Java BasicNetwork.reset方法代码示例

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
private BasicNetwork createNeuralNetwork() {
  BasicNetwork network = new BasicNetwork();

  // input layer
  network.addLayer(new BasicLayer(null, true, inputLayerSize));

  // hidden layer
  network.addLayer(new BasicLayer(new ActivationSigmoid(), true, inputLayerSize / 6));
  network.addLayer(new BasicLayer(new ActivationSigmoid(), true, inputLayerSize / 6 / 4));

  // output layer
  network.addLayer(new BasicLayer(new ActivationSigmoid(), false, outputLayerSize));

  network.getStructure().finalizeStructure();
  network.reset();

  return network;
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
/**
 * The main method.
 * @param args No arguments are used.
 */
public static void main(final String args[]) {

    // create a neural network, without using a factory
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
    network.reset();

    // create training data
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

    // train the neural network
    final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

    int epoch = 1;

    do {
        train.iteration();
        System.out.println("Epoch #" + epoch + " Error:" + train.getError());
        epoch++;
    } while(train.getError() > 0.01);
    train.finishTraining();

    // test the neural network
    System.out.println("Neural Network Results:");
    for(MLDataPair pair: trainingSet ) {
        final MLData output = network.compute(pair.getInput());
        System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
                + ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
    }

    Encog.getInstance().shutdown();
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
public static NeuralAgent generateAgent(EnvironmentMap map)
{
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(NeuralConstants.INPUT_NEURON_COUNT));
    network.addLayer(new BasicLayer(60));
    network.addLayer(new BasicLayer(30));
    network.addLayer(new BasicLayer(NeuralConstants.OUTPUT_NEURON_COUNT));
    network.getStructure().finalizeStructure();
    network.reset();

    NeuralAgent agent = new NeuralAgent(network, map);
    return agent;
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
private BasicNetwork getNetwork() {
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(new ActivationSigmoid(), true, INPUTS.length));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 25));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), true, OUTPUTS.length));
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
/** Neural Network structure initialization */
public BackPropagationNeuralNet() {
  iterations = new ArrayList<>();
  errors = new ArrayList<>();
  
  network = new BasicNetwork();
  network.addLayer(new BasicLayer(null, true, 4));
  network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 10));
  network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
  network.getStructure().finalizeStructure();
  network.reset();
  new ConsistentRandomizer(-1, 1, 500).randomize(network);
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
/** Neural Network structure initialization */
public ResilientPropagationNeuralNet() {
  iterations = new ArrayList<>();
  errors = new ArrayList<>();
  network = new BasicNetwork();
  network.addLayer(new BasicLayer(null, true, 4));
  network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 10));
  network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
  network.getStructure().finalizeStructure();
  network.reset();
  new ConsistentRandomizer(-1, 1, 500).randomize(network);
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
/**
 * Generate basic NN network object
 */
public static BasicNetwork generateNetwork(int in, int hidden, int out) {
    final BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(new ActivationLinear(), true, in));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), true, hidden));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false, out));

    network.getStructure().finalizeStructure();
    network.reset();
    return network;
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
/**
 * The main method.
 * @param args No arguments are used.
 */
public static void main(final String args[]) {

	// create a neural network, without using a factory
	BasicNetwork network = new BasicNetwork();
	network.addLayer(new BasicLayer(null,true,2));
	network.addLayer(new BasicLayer(new ActivationSigmoid(),true,3));
	network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
	network.getStructure().finalizeStructure();
	network.reset();

	// create training data
	MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);

	// train the neural network
	final ResilientPropagation train = new ResilientPropagation(network, trainingSet);

	int epoch = 1;

	do {
		train.iteration();
		System.out.println("Epoch #" + epoch + " Error:" + train.getError());
		epoch++;
	} while(train.getError() > 0.01);
	train.finishTraining();

	// test the neural network
	System.out.println("Neural Network Results:");
	for(MLDataPair pair: trainingSet ) {
		final MLData output = network.compute(pair.getInput());
		System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
				+ ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
	}

	Encog.getInstance().shutdown();
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
private BasicNetwork buildNetwork(NeuralDataSet trainingSet, int hidden, int size, Class<?> activation) throws InstantiationException, IllegalAccessException {
	BasicNetwork network = new BasicNetwork();
	this.currentNoOfHiddenNode = hidden;
	network.addLayer(new BasicLayer((ActivationFunction)activation.newInstance(), true, size));
	
	network.addLayer(new BasicLayer((ActivationFunction)activation.newInstance(), true, hidden));
	
	network.addLayer(new BasicLayer((ActivationFunction)activation.newInstance(), true, 1));
	network.getStructure().finalizeStructure();
	network.reset();
	

	return network;
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
private BasicNetwork getNetwork(ExampleSet exampleSet) throws OperatorException {
    BasicNetwork network = new BasicNetwork();

    // input layer
    network.addLayer(new FeedforwardLayer(exampleSet.getAttributes().size()));


    // hidden layers
    log("No hidden layers defined. Using default hidden layers.");
    int layerSize = getParameterAsInt(PARAMETER_DEFAULT_HIDDEN_LAYER_SIZE);
    if (layerSize <= 0)
        layerSize = getDefaultLayerSize(exampleSet);
    for (int p = 0; p < getParameterAsInt(PARAMETER_DEFAULT_NUMBER_OF_HIDDEN_LAYERS); p++) {
        network.addLayer(new FeedforwardLayer(layerSize));
    }


    // output layer
    if (exampleSet.getAttributes().getLabel().isNominal()) {
        network.addLayer(new FeedforwardLayer(new ActivationSigmoid(), 1));
    } else {
        network.addLayer(new FeedforwardLayer(new ActivationLinear(), 1));
    }

    network.reset(RandomGenerator.getRandomGenerator(getParameterAsBoolean(RandomGenerator.PARAMETER_USE_LOCAL_RANDOM_SEED), getParameterAsInt(RandomGenerator.PARAMETER_LOCAL_RANDOM_SEED)));

    return network;
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
public static void test(double[][] inputValues, double[][] outputValues)
{
    NeuralDataSet trainingSet = new BasicNeuralDataSet(inputValues, outputValues);
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 4));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false, 1000));
    network.addLayer(new BasicLayer(new ActivationLinear(), false, 1));
    network.getStructure().finalizeStructure();
    network.reset();
    final Train train = new ResilientPropagation(network, trainingSet);
    int epoch = 1;
    do
    {
        train.iteration();
        System.out.println("Epoch #" + epoch + " Error:" + train.getError());
        epoch++;
    }
    while(epoch < 10000);
    System.out.println("Neural Network Results:");
    for(MLDataPair pair : trainingSet)
    {
        final MLData output = network.compute(pair.getInput());
        System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1) + ", actual="
            + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));

    }
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
public void addLayer(ActivationFunction func, int nodes){
    
    if(params.size() > 0) {
        buildNetwork();
    
        transformData(); 
    } else {
        intermediateDataset = this.dataset;
    }
    
    network = new BasicNetwork();
    
    network.addLayer(new BasicLayer(new ActivationLinear(), true, intermediateDataset.getInputSize()));
    
    network.addLayer(new BasicLayer(func, true, nodes));
    
    network.addLayer(new BasicLayer(new ActivationTANH(), false, intermediateDataset.getIdealSize()));
    
    network.getStructure().finalizeStructure();
    
    network.reset();
    
    train(nodes);
}
 

import org.encog.neural.networks.BasicNetwork; //导入方法依赖的package包/类
/**
 * Create new artificial neural network.
 * 
 * @param inputSize
 *            Size of the input layer.
 * @param hiddenSize
 *            Size of the hidden layer.
 * @param outputSize
 *            Size of the output layer.
 * 
 * @return Neural network created object.
 */
public static BasicNetwork newNetwork(int inputSize, int hiddenSize, int outputSize) {
	BasicNetwork net = new BasicNetwork();

	net.addLayer(new BasicLayer(null, true, inputSize));
	net.addLayer(new BasicLayer(new ActivationSigmoid(), true, hiddenSize));
	net.addLayer(new BasicLayer(new ActivationSigmoid(), false, outputSize));
	net.getStructure().finalizeStructure();
	net.reset();

	return net;
}