int main(int * argc, char ** argv){
initialize_unit();
while(1){
calc_error(input_data, output_data);
back_propagation();
if(check_loop())break;
}
release_unit();
}
void network::SGD(train_couple* mini_batch, int batch_size, double eta,int n)
{
matrix* X=extract_X(mini_batch,batch_size);
matrix* Y=extract_Y(mini_batch,batch_size);
matrix* Gw_sum=prepare_Gw();
matrix* Gb_sum=prepare_Gb();
for (int i=0; i<batch_size; i++){
matrix* Gw=prepare_Gw();
matrix* Gb=prepare_Gb();
back_propagation(X[i],Y[i],Gw,Gb);
for (int l=0; l<layers_count; l++){
Gw_sum[l]=Gw_sum[l]+Gw[l];
Gb_sum[l]=Gb_sum[l]+Gb[l];
}
delete [] Gw;
delete [] Gb;
}
/////Averaging
for (int l=0; l<layers_count; l++){
Gw_sum[l]=Gw_sum[l]*(n/batch_size);
Gb_sum[l]=Gb_sum[l]*(n/batch_size);
}
for (int l=0; l<layers_count; l++){
w[l]=w[l]-eta*Gw_sum[l] ;
b[l]=b[l]-eta*Gb_sum[l] ;
}
delete [] X;
delete [] Y;
delete [] Gw_sum;
delete [] Gb_sum;
}