[jira] [Commented] (MAHOUT-976) Implement Multilayer Perceptron

["Christian Herta (Commented) (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13240231#comment-13240231 ] 

Christian Herta commented on MAHOUT-976:
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Until now I have implemented only autoencoder code which is very special for multilayer perceptrons: weight changes for the constraint "sparse autoencoder". 
I agree that it's a good idea to have a general autoencoder framework, e.g. for stacking autoencoders and for training autoencoders.
When I have some time I will have a look how to get rbm and mlp autoencoders together.   
                
> Implement Multilayer Perceptron
> -------------------------------
>
>                 Key: MAHOUT-976
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-976
>             Project: Mahout
>          Issue Type: New Feature
>    Affects Versions: 0.7
>            Reporter: Christian Herta
>            Priority: Minor
>              Labels: multilayer, networks, neural, perceptron
>         Attachments: MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch
>
>   Original Estimate: 80h
>  Remaining Estimate: 80h
>
> Implement a multi layer perceptron
>  * via Matrix Multiplication
>  * Learning by Backpropagation; implementing tricks by Yann LeCun et al.: "Efficent Backprop"
>  * arbitrary number of hidden layers (also 0  - just the linear model)
>  * connection between proximate layers only 
>  * different cost and activation functions (different activation function in each layer) 
>  * test of backprop by gradient checking 
>  * normalization of the inputs (storeable) as part of the model
>  
> First:
>  * implementation "stocastic gradient descent" like gradient machine
>  * simple gradient descent incl. momentum
> Later (new jira issues):  
>  * Distributed Batch learning (see below)  
>  * "Stacked (Denoising) Autoencoder" - Feature Learning
>  * advanced cost minimazation like 2nd order methods, conjugate gradient etc.
> Distribution of learning can be done by (batch learning):
>  1 Partioning of the data in x chunks 
>  2 Learning the weight changes as matrices in each chunk
>  3 Combining the matrixes and update of the weights - back to 2
> Maybe this procedure can be done with random parts of the chunks (distributed quasi online learning). 
> Batch learning with delta-bar-delta heuristics for adapting the learning rates.    
>  

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