According to the documents, we can create a Tensor with the data in ‘list’ object like this:
a = torch.FloatTensor([[1, 2, 3], [4, 5, 6]])

However, when I’m trying to initialize FloatTensor with list object in the following way:
def load_data_from_file(file_name='data.csv'):
    x = []
    y = []
    with open(file_name) as f:
        f_csv = csv.reader(f)
        headers = next(f_csv)
        for row in f_csv:
            x.append(row[3:9])
            y.append(row[9])
    return x, y


def convert2tensor(x):
    x = torch.FloatTensor(x)
    return x


def load_train_test(file_name='data.csv'):
    x, y = load_data_from_file(file_name)
    train_x = convert2tensor(x[:45])
    train_y = convert2tensor(y[:45])
    test_x = convert2tensor(x[45:])
    test_y = convert2tensor(y[45:])
    return Variable(train_x), Variable(train_y), Variable(test_x), Variable(test_y)

A runtime error occurs:
Traceback (most recent call last):
  File "/home/shawn/PythonWS/CDX/utils.py", line 32, in <module>
    x, y, x1, y1 = load_train_test()
  File "/home/shawn/PythonWS/CDX/utils.py", line 26, in load_train_test
    train_x = convert2tensor(x[:45])
  File "/home/shawn/PythonWS/CDX/utils.py", line 20, in convert2tensor
    x = torch.FloatTensor(x)
RuntimeError: already counted a million dimensions in a given sequence. Most likely your items are also sequences and there's no way to infer how many dimension should the tensor have

How could I solve this problem?