from .adam import Adam
[docs]class AnnealedAdam(Adam):
"""Implements Adam algorithm with learning rate annealing and optional L1 penalty.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
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)
lambda_l1 (float, optional): L1 penalty (default: 0)
weight_decay (float, optional): L2 penalty (weight decay) (default: 0)
lr_decay(float, optional): decay learning rate by this factor after every step
(default: 1.)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
lambda_l1=0, weight_decay=0, lr_decay=1.):
defaults = dict(lr=lr, betas=betas, eps=eps,
lambda_l1=lambda_l1, weight_decay=weight_decay,
lr_decay=lr_decay)
super(AnnealedAdam, self).__init__(params, **defaults)
[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.
"""
# Do an optimization step
super(AnnealedAdam, self).step(closure=closure)
# Update learning rate
for group in self.param_groups:
group['lr'] *= group['lr_decay']