A Toolkit for Analysis of Deep Learning Experiments

作者：O'Donoghue Jim^*; Roantree Mark

来源：15th International Symposium on Intelligent Data Analysis (IDA), 2016-10-13 to 2016-10-15.

DOI：10.1007/978-3-319-46349-0_12

Learning experiments are complex procedures which generate high volumes of data due to the number of updates which occur during training and the number of trials necessary for hyper-parameter selection. Often during runtime, interim result data is purged as the experiment progresses. This purge makes rolling-back to interim experiments, restarting at a specific point or discovering trends and patterns in parameters, hyper-parameters or results almost impossible given a large experiment or experiment set. In this research, we present a data model which captures all aspects of a deep learning experiment and through an application programming interface provides a simple means of storing, retrieving and analysing parameter settings and interim results at any point in the experiment. This has the further benefit of a high level of interoperability and sharing across machine learning researchers who can use the model and its interface for data management.