CNN_400x.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00c42eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import datasets, transforms, models \n",
    "from torchvision.utils import make_grid#\n",
    "import torchvision\n",
    "import os\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6225f321",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_datasets():\n",
    "\n",
    "    transform = transforms.Compose([\n",
    "        transforms.Resize((256,256)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "    ])\n",
    "    \n",
    "    trainset = torchvision.datasets.ImageFolder(root='Breast_cancer_400/train', transform=transform)\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "    validset = torchvision.datasets.ImageFolder(root='Breast_cancer_400/valid', transform=transform)\n",
    "    validloader = torch.utils.data.DataLoader(validset, batch_size=32,\n",
    "                                         shuffle=True, num_workers=2)\n",
    "    testset = torchvision.datasets.ImageFolder(root='Breast_cancer_400/test', transform=transform)\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=32,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "    return trainloader,validloader, testloader\n",
    "\n",
    "classes = ('Adenosis', 'Ductal_Carcinoma', 'Fibroadenoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma', 'Phyllodes_tumor', 'Tubular_adenoma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8e613da",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainloader,validloader, testloader= build_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "572cc8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNN(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 96, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
       "    (1): ReLU(inplace=True)\n",
       "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "    (4): ReLU(inplace=True)\n",
       "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (6): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (7): ReLU(inplace=True)\n",
       "    (8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (9): ReLU(inplace=True)\n",
       "    (10): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace=True)\n",
       "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (maxpool): MaxPool2d(kernel_size=(6, 6), stride=(6, 6), padding=0, dilation=1, ceil_mode=False)\n",
       "  (classifier): Sequential(\n",
       "    (0): Dropout(p=0.5, inplace=False)\n",
       "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
       "    (2): ReLU(inplace=True)\n",
       "    (3): Dropout(p=0.5, inplace=False)\n",
       "    (4): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "    (5): ReLU(inplace=True)\n",
       "    (6): Linear(in_features=1024, out_features=8, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "# define the CNN architecture\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self, num_classes: int = 8, dropout: float = 0.5) -> None:\n",
    "        super().__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(96, 256, kernel_size=5, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(256, 384, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(384, 384, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "        )\n",
    "        self.maxpool = nn.MaxPool2d((6, 6))\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(256 * 6 * 6, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout(p=dropout),\n",
    "            nn.Linear(4096, 1024),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(1024, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        x = self.features(x)\n",
    "        x = self.maxpool(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "net = CNN()\n",
    "net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41b2d702",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "670f75e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_accuracy(y_pred, y):\n",
    "    top_pred = y_pred.argmax(1, keepdim = True)\n",
    "    correct = top_pred.eq(y.view_as(top_pred)).sum()\n",
    "    acc = correct.float() / y.shape[0]\n",
    "    return acc\n",
    "\n",
    "def train(model, iterator, optimizer, criterion, device):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    model.train()\n",
    "    for (x, y) in iterator:\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        y_pred = model(x)\n",
    "\n",
    "        loss = criterion(y_pred, y)\n",
    "\n",
    "        acc = calculate_accuracy(y_pred, y)\n",
    "\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc.item()\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)\n",
    "\n",
    "def valid(model,iterator):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "\n",
    "    model.eval()\n",
    "\n",
    "    for (x, y) in iterator:\n",
    "        x = x.to(device)\n",
    "        y = y.to(device)\n",
    "        y_pred = model(x)\n",
    "        loss = criterion(y_pred, y)\n",
    "        acc = calculate_accuracy(y_pred, y)\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        epoch_acc += acc.item()\n",
    "\n",
    "    return epoch_loss / len(iterator), epoch_acc / len(iterator)\n",
    "\n",
    "\n",
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d3ace52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Epoch Time: 1m 33s\n",
      "\tTrain Loss: 1.799 | Train Acc: 40.52%\n",
      "\tValid Loss: 1.816 | Valid Acc: 43.50%\n",
      "Epoch: 02 | Epoch Time: 1m 49s\n",
      "\tTrain Loss: 1.676 | Train Acc: 43.53%\n",
      "\tValid Loss: 1.849 | Valid Acc: 43.59%\n",
      "Epoch: 03 | Epoch Time: 1m 51s\n",
      "\tTrain Loss: 1.627 | Train Acc: 43.68%\n",
      "\tValid Loss: 1.819 | Valid Acc: 43.50%\n",
      "Epoch: 04 | Epoch Time: 1m 53s\n",
      "\tTrain Loss: 1.622 | Train Acc: 44.47%\n",
      "\tValid Loss: 1.810 | Valid Acc: 43.86%\n",
      "Epoch: 05 | Epoch Time: 1m 52s\n",
      "\tTrain Loss: 1.588 | Train Acc: 47.77%\n",
      "\tValid Loss: 1.919 | Valid Acc: 20.33%\n",
      "Epoch: 06 | Epoch Time: 1m 48s\n",
      "\tTrain Loss: 1.530 | Train Acc: 48.35%\n",
      "\tValid Loss: 1.743 | Valid Acc: 32.78%\n",
      "Epoch: 07 | Epoch Time: 1m 47s\n",
      "\tTrain Loss: 1.455 | Train Acc: 51.44%\n",
      "\tValid Loss: 1.653 | Valid Acc: 44.51%\n",
      "Epoch: 08 | Epoch Time: 1m 47s\n",
      "\tTrain Loss: 1.413 | Train Acc: 51.80%\n",
      "\tValid Loss: 1.614 | Valid Acc: 47.71%\n",
      "Epoch: 09 | Epoch Time: 1m 33s\n",
      "\tTrain Loss: 1.226 | Train Acc: 55.46%\n",
      "\tValid Loss: 1.390 | Valid Acc: 55.77%\n",
      "Epoch: 10 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 1.098 | Train Acc: 59.91%\n",
      "\tValid Loss: 1.528 | Valid Acc: 56.23%\n",
      "Epoch: 11 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.872 | Train Acc: 68.03%\n",
      "\tValid Loss: 1.185 | Valid Acc: 74.08%\n",
      "Epoch: 12 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.701 | Train Acc: 74.78%\n",
      "\tValid Loss: 1.156 | Valid Acc: 81.04%\n",
      "Epoch: 13 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.538 | Train Acc: 82.76%\n",
      "\tValid Loss: 1.179 | Valid Acc: 82.88%\n",
      "Epoch: 14 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.420 | Train Acc: 85.92%\n",
      "\tValid Loss: 1.221 | Valid Acc: 84.34%\n",
      "Epoch: 15 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.315 | Train Acc: 90.52%\n",
      "\tValid Loss: 1.161 | Valid Acc: 86.54%\n",
      "Epoch: 16 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.290 | Train Acc: 91.09%\n",
      "\tValid Loss: 1.256 | Valid Acc: 84.89%\n",
      "Epoch: 17 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.177 | Train Acc: 94.25%\n",
      "\tValid Loss: 1.269 | Valid Acc: 85.90%\n",
      "Epoch: 18 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.223 | Train Acc: 92.89%\n",
      "\tValid Loss: 1.432 | Valid Acc: 86.26%\n",
      "Epoch: 19 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.160 | Train Acc: 95.19%\n",
      "\tValid Loss: 1.035 | Valid Acc: 84.62%\n",
      "Epoch: 20 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.171 | Train Acc: 95.11%\n",
      "\tValid Loss: 1.009 | Valid Acc: 85.16%\n",
      "Epoch: 21 | Epoch Time: 1m 14s\n",
      "\tTrain Loss: 0.106 | Train Acc: 95.91%\n",
      "\tValid Loss: 0.967 | Valid Acc: 86.81%\n",
      "Epoch: 22 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.068 | Train Acc: 97.56%\n",
      "\tValid Loss: 1.186 | Valid Acc: 85.44%\n",
      "Epoch: 23 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.073 | Train Acc: 97.13%\n",
      "\tValid Loss: 1.265 | Valid Acc: 84.62%\n",
      "Epoch: 24 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.066 | Train Acc: 97.34%\n",
      "\tValid Loss: 1.592 | Valid Acc: 86.54%\n",
      "Epoch: 25 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.055 | Train Acc: 97.92%\n",
      "\tValid Loss: 1.444 | Valid Acc: 86.54%\n",
      "Epoch: 26 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.069 | Train Acc: 97.20%\n",
      "\tValid Loss: 1.123 | Valid Acc: 86.17%\n",
      "Epoch: 27 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.059 | Train Acc: 97.27%\n",
      "\tValid Loss: 1.265 | Valid Acc: 85.35%\n",
      "Epoch: 28 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.059 | Train Acc: 97.49%\n",
      "\tValid Loss: 1.357 | Valid Acc: 85.99%\n",
      "Epoch: 29 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.051 | Train Acc: 97.84%\n",
      "\tValid Loss: 1.486 | Valid Acc: 85.35%\n",
      "Epoch: 30 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.046 | Train Acc: 97.27%\n",
      "\tValid Loss: 1.489 | Valid Acc: 85.44%\n",
      "Epoch: 31 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.044 | Train Acc: 97.63%\n",
      "\tValid Loss: 1.476 | Valid Acc: 86.26%\n",
      "Epoch: 32 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.126 | Train Acc: 96.48%\n",
      "\tValid Loss: 1.094 | Valid Acc: 86.17%\n",
      "Epoch: 33 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.072 | Train Acc: 96.98%\n",
      "\tValid Loss: 1.361 | Valid Acc: 85.71%\n",
      "Epoch: 34 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.051 | Train Acc: 97.84%\n",
      "\tValid Loss: 1.326 | Valid Acc: 84.80%\n",
      "Epoch: 35 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.090 | Train Acc: 96.26%\n",
      "\tValid Loss: 1.357 | Valid Acc: 85.16%\n",
      "Epoch: 36 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.061 | Train Acc: 96.98%\n",
      "\tValid Loss: 1.713 | Valid Acc: 86.81%\n",
      "Epoch: 37 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.058 | Train Acc: 97.63%\n",
      "\tValid Loss: 1.391 | Valid Acc: 84.62%\n",
      "Epoch: 38 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.046 | Train Acc: 97.70%\n",
      "\tValid Loss: 1.635 | Valid Acc: 84.34%\n",
      "Epoch: 39 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.048 | Train Acc: 97.70%\n",
      "\tValid Loss: 1.624 | Valid Acc: 83.42%\n",
      "Epoch: 40 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.089 | Train Acc: 97.34%\n",
      "\tValid Loss: 0.888 | Valid Acc: 86.81%\n",
      "Epoch: 41 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.111 | Train Acc: 96.70%\n",
      "\tValid Loss: 1.086 | Valid Acc: 87.00%\n",
      "Epoch: 42 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.069 | Train Acc: 96.55%\n",
      "\tValid Loss: 1.607 | Valid Acc: 85.16%\n",
      "Epoch: 43 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.089 | Train Acc: 96.62%\n",
      "\tValid Loss: 1.270 | Valid Acc: 85.99%\n",
      "Epoch: 44 | Epoch Time: 1m 14s\n",
      "\tTrain Loss: 0.046 | Train Acc: 97.56%\n",
      "\tValid Loss: 1.367 | Valid Acc: 85.53%\n",
      "Epoch: 45 | Epoch Time: 1m 23s\n",
      "\tTrain Loss: 0.089 | Train Acc: 96.91%\n",
      "\tValid Loss: 1.119 | Valid Acc: 84.52%\n",
      "Epoch: 46 | Epoch Time: 1m 24s\n",
      "\tTrain Loss: 0.041 | Train Acc: 98.06%\n",
      "\tValid Loss: 1.313 | Valid Acc: 84.62%\n",
      "Epoch: 47 | Epoch Time: 1m 23s\n",
      "\tTrain Loss: 0.045 | Train Acc: 97.63%\n",
      "\tValid Loss: 1.429 | Valid Acc: 84.25%\n",
      "Epoch: 48 | Epoch Time: 1m 21s\n",
      "\tTrain Loss: 0.048 | Train Acc: 97.49%\n",
      "\tValid Loss: 1.445 | Valid Acc: 84.25%\n",
      "Epoch: 49 | Epoch Time: 1m 14s\n",
      "\tTrain Loss: 0.042 | Train Acc: 97.77%\n",
      "\tValid Loss: 1.576 | Valid Acc: 85.16%\n",
      "Epoch: 50 | Epoch Time: 1m 33s\n",
      "\tTrain Loss: 0.040 | Train Acc: 97.84%\n",
      "\tValid Loss: 1.564 | Valid Acc: 83.24%\n",
      "Epoch: 51 | Epoch Time: 1m 25s\n",
      "\tTrain Loss: 0.038 | Train Acc: 97.99%\n",
      "\tValid Loss: 1.576 | Valid Acc: 84.62%\n",
      "Epoch: 52 | Epoch Time: 1m 18s\n",
      "\tTrain Loss: 0.040 | Train Acc: 97.77%\n",
      "\tValid Loss: 1.593 | Valid Acc: 85.16%\n",
      "Epoch: 53 | Epoch Time: 1m 16s\n",
      "\tTrain Loss: 0.041 | Train Acc: 97.77%\n",
      "\tValid Loss: 1.594 | Valid Acc: 85.90%\n",
      "Epoch: 54 | Epoch Time: 1m 13s\n",
      "\tTrain Loss: 0.036 | Train Acc: 97.99%\n",
      "\tValid Loss: 1.524 | Valid Acc: 85.71%\n",
      "Epoch: 55 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.049 | Train Acc: 97.41%\n",
      "\tValid Loss: 1.652 | Valid Acc: 83.88%\n",
      "Epoch: 56 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.061 | Train Acc: 97.05%\n",
      "\tValid Loss: 3.098 | Valid Acc: 82.05%\n",
      "Epoch: 57 | Epoch Time: 1m 29s\n",
      "\tTrain Loss: 0.084 | Train Acc: 97.49%\n",
      "\tValid Loss: 1.323 | Valid Acc: 86.26%\n",
      "Epoch: 58 | Epoch Time: 1m 12s\n",
      "\tTrain Loss: 0.057 | Train Acc: 97.27%\n",
      "\tValid Loss: 1.493 | Valid Acc: 84.89%\n",
      "Epoch: 59 | Epoch Time: 1m 21s\n",
      "\tTrain Loss: 0.051 | Train Acc: 97.70%\n",
      "\tValid Loss: 1.301 | Valid Acc: 85.16%\n",
      "Epoch: 60 | Epoch Time: 1m 16s\n",
      "\tTrain Loss: 0.049 | Train Acc: 97.13%\n",
      "\tValid Loss: 1.422 | Valid Acc: 86.26%\n",
      "Epoch: 61 | Epoch Time: 1m 13s\n",
      "\tTrain Loss: 0.038 | Train Acc: 97.92%\n",
      "\tValid Loss: 1.659 | Valid Acc: 87.36%\n",
      "Epoch: 62 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.037 | Train Acc: 98.42%\n",
      "\tValid Loss: 1.409 | Valid Acc: 87.00%\n",
      "Epoch: 63 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.040 | Train Acc: 97.63%\n",
      "\tValid Loss: 1.430 | Valid Acc: 87.00%\n",
      "Epoch: 64 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.035 | Train Acc: 97.70%\n",
      "\tValid Loss: 1.472 | Valid Acc: 87.55%\n",
      "Epoch: 65 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.067 | Train Acc: 96.98%\n",
      "\tValid Loss: 1.552 | Valid Acc: 83.97%\n",
      "Epoch: 66 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.068 | Train Acc: 96.77%\n",
      "\tValid Loss: 1.518 | Valid Acc: 86.26%\n",
      "Epoch: 67 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.059 | Train Acc: 97.56%\n",
      "\tValid Loss: 1.268 | Valid Acc: 86.81%\n",
      "Epoch: 68 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.044 | Train Acc: 97.27%\n",
      "\tValid Loss: 1.429 | Valid Acc: 85.71%\n",
      "Epoch: 69 | Epoch Time: 1m 11s\n",
      "\tTrain Loss: 0.045 | Train Acc: 97.92%\n",
      "\tValid Loss: 1.411 | Valid Acc: 85.35%\n",
      "Epoch: 70 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.041 | Train Acc: 97.63%\n",
      "\tValid Loss: 1.554 | Valid Acc: 85.44%\n",
      "Epoch: 71 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.034 | Train Acc: 98.06%\n",
      "\tValid Loss: 1.634 | Valid Acc: 85.99%\n",
      "Epoch: 72 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.41%\n",
      "\tValid Loss: 1.574 | Valid Acc: 85.71%\n",
      "Epoch: 73 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.033 | Train Acc: 98.20%\n",
      "\tValid Loss: 1.602 | Valid Acc: 85.62%\n",
      "Epoch: 74 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.032 | Train Acc: 98.20%\n",
      "\tValid Loss: 1.571 | Valid Acc: 84.62%\n",
      "Epoch: 75 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.034 | Train Acc: 97.77%\n",
      "\tValid Loss: 1.554 | Valid Acc: 85.44%\n",
      "Epoch: 76 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.037 | Train Acc: 97.70%\n",
      "\tValid Loss: 1.742 | Valid Acc: 85.71%\n",
      "Epoch: 77 | Epoch Time: 1m 10s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.34%\n",
      "\tValid Loss: 1.659 | Valid Acc: 83.70%\n",
      "Epoch: 78 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.042 | Train Acc: 98.13%\n",
      "\tValid Loss: 1.490 | Valid Acc: 85.07%\n",
      "Epoch: 79 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.043 | Train Acc: 98.13%\n",
      "\tValid Loss: 1.910 | Valid Acc: 84.07%\n",
      "Epoch: 80 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.041 | Train Acc: 97.84%\n",
      "\tValid Loss: 1.546 | Valid Acc: 84.89%\n",
      "Epoch: 81 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.052 | Train Acc: 97.13%\n",
      "\tValid Loss: 1.766 | Valid Acc: 85.44%\n",
      "Epoch: 82 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.044 | Train Acc: 97.70%\n",
      "\tValid Loss: 1.926 | Valid Acc: 85.99%\n",
      "Epoch: 83 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.053 | Train Acc: 97.34%\n",
      "\tValid Loss: 1.466 | Valid Acc: 84.52%\n",
      "Epoch: 84 | Epoch Time: 1m 9s\n",
      "\tTrain Loss: 0.045 | Train Acc: 97.41%\n",
      "\tValid Loss: 1.509 | Valid Acc: 85.99%\n",
      "Epoch: 85 | Epoch Time: 1m 31s\n",
      "\tTrain Loss: 0.039 | Train Acc: 97.41%\n",
      "\tValid Loss: 1.632 | Valid Acc: 86.36%\n",
      "Epoch: 86 | Epoch Time: 2m 8s\n",
      "\tTrain Loss: 0.041 | Train Acc: 97.34%\n",
      "\tValid Loss: 1.418 | Valid Acc: 85.71%\n",
      "Epoch: 87 | Epoch Time: 1m 50s\n",
      "\tTrain Loss: 0.048 | Train Acc: 97.49%\n",
      "\tValid Loss: 1.373 | Valid Acc: 85.71%\n",
      "Epoch: 88 | Epoch Time: 1m 49s\n",
      "\tTrain Loss: 0.031 | Train Acc: 97.99%\n",
      "\tValid Loss: 1.512 | Valid Acc: 86.17%\n",
      "Epoch: 89 | Epoch Time: 1m 52s\n",
      "\tTrain Loss: 0.032 | Train Acc: 98.20%\n",
      "\tValid Loss: 1.558 | Valid Acc: 85.44%\n",
      "Epoch: 90 | Epoch Time: 1m 54s\n",
      "\tTrain Loss: 0.031 | Train Acc: 97.84%\n",
      "\tValid Loss: 1.623 | Valid Acc: 85.99%\n",
      "Epoch: 91 | Epoch Time: 1m 51s\n",
      "\tTrain Loss: 0.035 | Train Acc: 97.92%\n",
      "\tValid Loss: 1.838 | Valid Acc: 86.26%\n",
      "Epoch: 92 | Epoch Time: 1m 47s\n",
      "\tTrain Loss: 0.045 | Train Acc: 97.27%\n",
      "\tValid Loss: 2.650 | Valid Acc: 84.89%\n",
      "Epoch: 93 | Epoch Time: 1m 47s\n",
      "\tTrain Loss: 0.083 | Train Acc: 96.70%\n",
      "\tValid Loss: 1.654 | Valid Acc: 86.81%\n",
      "Epoch: 94 | Epoch Time: 1m 51s\n",
      "\tTrain Loss: 0.065 | Train Acc: 97.05%\n",
      "\tValid Loss: 1.852 | Valid Acc: 84.34%\n",
      "Epoch: 95 | Epoch Time: 1m 52s\n",
      "\tTrain Loss: 0.042 | Train Acc: 97.41%\n",
      "\tValid Loss: 1.761 | Valid Acc: 84.89%\n",
      "Epoch: 96 | Epoch Time: 1m 52s\n",
      "\tTrain Loss: 0.034 | Train Acc: 97.27%\n",
      "\tValid Loss: 1.730 | Valid Acc: 84.89%\n",
      "Epoch: 97 | Epoch Time: 1m 48s\n",
      "\tTrain Loss: 0.032 | Train Acc: 97.70%\n",
      "\tValid Loss: 1.792 | Valid Acc: 84.62%\n",
      "Epoch: 98 | Epoch Time: 1m 47s\n",
      "\tTrain Loss: 0.033 | Train Acc: 97.99%\n",
      "\tValid Loss: 1.785 | Valid Acc: 85.44%\n",
      "Epoch: 99 | Epoch Time: 1m 48s\n",
      "\tTrain Loss: 0.033 | Train Acc: 97.41%\n",
      "\tValid Loss: 1.874 | Valid Acc: 85.16%\n",
      "Epoch: 100 | Epoch Time: 1m 49s\n",
      "\tTrain Loss: 0.035 | Train Acc: 97.92%\n",
      "\tValid Loss: 1.944 | Valid Acc: 85.53%\n",
      "Finished Training\n",
      "Accuracy of the network test images: 85 %\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import time\n",
    "\n",
    "train_l = []\n",
    "train_a = []\n",
    "valid_l = []\n",
    "valid_a = []\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "    for epoch in range(100):\n",
    "        start_time = time.monotonic()\n",
    "        train_loss, train_acc = train(net, trainloader, optimizer, criterion, device)\n",
    "        valid_loss, valid_acc = valid(net, validloader)\n",
    "        \n",
    "        train_l.append(train_loss)\n",
    "        train_a.append(train_acc)\n",
    "        valid_l.append(valid_loss)\n",
    "        valid_a.append(valid_acc)\n",
    "        \n",
    "        end_time = time.monotonic()\n",
    "        epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "\n",
    "        print(f'Epoch: {epoch + 1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "        print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc * 100:.2f}%')\n",
    "        print(f'\\tValid Loss: {valid_loss:.3f} | Valid Acc: {valid_acc * 100:.2f}%')\n",
    "    print('Finished Training')\n",
    "\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for data in testloader:\n",
    "            images, labels = data\n",
    "            outputs = net(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "    print('Accuracy of the network test images: %d %%' % (\n",
    "            100 * correct / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6f82d796",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.434981685090851, 0.4358974361157679, 0.434981685090851, 0.43864468886302066, 0.2032967032967033, 0.32783882794799385, 0.44505494505494503, 0.47710622732455915, 0.5576923076923077, 0.5622710624893943, 0.7408424910608229, 0.8104395604395604, 0.8287545789729108, 0.8434065934065934, 0.8653846153846154, 0.8489010989010989, 0.858974359192691, 0.8626373626373627, 0.8461538461538461, 0.8516483516483516, 0.8681318681318682, 0.8543956043956044, 0.8461538461538461, 0.8653846153846154, 0.8653846153846154, 0.8617216119399438, 0.8534798536981855, 0.8598901098901099, 0.8534798536981855, 0.8543956043956044, 0.8626373626373627, 0.8617216119399438, 0.8571428571428571, 0.84798534820368, 0.8516483516483516, 0.8681318681318682, 0.8461538461538461, 0.8434065934065934, 0.8342490844674163, 0.8681318681318682, 0.869963370181702, 0.8516483516483516, 0.8598901098901099, 0.8553113554205213, 0.8452380954564273, 0.8461538461538461, 0.8424908427091745, 0.8424908427091745, 0.8516483516483516, 0.8324175824175825, 0.8461538461538461, 0.8516483516483516, 0.858974359192691, 0.8571428571428571, 0.8388278389370049, 0.8205128207311525, 0.8626373626373627, 0.8489010989010989, 0.8516483516483516, 0.8626373626373627, 0.8736263736263736, 0.869963370181702, 0.869963370181702, 0.8754578756762075, 0.8397435899619218, 0.8626373626373627, 0.8681318681318682, 0.8571428571428571, 0.8534798536981855, 0.8543956043956044, 0.8598901098901099, 0.8571428571428571, 0.8562271064454383, 0.8461538461538461, 0.8543956043956044, 0.8571428571428571, 0.836996337214669, 0.8507326009509327, 0.8406593406593407, 0.8489010989010989, 0.8543956043956044, 0.8598901098901099, 0.8452380954564273, 0.8598901098901099, 0.8635531136622796, 0.8571428571428571, 0.8571428571428571, 0.8617216119399438, 0.8543956043956044, 0.8598901098901099, 0.8626373626373627, 0.8489010989010989, 0.8681318681318682, 0.8434065934065934, 0.8489010989010989, 0.8489010989010989, 0.8461538461538461, 0.8543956043956044, 0.8516483516483516, 0.8553113554205213]\n"
     ]
    }
   ],
   "source": [
    "print(valid_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1ce5efe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb75dc61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(train_a,label='Train Accuracy',linewidth=2.5)\n",
    "plt.plot(valid_a,label='Valid Accuracy',linewidth=2.5)\n",
    "plt.xlabel('Epoch',size=20)\n",
    "plt.ylabel('Accuracy',size=20)\n",
    "plt.legend(fontsize = 20)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e10d7ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for class Adenosis is: 71.4 %\n",
      "Accuracy for class Fibroadenoma is: 92.4 %\n",
      "Accuracy for class Phyllodes_tumor is: 87.2 %\n",
      "Accuracy for class Tubular_adenoma is: 77.8 %\n",
      "Accuracy for class Ductal_Carcinoma is: 81.8 %\n",
      "Accuracy for class Lobular_Carcinoma is: 78.6 %\n",
      "Accuracy for class Mucinous_Carcinoma is: 78.3 %\n",
      "Accuracy for class Papillary_Carcinoma is: 80.8 %\n"
     ]
    }
   ],
   "source": [
    "classes = ('Adenosis', 'Fibroadenoma', 'Phyllodes_tumor' ,'Tubular_adenoma','Ductal_Carcinoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma')\n",
    "\n",
    "# prepare to count predictions for each class\n",
    "correct_pred = {classname: 0 for classname in classes}\n",
    "total_pred = {classname: 0 for classname in classes}\n",
    "\n",
    "# again no gradients needed\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = net(images)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        # collect the correct predictions for each class\n",
    "        for label, prediction in zip(labels, predictions):\n",
    "            if label == prediction:\n",
    "                correct_pred[classes[label]] += 1\n",
    "            total_pred[classes[label]] += 1\n",
    "\n",
    "\n",
    "# print accuracy for each class\n",
    "for classname, correct_count in correct_pred.items():\n",
    "    accuracy = 100 * float(correct_count) / total_pred[classname]\n",
    "    print(\"Accuracy for class {:5s} is: {:.1f} %\".format(classname,\n",
    "                                                   accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ee57ac75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[ 15   2   0   0   0   1   2   1]\n",
      " [  1 146   3   1   0   0   2   5]\n",
      " [  1   4  41   0   1   0   0   0]\n",
      " [  0   5   0  21   1   0   0   0]\n",
      " [  0   4   0   0  27   2   0   0]\n",
      " [  0   5   0   0   0  22   0   1]\n",
      " [  0   1   1   0   1   2  18   0]\n",
      " [  0   3   1   0   1   0   0  21]]\n",
      "F1 score: 0.856749\n",
      "Accuracy score: 0.856749\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "\n",
    "def test_label_predictions(model, device, test_loader):\n",
    "    model.eval()\n",
    "    actuals = []\n",
    "    predictions = []\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            prediction = output.argmax(dim=1, keepdim=True)\n",
    "            actuals.extend(target.view_as(prediction))\n",
    "            predictions.extend(prediction)\n",
    "    return [i.item() for i in actuals], [i.item() for i in predictions]\n",
    "\n",
    "actuals, predictions = test_label_predictions(net, device, testloader)\n",
    "print('Confusion matrix:')\n",
    "print(sklearn.metrics.confusion_matrix(actuals, predictions))\n",
    "print('F1 score: %f' % sklearn.metrics.f1_score(actuals, predictions, average='micro'))\n",
    "print('Accuracy score: %f' % sklearn.metrics.accuracy_score(actuals, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66077f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [[ 15 ,  2 ,  0 ,  0  , 0 ,  1 ,  2 ,  1],\n",
    "     [  1 ,146,   3 ,  1 ,  0 ,  0 ,  2   ,5],\n",
    "     [  1  , 4 , 41 ,  0 ,  1  , 0  , 0  , 0],\n",
    "     [  0  , 5  , 0 , 21,   1 ,  0  , 0,   0],\n",
    "     [  0  , 4  , 0   ,0  ,27,   2,   0,   0],\n",
    "     [  0   ,5 ,  0 ,  0 ,  0 , 22 ,  0  , 1],\n",
    "     [  0 ,  1,   1 ,  0 ,  1   ,2  ,18 ,  0],\n",
    "     [  0  , 3  , 1,   0   ,1  , 0,   0,  21]]\n",
    "\n",
    "b = [[ 15/21,   2/21 ,  0  , 0 ,  0 ,  1/21  , 2/21  , 1/21],\n",
    "     [  1/158, 146/158 ,  3/158,   1/158 ,  0 ,  0  , 2/158 ,  5/158],\n",
    "     [  1/47  , 4/47,  41/47,   0 ,  1/47 ,  0 ,  0  , 0],\n",
    "     [  0  , 5/27,   0  ,21/27 ,  1/27  , 0 ,  0 ,  0],\n",
    "     [  0  , 4/33 ,  0 ,  0 , 27/33 ,  2/33,   0 ,  0],\n",
    "     [  0 ,  5/28  , 0   ,0  , 0 , 22/28,   0,   1/28],\n",
    "     [  0 ,  1/23 ,  1/23 ,  0  , 1/23 ,  2/23,  18/23 ,  0],\n",
    "     [  0 ,  3/26 ,  1/26,   0 ,  1/26,   0 ,  0 , 21/26]]\n",
    "\n",
    "\n",
    "c = [[ 18 ,  1 ,  0 ,  1  , 0 ,  1 ,  0 ,  0],\n",
    "     [  4 ,135,   7 ,  2 ,  1 ,  0 ,  4   ,5],     \n",
    "     [  0  , 1 , 43 ,  0 ,  2  , 0  , 1  , 0],     \n",
    "     [  0  , 1  , 0 , 25,   0 ,  0  , 0,   1],    \n",
    "     [  0  , 1  , 0   ,1  ,30,   0,   0,   1],     \n",
    "     [  0   ,1 ,  0 ,  0 ,  1 , 26 ,  0  , 0],     \n",
    "     [  0 ,  1,   0 ,  0 ,  0   ,1  ,21 ,  0],     \n",
    "     [  0  , 1  , 0,   0   ,1  , 0,   0,  24]]\n",
    "\n",
    "d = [[ 18/21,   1/21 ,  0  , 1/21 ,  0 ,  1/21  , 0  , 0],     \n",
    "     [  4/158, 135/158 ,  7/158,   2/158 ,  1/158 ,  0  , 4/158 ,  5/158],     \n",
    "     [  0/47  , 1/47,  43/47,   0 ,  2/47 ,  0 ,  1/47  , 0],     \n",
    "     [  0  , 1/27,   0  ,25/27 ,  0/27  , 0 ,  0 ,  1/27],     \n",
    "     [  0  , 1/33 ,  0 ,  1/33 , 30/33 ,  0,   0 ,  1/33],     \n",
    "     [  0 ,  1/28  , 0   ,0  , 1/28 , 26/28,   0,   0],     \n",
    "     [  0 ,  2/23 ,  0 ,  0  , 0 ,  1/23,  20/23 ,  0],     \n",
    "     [  0 ,  1/26 ,  0,   0 ,  1/26,   0 ,  1/26 , 23/26]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4f19c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'confusion Matrix x400 ohne Augmentation'}>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
    "                np.array(a).flatten()]\n",
    "\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
    "                     np.array(b).flatten()]\n",
    "\n",
    "labels = [f\"{v1}\\n{v2}\" for v1, v2 in\n",
    "          zip(group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(8,8)\n",
    "\n",
    "categories = ['Adenosis', 'Ductal_Carcinoma', 'Fibroadenoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma', 'Phyllodes_tumor', 'Tubular_adenoma']\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.title('confusion Matrix x400 ohne Augmentation')\n",
    "sns.heatmap(b, annot = labels, fmt='', xticklabels = categories, yticklabels = categories)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c63b0101",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'confusion Matrix x400 mit Data Augmentation'}>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group_counts = [\"{0:0.0f}\".format(value) for value in\n",
    "                np.array(c).flatten()]\n",
    "\n",
    "group_percentages = [\"{0:.2%}\".format(value) for value in\n",
    "                     np.array(d).flatten()]\n",
    "\n",
    "labels = [f\"{v1}\\n{v2}\" for v1, v2 in\n",
    "          zip(group_counts,group_percentages)]\n",
    "\n",
    "labels = np.asarray(labels).reshape(8,8)\n",
    "\n",
    "categories = ['Adenosis', 'Ductal_Carcinoma', 'Fibroadenoma', 'Lobular_Carcinoma',\n",
    "               'Mucinous_Carcinoma', 'Papillary_Carcinoma', 'Phyllodes_tumor', 'Tubular_adenoma']\n",
    "\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.title('confusion Matrix x400 mit Data Augmentation')\n",
    "sns.heatmap(d, annot = labels, fmt='', xticklabels = categories, yticklabels = categories)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df802dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6d23f1a2",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "def test_class_probabilities(model, device, test_loader, which_class):\n",
    "    model.eval()\n",
    "    actuals = []\n",
    "    probabilities = []\n",
    "    with torch.no_grad():\n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            prediction = output.argmax(dim=1, keepdim=True)\n",
    "            actuals.extend(target.view_as(prediction) == which_class)\n",
    "            probabilities.extend(np.exp(output[:, which_class]))\n",
    "    return [i.item() for i in actuals], [i.item() for i in probabilities]\n",
    "\n",
    "which_class = 5\n",
    "actuals, class_probabilities = test_class_probabilities(net, device, testloader, which_class)\n",
    "\n",
    "fpr, tpr, _ = sklearn.metrics.roc_curve(actuals, class_probabilities)\n",
    "roc_auc = sklearn.metrics.auc(fpr, tpr)\n",
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, 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 for label= %d ' % which_class)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b0e999f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5fde244",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}