Cross Entropy in PyTorch

In your example you are treating output [0, 0, 0, 1] as probabilities as required by the mathematical definition of cross entropy. But PyTorch treats them as outputs, that don’t need to sum to 1, and need to be first converted into probabilities for which it uses the softmax function.

So H(p, q) becomes:

H(p, softmax(output))

Translating the output [0, 0, 0, 1] into probabilities:

softmax([0, 0, 0, 1]) = [0.1749, 0.1749, 0.1749, 0.4754]

whence:

-log(0.4754) = 0.7437