contrib.learn.evaluate
tf.contrib.learn.evaluate
tf.contrib.learn.evaluate
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 )
Defined in tensorflow/contrib/learn/python/learn/graph_actions.py
.
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
.
Args:
-
graph
: AGraph
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 beNone
if the graph doesn't require loading any variables. -
eval_dict
: Adict
mapping string names to tensors to evaluate. It is evaluated in every logging step. The result of the final evaluation is returned. Ifupdate_op
is None, then it's evaluated in every step. Ifmax_steps
isNone
, this should depend on a reader that will raise an end-of-input exception when the inputs are exhausted. -
update_op
: ATensor
which is run in every step. -
global_step_tensor
: AVariable
containing the global step. IfNone
, one is extracted from the graph using the same logic as inSupervisor
. 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 everylog_every_steps
evaluation steps. The logs contain theeval_dict
and timing information. -
feed_fn
: A function that is called every iteration to produce afeed_dict
passed tosession.run
calls. Optional. -
max_steps
: Integer. Evaluateeval_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
: ifoutput_dir
is empty.
© 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