Day 10 100 Learning Rate Schedulers Medium

Welcome back to Day 10 of our “100 Days of Deep Dive into Machine Learning” series! Yesterday, we looked at Gradient Clipping and how it prevents training instability. Today, we’re tackling another powerful training tool: Learning Rate Schedulers – dynamic strategies that fine-tune how fast your model learns as training progresses. The learning rate is arguably the most important hyperparameter in training neural networks. But the same learning rate throughout training? That’s like sprinting the entire marathon.

Learning Rate Schedulers help your model: • Converge gently toward optimal solutions In short: schedulers teach your model when to slow down for better results. 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. Sarah Lee AI generated Llama-4-Maverick-17B-128E-Instruct-FP8 6 min read · June 10, 2025 Learning rate schedulers are a crucial component in optimizing the performance of machine learning models. By adjusting the learning rate during training, these schedulers can significantly improve the convergence and accuracy of models.

In this article, we will explore the concept of learning rate schedulers, their types, and how to choose the right one for your model. We will also discuss best practices for implementing learning rate schedulers and provide examples of successful implementations. A learning rate scheduler is a technique used to adjust the learning rate of a model during training. The learning rate is a hyperparameter that controls how quickly a model learns from the training data. A high learning rate can lead to fast convergence but may also cause the model to overshoot the optimal solution. On the other hand, a low learning rate can result in more stable convergence but may require more training iterations.

There are several types of learning rate schedulers, including: Learning rate schedulers work by adjusting the learning rate according to a predefined schedule. The schedule can be based on the number of training iterations, the model's performance on the validation set, or other factors. The goal is to adjust the learning rate to optimize the model's convergence and accuracy. Last Updated on July 25, 2023 by Editorial Team In my previous Medium article, I talked about the crucial role that the learning rate plays in training Machine Learning and Deep Learning models.

In the article, I listed the learning rate scheduler as one way to optimize the learning rate for optimal performance. Today, I will delve and go deeper into the concept of learning rate schedulers and explain how they work. But first (as usual), I’ll begin with a relatable story to explore the topic. Kim is a dedicated and hard-working teacher who has always wanted to achieve a better balance between her professional and personal life but has always struggled to find enough time for all of her... This led to her having feelings of stress and burnout. In addition to her teaching duties, she must also grade students’ homework, review her syllabus and lesson notes, and attend to other important tasks.

Backed by her determination to take full control of her schedule, Kim decided to create a daily to-do list in which she prioritized her most important tasks and allocated time slots for each of... At work, she implemented a strict schedule based on her existing teaching timetable. She also dedicated specific times to review homework, preparing lessons, and attending to other out-of-class responsibilities. At home, Kim continued to manage her time wisely by scheduling time for exercise, cooking and spending quality time with friends. She also made sure to carve out time for herself, such as reading or taking relaxing baths, to recharge and maintain her energy levels. Staying true to her schedule, she experienced significant improvements in her performance and overall well-being.

She was able to accomplish more and feel less stressed, and she was able to spend more quality time with friends, engage in fulfilling activities, and make time for self-care. 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. 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. The learning rate is a critical hyperparameter in model training because it directly controls the size of the steps taken to minimize the loss function during optimization. Constant LR: A constant learning rate is a straightforward approach where the learning rate remains unchanged throughout the entire training process of a neural network. Using a constant learning rate is like driving with the same speed all the time, no matter if the road is smooth or full of bumps.

It’s simple the learning rate never changes during training. But training a neural network is messy: weights change, parameters move, and the model keeps learning new things. If the learning rate stays the same, the model can make mistakes, overfit, or just not learn as well as it could. It’s like trying to run a race in flip-flops not impossible, but definitely not the best idea. A code example of Constant LR in TF and Pytorch : LR Schedulers : A learning rate (LR) scheduler is an algorithm that dynamically adjusts the learning rate during the training of a machine learning model, typically based on a predefined schedule or the model’s...

In the realm of deep learning, the learning rate is a critical hyperparameter that determines the step size at which the model's parameters are updated during training. An inappropriate learning rate can lead to slow convergence or even divergence of the training process. PyTorch, a popular deep learning framework, provides a variety of learning rate schedulers that can dynamically adjust the learning rate during training, helping to improve the training efficiency and model performance. In this blog post, we will explore the fundamental concepts, usage methods, common practices, and best practices of the best learning rate schedulers in PyTorch. A learning rate scheduler is a mechanism that adjusts the learning rate of an optimizer during the training process. The main idea behind using a learning rate scheduler is to start with a relatively large learning rate to quickly converge to a region close to the optimal solution and then gradually reduce the...

The general workflow of using a learning rate scheduler in PyTorch is as follows: StepLR reduces the learning rate by a fixed factor (gamma) every step_size epochs. MultiStepLR reduces the learning rate by a fixed factor (gamma) at specified epochs (milestones). 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.

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