o zi# @s<ddlZddlmZddlmZmZmZGdddeZdS)N) Optimizer)OptFloatOptLossClosureParamscs^eZdZdZ     ddedededed ed ed d ffd d Zdded efddZ Z S)SGDLRDa@Implements stochastic gradient descent (optionally with momentum). Nesterov momentum is based on the formula from `On the importance of initialization and momentum in deep learning`__. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float): learning rate momentum (float, optional): momentum factor (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) dampening (float, optional): dampening for momentum (default: 0) Example: >>> optimizer = SGD(model.parameters(), lr=0.1, momentum=0.9) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf {Gz?rparamslrmomentum dampening weight_decaydropoutreturnNcs|dkr td||dkrtd||dkr!td||dkr,td|t|||||d}tt|||dS)Nr zInvalid learning rate: {}zInvalid momentum value: {}zInvalid dampening value: {}rzInvalid weight_decay value: {})r r r rr) ValueErrorformatdictsuperr__init__)selfr r r r rrdefaults __class__J/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/sgdlrd.pyrs$ zSGDLRD.__init__closurecCsd}|dur |}|jD]t}|d}|d}|d}|dD]a}|jdur&q|jj}t|j|d} t| } |dkrD|||j|dkrl|j|} d| vr]t| } | d<n| d} | |d |||d | } | | }|jd |qq |S) zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nrr r r rrmomentum_bufferrr ) param_groupsgraddatatorch ones_like bernoulliadd_stateclonedetachmul_)rrlossgrouprr r pd_pmmask param_statebuf lr_dropoutI_bufrrrstep;s<        z SGDLRD.step)rrrrr )N) __name__ __module__ __qualname____doc__rfloatrrrr4 __classcell__rrrrrs.r)r"torch.optim.optimizerrtypesrrrrrrrrs