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From Yexi Jiang <yexiji...@gmail.com>
Subject Re: [jira] [Assigned] (MAHOUT-1388) Add command line support and logging for MLP
Date Mon, 24 Mar 2014 20:04:49 GMT
The patch is already available.


2014-03-23 1:01 GMT-04:00 Suneel Marthi (JIRA) <jira@apache.org>:

>
>      [
> https://issues.apache.org/jira/browse/MAHOUT-1388?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel]
>
> Suneel Marthi reassigned MAHOUT-1388:
> -------------------------------------
>
>     Assignee: Suneel Marthi
>
> > 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
> >            Assignee: Suneel Marthi
> >              Labels: mlp, sgd
> >             Fix For: 1.0
> >
> >         Attachments: Mahout-1388.patch, Mahout-1388.patch
> >
> >
> > The user should have the ability to run the Perceptron from the command
> line.
> > There are two programs to execute 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 for training are as follows:
> > ------------------------------------------------
> > --input -i (input data)
> > --skipHeader -sk // whether to skip the first row, this parameter is
> optional
> > --labels -labels // the labels of the instances, separated by
> whitespace. Take the iris dataset for example, the labels are 'setosa
> versicolor virginica'.
> > --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
> > --update -u // whether to incremental update the model, if this
> parameter is not given, train the model from scratch
> > --output -o           // this is only useful in labeling mode
> > --layersize -ls (no. of units per hidden layer) // use whitespace
> separated number to indicate the number of neurons in each layer (including
> input layer and output layer), e.g. '5 3 2'.
> > --squashingFunction -sf // currently only supports Sigmoid
> > --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 0.1 learning rate, 0.1 momentum rate, and 0.01 regularization weight,
> the parameter would be:
> > mlp -i /tmp/training-data.csv -labels setosa versicolor virginica -o
> /tmp/model.model -ls 5,3,1 -l 0.1 -m 0.1 -r 0.01
> > This command would read the training data from /tmp/training-data.csv
> and write the trained model to /tmp/model.model.
> > The parameters for labeling is as follows:
> > -------------------------------------------------------------
> > --input -i // input file path
> > --columnRange -cr // the range of column used for feature, start from 0
> and separated by whitespace, e.g. 0 5
> > --format -f // the format of input file, currently only supports csv
> > --model -mo // the file path of the model
> > --output -o // the output path for the results
> > -------------------------------------------------------------
> > If a user need to use an existing model, it will use the following
> command:
> > mlp -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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>



-- 
------
Yexi Jiang,
ECS 251,  yjian004@cs.fiu.edu
School of Computer and Information Science,
Florida International University
Homepage: http://users.cis.fiu.edu/~yjian004/

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