bool MLP::learn(realnumber ME, realnumber MT, realnumber LR, bool ALR, realnumber lambda, realnumber lambda1, realnumber lambda2)
// learn permet de réaliser l'apprentissage du MLP
{
/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
* *
* A IMPLEMENTER *
* *
* normaliser les données d'entrainement *
* erreur en dessous de laquelle un exemple n'est plus traité *
* weight decay *
* OK: variation du taux d'apprentissage (algo de Vogl) OU poids distinct pour chaque connexion (Sanossian & Evans) *
* élagage *
* injection de bruit *
* ensemble de validation *
* early stop *
* *
* *
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
/*
* ME = MAX_ERROR
* MT = MAX_TIME
* LR = LEARNING_RATE
* ALR = ADAPTATIVELR (adaptative learning rate)
*/
if (isSet())
{
integer index, compteur = 0;
realnumber nextDisplayTime = 0,
newMQE = MQE(lambda, lambda1, lambda2),
oldMQE = newMQE;
clock_t start = clock();
displayInfo(lambda, lambda1, lambda2);
display("learning starting...");
// pour la suite: "index" est le numéro de l'exemple que l'on est en train de traiter
// et "j" est le numéro de la couche
while(newMQE > ME && (clock() - start) / (realnumber)CLOCKS_PER_SEC < MT)
{
// affiche "mqe" et "m_learningRate" si le dernier affichage date de plus d'une seconde
displayMQE(start, nextDisplayTime, newMQE, LR);
// présente un exemple au hasard pour l'apprendre
index = rand()% m_input.cols(); // ATTENTION! A améliorer
saveWeights();
weightDecay(lambda, lambda1, lambda2);
modifyWeights(index, LR);
// on vérifie s'ils sont meilleurs que les anciens, sinon on revient en arrière
newMQE = MQE(lambda, lambda1, lambda2);
modifyLearningRate(LR, ALR, oldMQE, newMQE);
compteur++;
}
display("learning finished! \n");
display("Iterations: " + toStr(int(compteur)) + "; Temps en secondes : " + toStr ((clock() - start) / (realnumber)CLOCKS_PER_SEC) + "");
displayInfo(lambda, lambda1, lambda2);
return (newMQE <= ME);
}
else
return 0;
}
int main(int argc,char *argv[]){
int i,j,k,p,q;
char *line;
int BUFSIZE=100;
ssize_t read;
float elapsed_time;
struct timeval start_time, end_time;
line=(char *)malloc(sizeof(char)*BUFSIZE + 1);
int nx = atoi(argv[1]);
int ny = atoi(argv[2]);
int nz = atoi(argv[3]);
TYPE xlo = (TYPE)atof(argv[4]);
TYPE ylo = (TYPE)atof(argv[5]);
TYPE zlo = (TYPE)atof(argv[6]);
TYPE h = (TYPE)atof(argv[7]);
TYPE a = (TYPE)atof(argv[8]);
int generateTargetVariogram = atoi(argv[9]);
int useNscore = atoi(argv[10]);
int iters=atoi(argv[11]);
int report=100;
int updateTemperature=atoi(argv[12]);
TYPE tol=atof(argv[13]); // percentage of decrease w/r initial cost
TYPE temperatureInitial=atof(argv[14]);
int bufferNodes=ceil(a/h);
int neighRadius=bufferNodes;
int neighSide=2*neighRadius+1;
int nxExt=nx+2*bufferNodes;
int nyExt=ny+2*bufferNodes;
int nzExt=nz+2*bufferNodes;
//#ifdef OCTAVE
// printf("image=zeros(%d,%d,%d);\n",nx,ny,nz);
//#else
//#ifdef GSLIB
// printf("Test\n4\nX\nY\nZ\ndata\n");
//#endif
//#endif
latticeParams params;
params.nx=nx;
params.ny=ny;
params.nz=nz;
params.xlo=xlo;
params.ylo=ylo;
params.zlo=zlo;
params.h=h;
TYPE *image;
//image = genRandomImage(params.nx+2*bufferNodes,params.ny+2*bufferNodes,params.nz+2*bufferNodes);
image = genRandomImage(nxExt,nyExt,nzExt);
int initialAddress = bufferNodes*nyExt*nxExt +
bufferNodes*nxExt +
bufferNodes;
TYPE *imageZero = image+initialAddress;
TYPE distanceValue[MAXDISTVALS];
int distanceIndex[MAXDISTVALS];
for(i=0;i<MAXDISTVALS;i++){
distanceValue[i]=-1.0;
distanceIndex[i]=-1;
}
FILE *fpweight, *fptargetvariogram;
fpweight=fopen("weightout.dat","r");
if(generateTargetVariogram){
int retTarget = genTargetVariogram(nx,ny,nz,xlo,ylo,zlo,h,a,imageZero,useNscore);
fptargetvariogram=fopen("targetvariogram.dat","r");
}
else{
if(useNscore){
//fptargetvariogram=fopen("gamv_Cu_vertical_nscore.out","r");
fptargetvariogram=fopen("gamv_Cu_omnihoriz_nscore.out","r");
}
else{
//fptargetvariogram=fopen("gamv_Cu_vertical.out","r");
fptargetvariogram=fopen("gamv_Cu_omnihoriz.out","r");
}
}
int nlags,num;
if((fgets(line,BUFSIZE,fptargetvariogram))==0){
fprintf(stderr,"ERROR: variogram nlags not specified in variogram file.\n");
return 1;
}
sscanf(line,"%d\n",&nlags);
TYPE *expVariogram = (TYPE *)malloc(sizeof(TYPE)*nlags);
int *npairs=(int *)malloc(sizeof(int)*nlags);
TYPE *lagDistance = (TYPE *)malloc(sizeof(TYPE)*nlags);
TYPE vgval,lagval;
i=0;
while((fgets(line,BUFSIZE,fptargetvariogram))!=0){
sscanf(line,"%lf %d %lf",&vgval,&num,&lagval);
expVariogram[i]=vgval;
npairs[i]=num;
lagDistance[i]=lagval;
//printf("%f %d\n",vgval,num);
i++;
}
fprintf(stderr,"experimental variogram and number of pairs loaded.\n");
fclose(fptargetvariogram);
int side;
if((fgets(line,BUFSIZE,fpweight))==0){
fprintf(stderr,"ERROR: weights cell value not specified in weight file.\n");
return 2;
}
sscanf(line,"%d\n",&side);
//printf("%d\n",side);
nweight = side*side*side;
TYPE *weight = (TYPE *)malloc(sizeof(TYPE)*nweight);
TYPE *weightTemp = (TYPE *)malloc(sizeof(TYPE)*nweight);
TYPE *weightPerturbed = (TYPE *)malloc(sizeof(TYPE)*nweight);
TYPE *weightPerturbedTemp = (TYPE *)malloc(sizeof(TYPE)*nweight);
int midside;
TYPE val;
midside=ceil(side/2);
//midsidesqrt3=ceil((TYPE)(side/2)*(TYPE)sqrt(3.0));
// esto funciona con D+1 weights
//int distance;
////for(i=0;k<=midside;k++){
//while((fgets(line,BUFSIZE,fpweight))!=0){
// sscanf(line,"%d %lf",&distance,&val);
// if(distance>=MAXDISTVALS){
// printf("ERROR: too many distance-weight pair in weights input file.\n");
// return 2;
// }
// distanceValue[distance]=val;
// printf("distanceValue(%d)=%f;\n",distance,distanceValue[distance]);
//}
////}
//int idiag;
//for(k=0;k<=midside;k++){
// for(j=0;j<=midside;j++){
// for(i=0;i<=midside;i++){
// idiag = nearbyint(sqrt((TYPE)(i*i+j*j+k*k)));
// //if(idiag<=midside){
// weight[i + j*side + k*side*side]=distanceValue[idiag];
// //}
// //else{
// // weight[i + j*side + k*side*side]=0.0;
// //}
// //printf("weight(%d,%d,%d)=%f;\n",i+1,j+1,k+1,weight[i + j*side + k*side*side]);
// }
// }
//}
// esto funciona con (D+1)^3 weights
//for(k=0;k<=midside;k++){
// for(j=0;j<=midside;j++){
// for(i=0;i<=midside;i++){
// if((fgets(line,BUFSIZE,fpweight))==0){
// printf("ERROR: weights cell value not specified in weight file.\n");
// return 2;
// }
// sscanf(line,"%lf",&val);
// weight[i + j*side + k*side*side]=val;
// //printf("%d %d %d %f\n",i,j,k,val);
// }
// }
//}
TYPE xcoord1,ycoord1,zcoord1;
TYPE xcoord2,ycoord2,zcoord2,dist;
getCoordinates(0,0,0,params,&xcoord1,&ycoord1,&zcoord1);
int distCounter=0;
while((fgets(line,BUFSIZE,fpweight))!=0){
sscanf(line,"%d %d %d %lf",&i,&j,&k,&val);
weight[i + j*side + k*side*side]=val;
getCoordinates(i-neighRadius,j-neighRadius,k-neighRadius,params,&xcoord2,&ycoord2,&zcoord2);
dist = sqrt( (xcoord1-xcoord2)*(xcoord1-xcoord2)+
(ycoord1-ycoord2)*(ycoord1-ycoord2)+
(zcoord1-zcoord2)*(zcoord1-zcoord2));
if(existValue(distanceValue,MAXDISTVALS,dist)==-1){
distanceValue[distCounter]=dist;
distCounter++;
}
//printf("%d %d %d %f\n",i,j,k,val);
}
quickSort(distanceValue,0,distCounter-1);
//for(i=0;i<distCounter;i++)
// printf("%f ",distanceValue[i]);
//printf("\n");
fprintf(stderr,"weights (octant) loaded.\n");
fclose(fpweight);
int ieff,jeff,keff;
for(k=0;k<side;k++){
for(j=0;j<side;j++){
for(i=0;i<side;i++){
getCoordinates(i-neighRadius,j-neighRadius,k-neighRadius,params,&xcoord2,&ycoord2,&zcoord2);
dist = sqrt( (xcoord1-xcoord2)*(xcoord1-xcoord2)+
(ycoord1-ycoord2)*(ycoord1-ycoord2)+
(zcoord1-zcoord2)*(zcoord1-zcoord2));
for(p=0;p<distCounter;p++){
if(dist==distanceValue[p]){
distanceIndex[p]=i + j*side + k*side*side;
}
}
if(!(i<=midside && j<=midside && k<=midside)){
ieff=i;
jeff=j;
keff=k;
if(i>midside){
ieff=side-i-1;
}
if(j>midside){
jeff=side-j-1;
}
if(k>midside){
keff=side-k-1;
}
weight[i + j*side + k*side*side] = weight[ieff + jeff*side + keff*side*side];
}
//printf("%d %d %d %f\n",i,j,k,weight[i + j*side + k*side*side]);
//printf("weight(%d,%d,%d)=%f;\n",i+1,j+1,k+1,weight[i + j*side + k*side*side]);
weightPerturbed[i + j*side + k*side*side] = weight[i + j*side + k*side*side];
}
}
}
//for(i=0;i<distCounter;i++)
// fprintf(stderr,"%f ",distanceValue[i]);
//printf("\n");
//for(i=0;i<distCounter;i++)
// fprintf(stderr,"%d ",distanceIndex[i]);
//printf("\n");
fprintf(stderr,"weights loaded.\n");
//return 1;
int ret = genMovingAverageImage(imageZero,weight,nx,ny,nz,nxExt,nyExt,nzExt,neighRadius,neighSide,params,0,100);
fprintf(stderr,"starting cost function calculation...\n");
TYPE cost = costFunctionGSLIB(nlags,expVariogram,npairs,0,useNscore);
fprintf(stderr,"stoping cost function calculation...\n");
fprintf(stderr,"initial cost=%f\n",cost);
TYPE initialCost = cost;
TYPE tempCost=0.0;
TYPE currentCost= cost;
TYPE currentCostPrev= 0.0;
//int iters=6000;
//int iters=12000;
//int iters=100;
//int iters=2000;
//int report=100;
//int updateTemperature=100;
//int updateTemperature=750;
//int updateTemperature=(int)(0.05*((double)iters));
//int updateTemperature=600;
int resetCounter=0;
int averageCounter=0;
TYPE modifiedRadius;
//TYPE tol=0.01; // percentage of decrease w/r initial cost
TYPE newval,oldval;
//TYPE temperatureInitial=0.000075;
//TYPE temperatureInitial=0.0003;
//TYPE temperatureInitial=0.01; // 2D gaussian-wrong to gaussian works
//TYPE temperatureInitial=0.005; // 2D gaussian to circular works
//TYPE temperatureInitial=0.001; // 2D circular to gaussian works
//TYPE temperatureInitial=0.01; // real data works
//TYPE temperatureInitial=1.0; //
//TYPE temperatureInitial=0.1; //
//TYPE temperatureInitial=0.01; //
//TYPE temperatureInitial=0.001; //
//TYPE temperatureInitial=0.0001; //
//TYPE temperatureInitial=0.00001; //
//TYPE temperatureInitial=0.05; //
TYPE temperature=temperatureInitial;
TYPE probability, ran;
int averageFlag=0;
int lastState=0;
gettimeofday( &start_time, NULL );
//for(i=1;i<=1;i++){
for(i=1;i<=iters;i++){
if((currentCost/initialCost)<=tol){
fprintf(stdout,"STOP: convergence achieved. Congratulations!\n");
modifiedRadius = modifyWeights(
weight,
neighRadius,
neighSide,
distanceValue,
distanceIndex,
distCounter,
params,
2,
0,
0.0,
&newval,
&oldval,
i);
return 0;
}
else{
//if(i%50==0)
fprintf(stdout,"ITERATION %d:\t",i);
// smoothing of weights
//if(i%(iters/10)==0 || i==iters){
if(lastState==100){
modifiedRadius = averageWeights(
weightPerturbed,
weight,
neighRadius,
neighSide,
distanceValue,
distanceIndex,
distCounter,
params,
0,
0,
0.0,
&newval,
&oldval,
i);
ret = genMovingAverageImage(imageZero,weightPerturbed,nx,ny,nz,nxExt,nyExt,nzExt,neighRadius,neighSide,params,i,report);
averageFlag=1;
lastState=0;
}
modifiedRadius = modifyWeights(
weightPerturbed,
neighRadius,
neighSide,
distanceValue,
distanceIndex,
distCounter,
params,
0,
0,
0.0,
&newval,
&oldval,
i);
ret = genMovingAverageImage(imageZero,weightPerturbed,nx,ny,nz,nxExt,nyExt,nzExt,neighRadius,neighSide,params,i,report);
tempCost = costFunctionGSLIB(nlags,expVariogram,npairs,i,useNscore);
//printf("tempCost=%f\t(%1.6f)\t",tempCost,tempCost/initialCost);
//if(i%200==0){
if(i%updateTemperature==0){
//temperature=temperature*0.1;
temperature=temperature*0.5;
//temperature=temperature*0.9;
resetCounter++;
//if(resetCounter==10){
//if(resetCounter==3){
if(resetCounter==5){
//temperature=temperatureInitial*0.9;
temperature=temperatureInitial;
resetCounter=0;
}
}
probability = tempCost<currentCost?1.0:exp((currentCost-tempCost)/(currentCost*temperature));
ran=((TYPE)rand()/(TYPE)RAND_MAX);
//fprintf(stderr,"probability=%f>%f?%d (%f)\n",probability,ran,probability>ran,(currentCost-tempCost)/(temperature) );
//if(tempCost<currentCost){
if(probability>ran){
//accept modification
//weight[modifiedIndex] = weightPerturbed[modifiedIndex];
//if(i%50==0)
if(averageFlag==0){
fprintf(stdout,"accepted(prob=%f,r=%f,new=%f,old=%f)\ttempCost=%f\tcurrCost=%f\t%f\n",probability,modifiedRadius,newval,oldval,tempCost,currentCost,currentCost/initialCost);
}
else{
fprintf(stdout,"accepted(prob=%f,r=%f,new=%f,old=%f)\ttempCost=%f\tcurrCost=%f\t%f\t(averaged)\n",probability,modifiedRadius,newval,oldval,tempCost,currentCost,currentCost/initialCost);
averageFlag=0;
}
ret = modifyWeights(
weight,
neighRadius,
neighSide,
distanceValue,
distanceIndex,
distCounter,
params,
1,
modifiedRadius,
newval,
NULL,
NULL,
i);
currentCost = tempCost;
int sysret = system("cp currentdistanceweights.dat currentbestdistanceweights.dat");
sysret = system("cp currentvariogram.dat currentbestvariogram.dat");
lastState++;
}
else{
//reject modification
//weightPerturbed[modifiedIndex] = weight[modifiedIndex];
//if(i%50==0)
if(averageFlag==0){
fprintf(stdout,"rejected(prob=%f,r=%f,new=%f,old=%f)\ttempCost=%f\tcurrCost=%f\t%f\n",probability,modifiedRadius,newval,oldval,tempCost,currentCost,currentCost/initialCost);
}
else{
fprintf(stdout,"rejected(prob=%f,r=%f,new=%f,old=%f)\ttempCost=%f\tcurrCost=%f\t%f\t(averaged)\n",probability,modifiedRadius,newval,oldval,tempCost,currentCost,currentCost/initialCost);
averageFlag=0;
}
ret = modifyWeights(
weightPerturbed,
neighRadius,
neighSide,
distanceValue,
distanceIndex,
distCounter,
params,
1,
modifiedRadius,
oldval,
NULL,
NULL,
i);
/*for(i=0;i<nweight;i++){
weightPerturbed[i]=weight[i];
}*/
lastState=0;
}
}
}
gettimeofday( &end_time, NULL );
elapsed_time = end_time.tv_sec - start_time.tv_sec + (end_time.tv_usec - start_time.tv_usec ) / 1e6;
printf("elapsed_time=%f\n",elapsed_time/3.0f);
//return 1;
//printf("starting cost function calculation...\n");
//TYPE value = costFunction(nlags,expVariogram,npairs,imageZero,nx,ny,nz,bufferNodes);
//printf("stoping cost function calculation...\n");
//printf("%f\n",value);
free(expVariogram);
free(lagDistance);
free(npairs);
free(weight);
free(weightTemp);
free(weightPerturbed);
free(weightPerturbedTemp);
freeRandomImage(image);
free(line);
return 0;
}
