Difference between CrossEntropyLoss and NNLLoss with log_softmax in PyTorch?

Question

When I am building a classifier in PyTorch, I have 2 options to do

1. Using the nn.CrossEntropyLoss without any modification in the model
2. Using the nn.NNLLoss with F.log_softmax added as the last layer in the model

So there are two approaches.

Now, what approach should anyone use, and why?

Answer 1

They're the same.

If you check the implementation, you will find that it calls nll_loss after applying log_softmax on the incoming arguments.

return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)

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Edit: seems like the links are now broken, here's the C++ implementation which shows the same information.