Fix pre/post-training evaluation to use same batch in nn_tutorial

The tutorial was comparing loss on different batches:
- Pre-training: evaluated on first 64 instances (batch 0)
- Post-training: evaluated on last batch from training loop

This made the comparison misleading as it wasn't measuring improvement
on the same data.

Changes:
- Save the initial batch (xb_initial, yb_initial) after first evaluation
- Use the saved initial batch for post-training evaluation
- Added clarifying comment about fair comparison
- Now both evaluations use the same data (first 64 training instances)

This provides an accurate before/after comparison showing the model's
improvement on the same batch of data.

Author: patrocinio

beginner_source/nn_tutorial.py

@@ -174,6 +174,10 @@ def nll(input, target):
 yb = y_train[0:bs]
 print(loss_func(preds, yb))
 
+# Save the first batch for comparison after training
+xb_initial = xb
+yb_initial = yb
+
 
 ###############################################################################
 # Let's also implement a function to calculate the accuracy of our model.
@@ -244,9 +248,10 @@ def accuracy(out, yb):
 #
 # Let's check the loss and accuracy and compare those to what we got
 # earlier. We expect that the loss will have decreased and accuracy to
-# have increased, and they have.
+# have increased, and they have. We evaluate on the same initial batch
+# we used before training for a fair comparison.
 
-print(loss_func(model(xb), yb), accuracy(model(xb), yb))
+print(loss_func(model(xb_initial), yb_initial), accuracy(model(xb_initial), yb_initial))
 
 ###############################################################################
 # Using ``torch.nn.functional``