tf.keras.losses.binary_crossentropy

Computes the binary crossentropy loss.

View aliases

Main aliases

tf.keras.metrics.binary_crossentropy, tf.losses.binary_crossentropy, tf.metrics.binary_crossentropy

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.losses.binary_crossentropy, tf.compat.v1.keras.metrics.binary_crossentropy

tf.keras.losses.binary_crossentropy(
    y_true, y_pred, from_logits=False, label_smoothing=0
)

Usage:

y_true = [[0, 1], [0, 0]]
y_pred = [[0.6, 0.4], [0.4, 0.6]]
loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
assert loss.shape == (2,)
loss.numpy()
array([0.916 , 0.714], dtype=float32)

Args
y_true	Ground truth values. shape = [batch_size, d0, .. dN].
y_pred	The predicted values. shape = [batch_size, d0, .. dN].
from_logits	Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
label_smoothing	Float in [0, 1]. If > 0 then smooth the labels.

Returns
Binary crossentropy loss value. shape = [batch_size, d0, .. dN-1].