Yexi Jiang created MAHOUT-1388:
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Summary: 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
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:
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--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,
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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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