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Automated g4 rollback of changelist 170892257

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PiperOrigin-RevId: 170919783
This commit is contained in:
Gunhan Gulsoy 2017-10-03 14:59:25 -07:00 committed by TensorFlower Gardener
parent 66df43d09c
commit 435b31b9fc
2 changed files with 0 additions and 26 deletions
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4
tensorflow/python/estimator/training.py
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@ -392,10 +392,6 @@ class _TrainingExecutor(object):
metrics = evaluator.evaluate_and_export() metrics = evaluator.evaluate_and_export()
if not metrics:
# This is unexpected. Training should always end with a new checkpoint.
raise RuntimeError('There was no new checkpoint after the training.')
if _should_stop_local_train(metrics[ops.GraphKeys.GLOBAL_STEP]): if _should_stop_local_train(metrics[ops.GraphKeys.GLOBAL_STEP]):
break break

22
tensorflow/python/estimator/training_test.py
View File

@ -50,7 +50,6 @@ _INVALID_NAME_MSG = '`name` must be string'
_INVALID_EVAL_DELAY_SECS_MSG = 'Must specify delay_secs >= 0' _INVALID_EVAL_DELAY_SECS_MSG = 'Must specify delay_secs >= 0'
_INVALID_EVAL_THROTTLE_SECS_MSG = 'Must specify throttle_secs >= 0' _INVALID_EVAL_THROTTLE_SECS_MSG = 'Must specify throttle_secs >= 0'
_INVALID_ESTIMATOR_MSG = '`estimator` must have type `tf.estimator.Estimator`' _INVALID_ESTIMATOR_MSG = '`estimator` must have type `tf.estimator.Estimator`'
_STALE_CHECKPOINT_MSG = 'There was no new checkpoint after the training.'
_INVALID_EXPORT_STRATEGY_MSG = '`export_strategies` must be an ExportStrategy' _INVALID_EXPORT_STRATEGY_MSG = '`export_strategies` must be an ExportStrategy'
_DUPLICATE_STRATEGY_NAMES_MSG = '`export_strategies` must have unique names.' _DUPLICATE_STRATEGY_NAMES_MSG = '`export_strategies` must have unique names.'
_INVALID_TRAIN_SPEC_MSG = '`train_spec` must have type `tf.estimator.TrainSpec`' _INVALID_TRAIN_SPEC_MSG = '`train_spec` must have type `tf.estimator.TrainSpec`'
@ -1025,27 +1024,6 @@ class TrainingExecutorRunLocalTest(test.TestCase):
self.assertEqual(3, mock_est.evaluate.call_count) self.assertEqual(3, mock_est.evaluate.call_count)
self.assertEqual(3, mock_est.times_export_fn_was_called) self.assertEqual(3, mock_est.times_export_fn_was_called)
def test_handles_no_new_checkpoint_found(self):
mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
mock_est.latest_checkpoint.return_value = (
'no_new_checkpoints_after_the_first_train_step')
train_spec = training.TrainSpec(
input_fn=lambda: 1, max_steps=300, hooks=[_FakeHook()])
eval_spec = training.EvalSpec(
input_fn=lambda: 1, hooks=[_FakeHook()], throttle_secs=100)
# It was going to be called 3 times.
mock_est.evaluate.side_effect = [{
_GLOBAL_STEP_KEY: train_spec.max_steps - 100
}, {
_GLOBAL_STEP_KEY: train_spec.max_steps - 50
}, {
_GLOBAL_STEP_KEY: train_spec.max_steps
}]
executor = training._TrainingExecutor(mock_est, train_spec, eval_spec)
with self.assertRaisesRegexp(RuntimeError, _STALE_CHECKPOINT_MSG):
executor.run_local()
def test_train_and_evaluate_args(self): def test_train_and_evaluate_args(self):
mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/') mock_est = test.mock.Mock(spec=estimator_lib.Estimator, model_dir='path/')
mock_est.latest_checkpoint.return_value = 'checkpoint_path/' mock_est.latest_checkpoint.return_value = 'checkpoint_path/'