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

Model Size Classifier Layer(s) Replacement Layer(s)
ResNet 224 model.fc nn.Linear(512,num_classes)
DenseNet 224 model.classifier nn.Linear(1024,num_classes)
SqueezeNet 224 model.classifier[1] nn.Conv2d(512,num_classes,
kernel_size=1,stride=1)
"Why there are two layers for the InceptionV3 model?"
The Inception model is a special case because it has auxiliary classifiers. We’ll
discuss them later in this chapter.
Model Configuration
What’s missing in the model configuration? A loss function and an optimizer. A
multiclass classification problem, when the model produces logits, requires
nn.CrossEntropyLoss() as the loss function. For the optimizer, let’s use Adam with
the "Karpathy Constant" (3e-4) as its learning rate.
Model Configuration
1 torch.manual_seed(17)
2 multi_loss_fn = nn.CrossEntropyLoss(reduction='mean')
3 optimizer_alex = optim.Adam(alex.parameters(), lr=3e-4)
Cool, the model configuration is taken care of, so we can turn our attention to the…
Data Preparation
This step is quite similar to what we did in the previous chapter (we’re still using
the Rock Paper Scissors dataset), except for one key difference: We will use
different parameters for standardizing the images.
"So we’re not computing statistics for the images in our dataset
anymore?"
Nope!
"Why not?" Transfer Learning in Practice | 513