tf.keras.losses

TensorFlow 1 version

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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
)

Used in the notebooks

Used in the tutorials
Wiki Talk Comments Toxicity Prediction

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]`.

TensorFlow

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tf.keras / losses / losses.Overview

View aliases

Used in the notebooks

Standalone usage:

Args

Returns