Update model.py

model.py

@@ -3,32 +3,22 @@ import torchvision
 
 from torch import nn
 
-
-def create_effnetb2_model(num_classes:int=3, 
+def create_effnetb2_model(num_classes:int=3, # default output classes = 3 (pizza, steak, sushi)
                           seed:int=42):
-    """Creates an EfficientNetB2 feature extractor model and transforms.
-    Args:
-        num_classes (int, optional): number of classes in the classifier head. 
-            Defaults to 3.
-        seed (int, optional): random seed value. Defaults to 42.
-    Returns:
-        model (torch.nn.Module): EffNetB2 feature extractor model. 
-        transforms (torchvision.transforms): EffNetB2 image transforms.
-    """
-    # Create EffNetB2 pretrained weights, transforms and model
-    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
-    transforms = weights.transforms()
-    model = torchvision.models.efficientnet_b2(weights=weights)
+  # 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights=weights)
+
+  # 4. Freeze all layers in the base model
+  for param in model.parameters():
+    param.requires_grad = False
 
-    # Freeze all layers in base model
-    for param in model.parameters():
-        param.requires_grad = False
+  # 5. Change classifier head with random seed for reproducibility
+  torch.manual_seed(seed)
+  model.classifier = nn.Sequential(
+      nn.Dropout(p=0.3, inplace=True),
+      nn.Linear(in_features=1408, out_features=num_classes)
+  )
 
-    # Change classifier head with random seed for reproducibility
-    torch.manual_seed(seed)
-    model.classifier = nn.Sequential(
-        nn.Dropout(p=0.3, inplace=True),
-        nn.Linear(in_features=1408, out_features=num_classes),
-    )
-    
-    return model, transforms
+  return model, transforms