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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for early_stopping."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tempfile
from absl.testing import parameterized
from tensorflow.contrib.estimator.python.estimator import early_stopping
from tensorflow.python.estimator import estimator
from tensorflow.python.estimator import run_config
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.platform import test
from tensorflow.python.training import monitored_session
from tensorflow.python.training import training_util
class _FakeRunConfig(run_config.RunConfig):
def __init__(self, is_chief):
super(_FakeRunConfig, self).__init__()
self._is_chief = is_chief
@property
def is_chief(self):
return self._is_chief
def _dummy_model_fn(features, labels, params):
_, _, _ = features, labels, params
class _FakeEstimator(estimator.Estimator):
"""Fake estimator for testing."""
def __init__(self, config):
super(_FakeEstimator, self).__init__(
model_fn=_dummy_model_fn, config=config)
def _write_events(eval_dir, params):
"""Test helper to write events to summary files."""
for steps, loss, accuracy in params:
estimator._write_dict_to_summary(eval_dir, {
'loss': loss,
'accuracy': accuracy,
}, steps)
class ReadEvalMetricsTest(test.TestCase):
def test_read_eval_metrics(self):
eval_dir = tempfile.mkdtemp()
_write_events(
eval_dir,
[
# steps, loss, accuracy
(1000, 1, 2),
(2000, 3, 4),
(3000, 5, 6),
])
self.assertEqual({
1000: {
'loss': 1,
'accuracy': 2
},
2000: {
'loss': 3,
'accuracy': 4
},
3000: {
'loss': 5,
'accuracy': 6
},
}, early_stopping.read_eval_metrics(eval_dir))
def test_read_eval_metrics_when_no_events(self):
eval_dir = tempfile.mkdtemp()
self.assertTrue(os.path.exists(eval_dir))
# No error should be raised when eval directory exists with no event files.
self.assertEqual({}, early_stopping.read_eval_metrics(eval_dir))
os.rmdir(eval_dir)
self.assertFalse(os.path.exists(eval_dir))
# No error should be raised when eval directory does not exist.
self.assertEqual({}, early_stopping.read_eval_metrics(eval_dir))
class EarlyStoppingHooksTest(test.TestCase, parameterized.TestCase):
def setUp(self):
config = _FakeRunConfig(is_chief=True)
self._estimator = _FakeEstimator(config=config)
eval_dir = self._estimator.eval_dir()
os.makedirs(eval_dir)
_write_events(
eval_dir,
[
# steps, loss, accuracy
(1000, 0.8, 0.5),
(2000, 0.7, 0.6),
(3000, 0.4, 0.7),
(3500, 0.41, 0.68),
])
def run_session(self, hooks, should_stop):
hooks = hooks if isinstance(hooks, list) else [hooks]
with ops.Graph().as_default():
training_util.create_global_step()
no_op = control_flow_ops.no_op()
with monitored_session.SingularMonitoredSession(hooks=hooks) as mon_sess:
mon_sess.run(no_op)
self.assertEqual(mon_sess.should_stop(), should_stop)
@parameterized.parameters((0.8, 0, False), (0.6, 4000, False), (0.6, 0, True))
def test_stop_if_higher_hook(self, threshold, min_steps, should_stop):
self.run_session(
early_stopping.stop_if_higher_hook(
self._estimator,
metric_name='accuracy',
threshold=threshold,
min_steps=min_steps), should_stop)
@parameterized.parameters((0.3, 0, False), (0.5, 4000, False), (0.5, 0, True))
def test_stop_if_lower_hook(self, threshold, min_steps, should_stop):
self.run_session(
early_stopping.stop_if_lower_hook(
self._estimator,
metric_name='loss',
threshold=threshold,
min_steps=min_steps), should_stop)
@parameterized.parameters((1500, 0, False), (500, 4000, False),
(500, 0, True))
def test_stop_if_no_increase_hook(self, max_steps, min_steps, should_stop):
self.run_session(
early_stopping.stop_if_no_increase_hook(
self._estimator,
metric_name='accuracy',
max_steps_without_increase=max_steps,
min_steps=min_steps), should_stop)
@parameterized.parameters((1500, 0, False), (500, 4000, False),
(500, 0, True))
def test_stop_if_no_decrease_hook(self, max_steps, min_steps, should_stop):
self.run_session(
early_stopping.stop_if_no_decrease_hook(
self._estimator,
metric_name='loss',
max_steps_without_decrease=max_steps,
min_steps=min_steps), should_stop)
@parameterized.parameters((1500, 0.3, False), (1500, 0.5, True),
(500, 0.3, True))
def test_multiple_hooks(self, max_steps, loss_threshold, should_stop):
self.run_session([
early_stopping.stop_if_no_decrease_hook(
self._estimator,
metric_name='loss',
max_steps_without_decrease=max_steps),
early_stopping.stop_if_lower_hook(
self._estimator, metric_name='loss', threshold=loss_threshold)
], should_stop)
@parameterized.parameters(False, True)
def test_make_early_stopping_hook(self, should_stop):
self.run_session([
early_stopping.make_early_stopping_hook(
self._estimator, should_stop_fn=lambda: should_stop)
], should_stop)
def test_make_early_stopping_hook_typeerror(self):
with self.assertRaises(TypeError):
early_stopping.make_early_stopping_hook(
estimator=object(), should_stop_fn=lambda: True)
def test_make_early_stopping_hook_valueerror(self):
with self.assertRaises(ValueError):
early_stopping.make_early_stopping_hook(
self._estimator,
should_stop_fn=lambda: True,
run_every_secs=60,
run_every_steps=100)
class StopOnPredicateHookTest(test.TestCase):
def test_stop(self):
hook = early_stopping._StopOnPredicateHook(
should_stop_fn=lambda: False, run_every_secs=0)
with ops.Graph().as_default():
training_util.create_global_step()
no_op = control_flow_ops.no_op()
with monitored_session.SingularMonitoredSession(hooks=[hook]) as mon_sess:
mon_sess.run(no_op)
self.assertFalse(mon_sess.should_stop())
self.assertFalse(mon_sess.raw_session().run(hook._stop_var))
hook = early_stopping._StopOnPredicateHook(
should_stop_fn=lambda: True, run_every_secs=0)
with ops.Graph().as_default():
training_util.create_global_step()
no_op = control_flow_ops.no_op()
with monitored_session.SingularMonitoredSession(hooks=[hook]) as mon_sess:
mon_sess.run(no_op)
self.assertTrue(mon_sess.should_stop())
self.assertTrue(mon_sess.raw_session().run(hook._stop_var))
class CheckForStoppingHookTest(test.TestCase):
def test_stop(self):
hook = early_stopping._CheckForStoppingHook()
with ops.Graph().as_default():
no_op = control_flow_ops.no_op()
assign_op = state_ops.assign(early_stopping._get_or_create_stop_var(),
True)
with monitored_session.SingularMonitoredSession(hooks=[hook]) as mon_sess:
mon_sess.run(no_op)
self.assertFalse(mon_sess.should_stop())
mon_sess.run(assign_op)
self.assertTrue(mon_sess.should_stop())
if __name__ == '__main__':
test.main()
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