LostTech.TensorFlow : API Documentation

Type SingularMonitoredSession

Namespace tensorflow.train

Parent _MonitoredSession

Interfaces ISingularMonitoredSession

Session-like object that handles initialization, restoring, and hooks.

Please note that this utility is not recommended for distributed settings. For distributed settings, please use `tf.compat.v1.train.MonitoredSession`. The differences between `MonitoredSession` and `SingularMonitoredSession` are:

* `MonitoredSession` handles `AbortedError` and `UnavailableError` for distributed settings, but `SingularMonitoredSession` does not. * `MonitoredSession` can be created in `chief` or `worker` modes. `SingularMonitoredSession` is always created as `chief`. * You can access the raw `tf.compat.v1.Session` object used by `SingularMonitoredSession`, whereas in MonitoredSession the raw session is private. This can be used: - To `run` without hooks. - To save and restore. * All other functionality is identical.

Example usage: Initialization: At creation time the hooked session does following things in given order:

* calls `hook.begin()` for each given hook * finalizes the graph via `scaffold.finalize()` * create session * initializes the model via initialization ops provided by `Scaffold` * restores variables if a checkpoint exists * launches queue runners

Run: When `run()` is called, the hooked session does following things:

* calls `hook.before_run()` * calls TensorFlow `session.run()` with merged fetches and feed_dict * calls `hook.after_run()` * returns result of `session.run()` asked by user

Exit: At the `close()`, the hooked session does following things in order:

* calls `hook.end()` * closes the queue runners and the session * suppresses `OutOfRange` error which indicates that all inputs have been processed if the `SingularMonitoredSession` is used as a context.
Show Example
saver_hook = CheckpointSaverHook(...)
            summary_hook = SummarySaverHook(...)
            with SingularMonitoredSession(hooks=[saver_hook, summary_hook]) as sess:
              while not sess.should_stop():



Public instance methods

object raw_session()

Returns underlying `TensorFlow.Session` object.

object raw_session_dyn()

Returns underlying `TensorFlow.Session` object.

Public static methods

SingularMonitoredSession NewDyn(object hooks, object scaffold, ImplicitContainer<T> master, object config, object checkpoint_dir, ImplicitContainer<T> stop_grace_period_secs, object checkpoint_filename_with_path)

Creates a SingularMonitoredSession.
object hooks
An iterable of `SessionRunHook' objects.
object scaffold
A `Scaffold` used for gathering or building supportive ops. If not specified a default one is created. It's used to finalize the graph.
ImplicitContainer<T> master
`String` representation of the TensorFlow master to use.
object config
`ConfigProto` proto used to configure the session.
object checkpoint_dir
A string. Optional path to a directory where to restore variables.
ImplicitContainer<T> stop_grace_period_secs
Number of seconds given to threads to stop after `close()` has been called.
object checkpoint_filename_with_path
A string. Optional path to a checkpoint file from which to restore variables.

Public properties

object graph get;

object graph_dyn get;

object PythonObject get;