# Should we use only one-hot-vector for LSTM input/outputs?

1. Should we convert our inputs to on-hot-vectors and expect one-hot-vectors as output? I mean can we feed LSTM with a vector like x=[12, -234, 54 , 78 , 12 , 6], and have a label vector like this: y=[13, -230, 50, 80 , 9 , 7]? (And we don't use one-hot-vectors at all). Will such network work properly? Or it's better to convert inputs/outputs to a one-hot-vector and this is essence of LSTM?

2. If feeding LSTM with one-hot-vector isn't a necessary rule, and we like to feed our network with such vectors in my previous question, should we again use softmax() function for out outputs? Or we can use better options for such problem(or even don't use any functions there)? If we must(or better) to use softmax, how can we interpret it's result?

3. If it's better to convert our inputs/outputs to one-hot-vectors, can we use two or three hot vectors(I mean: x =[1,0,0,1,0,0] or x=[0,1,1,1,0,0])? Does this work properly or it disrupts the LSTM performance?