| |
| |
|
|
| 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 |
