Is a LSTM parameter to sequence regression possible?

1 次查看（过去 30 天）
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?

请先登录，再回答此问题。

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

类别

在 Help Center 和 File Exchange 中查找有关 Sequence and Numeric Feature Data Workflows 的更多信息

标签

产品


版本

R2020a

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by