optax.losses.sigmoid_binary_cross_entropy

optax.losses.sigmoid_binary_cross_entropy(logits: jax.typing.ArrayLike, labels: jax.typing.ArrayLike) → Array

Computes element-wise sigmoid cross entropy given logits and labels.

This function can be used for binary or multiclass classification (where each class is an independent binary prediction and different classes are not mutually exclusive e.g. predicting that an image contains both a cat and a dog.)

Because this function is overloaded, please ensure your logits and labels are compatible with each other. If you’re passing in binary labels (values in {0, 1}), ensure your logits correspond to class 1 only. If you’re passing in per-class target probabilities or one-hot labels, please ensure your logits are also multiclass. Be particularly careful if you’re relying on implicit broadcasting to reshape logits or labels.

Parameters:

• logits – Each element is the unnormalized log probability of a binary prediction. See note about compatibility with labels above.

• labels – Binary labels whose values are {0,1} or multi-class target probabilities. See note about compatibility with logits above.

Returns:

cross entropy for each binary prediction, same shape as logits.

References

Goodfellow et al, Deep Learning, 2016