Statistically Significant Stopping of Neural Network Training

• URL: http://arxiv.org/abs/2103.01205v1
• Date: Mon, 1 Mar 2021 18:51:16 GMT
• Title: Statistically Significant Stopping of Neural Network Training
• Authors: Justin K. Terry, Mario Jayakumar, Kusal De Alwis
• Abstract summary: We introduce a statistical significance test to determine if a neural network has stopped learning. We use this as the basis of a new learning rate scheduler.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The general approach taken when training deep learning classifiers is to save the parameters after every few iterations, train until either a human observer or a simple metric-based heuristic decides the network isn't learning anymore, and then backtrack and pick the saved parameters with the best validation accuracy. Simple methods are used to determine if a neural network isn't learning anymore because, as long as it's well after the optimal values are found, the condition doesn't impact the final accuracy of the model. However from a runtime perspective, this is of great significance to the many cases where numerous neural networks are trained simultaneously (e.g. hyper-parameter tuning). Motivated by this, we introduce a statistical significance test to determine if a neural network has stopped learning. This stopping criterion appears to represent a happy medium compared to other popular stopping criterions, achieving comparable accuracy to the criterions that achieve the highest final accuracies in 77% or fewer epochs, while the criterions which stop sooner do so with an appreciable loss to final accuracy. Additionally, we use this as the basis of a new learning rate scheduler, removing the need to manually choose learning rate schedules and acting as a quasi-line search, achieving superior or comparable empirical performance to existing methods.

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