Learning Rate Schedulers Towards Ai

Last Updated on July 25, 2023 by Editorial Team In my previous Medium article, I talked about the crucial role that the learning rate plays in training Machine Learning and Deep Learning models. In the article, I listed the learning rate scheduler as one way to optimize the learning rate for optimal performance. Today, I will delve and go deeper into the concept of learning rate schedulers and explain how they work. But first (as usual), I’ll begin with a relatable story to explore the topic. Kim is a dedicated and hard-working teacher who has always wanted to achieve a better balance between her professional and personal life but has always struggled to find enough time for all of her...

This led to her having feelings of stress and burnout. In addition to her teaching duties, she must also grade students’ homework, review her syllabus and lesson notes, and attend to other important tasks. Backed by her determination to take full control of her schedule, Kim decided to create a daily to-do list in which she prioritized her most important tasks and allocated time slots for each of... At work, she implemented a strict schedule based on her existing teaching timetable. She also dedicated specific times to review homework, preparing lessons, and attending to other out-of-class responsibilities. At home, Kim continued to manage her time wisely by scheduling time for exercise, cooking and spending quality time with friends.

She also made sure to carve out time for herself, such as reading or taking relaxing baths, to recharge and maintain her energy levels. Staying true to her schedule, she experienced significant improvements in her performance and overall well-being. She was able to accomplish more and feel less stressed, and she was able to spend more quality time with friends, engage in fulfilling activities, and make time for self-care. 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. In the realm of deep learning, PyTorch stands as a beacon, illuminating the path for researchers and practitioners to traverse the complex landscapes of artificial intelligence. Its dynamic computational graph and user-friendly interface have solidified its position as a preferred framework for developing neural networks. As we delve into the nuances of model training, one essential aspect that demands meticulous attention is the learning rate.

To navigate the fluctuating terrains of optimization effectively, PyTorch introduces a potent ally—the learning rate scheduler. This article aims to demystify the PyTorch learning rate scheduler, providing insights into its syntax, parameters, and indispensable role in enhancing the efficiency and efficacy of model training. PyTorch, an open-source machine learning library, has gained immense popularity for its dynamic computation graph and ease of use. Developed by Facebook's AI Research lab (FAIR), PyTorch has become a go-to framework for building and training deep learning models. Its flexibility and dynamic nature make it particularly well-suited for research and experimentation, allowing practitioners to iterate swiftly and explore innovative approaches in the ever-evolving field of artificial intelligence. At the heart of effective model training lies the learning rate—a hyperparameter crucial for controlling the step size during optimization.

PyTorch provides a sophisticated mechanism, known as the learning rate scheduler, to dynamically adjust this hyperparameter as the training progresses. The syntax for incorporating a learning rate scheduler into your PyTorch training pipeline is both intuitive and flexible. At its core, the scheduler is integrated into the optimizer, working hand in hand to regulate the learning rate based on predefined policies. The typical syntax for implementing a learning rate scheduler involves instantiating an optimizer and a scheduler, then stepping through epochs or batches, updating the learning rate accordingly. The versatility of the scheduler is reflected in its ability to accommodate various parameters, allowing practitioners to tailor its behavior to meet specific training requirements. The importance of learning rate schedulers becomes evident when considering the dynamic nature of model training.

As models traverse complex loss landscapes, a fixed learning rate may hinder convergence or cause overshooting. Learning rate schedulers address this challenge by adapting the learning rate based on the model's performance during training. This adaptability is crucial for avoiding divergence, accelerating convergence, and facilitating the discovery of optimal model parameters. The provided test accuracy of approximately 95.6% suggests that the trained neural network model performs well on the test set. 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. 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. When training neural networks, one of the most critical hyperparameters to tune is the learning rate (LR). The learning rate determines how much the model weights are updated in response to the gradient of the loss function during backpropagation. While a high learning rate might cause the training process to overshoot the optimal parameters, a low learning rate can make the process frustratingly slow or get the model stuck in suboptimal local minima. A learning rate scheduler dynamically adjusts the learning rate during training, offering a systematic way to balance the trade-off between convergence speed and stability.

Instead of manually tuning the learning rate, schedulers automate its adjustment based on a predefined strategy or the model’s performance metrics, enhancing the efficiency and performance of the training process. Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 6 min read · June 10, 2025 The learning rate is a crucial hyperparameter in machine learning (ML) that controls how quickly a model learns from the training data. It determines the step size of each iteration when optimizing the model's parameters using gradient descent. A suitable learning rate is essential for achieving optimal performance, as it directly influences the convergence rate and stability of the training process. The learning rate plays a pivotal role in model training, as it affects the model's ability to:

Using a fixed learning rate throughout the training process can be limiting, as it may not adapt to the changing needs of the model. Some challenges associated with fixed learning rates include: To address the challenges associated with fixed learning rates, learning rate schedulers were introduced. A learning rate scheduler is a technique that adjusts the learning rate during the training process based on a predefined schedule or criteria. The primary goal of a learning rate scheduler is to adapt the learning rate to the model's needs, ensuring optimal convergence and performance. Log in or create a free Lightning.ai account to track your progress and access additional course materials Get Started →

Tuner documentation for learning rate finding configure_optimizers dictionary documentation CosineAnnealingWarmRestarts documentation In this lecture, we introduced three different kinds of learning rate schedulers: step schedulers, on-plateau schedulers, and cosine decay schedulers. They all have in common that they decay the learning rate over time to achieve better annealing — making the loss less jittery or jumpy towards the end of the training. Learning rate schedulers are an essential tool in fine-tuning machine learning hyperparameters during neural network training.

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