I have recently begun researching LSTM networks, as I have finished my GA and am looking to progress to something more difficult. I believe I am using the classic LSTM (if that makes any sense) and have a few questions.

Do I need LSTM units everywhere in the network? For example, can I only use LSTM units for the first and last layer and use feedforward units everywhere else?

How do I go about implementing bias values into an LSTM?

Assuming I create a network that predicts the next few words of a sentence, does that mean my outputs should be every possible word that the network could conceivably use?