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What you need to know to train recurrent neural networks to make Flip Flops memories and more

2020-10-15 16:25:29
Cecilia Jarne


tract: Training neural networks to perform different tasks is relevant across various disciplines that go beyond Machine Learning. In particular, Recurrent Neural Networks (RNN) are of great interest to different scientific communities. Open-source frameworks dedicated to Machine Learning such as Tensorflow \cite{chollet2015keras} and Keras \cite{tensorflow2015-whitepaper} has produced significative changes in the development of technologies that we currently use. One relevant problem that can be approach is how to build the models for the study of dynamical systems, and how to extract the relevant information to be able to answer the scientific questions of interest. The purpose of the present work is to contribute to this aim by using a temporal processing task, in this case, a 3-bit Flip Flop memory, to show the modeling procedure in every step: from equations to the software code, using Tensorflow and Keras. The obtained networks are analyzed to describe the dynamics and to show different visualization and analysis tools. The code developed in this premier is provided to be used as a base for model other systems.

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