Unfortunately, not yet... Deep learning support (autoencoders, RBMs) is on the roadmap for 1.6 <https://issues.apache.org/jira/browse/SPARK-10324> though, and there is a spark package <http://spark-packages.org/package/rakeshchalasani/MLlib-dropout> for dropout regularized logistic regression.
On Mon, Sep 7, 2015 at 3:15 PM, Ruslan Dautkhanov <dautkha...@gmail.com> wrote: > Thanks! > > It does not look Spark ANN yet supports dropout/dropconnect or any other > techniques that help avoiding overfitting? > http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf > https://cs.nyu.edu/~wanli/dropc/dropc.pdf > > ps. There is a small copy-paste typo in > > https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/ann/BreezeUtil.scala#L43 > should read B&C :) > > > > -- > Ruslan Dautkhanov > > On Mon, Sep 7, 2015 at 12:47 PM, Feynman Liang <fli...@databricks.com> > wrote: > >> Backprop is used to compute the gradient here >> <https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala#L579-L584>, >> which is then optimized by SGD or LBFGS here >> <https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/ann/Layer.scala#L878> >> >> On Mon, Sep 7, 2015 at 11:24 AM, Nick Pentreath <nick.pentre...@gmail.com >> > wrote: >> >>> Haven't checked the actual code but that doc says "MLPC employes >>> backpropagation for learning the model. .."? >>> >>> >>> >>> — >>> Sent from Mailbox <https://www.dropbox.com/mailbox> >>> >>> >>> On Mon, Sep 7, 2015 at 8:18 PM, Ruslan Dautkhanov <dautkha...@gmail.com> >>> wrote: >>> >>>> http://people.apache.org/~pwendell/spark-releases/latest/ml-ann.html >>>> >>>> Implementation seems missing backpropagation? >>>> Was there is a good reason to omit BP? >>>> What are the drawbacks of a pure feedforward-only ANN? >>>> >>>> Thanks! >>>> >>>> >>>> -- >>>> Ruslan Dautkhanov >>>> >>> >>> >> >
