Calculates the categorical cross entropy.

Inherits From: Metric

The metric computes the cross entropy when there are multiple classes. It outputs a numpy array.

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. For example, if 0.1, use 0.1 / num_classes for non-target labels and 0.9 + 0.1 / num_classes for target labels.

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.