void Levenshtein::compute()
{
reset_weights();
DynProg::compute();
return;
}
/*--------------------------------------------------------*/
void AzsSvrg::_train_test()
{
if (rseed > 0) {
srand(rseed); /* initialize the random seed */
}
/*--- initialization ---*/
int dim = m_trn_x->rowNum();
reset_weights(dim);
/*--- iterate ... ---*/
AzTimeLog::print("--- Training begins ... ", log_out);
AzsSvrgData_fast prev_fast;
AzsSvrgData_compact prev_compact;
int ite;
for (ite = 0; ite < ite_num; ++ite) {
if (do_show_timing) AzTimeLog::print("--- iteration#", ite+1, log_out);
if (doing_svrg(ite) && (ite-sgd_ite) % svrg_interval == 0) {
if (do_show_timing) AzTimeLog::print("Computing gradient average ... ", log_out);
if (do_compact) get_avg_gradient_compact(&prev_compact);
else get_avg_gradient_fast(&prev_fast);
}
if (do_show_timing) AzTimeLog::print("Updating weights ... ", log_out);
AzIntArr ia_dxs;
const int *dxs = gen_seq(dataSize(), ia_dxs);
int ix;
for (ix = 0; ix < dataSize(); ++ix) {
int dx = dxs[ix]; /* data point index */
AzDvect v_deriv(class_num);
get_deriv(dx, &v_deriv); /* compute the derivatives */
if (doing_svrg(ite)) {
if (do_compact) updateDelta_svrg_compact(dx, &v_deriv, prev_compact);
else updateDelta_svrg_fast(dx, &v_deriv, prev_fast);
}
else {
updateDelta_sgd(dx, &v_deriv);
}
flushDelta();
}
show_perf(ite);
}
if (do_show_timing) AzTimeLog::print("--- End of training ... ", log_out);
/*--- write predictions to a file if requested ---*/
if (s_pred_fn.length() > 0) {
AzTimeLog::print("Writing predictions to ", s_pred_fn.c_str(), log_out);
write_pred(m_tst_x, s_pred_fn.c_str());
}
}
