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通用图像识别的神经网络代码描述
作者：武汉SEO闵涛  文章来源：敏韬网  点击数1749  更新时间：2009/4/23 18:27:00  文章录入：mintao  责任编辑：mintao

写人脸检测程序的时候顺带写的，网络格式是靠读入一个文件定义的，文件的格式如下：

输入图像长 输入图像宽 隐层神经元个数 输出神经元个数
不同网络结构数量
[连接位置不同的隐层神经元的个数 连接的隐层神经元个数]
[隐层神经元连接的输入神经元的位置表]

下面是一个例子：

24 28 52 1
3
16 32
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8
5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8
5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8
5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8
5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8
5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8
5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8
9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12
9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12
9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12
9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12
9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12
9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12
9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12
13 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 16
13 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 16
13 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 16
13 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 16
13 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 16
13 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 16
13 13 13 13 13 13 14 14 14 14 14 14 15 15 15 15 15 15 16 16 16 16 16 16
4 8
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
6 12
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6

下面是程序代码：

type

  TSingleExtendedArray = array of extended;
  TDoubleExtendedArray = array of array of extended;

  TSamples = packed record
    Ins: TSingleExtendedArray;
    Outs: TSingleExtendedArray;
  end;

type

  TGraphicBpnn = class
  private
    procedure BackPropagate(t: TSingleExtendedArray; n, m: extended);
    function UpDate(inputs: TSingleExtendedArray): extended;
  public
    samplecounts, TestCounts: longint;
    procedure AddToTrain(Ins, Outs: TSingleExtendedArray);
    procedure AddToTest(Ins, Outs: TSingleExtendedArray);
    procedure SaveToFile(FileName: string);
    procedure LoadFromFile(FileName: string);
    procedure Train(n, m: extended);
    function Init(FileName: string): boolean;
    function Predict(Ins: TSingleExtendedArray): extended;
    function Test: extended;
    destructor Destroy; override;
  private
    nI, nH, nO: longint;
    aI, aH, aO, Output_Deltas, Hidden_Deltas: TSingleExtendedArray;
    wI, wO, cI, cO: TDoubleExtendedArray;
    Connections: array of array of boolean;
    Samples: array of TSamples;
    TestSet: array of TSamples;
  end;

implementation

function TGraphicBpnn.Init(FileName: string): boolean;
var
  i, j, k, fi, fj: longint;
  nIw, nIh, RopMax, RopNum, RopTypes: longint;
  RopMap: array of longint;
begin
  AssignFile(Input, FileName);
  ReSet(Input);
  Readln(Input, nIw, nIh, nH, nO);
  nI := nIw * nIh;
  setlength(aI, nI);
  setlength(aH, nH);
  setlength(aO, nO);
  for i := 0 to nI - 1 do aI[i] := 1;
  for i := 0 to nH - 1 do aH[i] := 1;
  for i := 0 to nO - 1 do aO[i] := 1;

  setlength(wI, nI, nH);
  setlength(wO, nH, nO);
  setlength(cI, nI, nH);
  setlength(cO, nH, nO);
  setlength(Connections, nI, nH);

  for i := 0 to nI - 1 do
    for j := 0 to nH - 1 do
      Connections[i, j] := False;

  Readln(RopTypes); fj := 0;
  for k := 1 to RopTypes do begin
    Readln(RopMax, RopNum);
    setlength(RopMap, nI);
    fi := 0;
    for i := 1 to nIh do begin
      for j := 1 to nIw do begin
        Read(RopMap[fi]);
        Inc(fi);
      end;
      Readln;
    end;
    fi := 0;
    for i := 1 to RopNum do begin
      Inc(fi);
      if fi > RopMax then fi := 1;
      for j := 0 to nI - 1 do
        if RopMap[j] = fi then Connections[j, fj] := true;
      Inc(fj);
    end;
  end;

  setlength(Output_Deltas, nO);
  setlength(Hidden_Deltas, nH);

  randomize;
  for i := 0 to nI - 1 do
    for j := 0 to nH - 1 do begin
      cI[i, j] := 0;
      wI[i, j] := random(40000) / 10000 - 2;
    end;

  for i := 0 to nH - 1 do
    for j := 0 to nO - 1 do begin
      cO[i, j] := 0;
      wO[i, j] := random(40000) / 10000 - 2;
    end;

  setlength(Samples, $100); setlength(TestSet, $100);
  samplecounts := 0; TestCounts := 0;
  CloseFile(Input);
end;

procedure TGraphicBpnn.BackPropagate(t: TSingleExtendedArray; n, m: extended);
var
  i, j, k: Longint;
  Sum, Change: extended;
begin
  for i := 0 to nO - 1 do
    Output_Deltas[i] := aO[i] * (1 - aO[i]) * (t[i] - aO[i]);

  for j := 0 to nH - 1 do begin
    Sum := 0;
    for k := 0 to nO - 1 do
      Sum := Sum + Output_Deltas[k] * wO[j, k];
    Hidden_Deltas[j] := aH[j] * (1 - aH[j]) * Sum;
  end;

  for j := 0 to nH - 1 do
    for k := 0 to nO - 1 do begin
      Change := Output_Deltas[k] * aH[j];
      wO[j, k] := wO[j, k] + n * Change + m * cO[j, k];
      cO[j,

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