A toolkit for analysis of deep learning experiments
O'Donoghue, Jim and Roantree, Mark
(2016)
A toolkit for analysis of deep learning experiments.
In: The 15th International Symposium on Intelligent Data Analysis, 13-15 Oct 2016, Stockholm, Sweden.
Learning experiments are complex procedures which gener-
ate 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, hyperparameters 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.