tf.contrib.training.evaluate_repeatedly

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Repeatedly searches for a checkpoint in checkpoint_dir and evaluates it.

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
)

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.

checkpoint_dir The directory where checkpoints are stored.
master The address of the TensorFlow master.
scaffold An tf.compat.v1.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.estimator.SessionRunHook callbacks which are run inside the evaluation loop.
config An instance of tf.compat.v1.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 number of seconds 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.

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