Adaptive learning rate methodologies and clipping mechanisms based upon gradient entropy

Serving as a pivotal parameter during deep learning training processes, learning rate plays a crucial role regarding training efficiency. Traditional optimizers confront challenges such as sluggish convergence and unstable precision; particularly across large-scale datasets, solitary adaptive mechanisms easily lead to premature learning rate decay and training instability, thereby undermining model generalization capabilities. Present research proposes an optimizer enhancement method based upon entropy-modulated mechanisms and multi-strategy integration, improving adaptive capacity of learning rate regulation across diverse training phases through introduction of entropy-aware learning rate scheduling mechanisms while effectively bolstering optimizer responsiveness toward training dynamics and structural complexity. Constructing entropy-modulated optimizers incorporates dual factors of gradient perturbation rate and information entropy to achieve real-time response toward training dynamics, while multi-scale entropy-aware clipping mechanisms are designed to alleviate gradient explosion and structural instability within deep networks. Validated through experiments across multiple public datasets, results demonstrate that present method effectively enhances model robustness and convergence stability.