Update docstring for SVD to emphasize that output argument order differs from numpy.linalg.svd.

Change: 144375998

1 files changed, 7 insertions, 1 deletions

diff --git a/tensorflow/python/ops/linalg_ops.py b/tensorflow/python/ops/linalg_ops.py
index d331017f95..595c645cbb 100644
--- a/tensorflow/python/ops/linalg_ops.py
+++ b/tensorflow/python/ops/linalg_ops.py
@@ -264,6 +264,12 @@ def svd(tensor, full_matrices=False, compute_uv=True, name=None):
     v: Left singular vectors. If `full_matrices` is `False` (default) then
       shape is `[..., N, P]`. If `full_matrices` is `True` then shape is
       `[..., N, N]`. Not returned if `compute_uv` is `False`.
+
+  @compatibility(numpy)
+  Mostly equivalent to numpy.linalg.svd, except that the order of output
+  arguments here is `s`, `u`, `v` when `compute_uv` is `True`, as opposed to
+  `u`, `s`, `v` for numpy.linalg.svd.
+  @end_compatibility
   """
   # pylint: disable=protected-access
   s, u, v = gen_linalg_ops._svd(
@@ -324,7 +330,7 @@ def norm(tensor, ord='euclidean', axis=None, keep_dims=False, name=None):
     ValueError: If `ord` or `axis` is invalid.
 
   @compatibility(numpy)
-  Mostly equivalent to np.linalg.norm.
+  Mostly equivalent to numpy.linalg.norm.
   Not supported: ord <= 0, 2-norm for matrices, nuclear norm.
   Other differences:
     a) If axis is `None`, treats the the flattened `tensor` as a vector