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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
()
...
...
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