Deep Learning Optimizers 3 Learning Rate Schedulers Ipynb At Main Gith

There was an error while loading. Please reload this page. A repository to make available and organize the codes developed during the execution of a technical note on Medium about Optimization in Deep Learning. These codes enable practical visualization of the theoretical concepts covered in the work, this is part of the coursework for the Machine Learning course by professor Ivanovitch Medeiros. The code in the .ipynb files can be found under 'files' in this repository or accessed directly through these Google Colab links: 1.

Visualizando Gradientes Adaptados: Code to help visualize the changes in gradients, corrected gradients, and adapted gradients throughout model training, using EWMA and the Adam optimizer. 2. SGD Momentum e Nesterov: Code to help compare the behavior of SGD optimizer in three ways: normal, with momentum, and with Nesterov momentum. Analyzing gradients, path and loss functions. 3. Learning Rate Schedulers: Code to help understand the differences in a model training using learning rate schedulers, specifically StepLR and CyclicLR.

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: 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.

Researchers generally agree that neural network models are difficult to train. One of the biggest issues is the large number of hyperparameters to specify and optimize. The list goes on, including the number of hidden layers, activation functions, optimizers, learning rate, and regularization. Tuning these hyperparameters can significantly improve neural network models. For us, as data scientists, building neural network models is about solving an optimization problem. We want to find the minima (global or sometimes local) of the objective function by gradient-based methods, such as gradient descent.

Of all the gradient descent hyperparameters, the learning rate is one of the most critical ones for good model performance. In this article, we will explore this parameter and explain why scheduling our learning rate during model training is crucial. Moving from there, we’ll see how to schedule learning rates by implementing and using various schedulers in Keras. We will then create experiments in neptune.ai to compare how these schedulers perform. What is the learning rate, and what does it do to a neural network? The learning rate (or step size) is explained as the magnitude of change/update to model weights during the backpropagation training process.

As a configurable hyperparameter, the learning rate is usually specified as a positive value less than 1.0. There was an error while loading. Please reload this page. 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:

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