def normal_like(x, mean=0.0, scale=1.0, seed=auto_select, name=''):
"""normal_like(x, mean=0.0, scale=1.0, seed=auto_select, name='')
Generates samples from the normal distribution with mean `mean` and standard deviation `scale`.
Args:
x: cntk variable (input, output, parameter, or constant) from which to copy the shape, data type, and dynamic axes.
mean (float): mean of the distribution
scale (float): scale (standard deviation) of the distribution
seed (int): pseudo random number generator seed (default: automatically select a unique seed)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
Examples:
>>> x = C.parameter((2,3,4))
>>> z = C.random.normal_like(x, seed=98052)
>>> z.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code
array([[[ 1.803254, 0.995395, -0.631974, -1.73672 ],
[ 0.005615, -0.340025, -0.011913, -0.236371],
[-1.207685, -0.495846, 0.037022, -1.220596]],
<BLANKLINE>
[[ 0.872981, 0.654405, -0.111421, -0.544074],
[ 1.543746, -0.63555 , -1.072869, -0.379701],
[ 0.592069, -1.035192, 1.679303, -0.391963]]], dtype=float32)
"""
from cntk.cntk_py import normal_random_like
x = sanitize_input(x)
return normal_random_like(x, mean, scale, seed, name)
def normal_like(x, mean=0.0, scale=1.0, seed=auto_select, name=''):
"""normal_like(x, mean=0.0, scale=1.0, seed=auto_select, name='')
Generates samples from the normal distribution with mean `mean` and standard deviation `scale`.
Args:
x: cntk variable (input, output, parameter, or constant) from which to copy the shape, data type, and dynamic axes.
mean (float): mean of the distribution
scale (float): scale (standard deviation) of the distribution
seed (int): pseudo random number generator seed (default: automatically select a unique seed)
name (str, optional): the name of the Function instance in the network
Returns:
:class:`~cntk.ops.functions.Function`
Examples:
>>> x = C.parameter((2,3,4))
>>> z = C.random.normal_like(x, seed=98052)
>>> z.eval(device=C.cpu()) # explicitly setting cpu because this is tested on multiple platforms; leave it unspecified in your code
array([[[ 1.803254, 0.995395, -0.631974, -1.73672 ],
[ 0.005615, -0.340025, -0.011913, -0.236371],
[-1.207685, -0.495846, 0.037022, -1.220596]],
<BLANKLINE>
[[ 0.872981, 0.654405, -0.111421, -0.544074],
[ 1.543746, -0.63555 , -1.072869, -0.379701],
[ 0.592069, -1.035192, 1.679303, -0.391963]]], dtype=float32)
"""
from cntk.cntk_py import normal_random_like
x = sanitize_input(x)
return normal_random_like(x, mean, scale, seed, name)