Hello,

I met a problem when using backward() to do backpropagation. The something strange came.

I define a simple network with only 1 linear layer `nn.Linear(3,1)`

and used `load_state_dict`

to set the weights to be [1,1,1] and bias [0].

Then I passed

```
x = net(torch.tensor([1,2,3],dtype=torch.float))
L = x
L.backward()
```

Then I used `optim.step()`

to update the weights and found that all weights changed by 0.01 (my learning rate). Should not the weights get updated differently? I got confused…

Here is a screen shot: