text: #! /usr/bin/python3
  
  import torch
  from torchvision import datasets
  from torchvision.transforms import ToTensor
  import neuralnetwork
  
  classes = [
      "T-shirt/top",
      "Trouser",
      "Pullover",
      "Dress",
      "Coat",
      "Sandal",
      "Shirt",
      "Sneaker",
      "Bag",
      "Ankle boot",
  ]
  
  model = neuralnetwork.CNN()
  model.load_state_dict(torch.load("model.pth"))
  
  # Download test data from open datasets.
  test_data = datasets.FashionMNIST(
      root="data",
      train=False,
      download=True,
      transform=ToTensor(),
  )
  
  model.eval()
  
  correct = 0
  all = 0
  
  for X, y in test_data:
      with torch.no_grad():
          pred = model(X[None, ...])
          predicted, actual = classes[pred[0].argmax(0)], classes[y]
  
          all += 1
  
          if predicted == actual:
              correct += 1
  
  print(f'Got correct: "{correct/all}"')
  