神经网络一个简单实例

OpenCV的ml模块实现了人工神经网络（Artificial Neural Networks，ANN）最典型的多层感知器（multi-layer perceptrons, MLP）模型。

由于ml模型实现的算法都继承自统一的CvStatModel基类，其训练和预测的接口都是train(),predict()，非常简单。

下面来看神经网络CvANN_MLP 的使用~定义神经网络及参数：[cpp]view plain copy1.//Setup the BPNetwork2. CvANN_MLP bp;3.// Set up BPNetwork's parameters4. CvANN_MLP_TrainParams params;5. params.train_method=CvANN_MLP_TrainParams::BACKPROP;6. params.bp_dw_scale=0.1;7. params.bp_moment_scale=0.1;8.//params.train_method=CvANN_MLP_TrainParams::RPROP;9.//params.rp_dw0 = 0.1;10.//params.rp_dw_plus = 1.2;11.//params.rp_dw_minus = 0.5;12.//params.rp_dw_min = FLT_EPSILON;13.//params.rp_dw_max = 50.;可以直接定义CvANN_MLP神经网络，并设置其参数。

BACKPROP表示使用back-propagation的训练方法，RPROP即最简单的propagation训练方法。

使用BACKPROP有两个相关参数：bp_dw_scale即bp_moment_scale：使用PRPOP有四个相关参数：rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min,rp_dw_max：上述代码中为其默认值。

设置网络层数，训练数据：[cpp]view plain copy1.// Set up training data2.float labels[3][5] = {{0,0,0,0,0},{1,1,1,1,1},{0,0,0,0,0}};3. Mat labelsMat(3, 5, CV_32FC1, labels);4.5.float trainingData[3][5] = { {1,2,3,4,5},{111,112,113,114,115}, {21,22,23,24,25} };6. Mat trainingDataMat(3, 5, CV_32FC1, trainingData);7. Mat layerSizes=(Mat_<int>(1,5) << 5,2,2,2,5);8. bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM);//CvANN_MLP::SIGMOID_SYM9.//CvANN_MLP::GAUSSIAN10.//CvANN_MLP::IDENTITY11. bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);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。

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和阶跃函数。

使用训练好的网络结构分类新的数据：然后直接使用predict函数，就可以预测新的节点：[cpp]view plain copy1.Mat sampleMat = (Mat_<float>(1,5) << i,j,0,0,0);2. Mat responseMat;3. bp.predict(sampleMat,responseMat);完整程序代码：[cpp]view plain copy1.//The example of using BPNetwork in OpenCV2.//Coded by L. Wei3.#include <opencv2/core/core.hpp>4.#include <opencv2/highgui/highgui.hpp>5.#include <opencv2/ml/ml.hpp>6.#include <iostream>7.#include <string>8.ing namespace std;ing namespace cv;11.12.int main()13.{14.//Setup the BPNetwork15. CvANN_MLP bp;16.// Set up BPNetwork's parameters17. CvANN_MLP_TrainParams params;18. params.train_method=CvANN_MLP_TrainParams::BACKPROP;19. params.bp_dw_scale=0.1;20. params.bp_moment_scale=0.1;21.//params.train_method=CvANN_MLP_TrainParams::RPROP;22.//params.rp_dw0 = 0.1;23.//params.rp_dw_plus = 1.2;24.//params.rp_dw_minus = 0.5;25.//params.rp_dw_min = FLT_EPSILON;26.//params.rp_dw_max = 50.;27.28.// Set up training data29.float labels[3][5] = {{0,0,0,0,0},{1,1,1,1,1},{0,0,0,0,0}};30. Mat labelsMat(3, 5, CV_32FC1, labels);31.32.float trainingData[3][5] = { {1,2,3,4,5},{111,112,113,114,115}, {21,22,23,24,25} };33. Mat trainingDataMat(3, 5, CV_32FC1, trainingData);34. Mat layerSizes=(Mat_<int>(1,5) << 5,2,2,2,5);35. bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM);//CvANN_MLP::SIGMOID_SYM36.//CvANN_MLP::GAUSSIAN37.//CvANN_MLP::IDENTITY38. bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);39.40.41.// Data for visual representation42.int width = 512, height = 512;43. Mat image = Mat::zeros(height, width, CV_8UC3);44. Vec3b green(0,255,0), blue (255,0,0);45.// Show the decision regions given by the SVM46.for (int i = 0; i < image.rows; ++i)47.for (int j = 0; j < image.cols; ++j)48. {49. Mat sampleMat = (Mat_<float>(1,5) << i,j,0,0,0);50. Mat responseMat;51. bp.predict(sampleMat,responseMat);52.float* p=responseMat.ptr<float>(0);53.float response=0.0f;54.for(int k=0;k<5;i++){55.// cout<<p[k]<<" ";56. response+=p[k];57. }58.if (response >2)59. image.at<Vec3b>(j, i) = green;60.else61. image.at<Vec3b>(j, i) = blue;62. }63.64.// Show the training data65.int thickness = -1;66.int lineType = 8;67. circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness,lineType);68. circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness,lineType);69. circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness,lineType);70. circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness,lineType);71.72. imwrite("result.png", image); // save the image73.74. imshow("BP Simple Example", image); // show it to the user75. waitKey(0);76.77.}结果：。