diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2018-09-10 14:37:06 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-09-10 15:04:14 -0700 |
| commit | b828f89263e054bfa7c7a808cab1506834ab906d (patch) | |
| tree | e31816a6850d177306f19ee8670e0836060fcfc9 /tensorflow | |
| parent | acf0ee82092727afc2067316982407cf5e496f75 (diff) | |
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: 212336464
Diffstat (limited to 'tensorflow')
24 files changed, 242 insertions, 242 deletions
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py index 4278a30ba9..46dfbdefeb 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py @@ -331,7 +331,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testObliviousEnsemble(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree. tree1 = tree_ensemble_config.trees.add() @@ -1399,7 +1399,7 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([0, 0], result.eval()) def testObliviousTreeNonFinalized(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Depth 3 tree. tree1 = tree_ensemble_config.trees.add() diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py index b3e4c2e5f7..86fd5770a0 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py @@ -411,7 +411,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEmptyEnsembleObliviousCase(self): """Test growing an empty ensemble in the oblivious case.""" - with self.test_session() as session: + with self.cached_session() as session: # Create empty ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -1620,7 +1620,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsembleTreeLayerByLayerObliviousCase(self): """Test growing an existing ensemble with the last tree not finalized.""" - with self.test_session() as session: + with self.cached_session() as session: # Create existing ensemble with one root split tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge( @@ -1810,7 +1810,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsembleWithEmptyNodesMiddleCase(self): """Test case: The middle existing leaves don't have examples.""" - with self.test_session() as session: + with self.cached_session() as session: tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge( """ @@ -2071,7 +2071,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsembleWithEmptyNodesBorderCase(self): """Test case: The first and last existing leaves don't have examples.""" - with self.test_session() as session: + with self.cached_session() as session: tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge( """ diff --git a/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer_test.py b/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer_test.py index 9b4bf62710..3e25079e02 100644 --- a/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer_test.py +++ b/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer_test.py @@ -75,7 +75,7 @@ class ExternalRegretOptimizerTest(test.TestCase): multipliers3 = standard_ops.constant([0.4, 0.7, -0.2, 0.5, 0.1]) expected_projected_multipliers3 = np.array([0.2, 0.5, 0.0, 0.3, 0.0]) - with self.test_session() as session: + with self.cached_session() as session: projected_multipliers1 = session.run( external_regret_optimizer._project_multipliers_wrt_euclidean_norm( multipliers1, 1.0)) @@ -122,7 +122,7 @@ class ExternalRegretOptimizerTest(test.TestCase): ] multipliers = [] - with self.test_session() as session: + with self.cached_session() as session: session.run(standard_ops.global_variables_initializer()) while len(multipliers) < len(expected_multipliers): multipliers.append(session.run(optimizer.lagrange_multipliers)) diff --git a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer_test.py b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer_test.py index 34c4543dca..df0eced631 100644 --- a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer_test.py +++ b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer_test.py @@ -97,7 +97,7 @@ class SwapRegretOptimizerTest(test.TestCase): matrix1 = np.matrix([[0.6, 0.1, 0.1], [0.0, 0.6, 0.9], [0.4, 0.3, 0.0]]) matrix2 = np.matrix([[0.4, 0.4, 0.2], [0.2, 0.1, 0.5], [0.4, 0.5, 0.3]]) - with self.test_session() as session: + with self.cached_session() as session: eigenvector1 = session.run( swap_regret_optimizer._maximal_eigenvector_power_method( standard_ops.constant(matrix1))) @@ -119,7 +119,7 @@ class SwapRegretOptimizerTest(test.TestCase): expected_projected_matrix = np.array([[0.6, 0.1, 0.1], [0.0, 0.6, 0.9], [0.4, 0.3, 0.0]]) - with self.test_session() as session: + with self.cached_session() as session: projected_matrix = session.run( swap_regret_optimizer._project_stochastic_matrix_wrt_euclidean_norm( matrix)) @@ -134,7 +134,7 @@ class SwapRegretOptimizerTest(test.TestCase): expected_projected_matrix = np.array([[0.4, 0.4, 0.2], [0.2, 0.1, 0.5], [0.4, 0.5, 0.3]]) - with self.test_session() as session: + with self.cached_session() as session: projected_matrix = session.run( standard_ops.exp( swap_regret_optimizer. @@ -165,7 +165,7 @@ class SwapRegretOptimizerTest(test.TestCase): ] matrices = [] - with self.test_session() as session: + with self.cached_session() as session: session.run(standard_ops.global_variables_initializer()) while len(matrices) < len(expected_matrices): matrices.append(session.run(optimizer.stochastic_matrix)) @@ -198,7 +198,7 @@ class SwapRegretOptimizerTest(test.TestCase): ] matrices = [] - with self.test_session() as session: + with self.cached_session() as session: session.run(standard_ops.global_variables_initializer()) while len(matrices) < len(expected_matrices): matrices.append(session.run(optimizer.stochastic_matrix)) diff --git a/tensorflow/contrib/data/python/kernel_tests/optimization/latency_all_edges_test.py b/tensorflow/contrib/data/python/kernel_tests/optimization/latency_all_edges_test.py index 1850b6921a..db380c02a9 100644 --- a/tensorflow/contrib/data/python/kernel_tests/optimization/latency_all_edges_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/optimization/latency_all_edges_test.py @@ -40,7 +40,7 @@ class OptimizeStatsDatasetTest(stats_dataset_test_base.StatsDatasetTestBase): get_next = iterator.get_next() summary_t = stats_aggregator.get_summary() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(iterator.initializer) self.assertEqual(1 * 1, sess.run(get_next)) with self.assertRaises(errors.OutOfRangeError): diff --git a/tensorflow/contrib/data/python/kernel_tests/optimization/map_and_filter_fusion_test.py b/tensorflow/contrib/data/python/kernel_tests/optimization/map_and_filter_fusion_test.py index 6a7ef877f9..dde115925e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/optimization/map_and_filter_fusion_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/optimization/map_and_filter_fusion_test.py @@ -74,7 +74,7 @@ class MapAndFilterFusionTest(test.TestCase, parameterized.TestCase): dataset = dataset.prefetch(0).apply(optimization.optimize(["map_fusion"])) iterator = dataset.make_one_shot_iterator() get_next = iterator.get_next() - with self.test_session() as sess: + with self.cached_session() as sess: for x in range(5): result = sess.run(get_next) r = x @@ -131,7 +131,7 @@ class MapAndFilterFusionTest(test.TestCase, parameterized.TestCase): def _testMapAndFilter(self, dataset, function, predicate): iterator = dataset.make_one_shot_iterator() get_next = iterator.get_next() - with self.test_session() as sess: + with self.cached_session() as sess: for x in range(10): r = function(x) if isinstance(r, tuple): diff --git a/tensorflow/contrib/eager/python/evaluator_test.py b/tensorflow/contrib/eager/python/evaluator_test.py index 7d2274db9b..48d093e075 100644 --- a/tensorflow/contrib/eager/python/evaluator_test.py +++ b/tensorflow/contrib/eager/python/evaluator_test.py @@ -117,7 +117,7 @@ class EvaluatorTest(test.TestCase): self.assertEqual(6.0, results["mean"].numpy()) def testDatasetGraph(self): - with context.graph_mode(), ops.Graph().as_default(), self.test_session(): + with context.graph_mode(), ops.Graph().as_default(), self.cached_session(): e = SimpleEvaluator(IdentityModel()) ds = dataset_ops.Dataset.from_tensor_slices([3.0, 5.0, 7.0, 9.0]) init_op, call_op, results_op = e.evaluate_on_dataset(ds) @@ -126,7 +126,7 @@ class EvaluatorTest(test.TestCase): self.assertEqual(6.0, results["mean"]) def testWriteSummariesGraph(self): - with context.graph_mode(), ops.Graph().as_default(), self.test_session(): + with context.graph_mode(), ops.Graph().as_default(), self.cached_session(): e = SimpleEvaluator(IdentityModel()) ds = dataset_ops.Dataset.from_tensor_slices([3.0, 5.0, 7.0, 9.0]) training_util.get_or_create_global_step() diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py index dcc7b71d79..9d2d172752 100644 --- a/tensorflow/contrib/eager/python/metrics_test.py +++ b/tensorflow/contrib/eager/python/metrics_test.py @@ -216,7 +216,7 @@ class MetricsTest(test.TestCase): self.assertEqual(m1.numer.name, "has_space/numer:0") def testGraphWithPlaceholder(self): - with context.graph_mode(), self.test_session() as sess: + with context.graph_mode(), self.cached_session() as sess: m = metrics.Mean() p = array_ops.placeholder(dtypes.float32) accumulate = m(p) @@ -309,7 +309,7 @@ class MetricsTest(test.TestCase): self.assertTrue(old_numer is m.numer) def testMetricsChain(self): - with context.graph_mode(), self.test_session(): + with context.graph_mode(), self.cached_session(): m1 = metrics.Mean() m2 = metrics.Mean(name="m2") update_m2 = m2(3.0) diff --git a/tensorflow/contrib/framework/python/framework/checkpoint_utils_test.py b/tensorflow/contrib/framework/python/framework/checkpoint_utils_test.py index 4f591367fd..77a424145a 100644 --- a/tensorflow/contrib/framework/python/framework/checkpoint_utils_test.py +++ b/tensorflow/contrib/framework/python/framework/checkpoint_utils_test.py @@ -82,7 +82,7 @@ class CheckpointsTest(test.TestCase): def testNoTensor(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: _, _, _, _ = _create_checkpoints(session, checkpoint_dir) with self.assertRaises(errors_impl.OpError): self.assertAllEqual( @@ -90,7 +90,7 @@ class CheckpointsTest(test.TestCase): def testGetTensor(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: v1, v2, v3, v4 = _create_checkpoints(session, checkpoint_dir) self.assertAllEqual( checkpoint_utils.load_variable(checkpoint_dir, "var1"), v1) @@ -103,7 +103,7 @@ class CheckpointsTest(test.TestCase): def testGetAllVariables(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: _create_checkpoints(session, checkpoint_dir) self.assertEqual( checkpoint_utils.list_variables(checkpoint_dir), @@ -112,7 +112,7 @@ class CheckpointsTest(test.TestCase): def testInitFromCheckpoint(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: v1, v2, v3, v4 = _create_checkpoints(session, checkpoint_dir) # New graph and session. @@ -146,7 +146,7 @@ class CheckpointsTest(test.TestCase): def testInitWithScopeDoesNotCaptureSuffixes(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: _, _, _, v4 = _create_checkpoints(session, checkpoint_dir) with ops.Graph().as_default() as g: @@ -165,7 +165,7 @@ class CheckpointsTest(test.TestCase): def testInitFromRootCheckpoint(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: v1, v2, v3, v4 = _create_checkpoints(session, checkpoint_dir) # New graph and session. @@ -189,7 +189,7 @@ class CheckpointsTest(test.TestCase): def testInitToRootCheckpoint(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: v1, v2, v3, v4 = _create_checkpoints(session, checkpoint_dir) # New graph and session. @@ -212,7 +212,7 @@ class CheckpointsTest(test.TestCase): def testInitFromPartitionVar(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: v1 = _create_partition_checkpoints(session, checkpoint_dir) # New graph and session. @@ -266,7 +266,7 @@ class CheckpointsTest(test.TestCase): def testInitFromCheckpointMissing(self): checkpoint_dir = self.get_temp_dir() - with self.test_session() as session: + with self.cached_session() as session: _, _, _, _ = _create_checkpoints(session, checkpoint_dir) # New graph and session. diff --git a/tensorflow/contrib/framework/python/framework/tensor_util_test.py b/tensorflow/contrib/framework/python/framework/tensor_util_test.py index 2479fe5b8d..b1820c10c8 100644 --- a/tensorflow/contrib/framework/python/framework/tensor_util_test.py +++ b/tensorflow/contrib/framework/python/framework/tensor_util_test.py @@ -39,7 +39,7 @@ from tensorflow.python.platform import test class LocalVariabletest(test.TestCase): def test_local_variable(self): - with self.test_session() as sess: + with self.cached_session() as sess: self.assertEquals([], variables_lib.local_variables()) value0 = 42 variables_lib2.local_variable(value0) @@ -55,7 +55,7 @@ class LocalVariabletest(test.TestCase): class ReduceSumNTest(test.TestCase): def test_reduce_sum_n(self): - with self.test_session(): + with self.cached_session(): a = constant_op.constant(1) b = constant_op.constant([2]) c = constant_op.constant([[3, 4], [5, 6]]) @@ -119,13 +119,13 @@ class WithShapeTest(test.TestCase): })) def test_with_shape_invalid_expected_shape(self): - with self.test_session(): + with self.cached_session(): self.assertRaisesRegexp(ValueError, "Invalid rank", tensor_util.with_shape, [[1], [2]], constant_op.constant(1.0)) def test_with_shape_invalid_type(self): - with self.test_session(): + with self.cached_session(): self.assertRaisesRegexp(ValueError, "Invalid dtype", tensor_util.with_shape, [1.1], constant_op.constant([1.0])) @@ -138,7 +138,7 @@ class WithShapeTest(test.TestCase): constant_op.constant(1.0)) def test_with_shape_0(self): - with self.test_session(): + with self.cached_session(): value = 42 shape = [0] unexpected_shapes = [[1], [2], [1, 1]] @@ -150,7 +150,7 @@ class WithShapeTest(test.TestCase): unexpected_shapes) def test_with_shape_1(self): - with self.test_session(): + with self.cached_session(): value = [42] shape = [1] unexpected_shapes = [[0], [2], [1, 1]] @@ -162,7 +162,7 @@ class WithShapeTest(test.TestCase): unexpected_shapes) def test_with_shape_2(self): - with self.test_session(): + with self.cached_session(): value = [42, 43] shape = [2] unexpected_shapes = [[0], [1], [2, 1]] @@ -174,7 +174,7 @@ class WithShapeTest(test.TestCase): unexpected_shapes) def test_with_shape_2x2(self): - with self.test_session(): + with self.cached_session(): value = [[42, 43], [44, 45]] shape = [2, 2] unexpected_shapes = [[0], [1], [2, 1]] @@ -196,7 +196,7 @@ class WithShapeTest(test.TestCase): np.testing.assert_array_equal(value, tensor_with_shape.eval()) def test_with_shape_none(self): - with self.test_session(): + with self.cached_session(): tensor_no_shape = array_ops.placeholder(dtypes.float32) compatible_shape = [2, 2] @@ -220,7 +220,7 @@ class WithShapeTest(test.TestCase): @test_util.enable_c_shapes def test_with_shape_partial(self): - with self.test_session(): + with self.cached_session(): tensor_partial_shape = array_ops.placeholder(dtypes.float32) tensor_partial_shape.set_shape([None, 2]) diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py index 9f5fee4542..e3c780ac1a 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl_test.py @@ -51,7 +51,7 @@ class _LossesTest(object): loss = self._g_loss_fn(self._discriminator_gen_outputs) self.assertEqual(self._discriminator_gen_outputs.dtype, loss.dtype) self.assertEqual(self._generator_loss_name, loss.op.name) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss, loss.eval(), 5) def test_discriminator_all_correct(self): @@ -59,7 +59,7 @@ class _LossesTest(object): self._discriminator_real_outputs, self._discriminator_gen_outputs) self.assertEqual(self._discriminator_gen_outputs.dtype, loss.dtype) self.assertEqual(self._discriminator_loss_name, loss.op.name) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss, loss.eval(), 5) def test_generator_loss_collection(self): @@ -90,7 +90,7 @@ class _LossesTest(object): loss = self._g_loss_fn( array_ops.reshape(self._discriminator_gen_outputs, [2, 2])) self.assertEqual(self._discriminator_gen_outputs.dtype, loss.dtype) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss, loss.eval(), 5) def test_discriminator_patch(self): @@ -98,7 +98,7 @@ class _LossesTest(object): array_ops.reshape(self._discriminator_real_outputs, [2, 2]), array_ops.reshape(self._discriminator_gen_outputs, [2, 2])) self.assertEqual(self._discriminator_gen_outputs.dtype, loss.dtype) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss, loss.eval(), 5) def test_generator_loss_with_placeholder_for_logits(self): @@ -108,7 +108,7 @@ class _LossesTest(object): loss = self._g_loss_fn(logits, weights=weights) self.assertEqual(logits.dtype, loss.dtype) - with self.test_session() as sess: + with self.cached_session() as sess: loss = sess.run(loss, feed_dict={ logits: [[10.0, 4.4, -5.5, 3.6]], @@ -125,7 +125,7 @@ class _LossesTest(object): logits, logits2, real_weights=real_weights, generated_weights=generated_weights) - with self.test_session() as sess: + with self.cached_session() as sess: loss = sess.run(loss, feed_dict={ logits: [self._discriminator_real_outputs_np], @@ -136,7 +136,7 @@ class _LossesTest(object): def test_generator_with_python_scalar_weight(self): loss = self._g_loss_fn( self._discriminator_gen_outputs, weights=self._weights) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss * self._weights, loss.eval(), 4) @@ -144,14 +144,14 @@ class _LossesTest(object): loss = self._d_loss_fn( self._discriminator_real_outputs, self._discriminator_gen_outputs, real_weights=self._weights, generated_weights=self._weights) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss * self._weights, loss.eval(), 4) def test_generator_with_scalar_tensor_weight(self): loss = self._g_loss_fn(self._discriminator_gen_outputs, weights=constant_op.constant(self._weights)) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss * self._weights, loss.eval(), 4) @@ -160,7 +160,7 @@ class _LossesTest(object): loss = self._d_loss_fn( self._discriminator_real_outputs, self._discriminator_gen_outputs, real_weights=weights, generated_weights=weights) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss * self._weights, loss.eval(), 4) @@ -284,7 +284,7 @@ class ACGANLossTest(test.TestCase): self.assertEqual( self._discriminator_gen_classification_logits.dtype, loss.dtype) self.assertEqual(self._generator_loss_name, loss.op.name) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss, loss.eval(), 5) def test_discriminator_all_correct(self): @@ -292,7 +292,7 @@ class ACGANLossTest(test.TestCase): self.assertEqual( self._discriminator_gen_classification_logits.dtype, loss.dtype) self.assertEqual(self._discriminator_loss_name, loss.op.name) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss, loss.eval(), 5) def test_generator_loss_collection(self): @@ -319,14 +319,14 @@ class ACGANLossTest(test.TestCase): patch_args = {x: array_ops.reshape(y, [2, 2, 4]) for x, y in self._generator_kwargs.items()} loss = self._g_loss_fn(**patch_args) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss, loss.eval(), 5) def test_discriminator_patch(self): patch_args = {x: array_ops.reshape(y, [2, 2, 4]) for x, y in self._discriminator_kwargs.items()} loss = self._d_loss_fn(**patch_args) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss, loss.eval(), 5) def test_generator_loss_with_placeholder_for_logits(self): @@ -334,7 +334,7 @@ class ACGANLossTest(test.TestCase): one_hot_labels = array_ops.placeholder(dtypes.int32, shape=(None, 4)) loss = self._g_loss_fn(gen_logits, one_hot_labels) - with self.test_session() as sess: + with self.cached_session() as sess: loss = sess.run( loss, feed_dict={ gen_logits: self._discriminator_gen_classification_logits_np, @@ -349,7 +349,7 @@ class ACGANLossTest(test.TestCase): loss = self._d_loss_fn(gen_logits, real_logits, one_hot_labels) - with self.test_session() as sess: + with self.cached_session() as sess: loss = sess.run( loss, feed_dict={ gen_logits: self._discriminator_gen_classification_logits_np, @@ -360,7 +360,7 @@ class ACGANLossTest(test.TestCase): def test_generator_with_python_scalar_weight(self): loss = self._g_loss_fn(weights=self._weights, **self._generator_kwargs) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss * self._weights, loss.eval(), 4) @@ -368,14 +368,14 @@ class ACGANLossTest(test.TestCase): loss = self._d_loss_fn( real_weights=self._weights, generated_weights=self._weights, **self._discriminator_kwargs) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss * self._weights, loss.eval(), 4) def test_generator_with_scalar_tensor_weight(self): loss = self._g_loss_fn( weights=constant_op.constant(self._weights), **self._generator_kwargs) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_g_loss * self._weights, loss.eval(), 4) @@ -383,7 +383,7 @@ class ACGANLossTest(test.TestCase): weights = constant_op.constant(self._weights) loss = self._d_loss_fn(real_weights=weights, generated_weights=weights, **self._discriminator_kwargs) - with self.test_session(): + with self.cached_session(): self.assertAlmostEqual(self._expected_d_loss * self._weights, loss.eval(), 4) @@ -404,7 +404,7 @@ class _PenaltyTest(object): loss = self._penalty_fn(**self._kwargs) self.assertEqual(self._expected_dtype, loss.dtype) self.assertEqual(self._expected_op_name, loss.op.name) - with self.test_session(): + with self.cached_session(): variables.global_variables_initializer().run() self.assertAlmostEqual(self._expected_loss, loss.eval(), 6) @@ -419,13 +419,13 @@ class _PenaltyTest(object): def test_python_scalar_weight(self): loss = self._penalty_fn(weights=2.3, **self._kwargs) - with self.test_session(): + with self.cached_session(): variables.global_variables_initializer().run() self.assertAlmostEqual(self._expected_loss * 2.3, loss.eval(), 3) def test_scalar_tensor_weight(self): loss = self._penalty_fn(weights=constant_op.constant(2.3), **self._kwargs) - with self.test_session(): + with self.cached_session(): variables.global_variables_initializer().run() self.assertAlmostEqual(self._expected_loss * 2.3, loss.eval(), 3) @@ -472,7 +472,7 @@ class GradientPenaltyTest(test.TestCase, _PenaltyTest): self._kwargs['discriminator_scope']) self.assertEqual(generated_data.dtype, loss.dtype) - with self.test_session() as sess: + with self.cached_session() as sess: variables.global_variables_initializer().run() loss = sess.run(loss, feed_dict={ @@ -494,7 +494,7 @@ class GradientPenaltyTest(test.TestCase, _PenaltyTest): one_sided=True) self.assertEqual(generated_data.dtype, loss.dtype) - with self.test_session() as sess: + with self.cached_session() as sess: variables.global_variables_initializer().run() loss = sess.run(loss, feed_dict={ @@ -516,7 +516,7 @@ class GradientPenaltyTest(test.TestCase, _PenaltyTest): self._kwargs['discriminator_scope'], target=2.0) - with self.test_session() as sess: + with self.cached_session() as sess: variables.global_variables_initializer().run() loss = sess.run( loss, diff --git a/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py b/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py index a559bbfa11..25d74a8c23 100644 --- a/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py +++ b/tensorflow/contrib/gan/python/losses/python/tuple_losses_test.py @@ -118,7 +118,7 @@ def add_loss_consistency_test(test_class, loss_name_str, loss_args): def consistency_test(self): self.assertEqual(arg_loss.__name__, tuple_loss.__name__) - with self.test_session(): + with self.cached_session(): self.assertEqual(arg_loss(**loss_args).eval(), tuple_loss(_tuple_from_dict(loss_args)).eval()) @@ -241,7 +241,7 @@ class StarGANLossWrapperTest(test.TestCase): self.discriminator_generated_data_source_predication) wrapped_loss_result_tensor = wrapped_loss_fn(self.model) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) loss_result, wrapped_loss_result = sess.run( [loss_result_tensor, wrapped_loss_result_tensor]) @@ -257,7 +257,7 @@ class StarGANLossWrapperTest(test.TestCase): self.discriminator_generated_data_source_predication) wrapped_loss_result_tensor = wrapped_loss_fn(self.model) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) loss_result, wrapped_loss_result = sess.run( [loss_result_tensor, wrapped_loss_result_tensor]) @@ -282,7 +282,7 @@ class StarGANLossWrapperTest(test.TestCase): discriminator_scope=self.discriminator_scope) wrapped_loss_result_tensor = wrapped_loss_fn(self.model) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) loss_result, wrapped_loss_result = sess.run( [loss_result_tensor, wrapped_loss_result_tensor]) diff --git a/tensorflow/contrib/learn/python/learn/ops/ops_test.py b/tensorflow/contrib/learn/python/learn/ops/ops_test.py index 80d4923db3..ff190110c1 100644 --- a/tensorflow/contrib/learn/python/learn/ops/ops_test.py +++ b/tensorflow/contrib/learn/python/learn/ops/ops_test.py @@ -33,7 +33,7 @@ class OpsTest(test.TestCase): """Ops tests.""" def test_softmax_classifier(self): - with self.test_session() as session: + with self.cached_session() as session: features = array_ops.placeholder(dtypes.float32, [None, 3]) labels = array_ops.placeholder(dtypes.float32, [None, 2]) weights = constant_op.constant([[0.1, 0.1], [0.1, 0.1], [0.1, 0.1]]) @@ -52,7 +52,7 @@ class OpsTest(test.TestCase): ids_shape = (2, 3, 4) embeds = np.random.randn(n_embed, d_embed) ids = np.random.randint(0, n_embed, ids_shape) - with self.test_session(): + with self.cached_session(): embed_np = embeds[ids] embed_tf = ops.embedding_lookup(embeds, ids).eval() self.assertEqual(embed_np.shape, embed_tf.shape) @@ -60,7 +60,7 @@ class OpsTest(test.TestCase): def test_categorical_variable(self): random_seed.set_random_seed(42) - with self.test_session() as sess: + with self.cached_session() as sess: cat_var_idx = array_ops.placeholder(dtypes.int64, [2, 2]) embeddings = ops.categorical_variable( cat_var_idx, n_classes=5, embedding_size=10, name="my_cat_var") diff --git a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py index 95aec61955..5a7e4ebfea 100644 --- a/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py +++ b/tensorflow/contrib/learn/python/learn/ops/seq2seq_ops_test.py @@ -31,7 +31,7 @@ class Seq2SeqOpsTest(test.TestCase): """Sequence-to-sequence tests.""" def test_sequence_classifier(self): - with self.test_session() as session: + with self.cached_session() as session: decoding = [ array_ops.placeholder(dtypes.float32, [2, 2]) for _ in range(3) ] @@ -60,7 +60,7 @@ class Seq2SeqOpsTest(test.TestCase): def test_seq2seq_inputs(self): inp = np.array([[[1, 0], [0, 1], [1, 0]], [[0, 1], [1, 0], [0, 1]]]) out = np.array([[[0, 1, 0], [1, 0, 0]], [[1, 0, 0], [0, 1, 0]]]) - with self.test_session() as session: + with self.cached_session() as session: x = array_ops.placeholder(dtypes.float32, [2, 3, 2]) y = array_ops.placeholder(dtypes.float32, [2, 2, 3]) in_x, in_y, out_y = ops.seq2seq_inputs(x, y, 3, 2) @@ -77,7 +77,7 @@ class Seq2SeqOpsTest(test.TestCase): [[0, 0, 0], [0, 0, 0]]]) def test_rnn_decoder(self): - with self.test_session(): + with self.cached_session(): decoder_inputs = [ array_ops.placeholder(dtypes.float32, [2, 2]) for _ in range(3) ] diff --git a/tensorflow/contrib/specs/python/specs_test.py b/tensorflow/contrib/specs/python/specs_test.py index 9a4ad36793..b7ce6aa20a 100644 --- a/tensorflow/contrib/specs/python/specs_test.py +++ b/tensorflow/contrib/specs/python/specs_test.py @@ -38,7 +38,7 @@ def _rand(*size): class SpecsTest(test.TestCase): def testSimpleConv(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(1, 18, 19, 5)) spec = "net = Cr(64, [5, 5])" outputs = specs.create_net(spec, inputs) @@ -53,7 +53,7 @@ class SpecsTest(test.TestCase): def testUnary(self): # This is just a quick and dirty check that these ops exist # and work as unary ops. - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(17, 55)) spec = "net = Do(0.5) | Bn | Unit(1) | Relu | Sig | Tanh | Smax" outputs = specs.create_net(spec, inputs) @@ -63,7 +63,7 @@ class SpecsTest(test.TestCase): self.assertEqual(tuple(result.shape), (17, 55)) def testAdd(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(17, 55)) spec = "net = Fs(10) + Fr(10)" outputs = specs.create_net(spec, inputs) @@ -77,7 +77,7 @@ class SpecsTest(test.TestCase): "<> variablev2 dot variablev2 biasadd relu add") def testMpPower(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(1, 64, 64, 5)) spec = "M2 = Mp([2, 2]); net = M2**3" outputs = specs.create_net(spec, inputs) @@ -90,7 +90,7 @@ class SpecsTest(test.TestCase): "_ maxpool maxpool maxpool") def testAbbrevPower(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(1, 64, 64, 5)) spec = "C3 = Cr([3, 3]); M2 = Mp([2, 2]); net = (C3(5) | M2)**3" outputs = specs.create_net(spec, inputs) @@ -106,7 +106,7 @@ class SpecsTest(test.TestCase): " biasadd relu maxpool") def testAbbrevPower2(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(1, 64, 64, 5)) spec = "C3 = Cr(_1=[3, 3]); M2 = Mp([2, 2]);" spec += "net = (C3(_0=5) | M2)**3" @@ -123,7 +123,7 @@ class SpecsTest(test.TestCase): " maxpool") def testConc(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(10, 20)) spec = "net = Conc(1, Fs(20), Fs(10))" outputs = specs.create_net(spec, inputs) @@ -137,7 +137,7 @@ class SpecsTest(test.TestCase): "<> variablev2 dot variablev2 biasadd sig _ concatv2") def testImport(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(10, 20)) spec = ("S = Import('from tensorflow.python.ops" + " import math_ops; f = math_ops.sigmoid')") @@ -150,7 +150,7 @@ class SpecsTest(test.TestCase): self.assertEqual(summaries.tf_spec_structure(spec, inputs), "_ sig sig") def testKeywordRestriction(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(10, 20)) spec = "import re; net = Conc(1, Fs(20), Fs(10))" self.assertRaises(ValueError, lambda: specs.create_net(spec, inputs)) @@ -179,7 +179,7 @@ class SpecsTest(test.TestCase): # XXX: the cleverness of this code is over 9000 # TODO: original author please fix def DISABLED_testVar(self): - with self.test_session() as sess: + with self.cached_session() as sess: with specs.ops: # pylint: disable=undefined-variable v = Var("test_var", @@ -196,7 +196,7 @@ class SpecsTest(test.TestCase): # XXX: the cleverness of this code is over 9000 # TODO: original author please fix def DISABLED_testShared(self): - with self.test_session(): + with self.cached_session(): with specs.ops: # pylint: disable=undefined-variable f = Shared(Fr(100)) diff --git a/tensorflow/contrib/specs/python/summaries_test.py b/tensorflow/contrib/specs/python/summaries_test.py index 34ff4bc8ca..b82ba06d3f 100644 --- a/tensorflow/contrib/specs/python/summaries_test.py +++ b/tensorflow/contrib/specs/python/summaries_test.py @@ -34,7 +34,7 @@ def _rand(*size): class SummariesTest(test.TestCase): def testStructure(self): - with self.test_session(): + with self.cached_session(): inputs_shape = (1, 18, 19, 5) inputs = constant_op.constant(_rand(*inputs_shape)) spec = "net = Cr(64, [5, 5])" @@ -48,7 +48,7 @@ class SummariesTest(test.TestCase): "_ variablev2 conv variablev2 biasadd relu") def testStructureFromTensor(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(1, 18, 19, 5)) spec = "net = Cr(64, [5, 5])" outputs = specs.create_net(spec, inputs) @@ -60,7 +60,7 @@ class SummariesTest(test.TestCase): "_ variablev2 conv variablev2 biasadd relu") def testPrint(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(1, 18, 19, 5)) spec = "net = Cr(64, [5, 5])" outputs = specs.create_net(spec, inputs) @@ -70,7 +70,7 @@ class SummariesTest(test.TestCase): summaries.tf_spec_print(spec, inputs) def testSummary(self): - with self.test_session(): + with self.cached_session(): inputs = constant_op.constant(_rand(1, 18, 19, 5)) spec = "net = Cr(64, [5, 5])" outputs = specs.create_net(spec, inputs) diff --git a/tensorflow/python/data/util/convert_test.py b/tensorflow/python/data/util/convert_test.py index 6a67093e48..89c3afb296 100644 --- a/tensorflow/python/data/util/convert_test.py +++ b/tensorflow/python/data/util/convert_test.py @@ -30,28 +30,28 @@ class ConvertTest(test.TestCase): def testInteger(self): resp = convert.optional_param_to_tensor("foo", 3) - with self.test_session() as sess: + with self.cached_session() as sess: self.assertEqual(3, sess.run(resp)) def testIntegerDefault(self): resp = convert.optional_param_to_tensor("foo", None) - with self.test_session() as sess: + with self.cached_session() as sess: self.assertEqual(0, sess.run(resp)) def testStringDefault(self): resp = convert.optional_param_to_tensor("bar", None, "default", dtypes.string) - with self.test_session() as sess: + with self.cached_session() as sess: self.assertEqual(compat.as_bytes("default"), sess.run(resp)) def testString(self): resp = convert.optional_param_to_tensor("bar", "value", "default", dtypes.string) - with self.test_session() as sess: + with self.cached_session() as sess: self.assertEqual(compat.as_bytes("value"), sess.run(resp)) def testPartialShapeToTensorKnownDimension(self): - with self.test_session() as sess: + with self.cached_session() as sess: self.assertAllEqual([1], sess.run(convert.partial_shape_to_tensor( tensor_shape.TensorShape([1])))) self.assertAllEqual([1], sess.run(convert.partial_shape_to_tensor((1,)))) @@ -60,7 +60,7 @@ class ConvertTest(test.TestCase): constant_op.constant([1], dtype=dtypes.int64)))) def testPartialShapeToTensorUnknownDimension(self): - with self.test_session() as sess: + with self.cached_session() as sess: self.assertAllEqual([-1], sess.run(convert.partial_shape_to_tensor( tensor_shape.TensorShape([None])))) self.assertAllEqual([-1], sess.run(convert.partial_shape_to_tensor( @@ -84,7 +84,7 @@ class ConvertTest(test.TestCase): convert.partial_shape_to_tensor(constant_op.constant([1., 1.])) def testPartialShapeToTensorMultipleDimensions(self): - with self.test_session() as sess: + with self.cached_session() as sess: self.assertAllEqual([3, 6], sess.run(convert.partial_shape_to_tensor( tensor_shape.TensorShape([3, 6])))) self.assertAllEqual([3, 6], sess.run(convert.partial_shape_to_tensor( @@ -113,7 +113,7 @@ class ConvertTest(test.TestCase): constant_op.constant([-1, -1], dtype=dtypes.int64)))) def testPartialShapeToTensorScalar(self): - with self.test_session() as sess: + with self.cached_session() as sess: self.assertAllEqual([], sess.run(convert.partial_shape_to_tensor( tensor_shape.TensorShape([])))) self.assertAllEqual([], sess.run(convert.partial_shape_to_tensor(()))) diff --git a/tensorflow/python/data/util/sparse_test.py b/tensorflow/python/data/util/sparse_test.py index d49b3ff34b..056b32480f 100644 --- a/tensorflow/python/data/util/sparse_test.py +++ b/tensorflow/python/data/util/sparse_test.py @@ -291,7 +291,7 @@ class SparseTest(test.TestCase): self.assertEqual(a, b) return self.assertTrue(isinstance(b, sparse_tensor.SparseTensor)) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(a.eval().indices, b.eval().indices) self.assertAllEqual(a.eval().values, b.eval().values) self.assertAllEqual(a.eval().dense_shape, b.eval().dense_shape) diff --git a/tensorflow/python/estimator/canned/boosted_trees_test.py b/tensorflow/python/estimator/canned/boosted_trees_test.py index 08026a93c5..6e28c72151 100644 --- a/tensorflow/python/estimator/canned/boosted_trees_test.py +++ b/tensorflow/python/estimator/canned/boosted_trees_test.py @@ -1560,7 +1560,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): ops.reset_default_graph() expected_first, expected_second, expected_third = ( self._get_expected_ensembles_for_classification()) - with self.test_session() as sess: + with self.cached_session() as sess: # Train with train_in_memory mode. with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( @@ -1593,7 +1593,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): expected_first, expected_second, expected_third, expected_forth = ( self._get_expected_ensembles_for_classification_with_bias()) - with self.test_session() as sess: + with self.cached_session() as sess: with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( boosted_trees._create_classification_head(n_classes=2), @@ -1633,7 +1633,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): ops.reset_default_graph() expected_first, expected_second, expected_third = ( self._get_expected_ensembles_for_classification()) - with self.test_session() as sess: + with self.cached_session() as sess: # Train without train_in_memory mode. with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( @@ -1666,7 +1666,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): expected_first, expected_second, expected_third, expected_forth = ( self._get_expected_ensembles_for_classification_with_bias()) - with self.test_session() as sess: + with self.cached_session() as sess: with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( boosted_trees._create_classification_head(n_classes=2), @@ -1704,7 +1704,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): ops.reset_default_graph() expected_first, expected_second, expected_third = ( self._get_expected_ensembles_for_regression()) - with self.test_session() as sess: + with self.cached_session() as sess: # Train with train_in_memory mode. with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( @@ -1734,7 +1734,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): ops.reset_default_graph() expected_first, expected_second, expected_third, expected_forth = ( self._get_expected_ensembles_for_regression_with_bias()) - with self.test_session() as sess: + with self.cached_session() as sess: # Train with train_in_memory mode. with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( @@ -1774,7 +1774,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): ops.reset_default_graph() expected_first, expected_second, expected_third = ( self._get_expected_ensembles_for_regression()) - with self.test_session() as sess: + with self.cached_session() as sess: # Train without train_in_memory mode. with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( @@ -1804,7 +1804,7 @@ class ModelFnTests(test_util.TensorFlowTestCase): ops.reset_default_graph() expected_first, expected_second, expected_third, expected_forth = ( self._get_expected_ensembles_for_regression_with_bias()) - with self.test_session() as sess: + with self.cached_session() as sess: # Train with train_in_memory mode. with sess.graph.as_default(): train_op, ensemble_serialized = self._get_train_op_and_ensemble( diff --git a/tensorflow/python/estimator/canned/head_test.py b/tensorflow/python/estimator/canned/head_test.py index bd2e0ae943..de9c84d2ef 100644 --- a/tensorflow/python/estimator/canned/head_test.py +++ b/tensorflow/python/estimator/canned/head_test.py @@ -260,7 +260,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): features={'x': np.array(((30.,), (42.,),))}, mode=model_fn.ModeKeys.PREDICT, logits=logits_placeholder) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(errors.OpError, 'logits shape'): spec.predictions[prediction_keys.PredictionKeys.PROBABILITIES].eval({ logits_placeholder: logits_2x2 @@ -293,7 +293,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_placeholder, labels=labels_placeholder)[0] - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'\[expected_labels_shape: \] \[2 1\] \[labels_shape: \] \[2 2\]'): @@ -347,14 +347,14 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_placeholder, labels=labels_placeholder)[0] - with self.test_session(): + with self.cached_session(): with self.assertRaisesOpError('Labels must <= n_classes - 1'): training_loss.eval({ labels_placeholder: labels_2x1_with_large_id, logits_placeholder: logits_2x3 }) - with self.test_session(): + with self.cached_session(): with self.assertRaisesOpError('Labels must >= 0'): training_loss.eval({ labels_placeholder: labels_2x1_with_negative_id, @@ -413,7 +413,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_placeholder, labels=labels_placeholder)[0] - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'\[expected_labels_shape: \] \[2 1\] \[labels_shape: \] \[3 1\]'): @@ -449,7 +449,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): spec.export_outputs.keys()) # Assert predictions and export_outputs. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) predictions = sess.run(spec.predictions) @@ -484,7 +484,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.PREDICT, logits=logits) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertAllEqual( expected_classes, @@ -510,7 +510,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.PREDICT, logits=logits) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) predictions = sess.run(spec.predictions) self.assertAllClose(logits, @@ -534,7 +534,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) @@ -561,7 +561,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_input, labels=labels_input)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(np.sum(loss), actual_training_loss.eval()) @@ -581,7 +581,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -632,7 +632,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, and metrics. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -698,7 +698,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, and metrics. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -727,7 +727,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) @@ -755,7 +755,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): } tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} update_ops = {k: spec.eval_metric_ops[k][1] for k in spec.eval_metric_ops} @@ -804,7 +804,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert loss, and metrics. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -837,7 +837,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): logits=logits, labels=labels) tol = 1e-2 - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=tol, atol=tol) @@ -866,7 +866,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): logits=logits, labels=labels) tol = 1e-2 - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=tol, atol=tol) @@ -921,7 +921,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, train_op, and summaries. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run((spec.loss, spec.train_op, @@ -962,7 +962,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): optimizer=_Optimizer()) tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss, train_result = sess.run((spec.loss, spec.train_op)) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) @@ -992,7 +992,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): labels=np.array(((1,), (1,)), dtype=np.int64), train_op_fn=_train_op_fn) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) sess.run(spec.train_op) w_value, t_value = sess.run([w, t]) @@ -1023,7 +1023,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert summaries. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) summary_str = sess.run(spec.scaffold.summary_op) @@ -1064,7 +1064,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, train_op, and summaries. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run((spec.loss, spec.train_op, @@ -1104,7 +1104,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): logits=logits, labels=labels_rank_1) tol = 1e-2 - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=tol, atol=tol) @@ -1153,7 +1153,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, train_op, and summaries. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run((spec.loss, spec.train_op, @@ -1183,7 +1183,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) @@ -1211,7 +1211,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): train_op_fn=_train_op_fn) tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss = sess.run(spec.loss) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) @@ -1253,7 +1253,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, train_op, and summaries. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run((spec.loss, spec.train_op, @@ -1292,7 +1292,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): logits=logits, labels=labels) tol = 1e-2 - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=tol, atol=tol) @@ -1327,7 +1327,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, train_op, and summaries. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss, train_result = sess.run((spec.loss, spec.train_op)) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) @@ -1353,7 +1353,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): logits=logits, labels=labels, train_op_fn=_no_op_train_fn) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -1380,7 +1380,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): logits=logits, labels=labels, train_op_fn=_no_op_train_fn) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -1413,7 +1413,7 @@ class MultiClassHeadWithSoftmaxCrossEntropyLoss(test.TestCase): # Assert predictions, loss, and metrics. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} update_ops = {k: spec.eval_metric_ops[k][1] for k in spec.eval_metric_ops} @@ -1506,7 +1506,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): features={'x': np.array(((42.,),))}, mode=model_fn.ModeKeys.PREDICT, logits=logits_placeholder) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(errors.OpError, 'logits shape'): spec.predictions[prediction_keys.PredictionKeys.PROBABILITIES].eval({ logits_placeholder: logits_2x2 @@ -1536,7 +1536,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_placeholder, labels=labels_placeholder)[0] - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'\[expected_labels_shape: \] \[2 1\] \[labels_shape: \] \[2 2\]'): @@ -1577,7 +1577,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_placeholder, labels=labels_placeholder)[0] - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'\[expected_labels_shape: \] \[3 1\] \[labels_shape: \] \[2 1\]'): @@ -1585,7 +1585,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): labels_placeholder: values_2x1, logits_placeholder: values_3x1 }) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'\[expected_labels_shape: \] \[2 1\] \[labels_shape: \] \[3 1\]'): @@ -1624,7 +1624,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) predictions = sess.run(spec.predictions) @@ -1660,7 +1660,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.PREDICT, logits=logits) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertAllEqual( expected_classes, @@ -1680,7 +1680,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) @@ -1733,7 +1733,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -1808,7 +1808,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): } # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -1832,7 +1832,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(41., training_loss.eval()) @@ -1849,7 +1849,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): logits=logits, labels=labels) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -1877,7 +1877,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) @@ -1924,7 +1924,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): } self.assertItemsEqual(expected_metrics.keys(), spec.eval_metric_ops.keys()) tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -1957,7 +1957,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(expected_training_loss, training_loss.eval()) self.assertAllClose(expected_unreduced_loss, unreduced_loss.eval()) @@ -1983,7 +1983,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(expected_training_loss, training_loss.eval()) self.assertAllClose(expected_unreduced_loss, unreduced_loss.eval()) @@ -2011,7 +2011,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_input, labels=labels_input)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(np.sum(loss), actual_training_loss.eval()) @@ -2031,7 +2031,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -2086,7 +2086,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run((spec.loss, spec.train_op, @@ -2126,7 +2126,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): labels=labels, optimizer=_Optimizer()) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss, train_result = sess.run((spec.loss, spec.train_op)) self.assertAllClose(expected_loss, loss) @@ -2153,7 +2153,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): labels=np.array(((1,), (1,),), dtype=np.float64), train_op_fn=_train_op_fn) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) sess.run(spec.train_op) w_value, t_value = sess.run([w, t]) @@ -2182,7 +2182,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): labels=labels, train_op_fn=_train_op_fn) # Assert summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) summary_str = sess.run(spec.scaffold.summary_op) @@ -2227,7 +2227,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): regularization_losses=regularization_losses) # Assert predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run((spec.loss, spec.train_op, @@ -2254,7 +2254,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'Labels must <= n_classes - 1'): - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) training_loss.eval() @@ -2277,7 +2277,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) @@ -2309,7 +2309,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): train_op_fn=_train_op_fn) # Assert predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss, train_result = sess.run((spec.loss, spec.train_op)) self.assertAlmostEqual(expected_loss, loss, delta=1.e-5) @@ -2334,7 +2334,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), rtol=1e-2, atol=1e-2) @@ -2360,7 +2360,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): expected_loss = 1.2484322 # Assert loss. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) update_ops = {k: spec.eval_metric_ops[k][1] for k in spec.eval_metric_ops} @@ -2385,7 +2385,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): logits=logits) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) predictions = sess.run(spec.predictions) self.assertAllClose( @@ -2447,7 +2447,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): self.assertItemsEqual(expected_metrics.keys(), spec.eval_metric_ops.keys()) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} update_ops = {k: spec.eval_metric_ops[k][1] for k in spec.eval_metric_ops} @@ -2483,7 +2483,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels_rank_1) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), @@ -2531,7 +2531,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): self.assertIsNotNone(spec.train_op) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run(( @@ -2577,7 +2577,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): self.assertIsNotNone(spec.train_op) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) loss, train_result, summary_str = sess.run(( @@ -2612,7 +2612,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): logits=logits, labels=labels) tol = 1e-2 - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose( expected_training_loss, training_loss.eval(), @@ -2649,7 +2649,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): # Assert predictions, loss, train_op, and summaries. tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss, train_result = sess.run((spec.loss, spec.train_op)) self.assertAllClose(expected_loss, loss, rtol=tol, atol=tol) @@ -2675,7 +2675,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): logits=logits, labels=labels, train_op_fn=_no_op_train_fn) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -2700,7 +2700,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): logits=logits, labels=labels, train_op_fn=_no_op_train_fn) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -2744,7 +2744,7 @@ class BinaryLogisticHeadWithSigmoidCrossEntropyLossTest(test.TestCase): } tol = 1e-2 - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} update_ops = {k: spec.eval_metric_ops[k][1] for k in spec.eval_metric_ops} @@ -2825,7 +2825,7 @@ class RegressionHead(test.TestCase): features={'x': np.array(((42.,),))}, mode=model_fn.ModeKeys.PREDICT, logits=logits_placeholder) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(errors.OpError, 'logits shape'): spec.predictions[prediction_keys.PredictionKeys.PREDICTIONS].eval({ logits_placeholder: logits_1d @@ -2857,7 +2857,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_placeholder, labels=labels_placeholder) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(errors.OpError, 'logits shape'): spec.loss.eval({ labels_placeholder: values_3d, @@ -2868,7 +2868,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_placeholder, labels=labels_placeholder)[0] - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'\[expected_labels_shape: \] \[2 3\] \[labels_shape: \] \[2 1\]'): @@ -2908,7 +2908,7 @@ class RegressionHead(test.TestCase): logits=logits_placeholder, labels=labels_placeholder, train_op_fn=lambda x: x) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(errors.OpError, 'logits shape'): spec.loss.eval({ labels_placeholder: values_3d, @@ -2919,7 +2919,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits_placeholder, labels=labels_placeholder)[0] - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( errors.InvalidArgumentError, r'\[expected_labels_shape: \] \[2 3\] \[labels_shape: \] \[2 1\]'): @@ -2957,7 +2957,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions. - with self.test_session(): + with self.cached_session(): _initialize_variables(self, spec.scaffold) self.assertAllClose(logits, spec.predictions[prediction_key].eval()) self.assertAllClose( @@ -2992,7 +2992,7 @@ class RegressionHead(test.TestCase): spec.export_outputs.keys()) # Assert predictions. - with self.test_session(): + with self.cached_session(): _initialize_variables(self, spec.scaffold) self.assertAllClose( expected_predictions, spec.predictions[keys.PREDICTIONS].eval()) @@ -3019,7 +3019,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) # loss = [(43-45)^2, (44-41)] = [4, 9] self.assertAllClose(13., training_loss.eval()) @@ -3045,7 +3045,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits_input, labels=labels_input)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(np.sum(loss), actual_training_loss.eval()) @@ -3064,7 +3064,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -3112,7 +3112,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) loss_mean_value_op, loss_mean_update_op = spec.eval_metric_ops[ @@ -3180,7 +3180,7 @@ class RegressionHead(test.TestCase): } # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) value_ops = {k: spec.eval_metric_ops[k][0] for k in spec.eval_metric_ops} @@ -3212,7 +3212,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(expected_training_loss, training_loss.eval()) self.assertAllClose(expected_unreduced_loss, unreduced_loss.eval()) @@ -3237,7 +3237,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(expected_training_loss, training_loss.eval()) self.assertAllClose(expected_unreduced_loss, unreduced_loss.eval()) @@ -3294,7 +3294,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) predictions, loss, train_result, summary_str = sess.run(( @@ -3337,7 +3337,7 @@ class RegressionHead(test.TestCase): labels=labels, optimizer=_Optimizer()) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss, train_result = sess.run((spec.loss, spec.train_op)) self.assertAllClose(expected_loss, loss) @@ -3364,7 +3364,7 @@ class RegressionHead(test.TestCase): labels=np.array(((43.,), (44.,),), dtype=np.float64), train_op_fn=_train_op_fn) - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) sess.run(spec.train_op) w_value, t_value = sess.run([w, t]) @@ -3394,7 +3394,7 @@ class RegressionHead(test.TestCase): train_op_fn=_train_op_fn) # Assert summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) summary_str = sess.run(spec.scaffold.summary_op) @@ -3441,7 +3441,7 @@ class RegressionHead(test.TestCase): regularization_losses=regularization_losses) # Assert predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) prediction_key = prediction_keys.PredictionKeys.PREDICTIONS @@ -3487,7 +3487,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) loss_mean_value_op, loss_mean_update_op = spec.eval_metric_ops[ @@ -3523,7 +3523,7 @@ class RegressionHead(test.TestCase): labels=np.array(((35,), (42,), (45,)), dtype=np.int32)) # Assert loss. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) loss = sess.run(spec.loss) # loss = 1*(35-45)^2 + .1*(42-41)^2 + 1.5*(45-44)^2 = 100+.1+1.5 = 101.6 @@ -3565,7 +3565,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) predictions, loss, train_result, summary_str = sess.run(( @@ -3600,7 +3600,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels_rank_1) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(expected_training_loss, training_loss.eval()) self.assertAllClose(expected_unreduced_loss, unreduced_loss.eval()) @@ -3648,7 +3648,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) predictions, loss, train_result, summary_str = sess.run(( @@ -3679,7 +3679,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.EVAL, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) # loss = [(35-45)^2, (42-41)^2, (45-44)^2] = [100, 1, 1]. # weighted sum loss = 1 * 100 + .1 * 1 + 1.5 * 1 = 101.6 @@ -3718,7 +3718,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Assert predictions, loss, and metrics. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNone(spec.scaffold.summary_op) loss_mean_value_op, loss_mean_update_op = spec.eval_metric_ops[ @@ -3750,7 +3750,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels)[0] - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) # loss = [(35-45)^2, (42-41)^2, (45-44)^2] = [100, 1, 1]. # weighted sum loss = 1 * 100 + .1 * 1 + 1.5 * 1 = 101.6 @@ -3796,7 +3796,7 @@ class RegressionHead(test.TestCase): _assert_no_hooks(self, spec) # Evaluate predictions, loss, train_op, and summaries. - with self.test_session() as sess: + with self.cached_session() as sess: _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) predictions, loss, train_result, summary_str = sess.run(( @@ -3857,7 +3857,7 @@ class RegressionHead(test.TestCase): self.assertIsNone(spec.train_op) _assert_no_hooks(self, spec) - with self.test_session() as sess: + with self.cached_session() as sess: # Finalize graph and initialize variables. _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) @@ -3915,7 +3915,7 @@ class RegressionHead(test.TestCase): self.assertEqual(dtypes.float32, spec.loss.dtype) self.assertIsNotNone(spec.train_op) - with self.test_session() as sess: + with self.cached_session() as sess: # Finalize graph and initialize variables. _initialize_variables(self, spec.scaffold) self.assertIsNotNone(spec.scaffold.summary_op) @@ -3955,7 +3955,7 @@ class RegressionHead(test.TestCase): mode=model_fn.ModeKeys.TRAIN, logits=logits, labels=labels) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(expected_training_loss, training_loss.eval()) self.assertAllClose(expected_unreduced_loss, unreduced_loss.eval()) @@ -3988,7 +3988,7 @@ class RegressionHead(test.TestCase): logits=logits, labels=labels, train_op_fn=_train_op_fn) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) self.assertAllClose(expected_loss, spec.loss.eval()) @@ -4013,7 +4013,7 @@ class RegressionHead(test.TestCase): logits=logits, labels=labels, train_op_fn=_no_op_train_fn) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, @@ -4042,7 +4042,7 @@ class RegressionHead(test.TestCase): logits=logits, labels=labels, train_op_fn=_no_op_train_fn) - with self.test_session(): + with self.cached_session(): _initialize_variables(self, monitored_session.Scaffold()) with self.assertRaisesRegexp( errors.InvalidArgumentError, diff --git a/tensorflow/python/estimator/inputs/numpy_io_test.py b/tensorflow/python/estimator/inputs/numpy_io_test.py index 4e7b00b307..632908415f 100644 --- a/tensorflow/python/estimator/inputs/numpy_io_test.py +++ b/tensorflow/python/estimator/inputs/numpy_io_test.py @@ -42,7 +42,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = np.arange(-32, -28) - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) features, target = input_fn() @@ -68,7 +68,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = np.arange(-32, -30) - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=128, shuffle=False, num_epochs=2) features, target = input_fn() @@ -93,7 +93,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = np.arange(-32, -28) - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=0) features, target = input_fn() @@ -114,7 +114,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = np.arange(-32, -27) - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=batch_size, shuffle=False, num_epochs=1) features, target = input_fn() @@ -150,7 +150,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = np.arange(-32, -29) - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=batch_size, shuffle=False, num_epochs=3) features, target = input_fn() @@ -196,7 +196,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = np.arange(-32, -28) - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=batch_size, shuffle=False, num_epochs=1) features, target = input_fn() @@ -221,7 +221,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = np.arange(-32, -30) - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) features, target = input_fn() @@ -240,7 +240,7 @@ class NumpyIoTest(test.TestCase): def testNumpyInputFnWithXAsNonDict(self): x = list(range(32, 36)) y = np.arange(4) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(TypeError, 'x must be a dict or array'): failing_input_fn = numpy_io.numpy_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) @@ -249,7 +249,7 @@ class NumpyIoTest(test.TestCase): def testNumpyInputFnWithXIsEmptyDict(self): x = {} y = np.arange(4) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(ValueError, 'x cannot be an empty'): failing_input_fn = numpy_io.numpy_input_fn(x, y, shuffle=False) failing_input_fn() @@ -257,7 +257,7 @@ class NumpyIoTest(test.TestCase): def testNumpyInputFnWithXIsEmptyArray(self): x = np.array([[], []]) y = np.arange(4) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(ValueError, 'x cannot be an empty'): failing_input_fn = numpy_io.numpy_input_fn(x, y, shuffle=False) failing_input_fn() @@ -268,7 +268,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = None - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) features_tensor = input_fn() @@ -291,7 +291,7 @@ class NumpyIoTest(test.TestCase): def testNumpyInputFnWithNonBoolShuffle(self): x = np.arange(32, 36) y = np.arange(4) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(ValueError, 'shuffle must be provided and explicitly ' 'set as boolean'): @@ -303,7 +303,7 @@ class NumpyIoTest(test.TestCase): x = {'__target_key__': array} y = np.arange(4) - with self.test_session(): + with self.cached_session(): input_fn = numpy_io.numpy_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) input_fn() @@ -318,7 +318,7 @@ class NumpyIoTest(test.TestCase): x_mismatch_length = {'a': np.arange(1), 'b': b} y_longer_length = np.arange(10) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( ValueError, 'Length of tensors in x and y is mismatched.'): failing_input_fn = numpy_io.numpy_input_fn( @@ -341,7 +341,7 @@ class NumpyIoTest(test.TestCase): x = {'a': a, 'b': b} y = {'y1': np.arange(-32, -28), 'y2': np.arange(32, 28, -1)} - with self.test_session() as session: + with self.cached_session() as session: input_fn = numpy_io.numpy_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) features_tensor, targets_tensor = input_fn() @@ -369,7 +369,7 @@ class NumpyIoTest(test.TestCase): b = np.arange(32, 36) x = {'a': a, 'b': b} y = {} - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(ValueError, 'y cannot be empty'): failing_input_fn = numpy_io.numpy_input_fn(x, y, shuffle=False) failing_input_fn() @@ -379,7 +379,7 @@ class NumpyIoTest(test.TestCase): b = np.arange(32, 36) x = {'a': a, 'b': b} y = {'y1': np.arange(-32, -28), 'a': a, 'y2': np.arange(32, 28, -1), 'b': b} - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( ValueError, '2 duplicate keys are found in both x and y'): failing_input_fn = numpy_io.numpy_input_fn(x, y, shuffle=False) diff --git a/tensorflow/python/estimator/inputs/pandas_io_test.py b/tensorflow/python/estimator/inputs/pandas_io_test.py index 6f13bc95d2..9e69fc72dc 100644 --- a/tensorflow/python/estimator/inputs/pandas_io_test.py +++ b/tensorflow/python/estimator/inputs/pandas_io_test.py @@ -102,7 +102,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_ProducesExpectedOutputs(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrame() input_fn = pandas_io.pandas_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) @@ -116,7 +116,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFnWhenYIsDataFrame_ProducesExpectedOutput(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrameWithYAsDataFrame() input_fn = pandas_io.pandas_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) @@ -131,7 +131,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFnYIsDataFrame_HandlesOverlappingColumns(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrameWithYAsDataFrame() y = y.rename(columns={'a_target': 'a', 'b_target': 'b'}) input_fn = pandas_io.pandas_input_fn( @@ -147,7 +147,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFnYIsDataFrame_HandlesOverlappingColumnsInTargets(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrameWithYAsDataFrame() y = y.rename(columns={'a_target': 'a', 'b_target': 'a_n'}) input_fn = pandas_io.pandas_input_fn( @@ -163,7 +163,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_ProducesOutputsForLargeBatchAndMultipleEpochs(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: index = np.arange(100, 102) a = np.arange(2) b = np.arange(32, 34) @@ -191,7 +191,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_ProducesOutputsWhenDataSizeNotDividedByBatchSize(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: index = np.arange(100, 105) a = np.arange(5) b = np.arange(32, 37) @@ -230,7 +230,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_OnlyX(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, _ = self.makeTestDataFrame() input_fn = pandas_io.pandas_input_fn( x, y=None, batch_size=2, shuffle=False, num_epochs=1) @@ -243,7 +243,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_ExcludesIndex(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrame() input_fn = pandas_io.pandas_input_fn( x, y, batch_size=2, shuffle=False, num_epochs=1) @@ -266,7 +266,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_RespectsEpoch_NoShuffle(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrame() input_fn = pandas_io.pandas_input_fn( x, y, batch_size=4, shuffle=False, num_epochs=1) @@ -276,7 +276,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_RespectsEpoch_WithShuffle(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrame() input_fn = pandas_io.pandas_input_fn( x, y, batch_size=4, shuffle=True, num_epochs=1) @@ -286,7 +286,7 @@ class PandasIoTest(test.TestCase): def testPandasInputFn_RespectsEpoch_WithShuffleAutosize(self): if not HAS_PANDAS: return - with self.test_session() as session: + with self.cached_session() as session: x, y = self.makeTestDataFrame() input_fn = pandas_io.pandas_input_fn( x, y, batch_size=2, shuffle=True, queue_capacity=None, num_epochs=2) @@ -297,7 +297,7 @@ class PandasIoTest(test.TestCase): if not HAS_PANDAS: return x, y = self.makeTestDataFrame() - with self.test_session() as session: + with self.cached_session() as session: input_fn = pandas_io.pandas_input_fn( x, y, batch_size=3, shuffle=False, num_epochs=1) diff --git a/tensorflow/python/training/checkpointable/tracking_test.py b/tensorflow/python/training/checkpointable/tracking_test.py index e85f812ce2..a44c570fb9 100644 --- a/tensorflow/python/training/checkpointable/tracking_test.py +++ b/tensorflow/python/training/checkpointable/tracking_test.py @@ -165,7 +165,7 @@ class InterfaceTests(test.TestCase): self.assertEqual([c], a.attribute["c"].layers) checkpoint = util.Checkpoint(a=a) save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) - with self.test_session(): + with self.cached_session(): checkpoint.restore(save_path).assert_consumed().initialize_or_restore() @test_util.run_in_graph_and_eager_modes diff --git a/tensorflow/python/training/checkpointable/util_test.py b/tensorflow/python/training/checkpointable/util_test.py index 0d32d21426..f8b5bd8501 100644 --- a/tensorflow/python/training/checkpointable/util_test.py +++ b/tensorflow/python/training/checkpointable/util_test.py @@ -384,7 +384,7 @@ class CheckpointingTests(test.TestCase): saver = saver_lib.Saver(var_list=[v]) test_dir = self.get_temp_dir() prefix = os.path.join(test_dir, "ckpt") - with self.test_session() as sess: + with self.cached_session() as sess: self.evaluate(v.non_dep_variable.assign(42.)) save_path = saver.save(sess, prefix) self.evaluate(v.non_dep_variable.assign(43.)) |