unrecognized method property or field Labels for class augmentdatastore?

I am tring to train the model on .mat dataset. i have train the model sucessfully but when i tried to find the accuracy i got the error.

imds = imageDatastore('D:\yellow\img-data\iqmat\', 'FileExtensions', '.mat', 'IncludeSubfolders',true, ...
                      'LabelSource','foldernames',...
                      'ReadFcn',@matReader);
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7, 'randomized');
inputSize = lgraph_1.Layers(1).InputSize;
[learnableLayer,classLayer] = findLayersToReplace(lgraph_1);
numClasses = numel(categories(imdsTrain.Labels));

if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer')
    newLearnableLayer = fullyConnectedLayer(numClasses, ...
        'Name','new_fc', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
    
elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer')
    newLearnableLayer = convolution2dLayer(1,numClasses, ...
        'Name','new_conv', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
end

lgraph_1 = replaceLayer(lgraph_1,learnableLayer.Name,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph_1 = replaceLayer(lgraph_1,classLayer.Name,newClassLayer);
imdsTrain = augmentedImageDatastore([224,224],imdsTrain);
imdsValidation = augmentedImageDatastore([224,224],imdsValidation);
miniBatchSize =8;
valFrequency = floor(numel(imdsTrain.Files)/miniBatchSize);
checkpointPath = pwd;
options = trainingOptions('sgdm', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',100, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',valFrequency, ...
    'Verbose',false, ...
    'Plots','training-progress', ...
    'CheckpointPath',checkpointPath,...
    'ExecutionEnvironment','gpu');
net = trainNetwork(imdsTrain,lgraph_1,options);
[YPred,probs] = classify(net,imdsValidation);
accuracy = mean(YPred == imdsValidation.Labels)

error:

unrecognized method property or field Labels for class augmentdatastore

Answer 1

[Walter Roberson]

augmentedImageDatastore() does not record the labels of the input data store.

You currently have

imdsValidation = augmentedImageDatastore([224,224],imdsValidation);

which takes imdsValidation (an image data store that has labels) as input, and you write to the same variable... but augmentedImageDatastore does not carry the labels.

If you wrote to a different variable, then when you got to

accuracy = mean(YPred == imdsValidation.Labels)

you could be referring to the unaugmented data store that still has the labels.

[john karli]

How can i resolve it.

[Walter Roberson]

imds = imageDatastore('D:\yellow\img-data\iqmat\', 'FileExtensions', '.mat', 'IncludeSubfolders',true, ...
                      'LabelSource','foldernames',...
                      'ReadFcn',@matReader);
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7, 'randomized');
inputSize = lgraph_1.Layers(1).InputSize;
[learnableLayer,classLayer] = findLayersToReplace(lgraph_1);
numClasses = numel(categories(imdsTrain.Labels));

if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer')
    newLearnableLayer = fullyConnectedLayer(numClasses, ...
        'Name','new_fc', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
    
elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer')
    newLearnableLayer = convolution2dLayer(1,numClasses, ...
        'Name','new_conv', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
end

lgraph_1 = replaceLayer(lgraph_1,learnableLayer.Name,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph_1 = replaceLayer(lgraph_1,classLayer.Name,newClassLayer);
imdsTrain = augmentedImageDatastore([224,224],imdsTrain);
imdsValidation_aug = augmentedImageDatastore([224,224],imdsValidation);  %HERE
miniBatchSize =8;
valFrequency = floor(numel(imdsTrain.Files)/miniBatchSize);
checkpointPath = pwd;
options = trainingOptions('sgdm', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',100, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation_aug, ...                           %HERE
    'ValidationFrequency',valFrequency, ...
    'Verbose',false, ...
    'Plots','training-progress', ...
    'CheckpointPath',checkpointPath,...
    'ExecutionEnvironment','gpu');
net = trainNetwork(imdsTrain,lgraph_1,options);
[YPred,probs] = classify(net,imdsValidation);
accuracy = mean(YPred == imdsValidation.Labels)

I marked the two places where "If you wrote to a different variable"

[john karli]

Thanks Respected Sir

[john karli]

When i run the below line

[YPred,probs] = classify(net,imdsValidation);

i got the error

Error using DAGNetwork/classify. (line 175)
Incorrect input size. The input images must have size of [224,224,2]

[Walter Roberson]

imds = imageDatastore('D:\yellow\img-data\iqmat\', 'FileExtensions', '.mat', 'IncludeSubfolders',true, ...
                      'LabelSource','foldernames',...
                      'ReadFcn',@matReader);
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7, 'randomized');
inputSize = lgraph_1.Layers(1).InputSize;
[learnableLayer,classLayer] = findLayersToReplace(lgraph_1);
numClasses = numel(categories(imdsTrain.Labels));

if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer')
    newLearnableLayer = fullyConnectedLayer(numClasses, ...
        'Name','new_fc', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
    
elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer')
    newLearnableLayer = convolution2dLayer(1,numClasses, ...
        'Name','new_conv', ...
        'WeightLearnRateFactor',10, ...
        'BiasLearnRateFactor',10);
end

lgraph_1 = replaceLayer(lgraph_1,learnableLayer.Name,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph_1 = replaceLayer(lgraph_1,classLayer.Name,newClassLayer);
imdsTrain = augmentedImageDatastore([224,224],imdsTrain);
imdsValidation_aug = augmentedImageDatastore([224,224],imdsValidation);  %HERE
miniBatchSize =8;
valFrequency = floor(numel(imdsTrain.Files)/miniBatchSize);
checkpointPath = pwd;
options = trainingOptions('sgdm', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',100, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation_aug, ...                           %HERE
    'ValidationFrequency',valFrequency, ...
    'Verbose',false, ...
    'Plots','training-progress', ...
    'CheckpointPath',checkpointPath,...
    'ExecutionEnvironment','gpu');
net = trainNetwork(imdsTrain,lgraph_1,options);
[YPred,probs] = classify(net,imdsValidation_aug);
accuracy = mean(YPred == imdsValidation.Labels)

[john karli]

thanks