BCEWithLogitsLSLoss#
- class torch_uncertainty.losses.BCEWithLogitsLSLoss(weight=None, reduction='mean', label_smoothing=0.0)[source]#
Binary Cross Entropy with Logits Loss with label smoothing.
The original PyTorch implementation of the BCEWithLogitsLoss does not support label smoothing. This implementation adds label smoothing to the BCEWithLogitsLoss.
- Parameters:
weight (Tensor, optional) – A manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size “nbatch”. Defaults to
None
.reduction (str, optional) – Specifies the reduction to apply to the output:
'none'
|'mean'
|'sum
’.'none'
: no reduction will be applied,'mean'
: the sum of the output will be divided by the number of elements in the output,'sum'
: the output will be summed. Defaults to'mean'
.label_smoothing (float, optional) – The label smoothing factor. Defaults to
0.0
.