tf.contrib.learn.evaluate(*args, **kwargs)
See the guide: Learn (contrib) > Graph actions
Evaluate a model loaded from a checkpoint. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2017-02-15. Instructions for updating: graph_actions.py will be deleted. Use tf.train.* utilities instead. You can use learn/estimators/estimator.py as an example.
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
.
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.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.
ValueError
: if output_dir
is empty.Defined in tensorflow/python/util/deprecation.py
.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/learn/evaluate