Sorry if this seems like a basic question.
From my understanding, one of the advantages of sequence models like RNNs is that they can handle variable length input sequences. For example, if I'm doing sentiment classification on tweets, I can scale the number of RNN units to match the number of words in my input.
But a lot of implementations of NLP projects that I have seen seem to fix the input length to be fed into Keras. They find the longest length input and pad the others to match.
Is this required theoretically? I would assume not. If it is not then is it to fix the computation graph a priori? Or can keras handle variable length inputs? I am a little confused. Any help appreciated. Thanks in. advance.