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83d7261b
编写于
9月 28, 2017
作者:
Y
Yu Yang
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差异文件
Merge branch 'develop' of github.com:baidu/Paddle into stable_elemwise_mul
上级
b9c86372
b9336e6f
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4
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-0
paddle/operators/sigmoid_cross_entropy_with_logits_op.cc
paddle/operators/sigmoid_cross_entropy_with_logits_op.cc
+150
-0
paddle/operators/sigmoid_cross_entropy_with_logits_op.cu
paddle/operators/sigmoid_cross_entropy_with_logits_op.cu
+24
-0
paddle/operators/sigmoid_cross_entropy_with_logits_op.h
paddle/operators/sigmoid_cross_entropy_with_logits_op.h
+75
-0
python/paddle/v2/framework/tests/test_sigmoid_cross_entropy_with_logits_op.py
...mework/tests/test_sigmoid_cross_entropy_with_logits_op.py
+66
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未找到文件。
paddle/operators/sigmoid_cross_entropy_with_logits_op.cc
0 → 100644
浏览文件 @
83d7261b
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/sigmoid_cross_entropy_with_logits_op.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
SigmoidCrossEntropyWithLogitsOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Labels"
),
"Input(Labels) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) should be not null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Labels"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
labels_dims
.
size
(),
2
,
"Input(Labels)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
labels_dims
[
0
],
"The 1st dimension of Input(X) and Input(Labels) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
labels_dims
[
1
],
"The 2nd dimension of Input(X) and Input(Labels) should "
"be equal."
);
ctx
->
SetOutputDim
(
"Out"
,
x_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
class
SigmoidCrossEntropyWithLogitsGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Labels"
),
"Input(Labels) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) shoudl be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Output(X@GRAD) should be not null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
labels_dims
=
ctx
->
GetInputDim
(
"Labels"
);
auto
dout_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
labels_dims
.
size
(),
2
,
"Input(Labels)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
dout_dims
.
size
(),
2
,
"Input(Out@Grad)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
labels_dims
[
0
],
"The 1st dimension of Input(X) and Input(Labels) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
labels_dims
[
1
],
"The 2nd dimension of Input(X) and Input(Labels) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
dout_dims
[
0
],
"The 1st dimension of Input(X) and Input(Out@Grad) "
"should be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
1
],
dout_dims
[
1
],
"The 2nd dimension of Input(X) and Input(Out@Grad) "
"should be equal."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
x_dims
);
}
};
class
SigmoidCrossEntropyWithLogitsOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SigmoidCrossEntropyWithLogitsOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
"where N is the batch size and D is the number of classes. "
"This input is a tensor of logits computed by the previous "
" operator. Logits are unscaled log probabilities given as "
"log(p/(1-p))."
);
AddInput
(
"Labels"
,
"(Tensor, default Tensor<float>), a 2-D tensor of the same type "
"and shape as X. This input is a tensor of probabalistic labels "
"for each logit"
);
AddOutput
(
"Out"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D "
" of elementwise logistic losses."
);
AddComment
(
R"DOC(
SigmoidCrossEntropyWithLogits Operator.
This measures the elementwise probability error in discrete classification tasks
in which each class is independent. This can be thought of as predicting labels
for a data-point that are not mutually exclusive. For example, a news article
can be about politics, technology or sports at the same time or none of these.
The logistic loss is given as follows:
loss = -Labels * log(sigmoid(X)) - (1 - Labels) * log(1 - sigmoid(X))
We know that sigmoid(X) = (1 / (1 + exp(-X))). By substituting this we get
loss = X - X * Labels + log(1 + exp(-X))
For stability and to prevent overflow of exp(-X) when X < 0,
we can reformulate the loss as follows:
loss = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
Both the input `X` and `Labels` can carry the LoD (Level of Details) information.
However the output only shares the LoD with input `X`.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sigmoid_cross_entropy_with_logits
,
ops
::
SigmoidCrossEntropyWithLogitsOp
,
ops
::
SigmoidCrossEntropyWithLogitsOpMaker
,
sigmoid_cross_entropy_with_logits_grad
,
ops
::
SigmoidCrossEntropyWithLogitsGradOp
);
REGISTER_OP_CPU_KERNEL
(
sigmoid_cross_entropy_with_logits
,
ops
::
SigmoidCrossEntropyWithLogitsKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
sigmoid_cross_entropy_with_logits_grad
,
ops
::
SigmoidCrossEntropyWithLogitsGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/sigmoid_cross_entropy_with_logits_op.cu
0 → 100644
浏览文件 @
83d7261b
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/sigmoid_cross_entropy_with_logits_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sigmoid_cross_entropy_with_logits
,
ops
::
SigmoidCrossEntropyWithLogitsKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
sigmoid_cross_entropy_with_logits_grad
,
ops
::
SigmoidCrossEntropyWithLogitsGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/sigmoid_cross_entropy_with_logits_op.h
0 → 100644
浏览文件 @
83d7261b
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
// Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
template
<
typename
Place
,
typename
T
>
class
SigmoidCrossEntropyWithLogitsKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
const
framework
::
Tensor
*
Labels
=
context
.
Input
<
framework
::
Tensor
>
(
"Labels"
);
framework
::
Tensor
*
Out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
Out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
labels
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Labels
);
auto
out
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Out
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
// term1 = max(x, 0)
auto
term1
=
x
.
cwiseMax
(
static_cast
<
T
>
(
0
));
// term2 = x * labels
auto
term2
=
x
*
labels
;
// term3 = log(1 + exp(-abs(x)))
auto
term3
=
(
static_cast
<
T
>
(
1
)
+
(
-
(
x
.
abs
())).
exp
()).
log
();
out
.
device
(
place
)
=
term1
-
term2
+
term3
;
}
};
// dX = sigmoid(X) - labels
template
<
typename
Place
,
typename
T
>
class
SigmoidCrossEntropyWithLogitsGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
X
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
const
framework
::
Tensor
*
Labels
=
context
.
Input
<
framework
::
Tensor
>
(
"Labels"
);
const
framework
::
Tensor
*
dOut
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
framework
::
Tensor
*
dX
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
X
);
auto
labels
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
Labels
);
auto
dout
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dOut
);
auto
dx
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dX
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
sigmoid_x
=
static_cast
<
T
>
(
1
)
/
(
static_cast
<
T
>
(
1
)
+
(
-
x
).
exp
());
dx
.
device
(
place
)
=
dout
*
(
sigmoid_x
-
labels
);
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_sigmoid_cross_entropy_with_logits_op.py
0 → 100644
浏览文件 @
83d7261b
import
numpy
as
np
from
op_test
import
OpTest
from
scipy.special
import
logit
from
scipy.special
import
expit
class
TestSigmoidCrossEntropyWithLogitsOp1
(
OpTest
):
'''Test sigmoid_cross_entropy_with_logit_op with binary labels
'''
def
setUp
(
self
):
self
.
op_type
=
"sigmoid_cross_entropy_with_logits"
batch_size
=
64
num_classes
=
20
self
.
inputs
=
{
'X'
:
logit
(
np
.
random
.
uniform
(
0
,
1
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)),
'Labels'
:
np
.
random
.
randint
(
0
,
2
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
sigmoid_X
=
expit
(
self
.
inputs
[
'X'
])
term1
=
self
.
inputs
[
'Labels'
]
*
np
.
log
(
sigmoid_X
)
term2
=
(
1
-
self
.
inputs
[
'Labels'
])
*
np
.
log
(
1
-
sigmoid_X
)
self
.
outputs
=
{
'Out'
:
-
term1
-
term2
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
class
TestSigmoidCrossEntropyWithLogitsOp2
(
OpTest
):
'''Test sigmoid_cross_entropy_with_logit_op with probabalistic labels
'''
def
setUp
(
self
):
self
.
op_type
=
"sigmoid_cross_entropy_with_logits"
batch_size
=
64
num_classes
=
20
self
.
inputs
=
{
'X'
:
logit
(
np
.
random
.
uniform
(
0
,
1
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)),
'Labels'
:
np
.
random
.
uniform
(
0
,
1
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)
}
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
sigmoid_X
=
expit
(
self
.
inputs
[
'X'
])
term1
=
self
.
inputs
[
'Labels'
]
*
np
.
log
(
sigmoid_X
)
term2
=
(
1
-
self
.
inputs
[
'Labels'
])
*
np
.
log
(
1
-
sigmoid_X
)
self
.
outputs
=
{
'Out'
:
-
term1
-
term2
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'Out'
)
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