AI入门第一脚 BP神经网络

神经网络沉静了多年，突然复活了，根本原因是异或问题得到解决。验证异或也应为实践神经网络的第一步。

异或分为四组： {0,0}，{0,1}，{1,0}，{1,1}

对应结果为： {{0}, {1}, {1}, {0}}

上码：

// ANN_XORDemo.cpp : 定义控制台应用程序的入口点。

#include "stdafx.h"

#include<opencv2/opencv.hpp>

#include <iostream>

#include <string>

using namespace std;

using namespace cv;

using namespace ml;

int main()

{

float output[4][1] = { { 0 },{1.0 },{ 1.0 },{ 0 } };

Mat outputMat(4, 1, CV_32FC1, output);

cout << "output-> " << outputMat << endl;

float input[4][2] = { { 0,0 },{0,1.0 },{ 1.0,0 },{ 1.0,1.0 } };

Mat trainingMat(4, 2, CV_32FC1, input);

cout << "input->" << trainingMat << endl;

Mat layer = (Mat_<int>(1, 3) << 2, 2, 1);

Ptr<ANN_MLP> xor = ANN_MLP::create();

xor->setLayerSizes(layer);//

xor->setActivationFunction(ANN_MLP::SIGMOID_SYM);

xor->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON));

xor->setTrainMethod(ANN_MLP::BACKPROP, 0.1);

Ptr<TrainData> trData = TrainData::create(trainingMat, ROW_SAMPLE, outputMat);

xor->train(trData);

Mat sample = (Mat_<float>(1, 2) << 1, 1);

Mat response;

xor->predict(sample, response);

cout << "sample-> " << sample << endl;

cout << "1,1 ->" << response << endl;

sample = (Mat_<float>(1, 2) << 0, 1);

xor->predict(sample, response);

cout << "sample-> " << sample << endl;

cout << "0,1 ->" << response << endl;

sample = (Mat_<float>(1, 2) << 1, 0);

xor->predict(sample, response);

cout << "sample-> " << sample << endl;

cout << "1,0 ->" << response << endl;

sample = (Mat_<float>(1, 2) << 0, 0);

xor->predict(sample, response);

cout << "sample-> " << sample << endl;

cout << "0,0 ->" << response << endl;

return 0;

}

AI入门第一脚 BP神经网络

结论：{1,1}，{0,0}得到结果为0.001…， {1,0}，{0,1}的结果为0.999….结果分别非常逼近异或结果0,1.识别率为100%。