Pytorch Change The Learning Rate Based On Number Of Epochs

Communities for your favorite technologies. Explore all Collectives Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work. Bring the best of human thought and AI automation together at your work. Learn more

Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. Created On: Jun 13, 2025 | Last Updated On: Aug 24, 2025 torch.optim is a package implementing various optimization algorithms. Most commonly used methods are already supported, and the interface is general enough, so that more sophisticated ones can also be easily integrated in the future. To use torch.optim you have to construct an optimizer object that will hold the current state and will update the parameters based on the computed gradients.

To construct an Optimizer you have to give it an iterable containing the parameters (all should be Parameter s) or named parameters (tuples of (str, Parameter)) to optimize. Then, you can specify optimizer-specific options such as the learning rate, weight decay, etc. In the realm of deep learning, PyTorch stands as a beacon, illuminating the path for researchers and practitioners to traverse the complex landscapes of artificial intelligence. Its dynamic computational graph and user-friendly interface have solidified its position as a preferred framework for developing neural networks. As we delve into the nuances of model training, one essential aspect that demands meticulous attention is the learning rate. To navigate the fluctuating terrains of optimization effectively, PyTorch introduces a potent ally—the learning rate scheduler.

This article aims to demystify the PyTorch learning rate scheduler, providing insights into its syntax, parameters, and indispensable role in enhancing the efficiency and efficacy of model training. PyTorch, an open-source machine learning library, has gained immense popularity for its dynamic computation graph and ease of use. Developed by Facebook's AI Research lab (FAIR), PyTorch has become a go-to framework for building and training deep learning models. Its flexibility and dynamic nature make it particularly well-suited for research and experimentation, allowing practitioners to iterate swiftly and explore innovative approaches in the ever-evolving field of artificial intelligence. At the heart of effective model training lies the learning rate—a hyperparameter crucial for controlling the step size during optimization. PyTorch provides a sophisticated mechanism, known as the learning rate scheduler, to dynamically adjust this hyperparameter as the training progresses.

The syntax for incorporating a learning rate scheduler into your PyTorch training pipeline is both intuitive and flexible. At its core, the scheduler is integrated into the optimizer, working hand in hand to regulate the learning rate based on predefined policies. The typical syntax for implementing a learning rate scheduler involves instantiating an optimizer and a scheduler, then stepping through epochs or batches, updating the learning rate accordingly. The versatility of the scheduler is reflected in its ability to accommodate various parameters, allowing practitioners to tailor its behavior to meet specific training requirements. The importance of learning rate schedulers becomes evident when considering the dynamic nature of model training. As models traverse complex loss landscapes, a fixed learning rate may hinder convergence or cause overshooting.

Learning rate schedulers address this challenge by adapting the learning rate based on the model's performance during training. This adaptability is crucial for avoiding divergence, accelerating convergence, and facilitating the discovery of optimal model parameters. The provided test accuracy of approximately 95.6% suggests that the trained neural network model performs well on the test set. This article is a guide to PyTorch Learning Rate Scheduler and aims to explain how to Adjust the Learning Rate in PyTorch using the Learning Rate Scheduler. We learn about what an optimal learning rate means and how to find the optimal learning rate for training various model architectures. Learning rate is one of the most important hyperparameters to tune when training deep neural networks.

A good learning rate is crucial to find an optimal solution during the training of neural networks. To manually tune the learning rate by observing metrics like the model's loss curve, would require some amount of bookkeeping and babysitting on the observer's part. Also, rather than going with a constant learning rate throughout the training routine, it is almost always a better idea to adjust the learning rate and adapt according to some criterion like the number... Learning rate is a hyperparameter that controls the speed at which a neural network learns by updating its parameters. When I set the learning rate and find the accuracy cannot increase after training few epochs If I want to use a step decay: reduce the learning rate by a factor of 10 every 5 epochs, how can I do so?

Although details about this optimizer are beyond the scope of this article, it's worth mentioning that Adam updates a learning rate separately for each model parameter/weight. This implies that with Adam, the learning rate may first increase at early layers, and thus help improve the efficiency of deep neural networks. LAMBDA LR Sets the learning rate of each parameter group to the initial lr times a given function. You can use learning rate scheduler torch.optim.lr_scheduler.StepLR In PyTorch, there are several ways to adjust the learning rate. The above are several common methods for adjusting the learning rate, which can be chosen based on the actual situation when training a neural network.

Welcome to the first lesson of the Advanced Neural Tuning course. In this course, you will learn how to make your neural networks train more efficiently and achieve better results by using advanced optimization techniques. We will start with a key concept: learning rate scheduling. The learning rate is a crucial parameter in training neural networks. It controls how much the model's weights are updated during each step of training. If the learning rate is too high, the model might not learn well and could even diverge.

If it is too low, training can be very slow and might get stuck before reaching a good solution. Learning rate scheduling is a technique in which you change the learning rate during training instead of keeping it constant. This can help your model learn faster at the beginning and fine-tune its weights as training progresses. In this lesson, you will learn how to use a popular learning rate scheduler in PyTorch called StepLR. The StepLR scheduler is a simple but effective way to adjust the learning rate as your model trains. In PyTorch, StepLR reduces the learning rate by a certain factor every fixed number of epochs.

This helps the model make big updates early on and then smaller, more careful updates as it gets closer to a good solution. The two main parameters for StepLR are step_size and gamma. The step_size tells the scheduler how many epochs to wait before reducing the learning rate. The gamma parameter is the factor by which the learning rate is multiplied each time it is reduced. For example, if your initial learning rate is 0.1, your step_size is 10, and your gamma is 0.1, then after 10 epochs, the learning rate will become 0.01. Learning Rate is an important hyperparameter in Gradient Descent.

Its value determines how fast the Neural Network would converge to minima. Usually, we choose a learning rate and depending on the results change its value to get the optimal value for LR. If the learning rate is too low for the Neural Network the process of convergence would be very slow and if it's too high the converging would be fast but there is a chance... So we usually tune our parameters to find the best value for the learning rate. But is there a way we can improve this process? Instead of taking a constant learning rate, we can start with a higher value of LR and then keep decreasing its value periodically after certain iterations.

This way we can initially have faster convergence whilst reducing the chances of overshooting the loss. In order to implement this we can use various scheduler in optim library in PyTorch. The format of a training loop is as following:- PyTorch provides several methods to adjust the learning rate based on the number of epochs. Let's have a look at a few of them:- For this tutorial we are going to be using MNIST dataset, so we’ll start by loading our data and defining the model afterwards.

Its recommended that you know how to create and train a Neural Network in PyTorch. Let's start by loading our data. Now that we have our dataloader ready we can now proceed to create our model. PyTorch model follows the following format:- In the field of deep learning, the learning rate is a crucial hyperparameter that determines the step size at each iteration while updating the model's parameters during the training process. An appropriate learning rate can significantly speed up the convergence of the model and improve its performance.

PyTorch, a popular deep learning framework, provides several ways to adjust the learning rate within a training loop. In this blog post, we will explore the fundamental concepts, usage methods, common practices, and best practices of changing the learning rate in a PyTorch for loop. The learning rate controls how much we update the model's parameters in response to the estimated error each time the model parameters are updated. A large learning rate may cause the model to overshoot the optimal solution and fail to converge, while a small learning rate may lead to slow convergence or getting stuck in local minima. In PyTorch, the learning rate is set when initializing an optimizer, such as torch.optim.SGD or torch.optim.Adam. During the training process, we can change the learning rate either manually or by using learning rate schedulers provided by PyTorch.

The simplest way to change the learning rate is to manually adjust it within the training loop. Each optimizer in PyTorch has a param_groups attribute, which is a list of dictionaries. Each dictionary represents a parameter group and contains information such as the learning rate. PyTorch provides several built - in learning rate schedulers in the torch.optim.lr_scheduler module. These schedulers can automatically adjust the learning rate based on certain rules.

People Also Search

• Pytorch Change the learning rate based on number of epochs
• torch.optim — PyTorch 2.9 documentation
• Understanding PyTorch Learning Rate Scheduling - GeeksforGeeks
• How to Choose the Right Learning Rate in Deep Learning (with PyTorch ...
• How to Adjust Learning Rate in Pytorch - Scaler Topics
• PyTorch Learning Rate Adjustment Guide - Blog - Silicon Cloud
• Learning Rate Scheduling with PyTorch StepLR | CodeSignal Learn
• Adjusting Learning Rate of a Neural Network in PyTorch
• How to Change Learning Rate in PyTorch for Loop