本文整理汇总了Java中org.neuroph.core.NeuralNetwork.createFromFile方法的典型用法代码示例。如果您正苦于以下问题：Java NeuralNetwork.createFromFile方法的具体用法？Java NeuralNetwork.createFromFile怎么用？Java NeuralNetwork.createFromFile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.neuroph.core.NeuralNetwork的用法示例。
在下文中一共展示了NeuralNetwork.createFromFile方法的4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: testLearnedNeuralNet
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
private static void testLearnedNeuralNet(DataSet trainingSet, DataSet testSet) {
NeuralNetwork nnet = NeuralNetwork.createFromFile(NNET_NAME);
System.out.println("Testing loaded neural network");
//testNeuralNet(nnet, trainingSet, "Training set");
testNeuralNet(nnet, testSet, "Test set");
for(Map.Entry<Integer, Integer> entry : errorMap.entrySet()) {
//System.out.println(entry.getKey() + " " + entry.getValue());
}
}
示例2: testLearnedNeuralNet
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
private static void testLearnedNeuralNet(DataSet trainingSet, DataSet testSet) {
NeuralNetwork nnet = NeuralNetwork.createFromFile(NNET_NAME);
System.out.println("Testing loaded neural network");
//testNeuralNet(nnet, trainingSet, "Training set");
testNeuralNet(nnet, testSet, "Test set");
for(Map.Entry<Integer, Integer> entry : errorMap.entrySet()) {
System.out.println(entry.getKey() + " " + entry.getValue());
}
}
示例3: testLearnedNeuralNet
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
private static void testLearnedNeuralNet(DataSet trainingSet, DataSet testSet) {
NeuralNetwork nnet = NeuralNetwork.createFromFile(NNET_NAME);
System.out.println("Testing loaded neural network");
//testNeuralNet(nnet, trainingSet, "Training set");
testNeuralNet(nnet, testSet, "Test set");
// for(Map.Entry<Integer, Integer> entry : errorMap.entrySet()) {
// System.out.println(entry.getKey() + " " + entry.getValue());
// }
}
示例4: main
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static void main(String[] args) {
// create new perceptron network
NeuralNetwork neuralNetwork = new Perceptron(3, 1);
// create training set
DataSet trainingSet = new DataSet(3, 1);
// add training data to training set (logical OR function)
trainingSet.addRow(new DataSetRow(new double[] { 0, 0 ,0}, new double[] { 0 }));
trainingSet.addRow(new DataSetRow(new double[] { 0, 1 ,0}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 0 ,0}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 1 ,0}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 0, 0 ,1}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 0, 1 ,1}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 0 ,1}, new double[] { 1 }));
trainingSet.addRow(new DataSetRow(new double[] { 1, 1 ,1}, new double[] { 1 }));
// learn the training set
neuralNetwork.learn(trainingSet);
// save the trained network into file
neuralNetwork.save("or_perceptron.nnet");
// load the saved network
NeuralNetwork neuralNetwork1 = NeuralNetwork.createFromFile("or_perceptron.nnet");
// set network input
neuralNetwork1.setInput(1, 1,1);
// calculate network
neuralNetwork1.calculate();
// get network output
double[] networkOutput = neuralNetwork1.getOutput();
System.out.println(Arrays.toString(networkOutput));
}