lr = 0.1

num_epochs = 3

net = linreg

loss = squared_loss

num_inputs = 2

num_examples = 1000

features = torch.randn(size = (num_examples,num_inputs))

w = Variable(tdist.Normal(torch.tensor([0.0]),torch.tensor([0.01])).sample((num_inputs,)),requires_grad=True)

b = Variable(torch.zeros(size = (1,1)),requires_grad=True)

l =torch.empty(10,1, requires_grad=False)

for epoch in range(num_epochs):

i = 0

for X, y in data_iter(batch_size,features,labels):

i += 1

l[:] = loss(net(X,w,b),y)

```
if i ==1 :
l.backward(torch.ones(l.size()))
sgd([w],lr,batch_size)
```

train_loss = loss(net(features,w,b),labels)

print(‘epoch %d, loss %f’ % (epoch + 1, train_loss.mean()))

i want solve problem what should do … please save me

i think Variable w has something wrong