MedNISTClassify.ipynb

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      "name": "MedNISTClassify.ipynb",
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    {
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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/RSNA/MagiciansCorner/blob/master/MedNISTClassify.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbHU-1U_HE8x",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "hide_input": false,
        "id": "0kCscBj8rPbg",
        "colab_type": "text"
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      "source": [
        "# Classify the type of radiological image using the MedNIST collection\n",
        "\n",
        "###by: Bradley J Erickson, MD PhD\n",
        "*Copyright 2019\n",
        "\n",
        "###See corresponding Radiology: AI article here:\n",
        "https://pubs.rsna.org/doi/10.1148/ryai.2019190072\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "hide_input": true,
        "id": "n0yBAzDzrPbh",
        "colab_type": "text"
      },
      "source": [
        "In this tutorial we will:\n",
        "1) download and unzip 6 different classes of radiological images (CT Head, CT Chest, CT Abdomen, MRBrain, MRBreast, and Chest X-Ray). \n",
        "2) We will use a Convolutional Neural Network (CNN) pretrained on routine photgraphic images and using the ResNet 34 architecture to classify each of the 3 types of images\n",
        "3) We will review the performance of the system, note the 'worst' errors, and consider how we might improve performance\n"
      ]
    },
    {
      "cell_type": "code",
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        "id": "EsCBNolBrPbi",
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          "height": 697
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      },
      "source": [
        "#Cell 1\n",
        "# first, need to install and then import the fastai library\n",
        "!pip3 install fastai\n",
        "from fastai.vision import *"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "JWMnwRXA0vfv",
        "colab_type": "code",
        "outputId": "952335ef-6605-407a-84d4-2bc0b7a90db2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "#Cell 2\n",
        "# clean out any old data just to be sure, such as if re-running cells\n",
        "!rm -rf MagiciansCorner\n",
        "!rm -rf images\n",
        "\n",
        "!wget --no-check-certificate 'https://docs.google.com/uc?export=download&id=1mqgBKTB0MtGf8Fhc8HaedJyiD8yMoXOh' -O ./MedNIST.zip\n",
        "\n",
        "!mkdir images\n",
        "!cd images; unzip -q \"../MedNIST.zip\" \n",
        "!rm -rf MagiciansCorner\n",
        "# get rid of MAC garbage stuff\n",
        "!rm -rf ./images/__MACOSX\n",
        "!ls images\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-11-21 16:00:26--  https://docs.google.com/uc?export=download&id=1mqgBKTB0MtGf8Fhc8HaedJyiD8yMoXOh\n",
            "Resolving docs.google.com (docs.google.com)... 108.177.15.101, 108.177.15.139, 108.177.15.113, ...\n",
            "Connecting to docs.google.com (docs.google.com)|108.177.15.101|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-0s-60-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/qfafkhb48aqksmb5sk3h834sm1a6k44t/1574352000000/16160187475894979440/*/1mqgBKTB0MtGf8Fhc8HaedJyiD8yMoXOh?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2019-11-21 16:00:36--  https://doc-0s-60-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/qfafkhb48aqksmb5sk3h834sm1a6k44t/1574352000000/16160187475894979440/*/1mqgBKTB0MtGf8Fhc8HaedJyiD8yMoXOh?e=download\n",
            "Resolving doc-0s-60-docs.googleusercontent.com (doc-0s-60-docs.googleusercontent.com)... 74.125.133.132, 2a00:1450:400c:c07::84\n",
            "Connecting to doc-0s-60-docs.googleusercontent.com (doc-0s-60-docs.googleusercontent.com)|74.125.133.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2022144 (1.9M) [application/zip]\n",
            "Saving to: ‘./MedNIST.zip’\n",
            "\n",
            "./MedNIST.zip       100%[===================>]   1.93M  --.-KB/s    in 0.01s   \n",
            "\n",
            "2019-11-21 16:00:36 (146 MB/s) - ‘./MedNIST.zip’ saved [2022144/2022144]\n",
            "\n",
            "CTAbd  CTChest\tCTHead\tCXR  MRBrain  MRBreast\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qudSFXxQ7whs",
        "colab_type": "code",
        "outputId": "4b72c52d-640e-4e84-cb7a-0d7ef85a7320",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Cell 3\n",
        "classes_dir = \"./images\"\n",
        "flist = os.listdir(classes_dir)\n",
        "print (flist)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['CTHead', 'MRBreast', 'MRBrain', 'CTChest', 'CXR', 'CTAbd']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0r1wat14rPbr",
        "colab_type": "text"
      },
      "source": [
        "### We have already gone through and converted the DICOM images to JPEG (grayscale) images, and also sized so they are all 64x64 (if we kept CXRs as some size other than 64x64, it would cause the CNN to fail--all image MUST be the same size. The next article will explain more about this.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vDYOXZsPrPcR",
        "colab_type": "text"
      },
      "source": [
        "## View data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md0NwvtbrPcS",
        "colab_type": "code",
        "outputId": "8504dca8-3450-4d30-bcf1-947bb0b010df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Cell 4\n",
        "np.random.seed(42)\n",
        "data = ImageDataBunch.from_folder(classes_dir, train=\".\", valid_pct=0.2,\n",
        "        ds_tfms=get_transforms(), size=64, num_workers=4).normalize(imagenet_stats)\n",
        "data.classes\n",
        "data.classes, data.c, len(data.train_ds), len(data.valid_ds)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['CTAbd', 'CTChest', 'CTHead', 'CXR', 'MRBrain', 'MRBreast'], 6, 1440, 360)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "In_a6GdirPcZ",
        "colab_type": "text"
      },
      "source": [
        "Good! Let's take a look at some of our pictures then."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9116qi8XrPcg",
        "colab_type": "code",
        "outputId": "c4db0b97-ae64-4be4-c769-ef6146c1e773",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        }
      },
      "source": [
        "#Cell 5\n",
        "data.show_batch(rows=3, figsize=(7,8))\n",
        "\n",
        "def get_img(img_url): return open_image(img_url)\n",
        "\n",
        "# Function that displays many transformations of an image\n",
        "def plots_of_one_image(img_url, tfms, rows=1, cols=3, width=15, height=5, **kwargs):\n",
        "    img = get_img(img_url)\n",
        "    [img.apply_tfms(tfms, **kwargs).show(ax=ax)\n",
        "         for i,ax in enumerate(plt.subplots(rows,cols,figsize=(width,height))[1].flatten())]\n",
        "tfms = get_transforms(flip_vert=False,                # flip vertical and horizontal\n",
        "                      max_rotate=20.0,                # rotation between -30° and 30°\n",
        "                      max_zoom=1.2)                   # zoom between 1 and 1.2\n",
        "# Uncomment the line below to turn off augmentation (sets the transformations to nothing. Note that you will still see many images, but they are all the same\n",
        "# tfms=[[],[]]\n",
        "\n",
        "# Uncomment these 3 lines to show examples of artificial/augmented images from 1 starting image\n",
        "# note that 00000124.jpg is my randomly selected head CT\n",
        "# all displayed images are variants of that 1 image\n",
        "#plots_of_one_image('./images/MRBrain/00000129.jpg',tfms[0],9,14,11,7, size=64)\n",
        "#plt.subplots_adjust(left=0, bottom=0,wspace=0, hspace=0)\n",
        "#plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6NEab4wtHVfL+bKnE1quMWCtwzh+xCD6ds0+tHMXObJrIsgge6+HWJWnsPx6L\nBH6smQeeJEmSJEmTI1XgkRpxP1zLpwyoJc/7dgXpvg5e67w+98swukfhehW3qL55K5fXR7O1L04a\nfaVEO/qIkLbUozpX0Khz2uO+GT92z7+dGhm6L95f3d/OdakV01IUOGqC9uILdZUnrVpjPIKa6kzu\ni4WpXPgobsItBfhyyZsmNx0fXd1nWhX9WvuKYhyA46xzu+nDKG/8hh4r4hYO8DiQ1n3veeC9uAqP\nC6jvd1fatB9lVav148RUnYDW93U8ERZDr+Ef1SrwbW7a9w1+30VqOcpEqj/j9yGyKPk2zySpwJMk\nSZJkgWzEB+4+7+hVWh05oSJR4IwIPeIVovlfpVVlEPm8XeE69YjVoxvdr04OKrBvn9mm5et3Ve7V\n6FwlR5WBPE6g/s7f+z6iWdRaCryn+jaN+72ovIbq4Bjr6+vZD35O8Zn5tj0PdZ08cI9yZR9EUVNZ\nDJ99rToham/P3+6R46hWaaxdjgL3PG/vr17L3a1oNRyD10L3+upRxcBW9DPnB0sFM08RQ9A6b8eB\nuQrcn1nSeC/w/ML649c0six5pbNNESnwqC5Dy3LAMfjvRLSPVOBJkiRJkjTZKgXeGs25r8Ijxn1U\n7746XltR0uzHZ5KKZiOLagi3on6jnHKUhEe6M7L1qNr6+Hvzp7MuKoVoySgqvSYanXsUOqrJo+W9\nklH9mSuAbfGFewQ5vmQffbcyF1xhez16V7zeX1tWEFfnUZ6050SjwFGrdfujOIS584LTJlR/rcCZ\nRYz+5nEntC+aj96rFdZ9D5+rH1OklNimW4jqbbIMvnBiHVDm2+KvPVNMzSxXf15bGjk33Muew+91\nBvz9VG2PoySKiveoc88yqX8v6FtYJbzfbOK5lgo8SZIkSRZI/oAnSZIkyQI5UvtGFEwRhfD7/zWY\nwzBjeGEULzDgKVj1Op5q42b6qLTqnCCFqEBLlELntFKCvL0+ZWpk7vZt1iZDL9DiaWKY+OvCMj3c\njOzt3xZTugfseXBjbUarSyvW3HDDDZLGqUhJJwOuibs16mvDucbt4yl7XG+24YFlFNuop8XkWHpp\nNJ42yLa57l4itl7Gg9XcLOnTiEZFWOo2uTnbz4Xfi5ELreWO4/wQzObX5GwhKqhTT+CBWwn8vEap\nqv79ponKxkbPJn9e1//Tx7jfOGbcTNH0yWeCVOBJkiRJskA2MplJL4iipTpdzXk6T1Q6NSpqUsPo\n3aeL8wCgKIWoVYzD14kCeRjtoux8KtAaPzYPBoraEKXWtK6Dj5xpD8obRRZNN1lfOw9icqvIUYxQ\n5+AK1/tMfUx+zbl+LMN1jAJZ8DApAAAgAElEQVTQ/JzXQT6RevHr7eqZ/kohFdK7agi8Ab8X2Rep\nYF4sBotCbYHxoCdP2fR9+VSOflx1n6cwDqlebklolV+tl/P+W7eXa0YwG+sc5SQUR0kUvBZNTNRK\nIwN/Pnk/9+dxNLnTURNZe/z3xZ+Z9fG5AseS4yWMo755JkgFniRJkiQL5EgVeDQqmyoK78oVVeev\n7rvzCRS8DdKolkhV4RVFE6WVRaPMGlclPhpzdeXtxtdUnxtXZizjo0lGzT7F6tSELtGIGiXGxBmt\ngjjS6uT29TG5NcEV2abwwiheirRVWMjb7hYILzXpqX4eW9DqO5w39++Cl9BlG60pHj19Dfye86k2\n8XmTKlZP6ALRVKQ9nzivXnK17g/sFwXOvelK3M8j952nldVwTbjPt2XCjRq3Wrmqaz0XPDbGLSG+\nrcgyVvvAfUKfyEIYpYtti7WtVz42KmNc478prfPV2jbQ3+o4mui56umoEdvXc5MkSZIk6XKkCtxV\nZ1RQvlUYJRrN+wi75UeXxpF7XS6R/4m05L0rbkZMfO8j3lpF+X5dibtvjuUj5d0aafsoGMXlpV6n\nlKTj59GtDLQL9eeFadxa0TqGVlT3JnGLRuQXq0fMXizHC/AwonY15FYTj6qut8Er/Y0Sr0x8gzr1\nSW3Ydq2W+c6j4IG+w7Y/+9nPSlqdKKXlU3bfaRQPUU+AIvXLutb/uwLxyHdXLP5safX56BmxaT+t\ntDqREtYhrhvHWd+v3peiLA+P34gmlaqfIx4f0LOibpvvG3rtiGJUajyDiFePe/HyybzHMlpH9hO3\n4vFCkQXZSQWeJEmSJAvkSBW4q9A5pQsj34T7z9xX7jm1LdUf5aNHUec+lemUX8dHtz46Y5Tr/j9G\n3CinVg4yE6J4RHgU7ev79u9b+PkjKpj2oYCIxPRc9HoZzp/nBW+au+++W9I4IvaRs0ctS6uTunBe\nGEmDX3fWc8tGfQ94n/ZJNtiHK1gvY1n7zD2TAlxxsA+Pn/CyqLW697xhtzZEvny3xLmvVhr7k0cH\n+7HS7miynfq4I8U/R30dFfj63fdMX+A9sQHS6n3u52huHQafhEaKI6pdGfo17U37ukT8mDhPHmvU\nO+b6nl+npkiLVOBJkiRJskA2qsCjSTamRs0+Wnd/tX/vI8J6JOkVt3xE5dtE8U6NuKLRrud/+8jW\nffQoXfbpbZdGtcQ+vF1RxGjrPLsvHt8or+7/wYLgVpRWtPK2jsI/+tGPSpK+5Vu+RdKq37UVZ8Ey\n0XVyRRhVrmpdM/73yRLwR+P7dgsW65HXfOLEidPbvPzyy/cs4/cg6hnl50r35MmTez6v/aMexRxV\nCuR8ci78nva21MfiCsUrrkWTm7RUNf9zDrhGqP1tiEbnmqO4PRYBy1ytwJ2eAocoer2O+XCrXs9a\n4dd0WyYzOQyiqnOeBw7R71rL6gbRtKwRm++xSZIkSZKszZEOj6IqPlPTXLaUs7Q6faVHSUfVoGrF\n49HljKS89nnkx26N2N3Xzrb81X2jruy8BnF9rBybR6z6tKhRJbYppeGjTK8wxr5QiSzHea/V6rbV\nQ3Y+9alPSVqdEhW4JqhsaRwxo5TcouKWCbdIuHqeigNB1bP/KAqWvnLTTTdJkq644orT2/Cc4Nqi\nU7eX+8Lr//uUifU58mvPq1sSaCfWHFcktBHVWR+D73dujfQppUi7iV8gjmMbphP1yGauOefU611I\ncVZMdJ/7c8GzLmofuFtZouptENVX39ZnwDr4OfB7BnqVRmurRDRl9dz5IlKBJ0mSJMkC2Ugltihi\ncSpyNKo9zSjaVYDXWXaVLY1+P9+mR826SplSsD5ycr+6WxIYUXs7W6M4opAZlaNofOTsUcHg57ce\nFbtVxEeLHLPPRsa5ac3o5NXHYFtmI6PNnnuP4kVBtvz6HiXNdUOx4qP0GA2vp1yfA7fGuP/QI8r5\nngpyV1555Z52S6v5pB7V7bnk3E+0heOaE59Cf3TrjVsBPNofBUy+uzT67v3aRJkWHlPgfuAatklM\nAfvaBgUeWRJaMzWCn5uo+mOkDOfch1EEf6S4/fU4KPCobgRE6jk6R/X/UbxCKvAkSZIkOYYcqQJ3\n5RfVk27lCaP0ompeHuHno3/3d9f/8537kD3iNZoPvBWFHs0+5koXFeg+05bKR12gxD0C2n15rrYi\nJVzjNaZd7VOxi7awz5Z1whVkVHd4U6AI6Qdeo3uqGpLn6XNMN998s6Rx3mzPT3bLTEuBu98zqmRG\nu6kL4Gq5brtbadgX7eS6+qxjU7XbI2sN54R+6bOsuZ8XC0KtwLGOYcnw+yaKjZmK4vV2Ue+dvu0W\nq00Q1aKYuneiOhZRJbbo/nPL6FQ7InUZVbk7Dnif8+qKXtshisNonf8oYr1XM+P4neUkSZIkOQs4\nUgXuo3lXOB7NJ636XKI5kvH7uVL3+r51jqNH3EbzZEcVm1p+TPdZ9dS7z2jjPiMUrtT240ujgvA6\n2+4znYqi9xxZtuWvkW+RfdZti+Zu9muzKfCBPvroo5LGa+BWCD/f0moVPM9gIIoaZRvVR67969Gs\nbR5/gJ/92muvlTQq8FZutPdt9sexU/uc93XEfb3vVu32KO/b1bLXeWab9G180HXmRXQPertcqXj/\nrM8F7eCaYH3AouV14jeB+7qj6OTWHPXQ859GStzz42s8Fibad7Sv44Q/H7y+gVuipiyOkZXEY2Yi\nUoEnSZIkyQLZqAKP7Pstv5VH6LpyjWaEAa9aJa2qkyiC0v3ZkVJvfdbzZfnxuM9ySqWyLr48zivq\nMIpsb9Up9tmmON+cL3yRqBX3F7Yip7d99M15wO9LFLdfi9oH3cu15/vXvva1kqQHHnhAUlwNr/7c\n1TznnPf4inklb3pq7muPwaAvcF1R9T4vscd/QMu36TEtbl1im1jJ2AZ+bqqu1fuin0WVFf1ejOr7\n1+cX64JbXrh/sGxsEreE+TOzlUscZZD4NqOodNbz+drr76J5DaL2T8UzLRV/nkUK/CD+/4xCT5Ik\nSZKzgPwBT5IkSZIFslUm9FbYvQcMeMCbBxhF4fieViaN5k7M8J4K5ebHqAhGy8zh5hRvh5sAfRte\nclVaDfojCMhNX5gKWe7CCy/cs01MPrW50VOYgHa7ycy30Sqb6qbUbTOnYab1FLl6MhCpnVbjZmvO\nOeeYwLLbbrtNknTHHXdIGs21vj1pvNa0h+8wNRPs5dOM+kQ/tcnfgze9DCTb9pTEOe6uXslIvz/Y\nJ21h37yv+w7XxlNNfZIMT02lvXxfB2ThJsBlggmd95jyN4nfb+7agjllnHtmWP/eTcL1Zz7Bkwfb\n+nWJJrrZFnpBkq1z50HW3DNeIjgKQGwVs/Hr6P25Z45PBZ4kSZIkC2Qjc735NI3+OidFIirc4qMf\nL2BRb9tVvBce8dGkj/pbk917QEmUPubFLBjFQasEYVTe1NOfUB3+3guCtNrt1wBFFpWA5dX31ToW\nZ9OKnLYTaEbqF8rQr7e0GrDiAUJcRwK2KOxCiVJPy2spE/oblgDUPGlWBFu5KmqpBi/E432AbXox\nCq57FJjWOvYocMn7uqe/0WfqiVb8/LjFyi0Dbqljvfp4uQYEB6LIvZjNJvGiN/7ep4Vt4eff1VyU\nVso2OU/SeI6uvvrqPdvw5xPBkB5UjCVlW6YV9edvL7Cvvpc4Npahv3hKaVQ2tvV5VLhnzsRTUirw\nJEmSJFkkRzos8tGF+01RRK3RmvujfVKIXglVT/upiVJmXL17YZSWgoyUN9vybXAcXlqzlabho8Jo\nGkFvLwrHp3usR/FuBXF/LJ+7z8t95y3ryUEmUTiT4ENGgT3yyCOSRtWDz7n2fXlBE4/BwC/GtWHU\n/rrXvU7SqJ7ZV2154TzRLiwBnvrlFhi/N+qCJFOFWOrvowI+7outVURPHXA8xGqQpkfJVNrCOav7\nvFubXKGwTnQdOK/4t6XxnKOcPIUOZb4NRAU+Wsqwm2pkyttx5Vj3yeg8o9LdCujPgU0XawK30EZF\nuaZ80F72OlLgbg2aOgcHjQ1IBZ4kSZIkC2QjCtxH81EkY72OK1jwyFZXIz7tY62afdtRmdNoKr/W\nBAOuFHwk6mrZ2zA1mo4mlohG2FF5VI8QrZfxcoru43bfo/vCW1HVztwiBWcajtGj0IlOJnq/Lr/r\nipV1/ZxSLAS/ukeO4/+tS5d6UZ+omI+P9P0a1WrUfdstv760WkzFYxlQGbXVJro3ufewNlx11VV7\n9gFu1anb5JY19837feb9EDVdq2ovF+s+34cfflibJrKyrTPlJ0Qln6OCI1yHuk9yzjhXWFO4R4A+\n52VztyUDJbL++nPaqbOWiGvhXLgVrfVcrVnnGmYhlyRJkiQ5xmw0NNCV5FREro8a8Uf4lKA+8ov8\nhfVnc1WzM2fqvF5upk/OMjXlZ6uMojSOfmmv+zH9uFr7cjXEqNIzBqIMgqlpUP36bosCp+34Qjlm\nRtYc6+tf//rT66DKW1MvSqu592yT/G/6qUeS19uMfGee4eB53/59/T/7iSZsoU9hKfBoepRYHdXt\n5XO5F3klip5YAtZFRbuCb/V1j8XgGF0NsU3UI75voqDr77xMKwq8joLfFFF+8tRzYe42I/Xpft9a\ngRMfwvn33H3uHVfa26K8IbI+ROeGz+ln0njvMAEQr34PRTncredd77clFXiSJEmSHEM2osDdlxv5\nDGpcUbu/zX3frp5bvo4oB6+Xu93yfUOUj+7LegS++7xaKrk30QnHjsJgJO0Tufg+Wvt3n6IrTldo\nqKvaZxSNIlv585uA9qEiUGIcE+rj9ttvP70OkdSoSxRJVEHLo6Y5T62YDKwYtc+9XibKOvB91dfA\n2+OTAvl7+gjHybngODlX0qqlAOWNdQHLkOdmR1US6+eA97coXoXz6ZHkWFHqnGZiHLg/aAfvtyEP\nHHrKe8pa0cv66FUMq6PQPUeemA584V4vANim+8Q3hbfPfx/cGsw9iCVNGvsesRL0tchq4op8SoHP\nqQTXIhV4kiRJkiyQjdRC92pKHtlcR7pG0232Xn35lr86+i56759PjXinVHpNlO/Zmh7RlZir+Mhv\nyGgyylOs8W27gvMKV+4bb6n6qRzWTeJ5yLXvTxqPFXVXr+MWC9Sn+/6i+INWzICr+G4VJuvrnqcq\njYrZ87rdbx3V4mcfKPGWSga39Hh2SaTCfArbVjuwKrAtVDMqCKVIrAHXslbVqHHfj2chbJLIUhfF\nkUytG1XKg+g+rOMcOK+8YplBmXK+PcPF971pBd575vj9Sn+vp5jlXkKBRxXY1jnfUWW4VOBJkiRJ\ncow5UgXuow0fobuvrLVOlKPd82O3fOC9aPNoppiperrRiCmyEPiobWqE7ao4igx3JdfK0a6Xb+3X\nI7R9REhbUC2ukKRR6UQ+om0B9YCqQ6m1qvexzGc+8xlJ42ickToR1+4H9voFrcpmnmUQVRmMRuee\njyqtWmm4rrTLa227qmZfnulQ799r70f3SeRr5XOsAfUynm+P6sEqQnQ8SpBrRzvryPJWLrs03h91\nxPqmiKwu0bmsP4uWbc3yKK1ak/zel8bzSsT1ddddJ2n0gaNU3de9bfd49PzyewxrJXME1PEk999/\nv6Qxw8GtjtH8CAfJ4e+RCjxJkiRJFshGotCjSOdWPe0omrwXET4ncrxXr3ed6HPHIzw9it5rn7tV\nojWfsddHj/IN3a/JNtwXWY/6XC1HueRRdbqW/3Jb878j8Kdynlv+YffXogx5j1JBoZBXTRUyfIde\n31xaVb+92gBzlFZUL9336Qo8UhWte8WViMdLeN/27JOWBcznWcfSg/KmEpjPssZ14P6po+Y9l9zP\nQb3spug9W+Zk00T3mz8n/Bq3sgF8DnWi+6+//npJY3YC1gt/fmzLLGQed+V9DwsTljPu09q6+uCD\nD0oaz0lUt8Gjz+FMxAClAk+SJEmSBbLR4ZEr75biiHJG/fu5OcVzojf9895r3SZvR5TH6kqIEaDP\nzNaKQne/IK/uw4qigVtzUUcqKcpLj6Jl65mwPJr6INWkziS0nZE15/Oyyy6TtFdFoNK8dr3Pe4ya\n53OUOT42Xmu/r1e58vnqfSa7KMp1zjzvPRUf1Seoidrhy0Z1ADhX9Mc6C8BnCiMKms99lizv4/i7\na7+2z+Xu7fW50jfBus+iqe98W249iSqz1dfPI/SJObjhhhskrVYU9P5GX55T6yNiPzE0UcxUdD/g\n88ZSRt+sa75jhaBPRvdUZPFYpxLbXJWeCjxJkiRJFshGotDBc4ynfASRX6eXazdn5NqLAJ+rxKfW\n8RzZKNrUR3W1+otmnwL8rmwDNeznt5WX6/nLvZxz95e1rCe9Y902Xzj+009/+tOSViPLpVWfnkez\nRlYPFCPKEAtMvT2vl47ydkXOuv7q39f/z53lyi0vHvNQM/de877i6thVtTRGk/Ndqy5Ca1+0G4tH\nndvdqntQ4zEmm8Bng4uq7dX3/ly/eWQJmdoO+8H3TdYFKpQYD9pLpPbctrWIMorcwti6nh7z4f3X\nn/U8M/Hpc8/j77733ntPb9tns+OaeCaH3ztTv1G9zIAeqcCTJEmSZIHkD3iSJEmSLJAjNaFHk7xP\nFXuPJgOJTAyR+bu1nWjbEXMKuUTTS0aFXKISlr5+vW03fbmZyM1vXjijVaqTbfgUlb3gNZ8kAzNw\nax0P1OuZNM80UZ/CtIvZsC6n6OZI3BQ+cQd44KEXKKmvAfv1fokZ3IPZPMjNTe3S6j3nqS4eVOmB\nTlGxirp9LXePNJqvowAzAtMwTdb3jt8f7qrxyWC8qBEm9Do1zO9Nd8cdJNDqsPB0J3ebTZnQ/X6L\nJivx9aZgHe5rTOicX9KtcDOxLwK+COZsuQK9HX7Nfapiv/Ye1Fmv4886L9BCgCqlYQkgpb0c58mT\nJ09vm++i87ofl+BBA3tTgSdJkiTJAjlSBR4FJbgCn0qRgN7IZZ2iK9E6c7fRUvXezqhsa6TYW+9d\ngUfBHq2CDHVboP7epwn1aQJ9Xx5sw2sdNOQT0UxNorIJepM5uMqQpEsvvVTSqpqMLCeuml3JtiYH\n8XPuE6A4ft1rBe6BblFwj6c1RuVbWylqrtL9envBJn9tEaWWRgFNrhDpx1Pppa5mtyGoMnouRKq1\n99lhQT8nreqBBx6QNN4bV111lSTp2muvlTRapjjH9RSlfv0j655fjyggrYbvsJrRr3mP8mZaVO5P\nrF8Er6G8H3roodPbxmLkAYW9qUCnfkeiPjc1UVZNKvAkSZIkWSAb8YH7ZA1TCjzyM/h0h+v6yqP9\nzcGXr1VyNGp0ojQtV60ta4TvqzXphjQqSR8xcu5qJezTg7py6/nZWmU8vXwnPqSoBOy2QPvcHyaN\nI3l8apH65Bz3rDi1AndFAtF0rFFqX2uSGvD+5n52v5+ifdb0rqPHVbBPLAWu2FvtBJbFt+0FRrzM\n5VRKqls6toGobW5Nm3rmnAm4DihVFDhKm/6OAscnzuf4xKXV6XujyXAiy+LUM4c+hWWAVwq04Oum\nv2O5YXIiUkhJh0N11+0FvzeiuI1osqzWMaQPPEmSJEnOAjaiwD1yl1GHq5YaH+2sG0HeYt0CLev4\n2SMF44rct9EqsuL4OfDz5vvEf+VRnDWMgtnm1VdfLWn0XTES9bKt4P7O+hh8YhRfZ1uhvag7aYxM\nx4fGSN6LS0RTNUZR4fW2XGFExXR6GQItXM20rltrX63+25vm1CfIifppSy1HJZR5jxpCQXGNvK9P\nsU3KG2iTX4+ogI0UW2z8PB9Gu7gn8IXzDHcLDtHdfp9I47XDWoKq9xgPt9TOmSIaBU6JYp80iH1g\nXUNp33nnnZKke+65R9JodWM5KS4R7H00ildo+cznFiSLSAWeJEmSJAtkI1HojKwYvfk0fnNGH5Ff\nYW50eus7/7z3OseHOzX6klZ9IHNyCiO/CecVlcyokxGvl1itlbirIc/Z9lGwT5ji0z/W23f1MGVd\n2CY49toPxnnhHBKV7sfKq19XjzGY8tFGU9FG2RutvhNZAqJ13FIALZ90FO/hkdScM1coU35qt1B4\nLQNKraKQeH+QCUm2QZFzjlyB+/vWs+cwFbfDNrm/sXhwH/DqGRPXXHONpLHkqjSWLyUnGwXOtfXY\nHu/3PHNalkSvB8Ar/cOj6SmVihL3Erz1ee+d315tkjlR6FGsUUQq8CRJkiRZIBuZzMRzZOsp/5xo\nJNLyf8zZ99R3+32t6eVrzvWzt3z8kf+/NzL0qluMKuttM9plFIxvEfXpkaJR9ap6m57XPBU9ug34\n+WzlrXNezjvvPEnjOUVVRIqWa4ZCZIRPVGxrv96X/Dq64m351SGaxMSvo1sOPJOgxs+PrxtNg+rL\nQb0Pb5dPL8r5i/ynU/RUzyZxH3jkE5+awjjiIMfnzw7uA7eqoNC9ngQxNdKo1snk8EmXfJ/ROWhN\nA8v+6CduqcFy4BXl+J5+FvVRafU5DN7+KN6pta3o81TgSZIkSXIM2YgCd1/G1HSi0QglikJfR/Ee\nNPo8et9q99x2uV+npcD9FfXBeWQb/j7Ku6+/45URNgqHdVpKrN5WPer0Y/bo6W1T4DDVLkboVGhy\nPx3K3BUk/RWl7lMQTrXD/edRRb7W/ROp816NflfqrTr8XovB+1WkUDzH2adDlUYFxysKyRUT3x+k\njvk2KG9o5fK33s+JQj9IjW7Hnx0+9wHvPbKc7+ua4tRS8NxsFLn3RY4dNe19o67y5hHurMN7Xv35\n1qsK1/rOrQ+u2nvPSv+/td9U4EmSJElyDDlSBQ7RCGUq0jVSx9Fofx11t1/lPbWt6HNUS1RNy6OT\nW1W1Ih94dDyRumqtEykAV26MWD1qnchSaVRUjHZRr9ua/x359Wv4jpH8fffdJ2k8LzfddJOk0a+H\nAsBn6xXu6n35OfZ2RX7rqah+71/eN9yPHsVVtOqF99Z1i0t03Vu13lFG7rMkD98rBzrb5NdehyhO\nYKoSm697JnCfvGddcD9wj+OL5npRmU0a7w2UN1YpFLjHgrjlhn7Cvutz4xYb933TvmiWvXWsFr3M\nDd9GS5G7VS3K/olIBZ4kSZIkC2QjeeCOz2zFKESKR0KuKObmFh/mKLVVHzqqh9tT5nOi6T2XvFeZ\nyP2W7lNqnVs+i3KOXU2hsvFnkRdd7w+/1JxZqJYC5wmFjU8cVXHddddJkk6cOCFpVCSuGuq+46or\nmnXO2zCV+RBZYyI1D3OyPKL2Rn0/mk2Kc1P7MlFuZEGg8HyWvOPKfmYjO5PWBq61x1K4SuYaut+6\nVR/Co9B57zE7PlMizw9+L+oodJbhs2jms/3g63p8i7fXn79Uhau34/24VwPfSQWeJEmSJAtkIwrc\nR4+MXNwvIc2vFdurUzulvHs5d91IwAl/VG//UYW21rajiOBeXfh1zkF0vlHa+KsYJdMWn/+3Xhb/\nFyNs/MG14tokPX/p1Hnj+PGxUUuZ80X+KxYKVxN1FcJoDuyoHVPR5xFTefv1NqKMganaB1Ft9qiq\nmqs0VHf9v+cVH3flzTPQn4ncX/5eGu8r+lIvG2A/9HKeexkHWFDqZb3GQFT73PuLb7tuS1R7Yr/K\nu5XR4d9F2RVumfJr2FrHLR2pwJMkSZLkGJI/4EmSJEmyQLbChI7pxMt1SrEJBDOEF5OYYy6ey2Gm\nlbm5Mwo0mzJZukkmMqEfJGAuKlaAmQuTMNMEcv4JGqnN4r4frq9PrrJpeua1qe85TxwbkyJgzsRd\n4EFtvNapUz7tKt+5a8nTbKbcRb1SjWwjSoGZ2nYUVNe7L+gjTCjBK+mG0phG5m4F6Lk9lpY+Bj4Z\nB8fhgVt1oK8/OzDVsuzc63Imqa+Hu1xapZ1b6/bcqGea6F7ouTZ98q66n/ux+zZ6z8hU4EmSJEmy\nQDaiwF0xEoTRSq2B/UzVNpd1S6muM41ob5vRtlqlK6MRaGTZWKdMX6RofMIA1BLKEqVJ0FatDJhC\nkO8oocio06eRXRI9BUgBiTvvvFPSeN4IZrvlllskjakl0mqgIHgqjAfzuGJvBd70CiHRDld6kUWm\ntQ3wABwsLryirglsIgCQYLZ63Z4qO254eVCOn+vCOeReqpfhnHAPRtaLTdAKxlq32E70PGs9x84E\nbgnwYLvIQsrvG/DslFZT0SJLbUQq8CRJkiRZIEeqwH3E4mkErVF3pH49NaVXxm6d0VqkWvaj9ntl\nTqFXPrO1bs8X7kz5M71EYpT6w2Qd+MLxt7kKlMbrimqgCAfv6wIMm+RMlN1kmz65AoVJOBf4wiXp\nsssukzSqdM4f1xWFzqtP/uA+dGnVj+4+N/ff+dSfnupXT7bjqYQoPvbJMbqPm889buK4p4jNAcuN\nF8nhHHMOiUGRVuOAttE6MZWONdfyOccHHi1zGMo82rZbnFxVU16ae6+2NPk2fSKqLKWaJEmSJMeQ\njSpwRhuM+hmRz1HJrja9jN1+VHLvdc6EKT7ii977cUTR51NR6L11IpXfOh5XdfjRfNpLRo8ULPES\nobW/x8uIblsZzJ5feAovOYtlws+bq2iHEqySdOrUKUlj4RuUOP5ptumTxPgEKfU1oLQr7XQljlom\nHoFt0AZfj2tYr+v38R133CFpdepPfN0o7m3wzW4b9IHIWsY5rC0h+MO5hjxHt+n8tpTk3GlQe/FC\nU9Nz9uKEerSev1GZW56B7Iv79cILL5Q0WuHq55+3xwv4RFOSQirwJEmSJFkgR6rAI/+qT7HZGlFF\npVIhUt5zFO3c973Pe99NtWcd/3rPv9/zkXscgTSOFlF7+LZRVURO1uUupdW81Lr90dR9c/LRj4K5\neaX1eaLtqB78W0zi4q8+onZLRT0a9ylaOV+ce861+8L9vqrxa86I3q831hJUAq8cb0sJEE1+8uRJ\nSaPyQ3HTblT7frIOttGfeybxuBC/p+kjdSQzsRNcDywzHr+ySdaxaoE/CyOLwpw6DYdB9NzyyaG4\nV1DerMd1mfoN6k1i4ybbWU8AACAASURBVKQCT5IkSZIFUrZhdJYkSZIkyXqkAk+SJEmSBZI/4EmS\nJEmyQPIHPEmSJEkWSP6AJ0mSJMkCyR/wJEmSJFkg+QOeJEmSJAskf8CTJEmSZIHkD3iSJEmSLJD8\nAU+SJEmSBZI/4EmSJEmyQPIHPEmSJEkWSP6AJ0mSJMkCyR/wJEmSJFkg+QOeJEmSJAskf8CTJEmS\nZIHkD3iSJEmSLJD8AU+SJEmSBZI/4EmSJEmyQPIHPEmSJEkWSP6AJ0mSJMkCyR/wJEmSJFkg+QOe\nJEmSJAskf8CTJEmSZIHkD3iSJEmSLJD8AU+SJEmSBZI/4EmSJEmyQPIHPEmSJEkWSP6AJ0mSJMkC\nyR/wJEmSJFkg+QOeJEmSJAskf8CTJEmSZIHkD3iSJEmSLJD8AU+SJEmSBZI/4EmSJEmyQPIHPEmS\nJEkWSP6AJ0mSJMkCyR/wJEmSJFkg+QOeJEmSJAskf8CPEaWUoZRy86bbkSRJkpx58gd8BqWU7yyl\nfKSU8kwp5aFSyodLKb+z+/6ZUsoLpZQvVu8/XEq5YfcH9SW2rX9XSvnJTR1LsgxKKZ/Z7Vcn7POP\n7farG3b70gu7fe6xUspvl1JeXy37faWUL1X98u5Syv909Edz+nj+6ib2nWwXwfP0baWU95VSfsuW\nfX8p5Td2//+GUsqXd9d7upRyRynlXZs5iu0gf8A7lFLeLen9kn5K0hWSrpP0M5I+NgzDq4dhePXu\nd7/E+2EY3r65FifHiHsk/W3elFLeJOlVtsxP7/bBqyWdlPR/2/d/WPXTvynpp0spf6m1Mx9sJslh\nM/E8/RuS/qmkm/hRLqW8VdL3SvoH1SYe3O3LF0j6QUn/tpTyFUd3BNtF/oBPUEq5UNJPSPr+YRh+\ndRiGZ4dh+OIwDL8+DMN7Dmkfbyml/EEp5YlSyp+VUr6h+u5dpZS/2B1t3l1K+fu27nt2R7APllL+\nzmG0J9kqPijpe6r33yvp/2ktOAzDc5J+WdJXRRsbhuFjkv5C0q2SVFmJ/m4p5T5Jv7v7+b76ZCnl\nRCnlN3bXe6yU8vullHNKKR/UzoP613fV0z/e5/lIFkzveToMw+cl/T1J7y2lXC/p5yT9yDAMD/i2\nhh0+JOkxSV95lMexTeQP+DRvlfQKSf/hTGy8lHK1pN+U9JOSLpH0w5J+pZRy2e4ipyR9q3ZGm++S\n9K9KKV+9u+5f313+myTdIinNk8ePP5J0QSnl1lLKuZL+e0m/0FqwlHKedtT6p6ONlVK+VtLrJH3E\nvvp67fyof/NB+qSkH5L0gKTLtKOuflQ7z9rvlnSfpG/btQb89PxTkBwjus/TYRj+s6R/L+mjkh6W\n9LOt5XYHht8u6YQm+vxxJ3/Ap7lU0qPDMLx4gG08uqtIniilPCHpO6vvvkvSh4Zh+NAwDF8ehuG3\ntfNwfYckDcPwm8Mw3LU72vw9Sb8l6et21/0OST8/DMPHh2F4VtKPH6CNyfaCCv8m7ajnk/b9D+/2\nq6clvU3Sd9v3b9nte09L+uPd7X3KlvnxXTX0nA7WJ78o6TWSrt9VVr8/DMNw8FOQHBPmPk9/f3fZ\nX2z0n6t2+/tz2hkIvHvXsnRWkj/g03xO0okD+gZPDMNwEX+SfrH67npJf8t+4N+mnYegSilvL6X8\n0a458gntPEQJarpK0v3Vtu49QBuT7eWD2hn0fZ/a5vP37varG7TzUHN/4B/t9r3zJV0p6Q3a8T/W\n1P3oIH3yX2pHDf3Wrnn9R/Z1xMlxpfs8LaVcKum92vGT/0Qp5SJb5MHd/n6BpH8t6RvPVGOXQP6A\nT/OHkp6X9M4ztP37JX2w/oEfhuG8YRj+RSnl5ZJ+RTud+YrdTvshSWV33YckXVtt67oz1MZkgwzD\ncK92gtneIelXJ5a7T9L/LOn/KKW8Mljms9rpU9/mX1X/77tPDsPw9DAMPzQMw02Svl3Su0spf6Wx\nj+TsZM7z9P2S/tMwDD8o6b9op6+tMAzD85L+iaQ3lVLO1PN568kf8AmGYXhS0j+T9G9KKe8spbyq\nlPLSXRVyGH68X5D0baWUby6lnFtKecVuqsQ1kl4m6eWSHpH0Yinl7ZL+WrXuL0v6vlLKbaWUV0n6\n54fQnmQ7+buSvnHXVRKya+5+UNL/2Pp+V938d5I+MbGZfffJUsq3llJuLqUUSU9K+pKkL+9+/VlJ\nN8041uSY0nuellLeoR1X0bt3V/lHkt5ZSvnLwfZekPS+3W2eleQPeIdhGN6nnQ71Y9p5cN0v6R9K\n+rVD2Pb92kmf+NFq2++RdM4wDE9L+gHt/FA/rh0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hARDbMrr3QD4KHQBWB46LlDqmPyWY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MGMiBkDkzjpYZgwMQXyO6ZAN3tLm2kBHoyD2RDYt5uz+L6eWN7LsbIPTDJ33nmnpNEERlAb54Dl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\n",
            "text/plain": [
              "<Figure size 504x576 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gY-nJzEarPcn",
        "colab_type": "text"
      },
      "source": [
        "## Train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUKWKC7yrPco",
        "colab_type": "code",
        "outputId": "b46df8e0-4bcd-4220-db5e-99e2cd1c2030",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Cell 6\n",
        "learn = cnn_learner(data, models.resnet34, metrics=error_rate)\n",
        "#learn = cnn_learner(data, models.resnet50, metrics=error_rate)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/checkpoints/resnet34-333f7ec4.pth\n",
            "100%|██████████| 87306240/87306240 [00:00<00:00, 103651523.23it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uj9t1CDFrPcs",
        "colab_type": "code",
        "outputId": "44af328a-d54d-4927-98fa-11e34d0663ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "#Cell 7\n",
        "learn.fit_one_cycle(3)\n",
        "learn.save(\"MedNIST-34-1\")"
      ],
      "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>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.016704</td>\n",
              "      <td>0.267820</td>\n",
              "      <td>0.091667</td>\n",
              "      <td>00:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.494094</td>\n",
              "      <td>0.125997</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.309165</td>\n",
              "      <td>0.057508</td>\n",
              "      <td>0.011111</td>\n",
              "      <td>00:04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72n4nM0mbyk6",
        "colab_type": "text"
      },
      "source": [
        "#Evaluation\n",
        "* During the training process, the data is split into 3 parts: training, testing and validation. The training data is used to adjust the weights. The GPU does not have enough RAM to store the entire training set of images, so it is split into 'batches'. When all of the images have been used once for training, then an 'epoch' has passed. Once trained for that epoch, it evaluates how well it has learned using the 'test' data set. The performance on the training set is the train_loss and the performance on the validation set is the valid_loss, and the error_rate is also the percentage of cases wrong in the validation set.\n",
        "* It is common practice that after 'acceptable' performance is achieved on the vclidation set, that the system is tested on the 'test' data, and that is what is considered the 'real' performance.\n",
        "* Note that some use 'test' for what is called validation here, and vice versa.\n",
        "\n",
        "* But sometimes the overall error rate doesn't really tell the story. We might care more about false positives than false negatives, and vica versa. Looking at early results can provide valuable insight into the training process, and how to improve results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGIeWkp3bw1R",
        "colab_type": "code",
        "outputId": "24e8909f-9859-415d-88a7-197119056a5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "#Cell 8\n",
        "interp = ClassificationInterpretation.from_learner(learn)\n",
        "interp.plot_confusion_matrix()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WLWTJ4sVs2bKFcWPH0KfvWaHCZV18gJUrVrBubdTxuWnTJl55+d8c3q5dojmk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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6o6WLPNelGa",
        "colab_type": "text"
      },
      "source": [
        "# Looking closer\n",
        "* The confusion matrix shows that there is more confusion between chests and abdomens than with heads. Does that surprise you?\n",
        "* Lets look a little closer at those. FastAI has a nice function that can show you the cases the it did the worst on. Think about that--there are 'errors' but what are the worst errors?\n",
        "* Well, the class assigned to an image is the class that gets the highest score. So the 'worst' would be those where the score for the correct class was lowest. The function 'plot_top_losses' will show the predicted class, the real class, and the score, as well as the image for the N \n",
        "(in our case, 9) worst scored cases.\n",
        "* The second line of code in the cell shows another nice feature of FastAI: to get documentation on any function, just type 'doc(function)' and it will print the documentation for that function. AND it also has a link you can click to then see teh actual source code that implements that function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j04p1iASeZe3",
        "colab_type": "code",
        "outputId": "c80cceea-967c-400c-a718-bd34bdae2859",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        }
      },
      "source": [
        "#Cell 9\n",
        "interp.plot_top_losses(9, figsize=(10,10))\n",
        "doc(interp.plot_top_losses)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<h4 id=\"_cl_int_plot_top_losses\" class=\"doc_header\"><code>_cl_int_plot_top_losses</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/vision/learner.py#L137\" class=\"source_link\" style=\"float:right\">[source]</a><a class=\"source_link\" data-toggle=\"collapse\" data-target=\"#_cl_int_plot_top_losses-pytest\" style=\"float:right; padding-right:10px\">[test]</a></h4><blockquote><p><code>_cl_int_plot_top_losses</code>(<strong><code>k</code></strong>, <strong><code>largest</code></strong>=<strong><em><code>True</code></em></strong>, <strong><code>figsize</code></strong>=<strong><em><code>(12, 12)</code></em></strong>, <strong><code>heatmap</code></strong>:<code>bool</code>=<strong><em><code>None</code></em></strong>, <strong><code>heatmap_thresh</code></strong>:<code>int</code>=<strong><em><code>16</code></em></strong>, <strong><code>return_fig</code></strong>:<code>bool</code>=<strong><em><code>None</code></em></strong>) → <code>Optional</code>[<code>Figure</code>]</p>\n",
              "</blockquote>\n",
              "<div class=\"collapse\" id=\"_cl_int_plot_top_losses-pytest\"><div class=\"card card-body pytest_card\"><a type=\"button\" data-toggle=\"collapse\" data-target=\"#_cl_int_plot_top_losses-pytest\" class=\"close\" aria-label=\"Close\"><span aria-hidden=\"true\">&times;</span></a><p>No tests found for <code>_cl_int_plot_top_losses</code>. To contribute a test please refer to <a href=\"/dev/test.html\">this guide</a> and <a href=\"https://forums.fast.ai/t/improving-expanding-functional-tests/32929\">this discussion</a>.</p></div></div><p>Show images in <code>top_losses</code> along with their prediction, actual, loss, and probability of actual class.</p>\n",
              "<p><a href=\"https://docs.fast.ai/vision.learner.html#_cl_int_plot_top_losses\" target=\"_blank\" rel=\"noreferrer noopener\">Show in docs</a></p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EpFmIvpmymfVz2fdH4Xl6Lel6bVaWhf4mTVP7LKXM++GtoTLGGGOM6cmCaqio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Um+h0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\nVwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAfypUMmf_ea",
        "colab_type": "text"
      },
      "source": [
        "# What do we see?\n",
        "* Most of the errant classes are slices that contain BOTH lung and abdomen. \n",
        "* This is an important point: Data preparation and curation is critical to getting good results\n",
        "* We can argue about how to handle these cases. The correct answer probably depends on your use case. The point is that without seeing these error cases, fyou might never know what was going wrong...\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rpC_If1rPc0",
        "colab_type": "code",
        "outputId": "81c0262b-94b3-43e9-9b80-9547dfe58841",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "#Cell 10\n",
        "learn.unfreeze()\n",
        "learn.lr_find()\n",
        "learn.recorder.plot()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "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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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oplN8lro3Rs-",
        "colab_type": "text"
      },
      "source": [
        "# Extra credit:\n",
        "* We 'cheated' by starting with a network that was already trained on more than 1,000,000 images. That means the system really only had to learn the specific features of these body parts, but the lower level features like edges and lines were already 'known' to be important to the network.\n",
        "* On the other hand, the 'pretrained' network was trained on photographic images, which are color, not gray scale, and had a matrix size other than 64x64. \n",
        "* While we could start from scratch, a better option might be to use the pre-trained values, but allow any of the weights and kernels in the network to be changed, and that is what 'unfreeze' does. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2byn4o2FrPc8",
        "colab_type": "code",
        "outputId": "0d361539-28c9-412d-b9d5-b7a579af2aae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "#Cell 1\n",
        "learn.fit_one_cycle(5, max_lr=slice(3e-6,3e-5))\n",
        "learn.save(\"Unfreeze-34-1\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>error_rate</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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}