torch (version 0.3.0)

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.

Examples

# NOT RUN {
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)


}
# }