LostTech.TensorFlow : API Documentation

Type IExperiment

Namespace tensorflow.contrib.learn

Interfaces IPythonObjectContainer

Methods

Properties

Public instance methods

object continuous_eval(object delay_secs, object throttle_delay_secs, object evaluate_checkpoint_only_once, object continuous_eval_predicate_fn, object name)

object continuous_eval_on_train_data(object delay_secs, object throttle_delay_secs, object continuous_eval_predicate_fn, object name)

object continuous_train_and_eval(object continuous_eval_predicate_fn)

object extend_train_hooks(object additional_hooks)

object local_run()

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-23. Instructions for updating: local_run will be renamed to train_and_evaluate and the new default behavior will be to run evaluation every time there is a new checkpoint.

object reset_export_strategies(object new_export_strategies)

object run_std_server()

Starts a TensorFlow server and joins the serving thread.

Typically used for parameter servers.

object test()

Tests training, evaluating and exporting the estimator for a single step.
Returns
object
The result of the `evaluate` call to the `Estimator`.

object train(object delay_secs)

object train_and_evaluate()

Interleaves training and evaluation.

The frequency of evaluation is controlled by the constructor arg `min_eval_frequency`. When this parameter is 0, evaluation happens only after training has completed. Note that evaluation cannot happen more frequently than checkpoints are taken. If no new snapshots are available when evaluation is supposed to occur, then evaluation doesn't happen for another `min_eval_frequency` steps (assuming a checkpoint is available at that point). Thus, settings `min_eval_frequency` to 1 means that the model will be evaluated everytime there is a new checkpoint.

This is particular useful for a "Master" task in the cloud, whose responsibility it is to take checkpoints, evaluate those checkpoints, and write out summaries. Participating in training as the supervisor allows such a task to accomplish the first and last items, while performing evaluation allows for the second.
Returns
object
The result of the `evaluate` call to the `Estimator` as well as the export results using the specified `ExportStrategy`.

Public properties

object estimator get;

object eval_metrics get;

object eval_steps get;

object train_steps get;