diff options
Diffstat (limited to 'tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py')
| -rw-r--r-- | tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py | 23 |
1 files changed, 12 insertions, 11 deletions
diff --git a/tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py b/tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py index 02d2524b66..c0de42b15b 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py +++ b/tensorflow/contrib/timeseries/python/timeseries/math_utils_test.py @@ -55,7 +55,7 @@ class MathUtilsTest(test.TestCase): running_sum = running_sum + current_contribution # pylint: enable=g-no-augmented-assignment transition_power = numpy.dot(transition, transition_power) - with self.test_session(): + with self.cached_session(): self.assertAllClose(result, math_utils.power_sums_tensor( array_size, transition, addition).eval()) @@ -66,7 +66,7 @@ class MathUtilsTest(test.TestCase): result = [] for i in range(powers.shape[0]): result.append(numpy.linalg.matrix_power(matrix, powers[i])) - with self.test_session(): + with self.cached_session(): self.assertAllClose(result, math_utils.matrix_to_powers(matrix, powers).eval(), rtol=1e-5, @@ -78,7 +78,7 @@ class MathUtilsTest(test.TestCase): result = [] for i in range(batch.shape[0]): result.append(numpy.linalg.matrix_power(batch[i], powers[i])) - with self.test_session(): + with self.cached_session(): # TODO(allenl): Numerical errors seem to be creeping in. Maybe it can be # made slightly more stable? self.assertAllClose(result, @@ -91,7 +91,7 @@ class MathUtilsTest(test.TestCase): left_transpose = numpy.transpose(left, [0, 2, 1]) right = numpy.random.normal(size=[2, 3]).astype(numpy.float32) expected_result = numpy.dot(left, right) - with self.test_session(): + with self.cached_session(): self.assertAllClose(expected_result, math_utils.batch_times_matrix( left, right).eval()) @@ -114,7 +114,7 @@ class MathUtilsTest(test.TestCase): right_transpose = numpy.transpose(right, [0, 2, 1]) expected_result = numpy.transpose(numpy.dot(right_transpose, left.T), [0, 2, 1]) - with self.test_session(): + with self.cached_session(): self.assertAllClose(expected_result, math_utils.matrix_times_batch( left, right).eval()) @@ -132,7 +132,7 @@ class MathUtilsTest(test.TestCase): adj_x=True, adj_y=True).eval()) def test_make_diagonal_undefined_shapes(self): - with self.test_session(): + with self.cached_session(): completely_undefined = array_ops.placeholder(dtype=dtypes.float32) partly_undefined = array_ops.placeholder( shape=[None, None], dtype=dtypes.float32) @@ -152,7 +152,7 @@ class MathUtilsTest(test.TestCase): [5., 6.]]})) def test_make_diagonal_mostly_defined_shapes(self): - with self.test_session(): + with self.cached_session(): mostly_defined = array_ops.placeholder( shape=[None, 2], dtype=dtypes.float32) blocked = math_utils.block_diagonal([[[2.]], @@ -192,7 +192,7 @@ class TestMakeToeplitzMatrix(test.TestCase): def _test_make_toeplitz_matrix(self, inputs, output_expected): output_tf = math_utils.make_toeplitz_matrix(inputs) - with self.test_session() as sess: + with self.cached_session() as sess: output_tf_np = sess.run(output_tf) self.assertAllClose(output_tf_np, output_expected) @@ -201,13 +201,13 @@ class TestMakeCovarianceMatrix(test.TestCase): def test_zero_size_matrix(self): raw = numpy.zeros([0, 0]) - with self.test_session(): + with self.cached_session(): constructed = math_utils.sign_magnitude_positive_definite(raw=raw).eval() self.assertEqual((0, 0), constructed.shape) def test_sign_magnitude_positive_definite(self): for dtype in [dtypes.float32, dtypes.float64]: - with self.test_session(): + with self.cached_session(): matrix_tensor = math_utils.sign_magnitude_positive_definite( raw=constant_op.constant([[-1., -2.], [3., 4.]], dtype=dtype), off_diagonal_scale=constant_op.constant(-1., dtype=dtype), @@ -230,7 +230,8 @@ class TestLookupTable(test.TestCase): name="test_lookup") def stack_tensor(base_tensor): return array_ops.stack([base_tensor + 1, base_tensor + 2]) - with self.test_session() as session: + + with self.cached_session() as session: ((float_output, double_output), int_output) = session.run( hash_table.lookup([2, 1, 0])) def expected_output_before_insert(base_tensor): |