diff --git a/docs/Blaise_99_100_UK_12P_Max_Kleiner_V_Nr_Lazarus.pdf b/docs/Blaise_99_100_UK_12P_Max_Kleiner_V_Nr_Lazarus.pdf new file mode 100644 index 00000000..1279d5cf Binary files /dev/null and b/docs/Blaise_99_100_UK_12P_Max_Kleiner_V_Nr_Lazarus.pdf differ diff --git a/docs/SimpleMNist_mX4.pdf b/docs/SimpleMNist_mX4.pdf new file mode 100644 index 00000000..714a98aa Binary files /dev/null and b/docs/SimpleMNist_mX4.pdf differ diff --git a/docs/VisualLearn_XOR_Demo_maXbox.pdf b/docs/VisualLearn_XOR_Demo_maXbox.pdf new file mode 100644 index 00000000..b785be2c Binary files /dev/null and b/docs/VisualLearn_XOR_Demo_maXbox.pdf differ diff --git a/examples/1076_CAI_UVisualGAN2tester_MNIST2.pas b/examples/1076_CAI_UVisualGAN2tester_MNIST2.pas new file mode 100644 index 00000000..5a2df18e --- /dev/null +++ b/examples/1076_CAI_UVisualGAN2tester_MNIST2.pas @@ -0,0 +1,1358 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit uvisualgan_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +var csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer, lblfilename, lblfile: TLabel; + //ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + //GrBoxNeurons: TGroupBox; + //ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + FDisplay: TNNetVolume; + ImgTrainingVolumes, ImgTestVolumes, ImgValidationVolumes: TNNetVolumeList; + FRealPairs, FGeneratedPairs: TNNetVolumePairList; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FGenerative: THistoricalNets; + FGeneratives: TNNetDataParallelism; + FDiscriminator, FDiscriminatorClone: TNNet; + FBaseName: string; + FColorEncoding: byte; + FRandomSizeX, FRandomSizeY, FRandomDepth: integer; + FLearningRateProportion: TNeuralFloat; + + //iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + + procedure Learn(Sender: TObject); + procedure TFormVisualLearningLearn2( Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + + function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; + procedure GetDiscriminatorTrainingProc(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure DiscriminatorOnAfterEpoch(Sender: TObject); + procedure DiscriminatorOnAfterStep(Sender: TObject); + procedure DiscriminatorAugmentation(pInput: TNNetVolume; ThreadId: integer); + //procedure Learn(Sender: TObject); + //procedure SaveScreenshot(filename: string); + procedure BuildTrainingPairs(); + procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); + procedure DiscriminatorOnStart(Sender: TObject); + procedure TFormVisualLearningSendStop; + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure TFormVisualLearningLearn2( Sender: TObject); +var + NeuronMultiplier: integer; +begin + FRandomSizeX := 4; + FRandomSizeY := 4; + FRandomDepth := StrToInt(ComboComplexity.Text); + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'ART'+IntToStr(FRandomDepth)+'-'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= fbasename +'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName := fbasename+ 'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + if not(Assigned(ImgValidationVolumes)) then + begin + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, FColorEncoding); + end; + + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FGenerative := THistoricalNets.Create(); + FDiscriminator := TNNet.Create(); + + FLearningRateProportion := csLearningRates[ComboLearningRate.ItemIndex]; + + if Not(FileExists(FBaseName+'generative.nn')) then begin + WriteLn('Creating generative.'); + FGenerative.AddLayer49([ + TNNetInput.Create4(FRandomSizeX, FRandomSizeY, FRandomDepth), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), //4x4 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), //8x8 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(4,0,0), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,5,2,1,0), //32x32 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,1,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + (* + FGenerative.AddLayer([ + TNNetInput.Create(40, 40, 3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + *) + FGenerative.Layers[FGenerative.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading generative.'); + FGenerative.LoadFromFile(FBaseName+'generative.nn'); + end; + FGenerative.DebugStructure(); + FGenerative.SetLearningRate(0.001,0.9); + FGenerative.SetL2Decay(0.0); + + if Not(FileExists(FBaseName+'discriminator.nn')) then + begin + WriteLn('Creating discriminator.'); + (* + FDiscriminator.AddLayer([ + TNNetInput.Create(32,32,3), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 16x16 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 8x8 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 4x4 + TNNetSELU.Create(), +// TNNetDropout.Create(0.4), + TNNetFullConnectLinear.Create(2), + TNNetSoftMax.Create() + ]);*) + FDiscriminator.AddLayer49([ + TNNetInput.Create4(32,32,3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 15x15 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 7x7 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + // downsample to 3x3 + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(128,0), + TNNetFullConnectLinear.Create28(2,0), + TNNetSoftMax.Create() + ]); + + FDiscriminator.Layers[FDiscriminator.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading discriminator.'); + FDiscriminator.LoadFromFile(FBaseName+'discriminator.nn'); + TNNetInput(FDiscriminator.Layers[0]).EnableErrorCollection; + FDiscriminator.DebugStructure(); + FDiscriminator.DebugWeights(); + end; + FDiscriminator.DebugStructure(); + + FDiscriminatorClone := FDiscriminator.Clone(); + TNNetInput(FDiscriminatorClone.Layers[0]).EnableErrorCollection; + + FFit.EnableClassComparison(); + FFit.OnAfterEpoch := @{Self.}DiscriminatorOnAfterEpoch; + FFit.OnAfterStep := @{Self.}DiscriminatorOnAfterStep; + FFit.OnStart := @{Self.}DiscriminatorOnStart; + FFit.DataAugmentationFn := @{Self.}DiscriminatorAugmentation; + FFit.LearningRateDecay := 0.00001; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.001; + FFit.FileNameBase := FBaseName+'GenerativeNeuralAPI'; + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FGenerative.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: FFit.MaxThreadNum := 1; + //FFit.FitLoading(FDiscriminator, 64*10, 500, 500, 64, 35000, @GetDiscriminatorTrainingPair, nil, nil); // This line does the same as below + FFit.FitLoading1(FDiscriminator, 64*10, 500, 500, 64, 35000, + @GetDiscriminatorTrainingProc, nil, nil); // This line does the same as above + + if Assigned(FGeneratives) then begin + //FreeAndNil(FGeneratives); + FGeneratives.Free; + FGeneratives:= Nil; + end; + FGenerative.Free; + FDiscriminator.Free; + FDiscriminatorClone.Free; +end; + + +procedure TFormVisualLearningSendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure DiscriminatorOnStart(Sender: TObject); +begin + FGeneratives := TNNetDataParallelism.Create74(FGenerative, FFit.MaxThreadNum, true); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FGeneratives.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + BuildTrainingPairs(); +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + //FDisplay : TNNetVolume; +begin + //FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + //FDisplay.Free; +end; + + +function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; +var + RandomValue, RandomPos: integer; + LocalPair: TNNetVolumePair; +begin + if (FRealPairs.Count = 0) then + begin + WriteLn('Error: discriminator real pairs have no element'); + Result := nil; + exit; + end; + + if FGeneratedPairs.Count = 0 then + begin + WriteLn('Error: discriminator generated/fake pairs have no element'); + Result := nil; + exit; + end; + + RandomValue := Random(1000); + if RandomValue < 500 then + begin + RandomPos := Random(FRealPairs.Count); + Result := FRealPairs[RandomPos]; + Result.O.SetClassForSoftMax(1); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Real Pair index '+itoa(RandomPos)+ + 'has wrong size:'+itoa( Result.I.Size)); + end; + //Debug Only: if (Random(100)=0) then DisplayInputImage(Result.I, FColorEncoding); + end + else + begin + LocalPair := FGeneratedPairs[ThreadId]; + LocalPair.I.Resize(FRandomSizeX, FRandomSizeY, FRandomDepth); + LocalPair.I.Randomize(0,0,0); + LocalPair.I.NormalizeMax(2); + //FGeneratives: TNNetDataParallelism; + //RegisterProperty('Items', 'TNNet Integer', iptrw); + //LocalPair: TNNetVolumePair; + //RegisterMethod('Procedure Compute65(pInput:TNNetVolume;FromLayerIdx:integer);'); + FGeneratives[ThreadId].Compute65(LocalPair.I,0); + FGeneratives[ThreadId].GetOutput(LocalPair.I); + Result := LocalPair; + Result.O.SetClassForSoftMax(0); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Generated Pair has wrong size:'+itoa( Result.I.Size)); + end; + end; +end; + +procedure GetDiscriminatorTrainingProc(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + LocalPair: TNNetVolumePair; +begin + LocalPair := GetDiscriminatorTrainingPair(Idx, ThreadId); + pInput.Copy76(LocalPair.I); + pOutput.Copy76(LocalPair.O); +end; + +procedure DiscriminatorOnAfterEpoch(Sender: TObject); +var + LoopCnt, MaxLoop: integer; + ExpectedDiscriminatorOutput, Transitory, DiscriminatorFound, GenerativeInput: TNNetVolume; + Error: TNeuralFloat; +begin + if (FFit.TrainingAccuracy <= 0.745) or FFit.ShouldQuit + then exit; + WriteLn('Training Generative Start.'); + ExpectedDiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + ExpectedDiscriminatorOutput.SetClassForSoftMax(1); + DiscriminatorFound := TNNetVolume.Create3(ExpectedDiscriminatorOutput); + //Create3( Original : TVolume);'); + //Create6( pSize : integer; c : TNeuralFloat);') + Transitory := TNNetVolume.Create3(FDiscriminatorClone.Layers[0].OutputError); + GenerativeInput := TNNetVolume.Create0(FRandomSizeX, FRandomSizeY, FRandomDepth,0); + FDiscriminatorClone.CopyWeights(FDiscriminator); + FGenerative.SetBatchUpdate(true); + FGenerative.SetLearningRate(FFit.CurrentLearningRate*FLearningRateProportion, 0); + FGenerative.SetL2Decay(0.00001); + FDiscriminatorClone.SetBatchUpdate(true); + FDiscriminatorClone.SetL2Decay(0.0); + MaxLoop := Round(100 * (1/FLearningRateProportion)); + begin + Error := 0; + FDiscriminatorClone.RefreshDropoutMask(); + for LoopCnt := 1 to MaxLoop do + begin + if FFit.ShouldQuit then break; + FDiscriminatorClone.ClearDeltas(); + FDiscriminatorClone.ClearInertia(); + GenerativeInput.Randomize(0,0,0); + GenerativeInput.NormalizeMax(2); + if FGenerative.Layers[0].Output.Size<>GenerativeInput.Size then + begin + Write('.'); + FGenerative.Layers[0].Output.ReSize10(GenerativeInput); + FGenerative.Layers[0].OutputError.ReSize10(GenerativeInput); + end; + FGenerative.Compute65(GenerativeInput,0); + FGenerative.GetOutput(Transitory); + FDiscriminatorClone.Compute65(Transitory,0); + FDiscriminatorClone.GetOutput(DiscriminatorFound); + FDiscriminatorClone.Backpropagate69(ExpectedDiscriminatorOutput); + //Error += ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Error:= error +ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Transitory.Sub64(FDiscriminatorClone.Layers[0].OutputError); + FGenerative.Backpropagate69(Transitory); + FGenerative.NormalizeMaxAbsoluteDelta(0.001); + FGenerative.UpdateWeights(); + if LoopCnt mod 10 = 0 then TFormVisualLearningProcessMessages(); + if LoopCnt mod 100 = 0 then TFormVisualLearningDisplayInputImage(Transitory, FColorEncoding); + end; + FDiscriminatorClone.Layers[0].OutputError.PrintDebug(); + WriteLn('Clone.Layers end'); + WriteLn('Generative error:'+flots(Error))//:6:4); + end; + //Debug: + //FGenerative.DebugErrors(); + //FGenerative.DebugWeights(); + //FDiscriminatorClone.DebugWeights(); + FGeneratives.CopyWeights(FGenerative); + GenerativeInput.Free; + ExpectedDiscriminatorOutput.Free; + Transitory.Free; + DiscriminatorFound.Free; + if FFit.CurrentEpoch mod 100 = 0 then + begin + WriteLn('Saving '+ FBaseName); + FGenerative.SaveToFile(FBaseName+'generative.nn'); + FDiscriminator.SaveToFile(FBaseName+'discriminator.nn'); + SaveScreenshot(FBaseName+'cai-neural-gan.bmp'); + end; + WriteLn('Training Generative Finish with:'+flots( Error)); //:6:4); + //DisplayInputImage(FRealPairs[Random(FRealPairs.Count)].I, FColorEncoding); +end; + +procedure DiscriminatorOnAfterStep(Sender: TObject); +begin + LabClassRate.Caption := + StrPadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4,'-'); + TFormVisualLearningProcessMessages(); +end; + +procedure DiscriminatorAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin + if Random(1000)>500 then pInput.FlipX(); + //if Random(1000)>750 then pInput.MakeGray(FColorEncoding); +end; + +procedure BuildTrainingPairs(); +var + FakePairCnt: integer; + ImgTrainingVolume: TNNetVolume; + DiscriminatorOutput, GenerativeOutput: TNNetVolume; +begin + DiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + GenerativeOutput := TNNetVolume.Create0(32, 32, 3,0); + //if FRealPairs.Count = 0 then + //begin + (* + for ImgTrainingVolume in ImgTrainingVolumes do fix + begin + if ImgTrainingVolume.Tag = 5 then //5 + begin + DiscriminatorOutput.SetClassForSoftMax(1); + FRealPairs.Add + ( + TNNetVolumePair.CreateCopying + ( + ImgTrainingVolume, + DiscriminatorOutput + ) + ); + //Debug only: if (Random(100)=0) then DisplayInputImage(ImgTrainingVolume, FColorEncoding); + end + end; + end;*) + + DiscriminatorOutput.SetClassForSoftMax(0); + + if FGeneratedPairs.Count < FFit.MaxThreadNum then + begin + for FakePairCnt := 1 to FFit.MaxThreadNum do + begin + FGeneratedPairs.Add + ( + TNNetVolumePair.CreateCopying83 + ( + GenerativeOutput, + DiscriminatorOutput + ) + ); + end; + end; + ImgTrainingVolumes.Cleartag; + ImgValidationVolumes.Cleartag; + ImgTestVolumes.Cleartag; + GenerativeOutput.Free; + DiscriminatorOutput.Free; +end; + + + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then begin + if FileExists(OpenDialogNN.FileName) then begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + lblfile.caption:= extractfilename(filename); + if not(FileExists(FileName)) then begin + if FileName = 'SimpleImageClassifier.nn' then begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; + + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + NeuralInitCriticalSection(FCritSec); + ImgTrainingVolumes := nil; + ImgTestVolumes := nil; + ImgValidationVolumes := nil; + FGeneratives := nil; + FRealPairs := TNNetVolumePairList.Create(true); + FGeneratedPairs := TNNetVolumePairList.Create(true); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + (* CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}128, + {InputSize=}32, {FFilterSize=}64, {ImagesPerRow=}16 + ); *) + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + //ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); + TFormVisualLearningSendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + if Assigned(ImgValidationVolumes) then ImgValidationVolumes.Free; + if Assigned(ImgTestVolumes) then ImgTestVolumes.Free; + if Assigned(ImgTrainingVolumes) then ImgTrainingVolumes.Free; + FreeNeuronImages(aImage, aLabelX, aLabelY); + NeuralDoneCriticalSection(FCritSec); + FRealPairs.Free; + FGeneratedPairs.Free; + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + writeln('destroy neural neurons all done'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+ + flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('Neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Visual Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + lblfilename:= TLabel.create(self) + with lblfilename do begin + parent:= formvisuallearning; + Left := 890 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + lblfile:= TLabel.create(self) + with lblfile do begin + parent:= formvisuallearning; + Left := 940 + Height := 20 + font.size:= 10; + Top := 19 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 17 + Width := 264 + Enabled := False + font.size:= 11; + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage2(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +function NeuronForceMinMax(x, pMin, pMax: TNeuralFloat): TNeuralFloat; +begin + if (x>pMax) then Result := pMax + else if (x + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + + procedure TTestCNNAlgo.DoRun; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if Not(CheckMNISTFile('train')) or Not(CheckMNISTFile('t10k')) then + begin + Terminate; + exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + NN.AddLayer([ + TNNetInput.Create(28, 28, 1), + TNNetConvolutionLinear.Create(32, 5, 2, 1, 1), + TNNetMaxPool.Create(4), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetFullConnectReLU.Create(32), + TNNetFullConnectReLU.Create(32), + TNNetDropout.Create(0.2), + TNNetMaxPool.Create(2), + TNNetFullConnectLinear.Create(10), + TNNetSoftMax.Create() + ]); + CreateMNISTVolumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, 'train', 't10k'); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'SimpleMNist'; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.HasFlipX := false; + NeuralFit.HasFlipY := false; + NeuralFit.MaxCropSize := 4; + { NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}//10, {batchsize=}128, {epochs=}20); } + { NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + Terminate; + end; } + + +//} diff --git a/examples/1076_CAI_UVisualGAN2tester_MNIST2test.pas b/examples/1076_CAI_UVisualGAN2tester_MNIST2test.pas new file mode 100644 index 00000000..cef98fe4 --- /dev/null +++ b/examples/1076_CAI_UVisualGAN2tester_MNIST2test.pas @@ -0,0 +1,1358 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit uvisualgan_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +var csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer, lblfilename, lblfile: TLabel; + //ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + //GrBoxNeurons: TGroupBox; + //ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + FDisplay: TNNetVolume; + ImgTrainingVolumes, ImgTestVolumes, ImgValidationVolumes: TNNetVolumeList; + FRealPairs, FGeneratedPairs: TNNetVolumePairList; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FGenerative: THistoricalNets; + FGeneratives: TNNetDataParallelism; + FDiscriminator, FDiscriminatorClone: TNNet; + FBaseName: string; + FColorEncoding: byte; + FRandomSizeX, FRandomSizeY, FRandomDepth: integer; + FLearningRateProportion: TNeuralFloat; + + //iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + + procedure Learn(Sender: TObject); + procedure TFormVisualLearningLearn2( Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + + function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; + procedure GetDiscriminatorTrainingProc(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure DiscriminatorOnAfterEpoch(Sender: TObject); + procedure DiscriminatorOnAfterStep(Sender: TObject); + procedure DiscriminatorAugmentation(pInput: TNNetVolume; ThreadId: integer); + //procedure Learn(Sender: TObject); + //procedure SaveScreenshot(filename: string); + procedure BuildTrainingPairs(); + procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); + procedure DiscriminatorOnStart(Sender: TObject); + procedure TFormVisualLearningSendStop; + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure TFormVisualLearningLearn2( Sender: TObject); +var + NeuronMultiplier: integer; +begin + FRandomSizeX := 4; + FRandomSizeY := 4; + FRandomDepth := StrToInt(ComboComplexity.Text); + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'ART'+IntToStr(FRandomDepth)+'-'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= fbasename +'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName := fbasename+ 'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + if not(Assigned(ImgValidationVolumes)) then + begin + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, FColorEncoding); + end; + + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FGenerative := THistoricalNets.Create(); + FDiscriminator := TNNet.Create(); + + FLearningRateProportion := csLearningRates[ComboLearningRate.ItemIndex]; + + if Not(FileExists(FBaseName+'generative.nn')) then begin + WriteLn('Creating generative.'); + FGenerative.AddLayer49([ + TNNetInput.Create4(FRandomSizeX, FRandomSizeY, FRandomDepth), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), //4x4 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), //8x8 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(4,0,0), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,5,2,1,0), //32x32 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,1,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + (* + FGenerative.AddLayer([ + TNNetInput.Create(40, 40, 3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + *) + FGenerative.Layers[FGenerative.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading generative.'); + FGenerative.LoadFromFile(FBaseName+'generative.nn'); + end; + FGenerative.DebugStructure(); + FGenerative.SetLearningRate(0.001,0.9); + FGenerative.SetL2Decay(0.0); + + if Not(FileExists(FBaseName+'discriminator.nn')) then + begin + WriteLn('Creating discriminator.'); + (* + FDiscriminator.AddLayer([ + TNNetInput.Create(32,32,3), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 16x16 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 8x8 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 4x4 + TNNetSELU.Create(), +// TNNetDropout.Create(0.4), + TNNetFullConnectLinear.Create(2), + TNNetSoftMax.Create() + ]);*) + FDiscriminator.AddLayer49([ + TNNetInput.Create4(32,32,3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 15x15 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 7x7 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + // downsample to 3x3 + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(128,0), + TNNetFullConnectLinear.Create28(2,0), + TNNetSoftMax.Create() + ]); + + FDiscriminator.Layers[FDiscriminator.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading discriminator.'); + FDiscriminator.LoadFromFile(FBaseName+'discriminator.nn'); + TNNetInput(FDiscriminator.Layers[0]).EnableErrorCollection; + FDiscriminator.DebugStructure(); + FDiscriminator.DebugWeights(); + end; + FDiscriminator.DebugStructure(); + + FDiscriminatorClone := FDiscriminator.Clone(); + TNNetInput(FDiscriminatorClone.Layers[0]).EnableErrorCollection; + + FFit.EnableClassComparison(); + FFit.OnAfterEpoch := @{Self.}DiscriminatorOnAfterEpoch; + FFit.OnAfterStep := @{Self.}DiscriminatorOnAfterStep; + FFit.OnStart := @{Self.}DiscriminatorOnStart; + FFit.DataAugmentationFn := @{Self.}DiscriminatorAugmentation; + FFit.LearningRateDecay := 0.00001; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.001; + FFit.FileNameBase := FBaseName+'GenerativeNeuralAPI'; + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FGenerative.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: FFit.MaxThreadNum := 1; + //FFit.FitLoading(FDiscriminator, 64*10, 500, 500, 64, 35000, @GetDiscriminatorTrainingPair, nil, nil); // This line does the same as below + FFit.FitLoading1(FDiscriminator, 64*10, 500, 500, 64, 35000, + @GetDiscriminatorTrainingProc, nil, nil); // This line does the same as above + + if Assigned(FGeneratives) then begin + //FreeAndNil(FGeneratives); + FGeneratives.Free; + FGeneratives:= Nil; + end; + FGenerative.Free; + FDiscriminator.Free; + FDiscriminatorClone.Free; +end; + + +procedure TFormVisualLearningSendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure DiscriminatorOnStart(Sender: TObject); +begin + FGeneratives := TNNetDataParallelism.Create74(FGenerative, FFit.MaxThreadNum, true); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FGeneratives.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + BuildTrainingPairs(); +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + //FDisplay : TNNetVolume; +begin + //FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + //FDisplay.Free; +end; + + +function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; +var + RandomValue, RandomPos: integer; + LocalPair: TNNetVolumePair; +begin + if (FRealPairs.Count = 0) then + begin + WriteLn('Error: discriminator real pairs have no element'); + Result := nil; + exit; + end; + + if FGeneratedPairs.Count = 0 then + begin + WriteLn('Error: discriminator generated/fake pairs have no element'); + Result := nil; + exit; + end; + + RandomValue := Random(1000); + if RandomValue < 500 then + begin + RandomPos := Random(FRealPairs.Count); + Result := FRealPairs[RandomPos]; + Result.O.SetClassForSoftMax(1); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Real Pair index '+itoa(RandomPos)+ + 'has wrong size:'+itoa( Result.I.Size)); + end; + //Debug Only: if (Random(100)=0) then DisplayInputImage(Result.I, FColorEncoding); + end + else + begin + LocalPair := FGeneratedPairs[ThreadId]; + LocalPair.I.Resize(FRandomSizeX, FRandomSizeY, FRandomDepth); + LocalPair.I.Randomize(0,0,0); + LocalPair.I.NormalizeMax(2); + //FGeneratives: TNNetDataParallelism; + //RegisterProperty('Items', 'TNNet Integer', iptrw); + //LocalPair: TNNetVolumePair; + //RegisterMethod('Procedure Compute65(pInput:TNNetVolume;FromLayerIdx:integer);'); + FGeneratives[ThreadId].Compute65(LocalPair.I,0); + FGeneratives[ThreadId].GetOutput(LocalPair.I); + Result := LocalPair; + Result.O.SetClassForSoftMax(0); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Generated Pair has wrong size:'+itoa( Result.I.Size)); + end; + end; +end; + +procedure GetDiscriminatorTrainingProc(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + LocalPair: TNNetVolumePair; +begin + LocalPair := GetDiscriminatorTrainingPair(Idx, ThreadId); + pInput.Copy76(LocalPair.I); + pOutput.Copy76(LocalPair.O); +end; + +procedure DiscriminatorOnAfterEpoch(Sender: TObject); +var + LoopCnt, MaxLoop: integer; + ExpectedDiscriminatorOutput, Transitory, DiscriminatorFound, GenerativeInput: TNNetVolume; + Error: TNeuralFloat; +begin + if (FFit.TrainingAccuracy <= 0.745) or FFit.ShouldQuit + then exit; + WriteLn('Training Generative Start.'); + ExpectedDiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + ExpectedDiscriminatorOutput.SetClassForSoftMax(1); + DiscriminatorFound := TNNetVolume.Create3(ExpectedDiscriminatorOutput); + //Create3( Original : TVolume);'); + //Create6( pSize : integer; c : TNeuralFloat);') + Transitory := TNNetVolume.Create3(FDiscriminatorClone.Layers[0].OutputError); + GenerativeInput := TNNetVolume.Create0(FRandomSizeX, FRandomSizeY, FRandomDepth,0); + FDiscriminatorClone.CopyWeights(FDiscriminator); + FGenerative.SetBatchUpdate(true); + FGenerative.SetLearningRate(FFit.CurrentLearningRate*FLearningRateProportion, 0); + FGenerative.SetL2Decay(0.00001); + FDiscriminatorClone.SetBatchUpdate(true); + FDiscriminatorClone.SetL2Decay(0.0); + MaxLoop := Round(100 * (1/FLearningRateProportion)); + begin + Error := 0; + FDiscriminatorClone.RefreshDropoutMask(); + for LoopCnt := 1 to MaxLoop do + begin + if FFit.ShouldQuit then break; + FDiscriminatorClone.ClearDeltas(); + FDiscriminatorClone.ClearInertia(); + GenerativeInput.Randomize(0,0,0); + GenerativeInput.NormalizeMax(2); + if FGenerative.Layers[0].Output.Size<>GenerativeInput.Size then + begin + Write('.'); + FGenerative.Layers[0].Output.ReSize10(GenerativeInput); + FGenerative.Layers[0].OutputError.ReSize10(GenerativeInput); + end; + FGenerative.Compute65(GenerativeInput,0); + FGenerative.GetOutput(Transitory); + FDiscriminatorClone.Compute65(Transitory,0); + FDiscriminatorClone.GetOutput(DiscriminatorFound); + FDiscriminatorClone.Backpropagate69(ExpectedDiscriminatorOutput); + //Error += ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Error:= error +ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Transitory.Sub64(FDiscriminatorClone.Layers[0].OutputError); + FGenerative.Backpropagate69(Transitory); + FGenerative.NormalizeMaxAbsoluteDelta(0.001); + FGenerative.UpdateWeights(); + if LoopCnt mod 10 = 0 then TFormVisualLearningProcessMessages(); + if LoopCnt mod 100 = 0 then TFormVisualLearningDisplayInputImage(Transitory, FColorEncoding); + end; + FDiscriminatorClone.Layers[0].OutputError.PrintDebug(); + WriteLn('Clone.Layers end'); + WriteLn('Generative error:'+flots(Error))//:6:4); + end; + //Debug: + //FGenerative.DebugErrors(); + //FGenerative.DebugWeights(); + //FDiscriminatorClone.DebugWeights(); + FGeneratives.CopyWeights(FGenerative); + GenerativeInput.Free; + ExpectedDiscriminatorOutput.Free; + Transitory.Free; + DiscriminatorFound.Free; + if FFit.CurrentEpoch mod 100 = 0 then + begin + WriteLn('Saving '+ FBaseName); + FGenerative.SaveToFile(FBaseName+'generative.nn'); + FDiscriminator.SaveToFile(FBaseName+'discriminator.nn'); + SaveScreenshot(FBaseName+'cai-neural-gan.bmp'); + end; + WriteLn('Training Generative Finish with:'+flots( Error)); //:6:4); + //DisplayInputImage(FRealPairs[Random(FRealPairs.Count)].I, FColorEncoding); +end; + +procedure DiscriminatorOnAfterStep(Sender: TObject); +begin + LabClassRate.Caption := + StrPadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4,'-'); + TFormVisualLearningProcessMessages(); +end; + +procedure DiscriminatorAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin + if Random(1000)>500 then pInput.FlipX(); + //if Random(1000)>750 then pInput.MakeGray(FColorEncoding); +end; + +procedure BuildTrainingPairs(); +var + FakePairCnt: integer; + ImgTrainingVolume: TNNetVolume; + DiscriminatorOutput, GenerativeOutput: TNNetVolume; +begin + DiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + GenerativeOutput := TNNetVolume.Create0(32, 32, 3,0); + //if FRealPairs.Count = 0 then + //begin + (* + for ImgTrainingVolume in ImgTrainingVolumes do fix + begin + if ImgTrainingVolume.Tag = 5 then //5 + begin + DiscriminatorOutput.SetClassForSoftMax(1); + FRealPairs.Add + ( + TNNetVolumePair.CreateCopying + ( + ImgTrainingVolume, + DiscriminatorOutput + ) + ); + //Debug only: if (Random(100)=0) then DisplayInputImage(ImgTrainingVolume, FColorEncoding); + end + end; + end;*) + + DiscriminatorOutput.SetClassForSoftMax(0); + + if FGeneratedPairs.Count < FFit.MaxThreadNum then + begin + for FakePairCnt := 1 to FFit.MaxThreadNum do + begin + FGeneratedPairs.Add + ( + TNNetVolumePair.CreateCopying83 + ( + GenerativeOutput, + DiscriminatorOutput + ) + ); + end; + end; + ImgTrainingVolumes.Cleartag; + ImgValidationVolumes.Cleartag; + ImgTestVolumes.Cleartag; + GenerativeOutput.Free; + DiscriminatorOutput.Free; +end; + + + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then begin + if FileExists(OpenDialogNN.FileName) then begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + lblfile.caption:= extractfilename(filename); + if not(FileExists(FileName)) then begin + if FileName = 'SimpleImageClassifier.nn' then begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; + + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + NeuralInitCriticalSection(FCritSec); + ImgTrainingVolumes := nil; + ImgTestVolumes := nil; + ImgValidationVolumes := nil; + FGeneratives := nil; + FRealPairs := TNNetVolumePairList.Create(true); + FGeneratedPairs := TNNetVolumePairList.Create(true); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + (* CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}128, + {InputSize=}32, {FFilterSize=}64, {ImagesPerRow=}16 + ); *) + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + //ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); + TFormVisualLearningSendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + if Assigned(ImgValidationVolumes) then ImgValidationVolumes.Free; + if Assigned(ImgTestVolumes) then ImgTestVolumes.Free; + if Assigned(ImgTrainingVolumes) then ImgTrainingVolumes.Free; + FreeNeuronImages(aImage, aLabelX, aLabelY); + NeuralDoneCriticalSection(FCritSec); + FRealPairs.Free; + FGeneratedPairs.Free; + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + writeln('destroy neural neurons all done'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+ + flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('Neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Visual Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + lblfilename:= TLabel.create(self) + with lblfilename do begin + parent:= formvisuallearning; + Left := 890 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + lblfile:= TLabel.create(self) + with lblfile do begin + parent:= formvisuallearning; + Left := 940 + Height := 20 + font.size:= 10; + Top := 19 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 17 + Width := 264 + Enabled := False + font.size:= 11; + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage2(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +function NeuronForceMinMax(x, pMin, pMax: TNeuralFloat): TNeuralFloat; +begin + if (x>pMax) then Result := pMax + else if (x + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + + procedure TTestCNNAlgo.DoRun; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if Not(CheckMNISTFile('train')) or Not(CheckMNISTFile('t10k')) then + begin + Terminate; + exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + NN.AddLayer([ + TNNetInput.Create(28, 28, 1), + TNNetConvolutionLinear.Create(32, 5, 2, 1, 1), + TNNetMaxPool.Create(4), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetFullConnectReLU.Create(32), + TNNetFullConnectReLU.Create(32), + TNNetDropout.Create(0.2), + TNNetMaxPool.Create(2), + TNNetFullConnectLinear.Create(10), + TNNetSoftMax.Create() + ]); + CreateMNISTVolumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, 'train', 't10k'); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'SimpleMNist'; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.HasFlipX := false; + NeuralFit.HasFlipY := false; + NeuralFit.MaxCropSize := 4; + { NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}//10, {batchsize=}128, {epochs=}20); } + { NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + Terminate; + end; } + + +//} diff --git a/examples/1076_visuallearner_gradient_conlinear2_MNIST.png b/examples/1076_visuallearner_gradient_conlinear2_MNIST.png new file mode 100644 index 00000000..b5fa7948 Binary files /dev/null and b/examples/1076_visuallearner_gradient_conlinear2_MNIST.png differ diff --git a/examples/1076_visuallearner_gradient_conlinear_MNIST.png b/examples/1076_visuallearner_gradient_conlinear_MNIST.png new file mode 100644 index 00000000..5cca2f5d Binary files /dev/null and b/examples/1076_visuallearner_gradient_conlinear_MNIST.png differ diff --git a/examples/maXbox4Scripts/1065__CAI_2_SiImageClassifier21_Tutor_89_test4.txt b/examples/maXbox4Scripts/1065__CAI_2_SiImageClassifier21_Tutor_89_test4.txt new file mode 100644 index 00000000..638187e6 --- /dev/null +++ b/examples/maXbox4Scripts/1065__CAI_2_SiImageClassifier21_Tutor_89_test4.txt @@ -0,0 +1,1693 @@ +program CAI_NEURAL_API_Exec_47590_SimpleImageClassifier2_Tutor_89_Test2; + +//http://www.softwareschule.ch/examples/uPSI_NeuralNetworkCAI.txt +//http://www.softwareschule.ch/examples/uPSI_Neuralvolume.txt +//https://github.com/joaopauloschuler/neural-api/blob/master/examples/SimpleImageClassifier/SimpleImageClassifier.lpr +{fix done: not complete cause the neuralfil() has a writeln which troubles I/O 105 exception} +//https://ibrt.ch/delphi/neuralnets.html + +(* +const inputs : TBackInput = + ( // x1, x2 + ( 0.1, 0.1), // False, False + ( 0.1, 0.9), // False, True + ( 0.9, 0.1), // True, False + ( 0.9, 0.9) // True, True + ); + *) + +function CreateHypotenusePairList(MaxCnt: integer): TNNetVolumePairList; + var + Cnt: integer; + LocalX, LocalY, Hypotenuse: TNeuralFloat; + begin + Result := TNNetVolumePairList.Create(true); + for Cnt := 1 to MaxCnt do + begin + LocalX := Random(100); + LocalY := Random(100); + Hypotenuse := sqrt(LocalX*LocalX + LocalY*LocalY); + + Result.items[0]:= TNNetVolumePair.Create82( + TNNetVolume.Create1([LocalX, LocalY]), + TNNetVolume.Create1([Hypotenuse]) + ); + {Result.Add( + TNNetVolumePair.Create( + TNNetVolume.Create([LocalX, LocalY]), + TNNetVolume.Create([Hypotenuse]) + ) + ); } + end; + end; + + procedure TTestCNNAlgoDoRun; + var + NN: TNNet; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if not CheckCIFARFile() then + begin + //Terminate; + exit; + end; + WriteLn('Creating CNeural Network...'); + //TNNetConvolutionBase.Create36({Features=}64, {FeatureSize=}5, + // {Padding=}2, {Stride=}1, {SuppressBias=}1); + NN := TNNet.Create(); + //NN.verbose:= true; + NN.AddLayer49([ + TNNetInput.Create4(32, 32, 3), + TNNetConvolutionLinear.Create({Features=}64, {FeatureSize=}5, + {Padding=}2, {Stride=}1, {SuppressBias=}0), + //TNNetConvolutionReLU.Create({Features=}64, + // {FeatureSize=}5, {Padding=}2, {Stride=}1, {SuppressBias=}1), + TNNetMaxPool.Create44(4,0,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0), + TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0), + TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0), + TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0), + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(10,0), + TNNetSoftMax.Create() + ]); //*) + NN.DebugStructure(); + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, + ImgTestVolumes,csEncodeRGB); + + //TestBatch; + NeuralFit := TNeuralImageFit.Create; + try + NeuralFit.FileNameBase := 'SimpleImageClassifier-'+IntToStr(GetProcessId()); + neuralfit.verbose:= true; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}10, {batchsize=}64, {epochs=}50); + + finally + NeuralFit.Free; //*) + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + //Terminate; + end; + end; + + procedure TTestCNNAlgoDoRun2; + var + NN: TNNet; + NeuralFit: TNeuralImageFit; //TNeuralImageloadingFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if not CheckCIFARFile() then + begin + //Terminate; + exit; + end; + WriteLn('Creating CNeural Network...'); + //TNNetConvolutionBase.Create36({Features=}64, {FeatureSize=}5, + // {Padding=}2, {Stride=}1, {SuppressBias=}1); + NN := TNNet.Create(); + //NN.verbose:= true; + NN.AddLayer( TNNetInput.Create4(32, 32, 3)) + + NN.AddLayer( TNNetConvolutionLinear.Create({Features=}64, {FeatureSize=}5, + {Padding=}2, {Stride=}1, {SuppressBias=}0)); + //TNNetConvolutionReLU.Create({Features=}64, + // {FeatureSize=}5, {Padding=}2, {Stride=}1, {SuppressBias=}1), + NN.AddLayer( TNNetMaxPool.Create44(4,0,0)); + NN.AddLayer( TNNetMovingStdNormalization.Create()) + NN.AddLayer( TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0)); + NN.AddLayer( TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0)); + NN.AddLayer( TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0)); + NN.AddLayer( TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1, {SuppressBias=}0)); + NN.AddLayer( TNNetDropout.Create12(0.5,1)); + NN.AddLayer( TNNetMaxPool.Create44(2,0,0)); + NN.AddLayer( TNNetFullConnectLinear.Create28(10,0)); + NN.AddLayer( TNNetSoftMax.Create()); + //]); //*) + NN.DebugStructure(); + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, + ImgTestVolumes,csEncodeRGB); + + //TestBatch; + writeln('layers count '+itoa(nn.layers.count)); + NeuralFit := TNeuralImageFit.Create; + try + NeuralFit.FileNameBase := 'SimpleImageClassifier-'+IntToStr(GetProcessId()); + neuralfit.verbose:= true; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + //Neuralfit.RunTrainingBatch(TestSize: integer); + writeln(botostr(Neuralfit.hasresizing)); + //writeln(botostr(Neuralfit.count)); + writeln('totalsize '+itoa(ImgTrainingVolumes.gettotalsize )) + writeln('coint '+itoa(ImgTrainingVolumes.count )) + NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}10, {batchsize=}64, {epochs=}2); + + finally + NeuralFit.Free; //*) + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + //Terminate; + end; + end; + + procedure CreatesimpleCNN; + var NumClasses: byte; NN: TNNet; + InputVolume, PredictedVolume, vDesiredVolume: TNNetVolume; + //Example - How to Create Your Network + begin + NumClasses := 10; + NN := TNNet.Create(); + InputVolume:= TNNetVolume.create; + //inputvolume.resize + NN.AddLayer( TNNetInput.Create4(32,32,3) ); + NN.AddLayer( TNNetConvolutionReLU.Create( 16,5,0,0,1) ); + NN.AddLayer( TNNetMaxPool.Create44(2,0,0) ); + NN.AddLayer( TNNetConvolutionReLU.Create(128,5,0,0,1) ); + NN.AddLayer( TNNetMaxPool.Create44(2,0,0) ); + NN.AddLayer( TNNetConvolutionReLU.Create(128,5,0,0,1) ); + NN.AddLayer( TNNetFullConnectReLU.Create30(64,0) ); + NN.AddLayer( TNNetFullConnect.Create27(NumClasses, 0) ); + NN.AddLayer( TNNetSoftMax.Create()); + NN.SetLearningRate(0.01,0.8); + //Example - How to Train Your Network + // InputVolume and vDesiredVolume are of the type TNNetVolume + NN.DebugStructure(); + //NN.SaveDataToString; + //inputvolume.loadfromstring + //Function LoadImageFromFileIntoVolume( ImageFileName : string; V : TNNetVolume) : boolean'); + //LoadImageFromFileIntoVolume(exepath+'data_batch_1.bin', InputVolume); + //CreateVolumesFromImagesFromFolder + //CreateCifar10Volumes(inputvolume, nil, nil,csEncodeRGB); + NN.Compute65(InputVolume, 0); + //NN.GetOutput(PredictedVolume); + //vDesiredVolume.SetClassForReLU(DesiredClass); + //NN.Backpropagate69(vDesiredVolume); + InputVolume.Free; + writeln('InputVolume.Free;') + writeln(flots(PI)); + //LoadLibrary + end; + + +function TCustomApplicationFindOptionIndex(const S: String; + var Longopt: Boolean; StartAt: Integer): Integer; + +Var + SO,O : String; + I,P : Integer; + CaseSensitiveOptions: Boolean; + Params: array of string; + FEventLogFilter: TEventLogTypes; + FExceptObjectJS: JSValue; + FOnException: TExceptionEvent; + FTerminated: Boolean; + FTitle: String; + FOptionChar: Char; + FCaseSensitiveOptions: Boolean; + FStopOnException: Boolean; + FExceptionExitCode: Integer; + FExceptObject: Exception; + +begin + If Not CaseSensitiveOptions then + SO:=UpperCase(S) + else + SO:=S; + Result:=-1; + I:=StartAt; + if I=-1 then + I:=ParamCount; + While (Result=-1) and (I>0) do + begin + O:=Params[i]; + // - must be seen as an option value + If (Length(O)>1) and (O[1]=FOptionChar) then + begin + Delete(O,1,1); + LongOpt:=(Length(O)>0) and (O[1]=FOptionChar); + If LongOpt then + begin + Delete(O,1,1); + P:=Pos('=',O); + If (P<>0) then + O:=Copy(O,1,P-1); + end; + If Not CaseSensitiveOptions then + O:=UpperCase(O); + If (O=SO) then + Result:=i; + end; + Dec(i); + end; +end; + +function TCustomApplicationHasOption(const S,abs: String): Boolean; +Var + aB : Boolean; +begin + Result:=TCustomApplicationFindOptionIndex(S,aB,0)<>-1; +end; + +function TCustomApplicationGetOptionValue(const C: Char; const S: String + ): String; + +Var + B : Boolean; + I : integer; + capp: TCustomApplication; + +begin + capp:= TCustomApplication.create(self); + Result:=''; + //I:=TCustomApplicationFindOptionIndex(C,B,0); + //capp.GetOptionValues + I:=capp.FindOptionIndex(C,B,0); + If I=-1 then + //I:=TCustomApplicationFindOptionIndex(S,B,0); + I:=capp.FindOptionIndex(S,B,0); + //If I<>-1 then + //Result:=capp.GetOptionAtIndex(I,B); + capp.Free; +end; + +{/// Convolutional layer without activation function. + TNNetConvolutionLinear = class(TNNetConvolution) + public + constructor Create(pNumFeatures, pFeatureSize, pInputPadding, pStride: integer; pSuppressBias: integer = 0); override; + end; + + missing: (*----------------------------------------------------------------------------*) +procedure SIRegister_TNNetConvolutionLinear(CL: TPSPascalCompiler); +begin + //with RegClassS(CL,'TNNetConvolution', 'TNNetConvolutionLinear') do + with CL.AddClassN(CL.FindClass('TNNetConvolution'),'TNNetConvolutionLinear') do + begin + end; +end; + + RegisterVirtualConstructor(@TNNetConvolutionBaseCreate36_P, 'Create36'); + set to RegisterConstructor + + } + +procedure ReluTestingRunAlgo(ialgo: integer); +var NumClasses, aialgo: integer; + fileNameBase: string; + NN : TNNet; + NeuralFit: TNeuralImageFit; + I: integer; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + Volume: TNNetVolume; + //NumClasses: integer; + //fileNameBase: string; + TA,TB,TC: TNNetLayer; + TD: TNNetLayerFullConnectReLU; + TF: TNNetConvolution ; + netsoft:TNNetSoftMax; + farr: TNeuralFloatarray; + farr2: TNeuralFloatDynArr; + //TNNetLayerFullConnect = class(TNNetFullConnect); +begin + NumClasses := 10; + NN := TNNet.Create(); + fileNameBase := 'autosave-neuralnetwork_a'+IntToStr(iAlgo); + + try + case iAlgo of + 1: + begin + //RELU TESTING + TA:= NN.AddLayer(TNNetInput.Create4(32, 32, 3)); + //TA := NN.AddLayer(TNNetInput.Create4(32, 32, 3)); + {TF:=} NN.AddLayer(TNNetConvolutionReLU.Create(16, 5, 0, 0,1)); + //TB:= NN.AddLayer(TNNetMaxPool.Create44(2,0,0)); + TC:= NN.AddLayer(TNNetConvolutionReLU.Create(128, 5, 0, 0,1)); + NN.AddLayer(TNNetMaxPool.Create44(2,0,0)); + //NN.AddLayer(TNNetConvolutionReLU.Create(128, 5, 0, 0,1)); + // NN.AddLayer(TNNetLayerFullConnectReLU.Create30(64,0)); + + //NN.AddLayer(TNNetFullConnectReLU.Create30(64,10)); + NN.AddLayer(TNNetFullConnectReLU.Create28(64,10)); + NN.AddLayer(TNNetFullConnectLinear.Create27(NumClasses,10)); + NN.AddLayer(TNNetSoftMax.Create()); + writeln('layers '+itoa(NN.layers)); + //TNNetSoftMax.Create().compute(); + //netsoft:= TNNetSoftMax.Create() + //netsoft.compute(); + + //TF:= TNNetConvolution.create36(16, 5, 0, 0,1); + //TF.compute(); + //TB.compute(); + //TNNetFullConnectLinear.create28(); + writeln('choosen '+itoa(ialgo)); + end; + 2: writeln('no more'); + end; + + NN.DebugStructure(); + //NN.Compute63(ImgTestVolumes, ImgTestVolumes64); + + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, + ImgTestVolumes,csEncodeRGB); + + writeln('totalsize '+itoa(ImgTrainingVolumes.gettotalsize )) + writeln('coint '+itoa(ImgTrainingVolumes.count )) + + with TVolume.create() do begin + //setlength(farr, 3) + //setlength(raw, 3) + // setlength(fdata, 3) + farr:= fdata; + //farr[1]:= 6757.90; + //farr[2]:= 556757.90; + //raw[0]:= 6757.90; + //raw[1]:= 6757.90; + //raw[2]:= 556757.90; + //fdAta[1]:= farr; + //fdAta[0]:= 6767.8989; + //farr:= fdata[1]; + //writeln(floattostr(fdata[1])); + free + end; + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := fileNameBase; + NeuralFit.InitialLearningRate := 0.001; //0.001; + NeuralFit.Inertia := 0.2;; + NeuralFit.TargetAccuracy := 0.8; + neuralfit.verbose:= True; + writeln('momentum '+floattostr(neuralfit.momentum)) + //procedure TestDataParallelism(NN: TNNet); + TestDataParallelism(NN); + //TestConvolutionAPI ; + //NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, + // ImgTestVolumes, NumClasses, {batchsize=}128, {epochs=}2); + //ImgTestVolumes, NumClasses, {batchsize=}256, {epochs=}1); + + //CreateTokenizedStringList + + finally + NeuralFit.Free; //*) + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + //Terminate; + writeln('3 Volumes + NN freed...') + end; + //SyncWideInputQuery + //DarkenColor + //TCommandLineReader + //TCustomApplication + {with TVolume.create() do begin + farr[1]:= 6757.90; + farr[2]:= 556757.90; + fdAta[1]:=farr; + //writeln(floattostr(Fdata[1])); + free + end;} + end; + +type TBackInput = array[0..3] of array[0..1] of TNeuralFloat; +type TBackOutput = array[0..3] of array[0..2] of TNeuralFloat; + +// var Inputs: TBackInput; + // rOutput: TBackOutput; + + { +const inputs : TBackInput = + ( // x1, x2 + ( 0.1, 0.1), // False, False + ( 0.1, 0.9), // False, True + ( 0.9, 0.1), // True, False + ( 0.9, 0.9) // True, True + ); + +const reluoutputs : TBackOutput = + (// XOR, AND, OR + ( 0.1, 0.1, 0.1), + ( 0.8, 0.1, 0.8), + ( 0.8, 0.1, 0.8), + ( 0.1, 0.8, 0.8) + ); } + + procedure DefinelogicalMatrix(var inputs:TBackInput; var routput:TBackOutput); + begin + Inputs[0][0]:= 0.1; Inputs[0][1]:= 0.1; + Inputs[1][0]:= 0.1; Inputs[1][1]:= 0.9; + Inputs[2][0]:= 0.9; Inputs[2][1]:= 0.1; + Inputs[3][0]:= 0.9; Inputs[3][1]:= 0.9; + + routput[0][0]:= 0.1; routput[0][1]:= 0.1; routput[0][2]:= 0.1; + routput[1][0]:= 0.8; routput[1][1]:= 0.1; routput[1][2]:= 0.8; + routput[2][0]:= 0.8; routput[2][1]:= 0.1; routput[2][2]:= 0.8; + routput[3][0]:= 0.1; routput[3][1]:= 0.8; routput[3][2]:= 0.8; + end; + { : + ( 0.1, 0.1), // False, False + ( 0.1, 0.9), // False, True + ( 0.9, 0.1), // True, False + ( 0.9, 0.9) // True, True } + + procedure RunSimpleAlgo(); + var + NN: TNNet; + EpochCnt: integer; + Cnt: integer; + pOutPut: TNNetVolume; + vInputs: TBackInput; + vOutput: TBackOutput; + inputs: TBackInput; routput : TBackOutput; + Rate, Loss, ErrorSum : TNeuralFloat; + begin + definelogicalMatrix(inputs, routput); + NN := TNNet.Create(); + NN.AddLayer( TNNetInput.Create3(2) ); + NN.AddLayer( TNNetFullConnectReLU.Create30(3,0) ); + NN.AddLayer( TNNetFullConnectReLU.Create30(3,0) ); + NN.SetLearningRate(0.01, 0.9); + + vInputs := inputs; + vOutput := routput; + //constructor Create(pSizeX, pSizeY, pDepth: integer; c: T = 0); {$IFNDEF FPC} overload; {$ENDIF} + pOutPut := TNNetVolume.Create0(3,1,1,1); + for EpochCnt := 1 to 4000 do begin + for Cnt := Low(inputs) to High(inputs) do begin + NN.Compute68(vInputs[Cnt],0); + NN.GetOutput(pOutPut); + NN.Backpropagate70(vOutput[Cnt]); + if EpochCnt mod 300 = 0 then + WriteLn + ( + itoa(EpochCnt)+' x '+itoa(Cnt)+ + ' Output:'+ + format(' %5.2f',[poutPut.Raw[0]])+' '+ + format(' %5.2f',[poutPut.Raw[1]])+' '+ + format(' %5.2f',[poutPut.Raw[2]])+' '+ + (* pOutPut.Raw[1]:5:2,' ', + pOutPut.Raw[2]:5:2, *) + ' - Training/Desired Output:'+ + format('%5.2f',[vOutput[cnt][0]])+' '+ + format('%5.2f',[vOutput[cnt][1]])+' '+ + format('%5.2f',[vOutput[cnt][2]])+' ' + {vOutput[cnt][0]:5:2,' ', + vOutput[cnt][1]:5:2,' ' , + vOutput[cnt][2]:5:2,' ' } + ); + end; + if EpochCnt mod 300 = 0 then WriteLn(''); + end; + // TestBatch( NN, pOutPut, 10000, Rate, Loss, ErrorSum); + + //NN.DebugWeights(); + NN.DebugErrors(); + pOutPut.Free; + NN.Free; + Write('Press ENTER to exit.'); + //ReadLn; + end; + + + procedure TTestCNNAlgoDoRunClassifier89; + //Application.Title:='CIFAR-10 SELU Classification Example'; + var + NN: THistoricalNets; + NN2: TNNet; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + Rate, Loss, ErrorSum : TNeuralFloat; + begin + if not CheckCIFARFile() then begin + //Terminate; + //exit; + memo2.lines.add('TNNetConvolutionLinear CIFAR-10 Files missing!') + end; + WriteLn('Creating Neural Network...'); + NN:= THistoricalNets.Create(); + //TestDataParallelism( NN); + + NN.AddLayer(TNNetInput.Create4(32, 32, 3) ); + //Function InitSELU( Value : TNeuralFloat) : TNNetLayer'); + NN.AddLayer(TNNetConvolutionLinear.Create(64,5,2,1,1)).InitSELU(0).InitBasicPatterns(); + NN.AddLayer( TNNetMaxPool.Create44(4,0,0) ); + // NN.AddLayer( TNNetMaxPool.Create(4) ); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetMovingStdNormalization.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64,3,1,1,1)).InitSELU(0); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64,3,1,1,1)).InitSELU(0); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64,3,1,1,1)).InitSELU(0); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64,3,1,1,1)).InitSELU(0); + NN.AddLayer( TNNetDropout.Create12(0.5,1) ); + NN.AddLayer( TNNetMaxPool.Create44(2,0,0) ); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetFullConnectLinear.Create28(10,0) ); + {NN.AddLayer( TNNetDropout.Create(0.5) ); + NN.AddLayer( TNNetMaxPool.Create(2) ); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetFullConnectLinear.Create(10) );} + NN.AddLayer( TNNetSoftMax.Create() ); + memo2.lines.add('TNNetConvolutionLinear model add') + TestDataParallelism( NN); + memo2.lines.add('TestDataParallelism( NN) passed') + CheckCIFARFile() + try + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, + ImgTestVolumes, csEncodeRGB); + except + memo2.lines.add('TNNetConvolutionLinear CIFAR Files missing!') + end; + NeuralFit:= TNeuralImageFit.Create; + NeuralFit.FileNameBase:= 'ImageClassifierSELU_Tutor89_5'; + NeuralFit.InitialLearningRate:= 0.0004; + // SELU seems to work better with smaller learning rates. + NeuralFit.LearningRateDecay:= 0.03; + NeuralFit.StaircaseEpochs:= 10; + NeuralFit.Inertia:= 0.9; + NeuralFit.L2Decay:= 0.00001; + NeuralFit.verbose:= true; + try + NN.DebugStructure(); + nn.debugweights; + // Procedure TestBatch( NN : TNNet; ImgVolumes : TNNetVolumeList; SampleSize : integer; out Rate, Loss, ErrorSum : TNeuralFloat)'); + //Procedure TranslateCifar10VolumesToMachineAnimal( VolumeList : TNNetVolumeList)'); + writeln('volume list '+objtostr( ImgTestVolumes)); + //TranslateCifar10VolumesToMachineAnimal(ImgTestVolumes); + //ImgTestVolumes:= TNNetVolumeList.create(self); + //TestBatch( NN, ImgTestVolumes, 10000, Rate, Loss, ErrorSum); + writeln(format(' rate %4.2f loss %4.2f errorsum %4.2f ', + [Rate, Loss, ErrorSum])); + NN.DebugWeights(); + //NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, + // ImgTestVolumes, {NumClasses=}10,{batchsize=}64,{epochs=}3); + //TestBatch( NN, ImgTestVolumes, 10000, Rate, Loss, ErrorSum); + finally + NeuralFit.Free; + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + //Terminate; + writeln('3 Volumes + NN + NF freed...') + end; + end; + + + + procedure ReluTestingRunAlgo2(ialgo: integer); +var NumClasses, aialgo: integer; + fileNameBase: string; + NN : TNNet; + NeuralFit: TNeuralImageFit; //TNeuralFit; + I: integer; + ImgTrainingVolumes,ImgValidationVolumes,ImgTestVolumes: TNNetVolumeList; //PairList; + Volume: TNNetVolume; + //NumClasses: integer; + //fileNameBase: string; + TA,TB,TC: TNNetLayer; + TD: TNNetLayerFullConnectReLU; + TF: TNNetConvolution ; + netsoft:TNNetSoftMax; + Rate, Loss, ErrorSum : TNeuralFloat; + //TNNetLayerFullConnect = class(TNNetFullConnect); +begin + NumClasses := 10; + NN := TNNet.Create(); + fileNameBase := 'autosave-neuralnetwork_a'+IntToStr(iAlgo); + + try + case iAlgo of + 1: + begin + //RELU TESTING + TA:= NN.AddLayer(TNNetInput.Create4(32, 32, 3)); + //TA := NN.AddLayer(TNNetInput.Create4(32, 32, 3)); + {TF:=} NN.AddLayer(TNNetConvolutionReLU.Create(16, 5, 0, 0,1)); + TB:= NN.AddLayer(TNNetMaxPool.Create44(2,0,0)); + TC:= NN.AddLayer(TNNetConvolutionReLU.Create(128, 5, 0, 0,1)); + //NN.AddLayer(TNNetMaxPool.Create44(2,0,0)); + //NN.AddLayer(TNNetConvolutionReLU.Create(128, 5, 0, 0,1)); + // NN.AddLayer(TNNetLayerFullConnectReLU.Create30(64,0)); + + //NN.AddLayer(TNNetFullConnectReLU.Create30(64,10)); + NN.AddLayer(TNNetFullConnectReLU.Create28(64,2)); + NN.AddLayer(TNNetFullConnectLinear.Create27(NumClasses,10)); + NN.AddLayer(TNNetSoftMax.Create()); + writeln('layers '+itoa(NN.layers)); + //TNNetSoftMax.Create().compute(); + //netsoft:= TNNetSoftMax.Create() + //netsoft.compute(); + + //TF:= TNNetConvolution.create36(16, 5, 0, 0,1); + //TF.compute(); + //TB.compute(); + //TNNetFullConnectLinear.create28(); + writeln('choosen '+itoa(ialgo)); + end; + 2: writeln('no more'); + end; + + //NN.DebugStructure(); + + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, + ImgTestVolumes,csEncodeRGB); + + //writeln('totalsize '+itoa(ImgTrainingVolumes.gettotalsize )) + //writeln('coint '+itoa(ImgTrainingVolumes.count )) + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := fileNameBase; + NeuralFit.InitialLearningRate := 0.001;; + NeuralFit.Inertia := 0.2;; + NeuralFit.TargetAccuracy := 0.8; + //NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, + // ImgTestVolumes, {NumClasses=}10,{batchsize=}64,{epochs=}1); + //{batchsize=}128, {epochs=}2); + //TestBatch( NN, ImgTestVolumes, 1000, Rate, Loss, ErrorSum); + + //CreateTokenizedStringList + //neuralfit.runtestbatch + neuralfit.TrainingAccuracy; + + finally + NeuralFit.Free; //*) + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + //Terminate; + end; + //SyncWideInputQuery + //DarkenColor + //TCommandLineReader + //TCustomApplication + end; + + //https://sourceforge.net/p/cai/svncode/HEAD/tree/trunk/lazarus/experiments/visualCifar10test/uvisualcifar10test.pas + + procedure TFormVisualLearningLearn(Sender: TObject); +var + NN: TNNet; + I: integer; + ImgVolumes: TNNetVolumeList; + Volume: TNNetVolume; + pOutput, vOutput, vDisplay: TNNetVolume; + hit, miss: integer; + NumClasses: integer; + ErrorSum, LastError: TNeuralFloat; + startTime, totalTimeSeconds: double; + aImage: array of TImage; + NeuronCount: integer; + ImgIdx: integer; + MaxW, MinW: TNeuralFloat; + fileName: string; + firstNeuronalLayer: integer; + OpenDialogNN: TOpendialog; FormVisualLearning: TForm; + FRunning: boolean; + ButTest: TButton; + EdTestBinFile: TEdit; + ImgSample: TImage; + LabClassRate: TLabel; + LabTestFile: TLabel; + //OpenDialogNN: TOpenDialog; + + +begin + writeln('Creating Neural Network...'); + ImgVolumes := TNNetVolumeList.Create(true); + NumClasses := 10; + FRunning:= true; + ImgSample:= TImage.create(nil); + + //fileName := OpenDialogNN.FileName; //FormVisualLearning: TForm; + filename:= 'C:\maXbox\EKON_BASTA\EKON24\SimpleImageClassifierEKON25-5000.nn'; + + //-------------------------------------------------------------------- + // creates required volumes to store images + for I := 0 to 9999 do + begin + Volume := TNNetVolume.Create(); + ImgVolumes.Add(Volume); + end; + //-------------------------------------------------------------------- + + NN := TNNet.Create(); + + writeln('Loading neural network from file: '+fileName); + NN.LoadFromFile(fileName); + NN.EnableDropouts(false); + firstNeuronalLayer := NN.GetFirstNeuronalLayerIdx(1); + + pOutput := TNNetVolume.Create0(NumClasses,1,1,0); + vOutput := TNNetVolume.Create0(NumClasses,1,1,0); + vDisplay:= TNNetVolume.Create0(NumClasses,1,1,0); + + SetLength(aImage, NN.Layers[firstNeuronalLayer].Neurons.Count); + + for NeuronCount := 0 to NN.Layers[firstNeuronalLayer].Neurons.Count - 1 do + begin + aImage[NeuronCount] := TImage.Create(FormVisualLearning); + aImage[NeuronCount].Parent := FormVisualLearning; + aImage[NeuronCount].Width := NN.Layers[firstNeuronalLayer].Neurons[NeuronCount].Weights.SizeX; + aImage[NeuronCount].Height := NN.Layers[firstNeuronalLayer].Neurons[NeuronCount].Weights.SizeY; + aImage[NeuronCount].Top := (NeuronCount div 12) * 36 + 120; + aImage[NeuronCount].Left := (NeuronCount mod 12) * 36 + 32; + aImage[NeuronCount].Stretch:=true; + end; + + NN.DebugWeights(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+itoa( NN.CountLayers() ) ); + WriteLn(' Neurons:'+itoa( NN.CountNeurons() )); + WriteLn(' Weights:' +itoa (NN.CountWeights() )); + WriteLn('Computing...'); + + begin + hit := 0; + miss := 0; + ErrorSum := 0; + LastError := 0; + startTime := Now(); + loadCifar10Dataset(ImgVolumes, 'C:\maXbox\EKON_BASTA\EKON24\data_batch_1.bin',0,csEncodeRGB); + for I := 0 to ImgVolumes.Count - 1 do + begin + if not(FRunning) then Break; + ImgIdx := Random(ImgVolumes.Count); + //-- CAREFUL + NN.Compute65(ImgVolumes[ImgIdx],0); + NN.GetOutput(pOutput); + + vOutput.SetClassForReLU( ImgVolumes[ImgIdx].Tag ); // ReLU - no softmax + ErrorSum := errorsum+ vOutput.SumDiff(pOutput); + + if I mod 1000 = 0 then + begin + vDisplay.Copy38(ImgVolumes[ImgIdx]); + vDisplay.Mul26(64); + vDisplay.Add13(128); + + LoadVolumeIntoTImage(vDisplay, ImgSample, csEncodeRGB); + ImgSample.Width := 64; + ImgSample.Height := 64; + + for NeuronCount := 0 to NN.Layers[firstNeuronalLayer].Neurons.Count - 1 do + begin + MaxW := NN.Layers[firstNeuronalLayer].Neurons[NeuronCount].Weights.GetMax(); + MinW := NN.Layers[firstNeuronalLayer].Neurons[NeuronCount].Weights.GetMin(); + vDisplay.Copy38(NN.Layers[firstNeuronalLayer].Neurons[NeuronCount].Weights); + vDisplay.Mul26(256/(MaxW-MinW)); + vDisplay.Add13(128); + LoadVolumeIntoTImage(vDisplay, aImage[NeuronCount], csEncodeRGB); + aImage[NeuronCount].Width := 32; + aImage[NeuronCount].Height := 32; + end; + + Application.ProcessMessages(); + end; + + Application.ProcessMessages(); + + if pOutput.GetClass() = ImgVolumes[ImgIdx].Tag then + begin + Inc(Hit); + end + else + begin + Inc(Miss); + end; + + if (Hit>0) and (I>0) and ((I+1) mod 1000 = 0) then + begin + totalTimeSeconds := (Now() - startTime) * 24 * 60 * 60; + WriteLn + ( ''+ + itoa(I+1)+ ' Accuracy:'+flots(Hit/(Hit+Miss)) + (*' Error:', (ErrorSum-LastError):10:5, + ' Time:', totalTimeSeconds:6:2,'s', + ' Forward:', (NN.ForwardTime * 24 * 60 * 60):6:2,'s', + ' Backward:', (NN.BackwardTime * 24 * 60 * 60):6:2,'s' *) + ); + NN.ClearTime(); + + //LabClassRate.Caption := IntToStr( (Hit*100) div (Hit+Miss) )+'%'; + writeln(IntToStr( (Hit*100) div (Hit+Miss) )+'%'); + + startTime := Now(); + LastError := ErrorSum; + Application.ProcessMessages; + end; + + end; + + NN.DebugWeights(); + end; + + for NeuronCount := Low(aImage) to High(aImage) do + begin + aImage[NeuronCount].Free; + end; + + //LabClassRate.Caption := '0%'; + + vDisplay.Free; + NN.Free; + vOutput.Free; + pOutput.Free; + ImgVolumes.Free; + ImgSample.Free; +end; + + + + { testcnnalgo } + //https://sourceforge.net/p/cai/svncode/HEAD/tree/trunk/lazarus/experiments/testcnnalgo/testcnnalgo.lpr + procedure TTestCNNAlgoDoRunTester; + var + Algo, LearningRate, Inertia, Target: string; + iAlgo: integer; + fLearningRate, fInertia, fTarget: single; + //atx:TExternalTool; + //cust: TCustomApplication ; + begin + // quick check parameters + // parse parameters + {if HasOption('h', 'help') then + begin + WriteHelp; + end; } + + fLearningRate := 0.001; + if TCustomApplicationHasOption('l', 'learningrate') then + begin + LearningRate := TCustomApplicationGetOptionValue('l', 'learningrate'); + fLearningRate := StrToFloat(LearningRate); + end; + + fInertia := 0.9; + if TCustomApplicationHasOption('i', 'inertia') then + begin + Inertia := TCustomApplicationGetOptionValue('i', 'inertia'); + fInertia := StrToFloat(Inertia); + end; + + fTarget := 0.8; + if TCustomApplicationHasOption('t', 'target') then + begin + Target := TCustomApplicationGetOptionValue('t', 'target'); + fTarget := StrToFloat(Target); + end; + + if TCustomApplicationHasOption('a', 'algo') then + begin + Algo := TCustomApplicationGetOptionValue('a', 'algo'); + Writeln('Running algorithm:['+itoa(iAlgo)+']'); + iAlgo := StrToInt(Algo); + + if (iAlgo > 0) and (iAlgo < 14) then + begin + //RelutestingRunAlgo(iAlgo, fLearningRate, fInertia, fTarget); + RelutestingRunAlgo(iAlgo); + end + else + begin + WriteLn('Bad algorithm number:'+itoa(iAlgo)); + end; + //end + end + else + begin + {$IFDEF Release} + WriteHelp; + Write('Press ENTER to quit.'); + ReadLn(); + {$ELSE} + iAlgo := 3; + //TCustomApplicationRunAlgo(iAlgo, fLearningRate, fInertia, fTarget); + {$ENDIF} + end; + + //Terminate; + //Exit; + end; + + + + var st : string; + f1,f2 : double; + var A, B : TVectorClass; ax: TExtendedArray; aint64, aint64_: TInt64ArrayClass; + NN : TNNet; NumClasses: integer; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + NeuralFit : TNeuralImageFit; fLearningRate: double; + + TrainingPairs, ValidationPairs, TestPairs :TNNetVolumePairList; + ImgVolumes: TNNetVolumeList; + + //PythonEngine1.Free; + //myloadscript2:= filetostring(PYSCRIPT2); + + begin //@main + { + with TPythonEngine.Create(Nil) do begin + pythonhome:= 'C:\Users\breitsch\AppData\Local\Programs\Python\Python37-32\'; + try + loadDLL; + Println('Decimal: '+ + EvalStr('__import__("decimal").Decimal(0.1)')); + except + raiseError; + finally + free; + end; + end; } + + with TNNetDictionary.create(100) do begin + AddWordToDictionary('this bosx') + //StringToVolume + free + end; + + TestVectorClass; + + //with TExtendedArray.create([]) do begin + //assign + //end; + + ax:= TExtendedArray.create([]); + ax.appenditem(PI) + //ax.free; + + aint64:= TInt64ArrayClass.Create([1234]); + //aint64.clear; + //aint64.free; + aint64_:= TInt64ArrayClass.Create([12345678]); + + with TVectorClass.create([80.0]) do begin + //createinstance + //add(3500.9958) + add(3500.0) + add(3500.0) + //add TInt64ArrayClass + add6 (aint64); + add6 (aint64_); + add7(ax); + writeln(floattostr(PI)); + add(PI) + writeln('sum '+floattostr(sum)); + writeln('count '+itoa(count)); + //writeln(data) + + free; + end; //} + + aint64.clear; + aint64.free; + aint64_.free; + ax.clear; + ax.Free; + + {with TVectorClass.create do begin + //createinstance + //add(3500.9958) + add(3500.0) + //add6 + writeln(floattostr(PI)); + add(PI) + writeln('sum '+floattostr(sum)); + + //free; + end; //} + + A := TVectorClass.Create([]); + B := TVectorClass.Create([]); + A.Free; + B.Free; + + TestKMeans2; + //TestTNNetVolume; + //LoadPictureIntoVolume + + writeln(floattostr(SoftmaxDerivative( 0.087879))); + writeln(getparentprocessname2) + //register_PYthonGUI; + + //InverseCompareResult + + st := 'a number 12.35 and another 13.415'; + writeln('Total String parts = '+IntToStr(NumStringParts(st,#32))); + f1 := StrToFloatDef(GetStringPart(st,#32,3),0.0); + f2 := StrToFloatDef(GetStringPart(st,#32,6),0.0); + writeln('Float 1 = '+FloatToStr(F1)+' and Float 2 = '+FloatToStr(F2)); + + TestVectorClassExtended; + + with TMatrixClass.CreateSize(10,1) do begin + //SolveLinearSystem() + free + end; + + //procedure SIRegister_CurlHttpCodes(CL: TPSPascalCompiler); + writeln(itoa(HTTP_BAD_GATEWAY)) + + with TNNet.create do begin + //createlayer('TNNetDropout') + InitWeights(); + //AddLayer47(GetRandomLayer( )) + //GetRandomLayer( ); + writeln('GetBiasSum '+floattostr(GetBiasSum( ))); + free + end; + + (* + NN := TNNet.Create(); + NumClasses:= 2; + fLearningRate:= 0.009; + + NN.AddLayer49([ + TNNetInput.Create4(32, 32, 3), //32x32x3 Input Image + TNNetConvolutionReLU.Create({Features=}16, + {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0), + TNNetMaxPool.Create44({Size=}2, 0, 0), + TNNetConvolutionReLU.Create({Features=}32, + {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0), + TNNetMaxPool.Create44({Size=}2,0,0), + TNNetConvolutionReLU.Create({Features=}32, + {FeatureSize=}5, {Padding=}0, {Stride=}1, {SuppressBias=}0), + TNNetFullConnectReLU.Create30({Neurons=}32,0), + TNNetFullConnectLinear.Create28(NumClasses,0), + TNNetSoftMax.Create() + ]); + + + //Loading 10K images from file "data_batch_1.bin" ... + //ShowNeurons + //LoadNNLayersIntoCombo + //LoadTinyImageIntoTImage(var TI: TTinyImage; var Image: TImage); + + //function CheckCIFARFile():boolean; + //https://github.com/joaopauloschuler/neural-api/blob/master/neural/neuraldatasets.pas + + //procedure loadCifar10Dataset(ImgVolumes: TNNetVolumeList; idx:integer; base_pos:integer = 0; color_encoding: byte = csEncodeRGB); overload; + + //loadCifar10Dataset6(ImgVolumes, 1, 0, csEncodeRGB); //overload; + //if not (FileExists('data_batch_1.bin')) then --- files must be in root + //NN.DebugStructure(); + writeln('CheckCIFARFile() '+botostr(CheckCIFARFile())); + //CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes); + try + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, csEncodeRGB); + except + writeln(exceptiontostring(exceptiontype, exceptionparam)) + end + WriteLn('Neural Network will minimize error with:'); + WriteLn(' Layers: '+itoa( NN.CountLayers())); + WriteLn(' Neurons: '+itoa( NN.CountNeurons())); + WriteLn(' Weights: '+itoa( NN.CountWeights())); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.InitialLearningRate := fLearningRate; + NeuralFit.FileNameBase:= 'EKONSimpleImageClassifier2'+IntToStr(GetProcessId()); + NeuralFit.Inertia := 0.8; + NeuralFit.LearningRateDecay := 0.005; + NeuralFit.StaircaseEpochs := 17; + // NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + neuralfit.verbose:= true; + try + writeln(neuralfit.FileNameBase) + try + NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}10, {batchsize=}64, {epochs=}2); + except + writeln('writeln Exceptpn!!') + end; + + //NeuralFit.Fit(NN, ImgTrainingVolumes,ImgValidationVolumes,ImgTestVolumes, NumClasses, // {batchsize}128, {epochs}100) + + finally + NN.Free; + NeuralFit.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + writeln('all frees...'); + end; *) + + //TTestCNNAlgoDoRun2; + + //ReluTestingRunAlgo(1); + + //ReluTestingRunAlgo2(1); + + //TTestCNNAlgoDoRunClassifier89; + //CreateVolumesFromImagesFromFolder + + RunSimpleAlgo; + + writeln(itoa(neuralGetProcessId( ))); + writeln(itoa(GetProcessId( ))); + //CreatesimpleCNN ; + + //TFormVisualLearningLearn(self); + + { TrainingPairs := CreateHypotenusePairList(10000); + ValidationPairs := CreateHypotenusePairList(1000); + TestPairs := CreateHypotenusePairList(1000); } + +End. + +# Learning boolean functions Xor And Or + +This example has these main steps: +* Preparing training data +* Creating the neural network +* Fitting +* Printing a test result + +These are the inputs and expected outputs: +``` +const inputs : TBackInput = + ( // x1, x2 + ( 0.1, 0.1), // False, False + ( 0.1, 0.9), // False, True + ( 0.9, 0.1), // True, False + ( 0.9, 0.9) // True, True + ); + +const reluoutputs : TBackOutput = + (// XOR, AND, OR + ( 0.1, 0.1, 0.1), + ( 0.8, 0.1, 0.8), + ( 0.8, 0.1, 0.8), + ( 0.1, 0.8, 0.8) + ); +``` +The first row in `reluoutputs` has expected outputs for **XOR**, **AND** and **OR** boolean functions while the first row of +`inputs` contains inputs for these 3 boolean functions. All 3 boolean functions will be trained together in this example. + +This is how the training data is prepared with training pairs (input,output): +``` +TrainingPairs := TNNetVolumePairList.Create(); +... + for Cnt := Low(inputs) to High(inputs) do + begin + TrainingPairs.Add( + TNNetVolumePair.Create( + TNNetVolume.Create(vInputs[Cnt]), + TNNetVolume.Create(vOutput[Cnt]) + ) + ); + end; +``` +In this example, values smaller than 0.5 mean **false** while values bigger than 0.5 mean **true**. This is called **monopolar** encoding. + +CAI also supports bipolar encoding (-1, +1). Please have a look directly into the source code at these 2 methods: +* `EnableMonopolarHitComparison()` +* `EnableBipolarHitComparison()` + +This is how the neural network is created: +``` + NN := TNNet.Create(); + ... + NN.AddLayer( TNNetInput.Create(2) ); + NN.AddLayer( TNNetFullConnectReLU.Create(3) ); + NN.AddLayer( TNNetFullConnectReLU.Create(3) ); +``` + +As you can see, there is one input layer followed by 2 fully connected layers with 3 neurons each. + +This is how the fitting object is created and run: +``` +NFit := TNeuralFit.Create(); +... + NFit.InitialLearningRate := 0.01; + NFit.LearningRateDecay := 0; + NFit.L2Decay := 0; + NFit.Verbose := false; + NFit.HideMessages(); + NFit.Fit(NN, TrainingPairs, nil, nil, {batchsize=}4, {epochs=}3000); +``` + +The neural network is then tested for each input with: +``` + // tests the learning + for Cnt := Low(inputs) to High(inputs) do + begin + NN.Compute(vInputs[Cnt]); + NN.GetOutput(pOutPut); + WriteLn + ( + ' Output:', + pOutPut.Raw[0]:5:2,' ', + pOutPut.Raw[1]:5:2,' ', + pOutPut.Raw[2]:5:2, + ' - Training/Desired Output:', + vOutput[cnt][0]:5:2,' ', + vOutput[cnt][1]:5:2,' ' , + vOutput[cnt][2]:5:2,' ' + ); + end; +``` + + +Ref: simple logical learner +300 x 0 Output: 0.30 0.07 0.26 - Training/Desired Output: 0.10 0.10 0.10 +300 x 1 Output: 0.40 0.29 0.62 - Training/Desired Output: 0.80 0.10 0.80 +300 x 2 Output: 0.72 0.22 0.62 - Training/Desired Output: 0.80 0.10 0.80 +300 x 3 Output: 0.37 0.56 1.00 - Training/Desired Output: 0.10 0.80 0.80 + +600 x 0 Output: 0.29 0.02 0.23 - Training/Desired Output: 0.10 0.10 0.10 +600 x 1 Output: 0.52 0.28 0.62 - Training/Desired Output: 0.80 0.10 0.80 +600 x 2 Output: 0.74 0.15 0.69 - Training/Desired Output: 0.80 0.10 0.80 +600 x 3 Output: 0.27 0.66 0.96 - Training/Desired Output: 0.10 0.80 0.80 + +900 x 0 Output: 0.22 0.02 0.20 - Training/Desired Output: 0.10 0.10 0.10 +900 x 1 Output: 0.65 0.23 0.66 - Training/Desired Output: 0.80 0.10 0.80 +900 x 2 Output: 0.75 0.13 0.74 - Training/Desired Output: 0.80 0.10 0.80 +900 x 3 Output: 0.18 0.72 0.90 - Training/Desired Output: 0.10 0.80 0.80 + +1200 x 0 Output: 0.15 0.04 0.16 - Training/Desired Output: 0.10 0.10 0.10 +1200 x 1 Output: 0.73 0.18 0.72 - Training/Desired Output: 0.80 0.10 0.80 +1200 x 2 Output: 0.78 0.13 0.77 - Training/Desired Output: 0.80 0.10 0.80 +1200 x 3 Output: 0.13 0.75 0.85 - Training/Desired Output: 0.10 0.80 0.80 + +1500 x 0 Output: 0.11 0.06 0.12 - Training/Desired Output: 0.10 0.10 0.10 +1500 x 1 Output: 0.79 0.15 0.77 - Training/Desired Output: 0.80 0.10 0.80 +1500 x 2 Output: 0.80 0.12 0.79 - Training/Desired Output: 0.80 0.10 0.80 +1500 x 3 Output: 0.10 0.77 0.82 - Training/Desired Output: 0.10 0.80 0.80 + +1800 x 0 Output: 0.10 0.07 0.11 - Training/Desired Output: 0.10 0.10 0.10 +1800 x 1 Output: 0.80 0.13 0.79 - Training/Desired Output: 0.80 0.10 0.80 +1800 x 2 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +1800 x 3 Output: 0.10 0.78 0.81 - Training/Desired Output: 0.10 0.80 0.80 + +2100 x 0 Output: 0.10 0.09 0.11 - Training/Desired Output: 0.10 0.10 0.10 +2100 x 1 Output: 0.80 0.12 0.79 - Training/Desired Output: 0.80 0.10 0.80 +2100 x 2 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2100 x 3 Output: 0.10 0.79 0.80 - Training/Desired Output: 0.10 0.80 0.80 + +2400 x 0 Output: 0.10 0.09 0.10 - Training/Desired Output: 0.10 0.10 0.10 +2400 x 1 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2400 x 2 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2400 x 3 Output: 0.10 0.79 0.80 - Training/Desired Output: 0.10 0.80 0.80 + +2700 x 0 Output: 0.10 0.09 0.10 - Training/Desired Output: 0.10 0.10 0.10 +2700 x 1 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2700 x 2 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2700 x 3 Output: 0.10 0.80 0.80 - Training/Desired Output: 0.10 0.80 0.80 + +3000 x 0 Output: 0.10 0.10 0.10 - Training/Desired Output: 0.10 0.10 0.10 +3000 x 1 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 2 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 3 Output: 0.10 0.80 0.80 - Training/Desired Output: 0.10 0.80 0.80 + + +or another by 3000: +3000 x 0 Output: 0.11 0.00 0.11 - Training/Desired Output: 0.10 0.10 0.10 +3000 x 1 Output: 0.79 0.10 0.79 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 2 Output: 0.79 0.10 0.79 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 3 Output: 0.10 0.80 0.81 - Training/Desired Output: 0.10 0.80 0.80 + +Layer 0 Max Error: 0 Min Error: 0 Max ErrorD: 0 Min ErrorD: 0 TNNetInput 2,1,1 +debug errors else +Layer 1 Max Error: 0.000858666782733053 Min Error: -0.00092624151147902 Max ErrorD: 0 Min ErrorD: 0 TNNetFullConnectReLU 3,1,1 + Parent:0 +Layer 2 Max Error: 0.0012739896774292 Min Error: -0.00215935707092285 Max ErrorD: 0 Min ErrorD: 0 TNNetFullConnectReLU 3,1,1 + Parent:1 +Press ENTER to exit. + +3000 x 0 Output: 0.37 0.16 0.56 - Training/Desired Output: 0.10 0.10 0.10 +3000 x 1 Output: 0.77 0.10 0.82 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 2 Output: 0.59 0.00 0.55 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 3 Output: 0.11 0.72 0.57 - Training/Desired Output: 0.10 0.80 0.80 + +Ref: +Starting Validation. +VALIDATION RECORD! Saving NN at autosave-neuralnetwork_a1.nn +Debug SavetokenStructureToString: +Debug structuretostring: -1)TNNetInput:32;32;3;0;0;0;0;0#0)TNNetConvolutionReLU:16;5;0;0;1;0;0;0#1)TNNetMaxPool:2;2;0;0;0;0;0;0#2)TNNetConvolutionReLU:128;5;0;0;1;0;0;0#3)TNNetFullConnectLinear:64;1;1;2;0;0;0;0#4)TNNetFullConnect:10;1;1;10;0;0;0;0#5)TNNetSoftMax:0;0;0;0;0;0;0;0 +Epochs: 1 Examples seen:40000 Validation Accuracy: 0.1014 Validation Error: 1.7999 Validation Loss: 2.3027 Total time: 5.21min +Image mX4 FThreadNN[0].DebugWeights(); skipped... +Epoch time: 3.5000 minutes. 1 epochs: 0.0580 hours. +Epochs: 1. Working time: 0.09 hours. +CAI maXbox Neural Fit Finished. + mX4 executed: 04/11/2021 15:47:19 Runtime: 0:5:21.565 Memload: 41% use + +SELU Classifier +0 -1 0 +0 -1 0 +0 -1 0 +0 -1 0 +0 -1 0 +0 -1 0 +40000 of samples have been processed. +Starting Validation. + Epochs: 5 Examples seen:200000 Validation Accuracy: 0.0980 Validation Error: 1.8000 Validation Loss: 2.3028 Total time: 14.31min +Image mX4 FThreadNN[0].DebugWeights(); skipped... +Epoch time: 2.0000 minutes. 5 epochs: 0.1600 hours. +Epochs: 5. Working time: 0.24 hours. +CAI maXbox Neural Fit Finished. +3 Volumes + NN + NF freed... + mX4 executed: 04/11/2021 16:07:45 Runtime: 0:14:25.509 Memload: 43% use +PascalScript maXbox4 - RemObjects & SynEdit +C:\maXbox\works2021\maxbox4\examples\1065__CAI_2_SiImageClassifier21_Tutor_89_test2.txtOutput.txt as output file stored + +50 epochs: +40000 of samples have been processed. +Starting Validation. +Starting Testing. +Epoch time: 2.2000 minutes. 50 epochs: 1.8000 hours. +Epochs: 50. Working time: 2.14 hours. +CAI maXbox Neural Fit Finished. +3 Volumes + NN + NF freed... + mX4 executed: 04/11/2021 22:45:35 Runtime: 2:8:19.673 Memload: 41% use + + +epoch training accuracy training loss training error validation accuracy validation loss +1 0.0955 2.3005 1.7996 0.0977 2.3028 +2 0.0924 2.3026 1.8 0.0977 2.3028 +3 0.0919 2.3027 1.8 0.0977 2.3028 +4 0.0981 2.3063 1.8007 0.098 2.3027 + +47 0.0938 2.3036 1.8002 0.0977 +48 0.0997 2.3018 1.7998 0.0977 +49 0.0903 2.303 1.8001 0.0977 +50 0.0995 2.3036 1.8002 0.0977 + + + +Doc: Destroying it calls Py_Finalize, which frees all memory allocated by the Python DLL. + +Or, if you're just using the Python API without the VCL wrappers, you can probably just call Py_NewInterpreter on your TPythonInterface object to get a fresh execution environment without necessarily discarding everything done before. + +Example - How to create a simple fully forward connected network 3x3 +NN := TNNet.Create(); +NN.AddLayer( TNNetInput.Create(3) ); +NN.AddLayer( TNNetLayerFullConnectReLU.Create(3) ); +NN.AddLayer( TNNetLayerFullConnectReLU.Create(3) ); +NN.SetLearningRate(0.01,0.8); + +Evil eval() + +You should never pass untrusted source to the eval() directly. As it is quite easy for the malicious user to wreak havoc on your system. For example, the following code can be used to delete all the files from the system. + +1 >>> +2 eval('os.system("RM -RF /")') # command is deliberately capitalized +3 >>> + +procedure TNNet.LoadDataFromString(strData: string); +var + S: TStringList; + Cnt: integer; +begin + S := CreateTokenizedStringList(strData,'!'); + + if S.Count = FLayers.Count then + begin + if S.Count > 0 then + begin + for Cnt := 0 to S.Count - 1 do + begin + FLayers[Cnt].LoadDataFromString(S[Cnt]); + end; + end; + end + else + begin + FErrorProc + ( + 'Error while loading network: number of structure layers '+ + IntToStr(FLayers.Count)+' differ from data loaded layers '+ + IntToStr(S.Count) + ); + + +Ref: https://github.com/joaopauloschuler/neural-api + + +As we grow, we occasionally need to update our terms of service. To keep using Bitbucket Cloud, review and agree to the updated terms. To learn more, please see our Summary of Changes. + + + _od#HMM6&*MMMH::-_ + _dHMMMR??MMM? ""| `"'-?Hb_ + .~HMMMMMMMMHMMM#M? `*HMb. + ./?HMMMMMMMMMMM"*""" &MHb. + /'|MMMMMMMMMMM' - `*MHM\ + / |MMMMMMHHM'' .MMMHb + | 9HMMP .Hq, TMMMMMH + / |MM\,H-""&&6\__ `MMMMMMb +| `""HH#, \ - MMMMMMM| +| `HoodHMM###. `9MMMMMH +| .MMMMMMMM##\ `*"?HM +| .. ,HMMMMMMMMMMMo\. |M +| |MMMMMMMMMMMMMMMMHo |M +| ?MMMMMMMMMMMMMMMM* |H +|. `#MMMMMMMMMMMMM' .M| + \ `MMMMMMMMMMM* |P + `\ MMMMMMMMT"' ,H + `\ `MMMMMMH? ./ + \. |MMMH#" ,/ + `\. |MMP' ./' + `~\ `HM:.- . ,/' + "-\_ '_\ . _.-" + "-\-#odMM\_,oo==-" + + +normal + uninvolving = True neg : pos = 11.7 : 1.0 + avoids = True pos : neg = 11.7 : 1.0 + fascination = True pos : neg = 10.3 : 1.0 + astounding = True pos : neg = 10.3 : 1.0 + idiotic = True neg : pos = 9.8 : 1.0 + | n p | | n p | Stemmer more false positive + | e o | | e o | + | g s | | g s | +----+---------+ ----+---------+ +neg |<119>131 | neg |<110>140 | +pos | 5<245>| pos | 5<245>| +----+---------+ ----+---------+ +(row = reference; col = test) + +unit uPSI_neuraldatasets; +{ +mindset let mindset set + +} +interface + + + +uses + SysUtils + ,Classes + ,uPSComponent + ,uPSRuntime + ,uPSCompiler + ; + +type +(*----------------------------------------------------------------------------*) + TPSImport_neuraldatasets = class(TPSPlugin) + public + procedure CompileImport1(CompExec: TPSScript); override; + procedure ExecImport1(CompExec: TPSScript; const ri: TPSRuntimeClassImporter); override; + end; + + +{ compile-time registration functions } +procedure SIRegister_TClassesAndElements(CL: TPSPascalCompiler); +procedure SIRegister_TFileNameList(CL: TPSPascalCompiler); +procedure SIRegister_neuraldatasets(CL: TPSPascalCompiler); + +{ run-time registration functions } +procedure RIRegister_neuraldatasets_Routines(S: TPSExec); +procedure RIRegister_TClassesAndElements(CL: TPSRuntimeClassImporter); +procedure RIRegister_TFileNameList(CL: TPSRuntimeClassImporter); +procedure RIRegister_neuraldatasets(CL: TPSRuntimeClassImporter); + +procedure Register; + +implementation + + +uses + neuraldatasets, neuralnetworkCAI, neuralvolume , neuralthread ,math + ; + + +procedure Register; +begin + RegisterComponents('Pascal Script', [TPSImport_neuraldatasets]); +end; + +(* === compile-time registration functions === *) +(*----------------------------------------------------------------------------*) +procedure SIRegister_TClassesAndElements(CL: TPSPascalCompiler); +begin + //with RegClassS(CL,'TStringStringListVolume', 'TClassesAndElements') do + with CL.AddClassN(CL.FindClass('TStringStringListVolume'),'TClassesAndElements') do + begin + RegisterMethod('Constructor Create( )'); + Function CountElements( ) : integer'); + Procedure LoadFoldersAsClasses( FolderName : string; pImageSubFolder : string; SkipFirst : integer; SkipLast : integer)'); + Procedure LoadFoldersAsClassesProportional( FolderName : string; pImageSubFolder : string; fSkipFirst : TNeuralFloat; fLoadLen : TNeuralFloat)'); + Procedure LoadImages0( color_encoding : integer; NewSizeX : integer; NewSizeY : integer);'); + Procedure LoadClass_FilenameFromFolder( FolderName : string)'); + Function GetRandomClassId( ) : integer'); + Function GetClassesCount( ) : integer'); + Procedure GetRandomFileId( out ClassId : integer; out FileId : integer; StartPos : TNeuralFloat; Range : TNeuralFloat)'); + Procedure GetRandomFileName( out ClassId : integer; out FileName : string; StartPos : TNeuralFloat; Range : TNeuralFloat)'); + Procedure GetRandomImgVolumes( vInput, vOutput : TNNetVolume; StartPos : TNeuralFloat; Range : TNeuralFloat)'); + Function GetFileName( ClassId, ElementId : integer) : string'); + Procedure AddVolumesTo( Volumes : TNNetVolumeList; EmptySource : boolean)'); + Procedure AddFileNamesTo( FileNames : TFileNameList)'); + Procedure MakeMonopolar( Divisor : TNeuralFloat)'); + Function FileCountAtClassId( ClassId : integer) : integer'); + Procedure LoadImages_NTL( index, threadnum : integer)'); + end; +end; + +(*----------------------------------------------------------------------------*) +procedure SIRegister_TFileNameList(CL: TPSPascalCompiler); +begin + //with RegClassS(CL,'TStringListInt', 'TFileNameList') do + with CL.AddClassN(CL.FindClass('TStringListInt'),'TFileNameList') do + begin + Constructor Create( )'); + Procedure GetImageVolumePairFromId( ImageId : integer; vInput, vOutput : TNNetVolume; ThreadDangerous : boolean)'); + Procedure GetRandomImagePair( vInput, vOutput : TNNetVolume)'); + Function ThreadSafeLoadImageFromFileIntoVolume( ImageFileName : string; V : TNNetVolume) : boolean'); + RegisterProperty('ClassCount', 'integer', iptrw); + end; +end; + +(*----------------------------------------------------------------------------*) +procedure SIRegister_neuraldatasets(CL: TPSPascalCompiler); +begin + CL.AddTypeS('TTinyImageChannel','array [0..31] of array[0..31] of byte; '); + CL.AddTypeS('TTinyImageChannel1D','array [0..32 * 32 - 1] of byte; '); + CL.AddTypeS('TMNistImage','array [0..27] of array[0..27] of byte; '); + + //TTinyImageChannel1D = packed array [0..32 * 32 - 1] of byte; + //TMNistImage = packed array [0..27, 0..27] of byte; + + CL.AddTypeS('TTinyImage', 'record bLabel : byte; R : TTinyImageChannel; G : T' + +'TinyImageChannel; B : TTinyImageChannel; end'); + CL.AddTypeS('TCifar100Image', 'record bCoarseLabel : byte; bFineLabel : byte;' + +' R : TTinyImageChannel; G : TTinyImageChannel; B : TTinyImageChannel; end'); + CL.AddTypeS('TTinySingleChannelImage', 'record bLabel : byte; Grey : TTinyImageChannel; end'); + CL.AddTypeS('TTinySingleChannelImage1D', 'record bLabel : byte; Grey : TTinyImageChannel1D; end'); + //CL.AddTypeS('TTinySingleChannelImagePtr','^TTinySingleChannelImage// will not work'); + //CL.AddTypeS('TTinySingleChannelImage1DPtr', '^TTinySingleChannelImage1D // will not work'); + SIRegister_TFileNameList(CL); + SIRegister_TClassesAndElements(CL); + CL.AddDelphiFunction('Procedure CreateVolumesFromImagesFromFolder(out ImgTrainingVolumes,ImgValidationVolumes,ImgTestVolumes:TNNetVolumeList;FolderName,pImageSubFolder:string;color_encoding:integer;TrainingProp,ValidationProp,TestProp:single;'+ + 'NewSizeX:integer;NewSizeY:integer)'); + CL.AddDelphiFunction('Procedure CreateFileNameListsFromImagesFromFolder( out TrainingFileNames, ValidationFileNames, TestFileNames : TFileNameList; FolderName, pImageSubFolder : string; TrainingProp, ValidationProp, TestProp : single)'); + //Procedure LoadImageIntoVolume( M : TFPMemoryImage; Vol : TNNetVolume)'); + //Procedure LoadVolumeIntoImage( Vol : TNNetVolume; M : TFPMemoryImage)'); + Function LoadImageFromFileIntoVolume(ImageFileName:string; V:TNNetVolume): boolean'); + Function SaveImageFromVolumeIntoFile(V:TNNetVolume;ImageFileName:string) : boolean'); + Procedure ConfusionWriteCSVHeader(var CSVConfusion:TextFile;Labels array of string)'); + Procedure ConfusionWriteCSV( var CSVConfusion: TextFile; Vol:TNNetVolume; Digits : integer)'); + Procedure LoadTinyImageIntoNNetVolume1( var TI : TTinyImage; Vol : TNNetVolume);'); + Procedure LoadTinyImageIntoNNetVolume2(var TI: TCifar100Image; Vol : TNNetVolume);'); + Procedure LoadTinyImageIntoNNetVolume3( var TI : TMNistImage; Vol : TNNetVolume);'); + Procedure LoadNNetVolumeIntoTinyImage4( Vol : TNNetVolume; var TI : TTinyImage);'); + Procedure LoadNNetVolumeIntoTinyImage5( Vol:TNNetVolume; var TI : TCifar100Image);'); + Procedure LoadTinySingleChannelIntoNNetVolume( var SC : TTinySingleChannelImage; Vol : TNNetVolume)'); + Procedure TinyImageCreateGrey(var TI:TTinyImage;var TIGrey:TTinySingleChannelImage)'); + Procedure TinyImageHE( var TI, TIHE : TTinySingleChannelImage)'); + Procedure TinyImageVE( var TI, TIVE : TTinySingleChannelImage)'); + Procedure TinyImageRemoveZeroGradient(var TI:TTinySingleChannelImage;distance:byte)'); + Procedure TinyImageHVE( var TI, TIHE : TTinySingleChannelImage)'); + Function TinyImageTo1D( var TI:TTinySingleChannelImage): TTinySingleChannelImage1D'); + Procedure CreateCifar10Volumes( out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes : TNNetVolumeList; color_encoding : byte)'); + Procedure CreateCifar100Volumes( out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes : TNNetVolumeList; color_encoding : byte; Verbose : boolean)'); + Procedure CreateMNISTVolumes( out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes : TNNetVolumeList; TrainFileName, TestFileName : string; Verbose : boolean; IsFashion : boolean)'); + Procedure loadCifar10Dataset6( ImgVolumes : TNNetVolumeList; idx : integer; base_pos : integer; color_encoding : byte);'); + Procedure loadCifar10Dataset7( ImgVolumes : TNNetVolumeList; fileName : string; base_pos : integer; color_encoding : byte);'); + Procedure loadCifar100Dataset( ImgVolumes : TNNetVolumeList; fileName : string; color_encoding : byte; Verbose : boolean)'); + Procedure loadMNISTDataset( ImgVolumes : TNNetVolumeList; fileName : string; Verbose : boolean; IsFashion : boolean; MaxLabel : integer);'); + Function CheckCIFARFile( ) : boolean'); + Function CheckCIFAR100File( ) : boolean'); + Function CheckMNISTFile( fileName : string; IsFasion : boolean) : boolean'); + Procedure TestBatch( NN : TNNet; ImgVolumes : TNNetVolumeList; SampleSize : integer; out Rate, Loss, ErrorSum : TNeuralFloat)'); + Procedure TranslateCifar10VolumesToMachineAnimal( VolumeList : TNNetVolumeList)'); + Function SwapEndian( I : integer) : integer'); +end; + + +Loading 10K images from file "C:\maXbox\EKON_BASTA\EKON24\data_batch_1.bin" ... + GLOBAL MIN MAX -2 1.984375 +1000 Accuracy:0.61 +61% +2000 Accuracy:0.6145 +61% +3000 Accuracy:0.623333333333333 +62% +4000 Accuracy:0.6225 +62% +5000 Accuracy:0.6268 +62% +6000 Accuracy:0.625333333333333 +62% +7000 Accuracy:0.626571428571429 +62% +8000 Accuracy:0.62625 +62% +9000 Accuracy:0.628 +62% +10000 Accuracy:0.626 +62% +Layer 0 Max Output: 1.984375 Min Output: -1.984375 TNNetInput 32,32,3 + Times: 0s 0s +debug weights end..... +Layer 1 Neurons:64 Max Weight: 0.281952857971191 Min Weight: -0.269106924533844 Max Output: 4.69042015075684 Min Output: -5.31492280960083 TNNetConvolutionLinear 32,32,64 + Times: 0s 0s + Parent:0 +Layer 2 Max Output: 4.69042015075684 Min Output: -0.917664408683777 TNNetMaxPool 8,8,64 + Times: 0s 0s + Parent:1 +Layer 3 Neurons:1 Max Weight: 0.787006616592407 Min Weight: 0.61305570602417 Max Output: 5.18085145950317 Min Output: -1.94499015808105 TNNetMovingStdNormalization 8,8,64 + Times: 0s 0s + Parent:2 +Layer 4 Neurons:64 Max Weight: 0.136675611138344 Min Weight: -0.139890983700752 Max Output: 4.32235240936279 Min Output: 0 TNNetConvolutionReLU 8,8,64 + Times: 0s 0s + Parent:3 +Layer 5 Max Output: 4.32235240936279 Min Output: 0 TNNetDropout 8,8,64 + Times: 0s 0s + Parent:4 +Layer 6 Max Output: 4.32235240936279 Min Output: 0 TNNetMaxPool 4,4,64 + Times: 0s 0s + Parent:5 +Layer 7 Neurons:10 Max Weight: 0.192263349890709 Min Weight: -0.174276277422905 Max Output: 4.97360324859619 Min Output: -1.86302649974823 TNNetFullConnectLinear 10,1,1 + Times: 0s 0s + Parent:6 +Layer 8 Max Output: 0.917940378189087 Min Output: 0.0009856071555987 TNNetSoftMax 10,1,1 + Times: 0s 0s + Parent:7 + mX4 executed: 18/11/2021 10:04:05 Runtime: 0:3:52.522 Memload: 44% use +PascalScript maXbox4 - RemObjects & SynEdit + + + diff --git a/examples/maXbox4Scripts/1071_Newadds_V47610_Modules3.pas b/examples/maXbox4Scripts/1071_Newadds_V47610_Modules3.pas new file mode 100644 index 00000000..e9445069 --- /dev/null +++ b/examples/maXbox4Scripts/1071_Newadds_V47610_Modules3.pas @@ -0,0 +1,1134 @@ +unit U_PrimesFromDigits_mX4_Experimental476; + +//P:array of integer; of TIntPartition= class(Tobject) is missing! +//https://medium.com/@mohankrishnagupta/python-best-practices-4ad47c81b9bc + +interface + +(*uses + Windows, Messages, SysUtils, Classes, Graphics, Controls, Forms, Dialogs, + StdCtrls, + strutils, ShellAPI, ExtCtrls {manually added to access PosEx function}; *) + +type + TForm1 = {class(}TForm; + var + Panel1: TPanel; + aMemo1: TMemo; + Part1Btn: TButton; + Part2Btn: TButton; + aMemo2: TMemo; + aMemo3: TMemo; + image,image1,image2,image3,image4,sourceimage:TImage; JpegImage:TJpegImage; + StaticText1: TStaticText; + procedure TForm1FormActivate(Sender: TObject); + procedure TForm1Part1BtnClick(Sender: TObject); + procedure TForm1Part2BtnClick(Sender: TObject); + procedure TForm1Memo2Click(Sender: TObject); + procedure TForm1StaticText1Click(Sender: TObject); + + //public + { Public declarations } + var + count:integer; + baseparts:array of string; + basepartsindex: array of integer; {pointers to base partition} + parts:array of array of integer; + //partscounts:array of integer; + list:TStringlist; + combos: TComboSet; primes: TPrimes; defpartition: TIntPartition; + basepartscount:integer; + //function Callback:boolean; + function NextPartitionCallback:boolean; {receives partitions one per call} + procedure FindPrimeSets(index:integer; list:TStrings; var maxtoReturn:integer); + //end; + +var + Form1: TForm1; + +implementation + +//{$R *.DFM} + +//uses mathslib, ucombov2, uintegerpartition2; + +procedure TForm1FormActivate(Sender: TObject); +begin + list:=TStringlist.create; + list.sorted:=true; + combos:= TComboSet.create; + primes:= TPrimes.create; + defpartition:= Tintpartition.create; +end; + +{ CL.AddTypeS('TCombotype', '( ucombinations, uPermutations, CombinationsDown, Pe' + +'rmutationsDown, CombinationsCoLex, CombinationsCoLexDown, PermutationsRepe' + +'at, PermutationsWithRep, PermutationsRepeatDown, CombinationsWithrep, Comb' + +'inationsRepeat, CombinationsRepeatDown )');} + //http://www.delphibasics.co.uk/Article.asp?Name=Sets + +{*********** Part1BtnClick ************} +procedure TForm1Part1BtnClick(Sender: TObject); +var + i,primecount:integer; + p:int64; + act: TCombotype; + aSelected: TByteArray64; +begin + act:= upermutations; //CombinationsWithrep; //ord(TCombotype('')); + writeln('Comb Type is: '+intToStr(ord(act))); + //act:= TCombotype(ord(CombinationsDown)); //ord(TCombotype('')); + //act:= 2; + primecount:=0; + //combos.setup(9,9,TCombotype(ord(act))); + combos.setup(9,9,upermutations); + writeln(itoa(length(combos.selected))); + //for it:= 1 to length(combos.selected)-1 do + //amemo2.lines.add(itoa(aselected[it])); + with combos do + while getnext do + aselected:= selected; + //amemo2.lines.add(itoa(aselected[i])); + //if aselected[9] in [1,3,7,9] then + amemo2.lines.add(itoa(aselected[2])); + if aselected[9] = (1 or 3 or 7 or 9) then begin + p:=aselected[1]; + amemo2.lines.add(itoa(p)); + for i:=2 to 9 do begin + p:=10*p + aselected[i]; + amemo2.lines.add(itoa(aselected[i])); + end; + if primes.isprime(p) then begin + inc(primecount); + end; + end; + //amemo2.Clear; + showmessage(format('There are %d primes containing exactly one occurrence of the digits 1 through 9.' + +#13+'No matter how they are arranged, the sum of digits 1 though 9 is 45, a multiple of 3.' + +#13+'Any number whose digits sum to a multiple of 3 is divisible by 3. i.e. is not prime', + [primecount])); + +end; + +{************* NextPartitionCallback **********} +function NextPartitionCallback:boolean; +var + i:integer; + s:string; + P1:array of integer; +begin + result:=true; + with defpartition do + begin + {Save partition definitions for later use} + {we'll save all unique permutations of this partition} + s:=''; + p1:= p; + for i:=0 to partsize-1 do s:=s+','+inttostr(p1[partsize-1-i]); + inc(count); + inc(basepartscount); + delete(s,1,1); + baseparts[basepartscount]:=s; + if count>=length(parts) then + begin + setlength(parts,count+100); + setlength(basepartsindex,count+100); + end; + basepartsindex[count]:=basepartscount; + setlength(parts[count],partsize+1); + for i:=1 to partsize do parts[count][i]:=p1[partsize-i]; + parts[count][0]:=0; + end; + setlength(parts,count+1); +end; + + +{************** Part2BtnClick *************} +procedure TForm1Part2BtnClick(Sender: TObject); +var + N:integer; + i,j,k:integer; + startat:integer; + s:string; + p:integer; + ok:boolean; + pp:array[1..9] of integer; + pcount:integer; + temp,index:integer; + basepartscounts:array of integer; + totpartscount, dupcount:integer; + starttime:TDatetime; aSelected: TByteArray64; +begin + amemo2.Clear; + screen.Cursor:=crHourglass; + n:=defpartition.partitioncount(9,0); + setlength(parts,n+1); + setlength(baseparts,n+1); + setlength(basepartscounts,500); + setlength(basepartsindex,n+1); + count:=0; + dupcount:=0; + basepartscount:=0; + totpartscount:=0; + //defpartition.PartitionInit(9,0,NextPartitionCallback); + {now we have generated and saved all paritions, + generate permutations of 123456789 and break into + a set of numbers based on each partition. then check + each number in the set for "primeness"} + list.clear; + starttime:=now; + + combos.setup(9,9,upermutations); + + with combos do + while getnext do + aselected:= selected; + //if aselected[9] in [1,3,7,9] then begin + if aselected[9] = (1 or 3 or 7 or 9) then begin + for i:=1 to count do + begin + ok:=true; + startat:=1; + pcount:=0; + s:=''; + for j:=1 to high(parts[i]) do + begin {split this permutation into primes based on } + p:=0; + for k:=startat to startat+parts[i][j]-1 do + p:=10*p+aselected[k]; + + if not primes.isprime(p) then + begin + ok:=false; + break; + end; + inc(pcount); + pp[pcount]:=p; + startat:=startat+parts[i][j]; + end; + if OK then + begin + {sort this set of primes from smallest to largest value} + for j:=1 to pcount-1 do + for k:= j+1 to pcount do + if pp[j]>pp[k] then + begin + temp:=pp[j]; + pp[j]:=pp[k]; + pp[k]:=temp; + end; + s:=''; + for j:=1 to pcount do s:=s+','+inttostr(pp[j]); + delete(s,1,1); + if not list.find(s,index) then + begin {some sets may be duplicates, only save the first of those} + list.add(s); + (* + if baseparts[basepartsindex[i]]='1,2,2,2,2' + then memo2.lines.add(format('Sets of primes with lengths %s: %s', + [baseparts[basepartsindex[i]],s])); + *) + inc(totpartscount); + inc(parts[i][0]); {Keep counts by partition} + end + else inc(dupcount); + end; + end; + end; + + temp:=0; + for i:=1 to count do + begin + s:=''; + for j:=1 to high(parts[i]) do s:=s+','+inttostr(parts[i][j]); + delete(s,1,1); + memo2.lines.add(format('[%s] has %d prime sets', + [s,parts[i][0]{,baseparts[basepartsindex[i]]}])); + inc2(temp, parts[i][0]); + end; + + (* + {for debugging - add up the individual partition counts in "temp" to compare with total count} + temp:=0; + for i:= 1 to basepartscount do + begin + memo2.lines.add(format('For partition [%s], there are %d prime sets',[baseparts[i],basepartscounts[i]])); + inc(temp,basepartscounts[i]); + end; + *) + + screen.Cursor:=crdefault; + If totpartscount<>temp + then showmessage(format('Program error! Detail count(%.0n) <> Total count (%.0n)', + [0.0+temp,(now-starttime)*secsperday, 0.0+totpartscount])) + else + + showmessage(format('%.0n unique prime sets found in %.1f seconds' + +#13+'%d duplicate prime sets found', + [0.0+totpartscount,(now-starttime)*secsperday, dupcount])); + +end; + + +function LineClicked(Memo:TMemo):integer; +{For a click on a memo line return the line number (number is relative to 0)} +begin + with memo do result:=Perform(Em_LineFromChar,Selstart,0); +end; + + +function LinePositionClicked(Memo:TMemo):integer; +{When a TMemo line is clicked, return the character position + within the line (position is relative to 1)} +var LineIndex:integer; +begin + with memo do begin + LineIndex:=Perform(Em_LineIndex,Lineclicked(memo),0); + Result:=Selstart-Lineindex+1; + end; +end; + + +{************ FindPrimeSets ***************} +procedure FindPrimeSets(index:integer; list:TStrings; var maxtoreturn:Integer); +{Performs the same search as the original Part2 button except does it + for only the particular partitioning licked by the user} +var + j,k:integer; + ok:boolean; + startat,p,pcount,temp:integer; + pp:array[1..9] of integer; {primes found for a particular arrangement of digits} + s:string; + dummy:integer; aSelected: TByteArray64; +begin + combos.setup(9,9,upermutations); + with combos do + while getnext do + aSelected:= selected; + if aselected[9] in [1,3,7,9] then begin + ok:=true; + startat:=1; + pcount:=0; + s:=''; + for j:=1 to high(parts[index]) do + begin {split this permutation into primes based on } + p:=0; + for k:=startat to startat+parts[index][j]-1 do + p:=10*p+aselected[k]; + + if not primes.isprime(p) then + begin + ok:=false; + break; + end; + inc(pcount); + pp[pcount]:=p; + startat:=startat+parts[index][j]; + end; + if OK then begin + + {sort this set of primes from smallest to largest value} + {makes is easy to delete duplicate prime sets} + for j:=1 to pcount-1 do begin + for k:= j+1 to pcount do + if pp[j]>pp[k] then begin + temp:=pp[j]; + pp[j]:=pp[k]; + pp[k]:=temp; + end; + + s:=''; + for j:=1 to pcount do s:=s+','+inttostr(pp[j]); + delete(s,1,1); + {some sets may be duplicates, only save the first of those} + dummy:=list.IndexOf(s); + if dummy<0 then list.add(s); + if list.count>=maxtoreturn then break; + end; + end; + end; + maxtoreturn:=list.Count; +end; + +{**************** Memo2Click ************} +procedure TForm1Memo2Click(Sender: TObject); +var + lineNbr:integer; + i,n,n2:integer; + line,partition:string; + maxtoreturn:integer; +begin + LineNbr:=lineclicked(aMemo2); + line:=amemo2.lines[lineNbr]; + n:=pos('[',line); + if n>0 then + begin + n2:=posex(']',line,n+1); + partition:=copy(line,N+1, n2-n-1); + if baseparts[linenbr+1]=partition then + begin + amemo3.clear; + maxtoreturn:=100; + FindPrimeSets(linenbr+1, amemo3.lines, maxtoreturn); + end + else showmessage(format('Program error: Partition %s not found',[partition])); + end + else Showmessage('No partition found on clicked line'); +end; + +const + MAXX = 40 ; +type + ImgObj = record + Addrs : string; + X: Integer ; + Y: Integer ; + end; + +var + All : array[1..MAXX] of ImgObj ; + +procedure TForm1btn1Clickcanvas(Sender: TObject); +var + BuffBitmap :TBitmap ; + I,j,k: Integer; +begin + // set all bit maps + //.... + // draw 40 images + BuffBitmap := TBitmap.Create ; + for I := 1 to MAXX do begin + BuffBitmap.LoadFromFile(All[i].Addrs); + for j := 0 to BuffBitmap.Width-1 do + for k := 0 to BuffBitmap.Height-1 do + Self.Canvas.Pixels[All[i].X+j,All[i].Y+k]:=BuffBitmap.Canvas.Pixels[j,k]; + end; + BuffBitmap.free; +end; + +//https://thecodecave.com/treating-a-timages-loaded-jpeg-as-a-bitmap-in-delphi/ +procedure TForm5Image1Click(Sender: TObject; image1: TImage); +var + RS: TResourceStream; + MS: TMemoryStream; + JPGImage: TJPEGImage; var AName :String; +begin + JPGImage := TJPEGImage.Create; + try + aname:= 'MAXBOX4LOGO'; + RS:= TResourceStream.Create(hInstance, aname, 'JPEG'); + //MS:= TMemoryStream.create; + //GetResourceName( ObjStream : TStream; var AName :Str) :Bool + //writeln('resstream '+botostr(GetResourceName( MS, aname))); + //LoadGraphicFromResource(Graphic:TGraphic; const ResName:str; ResType:PChar); + //LoadGraphicFromResource(Image1.Picture.Graphic, aname, 'RT_RCDATA'); + //LoadGraphicFromResource(Image1.Picture.Graphic, aname, 'JPEG'); + try + JPGImage.LoadFromStream(RS); + Image1.Picture.Graphic := JPGImage; + finally + RS.Free; + //MS.Free; + end; + finally + JPGImage.Free; + end; +end; + +procedure loadPForm; +var abitm: TBitmap; SourceImage: TImage; JPGImage: TJPEGImage; + RS: TResourceStream; +begin + Form1:= TForm1.create(self) + with form1 do begin + SetBounds(317, 175, 1042, 648); + Caption := 'New Function tester Prime sets from Digits' + Color := clBtnFace + Font.Charset := DEFAULT_CHARSET + Font.Color := clWindowText + Font.Height := -14 + Font.Name := 'MS Sans Serif' + Font.Style := [] + OldCreateOrder := False + OnActivate := @TForm1FormActivate + PixelsPerInch := 120 + abitm:= TBitmap.create; + abitm.loadfromresourcename(0, 'MOON_FULL'); + show; + canvas.brush.bitmap:= abitm; + Canvas.FillRect(Rect(400,400,200,200)); + //Show; + TForm1FormActivate(self); + //TextHeight := 16 + Panel1:= TPanel.create(form1) + with panel1 do begin + parent:= form1; + SetBounds(0,0, 1024, 580); + Align := alClient + TabOrder := 0 + aMemo1:= TMemo.create(form1) + with amemo1 do begin + parent:= panel1 + Left := 23; Top := 16 + Width := 336; Height := 281 + Color := 14483455 + Lines.add ( + 'Primes from digits:' + +'' + +'Here'#39's another program answering fairly meaningless ' + +'questions but providing a good programming exercise. ' + +'' +CRLF+CRLF+ + +'Question 1:' + +' How many ways can digits 1 through 9 be rearranged to ' + +'form 9 digit prime numbers?' + +'' +CRLF+CRLF+ + +'Question 2:' + +'Using the dgits 1 thorugh 9, how many ways can ' + +'they be arranged into sets of numbers containing only ' + +'primes? (Each concatenation of N digits is treated as an' + +'N-digit number.) For example, one of the qualifying ' + + 'sets for partition [1,2,2,2,2] is the prime set : {5, 23, 47, 61' + + + ', ' + +'89}'); + TabOrder := 0 + end; + image:= TImage.create(form1) + with image do begin + parent:= panel1; + color:= clyellow; + setbounds(220,300,400,400) + picture.bitmap.loadfromresourcename(0, 'MOON_FULL'); + end; + image1:= TImage.create(form1) + sourceimage:= TImage.create(form1) + JpegImage := TJpegImage.Create; + //JpegImage.loadfromresourcename + // SourceImage.Picture.LoadFromFile(FullFileName); + SourceImage.Picture.bitmap.LoadFromresourcename(0, 'MAXBOXLOGO'); + //sourceimage.free; + //LoadJPEGResource(image1, 'MAXBOX4LOGO'); + //storeRCDATAResourcetofile('MAXBOX4LOGO', exepath+'maxbox4logo.jpg'); + writeln(ResourceNameToString('MAXBOX4LOGO')); + writeln(ResourceTypeToString('JPEG')) + aMemo3:= TMemo.create(form1) + //aMemo3:= TMemo.create(form1) + with amemo3 do begin + parent:= panel1; + Left := 832; Top := 16 + Width := 185; Height := 496 + ScrollBars := ssVertical + TabOrder := 4 + end; + with image1 do begin + parent:= panel1; + color:= clyellow; + setbounds(5,500,450,400) + picture.bitmap.loadfromresourcename(0, 'MAXBOXLOGO'); + //JpegImage.Assign(SourceImage.Picture.Graphic); + //SourceImage.Picture.Bitmap.Assign(JpegImage); + amemo3.lines.add('Res image 1 loaded') + end; + //TForm5Image1Click(self, image1) + //LoadJPEGResource(image1, 'MAXBOX4LOGO'); + sourceimage.free; + image2:= TImage.create(form1) + with image2 do begin + parent:= panel1; + color:= clyellow; + setbounds(450,500,400,400) + picture.bitmap.loadfromresourcename(0, 'MAXBOXLOGO'); + //JpegImage.Assign(SourceImage.Picture.Graphic); + //SourceImage.Picture.Bitmap.Assign(JpegImage); + amemo3.lines.add('Res image 2 logo loaded') + end; + TForm5Image1Click(self, image1) + image3:= TImage.create(form1) + with image3 do begin + parent:= panel1; + color:= clyellow; + setbounds(310,100,300,300) + //SourceImage.Picture.Bitmap.Assign(JpegImage); + JPGImage:= TJPEGImage.Create; + RS:= TResourceStream.Create(hInstance,'TRUTH','JPEG'); + try + JPGImage.LoadFromStream(RS); + Picture.Graphic:= JPGImage; + finally + RS.Free; + JPGImage.Free; + end; + amemo3.lines.add('Res image 3 truth loaded') + end; + amemo3.lines.add('Res image 4 moon loaded') + //TForm5Image1Click(self, image1) + image4:= TImage.create(form1) + with image4 do begin + parent:= panel1; //panel1; + color:= clyellow; + setbounds(5,300,300,300) + picture.bitmap.loadfromresourcename(0, 'EARTH'); + //JpegImage.Assign(SourceImage.Picture.Graphic); + //SourceImage.Picture.Bitmap.Assign(JpegImage); + amemo3.lines.add('Res image 4 EARTH logo loaded') + end; + part1Btn:= TButton.create(form1) + with part1btn do begin + parent:= panel1; + Left := 384; Top := 40 + Width := 161; Height := 31 + Caption := 'Answer Question 1' + TabOrder := 1 + OnClick := @TForm1Part1BtnClick + end; + part2Btn:= TButton.create(form1) + with part2btn do begin + parent:= panel1; + Left := 384 + Top := 80 + Width := 161 + Height := 31 + Caption := 'Answer Question 2' + TabOrder := 2 + OnClick := @Tform1Part2BtnClick; + end; + aMemo2:= TMemo.create(form1) + with amemo2 do begin + parent:= panel1; + Left := 585 + Top := 17 + Width := 232 + Height := 496 + Lines.add( + 'The "Answer Question 2" button ' + +'will show here a summary of prime ' + +'sets for each possible partitioning ' + +'for each arrangement of the digits ' + +'1 through 9.' + +''+CRLF+ + +'Click on any summary line to see ' + +'the prime sets for that partitioning. ' + +''+CRLF+CRLF+ + +'For each partition clicked, a ' + +'maximum of 100 sets will be ' + +'displayed in the area at right.') + ScrollBars := ssVertical + TabOrder := 3 + OnClick := @TForm1Memo2Click + end; + end; + StaticText1:= TStaticText.create(form1) + with statictext1 do begin + parent:= form1; + Left := 0 + Top := 580 + Width := 1024 + Height := 20 + Cursor := crHandPoint + Align := alBottom + Alignment := taCenter + Caption := 'maXbox4 new tester functions V 4.7.6.10' + Font.Charset := DEFAULT_CHARSET + Font.Color := clBlue + Font.Height := -17 + Font.Name := 'Arial' + Font.Style := [fsBold, fsUnderline] + ParentFont := False + TabOrder := 1 + OnClick := @Tform1StaticText1Click; + end; + end; + form1.canvas.brush.bitmap:= abitm; + form1.Canvas.FillRect(Rect(900,500,500,500)); +end; + +procedure TForm1StaticText1Click(Sender: TObject); +//var mbv: TCustomBitmap; +begin + //ShellExecute(Handle, 'open', 'http://www.delphiforfun.org/', + //nil, nil, SW_SHOWNORMAL) ; +end; + +procedure playWAVRes; +var + hFind, hRes: THandle; + Song : string; +begin + hFind := FindResource(HInstance, 'GHOST', 'WAV'); + if (hFind <> 0) then begin + hRes := LoadResource(HInstance, hFind); + if (hRes <> 0) then begin + Song := LockResource(hRes); + if Assigned(Song) then begin + SndPlaySound(Song, snd_ASync or snd_Memory); + end; + UnlockResource(hRes); + end; + FreeResource(hFind); + end; +end; + +procedure TForm2Button1Click(Sender: TObject); +var + Res: TResourceStream; +begin + Res := TResourceStream.Create(HInstance, 'APPLAUSE', 'RC_DATA'); + try + Res.Position := 0; + //SndPlaySound(Res.Memory, SND_MEMORY or SND_ASYNC or SND_LOOP); + finally + Res.Free; + end; +end; + +{Docu of main Neural Unit for maXbox: http://www.softwareschule.ch/examples/uPSI_NeuralNetworkCAI.txt +http://www.softwareschule.ch/examples/uPSI_neuralnetworkcai.txt +http://www.softwareschule.ch/examples/uPSI_neuralvolume.txt +http://www.softwareschule.ch/examples/uPSI_neuraldatasets.txt +http://www.softwareschule.ch/examples/uPSI_neuralfit.txt +Source https://github.com/joaopauloschuler/neural-api } + +procedure TTestFitLoadingGetTrainingPair(Idx: integer; ThreadId: integer; + pInput, pOutput: TNNetVolume); + var + LocalX, LocalY, Hypotenuse: TNeuralFloat; + Originals : array of TNeuralFloat; orig2: TByteArray; //array of byte; + NN: THistoricalNets; + begin + LocalX := Random(100); + LocalY := Random(100); + Hypotenuse := sqrt(LocalX*LocalX + LocalY*LocalY); + pInput.ReSize(2,1,1); + //pinput.create1([]) + //pinput.copy41(originals); + //pinput.copy42(orig2); + pInput.Data[0,0,0] := LocalX; + //pInput.FData[1] := LocalY; + pInput.Data[1,1,1] := LocalY; + pOutput.ReSize(1,1,1); + //pOutput.FData[0] := Hypotenuse; + pOutput.Data[0,0,0] := Hypotenuse; + end; + +procedure TTestFitLoadingGetTrainingPair2(Idx: integer; ThreadId: integer; + pInput, pOutput: TNNetVolume); + var + LocalX, LocalY, Hypotenuse: TNeuralFloat; + Originals : array of TNeuralFloat; orig2: TByteArray; //array of byte; + NN: THistoricalNets; + pres: TNeuralFloatArray; + begin + LocalX := Random(100); + LocalY := Random(100); + Hypotenuse := sqrt(LocalX*LocalX + LocalY*LocalY); + pInput.ReSize(2,1,1); + //pinput.create1([]) + //pinput.copy41(originals); + //pinput.copy42(orig2); + pres:= pinput.fdata; + writeln(floattostr(pres[0])); + pInput.Data[0,0,0] := LocalX; + pres[1]:= LocalY; + pInput.FData:= pres; + //pInput.FData[1] := LocalY; + pInput.Data[1,1,1] := LocalY; + pOutput.ReSize(1,1,1); + //pOutput.FData[0] := Hypotenuse; + pOutput.Data[0,0,0] := Hypotenuse; + end; + + + procedure TTestFitLoadingGetValidationPair(Idx: integer; ThreadId: integer; + pInput, pOutput: TNNetVolume); + begin + TTestFitLoadingGetTrainingPair(Idx, ThreadId, pInput, pOutput); + end; + + procedure TTestFitLoadingGetTestPair(Idx: integer; ThreadId: integer; + pInput, pOutput: TNNetVolume); + begin + TTestFitLoadingGetTrainingPair(Idx, ThreadId, pInput, pOutput); + end; + + //https://github.com/joaopauloschuler/neural-api/blob/master/examples/ImageClassifierSELU/ImageClassifierSELU.lpr + + {https://github.com/joaopauloschuler/neural-api/blob/master/examples/ImageClassifierSELU/ImageClassifierSELU.ipynb} + + procedure TTestCNNAlgoDoRunClassifier; + //Application.Title:='CIFAR-10 SELU Classification Example'; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if not CheckCIFARFile() then begin + //Terminate; + //exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + //TestDataParallelism( NN); + + NN.AddLayer(TNNetInput.Create4(32, 32, 3) ); + //Function InitSELU( Value : TNeuralFloat) : TNNetLayer'); + NN.AddLayer(TNNetConvolutionLinear.Create(64,5,2,1,1)).InitSELU(0).InitBasicPatterns(); + NN.AddLayer(TNNetMaxPool.Create44(4,0,0) ); + NN.AddLayer(TNNetSELU.Create() ); + NN.AddLayer(TNNetMovingStdNormalization.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64, 3, 1,1,1) ).InitSELU(0); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64, 3, 1,1,1) ).InitSELU(0); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64, 3, 1,1,1) ).InitSELU(0); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetConvolutionLinear.Create(64, 3, 1,1,1) ).InitSELU(0); + NN.AddLayer( TNNetDropout.Create12(0.5,1) ); + NN.AddLayer( TNNetMaxPool.Create44(2,0,0) ); + NN.AddLayer( TNNetSELU.Create() ); + NN.AddLayer( TNNetFullConnectLinear.Create28(10,0) ); + NN.AddLayer( TNNetSoftMax.Create() ); + amemo3.lines.add('TNNetConvolutionLinear model add') + TestDataParallelism( NN); + amemo3.lines.add('TestDataParallelism( NN) passed') + CheckCIFARFile() + try + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, + ImgTestVolumes, csEncodeRGB); + except + amemo3.lines.add('TNNetConvolutionLinear CIFAR Files missing!') + end; + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'ImageClassifierSELU'; + NeuralFit.InitialLearningRate := 0.0004; + // SELU seems to work better with smaller learning rates. + NeuralFit.LearningRateDecay := 0.03; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + //NN.DebugStructure(); + NN.DebugWeights(); + //NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, + // ImgTestVolumes, {NumClasses=}10, {batchsize=}64, {epochs=}50); + NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + //Terminate; + end; + + + type TBackInput = array[0..3] of array[0..1] of TNeuralFloat; + type TBackOutput = array[0..3] of array[0..2] of TNeuralFloat; + +// var Inputs: TBackInput; + // rOutput: TBackOutput; + + { +const inputs : TBackInput = + ( // x1, x2 + ( 0.1, 0.1), // False, False + ( 0.1, 0.9), // False, True + ( 0.9, 0.1), // True, False + ( 0.9, 0.9) // True, True + ); + +const reluoutputs : TBackOutput = + (// XOR, AND, OR + ( 0.1, 0.1, 0.1), + ( 0.8, 0.1, 0.8), + ( 0.8, 0.1, 0.8), + ( 0.1, 0.8, 0.8) + ); } + + procedure definelogicalMatrix(var inputs:TBackInput; var routput:TBackOutput); + begin + Inputs[0][0]:= 0.1; Inputs[0][1]:= 0.1; + Inputs[1][0]:= 0.1; Inputs[1][1]:= 0.9; + Inputs[2][0]:= 0.9; Inputs[2][1]:= 0.1; + Inputs[3][0]:= 0.9; Inputs[3][1]:= 0.9; + + routput[0][0]:= 0.1; routput[0][1]:= 0.1; routput[0][2]:= 0.1; + routput[1][0]:= 0.8; routput[1][1]:= 0.1; routput[1][2]:= 0.8; + routput[2][0]:= 0.8; routput[2][1]:= 0.1; routput[2][2]:= 0.8; + routput[3][0]:= 0.1; routput[3][1]:= 0.8; routput[3][2]:= 0.8; + end; + { : + ( 0.1, 0.1), // False, False + ( 0.1, 0.9), // False, True + ( 0.9, 0.1), // True, False + ( 0.9, 0.9) // True, True } + + procedure RunSimpleAlgo(); + var + NN: TNNet; + EpochCnt: integer; + Cnt: integer; + pOutPut: TNNetVolume; + vInputs: TBackInput; + vOutput: TBackOutput; + inputs: TBackInput; routput : TBackOutput; + Rate, Loss, ErrorSum : TNeuralFloat; + begin + definelogicalMatrix(inputs, routput); + NN := TNNet.Create(); + NN.AddLayer( TNNetInput.Create3(2) ); + NN.AddLayer( TNNetFullConnectReLU.Create30(3,0) ); + NN.AddLayer( TNNetFullConnectReLU.Create30(3,0) ); + NN.SetLearningRate(0.01, 0.9); + + vInputs := inputs; + vOutput := routput; + //constructor Create(pSizeX, pSizeY, pDepth: integer; c: T = 0); {$IFNDEF FPC} overload; {$ENDIF} + pOutPut := TNNetVolume.Create0(3,1,1,1); + for EpochCnt := 1 to 3000 do begin + for Cnt := Low(inputs) to High(inputs) do begin + NN.Compute68(vInputs[Cnt],0); + NN.GetOutput(pOutPut); + NN.Backpropagate70(vOutput[Cnt]); + if EpochCnt mod 300 = 0 then + WriteLn + ( + itoa(EpochCnt)+' x '+itoa(Cnt)+ + ' Output:'+ + format(' %5.2f',[poutPut.Raw[0]])+' '+ + format(' %5.2f',[poutPut.Raw[1]])+' '+ + format(' %5.2f',[poutPut.Raw[2]])+' '+ + (* pOutPut.Raw[1]:5:2,' ', + pOutPut.Raw[2]:5:2, *) + ' - Training/Desired Output:'+ + format('%5.2f',[vOutput[cnt][0]])+' '+ + format('%5.2f',[vOutput[cnt][1]])+' '+ + format('%5.2f',[vOutput[cnt][2]])+' ' + {vOutput[cnt][0]:5:2,' ', + vOutput[cnt][1]:5:2,' ' , + vOutput[cnt][2]:5:2,' ' } + ); + end; + if EpochCnt mod 300 = 0 then WriteLn(''); + end; + // TestBatch( NN, pOutPut, 10000, Rate, Loss, ErrorSum); + + //NN.DebugWeights(); + NN.DebugErrors(); + pOutPut.Free; + NN.Free; + Write('Press ENTER to exit.'); + amemo3.lines.add('definelogicalMatrix XOR, AND, & OR solved') + //ReadLn; + end; + + + function TCustomApplicationGetOptionValue(const C: Char; const S: String ): String; + +Var + B : Boolean; + I : integer; + capp: TCustomApplication; + +begin + capp:= TCustomApplication.create(self); + Result:=''; + //I:=TCustomApplicationFindOptionIndex(C,B,0); + //capp.GetOptionValues + I:=capp.FindOptionIndex(C,B,0); + If I=-1 then + //I:=TCustomApplicationFindOptionIndex(S,B,0); + I:=capp.FindOptionIndex(S,B,0); + //If I<>-1 then + //Result:=capp.GetOptionAtIndex(I,B); + capp.Free; +end; + + var asrlist, devicenames, env: TStringlist; abt: boolean; //GraphicClass: TGraphicClass; + sessionOptions: OLEVariant; + +begin //@main + + writeln('CPUspeed: '+cpuspeed+' Hz') + loadPForm ; + devicenames:= TStringlist.create; + //GetWaveOutDevices(DeviceNames); + GetMIDIOutDevices(devicenames); + + if devicenames.count>0 then // only play if wave device present + //PlaySound('MySound', HINSTANCE, SND_RESOURCE or SND_ASYNC); + PlaySound(('SOSUMI_WAV'), hInstance, SND_RESOURCE or SND_ASYNC); + writeln(devicenames.text) + devicenames.free; //*) + + env:= TStringlist.create; + with TCustomApplication.create(self) do begin + //run; + //GetEnvironmentList(env,true) + //writeln(env.text) + free + end; + env.Free; + + //writeln(botostr(PlayWaveResource('GHOST.WAV'))); + + //playWAVRes; + + playreswav('ghost','wav'); + playreswav('moon','wav'); //} + + TTestCNNAlgoDoRunClassifier; + //TestConvolutionAPI; + + RunSimpleAlgo(); + + //TestDataParallelism( NN : TNNet)'); + //sessionOptions := CreateOleObject('WinSCP.SessionOptions'); + //writeln('IsObjectActive '+botostr(VariantIsObject(sessionOptions))) + + (*asrlist::= TStringlist.create; + if LoadDFMFile2Strings('C:\maXbox\mX47464\maxbox4\web\mX47520\U_PrimesFromDigits.dfm', + asrlist, abt)= 0 then writeln(asrlist.text); + asrlist.Free; //*) + +End. + +//dfgfg kloi con-sole + +{-- v3 ++++ v4 +@@ -1,7 +1,7 @@ + +-Release Notes maXbox 4.7.5.90 V October 2021 mX47 ++Release Notes maXbox 4.7.5.90 VI November 2021 mX47 + +-Add 16 Units + 4 Tutorials ++Add 18 Units + 5 Tutorials + +1423 uPSI_SingleList.pas; TSingleListClass +1424 unit uPSI_AdMeter.pas; Async Professional +@@ -19,8 +19,10 @@ +1436 unit uPSI_neuraldatasetsv.pas CAI +1437 uPSI_flcFloats.pas FLC5 +1438 unit UBigIntsForFloatV4.pas DFF ++1439 unit uPSI_CustApp.pas Pas2js ++1440 unit uPSI_NeuralNetworkCAI.pas CAI + +-Total of Function Calls: 34780 +-SHA1: of 4.7.5.90 A1C85C7A69602F40F66C84E91196E25E44E0EC7A +-CRC32: 5BDDA952 30.4 MB (31,923,016 bytes) ++Total of Function Calls: 34807 ++SHA1: of 4.7.5.90 96DCDE2028125E00B67E42A801721AC513A5EAFC ++CRC32: BBC3A7E5 30.4 MB (31,974,216 bytes) + +Sent from sourceforge.net because you indicated interest in https://sourceforge.net/p/maxbox/news/2021/10/maxbox-47590-released-/} + + +{procedure SIRegister_TCustomApplication(CL: TPSPascalCompiler); +begin + //with RegClassS(CL,'TComponent', 'TCustomApplication') do + with CL.AddClassN(CL.FindClass('TComponent'),'TCustomApplication') do + begin + Constructor Create( AOwner : TComponent)'); + Procedure HandleException( Sender : TObject)'); + Procedure Initialize'); + Procedure Run'); + Procedure ShowException( E : Exception)'); + Procedure Terminate'); + Procedure Terminate1( AExitCode : Integer)'); + Function FindOptionIndex( const S : String; var Longopt : Boolean; StartAt : Integer) : Integer'); + Function GetOptionValue( const S : String) : String'); + Function GetOptionValue1( const C : Char; const S : String) : String'); + RegisterMethod('Function GetOptionValues( const C : Char; const S : String) : TStringDynArray'); + Runction HasOption( const S : String) : Boolean'); + RFunction HasOption1( const C : Char; const S : String) : Boolean'); + RegisterMethod('Function CheckOptions( const ShortOptions : String; const Longopts : TStrings; Opts, NonOpts : TStrings; AllErrors : Boolean) : String'); + RegisterMethod('Function CheckOptions1( const ShortOptions : String; const Longopts : array of string; Opts, NonOpts : TStrings; AllErrors : Boolean) : String'); + RegisterMethod('Function CheckOptions2( const ShortOptions : String; const Longopts : TStrings; AllErrors : Boolean) : String'); + RegisterMethod('Function CheckOptions3( const ShortOptions : String; const LongOpts : array of string; AllErrors : Boolean) : String'); + RegisterMethod('Function CheckOptions4( const ShortOptions : String; const LongOpts : String; AllErrors : Boolean) : String'); + RegisterMethod('Function GetNonOptions( const ShortOptions : String; const Longopts : array of string) : TStringDynArray'); + RegisterMethod('Procedure GetNonOptions1( const ShortOptions : String; const Longopts : array of string; NonOptions : TStrings)'); + RegisterMethod('Procedure GetEnvironmentList( List : TStrings; NamesOnly : Boolean)'); + RegisterMethod('Procedure GetEnvironmentList1( List : TStrings)'); + RegisterMethod('Procedure Log( EventType : TEventType; const Msg : String)'); + RegisterMethod('Procedure Log1( EventType : TEventType; const Fmt : String; const Args : array of string)'); + RegisterProperty('ExeName', 'string', iptr); + RegisterProperty('Terminated', 'Boolean', iptr); + RegisterProperty('Title', 'string', iptrw); + RegisterProperty('OnException', 'TExceptionEvent', iptrw); + RegisterProperty('ConsoleApplication', 'Boolean', iptr); + RegisterProperty('Location', 'String', iptr); + RegisterProperty('Params', 'String integer', iptr); + RegisterProperty('ParamCount', 'Integer', iptr); + RegisterProperty('EnvironmentVariable', 'String String', iptr); + RegisterProperty('OptionChar', 'Char', iptrw); + RegisterProperty('CaseSensitiveOptions', 'Boolean', iptrw); + RegisterProperty('StopOnException', 'Boolean', iptrw); + RegisterProperty('ExceptionExitCode', 'Longint', iptrw); + RegisterProperty('ExceptObject', 'Exception', iptrw); + RegisterProperty('ExceptObjectJS', 'JSValue', iptrw); + RegisterProperty('EventLogFilter', 'TEventLogTypes', iptrw); + end; +end; +} +(*procedure SIRegister_TComboSet(CL: TPSPascalCompiler); +begin + //with RegClassS(CL,'TObject', 'TComboSet') do + with CL.AddClassN(CL.FindClass('TObject'),'TComboSet') do begin + RegisterProperty('Selected', 'TByteArray64', iptrw); + RegisterProperty('RandomRank', 'int64', iptrw); + RegisterMethod('Procedure Setup( newR, newN : word; NewCtype : TComboType)'); + RegisterMethod('Function Getnext : boolean'); + RegisterMethod('Function GetNextCombo : boolean'); + RegisterMethod('Function GetNextPermute : boolean'); + RegisterMethod('Function GetNextComboWithRep : Boolean'); + RegisterMethod('Function GetNextPermuteWithRep : Boolean'); + RegisterMethod('Function GetCount : int64'); + RegisterMethod('Function GetR : integer'); + RegisterMethod('Function GetN : integer'); + RegisterMethod('Function GetCtype : TCombotype'); + RegisterMethod('Function GetNumberSubsets( const RPick, Number : word; const ACtype : TComboType) : int64'); + RegisterMethod('Function Binomial( const RPick, Number : integer) : int64'); + RegisterMethod('Function Factorial( const Number : integer) : int64'); + RegisterMethod('Function GetRCombo( const RPick, Number : integer) : int64'); + RegisterMethod('Function GetRepRCombo( const RPick, Number : integer) : int64'); + RegisterMethod('Function GetRPermute( const RPick, Number : integer) : int64'); + RegisterMethod('Function GetRepRPermute( const RPick, Number : integer) : int64'); + RegisterMethod('Procedure SetupR( NewR, NewN : word; NewCtype : TComboType)'); + RegisterMethod('Procedure SetupRFirstLast( NewR, NewN : word; NewCType : TComboType)'); + RegisterMethod('Function IsValidRSequence : boolean'); + RegisterMethod('Function ChangeRDirection : boolean'); + RegisterMethod('Function GetNextPrevR : boolean'); + RegisterMethod('Function NextR : boolean'); + RegisterMethod('Function NextLexRPermute : boolean'); + RegisterMethod('Function NextLexRepRPermute : boolean'); + RegisterMethod('Function NextLexRCombo : boolean'); + RegisterMethod('Function NextLexRepRCombo : boolean'); + RegisterMethod('Function NextCoLexRCombo : boolean'); + RegisterMethod('Function PrevR : boolean'); + RegisterMethod('Function PrevCoLexRCombo : boolean'); + RegisterMethod('Function PrevLexRepRPermute : boolean'); + RegisterMethod('Function PrevLexRPermute : boolean'); + RegisterMethod('Function PrevLexRCombo : boolean'); + RegisterMethod('Function PrevLexRepRCombo : boolean'); + RegisterMethod('Function RankR : int64'); + RegisterMethod('Function RankCoLexRCombo : int64'); + RegisterMethod('Function RankLexRCombo : int64'); + RegisterMethod('Function RankLexRepRCombo : int64'); + RegisterMethod('Function RankLexRPermute : int64'); + RegisterMethod('Function RankLexRepRPermute : int64'); + RegisterMethod('Function UnRankR( const Rank : int64) : boolean'); + RegisterMethod('Function UnRankCoLexRCombo( const Rank : int64) : boolean'); + RegisterMethod('Function UnRankLexRCombo( const Rank : int64) : boolean'); + RegisterMethod('Function UnRankLexRepRCombo( const Rank : int64) : boolean'); + RegisterMethod('Function UnRankLexRPermute( const Rank : int64) : boolean'); + RegisterMethod('Function UnRankLexRepRPermute( const Rank : int64) : boolean'); + RegisterMethod('Function RandomR( const RPick, Number : int64; const NewCtype : TComboType) : Boolean'); + RegisterMethod('Function RandomCoLexRCombo( const RPick, Number : int64) : Boolean'); + RegisterMethod('Function RandomLexRCombo( const RPick, Number : int64) : Boolean'); + RegisterMethod('Function RandomLexRepRCombo( const RPick, Number : int64) : Boolean'); + RegisterMethod('Function RandomLexRPermute( const RPick, Number : int64) : Boolean'); + RegisterMethod('Function RandomLexRepRPermute( const RPick, Number : int64) : Boolean'); + end; +end;*) + + + + diff --git a/examples/maXbox4Scripts/1074__CAI_3_visualautoencoder2.txt b/examples/maXbox4Scripts/1074__CAI_3_visualautoencoder2.txt new file mode 100644 index 00000000..b0286fc7 --- /dev/null +++ b/examples/maXbox4Scripts/1074__CAI_3_visualautoencoder2.txt @@ -0,0 +1,753 @@ +unit CAI_NEURAL_API_Exec_47590_SimpleImageClassifier2_Tutor_89_Test2; + +//http://www.softwareschule.ch/examples/uPSI_NeuralNetworkCAI.txt +//http://www.softwareschule.ch/examples/uPSI_Neuralvolume.txt +//https://github.com/joaopauloschuler/neural-api/blob/master/examples/SimpleImageClassifier/SimpleImageClassifier.lpr +{fix done: not complete cause the neuralfil() has a writeln which troubles I/O 105 exception} +//https://ibrt.ch/delphi/neuralnets.html + +interface + +//uses + {$ifdef unix} + cmem, // the c memory manager is on some systems much faster for multi-threading + {$endif} + {Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + ExtCtrls, Menus, neuralnetwork, neuralvolumev, neuraldatasets, neuraldatasetsv, + neuralvolume, MTProcs, math, neuralfit } + + {$ifdef OpenCL} + //, neuralopencl + {$endif} + // ; + +var + csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); +const + csGeneratorInputSize = 4; + csMaxDiscriminatorError = 0.1; + +type + + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + procedure ButLearnClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + FDisplay: TNNetVolume; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FAutoencoder: THistoricalNets; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FBaseName: string; + FColorEncoding: byte; + {$ifdef OpenCL} + FEasyOpenCL: TEasyOpenCL; + FHasOpenCL: boolean; + {$endif} + + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + FTrainImages: TClassesAndElements; + procedure GetTrainingData(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure AutoencoderOnAfterEpoch(Sender: TObject); + procedure AutoencoderOnAfterStep(Sender: TObject); + procedure TFormVisualLearningAutoencoderOnStart(Sender: TObject); + procedure AutoencoderAugmentation(pInput: TNNetVolume; ThreadId: integer); + procedure TFormVisualLearningLearn(Sender: TObject); + procedure SaveScreenshot(filename: string); + procedure DisplayInputImage(ImgInput: TNNetVolume; color_encoding: integer); //} + procedure SendStop; + //public + procedure TFormVisualLearningProcessMessages(); + //end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses strutils, LCLIntf, LCLType; + + +{ TFormVisualLearning } + +procedure ButLearnClick(Sender: TObject); +begin + if not CheckCIFARFile() then exit; + + if (FRunning) then + begin + SendStop; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + ChkBigNetwork.Enabled := false; + TFormVisualLearningLearn(Sender); + ChkBigNetwork.Enabled := true; + ButLearn.Caption := 'Restart'; + FRunning := false; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + SendStop; +end; + +procedure FormCreate(Sender: TObject); +begin + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + FTrainImages := TClassesAndElements.Create(); + neuralInitCriticalSection(FCritSec); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}32, + {InputSize=}64, {displaySize=}128, {ImagesPerRow=}8 + ); + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + SendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + FreeNeuronImages(aImage, aLabelX, aLabelY); + neuralDoneCriticalSection(FCritSec); + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + //FTrainImages.Free; +end; + +procedure DisplayInputImage(ImgInput: TNNetVolume; color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; +begin + FDisplay.Resize(ImgInput,0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then + begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + + FDisplay.NeuronalInputToRgbImg(color_encoding); + + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],csEncodeRGB); + aImage[FImageCnt].Width := 128; + aImage[FImageCnt].Height := 128; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); +end; //*) + +procedure TFormVisualLearningAutoencoderOnStart(Sender: TObject); +begin + // TODO +end; + +procedure SendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure TFormVisualLearningLearn( Sender: TObject); +var + NeuronMultiplier: integer; +begin + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'IMAGEART'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= Fbasename+ 'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName:= fbasename+'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + //if FTrainImages.Count = 0 then + begin + WriteLn('Loading Tiny ImageNet 200 file names.'); + FTrainImages.LoadFoldersAsClassesProportional('tiny-imagenet-200/train/','images',0,0.2); + //WriteLn('Tiny ImageNet 200 loaded classes: ',FTrainImages.Count,'. File names: ', FTrainImages.CountElements(),'.'); + WriteLn('Loading Tiny ImageNet 200 images.'); + //FTrainImages.LoadImages(FColorEncoding); + WriteLn('Loaded.'); + end; + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FAutoencoder := THistoricalNets.Create(); + + if Not(FileExists(FBaseName+'autoencoder.nn')) then begin + WriteLn('Creating auto encoder.'); + FAutoencoder.AddLayer49([ + TNNetInput.Create4(64, 64, 3), + TNNetConvolution.Create36(32 * NeuronMultiplier,3,1,2,1), //32x32 + TNNetConvolution.Create36(32 * NeuronMultiplier,3,1,1,1), + TNNetConvolution.Create36(32 * NeuronMultiplier,3,1,2,1), //16x16 + TNNetConvolution.Create36(32 * NeuronMultiplier,3,1,1,1), + TNNetConvolution.Create36(64 * NeuronMultiplier,3,1,2,0), //8x8 + TNNetConvolution.Create36(64 * NeuronMultiplier,3,1,1,0), + + TNNetConvolution.Create36(128 * NeuronMultiplier,3,1,2,1), //4x4 + TNNetConvolution.Create36(128 * NeuronMultiplier,3,1,1,1), + TNNetDeMaxPool.Create46(2,0), + TNNetConvolution.Create36(128 * NeuronMultiplier,5,2,1,1), //8x8 + TNNetConvolution.Create36(128 * NeuronMultiplier,3,1,1,1), + + TNNetDeMaxPool.Create46(2,0), + TNNetConvolution.Create36(32 * NeuronMultiplier,5,2,1,1), //16x16 + TNNetConvolution.Create36(32 * NeuronMultiplier,3,1,1,1), + TNNetDeMaxPool.Create46(2,0), + TNNetConvolution.Create36(32 * NeuronMultiplier,5,2,1,1), //32x32 + TNNetConvolution.Create36(32 * NeuronMultiplier,3,1,1,1), + TNNetDeMaxPool.Create46(2,0), + TNNetConvolution.Create36(32 * NeuronMultiplier,5,2,1,1), //64x64 + TNNetConvolution.Create36(32 * NeuronMultiplier,3,1,1,1), + TNNetConvolutionLinear.Create(3,1,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + end + else + begin + WriteLn('Loading auto encoder.'); + FAutoencoder.LoadFromFile(FBaseName+'autoencoder.nn'); + end; + FAutoencoder.DebugStructure(); + FAutoencoder.SetLearningRate(0.001,0.9); + FAutoencoder.SetL2Decay(0.0); + + FFit.OnAfterEpoch := @AutoencoderOnAfterEpoch; + FFit.OnAfterStep := @{Self.}AutoencoderOnAfterStep; + FFit.OnStart := @{Self.}TFormVisualLearningAutoencoderOnStart; + FFit.LearningRateDecay := 0.0; + FFit.L2Decay := 0.0; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.0001; + FFit.FileNameBase := FBaseName+'autoencoder'; + FFit.EnableBipolar99HitComparison(); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FAutoencoder.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: + FFit.MaxThreadNum := 2; + FFit.FitLoading1(FAutoencoder, {EpochSize=}10{FTrainImages.CountElements()}, 500, 500, {Batch=}64, {Epochs=}35000, @GetTrainingData, nil, nil); // This line does the same as above + + FAutoencoder.Free; +end; + +procedure GetTrainingData(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + ClassId, ImageId: integer; +begin + ClassId := FTrainImages.GetRandomClassId(); + //ImageId := FTrainImages.strings[ClassId].GetRandomIndex(); + //pInput.Copy38(FTrainImages.List[ClassId].List[ImageId]); + if Random(1000)>500 then pInput.FlipX(); + pOutput.Copy38(pInput); +end; + +procedure AutoencoderOnAfterEpoch(Sender: TObject); +begin +end; + +procedure AutoencoderOnAfterStep(Sender: TObject); +var + ClassId, ImageId: integer; +begin + //LabClassRate.Caption := PadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4); + TFormVisualLearningProcessMessages(); + //if FFit.CurrentStep mod 10 = 0 then + begin + ClassId := FTrainImages.GetRandomClassId(); + //ImageId := FTrainImages.List[ClassId].GetRandomIndex(); + //FFit.NN.Compute65(FTrainImages.List[ClassId].List[ImageId]); + DisplayInputImage(FFit.NN.GetLastLayer().Output, 0); + FFit.NN.GetLastLayer().Output.PrintDebug(); + end; +end; + +procedure AutoencoderAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin +end; + +procedure SaveScreenshot(filename: string); +begin + try + WriteLn(' Saving '+filename+'.'); + SaveHandleToBitmap(filename, Self.Handle); + except + // Nothing can be done if this fails. + end; +end; //*) + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + +//begin + //writeln(cpuspeed) + //procedure TestBackProp(); + //TestBackProp(); + +End. + +Ref: simple logical learner +300 x 0 Output: 0.30 0.07 0.26 - Training/Desired Output: 0.10 0.10 0.10 +300 x 1 Output: 0.40 0.29 0.62 - Training/Desired Output: 0.80 0.10 0.80 +300 x 2 Output: 0.72 0.22 0.62 - Training/Desired Output: 0.80 0.10 0.80 +300 x 3 Output: 0.37 0.56 1.00 - Training/Desired Output: 0.10 0.80 0.80 + +600 x 0 Output: 0.29 0.02 0.23 - Training/Desired Output: 0.10 0.10 0.10 +600 x 1 Output: 0.52 0.28 0.62 - Training/Desired Output: 0.80 0.10 0.80 +600 x 2 Output: 0.74 0.15 0.69 - Training/Desired Output: 0.80 0.10 0.80 +600 x 3 Output: 0.27 0.66 0.96 - Training/Desired Output: 0.10 0.80 0.80 + +900 x 0 Output: 0.22 0.02 0.20 - Training/Desired Output: 0.10 0.10 0.10 +900 x 1 Output: 0.65 0.23 0.66 - Training/Desired Output: 0.80 0.10 0.80 +900 x 2 Output: 0.75 0.13 0.74 - Training/Desired Output: 0.80 0.10 0.80 +900 x 3 Output: 0.18 0.72 0.90 - Training/Desired Output: 0.10 0.80 0.80 + +1200 x 0 Output: 0.15 0.04 0.16 - Training/Desired Output: 0.10 0.10 0.10 +1200 x 1 Output: 0.73 0.18 0.72 - Training/Desired Output: 0.80 0.10 0.80 +1200 x 2 Output: 0.78 0.13 0.77 - Training/Desired Output: 0.80 0.10 0.80 +1200 x 3 Output: 0.13 0.75 0.85 - Training/Desired Output: 0.10 0.80 0.80 + +1500 x 0 Output: 0.11 0.06 0.12 - Training/Desired Output: 0.10 0.10 0.10 +1500 x 1 Output: 0.79 0.15 0.77 - Training/Desired Output: 0.80 0.10 0.80 +1500 x 2 Output: 0.80 0.12 0.79 - Training/Desired Output: 0.80 0.10 0.80 +1500 x 3 Output: 0.10 0.77 0.82 - Training/Desired Output: 0.10 0.80 0.80 + +1800 x 0 Output: 0.10 0.07 0.11 - Training/Desired Output: 0.10 0.10 0.10 +1800 x 1 Output: 0.80 0.13 0.79 - Training/Desired Output: 0.80 0.10 0.80 +1800 x 2 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +1800 x 3 Output: 0.10 0.78 0.81 - Training/Desired Output: 0.10 0.80 0.80 + +2100 x 0 Output: 0.10 0.09 0.11 - Training/Desired Output: 0.10 0.10 0.10 +2100 x 1 Output: 0.80 0.12 0.79 - Training/Desired Output: 0.80 0.10 0.80 +2100 x 2 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2100 x 3 Output: 0.10 0.79 0.80 - Training/Desired Output: 0.10 0.80 0.80 + +2400 x 0 Output: 0.10 0.09 0.10 - Training/Desired Output: 0.10 0.10 0.10 +2400 x 1 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2400 x 2 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2400 x 3 Output: 0.10 0.79 0.80 - Training/Desired Output: 0.10 0.80 0.80 + +2700 x 0 Output: 0.10 0.09 0.10 - Training/Desired Output: 0.10 0.10 0.10 +2700 x 1 Output: 0.80 0.11 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2700 x 2 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +2700 x 3 Output: 0.10 0.80 0.80 - Training/Desired Output: 0.10 0.80 0.80 + +3000 x 0 Output: 0.10 0.10 0.10 - Training/Desired Output: 0.10 0.10 0.10 +3000 x 1 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 2 Output: 0.80 0.10 0.80 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 3 Output: 0.10 0.80 0.80 - Training/Desired Output: 0.10 0.80 0.80 + +Layer 0 Max Error: 0 Min Error: 0 Max ErrorD: 0 Min ErrorD: 0 TNNetInput 2,1,1 +debug errors else +Layer 1 Max Error: 0.000858666782733053 Min Error: -0.00092624151147902 Max ErrorD: 0 Min ErrorD: 0 TNNetFullConnectReLU 3,1,1 + Parent:0 +Layer 2 Max Error: 0.0012739896774292 Min Error: -0.00215935707092285 Max ErrorD: 0 Min ErrorD: 0 TNNetFullConnectReLU 3,1,1 + Parent:1 +Press ENTER to exit. + +3000 x 0 Output: 0.37 0.16 0.56 - Training/Desired Output: 0.10 0.10 0.10 +3000 x 1 Output: 0.77 0.10 0.82 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 2 Output: 0.59 0.00 0.55 - Training/Desired Output: 0.80 0.10 0.80 +3000 x 3 Output: 0.11 0.72 0.57 - Training/Desired Output: 0.10 0.80 0.80 + +Ref: +Starting Validation. +VALIDATION RECORD! Saving NN at autosave-neuralnetwork_a1.nn +Debug SavetokenStructureToString: +Debug structuretostring: -1)TNNetInput:32;32;3;0;0;0;0;0#0)TNNetConvolutionReLU:16;5;0;0;1;0;0;0#1)TNNetMaxPool:2;2;0;0;0;0;0;0#2)TNNetConvolutionReLU:128;5;0;0;1;0;0;0#3)TNNetFullConnectLinear:64;1;1;2;0;0;0;0#4)TNNetFullConnect:10;1;1;10;0;0;0;0#5)TNNetSoftMax:0;0;0;0;0;0;0;0 +Epochs: 1 Examples seen:40000 Validation Accuracy: 0.1014 Validation Error: 1.7999 Validation Loss: 2.3027 Total time: 5.21min +Image mX4 FThreadNN[0].DebugWeights(); skipped... +Epoch time: 3.5000 minutes. 1 epochs: 0.0580 hours. +Epochs: 1. Working time: 0.09 hours. +CAI maXbox Neural Fit Finished. + mX4 executed: 04/11/2021 15:47:19 Runtime: 0:5:21.565 Memload: 41% use + +SELU Classifier +0 -1 0 +0 -1 0 +0 -1 0 +0 -1 0 +0 -1 0 +0 -1 0 +40000 of samples have been processed. +Starting Validation. + Epochs: 5 Examples seen:200000 Validation Accuracy: 0.0980 Validation Error: 1.8000 Validation Loss: 2.3028 Total time: 14.31min +Image mX4 FThreadNN[0].DebugWeights(); skipped... +Epoch time: 2.0000 minutes. 5 epochs: 0.1600 hours. +Epochs: 5. Working time: 0.24 hours. +CAI maXbox Neural Fit Finished. +3 Volumes + NN + NF freed... + mX4 executed: 04/11/2021 16:07:45 Runtime: 0:14:25.509 Memload: 43% use +PascalScript maXbox4 - RemObjects & SynEdit +C:\maXbox\works2021\maxbox4\examples\1065__CAI_2_SiImageClassifier21_Tutor_89_test2.txtOutput.txt as output file stored + +50 epochs: +40000 of samples have been processed. +Starting Validation. +Starting Testing. +Epoch time: 2.2000 minutes. 50 epochs: 1.8000 hours. +Epochs: 50. Working time: 2.14 hours. +CAI maXbox Neural Fit Finished. +3 Volumes + NN + NF freed... + mX4 executed: 04/11/2021 22:45:35 Runtime: 2:8:19.673 Memload: 41% use + + +epoch training accuracy training loss training error validation accuracy validation loss +1 0.0955 2.3005 1.7996 0.0977 2.3028 +2 0.0924 2.3026 1.8 0.0977 2.3028 +3 0.0919 2.3027 1.8 0.0977 2.3028 +4 0.0981 2.3063 1.8007 0.098 2.3027 + +47 0.0938 2.3036 1.8002 0.0977 +48 0.0997 2.3018 1.7998 0.0977 +49 0.0903 2.303 1.8001 0.0977 +50 0.0995 2.3036 1.8002 0.0977 + + + +Doc: Destroying it calls Py_Finalize, which frees all memory allocated by the Python DLL. + +Or, if you're just using the Python API without the VCL wrappers, you can probably just call Py_NewInterpreter on your TPythonInterface object to get a fresh execution environment without necessarily discarding everything done before. + +Example - How to create a simple fully forward connected network 3x3 +NN := TNNet.Create(); +NN.AddLayer( TNNetInput.Create(3) ); +NN.AddLayer( TNNetLayerFullConnectReLU.Create(3) ); +NN.AddLayer( TNNetLayerFullConnectReLU.Create(3) ); +NN.SetLearningRate(0.01,0.8); + +Evil eval() + +You should never pass untrusted source to the eval() directly. As it is quite easy for the malicious user to wreak havoc on your system. For example, the following code can be used to delete all the files from the system. + +1 >>> +2 eval('os.system("RM -RF /")') # command is deliberately capitalized +3 >>> + +procedure TNNet.LoadDataFromString(strData: string); +var + S: TStringList; + Cnt: integer; +begin + S := CreateTokenizedStringList(strData,'!'); + + if S.Count = FLayers.Count then + begin + if S.Count > 0 then + begin + for Cnt := 0 to S.Count - 1 do + begin + FLayers[Cnt].LoadDataFromString(S[Cnt]); + end; + end; + end + else + begin + FErrorProc + ( + 'Error while loading network: number of structure layers '+ + IntToStr(FLayers.Count)+' differ from data loaded layers '+ + IntToStr(S.Count) + ); + + +Ref: https://github.com/joaopauloschuler/neural-api + + +As we grow, we occasionally need to update our terms of service. To keep using Bitbucket Cloud, review and agree to the updated terms. To learn more, please see our Summary of Changes. + + + _od#HMM6&*MMMH::-_ + _dHMMMR??MMM? ""| `"'-?Hb_ + .~HMMMMMMMMHMMM#M? `*HMb. + ./?HMMMMMMMMMMM"*""" &MHb. + /'|MMMMMMMMMMM' - `*MHM\ + / |MMMMMMHHM'' .MMMHb + | 9HMMP .Hq, TMMMMMH + / |MM\,H-""&&6\__ `MMMMMMb +| `""HH#, \ - MMMMMMM| +| `HoodHMM###. `9MMMMMH +| .MMMMMMMM##\ `*"?HM +| .. ,HMMMMMMMMMMMo\. |M +| |MMMMMMMMMMMMMMMMHo |M +| ?MMMMMMMMMMMMMMMM* |H +|. `#MMMMMMMMMMMMM' .M| + \ `MMMMMMMMMMM* |P + `\ MMMMMMMMT"' ,H + `\ `MMMMMMH? ./ + \. |MMMH#" ,/ + `\. |MMP' ./' + `~\ `HM:.- . ,/' + "-\_ '_\ . _.-" + "-\-#odMM\_,oo==-" + + +normal + uninvolving = True neg : pos = 11.7 : 1.0 + avoids = True pos : neg = 11.7 : 1.0 + fascination = True pos : neg = 10.3 : 1.0 + astounding = True pos : neg = 10.3 : 1.0 + idiotic = True neg : pos = 9.8 : 1.0 + | n p | | n p | Stemmer more false positive + | e o | | e o | + | g s | | g s | +----+---------+ ----+---------+ +neg |<119>131 | neg |<110>140 | +pos | 5<245>| pos | 5<245>| +----+---------+ ----+---------+ +(row = reference; col = test) + +unit uPSI_neuraldatasets; +{ +mindset let mindset set + +} +interface + + + +uses + SysUtils + ,Classes + ,uPSComponent + ,uPSRuntime + ,uPSCompiler + ; + +type +(*----------------------------------------------------------------------------*) + TPSImport_neuraldatasets = class(TPSPlugin) + public + procedure CompileImport1(CompExec: TPSScript); override; + procedure ExecImport1(CompExec: TPSScript; const ri: TPSRuntimeClassImporter); override; + end; + + +{ compile-time registration functions } +procedure SIRegister_TClassesAndElements(CL: TPSPascalCompiler); +procedure SIRegister_TFileNameList(CL: TPSPascalCompiler); +procedure SIRegister_neuraldatasets(CL: TPSPascalCompiler); + +{ run-time registration functions } +procedure RIRegister_neuraldatasets_Routines(S: TPSExec); +procedure RIRegister_TClassesAndElements(CL: TPSRuntimeClassImporter); +procedure RIRegister_TFileNameList(CL: TPSRuntimeClassImporter); +procedure RIRegister_neuraldatasets(CL: TPSRuntimeClassImporter); + +procedure Register; + +implementation + + +uses + neuraldatasets, neuralnetworkCAI, neuralvolume , neuralthread ,math + ; + + +procedure Register; +begin + RegisterComponents('Pascal Script', [TPSImport_neuraldatasets]); +end; + +(* === compile-time registration functions === *) +(*----------------------------------------------------------------------------*) +procedure SIRegister_TClassesAndElements(CL: TPSPascalCompiler); +begin + //with RegClassS(CL,'TStringStringListVolume', 'TClassesAndElements') do + with CL.AddClassN(CL.FindClass('TStringStringListVolume'),'TClassesAndElements') do + begin + RegisterMethod('Constructor Create( )'); + Function CountElements( ) : integer'); + Procedure LoadFoldersAsClasses( FolderName : string; pImageSubFolder : string; SkipFirst : integer; SkipLast : integer)'); + Procedure LoadFoldersAsClassesProportional( FolderName : string; pImageSubFolder : string; fSkipFirst : TNeuralFloat; fLoadLen : TNeuralFloat)'); + Procedure LoadImages0( color_encoding : integer; NewSizeX : integer; NewSizeY : integer);'); + Procedure LoadClass_FilenameFromFolder( FolderName : string)'); + Function GetRandomClassId( ) : integer'); + Function GetClassesCount( ) : integer'); + Procedure GetRandomFileId( out ClassId : integer; out FileId : integer; StartPos : TNeuralFloat; Range : TNeuralFloat)'); + Procedure GetRandomFileName( out ClassId : integer; out FileName : string; StartPos : TNeuralFloat; Range : TNeuralFloat)'); + Procedure GetRandomImgVolumes( vInput, vOutput : TNNetVolume; StartPos : TNeuralFloat; Range : TNeuralFloat)'); + Function GetFileName( ClassId, ElementId : integer) : string'); + Procedure AddVolumesTo( Volumes : TNNetVolumeList; EmptySource : boolean)'); + Procedure AddFileNamesTo( FileNames : TFileNameList)'); + Procedure MakeMonopolar( Divisor : TNeuralFloat)'); + Function FileCountAtClassId( ClassId : integer) : integer'); + Procedure LoadImages_NTL( index, threadnum : integer)'); + end; +end; + +(*----------------------------------------------------------------------------*) +procedure SIRegister_TFileNameList(CL: TPSPascalCompiler); +begin + //with RegClassS(CL,'TStringListInt', 'TFileNameList') do + with CL.AddClassN(CL.FindClass('TStringListInt'),'TFileNameList') do + begin + Constructor Create( )'); + Procedure GetImageVolumePairFromId( ImageId : integer; vInput, vOutput : TNNetVolume; ThreadDangerous : boolean)'); + Procedure GetRandomImagePair( vInput, vOutput : TNNetVolume)'); + Function ThreadSafeLoadImageFromFileIntoVolume( ImageFileName : string; V : TNNetVolume) : boolean'); + RegisterProperty('ClassCount', 'integer', iptrw); + end; +end; + +(*----------------------------------------------------------------------------*) +procedure SIRegister_neuraldatasets(CL: TPSPascalCompiler); +begin + CL.AddTypeS('TTinyImageChannel','array [0..31] of array[0..31] of byte; '); + CL.AddTypeS('TTinyImageChannel1D','array [0..32 * 32 - 1] of byte; '); + CL.AddTypeS('TMNistImage','array [0..27] of array[0..27] of byte; '); + + //TTinyImageChannel1D = packed array [0..32 * 32 - 1] of byte; + //TMNistImage = packed array [0..27, 0..27] of byte; + + CL.AddTypeS('TTinyImage', 'record bLabel : byte; R : TTinyImageChannel; G : T' + +'TinyImageChannel; B : TTinyImageChannel; end'); + CL.AddTypeS('TCifar100Image', 'record bCoarseLabel : byte; bFineLabel : byte;' + +' R : TTinyImageChannel; G : TTinyImageChannel; B : TTinyImageChannel; end'); + CL.AddTypeS('TTinySingleChannelImage', 'record bLabel : byte; Grey : TTinyImageChannel; end'); + CL.AddTypeS('TTinySingleChannelImage1D', 'record bLabel : byte; Grey : TTinyImageChannel1D; end'); + //CL.AddTypeS('TTinySingleChannelImagePtr','^TTinySingleChannelImage// will not work'); + //CL.AddTypeS('TTinySingleChannelImage1DPtr', '^TTinySingleChannelImage1D // will not work'); + SIRegister_TFileNameList(CL); + SIRegister_TClassesAndElements(CL); + CL.AddDelphiFunction('Procedure CreateVolumesFromImagesFromFolder(out ImgTrainingVolumes,ImgValidationVolumes,ImgTestVolumes:TNNetVolumeList;FolderName,pImageSubFolder:string;color_encoding:integer;TrainingProp,ValidationProp,TestProp:single;'+ + 'NewSizeX:integer;NewSizeY:integer)'); + CL.AddDelphiFunction('Procedure CreateFileNameListsFromImagesFromFolder( out TrainingFileNames, ValidationFileNames, TestFileNames : TFileNameList; FolderName, pImageSubFolder : string; TrainingProp, ValidationProp, TestProp : single)'); + //Procedure LoadImageIntoVolume( M : TFPMemoryImage; Vol : TNNetVolume)'); + //Procedure LoadVolumeIntoImage( Vol : TNNetVolume; M : TFPMemoryImage)'); + Function LoadImageFromFileIntoVolume(ImageFileName:string; V:TNNetVolume): boolean'); + Function SaveImageFromVolumeIntoFile(V:TNNetVolume;ImageFileName:string) : boolean'); + Procedure ConfusionWriteCSVHeader(var CSVConfusion:TextFile;Labels array of string)'); + Procedure ConfusionWriteCSV( var CSVConfusion: TextFile; Vol:TNNetVolume; Digits : integer)'); + Procedure LoadTinyImageIntoNNetVolume1( var TI : TTinyImage; Vol : TNNetVolume);'); + Procedure LoadTinyImageIntoNNetVolume2(var TI: TCifar100Image; Vol : TNNetVolume);'); + Procedure LoadTinyImageIntoNNetVolume3( var TI : TMNistImage; Vol : TNNetVolume);'); + Procedure LoadNNetVolumeIntoTinyImage4( Vol : TNNetVolume; var TI : TTinyImage);'); + Procedure LoadNNetVolumeIntoTinyImage5( Vol:TNNetVolume; var TI : TCifar100Image);'); + Procedure LoadTinySingleChannelIntoNNetVolume( var SC : TTinySingleChannelImage; Vol : TNNetVolume)'); + Procedure TinyImageCreateGrey(var TI:TTinyImage;var TIGrey:TTinySingleChannelImage)'); + Procedure TinyImageHE( var TI, TIHE : TTinySingleChannelImage)'); + Procedure TinyImageVE( var TI, TIVE : TTinySingleChannelImage)'); + Procedure TinyImageRemoveZeroGradient(var TI:TTinySingleChannelImage;distance:byte)'); + Procedure TinyImageHVE( var TI, TIHE : TTinySingleChannelImage)'); + Function TinyImageTo1D( var TI:TTinySingleChannelImage): TTinySingleChannelImage1D'); + Procedure CreateCifar10Volumes( out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes : TNNetVolumeList; color_encoding : byte)'); + Procedure CreateCifar100Volumes( out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes : TNNetVolumeList; color_encoding : byte; Verbose : boolean)'); + Procedure CreateMNISTVolumes( out ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes : TNNetVolumeList; TrainFileName, TestFileName: string; Verbose: boolean; IsFashion : boolean)'); + Procedure loadCifar10Dataset6( ImgVolumes : TNNetVolumeList; idx : integer; base_pos : integer; color_encoding : byte);'); + Procedure loadCifar10Dataset7( ImgVolumes : TNNetVolumeList; fileName : string; base_pos : integer; color_encoding : byte);'); + Procedure loadCifar100Dataset( ImgVolumes : TNNetVolumeList; fileName : string; color_encoding : byte; Verbose : boolean)'); + Procedure loadMNISTDataset( ImgVolumes : TNNetVolumeList; fileName : string; Verbose : boolean; IsFashion : boolean; MaxLabel : integer);'); + Function CheckCIFARFile( ) : boolean'); + Function CheckCIFAR100File( ) : boolean'); + Function CheckMNISTFile( fileName : string; IsFasion : boolean) : boolean'); + Procedure TestBatch( NN : TNNet; ImgVolumes : TNNetVolumeList; SampleSize : integer; out Rate, Loss, ErrorSum : TNeuralFloat)'); + Procedure TranslateCifar10VolumesToMachineAnimal( VolumeList : TNNetVolumeList)'); + Function SwapEndian( I : integer) : integer'); +end; + +compare the two files: + + + + diff --git a/examples/maXbox4Scripts/1076_CAI_UVisualGAN2.pas b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2.pas new file mode 100644 index 00000000..38e1ab77 --- /dev/null +++ b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2.pas @@ -0,0 +1,1242 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit uvisualgan_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +var csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer: TLabel; + //ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + //GrBoxNeurons: TGroupBox; + //ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + FDisplay: TNNetVolume; + ImgTrainingVolumes, ImgTestVolumes, ImgValidationVolumes: TNNetVolumeList; + FRealPairs, FGeneratedPairs: TNNetVolumePairList; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FGenerative: THistoricalNets; + FGeneratives: TNNetDataParallelism; + FDiscriminator, FDiscriminatorClone: TNNet; + FBaseName: string; + FColorEncoding: byte; + FRandomSizeX, FRandomSizeY, FRandomDepth: integer; + FLearningRateProportion: TNeuralFloat; + + //iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + + procedure Learn(Sender: TObject); + procedure TFormVisualLearningLearn2( Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + + function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; + procedure GetDiscriminatorTrainingProc(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure DiscriminatorOnAfterEpoch(Sender: TObject); + procedure DiscriminatorOnAfterStep(Sender: TObject); + procedure DiscriminatorAugmentation(pInput: TNNetVolume; ThreadId: integer); + //procedure Learn(Sender: TObject); + //procedure SaveScreenshot(filename: string); + procedure BuildTrainingPairs(); + procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); + procedure DiscriminatorOnStart(Sender: TObject); + procedure TFormVisualLearningSendStop; + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure TFormVisualLearningLearn2( Sender: TObject); +var + NeuronMultiplier: integer; +begin + FRandomSizeX := 4; + FRandomSizeY := 4; + FRandomDepth := StrToInt(ComboComplexity.Text); + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'ART'+IntToStr(FRandomDepth)+'-'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= fbasename +'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName := fbasename+ 'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + if not(Assigned(ImgValidationVolumes)) then + begin + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, FColorEncoding); + end; + + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FGenerative := THistoricalNets.Create(); + FDiscriminator := TNNet.Create(); + + FLearningRateProportion := csLearningRates[ComboLearningRate.ItemIndex]; + + if Not(FileExists(FBaseName+'generative.nn')) then + begin + WriteLn('Creating generative.'); + FGenerative.AddLayer49([ + TNNetInput.Create4(FRandomSizeX, FRandomSizeY, FRandomDepth), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), //4x4 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), //8x8 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(4,0,0), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,5,2,1,0), //32x32 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,1,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + (* + FGenerative.AddLayer([ + TNNetInput.Create(40, 40, 3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + *) + FGenerative.Layers[FGenerative.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading generative.'); + FGenerative.LoadFromFile(FBaseName+'generative.nn'); + end; + FGenerative.DebugStructure(); + FGenerative.SetLearningRate(0.001,0.9); + FGenerative.SetL2Decay(0.0); + + if Not(FileExists(FBaseName+'discriminator.nn')) then + begin + WriteLn('Creating discriminator.'); + (* + FDiscriminator.AddLayer([ + TNNetInput.Create(32,32,3), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 16x16 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 8x8 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 4x4 + TNNetSELU.Create(), +// TNNetDropout.Create(0.4), + TNNetFullConnectLinear.Create(2), + TNNetSoftMax.Create() + ]);*) + FDiscriminator.AddLayer49([ + TNNetInput.Create4(32,32,3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 15x15 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 7x7 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + // downsample to 3x3 + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(128,0), + TNNetFullConnectLinear.Create28(2,0), + TNNetSoftMax.Create() + ]); + + FDiscriminator.Layers[FDiscriminator.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading discriminator.'); + FDiscriminator.LoadFromFile(FBaseName+'discriminator.nn'); + TNNetInput(FDiscriminator.Layers[0]).EnableErrorCollection; + FDiscriminator.DebugStructure(); + FDiscriminator.DebugWeights(); + end; + FDiscriminator.DebugStructure(); + + FDiscriminatorClone := FDiscriminator.Clone(); + TNNetInput(FDiscriminatorClone.Layers[0]).EnableErrorCollection; + + FFit.EnableClassComparison(); + FFit.OnAfterEpoch := @{Self.}DiscriminatorOnAfterEpoch; + FFit.OnAfterStep := @{Self.}DiscriminatorOnAfterStep; + FFit.OnStart := @{Self.}DiscriminatorOnStart; + FFit.DataAugmentationFn := @{Self.}DiscriminatorAugmentation; + FFit.LearningRateDecay := 0.00001; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.001; + FFit.FileNameBase := FBaseName+'GenerativeNeuralAPI'; + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FGenerative.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: FFit.MaxThreadNum := 1; + //FFit.FitLoading(FDiscriminator, 64*10, 500, 500, 64, 35000, @GetDiscriminatorTrainingPair, nil, nil); // This line does the same as below + FFit.FitLoading1(FDiscriminator, 64*10, 500, 500, 64, 35000, + @GetDiscriminatorTrainingProc, nil, nil); // This line does the same as above + + if Assigned(FGeneratives) then begin + //FreeAndNil(FGeneratives); + FGeneratives.Free; + FGeneratives:= Nil; + end; + FGenerative.Free; + FDiscriminator.Free; + FDiscriminatorClone.Free; +end; + + +procedure TFormVisualLearningSendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure DiscriminatorOnStart(Sender: TObject); +begin + FGeneratives := TNNetDataParallelism.Create74(FGenerative, FFit.MaxThreadNum, true); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FGeneratives.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + BuildTrainingPairs(); +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + //FDisplay : TNNetVolume; +begin + //FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + //FDisplay.Free; +end; + + +function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; +var + RandomValue, RandomPos: integer; + LocalPair: TNNetVolumePair; +begin + if (FRealPairs.Count = 0) then + begin + WriteLn('Error: discriminator real pairs have no element'); + Result := nil; + exit; + end; + + if FGeneratedPairs.Count = 0 then + begin + WriteLn('Error: discriminator generated/fake pairs have no element'); + Result := nil; + exit; + end; + + RandomValue := Random(1000); + if RandomValue < 500 then + begin + RandomPos := Random(FRealPairs.Count); + Result := FRealPairs[RandomPos]; + Result.O.SetClassForSoftMax(1); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Real Pair index '+itoa(RandomPos)+ + 'has wrong size:'+itoa( Result.I.Size)); + end; + //Debug Only: if (Random(100)=0) then DisplayInputImage(Result.I, FColorEncoding); + end + else + begin + LocalPair := FGeneratedPairs[ThreadId]; + LocalPair.I.Resize(FRandomSizeX, FRandomSizeY, FRandomDepth); + LocalPair.I.Randomize(0,0,0); + LocalPair.I.NormalizeMax(2); + //FGeneratives: TNNetDataParallelism; + //RegisterProperty('Items', 'TNNet Integer', iptrw); + //LocalPair: TNNetVolumePair; + //RegisterMethod('Procedure Compute65( pInput : TNNetVolume; FromLayerIdx : integer);'); + FGeneratives[ThreadId].Compute65(LocalPair.I,0); + FGeneratives[ThreadId].GetOutput(LocalPair.I); + Result := LocalPair; + Result.O.SetClassForSoftMax(0); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Generated Pair has wrong size:'+itoa( Result.I.Size)); + end; + end; +end; + +procedure GetDiscriminatorTrainingProc(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + LocalPair: TNNetVolumePair; +begin + LocalPair := GetDiscriminatorTrainingPair(Idx, ThreadId); + pInput.Copy76(LocalPair.I); + pOutput.Copy76(LocalPair.O); +end; + +procedure DiscriminatorOnAfterEpoch(Sender: TObject); +var + LoopCnt, MaxLoop: integer; + ExpectedDiscriminatorOutput, Transitory, DiscriminatorFound, GenerativeInput: TNNetVolume; + Error: TNeuralFloat; +begin + if (FFit.TrainingAccuracy <= 0.745) or FFit.ShouldQuit + then exit; + WriteLn('Training Generative Start.'); + ExpectedDiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + ExpectedDiscriminatorOutput.SetClassForSoftMax(1); + DiscriminatorFound := TNNetVolume.Create3(ExpectedDiscriminatorOutput); + //Create3( Original : TVolume);'); + //Create6( pSize : integer; c : TNeuralFloat);') + Transitory := TNNetVolume.Create3(FDiscriminatorClone.Layers[0].OutputError); + GenerativeInput := TNNetVolume.Create0(FRandomSizeX, FRandomSizeY, FRandomDepth,0); + FDiscriminatorClone.CopyWeights(FDiscriminator); + FGenerative.SetBatchUpdate(true); + FGenerative.SetLearningRate(FFit.CurrentLearningRate*FLearningRateProportion, 0); + FGenerative.SetL2Decay(0.00001); + FDiscriminatorClone.SetBatchUpdate(true); + FDiscriminatorClone.SetL2Decay(0.0); + MaxLoop := Round(100 * (1/FLearningRateProportion)); + begin + Error := 0; + FDiscriminatorClone.RefreshDropoutMask(); + for LoopCnt := 1 to MaxLoop do + begin + if FFit.ShouldQuit then break; + FDiscriminatorClone.ClearDeltas(); + FDiscriminatorClone.ClearInertia(); + GenerativeInput.Randomize(0,0,0); + GenerativeInput.NormalizeMax(2); + if FGenerative.Layers[0].Output.Size<>GenerativeInput.Size then + begin + Write('.'); + FGenerative.Layers[0].Output.ReSize10(GenerativeInput); + FGenerative.Layers[0].OutputError.ReSize10(GenerativeInput); + end; + FGenerative.Compute65(GenerativeInput,0); + FGenerative.GetOutput(Transitory); + FDiscriminatorClone.Compute65(Transitory,0); + FDiscriminatorClone.GetOutput(DiscriminatorFound); + FDiscriminatorClone.Backpropagate69(ExpectedDiscriminatorOutput); + //Error += ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Error:= error +ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Transitory.Sub64(FDiscriminatorClone.Layers[0].OutputError); + FGenerative.Backpropagate69(Transitory); + FGenerative.NormalizeMaxAbsoluteDelta(0.001); + FGenerative.UpdateWeights(); + if LoopCnt mod 10 = 0 then TFormVisualLearningProcessMessages(); + if LoopCnt mod 100 = 0 then TFormVisualLearningDisplayInputImage(Transitory, FColorEncoding); + end; + FDiscriminatorClone.Layers[0].OutputError.PrintDebug(); + WriteLn('Clone.Layers end'); + WriteLn('Generative error:'+flots(Error))//:6:4); + end; + //Debug: + //FGenerative.DebugErrors(); + //FGenerative.DebugWeights(); + //FDiscriminatorClone.DebugWeights(); + FGeneratives.CopyWeights(FGenerative); + GenerativeInput.Free; + ExpectedDiscriminatorOutput.Free; + Transitory.Free; + DiscriminatorFound.Free; + if FFit.CurrentEpoch mod 100 = 0 then + begin + WriteLn('Saving '+ FBaseName); + FGenerative.SaveToFile(FBaseName+'generative.nn'); + FDiscriminator.SaveToFile(FBaseName+'discriminator.nn'); + SaveScreenshot(FBaseName+'cai-neural-gan.bmp'); + end; + WriteLn('Training Generative Finish with:'+flots( Error)); //:6:4); + //DisplayInputImage(FRealPairs[Random(FRealPairs.Count)].I, FColorEncoding); +end; + +procedure DiscriminatorOnAfterStep(Sender: TObject); +begin + LabClassRate.Caption := + StrPadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4,'-'); + TFormVisualLearningProcessMessages(); +end; + +procedure DiscriminatorAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin + if Random(1000)>500 then pInput.FlipX(); + //if Random(1000)>750 then pInput.MakeGray(FColorEncoding); +end; + +procedure BuildTrainingPairs(); +var + FakePairCnt: integer; + ImgTrainingVolume: TNNetVolume; + DiscriminatorOutput, GenerativeOutput: TNNetVolume; +begin + DiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + GenerativeOutput := TNNetVolume.Create0(32, 32, 3,0); + //if FRealPairs.Count = 0 then + //begin + (* + for ImgTrainingVolume in ImgTrainingVolumes do fix + begin + if ImgTrainingVolume.Tag = 5 then //5 + begin + DiscriminatorOutput.SetClassForSoftMax(1); + FRealPairs.Add + ( + TNNetVolumePair.CreateCopying + ( + ImgTrainingVolume, + DiscriminatorOutput + ) + ); + //Debug only: if (Random(100)=0) then DisplayInputImage(ImgTrainingVolume, FColorEncoding); + end + end; + end;*) + + DiscriminatorOutput.SetClassForSoftMax(0); + + if FGeneratedPairs.Count < FFit.MaxThreadNum then + begin + for FakePairCnt := 1 to FFit.MaxThreadNum do + begin + FGeneratedPairs.Add + ( + TNNetVolumePair.CreateCopying83 + ( + GenerativeOutput, + DiscriminatorOutput + ) + ); + end; + end; + ImgTrainingVolumes.Cleartag; + ImgValidationVolumes.Cleartag; + ImgTestVolumes.Cleartag; + GenerativeOutput.Free; + DiscriminatorOutput.Free; +end; + + + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then + begin + if FileExists(OpenDialogNN.FileName) then + begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + if not(FileExists(FileName)) then + begin + if FileName = 'SimpleImageClassifier.nn' then + begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else + begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; + + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + InitCriticalSection(FCritSec); + ImgTrainingVolumes := nil; + ImgTestVolumes := nil; + ImgValidationVolumes := nil; + FGeneratives := nil; + FRealPairs := TNNetVolumePairList.Create(true); + FGeneratedPairs := TNNetVolumePairList.Create(true); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + (* CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}128, + {InputSize=}32, {FFilterSize=}64, {ImagesPerRow=}16 + ); *) + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + //ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); + TFormVisualLearningSendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + if Assigned(ImgValidationVolumes) then ImgValidationVolumes.Free; + if Assigned(ImgTestVolumes) then ImgTestVolumes.Free; + if Assigned(ImgTrainingVolumes) then ImgTrainingVolumes.Free; + FreeNeuronImages(aImage, aLabelX, aLabelY); + DoneCriticalSection(FCritSec); + FRealPairs.Free; + FGeneratedPairs.Free; + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + writeln('destroy neural neurons all done'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 16 + Width := 264 + Enabled := False + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage2(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +function NeuronForceMinMax(x, pMin, pMax: TNeuralFloat): TNeuralFloat; +begin + if (x>pMax) then Result := pMax + else if (x + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + + procedure TTestCNNAlgo.DoRun; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if Not(CheckMNISTFile('train')) or Not(CheckMNISTFile('t10k')) then + begin + Terminate; + exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + NN.AddLayer([ + TNNetInput.Create(28, 28, 1), + TNNetConvolutionLinear.Create(32, 5, 2, 1, 1), + TNNetMaxPool.Create(4), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetFullConnectReLU.Create(32), + TNNetFullConnectReLU.Create(32), + TNNetDropout.Create(0.2), + TNNetMaxPool.Create(2), + TNNetFullConnectLinear.Create(10), + TNNetSoftMax.Create() + ]); + CreateMNISTVolumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, 'train', 't10k'); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'SimpleMNist'; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.HasFlipX := false; + NeuralFit.HasFlipY := false; + NeuralFit.MaxCropSize := 4; + { NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}//10, {batchsize=}128, {epochs=}20); } + { NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + Terminate; + end; } + + +//} diff --git a/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester.pas b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester.pas new file mode 100644 index 00000000..5929af9e --- /dev/null +++ b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester.pas @@ -0,0 +1,1317 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit uvisualgan_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +var csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer: TLabel; + //ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + //GrBoxNeurons: TGroupBox; + //ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + FDisplay: TNNetVolume; + ImgTrainingVolumes, ImgTestVolumes, ImgValidationVolumes: TNNetVolumeList; + FRealPairs, FGeneratedPairs: TNNetVolumePairList; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FGenerative: THistoricalNets; + FGeneratives: TNNetDataParallelism; + FDiscriminator, FDiscriminatorClone: TNNet; + FBaseName: string; + FColorEncoding: byte; + FRandomSizeX, FRandomSizeY, FRandomDepth: integer; + FLearningRateProportion: TNeuralFloat; + + //iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + + procedure Learn(Sender: TObject); + procedure TFormVisualLearningLearn2( Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + + function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; + procedure GetDiscriminatorTrainingProc(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure DiscriminatorOnAfterEpoch(Sender: TObject); + procedure DiscriminatorOnAfterStep(Sender: TObject); + procedure DiscriminatorAugmentation(pInput: TNNetVolume; ThreadId: integer); + //procedure Learn(Sender: TObject); + //procedure SaveScreenshot(filename: string); + procedure BuildTrainingPairs(); + procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); + procedure DiscriminatorOnStart(Sender: TObject); + procedure TFormVisualLearningSendStop; + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure TFormVisualLearningLearn2( Sender: TObject); +var + NeuronMultiplier: integer; +begin + FRandomSizeX := 4; + FRandomSizeY := 4; + FRandomDepth := StrToInt(ComboComplexity.Text); + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'ART'+IntToStr(FRandomDepth)+'-'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= fbasename +'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName := fbasename+ 'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + if not(Assigned(ImgValidationVolumes)) then + begin + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, FColorEncoding); + end; + + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FGenerative := THistoricalNets.Create(); + FDiscriminator := TNNet.Create(); + + FLearningRateProportion := csLearningRates[ComboLearningRate.ItemIndex]; + + if Not(FileExists(FBaseName+'generative.nn')) then + begin + WriteLn('Creating generative.'); + FGenerative.AddLayer49([ + TNNetInput.Create4(FRandomSizeX, FRandomSizeY, FRandomDepth), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), //4x4 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), //8x8 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(4,0,0), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,5,2,1,0), //32x32 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,1,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + (* + FGenerative.AddLayer([ + TNNetInput.Create(40, 40, 3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + *) + FGenerative.Layers[FGenerative.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading generative.'); + FGenerative.LoadFromFile(FBaseName+'generative.nn'); + end; + FGenerative.DebugStructure(); + FGenerative.SetLearningRate(0.001,0.9); + FGenerative.SetL2Decay(0.0); + + if Not(FileExists(FBaseName+'discriminator.nn')) then + begin + WriteLn('Creating discriminator.'); + (* + FDiscriminator.AddLayer([ + TNNetInput.Create(32,32,3), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 16x16 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 8x8 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 4x4 + TNNetSELU.Create(), +// TNNetDropout.Create(0.4), + TNNetFullConnectLinear.Create(2), + TNNetSoftMax.Create() + ]);*) + FDiscriminator.AddLayer49([ + TNNetInput.Create4(32,32,3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 15x15 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 7x7 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + // downsample to 3x3 + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(128,0), + TNNetFullConnectLinear.Create28(2,0), + TNNetSoftMax.Create() + ]); + + FDiscriminator.Layers[FDiscriminator.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading discriminator.'); + FDiscriminator.LoadFromFile(FBaseName+'discriminator.nn'); + TNNetInput(FDiscriminator.Layers[0]).EnableErrorCollection; + FDiscriminator.DebugStructure(); + FDiscriminator.DebugWeights(); + end; + FDiscriminator.DebugStructure(); + + FDiscriminatorClone := FDiscriminator.Clone(); + TNNetInput(FDiscriminatorClone.Layers[0]).EnableErrorCollection; + + FFit.EnableClassComparison(); + FFit.OnAfterEpoch := @{Self.}DiscriminatorOnAfterEpoch; + FFit.OnAfterStep := @{Self.}DiscriminatorOnAfterStep; + FFit.OnStart := @{Self.}DiscriminatorOnStart; + FFit.DataAugmentationFn := @{Self.}DiscriminatorAugmentation; + FFit.LearningRateDecay := 0.00001; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.001; + FFit.FileNameBase := FBaseName+'GenerativeNeuralAPI'; + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FGenerative.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: FFit.MaxThreadNum := 1; + //FFit.FitLoading(FDiscriminator, 64*10, 500, 500, 64, 35000, @GetDiscriminatorTrainingPair, nil, nil); // This line does the same as below + FFit.FitLoading1(FDiscriminator, 64*10, 500, 500, 64, 35000, + @GetDiscriminatorTrainingProc, nil, nil); // This line does the same as above + + if Assigned(FGeneratives) then begin + //FreeAndNil(FGeneratives); + FGeneratives.Free; + FGeneratives:= Nil; + end; + FGenerative.Free; + FDiscriminator.Free; + FDiscriminatorClone.Free; +end; + + +procedure TFormVisualLearningSendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure DiscriminatorOnStart(Sender: TObject); +begin + FGeneratives := TNNetDataParallelism.Create74(FGenerative, FFit.MaxThreadNum, true); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FGeneratives.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + BuildTrainingPairs(); +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + //FDisplay : TNNetVolume; +begin + //FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + //FDisplay.Free; +end; + + +function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; +var + RandomValue, RandomPos: integer; + LocalPair: TNNetVolumePair; +begin + if (FRealPairs.Count = 0) then + begin + WriteLn('Error: discriminator real pairs have no element'); + Result := nil; + exit; + end; + + if FGeneratedPairs.Count = 0 then + begin + WriteLn('Error: discriminator generated/fake pairs have no element'); + Result := nil; + exit; + end; + + RandomValue := Random(1000); + if RandomValue < 500 then + begin + RandomPos := Random(FRealPairs.Count); + Result := FRealPairs[RandomPos]; + Result.O.SetClassForSoftMax(1); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Real Pair index '+itoa(RandomPos)+ + 'has wrong size:'+itoa( Result.I.Size)); + end; + //Debug Only: if (Random(100)=0) then DisplayInputImage(Result.I, FColorEncoding); + end + else + begin + LocalPair := FGeneratedPairs[ThreadId]; + LocalPair.I.Resize(FRandomSizeX, FRandomSizeY, FRandomDepth); + LocalPair.I.Randomize(0,0,0); + LocalPair.I.NormalizeMax(2); + //FGeneratives: TNNetDataParallelism; + //RegisterProperty('Items', 'TNNet Integer', iptrw); + //LocalPair: TNNetVolumePair; + //RegisterMethod('Procedure Compute65( pInput : TNNetVolume; FromLayerIdx : integer);'); + FGeneratives[ThreadId].Compute65(LocalPair.I,0); + FGeneratives[ThreadId].GetOutput(LocalPair.I); + Result := LocalPair; + Result.O.SetClassForSoftMax(0); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Generated Pair has wrong size:'+itoa( Result.I.Size)); + end; + end; +end; + +procedure GetDiscriminatorTrainingProc(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + LocalPair: TNNetVolumePair; +begin + LocalPair := GetDiscriminatorTrainingPair(Idx, ThreadId); + pInput.Copy76(LocalPair.I); + pOutput.Copy76(LocalPair.O); +end; + +procedure DiscriminatorOnAfterEpoch(Sender: TObject); +var + LoopCnt, MaxLoop: integer; + ExpectedDiscriminatorOutput, Transitory, DiscriminatorFound, GenerativeInput: TNNetVolume; + Error: TNeuralFloat; +begin + if (FFit.TrainingAccuracy <= 0.745) or FFit.ShouldQuit + then exit; + WriteLn('Training Generative Start.'); + ExpectedDiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + ExpectedDiscriminatorOutput.SetClassForSoftMax(1); + DiscriminatorFound := TNNetVolume.Create3(ExpectedDiscriminatorOutput); + //Create3( Original : TVolume);'); + //Create6( pSize : integer; c : TNeuralFloat);') + Transitory := TNNetVolume.Create3(FDiscriminatorClone.Layers[0].OutputError); + GenerativeInput := TNNetVolume.Create0(FRandomSizeX, FRandomSizeY, FRandomDepth,0); + FDiscriminatorClone.CopyWeights(FDiscriminator); + FGenerative.SetBatchUpdate(true); + FGenerative.SetLearningRate(FFit.CurrentLearningRate*FLearningRateProportion, 0); + FGenerative.SetL2Decay(0.00001); + FDiscriminatorClone.SetBatchUpdate(true); + FDiscriminatorClone.SetL2Decay(0.0); + MaxLoop := Round(100 * (1/FLearningRateProportion)); + begin + Error := 0; + FDiscriminatorClone.RefreshDropoutMask(); + for LoopCnt := 1 to MaxLoop do + begin + if FFit.ShouldQuit then break; + FDiscriminatorClone.ClearDeltas(); + FDiscriminatorClone.ClearInertia(); + GenerativeInput.Randomize(0,0,0); + GenerativeInput.NormalizeMax(2); + if FGenerative.Layers[0].Output.Size<>GenerativeInput.Size then + begin + Write('.'); + FGenerative.Layers[0].Output.ReSize10(GenerativeInput); + FGenerative.Layers[0].OutputError.ReSize10(GenerativeInput); + end; + FGenerative.Compute65(GenerativeInput,0); + FGenerative.GetOutput(Transitory); + FDiscriminatorClone.Compute65(Transitory,0); + FDiscriminatorClone.GetOutput(DiscriminatorFound); + FDiscriminatorClone.Backpropagate69(ExpectedDiscriminatorOutput); + //Error += ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Error:= error +ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Transitory.Sub64(FDiscriminatorClone.Layers[0].OutputError); + FGenerative.Backpropagate69(Transitory); + FGenerative.NormalizeMaxAbsoluteDelta(0.001); + FGenerative.UpdateWeights(); + if LoopCnt mod 10 = 0 then TFormVisualLearningProcessMessages(); + if LoopCnt mod 100 = 0 then TFormVisualLearningDisplayInputImage(Transitory, FColorEncoding); + end; + FDiscriminatorClone.Layers[0].OutputError.PrintDebug(); + WriteLn('Clone.Layers end'); + WriteLn('Generative error:'+flots(Error))//:6:4); + end; + //Debug: + //FGenerative.DebugErrors(); + //FGenerative.DebugWeights(); + //FDiscriminatorClone.DebugWeights(); + FGeneratives.CopyWeights(FGenerative); + GenerativeInput.Free; + ExpectedDiscriminatorOutput.Free; + Transitory.Free; + DiscriminatorFound.Free; + if FFit.CurrentEpoch mod 100 = 0 then + begin + WriteLn('Saving '+ FBaseName); + FGenerative.SaveToFile(FBaseName+'generative.nn'); + FDiscriminator.SaveToFile(FBaseName+'discriminator.nn'); + SaveScreenshot(FBaseName+'cai-neural-gan.bmp'); + end; + WriteLn('Training Generative Finish with:'+flots( Error)); //:6:4); + //DisplayInputImage(FRealPairs[Random(FRealPairs.Count)].I, FColorEncoding); +end; + +procedure DiscriminatorOnAfterStep(Sender: TObject); +begin + LabClassRate.Caption := + StrPadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4,'-'); + TFormVisualLearningProcessMessages(); +end; + +procedure DiscriminatorAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin + if Random(1000)>500 then pInput.FlipX(); + //if Random(1000)>750 then pInput.MakeGray(FColorEncoding); +end; + +procedure BuildTrainingPairs(); +var + FakePairCnt: integer; + ImgTrainingVolume: TNNetVolume; + DiscriminatorOutput, GenerativeOutput: TNNetVolume; +begin + DiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + GenerativeOutput := TNNetVolume.Create0(32, 32, 3,0); + //if FRealPairs.Count = 0 then + //begin + (* + for ImgTrainingVolume in ImgTrainingVolumes do fix + begin + if ImgTrainingVolume.Tag = 5 then //5 + begin + DiscriminatorOutput.SetClassForSoftMax(1); + FRealPairs.Add + ( + TNNetVolumePair.CreateCopying + ( + ImgTrainingVolume, + DiscriminatorOutput + ) + ); + //Debug only: if (Random(100)=0) then DisplayInputImage(ImgTrainingVolume, FColorEncoding); + end + end; + end;*) + + DiscriminatorOutput.SetClassForSoftMax(0); + + if FGeneratedPairs.Count < FFit.MaxThreadNum then + begin + for FakePairCnt := 1 to FFit.MaxThreadNum do + begin + FGeneratedPairs.Add + ( + TNNetVolumePair.CreateCopying83 + ( + GenerativeOutput, + DiscriminatorOutput + ) + ); + end; + end; + ImgTrainingVolumes.Cleartag; + ImgValidationVolumes.Cleartag; + ImgTestVolumes.Cleartag; + GenerativeOutput.Free; + DiscriminatorOutput.Free; +end; + + + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then + begin + if FileExists(OpenDialogNN.FileName) then + begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + if not(FileExists(FileName)) then + begin + if FileName = 'SimpleImageClassifier.nn' then + begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else + begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; + + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + InitCriticalSection(FCritSec); + ImgTrainingVolumes := nil; + ImgTestVolumes := nil; + ImgValidationVolumes := nil; + FGeneratives := nil; + FRealPairs := TNNetVolumePairList.Create(true); + FGeneratedPairs := TNNetVolumePairList.Create(true); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + (* CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}128, + {InputSize=}32, {FFilterSize=}64, {ImagesPerRow=}16 + ); *) + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + //ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); + TFormVisualLearningSendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + if Assigned(ImgValidationVolumes) then ImgValidationVolumes.Free; + if Assigned(ImgTestVolumes) then ImgTestVolumes.Free; + if Assigned(ImgTrainingVolumes) then ImgTrainingVolumes.Free; + FreeNeuronImages(aImage, aLabelX, aLabelY); + DoneCriticalSection(FCritSec); + FRealPairs.Free; + FGeneratedPairs.Free; + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + writeln('destroy neural neurons all done'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 16 + Width := 264 + Enabled := False + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage2(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +function NeuronForceMinMax(x, pMin, pMax: TNeuralFloat): TNeuralFloat; +begin + if (x>pMax) then Result := pMax + else if (x + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + + procedure TTestCNNAlgo.DoRun; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if Not(CheckMNISTFile('train')) or Not(CheckMNISTFile('t10k')) then + begin + Terminate; + exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + NN.AddLayer([ + TNNetInput.Create(28, 28, 1), + TNNetConvolutionLinear.Create(32, 5, 2, 1, 1), + TNNetMaxPool.Create(4), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetFullConnectReLU.Create(32), + TNNetFullConnectReLU.Create(32), + TNNetDropout.Create(0.2), + TNNetMaxPool.Create(2), + TNNetFullConnectLinear.Create(10), + TNNetSoftMax.Create() + ]); + CreateMNISTVolumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, 'train', 't10k'); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'SimpleMNist'; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.HasFlipX := false; + NeuralFit.HasFlipY := false; + NeuralFit.MaxCropSize := 4; + { NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}//10, {batchsize=}128, {epochs=}20); } + { NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + Terminate; + end; } + + +//} diff --git a/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST.pas b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST.pas new file mode 100644 index 00000000..334fd630 --- /dev/null +++ b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST.pas @@ -0,0 +1,1340 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit uvisualgan_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +var csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer: TLabel; + //ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + //GrBoxNeurons: TGroupBox; + //ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + FDisplay: TNNetVolume; + ImgTrainingVolumes, ImgTestVolumes, ImgValidationVolumes: TNNetVolumeList; + FRealPairs, FGeneratedPairs: TNNetVolumePairList; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FGenerative: THistoricalNets; + FGeneratives: TNNetDataParallelism; + FDiscriminator, FDiscriminatorClone: TNNet; + FBaseName: string; + FColorEncoding: byte; + FRandomSizeX, FRandomSizeY, FRandomDepth: integer; + FLearningRateProportion: TNeuralFloat; + + //iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + + procedure Learn(Sender: TObject); + procedure TFormVisualLearningLearn2( Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + + function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; + procedure GetDiscriminatorTrainingProc(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure DiscriminatorOnAfterEpoch(Sender: TObject); + procedure DiscriminatorOnAfterStep(Sender: TObject); + procedure DiscriminatorAugmentation(pInput: TNNetVolume; ThreadId: integer); + //procedure Learn(Sender: TObject); + //procedure SaveScreenshot(filename: string); + procedure BuildTrainingPairs(); + procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); + procedure DiscriminatorOnStart(Sender: TObject); + procedure TFormVisualLearningSendStop; + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure TFormVisualLearningLearn2( Sender: TObject); +var + NeuronMultiplier: integer; +begin + FRandomSizeX := 4; + FRandomSizeY := 4; + FRandomDepth := StrToInt(ComboComplexity.Text); + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'ART'+IntToStr(FRandomDepth)+'-'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= fbasename +'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName := fbasename+ 'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + if not(Assigned(ImgValidationVolumes)) then + begin + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, FColorEncoding); + end; + + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FGenerative := THistoricalNets.Create(); + FDiscriminator := TNNet.Create(); + + FLearningRateProportion := csLearningRates[ComboLearningRate.ItemIndex]; + + if Not(FileExists(FBaseName+'generative.nn')) then + begin + WriteLn('Creating generative.'); + FGenerative.AddLayer49([ + TNNetInput.Create4(FRandomSizeX, FRandomSizeY, FRandomDepth), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), //4x4 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), //8x8 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(4,0,0), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,5,2,1,0), //32x32 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,1,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + (* + FGenerative.AddLayer([ + TNNetInput.Create(40, 40, 3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + *) + FGenerative.Layers[FGenerative.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading generative.'); + FGenerative.LoadFromFile(FBaseName+'generative.nn'); + end; + FGenerative.DebugStructure(); + FGenerative.SetLearningRate(0.001,0.9); + FGenerative.SetL2Decay(0.0); + + if Not(FileExists(FBaseName+'discriminator.nn')) then + begin + WriteLn('Creating discriminator.'); + (* + FDiscriminator.AddLayer([ + TNNetInput.Create(32,32,3), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 16x16 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 8x8 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), // downsample to 4x4 + TNNetSELU.Create(), +// TNNetDropout.Create(0.4), + TNNetFullConnectLinear.Create(2), + TNNetSoftMax.Create() + ]);*) + FDiscriminator.AddLayer49([ + TNNetInput.Create4(32,32,3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 15x15 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 7x7 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + // downsample to 3x3 + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(128,0), + TNNetFullConnectLinear.Create28(2,0), + TNNetSoftMax.Create() + ]); + + FDiscriminator.Layers[FDiscriminator.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading discriminator.'); + FDiscriminator.LoadFromFile(FBaseName+'discriminator.nn'); + TNNetInput(FDiscriminator.Layers[0]).EnableErrorCollection; + FDiscriminator.DebugStructure(); + FDiscriminator.DebugWeights(); + end; + FDiscriminator.DebugStructure(); + + FDiscriminatorClone := FDiscriminator.Clone(); + TNNetInput(FDiscriminatorClone.Layers[0]).EnableErrorCollection; + + FFit.EnableClassComparison(); + FFit.OnAfterEpoch := @{Self.}DiscriminatorOnAfterEpoch; + FFit.OnAfterStep := @{Self.}DiscriminatorOnAfterStep; + FFit.OnStart := @{Self.}DiscriminatorOnStart; + FFit.DataAugmentationFn := @{Self.}DiscriminatorAugmentation; + FFit.LearningRateDecay := 0.00001; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.001; + FFit.FileNameBase := FBaseName+'GenerativeNeuralAPI'; + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FGenerative.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: FFit.MaxThreadNum := 1; + //FFit.FitLoading(FDiscriminator, 64*10, 500, 500, 64, 35000, @GetDiscriminatorTrainingPair, nil, nil); // This line does the same as below + FFit.FitLoading1(FDiscriminator, 64*10, 500, 500, 64, 35000, + @GetDiscriminatorTrainingProc, nil, nil); // This line does the same as above + + if Assigned(FGeneratives) then begin + //FreeAndNil(FGeneratives); + FGeneratives.Free; + FGeneratives:= Nil; + end; + FGenerative.Free; + FDiscriminator.Free; + FDiscriminatorClone.Free; +end; + + +procedure TFormVisualLearningSendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure DiscriminatorOnStart(Sender: TObject); +begin + FGeneratives := TNNetDataParallelism.Create74(FGenerative, FFit.MaxThreadNum, true); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FGeneratives.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + BuildTrainingPairs(); +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + //FDisplay : TNNetVolume; +begin + //FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + //FDisplay.Free; +end; + + +function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; +var + RandomValue, RandomPos: integer; + LocalPair: TNNetVolumePair; +begin + if (FRealPairs.Count = 0) then + begin + WriteLn('Error: discriminator real pairs have no element'); + Result := nil; + exit; + end; + + if FGeneratedPairs.Count = 0 then + begin + WriteLn('Error: discriminator generated/fake pairs have no element'); + Result := nil; + exit; + end; + + RandomValue := Random(1000); + if RandomValue < 500 then + begin + RandomPos := Random(FRealPairs.Count); + Result := FRealPairs[RandomPos]; + Result.O.SetClassForSoftMax(1); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Real Pair index '+itoa(RandomPos)+ + 'has wrong size:'+itoa( Result.I.Size)); + end; + //Debug Only: if (Random(100)=0) then DisplayInputImage(Result.I, FColorEncoding); + end + else + begin + LocalPair := FGeneratedPairs[ThreadId]; + LocalPair.I.Resize(FRandomSizeX, FRandomSizeY, FRandomDepth); + LocalPair.I.Randomize(0,0,0); + LocalPair.I.NormalizeMax(2); + //FGeneratives: TNNetDataParallelism; + //RegisterProperty('Items', 'TNNet Integer', iptrw); + //LocalPair: TNNetVolumePair; + //RegisterMethod('Procedure Compute65( pInput : TNNetVolume; FromLayerIdx : integer);'); + FGeneratives[ThreadId].Compute65(LocalPair.I,0); + FGeneratives[ThreadId].GetOutput(LocalPair.I); + Result := LocalPair; + Result.O.SetClassForSoftMax(0); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Generated Pair has wrong size:'+itoa( Result.I.Size)); + end; + end; +end; + +procedure GetDiscriminatorTrainingProc(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + LocalPair: TNNetVolumePair; +begin + LocalPair := GetDiscriminatorTrainingPair(Idx, ThreadId); + pInput.Copy76(LocalPair.I); + pOutput.Copy76(LocalPair.O); +end; + +procedure DiscriminatorOnAfterEpoch(Sender: TObject); +var + LoopCnt, MaxLoop: integer; + ExpectedDiscriminatorOutput, Transitory, DiscriminatorFound, GenerativeInput: TNNetVolume; + Error: TNeuralFloat; +begin + if (FFit.TrainingAccuracy <= 0.745) or FFit.ShouldQuit + then exit; + WriteLn('Training Generative Start.'); + ExpectedDiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + ExpectedDiscriminatorOutput.SetClassForSoftMax(1); + DiscriminatorFound := TNNetVolume.Create3(ExpectedDiscriminatorOutput); + //Create3( Original : TVolume);'); + //Create6( pSize : integer; c : TNeuralFloat);') + Transitory := TNNetVolume.Create3(FDiscriminatorClone.Layers[0].OutputError); + GenerativeInput := TNNetVolume.Create0(FRandomSizeX, FRandomSizeY, FRandomDepth,0); + FDiscriminatorClone.CopyWeights(FDiscriminator); + FGenerative.SetBatchUpdate(true); + FGenerative.SetLearningRate(FFit.CurrentLearningRate*FLearningRateProportion, 0); + FGenerative.SetL2Decay(0.00001); + FDiscriminatorClone.SetBatchUpdate(true); + FDiscriminatorClone.SetL2Decay(0.0); + MaxLoop := Round(100 * (1/FLearningRateProportion)); + begin + Error := 0; + FDiscriminatorClone.RefreshDropoutMask(); + for LoopCnt := 1 to MaxLoop do + begin + if FFit.ShouldQuit then break; + FDiscriminatorClone.ClearDeltas(); + FDiscriminatorClone.ClearInertia(); + GenerativeInput.Randomize(0,0,0); + GenerativeInput.NormalizeMax(2); + if FGenerative.Layers[0].Output.Size<>GenerativeInput.Size then + begin + Write('.'); + FGenerative.Layers[0].Output.ReSize10(GenerativeInput); + FGenerative.Layers[0].OutputError.ReSize10(GenerativeInput); + end; + FGenerative.Compute65(GenerativeInput,0); + FGenerative.GetOutput(Transitory); + FDiscriminatorClone.Compute65(Transitory,0); + FDiscriminatorClone.GetOutput(DiscriminatorFound); + FDiscriminatorClone.Backpropagate69(ExpectedDiscriminatorOutput); + //Error += ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Error:= error +ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Transitory.Sub64(FDiscriminatorClone.Layers[0].OutputError); + FGenerative.Backpropagate69(Transitory); + FGenerative.NormalizeMaxAbsoluteDelta(0.001); + FGenerative.UpdateWeights(); + if LoopCnt mod 10 = 0 then TFormVisualLearningProcessMessages(); + if LoopCnt mod 100 = 0 then TFormVisualLearningDisplayInputImage(Transitory, FColorEncoding); + end; + FDiscriminatorClone.Layers[0].OutputError.PrintDebug(); + WriteLn('Clone.Layers end'); + WriteLn('Generative error:'+flots(Error))//:6:4); + end; + //Debug: + //FGenerative.DebugErrors(); + //FGenerative.DebugWeights(); + //FDiscriminatorClone.DebugWeights(); + FGeneratives.CopyWeights(FGenerative); + GenerativeInput.Free; + ExpectedDiscriminatorOutput.Free; + Transitory.Free; + DiscriminatorFound.Free; + if FFit.CurrentEpoch mod 100 = 0 then + begin + WriteLn('Saving '+ FBaseName); + FGenerative.SaveToFile(FBaseName+'generative.nn'); + FDiscriminator.SaveToFile(FBaseName+'discriminator.nn'); + SaveScreenshot(FBaseName+'cai-neural-gan.bmp'); + end; + WriteLn('Training Generative Finish with:'+flots( Error)); //:6:4); + //DisplayInputImage(FRealPairs[Random(FRealPairs.Count)].I, FColorEncoding); +end; + +procedure DiscriminatorOnAfterStep(Sender: TObject); +begin + LabClassRate.Caption := + StrPadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4,'-'); + TFormVisualLearningProcessMessages(); +end; + +procedure DiscriminatorAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin + if Random(1000)>500 then pInput.FlipX(); + //if Random(1000)>750 then pInput.MakeGray(FColorEncoding); +end; + +procedure BuildTrainingPairs(); +var + FakePairCnt: integer; + ImgTrainingVolume: TNNetVolume; + DiscriminatorOutput, GenerativeOutput: TNNetVolume; +begin + DiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + GenerativeOutput := TNNetVolume.Create0(32, 32, 3,0); + //if FRealPairs.Count = 0 then + //begin + (* + for ImgTrainingVolume in ImgTrainingVolumes do fix + begin + if ImgTrainingVolume.Tag = 5 then //5 + begin + DiscriminatorOutput.SetClassForSoftMax(1); + FRealPairs.Add + ( + TNNetVolumePair.CreateCopying + ( + ImgTrainingVolume, + DiscriminatorOutput + ) + ); + //Debug only: if (Random(100)=0) then DisplayInputImage(ImgTrainingVolume, FColorEncoding); + end + end; + end;*) + + DiscriminatorOutput.SetClassForSoftMax(0); + + if FGeneratedPairs.Count < FFit.MaxThreadNum then + begin + for FakePairCnt := 1 to FFit.MaxThreadNum do + begin + FGeneratedPairs.Add + ( + TNNetVolumePair.CreateCopying83 + ( + GenerativeOutput, + DiscriminatorOutput + ) + ); + end; + end; + ImgTrainingVolumes.Cleartag; + ImgValidationVolumes.Cleartag; + ImgTestVolumes.Cleartag; + GenerativeOutput.Free; + DiscriminatorOutput.Free; +end; + + + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then + begin + if FileExists(OpenDialogNN.FileName) then + begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + if not(FileExists(FileName)) then + begin + if FileName = 'SimpleImageClassifier.nn' then + begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else + begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; + + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + InitCriticalSection(FCritSec); + ImgTrainingVolumes := nil; + ImgTestVolumes := nil; + ImgValidationVolumes := nil; + FGeneratives := nil; + FRealPairs := TNNetVolumePairList.Create(true); + FGeneratedPairs := TNNetVolumePairList.Create(true); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + (* CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}128, + {InputSize=}32, {FFilterSize=}64, {ImagesPerRow=}16 + ); *) + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + //ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); + TFormVisualLearningSendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + if Assigned(ImgValidationVolumes) then ImgValidationVolumes.Free; + if Assigned(ImgTestVolumes) then ImgTestVolumes.Free; + if Assigned(ImgTrainingVolumes) then ImgTrainingVolumes.Free; + FreeNeuronImages(aImage, aLabelX, aLabelY); + DoneCriticalSection(FCritSec); + FRealPairs.Free; + FGeneratedPairs.Free; + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + writeln('destroy neural neurons all done'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 16 + Width := 264 + Enabled := False + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage2(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +function NeuronForceMinMax(x, pMin, pMax: TNeuralFloat): TNeuralFloat; +begin + if (x>pMax) then Result := pMax + else if (x + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + + procedure TTestCNNAlgo.DoRun; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if Not(CheckMNISTFile('train')) or Not(CheckMNISTFile('t10k')) then + begin + Terminate; + exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + NN.AddLayer([ + TNNetInput.Create(28, 28, 1), + TNNetConvolutionLinear.Create(32, 5, 2, 1, 1), + TNNetMaxPool.Create(4), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetFullConnectReLU.Create(32), + TNNetFullConnectReLU.Create(32), + TNNetDropout.Create(0.2), + TNNetMaxPool.Create(2), + TNNetFullConnectLinear.Create(10), + TNNetSoftMax.Create() + ]); + CreateMNISTVolumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, 'train', 't10k'); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'SimpleMNist'; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.HasFlipX := false; + NeuralFit.HasFlipY := false; + NeuralFit.MaxCropSize := 4; + { NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}//10, {batchsize=}128, {epochs=}20); } + { NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + Terminate; + end; } + + +//} diff --git a/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST2.pas b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST2.pas new file mode 100644 index 00000000..5a2df18e --- /dev/null +++ b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST2.pas @@ -0,0 +1,1358 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit uvisualgan_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +var csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer, lblfilename, lblfile: TLabel; + //ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + //GrBoxNeurons: TGroupBox; + //ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + FDisplay: TNNetVolume; + ImgTrainingVolumes, ImgTestVolumes, ImgValidationVolumes: TNNetVolumeList; + FRealPairs, FGeneratedPairs: TNNetVolumePairList; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FGenerative: THistoricalNets; + FGeneratives: TNNetDataParallelism; + FDiscriminator, FDiscriminatorClone: TNNet; + FBaseName: string; + FColorEncoding: byte; + FRandomSizeX, FRandomSizeY, FRandomDepth: integer; + FLearningRateProportion: TNeuralFloat; + + //iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + + procedure Learn(Sender: TObject); + procedure TFormVisualLearningLearn2( Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + + function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; + procedure GetDiscriminatorTrainingProc(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure DiscriminatorOnAfterEpoch(Sender: TObject); + procedure DiscriminatorOnAfterStep(Sender: TObject); + procedure DiscriminatorAugmentation(pInput: TNNetVolume; ThreadId: integer); + //procedure Learn(Sender: TObject); + //procedure SaveScreenshot(filename: string); + procedure BuildTrainingPairs(); + procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); + procedure DiscriminatorOnStart(Sender: TObject); + procedure TFormVisualLearningSendStop; + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure TFormVisualLearningLearn2( Sender: TObject); +var + NeuronMultiplier: integer; +begin + FRandomSizeX := 4; + FRandomSizeY := 4; + FRandomDepth := StrToInt(ComboComplexity.Text); + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'ART'+IntToStr(FRandomDepth)+'-'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= fbasename +'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName := fbasename+ 'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + if not(Assigned(ImgValidationVolumes)) then + begin + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, FColorEncoding); + end; + + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FGenerative := THistoricalNets.Create(); + FDiscriminator := TNNet.Create(); + + FLearningRateProportion := csLearningRates[ComboLearningRate.ItemIndex]; + + if Not(FileExists(FBaseName+'generative.nn')) then begin + WriteLn('Creating generative.'); + FGenerative.AddLayer49([ + TNNetInput.Create4(FRandomSizeX, FRandomSizeY, FRandomDepth), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), //4x4 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), //8x8 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(4,0,0), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,5,2,1,0), //32x32 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,1,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + (* + FGenerative.AddLayer([ + TNNetInput.Create(40, 40, 3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + *) + FGenerative.Layers[FGenerative.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading generative.'); + FGenerative.LoadFromFile(FBaseName+'generative.nn'); + end; + FGenerative.DebugStructure(); + FGenerative.SetLearningRate(0.001,0.9); + FGenerative.SetL2Decay(0.0); + + if Not(FileExists(FBaseName+'discriminator.nn')) then + begin + WriteLn('Creating discriminator.'); + (* + FDiscriminator.AddLayer([ + TNNetInput.Create(32,32,3), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 16x16 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 8x8 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 4x4 + TNNetSELU.Create(), +// TNNetDropout.Create(0.4), + TNNetFullConnectLinear.Create(2), + TNNetSoftMax.Create() + ]);*) + FDiscriminator.AddLayer49([ + TNNetInput.Create4(32,32,3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 15x15 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 7x7 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + // downsample to 3x3 + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(128,0), + TNNetFullConnectLinear.Create28(2,0), + TNNetSoftMax.Create() + ]); + + FDiscriminator.Layers[FDiscriminator.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading discriminator.'); + FDiscriminator.LoadFromFile(FBaseName+'discriminator.nn'); + TNNetInput(FDiscriminator.Layers[0]).EnableErrorCollection; + FDiscriminator.DebugStructure(); + FDiscriminator.DebugWeights(); + end; + FDiscriminator.DebugStructure(); + + FDiscriminatorClone := FDiscriminator.Clone(); + TNNetInput(FDiscriminatorClone.Layers[0]).EnableErrorCollection; + + FFit.EnableClassComparison(); + FFit.OnAfterEpoch := @{Self.}DiscriminatorOnAfterEpoch; + FFit.OnAfterStep := @{Self.}DiscriminatorOnAfterStep; + FFit.OnStart := @{Self.}DiscriminatorOnStart; + FFit.DataAugmentationFn := @{Self.}DiscriminatorAugmentation; + FFit.LearningRateDecay := 0.00001; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.001; + FFit.FileNameBase := FBaseName+'GenerativeNeuralAPI'; + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FGenerative.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: FFit.MaxThreadNum := 1; + //FFit.FitLoading(FDiscriminator, 64*10, 500, 500, 64, 35000, @GetDiscriminatorTrainingPair, nil, nil); // This line does the same as below + FFit.FitLoading1(FDiscriminator, 64*10, 500, 500, 64, 35000, + @GetDiscriminatorTrainingProc, nil, nil); // This line does the same as above + + if Assigned(FGeneratives) then begin + //FreeAndNil(FGeneratives); + FGeneratives.Free; + FGeneratives:= Nil; + end; + FGenerative.Free; + FDiscriminator.Free; + FDiscriminatorClone.Free; +end; + + +procedure TFormVisualLearningSendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure DiscriminatorOnStart(Sender: TObject); +begin + FGeneratives := TNNetDataParallelism.Create74(FGenerative, FFit.MaxThreadNum, true); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FGeneratives.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + BuildTrainingPairs(); +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + //FDisplay : TNNetVolume; +begin + //FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + //FDisplay.Free; +end; + + +function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; +var + RandomValue, RandomPos: integer; + LocalPair: TNNetVolumePair; +begin + if (FRealPairs.Count = 0) then + begin + WriteLn('Error: discriminator real pairs have no element'); + Result := nil; + exit; + end; + + if FGeneratedPairs.Count = 0 then + begin + WriteLn('Error: discriminator generated/fake pairs have no element'); + Result := nil; + exit; + end; + + RandomValue := Random(1000); + if RandomValue < 500 then + begin + RandomPos := Random(FRealPairs.Count); + Result := FRealPairs[RandomPos]; + Result.O.SetClassForSoftMax(1); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Real Pair index '+itoa(RandomPos)+ + 'has wrong size:'+itoa( Result.I.Size)); + end; + //Debug Only: if (Random(100)=0) then DisplayInputImage(Result.I, FColorEncoding); + end + else + begin + LocalPair := FGeneratedPairs[ThreadId]; + LocalPair.I.Resize(FRandomSizeX, FRandomSizeY, FRandomDepth); + LocalPair.I.Randomize(0,0,0); + LocalPair.I.NormalizeMax(2); + //FGeneratives: TNNetDataParallelism; + //RegisterProperty('Items', 'TNNet Integer', iptrw); + //LocalPair: TNNetVolumePair; + //RegisterMethod('Procedure Compute65(pInput:TNNetVolume;FromLayerIdx:integer);'); + FGeneratives[ThreadId].Compute65(LocalPair.I,0); + FGeneratives[ThreadId].GetOutput(LocalPair.I); + Result := LocalPair; + Result.O.SetClassForSoftMax(0); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Generated Pair has wrong size:'+itoa( Result.I.Size)); + end; + end; +end; + +procedure GetDiscriminatorTrainingProc(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + LocalPair: TNNetVolumePair; +begin + LocalPair := GetDiscriminatorTrainingPair(Idx, ThreadId); + pInput.Copy76(LocalPair.I); + pOutput.Copy76(LocalPair.O); +end; + +procedure DiscriminatorOnAfterEpoch(Sender: TObject); +var + LoopCnt, MaxLoop: integer; + ExpectedDiscriminatorOutput, Transitory, DiscriminatorFound, GenerativeInput: TNNetVolume; + Error: TNeuralFloat; +begin + if (FFit.TrainingAccuracy <= 0.745) or FFit.ShouldQuit + then exit; + WriteLn('Training Generative Start.'); + ExpectedDiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + ExpectedDiscriminatorOutput.SetClassForSoftMax(1); + DiscriminatorFound := TNNetVolume.Create3(ExpectedDiscriminatorOutput); + //Create3( Original : TVolume);'); + //Create6( pSize : integer; c : TNeuralFloat);') + Transitory := TNNetVolume.Create3(FDiscriminatorClone.Layers[0].OutputError); + GenerativeInput := TNNetVolume.Create0(FRandomSizeX, FRandomSizeY, FRandomDepth,0); + FDiscriminatorClone.CopyWeights(FDiscriminator); + FGenerative.SetBatchUpdate(true); + FGenerative.SetLearningRate(FFit.CurrentLearningRate*FLearningRateProportion, 0); + FGenerative.SetL2Decay(0.00001); + FDiscriminatorClone.SetBatchUpdate(true); + FDiscriminatorClone.SetL2Decay(0.0); + MaxLoop := Round(100 * (1/FLearningRateProportion)); + begin + Error := 0; + FDiscriminatorClone.RefreshDropoutMask(); + for LoopCnt := 1 to MaxLoop do + begin + if FFit.ShouldQuit then break; + FDiscriminatorClone.ClearDeltas(); + FDiscriminatorClone.ClearInertia(); + GenerativeInput.Randomize(0,0,0); + GenerativeInput.NormalizeMax(2); + if FGenerative.Layers[0].Output.Size<>GenerativeInput.Size then + begin + Write('.'); + FGenerative.Layers[0].Output.ReSize10(GenerativeInput); + FGenerative.Layers[0].OutputError.ReSize10(GenerativeInput); + end; + FGenerative.Compute65(GenerativeInput,0); + FGenerative.GetOutput(Transitory); + FDiscriminatorClone.Compute65(Transitory,0); + FDiscriminatorClone.GetOutput(DiscriminatorFound); + FDiscriminatorClone.Backpropagate69(ExpectedDiscriminatorOutput); + //Error += ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Error:= error +ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Transitory.Sub64(FDiscriminatorClone.Layers[0].OutputError); + FGenerative.Backpropagate69(Transitory); + FGenerative.NormalizeMaxAbsoluteDelta(0.001); + FGenerative.UpdateWeights(); + if LoopCnt mod 10 = 0 then TFormVisualLearningProcessMessages(); + if LoopCnt mod 100 = 0 then TFormVisualLearningDisplayInputImage(Transitory, FColorEncoding); + end; + FDiscriminatorClone.Layers[0].OutputError.PrintDebug(); + WriteLn('Clone.Layers end'); + WriteLn('Generative error:'+flots(Error))//:6:4); + end; + //Debug: + //FGenerative.DebugErrors(); + //FGenerative.DebugWeights(); + //FDiscriminatorClone.DebugWeights(); + FGeneratives.CopyWeights(FGenerative); + GenerativeInput.Free; + ExpectedDiscriminatorOutput.Free; + Transitory.Free; + DiscriminatorFound.Free; + if FFit.CurrentEpoch mod 100 = 0 then + begin + WriteLn('Saving '+ FBaseName); + FGenerative.SaveToFile(FBaseName+'generative.nn'); + FDiscriminator.SaveToFile(FBaseName+'discriminator.nn'); + SaveScreenshot(FBaseName+'cai-neural-gan.bmp'); + end; + WriteLn('Training Generative Finish with:'+flots( Error)); //:6:4); + //DisplayInputImage(FRealPairs[Random(FRealPairs.Count)].I, FColorEncoding); +end; + +procedure DiscriminatorOnAfterStep(Sender: TObject); +begin + LabClassRate.Caption := + StrPadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4,'-'); + TFormVisualLearningProcessMessages(); +end; + +procedure DiscriminatorAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin + if Random(1000)>500 then pInput.FlipX(); + //if Random(1000)>750 then pInput.MakeGray(FColorEncoding); +end; + +procedure BuildTrainingPairs(); +var + FakePairCnt: integer; + ImgTrainingVolume: TNNetVolume; + DiscriminatorOutput, GenerativeOutput: TNNetVolume; +begin + DiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + GenerativeOutput := TNNetVolume.Create0(32, 32, 3,0); + //if FRealPairs.Count = 0 then + //begin + (* + for ImgTrainingVolume in ImgTrainingVolumes do fix + begin + if ImgTrainingVolume.Tag = 5 then //5 + begin + DiscriminatorOutput.SetClassForSoftMax(1); + FRealPairs.Add + ( + TNNetVolumePair.CreateCopying + ( + ImgTrainingVolume, + DiscriminatorOutput + ) + ); + //Debug only: if (Random(100)=0) then DisplayInputImage(ImgTrainingVolume, FColorEncoding); + end + end; + end;*) + + DiscriminatorOutput.SetClassForSoftMax(0); + + if FGeneratedPairs.Count < FFit.MaxThreadNum then + begin + for FakePairCnt := 1 to FFit.MaxThreadNum do + begin + FGeneratedPairs.Add + ( + TNNetVolumePair.CreateCopying83 + ( + GenerativeOutput, + DiscriminatorOutput + ) + ); + end; + end; + ImgTrainingVolumes.Cleartag; + ImgValidationVolumes.Cleartag; + ImgTestVolumes.Cleartag; + GenerativeOutput.Free; + DiscriminatorOutput.Free; +end; + + + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then begin + if FileExists(OpenDialogNN.FileName) then begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + lblfile.caption:= extractfilename(filename); + if not(FileExists(FileName)) then begin + if FileName = 'SimpleImageClassifier.nn' then begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; + + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + NeuralInitCriticalSection(FCritSec); + ImgTrainingVolumes := nil; + ImgTestVolumes := nil; + ImgValidationVolumes := nil; + FGeneratives := nil; + FRealPairs := TNNetVolumePairList.Create(true); + FGeneratedPairs := TNNetVolumePairList.Create(true); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + (* CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}128, + {InputSize=}32, {FFilterSize=}64, {ImagesPerRow=}16 + ); *) + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + //ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); + TFormVisualLearningSendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + if Assigned(ImgValidationVolumes) then ImgValidationVolumes.Free; + if Assigned(ImgTestVolumes) then ImgTestVolumes.Free; + if Assigned(ImgTrainingVolumes) then ImgTrainingVolumes.Free; + FreeNeuronImages(aImage, aLabelX, aLabelY); + NeuralDoneCriticalSection(FCritSec); + FRealPairs.Free; + FGeneratedPairs.Free; + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + writeln('destroy neural neurons all done'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+ + flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('Neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Visual Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + lblfilename:= TLabel.create(self) + with lblfilename do begin + parent:= formvisuallearning; + Left := 890 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + lblfile:= TLabel.create(self) + with lblfile do begin + parent:= formvisuallearning; + Left := 940 + Height := 20 + font.size:= 10; + Top := 19 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 17 + Width := 264 + Enabled := False + font.size:= 11; + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage2(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +function NeuronForceMinMax(x, pMin, pMax: TNeuralFloat): TNeuralFloat; +begin + if (x>pMax) then Result := pMax + else if (x + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + + procedure TTestCNNAlgo.DoRun; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if Not(CheckMNISTFile('train')) or Not(CheckMNISTFile('t10k')) then + begin + Terminate; + exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + NN.AddLayer([ + TNNetInput.Create(28, 28, 1), + TNNetConvolutionLinear.Create(32, 5, 2, 1, 1), + TNNetMaxPool.Create(4), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetFullConnectReLU.Create(32), + TNNetFullConnectReLU.Create(32), + TNNetDropout.Create(0.2), + TNNetMaxPool.Create(2), + TNNetFullConnectLinear.Create(10), + TNNetSoftMax.Create() + ]); + CreateMNISTVolumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, 'train', 't10k'); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'SimpleMNist'; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.HasFlipX := false; + NeuralFit.HasFlipY := false; + NeuralFit.MaxCropSize := 4; + { NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}//10, {batchsize=}128, {epochs=}20); } + { NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + Terminate; + end; } + + +//} diff --git a/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST2test.pas b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST2test.pas new file mode 100644 index 00000000..cef98fe4 --- /dev/null +++ b/examples/maXbox4Scripts/1076_CAI_UVisualGAN2tester_MNIST2test.pas @@ -0,0 +1,1358 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit uvisualgan_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +var csLearningRates: array[0..2] of TNeuralFloat; // = (1, 0.1, 0.01); + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer, lblfilename, lblfile: TLabel; + //ButLearn: TButton; + ChkRunOnGPU: TCheckBox; + ChkBigNetwork: TCheckBox; + ComboLearningRate: TComboBox; + ComboComplexity: TComboBox; + //GrBoxNeurons: TGroupBox; + //ImgSample: TImage; + LabClassRate: TLabel; + LabComplexity: TLabel; + LabLearningRate: TLabel; + RadLAB: TRadioButton; + RadRGB: TRadioButton; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + FDisplay: TNNetVolume; + ImgTrainingVolumes, ImgTestVolumes, ImgValidationVolumes: TNNetVolumeList; + FRealPairs, FGeneratedPairs: TNNetVolumePairList; + FImageCnt: integer; + iEpochCount, iEpochCountAfterLoading: integer; + FGenerative: THistoricalNets; + FGeneratives: TNNetDataParallelism; + FDiscriminator, FDiscriminatorClone: TNNet; + FBaseName: string; + FColorEncoding: byte; + FRandomSizeX, FRandomSizeY, FRandomDepth: integer; + FLearningRateProportion: TNeuralFloat; + + //iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + FCritSec: TRTLCriticalSection; + FFit: TNeuralDataLoadingFit; + + procedure Learn(Sender: TObject); + procedure TFormVisualLearningLearn2( Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + + function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; + procedure GetDiscriminatorTrainingProc(Idx: integer; ThreadId: integer; pInput, pOutput: TNNetVolume); + procedure DiscriminatorOnAfterEpoch(Sender: TObject); + procedure DiscriminatorOnAfterStep(Sender: TObject); + procedure DiscriminatorAugmentation(pInput: TNNetVolume; ThreadId: integer); + //procedure Learn(Sender: TObject); + //procedure SaveScreenshot(filename: string); + procedure BuildTrainingPairs(); + procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); + procedure DiscriminatorOnStart(Sender: TObject); + procedure TFormVisualLearningSendStop; + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure TFormVisualLearningLearn2( Sender: TObject); +var + NeuronMultiplier: integer; +begin + FRandomSizeX := 4; + FRandomSizeY := 4; + FRandomDepth := StrToInt(ComboComplexity.Text); + {$ifdef OpenCL} + FHasOpenCL := false; + if ChkRunOnGPU.Checked then + begin + if FEasyOpenCL.GetPlatformCount() > 0 then + begin + FEasyOpenCL.SetCurrentPlatform(FEasyOpenCL.PlatformIds[0]); + if FEasyOpenCL.GetDeviceCount() > 0 then + begin + FHasOpenCL := true; + end; + end; + end; + {$endif} + if ChkBigNetwork.Checked + then NeuronMultiplier := 2 + else NeuronMultiplier := 1; + FBaseName := 'ART'+IntToStr(FRandomDepth)+'-'+IntToStr(NeuronMultiplier)+'-'; + if RadRGB.Checked then + begin + FColorEncoding := csEncodeRGB; + //FBaseName += 'RGB-'; + FBaseName:= fbasename +'RGB-'; + end + else + begin + FColorEncoding := csEncodeLAB; + FBaseName := fbasename+ 'LAB-'; + end; + Self.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Self.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + TFormVisualLearningProcessMessages(); + if not(Assigned(ImgValidationVolumes)) then + begin + CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, FColorEncoding); + end; + + iEpochCount := 0; + iEpochCountAfterLoading := 0; + + writeln('Creating Neural Networks...'); + FGenerative := THistoricalNets.Create(); + FDiscriminator := TNNet.Create(); + + FLearningRateProportion := csLearningRates[ComboLearningRate.ItemIndex]; + + if Not(FileExists(FBaseName+'generative.nn')) then begin + WriteLn('Creating generative.'); + FGenerative.AddLayer49([ + TNNetInput.Create4(FRandomSizeX, FRandomSizeY, FRandomDepth), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), //4x4 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), //8x8 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetDeMaxPool.Create44(4,0,0), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,5,2,1,0), //32x32 + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(32 * NeuronMultiplier,3,1,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,1,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + (* + FGenerative.AddLayer([ + TNNetInput.Create(40, 40, 3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + TNNetMovingStdNormalization.Create(), + TNNetConvolutionLinear.Create(3,3,0,1,0), + TNNetReLUL.Create(-40, +40) // Protection against overflow + ]); + *) + FGenerative.Layers[FGenerative.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading generative.'); + FGenerative.LoadFromFile(FBaseName+'generative.nn'); + end; + FGenerative.DebugStructure(); + FGenerative.SetLearningRate(0.001,0.9); + FGenerative.SetL2Decay(0.0); + + if Not(FileExists(FBaseName+'discriminator.nn')) then + begin + WriteLn('Creating discriminator.'); + (* + FDiscriminator.AddLayer([ + TNNetInput.Create(32,32,3), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 16x16 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 8x8 + TNNetSELU.Create(), + TNNetConvolutionLinear.Create(64 * NeuronMultiplier,3,1,2,0), + // downsample to 4x4 + TNNetSELU.Create(), +// TNNetDropout.Create(0.4), + TNNetFullConnectLinear.Create(2), + TNNetSoftMax.Create() + ]);*) + FDiscriminator.AddLayer49([ + TNNetInput.Create4(32,32,3), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 15x15 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(64 * NeuronMultiplier,3,0,1,0), + // downsample to 7x7 + TNNetMaxPool.Create44(2,0,0), + TNNetConvolutionReLU.Create(128 * NeuronMultiplier,3,0,1,0), + // downsample to 3x3 + TNNetDropout.Create12(0.5,1), + TNNetMaxPool.Create44(2,0,0), + TNNetFullConnectLinear.Create28(128,0), + TNNetFullConnectLinear.Create28(2,0), + TNNetSoftMax.Create() + ]); + + FDiscriminator.Layers[FDiscriminator.GetFirstImageNeuronalLayerIdx(0)].InitBasicPatterns(); + end + else + begin + WriteLn('Loading discriminator.'); + FDiscriminator.LoadFromFile(FBaseName+'discriminator.nn'); + TNNetInput(FDiscriminator.Layers[0]).EnableErrorCollection; + FDiscriminator.DebugStructure(); + FDiscriminator.DebugWeights(); + end; + FDiscriminator.DebugStructure(); + + FDiscriminatorClone := FDiscriminator.Clone(); + TNNetInput(FDiscriminatorClone.Layers[0]).EnableErrorCollection; + + FFit.EnableClassComparison(); + FFit.OnAfterEpoch := @{Self.}DiscriminatorOnAfterEpoch; + FFit.OnAfterStep := @{Self.}DiscriminatorOnAfterStep; + FFit.OnStart := @{Self.}DiscriminatorOnStart; + FFit.DataAugmentationFn := @{Self.}DiscriminatorAugmentation; + FFit.LearningRateDecay := 0.00001; + FFit.AvgWeightEpochCount := 1; + FFit.InitialLearningRate := 0.001; + FFit.FileNameBase := FBaseName+'GenerativeNeuralAPI'; + {$ifdef OpenCL} + if FHasOpenCL then + begin + FFit.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + FGenerative.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + //Debug only: FFit.MaxThreadNum := 1; + //FFit.FitLoading(FDiscriminator, 64*10, 500, 500, 64, 35000, @GetDiscriminatorTrainingPair, nil, nil); // This line does the same as below + FFit.FitLoading1(FDiscriminator, 64*10, 500, 500, 64, 35000, + @GetDiscriminatorTrainingProc, nil, nil); // This line does the same as above + + if Assigned(FGeneratives) then begin + //FreeAndNil(FGeneratives); + FGeneratives.Free; + FGeneratives:= Nil; + end; + FGenerative.Free; + FDiscriminator.Free; + FDiscriminatorClone.Free; +end; + + +procedure TFormVisualLearningSendStop; +begin + WriteLn('Sending STOP request'); + FFit.ShouldQuit := true; +end; + + +procedure DiscriminatorOnStart(Sender: TObject); +begin + FGeneratives := TNNetDataParallelism.Create74(FGenerative, FFit.MaxThreadNum, true); + {$ifdef OpenCL} + if FHasOpenCL then + begin + FGeneratives.EnableOpenCL(FEasyOpenCL.PlatformIds[0], FEasyOpenCL.Devices[0]); + end; + {$endif} + BuildTrainingPairs(); +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + //FDisplay : TNNetVolume; +begin + //FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + //FDisplay.Free; +end; + + +function GetDiscriminatorTrainingPair(Idx: integer; ThreadId: integer): TNNetVolumePair; +var + RandomValue, RandomPos: integer; + LocalPair: TNNetVolumePair; +begin + if (FRealPairs.Count = 0) then + begin + WriteLn('Error: discriminator real pairs have no element'); + Result := nil; + exit; + end; + + if FGeneratedPairs.Count = 0 then + begin + WriteLn('Error: discriminator generated/fake pairs have no element'); + Result := nil; + exit; + end; + + RandomValue := Random(1000); + if RandomValue < 500 then + begin + RandomPos := Random(FRealPairs.Count); + Result := FRealPairs[RandomPos]; + Result.O.SetClassForSoftMax(1); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Real Pair index '+itoa(RandomPos)+ + 'has wrong size:'+itoa( Result.I.Size)); + end; + //Debug Only: if (Random(100)=0) then DisplayInputImage(Result.I, FColorEncoding); + end + else + begin + LocalPair := FGeneratedPairs[ThreadId]; + LocalPair.I.Resize(FRandomSizeX, FRandomSizeY, FRandomDepth); + LocalPair.I.Randomize(0,0,0); + LocalPair.I.NormalizeMax(2); + //FGeneratives: TNNetDataParallelism; + //RegisterProperty('Items', 'TNNet Integer', iptrw); + //LocalPair: TNNetVolumePair; + //RegisterMethod('Procedure Compute65(pInput:TNNetVolume;FromLayerIdx:integer);'); + FGeneratives[ThreadId].Compute65(LocalPair.I,0); + FGeneratives[ThreadId].GetOutput(LocalPair.I); + Result := LocalPair; + Result.O.SetClassForSoftMax(0); + if Result.I.Size <> 32*32*3 then + begin + WriteLn('ERROR: Generated Pair has wrong size:'+itoa( Result.I.Size)); + end; + end; +end; + +procedure GetDiscriminatorTrainingProc(Idx: integer; + ThreadId: integer; pInput, pOutput: TNNetVolume); +var + LocalPair: TNNetVolumePair; +begin + LocalPair := GetDiscriminatorTrainingPair(Idx, ThreadId); + pInput.Copy76(LocalPair.I); + pOutput.Copy76(LocalPair.O); +end; + +procedure DiscriminatorOnAfterEpoch(Sender: TObject); +var + LoopCnt, MaxLoop: integer; + ExpectedDiscriminatorOutput, Transitory, DiscriminatorFound, GenerativeInput: TNNetVolume; + Error: TNeuralFloat; +begin + if (FFit.TrainingAccuracy <= 0.745) or FFit.ShouldQuit + then exit; + WriteLn('Training Generative Start.'); + ExpectedDiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + ExpectedDiscriminatorOutput.SetClassForSoftMax(1); + DiscriminatorFound := TNNetVolume.Create3(ExpectedDiscriminatorOutput); + //Create3( Original : TVolume);'); + //Create6( pSize : integer; c : TNeuralFloat);') + Transitory := TNNetVolume.Create3(FDiscriminatorClone.Layers[0].OutputError); + GenerativeInput := TNNetVolume.Create0(FRandomSizeX, FRandomSizeY, FRandomDepth,0); + FDiscriminatorClone.CopyWeights(FDiscriminator); + FGenerative.SetBatchUpdate(true); + FGenerative.SetLearningRate(FFit.CurrentLearningRate*FLearningRateProportion, 0); + FGenerative.SetL2Decay(0.00001); + FDiscriminatorClone.SetBatchUpdate(true); + FDiscriminatorClone.SetL2Decay(0.0); + MaxLoop := Round(100 * (1/FLearningRateProportion)); + begin + Error := 0; + FDiscriminatorClone.RefreshDropoutMask(); + for LoopCnt := 1 to MaxLoop do + begin + if FFit.ShouldQuit then break; + FDiscriminatorClone.ClearDeltas(); + FDiscriminatorClone.ClearInertia(); + GenerativeInput.Randomize(0,0,0); + GenerativeInput.NormalizeMax(2); + if FGenerative.Layers[0].Output.Size<>GenerativeInput.Size then + begin + Write('.'); + FGenerative.Layers[0].Output.ReSize10(GenerativeInput); + FGenerative.Layers[0].OutputError.ReSize10(GenerativeInput); + end; + FGenerative.Compute65(GenerativeInput,0); + FGenerative.GetOutput(Transitory); + FDiscriminatorClone.Compute65(Transitory,0); + FDiscriminatorClone.GetOutput(DiscriminatorFound); + FDiscriminatorClone.Backpropagate69(ExpectedDiscriminatorOutput); + //Error += ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Error:= error +ExpectedDiscriminatorOutput.SumDiff(DiscriminatorFound); + Transitory.Sub64(FDiscriminatorClone.Layers[0].OutputError); + FGenerative.Backpropagate69(Transitory); + FGenerative.NormalizeMaxAbsoluteDelta(0.001); + FGenerative.UpdateWeights(); + if LoopCnt mod 10 = 0 then TFormVisualLearningProcessMessages(); + if LoopCnt mod 100 = 0 then TFormVisualLearningDisplayInputImage(Transitory, FColorEncoding); + end; + FDiscriminatorClone.Layers[0].OutputError.PrintDebug(); + WriteLn('Clone.Layers end'); + WriteLn('Generative error:'+flots(Error))//:6:4); + end; + //Debug: + //FGenerative.DebugErrors(); + //FGenerative.DebugWeights(); + //FDiscriminatorClone.DebugWeights(); + FGeneratives.CopyWeights(FGenerative); + GenerativeInput.Free; + ExpectedDiscriminatorOutput.Free; + Transitory.Free; + DiscriminatorFound.Free; + if FFit.CurrentEpoch mod 100 = 0 then + begin + WriteLn('Saving '+ FBaseName); + FGenerative.SaveToFile(FBaseName+'generative.nn'); + FDiscriminator.SaveToFile(FBaseName+'discriminator.nn'); + SaveScreenshot(FBaseName+'cai-neural-gan.bmp'); + end; + WriteLn('Training Generative Finish with:'+flots( Error)); //:6:4); + //DisplayInputImage(FRealPairs[Random(FRealPairs.Count)].I, FColorEncoding); +end; + +procedure DiscriminatorOnAfterStep(Sender: TObject); +begin + LabClassRate.Caption := + StrPadLeft(IntToStr(Round(FFit.TrainingAccuracy*100))+'%',4,'-'); + TFormVisualLearningProcessMessages(); +end; + +procedure DiscriminatorAugmentation(pInput: TNNetVolume; + ThreadId: integer); +begin + if Random(1000)>500 then pInput.FlipX(); + //if Random(1000)>750 then pInput.MakeGray(FColorEncoding); +end; + +procedure BuildTrainingPairs(); +var + FakePairCnt: integer; + ImgTrainingVolume: TNNetVolume; + DiscriminatorOutput, GenerativeOutput: TNNetVolume; +begin + DiscriminatorOutput := TNNetVolume.Create0(2, 1, 1,0); + GenerativeOutput := TNNetVolume.Create0(32, 32, 3,0); + //if FRealPairs.Count = 0 then + //begin + (* + for ImgTrainingVolume in ImgTrainingVolumes do fix + begin + if ImgTrainingVolume.Tag = 5 then //5 + begin + DiscriminatorOutput.SetClassForSoftMax(1); + FRealPairs.Add + ( + TNNetVolumePair.CreateCopying + ( + ImgTrainingVolume, + DiscriminatorOutput + ) + ); + //Debug only: if (Random(100)=0) then DisplayInputImage(ImgTrainingVolume, FColorEncoding); + end + end; + end;*) + + DiscriminatorOutput.SetClassForSoftMax(0); + + if FGeneratedPairs.Count < FFit.MaxThreadNum then + begin + for FakePairCnt := 1 to FFit.MaxThreadNum do + begin + FGeneratedPairs.Add + ( + TNNetVolumePair.CreateCopying83 + ( + GenerativeOutput, + DiscriminatorOutput + ) + ); + end; + end; + ImgTrainingVolumes.Cleartag; + ImgValidationVolumes.Cleartag; + ImgTestVolumes.Cleartag; + GenerativeOutput.Free; + DiscriminatorOutput.Free; +end; + + + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then begin + if FileExists(OpenDialogNN.FileName) then begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + lblfile.caption:= extractfilename(filename); + if not(FileExists(FileName)) then begin + if FileName = 'SimpleImageClassifier.nn' then begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; + + FRunning := false; + FFit := TNeuralDataLoadingFit.Create(); + NeuralInitCriticalSection(FCritSec); + ImgTrainingVolumes := nil; + ImgTestVolumes := nil; + ImgValidationVolumes := nil; + FGeneratives := nil; + FRealPairs := TNNetVolumePairList.Create(true); + FGeneratedPairs := TNNetVolumePairList.Create(true); + FDisplay := TNNetVolume.Create(); + FImageCnt := 0; + (* CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + {ImageCount=}128, + {InputSize=}32, {FFilterSize=}64, {ImagesPerRow=}16 + ); *) + {$ifdef OpenCL} + FEasyOpenCL := TEasyOpenCL.Create(); + {$else} + //ChkRunOnGPU.Visible := false; + {$endif} +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); + TFormVisualLearningSendStop; + while FFit.Running do Application.ProcessMessages; + while FRunning do Application.ProcessMessages; + if Assigned(ImgValidationVolumes) then ImgValidationVolumes.Free; + if Assigned(ImgTestVolumes) then ImgTestVolumes.Free; + if Assigned(ImgTrainingVolumes) then ImgTrainingVolumes.Free; + FreeNeuronImages(aImage, aLabelX, aLabelY); + NeuralDoneCriticalSection(FCritSec); + FRealPairs.Free; + FGeneratedPairs.Free; + FDisplay.Free; + {$ifdef OpenCL}FEasyOpenCL.Free;{$endif} + FFit.Free; + writeln('destroy neural neurons all done'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+ + flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('Neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Visual Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + lblfilename:= TLabel.create(self) + with lblfilename do begin + parent:= formvisuallearning; + Left := 890 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + lblfile:= TLabel.create(self) + with lblfile do begin + parent:= formvisuallearning; + Left := 940 + Height := 20 + font.size:= 10; + Top := 19 + Width := 38 + Caption := 'Filename:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 17 + Width := 264 + Enabled := False + font.size:= 11; + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage2(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +function NeuronForceMinMax(x, pMin, pMax: TNeuralFloat): TNeuralFloat; +begin + if (x>pMax) then Result := pMax + else if (x + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + + procedure TTestCNNAlgo.DoRun; + var + NN: THistoricalNets; + NeuralFit: TNeuralImageFit; + ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList; + begin + if Not(CheckMNISTFile('train')) or Not(CheckMNISTFile('t10k')) then + begin + Terminate; + exit; + end; + WriteLn('Creating Neural Network...'); + NN := THistoricalNets.Create(); + NN.AddLayer([ + TNNetInput.Create(28, 28, 1), + TNNetConvolutionLinear.Create(32, 5, 2, 1, 1), + TNNetMaxPool.Create(4), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetConvolutionReLU.Create(32, 3, 1, 1, 1), + TNNetFullConnectReLU.Create(32), + TNNetFullConnectReLU.Create(32), + TNNetDropout.Create(0.2), + TNNetMaxPool.Create(2), + TNNetFullConnectLinear.Create(10), + TNNetSoftMax.Create() + ]); + CreateMNISTVolumes(ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, 'train', 't10k'); + + NeuralFit := TNeuralImageFit.Create; + NeuralFit.FileNameBase := 'SimpleMNist'; + NeuralFit.InitialLearningRate := 0.001; + NeuralFit.LearningRateDecay := 0.01; + NeuralFit.StaircaseEpochs := 10; + NeuralFit.Inertia := 0.9; + NeuralFit.L2Decay := 0.00001; + NeuralFit.HasFlipX := false; + NeuralFit.HasFlipY := false; + NeuralFit.MaxCropSize := 4; + { NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}//10, {batchsize=}128, {epochs=}20); } + { NeuralFit.Free; + + NN.Free; + ImgTestVolumes.Free; + ImgValidationVolumes.Free; + ImgTrainingVolumes.Free; + Terminate; + end; } + + +//} diff --git a/examples/maXbox4Scripts/1076_CAI_Ugradientascent.pas b/examples/maXbox4Scripts/1076_CAI_Ugradientascent.pas new file mode 100644 index 00000000..b73bfb60 --- /dev/null +++ b/examples/maXbox4Scripts/1076_CAI_Ugradientascent.pas @@ -0,0 +1,480 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit ugradientascent_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample: TImage; + LabLayer: TLabel; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + + iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + procedure Learn(Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then + begin + if FileExists(OpenDialogNN.FileName) then + begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + if not(FileExists(FileName)) then + begin + if FileName = 'SimpleImageClassifier.nn' then + begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else + begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.Free; +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 32 + Top := 120 + Width := 32 + Stretch := True + end; + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 16 + Width := 264 + Enabled := False + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + + +begin //@main + + writeln('CPUSpeed '+cpuspeed); + loadvisualneuralForm; + +End. + +{Doc: # Gradient Ascent Example + + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis)} diff --git a/examples/maXbox4Scripts/1076_CAI_Ugradientascent2.pas b/examples/maXbox4Scripts/1076_CAI_Ugradientascent2.pas new file mode 100644 index 00000000..f7b3c3c2 --- /dev/null +++ b/examples/maXbox4Scripts/1076_CAI_Ugradientascent2.pas @@ -0,0 +1,605 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit ugradientascent_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample, imgdirect: TImage; + LabLayer: TLabel; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + + iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + procedure Learn(Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then + begin + if FileExists(OpenDialogNN.FileName) then + begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + if not(FileExists(FileName)) then + begin + if FileName = 'SimpleImageClassifier.nn' then + begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else + begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; + writeln('form closed...'); +end; + +procedure FormCreate(Sender: TObject); +var ab: TBitmap; +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); + ab:= TBitmap.create; + ab.loadfromresourcename(0,'MOON_FULL'); + imgsample.picture.bitmap:= ab; + ab.loadfromresourcename(0,'MOON_FULL'); + imgdirect.picture.bitmap:= ab; + ab.Free; +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; + writeln('destroy neural images'); +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 50; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +ImgSample:= TImage.create(self); +imgdirect:= TImage.create(self); +with FormVisualLearning do begin + Left := 88 + Height := 704 + Top := 61 + Width := 1106 + Caption := 'CAI Gradient Ascent Example by maXbox4' + ClientHeight := 702 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 30 + Top := 15 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + //ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 445 + Height := 42 + Top := 120 + Width := 42 + Stretch := True + end; + //imgdirect:= TImage.create(self); + with imgdirect do begin + parent:= formvisuallearning; + Left := 545 + Height := 142 + Top := 120 + Width := 142 + Stretch := True + end; + + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 30 + Height := 30 + Top := 16 + Width := 160 + Caption := 'Load Neural Network...' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 30 + Top := 16 + Width := 264 + Enabled := False + ItemHeight := 25 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + //left := 488 + //top := 120 + end; + end; +end; + +procedure TFormVisualLearningDisplayInputImage(ImgInput: TNNetVolume; + fimagecnt, color_encoding: integer); +var + pMin0, pMax0: TNeuralFloat; + pMin1, pMax1: TNeuralFloat; + pMin2, pMax2: TNeuralFloat; + FDisplay : TNNetVolume; +begin + FDisplay := TNNetVolume.Create(); + FDisplay.Resize(ImgInput, 0,0); + FDisplay.Copy76(ImgInput); + + if color_encoding = csEncodeLAB then begin + FDisplay.GetMinMaxAtDepth(0, pMin0, pMax0); + FDisplay.GetMinMaxAtDepth(1, pMin1, pMax1); + FDisplay.GetMinMaxAtDepth(2, pMin2, pMax2); + pMax0 := MaxF(Abs(pMin0), Abs(pMax0)); + pMax1 := MaxF(Abs(pMin1), Abs(pMax1)); + pMax2 := MaxF(Abs(pMin2), Abs(pMax2)); + + if pMax0 > 2 then + begin + FDisplay.MulAtDepth27(0, 2/pMax0); + end; + + if pMax1 > 2 then + begin + FDisplay.MulAtDepth27(1, 2/pMax1); + end; + + if pMax2 > 2 then + begin + FDisplay.MulAtDepth27(2, 2/pMax2); + end; + end + else if FDisplay.GetMaxAbs() > 2 then + begin + FDisplay.NormalizeMax(2); + end; + + //Debug only: FDisplay.PrintDebugChannel(); + FDisplay.NeuronalInputToRgbImg(color_encoding); + LoadVolumeIntoTImage(FDisplay, aImage[FImageCnt],color_encoding); + aImage[FImageCnt].Width := 64; + aImage[FImageCnt].Height := 64; + TFormVisualLearningProcessMessages(); + FImageCnt := (FImageCnt + 1) mod Length(aImage); + FDisplay.Free; +end; + +procedure TForm1ConvertIcon2BitmapClick(Sender: TObject); +var + s : string; + Icon: TIcon; OpenDialog1: TOpenDialog; Image1: TImage; +begin + OpenDialog1.DefaultExt := '.ICO'; + OpenDialog1.Filter := 'icons (*.ico)|*.ICO'; + OpenDialog1.Options := [ofOverwritePrompt, ofFileMustExist, ofHideReadOnly ]; + if OpenDialog1.Execute then + begin + Icon := TIcon.Create; + try + Icon.Loadfromfile(OpenDialog1.FileName); + s:= ChangeFileExt(OpenDialog1.FileName,'.BMP'); + Image1.Width := Icon.Width; + Image1.Height := Icon.Height; + Image1.Canvas.Draw(0,0,Icon); + Image1.Picture.SaveToFile(s); + ShowMessage(OpenDialog1.FileName + ' Saved to ' + s); + finally + Icon.Free; + end; + end; +end; + +var ImgVolumes: TNNetVolumeList; + Volume: TNNetVolume; + +begin //@main + + writeln('CPUSpeed '+cpuspeed); + + loadvisualneuralForm; + + if CheckCIFARFile() then begin + ImgVolumes:= TNNetVolumeList.create(true); + // creates required volumes to store images + for it := 0 to 9999 do begin + Volume := TNNetVolume.Create(); + ImgVolumes.Add(Volume); + end; + //------------ + loadCifar10Dataset(ImgVolumes, + 'C:\maXbox\EKON_BASTA\EKON24\cifar-10-batches-bin\data_batch_1.bin',0, csEncodeRGB); + //imgdirect.clear; + LoadVolumeIntoImage( ImgVolumes[50], imgdirect); + writeln(itoa(imgvolumes[50].tag )) + //imgsample.refresh; + writeln(itoa(imgdirect.width)) + //imgsample.picture.savetofile (exepath+'cifarpic.bmp') + //openfile( exepath+'cifarpic.bmp'); + //SetLength(aImage, 9999); + //TFormVisualLearningDisplayInputImage(ImgVolumes[1], 1, csEncodeRGB); + end; + ImgVolumes.Free; //} + +End. + +{Doc: # Gradient Ascent Example + + +It's usually very hard to understand neuron by neuron how a neural network dedicated to image classification internally works. +One technique used to help with the understanding about what individual neurons represent is called Gradient Ascent. + +In this technique, an arbitrary neuron is required to activate and then the same backpropagation method used for learning is +applied to an input image producing an image that this neuron expects to see. + +To be able to run this example, you'll need to load an already trained neural network file and then select the layer you intend to visualize. + +Deeper convolutional layers tend to produce more complex patterns. Above image was produced from the a first convolutional layer. The following image was produced from a third convolutional layer. Notice that patterns are more complex. + + + +This is the API method used for an arbitrary neuron backpropagation (Gradient Ascent): +``` +procedure TNNet.BackpropagateFromLayerAndNeuron(LayerIdx, NeuronIdx: integer; Error: TNeuralFloat); +``` + +Errors on the input image aren't enabled by default. In this example, errors regarding the input image are enabled with this: +``` +TNNetInput(FNN.Layers[0]).EnableErrorCollection(); +``` + +Then, errors are added to the input with this: +``` +vInput.MulAdd(-1, FNN.Layers[0].OutputError); +FNN.ClearDeltas(); +FNN.ClearInertia(); +``` + +You can find more about Gradient Ascent at: +* [Lecture 12: Visualizing and Understanding - CS231n - Stanford](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture12.pdf) +* [Understanding Neural Networks Through Deep Visualization](http://yosinski.com/deepvis) + +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 188.44min +Epoch time: 3.2887 minutes. 50 epochs: 2.7406 hours. +Epochs: 50. Working time: 3.14 hours. +Loading SimpleImageClassifier-60.nn for final test. +Starting Testing. +Epochs: 50 Examples seen:2400000 Test Accuracy: 0.8481 Test Error: 0.4238 Test Loss: 0.4620 Total time: 189.71min +Loading best performing results SimpleImageClassifier-60.nn. +Finished. + +} diff --git a/examples/maXbox4Scripts/1076_SimpleImageClassifierEKON25_5000.nn b/examples/maXbox4Scripts/1076_SimpleImageClassifierEKON25_5000.nn new file mode 100644 index 00000000..bedccdf1 --- /dev/null +++ b/examples/maXbox4Scripts/1076_SimpleImageClassifierEKON25_5000.nn @@ -0,0 +1,2 @@ +-1)TNNetInput:32;32;3;0;0;0;0;0#0)TNNetConvolutionLinear:64;5;2;1;0;0;0;0#1)TNNetMaxPool:4;4;0;0;0;0;0;0#2)TNNetMovingStdNormalization:0;0;0;0;0;0;0;0#3)TNNetConvolutionReLU:64;3;1;1;0;0;0;0#4)TNNetDropout:2;0;0;0;0;0;0;0#5)TNNetMaxPool:2;2;0;0;0;0;0;0#6)TNNetFullConnectLinear:10;1;1;0;0;0;0;0#7)TNNetSoftMax:0;0;0;0;0;0;0;0 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diff --git a/examples/maXbox4Scripts/1076_ugradientascent.pas b/examples/maXbox4Scripts/1076_ugradientascent.pas new file mode 100644 index 00000000..e028844c --- /dev/null +++ b/examples/maXbox4Scripts/1076_ugradientascent.pas @@ -0,0 +1,436 @@ +{ +Copyright (C) 18 Joao Paulo Schwarz Schuler + +This program is free software; you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation; either version 2 of the License, or +any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License along +with this program; if not, write to the Free Software Foundation, Inc., +51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +} + +unit ugradientascent_mX476; + +//{$mode objfpc}{$H+} + +interface + +//uses + {$ifdef unix} + cmem; // the c memory manager is on some systems much faster for multi-threading + {$endif} + //Classes, SysUtils, FileUtil, Forms, Controls, Graphics, Dialogs, StdCtrls, + //ExtCtrls, neuralnetwork, neuralvolumev, neuraldatasets, + //neuralvolume; + +type + { TFormVisualLearning } + TFormVisualLearning = {class(}TForm; + var + ButLearn: TButton; + ButLoadFile: TButton; + ChkForceInputRange: TCheckBox; + ChkStrongInput: TCheckBox; + ComboLayer: TComboBox; + GrBoxNeurons: TGroupBox; + ImgSample: TImage; + LabLayer: TLabel; + OpenDialogNN: TOpenDialog; + procedure ButLearnClick(Sender: TObject); + procedure ButLoadFileClick(Sender: TObject); + procedure FormClose(Sender: TObject; var CloseAction: TCloseAction); + procedure FormCreate(Sender: TObject); + procedure FormDestroy(Sender: TObject); + //private + { private declarations } + var + FRunning: boolean; + aImage: array of TImage; + aLabelX, aLabelY: array of TLabel; + FileName: string; + + iEpochCount, iEpochCountAfterLoading: integer; + + FNN: TNNet; + FLastLayerIdx: integer; + FFilterSize: integer; + FImagesPerRow: integer; + FOutputSize: integer; + procedure Learn(Sender: TObject); + procedure TFormVisualLearningEnableComponents(flag: boolean); + procedure SaveScreenshot(pfilename: string); + procedure SaveNeuronsImage(pfilename: string); + //public + procedure TFormVisualLearningProcessMessages(); + // end; + +var + FormVisualLearning: TFormVisualLearning; + +implementation +//{$R *.lfm} + +//uses LCLIntf, LCLType, neuraldatasetsv; + +{ TFormVisualLearning } + +procedure ButLearnClick(Sender: TObject); +begin + if (FRunning) then + begin + FRunning := false; + end + else + begin + FRunning := true; + ButLearn.Caption := 'Stop'; + TFormVisualLearningEnableComponents(false); + Learn(Sender); + TFormVisualLearningEnableComponents(true); + FRunning := false; + end; + ButLearn.Caption := 'Restart'; +end; + +procedure ButLoadFileClick(Sender: TObject); +begin + if (OpenDialogNN.Execute()) then + begin + if FileExists(OpenDialogNN.FileName) then + begin + ButLearn.Enabled := false; + FileName := OpenDialogNN.FileName; + if not(FileExists(FileName)) then + begin + if FileName = 'SimpleImageClassifier.nn' then + begin + WriteLn('Please run the Simple Image Classifier example before running this example.'); + WriteLn('https://github.com/joaopauloschuler/neural-api/tree/master/examples/SimpleImageClassifier'); + end + else + begin + WriteLn('File not found:'+FileName); + end; + exit; + end; + + FNN.LoadFromFile(FileName); + FNN.DebugStructure(); + FNN.DebugWeights(); + FNN.SetBatchUpdate( true ); + FNN.SetLearningRate(0.01,0.0); + TNNetInput(FNN.Layers[0]).EnableErrorCollection(); + WriteLn('Neural network has: '); + WriteLn(' Layers: '+ itoa(FNN.CountLayers() )); + WriteLn(' Neurons:'+ itoa(FNN.CountNeurons() )); + WriteLn(' Weights:'+ itoa(FNN.CountWeights() )); + ButLearn.Enabled := true; + LoadNNLayersIntoCombo(FNN, ComboLayer); + end; + end; +end; + +procedure FormClose(Sender: TObject; + var CloseAction: TCloseAction); +begin + FRunning := false; + closeaction:= cafree; +end; + +procedure FormCreate(Sender: TObject); +begin + WriteLn('Creating Neural Network...'); + FNN := TNNet.Create(); + + FRunning := false; + SetLength(aImage, 0); + SetLength(aLabelX, 0); + SetLength(aLabelY, 0); +end; + +procedure FormDestroy(Sender: TObject); +begin + FRunning := false; + FreeNeuronImages(aImage, aLabelX, aLabelY); + FNN.Free; +end; + +procedure Learn(Sender: TObject); +var + vInput, pOutput, vDisplay: TNNetVolume; + OutputCount, K: integer; + InputSize: integer; + StatingSum, CurrentSum, InputForce: TNeuralFloat; + LayerHasDepth: boolean; + StopRatio: TNeuralFloat; +begin + iEpochCount := 0; + iEpochCountAfterLoading := 0; + FLastLayerIdx := ComboLayer.ItemIndex; + LayerHasDepth := (FNN.Layers[FLastLayerIdx].OutputError.Depth > 1); + if LayerHasDepth + then FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Depth + else FOutputSize := FNN.Layers[FLastLayerIdx].OutputError.Size; + + if FOutputSize > 32 then + begin + FFilterSize := 64; + FImagesPerRow := 16; + end + else + begin + FFilterSize := 128; + FImagesPerRow := 8; + end; + + //RegisterMethod('Constructor Create3( Original : TVolume);'); + vInput := TNNetVolume.Create3(FNN.Layers[0].Output); + vDisplay := TNNetVolume.Create3(vInput); + pOutput := TNNetVolume.Create3(FNN.GetLastLayer().Output); + InputSize := FNN.Layers[0].Output.SizeX; + + FreeNeuronImages(aImage, aLabelX, aLabelY); + CreateAscentImages + ( + GrBoxNeurons, + aImage, aLabelX, aLabelY, + FOutputSize, + InputSize, FFilterSize, FImagesPerRow + ); + + FormVisualLearning.Width := GrBoxNeurons.Left + GrBoxNeurons.Width + 10; + FormVisualLearning.Height := GrBoxNeurons.Top + GrBoxNeurons.Height + 10; + Application.ProcessMessages; + + if ChkForceInputRange.Checked + then StopRatio := 100 + else StopRatio := vInput.Size; + + for OutputCount := 0 to FOutputSize - 1 do + begin + //vInput.Randomize(10000, 5000, 5000000); + if LayerHasDepth then + begin + vInput.Fill(0.0); + vInput.AddSaltAndPepper(4, 2, -2, true); + end + else + begin + // RegisterMethod('Procedure Sub19( Value : TNeuralFloat);'); + vInput.RandomizeGaussian(2); vInput.Sub19(1); + end; + StatingSum := vInput.GetSumAbs(); + if ChkStrongInput.Checked + then InputForce := 2 + else InputForce := 0.002; + vInput.NormalizeMax(InputForce); + Write('Random Input: '); vInput.PrintDebug(); WriteLn(''); + for K := 1 to 10000 do + begin + FNN.Compute65(vInput,0); + FNN.GetOutput(pOutput); + FNN.BackpropagateFromLayerAndNeuron(FLastLayerIdx, OutputCount, 20); + vInput.MulAdd69(-1, FNN.Layers[0].OutputError); + if ChkForceInputRange.Checked then vInput.ForceMaxRange(2); + FNN.ClearDeltas(); + FNN.ClearInertia(); + if K mod 100 = 0 then + begin + vDisplay.Copy76(vInput); + vDisplay.NeuronalWeightToImg54(0); + LoadVolumeIntoTImage(vDisplay, aImage[OutputCount], 0); + aImage[OutputCount].Width := FFilterSize; + aImage[OutputCount].Height := FFilterSize; + Application.ProcessMessages(); + CurrentSum := vInput.GetSumAbs(); + WriteLn(flots(StatingSum)+' - '+flots(CurrentSum)+' - '+flots((CurrentSum/StatingSum))); + if (CurrentSum > StopRatio*StatingSum) or Not(LayerHasDepth) + then break + else vInput.AddSaltAndPepper(4, InputForce/10, -InputForce/10, true); + end; + Application.ProcessMessages(); + if Not(FRunning) then break; + end; + //FNN.DebugWeights(); + writeln('neural learn part has been finished!') + end; + + vDisplay.Free; + vInput.Free; + pOutput.Free; +end; + +procedure TFormVisualLearningEnableComponents(flag: boolean); +var + i : Integer; +begin + for i := 0 to FormVisualLearning.ComponentCount-1 do + begin + if (FormVisualLearning.Components[i] is TEdit) then + TEdit(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TComboBox) then + TComboBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TCheckBox) then + TCheckBox(FormVisualLearning.Components[i]).Enabled := flag; + + if (FormVisualLearning.Components[i] is TRadioButton) then + TRadioButton(FormVisualLearning.Components[i]).Enabled := flag; + end; + + Application.ProcessMessages; +end; + +procedure SaveScreenshot(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, Self.Handle); +end; + +procedure SaveNeuronsImage(pfilename: string); +begin + WriteLn(' Saving '+pfilename+'.'); + SaveHandleToBitmap(pfilename, GrBoxNeurons.Handle); +end; + +procedure TFormVisualLearningProcessMessages(); +begin + Application.ProcessMessages(); +end; + + +procedure loadvisualneuralForm; +begin +FormVisualLearning:= TFormVisualLearning.create(self); +with FormVisualLearning do begin + Left := 188 + Height := 754 + Top := 161 + Width := 1106 + Caption := 'CAI Gradient Ascent Example by maXbox4' + ClientHeight := 752 + ClientWidth := 1104 + //DesignTimePPI := 120 + OnClose := @FormClose; + OnCreate := @FormCreate; + OnDestroy := @FormDestroy; + //show; + FormCreate(Self); + ShoW; + Position := poScreenCenter + //LCLVersion := '2.0.2.0' + ButLearn:= TButton.create(self) + with butlearn do begin + parent:= formvisuallearning; + Left := 624 + Height := 25 + Top := 16 + Width := 112 + Caption := 'Start' + Enabled := False + OnClick := @ButLearnClick; + ParentFont := False + TabOrder := 0 + end; + ImgSample:= TImage.create(self) + with imgsample do begin + parent:= formvisuallearning; + Left := 440 + Height := 32 + Top := 120 + Width := 32 + Stretch := True + end; + GrBoxNeurons:= TGroupBox.create(self) + with grboxneurons do begin + parent:= formvisuallearning; + Left := 24 + Height := 312 + Top := 64 + Width := 344 + ParentFont := False + TabOrder := 1 + end; + //object ButLoadFile: TButton + ButLoadFile:= TButton.create(self) + with ButLoadFile do begin + parent:= formvisuallearning; + Left := 112 + Height := 25 + Top := 16 + Width := 160 + Caption := 'Load Neural Network' + OnClick := @ButLoadFileClick + ParentFont := False + TabOrder := 2 + end; + LabLayer:= TLabel.create(self) + with lablayer do begin + parent:= formvisuallearning; + Left := 296 + Height := 20 + Top := 21 + Width := 38 + Caption := 'Layer:' + ParentColor := False + end ; + ComboLayer:= TComboBox.create(self) + with combolayer do begin + parent:= formvisuallearning; + Left := 344 + Height := 28 + Top := 16 + Width := 264 + Enabled := False + ItemHeight := 20 + Style := csDropDownList + TabOrder := 3 + end ; + ChkStrongInput:= TCheckBox.create(self) + with chkstronginput do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 8 + Width := 106 + Caption := 'Strong Input' + Checked := True + State := cbChecked + TabOrder := 4 + end; + //object ChkForceInputRange: TCheckBox + ChkForceInputRange:= TCheckBox.create(self) + with ChkForceInputRange do begin + parent:= formvisuallearning; + Left := 768 + Height := 24 + Top := 32 + Width := 144 + Caption := 'Force Input Range' + TabOrder := 5 + end; + OpenDialogNN:= TOpenDialog.create(self); + with opendialogNN do begin + Title := 'Open existing Neural Network File' + Filter := 'Neural Network|*.nn' + left := 488 + top := 120 + end; + end; +end; + + +begin //@main + + writeln('CPUSpeed '+cpuspeed); + loadvisualneuralForm; + +End. diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient.png b/examples/maXbox4Scripts/1076_visuallearner_gradient.png new file mode 100644 index 00000000..d9792b0f Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster.png new file mode 100644 index 00000000..3e6d1c0a Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster25.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster25.png new file mode 100644 index 00000000..cf448c12 Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster25.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster2conv.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster2conv.png new file mode 100644 index 00000000..1a87e8b4 Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_EKONMaster2conv.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_colorascent.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_colorascent.png new file mode 100644 index 00000000..1ccbb0ca Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_colorascent.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_conlinear2_MNIST.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_conlinear2_MNIST.png new file mode 100644 index 00000000..b5fa7948 Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_conlinear2_MNIST.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_conlinear_MNIST.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_conlinear_MNIST.png new file mode 100644 index 00000000..5cca2f5d Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_conlinear_MNIST.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_maxpool_MNIST.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_maxpool_MNIST.png new file mode 100644 index 00000000..f610209d Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_maxpool_MNIST.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_normalization.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_normalization.png new file mode 100644 index 00000000..820592f9 Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_normalization.png differ diff --git a/examples/maXbox4Scripts/1076_visuallearner_gradient_notlearn.png b/examples/maXbox4Scripts/1076_visuallearner_gradient_notlearn.png new file mode 100644 index 00000000..2395dd42 Binary files /dev/null and b/examples/maXbox4Scripts/1076_visuallearner_gradient_notlearn.png differ diff --git a/examples/maXbox4Scripts/SimpleMNist_mX4.csv b/examples/maXbox4Scripts/SimpleMNist_mX4.csv new file mode 100644 index 00000000..083d654b --- /dev/null +++ b/examples/maXbox4Scripts/SimpleMNist_mX4.csv @@ -0,0 +1,31 @@ +epoch;training accuracy;training loss;training error;validation accuracy;validation loss;validation error;learning rate;time;test accuracy;test loss;test error +1;0.9384;0.16;0.1599;0.976;0.0725;0.073;0.001;389;;; 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+19;0.9916;0.0916;0.037;0.9912;0.0251;0.0238;0.0009044;7811;;; +20;0.9917;0.0371;0.0257;0.9908;0.0255;0.0237;0.0009044;8274;0.9966;0.0117;0.0093 +;;;;;;;;;;; +;;;;;;;;;;; +;"Image mX4 FThreadNN[0].DebugWeights(); skipped...";;;;;;;;;; +;Starting Testing.;;;;;;;;;; +;Epochs: 20 Examples seen:1200000 Test Accuracy: 0.9966 Test Error: 0.0093 Test Loss: 0.0117 Total time: 137.90min;;;;;;;;;; +;Epoch time: 5.9000 minutes. 20 epochs: 2.0000 hours.;;;;;;;;;; +;Epochs: 20. Working time: 2.30 hours.;;;;;;;;;; +;CAI maXbox Neural Fit Finished.;;;;;;;;;; +;mnist classifier done...;;;;;;;;;; +; mX4 executed: 19/11/2021 16:43:30 Runtime: 2:18:1.308 Memload: 46% use;;;;;;;;;; diff --git a/examples/maXbox4Scripts/SimpleMNist_mX4.nn b/examples/maXbox4Scripts/SimpleMNist_mX4.nn new file mode 100644 index 00000000..f156924a --- /dev/null +++ b/examples/maXbox4Scripts/SimpleMNist_mX4.nn @@ -0,0 +1,2 @@ +-1)TNNetInput:28;28;1;0;0;0;0;0#0)TNNetConvolutionLinear:32;5;2;1;1;0;0;0#1)TNNetMaxPool:4;4;0;0;0;0;0;0#2)TNNetConvolutionReLU:32;3;1;1;1;0;0;0#3)TNNetConvolutionReLU:32;3;1;1;1;0;0;0#4)TNNetFullConnectLinear:32;1;1;0;0;0;0;0#5)TNNetFullConnectLinear:32;1;1;0;0;0;0;0#6)TNNetDropout:5;1;0;0;0;0;0;0#7)TNNetMaxPool:2;2;0;0;0;0;0;0#8)TNNetFullConnectLinear:10;1;1;0;0;0;0;0#9)TNNetSoftMax:0;0;0;0;0;0;0;0 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diff --git a/examples/maXbox4Scripts/SimpleMNist_mX4.ods b/examples/maXbox4Scripts/SimpleMNist_mX4.ods new file mode 100644 index 00000000..33b179bd Binary files /dev/null and b/examples/maXbox4Scripts/SimpleMNist_mX4.ods differ diff --git a/examples/maXbox4Scripts/SimpleMNist_mX4.pdf b/examples/maXbox4Scripts/SimpleMNist_mX4.pdf new file mode 100644 index 00000000..4c82ec6b Binary files /dev/null and b/examples/maXbox4Scripts/SimpleMNist_mX4.pdf differ diff --git a/examples/maXbox4Scripts/SimpleMNist_mX4_2.pdf b/examples/maXbox4Scripts/SimpleMNist_mX4_2.pdf new file mode 100644 index 00000000..ad30181f Binary files /dev/null and b/examples/maXbox4Scripts/SimpleMNist_mX4_2.pdf differ diff --git a/examples/maXbox4Scripts/SimpleMNist_mX4_fakeNN.csv b/examples/maXbox4Scripts/SimpleMNist_mX4_fakeNN.csv new file mode 100644 index 00000000..2a9749ff --- /dev/null +++ b/examples/maXbox4Scripts/SimpleMNist_mX4_fakeNN.csv @@ -0,0 +1 @@ +epoch,training accuracy,training loss,training error,validation accuracy,validation loss,validation error,learning rate,time,tes \ No newline at end of file diff --git a/examples/maXbox4Scripts/SimpleMNist_mX4_fakeNN.nn b/examples/maXbox4Scripts/SimpleMNist_mX4_fakeNN.nn new file mode 100644 index 00000000..f6a8147b --- /dev/null +++ b/examples/maXbox4Scripts/SimpleMNist_mX4_fakeNN.nn @@ -0,0 +1,2 @@ +-1)TNNetInput:28;28;1;0;0;0;0;0#0)TNNetConvolutionLinear:32;5;2;1;1;0;0;0#1)TNNetMaxPool:4;4;0;0;0;0;0;0#2)TNNetConvolutionReLU:32;3;1;1;1;0;0;0#3)TNNetConvolutionReLU:32;3;1;1;1;0;0;0#4)TNNetFullConnectLinear:32;1;1;0;0;0;0;0#5)TNNetFullConnectLinear:32;1;1;0;0;0;0;0#6)TNNetDropout:5;1;0;0;0;0;0;0#7)TNNetMaxPool:2;2;0;0;0;0;0;0#8)TNNetFullConnectLinear:10;1;1;0;0;0;0;0#9)TNNetSoftMax:0;0;0;0;0;0;0;0 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diff --git a/examples/maXbox4Scripts/descript b/examples/maXbox4Scripts/descript new file mode 100644 index 00000000..fc1595d3 --- /dev/null +++ b/examples/maXbox4Scripts/descript @@ -0,0 +1,16 @@ +Subject: CAI Framework + +Docu of main Neural Unit for maXbox: +http://www.softwareschule.ch/examples/uPSI_NeuralNetworkCAI.txt +http://www.softwareschule.ch/examples/uPSI_neuralnetworkcai.txt +http://www.softwareschule.ch/examples/uPSI_neuralvolume.txt +http://www.softwareschule.ch/examples/uPSI_neuraldatasets.txt +http://www.softwareschule.ch/examples/uPSI_neuralfit.txt +Source https://github.com/joaopauloschuler/neural-api +https://github.com/joaopauloschuler/neural-api/blob/master/neural/neuralvolume.pas + +1441 unit uPSI_neuralgeneric.pas; CAI +1442 unit uPSI_neuralthread.pas; CAI +1443 unit uPSI_uSysTools; TuO +1444 unit upsi_neuralsets; mX4 +1445 unit uPSI_uWinNT.pas mX4 diff --git a/examples/maXbox4Scripts/pydemo13_cheatsheet_Tutorial_90.htm b/examples/maXbox4Scripts/pydemo13_cheatsheet_Tutorial_90.htm new file mode 100644 index 00000000..c4e426cf --- /dev/null +++ b/examples/maXbox4Scripts/pydemo13_cheatsheet_Tutorial_90.htm @@ -0,0 +1,404 @@ +Version:0.9 +StartHTML:0000000105 +EndHTML:0000087990 +StartFragment:0000001053 +EndFragment:0000087974 + + + + +mXScriptasHTML + + + + + +
PROGRAM SEPDemo_App_mX4_Python_Cheat_Sheet5_Tutorial_90;
+//Python Cheat Sheet: Functions and Tricks
+//http://www.softwareschule.ch/examples/cheatsheetpython.pdf
+//https://realpython.com/python-json/
+//https://wiki.freepascal.org/Developing_Python_Modules_with_Pascal#Minimum_Python_API
+{Purpose: Python Cheat Sheet: Functions and Tricks. }
+
+//<Constant declarations> 
+//Please check providers list below:['mymemory', 'microsoft', 'deepl', 'libre'].
+{TYPE <Type declarations> Pascal-Delphi-Python-Json-OLEAutomation} 
+
+Const PYHOME32 = 'C:\Users\max\AppData\Local\Programs\Python\Python36-32\'; 
+      PYDLL32  = 'C:\Users\max\AppData\Local\Programs\Python\Python36-32\python36.dll'; 
+
+Var  //<Variable declarations>
+  i: integer; eg: TPythonEngine;
+
+//<FUNCTION> //<PROCEDURE> 
+//Generate public key and private key
+
+Const PYBC =  'from bitcoin import *'+LF+
+              'my_private_key = random_key()'+LF+
+              'my_public_key = privtopub(my_private_key)'+LF+
+              'my_bitcoin_addr = pubtoaddr(my_public_key)'+LF+
+              'print(my_bitcoin_addr)';
+              
+Const USERAGENT = 'Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 '+
+                  '(KHTML, maXbox4) Chrome/24.0.1312.27 Safari/537.17'; 
+
+Const WEBURL= 'https://jsonplaceholder.typicode.com/todos'; 
+
+Const REXDEF= 'def striphtml(data):      '+LF+
+               '  p = re.compile(r"<.*?>")'+LF+
+               '  return p.sub("", data)  ';  
+               
+//https://gist.github.com/lkdocs/6519378               
+Const DEF_RSAKEYS= 'def generate_RSA(bits=2048): '+LF+
+          '  '''''+LF+
+          //'  Generate an RSA keypair with exponent of 65537 in PEM format'+LF+
+          //'  param: bits The key length in bits '+LF+
+          //'  Return private key and public key '+LF+
+          '  '''''+LF+
+          '  from Crypto.PublicKey import RSA '+LF+
+          '  new_key = RSA.generate(bits, e=65537)'+LF+ 
+          '  public_key = new_key.publickey().exportKey("PEM")'+LF+ 
+          '  private_key = new_key.exportKey("PEM")'+LF+ 
+          '  return private_key, public_key';
+               
+procedure GetJSONData;
+var  XMLhttp: OleVariant; // As Object Automation
+     ajt: TJson; JObj: TJsonObject2; JArray: TJsonArray2;
+     response,statuscode: string; cnt: integer;
+begin  
+  XMLhttp:= CreateOleObject('msxml2.xmlhttp')      
+  XMLhttp.Open('GET', WEBURL, False)   //False is async
+  //XMLhttp.setRequestHeader('Content-Type','application/x-www-form-urlencoded');
+  XMLhttp.setRequestHeader('Content-Type', 'application/json; charset=utf-8');
+  XMLhttp.Send();
+  response:= XMLhttp.responseText; //assign the data
+  statuscode:= XMLhttp.status; 
+  //writeln(statuscode +CRLF+ response)
+  ajt:= TJson.create(); 
+  try
+    ajt.parse(response);
+  except
+    writeln( 'Exception: <TJsonClass>"" parse error: {'+
+                  exceptiontostring(exceptiontype, exceptionparam)) 
+  end; 
+  JArray:= ajt.JsonArray;
+  writeln('Get all Titles: '+itoa(jarray.count));     
+  for cnt:= 0 to jarray.count-1 do
+    writeln(itoa(cnt)+' '+Jarray.items[cnt].asObject.values['title'].asString);   
+  ajt.Free;
+end;
+
+Begin  //@Main
+//<Executable statements>
+//https://www.amazon.com/Patterns-konkret-Max-Kleiner/dp/3935042469
+{ ISBN-10 ? : 3935042469  ISBN-13 ? : 978-3935042468} 
+
+  eg:= TPythonEngine.Create(Nil);
+  eg.pythonhome:= PYHOME32;
+  eg.opendll(PYDLL32)
+  //eng.IO:= pyMemo;
+  try
+    eg.Execstring('import base64'+LF+'import urllib.parse');
+    eg.Execstring('import urllib.request, os, textwrap, json, requests');
+    eg.Execstring(REXDEF);
+    
+   { eg.Execstring('import nacl');
+    eg.Execstring('from nacl.encoding import HexEncoder'+CRLF+
+                   'from nacl.exceptions import CryptoError'+CRLF+
+                   'from nacl.encoding import Base64Encoder'+CRLF+
+                   'from pydub import AudioSegment');  }           
+    
+   //eg.Execstring('from Crypto.PublicKey import RSA');                
+    
+   println(eg.evalStr('base64.b64decode("2e8WuEr0+5nc14VBxQrOl4ob6guOTySr")'));
+  //eng.Execstring('priv_key = nacl.public.PrivateKey.generate()');
+  //openWeb('http://www.softwareschule.ch/examples/cheatsheetpython.pdf');
+    
+  //# 1.map(func, iter) Executes the function on all elements of iterable
+   println(eg.evalStr('list(map( lambda x: x[0],["red","green","blue"]))'));
+   
+  //# 2.map(func, i1,...,Executes the function on all k elements of k iterables
+   println(eg.evalStr('list(map(lambda x,y: str(x)+" "+y + "s",[0,2,2],'+
+                                       '[ "apple" , "orange" , "banana" ]))'));
+    
+  //# 3.string.join(iter), Concatenates iterable elements separated by string
+   println(eg.evalStr('" marries " .join(list([ "Alice" , "Bob" ]))'));
+
+  //# 4.filter(func,iterable),Filters out elements in iterable for function returns False (or 0)
+   println(eg.evalStr('list(filter(lambda x: True if x> 17 else False,[1,15,17,18]))'));
+   
+  //# 5.string.strip(), Removes leading and trailing whitespaces of string
+   println(eg.evalStr('( " \n \t 42 \t " .strip())'));
+   
+  //# 6.sorted(iter), Sorts iterable in ascending order
+   println(eg.evalStr('sorted([ 8 , 3 , 2 , 42 , 5 ])'));
+   
+  //# 7.sorted(iter,key=key) , Sorts according to the key function in ascending order 
+   println(eg.evalStr('sorted([ 8,3,2,42,5 ], key=lambda x: 0 if x== 42 else x)'));
+   
+  //# 8.help(func) , Returns documentation of func
+  // println(eg.evalStr('help(''print'')'));
+   saveString(exepath+'pyhelp.py', 'help(''print'')');
+   print(getDosOutput('py '+exepath+'pyhelp.py', exePath));
+   
+  //# 9.zip(i1, i2, ...), Groups the i-th elements of iterators i1,i2,...together
+   println(eg.evalStr('list(zip([''Alice'',''Anna''],[''Bob'',''Jon'',''Frank'']))'));
+   
+  //# 10.Unzip, Equal to: 1) unpack the zipped list, 2) zip the result
+   println(eg.evalStr('list(zip(*[(''Alice'',''Bob''),(''Anna'',''Jon'')]))'));
+  
+  //# 11.enumerate(iter), Assigns a counter value to each element of iterable
+   println(eg.evalStr('list(enumerate(["Alice","Bob","Jon"]))'));
+   
+  //# 12.python -m http.server<P>,Want to share files between PC and phone?
+  //https://docs.python.org/3/library/http.server.html
+   //print(getDosOutput('py -m http.server<8080>', exePath));
+   //ExecuteShell('py', '-m http.server 8080');
+  
+  //# 13.Read comic Open the comic series xkcd in your web browser
+  //eg.Execstring('import antigravity');
+  
+  //# 14.Zen of Python import this
+   eg.execString('from this import *');
+   println('14. import this: '+CRLF+
+        StringReplace(eg.EvalStr('repr("".join([d.get(c,c) for c in s]))'),
+                                                '\n',CR+LF,[rfReplaceAll]));
+  
+  //# 15.Swapping numbers, Swapping vars is a breeze in Python. No offense, Java!
+  eg.execString('a, b = ''Jane'' , ''Alice'''+CRLF+'a, b = b, a');
+  println(eg.evalStr('a, b'));
+  
+  //# 16.Unpacking arguments, Use a sequence as function arguments!
+  eg.execString('def f (x, y, z) : return x + y * z');
+  println(eg.evalStr('f(*[ 1 , 3 , 4 ])'));
+  println(eg.evalStr('f(**{ ''z'': 4 , ''x'': 1 , ''y'': 3 })'));
+  
+  //# 17.Generate QRCode!
+  eg.Execstring('import psutil');
+  eg.Execstring('import pyqrcode');
+      
+  eg.Execstring('Qr_Code = pyqrcode.create("maXbox4")');
+  eg.Execstring('Qr_Code.svg("qrmx42.svg", scale=8)');
+  
+  //# 18.Checking Free RAM
+  
+  println('Virtual Mem: '+ 
+              eg.EvalStr('(__import__("psutil").virtual_memory())'));
+  
+ { eg.execString('import psutil, os')
+  eg.execString('adlst = []')
+  eg.execString('p = psutil.Process( os.getpid())');
+  eg.execString('for dll in p.memory_maps():'+CRLF+' print(dll.path)');
+  eg.execString('for dll in p.memory_maps():'+CRLF+' adlst.append(dll.path)');
+  //println(eg.evalStr(' print(dll.path)'));
+  println(eg.evalStr('p'));
+  println(eg.evalStr('adlst'));
+  println('dll list detect: '+
+             StringReplace(eg.EvalStr('adlst'),',',CR+LF,[rfReplaceAll]));   }
+    
+  // eg.Execstring('AudioSegment.from_wav(r"C:\maXbox\soundnvision\01_magix.wav").export(r"C:\maXbox\mX47464\maxbox4\web\G9\G9_magix.mp3", format="mp3")');           
+
+    { eng.Execstring(DEF_RSAKEYS);
+      eng.Execstring('d=generate_RSA(bits=2048)')
+      println('RSA Publickey '+eng.evalStr('d[1]'));  }
+   //# Get the maximum number of complete TODOs.
+   //println('user_max_complete = '+eng.evalStr('top_users[0][1]')); 
+                    
+  except
+    eg.raiseError;
+  finally
+    eg.Free;
+    //aPythonVersion.Free;
+  end;  
+  
+  //GetJSONData;  
+  //maXcalcF('2^64 /(60*60*24*365)')  
+//<Definitions>  
+End. 
+
+Ref:  https://www.sonarqube.org/features/multi-languages/python/
+ mX4 executed: 20/09/2021 19:20:40  Runtime: 0:0:2.782  Memload: 69% use
+
+C:\maXbox\mX39998\maxbox3>pip3 install -t C:\Users\max\AppData\Local\Programs\Py
+thon\Python36-32\Lib pydub
+Collecting pydub
+  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)
+Installing collected packages: pydub
+Successfully installed pydub-0.25.1
+
+C:\maXbox\mX39998\maxbox3>pip3 install -U -t C:\Users\max\AppData\Local\Programs
+\Python\Python36-32\Lib https://files.pythonhosted.org/packages/19/29/f7a38ee300
+83f2caa14cc77a6d34c4d5cfd1a69641e87bf1b3d6ba90d0ba/psutil-5.8.0-cp36-cp36m-win32
+.whl
+Collecting psutil==5.8.0
+  Using cached psutil-5.8.0-cp36-cp36m-win32.whl (240 kB)
+ERROR: distributed 2.22.0 has requirement cloudpickle>=1.5.0, but you'll have cl
+oudpickle 0.5.3 which is incompatible.
+Installing collected packages: psutil
+Successfully installed psutil-5.8.0
+
+C:\maXbox\mX39998\maxbox3>py
+Python 3.6.3 (v3.6.3:2c5fed8, Oct  3 2017, 18:11:49) [MSC v.1900 64 bit (AMD64
+ on win32
+Type "help", "copyright", "credits" or "license" for more information.
+>>> import psutil, os
+>>> p = psutil.Process( os.getpid())
+>>> for dll in p.memory_maps():
+...   print(dll.path)
+...
+C:\Users\max\AppData\Local\Programs\Python\Python36\python.exe
+C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_bz2.pyd
+C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_ctypes.pyd
+C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\select.pyd
+C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_socket.pyd
+C:\Users\max\AppData\Local\Programs\Python\Python36\python36.dll
+C:\Windows\System32\locale.nls
+C:\Windows\Globalization\Sorting\SortDefault.nls
+C:\Windows\System32\en-US\KernelBase.dll.mui
+C:\Windows\SysWOW64\en-US\kernel32.dll.mui
+C:\Windows\System32\ucrtbase.dll
+C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_lzma.pyd
+C:\Users\max\AppData\Local\Programs\Python\Python36\Lib\site-packages\psutil\_
+util_windows.cp36-win_amd64.pyd
+C:\Users\max\AppData\Local\Programs\Python\Python36\python3.dll
+C:\Windows\System32\api-ms-win-crt-filesystem-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-conio-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-process-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-environment-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-time-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-convert-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-string-l1-1-0.dll
+C:\Users\max\AppData\Local\Programs\Python\Python36\vcruntime140.dll
+C:\Windows\System32\api-ms-win-crt-heap-l1-1-0.dll
+C:\Windows\System32\pdh.dll
+C:\Windows\System32\api-ms-win-crt-locale-l1-1-0.dll
+C:\Windows\System32\version.dll
+C:\Windows\System32\winnsi.dll
+C:\Windows\System32\IPHLPAPI.DLL
+C:\Windows\System32\wtsapi32.dll
+C:\Windows\System32\api-ms-win-crt-stdio-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-math-l1-1-0.dll
+C:\Windows\System32\api-ms-win-crt-runtime-l1-1-0.dll
+C:\Windows\System32\rsaenh.dll
+C:\Windows\System32\cryptsp.dll
+C:\Windows\System32\bcrypt.dll
+C:\Windows\System32\bcryptprimitives.dll
+C:\Windows\System32\cryptbase.dll
+C:\Windows\System32\powrprof.dll
+C:\Windows\System32\KernelBase.dll
+C:\Windows\System32\sspicli.dll
+C:\Windows\System32\ole32.dll
+C:\Windows\System32\oleaut32.dll
+C:\Windows\System32\rpcrt4.dll
+C:\Windows\System32\msvcrt.dll
+C:\Windows\System32\ws2_32.dll
+C:\Windows\System32\user32.dll
+C:\Windows\System32\msctf.dll
+C:\Windows\System32\shlwapi.dll
+C:\Windows\System32\imm32.dll
+C:\Windows\System32\advapi32.dll
+C:\Windows\System32\shell32.dll
+C:\Windows\System32\kernel32.dll
+C:\Windows\System32\combase.dll
+C:\Windows\System32\gdi32.dll
+C:\Windows\System32\sechost.dll
+C:\Windows\System32\psapi.dll
+C:\Windows\System32\nsi.dll
+C:\Windows\System32\ntdll.dll
+>>>
+
+
+Then to convert any file from wav to mp3 just use pydub as
+import pydub
+sound = pydub.AudioSegment.from_wav("D:/example/apple.wav")
+sound.export("D:/example/apple.mp3", format="mp3")
+
+Exception: <class 'OSError'>: Cannot load native module 'Crypto.Hash._MD5': Trying '_MD5.cp36-win32.pyd': [WinError 126] The specified module could not be found, Trying '_MD5.pyd'
+
+Patterns konkret.
+ISBN-13: 9783935042468  ISBN-10: 3935042469
+Author: Kleiner, Max
+Binding: Paperback
+Publisher: Software + Support
+Published: September 2003
+
+https://mymemory.translated.net/doc/spec.php
+Hello PyWorld_, This data will be written on the file.
+Hola PyWorld_,&#10; Estos datos se escribirán en el archivo.
+
+Install a 32-bit package with a 64 pip installer -t (Target)
+C:\Users\max\AppData\Local\Programs\Python\Python36-32>pip3 install -t C:\Users\
+max\AppData\Local\Programs\Python\Python36-32\Lib bitcoin
+----File newtemplate.txt not exists - now saved!----
+
+
+Verifying EU Digital COVID-19 Certificate with Python CWT
+
+https://medium.com/@dajiaji/verifying-eu-digital-covid-19-certificate-with-python-cwt-fd3d5de27eed
+
+import cwt
+from cwt import Claims, load_pem_hcert_dsc
+
+# A DSC(Document Signing Certificate) issued by a CSCA (Certificate Signing Certificate Authority)
+# quoted from: https://github.com/eu-digital-green-certificates/dgc-testdata/blob/main/AT/2DCode/raw/1.json
+dsc = "-----BEGIN CERTIFICATE-----\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\n-----END CERTIFICATE-----"
+
+# An EUDCC (EU Digital COVID Certificate)
+# quoted from: https://github.com/eu-digital-green-certificates/dgc-testdata/blob/main/AT/2DCode/raw/1.json
+eudcc = bytes.fromhex("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")
+
+# 1. Loads a DSC as a COSEKey for verifying a signature in EUDCC.
+public_key = load_pem_hcert_dsc(dsc)
+
+# 2. Verifies and decodes a target EUDCC.
+decoded = cwt.decode(eudcc, keys=[public_key])
+
+# 3. Get the payload of the EUCC. It is a JSON-formatted Electronic Health Certificate as follows:
+claims = Claims.new(decoded)
+# claims.hcert[1] == decoded[-260][1] ==
+# {
+#     'v': [
+#         {
+#             'dn': 1,
+#             'ma': 'ORG-100030215',
+#             'vp': '1119349007',
+#             'dt': '2021-02-18',
+#             'co': 'AT',
+#             'ci': 'URN:UVCI:01:AT:10807843F94AEE0EE5093FBC254BD813#B',
+#             'mp': 'EU/1/20/1528',
+#             'is': 'Ministry of Health, Austria',
+#             'sd': 2,
+#             'tg': '840539006',
+#         }
+#     ],
+#     'nam': {
+#         'fnt': 'MUSTERFRAU<GOESSINGER',
+#         'fn': 'Musterfrau-Gößinger',
+#         'gnt': 'GABRIELE',
+#         'gn': 'Gabriele',
+#     },
+#     'ver': '1.0.0',
+#     'dob': '1998-02-26',
+# }
+
+       
+   
+   
+
+ \ No newline at end of file diff --git a/examples/maXbox4Scripts/pydemo13_cheatsheet_Tutorial_90.txt b/examples/maXbox4Scripts/pydemo13_cheatsheet_Tutorial_90.txt new file mode 100644 index 00000000..63737229 --- /dev/null +++ b/examples/maXbox4Scripts/pydemo13_cheatsheet_Tutorial_90.txt @@ -0,0 +1,371 @@ +PROGRAM SEPDemo_App_mX4_Python_Cheat_Sheet5_Tutorial_90; +//Python Cheat Sheet: Functions and Tricks +//http://www.softwareschule.ch/examples/cheatsheetpython.pdf +//https://realpython.com/python-json/ +//https://wiki.freepascal.org/Developing_Python_Modules_with_Pascal#Minimum_Python_API +{Purpose: Python Cheat Sheet: Functions and Tricks. } + +// +//Please check providers list below:['mymemory', 'microsoft', 'deepl', 'libre']. +{TYPE Pascal-Delphi-Python-Json-OLEAutomation} + +Const PYHOME32 = 'C:\Users\max\AppData\Local\Programs\Python\Python36-32\'; + PYDLL32 = 'C:\Users\max\AppData\Local\Programs\Python\Python36-32\python36.dll'; + +Var // + i: integer; eg: TPythonEngine; + +// // +//Generate public key and private key + +Const PYBC = 'from bitcoin import *'+LF+ + 'my_private_key = random_key()'+LF+ + 'my_public_key = privtopub(my_private_key)'+LF+ + 'my_bitcoin_addr = pubtoaddr(my_public_key)'+LF+ + 'print(my_bitcoin_addr)'; + +Const USERAGENT = 'Mozilla/5.0 (X11; Linux i686) AppleWebKit/537.17 '+ + '(KHTML, maXbox4) Chrome/24.0.1312.27 Safari/537.17'; + +Const WEBURL= 'https://jsonplaceholder.typicode.com/todos'; + +Const REXDEF= 'def striphtml(data): '+LF+ + ' p = re.compile(r"<.*?>")'+LF+ + ' return p.sub("", data) '; + +//https://gist.github.com/lkdocs/6519378 +Const DEF_RSAKEYS= 'def generate_RSA(bits=2048): '+LF+ + ' '''''+LF+ + //' Generate an RSA keypair with exponent of 65537 in PEM format'+LF+ + //' param: bits The key length in bits '+LF+ + //' Return private key and public key '+LF+ + ' '''''+LF+ + ' from Crypto.PublicKey import RSA '+LF+ + ' new_key = RSA.generate(bits, e=65537)'+LF+ + ' public_key = new_key.publickey().exportKey("PEM")'+LF+ + ' private_key = new_key.exportKey("PEM")'+LF+ + ' return private_key, public_key'; + +procedure GetJSONData; +var XMLhttp: OleVariant; // As Object Automation + ajt: TJson; JObj: TJsonObject2; JArray: TJsonArray2; + response,statuscode: string; cnt: integer; +begin + XMLhttp:= CreateOleObject('msxml2.xmlhttp') + XMLhttp.Open('GET', WEBURL, False) //False is async + //XMLhttp.setRequestHeader('Content-Type','application/x-www-form-urlencoded'); + XMLhttp.setRequestHeader('Content-Type', 'application/json; charset=utf-8'); + XMLhttp.Send(); + response:= XMLhttp.responseText; //assign the data + statuscode:= XMLhttp.status; + //writeln(statuscode +CRLF+ response) + ajt:= TJson.create(); + try + ajt.parse(response); + except + writeln( 'Exception: "" parse error: {'+ + exceptiontostring(exceptiontype, exceptionparam)) + end; + JArray:= ajt.JsonArray; + writeln('Get all Titles: '+itoa(jarray.count)); + for cnt:= 0 to jarray.count-1 do + writeln(itoa(cnt)+' '+Jarray.items[cnt].asObject.values['title'].asString); + ajt.Free; +end; + +Begin //@Main +// +//https://www.amazon.com/Patterns-konkret-Max-Kleiner/dp/3935042469 +{ ISBN-10 ? : 3935042469 ISBN-13 ? : 978-3935042468} + + eg:= TPythonEngine.Create(Nil); + eg.pythonhome:= PYHOME32; + eg.opendll(PYDLL32) + //eng.IO:= pyMemo; + try + eg.Execstring('import base64'+LF+'import urllib.parse'); + eg.Execstring('import urllib.request, os, textwrap, json, requests'); + eg.Execstring(REXDEF); + + { eg.Execstring('import nacl'); + eg.Execstring('from nacl.encoding import HexEncoder'+CRLF+ + 'from nacl.exceptions import CryptoError'+CRLF+ + 'from nacl.encoding import Base64Encoder'+CRLF+ + 'from pydub import AudioSegment'); } + + //eg.Execstring('from Crypto.PublicKey import RSA'); + + println(eg.evalStr('base64.b64decode("2e8WuEr0+5nc14VBxQrOl4ob6guOTySr")')); + //eng.Execstring('priv_key = nacl.public.PrivateKey.generate()'); + //openWeb('http://www.softwareschule.ch/examples/cheatsheetpython.pdf'); + + //# 1.map(func, iter) Executes the function on all elements of iterable + println(eg.evalStr('list(map( lambda x: x[0],["red","green","blue"]))')); + + //# 2.map(func, i1,...,Executes the function on all k elements of k iterables + println(eg.evalStr('list(map(lambda x,y: str(x)+" "+y + "s",[0,2,2],'+ + '[ "apple" , "orange" , "banana" ]))')); + + //# 3.string.join(iter), Concatenates iterable elements separated by string + println(eg.evalStr('" marries " .join(list([ "Alice" , "Bob" ]))')); + + //# 4.filter(func,iterable),Filters out elements in iterable for function returns False (or 0) + println(eg.evalStr('list(filter(lambda x: True if x> 17 else False,[1,15,17,18]))')); + + //# 5.string.strip(), Removes leading and trailing whitespaces of string + println(eg.evalStr('( " \n \t 42 \t " .strip())')); + + //# 6.sorted(iter), Sorts iterable in ascending order + println(eg.evalStr('sorted([ 8 , 3 , 2 , 42 , 5 ])')); + + //# 7.sorted(iter,key=key) , Sorts according to the key function in ascending order + println(eg.evalStr('sorted([ 8,3,2,42,5 ], key=lambda x: 0 if x== 42 else x)')); + + //# 8.help(func) , Returns documentation of func + // println(eg.evalStr('help(''print'')')); + saveString(exepath+'pyhelp.py', 'help(''print'')'); + print(getDosOutput('py '+exepath+'pyhelp.py', exePath)); + + //# 9.zip(i1, i2, ...), Groups the i-th elements of iterators i1,i2,...together + println(eg.evalStr('list(zip([''Alice'',''Anna''],[''Bob'',''Jon'',''Frank'']))')); + + //# 10.Unzip, Equal to: 1) unpack the zipped list, 2) zip the result + println(eg.evalStr('list(zip(*[(''Alice'',''Bob''),(''Anna'',''Jon'')]))')); + + //# 11.enumerate(iter), Assigns a counter value to each element of iterable + println(eg.evalStr('list(enumerate(["Alice","Bob","Jon"]))')); + + //# 12.python -m http.server

,Want to share files between PC and phone? + //https://docs.python.org/3/library/http.server.html + //print(getDosOutput('py -m http.server<8080>', exePath)); + //ExecuteShell('py', '-m http.server 8080'); + + //# 13.Read comic Open the comic series xkcd in your web browser + //eg.Execstring('import antigravity'); + + //# 14.Zen of Python import this + eg.execString('from this import *'); + println('14. import this: '+CRLF+ + StringReplace(eg.EvalStr('repr("".join([d.get(c,c) for c in s]))'), + '\n',CR+LF,[rfReplaceAll])); + + //# 15.Swapping numbers, Swapping vars is a breeze in Python. No offense, Java! + eg.execString('a, b = ''Jane'' , ''Alice'''+CRLF+'a, b = b, a'); + println(eg.evalStr('a, b')); + + //# 16.Unpacking arguments, Use a sequence as function arguments! + eg.execString('def f (x, y, z) : return x + y * z'); + println(eg.evalStr('f(*[ 1 , 3 , 4 ])')); + println(eg.evalStr('f(**{ ''z'': 4 , ''x'': 1 , ''y'': 3 })')); + + //# 17.Generate QRCode! + eg.Execstring('import psutil'); + eg.Execstring('import pyqrcode'); + + eg.Execstring('Qr_Code = pyqrcode.create("maXbox4")'); + eg.Execstring('Qr_Code.svg("qrmx42.svg", scale=8)'); + + //# 18.Checking Free RAM + + println('Virtual Mem: '+ + eg.EvalStr('(__import__("psutil").virtual_memory())')); + + { eg.execString('import psutil, os') + eg.execString('adlst = []') + eg.execString('p = psutil.Process( os.getpid())'); + eg.execString('for dll in p.memory_maps():'+CRLF+' print(dll.path)'); + eg.execString('for dll in p.memory_maps():'+CRLF+' adlst.append(dll.path)'); + //println(eg.evalStr(' print(dll.path)')); + println(eg.evalStr('p')); + println(eg.evalStr('adlst')); + println('dll list detect: '+ + StringReplace(eg.EvalStr('adlst'),',',CR+LF,[rfReplaceAll])); } + + // eg.Execstring('AudioSegment.from_wav(r"C:\maXbox\soundnvision\01_magix.wav").export(r"C:\maXbox\mX47464\maxbox4\web\G9\G9_magix.mp3", format="mp3")'); + + { eng.Execstring(DEF_RSAKEYS); + eng.Execstring('d=generate_RSA(bits=2048)') + println('RSA Publickey '+eng.evalStr('d[1]')); } + //# Get the maximum number of complete TODOs. + //println('user_max_complete = '+eng.evalStr('top_users[0][1]')); + + except + eg.raiseError; + finally + eg.Free; + //aPythonVersion.Free; + end; + + //GetJSONData; + //maXcalcF('2^64 /(60*60*24*365)') +// +End. + +Ref: https://www.sonarqube.org/features/multi-languages/python/ + mX4 executed: 20/09/2021 19:20:40 Runtime: 0:0:2.782 Memload: 69% use + +C:\maXbox\mX39998\maxbox3>pip3 install -t C:\Users\max\AppData\Local\Programs\Py +thon\Python36-32\Lib pydub +Collecting pydub + Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB) +Installing collected packages: pydub +Successfully installed pydub-0.25.1 + +C:\maXbox\mX39998\maxbox3>pip3 install -U -t C:\Users\max\AppData\Local\Programs +\Python\Python36-32\Lib https://files.pythonhosted.org/packages/19/29/f7a38ee300 +83f2caa14cc77a6d34c4d5cfd1a69641e87bf1b3d6ba90d0ba/psutil-5.8.0-cp36-cp36m-win32 +.whl +Collecting psutil==5.8.0 + Using cached psutil-5.8.0-cp36-cp36m-win32.whl (240 kB) +ERROR: distributed 2.22.0 has requirement cloudpickle>=1.5.0, but you'll have cl +oudpickle 0.5.3 which is incompatible. +Installing collected packages: psutil +Successfully installed psutil-5.8.0 + +C:\maXbox\mX39998\maxbox3>py +Python 3.6.3 (v3.6.3:2c5fed8, Oct 3 2017, 18:11:49) [MSC v.1900 64 bit (AMD64 + on win32 +Type "help", "copyright", "credits" or "license" for more information. +>>> import psutil, os +>>> p = psutil.Process( os.getpid()) +>>> for dll in p.memory_maps(): +... print(dll.path) +... +C:\Users\max\AppData\Local\Programs\Python\Python36\python.exe +C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_bz2.pyd +C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_ctypes.pyd +C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\select.pyd +C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_socket.pyd +C:\Users\max\AppData\Local\Programs\Python\Python36\python36.dll +C:\Windows\System32\locale.nls +C:\Windows\Globalization\Sorting\SortDefault.nls +C:\Windows\System32\en-US\KernelBase.dll.mui +C:\Windows\SysWOW64\en-US\kernel32.dll.mui +C:\Windows\System32\ucrtbase.dll +C:\Users\max\AppData\Local\Programs\Python\Python36\DLLs\_lzma.pyd +C:\Users\max\AppData\Local\Programs\Python\Python36\Lib\site-packages\psutil\_ +util_windows.cp36-win_amd64.pyd +C:\Users\max\AppData\Local\Programs\Python\Python36\python3.dll +C:\Windows\System32\api-ms-win-crt-filesystem-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-conio-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-process-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-environment-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-time-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-convert-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-string-l1-1-0.dll +C:\Users\max\AppData\Local\Programs\Python\Python36\vcruntime140.dll +C:\Windows\System32\api-ms-win-crt-heap-l1-1-0.dll +C:\Windows\System32\pdh.dll +C:\Windows\System32\api-ms-win-crt-locale-l1-1-0.dll +C:\Windows\System32\version.dll +C:\Windows\System32\winnsi.dll +C:\Windows\System32\IPHLPAPI.DLL +C:\Windows\System32\wtsapi32.dll +C:\Windows\System32\api-ms-win-crt-stdio-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-math-l1-1-0.dll +C:\Windows\System32\api-ms-win-crt-runtime-l1-1-0.dll +C:\Windows\System32\rsaenh.dll +C:\Windows\System32\cryptsp.dll +C:\Windows\System32\bcrypt.dll +C:\Windows\System32\bcryptprimitives.dll +C:\Windows\System32\cryptbase.dll +C:\Windows\System32\powrprof.dll +C:\Windows\System32\KernelBase.dll +C:\Windows\System32\sspicli.dll +C:\Windows\System32\ole32.dll +C:\Windows\System32\oleaut32.dll +C:\Windows\System32\rpcrt4.dll +C:\Windows\System32\msvcrt.dll +C:\Windows\System32\ws2_32.dll +C:\Windows\System32\user32.dll +C:\Windows\System32\msctf.dll +C:\Windows\System32\shlwapi.dll +C:\Windows\System32\imm32.dll +C:\Windows\System32\advapi32.dll +C:\Windows\System32\shell32.dll +C:\Windows\System32\kernel32.dll +C:\Windows\System32\combase.dll +C:\Windows\System32\gdi32.dll +C:\Windows\System32\sechost.dll +C:\Windows\System32\psapi.dll +C:\Windows\System32\nsi.dll +C:\Windows\System32\ntdll.dll +>>> + + +Then to convert any file from wav to mp3 just use pydub as +import pydub +sound = pydub.AudioSegment.from_wav("D:/example/apple.wav") +sound.export("D:/example/apple.mp3", format="mp3") + +Exception: : Cannot load native module 'Crypto.Hash._MD5': Trying '_MD5.cp36-win32.pyd': [WinError 126] The specified module could not be found, Trying '_MD5.pyd' + +Patterns konkret. +ISBN-13: 9783935042468 ISBN-10: 3935042469 +Author: Kleiner, Max +Binding: Paperback +Publisher: Software + Support +Published: September 2003 + +https://mymemory.translated.net/doc/spec.php +Hello PyWorld_, This data will be written on the file. +Hola PyWorld_, Estos datos se escribirán en el archivo. + +Install a 32-bit package with a 64 pip installer -t (Target) +C:\Users\max\AppData\Local\Programs\Python\Python36-32>pip3 install -t C:\Users\ +max\AppData\Local\Programs\Python\Python36-32\Lib bitcoin +----File newtemplate.txt not exists - now saved!---- + + +Verifying EU Digital COVID-19 Certificate with Python CWT + +https://medium.com/@dajiaji/verifying-eu-digital-covid-19-certificate-with-python-cwt-fd3d5de27eed + +import cwt +from cwt import Claims, load_pem_hcert_dsc + +# A DSC(Document Signing Certificate) issued by a CSCA (Certificate Signing Certificate Authority) +# quoted from: https://github.com/eu-digital-green-certificates/dgc-testdata/blob/main/AT/2DCode/raw/1.json +dsc = "-----BEGIN CERTIFICATE-----\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\n-----END CERTIFICATE-----" + +# An EUDCC (EU Digital COVID Certificate) +# quoted from: https://github.com/eu-digital-green-certificates/dgc-testdata/blob/main/AT/2DCode/raw/1.json +eudcc = bytes.fromhex("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") + +# 1. Loads a DSC as a COSEKey for verifying a signature in EUDCC. +public_key = load_pem_hcert_dsc(dsc) + +# 2. Verifies and decodes a target EUDCC. +decoded = cwt.decode(eudcc, keys=[public_key]) + +# 3. Get the payload of the EUCC. It is a JSON-formatted Electronic Health Certificate as follows: +claims = Claims.new(decoded) +# claims.hcert[1] == decoded[-260][1] == +# { +# 'v': [ +# { +# 'dn': 1, +# 'ma': 'ORG-100030215', +# 'vp': '1119349007', +# 'dt': '2021-02-18', +# 'co': 'AT', +# 'ci': 'URN:UVCI:01:AT:10807843F94AEE0EE5093FBC254BD813#B', +# 'mp': 'EU/1/20/1528', +# 'is': 'Ministry of Health, Austria', +# 'sd': 2, +# 'tg': '840539006', +# } +# ], +# 'nam': { +# 'fnt': 'MUSTERFRAU