log_cosh

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log_cosh#

class braintools.metric.log_cosh(predictions, targets=None)#

Calculates the log-cosh loss for a set of predictions.

log(cosh(x)) is approximately (x**2) / 2 for small x and abs(x) - log(2) for large x. It is a twice differentiable alternative to the Huber loss.

References

[Chen et al, 2019](https://openreview.net/pdf?id=rkglvsC9Ym)

Parameters:

  • predictions (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – a vector of arbitrary shape […].

  • targets (Array | ndarray | bool | number | bool | int | float | complex | Quantity | None) – a vector with shape broadcastable to that of predictions; if not provided then it is assumed to be a vector of zeros.

Return type:

Array | ndarray | bool | number | bool | int | float | complex | Quantity

Returns:

the log-cosh loss, with same shape as predictions.

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