def initialize_self_weights(self, num_in, scale_big, scale_small=0, W_to_exclude=[]):
from opt.utils.extra import sparsify_strict
for i, param in enumerate(self.W):
if i not in W_to_exclude:
param[:] = g.randn(*param.shape)
sparsify_strict(param, num_in, scale_big, scale_small)
return self
def initialize_self(self, num_in, scale_big,
scale_small=0.,
vars_to_exclude=[], vars=None):
for (var_name, var) in self.__dict__.iteritems():
if (var_name not in vars_to_exclude and
isinstance(var, g.garray) and
((vars is None) or (var_name in vars))):
sparsify_strict(var, num_in, scale_big, scale_small)
print('MRNN:sparsifying %s\n' % var_name)
return self