Source code for brainpy._src.initialize.regular_inits
# -*- coding: utf-8 -*-
from brainpy import math as bm, tools
from .base import _InterLayerInitializer
__all__ = [
'ZeroInit',
'Constant',
'OneInit',
'Identity',
]
[docs]
class ZeroInit(_InterLayerInitializer):
"""Zero initializer.
Initialize the weights with zeros.
"""
def __call__(self, shape, dtype=None):
shape = [tools.size2num(d) for d in shape]
return bm.zeros(shape, dtype=dtype)
def __repr__(self):
return self.__class__.__name__
[docs]
class Constant(_InterLayerInitializer):
"""Constant initializer.
Initialize the weights with the given values.
Parameters
----------
value : float, int, bm.ndarray
The value to specify.
"""
def __init__(self, value=1.):
super(Constant, self).__init__()
self.value = value
def __call__(self, shape, dtype=None):
shape = [tools.size2num(d) for d in shape]
return bm.ones(shape, dtype=dtype) * self.value
def __repr__(self):
return f'{self.__class__.__name__}(value={self.value})'
[docs]
class OneInit(Constant):
"""One initializer.
"""
pass
[docs]
class Identity(_InterLayerInitializer):
"""Returns the identity matrix.
This initializer was proposed in (Le, et al., 2015) [1]_.
Parameters
----------
value : float
The optional scaling factor.
Returns
-------
shape: tuple of int
The weight shape/size.
References
----------
.. [1] Le, Quoc V., Navdeep Jaitly, and Geoffrey E. Hinton. "A simple way to
initialize recurrent networks of rectified linear units." arXiv preprint
arXiv:1504.00941 (2015).
"""
def __init__(self, value=1.):
super(Identity, self).__init__()
self.value = value
def __call__(self, shape, dtype=None):
if isinstance(shape, int):
shape = (shape,)
elif isinstance(shape, (tuple, list)):
if len(shape) > 2:
raise ValueError(f'Only support initialize 2D weights for {self.__class__.__name__}.')
else:
raise ValueError(f'Only support shape of int, or tuple/list of int '
f'in {self.__class__.__name__}, but we got {shape}.')
shape = [tools.size2num(d) for d in shape]
return bm.eye(*shape, dtype=dtype) * self.value
def __repr__(self):
return f'{self.__class__.__name__}(value={self.value})'