java神经元网络框架（JOONE）

package com.cn.xn; import java.io.File; import java.io.FileOutputStream; import java.io.ObjectOutputStream; import java.io.Serializable; import org.joone.engine.FullSynapse; import org.joone.engine.Layer; import org.joone.engine.LinearLayer; import org.joone.engine.Monitor; import org.joone.engine.NeuralNetEvent; import org.joone.engine.NeuralNetListener; import org.joone.engine.SigmoidLayer; import org.joone.engine.learning.TeachingSynapse; import org.joone.io.FileInputSynapse; import org.joone.io.FileOutputSynapse; import org.joone.net.NeuralNet; import org.joone.net.NeuralNetLoader; public class AAA implements NeuralNetListener,Serializable { private static final long serialVersionUID = -3597853311948127352L; private FileInputSynapse inputStream = null; private NeuralNet nnet = null; public static void main(String args[]) { AAA xor = new AAA(); xor.initNeuralNet(); } protected void initNeuralNet() { // 三层：输入层 ，隐层，输出层 // create the three layers (using the sigmoid transfer function for the // hidden and the output layers) LinearLayer input = new LinearLayer(); SigmoidLayer hidden = new SigmoidLayer(); SigmoidLayer output = new SigmoidLayer(); // set their dimensions(神经元): // 设置每层神经元的个数 input.setRows(2); hidden.setRows(3); output.setRows(1); // Now build the neural net connecting the layers by creating the two // synapses(突触) // 三层需要两个突触对其进行连接,创建神经元 FullSynapse synapse_IH = new FullSynapse(); /* Input -> Hidden conn. */ FullSynapse synapse_HO = new FullSynapse(); /* Hidden -> Output conn. */ // 连接操作 // Next connect the input layer with the hidden layer: input.addOutputSynapse(synapse_IH); hidden.addInputSynapse(synapse_IH); // and then, the hidden layer with the output layer: hidden.addOutputSynapse(synapse_HO); output.addInputSynapse(synapse_HO); // need a NeuralNet object that will contain all the Layers of the // network // 创建一个网络来容纳各层 nnet = new NeuralNet(); nnet.addLayer(input, NeuralNet.INPUT_LAYER); nnet.addLayer(hidden, NeuralNet.HIDDEN_LAYER); nnet.addLayer(output, NeuralNet.OUTPUT_LAYER); Monitor monitor = nnet.getMonitor(); // 设定神经网络的学习率， monitor.setLearningRate(0.8); // 设定神经网络的动量 为 0.3 这两个变量与步长有关 monitor.setMomentum(0.3); monitor.addNeuralNetListener(this); // 输入流 inputStream = new FileInputSynapse(); /* The first two columns contain the input values */ inputStream.setAdvancedColumnSelector("1,2"); /* This is the file that contains the input data */ inputStream.setInputFile(new File("D:\\data\\joone\\XOR.txt")); // Next add the input synapse to the first layer. input.addInputSynapse(inputStream); TeachingSynapse trainer = new TeachingSynapse(); /* * Setting of the file containing the desired responses, provided by a * FileInputSynapse */ FileInputSynapse samples = new FileInputSynapse(); samples.setInputFile(new File("D:\\data\\joone\\XOR.txt")); /* The output values are on the third column of the file */ samples.setAdvancedColumnSelector("3"); trainer.setDesired(samples); output.addOutputSynapse(trainer); /* We attach the teacher to the network */ nnet.setTeacher(trainer); monitor.setTrainingPatterns(4); /* # of rows in the input file */ monitor.setTotCicles(100000); /* How many times the net must be trained*/ monitor.setLearning(true); /* The net must be trained */ nnet.go(); /* The network starts the training phase */ } @Override public void netStarted(NeuralNetEvent e) { System.out.println("Training..."); } @Override public void cicleTerminated(NeuralNetEvent e) { Monitor mon = (Monitor)e.getSource(); long c = mon.getCurrentCicle(); /* We want print the results every 100 epochs */ if (c % 100 == 0) System.out.println(c + " epochs remaining - RMSE = " + mon.getGlobalError()); } @Override public void netStopped(NeuralNetEvent e) { System.out.println("Training Stopped..."); long mystr = System.currentTimeMillis(); // 初始化当前的系统时间 System.out.println(mystr); saveNeuralNet("D:\\data\\xor.snet"); // 保存生成当前时间的myxor.snet神经网络 test(); } public void test(){ NeuralNet xorNNet = this.restoreNeuralNet("D:\\data\\xor.snet"); if (xorNNet != null) { // we get the output layer Layer output = xorNNet.getOutputLayer(); // we create an output synapse FileOutputSynapse fileOutput = new FileOutputSynapse(); fileOutput.setFileName("D:\\data\\xor_out.txt"); // we attach the output synapse to the last layer of the NN output.addOutputSynapse(fileOutput); // we run the neural network only once (1 cycle) in recall mode xorNNet.getMonitor().setTotCicles(1); xorNNet.getMonitor().setLearning(false); xorNNet.go(); } } @Override public void errorChanged(NeuralNetEvent e) { Monitor mon = (Monitor) e.getSource();// 得到监控层的信息 long c = mon.getCurrentCicle(); if (c % 100 == 0) System.out.println("Cycle: " + (mon.getTotCicles() - mon.getCurrentCicle()) + " RMSE:" + mon.getGlobalError()); // 输出 训练的次数和 rmse 均方误差 } public void saveNeuralNet(String fileName) { try { FileOutputStream stream = new FileOutputStream(fileName); ObjectOutputStream out = new ObjectOutputStream(stream); out.writeObject(nnet);// 写入nnet对象 out.close(); } catch (Exception excp) { excp.printStackTrace(); } } NeuralNet restoreNeuralNet(String fileName) { NeuralNetLoader loader = new NeuralNetLoader(fileName); NeuralNet nnet = loader.getNeuralNet(); return nnet; } @Override public void netStoppedError(NeuralNetEvent e, String error) { // TODO Auto-generated method stub } }