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-rw-r--r--dtrain/test/example/dtrain.ini3
-rw-r--r--dtrain/test/example/expected-output143
2 files changed, 74 insertions, 72 deletions
diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini
index e43d6b34..c8ac7c3f 100644
--- a/dtrain/test/example/dtrain.ini
+++ b/dtrain/test/example/dtrain.ini
@@ -5,7 +5,7 @@ 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=20 # stop epoch after 20 inputs
+stop_after=10 # stop epoch after 10 inputs
# interesting stuff
epochs=3 # run over input 3 times
@@ -19,3 +19,4 @@ filter=uniq # only unique entries in kbest (surface form)
pair_sampling=XYX
hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here
pair_threshold=0 # minimum distance in BLEU (this will still only use pairs with diff > 0)
+loss_margin=0
diff --git a/dtrain/test/example/expected-output b/dtrain/test/example/expected-output
index 08733dd4..25d2c069 100644
--- a/dtrain/test/example/expected-output
+++ b/dtrain/test/example/expected-output
@@ -15,7 +15,7 @@ State is 0 bytes for feature RuleNgramFeatures
feature: RuleShape (no config parameters)
Example feature: Shape_S00000_T00000
State is 0 bytes for feature RuleShape
-Seeding random number sequence to 380245307
+Seeding random number sequence to 1072059181
dtrain
Parameters:
@@ -27,6 +27,7 @@ Parameters:
filter 'uniq'
learning rate 0.0001
gamma 0
+ loss margin 0
pairs 'XYX'
hi lo 0.1
pair threshold 0
@@ -35,90 +36,90 @@ Parameters:
cdec cfg 'test/example/cdec.ini'
input 'test/example/nc-wmt11.1k.gz'
output '-'
- stop_after 20
+ stop_after 10
(a dot represents 10 inputs)
Iteration #1 of 3.
- .. 20
-Stopping after 20 input sentences.
+ . 10
+Stopping after 10 input sentences.
WEIGHTS
- Glue = -0.1015
- WordPenalty = -0.0152
- LanguageModel = +0.21493
- LanguageModel_OOV = -0.3257
- PhraseModel_0 = -0.050844
- PhraseModel_1 = +0.25074
- PhraseModel_2 = +0.27944
- PhraseModel_3 = -0.038384
- PhraseModel_4 = -0.12041
- PhraseModel_5 = +0.1047
- PhraseModel_6 = -0.1289
- PassThrough = -0.3094
+ Glue = -0.0293
+ WordPenalty = +0.049075
+ LanguageModel = +0.24345
+ LanguageModel_OOV = -0.2029
+ PhraseModel_0 = +0.0084102
+ PhraseModel_1 = +0.021729
+ PhraseModel_2 = +0.014922
+ PhraseModel_3 = +0.104
+ PhraseModel_4 = -0.14308
+ PhraseModel_5 = +0.0247
+ PhraseModel_6 = -0.012
+ PassThrough = -0.2161
---
- 1best avg score: 0.17508 (+0.17508)
- 1best avg model score: -1.2392 (-1.2392)
- avg # pairs: 1329.8
- avg # rank err: 649.1
- avg # margin viol: 677.5
- non0 feature count: 874
- avg list sz: 88.6
- avg f count: 85.643
-(time 0.25 min, 0.75 s/S)
+ 1best avg score: 0.16872 (+0.16872)
+ 1best avg model score: -1.8276 (-1.8276)
+ avg # pairs: 1121.1
+ avg # rank err: 555.6
+ avg # margin viol: 0
+ non0 feature count: 277
+ avg list sz: 77.2
+ avg f count: 90.96
+(time 0.1 min, 0.6 s/S)
Iteration #2 of 3.
- .. 20
+ . 10
WEIGHTS
- Glue = -0.0792
- WordPenalty = -0.056198
- LanguageModel = +0.31038
- LanguageModel_OOV = -0.4011
- PhraseModel_0 = +0.072188
- PhraseModel_1 = +0.11473
- PhraseModel_2 = +0.049774
- PhraseModel_3 = -0.18448
- PhraseModel_4 = -0.12092
- PhraseModel_5 = +0.1599
- PhraseModel_6 = -0.0606
- PassThrough = -0.3848
+ Glue = -0.3526
+ WordPenalty = +0.067576
+ LanguageModel = +1.155
+ LanguageModel_OOV = -0.2728
+ PhraseModel_0 = -0.025529
+ PhraseModel_1 = +0.095869
+ PhraseModel_2 = +0.094567
+ PhraseModel_3 = +0.12482
+ PhraseModel_4 = -0.36533
+ PhraseModel_5 = +0.1068
+ PhraseModel_6 = -0.1517
+ PassThrough = -0.286
---
- 1best avg score: 0.24015 (+0.065075)
- 1best avg model score: -10.131 (-8.8914)
- avg # pairs: 1324.7
- avg # rank err: 558.65
- avg # margin viol: 752.85
- non0 feature count: 1236
- avg list sz: 84.9
- avg f count: 88.306
-(time 0.22 min, 0.65 s/S)
+ 1best avg score: 0.18394 (+0.015221)
+ 1best avg model score: 3.205 (+5.0326)
+ avg # pairs: 1168.3
+ avg # rank err: 594.8
+ avg # margin viol: 0
+ non0 feature count: 543
+ avg list sz: 77.5
+ avg f count: 85.916
+(time 0.083 min, 0.5 s/S)
Iteration #3 of 3.
- .. 20
+ . 10
WEIGHTS
- Glue = -0.051
- WordPenalty = -0.077956
- LanguageModel = +0.33699
- LanguageModel_OOV = -0.4726
- PhraseModel_0 = +0.040228
- PhraseModel_1 = +0.18
- PhraseModel_2 = +0.15618
- PhraseModel_3 = -0.098908
- PhraseModel_4 = -0.036555
- PhraseModel_5 = +0.1619
- PhraseModel_6 = +0.0078
- PassThrough = -0.4563
+ Glue = -0.392
+ WordPenalty = +0.071963
+ LanguageModel = +0.81266
+ LanguageModel_OOV = -0.4177
+ PhraseModel_0 = -0.2649
+ PhraseModel_1 = -0.17931
+ PhraseModel_2 = +0.038261
+ PhraseModel_3 = +0.20261
+ PhraseModel_4 = -0.42621
+ PhraseModel_5 = +0.3198
+ PhraseModel_6 = -0.1437
+ PassThrough = -0.4309
---
- 1best avg score: 0.25527 (+0.015113)
- 1best avg model score: -13.906 (-3.7756)
- avg # pairs: 1356.3
- avg # rank err: 562.1
- avg # margin viol: 757.35
- non0 feature count: 1482
- avg list sz: 86.65
- avg f count: 87.475
-(time 0.23 min, 0.7 s/S)
+ 1best avg score: 0.2962 (+0.11225)
+ 1best avg model score: -36.274 (-39.479)
+ avg # pairs: 1109.6
+ avg # rank err: 515.9
+ avg # margin viol: 0
+ non0 feature count: 741
+ avg list sz: 77
+ avg f count: 88.982
+(time 0.083 min, 0.5 s/S)
Writing weights file to '-' ...
done
---
-Best iteration: 3 [SCORE 'stupid_bleu'=0.25527].
-This took 0.7 min.
+Best iteration: 3 [SCORE 'stupid_bleu'=0.2962].
+This took 0.26667 min.