File size: 2,851 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 69 70 71 72 73 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import math
class NAdam(torch.optim.Optimizer):
def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, momentum_decay=4e-3):
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))
if not 0.0 <= momentum_decay:
raise ValueError("Invalid momentum_decay value: {}".format(momentum_decay))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, momentum_decay=momentum_decay)
super(NAdam, self).__init__(params, defaults)
def __setstate__(self, state):
super(NAdam, 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('NAdam does not support sparse gradients')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['m_prev'] = torch.zeros_like(p.data)
state['v'] = torch.zeros_like(p.data)
m_prev, v = state['m_prev'], state['v']
beta1, beta2 = group['betas']
state['step'] += 1
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
if group['weight_decay'] != 0:
grad = grad.add(group['weight_decay'], p.data)
m = beta1 * m_prev + (1 - beta1) * grad
v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
m_hat = m / bias_correction1
v_hat = v / bias_correction2
denom = v_hat.sqrt().add_(group['eps'])
momentum_decay = group['momentum_decay']
m_prev.mul_(beta1).add_(1 - beta1, grad)
m_prev_hat = m_prev / bias_correction1
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, m_hat + momentum_decay * m_prev_hat, denom)
return loss |