optim_ignite_adagrad: LibTorch implementation of Adagrad

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

LibTorch implementation of Adagrad

Description

Proposed in Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

Usage

optim_ignite_adagrad(
  params,
  lr = 0.01,
  lr_decay = 0,
  weight_decay = 0,
  initial_accumulator_value = 0,
  eps = 1e-10
)

Arguments

params	(iterable): list of parameters to optimize or list parameter groups
lr	(float, optional): learning rate (default: 1e-2)
lr_decay	(float, optional): learning rate decay (default: 0)
weight_decay	(float, optional): weight decay (L2 penalty) (default: 0)
initial_accumulator_value	the initial value for the accumulator. (default: 0) Adagrad is an especially good optimizer for sparse data. It individually modifies learning rate for every single parameter, dividing the original learning rate value by sum of the squares of the gradients. It causes that the rarely occurring features get greater learning rates. The main downside of this method is the fact that learning rate may be getting small too fast, so that at some point a model cannot learn anymore.
eps	(float, optional): term added to the denominator to improve numerical stability (default: 1e-10)

Fields and Methods

See OptimizerIgnite.

Examples

if (torch_is_installed()) {
## Not run: 
optimizer <- optim_ignite_adagrad(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.