create a multi input multi output lstm in matlab

To create a multi-input multi-output LSTM network in MATLAB, we first need to define the network architecture, which includes the number of input and output features, the number of hidden units in the LSTM layer, and any additional layers such as fully connected layers.

Here's a code snippet to create a 2-input, 2-output LSTM network in MATLAB:

main.m
% Define input and output data
inputData = rand(10,2);
outputData = rand(10,2);

% Define LSTM network
numFeatures = 2;
numHiddenUnits = 10;

layers = [
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits, 'OutputMode', 'last')
    fullyConnectedLayer(2)
    regressionLayer
];

% Train LSTM network
opts = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.01, ...
    'MiniBatchSize',10, ...
    'SequenceLength','longest', ...
    'Shuffle','every-epoch', ...
    'Verbose',false);

net = trainNetwork(inputData, outputData, layers, opts);
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In this example, we define a 2-input (each with 10 time steps) and 2-output sequence (each with 10 time steps) LSTM network, where the output of the LSTM layer is passed through a fully connected layer and a regression output layer. We then train the network using the trainNetwork function and the specified training options opts.

Note that the sequenceInputLayer is used to specify the number of features in the input data, and the 'OutputMode' option is set to 'last' in the lstmLayer to only output the final LSTM hidden state.