1
在Mallet中训练数据时,处理由于OutOfMemoryError
而停止。 bin/mallet中的属性MEMORY
已被设置为3GB。培训文件output.mallet的大小仅为31 MB。我试图减少训练数据的大小。但它仍然抛出了同样的错误:Mallet:OutOfMemoryError:Java堆空间
[email protected]:~/dev/test_models/Mallet$ bin/mallet train-classifier --input output.mallet --trainer NaiveBayes --training-portion 0.0001 --num-trials 10
Training portion = 1.0E-4
Unlabeled training sub-portion = 0.0
Validation portion = 0.0
Testing portion = 0.9999
-------------------- Trial 0 --------------------
Trial 0 Training NaiveBayesTrainer with 7 instances
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
at cc.mallet.types.Multinomial$Estimator.setAlphabet(Multinomial.java:309)
at cc.mallet.classify.NaiveBayesTrainer.setup(NaiveBayesTrainer.java:251)
at cc.mallet.classify.NaiveBayesTrainer.trainIncremental(NaiveBayesTrainer.java:200)
at cc.mallet.classify.NaiveBayesTrainer.train(NaiveBayesTrainer.java:193)
at cc.mallet.classify.NaiveBayesTrainer.train(NaiveBayesTrainer.java:59)
at cc.mallet.classify.tui.Vectors2Classify.main(Vectors2Classify.java:415)
我想任何并欣赏帮助或见解这个问题
编辑:这是我的斌/槌文件。
#!/bin/bash
malletdir=`dirname $0`
malletdir=`dirname $malletdir`
cp=$malletdir/class:$malletdir/lib/mallet-deps.jar:$CLASSPATH
#echo $cp
MEMORY=10g
CMD=$1
shift
help()
{
cat <<EOF
Mallet 2.0 commands:
import-dir load the contents of a directory into mallet instances (one per file)
import-file load a single file into mallet instances (one per line)
import-svmlight load SVMLight format data files into Mallet instances
info get information about Mallet instances
train-classifier train a classifier from Mallet data files
classify-dir classify data from a single file with a saved classifier
classify-file classify the contents of a directory with a saved classifier
classify-svmlight classify data from a single file in SVMLight format
train-topics train a topic model from Mallet data files
infer-topics use a trained topic model to infer topics for new documents
evaluate-topics estimate the probability of new documents under a trained model
prune remove features based on frequency or information gain
split divide data into testing, training, and validation portions
bulk-load for big input files, efficiently prune vocabulary and import docs
Include --help with any option for more information
EOF
}
CLASS=
case $CMD in
import-dir) CLASS=cc.mallet.classify.tui.Text2Vectors;;
import-file) CLASS=cc.mallet.classify.tui.Csv2Vectors;;
import-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Vectors;;
info) CLASS=cc.mallet.classify.tui.Vectors2Info;;
train-classifier) CLASS=cc.mallet.classify.tui.Vectors2Classify;;
classify-dir) CLASS=cc.mallet.classify.tui.Text2Classify;;
classify-file) CLASS=cc.mallet.classify.tui.Csv2Classify;;
classify-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Classify;;
train-topics) CLASS=cc.mallet.topics.tui.TopicTrainer;;
infer-topics) CLASS=cc.mallet.topics.tui.InferTopics;;
evaluate-topics) CLASS=cc.mallet.topics.tui.EvaluateTopics;;
prune) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;
split) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;
bulk-load) CLASS=cc.mallet.util.BulkLoader;;
run) CLASS=$1; shift;;
*) echo "Unrecognized command: $CMD"; help; exit 1;;
esac
java -Xmx$MEMORY -ea -Djava.awt.headless=true -Dfile.encoding=UTF-8 -server -classpath "$cp" $CLASS "[email protected]"
还值得一提的是,我的原始培训文件有60,000项。当我减少项目数量(20,000个实例)时,培训将像正常一样运行,但使用大约10GB RAM。
您准确更改了哪个文件? bin/mallet或bin/mallet.sh? – mikep
bin/mallet和bin/mallet.bat –
是在其中一个对java的调用? – mikep