Learning Step Size Controllers for Robust Neural Network Training

This paper investigates algorithms to automatically adapt the learning rate of neural networks (NNs). Starting with stochastic gradient descent, a large variety of learning methods has been proposed for the NN setting. However, these methods are usually sensitive to the initial learning rate which has to be chosen by the experimenter. We investigate several features and show how an adaptive controller can adjust the learning rate without prior knowledge of the learning problem at hand.  Due to the recent successes of Neural Networks for tasks such as image classification (Krizhevsky, Sutskever, and Hinton 2012) and speech recognition (Hinton et al. 2012), the underlying gradient descent methods used for training have gained a renewed interest by the research community. Adding to the well known stochastic gradient descent and RMSprop methods (Tieleman and Hinton 2012), several new gradient based methods such as Adagrad (Duchi, Hazan, and Singer 2011) or Adadelta (Zeiler 2012) have been proposed. However, most of the proposed methods rely heavily on a good choice of an initial learning rate. Compounding this issue is the fact that the range of good learning rates for one problem is often small compared to the range of good learning rates across different problems, i.e., even an experienced experimenter often has to manually search for good problem-specific learning rates. A tempting alternative to manually searching for a good learning rate would be to learn a control policy that automatically adjusts the learning rate without further intervention using, for example, reinforcement learning techniques (Sutton and Barto 1998). Unfortunately, the success of learning such a controller from data is likely to depend heavily on the features made available to the learning algorithm. A wide array of reinforcement learning literature has shown the importance of good features in tasks ranging from Tetris (Thiery and Scherrer 2009) to haptile object identification (Kroemer, Lampert, and Peters 2011). Thus, the first step towards applying RL methods to control learning rates is to find good features. Subsequently, the main contributions of this paper are  Identifying informative features for the automatic control of the learning rate. • Proposing a learning setup for a controller that automatically adapts the step size of NN training algorithms. • Showing that the resulting controller generalizes across different tasks and architectures. Together, these contributions enable robust and efficient training of NNs without the need of manual step size tuning.  The goal of this paper is to develop an adaptive controller for the learning rate used in training algorithms such as Stochastic Gradient Descent (SGD) or RMSprop (Tieleman and Hinton 2012). We start with a general statement of the problem we are aiming to solve.  We are interested in finding the minimizer ω∗ = arg min ω F (X;ω), (1) where in our case ω represents the weight vector of the NN and X = {x1, . . . ,xN} is the set of N training examples (e.g., images and labels). The function F (·) sums over the function values induced by the individual inputs such that