optim_ignite_sgd {torch} | R Documentation |
LibTorch implementation of SGD
Description
Implements stochastic gradient descent (optionally with momentum). Nesterov momentum is based on the formula from On the importance of initialization and momentum in deep learning.
Usage
optim_ignite_sgd(
params,
lr = optim_required(),
momentum = 0,
dampening = 0,
weight_decay = 0,
nesterov = FALSE
)
Arguments
params |
(iterable): iterable of parameters to optimize or dicts defining parameter groups |
lr |
(float): learning rate |
momentum |
(float, optional): momentum factor (default: 0) |
dampening |
(float, optional): dampening for momentum (default: 0) |
weight_decay |
(float, optional): weight decay (L2 penalty) (default: 0) |
nesterov |
(bool, optional): enables Nesterov momentum (default: FALSE) |
Fields and Methods
See OptimizerIgnite
.
Examples
if (torch_is_installed()) {
## Not run:
optimizer <- optim_ignite_sgd(model$parameters(), lr = 0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()
## End(Not run)
}
[Package torch version 0.15.1 Index]