diff options
| -rw-r--r-- | training/dtrain/dtrain_net_interface.cc | 5 | ||||
| -rw-r--r-- | training/dtrain/dtrain_net_interface.h | 2 |
2 files changed, 5 insertions, 2 deletions
diff --git a/training/dtrain/dtrain_net_interface.cc b/training/dtrain/dtrain_net_interface.cc index f16b9304..77ccde55 100644 --- a/training/dtrain/dtrain_net_interface.cc +++ b/training/dtrain/dtrain_net_interface.cc @@ -143,7 +143,7 @@ main(int argc, char** argv) cerr << "[dtrain] learning ..." << endl; source = parts[0]; // debug -- - debug_output << "\"source\":\"" << source.substr(source.find_first_of(">")+1, source.find_last_of("<")-3) << "\"," << endl; + debug_output << "\"source\":\"" << source.substr(source.find_first_of(">")+2, source.find_last_of(">")-6) << "\"," << endl; debug_output << "\"target\":\"" << parts[1] << "\"," << endl; // -- debug parts.erase(parts.begin()); @@ -198,11 +198,14 @@ main(int argc, char** argv) // get pairs and update SparseVector<weight_t> updates; size_t num_up = CollectUpdates(samples, updates, margin); + debug_output << "\"1best_features\":\"" << (*samples)[0].f << "\"," << endl; + debug_output << "\"update_raw\":\"" << updates << "\"," << endl; updates *= eta_sparse; // apply learning rate for sparse features for (auto feat: dense_features) { // apply learning rate for dense features updates[FD::Convert(feat)] /= eta_sparse; updates[FD::Convert(feat)] *= eta; } + debug_output << "\"update\":\"" << updates << "\"," << endl; // debug -- debug_output << "\"num_up\":" << num_up << "," << endl; debug_output << "\"updated_features\":" << updates.size() << "," << endl; diff --git a/training/dtrain/dtrain_net_interface.h b/training/dtrain/dtrain_net_interface.h index debe8800..b201c7a3 100644 --- a/training/dtrain/dtrain_net_interface.h +++ b/training/dtrain/dtrain_net_interface.h @@ -66,7 +66,7 @@ dtrain_net_init(int argc, char** argv, po::variables_map* conf) ("learning_rate_sparse,l", po::value<weight_t>()->default_value(0.00001), "learning rate for sparse features") ("output_derivation,E", po::bool_switch()->default_value(false), "output derivation, not viterbi str") ("output_rules,R", po::bool_switch()->default_value(false), "also output rules") - ("dense_features,D", po::value<string>()->default_value("EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV Shape_S01111_T11011 Shape_S11110_T11011 Shape_S11100_T11000 Shape_S01110_T01110 Shape_S01111_T01111 Shape_S01100_T11000 Shape_S10000_T10000 Shape_S11100_T11100 Shape_S11110_T11110 Shape_S11110_T11010 Shape_S01100_T11100 Shape_S01000_T01000 Shape_S01010_T01010 Shape_S01111_T01011 Shape_S01100_T01100 Shape_S01110_T11010 Shape_S11000_T11000 Shape_S11000_T01100 IsSupportedOnline ForceRule"), + ("dense_features,D", po::value<string>()->default_value("EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV Shape_S01111_T11011 Shape_S11110_T11011 Shape_S11100_T11000 Shape_S01110_T01110 Shape_S01111_T01111 Shape_S01100_T11000 Shape_S10000_T10000 Shape_S11100_T11100 Shape_S11110_T11110 Shape_S11110_T11010 Shape_S01100_T11100 Shape_S01000_T01000 Shape_S01010_T01010 Shape_S01111_T01011 Shape_S01100_T01100 Shape_S01110_T11010 Shape_S11000_T11000 Shape_S11000_T01100 IsSupportedOnline NewRule KnownRule OOVFix"), "dense features") ("debug_output,d", po::value<string>()->default_value(""), "file for debug output"); po::options_description cl("Command Line Options"); |