{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NCOV19 X-ray Classifier.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP5bVIHFYCIyYBvI2lOz9C4",
      "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/ajsanjoaquin/COVID-19-Scanner/blob/master/NCOV19_X_ray_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e7geXMFyndA",
        "colab_type": "text"
      },
      "source": [
        "#Coronavirus 2019 (COVID-19) Classifer using Posteroanterior views (PA) of Chest Radiograph Images (CXR)\n",
        "Accompanying information [here](https://towardsdatascience.com/using-deep-learning-to-detect-ncov-19-from-x-ray-images-1a89701d1acd).\n",
        "\n",
        "NOTICE: This notebook is provided as-is with no guarantee of accurate diagnosis. The model was trained on heavily skewed data and is not suitable for deployment. It is currently meant to be a proof of concept for now. All images used were publicly accessible and usable at the time of training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZMVckwEAN3c",
        "colab_type": "code",
        "outputId": "9e9914b6-22e9-405c-e7c0-c8d1ff5c235a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "!git clone https://github.com/ieee8023/covid-chestxray-dataset.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'covid-chestxray-dataset'...\n",
            "remote: Enumerating objects: 41, done.\u001b[K\n",
            "remote: Counting objects:   2% (1/41)\u001b[K\rremote: Counting objects:   4% (2/41)\u001b[K\rremote: Counting objects:   7% (3/41)\u001b[K\rremote: Counting objects:   9% (4/41)\u001b[K\rremote: Counting objects:  12% (5/41)\u001b[K\rremote: Counting objects:  14% (6/41)\u001b[K\rremote: Counting objects:  17% (7/41)\u001b[K\rremote: Counting objects:  19% (8/41)\u001b[K\rremote: Counting objects:  21% (9/41)\u001b[K\rremote: Counting objects:  24% (10/41)\u001b[K\rremote: Counting objects:  26% (11/41)\u001b[K\rremote: Counting objects:  29% (12/41)\u001b[K\rremote: Counting objects:  31% (13/41)\u001b[K\rremote: Counting objects:  34% (14/41)\u001b[K\rremote: Counting objects:  36% (15/41)\u001b[K\rremote: Counting objects:  39% (16/41)\u001b[K\rremote: Counting objects:  41% (17/41)\u001b[K\rremote: Counting objects:  43% (18/41)\u001b[K\rremote: Counting objects:  46% (19/41)\u001b[K\rremote: Counting objects:  48% (20/41)\u001b[K\rremote: Counting objects:  51% (21/41)\u001b[K\rremote: Counting objects:  53% (22/41)\u001b[K\rremote: Counting objects:  56% (23/41)\u001b[K\rremote: Counting objects:  58% (24/41)\u001b[K\rremote: Counting objects:  60% (25/41)\u001b[K\rremote: Counting objects:  63% (26/41)\u001b[K\rremote: Counting objects:  65% (27/41)\u001b[K\rremote: Counting objects:  68% (28/41)\u001b[K\rremote: Counting objects:  70% (29/41)\u001b[K\rremote: Counting objects:  73% (30/41)\u001b[K\rremote: Counting objects:  75% (31/41)\u001b[K\rremote: Counting objects:  78% (32/41)\u001b[K\rremote: Counting objects:  80% (33/41)\u001b[K\rremote: Counting objects:  82% (34/41)\u001b[K\rremote: Counting objects:  85% (35/41)\u001b[K\rremote: Counting objects:  87% (36/41)\u001b[K\rremote: Counting objects:  90% (37/41)\u001b[K\rremote: Counting objects:  92% (38/41)\u001b[K\rremote: Counting objects:  95% (39/41)\u001b[K\rremote: Counting objects:  97% (40/41)\u001b[K\rremote: Counting objects: 100% (41/41)\u001b[K\rremote: Counting objects: 100% (41/41), done.\u001b[K\n",
            "remote: Compressing objects: 100% (36/36), done.\u001b[K\n",
            "remote: Total 352 (delta 16), reused 26 (delta 5), pack-reused 311\u001b[K\n",
            "Receiving objects: 100% (352/352), 58.02 MiB | 42.50 MiB/s, done.\n",
            "Resolving deltas: 100% (123/123), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLm4gvRD_9Te",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os, shutil\n",
        "from fastai.vision import *\n",
        "from fastai.widgets import ClassConfusion"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Nd3AHX5AULm",
        "colab_type": "text"
      },
      "source": [
        "#Preprocessing\n",
        "##Extracting Images "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9n9PzSeuCIRc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "metadata_path='covid-chestxray-dataset/metadata.csv'\n",
        "df=pd.read_csv(metadata_path)\n",
        "\n",
        "#types we're interested in\n",
        "covid_patients=df['finding']=='COVID-19'\n",
        "CT=df['view']=='CT'\n",
        "PA=df['view']=='PA'\n",
        "\n",
        "# %%\n",
        "df[covid_patients & CT].shape\n",
        "df[covid_patients & PA].shape\n",
        "# %%\n",
        "PA_covid=df[covid_patients & PA]\n",
        "Others=df[~covid_patients & PA]\n",
        "covid_files=[files for files in PA_covid['filename']]\n",
        "other_files=[files for files in Others['filename']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7BW9lAi0QDZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#our test folder. manually included files via upload.\n",
        "test_files=[file for file in sorted(os.listdir('test'))]\n",
        "df_test=pd.DataFrame(test_files, columns=['filename'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSShznGMAd7M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#create data folder and positive & negative cases folder, and test folder\n",
        "destpath = 'data/covid','data/other', 'data/test'\n",
        "srcpath = 'covid-chestxray-dataset/images'\n",
        "\n",
        "for root, dirs, files in os.walk(srcpath):\n",
        "  if not os.path.isdir(destpath[0]):\n",
        "    os.makedirs(destpath[0])\n",
        "  if not os.path.isdir(destpath[1]):\n",
        "    os.makedirs(destpath[1])\n",
        "  if not os.path.isdir(destpath[2]):\n",
        "    os.makedirs(destpath[2])\n",
        "  for file in files:\n",
        "    if file in covid_files:\n",
        "      shutil.copy(os.path.join(root, file),destpath[0])\n",
        "    if file in other_files:\n",
        "      shutil.copy(os.path.join(root,file),destpath[1])\n",
        "    if file in test_files:\n",
        "      shutil.copy(os.path.join(root,file)),destpath[2]\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXx92rbQF1K2",
        "colab_type": "code",
        "outputId": "d024b4f6-df26-48c5-bf35-95dc54ad550e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#see number of files\n",
        "path, dirs, files2 = os.walk(\"data/other\").__next__()\n",
        "path, dirs, files1 = os.walk(\"data/covid\").__next__()\n",
        "path, dirs, files3 = os.walk(\"data/test\").__next__()\n",
        "print(\"Number of images in Other: {}\".format(len(files2)),\"Number of images in Covid: {}\".format(len(files1)),\"Number of images in Test Set: {}\".format(len(files3)) )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of images in Other: 226 Number of images in Covid: 35\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eIwcpkbPT2_8",
        "colab_type": "text"
      },
      "source": [
        "## Loading and Splitting Data\n",
        "We first declare the labels to be used (corresponding with the folder names). We then wrap it around a dataloader from fastai. \n",
        "\n",
        "We allocate 20% of the data for validation, and we reserve a test set from a folder called \"test\". We resize all images to 512 x 512 pixels. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2v_TyrjQ5gO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classes=['covid','other']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5E51HCAQ6ph",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#include a test folder named test before running this block\n",
        "#function assumes test set is located in the path (first arg) by default\n",
        "data = ImageDataBunch.from_folder('data', train=\".\", valid_pct=0.25,test='test',\n",
        "        ds_tfms=get_transforms(), bs=8, size=512, num_workers=4).normalize(imagenet_stats)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMlHkvmQWmb4",
        "colab_type": "code",
        "outputId": "f4a52ff9-220b-4328-b9d0-e929706cb2fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data.classes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['covid', 'other']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FPm5_nyy60h",
        "colab_type": "code",
        "outputId": "74cb7b30-f588-4247-9f90-324cfe22f825",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#show size of our datasets\n",
        "print(len(data.train_ds),len(data.valid_ds),len(data.test_ds.x))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "196 65 78\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oL6USZoeV9Ol",
        "colab_type": "code",
        "outputId": "7959513d-4f1f-4ca4-9ff8-9da21ff3a46e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        }
      },
      "source": [
        "#sample of our images with labels\n",
        "data.show_batch(rows=5, figsize=(7,8))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mdsnvn2fMPnLkmZKsmptGYOG3s2Jn2Do2Go3M6QC2yHV7OWTeK5WKOaNyuaxa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\njtd0x4AC3+jxK+NE24l1q2zwefQHBoUQiHrx6JcIGNRrVQDJnKh3zoXx5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wAAIABJREFU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JBsNswjaocVZDT391rLSUsRpL1rQ6GNQq1y1js7OzTk0T2yVBLG43HY1GnmcCo8Z4m00oatYg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5bJZKzb7dr19bX9+7//u/3jP/6jnZ+f23w+95719GhHzzQFh84xbxqFarCA81Yn/lAaHeCi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PiG+bHx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\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os1DksAvMbGG77v/cX0cg3Fc/l7+tcAxOYKC6MZqODm37PTmMS/M8GKzANO6HaX/dc1Dq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p4IPBAJhHoulwsBB5xIrLNSqYQHyBwfPXqk4+Njff7556pUKjo8PFSxWFS9Xtfjx49jzpISihnF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\nV6xJjCdjylqCA2beec89xhynAdQhoumQRkQj8s+fa+cE9MgO7OXlZVpj6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            "text/plain": [
              "<Figure size 504x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0Jh0XPsevg3",
        "colab_type": "text"
      },
      "source": [
        "#Training\n",
        "We use a Resnet 50 for transfer learning.\n",
        "Initially we run the fit one cycle policy for a few epochs and then using fastai's **lrfinder** to find an optimal range for our learning rate.\n",
        "\n",
        "We use precision and recall to measure the incidents of false positives and false negatives, as well as AUC to account for performance given the skewed data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "676aAaiLglB6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "precision=Precision()\n",
        "recall=Recall()\n",
        "AUC=AUROC()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-hB0emxWHIW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn = cnn_learner(data, models.resnet50, metrics=(accuracy,precision,recall,AUC))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EODxAuYMLUhz",
        "colab_type": "code",
        "outputId": "4989cbbe-c903-43b4-c885-b25462553051",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79
        }
      },
      "source": [
        "learn.fit_one_cycle(1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
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              "  <tbody>\n",
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
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      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfwTNbHuaOY8",
        "colab_type": "text"
      },
      "source": [
        "At this stage, we realize the model is underfitting, so we continue to progressively increase the number of epochs from here on in an effort to reduce training loss while maintaining the low validation loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21zSYXbiMMOX",
        "colab_type": "code",
        "outputId": "5004a04e-19ee-4af4-b860-21490caf2861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "learn.fit_one_cycle(2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
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              "      <td>00:31</td>\n",
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              "    <tr>\n",
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              "      <td>0.609159</td>\n",
              "      <td>0.180127</td>\n",
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usBXPEH4hEs0",
        "colab_type": "code",
        "outputId": "8ea6ce5d-d043-448b-c60f-7a6a2753c4fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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              "      60.00% [3/5 01:13<00:49]\n",
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              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.367492</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.336237</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.360317</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='7' class='' max='24', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      29.17% [7/24 00:09<00:23 0.6484]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Icw+bfvsK3jBbyCqVKFp7AgFjljXCUJyTxsZFhfzxlRNTVqxTA0YCae/1kDeO4SiwptYW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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZY3yItbg2Gn",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Defining custom checkpoints\n",
        "#Customizing where our checkpoints are saved and loaded\n",
        "#if not os.path.isdir('checkpoints'): \n",
        " # os.mkdir('checkpoints')\n",
        "os.mkdir('check')\n",
        "def custom_path_save(self, name:PathOrStr, path='check', return_path:bool=False, with_opt:bool=True):\n",
        "        \"Save model and optimizer state (if `with_opt`) with `name` to `self.model_dir`.\"\n",
        "        # delete #  path = self.path/self.model_dir/f'{name}.pth'\n",
        "        # my addition: start\n",
        "        if path=='': path = self.path/self.model_dir/f'{name}.pth'\n",
        "        else: path = f'{path}/{name}.pth'\n",
        "        # end\n",
        "        if not with_opt: state = get_model(self.model).state_dict()\n",
        "        else: state = {'model': get_model(self.model).state_dict(), 'opt':self.opt.state_dict()}\n",
        "        torch.save(state, path)\n",
        "        if return_path: return path\n",
        "\n",
        "def custom_path_load(self, name:PathOrStr, path='check', device:torch.device=None, strict:bool=True, with_opt:bool=None,purge=False):\n",
        "        \"Load model and optimizer state (if `with_opt`) `name` from `self.model_dir` using `device`.\"\n",
        "        if device is None: device = self.data.device\n",
        "        # delete # state = torch.load(self.path/self.model_dir/f'{name}.pth', map_location=device)\n",
        "        # my addition: start\n",
        "        if path=='': path = self.path/self.model_dir/f'{name}.pth'\n",
        "        else: path = f'{path}/{name}.pth'\n",
        "        state = torch.load(path, map_location=device) \n",
        "        # end\n",
        "        if set(state.keys()) == {'model', 'opt'}:\n",
        "            get_model(self.model).load_state_dict(state['model'], strict=strict)\n",
        "            if ifnone(with_opt,True):\n",
        "                if not hasattr(self, 'opt'): opt = self.create_opt(defaults.lr, self.wd)\n",
        "                try:    self.opt.load_state_dict(state['opt'])\n",
        "                except: pass\n",
        "        else:\n",
        "            if with_opt: warn(\"Saved filed doesn't contain an optimizer state.\")\n",
        "            get_model(self.model).load_state_dict(state, strict=strict)\n",
        "        return self\n",
        "\n",
        "learn.save = custom_path_save.__get__(learn)\n",
        "learn.load = custom_path_load.__get__(learn)\n",
        "model_path ='check'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyPZ8xFIsStG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('Corona_model_stage1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLlcXkQWspWt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#learn.load('Corona_model_stage1')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qknwaf9au3qe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.unfreeze()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSgLIG_FtnLf",
        "colab_type": "code",
        "outputId": "b2057faa-5dd0-4cb9-adab-55cb34bacc2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "learn.fit_one_cycle(10, max_lr=slice(9e-07,1e-06))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.364490</td>\n",
              "      <td>0.198903</td>\n",
              "      <td>0.907692</td>\n",
              "      <td>0.912281</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.949686</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.399531</td>\n",
              "      <td>0.238177</td>\n",
              "      <td>0.861538</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.830189</td>\n",
              "      <td>0.987421</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.363702</td>\n",
              "      <td>0.195634</td>\n",
              "      <td>0.907692</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.886792</td>\n",
              "      <td>0.992138</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.378611</td>\n",
              "      <td>0.154305</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.944444</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.985849</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.397570</td>\n",
              "      <td>0.182065</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.974843</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.404387</td>\n",
              "      <td>0.157945</td>\n",
              "      <td>0.907692</td>\n",
              "      <td>0.943396</td>\n",
              "      <td>0.943396</td>\n",
              "      <td>0.982704</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.405342</td>\n",
              "      <td>0.164380</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.980769</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.982704</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.417745</td>\n",
              "      <td>0.136051</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990566</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.437893</td>\n",
              "      <td>0.137975</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.990566</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.402558</td>\n",
              "      <td>0.152316</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.945455</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.988994</td>\n",
              "      <td>00:32</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggsOKG5VQO3J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('Corona_model_stage2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGW3jeYGuxlC",
        "colab_type": "code",
        "outputId": "172b1aa9-ad52-4cd4-8753-e04f35451cd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "#confusion matrix for the first 2 iterations\n",
        "interp = ClassificationInterpretation.from_learner(learn)\n",
        "interp.plot_confusion_matrix()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ua9TVLefmu6cWnLKyKrkpsTNwe0QsBpD0V2CXRuZ7PCJezeeZAfQB3gb6A+PyFZh2wGsl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\nSPLUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eZlqAM5ysB7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ClassConfusion(interp, classes, is_ordered=False, figsize=(8,8))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rt5xT11WQZXo",
        "colab_type": "code",
        "outputId": "0ac84594-4184-453f-9954-679eae4c190f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='3' class='' max='5', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      60.00% [3/5 01:18<00:52]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.319931</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.326309</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.578660</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:26</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='1' class='' max='24', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      4.17% [1/24 00:04<01:46 0.6020]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JYl1hOVOe/pzFG4u5+6Ih9I6N4McvLbN+Cz9auK6I+OgwRtqkesYFriYIEZks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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxJO74OsQdSm",
        "colab_type": "code",
        "outputId": "9aec57ff-c761-4ff5-99b7-79fc466853b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        }
      },
      "source": [
        "learn.fit_one_cycle(30, max_lr=slice(6e-07,7e-06))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.329083</td>\n",
              "      <td>0.161188</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.924528</td>\n",
              "      <td>0.988994</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.322466</td>\n",
              "      <td>0.184026</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.905660</td>\n",
              "      <td>0.993711</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.377909</td>\n",
              "      <td>0.156069</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.993711</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.339729</td>\n",
              "      <td>0.185341</td>\n",
              "      <td>0.907692</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.886792</td>\n",
              "      <td>0.993711</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.343446</td>\n",
              "      <td>0.154403</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.987421</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.320960</td>\n",
              "      <td>0.201964</td>\n",
              "      <td>0.876923</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.849057</td>\n",
              "      <td>0.996855</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.314984</td>\n",
              "      <td>0.139085</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.993711</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.338542</td>\n",
              "      <td>0.123679</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998428</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.332734</td>\n",
              "      <td>0.143998</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.345055</td>\n",
              "      <td>0.122995</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>0.981481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.992138</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.351576</td>\n",
              "      <td>0.164527</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.943396</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.345132</td>\n",
              "      <td>0.130223</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.330823</td>\n",
              "      <td>0.123418</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.929825</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996855</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.325458</td>\n",
              "      <td>0.128031</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995283</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.338156</td>\n",
              "      <td>0.118844</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.998428</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.313690</td>\n",
              "      <td>0.128854</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.998428</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.311401</td>\n",
              "      <td>0.138623</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.995283</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.299370</td>\n",
              "      <td>0.132648</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.992138</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.297800</td>\n",
              "      <td>0.119331</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.996855</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.307549</td>\n",
              "      <td>0.114339</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.319106</td>\n",
              "      <td>0.146249</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.285886</td>\n",
              "      <td>0.118273</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.302488</td>\n",
              "      <td>0.115909</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.360066</td>\n",
              "      <td>0.117351</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.330640</td>\n",
              "      <td>0.170464</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.924528</td>\n",
              "      <td>0.998428</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.365404</td>\n",
              "      <td>0.135715</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.365287</td>\n",
              "      <td>0.129336</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.998428</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.327235</td>\n",
              "      <td>0.112275</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995283</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.335879</td>\n",
              "      <td>0.123840</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>0.981481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995283</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.295829</td>\n",
              "      <td>0.130496</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9hfQWiQYNfE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('Corona_model_stage3.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rnFypFAYYME",
        "colab_type": "code",
        "outputId": "7269b8df-e6fc-4ef4-a7a4-cc78d7060f7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='3' class='' max='5', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      60.00% [3/5 01:13<00:48]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.320863</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.398267</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.602363</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='10' class='' max='24', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      41.67% [10/24 00:12<00:17 0.7340]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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AV7z29VXVZOAa4EkRGVj3ZBG5wZNEUvLz81smYmNMo9yjqO3uIRg4mSByAe87gkRP2RGq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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcRZtawDYau4",
        "colab_type": "code",
        "outputId": "4b799512-2c44-453b-9b88-5364969f20a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "learn.fit_one_cycle(40, max_lr=slice(8e-06,1e-05))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.202330</td>\n",
              "      <td>0.129510</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.307139</td>\n",
              "      <td>0.130274</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.993711</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.329779</td>\n",
              "      <td>0.149713</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.987421</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.368552</td>\n",
              "      <td>0.165314</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.980769</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.987421</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.293627</td>\n",
              "      <td>0.108407</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.275742</td>\n",
              "      <td>0.113035</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.998428</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.292897</td>\n",
              "      <td>0.128666</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.988994</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.277679</td>\n",
              "      <td>0.121344</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.993711</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.318157</td>\n",
              "      <td>0.117274</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.990566</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.276515</td>\n",
              "      <td>0.137329</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.976415</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.272156</td>\n",
              "      <td>0.120699</td>\n",
              "      <td>0.984615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.250732</td>\n",
              "      <td>0.107218</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.984277</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.265247</td>\n",
              "      <td>0.144146</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.952830</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.261017</td>\n",
              "      <td>0.150637</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.973270</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.232559</td>\n",
              "      <td>0.148362</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.984277</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.231504</td>\n",
              "      <td>0.151517</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960692</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.229667</td>\n",
              "      <td>0.169188</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.922956</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.231852</td>\n",
              "      <td>0.221873</td>\n",
              "      <td>0.907692</td>\n",
              "      <td>0.960784</td>\n",
              "      <td>0.924528</td>\n",
              "      <td>0.955975</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.223735</td>\n",
              "      <td>0.206218</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.961538</td>\n",
              "      <td>0.943396</td>\n",
              "      <td>0.927673</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.238116</td>\n",
              "      <td>0.184144</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.951258</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.224600</td>\n",
              "      <td>0.175078</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.941824</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.248824</td>\n",
              "      <td>0.212263</td>\n",
              "      <td>0.907692</td>\n",
              "      <td>0.960784</td>\n",
              "      <td>0.924528</td>\n",
              "      <td>0.970126</td>\n",
              "      <td>00:31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.260683</td>\n",
              "      <td>0.154493</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.968553</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.218456</td>\n",
              "      <td>0.133802</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.977987</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.192157</td>\n",
              "      <td>0.162497</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.212321</td>\n",
              "      <td>0.146048</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.973270</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.205146</td>\n",
              "      <td>0.133541</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.984277</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.220228</td>\n",
              "      <td>0.159637</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.957547</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.196391</td>\n",
              "      <td>0.161086</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.937107</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.189515</td>\n",
              "      <td>0.152208</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.970126</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.203865</td>\n",
              "      <td>0.144759</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.963836</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.222767</td>\n",
              "      <td>0.148251</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.970126</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.223735</td>\n",
              "      <td>0.147994</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.954403</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.227146</td>\n",
              "      <td>0.142463</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.974843</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.213540</td>\n",
              "      <td>0.152646</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.963836</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.247148</td>\n",
              "      <td>0.158109</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.970126</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.214760</td>\n",
              "      <td>0.155666</td>\n",
              "      <td>0.969231</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.938679</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.215133</td>\n",
              "      <td>0.146378</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.973270</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.190748</td>\n",
              "      <td>0.151290</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.960692</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.210779</td>\n",
              "      <td>0.173059</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.952830</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxREqJWPfV6A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('Corona_model_stage4.pth')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJhH61YcfZ2O",
        "colab_type": "code",
        "outputId": "cb0d819d-d5a8-4243-f3be-ec062c52c26b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "learn.lr_find()\n",
        "learn.recorder.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='3' class='' max='5', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      60.00% [3/5 01:12<00:48]\n",
              "    </div>\n",
              "    \n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.207901</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.206814</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.478553</td>\n",
              "      <td>#na#</td>\n",
              "      <td>00:24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "                background: #F44336;\n",
              "            }\n",
              "        </style>\n",
              "      <progress value='7' class='' max='24', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      29.17% [7/24 00:08<00:20 0.6359]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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8OhFZKyKrReRbERns7O8lIsXO/tUi8rybcRpjfC9zn7NKrQ14B4wQt04sIsHAM8AkIANY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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PWLp5u5feF4",
        "colab_type": "code",
        "outputId": "2c5fe682-9e78-49f6-d3b8-82a83d85c837",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "learn.fit_one_cycle(5, max_lr=2e-06)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>auroc</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.147189</td>\n",
              "      <td>0.147491</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.957547</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.156640</td>\n",
              "      <td>0.138794</td>\n",
              "      <td>0.953846</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>0.971698</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.155739</td>\n",
              "      <td>0.169582</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.979560</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.160096</td>\n",
              "      <td>0.160149</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.948113</td>\n",
              "      <td>00:30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.187604</td>\n",
              "      <td>0.159831</td>\n",
              "      <td>0.938462</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.962264</td>\n",
              "      <td>0.966981</td>\n",
              "      <td>00:29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VYCH1GtlPus",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learn.save('Corona_model_stage5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMA2CnQZyPuT",
        "colab_type": "text"
      },
      "source": [
        "# Results on Validation Set and Predictions on Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWN8UExfQjeB",
        "colab_type": "code",
        "outputId": "9db80d2f-f754-4e92-9ae8-92633d1de58a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "interp = ClassificationInterpretation.from_learner(learn)\n",
        "interp.plot_confusion_matrix()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yHJdfeTXt27cvOo7VctCpv2Tx0hV07tCWe34zkucWvMb02S8VHauiyrnG0h+4KyJWRcRK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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNi_5tq3XRE1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds, _ = learn.get_preds(ds_type = DatasetType.Test, ordered=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6y4X9lguOgE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2znTCilq29M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#WARNING: PREDICTIONS ARE NOT SORTED AND DO NOT MATCH THEIR ACTUAL CORRESPONDING IMAGES\n",
        "'''\n",
        "model_classes = learn.data.classes\n",
        "preds = preds.tolist()\n",
        "confidences = [{c: p for c,p*100 in zip(model_classes, probs)} for probs in preds]\n",
        "final_df = pd.DataFrame({'ID_code': df_test['filename'], 'target': confidences})\n",
        "final_df.to_csv('NCOV_test_results.csv', header=True, index=False)\n",
        "'''\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKe9TohirATh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#safer to use a dictionary data structure\n",
        "#save predictions on test set in csv\n",
        "images={filename:open_image('data/test/'+filename) for filename in test_files}\n",
        "results={filename:learn.predict(images[filename]) for filename in test_files}\n",
        "final_df=pd.DataFrame.from_dict(results,orient='index')\n",
        "final_df.to_csv('NCOV_test_results.csv', header=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GXxvwbZpMbFj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}