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WT_02_supervised_learning.md

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walkthrough: supervised learning

implementation example

Supervised learning implementation

See below a full xor test of the neural network with supervised learning:

// prepare set of expected input values
double[][] in = {{0,0}, {1,0}, {0,1}, {1,1}};

// prepare corresponding expected output values
double[][] out = {{0}, {1}, {1}, {0}};

// create and configure neural network
Rectifier rectifier = Rectifier.SIGMOID
NeuralNetwork neuralNetwork = new NeuralNetwork.Builder(2, 4, 1);
    .setDefaultRectifier(rectifier)
    .setLearningRate(0.8)
    .setLearningRateOptimizer(LearningRateOptimizer.NONE)
    .build();

// train neural network with the input set, the corresponding expected output set and the training epochs
neuralNetwork.fit(in, out, 2000);

// use the trained NeuralNetwork to predict the output values given to the input set
System.out.println("test with rectifier: " + rectifier.getDescription());
System.out.println("combo 1: " + neuralNetwork.predict(in[0]));
System.out.println("combo 2: " + neuralNetwork.predict(in[1]));
System.out.println("combo 3: " + neuralNetwork.predict(in[2]));
System.out.println("combo 4: " + neuralNetwork.predict(in[3]));

Output:

test with recitifier: Sigmoid (SIGMOID)
combo 1: [0.05062227783220413]          // close to 0
combo 2: [0.9461083391423777]           // close to 1
combo 3: [0.9425030935131657]           // close to 1
combo 4: [0.07157249157075309]          // close to 0