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How to deal with Time Sequence Inputs for 1D Convolutional-LSTM networks.

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I am trying to combine two approach for Time Sequence Classification using deep learning.
The first one implement LSTM networks and it is described here:
The seccond apply convolutional networks and it is described here:
Following previous advices on ANSWERS I used the Deep Network builder object to recreate the main convolutional block of 2) as
Now my doubt is how should i format the accelerometry data for the input of this network?
My data are 42 features signals from accelerometry represented as 42xN°observations.
I tried to format the data as a sequence of images as 1x1x42xN°observations, and it seemed work but still my doubt remains.
Is this data format correct ? and if so:
It is correct to define 1x3 as dimension of the filter?
Thank in advance,
  3 Comments
krishna Chauhan
krishna Chauhan on 6 Jul 2020
@Mirko Job
did you find your answers sir?
I am dealing with sequence classification using TCN.
Mirko Job
Mirko Job on 7 Jul 2020
Edited: Mirko Job on 21 Jul 2020
@krishna Chauhan Yes, the problem is that you have to write your own custom training loop routine using dlarrays. So first you train TCN using the approach described in the link. Then you extract the Features dlarrays from the net manually before the Fully Connected part. Then you train a lstm net (see lstm function) from the elaborated temporale features obtained through TCN.

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