BAEL-771 (#2286)

* BAEL-771 * Corrected XOR from mislabeled AND * Unit tests added * Merged into libraries module - removed Neuroph module * Merged into libraries module - removed Neuroph module * Merged pom.xml * Merged pom.xml * libraries pom.xml - I removed a white space during merge so conflict persisted - here's the temporary reversion
2017-07-25 15:11:02 -07:00
parent a89462e7f1
commit d4f245a275
4 changed files with 167 additions and 2 deletions
@@ -0,0 +1,73 @@
+package com.baeldung.neuroph;
+
+import org.neuroph.core.Layer;
+import org.neuroph.core.NeuralNetwork;
+import org.neuroph.core.Neuron;
+import org.neuroph.core.data.DataSet;
+import org.neuroph.core.data.DataSetRow;
+import org.neuroph.nnet.learning.BackPropagation;
+import org.neuroph.util.ConnectionFactory;
+import org.neuroph.util.NeuralNetworkType;
+
+public class NeurophXOR {
+
+    public static NeuralNetwork assembleNeuralNetwork() {
+
+        Layer inputLayer = new Layer();
+        inputLayer.addNeuron(new Neuron());
+        inputLayer.addNeuron(new Neuron());
+
+        Layer hiddenLayerOne = new Layer();
+        hiddenLayerOne.addNeuron(new Neuron());
+        hiddenLayerOne.addNeuron(new Neuron());
+        hiddenLayerOne.addNeuron(new Neuron());
+        hiddenLayerOne.addNeuron(new Neuron());
+
+        Layer hiddenLayerTwo = new Layer();
+        hiddenLayerTwo.addNeuron(new Neuron());
+        hiddenLayerTwo.addNeuron(new Neuron());
+        hiddenLayerTwo.addNeuron(new Neuron());
+        hiddenLayerTwo.addNeuron(new Neuron());
+
+        Layer outputLayer = new Layer();
+        outputLayer.addNeuron(new Neuron());
+
+        NeuralNetwork ann = new NeuralNetwork();
+
+        ann.addLayer(0, inputLayer);
+        ann.addLayer(1, hiddenLayerOne);
+        ConnectionFactory.fullConnect(ann.getLayerAt(0), ann.getLayerAt(1));
+        ann.addLayer(2, hiddenLayerTwo);
+        ConnectionFactory.fullConnect(ann.getLayerAt(1), ann.getLayerAt(2));
+        ann.addLayer(3, outputLayer);
+        ConnectionFactory.fullConnect(ann.getLayerAt(2), ann.getLayerAt(3));
+        ConnectionFactory.fullConnect(ann.getLayerAt(0), ann.getLayerAt(ann.getLayersCount()-1), false);
+
+        ann.setInputNeurons(inputLayer.getNeurons());
+        ann.setOutputNeurons(outputLayer.getNeurons());
+
+        ann.setNetworkType(NeuralNetworkType.MULTI_LAYER_PERCEPTRON);
+        return ann;
+    }
+
+    public static NeuralNetwork trainNeuralNetwork(NeuralNetwork ann) {
+        int inputSize = 2;
+        int outputSize = 1;
+        DataSet ds = new DataSet(inputSize, outputSize);
+
+        DataSetRow rOne = new DataSetRow(new double[] {0, 1}, new double[] {1});
+        ds.addRow(rOne);
+        DataSetRow rTwo = new DataSetRow(new double[] {1, 1}, new double[] {0});
+        ds.addRow(rTwo);
+        DataSetRow rThree = new DataSetRow(new double[] {0, 0}, new double[] {0});
+        ds.addRow(rThree);
+        DataSetRow rFour = new DataSetRow(new double[] {1, 0}, new double[] {1});
+        ds.addRow(rFour);
+
+        BackPropagation backPropagation = new BackPropagation();
+        backPropagation.setMaxIterations(1000);
+
+        ann.learn(ds, backPropagation);
+        return ann;
+    }
+}