Pytorch Optimizers Adam Sgd

© 2025 ApX Machine LearningEngineered with @keyframes heartBeat { 0%, 100% { transform: scale(1); } 25% { transform: scale(1.3); } 50% { transform: scale(1.1); } 75% { transform: scale(1.2); } } 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 field of deep learning, optimization algorithms play a crucial role in training neural networks. They are responsible for adjusting the model's parameters to minimize the loss function, which in turn helps the model to learn from the data effectively. Two popular optimization algorithms are Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam). PyTorch, a widely - used deep learning framework, provides easy - to - use implementations of these algorithms. In this blog post, we will explore the fundamental concepts of Adam and SGD in PyTorch, their usage methods, common practices, and best practices.

SGD is one of the most basic and widely used optimization algorithms in deep learning. The core idea of SGD is to update the model's parameters in the opposite direction of the gradient of the loss function with respect to the parameters. Mathematically, for a parameter $\theta$ and a learning rate $\eta$, the update rule is given by: $\theta_{t+1}=\theta_{t}-\eta\nabla L(\theta_{t})$ where $\nabla L(\theta_{t})$ is the gradient of the loss function $L$ with respect to $\theta$ at time step $t$. The "stochastic" part comes from the fact that instead of computing the gradient over the entire dataset (which can be computationally expensive), SGD computes the gradient over a randomly selected mini - batch of...

Adam is an adaptive learning rate optimization algorithm that combines the advantages of two other optimization algorithms: AdaGrad and RMSProp. It computes adaptive learning rates for each parameter. Adam maintains two moving averages: the first - order moment (mean) and the second - order moment (uncentered variance) of the gradients. Welcome back to the Advanced Neural Tuning course. In the last lesson, you learned how adjusting the learning rate during training can help your neural network learn more efficiently. Now, we will focus on another key part of the training process: the optimizer.

The optimizer is the algorithm that updates the weights of your neural network based on the gradients calculated during backpropagation. Choosing the right optimizer can make a big difference in how quickly your model learns and how well it performs. Just as with learning rate scheduling, the optimizer you select can help your model achieve better results, sometimes with less effort. In this lesson, you will learn how to set up and compare two of the most popular optimizers in PyTorch: SGD and Adam. Before we look at the code, let’s briefly discuss what makes SGD and Adam different. SGD stands for Stochastic Gradient Descent.

It is one of the simplest and most widely used optimizers. With SGD, the model’s weights are updated in the direction that reduces the loss, using a fixed learning rate. While it is simple and effective, it can sometimes be slow to converge, especially if the learning rate is not set well. Adam, which stands for Adaptive Moment Estimation, is a more advanced optimizer. It keeps track of both the average of the gradients and the average of the squared gradients for each parameter. This allows Adam to adapt the learning rate for each parameter individually, often leading to faster and more stable training.

In practice, Adam is a good default choice for many deep learning problems, but it is still important to understand and compare it with simpler methods like SGD. When training machine learning models using PyTorch, selecting the right optimizer can significantly influence the performance and convergence of your model. PyTorch provides several optimization algorithms that come in handy for different types of problems. In this article, we will explore some of the most commonly used optimizers in PyTorch, discuss their properties, and help you choose the right one for your tasks. An optimizer adjusts the attributes of your neural network, such as weights and learning rate. It uses the information from the loss function to help the model iterate towards the most accurate prediction possible.

Essentially, it minimizes the loss function by adjusting model parameters, boosting performance. In PyTorch, several different optimizers are available in the torch.optim package. Some of the most popular ones include: SGD is one of the simplest types of optimizer. The key benefit of using SGD is its simplicity and ease of implementation. While SGD is a straightforward choice, it can be slow, especially when training large models or deep networks.

torch.optim is a package implementing various optimization algorithms in PyTorch. If you use PyTorch you can create your own optimizers in Python. PyTorch has default optimizers. Most famous is torch.optim.SGD, followed by torch.optim.Adam or torch.optim.AdamW. The original Adam algorithm was proposed in Adam: A Method for Stochastic Optimization. The AdamW variant was proposed in Decoupled Weight Decay Regularization.

Recently very popular is also torch.optim.LBFGS inspired by a matlab function minFunc. In PyTorch, an optimizer is a specific implementation of the optimization algorithm that is used to update the parameters of a neural network. The optimizer updates the parameters in such a way that the loss of the neural network is minimized. PyTorch provides various built-in optimizers such as SGD, Adam, Adagrad, etc. that can be used out of the box. However, in some cases, the built-in optimizers may not be suitable for a particular problem or may not perform well.

In such cases, one can create their own custom optimizer. A custom optimizer in PyTorch is a class that inherits from the torch.optim.Optimizer base class. The custom optimizer should implement the init and step methods. The init method is used to initialize the optimizer's internal state, and the step method is used to update the parameters of the model. In PyTorch, creating a custom optimizer is a two-step process. First, we need to create a class that inherits from the torch.optim.Optimizer class, and override the following methods:

The init method is used to initialize the optimizer's internal state. In this method, we define the hyperparameters of the optimizer and set the internal state. For example, let's say we want to create a custom optimizer that implements the Momentum optimization algorithm. The init method for this optimizer would look something like this: In the below example, we define the hyperparameters of the optimizer to be the learning rate lr and the momentum. We then call the super() method to initialize the internal state of the optimizer.

We also set up a state dictionary that we will use to store the velocity vector for each parameter. Implements stochastic gradient descent (optionally with momentum). Nesterov momentum is based on the formula from On the importance of initialization and momentum in deep learning. params (iterable) – iterable of parameters or named_parameters to optimize or iterable of dicts defining parameter groups. When using named_parameters, all parameters in all groups should be named lr (float, Tensor, optional) – learning rate (default: 1e-3)

momentum (float, optional) – momentum factor (default: 0) Optimizers determine how neural networks learn by updating parameters to minimize loss. The choice of optimizer significantly affects training speed and final performance. Watch how different optimizers navigate toward the minimum of a simple quadratic function: Step 0 / 100 | SGD Loss: 9.0000 | Momentum Loss: 9.0000 | Adam Loss: 9.0000 The simplest optimizer.

Updates parameters directly proportional to the gradient magnitude. Where α is learning rate and ∇f(θ) is the gradient

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