Yes! I’m using nn.CrossEntropyLoss as in the tutorial. I see, since it’s a log softmax, the output is not strictly a probability distribution, right? That’s why I’m getting values greater than 1?
I mean, in the tutorial, in this block of code
with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    print(outputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

The print returns an array with values greater than 1… That’s why I’m confused.