mlp #

implementation of a multilayer perceptron which can be trained using the backpropagation algorithm

installation ##

npm install mlp

Using browserify, mlp can be used in a browser as well.

how to use ##

$3

// create the perceptron
var MLP = require('mlp');
var mlp = new MLP(2,3);

// add hidden layers and initialize
mlp.addHiddenLayer(5);
mlp.addHiddenLayer(5);
mlp.init();

The amount and dimensions of the hidden layers has to correspond with the problem,
you want to solve (think about overfitting)...

$3

// create a training set
mlp.addToTrainingSet([2, 1], [1, 0, 0]);
mlp.addToTrainingSet([0, 1], [1, 0, 0]);
mlp.addToTrainingSet([3, 4], [0, 1, 0]);
mlp.addToTrainingSet([2, 3], [0, 1, 0]);
mlp.addToTrainingSet([1, 0], [0, 0, 1]);
mlp.addToTrainingSet([3, 1], [0, 0, 1]);

// train the perceptron
var learnRate = 0.5;
var error = Number.MAX_VALUE;
while (error > 0.01) {
error = mlp.train(learnRate);
}

Depending on the training set, the learn rate and the structure of the perceptron, this step may take a while.
If necessary, you can add additional exit conditions to the loop (e.g. a maximum number of iterations).

If you want to restart the training, you have to clear the training set and to reset the weights:

mlp.clearTrainingSet();
mlp.resetWeights();

$3

var elementToClassify = [1, 1];
var classification = mlp.classify(elementToClassify);

The vector classification contains in each component the probability
that the element belongs to the associated class.

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After training the perceptron, you can export the weights to save them for later use:

var data = mlp.exportToJson();

The export is a simple JSON object that can be imported again as follows:

mlp.setWeights(data);

Changelog

$3

Add methods exportToJson and setWeights to export and import a perceptron.

License

Copyright (c) 2015-2016 Ulf Biallas. Licensed under the [MIT license][MIT].
[MIT]: https://github.com/ulfbiallas/npm-mlp/blob/master/LICENSE