 0.1446 - val_accuracy: 0.9667\n",
            "Epoch 8/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.1689 - accuracy: 0.9330 - val_loss: 0.1231 - val_accuracy: 0.9833\n",
            "Epoch 9/10\n",
            "7/7 [==============================] - 53s 8s/step - loss: 0.1248 - accuracy: 0.9598 - val_loss: 0.0690 - val_accuracy: 0.9667\n",
            "Epoch 10/10\n",
            "7/7 [==============================] - 55s 8s/step - loss: 0.1060 - accuracy: 0.9598 - val_loss: 0.0839 - val_accuracy: 0.9667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "964YnzRd5eKZ",
        "colab_type": "text"
      },
      "source": [
        "# Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0dgKAF5ez4F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(\"model_adv.h5\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmTX4AYNiS_z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "8c96332a-16ae-46e5-ca12-fb05e44e85aa"
      },
      "source": [
        "model.evaluate_generator(train_generator)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-15-e4ade065aa26>:1: Model.evaluate_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.evaluate, which supports generators.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.11961957067251205, 0.9642857313156128]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzjG1tR3iTuV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dc3531d6-14e2-4662-e350-60f2be8da143"
      },
      "source": [
        "model.evaluate_generator(validation_generator)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08385119587182999, 0.9666666388511658]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjex5QIz5rcY",
        "colab_type": "text"
      },
      "source": [
        "# Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3uZmY7X5v2B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model= load_model('model_adv.h5')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o_o1IRviq0y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pADQRDZ_itUT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "313e6c21-a179-4929-f696-1ddf82f40d6d"
      },
      "source": [
        "train_generator.class_indices"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Covid': 0, 'Normal': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAWkRmAEiuci",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_actual= []\n",
        "y_test= []"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMXHiNevi4Kr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "a9a685b0-68d5-4d84-aaec-19631586dcd6"
      },
      "source": [
        "for i in os.listdir(\"./CovidDataset/Val/Normal/\"):\n",
        "  img= image.load_img(\"./CovidDataset/Val/Normal/\"+i, target_size=(224,224))\n",
        "  img= image.img_to_array(img)\n",
        "  img= np.expand_dims(img, axis=0)\n",
        "  p= model.predict_classes(img)\n",
        "  y_test.append(p[0,0])\n",
        "  y_actual.append(1)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-21-70d307b7a22c>:5: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O7j9Y7Qi_jn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in os.listdir(\"./CovidDataset/Val/Covid/\"):\n",
        "  img= image.load_img(\"./CovidDataset/Val/Covid/\"+i, target_size=(224,224))\n",
        "  img= image.img_to_array(img)\n",
        "  img= np.expand_dims(img, axis=0)\n",
        "  p= model.predict_classes(img)\n",
        "  y_test.append(p[0,0])\n",
        "  y_actual.append(0)\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aljG_6IPjCCP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_actual= np.array(y_actual)\n",
        "y_test = np.array(y_test)\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2OsZlzKjGSB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3T_HrcapjJ0l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cm= confusion_matrix(y_actual, y_test)\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qmgfar6kXkY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "a0cb65e3-5862-475e-82c4-bc20706db21f"
      },
      "source": [
        "cm"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[30,  0],\n",
              "       [ 2, 28]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbBHmLgWjNiB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "4e7ecb33-5cac-4964-9d1a-0544daef89f4"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ux09AOzHjVJ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e987140e-0e86-423d-d729-116bc467378c"
      },
      "source": [
        "sns.heatmap(cm, cmap=\"plasma\", annot= True)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ffb34128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}