Unable to perform assignment because the size of the left side is 1-by-1 and the size of the right side is 1-by-3. How to solve this?

clc;
clear all;
outputFolder=fullfile('recycle101');
rootFolder=fullfile(outputFolder,'recycle');
categories={'can','plastic','drinkcartonbox'};

imds=imageDatastore(fullfile(rootFolder,categories),'LabelSource','foldernames');
tbl=countEachLabel(imds)
minSetCount=min(tbl{:,2});
imds=splitEachLabel(imds,minSetCount,'randomize');

countEachLabel(imds);

can=find(imds.Labels=='can',1); %randomly choose file for can
plastic=find(imds.Labels=='plastic',1);
drinkcartonbox=find(imds.Labels=='drinkcartonbox',1);

figure %plot iamge that was pick randomly
subplot(2,2,1);
imshow(readimage(imds,can));
subplot(2,2,2);
imshow(readimage(imds,plastic));
subplot(2,2,3);
imshow(readimage(imds,drinkcartonbox));

net=resnet50();
figure
plot(net)
title('Architecture of ResNet-50')
set(gca,'YLim',[150 170]);

net.Layers(1);
net.Layers(end)

numel(net.Layers(end).ClassNames)
[trainingSet,testSet]=splitEachLabel(imds,0.3,'randomize');

imageSize=net.Layers(1).InputSize;

augmentedTrainingSet=augmentedImageDatastore(imageSize,...
    trainingSet,'ColorPreprocessing','gray2rgb');

augmentedTestSet=augmentedImageDatastore(imageSize,...
    testSet,'ColorPreprocessing','gray2rgb');

w1=net.Layers(2).Weights;
w1=mat2gray(w1);

figure
montage(w1)
title('First Concolutional Layer Weight')

featureLayer='fc1000';
trainingFeatures=activations(net,...
    augmentedTrainingSet,featureLayer,'MiniBatchSize',32,'OutputAs','columns');

trainingLabels=trainingSet.Labels;
classifier=fitcecoc(trainingFeatures,trainingLabels,...
    'Learner','Linear','Coding','onevsall','ObservationsIn','columns');

testFeatures=activations(net,...
    augmentedTestSet,featureLayer,'MiniBatchSize',32,'OutputAs','columns');

predictLabels=predict(classifier,testFeatures,'ObservationsIn','columns');

testLabels=testSet.Labels;
confMat=confusionmat(testLabels,predictLabels);
confMat=bsxfun(@rdivide,confMat,sum(confMat,2));

z=mean(diag(confMat));
Accuracy=sum( diag (confMat)) / sum (confMat(:)) *100

[XTrain,YTrain] = digitTrain4DArrayData;

idx = randperm(size(XTrain,4),403);
XValidation = XTrain(:,:,:,idx);
XTrain(:,:,:,idx) = [];
YValidation = YTrain(idx);
YTrain(idx) = [];
layers = [
    imageInputLayer([28 28 1])
    
    convolution2dLayer(3,8,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    maxPooling2dLayer(2,'Stride',2)
    
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer   
    
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

options = trainingOptions('sgdm', ...
    'MaxEpochs',8, ...
    'ValidationData',{XValidation,YValidation}, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
NET = trainNetwork(XTrain,YTrain,layers,options);
load fisheriris
species = categorical(species);
G = categories(species) % Class names
numClasses = size(G,1);
H = countcats(species) % Number of observations in each class
rng('default') % For reproducibility
cv = cvpartition(species,'KFold',5,'Stratify',false) 

numFolds = cv.NumTestSets;
nTestData = zeros(numFolds,numClasses);
for i = 1:numFolds
    testClasses = species(cv.test(i));
    nCounts = countcats(testClasses); % Number of test set observations in each class
    nTestData(i,:) = nCounts';
end

bar(nTestData)
xlabel('Test Set (Fold)')
ylabel('Number of Observations')
title('Nonstratified Partition')
legend(g)

[Dave B]

could you reduce this to the relevant part?

[Image Analyst]

You forgot to give the actual error - the line of code that caused the error. You did not copy ALL the red text. Tell us what it says. But in the mean time, one of those lines is trying to stick a stretch of 3 numbers into an array element that can contain only one number, like it says. But because you didn't tell us the line of code, we don't know which line, but you should.

TUTORIAL: How to ask a question (on Answers) and get a fast answer

[TAN HOR YAN]

load fisheriris

species = categorical(species);

G = categories(species) % Class names

numClasses = size(G,1);

H = countcats(species) % Number of observations in each class

rng('default') % For reproducibility

cv = cvpartition(species,'KFold',5,'Stratify',false)

numFolds = cv.NumTestSets;

nTestData = zeros(numFolds,numClasses);

for i = 1:numFolds

testClasses = species(cv.test(i));

nCounts = countcats(testClasses); % Number of test set observations in each class

nTestData(i,:) = nCounts';

end

bar(nTestData)

xlabel('Test Set (Fold)')

ylabel('Number of Observations')

title('Nonstratified Partition')

legend(g)

The error coding

[TAN HOR YAN]

when run until the end command, it shows

Unable to perform assignment because the size of the left side is 1-by-1 and the size of the right side is 1-by-3.

[Image Analyst]

That's not all the red text. I know because the red text would give a line number and the actual line of code. Again, read the link I gave you and then paste ALL THE RED TEXT back here.

The code you gave two comments ago runs fine up until the

legend(g);

line because you didn't define g. There is no error about trying to put a 3 element vector into a single element.

[Dave B]

@TAN HOR YAN - are you sure you got the relevant code snippet? This bit runs okay (I updated the g to G because, as noted, there's no g defined).

load fisheriris
species = categorical(species);
G = categories(species) % Class names

G = 3×1 cell array
    {'setosa'    }
    {'versicolor'}
    {'virginica' }

numClasses = size(G,1);
H = countcats(species) % Number of observations in each class

H = 3×1

50 50 50

rng('default') % For reproducibility
cv = cvpartition(species,'KFold',5,'Stratify',false) 

cv = 
K-fold cross validation partition
   NumObservations: 150
       NumTestSets: 5
         TrainSize: 120  120  120  120  120
          TestSize: 30  30  30  30  30

numFolds = cv.NumTestSets;
nTestData = zeros(numFolds,numClasses);
for i = 1:numFolds
    testClasses = species(cv.test(i));
    nCounts = countcats(testClasses); % Number of test set observations in each class
    nTestData(i,:) = nCounts';
end
bar(nTestData)
xlabel('Test Set (Fold)')
ylabel('Number of Observations')
title('Nonstratified Partition')
legend(G)