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tf.contrib.training.evaluate_repeatedly

tf.contrib.training.evaluate_repeatedly(
    checkpoint_dir,
    master='',
    scaffold=None,
    eval_ops=None,
    feed_dict=None,
    final_ops=None,
    final_ops_feed_dict=None,
    eval_interval_secs=60,
    hooks=None,
    config=None,
    max_number_of_evaluations=None,
    timeout=None,
    timeout_fn=None
)

Defined in tensorflow/contrib/training/python/training/evaluation.py.

Repeatedly searches for a checkpoint in checkpoint_dir and evaluates it.

During a single evaluation, the eval_ops is run until the session is interrupted or requested to finish. This is typically requested via a tf.contrib.training.StopAfterNEvalsHook which results in eval_ops running the requested number of times.

Optionally, a user can pass in final_ops, a single Tensor, a list of Tensors or a dictionary from names to Tensors. The final_ops is evaluated a single time after eval_ops has finished running and the fetched values of final_ops are returned. If final_ops is left as None, then None is returned.

One may also consider using a tf.contrib.training.SummaryAtEndHook to record summaries after the eval_ops have run. If eval_ops is None, the summaries run immediately after the model checkpoint has been restored.

Note that evaluate_once creates a local variable used to track the number of evaluations run via tf.contrib.training.get_or_create_eval_step. Consequently, if a custom local init op is provided via a scaffold, the caller should ensure that the local init op also initializes the eval step.

Args:

  • checkpoint_dir: The directory where checkpoints are stored.
  • master: The address of the TensorFlow master.
  • scaffold: An tf.train.Scaffold instance for initializing variables and restoring variables. Note that scaffold.init_fn is used by the function to restore the checkpoint. If you supply a custom init_fn, then it must also take care of restoring the model from its checkpoint.
  • eval_ops: A single Tensor, a list of Tensors or a dictionary of names to Tensors, which is run until the session is requested to stop, commonly done by a tf.contrib.training.StopAfterNEvalsHook.
  • feed_dict: The feed dictionary to use when executing the eval_ops.
  • final_ops: A single Tensor, a list of Tensors or a dictionary of names to Tensors.
  • final_ops_feed_dict: A feed dictionary to use when evaluating final_ops.
  • eval_interval_secs: The minimum number of seconds between evaluations.
  • hooks: List of tf.train.SessionRunHook callbacks which are run inside the evaluation loop.
  • config: An instance of tf.ConfigProto that will be used to configure the Session. If left as None, the default will be used.
  • max_number_of_evaluations: The maximum times to run the evaluation. If left as None, then evaluation runs indefinitely.
  • timeout: The maximum amount of time to wait between checkpoints. If left as None, then the process will wait indefinitely.
  • timeout_fn: Optional function to call after a timeout. If the function returns True, then it means that no new checkpoints will be generated and the iterator will exit. The function is called with no arguments.

Returns:

The fetched values of final_ops or None if final_ops is None.