Dear Chris,
Thank you for the reply.
As best of my understanding on debugging KERAS code, the merge operation is performed after every layer in a multi-layer bidirectional LSTM/GRU model in KERAS. And I am trying to replicate the same behavior in PyTorch.
Also, thank you for the code snippet. PyTorch only allows to merge mode as ‘concat’ (by default), wouldn’t it be good to have the merge mode configurable, so the programmer could pick any between {‘sum’, ‘mul’, ‘concat’, ‘ave’, None}? My concern is not how can I perform merge mode as {‘sum’, ‘mul’, ‘concat’, ‘ave’, None}, but more from execution time performance perspective.
Thanks,