tf.contrib.learn.evaluate

tf.contrib.learn.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 Tensors 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.