How to correctly export Weight and Bias value of Backpropagation neural network into another programming language (Java)

Question

I created backpropagation Neural Network using Matlab. I tried to implement XOR gate using Matlab, then getting its weight and bias to create neural network in java. Network consist of 2 input neuron, 2 hidden layer each using 2 neuron and 1 output neuron. After train network, i got following weight and bias :

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;

I simulate it using 1 and 1 as input got following result :

>> f = [1;1]

f =

     1
     1

>> sim(net,f)

ans =

   -0.1639

Then I tried to make simple java code to count this neural network. My code :

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;
    }
}

But it got result as following :

-1.3278721528152158

My question, is there any error or my mistake in exporting weight and bias value from matlab to java? Maybe I made mistake in my java program? Thank you verymuch..

Answer 1

I think the problem is the normalization: http://www.mathworks.com/matlabcentral/answers/14590

If you work with 0,1 inputs, you have to use the f(x)=2*x-1 normalization function, which transforms the values to the [-1; 1] interval, then g(x)=(x+1)/2 to transform back the output to [0; 1]. Pseudocode:

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

I tried this with an other network and worked for me.