VariationalChain¶

class tfsnippet.VariationalChain(variational, model, log_joint=None, latent_names=None, latent_axis=None)¶

Bases: object

Chain of the variational and model nets for variational inference.

In the context of variational inference, it is a common usage for chaining the variational net and the model net, by feeding the samples of latent variables from the variational net as the observations of the model net. VariationalChain holds the BayesianNet instances of the variational and the model nets, and the VariationalInference object for this chain.

See also

tfsnippet.bayes.BayesianNet.variational_chain()

Attributes Summary

`latent_axis`	Get the axes of sampling dimensions of latent variables.
`latent_names`	Get the names of the latent variables for variational inference.
`log_joint`	Get the log-joint of the model.
`model`	Get the model net.
`variational`	Get the variational net.
`vi`	Get the variational inference object.

Attributes Documentation

latent_axis¶: Get the axes of sampling dimensions of latent variables.

latent_names¶

Get the names of the latent variables for variational inference.

Returns:	The names of the latent variables.
Return type:	tuple[str]

log_joint¶

Get the log-joint of the model.

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

model¶

Get the model net.

Returns:	The model net.
Return type:	BayesianNet

variational¶

Get the variational net.

Returns:	The variational net.
Return type:	BayesianNet

vi¶

Get the variational inference object.

Returns:	The variational inference object.
Return type:	VariationalInference