Loss Functions

Additional loss functions

BCEWithLogitsLoss

 BCEWithLogitsLoss (weight:Tensor|None=None, reduction:str='mean',
                    pos_weight:Tensor|None=None, thresh:float=0.5)

Like nn.BCEWithLogitsLoss, but with ‘batchmean’ reduction from MosiacML. batchmean scales loss by the batch size which results in larger loss values more similar to nn.CrossEntropy then mean reduction.

	Type	Default	Details
weight	Tensor \| None	None	Rescaling weight for each class
reduction	str	mean	Pytorch reduction to apply to loss output. Also supports ‘batchmean’.
pos_weight	Tensor \| None	None	Weight of positive examples
thresh	float	0.5	Threshold for `decodes`

source

ClassBalancedCrossEntropyLoss

 ClassBalancedCrossEntropyLoss (samples_per_class:Tensor|Listy[int],
                                beta:float=0.99, ignore_index:int=-100,
                                reduction:str='mean',
                                label_smoothing:float=0.0, axis:int=-1)

Class Balanced Cross Entropy Loss, from https://arxiv.org/abs/1901.05555.

	Type	Default	Details
samples_per_class	Tensor \| Listy[int]		Number of samples per class
beta	float	0.99	Rebalance factor, usually between [0.9, 0.9999]
ignore_index	int	-100	Target value which is ignored and doesn’t contribute to gradient
reduction	str	mean	Pytorch reduction to apply to loss output
label_smoothing	float	0.0	Convert hard targets to soft targets, defaults to no smoothing
axis	int	-1	ArgMax axis for fastai `decodes``

source

ClassBalancedBCEWithLogitsLoss

 ClassBalancedBCEWithLogitsLoss (samples_per_class:Tensor|Listy[int],
                                 beta:float=0.99, reduction:str='mean',
                                 pos_weight:Tensor|None=None,
                                 thresh:float=0.5)

Class Balanced BCE With Logits Loss, from https://arxiv.org/abs/1901.05555 with ‘batchmean’ reduction

	Type	Default	Details
samples_per_class	Tensor \| Listy[int]		Number of samples per class
beta	float	0.99	Rebalance factor, usually between [0.9, 0.9999]
reduction	str	mean	Pytorch reduction to apply to loss output. Also supports ‘batchmean’.
pos_weight	Tensor \| None	None	BCE Weight of positive examples
thresh	float	0.5	Threshold for fastai `decodes`