args = parser.parse_args()
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")
input_channels = 2
n_classes = 1
batch_size = args.batch_size
seq_length = args.seq_len
epochs = args.epochs
print(args)
print("Producing data...")
X_train, Y_train = data_generator(50000, seq_length)
X_test, Y_test = data_generator(1000, seq_length)
# Note: We use a very simple setting here (assuming all levels have the same # of channels.
channel_sizes = [args.nhid]*args.levels
kernel_size = args.ksize
dropout = args.dropout
model = TCN(input_channels, n_classes, channel_sizes, kernel_size=kernel_size, dropout=dropout)
if args.cuda:
model.cuda()
X_train = X_train.cuda()
Y_train = Y_train.cuda()
X_test = X_test.cuda()
Y_test = Y_test.cuda()
args = parser.parse_args()
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")
input_channels = 2
n_classes = 1
batch_size = args.batch_size
seq_length = args.seq_len
epochs = args.epochs
print(args)
print("Producing data...")
X_train, Y_train = data_generator(50000, seq_length)
X_test, Y_test = data_generator(1000, seq_length)
# Note: We use a very simple setting here (assuming all levels have the same # of channels.
channel_sizes = [args.nhid]*args.levels
kernel_size = args.ksize
dropout = args.dropout
model = TCN(input_channels, n_classes, channel_sizes, kernel_size=kernel_size, dropout=dropout)
if args.cuda:
model.cuda()
X_train = X_train.cuda()
Y_train = Y_train.cuda()
X_test = X_test.cuda()
Y_test = Y_test.cuda()