Add the pytorch version of the linear regression as a comment. (#163)

* Add the pytorch version of the linear regression.

* Typo.

candle-nn/tests/optim.rs

@@ -18,6 +18,30 @@ fn sgd_optim() -> Result<()> {
     Ok(())
 }
 
+/* The results of this test have been checked against the following PyTorch code.
+    import torch
+    from torch import optim
+
+    w_gen = torch.tensor([[3., 1.]])
+    b_gen = torch.tensor([-2.])
+
+    sample_xs = torch.tensor([[2., 1.], [7., 4.], [-4., 12.], [5., 8.]])
+    sample_ys = sample_xs.matmul(w_gen.t()) + b_gen
+
+    m = torch.nn.Linear(2, 1)
+    with torch.no_grad():
+        m.weight.zero_()
+        m.bias.zero_()
+    optimizer = optim.SGD(m.parameters(), lr=0.004, momentum=0.)
+    for _step in range(1000):
+        optimizer.zero_grad()
+        ys = m(sample_xs)
+        loss = ((ys - sample_ys)**2).sum()
+        loss.backward()
+        optimizer.step()
+    print(m.weight)
+    print(m.bias)
+*/
 #[test]
 fn sgd_linear_regression() -> Result<()> {
     // Generate some linear data, y = 3.x1 + x2 - 2.