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体验新版 GitCode,发现更多精彩内容 >>
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6b3c33d0
编写于
6月 22, 2017
作者:
C
Cao Ying
提交者:
GitHub
6月 22, 2017
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差异文件
Merge pull request #2523 from emailweixu/rnn_evaluator
evaluator for recurrent group.
上级
d524acc6
02cc7d90
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
38 addition
and
21 deletion
+38
-21
paddle/gserver/gradientmachines/NeuralNetwork.cpp
paddle/gserver/gradientmachines/NeuralNetwork.cpp
+34
-1
paddle/gserver/gradientmachines/NeuralNetwork.h
paddle/gserver/gradientmachines/NeuralNetwork.h
+2
-0
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
+2
-18
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
+0
-2
未找到文件。
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @
6b3c33d0
...
...
@@ -321,7 +321,7 @@ public:
}
}
virtual
void
eval
(
const
NeuralNetwork
&
nn
)
{
virtual
void
eval
(
const
NeuralNetwork
&
nn
)
override
{
for
(
auto
&
evaluator
:
evaluators_
)
{
evaluator
->
eval
(
nn
);
}
...
...
@@ -396,6 +396,30 @@ private:
}
};
class
SubnetEvaluator
:
public
CombinedEvaluator
{
public:
SubnetEvaluator
(
const
std
::
string
&
layerName
,
std
::
unique_ptr
<
Evaluator
>&&
evaluator
)
:
layerName_
(
layerName
)
{
addEvaluator
(
std
::
move
(
evaluator
));
}
virtual
void
eval
(
const
NeuralNetwork
&
nn
)
override
{
const
LayerPtr
&
layer
=
nn
.
getLayer
(
layerName_
);
CHECK
(
layer
)
<<
"Nonexisted layer: "
<<
layerName_
<<
" in submodel "
<<
nn
.
getName
();
bool
accessed
=
false
;
layer
->
accessSubNetwork
([
this
,
&
accessed
](
NeuralNetwork
&
subnet
)
{
subnet
.
eval
(
evaluators_
[
0
].
get
());
accessed
=
true
;
});
CHECK
(
accessed
)
<<
"There is no subnetwork for layer "
<<
layerName_
<<
" in submodel "
<<
nn
.
getName
();
}
protected:
std
::
string
layerName_
;
};
Evaluator
*
NeuralNetwork
::
makeEvaluator
()
const
{
CombinedEvaluator
*
combinedEvaluator
=
new
CombinedEvaluator
();
auto
subModelConfig
=
std
::
find_if
(
config_
.
sub_models
().
begin
(),
...
...
@@ -422,6 +446,15 @@ Evaluator* NeuralNetwork::makeEvaluator() const {
combinedEvaluator
->
addEvaluator
(
std
::
move
(
evaluator
));
}
}
for
(
auto
&
layer
:
layers_
)
{
layer
->
accessSubNetwork
(
[
layer
,
combinedEvaluator
](
NeuralNetwork
&
subnet
)
{
std
::
unique_ptr
<
Evaluator
>
subEvaluator
(
new
SubnetEvaluator
(
layer
->
getName
(),
std
::
unique_ptr
<
Evaluator
>
(
subnet
.
makeEvaluator
())));
combinedEvaluator
->
addEvaluator
(
std
::
move
(
subEvaluator
));
});
}
}
else
{
for
(
const
EvaluatorConfig
&
evalConfig
:
config_
.
evaluators
())
{
std
::
unique_ptr
<
Evaluator
>
evaluator
(
Evaluator
::
create
(
evalConfig
));
...
...
paddle/gserver/gradientmachines/NeuralNetwork.h
浏览文件 @
6b3c33d0
...
...
@@ -129,6 +129,8 @@ public:
static
NeuralNetwork
*
newNeuralNetwork
(
const
std
::
string
&
name
=
""
,
NeuralNetwork
*
rootNetwork
=
nullptr
);
const
std
::
string
&
getName
()
const
{
return
subModelName_
;
}
protected:
/**
* The constructor of NeuralNetwork.
...
...
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
浏览文件 @
6b3c33d0
...
...
@@ -288,10 +288,6 @@ void RecurrentGradientMachine::init(
parameterIds_
.
push_back
(
para
->
getID
());
}
}
if
(
subModelConfig
->
evaluator_names_size
()
>
0
)
{
evaluator_
.
reset
(
frames_
[
0
]
->
makeEvaluator
());
}
}
void
RecurrentGradientMachine
::
resizeOrCreateFrames
(
int
numFrames
)
{
...
...
@@ -562,9 +558,6 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
std
::
vector
<
Argument
>
outArgs
;
frames_
[
i
]
->
forward
(
inArgs
,
&
outArgs
,
passType
);
}
if
(
evaluator_
&&
passType
==
PASS_TEST
)
{
this
->
eval
(
evaluator_
.
get
());
}
reorganizeOutput
(
passType
);
}
...
...
@@ -581,11 +574,6 @@ void RecurrentGradientMachine::backward(const UpdateCallback& callback) {
for
(
auto
&
memoryFrameLine
:
memoryFrameLines_
)
{
memoryFrameLine
.
bootLayer
->
backward
(
nullptr
);
}
// call printers here so the gradient can be printed
if
(
evaluator_
)
{
this
->
eval
(
evaluator_
.
get
());
}
}
void
RecurrentGradientMachine
::
forwardBackward
(
...
...
@@ -599,9 +587,9 @@ void RecurrentGradientMachine::forwardBackward(
void
RecurrentGradientMachine
::
eval
(
Evaluator
*
evaluator
)
const
{
// call printers frame by frame
for
(
int
i
=
0
;
i
<
maxSequenceLength_
;
++
i
)
{
LOG
(
INFO
)
<<
"Recurrent Layer Group eval frame "
<<
i
<<
" begin"
;
VLOG
(
2
)
<<
"Recurrent Layer Group eval frame "
<<
i
<<
" begin"
;
evaluator
->
eval
(
*
(
frames_
[
i
].
get
()));
LOG
(
INFO
)
<<
"Recurrent Layer Group eval frame "
<<
i
<<
" end"
;
VLOG
(
2
)
<<
"Recurrent Layer Group eval frame "
<<
i
<<
" end"
;
}
}
...
...
@@ -1097,10 +1085,6 @@ void RecurrentGradientMachine::oneWaySearch(size_t batchSize) {
copyDataOutlinkFrame
(
machineCur
);
// call value printer
if
(
evaluator_
)
{
evaluator_
->
eval
(
*
(
frames_
[
machineCur
].
get
()));
}
// check eos
const
IVectorPtr
&
eosVec
=
eosFrameLine_
->
layers
[
machineCur
]
->
getOutput
().
ids
;
...
...
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
浏览文件 @
6b3c33d0
...
...
@@ -429,8 +429,6 @@ protected:
std
::
vector
<
int
>
parameterIds_
;
// parameters actually used by this Layer Group
std
::
unique_ptr
<
Evaluator
>
evaluator_
;
// frame printers in this layer group
// store final argument of outFrameLines_
std
::
vector
<
Argument
>
dataArgs_
;
// store each frame's output argument of outFrameLines_
...
...
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