def build_model(args, i_cv):
args.net = ARCHI(n_in=29, n_out=2, n_unit=args.n_unit)
args.adv_net = ARCHI(n_in=2, n_out=2, n_unit=args.n_unit)
args.net_optimizer = get_optimizer(args)
args.adv_optimizer = get_optimizer(args, args.adv_net)
args.net_criterion = WeightedCrossEntropyLoss()
args.adv_criterion = WeightedGaussEntropyLoss()
model = get_model(args, PivotClassifier)
model.set_info(DATA_NAME, BENCHMARK_NAME, i_cv)
return model
def build_model(args, i_cv):
args.net = ARCHI(n_in=3, n_out=2, n_unit=args.n_unit)
args.optimizer = get_optimizer(args)
model = get_model(args, NeuralNetClassifier)
model.base_name = "DataAugmentation"
model.set_info(DATA_NAME, BENCHMARK_NAME, i_cv)
return model
def build_model(args, i_cv):
args.net = ARCHI(n_in=29, n_out=N_BINS, n_unit=args.n_unit)
args.optimizer = get_optimizer(args)
args.criterion = HiggsLoss()
model = get_model(args, Inferno)
model.set_info(DATA_NAME, BENCHMARK_NAME, i_cv)
return model
def load_some_NN(i_cv=0, cuda=False):
from model.neural_network import NeuralNetClassifier
from archi.classic import L4 as ARCHI
import torch.optim as optim
n_unit = 200
net = ARCHI(n_in=29, n_out=2, n_unit=n_unit)
optimizer = optim.Adam(net.parameters(), lr=1e-3)
model = NeuralNetClassifier(net, optimizer, n_steps=15000, batch_size=10000, cuda=cuda)
model.set_info(DATA_NAME, BENCHMARK_NAME, i_cv)
print(f"loading {model.model_path}")
model.load(model.model_path)
return model
def build_model(args, i_cv):
args.net = ARCHI(n_in=1, n_out=2, n_unit=args.n_unit)
args.optimizer = get_optimizer(args)
model = get_model(args, TangentPropClassifier)
model.set_info(DATA_NAME, BENCHMARK_NAME, i_cv)
return model
def build_model(args, i_cv):
args.net = ARCHI(n_in=1, n_out=2, n_unit=args.n_unit)
args.optimizer = get_optimizer(args)
model = get_model(args, NeuralNetClassifier)
model.set_info(DATA_NAME, f"{BENCHMARK_NAME}/learning_curve", i_cv)
return model
