madgrad (version 0.1.0)

optim_madgrad: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization.

Description

MADGRAD is a general purpose optimizer that can be used in place of SGD or Adam may converge faster and generalize better. Currently GPU-only. Typically, the same learning rate schedule that is used for SGD or Adam may be used. The overall learning rate is not comparable to either method and should be determined by a hyper-parameter sweep.

Usage

optim_madgrad(params, lr = 0.01, momentum = 0.9, weight_decay = 0, eps = 1e-06)

Arguments

params

(list): List of parameters to optimize.

lr

(float): Learning rate (default: 1e-2).

momentum

(float): Momentum value in the range [0,1) (default: 0.9).

weight_decay

(float): Weight decay, i.e. a L2 penalty (default: 0).

eps

(float): Term added to the denominator outside of the root operation to improve numerical stability. (default: 1e-6).

Value

An optimizer object implementing the step and zero_grad methods.

Details

MADGRAD requires less weight decay than other methods, often as little as zero. Momentum values used for SGD or Adam's beta1 should work here also.

On sparse problems both weight_decay and momentum should be set to 0. (not yet supported in the R implementation).

Examples

# NOT RUN {
if (torch::torch_is_installed()) {
library(torch)
x <- torch_randn(1, requires_grad = TRUE)
opt <- optim_madgrad(x)
for (i in 1:100) {
  opt$zero_grad()
  y <- x^2
  y$backward()
  opt$step()
}
all.equal(x$item(), 0, tolerance = 1e-9)
}

# }