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torch.optim.rmsprop.RMSprop Class Reference
Inheritance diagram for torch.optim.rmsprop.RMSprop:

Public Member Functions

def __init__ (self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False)
def __setstate__ (self, state)
def step (self, closure=None)

Detailed Description

Implements RMSprop algorithm.

Proposed by G. Hinton in his
`course <>`_.

The centered version first appears in `Generating Sequences
With Recurrent Neural Networks <>`_.

    params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
    lr (float, optional): learning rate (default: 1e-2)
    momentum (float, optional): momentum factor (default: 0)
    alpha (float, optional): smoothing constant (default: 0.99)
    eps (float, optional): term added to the denominator to improve
        numerical stability (default: 1e-8)
    centered (bool, optional) : if ``True``, compute the centered RMSProp,
        the gradient is normalized by an estimation of its variance
    weight_decay (float, optional): weight decay (L2 penalty) (default: 0)

Definition at line 5 of file

Member Function Documentation

def torch.optim.rmsprop.RMSprop.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 49 of file

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