Resources/Session 8/Resnet.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcQs_8GpaHSb",
        "outputId": "d4a43f44-f4f9-4161-a9d5-59f948d91565"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YPFc3W9gwn9h"
      },
      "outputs": [],
      "source": [
        "# Set up CUDA in OS\n",
        "import os\n",
        "os.environ['CUDA_LAUNCH_BLOCKING'] = '1'\n",
        "# Import libabries\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "import seaborn as sn\n",
        "import pandas as pd\n",
        "import torchvision\n",
        "from torchvision import *\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torchvision.io import read_image\n",
        "import torchvision.transforms as T\n",
        "from torchvision import datasets, models, transforms\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import copy\n",
        "\n",
        "# Ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "uJAoDg4sRehE",
        "outputId": "413080ca-72c8-4cd5-82a3-47ebde507b53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J_RAhjs4RpvD"
      },
      "outputs": [],
      "source": [
        "transforms_train = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "    transforms.RandomResizedCrop(224),\n",
        "    transforms.RandomHorizontalFlip(), # data augmentation\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # normalization\n",
        "])\n",
        "transforms_test = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),\n",
        "     transforms.CenterCrop((224, 224)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mqkYqsWMdu71"
      },
      "outputs": [],
      "source": [
        "train_dir = \"/content/drive/MyDrive/Resnet Skin Cancer Dataset/train-20240702T094320Z-001/train\"\n",
        "test_dir = \"/content/drive/MyDrive/Resnet Skin Cancer Dataset/test-20240702T102548Z-001/test\"\n",
        "train_classa_dir = \"/content/drive/MyDrive/Resnet Skin Cancer Dataset/train-20240702T094320Z-001/train/benign\"\n",
        "train_classb_dir = \"/content/drive/MyDrive/Resnet Skin Cancer Dataset/train-20240702T094320Z-001/train/malignant\"\n",
        "test_classa_dir = \"/content/drive/MyDrive/Resnet Skin Cancer Dataset/test-20240702T102548Z-001/test/benign\"\n",
        "test_classb_dir = \"/content/drive/MyDrive/Resnet Skin Cancer Dataset/test-20240702T102548Z-001/test/malignant\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "wDCBjnq_RvxA"
      },
      "outputs": [],
      "source": [
        "train_dataset = datasets.ImageFolder(train_dir, transforms_train)\n",
        "test_dataset = datasets.ImageFolder(test_dir, transforms_test)\n",
        "\n",
        "train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=12, shuffle=True, num_workers=0)\n",
        "test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=12, shuffle=False, num_workers=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lz6BupNXR1sp",
        "outputId": "309ee11c-da86-4b0b-deeb-61edd03f743a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 2087\n",
            "Test dataset size: 660\n",
            "Class names: ['benign', 'malignant']\n"
          ]
        }
      ],
      "source": [
        "print('Train dataset size:', len(train_dataset))\n",
        "print('Test dataset size:', len(test_dataset))\n",
        "class_names = train_dataset.classes\n",
        "print('Class names:', class_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "ym18B412Vbwj",
        "outputId": "f74931f0-7770-4cd2-b920-f4e4e5624294"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.rcParams['figure.figsize'] = [12, 8]\n",
        "plt.rcParams['figure.dpi'] = 60\n",
        "plt.rcParams.update({'font.size': 20})\n",
        "def imshow(input, title):\n",
        "    # torch.Tensor => numpy\n",
        "    input = input.numpy().transpose((1, 2, 0))\n",
        "    # undo image normalization\n",
        "    mean = np.array([0.485, 0.456, 0.406])\n",
        "    std = np.array([0.229, 0.224, 0.225])\n",
        "    input = std * input + mean\n",
        "    input = np.clip(input, 0, 1)\n",
        "    # display images\n",
        "    plt.imshow(input)\n",
        "    plt.title(title)\n",
        "    plt.show()\n",
        "# load a batch of train image\n",
        "iterator = iter(train_dataloader)\n",
        "# visualize a batch of train image\n",
        "inputs, classes = next(iterator)\n",
        "out = torchvision.utils.make_grid(inputs[:4])\n",
        "imshow(out, title=[class_names[x] for x in classes[:4]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyZT7Ac0R9ga",
        "outputId": "91239d0b-47fa-4142-ee04-078d9086f3f1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\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",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "model = models.resnet18(pretrained=True)\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mR23nWH0R_wZ",
        "outputId": "93c6c41a-efa0-415e-fa02-7a8e302015dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of features from pre-trained model 512\n"
          ]
        }
      ],
      "source": [
        "num_features = model.fc.in_features\n",
        "print('Number of features from pre-trained model', num_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNG7uI83UAZO"
      },
      "outputs": [],
      "source": [
        "for param in model.parameters():\n",
        "    param.requires_grad = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AvwXM--NUTDQ"
      },
      "outputs": [],
      "source": [
        "model.fc = nn.Linear(num_features, 2)\n",
        "model = model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6uQGJr2eYt_",
        "outputId": "e5d7898b-ae3a-4dfa-bc2f-f78e448e374a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ResNet(\n",
              "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
              "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "  (relu): ReLU(inplace=True)\n",
              "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
              "  (layer1): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer2): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\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",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer3): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (layer4): Sequential(\n",
              "    (0): BasicBlock(\n",
              "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (downsample): Sequential(\n",
              "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
              "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      )\n",
              "    )\n",
              "    (1): BasicBlock(\n",
              "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "      (relu): ReLU(inplace=True)\n",
              "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "    )\n",
              "  )\n",
              "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
              "  (fc): Linear(in_features=512, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21EMyYybUZn0"
      },
      "outputs": [],
      "source": [
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hxkk4IYzUcGC"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(42)\n",
        "torch.cuda.manual_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8R6l5U-Ub9d",
        "outputId": "0101e0ab-c12f-41b6-ff63-424fbfe03954"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0 running\n",
            "[Train #1] Loss: 0.0476 Acc: 71.9693% Time: 16.4732s\n",
            "[Test #1] Loss: 0.0408 Acc: 74.2424% Time: 20.6285s\n",
            "Epoch 1 running\n",
            "[Train #2] Loss: 0.0414 Acc: 76.4255% Time: 37.5200s\n",
            "[Test #2] Loss: 0.0372 Acc: 78.0303% Time: 41.8437s\n",
            "Epoch 2 running\n",
            "[Train #3] Loss: 0.0387 Acc: 78.3421% Time: 58.2507s\n",
            "[Test #3] Loss: 0.0365 Acc: 75.6061% Time: 62.5667s\n",
            "Epoch 3 running\n",
            "[Train #4] Loss: 0.0385 Acc: 77.8630% Time: 79.6707s\n",
            "[Test #4] Loss: 0.0339 Acc: 81.2121% Time: 83.8412s\n",
            "Epoch 4 running\n",
            "[Train #5] Loss: 0.0382 Acc: 77.6234% Time: 100.0558s\n",
            "[Test #5] Loss: 0.0353 Acc: 76.6667% Time: 104.5546s\n",
            "Epoch 5 running\n",
            "[Train #6] Loss: 0.0373 Acc: 78.7734% Time: 121.1914s\n",
            "[Test #6] Loss: 0.0337 Acc: 79.2424% Time: 125.3117s\n",
            "Epoch 6 running\n",
            "[Train #7] Loss: 0.0360 Acc: 79.9713% Time: 141.7661s\n",
            "[Test #7] Loss: 0.0334 Acc: 79.5455% Time: 146.4830s\n",
            "Epoch 7 running\n",
            "[Train #8] Loss: 0.0355 Acc: 79.6838% Time: 162.9430s\n",
            "[Test #8] Loss: 0.0335 Acc: 78.1818% Time: 167.1281s\n",
            "Epoch 8 running\n",
            "[Train #9] Loss: 0.0353 Acc: 80.4504% Time: 184.6046s\n",
            "[Test #9] Loss: 0.0352 Acc: 76.0606% Time: 189.0355s\n",
            "Epoch 9 running\n",
            "[Train #10] Loss: 0.0363 Acc: 80.4504% Time: 205.5408s\n",
            "[Test #10] Loss: 0.0352 Acc: 76.8182% Time: 209.6658s\n",
            "Epoch 10 running\n",
            "[Train #11] Loss: 0.0353 Acc: 80.7858% Time: 226.6659s\n",
            "[Test #11] Loss: 0.0342 Acc: 78.4848% Time: 230.8115s\n",
            "Epoch 11 running\n",
            "[Train #12] Loss: 0.0351 Acc: 79.8275% Time: 247.3054s\n",
            "[Test #12] Loss: 0.0324 Acc: 80.7576% Time: 251.7208s\n",
            "Epoch 12 running\n",
            "[Train #13] Loss: 0.0350 Acc: 80.4983% Time: 268.4798s\n",
            "[Test #13] Loss: 0.0342 Acc: 78.3333% Time: 272.6322s\n",
            "Epoch 13 running\n",
            "[Train #14] Loss: 0.0351 Acc: 79.8754% Time: 288.8364s\n",
            "[Test #14] Loss: 0.0328 Acc: 80.4545% Time: 293.5443s\n",
            "Epoch 14 running\n",
            "[Train #15] Loss: 0.0344 Acc: 80.3067% Time: 309.9841s\n",
            "[Test #15] Loss: 0.0330 Acc: 78.6364% Time: 314.1076s\n",
            "Epoch 15 running\n",
            "[Train #16] Loss: 0.0359 Acc: 80.5462% Time: 330.7709s\n",
            "[Test #16] Loss: 0.0323 Acc: 81.0606% Time: 335.3166s\n",
            "Epoch 16 running\n",
            "[Train #17] Loss: 0.0344 Acc: 80.4025% Time: 351.7690s\n",
            "[Test #17] Loss: 0.0321 Acc: 81.9697% Time: 356.0742s\n",
            "Epoch 17 running\n",
            "[Train #18] Loss: 0.0350 Acc: 80.9296% Time: 373.8613s\n",
            "[Test #18] Loss: 0.0325 Acc: 79.0909% Time: 378.1869s\n",
            "Epoch 18 running\n",
            "[Train #19] Loss: 0.0345 Acc: 79.7796% Time: 395.2331s\n",
            "[Test #19] Loss: 0.0323 Acc: 82.4242% Time: 400.0344s\n",
            "Epoch 19 running\n",
            "[Train #20] Loss: 0.0353 Acc: 79.9713% Time: 418.1251s\n",
            "[Test #20] Loss: 0.0331 Acc: 78.3333% Time: 422.4191s\n",
            "Epoch 20 running\n",
            "[Train #21] Loss: 0.0365 Acc: 79.3963% Time: 439.4453s\n",
            "[Test #21] Loss: 0.0329 Acc: 79.0909% Time: 443.8887s\n",
            "Epoch 21 running\n",
            "[Train #22] Loss: 0.0345 Acc: 80.3546% Time: 460.2849s\n",
            "[Test #22] Loss: 0.0320 Acc: 80.9091% Time: 464.4366s\n",
            "Epoch 22 running\n",
            "[Train #23] Loss: 0.0336 Acc: 81.6004% Time: 481.6058s\n",
            "[Test #23] Loss: 0.0325 Acc: 79.6970% Time: 485.7651s\n",
            "Epoch 23 running\n",
            "[Train #24] Loss: 0.0327 Acc: 81.3129% Time: 502.2191s\n",
            "[Test #24] Loss: 0.0321 Acc: 81.5152% Time: 506.5891s\n",
            "Epoch 24 running\n",
            "[Train #25] Loss: 0.0327 Acc: 81.6004% Time: 523.4852s\n",
            "[Test #25] Loss: 0.0316 Acc: 82.4242% Time: 527.6254s\n",
            "Epoch 25 running\n",
            "[Train #26] Loss: 0.0346 Acc: 80.3067% Time: 544.0830s\n",
            "[Test #26] Loss: 0.0317 Acc: 82.2727% Time: 548.7696s\n",
            "Epoch 26 running\n",
            "[Train #27] Loss: 0.0340 Acc: 81.5525% Time: 565.4160s\n",
            "[Test #27] Loss: 0.0327 Acc: 78.3333% Time: 569.6426s\n",
            "Epoch 27 running\n",
            "[Train #28] Loss: 0.0325 Acc: 82.6545% Time: 586.1855s\n",
            "[Test #28] Loss: 0.0315 Acc: 81.2121% Time: 590.8293s\n",
            "Epoch 28 running\n",
            "[Train #29] Loss: 0.0332 Acc: 81.7920% Time: 607.3192s\n",
            "[Test #29] Loss: 0.0312 Acc: 82.2727% Time: 611.4115s\n",
            "Epoch 29 running\n",
            "[Train #30] Loss: 0.0343 Acc: 80.7858% Time: 628.5480s\n",
            "[Test #30] Loss: 0.0316 Acc: 82.4242% Time: 632.9830s\n"
          ]
        }
      ],
      "source": [
        "#### Train model\n",
        "train_loss=[]\n",
        "train_accuary=[]\n",
        "test_loss=[]\n",
        "test_accuary=[]\n",
        "\n",
        "num_epochs = 30   #(set no of epochs)\n",
        "start_time = time.time() #(for showing time)\n",
        "# Start loop\n",
        "for epoch in range(num_epochs): #(loop for every epoch)\n",
        "    print(\"Epoch {} running\".format(epoch)) #(printing message)\n",
        "    \"\"\" Training Phase \"\"\"\n",
        "    model.train()    #(training model)\n",
        "    running_loss = 0.   #(set loss 0)\n",
        "    running_corrects = 0\n",
        "    # load a batch data of images\n",
        "    for i, (inputs, labels) in enumerate(train_dataloader):\n",
        "        inputs = inputs.to(device)\n",
        "        labels = labels.to(device)\n",
        "        # forward inputs and get output\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(inputs)\n",
        "        _, preds = torch.max(outputs, 1)\n",
        "        loss = criterion(outputs, labels)\n",
        "        # get loss value and update the network weights\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        running_loss += loss.item()\n",
        "        running_corrects += torch.sum(preds == labels.data).item()\n",
        "    epoch_loss = running_loss / len(train_dataset)\n",
        "    epoch_acc = running_corrects / len(train_dataset) * 100.\n",
        "    # Append result\n",
        "    train_loss.append(epoch_loss)\n",
        "    train_accuary.append(epoch_acc)\n",
        "    # Print progress\n",
        "    print('[Train #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch+1, epoch_loss, epoch_acc, time.time() -start_time))\n",
        "    \"\"\" Testing Phase \"\"\"\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        running_loss = 0.\n",
        "        running_corrects = 0\n",
        "        for inputs, labels in test_dataloader:\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs)\n",
        "            _, preds = torch.max(outputs, 1)\n",
        "            loss = criterion(outputs, labels)\n",
        "            running_loss += loss.item()\n",
        "            running_corrects += torch.sum(preds == labels.data).item()\n",
        "        epoch_loss = running_loss / len(test_dataset)\n",
        "        epoch_acc = running_corrects / len(test_dataset) * 100.\n",
        "        # Append result\n",
        "        test_loss.append(epoch_loss)\n",
        "        test_accuary.append(epoch_acc)\n",
        "        # Print progress\n",
        "        print('[Test #{}] Loss: {:.4f} Acc: {:.4f}% Time: {:.4f}s'.format(epoch+1, epoch_loss, epoch_acc, time.time()- start_time))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bAZuJzvjVSia"
      },
      "outputs": [],
      "source": [
        "y_pred = []\n",
        "y_true = []\n",
        "\n",
        "num_epochs = 30   #(set no of epochs)\n",
        "start_time = time.time() #(for showing time)\n",
        "# Start loop\n",
        "for epoch in range(num_epochs): #(loop for every epoch)\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        for inputs, labels in test_dataloader:\n",
        "            inputs = inputs.to(device)\n",
        "            labels = labels.to(device)\n",
        "            outputs = model(inputs) # Feed Network\n",
        "            outputs = (torch.max(torch.exp(outputs), 1)[1]).data.cpu().numpy()\n",
        "            y_pred.extend(outputs) # Save Prediction\n",
        "            labels = labels.data.cpu().numpy()\n",
        "            y_true.extend(labels) # Save Truth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gsotnxo15r57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "563e3161-8004-4fba-e99e-09109a6d693e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot\n",
        "plt.figure(figsize=(6,6))\n",
        "plt.plot(np.arange(1,num_epochs+1), train_accuary,'-o')\n",
        "plt.plot(np.arange(1,num_epochs+1), test_accuary,'-o')\n",
        "plt.ylim(70,100)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Train','Test'])\n",
        "plt.title('Train vs Test Accuracy over time')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "2RXXbADGVVM9",
        "outputId": "137976e9-8573-4812-f0c8-fabde1ff23b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on Training set:  0.8242424242424242\n",
            "Confusion matrix: \n",
            " [[8670 2130]\n",
            " [1350 7650]]\n",
            "Classification report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.80      0.83     10800\n",
            "           1       0.78      0.85      0.81      9000\n",
            "\n",
            "    accuracy                           0.82     19800\n",
            "   macro avg       0.82      0.83      0.82     19800\n",
            "weighted avg       0.83      0.82      0.82     19800\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Confusion matrix for Skin Cancer classification '}>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 420x420 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "classes = test_dataset.classes\n",
        "# Build confusion matrix\n",
        "print(\"Accuracy on Training set: \",accuracy_score(y_true, y_pred))\n",
        "print('Confusion matrix: \\n', confusion_matrix(y_true, y_pred))\n",
        "print('Classification report: \\n', classification_report(y_true, y_pred))\n",
        "# Plot\n",
        "cf_matrix = confusion_matrix(y_true, y_pred)\n",
        "df_cm = pd.DataFrame(cf_matrix, index = [i for i in classes], columns = [i for i in classes])\n",
        "plt.figure(figsize = (7,7))\n",
        "plt.title(\"Confusion matrix for Skin Cancer classification \")\n",
        "sn.heatmap(df_cm, annot=True)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}