lr_lambda: Sets the learning rate of each parameter group to the initial...

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.

Description

Sets the learning rate of each parameter group to the initial lr times a given function. When last_epoch=-1, sets initial lr as lr.

Usage

lr_lambda(optimizer, lr_lambda, last_epoch = -1, verbose = FALSE)

Arguments

optimizer	(Optimizer): Wrapped optimizer.
lr_lambda	(function or list): A function which computes a multiplicative factor given an integer parameter epoch, or a list of such functions, one for each group in optimizer.param_groups.
last_epoch	(int): The index of last epoch. Default: -1.
verbose	(bool): If TRUE, prints a message to stdout for each update. Default: FALSE.

Examples

if (torch_is_installed()) {
# Assuming optimizer has two groups.
lambda1 <- function(epoch) epoch %/% 30
lambda2 <- function(epoch) 0.95^epoch
## Not run: 
scheduler <- lr_lambda(optimizer, lr_lambda = list(lambda1, lambda2))
for (epoch in 1:100) {
  train(...)
  validate(...)
  scheduler$step()
}

## End(Not run)

}

torch documentation built on May 29, 2024, 9:54 a.m.