Hello,

I am a little confused by what a Loss function produces.

I was looking at this post: Multi Label Classification in pytorch - #45 by ptrblck

And tried to recreate it to understand the loss value calculated. So I constructed a perfect output for a given target:

```
from torch.nn.modules.loss import BCEWithLogitsLoss
loss_function = BCEWithLogitsLoss()
# Given are 2 classes
output_tensor = Tensor([[0.0, 1.0]]) # Output of my nn
target_tensor = Tensor([[0.0, 1.0]]) # Target result which is identical to output
loss = loss_function(output_tensor, target_tensor)
# Shouldn't this yield no loss at all?
print(loss.item())
# Output: 0.5032044053077698
```

If I understand correctly, then this should yield a loss of `0.0`

, because the target is identical to the output.

But I get: `0.5032044053077698`

.

Did I miss something?

With best regards,

Patrick