本文整理汇总了Java中org.neuroph.core.NeuralNetwork类的典型用法代码示例。如果您正苦于以下问题：Java NeuralNetwork类的具体用法？Java NeuralNetwork怎么用？Java NeuralNetwork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。
NeuralNetwork类属于org.neuroph.core包，在下文中一共展示了NeuralNetwork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: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:26，代码来源:OneToOneHVTest.java
示例2: testLearnedNeuralNet
import org.neuroph.core.NeuralNetwork; //导入依赖的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());
}
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:10，代码来源:OneToOneHVTest.java
示例3: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:27，代码来源:EnglishOneToOneHorizontalTest_SmallTest.java
示例4: startCheck
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
@FXML
private void startCheck(ActionEvent event) throws IOException {
if (nnetSrc == null || imgSrc == null) {
Calert.showAlert("Invalid Data", "Select Required Files", Alert.AlertType.ERROR);
return;
}
try {
nnet = NeuralNetwork.load(new FileInputStream(nnetSrc)); // load trained neural network saved with Neuroph Studio
System.out.println("Learning Rule = " + nnet.getLearningRule());
ImageRecognitionPlugin imageRecognition = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class); // get the
HashMap<String, Double> output = imageRecognition.recognizeImage(ImageIO.read(imgSrc));
if (output == null) {
System.err.println("Image Recognition Failed");
}
double real = output.get("real");
double fake = output.get("faked");
System.out.println(output.toString());
Calert.showAlert("Result", "Real = " + real + "\nFake = " + fake, Alert.AlertType.INFORMATION);
} catch (FileNotFoundException ex) {
Logger.getLogger(SingleImageAnalyzerController.class.getName()).log(Level.SEVERE, null, ex);
}
}
开发者ID:afsalashyana，项目名称:FakeImageDetection，代码行数:23，代码来源:SingleImageAnalyzerController.java
示例5: main
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
public static void main(String[] args) {
try {
System.out.println("usage java -jar nn.jar image_to_be_processed file_of_neural_network");
System.out.println("Loading Image....");
image = ImageIO.read(new File(args[0]));
System.out.println("Loading NN....");
File NNetwork = new File(args[1]);
if (!NNetwork.exists()) {
System.err.println("Cant Find NN");
return;
}
nnet = NeuralNetwork.load(new FileInputStream(NNetwork)); // load trained neural network saved with Neuroph Studio
System.out.println("Load Image Recog Plugin....");
imageRecognition = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class); // get the image recognition plugin from neural network
System.out.println("Recognize Image....");
HashMap<String, Double> output = imageRecognition.recognizeImage(image);
System.out.println("Output is....");
System.out.println(output.toString());
} catch (IOException ex) {
Logger.getLogger(NeuralNetProcessor.class.getName()).log(Level.SEVERE, null, ex);
}
}
开发者ID:afsalashyana，项目名称:FakeImageDetection，代码行数:23，代码来源:NeuralNetProcessor.java
示例6: doRun
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
@Override
public void doRun() {
try {
System.out.println("Starting training thread....." + sampleDimension.toString() + " and " + imageLabels.toString());
HashMap<String, BufferedImage> imagesMap = new HashMap<String, BufferedImage>();
for (File file : srcDirectory.listFiles()) {
imageLabels.add(FilenameUtils.removeExtension(file.getName()));
if (sampleDimension.getWidth() > 0 && sampleDimension.getHeight() > 0) {
Double w = sampleDimension.getWidth();
Double h = sampleDimension.getHeight();
imagesMap.put(file.getName(), ImageUtilities.resizeImage(ImageUtilities.loadImage(file), w.intValue(), h.intValue()));
}
}
Map<String, FractionRgbData> imageRgbData = ImageUtilities.getFractionRgbDataForImages(imagesMap);
DataSet learningData = ImageRecognitionHelper.createRGBTrainingSet(imageLabels, imageRgbData);
nnet = NeuralNetwork.load(new FileInputStream(nnFile)); //Load NNetwork
MomentumBackpropagation mBackpropagation = (MomentumBackpropagation) nnet.getLearningRule();
mBackpropagation.setLearningRate(learningRate);
mBackpropagation.setMaxError(maxError);
mBackpropagation.setMomentum(momentum);
System.out.println("Network Information\nLabel = " + nnet.getLabel()
+ "\n Input Neurons = " + nnet.getInputsCount()
+ "\n Number of layers = " + nnet.getLayersCount()
);
mBackpropagation.addListener(this);
System.out.println("Starting training......");
nnet.learn(learningData, mBackpropagation);
//Training Completed
listener.batchImageTrainingCompleted();
} catch (FileNotFoundException ex) {
System.out.println(ex.getMessage() + "\n" + ex.getLocalizedMessage());
}
}
开发者ID:afsalashyana，项目名称:FakeImageDetection，代码行数:39，代码来源:BatchImageTrainer.java
示例7: main
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
/**
* Runs this sample
*/
public static void main(String args[]) {
// create training set (logical AND 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[]{0}));
trainingSet.addRow(new DataSetRow(new double[]{1, 0}, new double[]{0}));
trainingSet.addRow(new DataSetRow(new double[]{1, 1}, new double[]{1}));
// create perceptron neural network
NeuralNetwork myPerceptron = new Perceptron(2, 1);
// learn the training set
myPerceptron.learn(trainingSet);
// test perceptron
System.out.println("Testing trained perceptron");
testNeuralNetwork(myPerceptron, trainingSet);
// save trained perceptron
myPerceptron.save("mySamplePerceptron.nnet");
// load saved neural network
NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");
// test loaded neural network
System.out.println("Testing loaded perceptron");
testNeuralNetwork(loadedPerceptron, trainingSet);
}
开发者ID:East196，项目名称:maker，代码行数:29，代码来源:PerceptronSample.java
示例8: SimulatedAnnealingLearning
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
/**
* Construct a simulated annleaing trainer for a feedforward neural network.
*
* @param network
* The neural network to be trained.
* @param startTemp
* The starting temperature.
* @param stopTemp
* The ending temperature.
* @param cycles
* The number of cycles in a training iteration.
*/
public SimulatedAnnealingLearning(final NeuralNetwork network,
final double startTemp, final double stopTemp, final int cycles) {
this.network = network;
this.temperature = startTemp;
this.startTemperature = startTemp;
this.stopTemperature = stopTemp;
this.cycles = cycles;
this.weights = new double[NeuralNetworkCODEC
.determineArraySize(network)];
this.bestWeights = new double[NeuralNetworkCODEC
.determineArraySize(network)];
NeuralNetworkCODEC.network2array(network, this.weights);
NeuralNetworkCODEC.network2array(network, this.bestWeights);
}
开发者ID:fiidau，项目名称:Y-Haplogroup-Predictor，代码行数:29，代码来源:SimulatedAnnealingLearning.java
示例9: setDefaultIO
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
/**
* Sets default input and output neurons for network (first layer as input,
* last as output)
*/
public static void setDefaultIO(NeuralNetwork nnet) {
ArrayList<Neuron> inputNeuronsList = new ArrayList<Neuron>();
Layer firstLayer = nnet.getLayerAt(0);
for (Neuron neuron : firstLayer.getNeurons() ) {
if (!(neuron instanceof BiasNeuron)) { // dont set input to bias neurons
inputNeuronsList.add(neuron);
}
}
Neuron[] inputNeurons = new Neuron[inputNeuronsList.size()];
inputNeurons = inputNeuronsList.toArray(inputNeurons);
Neuron[] outputNeurons = ((Layer) nnet.getLayerAt(nnet.getLayersCount()-1)).getNeurons();
nnet.setInputNeurons(inputNeurons);
nnet.setOutputNeurons(outputNeurons);
}
开发者ID:fiidau，项目名称:Y-Haplogroup-Predictor，代码行数:21，代码来源:NeuralNetworkFactory.java
示例10: testLearnedNeuralNet
import org.neuroph.core.NeuralNetwork; //导入依赖的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());
}
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:10，代码来源:OneToOneNonUniqueDiagonalTest.java
示例11: main
import org.neuroph.core.NeuralNetwork; //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12: testNeuralNetwork
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
public static void testNeuralNetwork(NeuralNetwork nnet, DataSet testSet) {
for(DataSetRow dataRow : testSet.getRows()) {
nnet.setInput(dataRow.getInput());
nnet.calculate();
double[ ] networkOutput = nnet.getOutput();
System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );
System.out.println(" Output: " + Arrays.toString(networkOutput) );
}
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:12，代码来源:TestLearn.java
示例13: testLearnedNeuralNet
import org.neuroph.core.NeuralNetwork; //导入依赖的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());
// }
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:10，代码来源:EnglishOneToOneHorizontalTest_SmallTest.java
示例14: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
// System.out.println(Arrays.toString(networkOutput));
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:29，代码来源:EnglishOneToOneDiagonalCrossTest_SmallTest.java
示例15: testNeuralNet
import org.neuroph.core.NeuralNetwork; //导入依赖的package包/类
private static void testNeuralNet(NeuralNetwork nnet, DataSet dataSet, String setName) {
int counter = 0;
for (DataSetRow row : dataSet.getRows()) {
nnet.setInput(row.getInput());
nnet.calculate();
double[] networkOutput = nnet.getOutput();
//System.out.println(Arrays.toString(networkOutput));
if (isOutputSame(networkOutput, row.getDesiredOutput())) {
counter++;
} else {
for (int i = 0; i < row.getDesiredOutput().length; i++) {
if (row.getDesiredOutput()[i] == 1) {
Integer d = errorMap.get(i);
if (d == null) {
errorMap.put(i, 1);
} else {
errorMap.put(i, ++d);
}
break;
}
}
}
}
System.out.println(setName + " success rate: " + (counter / (double) dataSet.size()));
}
开发者ID:fgulan，项目名称:final-thesis，代码行数:28，代码来源:OneToOneHorizontalTest_SmallTest.java
注：本文中的org.neuroph.core.NeuralNetwork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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