本文整理汇总了Java中org.encog.neural.networks.training.propagation.resilient.ResilientPropagation.finishTraining方法的典型用法代码示例。如果您正苦于以下问题：Java ResilientPropagation.finishTraining方法的具体用法？Java ResilientPropagation.finishTraining怎么用？Java ResilientPropagation.finishTraining使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.encog.neural.networks.training.propagation.resilient.ResilientPropagation的用法示例。
在下文中一共展示了ResilientPropagation.finishTraining方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: main
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //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();
}
示例2: main
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation; //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();
}