Table Of Contents
Table Of Contents


Gluon provides pre-defined loss functions in the mxnet.gluon.parameter module.

losses for training neural networks

Loss(weight, batch_axis, **kwargs) Base class for loss.
L2Loss([weight, batch_axis]) Calculates the mean squared error between pred and label.
L1Loss([weight, batch_axis]) Calculates the mean absolute error between pred and label.
SigmoidBinaryCrossEntropyLoss([…]) The cross-entropy loss for binary classification.
SoftmaxCrossEntropyLoss([axis, …]) Computes the softmax cross entropy loss.
KLDivLoss([from_logits, axis, weight, …]) The Kullback-Leibler divergence loss.
HuberLoss([rho, weight, batch_axis]) Calculates smoothed L1 loss that is equal to L1 loss if absolute error exceeds rho but is equal to L2 loss otherwise.
HingeLoss([margin, weight, batch_axis]) Calculates the hinge loss function often used in SVMs:
SquaredHingeLoss([margin, weight, batch_axis]) Calculates the soft-margin loss function used in SVMs:
LogisticLoss([weight, batch_axis, label_format]) Calculates the logistic loss (for binary losses only):
TripletLoss([margin, weight, batch_axis]) Calculates triplet loss given three input tensors and a positive margin.
CTCLoss([layout, label_layout, weight]) Connectionist Temporal Classification Loss.