tf.contrib.learn.Experiment

Class Experiment

Defined in tensorflow/contrib/learn/python/learn/experiment.py.

See the guide: Learn (contrib) > Distributed training utilities

Experiment is a class containing all information needed to train a model.

THIS CLASS IS DEPRECATED. See contrib/learn/README.md for general migration instructions.

After an experiment is created (by passing an Estimator and inputs for training and evaluation), an Experiment instance knows how to invoke training and eval loops in a sensible fashion for distributed training.

Properties

estimator

eval_metrics

eval_steps

train_steps

Methods

__init__

__init__(
    estimator,
    train_input_fn,
    eval_input_fn,
    eval_metrics=None,
    train_steps=None,
    eval_steps=100,
    train_monitors=None,
    eval_hooks=None,
    local_eval_frequency=None,
    eval_delay_secs=120,
    continuous_eval_throttle_secs=60,
    min_eval_frequency=None,
    delay_workers_by_global_step=False,
    export_strategies=None,
    train_steps_per_iteration=None,
    checkpoint_and_export=False,
    saving_listeners=None,
    check_interval_secs=5
)

Constructor for Experiment. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please switch to tf.estimator.train_and_evaluate. You will also have to convert to a tf.estimator.Estimator.

Creates an Experiment instance. None of the functions passed to this constructor are executed at construction time. They are stored and used when a method is executed which requires it.

Args:

  • estimator: Object implementing Estimator interface, which could be a combination of tf.contrib.learn.Trainable and tf.contrib.learn.Evaluable (deprecated), or tf.estimator.Estimator.
  • train_input_fn: function, returns features and labels for training.
  • eval_input_fn: function, returns features and labels for evaluation. If eval_steps is None, this should be configured only to produce for a finite number of batches (generally, 1 epoch over the evaluation data).
  • eval_metrics: dict of string, metric function. If None, default set is used. This should be None if the estimator is tf.estimator.Estimator. If metrics are provided they will be appended to the default set.
  • train_steps: Perform this many steps of training. None, the default, means train forever.
  • eval_steps: evaluate runs until input is exhausted (or another exception is raised), or for eval_steps steps, if specified.
  • train_monitors: A list of monitors to pass to the Estimator's fit function.
  • eval_hooks: A list of SessionRunHook hooks to pass to the Estimator's evaluate function.
  • local_eval_frequency: (applies only to local_run) Frequency of running eval in steps. If None, runs evaluation only at the end of training.
  • eval_delay_secs: Start evaluating after waiting for this many seconds.
  • continuous_eval_throttle_secs: Do not re-evaluate unless the last evaluation was started at least this many seconds ago for continuous_eval().
  • min_eval_frequency: (applies only to train_and_evaluate). the minimum number of steps between evaluations. Of course, evaluation does not occur if no new snapshot is available, hence, this is the minimum. If 0, the evaluation will only happen after training. If None, defaults to 1. To avoid checking for new checkpoints too frequent, the interval is further limited to be at least check_interval_secs between checks.
  • delay_workers_by_global_step: if True delays training workers based on global step instead of time.
  • export_strategies: Iterable of ExportStrategys, or a single one, or None.
  • train_steps_per_iteration: (applies only to continuous_train_and_eval). Perform this many (integer) number of train steps for each training-evaluation iteration. With a small value, the model will be evaluated more frequently with more checkpoints saved. If None, will use a default value (which is smaller than train_steps if provided).
  • checkpoint_and_export: (applies only to train_and_evaluate). If True, performs intermediate model checkpoints and exports during the training process, rather than only once model training is complete. This parameter is experimental and may be changed or removed in the future. Setting this parameter leads to the following: the value of min_eval_frequency will be ignored, and the number of steps between evaluations and exports will instead be determined by the Estimator configuration parameters save_checkpoints_secs and save_checkpoints_steps. Also, this parameter leads to the creation of a default CheckpointSaverHook instead of a ValidationMonitor, so the provided train_monitors will need to be adjusted accordingly.
  • saving_listeners: list of CheckpointSaverListener objects. Used by tf.estimator.Estimator for callbacks that run immediately before or after checkpoint savings.
  • check_interval_secs: Minimum time between subsequent checks for a new checkpoint. This mostly applies if both min_eval_frequency and the time spent per training step is low.

Raises:

  • ValueError: if estimator does not implement Estimator interface, or if export_strategies has the wrong type.

continuous_eval

continuous_eval(
    delay_secs=None,
    throttle_delay_secs=None,
    evaluate_checkpoint_only_once=True,
    continuous_eval_predicate_fn=None,
    name='continuous'
)

continuous_eval_on_train_data

continuous_eval_on_train_data(
    delay_secs=None,
    throttle_delay_secs=None,
    continuous_eval_predicate_fn=None,
    name='continuous_on_train_data'
)

continuous_train_and_eval

continuous_train_and_eval(
    *args,
    **kwargs
)

Interleaves training and evaluation. (experimental)

THIS FUNCTION IS EXPERIMENTAL. It may change or be removed at any time, and without warning.

The frequency of evaluation is controlled by the train_steps_per_iteration (via constructor). The model will be first trained for train_steps_per_iteration, and then be evaluated in turns.

This method is intended for single machine usage.

This differs from train_and_evaluate as follows:

  1. The procedure will have train and evaluation in turns. The model will be trained for a number of steps (usually smaller than train_steps if provided) and then be evaluated. train_and_evaluate will train the model for train_steps (no small training iterations).

  2. Due to the different approach this schedule takes, it leads to two differences in resource control. First, the resources (e.g., memory) used by training will be released before evaluation (train_and_evaluate takes double resources). Second, more checkpoints will be saved as a checkpoint is generated at the end of each training iteration.

  3. As the estimator.train starts from scratch (new graph, new states for input, etc) at each iteration, it is recommended to have the train_steps_per_iteration larger. It is also recommended to shuffle your input.

Args:

  • continuous_eval_predicate_fn: A predicate function determining whether to continue eval after each iteration. A predicate_fn has one of the following signatures:
    • (eval_results) -> boolean
    • (eval_results, checkpoint_path) -> boolean Where eval_results is the dictionary of metric evaluations and checkpoint_path is the path to the checkpoint containing the parameters on which that evaluation was based. At the beginning of evaluation, the passed eval_results and checkpoint_path will be None so it's expected that the predicate function handles that gracefully. When predicate_fn is not specified, continuous eval will run in an infinite loop (if train_steps is None). or exit once global step reaches train_steps.

Returns:

A tuple of the result of the evaluate call to the Estimator and the export results using the specified ExportStrategy.

Raises:

  • ValueError: if continuous_eval_predicate_fn is neither None nor callable.

evaluate

evaluate(
    delay_secs=None,
    name=None
)

Evaluate on the evaluation data.

Runs evaluation on the evaluation data and returns the result. Runs for self._eval_steps steps, or if it's None, then run until input is exhausted or another exception is raised. Start the evaluation after delay_secs seconds, or if it's None, defaults to using self._eval_delay_secs seconds.

Args:

  • delay_secs: Start evaluating after this many seconds. If None, defaults to using self._eval_delays_secs.
  • name: Gives the name to the evauation for the case multiple evaluation is run for the same experiment.

Returns:

The result of the evaluate call to the Estimator.

extend_train_hooks

extend_train_hooks(additional_hooks)

Extends the hooks for training.

local_run

local_run()

DEPRECATED FUNCTION

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.

reset_export_strategies

reset_export_strategies(new_export_strategies=None)

Resets the export strategies with the new_export_strategies.

Args:

  • new_export_strategies: A new list of ExportStrategys, or a single one, or None.

Returns:

The old export strategies.

run_std_server

run_std_server()

Starts a TensorFlow server and joins the serving thread.

Typically used for parameter servers.

Raises:

  • ValueError: if not enough information is available in the estimator's config to create a server.

test

test()

Tests training, evaluating and exporting the estimator for a single step.

Returns:

The result of the evaluate call to the Estimator.

train

train(delay_secs=None)

Fit the estimator using the training data.

Train the estimator for self._train_steps steps, after waiting for delay_secs seconds. If self._train_steps is None, train forever.

Args:

  • delay_secs: Start training after this many seconds.

Returns:

The trained estimator.

train_and_evaluate

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:

The result of the evaluate call to the Estimator as well as the export results using the specified ExportStrategy.