Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

StepByStep Method
def correct(self, x, y, threshold=.5):
self.model.eval()
yhat = self.model(x.to(self.device))
y = y.to(self.device)
self.model.train()
# We get the size of the batch and the number of classes
# (only 1, if it is binary)
n_samples, n_dims = yhat.shape
if n_dims > 1:
# In a multiclass classification, the largest logit
# always wins, so we don't bother getting probabilities
# This is PyTorch's version of argmax,
# but it returns a tuple: (max value, index of max value)
_, predicted = torch.max(yhat, 1)
else:
n_dims += 1
# In binary classification, we NEED to check if the
# last layer is a sigmoid (and then it produces probs)
if isinstance(self.model, nn.Sequential) and \
isinstance(self.model[-1], nn.Sigmoid):
predicted = (yhat > threshold).long()
# or something else (logits), which we need to convert
# using a sigmoid
else:
predicted = (torch.sigmoid(yhat) > threshold).long()
# How many samples got classified
correctly for each class
result = []
for c in range(n_dims):
n_class = (y == c).sum().item()
n_correct = (predicted[y == c] == c).sum().item()
result.append((n_correct, n_class))
return torch.tensor(result)
setattr(StepByStep, 'correct', correct)
If the labels have two or more columns, it means we’re dealing with a multiclass
408 | Chapter 5: Convolutions