tf.keras.losses.binary_crossentropy

Computes the binary crossentropy loss.

View aliases

Main aliases

tf.keras.metrics.binary_crossentropy

tf.keras.losses.binary_crossentropy(
    y_true, y_pred, from_logits=False, label_smoothing=0.0, axis=-1
)

Used in the notebooks

Used in the tutorials
Wiki Talk Comments Toxicity Prediction

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 by squeezing them towards 0.5, that is, using 1. - 0.5 * label_smoothing for the target class and 0.5 * label_smoothing for the non-target class.
axis	The axis along which the mean is computed. Defaults to -1.

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

Example:

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