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Learn How Learning Rate Scheduling And Adaptive Optimizers Are Used Fo

Leo Migdal
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learn how learning rate scheduling and adaptive optimizers are used fo

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.

Adaptive Optimization in Machine Learning is a set of techniques that automatically adjust the learning rate during training. Unlike traditional methods like basic SGD that use a fixed learning rate adaptive optimizers like Adam, RMSprop, Adagrad change the learning rate for each parameter based on the data and gradient history. This makes training faster, more stable and often easier especially for deep learning tasks and models with complex or sparse data. Adaptive optimization refers to a class of optimization algorithms that automatically modify learning rates based on the characteristics of the data and gradients. These optimizers aim to: They do this by maintaining and updating internal state variables allowing them to scale the updates differently for each parameter.

A Gentle Guide to boosting model training and hyperparameter tuning with Optimizers and Schedulers, in Plain English Optimizers are a critical component of neural network architecture. And Schedulers are a vital part of your deep learning toolkit. During training, they play a key role in helping the network learn to make better predictions. But what ‘knobs’ do they have to control their behavior? And how can you make the best use of them to tune hyperparameters to improve the performance of your model?

When defining your model there are a few important choices to be made – how to prepare the data, the model architecture, and the loss function. And then when you train it, you have to pick the Optimizer and optionally, a Scheduler. Very often, we might end up simply choosing our "favorite" optimizer for most of our projects – probably SGD or Adam. We add it and forget about it because it is a single line of code. And for many simpler applications, that works just fine. A long long time ago, almost all neural networks were trained using a fixed learning rate and the stochastic gradient descent (SGD) optimizer.

Then the whole deep learning revolution thing happened, leading to a whirlwind of new techniques and ideas. In the area of model optimization, the two most influential of these new ideas have been learning rate schedulers and adaptive optimizers. In this chapter, we will discuss the history of learning rate schedulers and optimizers, leading up to the two techniques best-known among practitioners today: OneCycleLR and the Adam optimizer. We will discuss the relative merits of these two techniques. TLDR: you can stick to Adam (or one of its derivatives) during the development stage of the project, but you should try additionally incorporating OneCycleLR into your model as well eventually. All optimizers have a learning rate hyperparameter, which is one of the most important hyperparameters affecting model performance.

Okay, let's break down learning rate schedules and why they're a crucial part of training neural networks, especially when combined with adaptive optimizers. A learning rate schedule is a strategy for adjusting the learning rate during the training process of a machine learning model, particularly a neural network. The learning rate is a hyperparameter that controls the step size taken during each iteration of gradient descent. It essentially determines how much the model's weights are updated based on the calculated error. Here are some popular types of learning rate schedules: Why are Learning Rate Schedules Used with Adaptive Optimizers?

This is where it gets really interesting. Adaptive optimizers (like Adam, RMSprop, Adagrad, etc.) are designed to automatically adjust the learning rate for each parameter in the model. They do this based on the history of gradients for that specific parameter. However, even with adaptive optimizers, a learning rate schedule can still be beneficial. © 2025 ApX Machine LearningEngineered with @keyframes heartBeat { 0%, 100% { transform: scale(1); } 25% { transform: scale(1.3); } 50% { transform: scale(1.1); } 75% { transform: scale(1.2); } } 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.

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