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24458ae3
编写于
12月 27, 2017
作者:
Y
Yibing Liu
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into profiler_tool
上级
5837166c
94096ae5
变更
13
展开全部
隐藏空白更改
内联
并排
Showing
13 changed file
with
411 addition
and
338 deletion
+411
-338
paddle/framework/CMakeLists.txt
paddle/framework/CMakeLists.txt
+4
-4
paddle/framework/data_transform.h
paddle/framework/data_transform.h
+7
-8
paddle/framework/operator.cc
paddle/framework/operator.cc
+33
-1
paddle/operators/activation_op.cc
paddle/operators/activation_op.cc
+59
-59
paddle/operators/activation_op.h
paddle/operators/activation_op.h
+208
-179
paddle/operators/softmax_op.cc
paddle/operators/softmax_op.cc
+11
-11
paddle/operators/softmax_op.h
paddle/operators/softmax_op.h
+6
-6
python/paddle/v2/fluid/io.py
python/paddle/v2/fluid/io.py
+14
-2
python/paddle/v2/fluid/layer_helper.py
python/paddle/v2/fluid/layer_helper.py
+1
-1
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+10
-9
python/paddle/v2/fluid/tests/test_activation_op.py
python/paddle/v2/fluid/tests/test_activation_op.py
+54
-54
python/paddle/v2/fluid/tests/test_net.py
python/paddle/v2/fluid/tests/test_net.py
+2
-2
python/paddle/v2/fluid/tests/test_softmax_op.py
python/paddle/v2/fluid/tests/test_softmax_op.py
+2
-2
未找到文件。
paddle/framework/CMakeLists.txt
浏览文件 @
24458ae3
...
...
@@ -21,6 +21,8 @@ cc_test(variable_test SRCS variable_test.cc)
cc_library
(
scope SRCS scope.cc DEPS glog
)
cc_test
(
scope_test SRCS scope_test.cc DEPS scope
)
cc_library
(
data_transform SRCS data_transform.cc DEPS tensor framework_proto
)
cc_test
(
data_transform_test SRCS data_transform_test.cc DEPS data_transform device_context
)
cc_library
(
attribute SRCS attribute.cc DEPS framework_proto
)
cc_test
(
program_desc_test SRCS program_desc_test.cc DEPS proto_desc
...
...
@@ -29,7 +31,8 @@ cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
cc_test
(
op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker
)
cc_library
(
op_info SRCS op_info.cc DEPS attribute framework_proto
)
cc_library
(
shape_inference SRCS shape_inference.cc DEPS ddim attribute
)
cc_library
(
operator SRCS operator.cc DEPS op_info device_context tensor scope glog shape_inference
)
cc_library
(
operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform
)
cc_test
(
operator_test SRCS operator_test.cc DEPS operator op_registry init
)
cc_library
(
proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog
)
...
...
@@ -65,6 +68,3 @@ cc_library(init SRCS init.cc DEPS gflags device_context place stringpiece)
cc_test
(
init_test SRCS init_test.cc DEPS init
)
cc_test
(
op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto
)
cc_library
(
data_transform SRCS data_transform.cc DEPS tensor framework_proto
)
cc_test
(
data_transform_test SRCS data_transform_test.cc DEPS data_transform device_context
)
paddle/framework/data_transform.h
浏览文件 @
24458ae3
...
...
@@ -32,17 +32,16 @@ using DataTransformFN =
const
Variable
&
in
,
Variable
*
out
)
>
;
using
KernelTypePair
=
std
::
pair
<
OpKernelType
,
OpKernelType
>
;
static
void
hash_combine
(
std
::
size_t
&
seed
,
const
OpKernelType
&
t
)
{
OpKernelType
::
Hash
kernel_type_hasher
;
seed
^=
kernel_type_hasher
(
t
)
+
0x9e3779b9
+
(
seed
<<
6
)
+
(
seed
>>
2
);
}
struct
KernelTypePairHash
{
static
void
HashCombine
(
const
OpKernelType
&
t
,
std
::
size_t
*
seed
)
{
OpKernelType
::
Hash
kernel_type_hasher
;
(
*
seed
)
^=
kernel_type_hasher
(
t
)
+
0x9e3779b9
+
(
*
seed
<<
6
)
+
(
*
seed
>>
2
);
}
size_t
operator
()(
const
KernelTypePair
&
kernel_pair
)
const
{
std
::
size_t
seed
=
0
;
hash_combine
(
seed
,
kernel_pair
.
first
);
hash_combine
(
seed
,
kernel_pair
.
second
);
HashCombine
(
kernel_pair
.
first
,
&
seed
);
HashCombine
(
kernel_pair
.
second
,
&
seed
);
return
seed
;
}
};
...
...
paddle/framework/operator.cc
浏览文件 @
24458ae3
...
...
@@ -15,6 +15,7 @@ limitations under the License. */
#include <algorithm>
#include <atomic>
#include "paddle/framework/data_transform.h"
#include "paddle/framework/executor.h"
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/operator.h"
...
...
@@ -411,7 +412,38 @@ void OperatorWithKernel::Run(const Scope& scope,
expected_kernel_key
);
}
kernel_iter
->
second
->
Compute
(
ctx
);
if
(
actual_kernel_key
==
expected_kernel_key
)
{
kernel_iter
->
second
->
Compute
(
ctx
);
}
else
{
Scope
&
op_scope
=
scope
.
NewScope
();
auto
input_vars
=
this
->
InputVars
();
for
(
auto
var_name
:
input_vars
)
{
op_scope
.
Var
(
var_name
);
}
// TODO(qijun) get appropriate DeviceContext from DeviceContext pool
platform
::
DeviceContext
*
trans_dev_ctx
=
nullptr
;
std
::
vector
<
platform
::
DeviceContext
*>
trans_dev_ctx_vec
{
trans_dev_ctx
};
// TODO(qijun) get appropriate DataTransformFN from global map
framework
::
DataTransformFN
trans_fun
=
nullptr
;
// Wait for transform starting
dev_ctx
->
Wait
();
for
(
auto
var_name
:
input_vars
)
{
trans_fun
(
trans_dev_ctx_vec
,
*
(
scope
.
FindVar
(
var_name
)),
op_scope
.
FindVar
(
var_name
));
}
// Wait for data transform finishing
for
(
auto
ctx
:
trans_dev_ctx_vec
)
{
ctx
->
Wait
();
}
// Create a new ExecutionContext
ExecutionContext
op_ctx
(
*
this
,
op_scope
,
*
dev_ctx
);
kernel_iter
->
second
->
Compute
(
op_ctx
);
}
}
OpKernelType
OperatorWithKernel
::
GetActualKernelType
(
...
...
paddle/operators/activation_op.cc
浏览文件 @
24458ae3
...
...
@@ -22,8 +22,8 @@ class ActivationOp : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
ctx
->
SetOutputDim
(
"
Y
"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"
Y
"
);
ctx
->
SetOutputDim
(
"
Out
"
,
ctx
->
GetInputDim
(
"X"
));
ctx
->
ShareLoD
(
"X"
,
/*->*/
"
Out
"
);
}
};
...
...
@@ -32,7 +32,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"
Y
"
));
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"
Out
"
));
}
};
...
...
@@ -41,11 +41,11 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
SigmoidOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Sigmoid operator"
);
AddOutput
(
"
Y
"
,
"Output of Sigmoid operator"
);
AddOutput
(
"
Out
"
,
"Output of Sigmoid operator"
);
AddComment
(
R"DOC(
Sigmoid Activation Operator
$$
y
= \frac{1}{1 + e^{-x}}$$
$$
out
= \frac{1}{1 + e^{-x}}$$
)DOC"
);
}
...
...
@@ -56,11 +56,11 @@ class LogSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
LogSigmoidOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of LogSigmoid operator"
);
AddOutput
(
"
Y
"
,
"Output of LogSigmoid operator"
);
AddOutput
(
"
Out
"
,
"Output of LogSigmoid operator"
);
AddComment
(
R"DOC(
Logsigmoid Activation Operator
$$
y
= \log \frac{1}{1 + e^{-x}}$$
$$
out
= \log \frac{1}{1 + e^{-x}}$$
)DOC"
);
}
...
...
@@ -71,11 +71,11 @@ class ExpOpMaker : public framework::OpProtoAndCheckerMaker {
ExpOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Exp operator"
);
AddOutput
(
"
Y
"
,
"Output of Exp operator"
);
AddOutput
(
"
Out
"
,
"Output of Exp operator"
);
AddComment
(
R"DOC(
Exp Activation Operator.
$
y
= e^x$
$
out
= e^x$
)DOC"
);
}
...
...
@@ -86,11 +86,11 @@ class ReluOpMaker : public framework::OpProtoAndCheckerMaker {
ReluOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Relu operator"
);
AddOutput
(
"
Y
"
,
"Output of Relu operator"
);
AddOutput
(
"
Out
"
,
"Output of Relu operator"
);
AddComment
(
R"DOC(
Relu Activation Operator.
$
y
= \max(x, 0)$
$
out
= \max(x, 0)$
)DOC"
);
}
...
...
@@ -101,12 +101,12 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
LeakyReluOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of LeakyRelu operator"
);
AddOutput
(
"
Y
"
,
"Output of LeakyRelu operator"
);
AddOutput
(
"
Out
"
,
"Output of LeakyRelu operator"
);
AddAttr
<
float
>
(
"alpha"
,
"The small negative slope"
).
SetDefault
(
0.02
f
);
AddComment
(
R"DOC(
LeakyRelu Activation Operator.
$
y
= \max(x, \alpha * x)$
$
out
= \max(x, \alpha * x)$
)DOC"
);
}
...
...
@@ -117,13 +117,13 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
SoftShrinkOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Softshrink operator"
);
AddOutput
(
"
Y
"
,
"Output of Softshrink operator"
);
AddOutput
(
"
Out
"
,
"Output of Softshrink operator"
);
AddAttr
<
float
>
(
"lambda"
,
"non-negative offset"
).
SetDefault
(
0.5
f
);
AddComment
(
R"DOC(
Softshrink Activation Operator.
$$
y
= \begin{cases}
out
= \begin{cases}
x - \lambda, \text{if } x > \lambda \\
x + \lambda, \text{if } x < -\lambda \\
0, \text{otherwise}
...
...
@@ -139,11 +139,11 @@ class TanhOpMaker : public framework::OpProtoAndCheckerMaker {
TanhOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Tanh operator"
);
AddOutput
(
"
Y
"
,
"Output of Tanh operator"
);
AddOutput
(
"
Out
"
,
"Output of Tanh operator"
);
AddComment
(
R"DOC(
Tanh Activation Operator.
$$
y
= \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$
out
= \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC"
);
}
...
...
@@ -154,11 +154,11 @@ class TanhShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
TanhShrinkOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of TanhShrink operator"
);
AddOutput
(
"
Y
"
,
"Output of TanhShrink operator"
);
AddOutput
(
"
Out
"
,
"Output of TanhShrink operator"
);
AddComment
(
R"DOC(
TanhShrink Activation Operator.
$$
y
= x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$
out
= x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC"
);
}
...
...
@@ -169,14 +169,14 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
HardShrinkOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of HardShrink operator"
);
AddOutput
(
"
Y
"
,
"Output of HardShrink operator"
);
AddOutput
(
"
Out
"
,
"Output of HardShrink operator"
);
AddAttr
<
float
>
(
"threshold"
,
"The value of threshold for HardShrink"
)
.
SetDefault
(
0.5
f
);
AddComment
(
R"DOC(
HardShrink Activation Operator.
$$
y
= \begin{cases}
out
= \begin{cases}
x, \text{if } x > \lambda \\
x, \text{if } x < -\lambda \\
0, \text{otherwise}
...
...
@@ -192,11 +192,11 @@ class SqrtOpMaker : public framework::OpProtoAndCheckerMaker {
SqrtOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Sqrt operator"
);
AddOutput
(
"
Y
"
,
"Output of Sqrt operator"
);
AddOutput
(
"
Out
"
,
"Output of Sqrt operator"
);
AddComment
(
R"DOC(
Sqrt Activation Operator.
$
y
= \sqrt{x}$
$
out
= \sqrt{x}$
)DOC"
);
}
...
...
@@ -207,11 +207,11 @@ class AbsOpMaker : public framework::OpProtoAndCheckerMaker {
AbsOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Abs operator"
);
AddOutput
(
"
Y
"
,
"Output of Abs operator"
);
AddOutput
(
"
Out
"
,
"Output of Abs operator"
);
AddComment
(
R"DOC(
Abs Activation Operator.
$
y
= |x|$
$
out
= |x|$
)DOC"
);
}
...
...
@@ -222,11 +222,11 @@ class CeilOpMaker : public framework::OpProtoAndCheckerMaker {
CeilOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Ceil operator"
);
AddOutput
(
"
Y
"
,
"Output of Ceil operator"
);
AddOutput
(
"
Out
"
,
"Output of Ceil operator"
);
AddComment
(
R"DOC(
Ceil Activation Operator.
$
y
= ceil(x)$
$
out
= ceil(x)$
)DOC"
);
}
...
...
@@ -237,11 +237,11 @@ class FloorOpMaker : public framework::OpProtoAndCheckerMaker {
FloorOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Floor operator"
);
AddOutput
(
"
Y
"
,
"Output of Floor operator"
);
AddOutput
(
"
Out
"
,
"Output of Floor operator"
);
AddComment
(
R"DOC(
Floor Activation Operator.
$
y
= floor(x)$
$
out
= floor(x)$
)DOC"
);
}
...
...
@@ -252,11 +252,11 @@ class RoundOpMaker : public framework::OpProtoAndCheckerMaker {
RoundOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Round operator"
);
AddOutput
(
"
Y
"
,
"Output of Round operator"
);
AddOutput
(
"
Out
"
,
"Output of Round operator"
);
AddComment
(
R"DOC(
Round Activation Operator.
$
y
= [x]$
$
out
= [x]$
)DOC"
);
}
...
...
@@ -267,11 +267,11 @@ class ReciprocalOpMaker : public framework::OpProtoAndCheckerMaker {
ReciprocalOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Reciprocal operator"
);
AddOutput
(
"
Y
"
,
"Output of Reciprocal operator"
);
AddOutput
(
"
Out
"
,
"Output of Reciprocal operator"
);
AddComment
(
R"DOC(
Reciprocal Activation Operator.
$$
y
= \frac{1}{x}$$
$$
out
= \frac{1}{x}$$
)DOC"
);
}
...
...
@@ -282,11 +282,11 @@ class LogOpMaker : public framework::OpProtoAndCheckerMaker {
LogOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Log operator"
);
AddOutput
(
"
Y
"
,
"Output of Log operator"
);
AddOutput
(
"
Out
"
,
"Output of Log operator"
);
AddComment
(
R"DOC(
Log Activation Operator.
$
y
= \ln(x)$
$
out
= \ln(x)$
Natural logarithm of x.
...
...
@@ -299,11 +299,11 @@ class SquareOpMaker : public framework::OpProtoAndCheckerMaker {
SquareOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Square operator"
);
AddOutput
(
"
Y
"
,
"Output of Square operator"
);
AddOutput
(
"
Out
"
,
"Output of Square operator"
);
AddComment
(
R"DOC(
Square Activation Operator.
$
y
= x^2$
$
out
= x^2$
)DOC"
);
}
...
...
@@ -314,11 +314,11 @@ class SoftplusOpMaker : public framework::OpProtoAndCheckerMaker {
SoftplusOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Softplus operator"
);
AddOutput
(
"
Y
"
,
"Output of Softplus operator"
);
AddOutput
(
"
Out
"
,
"Output of Softplus operator"
);
AddComment
(
R"DOC(
Softplus Activation Operator.
$
y
= \ln(1 + e^{x})$
$
out
= \ln(1 + e^{x})$
)DOC"
);
}
...
...
@@ -329,11 +329,11 @@ class SoftsignOpMaker : public framework::OpProtoAndCheckerMaker {
SoftsignOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Softsign operator"
);
AddOutput
(
"
Y
"
,
"Output of Softsign operator"
);
AddOutput
(
"
Out
"
,
"Output of Softsign operator"
);
AddComment
(
R"DOC(
Softsign Activation Operator.
$$
y
= \frac{x}{1 + |x|}$$
$$
out
= \frac{x}{1 + |x|}$$
)DOC"
);
}
...
...
@@ -344,7 +344,7 @@ class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
BReluOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of BRelu operator"
);
AddOutput
(
"
Y
"
,
"Output of BRelu operator"
);
AddOutput
(
"
Out
"
,
"Output of BRelu operator"
);
AddAttr
<
float
>
(
"t_min"
,
"The min marginal value of BRelu"
)
.
SetDefault
(
static_cast
<
float
>
(
0
));
AddAttr
<
float
>
(
"t_max"
,
"The max marginal value of BRelu"
)
...
...
@@ -352,7 +352,7 @@ class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
BRelu Activation Operator.
$
y
= \max(\min(x, t_{min}), t_{max})$
$
out
= \max(\min(x, t_{min}), t_{max})$
)DOC"
);
}
...
...
@@ -363,13 +363,13 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
SoftReluOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of SoftRelu operator"
);
AddOutput
(
"
Y
"
,
"Output of SoftRelu operator"
);
AddOutput
(
"
Out
"
,
"Output of SoftRelu operator"
);
AddAttr
<
float
>
(
"threshold"
,
"The threshold value of SoftRelu"
)
.
SetDefault
(
40.0
f
);
AddComment
(
R"DOC(
SoftRelu Activation Operator.
$
y
= \ln(1 + \exp(\max(\min(x, threshold), threshold))$
$
out
= \ln(1 + \exp(\max(\min(x, threshold), threshold))$
)DOC"
);
}
...
...
@@ -380,7 +380,7 @@ class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
ELUOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of ELU operator"
);
AddOutput
(
"
Y
"
,
"Output of ELU operator"
);
AddOutput
(
"
Out
"
,
"Output of ELU operator"
);
AddAttr
<
float
>
(
"alpha"
,
"The alpha value of ELU"
).
SetDefault
(
1.0
f
);
AddComment
(
R"DOC(
ELU Activation Operator.
...
...
@@ -388,7 +388,7 @@ ELU Activation Operator.
Applies the following element-wise computation on the input according to
https://arxiv.org/abs/1511.07289.
$
y
= \max(0, x) + \min(0, \alpha * (e^x - 1))$
$
out
= \max(0, x) + \min(0, \alpha * (e^x - 1))$
)DOC"
);
}
...
...
@@ -399,13 +399,13 @@ class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
Relu6OpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Relu6 operator"
);
AddOutput
(
"
Y
"
,
"Output of Relu6 operator"
);
AddOutput
(
"
Out
"
,
"Output of Relu6 operator"
);
AddAttr
<
float
>
(
"threshold"
,
"The threshold value of Relu6"
)
.
SetDefault
(
6.0
f
);
AddComment
(
R"DOC(
Relu6 Activation Operator.
$
y
= \min(\max(0, x), 6)$
$
out
= \min(\max(0, x), 6)$
)DOC"
);
}
...
...
@@ -416,12 +416,12 @@ class PowOpMaker : public framework::OpProtoAndCheckerMaker {
PowOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Pow operator"
);
AddOutput
(
"
Y
"
,
"Output of Pow operator"
);
AddOutput
(
"
Out
"
,
"Output of Pow operator"
);
AddAttr
<
float
>
(
"factor"
,
"The exponential factor of Pow"
).
SetDefault
(
1.0
f
);
AddComment
(
R"DOC(
Pow Activation Operator.
$
y
= x^{factor}$
$
out
= x^{factor}$
)DOC"
);
}
...
...
@@ -432,7 +432,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
STanhOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of STanh operator"
);
AddOutput
(
"
Y
"
,
"Output of STanh operator"
);
AddOutput
(
"
Out
"
,
"Output of STanh operator"
);
AddAttr
<
float
>
(
"scale_a"
,
"The scale parameter of a for the input"
)
.
SetDefault
(
2.0
f
/
3.0
f
);
AddAttr
<
float
>
(
"scale_b"
,
"The scale parameter of b for the input"
)
...
...
@@ -440,7 +440,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
STanh Activation Operator.
$$
y
= b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
$$
out
= b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
)DOC"
);
}
...
...
@@ -451,14 +451,14 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
ThresholdedReluOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of ThresholdedRelu operator"
);
AddOutput
(
"
Y
"
,
"Output of ThresholdedRelu operator"
);
AddOutput
(
"
Out
"
,
"Output of ThresholdedRelu operator"
);
AddAttr
<
float
>
(
"threshold"
,
"The threshold location of activation"
)
.
SetDefault
(
1.0
f
);
AddComment
(
R"DOC(
ThresholdedRelu Activation Operator.
$$
y
= \begin{cases}
out
= \begin{cases}
x, \text{if } x > threshold \\
0, \text{otherwise}
\end{cases}
...
...
@@ -473,7 +473,7 @@ class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
HardSigmoidOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of HardSigmoid operator"
);
AddOutput
(
"
Y
"
,
"Output of HardSigmoid operator"
);
AddOutput
(
"
Out
"
,
"Output of HardSigmoid operator"
);
AddAttr
<
float
>
(
"slope"
,
"Slope for linear approximation of sigmoid"
)
.
SetDefault
(
0.2
f
);
AddAttr
<
float
>
(
"offset"
,
"Offset for linear approximation of sigmoid"
)
...
...
@@ -484,7 +484,7 @@ HardSigmoid Activation Operator.
Segment-wise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391),
which is much faster than sigmoid.
$
y
= \max(0, \min(1, slope * x + shift))$
$
out
= \max(0, \min(1, slope * x + shift))$
The slope should be positive. The offset can be either positive or negative.
The default slope and shift are set according to the above reference.
...
...
@@ -499,12 +499,12 @@ class SwishOpMaker : public framework::OpProtoAndCheckerMaker {
SwishOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
framework
::
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of Swish operator"
);
AddOutput
(
"
Y
"
,
"Output of Swish operator"
);
AddOutput
(
"
Out
"
,
"Output of Swish operator"
);
AddAttr
<
float
>
(
"beta"
,
"Constant beta of swish operator"
).
SetDefault
(
1.0
f
);
AddComment
(
R"DOC(
Swish Activation Operator.
$$
y
= \frac{x}{1 + e^{- \beta x}}$$
$$
out
= \frac{x}{1 + e^{- \beta x}}$$
)DOC"
);
}
...
...
paddle/operators/activation_op.h
浏览文件 @
24458ae3
此差异已折叠。
点击以展开。
paddle/operators/softmax_op.cc
浏览文件 @
24458ae3
...
...
@@ -24,13 +24,13 @@ class SoftmaxOp : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SoftmaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"
Y
"
),
"Output(
Y
) of SoftmaxOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"
Out
"
),
"Output(
Out
) of SoftmaxOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE
(
x_dims
.
size
()
==
2UL
,
"The input of softmax op must be a matrix."
);
ctx
->
SetOutputDim
(
"
Y
"
,
x_dims
);
ctx
->
SetOutputDim
(
"
Out
"
,
x_dims
);
}
};
...
...
@@ -41,7 +41,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput
(
"X"
,
"The input tensor of softmax. "
"2-D with shape [batch_size, input_feature_dimensions]."
);
AddOutput
(
"
Y
"
,
"The normalized values with the same shape as X."
);
AddOutput
(
"
Out
"
,
"The normalized values with the same shape as X."
);
AddComment
(
R"DOC(
Softmax Operator.
...
...
@@ -59,7 +59,7 @@ exponential values of all the other dimensions is the output of the softmax
operator.
For each row $i$ and each column $j$ in Input(X), we have:
$$
Y
[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
$$
Out
[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
)DOC"
);
}
...
...
@@ -70,12 +70,12 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"
Y"
),
"Input(Y
) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"
Y
"
)),
"Input(
Y
@GRAD) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"
Y
"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"
Y
"
)),
"Input(
Y
) and its gradients should have a same shape."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"
Out"
),
"Input(Out
) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"
Out
"
)),
"Input(
Out
@GRAD) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
->
GetInputDim
(
"
Out
"
),
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"
Out
"
)),
"Input(
Out
) and its gradients should have a same shape."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
...
...
paddle/operators/softmax_op.h
浏览文件 @
24458ae3
...
...
@@ -26,13 +26,13 @@ class SoftmaxKernel : public framework::OpKernel<T> {
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
X
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
Y
=
context
.
Output
<
Tensor
>
(
"Y
"
);
auto
*
Out
=
context
.
Output
<
Tensor
>
(
"Out
"
);
// allocate memory on device.
Y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
SoftmaxFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
X
,
Y
);
context
.
template
device_context
<
DeviceContext
>(),
X
,
Out
);
}
};
...
...
@@ -40,15 +40,15 @@ template <typename DeviceContext, typename T>
class
SoftmaxGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
Y
=
context
.
Input
<
Tensor
>
(
"Y
"
);
auto
*
d
Y
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y
"
));
auto
*
Out
=
context
.
Input
<
Tensor
>
(
"Out
"
);
auto
*
d
Out
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out
"
));
auto
*
dX
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
// allocate memory on device.
dX
->
mutable_data
<
T
>
(
context
.
GetPlace
());
math
::
SoftmaxGradFunctor
<
DeviceContext
,
T
>
()(
context
.
template
device_context
<
DeviceContext
>(),
Y
,
dY
,
dX
);
context
.
template
device_context
<
DeviceContext
>(),
Out
,
dOut
,
dX
);
}
};
...
...
python/paddle/v2/fluid/io.py
浏览文件 @
24458ae3
...
...
@@ -180,10 +180,22 @@ def save_inference_model(dirname,
:return: None
"""
if
isinstance
(
feeded_var_names
,
basestring
):
feeded_var_names
=
[
feeded_var_names
]
else
:
if
not
(
bool
(
feeded_var_names
)
and
all
(
isinstance
(
name
,
basestring
)
for
name
in
feeded_var_names
)):
raise
ValueError
(
"'feed_var_names' should be a list of str."
)
if
isinstance
(
target_vars
,
Variable
):
feeded_var_names
=
[
feeded_var_names
]
else
:
if
not
(
bool
(
target_vars
)
and
all
(
isinstance
(
var
,
Variable
)
for
var
in
target_vars
)):
raise
ValueError
(
"'target_vars' should be a list of Variable."
)
if
main_program
is
None
:
main_program
=
default_main_program
()
if
not
isinstance
(
target_vars
,
list
):
target_vars
=
[
target_vars
]
if
not
os
.
path
.
isdir
(
dirname
):
os
.
makedirs
(
dirname
)
...
...
python/paddle/v2/fluid/layer_helper.py
浏览文件 @
24458ae3
...
...
@@ -184,7 +184,7 @@ class LayerHelper(object):
self
.
append_op
(
type
=
act_type
,
inputs
=
{
"X"
:
[
input_var
]},
outputs
=
{
"
Y
"
:
[
tmp
]},
outputs
=
{
"
Out
"
:
[
tmp
]},
attrs
=
act
)
return
tmp
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
24458ae3
...
...
@@ -386,7 +386,8 @@ def square_error_cost(input, label, **kwargs):
square_out
=
helper
.
create_tmp_variable
(
dtype
=
input
.
dtype
)
helper
.
append_op
(
type
=
'square'
,
inputs
=
{
'X'
:
[
minus_out
]},
outputs
=
{
'Y'
:
[
square_out
]})
type
=
'square'
,
inputs
=
{
'X'
:
[
minus_out
]},
outputs
=
{
'Out'
:
[
square_out
]})
return
square_out
...
...
@@ -604,7 +605,7 @@ def sequence_pool(input, pool_type, **kwargs):
sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
...
...
@@ -616,7 +617,7 @@ def sequence_pool(input, pool_type, **kwargs):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
...
...
@@ -654,7 +655,7 @@ def sequence_first_step(input, **kwargs):
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
...
...
@@ -664,7 +665,7 @@ def sequence_first_step(input, **kwargs):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_first_step = fluid.layers.sequence_first_step(input=x)
...
...
@@ -687,7 +688,7 @@ def sequence_last_step(input, **kwargs):
out.dim = [3, 1]
with condition len(x.lod[-1]) - 1 == out.dims[0]
out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
...
...
@@ -697,7 +698,7 @@ def sequence_last_step(input, **kwargs):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[7, 1],
dtype='float32', lod_level=1)
x_last_step = fluid.layers.sequence_last_step(input=x)
...
...
@@ -1132,7 +1133,7 @@ def reduce_sum(input, dim=None, keep_dim=False):
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
...
...
@@ -1176,7 +1177,7 @@ def reduce_mean(input, dim=None, keep_dim=False):
Returns:
Variable: The reduced Tensor variable.
Examples:
.. code-block:: python
...
...
python/paddle/v2/fluid/tests/test_activation_op.py
浏览文件 @
24458ae3
...
...
@@ -10,13 +10,13 @@ class TestExp(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
exp
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
exp
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestSigmoid
(
OpTest
):
...
...
@@ -25,13 +25,13 @@ class TestSigmoid(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
1
/
(
1
+
np
.
exp
(
-
self
.
inputs
[
'X'
]))}
self
.
outputs
=
{
'
Out
'
:
1
/
(
1
+
np
.
exp
(
-
self
.
inputs
[
'X'
]))}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.008
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.008
)
class
TestLogSigmoid
(
OpTest
):
...
...
@@ -40,13 +40,13 @@ class TestLogSigmoid(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
-
1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
log
(
1
/
(
1
+
np
.
exp
(
-
self
.
inputs
[
'X'
])))}
self
.
outputs
=
{
'
Out
'
:
np
.
log
(
1
/
(
1
+
np
.
exp
(
-
self
.
inputs
[
'X'
])))}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.008
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.008
)
class
TestTanh
(
OpTest
):
...
...
@@ -55,13 +55,13 @@ class TestTanh(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
tanh
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
tanh
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestTanhShrink
(
OpTest
):
...
...
@@ -70,13 +70,13 @@ class TestTanhShrink(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
10
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
self
.
inputs
[
'X'
]
-
np
.
tanh
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
self
.
inputs
[
'X'
]
-
np
.
tanh
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.008
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.008
)
class
TestHardShrink
(
OpTest
):
...
...
@@ -90,13 +90,13 @@ class TestHardShrink(OpTest):
t
=
np
.
copy
(
x
)
t
[(
t
>=
-
threshold
)
&
(
t
<=
threshold
)]
=
0
self
.
outputs
=
{
'
Y
'
:
t
}
self
.
outputs
=
{
'
Out
'
:
t
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.005
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.005
)
class
TestSoftShrink
(
OpTest
):
...
...
@@ -110,13 +110,13 @@ class TestSoftShrink(OpTest):
y
=
np
.
copy
(
self
.
inputs
[
'X'
])
y
=
(
y
<
-
lambda_val
)
*
(
y
+
lambda_val
)
+
(
y
>
lambda_val
)
*
(
y
-
lambda_val
)
self
.
outputs
=
{
'
Y
'
:
y
}
self
.
outputs
=
{
'
Out
'
:
y
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestSqrt
(
OpTest
):
...
...
@@ -125,13 +125,13 @@ class TestSqrt(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
sqrt
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
sqrt
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestAbs
(
OpTest
):
...
...
@@ -144,13 +144,13 @@ class TestAbs(OpTest):
# we should avoid this
x
[
np
.
abs
(
x
)
<
0.005
]
=
0.02
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'
Y
'
:
np
.
abs
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
abs
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestCeil
(
OpTest
):
...
...
@@ -158,13 +158,13 @@ class TestCeil(OpTest):
self
.
op_type
=
"ceil"
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
4
,
4
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'
Y
'
:
np
.
ceil
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
ceil
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestFloor
(
OpTest
):
...
...
@@ -173,13 +173,13 @@ class TestFloor(OpTest):
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
4
,
4
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
}
# numpy floor need +1
self
.
outputs
=
{
'
Y
'
:
np
.
floor
(
self
.
inputs
[
'X'
])
+
1.0
}
self
.
outputs
=
{
'
Out
'
:
np
.
floor
(
self
.
inputs
[
'X'
])
+
1.0
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestRound
(
OpTest
):
...
...
@@ -187,13 +187,13 @@ class TestRound(OpTest):
self
.
op_type
=
"round"
x
=
np
.
random
.
uniform
(
-
1
,
1
,
[
4
,
4
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'
Y
'
:
np
.
round
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
round
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestRelu
(
OpTest
):
...
...
@@ -203,13 +203,13 @@ class TestRelu(OpTest):
# The same reason with TestAbs
x
[
np
.
abs
(
x
)
<
0.005
]
=
0.02
self
.
inputs
=
{
'X'
:
x
}
self
.
outputs
=
{
'
Y
'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
0
)}
self
.
outputs
=
{
'
Out
'
:
np
.
maximum
(
self
.
inputs
[
'X'
],
0
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestBRelu
(
OpTest
):
...
...
@@ -227,13 +227,13 @@ class TestBRelu(OpTest):
t
=
np
.
copy
(
x
)
t
[
t
<
t_min
]
=
t_min
t
[
t
>
t_max
]
=
t_max
self
.
outputs
=
{
'
Y
'
:
t
}
self
.
outputs
=
{
'
Out
'
:
t
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.02
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.02
)
class
TestRelu6
(
OpTest
):
...
...
@@ -248,14 +248,14 @@ class TestRelu6(OpTest):
self
.
inputs
=
{
'X'
:
x
}
self
.
attrs
=
{
'threshold'
:
threshold
}
self
.
outputs
=
{
'
Y
'
:
np
.
minimum
(
np
.
maximum
(
self
.
inputs
[
'X'
],
0
),
threshold
)
'
Out
'
:
np
.
minimum
(
np
.
maximum
(
self
.
inputs
[
'X'
],
0
),
threshold
)
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.02
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.02
)
class
TestSoftRelu
(
OpTest
):
...
...
@@ -271,13 +271,13 @@ class TestSoftRelu(OpTest):
t
=
np
.
copy
(
x
)
t
[
t
<
-
threshold
]
=
-
threshold
t
[
t
>
threshold
]
=
threshold
self
.
outputs
=
{
'
Y
'
:
np
.
log
((
np
.
exp
(
t
)
+
1
))}
self
.
outputs
=
{
'
Out
'
:
np
.
log
((
np
.
exp
(
t
)
+
1
))}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.02
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.02
)
class
TestELU
(
OpTest
):
...
...
@@ -290,27 +290,27 @@ class TestELU(OpTest):
self
.
inputs
=
{
'X'
:
x
}
self
.
attrs
=
{
'alpha'
:
alpha
}
self
.
outputs
=
{
'
Y
'
:
np
.
maximum
(
0
,
x
)
+
np
.
minimum
(
0
,
alpha
*
(
np
.
exp
(
x
)
-
1
))
'
Out
'
:
np
.
maximum
(
0
,
x
)
+
np
.
minimum
(
0
,
alpha
*
(
np
.
exp
(
x
)
-
1
))
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.02
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.02
)
class
TestReciprocal
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"reciprocal"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
1
,
2
,
[
11
,
17
]).
astype
(
"float32"
)}
self
.
outputs
=
{
'
Y
'
:
np
.
reciprocal
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
reciprocal
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.01
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.01
)
class
TestLog
(
OpTest
):
...
...
@@ -319,13 +319,13 @@ class TestLog(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
log
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
log
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestSquare
(
OpTest
):
...
...
@@ -334,13 +334,13 @@ class TestSquare(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
square
(
self
.
inputs
[
'X'
])}
self
.
outputs
=
{
'
Out
'
:
np
.
square
(
self
.
inputs
[
'X'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestPow
(
OpTest
):
...
...
@@ -348,13 +348,13 @@ class TestPow(OpTest):
self
.
op_type
=
"pow"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
1
,
2
,
[
11
,
17
]).
astype
(
"float32"
)}
self
.
attrs
=
{
'factor'
:
3.0
}
self
.
outputs
=
{
'
Y
'
:
np
.
power
(
self
.
inputs
[
'X'
],
3
)}
self
.
outputs
=
{
'
Out
'
:
np
.
power
(
self
.
inputs
[
'X'
],
3
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.02
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.02
)
class
TestSTanh
(
OpTest
):
...
...
@@ -366,13 +366,13 @@ class TestSTanh(OpTest):
scale_a
=
2.0
/
3.0
scale_b
=
1.7159
self
.
attrs
=
{
'scale_a'
:
scale_a
,
'scale_b'
:
scale_b
}
self
.
outputs
=
{
'
Y
'
:
scale_b
*
np
.
tanh
(
self
.
inputs
[
'X'
]
*
scale_a
)}
self
.
outputs
=
{
'
Out
'
:
scale_b
*
np
.
tanh
(
self
.
inputs
[
'X'
]
*
scale_a
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestSoftplus
(
OpTest
):
...
...
@@ -381,13 +381,13 @@ class TestSoftplus(OpTest):
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
-
1
,
1
,
[
11
,
17
]).
astype
(
"float64"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
log
(
1
+
np
.
exp
(
self
.
inputs
[
'X'
]))}
self
.
outputs
=
{
'
Out
'
:
np
.
log
(
1
+
np
.
exp
(
self
.
inputs
[
'X'
]))}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestSoftsign
(
OpTest
):
...
...
@@ -397,14 +397,14 @@ class TestSoftsign(OpTest):
'X'
:
np
.
random
.
uniform
(
-
1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
divide
(
self
.
inputs
[
'X'
],
1
+
np
.
abs
(
self
.
inputs
[
'X'
]))
'
Out
'
:
np
.
divide
(
self
.
inputs
[
'X'
],
1
+
np
.
abs
(
self
.
inputs
[
'X'
]))
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.007
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.007
)
class
TestThresholdedRelu
(
OpTest
):
...
...
@@ -419,13 +419,13 @@ class TestThresholdedRelu(OpTest):
self
.
inputs
=
{
'X'
:
X
}
self
.
attrs
=
{
'threshold'
:
threshold
}
self
.
outputs
=
{
'
Y
'
:
(
X
>
threshold
)
*
X
}
self
.
outputs
=
{
'
Out
'
:
(
X
>
threshold
)
*
X
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
self
.
relative_error
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
self
.
relative_error
)
class
TestHardSigmoid
(
OpTest
):
...
...
@@ -447,13 +447,13 @@ class TestHardSigmoid(OpTest):
upper_threshold
-
0.2
temp
=
X
*
slope
+
offset
self
.
outputs
=
{
'
Y
'
:
np
.
maximum
(
0.0
,
np
.
minimum
(
1.0
,
temp
))}
self
.
outputs
=
{
'
Out
'
:
np
.
maximum
(
0.0
,
np
.
minimum
(
1.0
,
temp
))}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.002
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.002
)
class
TestSwish
(
OpTest
):
...
...
@@ -462,13 +462,13 @@ class TestSwish(OpTest):
X
=
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
X
}
self
.
attrs
=
{
'beta'
:
2.3
}
self
.
outputs
=
{
'
Y
'
:
X
*
expit
(
self
.
attrs
[
'beta'
]
*
X
)}
self
.
outputs
=
{
'
Out
'
:
X
*
expit
(
self
.
attrs
[
'beta'
]
*
X
)}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
,
max_relative_error
=
0.008
)
self
.
check_grad
([
'X'
],
'
Out
'
,
max_relative_error
=
0.008
)
if
__name__
==
"__main__"
:
...
...
python/paddle/v2/fluid/tests/test_net.py
浏览文件 @
24458ae3
...
...
@@ -7,7 +7,7 @@ def fc(X, W, Y):
ret_v
=
core
.
Net
.
create
()
ret_v
.
append_op
(
Operator
(
"mul"
,
X
=
"X"
,
Y
=
"W"
,
Out
=
"pre_activation"
))
ret_v
.
append_op
(
Operator
(
"sigmoid"
,
X
=
"pre_activation"
,
Y
=
Y
))
ret_v
.
append_op
(
Operator
(
"sigmoid"
,
X
=
"pre_activation"
,
Out
=
Y
))
ret_v
.
complete_add_op
(
True
)
return
ret_v
...
...
@@ -30,7 +30,7 @@ Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}.
Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}.
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{
Y
[fc.out]}.
Op(sigmoid), inputs:{X[pre_activation]}, outputs:{
Out
[fc.out]}.
'''
self
.
assertEqual
(
expected
,
"
\n
"
+
str
(
net
))
...
...
python/paddle/v2/fluid/tests/test_softmax_op.py
浏览文件 @
24458ae3
...
...
@@ -17,14 +17,14 @@ class TestSoftmaxOp(OpTest):
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
10
,
10
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'
Y
'
:
np
.
apply_along_axis
(
stable_softmax
,
1
,
self
.
inputs
[
'X'
])
'
Out
'
:
np
.
apply_along_axis
(
stable_softmax
,
1
,
self
.
inputs
[
'X'
])
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
'
Y
'
)
self
.
check_grad
([
'X'
],
'
Out
'
)
if
__name__
==
"__main__"
:
...
...
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