File size: 2,595 Bytes
3cd1076 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
from torch.optim.optimizer import Optimizer
class Adamax(Optimizer):
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)
def __setstate__(self, state):
super(Adamax, self).__setstate__(state)
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
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.data)
state['exp_inf'] = torch.zeros_like(p.data)
exp_avg, exp_inf = state['exp_avg'], state['exp_inf']
beta1, beta2 = group['betas']
state['step'] += 1
if group['weight_decay'] != 0:
grad = grad.add(group['weight_decay'], p.data)
# Update biased first moment estimate
exp_avg.mul_(beta1).add_(1 - beta1, grad)
# Update the exponentially weighted infinity norm
norm_buf = torch.cat([
exp_inf.mul_(beta2).unsqueeze(0),
grad.abs().unsqueeze_(0)
], 0)
torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
bias_correction = 1 - beta1 ** state['step']
clr = group['lr'] / bias_correction
p.data.addcdiv_(-clr, exp_avg, exp_inf + group['eps'])
return loss |