Comments The Best Learning Rate Schedules Substack

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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. Practical and powerful tips for setting the learning rate... Great article Cameron! One question: With the triangular or cyclical LR, how to deal with early stopping criteria while training?

Early stopping will stop the training if the validation loss is consistently high for x epochs. The general recommendation from [1] and [2] would be to make sure early stopping occurs at the end of a decay cycle. Your performance always degrades a bit as you increase the learning rate, then reaches a maximum after the learning rate is decreased again and reaches the end of a decay cycle. The performance improves after each successive cycle, but the peaks always occur at the end of the cycle. So, when using cyclical learning rates you always want to stop training at the end of a cycle. You can combine this with early stopping by only testing the early stopping criteria as you approach the end of a decay cycle.

Or, you could simply record validation accuracy after each cycle and stop training once you've performed a sufficient number of cycles for the performance to reach a plateau. 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. When training neural networks, one of the most critical hyperparameters is the learning rate (η). It controls how much the model updates its parameters in response to the computed gradient during optimization. Choosing the right learning rate is crucial for achieving optimal model performance, as it directly affects convergence speed, stability, and the generalization ability of the network. The learning rate determines how quickly or slowly a neural network learns from data. It plays a key role in finding the optimal set of weights that minimize the loss function.

A well-chosen learning rate ensures: Choosing an inappropriate learning rate can lead to several issues: The learning rate (η) is a fundamental hyperparameter in gradient-based optimization methods like Stochastic Gradient Descent (SGD) and its variants. It determines the step size in updating the model parameters (θ) during training. The standard gradient descent algorithm updates model parameters using the following formula: 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. 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 Natural Language Processing (NLP) tasks. 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. In this article, we will discuss the best practices for implementing learning rate scheduling in NLP projects, including choosing the right learning rate schedule, tuning hyperparameters, and monitoring model performance.

Choosing the right learning rate schedule is essential for achieving optimal model performance. The learning rate schedule determines how the learning rate changes during training, and different schedules can significantly impact the model's convergence and accuracy. When selecting a learning rate schedule, several factors should be considered, including: Several learning rate scheduling techniques are commonly used in NLP, including: 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 a deep learning model, setting an appropriate learning rate is crucial.

Typically kept constant, the learning rate governs the size of parameter updates during each training iteration. However, with vast training data, a small learning rate can slow convergence towards the optimal solution, hampering exploration of the parameter space and risking entrapment in local minima. Conversely, a larger learning rate may destabilize the optimization process, leading to overshooting and convergence difficulties. To address these challenges, fixed learning rates may not suffice. Instead, employing dynamic learning rate schedulers proves beneficial. These schedulers enable adjusting the learning rate throughout training, facilitating larger strides during initial optimization phases and smaller steps as convergence approaches.

Think of it as sprinting towards Mordor but proceeding cautiously near Mount Doom. Learning rate schedulers come in various types, each tailored to different training scenarios. By dynamically adapting the learning rate, these schedulers optimize the training process for improved convergence and model performance. Let’s explore some common types with accompanying Python code examples: 2. ReduceLROnPlateau: Learning rate is reduced when a monitored quantity has stopped improving.

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