I have a couple of questions.
I have data of the following shape:
(32, 64, 11)
where 32 is the batch size, 64 is a sequence length and 11 is the number of features. each sample of mine is 64X11, and has a label of 0 or 1.
I’d like to predict when a sequence has a label of “1”.
I’m trying to use a simple architecture with
conv1D → ReLU → flatten → linear → sigmoid.
For the Conv1D I thought that since it is a multi variate time series prediction, and each row in my data is a second, I think that the number of in channels should be the number of features, since that way it will process all of the features concurrently, (I don’t have any spatial things in my data, it doesn’t matter if a column is in index 0 or 9, as it is important in image with pixels.
I can't get to decide how to “initialize” the conv1D parameters. Currently I think the number of channels should be the number of features and not 1, as the reason I just explained, but unsure of it.
Secondly, should the loss function be BCELOSS or something else? assuming that my labels are 0 or 1, and the prediction for me is I want the model to provide a probability of belonging to class with label 1.
A lot of thanks.
