tf.keras.metrics.BinaryCrossentropy

Computes the crossentropy metric between the labels and predictions.

Inherits From: Mean, Metric, Layer, Module

This is the crossentropy metric class to be used when there are only two label classes (0 and 1).

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
from_logits (Optional )Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
label_smoothing (Optional) Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. label_smoothing=0.2 means that we will use a value of 0.1 for label 0 and 0.9 for label 1".

Standalone usage:

m = tf.keras.metrics.BinaryCrossentropy()
m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
m.result().numpy()
0.81492424
m.reset_state()
m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
               sample_weight=[1, 0])
m.result().numpy()
0.9162905

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[tf.keras.metrics.BinaryCrossentropy()])

Methods

reset_state

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Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

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Computes and returns the metric value tensor.

Resu