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https://issues.apache.org/jira/browse/SPARK-16377?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Nicholas Chammas updated SPARK-16377:
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Component/s: (was: MLilb)
MLlib
> Spark MLlib: MultilayerPerceptronClassifier - error while training
> ------------------------------------------------------------------
>
> Key: SPARK-16377
> URL: https://issues.apache.org/jira/browse/SPARK-16377
> Project: Spark
> Issue Type: Bug
> Components: ML, MLlib
> Affects Versions: 1.5.2
> Reporter: Mikhail Shiryaev
>
> Hi,
> I am trying to train model by MultilayerPerceptronClassifier.
> It works on sample data from
> data/mllib/sample_multiclass_classification_data.txt with 4 features, 3
> classes and layers [4, 4, 3].
> But when I try to use other input files with other features and classes (from
> here for example:
> https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html)
> then I get errors.
> Example:
> Input file aloi (128 features, 1000 classes, layers [128, 128, 1000]):
> with block size = 1:
> ERROR StrongWolfeLineSearch: Encountered bad values in function evaluation.
> Decreasing step size to Infinity
> ERROR LBFGS: Failure! Resetting history: breeze.optimize.FirstOrderException:
> Line search failed
> ERROR LBFGS: Failure again! Giving up and returning. Maybe the objective is
> just poorly behaved?
> with default block size = 128:
> java.lang.ArrayIndexOutOfBoundsException
> at java.lang.System.arraycopy(Native Method)
> at
> org.apache.spark.ml.ann.DataStacker$$anonfun$3$$anonfun$apply$3$$anonfun$apply$4.apply(Layer.scala:629)
>
> at
> org.apache.spark.ml.ann.DataStacker$$anonfun$3$$anonfun$apply$3$$anonfun$apply$4.apply(Layer.scala:628)
>
> at scala.collection.immutable.List.foreach(List.scala:381)
> at
> org.apache.spark.ml.ann.DataStacker$$anonfun$3$$anonfun$apply$3.apply(Layer.scala:628)
>
> at
> org.apache.spark.ml.ann.DataStacker$$anonfun$3$$anonfun$apply$3.apply(Layer.scala:624)
>
> Even if I modify sample_multiclass_classification_data.txt file (rename all
> 4-th features to 5-th) and run with layers [5, 5, 3] then I also get the same
> errors as for file above.
> So to resume:
> I can't run training with default block size and with more than 4 features.
> If I set block size to 1 then some actions are happened but I get errors
> from LBFGS.
> It is reproducible with Spark 1.5.2 and from master branch on github (from
> 4-th July).
> Did somebody already met with such behavior?
> Is there bug in MultilayerPerceptronClassifier or I use it incorrectly?
> Thanks.
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