Understanding Slanted Triangular Learning Rates
Slanted Triangular Learning Rates (STLR) is a variant of Triangular Learning Rates, originally introduced by Leslie N. Smith in 2015, to improve the performance of deep learning models. It is a learning rate schedule that gradually increases and decreases the learning rate during training, in order provide a smoother learning curve.
Machine learning algorithms are designed to learn from data that is fed into them. The process of learning involves evaluating how well a model performs on a given dataset, adjusting the model parameters such that the performance improves, and repeating this process until the performance reaches a satisfactory level. The parameter updates in a neural network depend on the gradient of loss with respect to parameters, which is controlled by the learning rate. Therefore, the choice of learning rate schedule can greatly impact the performance of the model.
Triangular Learning Rates
Triangular Learning Rates (TLR) is a popular learning rate scheduling method, where learning rate is gradually increased and then decreased over a number of iterations. In the first half, the learning rate gradually increases linearly from a low value to a high value. In the second half, it gradually decreases back to the low value. The idea behind this is to prevent the model from getting stuck in a local minima, and ensure that the weights of the model are updated in a consistent manner.
Despite its effectiveness in practice, TLR has two potential drawbacks. Firstly, because the period of increase and decrease is symmetrical, the model spends a significant amount of time at a high learning rate, which can lead to unstable optimization. Secondly, the direction of the gradient can change abruptly at the boundaries of the triangular region, leading to undesirable jumps in optimization.
Slanted Triangular Learning Rates
Slanted Triangular Learning Rates (STLR) addresses the aforementioned drawbacks of TLR by using a more aggressive learning rate schedule, with a shorter increase period and a longer decay period. During the first half of training, the learning rate increases linearly from a low value to a high value over a set number of iterations. In the second half, the learning rate decreases linearly back to the low value over a much longer period of time, often with a multiple of the period of increase. The difference in length of the increase and decrease periods gives STLR a slanted shape, hence the name.
The advantage of STLR over TLR is that it spends less time at high learning rates, which can prevent undesired jumps in optimization. Additionally, the longer decay period allows the model to perform more stable updates and converge to a better minima.
Implementing STLR
STLR can be easily implemented in most deep learning frameworks, by specifying the learning rate schedule for the optimizer. The implementation details may vary depending on the framework, but the general idea remains the same.
Here is an example implementation of STLR using Python and PyTorch:
optimizer = optim.SGD(model.parameters(), lr=0.1) scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.1, total_steps=1000, pct_start=0.3, anneal_strategy='linear')
In this example, we create an optimizer object with an initial learning rate of 0.1. We then create a learning rate scheduler object using the OneCycleLR class provided by PyTorch. The max_lr argument specifies the highest learning rate to be reached during the increase period, and total_steps specifies the total number of iterations to be performed. The pct_start argument specifies the percentage of iterations to be used for the increase period, which in this example is set to 30%. Finally, the anneal_strategy argument is set to 'linear' to ensure that the learning rate is decreased linearly during the decay period.
In Conclusion
Slanted Triangular Learning Rates is an adaptive learning rate scheduling method that has been shown to improve the convergence rate and generalization performance of deep learning models. By gradually increasing and then decreasing the learning rate during training, STLR provides the model with sufficient exploration and exploitation capabilities, while reducing the risk of unwanted jumps and oscillations in optimization. STLR can be easily implemented in most modern deep learning frameworks, and is recommended for use in training deep neural networks.
