Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
O
old_macaon
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Code
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Deploy
Releases
Container registry
Model registry
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
GitLab community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Franck Dary
old_macaon
Commits
9093a1fb
Commit
9093a1fb
authored
6 years ago
by
Franck Dary
Browse files
Options
Downloads
Patches
Plain Diff
started wordking on dynamical oracle
parent
25dd7173
No related branches found
No related tags found
No related merge requests found
Changes
3
Show whitespace changes
Inline
Side-by-side
Showing
3 changed files
trainer/include/Trainer.hpp
+12
-1
12 additions, 1 deletion
trainer/include/Trainer.hpp
trainer/src/Trainer.cpp
+166
-2
166 additions, 2 deletions
trainer/src/Trainer.cpp
trainer/src/macaon_train.cpp
+4
-1
4 additions, 1 deletion
trainer/src/macaon_train.cpp
with
182 additions
and
4 deletions
trainer/include/Trainer.hpp
+
12
−
1
View file @
9093a1fb
...
@@ -62,6 +62,16 @@ class Trainer
...
@@ -62,6 +62,16 @@ class Trainer
/// @param mustShuffle Will the examples be shuffled after every epoch ?
/// @param mustShuffle Will the examples be shuffled after every epoch ?
void
trainBatched
(
int
nbIter
,
int
batchSize
,
bool
mustShuffle
);
void
trainBatched
(
int
nbIter
,
int
batchSize
,
bool
mustShuffle
);
/// @brief Train the TransitionMachine one example at a time.
///
/// For each epoch all the Classifier of the TransitionMachine are fed all the
/// training examples, at the end of the epoch Classifier are evaluated on
/// the devBD if available, and each Classifier will be saved only if its score
/// on the current epoch is its all time best.\n
/// When a Classifier is saved that way, all the Dict involved are also saved.
/// @param nbIter The number of epochs.
void
trainUnbatched
(
int
nbIter
);
/// @brief Uses a TM and a config to create the TrainingExamples that will be used during training.
/// @brief Uses a TM and a config to create the TrainingExamples that will be used during training.
///
///
/// @param config The config to use.
/// @param config The config to use.
...
@@ -129,7 +139,8 @@ void processAllExamples(
...
@@ -129,7 +139,8 @@ void processAllExamples(
/// @param nbIter The number of training epochs.
/// @param nbIter The number of training epochs.
/// @param batchSize The size of each batch.
/// @param batchSize The size of each batch.
/// @param mustShuffle Will the examples be shuffled after every epoch ?
/// @param mustShuffle Will the examples be shuffled after every epoch ?
void
train
(
int
nbIter
,
int
batchSize
,
bool
mustShuffle
);
/// @param batched True if we feed the training algorithm with batches of examples
void
train
(
int
nbIter
,
int
batchSize
,
bool
mustShuffle
,
bool
batched
);
};
};
#endif
#endif
This diff is collapsed.
Click to expand it.
trainer/src/Trainer.cpp
+
166
−
2
View file @
9093a1fb
...
@@ -202,8 +202,172 @@ void Trainer::trainBatched(int nbIter, int batchSize, bool mustShuffle)
...
@@ -202,8 +202,172 @@ void Trainer::trainBatched(int nbIter, int batchSize, bool mustShuffle)
}
}
}
}
void
Trainer
::
train
(
int
nbIter
,
int
batchSize
,
bool
mustShuffle
)
void
Trainer
::
train
Unbatched
(
int
nbIter
)
{
{
std
::
map
<
Classifier
*
,
TrainingExamples
>
devExamples
;
fprintf
(
stderr
,
"Training of
\'
%s
\'
:
\n
"
,
tm
.
name
.
c_str
());
if
(
devBD
&&
devConfig
)
devExamples
=
getExamplesByClassifier
(
*
devConfig
);
auto
&
classifiers
=
tm
.
getClassifiers
();
for
(
Classifier
*
cla
:
classifiers
)
if
(
cla
->
needsTrain
())
cla
->
printTopology
(
stderr
);
std
::
map
<
std
::
string
,
std
::
vector
<
float
>
>
trainScores
;
std
::
map
<
std
::
string
,
std
::
vector
<
float
>
>
devScores
;
std
::
map
<
std
::
string
,
int
>
bestIter
;
Dict
::
saveDicts
(
expPath
,
""
);
for
(
int
i
=
0
;
i
<
nbIter
;
i
++
)
{
tm
.
reset
();
trainConfig
.
reset
();
std
::
map
<
std
::
string
,
std
::
pair
<
int
,
int
>
>
nbExamplesTrain
;
std
::
map
<
std
::
string
,
std
::
pair
<
int
,
int
>
>
nbExamplesDev
;
int
nbTreated
=
0
;
while
(
!
trainConfig
.
isFinal
())
{
TransitionMachine
::
State
*
currentState
=
tm
.
getCurrentState
();
Classifier
*
classifier
=
currentState
->
classifier
;
trainConfig
.
setCurrentStateName
(
&
currentState
->
name
);
Dict
::
currentClassifierName
=
classifier
->
name
;
classifier
->
initClassifier
(
trainConfig
);
if
(
debugMode
)
{
trainConfig
.
printForDebug
(
stderr
);
fprintf
(
stderr
,
"State :
\'
%s
\'\n
"
,
currentState
->
name
.
c_str
());
}
int
neededActionIndex
=
classifier
->
getOracleActionIndex
(
trainConfig
);
std
::
string
neededActionName
=
classifier
->
getActionName
(
neededActionIndex
);
if
(
debugMode
)
{
fprintf
(
stderr
,
"Action : %s
\n
"
,
neededActionName
.
c_str
());
fprintf
(
stderr
,
"
\n
"
);
}
if
(
classifier
->
needsTrain
())
{
TrainingExamples
example
;
example
.
add
(
classifier
->
getFeatureDescription
(
trainConfig
),
neededActionIndex
);
int
score
=
classifier
->
trainOnBatch
(
example
);
nbExamplesTrain
[
classifier
->
name
].
first
++
;
nbExamplesTrain
[
classifier
->
name
].
second
+=
score
;
}
auto
weightedActions
=
classifier
->
weightActions
(
trainConfig
);
if
(
debugMode
)
{
Classifier
::
printWeightedActions
(
stderr
,
weightedActions
);
fprintf
(
stderr
,
"
\n
"
);
}
std
::
string
&
predictedAction
=
weightedActions
[
0
].
second
.
second
;
Action
*
action
=
classifier
->
getAction
(
predictedAction
);
for
(
unsigned
int
i
=
0
;
i
<
weightedActions
.
size
();
i
++
)
{
predictedAction
=
weightedActions
[
i
].
second
.
second
;
action
=
classifier
->
getAction
(
predictedAction
);
if
(
weightedActions
[
i
].
first
)
break
;
}
if
(
!
action
->
appliable
(
trainConfig
))
{
fprintf
(
stderr
,
"ERROR (%s) : action
\'
%s
\'
is not appliable. Aborting
\n
"
,
ERRINFO
,
predictedAction
.
c_str
());
exit
(
1
);
}
if
(
nbTreated
%
1000
==
0
)
fprintf
(
stderr
,
"%d - %s
\n
"
,
nbTreated
,
predictedAction
.
c_str
());
nbTreated
++
;
action
->
apply
(
trainConfig
);
TransitionMachine
::
Transition
*
transition
=
tm
.
getTransition
(
predictedAction
);
tm
.
takeTransition
(
transition
);
trainConfig
.
moveHead
(
transition
->
headMvt
);
}
devConfig
->
reset
();
tm
.
reset
();
while
(
!
devConfig
->
isFinal
())
{
TransitionMachine
::
State
*
currentState
=
tm
.
getCurrentState
();
Classifier
*
classifier
=
currentState
->
classifier
;
devConfig
->
setCurrentStateName
(
&
currentState
->
name
);
Dict
::
currentClassifierName
=
classifier
->
name
;
classifier
->
initClassifier
(
*
devConfig
);
int
neededActionIndex
=
classifier
->
getOracleActionIndex
(
*
devConfig
);
std
::
string
neededActionName
=
classifier
->
getActionName
(
neededActionIndex
);
auto
weightedActions
=
classifier
->
weightActions
(
*
devConfig
);
std
::
string
&
predictedAction
=
weightedActions
[
0
].
second
.
second
;
Action
*
action
=
classifier
->
getAction
(
predictedAction
);
for
(
unsigned
int
i
=
0
;
i
<
weightedActions
.
size
();
i
++
)
{
predictedAction
=
weightedActions
[
i
].
second
.
second
;
action
=
classifier
->
getAction
(
predictedAction
);
if
(
weightedActions
[
i
].
first
)
break
;
}
if
(
!
action
->
appliable
(
trainConfig
))
{
fprintf
(
stderr
,
"ERROR (%s) : action
\'
%s
\'
is not appliable. Aborting
\n
"
,
ERRINFO
,
predictedAction
.
c_str
());
exit
(
1
);
}
if
(
classifier
->
needsTrain
())
{
nbExamplesDev
[
classifier
->
name
].
first
++
;
nbExamplesDev
[
classifier
->
name
].
second
+=
neededActionName
==
predictedAction
?
1
:
0
;
}
action
->
apply
(
*
devConfig
);
TransitionMachine
::
Transition
*
transition
=
tm
.
getTransition
(
predictedAction
);
tm
.
takeTransition
(
transition
);
devConfig
->
moveHead
(
transition
->
headMvt
);
}
printIterationScores
(
stderr
,
nbExamplesTrain
,
nbExamplesDev
,
trainScores
,
devScores
,
bestIter
,
nbIter
,
i
);
for
(
Classifier
*
cla
:
classifiers
)
if
(
cla
->
needsTrain
())
if
(
bestIter
[
cla
->
name
]
==
i
)
{
cla
->
save
(
expPath
+
cla
->
name
+
".model"
);
Dict
::
saveDicts
(
expPath
,
cla
->
name
);
}
}
}
void
Trainer
::
train
(
int
nbIter
,
int
batchSize
,
bool
mustShuffle
,
bool
batched
)
{
if
(
batched
)
trainBatched
(
nbIter
,
batchSize
,
mustShuffle
);
trainBatched
(
nbIter
,
batchSize
,
mustShuffle
);
else
trainUnbatched
(
nbIter
);
}
}
This diff is collapsed.
Click to expand it.
trainer/src/macaon_train.cpp
+
4
−
1
View file @
9093a1fb
...
@@ -49,6 +49,8 @@ po::options_description getOptionsDescription()
...
@@ -49,6 +49,8 @@ po::options_description getOptionsDescription()
"The random seed that will initialize RNG"
)
"The random seed that will initialize RNG"
)
(
"duplicates"
,
po
::
value
<
bool
>
()
->
default_value
(
true
),
(
"duplicates"
,
po
::
value
<
bool
>
()
->
default_value
(
true
),
"Remove identical training examples"
)
"Remove identical training examples"
)
(
"batched"
,
po
::
value
<
bool
>
()
->
default_value
(
true
),
"Uses batch of training examples"
)
(
"shuffle"
,
po
::
value
<
bool
>
()
->
default_value
(
true
),
(
"shuffle"
,
po
::
value
<
bool
>
()
->
default_value
(
true
),
"Shuffle examples after each iteration"
);
"Shuffle examples after each iteration"
);
...
@@ -116,6 +118,7 @@ int main(int argc, char * argv[])
...
@@ -116,6 +118,7 @@ int main(int argc, char * argv[])
int
batchSize
=
vm
[
"batchsize"
].
as
<
int
>
();
int
batchSize
=
vm
[
"batchsize"
].
as
<
int
>
();
int
randomSeed
=
vm
[
"seed"
].
as
<
int
>
();
int
randomSeed
=
vm
[
"seed"
].
as
<
int
>
();
bool
mustShuffle
=
vm
[
"shuffle"
].
as
<
bool
>
();
bool
mustShuffle
=
vm
[
"shuffle"
].
as
<
bool
>
();
bool
batched
=
vm
[
"batched"
].
as
<
bool
>
();
bool
removeDuplicates
=
vm
[
"duplicates"
].
as
<
bool
>
();
bool
removeDuplicates
=
vm
[
"duplicates"
].
as
<
bool
>
();
bool
debugMode
=
vm
.
count
(
"debug"
)
==
0
?
false
:
true
;
bool
debugMode
=
vm
.
count
(
"debug"
)
==
0
?
false
:
true
;
...
@@ -156,7 +159,7 @@ int main(int argc, char * argv[])
...
@@ -156,7 +159,7 @@ int main(int argc, char * argv[])
}
}
trainer
->
expPath
=
expPath
;
trainer
->
expPath
=
expPath
;
trainer
->
train
(
nbIter
,
batchSize
,
mustShuffle
);
trainer
->
train
(
nbIter
,
batchSize
,
mustShuffle
,
batched
);
return
0
;
return
0
;
}
}
...
...
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment