backprop.cpp

常用的模型和算法介绍。有BP神经网络的C语言实现

/*
 ******************************************************************
 * 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 INITZERO
	bpnn_zero_weights(newnet->input_weights, n_in, n_hidden);
#else
	bpnn_randomize_weights(newnet->input_weights, n_in, n_hidden);
#endif
	bpnn_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);
}