contrib.training.evaluate_once

tf.contrib.training.evaluate_once

tf.contrib.training.evaluate_once

evaluate_once(
    checkpoint_path,
    master='',
    scaffold=None,
    eval_ops=None,
    feed_dict=None,
    final_ops=None,
    final_ops_feed_dict=None,
    hooks=None,
    config=None
)

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

Evaluates the model at the given checkpoint path.

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 immedietly 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_path: The path to a checkpoint to use for evaluation.
• master: The BNS 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.
• 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.

Returns:

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