Hello,
My network has Softmax activation plus a Cross-Entropy loss, which some refer to Categorical Cross-Entropy loss. See:

Softmax_Cross-Entropy1162×236 48.9 KB

In binary classification, do I need one-hot encoding to work in a network like this in PyTorch? I am using Integer Encoding. Just as matter of fact, here are some outputs WITHOUT Softmax activation (batch = 4):
outputs:  tensor([[ 0.2439,  0.0890],
[ 0.2258,  0.1119],
[-0.2149,  0.2282],
[ 0.0222, -0.1259]]
And here are some outputs WITH Softmax (Softmax activation before Cross-Entropy):
outputs:  tensor([[0.3662, 0.6338],
[0.4209, 0.5791],
[0.4611, 0.5389],
[0.5497, 0.4503]]
As expected, the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1. Honestly, I see no remarkable in the loss functions in both situations.
But, the whole point is to know whether I can use integer encoding with Softmax + Cross-Entropy in PyTorch.
Thanks.