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体验新版 GitCode,发现更多精彩内容 >>
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39bce49b
编写于
11月 20, 2017
作者:
W
wanghaox
浏览文件
操作
浏览文件
下载
差异文件
fix some comments
上级
36035467
c6ec26df
变更
22
隐藏空白更改
内联
并排
Showing
22 changed file
with
683 addition
and
44 deletion
+683
-44
CMakeLists.txt
CMakeLists.txt
+1
-1
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+5
-0
paddle/framework/backward.cc
paddle/framework/backward.cc
+3
-8
paddle/framework/backward_test.cc
paddle/framework/backward_test.cc
+7
-0
paddle/framework/data_type.h
paddle/framework/data_type.h
+5
-0
paddle/gserver/layers/L2DistanceLayer.cpp
paddle/gserver/layers/L2DistanceLayer.cpp
+91
-0
paddle/gserver/layers/L2DistanceLayer.h
paddle/gserver/layers/L2DistanceLayer.h
+52
-0
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+20
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+5
-0
paddle/operators/logical_op.cc
paddle/operators/logical_op.cc
+153
-0
paddle/operators/logical_op.cu
paddle/operators/logical_op.cu
+24
-0
paddle/operators/logical_op.h
paddle/operators/logical_op.h
+93
-0
paddle/operators/sequence_slice_op.h
paddle/operators/sequence_slice_op.h
+3
-3
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+30
-14
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+48
-1
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
.../paddle/trainer_config_helpers/tests/configs/file_list.sh
+3
-2
python/paddle/trainer_config_helpers/tests/configs/protostr/test_l2_distance_layer.protostr
...rs/tests/configs/protostr/test_l2_distance_layer.protostr
+39
-0
python/paddle/trainer_config_helpers/tests/configs/test_l2_distance_layer.py
...er_config_helpers/tests/configs/test_l2_distance_layer.py
+7
-0
python/paddle/v2/fluid/tests/test_logical_op.py
python/paddle/v2/fluid/tests/test_logical_op.py
+35
-0
python/paddle/v2/fluid/tests/test_optimizer.py
python/paddle/v2/fluid/tests/test_optimizer.py
+40
-8
python/paddle/v2/fluid/tests/test_program.py
python/paddle/v2/fluid/tests/test_program.py
+9
-5
python/paddle/v2/fluid/tests/test_regularizer.py
python/paddle/v2/fluid/tests/test_regularizer.py
+10
-2
未找到文件。
CMakeLists.txt
浏览文件 @
39bce49b
...
...
@@ -109,7 +109,7 @@ else()
endif
()
set
(
WITH_MKLML
${
WITH_MKL
}
)
if
(
WITH_MKL AND
${
AVX2_FOUND
}
)
if
(
WITH_MKL AND
AVX2_FOUND
)
set
(
WITH_MKLDNN ON
)
else
()
message
(
STATUS
"Do not have AVX2 intrinsics and disabled MKL-DNN"
)
...
...
doc/api/v2/config/layer.rst
浏览文件 @
39bce49b
...
...
@@ -382,6 +382,11 @@ cos_sim
.. autoclass:: paddle.v2.layer.cos_sim
:noindex:
l2_distance
-----------
.. autoclass:: paddle.v2.layer.l2_distance
:noindex:
trans
-----
.. autoclass:: paddle.v2.layer.trans
...
...
paddle/framework/backward.cc
浏览文件 @
39bce49b
...
...
@@ -513,19 +513,14 @@ ParamGradInfoMap AppendBackward(
const
int
root_block_idx
=
0
;
auto
root_block
=
program_desc
.
MutableBlock
(
root_block_idx
);
// insert fill one op for target
// TODO(qiao) add some check to the target.
std
::
string
fill_one_op_out
=
GradVarName
(
target
.
Name
());
std
::
vector
<
int64_t
>
target_shape_desc
=
target
.
Shape
();
std
::
vector
<
int
>
target_shape
;
std
::
transform
(
target_shape_desc
.
begin
(),
target_shape_desc
.
end
(),
std
::
back_inserter
(
target_shape
),
[](
int64_t
dim
)
{
return
static_cast
<
int
>
(
dim
);
});
bool
is_scalar
=
target
.
Shape
()
==
std
::
vector
<
int64_t
>
{
1
};
PADDLE_ENFORCE
(
is_scalar
,
"target should be scalar"
);
VLOG
(
3
)
<<
"backward from loss="
<<
target
.
Name
()
<<
" data_type="
<<
target
.
GetDataType
();
std
::
unique_ptr
<
OpDescBind
>
fill_one_op
(
new
OpDescBind
(
"fill_constant"
,
{},
{{
"Out"
,
{
fill_one_op_out
}}},
{{
"shape"
,
target_shape
},
{{
"shape"
,
std
::
vector
<
int
>
{
1
}
},
{
"value"
,
static_cast
<
float
>
(
1.0
)},
{
"data_type"
,
target
.
GetDataType
()}}));
// infer var type of fill_one_op
...
...
paddle/framework/backward_test.cc
浏览文件 @
39bce49b
...
...
@@ -508,6 +508,7 @@ TEST(Backward, simple_single_op) {
op
->
SetOutput
(
"Out"
,
{
"out"
});
auto
target
=
f
::
VarDescBind
(
"out"
);
target
.
SetShape
({
1
});
auto
var_to_grad
=
AppendBackward
(
program
,
target
,
{});
ASSERT_EQ
(
block
->
AllOps
().
size
(),
3UL
);
...
...
@@ -544,6 +545,7 @@ TEST(Backward, default_attribute) {
op
->
CheckAttrs
();
auto
target
=
f
::
VarDescBind
(
"out"
);
target
.
SetShape
({
1
});
AppendBackward
(
program
,
target
,
{});
ASSERT_EQ
(
block
->
AllOps
().
size
(),
3UL
);
...
...
@@ -581,6 +583,7 @@ TEST(Backward, simple_mult_op) {
op3
->
SetOutput
(
"Out"
,
{
"out3"
});
auto
target
=
f
::
VarDescBind
(
"out3"
);
target
.
SetShape
({
1
});
size_t
forward_len
=
block
->
AllOps
().
size
();
auto
var_to_grad
=
AppendBackward
(
program
,
target
,
{});
...
...
@@ -670,6 +673,7 @@ TEST(Backward, intermedia_var_no_grad) {
op4
->
SetOutput
(
"Out"
,
{
"out4"
});
auto
target
=
f
::
VarDescBind
(
"out4"
);
target
.
SetShape
({
1
});
size_t
forward_len
=
block
->
AllOps
().
size
();
auto
var_to_grad
=
AppendBackward
(
program
,
target
,
{
"out3"
});
...
...
@@ -730,6 +734,7 @@ TEST(Backward, var_no_grad) {
op2
->
SetOutput
(
"Z"
,
{
"z2"
});
auto
target
=
f
::
VarDescBind
(
"z2"
);
target
.
SetShape
({
1
});
size_t
forward_len
=
block
->
AllOps
().
size
();
auto
var_to_grad
=
AppendBackward
(
program
,
target
,
{
"z1"
});
...
...
@@ -810,6 +815,7 @@ TEST(Backward, shared_var) {
op3
->
SetOutput
(
"Out"
,
{
"out3"
});
auto
target
=
f
::
VarDescBind
(
"out3"
);
target
.
SetShape
({
1
});
size_t
forward_len
=
block
->
AllOps
().
size
();
auto
var_to_grad
=
AppendBackward
(
program
,
target
,
{});
...
...
@@ -888,6 +894,7 @@ TEST(Backward, half_backward) {
op1
->
SetOutput
(
"Out"
,
{
"out"
});
auto
target
=
f
::
VarDescBind
(
"out"
);
target
.
SetShape
({
1
});
size_t
forward_len
=
block
->
AllOps
().
size
();
auto
var_to_grad
=
AppendBackward
(
program
,
target
,
{
"b"
});
f
::
OpDescBind
*
fill_op
=
block
->
AllOps
()[
forward_len
];
...
...
paddle/framework/data_type.h
浏览文件 @
39bce49b
...
...
@@ -46,6 +46,8 @@ inline std::type_index ToTypeIndex(DataType type) {
return
typeid
(
int
);
case
DataType
::
INT64
:
return
typeid
(
int64_t
);
case
DataType
::
BOOL
:
return
typeid
(
bool
);
default:
PADDLE_THROW
(
"Not support type %d"
,
type
);
}
...
...
@@ -66,6 +68,9 @@ inline void VisitDataType(DataType type, Visitor visitor) {
case
DataType
::
INT64
:
visitor
.
template
operator
()
<
int64_t
>();
break
;
case
DataType
::
BOOL
:
visitor
.
template
operator
()
<
bool
>();
break
;
default:
PADDLE_THROW
(
"Not supported"
);
}
...
...
paddle/gserver/layers/L2DistanceLayer.cpp
0 → 100644
浏览文件 @
39bce49b
/* 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 "L2DistanceLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace
paddle
{
REGISTER_LAYER
(
l2_distance
,
L2DistanceLayer
);
bool
L2DistanceLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
/* Initialize the basic parent class */
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
inputLayers_
.
size
(),
2UL
)
<<
"The L2DistanceLayer accepts two and "
<<
"only two inputs."
;
CHECK_EQ
(
getSize
(),
1UL
)
<<
"The output dimensionality of L2DistanceLayer "
<<
"is fixed to be 1."
;
return
true
;
}
void
L2DistanceLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
const
auto
inV1
=
getInputValue
(
0
);
const
auto
inV2
=
getInputValue
(
1
);
CHECK
(
inV1
&&
inV2
);
CHECK_EQ
(
inV1
->
getHeight
(),
inV2
->
getHeight
())
<<
"The height of two inputs of this layer must be the same."
;
CHECK_EQ
(
inV1
->
getWidth
(),
inV2
->
getWidth
())
<<
"The width of two inputs of this layer must be the same."
;
int
batchSize
=
inV1
->
getHeight
();
int
output_dim
=
getSize
();
{
REGISTER_TIMER_INFO
(
"L2DistanceBpAtvTimer"
,
getName
().
c_str
());
reserveOutput
(
batchSize
,
output_dim
);
auto
outV
=
getOutputValue
();
CHECK
(
outV
)
<<
"The output matrix should not be null."
;
Matrix
::
resizeOrCreate
(
inputSub_
,
inV1
->
getHeight
(),
inV1
->
getWidth
(),
false
,
useGpu_
);
inputSub_
->
assign
(
*
inV1
);
inputSub_
->
sub
(
*
inV2
);
outV
->
sumOfProducts
(
*
inputSub_
,
*
inputSub_
,
1
,
0
);
outV
->
sqrt2
(
*
outV
);
}
}
void
L2DistanceLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
const
auto
outG
=
getOutputGrad
();
const
auto
outV
=
getOutputValue
();
CHECK
(
outG
&&
outV
);
auto
inGrad1
=
getInputGrad
(
0
);
auto
inGrad2
=
getInputGrad
(
1
);
{
REGISTER_TIMER_INFO
(
"L2DistanceBpAtvTimer"
,
getName
().
c_str
());
if
(
inGrad1
||
inGrad2
)
{
outV
->
scalarDiv
(
*
outV
,
1.
);
outV
->
dotMul
(
*
outG
,
*
outV
);
}
if
(
inGrad1
)
inGrad1
->
addRowScale
(
0
,
*
inputSub_
,
*
outV
);
if
(
inGrad2
)
{
inputSub_
->
mulScalar
(
-
1.
);
inGrad2
->
addRowScale
(
0
,
*
inputSub_
,
*
outV
);
}
}
}
}
// namespace paddle
paddle/gserver/layers/L2DistanceLayer.h
0 → 100644
浏览文件 @
39bce49b
/* 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 "Layer.h"
#include "paddle/math/Matrix.h"
namespace
paddle
{
/**
* @brief The layer calculates the l2 distance between two input vectors.
* \f[
* f(\bf{x}, \bf{y}) = \sqrt{\sum_{i=1}^D(x_i - y_i)}
* \f]
*
* - Input1: A vector (batchSize * dataDim)
* - Input2: A vector (batchSize * dataDim)
* - Output: A vector (batchSize * 1)
*
* The configuration api is: l2_distance_layer.
*/
class
L2DistanceLayer
:
public
Layer
{
public:
explicit
L2DistanceLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
~
L2DistanceLayer
()
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forward
(
PassType
passType
)
override
;
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
)
override
;
private:
// Store the result of subtracting Input2 from Input1 in forward computation,
// which will be reused in backward computation.
MatrixPtr
inputSub_
;
};
}
// namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
39bce49b
...
...
@@ -583,6 +583,7 @@ TEST(Layer, maxoutLayer) {
testLayerGrad
(
config
,
"maxout"
,
10
,
false
,
useGpu
);
}
}
void
testFcLayer
(
string
format
,
size_t
nnz
)
{
TestConfig
config
;
config
.
biasSize
=
1024
;
...
...
@@ -2444,6 +2445,25 @@ TEST(Layer, ScaleSubRegionLayer) {
}
}
TEST
(
Layer
,
L2DistanceLayer
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"l2_distance"
);
config
.
layerConfig
.
set_size
(
1
);
config
.
biasSize
=
0
;
const
size_t
input_dim
=
27
;
const
size_t
batch_size
=
11
;
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_0"
,
input_dim
,
0
});
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_1"
,
input_dim
,
0
});
config
.
layerConfig
.
add_inputs
();
config
.
layerConfig
.
add_inputs
();
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"l2_distance"
,
batch_size
,
false
,
useGpu
);
}
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
39bce49b
...
...
@@ -87,6 +87,11 @@ function(op_library TARGET)
file
(
APPEND
${
pybind_file
}
"USE_OP(pool2d_cudnn);
\n
"
)
endif
()
if
(
"
${
TARGET
}
"
STREQUAL
"logical_op"
)
set
(
pybind_flag 1
)
file
(
APPEND
${
pybind_file
}
"USE_OP(logical_and);
\n
"
)
endif
()
# pool_with_index_op contains several operators
if
(
"
${
TARGET
}
"
STREQUAL
"pool_with_index_op"
)
set
(
pybind_flag 1
)
...
...
paddle/operators/logical_op.cc
0 → 100644
浏览文件 @
39bce49b
/* 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. */
#include "paddle/operators/logical_op.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
OpComment
>
class
BinaryLogicalOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
BinaryLogicalOpProtoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
OpComment
comment
;
AddInput
(
"X"
,
string
::
Sprintf
(
"(LoDTensor) Left hand operand of %s operator"
,
comment
.
type
));
AddInput
(
"Y"
,
string
::
Sprintf
(
"(LoDTensor) Right hand operand of %s operator"
,
comment
.
type
));
AddOutput
(
"Out"
,
string
::
Sprintf
(
"(LoDTensor) n-dim bool tensor. Each element is %s"
,
comment
.
equation
));
AddComment
(
string
::
Sprintf
(
R"DOC(%s Operator
It operates element-wise on X and Y, and returns the Out. X, Y and Out are N-dim boolean tensors.
Each element of Out is calculated by %s
)DOC"
,
comment
.
type
,
comment
.
equation
));
}
};
template
<
typename
OpComment
>
class
UnaryLogicalOpProtoMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
UnaryLogicalOpProtoMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
OpComment
comment
;
AddInput
(
"X"
,
string
::
Sprintf
(
"(LoDTensor) Operand of %s operator"
,
comment
.
type
));
AddOutput
(
"Out"
,
string
::
Sprintf
(
"(LoDTensor) n-dim bool tensor. Each element is %s"
,
comment
.
equation
));
AddComment
(
string
::
Sprintf
(
R"DOC(%s Operator
It operates element-wise on X, and returns the Out. X and Out are N-dim boolean tensors.
Each element of Out is calculated by %s
)DOC"
,
comment
.
type
,
comment
.
equation
));
}
};
template
<
typename
OpComment
>
class
BinaryLogicalOpInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
OpComment
comment
;
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
),
"Input(X) of %s operator must not be null"
,
comment
.
type
);
PADDLE_ENFORCE
(
context
->
HasInput
(
"Y"
),
"Input(Y) of %s operator must not be null"
,
comment
.
type
);
auto
dim_x
=
context
->
GetInputDim
(
"X"
);
auto
dim_y
=
context
->
GetInputDim
(
"Y"
);
PADDLE_ENFORCE_EQ
(
framework
::
product
(
dim_x
),
framework
::
product
(
dim_y
),
"The number of elements in X and Y should be same"
);
context
->
SetOutputDim
(
"Out"
,
context
->
GetInputDim
(
"X"
));
context
->
ShareLoD
(
"X"
,
"Out"
);
}
};
template
<
typename
OpComment
>
class
UnaryLogicalOpInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
context
)
const
override
{
OpComment
comment
;
PADDLE_ENFORCE
(
context
->
HasInput
(
"X"
),
"Input(X) of %s operator must not be null"
,
comment
.
type
);
auto
dim_x
=
context
->
GetInputDim
(
"X"
);
context
->
SetOutputDim
(
"Out"
,
context
->
GetInputDim
(
"X"
));
context
->
ShareLoD
(
"X"
,
"Out"
);
}
};
class
LogicalOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
OpKernelType
kt
=
OperatorWithKernel
::
GetKernelType
(
ctx
);
// LogicalOp kernel's device type is decided by input tensor place
kt
.
place_
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
place
();
return
kt
;
}
};
}
// namespace operators
}
// namespace paddle
#define REGISTER_BINARY_LOGICAL_OP(op_type, _equation) \
struct _##op_type##Comment { \
static char type[]; \
static char equation[]; \
}; \
char _##op_type##Comment::type[]{#op_type}; \
char _##op_type##Comment::equation[]{_equation}; \
REGISTER_OPERATOR( \
op_type, ::paddle::operators::LogicalOp, \
::paddle::operators::BinaryLogicalOpProtoMaker<_##op_type##Comment>, \
::paddle::operators::BinaryLogicalOpInferShape<_##op_type##Comment>, \
::paddle::framework::EmptyGradOpMaker);
#define REGISTER_UNARY_LOGICAL_OP(op_type, _equation) \
struct _##op_type##Comment { \
static char type[]; \
static char equation[]; \
}; \
char _##op_type##Comment::type[]{#op_type}; \
char _##op_type##Comment::equation[]{_equation}; \
REGISTER_OPERATOR( \
op_type, ::paddle::operators::LogicalOp, \
::paddle::operators::UnaryLogicalOpProtoMaker<_##op_type##Comment>, \
::paddle::operators::UnaryLogicalOpInferShape<_##op_type##Comment>, \
::paddle::framework::EmptyGradOpMaker);
REGISTER_BINARY_LOGICAL_OP
(
logical_and
,
"Out = X && Y"
);
REGISTER_BINARY_LOGICAL_KERNEL
(
logical_and
,
CPU
,
paddle
::
operators
::
LogicalAndFunctor
);
REGISTER_BINARY_LOGICAL_OP
(
logical_or
,
"Out = X && Y"
);
REGISTER_BINARY_LOGICAL_KERNEL
(
logical_or
,
CPU
,
paddle
::
operators
::
LogicalOrFunctor
);
REGISTER_UNARY_LOGICAL_OP
(
logical_not
,
"Out = !X"
);
REGISTER_UNARY_LOGICAL_KERNEL
(
logical_not
,
CPU
,
paddle
::
operators
::
LogicalNotFunctor
);
REGISTER_BINARY_LOGICAL_OP
(
logical_xor
,
"Out = (X || Y) && !(X && Y)"
);
REGISTER_BINARY_LOGICAL_KERNEL
(
logical_xor
,
CPU
,
paddle
::
operators
::
LogicalXorFunctor
);
paddle/operators/logical_op.cu
0 → 100644
浏览文件 @
39bce49b
/* 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. */
#include "paddle/operators/logical_op.h"
REGISTER_BINARY_LOGICAL_KERNEL
(
logical_and
,
GPU
,
paddle
::
operators
::
LogicalAndFunctor
);
REGISTER_BINARY_LOGICAL_KERNEL
(
logical_or
,
GPU
,
paddle
::
operators
::
LogicalOrFunctor
);
REGISTER_UNARY_LOGICAL_KERNEL
(
logical_not
,
GPU
,
paddle
::
operators
::
LogicalNotFunctor
);
REGISTER_BINARY_LOGICAL_KERNEL
(
logical_xor
,
GPU
,
paddle
::
operators
::
LogicalXorFunctor
);
paddle/operators/logical_op.h
0 → 100644
浏览文件 @
39bce49b
/* 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 <math.h>
#include <type_traits>
#include "paddle/framework/op_registry.h"
#include "paddle/platform/transform.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
struct
LogicalAndFunctor
{
using
ELEM_TYPE
=
T
;
HOSTDEVICE
bool
operator
()(
const
T
&
a
,
const
T
&
b
)
const
{
return
a
&&
b
;
}
};
template
<
typename
T
>
struct
LogicalOrFunctor
{
using
ELEM_TYPE
=
T
;
HOSTDEVICE
bool
operator
()(
const
T
&
a
,
const
T
&
b
)
const
{
return
a
||
b
;
}
};
template
<
typename
T
>
struct
LogicalNotFunctor
{
using
ELEM_TYPE
=
T
;
HOSTDEVICE
bool
operator
()(
const
T
&
a
)
const
{
return
!
a
;
}
};
template
<
typename
T
>
struct
LogicalXorFunctor
{
using
ELEM_TYPE
=
T
;
HOSTDEVICE
bool
operator
()(
const
T
&
a
,
const
T
&
b
)
const
{
return
(
a
||
b
)
&&
!
(
a
&&
b
);
}
};
template
<
typename
Place
,
typename
Functor
>
class
BinaryLogicalOpKernel
:
public
framework
::
OpKernel
<
typename
Functor
::
ELEM_TYPE
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
using
T
=
typename
Functor
::
ELEM_TYPE
;
auto
*
x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
y
=
context
.
Input
<
framework
::
Tensor
>
(
"Y"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
Functor
binary_func
;
platform
::
Transform
<
Place
>
trans
;
trans
(
context
.
device_context
(),
x
->
data
<
T
>
(),
x
->
data
<
T
>
()
+
x
->
numel
(),
y
->
data
<
T
>
(),
out
->
mutable_data
<
bool
>
(
context
.
GetPlace
()),
binary_func
);
}
};
template
<
typename
Place
,
typename
Functor
>
class
UnaryLogicalOpKernel
:
public
framework
::
OpKernel
<
typename
Functor
::
ELEM_TYPE
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
using
T
=
typename
Functor
::
ELEM_TYPE
;
auto
*
x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
Functor
unary_func
;
platform
::
Transform
<
Place
>
trans
;
trans
(
context
.
device_context
(),
x
->
data
<
T
>
(),
x
->
data
<
T
>
()
+
x
->
numel
(),
out
->
mutable_data
<
bool
>
(
context
.
GetPlace
()),
unary_func
);
}
};
}
// namespace operators
}
// namespace paddle
#define REGISTER_BINARY_LOGICAL_KERNEL(op_type, dev, functor) \
REGISTER_OP_##dev##_KERNEL( \
op_type, ::paddle::operators::BinaryLogicalOpKernel< \
::paddle::platform::dev##Place, functor<bool>>);
#define REGISTER_UNARY_LOGICAL_KERNEL(op_type, dev, functor) \
REGISTER_OP_##dev##_KERNEL( \
op_type, ::paddle::operators::UnaryLogicalOpKernel< \
::paddle::platform::dev##Place, functor<bool>>);
paddle/operators/sequence_slice_op.h
浏览文件 @
39bce49b
...
...
@@ -77,13 +77,13 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
PADDLE_ENFORCE_LT
(
0
,
offset_data
[
i
],
"The offset
must greater than zero
"
)
"The offset
[%d] must greater than zero.
"
)
PADDLE_ENFORCE_LT
(
0
,
length_data
[
i
],
"The length must greater than zero
"
)
"The length[%d] must greater than zero.
"
)
PADDLE_ENFORCE_LT
(
lod
[
0
][
i
]
+
offset_data
[
i
]
+
length_data
[
i
],
lod
[
0
][
i
+
1
],
"The target tensor's length overflow"
)
"The target tensor's length overflow
.
"
)
}
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
39bce49b
...
...
@@ -3342,6 +3342,20 @@ class RowL2NormLayer(LayerBase):
self
.
set_layer_size
(
input_layer
.
size
)
@
config_layer
(
'cos'
)
class
CosSimLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
cos_scale
=
1
,
device
=
None
):
super
(
CosSimLayer
,
self
).
__init__
(
name
,
'cos'
,
1
,
inputs
=
inputs
,
device
=
device
)
config_assert
(
len
(
self
.
inputs
)
==
2
,
'The CosSimLayer expects two and only two inputs.'
)
config_assert
(
self
.
get_input_layer
(
0
).
size
==
self
.
get_input_layer
(
1
).
size
,
'The two inputs of CosSimLayer must have the same dimensionality.'
)
self
.
config
.
cos_scale
=
cos_scale
@
config_layer
(
'cos_vm'
)
class
CosSimVecMatLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
size
,
inputs
,
cos_scale
=
1.0
,
device
=
None
):
...
...
@@ -3349,10 +3363,24 @@ class CosSimVecMatLayer(LayerBase):
name
,
'cos_vm'
,
size
,
inputs
=
inputs
,
device
=
device
)
self
.
config
.
cos_scale
=
cos_scale
config_assert
(
len
(
self
.
inputs
)
==
2
,
'
CosSimVecMatLayer must have 2 inputs
'
)
len
(
self
.
inputs
)
==
2
,
'
The CosSimVecMatLayer must have 2 inputs.
'
)
config_assert
(
size
*
self
.
get_input_layer
(
0
).
size
==
self
.
get_input_layer
(
1
).
size
,
'Wrong input size for CosSimVecMatLayer'
)
'Wrong input size for CosSimVecMatLayer.'
)
@
config_layer
(
'l2_distance'
)
class
L2DistanceLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
device
=
None
):
super
(
L2DistanceLayer
,
self
).
__init__
(
name
,
'l2_distance'
,
1
,
inputs
=
inputs
,
device
=
device
)
config_assert
(
len
(
self
.
inputs
)
==
2
,
(
'The L2DistanceLayer must have '
'and only have 2 inputs.'
))
config_assert
(
self
.
get_input_layer
(
0
).
size
==
self
.
get_input_layer
(
1
).
size
,
(
'Two inputs of the L2DistanceLayer must have '
'the same dimensionality.'
))
@
config_layer
(
'sampling_id'
)
...
...
@@ -3396,18 +3424,6 @@ class AverageLayer(LayerBase):
self
.
create_bias_parameter
(
bias
,
self
.
config
.
size
)
@
config_layer
(
'cos'
)
class
CosSimLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
cos_scale
=
1
,
device
=
None
):
super
(
CosSimLayer
,
self
).
__init__
(
name
,
'cos'
,
1
,
inputs
=
inputs
,
device
=
device
)
config_assert
(
len
(
self
.
inputs
)
==
2
,
'CosSimLayer must have 2 inputs'
)
config_assert
(
self
.
get_input_layer
(
0
).
size
==
self
.
get_input_layer
(
1
).
size
,
'inputs of CosSimLayer must have same dim'
)
self
.
config
.
cos_scale
=
cos_scale
@
config_layer
(
'tensor'
)
class
TensorLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
size
,
inputs
,
bias
=
True
,
**
xargs
):
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
39bce49b
...
...
@@ -51,6 +51,7 @@ __all__ = [
'last_seq'
,
'first_seq'
,
'cos_sim'
,
'l2_distance_layer'
,
'hsigmoid'
,
'conv_projection'
,
'square_error_cost'
,
...
...
@@ -168,6 +169,7 @@ class LayerType(object):
COST
=
'cost'
COSINE_SIM_VEC
=
'cos_vm'
COSINE_SIM
=
'cos'
L2_DISTANCE
=
'l2_distance'
HSIGMOID
=
'hsigmoid'
CONV_LAYER
=
'conv'
CONVTRANS_LAYER
=
'convt'
...
...
@@ -2334,6 +2336,51 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
return
LayerOutput
(
name
,
LayerType
.
COSINE_SIM
,
parents
=
[
a
,
b
],
size
=
size
)
@
wrap_name_default
()
@
layer_support
()
def
l2_distance_layer
(
x
,
y
,
name
=
None
,
layer_attr
=
None
):
"""
This layer calculates and returns the Euclidean distance between two input
vectors x and y. The equation is as follows:
.. math::
l2_distance(
\\
mathbf{x},
\\
mathbf{y}) =
\\
sqrt{
\\
sum_{i=1}^D(x_i - y_i)}
The output size of this layer is fixed to be 1. Note that the above
computation is for one sample. Multiple samples are processed in one batch.
The example usage is:
.. code-block:: python
l2_sim = l2_distance(x=layer1, y=layer2)
:param name: The name of this layer. It is optional.
:type name: basestring
:param x: The first input x for this layer, whose output is a matrix with
dimensionality N x D. N is the sample number in a mini-batch.
D is the dimensionality of x's output.
:type x: LayerOutput
:param y: The second input y for this layer, whose output is a matrix with
dimensionality N x D. N is the sample number in a mini-batch.
D is the dimensionality of y's output.
:type y: LayerOutput
:param layer_attr: The extra layer attributes, for example, drop rate.
See ExtraLayerAttribute for more details.
:type layer_attr: ExtraLayerAttribute
:return: The returned LayerOutput object.
:rtype: LayerOutput
"""
assert
isinstance
(
x
,
LayerOutput
)
and
isinstance
(
y
,
LayerOutput
)
Layer
(
name
=
name
,
type
=
LayerType
.
L2_DISTANCE
,
inputs
=
[
x
.
name
,
y
.
name
],
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
LayerType
.
L2_DISTANCE
,
parents
=
[
x
,
y
],
size
=
1
)
@
wrap_name_default
()
@
wrap_bias_attr_default
(
has_bias
=
True
)
@
wrap_param_attr_default
()
...
...
@@ -3873,7 +3920,7 @@ def recurrent_layer(input,
:type input: LayerOutput
:param act: Activation type. TanhActivation is the default activation.
:type act: BaseActivation
:param bias_attr: The parameter attribute for bias. If this parameter is set to
:param bias_attr: The parameter attribute for bias. If this parameter is set to
False or an object whose type is not ParameterAttribute,
no bias is defined. If the parameter is set to True,
the bias is initialized to zero.
...
...
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
39bce49b
...
...
@@ -10,7 +10,8 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer
test_dot_prod_layer
)
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_scale_sub_region_layer test_dot_prod_layer test_l2_distance_layer
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_l2_distance_layer.protostr
0 → 100644
浏览文件 @
39bce49b
type: "nn"
layers {
name: "x"
type: "data"
size: 128
active_type: ""
}
layers {
name: "y"
type: "data"
size: 128
active_type: ""
}
layers {
name: "__l2_distance_layer_0__"
type: "l2_distance"
size: 1
active_type: ""
inputs {
input_layer_name: "x"
}
inputs {
input_layer_name: "y"
}
}
input_layer_names: "x"
input_layer_names: "y"
output_layer_names: "__l2_distance_layer_0__"
sub_models {
name: "root"
layer_names: "x"
layer_names: "y"
layer_names: "__l2_distance_layer_0__"
input_layer_names: "x"
input_layer_names: "y"
output_layer_names: "__l2_distance_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_l2_distance_layer.py
0 → 100644
浏览文件 @
39bce49b
from
paddle.trainer_config_helpers
import
*
outputs
(
l2_distance_layer
(
x
=
data_layer
(
name
=
'x'
,
size
=
128
),
y
=
data_layer
(
name
=
'y'
,
size
=
128
)))
python/paddle/v2/fluid/tests/test_logical_op.py
0 → 100644
浏览文件 @
39bce49b
import
op_test
import
unittest
import
numpy
as
np
def
create_test_class
(
op_type
,
callback
,
binary_op
=
True
):
class
Cls
(
op_test
.
OpTest
):
def
setUp
(
self
):
a
=
np
.
random
.
choice
(
a
=
[
True
,
False
],
size
=
(
10
,
7
)).
astype
(
bool
)
if
binary_op
:
b
=
np
.
random
.
choice
(
a
=
[
True
,
False
],
size
=
(
10
,
7
)).
astype
(
bool
)
c
=
callback
(
a
,
b
)
else
:
c
=
callback
(
a
)
self
.
outputs
=
{
'Out'
:
c
}
self
.
op_type
=
op_type
if
binary_op
:
self
.
inputs
=
{
'X'
:
a
,
'Y'
:
b
}
else
:
self
.
inputs
=
{
'X'
:
a
}
def
test_output
(
self
):
self
.
check_output
()
Cls
.
__name__
=
op_type
globals
()[
op_type
]
=
Cls
create_test_class
(
'logical_and'
,
lambda
_a
,
_b
:
np
.
logical_and
(
_a
,
_b
))
create_test_class
(
'logical_or'
,
lambda
_a
,
_b
:
np
.
logical_or
(
_a
,
_b
))
create_test_class
(
'logical_not'
,
lambda
_a
:
np
.
logical_not
(
_a
),
False
)
create_test_class
(
'logical_xor'
,
lambda
_a
,
_b
:
np
.
logical_xor
(
_a
,
_b
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/fluid/tests/test_optimizer.py
浏览文件 @
39bce49b
...
...
@@ -16,14 +16,18 @@ class TestOptimizer(unittest.TestCase):
dtype
=
"float32"
,
shape
=
[
10
,
8
],
lod_level
=
0
,
name
=
"mul.y"
)
mul_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
5
,
8
],
lod_level
=
0
,
name
=
"mul.out"
)
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
mul_x
,
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
sgd_optimizer
=
optimizer
.
SGDOptimizer
(
learning_rate
=
0.01
)
opts
=
sgd_optimizer
.
minimize
(
m
ul
_out
,
init_program
)
opts
=
sgd_optimizer
.
minimize
(
m
ean
_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
1
)
sgd_op
=
opts
[
0
]
self
.
assertEqual
(
sgd_op
.
type
,
"sgd"
)
...
...
@@ -44,12 +48,16 @@ class TestOptimizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
global_step
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"step"
)
learning_rate
=
0.01
sgd_optimizer
=
optimizer
.
SGDOptimizer
(
learning_rate
=
learning_rate
,
global_step
=
global_step
)
opts
=
sgd_optimizer
.
minimize
(
m
ul
_out
,
init_program
)
opts
=
sgd_optimizer
.
minimize
(
m
ean
_out
,
init_program
)
self
.
assertEqual
(
len
(
opts
),
2
)
sgd_op
=
opts
[
0
]
self
.
assertEqual
(
sgd_op
.
type
,
"sgd"
)
...
...
@@ -90,7 +98,11 @@ class TestMomentumOptimizer(unittest.TestCase):
learning_rate
=
0.01
momentum_optimizer
=
self
.
MockMomentum
(
learning_rate
=
learning_rate
,
momentum
=
0.2
)
params_grads
=
append_backward_ops
(
mul_out
)
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
params_grads
=
append_backward_ops
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
momentum_optimizer
.
get_accumulators
()),
0
)
opts
=
momentum_optimizer
.
create_optimization_pass
(
...
...
@@ -132,10 +144,14 @@ class TestMomentumOptimizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
learning_rate
=
0.01
momentum_optimizer
=
self
.
MockMomentum
(
learning_rate
=
learning_rate
,
momentum
=
0.2
,
use_nesterov
=
True
)
params_grads
=
append_backward_ops
(
m
ul
_out
)
params_grads
=
append_backward_ops
(
m
ean
_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
momentum_optimizer
.
get_accumulators
()),
0
)
opts
=
momentum_optimizer
.
create_optimization_pass
(
...
...
@@ -186,10 +202,14 @@ class TestAdagradOptimizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
learning_rate
=
0.01
adagrad_optimizer
=
self
.
MockAdagrad
(
learning_rate
=
learning_rate
,
epsilon
=
1.0e-6
)
params_grads
=
append_backward_ops
(
m
ul
_out
)
params_grads
=
append_backward_ops
(
m
ean
_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adagrad_optimizer
.
get_accumulators
()),
0
)
opts
=
adagrad_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
...
...
@@ -242,10 +262,14 @@ class TestAdamOptimizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
learning_rate
=
0.01
adam_optimizer
=
self
.
MockAdam
(
learning_rate
=
learning_rate
,
beta1
=
0.9
,
beta2
=
0.999
)
params_grads
=
append_backward_ops
(
m
ul
_out
)
params_grads
=
append_backward_ops
(
m
ean
_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adam_optimizer
.
get_accumulators
()),
0
)
opts
=
adam_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
...
...
@@ -300,10 +324,14 @@ class TestAdamaxOptimizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
learning_rate
=
0.01
adamax_optimizer
=
self
.
MockAdamax
(
learning_rate
=
learning_rate
,
beta1
=
0.9
,
beta2
=
0.999
)
params_grads
=
append_backward_ops
(
m
ul
_out
)
params_grads
=
append_backward_ops
(
m
ean
_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
adamax_optimizer
.
get_accumulators
()),
0
)
opts
=
adamax_optimizer
.
create_optimization_pass
(
params_grads
,
mul_out
,
...
...
@@ -355,10 +383,14 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
learning_rate
=
0.01
decayed_adagrad_optimizer
=
self
.
MockDecayedAdagrad
(
learning_rate
=
learning_rate
,
decay
=
0.95
,
epsilon
=
1.0e-6
)
params_grads
=
append_backward_ops
(
m
ul
_out
)
params_grads
=
append_backward_ops
(
m
ean
_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
self
.
assertEqual
(
len
(
decayed_adagrad_optimizer
.
get_accumulators
()),
0
)
opts
=
decayed_adagrad_optimizer
.
create_optimization_pass
(
...
...
python/paddle/v2/fluid/tests/test_program.py
浏览文件 @
39bce49b
import
unittest
import
paddle.v2.fluid.core
as
core
from
paddle.v2.fluid.framework
import
Program
from
paddle.v2.fluid.framework
import
g_main_program
...
...
@@ -98,21 +97,26 @@ class TestProgram(unittest.TestCase):
"Y"
:
add_y
},
outputs
=
{
"Out"
:
add_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
add_out
},
outputs
=
{
"Out"
:
mean_out
})
self
.
assertEqual
(
mul_op
.
idx
,
0
)
self
.
assertEqual
(
add_op
.
idx
,
1
)
param_to_grad
=
prog
.
append_backward
(
add
_out
,
set
())
param_to_grad
=
prog
.
append_backward
(
mean
_out
,
set
())
def
grad_name
(
name
):
return
name
+
"@GRAD"
for
var_name
in
(
"mul.x"
,
"mul.y"
,
"mul.out"
,
"add.y"
,
"add.out"
):
for
var_name
in
(
"mul.x"
,
"mul.y"
,
"mul.out"
,
"add.y"
,
"add.out"
,
"mean.out"
):
self
.
assertEqual
(
param_to_grad
[
var_name
][
0
],
grad_name
(
var_name
))
self
.
assertEqual
(
param_to_grad
[
var_name
][
1
],
0
)
expect_ops
=
[
"mul"
,
"elementwise_add"
,
"
fill_constant"
,
"elementwise_add
_grad"
,
"mul_grad"
"mul"
,
"elementwise_add"
,
"
mean"
,
"fill_constant"
,
"mean
_grad"
,
"
elementwise_add_grad"
,
"
mul_grad"
]
actual_ops
=
[]
for
op
in
block
.
ops
:
...
...
python/paddle/v2/fluid/tests/test_regularizer.py
浏览文件 @
39bce49b
...
...
@@ -29,7 +29,11 @@ class TestL2DecayRegularizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
params_grads
=
append_backward_ops
(
mul_out
)
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
params_grads
=
append_backward_ops
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
count_ops
=
len
(
block
.
ops
)
params_grads
=
optimizer
.
append_regularization_ops
(
params_grads
)
...
...
@@ -62,7 +66,11 @@ class TestL1DecayRegularizer(unittest.TestCase):
"Y"
:
mul_y
},
outputs
=
{
"Out"
:
mul_out
},
attrs
=
{
"x_num_col_dims"
:
1
})
params_grads
=
append_backward_ops
(
mul_out
)
mean_out
=
block
.
create_var
(
dtype
=
"float32"
,
shape
=
[
1
],
lod_level
=
0
,
name
=
"mean.out"
)
block
.
append_op
(
type
=
"mean"
,
inputs
=
{
"X"
:
mul_out
},
outputs
=
{
"Out"
:
mean_out
})
params_grads
=
append_backward_ops
(
mean_out
)
self
.
assertEqual
(
len
(
params_grads
),
1
)
count_ops
=
len
(
block
.
ops
)
params_grads
=
optimizer
.
append_regularization_ops
(
params_grads
)
...
...
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