tf.keras.losses.binary_crossentropy

Computes the binary crossentropy loss.

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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
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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)

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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/losses/binary_crossentropy