  Recurrent neural networks (RNNs) are non-linear dynamic systems. Previous
work believes that RNN may suffer from the phenomenon of chaos, where the
system is sensitive to initial states and unpredictable in the long run. In
this paper, however, we perform a systematic empirical analysis, showing that a
vanilla or long short term memory (LSTM) RNN does not exhibit chaotic behavior
along the training process in real applications such as text generation. Our
findings suggest that future work in this direction should address the other
side of non-linear dynamics for RNN.
