Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
BaiXuePrincess
Paddle
提交
81d24295
P
Paddle
项目概览
BaiXuePrincess
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
81d24295
编写于
1月 06, 2018
作者:
T
Travis CI
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Deploy to GitHub Pages:
0b6c5e6d
上级
f015d512
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
6 addition
and
6 deletion
+6
-6
develop/doc/api/v2/fluid/layers.html
develop/doc/api/v2/fluid/layers.html
+3
-3
develop/doc_cn/api/v2/fluid/layers.html
develop/doc_cn/api/v2/fluid/layers.html
+3
-3
未找到文件。
develop/doc/api/v2/fluid/layers.html
浏览文件 @
81d24295
...
...
@@ -259,14 +259,14 @@ into a 2-dimensional matrix. The parameter
is flattened: the first
<cite>
num_flatten_dims
</cite>
dimensions will be flatten to form the first
dimension of the final matrix (height of the
matrix), and the rest
<cite>
rank(X) - num_
col
_dims
</cite>
matrix), and the rest
<cite>
rank(X) - num_
flatten
_dims
</cite>
dimensions are flattened to form the second
dimension of the final matrix (width of the matrix).
For example, suppose
<cite>
X
</cite>
is a 6-dimensional tensor
with a shape [2, 3, 4, 5, 6], and
<cite>
x_num_col
_dims
</cite>
= 3. Then, the flattened matrix
<cite>
num_flatten
_dims
</cite>
= 3. Then, the flattened matrix
will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
By default,
<cite>
x_num_col
_dims
</cite>
is set to 1.
</li>
By default,
<cite>
num_flatten
_dims
</cite>
is set to 1.
</li>
<li><strong>
param_attr
</strong>
(
<em>
ParamAttr|list
</em>
)
–
The parameter attribute for learnable
parameters/weights of the fully connected
layer.
</li>
...
...
develop/doc_cn/api/v2/fluid/layers.html
浏览文件 @
81d24295
...
...
@@ -272,14 +272,14 @@ into a 2-dimensional matrix. The parameter
is flattened: the first
<cite>
num_flatten_dims
</cite>
dimensions will be flatten to form the first
dimension of the final matrix (height of the
matrix), and the rest
<cite>
rank(X) - num_
col
_dims
</cite>
matrix), and the rest
<cite>
rank(X) - num_
flatten
_dims
</cite>
dimensions are flattened to form the second
dimension of the final matrix (width of the matrix).
For example, suppose
<cite>
X
</cite>
is a 6-dimensional tensor
with a shape [2, 3, 4, 5, 6], and
<cite>
x_num_col
_dims
</cite>
= 3. Then, the flattened matrix
<cite>
num_flatten
_dims
</cite>
= 3. Then, the flattened matrix
will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
By default,
<cite>
x_num_col
_dims
</cite>
is set to 1.
</li>
By default,
<cite>
num_flatten
_dims
</cite>
is set to 1.
</li>
<li><strong>
param_attr
</strong>
(
<em>
ParamAttr|list
</em>
)
–
The parameter attribute for learnable
parameters/weights of the fully connected
layer.
</li>
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录