diff options
Diffstat (limited to 'dtrain/test')
| -rw-r--r-- | dtrain/test/example/dtrain.ini | 8 | ||||
| -rw-r--r-- | dtrain/test/example/expected-output | 128 |
2 files changed, 55 insertions, 81 deletions
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini index 8338b2d3..72d50ca1 100644 --- a/dtrain/test/example/dtrain.ini +++ b/dtrain/test/example/dtrain.ini @@ -1,18 +1,18 @@ input=test/example/nc-wmt11.1k.gz # use '-' for STDIN output=- # a weights file (add .gz for gzip compression) or STDOUT '-' -select_weights=VOID # don't output weights +select_weights=VOID # don't output weights decoder_config=test/example/cdec.ini # config for cdec # weights for these features will be printed on each iteration print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough tmp=/tmp -stop_after=100 # stop epoch after 10 inputs +stop_after=10 # stop epoch after 10 inputs # interesting stuff -epochs=3 # run over input 3 times +epochs=2 # run over input 2 times k=100 # use 100best lists N=4 # optimize (approx) BLEU4 scorer=stupid_bleu # use 'stupid' BLEU+1 -learning_rate=0.0001 # learning rate +learning_rate=1.0 # learning rate, don't care if gamma=0 (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/dtrain/test/example/expected-output b/dtrain/test/example/expected-output index 43798484..05326763 100644 --- a/dtrain/test/example/expected-output +++ b/dtrain/test/example/expected-output @@ -4,17 +4,17 @@ Reading test/example/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 2108658507 +Seeding random number sequence to 2912000813 dtrain Parameters: k 100 N 4 - T 3 + T 2 scorer 'stupid_bleu' sample from 'kbest' filter 'uniq' - learning rate 0.0001 + learning rate 1 gamma 0 loss margin 0 pairs 'XYX' @@ -26,90 +26,64 @@ Parameters: cdec cfg 'test/example/cdec.ini' input 'test/example/nc-wmt11.1k.gz' output '-' - stop_after 100 + stop_after 10 (a dot represents 10 inputs) -Iteration #1 of 3. - .......... 100 -Stopping after 100 input sentences. +Iteration #1 of 2. + . 10 +Stopping after 10 input sentences. WEIGHTS - Glue = -0.236 - WordPenalty = +0.056111 - LanguageModel = +0.71011 - LanguageModel_OOV = -0.489 - PhraseModel_0 = -0.21332 - PhraseModel_1 = -0.13038 - PhraseModel_2 = +0.085148 - PhraseModel_3 = -0.16982 - PhraseModel_4 = -0.026332 - PhraseModel_5 = +0.2133 - PhraseModel_6 = +0.1002 - PassThrough = -0.5541 + Glue = -637 + WordPenalty = +1064 + LanguageModel = +1175.3 + LanguageModel_OOV = -1437 + PhraseModel_0 = +1935.6 + PhraseModel_1 = +2499.3 + PhraseModel_2 = +964.96 + PhraseModel_3 = +1410.8 + PhraseModel_4 = -5977.9 + PhraseModel_5 = +522 + PhraseModel_6 = +1089 + PassThrough = -1308 --- - 1best avg score: 0.16928 (+0.16928) - 1best avg model score: 2.4454 (+2.4454) - avg # pairs: 1616.2 - avg # rank err: 769.6 + 1best avg score: 0.16963 (+0.16963) + 1best avg model score: 64485 (+64485) + avg # pairs: 1494.4 + avg # rank err: 702.6 avg # margin viol: 0 - non0 feature count: 4068 - avg list sz: 96.65 - avg f count: 118.01 -(time 1.3 min, 0.79 s/S) + non0 feature count: 528 + avg list sz: 85.7 + avg f count: 102.75 +(time 0.083 min, 0.5 s/S) -Iteration #2 of 3. - .......... 100 +Iteration #2 of 2. + . 10 WEIGHTS - Glue = -0.1721 - WordPenalty = -0.14132 - LanguageModel = +0.56023 - LanguageModel_OOV = -0.6786 - PhraseModel_0 = +0.14155 - PhraseModel_1 = +0.34218 - PhraseModel_2 = +0.22954 - PhraseModel_3 = -0.24762 - PhraseModel_4 = -0.25848 - PhraseModel_5 = -0.0453 - PhraseModel_6 = -0.0264 - PassThrough = -0.7436 + Glue = -1196 + WordPenalty = +809.52 + LanguageModel = +3112.1 + LanguageModel_OOV = -1464 + PhraseModel_0 = +3895.5 + PhraseModel_1 = +4683.4 + PhraseModel_2 = +1092.8 + PhraseModel_3 = +1079.6 + PhraseModel_4 = -6827.7 + PhraseModel_5 = -888 + PhraseModel_6 = +142 + PassThrough = -1335 --- - 1best avg score: 0.19585 (+0.02657) - 1best avg model score: -16.311 (-18.757) - avg # pairs: 1475.8 - avg # rank err: 668.48 + 1best avg score: 0.277 (+0.10736) + 1best avg model score: -3110.5 (-67595) + avg # pairs: 1144.2 + avg # rank err: 529.1 avg # margin viol: 0 - non0 feature count: 6300 - avg list sz: 96.08 - avg f count: 114.92 -(time 1.3 min, 0.76 s/S) - -Iteration #3 of 3. - .......... 100 -WEIGHTS - Glue = -0.1577 - WordPenalty = -0.086902 - LanguageModel = +0.30136 - LanguageModel_OOV = -0.7848 - PhraseModel_0 = +0.11743 - PhraseModel_1 = +0.11142 - PhraseModel_2 = -0.0053865 - PhraseModel_3 = -0.18731 - PhraseModel_4 = -0.67144 - PhraseModel_5 = +0.1236 - PhraseModel_6 = -0.2665 - PassThrough = -0.8498 - --- - 1best avg score: 0.20034 (+0.0044978) - 1best avg model score: -7.2775 (+9.0336) - avg # pairs: 1578.6 - avg # rank err: 705.77 - avg # margin viol: 0 - non0 feature count: 7313 - avg list sz: 96.84 - avg f count: 124.48 -(time 1.5 min, 0.9 s/S) + non0 feature count: 859 + avg list sz: 74.9 + avg f count: 112.84 +(time 0.067 min, 0.4 s/S) Writing weights file to '-' ... done --- -Best iteration: 3 [SCORE 'stupid_bleu'=0.20034]. -This took 4.0833 min. +Best iteration: 2 [SCORE 'stupid_bleu'=0.277]. +This took 0.15 min. |