lstm_classifier.setLstm(lstm)
#CONFIGURACAO DA REDE NEURAL
rna = RnaModule()
rna.setNumberNeuronsImputLayer(20)
rna.setActivationFunctionImputLayer("tanh")
rna.setImputDimNeurons(20)
rna.setNumberNeuronsHiddenLayer(20)
rna.setActivationFunctionHiddenLayer("tanh")
rna.setNumberNeuronsOutputLayer(1)
rna.setActivationFunctionOutputLayer("tanh")
rna_classifier = RnaClassifier()
rna_classifier.setRna(rna)
#METODO HIBRIDO
hybrid_classifier = HybridClassifier()
hybrid_classifier.setPercentilFaixaSup(25)
hybrid_classifier.setPercentilFaixaInf(75)
hybrid_classifier.setRna(rna)
hybrid_classifier.setKnn(knn)
#PREPROCESSADOR PARA ATRIBUTOS CATEGORICOS
preprocessor = Preprocessor()
#preprocessor.setColumnsCategory(['protocol_type','service','flag'])
preprocessor.setColumnsCategory(['service', 'flag'])
evaluate = EvaluateModule()
cross = CrossValidation()
#DEFINIR A ITERACAO QUE O CROSS VALIDATION ESTA
cross.setIteration(1)
sys.stderr = sys.stdout
parser = get_parser()
args = parser.parse_args()
torch.set_num_threads(args.workers)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
utils.set_use_gpu(args.gpu, not args.no_bench_mode)
utils.set_use_half(args.half)
utils.show_args(args)
data_loader = load_dataset(args, train=True)
model = utils.enable_cuda(HybridClassifier())
if args.half:
model = network_to_half(model)
criterion = utils.enable_cuda(HybridLoss())
optimizer = torch.optim.SGD(
model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay
)
optimizer = OptimizerAdapter(
optimizer,
half=args.half,
