Adagrad¶
- class Adagrad(params, lr=0.01, lr_decay=0.0, eps=1e-10, weight_decay=0.0)[source]¶
Implements Adagrad algorithm proposed in “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization”.
- Parameters
params (Union[Iterable[Parameter], dict]) – iterable of parameters to optimize or dicts defining parameter groups.
lr (float) – coefficient that scales delta before it is applied to the parameters. Default: 1e-2.
lr_decay (float) – learning rate decay. Default: 0.
eps (float) – term added to the denominator to improve numerical stability. Default: 1e-10.
weight_decay (float) – weight decay (L2 penalty). Default: 0.
- Returns
An instance of the Adagrad optimizer.