title: Confusion matrix and test accuracy for PyTorch Transfer Learning tutorial
body: <p>Following the Pytorch Transfer learning tutorial, I am interested in reporting only train and test accuracy as well as confusion matrix (say using sklearn confusionmatrix). How can I do that? The current tutorial only reports train/val accuracy and I am having hard time figuring how to incorporate the sklearn confusionmatrix code there. Link to original tutorial here: <a href="https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html" rel="noreferrer">https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html</a></p>
  
  <pre><code>%matplotlib inline
  from graphviz import Digraph
  import torch
  from torch.autograd import Variable
  # Author: Sasank Chilamkurthy
  
  from __future__ import print_function, division
  
  import torch
  import torch.nn as nn
  import torch.optim as optim
  from torch.optim import lr_scheduler
  import numpy as np
  import torchvision
  from torchvision import datasets, models, transforms
  import matplotlib.pyplot as plt
  import time
  import os
  import copy
  
  plt.ion()
  data_transforms = {
      'train': transforms.Compose([
          transforms.RandomResizedCrop(224),
          transforms.RandomHorizontalFlip(),
          transforms.ToTensor(),
          transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      ]),
      'val': transforms.Compose([
          transforms.Resize(256),
          transforms.CenterCrop(224),
          transforms.ToTensor(),
          transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
      ]),
  }
  
  
  data_dir = "images"
  image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                            data_transforms[x])
                    for x in ['train', 'val']}
  dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                               shuffle=True, num_workers=4)
                for x in ['train', 'val']}
  dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
  class_names = image_datasets['train'].classes
  
  device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
  
  def imshow(inp, title=None):
      """Imshow for Tensor."""
      inp = inp.numpy().transpose((1, 2, 0))
      mean = np.array([0.485, 0.456, 0.406])
      std = np.array([0.229, 0.224, 0.225])
      inp = std * inp + mean
      inp = np.clip(inp, 0, 1)
      plt.imshow(inp)
      if title is not None:
          plt.title(title)
      plt.pause(0.001)  # pause a bit so that plots are updated
  
  
  # Get a batch of training data
  inputs, classes = next(iter(dataloaders['train']))
  
  # Make a grid from batch
  out = torchvision.utils.make_grid(inputs)
  
  imshow(out, title=[class_names[x] for x in classes])
  
  def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
      since = time.time()
  
      best_model_wts = copy.deepcopy(model.state_dict())
      best_acc = 0.0
  
      for epoch in range(num_epochs):
          print('Epoch {}/{}'.format(epoch, num_epochs - 1))
          print('-' * 10)
  
          # Each epoch has a training and validation phase
          for phase in ['train', 'val']:
              if phase == 'train':
                  scheduler.step()
                  model.train()  # Set model to training mode
              else:
                  model.eval()   # Set model to evaluate mode
  
              running_loss = 0.0
              running_corrects = 0
  
              # Iterate over data.
              for inputs, labels in dataloaders[phase]:
                  inputs = inputs.to(device)
                  labels = labels.to(device)
  
                  # zero the parameter gradients
                  optimizer.zero_grad()
  
                  # forward
                  # track history if only in train
                  with torch.set_grad_enabled(phase == 'train'):
                      outputs = model(inputs)
                      _, preds = torch.max(outputs, 1)
                      loss = criterion(outputs, labels)
  
                      # backward + optimize only if in training phase
                      if phase == 'train':
                          loss.backward()
                          optimizer.step()
  
                  # statistics
                  running_loss += loss.item() * inputs.size(0)
                  running_corrects += torch.sum(preds == labels.data)
  
              epoch_loss = running_loss / dataset_sizes[phase]
              epoch_acc = running_corrects.double() / dataset_sizes[phase]
  
              print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                  phase, epoch_loss, epoch_acc))
  
              # deep copy the model
              if phase == 'val' and epoch_acc &gt; best_acc:
                  best_acc = epoch_acc
                  best_model_wts = copy.deepcopy(model.state_dict())
  
          print()
  
      time_elapsed = time.time() - since
      print('Training complete in {:.0f}m {:.0f}s'.format(
          time_elapsed // 60, time_elapsed % 60))
      print('Best val Acc: {:4f}'.format(best_acc))
  
      # load best model weights
      model.load_state_dict(best_model_wts)
      return model
  
  def visualize_model(model, num_images=6):
      was_training = model.training
      model.eval()
      images_so_far = 0
      fig = plt.figure()
  
      with torch.no_grad():
          for i, (inputs, labels) in enumerate(dataloaders['val']):
              inputs = inputs.to(device)
              labels = labels.to(device)
  
              outputs = model(inputs)
              _, preds = torch.max(outputs, 1)
  
              for j in range(inputs.size()[0]):
                  images_so_far += 1
                  ax = plt.subplot(num_images//2, 2, images_so_far)
                  ax.axis('off')
                  ax.set_title('predicted: {}'.format(class_names[preds[j]]))
                  imshow(inputs.cpu().data[j])
  
                  if images_so_far == num_images:
                      model.train(mode=was_training)
                      return
          model.train(mode=was_training)
  
  model_ft = models.resnet18(pretrained=True)
  num_ftrs = model_ft.fc.in_features
  model_ft.fc = nn.Linear(num_ftrs, 9)
  
  model_ft = model_ft.to(device)
  
  criterion = nn.CrossEntropyLoss()
  
  # Observe that all parameters are being optimized
  optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
  
  # Decay LR by a factor of 0.1 every 7 epochs
  exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
  
  model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
                         num_epochs=25)
  
  visualize_model(model_ft)
  </code></pre>