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

Model Configuration
1 optimizer_model = optim.Adam(model.parameters(), lr=3e-4)
2 sbs_incep = StepByStep(model, inception_loss, optimizer_model)
"Wait, aren’t we pre-processing the dataset this time?"
Unfortunately, no. The preprocessed_dataset() function cannot handle multiple
outputs. Instead of making the process convoluted in order to handle the
peculiarities of the Inception model, I am sticking with the simpler (yet slower) way
of training the last layer while it is still attached to the rest of the model.
The Inception model is also different from the others in its expected input size: 299
instead of 224. So, we need to recreate the data loaders accordingly:
Data Preparation
1 normalizer = Normalize(mean=[0.485, 0.456, 0.406],
2 std=[0.229, 0.224, 0.225])
3
4 composer = Compose([Resize(299),
5 ToTensor(),
6 normalizer])
7
8 train_data = ImageFolder(root='rps', transform=composer)
9 val_data = ImageFolder(root='rps-test-set', transform=composer)
10 # Builds a loader of each set
11 train_loader = DataLoader(
12 train_data, batch_size=16, shuffle=True)
13 val_loader = DataLoader(val_data, batch_size=16)
We’re ready, so let’s train our model for a single epoch and evaluate the result:
Model Training
1 sbs_incep.set_loaders(train_loader, val_loader)
2 sbs_incep.train(1)
StepByStep.loader_apply(val_loader, sbs_incep.correct)
Auxiliary Classifiers (Side-Heads) | 523