If my understanding of an LSTM is correct then the output from each LSTM unit is the hidden state from that layer. For the final layer if I wanted to predict e.g. a scalar real number, would I want to add a dense layer with 1 neuron or is it recommended to have a final LSTM layer where the output has just one hidden unit (i.e. the output dimension of the final hidden state is 1)? If we didn't add the dense layer, then the output from the hidden layer I believe would be between (-1,1), if you use the traditional activations in the LSTM unit.
Apologies if I've used wrong terminology, there seems to be some inconsistency with LSTM's when going between literature and definition in TensorFlow etc.