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uniform.py
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/
uniform.py
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import numpy
from chainer import cuda
from chainer import initializer
# Original code forked from MIT licensed keras project
# https://github.com/fchollet/keras/blob/master/keras/initializations.py
class Uniform(initializer.Initializer):
"""Initializes array with a scaled uniform distribution.
Each element of the array is initialized by the value drawn
independently from uniform distribution :math:`[-scale, scale]`.
Attributes:
~Uniform.scale (float): A constant that determines the
scale of the uniform distribution.
~Uniform.dtype: Data type specifier.
"""
def __init__(self, scale=0.05, dtype=None):
self.scale = scale
super(Uniform, self).__init__(dtype)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
xp = cuda.get_array_module(array)
array[...] = xp.random.uniform(
low=-self.scale, high=self.scale, size=array.shape)
class LeCunUniform(initializer.Initializer):
"""Initializes array with a scaled uniform distribution.
Each element of the array is initialized by the value drawn
independently from uniform distribution :math:`[-s, s]`
where :math:`s = scale \\times \\sqrt{\\frac{3}{fan_{in}}}`.
Here :math:`fan_{in}` is the number of input units.
Reference: LeCun 98, Efficient Backprop
http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
Attributes:
~LeCunUniform.scale (float): A constant that determines the
scale of the uniform distribution.
~LeCunUniform.dtype: Data type specifier.
"""
def __init__(self, scale=1.0, dtype=None):
self.scale = scale
super(LeCunUniform, self).__init__(dtype)
def __call__(self, array):
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(3. / fan_in)
Uniform(s)(array)
class GlorotUniform(initializer.Initializer):
"""Initializes array with a scaled uniform distribution.
Each element of the array is initialized by the value drawn
independently from uniform distribution :math:`[-s, s]`
where :math:`s = scale \\times \\sqrt{\\frac{6}{fan_{in} + fan_{out}}}`.
Here, :math:`fan_{in}` and :math:`fan_{out}` are the number of
input and output units, respectively.
Attributes:
~GlorotUniform.scale (float): A constant that determines the
scale of the uniform distribution.
~GlorotUniform.dtype: Data type specifier.
"""
def __init__(self, scale=1.0, dtype=None):
self.scale = scale
super(GlorotUniform, self).__init__(dtype)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(6. / (fan_in + fan_out))
Uniform(s)(array)
class HeUniform(initializer.Initializer):
"""Initializes array with scaled uniform distribution.
Each element of the array is initialized by the value drawn
independently from uniform distribution :math:`[-s, s]`
where :math:`s = scale \\times \\sqrt{\\frac{6}{fan_{in}}}`.
Here, :math:`fan_{in}` is the number of input units.
Attributes:
~HeUniform.scale (float): A constant that determines the
scale of the uniform distribution.
~HeUniform.dtype: Data type specifier.
"""
def __init__(self, scale=1.0, dtype=None):
self.scale = scale
super(HeUniform, self).__init__(dtype)
def __call__(self, array):
if self.dtype is not None:
assert array.dtype == self.dtype
fan_in, fan_out = initializer.get_fans(array.shape)
s = self.scale * numpy.sqrt(6. / fan_in)
Uniform(s)(array)