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-rw-r--r--training/mira/kbest_cut_mira.cc26
-rw-r--r--training/mira/kbest_mira.cc8
2 files changed, 21 insertions, 13 deletions
diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc
index 724b1853..c3f7891b 100644
--- a/training/mira/kbest_cut_mira.cc
+++ b/training/mira/kbest_cut_mira.cc
@@ -96,7 +96,7 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("weights_output,O",po::value<string>(),"Directory to write weights to")
("output_dir,D",po::value<string>(),"Directory to place output in")
("decoder_config,c",po::value<string>(),"Decoder configuration file")
- ("verbose,v",po::value<bool>()->zero_tokens(),"verbose stderr output");
+ ("verbose,v",po::value<bool>()->zero_tokens(),"Verbose stderr output");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
@@ -104,7 +104,7 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description dconfig_options, dcmdline_options;
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
-
+
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
if (conf->count("config")) {
ifstream config((*conf)["config"].as<string>().c_str());
@@ -229,14 +229,15 @@ void CuttingPlane(vector<boost::shared_ptr<HypothesisInfo> >* cur_c, bool* again
}
-double ComputeDelta(vector<boost::shared_ptr<HypothesisInfo> >* cur_p, double max_step_size,vector<weight_t> dense_weights )
+double ComputeDelta(vector<boost::shared_ptr<HypothesisInfo> >* cur_p, double max_step_size,vector<weight_t> dense_weights, bool verbose = true )
{
vector<boost::shared_ptr<HypothesisInfo> >& cur_pair = *cur_p;
double loss = cur_pair[0]->oracle_loss - cur_pair[1]->oracle_loss;
double margin = -(cur_pair[0]->oracleN->features.dot(dense_weights)- cur_pair[0]->features.dot(dense_weights)) + (cur_pair[1]->oracleN->features.dot(dense_weights) - cur_pair[1]->features.dot(dense_weights));
const double num = margin + loss;
- cerr << "LOSS: " << num << " Margin:" << margin << " BLEUL:" << loss << " " << cur_pair[1]->features.dot(dense_weights) << " " << cur_pair[0]->features.dot(dense_weights) <<endl;
+ if (verbose)
+ cerr << "LOSS: " << num << " Margin:" << margin << " BLEUL:" << loss << " " << cur_pair[1]->features.dot(dense_weights) << " " << cur_pair[0]->features.dot(dense_weights) <<endl;
SparseVector<double> diff = cur_pair[0]->features;
@@ -704,7 +705,8 @@ int main(int argc, char** argv) {
SparseVector<double> old_lambdas = lambdas;
tot.clear();
tot += lambdas;
- cerr << "PASS " << cur_pass << " " << endl << lambdas << endl;
+ if (VERBOSE)
+ cerr << "PASS " << cur_pass << " " << endl << lambdas << endl;
ScoreP acc, acc_h, acc_f;
while(*in) {
@@ -841,7 +843,7 @@ int main(int argc, char** argv) {
cur_pair.clear();
cur_pair.push_back(cur_constraint[j]);
cur_pair.push_back(cur_constraint[i]);
- double delta = ComputeDelta(&cur_pair,max_step_size, dense_weights);
+ double delta = ComputeDelta(&cur_pair,max_step_size, dense_weights, VERBOSE);
if (delta == 0) optimize_again = false;
cur_constraint[j]->alpha += delta;
@@ -865,7 +867,7 @@ int main(int argc, char** argv) {
}
else if(optimizer == 2 || optimizer == 3) //PA and Cutting Plane MIRA update
{
- bool DEBUG_SMO= true;
+ bool DEBUG_SMO= false;
vector<boost::shared_ptr<HypothesisInfo> > cur_constraint;
cur_constraint.push_back(cur_good_v[0]); //add oracle to constraint set
bool optimize_again = true;
@@ -914,7 +916,7 @@ int main(int argc, char** argv) {
continue;
} //pair is undefined so we are done with this smo
- double delta = ComputeDelta(&cur_pair,max_step_size, dense_weights);
+ double delta = ComputeDelta(&cur_pair,max_step_size, dense_weights, VERBOSE);
cur_pair[0]->alpha += delta;
cur_pair[1]->alpha -= delta;
@@ -928,7 +930,7 @@ int main(int argc, char** argv) {
//reload weights based on update
dense_weights.clear();
lambdas.init_vector(&dense_weights);
- if (dense_weights.size() < 500)
+ if (VERBOSE && dense_weights.size() < 500)
ShowLargestFeatures(dense_weights);
dense_w_local = dense_weights;
iter++;
@@ -968,12 +970,14 @@ int main(int argc, char** argv) {
for(int u=0;u!=cur_constraint.size();u++)
{
- cerr << "alpha=" << cur_constraint[u]->alpha << " hope=" << cur_constraint[u]->hope << " fear=" << cur_constraint[u]->fear << endl;
+ if (VERBOSE)
+ cerr << "alpha=" << cur_constraint[u]->alpha << " hope=" << cur_constraint[u]->hope << " fear=" << cur_constraint[u]->fear << endl;
temp_objective += cur_constraint[u]->alpha * cur_constraint[u]->fear;
}
objective += temp_objective;
- cerr << "SENT OBJ: " << temp_objective << " NEW OBJ: " << objective << endl;
+ if (VERBOSE)
+ cerr << "SENT OBJ: " << temp_objective << " NEW OBJ: " << objective << endl;
}
diff --git a/training/mira/kbest_mira.cc b/training/mira/kbest_mira.cc
index 2868de0c..07718a7f 100644
--- a/training/mira/kbest_mira.cc
+++ b/training/mira/kbest_mira.cc
@@ -57,7 +57,8 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("sample_forest,f", "Instead of a k-best list, sample k hypotheses from the decoder's forest")
("sample_forest_unit_weight_vector,x", "Before sampling (must use -f option), rescale the weight vector used so it has unit length; this may improve the quality of the samples")
("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
- ("decoder_config,c",po::value<string>(),"Decoder configuration file");
+ ("decoder_config,c",po::value<string>(),"Decoder configuration file")
+ ("verbose,v", po::value<bool>()->zero_tokens(), "verbose stderr output");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
@@ -188,6 +189,8 @@ int main(int argc, char** argv) {
po::variables_map conf;
if (!InitCommandLine(argc, argv, &conf)) return 1;
+ const bool VERBOSE = conf.count("verbose");
+
if (conf.count("random_seed"))
rng.reset(new MT19937(conf["random_seed"].as<uint32_t>()));
else
@@ -254,7 +257,8 @@ int main(int argc, char** argv) {
if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; }
if (corpus.size() == cur_sent) {
cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n";
- Weights::ShowLargestFeatures(dense_weights);
+ if (VERBOSE)
+ Weights::ShowLargestFeatures(dense_weights);
cur_sent = 0;
tot_loss = 0;
dots = 0;
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-22 06:42:17 +0000