image resize deep learning

i have problem in image size in matlab

Error using trainNetwork (line 183)

Unexpected image size: All images must have the same size.

Error in neuralkadoplnenienulami (line 58)

net = trainNetwork(imgsTrain,layers,options);

%Trenovacie data pre siet
trainingDataPath = fullfile('C:\Users\amjad\Desktop\COVID-19-master\doplnenie nulami (1)\Triedenie');
trainingImages = imageDatastore(trainingDataPath,'IncludeSubfolders',true,'LabelSource','foldernames');

% Zobrazenie nahodnych trenovacich dat
figure;
perm = randperm(169,16);
for i = 1:16
    subplot(4,4,i);
    imshow(trainingImages.Files{perm(i)});
end
% Pocet fotografii v jednotlivych triedach
labelCount = countEachLabel(trainingImages);

% Velkost trenovacich fotiek
img = readimage(trainingImages,1);
[sizeR, SizeC] = size(img);

%specifikovanie poctu trenovacich a validacnych dat

numTrainData = 75;
[imgsTrain,imgsValidation] = splitEachLabel(trainingImages,numTrainData,'randomize');


%Definovanie architektury siete
layers = [
    imageInputLayer([sizeR SizeC 1])
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(1,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2,'Stride',2)
   
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer  
    
    fullyConnectedLayer(2)
    softmaxLayer
    classificationLayer];

options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',20, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imgsValidation, ...
    'Verbose',false, ...
    'Plots','training-progress');

net = trainNetwork(imgsTrain,layers,options);

%Klasifikacia dat s vyhodnotenim
clasifydataPath = fullfile('C:\Users\amjad\Desktop\COVID-19-master\doplnenie nulami (1)\Klasifikacia');
clasifyingImages = imageDatastore(clasifydataPath,'IncludeSubfolders',true,'LabelSource','foldernames');

YPred = classify(net,clasifyingImages);
YValidation = clasifyingImages.Labels;

accuracy = sum(YPred == YValidation)/numel(YValidation);
missclassifaction  = find(YPred ~= YValidation);
missclassifaction = transpose(missclassifaction);

FP = sum(missclassifaction (:)>8);
FN = sum(missclassifaction (:)<9);
TN = 8-FP;
TP = 8-FN;

[KALYAN ACHARJYA]

Dear Amjad, before train the model, why do not perfoem just 2 line code to make all images in same size.

[amjad hammad]

i have chabged all images to same sizes using resize program

Please if you have code to resize images can you provide me

Answer 1

[Walter Roberson]

https://www.mathworks.com/help/deeplearning/ref/augmentedimagedatastore.html

You use an augmented image data store. That allows you to specify an output size. It also allows you to set rgb or grayscale and will automatically convert images as needed.