optim_adamw function

Implements AdamW algorithm

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

optim_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)