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
s920243400
PaddleDetection
提交
650f7791
P
PaddleDetection
项目概览
s920243400
/
PaddleDetection
与 Fork 源项目一致
Fork自
PaddlePaddle / PaddleDetection
通知
2
Star
0
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
PaddleDetection
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
650f7791
编写于
2月 01, 2017
作者:
H
helinwang
提交者:
GitHub
2月 01, 2017
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #1243 from Haichao-Zhang/updated_comments_for_gru_and_lstm_group
updated comments for gru_group and lstm_group in networks.py
上级
786faabf
e1d074ab
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
7 addition
and
7 deletion
+7
-7
python/paddle/trainer_config_helpers/networks.py
python/paddle/trainer_config_helpers/networks.py
+7
-7
未找到文件。
python/paddle/trainer_config_helpers/networks.py
浏览文件 @
650f7791
...
...
@@ -737,12 +737,12 @@ def lstmemory_group(input,
lstm_layer_attr
=
None
,
get_output_layer_attr
=
None
):
"""
lstm_group is a recurrent layer group version Long Short Term Memory. It
lstm_group is a recurrent layer group version
of
Long Short Term Memory. It
does exactly the same calculation as the lstmemory layer (see lstmemory in
layers.py for the maths) does. A promising benefit is that LSTM memory
cell states, or hidden states in every time step are accessible to
for
the
cell states, or hidden states in every time step are accessible to the
user. This is especially useful in attention model. If you do not need to
access t
o t
he internal states of the lstm, but merely use its outputs,
access the internal states of the lstm, but merely use its outputs,
it is recommended to use the lstmemory, which is relatively faster than
lstmemory_group.
...
...
@@ -878,11 +878,11 @@ def gru_group(input,
gate_act
=
None
,
gru_layer_attr
=
None
):
"""
gru_group is a recurrent layer group version Gated Recurrent Unit. It
gru_group is a recurrent layer group version
of
Gated Recurrent Unit. It
does exactly the same calculation as the grumemory layer does. A promising
benefit is that gru hidden s
ates are accessible to for
the user. This is
especially useful in attention model. If you do not need to access
to
any internal state, but merely use the outputs of a GRU, it is recomm
a
nded
benefit is that gru hidden s
tates are accessible to
the user. This is
especially useful in attention model. If you do not need to access
any internal state, but merely use the outputs of a GRU, it is recomm
e
nded
to use the grumemory, which is relatively faster.
Please see grumemory in layers.py for more detail about the maths.
...
...
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