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| import torch | |
| from torch.optim import Optimizer | |
| class Nadam(Optimizer): | |
| """Implements Nadam algorithm (a variant of Adam based on Nesterov momentum). | |
| It has been proposed in `Incorporating Nesterov Momentum into Adam`__. | |
| Arguments: | |
| params (iterable): iterable of parameters to optimize or dicts defining | |
| parameter groups | |
| lr (float, optional): learning rate (default: 2e-3) | |
| betas (Tuple[float, float], optional): coefficients used for computing | |
| running averages of gradient and its square | |
| eps (float, optional): term added to the denominator to improve | |
| numerical stability (default: 1e-8) | |
| weight_decay (float, optional): weight decay (L2 penalty) (default: 0) | |
| schedule_decay (float, optional): momentum schedule decay (default: 4e-3) | |
| __ http://cs229.stanford.edu/proj2015/054_report.pdf | |
| __ http://www.cs.toronto.edu/~fritz/absps/momentum.pdf | |
| Originally taken from: https://github.com/pytorch/pytorch/pull/1408 | |
| NOTE: Has potential issues but does work well on some problems. | |
| """ | |
| def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, | |
| weight_decay=0, schedule_decay=4e-3): | |
| defaults = dict(lr=lr, betas=betas, eps=eps, | |
| weight_decay=weight_decay, schedule_decay=schedule_decay) | |
| super(Nadam, self).__init__(params, defaults) | |
| def step(self, closure=None): | |
| """Performs a single optimization step. | |
| Arguments: | |
| closure (callable, optional): A closure that reevaluates the model | |
| and returns the loss. | |
| """ | |
| 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] | |
| # State initialization | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| state['m_schedule'] = 1. | |
| state['exp_avg'] = grad.new().resize_as_(grad).zero_() | |
| state['exp_avg_sq'] = grad.new().resize_as_(grad).zero_() | |
| # Warming momentum schedule | |
| m_schedule = state['m_schedule'] | |
| schedule_decay = group['schedule_decay'] | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| beta1, beta2 = group['betas'] | |
| eps = group['eps'] | |
| state['step'] += 1 | |
| t = state['step'] | |
| if group['weight_decay'] != 0: | |
| grad = grad.add(group['weight_decay'], p.data) | |
| momentum_cache_t = beta1 * \ | |
| (1. - 0.5 * (0.96 ** (t * schedule_decay))) | |
| momentum_cache_t_1 = beta1 * \ | |
| (1. - 0.5 * (0.96 ** ((t + 1) * schedule_decay))) | |
| m_schedule_new = m_schedule * momentum_cache_t | |
| m_schedule_next = m_schedule * momentum_cache_t * momentum_cache_t_1 | |
| state['m_schedule'] = m_schedule_new | |
| # Decay the first and second moment running average coefficient | |
| exp_avg.mul_(beta1).add_(1. - beta1, grad) | |
| exp_avg_sq.mul_(beta2).addcmul_(1. - beta2, grad, grad) | |
| exp_avg_sq_prime = exp_avg_sq / (1. - beta2 ** t) | |
| denom = exp_avg_sq_prime.sqrt_().add_(eps) | |
| p.data.addcdiv_(-group['lr'] * (1. - momentum_cache_t) / (1. - m_schedule_new), grad, denom) | |
| p.data.addcdiv_(-group['lr'] * momentum_cache_t_1 / (1. - m_schedule_next), exp_avg, denom) | |
| return loss | |