Function evaluate

tensorflow/contrib/learn/python/learn/graph_actions.py:478–628

Evaluate a model loaded from a checkpoint. Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval loop for `max_steps` steps, or until an exception (generally, an end-of-input signal from a

(graph,
             output_dir,
             checkpoint_path,
             eval_dict,
             update_op=None,
             global_step_tensor=None,
             supervisor_master='',
             log_every_steps=10,
             feed_fn=None,
             max_steps=None)

@_graph_action_deprecation
def evaluate(graph,
             output_dir,
             checkpoint_path,
             eval_dict,
             update_op=None,
             global_step_tensor=None,
             supervisor_master='',
             log_every_steps=10,
             feed_fn=None,
             max_steps=None):
  """Evaluate a model loaded from a checkpoint.

  Given `graph`, a directory to write summaries to (`output_dir`), a checkpoint
  to restore variables from, and a `dict` of `Tensor`s to evaluate, run an eval
  loop for `max_steps` steps, or until an exception (generally, an
  end-of-input signal from a reader operation) is raised from running
  `eval_dict`.

  In each step of evaluation, all tensors in the `eval_dict` are evaluated, and
  every `log_every_steps` steps, they are logged. At the very end of evaluation,
  a summary is evaluated (finding the summary ops using `Supervisor`'s logic)
  and written to `output_dir`.

  Args:
    graph: A `Graph` to train. It is expected that this graph is not in use
      elsewhere.
    output_dir: A string containing the directory to write a summary to.
    checkpoint_path: A string containing the path to a checkpoint to restore.
      Can be `None` if the graph doesn't require loading any variables.
    eval_dict: A `dict` mapping string names to tensors to evaluate. It is
      evaluated in every logging step. The result of the final evaluation is
      returned. If `update_op` is None, then it's evaluated in every step. If
      `max_steps` is `None`, this should depend on a reader that will raise an
      end-of-input exception when the inputs are exhausted.
    update_op: A `Tensor` which is run in every step.
    global_step_tensor: A `Variable` containing the global step. If `None`,
      one is extracted from the graph using the same logic as in `Supervisor`.
      Used to place eval summaries on training curves.
    supervisor_master: The master string to use when preparing the session.
    log_every_steps: Integer. Output logs every `log_every_steps` evaluation
      steps. The logs contain the `eval_dict` and timing information.
    feed_fn: A function that is called every iteration to produce a `feed_dict`
      passed to `session.run` calls. Optional.
    max_steps: Integer. Evaluate `eval_dict` this many times.

  Returns:
    A tuple `(eval_results, global_step)`:
    eval_results: A `dict` mapping `string` to numeric values (`int`, `float`)
      that are the result of running eval_dict in the last step. `None` if no
      eval steps were run.
    global_step: The global step this evaluation corresponds to.

  Raises:
    ValueError: if `output_dir` is empty.
  """
  if not output_dir:
    raise ValueError('Output directory should be non-empty %s.' % output_dir)
  with graph.as_default():