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import torch
from .optimizer import Optimizer
class RMSprop(Optimizer):
"""Implements RMSprop algorithm.
Proposed by G. Hinton in his
`course <http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_.
The centered version first appears in `Generating Sequences
With Recurrent Neural Networks <https://arxiv.org/pdf/1308.0850v5.pdf>`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
momentum (float, optional): momentum factor (default: 0)
alpha (float, optional): smoothing constant (default: 0.99)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
centered (bool, optional) : if True, compute the centered RMSProp,
the gradient is normalized by an estimation of its variance
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
"""
def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=False):
defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay)
super(RMSprop, self).__init__(params, defaults)
def __setstate__(self, state):
super(RMSprop, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('momentum', 0)
group.setdefault('centered', False)
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
if grad.is_sparse:
raise RuntimeError('RMSprop does not support sparse gradients')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['square_avg'] = torch.zeros_like(p.data)
if group['momentum'] > 0:
state['momentum_buffer'] = torch.zeros_like(p.data)
if group['centered']:
state['grad_avg'] = torch.zeros_like(p.data)
square_avg = state['square_avg']
alpha = group['alpha']
state['step'] += 1
if group['weight_decay'] != 0:
grad = grad.add(group['weight_decay'], p.data)
square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)
if group['centered']:
grad_avg = state['grad_avg']
grad_avg.mul_(alpha).add_(1 - alpha, grad)
avg = square_avg.addcmul(-1, grad_avg, grad_avg).sqrt().add_(group['eps'])
else:
avg = square_avg.sqrt().add_(group['eps'])
if group['momentum'] > 0:
buf = state['momentum_buffer']
buf.mul_(group['momentum']).addcdiv_(grad, avg)
p.data.add_(-group['lr'], buf)
else:
p.data.addcdiv_(-group['lr'], grad, avg)
return loss
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