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
PaddlePaddle
Paddle
提交
55b96287
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
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
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
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
55b96287
编写于
2月 23, 2017
作者:
Q
qiaolongfei
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
support rnn
上级
ac712688
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
79 addition
and
8 deletion
+79
-8
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+79
-8
未找到文件。
python/paddle/v2/layer.py
浏览文件 @
55b96287
...
...
@@ -73,6 +73,7 @@ from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config
as
__parse__
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
import
activation
import
data_type
__all__
=
[
...
...
@@ -97,10 +98,11 @@ def parse_network(*outputs):
class
Layer
(
object
):
def
__init__
(
self
,
name
,
parent_layers
):
def
__init__
(
self
,
name
,
parent_layers
,
step_input
=
None
):
assert
isinstance
(
parent_layers
,
dict
)
assert
isinstance
(
name
,
basestring
)
self
.
name
=
name
self
.
step_input
=
step_input
self
.
__parent_layers__
=
parent_layers
def
to_proto
(
self
,
context
):
...
...
@@ -116,8 +118,14 @@ class Layer(object):
else
:
v1_layer
=
map
(
lambda
x
:
x
.
to_proto
(
context
=
context
),
self
.
__parent_layers__
[
layer_name
])
if
layer_name
==
"input"
and
self
.
step_input
is
not
None
:
v1_layer
.
insert
(
0
,
self
.
step_input
)
kwargs
[
layer_name
]
=
v1_layer
# memory may have the same name with some layer
if
isinstance
(
self
,
MemoryV2
):
return
self
.
to_proto_impl
(
**
kwargs
)
if
self
.
name
not
in
context
:
context
[
self
.
name
]
=
self
.
to_proto_impl
(
**
kwargs
)
return
context
[
self
.
name
]
...
...
@@ -133,7 +141,7 @@ def __convert_to_v2__(method_name, name_prefix, parent_names):
wrapper
=
None
class
V2LayerImpl
(
Layer
):
def
__init__
(
self
,
name
=
None
,
**
kwargs
):
def
__init__
(
self
,
name
=
None
,
step_input
=
None
,
**
kwargs
):
parent_layers
=
dict
()
other_kwargs
=
dict
()
for
pname
in
parent_names
:
...
...
@@ -143,7 +151,7 @@ def __convert_to_v2__(method_name, name_prefix, parent_names):
if
key
not
in
parent_names
:
other_kwargs
[
key
]
=
kwargs
[
key
]
super
(
V2LayerImpl
,
self
).
__init__
(
name
,
parent_layers
)
super
(
V2LayerImpl
,
self
).
__init__
(
name
,
parent_layers
,
step_input
)
self
.
__other_kwargs__
=
other_kwargs
if
wrapper
is
not
None
:
...
...
@@ -186,6 +194,22 @@ class DataLayerV2(Layer):
return
getattr
(
conf_helps
,
self
.
__method_name__
)(
name
=
self
.
name
,
**
args
)
class
MemoryV2
(
Layer
):
def
__init__
(
self
,
name
,
size
,
**
kwargs
):
self
.
name
=
name
self
.
size
=
size
self
.
__kwargs__
=
kwargs
super
(
MemoryV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__kwargs__
:
args
[
each
]
=
self
.
__kwargs__
[
each
]
return
conf_helps
.
memory
(
name
=
self
.
name
,
size
=
self
.
size
,
**
args
)
data
=
DataLayerV2
fc
=
__convert_to_v2__
(
'fc_layer'
,
name_prefix
=
'fc'
,
parent_names
=
[
'input'
])
max_id
=
__convert_to_v2__
(
...
...
@@ -198,6 +222,13 @@ cross_entropy_cost = __convert_to_v2__(
'cross_entropy'
,
name_prefix
=
'cross_entropy'
,
parent_names
=
[
'input'
,
'label'
])
embedding
=
__convert_to_v2__
(
'embedding_layer'
,
name_prefix
=
'embedding'
,
parent_names
=
[
'input'
])
last_seq
=
__convert_to_v2__
(
'last_seq'
,
name_prefix
=
'last_seq'
,
parent_names
=
[
'input'
])
recurrent_group
=
__convert_to_v2__
(
'recurrent_group'
,
name_prefix
=
'recurrent_layer'
,
parent_names
=
[
'input'
])
memory
=
MemoryV2
if
__name__
==
'__main__'
:
pixel
=
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
...
...
@@ -208,8 +239,48 @@ if __name__ == '__main__':
cost1
=
classification_cost
(
input
=
inference
,
label
=
label
)
cost2
=
cross_entropy_cost
(
input
=
inference
,
label
=
label
)
print
parse_network
(
cost1
)
print
parse_network
(
cost2
)
print
parse_network
(
cost1
,
cost2
)
print
parse_network
(
cost2
)
print
parse_network
(
inference
,
maxid
)
mem
=
memory
(
name
=
"rnn_state"
,
size
=
10
)
# print parse_network(cost1)
# print parse_network(cost2)
# print parse_network(cost1, cost2)
# print parse_network(cost2)
# print parse_network(inference, maxid)
dict_dim
=
10
word_dim
=
8
hidden_dim
=
8
label_dim
=
3
def
step
(
y
):
mem
=
conf_helps
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
conf_helps
.
fc_layer
(
input
=
[
y
,
mem
],
size
=
hidden_dim
,
act
=
activation
.
Tanh
(),
bias_attr
=
True
,
name
=
"rnn_state"
)
return
out
def
test
():
data1
=
conf_helps
.
data_layer
(
name
=
"word"
,
size
=
dict_dim
)
embd
=
conf_helps
.
embedding_layer
(
input
=
data1
,
size
=
word_dim
)
conf_helps
.
recurrent_group
(
name
=
"rnn"
,
step
=
step
,
input
=
embd
)
# print __parse__(test)
# yyyyyyyy
def
new_step
(
y
):
mem
=
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
fc
(
input
=
[
mem
],
step_input
=
y
,
size
=
hidden_dim
,
act
=
activation
.
Tanh
(),
bias_attr
=
True
,
name
=
"rnn_state"
)
return
out
.
to_proto
(
dict
())
data1
=
data
(
name
=
"word"
,
type
=
data_type
.
integer_value
(
dict_dim
))
embd
=
embedding
(
input
=
data1
,
size
=
word_dim
)
aaa
=
recurrent_group
(
name
=
"rnn"
,
step
=
new_step
,
input
=
embd
)
print
parse_network
(
aaa
)
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