Adaptive learning rate for neural network - Teo, Chee Seong (Nanyang Technological University, 2021)
The learning rate is one of the most important hyper-parameters to tune in a neural network and Deep Learning. The right choice of learning rate results in a better model and faster convergence during the learning process. Time is often wasted on selecting and tuning the learning rate. The purpose of this thesis is to present the Armijo learning rate (LR) to eliminate the need of manually selecting and tuning the learning rate. We first introduce related information to our work, including the foundation of the neural network. We discuss some current methods on selecting learning rate and propose the Armijo LR. We evaluate the Armijo LR with the current methods and evaluate their performance on some image classification data sets.