create model.py

model.py

@@ -0,0 +1,27 @@
+
+import torch
+import torchvision
+
+from torch import nn
+
+def create_effnetb2_model(num_classes: int=3,
+                          seed: int =42):
+
+  #weights, transforms and model instance
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+
+  model = torchvision.models.efficientnet_b2(weights= weights)
+
+  # freeze all layers of base model
+  for param in model.parameters():
+    param.requires_grad = False  # i.e don't keep track of gradients
+
+    # change classifier head
+    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