Hello community, I’m coming from mxnet so rather new to pytorch. I am a bit surprised by the nn.Linear layer. For testing purposes I did a simple linear regression and it works perfectly well when using tensors, yet when I replace the tensor approach by using directly the Linear layer it fails.

More specifically, I try to fit a simple linear relation y = x * beta. When using tensors it works fine (note I do not need to code a class here but just to be similar to Linear net) but fails when I replace the tensor by a nn.Linear layer

*Tensor approach*

```
# various constants data and learning
nb_dim = 5
sample_size = 300
noise_level = 0.0001
lr = 0.05
num_epochs = 50
# dummy data for test
x = np.random.normal(size=(sample_size, nb_dim))
beta = 10 * (np.random.rand(nb_dim) - 0.5)
noise = np.random.rand(sample_size)
yn = np.dot(x, beta) + noise_level * noise
# numpy -> torch tensor
xn_ = torch.from_numpy(x).float()
yn_ = torch.from_numpy(yn).float()
# linear net y = x * beta
class Net(torch.nn.Module):
def __init__(self, dim_in):
super(Net, self).__init__()
self.layer = torch.autograd.Variable(torch.randn(dim_in), requires_grad=True)
def forward(self, v):
return torch.matmul(v, self.layer)
def parameters(self):
return [self.layer]
net = Net(len(beta))
loss = torch.nn.MSELoss()
optimizer = torch.optim.SGD(net.parameters(), lr=lr)
losses = []
for e in range(num_epochs):
output = net(xn_)
l = loss(output, yn_)
l.backward()
losses.append(l.data.numpy())
optimizer.step()
optimizer.zero_grad()
```

**And it works fine**

losses are going to 0 and I find back the original beta with a small error.

*Using Linear Layer*

Yet if I change the Net class and use a Linear layer instead

```
class Net(torch.nn.Module):
def __init__(self, dim_in):
super(Net, self).__init__()
self.layer = torch.nn.Linear(dim_in, 1, bias=False)
def forward(self, v):
return self.layer(v)
```

then the **losses does not converge to 0** and the beta is far from the real value

I am sure I am doing something incorrect but I can’t see where ?

Thanks a lot for your help