Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change

Abstract

A multiplicative constant scaling factor is often applied to the model output to adjust the dynamics of neural network parameters. This has been used as one of the key interventions in an empirical study of lazy and active behavior. However, we show that the combination of such scaling and a commonly used adaptive learning rate optimizer strongly affects the training behavior of the neural network. This is problematic as it can cause \emph{unintended behavior} of neural networks, resulting in the misinterpretation of experimental results. Specifically, for some scaling settings, the effect of the adaptive learning rate disappears or is strongly influenced by the scaling factor. To avoid the unintended effect, we present a modification of an optimization algorithm and demonstrate remarkable differences between adaptive learning rate optimization and simple gradient descent, especially with a small ($<1.0$) scaling factor.

References in corpus (10)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
• A Convergence Theory for Deep Learning via Over-Parameterization
• A Mean Field View of the Landscape of Two-Layers Neural Networks
• On Lazy Training in Differentiable Programming
• Scaling description of generalization with number of parameters in deep learning
• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
• The large learning rate phase of deep learning: the catapult mechanism
• Kernel and Rich Regimes in Overparametrized Models
• Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK