"如何用70行Java代码实现深度神经网络算法" 的delphi版本

=====ann.pas源程序===================================

{

by 阿甘 2016.2.23

参考自此篇文档

如何用70行Java代码实现深度神经网络算法
http://geek.csdn.net/news/detail/56086
原文中的代码作者:fourinone

原文中的代码是用java写的，现移植为delphi 供参考

略作修改：权重不含输出层

}

unit ann;

interface

type

Tdbarr=array of double;

TBpDeep=class

private

layer:array of array of double;

layerErr:array of array of double;

layer_weight:array of array of array of double;

layer_weight_delta:array of array of array of double;

mobp,rate:double;

procedure updateWeight(tar:array of double);

public

constructor Create(layernum:array of integer;rate,mobp:double);

function computeout(input:array of double):Tdbarr;

procedure train(input,tar:array of double);

end;

implementation

constructor TBpDeep.Create(layernum:array of integer;rate,mobp:double);

var

a,i,j,k:integer;

begin

self.rate:=rate;

self.mobp:=mobp;

a:=length(layernum);

if a<2 then exit;

setlength(layer,a);

setlength(layerErr,a);

setlength(layer_weight,a-1);

setlength(layer_weight_delta,a-1);

Randomize;

for k:=0 to a-1 do

begin

setlength(layer[k],layernum[k]);

setlength(layerErr[k],layernum[k]);

if k+1<a then

begin

setlength(layer_weight[k],layernum[k]+1,layernum[k+1]);

setlength(layer_weight_delta[k],layernum[k]+1,layernum[k+1]);

for j:=0 to layernum[k]-1 do

for i:=0 to layernum[k+1]-1 do

layer_weight[k][j][i]:=Random;//随机初始化权重

end;

end;

end;

function TBpDeep.computeout(input:array of double):Tdbarr;

var

i,j,k:integer;

z:double;

begin

for k:=1 to high(layer) do

for j:=0 to high(layer[k]) do

begin

z:=layer_weight[k-1][length(layer[k-1])][j];

for i:=0 to high(layer[k-1]) do

begin

if k=1 then layer[k-1][i]:=input[i];

z:=z+layer_weight[k-1][i][j]*layer[k-1][i];

end;

layer[k][j]:=1/(1+exp(-z));

end;

result:=Tdbarr(layer[high(layer)]);

end;

procedure TBpDeep.updateWeight(tar:array of double);

var

i,j,k:integer;

z:double;

begin

k:=high(layer);

for j:=0 to high(layererr[k]) do layerErr[k][j]:=layer[k][j]*(1-layer[k][j])*(tar[j]-layer[k][j]);

while k>0 do

begin

dec(k);

for j:=0 to high(layererr[k]) do

begin

z:=0;

for i:=0 to high(layererr[k+1]) do

begin

if z+k>0 then z:=layerErr[k+1][i]*layer_weight[k][j][i]

else z:=0;

layer_weight_delta[k][j][i]:=mobp*layer_weight_delta[k][j][i]+rate*layerErr[k+1][i]*layer[k][j];//隐含层动量调整

layer_weight[k][j][i]:=layer_weight[k][j][i]+layer_weight_delta[k][j][i];//隐含层权重调整

if j=high(layererr[k]) then

begin

layer_weight_delta[k][j+1][i]:=mobp*layer_weight_delta[k][j+1][i]+rate*layerErr[k+1][i];//截距动量调整

layer_weight[k][j+1][i]:=layer_weight[k][j+1][i]+layer_weight_delta[k][j+1][i];//截距权重调整

end;

end;

layerErr[k][j]:=z*layer[k][j]*(1-layer[k][j]);//记录误差

end;

end;

end;

procedure TBpDeep.train(input,tar:array of double);

begin

computeout(input);

updateWeight(tar);

end;

end.

========================================

测试代码：

procedure TForm1.bttrainClick(Sender: TObject);//训练

var

i,k:integer;

const

data:array[0..3] of array[0..1] of double=((1,2),(2,2),(1,1),(2,1));

tar:array[0..3] of array[0..1] of double=((1,0),(0,1),(0,1),(1,0));

begin

for k:=0 to 499 do

for i:=0 to high(data) do

bp.train(data[i], tar[i]);

end;

procedure TForm1.btcomputeClick(Sender: TObject);//识别

var

rst:Tdbarr;

x:array[0..1] of double;

begin

x[0]:=strtofloat(xx.Text);

x[1]:=strtofloat(yy.Text);

rst:=bp.computeout(x);

memo1.Lines.Append(floattostr(rst[0])+' '+floattostr(rst[1]));

end;

procedure TForm1.FormCreate(Sender: TObject);//初始化

begin

bp:=TBpdeep.Create([2,10,2],0.15,0.8);

end;

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