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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 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
from torch.optim.optimizer import Optimizer
class Adam(Optimizer):
def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=1e-8):
defaults = dict(lr=lr, betas=betas, eps=eps)
super(Adam, self).__init__(params, defaults)
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
state = self.state[p]
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p.data)
state['exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * (bias_correction2 ** 0.5) / bias_correction1
denom = exp_avg_sq.sqrt().add_(group['eps'])
p.data.addcdiv_(-step_size, exp_avg, denom)
return loss |