Effective Neural Network Training With Adaptive Learning Rate Based On

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. Adaptive learning rate schedulers have become a crucial component in the training of artificial neural networks (ANNs). By dynamically adjusting the learning rate during training, these schedulers can significantly improve the convergence speed and accuracy of ANNs. In this article, we will explore the different types of adaptive learning rate schedulers and provide practical implementation details on how to implement them in popular deep learning frameworks. The traditional stochastic gradient descent (SGD) algorithm has been widely used for training ANNs.

However, it can be slow to converge and may get stuck in local minima. To address this issue, adaptive learning rate schedulers have been introduced. These schedulers adjust the learning rate during training based on the magnitude of the gradient or the number of iterations. There are several types of adaptive learning rate schedulers that can be used, including: Here’s an example implementation of the exponential decay schedule in PyTorch: Adaptive learning rate schedulers are an essential component in the training of ANNs.

By dynamically adjusting the learning rate during training, these schedulers can significantly improve convergence speed and accuracy. In this article, we explored different types of adaptive learning rate schedulers and provided practical implementation details for implementing them in popular deep learning frameworks. arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community?

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