A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks

TL;DR

This paper introduces a new training algorithm combining gradient clipping and standard gradients, along with a novel squashing activation function, to ensure stability and consistency in deep neural network training across supervised, unsupervised, and reinforcement learning tasks.

Contribution

The paper proposes a provably stable training algorithm for deep networks using gradient clipping on the output layer and a new activation function, tGELU, to prevent vanishing gradients and improve stability.

Findings

Training stability is improved with low variance in updates.

Reinforcement learning benefits include omission of target networks.

Classification tasks show reduced variance and smoother loss reduction.

Abstract

We present a novel algorithm for training deep neural networks in supervised (classification and regression) and unsupervised (reinforcement learning) scenarios. This algorithm combines the standard stochastic gradient descent and the gradient clipping method. The output layer is updated using clipped gradients, the rest of the neural network is updated using standard gradients. Updating the output layer using clipped gradient stabilizes it. We show that the remaining layers are automatically stabilized provided the neural network is only composed of squashing (compact range) activations. We also present a novel squashing activation function - it is obtained by modifying a Gaussian Error Linear Unit (GELU) to have compact range - we call it Truncated GELU (tGELU). Unlike other squashing activations, such as sigmoid, the range of tGELU can be explicitly specified. As a consequence, the problem of vanishing gradients that arise due to a small range, e.g., in the case of a sigmoid activation, is eliminated. We prove that a NN composed of squashing activations (tGELU, sigmoid, etc.), when updated using the algorithm presented herein, is numerically stable and has consistent performance (low variance). The theory is supported by extensive experiments. Within reinforcement learning, as a consequence of our study, we show that target networks in Deep Q-Learning can be omitted, greatly speeding up learning and alleviating memory requirements. Cross-entropy based classification algorithms that suffer from high variance issues are more consistent when trained using our framework. One symptom of numerical instability in training is the high variance of the neural network update values. We show, in theory and through experiments, that our algorithm updates have low variance, and the training loss reduces in a smooth manner.