Package Exports
- ml-fnn
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Readme
Feedforward Neural Network
A implementation of feedforward neural networks in javascript based on the mrbo answer found here:
[Implementation] (http://stackoverflow.com/questions/9951487/implementing-a-neural-network-in-java-training-and-backpropagation-issues)
Methods
new FeedforwardNeuralNetwork([LayersSize])
Arguments
layersSize- Array of numbers with sizes of each layer.
Example
var fnn = new FeedforwardNeuralNetwork();train(trainingSet, predictions, iterations, learningRate, momentum)
Train the Neural Network with a given training set, predictions, learning rate and a momentum (Regularization term).
Arguments
trainingSet- A matrix of the training set.predictions- A matrix of predictions with the same size of rows of the trainingSet.options- A Javascript object with the configuration of the FNN.
Options
hiddenLayers- Array with the size of each hidden layer in the FNN.iterations- Maximum number of iterations of the algorithm.learningRate- The learning rate (number).momentum- The regularization term (number).
Example
var trainingSet = [[0, 0], [0, 1], [1, 0], [1, 1]];
var predictions = [[0], [0], [0], [1]];
var options = {
hiddenLayers: [4],
iterations: 100,
learningRate: 0.3,
momentum: 0.3
};
fnn.train(trainingSet, predictions, options);predict(dataset)
Predict the values of the dataset.
Arguments
dataset- A matrix that contains the dataset.
Example
var dataset = [[0, 0], [0, 1], [1, 0], [1, 1]];
var ans = fnn.predict(dataset);export()
Exports the actual Neural Network to an Javascript Object.
load(model)
Returns a new Neural Network with the given model.
Arguments
model- Javascript Object generated from export() function.