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
5905d0e8
P
Paddle
项目概览
PaddlePaddle
/
Paddle
11 个月 前同步成功
通知
2289
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
体验新版 GitCode,发现更多精彩内容 >>
提交
5905d0e8
编写于
3月 07, 2017
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of github.com:baidu/Paddle into feature/better_infer_interface
上级
0d2d419a
79e95c1f
变更
18
隐藏空白更改
内联
并排
Showing
18 changed file
with
416 addition
and
367 deletion
+416
-367
demo/image_classification/api_v2_train.py
demo/image_classification/api_v2_train.py
+9
-8
demo/introduction/api_train_v2.py
demo/introduction/api_train_v2.py
+11
-11
demo/mnist/.gitignore
demo/mnist/.gitignore
+3
-0
demo/mnist/api_train_v2.py
demo/mnist/api_train_v2.py
+18
-5
demo/semantic_role_labeling/api_train_v2.py
demo/semantic_role_labeling/api_train_v2.py
+3
-3
demo/sentiment/train_v2.py
demo/sentiment/train_v2.py
+9
-14
demo/seqToseq/api_train_v2.py
demo/seqToseq/api_train_v2.py
+98
-68
demo/seqToseq/seqToseq_net_v2.py
demo/seqToseq/seqToseq_net_v2.py
+0
-92
python/paddle/trainer_config_helpers/layer_math.py
python/paddle/trainer_config_helpers/layer_math.py
+1
-0
python/paddle/trainer_config_helpers/tests/configs/math_ops.py
...n/paddle/trainer_config_helpers/tests/configs/math_ops.py
+2
-1
python/paddle/trainer_config_helpers/tests/configs/protostr/math_ops.protostr
...r_config_helpers/tests/configs/protostr/math_ops.protostr
+24
-8
python/paddle/v2/data_feeder.py
python/paddle/v2/data_feeder.py
+18
-6
python/paddle/v2/dataset/wmt14.py
python/paddle/v2/dataset/wmt14.py
+75
-112
python/paddle/v2/inference.py
python/paddle/v2/inference.py
+6
-17
python/paddle/v2/parameters.py
python/paddle/v2/parameters.py
+70
-1
python/paddle/v2/tests/run_tests.sh
python/paddle/v2/tests/run_tests.sh
+1
-1
python/paddle/v2/tests/test_parameters.py
python/paddle/v2/tests/test_parameters.py
+60
-0
python/paddle/v2/trainer.py
python/paddle/v2/trainer.py
+8
-20
未找到文件。
demo/image_classification/api_v2_train.py
浏览文件 @
5905d0e8
...
...
@@ -13,9 +13,10 @@
# limitations under the License
import
sys
import
paddle.v2
as
paddle
from
api_v2_vgg
import
vgg_bn_drop
from
api_v2_resnet
import
resnet_cifar10
def
main
():
...
...
@@ -23,16 +24,16 @@ def main():
classdim
=
10
# PaddlePaddle init
paddle
.
init
(
use_gpu
=
Tru
e
,
trainer_count
=
1
)
paddle
.
init
(
use_gpu
=
Fals
e
,
trainer_count
=
1
)
image
=
paddle
.
layer
.
data
(
name
=
"image"
,
type
=
paddle
.
data_type
.
dense_vector
(
datadim
))
# Add neural network config
# option 1. resnet
net
=
resnet_cifar10
(
image
,
depth
=
32
)
#
net = resnet_cifar10(image, depth=32)
# option 2. vgg
#
net = vgg_bn_drop(image)
net
=
vgg_bn_drop
(
image
)
out
=
paddle
.
layer
.
fc
(
input
=
net
,
size
=
classdim
,
...
...
@@ -68,8 +69,8 @@ def main():
result
=
trainer
.
test
(
reader
=
paddle
.
batch
(
paddle
.
dataset
.
cifar
.
test10
(),
batch_size
=
128
),
reader_dict
=
{
'image'
:
0
,
'label'
:
1
})
feeding
=
{
'image'
:
0
,
'label'
:
1
})
print
"
\n
Test with Pass %d, %s"
%
(
event
.
pass_id
,
result
.
metrics
)
# Create trainer
...
...
@@ -83,8 +84,8 @@ def main():
batch_size
=
128
),
num_passes
=
5
,
event_handler
=
event_handler
,
reader_dict
=
{
'image'
:
0
,
'label'
:
1
})
feeding
=
{
'image'
:
0
,
'label'
:
1
})
if
__name__
==
'__main__'
:
...
...
demo/introduction/api_train_v2.py
浏览文件 @
5905d0e8
...
...
@@ -30,26 +30,26 @@ def main():
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
100
==
0
:
print
"Pass %d, Batch %d, Cost %f
, %s
"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
)
print
"Pass %d, Batch %d, Cost %f"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
)
if
isinstance
(
event
,
paddle
.
event
.
EndPass
):
result
=
trainer
.
test
(
reader
=
paddle
.
reader
.
batched
(
uci_housing
.
test
(),
batch_size
=
2
),
reader_dict
=
{
'x'
:
0
,
if
(
event
.
pass_id
+
1
)
%
10
==
0
:
result
=
trainer
.
test
(
reader
=
paddle
.
batch
(
uci_housing
.
test
(),
batch_size
=
2
),
feeding
=
{
'x'
:
0
,
'y'
:
1
})
if
event
.
pass_id
%
10
==
0
:
print
"Test %d, %s"
%
(
event
.
pass_id
,
result
.
metrics
)
print
"Test %d, %.2f"
%
(
event
.
pass_id
,
result
.
cost
)
# training
trainer
.
train
(
reader
=
paddle
.
reader
.
batched
(
reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
uci_housing
.
train
(),
buf_size
=
500
),
batch_size
=
2
),
reader_dict
=
{
'x'
:
0
,
'y'
:
1
},
feeding
=
{
'x'
:
0
,
'y'
:
1
},
event_handler
=
event_handler
,
num_passes
=
30
)
...
...
demo/mnist/.gitignore
浏览文件 @
5905d0e8
...
...
@@ -5,3 +5,6 @@ plot.png
train.log
*pyc
.ipynb_checkpoints
params.pkl
params.tar
params.tar.gz
demo/mnist/api_train_v2.py
浏览文件 @
5905d0e8
import
paddle.v2
as
paddle
import
gzip
def
softmax_regression
(
img
):
...
...
@@ -71,7 +72,11 @@ def main():
cost
=
paddle
.
layer
.
classification_cost
(
input
=
predict
,
label
=
label
)
parameters
=
paddle
.
parameters
.
create
(
cost
)
try
:
with
gzip
.
open
(
'params.tar.gz'
,
'r'
)
as
f
:
parameters
=
paddle
.
parameters
.
Parameters
.
from_tar
(
f
)
except
IOError
:
parameters
=
paddle
.
parameters
.
create
(
cost
)
optimizer
=
paddle
.
optimizer
.
Momentum
(
learning_rate
=
0.1
/
128.0
,
...
...
@@ -86,10 +91,18 @@ def main():
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
100
==
0
:
print
"Pass %d, Batch %d, Cost %f, %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
)
if
isinstance
(
event
,
paddle
.
event
.
EndPass
):
if
event
.
batch_id
%
1000
==
0
:
result
=
trainer
.
test
(
reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
256
))
print
"Pass %d, Batch %d, Cost %f, %s, Testing metrics %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
,
result
.
metrics
)
with
gzip
.
open
(
'params.tar.gz'
,
'w'
)
as
f
:
parameters
.
to_tar
(
f
)
elif
isinstance
(
event
,
paddle
.
event
.
EndPass
):
result
=
trainer
.
test
(
reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
128
))
print
"Test with Pass %d, Cost %f, %s
\n
"
%
(
...
...
demo/semantic_role_labeling/api_train_v2.py
浏览文件 @
5905d0e8
...
...
@@ -163,11 +163,11 @@ def main():
update_equation
=
optimizer
)
parameters
.
set
(
'emb'
,
load_parameter
(
conll05
.
get_embedding
(),
44068
,
32
))
trn_reader
=
paddle
.
reader
.
batched
(
trn_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
conll05
.
test
(),
buf_size
=
8192
),
batch_size
=
10
)
reader_dict
=
{
feeding
=
{
'word_data'
:
0
,
'ctx_n2_data'
:
1
,
'ctx_n1_data'
:
2
,
...
...
@@ -183,7 +183,7 @@ def main():
reader
=
trn_reader
,
event_handler
=
event_handler
,
num_passes
=
10000
,
reader_dict
=
reader_dict
)
feeding
=
feeding
)
if
__name__
==
'__main__'
:
...
...
demo/sentiment/train_v2.py
浏览文件 @
5905d0e8
...
...
@@ -18,11 +18,7 @@ from paddle.trainer_config_helpers.poolings import MaxPooling
import
paddle.v2
as
paddle
def
convolution_net
(
input_dim
,
class_dim
=
2
,
emb_dim
=
128
,
hid_dim
=
128
,
is_predict
=
False
):
def
convolution_net
(
input_dim
,
class_dim
=
2
,
emb_dim
=
128
,
hid_dim
=
128
):
data
=
paddle
.
layer
.
data
(
"word"
,
paddle
.
data_type
.
integer_value_sequence
(
input_dim
))
emb
=
paddle
.
layer
.
embedding
(
input
=
data
,
size
=
emb_dim
)
...
...
@@ -42,8 +38,7 @@ def stacked_lstm_net(input_dim,
class_dim
=
2
,
emb_dim
=
128
,
hid_dim
=
512
,
stacked_num
=
3
,
is_predict
=
False
):
stacked_num
=
3
):
"""
A Wrapper for sentiment classification task.
This network uses bi-directional recurrent network,
...
...
@@ -110,7 +105,7 @@ def stacked_lstm_net(input_dim,
if
__name__
==
'__main__'
:
# init
paddle
.
init
(
use_gpu
=
Tru
e
,
trainer_count
=
4
)
paddle
.
init
(
use_gpu
=
Fals
e
,
trainer_count
=
4
)
# network config
print
'load dictionary...'
...
...
@@ -143,11 +138,11 @@ if __name__ == '__main__':
sys
.
stdout
.
flush
()
if
isinstance
(
event
,
paddle
.
event
.
EndPass
):
result
=
trainer
.
test
(
reader
=
paddle
.
reader
.
batched
(
reader
=
paddle
.
batch
(
lambda
:
paddle
.
dataset
.
imdb
.
test
(
word_dict
),
batch_size
=
128
),
reader_dict
=
{
'word'
:
0
,
'label'
:
1
})
feeding
=
{
'word'
:
0
,
'label'
:
1
})
print
"
\n
Test with Pass %d, %s"
%
(
event
.
pass_id
,
result
.
metrics
)
# create trainer
...
...
@@ -156,11 +151,11 @@ if __name__ == '__main__':
update_equation
=
adam_optimizer
)
trainer
.
train
(
reader
=
paddle
.
reader
.
batched
(
reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
lambda
:
paddle
.
dataset
.
imdb
.
train
(
word_dict
),
buf_size
=
1000
),
batch_size
=
100
),
event_handler
=
event_handler
,
reader_dict
=
{
'word'
:
0
,
'label'
:
1
},
feeding
=
{
'word'
:
0
,
'label'
:
1
},
num_passes
=
10
)
demo/seqToseq/api_train_v2.py
浏览文件 @
5905d0e8
import
os
import
paddle.v2
as
paddle
from
seqToseq_net_v2
import
seqToseq_net_v2
# Data Definiation.
# TODO:This code should be merged to dataset package.
data_dir
=
"./data/pre-wmt14"
src_lang_dict
=
os
.
path
.
join
(
data_dir
,
'src.dict'
)
trg_lang_dict
=
os
.
path
.
join
(
data_dir
,
'trg.dict'
)
source_dict_dim
=
len
(
open
(
src_lang_dict
,
"r"
).
readlines
())
target_dict_dim
=
len
(
open
(
trg_lang_dict
,
"r"
).
readlines
())
def
read_to_dict
(
dict_path
):
with
open
(
dict_path
,
"r"
)
as
fin
:
out_dict
=
{
line
.
strip
():
line_count
for
line_count
,
line
in
enumerate
(
fin
)
}
return
out_dict
src_dict
=
read_to_dict
(
src_lang_dict
)
trg_dict
=
read_to_dict
(
trg_lang_dict
)
train_list
=
os
.
path
.
join
(
data_dir
,
'train.list'
)
test_list
=
os
.
path
.
join
(
data_dir
,
'test.list'
)
UNK_IDX
=
2
START
=
"<s>"
END
=
"<e>"
def
_get_ids
(
s
,
dictionary
):
words
=
s
.
strip
().
split
()
return
[
dictionary
[
START
]]
+
\
[
dictionary
.
get
(
w
,
UNK_IDX
)
for
w
in
words
]
+
\
[
dictionary
[
END
]]
def
train_reader
(
file_name
):
def
reader
():
with
open
(
file_name
,
'r'
)
as
f
:
for
line_count
,
line
in
enumerate
(
f
):
line_split
=
line
.
strip
().
split
(
'
\t
'
)
if
len
(
line_split
)
!=
2
:
continue
src_seq
=
line_split
[
0
]
# one source sequence
src_ids
=
_get_ids
(
src_seq
,
src_dict
)
trg_seq
=
line_split
[
1
]
# one target sequence
trg_words
=
trg_seq
.
split
()
trg_ids
=
[
trg_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
trg_words
]
# remove sequence whose length > 80 in training mode
if
len
(
src_ids
)
>
80
or
len
(
trg_ids
)
>
80
:
continue
trg_ids_next
=
trg_ids
+
[
trg_dict
[
END
]]
trg_ids
=
[
trg_dict
[
START
]]
+
trg_ids
yield
src_ids
,
trg_ids
,
trg_ids_next
return
reader
def
seqToseq_net
(
source_dict_dim
,
target_dict_dim
):
### Network Architecture
word_vector_dim
=
512
# dimension of word vector
decoder_size
=
512
# dimension of hidden unit in GRU Decoder network
encoder_size
=
512
# dimension of hidden unit in GRU Encoder network
#### Encoder
src_word_id
=
paddle
.
layer
.
data
(
name
=
'source_language_word'
,
type
=
paddle
.
data_type
.
integer_value_sequence
(
source_dict_dim
))
src_embedding
=
paddle
.
layer
.
embedding
(
input
=
src_word_id
,
size
=
word_vector_dim
,
param_attr
=
paddle
.
attr
.
ParamAttr
(
name
=
'_source_language_embedding'
))
src_forward
=
paddle
.
networks
.
simple_gru
(
input
=
src_embedding
,
size
=
encoder_size
)
src_backward
=
paddle
.
networks
.
simple_gru
(
input
=
src_embedding
,
size
=
encoder_size
,
reverse
=
True
)
encoded_vector
=
paddle
.
layer
.
concat
(
input
=
[
src_forward
,
src_backward
])
#### Decoder
with
paddle
.
layer
.
mixed
(
size
=
decoder_size
)
as
encoded_proj
:
encoded_proj
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
encoded_vector
)
backward_first
=
paddle
.
layer
.
first_seq
(
input
=
src_backward
)
with
paddle
.
layer
.
mixed
(
size
=
decoder_size
,
act
=
paddle
.
activation
.
Tanh
())
as
decoder_boot
:
decoder_boot
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
backward_first
)
def
gru_decoder_with_attention
(
enc_vec
,
enc_proj
,
current_word
):
decoder_mem
=
paddle
.
layer
.
memory
(
name
=
'gru_decoder'
,
size
=
decoder_size
,
boot_layer
=
decoder_boot
)
context
=
paddle
.
networks
.
simple_attention
(
encoded_sequence
=
enc_vec
,
encoded_proj
=
enc_proj
,
decoder_state
=
decoder_mem
)
with
paddle
.
layer
.
mixed
(
size
=
decoder_size
*
3
)
as
decoder_inputs
:
decoder_inputs
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
context
)
decoder_inputs
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
current_word
)
gru_step
=
paddle
.
layer
.
gru_step
(
name
=
'gru_decoder'
,
input
=
decoder_inputs
,
output_mem
=
decoder_mem
,
size
=
decoder_size
)
with
paddle
.
layer
.
mixed
(
size
=
target_dict_dim
,
bias_attr
=
True
,
act
=
paddle
.
activation
.
Softmax
())
as
out
:
out
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
gru_step
)
return
out
decoder_group_name
=
"decoder_group"
group_input1
=
paddle
.
layer
.
StaticInputV2
(
input
=
encoded_vector
,
is_seq
=
True
)
group_input2
=
paddle
.
layer
.
StaticInputV2
(
input
=
encoded_proj
,
is_seq
=
True
)
group_inputs
=
[
group_input1
,
group_input2
]
trg_embedding
=
paddle
.
layer
.
embedding
(
input
=
paddle
.
layer
.
data
(
name
=
'target_language_word'
,
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
)),
size
=
word_vector_dim
,
param_attr
=
paddle
.
attr
.
ParamAttr
(
name
=
'_target_language_embedding'
))
group_inputs
.
append
(
trg_embedding
)
# For decoder equipped with attention mechanism, in training,
# target embeding (the groudtruth) is the data input,
# while encoded source sequence is accessed to as an unbounded memory.
# Here, the StaticInput defines a read-only memory
# for the recurrent_group.
decoder
=
paddle
.
layer
.
recurrent_group
(
name
=
decoder_group_name
,
step
=
gru_decoder_with_attention
,
input
=
group_inputs
)
lbl
=
paddle
.
layer
.
data
(
name
=
'target_language_next_word'
,
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
decoder
,
label
=
lbl
)
return
cost
def
main
():
paddle
.
init
(
use_gpu
=
False
,
trainer_count
=
1
)
# source and target dict dim.
dict_size
=
30000
source_dict_dim
=
target_dict_dim
=
dict_size
# define network topology
cost
=
seqToseq_net
_v2
(
source_dict_dim
,
target_dict_dim
)
cost
=
seqToseq_net
(
source_dict_dim
,
target_dict_dim
)
parameters
=
paddle
.
parameters
.
create
(
cost
)
# define optimize method and trainer
...
...
@@ -80,15 +110,15 @@ def main():
update_equation
=
optimizer
)
# define data reader
reader_dict
=
{
feeding
=
{
'source_language_word'
:
0
,
'target_language_word'
:
1
,
'target_language_next_word'
:
2
}
wmt14_reader
=
paddle
.
reader
.
batched
(
wmt14_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
train_reader
(
"data/pre-wmt14/train/train"
),
buf_size
=
8192
),
paddle
.
dataset
.
wmt14
.
train
(
dict_size
=
dict_size
),
buf_size
=
8192
),
batch_size
=
5
)
# define event_handler callback
...
...
@@ -103,7 +133,7 @@ def main():
reader
=
wmt14_reader
,
event_handler
=
event_handler
,
num_passes
=
10000
,
reader_dict
=
reader_dict
)
feeding
=
feeding
)
if
__name__
==
'__main__'
:
...
...
demo/seqToseq/seqToseq_net_v2.py
已删除
100644 → 0
浏览文件 @
0d2d419a
import
paddle.v2
as
paddle
def
seqToseq_net_v2
(
source_dict_dim
,
target_dict_dim
):
### Network Architecture
word_vector_dim
=
512
# dimension of word vector
decoder_size
=
512
# dimension of hidden unit in GRU Decoder network
encoder_size
=
512
# dimension of hidden unit in GRU Encoder network
#### Encoder
src_word_id
=
paddle
.
layer
.
data
(
name
=
'source_language_word'
,
type
=
paddle
.
data_type
.
integer_value_sequence
(
source_dict_dim
))
src_embedding
=
paddle
.
layer
.
embedding
(
input
=
src_word_id
,
size
=
word_vector_dim
,
param_attr
=
paddle
.
attr
.
ParamAttr
(
name
=
'_source_language_embedding'
))
src_forward
=
paddle
.
networks
.
simple_gru
(
input
=
src_embedding
,
size
=
encoder_size
)
src_backward
=
paddle
.
networks
.
simple_gru
(
input
=
src_embedding
,
size
=
encoder_size
,
reverse
=
True
)
encoded_vector
=
paddle
.
layer
.
concat
(
input
=
[
src_forward
,
src_backward
])
#### Decoder
with
paddle
.
layer
.
mixed
(
size
=
decoder_size
)
as
encoded_proj
:
encoded_proj
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
encoded_vector
)
backward_first
=
paddle
.
layer
.
first_seq
(
input
=
src_backward
)
with
paddle
.
layer
.
mixed
(
size
=
decoder_size
,
act
=
paddle
.
activation
.
Tanh
())
as
decoder_boot
:
decoder_boot
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
backward_first
)
def
gru_decoder_with_attention
(
enc_vec
,
enc_proj
,
current_word
):
decoder_mem
=
paddle
.
layer
.
memory
(
name
=
'gru_decoder'
,
size
=
decoder_size
,
boot_layer
=
decoder_boot
)
context
=
paddle
.
networks
.
simple_attention
(
encoded_sequence
=
enc_vec
,
encoded_proj
=
enc_proj
,
decoder_state
=
decoder_mem
)
with
paddle
.
layer
.
mixed
(
size
=
decoder_size
*
3
)
as
decoder_inputs
:
decoder_inputs
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
context
)
decoder_inputs
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
current_word
)
gru_step
=
paddle
.
layer
.
gru_step
(
name
=
'gru_decoder'
,
input
=
decoder_inputs
,
output_mem
=
decoder_mem
,
size
=
decoder_size
)
with
paddle
.
layer
.
mixed
(
size
=
target_dict_dim
,
bias_attr
=
True
,
act
=
paddle
.
activation
.
Softmax
())
as
out
:
out
+=
paddle
.
layer
.
full_matrix_projection
(
input
=
gru_step
)
return
out
decoder_group_name
=
"decoder_group"
group_input1
=
paddle
.
layer
.
StaticInputV2
(
input
=
encoded_vector
,
is_seq
=
True
)
group_input2
=
paddle
.
layer
.
StaticInputV2
(
input
=
encoded_proj
,
is_seq
=
True
)
group_inputs
=
[
group_input1
,
group_input2
]
trg_embedding
=
paddle
.
layer
.
embedding
(
input
=
paddle
.
layer
.
data
(
name
=
'target_language_word'
,
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
)),
size
=
word_vector_dim
,
param_attr
=
paddle
.
attr
.
ParamAttr
(
name
=
'_target_language_embedding'
))
group_inputs
.
append
(
trg_embedding
)
# For decoder equipped with attention mechanism, in training,
# target embeding (the groudtruth) is the data input,
# while encoded source sequence is accessed to as an unbounded memory.
# Here, the StaticInput defines a read-only memory
# for the recurrent_group.
decoder
=
paddle
.
layer
.
recurrent_group
(
name
=
decoder_group_name
,
step
=
gru_decoder_with_attention
,
input
=
group_inputs
)
lbl
=
paddle
.
layer
.
data
(
name
=
'target_language_next_word'
,
type
=
paddle
.
data_type
.
integer_value_sequence
(
target_dict_dim
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
decoder
,
label
=
lbl
)
return
cost
python/paddle/trainer_config_helpers/layer_math.py
浏览文件 @
5905d0e8
...
...
@@ -39,6 +39,7 @@ register_unary_math_op('abs', act.AbsActivation())
register_unary_math_op
(
'sigmoid'
,
act
.
SigmoidActivation
())
register_unary_math_op
(
'tanh'
,
act
.
TanhActivation
())
register_unary_math_op
(
'square'
,
act
.
SquareActivation
())
register_unary_math_op
(
'relu'
,
act
.
ReluActivation
())
def
add
(
layeroutput
,
other
):
...
...
python/paddle/trainer_config_helpers/tests/configs/math_ops.py
浏览文件 @
5905d0e8
...
...
@@ -7,8 +7,9 @@ x = layer_math.exp(x)
x
=
layer_math
.
log
(
x
)
x
=
layer_math
.
abs
(
x
)
x
=
layer_math
.
sigmoid
(
x
)
x
=
layer_math
.
tanh
(
x
)
x
=
layer_math
.
square
(
x
)
x
=
layer_math
.
square
(
x
)
x
=
layer_math
.
relu
(
x
)
y
=
1
+
x
y
=
y
+
1
y
=
x
+
y
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/math_ops.protostr
浏览文件 @
5905d0e8
...
...
@@ -65,13 +65,28 @@ layers {
}
}
}
layers {
name: "__tanh_0__"
type: "mixed"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__sigmoid_0__"
proj_conf {
type: "identity"
name: "___tanh_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__square_0__"
type: "mixed"
size: 100
active_type: "square"
inputs {
input_layer_name: "__
sigmoid
_0__"
input_layer_name: "__
tanh
_0__"
proj_conf {
type: "identity"
name: "___square_0__.w0"
...
...
@@ -81,15 +96,15 @@ layers {
}
}
layers {
name: "__
square_1
__"
name: "__
relu_0
__"
type: "mixed"
size: 100
active_type: "
square
"
active_type: "
relu
"
inputs {
input_layer_name: "__square_0__"
proj_conf {
type: "identity"
name: "___
square_1
__.w0"
name: "___
relu_0
__.w0"
input_size: 100
output_size: 100
}
...
...
@@ -101,7 +116,7 @@ layers {
size: 100
active_type: ""
inputs {
input_layer_name: "__
square_1
__"
input_layer_name: "__
relu_0
__"
}
slope: 1.0
intercept: 1
...
...
@@ -123,7 +138,7 @@ layers {
size: 100
active_type: ""
inputs {
input_layer_name: "__
square_1
__"
input_layer_name: "__
relu_0
__"
proj_conf {
type: "identity"
name: "___mixed_0__.w0"
...
...
@@ -147,7 +162,7 @@ layers {
size: 100
active_type: ""
inputs {
input_layer_name: "__
square_1
__"
input_layer_name: "__
relu_0
__"
}
slope: -1.0
intercept: 0.0
...
...
@@ -339,8 +354,9 @@ sub_models {
layer_names: "__log_0__"
layer_names: "__abs_0__"
layer_names: "__sigmoid_0__"
layer_names: "__tanh_0__"
layer_names: "__square_0__"
layer_names: "__
square_1
__"
layer_names: "__
relu_0
__"
layer_names: "__slope_intercept_layer_0__"
layer_names: "__slope_intercept_layer_1__"
layer_names: "__mixed_0__"
...
...
python/paddle/v2/data_feeder.py
浏览文件 @
5905d0e8
...
...
@@ -14,11 +14,18 @@
from
py_paddle
import
DataProviderConverter
import
data_type
import
paddle.trainer.PyDataProvider2
as
pydp2
__all__
=
[
'DataFeeder'
]
def
default_feeding_map
(
data_types
):
reader_dict
=
dict
()
for
i
,
tp
in
enumerate
(
data_types
):
reader_dict
[
tp
[
0
]]
=
i
return
reader_dict
class
DataFeeder
(
DataProviderConverter
):
"""
DataFeeder converts the data returned by paddle.reader into a data structure
...
...
@@ -60,16 +67,21 @@ class DataFeeder(DataProviderConverter):
:type data_types: list
:param reader_dict: A dictionary to specify the position of each data
in the input data.
:type
reader_dict
: dict
:type
feeding
: dict
"""
def
__init__
(
self
,
data_types
,
reader_dict
):
def
__init__
(
self
,
data_types
,
feeding
=
None
):
self
.
input_names
=
[]
input_types
=
[]
self
.
reader_dict
=
reader_dict
if
feeding
is
None
:
feeding
=
default_feeding_map
(
data_types
)
self
.
feeding
=
feeding
for
each
in
data_types
:
self
.
input_names
.
append
(
each
[
0
])
assert
isinstance
(
each
[
1
],
data_type
.
InputType
)
if
not
isinstance
(
each
[
1
],
pydp2
.
InputType
):
raise
TypeError
(
"second item in each data_type should be an "
"InputType"
)
input_types
.
append
(
each
[
1
])
DataProviderConverter
.
__init__
(
self
,
input_types
)
...
...
@@ -90,7 +102,7 @@ class DataFeeder(DataProviderConverter):
for
each
in
data
:
reorder
=
[]
for
name
in
self
.
input_names
:
reorder
.
append
(
each
[
self
.
reader_dict
[
name
]])
reorder
.
append
(
each
[
self
.
feeding
[
name
]])
retv
.
append
(
reorder
)
return
retv
...
...
python/paddle/v2/dataset/wmt14.py
浏览文件 @
5905d0e8
...
...
@@ -14,129 +14,92 @@
"""
wmt14 dataset
"""
import
paddle.v2.dataset.common
import
tarfile
import
os.path
import
itertools
import
paddle.v2.dataset.common
__all__
=
[
'train'
,
'test'
,
'build_dict'
]
URL_DEV_TEST
=
'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
MD5_DEV_TEST
=
'7d7897317ddd8ba0ae5c5fa7248d3ff5'
URL_TRAIN
=
'http://localhost:8000/train.tgz'
MD5_TRAIN
=
'72de99da2830ea5a3a2c4eb36092bbc7'
def
word_count
(
f
,
word_freq
=
None
):
add
=
paddle
.
v2
.
dataset
.
common
.
dict_add
if
word_freq
==
None
:
word_freq
=
{}
for
l
in
f
:
for
w
in
l
.
strip
().
split
():
add
(
word_freq
,
w
)
add
(
word_freq
,
'<s>'
)
add
(
word_freq
,
'<e>'
)
return
word_freq
def
get_word_dix
(
word_freq
):
TYPO_FREQ
=
50
word_freq
=
filter
(
lambda
x
:
x
[
1
]
>
TYPO_FREQ
,
word_freq
.
items
())
word_freq_sorted
=
sorted
(
word_freq
,
key
=
lambda
x
:
(
-
x
[
1
],
x
[
0
]))
words
,
_
=
list
(
zip
(
*
word_freq_sorted
))
word_idx
=
dict
(
zip
(
words
,
xrange
(
len
(
words
))))
word_idx
[
'<unk>'
]
=
len
(
words
)
return
word_idx
def
get_word_freq
(
train
,
dev
):
word_freq
=
word_count
(
train
,
word_count
(
dev
))
if
'<unk>'
in
word_freq
:
# remove <unk> for now, since we will set it as last index
del
word_freq
[
'<unk>'
]
return
word_freq
def
build_dict
():
base_dir
=
'./wmt14-data'
train_en_filename
=
base_dir
+
'/train/train.en'
train_fr_filename
=
base_dir
+
'/train/train.fr'
dev_en_filename
=
base_dir
+
'/dev/ntst1213.en'
dev_fr_filename
=
base_dir
+
'/dev/ntst1213.fr'
if
not
os
.
path
.
exists
(
train_en_filename
)
or
not
os
.
path
.
exists
(
train_fr_filename
):
with
tarfile
.
open
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
))
as
tf
:
tf
.
extractall
(
base_dir
)
if
not
os
.
path
.
exists
(
dev_en_filename
)
or
not
os
.
path
.
exists
(
dev_fr_filename
):
with
tarfile
.
open
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_DEV_TEST
,
'wmt14'
,
MD5_DEV_TEST
))
as
tf
:
tf
.
extractall
(
base_dir
)
f_en
=
open
(
train_en_filename
)
f_fr
=
open
(
train_fr_filename
)
f_en_dev
=
open
(
dev_en_filename
)
f_fr_dev
=
open
(
dev_fr_filename
)
word_freq_en
=
get_word_freq
(
f_en
,
f_en_dev
)
word_freq_fr
=
get_word_freq
(
f_fr
,
f_fr_dev
)
f_en
.
close
()
f_fr
.
close
()
f_en_dev
.
close
()
f_fr_dev
.
close
()
return
get_word_dix
(
word_freq_en
),
get_word_dix
(
word_freq_fr
)
def
reader_creator
(
directory
,
path_en
,
path_fr
,
URL
,
MD5
,
dict_en
,
dict_fr
):
# this is a small set of data for test. The original data is too large and will be add later.
URL_TRAIN
=
'http://paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz'
MD5_TRAIN
=
'a755315dd01c2c35bde29a744ede23a6'
START
=
"<s>"
END
=
"<e>"
UNK
=
"<unk>"
UNK_IDX
=
2
def
__read_to_dict__
(
tar_file
,
dict_size
):
def
__to_dict__
(
fd
,
size
):
out_dict
=
dict
()
for
line_count
,
line
in
enumerate
(
fd
):
if
line_count
<
size
:
out_dict
[
line
.
strip
()]
=
line_count
else
:
break
return
out_dict
with
tarfile
.
open
(
tar_file
,
mode
=
'r'
)
as
f
:
names
=
[
each_item
.
name
for
each_item
in
f
if
each_item
.
name
.
endswith
(
"src.dict"
)
]
assert
len
(
names
)
==
1
src_dict
=
__to_dict__
(
f
.
extractfile
(
names
[
0
]),
dict_size
)
names
=
[
each_item
.
name
for
each_item
in
f
if
each_item
.
name
.
endswith
(
"trg.dict"
)
]
assert
len
(
names
)
==
1
trg_dict
=
__to_dict__
(
f
.
extractfile
(
names
[
0
]),
dict_size
)
return
src_dict
,
trg_dict
def
reader_creator
(
tar_file
,
file_name
,
dict_size
):
def
reader
():
if
not
os
.
path
.
exists
(
path_en
)
or
not
os
.
path
.
exists
(
path_fr
):
with
tarfile
.
open
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL
,
'wmt14'
,
MD5
))
as
tf
:
tf
.
extractall
(
directory
)
f_en
=
open
(
path_en
)
f_fr
=
open
(
path_fr
)
UNK_en
=
dict_en
[
'<unk>'
]
UNK_fr
=
dict_fr
[
'<unk>'
]
for
en
,
fr
in
itertools
.
izip
(
f_en
,
f_fr
):
src_ids
=
[
dict_en
.
get
(
w
,
UNK_en
)
for
w
in
en
.
strip
().
split
()]
tar_ids
=
[
dict_fr
.
get
(
w
,
UNK_fr
)
for
w
in
[
'<s>'
]
+
fr
.
strip
().
split
()
+
[
'<e>'
]
src_dict
,
trg_dict
=
__read_to_dict__
(
tar_file
,
dict_size
)
with
tarfile
.
open
(
tar_file
,
mode
=
'r'
)
as
f
:
names
=
[
each_item
.
name
for
each_item
in
f
if
each_item
.
name
.
endswith
(
file_name
)
]
# remove sequence whose length > 80 in training mode
if
len
(
src_ids
)
==
0
or
len
(
tar_ids
)
<=
1
or
len
(
src_ids
)
>
80
or
len
(
tar_ids
)
>
80
:
continue
yield
src_ids
,
tar_ids
[:
-
1
],
tar_ids
[
1
:]
f_en
.
close
()
f_fr
.
close
()
for
name
in
names
:
for
line
in
f
.
extractfile
(
name
):
line_split
=
line
.
strip
().
split
(
'
\t
'
)
if
len
(
line_split
)
!=
2
:
continue
src_seq
=
line_split
[
0
]
# one source sequence
src_words
=
src_seq
.
split
()
src_ids
=
[
src_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
[
START
]
+
src_words
+
[
END
]
]
trg_seq
=
line_split
[
1
]
# one target sequence
trg_words
=
trg_seq
.
split
()
trg_ids
=
[
trg_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
trg_words
]
# remove sequence whose length > 80 in training mode
if
len
(
src_ids
)
>
80
or
len
(
trg_ids
)
>
80
:
continue
trg_ids_next
=
trg_ids
+
[
trg_dict
[
END
]]
trg_ids
=
[
trg_dict
[
START
]]
+
trg_ids
yield
src_ids
,
trg_ids
,
trg_ids_next
return
reader
def
train
(
dict_en
,
dict_fr
):
directory
=
'./wmt14-data'
return
reader_creator
(
directory
,
directory
+
'/train/train.en'
,
directory
+
'/train/train.fr'
,
URL_TRAIN
,
MD5_TRAIN
,
dict_en
,
dict_fr
)
def
train
(
dict_size
):
return
reader_creator
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
),
'train/train'
,
dict_size
)
def
test
(
dict_en
,
dict_fr
):
directory
=
'./wmt14-data'
return
reader_creator
(
directory
,
directory
+
'/dev/ntst1213.en'
,
directory
+
'/dev/ntst1213.fr'
,
URL_DEV_TEST
,
MD5_DEV_TEST
,
dict_en
,
dict_fr
)
def
test
(
dict_size
):
return
reader_creator
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
),
'test/test'
,
dict_size
)
python/paddle/v2/inference.py
浏览文件 @
5905d0e8
...
...
@@ -21,13 +21,8 @@ class Inference(object):
self
.
__gradient_machine__
=
gm
self
.
__data_types__
=
topo
.
data_type
()
def
iter_infer
(
self
,
input
=
None
,
batch_size
=
None
,
reader
=
None
,
reader_dict
=
None
):
if
reader_dict
is
None
:
reader_dict
=
self
.
default_reader_dict
()
def
iter_infer
(
self
,
input
=
None
,
batch_size
=
None
,
reader
=
None
,
feeding
=
None
):
if
reader
is
None
:
assert
input
is
not
None
and
isinstance
(
input
,
collections
.
Iterable
)
...
...
@@ -51,7 +46,7 @@ class Inference(object):
raise
ValueError
(
"User should set either input or reader, "
"should not set them both."
)
feeder
=
DataFeeder
(
self
.
__data_types__
,
reader_dict
)
feeder
=
DataFeeder
(
self
.
__data_types__
,
feeding
)
self
.
__gradient_machine__
.
start
()
for
data_batch
in
reader
():
yield
self
.
__gradient_machine__
.
forwardTest
(
feeder
(
data_batch
))
...
...
@@ -74,19 +69,13 @@ class Inference(object):
else
:
return
retv
def
default_reader_dict
(
self
):
reader_dict
=
dict
()
for
i
,
tp
in
enumerate
(
self
.
__data_types__
):
reader_dict
[
tp
[
0
]]
=
i
return
reader_dict
def
infer
(
output
,
parameters
,
input
=
None
,
batch_size
=
None
,
reader
=
None
,
reader_dict
=
None
,
feeding
=
None
,
field
=
'value'
):
"""
Infer a neural network by given neural network output and parameters. The
...
...
@@ -113,7 +102,7 @@ def infer(output,
:param reader: input data reader creator in batch. If this field is set, the
`input` and `batch_size` will be ignored.
:type reader: callable
:param
reader_dict
: Reader dictionary. Default could generate from input
:param
feeding
: Reader dictionary. Default could generate from input
value.
:param field: The prediction field. It should in [`value`, `ids`]. `value`
means return the prediction probabilities, `ids` means return
...
...
@@ -129,4 +118,4 @@ def infer(output,
input
=
input
,
batch_size
=
batch_size
,
reader
=
reader
,
reader_dict
=
reader_dict
)
feeding
=
feeding
)
python/paddle/v2/parameters.py
浏览文件 @
5905d0e8
import
numpy
as
np
import
py_paddle.swig_paddle
as
api
from
paddle.proto.ParameterConfig_pb2
import
ParameterConfig
import
struct
import
tarfile
import
cStringIO
from
topology
import
Topology
__all__
=
[
'Parameters'
,
'create'
]
...
...
@@ -122,6 +124,12 @@ class Parameters(object):
if
len
(
self
.
__gradient_machines__
)
==
0
:
# create new parameter in python numpy.
if
len
(
self
.
__tmp_params__
)
!=
0
:
ret_list
=
[
mat
for
name
,
mat
in
self
.
__tmp_params__
if
name
==
key
]
if
len
(
ret_list
)
==
1
:
return
ret_list
[
0
]
return
np
.
ndarray
(
shape
=
shape
,
dtype
=
np
.
float32
)
else
:
for
each_gradient_machine
in
self
.
__gradient_machines__
:
...
...
@@ -228,6 +236,67 @@ class Parameters(object):
self
.
__gradient_machines__
.
append
(
gradient_machine
)
def
serialize
(
self
,
name
,
f
):
"""
:param name:
:param f:
:type f: file
:return:
"""
param
=
self
.
get
(
name
)
size
=
reduce
(
lambda
a
,
b
:
a
*
b
,
param
.
shape
)
f
.
write
(
struct
.
pack
(
"IIQ"
,
0
,
4
,
size
))
param
=
param
.
astype
(
np
.
float32
)
f
.
write
(
param
.
tobytes
())
def
deserialize
(
self
,
name
,
f
):
"""
:param name:
:param f:
:type f: file
:return:
"""
f
.
read
(
16
)
# header
arr
=
np
.
frombuffer
(
f
.
read
(),
dtype
=
np
.
float32
)
self
.
set
(
name
,
arr
.
reshape
(
self
.
get_shape
(
name
)))
def
to_tar
(
self
,
f
):
tar
=
tarfile
.
TarFile
(
fileobj
=
f
,
mode
=
'w'
)
for
nm
in
self
.
names
():
buf
=
cStringIO
.
StringIO
()
self
.
serialize
(
nm
,
buf
)
tarinfo
=
tarfile
.
TarInfo
(
name
=
nm
)
buf
.
seek
(
0
)
tarinfo
.
size
=
len
(
buf
.
getvalue
())
tar
.
addfile
(
tarinfo
,
buf
)
conf
=
self
.
__param_conf__
[
nm
]
confStr
=
conf
.
SerializeToString
()
tarinfo
=
tarfile
.
TarInfo
(
name
=
"%s.protobuf"
%
nm
)
tarinfo
.
size
=
len
(
confStr
)
buf
=
cStringIO
.
StringIO
(
confStr
)
buf
.
seek
(
0
)
tar
.
addfile
(
tarinfo
,
fileobj
=
buf
)
@
staticmethod
def
from_tar
(
f
):
params
=
Parameters
()
tar
=
tarfile
.
TarFile
(
fileobj
=
f
,
mode
=
'r'
)
for
finfo
in
tar
:
assert
isinstance
(
finfo
,
tarfile
.
TarInfo
)
if
finfo
.
name
.
endswith
(
'.protobuf'
):
f
=
tar
.
extractfile
(
finfo
)
conf
=
ParameterConfig
()
conf
.
ParseFromString
(
f
.
read
())
params
.
__append_config__
(
conf
)
for
param_name
in
params
.
names
():
f
=
tar
.
extractfile
(
param_name
)
params
.
deserialize
(
param_name
,
f
)
return
params
def
__get_parameter_in_gradient_machine__
(
gradient_machine
,
name
):
"""
...
...
python/paddle/v2/tests/run_tests.sh
浏览文件 @
5905d0e8
...
...
@@ -22,7 +22,7 @@ cd $SCRIPTPATH
$1
-m
pip
install
../../../../paddle/dist/
*
.whl
test_list
=
"test_data_feeder.py"
test_list
=
"test_data_feeder.py
test_parameters.py
"
export
PYTHONPATH
=
$PWD
/../../../../python/
...
...
python/paddle/v2/tests/test_parameters.py
0 → 100644
浏览文件 @
5905d0e8
import
unittest
import
sys
try
:
import
py_paddle
del
py_paddle
except
ImportError
:
print
>>
sys
.
stderr
,
"It seems swig of Paddle is not installed, this "
\
"unittest will not be run."
sys
.
exit
(
0
)
import
paddle.v2.parameters
as
parameters
from
paddle.proto.ParameterConfig_pb2
import
ParameterConfig
import
random
import
cStringIO
import
numpy
def
__rand_param_config__
(
name
):
conf
=
ParameterConfig
()
conf
.
name
=
name
size
=
1
for
i
in
xrange
(
2
):
dim
=
random
.
randint
(
1
,
1000
)
conf
.
dims
.
append
(
dim
)
size
*=
dim
conf
.
size
=
size
assert
conf
.
IsInitialized
()
return
conf
class
TestParameters
(
unittest
.
TestCase
):
def
test_serialization
(
self
):
params
=
parameters
.
Parameters
()
params
.
__append_config__
(
__rand_param_config__
(
"param_0"
))
params
.
__append_config__
(
__rand_param_config__
(
"param_1"
))
for
name
in
params
.
names
():
param
=
params
.
get
(
name
)
param
[:]
=
numpy
.
random
.
uniform
(
-
1.0
,
1.0
,
size
=
params
.
get_shape
(
name
))
params
.
set
(
name
,
param
)
tmp_file
=
cStringIO
.
StringIO
()
params
.
to_tar
(
tmp_file
)
tmp_file
.
seek
(
0
)
params_dup
=
parameters
.
Parameters
.
from_tar
(
tmp_file
)
self
.
assertEqual
(
params_dup
.
names
(),
params
.
names
())
for
name
in
params
.
names
():
self
.
assertEqual
(
params
.
get_shape
(
name
),
params_dup
.
get_shape
(
name
))
p0
=
params
.
get
(
name
)
p1
=
params_dup
.
get
(
name
)
self
.
assertTrue
(
numpy
.
isclose
(
p0
,
p1
).
all
())
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/trainer.py
浏览文件 @
5905d0e8
...
...
@@ -57,11 +57,11 @@ class SGD(object):
self
.
__topology_in_proto__
,
api
.
CREATE_MODE_NORMAL
,
self
.
__optimizer__
.
enable_types
())
assert
isinstance
(
gm
,
api
.
GradientMachine
)
parameters
.
append_gradient_machine
(
gm
)
self
.
__gradient_machine__
=
gm
self
.
__gradient_machine__
.
randParameters
()
parameters
.
append_gradient_machine
(
gm
)
def
train
(
self
,
reader
,
num_passes
=
1
,
event_handler
=
None
,
reader_dict
=
None
):
def
train
(
self
,
reader
,
num_passes
=
1
,
event_handler
=
None
,
feeding
=
None
):
"""
Training method. Will train num_passes of input data.
...
...
@@ -70,14 +70,13 @@ class SGD(object):
:param event_handler: Event handler. A method will be invoked when event
occurred.
:type event_handler: (BaseEvent) => None
:param feeding: Feeding is a map of neural network input name and array
index that reader returns.
:type feeding: dict
:return:
"""
if
event_handler
is
None
:
event_handler
=
default_event_handler
if
reader_dict
is
None
:
reader_dict
=
self
.
default_reader_dict
()
__check_train_args__
(
**
locals
())
updater
=
self
.
__optimizer__
.
create_local_updater
()
...
...
@@ -89,9 +88,7 @@ class SGD(object):
pass_evaluator
=
self
.
__gradient_machine__
.
makeEvaluator
()
assert
isinstance
(
pass_evaluator
,
api
.
Evaluator
)
out_args
=
api
.
Arguments
.
createArguments
(
0
)
feeder
=
DataFeeder
(
self
.
__data_types__
,
reader_dict
)
feeder
=
DataFeeder
(
self
.
__data_types__
,
feeding
)
for
pass_id
in
xrange
(
num_passes
):
event_handler
(
v2_event
.
BeginPass
(
pass_id
))
pass_evaluator
.
start
()
...
...
@@ -125,17 +122,8 @@ class SGD(object):
event_handler
(
v2_event
.
EndPass
(
pass_id
,
evaluator
=
pass_evaluator
))
self
.
__gradient_machine__
.
finish
()
def
default_reader_dict
(
self
):
reader_dict
=
dict
()
for
i
,
tp
in
enumerate
(
self
.
__data_types__
):
reader_dict
[
tp
[
0
]]
=
i
return
reader_dict
def
test
(
self
,
reader
,
reader_dict
=
None
):
if
reader_dict
is
None
:
reader_dict
=
self
.
default_reader_dict
()
feeder
=
DataFeeder
(
self
.
__data_types__
,
reader_dict
)
def
test
(
self
,
reader
,
feeding
=
None
):
feeder
=
DataFeeder
(
self
.
__data_types__
,
feeding
)
evaluator
=
self
.
__gradient_machine__
.
makeEvaluator
()
out_args
=
api
.
Arguments
.
createArguments
(
0
)
evaluator
.
start
()
...
...
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