感知器的总和不能正常工作。获得大额总结

Question

所以我有一个run方法，它总结了人工神经网络中边缘的权重和输入节点的阈值。

有点像这样：

现在我的测试感知器应该产生-3的总和，但是我的值为1176！这是怎么回事？

这是我为run（）方法，构造函数和主要方法编写的代码。

构造

    public class Perceptron {

//We want to create a variable which will represent the number of weighted edges
//in the 2-dimensional array.
protected int num_weighted_Edges;

//Inside this class we want to create a data field which is a 
//2-D array of WeightedEdges. Since the weightedEdges will be in 
//double data type, we will create a double type 2-dimensional
//array.
protected WeightedEdge[][] weightedEdges; 

protected int[] weights;

//We set a double field named eta equal to 0.05.
protected double eta = 0.05;

//We initialize a constructor which only takes a parameter int n.
public Perceptron(int n){

    //We want to create a new graph which will have n + 1 vertices
    //, where we also want vertex 0 to act like the output node
    //as in a neural network.
    this.num_weighted_Edges = n;

    weights = new int[num_weighted_Edges];

    //First we need to verify that n is a positive real number
    if (num_weighted_Edges < 0){
        throw new RuntimeException("You cannot have a perceptron of negative value");
    }
    else {
        //Test code for testing if this code works.
        System.out.println("A perceptron of " + num_weighted_Edges + " input nodes, and 1 output node was created");
    }

    //Now we create a graph object with "n" number of vertices.
    weightedEdges = new WeightedEdge[num_weighted_Edges + 1][num_weighted_Edges + 1];

    //Create a for loop that will iterate the weightedEdges array.
    //We want to create the weighted edges from vertex 1 and not vertex 0
    //since vertex 0 will be the output node, so we set i = 1.
    for (int i = 1; i < weightedEdges.length; i++){
        for (int j = 0; j < weightedEdges[i].length; j++){
            //This will create a weighted edge in between [1][0]...[2][0]...[3][0]
            //The weighted edge will have a random value between -1 and 1 assigned to it.
            weightedEdges[i][0] = new WeightedEdge(i, j, 1);
        }
    }

}

这是我的run（）方法：

    //This method will take the input nodes, do a quick verification check on it and
//sum up the weights using the simple threshold function described in class to return
//either a 1 or -1. 1 meaning fire, and -1 not firing. 
public int run(int[] weights){
    //So this method will act like the summation function. It will take the int parameters
    //you put into the parameter field and multiply it times the input nodes in the
    //weighted edge 2 d array.

    //Setup a summation counter.
    int sum = 0;

    if (weights.length != num_weighted_Edges){
        throw new RuntimeException("Array coming in has to equal the number of input nodes");
    }
    else {
        //We iterate the weights array and use the sum counter to sum up weights.
        for (int i = 0; i < weights.length; i++){
            //Create a nested for loop which will iterate over the input nodes
            for ( int j = 1; j < weightedEdges.length; j++){
                for (int k = 0; k < weightedEdges[j].length; k++){
                    //This takes the weights and multiplies it times the value in the 
                    //input nodes. The sum should equal greater than 0 or less than 0.
                    sum += (int) ((weightedEdges[j][0].getWeight()) * i);
                    //Here the plus equals sign takes the product of (weightedEdges[j][0] * i) and 
                    //then adds it to the previous value. 
                }
            }
        }
    }

    System.out.println(sum);

    //If the sum is greater than 0, we fire the neuron by returning 1.
    if (sum > 0){
        //System.out.println(1); test code
        return 1;
    }
    //Else we don't fire and return -1.
    else {
        //System.out.println(-1); test code
        return -1;
    }

}

这是我的主要方法：

    //Main method which will stimulate the artificial neuron (perceptron, which is the
//simplest type of neuron in an artificial network). 
public static void main(String[] args){

    //Create a test perceptron with a user defined set number of nodes.
    Perceptron perceptron = new Perceptron(7);

    //Create a weight object that creates an edge between vertices 1 and 2
    //with a weight of 1.5
    WeightedEdge weight = new WeightedEdge(1, 2, 1.5);

    //These methods work fine.
    weight.getStart();
    weight.getEnd();
    weight.setWeight(2.0);

    //Test to see if the run class works. (Previously was giving a null pointer, but
    //fixed now)
    int[] test_weight_Array = {-1, -1, -1, -1, -1, 1, 1};

    //Tested and works to return output of 1 or -1. Also catches exceptions.
    perceptron.run(test_weight_Array);

    //Testing a 2-d array to see if the train method works.
    int[][] test_train_Array = {{1}, {-1}, {1}, {1}, {1}, {1}, {1}, {1}};

    //Works and catches exceptions.
    perceptron.train(test_train_Array);

}

    }

Answer 1

我认为你应该改变

sum += (int) ((weightedEdges[j][0].getWeight()) * i);

到

sum += (int) ((weightedEdges[j][k].getWeight()) * i);