Hopfield神经网络实现污染字体的识别

时间:2022-08-11 19:33:37

这个网络的内部使用的是hebb学习规则

贴上两段代码:

  

package geym.nn.hopfiled;

import java.util.Arrays;

import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.nnet.Hopfield;
import org.neuroph.nnet.comp.neuron.InputOutputNeuron;
import org.neuroph.nnet.learning.HopfieldLearning;
import org.neuroph.util.NeuronProperties;
import org.neuroph.util.TransferFunctionType; /**
* 识别0 1 2 使用hopfield 全连接结构
* @author Administrator
*
*/
public class HopfieldSample2 { public static double[] format(double[] data){
for(int i=0;i<data.length;i++){
if(data[i]==0)data[i]=-1;
}
return data;
} public static void main(String args[]) {
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.setProperty("neuronType", InputOutputNeuron.class);
neuronProperties.setProperty("bias", new Double(0.0D));
neuronProperties.setProperty("transferFunction", TransferFunctionType.STEP);
neuronProperties.setProperty("transferFunction.yHigh", new Double(1.0D));
neuronProperties.setProperty("transferFunction.yLow", new Double(-1.0D)); // create training set (H and T letter in 3x3 grid)
DataSet trainingSet = new DataSet(30);
trainingSet.addRow(new DataSetRow(format(new double[] {
0,1,1,1,1,0,
1,0,0,0,0,1,
1,0,0,0,0,1,
1,0,0,0,0,1,
0,1,1,1,1,0}))); // trainingSet.addRow(new DataSetRow(format(new double[] {
0,0,0,0,0,0,
1,0,0,0,0,0,
1,1,1,1,1,1,
0,0,0,0,0,0,
0,0,0,0,0,0}))); // trainingSet.addRow(new DataSetRow(format(new double[] {
1,0,0,0,0,0,
1,0,0,1,1,1,
1,0,0,1,0,1,
1,0,0,1,0,1,
0,1,1,0,0,1}))); //2 // create hopfield network
Hopfield myHopfield = new Hopfield(30, neuronProperties);
myHopfield.setLearningRule(new StandHopfieldLearning());
// learn the training set
myHopfield.learn(trainingSet); // test hopfield network
System.out.println("Testing network"); // add one more 'incomplete' H pattern for testing - it will be
// recognized as H
// DataSetRow h=new DataSetRow(new double[] { 1, 0, 0, 1, 0, 1, 1, 0, 1
// });
// DataSetRow h=new DataSetRow(new double[] { 1, 0, 0, 1, 0, 1, 1, 0, 1
// });
DataSetRow h = new DataSetRow(format(new double[] {
1,0,0,0,0,0,
1,0,0,1,1,1,
1,0,0,1,0,1,
1,0,0,1,0,0,
0,1,1,0,0,1})); // 2 bad
trainingSet.addRow(h); myHopfield.setInput(h.getInput()); double[] networkOutput = null;
double[] preNetworkOutput = null;
while (true) {
myHopfield.calculate();
networkOutput = myHopfield.getOutput();
if (preNetworkOutput == null) {
preNetworkOutput = networkOutput;
continue;
}
if (Arrays.equals(networkOutput, preNetworkOutput)) {
break;
}
preNetworkOutput = networkOutput;
} System.out.print("Input: " + Arrays.toString(h.getInput()));
System.out.println(" Output: " + Arrays.toString(networkOutput)); System.out.println(Arrays.equals(format(new double[] {
1,0,0,0,0,0,
1,0,0,1,1,1,
1,0,0,1,0,1,
1,0,0,1,0,1,
0,1,1,0,0,1}), networkOutput));
} }

下面就是StandHopfieldLearning类的实现,里面标红的地方就是hebb学习规则,权重为输入和输出的乘积:

  

package com.cgjr.com.hopfield;

import org.neuroph.core.Connection;
import org.neuroph.core.Layer;
import org.neuroph.core.Neuron;
import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.core.learning.LearningRule; /**
* Learning algorithm for the Hopfield neural network.
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class StandHopfieldLearning extends LearningRule { /**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L; /**
* Creates new HopfieldLearning
*/
public StandHopfieldLearning() {
super();
} /**
* Calculates weights for the hopfield net to learn the specified training
* set
*
* @param trainingSet
* training set to learn
*/
public void learn(DataSet trainingSet) {
int M = trainingSet.size();
int N = neuralNetwork.getLayerAt(0).getNeuronsCount();
Layer hopfieldLayer = neuralNetwork.getLayerAt(0); for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
if (j == i)
continue;
Neuron ni = hopfieldLayer.getNeuronAt(i);
Neuron nj = hopfieldLayer.getNeuronAt(j);
Connection cij = nj.getConnectionFrom(ni);
Connection cji = ni.getConnectionFrom(nj); double wij=0;
for(int k = 0;k < M;k++){
DataSetRow row=trainingSet.getRowAt(k);
double[] inputs=row.getInput();
wij+=inputs[i]*inputs[j];//Hebb学习规则
}
cij.getWeight().setValue(wij);
cji.getWeight().setValue(wij);
}// j
} // i } }