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495861f5
编写于
3月 02, 2017
作者:
Q
qiaolongfei
浏览文件
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电子邮件补丁
差异文件
add v2 demo to seqtoseq, fix __dfs_travel__ for v2 layers
上级
061e743c
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
202 addition
and
2 deletion
+202
-2
demo/seqToseq/api_train_v2.py
demo/seqToseq/api_train_v2.py
+106
-0
demo/seqToseq/seqToseq_net_v2.py
demo/seqToseq/seqToseq_net_v2.py
+90
-0
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+1
-1
python/paddle/v2/topology.py
python/paddle/v2/topology.py
+5
-1
未找到文件。
demo/seqToseq/api_train_v2.py
0 → 100644
浏览文件 @
495861f5
import
os
import
paddle.v2
as
paddle
from
seqToseq_net_v2
import
seqToseq_net_v2
### Data Definiation
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
main
():
paddle
.
init
(
use_gpu
=
False
,
trainer_count
=
1
)
# reader = train_reader("data/pre-wmt14/train/train")
# define network topology
cost
=
seqToseq_net_v2
(
source_dict_dim
,
target_dict_dim
)
parameters
=
paddle
.
parameters
.
create
(
cost
)
optimizer
=
paddle
.
optimizer
.
Adam
(
batch_size
=
50
,
learning_rate
=
5e-4
)
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
)
trainer
=
paddle
.
trainer
.
SGD
(
cost
=
cost
,
parameters
=
parameters
,
update_equation
=
optimizer
)
reader_dict
=
{
'source_language_word'
:
0
,
'target_language_word'
:
1
,
'target_language_next_word'
:
2
}
trn_reader
=
paddle
.
reader
.
batched
(
paddle
.
reader
.
shuffle
(
train_reader
(
"data/pre-wmt14/train/train"
),
buf_size
=
8192
),
batch_size
=
10
)
trainer
.
train
(
reader
=
trn_reader
,
event_handler
=
event_handler
,
num_passes
=
10000
,
reader_dict
=
reader_dict
)
if
__name__
==
'__main__'
:
main
()
demo/seqToseq/seqToseq_net_v2.py
0 → 100644
浏览文件 @
495861f5
import
paddle.v2.activation
as
activation
import
paddle.v2.attr
as
attr
import
paddle.v2.data_type
as
data_type
import
paddle.v2.layer
as
layer
import
paddle.v2.networks
as
networks
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
=
layer
.
data
(
name
=
'source_language_word'
,
type
=
data_type
.
dense_vector
(
source_dict_dim
))
src_embedding
=
layer
.
embedding
(
input
=
src_word_id
,
size
=
word_vector_dim
,
param_attr
=
attr
.
ParamAttr
(
name
=
'_source_language_embedding'
))
src_forward
=
networks
.
simple_gru
(
input
=
src_embedding
,
size
=
encoder_size
)
src_backward
=
networks
.
simple_gru
(
input
=
src_embedding
,
size
=
encoder_size
,
reverse
=
True
)
encoded_vector
=
layer
.
concat
(
input
=
[
src_forward
,
src_backward
])
#### Decoder
with
layer
.
mixed
(
size
=
decoder_size
)
as
encoded_proj
:
encoded_proj
+=
layer
.
full_matrix_projection
(
input
=
encoded_vector
)
backward_first
=
layer
.
first_seq
(
input
=
src_backward
)
with
layer
.
mixed
(
size
=
decoder_size
,
act
=
activation
.
Tanh
())
as
decoder_boot
:
decoder_boot
+=
layer
.
full_matrix_projection
(
input
=
backward_first
)
def
gru_decoder_with_attention
(
enc_vec
,
enc_proj
,
current_word
):
decoder_mem
=
layer
.
memory
(
name
=
'gru_decoder'
,
size
=
decoder_size
,
boot_layer
=
decoder_boot
)
context
=
networks
.
simple_attention
(
encoded_sequence
=
enc_vec
,
encoded_proj
=
enc_proj
,
decoder_state
=
decoder_mem
)
with
layer
.
mixed
(
size
=
decoder_size
*
3
)
as
decoder_inputs
:
decoder_inputs
+=
layer
.
full_matrix_projection
(
input
=
context
)
decoder_inputs
+=
layer
.
full_matrix_projection
(
input
=
current_word
)
gru_step
=
layer
.
gru_step
(
name
=
'gru_decoder'
,
input
=
decoder_inputs
,
output_mem
=
decoder_mem
,
size
=
decoder_size
)
with
layer
.
mixed
(
size
=
target_dict_dim
,
bias_attr
=
True
,
act
=
activation
.
Softmax
())
as
out
:
out
+=
layer
.
full_matrix_projection
(
input
=
gru_step
)
return
out
decoder_group_name
=
"decoder_group"
group_input1
=
layer
.
StaticInputV2
(
input
=
encoded_vector
,
is_seq
=
True
)
group_input2
=
layer
.
StaticInputV2
(
input
=
encoded_proj
,
is_seq
=
True
)
group_inputs
=
[
group_input1
,
group_input2
]
trg_embedding
=
layer
.
embedding
(
input
=
layer
.
data
(
name
=
'target_language_word'
,
type
=
data_type
.
dense_vector
(
target_dict_dim
)),
size
=
word_vector_dim
,
param_attr
=
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
=
layer
.
recurrent_group
(
name
=
decoder_group_name
,
step
=
gru_decoder_with_attention
,
input
=
group_inputs
)
lbl
=
layer
.
data
(
name
=
'target_language_next_word'
,
type
=
data_type
.
dense_vector
(
target_dict_dim
))
cost
=
layer
.
classification_cost
(
input
=
decoder
,
label
=
lbl
)
return
cost
python/paddle/v2/layer.py
浏览文件 @
495861f5
...
...
@@ -262,7 +262,7 @@ class StaticInputV2(object):
self
.
input
=
input
self
.
is_seq
=
is_seq
self
.
size
=
size
# TODO(
qiaolongfei): add size
# TODO(
add size check)
# assert input.size is not None or size is not None
...
...
python/paddle/v2/topology.py
浏览文件 @
495861f5
...
...
@@ -17,6 +17,7 @@ import collections
from
paddle.proto.ModelConfig_pb2
import
ModelConfig
import
layer
as
v2_layer
from
layer
import
WithExtraParent
__all__
=
[
'Topology'
]
...
...
@@ -40,7 +41,10 @@ def __bfs_travel__(callback, *layers):
__break__
=
callback
(
each_layer
)
if
__break__
:
return
__bfs_travel__
(
callback
,
*
each_layer
.
__parent_layers__
.
values
())
__layers__
=
each_layer
.
__parent_layers__
.
values
()
if
isinstance
(
each_layer
,
WithExtraParent
):
__layers__
=
__layers__
+
each_layer
.
extra_parent
()
__bfs_travel__
(
callback
,
*
__layers__
)
class
Topology
(
object
):
...
...
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