How should I treat my input matrix and target matrix for 1D regression problem with CNN?
Suppose I have EMG signals with 760000 points (samples) and I've collected data from 8 muscles (features). So, I have a matrix 760000-by-8. My target is a matrix 760000-by-1. Imagine that I have 1 trial for each 5 persons. So, I'll have for each person a 760000-by-8 matrix.
In the documentation, it says that CNN treats the input data as an image, so it expects an input like h-by-w-by-c-by-N.
In this case, should I consider my features as the number of channels or the width of my input "image"? And N should be 5?
I.e., should I rearrage my training data as 760000-by-1-by-8-by-5 or 760000-by-8-by-1-by-5? Reading the documentation e some questions about this issue, I do not fully understand how should I give my data to the trainNetwork function.
Another question is about the fully connected layer after the CNN feature extractor layers. After I have my feature maps, as this is a regression problem and my target is a 760000-by-1 signal, the output size of my fully connected layer should be 760000?
I'm always having a size issue, saying that it is required a very long array and matlab would become unresponsive.
Thanks in advance