@smth. By “data streams” do you mean the discrete time intervals? e.g., say I have data at 10 discrete time intervals, your statement would mean in_channels = 10? If so, that is something that I have considered, but I am not sure if it is suitable for my application.
I am trying to build a CNN for a computational physics application. Essentially, my CNN input is temperature values. For each input sample, the temperature is 3-D spatially- and temporally-dependent. I have the temperature at M locations for each discrete time. I have N discrete times. So for each input sample, I have NM features or Nm temperature values.
I am trying to figure out how to wrangle this data so that I can feed it into the CNN. What I am thinking of doing is having a NxMx1 tensor, where N = width, M = height, 1 = in_channel. So, in this setup, the number of data streams does not correspond to in_channel. If I were to make the number of data streams correspond to in_channel, i.e., in_channel = N, then my width = 1, and height = M. So the image becomes just a column as opposed to a NxM matrix/2nd order tensor.
Because of the temporal and spatial dependency nature of temperature (i.e., temperature values are similar spatial locations more similar than at disparate spatial locations. and temperature values at similar times are more similar than at disparate times), I think the former structure, with in_channels = 1, would be more suitable.