Exploring the Optimized Value of Each Hyperparameter in Various Gradient Descent Algorithms
This work addresses hyperparameter tuning for machine learning practitioners, but it appears incremental as it builds on existing gradient descent methods without introducing a new paradigm.
The study tackled the problem of hyperparameter selection in gradient descent algorithms by proposing an analytical framework to determine optimal values, resulting in higher efficiency and lower errors in convergence.
In the recent years, various gradient descent algorithms including the methods of gradient descent, gradient descent with momentum, adaptive gradient (AdaGrad), root-mean-square propagation (RMSProp) and adaptive moment estimation (Adam) have been applied to the parameter optimization of several deep learning models with higher accuracies or lower errors. These optimization algorithms may need to set the values of several hyperparameters which include a learning rate, momentum coefficients, etc. Furthermore, the convergence speed and solution accuracy may be influenced by the values of hyperparameters. Therefore, this study proposes an analytical framework to use mathematical models for analyzing the mean error of each objective function based on various gradient descent algorithms. Moreover, the suitable value of each hyperparameter could be determined by minimizing the mean error. The principles of hyperparameter value setting have been generalized based on analysis results for model optimization. The experimental results show that higher efficiency convergences and lower errors can be obtained by the proposed method.