Training a Two Layer ReLU Network Analytically

Abstract

Neural networks are usually trained with different variants of gradient descent based optimization algorithms such as stochastic gradient descent or the Adam optimizer. Recent theoretical work states that the critical points (where the gradient of the loss is zero) of two-layer ReLU networks with the square loss are not all local minima. However, in this work we will explore an algorithm for training two-layer neural networks with ReLU-like activation and the square loss that alternatively finds the critical points of the loss function analytically for one layer while keeping the other layer and the neuron activation pattern fixed. Experiments indicate that this simple algorithm can find deeper optima than Stochastic Gradient Descent or the Adam optimizer, obtaining significantly smaller training loss values on four out of the five real datasets evaluated. Moreover, the method is faster than the gradient descent methods and has virtually no tuning parameters.

arXiv comment17 pages, 11 figures

References in corpus (6)

• The Global Landscape of Neural Networks: An Overview
• Learning Neural Networks with Two Nonlinear Layers in Polynomial Time
• Learning Two Layer Rectified Neural Networks in Polynomial Time
• Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks
• Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions
• Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations