Skip to contents

In the training phase, computes individual losses with regard to two targets, weights them item-wise, and averages the linear combinations to yield the mean batch loss. For validation and testing, defers to the passed-in loss.

## Usage

nn_mixup_loss(loss)

## Arguments

loss

the underlying loss nn_module to call. It must support the reduction field. During training the attribute will be changed to 'none' so we get the loss for individual observations. See for for example documentation for the reduction argument in torch::nn_cross_entropy_loss().

## Details

It should be used together with luz_callback_mixup().

## See also

luz_callback_mixup()