From 4a533f6c8b4758b91122e445fb0d91709f062b26 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 2 Nov 2016 14:24:29 -0800 Subject: Update generated Python Op docs. Change: 138001884 --- .../g3doc/api_docs/python/functions_and_classes/shard3/tf.svd.md | 8 ++++---- tensorflow/g3doc/api_docs/python/math_ops.md | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.svd.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.svd.md index a11df39a13..b985bd7e58 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.svd.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.svd.md @@ -1,4 +1,4 @@ -### `tf.svd(tensor, compute_uv=True, full_matrices=False, name=None)` {#svd} +### `tf.svd(tensor, full_matrices=False, compute_uv=True, name=None)` {#svd} Computes the singular value decompositions of one or more matrices. @@ -20,12 +20,12 @@ s = svd(a, compute_uv=False) * `matrix`: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and `N`. -* `compute_uv`: If `True` then left and right singular vectors will be - computed and returned in `u` and `v`, respectively. Otherwise, only the - singular values will be computed, which can be significantly faster. * `full_matrices`: If true, compute full-sized `u` and `v`. If false (the default), compute only the leading `P` singular vectors. Ignored if `compute_uv` is `False`. +* `compute_uv`: If `True` then left and right singular vectors will be + computed and returned in `u` and `v`, respectively. Otherwise, only the + singular values will be computed, which can be significantly faster. * `name`: string, optional name of the operation. ##### Returns: diff --git a/tensorflow/g3doc/api_docs/python/math_ops.md b/tensorflow/g3doc/api_docs/python/math_ops.md index 71e6b9a26f..bc40bfa404 100644 --- a/tensorflow/g3doc/api_docs/python/math_ops.md +++ b/tensorflow/g3doc/api_docs/python/math_ops.md @@ -1832,7 +1832,7 @@ Computes the eigenvalues of one or more self-adjoint matrices. - - - -### `tf.svd(tensor, compute_uv=True, full_matrices=False, name=None)` {#svd} +### `tf.svd(tensor, full_matrices=False, compute_uv=True, name=None)` {#svd} Computes the singular value decompositions of one or more matrices. @@ -1854,12 +1854,12 @@ s = svd(a, compute_uv=False) * `matrix`: `Tensor` of shape `[..., M, N]`. Let `P` be the minimum of `M` and `N`. -* `compute_uv`: If `True` then left and right singular vectors will be - computed and returned in `u` and `v`, respectively. Otherwise, only the - singular values will be computed, which can be significantly faster. * `full_matrices`: If true, compute full-sized `u` and `v`. If false (the default), compute only the leading `P` singular vectors. Ignored if `compute_uv` is `False`. +* `compute_uv`: If `True` then left and right singular vectors will be + computed and returned in `u` and `v`, respectively. Otherwise, only the + singular values will be computed, which can be significantly faster. * `name`: string, optional name of the operation. ##### Returns: -- cgit v1.2.3