Averaged Stochastic Gradient Descent optimizer

Proposed in Acceleration of stochastic approximation by averaging

Usage

optim_asgd(
  params,
  lr = 0.01,
  lambda = 1e-04,
  alpha = 0.75,
  t0 = 1e+06,
  weight_decay = 0
)

Arguments

params

(iterable): iterable of parameters to optimize or lists defining parameter groups

lr

(float): learning rate

lambda

(float, optional): decay term (default: 1e-4)

alpha

(float, optional): power for eta update (default: 0.75)

t0

(float, optional): point at which to start averaging (default: 1e6)

weight_decay

(float, optional): weight decay (L2 penalty) (default: 0)

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()) {
if (FALSE) {
optimizer <- optim_asgd(model$parameters(), lr = 0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()
}

}