BP神经网络原理及C++代码实现

                               

                               

                               

                               

                               

                               

                               

                               

                               

                               

                               

                               

                               

                               

                               

                             

//ann_bp.h//#ifndef _ANN_BP_H_#define _ANN_BP_H_#include <assert.h>#include <stdlib.h>#include <iostream>#include <string.h>#include <windows.h>#include <ctime>class Ann_bp{public:explicit Ann_bp(int _SampleN, int nNIL, int nNOL, const int nNHL, float _sR = 0.2);~Ann_bp();void train(int _sampleNum, float** _trainMat, int** _labelMat);void predict(float* in, float* proba);private:int numNodesInputLayer;int numNodesOutputLayer;int numNodesHiddenLayer;int SampleCount;               //总的训练样本数double ***weights;            //网络权值double **bias;                 //网络偏置float studyRate;               //学习速率double *hidenLayerOutput;     //隐藏层各结点的输出值double *outputLayerOutput;     //输出层各结点的输出值double ***allDeltaBias;        //所有样本的偏置更新量double ****allDeltaWeights;    //所有样本的权值更新量double **outputMat;            //所有样本的输出层输出void train_vec(const float* _trainVec, const int* _labelVec, int index);double sigmoid(double x){ return 1 / (1 + exp(-1 * x)); }bool Ann_bp::isNotConver(const int _sampleNum, int** _labelMat, double _thresh);};#endif

//ann_bp.cpp//#include "ann_bp.h"#include <math.h>Ann_bp::Ann_bp(int _SampleN, int nNIL, int nNOL, const int nNHL, float _sR) :SampleCount(_SampleN), numNodesInputLayer(nNIL), numNodesOutputLayer(nNOL),numNodesHiddenLayer(nNHL), studyRate(_sR){//创建权值空间,并初始化srand(time(NULL));weights = new double**[2];weights[0] = new double *[numNodesInputLayer];for (int i = 0; i < numNodesInputLayer; ++i){weights[0][i] = new double[numNodesHiddenLayer];for (int j = 0; j <numNodesHiddenLayer; ++j){weights[0][i][j] = (rand() % (2000) / 1000.0 - 1); //-1到1之间}}weights[1] = new double *[numNodesHiddenLayer];for (int i = 0; i < numNodesHiddenLayer; ++i){weights[1][i] = new double[numNodesOutputLayer];for (int j = 0; j < numNodesOutputLayer; ++j){weights[1][i][j] = (rand() % (2000) / 1000.0 - 1); //-1到1之间}}//创建偏置空间，并初始化bias = new double *[2];bias[0] = new double[numNodesHiddenLayer];for (int i = 0; i < numNodesHiddenLayer; ++i){bias[0][i] = (rand() % (2000) / 1000.0 - 1); //-1到1之间}bias[1] = new double[numNodesOutputLayer];for (int i = 0; i < numNodesOutputLayer; ++i){bias[1][i] = (rand() % (2000) / 1000.0 - 1); //-1到1之间}//创建隐藏层各结点的输出值空间hidenLayerOutput = new double[numNodesHiddenLayer];//创建输出层各结点的输出值空间outputLayerOutput = new double[numNodesOutputLayer];//创建所有样本的权值更新量存储空间allDeltaWeights = new double ***[_SampleN];for (int k = 0; k < _SampleN; ++k){allDeltaWeights[k] = new double**[2];allDeltaWeights[k][0] = new double *[numNodesInputLayer];for (int i = 0; i < numNodesInputLayer; ++i){allDeltaWeights[k][0][i] = new double[numNodesHiddenLayer];}allDeltaWeights[k][1] = new double *[numNodesHiddenLayer];for (int i = 0; i < numNodesHiddenLayer; ++i){allDeltaWeights[k][1][i] = new double[numNodesOutputLayer];}}//创建所有样本的偏置更新量存储空间allDeltaBias = new double **[_SampleN];for (int k = 0; k < _SampleN; ++k){allDeltaBias[k] = new double *[2];allDeltaBias[k][0] = new double[numNodesHiddenLayer];allDeltaBias[k][1] = new double[numNodesOutputLayer];}//创建存储所有样本的输出层输出空间outputMat = new double*[ _SampleN ];for (int k = 0; k < _SampleN; ++k){outputMat[k] = new double[ numNodesOutputLayer ];}}Ann_bp::~Ann_bp(){//释放权值空间for (int i = 0; i < numNodesInputLayer; ++i)delete[] weights[0][i];for (int i = 1; i < numNodesHiddenLayer; ++i)delete[] weights[1][i];for (int i = 0; i < 2; ++i)delete[] weights[i];delete[] weights;//释放偏置空间for (int i = 0; i < 2; ++i)delete[] bias[i];delete[] bias;//释放所有样本的权值更新量存储空间for (int k = 0; k < SampleCount; ++k){for (int i = 0; i < numNodesInputLayer; ++i)delete[] allDeltaWeights[k][0][i];for (int i = 1; i < numNodesHiddenLayer; ++i)delete[] allDeltaWeights[k][1][i];for (int i = 0; i < 2; ++i)delete[] allDeltaWeights[k][i];delete[] allDeltaWeights[k];}delete[] allDeltaWeights;//释放所有样本的偏置更新量存储空间for (int k = 0; k < SampleCount; ++k){for (int i = 0; i < 2; ++i)delete[] allDeltaBias[k][i];delete[] allDeltaBias[k];}delete[] allDeltaBias;//释放存储所有样本的输出层输出空间for (int k = 0; k < SampleCount; ++k)delete[] outputMat[k];delete[] outputMat;}void Ann_bp::train(const int _sampleNum, float** _trainMat, int** _labelMat){double thre = 1e-4;for (int i = 0; i < _sampleNum; ++i){train_vec(_trainMat[i], _labelMat[i], i);}int tt = 0;while (isNotConver(_sampleNum, _labelMat, thre) && tt<100000){tt++;//调整权值for (int index = 0; index < _sampleNum; ++index){for (int i = 0; i < numNodesInputLayer; ++i){for (int j = 0; j < numNodesHiddenLayer; ++j){weights[0][i][j] -= studyRate* allDeltaWeights[index][0][i][j];}}for (int i = 0; i < numNodesHiddenLayer; ++i){for (int j = 0; j < numNodesOutputLayer; ++j){weights[1][i][j] -= studyRate* allDeltaWeights[index][1][i][j];}}}for (int index = 0; index < _sampleNum; ++index){for (int i = 0; i < numNodesHiddenLayer; ++i){bias[0][i] -= studyRate* allDeltaBias[index][0][i];}for (int i = 0; i < numNodesOutputLayer; ++i){bias[1][i] -= studyRate*allDeltaBias[index][1][i];}}for (int i = 0; i < _sampleNum; ++i){train_vec(_trainMat[i], _labelMat[i], i);}}printf("训练权值和偏置成功了！\n");}void Ann_bp::train_vec(const float* _trainVec, const int* _labelVec, int index){//计算各隐藏层结点的输出for (int i = 0; i < numNodesHiddenLayer; ++i){double z = 0.0;for (int j = 0; j < numNodesInputLayer; ++j){z += _trainVec[j] * weights[0][j][i];}z += bias[0][i];hidenLayerOutput[i] = sigmoid(z);}//计算输出层结点的输出值for (int i = 0; i < numNodesOutputLayer; ++i){double z = 0.0;for (int j = 0; j < numNodesHiddenLayer; ++j){z += hidenLayerOutput[j] * weights[1][j][i];}z += bias[1][i];outputLayerOutput[i] = sigmoid(z);outputMat[index][i] = outputLayerOutput[i];}//计算偏置及权重更新量，但不更新for (int j = 0; j <numNodesOutputLayer; ++j){allDeltaBias[index][1][j] = (-0.1)*(_labelVec[j] - outputLayerOutput[j])*outputLayerOutput[j]* (1 - outputLayerOutput[j]);for (int i = 0; i < numNodesHiddenLayer; ++i){allDeltaWeights[index][1][i][j] = allDeltaBias[index][1][j] * hidenLayerOutput[i];}}for (int j = 0; j < numNodesHiddenLayer; ++j){double z = 0.0;for (int k = 0; k < numNodesOutputLayer; ++k){z += weights[1][j][k] * allDeltaBias[index][1][k];}allDeltaBias[index][0][j] = z*hidenLayerOutput[j] * (1 - hidenLayerOutput[j]);for (int i = 0; i < numNodesInputLayer; ++i){allDeltaWeights[index][0][i][j] = allDeltaBias[index][0][j] * _trainVec[i];}}}bool Ann_bp::isNotConver(const int _sampleNum,int** _labelMat, double _thresh){double lossFunc = 0.0;for (int k = 0; k < _sampleNum; ++k){double loss = 0.0;for (int t = 0; t < numNodesOutputLayer; ++t){loss += (outputMat[k][t] - _labelMat[k][t])*(outputMat[k][t] - _labelMat[k][t]);}lossFunc += (1.0 / 2)*loss;}lossFunc = lossFunc / _sampleNum;//for (int k = 0; k < _sampleNum; ++k){//for (int i = 0; i< numNodesOutputLayer; ++i){//std::cout << outputMat[k][i] << " " ;//}//std::cout << std::endl;//}////第几次时的损失函数值//////static int tt = 0;printf("第%d次训练：", ++tt);printf("%0.12f\n", lossFunc);if (lossFunc > _thresh)return true;return false;}void Ann_bp::predict(float* in, float* proba){////////输出训练得到的权值//std::cout << "\n输出训练得到的权值:\n";//for (int i = 0; i < numNodesInputLayer; ++i){//for (int j = 0; j < numNodesHiddenLayer; ++j)//std::cout <<weights[0][i][j] << " ";//}//std::cout << "\n\n\n";//for (int i = 0; i < numNodesHiddenLayer; ++i){//for (int j = 0; j < numNodesOutputLayer; ++j)//std::cout<< weights[1][i][j] << " ";//}//std::cout << "\n输出训练得到的偏置:\n";//for (int i = 0; i < numNodesHiddenLayer; ++i)//std::cout << bias[0][i] << " ";//std::cout << "\n\n\n";//for (int j = 0; j < numNodesOutputLayer; ++j)//std::cout << bias[1][j] << " ";//Sleep(5000);//计算各隐藏层结点的输出for (int i = 0; i < numNodesHiddenLayer; ++i){double z = 0.0;for (int j = 0; j < numNodesInputLayer; ++j){z += in[j] * weights[0][j][i];}z += bias[0][i];hidenLayerOutput[i] = sigmoid(z);}//计算输出层结点的输出值for (int i = 0; i < numNodesOutputLayer; ++i){double z = 0.0;for (int j = 0; j < numNodesHiddenLayer; ++j){z += hidenLayerOutput[j] * weights[1][j][i];}z += bias[1][i];outputLayerOutput[i] = sigmoid(z);std::cout << outputLayerOutput[i] << " ";}}

//main.cpp#include "ann_bp.h"int main(){const int hidnodes = 8; //单层隐藏层的结点数const int inNodes = 10;   //输入层结点数const int outNodes = 5;  //输出层结点数const int trainClass = 5; //5个类别const int numPerClass = 30;  //每个类别30个样本点int sampleN = trainClass*numPerClass;     //每类训练样本数为30，5个类别，总的样本数为150float **trainMat = new float*[sampleN];                         //生成训练样本for (int k = 0; k < trainClass; ++k){for (int i = k * numPerClass; i < (k + 1) * numPerClass; ++i){trainMat[i] = new float[inNodes];for (int j = 0; j < inNodes; ++j){trainMat[i][j] = rand() % 1000 / 10000.0 + 0.1*(2 * k + 1);}}}int **labelMat = new int*[sampleN]; //生成标签矩阵for (int k = 0; k < trainClass; ++k){for (int i = k * numPerClass; i < (k + 1) * numPerClass; ++i){labelMat[i] = new int[outNodes];for (int j = 0; j <trainClass; ++j){if (j == k)labelMat[i][j] = 1;elselabelMat[i][j] = 0;}}}Ann_bp ann_classify(sampleN, inNodes, outNodes, hidnodes, 0.12);  //输入层为10个结点，输出层5个结点，单层隐藏层ann_classify.train(sampleN, trainMat, labelMat);for (int i = 0; i < 30; ++i){ann_classify.predict(trainMat[ i+120], NULL);std::cout << std::endl;}//释放内存for (int i = 0; i < sampleN; ++i)delete[] trainMat[i];delete[] trainMat;for (int i = 0; i < sampleN; ++i)delete[] labelMat[i];delete[] labelMat;return 0;}