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: 213326581

2 files changed, 19 insertions, 19 deletions

diff --git a/tensorflow/contrib/metrics/python/kernel_tests/histogram_ops_test.py b/tensorflow/contrib/metrics/python/kernel_tests/histogram_ops_test.py
index 1d18d6beff..bed1ecb71c 100644
--- a/tensorflow/contrib/metrics/python/kernel_tests/histogram_ops_test.py
+++ b/tensorflow/contrib/metrics/python/kernel_tests/histogram_ops_test.py
@@ -31,21 +31,21 @@ class Strict1dCumsumTest(test.TestCase):
   """Test this private function."""
 
   def test_empty_tensor_returns_empty(self):
-    with self.test_session():
+    with self.cached_session():
       tensor = constant_op.constant([])
       result = histogram_ops._strict_1d_cumsum(tensor, 0)
       expected = constant_op.constant([])
       np.testing.assert_array_equal(expected.eval(), result.eval())
 
   def test_length_1_tensor_works(self):
-    with self.test_session():
+    with self.cached_session():
       tensor = constant_op.constant([3], dtype=dtypes.float32)
       result = histogram_ops._strict_1d_cumsum(tensor, 1)
       expected = constant_op.constant([3], dtype=dtypes.float32)
       np.testing.assert_array_equal(expected.eval(), result.eval())
 
   def test_length_3_tensor_works(self):
-    with self.test_session():
+    with self.cached_session():
       tensor = constant_op.constant([1, 2, 3], dtype=dtypes.float32)
       result = histogram_ops._strict_1d_cumsum(tensor, 3)
       expected = constant_op.constant([1, 3, 6], dtype=dtypes.float32)
@@ -58,7 +58,7 @@ class AUCUsingHistogramTest(test.TestCase):
     self.rng = np.random.RandomState(0)
 
   def test_empty_labels_and_scores_gives_nan_auc(self):
-    with self.test_session():
+    with self.cached_session():
       labels = constant_op.constant([], shape=[0], dtype=dtypes.bool)
       scores = constant_op.constant([], shape=[0], dtype=dtypes.float32)
       score_range = [0, 1.]
@@ -155,7 +155,7 @@ class AUCUsingHistogramTest(test.TestCase):
         from synthetic data.
     """
     score_range = [0, 1.] or score_range
-    with self.test_session():
+    with self.cached_session():
       labels = array_ops.placeholder(dtypes.bool, shape=[num_records])
       scores = array_ops.placeholder(dtypes.float32, shape=[num_records])
       auc, update_op = histogram_ops.auc_using_histogram(
diff --git a/tensorflow/contrib/metrics/python/metrics/classification_test.py b/tensorflow/contrib/metrics/python/metrics/classification_test.py
index 3d0b81c1be..d6a670f97b 100644
--- a/tensorflow/contrib/metrics/python/metrics/classification_test.py
+++ b/tensorflow/contrib/metrics/python/metrics/classification_test.py
@@ -34,7 +34,7 @@ from tensorflow.python.platform import test
 class ClassificationTest(test.TestCase):
 
   def testAccuracy1D(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       pred = array_ops.placeholder(dtypes.int32, shape=[None])
       labels = array_ops.placeholder(dtypes.int32, shape=[None])
       acc = classification.accuracy(pred, labels)
@@ -44,7 +44,7 @@ class ClassificationTest(test.TestCase):
       self.assertEqual(result, 0.5)
 
   def testAccuracy1DBool(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       pred = array_ops.placeholder(dtypes.bool, shape=[None])
       labels = array_ops.placeholder(dtypes.bool, shape=[None])
       acc = classification.accuracy(pred, labels)
@@ -54,7 +54,7 @@ class ClassificationTest(test.TestCase):
       self.assertEqual(result, 0.5)
 
   def testAccuracy1DInt64(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       pred = array_ops.placeholder(dtypes.int64, shape=[None])
       labels = array_ops.placeholder(dtypes.int64, shape=[None])
       acc = classification.accuracy(pred, labels)
@@ -64,7 +64,7 @@ class ClassificationTest(test.TestCase):
       self.assertEqual(result, 0.5)
 
   def testAccuracy1DString(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       pred = array_ops.placeholder(dtypes.string, shape=[None])
       labels = array_ops.placeholder(dtypes.string, shape=[None])
       acc = classification.accuracy(pred, labels)
@@ -87,7 +87,7 @@ class ClassificationTest(test.TestCase):
       classification.accuracy(pred, labels)
 
   def testAccuracy1DWeighted(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       pred = array_ops.placeholder(dtypes.int32, shape=[None])
       labels = array_ops.placeholder(dtypes.int32, shape=[None])
       weights = array_ops.placeholder(dtypes.float32, shape=[None])
@@ -101,7 +101,7 @@ class ClassificationTest(test.TestCase):
       self.assertEqual(result, 0.5)
 
   def testAccuracy1DWeightedBroadcast(self):
-    with self.test_session() as session:
+    with self.cached_session() as session:
       pred = array_ops.placeholder(dtypes.int32, shape=[None])
       labels = array_ops.placeholder(dtypes.int32, shape=[None])
       weights = array_ops.placeholder(dtypes.float32, shape=[])
@@ -161,7 +161,7 @@ class F1ScoreTest(test.TestCase):
         (10, 3), maxval=2, dtype=dtypes.int64, seed=2)
     f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.local_variables_initializer())
 
       # Run several updates.
@@ -176,7 +176,7 @@ class F1ScoreTest(test.TestCase):
   def testAllCorrect(self):
     inputs = np.random.randint(0, 2, size=(100, 1))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       predictions = constant_op.constant(inputs, dtype=dtypes.float32)
       labels = constant_op.constant(inputs)
       f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3)
@@ -191,7 +191,7 @@ class F1ScoreTest(test.TestCase):
         [1, 0, 1, 0], shape=(1, 4), dtype=dtypes.float32)
     labels = constant_op.constant([0, 1, 1, 0], shape=(1, 4))
     f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=1)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.local_variables_initializer())
       sess.run([f1_op])
       # Threshold 0 will have around 0.5 precision and 1 recall yielding an F1
@@ -201,7 +201,7 @@ class F1ScoreTest(test.TestCase):
   def testAllIncorrect(self):
     inputs = np.random.randint(0, 2, size=(10000, 1))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       predictions = constant_op.constant(inputs, dtype=dtypes.float32)
       labels = constant_op.constant(1 - inputs, dtype=dtypes.float32)
       f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3)
@@ -214,7 +214,7 @@ class F1ScoreTest(test.TestCase):
       self.assertAlmostEqual(2 * 0.5 * 1 / (1 + 0.5), f1.eval(), places=2)
 
   def testWeights1d(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       predictions = constant_op.constant(
           [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes.float32)
       labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
@@ -228,7 +228,7 @@ class F1ScoreTest(test.TestCase):
       self.assertAlmostEqual(1.0, f1.eval(), places=5)
 
   def testWeights2d(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       predictions = constant_op.constant(
           [[1, 0], [1, 0]], shape=(2, 2), dtype=dtypes.float32)
       labels = constant_op.constant([[0, 1], [1, 0]], shape=(2, 2))
@@ -242,7 +242,7 @@ class F1ScoreTest(test.TestCase):
       self.assertAlmostEqual(1.0, f1.eval(), places=5)
 
   def testZeroLabelsPredictions(self):
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       predictions = array_ops.zeros([4], dtype=dtypes.float32)
       labels = array_ops.zeros([4])
       f1, f1_op = classification.f1_score(predictions, labels, num_thresholds=3)
@@ -300,7 +300,7 @@ class F1ScoreTest(test.TestCase):
     f1, f1_op = classification.f1_score(tf_labels, tf_predictions,
                                         num_thresholds=3)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.local_variables_initializer())
       for _ in range(num_batches):
         sess.run([f1_op])