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‎examples/ResNet/CaiResNet20.lpi

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<?xml version="1.0" encoding="UTF-8"?>
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<CONFIG>
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<Title Value="CIFAR-10 Classification Example"/>
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‎examples/ResNet/CaiResNet20.lpr

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program CaiResNet20;
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(*
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Coded by Joao Paulo Schwarz Schuler.
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https://github.com/joaopauloschuler/neural-api
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*)
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{$mode objfpc}{$H+}
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uses {$IFDEF UNIX} {$IFDEF UseCThreads}
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cthreads, {$ENDIF} {$ENDIF}
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Classes, SysUtils, CustApp, neuralnetwork, neuralvolume,
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Math, neuraldatasets, neuralfit, neuralthread;
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type
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TTestCNNAlgo = class(TCustomApplication)
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protected
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procedure DoRun; override;
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end;
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const
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// Padding and cropping constants.
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csPadding = 4;
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csCropSize = csPadding * 2;
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procedure CaiOptimizedResnetUnit(pNN: TNNet; pNeurons: integer);
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var
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PreviousLayer, ShortCut, LongPath: TNNetLayer;
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Stride: integer;
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begin
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PreviousLayer := pNN.GetLastLayer();
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if PreviousLayer.Output.Depth = pNeurons
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then Stride := 1
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else Stride := 2;
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LongPath := pNN.AddLayer([
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TNNetConvolutionLinear.Create(pNeurons, {featuresize}3, {padding}1, Stride),
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TNNetReLU.Create(),
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TNNetConvolutionLinear.Create(pNeurons, {featuresize}3, {padding}1, {stride}1),
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TNNetReLUL.Create(-3, 3, 0)
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]);
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if PreviousLayer.Output.Depth = pNeurons then
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begin
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pNN.AddLayer( TNNetSum.Create([PreviousLayer, LongPath]) );
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end
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else
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begin
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ShortCut := pNN.AddLayerAfter([
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TNNetConvolutionReLU.Create(pNeurons, {featuresize}3, {padding}1, Stride)
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], PreviousLayer);
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pNN.AddLayer( TNNetSum.Create([ShortCut, LongPath]) );
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end;
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end;
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procedure TTestCNNAlgo.DoRun;
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var
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NN: THistoricalNets;
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NeuralFit: TNeuralImageFit;
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ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList;
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ModuleCount, ModuleNumber: integer;
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begin
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ModuleNumber := 3;
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if not CheckCIFARFile() then
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begin
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Terminate;
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exit;
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end;
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WriteLn('Creating Neural Network...');
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NN := THistoricalNets.Create();
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NN.AddLayer(TNNetInput.Create(32, 32, 3));
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NN.AddLayer([
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TNNetConvolutionLinear.Create({neurons=}16, {featuresize}3, {padding}1, {stride}1),
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TNNetReLU6.Create()
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]);
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for ModuleCount := 1 to ModuleNumber do CaiOptimizedResnetUnit(NN, 16);
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for ModuleCount := 1 to ModuleNumber do CaiOptimizedResnetUnit(NN, 32);
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for ModuleCount := 1 to ModuleNumber do CaiOptimizedResnetUnit(NN, 64);
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NN.AddLayer([
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TNNetAvgChannel.Create(),
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TNNetFullConnectLinear.Create(10), // The original implementation uses avg pooling.
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TNNetSoftMax.Create()
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]);
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NN.DebugStructure();
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CreateCifar10Volumes(ImgTrainingVolumes, ImgValidationVolumes,
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ImgTestVolumes, csEncodeRGB, {ValidationSampleSize=}2000);
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// Add padding to dataset
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WriteLn
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(
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'Original image size: ',
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ImgTrainingVolumes[0].SizeX,',',
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ImgTrainingVolumes[0].SizeY,' px.'
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);
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ImgTrainingVolumes.AddPadding(csPadding);
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ImgValidationVolumes.AddPadding(csPadding);
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ImgTestVolumes.AddPadding(csPadding);
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WriteLn
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(
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'New image size after padding: ',
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ImgTrainingVolumes[0].SizeX,',',
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ImgTrainingVolumes[0].SizeY,' px.'
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);
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NeuralFit := TNeuralImageFit.Create;
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// Enable cropping while fitting.
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NeuralFit.HasImgCrop := true;
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NeuralFit.MaxCropSize := csCropSize;
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NeuralFit.FileNameBase := 'SimpleImageClassifier-'+IntToStr(GetProcessId());
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NeuralFit.InitialLearningRate := 0.001;
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NeuralFit.LearningRateDecay := 0.01;
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NeuralFit.StaircaseEpochs := 10;
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NeuralFit.Inertia := 0.9;
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NeuralFit.L2Decay := 0.00001;
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NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}10, {batchsize=}32, {epochs=}50);
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NeuralFit.Free;
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ReadLn();
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NN.Free;
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ImgTestVolumes.Free;
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ImgValidationVolumes.Free;
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ImgTrainingVolumes.Free;
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Terminate;
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end;
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var
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Application: TTestCNNAlgo;
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begin
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Application := TTestCNNAlgo.Create(nil);
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Application.Title:='CIFAR-10 Classification Example';
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Application.Run;
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Application.Free;
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end.

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