[jira] [Commented] (MAHOUT-1388) Add command line support and logging for MLP

["Suneel Marthi (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-1388?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13856592#comment-13856592 ] 

Suneel Marthi commented on MAHOUT-1388:
---------------------------------------

[~yxjiang] Also please provide adequate Logging statements in ur code. Would u be only supporting CSV as input format? 

> Add command line support and logging for MLP
> --------------------------------------------
>
>                 Key: MAHOUT-1388
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-1388
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Classification
>    Affects Versions: 1.0
>            Reporter: Yexi Jiang
>              Labels: mlp, sgd
>             Fix For: 1.0
>
>
> The user should have the ability to run the Perceptron from the command line.
> There are two modes for MLP, the training and labeling, the first one takes the data as input and outputs the model, the second one takes the model and unlabeled data as input and outputs the results.
> The parameters are as follows:
> ------------------------------------------------
> --mode -mo // train or label
> --input -i (input data)
> --model -mo  // in training mode, this is the location to store the model (if the specified location has an existing model, it will update the model through incremental learning), in labeling mode, this is the location to store the result
> --output -o           // this is only useful in labeling mode
> --layersize -ls (no. of units per hidden layer) // use comma separated number to indicate the number of neurons in each layer (including input layer and output layer)
> --momentum -m 
> --learningrate -l
> --regularizationweight -r
> --costfunction -cf   // the type of cost function,
> ------------------------------------------------
> For example, train a 3-layer (including input, hidden, and output) MLP with Minus_Square cost function, 0.1 learning rate, 0.1 momentum rate, and 0.01 regularization weight, the parameter would be:
> mlp -mo train -i /tmp/training-data.csv -o /tmp/model.model -ls 5,3,1 -l 0.1 -m 0.1 -r 0.01 -cf minus_squared
> This command would read the training data from /tmp/training-data.csv and write the trained model to /tmp/model.model.
> If a user need to use an existing model, it will use the following command:
> mlp -mo label -i /tmp/unlabel-data.csv -m /tmp/model.model -o /tmp/label-result
> Moreover, we should be providing default values if the user does not specify any. 



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