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) { # \dontrun{
optimizer <- optim_ignite_rmsprop(model$parameters(), lr = 0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()
} # }
}