tf.contrib.distributions.kl(dist_a, dist_b, allow_nan=False, name=None)

tf.contrib.distributions.kl(dist_a, dist_b, allow_nan=False, name=None)

See the guide: Statistical Distributions (contrib) > Kullback-Leibler Divergence

Get the KL-divergence KL(dist_a || dist_b).

If there is no KL method registered specifically for type(dist_a) and type(dist_b), then the class hierarchies of these types are searched.

If one KL method is registered between any pairs of classes in these two parent hierarchies, it is used.

If more than one such registered method exists, the method whose registered classes have the shortest sum MRO paths to the input types is used.

If more than one such shortest path exists, the first method identified in the search is used (favoring a shorter MRO distance to type(dist_a)).

Args:

• dist_a: The first distribution.
• dist_b: The second distribution.
• allow_nan: If False (default), a runtime error is raised if the KL returns NaN values for any batch entry of the given distributions. If True, the KL may return a NaN for the given entry.
• name: (optional) Name scope to use for created operations.

Returns:

A Tensor with the batchwise KL-divergence between dist_a and dist_b.

Raises:

• NotImplementedError: If no KL method is defined for distribution types of dist_a and dist_b.

Defined in tensorflow/contrib/distributions/python/ops/kullback_leibler.py.