sqn_param¶
sqn_param
¶
Classes¶
StochasticQuasiNewtonParam (BaseParam)
¶
Parameters used for stochastic quasi-newton method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
update_interval_L |
int, default: 3 |
Set how many iteration to update hess matrix |
3 |
memory_M |
int, default: 5 |
Stack size of curvature information, i.e. y_k and s_k in the paper. |
5 |
sample_size |
int, default: 5000 |
Sample size of data that used to update Hess matrix |
5000 |
Source code in federatedml/param/sqn_param.py
class StochasticQuasiNewtonParam(BaseParam):
"""
Parameters used for stochastic quasi-newton method.
Parameters
----------
update_interval_L : int, default: 3
Set how many iteration to update hess matrix
memory_M : int, default: 5
Stack size of curvature information, i.e. y_k and s_k in the paper.
sample_size : int, default: 5000
Sample size of data that used to update Hess matrix
"""
def __init__(self, update_interval_L=3, memory_M=5, sample_size=5000, random_seed=None):
super().__init__()
self.update_interval_L = update_interval_L
self.memory_M = memory_M
self.sample_size = sample_size
self.random_seed = random_seed
def check(self):
descr = "hetero sqn param's"
self.check_positive_integer(self.update_interval_L, descr)
self.check_positive_integer(self.memory_M, descr)
self.check_positive_integer(self.sample_size, descr)
if self.random_seed is not None:
self.check_positive_integer(self.random_seed, descr)
return True
__init__(self, update_interval_L=3, memory_M=5, sample_size=5000, random_seed=None)
special
¶
Source code in federatedml/param/sqn_param.py
def __init__(self, update_interval_L=3, memory_M=5, sample_size=5000, random_seed=None):
super().__init__()
self.update_interval_L = update_interval_L
self.memory_M = memory_M
self.sample_size = sample_size
self.random_seed = random_seed
check(self)
¶
Source code in federatedml/param/sqn_param.py
def check(self):
descr = "hetero sqn param's"
self.check_positive_integer(self.update_interval_L, descr)
self.check_positive_integer(self.memory_M, descr)
self.check_positive_integer(self.sample_size, descr)
if self.random_seed is not None:
self.check_positive_integer(self.random_seed, descr)
return True
Last update: 2021-11-24