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
DMLL
Paddle
提交
55b96287
P
Paddle
项目概览
DMLL
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
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
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
体验新版 GitCode,发现更多精彩内容 >>
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