no difference


-F.crossentropy is not bounded and minimizing loss will not converge
-F.crossentropy(logits, label) is not equal to  F.crossentropy(-logits, label)



crossentropy is equivalent to the combination of LogSoftmax and NLLLoss
So logits input first go through LogSoftMax.
And for F.crossentropy(-logits, labels) think it this way: minimizing it will return estimator with minimum cross entropy between softmax(-logits) and lables .

are these two represents to maximize the cross entropy?

I don’t think so.