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b7a6cc9c
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体验新版 GitCode,发现更多精彩内容 >>
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b7a6cc9c
编写于
8月 21, 2017
作者:
G
Guo Sheng
提交者:
GitHub
8月 21, 2017
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差异文件
Merge pull request #3560 from guoshengCS/add-ShiftLayer
Add ScaleShiftLayer
上级
9871c6db
f6dc56aa
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
265 addition
and
1 deletion
+265
-1
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+5
-0
paddle/gserver/layers/ScaleShiftLayer.cpp
paddle/gserver/layers/ScaleShiftLayer.cpp
+107
-0
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+15
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+14
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+42
-0
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
.../paddle/trainer_config_helpers/tests/configs/file_list.sh
+1
-1
python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr
...rs/tests/configs/protostr/test_scale_shift_layer.protostr
+72
-0
python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py
...er_config_helpers/tests/configs/test_scale_shift_layer.py
+9
-0
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
b7a6cc9c
...
...
@@ -362,6 +362,11 @@ trans
.. autoclass:: paddle.v2.layer.trans
:noindex:
scale_shift
-----------
.. autoclass:: paddle.v2.layer.scale_shift
:noindex:
Sampling Layers
===============
...
...
paddle/gserver/layers/ScaleShiftLayer.cpp
0 → 100644
浏览文件 @
b7a6cc9c
/* 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 "Layer.h"
namespace
paddle
{
/**
* A layer applies a linear transformation to each element in each row of
* the input matrix. For each element, the layer first re-scale it and then
* adds a bias to it.
*
* \f[
* y = wx + b
* \f]
*
* Here, w is the scale and b is the bias. Both w and b are trainable scalars.
*
*/
class
ScaleShiftLayer
:
public
Layer
{
protected:
std
::
unique_ptr
<
Weight
>
scale_
;
std
::
unique_ptr
<
Weight
>
offset_
;
public:
explicit
ScaleShiftLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forward
(
PassType
passType
)
override
;
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
)
override
;
};
REGISTER_LAYER
(
scale_shift
,
ScaleShiftLayer
);
bool
ScaleShiftLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
inputLayers_
.
size
(),
1U
);
scale_
.
reset
(
new
Weight
(
1
,
1
,
parameters_
[
0
]));
if
(
biasParameter_
.
get
()
!=
NULL
)
{
offset_
=
std
::
unique_ptr
<
Weight
>
(
new
Weight
(
1
,
1
,
biasParameter_
));
}
return
true
;
}
void
ScaleShiftLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
MatrixPtr
inV
=
getInputValue
(
0
);
resetOutput
(
inV
->
getHeight
(),
inV
->
getWidth
());
MatrixPtr
outV
=
getOutputValue
();
real
scaleValue
=
scale_
->
getW
()
->
getElement
(
0
,
0
);
outV
->
mulScalar
(
*
inV
,
scaleValue
);
if
(
offset_
)
{
real
offsetValue
=
offset_
->
getW
()
->
getElement
(
0
,
0
);
outV
->
add
(
offsetValue
);
}
}
void
ScaleShiftLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
MatrixPtr
inV
=
getInputValue
(
0
);
MatrixPtr
inG
=
getInputGrad
(
0
);
MatrixPtr
outV
=
getOutputValue
();
MatrixPtr
outG
=
getOutputGrad
();
/* Calculate the parameter gradient for the current layer */
if
(
scale_
->
getWGrad
())
{
MatrixPtr
rowSumMtx
;
Matrix
::
resizeOrCreate
(
rowSumMtx
,
outG
->
getHeight
(),
1
,
false
,
useGpu_
);
// this_i = scaleDest * this_i + scaleSum * \sum_j b_{ij} * c_{ij}
rowSumMtx
->
sumOfProducts
(
/* b= */
*
inV
,
/* c= */
*
outG
,
/* scaleSum= */
1
,
/* scaleDest= */
0.
);
// this_i = scaleDest * this_i + scaleSum * \sum_j b_{ji}
scale_
->
getWGrad
()
->
sumCols
(
/* b= */
*
rowSumMtx
,
/* scaleSum= */
1.
,
/* scaleDest= */
1.
);
scale_
->
getParameterPtr
()
->
incUpdate
(
callback
);
}
if
(
offset_
&&
offset_
->
getWGrad
())
{
MatrixPtr
rowSumMtx
;
Matrix
::
resizeOrCreate
(
rowSumMtx
,
outG
->
getHeight
(),
1
,
false
,
useGpu_
);
rowSumMtx
->
sumRows
(
*
outG
,
1.
,
0.
);
offset_
->
getWGrad
()
->
sumCols
(
*
rowSumMtx
,
1.
,
1.
);
offset_
->
getParameterPtr
()
->
incUpdate
(
callback
);
}
/* Calculate the input layers error */
if
(
inG
)
{
real
scaleValue
=
scale_
->
getW
()
->
getElement
(
0
,
0
);
inG
->
add
(
*
outG
,
scaleValue
);
}
}
}
// namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
b7a6cc9c
...
...
@@ -2007,6 +2007,21 @@ TEST(Layer, RowL2NormLayer) {
}
}
TEST
(
Layer
,
ScaleShiftLayer
)
{
const
size_t
batchSize
=
16
;
const
size_t
size
=
32
;
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"scale_shift"
);
config
.
layerConfig
.
set_size
(
size
);
config
.
biasSize
=
1
;
config
.
inputDefs
.
push_back
(
{
INPUT_DATA
,
"input"
,
/* dim= */
size
,
/* paraSize= */
1
});
config
.
layerConfig
.
add_inputs
();
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"scale_shift"
,
batchSize
,
false
,
useGpu
,
false
);
}
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
b7a6cc9c
...
...
@@ -2232,6 +2232,20 @@ class ClipLayer(LayerBase):
self
.
config
.
inputs
[
0
].
clip_conf
.
max
=
max
@
config_layer
(
'scale_shift'
)
class
ScaleShiftLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
bias
=
True
,
**
xargs
):
super
(
ScaleShiftLayer
,
self
).
__init__
(
name
,
'scale_shift'
,
0
,
inputs
=
inputs
,
**
xargs
)
config_assert
(
len
(
self
.
inputs
)
==
1
,
'ScaleShiftLayer must have one and only one input.'
)
input_layer
=
self
.
get_input_layer
(
0
)
self
.
set_layer_size
(
input_layer
.
size
)
self
.
create_input_parameter
(
0
,
1
,
[
1
,
1
])
self
.
create_bias_parameter
(
bias
,
1
)
# key: cost type
# value: cost class
g_cost_map
=
{}
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
b7a6cc9c
...
...
@@ -133,6 +133,7 @@ __all__ = [
'clip_layer'
,
'slice_projection'
,
'kmax_sequence_score_layer'
,
'scale_shift_layer'
,
]
...
...
@@ -230,6 +231,7 @@ class LayerType(object):
CLIP_LAYER
=
'clip'
KMAX_SEQ_SCORE
=
'kmax_seq_score'
SCALE_SHIFT_LAYER
=
'scale_shift'
@
staticmethod
def
is_layer_type
(
type_name
):
...
...
@@ -6210,3 +6212,43 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1):
return
LayerOutput
(
name
,
LayerType
.
KMAX_SEQ_SCORE
,
parents
=
[
input
],
size
=
input
.
size
)
@
wrap_name_default
(
"scale_shift"
)
@
wrap_param_attr_default
()
@
wrap_bias_attr_default
()
def
scale_shift_layer
(
input
,
name
=
None
,
param_attr
=
None
,
bias_attr
=
None
):
"""
A layer applies a linear transformation to each element in each row of
the input matrix. For each element, the layer first re-scale it and then
adds a bias to it.
This layer is very like the SlopeInterceptLayer, except the scale and
bias are trainable.
.. math::
y = w * x + b
.. code-block:: python
scale_shift = scale_shift_layer(input=input_layer, bias_attr=False)
:param name: The Layer Name.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput.
:param param_attr: The parameter attribute of scaling.
:type param_attr: ParameterAttribute
:param bias_attr: The parameter attribute of shifting.
:type bias_attr: ParameterAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer
(
name
=
name
,
type
=
LayerType
.
SCALE_SHIFT_LAYER
,
inputs
=
Input
(
input
.
name
,
**
param_attr
.
attr
),
bias
=
ParamAttr
.
to_bias
(
bias_attr
))
return
LayerOutput
(
name
,
LayerType
.
SCALE_SHIFT_LAYER
,
parents
=
[
input
],
size
=
input
.
size
)
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
b7a6cc9c
...
...
@@ -8,6 +8,6 @@ test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_seq_select_layers
)
test_kmax_seq_socre_layer test_seq_select_layers
test_scale_shift_layer
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr
0 → 100644
浏览文件 @
b7a6cc9c
type: "nn"
layers {
name: "data"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__scale_shift_0__"
type: "scale_shift"
size: 100
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___scale_shift_0__.w0"
}
}
layers {
name: "__scale_shift_1__"
type: "scale_shift"
size: 100
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___scale_shift_1__.w0"
}
bias_parameter_name: "___scale_shift_1__.wbias"
}
parameters {
name: "___scale_shift_0__.w0"
size: 1
initial_mean: 0.0
initial_std: 1.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___scale_shift_1__.w0"
size: 1
initial_mean: 0.0
initial_std: 1.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___scale_shift_1__.wbias"
size: 1
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__scale_shift_0__"
output_layer_names: "__scale_shift_1__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__scale_shift_0__"
layer_names: "__scale_shift_1__"
input_layer_names: "data"
output_layer_names: "__scale_shift_0__"
output_layer_names: "__scale_shift_1__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py
0 → 100644
浏览文件 @
b7a6cc9c
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'data'
,
size
=
100
)
scale
=
scale_shift_layer
(
input
=
data
,
bias_attr
=
False
)
scale_shift
=
scale_shift_layer
(
input
=
data
)
outputs
(
scale
,
scale_shift
)
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