I want to get the gradient of output w.r.t. model parameters. For example, y = xW where x is a vector of size 1x5, and W is a vector of size 5x1, and W is model parameter. I don’t have a loss function, just want to get the gradient of y w.r.t W. Obviously, in this case, dy/dW should be x. However, using pytorch backward, the value I got is wrong. What’s the reason? Here is my code.

```
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as torchF
from torch.autograd import Variable
class Net(nn.Module):
def __init__(self, dim_action=1): # assumes a [256,256,3] input
super(Net, self).__init__()
self.fc = nn.Linear(5,1, bias=False)
def forward(self,x):
x = self.fc(x)
return x
net = Net()
net.double()
x = Variable(torch.from_numpy(np.array([1.0,2.0,3.0,4.0,5.0]).reshape((1,5))))
out = net(x)
outvalue = np.array(out.data)
net.zero_grad()
y = torch.randn(1,1)
y.data = outvalue
y = y.double()
out.backward(y)
for f in net.parameters():
print('data is')
print(f.data)
print('grad is')
print(f.grad)
```

and the output is,

data is

```
-0.3839 -0.1894 -0.3376 -0.3934 -0.2313
[torch.DoubleTensor of size 1x5]
grad is
Variable containing:
0.2647 0.5293 0.7940 1.0587 1.3233
[torch.DoubleTensor of size 1x5]
```