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)
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)