Refer to the documentation of the Input Arguments: sequences & responses of the trainNetwork function for the syntax
net = trainNetwork(sequences,responses,layers,options) to know the format of the training data.
You can execute the following code to understand the data format:
inputSize = [528 1 1];
filterSize = [64 1];
numFilters = 50;
numClasses = 12;
numHiddenUnits = 200;
numResponses = numClasses;
numFeatures = 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')
lstmLayer(numHiddenUnits,'OutputMode','sequence','Name','lstm')
reluLayer('Name','relu2')
fullyConnectedLayer(numFeatures*2, 'Name','fc1')
reluLayer('Name','relu3')
fullyConnectedLayer(numFeatures*2, 'Name','fc2')
reluLayer('Name','relu4')
fullyConnectedLayer(numFeatures*2, 'Name','fc3')
fullyConnectedLayer(numClasses, 'Name','fcl')
regressionLayer('Name','regression')];
lgraph = layerGraph(layers);
lgraph = connectLayers(lgraph,'fold/miniBatchSize','unfold/miniBatchSize');
analyzeNetwork(lgraph)
%%
numTrainSamples = 50;
trainData = arrayfun(@(x)rand([inputSize(:)' 1]),1:numTrainSamples,'UniformOutput',false)';
trainLabels = arrayfun(@(x)rand(numResponses,1),1:numTrainSamples,'UniformOutput',false)';
size(trainData)
size(trainLabels)
%%
options = trainingOptions('adam', ...
'InitialLearnRate',0.005, ...
'LearnRateSchedule','piecewise',...
'Verbose',1, ...
'Plots','training-progress');
net = trainNetwork(trainData,trainLabels,lgraph,options);
