It has been proposed in Adam: A Method for Stochastic Optimization.
Usage
optim_adam(
params,
lr = 0.001,
betas = c(0.9, 0.999),
eps = 1e-08,
weight_decay = 0,
amsgrad = FALSE
)Arguments
- params
(iterable): iterable of parameters to optimize or dicts defining parameter groups
- lr
(float, optional): learning rate (default: 1e-3)
- betas
(
Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999))- eps
(float, optional): term added to the denominator to improve numerical stability (default: 1e-8)
- weight_decay
(float, optional): weight decay (L2 penalty) (default: 0)
- amsgrad
(boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond (default: FALSE)
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 $cuda()
will be different objects from those before the call. In general, you
should make sure that the objects pointed to by model parameters subject
to optimization remain the same over the whole lifecycle of optimizer
creation and usage.
Examples
if (torch_is_installed()) {
if (FALSE) {
optimizer <- optim_adam(model$parameters(), lr = 0.1)
optimizer$zero_grad()
loss_fn(model(input), target)$backward()
optimizer$step()
}
}