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torch.optim.adamax.Adamax Class Reference
Inheritance diagram for torch.optim.adamax.Adamax:

Public Member Functions

def __init__ (self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0)
def step (self, closure=None)

Detailed Description

Implements Adamax algorithm (a variant of Adam based on infinity norm).

It has been proposed in `Adam: A Method for Stochastic Optimization`__.

    params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
    lr (float, optional): learning rate (default: 2e-3)
    betas (Tuple[float, float], optional): coefficients used for computing
        running averages of gradient and its square
    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)


Definition at line 5 of file

Member Function Documentation

def torch.optim.adamax.Adamax.step (   self,
  closure = None 
Performs a single optimization step.

    closure (callable, optional): A closure that reevaluates the model
and returns the loss.

Definition at line 39 of file

The documentation for this class was generated from the following file: