Source code for torch.optim.asgd

import math
import torch
from .optimizer import Optimizer

[docs]class ASGD(Optimizer): """Implements Averaged Stochastic Gradient Descent. It has been proposed in `Acceleration of stochastic approximation by averaging`_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) lambd (float, optional): decay term (default: 1e-4) alpha (float, optional): power for eta update (default: 0.75) t0 (float, optional): point at which to start averaging (default: 1e6) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) .. _Acceleration of stochastic approximation by averaging: """ def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) defaults = dict(lr=lr, lambd=lambd, alpha=alpha, t0=t0, weight_decay=weight_decay) super(ASGD, 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('ASGD does not support sparse gradients') state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 state['eta'] = group['lr'] state['mu'] = 1 state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['step'] += 1 if group['weight_decay'] != 0: grad = grad.add(p, alpha=group['weight_decay']) # decay term p.mul_(1 - group['lambd'] * state['eta']) # update parameter p.add_(grad, alpha=-state['eta']) # averaging if state['mu'] != 1: state['ax'].add_(p.sub(state['ax']).mul(state['mu'])) else: state['ax'].copy_(p) # update eta and mu state['eta'] = (group['lr'] / math.pow((1 + group['lambd'] * group['lr'] * state['step']), group['alpha'])) state['mu'] = 1 / max(1, state['step'] - group['t0']) return loss


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