Cyclical Learning Rates for Training Neural Networks


"Cyclical Learning Rates for Training Neural Networks" introduced the concept of cyclical learning rates, a novel method of adjusting the learning rate during training.

Introduction

Typically, when training a neural network, a constant learning rate or a learning rate with a predetermined schedule {such as step decay or exponential decay} is used. However, these approaches may not always be optimal. A learning rate that is too high can cause training to diverge, while a learning rate that is too low can slow down training or cause the model to get stuck in poor local minima.

In this paper, Leslie N. Smith introduced the concept of cyclical learning rates {CLR}, where the learning rate is varied between a lower bound and an upper bound in a cyclical manner. This approach aims to combine the benefits of both high and low learning rates.

Cyclical Learning Rates

In the CLR approach, the learning rate is cyclically varied between reasonable boundary values. The learning rate increases linearly or exponentially from a lower bound to an upper bound, and then decreases again. This cycle is repeated for the entire duration of the training process.

Mathematically, the learning rate for a given iteration can be calculated as:

[ \text{lr}(t) = \text{lr}{\text{min}} + 0.5 \left( \text{lr}{\text{max}} - \text{lr}{\text{min}} \right) \left( 1 + \cos\left( \frac{T{\text{cur}}}{T} \pi \right) \right) ]

where:

Experimental Results

The author tested the CLR method on various datasets and neural network architectures, including CIFAR-10, CIFAR-100, and ImageNet. The results showed that CLR can lead to faster convergence and improved generalization performance compared to traditional learning rate schedules.

Implications

The concept of cyclical learning rates has significant implications for the field of machine learning:

Limitations

While CLR is a powerful tool, it's not without its limitations:

Conclusion

In conclusion, "Cyclical Learning Rates for Training Neural Networks" made a significant contribution to the field of machine learning by introducing a novel approach to adjust the learning rate during training. The concept of cyclical learning rates has since been widely adopted and implemented in various deep learning libraries.



Tags: Optimization, 2017
👁️ 790
hills
20:26
30.05.23
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