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C-LSTM Input of 4D to predict 12X1 values

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TROY TULLY
TROY TULLY on 17 Mar 2021
Answered: Srivardhan Gadila on 28 Mar 2021
I am trying to use a C-LSTM to predict 12 values for each time step with regression.
I'm happy to provide as much information as I can. However, to me, it seems clear that I'm providing the correct inputs for the C-LSTM architecture...
Please help! This is important medical research!
Thank you all.
I tried it with two other formats, one sort of worked (RMSE = .54)
and then I tried it with a cell array around each variable such that the training and validation are just one cell array with a time series of 528*n and kinematics is 12*n and somehow that worked
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');
Here are my layers
inputsize is 528 1 1
filtersize is 64 1
numFilters is 50
numclasses is 12

Answers (1)

Srivardhan Gadila
Srivardhan Gadila on 28 Mar 2021
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);

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