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Add 'log_progress' argument for tf.estimator.Estimator's evaluate function (#13695)

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* Add  argument for tf.estimator.Estimator's evaluate function

* add log_progress argument to ._convert_eval_steps_to_hooks for TPU estimator

* log only every 10th step if more than 100 iterations in _StopAfterNEvalsHook

* ensure last step is logged and aim for 10 outputs total
This commit is contained in:
Urs Köster 2017-10-31 23:38:10 -07:00 committed by gunan
parent 07a91dac54
commit fa9d8aab41
1 changed files with 7 additions and 1 deletions
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8
tensorflow/python/training/evaluation.py
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@ -19,6 +19,7 @@ from __future__ import division
from __future__ import print_function
import time
import math
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
@ -91,6 +92,9 @@ class _StopAfterNEvalsHook(session_run_hook.SessionRunHook):
self._num_evals = num_evals
self._evals_completed = None
self._log_progress = log_progress
# Reduce logging frequency if there are 20 or more evaluations.
self._log_frequency = (1 if (num_evals is None or num_evals < 20)
else math.floor(num_evals / 10.))
def _set_evals_completed_tensor(self, updated_eval_step):
self._evals_completed = updated_eval_step
@ -106,7 +110,9 @@ class _StopAfterNEvalsHook(session_run_hook.SessionRunHook):
if self._num_evals is None:
logging.info('Evaluation [%d]', evals_completed)
else:
logging.info('Evaluation [%d/%d]', evals_completed, self._num_evals)
if ((evals_completed % self._log_frequency) == 0 or
(self._num_evals == evals_completed)):
logging.info('Evaluation [%d/%d]', evals_completed, self._num_evals)
if self._num_evals is not None and evals_completed >= self._num_evals:
run_context.request_stop()