c# Bp神经网络

 使用了周志华老师写的机器学习中的神经网络的公式，具体公式可以看《机器学习》--周志华第103页，用的时标准BP算法

具体代码在下面，注释明确

 

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;

namespace mssbyi
{
  //Random Random = new Random();
  class BpDeep
  {
    Random Random = new Random();
    //构造函数，构造bp网络，n为学习率,MST为最大学习次数
    public BpDeep(List<int> net_size, double n, int MST, double min_error)
    {
      //模型参数传入
      inputSize = net_size[0];
      numLaters = net_size.Count;
      netSize = net_size;
      outputSize = net_size.Last();
      N = n;
      MaxStudyTimes = MST;
      //设置输入层
      layers.Add(new layer(netSize[0], netSize[1], n));
      //初始化隐藏层
      for (int i = 1; i < numLaters - 1; i++)
      {
        layers.Add(new layer(netSize[i], netSize[i + 1], n));
      }
      //初始化输出层
      layers.Add(new layer(netSize.Last(), 0, n));//后期需要设置分类数量！！！！！！！！！！！
      error = 100;//初使可以用很大的值
      minError = min_error;
    }
    //正向传播
    public void forward(List<double> input)
    {
      layers[0].Y = input;
      for (int i = 1; i < numLaters; i++)
      {
        layers[i].forward(layers[i - 1]);
      }
    }
    //反向传播
    public void backward(List<double> output_real)
    {
      //计算误差


      //计算误差和输出层神经元梯度项
      List<double> g = new List<double>();
      for (int i = 0; i < outputSize; i++)
      {
        g.Add((layers.Last().Y[i] * (1 - layers.Last().Y[i]) * (output_real[i] - layers.Last().Y[i])));
        error += 0.5 * Math.Pow((output_real[i] - layers.Last().Y[i]), 2);
      }
      //设置输出层的阈值
      List<double> db_last = new List<double>();
      for (int i = 0; i < layers[numLaters - 1].numNeuron; i++)
      {
        db_last.Add(-1 * N * g[i]);
        //db_last[i] = -1 * N * g[i];
      }
      layers[numLaters - 1].setDB = db_last;
     
      //计算最后一个隐藏层的神经元梯度项
      List<double> e = new List<double>();
      for (int i = 0; i < layers[numLaters - 2].numNeuron; i++)
      {
        double sum = 0;
        for (int j = 0; j < outputSize; j++)
        {
          sum += layers[numLaters - 2].getW[i][j] * g[j];
        }
        e.Add(layers[numLaters - 2].Y[i] * (1 - layers[numLaters