A comparison of LSTM and GRU networks for learning symbolic sequences
This work provides insights for practitioners tuning RNNs on sequence tasks, but it is incremental as it compares existing methods without introducing new ones.
The study compared LSTM and GRU networks for learning symbolic sequences, finding that GRUs outperform LSTMs on low-complexity sequences while LSTMs perform better on high-complexity ones, with depth not necessarily improving memorization under constrained training time.
We explore the architecture of recurrent neural networks (RNNs) by studying the complexity of string sequences it is able to memorize. Symbolic sequences of different complexity are generated to simulate RNN training and study parameter configurations with a view to the network's capability of learning and inference. We compare Long Short-Term Memory (LSTM) networks and gated recurrent units (GRUs). We find that an increase in 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 hyper-parameters to be tuned. Generally, GRUs outperform LSTM networks on low-complexity sequences while on high-complexity sequences LSTMs perform better.