torch (version 0.14.2)

optim_ignite_adamw: LibTorch implementation of AdamW

Description

For further details regarding the algorithm we refer to Decoupled Weight Decay Regularization

Usage

optim_ignite_adamw(
  params,
  lr = 0.001,
  betas = c(0.9, 0.999),
  eps = 1e-08,
  weight_decay = 0.01,
  amsgrad = FALSE
)

Arguments

params

(iterable): iterable of parameters to optimize or dicts defining parameter groups

lr

(float, optional): learning rate (default: 1e-3)

betas

(Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999))

eps

(float, optional): term added to the denominator to improve numerical stability (default: 1e-8)

weight_decay

(float, optional): weight decay (L2 penalty) (default: 0)

amsgrad

(boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond (default: FALSE)

Fields and Methods

See OptimizerIgnite.

Examples

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