本文整理汇总了Java中org.neuroph.nnet.MultiLayerPerceptron类的典型用法代码示例。如果您正苦于以下问题：Java MultiLayerPerceptron类的具体用法？Java MultiLayerPerceptron怎么用？Java MultiLayerPerceptron使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
MultiLayerPerceptron类属于org.neuroph.nnet包，在下文中一共展示了MultiLayerPerceptron类的15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，您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: main
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
/**
* Runs this sample
*/
public static void main(String[] args) {
// get the path to file with data
String inputFileName = IrisClassificationSample.class.getResource("data/iris_data_normalised.txt").getFile();
// create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(4, 16, 3);
// create training set from file
DataSet irisDataSet = DataSet.createFromFile(inputFileName, 4, 3, ",");
// train the network with training set
neuralNet.learn(irisDataSet);
System.out.println("Done training.");
System.out.println("Testing network...");
testNeuralNetwork(neuralNet, irisDataSet);
}
开发者ID:East196，项目名称:maker，代码行数:20，代码来源:IrisClassificationSample.java
示例2: createMLPerceptron
import org.neuroph.nnet.MultiLayerPerceptron; //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;
}
开发者ID:fiidau，项目名称:Y-Haplogroup-Predictor，代码行数:30，代码来源:NeuralNetworkFactory.java
示例3: AnimalNetwork
import org.neuroph.nnet.MultiLayerPerceptron; //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();
initializeQuestions();
animal_network = new MultiLayerPerceptron(TransferFunctionType.SIGMOID,Data.INPUTUNITS,Data.HIDDENUNITS,Data.OUTPUTUNITS);
animal_network.setNetworkType(NeuralNetworkType.MULTI_LAYER_PERCEPTRON);
animal_network.randomizeWeights(); //randomize weights
// set parameters
((LMS) animal_network.getLearningRule()).setMaxError(MAXERROR);//0-1
((LMS) animal_network.getLearningRule()).setLearningRate(LEARNINGRATE);//0-1
((LMS) animal_network.getLearningRule()).setMaxIterations(MAXITERATIONS);//0-1
MomentumBackpropagation backpropogation = new MomentumBackpropagation(); // define momentum
backpropogation.setMomentum(0.7); // set momentum
animal_network.setLearningRule(backpropogation);
}
开发者ID:eldemcan，项目名称:20q，代码行数:24，代码来源:AnimalNetwork.java
示例4: startLearning
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
public void startLearning() {
Thread t1 = new Thread(new Runnable() {
public void run() {
console.addLog("Loading test set");
testSet = loader.loadDataSet(testSetPath);
console.addLog("Test set loaded");
console.addLog("Loading training set");
trainingSet = loader.loadDataSet(trainingSetPath);
console.addLog("Training set loaded. Input size: " + trainingSet.getInputSize() +
" Output size: " + trainingSet.getOutputSize());
nnet = new MultiLayerPerceptron(TransferFunctionType.SIGMOID,
trainingSet.getInputSize(), 86, 86, trainingSet.getOutputSize());
MomentumBackpropagation backPropagation = new MomentumBackpropagation();
backPropagation.setLearningRate(learningRate);
backPropagation.setMomentum(momentum);
LearningTestSetEvaluator evaluator =
new LearningTestSetEvaluator(nnetName, testSet, trainingSet, console);
backPropagation.addListener(evaluator);
backPropagation.addListener(new LearningEventListener() {
@Override
public void handleLearningEvent(LearningEvent event) {
if (event.getEventType() == LearningEvent.Type.LEARNING_STOPPED) {
listeners.forEach((listener) -> listener.learningStopped(LearningNetTask.this));
}
}
});
nnet.setLearningRule(backPropagation);
console.addLog("Started neural net learning with momentum: "
+ momentum + ", learning rate: " + learningRate);
nnet.learnInNewThread(trainingSet);
}
});
t1.start();
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:39，代码来源:LearningNetTask.java
示例5: learnNeuralNet
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
private static void learnNeuralNet(DataSet trainingSet, DataSet testSet) {
TestSetEvaluator testEvaluator = new TestSetEvaluator(NNET_NAME, testSet, trainingSet);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, INPUT_LAYER, 86, 86, OUTPUT_LAYER);
MomentumBackpropagation bp = new MomentumBackpropagation();
bp.setLearningRate(LEARINING_RATE);
bp.setMomentum(MOMENTUM);
bp.addListener(testEvaluator);
nnet.setLearningRule(bp);
nnet.learn(trainingSet);
nnet.save(NNET_NAME + "last");
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:14，代码来源:OneToOneHVTest.java
示例6: learnNeuralNet
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
private static void learnNeuralNet(DataSet trainingSet, DataSet testSet) {
TestSetEvaluator testEvaluator = new TestSetEvaluator(NNET_NAME, testSet, trainingSet);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, INPUT_LAYER, 140, OUTPUT_LAYER);
MomentumBackpropagation bp = new MomentumBackpropagation();
bp.setLearningRate(LEARINING_RATE);
bp.setMomentum(MOMENTUM);
bp.addListener(testEvaluator);
nnet.setLearningRule(bp);
nnet.learn(trainingSet);
nnet.save(NNET_NAME + "last");
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:14，代码来源:OneToOneNonUniqueDiagonalTest.java
示例7: learnNeuralNet
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
private static void learnNeuralNet(DataSet trainingSet, DataSet testSet) {
TestSetEvaluator testEvaluator = new TestSetEvaluator(NNET_NAME, testSet, trainingSet);
MultiLayerPerceptron nnet = new MultiLayerPerceptron(TransferFunctionType.SIGMOID, INPUT_LAYER, 76, 76, OUTPUT_LAYER);
MomentumBackpropagation bp = new MomentumBackpropagation();
bp.setLearningRate(LEARINING_RATE);
bp.setMomentum(MOMENTUM);
bp.addListener(testEvaluator);
nnet.setLearningRule(bp);
nnet.learn(trainingSet);
nnet.save(NNET_NAME + "last");
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:14，代码来源:OneToOneDiagonalTest.java
示例8: main
import org.neuroph.nnet.MultiLayerPerceptron; //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);
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:31，代码来源:TestLearn.java
示例9: main
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
/**
* Runs this sample
*/
public static void main(String[] args) throws FileNotFoundException, IOException, ClassNotFoundException, SQLException {
// create neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1);
// Load the database driver
Class.forName("sun.jdbc.odbc.JdbcOdbcDriver");
// Get a connection to the database
String dbName = "neuroph";
String dbUser = "root";
String dbPass = "";
// create a connection to database
Connection connection = DriverManager.getConnection("jdbc:mysql://localhost:3306/" + dbName, dbUser, dbPass);
// ise this sql to get input from database table
String inputSql = "SELECT * FROM input_data";
// create dinput adapter using specidfied database connection and sql query
JDBCInputAdapter in = new JDBCInputAdapter(connection, inputSql);
String outputTable = "output_data"; // write output to this table
// create output adapter using specified connection and output table
JDBCOutputAdapter out = new JDBCOutputAdapter(connection, outputTable);
double[] input;
// read input using input adapter
while ((input = in.readInput()) != null) {
neuralNet.setInput(input);
neuralNet.calculate();
double[] output = neuralNet.getOutput();
// and write output using output aadapter
out.writeOutput(output);
}
in.close();
out.close();
connection.close();
}
开发者ID:East196，项目名称:maker，代码行数:41，代码来源:JDBCSample.java
示例10: run
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
/**
* Runs this sample
*/
public void run() {
// 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.TANH, 2, 3, 1);
// enable batch if using MomentumBackpropagation
if( myMlPerceptron.getLearningRule() instanceof MomentumBackpropagation ){
((MomentumBackpropagation)myMlPerceptron.getLearningRule()).setBatchMode(true);
((MomentumBackpropagation)myMlPerceptron.getLearningRule()).setMaxError(0.00001);
}
LearningRule learningRule = myMlPerceptron.getLearningRule();
learningRule.addListener(this);
// learn the training set
System.out.println("Training neural network...");
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.load("myMlPerceptron.nnet");
// test loaded neural network
System.out.println("Testing loaded neural network");
testNeuralNetwork(loadedMlPerceptron, trainingSet);
}
开发者ID:East196，项目名称:maker，代码行数:42，代码来源:XorMultiLayerPerceptronSample.java
示例11: main
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
/**
* Runs this sample
*/
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);
// set ResilientPropagation learning rule
myMlPerceptron.setLearningRule(new ResilientPropagation());
// learn the training set
System.out.println("Training neural network...");
myMlPerceptron.learn(trainingSet);
int iterations = ((SupervisedLearning)myMlPerceptron.getLearningRule()).getCurrentIteration();
System.out.println("Learned in "+iterations+" iterations");
// test perceptron
System.out.println("Testing trained neural network");
testNeuralNetwork(myMlPerceptron, trainingSet);
}
开发者ID:East196，项目名称:maker，代码行数:30，代码来源:XorResilientPropagationSample.java
示例12: main
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
/**
* Runs this sample
*/
public static void main(String[] args) {
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1);
// neuralNet.randomizeWeights(new WeightsRandomizer());
// neuralNet.randomizeWeights(new RangeRandomizer(0.1, 0.9));
// neuralNet.randomizeWeights(new GaussianRandomizer(0.4, 0.3));
neuralNet.randomizeWeights(new NguyenWidrowRandomizer(0.3, 0.7));
printWeights(neuralNet);
neuralNet.randomizeWeights(new DistortRandomizer(0.5));
printWeights(neuralNet);
}
开发者ID:East196，项目名称:maker，代码行数:16，代码来源:RandomizationSample.java
示例13: prepareTest
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
/**
* Benchmrk preparation consists of training set and neural networ creatiion.
* This method generates training set with 100 rows, where every row has 10 input and 5 output elements
* Neural network has two hiddden layers with 8 and 7 neurons, and runs learning rule for 2000 iterations
*/
@Override
public void prepareTest() {
int trainingSetSize = 100;
int inputSize = 10;
int outputSize = 5;
this.trainingSet = new DataSet(inputSize, outputSize);
for (int i = 0; i < trainingSetSize; i++) {
double input[] = new double[inputSize];
for( int j=0; j<inputSize; j++)
input[j] = Math.random();
double output[] = new double[outputSize];
for( int j=0; j<outputSize; j++)
output[j] = Math.random();
DataSetRow trainingSetRow = new DataSetRow(input, output);
trainingSet.addRow(trainingSetRow);
}
network = new MultiLayerPerceptron(inputSize, 8, 7, outputSize);
((MomentumBackpropagation)network.getLearningRule()).setMaxIterations(2000);
}
开发者ID:fiidau，项目名称:Y-Haplogroup-Predictor，代码行数:32，代码来源:MyBenchmarkTask.java
示例14: AnimalNetwork
import org.neuroph.nnet.MultiLayerPerceptron; //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());
}
开发者ID:eldemcan，项目名称:20q，代码行数:20，代码来源:AnimalNetwork.java
示例15: main
import org.neuroph.nnet.MultiLayerPerceptron; //导入依赖的package包/类
public static void main(String[] args) {
// create training set (extending XOR sample)
DataSet trainingSet = new DataSet(2, 1);
trainingSet.addRow(new DataSetRow(new double[]{0, 0}, new double[]{1}));
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}));
trainingSet.addRow(new DataSetRow(new double[]{2, 2}, new double[]{-1}));
trainingSet.addRow(new DataSetRow(new double[]{1, 2}, new double[]{-1}));
trainingSet.addRow(new DataSetRow(new double[]{1, 3}, new double[]{-1}));
trainingSet.addRow(new DataSetRow(new double[]{2, 2}, new double[]{-1}));
trainingSet.addRow(new DataSetRow(new double[]{2, 42}, new double[]{-1}));
// create multi layer perceptron
MultiLayerPerceptron myMlPerceptron = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 9, 1);
// learn the training set
long start = System.currentTimeMillis();
myMlPerceptron.learn(trainingSet);
long time = System.currentTimeMillis()-start;
System.out.println("It took: "+time+" ms");
// test perceptron
System.out.println("Testing trained neural network");
testNeuralNetwork(myMlPerceptron, trainingSet);
// save trained neural network
myMlPerceptron.save("myMlPerceptron.nnet");
// load saved neural network
FileInputStream stream;
try {
stream = new FileInputStream("myMlPerceptron.nnet");
NeuralNetwork loadedMlPerceptron = NeuralNetwork.load(stream);
// test loaded neural network
System.out.println("Testing loaded neural network");
testNeuralNetwork(loadedMlPerceptron, trainingSet);
System.out.println("Testing unknown input");
testNeuralNetwork(loadedMlPerceptron, new DataSetRow(new double[]{2, 30}));
} catch (FileNotFoundException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
开发者ID:yuripourre，项目名称:neuro-project，代码行数:57，代码来源:MultiLayerPerceptronSample.java
注：本文中的org.neuroph.nnet.MultiLayerPerceptron类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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