Question

我使用Matlab创建了反向传播神经网络。我尝试使用Matlab实现XOR门，然后获得它的重量和偏见来在java中创建神经网络。网络由2个输入神经元组成，2个隐藏层各使用2个神经元和1个输出神经元。在火车网络之后，我得到了以下重量和偏见：

clear;
clc;
i = [0 0 1 1; 0 1 0 1];
o = [0 1 1 0];
net = newff(i,o,{2,2},{'tansig','logsig','purelin'});
net.IW{1,1} = [
    -5.5187   -5.4490;
     3.7332    2.7697
];
net.LW{2,1} = [
   -2.8093   -3.0692;
   -1.6685    6.7527
];
net.LW{3,2} = [
    -4.9318   -0.9651
];
net.b{1,1} = [
    2.1369;
    2.6529
];
net.b{2,1} = [
    -0.2274;
    -4.9512
];
net.b{3,1} = [
    1.4848
];

input  = net.IW{1,1};
layer  = net.LW{2,1};
output = net.LW{3,2};

biasinput = net.b{1,1};
biaslayer = net.b{2,1};
biasoutput= net.b{3,1};


a = sim(net,i);
a;

我使用1和1模拟它，因为输入得到以下结果：

>> f = [1;1]

f =

     1
     1

>> sim(net,f)

ans =

   -0.1639

然后我尝试制作简单的java代码来计算这个神经网络。我的代码：

public class Xor {

    //Value of neuron
    static double[] neuroninput    = new double[2];
    static double[] neuronhidden1  = new double[2];
    static double[] neuronhidden2  = new double[2];
    static double[] neuronoutput   = new double[2];

    //Weight variable init
    //For first hidden layer
    static double[] weighthidden11 = new double[2];
    static double[] weighthidden12 = new double[2];

    //for second hidden layer
    static double[] weighthidden21 = new double[2];
    static double[] weighthidden22 = new double[2];

    //for output layer
    static double[] weightoutput   = new double[2];
    //End of weight variable init

    //Bias value input
    static double[] biashidden1    = new double[2];
    static double[] biashidden2    = new double[2];
    static double[] biasoutput     = new double[1];

    public static void main(String[] args) {
        neuroninput[0] = 1;
        neuroninput[1] = 1;

        weighthidden11[0] = -5.5187;
        weighthidden11[1] = -5.4490;
        weighthidden12[0] =  3.7332;
        weighthidden12[1] =  2.7697;

        weighthidden21[0] = -2.8093;
        weighthidden21[1] = -3.0692;
        weighthidden22[0] = -1.6685;
        weighthidden22[1] =  6.7527;

        weightoutput[0]    = -4.9318;
        weightoutput[1]    = -0.9651;

        biashidden1[0] = 2.1369;
        biashidden1[1] = 2.6529;

        biashidden2[0] = -0.2274;
        biashidden2[1] = -4.9512;

        biasoutput[0]  = 1.4848;

        //Counting each neuron (Feed forward)
        neuronhidden1[0] = sigma(neuroninput,weighthidden11,biashidden1[0]);
        neuronhidden1[0] = tansig(neuronhidden1[0]);

        neuronhidden1[1] = sigma(neuroninput,weighthidden12,biashidden1[1]);
        neuronhidden1[1] = tansig(neuronhidden1[1]);


        neuronhidden2[0] = sigma(neuronhidden1,weighthidden21,biashidden2[0]);
        neuronhidden2[0] = logsig(neuronhidden2[0]);

        neuronhidden2[1] = sigma(neuronhidden1,weighthidden22,biashidden2[1]);
        neuronhidden2[1] = logsig(neuronhidden2[1]);

        neuronoutput[0] = sigma(neuronhidden2,weightoutput,biasoutput[0]);
        neuronoutput[0] = purelin(neuronoutput[0]);
        System.out.println(neuronoutput[0]);
    }

    static double tansig(double x) {
        double value = 0;
        value = (Math.exp(x) - Math.exp(-x)) / (Math.exp(x) + Math.exp(-x));
        return value;
    }

    static double logsig(double x) {
        double value = 0;
        value = 1 / (1+Math.exp(-x));
        return value;
    }

    static double purelin(double x) {
        double value = x;
        return value;
    }

    static double sigma(double[] val, double[] weight, double hidden) {
        double value = 0;
        for (int i = 0; i < val.length; i++) {
            value += (val[i] * weight[i]);
            //System.out.println(val[i]);
        }
        value += hidden;
        return value;
    }
}

但结果如下：

-1.3278721528152158

我的问题是，从matlab到java导出权重和偏差值是否有任何错误或错误？也许我在我的java程序中犯了错误？非常感谢你..

Answer 1

我认为问题在于规范化： http://www.mathworks.com/matlabcentral/answers/14590

如果使用0,1输入，则必须使用f（x）= 2 * x-1归一化函数，该函数将值转换为[-1; 1]间隔，然后g（x）=（x + 1）/ 2将输出转换回[0; 1]。伪代码：

g( java_net( f(x), f(y) ) ) = matlab_net(x, y)

我尝试使用其他网络并为我工作。

Answer 2

您的问题肯定与您的JAPA版本的Matlab sim()命令有关。

这是一个复杂的Matlab命令，其中有许多设置会影响要模拟的网络架构。为了使调试更容易，请尝试在Matlab中自己实现sim（）命令。可能会减少层数，直到您在sim() - builtin和您自己的sim版本之间匹配Matlab。当它工作时转换为JAVA。

修改

在Matlab中重新实现sim()函数的原因是，如果你不能在这里实现它，你将无法在JAVA中正确实现它。使用Matlab矢量符号很容易实现前馈网络。

如何正确地将反向传播神经网络的权重和偏差值导出到另一种编程语言（Java）

2 个答案: