diff options
Diffstat (limited to 'pro-train')
| -rw-r--r-- | pro-train/Makefile.am | 2 | ||||
| -rw-r--r-- | pro-train/mr_pro_reduce.cc | 9 |
2 files changed, 7 insertions, 4 deletions
diff --git a/pro-train/Makefile.am b/pro-train/Makefile.am index 11d26211..a98dd245 100644 --- a/pro-train/Makefile.am +++ b/pro-train/Makefile.am @@ -8,6 +8,6 @@ mr_pro_map_SOURCES = mr_pro_map.cc mr_pro_map_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz mr_pro_reduce_SOURCES = mr_pro_reduce.cc -mr_pro_reduce_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/training/optimize.o $(top_srcdir)/training/liblbfgs/liblbfgs.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a -lz +mr_pro_reduce_LDADD = $(top_srcdir)/training/liblbfgs/liblbfgs.a $(top_srcdir)/utils/libutils.a -lz AM_CPPFLAGS = -W -Wall -Wno-sign-compare $(GTEST_CPPFLAGS) -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval -I$(top_srcdir)/training diff --git a/pro-train/mr_pro_reduce.cc b/pro-train/mr_pro_reduce.cc index d3fb8026..9698bb5d 100644 --- a/pro-train/mr_pro_reduce.cc +++ b/pro-train/mr_pro_reduce.cc @@ -25,6 +25,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) { opts.add_options() ("weights,w", po::value<string>(), "Weights from previous iteration (used as initialization and interpolation") ("regularization_strength,C",po::value<double>()->default_value(500.0), "l2 regularization strength") + ("l1",po::value<double>()->default_value(0.0), "l1 regularization strength") ("regularize_to_weights,y",po::value<double>()->default_value(5000.0), "Differences in learned weights to previous weights are penalized with an l2 penalty with this strength; 0.0 = no effect") ("memory_buffers,m",po::value<unsigned>()->default_value(100), "Number of memory buffers (LBFGS)") ("min_reg,r",po::value<double>()->default_value(0.01), "When tuning (-T) regularization strength, minimum regularization strenght") @@ -180,12 +181,13 @@ struct ProLoss { double LearnParameters(const vector<pair<bool, SparseVector<weight_t> > >& training, const vector<pair<bool, SparseVector<weight_t> > >& testing, const double C, + const double C1, const double T, const unsigned memory_buffers, const vector<weight_t>& prev_x, vector<weight_t>* px) { ProLoss loss(training, testing, C, T, prev_x); - LBFGS<ProLoss> lbfgs(px, loss, 0.0, memory_buffers); + LBFGS<ProLoss> lbfgs(px, loss, C1, memory_buffers); lbfgs.MinimizeFunction(); return loss.tppl; } @@ -203,6 +205,7 @@ int main(int argc, char** argv) { const double min_reg = conf["min_reg"].as<double>(); const double max_reg = conf["max_reg"].as<double>(); double C = conf["regularization_strength"].as<double>(); // will be overridden if parameter is tuned + double C1 = conf["l1"].as<double>(); // will be overridden if parameter is tuned const double T = conf["regularize_to_weights"].as<double>(); assert(C >= 0.0); assert(min_reg >= 0.0); @@ -239,7 +242,7 @@ int main(int argc, char** argv) { cerr << "SWEEP FACTOR: " << sweep_factor << endl; while(C < max_reg) { cerr << "C=" << C << "\tT=" <<T << endl; - tppl = LearnParameters(training, testing, C, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x); + tppl = LearnParameters(training, testing, C, C1, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x); sp.push_back(make_pair(C, tppl)); C *= sweep_factor; } @@ -262,7 +265,7 @@ int main(int argc, char** argv) { } C = sp[best_i].first; } // tune regularizer - tppl = LearnParameters(training, testing, C, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x); + tppl = LearnParameters(training, testing, C, C1, T, conf["memory_buffers"].as<unsigned>(), prev_x, &x); if (conf.count("weights")) { for (int i = 1; i < x.size(); ++i) { x[i] = (x[i] * psi) + prev_x[i] * (1.0 - psi); |