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