ANN—— Artificial Neural Networks 人工神经网络

//定义人工神经网络    CvANN_MLP bp;     // Set up BPNetwork's parameters    CvANN_MLP_TrainParams params;    params.train_method=CvANN_MLP_TrainParams::BACKPROP;    params.bp_dw_scale=0.1;    params.bp_moment_scale=0.1;    //params.train_method=CvANN_MLP_TrainParams::RPROP;    //params.rp_dw0 = 0.1;     //params.rp_dw_plus = 1.2;     //params.rp_dw_minus = 0.5;    //params.rp_dw_min = FLT_EPSILON;     //params.rp_dw_max = 50.;

两种训练方法：BACKPROP 与 RPROP

BACKPROP的两个参数：

RPROP的四个参数：

 

//  training data    float labels[3][5] = {
   {
   0,0,0,0,0},{
   1,1,1,1,1},{
   0,0,0,0,0}};    Mat labelsMat(3, 5, CV_32FC1, labels);    float trainingData[3][5] = { {
   1,2,3,4,5},{
   111,112,113,114,115}, {
   21,22,23,24,25} };    Mat trainingDataMat(3, 5, CV_32FC1, trainingData);// layerSizes设置了有三个隐含层的网络结构：输入层，三个隐含层，输出层。输入层和输出层节点数均为5，中间隐含层每层有两个节点    Mat layerSizes=(Mat_
     
      (1,5) << 5,2,2,2,5);//create第二个参数可以设置每个神经节点的激活函数，默认为CvANN_MLP::SIGMOID_SYM，即Sigmoid函数//同时提供的其他激活函数有Gauss（CvANN_mlp::GAUSSIAN）和阶跃函数(CvANN_MLP::IDENTITY)。
     

bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM);   //CvANN_MLP::SIGMOID_SYM   bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);

//预测新节点Mat sampleMat = (Mat_
     
      (1,5) << i,j,0,0,0);              Mat responseMat;              bp.predict(sampleMat,responseMat);
     

float CvANN_MLP::predict(constMat&inputs,Mat&outputs)

图像进行特征提取，把它保存在inputs里，通过调用predict函数，我们得到一个输出向量，它是一个1*nClass的行向量，

其中每一列说明它与该类的相似程度（0-1之间），也可以说是置信度。我们只用对output求一个最大值，就可得到结果。

完整代码：

#include 
     
        #include 
      
         #include 
       
          #include 
        
           #include 
         
            using namespace std;  using namespace cv;  int main()  {      CvANN_MLP bp;           CvANN_MLP_TrainParams params;      params.train_method=CvANN_MLP_TrainParams::BACKPROP;  //(Back Propagation,BP)反向传播算法    params.bp_dw_scale=0.1;      params.bp_moment_scale=0.1;      float labels[10][2] = {
   {
   0.9,0.1},{
   0.1,0.9},{
   0.9,0.1},{
   0.1,0.9},{
   0.9,0.1},{
   0.9,0.1},{
   0.1,0.9},{
   0.1,0.9},{
   0.9,0.1},{
   0.9,0.1}};      //这里对于样本标记为0.1和0.9而非0和1，主要是考虑到sigmoid函数的输出为一般为0和1之间的数，只有在输入趋近于-∞和+∞才逐渐趋近于0和1，而不可能达到。    Mat labelsMat(10, 2, CV_32FC1, labels);      float trainingData[10][2] = { {
   11,12},{
   111,112}, {
   21,22}, {
   211,212},{
   51,32}, {
   71,42}, {
   441,412},{
   311,312}, {
   41,62}, {
   81,52} };      Mat trainingDataMat(10, 2, CV_32FC1, trainingData);      Mat layerSizes=(Mat_
          
           (1,5) << 2, 2, 2, 2, 2); //5层：输入层，3层隐藏层和输出层，每层均为两个perceptron bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM); bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params); int width = 512, height = 512; Mat image = Mat::zeros(height, width, CV_8UC3); Vec3b green(0,255,0), blue (255,0,0); for (int i = 0; i < image.rows; ++i) { for (int j = 0; j < image.cols; ++j) { Mat sampleMat = (Mat_
           
            (1,2) << i,j); Mat responseMat; bp.predict(sampleMat,responseMat); float* p=responseMat.ptr
            
             (0); // if (p[0] > p[1]) { image.at
             
              (j, i) = green; } else { image.at
              
               (j, i) = blue; } } } // Show the training data int thickness = -1; int lineType = 8; circle( image, Point(111, 112), 5, Scalar( 0, 0, 0), thickness, lineType); circle( image, Point(211, 212), 5, Scalar( 0, 0, 0), thickness, lineType); circle( image, Point(441, 412), 5, Scalar( 0, 0, 0), thickness, lineType); circle( image, Point(311, 312), 5, Scalar( 0, 0, 0), thickness, lineType); circle( image, Point(11, 12), 5, Scalar(255, 255, 255), thickness, lineType); circle( image, Point(21, 22), 5, Scalar(255, 255, 255), thickness, lineType); circle( image, Point(51, 32), 5, Scalar(255, 255, 255), thickness, lineType); circle( image, Point(71, 42), 5, Scalar(255, 255, 255), thickness, lineType); circle( image, Point(41, 62), 5, Scalar(255, 255, 255), thickness, lineType); circle( image, Point(81, 52), 5, Scalar(255, 255, 255), thickness, lineType); imwrite("result.png", image); // save the image imshow("BP Simple Example", image); // show it to the user waitKey(0); return 0;}