optim_ignite_sgd: LibTorch implementation of SGD

In torch: Tensors and Neural Networks with 'GPU' Acceleration

LibTorch implementation of SGD

Description

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.

Usage

optim_ignite_sgd(
  params,
  lr = optim_required(),
  momentum = 0,
  dampening = 0,
  weight_decay = 0,
  nesterov = FALSE
)

Arguments

params	(iterable): iterable of parameters to optimize or dicts defining parameter groups
lr	(float): learning rate
momentum	(float, optional): momentum factor (default: 0)
dampening	(float, optional): dampening for momentum (default: 0)
weight_decay	(float, optional): weight decay (L2 penalty) (default: 0)
nesterov	(bool, optional): enables Nesterov momentum (default: FALSE)

Fields and Methods

See OptimizerIgnite.

Examples

if (torch_is_installed()) {
## Not run: 
optimizer <- optim_ignite_sgd(model$parameters(), lr = 0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()

## End(Not run)
}

torch documentation built on Aug. 21, 2025, 5:50 p.m.