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fc68290b
编写于
10月 26, 2017
作者:
Q
Qiao Longfei
提交者:
GitHub
10月 26, 2017
浏览文件
操作
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电子邮件补丁
差异文件
update _create_op_func_ and support generate dropout layer (#5134)
上级
aa379ccb
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
30 addition
and
12 deletion
+30
-12
paddle/operators/dropout_op.cc
paddle/operators/dropout_op.cc
+5
-5
paddle/operators/dropout_op.h
paddle/operators/dropout_op.h
+2
-2
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+23
-5
未找到文件。
paddle/operators/dropout_op.cc
浏览文件 @
fc68290b
...
...
@@ -30,7 +30,7 @@ class DropoutOp : public framework::OperatorWithKernel {
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
ctx
->
SetOutputDim
(
"Out"
,
x_dims
);
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"is_training"
)
==
1
)
{
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"is_training"
)
==
true
)
{
ctx
->
SetOutputDim
(
"Mask"
,
x_dims
);
}
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
...
...
@@ -43,7 +43,7 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
DropoutOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddAttr
<
AttrType
>
(
"dropout_prob"
,
"Probability of setting units to zero."
)
AddAttr
<
float
>
(
"dropout_prob"
,
"Probability of setting units to zero."
)
.
SetDefault
(
.5
f
);
AddAttr
<
bool
>
(
"is_training"
,
"Whether in training phase."
).
SetDefault
(
true
);
AddAttr
<
int
>
(
"seed"
,
"Dropout random seed."
).
SetDefault
(
0
);
...
...
@@ -69,7 +69,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE_EQ
(
ctx
->
Attrs
().
Get
<
bool
>
(
"is_training"
),
1
,
PADDLE_ENFORCE_EQ
(
ctx
->
Attrs
().
Get
<
bool
>
(
"is_training"
),
true
,
"GradOp is only callable when is_training is true"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
...
...
@@ -77,8 +77,8 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) must not be null."
);
PADDLE_ENFORCE_GE
(
ctx
->
Attrs
().
Get
<
AttrType
>
(
"dropout_prob"
),
0
);
PADDLE_ENFORCE_LE
(
ctx
->
Attrs
().
Get
<
AttrType
>
(
"dropout_prob"
),
1
);
PADDLE_ENFORCE_GE
(
ctx
->
Attrs
().
Get
<
float
>
(
"dropout_prob"
),
0
);
PADDLE_ENFORCE_LE
(
ctx
->
Attrs
().
Get
<
float
>
(
"dropout_prob"
),
1
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
out_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
PADDLE_ENFORCE_EQ
(
x_dims
,
out_dims
,
...
...
paddle/operators/dropout_op.h
浏览文件 @
fc68290b
...
...
@@ -33,7 +33,7 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
auto
*
y
=
context
.
Output
<
Tensor
>
(
"Out"
);
const
auto
*
x_data
=
x
->
data
<
T
>
();
auto
*
y_data
=
y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
AttrType
dropout_prob
=
context
.
Attr
<
AttrType
>
(
"dropout_prob"
);
float
dropout_prob
=
context
.
Attr
<
float
>
(
"dropout_prob"
);
if
(
context
.
Attr
<
bool
>
(
"is_training"
))
{
auto
*
mask
=
context
.
Output
<
Tensor
>
(
"Mask"
);
...
...
@@ -41,7 +41,7 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
int
seed
=
context
.
Attr
<
int
>
(
"seed"
);
std
::
minstd_rand
engine
;
engine
.
seed
(
seed
);
std
::
uniform_real_distribution
<
AttrType
>
dist
(
0
,
1
);
std
::
uniform_real_distribution
<
float
>
dist
(
0
,
1
);
size_t
size
=
framework
::
product
(
mask
->
dims
());
for
(
size_t
i
=
0
;
i
<
size
;
++
i
)
{
if
(
dist
(
engine
)
<
dropout_prob
)
{
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
fc68290b
...
...
@@ -97,15 +97,28 @@ def _convert_(name):
def
_create_op_func_
(
op_type
):
op_proto
=
OpProtoHolder
.
instance
().
get_op_proto
(
op_type
)
if
len
(
op_proto
.
outputs
)
!=
1
:
not_intermediate_outputs
=
\
filter
(
lambda
output
:
not
output
.
intermediate
,
op_proto
.
outputs
)
intermediate_outputs
=
\
filter
(
lambda
output
:
output
.
intermediate
,
op_proto
.
outputs
)
if
len
(
not_intermediate_outputs
)
!=
1
:
raise
ValueError
(
"Only one output operator can be automatically generated"
)
"Only one not intermediate output operator can be automatically generated"
)
if
op_proto
.
outputs
[
0
].
duplicable
:
if
not_intermediate_
outputs
[
0
].
duplicable
:
raise
ValueError
(
"Only not duplicable op can be automatically generated"
)
o_name
=
op_proto
.
outputs
[
0
].
name
for
output
in
intermediate_outputs
:
if
output
.
duplicable
:
raise
ValueError
(
"Only when all intermediate ops are not duplicable, "
"this op can be automatically generated"
)
o_name
=
not_intermediate_outputs
[
0
].
name
intermediate_output_names
=
[
output
.
name
for
output
in
intermediate_outputs
]
def
func
(
**
kwargs
):
helper
=
LayerHelper
(
op_type
,
**
kwargs
)
...
...
@@ -128,9 +141,13 @@ def _create_op_func_(op_type):
"operator {0} must input same dtype"
.
format
(
op_type
))
inputs
[
ipt
.
name
]
=
val
outputs
=
dict
()
out
=
helper
.
create_tmp_variable
(
dtype
=
dtype
)
outputs
[
o_name
]
=
[
out
]
for
name
in
intermediate_output_names
:
outputs
[
name
]
=
[
helper
.
create_tmp_variable
(
dtype
=
dtype
)]
helper
.
append_op
(
type
=
op_type
,
inputs
=
inputs
,
outputs
=
{
o_name
:
[
out
]}
,
attrs
=
kwargs
)
type
=
op_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
kwargs
)
return
out
func
.
__name__
=
op_type
...
...
@@ -141,6 +158,7 @@ def _create_op_func_(op_type):
_create_op_func_
(
'mean'
)
_create_op_func_
(
'mul'
)
_create_op_func_
(
'dropout'
)
def
concat
(
input
,
axis
,
program
=
None
,
init_program
=
None
):
...
...
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