tf.keras.losses.KLD

Computes Kullback-Leibler divergence loss between y_true and y_pred.

View aliases

Main aliases

tf.keras.losses.kld, tf.keras.losses.kullback_leibler_divergence, tf.keras.metrics.KLD, tf.keras.metrics.kld, tf.keras.metrics.kullback_leibler_divergence

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.losses.KLD, tf.compat.v1.keras.losses.kld, tf.compat.v1.keras.losses.kullback_leibler_divergence, tf.compat.v1.keras.metrics.KLD, tf.compat.v1.keras.metrics.kld, tf.compat.v1.keras.metrics.kullback_leibler_divergence, `tf.compat.v2.keras.losses.KLD`, `tf.compat.v2.keras.losses.kld`, `tf.compat.v2.keras.losses.kullback_leibler_divergence`, `tf.compat.v2.keras.metrics.KLD`, `tf.compat.v2.keras.metrics.kld`, `tf.compat.v2.keras.metrics.kullback_leibler_divergence`, `tf.compat.v2.losses.KLD`, `tf.compat.v2.losses.kld`, `tf.compat.v2.losses.kullback_leibler_divergence`, `tf.compat.v2.metrics.KLD`, `tf.compat.v2.metrics.kld`, `tf.compat.v2.metrics.kullback_leibler_divergence`

tf.keras.losses.KLD(
    y_true, y_pred
)

loss = y_true * log(y_true / y_pred)

See: https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence

Usage:

loss = tf.keras.losses.KLD([.4, .9, .2], [.5, .8, .12])
print('Loss: ', loss.numpy())  # Loss: 0.11891246

Args
y_true	Tensor of true targets.
y_pred	Tensor of predicted targets.

Returns
A Tensor with loss.

Raises
TypeError	If y_true cannot be cast to the y_pred.dtype.