经典的BPN带动量的误差反向传播神经网络类代码示例

初始代码来自：

/*

 ******************************************************************
 * HISTORY
 * 15-Oct-94  Jeff Shufelt (js), Carnegie Mellon University
 *      Prepared for 15-681, Fall 1994.
 *
 ******************************************************************

 */

大众修改版：

/* ****************************************************************** * backprop.h ****************************************************************** */#ifndef _BACKPROP_H_#define _BACKPROP_H_#define BIGRND 0x7fffffff/*** 神经网络的数据结构。网络被假定为一个全连接的3层前向结构， 每层的单元0是阈值，这意味着真正的神经元编号为1－n ***/typedef struct {int input_n;                  /* 输入层的神经元个数 */int hidden_n;                 /* 隐含层的神经元个数 */int output_n;                 /* 输出层的神经元个数 */double *input_units;          /* 输入层的神经元 */double *hidden_units;         /* 隐藏层的神经元 */double *output_units;         /* 输出曾的神经元 */double *hidden_delta;         /* 隐藏层的误差 */double *output_delta;         /* 输出层的误差 */double *target;               /* 目标向量 */double **input_weights;       /* 输入层到隐藏层的连接权 */double **hidden_weights;      /* 隐藏层到输出曾的连接权 */                                /*** 下面两个在迭代时使用 ***/double **input_prev_weights;  /* 前次输入层导隐藏层权值的改变 */double **hidden_prev_weights; /* 前次隐藏层导输出层权值的改变 */} BPNN;/*** 用户接口 ***//* 初始化随机数种子 */void bpnn_initialize(int seed);/* 创建BP网络 */BPNN *bpnn_create(int n_in, int n_hidden, int n_out);/* 释放BP网络所占地内存空间 */void bpnn_free(BPNN *net);/* 训练BP网络 */void bpnn_train(BPNN *net, double eta, double momentum, double *eo, double *eh);/* 前向运算 */void bpnn_feedforward(BPNN* net);/* 保存BP网络到文件中 */void bpnn_save(BPNN *net, char *filename);/* 从文件中读取BP网络参数 */BPNN *bpnn_read(char *filename);//随机初始化权值void bpnn_randomize_weights(double **w, int m, int n);#endif

/* ****************************************************************** * backprop.cpp ****************************************************************** */#include "StdAfx.h"#include <stdio.h>#include "backprop.h"#include <math.h>#include <stdlib.h>#define ABS(x)          (((x) > 0.0) ? (x) : (-(x)))/* 宏定义：快速拷贝 */#define fastcopy(to,from,len)\{\register char *_to,*_from;\register int _i,_l;\_to = (char *)(to);\_from = (char *)(from);\_l = (len);\for (_i = 0; _i < _l; _i++) *_to++ = *_from++;\}/*** 返回0－1的双精度随机数 ***/double drnd(){return ((double) rand() / (double) BIGRND);}/*** 返回-1.0到1.0之间的双精度随机数 ***/double dpn1(){return ((drnd() * 2.0) - 1.0);}/*** 作用函数，目前是S型函数 ***/double squash(double x){return (1.0 / (1.0 + exp(-x)));}/*** 申请1维双精度实数数组 ***/double *alloc_1d_dbl(int n){double *new1;new1 = (double *) malloc ((unsigned) (n * sizeof (double)));if (new1 == NULL) {printf("ALLOC_1D_DBL: Couldn't allocate array of doubles\n");return (NULL);}return (new1);}/*** 申请2维双精度实数数组 ***/double **alloc_2d_dbl(int m, int n){int i;double **new1;new1 = (double **) malloc ((unsigned) (m * sizeof (double *)));if (new1 == NULL) {printf("ALLOC_2D_DBL: Couldn't allocate array of dbl ptrs\n");return (NULL);}for (i = 0; i < m; i++) {new1[i] = alloc_1d_dbl(n);}return (new1);}/*** 随机初始化权值 ***/void bpnn_randomize_weights(double **w, int m, int n){int i, j;for (i = 0; i <= m; i++) {for (j = 0; j <= n; j++) {w[i][j] = dpn1();}}}/*** 0初始化权值 ***/void bpnn_zero_weights(double **w, int m, int n){int i, j;for (i = 0; i <= m; i++) {for (j = 0; j <= n; j++) {w[i][j] = 0.0;}}}/*** 设置随机数种子 ***/void bpnn_initialize(int seed){printf("Random number generator seed: %d\n", seed);srand(seed);}/*** 创建BP网络 ***/BPNN *bpnn_internal_create(int n_in, int n_hidden, int n_out){BPNN *newnet;newnet = (BPNN *) malloc (sizeof (BPNN));if (newnet == NULL) {printf("BPNN_CREATE: Couldn't allocate neural network\n");return (NULL);}newnet->input_n = n_in;newnet->hidden_n = n_hidden;newnet->output_n = n_out;newnet->input_units = alloc_1d_dbl(n_in + 1);newnet->hidden_units = alloc_1d_dbl(n_hidden + 1);newnet->output_units = alloc_1d_dbl(n_out + 1);newnet->hidden_delta = alloc_1d_dbl(n_hidden + 1);newnet->output_delta = alloc_1d_dbl(n_out + 1);newnet->target = alloc_1d_dbl(n_out + 1);newnet->input_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);newnet->hidden_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);newnet->input_prev_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);newnet->hidden_prev_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);return (newnet);}/* 释放BP网络所占地内存空间 */void bpnn_free(BPNN *net){int n1, n2, i;n1 = net->input_n;n2 = net->hidden_n;free((char *) net->input_units);free((char *) net->hidden_units);free((char *) net->output_units);free((char *) net->hidden_delta);free((char *) net->output_delta);free((char *) net->target);for (i = 0; i <= n1; i++) {free((char *) net->input_weights[i]);free((char *) net->input_prev_weights[i]);}free((char *) net->input_weights);free((char *) net->input_prev_weights);for (i = 0; i <= n2; i++) {free((char *) net->hidden_weights[i]);free((char *) net->hidden_prev_weights[i]);}free((char *) net->hidden_weights);free((char *) net->hidden_prev_weights);free((char *) net);}/***  创建一个BP网络，并初始化权值***/BPNN *bpnn_create(int n_in, int n_hidden, int n_out){BPNN *newnet;newnet = bpnn_internal_create(n_in, n_hidden, n_out);#ifdef INITZERObpnn_zero_weights(newnet->input_weights, n_in, n_hidden);#elsebpnn_randomize_weights(newnet->input_weights, n_in, n_hidden);#endifbpnn_randomize_weights(newnet->hidden_weights, n_hidden, n_out);bpnn_zero_weights(newnet->input_prev_weights, n_in, n_hidden);bpnn_zero_weights(newnet->hidden_prev_weights, n_hidden, n_out);return (newnet);}void bpnn_layerforward(double *l1, double *l2, double **conn, int n1, int n2){double sum;int j, k;/*** 设置阈值 ***/l1[0] = 1.0;/*** 对于第二层的每个神经元 ***/for (j = 1; j <= n2; j++) {/*** 计算输入的加权总和 ***/sum = 0.0;for (k = 0; k <= n1; k++) {sum += conn[k][j] * l1[k];}l2[j] = squash(sum);}}/* 输出误差 */void bpnn_output_error(double *delta, double *target, double *output, int nj, double *err){int j;double o, t, errsum;errsum = 0.0;for (j = 1; j <= nj; j++) {o = output[j];t = target[j];delta[j] = o * (1.0 - o) * (t - o);errsum += ABS(delta[j]);}*err = errsum;}/* 隐含层误差 */void bpnn_hidden_error(double* delta_h, int nh, double *delta_o, int no, double **who, double *hidden, double *err){int j, k;double h, sum, errsum;errsum = 0.0;for (j = 1; j <= nh; j++) {h = hidden[j];sum = 0.0;for (k = 1; k <= no; k++) {sum += delta_o[k] * who[j][k];}delta_h[j] = h * (1.0 - h) * sum;errsum += ABS(delta_h[j]);}*err = errsum;}/* 调整权值 */void bpnn_adjust_weights(double *delta, int ndelta, double *ly, int nly, double** w, double **oldw, double eta, double momentum){double new_dw;int k, j;ly[0] = 1.0;for (j = 1; j <= ndelta; j++) {for (k = 0; k <= nly; k++) {new_dw = ((eta * delta[j] * ly[k]) + (momentum * oldw[k][j]));w[k][j] += new_dw;oldw[k][j] = new_dw;}}}/* 进行前向运算 */void bpnn_feedforward(BPNN* net){int in, hid, out;in = net->input_n;hid = net->hidden_n;out = net->output_n;/*** Feed forward input activations. ***/bpnn_layerforward(net->input_units, net->hidden_units,net->input_weights, in, hid);bpnn_layerforward(net->hidden_units, net->output_units,net->hidden_weights, hid, out);}/* 训练BP网络 */void bpnn_train(BPNN *net, double eta, double momentum, double *eo, double *eh){int in, hid, out;double out_err, hid_err;in = net->input_n;hid = net->hidden_n;out = net->output_n;/*** 前向输入激活 ***/bpnn_layerforward(net->input_units, net->hidden_units,net->input_weights, in, hid);bpnn_layerforward(net->hidden_units, net->output_units,net->hidden_weights, hid, out);/*** 计算隐含层和输出层误差 ***/bpnn_output_error(net->output_delta, net->target, net->output_units,out, &out_err);bpnn_hidden_error(net->hidden_delta, hid, net->output_delta, out,net->hidden_weights, net->hidden_units, &hid_err);*eo = out_err;*eh = hid_err;/*** 调整输入层和隐含层权值 ***/bpnn_adjust_weights(net->output_delta, out, net->hidden_units, hid,net->hidden_weights, net->hidden_prev_weights, eta, momentum);bpnn_adjust_weights(net->hidden_delta, hid, net->input_units, in,net->input_weights, net->input_prev_weights, eta, momentum);}/* 保存BP网络 */void bpnn_save(BPNN *net, char *filename){int n1, n2, n3, i, j, memcnt;double dvalue, **w;char *mem;FILE *fd;if ((fd = fopen(filename, "w")) == NULL) {printf("BPNN_SAVE: Cannot create '%s'\n", filename);return;}n1 = net->input_n;  n2 = net->hidden_n;  n3 = net->output_n;printf("Saving %dx%dx%d network to '%s'\n", n1, n2, n3, filename);fflush(stdout);fwrite((char *) &n1, sizeof(int), 1, fd);fwrite((char *) &n2, sizeof(int), 1, fd);fwrite((char *) &n3, sizeof(int), 1, fd);memcnt = 0;w = net->input_weights;mem = (char *) malloc ((unsigned) ((n1+1) * (n2+1) * sizeof(double)));for (i = 0; i <= n1; i++) {for (j = 0; j <= n2; j++) {dvalue = w[i][j];fastcopy(&mem[memcnt], &dvalue, sizeof(double));memcnt += sizeof(double);}}fwrite(mem, (n1+1) * (n2+1) * sizeof(double), 1, fd);free(mem);memcnt = 0;w = net->hidden_weights;mem = (char *) malloc ((unsigned) ((n2+1) * (n3+1) * sizeof(double)));for (i = 0; i <= n2; i++) {for (j = 0; j <= n3; j++) {dvalue = w[i][j];fastcopy(&mem[memcnt], &dvalue, sizeof(double));memcnt += sizeof(double);}}fwrite(mem, (n2+1) * (n3+1) * sizeof(double), 1, fd);free(mem);fclose(fd);return;}/* 从文件中读取BP网络 */BPNN *bpnn_read(char *filename){char *mem;BPNN *new1;int n1, n2, n3, i, j, memcnt;FILE *fd;if ((fd = fopen(filename, "r")) == NULL) {return (NULL);}printf("Reading '%s'\n", filename);  fflush(stdout);fread((char *) &n1, sizeof(int), 1, fd);fread((char *) &n2, sizeof(int), 1, fd);fread((char *) &n3, sizeof(int), 1, fd);new1 = bpnn_internal_create(n1, n2, n3);printf("'%s' contains a %dx%dx%d network\n", filename, n1, n2, n3);printf("Reading input weights...");  fflush(stdout);memcnt = 0;mem = (char *) malloc ((unsigned) ((n1+1) * (n2+1) * sizeof(double)));fread( mem, (n1+1) * (n2+1) * sizeof(double), 1, fd);for (i = 0; i <= n1; i++) {for (j = 0; j <= n2; j++) {fastcopy(&(new1->input_weights[i][j]), &mem[memcnt], sizeof(double));memcnt += sizeof(double);}}free(mem);printf("Done\nReading hidden weights...");  fflush(stdout);memcnt = 0;mem = (char *) malloc ((unsigned) ((n2+1) * (n3+1) * sizeof(double)));fread( mem, (n2+1) * (n3+1) * sizeof(double), 1, fd);for (i = 0; i <= n2; i++) {for (j = 0; j <= n3; j++) {fastcopy(&(new1->hidden_weights[i][j]), &mem[memcnt], sizeof(double));memcnt += sizeof(double);}}free(mem);fclose(fd);printf("Done\n");  fflush(stdout);bpnn_zero_weights(new1->input_prev_weights, n1, n2);bpnn_zero_weights(new1->hidden_prev_weights, n2, n3);return (new1);}

使用该BP神经网络识别数字1-9的例子：