composer.algorithms.functional.smooth_labels

composer.algorithms.functional.smooth_labels(logits: torch.Tensor, targets: torch.Tensor, alpha: float)

Shrinks targets towards a uniform distribution to counteract label noise as in Szegedy et al..

This is computed by (1 - alpha) * targets + alpha * smoothed_targets where smoothed_targets is a vector of ones.

Parameters

• logits – Output of the model. Tensor of shape (N, C, d1, …, dn) for N examples and C classes, and d1, …, dn extra dimensions.

• targets – Tensor of shape (N) containing integers 0 <= i <= C-1 specifying the target labels for each example.

• alpha – Strength of the label smoothing, in [0, 1]. alpha=0 means no label smoothing, and alpha=1 means maximal smoothing (targets are ignored).