Using output cell and hidden states of one LSTM cell as input states for another

Question

Typically, when discussing stacking LSTMs (with independent weights), the cell and hidden states are unique to each individual cell and not shared between them. Each LSTM cell operates independently with its own set of states.

Is there any reason for using the output cell state and hidden state of one LSTM cell as the input cell state and hidden state for another LSTM cell? Does this have any logic?

I had in mind a model that only receives one vector/single timestep as input (not a sequence), but I wanted to keep memory between consecutive iterations of the model (using stateful=True intf.keras.layers.LSTM ).

Answer 1

what is your goal? there are three states for lstm: a memory state, a forgot state, and a transfer state. the advantage of lstm over recurrent is the memory state allowing for long term memory. the forget state removes noise from the.network making it more efficient for non contributing states.