tf.keras.metrics.BinaryCrossentropy

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Computes the crossentropy metric between the labels and predictions.

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Main aliases

tf.metrics.BinaryCrossentropy

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.BinaryCrossentropy

tf.keras.metrics.BinaryCrossentropy(
    name='binary_crossentropy', dtype=None, from_logits=False, label_smoothing=0
)

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

Args
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_states()
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_states

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reset_states()

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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result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

update_state

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update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args
y_true	Ground truth values. shape = [batch_size, d0, .. dN].
y_pred	The predicted values. shape = [batch_size, d0, .. dN].
sample_weight	Optional sample_weight acts as a coefficient for the metric. If a scalar is provided, then the metric is simply scaled by the given value. If sample_weight is a tensor of size [batch_size], then the metric for each sample of the batch is rescaled by the corresponding element in the sample_weight vector. If the shape of sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to this shape), then each metric element of y_pred is scaled by the corresponding value of sample_weight. (Note on dN-1: all metric functions reduce by 1 dimension, usually the last axis (-1)).

Returns
Update op.