A comparison of LSTM and GRU networks for learning symbolic sequences [article]

Roberto Cahuantzi, Xinye Chen, Stefan Güttel
2021 arXiv   pre-print
We explore relations between the hyper-parameters of a recurrent neural network (RNN) and the complexity of string sequences it is able to memorize. We compare long short-term memory (LSTM) networks and gated recurrent units (GRUs). We find that an increase of RNN depth does not necessarily result in better memorization capability when the training time is constrained. Our results also indicate that the learning rate and the number of units per layer are among the most important
more » ... to be tuned. Generally, GRUs outperform LSTM networks on low complexity sequences while on high complexity sequences LSTMs perform better.
arXiv:2107.02248v1 fatcat:ilsxako76fgodm2aukyworvjl4