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: 212725342

24 files changed, 150 insertions, 150 deletions

diff --git a/tensorflow/contrib/slim/python/slim/data/dataset_data_provider_test.py b/tensorflow/contrib/slim/python/slim/data/dataset_data_provider_test.py
index 1bb6fbc570..795de6a408 100644
--- a/tensorflow/contrib/slim/python/slim/data/dataset_data_provider_test.py
+++ b/tensorflow/contrib/slim/python/slim/data/dataset_data_provider_test.py
@@ -88,7 +88,7 @@ class DatasetDataProviderTest(test.TestCase):
     height = 300
     width = 280
 
-    with self.test_session():
+    with self.cached_session():
       test_dataset = _create_tfrecord_dataset(dataset_dir)
       provider = dataset_data_provider.DatasetDataProvider(test_dataset)
       key, image, label = provider.get(['record_key', 'image', 'label'])
@@ -111,7 +111,7 @@ class DatasetDataProviderTest(test.TestCase):
     height = 300
     width = 280
 
-    with self.test_session():
+    with self.cached_session():
       provider = dataset_data_provider.DatasetDataProvider(
           _create_tfrecord_dataset(dataset_dir))
     [image] = provider.get(['image'])
@@ -128,7 +128,7 @@ class DatasetDataProviderTest(test.TestCase):
     dataset_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(),
                                                        'tfrecord_dataset'))
 
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaises(ValueError):
         dataset_data_provider.DatasetDataProvider(
             _create_tfrecord_dataset(dataset_dir), record_key='image')
diff --git a/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py b/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py
index ea8cc0ff61..c457d44e07 100644
--- a/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py
+++ b/tensorflow/contrib/slim/python/slim/data/parallel_reader_test.py
@@ -39,7 +39,7 @@ class ParallelReaderTest(test.TestCase):
     ops.reset_default_graph()
 
   def _verify_all_data_sources_read(self, shared_queue):
-    with self.test_session():
+    with self.cached_session():
       tfrecord_paths = test_utils.create_tfrecord_files(
           self.get_temp_dir(), num_files=3)
 
@@ -76,7 +76,7 @@ class ParallelReaderTest(test.TestCase):
     self.assertEquals(count0 + count1 + count2, num_reads)
 
   def _verify_read_up_to_out(self, shared_queue):
-    with self.test_session():
+    with self.cached_session():
       num_files = 3
       num_records_per_file = 7
       tfrecord_paths = test_utils.create_tfrecord_files(
@@ -161,7 +161,7 @@ class ParallelReadTest(test.TestCase):
     ops.reset_default_graph()
 
   def testTFRecordReader(self):
-    with self.test_session():
+    with self.cached_session():
       self._tfrecord_paths = test_utils.create_tfrecord_files(
           self.get_temp_dir(), num_files=3)
 
@@ -188,7 +188,7 @@ class SinglePassReadTest(test.TestCase):
     ops.reset_default_graph()
 
   def testOutOfRangeError(self):
-    with self.test_session():
+    with self.cached_session():
       [tfrecord_path] = test_utils.create_tfrecord_files(
           self.get_temp_dir(), num_files=1)
 
@@ -196,7 +196,7 @@ class SinglePassReadTest(test.TestCase):
         tfrecord_path, reader_class=io_ops.TFRecordReader)
     init_op = variables.local_variables_initializer()
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(init_op)
       with queues.QueueRunners(sess):
         num_reads = 11
@@ -205,7 +205,7 @@ class SinglePassReadTest(test.TestCase):
             sess.run([key, value])
 
   def testTFRecordReader(self):
-    with self.test_session():
+    with self.cached_session():
       [tfrecord_path] = test_utils.create_tfrecord_files(
           self.get_temp_dir(), num_files=1)
 
@@ -213,7 +213,7 @@ class SinglePassReadTest(test.TestCase):
         tfrecord_path, reader_class=io_ops.TFRecordReader)
     init_op = variables.local_variables_initializer()
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(init_op)
       with queues.QueueRunners(sess):
         flowers = 0
diff --git a/tensorflow/contrib/slim/python/slim/data/prefetch_queue_test.py b/tensorflow/contrib/slim/python/slim/data/prefetch_queue_test.py
index 6c3e57c47d..7caa42dcb9 100644
--- a/tensorflow/contrib/slim/python/slim/data/prefetch_queue_test.py
+++ b/tensorflow/contrib/slim/python/slim/data/prefetch_queue_test.py
@@ -37,7 +37,7 @@ from tensorflow.python.training import queue_runner_impl
 class PrefetchQueueTest(test.TestCase):
 
   def testOneThread(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       batch_size = 10
       image_size = 32
       num_batches = 5
@@ -74,7 +74,7 @@ class PrefetchQueueTest(test.TestCase):
         thread.join()
 
   def testMultiThread(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       batch_size = 10
       image_size = 32
       num_batches = 5
@@ -114,7 +114,7 @@ class PrefetchQueueTest(test.TestCase):
         thread.join()
 
   def testMultipleDequeue(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       batch_size = 10
       image_size = 32
       num_batches = 4
@@ -162,7 +162,7 @@ class PrefetchQueueTest(test.TestCase):
         prefetch_queue.prefetch_queue([variable_tensor])
 
   def testDynamicPad(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       # Create 3 tensors of variable but compatible shapes.
       var_shape = [None, 2]
       p1 = constant_op.constant([[1, 2], [3, 4]])
diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
index 826242c9d7..3114949b82 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
@@ -45,7 +45,7 @@ class TFExampleDecoderTest(test.TestCase):
         int64_list=feature_pb2.Int64List(value=ndarray.flatten().tolist()))
 
   def _EncodedBytesFeature(self, tf_encoded):
-    with self.test_session():
+    with self.cached_session():
       encoded = tf_encoded.eval()
 
     def BytesList(value):
@@ -133,7 +133,7 @@ class TFExampleDecoderTest(test.TestCase):
     tf_image = self.DecodeExample(serialized_example, item_handler,
                                   image_format)
 
-    with self.test_session():
+    with self.cached_session():
       decoded_image = tf_image.eval()
 
       # We need to recast them here to avoid some issues with uint8.
@@ -265,7 +265,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'labels':
@@ -296,7 +296,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'array': parsing_ops.FixedLenFeature(np_array.shape, dtypes.float32)
@@ -319,7 +319,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'array': parsing_ops.FixedLenFeature(np_array.shape, dtypes.int64)
@@ -342,7 +342,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64),
@@ -366,7 +366,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'labels':
@@ -390,7 +390,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64),
@@ -423,7 +423,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'image': parsing_ops.VarLenFeature(dtype=dtypes.float32),
@@ -468,7 +468,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'image': parsing_ops.VarLenFeature(dtype=dtypes.float32),
@@ -505,7 +505,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64),
@@ -536,7 +536,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64),
@@ -567,7 +567,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64),
@@ -598,7 +598,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
       keys_to_features = {
           'indices': parsing_ops.VarLenFeature(dtype=dtypes.int64),
@@ -625,7 +625,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
 
       keys_to_features = {
@@ -657,7 +657,7 @@ class TFExampleDecoderTest(test.TestCase):
 
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
 
       keys_to_features = {
@@ -692,7 +692,7 @@ class TFExampleDecoderTest(test.TestCase):
       image, serialized_example = self.GenerateImage(
           image_format=image_encoding, image_shape=image_shape)
 
-      with self.test_session():
+      with self.cached_session():
 
         def ConditionalDecoding(keys_to_tensors):
           """See base class."""
@@ -759,7 +759,7 @@ class TFExampleDecoderTest(test.TestCase):
             }))
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
 
       keys_to_features = {
@@ -800,7 +800,7 @@ class TFExampleDecoderTest(test.TestCase):
             }))
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
 
       keys_to_features = {
@@ -837,7 +837,7 @@ class TFExampleDecoderTest(test.TestCase):
     image, _ = self.GenerateImage(
         image_format=image_format, image_shape=image_shape)
     tf_encoded = self._Encoder(image, image_format)
-    with self.test_session():
+    with self.cached_session():
       tf_string = tf_encoded.eval()
 
     example = example_pb2.Example(
@@ -852,7 +852,7 @@ class TFExampleDecoderTest(test.TestCase):
             }))
     serialized_example = example.SerializeToString()
 
-    with self.test_session():
+    with self.cached_session():
       serialized_example = array_ops.reshape(serialized_example, shape=[])
 
       decoder = tfexample_decoder.TFExampleDecoder(
@@ -885,7 +885,7 @@ class TFExampleDecoderTest(test.TestCase):
     table = lookup_ops.index_table_from_tensor(
         constant_op.constant(['dog', 'guinea pig', 'cat']))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(lookup_ops.tables_initializer())
 
       serialized_example = array_ops.reshape(serialized_example, shape=[])
@@ -943,7 +943,7 @@ class TFExampleDecoderTest(test.TestCase):
     decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                  items_to_handlers)
     obtained_class_ids_each_example = []
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(lookup_ops.tables_initializer())
       for example in [example1, example2, example3]:
         serialized_example = array_ops.reshape(
diff --git a/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py b/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py
index 4707dc2229..8fcd7aeef6 100644
--- a/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py
+++ b/tensorflow/contrib/solvers/python/kernel_tests/lanczos_test.py
@@ -47,7 +47,7 @@ def _get_lanczos_tests(dtype_, use_static_shape_, shape_, orthogonalize_,
         low=-1.0, high=1.0, size=np.prod(shape_)).reshape(shape_).astype(dtype_)
     tol = 1e-12 if dtype_ == np.float64 else 1e-5
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       if use_static_shape_:
         a = constant_op.constant(a_np)
       else:
diff --git a/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py b/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py
index a73642716b..2a9100903a 100644
--- a/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py
+++ b/tensorflow/contrib/solvers/python/kernel_tests/least_squares_test.py
@@ -47,7 +47,7 @@ def _get_least_squares_tests(dtype_, use_static_shape_, shape_):
         low=-1.0, high=1.0, size=shape_[0]).astype(dtype_)
     tol = 1e-12 if dtype_ == np.float64 else 1e-6
     max_iter = 20
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       if use_static_shape_:
         a = constant_op.constant(a_np)
         rhs = constant_op.constant(rhs_np)
diff --git a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py
index a1282847be..a0e6eb87bc 100644
--- a/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py
+++ b/tensorflow/contrib/solvers/python/kernel_tests/linear_equations_test.py
@@ -54,7 +54,7 @@ def _get_linear_equations_tests(dtype_, use_static_shape_, shape_):
     x_np = np.zeros_like(rhs_np)
     tol = 1e-6 if dtype_ == np.float64 else 1e-3
     max_iter = 20
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       if use_static_shape_:
         a = constant_op.constant(a_np)
         rhs = constant_op.constant(rhs_np)
diff --git a/tensorflow/contrib/solvers/python/kernel_tests/util_test.py b/tensorflow/contrib/solvers/python/kernel_tests/util_test.py
index 5d7534657b..57b4996689 100644
--- a/tensorflow/contrib/solvers/python/kernel_tests/util_test.py
+++ b/tensorflow/contrib/solvers/python/kernel_tests/util_test.py
@@ -33,7 +33,7 @@ class UtilTest(test.TestCase):
       a_np = np.array([[1., 2.], [3., 4.], [5., 6.]], dtype=dtype)
       x_np = np.array([[2.], [-3.]], dtype=dtype)
       y_np = np.array([[2], [-3.], [5.]], dtype=dtype)
-      with self.test_session() as sess:
+      with self.cached_session() as sess:
         if use_static_shape_:
           a = constant_op.constant(a_np, dtype=dtype)
           x = constant_op.constant(x_np, dtype=dtype)
@@ -68,7 +68,7 @@ class UtilTest(test.TestCase):
       a_np = np.array([[1., 2.], [3., 4.], [5., 6.]], dtype=dtype)
       x_np = np.array([[2.], [-3.]], dtype=dtype)
       y_np = np.array([[2], [-3.], [5.]], dtype=dtype)
-      with self.test_session() as sess:
+      with self.cached_session() as sess:
         if use_static_shape_:
           a = constant_op.constant(a_np, dtype=dtype)
           x = constant_op.constant(x_np, dtype=dtype)
@@ -101,7 +101,7 @@ class UtilTest(test.TestCase):
     self._testIdentityOperator(False)
 
   def testL2Norm(self):
-    with self.test_session():
+    with self.cached_session():
       x_np = np.array([[2], [-3.], [5.]])
       x_norm_np = np.linalg.norm(x_np)
       x_normalized_np = x_np / x_norm_np
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py
index 53d7340e85..a77c507d9b 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor_test.py
@@ -61,7 +61,7 @@ class FilteringStepPostprocessorTest(test.TestCase):
       expected_state = [[[80.], [20.]],
                         [1., 6.],
                         [-1, -2]]
-      with self.test_session():
+      with self.cached_session():
         for interpolated, expected in zip(interpolated_state, expected_state):
           self.assertAllClose(expected, interpolated.eval())
         self.assertGreater(0., updated_outputs["anomaly_score"][0].eval())
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py
index 57f29f3c7f..f636126a33 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter_test.py
@@ -98,7 +98,7 @@ class MultivariateTests(test.TestCase):
         observation_model=observation_model,
         predicted_observations=(observed_mean, observed_var),
         observation_noise=observation_noise_covariance)
-    with self.test_session() as session:
+    with self.cached_session() as session:
       evaled_state = numpy.array([[1., 1., 1., 1.]])
       evaled_state_var = numpy.eye(4)[None]
       for i in range(500):
@@ -136,7 +136,7 @@ class KalmanFilterNonBatchTest(test.TestCase):
 
   def test_observed_from_state(self):
     """Compare observation mean and noise to hand-computed values."""
-    with self.test_session():
+    with self.cached_session():
       state = constant_op.constant([[2., 1.]])
       state_var = constant_op.constant([[[4., 0.], [0., 3.]]])
       observed_mean, observed_var = self.kalman_filter.observed_from_state(
@@ -171,7 +171,7 @@ class KalmanFilterNonBatchTest(test.TestCase):
             observation_model=observation_model,
             predicted_observations=predicted_observations,
             observation_noise=observation_noise))
-    with self.test_session() as session:
+    with self.cached_session() as session:
       evaled_state, evaled_state_var = session.run([state, state_var])
       for _ in range(300):
         evaled_state, evaled_state_var = session.run(
@@ -231,7 +231,7 @@ class KalmanFilterNonBatchTest(test.TestCase):
 
   def test_predict_state_mean(self):
     """Compare state mean transitions with simple hand-computed values."""
-    with self.test_session():
+    with self.cached_session():
       state = constant_op.constant([[4., 2.]])
       state = self.kalman_filter.predict_state_mean(
           state, self.transition_fn([1]))
@@ -245,7 +245,7 @@ class KalmanFilterNonBatchTest(test.TestCase):
 
   def test_predict_state_var(self):
     """Compare a variance transition with simple hand-computed values."""
-    with self.test_session():
+    with self.cached_session():
       state_var = constant_op.constant([[[1., 0.], [0., 2.]]])
       state_var = self.kalman_filter.predict_state_var(
           state_var, self.transition_fn([1]), self.power_sum_fn([1]))
@@ -259,7 +259,7 @@ class KalmanFilterNonBatchTest(test.TestCase):
     Tests that correct values have high probability and incorrect values
     have low probability when there is low uncertainty.
     """
-    with self.test_session():
+    with self.cached_session():
       state = constant_op.constant([[4., 2.]])
       state_var = constant_op.constant([[[0.0001, 0.], [0., 0.0001]]])
       observation = constant_op.constant([[
@@ -289,7 +289,7 @@ class KalmanFilterNonBatchTest(test.TestCase):
       self.assertGreater(first_log_prob.eval()[0], numpy.log(0.99))
 
   def test_predict_n_ahead_mean(self):
-    with self.test_session():
+    with self.cached_session():
       original_state = constant_op.constant([[4., 2.]])
       n = 5
       iterative_state = original_state
@@ -304,7 +304,7 @@ class KalmanFilterNonBatchTest(test.TestCase):
             self.transition_fn([1]))
 
   def test_predict_n_ahead_var(self):
-    with self.test_session():
+    with self.cached_session():
       original_var = constant_op.constant([[[2., 3.], [4., 5.]]])
       n = 5
       iterative_var = original_var
@@ -330,7 +330,7 @@ class KalmanFilterBatchTest(test.TestCase):
     Tests that correct values have high probability and incorrect values
     have low probability when there is low uncertainty.
     """
-    with self.test_session():
+    with self.cached_session():
       state = constant_op.constant([[4., 2.], [5., 3.], [6., 4.]])
       state_var = constant_op.constant(3 * [[[0.0001, 0.], [0., 0.0001]]])
       observation = constant_op.constant([
@@ -378,7 +378,7 @@ class KalmanFilterBatchTest(test.TestCase):
       self.assertLess(third_log_prob.sum(), numpy.log(0.01))
 
   def test_predict_n_ahead_mean(self):
-    with self.test_session():
+    with self.cached_session():
       kf = kalman_filter.KalmanFilter()
       transition_fn, _ = _powers_and_sums_from_transition_matrix(
           state_transition=STATE_TRANSITION,
@@ -396,7 +396,7 @@ class KalmanFilterBatchTest(test.TestCase):
       self.assertAllClose(state2.eval()[2], batch_eval[2])
 
   def test_predict_n_ahead_var(self):
-    with self.test_session():
+    with self.cached_session():
       kf = kalman_filter.KalmanFilter()
       transition_fn, power_sum_fn = _powers_and_sums_from_transition_matrix(
           state_transition=STATE_TRANSITION,
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py
index c2eaa78493..80126ac786 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model_test.py
@@ -96,7 +96,7 @@ class ConstructionTests(test.TestCase):
           },
           mode=estimator_lib.ModeKeys.TRAIN)
       initializer = variables.global_variables_initializer()
-      with self.test_session() as sess:
+      with self.cached_session() as sess:
         sess.run([initializer])
         outputs.loss.eval()
 
@@ -114,7 +114,7 @@ class ConstructionTests(test.TestCase):
           },
           mode=estimator_lib.ModeKeys.TRAIN)
       initializer = variables.global_variables_initializer()
-      with self.test_session() as sess:
+      with self.cached_session() as sess:
         sess.run([initializer])
         outputs.loss.eval()
 
@@ -144,7 +144,7 @@ class GapTests(test.TestCase):
         state=math_utils.replicate_state(
             start_state=random_model.get_start_state(),
             batch_size=array_ops.shape(times)[0]))
-    with self.test_session() as session:
+    with self.cached_session() as session:
       variables.global_variables_initializer().run()
       coordinator = coordinator_lib.Coordinator()
       queue_runner_impl.start_queue_runners(session, coord=coordinator)
@@ -250,7 +250,7 @@ class StateSpaceEquivalenceTests(test.TestCase):
       self.assertAllClose(combined_value, split_predict[prediction_key])
 
   def _equivalent_to_single_model_test_template(self, model_generator):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       random_model = RandomStateSpaceModel(
           state_dimension=5,
           state_noise_dimension=4,
@@ -374,7 +374,7 @@ class PredictionTests(test.TestCase):
               math_utils.replicate_state(
                   start_state=random_model.get_start_state(), batch_size=1)
       })
-      with self.test_session():
+      with self.cached_session():
         variables.global_variables_initializer().run()
         predicted_mean = prediction_dict["mean"].eval()
         predicted_covariance = prediction_dict["covariance"].eval()
@@ -404,7 +404,7 @@ class PredictionTests(test.TestCase):
           feature_keys.PredictionFeatures.TIMES: [[5, 7, 8]],
           feature_keys.PredictionFeatures.STATE_TUPLE: model_outputs.end_state
       })
-      with self.test_session():
+      with self.cached_session():
         variables.global_variables_initializer().run()
         predicted_mean = predictions["mean"].eval()
         predicted_covariance = predictions["covariance"].eval()
@@ -428,7 +428,7 @@ class ExogenousTests(test.TestCase):
             state=[
                 array_ops.ones(shape=[1, 5]), original_covariance[None], [0]
             ])
-        with self.test_session() as session:
+        with self.cached_session() as session:
           variables.global_variables_initializer().run()
           evaled_new_covariance, evaled_original_covariance = session.run(
               [new_covariance[0], original_covariance])
@@ -454,7 +454,7 @@ class ExogenousTests(test.TestCase):
                 -array_ops.ones(shape=[1, 5], dtype=dtype),
                 original_covariance[None], [0]
             ])
-        with self.test_session() as session:
+        with self.cached_session() as session:
           variables.global_variables_initializer().run()
           evaled_new_covariance, evaled_original_covariance = session.run(
               [new_covariance[0], original_covariance])
@@ -519,7 +519,7 @@ class PosteriorTests(test.TestCase):
         model=stub_model, data=data, true_parameters=true_params)
 
   def test_exact_posterior_recovery_no_transition_noise(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       stub_model, data, true_params = self._get_single_model()
       input_fn = input_pipeline.WholeDatasetInputFn(
           input_pipeline.NumpyReader(data))
@@ -559,7 +559,7 @@ class PosteriorTests(test.TestCase):
           posterior_times)
 
   def test_chained_exact_posterior_recovery_no_transition_noise(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       stub_model, data, true_params = self._get_single_model()
       chunk_size = 10
       input_fn = test_utils.AllWindowInputFn(
@@ -748,7 +748,7 @@ class MultivariateTests(test.TestCase):
         },
         mode=estimator_lib.ModeKeys.TRAIN)
     initializer = variables.global_variables_initializer()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run([initializer])
       outputs.loss.eval()
 
diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py
index 84885d5c9a..e8875f4eb9 100644
--- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py
+++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/varma_test.py
@@ -46,7 +46,7 @@ class MakeModelTest(test.TestCase):
         },
         mode=estimator_lib.ModeKeys.TRAIN)
     initializer = variables.global_variables_initializer()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run([initializer])
       outputs.loss.eval()
 
@@ -65,7 +65,7 @@ class MakeModelTest(test.TestCase):
         },
         mode=estimator_lib.ModeKeys.TRAIN)
     initializer = variables.global_variables_initializer()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run([initializer])
       outputs.loss.eval()
 
@@ -85,7 +85,7 @@ class MakeModelTest(test.TestCase):
             TrainEvalFeatures.VALUES: constant_op.constant([[[1.], [2.]]])},
         mode=estimator_lib.ModeKeys.TRAIN)
     initializer = variables.global_variables_initializer()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run([initializer])
       outputs.loss.eval()
 
diff --git a/tensorflow/examples/speech_commands/freeze_test.py b/tensorflow/examples/speech_commands/freeze_test.py
index c8de6c2152..0c7ca9bc01 100644
--- a/tensorflow/examples/speech_commands/freeze_test.py
+++ b/tensorflow/examples/speech_commands/freeze_test.py
@@ -25,7 +25,7 @@ from tensorflow.python.platform import test
 class FreezeTest(test.TestCase):
 
   def testCreateInferenceGraphWithMfcc(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       freeze.create_inference_graph(
           wanted_words='a,b,c,d',
           sample_rate=16000,
@@ -44,7 +44,7 @@ class FreezeTest(test.TestCase):
       self.assertEqual(1, ops.count('Mfcc'))
 
   def testCreateInferenceGraphWithoutMfcc(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       freeze.create_inference_graph(
           wanted_words='a,b,c,d',
           sample_rate=16000,
@@ -63,7 +63,7 @@ class FreezeTest(test.TestCase):
       self.assertEqual(0, ops.count('Mfcc'))
 
   def testFeatureBinCount(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       freeze.create_inference_graph(
           wanted_words='a,b,c,d',
           sample_rate=16000,
diff --git a/tensorflow/examples/speech_commands/input_data_test.py b/tensorflow/examples/speech_commands/input_data_test.py
index 2e551be9a2..aa4e807779 100644
--- a/tensorflow/examples/speech_commands/input_data_test.py
+++ b/tensorflow/examples/speech_commands/input_data_test.py
@@ -32,7 +32,7 @@ from tensorflow.python.platform import test
 class InputDataTest(test.TestCase):
 
   def _getWavData(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sample_data = tf.zeros([32000, 2])
       wav_encoder = contrib_audio.encode_wav(sample_data, 16000)
       wav_data = sess.run(wav_encoder)
@@ -75,7 +75,7 @@ class InputDataTest(test.TestCase):
       self._saveTestWavFile(file_path, wav_data)
     model_settings = models.prepare_model_settings(
         4, 16000, 1000, window_length_ms, 20, 40, preprocess)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       audio_processor = input_data.AudioProcessor(
           "", wav_dir, 10, 10, ["a", "b"], 10, 10, model_settings, tmp_dir)
       result_data, result_labels = audio_processor.get_data(
diff --git a/tensorflow/examples/speech_commands/label_wav_test.py b/tensorflow/examples/speech_commands/label_wav_test.py
index 80ca774706..f0af2a4798 100644
--- a/tensorflow/examples/speech_commands/label_wav_test.py
+++ b/tensorflow/examples/speech_commands/label_wav_test.py
@@ -30,7 +30,7 @@ from tensorflow.python.platform import test
 class LabelWavTest(test.TestCase):
 
   def _getWavData(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sample_data = tf.zeros([1000, 2])
       wav_encoder = contrib_audio.encode_wav(sample_data, 16000)
       wav_data = sess.run(wav_encoder)
diff --git a/tensorflow/examples/speech_commands/models_test.py b/tensorflow/examples/speech_commands/models_test.py
index 0c373967ed..04478c0962 100644
--- a/tensorflow/examples/speech_commands/models_test.py
+++ b/tensorflow/examples/speech_commands/models_test.py
@@ -49,7 +49,7 @@ class ModelsTest(test.TestCase):
 
   def testCreateModelConvTraining(self):
     model_settings = self._modelSettings()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
       logits, dropout_prob = models.create_model(fingerprint_input,
                                                  model_settings, "conv", True)
@@ -60,7 +60,7 @@ class ModelsTest(test.TestCase):
 
   def testCreateModelConvInference(self):
     model_settings = self._modelSettings()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
       logits = models.create_model(fingerprint_input, model_settings, "conv",
                                    False)
@@ -69,7 +69,7 @@ class ModelsTest(test.TestCase):
 
   def testCreateModelLowLatencyConvTraining(self):
     model_settings = self._modelSettings()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
       logits, dropout_prob = models.create_model(
           fingerprint_input, model_settings, "low_latency_conv", True)
@@ -80,7 +80,7 @@ class ModelsTest(test.TestCase):
 
   def testCreateModelFullyConnectedTraining(self):
     model_settings = self._modelSettings()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
       logits, dropout_prob = models.create_model(
           fingerprint_input, model_settings, "single_fc", True)
@@ -91,7 +91,7 @@ class ModelsTest(test.TestCase):
 
   def testCreateModelBadArchitecture(self):
     model_settings = self._modelSettings()
-    with self.test_session():
+    with self.cached_session():
       fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
       with self.assertRaises(Exception) as e:
         models.create_model(fingerprint_input, model_settings,
@@ -100,7 +100,7 @@ class ModelsTest(test.TestCase):
 
   def testCreateModelTinyConvTraining(self):
     model_settings = self._modelSettings()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       fingerprint_input = tf.zeros([1, model_settings["fingerprint_size"]])
       logits, dropout_prob = models.create_model(
           fingerprint_input, model_settings, "tiny_conv", True)
diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py
index 266af56611..2f271c4f50 100644
--- a/tensorflow/python/keras/backend_test.py
+++ b/tensorflow/python/keras/backend_test.py
@@ -279,7 +279,7 @@ class BackendUtilsTest(test.TestCase):
           keras.backend.get_session().run(fetches=[x, y]), [30., 40.])
 
   def test_function_tf_run_options_with_run_metadata(self):
-    with self.test_session():
+    with self.cached_session():
       x_placeholder = keras.backend.placeholder(shape=())
       y_placeholder = keras.backend.placeholder(shape=())
 
diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py
index 7675a6586f..b6fae19823 100644
--- a/tensorflow/python/keras/callbacks_test.py
+++ b/tensorflow/python/keras/callbacks_test.py
@@ -63,7 +63,7 @@ class KerasCallbacksTest(test.TestCase):
     if h5py is None:
       return  # Skip test if models cannot be saved.
 
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
 
       temp_dir = self.get_temp_dir()
@@ -226,7 +226,7 @@ class KerasCallbacksTest(test.TestCase):
           mode='unknown')
 
   def test_EarlyStopping(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(123)
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
@@ -265,7 +265,7 @@ class KerasCallbacksTest(test.TestCase):
             verbose=0)
 
   def test_EarlyStopping_reuse(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
       patience = 3
       data = np.random.random((100, 1))
@@ -287,7 +287,7 @@ class KerasCallbacksTest(test.TestCase):
       assert len(hist.epoch) >= patience
 
   def test_EarlyStopping_with_baseline(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
       baseline = 0.5
       (data, labels), _ = testing_utils.get_test_data(
@@ -321,7 +321,7 @@ class KerasCallbacksTest(test.TestCase):
     monitor.on_epoch_end(0, logs={'loss': 0.})
 
   def test_LearningRateScheduler(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
@@ -368,7 +368,7 @@ class KerasCallbacksTest(test.TestCase):
               model.optimizer.lr)) - 0.01 / 4) < keras.backend.epsilon()
 
   def test_ReduceLROnPlateau(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
@@ -470,7 +470,7 @@ class KerasCallbacksTest(test.TestCase):
     self.assertEqual(reduce_on_plateau.min_delta, 1e-13)
 
   def test_CSVLogger(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
       temp_dir = self.get_temp_dir()
       self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
@@ -549,7 +549,7 @@ class KerasCallbacksTest(test.TestCase):
     tmpdir = self.get_temp_dir()
     self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
 
-    with self.test_session():
+    with self.cached_session():
       fp = os.path.join(tmpdir, 'test.csv')
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
@@ -601,7 +601,7 @@ class KerasCallbacksTest(test.TestCase):
       assert 'nan' in values[-1], 'The last epoch was not logged.'
 
   def test_TerminateOnNaN(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
@@ -666,7 +666,7 @@ class KerasCallbacksTest(test.TestCase):
         i %= max_batch_index
 
     # case: Sequential
-    with self.test_session():
+    with self.cached_session():
       model = keras.models.Sequential()
       model.add(
           keras.layers.Dense(
@@ -743,7 +743,7 @@ class KerasCallbacksTest(test.TestCase):
     tmpdir = self.get_temp_dir()
     self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
 
-    with self.test_session():
+    with self.cached_session():
       filepath = os.path.join(tmpdir, 'logs')
 
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
@@ -815,7 +815,7 @@ class KerasCallbacksTest(test.TestCase):
     tmpdir = self.get_temp_dir()
     self.addCleanup(shutil.rmtree, tmpdir, ignore_errors=True)
 
-    with self.test_session():
+    with self.cached_session():
       filepath = os.path.join(tmpdir, 'logs')
 
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
@@ -925,7 +925,7 @@ class KerasCallbacksTest(test.TestCase):
     y_test = keras.utils.to_categorical(y_test)
     y_train = keras.utils.to_categorical(y_train)
 
-    with self.test_session():
+    with self.cached_session():
       model = keras.models.Sequential()
       model.add(
           keras.layers.Dense(
@@ -969,7 +969,7 @@ class KerasCallbacksTest(test.TestCase):
       while True:
         yield x, y
 
-    with self.test_session():
+    with self.cached_session():
       model = testing_utils.get_small_sequential_mlp(
           num_hidden=10, num_classes=10, input_dim=100)
       model.compile(
@@ -1011,7 +1011,7 @@ class KerasCallbacksTest(test.TestCase):
       os.name == 'nt',
       'use_multiprocessing=True does not work on windows properly.')
   def test_LambdaCallback(self):
-    with self.test_session():
+    with self.cached_session():
       np.random.seed(1337)
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
@@ -1055,7 +1055,7 @@ class KerasCallbacksTest(test.TestCase):
       assert not t.is_alive()
 
   def test_TensorBoard_with_ReduceLROnPlateau(self):
-    with self.test_session():
+    with self.cached_session():
       temp_dir = self.get_temp_dir()
       self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
 
@@ -1194,7 +1194,7 @@ class KerasCallbacksTest(test.TestCase):
   def test_RemoteMonitorWithJsonPayload(self):
     if requests is None:
       self.skipTest('`requests` required to run this test')
-    with self.test_session():
+    with self.cached_session():
       (x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
           train_samples=TRAIN_SAMPLES,
           test_samples=TEST_SAMPLES,
diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py
index 71c1987cee..3a1b00041f 100644
--- a/tensorflow/python/keras/model_subclassing_test.py
+++ b/tensorflow/python/keras/model_subclassing_test.py
@@ -463,7 +463,7 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 10
     input_dim = 50
 
-    with self.test_session():
+    with self.cached_session():
       model = SimpleTestModel(num_classes=num_classes,
                               use_dp=True,
                               use_bn=True)
@@ -481,7 +481,7 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 10
     input_dim = 50
 
-    with self.test_session():
+    with self.cached_session():
       model = MultiIOTestModel(num_classes=num_classes,
                                use_dp=True,
                                use_bn=True)
@@ -501,7 +501,7 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 10
     input_dim = 50
 
-    with self.test_session():
+    with self.cached_session():
       model = SimpleTestModel(num_classes=num_classes, use_dp=True, use_bn=True)
       model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
 
@@ -521,7 +521,7 @@ class ModelSubclassingTest(test.TestCase):
     num_samples = 1000
     input_dim = 50
 
-    with self.test_session():
+    with self.cached_session():
       model = MultiIOTestModel(num_classes=num_classes,
                                use_dp=True,
                                use_bn=True)
@@ -610,7 +610,7 @@ class ModelSubclassingTest(test.TestCase):
       def call(self, x):
         return self.bn(self.fc(x))
 
-    with self.test_session():
+    with self.cached_session():
       model = TestModel1()
 
       x = array_ops.ones(shape=[100, 784], dtype='float32')
@@ -631,7 +631,7 @@ class ModelSubclassingTest(test.TestCase):
       def call(self, x):
         return self.bn(self.fc(x))
 
-    with self.test_session():
+    with self.cached_session():
       model = TestModel2()
 
       x = array_ops.ones(shape=[100, 784], dtype='float32')
@@ -655,7 +655,7 @@ class ModelSubclassingTest(test.TestCase):
       def call(self, x):
         return self.bn(self.fc(x))
 
-    with self.test_session():
+    with self.cached_session():
       model = TestModel3()
 
       x = array_ops.ones(shape=[100, 784], dtype='float32')
diff --git a/tensorflow/python/keras/optimizers_test.py b/tensorflow/python/keras/optimizers_test.py
index 9a68fc0e35..8d7493462e 100644
--- a/tensorflow/python/keras/optimizers_test.py
+++ b/tensorflow/python/keras/optimizers_test.py
@@ -85,23 +85,23 @@ def _test_optimizer(optimizer, target=0.75):
 class KerasOptimizersTest(test.TestCase):
 
   def test_sgd(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.SGD(lr=0.01,
                                            momentum=0.9,
                                            nesterov=True))
 
   def test_rmsprop(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.RMSprop())
       _test_optimizer(keras.optimizers.RMSprop(decay=1e-3))
 
   def test_adagrad(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.Adagrad())
       _test_optimizer(keras.optimizers.Adagrad(decay=1e-3))
 
   def test_adadelta(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.Adadelta(), target=0.6)
       # Accuracy seems dependent on the initialization. Even adding tf.Print
       # nodes in the graph seemed to affect the initialization seed, and hence
@@ -109,28 +109,28 @@ class KerasOptimizersTest(test.TestCase):
       _test_optimizer(keras.optimizers.Adadelta(decay=1e-3), target=0.4)
 
   def test_adam(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.Adam())
       _test_optimizer(keras.optimizers.Adam(decay=1e-3))
       _test_optimizer(keras.optimizers.Adam(amsgrad=True))
 
   def test_adamax(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.Adamax())
       _test_optimizer(keras.optimizers.Adamax(decay=1e-3))
 
   def test_nadam(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.Nadam())
 
   def test_clipnorm(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.SGD(lr=0.01,
                                            momentum=0.9,
                                            clipnorm=0.5))
 
   def test_clipvalue(self):
-    with self.test_session():
+    with self.cached_session():
       _test_optimizer(keras.optimizers.SGD(lr=0.01,
                                            momentum=0.9,
                                            clipvalue=0.5))
@@ -158,7 +158,7 @@ class KerasOptimizersTest(test.TestCase):
 
   @test_util.run_in_graph_and_eager_modes
   def test_tfoptimizer_iterations(self):
-    with self.test_session():
+    with self.cached_session():
       optimizer = keras.optimizers.TFOptimizer(AdamOptimizer(0.01))
       model = keras.models.Sequential()
       model.add(keras.layers.Dense(
diff --git a/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py
index 4e31b1ea2a..dee96102fb 100644
--- a/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py
+++ b/tensorflow/python/kernel_tests/boosted_trees/prediction_ops_test.py
@@ -30,7 +30,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testCachedPredictionOnEmptyEnsemble(self):
     """Tests that prediction on a dummy ensemble does not fail."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       # Create a dummy ensemble.
       tree_ensemble = boosted_trees_ops.TreeEnsemble(
           'ensemble', serialized_proto='')
@@ -63,7 +63,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testNoCachedPredictionButTreeExists(self):
     """Tests that predictions are updated once trees are added."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -129,7 +129,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testCachedPredictionIsCurrent(self):
     """Tests that prediction based on previous node in the tree works."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -201,7 +201,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testCachedPredictionFromTheSameTree(self):
     """Tests that prediction based on previous node in the tree works."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -315,7 +315,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testCachedPredictionFromPreviousTree(self):
     """Tests the predictions work when we have cache from previous trees."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -447,7 +447,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testCachedPredictionFromTheSameTreeWithPostPrunedNodes(self):
     """Tests that prediction based on previous node in the tree works."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -577,7 +577,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testCachedPredictionFromThePreviousTreeWithPostPrunedNodes(self):
     """Tests that prediction based on previous node in the tree works."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -722,7 +722,7 @@ class TrainingPredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testCachedPredictionTheWholeTreeWasPruned(self):
     """Tests that prediction based on previous node in the tree works."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -794,7 +794,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testPredictionOnEmptyEnsemble(self):
     """Tests that prediction on a empty ensemble does not fail."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       # Create an empty ensemble.
       tree_ensemble = boosted_trees_ops.TreeEnsemble(
           'ensemble', serialized_proto='')
@@ -816,7 +816,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase):
 
   def testPredictionMultipleTree(self):
     """Tests the predictions work when we have multiple trees."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -930,7 +930,7 @@ class FeatureContribsOpsTest(test_util.TensorFlowTestCase):
 
   def testContribsMultipleTree(self):
     """Tests that the contribs work when we have multiple trees."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge(
           """
diff --git a/tensorflow/python/kernel_tests/boosted_trees/resource_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/resource_ops_test.py
index d5f0c22d6e..65bb9ab55f 100644
--- a/tensorflow/python/kernel_tests/boosted_trees/resource_ops_test.py
+++ b/tensorflow/python/kernel_tests/boosted_trees/resource_ops_test.py
@@ -31,7 +31,7 @@ class ResourceOpsTest(test_util.TensorFlowTestCase):
   """Tests resource_ops."""
 
   def testCreate(self):
-    with self.test_session():
+    with self.cached_session():
       ensemble = boosted_trees_ops.TreeEnsemble('ensemble')
       resources.initialize_resources(resources.shared_resources()).run()
       stamp_token = ensemble.get_stamp_token()
@@ -44,7 +44,7 @@ class ResourceOpsTest(test_util.TensorFlowTestCase):
       self.assertAllEqual([0, 1], nodes_range.eval())
 
   def testCreateWithProto(self):
-    with self.test_session():
+    with self.cached_session():
       ensemble_proto = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge(
           """
@@ -161,7 +161,7 @@ class ResourceOpsTest(test_util.TensorFlowTestCase):
       self.assertAllEqual([16, 19], nodes_range.eval())
 
   def testSerializeDeserialize(self):
-    with self.test_session():
+    with self.cached_session():
       # Initialize.
       ensemble = boosted_trees_ops.TreeEnsemble('ensemble', stamp_token=5)
       resources.initialize_resources(resources.shared_resources()).run()
diff --git a/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py
index 568e695fd5..09e9cfa3af 100644
--- a/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py
+++ b/tensorflow/python/kernel_tests/boosted_trees/stats_ops_test.py
@@ -30,7 +30,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testCalculateBestGainsWithoutRegularization(self):
     """Testing Gain calculation without any regularization."""
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       max_splits = 7
       node_id_range = [1, 3]  # node 1 through 2 will be processed.
       stats_summary_list = [
@@ -78,7 +78,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testCalculateBestGainsWithL2(self):
     """Testing Gain calculation with L2."""
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       max_splits = 7
       node_id_range = [1, 3]  # node 1 through 2 will be processed.
       stats_summary_list = [
@@ -126,7 +126,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testCalculateBestGainsWithL1(self):
     """Testing Gain calculation with L1."""
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       max_splits = 7
       node_id_range = [1, 3]  # node 1 through 2 will be processed.
       stats_summary_list = [
@@ -177,7 +177,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testCalculateBestGainsWithTreeComplexity(self):
     """Testing Gain calculation with L2."""
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       max_splits = 7
       node_id_range = [1, 3]  # node 1 through 2 will be processed.
       stats_summary_list = [
@@ -229,7 +229,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testCalculateBestGainsWithMinNodeWeight(self):
     """Testing Gain calculation without any regularization."""
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       max_splits = 7
       node_id_range = [1, 3]  # node 1 through 2 will be processed.
       stats_summary_list = [
@@ -276,7 +276,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testCalculateBestGainsWithMinNodeWeightNoSplitOnFeturePossible(self):
     """Testing Gain calculation without any regularization."""
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       max_splits = 7
       node_id_range = [1, 3]  # node 1 through 2 will be processed.
       stats_summary_list = [
@@ -329,7 +329,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testMakeStatsSummarySimple(self):
     """Simple test for MakeStatsSummary."""
-    with self.test_session():
+    with self.cached_session():
       self.assertAllClose([[[[1., 5.], [2., 6.]], [[3., 7.], [4., 8.]]]],
                           boosted_trees_ops.make_stats_summary(
                               node_ids=[0, 0, 1, 1],
@@ -341,7 +341,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testMakeStatsSummaryAccumulate(self):
     """Tests that Summary actually accumulates."""
-    with self.test_session():
+    with self.cached_session():
       max_splits = 3
       num_buckets = 4
       node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
@@ -363,7 +363,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
 
   def testMakeStatsSummaryMultipleFeatures(self):
     """Tests that MakeStatsSummary works for multiple features."""
-    with self.test_session():
+    with self.cached_session():
       max_splits = 3
       num_buckets = 4
       node_ids = [1, 1, 2, 2, 1, 1, 2, 0]
@@ -392,7 +392,7 @@ class StatsOpsTest(test_util.TensorFlowTestCase):
           result.eval())
 
   def _verify_precision(self, length):
-    with self.test_session():
+    with self.cached_session():
       max_splits = 1
       num_buckets = 1
       node_ids = array_ops.fill([length], 0)
diff --git a/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py b/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py
index d55240297a..ea022820e4 100644
--- a/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py
+++ b/tensorflow/python/kernel_tests/boosted_trees/training_ops_test.py
@@ -32,7 +32,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testGrowWithEmptyEnsemble(self):
     """Test growing an empty ensemble."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       # Create empty ensemble.
       tree_ensemble = boosted_trees_ops.TreeEnsemble('ensemble')
       tree_ensemble_handle = tree_ensemble.resource_handle
@@ -141,7 +141,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testBiasCenteringOnEmptyEnsemble(self):
     """Test growing with bias centering on an empty ensemble."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       # Create empty ensemble.
       tree_ensemble = boosted_trees_ops.TreeEnsemble('ensemble')
       tree_ensemble_handle = tree_ensemble.resource_handle
@@ -184,7 +184,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testGrowExistingEnsembleTreeNotFinalized(self):
     """Test growing an existing ensemble with the last tree not finalized."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -368,7 +368,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testGrowExistingEnsembleTreeFinalized(self):
     """Test growing an existing ensemble with the last tree finalized."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -517,7 +517,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testPrePruning(self):
     """Test growing an existing ensemble with pre-pruning."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge("""
         trees {
@@ -673,7 +673,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testMetadataWhenCantSplitDueToEmptySplits(self):
     """Test that the metadata is updated even though we can't split."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge(
           """
@@ -784,7 +784,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testMetadataWhenCantSplitDuePrePruning(self):
     """Test metadata is updated correctly when no split due to prepruning."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       text_format.Merge(
           """
@@ -919,7 +919,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testPostPruningOfSomeNodes(self):
     """Test growing an ensemble with post-pruning."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       # Create empty ensemble.
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       tree_ensemble = boosted_trees_ops.TreeEnsemble(
@@ -1253,7 +1253,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testPostPruningOfAllNodes(self):
     """Test growing an ensemble with post-pruning, with all nodes are pruned."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       # Create empty ensemble.
       # Create empty ensemble.
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
@@ -1436,7 +1436,7 @@ class UpdateTreeEnsembleOpTest(test_util.TensorFlowTestCase):
 
   def testPostPruningChangesNothing(self):
     """Test growing an ensemble with post-pruning with all gains >0."""
-    with self.test_session() as session:
+    with self.cached_session() as session:
       # Create empty ensemble.
       tree_ensemble_config = boosted_trees_pb2.TreeEnsemble()
       tree_ensemble = boosted_trees_ops.TreeEnsemble(