how to set input for fully connected layer

Hello. I extracted features from video dataset and saved them in a cell. Because each video has different number of frames, size of feature matrixes are different (plz see the attached picture). I want to use these features as input for net_fc in last line of following code, but this error happens: ''Error using trainNetwork invalid training data. Responses must be a vector of categorical responses, or a cell array of categorical response sequences. can any one plz help me to set my input for the nt_fc? thanks so much...

clc;clear;close

DD = load( 'D:\simulation\ALL1\TRAIN_DATA_taranhade.mat');
sequences = DD.sequences;
YTrain = ['a'; 'a'; 'a'; 'a'; 'a'; 'a';...
    'b'; 'b'; 'b'; 'b'; 'b'; 'b';...
    'c'; 'c'; 'c'; 'c'; 'c'; 'c';...
    'd'; 'd'; 'd'; 'd'; 'd'; 'd';...
    'e'; 'e'; 'e'; 'e'; 'e'; 'e';...
    'f'; 'f'; 'f'; 'f'; 'f'; 'f';...
    'g'; 'g'; 'g'; 'g'; 'g'; 'g';...
    'h'; 'h'; 'h'; 'h'; 'h'; 'h'];
numObservations = numel(sequences);
idx = randperm(numObservations);
N = floor(0.9 * numObservations);
idxTrain = idx(1:N);
sequencesTrain = sequences(idxTrain);
labelsTrain = YTrain(idxTrain);

idxValidation = idx(N+1:end);
sequencesValidation = sequences(idxValidation);
labelsValidation = YTrain(idxValidation);
numFeatures = size(sequencesTrain{1},1);
numClasses = 8;%numel(categories((labelsTrain)));
% analyzeNetwork
layers = [
    sequenceInputLayer(numFeatures,'Name','sequence')
    fullyConnectedLayer(128,'Name','fc1');
    reluLayer
    fullyConnectedLayer(numClasses);
    reluLayer
    softmaxLayer('Name','soft1')
    classificationLayer('Name','classification')];

miniBatchSize = 16;
numObservations = numel(sequencesTrain);
numIterationsPerEpoch = floor(numObservations / miniBatchSize);

options = trainingOptions('adam', ...
    'MiniBatchSize',miniBatchSize, ...
    'InitialLearnRate',1e-4, ...
    'GradientThreshold',2, ...
    'Shuffle','every-epoch', ...
    'ValidationData',{sequencesValidation,labelsValidation}, ...
    'ValidationFrequency',numIterationsPerEpoch, ...
    'Plots','training-progress', ...
    'Verbose',false);

net_fc = trainNetwork(sequencesTrain,labelsTrain,layers,options);