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1d2025c9
编写于
3月 03, 2017
作者:
H
hedaoyuan
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Use the sequence_conv_pool define inside the networks.py
上级
d194ce73
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
59 addition
and
138 deletion
+59
-138
demo/sentiment/train_v2.py
demo/sentiment/train_v2.py
+57
-138
python/paddle/v2/config_base.py
python/paddle/v2/config_base.py
+2
-0
未找到文件。
demo/sentiment/train_v2.py
浏览文件 @
1d2025c9
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
sys
from
os.path
import
join
as
join_path
import
paddle.trainer_config_helpers.attrs
as
attrs
from
paddle.trainer_config_helpers.poolings
import
MaxPooling
import
paddle.v2.layer
as
layer
import
paddle.v2.activation
as
activation
import
paddle.v2.data_type
as
data_type
import
paddle.v2.dataset.imdb
as
imdb
import
paddle.v2
as
paddle
def
sequence_conv_pool
(
input
,
input_size
,
context_len
,
hidden_size
,
name
=
None
,
context_start
=
None
,
pool_type
=
None
,
context_proj_layer_name
=
None
,
context_proj_param_attr
=
False
,
fc_layer_name
=
None
,
fc_param_attr
=
None
,
fc_bias_attr
=
None
,
fc_act
=
None
,
pool_bias_attr
=
None
,
fc_attr
=
None
,
context_attr
=
None
,
pool_attr
=
None
):
"""
Text convolution pooling layers helper.
Text input => Context Projection => FC Layer => Pooling => Output.
:param name: name of output layer(pooling layer name)
:type name: basestring
:param input: name of input layer
:type input: LayerOutput
:param context_len: context projection length. See
context_projection's document.
:type context_len: int
:param hidden_size: FC Layer size.
:type hidden_size: int
:param context_start: context projection length. See
context_projection's context_start.
:type context_start: int or None
:param pool_type: pooling layer type. See pooling_layer's document.
:type pool_type: BasePoolingType.
:param context_proj_layer_name: context projection layer name.
None if user don't care.
:type context_proj_layer_name: basestring
:param context_proj_param_attr: context projection parameter attribute.
None if user don't care.
:type context_proj_param_attr: ParameterAttribute or None.
:param fc_layer_name: fc layer name. None if user don't care.
:type fc_layer_name: basestring
:param fc_param_attr: fc layer parameter attribute. None if user don't care.
:type fc_param_attr: ParameterAttribute or None
:param fc_bias_attr: fc bias parameter attribute. False if no bias,
None if user don't care.
:type fc_bias_attr: ParameterAttribute or None
:param fc_act: fc layer activation type. None means tanh
:type fc_act: BaseActivation
:param pool_bias_attr: pooling layer bias attr. None if don't care.
False if no bias.
:type pool_bias_attr: ParameterAttribute or None.
:param fc_attr: fc layer extra attribute.
:type fc_attr: ExtraLayerAttribute
:param context_attr: context projection layer extra attribute.
:type context_attr: ExtraLayerAttribute
:param pool_attr: pooling layer extra attribute.
:type pool_attr: ExtraLayerAttribute
:return: output layer name.
:rtype: LayerOutput
"""
# Set Default Value to param
context_proj_layer_name
=
"%s_conv_proj"
%
name
\
if
context_proj_layer_name
is
None
else
context_proj_layer_name
with
layer
.
mixed
(
name
=
context_proj_layer_name
,
size
=
input_size
*
context_len
,
act
=
activation
.
Linear
(),
layer_attr
=
context_attr
)
as
m
:
m
+=
layer
.
context_projection
(
input
=
input
,
context_len
=
context_len
,
context_start
=
context_start
,
padding_attr
=
context_proj_param_attr
)
fc_layer_name
=
"%s_conv_fc"
%
name
\
if
fc_layer_name
is
None
else
fc_layer_name
fl
=
layer
.
fc
(
name
=
fc_layer_name
,
input
=
m
,
size
=
hidden_size
,
act
=
fc_act
,
layer_attr
=
fc_attr
,
param_attr
=
fc_param_attr
,
bias_attr
=
fc_bias_attr
)
return
layer
.
pooling
(
name
=
name
,
input
=
fl
,
pooling_type
=
pool_type
,
bias_attr
=
pool_bias_attr
,
layer_attr
=
pool_attr
)
def
convolution_net
(
input_dim
,
class_dim
=
2
,
emb_dim
=
128
,
hid_dim
=
128
,
is_predict
=
False
):
data
=
layer
.
data
(
"word"
,
data_type
.
integer_value_sequence
(
input_dim
))
emb
=
layer
.
embedding
(
input
=
data
,
size
=
emb_dim
)
conv_3
=
sequence_conv_pool
(
input
=
emb
,
input_size
=
emb_dim
,
context_len
=
3
,
hidden_size
=
hid_dim
)
conv_4
=
sequence_conv_pool
(
input
=
emb
,
input_size
=
emb_dim
,
context_len
=
4
,
hidden_size
=
hid_dim
)
output
=
layer
.
fc
(
input
=
[
conv_3
,
conv_4
],
size
=
class_dim
,
act
=
activation
.
Softmax
())
lbl
=
layer
.
data
(
"label"
,
data_type
.
integer_value
(
2
))
cost
=
layer
.
classification_cost
(
input
=
output
,
label
=
lbl
)
data
=
paddle
.
layer
.
data
(
"word"
,
paddle
.
data_type
.
integer_value_sequence
(
input_dim
))
emb
=
paddle
.
layer
.
embedding
(
input
=
data
,
size
=
emb_dim
)
conv_3
=
paddle
.
networks
.
sequence_conv_pool
(
input
=
emb
,
context_len
=
3
,
hidden_size
=
hid_dim
)
conv_4
=
paddle
.
networks
.
sequence_conv_pool
(
input
=
emb
,
context_len
=
4
,
hidden_size
=
hid_dim
)
output
=
paddle
.
layer
.
fc
(
input
=
[
conv_3
,
conv_4
],
size
=
class_dim
,
act
=
paddle
.
activation
.
Softmax
())
lbl
=
paddle
.
layer
.
data
(
"label"
,
paddle
.
data_type
.
integer_value
(
2
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
output
,
label
=
lbl
)
return
cost
...
...
@@ -152,24 +66,28 @@ def stacked_lstm_net(input_dim,
lstm_para_attr
=
attrs
.
ParameterAttribute
(
initial_std
=
0.
,
learning_rate
=
1.
)
para_attr
=
[
fc_para_attr
,
lstm_para_attr
]
bias_attr
=
attrs
.
ParameterAttribute
(
initial_std
=
0.
,
l2_rate
=
0.
)
relu
=
activation
.
Relu
()
linear
=
activation
.
Linear
()
data
=
layer
.
data
(
"word"
,
data_type
.
integer_value_sequence
(
input_dim
))
emb
=
layer
.
embedding
(
input
=
data
,
size
=
emb_dim
)
fc1
=
layer
.
fc
(
input
=
emb
,
size
=
hid_dim
,
act
=
linear
,
bias_attr
=
bias_attr
)
lstm1
=
layer
.
lstmemory
(
relu
=
paddle
.
activation
.
Relu
()
linear
=
paddle
.
activation
.
Linear
()
data
=
paddle
.
layer
.
data
(
"word"
,
paddle
.
data_type
.
integer_value_sequence
(
input_dim
))
emb
=
paddle
.
layer
.
embedding
(
input
=
data
,
size
=
emb_dim
)
fc1
=
paddle
.
layer
.
fc
(
input
=
emb
,
size
=
hid_dim
,
act
=
linear
,
bias_attr
=
bias_attr
)
lstm1
=
paddle
.
layer
.
lstmemory
(
input
=
fc1
,
act
=
relu
,
bias_attr
=
bias_attr
,
layer_attr
=
layer_attr
)
inputs
=
[
fc1
,
lstm1
]
for
i
in
range
(
2
,
stacked_num
+
1
):
fc
=
layer
.
fc
(
input
=
inputs
,
size
=
hid_dim
,
act
=
linear
,
param_attr
=
para_attr
,
bias_attr
=
bias_attr
)
lstm
=
layer
.
lstmemory
(
fc
=
paddle
.
layer
.
fc
(
input
=
inputs
,
size
=
hid_dim
,
act
=
linear
,
param_attr
=
para_attr
,
bias_attr
=
bias_attr
)
lstm
=
paddle
.
layer
.
lstmemory
(
input
=
fc
,
reverse
=
(
i
%
2
)
==
0
,
act
=
relu
,
...
...
@@ -177,16 +95,16 @@ def stacked_lstm_net(input_dim,
layer_attr
=
layer_attr
)
inputs
=
[
fc
,
lstm
]
fc_last
=
layer
.
pooling
(
input
=
inputs
[
0
],
pooling_type
=
MaxPooling
())
lstm_last
=
layer
.
pooling
(
input
=
inputs
[
1
],
pooling_type
=
MaxPooling
())
output
=
layer
.
fc
(
input
=
[
fc_last
,
lstm_last
],
size
=
class_dim
,
act
=
activation
.
Softmax
(),
bias_attr
=
bias_attr
,
param_attr
=
para_attr
)
fc_last
=
paddle
.
layer
.
pooling
(
input
=
inputs
[
0
],
pooling_type
=
MaxPooling
())
lstm_last
=
paddle
.
layer
.
pooling
(
input
=
inputs
[
1
],
pooling_type
=
MaxPooling
())
output
=
paddle
.
layer
.
fc
(
input
=
[
fc_last
,
lstm_last
],
size
=
class_dim
,
act
=
paddle
.
activation
.
Softmax
(),
bias_attr
=
bias_attr
,
param_attr
=
para_attr
)
lbl
=
layer
.
data
(
"label"
,
data_type
.
integer_value
(
2
))
cost
=
layer
.
classification_cost
(
input
=
output
,
label
=
lbl
)
lbl
=
paddle
.
layer
.
data
(
"label"
,
paddle
.
data_type
.
integer_value
(
2
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
output
,
label
=
lbl
)
return
cost
...
...
@@ -196,7 +114,7 @@ if __name__ == '__main__':
# network config
print
'load dictionary...'
word_dict
=
imdb
.
word_dict
()
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
dict_dim
=
len
(
word_dict
)
class_dim
=
2
...
...
@@ -226,7 +144,8 @@ if __name__ == '__main__':
if
isinstance
(
event
,
paddle
.
event
.
EndPass
):
result
=
trainer
.
test
(
reader
=
paddle
.
reader
.
batched
(
lambda
:
imdb
.
test
(
word_dict
),
batch_size
=
128
),
lambda
:
paddle
.
dataset
.
imdb
.
test
(
word_dict
),
batch_size
=
128
),
reader_dict
=
{
'word'
:
0
,
'label'
:
1
})
print
"
\n
Test with Pass %d, %s"
%
(
event
.
pass_id
,
result
.
metrics
)
...
...
@@ -239,7 +158,7 @@ if __name__ == '__main__':
trainer
.
train
(
reader
=
paddle
.
reader
.
batched
(
paddle
.
reader
.
shuffle
(
lambda
:
imdb
.
train
(
word_dict
),
buf_size
=
1000
),
lambda
:
paddle
.
dataset
.
imdb
.
train
(
word_dict
),
buf_size
=
1000
),
batch_size
=
100
),
event_handler
=
event_handler
,
reader_dict
=
{
'word'
:
0
,
...
...
python/paddle/v2/config_base.py
浏览文件 @
1d2025c9
...
...
@@ -93,6 +93,8 @@ def __convert_to_v2__(method_name, parent_names, is_default_name=True):
name
=
kwargs
.
get
(
'name'
,
None
)
super
(
V2LayerImpl
,
self
).
__init__
(
name
,
parent_layers
)
if
kwargs
.
has_key
(
'size'
):
self
.
size
=
kwargs
[
'size'
]
self
.
__other_kwargs__
=
other_kwargs
if
wrapper
is
not
None
:
...
...
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