ed.ReparameterizationEntropyKLqp

Class ReparameterizationEntropyKLqp

Inherits From: VariationalInference

Aliases:

  • Class ed.ReparameterizationEntropyKLqp
  • Class ed.inferences.ReparameterizationEntropyKLqp

Defined in edward/inferences/klqp.py.

Variational inference with the KL divergence

\(\text{KL}( q(z; \lambda) \| p(z \mid x) ).\)

This class minimizes the objective using the reparameterization gradient and an analytic entropy term.

Methods

init

__init__(
    *args,
    **kwargs
)

build_loss_and_gradients

build_loss_and_gradients(var_list)

finalize

finalize()

Function to call after convergence.

initialize

initialize(
    n_samples=1,
    *args,
    **kwargs
)

Initialize inference algorithm. It initializes hyperparameters and builds ops for the algorithm's computation graph.

Args:

  • n_samples: int, optional. Number of samples from variational model for calculating stochastic gradients.
print_progress(info_dict)

Print progress to output.

run

run(
    variables=None,
    use_coordinator=True,
    *args,
    **kwargs
)

A simple wrapper to run inference.

  1. Initialize algorithm via initialize.
  2. (Optional) Build a TensorFlow summary writer for TensorBoard.
  3. (Optional) Initialize TensorFlow variables.
  4. (Optional) Start queue runners.
  5. Run update for self.n_iter iterations.
  6. While running, print_progress.
  7. Finalize algorithm via finalize.
  8. (Optional) Stop queue runners.

To customize the way inference is run, run these steps individually.

Args:

  • variables: list, optional. A list of TensorFlow variables to initialize during inference. Default is to initialize all variables (this includes reinitializing variables that were already initialized). To avoid initializing any variables, pass in an empty list.
  • use_coordinator: bool, optional. Whether to start and stop queue runners during inference using a TensorFlow coordinator. For example, queue runners are necessary for batch training with file readers. *args, **kwargs: Passed into initialize.

update

update(feed_dict=None)

Run one iteration of optimization.

Args:

  • feed_dict: dict, optional. Feed dictionary for a TensorFlow session run. It is used to feed placeholders that are not fed during initialization.

Returns:

dict. Dictionary of algorithm-specific information. In this case, the loss function value after one iteration.