diff options
Diffstat (limited to 'tensorflow/contrib/layers/python/layers/optimizers_test.py')
| -rw-r--r-- | tensorflow/contrib/layers/python/layers/optimizers_test.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/tensorflow/contrib/layers/python/layers/optimizers_test.py b/tensorflow/contrib/layers/python/layers/optimizers_test.py index 0f037e24ad..29dede2a49 100644 --- a/tensorflow/contrib/layers/python/layers/optimizers_test.py +++ b/tensorflow/contrib/layers/python/layers/optimizers_test.py @@ -165,7 +165,7 @@ class OptimizersTest(test.TestCase): def testGradientNoise(self): random_seed.set_random_seed(42) - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -182,7 +182,7 @@ class OptimizersTest(test.TestCase): def testGradientNoiseWithClipping(self): random_seed.set_random_seed(42) - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -198,7 +198,7 @@ class OptimizersTest(test.TestCase): self.assertEqual(global_step_value, 1) def testGradientClip(self): - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -213,7 +213,7 @@ class OptimizersTest(test.TestCase): self.assertEqual(global_step_value, 1) def testAdaptiveGradientClip(self): - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() clip_gradients = optimizers_lib.adaptive_clipping_fn() train = optimizers_lib.optimize_loss( @@ -234,7 +234,7 @@ class OptimizersTest(test.TestCase): self.assertEqual(2, var_count) def testGradientMultiply(self): - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -433,7 +433,7 @@ class OptimizersTest(test.TestCase): class AdaptiveClipping(test.TestCase): def testAverages(self): - with self.test_session() as session: + with self.cached_session() as session: scale = 2. grad = array_ops.ones([3, 4]) * scale log_norm = np.log(np.sqrt(scale**2 * grad.get_shape().num_elements())) @@ -463,7 +463,7 @@ class AdaptiveClipping(test.TestCase): self.assertAlmostEqual(float(sq_mean), log_norm**2, places=4) def testClip(self): - with self.test_session() as session: + with self.cached_session() as session: spike = 1000. multiplier = array_ops.placeholder(dtypes.float32, [], "multiplier") step = array_ops.placeholder(dtypes.int32, [], "step") |