In the pytorch docs, it says for cross entropy loss:

input has to be a Tensor of size (minibatch, C)

Does this mean that for binary (0,1) prediction, the input must be converted into an (N,2) tensor where the second dimension is equal to (1-p)?

So for instance if I predict 0.75 for a class with target 1 (true), would I have to stack two values (0.75; 0.25) on top of each other as input?


In Pytorch you can use cross-entropy loss for a binary classification task. You need to make sure to have two neurons in the final layer of the model. Make sure that you do not add a softmax function.

Use the below for resources:




There is a specific loss in pytorch for your case :

  • Just use the binary cross entropy loss

See the documentation here and here.


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