def _initializer_for(init, rank_params=None):
if init is None:
raise ValueError("init parameter cannot be None")
# if default then select
if init is _default_sentinel_init:
init = _current_default_options.init
elif init is _default_sentinel_init_bias:
init = _current_default_options.init_bias
# scalar constant: that's it, nothing further to do here
if np.isscalar(init):
# BUGBUG: this is sometimes required when dimensions are unknown; shouldn't.
from _cntk_py import constant_initializer
return constant_initializer(init)
# return init # TODO: change to this once this works, e.g. for layers.BatchNormalization()
# implant additional rank parameters
if rank_params:
from cntk.initializer import initializer_with_rank
init = initializer_with_rank(init, **rank_params)
return init
def _initializer_for(init, rank_params=None):
if init is None:
raise ValueError("init parameter cannot be None")
# scalar constant: that's it, nothing further to do here
if np.isscalar(init):
# BUGBUG: this is sometimes required when dimensions are unknown; shouldn't.
from _cntk_py import constant_initializer
return constant_initializer(init)
#return init # TODO: change to this once this works, e.g. for layers.BatchNormalization()
# implant additional rank parameters
if rank_params:
from cntk.initializer import initializer_with_rank
init = initializer_with_rank(init, **rank_params)
return init