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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