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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 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
from torch.optim.optimizer import Optimizer
class Adadelta(Optimizer):
def __init__(self, params, lr=1.0, rho=0.95, eps=1e-6):
defaults = dict(lr=lr, rho=rho, eps=eps)
super(Adadelta, 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['square_avg'] = torch.zeros_like(p.data)
state['acc_delta'] = torch.zeros_like(p.data)
square_avg, acc_delta = state['square_avg'], state['acc_delta']
rho, eps = group['rho'], group['eps']
state['step'] += 1
square_avg.mul_(rho).addcmul_(1 - rho, grad, grad)
std = square_avg.add(eps).sqrt_()
delta = acc_delta.add(eps).sqrt_().div_(std).mul_(grad)
p.data.add_(-delta)
acc_delta.mul_(rho).addcmul_(1 - rho, delta, delta)
return loss |