sequence to sequence classification output sequence is to small and have the same size

Hi,

inputSize = [86 5 1];
filterSize = 3;
numFilters = 5;
numHiddenUnits = 256;
numClasses = 27;
maxEpochs=20;

% Netzoptionen

options = trainingOptions('adam', ...
    'ExecutionEnvironment','gpu', ...
    'GradientThresholdMethod','l2norm', ...
    'Shuffle','never', ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',1000, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.01, ...
    'SequenceLength','longest', ...
    'Verbose',false, ...    
    'Plots','training-progress', ...
    'LearnRateSchedule','none', ...
    'LearnRateDropFactor',0.01, ...
    'LearnRateDropPeriod',10);

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')
    
    bilstmLayer(numHiddenUnits,'OutputMode','sequence','Name','lstm')
    bilstmLayer(numHiddenUnits,'OutputMode','sequence','Name','lstm2')
    
    fullyConnectedLayer(numClasses, 'Name','fc')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classification')];
   

I have the network with these settings Trained with image sequences and categorical labels with the same sequence length. No errors! When classifying to test with the same sequences, unfortunately, I always get 1 × 5 categorical array, no matter how big the sequences are.

But I want that the output sequence has the same size as the input