我一直在遵循一个关于创建一个简单的三层神经网络的教程，可以在这里找到，我的代码几乎是一对一的时刻。在本教程中，他们的网络计算是在他们的“矩阵”类中完成的。另一节课叫我“数学”。他们的榜样行得通，而我的榜样不行。我想知道为什么会这样，因为在我看来，它应该工作。为了便于查找，我将包括教程的类和我的。
示例代码：

public class Matrix {
double[][] data;
int rows,cols;

public Matrix(int row, int col) {
    data = new double[row][col];
    rows = row;
    cols = col;
    for (int i = 0; i < row; i++) {
        for (int j = 0; j < col; j++) {
            data[i][j] = Math.random() * 2 - 1;
        }
    }
}        
public void add(Matrix B){
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++) {
            this.data[i][j] += B.data[i][j];
        }
    }
}

public static Matrix sub(Matrix A, Matrix B){
    Matrix temp = new Matrix(A.rows, A.cols);
    for (int i = 0; i < A.rows; i++) {
        for (int j = 0; j < A.cols; j++) {
            temp.data[i][j] = A.data[i][j] - B.data[i][j];
        }
    }
    return temp;
}

public void multiplyElements(Matrix a){
    for (int i = 0; i < a.rows; i++) {
        for (int j = 0; j < a.cols; j++) {
            this.data[i][j] *= a.data[i][j];
        }
    }
}

public void multiplyScalar(double a){
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++) {
            this.data[i][j] *= a;
        }
    }
}

public static Matrix DotProduct(Matrix A,Matrix B){
    Matrix temp = new Matrix(A.rows, B.cols);
    for (int i = 0; i < temp.rows; i++) {
        for (int j = 0; j < temp.cols; j++) {
            double sum = 0;
            for (int k = 0; k < A.cols; k++) {
                sum += A.data[i][k] * B.data[k][j];
            }
            temp.data[i][j] = sum;
        }
    }
    return temp;
}

public void Activate(){
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++)
            this.data[i][j] = 1 / (1 + Math.exp(-this.data[i][j]));
    }
}

public static Matrix Transpose(Matrix A){
    Matrix temp = new Matrix(A.cols, A.rows);
    for (int i = 0; i < A.rows; i++) {
        for (int j = 0; j < A.cols; j++) {
            temp.data[j][i] = A.data[i][j];
        }
    }
    return temp;
}

public static Matrix Clone(double[] A){
    Matrix temp = new Matrix(A.length,1);
    for(int i =0;i<A.length;i++)
        temp.data[i][0]=A[i];
    return temp;
}

public Matrix DxActivate(){
    Matrix temp = new Matrix(rows, cols);
    for (int i = 0; i < rows; i++) {
        for (int j = 0; j < cols; j++)
            temp.data[i][j] = this.data[i][j] * (1 - this.data[i][j]);
    }
    return temp;
}

我的代码：

public class Matrix {
double[][] data;
int rows, cols;
static Maths M = new Maths();

public Matrix(int row, int col) {
    data = new double[row][col];
    rows = row;
    cols = col;
    for (int i = 0; i < row; i++) {
        for (int j = 0; j < col; j++) {
            data[i][j] = Math.random() * 2 - 1;
        }
    }
}

public void add(Matrix B) {
    this.data = M.AddIndices(this.data, B.data);
}

public static Matrix sub(Matrix A, Matrix B) {
    Matrix temp = new Matrix(A.data.length, A.data[0].length);
    temp.data = M.SubtractIndices(A.data, B.data);
    return temp;
}

public void multiplyElements(Matrix B) {
    this.data = M.MultiplyIndexes(this.data, B.data);
}

public void multiplyScalar(double S) {
    this.data = M.MultiplyByScalar(this.data, S);
}

public static Matrix DotProduct(Matrix A, Matrix B) {
    Matrix temp = new Matrix(A.data.length, B.data[0].length);
    temp.data = M.DotProduct(A.data, B.data);
    return temp;
}

public void Activate() {
    this.data = M.ApplySigmoid(this.data);
}

public Matrix Transpose() {
    Matrix temp = new Matrix(this.data[0].length, this.data.length);
    temp.data = M.Transpose(this.data);
    return temp;
}

public static Matrix Clone(double[] A) {
    Matrix temp = new Matrix(A.length, 1);
    for (int i = 0; i < A.length; i++)
        temp.data[i][0] = A[i];
    return temp;
}

public Matrix DxActivate() {
    Matrix temp = new Matrix(this.data.length, this.data[0].length);
    temp.data = M.Apply_dx_Sigmoid(this.data);
    return temp;
}

}
在每次调用数学类m中的函数时，该函数都将返回一个二维双数组。所调用的每个函数都与示例代码中的函数相同，只是创建了一个2d双数组，而不是创建一个矩阵示例。
我相信问题在于数据是如何传递/存储的，但这并不是特别重要，因为对于我来说，作为一个简单的temp.data=returning数组，应该正确地存储它。
谢谢你的帮助和知识。
编辑：我发现了我的问题，我的点积函数工作不正常。谢谢您