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A Fortran-Keras Deep Learning Bridge for Scientific Computing
Scientific Programming ( IF 1.672 ) Pub Date : 2020-08-28 , DOI: 10.1155/2020/8888811
Jordan Ott 1 , Mike Pritchard 2 , Natalie Best 3 , Erik Linstead 3 , Milan Curcic 4 , Pierre Baldi 1
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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.

中文翻译:

用于科学计算的 Fortran-Keras 深度学习桥梁

人工神经网络的实现通常是通过高级编程语言(如 Python)和易于使用的深度学习库(如 Keras)来实现的。这些软件库预装了各种网络架构,提供自动微分,并支持 GPU 以实现快速高效的计算。因此,深度学习从业者更喜欢用 Python 训练神经网络模型,这些工具很容易获得。然而,许多大型科学计算项目是用 Fortran 编写的,很难与现代深度学习方法相结合。为了缓解这个问题,我们引入了一个软件库,Fortran-Keras Bridge (FKB)。这种双向桥梁将深度学习资源丰富的环境与稀缺的环境连接起来。该论文描述了 FKB 提供的几个独特功能,例如可定制的层、损失函数和网络集成。本文以一个案例研究结束,该案例研究应用 FKB 来解决有关全球气候模拟实验方法稳健性的开放性问题,其中子网格物理外包给深度神经网络仿真器。在这种情况下,FKB 支持对 100 多个子网格云和辐射物理的候选模型进行超参数搜索,最初在 Keras 中实现,然后在 Fortran 中传输和使用。这样的过程允许评估模型的紧急行为,即当拟合缺陷与明确的行星尺度流体动力学耦合时。结果揭示了离线验证错误和在线性能之间以前没有意识到的强关系,其中优化器的选择出人意料地至关重要。这揭示了许多神经网络架构在稳定性方面产生了相当大的改进,包括一些减少了错误的神经网络架构,对于一个特别具有挑战性的训练数据集。
更新日期:2020-08-28
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