Multi variable prediction LSTM
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Hi,
I am trying to train a network on a dataset. I would like to predict the coefficients of a spline that represent a contact force between to impacting objects and the total contact time. The spline coefficient vector has variable length depending on the case but as of now I get predictions of the same length ( set to the maximum lenght of the coefficients vector). This is the code I've written
inputTable = table;
predictorNames = {'v_i', 'E_tip', 'rho_tip', 'v_tip', 'Y_tip', ...
'Radius', 'E_plate', 'rho_plate', 'v_plate', 'Y_plate', 'Insulator'};
predictors = inputTable{:, predictorNames}';
maxLen = max(cellfun(@numel, inputTable.Sp));
responseCoeffs = zeros(maxLen, length(inputTable.Sp));
scalarResponse = inputTable.ContactTime;
for i = 1:length(inputTable.Sp)
coeffs = inputTable.Sp{i};
responseCoeffs(1:numel(coeffs), i) = coeffs;
end
predictors = normalize(predictors, 2);
responseCoeffs = normalize(responseCoeffs, 2);
scalarResponse = normalize(scalarResponse);
numFeatures = size(predictors, 1);
numSplineCoeffs = maxLen;
% LSTM
layers = [
sequenceInputLayer(numFeatures)
lstmLayer(50, 'OutputMode', 'sequence')
fullyConnectedLayer(100)
reluLayer
fullyConnectedLayer(numSplineCoeffs + 1)
regressionLayer
];
options = trainingOptions('adam', ...
'MaxEpochs', 1000, ...
'MiniBatchSize', 32, ...
'Shuffle', 'every-epoch', ...
'Plots', 'training-progress', ...
'Verbose', true);
fullResponse = [responseCoeffs; scalarResponse'];
net = trainNetwork(predictors, fullResponse, layers, options);
This is my first time trying to train a network so any advice even on where to find more information on how to select the corret type of network would be much appreciated.
Thanks in advance!
采纳的回答
praguna manvi
2025-3-12
0 个投票
Choosing the right architecture depends on the complexity of the problem. Here are some resources that discuss which approach to take based on the type of problem:
- Deep Learning Tips and Tricks: https://www.mathworks.com/help/deeplearning/ug/deep-learning-tips-and-tricks.html
- Choosing a Network Architecture: https://www.mathworks.com/campaigns/offers/next/all-about-choosing-a-network-architecture.html
Since you have only 1800 samples, it would be easier to fit them with a lighter network, as more parameters require more data. Also, since R2023b, it is recommended to use "trainnet" instead. Refer to the following link for more examples:
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