Class | Description |
---|---|
AbstractCompositeLoss | |
ElasticNetWeightDecay |
ElasticWeightDecay calculates L1+L2 penalty of a set of parameters. |
HingeLoss |
HingeLoss is a type of Loss . |
L1Loss |
L1Loss calculates L1 loss between label and prediction. |
L1WeightDecay |
L1WeightDecay calculates L1 penalty of a set of parameters. |
L2Loss |
Calculates L2Loss between label and prediction, a.k.a.
|
L2WeightDecay |
L2WeightDecay calculates L2 penalty of a set of parameters. |
Loss |
Loss functions (or Cost functions) are used to evaluate the model predictions against true labels
for optimization.
|
MaskedSoftmaxCrossEntropyLoss |
MaskedSoftmaxCrossEntropyLoss is an implementation of Loss that only considers a
specific number of values for the loss computations, and masks the rest according to the given
sequence. |
SigmoidBinaryCrossEntropyLoss |
SigmoidBinaryCrossEntropyLoss is a type of Loss . |
SimpleCompositeLoss | |
SingleShotDetectionLoss |
SingleShotDetectionLoss is an implementation of Loss . |
SoftmaxCrossEntropyLoss |
SoftmaxCrossEntropyLoss is a type of Loss that calculates the softmax cross
entropy loss. |