Zusammenfassung
Implementing artificial neural networks is commonly achieved via high-level
programming languages like Python and easy-to-use deep learning libraries like
Keras. These software libraries come pre-loaded 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 optimizer
proves unexpectedly critical. This reveals many neural network architectures
that produce considerable improvements in stability including some with reduced
error, for an especially challenging training dataset.
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