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665eb015
编写于
11月 13, 2017
作者:
P
peterzhang2029
浏览文件
操作
浏览文件
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差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into bi_tensor_prod_op
上级
ab41648c
5fe97469
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
179 addition
and
17 deletion
+179
-17
paddle/platform/call_once.h
paddle/platform/call_once.h
+13
-11
python/paddle/v2/framework/layer_helper.py
python/paddle/v2/framework/layer_helper.py
+6
-5
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+50
-1
python/paddle/v2/framework/tests/test_understand_sentiment_dynamic_lstm.py
...framework/tests/test_understand_sentiment_dynamic_lstm.py
+110
-0
未找到文件。
paddle/platform/call_once.h
浏览文件 @
665eb015
...
...
@@ -27,20 +27,22 @@ namespace platform {
This wrap is a hack to avoid this bug.
*/
template
<
class
Callable
,
class
...
Args
>
template
<
typename
Callable
,
typename
...
Args
>
inline
void
call_once
(
std
::
once_flag
&
flag
,
Callable
&&
f
,
Args
&&
...
args
)
{
bool
good
=
false
;
std
::
exception
ex
;
std
::
call_once
(
flag
,
[
&
]()
{
try
{
f
(
args
...);
good
=
true
;
}
catch
(
const
std
::
exception
&
e
)
{
ex
=
e
;
}
catch
(...)
{
ex
=
std
::
runtime_error
(
"excption caught in call_once"
);
}
});
std
::
call_once
(
flag
,
[
&
](
Args
&&
...
args
)
{
try
{
f
(
args
...);
good
=
true
;
}
catch
(
const
std
::
exception
&
e
)
{
ex
=
e
;
}
catch
(...)
{
ex
=
std
::
runtime_error
(
"excption caught in call_once"
);
}
},
args
...);
if
(
!
good
)
{
throw
std
::
exception
(
ex
);
}
...
...
python/paddle/v2/framework/layer_helper.py
浏览文件 @
665eb015
...
...
@@ -4,7 +4,7 @@ import itertools
from
paddle.v2.framework.framework
import
Variable
,
g_main_program
,
\
g_startup_program
,
unique_name
,
Program
from
paddle.v2.framework.initializer
import
ConstantInitializer
,
\
UniformInitializer
UniformInitializer
,
XavierInitializer
class
LayerHelper
(
object
):
...
...
@@ -61,7 +61,7 @@ class LayerHelper(object):
@
property
def
param_attr
(
self
):
default
=
{
'name'
:
None
,
'initializer'
:
Uniform
Initializer
()}
default
=
{
'name'
:
None
,
'initializer'
:
Xavier
Initializer
()}
actual
=
self
.
kwargs
.
get
(
'param_attr'
,
None
)
if
actual
is
None
:
actual
=
default
...
...
@@ -70,10 +70,11 @@ class LayerHelper(object):
actual
[
default_field
]
=
default
[
default_field
]
return
actual
@
property
def
bias_attr
(
self
):
default
=
{
'name'
:
None
,
'initializer'
:
Constant
Initializer
()}
default
=
{
'name'
:
None
,
'initializer'
:
Xavier
Initializer
()}
bias_attr
=
self
.
kwargs
.
get
(
'bias_attr'
,
None
)
if
bias_attr
is
Tru
e
:
if
bias_attr
is
Non
e
:
bias_attr
=
default
if
isinstance
(
bias_attr
,
dict
):
...
...
@@ -166,7 +167,7 @@ class LayerHelper(object):
num_flatten_dims
=
1
size
=
list
(
input_var
.
shape
[
num_flatten_dims
:])
bias_attr
=
self
.
bias_attr
()
bias_attr
=
self
.
bias_attr
if
not
bias_attr
:
return
input_var
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
665eb015
...
...
@@ -16,7 +16,7 @@ __all__ = [
def
fc
(
input
,
size
,
param_attr
=
None
,
bias_attr
=
Tru
e
,
bias_attr
=
Non
e
,
name
=
None
,
act
=
None
,
num_flatten_dims
=
1
,
...
...
@@ -125,6 +125,55 @@ def embedding(input,
return
tmp
# TODO(qijun): expose H0 and C0
def
dynamic_lstm
(
input
,
size
,
data_type
=
'float32'
,
param_attr
=
None
,
bias_attr
=
None
,
use_peepholes
=
True
,
is_reverse
=
False
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'tanh'
,
candidate_activation
=
'tanh'
,
main_program
=
None
,
startup_program
=
None
):
helper
=
LayerHelper
(
'lstm'
,
**
locals
())
size
=
size
/
4
weight
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
[
size
,
4
*
size
],
dtype
=
data_type
)
bias_size
=
[
1
,
7
*
size
]
if
not
use_peepholes
:
bias_size
[
1
]
=
4
*
size
bias
=
helper
.
create_parameter
(
attr
=
helper
.
bias_attr
,
shape
=
bias_size
,
dtype
=
data_type
,
suffix
=
'b'
)
hidden
=
helper
.
create_tmp_variable
(
data_type
)
cell
=
helper
.
create_tmp_variable
(
data_type
)
batch_gate
=
helper
.
create_tmp_variable
(
data_type
)
batch_cell_pre_act
=
helper
.
create_tmp_variable
(
data_type
)
helper
.
append_op
(
type
=
'lstm'
,
inputs
=
{
'Input'
:
input
,
'Weight'
:
weight
,
'Bias'
:
bias
},
outputs
=
{
'Hidden'
:
hidden
,
'Cell'
:
cell
,
'BatchGate'
:
batch_gate
,
'BatchCellPreAct'
:
batch_cell_pre_act
},
attrs
=
{
'use_peepholes'
:
use_peepholes
,
'is_reverse'
:
is_reverse
,
'gate_activation'
:
gate_activation
,
'cell_activation'
:
cell_activation
,
'candidate_activation'
:
candidate_activation
})
return
hidden
,
cell
def
data
(
name
,
shape
,
data_type
=
'float32'
,
...
...
python/paddle/v2/framework/tests/test_understand_sentiment_dynamic_lstm.py
0 → 100644
浏览文件 @
665eb015
import
paddle.v2
as
paddle
import
paddle.v2.framework.layers
as
layers
import
paddle.v2.framework.nets
as
nets
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.optimizer
as
optimizer
from
paddle.v2.framework.framework
import
Program
,
g_main_program
,
g_startup_program
from
paddle.v2.framework.executor
import
Executor
import
numpy
as
np
def
stacked_lstm_net
(
input_dim
,
class_dim
=
2
,
emb_dim
=
128
,
hid_dim
=
512
,
stacked_num
=
3
):
assert
stacked_num
%
2
==
1
data
=
layers
.
data
(
name
=
"words"
,
shape
=
[
1
],
data_type
=
"int64"
)
label
=
layers
.
data
(
name
=
"label"
,
shape
=
[
1
],
data_type
=
"int64"
)
emb
=
layers
.
embedding
(
input
=
data
,
size
=
[
input_dim
,
emb_dim
])
# add bias attr
# TODO(qijun) linear act
fc1
=
layers
.
fc
(
input
=
emb
,
size
=
hid_dim
)
lstm1
,
cell1
=
layers
.
dynamic_lstm
(
input
=
fc1
,
size
=
hid_dim
)
inputs
=
[
fc1
,
lstm1
]
for
i
in
range
(
2
,
stacked_num
+
1
):
fc
=
layers
.
fc
(
input
=
inputs
,
size
=
hid_dim
)
lstm
,
cell
=
layers
.
dynamic_lstm
(
input
=
fc
,
size
=
hid_dim
,
is_reverse
=
(
i
%
2
)
==
0
)
inputs
=
[
fc
,
lstm
]
fc_last
=
layers
.
sequence_pool
(
input
=
inputs
[
0
],
pool_type
=
'max'
)
lstm_last
=
layers
.
sequence_pool
(
input
=
inputs
[
1
],
pool_type
=
'max'
)
prediction
=
layers
.
fc
(
input
=
[
fc_last
,
lstm_last
],
size
=
class_dim
,
act
=
'softmax'
)
cost
=
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_cost
=
layers
.
mean
(
x
=
cost
)
adam_optimizer
=
optimizer
.
AdamOptimizer
(
learning_rate
=
0.002
)
opts
=
adam_optimizer
.
minimize
(
avg_cost
)
acc
=
layers
.
accuracy
(
input
=
prediction
,
label
=
label
)
return
avg_cost
,
acc
def
to_lodtensor
(
data
,
place
):
seq_lens
=
[
len
(
seq
)
for
seq
in
data
]
cur_len
=
0
lod
=
[
cur_len
]
for
l
in
seq_lens
:
cur_len
+=
l
lod
.
append
(
cur_len
)
flattened_data
=
np
.
concatenate
(
data
,
axis
=
0
).
astype
(
"int64"
)
flattened_data
=
flattened_data
.
reshape
([
len
(
flattened_data
),
1
])
res
=
core
.
LoDTensor
()
res
.
set
(
flattened_data
,
place
)
res
.
set_lod
([
lod
])
return
res
def
main
():
BATCH_SIZE
=
100
PASS_NUM
=
5
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
print
"load word dict successfully"
dict_dim
=
len
(
word_dict
)
class_dim
=
2
cost
,
acc
=
stacked_lstm_net
(
input_dim
=
dict_dim
,
class_dim
=
class_dim
)
train_data
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
imdb
.
train
(
word_dict
),
buf_size
=
1000
),
batch_size
=
BATCH_SIZE
)
place
=
core
.
CPUPlace
()
exe
=
Executor
(
place
)
exe
.
run
(
g_startup_program
)
for
pass_id
in
xrange
(
PASS_NUM
):
for
data
in
train_data
():
tensor_words
=
to_lodtensor
(
map
(
lambda
x
:
x
[
0
],
data
),
place
)
label
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int64"
)
label
=
label
.
reshape
([
BATCH_SIZE
,
1
])
tensor_label
=
core
.
LoDTensor
()
tensor_label
.
set
(
label
,
place
)
outs
=
exe
.
run
(
g_main_program
,
feed
=
{
"words"
:
tensor_words
,
"label"
:
tensor_label
},
fetch_list
=
[
cost
,
acc
])
cost_val
=
np
.
array
(
outs
[
0
])
acc_val
=
np
.
array
(
outs
[
1
])
print
(
"cost="
+
str
(
cost_val
)
+
" acc="
+
str
(
acc_val
))
if
cost_val
<
1.0
and
acc_val
>
0.7
:
exit
(
0
)
exit
(
1
)
if
__name__
==
'__main__'
:
main
()
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