Simulated Annealing for ANN_MLP training method (#10213)
* Simulated Annealing for ANN_MLP training method * EXPECT_LT * just to test new data * manage RNG * Try again * Just run buildbot with new data * try to understand * Test layer * New data- new test * Force RNG in backprop * Use Impl to avoid virtual method * reset all weights * try to solve ABI * retry * ABI solved? * till problem with dynamic_cast * Something is wrong * Solved? * disable backprop test * remove ANN_MLP_ANNEALImpl * Disable weight in varmap * Add example for SimulatedAnnealing
This commit is contained in:
LaurentBerger
committed by
Vadim Pisarevsky
Vadim Pisarevsky
parent
6df8ac0342
commit
7ad308ea47
@@ -1406,13 +1406,14 @@ public:
|
||||
/** Available training methods */
|
||||
enum TrainingMethods {
|
||||
BACKPROP=0, //!< The back-propagation algorithm.
|
||||
RPROP=1 //!< The RPROP algorithm. See @cite RPROP93 for details.
|
||||
RPROP = 1, //!< The RPROP algorithm. See @cite RPROP93 for details.
|
||||
ANNEAL = 2 //!< The simulated annealing algorithm. See @cite Kirkpatrick83 for details.
|
||||
};
|
||||
|
||||
/** Sets training method and common parameters.
|
||||
@param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
|
||||
@param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP
|
||||
@param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP.
|
||||
@param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
|
||||
@param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.
|
||||
*/
|
||||
CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0;
|
||||
|
||||
@@ -1499,6 +1500,34 @@ public:
|
||||
/** @copybrief getRpropDWMax @see getRpropDWMax */
|
||||
CV_WRAP virtual void setRpropDWMax(double val) = 0;
|
||||
|
||||
/** ANNEAL: Update initial temperature.
|
||||
It must be \>=0. Default value is 10.*/
|
||||
/** @see setAnnealInitialT */
|
||||
CV_WRAP double getAnnealInitialT() const;
|
||||
/** @copybrief getAnnealInitialT @see getAnnealInitialT */
|
||||
CV_WRAP void setAnnealInitialT(double val);
|
||||
|
||||
/** ANNEAL: Update final temperature.
|
||||
It must be \>=0 and less than initialT. Default value is 0.1.*/
|
||||
/** @see setAnnealFinalT */
|
||||
CV_WRAP double getAnnealFinalT() const;
|
||||
/** @copybrief getAnnealFinalT @see getAnnealFinalT */
|
||||
CV_WRAP void setAnnealFinalT(double val);
|
||||
|
||||
/** ANNEAL: Update cooling ratio.
|
||||
It must be \>0 and less than 1. Default value is 0.95.*/
|
||||
/** @see setAnnealCoolingRatio */
|
||||
CV_WRAP double getAnnealCoolingRatio() const;
|
||||
/** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */
|
||||
CV_WRAP void setAnnealCoolingRatio(double val);
|
||||
|
||||
/** ANNEAL: Update iteration per step.
|
||||
It must be \>0 . Default value is 10.*/
|
||||
/** @see setAnnealItePerStep */
|
||||
CV_WRAP int getAnnealItePerStep() const;
|
||||
/** @copybrief getAnnealItePerStep @see getAnnealItePerStep */
|
||||
CV_WRAP void setAnnealItePerStep(int val);
|
||||
|
||||
/** possible activation functions */
|
||||
enum ActivationFunctions {
|
||||
/** Identity function: \f$f(x)=x\f$ */
|
||||
@@ -1838,6 +1867,111 @@ CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, Out
|
||||
CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
|
||||
OutputArray samples, OutputArray responses);
|
||||
|
||||
/** @brief Artificial Neural Networks - Multi-Layer Perceptrons.
|
||||
|
||||
@sa @ref ml_intro_ann
|
||||
*/
|
||||
class CV_EXPORTS_W ANN_MLP_ANNEAL : public ANN_MLP
|
||||
{
|
||||
public:
|
||||
/** @see setAnnealInitialT */
|
||||
CV_WRAP virtual double getAnnealInitialT() const;
|
||||
/** @copybrief getAnnealInitialT @see getAnnealInitialT */
|
||||
CV_WRAP virtual void setAnnealInitialT(double val);
|
||||
|
||||
/** ANNEAL: Update final temperature.
|
||||
It must be \>=0 and less than initialT. Default value is 0.1.*/
|
||||
/** @see setAnnealFinalT */
|
||||
CV_WRAP virtual double getAnnealFinalT() const;
|
||||
/** @copybrief getAnnealFinalT @see getAnnealFinalT */
|
||||
CV_WRAP virtual void setAnnealFinalT(double val);
|
||||
|
||||
/** ANNEAL: Update cooling ratio.
|
||||
It must be \>0 and less than 1. Default value is 0.95.*/
|
||||
/** @see setAnnealCoolingRatio */
|
||||
CV_WRAP virtual double getAnnealCoolingRatio() const;
|
||||
/** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */
|
||||
CV_WRAP virtual void setAnnealCoolingRatio(double val);
|
||||
|
||||
/** ANNEAL: Update iteration per step.
|
||||
It must be \>0 . Default value is 10.*/
|
||||
/** @see setAnnealItePerStep */
|
||||
CV_WRAP virtual int getAnnealItePerStep() const;
|
||||
/** @copybrief getAnnealItePerStep @see getAnnealItePerStep */
|
||||
CV_WRAP virtual void setAnnealItePerStep(int val);
|
||||
|
||||
|
||||
/** @brief Creates empty model
|
||||
|
||||
Use StatModel::train to train the model, Algorithm::load\<ANN_MLP\>(filename) to load the pre-trained model.
|
||||
Note that the train method has optional flags: ANN_MLP::TrainFlags.
|
||||
*/
|
||||
// CV_WRAP static Ptr<ANN_MLP> create();
|
||||
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
* Simulated annealing solver *
|
||||
\****************************************************************************************/
|
||||
|
||||
/** @brief The class implements simulated annealing for optimization.
|
||||
@cite Kirkpatrick83 for details
|
||||
*/
|
||||
class CV_EXPORTS SimulatedAnnealingSolver : public Algorithm
|
||||
{
|
||||
public:
|
||||
SimulatedAnnealingSolver() { init(); };
|
||||
~SimulatedAnnealingSolver();
|
||||
/** Give energy value for a state of system.*/
|
||||
virtual double energy() =0;
|
||||
/** Function which change the state of system (random pertubation).*/
|
||||
virtual void changedState() = 0;
|
||||
/** Function to reverse to the previous state.*/
|
||||
virtual void reverseChangedState() = 0;
|
||||
/** Simulated annealing procedure. */
|
||||
int run();
|
||||
/** Set intial temperature of simulated annealing procedure.
|
||||
*@param x new initial temperature. x\>0
|
||||
*/
|
||||
void setInitialTemperature(double x);
|
||||
/** Set final temperature of simulated annealing procedure.
|
||||
*@param x new final temperature value. 0\<x\<initial temperature
|
||||
*/
|
||||
void setFinalTemperature(double x);
|
||||
double getFinalTemperature();
|
||||
/** Set setCoolingRatio of simulated annealing procedure : T(t) = coolingRatio * T(t-1).
|
||||
* @param x new cooling ratio value. 0\<x\<1
|
||||
*/
|
||||
void setCoolingRatio(double x);
|
||||
/** Set number iteration per temperature step.
|
||||
* @param ite number of iteration per temperature step ite \> 0
|
||||
*/
|
||||
void setIterPerStep(int ite);
|
||||
struct Impl;
|
||||
protected :
|
||||
void init();
|
||||
Impl* impl;
|
||||
};
|
||||
struct SimulatedAnnealingSolver::Impl
|
||||
{
|
||||
RNG rEnergy;
|
||||
double coolingRatio;
|
||||
double initialT;
|
||||
double finalT;
|
||||
int iterPerStep;
|
||||
Impl()
|
||||
{
|
||||
initialT = 2;
|
||||
finalT = 0.1;
|
||||
coolingRatio = 0.95;
|
||||
iterPerStep = 100;
|
||||
refcount = 1;
|
||||
}
|
||||
int refcount;
|
||||
~Impl() { refcount--;CV_Assert(refcount==0); }
|
||||
};
|
||||
|
||||
|
||||
//! @} ml
|
||||
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user