PlzHelp:Invalid training data. Predictors must be a N-by-1 cell array of sequences, where N is the number of sequences. All sequences must have the same feature dimension and

inputSize = [224 224 3];
filterSize = 5;
numFilters = 20;
numHiddenUnits = 200;
numClasses = 4;

layers = [ ...
    sequenceInputLayer(inputSize,'Name','input')
    
    sequenceFoldingLayer('Name','fold')
    
    convolution2dLayer(filterSize,numFilters,'Name','conv')
    batchNormalizationLayer('Name','bn')
    reluLayer('Name','relu')
    
    sequenceUnfoldingLayer('Name','unfold')
    flattenLayer('Name','flatten')
    
    lstmLayer(numHiddenUnits,'OutputMode','last','Name','lstm')
    
    fullyConnectedLayer(numClasses, 'Name','fc')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classification')];

lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');

miniBatchSize = 32;
options = trainingOptions("adam", ...
    'MaxEpochs',3, ...
    'MiniBatchSize',32, ...
    'InitialLearnRate',0.005, ...
    'LearnRateDropPeriod',2, ...
    'LearnRateSchedule',"piecewise", ...
    'L2Regularization',5e-4, ...
    'SequencePaddingDirection',"left", ...
    'Shuffle',"every-epoch", ...
    'ValidationFrequency',floor(numel(imdsTrain.Files)/miniBatchSize), ...
    'ValidationData',{imdsValidation,imdsValidation.Labels}, ...
    'Verbose',false, ...
    'Plots',"training-progress");

% 
net = trainNetwork(imdsTrain.Files,imdsTrain.Labels,lgraph,options);

Answer 1

[Cris LaPierre]

The error is in how the data is organized in imdsTrain.Labels

It must conform to the following specifications

https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html#mw_d0b3a2e4-09a0-42f9-a273-2bb25956fe66

[Paromita Kundu]

I'm truly grateful for your help. The code is finally working flawlessly. May God bless you abundantly for your assistance.