Source code for torch.optim.asgd

import math
import torch
from . import _functional as F
from .optimizer import Optimizer

[docs]class ASGD(Optimizer): """Implements Averaged Stochastic Gradient Descent. It has been proposed in `Acceleration of stochastic approximation by averaging`_. Args: 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. Args: 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: params_with_grad = [] grads = [] mus = [] axs = [] etas = [] state_steps = [] for p in group['params']: if p.grad is not None: params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError('ASGD does not support sparse gradients') grads.append(p.grad) 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) mus.append(state['mu']) axs.append(state['ax']) etas.append(state['eta']) state['step'] += 1 state_steps.append(state['step']) F.asgd(params_with_grad, grads, axs, mus, etas, weight_decay=group['weight_decay'], lambd=group['lambd']) # update eta and mu for p, mu, eta in zip(params_with_grad, mus, etas): state = self.state[p] 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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