Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks

• URL: http://arxiv.org/abs/2412.11400v1
• Date: Mon, 16 Dec 2024 02:57:23 GMT
• Title: Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks
• Authors: Naoki Sato, Koshiro Izumi, Hideaki Iiduka,
• Abstract summary: A scaled conjugate gradient method is proposed for solving non optimization problems with deep neural networks. The proposed method is shown to be superior to that of the conjugate gradient method in practical applications of image and text classification.
• Score: 0.6144680854063939
• License:
• Abstract: A scaled conjugate gradient method that accelerates existing adaptive methods utilizing stochastic gradients is proposed for solving nonconvex optimization problems with deep neural networks. It is shown theoretically that, whether with constant or diminishing learning rates, the proposed method can obtain a stationary point of the problem. Additionally, its rate of convergence with diminishing learning rates is verified to be superior to that of the conjugate gradient method. The proposed method is shown to minimize training loss functions faster than the existing adaptive methods in practical applications of image and text classification. Furthermore, in the training of generative adversarial networks, one version of the proposed method achieved the lowest Frechet inception distance score among those of the adaptive methods.

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