How does one track the train AND test error during training?

[Brando_Miranda (MirandaAgent)]

if there was any error issues it was probably how the errors were being tracked. Use the following:

For the fix check:

How does one manually compute the error of the whole data set in pytorch?

How does the Cifar10 tutorial make sure to use the test set is actually the test set if both use the same path to load both test and train?

I had a look at your code and it seems your error calculation does overflow. In this method you are calculating the error: def error_criterion(outputs,labels): max_vals, max_indices = torch.max(outputs,1) train_error = (max_indices != labels).sum().data[0]/max_indices.size()[0] return train_error The comparision (max_indices != labels) returns a torch.ByteTensor, which can overflow using your batch size of 10000. Adding a .float to this line (max_indices != labels).float().sum().…