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)