AdamZ: An Enhanced Optimisation Method for Neural Network Training

TL;DR

AdamZ is an improved optimizer for neural networks that adaptively adjusts learning rates to better handle overshooting and stagnation, resulting in more precise model training.

Contribution

It introduces a dynamic learning rate adjustment mechanism in AdamZ, addressing common training challenges with hyperparameter-guided modifications.

Findings

Consistently reduces loss function more effectively.

Maintains optimal learning rates across diverse tasks.

Enhances model performance despite slightly longer training times.

Abstract

AdamZ is an advanced variant of the Adam optimiser, developed to enhance convergence efficiency in neural network training. This optimiser dynamically adjusts the learning rate by incorporating mechanisms to address overshooting and stagnation, that are common challenges in optimisation. Specifically, AdamZ reduces the learning rate when overshooting is detected and increases it during periods of stagnation, utilising hyperparameters such as overshoot and stagnation factors, thresholds, and patience levels to guide these adjustments. While AdamZ may lead to slightly longer training times compared to some other optimisers, it consistently excels in minimising the loss function, making it particularly advantageous for applications where precision is critical. Benchmarking results demonstrate the effectiveness of AdamZ in maintaining optimal learning rates, leading to improved model performance across diverse tasks.