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A deep learning, cross platform ML framework
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torch.optim.rprop.Rprop Class Reference
Inheritance diagram for torch.optim.rprop.Rprop:

Public Member Functions

def __init__ (self, params, lr=1e-2, etas=(0.5, 1.2), step_sizes=(1e-6, 50))
def step (self, closure=None)

Detailed Description

Implements the resilient backpropagation algorithm.

    params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
    lr (float, optional): learning rate (default: 1e-2)
    etas (Tuple[float, float], optional): pair of (etaminus, etaplis), that
        are multiplicative increase and decrease factors
        (default: (0.5, 1.2))
    step_sizes (Tuple[float, float], optional): a pair of minimal and
        maximal allowed step sizes (default: (1e-6, 50))

Definition at line 6 of file

Member Function Documentation

def torch.optim.rprop.Rprop.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 29 of file

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