unrecognized method property or field Labels for class augmentdatastore?

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john karli 2021-12-14

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1610430-unrecognized-method-property-or-field-labels-for-class-augmentdatastore

评论： john karli 2021-12-15

采纳的回答： Walter Roberson

在 MATLAB Online 中打开

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

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采纳的回答

Walter Roberson 2021-12-14

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1610430-unrecognized-method-property-or-field-labels-for-class-augmentdatastore#answer_854615

在 MATLAB Online 中打开

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.

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Walter Roberson 2021-12-14

在 MATLAB Online 中打开

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"

Walter Roberson 2021-12-15

在 MATLAB Online 中打开

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 2021-12-15

thanks

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