smooth_labels

class brainpy.losses.smooth_labels(labels, alpha)

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

[Müller et al, 2019](https://arxiv.org/pdf/1906.02629.pdf)

Parameters:

• labels – one hot labels to be smoothed.

• alpha (float) – the smoothing factor, the greedy category with be assigned probability (1-alpha) + alpha / num_categories

Return type:

Array

Returns:

a smoothed version of the one hot input labels.