Since LogSoftmax is idempotent, you’ll get the same output as shown by @Eta_C’s example.
Internally nn.CrossEntropyLoss will apply another F.log_softmax on the inputs.
However, I would recommend to stick to:


nn.LogSoftmax + nn.NLLLoss or
raw logits + nn.CrossentropyLoss


as you won’t get any benefit from it using these loss functions.