def new_hyperloss(reg, i_hyper, cur_train_data, cur_valid_data):
RS = RandomState((seed, i_hyper, "hyperloss"))
w_vect_0 = RS.randn(N_weights) * init_scales
w_vect_final = train_z(loss_fun, cur_train_data, w_vect_0, reg)
return loss_fun(w_vect_final, **cur_valid_data)
