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import torch
import torchvision
from torch import nn
def create_effnetb2_model(num_classes:int=3,
seed:int=42):
# Create Effnet pretrained model
weights= torchvision.models.EfficientNet_B2_Weights.DEFAULT
transforms= weights.transforms()
model= torchvision.models.efficientnet_b2(weights=weights)
# Freeze all layers in the base model
for param in model.parameters():
param.requires_grad= False
# Change the classifier layer
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
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