Sparsemax loss function [1].
loss_sparsemax( from_logits = TRUE, reduction = tf$keras$losses$Reduction$SUM_OVER_BATCH_SIZE, name = "sparsemax_loss" )
| from_logits | Whether y_pred is expected to be a logits tensor. Default is True, meaning y_pred is the logits. |
|---|---|
| reduction | (Optional) Type of tf$keras$losses$Reduction to apply to loss. Default value is SUM_OVER_BATCH_SIZE. |
| name | Optional name for the op. |
A `Tensor`. Has the same type as `logits`.
Computes the generalized multi-label classification loss for the sparsemax function. The implementation is a reformulation of the original loss function such that it uses the sparsemax properbility output instead of the internal au variable. However, the output is identical to the original loss function. [1]: https://arxiv.org/abs/1602.02068