void DBN::learn(){
teacher->monitor->teacher = teacher;
int level = 0;
for (auto input:inputs) rc_MLP->add(input);
while (1){
RBM *rbm = make_rbm_level(level);
for (auto edge:(rbm->edges)) rc_MLP->add((Connection*)edge);
if (rbm->edges.size() == 0) break;
//if (level > 0) rbm->toggle_noise();
rbm->teacher = teacher;
rbm->learn();
//rbm->turn_off();
std::cout << "Done training " << level << " layer." << std::endl;
++level;
}
}
int main (int argc, const char * argv[])
{
//--------------RNG INIT STUFF
srand((unsigned)time(0));
long seed;
r = gsl_rng_alloc (gsl_rng_rand48); // pick random number generator
seed = time (NULL) * getpid();
gsl_rng_set (r, seed); // set seed
//--------------
//LOAD DATASET and INIT
DataSet data1;
data1.loadfMRI(true,true,true);
//DataSet data2;
//data2.loadstim();
//---------DONE INIT
//--------- GSL TESTS GO HERE ----------------
//INIT RBM
GaussianLayer baselayer((int)data1.train->size2);
//SigmoidLayer stimuluslayer((int)data2.train->size2);
//stimuluslayer.noise = .2;
ReLULayer hiddenlayer(32);
//ReLULayer hiddenlayer2(16);
InputEdge ie1(&data1);
//InputEdge ie2(&data2);
baselayer.input_edge = &ie1;
//stimuluslayer.input_edge = &ie2;
Connection c1(&baselayer, &hiddenlayer);
c1.learning_rate = 0.0000001;
c1.decay = 0.0000002;
//Connection c2(&stimuluslayer, &hiddenlayer);
//c2.learning_rate = 0.000000005;
//c2.decay = 0;
//Connection c3(&hiddenlayer, &hiddenlayer2);
//c3.learning_rate = 0.00001;
//c3.decay = 0.000001;
//--------------
float momentum = 0.65;
float k = 1;
float batchsize = 1;
float epochs = 4000;
//LEARNING!!!!!!!!!
ContrastiveDivergence cdLearner(momentum, k, batchsize, epochs);
Connection_Learning_Monitor monitor(&c1);
cdLearner.monitor = &monitor;
RBM rbm;
rbm.add_connection(&c1);
//rbm.add_connection(&c2);
rbm.teacher = &cdLearner;
rbm.learn();
/*
DBN dbn;
dbn.add_connection(&c1);
dbn.add_connection(&c3);
dbn.teacher = &cdLearner;
dbn.finish_setup();
dbn.pathways[0]->setup_viz();
dbn.viz = (dbn.pathways[0])->viz;
dbn.viz->initViz();
dbn.viz->thresh = .02;
dbn.viz->scale = 1;
dbn.learn();
std::cout << "Done Learning! " << std::endl;
*/
return 0;
}