How to use the BCELoss in PyTorch?

Update

The BCELoss function did not use to be numerically stable. See this issue https://github.com/pytorch/pytorch/issues/751. However, this issue has been resolved with Pull #1792, so that BCELoss is numerically stable now!

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Old answer

If you build PyTorch from source, you can use the numerically stable function BCEWithLogitsLoss(contributed in https://github.com/pytorch/pytorch/pull/1792), which takes logits as input.

Otherwise, you can use the following function (contributed by yzgao in the above issue):

class StableBCELoss(nn.modules.Module):
       def __init__(self):
             super(StableBCELoss, self).__init__()
       def forward(self, input, target):
             neg_abs = - input.abs()
             loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
             return loss.mean()

You might want to use a sigmoid layer at the end of the network. In that way the number would represent probabilities. Also make sure that the targets are binary numbers. If you post your complete code we might help more.