Learning Rate Schedulers A Deep Dive Numberanalytics Com

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. 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. 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. 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. Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 5 min read · May 28, 2025 Learning rate scheduling is a crucial aspect of training machine learning models, particularly deep neural networks. The learning rate determines how quickly a model learns from the training data, and adjusting it during training can significantly impact the model's performance and convergence. Learning rate scheduling refers to the process of adjusting the learning rate during the training process.

The learning rate is a hyperparameter that controls how quickly the model updates its parameters based on the gradient of the loss function. A high learning rate can lead to fast convergence but may also cause the model to overshoot the optimal solution, while a low learning rate can result in slow convergence or getting stuck in... "The learning rate is probably the most important hyperparameter in deep learning." - 1 There are several types of learning rate schedulers, each with its strengths and weaknesses. Some of the most commonly used schedulers include: 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 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:

Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 6 min read · June 11, 2025 Learning rate scheduling is a crucial aspect of training deep learning models, particularly in robotics and machine learning applications. The learning rate determines how quickly a model learns from the data, and adjusting it during training can significantly impact the model's performance. In this section, we will explore advanced learning rate scheduling techniques, including cyclical learning rates, warm restarts, and learning rate scheduling with momentum. Cyclical learning rates involve oscillating the learning rate between a minimum and maximum value during training. This technique has been shown to improve the convergence of deep learning models and prevent overfitting 1.

The benefits of cyclical learning rates include: The following Mermaid graph illustrates the cyclical learning rate schedule: Warm restarts involve restarting the training process with a lower learning rate after a certain number of iterations. This technique has been shown to improve the convergence of deep learning models and prevent overfitting 2. The benefits of warm restarts include:

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