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: 212348850

4 files changed, 16 insertions, 16 deletions

diff --git a/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py b/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
index bb5140aeb3..6aa62fb82e 100644
--- a/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
+++ b/tensorflow/contrib/factorization/python/ops/factorization_ops_test.py
@@ -126,7 +126,7 @@ class WalsModelTest(test.TestCase):
     observed *= num_rows / 3. if test_rows else num_cols / 2.
     want_weight_sum = unobserved + observed
 
-    with ops.Graph().as_default(), self.test_session() as sess:
+    with ops.Graph().as_default(), self.cached_session() as sess:
       wals_model = factorization_ops.WALSModel(
           input_rows=num_rows,
           input_cols=num_cols,
@@ -161,7 +161,7 @@ class WalsModelTest(test.TestCase):
   def _run_test_process_input(self,
                               use_factors_weights_cache,
                               compute_loss=False):
-    with ops.Graph().as_default(), self.test_session() as sess:
+    with ops.Graph().as_default(), self.cached_session() as sess:
       self._wals_inputs = self.sparse_input()
       sp_feeder = array_ops.sparse_placeholder(dtypes.float32)
       num_rows = 5
@@ -330,7 +330,7 @@ class WalsModelTest(test.TestCase):
   def _run_test_process_input_transposed(self,
                                          use_factors_weights_cache,
                                          compute_loss=False):
-    with ops.Graph().as_default(), self.test_session() as sess:
+    with ops.Graph().as_default(), self.cached_session() as sess:
       self._wals_inputs = self.sparse_input()
       sp_feeder = array_ops.sparse_placeholder(dtypes.float32)
       num_rows = 5
@@ -505,7 +505,7 @@ class WalsModelTest(test.TestCase):
   # trigger the more efficient ALS updates.
   # Here we test that those two give identical results.
   def _run_test_als(self, use_factors_weights_cache):
-    with ops.Graph().as_default(), self.test_session():
+    with ops.Graph().as_default(), self.cached_session():
       self._wals_inputs = self.sparse_input()
       col_init = np.random.rand(7, 3)
       als_model = factorization_ops.WALSModel(
@@ -583,7 +583,7 @@ class WalsModelTest(test.TestCase):
           atol=1e-2)
 
   def _run_test_als_transposed(self, use_factors_weights_cache):
-    with ops.Graph().as_default(), self.test_session():
+    with ops.Graph().as_default(), self.cached_session():
       self._wals_inputs = self.sparse_input()
       col_init = np.random.rand(7, 3)
       als_model = factorization_ops.WALSModel(
@@ -673,7 +673,7 @@ class WalsModelTest(test.TestCase):
     rows = 15
     cols = 11
     dims = 3
-    with ops.Graph().as_default(), self.test_session():
+    with ops.Graph().as_default(), self.cached_session():
       data = np.dot(np.random.rand(rows, 3), np.random.rand(
           3, cols)).astype(np.float32) / 3.0
       indices = [[i, j] for i in xrange(rows) for j in xrange(cols)]
@@ -703,7 +703,7 @@ class WalsModelTest(test.TestCase):
     cols = 11
     dims = 3
 
-    with ops.Graph().as_default(), self.test_session():
+    with ops.Graph().as_default(), self.cached_session():
       data = np.dot(np.random.rand(rows, 3), np.random.rand(
           3, cols)).astype(np.float32) / 3.0
       indices = [[i, j] for i in xrange(rows) for j in xrange(cols)]
@@ -736,7 +736,7 @@ class WalsModelTest(test.TestCase):
     def keep_index(x):
       return not (x[0] + x[1]) % 4
 
-    with ops.Graph().as_default(), self.test_session():
+    with ops.Graph().as_default(), self.cached_session():
       row_wts = 0.1 + np.random.rand(rows)
       col_wts = 0.1 + np.random.rand(cols)
       data = np.dot(np.random.rand(rows, 3), np.random.rand(
diff --git a/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py b/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py
index 888c3c238c..112e4d289b 100644
--- a/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py
+++ b/tensorflow/contrib/factorization/python/ops/gmm_ops_test.py
@@ -99,7 +99,7 @@ class GmmOpsTest(test.TestCase):
     logging.info('Numpy took %f', time.time() - start_time)
 
     start_time = time.time()
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       op = gmm_ops._covariance(
           constant_op.constant(
               data.T, dtype=dtypes.float32), False)
@@ -120,7 +120,7 @@ class GmmOpsTest(test.TestCase):
     graph = ops.Graph()
     with graph.as_default() as g:
       g.seed = 5
-      with self.test_session() as sess:
+      with self.cached_session() as sess:
         data = constant_op.constant(self.data, dtype=dtypes.float32)
         loss_op, scores, assignments, training_op, init_op, _ = gmm_ops.gmm(
             data, 'random', num_classes, random_seed=self.seed)
@@ -144,7 +144,7 @@ class GmmOpsTest(test.TestCase):
   def testParams(self):
     """Tests that the params work as intended."""
     num_classes = 2
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       # Experiment 1. Update weights only.
       data = constant_op.constant(self.data, dtype=dtypes.float32)
       gmm_tool = gmm_ops.GmmAlgorithm([data], num_classes,
diff --git a/tensorflow/contrib/factorization/python/ops/kmeans_test.py b/tensorflow/contrib/factorization/python/ops/kmeans_test.py
index 88eb9cf692..1ab5418fe4 100644
--- a/tensorflow/contrib/factorization/python/ops/kmeans_test.py
+++ b/tensorflow/contrib/factorization/python/ops/kmeans_test.py
@@ -232,7 +232,7 @@ class KMeansTest(KMeansTestBase):
     self.assertEqual(features.shape, parsed_feature_dict.shape)
     self.assertEqual(features.dtype, parsed_feature_dict.dtype)
     # Then check that running the tensor yields the original list of points.
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       parsed_points = sess.run(parsed_feature_dict)
       self.assertAllEqual(self.points, parsed_points)
 
diff --git a/tensorflow/contrib/factorization/python/ops/wals_test.py b/tensorflow/contrib/factorization/python/ops/wals_test.py
index 31820a18b4..9bdbd05015 100644
--- a/tensorflow/contrib/factorization/python/ops/wals_test.py
+++ b/tensorflow/contrib/factorization/python/ops/wals_test.py
@@ -336,7 +336,7 @@ class WALSMatrixFactorizationTest(test.TestCase):
     loss = self._model.evaluate(
         input_fn=eval_input_fn_row, steps=self._num_rows)['loss']
 
-    with self.test_session():
+    with self.cached_session():
       true_loss = self.calculate_loss()
 
     self.assertNear(
@@ -354,7 +354,7 @@ class WALSMatrixFactorizationTest(test.TestCase):
     loss = self._model.evaluate(
         input_fn=eval_input_fn_col, steps=self._num_cols)['loss']
 
-    with self.test_session():
+    with self.cached_session():
       true_loss = self.calculate_loss()
 
     self.assertNear(
@@ -440,7 +440,7 @@ class SweepHookTest(test.TestCase):
                          math_ops.logical_not(is_row_sweep_var)))
     mark_sweep_done = state_ops.assign(is_sweep_done_var, True)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sweep_hook = wals_lib._SweepHook(
           is_row_sweep_var,
           is_sweep_done_var,
@@ -491,7 +491,7 @@ class StopAtSweepHookTest(test.TestCase):
     train_op = state_ops.assign_add(completed_sweeps, 1)
     hook.begin()
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run([variables.global_variables_initializer()])
       mon_sess = monitored_session._HookedSession(sess, [hook])
       mon_sess.run(train_op)