diff options
| -rw-r--r-- | mira/kbest_mira.cc | 23 |
1 files changed, 18 insertions, 5 deletions
diff --git a/mira/kbest_mira.cc b/mira/kbest_mira.cc index 60703273..ae54c807 100644 --- a/mira/kbest_mira.cc +++ b/mira/kbest_mira.cc @@ -53,6 +53,7 @@ void ShowLargestFeatures(const vector<double>& w) { mid += (w.size() > 10 ? 10 : w.size()); partial_sort(fnums.begin(), mid, fnums.end(), FComp(w)); cerr << "TOP FEATURES:"; + --mid; for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) { cerr << ' ' << FD::Convert(*i) << '=' << w[*i]; } @@ -207,15 +208,17 @@ int main(int argc, char** argv) { TrainingObserver observer(conf["k_best_size"].as<int>(), ds, &oracles); int cur_sent = 0; int lcount = 0; + int normalizer = 0; double tot_loss = 0; int dots = 0; int cur_pass = 0; vector<double> dense_weights; SparseVector<double> tot; tot += lambdas; // initial weights - lcount++; // count for initial weights + normalizer++; // count for initial weights int max_iteration = conf["passes"].as<int>() * corpus.size(); string msg = "# MIRA tuned weights"; + string msga = "# MIRA tuned weights AVERAGED"; while (lcount <= max_iteration) { dense_weights.clear(); weights.InitFromVector(lambdas); @@ -223,16 +226,25 @@ int main(int argc, char** argv) { decoder.SetWeights(dense_weights); if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; } if (corpus.size() == cur_sent) { - cur_sent = 0; cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n"; + ShowLargestFeatures(dense_weights); + cur_sent = 0; tot_loss = 0; dots = 0; ostringstream os; os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz"; weights.WriteToFile(os.str(), true, &msg); + SparseVector<double> x = tot; + x /= normalizer; + ostringstream sa; + sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz"; + Weights ww; + ww.InitFromVector(x); + ww.WriteToFile(sa.str(), true, &msga); ++cur_pass; + } else if (cur_sent == 0) { + cerr << "PASS " << (lcount / corpus.size() + 1) << endl; } - if (cur_sent == 0) { cerr << "PASS " << (lcount / corpus.size() + 1) << endl << lambdas << endl; } decoder.SetId(cur_sent); decoder.Decode(corpus[cur_sent], &observer); // update oracles const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis(); @@ -255,16 +267,17 @@ int main(int argc, char** argv) { } } tot += lambdas; + ++normalizer; ++lcount; ++cur_sent; } cerr << endl; weights.WriteToFile("weights.mira-final.gz", true, &msg); - tot /= lcount; + tot /= normalizer; weights.InitFromVector(tot); msg = "# MIRA tuned weights (averaged vector)"; weights.WriteToFile("weights.mira-final-avg.gz", true, &msg); - cerr << "Optimization complete.\\AVERAGED WEIGHTS: weights.mira-final-avg.gz\n"; + cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n"; return 0; } |