optim_adamw: AdamW optimizer

In torchopt: Advanced Optimizers for Torch

AdamW optimizer

Description

R implementation of the AdamW optimizer proposed by Loshchilov & Hutter (2019). We used the pytorch implementation developed by Collin Donahue-Oponski available at: https://gist.github.com/colllin/0b146b154c4351f9a40f741a28bff1e3

From the abstract by the paper by Loshchilov & Hutter (2019): L2 regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is not the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L2 regularization (often calling it “weight decay” in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by decoupling the weight decay from the optimization steps taken w.r.t. the loss function

Usage

optim_adamw(
  params,
  lr = 0.01,
  betas = c(0.9, 0.999),
  eps = 1e-08,
  weight_decay = 1e-06
)

Arguments

params	List of parameters to optimize.
lr	Learning rate (default: 1e-3)
betas	Coefficients computing running averages of gradient and its square (default: (0.9, 0.999))
eps	Term added to the denominator to improve numerical stability (default: 1e-8)
weight_decay	Weight decay (L2 penalty) (default: 1e-6)

Value

A torch optimizer object implementing the step method.

Author(s)

Gilberto Camara, [e-mail]

Rolf Simoes, [e-mail]

Felipe Souza, [e-mail]

Alber Sanchez, [e-mail]

References

Ilya Loshchilov, Frank Hutter, "Decoupled Weight Decay Regularization", International Conference on Learning Representations (ICLR) 2019. https://arxiv.org/abs/1711.05101

Examples

if (torch::torch_is_installed()) {
# function to demonstrate optimization
beale <- function(x, y) {
    log((1.5 - x + x * y)^2 + (2.25 - x - x * y^2)^2 + (2.625 - x + x * y^3)^2)
 }
# define optimizer
optim <- torchopt::optim_adamw
# define hyperparams
opt_hparams <- list(lr = 0.01)

# starting point
x0 <- 3
y0 <- 3
# create tensor
x <- torch::torch_tensor(x0, requires_grad = TRUE)
y <- torch::torch_tensor(y0, requires_grad = TRUE)
# instantiate optimizer
optim <- do.call(optim, c(list(params = list(x, y)), opt_hparams))
# run optimizer
steps <- 400
x_steps <- numeric(steps)
y_steps <- numeric(steps)
for (i in seq_len(steps)) {
    x_steps[i] <- as.numeric(x)
    y_steps[i] <- as.numeric(y)
    optim$zero_grad()
    z <- beale(x, y)
    z$backward()
    optim$step()
}
print(paste0("starting value = ", beale(x0, y0)))
print(paste0("final value = ", beale(x_steps[steps], y_steps[steps])))
}

torchopt documentation built on June 7, 2023, 6:10 p.m.