Input shape for the LSTM model

Train dataset X has a shape of 1x50000 and each of 50000 elements has 5x1 data. Train dataset Y also has a shape of 1x50000 and each elements has 1x1 data. I wonder if the shape of the two datasets are valid to train lstm model like below because it keeps giving me an error msg " Invalid training data. Responses must be a matrix of numeric responses, or a N-by-1 cell array of sequences, where N is the number of sequences. The feature dimension of all sequences must be the same.

numFeatures = 5;
numHiddenUnits = 200;
numClasses = 1;

layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits)
    dropoutLayer(0.5)
    fullyConnectedLayer(numClasses)
    regressionLayer];

options = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'ValidationData',{test_x, test_y}, ...
    'GradientThreshold',1, ...
    'Verbose',1, ...
    'Shuffle','never', ...
    'ExecutionEnvironment','gpu', ...
    'Plots','training-progress');

net = trainNetwork(train_x, train_y, layers, options);

Thank you for help in advance.

[KSSV]

Check the given working examples in the doc and try to understand. Did you try to run given examples?

Answer 1

[Prince Kumar]

Your train dataset X and Y should have dimension 50000x1.

Refer the example in this article. It has a sequence classification example.

https://in.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html#d123e106953