스크립트의 함수 정의는 파일의 끝에 표시되어야 합니다. "preprocessMiniBatch" 함수 정의 뒤에 있는 모든 명령문을 첫 번째 로컬 함수 정의 앞으로 이동하십시오.

GNN CODE에서 output image size를 변경하기 위해 다음과같은 아래에 imds 코드 부터 실행을 진행하면

mbq = minibatchqueue(augimds, ...

MiniBatchSize=miniBatchSize, ...

PartialMiniBatch="discard", ...

MiniBatchFcn=@preprocessMiniBatch, ...

MiniBatchFormat="SSCB");

해당 내용에서 다음과 같은 문제가 발생됩니다.

[스크립트의 함수 정의는 파일의 끝에 표시되어야 합니다.

"preprocessMiniBatch" 함수 정의 뒤에 있는 모든 명령문을 첫 번째 로컬 함수 정의 앞으로 이동하십시오.]

이를 해결 할수 있는 방법이 있나요?

imds = imageDatastore("C:\Users\COMPUTER\Documents\MATLAB\lololo", IncludeSubfolders=true);

augmenter = imageDataAugmenter('RandXReflection', true);

augimds = augmentedImageDatastore([128 128], imds, 'DataAugmentation', augmenter);

numLatentInputs = 100;

projectionSize = [8 8 512]; % 조정된 값

filterSize = 5;

numFilters = 64;

layersGenerator = [

featureInputLayer(numLatentInputs, 'InputSize', [1 1 1 numLatentInputs])

projectAndReshapeLayer(projectionSize)

transposedConv2dLayer(filterSize, 4*numFilters, 'Name', 'tconv1')

batchNormalizationLayer('Name', 'bn1')

reluLayer('Name', 'relu1')

transposedConv2dLayer(filterSize, 2*numFilters, 'Stride', 2, 'Cropping', 'same', 'Name', 'tconv2')

batchNormalizationLayer('Name', 'bn2')

reluLayer('Name', 'relu2')

transposedConv2dLayer(filterSize, numFilters, 'Stride', 2, 'Cropping', 'same', 'Name', 'tconv3')

batchNormalizationLayer('Name', 'bn3')

reluLayer('Name', 'relu3')

transposedConv2dLayer(filterSize, 3, 'Stride', 2, 'Cropping', 'same', 'Name', 'tconv4')

tanhLayer('Name', 'tanh')];

netG = dlnetwork(layersGenerator);

dropoutProb = 0.5;

numFilters = 64;

scale = 0.2;

inputSize = [128 128 3]; % 이미지 크기 변경

filterSize = 5;

layersDiscriminator = [

imageInputLayer(inputSize, Normalization="none")

dropoutLayer(dropoutProb)

convolution2dLayer(filterSize, numFilters, Stride=2, Padding="same")

leakyReluLayer(scale)

convolution2dLayer(filterSize, 2*numFilters, Stride=2, Padding="same")

batchNormalizationLayer

leakyReluLayer(scale)

convolution2dLayer(filterSize, 4*numFilters, Stride=2, Padding="same")

batchNormalizationLayer

leakyReluLayer(scale)

convolution2dLayer(filterSize, 8*numFilters, Stride=2, Padding="same")

batchNormalizationLayer

leakyReluLayer(scale)

convolution2dLayer(4, 1)

sigmoidLayer];

netD = dlnetwork(layersDiscriminator);

dropoutProb = 0.5;

numFilters = 64;

scale = 0.2;

inputSize = [512 512 3];

filterSize = 5;

layersDiscriminator = [

imageInputLayer(inputSize,Normalization="none")

dropoutLayer(dropoutProb)

convolution2dLayer(filterSize,numFilters,Stride=2,Padding="same")

leakyReluLayer(scale)

convolution2dLayer(filterSize,2*numFilters,Stride=2,Padding="same")

batchNormalizationLayer

leakyReluLayer(scale)

convolution2dLayer(filterSize,4*numFilters,Stride=2,Padding="same")

batchNormalizationLayer

leakyReluLayer(scale)

convolution2dLayer(filterSize,8*numFilters,Stride=2,Padding="same")

batchNormalizationLayer

leakyReluLayer(scale)

convolution2dLayer(4,1)

sigmoidLayer];

netD = dlnetwork(layersDiscriminator);

numEpochs = 100;

miniBatchSize = 128;

learnRate = 0.0002;

gradientDecayFactor = 0.5;

squaredGradientDecayFactor = 0.999;

flipProb = 0.35;

validationFrequency = 100;

augimds.MiniBatchSize = miniBatchSize;

function X = preprocessMiniBatch(data)

% Concatenate mini-batch

X = cat(4, data{:});

% Rescale the images in the range [-1 1].

X = rescale(X, -1, 1, 'InputMin', 0, 'InputMax', 255);

end

mbq = minibatchqueue(augimds, ...

MiniBatchSize=miniBatchSize, ...

PartialMiniBatch="discard", ...

MiniBatchFcn=@preprocessMiniBatch, ...

MiniBatchFormat="SSCB");

trailingAvgG = [];

trailingAvgSqG = [];

trailingAvg = [];

trailingAvgSqD = [];

numValidationImages = 5;

ZValidation = randn(numLatentInputs,numValidationImages,"single");

ZValidation = dlarray(ZValidation,"CB");

if canUseGPU

ZValidation = gpuArray(ZValidation);

end

f = figure;

f.Position(3) = 2*f.Position(3);

imageAxes = subplot(1,2,1);

scoreAxes = subplot(1,2,2);

C = colororder;

lineScoreG = animatedline(scoreAxes,Color=C(1,:));

lineScoreD = animatedline(scoreAxes,Color=C(2,:));

legend("Generator","Discriminator");

ylim([0 1])

xlabel("Iteration")

ylabel("Score")

grid on

iteration = 0;

start = tic;

% Loop over epochs.

for epoch = 1:numEpochs

% Reset and shuffle datastore.

shuffle(mbq);

% Loop over mini-batches.

while hasdata(mbq)

iteration = iteration + 1;

% Read mini-batch of data.

X = next(mbq);

% Generate latent inputs for the generator network. Convert to

% dlarray and specify the format "CB" (channel, batch). If a GPU is

% available, then convert latent inputs to gpuArray.

Z = randn(numLatentInputs, miniBatchSize, 'single');

Z = dlarray(Z, 'CB');

if canUseGPU

Z = gpuArray(Z);

end

% Evaluate the gradients of the loss with respect to the learnable

% parameters, the generator state, and the network scores using

% dlfeval and the modelLoss function.

[lossG, lossD, gradientsG, gradientsD, stateG, scoreG, scoreD] = ...

dlfeval(@modelLoss, netG, netD, X, Z, flipProb);

netG.State = stateG;

% Update the discriminator network parameters.

[netD, trailingAvg, trailingAvgSqD] = adamupdate(netD, gradientsD, ...

trailingAvg, trailingAvgSqD, iteration, ...

learnRate, gradientDecayFactor, squaredGradientDecayFactor);

% Update the generator network parameters.

[netG, trailingAvgG, trailingAvgSqG] = adamupdate(netG, gradientsG, ...

trailingAvgG, trailingAvgSqG, iteration, ...

learnRate, gradientDecayFactor, squaredGradientDecayFactor);

% Every validationFrequency iterations, display batch of generated

% images using the held-out generator input.

if mod(iteration, validationFrequency) == 0 || iteration == 1

% Generate images using the held-out generator input.

XGeneratedValidation = predict(netG, ZValidation);

% Tile and rescale the images in the range [0 1].

I = imtile(extractdata(XGeneratedValidation));

I = rescale(I);

% Display the images.

subplot(1, 2, 1);

image(imageAxes, I)

xticklabels([]);

yticklabels([]);

title("Generated Images");

end

% Update the scores plot.

subplot(1, 2, 2)

lossG = double(extractdata(lossG));

addpoints(lineScoreG, iteration, lossG);

lossD = double(extractdata(lossD));

addpoints(lineScoreD, iteration, lossD);

% Update the title with training progress information.

D = duration(0, 0, toc(start), 'Format', 'hh:mm:ss');

title(...

"Epoch: " + epoch + ", " + ...

"Iteration: " + iteration + ", " + ...

"Elapsed: " + string(D))

drawnow

end

end

% 나머지 코드...

numObservations = 5;

ZNew = randn(numLatentInputs, numObservations, "single");

ZNew = dlarray(ZNew, "CB");

if canUseGPU

ZNew = gpuArray(ZNew);

end

XGeneratedNew = predict(netG, ZNew);

I = imtile(extractdata(XGeneratedNew));

I = rescale(I);

figure

image(I)

axis off

title("Generated Images")

numImagesToSave = 1; % 저장할 이미지 개수

outputFolder = 'C:\Users\COMPUTER\Documents\MATLAB\pixgan\rpgan'; % 이미지를 저장할 폴더 경로

% 새 폴더 생성

if ~exist(outputFolder, 'dir')

mkdir(outputFolder);

end

% 생성된 이미지를 개별로 저장

for i = 1:numImagesToSave

generatedImage = extractdata(XGeneratedNew(:, :, :, i));

% 이미지 스케일 조정 및 uint8로 변환

generatedImage = uint8(255 * mat2gray(generatedImage));

imwrite(generatedImage, fullfile(outputFolder, strcat('generated_image_', num2str(i), '.jpg')));

end

Answer 1

[lazymatlab]

함수는 스크립트 중간에 정의될 수 없습니다.

코드 중 아래 부분을 스크립트 맨 뒤로 옮기고 실행해보세요.

function X = preprocessMiniBatch(data)
    % Concatenate mini-batch
    X = cat(4, data{:});
    % Rescale the images in the range [-1 1].
    X = rescale(X, -1, 1, 'InputMin', 0, 'InputMax', 255);
end