A Fortran-Keras Deep Learning Bridge for Scientific Computing

Authors

Jordan Ott, University of California, Irvine
Mike Pritchard, University of California, Irvine
Natalie Best, Chapman University
Erik Linstead, Chapman UniversityFollow
Milan Curcic, University of Miam
Pierre Baldi, University of California, Irvine

Document Type

Article

Publication Date

8-28-2020

Abstract

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.

Comments

This article was originally published in Scientific Programming in 2020. https://doi.org/10.1155/2020/8888811

Recommended Citation

Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi, "A Fortran-Keras Deep Learning Bridge for Scientific Computing", Scientific Programming, vol. 2020, Article ID 8888811, 13 pages, 2020. https://doi.org/10.1155/2020/8888811

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.