optim_ignite_rmsprop: LibTorch implementation of RMSprop

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

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()) {
## Not run: 
optimizer <- optim_ignite_rmsprop(model$parameters(), lr = 0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()

## End(Not run)
}

torch documentation built on Aug. 21, 2025, 5:50 p.m.