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) 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?