本文整理汇总了Java中org.neuroph.core.NeuralNetwork.load方法的典型用法代码示例。如果您正苦于以下问题:Java NeuralNetwork.load方法的具体用法?Java NeuralNetwork.load怎么用?Java NeuralNetwork.load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.load方法的12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: startCheck
import org.neuroph.core.NeuralNetwork; //导入方法依赖的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);
}
}
示例2: main
import org.neuroph.core.NeuralNetwork; //导入方法依赖的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);
}
}
示例3: doRun
import org.neuroph.core.NeuralNetwork; //导入方法依赖的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());
}
}
示例4: main
import org.neuroph.core.NeuralNetwork; //导入方法依赖的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);
}
示例5: doRun
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
@Override
public void doRun() {
try {
//Bypass network reload during comeback through home button
if (nnet == null) {
File NNetwork = new File(ConstantObjects.neuralNetworkPath);
System.out.println("Nueral network loaded = " + NNetwork.getAbsolutePath());
if (!NNetwork.exists()) {
notifyUser();
return;
}
nnet = NeuralNetwork.load(new FileInputStream(NNetwork)); // load trained neural network saved with Neuroph Studio
System.out.println("Learning Rule = " + nnet.getLearningRule());
imageRecognition = (ImageRecognitionPlugin) nnet.getPlugin(ImageRecognitionPlugin.class); // get the image recognition plugin from neural network
}
HashMap<String, Double> output = imageRecognition.recognizeImage(image);
if (output == null) {
System.err.println("Image Recognition Failed");
}
System.out.println(output.toString());
listener.neuralnetProcessCompleted(output);
} catch (Exception ex) {
Logger.getLogger(NeuralNetProcessor.class.getName()).log(Level.SEVERE, null, ex);
listener.neuralnetProcessCompleted(null);
}
}
示例6: run
import org.neuroph.core.NeuralNetwork; //导入方法依赖的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);
}
示例7: main
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Runs this sample
*/
public static void main(String[] args) throws FileNotFoundException, IOException {
// create neural network
//MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1);
// use file provided in org.neuroph.sample.data package
String inputFileName = FileIOSample.class.getResource("data/xor_data.txt").getFile();
// create file input adapter using specifed file
FileInputAdapter fileIn = new FileInputAdapter(inputFileName);
// create file output adapter using specified file name
FileOutputAdapter fileOut = new FileOutputAdapter("some_output_file.txt");
NeuralNetwork neuralNet = NeuralNetwork.load("myMlPerceptron.nnet");
double[] input; // input buffer used for reading network input from file
// read network input using input adapter
while( (input = fileIn.readInput()) != null) {
// feed neywork with input
neuralNet.setInput(input);
// calculate network ...
neuralNet.calculate();
// .. and get network output
double[] output = neuralNet.getOutput();
// write network output using output adapter
fileOut.writeOutput(output);
}
// close input and output files
fileIn.close();
fileOut.close();
// Also note that shorter way for this is using org.neuroph.util.io.IOHelper class
}
示例8: FIDNetworkAnalyser
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public FIDNetworkAnalyser(String nSourceFile) throws FileNotFoundException {
nnet = NeuralNetwork.load(new FileInputStream(nSourceFile)); // load trained neural network saved with Neuroph Studio
}
示例9: main
import org.neuroph.core.NeuralNetwork; //导入方法依赖的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();
}
}
示例10: predict
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static String predict(int[] dys,String HG) {
int[] TUNING=YSubCladeUtil.getTuning(HG);
int[] BITS=YSubCladeUtil.getBits(HG);
String[] OUTPUT=YSubCladeUtil.getOutput(HG);
int total_bits=0;
for(int i=0;i<BITS.length;i++)
total_bits+=BITS[i];
String[] binary_str=new String[12];
StringBuffer sb=new StringBuffer();
for(int i=0;i<12;i++)
{
binary_str[i]="00000"+Integer.toBinaryString(dys[i]-TUNING[i]);
binary_str[i]=binary_str[i].substring(binary_str[i].length()-BITS[i]);
sb.append(binary_str[i]);
}
String input=sb.toString();
double[] input_normalized=new double[total_bits];
for(int i=0;i<total_bits;i++)
{
if(input.charAt(i)=='0')
input_normalized[i]=0;
else if(input.charAt(i)=='1')
input_normalized[i]=1;
else
throw new RuntimeException("Error:Input data not normalized.");
}
NeuralNetwork neuralNetwork = NeuralNetwork.load(YHaploEntryDlg.class.getResourceAsStream("/fc/id/au/haplogroups/"+HG+".nnet"));
neuralNetwork.setInput(input_normalized);
neuralNetwork.calculate();
double[] networkOutput = neuralNetwork.getOutput();
sb.setLength(0);
for(int i=0;i<networkOutput.length;i++)
{
sb.append((Math.round(networkOutput[i])));
}
String output=sb.toString();
int value=Integer.parseInt(output, 2);
String haplogroup=OUTPUT[value];
return haplogroup;
}
示例11: predictHaplogroup
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
public static String predictHaplogroup(int[] dys) {
try
{
//int[] dys={13, 21, 15, 10, 13, 17, 11, 13, 12, 14, 11, 30};//felix
//int[] dys={13,22,15,10,12,17,11,13,12,12,11,29}; //E
/*
System.out.print("12 Marker STR Values:\t");
for(int i=0;i<dys.length;i++)
{
System.out.print(dys[i]+" ");
}
System.out.println();
*/
String[] binary_str=new String[12];
StringBuffer sb=new StringBuffer();
for(int i=0;i<12;i++)
{
binary_str[i]="00000"+Integer.toBinaryString(dys[i]-TUNING[i]);
binary_str[i]=binary_str[i].substring(binary_str[i].length()-BITS[i]);
sb.append(binary_str[i]);
}
String input=sb.toString();
//System.out.println("Normalized Input:\t"+input);
double[] input_normalized=new double[48];
for(int i=0;i<48;i++)
{
if(input.charAt(i)=='0')
input_normalized[i]=0;
else if(input.charAt(i)=='1')
input_normalized[i]=1;
else
throw new RuntimeException("Error:Input data not normalized.");
}
NeuralNetwork neuralNetwork = NeuralNetwork.load(YHaploEntryDlg.class.getResourceAsStream("/fc/id/au/HapNN72.nnet"));
neuralNetwork.setInput(input_normalized);
neuralNetwork.calculate();
double[] networkOutput = neuralNetwork.getOutput();
sb.setLength(0);
for(int i=0;i<networkOutput.length;i++)
{
sb.append((Math.round(networkOutput[i])));
}
String output=sb.toString();
//System.out.println("Normalized Output:\t"+output);
int value=Integer.parseInt(output, 2)+65;
String haplogroup=((char)value)+"";
//System.out.println("Predicted Haplogroup:\t"+haplogroup);
return haplogroup;
}
catch (Exception e)
{
JOptionPane.showMessageDialog(null, "Error:"+e.getMessage(),"Error",JOptionPane.ERROR_MESSAGE);
return "?";
}
}
示例12: loadNetwork
import org.neuroph.core.NeuralNetwork; //导入方法依赖的package包/类
/**
* Load network.
*
* @param name the name
*/
public void loadNetwork(String name){
animal_network=NeuralNetwork.load(name);
System.out.println("load is completed");
}