Challenge_2_Final_Code.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVfOV4Uv0hN-"
      },
      "source": [
        "Code Devised From -\n",
        "https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBRhbx4y3o4k"
      },
      "source": [
        "## Import libraries"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VyDWPA_BVuZd"
      },
      "source": [
        "For Adilina - dataset is in UdG Account\n",
        "\n",
        "For Anwai - dataset is in UdG Account"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9KLFTlhE3es",
        "outputId": "eb30effe-39a1-422c-8a3e-62bb56a068fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: barbar in /usr/local/lib/python3.7/dist-packages (0.2.1)\n",
            "Requirement already satisfied: efficientnet-pytorch in /usr/local/lib/python3.7/dist-packages (0.7.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from efficientnet-pytorch) (1.10.0+cu111)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->efficientnet-pytorch) (3.10.0.2)\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import glob\n",
        "import random\n",
        "import skimage\n",
        "import imageio\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import seaborn as sns\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import scipy.ndimage\n",
        "from scipy import misc, stats\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, accuracy_score\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "from torch import nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "%matplotlib inline\n",
        "%pip install barbar \n",
        "%pip install --upgrade efficientnet-pytorch\n",
        "from barbar import Bar\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device\n",
        "\n",
        "from google.colab import drive\n",
        "drive._mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uquU7jReFmSc"
      },
      "source": [
        "## Configuration Options and Hyper Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ioy1TTbJFevo"
      },
      "outputs": [],
      "source": [
        "# Directories of the HAM10000 Dataset \n",
        "main_dir  = '/content/drive/MyDrive/CAD/Project - 2/Challenge 2/'\n",
        "train_dir = main_dir + 'data/train/'\n",
        "val_dir   = main_dir + 'data/val/'\n",
        "# test_dir  = main_dir + 'data/test/'\n",
        "\n",
        "# Classes of the Challenge 2\n",
        "classes   = ['bcc','bkl', 'mel']\n",
        "\n",
        "# Normalization Mean and Standard Deviation Values for the Pretrained Weights on Imagenet Dataset\n",
        "norm_mean = (0.485, 0.456, 0.406)\n",
        "norm_std  = (0.229, 0.224, 0.225)\n",
        "\n",
        "# Hyperparameters\n",
        "num_size              = n # where n is the batch size you want, either the same for all the datasets or your individual choices.\n",
        "batch_size            = num_size \n",
        "validation_batch_size = num_size\n",
        "test_batch_size       = num_size\n",
        "\n",
        "num_epochs            = 100\n",
        "num_classes           = len(classes)\n",
        "learning_rate         = 1e-5\n",
        "patience              = 10\n",
        "\n",
        "# Image Size\n",
        "height                = 450\n",
        "width                 = 600\n",
        "\n",
        "# Dimension of the CenterCrop Transformation (448,224,256)\n",
        "centercrop            = c # depends on the configuration you prefer\n",
        "\n",
        "# Weighting of the Class Imbalance\n",
        "weights               = [2.0, 1.0, 1.0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_eUcMLVF3co"
      },
      "source": [
        "## Data Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K5L8-TDoF0uV"
      },
      "outputs": [],
      "source": [
        "# Specifying the Transformations for the Training Images\n",
        "transform_train = transforms.Compose([\n",
        "                    transforms.Resize((height,width)),\n",
        "                    # transforms.CenterCrop(centercrop),\n",
        "                    transforms.RandomAffine(90,shear=20,scale=(0.8,1.2)),\n",
        "                    transforms.RandomVerticalFlip(),\n",
        "                    transforms.RandomHorizontalFlip(),\n",
        "                    transforms.ColorJitter(brightness=(0.7,1.3), contrast=(0.7,1.3), saturation=(0.7,1.3)),\n",
        "                    # transforms.RandomRotation(degrees=90),\n",
        "                    # transforms.RandomInvert(),\n",
        "                    transforms.ToTensor(),\n",
        "                    transforms.Normalize(norm_mean, norm_std),\n",
        "                    ])\n",
        "\n",
        "# Specifying the Transformations for the Validation Images\n",
        "transform_val = transforms.Compose([\n",
        "                    transforms.Resize((height,width)),\n",
        "                    # transforms.CenterCrop(centercrop),\n",
        "                    transforms.ToTensor(),\n",
        "                    transforms.Normalize(norm_mean, norm_std),\n",
        "                    ])\n",
        "\n",
        "# Specifying the Transformations for the Testing Images\n",
        "transform_test = transforms.Compose([\n",
        "                    transforms.Resize((height,width)),\n",
        "                    # transforms.CenterCrop(centercrop),\n",
        "                    transforms.ToTensor(),\n",
        "                    transforms.Normalize(norm_mean, norm_std),\n",
        "                    ])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeoM6dSnF8Er"
      },
      "source": [
        "Data Generators"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UD4WKvPIF6Md"
      },
      "outputs": [],
      "source": [
        "# Datasets\n",
        "traindataset = torchvision.datasets.ImageFolder(root = train_dir, transform = transform_train)\n",
        "valdataset   = torchvision.datasets.ImageFolder(root = val_dir, transform = transform_val)\n",
        "# testdataset  = torchvision.datasets.ImageFolder(root = val_dir, transform = transform_test)\n",
        "\n",
        "# Dataloaders\n",
        "train_data_loader      = torch.utils.data.DataLoader(traindataset, batch_size = batch_size, shuffle = True)\n",
        "validation_data_loader = torch.utils.data.DataLoader(valdataset, batch_size = validation_batch_size, shuffle = False)\n",
        "# test_data_loader       = torch.utils.data.DataLoader(testdataset, batch_size = test_batch_size, shuffle = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0bzCHyM9GAbG"
      },
      "source": [
        "Visualising the Transformation Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "0OwDo-3dF-ZW",
        "outputId": "ffd33396-e8d8-4476-ab99-b1fbd05eaa8a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  bkl,    mel,    mel,    mel,    bkl,    bkl,    bkl,    bcc,    mel,    bcc,    mel,    bkl,    mel,    bcc,    bcc,    bcc,    bcc,    bkl,    bcc,    mel,    bcc,    bkl,    bkl,    mel, \n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 720x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Function to Show Image\n",
        "fig = plt.figure(figsize=(10, 15))\n",
        "def imshow(img):\n",
        "    img = img / 2 + 0.5     # unnormalise\n",
        "    npimg = img.numpy()\n",
        "    plt.axis('off')\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "# Obtain Random Training Images\n",
        "dataiter       = iter(train_data_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# Show the Images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "\n",
        "print(' '.join('%5s, ' % classes[labels[j]] for j in range(len(labels))))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbmm5fdkGHyk"
      },
      "source": [
        "## Architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWOPnhmlOaN3",
        "outputId": "975a2557-ebec-47d2-fcd8-54a64fdbedc0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RegNet(\n",
            "  (stem): SimpleStemIN(\n",
            "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "  )\n",
            "  (trunk_output): Sequential(\n",
            "    (block1): AnyStage(\n",
            "      (block1-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=4, bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (block2): AnyStage(\n",
            "      (block2-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=8, bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block2-1): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=8, bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block2-2): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=8, bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (block3): AnyStage(\n",
            "      (block3-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(128, 288, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(128, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-1): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-2): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-3): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-4): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-5): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-6): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (block4): AnyStage(\n",
            "      (block4-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(288, 672, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-1): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-2): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-3): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-4): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (fc): Linear(in_features=672, out_features=1000, bias=True)\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "dummy_net = torchvision.models.regnet_x_800mf(pretrained = True) \n",
        "print(dummy_net)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "-o1CPZvjm-yc",
        "outputId": "3b0a18ab-3629-4ce6-8cd0-55e5be11cf58"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'(classifier): Sequential(\\n    (0): Dropout(p=0.5, inplace=False)\\n    (1): Linear(in_features=9216, out_features=4096, bias=True)\\n    (2): ReLU(inplace=True)\\n    (3): Dropout(p=0.5, inplace=False)\\n    (4): Linear(in_features=4096, out_features=4096, bias=True)\\n    (5): ReLU(inplace=True)\\n    (6): Linear(in_features=4096, out_features=1000, bias=True)'"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Alexnet Final Layers Sample - How to Play with them in the Necessary Structure\n",
        "\"\"\"(classifier): Sequential(\n",
        "    (0): Dropout(p=0.5, inplace=False)\n",
        "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
        "    (2): ReLU(inplace=True)\n",
        "    (3): Dropout(p=0.5, inplace=False)\n",
        "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
        "    (5): ReLU(inplace=True)\n",
        "    (6): Linear(in_features=4096, out_features=1000, bias=True)\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYcs72pYBJGf",
        "outputId": "7b46d900-0c6b-4722-9b3f-2ad822387ca1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RegNet(\n",
            "  (stem): SimpleStemIN(\n",
            "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "    (2): ReLU(inplace=True)\n",
            "  )\n",
            "  (trunk_output): Sequential(\n",
            "    (block1): AnyStage(\n",
            "      (block1-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=4, bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (block2): AnyStage(\n",
            "      (block2-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=8, bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block2-1): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=8, bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block2-2): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=8, bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (block3): AnyStage(\n",
            "      (block3-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(128, 288, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(128, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-1): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-2): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-3): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-4): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-5): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block3-6): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=18, bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(288, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (block4): AnyStage(\n",
            "      (block4-0): ResBottleneckBlock(\n",
            "        (proj): ConvNormActivation(\n",
            "          (0): Conv2d(288, 672, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(288, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-1): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-2): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-3): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "      (block4-4): ResBottleneckBlock(\n",
            "        (f): BottleneckTransform(\n",
            "          (a): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (b): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=42, bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (2): ReLU(inplace=True)\n",
            "          )\n",
            "          (c): ConvNormActivation(\n",
            "            (0): Conv2d(672, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (activation): ReLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
            "  (fc): Linear(in_features=672, out_features=3, bias=True)\n",
            ")\n",
            "/content/drive/MyDrive/CAD/Project - 2/Challenge 2/pytorch-multiclass-models/try-cad2-checkpoint-epoch100-batchsize24-regnet_x_800mf.pt\n"
          ]
        }
      ],
      "source": [
        "# The Choice of our Architecture to Train\n",
        "model = \"regnet_x_800mf\"\n",
        "\n",
        "# Replacing the Last Layer(s) of \"x\"net to Match our Number of Classes\n",
        "net    = torchvision.models.regnet_y_800mf(pretrained = True)\n",
        "net.fc = nn.Linear(784, num_classes)\n",
        "\n",
        "print(net)\n",
        "net = net.to(device)\n",
        "\n",
        "# Number of Loops over the Dataset (try-cad2 have no architecture changes, hence the final models)\n",
        "model_save_dir = main_dir + 'pytorch-multiclass-models/try-cad2-checkpoint-epoch{0}-batchsize{1}-{2}.pt'.format(num_epochs, num_size, str(model))\n",
        "print(model_save_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\"\"\"# If the model is to be used as a Feature Extractor, setting its parameters to non-trainable (by default, trainable)\n",
        "for param in model.parameters():\n",
        "    param.requires_grad = False\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IwCGjZFNGPmp"
      },
      "source": [
        "## Functions for Metrics and Validation Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zwQwPjBMGJxd"
      },
      "outputs": [],
      "source": [
        "def get_accuracy(predicted, labels):\n",
        "    batch_len, correct= 0, 0\n",
        "    batch_len = labels.size(0)\n",
        "    correct = (predicted == labels).sum().item()\n",
        "    return batch_len, correct\n",
        "\n",
        "def evaluate(model, val_loader):\n",
        "    losses= 0\n",
        "    num_samples_total=0\n",
        "    correct_total=0\n",
        "    model.eval()\n",
        "    for inputs, labels in val_loader:\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "        out = model(inputs)\n",
        "        _, predicted = torch.max(out, 1)\n",
        "        loss = criterion(out, labels)\n",
        "        losses += loss.item()\n",
        "        b_len, corr = get_accuracy(predicted, labels)\n",
        "        num_samples_total +=b_len\n",
        "        correct_total +=corr\n",
        "    accuracy = correct_total/num_samples_total\n",
        "    losses = losses/len(val_loader)\n",
        "    return losses, accuracy\n",
        "\n",
        "class EarlyStopping:\n",
        "    \"\"\"Early Stops the Training if the Validation Loss doesn't improve after a predefined Patience.\"\"\"\n",
        "    def __init__(self, patience=7, verbose=False, delta=0):\n",
        "        \n",
        "        self.patience     = patience\n",
        "        self.verbose      = verbose\n",
        "        self.counter      = 0\n",
        "        self.best_score   = None\n",
        "        self.early_stop   = False\n",
        "        self.val_loss_min = np.Inf\n",
        "        self.delta        = delta\n",
        "\n",
        "    def __call__(self, val_loss, model):\n",
        "\n",
        "        score = -val_loss\n",
        "\n",
        "        if self.best_score is None:\n",
        "            self.best_score = score\n",
        "            self.save_checkpoint(val_loss, model)\n",
        "        elif score < self.best_score + self.delta:\n",
        "            self.counter += 1\n",
        "            print(f'EarlyStopping Counter: {self.counter} out of {self.patience}')\n",
        "            if self.counter >= self.patience:\n",
        "                self.early_stop = True\n",
        "        else:\n",
        "            self.best_score = score\n",
        "            self.save_checkpoint(val_loss, model)\n",
        "            self.counter = 0\n",
        "\n",
        "    def save_checkpoint(self, val_loss, model):\n",
        "        '''Saves model when validation loss decrease.'''\n",
        "        if self.verbose:\n",
        "            print(f'Validation Loss Decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving Model ...')\n",
        "        torch.save(model.state_dict(), model_save_dir)\n",
        "        self.val_loss_min = val_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtR0iHGbGUCf"
      },
      "source": [
        "## Training Progress"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VO-NeM6YH-ss"
      },
      "outputs": [],
      "source": [
        "# Clearing the GPU Cache\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nA7Mk1wQyQGQ",
        "outputId": "17478464-4b11-406d-8a84-56602137c6dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting Training with Network: regnet_x_800mf, Loading Checkpoint Status : False\n"
          ]
        }
      ],
      "source": [
        "load_checkpoint  = False\n",
        "print(\"Starting Training with Network: \" + model + \", Loading Checkpoint Status : \" + str(load_checkpoint))\n",
        "\n",
        "# Optimizer and Loss Configurations\n",
        "class_weights = torch.FloatTensor(weights).to(device)\n",
        "criterion = nn.CrossEntropyLoss(weight=class_weights) # weight=class_weights\n",
        "optimizer = optim.Adam(net.parameters(), lr=1e-5)\n",
        "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,'min')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Qdib0A1GSSO",
        "outputId": "2de8edcd-1acf-4197-d421-e9a6700e3ffb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2000/2000: [===============================>] - ETA 2.5s\n",
            "Epoch: 1\n",
            "Training Loss: 1.055 , Training Accuracy:0.393 => Validation Loss: 1.014, Validation Accuracy: 0.512 \n",
            "Validation Loss Decreased (inf --> 1.014461).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 2\n",
            "Training Loss: 0.808 , Training Accuracy:0.612 => Validation Loss: 0.712, Validation Accuracy: 0.684 \n",
            "Validation Loss Decreased (1.014461 --> 0.711941).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 3\n",
            "Training Loss: 0.613 , Training Accuracy:0.721 => Validation Loss: 0.592, Validation Accuracy: 0.756 \n",
            "Validation Loss Decreased (0.711941 --> 0.591894).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 4\n",
            "Training Loss: 0.535 , Training Accuracy:0.742 => Validation Loss: 0.513, Validation Accuracy: 0.782 \n",
            "Validation Loss Decreased (0.591894 --> 0.513097).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 5\n",
            "Training Loss: 0.481 , Training Accuracy:0.782 => Validation Loss: 0.499, Validation Accuracy: 0.798 \n",
            "Validation Loss Decreased (0.513097 --> 0.499282).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 6\n",
            "Training Loss: 0.455 , Training Accuracy:0.789 => Validation Loss: 0.459, Validation Accuracy: 0.816 \n",
            "Validation Loss Decreased (0.499282 --> 0.458882).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 7\n",
            "Training Loss: 0.400 , Training Accuracy:0.827 => Validation Loss: 0.454, Validation Accuracy: 0.808 \n",
            "Validation Loss Decreased (0.458882 --> 0.454040).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 8\n",
            "Training Loss: 0.400 , Training Accuracy:0.825 => Validation Loss: 0.429, Validation Accuracy: 0.820 \n",
            "Validation Loss Decreased (0.454040 --> 0.428859).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 9\n",
            "Training Loss: 0.359 , Training Accuracy:0.834 => Validation Loss: 0.396, Validation Accuracy: 0.828 \n",
            "Validation Loss Decreased (0.428859 --> 0.396104).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 10\n",
            "Training Loss: 0.370 , Training Accuracy:0.826 => Validation Loss: 0.386, Validation Accuracy: 0.822 \n",
            "Validation Loss Decreased (0.396104 --> 0.385563).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 11\n",
            "Training Loss: 0.327 , Training Accuracy:0.854 => Validation Loss: 0.406, Validation Accuracy: 0.822 \n",
            "EarlyStopping Counter: 1 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 12\n",
            "Training Loss: 0.314 , Training Accuracy:0.857 => Validation Loss: 0.374, Validation Accuracy: 0.840 \n",
            "Validation Loss Decreased (0.385563 --> 0.373787).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 13\n",
            "Training Loss: 0.305 , Training Accuracy:0.866 => Validation Loss: 0.378, Validation Accuracy: 0.844 \n",
            "EarlyStopping Counter: 1 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 14\n",
            "Training Loss: 0.314 , Training Accuracy:0.855 => Validation Loss: 0.431, Validation Accuracy: 0.820 \n",
            "EarlyStopping Counter: 2 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 15\n",
            "Training Loss: 0.280 , Training Accuracy:0.872 => Validation Loss: 0.471, Validation Accuracy: 0.810 \n",
            "EarlyStopping Counter: 3 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 16\n",
            "Training Loss: 0.307 , Training Accuracy:0.867 => Validation Loss: 0.422, Validation Accuracy: 0.826 \n",
            "EarlyStopping Counter: 4 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 17\n",
            "Training Loss: 0.277 , Training Accuracy:0.880 => Validation Loss: 0.460, Validation Accuracy: 0.822 \n",
            "EarlyStopping Counter: 5 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 18\n",
            "Training Loss: 0.255 , Training Accuracy:0.890 => Validation Loss: 0.338, Validation Accuracy: 0.852 \n",
            "Validation Loss Decreased (0.373787 --> 0.338325).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 19\n",
            "Training Loss: 0.246 , Training Accuracy:0.895 => Validation Loss: 0.387, Validation Accuracy: 0.834 \n",
            "EarlyStopping Counter: 1 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 20\n",
            "Training Loss: 0.247 , Training Accuracy:0.883 => Validation Loss: 0.355, Validation Accuracy: 0.870 \n",
            "EarlyStopping Counter: 2 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 21\n",
            "Training Loss: 0.238 , Training Accuracy:0.893 => Validation Loss: 0.353, Validation Accuracy: 0.852 \n",
            "EarlyStopping Counter: 3 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 22\n",
            "Training Loss: 0.231 , Training Accuracy:0.892 => Validation Loss: 0.325, Validation Accuracy: 0.872 \n",
            "Validation Loss Decreased (0.338325 --> 0.325045).  Saving Model ...\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 23\n",
            "Training Loss: 0.215 , Training Accuracy:0.906 => Validation Loss: 0.360, Validation Accuracy: 0.858 \n",
            "EarlyStopping Counter: 1 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 24\n",
            "Training Loss: 0.213 , Training Accuracy:0.906 => Validation Loss: 0.345, Validation Accuracy: 0.874 \n",
            "EarlyStopping Counter: 2 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 25\n",
            "Training Loss: 0.209 , Training Accuracy:0.909 => Validation Loss: 0.368, Validation Accuracy: 0.870 \n",
            "EarlyStopping Counter: 3 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 26\n",
            "Training Loss: 0.203 , Training Accuracy:0.914 => Validation Loss: 0.504, Validation Accuracy: 0.824 \n",
            "EarlyStopping Counter: 4 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 27\n",
            "Training Loss: 0.209 , Training Accuracy:0.904 => Validation Loss: 0.356, Validation Accuracy: 0.868 \n",
            "EarlyStopping Counter: 5 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 28\n",
            "Training Loss: 0.209 , Training Accuracy:0.910 => Validation Loss: 0.342, Validation Accuracy: 0.872 \n",
            "EarlyStopping Counter: 6 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 29\n",
            "Training Loss: 0.180 , Training Accuracy:0.923 => Validation Loss: 0.345, Validation Accuracy: 0.872 \n",
            "EarlyStopping Counter: 7 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 30\n",
            "Training Loss: 0.190 , Training Accuracy:0.914 => Validation Loss: 0.330, Validation Accuracy: 0.876 \n",
            "EarlyStopping Counter: 8 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 31\n",
            "Training Loss: 0.184 , Training Accuracy:0.919 => Validation Loss: 0.373, Validation Accuracy: 0.854 \n",
            "EarlyStopping Counter: 9 out of 10\n",
            "2000/2000: [===============================>] - ETA 2.4s\n",
            "Epoch: 32\n",
            "Training Loss: 0.191 , Training Accuracy:0.912 => Validation Loss: 0.348, Validation Accuracy: 0.856 \n",
            "EarlyStopping Counter: 10 out of 10\n",
            "Early Stopping\n",
            "Finished Training\n"
          ]
        }
      ],
      "source": [
        "if load_checkpoint == False:\n",
        "  #net.load_state_dict(torch.load('/content/drive/MyDrive/CAD/Project - 2/Challenge 2/pytorch-multiclass-models/checkpoint-epoch-{0}.pt'.format(num_epochs)))\n",
        "  #print(\"Here\")\n",
        "\n",
        "  # Initializing the early_stopping\n",
        "  early_stopping = EarlyStopping(patience=patience, verbose=True)\n",
        "\n",
        "  accuracy      = []\n",
        "  val_accuracy  = []\n",
        "  losses        = []\n",
        "  val_losses    = []\n",
        "  best_accuracy = 0\n",
        "\n",
        "  for epoch in range(num_epochs):\n",
        "      running_loss      = 0.0\n",
        "      correct_total     = 0.0\n",
        "      num_samples_total = 0.0\n",
        "      for i, data in enumerate(Bar(train_data_loader)):\n",
        "          # Getting the Inputs\n",
        "          inputs, labels = data\n",
        "          inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "          # Setting the Parameter Gradients to Zero\n",
        "          optimizer.zero_grad()\n",
        "\n",
        "          # Forward Pass + Backward Pass + Optimize\n",
        "          outputs = net(inputs)\n",
        "          loss    = criterion(outputs, labels)\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "          \n",
        "          # Computing Accuracy\n",
        "          _, predicted = torch.max(outputs, 1)\n",
        "          b_len, corr = get_accuracy(predicted, labels)\n",
        "          num_samples_total +=b_len\n",
        "          correct_total +=corr\n",
        "          running_loss += loss.item()\n",
        "\n",
        "      running_loss /= len(train_data_loader)\n",
        "      train_accuracy = correct_total/num_samples_total\n",
        "      val_loss, val_acc = evaluate(net, validation_data_loader)\n",
        "      scheduler.step(val_loss)\n",
        "      \n",
        "      print('Epoch: %d' %(epoch+1))\n",
        "      print('Training Loss: %.3f , Training Accuracy:%.3f => Validation Loss: %.3f, Validation Accuracy: %.3f ' %(running_loss, train_accuracy,val_loss, val_acc))\n",
        "\n",
        "      losses.append(running_loss)     # List Containing the Training Losses\n",
        "      val_losses.append(val_loss)     # List Containing the Validation Losses\n",
        "      accuracy.append(train_accuracy) # List Containing the Training Accuracy\n",
        "      val_accuracy.append(val_acc)    # List Containing the Validation Accuracy        \n",
        "\n",
        "      # early_stopping needs Validation Loss to check if it decreases and if so, it makes a Checkpoint of the Current Model\n",
        "      early_stopping(val_loss, net)\n",
        "      \n",
        "      if early_stopping.early_stop:\n",
        "          print('Early Stopping')\n",
        "          break\n",
        "\n",
        "  print('Finished Training')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pyix8nUeI3hN"
      },
      "source": [
        "Training and Validation Accuracy Curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "20YNPFtBHodO",
        "outputId": "5cefed61-1f11-4f21-bc33-f48ce45e92a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f81da0818d0>"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#tmpdir = \"/content/\"\n",
        "plt.figure()\n",
        "plt.plot(accuracy)\n",
        "plt.plot(val_accuracy)\n",
        "plt.title('Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "#plt.savefig(\"{}/accuracy.png\".format(tmpdir))\n",
        "\n",
        "# Training and Validation Loss\n",
        "plt.figure()\n",
        "plt.plot(losses)\n",
        "plt.plot(val_losses)\n",
        "plt.title('Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "#plt.savefig(\"{}/loss.png\".format(tmpdir))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eEJjBYvJJC1l"
      },
      "source": [
        "## Testing Accuracy and Confusion Matrix\n",
        "Validation Accuracy and Confusion Matrix without Test Time Augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xf8MAgaeWgEi",
        "outputId": "b571c98e-b34f-4301-f3de-c959fc211d5f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Loading the best model\n",
        "net.load_state_dict(torch.load(model_save_dir))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9t746QnGI_0T",
        "outputId": "ad33b4f0-c5e6-4eae-e29d-a8330ac74da5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of the Network on the Validation Images: 87 %\n"
          ]
        }
      ],
      "source": [
        "#net.load_state_dict(torch.load('/content/drive/MyDrive/CAD/Project - 2/Challenge 2/pytorch-multiclass-models/checkpoint-epoch-{0}.pt'.format(num_epochs)))\n",
        "\n",
        "correct = 0\n",
        "total = 0\n",
        "net.eval()\n",
        "\n",
        "with torch.no_grad():\n",
        "    for data in validation_data_loader:\n",
        "        images, labels = data\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "print('Accuracy of the Network on the Validation Images: %d %%' % (\n",
        "    100 * correct / total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tvMgAWg8AJ3",
        "outputId": "baac0d62-a094-43cf-ef33-31773ae96664"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[ 94.,   4.,   2.],\n",
            "        [  9., 161.,  30.],\n",
            "        [  4.,  15., 181.]]) \n",
            "\n",
            "500.0\n",
            "bcc - recall :  0.8785047  precision :  0.94  accuracy :  0.962\n",
            "bkl - recall :  0.89444447  precision :  0.805  accuracy :  0.884\n",
            "mel - recall :  0.84976524  precision :  0.905  accuracy :  0.898\n"
          ]
        }
      ],
      "source": [
        "confusion_matrix = torch.zeros(len(classes), len(classes))\n",
        "with torch.no_grad():\n",
        "    for data in validation_data_loader:\n",
        "        images, labels = data\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        for t, p in zip(labels.view(-1), predicted.view(-1)):\n",
        "                confusion_matrix[t.long(), p.long()] += 1\n",
        "\n",
        "print(confusion_matrix, \"\\n\")\n",
        "cm = confusion_matrix.numpy()\n",
        "\n",
        "recall_bcc = cm[0][0]/np.sum(cm[:,0])\n",
        "recall_bkl = cm[1][1]/np.sum(cm[:,1])\n",
        "recall_mel = cm[2][2]/np.sum(cm[:,2])\n",
        "\n",
        "precision_bcc = cm[0][0]/np.sum(cm[0])\n",
        "precision_bkl = cm[1][1]/np.sum(cm[1])\n",
        "precision_mel = cm[2][2]/np.sum(cm[2])\n",
        "\n",
        "total = np.sum(cm)\n",
        "print(total)\n",
        "accuracy_bcc = cm[0][0] + (total - np.sum(cm[0]) - np.sum(cm[:,0]) + cm[0][0])\n",
        "accuracy_bkl = cm[1][1] + (total - np.sum(cm[1]) - np.sum(cm[:,1]) + cm[1][1])\n",
        "accuracy_mel = cm[2][2] + (total - np.sum(cm[2]) - np.sum(cm[:,2]) + cm[2][2])\n",
        "\n",
        "print(\"bcc - recall : \",recall_bcc, \" precision : \",precision_bcc, \" accuracy : \",(accuracy_bcc/total))\n",
        "print(\"bkl - recall : \",recall_bkl, \" precision : \",precision_bkl, \" accuracy : \",(accuracy_bkl/total))\n",
        "print(\"mel - recall : \",recall_mel, \" precision : \",precision_mel, \" accuracy : \",(accuracy_mel/total))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "38gUjkwZ8Dk9",
        "outputId": "a73c3910-8340-4af0-98c0-fc7d7a0e0e14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.872\n"
          ]
        },
        {
          "data": {
            "image/png": 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CRHdFAj8EHj5k28NprlKXJKln0jO2zcBrk1wEfKCqrkxyG+BZwMuBo8fZOEnqHLsiV9ybgd8A/h74uyRX0UyjFZrzZ28eY9skqXsMbCurqqaA5yV5K3AQM9exnVJV5461cZKkiTTRgQ0gydbAI4ADaKbT+ilwU5Lzq+r6sTZOkrpmlS/QXgkTPXiknRfsezQ3orsHcFP7eAywZcAN6SRJy7HKd9BeCZOesW0CLgceVlU/ml6ZZG/gX2ju1TZs1KQkaR2a9MB2AHBYf1ADqKofJXkt8JHxNEuSOqoD17FNemA7n+YWBYNsQ3M7A0nSqHRgVOREn2MDjgTekORmtwpP8kDg9cArxtIqSdLEmriMLckZNPfcmbYR+Gp7kfZFNNNo7Uoz7P8vAO/JJkmj0oGMbeICG3AWNw9sZ42rIZK07nRguP/EBbaqOnzcbZAkrV0TF9gkSeNTU46KlCR1SQfOsU36qEhJkhbFjE2SNMPBI5KkTunAOTa7IiVJnWLGJkma0YHBIwY2SdIMA5skqVM6MLu/59gkSZ1ixiZJmmFXpCSpUxzuL0nSZDFjkyTNcOYRSVKn2BUpSdJkMWOTJPWUoyIlSZ1iV6QkSZPFjE2SNMNRkZKkTrErUpKkyWLGJkma4ahISVKn2BUpSdJkMWOTJM1wVKQkqVPsipQkabKYsUmSepwrUpLULXZFSpI0WczYJEkzOpCxGdgkSTM6MNzfrkhJUqeYsUmSZtgVKUnqkupAYLMrUpLUKWZskqQZHcjYDGySpBkdmHnErkhJUqeYsUmSZtgVKUnqlA4ENrsiJUmdYsYmSeqpWvsZm4FNkjTDrkhJkiaLGZskaUYHMrZ1Edjutt/Txt0EzeHy418y7iZoDo98wT+PuwlaRc4VKUnShFkXGZskaYE6kLEZ2CRJM9b+VJF2RUqSusWMTZLU04XBIwY2SdKMDgQ2uyIlSZ1ixiZJmuHgEUlSl9RUjWxZiCSPS3JOki1JjhxS5pAkZyc5K8lH5qvTjE2SNBZJNgDHAI8GLgDOSHJCVZ3dV+YuwCuBh1TVZUl2na9eMzZJ0oypES7zuz+wparOq6rrgY8BT5lV5vnAMVV1GUBVXTRfpQY2SVLPKLsikxyR5My+5YhZf24P4Md9ry9o1/W7K3DXJKcn+fckj5vvGOyKlCStiKraBGxaZjVbAXcBDgT2BL6S5J5V9cu5dpAkqbG6oyIvBPbqe71nu67fBcDXq+oG4AdJzqUJdGcMq9SuSElST02NblmAM4C7JLljkq2BQ4ETZpX5LE22RpJdaLomz5urUjM2SdKMVczYqurGJC8CTgI2AMdW1VlJXgecWVUntNsek+Rs4Cbgz6vqkrnqNbBJksamqk4ETpy17jV9zwt4SbssiIFNktSzwC7EiWZgkyTN6EBgc/CIJKlTzNgkST12RUqSOqULgc2uSElSp5ixSZJ6upCxGdgkSTMq427BstkVKUnqFDM2SVKPXZGSpE6pKbsiJUmaKGZskqQeuyIlSZ1SjoqUJGmymLFJknrsipQkdYqjIiVJmjBmbJKknqpxt2D5DGySpB67IiVJmjBmbJKkni5kbEsKbEn2BF4CPBbYB9imqrbq274j8MdAAW+tqhtH0FZJ0gpbl+fYkjwa+ASwEZgO7Td7K6rqsiRPBe4LnAWcsMx2SpK0IIs6x5ZkL+BTwA7APwNPBy4bUvxYmsD3xOU0UJK0emoqI1vGZbEZ20uB7YFPVNWhAEmOGVL2pPbxfktsmyRpla3HuSIfS9Pt+JfzFayqHwDXAXdcQrskSVqSxWZsewPXVNX3Flj+SppuS0nSGrAe54qcAjYspGCSrWgGmFyx2EZJksZjah12Rf4QuFWSvRdQ9uHALYGFZneSJC3bYgPbye3jH81VKMktgb+iOR/3+SW0S5I0BlUZ2TIuiw1sfwNcD7w0yXMHFUhyH5oA+ADgV8A/LKuFkqRV04Xh/osKbFX1Q+B5NOfZNiX5ObAjQJKvJrkQOAN4GHAj8Kyquni0TZYkabhFzzxSVccluQg4Brhz36YH9j3fAvxRVZ2yzPZJklbRupxSC6CqvpTkbjQDRB4C3IEmi/sZcDpwalXdNLJWSpJWxbqdBBmgqgr413aRJGkieNsaSVJPF65jM7BJknq6MFfkogJbkqUMBqmqOngJ+0mStGiLzdgOXGC56XE1Yda92uaT5O6LKV9VZy+mvCRpuPU4KvLoebbvQHNh9oOAS4B3AYsdHfltFhYMp4PmguaulCTNb92dY6uq+QIbAEkeCXwauHtVPX2RbTpokeUlSepZkcEjVXVKkhcDxyZ5XlW9bxH79i4fSJL2soKBkmy/zKZKkvp0YfDIYueKXIyP03RDPm8ZdWwetiHJLsCpy6hbkjRL1eiWcVmxwFZV1wJXAfsvo5rHJHnX7JVJdge+wsoGZknSGrRi17El2YNmMMmVy6jm0cCpSa6tqj9r690XOAX4OfD4ZTZTktRn3Q0eWagkt2bmdjX/s9R6qurbSR4DfDnJNcCHgC8B5wJPqqqr5mjDEcARADtvtycbt9llqc2QpHWjC+fYFnuB9mvmKbINsBfwWGBnmuH4xyytaY2q+maSxwNfBP6U5rza06vqunn22wRsArjTLvfuwJUZkqSFWGzGdhQLv8ZsCnhDVX1kMX8gyf8asulzNF2TJwPPTQLNrCa/dg5OkrQ067Er8ivMHdhuBC4D/gv4RFV9bwlteuc82/+m73nRXAQuSRqBLnRvLfYC7QNXqB39f8ORjpI0JusxY1t1SbYGDgfuD+wO/BT4OvCBqrp+jE2TJE2gRWVHSaaS3JjkzivVoFl/b3/gezQDUO5Bc8H3PdrXWxY7YbIkaW5VGdkyLovN2K4BbqiqLSvRmAE2AZcDD6uqH02vTLI38C/Au4GHr1JbJKnzpsbdgBFY7PmsC4BbrkRDhjgAeE1/UANoX78WuN8qtkWStAYsNrB9DtgmySNWojEDnE9zbdwg2wA/GrJNkrQERUa2jMtiA9sbgV8A72rna1xpRwJvSPKA/pVJHgi8HnjFKrRBktaNqRrdMi6LPce2P/AqmmvJzk7yIeB04CLmuKFoVX1loX8gyRnc/FKKjcBXk1zU/p1d2+US4C+Azy7yGCRJHTZnYEvyLOCaqvpku+o0bh50Xtguc6n5/s4sZ836G2ctYl9J0jJMjbELcVTmCzibaa4b+2TfusUe9aLKV9Xhi6xfkjQi4zw3NioLyaR6R+msIJKkSTfxM49IklZPF65jM7BJknq60BVp16IkqVPM2CRJPeulK/L2SYZeo7YAVVUGUElaA9ZLYIPFD/GXJGksFhLYrgLevtINkSSNXxcGjywksF1ZVUeveEskSWM3tfbjmqMiJUnd4qAOSVLPepgrUpK0jozxbjMjY1ekJKlTDGySpJ6pES4LkeRxSc5JsiXJkXOUe1qSSnLAfHXO2RXpbP6StL5MZfXOsSXZABwDPBq4ADgjyQlVdfasctsDLwa+vpB6DVySpHG5P7Clqs6rquuBjwFPGVDu9cCbgWsXUqmBTZLUUyNckhyR5My+5YhZf24P4Md9ry9o1/UkuQ+wV1V9bqHH4KhISVLPKOeKrKpNwKal7p/kFsA7gMMXs58ZmyRpXC4E9up7vWe7btr2wD2A05KcDzwQOGG+ASRmbJKknlWeUusM4C5J7kgT0A4FnjG9saouB3aZfp3kNOBlVXXmXJUa2CRJPas580hV3ZjkRcBJwAbg2Ko6K8nrgDOr6oSl1GtgkySNTVWdCJw4a91rhpQ9cCF1GtgkST1dmFLLwCZJ6vG2NZIkTRgzNklSzyivYxsXA5skqacL59jsipQkdYoZmySppwuDRwxskqSeLpxjsytSktQpZmySpJ4uZGwGNklST3XgHJtdkZKkTjFjkyT12BUpSeqULgQ2uyIlSZ1ixiZJ6unClFoGNklSTxdmHrErUpLUKWZskqSeLgweMbBJknq6ENjsipQkdYoZmySpx1GRkqRO6cKoSAObJKnHc2ySJE0YMzZJUo/n2NaIi6+5fNxN0Bzu/Mx/HHcTNIcf/Mf7x90EraKpDoQ2uyIlSZ2yLjI2SdLCdGHwiIFNktSz9jsi7YqUJHWMGZskqceuSElSp3Rh5hG7IiVJnWLGJknq6cJ1bAY2SVLP2g9rdkVKkjrGjE2S1OOoSElSp3ThHJtdkZKkTjFjkyT1rP18zcAmSerThXNsdkVKkjrFjE2S1NOFwSMGNklSz9oPa3ZFSpI6xoxNktTThcEjBjZJUk91oDPSrkhJUqeYsUmSeuyKlCR1SheG+9sVKUnqFDM2SVLP2s/XDGySpD52RUqSNGHM2CRJPY6KlCR1ihdoS5I0YczYJEk9dkVKkjrFrkhJkiaMGZskqceuSElSp0yVXZGSJE0UMzZJUs/az9cMbJKkPs4VKUnShDFjkyT1dOE6NgObJKmnC8P97YqUJHWKGZskqacLg0cMbJKkni6cY7MrUpLUKQY2SVLP1AiXhUjyuCTnJNmS5MgB21+S5Owk/53ky0n2ma9OA5skqaeqRrbMJ8kG4Bjg8cDdgcOS3H1WsW8CB1TVbwGfAt4yX70GNknSuNwf2FJV51XV9cDHgKf0F6iqU6vq6vblvwN7zlepgU2S1DNFjWxJckSSM/uWI2b9uT2AH/e9vqBdN8xzgc/PdwyOipQk9YzyAu2q2gRsGkVdSf4QOAB4xHxlDWySpJ5VHu5/IbBX3+s923U3k+RRwKuAR1TVdfNValekJGlczgDukuSOSbYGDgVO6C+Q5N7Ae4AnV9VFC6nUjE2S1LOaM49U1Y1JXgScBGwAjq2qs5K8Djizqk4A3grcBvhkEoAfVdWT56p34gJbkk8sonhV1R+sWGMkaZ1ZyDD9Ef+9E4ETZ617Td/zRy22zokLbMDtxt0ASdLaNXGBraoOGncbJGm96sJtayYusA2TpnN1d+Ciqrpx3O2RpC5yEuRVkOQJSb4OXEtzId9vtevf217XIElSz0QHtiTPohn6+V3gCCB9m8+luQpdkjQio5x5ZFwmOrDRXJD31qp6NvDhWdvOopk0U5I0Iqs5CfJKmfTAtg/wpSHbrgU2rmJbJElrwKQHth8D9x6y7QBgyyq2RZI6z67IlfePwGvbQSK3btclycHAy4H3Dtuxf1bp62+8YhWaKklrX43wv3GZ9OH+b6aZIPMDwE3tuq/STL3ynqr6u2E79s8qvXG7O6398auSpAWZ6MBWzdnHFyZ5B3AwsAtwKXBKVZ071sZJUgdNjXHQx6hMdGDrsxXN+bQL2td3TnJn6M0zJkkagbUf1iY8sCW5J/BRYH9ufg3btKLplpQkCZjwwAYcC9wA/A5Nxnb9eJsjSd02ztGMozLpgW1/4GlVddK4GyJJ60EXAtukD/f/BrD3uBshSVo7Jj1jOwL4aJKrgVOBX84uUFVXr3qrJKmjxjkV1qhMemC7GDgf+OAcZRw8Ikkj0oWuyEkPbB8GHgS8DQePSJIWYNID20HA86vqI+NuiCStB1240eikB7bzAc+hSdIq6cI5tkkfFfnnwKuS7DvmdkiS1ohJz9iOphnuf26S8xk8KvL+q90oSeoqB4+svG+3iyRpFXShK3KiA1tVPWfcbZAkrS0THdgkSavLrkhJUqd0Ybj/pI+KlCRpUczYJEk93kFbktQpdkVKkjRhzNgkST12RUqSOsWuSEmSJowZmySpx65ISVKn2BUpSdKEMWOTJPXYFSlJ6hS7IiVJmjBmbJKknqqpcTdh2QxskqSeLtyPza5ISVKnmLFJknrKUZGSpC6xK1KSpAljxiZJ6rErUpLUKV2YecSuSElSp5ixSZJ6ujClloFNktTjOTZJUqc43F+SpAljxiZJ6rErUpLUKQ73lyRpwpixSZJ67IqUJHWKoyIlSZowZmySpB67IiVJneKoSEmSJowZmySpx0mQJUmdYlekJEkTxoxNktTjqEhJUqd04RybXZGSpE4xY5Mk9dgVKUnqlC4ENrsiJUmdYsYmSepZ+/kapAtp53qT5Iiq2jTudmgwP5/J52fUbXZFrk1HjLsBmpOfz+TzM+owA5skqVMMbJKkTjGwrU2eG5hsfj6Tz8+owxw8IknqFDM2SVKnGOSDjSgAAAppSURBVNgkSZ1iYJsQSTYnOXPc7dDcFvI5JakkL5pj+4FtmXuMvoUapSSnJfnUuNuhxTGwSZI6xcAmSeoUA9uESfLUJN9Ncm2Sf0ty975tG5K8Msm5Sa5LckGSzbP2/90k30hyTZJLkpyYZJ9VP5COm+tzGlD2Hkl+luRDSTasZjvXg+nu4SRPTHJ2kquTfC7JTknunOTUJFe1ZX6rb79bJDkyyZb239O5SZ49zmPRaBjYJss+wDuA1wPPAHYATkqyTbv9PcDRwCeA3wFeCmw7vXOSZwKfBr4PHAI8BzgXuN0qtX+9mO9z6klyb+A04ATg2VV10yq2cz3ZG3gd8Gqa6bIeTHOt2sfa5ek0k75/LEnaff6+Lb8JeCLwGeDYJL+zuk3XqDm7/2TZBXhKVX0VIMl/0ASpw5OcBjwXeHFV/V3fPh9vy94CeBPwmao6rG/7CavR8HVm6OcEvHu6UJIHAF8APkTzuXnR6MrZCXhQVX0foM3M/pzmx8QH23UBPgfsl+QG4I+B51TVB9o6Tk6yO/Ba4F9W+wA0OmZsk+Wi6S9LgKr6IfAfwP2Bg9rVm4fsezfgDsD7V7KBAub+nKY9BPgSsKmq/tSgtuLOnw5qrS3t4ykD1u0BHAxMAZ9JstX0AnwZuJddxmubGdtkuWjIut2BnYGrquqKIfvu3D7+dCUappuZ63Oa9hiaf18fXJUW6ZezXl8/YP30um1osu4NwOVD6tsduGBkrdOqMrBNll2HrDsLuATYLsnGIcHtkvZx9wHbNFpzfU7T3gA8CvhikodV1Xmr0jIt1KXAjTSZ9dSA7YN+vGiNsCtysuya5MHTL5LsDdwH+AYzXSrPGrLvOcCFgKO6Vt5cn9O0G2gGLJwLfDnJHqvbRM3jFJqMbYeqOnPAcv18FWhymbFNlouBDyd5NXANzQjIi4DNVXVtkk3A25PsCnwFuC3w9Ko6tKqmkrwcOC7JccBHae7y/kjgo1XlrCajM/Rz6i9UVdckeRJwMs3AhIdX1S9Wu7H6dVV1TpJ304ySfAtwJk0X5W8Cd62q5421gVoWA9tk+SHw1zSjG/eh+cf2jKq6tt3+v9oyzwOOpPky/eL0zlX1kSTXAq8CPgVcBfw74JfpaM33OfVU1ZVJHg+cSnNJwEGzy2hsXkiTUT+f5lKBK4CzgX8cZ6O0fN62RpLUKZ5jkyR1ioFNktQpBjZJUqcY2CRJnWJgkyR1ioFNktQpBjZpkZKclqSSHDVg2/nttsNXv2Urqz2uSnLguNsizcXAplWX5Ki+L8n+5dr25qknJDmk775Z61aSfdv366hxt0VaK5x5ROP2877nO9DcUmQP4Ek096H73aq6biwtW5rvA9cyfNb4xdqX5v5gAEeNqE6p08zYNFZVtdv0AmwH3IPmPmYAj6eZJX/NqKqDq2q/qvrMuNsirVcGNk2MqpqqqrOAJzNzU8gXtDeAlKQFMbBp4rSTCX+yfbk9sF97rmn6XNy+SX4jyaYkP0hyXZLz++tIcosk/1+SE5P8PMn1SX6R5ItJDpvr/F2SDUn+JMl/JrkqyaXtgJGnz9f2hQweSfKAJO9PsiXJ1UmuSHJ2kmOTPLa/LprJk6dfzz4nuXlA3dsnOTLJ19p2X5fkx0k+luRB87R9xyRvTfL99nznT5N8Msl95ztuaZL4S1iTqv/uxRuBK/tePxh4D3Ab4Gqae5/1JNkJ+Azw8L7Vl9PcNfnR7XJokt+ffd+tJLcC/gmYDjBTNHdefjjwiCRvXuoBJdkAvAP4077VV9Hc8HI/YH/g92huRwTNXRk2Aju2r/vPR04fU3/99wL+GdizXXUTzfuzJ/AHwCFJXlVVbxzQtn2B02juVgDNMW9Lc0+5Jyf5/QUfqDRmZmyaVPv2Pb901rb30Nyt+n5VtV1V3QZ4DPSCx6dpAtG3aAahbFdVt6UJhM+mud3Pk4FBQeqNNEGtgFcDO1bVjsBuwLuAVwD3WuIx/TUzQe1Y4G5VdZuq2okmeD0V+MJ04aq6H02gm36926zlxdPbkuwOnEQTxD4NHADcuqo2ArcHXk8T6P46yVP7G9W+Z5+kCWqXAYfQvGc70Nyf7OvAB5Z4zNLqqyoXl1VdaEb3VfO/38DtG2nuBl7AJTQ/wPad3gc4H7jNkH2f2Zb5Ds3dkQeVuS9NJnYdsGvf+jvQZH8FvG7Ivh/pa8dRA7af3247fNb6u9IElgLevIj36sC53qu+cv/YljtujjJ/1pb51qz1h/Qd08ED9tuW5pzndJkDx/3/kIvLXIsZmyZGktsmORg4hSbIAPxtVU3NKvrOqrqSwZ7bPr6rqgYOua+q/6DJ+LYG+m/8+XSa7vlrgLcNqf+oOQ9iuGfTBOhLmBm+PxJJtgGe0b6cq6v0g+3jbye5fd/6Q9vH06vqy7N3qqqrgbcsu6HSKvEcm8YqyVx3uv0w8FcD1p8+pK4NwAPbl0cl+Ys56t6pfdynb90B7eOZVXXFoJ2q6twkF9Jca7cYD24fv1QD7rS9TPcFtmmff3GB17Xvw8w5u+njPmWO8nNtkyaKgU3j1j8g4jrgYuCbNF1qpw7ehYuGrN8JuFX7fMchZWbbtu/5ru3jhfPscwGLD2y7tY8/XOR+C3GHvue3H1rq5hZ73BfMsU2aKAY2jVU1F2Yv1k1D1m/oe/74qvrCkHLjMFdmulz9x33rFcgIpTXFc2zqkktohs7DzbsYF2o6E5wvG1tstgbws/ZxKe1aaN1LrX8hx72UY5bGwsCmzqiqG4BvtC+ftIQqzmwfD0hym0EFktyFmevEFuOr7eOj28EeC9UbODPHReVn0Fx3Bss77oPmKPPIJdQrjYWBTV2zqX18QpInzFWwvZC73/E03Zy3Bl42ZLfXLLFdm9u6dwaOXsR+/YNYbjuoQFVdRXMZAsArkuw9V4UDjvvj7eNDB92SJsmtgT9fUGulCWBgU9d8GDgZCPCZJK9O0htckWS7JAclOQY4r3/HqroQOKZ9+ZdJXplk+3a/2yV5J/CHLGHm/qraAry1ffnyJO9rs7/pdm1M8gdJZk+efC4z2djz5sja/gL4Cc3sKl9L8szptve1/2lt/R+dte/xwH9OP2/LbWj32x/4PHC7RR2wNE7jvpDOZf0tzHOB9pB99mXmAuF95ym7kWZqqepbLqeZVWOqb90NA/bdhubuAtNlbqSZ+WR6vzfRTD21qAu0220bgHfOatevZtX/ywH7va+v/FU0IyvPB942q9z+wDl9ZW+iOe945ay/+aUBf+NOwI/6ylwL/LJ9fh3NTC1eoO2yJhYzNnVOVV1RVU8CnkDTzfYjmssAtqUZ0v5F4JXA3Qbsey3N7XJeTDMl1/U02d//BQ6pqiOX0a6bqupFwEOB49p23bKt/2ya2UOeNmDXF9L8GPif9vXeNINEdplV/3eA3wJe0B7jxTRBPjQzh3wSOIJmppHZbTuPZqqwdwA/aPe5FvgU8OCqOmFpRy2tvlSt5ChkSZJWlxmbJKlTDGySpE4xsEmSOsXAJknqFAObJKlTDGySpE4xsEmSOsXAJknqFAObJKlTDGySpE75f2GXqgzFFN5iAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig,ax= plt.subplots(figsize=(7,7))\n",
        "sns.heatmap(cm / (cm.astype(np.float).sum(axis=1) + 1e-9), annot=False, ax=ax)\n",
        "\n",
        "# labels, title and ticks\n",
        "ax.set_xlabel('Predicted', size=25);\n",
        "ax.set_ylabel('True', size=25); \n",
        "ax.set_title('Confusion Matrix', size=25); \n",
        "ax.xaxis.set_ticklabels(['bcc','bkl','mel'], size=15); \n",
        "ax.yaxis.set_ticklabels(['bcc','bkl','mel'], size=15);\n",
        "print(correct/total)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Challenge 2 - Final Code.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}