optax.losses.smooth_labels

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optax.losses.smooth_labels#

optax.losses.smooth_labels(
labels: jax.typing.ArrayLike,
alpha: jax.typing.ArrayLike,
*,
axis: int | tuple[int, ...] | None = -1,
where: TypeAliasForwardRef('jax.typing.ArrayLike') | None = None,
) → Array[source]#

Apply label smoothing.

Label smoothing is often used in combination with a cross-entropy loss. Smoothed labels favor small logit gaps, and it has been shown that this can provide better model calibration by preventing overconfident predictions.

Parameters:

  • labels – One hot labels to be smoothed.

  • alpha – The smoothing factor.

  • axis – Axis or axes along which to compute.

  • where – Elements to include in the computation.

Returns:

a smoothed version of the one hot input labels.

References

Muller et al, When does label smoothing help?, 2019

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