bplayer.cpp

BP人工神经网络训练源码

// BPLayer.cpp: implementation of the CBPLayer class.
//
//////////////////////////////////////////////////////////////////////

#include "stdafx.h"
#include "trainbp.h"
#include "BPLayer.h"

#ifdef _DEBUG
#undef THIS_FILE
static char THIS_FILE[]=__FILE__;
#define new DEBUG_NEW
#endif

//////////////////////////////////////////////////////////////////////
// Construction/Destruction
//////////////////////////////////////////////////////////////////////

CBPLayer::CBPLayer()
{

}

CBPLayer::~CBPLayer()
{

}

CMatrix CBPLayer::DeltaF()
{
	CMatrix temp;
	switch( FunctionType )
	{
	case CBPLayer::purelin:
		temp.Ones(V.RowNo(),V.ColNo());
		return temp;
	case CBPLayer::logsig:
		temp=V.Mul(1-V);
		return temp;
	case CBPLayer::tansig:
		temp=1.0-V.Mul(V);
		return temp;
	default:
		VERIFY(FALSE);
	}
	return temp;
	
}

void CBPLayer::initiate(unsigned int NumIn, unsigned int NumOut, unsigned int Mode, double l)
{
	unsigned int i,j;
	
	lr=l;//学习速率
	FunctionType=(FunctionMode)Mode;
	srand( (unsigned)time( NULL ) );
	
	W.SetSize(NumOut,NumIn);
	for( i=0; i<W.RowNo(); i++)
		for( j=0; j<W.ColNo(); j++)
			W(i,j)=rand();
	W=W-double(RAND_MAX/2);
	W/=double(RAND_MAX/2);

	B.SetSize(NumOut,1);
	for( i=0; i<B.RowNo(); i++)
		for( j=0; j<B.ColNo(); j++)
			B(i,j)=rand();
	B=B-double(RAND_MAX/2);
	B/=double(RAND_MAX/2);

}

void CBPLayer::ForWard(CMatrix &P1)
{
	CMatrix t1(1,P1.ColNo());
	t1.Ones();//置1
	
	P=&P1;
	
	V=W*P1+B*t1;
	F(); //Y=F(V)
	return ;
}

CMatrix CBPLayer::BackWard(CMatrix &E)
{
	CMatrix t1(E.ColNo(), 1);
	t1.Ones();

	D=DeltaF().Mul(E);
	CMatrix dw=D*(~(*P))*lr;
	CMatrix db=D*t1*lr;

	D=~W*D;
	W+=dw;//更新权系数
	B+=db;

	return D;
}

void CBPLayer::F()
{
	switch( FunctionType )
	{
	case CBPLayer::purelin:
		Y=V;
		return;
	case CBPLayer::logsig:
		Y=(1+Exp(-V)).Div();//1./(1+exp(-v))
		return;
	case CBPLayer::tansig:
		Y=(1+Exp(-2*V)).Div()*2.0 -1.0;//2.0./(1+exp(-2v))
		return;
	default:
		VERIFY(FALSE);
	}
	
}

//////////////////////////////////////////////////////////////////////
// CBPNet Class
//////////////////////////////////////////////////////////////////////

//////////////////////////////////////////////////////////////////////
// Construction/Destruction
//////////////////////////////////////////////////////////////////////

CBPNet::CBPNet()
{

}

CBPNet::~CBPNet()
{

}

void CBPNet::init(CEdit * edit_message,double m_lr,int m_num)
{
	CString message;
	m_message=edit_message;
	//m_message->SetLimitText( 40000 );
	m_message->SetSel( 0, -1);

	m_message->Clear();
	m_message->ReplaceSel("三层结构\r\n第一层：1输入，5输出，logsis\r\n");
	//m_message->LineScroll(1);
	//m_message->ReplaceSel("");
	m_message->ReplaceSel("第二层：5输入，1输出，purelin\r\n");
	P.SetSize(1,20);//21个输入数据
	T.SetSize(1,20);//21个输出数据
	m_message->ReplaceSel("公式为e(-d/10)sin(d)\r\n");
	m_message->ReplaceSel("21个输入数据 21个输出数据\r\n");
	lr=m_lr;
	l1.initiate(1, 25, CBPLayer::tansig,lr/5);//1输入，5输出，logsis
	l2.initiate(25, 1, CBPLayer::purelin,lr);//5输入，1输出，purelin

	err_goal=0.01;
	max_epoch=m_num;//运行100次
	message.Format("学习速率：%f 运行%d次\r\n\r\n",lr,m_num);
	m_message->ReplaceSel(message);


	//初始化输入、输出数据
	unsigned int i;
	double d;

	for(i=0,d=0.0; i<20; i++,d+=4*3.14159265/20)
	{
		P(0,i)=d;
		T(0,i)=exp(-d/10.0)*sin(d);//公式为e(-d/10)sin(d) 
	}

}

void CBPNet::ForWard()
{
	l1.ForWard(P);
	l2.ForWard(l1.Y);
	E=T-l2.Y;
	sse=E.Norm();
}

void CBPNet::BackWard()
{
	l1.BackWard(l2.BackWard(E));
}

void CBPNet::Train()
{
	int i;
	CString message;
	for(i=0; i<max_epoch; i++)
	{
		ForWard();
		if(sse<err_goal)break;
		BackWard();
		message.Format( "运行次数 = %d, 误差 = %f\r\n", i, sse );
		m_message->ReplaceSel(message);
	}

}