cross_entropy: Cross Entropy Loss

In mlr3torch: Deep Learning with 'mlr3'

Cross Entropy Loss

Description

The cross_entropy loss function selects the multi-class (nn_cross_entropy_loss) or binary (nn_bce_with_logits_loss) cross entropy loss based on the number of classes. Because of this, there is a slight reparameterization of the loss arguments, see Parameters.

Parameters

• class_weight:: torch_tensor
  The class weights. For multi-class problems, this must be a torch_tensor of length num_classes (and is passed as argument weight to nn_cross_entropy_loss). For binary problems, this must be a scalar (and is passed as argument pos_weight to nn_bce_with_logits_loss).

• ignore_index:: integer(1)
  Index of the class which to ignore and which does not contribute to the gradient. This is only available for multi-class loss.

• reduction :: character(1)
  The reduction to apply. Is either "mean" or "sum" and passed as argument reduction to either loss function. The default is "mean".

Examples

loss = t_loss("cross_entropy")
# multi-class
multi_ce = loss$generate(tsk("iris"))
multi_ce

# binary
binary_ce = loss$generate(tsk("sonar"))
binary_ce

mlr3torch documentation built on Aug. 26, 2025, 5:09 p.m.