我正在构建一个测试神经网络，但它肯定不起作用。我的主要问题是反向传播。通过我的研究，我知道使用sigmoid函数很容易。因此，我通过(1-输出)(输出)(目标值-输出)更新每个权重，但这样做的问题是如果输出为1而目标值不是呢？如果在某个时刻输出为1，则权重更新将始终为0...目前我只是尝试让它添加来自2个输入神经元的输入，因此最佳权重应该只是1，因为输出神经元只是将其输入相加。我确定我在很多地方搞砸了，但这是我的代码：
public class Main {
public static void main(String[] args) {
Double[] inputs = {1.0, 2.0};
ArrayList<Double> answers = new ArrayList<Double>();
answers.add(3.0);
net myNeuralNet = new net(2, 1, answers);
for(int i=0; i<200; i++){
myNeuralNet.setInputs(inputs);
myNeuralNet.start();
myNeuralNet.backpropagation();
myNeuralNet.printOutput();
System.out.println("*****");
for(int j=0; j<myNeuralNet.getOutputs().size(); j++){
myNeuralNet.getOutputs().get(j).resetInput();
myNeuralNet.getOutputs().get(j).resetOutput();
myNeuralNet.getOutputs().get(j).resetNumCalled();
}
}
}
}
package myneuralnet;
import java.util.ArrayList;
public class net {
private ArrayList<neuron> inputLayer;
private ArrayList<neuron> outputLayer;
private ArrayList<Double> answers;
public net(Integer numInput, Integer numOut, ArrayList<Double> answers){
inputLayer = new ArrayList<neuron>();
outputLayer = new ArrayList<neuron>();
this.answers = answers;
for(int i=0; i<numOut; i++){
outputLayer.add(new neuron(true));
}
for(int i=0; i<numInput; i++){
ArrayList<Double> randomWeights = createRandomWeights(numInput);
inputLayer.add(new neuron(outputLayer, randomWeights, -100.00, true));
}
for(int i=0; i<numOut; i++){
outputLayer.get(i).setBackConn(inputLayer);
}
}
public ArrayList<neuron> getOutputs(){
return outputLayer;
}
public void backpropagation(){
for(int i=0; i<answers.size(); i++){
neuron iOut = outputLayer.get(i);
ArrayList<neuron> iOutBack = iOut.getBackConn();
Double iSigDeriv = (1-iOut.getOutput())*iOut.getOutput();
Double iError = (answers.get(i) - iOut.getOutput());
System.out.println("Answer: "+answers.get(i) + " iOut: "+iOut.getOutput()+" Error: "+iError+" Sigmoid: "+iSigDeriv);
for(int j=0; j<iOutBack.size(); j++){
neuron jNeuron = iOutBack.get(j);
Double ijWeight = jNeuron.getWeight(i);
System.out.println("ijWeight: "+ijWeight);
System.out.println("jNeuronOut: "+jNeuron.getOutput());
jNeuron.setWeight(i, ijWeight+(iSigDeriv*iError*jNeuron.getOutput()));
}
}
for(int i=0; i<inputLayer.size(); i++){
inputLayer.get(i).resetInput();
inputLayer.get(i).resetOutput();
}
}
public ArrayList<Double> createRandomWeights(Integer size){
ArrayList<Double> iWeight = new ArrayList<Double>();
for(int i=0; i<size; i++){
Double randNum = (2*Math.random())-1;
iWeight.add(randNum);
}
return iWeight;
}
public void setInputs(Double[] is){
for(int i=0; i<is.length; i++){
inputLayer.get(i).setInput(is[i]);
}
for(int i=0; i<outputLayer.size(); i++){
outputLayer.get(i).resetInput();
}
}
public void start(){
for(int i=0; i<inputLayer.size(); i++){
inputLayer.get(i).fire();
}
}
public void printOutput(){
for(int i=0; i<outputLayer.size(); i++){
System.out.println(outputLayer.get(i).getOutput().toString());
}
}
}
package myneuralnet;
import java.util.ArrayList;
public class neuron {
private ArrayList<neuron> connections;
private ArrayList<neuron> backconns;
private ArrayList<Double> weights;
private Double threshold;
private Double input;
private Boolean isOutput = false;
private Boolean isInput = false;
private Double totalSignal;
private Integer numCalled;
private Double myOutput;
public neuron(ArrayList<neuron> conns, ArrayList<Double> weights, Double threshold){
this.connections = conns;
this.weights = weights;
this.threshold = threshold;
this.totalSignal = 0.00;
this.numCalled = 0;
this.backconns = new ArrayList<neuron>();
this.input = 0.00;
}
public neuron(ArrayList<neuron> conns, ArrayList<Double> weights, Double threshold, Boolean isin){
this.connections = conns;
this.weights = weights;
this.threshold = threshold;
this.totalSignal = 0.00;
this.numCalled = 0;
this.backconns = new ArrayList<neuron>();
this.input = 0.00;
this.isInput = isin;
}
public neuron(Boolean tf){
this.connections = new ArrayList<neuron>();
this.weights = new ArrayList<Double>();
this.threshold = 0.00;
this.totalSignal = 0.00;
this.numCalled = 0;
this.isOutput = tf;
this.backconns = new ArrayList<neuron>();
this.input = 0.00;
}
public void setInput(Double input){
this.input = input;
}
public void setOut(Boolean tf){
this.isOutput = tf;
}
public void resetNumCalled(){
numCalled = 0;
}
public void setBackConn(ArrayList<neuron> backs){
this.backconns = backs;
}
public Double getOutput(){
return myOutput;
}
public Double getInput(){
return totalSignal;
}
public Double getRealInput(){
return input;
}
public ArrayList<Double> getWeights(){
return weights;
}
public ArrayList<neuron> getBackConn(){
return backconns;
}
public Double getWeight(Integer i){
return weights.get(i);
}
public void setWeight(Integer i, Double d){
weights.set(i, d);
}
public void setOutput(Double d){
myOutput = d;
}
public void activation(Double myInput){
numCalled++;
totalSignal += myInput;
if(numCalled==backconns.size() && isOutput){
System.out.println("Total Sig: "+totalSignal);
setInput(totalSignal);
setOutput(totalSignal);
}
}
public void activation(){
Double activationValue = 1 / (1 + Math.exp(input));
setInput(activationValue);
fire();
}
public void fire(){
for(int i=0; i<connections.size(); i++){
Double iWeight = weights.get(i);
neuron iConn = connections.get(i);
myOutput = (1/(1+(Math.exp(-input))))*iWeight;
iConn.activation(myOutput);
}
}
public void resetInput(){
input = 0.00;
totalSignal = 0.00;
}
public void resetOutput(){
myOutput = 0.00;
}
}
好的，这是很多代码，让我解释一下。目前为止，网络很简单，只有一个输入层和一个输出层 --- 我想稍后添加一个隐藏层，但现在我正在小步前进。每个层都是神经元的arraylist。输入神经元装载了输入，在这个例子中是1和2。这些神经元发射，计算输入的sigmoid并将其输出到输出神经元，然后将它们相加并存储该值。然后网络通过取（答案-输出）(输出)(1-输出)（特定输入神经元的输出）进行反向传播，并相应地更新权重。很多时候，它会循环运行，我得到无限大，这似乎与负权重或sigmoid相关。当这种情况不发生时，它收敛于1，由于（1-输出1）为0，我的权重停止更新。
numCalled和totalSignal值只是为了使算法等待所有神经元输入才继续。我知道我这样做有点奇怪，但神经元类有一个名为connections的神经元arraylist，用于保存它正向连接到的神经元。另一个名为backconns的arraylist保存反向连接。我也应该更新正确的权重，因为我获取i和j之间的所有反向连接，但在所有神经元j（i上面的层）中，我只拉取了权重i。我为混乱道歉 --- 我已经尝试了很多东西，花了数小时的时间，仍然无法弄清楚。非常感谢任何帮助！