bp神經網路的實現C++

標籤：cout   ++   div   c++   ror   double   code   bp神經網路   val   

#include <iostream>#include<stdlib.h>#include <math.h>using namespace std;#define  innode 2  #define  hiddennode 10#define  outnode 1 #define  sample 4class bpnet{public:    double w1[hiddennode][innode];    double w2[outnode][hiddennode];    double b1[hiddennode];    double b2[outnode];    double e;    double error;    double lr;    bpnet();    ~bpnet();    void init();    double randval(double low, double high);    void initw(double w[], int n);    void train(double p[sample][innode], double t[sample][outnode]);    double sigmod(double y);    double dsigmod(double y);    void predict(double p[]);};double bpnet::dsigmod(double y){    return y*(1 - y);}double bpnet::sigmod(double y){    return 1.0 / (1 + exp(-y));}double bpnet::randval(double low, double high){    double val;    val = ((double)rand() / (double)RAND_MAX)*(high - low) + low;    return val;}void bpnet::initw(double w[], int n){    for (int i = 0; i < n; i++)    {        w[i] = randval(-0.01, 0.01);    }}void bpnet::init(){    initw((double*)w1, hiddennode*innode);    initw((double*)w2, hiddennode*outnode);    initw(b1, hiddennode);    initw(b2, outnode);}void bpnet::train(double p[sample][innode], double t[sample][outnode]){    double hiddenerr[hiddennode];    double outerr[outnode];    double hiddenin[hiddennode];    double hiddenout[hiddennode];    double outin[outnode];    double outout[outnode];    double x[innode];    double d[outnode];    for (int k = 0; k < sample; k++)    {        for (int i = 0; i < innode; i++)        {            x[i] = p[k][i];        }        for (int i = 0; i < outnode; i++)        {            d[i] = t[k][i];        }        for (int i = 0; i < hiddennode; i++)        {            hiddenin[i] = 0.0;            for (int j = 0; j < innode; j++)            {                hiddenin[i] += w1[i][j] * x[j];            }            hiddenout[i] = sigmod(hiddenin[i] + b1[i]);        }        for (int i = 0; i < outnode; i++)        {            outin[i] = 0.0;            for (int j = 0; j < hiddennode; j++)            {                outin[i] += w2[i][j] * hiddenout[j];            }            outout[i] = sigmod(outin[i] + b2[i]);        }        for (int i = 0; i < outnode; i++)        {            outerr[i] = (d[i] - outout[i])*dsigmod(outout[i]);            for (int j = 0; j < hiddennode; j++)            {                w2[i][j] += lr*outerr[i] * hiddenout[j];            }        }        for (int i = 0; i < hiddennode; i++)        {            hiddenerr[i] = 0.0;            for (int j = 0; j < outnode; j++)            {                hiddenerr[i] += w2[j][i] * outerr[j];            }            hiddenerr[i] = hiddenerr[i] * dsigmod(hiddenout[i]);            for (int j = 0; j < innode; j++)            {                w1[i][j] += lr*hiddenerr[i] * x[j];            }        }        for (int i = 0; i < outnode; i++)        {            e += pow((d[i] - outout[i]), 2);        }        error = e / 2.0;        for (int i = 0; i < outnode; i++)        {            b2[i]=lr*outerr[i];        }        for (int i = 0; i < hiddennode; i++)        {            b1[i] =hiddenerr[i] * lr;        }    }}void bpnet::predict(double p[]){    double hiddenin[hiddennode];    double hiddenout[hiddennode];    double outin[outnode];    double outout[outnode];    double x[innode];    for (int i = 0; i < innode; i++)    {        x[i] = p[i];    }    for (int i = 0; i < hiddennode; i++)    {        hiddenin[i] = 0.0;        for (int j = 0; j < innode; j++)        {            hiddenin[i] += w1[i][j] * x[j];        }        hiddenout[i] = sigmod(hiddenin[i] + b1[i]);    }    for (int i = 0; i < outnode; i++)    {        outin[i] = 0.0;        for (int j = 0; j < hiddennode; j++)        {            outin[i] += w2[i][j] * hiddenout[j];        }        outout[i] = sigmod(outin[i] + b2[i]);    }    for (int i = 0; i < outnode; i++)    {        cout << "the prediction is"<<outout[i] << endl;    }}bpnet::bpnet(){    e = 0.0;    error = 1.0;    lr = 0.4;}bpnet::~bpnet(){}double X[sample][innode] = {    {1,1},    {1,0},    {0,1},    {0,0}};double Y[sample][outnode] = {    {1},    {0},    {0},    {1}};int main(){    bpnet bp;    bp.init();    int times = 0;    while (bp.error > 0.001&&times <10000)    {        bp.e = 0.0;        times++;        bp.train(X, Y);    }    double m[2] = { 0,1 };    bp.predict(m);    return 0;}

 

bp神經網路的實現C++