Java中的XOR神经网络

我试图实现和训练一个五神经元神经网络,其反向传播用于Java中的XOR函数。 我的代码(请原谅它的可怕性):

public class XORBackProp {

private static final int MAX_EPOCHS = 500;

//weights
private static double w13, w23, w14, w24, w35, w45;
private static double theta3, theta4, theta5;
//neuron outputs
private static double gamma3, gamma4, gamma5;
//neuron error gradients
private static double delta3, delta4, delta5;
//weight corrections
private static double dw13, dw14, dw23, dw24, dw35, dw45, dt3, dt4, dt5;
//learning rate
private static double alpha = 0.1;
private static double error;
private static double sumSqrError;
private static int epochs = 0;
private static boolean loop = true;

private static double sigmoid(double exponent)
{
    return (1.0/(1 + Math.pow(Math.E, (-1) * exponent)));
}

private static void activateNeuron(int x1, int x2, int gd5)
{
    gamma3 = sigmoid(x1*w13 + x2*w23 - theta3);
    gamma4 = sigmoid(x1*w14 + x2*w24 - theta4);
    gamma5 = sigmoid(gamma3*w35 + gamma4*w45 - theta5);

    error = gd5 - gamma5;

    weightTraining(x1, x2);
}

private static void weightTraining(int x1, int x2)
{
    delta5 = gamma5 * (1 - gamma5) * error;
    dw35 = alpha * gamma3 * delta5;
    dw45 = alpha * gamma4 * delta5;
    dt5 = alpha * (-1) * delta5;

    delta3 = gamma3 * (1 - gamma3) * delta5 * w35;
    delta4 = gamma4 * (1 - gamma4) * delta5 * w45;

    dw13 = alpha * x1 * delta3;
    dw23 = alpha * x2 * delta3;
    dt3 = alpha * (-1) * delta3;
    dw14 = alpha * x1 * delta4;
    dw24 = alpha * x2 * delta4;
    dt4 = alpha * (-1) * delta4;

    w13 = w13 + dw13;
    w14 = w14 + dw14;
    w23 = w23 + dw23;
    w24 = w24 + dw24;
    w35 = w35 + dw35;
    w45 = w45 + dw45;
    theta3 = theta3 + dt3;
    theta4 = theta4 + dt4;
    theta5 = theta5 + dt5;
}

public static void main(String[] args)
{

    w13 = 0.5;
    w14 = 0.9;
    w23 = 0.4;
    w24 = 1.0;
    w35 = -1.2;
    w45 = 1.1;
    theta3 = 0.8;
    theta4 = -0.1;
    theta5 = 0.3;

    System.out.println("XOR Neural Network");

    while(loop)
    {
        activateNeuron(1,1,0);
        sumSqrError = error * error;
        activateNeuron(0,1,1);
        sumSqrError += error * error;
        activateNeuron(1,0,1);
        sumSqrError += error * error;
        activateNeuron(0,0,0);
        sumSqrError += error * error;

        epochs++;

        if(epochs >= MAX_EPOCHS)
        {
            System.out.println("Learning will take more than " + MAX_EPOCHS + " epochs, so program has terminated.");
            System.exit(0);
        }

        System.out.println(epochs + " " + sumSqrError);

        if (sumSqrError < 0.001)
        {
            loop = false;
        }
    }
}
}

如果它有帮助,这里是网络图。

所有权重的初始值和学习率都是从我的教科书中的一个例子中直接获得的。 目标是训练网络,直到平方误差的总和小于.001。 教科书还给出了第一次迭代之后所有权重的值(1,1,0),并且我测试了我的代码,其结果与教科书的结果完美匹配。 但根据这本书,这应该只需要224个时代的融合。 但是当我运行它时,它总是达到MAX_EPOCHS,除非它被设置为几千。 我究竟做错了什么?


    //Add this in the constants declaration section.
    private static double alpha = 3.8, g34 = 0.13, g5 = 0.21;

    // Add this in activate neuron
    gamma3 = sigmoid(x1 * w13 + x2 * w23 - theta3);
    gamma4 = sigmoid(x1 * w14 + x2 * w24 - theta4);        
    if (gamma3 > 1 - g34 ) {gamma3 = 1;}
    if (gamma3 < g34) {gamma3 = 0;}
    if (gamma4 > 1- g34) {gamma4 = 1;}
    if (gamma4 < g34) {gamma4 = 0;}   
    gamma5 = sigmoid(gamma3 * w35 + gamma4 * w45 - theta5);
    if (gamma5 > 1 - g5) {gamma5 = 1;}
    if (gamma5 < g5) {gamma5 = 0;}

ANN应该在66次迭代中学习,但是处于分歧的边缘。


尝试使gamma3,gamma4,gamma5四舍五入,同时在激活阶段为instace:

if (gamma3 > 0.7) gamma3 = 1;
if (gamma3 < 0.3) gamma3 = 0;

并升起一点点learnig变量(alpha)

alpha = 0.2;

学习在466个时代结束。

当然,如果你做出更大的舍入和更高的alpha值,你可以取得比224更好的结果。


这个网络的整个目的是说明在分组不是基于“top = yes,bottom = no”的情况下如何处理这种情况,而是存在一条中心线(经过点(0,1)和(1 ,在这种情况下为0),如果值接近该行,则答案是“是”,而如果它很远,则答案是“否”。 你不能将这样的系统只聚集一层。 但是,两层就足够了。

链接地址: http://www.djcxy.com/p/58221.html

上一篇: XOR Neural Network in Java

下一篇: JAXB extend generated code with extended object factory