Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves
By Tobias Domhan, Jost Tobias Springenberg, and Frank Hutter, 2014. University of Freiburg, Freiburg, Germany {domhant,springj,fh}@cs.uni-freiburg.de
Experienced human experts in deep learning commonly rely on a large “bag of tricks” to determine model hyperparameters (Bengio, 2012), as well as learning rates for stochastic gradient descent (SGD) (LeCun et al., 1998; Bottou, 2012). Using this acquired knowledge they can often tell after a few SGD iterations whether the training procedure will converge to a model with competitive performance or not. To save time, they then prematurely terminate runs expected to perform poorly. Automating this manual trick would be valuable for speeding up both structure search (Bergstra et al., 2011, 2013; Swersky et al., 2013) and hyperparameter optimization (Snoek et al., 2012; Eggensperger et al., 2013), which are currently often prohibitively expensive (Krizhevsky et al., 2012). We take a first step towards this goal by studying methods for extrapolating from the first part of a learning curve to its remainder. Preliminary results indicate that such predictions can be quite accurate and enable the early termination of poor runs.
Copyright by Tobias Domhan, Jost Tobias Springenberg, and Frank Hutter, 2014
Orion Implementation Copyright by Dendi Suhubdy, 2018