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Cross Entropy Loss

Source: R/TorchLoss.R
cross_entropy.Rd

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
#> An `nn_module` containing 0 parameters.

# binary
binary_ce = loss$generate(tsk("sonar"))
binary_ce
#> An `nn_module` containing 0 parameters.