torch (version 0.14.2)

optim_ignite_rmsprop: LibTorch implementation of RMSprop

Description

Proposed by G. Hinton in his course.

Usage

optim_ignite_rmsprop(
  params,
  lr = 0.01,
  alpha = 0.99,
  eps = 1e-08,
  weight_decay = 0,
  momentum = 0,
  centered = FALSE
)

Arguments

params

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

lr

(float, optional): learning rate (default: 1e-2)

alpha

(float, optional): smoothing constant (default: 0.99)

eps

(float, optional): term added to the denominator to improve numerical stability (default: 1e-8)

weight_decay

optional weight decay penalty. (default: 0)

momentum

(float, optional): momentum factor (default: 0)

centered

(bool, optional) : if TRUE, compute the centered RMSProp, the gradient is normalized by an estimation of its variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0)

Fields and Methods

See OptimizerIgnite.

Examples

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