Title: Simplified Long Short-term Memory Recurrent Neural Networks: part I,
Abstract: We present five variants of the standard Long Short-term Memory (LSTM)
recurrent neural networks by uniformly reducing blocks of adaptive parameters
in the gating mechanisms. For simplicity, we refer to these models as LSTM1,
LSTM2, LSTM3, LSTM4, and LSTM5, respectively. Such parameter-reduced variants
enable speeding up data training computations and would be more suitable for
implementations onto constrained embedded platforms. We comparatively evaluate
and verify our five variant models on the classical MNIST dataset and
demonstrate that these variant models are comparable to a standard
implementation of the LSTM model while using less number of parameters.
Moreover, we observe that in some cases the standard LSTM's accuracy
performance will drop after a number of epochs when using the ReLU
nonlinearity; in contrast, however, LSTM3, LSTM4 and LSTM5 will retain their
performance.