[jira] [Commented] (MADLIB-1210) Add momentum methods to MLP

["Frank McQuillan (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MADLIB-1210?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16468098#comment-16468098 ] 

Frank McQuillan commented on MADLIB-1210:
-----------------------------------------

If anyone knows of a good data set to demonstrate the virtues of momentum and Nesterov momentum for MLP with gradient descent, please add link(s) to this JIRA.

> Add momentum methods to MLP
> ---------------------------
>
>                 Key: MADLIB-1210
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1210
>             Project: Apache MADlib
>          Issue Type: New Feature
>          Components: Module: Neural Networks
>            Reporter: Frank McQuillan
>            Priority: Major
>             Fix For: v1.15
>
>
> Story
> As a data scientist,
> I want to use momentum methods in MLP,
> so that I get significantly better convergence behavior.
> Details
> Adding momentum will get the MADlib MLP algorithm closer to state of the art.
> 1) Implement momentum term, default value ~0.9
> Ref [1]:
> "Momentum update is another approach that almost always enjoys better converge rates on deep networks." 
> 2) Implement Nesterov momentum, default TRUE
> Ref [2]:
> "Nesterov Momentum is a slightly different version of the momentum update that has recently been gaining popularity. It enjoys stronger theoretical converge guarantees for convex functions and in practice it also consistently works slightly better than standard momentum."
> Interface
> There are 2 new optimization params for momentum, which apply for both 
> classification and regression:
> {code}
> 'learning_rate_init = <value>,
> learning_rate_policy = <value>,
> gamma = <value>,
> power = <value>,
> iterations_per_step = <value>,
> n_iterations = <value>,
> n_tries = <value>,
> lambda = <value>,
> tolerance = <value>,
> batch_size = <value>,
> n_epochs = <value>,
> momentum = <value>,
> nesterov_momentum= <value>'
> momentum
> Default: 0.9. Momentum can help accelerate learning and 
> avoid local minima when using gradient descent. Value must be in the 
> range 0 to 1, where 0 means no momentum.
> nesterov_momentum
> Default: TRUE. Nesterov momentum can provide better results than using
> classical momentum alone, due to its look ahead characteristics.  
> In classical momentum you first correct velocity and step with that 
> velocity, whereas in Nesterov momentum you first step in the velocity 
> direction then make a correction to the velocity vector based on 
> new location.
> Nesterov momentum is only used when the 'momentum' parameter is > 0.
> {code}
> Open questions
> 1) Does momentum and Nesterov momentum work equally well with and without mini-batching?
> Is there any guidance we need to give to users on this?
> Acceptance
> [1] Find/create a dataset that can be used to compare the usefulness of momentum with and without Nesterov, mini-batch, and SGD. This usefulness can be compared based on convergence, which includes both speed and accuracy.
> References
> [1] http://cs231n.github.io/neural-networks-3/#sgd
> [2] http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf, a link from previous source.
> [3] http://ruder.io/optimizing-gradient-descent/index.html#gradientdescentoptimizationalgorithms
> [4] http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
> [5] https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf



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