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99369d43
编写于
3月 02, 2019
作者:
D
dengkaipeng
浏览文件
操作
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电子邮件补丁
差异文件
fix doc. test=develop
上级
40405d13
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
4 addition
and
3 deletion
+4
-3
paddle/fluid/operators/kldiv_loss_op.cc
paddle/fluid/operators/kldiv_loss_op.cc
+2
-2
paddle/fluid/operators/kldiv_loss_op.h
paddle/fluid/operators/kldiv_loss_op.h
+2
-1
未找到文件。
paddle/fluid/operators/kldiv_loss_op.cc
浏览文件 @
99369d43
...
@@ -48,7 +48,7 @@ class KLDivLossOp : public framework::OperatorWithKernel {
...
@@ -48,7 +48,7 @@ class KLDivLossOp : public framework::OperatorWithKernel {
if
(
"none"
==
reduction
)
{
if
(
"none"
==
reduction
)
{
ctx
->
SetOutputDim
(
"Loss"
,
dim_x
);
ctx
->
SetOutputDim
(
"Loss"
,
dim_x
);
}
else
{
}
else
{
ctx
->
SetOutputDim
(
"Loss"
,
framework
::
make_ddim
({
1
})
);
ctx
->
SetOutputDim
(
"Loss"
,
{
1
}
);
}
}
}
}
...
@@ -81,7 +81,7 @@ class KLDivLossOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -81,7 +81,7 @@ class KLDivLossOpMaker : public framework::OpProtoAndCheckerMaker {
"The reduction type to apply to the output, available types "
"The reduction type to apply to the output, available types "
"are 'none' | 'batchmean' | 'mean' | 'sum', 'none' for no "
"are 'none' | 'batchmean' | 'mean' | 'sum', 'none' for no "
"reduction, 'batchmean' for the sum of output divided by "
"reduction, 'batchmean' for the sum of output divided by "
"batch
mean
size, 'mean' for the average valud of all output, "
"batch size, 'mean' for the average valud of all output, "
"'sum' for the sum of the output."
)
"'sum' for the sum of the output."
)
.
SetDefault
(
"mean"
);
.
SetDefault
(
"mean"
);
...
...
paddle/fluid/operators/kldiv_loss_op.h
浏览文件 @
99369d43
...
@@ -104,7 +104,8 @@ class KLDivLossGradKernel : public framework::OpKernel<T> {
...
@@ -104,7 +104,8 @@ class KLDivLossGradKernel : public framework::OpKernel<T> {
auto
loss_grad_expand
=
loss_grad_t
.
broadcast
(
Array1
(
expand
));
auto
loss_grad_expand
=
loss_grad_t
.
broadcast
(
Array1
(
expand
));
auto
grad_t
=
target_t
*
loss_grad_expand
;
auto
grad_t
=
target_t
*
loss_grad_expand
;
input_grad_t
.
device
(
place
)
=
target_t
.
binaryExpr
(
grad_t
,
KLDivLossBackward
<
T
>
());
input_grad_t
.
device
(
place
)
=
target_t
.
binaryExpr
(
grad_t
,
KLDivLossBackward
<
T
>
());
if
(
"mean"
==
reduction
)
{
if
(
"mean"
==
reduction
)
{
input_grad_t
.
device
(
place
)
=
input_grad_t
/
static_cast
<
T
>
(
numel
);
input_grad_t
.
device
(
place
)
=
input_grad_t
/
static_cast
<
T
>
(
numel
);
...
...
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