View source on GitHub
|
Get the KL-divergence KL(distribution_a || distribution_b).
tfp.substrates.jax.distributions.kl_divergence(
distribution_a, distribution_b, allow_nan_stats=True, name=None
)
If there is no KL method registered specifically for type(distribution_a)
and type(distribution_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(distribution_a)).
Args | |
|---|---|
distribution_a
|
The first distribution. |
distribution_b
|
The second distribution. |
allow_nan_stats
|
Python bool, default True. When True,
statistics (e.g., mean, mode, variance) use the value "NaN" to
indicate the result is undefined. When False, an exception is raised
if one or more of the statistic's batch members are undefined.
|
name
|
Python str name prefixed to Ops created by this class.
|
Returns | |
|---|---|
A Tensor with the batchwise KL-divergence between distribution_a
and distribution_b.
|
Raises | |
|---|---|
NotImplementedError
|
If no KL method is defined for distribution types
of distribution_a and distribution_b.
|
Built-in KL(distribution_a || distribution_b) registrations:
distribution_a || distribution_b
====================================================================================
Bernoulli || Bernoulli
Beta || Beta
Blockwise || Blockwise
Categorical || Categorical
Cauchy || Cauchy
Chi || Chi
Chi2 || Chi2
Chi2 || Gamma
+ Exponential
ContinuousBernoulli || ContinuousBernoulli
Dirichlet || Dirichlet
ExpGamma || ExpGamma
ExpInverseGamma || ExpInverseGamma
Gamma || Chi2
Exponential +
Gamma || Gamma
Exponential + + Exponential
GaussianProcess || MultivariateNormalLinearOperator
GaussianProcessRegressionModel + + MultivariateNormalDiag
VariationalGaussianProcess + + MultivariateNormalDiagPlusLowRank
+ MultivariateNormalFullCovariance
+ MultivariateNormalTriL
GaussianProcess || Normal
GaussianProcessRegressionModel +
VariationalGaussianProcess +
Gumbel || Gumbel
HalfNormal || HalfNormal
Independent || Independent
JointDistributionSequential || JointDistributionSequential
JointDistributionNamed + + JointDistributionNamed
JointDistributionNamedAutoBatched + + JointDistributionNamedAutoBatched
JointDistributionSequentialAutoBatched + + JointDistributionSequentialAutoBatched
Laplace || Laplace
LogNormal || LogNormal
LogitNormal || LogitNormal
Moyal || Moyal
MultivariateNormalLinearOperator || GaussianProcess
MultivariateNormalDiag + + GaussianProcessRegressionModel
MultivariateNormalDiagPlusLowRank + + VariationalGaussianProcess
MultivariateNormalFullCovariance +
MultivariateNormalTriL +
MultivariateNormalLinearOperator || MultivariateNormalLinearOperator
MultivariateNormalDiag + + MultivariateNormalDiag
MultivariateNormalDiagPlusLowRank + + MultivariateNormalDiagPlusLowRank
MultivariateNormalFullCovariance + + MultivariateNormalFullCovariance
MultivariateNormalTriL + + MultivariateNormalTriL
Normal || GaussianProcess
+ GaussianProcessRegressionModel
+ VariationalGaussianProcess
Normal || Normal
OneHotCategorical || OneHotCategorical
OrderedLogistic || OrderedLogistic
Pareto || Pareto
PowerSpherical || SphericalUniform
PowerSpherical || VonMisesFisher
ProbitBernoulli || ProbitBernoulli
Sample || Sample
TransformedDistribution || TransformedDistribution
Chi + + Chi
ExpInverseGamma + + ExpInverseGamma
GeneralizedExtremeValue + + GeneralizedExtremeValue
Gumbel + + Gumbel
JohnsonSU + + JohnsonSU
Kumaraswamy + + Kumaraswamy
13 more + + 13 more
Uniform || Uniform
VonMises || VonMises
VonMisesFisher || SphericalUniform
Weibull || Weibull
_BaseDeterministic || Distribution
Deterministic + + Autoregressive
VectorDeterministic + + BatchReshape
+ Bates
+ Bernoulli
+ Beta
+ BetaBinomial
+ 94 more
_Cast || _Cast