diff options
Diffstat (limited to 'dtrain/dtrain.cc')
| -rw-r--r-- | dtrain/dtrain.cc | 22 |
1 files changed, 18 insertions, 4 deletions
diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc index 8b1fc953..717d47a2 100644 --- a/dtrain/dtrain.cc +++ b/dtrain/dtrain.cc @@ -33,6 +33,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("fselect", po::value<weight_t>()->default_value(-1), "TODO select top x percent (or by threshold) of features after each epoch") ("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") #ifdef DTRAIN_LOCAL ("refs,r", po::value<string>(), "references in local mode") #endif @@ -134,6 +135,8 @@ main(int argc, char** argv) const string select_weights = cfg["select_weights"].as<string>(); const float hi_lo = cfg["hi_lo"].as<float>(); const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>(); + weight_t loss_margin = cfg["loss_margin"].as<weight_t>(); + 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; bool average = false; @@ -160,6 +163,8 @@ main(int argc, char** argv) scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer); } else if (scorer_str == "smooth_bleu") { scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer); + } else if (scorer_str == "smooth_single_bleu") { + scorer = dynamic_cast<SmoothSingleBleuScorer*>(new SmoothSingleBleuScorer); } else if (scorer_str == "approx_bleu") { scorer = dynamic_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d)); } else { @@ -220,7 +225,7 @@ main(int argc, char** argv) grammar_buf_out.open(grammar_buf_fn.c_str()); #endif - unsigned in_sz = UINT_MAX; // input index, input size + unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size vector<pair<score_t, score_t> > all_scores; score_t max_score = 0.; unsigned best_it = 0; @@ -242,6 +247,7 @@ main(int argc, char** argv) if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl; else cerr << setw(25) << "learning rate " << "bleu diff" << endl; cerr << setw(25) << "gamma " << gamma << endl; + cerr << setw(25) << "loss margin " << loss_margin << endl; cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl; if (pair_sampling == "XYX") cerr << setw(25) << "hi lo " << hi_lo << endl; @@ -424,12 +430,18 @@ main(int argc, char** argv) for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); it != pairs.end(); it++) { +#ifdef DTRAIN_FASTER_PERCEPTRON + bool rank_error = true; // pair filtering already did this for us + rank_errors++; + score_t margin = std::numeric_limits<float>::max(); +#else bool rank_error = it->first.model <= it->second.model; if (rank_error) rank_errors++; - score_t margin = fabs(it->first.model - it->second.model); - if (!rank_error && margin < 1) margin_violations++; + score_t margin = fabs(fabs(it->first.model) - fabs(it->second.model)); + if (!rank_error && margin < loss_margin) margin_violations++; +#endif if (scale_bleu_diff) eta = it->first.score - it->second.score; - if (rank_error || (gamma && margin<1)) { + if (rank_error || margin < loss_margin) { SparseVector<weight_t> diff_vec = it->first.f - it->second.f; lambdas.plus_eq_v_times_s(diff_vec, eta); if (gamma) @@ -534,8 +546,10 @@ main(int argc, char** argv) cerr << _np << npairs/(float)in_sz << endl; cerr << " avg # rank err: "; cerr << rank_errors/(float)in_sz << endl; +#ifndef DTRAIN_FASTER_PERCEPTRON cerr << " avg # margin viol: "; cerr << margin_violations/(float)in_sz << endl; +#endif cerr << " non0 feature count: " << nonz << endl; cerr << " avg list sz: " << list_sz/(float)in_sz << endl; cerr << " avg f count: " << f_count/(float)list_sz << endl; |