Hi, RJ https://github.com/avulanov/spark/blob/neuralnetwork/mllib/src/main/scala/org/apache/spark/mllib/classification/NeuralNetwork.scala
Unit tests are in the same branch. Alexander From: RJ Nowling [mailto:rnowl...@gmail.com] Sent: Tuesday, August 26, 2014 6:59 PM To: Ulanov, Alexander Cc: dev@spark.apache.org Subject: Re: Gradient descent and runMiniBatchSGD Hi Alexander, Can you post a link to the code? RJ On Tue, Aug 26, 2014 at 6:53 AM, Ulanov, Alexander <alexander.ula...@hp.com<mailto:alexander.ula...@hp.com>> wrote: Hi, I've implemented back propagation algorithm using Gradient class and a simple update using Updater class. Then I run the algorithm with mllib's GradientDescent class. I have troubles in scaling out this implementation. I thought that if I partition my data into the number of workers then performance will increase, because each worker will run a step of gradient descent on its partition of data. But this does not happen and each worker seems to process all data (if miniBatchFraction == 1.0 as in mllib's logisic regression implementation). For me, this doesn't make sense, because then only single Worker will provide the same performance. Could someone elaborate on this and correct me if I am wrong. How can I scale out the algorithm with many Workers? Best regards, Alexander -- em rnowl...@gmail.com<mailto:rnowl...@gmail.com> c 954.496.2314
