My input features for LSTM are different dimension, Is it possible to combine and train the network
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Manoj Devaraju
am 21 Mai 2022
Kommentiert: Manoj Devaraju
am 26 Mai 2022
I have 6 input features for trining the LSTM network, but 1st coloumn has 2x28(contain 2 feature of 28 length) cells by 100 and all four are 1x100 each, Is it possible to combine all the feature and give it to network. since for LSTM we are using (sequenceInputLayer(N)) as input layer. Is there any alternate solution to this.
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Hiro Yoshino
am 24 Mai 2022
No, you cannot do that I think.
Why don't you conver the singular values into series data by using remat and put them under the 2x28 series data so you can use LSTM?
Another way to do this is creating a network with multi-inputs.
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