try to implement the image to image regression using CAE but except digit dataset its not working for any other dataset. thank you.

this is the code code :https://in.mathworks.com/help/deeplearning/ug/image-to-image-regression-using-deep-learning.html

only the dataset has changed but output is not shown .

clc;

clear all;

close all;

imagefile=fullfile('C:\Users\dibya\Documents\images\kodak');

imds=imageDatastore(imagefile,...

'IncludeSubfolders',true,...

'LabelSource','Foldernames')

imds.ReadSize = 70;

rng(0);

imds = shuffle(imds);

[imdsTrain,imdsVal,imdsTest] = splitEachLabel(imds,0.8,0.1);

dsTrainNoisy = transform(imdsTrain,@addNoise);

dsValNoisy = transform(imdsVal,@addNoise);

dsTestNoisy = transform(imdsTest,@addNoise);

dsTrain = combine(dsTrainNoisy,imdsTrain);

dsVal = combine(dsValNoisy,imdsVal);

dsTest = combine(dsTestNoisy,imdsTest);

dsTrain = transform(dsTrain,@commonPreprocessing);

dsVal = transform(dsVal,@commonPreprocessing);

dsTest = transform(dsTest,@commonPreprocessing);

dsTrain = transform(dsTrain,@augmentImages);

exampleData = preview(dsTrain);

inputs = exampleData(:,1);

responses = exampleData(:,2);

minibatch = cat(2,inputs,responses);

montage(minibatch','Size',[8 2])

title('Inputs (Left) and Responses (Right)')

imageLayer = imageInputLayer([32,32,1]);

encodingLayers = [ ...

convolution2dLayer(3,16,'Padding','same'), ...

reluLayer, ...

maxPooling2dLayer(2,'Padding','same','Stride',2), ...

convolution2dLayer(3,8,'Padding','same'), ...

reluLayer, ...

maxPooling2dLayer(2,'Padding','same','Stride',2), ...

convolution2dLayer(3,8,'Padding','same'), ...

reluLayer, ...

maxPooling2dLayer(2,'Padding','same','Stride',2)];

% % % % % % % % % % Decoder

decodingLayers = [ ...

createUpsampleTransponseConvLayer(2,8), ...

reluLayer, ...

createUpsampleTransponseConvLayer(2,8), ...

reluLayer, ...

createUpsampleTransponseConvLayer(2,16), ...

reluLayer, ...

convolution2dLayer(3,1,'Padding','same'), ...

clippedReluLayer(1.0), ...

regressionLayer];

layers = [imageLayer,encodingLayers,decodingLayers];

% % % % training

options = trainingOptions('adam', ...

'MaxEpochs',100, ...

'MiniBatchSize',imds.ReadSize, ...

'ValidationData',dsVal, ...

'Shuffle','never', ...

'Plots','training-progress', ...

'Verbose',false);

net = trainNetwork(dsTrain,layers,options);

ypred = predict(net,dsTest);

inputImageExamples = preview(dsTest);

montage({inputImageExamples{1},ypred(:,:,:,1)});

ref = inputImageExamples{1,2};

originalNoisyImage = inputImageExamples{1,1};

psnrNoisy = psnr(originalNoisyImage,ref)

psnrDenoised = psnr(ypred(:,:,:,1),ref)

%

%

% % % % % % % % % % helping function

function dataOut = addNoise(data)

dataOut = data;

for idx = 1:size(data,1)

dataOut{idx} = imnoise(data{idx},'salt & pepper');

end

end

function dataOut = commonPreprocessing(data)

dataOut = cell(size(data));

for col = 1:size(data,2)

for idx = 1:size(data,1)

temp = single(data{idx,col});

temp = imresize(temp,[32,32]);

temp = rescale(temp);

dataOut{idx,col} = temp;

end

end

end

function dataOut = augmentImages(data)

dataOut = cell(size(data));

for idx = 1:size(data,1)

rot90Val = randi(4,1,1)-1;

dataOut(idx,:) = {rot90(data{idx,1},rot90Val),rot90(data{idx,2},rot90Val)};

end

end

function out = createUpsampleTransponseConvLayer(factor,numFilters)

filterSize = 2*factor - mod(factor,2);

cropping = (factor-mod(factor,2))/2;

numChannels = 1;

out = transposedConv2dLayer(filterSize,numFilters, ...

'NumChannels',numChannels,'Stride',factor,'Cropping',cropping);

end