class_docstr: Implementation of `Eve:  A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates <https://arxiv.org/pdf/1611.01505.pdf>`_
class_docstr_tok: ["Implementation","of","`","Eve",":","A","Gradient","Based","Optimization","Method","with","Locally","and","Globally","Adaptive","Learning","Rates","<","https",":","\/\/arxiv.org\/pdf\/1611.01505.pdf",">","`","_"]
code: def __init__(
      self,
      params,
      lr=1e-3,
      betas=(0.9, 0.999, 0.999),
      eps=1e-8,
      k=0.1,
      K=10,
      weight_decay=0,
  ):
      defaults = dict(
          lr=lr,
          betas=betas,
          eps=eps,
          k=k,
          K=K,
          weight_decay=weight_decay,
      )
      super(Eve, self).__init__(params, defaults)
  
imports: import math
  from torch.optim.optimizer import Optimizer
label_desc: Use image node_id 1 to create a new Eve object from inherited base classes: Optimizer with example: obj = Eve(params, lr, betas, eps, k, K, weight_decay)