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