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import chainer
from chainer import cuda
class Lasso(object):
"""Optimizer/UpdateRule hook function for Lasso regularization.
This hook function adds a scaled parameter to the sign of each weight.
It can be used as a regularization.
rate (float): Coefficient for the weight decay.
~optimizer_hooks.Lasso.rate (float): Coefficient for the weight decay.
~optimizer_hooks.Lasso.timing (string): Specifies
when this hook should be called by
the Optimizer/UpdateRule. Valid values are 'pre'
(before any updates) and 'post' (after any updates).
~optimizer_hooks.Lasso.call_for_each_param (bool): Specifies
if this hook is called for each parameter (``True``)
or only once (``False``) by an optimizer to
which this hook is registered. This function does
not expect users to switch the value from default one,
which is `True`.
.. versionadded:: 4.0.0
The *timing* parameter.
name = 'Lasso'
call_for_each_param = True
timing = 'pre'
def __init__(self, rate):
self.rate = rate
def __call__(self, rule, param):
p, g =, param.grad
if p is None or g is None:
with chainer.using_device(param.device):
xp = param.device.xp
sign = xp.sign(p)
if xp is cuda.cupy:
kernel = cuda.elementwise(
'T s, T decay', 'T g', 'g += decay * s', 'lasso')
kernel(sign, self.rate, g)
g += self.rate * sign
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