Source code for torch.optim.adamax

import torch
from .optimizer import Optimizer

[docs]class Adamax(Optimizer): """Implements Adamax algorithm (a variant of Adam based on infinity norm). It has been proposed in `Adam: A Method for Stochastic Optimization`__. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 2e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) __ """ def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) super(Adamax, self).__init__(params, defaults)
[docs] @torch.no_grad() def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad if grad.is_sparse: raise RuntimeError('Adamax does not support sparse gradients') state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['exp_inf'] = torch.zeros_like(p, memory_format=torch.preserve_format) exp_avg, exp_inf = state['exp_avg'], state['exp_inf'] beta1, beta2 = group['betas'] eps = group['eps'] state['step'] += 1 if group['weight_decay'] != 0: grad = grad.add(p, alpha=group['weight_decay']) # Update biased first moment estimate. exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # Update the exponentially weighted infinity norm. norm_buf =[ exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0) ], 0) torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, bias_correction = 1 - beta1 ** state['step'] clr = group['lr'] / bias_correction p.addcdiv_(exp_avg, exp_inf, value=-clr) return loss


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