Fixes #2083 - explain model.eval, torch.no_grad (#2400)

Co-authored-by: Svetlana Karslioglu <[email protected]>

Author: zabboud

beginner_source/basics/optimization_tutorial.py

@@ -149,6 +149,9 @@ def forward(self, x):
 
 def train_loop(dataloader, model, loss_fn, optimizer):
     size = len(dataloader.dataset)
+    # Set the model to training mode - important for batch normalization and dropout layers
+    # Unnecessary in this situation but added for best practices
+    model.train()
     for batch, (X, y) in enumerate(dataloader):
         # Compute prediction and loss
         pred = model(X)
@@ -165,10 +168,15 @@ def train_loop(dataloader, model, loss_fn, optimizer):
 
 
 def test_loop(dataloader, model, loss_fn):
+    # Set the model to evaluation mode - important for batch normalization and dropout layers
+    # Unnecessary in this situation but added for best practices
+    model.eval()
     size = len(dataloader.dataset)
     num_batches = len(dataloader)
     test_loss, correct = 0, 0
 
+    # Evaluating the model with torch.no_grad() ensures that no gradients are computed during test mode
+    # also serves to reduce unnecessary gradient computations and memory usage for tensors with requires_grad=True
     with torch.no_grad():
         for X, y in dataloader:
             pred = model(X)