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# Source code for torch.optim.asgd

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

[docs]class ASGD(Optimizer):

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:
https://dl.acm.org/citation.cfm?id=131098
"""

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)

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:
loss = closure()

for group in self.param_groups:
mus = []
axs = []
etas = []
state_steps = []

for p in group['params']:
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)

mus.append(state['mu'])
axs.append(state['ax'])
etas.append(state['eta'])

state['step'] += 1
state_steps.append(state['step'])

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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