parameters: (checkpoint_path,
                     master='',
                     scaffold=None,
                     eval_ops=None,
                     feed_dict=None,
                     final_ops=None,
                     final_ops_feed_dict=None,
                     hooks=None,
                     config=None)
return_statement: return final_ops_hook.final_ops_values
docstring: 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 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_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`.
docstring_summary: Evaluates the model at the given checkpoint path.
docstring_tokens: Evaluates the model at the given checkpoint path .
function: def _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):
    """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 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_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`.
    """
    eval_step = _get_or_create_eval_step()
  
    # Prepare the run hooks.
    hooks = list(hooks or [])
  
    if eval_ops is not None:
      update_eval_step = state_ops.assign_add(eval_step, 1, use_locking=True)
  
      if isinstance(eval_ops, dict):
        eval_ops['update_eval_step'] = update_eval_step
      elif isinstance(eval_ops, (tuple, list)):
        eval_ops = list(eval_ops) + [update_eval_step]
      else:
        eval_ops = [eval_ops, update_eval_step]
  
      eval_step_value = _get_latest_eval_step_value(eval_ops)
  
      for h in hooks:
        if isinstance(h, _StopAfterNEvalsHook):
          h._set_evals_completed_tensor(eval_step_value)  # pylint: disable=protected-access
  
    logging.info('Starting evaluation at ' + time.strftime('%Y-%m-%d-%H:%M:%S',
                                                           time.gmtime()))
  
    # Prepare the session creator.
    session_creator = monitored_session.ChiefSessionCreator(
        scaffold=scaffold,
        checkpoint_filename_with_path=checkpoint_path,
        master=master,
        config=config)
  
    final_ops_hook = basic_session_run_hooks.FinalOpsHook(
        final_ops, final_ops_feed_dict)
    hooks.append(final_ops_hook)
  
    with monitored_session.MonitoredSession(
        session_creator=session_creator, hooks=hooks) as session:
      if eval_ops is not None:
        while not session.should_stop():
          session.run(eval_ops, feed_dict)
  
    logging.info('Finished evaluation at ' + time.strftime('%Y-%m-%d-%H:%M:%S',
                                                           time.gmtime()))
    return final_ops_hook.final_ops_values
function_tokens: def _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 ) : eval_step = _get_or_create_eval_step ( ) # Prepare the run hooks. hooks = list ( hooks or [ ] ) if eval_ops is not None : update_eval_step = state_ops . assign_add ( eval_step , 1 , use_locking = True ) if isinstance ( eval_ops , dict ) : eval_ops [ 'update_eval_step' ] = update_eval_step elif isinstance ( eval_ops , ( tuple , list ) ) : eval_ops = list ( eval_ops ) + [ update_eval_step ] else : eval_ops = [ eval_ops , update_eval_step ] eval_step_value = _get_latest_eval_step_value ( eval_ops ) for h in hooks : if isinstance ( h , _StopAfterNEvalsHook ) : h . _set_evals_completed_tensor ( eval_step_value ) # pylint: disable=protected-access logging . info ( 'Starting evaluation at ' + time . strftime ( '%Y-%m-%d-%H:%M:%S' , time . gmtime ( ) ) ) # Prepare the session creator. session_creator = monitored_session . ChiefSessionCreator ( scaffold = scaffold , checkpoint_filename_with_path = checkpoint_path , master = master , config = config ) final_ops_hook = basic_session_run_hooks . FinalOpsHook ( final_ops , final_ops_feed_dict ) hooks . append ( final_ops_hook ) with monitored_session . MonitoredSession ( session_creator = session_creator , hooks = hooks ) as session : if eval_ops is not None : while not session . should_stop ( ) : session . run ( eval_ops , feed_dict ) logging . info ( 'Finished evaluation at ' + time . strftime ( '%Y-%m-%d-%H:%M:%S' , time . gmtime ( ) ) ) return final_ops_hook . final_ops_values