Cosineannealinglr Pytorch 2 9 Documentation

Set the learning rate of each parameter group using a cosine annealing schedule. The learning rate is updated recursively using: This implements a recursive approximation of the closed-form schedule proposed in SGDR: Stochastic Gradient Descent with Warm Restarts: ηt\eta_tηt​ is the learning rate at step ttt TcurT_{cur}Tcur​ is the number of epochs since the last restart 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. PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.

Features described in this documentation are classified by release status: Stable (API-Stable): These features will be maintained long-term and there should generally be no major performance limitations or gaps in documentation. We also expect to maintain backwards compatibility (although breaking changes can happen and notice will be given one release ahead of time). Unstable (API-Unstable): Encompasses all features that are under active development where APIs may change based on user feedback, requisite performance improvements or because coverage across operators is not yet complete. The APIs and performance characteristics of these features may change. In the field of deep learning, optimizing the learning rate is crucial for training efficient and effective models.

The learning rate determines the step size at which the model's parameters are updated during the training process. A fixed learning rate can often lead to sub - optimal results, either converging too slowly or overshooting the optimal solution. Cosine annealing is a learning rate scheduling technique that addresses these issues by adjusting the learning rate in a cosine - shaped curve over the training epochs. PyTorch, a popular deep learning framework, provides built - in support for cosine annealing. This blog post aims to provide a detailed overview of cosine annealing in PyTorch, including its fundamental concepts, usage methods, common practices, and best practices. The basic idea behind cosine annealing is to decrease the learning rate in a smooth, periodic manner.

The formula for cosine annealing is given by: [ \eta_{t}=\eta_{\min}+\frac{1}{2}(\eta_{\max}-\eta_{\min})(1 + \cos(\frac{T_{cur}}{T_{max}}\pi)) ] At the beginning of a cycle ((T_{cur} = 0)), the cosine function is at its maximum value ((\cos(0)=1)), and the learning rate is set to (\eta_{\max}). As the number of epochs progresses, the cosine function decreases, and so does the learning rate. At the end of the cycle ((T_{cur}=T_{max})), the cosine function is at its minimum value ((\cos(\pi)= - 1)), and the learning rate reaches (\eta_{\min}). First, we need to import the necessary PyTorch libraries.

In machine learning, particularly in deep learning, optimizing model performance requires not only selecting the right architecture but also fine-tuning the learning process. One of the essential aspects of training models effectively is managing the learning rate — a parameter that determines how much a model’s weights are adjusted with respect to the loss gradient during each... Too high a learning rate can lead to unstable training, while too low a rate may result in slow convergence or getting stuck in local minima. Here’s where learning rate schedulers come in. Learning rate schedulers are tools that dynamically adjust the learning rate as training progresses, helping models converge more efficiently and often to a better solution. These schedulers work by modifying the learning rate over time based on predefined rules or performance metrics.

For instance, a learning rate scheduler might decrease the rate over time to allow the model to take smaller, more refined steps as it nears optimal solutions. Others might increase the learning rate at strategic points to help the model escape plateaus in the loss landscape. The goal is to balance stability and speed, helping models reach an optimal solution faster and more reliably. In PyTorch, learning rate schedulers are built directly into the library, making it easy for users to experiment with different scheduling strategies and tailor them to their specific needs. PyTorch offers a range of scheduling options — from basic, predefined schedules like StepLR, which decreases the learning rate by a factor at regular intervals, to more sophisticated ones like ReduceLROnPlateau, which reduces the... These schedulers are flexible, allowing us to customize parameters like learning rate decay rates, milestones, and conditions, making them a powerful tool in fine-tuning model performance.

With PyTorch’s straightforward approach, integrating a learning rate scheduler into our model’s training loop becomes almost seamless, giving us the advantage of dynamically managing learning rates without needing extensive code modifications. In this guide, I’ll dive deeper into one specific type of learning rate scheduler: the Cosine Annealing learning rate scheduler. Cosine annealing schedulers adjust the learning rate following a cosine curve, gradually reducing the rate over each cycle. This smooth decay pattern can help stabilize training, especially for models that may otherwise oscillate around suboptimal solutions. The cosine learning rate scheduler is particularly useful for scenarios where we want to fine-tune the model more carefully as it approaches convergence. It’s designed to lower the learning rate more gradually than step or exponential decay schedulers, and it often includes a restart mechanism, where the learning rate resets to its initial value at regular intervals...

This restart helps the model escape from potential local minima by periodically taking larger steps, enabling it to search more thoroughly across the loss landscape. I am still new to PyTorch and I am going off this link: https://pytorch.org/docs/stable/optim.html I don’t see many examples of it being applied online so this is how I thought it should look. Then in my training loop, I have it set up like so: For the training loop, I even tried a different approach such as: Where after every epoch it gets adjusted. An example of implement Cosine Annealing + warm restarts can be found here.

Set the learning rate of each parameter group using a cosine annealing schedule, whereηmax\eta_{max}is set to the initial lr andTcurT_{cur}is the number of epochs since the last restart in SGDR: When last_epoch=-1, sets initial lr as lr. Notice that because the schedule is defined recursively, the learning rate can be simultaneously modified outside this scheduler by other operators. If the learning rate is set solely by this scheduler, the learning rate at each step becomes: It has been proposed in SGDR: Stochastic Gradient Descent with Warm Restarts. Note that this only implements the cosine annealing part of SGDR, and not the restarts.

Return last computed learning rate by current scheduler. state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict(). There was an error while loading. Please reload this page. This formula does not incorporate the learning rate of the last step, is the same as the "If the learning rate is set solely by this scheduler" formula below, and does not seem to...

I think the correct formula should be something like: cc @svekars @sekyondaMeta @AlannaBurke @vincentqb @jbschlosser @albanD @janeyx99 @crcrpar Set the learning rate of each parameter group using a cosine annealing schedule. The learning rate is updated recursively using: This implements a recursive approximation of the closed-form schedule proposed in SGDR: Stochastic Gradient Descent with Warm Restarts: ηt\eta_tηt​ is the learning rate at step ttt

TcurT_{cur}Tcur​ is the number of epochs since the last restart

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