Hi all,
I am currently trying to train a network for a regression task, basically I need to map a variable-length sequence (say a spectrogram) to a fixed size vector. My baseline model was a GRU (many-to-one) which mapped the sequence to a vector, and then fed it to a Linear layer for the output, this didn’t go very well. Which led me to believe that perhaps I should shrink the spectrogram before handing it over to the GRU. This actually helped a lot.
My problem is, with the baseline GRU model I just used PackedSequence because I knew the original lengths of the sequences (which are need to create a PackedSequence). But now, after the sequences first pass through a conv2d layer, I “lose” that information and no longer know the length of sequences. So my question is - what it the common practice in such cases? I am open to other architectures as well btw.
Thanks,
Felix.