tf.keras.losses.KLD

Computes Kullback-Leibler divergence loss between y_true and y_pred.

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

Standalone usage:

y_true = np.random.randint(0, 2, size=(2, 3)).astype(np.float64)
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.kullback_leibler_divergence(y_true, y_pred)
assert loss.shape == (2,)
y_true = tf.keras.backend.clip(y_true, 1e-7, 1)
y_pred = tf.keras.backend.clip(y_pred, 1e-7, 1)
assert np.array_equal(
    loss.numpy(), np.sum(y_true * np.log(y_true / y_pred), axis=-1))

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`.

© 2020 The TensorFlow Authors. All rights reserved.
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/KLD

tf.keras.losses.KLD

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Standalone usage: