An Approach to Tuning Hyperparameters in Parallel: A Performance Study Using Climate Data CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
An Approach to Tuning Hyperparameters in Parallel: A Performance Study Using Climate Data CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
复制标题
并行调整超参数的方法：使用气候数据的性能研究网络培训：大数据高性能计算大气科学
DOI：
10.13016/m2dxhb-r86g
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发表时间：
2019
影响因子：
22.7
通讯作者：
Bin Wang
中科院分区：
文献类型：
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作者：
Charlie Becker;W. Mayfield;S. Murphy;Bin Wang
由于天气模拟具有极大的复杂性，利用现有模型预测强风暴和恶劣天气状况可能很困难。例如，在预测龙卷风时，当试图快速将一种天气模式归类为龙卷风或非龙卷风时必须谨慎。因此，可以使用机器学习快速对这些天气模式进行分类，但在保持预测时间的同时必须非常小心以获得最大的准确性。然后我们利用TensorFlow和Keras创建一个确定超参数的通用框架，并将其用于训练一个卷积神经网络，该网络基于涡度等重要因素专门对风暴进行龙卷风或非龙卷风分类。通过检查针对少量应用数据的准确性和训练时间，我们展示了我们的框架确定批量大小、轮数和学习率的最佳超参数值的能力。在训练时间方面，我们同时利用了CPU和GPU，并且发现GPU在训练各种网络所花费的时间上的性能远远优于CPU。
The ability to predict violent storms and bad weather conditions with current models can be difficult due to the immense complexity associated with weather simulation. For example when predicting a tornado caution must be used when attempting to quickly classify a weather pattern as tornadic or not tornadic. Thus one can use machine learning to quickly classify these weather patterns but great care must be taken to obtain the maximal amount of accuracy while maintaining prediction wall time. We then create a general framework for determining hyperparameters with tensorflow and keras and use it for training a convolutional neural network that specializes in classifying storms as tornadic or not tornadic based on important factors like vorticity. We demonstrate our framework’s ability to determine optimal hyperparameter values for batch size, epochs, and learning rate by examining accuracy and training time with regards to a small amount of application data. In the context of training time we leverage both CPUs and GPUs and found the performance of GPUs to be vastly superior in time taken to train the various networks than CPUs.