Hi Chris
This concatenation and dense network action works great if you only want to use the final layer, hidden state. But how about when you need to reuse all layer hidden-states (both h and c) e.g. when using the 3 layer bidirectional encoder LSTM h_n–> 3-layer unidirectional decoder LSTM?
I assume I would then have to reshape using h_n.view() to get the grouped bidirectional hidden_states per layer, then iterating over each layer, concatenate the fw & bw states, push each concatenated layer through a dense network to get the final hidden_states variable. Another question is whether this is also necessary for the c_state of the LSTM.