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-rw-r--r--training/mira/kbest_cut_mira.cc8
1 files changed, 6 insertions, 2 deletions
diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc
index 56206593..724b1853 100644
--- a/training/mira/kbest_cut_mira.cc
+++ b/training/mira/kbest_cut_mira.cc
@@ -95,7 +95,8 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("stream,t", "Stream mode (used for realtime)")
("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");
+ ("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")
@@ -621,6 +622,7 @@ int main(int argc, char** argv) {
vector<string> corpus;
+ const bool VERBOSE = conf.count("verbose");
const string metric_name = conf["mt_metric"].as<string>();
optimizer = conf["optimizer"].as<int>();
fear_select = conf["fear"].as<int>();
@@ -783,7 +785,8 @@ int main(int argc, char** argv) {
double margin = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights);
double mt_loss = (cur_good.mt_metric - cur_bad.mt_metric);
const double loss = margin + mt_loss;
- cerr << "LOSS: " << loss << " Margin:" << margin << " BLEUL:" << mt_loss << " " << cur_bad.features.dot(dense_weights) << " " << cur_good.features.dot(dense_weights) <<endl;
+ cerr << "LOSS: " << loss << " Margin:" << margin << " BLEUL:" << mt_loss << endl;
+ if (VERBOSE) cerr << cur_bad.features.dot(dense_weights) << " " << cur_good.features.dot(dense_weights) << endl;
if (loss > 0.0 || !checkloss) {
SparseVector<double> diff = cur_good.features;
diff -= cur_bad.features;
@@ -920,6 +923,7 @@ int main(int argc, char** argv) {
lambdas += (cur_pair[1]->features) * step_size;
lambdas -= (cur_pair[0]->features) * step_size;
+ if (VERBOSE) cerr << " Lambdas " << lambdas << endl;
//reload weights based on update
dense_weights.clear();