image_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "giufhIkbgTgH"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import random \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "from cvxopt import matrix, solvers\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics.pairwise import rbf_kernel\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms, models\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import *\n",
    "from tqdm import tqdm \n",
    "import warnings "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IXiFge9Hkjga",
    "outputId": "91777062-a0f4-4f30-af70-4867fbd66f60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6SuGE7Qe97pM"
   },
   "source": [
    "VGG Network "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yW_tHw56kj0b",
    "outputId": "589342a2-83e5-4f5c-eaf8-8e75262763dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************\n",
      "CUDA is found! Tranining on Tesla P100-PCIE-16GB.......\n"
     ]
    }
   ],
   "source": [
    "# check pytorch cuda and use cuda if possible\n",
    "device = torch.cuda.is_available()\n",
    "print('*' * 50)\n",
    "if torch.cuda.is_available():\n",
    "    print('CUDA is found! Tranining on %s.......'%torch.cuda.get_device_name(0))\n",
    "else:\n",
    "    warnings.warn('CUDA not found! Training may be slow......')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "wwP8va9Xnjvg"
   },
   "outputs": [],
   "source": [
    "path = '/content/drive/My Drive/Covid19-dataset'\n",
    "\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.Resize(size=(224, 224)),\n",
    "    transforms.RandomHorizontalFlip(),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "])\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.Resize(size=(224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "])\n",
    "\n",
    "train_data = datasets.ImageFolder(path + '/train', train_transform)\n",
    "test_data = datasets.ImageFolder(path + '/test', test_transform)\n",
    "\n",
    "classes = train_data.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "QZyr3EV257_c",
    "outputId": "45fe58b0-1cd0-4bc2-f0ef-cce0e8c940e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes: ['Covid', 'Normal', 'Viral Pneumonia'] \n",
      "Training samples: 251\n",
      "Counts: {'Covid': 111, 'Normal': 70, 'Viral Pneumonia': 70}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bU47cR/dV4HEZXN0SgO6t+D3AyUn2SjLG4FX+mq7LJcDpwIuAL0+zz6uA3+32dRyDt4zqqZtKuJSff0v8dR4afZ4GXN3jEE9kEGT3dvOux+2i/x6tqu4D1gMXsPNR+1T78tA1pt4wD2WtA05M8ssASQ5IsuOKiduTPCPJY4BXz3K/1zCYZlvczeGfyuDd+Ez9ErAtyWOZ2TTRnDHcR9S9Yr8aODaDj0LeAryfwVv9G4EbGLwAvKuq7uw2uwJ4MfCV7lr3U70POLrb12uA787zw3g0+RCDS8Hu8Gbg9CQ3Aq8D3jrqjrs/pF/HYF7508C/9qhzT3Exg78rzTTcPwC8v5tmmY/LntwK/DFwRXdOrwQO6lafyWD65OvAtlnud1u3/XoGv9ObqmrtLHbxJ8BGBj8T35rNsfvy8gOS1CBH7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNej/ANvfse3/rVyBAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes_count = { c:0 for c in classes}\n",
    "for _, label in train_data:\n",
    "    classes_count[classes[label]] +=1\n",
    "\n",
    "print(f'Classes: {classes} ')\n",
    "print(f'Training samples: {len(train_data)}')\n",
    "print(f'Counts: {classes_count}')\n",
    "plt.bar(classes_count.keys(), classes_count.values(), alpha=0.3 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "AQ_C2oPEtIsd",
    "outputId": "0345e158-6247-467d-f788-c927d7247ffc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes: ['Covid', 'Normal', 'Viral Pneumonia']\n",
      "Testing samples: 66\n",
      "Counts: {'Covid': 26, 'Normal': 20, 'Viral Pneumonia': 20}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6G/hj4Npun14HrO7uPpveqY7PAdsXuN7t3eOvp/d3enNVXbWAVfwJcAu9n4kvLWTbo/Ct9JLUKI/AJalRBlySGmXAJalRBlySGmXAJalRBlySGmXAJalR/w8P+2wqQw2VAwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes_count_test = { c:0 for c in classes}\n",
    "for _, label in test_data:\n",
    "    classes_count_test[classes[label]] +=1\n",
    "\n",
    "print(f'Classes: {classes}')\n",
    "print(f'Testing samples: {len(test_data)}')\n",
    "print(f'Counts: {classes_count_test}')\n",
    "plt.bar(classes_count_test.keys(), classes_count_test.values(), alpha=0.3 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7OCNt3Bc8i5c"
   },
   "outputs": [],
   "source": [
    "batch_size = 10\n",
    "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 144
    },
    "id": "lZ8ieCiRJqWd",
    "outputId": "12b05fc1-2ca7-4f40-c720-b75f81dbab26"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([10, 3, 224, 224])\n",
      "Samples from the training data: \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images, labels = next(iter(train_loader))\n",
    "print(images.shape)\n",
    "print(\"Samples from the training data: \")\n",
    "\n",
    "plt.figure(figsize=(8, 4))\n",
    "for i, image in enumerate(images):\n",
    "    plt.subplot(1, 6, i + 1, xticks=[], yticks=[])\n",
    "    image = image.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    image = image * std + mean\n",
    "    image = np.clip(image, 0., 1.)\n",
    "    plt.imshow(image)\n",
    "    plt.xlabel(classes[int(labels[i].numpy())])\n",
    "    if(i == 4):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1lohMsfJPLzy"
   },
   "outputs": [],
   "source": [
    "class VGGNet(nn.Module):\n",
    "    def __init__ (self):\n",
    "        super(VGGNet,self).__init__()\n",
    "\n",
    "        self.conv1_1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1, stride=1)\n",
    "\n",
    "        self.conv2_1 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1, stride=1)\n",
    "\n",
    "        self.conv3_1 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1, stride=1)\n",
    "\n",
    "        self.conv4_1 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1, stride=1)\n",
    "        self.conv4_2 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1)\n",
    "        self.conv4_3 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1, stride=1)\n",
    "\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "\n",
    "        self.fc1 = nn.Linear(4608, 1024)\n",
    "        self.fc2 = nn.Linear(1024, 1024)\n",
    "        self.fc3 = nn.Linear(1024, 3)\n",
    "\n",
    "    def forward(self , x):\n",
    "               \n",
    "        x = F.relu(self.conv1_1(x))\n",
    "        x = self.maxpool(x)\n",
    "        x = F.relu(self.conv2_1(x))\n",
    "        x = self.maxpool(x)\n",
    "        x = F.relu(self.conv3_1(x))\n",
    "        x = self.maxpool(x)\n",
    "        x = F.relu(self.conv4_1(x))\n",
    "        x = self.maxpool(x)\n",
    "        x = F.relu(self.conv4_2(x))\n",
    "        x = self.maxpool(x)\n",
    "        x = F.relu(self.conv4_3(x))\n",
    "        x = self.maxpool(x)\n",
    "        x = x.reshape(x.shape[0], -1)\n",
    "        \n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mANKb2a5KidS"
   },
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "model = VGGNet()\n",
    "model.to(device)\n",
    "\n",
    "error = nn.CrossEntropyLoss()\n",
    "learning_rate = 0.000008\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "num_epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U_9uE759GHjP",
    "outputId": "53b1447a-f9b9-4b04-b8ad-74f9cf9d9e2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VGGNet(\n",
      "  (conv1_1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv2_1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv3_1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv4_1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv4_2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv4_3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (fc1): Linear(in_features=4608, out_features=1024, bias=True)\n",
      "  (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
      "  (fc3): Linear(in_features=1024, out_features=3, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lTX9yb-sPiJp"
   },
   "outputs": [],
   "source": [
    "def train(model, loader):\n",
    "    \n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = error(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / (i + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2opSfc3WPk3c"
   },
   "outputs": [],
   "source": [
    "def evaluate(model, loader):\n",
    "    \n",
    "    model.eval()\n",
    "    epoch_loss = 0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    #with torch.no_grad():\n",
    "    for i, (images, labels) in enumerate(loader):\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "        outputs = model(images)\n",
    "        loss = error(outputs, labels)\n",
    "        epoch_loss += loss.data\n",
    "        predicted = torch.argmax(outputs, 1)\n",
    "        total += len(labels)\n",
    "        correct += (predicted == labels).sum()\n",
    "\n",
    "    accuracy = 100 * correct / float(total)\n",
    "\n",
    "    return epoch_loss / (i + 1), accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xKGp0Dm2SQll",
    "outputId": "d9730188-5456-490d-901e-8926bbf7d210"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 1/20 [00:15<04:51, 15.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1\n",
      "\tTrain Loss: 1.093\n",
      "\tTest Loss: 1.094\n",
      "\tTest accuracy: 39.3939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 10%|█         | 2/20 [00:30<04:35, 15.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2\n",
      "\tTrain Loss: 1.086\n",
      "\tTest Loss: 1.086\n",
      "\tTest accuracy: 39.3939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 15%|█▌        | 3/20 [00:45<04:19, 15.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3\n",
      "\tTrain Loss: 1.067\n",
      "\tTest Loss: 1.051\n",
      "\tTest accuracy: 51.5152\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 20%|██        | 4/20 [01:01<04:04, 15.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4\n",
      "\tTrain Loss: 0.939\n",
      "\tTest Loss: 0.819\n",
      "\tTest accuracy: 72.7273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 25%|██▌       | 5/20 [01:16<03:49, 15.30s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5\n",
      "\tTrain Loss: 0.553\n",
      "\tTest Loss: 0.586\n",
      "\tTest accuracy: 71.2121\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 30%|███       | 6/20 [01:31<03:33, 15.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 6\n",
      "\tTrain Loss: 0.353\n",
      "\tTest Loss: 0.597\n",
      "\tTest accuracy: 74.2424\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 35%|███▌      | 7/20 [01:46<03:18, 15.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7\n",
      "\tTrain Loss: 0.319\n",
      "\tTest Loss: 0.466\n",
      "\tTest accuracy: 78.7879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|████      | 8/20 [02:02<03:02, 15.23s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 8\n",
      "\tTrain Loss: 0.246\n",
      "\tTest Loss: 0.448\n",
      "\tTest accuracy: 81.8182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 45%|████▌     | 9/20 [02:17<02:47, 15.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 9\n",
      "\tTrain Loss: 0.241\n",
      "\tTest Loss: 0.491\n",
      "\tTest accuracy: 77.2727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 10/20 [02:32<02:32, 15.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10\n",
      "\tTrain Loss: 0.244\n",
      "\tTest Loss: 0.385\n",
      "\tTest accuracy: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 55%|█████▌    | 11/20 [02:47<02:16, 15.21s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 11\n",
      "\tTrain Loss: 0.254\n",
      "\tTest Loss: 0.455\n",
      "\tTest accuracy: 78.7879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|██████    | 12/20 [03:02<02:01, 15.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 12\n",
      "\tTrain Loss: 0.275\n",
      "\tTest Loss: 0.505\n",
      "\tTest accuracy: 78.7879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 65%|██████▌   | 13/20 [03:18<01:46, 15.24s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 13\n",
      "\tTrain Loss: 0.223\n",
      "\tTest Loss: 0.410\n",
      "\tTest accuracy: 84.8485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 70%|███████   | 14/20 [03:33<01:31, 15.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 14\n",
      "\tTrain Loss: 0.209\n",
      "\tTest Loss: 0.391\n",
      "\tTest accuracy: 84.8485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 75%|███████▌  | 15/20 [03:48<01:16, 15.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 15\n",
      "\tTrain Loss: 0.217\n",
      "\tTest Loss: 0.461\n",
      "\tTest accuracy: 89.3939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 80%|████████  | 16/20 [04:04<01:01, 15.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 16\n",
      "\tTrain Loss: 0.231\n",
      "\tTest Loss: 0.500\n",
      "\tTest accuracy: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 85%|████████▌ | 17/20 [04:19<00:45, 15.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 17\n",
      "\tTrain Loss: 0.166\n",
      "\tTest Loss: 0.378\n",
      "\tTest accuracy: 87.8788\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|█████████ | 18/20 [04:34<00:30, 15.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 18\n",
      "\tTrain Loss: 0.158\n",
      "\tTest Loss: 0.373\n",
      "\tTest accuracy: 83.3333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 95%|█████████▌| 19/20 [04:49<00:15, 15.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 19\n",
      "\tTrain Loss: 0.165\n",
      "\tTest Loss: 0.503\n",
      "\tTest accuracy: 81.8182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [05:05<00:00, 15.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20\n",
      "\tTrain Loss: 0.155\n",
      "\tTest Loss: 0.364\n",
      "\tTest accuracy: 86.3636\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "loss_train = []\n",
    "accuracy_list = []\n",
    "epoch_list = []\n",
    "\n",
    "for epoch in tqdm(range(num_epochs)):\n",
    "    \n",
    "    train_loss = train(model, train_loader)\n",
    "    test_loss, accuracy = evaluate(model, test_loader)\n",
    "    \n",
    "    loss_train.append(train_loss)\n",
    "    accuracy_list.append(accuracy)\n",
    "    epoch_list.append(epoch)\n",
    "\n",
    "    print(f'Epoch: {epoch + 1}')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f}')\n",
    "    print(f'\\tTest Loss: {test_loss:.3f}')\n",
    "    print(f'\\tTest accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "_-8Vug1EkkpB",
    "outputId": "f72a6921-f747-4db7-8bb4-9e0fd5579ab1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_list, loss_train)\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.ylabel(\"Train loss\")\n",
    "plt.title(\"Train Loss vs Number of epochs\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(epoch_list, accuracy_list, color = \"red\")\n",
    "plt.xlabel(\"Number of epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy vs Number of epochs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-CpmkI_jq16A"
   },
   "outputs": [],
   "source": [
    "predict_list = torch.tensor([]).to(device)\n",
    "test_labels_list = torch.tensor([]).to(device)\n",
    "\n",
    "for i, (images, labels) in enumerate(test_loader):\n",
    "    images, labels = images.to(device), labels.to(device)\n",
    "    outputs = model(images)\n",
    "    preds = torch.argmax(outputs, 1)\n",
    "\n",
    "    predict_list = torch.cat((predict_list, preds), dim=-1)\n",
    "    test_labels_list = torch.cat((test_labels_list, labels), dim=-1)\n",
    "\n",
    "predict_list = predict_list.cpu().numpy()\n",
    "test_labels_list = test_labels_list.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 463
    },
    "id": "IYDcoL9gy9pX",
    "outputId": "599fb9ac-95a9-4468-85a0-da862ad8cddd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.8636363636363636\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f53fc54e110>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Test accuracy: \", accuracy_score(predict_list, test_labels_list))\n",
    "\n",
    "cmt = confusion_matrix(test_labels_list, predict_list)\n",
    "df_cmt = pd.DataFrame(cmt, index = [i for i in train_data.classes], columns = [j for j in train_data.classes])\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(df_cmt, annot=True, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2CAbUlgM_J-X"
   },
   "source": [
    "SVM "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "JgjCj9iRzXHJ"
   },
   "outputs": [],
   "source": [
    "path_train = path + '/train'\n",
    "path_test = path + '/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "WotGOU54OntL"
   },
   "outputs": [],
   "source": [
    "training_data = []\n",
    "training_label = []\n",
    "\n",
    "testing_data = []\n",
    "testing_label = []\n",
    "img_size = 224\n",
    "\n",
    "for class_ in classes:\n",
    "    train_path = os.path.join(path_train, class_)\n",
    "    class_label = classes.index(class_)\n",
    "    for img in os.listdir(train_path):\n",
    "        img_array = cv2.imread(os.path.join(train_path, img))\n",
    "        new_array = cv2.resize(img_array,(img_size, img_size))\n",
    "        training_data.append(new_array)\n",
    "        training_label.append(class_label)\n",
    "\n",
    "for class_ in classes:\n",
    "    test_path = os.path.join(path_test, class_)\n",
    "    class_label = classes.index(class_)\n",
    "    for img in os.listdir(test_path):\n",
    "        img_array = cv2.imread(os.path.join(test_path, img))\n",
    "        new_array = cv2.resize(img_array,(img_size, img_size))\n",
    "        testing_data.append(new_array)\n",
    "        testing_label.append(class_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "f4UnYCABO-lI"
   },
   "outputs": [],
   "source": [
    "X_train = np.array(training_data).reshape(len(training_data), -1)\n",
    "y_train = np.array(training_label)\n",
    "\n",
    "X_test = np.array(testing_data).reshape(len(testing_data), -1)\n",
    "y_test = np.array(testing_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "VIk0egc-dLeV"
   },
   "outputs": [],
   "source": [
    "def train_svm(X, y, kernel, C = 10):\n",
    "\n",
    "    m, n = X.shape\n",
    "    y = y.reshape(-1,1) * 1.\n",
    "    if kernel == 'rbf':\n",
    "        K = rbf_kernel(X, gamma = 10)\n",
    "        H = y * K \n",
    "        H = H * y.T\n",
    "    else:\n",
    "        K = linear_kernel(X)\n",
    "        H = y * K \n",
    "        H = H * y.T\n",
    "    P = matrix(H)\n",
    "    q = matrix(-np.ones((m, 1)))\n",
    "    G = matrix(np.vstack((np.eye(m)*-1, np.eye(m))))\n",
    "    h = matrix(np.hstack((np.zeros(m), np.ones(m) * C)))\n",
    "    A = matrix(y.reshape(1, -1))\n",
    "    b = matrix(np.zeros(1))\n",
    "    \n",
    "    solvers.options['show_progress'] = False\n",
    "    #solvers.options['abstol'] = 1e-10\n",
    "    #solvers.options['reltol'] = 1e-10\n",
    "    #solvers.options['feastol'] = 1e-10\n",
    "    return solvers.qp(P, q, G, h, A, b)\n",
    "\n",
    "def get_parameters(X, y, alphas):\n",
    "\n",
    "    y = y.reshape(-1,1)\n",
    "    threshold = 1e-12 # Values greater than zero (some floating point tolerance)\n",
    "    w = ((y * alphas).T @ X).reshape(-1,1)\n",
    "    S = (alphas > threshold).flatten()\n",
    "    b = y[S] - np.dot(X[S], w)\n",
    "\n",
    "    return w, b[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "j0JHr_fLAFIu"
   },
   "outputs": [],
   "source": [
    "y_train_1 = []\n",
    "y_train_2 = []\n",
    "y_train_3 = []\n",
    "for i in range(y_train.shape[0]):\n",
    "    if y_train[i] == 0:\n",
    "        y_train_1.append(1)\n",
    "    else:\n",
    "        y_train_1.append(-1)\n",
    "y_train_1 = np.array(y_train_1)\n",
    "\n",
    "for i in range(y_train.shape[0]):\n",
    "    if y_train[i] == 1:\n",
    "        y_train_2.append(1)\n",
    "    else:\n",
    "        y_train_2.append(-1)\n",
    "y_train_2 = np.array(y_train_2)\n",
    "\n",
    "for i in range(y_train.shape[0]):\n",
    "    if y_train[i] == 2:\n",
    "        y_train_3.append(1)\n",
    "    else:\n",
    "        y_train_3.append(-1)\n",
    "y_train_3 = np.array(y_train_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "mS_Tx83vD5ma"
   },
   "outputs": [],
   "source": [
    "sol0 = train_svm(X_train, y_train_1, 'rb')\n",
    "alphas0 = np.array(sol0['x'])\n",
    "w0, b0 = get_parameters(X_train, y_train_1, alphas0)\n",
    "\n",
    "sol1 = train_svm(X_train, y_train_2, 'rb')\n",
    "alphas1 = np.array(sol1['x'])\n",
    "w1, b1 = get_parameters(X_train, y_train_2, alphas1)\n",
    "\n",
    "sol2 = train_svm(X_train, y_train_3, 'rb')\n",
    "alphas2 = np.array(sol2['x'])\n",
    "w2, b2 = get_parameters(X_train, y_train_3, alphas2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "hh5GkOUBESJh"
   },
   "outputs": [],
   "source": [
    "pred_test = []\n",
    "count = 0\n",
    "for data in X_test:\n",
    "    count += 1\n",
    "    data = data.reshape(1, -1)\n",
    "    res_0 = np.dot(data, w0) + b0\n",
    "    res_1 = np.dot(data, w1) + b1\n",
    "    res_2 = np.dot(data, w2) + b2\n",
    "\n",
    "    res_list = [res_0, res_1, res_2]\n",
    "    tmp = max(res_list)\n",
    "    index = res_list.index(tmp)\n",
    "    pred_test.append(index)\n",
    "\n",
    "pred_test = np.array(pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "id": "xxkcBOnnI36n",
    "outputId": "2167788c-fa81-44cc-a08b-d818cbb788b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.7272727272727273\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Test accuracy: \", accuracy_score(y_test, pred_test))\n",
    "\n",
    "mat = confusion_matrix(y_test, pred_test)\n",
    "df_mat = pd.DataFrame(mat, index = [i for i in train_data.classes], columns = [j for j in train_data.classes])\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(df_mat, annot=True, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "LCL6Ck_LJjeE"
   },
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from scipy import interp\n",
    "from itertools import cycle\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Learn to predict each class against the other\n",
    "classifier = OneVsRestClassifier(\n",
    "    svm.SVC(kernel=\"linear\", probability=True, random_state=0, C=10)\n",
    ")\n",
    "y_score = classifier.fit(X_train, y_train).decision_function(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "6ciEUmkxmjNE",
    "outputId": "1adcba8c-c6a0-4eb6-aee4-9f15905ff9f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/5k/9cnbg8c917l1k8mldgxq2cmm0000gn/T/ipykernel_3108/2761096043.py:19: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n",
      "  mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "\n",
    "class_labels = ['Covid', 'Normal', 'Viral Pneumonia']\n",
    "y_test_2 = label_binarize(y_test, classes=[0, 1, 2])\n",
    "n_classes = y_test_2.shape[1]\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test_2[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "\n",
    "# First aggregate all false positive rates\n",
    "all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n",
    "\n",
    "# Then interpolate all ROC curves at this points\n",
    "mean_tpr = np.zeros_like(all_fpr)\n",
    "for i in range(n_classes):\n",
    "    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n",
    "\n",
    "# Finally average it and compute AUC\n",
    "mean_tpr /= n_classes\n",
    "\n",
    "# Plot all ROC curves\n",
    "colors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\n",
    "for i, color in zip(range(n_classes), colors):\n",
    "    plt.plot(\n",
    "        fpr[i],\n",
    "        tpr[i],\n",
    "        color=color,\n",
    "        lw=2,\n",
    "        label=\"ROC curve of {0} class (area = {1:0.2f})\".format(class_labels[i], roc_auc[i]),\n",
    "    )\n",
    "\n",
    "plt.plot([0, 1], [0, 1], \"k--\", lw=2)\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 Curve of soft margin SVM for multiclass with linear kernel\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
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