torch (version 0.14.2)

optim_ignite_sgd: 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()) {
if (FALSE) {
optimizer <- optim_ignite_sgd(model$parameters(), lr = 0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()
}
}