parser.add_argument('--data', type=str, default='Nott', help='the dataset to run (default: Nott)') parser.add_argument('--seed', type=int, default=1111, help='random seed (default: 1111)') args = parser.parse_args() # Set the random seed manually for reproducibility. torch.manual_seed(args.seed) if torch.cuda.is_available(): if not args.cuda: print("WARNING: You have a CUDA device, so you should probably run with --cuda") print(args) input_size = 88 X_train, X_valid, X_test = data_generator(args.data) n_channels = [args.nhid] * args.levels kernel_size = args.ksize dropout = args.dropout model = TCN(input_size, input_size, n_channels, kernel_size, dropout=args.dropout) if args.cuda: model.cuda() criterion = nn.CrossEntropyLoss() lr = args.lr optimizer = getattr(optim, args.optim)(model.parameters(), lr=lr)