Upload model.py

model.py

@@ -0,0 +1,24 @@
+import torch
+import torchvision
+from torch import nn
+
+
+def create_effnetb2_model(num_classese: int = 101,  # default output classes = 3 (pizza, steak , sushi)
+                          seed: int = 42):
+    # 1, 2, 3 Create EffNetB2 pretained 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
+
+    # 5. Change classifier head with random seed for reproducibility
+        torch.manual_seed(seed)
+        model.classifier = nn.Sequential(
+            nn.Dropout(p=.3, inplace=True),
+            nn.Linear(in_features=1408, out_features=num_classese)
+        )
+
+    return model, transforms