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4f6e9e3a
编写于
12月 19, 2018
作者:
H
heqiaozhi
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teacher student sigmoid loss
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paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc
paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc
+256
-0
paddle/fluid/operators/teacher_student_sigmoid_loss_op.h
paddle/fluid/operators/teacher_student_sigmoid_loss_op.h
+25
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+42
-0
python/paddle/fluid/tests/unittests/test_teacher_student_sigmoid_loss_op.py
...d/tests/unittests/test_teacher_student_sigmoid_loss_op.py
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paddle/fluid/operators/teacher_student_sigmoid_loss_op.cc
0 → 100644
浏览文件 @
4f6e9e3a
/* 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/fluid/operators/teacher_student_sigmoid_loss_op.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
class
TeacherStudentSigmoidLossOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Y"
),
"Output(Y) should be not null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2UL
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
2UL
,
"Input(Label)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
label_dims
[
0
],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
label_dims
[
1
],
1UL
,
"The 2nd dimension of "
"Input(Label) should be 1."
);
ctx
->
SetOutputDim
(
"Y"
,
{
x_dims
[
0
],
1
});
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Y"
);
}
protected:
// Explicitly set that the data type of computation kernel of
// teacher_student_sigmoid_loss
// is determined by its input "X".
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
(),
ctx
.
device_context
());
}
};
class
TeacherStudentSigmoidLossGradientOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Label"
),
"Input(Label) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Y"
)),
"Input(Y@GRAD) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Output(X@GRAD) should be not null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
label_dims
=
ctx
->
GetInputDim
(
"Label"
);
auto
dy_dims
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
2
,
"Input(X)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
dy_dims
.
size
(),
2
,
"Input(Y@Grad)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
label_dims
.
size
(),
2
,
"Input(Label)'s rank should be 2."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
label_dims
[
0
],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
dy_dims
[
0
],
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal."
);
PADDLE_ENFORCE_EQ
(
dy_dims
[
1
],
1
,
"The 2nd dimension of Input(Y@Grad) should be 1."
);
PADDLE_ENFORCE_EQ
(
label_dims
[
1
],
1
,
"When Attr(soft_label) == false, the 2nd dimension of "
"Input(Label) should be 1."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
x_dims
);
ctx
->
ShareLoD
(
"X"
,
framework
::
GradVarName
(
"X"
));
}
protected:
// Explicitly set that the data type of computation kernel of
// teacher_student_sigmoid_loss
// is determined by its input "X".
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
(),
ctx
.
device_context
());
}
};
class
TeacherStudentSigmoidLossOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape [N x 1],"
" where N is the batch size and D is the output. "
"This input is a probability computed by the previous operator, "
"which is almost always the result of a softmax operator."
);
AddInput
(
"Label"
,
"(Tensor), the ground truth which is a 2-D tensor. "
"Label is a Tensor<float> with shape [N x 1]. "
);
AddOutput
(
"Y"
,
"(Tensor, default Tensor<float>), a 2-D tensor with shape "
"[N x 1]. The teacher student sigmoid loss."
);
AddAttr
<
float
>
(
"soft_max_up_bound"
,
"fp32, default 15.0"
).
SetDefault
(
15.0
);
AddAttr
<
float
>
(
"soft_max_lower_bound"
,
"fp32, default -15.0"
)
.
SetDefault
(
-
15.0
);
AddComment
(
R"DOC(
TeacherStudentSigmoidLoss Operator.
TeacherStudentSigmoidLoss Operator.
It's similarity to SigmoidCrossEntropyWithLogits Operator. The difference is that
we add another label(z') to original.
loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
z is click or not
z' is value q of feed_fine
label = {-2, -1, [0, 2]}
when z' is not exist, clk = 0 : label = -2;
when z' is not exist, clk = 1 : label = -1;
when z' is exist , clk = 0 : label = 0 + z';
when z' is exist , clk = 1 : label = 1 + z';
)DOC"
);
}
};
// template <typename DeviceContext, typename T>
template
<
typename
T
>
class
TeacherStudentSigmoidLossOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_cpu_place
(
context
.
GetPlace
()),
"This kernel only runs on CPU."
);
Tensor
*
y
=
context
.
Output
<
Tensor
>
(
"Y"
);
const
Tensor
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
T
*
y_data
=
y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
x_data
=
x
->
data
<
T
>
();
const
T
*
label_data
=
labels
->
data
<
T
>
();
int64_t
batch_size
=
x
->
dims
()[
0
];
// loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' +
// log(1 + exp(-abs(x)))
// z is click or not
// z' is value q of feed_fine
// label = {-2, -1, [0, 2]}
// when z' is not exist, clk = 0 : label = -2;
// when z' is not exist, clk = 1 : label = -1;
// when z' is exist , clk = 0 : label = 0 + z';
// when z' is exist , clk = 1 : label = 1 + z';
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
if
(
label_data
[
i
]
<
-
1.0
)
{
y_data
[
i
]
=
(
x_data
[
i
]
>
0
?
x_data
[
i
]
:
0.0
)
+
log
(
1.0
+
exp
(
-
fabs
(
x_data
[
i
])));
}
else
if
(
label_data
[
i
]
<
0.0
)
{
y_data
[
i
]
=
(
x_data
[
i
]
>
0
?
x_data
[
i
]
:
0.0
)
-
x_data
[
i
]
+
log
(
1.0
+
exp
(
-
fabs
(
x_data
[
i
])));
}
else
if
(
label_data
[
i
]
<
1.0
)
{
y_data
[
i
]
=
(
x_data
[
i
]
>
0
?
x_data
[
i
]
:
0.0
)
+
log
(
1.0
+
exp
(
-
fabs
(
x_data
[
i
])))
+
(
x_data
[
i
]
>
0
?
x_data
[
i
]
:
0.0
)
-
x_data
[
i
]
*
label_data
[
i
]
+
log
(
1.0
+
exp
(
-
fabs
(
x_data
[
i
])));
}
else
{
y_data
[
i
]
=
(
x_data
[
i
]
>
0
?
x_data
[
i
]
:
0.0
)
-
x_data
[
i
]
+
log
(
1.0
+
exp
(
-
fabs
(
x_data
[
i
])))
+
(
x_data
[
i
]
>
0
?
x_data
[
i
]
:
0.0
)
-
x_data
[
i
]
*
(
label_data
[
i
]
-
1.0
)
+
log
(
1.0
+
exp
(
-
fabs
(
x_data
[
i
])));
}
}
}
};
template
<
typename
T
>
class
TeacherStudentSigmoidLossGradOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
Tensor
*
dx
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
T
*
dx_data
=
dx
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
Tensor
*
labels
=
context
.
Input
<
Tensor
>
(
"Label"
);
const
T
*
label_data
=
labels
->
data
<
T
>
();
T
soft_max_up_bound
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"soft_max_up_bound"
));
T
soft_max_lower_bound
=
static_cast
<
T
>
(
context
.
Attr
<
float
>
(
"soft_max_lower_bound"
));
int64_t
batch_size
=
x
->
dims
()[
0
];
const
framework
::
Tensor
*
dOut
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
const
T
*
dout_data
=
dOut
->
data
<
T
>
();
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
T
sum_val
=
x_data
[
i
];
if
(
sum_val
>
soft_max_up_bound
)
{
sum_val
=
soft_max_up_bound
;
}
else
{
if
(
sum_val
<
soft_max_lower_bound
)
{
sum_val
=
soft_max_lower_bound
;
}
}
T
pred
=
1.0
/
(
1.0
+
exp
(
-
sum_val
));
if
(
label_data
[
i
]
<
-
1.0
)
{
dx_data
[
i
]
=
0.0
-
pred
;
}
else
if
(
label_data
[
i
]
<
0.0
)
{
dx_data
[
i
]
=
1.0
-
pred
;
}
else
{
dx_data
[
i
]
=
label_data
[
i
]
-
2.0
*
pred
;
}
if
(
sum_val
>=
soft_max_up_bound
||
sum_val
<=
soft_max_lower_bound
)
{
dx_data
[
i
]
=
0
;
}
dx_data
[
i
]
*=
dout_data
[
i
]
*
-
1
;
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
teacher_student_sigmoid_loss
,
ops
::
TeacherStudentSigmoidLossOp
,
ops
::
TeacherStudentSigmoidLossOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
teacher_student_sigmoid_loss_grad
,
ops
::
TeacherStudentSigmoidLossGradientOp
);
REGISTER_OP_CPU_KERNEL
(
teacher_student_sigmoid_loss
,
ops
::
TeacherStudentSigmoidLossOpKernel
<
float
>
,
ops
::
TeacherStudentSigmoidLossOpKernel
<
double
>
);
REGISTER_OP_CPU_KERNEL
(
teacher_student_sigmoid_loss_grad
,
ops
::
TeacherStudentSigmoidLossGradOpKernel
<
float
>
,
ops
::
TeacherStudentSigmoidLossGradOpKernel
<
double
>
);
paddle/fluid/operators/teacher_student_sigmoid_loss_op.h
0 → 100644
浏览文件 @
4f6e9e3a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
4f6e9e3a
...
...
@@ -176,6 +176,7 @@ __all__ = [
'get_tensor_from_selected_rows'
,
'lstm'
,
'psroi_pool'
,
'teacher_student_sigmoid_loss'
,
]
kIgnoreIndex
=
-
100
...
...
@@ -9184,6 +9185,47 @@ def log_loss(input, label, epsilon=1e-4, name=None):
return
loss
def
teacher_student_sigmoid_loss
(
input
,
label
,
soft_max_up_bound
=
15.0
,
soft_max_lower_bound
=-
15.0
):
"""
**Teacher Student Log Loss Layer**
This layer accepts input predictions and target label and returns the
teacher_student loss.
.. math::
loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
Args:
input (Variable|list): a 2-D tensor with shape [N x 1], where N is the
batch size. This input is a probability computed
by the previous operator.
label (Variable|list): the ground truth which is a 2-D tensor with
shape [N x 1], where N is the batch size.
soft_max_up_bound (float): if input > soft_max_up_bound, will be bound
soft_max_lower_bound (float): if input < soft_max_lower_bound, will be bound
Returns:
Variable: A 2-D tensor with shape [N x 1], the teacher_student_sigmoid_loss.
Examples:
.. code-block:: python
cost = fluid.layers.teacher_student_sigmoid_loss(input=similarity, label=label)
"""
helper
=
LayerHelper
(
'teacher_student_sigmoid_loss'
,
**
locals
())
out
=
helper
.
create_variable
(
dtype
=
input
.
dtype
)
helper
.
append_op
(
type
=
'teacher_student_sigmoid_loss'
,
inputs
=
{
'X'
:
[
input
],
'Label'
:
[
label
]},
outputs
=
{
'Y'
:
[
out
]},
attrs
=
{
"soft_max_lower_bound"
:
float
(
soft_max_lower_bound
),
\
"soft_max_up_bound"
:
float
(
soft_max_up_bound
)})
return
out
def
add_position_encoding
(
input
,
alpha
,
beta
,
name
=
None
):
"""
**Add Position Encoding Layer**
...
...
python/paddle/fluid/tests/unittests/test_teacher_student_sigmoid_loss_op.py
0 → 100644
浏览文件 @
4f6e9e3a
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import
numpy
as
np
from
math
import
log
from
math
import
exp
from
op_test
import
OpTest
from
scipy.special
import
logit
from
scipy.special
import
expit
import
unittest
class
TestTeacherStudentSigmoidLossOp
(
OpTest
):
"""
Test teacher_student_sigmoid_loss with discrete one-hot labels.
"""
def
setUp
(
self
):
"""
ut
"""
self
.
op_type
=
"teacher_student_sigmoid_loss"
batch_size
=
16
num_classes
=
1
self
.
inputs
=
{
'X'
:
logit
(
np
.
random
.
uniform
(
0
,
1
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)),
'Label'
:
np
.
random
.
uniform
(
0
,
2
,
(
batch_size
,
num_classes
))
.
astype
(
"float32"
)
}
outs
=
[]
for
index
,
label
in
enumerate
(
self
.
inputs
[
"Label"
]):
x
=
self
.
inputs
[
"X"
][
index
]
if
label
<
-
1.0
:
outs
.
append
(
max
(
x
,
0.0
)
+
log
(
1.0
+
exp
(
-
abs
(
x
))))
elif
label
<
0.0
:
outs
.
append
(
max
(
x
,
0.0
)
-
x
+
log
(
1.0
+
exp
(
-
abs
(
x
))))
elif
label
<
1.0
:
outs
.
append
(
max
(
x
,
0.0
)
+
log
(
1.0
+
exp
(
-
abs
(
x
)))
+
\
max
(
x
,
0.0
)
-
x
*
label
+
log
(
1.0
+
exp
(
-
abs
(
x
))))
#print "33 python x:", x, "python label:", label, "term1:", max(x, 0.0) + log(1.0 + exp(-abs(x))), "term2:", max(x, 0.0) - x * label + log(1.0 + exp(-abs(x)))
else
:
outs
.
append
(
max
(
x
,
0.0
)
-
x
+
log
(
1.0
+
exp
(
-
abs
(
x
)))
+
\
max
(
x
,
0.0
)
-
x
*
(
label
-
1.0
)
+
log
(
1.0
+
exp
(
-
abs
(
x
))))
#print "44 python x:", x, "python label:", label, "term1:", max(x, 0.0) - x + log(1.0 + exp(-abs(x))), "term2:", max(x, 0.0) - x * (label - 1.0) + log(1.0 + exp(-abs(x)))
self
.
outputs
=
{
'Y'
:
np
.
array
(
outs
)}
def
test_check_output
(
self
):
"""
ut
"""
self
.
check_output
()
def
test_check_grad
(
self
):
"""
ut
"""
self
.
check_grad
([
"X"
],
"Y"
,
numeric_grad_delta
=
0.005
)
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