help='random seed(default:1)')
parser.add_argument(
'-log-interval',
type=int,
default=1,
metavar='N',
help='how many batches to wait before logging training status')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 3, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(DataUtils.ECGDataset(train_path,
test_path,
train=True),
batch_size=args.train_batch_size,
drop_last=True,
shuffle=True)
test_loader = torch.utils.data.DataLoader(DataUtils.ECGDataset(train_path,
test_path,
test=True),
batch_size=args.test_batch_size,
drop_last=True,
shuffle=True)
#model = LSTM(28*28, 64, 10) MNIST dataset
#model = LSTM(140, 64, 5)
#model = FC(28 * 28, 300, 100, 10)
#model = TTRNN([4,7,4,7], [4,2,4,4], [1,3,4,2,1], 1, 0.8, 'ttgru')
#model = RNN([2,5,2,7], [4,4,2,4], [1,2,5,3,1], 0.8, 5)
