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From "Yexi Jiang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HAMA-760) Add new features to existing Multi Layer Perceptron
Date Sun, 09 Jun 2013 21:47:20 GMT

     [ https://issues.apache.org/jira/browse/HAMA-760?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Yexi Jiang updated HAMA-760:
----------------------------

    Attachment: HAMA-760.patch

I have added the new features as well as the corresponding test cases.
Since I didn't touch any code other than the ones in ml package, I only tested the ml package.

The following is the output.

[INFO] Apache Hama parent POM ............................ SUCCESS [0.974s]
[INFO] core .............................................. SUCCESS [3.188s]
[INFO] graph ............................................. SUCCESS [0.584s]
[INFO] machine learning .................................. SUCCESS [34.567s]
[INFO] examples .......................................... SUCCESS [0.469s]
[INFO] yarn .............................................. SUCCESS [1.622s]
[INFO] hama-dist ......................................... SUCCESS [0.011s]
[INFO] ------------------------------------------------------------------------
[INFO] BUILD SUCCESS
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 41.966s
[INFO] Finished at: Sun Jun 09 17:42:33 EDT 2013
[INFO] Final Memory: 27M/981M

                
> Add new features to existing Multi Layer Perceptron
> ---------------------------------------------------
>
>                 Key: HAMA-760
>                 URL: https://issues.apache.org/jira/browse/HAMA-760
>             Project: Hama
>          Issue Type: New Feature
>            Reporter: Yexi Jiang
>            Assignee: Yexi Jiang
>              Labels: features, machine_learning, mlp
>         Attachments: HAMA-760.patch
>
>
> Current MultiLayerPerceptron has only implemented the basic features of a Multi Layer
Perceptron.
> There are still several features need to be implemented.
> In the next step the following features should be added:
> 1) add more cost functions such as cross entropy.
> 2) add momentum and regularization.
> 3) make the training method in MLP be public to allow user to use MLP in a standalone
algorithm.
> 4) add more test cases on other applications, at least one with regression and one with
classification. 

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