| |
| |
|
|
| 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 |
