Deep Learning Practice End Term: 22 December 2024 (September 2024 term)

1import torch
2import torch.nn as nn
3from torchvision.models import vgg16, vgg19
4
5# Block A: VGGNet-16 with modified classification head
6class VGG16Modified(nn.Module):
7    def __init__(self, num_classes=10):
8        super(VGG16Modified, self).__init__()
9        self.vgg16 = vgg16(pretrained=True)
10        self.vgg16.classifier[6] = nn.Linear(4096, num_classes)
11
12    def forward(self, x):
13        return self.vgg16(x)
14
15model_a = VGG16Modified()
16
17# Block B: VGGNet-19 with modified classification head
18class VGG19Modified(nn.Module):
19    def __init__(self, num_classes=10):
20        super(VGG19Modified, self).__init__()
21        self.vgg19 = vgg19(pretrained=True)
22        self.vgg19.classifier[6] = nn.Linear(4096, num_classes)
23
24    def forward(self, x):
25        return self.vgg19(x)
26
27model_b = VGG19Modified()
28
29# Block C: Loading VGGNet-16 and freezing feature extraction layers
30vgg16_model = vgg16(pretrained=True)
31for param in vgg16_model.features.parameters():
32    param.requires_grad = False
33vgg16_model.classifier[6] = nn.Linear(4096, 10)
34
35# Block D: VGGNet-19 with feature extraction layers unfrozen
36vgg19_model = vgg19(pretrained=True)
37vgg19_model.classifier[6] = nn.Linear(4096, 10)