Java BackPropagation类代码示例

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
/**
 * Creates and returns a new instance of Multi Layer Perceptron
 * @param layersStr space separated number of neurons in layers
 * @param transferFunctionType transfer function type for neurons
 * @return instance of Multi Layer Perceptron
 */
public static MultiLayerPerceptron createMLPerceptron(String layersStr, TransferFunctionType transferFunctionType, Class learningRule,  boolean useBias, boolean connectIO) {
	ArrayList<Integer> layerSizes = VectorParser.parseInteger(layersStr);
               NeuronProperties neuronProperties = new NeuronProperties(transferFunctionType, useBias);
	MultiLayerPerceptron nnet = new MultiLayerPerceptron(layerSizes, neuronProperties);
               
               // set learning rule - TODO: use reflection here
               if (learningRule.getName().equals(BackPropagation.class.getName()))  {
                   nnet.setLearningRule(new BackPropagation());
               } else if (learningRule.getName().equals(MomentumBackpropagation.class.getName())) {
                   nnet.setLearningRule(new MomentumBackpropagation());
               } else if (learningRule.getName().equals(DynamicBackPropagation.class.getName())) {
                   nnet.setLearningRule(new DynamicBackPropagation());
               } else if (learningRule.getName().equals(ResilientPropagation.class.getName())) {
                   nnet.setLearningRule(new ResilientPropagation());
               } 

               // connect io
               if (connectIO) {
                   nnet.connectInputsToOutputs();
               }

	return nnet;
}
 

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
public static void main(String[] args) {

// create training set (logical XOR function)
        DataSet trainingSet = new DataSet(2, 1);
        trainingSet.addRow(new DataSetRow(new double[]{0, 0}, new double[]{0}));
        trainingSet.addRow(new DataSetRow(new double[]{0, 1}, new double[]{1}));
        trainingSet.addRow(new DataSetRow(new double[]{1, 0}, new double[]{1}));
        trainingSet.addRow(new DataSetRow(new double[]{1, 1}, new double[]{0}));


// create multi layer perceptron
        MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, 2, 3, 1);
        myMlPerceptron.setLearningRule(new BackPropagation());
// learn the training set
        myMlPerceptron.learn(trainingSet);
// test perceptron
        System.out.println("Testing trained neural network");
        testNeuralNetwork(myMlPerceptron, trainingSet);

// save trained neural network
        myMlPerceptron.save("myMlPerceptron.nnet");

// load saved neural network
        NeuralNetwork loadedMlPerceptron = NeuralNetwork.createFromFile("myMlPerceptron.nnet");

// test loaded neural network
        System.out.println("Testing loaded neural network");
        testNeuralNetwork(loadedMlPerceptron, trainingSet);

    }
 

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
/**
 * Instantiates a new animal network.
 *
 * @param input the input
 * @param hidden the hidden
 * @param output the output
 */
public AnimalNetwork(int input,int hidden,int output) {
	super();
	System.out.println("network is created");
	initializeNeurons();
	animal_network = new MultiLayerPerceptron(TransferFunctionType.SIGMOID,Data.INPUTUNITS,Data.HIDDENUNITS,Data.OUTPUTUNITS);
	animal_network.setNetworkType(NeuralNetworkType.MULTI_LAYER_PERCEPTRON);
	animal_network.randomizeWeights();  //randomize weights 
	((LMS) animal_network.getLearningRule()).setMaxError(MAXERROR);//0-1
	((LMS) animal_network.getLearningRule()).setLearningRate(LEARNINGRATE);//0-1
	((LMS) animal_network.getLearningRule()).setMaxIterations(MAXITERATIONS);//0-1
	animal_network.setLearningRule(new BackPropagation());
}
 

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
@Override
public void handleLearningEvent(LearningEvent event) {
    BackPropagation bp = (BackPropagation) event.getSource();
    System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
    listener.batchImageTrainingUpdate(bp.getCurrentIteration(), bp.getTotalNetworkError());
}
 

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
@Override
public void handleLearningEvent(LearningEvent event) {
    BackPropagation bp = (BackPropagation) event.getSource();
    System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
}
 

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
@Override
public void handleLearningEvent(LearningEvent event) {
    BackPropagation bp = (BackPropagation)event.getSource();
    System.out.println(bp.getCurrentIteration() + ". iteration : "+ bp.getTotalNetworkError());
}
 

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
@Override
public void handleLearningEvent(LearningEvent event) {
	BackPropagation bp = (BackPropagation) event.getSource();
	System.out.println(bp.getCurrentIteration() + ". iteration : " + bp.getTotalNetworkError());
}
 

import org.neuroph.nnet.learning.BackPropagation; //导入依赖的package包/类
@Override
public void handleLearningEvent(LearningEvent event) {
	BackPropagation bp = (BackPropagation) event.getSource();
	System.out.println(bp.getCurrentIteration() + ". iteration : "
			+ bp.getTotalNetworkError());
}