An Approach to Tuning Hyperparameters in Parallel: An Approach to Tuning Hyperparameters in Parallel

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dc.contributor.advisorGobbert, Matthias K
dc.contributor.authorBarajas, Carlos Alexander
dc.contributor.departmentMathematics and Statistics
dc.contributor.programMathematics and Statistics
dc.date.accessioned2021-09-01T13:55:52Z
dc.date.available2021-09-01T13:55:52Z
dc.date.issued2019-01-01
dc.description.abstractPredicting violent storms and dangerous weather conditions, for instance predicting tornados, is an important application for public safety. Using numerical weather simulations to classify a weather pattern as tornadic or not tornadic can take a long time due to the immense complexity associated with current models. Machine learning has the potential to classify tornadic weather patterns much more quickly, since a trained model can classify a storm in seconds when provided data. This allows for alerts to be issued more rapidly such that there is more time for the public to respond to the alert. Preprocessing methods must be applied to the natural data prior to training to prevent inaccuracies. Neural networks must have an equal balance of all classifiable cases for accurate prediction of each case. Different data augmentation approaches have been proposed to fix data imbalances. With our specialized HPC enabled framework, we examine the wall time difference of live data augmentation method versus the use of preaugmented data, when used with a convolutional neural network under various hyperparameter configurations. We also compare CPU and GPU based training over varying sizes of augmented data sets. Then we examine the wall time impact associated with varying the number of GPUs used for training a convolutional neural network. Finally, we create a new data augmentation system by implementing a generative adversarial network and qualitatively compare its output to natural data.
dc.formatapplication:pdf
dc.genretheses
dc.identifierdoi:10.13016/m2wiwj-hlrq
dc.identifier.other12104
dc.identifier.urihttp://hdl.handle.net/11603/22911
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics and Statistics, Applied Department Collection
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.sourceOriginal File Name: Barajas_umbc_0434M_12104.pdf
dc.subjectconvolutional neural networks
dc.subjectdeep learning
dc.subjectGAN
dc.subjectgenerative adversarial networks
dc.subjectscientific computing
dc.subjectWeather classification
dc.titleAn Approach to Tuning Hyperparameters in Parallel: An Approach to Tuning Hyperparameters in Parallel
dc.typeText
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