Day 11 Learning Rate Schedulers Linkedin

In deep learning, the learning rate is a hyperparameter that determines the step size at each iteration while moving towards a minimum of the loss function. A fixed learning rate can be problematic: A learning rate scheduler (or learning rate policy) dynamically adjusts the learning rate during training. The goal is often to start with a relatively high learning rate to explore the loss landscape quickly and then gradually decrease it to fine-tune the model and converge more precisely. Concept: StepLR is one of the simplest and most commonly used learning rate schedulers. It decreases the learning rate by a fixed factor at predefined epochs.

Example: If your initial learning rate is 0.01, step_size=30, and gamma=0.1, the learning rate will be 0.01 for the first 30 epochs, then 0.001 for the next 30, then 0.0001, and so on. Pros: Easy to tune (step_size and gamma), predictable behavior.Cons: The sudden drops can sometimes lead to instability or require careful tuning. 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. 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... 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 11.6 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 11.5 discussed this in some detail and we analyzed performance guarantees in Section 11.4. 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 4.8 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. 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: 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 11.6 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 11.5 discussed this in some detail and we analyzed performance guarantees in Section 11.4. 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 4.8 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 (), 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 8 min read · June 14, 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 a challenging task, requiring careful tuning of hyperparameters to achieve optimal performance. One crucial hyperparameter that significantly impacts the training process is the learning rate. In this article, we will explore the concept of learning rate schedulers and their role in optimizing deep learning models. A learning rate scheduler is a technique used to adjust the learning rate during the training process.

The learning rate determines the step size of each update in the gradient descent algorithm, and adjusting it can significantly impact the convergence of the model. In this section, we will discuss how to implement learning rate schedulers in popular deep learning frameworks such as PyTorch, TensorFlow, and Keras. PyTorch provides a variety of learning rate schedulers through its torch.optim.lr_scheduler module. Some of the most commonly used schedulers include: Here is an example of how to use the StepLR scheduler in PyTorch: 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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