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-rw-r--r--training/dtrain/examples/standard/cdec.ini2
-rw-r--r--training/dtrain/examples/standard/expected-output115
-rw-r--r--training/mira/kbest_cut_mira.cc8
-rwxr-xr-xtraining/mira/mira.py4
4 files changed, 69 insertions, 60 deletions
diff --git a/training/dtrain/examples/standard/cdec.ini b/training/dtrain/examples/standard/cdec.ini
index 6cba9e1e..3330dd71 100644
--- a/training/dtrain/examples/standard/cdec.ini
+++ b/training/dtrain/examples/standard/cdec.ini
@@ -21,7 +21,7 @@ feature_function=RuleIdentityFeatures
feature_function=RuleSourceBigramFeatures
feature_function=RuleTargetBigramFeatures
feature_function=RuleShape
-feature_function=RuleWordAlignmentFeatures
+feature_function=LexicalFeatures 1 1 1
#feature_function=SourceSpanSizeFeatures
#feature_function=SourceWordPenalty
#feature_function=SpanFeatures
diff --git a/training/dtrain/examples/standard/expected-output b/training/dtrain/examples/standard/expected-output
index fa831221..2460cfbb 100644
--- a/training/dtrain/examples/standard/expected-output
+++ b/training/dtrain/examples/standard/expected-output
@@ -4,7 +4,8 @@ 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 4138446869
+T=1 I=1 D=1
+Seeding random number sequence to 2327685089
dtrain
Parameters:
@@ -36,87 +37,87 @@ Iteration #1 of 3.
. 10
Stopping after 10 input sentences.
WEIGHTS
- Glue = -80.3
- WordPenalty = -51.247
- LanguageModel = +282.46
- LanguageModel_OOV = -85.8
- PhraseModel_0 = -100.06
- PhraseModel_1 = -98.692
- PhraseModel_2 = -9.4958
- PhraseModel_3 = +18.535
- PhraseModel_4 = +62.35
- PhraseModel_5 = +7
- PhraseModel_6 = +31.4
- PassThrough = -126.5
+ Glue = +6.9
+ WordPenalty = -46.426
+ LanguageModel = +535.12
+ LanguageModel_OOV = -123.5
+ PhraseModel_0 = -160.73
+ PhraseModel_1 = -350.13
+ PhraseModel_2 = -187.81
+ PhraseModel_3 = +172.04
+ PhraseModel_4 = +0.90108
+ PhraseModel_5 = +21.6
+ PhraseModel_6 = +67.2
+ PassThrough = -149.7
---
- 1best avg score: 0.25631 (+0.25631)
- 1best avg model score: -4843.6 (-4843.6)
- avg # pairs: 744.4
+ 1best avg score: 0.23327 (+0.23327)
+ 1best avg model score: -9084.9 (-9084.9)
+ avg # pairs: 780.7
avg # rank err: 0 (meaningless)
avg # margin viol: 0
k-best loss imp: 100%
- non0 feature count: 1274
+ non0 feature count: 1389
avg list sz: 91.3
- avg f count: 143.72
-(time 0.4 min, 2.4 s/S)
+ avg f count: 146.2
+(time 0.37 min, 2.2 s/S)
Iteration #2 of 3.
. 10
WEIGHTS
- Glue = -117.4
- WordPenalty = -99.584
- LanguageModel = +395.05
- LanguageModel_OOV = -136.8
- PhraseModel_0 = +40.614
- PhraseModel_1 = -123.29
- PhraseModel_2 = -152
- PhraseModel_3 = -161.13
- PhraseModel_4 = -76.379
- PhraseModel_5 = +39.1
- PhraseModel_6 = +137.7
- PassThrough = -162.1
+ Glue = -43
+ WordPenalty = -22.019
+ LanguageModel = +591.53
+ LanguageModel_OOV = -252.1
+ PhraseModel_0 = -120.21
+ PhraseModel_1 = -43.589
+ PhraseModel_2 = +73.53
+ PhraseModel_3 = +113.7
+ PhraseModel_4 = -223.81
+ PhraseModel_5 = +64
+ PhraseModel_6 = +54.8
+ PassThrough = -331.1
---
- 1best avg score: 0.26751 (+0.011198)
- 1best avg model score: -10061 (-5216.9)
- avg # pairs: 639.1
+ 1best avg score: 0.29568 (+0.062413)
+ 1best avg model score: -15879 (-6794.1)
+ avg # pairs: 566.1
avg # rank err: 0 (meaningless)
avg # margin viol: 0
k-best loss imp: 100%
- non0 feature count: 1845
+ non0 feature count: 1931
avg list sz: 91.3
- avg f count: 139.88
-(time 0.35 min, 2.1 s/S)
+ avg f count: 139.89
+(time 0.33 min, 2 s/S)
Iteration #3 of 3.
. 10
WEIGHTS
- Glue = -101.1
- WordPenalty = -139.97
- LanguageModel = +327.98
- LanguageModel_OOV = -234.7
- PhraseModel_0 = -144.49
- PhraseModel_1 = -263.88
- PhraseModel_2 = -149.25
- PhraseModel_3 = -38.805
- PhraseModel_4 = +50.575
- PhraseModel_5 = -52.4
- PhraseModel_6 = +41.6
- PassThrough = -230.2
+ Glue = -44.3
+ WordPenalty = -131.85
+ LanguageModel = +230.91
+ LanguageModel_OOV = -285.4
+ PhraseModel_0 = -194.27
+ PhraseModel_1 = -294.83
+ PhraseModel_2 = -92.043
+ PhraseModel_3 = -140.24
+ PhraseModel_4 = +85.613
+ PhraseModel_5 = +238.1
+ PhraseModel_6 = +158.7
+ PassThrough = -359.6
---
- 1best avg score: 0.36222 (+0.094717)
- 1best avg model score: -17416 (-7355.5)
- avg # pairs: 661.2
+ 1best avg score: 0.37375 (+0.078067)
+ 1best avg model score: -14519 (+1359.7)
+ avg # pairs: 545.4
avg # rank err: 0 (meaningless)
avg # margin viol: 0
k-best loss imp: 100%
- non0 feature count: 2163
+ non0 feature count: 2218
avg list sz: 91.3
- avg f count: 132.53
-(time 0.33 min, 2 s/S)
+ avg f count: 137.77
+(time 0.35 min, 2.1 s/S)
Writing weights file to '-' ...
done
---
-Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.36222].
-This took 1.0833 min.
+Best iteration: 3 [SCORE 'fixed_stupid_bleu'=0.37375].
+This took 1.05 min.
diff --git a/training/mira/kbest_cut_mira.cc b/training/mira/kbest_cut_mira.cc
index e0b6eecb..9de57f5f 100644
--- a/training/mira/kbest_cut_mira.cc
+++ b/training/mira/kbest_cut_mira.cc
@@ -95,7 +95,8 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("stream,t", "Stream mode (used for realtime)")
("weights_output,O",po::value<string>(),"Directory to write weights to")
("output_dir,D",po::value<string>(),"Directory to place output in")
- ("decoder_config,c",po::value<string>(),"Decoder configuration file");
+ ("decoder_config,c",po::value<string>(),"Decoder configuration file")
+ ("verbose,v",po::value<bool>()->zero_tokens(),"verbose stderr output");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
@@ -629,6 +630,7 @@ int main(int argc, char** argv) {
vector<string> corpus;
+ const bool VERBOSE = conf.count("verbose");
const string metric_name = conf["mt_metric"].as<string>();
optimizer = conf["optimizer"].as<int>();
fear_select = conf["fear"].as<int>();
@@ -792,7 +794,8 @@ int main(int argc, char** argv) {
double margin = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights);
double mt_loss = (cur_good.mt_metric - cur_bad.mt_metric);
const double loss = margin + mt_loss;
- cerr << "LOSS: " << loss << " Margin:" << margin << " BLEUL:" << mt_loss << " " << cur_bad.features.dot(dense_weights) << " " << cur_good.features.dot(dense_weights) <<endl;
+ cerr << "LOSS: " << loss << " Margin:" << margin << " BLEUL:" << mt_loss << endl;
+ if (VERBOSE) cerr << cur_bad.features.dot(dense_weights) << " " << cur_good.features.dot(dense_weights) << endl;
if (loss > 0.0 || !checkloss) {
SparseVector<double> diff = cur_good.features;
diff -= cur_bad.features;
@@ -929,6 +932,7 @@ int main(int argc, char** argv) {
lambdas += (cur_pair[1]->features) * step_size;
lambdas -= (cur_pair[0]->features) * step_size;
+ if (VERBOSE) cerr << " Lambdas " << lambdas << endl;
//reload weights based on update
dense_weights.clear();
diff --git a/training/mira/mira.py b/training/mira/mira.py
index c84a8cff..1861da1a 100755
--- a/training/mira/mira.py
+++ b/training/mira/mira.py
@@ -143,6 +143,8 @@ def main():
parser.add_argument('--pass-suffix',
help='multipass decoding iteration. see documentation '
'at www.cdec-decoder.org for more information')
+ parser.add_argument('-v', '--verbose',
+ help='more verbose mira optimizers')
args = parser.parse_args()
args.metric = args.metric.upper()
@@ -352,6 +354,8 @@ def optimize(args, script_dir, dev_size):
decoder_cmd += ' -a'
if not args.no_pseudo:
decoder_cmd += ' -e'
+ if args.verbose:
+ decoder_cmd += ' -v'
#always use fork
parallel_cmd = '{0} --use-fork -e {1} -j {2} --'.format(
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-11 08:52:02 +0000