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tf.keras.losses.binary_crossentropy

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Computes the binary crossentropy loss.

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

Standalone 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].

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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/losses/binary_crossentropy