diff options
| -rw-r--r-- | training/dtrain/dtrain.cc | 36 | ||||
| -rw-r--r-- | training/dtrain/examples/standard/dtrain.ini | 2 | ||||
| -rw-r--r-- | training/dtrain/examples/standard/expected-output | 112 |
3 files changed, 80 insertions, 70 deletions
diff --git a/training/dtrain/dtrain.cc b/training/dtrain/dtrain.cc index 441e2cd7..0a27a068 100644 --- a/training/dtrain/dtrain.cc +++ b/training/dtrain/dtrain.cc @@ -414,6 +414,12 @@ main(int argc, char** argv) score_t kbest_loss_first, kbest_loss_last = 0.0; + for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); + it != pairs.end(); it++) { + score_t model_diff = it->first.model - it->second.model; + kbest_loss_first += max(0.0, -1.0 * model_diff); + } + for (int ki=0; ki < repeat; ki++) { score_t kbest_loss = 0.0; // test-k-best @@ -520,21 +526,22 @@ main(int argc, char** argv) } } - if (ki==0) kbest_loss_first = kbest_loss; if (ki==repeat-1) { // done kbest_loss_last = kbest_loss; - score_t best_score = -1.; - score_t best_model = -std::numeric_limits<score_t>::max(); - unsigned best_idx; - for (unsigned i=0; i < samples->size(); i++) { - score_t s = lambdas.dot((*samples)[i].f); - if (s > best_model) { - best_idx = i; - best_model = s; + if (repeat > 1) { + score_t best_score = -1.; + score_t best_model = -std::numeric_limits<score_t>::max(); + unsigned best_idx; + for (unsigned i=0; i < samples->size(); i++) { + score_t s = lambdas.dot((*samples)[i].f); + if (s > best_model) { + best_idx = i; + best_model = s; + } } + score_sum += (*samples)[best_idx].score; + model_sum += best_model; } - score_sum += (*samples)[best_idx].score; - model_sum += best_model; } } // repeat @@ -588,15 +595,14 @@ main(int argc, char** argv) cerr << _p << " (" << model_diff << ")" << endl; cerr << " avg # pairs: "; cerr << _np << npairs/(float)in_sz << endl; - cerr << " avg # margin viol: "; - cerr << margin_violations/(float)in_sz << endl; cerr << " avg # rank err: "; cerr << rank_errors/(float)in_sz; if (faster_perceptron) cerr << " (meaningless)"; cerr << endl; + cerr << " avg # margin viol: "; + cerr << margin_violations/(float)in_sz << endl; if (batch) cerr << " batch loss: " << batch_loss << endl; - if (repeat > 1) cerr << " k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl; - + cerr << " k-best loss imp: " << ((float)kbest_loss_improve/in_sz)*100 << "%" << endl; 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; diff --git a/training/dtrain/examples/standard/dtrain.ini b/training/dtrain/examples/standard/dtrain.ini index ef022469..fc83f08e 100644 --- a/training/dtrain/examples/standard/dtrain.ini +++ b/training/dtrain/examples/standard/dtrain.ini @@ -15,7 +15,7 @@ epochs=3 # run over input 3 times k=100 # use 100best lists N=4 # optimize (approx) BLEU4 scorer=fixed_stupid_bleu # use 'stupid' BLEU+1 -learning_rate=0.0001 # learning rate, don't care if gamma=0 (perceptron) and loss_margin=0 (not margin perceptron) +learning_rate=0.1 # learning rate, don't care if gamma=0 (perceptron) and loss_margin=0 (not margin perceptron) gamma=0 # use SVM reg sample_from=kbest # use kbest lists (as opposed to forest) filter=uniq # only unique entries in kbest (surface form) diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output index a35bbe6f..75f47337 100644 --- a/training/dtrain/examples/standard/expected-output +++ b/training/dtrain/examples/standard/expected-output @@ -4,17 +4,18 @@ Reading ./nc-wmt11.en.srilm.gz ----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 **************************************************************************************************** Example feature: Shape_S00000_T00000 -Seeding random number sequence to 4049211323 +Seeding random number sequence to 3751911392 dtrain Parameters: k 100 N 4 T 3 + batch 0 scorer 'fixed_stupid_bleu' sample from 'kbest' filter 'uniq' - learning rate 1 + learning rate 0.1 gamma 0 loss margin 0 faster perceptron 1 @@ -25,9 +26,9 @@ Parameters: l1 reg 0 'none' pclr no max pairs 4294967295 + repeat 1 cdec cfg './cdec.ini' - input './nc-wmt11.de.gz' - refs './nc-wmt11.en.gz' + input './nc-wmt11.gz' output '-' stop_after 10 (a dot represents 10 inputs) @@ -35,25 +36,26 @@ Iteration #1 of 3. . 10 Stopping after 10 input sentences. WEIGHTS - Glue = -1100 - WordPenalty = -82.082 - LanguageModel = -3199.1 - LanguageModel_OOV = -192 - PhraseModel_0 = +3128.2 - PhraseModel_1 = -1610.2 - PhraseModel_2 = -4336.5 - PhraseModel_3 = +2910.3 - PhraseModel_4 = +2523.2 - PhraseModel_5 = +506 - PhraseModel_6 = +1467 - PassThrough = -387 + Glue = -110 + WordPenalty = -8.2082 + LanguageModel = -319.91 + LanguageModel_OOV = -19.2 + PhraseModel_0 = +312.82 + PhraseModel_1 = -161.02 + PhraseModel_2 = -433.65 + PhraseModel_3 = +291.03 + PhraseModel_4 = +252.32 + PhraseModel_5 = +50.6 + PhraseModel_6 = +146.7 + PassThrough = -38.7 --- 1best avg score: 0.16966 (+0.16966) - 1best avg model score: 2.9874e+05 (+2.9874e+05) - avg # pairs: 906.3 (meaningless) - avg # rank err: 906.3 + 1best avg model score: 29874 (+29874) + avg # pairs: 906.3 + avg # rank err: 0 (meaningless) avg # margin viol: 0 - non0 feature count: 825 + k-best loss imp: 100% + non0 feature count: 832 avg list sz: 91.3 avg f count: 139.77 (time 0.35 min, 2.1 s/S) @@ -61,25 +63,26 @@ WEIGHTS Iteration #2 of 3. . 10 WEIGHTS - Glue = -1221 - WordPenalty = +836.89 - LanguageModel = +2332.3 - LanguageModel_OOV = -1451 - PhraseModel_0 = +1507.2 - PhraseModel_1 = -2728.4 - PhraseModel_2 = -4183.6 - PhraseModel_3 = +1816.3 - PhraseModel_4 = -2894.7 - PhraseModel_5 = +1403 - PhraseModel_6 = +35 - PassThrough = -1097 + Glue = -122.1 + WordPenalty = +83.689 + LanguageModel = +233.23 + LanguageModel_OOV = -145.1 + PhraseModel_0 = +150.72 + PhraseModel_1 = -272.84 + PhraseModel_2 = -418.36 + PhraseModel_3 = +181.63 + PhraseModel_4 = -289.47 + PhraseModel_5 = +140.3 + PhraseModel_6 = +3.5 + PassThrough = -109.7 --- 1best avg score: 0.17399 (+0.004325) - 1best avg model score: 49369 (-2.4937e+05) - avg # pairs: 662.4 (meaningless) - avg # rank err: 662.4 + 1best avg model score: 4936.9 (-24937) + avg # pairs: 662.4 + avg # rank err: 0 (meaningless) avg # margin viol: 0 - non0 feature count: 1235 + k-best loss imp: 100% + non0 feature count: 1240 avg list sz: 91.3 avg f count: 125.11 (time 0.27 min, 1.6 s/S) @@ -87,32 +90,33 @@ WEIGHTS Iteration #3 of 3. . 10 WEIGHTS - Glue = -1574 - WordPenalty = -17.372 - LanguageModel = +6861.8 - LanguageModel_OOV = -3997 - PhraseModel_0 = -398.76 - PhraseModel_1 = -3419.6 - PhraseModel_2 = -3186.7 - PhraseModel_3 = +1050.8 - PhraseModel_4 = -2902.7 - PhraseModel_5 = -486 - PhraseModel_6 = -436 - PassThrough = -2985 + Glue = -157.4 + WordPenalty = -1.7372 + LanguageModel = +686.18 + LanguageModel_OOV = -399.7 + PhraseModel_0 = -39.876 + PhraseModel_1 = -341.96 + PhraseModel_2 = -318.67 + PhraseModel_3 = +105.08 + PhraseModel_4 = -290.27 + PhraseModel_5 = -48.6 + PhraseModel_6 = -43.6 + PassThrough = -298.5 --- 1best avg score: 0.30742 (+0.13343) - 1best avg model score: -1.5393e+05 (-2.0329e+05) - avg # pairs: 623.8 (meaningless) - avg # rank err: 623.8 + 1best avg model score: -15393 (-20329) + avg # pairs: 623.8 + avg # rank err: 0 (meaningless) avg # margin viol: 0 - non0 feature count: 1770 + k-best loss imp: 100% + non0 feature count: 1776 avg list sz: 91.3 avg f count: 118.58 -(time 0.25 min, 1.5 s/S) +(time 0.28 min, 1.7 s/S) Writing weights file to '-' ... done --- Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.30742]. -This took 0.86667 min. +This took 0.9 min. |