tf.keras.metrics.KLDivergence

Computes Kullback-Leibler divergence metric between y_true and y_pred.

Main aliases

tf.metrics.KLDivergence

Compat aliases for migration

See Migration guide for more details.

tf.keras.metrics.KLDivergence(
    name='kullback_leibler_divergence', dtype=None
)

metric = y_true * log(y_true / y_pred)

m = tf.keras.metrics.KLDivergence()
m.update_state([.4, .9, .2], [.5, .8, .12])
print('Final result: ', m.result().numpy())  # Final result: -0.043

Usage with tf.keras API:

model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.KLDivergence()])

Args
`fn`	The metric function to wrap, with signature `fn(y_true, y_pred, **kwargs)`.
`name`	(Optional) string name of the metric instance.
`dtype`	(Optional) data type of the metric result.
`**kwargs`	The keyword arguments that are passed on to `fn`.

Methods

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates metric statistics.

y_true and y_pred should have the same shape.

Args
`y_true`	The ground truth values.
`y_pred`	The predicted values.
`sample_weight`	Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

Returns
Update op.