輸入圖像長 輸入圖像寬 隱層神經元個數 輸出神經元個數
不同網絡結構數量
[連接位置不同的隱層神經元的個數 連接的隱層神經元個數]
[隱層神經元連接的輸入神經元的位置表]
下面是一個例子：
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
下面是程序代碼：
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, k] := Change;
end;
for i := 0 to nI - 1 do
for j := 0 to nH - 1 do
if Connections[i, j] then begin
Change := Hidden_Deltas[j] * aI[i];
wI[i, j] := wI[i, j] + n * Change + m * cI[i, j];
cI[i, j] := Change;
end;
end;
function TGraphicBpnn.UpDate(inputs: TSingleExtendedArray): extended;
var
i, j, k: Longint;
Sum: extended;
begin
for i := 0 to nI - 1 do
aI[i] := Inputs[i];
for j := 0 to nH - 1 do begin
Sum := 0;
for i := 0 to nI - 1 do
if Connections[i, j] then
Sum := Sum + aI[i] * wI[i, j];
aH[j] := 1 / (1 + Exp(-Sum));
end;
for k := 0 to nO - 1 do begin
Sum := 0;
for j := 0 to nH - 1 do
Sum := Sum + aH[j] * wO[j, k];
aO[k] := 1 / (1 + Exp(-Sum));
end;
UpDate := aO[0];
end;
procedure TGraphicBpnn.Train(n, m: extended);
var i: Longint;
begin
for i := 0 to samplecounts - 1 do begin
UpDate(Samples[i].Ins);
BackPropagate(Samples[i].Outs, n, m);
end;
end;
procedure TGraphicBpnn.AddToTrain(Ins, Outs: TSingleExtendedArray);
var i: longint;
begin
if samplecounts > High(Samples) then setlength(Samples, samplecounts + $100);
setlength(Samples[samplecounts].Ins, nI);
setlength(Samples[samplecounts].Outs, nO);
for i := 0 to nI - 1 do Samples[samplecounts].Ins[i] := Ins[i];
for i := 0 to nO - 1 do Samples[samplecounts].Outs[i] := Outs[i];
Inc(samplecounts);
end;
procedure TGraphicBpnn.AddToTest(Ins, Outs: TSingleExtendedArray);
var i: longint;
begin
if TestCounts > High(TestSet) then setlength(TestSet, TestCounts + $100);
setlength(TestSet[TestCounts].Ins, nI);
setlength(TestSet[TestCounts].Outs, nO);
for i := 0 to nI - 1 do TestSet[TestCounts].Ins[i] := Ins[i];
for i := 0 to nO - 1 do TestSet[TestCounts].Outs[i] := Outs[i];
Inc(TestCounts);
end;
procedure TGraphicBpnn.SaveToFile(FileName: string);
var
i, j, k: longint;
SaveStream: TMemoryStream;
begin
SaveStream := TMemoryStream.Create;
SaveStream.Seek(0, 0);
for i := 0 to nI - 1 do
for j := 0 to nH - 1 do begin
SaveStream.Write(wI[i, j], sizeof(wI[i, j]));
SaveStream.Write(cI[i, j], sizeof(cI[i, j]));
end;
for j := 0 to nH - 1 do
for k := 0 to nO - 1 do begin
SaveStream.Write(wO[j, k], sizeof(wO[j, k]));
SaveStream.Write(cO[j, k], sizeof(cO[j, k]));
end;
SaveStream.SaveToFile(FileName);
SaveStream.Free;
end;
procedure TGraphicBpnn.LoadFromFile(FileName: string);
var
i, j, k: longint;
ReadStream: TMemoryStream;
begin
ReadStream := TMemoryStream.Create;
ReadStream.LoadFromFile(FileName);
ReadStream.Seek(0, 0);
for i := 0 to nI - 1 do
for j := 0 to nH - 1 do begin
ReadStream.Read(wI[i, j], sizeof(wI[i, j]));
ReadStream.Read(cI[i, j], sizeof(cI[i, j]));
end;
for j := 0 to nH - 1 do
for k := 0 to nO - 1 do begin
ReadStream.Read(wO[j, k], sizeof(wO[j, k]));
ReadStream.Read(cO[j, k], sizeof(cO[j, k]));
end;
ReadStream.Free;
end;
function TGraphicBpnn.Predict(Ins: TSingleExtendedArray): extended;
begin
try
Predict := Update(Ins);
except
Predict := 0;
end;
end;
function TGraphicBpnn.Test: extended;
var
PreRet: extended;
i, Counts, Ret: longint;
begin
Counts := 0;
for i := 0 to TestCounts - 1 do begin
PreRet := Predict(TestSet[i].Ins);
if PreRet > 0.5 then Ret := 1 else Ret := 0;
if Ret = TestSet[i].Outs[0] then Inc(Counts);
end;
Result := Counts / TestCounts;
end;
destructor TGraphicBpnn.Destroy;
begin
setlength(aI, 0);
setlength(aH, 0);
setlength(aO, 0);
setlength(Output_Deltas, 0);
setlength(Hidden_Deltas, 0);
setlength(wI, 0, 0);
setlength(wO, 0, 0);
setlength(cI, 0, 0);
setlength(cO, 0, 0);
setlength(Connections, 0, 0);
setlength(Samples, 0);
inherited;
end;
