tf.keras.metrics.BinaryCrossentropy

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Computes the crossentropy metric between the labels and predictions.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.BinaryCrossentropy, `tf.compat.v2.keras.metrics.BinaryCrossentropy`, `tf.compat.v2.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).

Usage:

m = tf.keras.metrics.BinaryCrossentropy()
m.update_state([1., 0., 1., 0.], [1., 1., 1., 0.])

# EPSILON = 1e-7, y = y_true, y` = y_pred, Y_MAX = 0.9999999
# y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
# y` = [Y_MAX, Y_MAX, Y_MAX, EPSILON]

# Metric = -(y log(y` + EPSILON) + (1 - y) log(1 - y` + EPSILON))
#        = [-log(Y_MAX + EPSILON), -log(1 - Y_MAX + EPSILON),
#           -log(Y_MAX + EPSILON), -log(1)]
#        = [(0 + 15.33) / 2, (0 + 0) / 2]
# Reduced metric = 7.665 / 2

print('Final result: ', m.result().numpy())  # Final result: 3.833

Usage with tf.keras API:

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

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"

Methods

reset_states

View source

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

View source

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	The ground truth values.
y_pred	The predicted values.
sample_weight	Optional weighting of each example. Defaults to 1. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true.

Returns
Update op.