diff options
author | A. Unique TensorFlower <gardener@tensorflow.org> | 2018-09-10 14:36:05 -0700 |
---|---|---|
committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-09-10 14:43:27 -0700 |
commit | 55ad6406b8e0e1f50d27f619aa150cc2f827311a (patch) | |
tree | f198767fb56e4eb32f177102a2fffe41b7da5f43 /tensorflow/contrib/layers | |
parent | 4cbe494e87437213a7cb464ec23c12cb5788eb66 (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: 212336206
Diffstat (limited to 'tensorflow/contrib/layers')
11 files changed, 352 insertions, 352 deletions
diff --git a/tensorflow/contrib/layers/python/layers/embedding_ops_test.py b/tensorflow/contrib/layers/python/layers/embedding_ops_test.py index 7ede193029..124515e5a6 100644 --- a/tensorflow/contrib/layers/python/layers/embedding_ops_test.py +++ b/tensorflow/contrib/layers/python/layers/embedding_ops_test.py @@ -109,7 +109,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): return sparse_ids, sparse_weights def test_safe_embedding_lookup_sparse_return_zero_vector(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() sparse_ids, sparse_weights = self._ids_and_weights_2d() @@ -122,7 +122,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): 3.0, [0] * 4, [0] * 4, embedding_weights[0][2], [0] * 4]) def test_safe_embedding_lookup_sparse_return_special_vector(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() sparse_ids, sparse_weights = self._ids_and_weights_2d() @@ -136,7 +136,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): embedding_weights[0][2], embedding_weights[0][3]]) def test_safe_embedding_lookup_sparse_no_weights(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() sparse_ids, _ = self._ids_and_weights_2d() @@ -150,7 +150,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): embedding_weights[0][0] + embedding_weights[0][1]) / 2.0]) def test_safe_embedding_lookup_sparse_partitioned(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights(num_shards=3) sparse_ids, _ = self._ids_and_weights_2d() @@ -164,7 +164,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): (embedding_weights[0] + embedding_weights[1]) / 2.0]) def test_safe_embedding_lookup_sparse_partitioned_inconsistent_weights(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights(num_shards=3) sparse_ids, sparse_weights = self._ids_and_weights_2d() @@ -179,7 +179,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): embedding_weights, sparse_ids, sparse_weights) def test_safe_embedding_lookup_sparse_3d_return_zero_vector(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() sparse_ids, sparse_weights = self._ids_and_weights_3d() @@ -192,7 +192,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): ], [embedding_weights[0][2], [0] * 4, [0] * 4]]) def test_safe_embedding_lookup_sparse_3d_return_special_vector(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() sparse_ids, sparse_weights = self._ids_and_weights_3d() @@ -208,7 +208,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): ]]) def test_safe_embedding_lookup_sparse_3d_no_weights(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() sparse_ids, _ = self._ids_and_weights_3d() @@ -224,7 +224,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): ]]) def test_safe_embedding_lookup_sparse_3d_partitioned(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights(num_shards=3) sparse_ids, _ = self._ids_and_weights_3d() @@ -241,7 +241,7 @@ class SafeEmbeddingLookupSparseTest(test.TestCase): def test_safe_embedding_lookup_sparse_3d_partitioned_inconsistent_weights( self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights(num_shards=3) sparse_ids, sparse_weights = self._ids_and_weights_3d() @@ -276,7 +276,7 @@ class ScatteredEmbeddingLookupTest(test.TestCase): return embedding_weights def test_scattered_embedding_consistency(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() values = constant_op.constant(["foo", "foo"]) @@ -288,7 +288,7 @@ class ScatteredEmbeddingLookupTest(test.TestCase): embedding_lookup_result[1]) def test_scattered_embedding_multiple_partition(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights(num_shards=7) values = constant_op.constant([4, 4, 5]) @@ -304,7 +304,7 @@ class ScatteredEmbeddingLookupTest(test.TestCase): self.assertGreater(embedding_diff, 0) def test_scattered_embedding_coverage(self): - with self.test_session(): + with self.cached_session(): size = 8 embedding_weights = self._random_weights(size=size, num_shards=3) values = constant_op.constant(["foo"]) @@ -316,7 +316,7 @@ class ScatteredEmbeddingLookupTest(test.TestCase): self.assertEqual(len(np.unique(embedding_lookup_result[0])), size) def test_scattered_embedding_multi_dimension(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() values = constant_op.constant([["foo", "bar", "bar"], ["bar", "bar", "foo"]]) @@ -329,7 +329,7 @@ class ScatteredEmbeddingLookupTest(test.TestCase): embedding_lookup_result[1][2]) def test_scattered_embedding_lookup_sparse(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights(num_shards=3) sparse_tensor = sparse_tensor_lib.SparseTensor( values=["foo", "bar", "foo", "bar"], @@ -358,7 +358,7 @@ class ScatteredEmbeddingLookupTest(test.TestCase): embeds = np.random.randn(n_embed, d_embed) idx = np.random.randint(0, n_embed, idx_shape) - with self.test_session(): + with self.cached_session(): embedded_np = embeds[idx] embedded_tf = embedding_ops.embedding_lookup_unique(embeds, idx).eval() @@ -370,7 +370,7 @@ class ScatteredEmbeddingLookupTest(test.TestCase): idx = np.random.randint(0, 5, 10) idx2d = np.random.randint(0, 5, (10, 2)) - with self.test_session(): + with self.cached_session(): embedded_np = embeds[idx] embedded_np2d = embeds[idx2d] embedded_tf = embedding_ops.embedding_lookup_unique(embeds, idx).eval() @@ -408,7 +408,7 @@ class SampledScatteredEmbeddingLookupTest(test.TestCase): return embedding_weights def test_hashed_embedding_consistency(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() values = constant_op.constant(["foo", "foo"]) # The first three sampled_candidates are equal, so the first three @@ -429,7 +429,7 @@ class SampledScatteredEmbeddingLookupTest(test.TestCase): embedding_lookup_result[1][3]) def test_hashed_embedding_multi_dimension(self): - with self.test_session(): + with self.cached_session(): embedding_weights = self._random_weights() values = constant_op.constant([["foo", "bar", "bar"], ["bar", "bar", "foo"]]) @@ -467,7 +467,7 @@ class SampledScatteredEmbeddingLookupSparseTest(test.TestCase): def test_output_shape(self): """Verifies the shape of the output tensor.""" - with self.test_session(): + with self.cached_session(): sp_values = sparse_tensor_lib.SparseTensor( values=["a", "a", "b", "c", "d", "e", "f"], indices=[[1, 0], [2, 0], [2, 1], [2, 2], [2, 3], [2, 4], [2, 5]], @@ -481,7 +481,7 @@ class SampledScatteredEmbeddingLookupSparseTest(test.TestCase): def test_output_values(self): """Verifies the values in a trivial case.""" - with self.test_session(): + with self.cached_session(): sp_values = sparse_tensor_lib.SparseTensor( values=["a"], indices=[[1, 0]], dense_shape=[3, 1]) params = constant_op.constant([.1, .2, .3]) @@ -495,7 +495,7 @@ class SampledScatteredEmbeddingLookupSparseTest(test.TestCase): def test_output_values_with_sampled_candidates(self): """Verifies the values for given sampled_candidates.""" - with self.test_session(): + with self.cached_session(): sp_values = sparse_tensor_lib.SparseTensor( values=["a", "a", "b", "c", "d", "e", "f"], indices=[[1, 0], [2, 0], [2, 1], [2, 2], [2, 3], [2, 4], [2, 5]], @@ -520,7 +520,7 @@ class SampledScatteredEmbeddingLookupSparseTest(test.TestCase): def test_output_values_with_sign_hash(self): """Verifies the values in a trivial case with hash_signs=True.""" - with self.test_session(): + with self.cached_session(): sp_values = sparse_tensor_lib.SparseTensor( values=["a"], indices=[[1, 0]], dense_shape=[3, 1]) params = constant_op.constant([.1, .1, .1]) @@ -537,7 +537,7 @@ class SampledScatteredEmbeddingLookupSparseTest(test.TestCase): def test_distributive_property(self): """Verifies the distributive property of matrix multiplication.""" - with self.test_session(): + with self.cached_session(): params = constant_op.constant([.1, .2, .3]) sp_values_a = sparse_tensor_lib.SparseTensor( values=["a"], indices=[[0, 0]], dense_shape=[3, 1]) @@ -710,7 +710,7 @@ class EmbeddingLookupSparseWithDistributedAggregationTest(test.TestCase): [1, 5], ["sum", "mean", "sqrtn"], [dtypes.float32, dtypes.float64], [True, False]): - with self.test_session(): + with self.cached_session(): p, params, feed_dict = _EmbeddingParams( num_shards, vocab_size, shape=param_shape, dtype=dtype) embedding_sum = \ @@ -749,7 +749,7 @@ class EmbeddingLookupSparseWithDistributedAggregationTest(test.TestCase): for num_shards, combiner, dtype, ignore_weights in itertools.product( [1, 3], ["sum", "mean", "sqrtn"], [dtypes.float32, dtypes.float64], [True, False]): - with self.test_session(): + with self.cached_session(): x, params, _ = _EmbeddingParams( num_shards, vocab_size, shape=param_shape, dtype=dtype) @@ -767,7 +767,7 @@ class EmbeddingLookupSparseWithDistributedAggregationTest(test.TestCase): self.assertLess(err, 1e-5 if dtype == dtypes.float64 else 2e-3) def testIncompatibleShapes(self): - with self.test_session(): + with self.cached_session(): x, _, _ = _EmbeddingParams(1, 10, dtype=dtypes.float32) sp_ids = sparse_tensor_lib.SparseTensor( constant_op.constant([[0, 0], [0, 1], [1, 0]], dtypes.int64), diff --git a/tensorflow/contrib/layers/python/layers/encoders_test.py b/tensorflow/contrib/layers/python/layers/encoders_test.py index e8528e9890..1a2aa710d5 100644 --- a/tensorflow/contrib/layers/python/layers/encoders_test.py +++ b/tensorflow/contrib/layers/python/layers/encoders_test.py @@ -34,14 +34,14 @@ def _get_const_var(name, shape, value): class EncodersTest(test.TestCase): def testBowEncoderSparse(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[0, 1], [2, 3]] enc = encoders.bow_encoder(docs, 4, 3) sess.run(variables.global_variables_initializer()) self.assertAllEqual([2, 3], enc.eval().shape) def testBowEncoderSparseTensor(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[0, 1], [2, 3]] sparse_docs = sparse_ops.dense_to_sparse_tensor(docs) enc = encoders.bow_encoder(sparse_docs, 4, 3) @@ -49,28 +49,28 @@ class EncodersTest(test.TestCase): self.assertAllEqual([2, 3], enc.eval().shape) def testBowEncoderSparseEmptyRow(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[0, 1], [2, 3], [0, 0]] enc = encoders.bow_encoder(docs, 4, 5) sess.run(variables.global_variables_initializer()) self.assertAllEqual([3, 5], enc.eval().shape) def testBowEncoderDense(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[0, 1], [2, 3], [0, 0], [0, 0]] enc = encoders.bow_encoder(docs, 4, 3, sparse_lookup=False) sess.run(variables.global_variables_initializer()) self.assertAllEqual([4, 3], enc.eval().shape) def testBowEncoderSparseTensorDenseLookup(self): - with self.test_session(): + with self.cached_session(): docs = [[0, 1]] sparse_docs = sparse_ops.dense_to_sparse_tensor(docs) with self.assertRaises(TypeError): encoders.bow_encoder(sparse_docs, 4, 3, sparse_lookup=False) def testBowEncodersSharingEmbeddings(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[0, 1], [2, 3]] enc_1 = encoders.bow_encoder(docs, 4, 3, scope='test') enc_2 = encoders.bow_encoder(docs, 4, 3, scope='test', reuse=True) @@ -79,7 +79,7 @@ class EncodersTest(test.TestCase): self.assertAllEqual(avg_1, avg_2) def testBowEncodersSharingEmbeddingsInheritedScopes(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[0, 1], [2, 3]] with variable_scope.variable_scope('test'): enc_1 = encoders.bow_encoder(docs, 4, 3) @@ -90,7 +90,7 @@ class EncodersTest(test.TestCase): self.assertAllEqual(avg_1, avg_2) def testBowEncodersSharingEmbeddingsSharedScope(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[0, 1], [2, 3]] enc_1 = encoders.bow_encoder(docs, 4, 3, scope='bow') variable_scope.get_variable_scope().reuse_variables() @@ -100,7 +100,7 @@ class EncodersTest(test.TestCase): self.assertAllEqual(avg_1, avg_2) def testBowEncoderReuseEmbeddingsVariable(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[1, 1], [2, 3]] with variable_scope.variable_scope('test'): v = _get_const_var('embeddings', (4, 3), @@ -111,7 +111,7 @@ class EncodersTest(test.TestCase): self.assertAllClose([[3., 4., 5.], [7.5, 8.5, 9.5]], enc.eval()) def testEmbedSequence(self): - with self.test_session() as sess: + with self.cached_session() as sess: docs = [[1, 1], [2, 3]] with variable_scope.variable_scope('test'): v = _get_const_var('embeddings', (4, 3), diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py b/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py index e6bbd86ab7..6fb4b9ff35 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py +++ b/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py @@ -49,7 +49,7 @@ class TransformerTest(test.TestCase): real_valued = feature_column.real_valued_column("price") features = {"price": constant_op.constant([[20.], [110], [-3]])} output = feature_column_ops._Transformer(features).transform(real_valued) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(output.eval(), [[20.], [110], [-3]]) def testSparseRealValuedColumnIdentityTransformation(self): @@ -60,7 +60,7 @@ class TransformerTest(test.TestCase): features = {"rating": rating_tensor} output = feature_column_ops._Transformer(features).transform( sparse_real_valued) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(output.values.eval(), rating_tensor.values.eval()) self.assertAllEqual(output.indices.eval(), rating_tensor.indices.eval()) self.assertAllEqual(output.dense_shape.eval(), @@ -80,7 +80,7 @@ class TransformerTest(test.TestCase): [sparse_real_valued]) self.assertTrue(sparse_real_valued in output_dict) output = output_dict[sparse_real_valued] - with self.test_session(): + with self.cached_session(): self.assertArrayNear(output.values.eval(), [4.0, 25.0], 1e-5) self.assertAllEqual(output.indices.eval(), rating_tensor.indices.eval()) self.assertAllEqual(output.dense_shape.eval(), @@ -97,7 +97,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[bucket]) self.assertEqual(len(output), 1) self.assertIn(bucket, output) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(output[bucket].eval(), [[2], [3], [0]]) def testBucketizedColumnWithMultiDimensions(self): @@ -109,7 +109,7 @@ class TransformerTest(test.TestCase): "price": constant_op.constant([[20., 110], [110., 20], [-3, -3]]) } output = feature_column_ops._Transformer(features).transform(bucket) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(output.eval(), [[2, 3], [3, 2], [0, 0]]) def testCachedTransformation(self): @@ -118,7 +118,7 @@ class TransformerTest(test.TestCase): # buckets 2, 3, 0 features = {"price": constant_op.constant([[20.], [110], [-3]])} transformer = feature_column_ops._Transformer(features) - with self.test_session() as sess: + with self.cached_session() as sess: transformer.transform(bucket) num_of_ops = len(sess.graph.get_operations()) # Verify that the second call to transform the same feature @@ -138,7 +138,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[hashed_sparse]) self.assertEqual(len(output), 1) self.assertIn(hashed_sparse, output) - with self.test_session(): + with self.cached_session(): self.assertEqual(output[hashed_sparse].values.dtype, dtypes.int64) self.assertTrue( all(x < 10 and x >= 0 for x in output[hashed_sparse].values.eval())) @@ -161,7 +161,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[hashed_sparse]) self.assertEqual(len(output), 1) self.assertIn(hashed_sparse, output) - with self.test_session(): + with self.cached_session(): self.assertEqual(output[hashed_sparse].values.dtype, dtypes.int64) self.assertTrue( all(x < 10 and x >= 0 for x in output[hashed_sparse].values.eval())) @@ -177,7 +177,7 @@ class TransformerTest(test.TestCase): features = {"wire": wire_tensor} output = feature_column_ops._Transformer(features).transform(hashed_sparse) - with self.test_session(): + with self.cached_session(): # While the input is a dense Tensor, the output should be a SparseTensor. self.assertIsInstance(output, sparse_tensor.SparseTensor) self.assertEqual(output.values.dtype, dtypes.int64) @@ -203,7 +203,7 @@ class TransformerTest(test.TestCase): self.assertEqual(len(output), 2) self.assertIn(hashed_sparse, output) self.assertIn(wire_embedding, output) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(output[wire_embedding].indices.eval(), wire_tensor.indices.eval()) self.assertAllEqual(output[wire_embedding].dense_shape.eval(), [2, 2]) @@ -223,7 +223,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[keys_sparse]) self.assertEqual(len(output), 1) self.assertIn(keys_sparse, output) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() self.assertEqual(output[keys_sparse].values.dtype, dtypes.int64) self.assertAllEqual(output[keys_sparse].values.eval(), [1, 2, 0]) @@ -241,7 +241,7 @@ class TransformerTest(test.TestCase): features = {"wire": wire_tensor} output = feature_column_ops._Transformer(features).transform(keys_sparse) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() # While the input is a dense Tensor, the output should be a SparseTensor. self.assertIsInstance(output, sparse_tensor.SparseTensor) @@ -264,7 +264,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[hashed_sparse]) self.assertEqual(len(output), 1) self.assertIn(hashed_sparse, output) - with self.test_session(): + with self.cached_session(): self.assertEqual(output[hashed_sparse].values.dtype, dtypes.int32) self.assertTrue( all(x < 10 and x >= 0 for x in output[hashed_sparse].values.eval())) @@ -282,7 +282,7 @@ class TransformerTest(test.TestCase): wire_tensor = constant_op.constant([[100, 0], [1, 25]]) features = {"wire": wire_tensor} output = feature_column_ops._Transformer(features).transform(hashed_sparse) - with self.test_session(): + with self.cached_session(): # While the input is a dense Tensor, the output should be a SparseTensor. self.assertIsInstance(output, sparse_tensor.SparseTensor) self.assertEqual(output.values.dtype, dtypes.int32) @@ -310,7 +310,7 @@ class TransformerTest(test.TestCase): self.assertEqual(len(output), 1) self.assertIn(weighted_ids, output) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() self.assertAllEqual(output[weighted_ids][0].dense_shape.eval(), ids_tensor.dense_shape.eval()) @@ -340,7 +340,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[vocab_sparse]) self.assertEqual(len(output), 1) self.assertIn(vocab_sparse, output) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64) self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0]) @@ -362,7 +362,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[vocab_sparse]) self.assertEqual(len(output), 1) self.assertIn(vocab_sparse, output) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64) self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0, 1]) @@ -386,7 +386,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[vocab_sparse]) self.assertEqual(len(output), 1) self.assertIn(vocab_sparse, output) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64) self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0]) @@ -408,7 +408,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[vocab_sparse]) self.assertEqual(len(output), 1) self.assertIn(vocab_sparse, output) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() self.assertEqual(output[vocab_sparse].values.dtype, dtypes.int64) self.assertAllEqual(output[vocab_sparse].values.eval(), [1, 2, 0, 1]) @@ -440,7 +440,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[country_language]) self.assertEqual(len(output), 1) self.assertIn(country_language, output) - with self.test_session(): + with self.cached_session(): self.assertEqual(output[country_language].values.dtype, dtypes.int64) self.assertTrue( all(x < 15 and x >= 0 for x in output[country_language].values.eval( @@ -467,7 +467,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[country_price]) self.assertEqual(len(output), 1) self.assertIn(country_price, output) - with self.test_session(): + with self.cached_session(): self.assertEqual(output[country_price].values.dtype, dtypes.int64) self.assertTrue( all(x < 15 and x >= 0 for x in output[country_price].values.eval())) @@ -498,7 +498,7 @@ class TransformerTest(test.TestCase): weights = column_to_variable[country_price][0] grad = array_ops.squeeze( gradients_impl.gradients(output, weights)[0].values) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertEqual(len(grad.eval()), 6) @@ -537,7 +537,7 @@ class TransformerTest(test.TestCase): features=features, feature_columns=[wire_country_price]) self.assertEqual(len(output), 1) self.assertIn(wire_country_price, output) - with self.test_session(): + with self.cached_session(): self.assertEqual(output[wire_country_price].values.dtype, dtypes.int64) self.assertTrue( all(x < 15 and x >= 0 for x in output[wire_country_price].values.eval( @@ -600,7 +600,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): columns = [one_hot_column, embedding_column, real_valued_column] output = feature_column_ops.input_from_feature_columns(features, columns) output_core = fc_core.input_layer(features, columns) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual(output.eval().shape, [3, 2 + 4 + 10]) @@ -626,7 +626,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): cols_to_outs = {} feature_column_ops.input_from_feature_columns( features, columns, cols_to_outs=cols_to_outs) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() for column in columns: @@ -637,7 +637,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): features = {"price": constant_op.constant([[20.], [110], [-3]])} output = feature_column_ops.input_from_feature_columns(features, [real_valued]) - with self.test_session(): + with self.cached_session(): self.assertAllClose(output.eval(), features["price"].eval()) # Verify cross compatibility: Core builder output should equal to contrib. self.assertAllClose(output.eval(), @@ -650,7 +650,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): } output = feature_column_ops.input_from_feature_columns(features, [real_valued]) - with self.test_session(): + with self.cached_session(): self.assertAllClose(output.eval(), features["price"].eval()) # Verify cross compatibility: Core builder output should equal to contrib. self.assertAllClose(output.eval(), @@ -662,7 +662,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): rating = np.array([[0., 1., 2., -1.], [3., 4., 5., 6.]]) features = {"rating": constant_op.constant(rating)} - with self.test_session() as sess: + with self.cached_session() as sess: output = sess.run(feature_column_ops.input_from_feature_columns( features, [var_len_real_valued])) self.assertAllClose(rating, output) @@ -673,7 +673,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): rating = np.array([[0, 1, 2, -1], [3, 4, 5, 6]]) features = {"rating": constant_op.constant(rating, dtype=dtypes.int64)} - with self.test_session() as sess: + with self.cached_session() as sess: output = sess.run(feature_column_ops.input_from_feature_columns( features, [var_len_real_valued])) self.assertAllClose(rating.astype(np.float32), output) @@ -684,7 +684,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): features = {"price": constant_op.constant([[20.], [110], [-3]])} output = feature_column_ops.input_from_feature_columns(features, [real_valued]) - with self.test_session(): + with self.cached_session(): self.assertAllClose(output.eval(), features["price"].eval() - 2) # Verify cross compatibility: Core builder output should equal to contrib. self.assertAllClose(output.eval(), @@ -698,7 +698,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): } output = feature_column_ops.input_from_feature_columns(features, [real_valued]) - with self.test_session(): + with self.cached_session(): self.assertAllClose(output.eval(), features["price"].eval() - 2) # Verify cross compatibility: Core builder output should equal to contrib. self.assertAllClose(output.eval(), @@ -713,7 +713,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): features = {"price": constant_op.constant([[20.], [110], [-3]])} output = feature_column_ops.input_from_feature_columns(features, [bucket]) expected = [[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]] - with self.test_session(): + with self.cached_session(): self.assertAllClose(output.eval(), expected) self.assertAllClose(output.eval(), fc_core.input_layer(features, [bucket]).eval()) @@ -729,7 +729,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): output = feature_column_ops.input_from_feature_columns(features, [bucket]) expected = [[0, 0, 1, 0, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 1, 0], [1, 0, 0, 0, 1, 0, 0, 0]] - with self.test_session(): + with self.cached_session(): self.assertAllClose(output.eval(), expected) self.assertAllClose(output.eval(), fc_core.input_layer(features, [bucket]).eval()) @@ -752,7 +752,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): output = feature_column_ops.input_from_feature_columns(features, [one_hot_column]) output_core = fc_core.input_layer(features, [one_hot_column]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual([[0, 0, 10., 0], [0, 20., 0, 0], [30., 0, 40., 0]], @@ -773,7 +773,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): [one_hot_sparse]) output_core = fc_core.input_layer(features, [one_hot_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual([[0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]], @@ -794,7 +794,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): [one_hot_sparse]) output_core = fc_core.input_layer(features, [one_hot_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual([[0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 1, 0]], @@ -816,7 +816,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): output = feature_column_ops.input_from_feature_columns(features, [one_hot_sparse]) output_core = fc_core.input_layer(features, [one_hot_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual([[0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 1, 0]], output.eval()) @@ -834,7 +834,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): output = feature_column_ops.input_from_feature_columns(features, [one_hot_sparse]) output_core = fc_core.input_layer(features, [one_hot_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual([3, 10], output.eval().shape) @@ -852,7 +852,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): output = feature_column_ops.input_from_feature_columns(features, [embeded_sparse]) output_core = fc_core.input_layer(features, [embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(output.eval().shape, [4, 10]) # Verify cross compatibility: Core builder output should equal to contrib. @@ -878,7 +878,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): features, [embedded_sparse], weight_collections=["my_collection_core"]) weights_core = ops.get_collection("my_collection_core") grad_core = gradients_impl.gradients(output_core, weights_core) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() gradient_values = [] gradient_values_core = [] @@ -907,7 +907,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): [embeded_sparse]) output_core = fc_core.input_layer(features, [embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() output_eval = output.eval() self.assertAllEqual(output_eval.shape, [2, 10]) @@ -935,7 +935,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): # Makes sure that trying to use different initializers with the same # embedding column explicitly fails. - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( ValueError, "Duplicate feature column key found for column: wire_embedding"): @@ -961,7 +961,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): [embeded_sparse]) output_core = fc_core.input_layer(features, [embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual(output.eval().shape, [2, 10]) @@ -986,7 +986,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): embeded_sparse = feature_column.embedding_column(weighted_ids, 10) output = feature_column_ops.input_from_feature_columns(features, [embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual(output.eval().shape, [2, 10]) @@ -1005,7 +1005,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): embeded_sparse = feature_column.embedding_column(crossed, 10) output = feature_column_ops.input_from_feature_columns(features, [embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(output.eval().shape, [2, 10]) @@ -1016,7 +1016,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): indices=[[0, 0], [1, 0], [1, 1]], dense_shape=[2, 2]) features = {"wire": wire_tensor} - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( ValueError, "Error creating input layer for column: wire"): variables_lib.global_variables_initializer().run() @@ -1035,7 +1035,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): indices=[[0, 0], [1, 0], [1, 1]], dense_shape=[2, 2]) features = {"ids": ids_tensor, "weights": weights_tensor} - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( ValueError, "Error creating input layer for column: ids_weighted_by_weights"): @@ -1053,7 +1053,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): indices=[[0, 0], [1, 0], [1, 1]], dense_shape=[2, 2]) features = {"aaa": wire_tensor, "bbb": wire_tensor} - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( ValueError, "Error creating input layer for column: aaa_X_bbb"): variables_lib.global_variables_initializer().run() @@ -1080,7 +1080,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): hashed_sparse, 10, initializer=init_ops.constant_initializer(133.7)) output = feature_column_ops.input_from_feature_columns( features, [real_valued, bucket, embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() # size of output = 3 (real_valued) + 2 * 4 (bucket) + 10 (embedding) = 21 self.assertAllEqual(output.eval().shape, [3, 21]) @@ -1099,7 +1099,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): initializer=init_ops.ones_initializer()) output = feature_column_ops.input_from_feature_columns(features, [embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() # score: (number of values) self.assertAllEqual(output.eval(), [[1.], [2.], [0.]]) @@ -1119,7 +1119,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): max_norm=0.5) output = feature_column_ops.input_from_feature_columns(features, [embedded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() # score: (number of values * 0.5) self.assertAllClose(output.eval(), [[0.5], [1.], [0.]]) @@ -1144,7 +1144,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): initializer=init_ops.ones_initializer()) output = feature_column_ops.input_from_feature_columns(features, [embeded_sparse]) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() # score: (sum of weights) @@ -1236,7 +1236,7 @@ class CreateInputLayersForDNNsTest(test.TestCase): # There should be one trainable variables for sparse_2 self.assertEqual(1, len(variables_lib.trainable_variables())) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() output_1_eval = output_1.eval() output_2_eval = output_2.eval() @@ -1295,7 +1295,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): model_input_tensor = feature_column_ops.sequence_input_from_feature_columns( columns_to_tensors, [measurement_column]) - with self.test_session() as sess: + with self.cached_session() as sess: model_inputs = sess.run(model_input_tensor) self.assertAllClose(measurement_input, model_inputs) @@ -1305,7 +1305,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): rating = np.array([[0., 1., 2., -1.], [3., 4., 5., 6.]]) features = {"rating": constant_op.constant(rating)} - with self.test_session() as sess: + with self.cached_session() as sess: output = sess.run( feature_column_ops.sequence_input_from_feature_columns( features, [var_len_real_valued])) @@ -1329,7 +1329,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): expected_shape = [batch_size, sequence_length, np.prod(dimensions)] reshaped_measurements = np.reshape(measurement_input, expected_shape) - with self.test_session() as sess: + with self.cached_session() as sess: model_inputs = sess.run(model_input_tensor) self.assertAllClose(reshaped_measurements, model_inputs) @@ -1350,7 +1350,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): model_input_tensor = feature_column_ops.sequence_input_from_feature_columns( columns_to_tensors, [measurement_column]) - with self.test_session() as sess: + with self.cached_session() as sess: model_inputs = sess.run(model_input_tensor) self.assertAllClose(normalizer(measurement_input), model_inputs) @@ -1373,7 +1373,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): expected_shape = [batch_size, sequence_length, np.prod(dimensions)] reshaped_measurements = np.reshape(measurement_input, expected_shape) - with self.test_session() as sess: + with self.cached_session() as sess: model_inputs = sess.run(model_input_tensor) self.assertAllClose(normalizer(reshaped_measurements), model_inputs) @@ -1395,7 +1395,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): model_input_tensor = feature_column_ops.sequence_input_from_feature_columns( columns_to_tensors, [one_hot_column]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() model_input = sess.run(model_input_tensor) @@ -1429,7 +1429,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): model_input_tensor = feature_column_ops.sequence_input_from_feature_columns( columns_to_tensors, [one_hot_column]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() model_input = sess.run(model_input_tensor) @@ -1459,7 +1459,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): model_input_tensor = feature_column_ops.sequence_input_from_feature_columns( columns_to_tensors, [embedded_column]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() model_input = sess.run(model_input_tensor) @@ -1488,7 +1488,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): model_input_tensor = feature_column_ops.sequence_input_from_feature_columns( columns_to_tensors, [embedded_column]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() model_input = sess.run(model_input_tensor) @@ -1518,7 +1518,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): embedding_weights = ops.get_collection("my_collection") gradient_tensor = gradients_impl.gradients(model_input_tensor, embedding_weights) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() model_input, gradients = sess.run([model_input_tensor, gradient_tensor]) @@ -1585,7 +1585,7 @@ class SequenceInputFromFeatureColumnTest(test.TestCase): columns_to_tensors, model_input_columns) self.assertEqual(dtypes.float32, model_input_tensor.dtype) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() model_input = sess.run(model_input_tensor) @@ -1622,7 +1622,7 @@ class WeightedSumTest(test.TestCase): logits, _, _ = feature_column_ops.weighted_sum_from_feature_columns( features, [hashed_sparse], num_outputs=5) logits_core = fc_core.linear_model(features, [hashed_sparse], units=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(logits.eval().shape, [2, 5]) # Verify cross compatibility: Core builder output should equal to contrib. @@ -1640,7 +1640,7 @@ class WeightedSumTest(test.TestCase): logits, _, _ = feature_column_ops.weighted_sum_from_feature_columns( features, [hashed_sparse], num_outputs=5) logits_core = fc_core.linear_model(features, [hashed_sparse], units=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(logits.eval().shape, [2, 5]) # Verify cross compatibility: Core builder output should equal to contrib. @@ -1654,7 +1654,7 @@ class WeightedSumTest(test.TestCase): logits, _, _ = feature_column_ops.weighted_sum_from_feature_columns( features, [hashed_sparse], num_outputs=5) logits_core = fc_core.linear_model(features, [hashed_sparse], units=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(logits.eval().shape, [2, 5]) # Verify cross compatibility: Core builder output should equal to contrib. @@ -1676,7 +1676,7 @@ class WeightedSumTest(test.TestCase): logits, _, _ = feature_column_ops.weighted_sum_from_feature_columns( features, [weighted_ids], num_outputs=5) logits_core = fc_core.linear_model(features, [weighted_ids], units=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual(logits.eval().shape, [2, 5]) @@ -1695,7 +1695,7 @@ class WeightedSumTest(test.TestCase): features, [weighted_ids], num_outputs=5) logits_core = fc_core.linear_model(features, [weighted_ids], units=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() self.assertAllEqual(logits.eval().shape, [2, 5]) @@ -1716,7 +1716,7 @@ class WeightedSumTest(test.TestCase): logits, _, _ = feature_column_ops.weighted_sum_from_feature_columns( features, [crossed], num_outputs=5) logits_core = fc_core.linear_model(features, [crossed], units=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(logits.eval().shape, [2, 5]) # Verify cross compatibility: Core builder output should equal to contrib. @@ -1730,7 +1730,7 @@ class WeightedSumTest(test.TestCase): dense_shape=[2, 2]) features = {"wire": wire_tensor} embeded_sparse = feature_column.embedding_column(hashed_sparse, 10) - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp( ValueError, "Error creating weighted sum for column: wire_embedding"): variables_lib.global_variables_initializer().run() @@ -1756,7 +1756,7 @@ class WeightedSumTest(test.TestCase): features, [movies], num_outputs=1)) logits_core = fc_core.linear_model(features, [movies]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.initialize_all_variables().run() lookup_ops.tables_initializer().run() @@ -1776,7 +1776,7 @@ class WeightedSumTest(test.TestCase): } logits, _, _ = feature_column_ops.weighted_sum_from_feature_columns( features, [real_valued], num_outputs=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(logits.eval().shape, [3, 5]) @@ -1789,7 +1789,7 @@ class WeightedSumTest(test.TestCase): } logits, _, _ = feature_column_ops.weighted_sum_from_feature_columns( features, [bucket], num_outputs=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(logits.eval().shape, [3, 5]) @@ -1814,7 +1814,7 @@ class WeightedSumTest(test.TestCase): features, [real_valued, bucket, hashed_sparse, crossed], num_outputs=5) output_core = fc_core.linear_model( features, [real_valued, bucket, hashed_sparse, crossed], units=5) - with self.test_session(): + with self.cached_session(): variables_lib.global_variables_initializer().run() self.assertAllEqual(output.eval().shape, [3, 5]) # Verify cross compatibility: Core builder output should equal to contrib. @@ -1837,7 +1837,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, bias = ( feature_column_ops.weighted_sum_from_feature_columns( features, [age, language], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -1877,7 +1877,7 @@ class WeightedSumTest(test.TestCase): features, [country, language], num_outputs=1)) # Assert that only a single weight is created. self.assertEqual(len(variables), 1) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -1941,7 +1941,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, bias = ( feature_column_ops.weighted_sum_from_feature_columns( features, [weighted_language], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -1969,7 +1969,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, bias = ( feature_column_ops.weighted_sum_from_feature_columns( features, [language], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -1992,7 +1992,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [movies], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2026,7 +2026,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [country_language], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2050,7 +2050,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [language_language], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2083,7 +2083,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [country_language], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2124,7 +2124,7 @@ class WeightedSumTest(test.TestCase): features, [country, language, country_language], num_outputs=1, scope=scope)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2161,7 +2161,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [country, age, incomes], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2197,7 +2197,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [country, age, height, incomes], num_outputs=5)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2228,7 +2228,7 @@ class WeightedSumTest(test.TestCase): feature_column_ops.weighted_sum_from_feature_columns( features, [bucket], num_outputs=1)) output_core = fc_core.linear_model(features, [bucket]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() # Cross compatibility: Core builder output should equal to contrib. @@ -2259,7 +2259,7 @@ class WeightedSumTest(test.TestCase): feature_column_ops.weighted_sum_from_feature_columns( features, [bucket, country], num_outputs=1)) output_core = fc_core.linear_model(features, [bucket, country]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() # Cross compatibility: Core builder output should equal to contrib. @@ -2290,7 +2290,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [bucket, country], num_outputs=5)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2326,7 +2326,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [country_price], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2365,7 +2365,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [country_language_price], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2389,7 +2389,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [product], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() product_weights = column_to_variable[product][0] @@ -2404,7 +2404,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [product], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() product_weights = column_to_variable[product][0] @@ -2419,7 +2419,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [product], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() product_weights = column_to_variable[product][0] @@ -2440,7 +2440,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [product], num_outputs=1)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() product_weights = column_to_variable[product][0] @@ -2452,7 +2452,7 @@ class WeightedSumTest(test.TestCase): features = {"age": constant_op.constant([[10.], [20.], [30.], [40.]])} output, _, bias = feature_column_ops.weighted_sum_from_feature_columns( features, [feature_column.real_valued_column("age")], num_outputs=3) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() sess.run(bias.assign([0.1, 0.2, 0.3])) @@ -2466,7 +2466,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [column], num_outputs=3)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() weights = column_to_variable[column][0] @@ -2490,7 +2490,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [column], num_outputs=3)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() weights = column_to_variable[column][0] @@ -2516,7 +2516,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [column], num_outputs=3)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2556,7 +2556,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [column], num_outputs=3)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2585,7 +2585,7 @@ class WeightedSumTest(test.TestCase): output, column_to_variable, _ = ( feature_column_ops.weighted_sum_from_feature_columns( features, [column], num_outputs=3)) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() lookup_ops.tables_initializer().run() @@ -2651,7 +2651,7 @@ class ParseExampleTest(test.TestCase): feature_columns=[bucket, wire_cast]) self.assertIn(bucket, output) self.assertIn(wire_cast, output) - with self.test_session(): + with self.cached_session(): lookup_ops.tables_initializer().run() self.assertAllEqual(output[bucket].eval(), [[2, 3, 0]]) self.assertAllEqual(output[wire_cast].indices.eval(), [[0, 0], [0, 1]]) @@ -2713,7 +2713,7 @@ class ParseExampleTest(test.TestCase): self.assertIn("measurements", seq) self.assertIsInstance(seq["measurements"], ops.Tensor) - with self.test_session() as sess: + with self.cached_session() as sess: location_val, wire_cast_val, measurement_val = sess.run( [ctx["location"], seq["wire_cast"], seq["measurements"]]) diff --git a/tensorflow/contrib/layers/python/layers/feature_column_test.py b/tensorflow/contrib/layers/python/layers/feature_column_test.py index eaaf9f8d5f..d90d6ecf7f 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column_test.py +++ b/tensorflow/contrib/layers/python/layers/feature_column_test.py @@ -201,7 +201,7 @@ class FeatureColumnTest(test.TestCase): b2 = feature_column_ops.input_from_feature_columns({ b[1]: input_tensor_c2 }, [b[1]]) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) b1_value = b1.eval() b2_value = b2.eval() @@ -230,7 +230,7 @@ class FeatureColumnTest(test.TestCase): e1 = feature_column_ops.input_from_feature_columns({ e[0]: input_tensor_c1 }, [e[0]]) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) d1_value = d1.eval() e1_value = e1.eval() @@ -340,7 +340,7 @@ class FeatureColumnTest(test.TestCase): with variable_scope.variable_scope("output_rank_{}".format(output_rank)): one_hot_output = one_hot._to_dnn_input_layer( id_tensor, output_rank=output_rank) - with self.test_session() as sess: + with self.cached_session() as sess: one_hot_value = sess.run(one_hot_output) expected_shape = (id_tensor_shape[:output_rank - 1] + [vocab_size]) self.assertEquals(expected_shape, list(one_hot_value.shape)) @@ -376,7 +376,7 @@ class FeatureColumnTest(test.TestCase): one_hot_output_shape = one_hot_output.get_shape().as_list() expected_shape = id_tensor_shape[:-1] + [vocab_size] self.assertEquals(expected_shape, one_hot_output_shape) - with self.test_session() as sess: + with self.cached_session() as sess: one_hot_value = sess.run(one_hot_output) self.assertEquals(expected_shape, list(one_hot_value.shape)) @@ -399,7 +399,7 @@ class FeatureColumnTest(test.TestCase): expected = np.array([[0., 1., 0., 0., 0., 0., 0., 1., 0., 0.], [0., 1., 0., 0., 0., 0., 0., 0., 0., 1.], [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]) - with self.test_session() as sess: + with self.cached_session() as sess: one_hot_value = sess.run(one_hot_output) self.assertTrue(np.array_equal(one_hot_value, expected)) @@ -440,7 +440,7 @@ class FeatureColumnTest(test.TestCase): } one_hot_tensor = feature_column_ops.input_from_feature_columns( features, [one_hot]) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) sess.run(lookup_ops.tables_initializer()) self.assertAllEqual([[2., 6., 0.]], one_hot_tensor.eval()) @@ -451,7 +451,7 @@ class FeatureColumnTest(test.TestCase): features = {"ids": constant_op.constant([["marlo", "unknown", "omar"]])} one_hot_tensor = feature_column_ops.input_from_feature_columns( features, [one_hot]) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) sess.run(lookup_ops.tables_initializer()) self.assertAllEqual([[1., 1., 0.]], one_hot_tensor.eval()) @@ -603,7 +603,7 @@ class FeatureColumnTest(test.TestCase): real_valued_output = real_valued_column._to_dnn_input_layer( constant_op.constant(real_valued_input, dtype=dtypes.float32), output_rank=output_rank) - with self.test_session() as sess: + with self.cached_session() as sess: real_valued_eval = sess.run(real_valued_output) expected_shape = ( input_shape[:output_rank - 1] + @@ -797,7 +797,7 @@ class FeatureColumnTest(test.TestCase): sparse_column.insert_transformed_feature(features) sparse_output = features[sparse_column] expected_shape = [batch_size, 1] - with self.test_session() as sess: + with self.cached_session() as sess: sparse_result = sess.run(sparse_output) self.assertEquals(expected_shape, list(sparse_result.dense_shape)) @@ -1110,7 +1110,7 @@ class FeatureColumnTest(test.TestCase): ckpt_dir = tempfile.mkdtemp(prefix=ckpt_dir_prefix) checkpoint_path = os.path.join(ckpt_dir, "model.ckpt") - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) saved_embedding = embeddings.eval() save.save(sess, checkpoint_path) @@ -1131,7 +1131,7 @@ class FeatureColumnTest(test.TestCase): embedding_col_initialized: input_tensor }, [embedding_col_initialized]) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) loaded_embedding = pretrained_embeddings.eval() @@ -1176,7 +1176,7 @@ class FeatureColumnTest(test.TestCase): ckpt_dir = tempfile.mkdtemp(prefix=ckpt_dir_prefix) checkpoint_path = os.path.join(ckpt_dir, "model.ckpt") - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) sess.run(assign_op) saved_col_weights = col_weights[crossed_col][0].eval() @@ -1201,7 +1201,7 @@ class FeatureColumnTest(test.TestCase): }, [crossed_col_initialized], 1)) col_weights_from_ckpt = col_weights[crossed_col_initialized][0] - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) loaded_col_weights = col_weights_from_ckpt.eval() diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py index 52c9c4f3be..85af9de4e4 100644 --- a/tensorflow/contrib/layers/python/layers/layers_test.py +++ b/tensorflow/contrib/layers/python/layers/layers_test.py @@ -281,7 +281,7 @@ class BiasAddTest(test.TestCase): def testCreate(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height, width, 3)) output = _layers.bias_add(images) self.assertEqual(output.op.name, 'BiasAdd/BiasAdd') @@ -289,7 +289,7 @@ class BiasAddTest(test.TestCase): def testCreateWithActivation(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = _layers.bias_add(images, activation_fn=nn_ops.relu) self.assertEqual(output.op.name, 'BiasAdd/Relu') @@ -298,7 +298,7 @@ class BiasAddTest(test.TestCase): def testCreateDimensions(self): dims = (2, 3, 4) shape = [5, 2, 3, 4] - with self.test_session(): + with self.cached_session(): for d in dims: input_shape = shape[:d] inputs = random_ops.random_uniform(input_shape, seed=1) @@ -311,7 +311,7 @@ class BiasAddTest(test.TestCase): class ConvolutionTest(test.TestCase): def testInvalidShape(self): - with self.test_session(): + with self.cached_session(): images_2d = random_ops.random_uniform((5, 7, 9, 3), seed=1) with self.assertRaisesRegexp( ValueError, 'Convolution expects input with rank 5, got 4'): @@ -323,14 +323,14 @@ class ConvolutionTest(test.TestCase): def testInvalidDataFormat(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp(ValueError, 'data_format'): layers_lib.convolution2d(images, 32, 3, data_format='CHWN') def testCreateConv(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height, width, 4)).astype(np.float32) output = layers_lib.convolution2d(images, 32, [3, 3]) self.assertEqual(output.op.name, 'Conv/Relu') @@ -342,7 +342,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvNCHW(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, 4, height, width)).astype(np.float32) output = layers_lib.convolution2d(images, 32, [3, 3], data_format='NCHW') self.assertEqual(output.op.name, 'Conv/Relu') @@ -354,7 +354,7 @@ class ConvolutionTest(test.TestCase): def testCreateSquareConv(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.convolution2d(images, 32, 3) self.assertEqual(output.op.name, 'Conv/Relu') @@ -362,7 +362,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvWithTensorShape(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.convolution2d(images, 32, images.get_shape()[1:3]) self.assertEqual(output.op.name, 'Conv/Relu') @@ -370,7 +370,7 @@ class ConvolutionTest(test.TestCase): def testCreateFullyConv(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 32), seed=1) output = layers_lib.convolution2d( images, 64, images.get_shape()[1:3], padding='VALID') @@ -381,7 +381,7 @@ class ConvolutionTest(test.TestCase): def testFullyConvWithCustomGetter(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): called = [0] def custom_getter(getter, *args, **kwargs): @@ -395,7 +395,7 @@ class ConvolutionTest(test.TestCase): def testCreateVerticalConv(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 4), seed=1) output = layers_lib.convolution2d(images, 32, [3, 1]) self.assertEqual(output.op.name, 'Conv/Relu') @@ -407,7 +407,7 @@ class ConvolutionTest(test.TestCase): def testCreateHorizontalConv(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 4), seed=1) output = layers_lib.convolution2d(images, 32, [1, 3]) self.assertEqual(output.op.name, 'Conv/Relu') @@ -417,7 +417,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvWithStride(self): height, width = 6, 8 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.convolution2d(images, 32, [3, 3], stride=2) self.assertEqual(output.op.name, 'Conv/Relu') @@ -427,7 +427,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvCreatesWeightsAndBiasesVars(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) - with self.test_session(): + with self.cached_session(): self.assertFalse(variables.get_variables('conv1/weights')) self.assertFalse(variables.get_variables('conv1/biases')) layers_lib.convolution2d(images, 32, [3, 3], scope='conv1') @@ -436,7 +436,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvWithScope(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.convolution2d(images, 32, [3, 3], scope='conv1') self.assertEqual(output.op.name, 'conv1/Relu') @@ -453,14 +453,14 @@ class ConvolutionTest(test.TestCase): def testCreateConvWithoutActivation(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.convolution2d(images, 32, [3, 3], activation_fn=None) self.assertEqual(output.op.name, 'Conv/BiasAdd') def testCreateConvValid(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.convolution2d(images, 32, [3, 3], padding='VALID') self.assertListEqual(output.get_shape().as_list(), [5, 5, 7, 32]) @@ -468,7 +468,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvWithWD(self): height, width = 7, 9 weight_decay = 0.01 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform((5, height, width, 3), seed=1) regularizer = regularizers.l2_regularizer(weight_decay) layers_lib.convolution2d( @@ -481,7 +481,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvNoRegularizers(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) layers_lib.convolution2d(images, 32, [3, 3]) self.assertEqual( @@ -489,7 +489,7 @@ class ConvolutionTest(test.TestCase): def testReuseVars(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) layers_lib.convolution2d(images, 32, [3, 3], scope='conv1') self.assertEqual(len(variables.get_variables()), 2) @@ -498,7 +498,7 @@ class ConvolutionTest(test.TestCase): def testNonReuseVars(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) layers_lib.convolution2d(images, 32, [3, 3]) self.assertEqual(len(variables.get_variables()), 2) @@ -507,7 +507,7 @@ class ConvolutionTest(test.TestCase): def testReuseConvWithWD(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) weight_decay = regularizers.l2_regularizer(0.01) with arg_scope( @@ -523,7 +523,7 @@ class ConvolutionTest(test.TestCase): def testConvWithBatchNorm(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 32), seed=1) with arg_scope( [layers_lib.convolution2d], @@ -539,7 +539,7 @@ class ConvolutionTest(test.TestCase): def testReuseConvWithBatchNorm(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 32), seed=1) with arg_scope( [layers_lib.convolution2d], @@ -557,7 +557,7 @@ class ConvolutionTest(test.TestCase): def testCreateConvCreatesWeightsAndBiasesVarsWithRateTwo(self): height, width = 7, 9 images = random_ops.random_uniform((5, height, width, 3), seed=1) - with self.test_session(): + with self.cached_session(): self.assertFalse(variables.get_variables('conv1/weights')) self.assertFalse(variables.get_variables('conv1/biases')) layers_lib.convolution2d(images, 32, [3, 3], rate=2, scope='conv1') @@ -573,7 +573,7 @@ class ConvolutionTest(test.TestCase): output = layers_lib.convolution2d( images, num_filters, [3, 3], rate=2, padding='SAME') self.assertListEqual(list(output.get_shape().as_list()), expected_size) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -587,7 +587,7 @@ class ConvolutionTest(test.TestCase): output = layers_lib.convolution2d( images, num_filters, [3, 3], rate=2, padding='VALID') self.assertListEqual(list(output.get_shape().as_list()), expected_size) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -601,7 +601,7 @@ class ConvolutionTest(test.TestCase): output = layers_lib.convolution2d( images, num_filters, [3, 3], rate=[2, 3], padding='VALID') self.assertListEqual(list(output.get_shape().as_list()), expected_size) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEquals(output.op.name, 'Conv/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -612,7 +612,7 @@ class ConvolutionTest(test.TestCase): expected_size = [None, None, None, num_filters] expected_size_dynamic = [5, 7, 9, num_filters] - with self.test_session(): + with self.cached_session(): images = array_ops.placeholder(np.float32, [None, None, None, input_size[3]]) output = layers_lib.convolution2d( @@ -651,7 +651,7 @@ class ConvolutionTest(test.TestCase): expected_size = [None, None, None, num_filters] expected_size_dynamic = [5, 5, 7, num_filters] - with self.test_session(): + with self.cached_session(): images = array_ops.placeholder(np.float32, [None, None, None, input_size[3]]) output = layers_lib.convolution2d( @@ -670,7 +670,7 @@ class ConvolutionTest(test.TestCase): images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.convolution2d( images, num_filters, [3, 3], rate=2, padding='VALID', scope='conv7') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'conv7/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -688,7 +688,7 @@ class ConvolutionTest(test.TestCase): padding='VALID', activation_fn=None, scope='conv7') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'conv7/BiasAdd') self.assertListEqual(list(output.eval().shape), expected_size) @@ -712,7 +712,7 @@ class Convolution2dTransposeTests(test.TestCase): def testInvalidDataFormat(self): height, width = 7, 9 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) with self.assertRaisesRegexp( ValueError, 'data_format has to be either NCHW or NHWC.'): @@ -915,7 +915,7 @@ class Convolution2dTransposeTests(test.TestCase): images, num_filters, [3, 3], stride=1, padding='SAME') self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size) @@ -929,7 +929,7 @@ class Convolution2dTransposeTests(test.TestCase): images, num_filters, [3, 3], stride=1, padding='VALID') self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size) @@ -944,7 +944,7 @@ class Convolution2dTransposeTests(test.TestCase): self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.get_shape().as_list()), expected_size) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size) @@ -958,7 +958,7 @@ class Convolution2dTransposeTests(test.TestCase): images, num_filters, [2, 2], stride=[2, 2], padding='SAME') self.assertListEqual(list(output.get_shape().as_list()), expected_size) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -971,7 +971,7 @@ class Convolution2dTransposeTests(test.TestCase): images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='VALID') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -984,7 +984,7 @@ class Convolution2dTransposeTests(test.TestCase): images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='SAME') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -997,7 +997,7 @@ class Convolution2dTransposeTests(test.TestCase): images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 2], stride=[2, 2], padding='VALID') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -1010,7 +1010,7 @@ class Convolution2dTransposeTests(test.TestCase): images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 1], padding='VALID') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -1023,7 +1023,7 @@ class Convolution2dTransposeTests(test.TestCase): images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 4], padding='VALID') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -1036,7 +1036,7 @@ class Convolution2dTransposeTests(test.TestCase): images = random_ops.random_uniform(input_size, seed=1) output = layers_lib.conv2d_transpose( images, num_filters, [2, 4], stride=[2, 5], padding='VALID') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') self.assertListEqual(list(output.eval().shape), expected_size) @@ -1083,7 +1083,7 @@ class Convolution2dTransposeTests(test.TestCase): images, num_filters, [3, 3], stride=[2, 2], padding='VALID') self.assertListEqual(output.get_shape().as_list(), expected_size) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertEqual(output.op.name, 'Conv2d_transpose/Relu') eval_output = output.eval({images: np.zeros(input_size, np.float32)}) @@ -1095,7 +1095,7 @@ class Convolution2dTransposeTests(test.TestCase): expected_size = [None, None, None, num_filters] expected_size_dynamic = [5, 18, 22, num_filters] - with self.test_session(): + with self.cached_session(): images = array_ops.placeholder(np.float32, [None, None, None, input_size[3]]) output = layers_lib.conv2d_transpose( @@ -1116,7 +1116,7 @@ class Convolution2dTransposeTests(test.TestCase): images, num_filters, [3, 3], stride=2, padding='VALID', scope='conv7') self.assertEqual(output.op.name, 'conv7/Relu') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size) @@ -1135,7 +1135,7 @@ class Convolution2dTransposeTests(test.TestCase): scope='conv7') self.assertEqual(output.op.name, 'conv7/BiasAdd') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertListEqual(list(output.eval().shape), expected_size) @@ -1146,7 +1146,7 @@ class Convolution2dTransposeTests(test.TestCase): stride = 2 padding = 'VALID' - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform(input_size, seed=1) output_deconv = layers_lib.conv2d_transpose( images, @@ -1184,7 +1184,7 @@ class ConvolutionInPlaneTest(test.TestCase): activation_fn=None) init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(init_op) result = sess.run(horz_gradients) expected = np.zeros((1, 10, 9, 1)) @@ -1201,7 +1201,7 @@ class ConvolutionInPlaneTest(test.TestCase): activation_fn=None) init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(init_op) result = sess.run( horz_gradients, feed_dict={ @@ -1225,7 +1225,7 @@ class ConvolutionInPlaneTest(test.TestCase): activation_fn=None) init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(init_op) result = sess.run(horz_gradients) @@ -1245,7 +1245,7 @@ class ConvolutionInPlaneTest(test.TestCase): activation_fn=None) init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(init_op) result = sess.run(horz_gradients) @@ -1267,7 +1267,7 @@ class ConvolutionInPlaneTest(test.TestCase): activation_fn=None) init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(init_op) result = sess.run(horz_gradients) @@ -1283,7 +1283,7 @@ class ConvolutionInPlaneTest(test.TestCase): activation_fn=None) init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(init_op) result = sess.run(vert_gradients) expected = np.zeros((1, 9, 10, 1)) @@ -1306,7 +1306,7 @@ class ConvolutionInPlaneTest(test.TestCase): activation_fn=None) init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(init_op) result = sess.run(vert_gradients) @@ -1314,7 +1314,7 @@ class ConvolutionInPlaneTest(test.TestCase): def testConv1dShape(self): width = 7 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, width, 3), seed=1) output = layers_lib.convolution1d(images, 32, 3) self.assertEqual(output.op.name, 'Conv/Relu') @@ -1322,7 +1322,7 @@ class ConvolutionInPlaneTest(test.TestCase): def testConvInferSpatialDims(self): depth, height, width = 7, 9, 11 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, width, 4)).astype(np.float32) output = layers_lib.convolution(images, 32, [3]) self.assertListEqual(output.get_shape().as_list(), [5, width, 32]) @@ -1344,7 +1344,7 @@ class DenseToSparseTest(test.TestCase): sparse = _layers.dense_to_sparse(tensor) dense = sparse_ops.sparse_to_dense(sparse.indices, sparse.dense_shape, sparse.values) - with self.test_session() as sess: + with self.cached_session() as sess: constant = sess.run(dense) self.assertAllEqual(expected_constant, constant) @@ -1353,7 +1353,7 @@ class DropoutTest(test.TestCase): def testCreateDropout(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height, width, 3)) output = _layers.dropout(images) self.assertEqual(output.op.name, 'Dropout/dropout_1/mul') @@ -1362,7 +1362,7 @@ class DropoutTest(test.TestCase): def testCreateDropoutWithConstantTrue(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): is_training = constant_op.constant(True) images = random_ops.random_uniform((5, height, width, 3), seed=1) output = _layers.dropout(images, is_training=is_training) @@ -1370,7 +1370,7 @@ class DropoutTest(test.TestCase): def testCreateDropoutWithConstantFalse(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): is_training = constant_op.constant(False) images = random_ops.random_uniform((5, height, width, 3), seed=1) output = _layers.dropout(images, is_training=is_training) @@ -1378,7 +1378,7 @@ class DropoutTest(test.TestCase): def testCreateDropoutWithPlaceholder(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): is_training = array_ops.placeholder(dtype=dtypes.bool, shape=[]) images = random_ops.random_uniform((5, height, width, 3), seed=1) output = _layers.dropout(images, is_training=is_training) @@ -1387,7 +1387,7 @@ class DropoutTest(test.TestCase): def testCollectOutputs(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = _layers.dropout(images, outputs_collections='outputs') c_output = ops.get_collection('outputs')[0] @@ -1396,7 +1396,7 @@ class DropoutTest(test.TestCase): def testDropout(self): height, width = 10, 10 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') num_elem_initial = math_ops.reduce_mean(math_ops.to_float(images > 0)) @@ -1409,7 +1409,7 @@ class DropoutTest(test.TestCase): def testDropoutSeed(self): """Test that providing the same seed produces the same result.""" height, width = 10, 10 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') output1 = _layers.dropout(images, seed=1) @@ -1418,7 +1418,7 @@ class DropoutTest(test.TestCase): def testCreateDropoutNoTraining(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') num_elem_initial = math_ops.reduce_mean(math_ops.to_float(images > 0)) @@ -1431,7 +1431,7 @@ class DropoutTest(test.TestCase): def testCreateFCFollowByDropout(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') output = _layers.fully_connected(images, 50) @@ -1445,7 +1445,7 @@ class DropoutTest(test.TestCase): def testCreateFCWithDropout(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') output = _layers.fully_connected( @@ -1475,7 +1475,7 @@ class FlattenTest(test.TestCase): def testCollectOutputs(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height, width, 3)) output = _layers.flatten(images, outputs_collections='outputs') c_output = ops.get_collection('outputs')[0] @@ -1484,7 +1484,7 @@ class FlattenTest(test.TestCase): def testFlatten4D(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') output = _layers.flatten(images) @@ -1494,7 +1494,7 @@ class FlattenTest(test.TestCase): def testFlatten3D(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width), seed=1, name='images') output = _layers.flatten(images) @@ -1504,7 +1504,7 @@ class FlattenTest(test.TestCase): def testFlattenBatchSize(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') inputs = array_ops.placeholder(dtypes.int32, (None, height, width, 3)) @@ -1516,7 +1516,7 @@ class FlattenTest(test.TestCase): def testUnknownDims(self): height = width = depth = 3 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform( (5, height, width, depth), seed=1, name='images') inputs = array_ops.placeholder(dtypes.int32, (None, None, None, None)) @@ -1551,7 +1551,7 @@ class PartialFlattenTest(test.TestCase): flattened_t = _layers._inner_flatten(inputs, new_rank) static_shape = flattened_t.get_shape().as_list() self.assertEqual(static_shape, expected_new_shape) - with self.test_session() as sess: + with self.cached_session() as sess: flattened = sess.run(flattened_t) np.testing.assert_array_equal(expected_flattened, flattened) @@ -1571,7 +1571,7 @@ class PartialFlattenTest(test.TestCase): flattened_t = _layers._inner_flatten(inputs_t, new_rank) - with self.test_session() as sess: + with self.cached_session() as sess: flattened = sess.run(flattened_t) np.testing.assert_array_equal(expected_indices, flattened.indices) @@ -1641,7 +1641,7 @@ class FCTest(test.TestCase): def testCreateFCWithScope(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): inputs = random_ops.random_uniform((5, height * width * 3), seed=1) output = _layers.fully_connected(inputs, 32, scope='fc1') self.assertEqual(output.op.name, 'fc1/Relu') @@ -1659,7 +1659,7 @@ class FCTest(test.TestCase): def testCreateFcCreatesWeightsAndBiasesVars(self): height, width = 3, 3 inputs = random_ops.random_uniform((5, height * width * 3), seed=1) - with self.test_session(): + with self.cached_session(): self.assertFalse(variables.get_variables('fc1/weights')) self.assertFalse(variables.get_variables('fc1/biases')) _layers.fully_connected(inputs, 32, scope='fc1') @@ -1669,7 +1669,7 @@ class FCTest(test.TestCase): def testReuseVars(self): height, width = 3, 3 inputs = random_ops.random_uniform((5, height * width * 3), seed=1) - with self.test_session(): + with self.cached_session(): _layers.fully_connected(inputs, 32, scope='fc1') self.assertEqual(len(variables.get_variables('fc1')), 2) _layers.fully_connected(inputs, 32, scope='fc1', reuse=True) @@ -1678,7 +1678,7 @@ class FCTest(test.TestCase): def testNonReuseVars(self): height, width = 3, 3 inputs = random_ops.random_uniform((5, height * width * 3), seed=1) - with self.test_session(): + with self.cached_session(): _layers.fully_connected(inputs, 32) self.assertEqual(len(variables.get_variables('fully_connected')), 2) _layers.fully_connected(inputs, 32) @@ -1713,14 +1713,14 @@ class FCTest(test.TestCase): def testCreateFCWithoutActivation(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): inputs = random_ops.random_uniform((5, height * width * 3), seed=1) output = _layers.fully_connected(inputs, 32, activation_fn=None) self.assertEqual(output.op.name, 'fully_connected/BiasAdd') def testCreateFCWithWD(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: inputs = random_ops.random_uniform((5, height * width * 3), seed=1) weight_decay = regularizers.l2_regularizer(0.01) _layers.fully_connected(inputs, 32, weights_regularizer=weight_decay) @@ -1732,7 +1732,7 @@ class FCTest(test.TestCase): def testCreateFCWithBD(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: inputs = random_ops.random_uniform((5, height * width * 3), seed=1) bias_decay = regularizers.l2_regularizer(0.01) _layers.fully_connected(inputs, 32, biases_regularizer=bias_decay) @@ -1744,7 +1744,7 @@ class FCTest(test.TestCase): def testCreateNoRegularizers(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): inputs = random_ops.random_uniform((5, height * width * 3), seed=1) _layers.fully_connected(inputs, 32) self.assertEqual( @@ -1752,7 +1752,7 @@ class FCTest(test.TestCase): def testReuseFCWithWD(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): inputs = random_ops.random_uniform((5, height * width * 3), seed=1) weight_decay = regularizers.l2_regularizer(0.01) _layers.fully_connected( @@ -1768,7 +1768,7 @@ class FCTest(test.TestCase): def testFCWithBatchNorm(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height * width * 3), seed=1) with arg_scope( [_layers.fully_connected], @@ -1786,7 +1786,7 @@ class FCTest(test.TestCase): def testReuseFCWithBatchNorm(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height * width * 3), seed=1) with arg_scope( [_layers.fully_connected], @@ -1844,7 +1844,7 @@ class BatchNormTest(test.TestCase): if dtype is None: dtype = dtypes.float32 height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height, width, 3)).astype( dtype.as_numpy_dtype) output = _layers.batch_norm(images, fused=fused) @@ -1866,7 +1866,7 @@ class BatchNormTest(test.TestCase): def _testCreateOpBetaRegularizer(self, fused=True): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): reg = lambda x: 0.1 * math_ops.reduce_sum(x) images = np.random.uniform(size=(5, height, width, 3)).astype('f') _layers.batch_norm(images, param_regularizers={'beta': reg}, fused=fused) @@ -1883,7 +1883,7 @@ class BatchNormTest(test.TestCase): def _testCreateOpGammaRegularizer(self, fused=True): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): reg = lambda x: 0.1 * math_ops.reduce_sum(x) images = np.random.uniform(size=(5, height, width, 3)).astype('f') _layers.batch_norm( @@ -1901,7 +1901,7 @@ class BatchNormTest(test.TestCase): def testCreateVariables(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _layers.batch_norm(images, scale=True) beta = variables.get_variables_by_name('beta')[0] @@ -1915,7 +1915,7 @@ class BatchNormTest(test.TestCase): def testMovingAverageVariables(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _layers.batch_norm(images, scale=True) self.assertEqual(len(variables.get_model_variables()), 4) @@ -1926,7 +1926,7 @@ class BatchNormTest(test.TestCase): def testMovingAverageVariablesZeroDebias(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _layers.batch_norm( images, scale=True, zero_debias_moving_mean=True, fused=False) @@ -1943,7 +1943,7 @@ class BatchNormTest(test.TestCase): def testUpdatesCollection(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _layers.batch_norm(images, updates_collections='my_update_ops') update_layers = ops.get_collection('my_update_ops') @@ -1971,7 +1971,7 @@ class BatchNormTest(test.TestCase): def testReuseVariables(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _layers.batch_norm(images, scale=True, scope='bn') _layers.batch_norm(images, scale=True, scope='bn', reuse=True) @@ -1986,7 +1986,7 @@ class BatchNormTest(test.TestCase): def testReuseUpdateOps(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) with arg_scope([_layers.batch_norm], updates_collections='update_ops'): _layers.batch_norm(images, scope='bn') @@ -1996,7 +1996,7 @@ class BatchNormTest(test.TestCase): def testCreateMovingVars(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _ = _layers.batch_norm(images) moving_mean = variables.get_variables('BatchNorm/moving_mean') @@ -2029,7 +2029,7 @@ class BatchNormTest(test.TestCase): moving_variance = variables.get_variables_by_name('moving_variance')[0] biased = variables.get_variables_by_name('biased')[0] local_step = variables.get_variables_by_name('local_step')[0] - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) self.assertAllClose(local_step.eval(), 0) self.assertAllClose(moving_mean.eval(), [0] * channels) @@ -2213,7 +2213,7 @@ class BatchNormTest(test.TestCase): def _testEvalMovingVars(self, zero_debias_moving_mean=False): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: image_shape = (10, height, width, 3) image_values = np.random.rand(*image_shape) expected_mean = np.mean(image_values, axis=(0, 1, 2)) @@ -2264,7 +2264,7 @@ class BatchNormTest(test.TestCase): height, width = 3, 3 batch_size = 10 channels = 3 - with self.test_session() as sess: + with self.cached_session() as sess: image_shape = (batch_size, height, width, channels) image_values = np.random.rand(*image_shape) expected_mean = np.mean(image_values, axis=(0, 1, 2)) @@ -2435,7 +2435,7 @@ class BatchNormTest(test.TestCase): def testNoUpdatesWhenIsTrainingFalse(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: image_shape = (10, height, width, 3) image_values = np.random.rand(*image_shape) images = constant_op.constant( @@ -2460,7 +2460,7 @@ class BatchNormTest(test.TestCase): def testNoneUpdatesCollectionNoTraining(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: image_shape = (10, height, width, 3) image_values = np.random.rand(*image_shape) images = constant_op.constant( @@ -2647,7 +2647,7 @@ class BatchNormTest(test.TestCase): def testCustomInitializer(self): height, width = 3, 3 channels = 3 - with self.test_session() as sess: + with self.cached_session() as sess: images = (np.ones((5, height, width, channels)) * 9.0).astype('f') beta = init_ops.constant_initializer( (np.ones(channels) * 5.0).astype('f')) @@ -2728,7 +2728,7 @@ class BatchNormTest(test.TestCase): def testBatchNormBeta(self): # Test case for 11673 - with self.test_session() as sess: + with self.cached_session() as sess: a_32 = array_ops.placeholder(dtypes.float32, shape=(10, 10, 10, 10)) _layers.batch_norm( a_32, center=False, data_format='NCHW', zero_debias_moving_mean=True) @@ -2739,7 +2739,7 @@ class BatchNormTest(test.TestCase): def testVariablesAreFloat32(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width, 3), seed=1, dtype=dtypes.float16) _layers.batch_norm(images, scale=True) @@ -2824,7 +2824,7 @@ class LayerNormTest(test.TestCase): def testCreateOp(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height, width, 3)) output = _layers.layer_norm(images) self.assertTrue(output.op.name.startswith('LayerNorm/batchnorm')) @@ -2832,7 +2832,7 @@ class LayerNormTest(test.TestCase): def testCreateVariables(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _layers.layer_norm(images) beta = variables.get_variables_by_name('beta')[0] @@ -2842,7 +2842,7 @@ class LayerNormTest(test.TestCase): def testReuseVariables(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) _layers.layer_norm(images, scope='ln') _layers.layer_norm(images, scope='ln', reuse=True) @@ -2853,7 +2853,7 @@ class LayerNormTest(test.TestCase): def testReuseVars(self): height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: image_shape = (10, height, width, 3) image_values = np.random.rand(*image_shape) images = constant_op.constant( @@ -2940,7 +2940,7 @@ class GDNTest(test.TestCase): def _runGDN(self, x, shape, inverse, data_format): inputs = array_ops.placeholder(dtypes.float32, shape) outputs = _layers.gdn(inputs, inverse=inverse, data_format=data_format) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() y, = sess.run([outputs], {inputs: x}) return y @@ -3152,14 +3152,14 @@ class MaxPool3DTest(test.TestCase): class OneHotEncodingTest(test.TestCase): def testOneHotEncodingCreate(self): - with self.test_session(): + with self.cached_session(): labels = np.array([0, 1, 2]) output = _layers.one_hot_encoding(labels, num_classes=3) self.assertEqual(output.op.name, 'OneHotEncoding/one_hot') self.assertListEqual(output.get_shape().as_list(), [3, 3]) def testCollectOutputs(self): - with self.test_session(): + with self.cached_session(): labels = constant_op.constant([0, 1, 2]) output = _layers.one_hot_encoding( labels, num_classes=3, outputs_collections='outputs') @@ -3168,14 +3168,14 @@ class OneHotEncodingTest(test.TestCase): self.assertEqual(c_output, output) def testOneHotEncoding(self): - with self.test_session(): + with self.cached_session(): labels = constant_op.constant([0, 1, 2]) one_hot_labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) output = _layers.one_hot_encoding(labels, num_classes=3) self.assertAllClose(output.eval(), one_hot_labels.eval()) def testOneHotEncodingInt32(self): - with self.test_session(): + with self.cached_session(): labels = constant_op.constant([0, 1, 2], dtype=dtypes.int32) one_hot_labels = constant_op.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) output = _layers.one_hot_encoding(labels, num_classes=3) @@ -3186,7 +3186,7 @@ class RepeatTests(test.TestCase): def testRepeat(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height, width, 3)).astype(np.float32) output = _layers.repeat(images, 3, layers_lib.conv2d, 32, [3, 3]) self.assertEqual(output.op.name, 'Repeat/convolution2d_3/Relu') @@ -3194,7 +3194,7 @@ class RepeatTests(test.TestCase): def testRepeatWithScope(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') output = _layers.repeat( @@ -3207,7 +3207,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateConvInt32(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width, 3), seed=1, dtype=dtypes.int32, maxval=12345) with self.assertRaisesRegexp(TypeError, 'non-floating point type'): @@ -3215,7 +3215,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateConvFloat32(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width, 3), seed=1, dtype=dtypes.float32) output = layers_lib.separable_conv2d(images, 32, [3, 3], 2) @@ -3224,7 +3224,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateDepthwiseConv(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.separable_conv2d(images, None, [3, 3], 2) self.assertEqual(output.op.name, 'SeparableConv2d/Relu') @@ -3233,7 +3233,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateConvCreatesWeightsAndBiasesVars(self): height, width = 3, 3 images = random_ops.random_uniform((5, height, width, 3), seed=1) - with self.test_session(): + with self.cached_session(): self.assertFalse(variables.get_variables('conv1/depthwise_weights')) self.assertFalse(variables.get_variables('conv1/pointwise_weights')) self.assertFalse(variables.get_variables('conv1/biases')) @@ -3245,7 +3245,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateAtrousConvCreatesWeightsAndBiasesVars(self): height, width = 3, 3 images = random_ops.random_uniform((5, height, width, 3), seed=1) - with self.test_session(): + with self.cached_session(): self.assertFalse(variables.get_variables('conv1/depthwise_weights')) self.assertFalse(variables.get_variables('conv1/pointwise_weights')) self.assertFalse(variables.get_variables('conv1/biases')) @@ -3257,7 +3257,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateDepthwiseConvCreatesWeightsAndBiasesVars(self): height, width = 3, 3 images = random_ops.random_uniform((5, height, width, 3), seed=1) - with self.test_session(): + with self.cached_session(): self.assertFalse(variables.get_variables('conv1/depthwise_weights')) self.assertFalse(variables.get_variables('conv1/pointwise_weights')) self.assertFalse(variables.get_variables('conv1/biases')) @@ -3268,14 +3268,14 @@ class SeparableConv2dTest(test.TestCase): def testCreateConvWithScope(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.separable_conv2d(images, 32, [3, 3], 6, scope='conv1') self.assertEqual(output.op.name, 'conv1/Relu') def testCreateConvWithoutActivation(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.separable_conv2d( images, 32, [3, 3], 8, activation_fn=None) @@ -3283,7 +3283,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateConvValid(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.separable_conv2d( images, 32, [3, 3], 2, padding='VALID') @@ -3291,7 +3291,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateAtrousConvValid(self): height, width = 5, 5 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.separable_conv2d( images, 32, [3, 3], 2, padding='VALID', rate=2) @@ -3299,7 +3299,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateDepthwiseConvValid(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.separable_conv2d( images, None, [3, 3], 2, padding='VALID') @@ -3307,7 +3307,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateAtrousDepthwiseConvValid(self): height, width = 5, 5 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) output = layers_lib.separable_conv2d( images, None, [3, 3], 2, padding='VALID', rate=2) @@ -3316,7 +3316,7 @@ class SeparableConv2dTest(test.TestCase): def testCreateConvWithWeightDecay(self): random_seed.set_random_seed(0) height, width = 3, 3 - with self.test_session() as sess: + with self.cached_session() as sess: images = random_ops.random_uniform((5, height, width, 3), seed=1) regularizer = regularizers.l2_regularizer(0.01) layers_lib.separable_conv2d( @@ -3360,7 +3360,7 @@ class SeparableConv2dTest(test.TestCase): def testReuseConvWithWeightDecay(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform((5, height, width, 3), seed=1) regularizer = regularizers.l2_regularizer(0.01) layers_lib.separable_conv2d( @@ -3419,7 +3419,7 @@ class SeparableConv2dTest(test.TestCase): normalizer_params={}, scope='conv1') init_op = variables_lib.global_variables_initializer() - with self.test_session() as sess: + with self.cached_session() as sess: images = np.random.rand(5, height, width, 3) sess.run(init_op) sess.run(net, feed_dict={images_placeholder: images}) @@ -3440,7 +3440,7 @@ class SeparableConv2dTest(test.TestCase): def testSepConvNCHW(self): for num_filters, correct_output_filters in zip((None, 5), (6, 5)): - with self.test_session(): + with self.cached_session(): batch, height, width = 4, 10, 12 kernel_dim, stride = 3, 2 images = random_ops.random_uniform((batch, 3, height, width), seed=1) @@ -3462,7 +3462,7 @@ class ScaleGradientTests(test.TestCase): """Simple tests of the scale_gradient function.""" def testBasic(self): - with self.test_session(): + with self.cached_session(): x = np.array([42], np.float32) gradient_scale = np.array([2], np.float32) @@ -3513,7 +3513,7 @@ class SoftmaxTests(test.TestCase): exp_prediction = np.array([[self.low, self.high], [0.5, 0.5], [self.high, self.low]]) - with self.test_session() as sess: + with self.cached_session() as sess: prediction = sess.run(prediction) self.assertAllClose(exp_prediction, prediction) @@ -3529,7 +3529,7 @@ class SoftmaxTests(test.TestCase): exp_prediction[1, 1, 1] = self.low prediction = _layers.softmax(logits) - with self.test_session() as sess: + with self.cached_session() as sess: prediction = sess.run(prediction) self.assertAllClose(exp_prediction, prediction) @@ -3547,7 +3547,7 @@ class SoftmaxTests(test.TestCase): exp_prediction[1, 1, 1] = self.low prediction = _layers.softmax(logit_placeholder) - with self.test_session() as sess: + with self.cached_session() as sess: prediction = sess.run(prediction, feed_dict=feed_dict) self.assertAllClose(exp_prediction, prediction) @@ -3575,7 +3575,7 @@ class SpatialSoftmaxTests(test.TestCase): features = array_ops.placeholder(dtypes.float32, shape=batch_shape) np_features = np.zeros(batch_shape, dtype=np.float32) spatial_softmax = _layers.spatial_softmax(features) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) @@ -3586,7 +3586,7 @@ class SpatialSoftmaxTests(test.TestCase): features = array_ops.placeholder(dtypes.float32, shape=batch_shape) np_features = np.zeros(batch_shape, dtype=np.float32) spatial_softmax = _layers.spatial_softmax(features, data_format='NCHW') - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) @@ -3613,7 +3613,7 @@ class SpatialSoftmaxTests(test.TestCase): nchannels) # Make sure expected location keypoints matches actual location keypoints. - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) @@ -3637,7 +3637,7 @@ class SpatialSoftmaxTests(test.TestCase): nchannels) # Make sure expected location keypoints matches actual location keypoints. - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) @@ -3669,7 +3669,7 @@ class SpatialSoftmaxTests(test.TestCase): batch_size, nchannels) # Make sure expected location keypoints matches actual location keypoints. - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features1} tf_keypoints1 = sess.run(spatial_softmax, feed_dict) @@ -3696,7 +3696,7 @@ class SpatialSoftmaxTests(test.TestCase): nchannels) # Make sure expected location keypoints matches actual location keypoints. - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) @@ -3719,7 +3719,7 @@ class SpatialSoftmaxTests(test.TestCase): nchannels) # Make sure expected location keypoints matches actual location keypoints. - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} keypoints = sess.run(spatial_softmax, feed_dict) @@ -3731,7 +3731,7 @@ class SpatialSoftmaxTests(test.TestCase): spatial_softmax = _layers.spatial_softmax(features) net = _layers.fully_connected(spatial_softmax, 10) np_features = np.zeros(batch_shape, dtype=np.float32) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables_lib.global_variables_initializer()) feed_dict = {features: np_features} sess.run(net, feed_dict) @@ -3741,7 +3741,7 @@ class StackTests(test.TestCase): def testStackFullyConnected(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = np.random.uniform(size=(5, height * width * 3)) output = _layers.stack(images, _layers.fully_connected, [10, 20, 30]) self.assertEqual(output.op.name, 'Stack/fully_connected_3/Relu') @@ -3749,7 +3749,7 @@ class StackTests(test.TestCase): def testStackFullyConnectedFailOnReuse(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('test', reuse=True): images = np.random.uniform(size=(5, height * width * 3)) with self.assertRaises(ValueError): @@ -3757,7 +3757,7 @@ class StackTests(test.TestCase): def testStackRelu(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height * width * 3), seed=1, name='images') output = _layers.stack(images, layers_lib.relu, [10, 20, 30]) @@ -3766,7 +3766,7 @@ class StackTests(test.TestCase): def testStackElu(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height * width * 3), seed=1, name='images') output = _layers.stack(images, layers_lib.elu, [10, 20, 30]) @@ -3775,7 +3775,7 @@ class StackTests(test.TestCase): def testStackConvolution2d(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') output = _layers.stack( @@ -3788,7 +3788,7 @@ class StackTests(test.TestCase): def testStackWithScope(self): height, width = 3, 3 - with self.test_session(): + with self.cached_session(): images = random_ops.random_uniform( (5, height, width, 3), seed=1, name='images') output = _layers.stack( @@ -3817,7 +3817,7 @@ class UnitNormTests(test.TestCase): del shape[dim] expected = np.ones(shape) - with self.test_session(): + with self.cached_session(): actual = norms.eval() self.assertAllClose(expected, actual, 1e-4, 1e-4) @@ -3849,7 +3849,7 @@ class UnitNormTests(test.TestCase): norms = math_ops.sqrt( math_ops.reduce_sum(math_ops.square(output), reduction_indices=dim)) - with self.test_session(): + with self.cached_session(): actual = norms.eval({image: placeholder_value}) self.assertAllClose(expected, actual, 1e-4, 1e-4) @@ -3875,7 +3875,7 @@ class PoincareNormalizeTest(test.TestCase): x_np = np.random.random_sample(x_shape).astype(np.float32) for dim in range(len(x_shape)): y_np = self._PoincareNormalize(x_np, dim, epsilon) - with self.test_session(): + with self.cached_session(): x_tf = constant_op.constant(x_np, name='x') y_tf = _layers.poincare_normalize(x_tf, dim, epsilon) y_tf_eval = y_tf.eval() @@ -3893,7 +3893,7 @@ class PoincareNormalizeTest(test.TestCase): x_np = np.random.random_sample(x_shape).astype(np.float32) dim = [1, 2] y_np = self._PoincareNormalize(x_np, dim, epsilon) - with self.test_session(): + with self.cached_session(): x_tf = constant_op.constant(x_np, name='x') y_tf = _layers.poincare_normalize(x_tf, dim, epsilon) y_tf_eval = y_tf.eval() @@ -3908,7 +3908,7 @@ class PoincareNormalizeTest(test.TestCase): np.random.seed(1) x_np = np.random.random_sample(x_shape).astype(np.float64) for dim in range(len(x_shape)): - with self.test_session(): + with self.cached_session(): x_tf = constant_op.constant(x_np, name='x') y_tf = _layers.poincare_normalize(x_tf, dim) err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf, @@ -4117,7 +4117,7 @@ class LegacyFullyConnectedTest(test.TestCase): # Empty x is common if someone masks their input with tf.boolean_mask in # order to drop missing entries, and in a particular batch all entries are # missing. - with self.test_session(): + with self.cached_session(): x = np.array([]).reshape(0, 3) self.assertEqual(0, array_ops.size(x).eval()) y = _layers.legacy_fully_connected(x, 2, activation_fn=nn_ops.softmax) @@ -4131,7 +4131,7 @@ class LegacyFullyConnectedTest(test.TestCase): y = _layers.legacy_fully_connected(x, 1) # in the output we still only know the 2nd and 3rd dimensions statically. self.assertEqual(y.get_shape().as_list(), [None, 4, 1]) - with self.test_session() as sess: + with self.cached_session() as sess: variables_lib.global_variables_initializer().run() # we can feed in input with first dimension 2 shape_value = sess.run( @@ -4162,7 +4162,7 @@ class LegacyFullyConnectedTest(test.TestCase): self._unknown_dim_invalid_input(last_dim=None) def test_1d_invalid_input(self): - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(ValueError, 'rank of x must be at least 2 not: 1'): x = constant_op.constant([[]], shape=[0]) diff --git a/tensorflow/contrib/layers/python/layers/normalization_test.py b/tensorflow/contrib/layers/python/layers/normalization_test.py index 55272e5fd1..c8d3c91b10 100644 --- a/tensorflow/contrib/layers/python/layers/normalization_test.py +++ b/tensorflow/contrib/layers/python/layers/normalization_test.py @@ -106,7 +106,7 @@ class InstanceNormTest(test.TestCase): images = random_ops.random_uniform(image_shape, seed=1) output_train = normalization.instance_norm(images, scope='IN') output_eval = normalization.instance_norm(images, scope='IN', reuse=True) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) # output_train and output_eval should be the same. train_np, eval_np = sess.run([output_train, output_eval]) @@ -130,7 +130,7 @@ class InstanceNormTest(test.TestCase): inputs = random_ops.random_uniform(input_shape, seed=0) * sigma + mu output_op = normalization.instance_norm( inputs, center=False, scale=False, data_format=data_format) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) outputs = sess.run(output_op) # Make sure that there are no NaNs @@ -287,7 +287,7 @@ class GroupNormTest(test.TestCase): output_train = normalization.group_norm(images, groups=2, scope='IN') output_eval = normalization.group_norm(images, groups=2, scope='IN', reuse=True) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) # output_train and output_eval should be the same. train_np, eval_np = sess.run([output_train, output_eval]) @@ -349,7 +349,7 @@ class GroupNormTest(test.TestCase): channels_axis=channels_axis, reduction_axes=reduction_axes, mean_close_to_zero=mean_close_to_zero) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) outputs = sess.run(output_op) # Make sure that there are no NaNs diff --git a/tensorflow/contrib/layers/python/layers/optimizers_test.py b/tensorflow/contrib/layers/python/layers/optimizers_test.py index 0f037e24ad..29dede2a49 100644 --- a/tensorflow/contrib/layers/python/layers/optimizers_test.py +++ b/tensorflow/contrib/layers/python/layers/optimizers_test.py @@ -165,7 +165,7 @@ class OptimizersTest(test.TestCase): def testGradientNoise(self): random_seed.set_random_seed(42) - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -182,7 +182,7 @@ class OptimizersTest(test.TestCase): def testGradientNoiseWithClipping(self): random_seed.set_random_seed(42) - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -198,7 +198,7 @@ class OptimizersTest(test.TestCase): self.assertEqual(global_step_value, 1) def testGradientClip(self): - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -213,7 +213,7 @@ class OptimizersTest(test.TestCase): self.assertEqual(global_step_value, 1) def testAdaptiveGradientClip(self): - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() clip_gradients = optimizers_lib.adaptive_clipping_fn() train = optimizers_lib.optimize_loss( @@ -234,7 +234,7 @@ class OptimizersTest(test.TestCase): self.assertEqual(2, var_count) def testGradientMultiply(self): - with self.test_session() as session: + with self.cached_session() as session: x, var, loss, global_step = _setup_model() train = optimizers_lib.optimize_loss( loss, @@ -433,7 +433,7 @@ class OptimizersTest(test.TestCase): class AdaptiveClipping(test.TestCase): def testAverages(self): - with self.test_session() as session: + with self.cached_session() as session: scale = 2. grad = array_ops.ones([3, 4]) * scale log_norm = np.log(np.sqrt(scale**2 * grad.get_shape().num_elements())) @@ -463,7 +463,7 @@ class AdaptiveClipping(test.TestCase): self.assertAlmostEqual(float(sq_mean), log_norm**2, places=4) def testClip(self): - with self.test_session() as session: + with self.cached_session() as session: spike = 1000. multiplier = array_ops.placeholder(dtypes.float32, [], "multiplier") step = array_ops.placeholder(dtypes.int32, [], "step") diff --git a/tensorflow/contrib/layers/python/layers/regularizers_test.py b/tensorflow/contrib/layers/python/layers/regularizers_test.py index 07191eeda7..51faba30c7 100644 --- a/tensorflow/contrib/layers/python/layers/regularizers_test.py +++ b/tensorflow/contrib/layers/python/layers/regularizers_test.py @@ -71,7 +71,7 @@ class RegularizerTest(test.TestCase): with self.assertRaises(ValueError): regularizers.l1_l2_regularizer(0.5, 0) - with self.test_session(): + with self.cached_session(): shape = [5, 5, 5] num_elem = 5 * 5 * 5 tensor = constant_op.constant(1.0, shape=shape) @@ -84,7 +84,7 @@ class RegularizerTest(test.TestCase): num_elem = 5 * 5 * 5 tensor = constant_op.constant(1.0, shape=shape) loss = regularizers.l1_l2_regularizer(0.0, 1.0)(tensor) - with self.test_session(): + with self.cached_session(): self.assertEquals(loss.op.name, 'l1_l2_regularizer') self.assertAlmostEqual(loss.eval(), num_elem / 2, 5) @@ -93,7 +93,7 @@ class RegularizerTest(test.TestCase): num_elem = 5 * 5 * 5 tensor = constant_op.constant(1.0, shape=shape) loss = regularizers.l1_l2_regularizer(1.0, 0.0)(tensor) - with self.test_session(): + with self.cached_session(): self.assertEquals(loss.op.name, 'l1_l2_regularizer') self.assertAlmostEqual(loss.eval(), num_elem, 5) @@ -104,7 +104,7 @@ class RegularizerTest(test.TestCase): self.assertEquals(loss, None) def testL1L2RegularizerWithScope(self): - with self.test_session(): + with self.cached_session(): shape = [5, 5, 5] num_elem = 5 * 5 * 5 tensor = constant_op.constant(1.0, shape=shape) @@ -142,7 +142,7 @@ class RegularizerTest(test.TestCase): array_weights_list = [[1.5], [2, 3, 4.2], [10, 42, 666.6]] tensor_weights_list = [constant_op.constant(x) for x in array_weights_list] expected = sum([2 * x for l in array_weights_list for x in l]) - with self.test_session(): + with self.cached_session(): result = regularizers.apply_regularization(dummy_regularizer, tensor_weights_list) self.assertAllClose(expected, result.eval()) @@ -151,7 +151,7 @@ class RegularizerTest(test.TestCase): regularizer = regularizers.l2_regularizer(0.0) array_weights_list = [[1.5], [2, 3, 4.2], [10, 42, 666.6]] tensor_weights_list = [constant_op.constant(x) for x in array_weights_list] - with self.test_session(): + with self.cached_session(): result = regularizers.apply_regularization(regularizer, tensor_weights_list) self.assertAllClose(0.0, result.eval()) @@ -161,7 +161,7 @@ class RegularizerTest(test.TestCase): tensor_weights_list = [ constant_op.constant(x) for x in [[1.5], [2, 3, 4.2], [10, 42, 666.6]] ] - with self.test_session(): + with self.cached_session(): with self.assertRaises(ValueError): regularizers.apply_regularization(non_scalar_regularizer, tensor_weights_list) diff --git a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py index c34b5a8017..2c7463acc0 100644 --- a/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py +++ b/tensorflow/contrib/layers/python/layers/rev_block_lib_test.py @@ -58,7 +58,7 @@ class RevBlockTest(test.TestCase): y1, y2 = block.forward(x1, x2) x1_inv, x2_inv = block.backward(y1, y2) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) x1, x2, x1_inv, x2_inv = sess.run([x1, x2, x1_inv, x2_inv]) @@ -81,7 +81,7 @@ class RevBlockTest(test.TestCase): x1, x2 = block.backward(y1, y2) y1_inv, y2_inv = block.forward(x1, x2) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) y1, y2, y1_inv, y2_inv = sess.run([y1, y2, y1_inv, y2_inv]) @@ -151,7 +151,7 @@ class RevBlockTest(test.TestCase): grads_rev = gradients_impl.gradients(loss_rev, wrt) grads = gradients_impl.gradients(loss, wrt) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) y_val, yd_val, gd_val, g_val = sess.run([y, y_rev, grads_rev, grads]) self.assertAllClose(y_val, yd_val) @@ -286,7 +286,7 @@ class RecomputeTest(test.TestCase): for out, scope_vars in outputs_and_vars: all_grads.append(gradients_impl.gradients(out, scope_vars)) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) outputs = list(zip(*outputs_and_vars))[0] outs, all_grads_val = sess.run([outputs, all_grads]) @@ -389,7 +389,7 @@ class RecomputeTest(test.TestCase): layer_list.append(math_ops.sqrt(concat_n_wrap(*layer_list))) grads = gradients_impl.gradients(layer_list[-1], layer_list[0]) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(grads) def testErrorOnClosedOverTensor(self): diff --git a/tensorflow/contrib/layers/python/layers/summaries_test.py b/tensorflow/contrib/layers/python/layers/summaries_test.py index a1ef06feec..2ec2af9d44 100644 --- a/tensorflow/contrib/layers/python/layers/summaries_test.py +++ b/tensorflow/contrib/layers/python/layers/summaries_test.py @@ -29,19 +29,19 @@ from tensorflow.python.platform import test class SummariesTest(test.TestCase): def test_summarize_scalar_tensor(self): - with self.test_session(): + with self.cached_session(): scalar_var = variables.Variable(1) summary_op = summaries_lib.summarize_tensor(scalar_var) self.assertEquals(summary_op.op.type, 'ScalarSummary') def test_summarize_multidim_tensor(self): - with self.test_session(): + with self.cached_session(): tensor_var = variables.Variable([1, 2, 3]) summary_op = summaries_lib.summarize_tensor(tensor_var) self.assertEquals(summary_op.op.type, 'HistogramSummary') def test_summarize_activation(self): - with self.test_session(): + with self.cached_session(): var = variables.Variable(1) op = array_ops.identity(var, name='SummaryTest') summary_op = summaries_lib.summarize_activation(op) @@ -52,7 +52,7 @@ class SummariesTest(test.TestCase): self.assertIn(u'SummaryTest/activation', names) def test_summarize_activation_relu(self): - with self.test_session(): + with self.cached_session(): var = variables.Variable(1) op = nn_ops.relu(var, name='SummaryTest') summary_op = summaries_lib.summarize_activation(op) @@ -64,7 +64,7 @@ class SummariesTest(test.TestCase): self.assertIn(u'SummaryTest/activation', names) def test_summarize_activation_relu6(self): - with self.test_session(): + with self.cached_session(): var = variables.Variable(1) op = nn_ops.relu6(var, name='SummaryTest') summary_op = summaries_lib.summarize_activation(op) @@ -77,7 +77,7 @@ class SummariesTest(test.TestCase): self.assertIn(u'SummaryTest/activation', names) def test_summarize_collection_regex(self): - with self.test_session(): + with self.cached_session(): var = variables.Variable(1) array_ops.identity(var, name='Test1') ops.add_to_collection('foo', array_ops.identity(var, name='Test2')) diff --git a/tensorflow/contrib/layers/python/layers/utils_test.py b/tensorflow/contrib/layers/python/layers/utils_test.py index a9bd89532a..34f63f5d86 100644 --- a/tensorflow/contrib/layers/python/layers/utils_test.py +++ b/tensorflow/contrib/layers/python/layers/utils_test.py @@ -42,7 +42,7 @@ class ConstantValueTest(test.TestCase): c = constant_op.constant(v) value = utils.constant_value(c) self.assertEqual(value, v) - with self.test_session(): + with self.cached_session(): self.assertEqual(c.eval(), v) def test_variable(self): @@ -60,7 +60,7 @@ class ConstantValueTest(test.TestCase): x = array_ops.identity(p) value = utils.constant_value(p) self.assertEqual(value, None) - with self.test_session(): + with self.cached_session(): self.assertEqual(x.eval(feed_dict={p: v}), v) @@ -80,7 +80,7 @@ class StaticCondTest(test.TestCase): expected = lambda v: b'fn1' if v else b'fn2' for v in [True, False, 1, 0]: o = utils.static_cond(v, fn1, fn2) - with self.test_session(): + with self.cached_session(): self.assertEqual(o.eval(), expected(v)) def test_variable(self): @@ -89,7 +89,7 @@ class StaticCondTest(test.TestCase): expected = lambda v: b'fn1' if v else b'fn2' for v in [True, False, 1, 0]: o = utils.static_cond(v, fn1, fn2) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) self.assertEqual(o.eval(), expected(v)) @@ -99,7 +99,7 @@ class StaticCondTest(test.TestCase): expected = lambda v: -1 if v else -2 for v in [True, False, 1, 0]: o = utils.static_cond(v, fn1, fn2) - with self.test_session(): + with self.cached_session(): self.assertEqual(o.eval(), expected(v)) @@ -119,7 +119,7 @@ class SmartCondStaticTest(test.TestCase): expected = lambda v: b'fn1' if v else b'fn2' for v in [True, False, 1, 0]: o = utils.smart_cond(constant_op.constant(v), fn1, fn2) - with self.test_session(): + with self.cached_session(): self.assertEqual(o.eval(), expected(v)) def test_variable(self): @@ -128,7 +128,7 @@ class SmartCondStaticTest(test.TestCase): expected = lambda v: b'fn1' if v else b'fn2' for v in [True, False, 1, 0]: o = utils.smart_cond(constant_op.constant(v), fn1, fn2) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) self.assertEqual(o.eval(), expected(v)) @@ -138,7 +138,7 @@ class SmartCondStaticTest(test.TestCase): expected = lambda v: -1 if v else -2 for v in [True, False, 1, 0]: o = utils.smart_cond(constant_op.constant(v), fn1, fn2) - with self.test_session(): + with self.cached_session(): self.assertEqual(o.eval(), expected(v)) @@ -151,7 +151,7 @@ class SmartCondDynamicTest(test.TestCase): p = array_ops.placeholder(dtypes.bool, []) for v in [True, False, 1, 0]: o = utils.smart_cond(p, fn1, fn2) - with self.test_session(): + with self.cached_session(): self.assertEqual(o.eval(feed_dict={p: v}), expected(v)) def test_constant(self): @@ -161,7 +161,7 @@ class SmartCondDynamicTest(test.TestCase): p = array_ops.placeholder(dtypes.bool, []) for v in [True, False, 1, 0]: o = utils.smart_cond(p, fn1, fn2) - with self.test_session(): + with self.cached_session(): self.assertEqual(o.eval(feed_dict={p: v}), expected(v)) def test_variable(self): @@ -171,7 +171,7 @@ class SmartCondDynamicTest(test.TestCase): p = array_ops.placeholder(dtypes.bool, []) for v in [True, False, 1, 0]: o = utils.smart_cond(p, fn1, fn2) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(variables.global_variables_initializer()) self.assertEqual(o.eval(feed_dict={p: v}), expected(v)) @@ -182,7 +182,7 @@ class SmartCondDynamicTest(test.TestCase): p = array_ops.placeholder(dtypes.bool, []) for v in [True, False, 1, 0]: o = utils.smart_cond(p, fn1, fn2) - with self.test_session(): + with self.cached_session(): self.assertEqual(o.eval(feed_dict={p: v}), expected(v)) |