Norberto Ritzmann Jr
74 lines
2.6 KiB
Java
74 lines
2.6 KiB
Java
package com.baeldung.neuroph;
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import org.neuroph.core.Layer;
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import org.neuroph.core.NeuralNetwork;
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import org.neuroph.core.Neuron;
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import org.neuroph.core.data.DataSet;
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import org.neuroph.core.data.DataSetRow;
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import org.neuroph.nnet.learning.BackPropagation;
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import org.neuroph.util.ConnectionFactory;
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import org.neuroph.util.NeuralNetworkType;
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public class NeurophXOR {
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public static NeuralNetwork assembleNeuralNetwork() {
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Layer inputLayer = new Layer();
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inputLayer.addNeuron(new Neuron());
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inputLayer.addNeuron(new Neuron());
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Layer hiddenLayerOne = new Layer();
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hiddenLayerOne.addNeuron(new Neuron());
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hiddenLayerOne.addNeuron(new Neuron());
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hiddenLayerOne.addNeuron(new Neuron());
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hiddenLayerOne.addNeuron(new Neuron());
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Layer hiddenLayerTwo = new Layer();
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hiddenLayerTwo.addNeuron(new Neuron());
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hiddenLayerTwo.addNeuron(new Neuron());
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hiddenLayerTwo.addNeuron(new Neuron());
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hiddenLayerTwo.addNeuron(new Neuron());
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Layer outputLayer = new Layer();
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outputLayer.addNeuron(new Neuron());
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NeuralNetwork ann = new NeuralNetwork();
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ann.addLayer(0, inputLayer);
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ann.addLayer(1, hiddenLayerOne);
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ConnectionFactory.fullConnect(ann.getLayerAt(0), ann.getLayerAt(1));
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ann.addLayer(2, hiddenLayerTwo);
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ConnectionFactory.fullConnect(ann.getLayerAt(1), ann.getLayerAt(2));
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ann.addLayer(3, outputLayer);
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ConnectionFactory.fullConnect(ann.getLayerAt(2), ann.getLayerAt(3));
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ConnectionFactory.fullConnect(ann.getLayerAt(0), ann.getLayerAt(ann.getLayersCount() - 1), false);
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ann.setInputNeurons(inputLayer.getNeurons());
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ann.setOutputNeurons(outputLayer.getNeurons());
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ann.setNetworkType(NeuralNetworkType.MULTI_LAYER_PERCEPTRON);
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return ann;
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}
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public static NeuralNetwork trainNeuralNetwork(NeuralNetwork ann) {
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int inputSize = 2;
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int outputSize = 1;
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DataSet ds = new DataSet(inputSize, outputSize);
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DataSetRow rOne = new DataSetRow(new double[] { 0, 1 }, new double[] { 1 });
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ds.addRow(rOne);
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DataSetRow rTwo = new DataSetRow(new double[] { 1, 1 }, new double[] { 0 });
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ds.addRow(rTwo);
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DataSetRow rThree = new DataSetRow(new double[] { 0, 0 }, new double[] { 0 });
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ds.addRow(rThree);
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DataSetRow rFour = new DataSetRow(new double[] { 1, 0 }, new double[] { 1 });
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ds.addRow(rFour);
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BackPropagation backPropagation = new BackPropagation();
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backPropagation.setMaxIterations(1000);
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ann.learn(ds, backPropagation);
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return ann;
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}
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}
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