NCOV19_X_ray_Classifier.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/imihassan/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."
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "!git clone https://github.com/ieee8023/covid-chestxray-dataset.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'covid-chestxray-dataset'...\n",
            "remote: Enumerating objects: 4, done.\u001b[K\n",
            "remote: Counting objects: 100% (4/4), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 631 (delta 0), reused 0 (delta 0), pack-reused 627\u001b[K\n",
            "Receiving objects: 100% (631/631), 84.29 MiB | 28.56 MiB/s, done.\n",
            "Resolving deltas: 100% (261/261), 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",
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          "height": 54
        },
        "outputId": "58790970-f5e4-4ce7-e0ca-5c81e0b79972"
      },
      "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",
        "print(covid_files)\n",
        "other_files=[files for files in Others['filename']]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['auntminnie-a-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'auntminnie-b-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'auntminnie-c-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'auntminnie-d-2020_01_28_23_51_6665_2020_01_28_Vietnam_coronavirus.jpeg', 'nejmc2001573_f1a.jpeg', 'nejmc2001573_f1b.jpeg', 'lancet-case2a.jpg', 'lancet-case2b.jpg', '1-s2.0-S0140673620303706-fx1_lrg.jpg', 'nCoV-radiol.2020200269.fig1-day7.jpeg', 'nejmoa2001191_f1-PA.jpeg', 'nejmoa2001191_f3-PA.jpeg', 'nejmoa2001191_f4.jpeg', 'nejmoa2001191_f5-PA.jpeg', 'ryct.2020200034.fig2.jpeg', 'ryct.2020200034.fig5-day0.jpeg', 'ryct.2020200034.fig5-day4.jpeg', 'ryct.2020200034.fig5-day7.jpeg', 'ryct.2020200028.fig1a.jpeg', 'radiol.2020200490.fig3.jpeg', '1-s2.0-S0929664620300449-gr2_lrg-a.jpg', '1-s2.0-S0929664620300449-gr2_lrg-b.jpg', '1-s2.0-S0929664620300449-gr2_lrg-c.jpg', '1-s2.0-S0929664620300449-gr2_lrg-d.jpg', 'covid-19-pneumonia-15-PA.jpg', 'covid-19-pneumonia-2.jpg', 'covid-19-pneumonia-7-PA.jpg', 'covid-19-pneumonia-14-PA.png', 'covid-19-pneumonia-12.jpg', '7C69C012-7479-493F-8722-ABC29C60A2DD.jpeg', '8FDE8DBA-CFBD-4B4C-B1A4-6F36A93B7E87.jpeg', '9C34AF49-E589-44D5-92D3-168B3B04E4A6.jpeg', 'F2DE909F-E19C-4900-92F5-8F435B031AC6.jpeg', '31BA3780-2323-493F-8AED-62081B9C383B.jpeg', '1312A392-67A3-4EBF-9319-810CF6DA5EF6.jpeg', '23E99E2E-447C-46E5-8EB2-D35D12473C39.png', '7AF6C1AF-D249-4BD2-8C26-449304105D03.jpeg', '1B734A89-A1BF-49A8-A1D3-66FAFA4FAC5D.jpeg', 'CD50BA96-6982-4C80-AE7B-5F67ACDBFA56.jpeg', '85E52EB3-56E9-4D67-82DA-DEA247C82886.jpeg', '6CB4EFC6-68FA-4CD5-940C-BEFA8DAFE9A7.jpeg', '01E392EE-69F9-4E33-BFCE-E5C968654078.jpeg', 'F63AB6CE-1968-4154-A70F-913AF154F53D.jpeg', '2C26F453-AF3B-4517-BB9E-802CF2179543.jpeg', '93FE0BB1-022D-4F24-9727-987A07975FFB.jpeg', 'F4341CE7-73C9-45C6-99C8-8567A5484B63.jpeg', 'E63574A7-4188-4C8D-8D17-9D67A18A1AFA.jpeg', '80446565-E090-4187-A031-9D3CEAA586C8.jpeg', 'all14238-fig-0001-m-b.jpg', 'all14238-fig-0001-m-c.jpg', '1-s2.0-S1684118220300608-main.pdf-001.jpg', '1-s2.0-S1684118220300608-main.pdf-002.jpg', 'ciaa199.pdf-001-a.png', 'ciaa199.pdf-001-b.png', 'ciaa199.pdf-001-c.png', 'kjr-21-e24-g001-l-a.jpg', 'kjr-21-e24-g002-l-a.jpg', 'kjr-21-e24-g003-l-a.jpg', '1-s2.0-S1684118220300682-main.pdf-002-a1.png', '1-s2.0-S1684118220300682-main.pdf-002-a2.png', '1-s2.0-S1684118220300682-main.pdf-003-b1.png', '1-s2.0-S1684118220300682-main.pdf-003-b2.png', 'gr1_lrg-a.jpg', 'gr1_lrg-b.jpg', 'FE9F9A5D-2830-46F9-851B-1FF4534959BE.jpeg', '41591_2020_819_Fig1_HTML.webp-day5.png', '41591_2020_819_Fig1_HTML.webp-day10.png', 'covid-19-pneumonia-19.jpg', 'kjr-21-e25-g001-l-a.jpg', '03BF7561-A9BA-4C3C-B8A0-D3E585F73F3C.jpeg', '353889E0-A1E8-4F9E-A0B8-F24F36BCFBFB.jpeg', 'figure1-5e75d0940b71e1b702629659-98-right.jpeg', 'figure1-5e71be566aa8714a04de3386-98-left.jpeg']\n"
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      "source": [
        "#our test folder. manually included files via upload.\n",
        "test_files=[file for file in sorted(os.listdir('covid-chestxray-dataset/test/'))]\n",
        "# print(test_files[1])\n",
        "df=pd.read_csv('covid-chestxray-dataset/test/' + test_files[1])\n",
        "# print(df)\n",
        "test_files=[files for files in df['Filename']]\n",
        "print(test_files)\n",
        "# df_test=pd.DataFrame(test_files, columns=['Filename'])\n",
        "# print(df_test)"
      ],
      "execution_count": 27,
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            "['1.jpg', '2.jpg', '2602ea3e16a6d7e22204ac78382d1c_jumbo.jpg', 'NORMAL2-IM-0195-0001.jpeg', 'NORMAL2-IM-0196-0001.jpeg', 'NORMAL2-IM-0198-0001.jpeg', 'NORMAL2-IM-0199-0001.jpeg', 'NORMAL2-IM-0201-0001.jpeg', 'NORMAL2-IM-0206-0001.jpeg', 'NORMAL2-IM-0207-0001.jpeg', 'NORMAL2-IM-0210-0001.jpeg', 'NORMAL2-IM-0213-0001.jpeg', 'NORMAL2-IM-0217-0001.jpeg', 'NORMAL2-IM-0219-0001.jpeg', 'NORMAL2-IM-0221-0001.jpeg', 'NORMAL2-IM-0222-0001.jpeg', 'NORMAL2-IM-0229-0001.jpeg', 'NORMAL2-IM-0232-0001.jpeg', 'NORMAL2-IM-0233-0001.jpeg', 'NORMAL2-IM-0237-0001.jpeg', 'NORMAL2-IM-0238-0001.jpeg', 'NORMAL2-IM-0241-0001.jpeg', 'NORMAL2-IM-0246-0001-0001.jpeg', 'NORMAL2-IM-0246-0001-0002.jpeg', 'NORMAL2-IM-0246-0001.jpeg', 'NORMAL2-IM-0249-0001.jpeg', 'NORMAL2-IM-0251-0001.jpeg', 'NORMAL2-IM-0252-0001.jpeg', 'NORMAL2-IM-0256-0001.jpeg', 'NORMAL2-IM-0259-0001.jpeg', 'NORMAL2-IM-0267-0001.jpeg', 'NORMAL2-IM-0271-0001.jpeg', 'NORMAL2-IM-0272-0001.jpeg', 'NORMAL2-IM-0273-0001.jpeg', 'NORMAL2-IM-0274-0001.jpeg', 'NORMAL2-IM-0275-0001.jpeg', 'NORMAL2-IM-0276-0001.jpeg', 'NORMAL2-IM-0277-0001.jpeg', 'NORMAL2-IM-0278-0001.jpeg', 'NORMAL2-IM-0279-0001.jpeg', 'NORMAL2-IM-0280-0001.jpeg', 'NORMAL2-IM-0281-0001.jpeg', 'NORMAL2-IM-0282-0001.jpeg', 'NORMAL2-IM-0283-0001.jpeg', 'NORMAL2-IM-0285-0001.jpeg', 'NORMAL2-IM-0286-0001.jpeg', 'NORMAL2-IM-0287-0001.jpeg', 'NORMAL2-IM-0288-0001.jpeg', 'NORMAL2-IM-0289-0001.jpeg', 'NORMAL2-IM-0290-0001.jpeg', 'NORMAL2-IM-0292-0001.jpeg', 'NORMAL2-IM-0294-0001.jpeg', 'NORMAL2-IM-0297-0001.jpeg', 'NORMAL2-IM-0300-0001.jpeg', 'NORMAL2-IM-0301-0001.jpeg', 'NORMAL2-IM-0302-0001.jpeg', 'NORMAL2-IM-0303-0001.jpeg', 'NORMAL2-IM-0304-0001.jpeg', 'NORMAL2-IM-0305-0001.jpeg', 'NORMAL2-IM-0307-0001.jpeg', 'NORMAL2-IM-0309-0001.jpeg', 'NORMAL2-IM-0310-0001.jpeg', 'NORMAL2-IM-0311-0001.jpeg', 'NORMAL2-IM-0312-0001.jpeg', 'NORMAL2-IM-0313-0001.jpeg', 'NORMAL2-IM-0315-0001.jpeg', 'NORMAL2-IM-0351-0001.jpeg', 'NORMAL2-IM-0352-0001.jpeg', 'NORMAL2-IM-0360-0001.jpeg', 'NORMAL2-IM-0361-0001.jpeg', 'NORMAL2-IM-0362-0001.jpeg', 'NORMAL2-IM-0370-0001.jpeg', 'NORMAL2-IM-0372-0001.jpeg', 'NORMAL2-IM-0373-0001.jpeg', 'NORMAL2-IM-0378-0001.jpeg', 'NORMAL2-IM-0380-0001.jpeg', 'NORMAL2-IM-0381-0001.jpeg', 'kjr-21-e24-g003-m.jpg']\n"
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      "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[1]:\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": "4b6c1244-f06e-4b4d-bd54-a69e88880206",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of images in Other: 23 Number of images in Covid: 73 Number of images in Test Set: 0\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": "2e51a9f7-350c-4bef-be73-0706efa13489",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data.classes"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['covid', 'other']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FPm5_nyy60h",
        "colab_type": "code",
        "outputId": "f301e36f-396b-42cb-f7fe-fb4e881cb98c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#show size of our datasets\n",
        "print(len(data.train_ds),len(data.valid_ds),len(data.test_ds.x))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "72 24 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oL6USZoeV9Ol",
        "colab_type": "code",
        "outputId": "f7b78907-3f1d-4de1-8ef7-3c6c55d16144",
        "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": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vFeD6L3/X4iQ6iSrkVbNTS/cxCEcnRyFiEjQFFwlDhbM+n/3juygY+RmFJjcG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xcANTkMlSXa2jWpF5UjMl9V2miKoNBAJFTRUdJ43sPxK1LbKoCtEtm2f0gDbxnxa5h3+7cuvDYy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jk9YG/EYcYsvB6HJ5KJt7dFhSl5fir/c1gbTx8h3t3d7YTem2YRpYABIhbh5rZdbChA3u3d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\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w7Lgehg/PREKaaX0/ixOnGGfmAONHMRhobQNev99PZT+J70K1O9wwHo9jMplUxso5IPQN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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": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "4f0ec20625fb41c88cc0c80720b9a228",
            "0afe3a2efa314c15b7726234757f07bc",
            "14ff60a2325f45c3b61b2ec6af6be333",
            "f062e41e4b7646cbb0e6852979c67f06",
            "f940cf5b2f684d28afaf4d16de91ebd4",
            "2af62a0c08d04d9cba48d4ddcfa3ba0d",
            "aee4fe1928c049e19a25c308d04879b0",
            "c23c9fc6f7874654b07a3b4592ad4846"
          ]
        },
        "outputId": "c0eeee68-4ea4-40c6-8fbb-2bd8e767a806"
      },
      "source": [
        "learn = cnn_learner(data, models.resnet50, metrics=(accuracy,precision,recall,AUC))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet50-19c8e357.pth\" to /root/.cache/torch/checkpoints/resnet50-19c8e357.pth\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4f0ec20625fb41c88cc0c80720b9a228",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=102502400), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EODxAuYMLUhz",
        "colab_type": "code",
        "outputId": "07c9a3a8-bee8-471f-e99a-099e4e00ca28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "learn.fit_one_cycle(1)"
      ],
      "execution_count": 25,
      "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",
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      "metadata": {
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        "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",
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        "colab": {
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      "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",
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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",
              "                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<00:49]\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>0.367492</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": {
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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": []
    }
  ]
}