o zi@sXddlZddlZddlmZddlmZddlmZmZm Z m Z dZ GdddeZ dS)N) Optimizer)Betas2OptFloatOptLossClosureParams)Yogics^eZdZdZ     ddeded ed ed ed ed dffdd Zdded e fddZ Z S)ra9Implements Yogi Optimizer Algorithm. It has been proposed in `Adaptive methods for Nonconvex Optimization`__. Arguments: params: iterable of parameters to optimize or dicts defining parameter groups lr: learning rate (default: 1e-2) betas: coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps: term added to the denominator to improve numerical stability (default: 0.001) initial_accumulator: initial values for first and second moments (default: 1e-6) weight_decay: weight decay (L2 penalty) (default: 0) Example: >>> import torch_optimizer as optim >>> optimizer = optim.Yogi(model.parameters(), lr=0.01) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization # noqa Note: Reference code: https://github.com/4rtemi5/Yogi-Optimizer_Keras {Gz?g?g+?MbP?ư>rparamslrbetasepsinitial_accumulator weight_decayreturnNcs|dkr td||dkrtd|d|dkr"dks,ntd|dd|dkr8dksBntd|d|dkrMtd |t|||||d }tt|||dS) NgzInvalid learning rate: {}zInvalid epsilon value: {}rg?z%Invalid beta parameter at index 0: {}rz%Invalid beta parameter at index 1: {}zInvalid weight_decay value: {})rrrrr) ValueErrorformatdictsuperr__init__)selfr rrrrrdefaults __class__H/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/yogi.pyr)s0   z Yogi.__init__closurecCsd}|dur |}|jD]}|dD]}|jdurq|jj}|jr%td|j|}t|dkrXd|d<tj t j |jt j d|d|d<tj t j |jt j d|d|d <|d|d }}|d \} } |dd 7<d | |d} d | |d} |d dkr|j |j|d d }|| j|d | d ||} |jt || | d |  d|t| |d}|d| }|jj||| dqq |S)zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nr zJYogi does not support sparse gradients, please consider SparseAdam insteadrstep) memory_formatrexp_avg exp_avg_sqrrr)alpha)valuerr) param_groupsgraddata is_sparse RuntimeErrorstatelennninit constant_torch empty_likepreserve_formataddmul_add_muladdcmul_signsqrtmathaddcdiv_)rrlossgrouppr'r+r"r#beta1beta2bias_correction1bias_correction2 grad_squareddenom step_sizerrrr Ls`          =z Yogi.step)r r r r r)N) __name__ __module__ __qualname____doc__rfloatrrrrr __classcell__rrrrr s.#r) r:r0torch.nnr-torch.optim.optimizerrtypesrrrr__all__rrrrrs