ConfidencePenaltyLoss#
- class torch_uncertainty.losses.ConfidencePenaltyLoss(reg_weight=1, reduction='mean', eps=1e-06)[source]#
The Confidence Penalty Loss.
- Parameters:
reg_weight (float, optional) – The weight of the regularization term.
reduction (str, optional) – specifies the reduction to apply to the output:
'none'
|'mean'
|'sum'
. Defaults to “mean”.eps (float, optional) – A small value to avoid numerical instability. Defaults to
1e-6.
References
[1] Gabriel Pereyra: Regularizing neural networks by penalizing confident output distributions.