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Adding Simple Tiny ImageNet 200 Example
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‎examples/SimpleTinyImageNet/SimpleTinyImageNet.lpi

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‎examples/SimpleTinyImageNet/SimpleTinyImageNet.pas

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///This file has an implementation to classify
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//the TinyImageNet 200 dataset: https://tiny-imagenet.herokuapp.com/ .
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program SimpleTinyImageNet;
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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, Math, neuraldatasets,
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neuralfit;
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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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procedure TTestCNNAlgo.DoRun;
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var
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NN: TNNet;
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NeuralFit: TNeuralImageFit;
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ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes: TNNetVolumeList;
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ProportionToLoad: Single;
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begin
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WriteLn('Creating Neural Network...');
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NN := THistoricalNets.Create();
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NN.AddLayer([
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TNNetInput.Create(64, 64, 3),
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TNNetConvolutionLinear.Create({Features=}64, {FeatureSize=}5, {Padding=}2, {Stride=}2),
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TNNetMaxPool.Create(2),
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TNNetMovingStdNormalization.Create(),
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TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1),
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TNNetConvolutionReLU.Create({Features=}64, {FeatureSize=}3, {Padding=}1, {Stride=}1),
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TNNetMaxPool.Create(2),
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TNNetConvolutionReLU.Create({Features=}128, {FeatureSize=}3, {Padding=}1, {Stride=}1),
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TNNetConvolutionReLU.Create({Features=}256, {FeatureSize=}3, {Padding=}1, {Stride=}1),
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TNNetConvolutionReLU.Create({Features=}512, {FeatureSize=}3, {Padding=}1, {Stride=}2),
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TNNetDropout.Create(0.5),
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TNNetMaxPool.Create(2),
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TNNetFullConnectLinear.Create(200),
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TNNetSoftMax.Create()
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]);
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NN.DebugStructure();
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// change ProportionToLoad to a smaller number if you don't have available 6GB of RAM.
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ProportionToLoad := 1;
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WriteLn('Loading ', Round(ProportionToLoad*100), '% of the Tiny ImageNet 200 dataset into memory.');
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CreateVolumesFromImagesFromFolder
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(
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ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes,
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{FolderName=}'tiny-imagenet-200/train', {pImageSubFolder=}'images',
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{color_encoding=}0{RGB},
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{TrainingProp=}0.9*ProportionToLoad,
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{ValidationProp=}0.05*ProportionToLoad,
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{TestProp=}0.05*ProportionToLoad
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);
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WriteLn
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(
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'Training Images:', ImgTrainingVolumes.Count,
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' Validation Images:', ImgValidationVolumes.Count,
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' Test Images:', ImgTestVolumes.Count
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);
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NeuralFit := TNeuralImageFit.Create;
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NeuralFit.FileNameBase := 'SimpleTinyImageNet200';
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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.CyclicalLearningRateLen := 100;
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NeuralFit.L2Decay := 0.00001;
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NeuralFit.Fit(NN, ImgTrainingVolumes, ImgValidationVolumes, ImgTestVolumes, {NumClasses=}200, {batchsize=}64, {epochs=}400);
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NeuralFit.Free;
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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:='Tiny ImageNet 200 Classification Example';
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Application.Run;
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Application.Free;
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end.

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