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
wzpei9
Paddle
提交
863cd9c7
P
Paddle
项目概览
wzpei9
/
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
1
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
1
Issue
1
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
体验新版 GitCode,发现更多精彩内容 >>
提交
863cd9c7
编写于
2月 06, 2018
作者:
W
wanghaoshuang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add comments to explain the empty result
上级
c2eb213c
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
20 addition
and
19 deletion
+20
-19
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+20
-19
未找到文件。
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
863cd9c7
...
...
@@ -410,12 +410,12 @@ def dynamic_lstmp(input,
"""
**Dynamic LSTMP Layer**
LSTMP (LSTM with recurrent projection) layer has a separate projection
layer after the LSTM layer, projecting the original hidden state to a
lower-dimensional one, which is proposed to reduce the number of total
parameters and furthermore computational complexity for the LSTM,
espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
LSTMP (LSTM with recurrent projection) layer has a separate projection
layer after the LSTM layer, projecting the original hidden state to a
lower-dimensional one, which is proposed to reduce the number of total
parameters and furthermore computational complexity for the LSTM,
espeacially for the case that the size of output units is relative
large (https://research.google.com/pubs/archive/43905.pdf).
The formula is as follows:
...
...
@@ -441,27 +441,27 @@ def dynamic_lstmp(input,
the matrix of weights from the input gate to the input).
* :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight
\
matrices for peephole connections. In our implementation,
\
we use vectors to reprenset these diagonal weight matrices.
we use vectors to reprenset these diagonal weight matrices.
* :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate
\
bias vector).
bias vector).
* :math:`\sigma`: The activation, such as logistic sigmoid function.
* :math:`i, f, o` and :math:`c`: The input gate, forget gate, output
\
gate, and cell activation vectors, respectively, all of which have
\
the same size as the cell output activation vector :math:`h`.
the same size as the cell output activation vector :math:`h`.
* :math:`h`: The hidden state.
* :math:`r`: The recurrent projection of the hidden state.
* :math:`r`: The recurrent projection of the hidden state.
* :math:`
\\
tilde{c_t}`: The candidate hidden state, whose
\
computation is based on the current input and previous hidden state.
* :math:`\odot`: The element-wise product of the vectors.
* :math:`\odot`: The element-wise product of the vectors.
* :math:`act_g` and :math:`act_h`: The cell input and cell output
\
activation functions and `tanh` is usually used for them.
activation functions and `tanh` is usually used for them.
* :math:`\overline{act_h}`: The activation function for the projection
\
output, usually using `identity` or same as :math:`act_h`.
Set `use_peepholes` to `False` to disable peephole connection. The formula
is omitted here, please refer to the paper
http://www.bioinf.jku.at/publications/older/2604.pdf for details.
Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
operations on the input :math:`x_{t}` are NOT included in this operator.
Users can choose to use fully-connected layer before LSTMP layer.
...
...
@@ -479,8 +479,8 @@ def dynamic_lstmp(input,
- Hidden-hidden weight = {:math:`W_{ch}, W_{ih},
\
W_{fh}, W_{oh}`}.
- The shape of hidden-hidden weight is (P x 4D),
where P is the projection size and D the hidden
- The shape of hidden-hidden weight is (P x 4D),
where P is the projection size and D the hidden
size.
- Projection weight = {:math:`W_{rh}`}.
- The shape of projection weight is (D x P).
...
...
@@ -525,9 +525,9 @@ def dynamic_lstmp(input,
hidden_dim, proj_dim = 512, 256
fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
act=None, bias_attr=None)
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4,
proj_size=proj_dim,
proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
size=hidden_dim * 4,
proj_size=proj_dim,
use_peepholes=False,
is_reverse=True,
cell_activation="tanh",
...
...
@@ -2525,7 +2525,8 @@ def ctc_greedy_decoder(input, blank, name=None):
interval [0, num_classes + 1).
Returns:
Variable: CTC greedy decode result.
Variable: CTC greedy decode result. If all the sequences in result were
empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1].
Examples:
.. code-block:: python
...
...
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