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https://issues.apache.org/jira/browse/MAHOUT-1388?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14001021#comment-14001021
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Sebastian Schelter commented on MAHOUT-1388:
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[~yxjiang] what's the status here?
> 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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