From: Ran <1012869439@qq.com>
Date: Fri, 19 Apr 2019 05:10:34 +0000 (-0700)
Subject: update documentation of PairwiseDistance#19241 (#19412)
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~141
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=940caed0d47aa7001308e7f64fd5000d06abd618;p=platform%2Fupstream%2Fpytorch.git

update documentation of PairwiseDistance#19241 (#19412)

Summary:
Fix the documentation of PairwiseDistance [#19241](https://github.com/pytorch/pytorch/issues/19241)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19412

Differential Revision: D14998271

Pulled By: soumith

fbshipit-source-id: bcb2aa46d3b3102c4480f2d24072a5e14b049888
---

diff --git a/torch/nn/modules/distance.py b/torch/nn/modules/distance.py
index f1e8722..9218992 100644
--- a/torch/nn/modules/distance.py
+++ b/torch/nn/modules/distance.py
@@ -7,24 +7,19 @@ from ..._jit_internal import weak_module, weak_script_method
 class PairwiseDistance(Module):
     r"""
     Computes the batchwise pairwise distance between vectors :math:`v_1`, :math:`v_2` using the p-norm:
-
     .. math ::
         \Vert x \Vert _p = \left( \sum_{i=1}^n  \vert x_i \vert ^ p \right) ^ {1/p}
-
     Args:
         p (real): the norm degree. Default: 2
         eps (float, optional): Small value to avoid division by zero.
             Default: 1e-6
-        keepdim (bool, optional): Determines whether or not to keep the batch dimension.
+        keepdim (bool, optional): Determines whether or not to keep the vector dimension.
             Default: False
-
     Shape:
         - Input1: :math:`(N, D)` where `D = vector dimension`
         - Input2: :math:`(N, D)`, same shape as the Input1
-        - Output: :math:`(N)`. If :attr:`keepdim` is ``False``, then :math:`(N, 1)`.
-
+        - Output: :math:`(N)`. If :attr:`keepdim` is ``True``, then :math:`(N, 1)`.
     Examples::
-
         >>> pdist = nn.PairwiseDistance(p=2)
         >>> input1 = torch.randn(100, 128)
         >>> input2 = torch.randn(100, 128)
@@ -46,22 +41,17 @@ class PairwiseDistance(Module):
 @weak_module
 class CosineSimilarity(Module):
     r"""Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along dim.
-
     .. math ::
         \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-
     Args:
         dim (int, optional): Dimension where cosine similarity is computed. Default: 1
         eps (float, optional): Small value to avoid division by zero.
             Default: 1e-8
-
     Shape:
         - Input1: :math:`(\ast_1, D, \ast_2)` where D is at position `dim`
         - Input2: :math:`(\ast_1, D, \ast_2)`, same shape as the Input1
         - Output: :math:`(\ast_1, \ast_2)`
-
     Examples::
-
         >>> input1 = torch.randn(100, 128)
         >>> input2 = torch.randn(100, 128)
         >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6)