An Open-source Implementation of LSTM and GRU in the Ptolemy Simulation Framework

Ptolemy II [1] is an open-source software framework for modelling, simulation and design of concurrent, heterogeneous, real-time systems, including distributed/parallel systems [2]. These systems can be described using the combination of different formal as well as computation models. Ptolemy also includes machine learning libraries providing support for particle filtering, model-predictive control, hidden Markov models, and various statistical analysis tools. However, one of the main problems Ptolemy users face is the lack of fundamental recurrent neural network structures. In this paper, an LSTM and a GRU recurrent neural network are implemented in Ptolemy II framework, in order to extend its machine learning library, enabling users to develop their complex recurrent neural networks in significantly less time. The presented work has been verified through a real-world weather forecasting use case; the results demonstrate that our approach has identical accuracy with one of the most widely used machine learning library (i.e. Keras) in all cases. To further increase the impact of our approach, the complete source code is freely distributed to the community.