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: 209703613

7 files changed, 38 insertions, 38 deletions

diff --git a/tensorflow/contrib/optimizer_v2/adadelta_test.py b/tensorflow/contrib/optimizer_v2/adadelta_test.py
index 31cfec0d50..4c94b66679 100644
--- a/tensorflow/contrib/optimizer_v2/adadelta_test.py
+++ b/tensorflow/contrib/optimizer_v2/adadelta_test.py
@@ -37,7 +37,7 @@ class AdadeltaOptimizerTest(test.TestCase):
     for dtype in [dtypes.half, dtypes.float32]:
       for grad in [0.2, 0.1, 0.01]:
         for lr in [1.0, 0.5, 0.1]:
-          with self.test_session():
+          with self.cached_session():
             var0_init = [1.0, 2.0]
             var1_init = [3.0, 4.0]
             if use_resource:
@@ -146,7 +146,7 @@ class AdadeltaOptimizerTest(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]], dtype=dtype)
         x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
         pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
diff --git a/tensorflow/contrib/optimizer_v2/adagrad_test.py b/tensorflow/contrib/optimizer_v2/adagrad_test.py
index 18191c3ef2..debaaaeeba 100644
--- a/tensorflow/contrib/optimizer_v2/adagrad_test.py
+++ b/tensorflow/contrib/optimizer_v2/adagrad_test.py
@@ -36,7 +36,7 @@ class AdagradOptimizerTest(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 AdagradOptimizerTest(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 AdagradOptimizerTest(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 AdagradOptimizerTest(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(
@@ -147,7 +147,7 @@ class AdagradOptimizerTest(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(
@@ -177,7 +177,7 @@ class AdagradOptimizerTest(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(
@@ -201,7 +201,7 @@ class AdagradOptimizerTest(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(
             [[
@@ -237,7 +237,7 @@ class AdagradOptimizerTest(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)
@@ -270,7 +270,7 @@ class AdagradOptimizerTest(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())
diff --git a/tensorflow/contrib/optimizer_v2/adam_test.py b/tensorflow/contrib/optimizer_v2/adam_test.py
index 1f079d9afc..b1ad0ade42 100644
--- a/tensorflow/contrib/optimizer_v2/adam_test.py
+++ b/tensorflow/contrib/optimizer_v2/adam_test.py
@@ -56,7 +56,7 @@ class AdamOptimizerTest(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)
@@ -122,7 +122,7 @@ 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(
@@ -215,7 +215,7 @@ class AdamOptimizerTest(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)
@@ -224,7 +224,7 @@ class AdamOptimizerTest(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)
@@ -261,7 +261,7 @@ class AdamOptimizerTest(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/optimizer_v2/gradient_descent_test.py b/tensorflow/contrib/optimizer_v2/gradient_descent_test.py
index ad9aef804f..4a77bce478 100644
--- a/tensorflow/contrib/optimizer_v2/gradient_descent_test.py
+++ b/tensorflow/contrib/optimizer_v2/gradient_descent_test.py
@@ -34,7 +34,7 @@ class GradientDescentOptimizerTest(test.TestCase):
 
   def testBasic(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)
@@ -57,7 +57,7 @@ class GradientDescentOptimizerTest(test.TestCase):
 
   def testBasicResourceVariable(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], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -82,7 +82,7 @@ class GradientDescentOptimizerTest(test.TestCase):
 
   def testMinimizeResourceVariable(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]], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0], dtype=dtype)
         x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
@@ -108,7 +108,7 @@ class GradientDescentOptimizerTest(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]], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0], dtype=dtype)
         x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
@@ -135,7 +135,7 @@ class GradientDescentOptimizerTest(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)
@@ -157,7 +157,7 @@ class GradientDescentOptimizerTest(test.TestCase):
 
   def testGradWrtRef(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         opt = gradient_descent.GradientDescentOptimizer(3.0)
         values = [1.0, 3.0]
         vars_ = [variables.Variable([v], dtype=dtype) for v in values]
@@ -168,7 +168,7 @@ class GradientDescentOptimizerTest(test.TestCase):
 
   def testWithGlobalStep(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         global_step = variables.Variable(0, trainable=False)
         var0 = variables.Variable([1.0, 2.0], dtype=dtype)
         var1 = variables.Variable([3.0, 4.0], dtype=dtype)
@@ -191,7 +191,7 @@ class GradientDescentOptimizerTest(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(
diff --git a/tensorflow/contrib/optimizer_v2/momentum_test.py b/tensorflow/contrib/optimizer_v2/momentum_test.py
index 24cdab4626..e69f12839e 100644
--- a/tensorflow/contrib/optimizer_v2/momentum_test.py
+++ b/tensorflow/contrib/optimizer_v2/momentum_test.py
@@ -123,7 +123,7 @@ class MomentumOptimizerTest(test.TestCase):
           ]), 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)
@@ -162,7 +162,7 @@ class MomentumOptimizerTest(test.TestCase):
 
   def testNesterovMomentum(self):
     for dtype in [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)
         var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
@@ -188,7 +188,7 @@ class MomentumOptimizerTest(test.TestCase):
 
   def testSparseNesterovMomentum(self):
     for dtype in [dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
         var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
         accum0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype)
@@ -282,7 +282,7 @@ class MomentumOptimizerTest(test.TestCase):
 
   def testTensorLearningRateAndMomentum(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)
@@ -435,7 +435,7 @@ class MomentumOptimizerTest(test.TestCase):
     return db_grad, db_out
 
   def testLikeDistBeliefMom01(self):
-    with self.test_session():
+    with self.cached_session():
       db_grad, db_out = self._dbParamsMom01()
       num_samples = len(db_grad)
       var0 = variables.Variable([0.0] * num_samples)
@@ -449,7 +449,7 @@ class MomentumOptimizerTest(test.TestCase):
 
   def testSparse(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.test_session():
+      with self.cached_session():
         var0 = variables.Variable(array_ops.zeros([4, 2], dtype=dtype))
         var1 = variables.Variable(constant_op.constant(1.0, dtype, [4, 2]))
         grads0 = ops.IndexedSlices(
@@ -518,7 +518,7 @@ class MomentumOptimizerTest(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)
diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py
index a44bfd1bfd..dd7f2f4405 100644
--- a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py
+++ b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py
@@ -61,7 +61,7 @@ class OptimizerTest(test.TestCase):
 
   def testAggregationMethod(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)
         cost = 5 * var0 + 3 * var1
@@ -86,7 +86,7 @@ class OptimizerTest(test.TestCase):
 
   def testPrecomputedGradient(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)
         cost = 5 * var0 + 3 * var1
@@ -212,7 +212,7 @@ class OptimizerTest(test.TestCase):
       sgd_op.apply_gradients(grads_and_vars)
 
   def testTrainOp(self):
-    with self.test_session():
+    with self.cached_session():
       var0 = variables.Variable([1.0, 2.0])
       var1 = variables.Variable([3.0, 4.0])
       cost = 5 * var0 + 3 * var1
@@ -225,7 +225,7 @@ class OptimizerTest(test.TestCase):
   def testConstraint(self):
     constraint_01 = lambda x: clip_ops.clip_by_value(x, -0.1, 0.)
     constraint_0 = lambda x: clip_ops.clip_by_value(x, 0., 1.)
-    with self.test_session():
+    with self.cached_session():
       var0 = variables.Variable([1.0, 2.0],
                                 constraint=constraint_01)
       var1 = variables.Variable([3.0, 4.0],
@@ -247,7 +247,7 @@ class OptimizerTest(test.TestCase):
       self.assertAllClose([0., 0.], var1.eval())
 
   def testStopGradients(self):
-    with self.test_session():
+    with self.cached_session():
       var0 = variables.Variable([1.0, 2.0], name='var0')
       var1 = variables.Variable([3.0, 4.0], name='var1')
       var0_id = array_ops.identity(var0)
diff --git a/tensorflow/contrib/optimizer_v2/rmsprop_test.py b/tensorflow/contrib/optimizer_v2/rmsprop_test.py
index 628d0418dd..44301ffe9e 100644
--- a/tensorflow/contrib/optimizer_v2/rmsprop_test.py
+++ b/tensorflow/contrib/optimizer_v2/rmsprop_test.py
@@ -162,7 +162,7 @@ class RMSPropOptimizerTest(test.TestCase, parameterized.TestCase):
 
   @parameterized.parameters([dtypes.float32, dtypes.float64])
   def testMinimizeSparseResourceVariable(self, dtype):
-    with self.test_session():
+    with self.cached_session():
       var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
       x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
       pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)
@@ -184,7 +184,7 @@ class RMSPropOptimizerTest(test.TestCase, parameterized.TestCase):
 
   @parameterized.parameters([dtypes.float32, dtypes.float64])
   def testMinimizeSparseResourceVariableCentered(self, dtype):
-    with self.test_session():
+    with self.cached_session():
       var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
       x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
       pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x)