The Madgrad package is an R port of the original madgrad by Aaron Defazio and Samy Jelassi. See the Arxiv paper for details on the method.
Madgrad is not yet on CRAN. The development version from GitHub can be installed with:
# install.packages("devtools")
devtools::install_github("mlverse/madgrad")This is a small example showing how to use madgrad with
torch to minimize a function, of course, madgrad is not the
best algorithm for this task and should work better for neural network
training.
library(madgrad)
library(torch)
torch_manual_seed(1)
f <- function(x, y) {
log((1.5 - x + x*y)^2 + (2.25 - x - x*(y^2))^2 + (2.625 - x + x*(y^3))^2)
}
x <- torch_tensor(-5, requires_grad = TRUE)
y <- torch_tensor(-2, requires_grad = TRUE)
opt <- optim_madgrad(params = list(x, y), lr = 0.1)
for (i in 1:100) {
opt$zero_grad()
z <- f(x, y)
z$backward()
opt$step()
}
x
#> torch_tensor
#> 2.2882
#> [ CPUFloatType{1} ]
y
#> torch_tensor
#> 0.2412
#> [ CPUFloatType{1} ]
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.