def load_calib_rescale():
args = lambda: None
args.n_unit = 80
args.optimizer_name = "adam"
args.beta1 = 0.5
args.beta2 = 0.9
args.learning_rate = 1e-4
args.n_samples = 1000
args.n_steps = 1000
args.batch_size = 20
args.net = CALIB_ARCHI(n_in=1, n_out=2, n_unit=args.n_unit)
args.optimizer = get_optimizer(args)
model = get_model(args, Regressor)
model.base_name = CALIB_RESCALE
model.set_info(DATA_NAME, BENCHMARK_NAME, 0)
model.load(model.model_path)
return model
def load_calib_lam(DATA_NAME, BENCHMARK_NAME):
from model.regressor import Regressor
from archi.reducer import A1AR8MR8L1 as CALIB_ARCHI
args = lambda: None
args.n_unit = 200
args.optimizer_name = "adam"
args.beta1 = 0.5
args.beta2 = 0.9
args.learning_rate = 1e-4
args.n_samples = 1000
args.n_steps = 2000
args.batch_size = 20
args.net = CALIB_ARCHI(n_in=3, n_out=2, n_unit=args.n_unit)
args.optimizer = get_optimizer(args)
model = get_model(args, Regressor)
model.base_name = "Calib_lam"
model.set_info(DATA_NAME, BENCHMARK_NAME, 0)
model.load(model.model_path)
return model