我正在尝试为 Java 中的 XOR 函数实现和训练一个具有反向传播的五神经元神经网络。我的代码(请原谅它的丑陋):
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;
}
}
}
}
如果有帮助,这里是diagram of the network .
所有权重和学习率的初始值都直接取 self 教科书中的一个例子。目标是训练网络,直到误差平方和小于 .001。教科书还给出了第一次迭代后所有权重的值 (1,1,0),我已经测试了我的代码,其结果与教科书的结果完全匹配。但根据书上的说法,这应该只需要 224 个 epoch 就可以收敛。但是当我运行它时,它总是达到 MAX_EPOCHS,除非它设置为几千。我做错了什么?
最佳答案
//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 应该学习 66 次迭代,但处于发散的边缘。
关于java - Java 中的 XOR 神经网络,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/9662746/