Move from deprecated self.test_session() to self.cached_session().

self.test_session() has been deprecated in 9962eb5e84b15e309410071b06c2ed2d6148ed44 as its name confuses readers of the test. Moving to cached_session() instead which is more explicit about:
* the fact that the session may be reused.
* the session is not closed even when doing a "with self.test_session()" statement.

PiperOrigin-RevId: 209836477

12 files changed, 51 insertions, 51 deletions

diff --git a/tensorflow/contrib/opt/python/training/adamax_test.py b/tensorflow/contrib/opt/python/training/adamax_test.py
index 5790d8a3f1..61d8b94eca 100644
--- a/tensorflow/contrib/opt/python/training/adamax_test.py
+++ b/tensorflow/contrib/opt/python/training/adamax_test.py
@@ -74,7 +74,7 @@ class AdaMaxOptimizerTest(test.TestCase):
 
   def doTestSparse(self, use_resource=False):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         # Initialize variables for numpy implementation.
         zero_slots = lambda: np.zeros((3), dtype=dtype.as_numpy_dtype)
         m0, v0, m1, v1 = zero_slots(), zero_slots(), zero_slots(), zero_slots()
@@ -142,7 +142,7 @@ class AdaMaxOptimizerTest(test.TestCase):
 
   def testSparseRepeatedIndices(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         repeated_index_update_var = variables.Variable(
             [[1.0], [2.0]], dtype=dtype)
         aggregated_update_var = variables.Variable(
@@ -233,7 +233,7 @@ class AdaMaxOptimizerTest(test.TestCase):
                              opt.get_slot(var=var0, name="m").name)
 
   def testBasic(self):
-    with self.test_session():
+    with self.cached_session():
       self.doTestBasic(use_resource=False)
 
   @test_util.run_in_graph_and_eager_modes(reset_test=True)
@@ -242,7 +242,7 @@ class AdaMaxOptimizerTest(test.TestCase):
 
   def testTensorLearningRate(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         # Initialize variables for numpy implementation.
         m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
         var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
@@ -278,7 +278,7 @@ class AdaMaxOptimizerTest(test.TestCase):
 
   def testSharing(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         # Initialize variables for numpy implementation.
         m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
         var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
diff --git a/tensorflow/contrib/opt/python/training/external_optimizer_test.py b/tensorflow/contrib/opt/python/training/external_optimizer_test.py
index 953586ee70..9997103016 100644
--- a/tensorflow/contrib/opt/python/training/external_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/external_optimizer_test.py
@@ -85,7 +85,7 @@ class ExternalOptimizerInterfaceTest(TestCase):
 
     optimizer = MockOptimizerInterface(loss)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
 
       optimizer.minimize(sess)
@@ -107,7 +107,7 @@ class ExternalOptimizerInterfaceTest(TestCase):
 
     optimizer = MockOptimizerInterface(loss)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
 
       initial_vector_val = sess.run(vector)
@@ -164,7 +164,7 @@ class ScipyOptimizerInterfaceTest(TestCase):
     optimizer = external_optimizer.ScipyOptimizerInterface(
         self._objective(x), method=method, options=options)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       optimizer.minimize(sess)
 
@@ -176,7 +176,7 @@ class ScipyOptimizerInterfaceTest(TestCase):
     x = variables.Variable(array_ops.zeros(dimension))
     optimizer = external_optimizer.ScipyOptimizerInterface(self._objective(x))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       optimizer.minimize(sess)
 
@@ -242,7 +242,7 @@ class ScipyOptimizerInterfaceTest(TestCase):
     optimizer = external_optimizer.ScipyOptimizerInterface(
         loss, equalities=equalities, inequalities=inequalities, method='SLSQP')
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       optimizer.minimize(sess)
       self.assertAllClose(np.ones(2), sess.run(vector))
@@ -260,7 +260,7 @@ class ScipyOptimizerInterfaceTest(TestCase):
     optimizer = external_optimizer.ScipyOptimizerInterface(
         loss, var_to_bounds=var_to_bounds)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       optimizer.minimize(sess)
       self.assertAllClose(np.ones(2), sess.run(vector))
@@ -277,7 +277,7 @@ class ScipyOptimizerInterfaceTest(TestCase):
     optimizer = external_optimizer.ScipyOptimizerInterface(
         loss, var_to_bounds=var_to_bounds)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       optimizer.minimize(sess)
       self.assertAllClose([0., 2.], sess.run(vector))
@@ -293,7 +293,7 @@ class ScipyOptimizerInterfaceTest(TestCase):
     optimizer = external_optimizer.ScipyOptimizerInterface(
         loss, method='SLSQP')
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       optimizer.minimize(sess)
       method = optimizer.optimizer_kwargs.get('method')
@@ -312,7 +312,7 @@ class ScipyOptimizerInterfaceTest(TestCase):
 
     optimizer = external_optimizer.ScipyOptimizerInterface(loss, method='SLSQP')
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
 
       initial_vector_val = sess.run(vector)
diff --git a/tensorflow/contrib/opt/python/training/ggt_test.py b/tensorflow/contrib/opt/python/training/ggt_test.py
index 1d2a79957b..1775edabb3 100644
--- a/tensorflow/contrib/opt/python/training/ggt_test.py
+++ b/tensorflow/contrib/opt/python/training/ggt_test.py
@@ -171,7 +171,7 @@ class GGTOptimizerTest(test.TestCase):
           self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))
 
   def testBasic(self):
-    with self.test_session():
+    with self.cached_session():
       self.doTestBasic(use_resource=False)
 
   @test_util.run_in_graph_and_eager_modes(reset_test=True)
diff --git a/tensorflow/contrib/opt/python/training/lars_optimizer_test.py b/tensorflow/contrib/opt/python/training/lars_optimizer_test.py
index d94249b994..b76db763da 100644
--- a/tensorflow/contrib/opt/python/training/lars_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/lars_optimizer_test.py
@@ -31,7 +31,7 @@ class LARSOptimizerTest(test.TestCase):
   def testLARSGradientOneStep(self):
     for _ in range(10):
       for dtype in [dtypes.float32, dtypes.float64]:
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
           shape = [3, 3]
           var_np = np.ones(shape)
           grad_np = np.ones(shape)
@@ -77,7 +77,7 @@ class LARSOptimizerTest(test.TestCase):
   def testLARSGradientMultiStep(self):
     for _ in range(10):
       for dtype in [dtypes.float32, dtypes.float64]:
-        with self.test_session() as sess:
+        with self.cached_session() as sess:
           shape = [3, 3]
           var_np = np.ones(shape)
           grad_np = np.ones(shape)
diff --git a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py
index a16857db7d..dc4c462ce4 100644
--- a/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/lazy_adam_optimizer_test.py
@@ -53,7 +53,7 @@ class AdamOptimizerTest(test.TestCase):
 
   def testSparse(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         # Initialize variables for numpy implementation.
         m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
         var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
@@ -109,7 +109,7 @@ class AdamOptimizerTest(test.TestCase):
 
   def testSparseRepeatedIndices(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         repeated_index_update_var = variables.Variable(
             [[1.0], [2.0]], dtype=dtype)
         aggregated_update_var = variables.Variable(
diff --git a/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py
index d15716f6f6..f22e724528 100644
--- a/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py
@@ -165,7 +165,7 @@ class MovingAverageOptimizerTest(test.TestCase):
             self.assertLess(avg_val1[i], orig_val1[i])
 
   def testFailWhenSaverCreatedBeforeInitialized(self):
-    with self.test_session():
+    with self.cached_session():
       var = variables.Variable([1.0], name='var', dtype=dtypes.float32)
       opt = moving_average_optimizer.MovingAverageOptimizer(
           gradient_descent.GradientDescentOptimizer(learning_rate=2.0))
@@ -187,7 +187,7 @@ class MovingAverageOptimizerTest(test.TestCase):
         self.apply_gradients_called = True
         return super(WrapperOptimizer, self).apply_gradients(*args, **kwargs)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       var = variables.Variable([1.2], name='var', dtype=dtypes.float32)
       loss = var ** 2
       wrapper_opt = WrapperOptimizer(learning_rate=2.0)
diff --git a/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py b/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py
index 618d8eb18d..904aa9ab13 100644
--- a/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py
+++ b/tensorflow/contrib/opt/python/training/multitask_optimizer_wrapper_test.py
@@ -34,7 +34,7 @@ class MultitaskOptimizerWrapperTest(test.TestCase):
   """
 
   def testWrapper(self):
-    with self.test_session():
+    with self.cached_session():
       var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
       var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
       grads0 = constant_op.constant([0.1, 0.1], dtype=dtypes.float32)
@@ -92,7 +92,7 @@ class MultitaskOptimizerWrapperTest(test.TestCase):
           self.evaluate(slot1))
 
   def testGradientClipping(self):
-    with self.test_session():
+    with self.cached_session():
       var0 = variables.Variable([1.0, 2.0], dtype=dtypes.float32)
       var1 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
       var2 = variables.Variable([3.0, 4.0], dtype=dtypes.float32)
diff --git a/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py b/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py
index 825c08a09a..85e05ce71c 100644
--- a/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/nadam_optimizer_test.py
@@ -53,7 +53,7 @@ class NadamOptimizerTest(test.TestCase):
 
   def doTestSparse(self, use_resource=False):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         # Initialize variables for numpy implementation.
         m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
         var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
@@ -106,7 +106,7 @@ class NadamOptimizerTest(test.TestCase):
 
   def doTestBasic(self, use_resource=False):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         # Initialize variables for numpy implementation.
         m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
         var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
diff --git a/tensorflow/contrib/opt/python/training/reg_adagrad_optimizer_test.py b/tensorflow/contrib/opt/python/training/reg_adagrad_optimizer_test.py
index ea56e1646a..c09e2ac76d 100644
--- a/tensorflow/contrib/opt/python/training/reg_adagrad_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/reg_adagrad_optimizer_test.py
@@ -36,7 +36,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def doTestBasic(self, use_locking=False, use_resource=False):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         if use_resource:
           var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
           var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
@@ -73,7 +73,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def testMinimizeSparseResourceVariable(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0 = resource_variable_ops.ResourceVariable(
             [[1.0, 2.0], [3.0, 4.0]], dtype=dtype)
         x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
@@ -92,7 +92,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def testTensorLearningRate(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0 = variables.Variable([1.0, 2.0], dtype=dtype)
         var1 = variables.Variable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -116,7 +116,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def testSparseBasic(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0 = variables.Variable([[1.0], [2.0]], dtype=dtype)
         var1 = variables.Variable([[3.0], [4.0]], dtype=dtype)
         grads0 = ops.IndexedSlices(
@@ -144,7 +144,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def testSparseRepeatedIndices(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         repeated_index_update_var = variables.Variable(
             [[1.0], [2.0]], dtype=dtype)
         aggregated_update_var = variables.Variable([[1.0], [2.0]], dtype=dtype)
@@ -170,7 +170,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def testSparseRepeatedIndicesResourceVariable(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var_repeated = resource_variable_ops.ResourceVariable(
             [1.0, 2.0], dtype=dtype)
         loss_repeated = math_ops.reduce_sum(
@@ -194,7 +194,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def testSparseStability(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         shape = [1, 6]
         var0 = variables.Variable(
             [[
@@ -230,7 +230,7 @@ class RegAdagradOptimizerTest(test.TestCase):
 
   def testSharing(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0 = variables.Variable([1.0, 2.0], dtype=dtype)
         var1 = variables.Variable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -263,7 +263,7 @@ class RegAdagradOptimizerTest(test.TestCase):
             np.array([2.715679168701172, 3.715679168701172]), var1.eval())
 
   def testDynamicShapeVariable_Ok(self):
-    with self.test_session():
+    with self.cached_session():
       v = variable_scope.get_variable(
           "v", initializer=constant_op.constant(1.), validate_shape=False)
       self.assertFalse(v.shape.is_fully_defined())
@@ -274,7 +274,7 @@ class RegAdagradOptimizerTest(test.TestCase):
   def testSkipUpdatingSlots(self):
     iav = 0.130005  # A value that works with float16
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0 = variables.Variable([1.0, 2.0], dtype=dtype)
         var1 = variables.Variable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -306,7 +306,7 @@ class RegAdagradOptimizerTest(test.TestCase):
   def testSparseSkipUpdatingSlots(self):
     iav = 0.130005  # A value that works with float16
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0 = variables.Variable([[1.0], [2.0]], dtype=dtype)
         var1 = variables.Variable([[3.0], [4.0]], dtype=dtype)
         grads0 = ops.IndexedSlices(
diff --git a/tensorflow/contrib/opt/python/training/shampoo_test.py b/tensorflow/contrib/opt/python/training/shampoo_test.py
index 2e0a202ae2..b3688ab181 100644
--- a/tensorflow/contrib/opt/python/training/shampoo_test.py
+++ b/tensorflow/contrib/opt/python/training/shampoo_test.py
@@ -52,7 +52,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     grad_np = np.random.rand(size)
     grad_np_2 = np.random.rand(size)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -103,7 +103,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     grad_np = np.random.rand(size[0], size[1])
     grad_np_2 = np.random.rand(size[0], size[1])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -162,7 +162,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     grad_np = np.random.rand(size[0], size[1], size[2])
     grad_np_2 = np.random.rand(size[0], size[1], size[2])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -240,7 +240,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     grad_np = np.random.rand(size)
     grad_np_2 = np.random.rand(size)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -294,7 +294,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     grad_np = np.random.rand(size[0], size[1])
     grad_np_2 = np.random.rand(size[0], size[1])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -365,7 +365,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
                                               replace=False))
     grad_np_2 = np.random.rand(sample_size_2, size[1])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -445,7 +445,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
                                             replace=False))
     grad_np = np.random.rand(sample_size, size[1], size[2])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -512,7 +512,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     gbar_decay = 0.9
     gbar_weight = 0.1
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -601,7 +601,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     mat_g3_a = np.eye(size[2])
     mat_g3 = np.zeros_like(mat_g3_a)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
@@ -672,7 +672,7 @@ class ShampooTest(test.TestCase, parameterized.TestCase):
     mat_g3_a = np.eye(size[2])
     mat_g3 = np.zeros_like(mat_g3_a)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       global_step = variables.Variable(
           0, dtype=dtypes.int64, use_resource=use_resource_var)
       var = variables.Variable(
diff --git a/tensorflow/contrib/opt/python/training/sign_decay_test.py b/tensorflow/contrib/opt/python/training/sign_decay_test.py
index c31cb924ea..3a84789afd 100644
--- a/tensorflow/contrib/opt/python/training/sign_decay_test.py
+++ b/tensorflow/contrib/opt/python/training/sign_decay_test.py
@@ -66,7 +66,7 @@ class SignDecaysTest(test.TestCase):
     linear_decay_fn = sign_decay.get_linear_decay_fn(num_training_steps)
 
     for step in range(0, 1000, 100):
-      with self.test_session():
+      with self.cached_session():
         tf_decayed = linear_decay_fn(step).eval()
         py_decayed = py_linear_decay_fn(num_training_steps)(step)
         self.assertAlmostEqual(tf_decayed, py_decayed, places=4)
@@ -78,7 +78,7 @@ class SignDecaysTest(test.TestCase):
         num_training_steps, num_periods=5, zero_after=2)
 
     for step in range(0, 1000, 100):
-      with self.test_session():
+      with self.cached_session():
         tf_decayed = cosine_decay_fn(step).eval()
         py_decayed = py_cosine_decay_fn(num_training_steps)(step)
         self.assertAlmostEqual(tf_decayed, py_decayed, places=4)
@@ -95,7 +95,7 @@ class SignDecaysTest(test.TestCase):
         num_training_steps, num_periods=5, zero_after=2)
 
     for step in range(0, 1000, 100):
-      with self.test_session():
+      with self.cached_session():
         tf_decayed = restart_decay_fn(step).eval()
         py_decayed = py_restart_decay_fn(num_training_steps)(step)
         self.assertAlmostEqual(tf_decayed, py_decayed, places=4)
diff --git a/tensorflow/contrib/opt/python/training/variable_clipping_optimizer_test.py b/tensorflow/contrib/opt/python/training/variable_clipping_optimizer_test.py
index fdda86b0b5..ff0ea8d766 100644
--- a/tensorflow/contrib/opt/python/training/variable_clipping_optimizer_test.py
+++ b/tensorflow/contrib/opt/python/training/variable_clipping_optimizer_test.py
@@ -158,7 +158,7 @@ class VariableClippingOptimizerTest(test.TestCase):
 
   def testDenseLocal(self):
     for dtype in [dtypes.float32, dtypes.float64, dtypes.half]:
-      with self.test_session():
+      with self.cached_session():
         var0, var1, update_op = self._setupDense(False, dtype)
         self._assertDenseCorrect(var0, var1, update_op)
 
@@ -171,7 +171,7 @@ class VariableClippingOptimizerTest(test.TestCase):
 
   def testSparseLocal(self):
     for dtype in [dtypes.float64, dtypes.float32, dtypes.half]:
-      with self.test_session():
+      with self.cached_session():
         var0, var1, update_op = self._setupSparse(False, dtype)
         self._assertSparseCorrect(var0, var1, update_op)