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: 213944355

2 files changed, 25 insertions, 25 deletions

diff --git a/tensorflow/contrib/kernel_methods/python/losses_test.py b/tensorflow/contrib/kernel_methods/python/losses_test.py
index 72507539f8..4d5cc24ce0 100644
--- a/tensorflow/contrib/kernel_methods/python/losses_test.py
+++ b/tensorflow/contrib/kernel_methods/python/losses_test.py
@@ -32,7 +32,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testInvalidLogitsShape(self):
     """An error is raised when logits have invalid shape."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([-1.0, 2.1], shape=(2,))
       labels = constant_op.constant([0, 1])
       with self.assertRaises(ValueError):
@@ -40,7 +40,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testInvalidLabelsShape(self):
     """An error is raised when labels have invalid shape."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
       labels = constant_op.constant([1, 0], shape=(1, 1, 2))
       with self.assertRaises(ValueError):
@@ -48,7 +48,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testInvalidWeightsShape(self):
     """An error is raised when weights have invalid shape."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
       labels = constant_op.constant([1, 0], shape=(2,))
       weights = constant_op.constant([1.5, 0.2], shape=(2, 1, 1))
@@ -57,7 +57,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testInvalidLabelsDtype(self):
     """An error is raised when labels have invalid shape."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
       labels = constant_op.constant([1, 0], dtype=dtypes.float32)
       with self.assertRaises(ValueError):
@@ -65,7 +65,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testNoneWeightRaisesValueError(self):
     """An error is raised when weights are None."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
       labels = constant_op.constant([1, 0])
       with self.assertRaises(ValueError):
@@ -73,7 +73,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testInconsistentLabelsAndWeightsShapesSameRank(self):
     """Error raised when weights and labels have same ranks, different sizes."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([-1.0, 2.1, 4.1], shape=(3, 1))
       labels = constant_op.constant([1, 0, 2], shape=(3, 1))
       weights = constant_op.constant([1.1, 2.0], shape=(2, 1))
@@ -82,7 +82,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testInconsistentLabelsAndWeightsShapesDifferentRank(self):
     """Error raised when weights and labels have different ranks and sizes."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([-1.0, 2.1], shape=(2, 1))
       labels = constant_op.constant([1, 0], shape=(2, 1))
       weights = constant_op.constant([1.1, 2.0, 2.8], shape=(3,))
@@ -91,7 +91,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testOutOfRangeLabels(self):
     """An error is raised when labels are not in [0, num_classes)."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.2, -1.4, -1.0], [1.4, 1.8, 4.0],
                                      [0.5, 1.8, -1.0]])
       labels = constant_op.constant([1, 0, 4])
@@ -101,7 +101,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testZeroLossInt32Labels(self):
     """Loss is 0 if true class logits sufficiently higher than other classes."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.2, -1.4, -1.0], [1.4, 1.8, 4.0],
                                      [0.5, 1.8, -1.0]])
       labels = constant_op.constant([0, 2, 1], dtype=dtypes.int32)
@@ -110,7 +110,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testZeroLossInt64Labels(self):
     """Loss is 0 if true class logits sufficiently higher than other classes."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[2.1, -0.4, -1.0], [1.4, 2.8, 4.0],
                                      [-0.5, 0.8, -1.0]])
       labels = constant_op.constant([0, 2, 1], dtype=dtypes.int64)
@@ -130,7 +130,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
     ]
 
     for batch_size, num_classes in logits_shapes:
-      with self.test_session():
+      with self.cached_session():
         logits = array_ops.placeholder(
             dtypes.float32, shape=(batch_size, num_classes))
         labels = array_ops.placeholder(dtypes.int32, shape=(batch_size,))
@@ -140,7 +140,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testCorrectPredictionsSomeClassesInsideMargin(self):
     """Loss is > 0 even if true class logits are higher than other classes."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.2, -1.4, 0.8], [1.4, 1.8, 4.0],
                                      [1.5, 1.8, -1.0]])
       labels = constant_op.constant([0, 2, 1])
@@ -150,7 +150,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testIncorrectPredictions(self):
     """Loss is >0 when an incorrect class has higher logits than true class."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[2.6, 0.4, 0.8], [1.4, 0.8, -1.0],
                                      [0.5, -1.8, 2.0]])
       labels = constant_op.constant([1, 0, 2])
@@ -162,7 +162,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testIncorrectPredictionsColumnLabels(self):
     """Same as above but labels is a rank-2 tensor."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
                                      [0.2, -1.8, 4.0]])
       labels = constant_op.constant([1, 0, 2], shape=(3, 1))
@@ -174,7 +174,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testIncorrectPredictionsZeroWeights(self):
     """Loss is 0 when all weights are missing even if predictions are wrong."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
                                      [0.2, -1.8, 4.0]])
       labels = constant_op.constant([1, 0, 2], shape=(3, 1))
@@ -185,7 +185,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testNonZeroLossWithPythonScalarWeights(self):
     """Weighted loss is correctly computed when weights is a python scalar."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
                                      [0.2, -1.8, 4.0]])
       labels = constant_op.constant([1, 0, 2], shape=(3, 1))
@@ -195,7 +195,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testNonZeroLossWithScalarTensorWeights(self):
     """Weighted loss is correctly computed when weights is a rank-0 tensor."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
                                      [0.2, -1.8, 4.0]])
       labels = constant_op.constant([1, 0, 2], shape=(3, 1))
@@ -205,7 +205,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testNonZeroLossWith1DTensorWeightsColumnLabels(self):
     """Weighted loss is correctly computed when weights is a rank-0 tensor."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
                                      [0.2, -1.8, 4.0]])
       labels = constant_op.constant([1, 0, 2], shape=(3, 1))
@@ -216,7 +216,7 @@ class SparseMulticlassHingeLossTest(test.TestCase):
 
   def testNonZeroLossWith2DTensorWeights1DLabelsSomeWeightsMissing(self):
     """Weighted loss is correctly computed when weights is a rank-0 tensor."""
-    with self.test_session():
+    with self.cached_session():
       logits = constant_op.constant([[1.6, -0.4, 0.8], [1.5, 0.8, -1.0],
                                      [0.2, -1.8, 4.0], [1.6, 1.8, -4.0]])
       labels = constant_op.constant([1, 0, 2, 1])
diff --git a/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py b/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py
index 2ff4d41d75..bad0a596a7 100644
--- a/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py
+++ b/tensorflow/contrib/kernel_methods/python/mappers/random_fourier_features_test.py
@@ -58,7 +58,7 @@ class RandomFourierFeatureMapperTest(TensorFlowTestCase):
   def testInvalidInputShape(self):
     x = constant_op.constant([[2.0, 1.0]])
 
-    with self.test_session():
+    with self.cached_session():
       rffm = RandomFourierFeatureMapper(3, 10)
       with self.assertRaisesWithPredicateMatch(
           dense_kernel_mapper.InvalidShapeError,
@@ -70,7 +70,7 @@ class RandomFourierFeatureMapperTest(TensorFlowTestCase):
     x2 = constant_op.constant([[1.0, -1.0, 2.0], [-1.0, 10.0, 1.0],
                                [4.0, -2.0, -1.0]])
 
-    with self.test_session():
+    with self.cached_session():
       rffm = RandomFourierFeatureMapper(3, 10, 1.0)
       mapped_x1 = rffm.map(x1)
       mapped_x2 = rffm.map(x2)
@@ -80,7 +80,7 @@ class RandomFourierFeatureMapperTest(TensorFlowTestCase):
   def testSameOmegaReused(self):
     x = constant_op.constant([[2.0, 1.0, 0.0]])
 
-    with self.test_session():
+    with self.cached_session():
       rffm = RandomFourierFeatureMapper(3, 100)
       mapped_x = rffm.map(x)
       mapped_x_copy = rffm.map(x)
@@ -93,7 +93,7 @@ class RandomFourierFeatureMapperTest(TensorFlowTestCase):
     y = constant_op.constant([[1.0, -1.0, 2.0]])
     stddev = 3.0
 
-    with self.test_session():
+    with self.cached_session():
       # The mapped dimension is fairly small, so the kernel approximation is
       # very rough.
       rffm1 = RandomFourierFeatureMapper(3, 100, stddev)
@@ -113,7 +113,7 @@ class RandomFourierFeatureMapperTest(TensorFlowTestCase):
     y = constant_op.constant([[1.0, -1.0, 2.0]])
     stddev = 3.0
 
-    with self.test_session():
+    with self.cached_session():
       # The mapped dimension is fairly small, so the kernel approximation is
       # very rough.
       rffm = RandomFourierFeatureMapper(3, 100, stddev, seed=0)
@@ -139,7 +139,7 @@ class RandomFourierFeatureMapperTest(TensorFlowTestCase):
 
     normalized_points = [nn.l2_normalize(point, dim=1) for point in points]
     total_absolute_error = 0.0
-    with self.test_session():
+    with self.cached_session():
       rffm = RandomFourierFeatureMapper(input_dim, mapped_dim, stddev, seed=0)
       # Cache mappings so that they are not computed multiple times.
       cached_mappings = dict((point, rffm.map(point))