diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/norm_op_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/norm_op_test.py | 16 |
1 files changed, 7 insertions, 9 deletions
diff --git a/tensorflow/python/kernel_tests/norm_op_test.py b/tensorflow/python/kernel_tests/norm_op_test.py index d85512fae6..3f71b326a2 100644 --- a/tensorflow/python/kernel_tests/norm_op_test.py +++ b/tensorflow/python/kernel_tests/norm_op_test.py @@ -37,17 +37,17 @@ class NormOpTest(test_lib.TestCase): def testBadOrder(self): matrix = [[0., 1.], [2., 3.]] - for ord_ in "foo", -7, -1.1, 0: + for ord_ in "fro", -7, -1.1, 0: with self.assertRaisesRegexp(ValueError, "'ord' must be a supported vector norm"): - linalg_ops.norm(matrix, ord="fro") + linalg_ops.norm(matrix, ord=ord_) - for ord_ in "foo", -7, -1.1, 0: + for ord_ in "fro", -7, -1.1, 0: with self.assertRaisesRegexp(ValueError, "'ord' must be a supported vector norm"): linalg_ops.norm(matrix, ord=ord_, axis=-1) - for ord_ in 1.1, 2: + for ord_ in "foo", -7, -1.1, 1.1: with self.assertRaisesRegexp(ValueError, "'ord' must be a supported matrix norm"): linalg_ops.norm(matrix, ord=ord_, axis=[-2, -1]) @@ -69,14 +69,14 @@ def _GetNormOpTest(dtype_, shape_, ord_, axis_, keep_dims_, use_static_shape_): if use_static_shape_: tf_matrix = constant_op.constant(matrix) tf_norm = linalg_ops.norm( - tf_matrix, ord=ord_, axis=axis_, keep_dims=keep_dims_) + tf_matrix, ord=ord_, axis=axis_, keepdims=keep_dims_) tf_norm_val = sess.run(tf_norm) else: tf_matrix = array_ops.placeholder(dtype_) tf_norm = linalg_ops.norm( - tf_matrix, ord=ord_, axis=axis_, keep_dims=keep_dims_) + tf_matrix, ord=ord_, axis=axis_, keepdims=keep_dims_) tf_norm_val = sess.run(tf_norm, feed_dict={tf_matrix: matrix}) - self.assertAllClose(np_norm, tf_norm_val) + self.assertAllClose(np_norm, tf_norm_val, rtol=1e-5, atol=1e-5) def Test(self): is_matrix_norm = (isinstance(axis_, tuple) or @@ -85,8 +85,6 @@ def _GetNormOpTest(dtype_, shape_, ord_, axis_, keep_dims_, use_static_shape_): if ((not is_matrix_norm and ord_ == "fro") or (is_matrix_norm and is_fancy_p_norm)): self.skipTest("Not supported by neither numpy.linalg.norm nor tf.norm") - if is_matrix_norm and ord_ == 2: - self.skipTest("Not supported by tf.norm") if ord_ == 'euclidean' or (axis_ is None and len(shape) > 2): self.skipTest("Not supported by numpy.linalg.norm") matrix = np.random.randn(*shape_).astype(dtype_) |