optim_adadelta function

Adadelta optimizer

It has been proposed in ADADELTA: An Adaptive Learning Rate Method

optim_adadelta(params, lr = 1, rho = 0.9, eps = 1e-06, weight_decay = 0)

Arguments

• params: (iterable): list of parameters to optimize or list defining parameter groups
• lr: (float, optional): learning rate (default: 1e-3)
• rho: (float, optional): coefficient used for computing a running average of squared gradients (default: 0.9)
• eps: (float, optional): term added to the denominator to improve numerical stability (default: 1e-6)
• weight_decay: (float, optional): weight decay (L2 penalty) (default: 0)

Note

According to the original paper, decaying average of the squared gradients is computed as follows:

RMS of previous squared gradients up to time t:

Adadelta update rule:

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()) {
## Not run:

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

}