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https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13658192#comment-13658192
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Sascha Jonas commented on HAMA-681:
-----------------------------------
Hi,
i am writing my master-thesis about distributing a Multilayer Perceptron with
Apache Hama under supervision of Prof. Herta.
Right now i am implementing batch gradient descent:
- each task calculates the delta Ws (for all connection matrices) for a mini
batch given
- the delta Ws are summed up and sended to a master
- the master sums up all delta Ws and calculates the average delta W
- the master calculates the new connection matrices
- the master sends the new connection matrices to all clients
- repeat above steps till number of iterations is reached
Restrictions:
- the model must fit into memory
TODO:
- implement a train batch method. right now i am using the train online method.
- implement stochastic gradient descent
- replace mahout math libraries with http://mikiobraun.github.io/jblas/ which
are 10x faster
You can find the alpha code here:
https://code.google.com/p/large-scale-mlp/
I think it would be great working together on this task.
> Multi Layer Perceptron
> -----------------------
>
> Key: HAMA-681
> URL: https://issues.apache.org/jira/browse/HAMA-681
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Reporter: Christian Herta
>
> Implementation of a Multilayer Perceptron (Neural Network)
> - Learning by Backpropagation
> - Distributed Learning
> The implementation should be the basis for the long range goals:
> - more efficent learning (Adagrad, L-BFGS)
> - High efficient distributed Learning
> - Autoencoder - Sparse (denoising) Autoencoder
> - Deep Learning
>
> ---
> Due to the overhead of Map-Reduce(MR) MR didn't seem to be the best strategy
> to distribute the learning of MLPs.
> Therefore the current implementation of the MLP (see MAHOUT-976) should be
> migrated to Hama. First all dependencies to Mahout (Matrix-Library) must be
> removed to get a standalone MLP Implementation. Then the Hama BSP programming
> model should be used to realize distributed learning.
> Different strategies of efficient synchronized weight updates has to be
> evaluated.
> Resources:
> Videos:
> - http://www.youtube.com/watch?v=ZmNOAtZIgIk
> - http://techtalks.tv/talks/57639/
> MLP and Deep Learning Tutorial:
> - http://www.stanford.edu/class/cs294a/
> Scientific Papers:
> - Google's "Brain" project:
> http://research.google.com/archive/large_deep_networks_nips2012.html
> - Neural Networks and BSP: http://ipdps.cc.gatech.edu/1998/biosp3/bispp4.pdf
> - http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
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