I have an input of the form 1000* 5* 1* 50 where 1000 is the batch size with 5 features, having 45* 1 input.
I have an output label of 1000* 1* 2. This is a regression task.
Now, I would like to take individual features into account for example using only feature 1 or only feature 2 or combination of feature 1 and 2 and so on.
How may I set the convolutions to be?
Until now, I am using Conv1d with 1000* 1* 50 input feature wise and then concatenating wherever required with another Conv1d.
What should I choose for the kernal size  and how to do this in one model? is there any option to choose which features I can select while using Conv2d?