Learning Rate Schedulers Rasbt Machine Learning Notes Deepwiki

Leo Migdal

-Nov 16, 2025, 10:57 PM

learning rate schedulers rasbt machine learning notes deepwiki

This page documents the learning rate schedulers implemented in the repository, their characteristics, and how they integrate with PyTorch Lightning. Learning rate scheduling is a technique for dynamically adjusting the learning rate during training to improve model convergence and performance. For implementation of neural network models, see Lightning Classifier Implementation. For hyperparameter tuning and optimization techniques, see Hyperparameter Tuning with Optuna. Learning rate scheduling is a critical technique in deep learning that adjusts the learning rate during training. The learning rate controls how much the model parameters change in response to the estimated error.

A proper learning rate schedule can lead to: The repository implements several common learning rate schedulers using PyTorch and PyTorch Lightning. The repository contains implementations and comparative experiments for the following types of learning rate schedulers: Collection of useful machine learning codes and snippets (originally intended for my personal use) 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. 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. Code example below uses validation loss as monitored quantity. 3. CosineAnnealingLR: Learning rate follows a cosine annealing schedule. This document provides a technical introduction to the machine-learning-notes repository, a collection of machine learning implementations and benchmarks created by Sebastian Raschka.

The repository serves as a practical resource for machine learning techniques, focusing on PyTorch, PyTorch Lightning, and performance optimization across different hardware platforms. The machine-learning-notes repository contains implementations covering several key areas: The repository leverages several key technologies: The following diagram illustrates the typical workflow patterns implemented in the repository: This section covers the mathematical foundations of machine learning algorithms, with a particular focus on matrix operations which form the basis of neural networks. For more details, see Fundamental Mathematics.

We saw in previous lectures that the Gradient Descent algorithm updates the parameters, or weights, in the form: Recall that the learning rate \(\alpha\) is the hyperparameter defining the step size on the parameters at each update. The learning rate \(\alpha\) is kept constant through the whole process of Gradient Descent. But we saw that the model’s performance could be drastrically affected by the learning rate value; if too small the descent would take ages to converge, too big it could explode and not converge... How to properly choose this crucial hyperparameter? In the florishing epoch (pun intended) of deep learning, new optimization techniques have emerged.

The two most influencial families are Learning Rate Schedulers and Adaptative Learning Rates. Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 6 min read · June 10, 2025 Deep learning models have revolutionized the field of artificial intelligence, achieving state-of-the-art results in various tasks such as image classification, natural language processing, and speech recognition. However, training these models can be challenging, and one of the key factors that affect their performance is the learning rate. In this article, we will explore the concept of learning rate schedulers, their theoretical foundations, practical applications, and advanced topics. The learning rate is a hyperparameter that controls how quickly a deep learning model learns from the training data.

It determines the step size of each update in the stochastic gradient descent (SGD) algorithm, which is commonly used to optimize deep learning models. 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. Mathematically, the update rule for SGD can be written as: \[w_{t+1} = w_t - \alpha \nabla L(w_t)\] There was an error while loading. Please reload this page.

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:

Learning Rate Schedulers Rasbt Machine Learning Notes Deepwiki

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