Hi,
How do you calculate the validation loss per iteration?
Say you’ve your train_loader
and test_loader
.
Normally for training per iteration to get the training loss, we do loss = criterion(outputs, labels
where criterion = nn.CrossEntropyLoss()
.
For validation per iteration,
test_loss_iter = []
for images, labels in test_loader:
images = Variable(images.view(-1, 784).cuda())
outputs = net(images)
test_loss = criterion(outputs, labels)
test_loss_iter.append(test_loss)
iteration_test_loss = np.mean(test_loss_iter)
But I get error AttributeError: 'torch.LongTensor' object has no attribute 'requires_grad'