void StochasticGD<ParameterType, SampleType, LabelType, SparseGradientType,
DenseGradientType>::Train() {
learnrateiter_ = this->LearningRate();
itercount_ = 0;
timeutil timer;
for (epochcount_ = 0; epochcount_ < this->MaxIter(); ++epochcount_) {
LOG(INFO) << "epoch " << epochcount_ << " start";
this->trainiter_->ResetBatch();
timer.tic();
TrainOneEpoch();
double secs = timer.toc();
LOG(INFO) << "batch costs " << secs;
LOG(INFO) << "evaluate on train set";
this->EvaluateOnSet(this->trainiter_->GetAllData(),
this->trainiter_->GetAllLabel());
if (this->testiter_->IsValid()) {
LOG(INFO) << "evaluate on test set";
this->EvaluateOnSet(this->testiter_->GetAllData(),
this->testiter_->GetAllLabel());
}
}
this->ResultStats(this->model_.GetParameters());
}
/**
* Function that get two vectors, one for the entries and another to the outs, and train one epoch:
*/
int Train( PtNet Net, int size, float *entry, int size2, float *out, float l_rate )
{
int Ret;
/* Inserting the entries: */
Ret = SetEntry( Net, size, entry);
/* Getting the wrong out values: */
ActivAll(Net);
/* Training one epoch: */
Ret = TrainOneEpoch( Net, out, size2 );
/* Updating the weights: */
UpdateWeights( Net, l_rate );
return Ret;
}
