You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Is the SoftDTWLoss function designed to accept arrays of time-series? Since during training and testing in Chainer, the loss is computed per batch (true and predicted), the input to the SoftDTWLoss is a batch of time-series labels and predictions. Does the SoftDTWLoss function consider the whole batch as 1 pair of time series or does it treat each pair of time-series (true and predicted) in a batch pair-wise?
Thank you.
The text was updated successfully, but these errors were encountered:
Hi! As you can see here, I'm looping over the batch in the example code. So SoftDTWLoss is between two time series, not between two batches of time series. I think back then I made this choice because the batch has to be a Python list (since each time series can have a different length).
So in order to get the loss of the whole batch, I need to iterate over all the sequences in the batch. And for each iteration, I need to reshape each sequence to (-1,1) first before getting the SoftDTWLoss for each pair of sequences to be compared? Thank you for clarifying.
SoftDTWLoss expects time series of shape (length, n_dimensions), so if your time series are one dimensional and have shape (length,), you indeed need a reshape.
Hello. Just wanted to clarify.
Is the SoftDTWLoss function designed to accept arrays of time-series? Since during training and testing in Chainer, the loss is computed per batch (true and predicted), the input to the SoftDTWLoss is a batch of time-series labels and predictions. Does the SoftDTWLoss function consider the whole batch as 1 pair of time series or does it treat each pair of time-series (true and predicted) in a batch pair-wise?
Thank you.
The text was updated successfully, but these errors were encountered: