Testing in loop as training

2

Hi,

A few things:

  • Variable is not needed anymore, you can have simply images = data.to('cuda:0')
  • You are missing the optimizer.zero_grad() before the backward ! You need to manually reset the weights to 0 when you pytorch (see discussion about this here: Why do we need to set the gradients manually to zero in pytorch? )
  • You should not use .data. If you want to compute things without tracking history, you can either use detach() as _, predicted = torch.max(outputs.detach(), 1) or wrap the computations in with torch.no_grad(): to compute predicted and correct.
  • You’re doing the right thing with .item() to accumulate the loss.
  • For the evaluattion, same thing about .data and Variable
  • You might be missing a model.eval() before the evaluation loop.
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