The Ultimate Guide To Learning Rate Schedulers

A Gentle Introduction to Learning Rate SchedulersImage by Author | ChatGPT Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach its full potential? The culprit might be your learning rate – arguably one of the most important hyperparameters in machine learning. While a fixed learning rate can work, it often leads to suboptimal results. Learning rate schedulers offer a more dynamic approach by automatically adjusting the learning rate during training. In this article, you’ll discover five popular learning rate schedulers through clear visualizations and hands-on examples.

You’ll learn when to use each scheduler, see their behavior patterns, and understand how they can improve your model’s performance. We’ll start with the basics, explore sklearn’s approach versus deep learning requirements, then move to practical implementation using the MNIST dataset. By the end, you’ll have both the theoretical understanding and practical code to start using learning rate schedulers in your own projects. Imagine you’re hiking down a mountain in thick fog, trying to reach the valley. The learning rate is like your step size – take steps too large, and you might overshoot the valley or bounce between mountainsides. Take steps too small, and you’ll move painfully slowly, possibly getting stuck on a ledge before reaching the bottom.

Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 5 min read · May 26, 2025 Choosing the right learning rate scheduler is a crucial step in training a deep learning model. The learning rate scheduler determines how the learning rate changes during training, which can significantly impact the model's performance. In this section, we'll discuss the factors to consider when choosing a learning rate scheduler, provide an overview of popular learning rate schedulers, and compare their strengths and weaknesses. When selecting a learning rate scheduler, there are several factors to consider: Some of the most popular learning rate schedulers include:

The step learning rate scheduler reduces the learning rate by a fixed factor at regular intervals. The learning rate is updated according to the following formula: Researchers generally agree that neural network models are difficult to train. One of the biggest issues is the large number of hyperparameters to specify and optimize. The list goes on, including the number of hidden layers, activation functions, optimizers, learning rate, and regularization. Tuning these hyperparameters can significantly improve neural network models.

For us, as data scientists, building neural network models is about solving an optimization problem. We want to find the minima (global or sometimes local) of the objective function by gradient-based methods, such as gradient descent. Of all the gradient descent hyperparameters, the learning rate is one of the most critical ones for good model performance. In this article, we will explore this parameter and explain why scheduling our learning rate during model training is crucial. Moving from there, we’ll see how to schedule learning rates by implementing and using various schedulers in Keras. We will then create experiments in neptune.ai to compare how these schedulers perform.

What is the learning rate, and what does it do to a neural network? The learning rate (or step size) is explained as the magnitude of change/update to model weights during the backpropagation training process. As a configurable hyperparameter, the learning rate is usually specified as a positive value less than 1.0. This newsletter is supported by Alegion. As a research scientist at Alegion, I work on a range of problems from online learning to diffusion models. Feel free to check out our data annotation platform or contact me about potential collaboration/opportunities!

Welcome to the Deep (Learning) Focus newsletter. Each issue picks a single topic in deep learning research and comprehensively overviews related research. Feel free to subscribe to the newsletter, share it, or follow me on twitter if you enjoy it! Anybody that has trained a neural network knows that properly setting the learning rate during training is a pivotal aspect of getting the neural network to perform well. Additionally, the learning rate is typically varied along the training trajectory according to some learning rate schedule. The choice of this schedule also has a large impact on the quality of training.

Most practitioners adopt a few, widely-used strategies for the learning rate schedule during training; e.g., step decay or cosine annealing. Many of these schedules are curated for a particular benchmark, where they have been determined empirically to maximize test accuracy after years of research. But, these strategies often fail to generalize to other experimental settings, raising an important question: what are the most consistent and useful learning rate schedules for training neural networks? Within this overview, we will look at recent research into various learning rate schedules that can be used to train neural networks. Such research has discovered numerous strategies for the learning rate that are both highly effective and easy to use; e.g., cyclical or triangular learning rate schedules. By studying these methods, we will arrive at several practical takeaways, providing simple tricks that can be immediately applied to improving neural network training.

So far we primarily focused on optimization algorithms for how to update the weight vectors rather than on the rate at which they are being updated. Nonetheless, adjusting the learning rate is often just as important as the actual algorithm. There are a number of aspects to consider: Most obviously the magnitude of the learning rate matters. If it is too large, optimization diverges, if it is too small, it takes too long to train or we end up with a suboptimal result. We saw previously that the condition number of the problem matters (see e.g., Section 12.6 for details).

Intuitively it is the ratio of the amount of change in the least sensitive direction vs. the most sensitive one. Secondly, the rate of decay is just as important. If the learning rate remains large we may simply end up bouncing around the minimum and thus not reach optimality. Section 12.5 discussed this in some detail and we analyzed performance guarantees in Section 12.4. In short, we want the rate to decay, but probably more slowly than \(\mathcal{O}(t^{-\frac{1}{2}})\) which would be a good choice for convex problems.

Another aspect that is equally important is initialization. This pertains both to how the parameters are set initially (review Section 5.4 for details) and also how they evolve initially. This goes under the moniker of warmup, i.e., how rapidly we start moving towards the solution initially. Large steps in the beginning might not be beneficial, in particular since the initial set of parameters is random. The initial update directions might be quite meaningless, too. Lastly, there are a number of optimization variants that perform cyclical learning rate adjustment.

This is beyond the scope of the current chapter. We recommend the reader to review details in Izmailov et al. (2018), e.g., how to obtain better solutions by averaging over an entire path of parameters. Welcome back to Day 10 of our “100 Days of Deep Dive into Machine Learning” series! Yesterday, we looked at Gradient Clipping and how it prevents training instability. Today, we’re tackling another powerful training tool: Learning Rate Schedulers – dynamic strategies that fine-tune how fast your model learns as training progresses.

The learning rate is arguably the most important hyperparameter in training neural networks. But the same learning rate throughout training? That’s like sprinting the entire marathon. Learning Rate Schedulers help your model: • Converge gently toward optimal solutions In short: schedulers teach your model when to slow down for better results.

Learning rate is one of the most important hyperparameters in the training of neural networks, impacting the speed and effectiveness of the learning process. A learning rate that is too high can cause the model to oscillate around the minimum, while a learning rate that is too low can cause the training process to be very slow or... This article provides a visual introduction to learning rate schedulers, which are techniques used to adapt the learning rate during training. In the context of machine learning, the learning rate is a hyperparameter that determines the step size at which an optimization algorithm (like gradient descent) proceeds while attempting to minimize the loss function. Now, let’s move on to learning rate schedulers. A learning rate scheduler is a method that adjusts the learning rate during the training process, often lowering it as the training progresses.

This helps the model to make large updates at the beginning of training when the parameters are far from their optimal values, and smaller updates later when the parameters are closer to their optimal... Several learning rate schedulers are widely used in practice. In this article, we will focus on three popular ones: Neural networks have many hyperparameters that affect the model’s performance. One of the essential hyperparameters is the learning rate (LR), which determines how much the model weights change between training steps. In the simplest case, the LR value is a fixed value between 0 and 1.

However, choosing the correct LR value can be challenging. On the one hand, a large learning rate can help the algorithm to converge quickly. But it can also cause the algorithm to bounce around the minimum without reaching it or even jumping over it if it is too large. On the other hand, a small learning rate can converge better to the minimum. However, the optimizer may take too long to converge or get stuck in a plateau if it is too small. One solution to help the algorithm converge quickly to an optimum is to use a learning rate scheduler.

A learning rate scheduler adjusts the learning rate according to a pre-defined schedule during the training process. One solution to help the algorithm converge quickly to an optimum is to use a learning rate scheduler. Usually, the learning rate is set to a higher value at the beginning of the training to allow faster convergence. As the training progresses, the learning rate is reduced to enable convergence to the optimum and thus leading to better performance. Reducing the learning rate over the training process is also known as annealing or decay. Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 6 min read · June 10, 2025

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