torch (version 0.14.2)

optimizer: Creates a custom optimizer

Description

When implementing custom optimizers you will usually need to implement the initialize and step methods. See the example section below for a full example.

Usage

optimizer(
  name = NULL,
  inherit = Optimizer,
  ...,
  private = NULL,
  active = NULL,
  parent_env = parent.frame()
)

Arguments

name

(optional) name of the optimizer

inherit

(optional) you can inherit from other optimizers to re-use some methods.

...

Pass any number of fields or methods. You should at least define the initialize and step methods. See the examples section.

private

(optional) a list of private methods for the optimizer.

active

(optional) a list of active methods for the optimizer.

parent_env

used to capture the right environment to define the class. The default is fine for most situations.

Warning

If you need to move a model to GPU via $cuda(), please do so before constructing optimizers for it. Parameters of a model after $cuda() will be different objects from those before the call. In general, you should make sure that the objects pointed to by model parameters subject to optimization remain the same over the whole lifecycle of optimizer creation and usage.

Examples

if (torch_is_installed()) {

# In this example we will create a custom optimizer
# that's just a simplified version of the `optim_sgd` function.

optim_sgd2 <- optimizer(
  initialize = function(params, learning_rate) {
    defaults <- list(
      learning_rate = learning_rate
    )
    super$initialize(params, defaults)
  },
  step = function() {
    with_no_grad({
      for (g in seq_along(self$param_groups)) {
        group <- self$param_groups[[g]]
        for (p in seq_along(group$params)) {
          param <- group$params[[p]]

          if (is.null(param$grad) || is_undefined_tensor(param$grad)) {
            next
          }

          param$add_(param$grad, alpha = -group$learning_rate)
        }
      }
    })
  }
)

x <- torch_randn(1, requires_grad = TRUE)
opt <- optim_sgd2(x, learning_rate = 0.1)
for (i in 1:100) {
  opt$zero_grad()
  y <- x^2
  y$backward()
  opt$step()
}
all.equal(x$item(), 0, tolerance = 1e-9)
}