VariationalInference¶

class tfsnippet.VariationalInference(log_joint, latent_log_probs, axis=None)¶

Class for variational inference.

Attributes Summary

`axis`	Get the axis or axes to be considered as the sampling dimensions of latent variables.
`evaluation`	Get the factory for evaluation outputs.
`latent_log_prob`	Get the summed log-density of latent variables.
`latent_log_probs`	Get the log-densities of latent variables.
`log_joint`	Get the log-joint of the model.
`lower_bound`	Get the factory for variational lower-bounds.
`training`	Get the factory for training objectives.

Methods Summary

`zs_elbo`()	Create a `zhusuan.variational.EvidenceLowerBoundObjective`, with pre-computed log-joint.
`zs_importance_weighted_objective`()	Create a `zhusuan.variational.ImportanceWeightedObjective`, with pre-computed log-joint.
`zs_klpq`()	Create a `zhusuan.variational.InclusiveKLObjective`, with pre-computed log-joint.
`zs_objective`(func, **kwargs)	Create a `zhusuan.variational.VariationalObjective` with pre-computed log-joint, by specified algorithm.

Attributes Documentation

axis¶: Get the axis or axes to be considered as the sampling dimensions of latent variables.

evaluation¶

Get the factory for evaluation outputs.

Returns:	The factory for evaluation outputs.
Return type:	VariationalEvaluation

latent_log_prob¶

Get the summed log-density of latent variables.

Returns:	The summed log-density of latent variables.
Return type:	tf.Tensor

latent_log_probs¶

Get the log-densities of latent variables.

Returns:	The log-densities of latent variables.
Return type:	tuple[tf.Tensor]

log_joint¶

Get the log-joint of the model.

Returns:	The log-joint of the model.
Return type:	tf.Tensor

lower_bound¶

Get the factory for variational lower-bounds.

Returns:	The factory for variational lower-bounds.
Return type:	VariationalLowerBounds

training¶

Get the factory for training objectives.

Returns:	The factory for training objectives.
Return type:	VariationalTrainingObjectives

Methods Documentation

zs_elbo()¶

Create a zhusuan.variational.EvidenceLowerBoundObjective, with pre-computed log-joint.

Returns:	The constructed per-data ELBO objective.
Return type:	zhusuan.variational.EvidenceLowerBoundObjective

zs_importance_weighted_objective()¶

Create a zhusuan.variational.ImportanceWeightedObjective, with pre-computed log-joint.

Returns:	The constructed per-data importance weighted objective.
Return type:	zhusuan.variational.ImportanceWeightedObjective

zs_klpq()¶

Create a zhusuan.variational.InclusiveKLObjective, with pre-computed log-joint.

Returns:	The constructed per-data inclusive KL objective.
Return type:	zhusuan.variational.InclusiveKLObjective

zs_objective(func, **kwargs)¶

Create a zhusuan.variational.VariationalObjective with pre-computed log-joint, by specified algorithm.

Parameters:

func – The variational algorithm from ZhuSuan. Supported functions are: 1. zhusuan.variational.elbo() 2. zhusuan.variational.importance_weighted_objective() 3. zhusuan.variational.klpq()
**kwargs – Named arguments passed to func.

Returns:

Return type:

zhusuan.variational.VariationalObjective