Java Code Examples for org.neuroph.core.NeuralNetwork#setOutputNeurons()

The following examples show how to use org.neuroph.core.NeuralNetwork#setOutputNeurons() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: BackPropagationTest.java    From NeurophFramework with Apache License 2.0 6 votes vote down vote up 
@Test
public void testCalculateErrorAndUpdateOutputNeurons() {
    NeuralNetwork<BackPropagation> nn = new NeuralNetwork<>();
    nn.setInputNeurons(new ArrayList<Neuron>() {
        {
            add(new Neuron());
            add(new Neuron());
        }
    });
    nn.setOutputNeurons(new ArrayList<Neuron>() {
        {
            add(new Neuron());
        }
    });
    nn.setLearningRule(instance);
    nn.getOutputNeurons().get(0).setDelta(1);
    instance.calculateErrorAndUpdateOutputNeurons(new double[]{0});
    assertTrue(nn.getOutputNeurons().get(0).getDelta() == 0);
    instance.calculateErrorAndUpdateOutputNeurons(new double[]{0.5});
    assertTrue(nn.getOutputNeurons().get(0).getDelta() == 0.5);
}
Example 2
Source File: BackPropagationTest.java    From NeurophFramework with Apache License 2.0 6 votes vote down vote up 
@Test
public void testUpdateNetworkWeights() {
    NeuralNetwork<BackPropagation> nn = new NeuralNetwork<>();
    nn.setInputNeurons(new ArrayList<Neuron>() {
        {
            add(new Neuron());
            add(new Neuron());
        }
    });
    nn.setOutputNeurons(new ArrayList<Neuron>() {
        {
            add(new Neuron());
        }
    });
    nn.setLearningRule(instance);
    BackPropagation bp1 = Mockito.spy(new BackPropagation());
    nn.setLearningRule(bp1);
    double[] weigths = {1, 2};
    bp1.calculateWeightChanges(weigths);
    Mockito.verify(bp1).calculateErrorAndUpdateOutputNeurons(weigths);
    Mockito.verify(bp1).calculateErrorAndUpdateHiddenNeurons();
}
Example 3
Source File: NeuralNetworkFactory.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
/**
 * Sets default input and output neurons for network (first layer as input,
 * last as output)
 */
public static void setDefaultIO(NeuralNetwork nnet) {
              ArrayList<Neuron> inputNeuronsList = new ArrayList<>();
               Layer firstLayer = nnet.getLayerAt(0);
               for (Neuron neuron : firstLayer.getNeurons() ) {
                   if (!(neuron instanceof BiasNeuron)) {  // dont set input to bias neurons
                       inputNeuronsList.add(neuron);
                   }
               }

	List<Neuron> outputNeurons = ((Layer) nnet.getLayerAt(nnet.getLayersCount()-1)).getNeurons();

	nnet.setInputNeurons(inputNeuronsList);
	nnet.setOutputNeurons(outputNeurons); 
}
Example 4
Source File: BackPropagationTest.java    From NeurophFramework with Apache License 2.0 5 votes vote down vote up 
@Test
public void testCalculateErrorAndUpdateHiddenNeurons() {
    NeuralNetwork<BackPropagation> nn = new NeuralNetwork<>();
    nn.setInputNeurons(new ArrayList<Neuron>() {
        {
            add(new Neuron());
            add(new Neuron());
        }
    });
    nn.setOutputNeurons(new ArrayList<Neuron>() {
        {
            add(new Neuron());
        }
    });
    nn.setLearningRule(instance);
    Layer l1 = new Layer();
    Layer l2 = new Layer();
    Layer l3 = new Layer();
    Neuron n = new Neuron();
    n.setDelta(0.5);
    Neuron n1 = new Neuron();
    Linear transfer = new Linear();
    n1.setTransferFunction(transfer);

    double weigth = 2;
    n.addInputConnection(new Connection(n1, n, weigth));

    assertTrue(0 == n1.getDelta());

    nn.addLayer(l1);
    nn.addLayer(l2);
    nn.addLayer(l3);
    l2.addNeuron(n1);

    instance.calculateErrorAndUpdateHiddenNeurons();

    assertTrue(instance.calculateHiddenNeuronError(n1) == n1.getDelta());
}
Example 5
Source File: NeurophXOR.java    From tutorials with MIT License 5 votes vote down vote up 
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;
    }