This is for you :-) http://techreports.cs.queensu.ca/files/1997-406.pdf
On Thu, Nov 15, 2012 at 6:09 PM, [email protected] <[email protected]> wrote: > Dear All, > what do you think about to scale out the learning of Multi Layer Perceptrons > (MLP) with BSPs? > I heard the talk of Tommaso at the apacheConAt first glance the pramming model > BSP seems to fit better the MapReduce for this purpose. > The basic idea is to distribute the backprop algorithm is the following: > > Distribution of learning can be done by (batch learning): > 1 Partioning of the data in x chunks > 2 On each working node: Learning the weight changes (as matrices) in each > chunk > 3 Combining the matrixes (weight changes) and simultaneous update of the > weights > in each node - back to 2 > > Maybe this procedure can be done with random parts of the chunks (distributed > quasi online learning). > > I wrote the (basic) backprob algorithm of a multi layer preceptron (see mahout > patch https://issues.apache.org/jira/browse/MAHOUT-976). It uses the Mahout > Matrix Library, which is under the hood the Colt Matrix Library from Cern. > > Probably using the Cern Matrix Library would also suitable for Hama. Then it > could be easy to port the MLP to Hama. > > What do you think about it? > > Thanks for you response. > > Cheers > Christian -- Best Regards, Edward J. Yoon @eddieyoon
