A Very Short Visual Introduction To Learning Rate Schedulers With Code

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: 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. 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:

A blog about data science and machine learning In deep learning, optimizing the learning rate is an important for training neural networks effectively. Learning rate schedulers in PyTorch adjust the learning rate during training to improve convergence and performance. This tutorial will guide you through implementing and using various learning rate schedulers in PyTorch. The tutorial covers: The learning rate is a critical hyperparameter in the training of machine learning models, particularly in neural networks and other iterative optimization algorithms.

It determines the step size at each iteration while moving towards a minimum of the loss function. Before you start, ensure you have the torch library installed: This command will download and install the necessary dependencies in your Python environment. This repo contains simple code for visualizing popular learning rate schedulers. The interactive interface allows to alter schedulers parameters and plot them on one canvas. Additionally, underlying Pytorch code to reproduce your tuned scheduler is generated.

This is aimed to help forming an intuition for setting lr scheduler in your DL project. To run the code with interactive Web interface: git clone https://github.com/NesterukSergey/pytorch_lr_scheduler_visualization.git cd pytorch_lr_scheduler_visualization python3 -m venv venv source venv/bin/activate pip install -r requirements.txt cd streamlit_server/ streamlit run __main__.py This will run streamlit server (default adress is: http://localhost:8501/). You can access it in your browser. Neural network training fails when learning rates stay constant throughout epochs.

Static learning rates cause slow convergence or unstable training dynamics. Learning rate scheduling solves this problem by adjusting rates during training. This guide compares three popular scheduling methods: cosine decay, linear decay, and exponential decay. You'll learn implementation details, performance characteristics, and selection criteria for each approach. Learning rate scheduling dynamically adjusts the learning rate during neural network training. The scheduler reduces learning rates as training progresses, allowing models to converge more effectively.

Key benefits of learning rate scheduling: Cosine decay follows a cosine curve pattern, starting high and gradually decreasing to zero. This smooth transition provides excellent convergence properties for deep learning models. 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. Neural networks have many hyperparameters that affect the model’s performance. One of the essential hyperparameters is the learning rate (LR), which determines how much the model weights change between training steps. In the simplest case, the LR value is a fixed value between 0 and 1. However, choosing the correct LR value can be challenging. On the one hand, a large learning rate can help the algorithm to converge quickly.

But it can also cause the algorithm to bounce around the minimum without reaching it or even jumping over it if it is too large. On the other hand, a small learning rate can converge better to the minimum. However, the optimizer may take too long to converge or get stuck in a plateau if it is too small. One solution to help the algorithm converge quickly to an optimum is to use a learning rate scheduler. A learning rate scheduler adjusts the learning rate according to a pre-defined schedule during the training process. One solution to help the algorithm converge quickly to an optimum is to use a learning rate scheduler.

Usually, the learning rate is set to a higher value at the beginning of the training to allow faster convergence. As the training progresses, the learning rate is reduced to enable convergence to the optimum and thus leading to better performance. Reducing the learning rate over the training process is also known as annealing or decay. The learning rate is a crucial hyperparameter that directly affects the future model’s performance. It represents the size of your model’s weight updates in search of the global minimal loss value. In short, learning rate schedulers are algorithms that allow you to control your model’s learning rate according to some pre-set schedule or based on performance improvements.

Gradient descent is an optimization technique that helps researchers detect the most optimal model weight values on training. An effective way to assess the model’s performance on training is to set the cost function, also called a loss function. In the Data Science field, such a function focuses on punishing a model for making errors by assigning some cost to mistakes. Thus, in theory, we can find out the position of our model on the loss function curve for each set of parameters. The weights that result in the minimal loss function lead to the best model performance. In the real world, we usually can not afford to check the model’s loss function for every possible set of parameters since the computation costs would be too high.

Therefore, starting with some random guess and then refining it in iterations makes sense. The algorithm is as follows:

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