Source code for slayerSNN.optimizer
import math
import torch
# from .optimizer import Optimizer
[docs]class Nadam(torch.optim.Optimizer):
'''
Implements Nadam algorithm. (Modified Adam from PyTorch_)
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: 1e-3).
* ``betas`` (Tuple[``float``, ``float``], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999)).
* ``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).
* ``amsgrad`` (``boolean``, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False).
.. _PyTorch:
https://pytorch.org/docs/stable/_modules/torch/optim/adam.html#Adam
.. _Incorporating Nesterov Momentum into Adam:
https://openreview.net/pdf?id=OM0jvwB8jIp57ZJjtNEZ
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
'''
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False):
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]))
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
super(Nadam, self).__init__(params, defaults)
def __setstate__(self, state):
super(Nadam, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
[docs] 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
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
if group['weight_decay'] != 0:
grad.add_(group['weight_decay'], p.data)
# 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)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
# only change is here
# p.data.addcdiv_(-step_size, exp_avg, denom)
p.data.addcdiv_(-step_size, beta1 * exp_avg + (1-beta1) * grad, denom)
return loss