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torch.optim.adadelta.Adadelta Class Reference
Inheritance diagram for torch.optim.adadelta.Adadelta:

Public Member Functions

def __init__ (self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0)
def step (self, closure=None)

Detailed Description

Implements Adadelta algorithm.

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

    params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
    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)
    lr (float, optional): coefficient that scale delta before it is applied
        to the parameters (default: 1.0)
    weight_decay (float, optional): weight decay (L2 penalty) (default: 0)


Definition at line 6 of file

Member Function Documentation

def torch.optim.adadelta.Adadelta.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 38 of file

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