Is a LSTM parameter to sequence regression possible?

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Linus Taenzer
Linus Taenzer 2020-6-18
回答： Divya Gaddipati 2020-6-23
Hello,
What happens if I have for example 30 different input parameters in a dataset and a corresponding signal as output and I want to predict this signal?
E.g. features are [X1, X2, X3, .... X30] and the label is a time dependent signal of length n [X31(t_1) X(31(t_2) X(31(t_3) .... X31(t_n)]
layers = [ ...
fullyConnectedLayer(30)
lstmLayer(numHiddenUnits,'OutputMode','sequence')
fullyConnectedLayer(n)
regressionLayer];
This did not work for me so far as I think there is a problem with the input layer?
Can someone help?

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回答（1 个）

Divya Gaddipati
Divya Gaddipati 2020-6-23
For a sequence input, you can use sequenceInputLayer.
sequenceInputLayer(featureDimension)
For more informatiom on sequenceInputLayer, refer to the following link:
https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.sequenceinputlayer.html
Here's an example on Sequence-to-Sequence regression:
https://www.mathworks.com/help/deeplearning/ug/sequence-to-sequence-regression-using-deep-learning.html

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