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Author: chao1224

mnist/main.py

@@ -99,12 +99,11 @@ def test():
  data, target = Variable(data, volatile=True), Variable(target)
  current_batch_size = data.data.size()[0]
  output = model(data)
- # F.nll_loss is averaged among each batch
- test_loss += F.nll_loss(output, target).data[0] * current_batch_size
+ test_loss += F.nll_loss(output, target).data[0] * current_batch_size # sum up batch loss
  pred = output.data.max(1)[1] # get the index of the max log-probability
  correct += pred.eq(target.data).cpu().sum()
 
- test_loss = test_loss # sum of loss function
+ test_loss = test_loss # sum of loss function over all data points
  test_loss /= len(test_loader.dataset)
  print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
  test_loss, correct, len(test_loader.dataset),