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4817ade | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | import torch
import torchvision
from torch import nn
def create_mobilenetv2_model(num_classes:int=5,
seed:int=42):
model=torch.load('model.pth')
model.to(device)
# Freeze all layers in base model
for param in model.parameters():
param.requires_grad = False
# 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 # Assuming 'transforms' is defined somewhere
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