DEVIATE: A Deep Learning Variance Testing Framework

Abstract

Deep learning (DL) training is nondeterministic and such nondeterminism was shown to cause significant variance of model accuracy (up to 10.8%). Such variance may affect the validity of the comparison of newly proposed DL techniques with baselines. To ensure such validity, DL researchers and practitioners must replicate their experiments multiple times with identical settings to quantify the variance of the proposed approaches and baselines. Replicating and measuring DL variances reliably and efficiently is challenging and understudied. We propose a ready-to-deploy framework DEVIATE that (1)measures DL training variance of a DL model with minimal manual efforts, and (2) provides statistical tests of both accuracy and variance. Specifically, DEVIATEautomaticallyanalyzes the DL training code and extracts monitored important metrics (such as accuracy and loss). In addition, DEVIATE performs popular statistical tests and provides users with a report of statistical p-values and effect sizes along with various confidence levels when comparing to selected baselines. We demonstrate the effectiveness of DEVIATE by performing case studies with adversarial training. Specifically, for an adversarial training process that uses the Fast Gradient Signed Method to generate adversarial examples as the training data, DEVIATEmeasures a max difference of accuracy among 8 identical training runs with fixed random seeds to be up to 5.1%.