Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Data Preparation
1 # ImageNet statistics
2 normalizer = Normalize(mean=[0.485, 0.456, 0.406],
3 std=[0.229, 0.224, 0.225])
4
5 composer = Compose([Resize(256),
6 CenterCrop(224),
7 ToTensor(),
8 normalizer])
9
10 train_data = ImageFolder(root='rps', transform=composer)
11 val_data = ImageFolder(root='rps-test-set', transform=composer)
12
13 # Builds a loader of each set
14 train_loader = DataLoader(
15 train_data, batch_size=16, shuffle=True
16 )
17 val_loader = DataLoader(val_data, batch_size=16)
This time, we’ll use the smallest version of the ResNet model (resnet18) and either
fine-tune it or use it as a feature extractor only.
Fine-Tuning
Model Configuration (1)
1 model = resnet18(pretrained=True)
2 torch.manual_seed(42)
3 model.fc = nn.Linear(512, 3)
There is no freezing since fine-tuning entails the training of all the weights, not only
those from the "top" layer.
Model Configuration (2)
1 multi_loss_fn = nn.CrossEntropyLoss(reduction='mean')
2 optimizer_model = optim.Adam(model.parameters(), lr=3e-4)
Putting It All Together | 555