Calculates the binary cross entropy.

Inherits From: Metric

The metric computes the cross entropy when there are only two label classes (0 and 1). See definition at:

name The name of the metric.
from_logits (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
label_smoothing Float in [0, 1]. If > 0 then smooth the labels by squeezing them towards 0.5 That is, using 1. - 0.5 * label_smoothing for the target class and 0.5 * label_smoothing for the non-target class.

compute_confidence_interval Whether to compute confidence intervals for this metric.

Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.



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Creates computations associated with metric.


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Returns serializable config.