Project_binary_classification_2022.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GishB/Deep-Learning-project/blob/main/Project_binary_classification_2022.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J71v_8JjTDF2"
      },
      "source": [
        "# Import all important libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "CKwe5Ka5TMtY"
      },
      "outputs": [],
      "source": [
        "#Import all libraries which is required for project tasks\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torch.utils.data as data\n",
        "from torch.utils.data import Dataset\n",
        "from torch.types import Device\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "import PIL\n",
        "from PIL import Image\n",
        "import torchvision\n",
        "from torchvision import datasets, transforms, utils\n",
        "from torchvision.datasets import ImageFolder\n",
        "\n",
        "\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcjwexHy4QUt"
      },
      "source": [
        "# Augumentation NORMAL chest X-rays. Increase number of NORMAL chest X-rays images from 1500 to 4622 pictures. Run just once and save in directory!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "esU72QZi4Vko"
      },
      "outputs": [],
      "source": [
        "# path_to_folder = '/Users/peterbkh/miniconda3/deep-learning-liege/NORMAL'\n",
        "# new_folder = 'test'\n",
        "\n",
        "\n",
        "\n",
        "# for i in os.listdir(path_to_folder):\n",
        "#   img_path = path_to_folder + '/' + i\n",
        "#   print(i)\n",
        "#   im = Image.open(img_path)\n",
        "#   im = im.convert(\"RGB\")\n",
        "\n",
        "#   vertical_img = im.transpose(method=Image.FLIP_TOP_BOTTOM)\n",
        "#   vertical_img.save(new_folder + \"/vertical_\" + i)\n",
        "\n",
        "#   hor_img = im.transpose(method=Image.FLIP_LEFT_RIGHT)\n",
        "#   hor_img.save(new_folder + \"/horisontal_\" + i)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRBh6_FoUnaE"
      },
      "source": [
        "# Import X-ray chest images from google drive directory. Here we have 4622 NORMAL and 4273 PNEUMONIA images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CIEtAaSwUvVm"
      },
      "outputs": [],
      "source": [
        "root_dir = '/content/drive/MyDrive/DATASET_FINAL/images' #root which contents all images in RGB\n",
        "csv_dir = '/content/drive/MyDrive/DATASET_FINAL/x_ray_new.csv' #root to csv file which content in 0 column images name and in 1 column class"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7k3ioCGbjqD-"
      },
      "source": [
        "# Dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2Qh9MnxpjtYQ"
      },
      "outputs": [],
      "source": [
        "class XRayChestDataset(Dataset):\n",
        "  def __init__(self, csv_file, root_dir, transform=None):\n",
        "    self.annotations = pd.read_csv(csv_file)\n",
        "    self.root_dir = root_dir\n",
        "    self.transform = transform\n",
        "  \n",
        "  def __len__(self):\n",
        "    return len(self.annotations)\n",
        "\n",
        "  def __getitem__(self, index):\n",
        "    img_path = os.path.join(self.root_dir, self.annotations.iloc[index, 0])\n",
        " #   image = io.imread(img_path)\n",
        "    image = Image.open(img_path).convert('RGB')\n",
        "    y_label = torch.tensor(int(self.annotations.iloc[index, 1]))\n",
        "\n",
        "    if self.transform:\n",
        "      image = self.transform(image)\n",
        "\n",
        "    return (image, y_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "nBUxWLx2zIm4"
      },
      "outputs": [],
      "source": [
        "#transform rule for data\n",
        "preprocess = transforms.Compose([\n",
        "  #do size 64 to 64 to make computations faster for computer\n",
        "  transforms.Resize(size=(64, 64)),\n",
        "  #do transform.ToTensor for dataset in pytorch\n",
        "  transforms.ToTensor(),\n",
        "  transforms.RandomRotation(degrees = 5),\n",
        "  transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
        "                                 std=[0.229, 0.224, 0.225])\n",
        "  ])\n",
        "\n",
        "#Dataset which we are gonna working with\n",
        "dataset = XRayChestDataset(csv_file = csv_dir, root_dir = root_dir, transform = preprocess)\n",
        "\n",
        "#split data to 70/20/10 sets\n",
        "number_of_images = len(dataset)\n",
        "\n",
        "number_of_images_train = int(number_of_images*0.7)\n",
        "number_of_images_test = int(number_of_images*0.2)\n",
        "number_of_images_valid = number_of_images - number_of_images_train - number_of_images_test\n",
        "\n",
        "train_set, test_set, valid_set = torch.utils.data.random_split(dataset, [number_of_images_train,\n",
        "                                                                         number_of_images_test,\n",
        "                                                                         number_of_images_valid])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "-1inPpU-0LCG"
      },
      "outputs": [],
      "source": [
        "#hint: DO NOT USE JUPYNER NOTEBOOK! IT DOSENT SUPPORT multiprocess. There is a lag when num_workers > 0!\n",
        "train_loader = data.DataLoader(dataset=train_set, batch_size=32, shuffle=True, num_workers=2)\n",
        "test_loader = data.DataLoader(dataset=test_set, batch_size=32, shuffle=False, num_workers=2)\n",
        "valid_loader = data.DataLoader(dataset=valid_set, batch_size=1, shuffle=False, num_workers=2) \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "kTS07oaRZn_I",
        "outputId": "76230fdd-7ab2-4389-d098-ad44689f3504"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NORMAL PNEUMONIA NORMAL PNEUMONIA PNEUMONIA PNEUMONIA NORMAL PNEUMONIA NORMAL NORMAL NORMAL NORMAL NORMAL NORMAL PNEUMONIA NORMAL NORMAL PNEUMONIA PNEUMONIA PNEUMONIA NORMAL PNEUMONIA PNEUMONIA PNEUMONIA PNEUMONIA PNEUMONIA PNEUMONIA NORMAL NORMAL NORMAL PNEUMONIA PNEUMONIA\n"
          ]
        }
      ],
      "source": [
        "classes = ('NORMAL', 'PNEUMONIA')\n",
        "\n",
        "def show_images(img):\n",
        "    plt.imshow(transforms.functional.to_pil_image(img))\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "images, labels = next(iter(train_loader))\n",
        "show_images(utils.make_grid(images))  # should look weird due to normalization\n",
        "print(*[classes[l] for l in labels])\n",
        "\n",
        "#Should working about 1 minute before it shows images and labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZGrtBdmaL_P"
      },
      "source": [
        "# Import models"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#All models are based on PyTorch documentation and https://github.com/aladdinpersson/Machine-Learning-Collection.git\n",
        "class MLP(nn.Module): \n",
        "    def __init__(self): \n",
        "        super().__init__()\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.relu = nn.ReLU()\n",
        "        self.fc1 = nn.Linear(12288, 4096) \n",
        "        self.fc2 = nn.Linear(4096, 2048)\n",
        "        self.fc3 = nn.Linear(2048, 1024)\n",
        "        self.fc4 = nn.Linear(1024,2)\n",
        "        \n",
        "    def forward(self, x): \n",
        "        x = self.fc1(self.flatten(x))\n",
        "        x = self.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc3(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc4(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "r8gjGymvm7x1"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CNN(nn.Module): \n",
        "    def __init__(self): \n",
        "        super().__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 6, 3)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))\n",
        "        self.conv2 = nn.Conv2d(6,16,3) \n",
        "        self.fc1 = nn.Linear(3136, 1024) \n",
        "        self.fc2 = nn.Linear(1024, 248)\n",
        "        self.drop = nn.Dropout(0.5)\n",
        "        self.fc3 = nn.Linear(248, 32)\n",
        "        self.fc4 = nn.Linear(32,2)\n",
        "        \n",
        "    def forward(self, x): \n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        x = x.reshape(x.shape[0], -1)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.drop(x)\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = self.drop(x)\n",
        "        x = F.relu(self.fc3(x))\n",
        "        x = self.drop(x)\n",
        "        x = self.fc4(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "ToEgcLVOnBfk"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "C9USjvHw-_yB"
      },
      "outputs": [],
      "source": [
        "class VGG16(nn.Module): \n",
        "    def __init__(self): \n",
        "        super().__init__()\n",
        "        self.conv2 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3,3), padding=(1,1))\n",
        "        self.conv2_0 = nn.Conv2d(in_channels=64, out_channels =64, kernel_size=(3,3), padding=(1,1))\n",
        "        self.relu = nn.ReLU()\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size= (2,2), stride = (2,2))\n",
        "        self.conv2_1 = nn.Conv2d(64,128, kernel_size=(3,3), padding=(1,1))\n",
        "        self.conv2_1_0 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=(1,1))\n",
        "        self.conv2_2 = nn.Conv2d(128,256, kernel_size=(3,3), padding=(1,1))\n",
        "        self.conv2_2_0 = nn.Conv2d(256,256, kernel_size=(3,3), padding=(1,1))\n",
        "        self.conv2_3 = nn.Conv2d(256,512, kernel_size=(3,3), padding=(1,1))\n",
        "        self.conv2_3_0 = nn.Conv2d(512,512, kernel_size= (3,3), padding=(1,1))\n",
        "        self.fcs = nn.Sequential(\n",
        "            nn.Linear(2048, 1024),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(1024, 1024),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(1024, 2)\n",
        "        )\n",
        "        \n",
        "    def forward(self, x): \n",
        "        x = self.relu(self.conv2(x))\n",
        "        x = self.maxpool(self.relu(self.conv2_0(x)))\n",
        "        x = self.relu(self.conv2_1(x))\n",
        "        x = self.maxpool(self.relu(self.conv2_1_0(x)))\n",
        "        x = self.relu(self.conv2_2(x))\n",
        "        x = self.relu(self.conv2_2_0(x))\n",
        "        x = self.maxpool(self.relu(self.conv2_2_0(x)))\n",
        "        x = self.relu(self.conv2_3(x))\n",
        "        x = self.relu(self.conv2_3_0(x))\n",
        "        x = self.maxpool(self.relu(self.conv2_3_0(x)))\n",
        "        x = self.relu(self.conv2_3_0(x))\n",
        "        x = self.relu(self.conv2_3_0(x))\n",
        "        x = self.maxpool(self.relu(self.conv2_3_0(x)))\n",
        "        x = x.reshape(x.shape[0], -1)\n",
        "        x = self.fcs(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class block(nn.Module):\n",
        "    def __init__(\n",
        "        self, in_channels, intermediate_channels, identity_downsample=None, stride=1\n",
        "    ):\n",
        "        super(block, self).__init__()\n",
        "        self.expansion = 4\n",
        "        self.conv1 = nn.Conv2d(\n",
        "            in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0, bias=False\n",
        "        )\n",
        "        self.bn1 = nn.BatchNorm2d(intermediate_channels)\n",
        "        self.conv2 = nn.Conv2d(\n",
        "            intermediate_channels,\n",
        "            intermediate_channels,\n",
        "            kernel_size=3,\n",
        "            stride=stride,\n",
        "            padding=1,\n",
        "            bias=False\n",
        "        )\n",
        "        self.bn2 = nn.BatchNorm2d(intermediate_channels)\n",
        "        self.conv3 = nn.Conv2d(\n",
        "            intermediate_channels,\n",
        "            intermediate_channels * self.expansion,\n",
        "            kernel_size=1,\n",
        "            stride=1,\n",
        "            padding=0,\n",
        "            bias=False\n",
        "        )\n",
        "        self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.identity_downsample = identity_downsample\n",
        "        self.stride = stride\n",
        "\n",
        "    def forward(self, x):\n",
        "        identity = x.clone()\n",
        "\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.bn2(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.conv3(x)\n",
        "        x = self.bn3(x)\n",
        "\n",
        "        if self.identity_downsample is not None:\n",
        "            identity = self.identity_downsample(identity)\n",
        "\n",
        "        x += identity\n",
        "        x = self.relu(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class ResNet(nn.Module):\n",
        "    def __init__(self, block, layers, image_channels, num_classes):\n",
        "        super(ResNet, self).__init__()\n",
        "        self.in_channels = 64\n",
        "        self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
        "        self.bn1 = nn.BatchNorm2d(64)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "\n",
        "        # Essentially the entire ResNet architecture are in these 4 lines below\n",
        "        self.layer1 = self._make_layer(\n",
        "            block, layers[0], intermediate_channels=64, stride=1\n",
        "        )\n",
        "        self.layer2 = self._make_layer(\n",
        "            block, layers[1], intermediate_channels=128, stride=2\n",
        "        )\n",
        "        self.layer3 = self._make_layer(\n",
        "            block, layers[2], intermediate_channels=256, stride=2\n",
        "        )\n",
        "        self.layer4 = self._make_layer(\n",
        "            block, layers[3], intermediate_channels=512, stride=2\n",
        "        )\n",
        "\n",
        "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
        "        self.fc = nn.Linear(512 * 4, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.bn1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.maxpool(x)\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = self.layer4(x)\n",
        "\n",
        "        x = self.avgpool(x)\n",
        "        x = x.reshape(x.shape[0], -1)\n",
        "        x = self.fc(x)\n",
        "\n",
        "        return x\n",
        "\n",
        "    def _make_layer(self, block, num_residual_blocks, intermediate_channels, stride):\n",
        "        identity_downsample = None\n",
        "        layers = []\n",
        "\n",
        "        # Either if we half the input space for ex, 56x56 -> 28x28 (stride=2), or channels changes\n",
        "        # we need to adapt the Identity (skip connection) so it will be able to be added\n",
        "        # to the layer that's ahead\n",
        "        if stride != 1 or self.in_channels != intermediate_channels * 4:\n",
        "            identity_downsample = nn.Sequential(\n",
        "                nn.Conv2d(\n",
        "                    self.in_channels,\n",
        "                    intermediate_channels * 4,\n",
        "                    kernel_size=1,\n",
        "                    stride=stride,\n",
        "                    bias=False\n",
        "                ),\n",
        "                nn.BatchNorm2d(intermediate_channels * 4),\n",
        "            )\n",
        "\n",
        "        layers.append(\n",
        "            block(self.in_channels, intermediate_channels, identity_downsample, stride)\n",
        "        )\n",
        "\n",
        "        # The expansion size is always 4 for ResNet 50,101,152\n",
        "        self.in_channels = intermediate_channels * 4\n",
        "\n",
        "        # For example for first resnet layer: 256 will be mapped to 64 as intermediate layer,\n",
        "        # then finally back to 256. Hence no identity downsample is needed, since stride = 1,\n",
        "        # and also same amount of channels.\n",
        "        for i in range(num_residual_blocks - 1):\n",
        "            layers.append(block(self.in_channels, intermediate_channels))\n",
        "\n",
        "        return nn.Sequential(*layers)\n",
        "\n",
        "\n",
        "def ResNet50(img_channel=3, num_classes=2):\n",
        "    return ResNet(block, [3, 4, 6, 3], img_channel, num_classes)"
      ],
      "metadata": {
        "id": "m7wFCgQ-mwRy"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GoogLeNet(nn.Module): \n",
        "    def __init__(self, in_channels = 3 , num_classes=2): \n",
        "        super(GoogLeNet, self).__init__()\n",
        "\n",
        "        self.conv1 = conv_block(in_channels=in_channels, out_channels=64, kernel_size=(7,7), stride=(2,2), padding=(3,3))\n",
        "        self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "        self.conv2 = conv_block(64, 192, kernel_size=3, stride=1, padding=1)\n",
        "        self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "\n",
        "        self.inception3a = Inception_block(192, 64, 96, 128, 16, 32, 32)\n",
        "        self.inception3b = Inception_block(256, 128, 128, 192, 32, 96, 64)\n",
        "        self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "\n",
        "        self.inception4a = Inception_block(480, 192, 96, 208, 16, 48, 64)\n",
        "        self.inception4b = Inception_block(512, 160, 112, 224, 24, 64, 64)\n",
        "        self.inception4c = Inception_block(512, 128, 128, 256, 24, 64, 64)\n",
        "        self.inception4d = Inception_block(512, 112, 144, 288, 32, 64, 64)\n",
        "        self.inception4e = Inception_block(528, 256, 160, 320, 32, 128, 128)\n",
        "        self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
        "        self.inception5a = Inception_block(832, 256, 160, 320, 32, 128, 128)\n",
        "        self.inception5b = Inception_block(832, 384, 192, 384, 48, 128, 128)\n",
        "\n",
        "        self.avgpool = nn.AvgPool2d(kernel_size = 2, stride=1)\n",
        "        self.dropout = nn.Dropout(p=0.5)\n",
        "        self.fc1 = nn.Linear(1024, 2)\n",
        "\n",
        "    def forward(self, x): \n",
        "        x = self.conv1(x)\n",
        "        x = self.maxpool1(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.maxpool2(x)\n",
        "        \n",
        "        x = self.inception3a(x)\n",
        "        x = self.inception3b(x)\n",
        "        x = self.maxpool3(x)\n",
        "\n",
        "        x = self.inception4a(x)\n",
        "        x = self.inception4b(x)\n",
        "        x = self.inception4c(x)\n",
        "        x = self.inception4d(x)\n",
        "        x = self.inception4e(x)\n",
        "        x = self.maxpool4(x)\n",
        "\n",
        "        x = self.inception5a(x)\n",
        "        x = self.inception5b(x)\n",
        "        x = self.avgpool(x)\n",
        "        x = x.reshape(x.shape[0], -1)\n",
        "        x = self.dropout(x)\n",
        "        x = self.fc1(x)\n",
        "        return x\n",
        "\n",
        "\n",
        "class Inception_block(nn.Module):\n",
        "    def __init__(\n",
        "        self, in_channels, out_1x1, red_3x3, out_3x3, red_5x5, out_5x5, out_1x1pool\n",
        "    ):\n",
        "        super(Inception_block, self).__init__()\n",
        "        self.branch1 = conv_block(in_channels, out_1x1, kernel_size=1)\n",
        "\n",
        "        self.branch2 = nn.Sequential(\n",
        "            conv_block(in_channels, red_3x3, kernel_size=1),\n",
        "            conv_block(red_3x3, out_3x3, kernel_size=3, padding=1),\n",
        "        )\n",
        "\n",
        "        self.branch3 = nn.Sequential(\n",
        "            conv_block(in_channels, red_5x5, kernel_size=1),\n",
        "            conv_block(red_5x5, out_5x5, kernel_size=5, padding=2),\n",
        "        )\n",
        "\n",
        "        self.branch4 = nn.Sequential(\n",
        "            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),\n",
        "            conv_block(in_channels, out_1x1pool, kernel_size=1),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return torch.cat(\n",
        "            [self.branch1(x), self.branch2(x), self.branch3(x), self.branch4(x)], 1\n",
        "        )\n",
        "\n",
        "\n",
        "class conv_block(nn.Module):\n",
        "    def __init__(self, in_channels, out_channels, **kwargs):\n",
        "        super(conv_block, self).__init__()\n",
        "        self.relu = nn.ReLU()\n",
        "        self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)\n",
        "        self.batchnorm = nn.BatchNorm2d(out_channels)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.relu(self.batchnorm(self.conv(x)))"
      ],
      "metadata": {
        "id": "vWc5_7CUTtVk"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Call all models directly here >>\n",
        "model_MLP = MLP()\n",
        "model_CNN = CNN()\n",
        "model_VGG16 = VGG16()\n",
        "model_ResNet = ResNet50()\n",
        "model_GoogleNet = GoogLeNet()"
      ],
      "metadata": {
        "id": "n9ps3hgITrRp"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "tjnMUw-ZZqkv"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "model_MLP.to(device)\n",
        "model_CNN.to(device)\n",
        "model_VGG16.to(device)\n",
        "model_ResNet.to(device)\n",
        "model_GoogleNet.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model_MLP.parameters(), lr = 0.01)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train, test and validate functions for models"
      ],
      "metadata": {
        "id": "OAiCsCUTX8N-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(num_epochs, model_number): #anynetwork \n",
        "\n",
        "    train_avg_loss = []\n",
        "    test_avg_loss = []\n",
        "    test_accuracy = []\n",
        "\n",
        "    for i in range(num_epochs):\n",
        "        train_losses = []\n",
        "        test_losses = []\n",
        "        \n",
        "        for x, y in train_loader:\n",
        "            x = x.to(device) # your code !\n",
        "            y = y.to(device) # your code !\n",
        "            \n",
        "            if model_number == 0:\n",
        "              pred = model_MLP(x)\n",
        "            elif model_number == 1:\n",
        "              pred = model_CNN(x)\n",
        "            elif model_number == 2:\n",
        "              pred = model_VGG16(x)\n",
        "            elif model_number == 3:\n",
        "              pred = model_ResNet(x)\n",
        "            elif model_number == 4:\n",
        "              pred = model_GoogleNet(x)\n",
        "            else:\n",
        "              pred = model_MLP(x)\n",
        "\n",
        "            loss = criterion(pred, y)\n",
        "            train_losses.append(loss.detach())\n",
        "            \n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "        with torch.no_grad():   \n",
        "            correct = 0\n",
        "\n",
        "            for x, y in test_loader:\n",
        "                x = x.to(device) # your code !!\n",
        "                y = y.to(device) # your code !!\n",
        "                \n",
        "                if model_number == 0:\n",
        "                  pred = model_MLP(x)\n",
        "                elif model_number == 1:\n",
        "                  pred = model_CNN(x)\n",
        "                elif model_number == 2:\n",
        "                  pred = model_VGG16(x)\n",
        "                elif model_number == 3:\n",
        "                  pred = model_ResNet(x)\n",
        "                elif model_number == 4:\n",
        "                  pred = model_GoogleNet(x)\n",
        "                else:\n",
        "                  pred = model_MLP(x)\n",
        "\n",
        "                loss = criterion(pred, y)\n",
        "                test_losses.append(loss)\n",
        "                \n",
        "                y_pred = pred.argmax(dim=-1)\n",
        "                correct = correct + (y_pred == y).sum()\n",
        "\n",
        "            accuracy = correct / len(test_set)\n",
        "\n",
        "        \n",
        "        train_losses = torch.as_tensor(train_losses) #UPDATE5.1 (avoid copy), it is important  for .append\n",
        "        test_losses = torch.as_tensor(test_losses) #UPDATE5.1 (avoid copy), it is important for .append\n",
        "        \n",
        "        mean_test_loss = torch.mean(test_losses) #UPDATE5.2 TO CONTROL PRINT\n",
        "        mean_train_loss = torch.mean(train_losses) #UPDATE5.2 TO CONTROL PRINT\n",
        "        \n",
        "        train_avg_loss.append(mean_train_loss) # your code UPDATE5\n",
        "        test_avg_loss.append(mean_test_loss) # your code UPDATE5\n",
        "        test_accuracy.append(accuracy) # your code UPDATE5\n",
        "        \n",
        "        print(f'This is {i} epoch. \\n Test loss = {mean_test_loss}, Train loss = {mean_train_loss}, Accuracy = {accuracy}')\n",
        "    test_accuracy = torch.tensor(test_accuracy)\n",
        "    return train_avg_loss, test_avg_loss, test_accuracy  # UPDATE4"
      ],
      "metadata": {
        "id": "9RhwRrojancR"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def sigmoid_output_transform(output):\n",
        "  y_pred = torch.sigmoid(output)\n",
        "  return y_pred\n",
        "\n",
        "def roc_auc_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor) -> float:\n",
        "    y_true = y_targets.cpu().numpy()\n",
        "    y_pred = y_preds.cpu().numpy()\n",
        "    return roc_auc_score(y_true, y_pred)\n",
        "\n",
        "def roc_auc_curve_compute_fn(y_preds: torch.Tensor, y_targets: torch.Tensor):\n",
        "    y_true = y_targets.numpy()\n",
        "    y_pred = y_preds.numpy()\n",
        "    return roc_curve(y_true, y_pred)"
      ],
      "metadata": {
        "id": "-4cRVFtzav7V"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validate_model(valid_loader, model_number):\n",
        "\n",
        "  true_positive = 0\n",
        "  true_negative = 0\n",
        "  false_negative = 0\n",
        "  false_positive = 0\n",
        "\n",
        "\n",
        "  #two list objects keep results of prediciton (probability) and real data class\n",
        "  list_pred = []\n",
        "  list_data_1 = []\n",
        "  \n",
        "  for x, y in valid_loader:\n",
        "    x = x.to(device) # data to device - important\n",
        "    y = y.to(device) # data to device - important\n",
        "                    \n",
        "    if model_number == 0:\n",
        "        pred = model_MLP(x)\n",
        "    elif model_number == 1:\n",
        "        pred = model_CNN(x)\n",
        "    elif model_number == 2:\n",
        "        pred = model_VGG16(x)\n",
        "    elif model_number == 3:\n",
        "        pred = model_ResNet(x)\n",
        "    elif model_number == 4:\n",
        "        pred = model_GoogleNet(x)\n",
        "    else:\n",
        "        pred = model_MLP(x)\n",
        "    \n",
        "    y_pred = pred.argmax(dim=-1)\n",
        "    y_pred_test = sigmoid_output_transform(y_pred)\n",
        "    list_pred.append(y_pred_test)    \n",
        "    list_data_1.append(y)\n",
        "\n",
        "  #Logic if - else for TP, TF, FP, FN\n",
        "    if y_pred == y:\n",
        "      if y == 1:\n",
        "        true_positive = true_positive + (y == 1).sum()\n",
        "      elif y == 0:\n",
        "        true_negative = true_negative + (y == 0).sum()\n",
        "    elif y_pred != y:\n",
        "      if y == 1:\n",
        "        false_positive = false_positive + (y == 1).sum()\n",
        "      elif y == 0:\n",
        "        false_negative = false_negative + (y == 0).sum()\n",
        "\n",
        "  # list objects to torch.tensor\n",
        "  list_pred = torch.tensor(list_pred) #this is predicted classes (probability)\n",
        "  list_data_1 = torch.tensor(list_data_1) #this is original classes\n",
        "\n",
        "  #Compute ROC-AUC parameters with def from scipy\n",
        "  auc = roc_auc_compute_fn(list_pred, list_data_1)\n",
        "  curve_test = roc_auc_curve_compute_fn(list_pred, list_data_1)\n",
        "\n",
        "  precision = true_positive / ( true_positive + false_positive )\n",
        "  recall = true_positive / ( true_positive + false_negative )\n",
        "  F1_score = precision*recall*2/(precision + recall)\n",
        "\n",
        "  plt.plot(curve_test[0], curve_test[1], color='darkorange',label='ROC curve (area = %0.2f)' % auc)\n",
        "  plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
        "  plt.xlim([0.0, 1.0])\n",
        "  plt.ylim([0.0, 1.05])\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.ylabel('True Positive Rate')\n",
        "  plt.title('ROC/AUC curve')\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.show()\n",
        "  print(f'F1 = {F1_score}, Precision = {precision}, Recall = {recall}, True positive = {true_positive}, True negative = {true_negative}')\n",
        "  print(f'False positive = {false_positive}, False negative = {false_negative}')\n",
        "  return F1_score, precision, recall, true_positive, true_negative, false_positive, false_negative, auc, curve_test"
      ],
      "metadata": {
        "id": "VPzhKlY1a46m"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Running models"
      ],
      "metadata": {
        "id": "fjGX6sCsFjHe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "bp7IGQJEaZmT",
        "outputId": "f080e948-246d-4ea9-da60-e629ee27e0a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "######################################### \n",
            " Start training\n",
            "Working with MLP\n",
            "This is 0 epoch. \n",
            " Test loss = 1.9360555410385132, Train loss = 229.197998046875, Accuracy = 0.855064332485199\n",
            "This is 1 epoch. \n",
            " Test loss = 0.25107061862945557, Train loss = 0.23507995903491974, Accuracy = 0.9210968017578125\n",
            "This is 2 epoch. \n",
            " Test loss = 0.15560822188854218, Train loss = 0.19417043030261993, Accuracy = 0.952434241771698\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-2a94773063fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Working with {list_models[i]}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m   \u001b[0mtrain_avg_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_avg_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#train model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m   \u001b[0mvalidate_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#check accuracy, F1, TP, FP and etc.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-15-22dcf86746d4>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(num_epochs, model_number)\u001b[0m\n\u001b[1;32m     31\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m                 \u001b[0mprofile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Optimizer.step#{}.step\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m    142\u001b[0m                    \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m                    \u001b[0mweight_decay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weight_decay'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m                    eps=group['eps'])\n\u001b[0m\u001b[1;32m    145\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/_functional.py\u001b[0m in \u001b[0;36madam\u001b[0;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad, beta1, beta2, lr, weight_decay, eps)\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_exp_avg_sqs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias_correction2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m             \u001b[0mdenom\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexp_avg_sq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias_correction2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0mstep_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlr\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mbias_correction1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "#Train block\n",
        "\n",
        "list_models = ['MLP', 'CNN', 'VGG16', 'ResNet', 'GoogleNet'] #All models which is used in the project\n",
        "num_epochs = 10 #Chose random number of epochs - 50 is fine to see how a model works with dataset\n",
        "\n",
        "#Save model parameters to plot model results\n",
        "dict_models_params = {}\n",
        "\n",
        "for i in range(0,5):\n",
        "  print('######################################### \\n Start training')\n",
        "  print(f'Working with {list_models[i]}')\n",
        "\n",
        "  train_avg_loss, test_avg_loss, test_accuracy = train(num_epochs, i) #train model\n",
        "  validate_results = validate_model(valid_loader, i) #check accuracy, F1, TP, FP and etc.\n",
        "\n",
        "  #Save params\n",
        "  dict_models_params[list_models[i]] = train_avg_loss, test_avg_loss, test_accuracy, validate_results\n",
        "  \n",
        "  #Save trained model\n",
        "  if i == 0:\n",
        "    model = model_MLP\n",
        "    path = '/content/drive/MyDrive/DATASET_FINAL/' + list_models[i]\n",
        "  elif i == 1:\n",
        "    model = model_CNN\n",
        "    path = '/content/drive/MyDrive/DATASET_FINAL/' + list_models[i]\n",
        "  elif i == 2:\n",
        "    model = model_VGG16\n",
        "    path = '/content/drive/MyDrive/DATASET_FINAL/' + list_models[i]    \n",
        "  elif i == 3:\n",
        "    model = model_ResNet \n",
        "    path = '/content/drive/MyDrive/DATASET_FINAL/' + list_models[i]    \n",
        "  elif i == 4:\n",
        "    model = model_GoogleNet\n",
        "    path = '/content/drive/MyDrive/DATASET_FINAL/' + list_models[i] \n",
        "\n",
        "  print(\"Model's state_dict:\")\n",
        "  for param_tensor in model.state_dict():\n",
        "    print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())\n",
        "  torch.save(model.state_dict(), path)\n",
        "  print('Finish training \\n #########################################')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emRVHF5RW3ZG"
      },
      "source": [
        "# Plot models results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PLPY0M5r3W2t"
      },
      "outputs": [],
      "source": [
        "for i in range(0,5):\n",
        "  params_temp = dict_models_params[list_models[i]]\n",
        "  fig, ax = plt.subplots(2,1, figsize=(15,15))\n",
        "  ax[0].set_title('Training Loss')\n",
        "  ax[0].plot(params_temp[0], label='Train average loss' +' '+ list_models[i])\n",
        "  ax[0].plot(params_temp[1], label='Test average loss' +' '+ list_models[i])\n",
        "  ax[1].plot(params_temp[2], label='Test accuracy' +' '+ list_models[i])\n",
        "  ax[1].set_ylim([0, 1])\n",
        "  ax[0].legend()\n",
        "  ax[0].set_xlabel('Number epochs')\n",
        "  ax[0].set_ylabel('Loss (Cross Entropy)')\n",
        "  ax[1].legend()\n",
        "  ax[1].set_xlabel('Number epochs')\n",
        "  ax[1].set_ylabel('Accuracy percent')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Z-YWkVSeUoZr"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Project_binary_classification_2022",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Qrf5G0y6AMHBpTS7n21_1i5gzNJhteVO",
      "authorship_tag": "ABX9TyOAV22CTyWB9Omw9Oo9Y8il",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}