Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
SummerGao.
Paddle
提交
47eb8691
P
Paddle
项目概览
SummerGao.
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
47eb8691
编写于
8月 29, 2017
作者:
T
Tao Luo
提交者:
GitHub
8月 29, 2017
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #3571 from luotao1/huber_loss
refine Huber loss, add huber_regression_cost
上级
58419e7c
b709af61
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
289 addition
and
80 deletion
+289
-80
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+8
-3
paddle/gserver/layers/CostLayer.cpp
paddle/gserver/layers/CostLayer.cpp
+98
-41
paddle/gserver/layers/CostLayer.h
paddle/gserver/layers/CostLayer.h
+48
-17
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+20
-2
proto/ModelConfig.proto
proto/ModelConfig.proto
+3
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+12
-1
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+72
-8
python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr
..._helpers/tests/configs/protostr/test_cost_layers.protostr
+22
-5
python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py
.../trainer_config_helpers/tests/configs/test_cost_layers.py
+3
-1
python/paddle/v2/tests/test_layer.py
python/paddle/v2/tests/test_layer.py
+3
-2
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
47eb8691
...
...
@@ -419,9 +419,14 @@ multi_binary_label_cross_entropy_cost
.. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost
:noindex:
huber_cost
----------
.. autoclass:: paddle.v2.layer.huber_cost
huber_regression_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_regression_cost
:noindex:
huber_classification_cost
-------------------------
.. autoclass:: paddle.v2.layer.huber_classification_cost
:noindex:
lambda_cost
...
...
paddle/gserver/layers/CostLayer.cpp
浏览文件 @
47eb8691
...
...
@@ -572,13 +572,8 @@ void MultiBinaryLabelCrossEntropy::backwardImp(Matrix& output,
}
}
//
// Huber loss for robust 2-classes classification
//
REGISTER_LAYER
(
huber
,
HuberTwoClass
);
bool
HuberTwoClass
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
bool
HuberCost
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
CostLayer
::
init
(
layerMap
,
parameterMap
);
if
(
useGpu_
)
{
tmpCpuInput_
.
reserve
(
inputLayers_
.
size
());
...
...
@@ -589,7 +584,7 @@ bool HuberTwoClass::init(const LayerMap& layerMap,
return
true
;
}
void
Huber
TwoClass
::
forwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
cost
)
{
void
Huber
Cost
::
forwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
cost
)
{
if
(
useGpu_
)
{
for
(
size_t
i
=
0
;
i
<
inputLayers_
.
size
();
i
++
)
{
tmpCpuInput_
[
i
].
resizeAndCopyFrom
(
...
...
@@ -597,13 +592,87 @@ void HuberTwoClass::forwardImp(Matrix& output, Argument& label, Matrix& cost) {
}
hl_stream_synchronize
(
HPPL_STREAM_DEFAULT
);
}
forwardImpIn
(
output
,
label
,
cost
);
}
void
HuberTwoClass
::
forwardImpIn
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
target
)
{
//
// Huber loss for robust regression.
//
REGISTER_LAYER
(
huber_regression
,
HuberRegressionLoss
);
bool
HuberRegressionLoss
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
HuberCost
::
init
(
layerMap
,
parameterMap
);
delta_
=
config_
.
delta
();
return
true
;
}
void
HuberRegressionLoss
::
forwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
target
)
{
HuberCost
::
forwardImp
(
output
,
label
,
target
);
size_t
numSamples
=
target
.
getHeight
();
size_t
dim
=
output
.
getWidth
();
CHECK
(
label
.
value
);
CHECK_EQ
((
*
label
.
value
).
getHeight
(),
numSamples
);
CHECK_EQ
(
output
.
getHeight
(),
numSamples
);
CHECK_EQ
(
dim
,
(
*
label
.
value
).
getWidth
());
CHECK_EQ
(
target
.
getWidth
(),
(
size_t
)
1
);
real
*
out
=
useGpu_
?
tmpCpuInput_
[
0
].
value
->
getData
()
:
output
.
getData
();
real
*
lbl
=
useGpu_
?
tmpCpuInput_
[
1
].
value
->
getData
()
:
(
*
label
.
value
).
getData
();
std
::
vector
<
real
>
cost
(
numSamples
,
0
);
for
(
size_t
i
=
0
;
i
<
numSamples
;
++
i
)
{
for
(
size_t
j
=
0
;
j
<
dim
;
++
j
)
{
int
index
=
i
*
dim
+
j
;
real
a
=
std
::
abs
(
lbl
[
index
]
-
out
[
index
]);
if
(
a
<=
delta_
)
cost
[
i
]
+=
a
*
a
/
2
;
else
cost
[
i
]
+=
delta_
*
(
a
-
delta_
/
2
);
}
}
target
.
copyFrom
(
cost
.
data
(),
numSamples
);
}
void
HuberRegressionLoss
::
backwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
outputG
)
{
size_t
numSamples
=
output
.
getHeight
();
size_t
dim
=
output
.
getWidth
();
real
*
out
=
useGpu_
?
tmpCpuInput_
[
0
].
value
->
getData
()
:
output
.
getData
();
real
*
lbl
=
useGpu_
?
tmpCpuInput_
[
1
].
value
->
getData
()
:
(
*
label
.
value
).
getData
();
real
*
grad
=
useGpu_
?
tmpCpuInput_
[
0
].
grad
->
getData
()
:
outputG
.
getData
();
for
(
size_t
i
=
0
;
i
<
numSamples
;
++
i
)
{
for
(
size_t
j
=
0
;
j
<
dim
;
++
j
)
{
int
index
=
i
*
dim
+
j
;
real
a
=
lbl
[
index
]
-
out
[
index
];
if
(
std
::
abs
(
a
)
<=
delta_
)
grad
[
index
]
+=
-
a
;
else
grad
[
index
]
+=
a
>
0
?
-
delta_
:
delta_
;
}
}
if
(
useGpu_
)
outputG
.
copyFrom
(
grad
,
numSamples
*
dim
);
}
//
// Huber loss for robust 2-classes classification
//
REGISTER_LAYER
(
huber_classification
,
HuberTwoClassification
);
bool
HuberTwoClassification
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
return
HuberCost
::
init
(
layerMap
,
parameterMap
);
}
void
HuberTwoClassification
::
forwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
target
)
{
HuberCost
::
forwardImp
(
output
,
label
,
target
);
size_t
numSamples
=
target
.
getHeight
();
CHECK
(
label
.
ids
);
CHECK_EQ
((
*
label
.
ids
).
getSize
(),
numSamples
);
CHECK_EQ
(
output
.
getHeight
(),
numSamples
);
CHECK_EQ
(
output
.
getWidth
(),
(
size_t
)
1
);
...
...
@@ -611,47 +680,35 @@ void HuberTwoClass::forwardImpIn(Matrix& output,
real
*
out
=
useGpu_
?
tmpCpuInput_
[
0
].
value
->
getData
()
:
output
.
getData
();
int
*
lbl
=
useGpu_
?
tmpCpuInput_
[
1
].
ids
->
getData
()
:
(
*
label
.
ids
).
getData
();
std
::
vector
<
real
>
cost
(
numSamples
);
std
::
vector
<
real
>
cost
(
numSamples
,
0
);
for
(
size_t
i
=
0
;
i
<
numSamples
;
++
i
)
{
int
y
=
2
*
lbl
[
i
]
-
1
;
if
(
out
[
i
]
*
y
<
-
1
)
cost
[
i
]
=
-
4
*
out
[
i
]
*
y
;
else
if
(
out
[
i
]
*
y
<
1
)
cost
[
i
]
=
(
1
-
out
[
i
]
*
y
)
*
(
1
-
out
[
i
]
*
y
);
else
cost
[
i
]
=
0
;
real
a
=
out
[
i
]
*
y
;
if
(
a
<
-
1
)
cost
[
i
]
=
-
4
*
a
;
else
if
(
a
<
1
)
cost
[
i
]
=
(
1
-
a
)
*
(
1
-
a
);
}
target
.
copyFrom
(
cost
.
data
(),
numSamples
);
}
void
HuberTwoClass
::
backwardImp
(
Matrix
&
outputValue
,
Argument
&
label
,
Matrix
&
outputGrad
)
{
if
(
useGpu_
)
{
backwardImpIn
(
*
tmpCpuInput_
[
0
].
value
,
tmpCpuInput_
[
1
],
*
tmpCpuInput_
[
0
].
grad
);
outputGrad
.
copyFrom
(
*
tmpCpuInput_
[
0
].
grad
);
}
else
{
backwardImpIn
(
outputValue
,
label
,
outputGrad
);
}
}
void
HuberTwoClass
::
backwardImpIn
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
outputG
)
{
void
HuberTwoClassification
::
backwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
outputG
)
{
size_t
numSamples
=
output
.
getHeight
();
real
*
out
=
output
.
getData
();
real
*
grad
=
outputG
.
getData
();
int
*
lbl
=
(
*
label
.
ids
)
.
getData
();
real
*
out
=
useGpu_
?
tmpCpuInput_
[
0
].
value
->
getData
()
:
output
.
getData
();
int
*
lbl
=
useGpu_
?
tmpCpuInput_
[
1
].
ids
->
getData
()
:
(
*
label
.
ids
)
.
getData
();
real
*
grad
=
useGpu_
?
tmpCpuInput_
[
0
].
grad
->
getData
()
:
outputG
.
getData
();
for
(
size_t
i
=
0
;
i
<
numSamples
;
++
i
)
{
int
y
=
2
*
lbl
[
i
]
-
1
;
if
(
y
*
out
[
i
]
<
-
1
)
real
a
=
out
[
i
]
*
y
;
if
(
a
<
-
1
)
grad
[
i
]
+=
-
4
*
y
;
else
if
(
y
*
out
[
i
]
<
1
)
grad
[
i
]
+=
-
2
*
(
1
-
y
*
out
[
i
]
)
*
y
;
else
if
(
a
<
1
)
grad
[
i
]
+=
-
2
*
(
1
-
a
)
*
y
;
}
if
(
useGpu_
)
outputG
.
copyFrom
(
grad
,
numSamples
);
}
/**
* This cost layer compute the sum of its input as loss.
* \f[
...
...
paddle/gserver/layers/CostLayer.h
浏览文件 @
47eb8691
...
...
@@ -304,37 +304,68 @@ public:
Matrix
&
outputGrad
)
override
;
};
/**
* Huber loss for robust 2-classes classification.
*
* For label={0, 1}, let y=2*label-1. Given output f, the loss is:
* \f[
* Loss =
* \left\{\begin{matrix}
* 4 * y * f & \textit{if} \ \ y* f < -1 \\
* (1 - y * f)^2 & \textit{if} \ \ -1 < y * f < 1 \\
* 0 & \textit{otherwise}
* \end{matrix}\right.
* \f]
/*
* A base layer for HuberRegressionLoss and HuberTwoClassification.
*/
class
HuberTwoClass
:
public
CostLayer
{
class
HuberCost
:
public
CostLayer
{
public:
std
::
vector
<
Argument
>
tmpCpuInput_
;
public:
explicit
HuberTwoClass
(
const
LayerConfig
&
config
)
:
CostLayer
(
config
)
{}
explicit
HuberCost
(
const
LayerConfig
&
config
)
:
CostLayer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
cost
)
override
;
void
forwardImpIn
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
cost
);
void
backwardImp
(
Matrix
&
outputValue
,
Argument
&
label
,
Matrix
&
outputGrad
)
{}
};
/**
* Huber loss for robust regression.
*
* Given output f(x), label y and delta, the loss is:
* Loss = 0.5 * (1 - y * f)^2, if abs(y - f) <= delta \\
* Loss = delta * abs(y - f) - 0.5 * delta^2, otherwise
*/
class
HuberRegressionLoss
:
public
HuberCost
{
public:
explicit
HuberRegressionLoss
(
const
LayerConfig
&
config
)
:
HuberCost
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
cost
)
override
;
void
backwardImp
(
Matrix
&
outputValue
,
Argument
&
label
,
Matrix
&
outputGrad
)
override
;
void
backwardImpIn
(
Matrix
&
outputValue
,
Argument
&
label
,
Matrix
&
outputGrad
);
protected:
real
delta_
;
};
/**
* Huber loss for robust 2-classes classification.
*
* For label={0, 1}, let y=2*label-1. Given output f(x), the loss is:
* Loss = 4 * y * f, if y* f < -1 \\
* Loss = (1 - y * f)^2, if -1 < y * f < 1 \\
* Loss = 0, otherwise
*/
class
HuberTwoClassification
:
public
HuberCost
{
public:
explicit
HuberTwoClassification
(
const
LayerConfig
&
config
)
:
HuberCost
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forwardImp
(
Matrix
&
output
,
Argument
&
label
,
Matrix
&
cost
)
override
;
void
backwardImp
(
Matrix
&
outputValue
,
Argument
&
label
,
Matrix
&
outputGrad
)
override
;
};
typedef
std
::
shared_ptr
<
CostLayer
>
CostLayerPtr
;
...
...
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
47eb8691
...
...
@@ -850,9 +850,27 @@ TEST(Layer, square_error_weighted) {
}
}
TEST
(
Layer
,
huber_regression_loss
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"huber_regression"
);
config
.
biasSize
=
0
;
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_0"
,
10
,
0
});
config
.
inputDefs
.
push_back
({
INPUT_DATA_TARGET
,
"layer_1"
,
10
,
0
});
config
.
layerConfig
.
add_inputs
();
config
.
layerConfig
.
add_inputs
();
for
(
auto
useGpu
:
{
false
,
true
})
{
for
(
auto
delta
:
{
1
,
3
,
5
})
{
config
.
layerConfig
.
set_delta
(
delta
);
testLayerGrad
(
config
,
"huber_regression"
,
100
,
/* trans */
false
,
useGpu
);
}
}
}
TEST
(
Layer
,
huber_two_class
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"huber"
);
config
.
layerConfig
.
set_type
(
"huber
_classification
"
);
config
.
biasSize
=
0
;
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_0"
,
1
,
0
});
...
...
@@ -861,7 +879,7 @@ TEST(Layer, huber_two_class) {
config
.
layerConfig
.
add_inputs
();
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"huber"
,
100
,
/* trans */
false
,
useGpu
);
testLayerGrad
(
config
,
"huber
_two_class
"
,
100
,
/* trans */
false
,
useGpu
);
}
}
...
...
proto/ModelConfig.proto
浏览文件 @
47eb8691
...
...
@@ -499,6 +499,9 @@ message LayerConfig {
optional
int32
axis
=
54
[
default
=
2
];
repeated
uint32
offset
=
55
;
repeated
uint32
shape
=
56
;
// for HuberRegressionLoss
optional
double
delta
=
57
[
default
=
1.0
];
}
message
EvaluatorConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
47eb8691
...
...
@@ -2274,7 +2274,7 @@ define_cost('PnpairValidation', 'pnpair-validation')
define_cost
(
'SumOfSquaresCostLayer'
,
'square_error'
)
define_cost
(
'MultiBinaryLabelCrossEntropy'
,
'multi_binary_label_cross_entropy'
)
define_cost
(
'SoftBinaryClassCrossEntropy'
,
'soft_binary_class_cross_entropy'
)
define_cost
(
'HuberTwoClass
'
,
'huber
'
)
define_cost
(
'HuberTwoClass
ification'
,
'huber_classification
'
)
define_cost
(
'SumCost'
,
'sum_cost'
)
define_cost
(
'SmoothL1Cost'
,
'smooth_l1'
)
...
...
@@ -2336,6 +2336,17 @@ class LambdaCost(LayerBase):
self
.
config
.
max_sort_size
=
max_sort_size
@
config_layer
(
'huber_regression'
)
class
HuberRegressionLoss
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
delta
=
1.
,
coeff
=
1.
,
device
=
None
):
super
(
HuberRegressionLoss
,
self
).
__init__
(
name
,
'huber_regression'
,
1
,
inputs
=
inputs
,
device
=
device
)
config_assert
(
len
(
self
.
inputs
)
==
2
,
'HuberRegression must have 2 inputs'
)
self
.
config
.
delta
=
delta
self
.
config
.
coeff
=
coeff
@
config_layer
(
'nce'
)
class
NCELayer
(
LayerBase
):
def
__init__
(
self
,
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
47eb8691
...
...
@@ -110,7 +110,8 @@ __all__ = [
'sum_cost'
,
'rank_cost'
,
'lambda_cost'
,
'huber_cost'
,
'huber_regression_cost'
,
'huber_classification_cost'
,
'block_expand_layer'
,
'maxout_layer'
,
'out_prod_layer'
,
...
...
@@ -220,7 +221,8 @@ class LayerType(object):
RANK_COST
=
'rank-cost'
LAMBDA_COST
=
'lambda_cost'
HUBER
=
'huber'
HUBER_REGRESSION
=
'huber_regression'
HUBER_CLASSIFICATION
=
'huber_classification'
CROSS_ENTROPY
=
'multi-class-cross-entropy'
CROSS_ENTROPY_WITH_SELFNORM
=
'multi_class_cross_entropy_with_selfnorm'
SOFT_BIN_CLASS_CROSS_ENTROPY
=
'soft_binary_class_cross_entropy'
...
...
@@ -5644,16 +5646,77 @@ def sum_cost(input, name=None, layer_attr=None):
@
wrap_name_default
()
@
layer_support
()
def
huber_cost
(
input
,
label
,
name
=
None
,
coeff
=
1.0
,
layer_attr
=
None
):
def
huber_regression_cost
(
input
,
label
,
name
=
None
,
delta
=
1.0
,
coeff
=
1.0
,
layer_attr
=
None
):
"""
In statistics, the Huber loss is a loss function used in robust regression,
that is less sensitive to outliers in data than the squared error loss.
Given a prediction f(x), a label y and :math:`\delta`, the loss function
is defined as:
.. math:
loss = 0.5*\left ( y-f(x)
\r
ight )^2, \left | y-f(x)
\r
ight |\leq \delta
loss = \delta \left | y-f(x)
\r
ight |-0.5\delta ^2, otherwise
The example usage is:
.. code-block:: python
cost = huber_regression_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
:param label: The input label.
:type input: LayerOutput.
:param name: The name of this layers. It is not necessary.
:type name: None|basestring.
:param delta: The difference between the observed and predicted values.
:type delta: float.
:param coeff: The coefficient affects the gradient in the backward.
:type coeff: float.
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
"""
assert
isinstance
(
input
,
LayerOutput
)
Layer
(
name
=
name
,
type
=
LayerType
.
HUBER_REGRESSION
,
inputs
=
[
input
.
name
,
label
.
name
],
delta
=
delta
,
coeff
=
coeff
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
LayerType
.
HUBER_REGRESSION
,
parents
=
[
input
,
label
],
size
=
1
)
@
wrap_name_default
()
@
layer_support
()
def
huber_classification_cost
(
input
,
label
,
name
=
None
,
coeff
=
1.0
,
layer_attr
=
None
):
"""
A loss layer for huber loss.
For classification purposes, a variant of the Huber loss called modified Huber
is sometimes used. Given a prediction f(x) (a real-valued classifier score) and
a true binary class label :math:`y\in \left \{-1, 1
\r
ight \}`, the modified Huber
loss is defined as:
.. math:
loss = \max \left ( 0, 1-yf(x)
\r
ight )^2, yf(x)\geq 1
loss = -4yf(x),
\t
ext{otherwise}
The example usage is:
.. code-block:: python
cost = huber_cost(input=input_layer,
label=label_layer)
cost = huber_classification_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
...
...
@@ -5673,11 +5736,12 @@ def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None):
assert
input
.
size
==
1
Layer
(
name
=
name
,
type
=
LayerType
.
HUBER
,
type
=
LayerType
.
HUBER
_CLASSIFICATION
,
inputs
=
[
input
.
name
,
label
.
name
],
coeff
=
coeff
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
LayerType
.
HUBER
,
parents
=
[
input
,
label
],
size
=
1
)
return
LayerOutput
(
name
,
LayerType
.
HUBER_CLASSIFICATION
,
parents
=
[
input
,
label
],
size
=
1
)
@
wrap_name_default
()
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr
浏览文件 @
47eb8691
...
...
@@ -167,6 +167,20 @@ layers {
softmax_selfnorm_alpha: 0.1
coeff: 1.0
}
layers {
name: "__huber_regression_cost_0__"
type: "huber_regression"
size: 1
active_type: ""
inputs {
input_layer_name: "input"
}
inputs {
input_layer_name: "labels"
}
coeff: 1.0
delta: 1.0
}
layers {
name: "huber_probs"
type: "data"
...
...
@@ -180,8 +194,8 @@ layers {
active_type: ""
}
layers {
name: "__huber_cost_0__"
type: "huber"
name: "__huber_c
lassification_c
ost_0__"
type: "huber
_classification
"
size: 1
active_type: ""
inputs {
...
...
@@ -300,7 +314,8 @@ output_layer_names: "__rank_cost_0__"
output_layer_names: "__lambda_cost_0__"
output_layer_names: "__cross_entropy_0__"
output_layer_names: "__cross_entropy_with_selfnorm_0__"
output_layer_names: "__huber_cost_0__"
output_layer_names: "__huber_regression_cost_0__"
output_layer_names: "__huber_classification_cost_0__"
output_layer_names: "__multi_binary_label_cross_entropy_0__"
output_layer_names: "__sum_cost_0__"
output_layer_names: "__nce_layer_0__"
...
...
@@ -324,9 +339,10 @@ sub_models {
layer_names: "__lambda_cost_0__"
layer_names: "__cross_entropy_0__"
layer_names: "__cross_entropy_with_selfnorm_0__"
layer_names: "__huber_regression_cost_0__"
layer_names: "huber_probs"
layer_names: "huber_label"
layer_names: "__huber_cost_0__"
layer_names: "__huber_c
lassification_c
ost_0__"
layer_names: "__multi_binary_label_cross_entropy_0__"
layer_names: "__sum_cost_0__"
layer_names: "__nce_layer_0__"
...
...
@@ -349,7 +365,8 @@ sub_models {
output_layer_names: "__lambda_cost_0__"
output_layer_names: "__cross_entropy_0__"
output_layer_names: "__cross_entropy_with_selfnorm_0__"
output_layer_names: "__huber_cost_0__"
output_layer_names: "__huber_regression_cost_0__"
output_layer_names: "__huber_classification_cost_0__"
output_layer_names: "__multi_binary_label_cross_entropy_0__"
output_layer_names: "__sum_cost_0__"
output_layer_names: "__nce_layer_0__"
...
...
python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py
浏览文件 @
47eb8691
...
...
@@ -33,7 +33,9 @@ outputs(
input
=
probs
,
label
=
xe_label
),
cross_entropy_with_selfnorm
(
input
=
probs
,
label
=
xe_label
),
huber_cost
(
huber_regression_cost
(
input
=
seq_in
,
label
=
labels
),
huber_classification_cost
(
input
=
data_layer
(
name
=
'huber_probs'
,
size
=
1
),
label
=
data_layer
(
...
...
python/paddle/v2/tests/test_layer.py
浏览文件 @
47eb8691
...
...
@@ -141,12 +141,13 @@ class CostLayerTest(unittest.TestCase):
cost8
=
layer
.
rank_cost
(
left
=
score
,
right
=
score
,
label
=
score
)
cost9
=
layer
.
lambda_cost
(
input
=
inference
,
score
=
score
)
cost10
=
layer
.
sum_cost
(
input
=
inference
)
cost11
=
layer
.
huber_cost
(
input
=
score
,
label
=
label
)
cost11
=
layer
.
huber_regression_cost
(
input
=
score
,
label
=
label
)
cost12
=
layer
.
huber_classification_cost
(
input
=
score
,
label
=
label
)
print
layer
.
parse_network
([
cost1
,
cost2
])
print
layer
.
parse_network
([
cost3
,
cost4
])
print
layer
.
parse_network
([
cost5
,
cost6
])
print
layer
.
parse_network
([
cost7
,
cost8
,
cost9
,
cost10
,
cost11
])
print
layer
.
parse_network
([
cost7
,
cost8
,
cost9
,
cost10
,
cost11
,
cost12
])
crf
=
layer
.
crf
(
input
=
inference
,
label
=
label
)
crf_decoding
=
layer
.
crf_decoding
(
input
=
inference
,
size
=
3
)
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录