From 515e44e5f5fb7b74c334573f2b3f135fa99d5aba Mon Sep 17 00:00:00 2001
From: ying <lcy.seso@gmail.com>
Date: Sat, 23 Dec 2017 19:51:12 +0800
Subject: [PATCH] fix doc.

---
 paddle/operators/transpose_op.cc | 25 +++++++++++++------------
 paddle/operators/unpool_op.cc    |  5 ++---
 2 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/paddle/operators/transpose_op.cc b/paddle/operators/transpose_op.cc
index 0109b8bc5c3..f18be384345 100644
--- a/paddle/operators/transpose_op.cc
+++ b/paddle/operators/transpose_op.cc
@@ -70,18 +70,19 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
 Transpose Operator.
 
 The input tensor will be permuted according to the axis values given.
-The op functions similar to how numpy.transpose works in python.
-For example:
- >> input = numpy.arange(6).reshape((2,3))
- >> input
- array([[0, 1, 2],
-        [3, 4, 5]])
- >> axis = [1, 0]
- >> output = input.transpose(axis)
- >> output
- array([[0, 3],
-        [1, 4],
-		[2, 5]])
+The op functions is similar to how numpy.transpose works in python.
+
+For example: input = numpy.arange(6).reshape((2,3))
+the input is:
+array([[0, 1, 2],
+      [3, 4, 5]])
+given axis is: [1, 0]
+
+output = input.transpose(axis)
+then the output is:
+array([[0, 3],
+       [1, 4],
+       [2, 5]])
 So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
 the output tensor shape will be (N, H, W, C)
 
diff --git a/paddle/operators/unpool_op.cc b/paddle/operators/unpool_op.cc
index 4ec8efd180d..1b682d5c72b 100644
--- a/paddle/operators/unpool_op.cc
+++ b/paddle/operators/unpool_op.cc
@@ -53,9 +53,8 @@ class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
         "(string), unpooling type, can be \"max\" for max-unpooling ")
         .InEnum({"max"});
     AddComment(R"DOC(
-"Input shape: $(N, C_{in}, H_{in}, W_{in})$, 
-Output shape: $(N, C_{out}, H_{out}, W_{out})$
-Where
+Input shape is: $(N, C_{in}, H_{in}, W_{in})$, Output shape is:
+$(N, C_{out}, H_{out}, W_{out})$, where
 $$
 H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\
 W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1]
-- 
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