c++实现mlp神经网络

之前一直用theano训练样本，最近需要转成c或c++实现。在网上参考了一下其它代码，还是喜欢c++。但是看了几份cpp代码之后，发现都多少有些bug，很不爽。由于本人编码能力较弱，还花了不少时间改正。另外又添加了写权值和读权值的功能，可以保存训练的结果。下面是代码实现的基本功能描述。

问题描述：

用cpp重写mlp，即普通的多层神经网络。需要实现多个隐藏层与输出层互连，分类层采用softmax分类。

测试例子：

测试例子为自己构造，将3位二进制转为10进制有8中可能，我分别让它们对应label0-7。例如：001对应的label为1,111对应的label为7，以此类推

下面看代码：

main.cpp

#include <iostream>
#include "NeuralNetwork.h"
#include "util.h"
using namespace std;
/*main函数中调用的两个函数功能一样
*将3位二进制分类成十进制
*test_lr用的是单层的softmax回归
*mlp是含有多个隐藏层的神经网络
*/
int main()
{
	cout << "****softmax****" << endl;
	test_lr();
	cout << "****mlp****" << endl;
	mlp();
    return 0;
}

test_lr函数为softmax测试，先看它的相关文件

LogisticRegression.h

#ifndef LOGISTICREGRESSIONLAYER
#define LOGISTICREGRESSIONLAYER

class LogisticRegression
{
public:
	LogisticRegression(int n_i, int i_o, int);
	~LogisticRegression();

	void forward_propagation(double* input_data);
	void back_propagation(double* input_data, double* label, double lr);
	void softmax(double* x);
	void printwb();
	void writewb(const char *pcname);
	long readwb(const char *pcname, long);
	void setwb(double ppdw[][3], double [8]);
	void train(double *x, double *y, double lr);

	int predict(double *);
	double cal_error(double **ppdtest, double* pdlabel, int ibatch);
	//double cal_error(double* label);
	void makeLabels(int* pimax, double (*pplabels)[8]);

	//本层前向传播的输出值，也是最终的预测值
	double* output_data;
	//反向传播时所需值
	double* delta;

public:
	int n_in;
	int n_out;
	int n_train;
	double** w;
	double* b;

};

void test_lr();
void testwb();
#endif

头文件中的testwb为测试权值的读写而测试的，可以先不用理会

LogisticRegression.cpp

#include <cmath>
#include <ctime>
#include <iostream>
#include "LogisticRegression.h"
#include "util.h"

using namespace std;

LogisticRegression::LogisticRegression(int n_i, int n_o, int n_t)
{
    n_in = n_i;
    n_out = n_o;
    n_train = n_t;

    w = new double* [n_out];
    for(int i = 0; i < n_out; ++i)
    {
        w[i] = new double [n_in];
    }
    b = new double [n_out];

    double a = 1.0 / n_in;

    srand((unsigned)time(NULL));
    for(int i = 0; i < n_out; ++i)
    {
        for(int j = 0; j < n_in; ++j)
            w[i][j] = uniform(-a, a);
        b[i] = uniform(-a, a);
    }

    delta = new double [n_out];
    output_data = new double [n_out];
}

LogisticRegression::~LogisticRegression()
{
    for(int i=0; i < n_out; i++)
		delete []w[i];
    delete[] w;
    delete[] b;
    delete[] output_data;
    delete[] delta;
}

void LogisticRegression::printwb()
{
    cout << "'****w****\n";
    for(int i = 0; i < n_out; ++i)
    {
        for(int j = 0; j < n_in; ++j)
            cout << w[i][j] << ' ';
        cout << endl;
            //w[i][j] = uniform(-a, a);
    }
    cout << "'****b****\n";
    for(int i = 0; i < n_out; ++i)
    {
        cout << b[i] << ' ';
    }
    cout << endl;
	cout << "'****output****\n";
    for(int i = 0; i < n_out; ++i)
    {
        cout << output_data[i] << ' ';
    }
    cout << endl;

}
void LogisticRegression::softmax(double* x)
{
    double _max = 0.0;
    double _sum = 0.0;

    for(int i = 0; i < n_out; ++i)
    {
        if(_max < x[i])
            _max = x[i];
    }
    for(int i = 0; i < n_out; ++i)
    {
        x[i] = exp(x[i]-_max);
        _sum += x[i];
    }

    for(int i = 0; i < n_out; ++i)
    {
        x[i] /= _sum;
    }
}

void LogisticRegression::forward_propagation(double* input_data)
{
    for(int i = 0; i < n_out; ++i)
    {
        output_data[i] = 0.0;
        for(int j = 0; j < n_in; ++j)
        {
            output_data[i] += w[i][j]*input_data[j];
        }
        output_data[i] += b[i];
    }

    softmax(output_data);
}

void LogisticRegression::back_propagation(double* input_data, double* label, double lr)
{
    for(int i = 0; i < n_out; ++i)
    {
        delta[i] = label[i] - output_data[i] ;
        for(int j = 0; j < n_in; ++j)
        {
            w[i][j] += lr * delta[i] * input_data[j] / n_train;
        }
        b[i] += lr * delta[i] / n_train;
    }
}

int LogisticRegression::predict(double *x)
{
	forward_propagation(x);
	cout << "***result is ***" << endl;
	int iresult = getMaxIndex(output_data, n_out);
	cout << iresult << endl;
	if (iresult == 1)
		printArr(output_data, n_out);
	return iresult;

}
void LogisticRegression::train(double *x, double *y, double lr)
{
    forward_propagation(x);
	back_propagation(x, y, lr);
}
//这个函数目前还用不到，大家不用看
double LogisticRegression::cal_error(double **ppdtest, double* pdlabel, int ibatch)
{
    double error = 0.0, dmax = 0;
	int imax = -1, ierrNum = 0;
	for (int i = 0; i < ibatch; ++i)
	{
		imax = predict(ppdtest[i]);
		if (imax != pdlabel[i])
			++ierrNum;
	}
	error = (double)ierrNum / ibatch;
	return error;
}
void LogisticRegression::writewb(const char *pcname)
{
	savewb(pcname, w, b, n_out, n_in);
}
long LogisticRegression::readwb(const char *pcname, long dstartpos)
{
	return loadwb(pcname, w, b, n_out, n_in, dstartpos);
}

void LogisticRegression::setwb(double ppdw[][3], double szib[8])
{
	for (int i = 0; i < n_out; ++i)
	{
		for (int j = 0; j < n_in; ++j)
			w[i][j] = ppdw[i][j];
		b[i] = szib[i];
	}
	cout << "setwb----------" << endl;
	printArrDouble(w, n_out, n_in);
	printArr(b, n_out);
}

void LogisticRegression::makeLabels(int* pimax, double (*pplabels)[8])
{
	for (int i = 0; i < n_train; ++i)
	{
		for (int j = 0; j < n_out; ++j)
			pplabels[i][j] = 0;
		int k = pimax[i];
		pplabels[i][k] = 1.0;
	}
}

void test_lr()
{
    srand(0);

    double learning_rate = 0.1;
    double n_epochs = 200;

    int test_N = 2;
    const int trainNum = 8, n_in = 3, n_out = 8;
	//int n_out = 2;
	double train_X[trainNum][n_in] = {
		{1, 1, 1},
		{1, 1, 0},
		{1, 0, 1},
		{1, 0, 0},
		{0, 1, 1},
		{0, 1, 0},
		{0, 0, 1},
		{0, 0, 0}
	};
	//szimax存储的是最大值的下标
	int szimax[trainNum];
	for (int i = 0; i < trainNum; ++i)
		szimax[i] = trainNum - i - 1;
	double train_Y[trainNum][n_out];

	// construct LogisticRegression
	LogisticRegression classifier(n_in, n_out, trainNum);

	classifier.makeLabels(szimax, train_Y);
	// train online
	for(int epoch=0; epoch<n_epochs; epoch++) {
		for(int i=0; i<trainNum; i++) {
            //classifier.trainEfficient(train_X[i], train_Y[i], learning_rate);
            classifier.train(train_X[i], train_Y[i], learning_rate);
        }
    }

	const char *pcfile = "test.wb";
	classifier.writewb(pcfile);

	LogisticRegression logistic(n_in, n_out, trainNum);
	logistic.readwb(pcfile, 0);
    // test data
    double test_X[2][n_out] = {
        {1, 0, 1},
        {0, 0, 1}
    };
	 // test
	cout << "before readwb ---------" << endl;
    for(int i=0; i<test_N; i++) {
		classifier.predict(test_X[i]);
        cout << endl;
    }
	cout << "after readwb ---------" << endl;
    for(int i=0; i<trainNum; i++) {
		logistic.predict(train_X[i]);
        cout << endl;
    }
    cout << "*********\n";

}
void testwb()
{

    int test_N = 2;
    const int trainNum = 8, n_in = 3, n_out = 8;
	//int n_out = 2;
	double train_X[trainNum][n_in] = {
		{1, 1, 1},
		{1, 1, 0},
		{1, 0, 1},
		{1, 0, 0},
		{0, 1, 1},
		{0, 1, 0},
		{0, 0, 1},
		{0, 0, 0}
	};
	double szib[n_out] = {1, 2, 3, 3, 3, 3, 2, 1};

	// construct LogisticRegression
	LogisticRegression classifier(n_in, n_out, trainNum);

	classifier.setwb(train_X, szib);

	const char *pcfile = "test.wb";
	classifier.writewb(pcfile);

	LogisticRegression logistic(n_in, n_out, trainNum);
	logistic.readwb(pcfile, 0);

}

下面是mlp神经网络组合起来的代码,这个就是将隐藏层与softmax层互连，当时调试了好久好久。。。

NeuralNetwork.h

#ifndef NEURALNETWORK_H
#define NEURALNETWORK_H

#include "HiddenLayer.h"
#include "LogisticRegression.h"

class NeuralNetwork
{
public:
    NeuralNetwork(int n, int n_i, int n_o, int nhl, int*hls);
    ~NeuralNetwork();

    void train(double** in_data, double** in_label, double lr, int epochs);
    void predict(double** in_data, int n);

	void writewb(const char *pcname);
	void readwb(const char *pcname);

private:
    int N; //样本数量
    int n_in; //输入维数
    int n_out; //输出维数
    int n_hidden_layer; //隐层数目
    int* hidden_layer_size; //中间隐层的大小 e.g. {3,4}表示有两个隐层，第一个有三个节点，第二个有4个节点

    HiddenLayer **sigmoid_layers;
    LogisticRegression *log_layer;
};

void mlp();

#endif

mlp()就是测试整个神经网络基本功能的代码，写的比较乱。基本就是先用一个mlp训练，然后保存权值。之后另一个mlp读取权值，预测结果。

NeuralNetwork.cpp

#include <iostream>
#include "NeuralNetwork.h"
#include "util.h"
//#include "HiddenLayer.h"
//#include "LogisticRegression.h"

using namespace std;

const int n_train = 8, innode = 3, outnode = 8;
NeuralNetwork::NeuralNetwork(int n, int n_i, int n_o, int nhl, int *hls)
{
	N = n;
	n_in = n_i;
	n_out = n_o;

	n_hidden_layer = nhl;
	hidden_layer_size = hls;

	//构造网络结构
	sigmoid_layers = new HiddenLayer* [n_hidden_layer];
	for(int i = 0; i < n_hidden_layer; ++i)
	{
		if(i == 0)
		{
			sigmoid_layers[i] = new HiddenLayer(n_in, hidden_layer_size[i]);//第一个隐层
		}
		else
		{
			sigmoid_layers[i] = new HiddenLayer(hidden_layer_size[i-1], hidden_layer_size[i]);//其他隐层
		}
	}

	log_layer = new LogisticRegression(hidden_layer_size[n_hidden_layer-1], n_out, N);//最后的softmax层
}

NeuralNetwork::~NeuralNetwork()
{
	//二维指针分配的对象不一定是二维数组
	for(int i = 0; i < n_hidden_layer; ++i)
		delete sigmoid_layers[i];  //删除的时候不能加[]
	delete[] sigmoid_layers;
	//log_layer只是一个普通的对象指针，不能作为数组delete
	delete log_layer;//删除的时候不能加[]
}

void NeuralNetwork::train(double** ppdinData, double** ppdinLabel, double dlr, int iepochs)
{
	printArrDouble(ppdinData, N, n_in);

	cout << "******label****" << endl;
	printArrDouble(ppdinLabel, N, n_out);

	//反复迭代样本iepochs次训练
	for(int epoch = 0; epoch < iepochs; ++epoch)
	{
		double e = 0.0;
		for(int i = 0; i < N; ++i)
		{
			//前向传播阶段
			for(int n = 0; n < n_hidden_layer; ++ n)
			{
				if(n == 0) //第一个隐层直接输入数据
				{
					sigmoid_layers[n]->forward_propagation(ppdinData[i]);
				}
				else //其他隐层用前一层的输出作为输入数据
				{
					sigmoid_layers[n]->forward_propagation(sigmoid_layers[n-1]->output_data);
				}
			}
			//softmax层使用最后一个隐层的输出作为输入数据
			log_layer->forward_propagation(sigmoid_layers[n_hidden_layer-1]->output_data);

			//e += log_layer->cal_error(ppdinLabel[i]);

			//反向传播阶段
			log_layer->back_propagation(sigmoid_layers[n_hidden_layer-1]->output_data, ppdinLabel[i], dlr);

			for(int n = n_hidden_layer-1; n >= 1; --n)
			{
				if(n == n_hidden_layer-1)
				{
					sigmoid_layers[n]->back_propagation(sigmoid_layers[n-1]->output_data,
						log_layer->delta, log_layer->w, log_layer->n_out, dlr, N);
				}
				else
				{
					double *pdinputData;
					pdinputData = sigmoid_layers[n-1]->output_data;

					sigmoid_layers[n]->back_propagation(pdinputData,
						sigmoid_layers[n+1]->delta, sigmoid_layers[n+1]->w, sigmoid_layers[n+1]->n_out, dlr, N);
				}
			}
			//这里该怎么写？
			if (n_hidden_layer > 1)
				sigmoid_layers[0]->back_propagation(ppdinData[i],
				sigmoid_layers[1]->delta, sigmoid_layers[1]->w, sigmoid_layers[1]->n_out, dlr, N);
			else
				sigmoid_layers[0]->back_propagation(ppdinData[i],
				log_layer->delta, log_layer->w, log_layer->n_out, dlr, N);
		}
		//if (epoch % 100 == 1)
		//cout << "iepochs number is " << epoch << "   cost function is " << e / (double)N << endl;
	}

}

void NeuralNetwork::predict(double** ppdata, int n)
{

	for(int i = 0; i < n; ++i)
	{
		for(int n = 0; n < n_hidden_layer; ++ n)
		{
			if(n == 0) //第一个隐层直接输入数据
			{
				sigmoid_layers[n]->forward_propagation(ppdata[i]);
			}
			else //其他隐层用前一层的输出作为输入数据
			{
				sigmoid_layers[n]->forward_propagation(sigmoid_layers[n-1]->output_data);
			}
		}
		//softmax层使用最后一个隐层的输出作为输入数据
		log_layer->predict(sigmoid_layers[n_hidden_layer-1]->output_data);
		//log_layer->forward_propagation(sigmoid_layers[n_hidden_layer-1]->output_data);
	}
}

void NeuralNetwork::writewb(const char *pcname)
{
	for(int i = 0; i < n_hidden_layer; ++i)
	{
		sigmoid_layers[i]->writewb(pcname);
	}
	log_layer->writewb(pcname);

}
void NeuralNetwork::readwb(const char *pcname)
{
	long dcurpos = 0, dreadsize = 0;
	for(int i = 0; i < n_hidden_layer; ++i)
	{
		dreadsize = sigmoid_layers[i]->readwb(pcname, dcurpos);
		cout << "hiddenlayer " << i + 1 << " read bytes: " << dreadsize << endl;
		if (-1 != dreadsize)
			dcurpos += dreadsize;
		else
		{
			cout << "read wb error from HiddenLayer" << endl;
			return;
		}
	}
	dreadsize = log_layer->readwb(pcname, dcurpos);
	if (-1 != dreadsize)
		dcurpos += dreadsize;
	else
	{
		cout << "read wb error from sofmaxLayer" << endl;
		return;
	}
}
//double **makeLabelSample(double **label_x)
double **makeLabelSample(double label_x[][outnode])
{
	double **pplabelSample;
	pplabelSample = new double*[n_train];
	for (int i = 0; i < n_train; ++i)
	{
		pplabelSample[i] = new double[outnode];
	}

	for (int i = 0; i < n_train; ++i)
	{
		for (int j = 0; j < outnode; ++j)
			pplabelSample[i][j] = label_x[i][j];
	}
	return pplabelSample;
}
double **maken_train(double train_x[][innode])
{
	double **ppn_train;
	ppn_train = new double*[n_train];
	for (int i = 0; i < n_train; ++i)
	{
		ppn_train[i] = new double[innode];
	}

	for (int i = 0; i < n_train; ++i)
	{
		for (int j = 0; j < innode; ++j)
			ppn_train[i][j] = train_x[i][j];
	}
	return ppn_train;
}
void mlp()
{
	//输入样本
	double X[n_train][innode]= {
		{0,0,0},{0,0,1},{0,1,0},{0,1,1},{1,0,0},{1,0,1},{1,1,0},{1,1,1}
	};

	double Y[n_train][outnode]={
		{1, 0, 0, 0, 0, 0, 0, 0},
		{0, 1, 0, 0, 0, 0, 0, 0},
		{0, 0, 1, 0, 0, 0, 0, 0},
		{0, 0, 0, 1, 0, 0, 0, 0},
		{0, 0, 0, 0, 1, 0, 0, 0},
		{0, 0, 0, 0, 0, 1, 0, 0},
		{0, 0, 0, 0, 0, 0, 1, 0},
		{0, 0, 0, 0, 0, 0, 0, 1},
	};
	const int ihiddenSize = 2;
	int phidden[ihiddenSize] = {5, 5};
	//printArr(phidden, 1);
	NeuralNetwork neural(n_train, innode, outnode, ihiddenSize, phidden);
	double **train_x, **ppdlabel;
	train_x = maken_train(X);
	//printArrDouble(train_x, n_train, innode);
	ppdlabel = makeLabelSample(Y);
	neural.train(train_x, ppdlabel, 0.1, 3500);
	cout<<"trainning complete..."<<endl;
	//pcname存放权值
	const char *pcname = "mlp55new.wb";
	neural.writewb(pcname);
	NeuralNetwork neural2(n_train, innode, outnode, ihiddenSize, phidden);
	cout<<"readwb start..."<<endl;
	neural2.readwb(pcname);
	cout<<"readwb end..."<<endl;
	neural.predict(train_x, n_train);
	cout << "----------after readwb________" << endl;
	neural2.predict(train_x, n_train);

	for (int i = 0; i != n_train; ++i)
	{
		delete []train_x[i];
		delete []ppdlabel[i];
	}
	delete []train_x;
	delete []ppdlabel;
	cout<<endl;
}

HiddenLayer.h

#ifndef HIDDENLAYER_H
#define HIDDENLAYER_H

class HiddenLayer{
public:
	HiddenLayer(int n_i, int n_o);
	~HiddenLayer();

	void forward_propagation(double* input_data);

	void back_propagation(double *pdinputData, double *pdnextLayerDelta,
        double** ppdnextLayerW, int iNextLayerOutNum, double dlr, int N);

	void writewb(const char *pcname);
	long readwb(const char *pcname, long);

	//本层前向传播的输出值,作为下一层的输入值
	double* output_data;
	//反向传播时所需值
	double* delta;

public:
	int n_in;
	int n_out;
	double** w;
	double*b;
};

#endif

HiddenLayer.cpp

#include <cmath>
#include <cstdlib>
#include <ctime>
#include <iostream>
#include "HiddenLayer.h"
#include "util.h"

using namespace std;

HiddenLayer::HiddenLayer(int n_i, int n_o)
{
	n_in  = n_i;
	n_out = n_o;

	w = new double* [n_out];
	for(int i = 0; i < n_out; ++i)
	{
		w[i] = new double [n_in];
	}
	b = new double [n_out];

	double a = 1.0 / n_in;

	srand((unsigned)time(NULL));
	for(int i = 0; i < n_out; ++i)
	{
		for(int j = 0; j < n_in; ++j)
			w[i][j] = uniform(-a, a);
		b[i] = uniform(-a, a);
	}

	delta = new double [n_out];
	output_data = new double [n_out];
}

HiddenLayer::~HiddenLayer()
{
	for(int i=0; i<n_out; i++)
		delete []w[i];
	delete[] w;
	delete[] b;
	delete[] output_data;
	delete[] delta;
}
void HiddenLayer::forward_propagation(double* pdinputData)
{
	for(int i = 0; i < n_out; ++i)
	{
		output_data[i] = 0.0;
		for(int j = 0; j < n_in; ++j)
		{
			output_data[i] += w[i][j]*pdinputData[j];
		}
		output_data[i] += b[i];

		output_data[i] = sigmoid(output_data[i]);
	}
}

void HiddenLayer::back_propagation(double *pdinputData, double *pdnextLayerDelta,
								   double** ppdnextLayerW, int iNextLayerOutNum, double dlr, int N)
{
	/*
	pdinputData          为输入数据
	*pdnextLayerDelta   为下一层的残差值delta,是一个大小为iNextLayerOutNum的数组
	**ppdnextLayerW      为此层到下一层的权值
	iNextLayerOutNum    实际上就是下一层的n_out
	dlr                  为学习率learning rate
	N                   为训练样本总数
	*/

	//sigma元素个数应与本层单元个数一致，而网上代码有误
	//作者是没有自己测试啊，测试啊
	//double* sigma = new double[iNextLayerOutNum];
	double* sigma = new double[n_out];
	//double sigma[10];
	for(int i = 0; i < n_out; ++i)
		sigma[i] = 0.0;

	for(int i = 0; i < iNextLayerOutNum; ++i)
	{
		for(int j = 0; j < n_out; ++j)
		{
			sigma[j] += ppdnextLayerW[i][j] * pdnextLayerDelta[i];
		}
	}
	//计算得到本层的残差delta
	for(int i = 0; i < n_out; ++i)
	{
		delta[i] = sigma[i] * output_data[i] * (1 - output_data[i]);
	}

	//调整本层的权值w
	for(int i = 0; i < n_out; ++i)
	{
		for(int j = 0; j < n_in; ++j)
		{
			w[i][j] += dlr * delta[i] * pdinputData[j];
		}
		b[i] += dlr * delta[i];
	}
	delete[] sigma;
}

void HiddenLayer::writewb(const char *pcname)
{
	savewb(pcname, w, b, n_out, n_in);
}
long HiddenLayer::readwb(const char *pcname, long dstartpos)
{
	return loadwb(pcname, w, b, n_out, n_in, dstartpos);
}

下面是一个工具文件

util.h

#ifndef UTIL_H
#define UTIL_H

typedef unsigned char BYTE;
double sigmoid(double x);

double uniform(double _min, double _max);
//void printArr(T *parr, int num);
//void printArrDouble(double **pparr, int row, int col);
void initArr(double *parr, int num);
int getMaxIndex(double *pdarr, int num);
void savewb(const char *pcname, double **ppw, double *pb,
			 int irow, int icol);
long loadwb(const char *pcname, double **ppw, double *pb,
			int irow, int icol, long dstartpos);
void readonefile(const char *pcname);
void writeonefile(const char *pcname);

template <typename T>
void printArr(T *parr, int num)
{
	cout << "****printArr****" << endl;

	for (int i = 0; i < num; ++i)
		cout << parr[i] << ' ';
	cout << endl;
}
template <typename T>
void printArrDouble(T **pparr, int row, int col)
{
	cout << "****printArrDouble****" << endl;
	for (int i = 0; i < row; ++i)
	{
		for (int j = 0; j < col; ++j)
		{
			cout << pparr[i][j] << ' ';
		}
		cout << endl;
	}
}

#endif

util.cpp

#include "util.h"
#include <iostream>
#include <ctime>
#include <cmath>

using namespace std;

int getMaxIndex(double *pdarr, int num)
{
	double dmax = -1;
	int imax = -1;
	for(int i = 0; i < num; ++i)
	{
		if (pdarr[i] > dmax)
		{
			dmax = pdarr[i];
			imax = i;
		}
	}
	return imax;
}

double sigmoid(double x)
{
	return 1.0/(1.0+exp(-x));
}

double uniform(double _min, double _max)
{
	return rand()/(RAND_MAX + 1.0) * (_max - _min) + _min;
}

void initArr(double *parr, int num)
{
	for (int i = 0; i < num; ++i)
		parr[i] = 0.0;
}

void savewb(const char *pcname, double **ppw, double *pb,
			 int irow, int icol)
{
	FILE *pf;
	if( (pf = fopen(pcname, "ab" )) == NULL )
	{
		printf( "File coulkd not be opened " );
		return;
	} 

	int isizeofelem = sizeof(double);
	for (int i = 0; i < irow; ++i)
	{
		if (fwrite((const void*)ppw[i], isizeofelem, icol, pf) != icol)
		{
			fputs ("Writing ppw error",stderr);
			return;
		}
	}
	if (fwrite((const void*)pb, isizeofelem, irow, pf) != irow)
	{
		fputs ("Writing ppw error",stderr);
		return;
	}
	fclose(pf);
}
long loadwb(const char *pcname, double **ppw, double *pb,
			int irow, int icol, long dstartpos)
{
	FILE *pf;
	long dtotalbyte = 0, dreadsize;
	if( (pf = fopen(pcname, "rb" )) == NULL )
	{
		printf( "File coulkd not be opened " );
		return -1;
	}
	//让文件指针偏移到正确位置
	fseek(pf, dstartpos , SEEK_SET);

	int isizeofelem = sizeof(double);
	for (int i = 0; i < irow; ++i)
	{
		dreadsize = fread((void*)ppw[i], isizeofelem, icol, pf);
		if (dreadsize != icol)
		{
			fputs ("Reading ppw error",stderr);
			return -1;
		}
		//每次成功读取，都要加到dtotalbyte中，最后返回
		dtotalbyte += dreadsize;
	}
	dreadsize = fread(pb, isizeofelem, irow, pf);
	if (dreadsize != irow)
	{
		fputs ("Reading pb error",stderr);
		return -1;
	}
	dtotalbyte += dreadsize;
	dtotalbyte *= isizeofelem;
	fclose(pf);
	return dtotalbyte;
}
void readonefile(const char *pcname)
{
	FILE *pf;
	if( (pf = fopen(pcname, "rb" )) == NULL )
	{
		printf( "File could not be opened " );
		return;
	} 

	/*int isizeofelem = sizeof(BYTE);
	BYTE ielem;*/
	int isizeofelem = sizeof(double);
	double ielem;
	while(1 == fread((void*)(&ielem), isizeofelem, 1, pf))
		cout << ielem << endl;

	fclose(pf);
}
void writeonefile(const char *pcname)
{
	FILE *pf;
	if( (pf = fopen(pcname, "wb" )) == NULL )
	{
		printf( "File could not be opened " );
		return;
	} 

	//int isizeofelem = sizeof(BYTE);
	//BYTE ielem = (BYTE)16;
	int isizeofelem = sizeof(int);
	int ielem = 16;
	if(1 == fwrite((void*)(&ielem), isizeofelem, 1, pf))
		cout << ielem << endl;

	fclose(pf);
}

至此代码已经贴完了，我测试是可以运行的。本人编码较少，如果有什么问题，请见谅。

这里有vs2008建的工程代码，如果不想自己建工程，可以下载运行即可，工程里面只是少了保存权值的函数而已。

下载地址： 点击打开链接