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| author |  Allen Lavoie <allenl@google.com> | 2018-05-09 11:28:30 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-05-09 13:08:46 -0700 |
| commit | 80ec58f7d6f59618aaf7da7e0465441c7c83bc1d (patch) | |
| tree | cd7135f89474d8913ddacefe060bf765351bbe5a /tensorflow/contrib/timeseries | |
| parent | 7baa9ffe735adfa11c987c435216943767530269 (diff) | |
TFTS: Make estimators_test non-flaky
Replaces a "loss decreased" check with basic shape checking (it should have been seeded already, so there's likely some race condition which I should track down...).
PiperOrigin-RevId: 196001526
Diffstat (limited to 'tensorflow/contrib/timeseries')
| -rw-r--r-- | tensorflow/contrib/timeseries/python/timeseries/estimators_test.py | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py index 706742ca28..983455f63d 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/estimators_test.py @@ -68,15 +68,16 @@ class TimeSeriesRegressorTest(test.TestCase): eval_input_fn = input_pipeline.RandomWindowInputFn( input_pipeline.NumpyReader(features), shuffle_seed=3, num_threads=1, batch_size=16, window_size=16) - first_estimator.train(input_fn=train_input_fn, steps=5) + first_estimator.train(input_fn=train_input_fn, steps=1) first_loss_before_fit = first_estimator.evaluate( input_fn=eval_input_fn, steps=1)["loss"] - first_estimator.train(input_fn=train_input_fn, steps=50) + self.assertAllEqual([], first_loss_before_fit.shape) + first_estimator.train(input_fn=train_input_fn, steps=1) first_loss_after_fit = first_estimator.evaluate( input_fn=eval_input_fn, steps=1)["loss"] - self.assertLess(first_loss_after_fit, first_loss_before_fit) + self.assertAllEqual([], first_loss_after_fit.shape) second_estimator = estimator_fn(model_dir, exogenous_feature_columns) - second_estimator.train(input_fn=train_input_fn, steps=2) + second_estimator.train(input_fn=train_input_fn, steps=1) whole_dataset_input_fn = input_pipeline.WholeDatasetInputFn( input_pipeline.NumpyReader(features)) whole_dataset_evaluation = second_estimator.evaluate( |