df = pd.read_csv('spirals.csv') data = torch.tensor(df.values, dtype=torch.float32) num_input = data.shape[1] - 1 full_input = data[:, 0:num_input] full_target = data[:, num_input:num_input + 1] train_dataset = torch.utils.data.TensorDataset(full_input, full_target) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=97) # choose network architecture if args.net == 'polar': net = PolarNet(args.hid) else: net = RawNet(args.hid) if list(net.parameters()): # initialize weight values for m in list(net.parameters()): m.data.normal_(0, args.init) # use Adam optimizer optimizer = torch.optim.Adam(net.parameters(), eps=0.000001, lr=args.lr, betas=(0.9, 0.999), weight_decay=0.0001)