代码笔记 | BP神经网络的C语言代码

来源：互联网发布：javascript 控制height 编辑：程序博客网时间：2024/05/21 11:12
 #include<stdlib.h>#include<time.h>#include<math.h>#include<stdio.h>  typedef enum {CUSTOM,SIGMOD} ActionType;  typedef double(*Function)(double);  typedef struct {    int cbSize;             //神经网络所占用的内存空间    int szLayer;            //层数    double eta;    double momentum;    int *layer;             //每层的结点数    ActionType actionType;  //激活函数类型    Function act;           //激活函数    Function actdiff;       //激活函数的导数    double **weights;       //权值    double **preWeights;    //前一时刻的权值    double **delta;         //误差值    double **theta;         //阈值    double **preTheta;      //前一时刻的阈值    double **output;        //每层结点的输出值    void* buffer[0];        //用于存储结点数、权值、前一时刻的权值、误差值、阈值、前一时刻的阈值、结点输出值的空间}BPAnn;  void MatXMat(double mat1[], double mat2[], double output[], int row, int column, int lcolrrow){    for (int i = 0; i < row; ++i)        for (int j = 0; j < column; ++j)        {            int pos = column * i + j;            output[pos] = 0;            for (int k = 0; k < lcolrrow; ++k)                output[pos] += mat1[lcolrrow * i + k] * mat2[column * k + j];        }}  //随机生成－1.0～1.0之间的随机浮点数double lfrand(){          static int randbit = 0;    if (!randbit)    {        srand((unsigned)time(0));        for (int i = RAND_MAX; i; i >>= 1, ++randbit);    }    unsigned long long lvalue = 0x4000000000000000L;    int i = 52 - randbit;    for (; i > 0; i -= randbit)        lvalue |= (unsigned long long)rand() << i;    lvalue |= (unsigned long long)rand() >> -i;    return *(double *)&lvalue - 3;}  double Sigmod(double x){    return 1 / (1 + exp(-x));}  double SigmodDiff(double y){    return y*(1 - y);}  static int GetCbSize(int szLayer,int layer[]){    int cbSize = sizeof(BPAnn);    cbSize += sizeof(int)*szLayer;    cbSize += sizeof(double *)*(szLayer*6-5);    cbSize += sizeof(double)*layer[0];    for (int i = 1; i < szLayer; ++i)    {        cbSize += sizeof(double)*layer[i] * layer[i - 1]*2;        cbSize += sizeof(double)*layer[i]*4;    }    return cbSize;}  void InitBPAnn(void *buffer){    BPAnn *pBPAnn = (BPAnn *)buffer;    switch (pBPAnn->actionType) {        case SIGMOD:            pBPAnn->act=Sigmod;            pBPAnn->actdiff = SigmodDiff;            break;                      default:            pBPAnn->act = 0;            pBPAnn->actdiff = 0;            break;    }          int szLayer = pBPAnn->szLayer;    pBPAnn->layer = (int *)pBPAnn->buffer;    pBPAnn->output = (double **)(pBPAnn->layer+szLayer);    pBPAnn->delta = pBPAnn->output+szLayer;    pBPAnn->weights = pBPAnn->delta + szLayer - 1;    pBPAnn->theta = pBPAnn->weights + szLayer - 1;    pBPAnn->preWeights = pBPAnn->theta + szLayer - 1;    pBPAnn->preTheta = pBPAnn->preWeights + szLayer - 1;          *(pBPAnn->output) = (double *)(pBPAnn->preTheta + szLayer - 1);    int *layer = pBPAnn->layer;    for (int i = 0; i < szLayer; ++i)        pBPAnn->output[i+1]=pBPAnn->output[i]+layer[i];    for (int i=0;i<szLayer - 1;++i)        pBPAnn->delta[i+1]=pBPAnn->delta[i]+layer[i+1];    for (int i = 0; i < szLayer - 1; ++i)        pBPAnn->weights[i+1]=pBPAnn->weights[i]+layer[i] * layer[i + 1];    for(int i=0;i<szLayer - 1;++i)        pBPAnn->theta[i+1]=pBPAnn->theta[i]+layer[i+1];    long long tmp = pBPAnn->theta[szLayer - 1]-pBPAnn->weights[0];    for(int i=0;i<szLayer - 1;++i)    {        pBPAnn->preWeights[i]=pBPAnn->weights[i]+tmp;        pBPAnn->preTheta[i] = pBPAnn->theta[i]+tmp;    }}  int SaveBPAnn(BPAnn *pBPAnn,const char *filename){    if(!pBPAnn) return 0;    FILE *fp =0;    if((fp = fopen(filename, "wb+")))    {        fwrite(pBPAnn, 1, pBPAnn->cbSize, fp);        return fclose(fp);    }    return 0;}  BPAnn* LoadBPAnn(const char *filename){    if(!filename) return 0;    FILE *fp = 0;    if((fp=fopen(filename, "rb+")))    {        int szfile = 0;        fread(&szfile, 4, 1, fp);        fseek(fp, 0, SEEK_SET);        BPAnn *pBPAnn = (BPAnn *)malloc(szfile);        if(fread(pBPAnn, 1, szfile, fp)<szfile) return 0;        InitBPAnn(pBPAnn);        return pBPAnn;    }    return 0;}  BPAnn* CreateBPAnn(double eta, double momentum, int layer[],int szLayer, ActionType actionType){    int cbSize = GetCbSize(szLayer, layer);    BPAnn* pBPAnn = (BPAnn *)malloc(cbSize);    pBPAnn->cbSize = cbSize;    pBPAnn->eta = eta;    pBPAnn->momentum = momentum;    pBPAnn->szLayer = szLayer;    pBPAnn->actionType = actionType;    pBPAnn->layer = (int *)pBPAnn->buffer;    for(int i=0;i<szLayer;++i)        pBPAnn->layer[i] = layer[i];    InitBPAnn(pBPAnn);    for(double *i=pBPAnn->weights[0];i!=pBPAnn->preWeights[0];++i)        *i=lfrand();    for(double *i=pBPAnn->preWeights[0];i!=(double *)((unsigned char *)pBPAnn+cbSize);++i)        *i=0;    return pBPAnn;}  int DestroyBPAnn(BPAnn *pBPAnn){    if (!pBPAnn) return 0;    free(pBPAnn);    return 1;}  static void LoadInput(double input[],BPAnn *pBPAnn){    for (int i = 0; i < pBPAnn->layer[0]; ++i)        pBPAnn->output[0][i] = input[i];}  static void LoadTarget(double target[], BPAnn *pBPAnn){    int lastIndex = pBPAnn->szLayer - 1;    double *delta = pBPAnn->delta[lastIndex - 1];    double *output = pBPAnn->output[lastIndex];    for (int i = 0; i < pBPAnn->layer[lastIndex]; ++i)        delta[i] = pBPAnn->actdiff(output[i])*(target[i] - output[i]);}  static void Forward(BPAnn *pBPAnn){    int lastIndex = pBPAnn->szLayer - 1;    int *layer = pBPAnn->layer;    double **weights = pBPAnn->weights;    double **output = pBPAnn->output;    double **theta = pBPAnn->theta;    Function act = pBPAnn->act;    for (int i = 0; i < lastIndex; ++i)    {        MatXMat(output[i], weights[i], output[i + 1], 1, layer[i + 1], layer[i]);        for (int j = 0; j < layer[i + 1]; ++j)            output[i + 1][j] = act(output[i + 1][j] + theta[i][j]);    }}  static void CalculateDelta(BPAnn *pBPAnn){    int lastIndex = pBPAnn->szLayer - 1;    int *layer = pBPAnn->layer;    double **weights = pBPAnn->weights;    double **output = pBPAnn->output;    double **delta = pBPAnn->delta;    Function actdiff = pBPAnn->actdiff;    for (int i = lastIndex-1; i > 0; --i)    {        MatXMat(weights[i], delta[i], delta[i - 1], layer[i], 1, layer[i + 1]);        for (int j = 0; j < layer[i]; ++j)            delta[i - 1][j] *= actdiff(output[i][j]);    }}  static void AdjustWeights(BPAnn *pBPAnn){    int lastIndex = pBPAnn->szLayer - 1;    int *layer = pBPAnn->layer;    double **weights = pBPAnn->weights;    double **output = pBPAnn->output;    double **delta = pBPAnn->delta;    double **preWeights = pBPAnn->preWeights;    double **theta = pBPAnn->theta;    double **preTheta = pBPAnn->preTheta;    double momentum = pBPAnn->momentum;    double eta = pBPAnn->eta;    for (int i = 0; i < lastIndex; ++i)    {        for (int j = 0; j < layer[i]; ++j)            for (int k = 0; k < layer[i + 1]; ++k)            {                int pos = j*layer[i + 1] + k;                preWeights[i][pos] = momentum * preWeights[i][pos] + eta * delta[i][k] * output[i][j];                weights[i][pos] += preWeights[i][pos];            }                  for (int j = 0; j < layer[i + 1]; ++j)        {            preTheta[i][j] = momentum*preTheta[i][j] + eta*delta[i][j];            theta[i][j] += preTheta[i][j];        }    }}  void Train(double input[], double target[],BPAnn *pBPAnn){    if(pBPAnn->act&&pBPAnn->actdiff)    {        LoadInput(input, pBPAnn);        Forward(pBPAnn);        LoadTarget(target,pBPAnn);        CalculateDelta(pBPAnn);        AdjustWeights(pBPAnn);    }}  void Predict(double input[],double output[],BPAnn *pBPAnn){    if(pBPAnn->act&&pBPAnn->actdiff)    {        int lastIndex = pBPAnn->szLayer - 1;        LoadInput(input, pBPAnn);        Forward(pBPAnn);        double *result = pBPAnn->output[lastIndex];        for (int i = 0; i < pBPAnn->layer[lastIndex]; ++i)            output[i] = result[i];    }}      static void ToBinary(unsigned x, unsigned n,double output[]){    for (unsigned i = 0, j = x; i < n; ++i, j >>= 1)        output[i] = j & 1;}  static unsigned FromBinary(double output[],unsigned n){    int result = 0;    for (int i = n - 1; i >= 0; --i)        result = result << 1 | (output[i] > 0.5) ;//对输出结果四舍五入，并通过二进制转换为数    return result;}  //使用神经网络进行异或运算，输入为2个0~32767之间的数，前15节点为第1个数二进制，后15节点为第2个数二进制，输出为结果的二进制static void TestXor(){    int layer[] = { 30,48,15 };    BPAnn *bp = CreateBPAnn(0.25, 0.9, layer, 3, SIGMOD);    double input[30], output[15];    int error = 0;    for (int j = 0; j < 100; ++j) {        for (int i = 0; i < 400; ++i)        {            unsigned x = rand() & 32767;            unsigned y = rand() & 32767;            ToBinary(x << 15 | y, 30, input);            ToBinary(x^y, 15, output);            Train(input, output, bp);        }        printf("%02d%%\\n", j + 1);    }    printf("\\nfinish train\\n");    for (int i = 0; i < 2000; ++i)    {        unsigned x = rand() & 32767;        unsigned y = rand() & 32767;        ToBinary(x << 15 | y, 30, input);        Predict(input, output, bp);        unsigned result = FromBinary(output, 15);        if(result != (x^y))        {            ++error;            printf("%u^%u=%u\\tpredict=%u\\n", x, y, x^y, result);        }    }    printf("error = %d\\n", error);    SaveBPAnn(bp,"BP.ann");    DestroyBPAnn(bp);}  int main(){    TestXor();    return  0;}
0 0