Dear Doosti and all,
First of all, thank you for your attention to my question and your time to answer me.
I will now describe my understanding, please correct me if I am still not getting it:
I need to first transpose my current tensor (239 groups each containing 49 rows/time stamps having 5 columns/features)
into a tensor of
5 rows (features) having 49 * 239 = 11 711 columns/time stamps (that represent 239 groups each having 49 columns)?
Then, as I wish to use 1 patient per batch (239 patients), for the batching I should slice the tensor 238 times, meaning that each batch dimension will be 5 (rows) x 49 (columns) and I will have 239 of these batches.
In this case my understanding is that the Conv1d would be:
torch.nn. Conv1d( in_channels: 5 (features/rows), out_channels: 5, kernel_size: I can try different numbers here, the kernel will be sliding over 49 time stamps in each out of 239 batches, stride: I can change, it is a kernel “step”, padding: depending on the kernel size might use padding, dilation: another hyperparameter I might tune, groups: not applicable for me, bias: bool = True, padding_mode: str = ‘zeros’* )
Thank you in advance for any comments and your reply, if any.
Sincerely,
Alice