diff options
Diffstat (limited to 'training')
| -rw-r--r-- | training/dtrain/dtrain.cc | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc index ae5b630a..4f29c499 100644 --- a/training/dtrain/dtrain.cc +++ b/training/dtrain/dtrain.cc @@ -12,7 +12,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) { po::options_description ini("Configuration File Options"); ini.add_options() - ("bitext,b", po::value<string>(), "bitext: 'src ||| tgt ||| tgt ||| ...'") + ("bitext,b", po::value<string>(), "bitext: 'src ||| tgt ||| tgt ||| ...'") ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT") ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)") ("decoder_config", po::value<string>(), "configuration file for cdec") @@ -31,12 +31,11 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("learning_rate", po::value<weight_t>()->default_value(1.0), "learning rate") ("gamma", po::value<weight_t>()->default_value(0.), "gamma for SVM (0 for perceptron)") ("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)") - ("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input") - ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010) UNTESTED") + ("rescale", po::value<bool>()->zero_tokens(), "(re)scale data and weight vector to unit length") + ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010)") ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength") ("fselect", po::value<weight_t>()->default_value(-1), "select top x percent (or by threshold) of features after each epoch NOT IMPLEMENTED") // TODO ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU") - ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair") ("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near") ("max_pairs", po::value<unsigned>()->default_value(std::numeric_limits<unsigned>::max()), "max. # of pairs per Sent.") ("pclr", po::value<string>()->default_value("no"), "use a (simple|adagrad) per-coordinate learning rate") @@ -106,6 +105,7 @@ main(int argc, char** argv) // handle most parameters po::variables_map cfg; if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong + bool quiet = false; if (cfg.count("quiet")) quiet = true; bool verbose = false; @@ -139,8 +139,6 @@ main(int argc, char** argv) bool batch = false; if (cfg.count("batch")) batch = true; if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max(); - bool scale_bleu_diff = false; - if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true; const string pclr = cfg["pclr"].as<string>(); bool average = false; if (select_weights == "avg") @@ -260,8 +258,7 @@ main(int argc, char** argv) cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl; if (sample_from == "kbest") cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl; - if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl; - else cerr << setw(25) << "learning rate " << "bleu diff" << endl; + cerr << setw(25) << "learning rate " << eta << endl; cerr << setw(25) << "gamma " << gamma << endl; cerr << setw(25) << "loss margin " << loss_margin << endl; cerr << setw(25) << "faster perceptron " << faster_perceptron << endl; @@ -438,6 +435,10 @@ main(int argc, char** argv) for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); it != pairs.end(); it++) { + if (rescale) { + it->first.f /= it->first.f.l2norm(); + it->second.f /= it->second.f.l2norm(); + } score_t model_diff = it->first.model - it->second.model; score_t loss = max(0.0, -1.0 * model_diff); losses.push_back(loss); @@ -475,7 +476,6 @@ main(int argc, char** argv) if (!rank_error && margin < loss_margin) margin_violations++; } if (rank_error && ki==0) rank_errors++; - if (scale_bleu_diff) eta = it->first.score - it->second.score; if (rank_error || margin < loss_margin) { SparseVector<weight_t> diff_vec = it->first.f - it->second.f; if (batch) { @@ -673,7 +673,7 @@ main(int argc, char** argv) if (!noup) { if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl; if (select_weights == "last" || average) { // last, average - WriteFile of(output_fn); // works with '-' + WriteFile of(output_fn); ostream& o = *of.stream(); o.precision(17); o << _np; |