How to create a completely (uniformly) random dataset on PyTorch
I need to run some experiments on custom datasets using pytorch. The question is, how can I create a dataset using torch.Dataloader? 

I have two lists, one is called Values and has a datapoint tensor at every entry, and the other one is called Labels, that has the corresponding label. What I did is the following: 

for i in range(samples):
dataset[i] = [values[i],labels[I]]


So I have a list with datapoint and respective label, and then tried the following: 

dataset = torch.tensor(dataset).float()
dataset = torch.utils.data.TensorDataset(dataset)

data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=100, shuffle=True, num_workers=4, pin_memory=True)


But, first of all, I get the error "Not a sequence" in the torch.tensor command, and second, I'm not sure this is the right way of creating one. Any suggestion? 

Thank you very much!

You do not need to overload DataLoader, but rather create a Dataset for your data.
For instance,

class MyDataset(Dataset):
  def __init__(self):
    super(MyDataset, self).__init__()
    # do stuff here?
    self.values = values
    self.labels = labels

  def __len__(self):
    return len(self.values)  # number of samples in the dataset

  def __getitem__(self, index):
    return self.values[index], self.labels[index]

