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

need to reattach the "top" layer:
Replacing Top Layer
1 model.fc = top_model
2 sbs_temp = StepByStep(model, None, None)
We can still create a separate instance of StepByStep for the full model so as to be
able to call its predict() or correct() methods (in this case, both loss function and
optimizers are set to None since we won’t be training the model anymore):
Evaluation
StepByStep.loader_apply(val_loader, sbs_temp.correct)
Output
tensor([[ 98, 124],
[124, 124],
[104, 124]])
We got the same results, as expected.
Recap
In this chapter, we’ve learned about the ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) and the many model architectures developed to tackle it
(AlexNet, VGG, Inception, and ResNet). We used their pre-trained weights to
perform transfer learning and either fine-tune or extract features for our own
classification task. Moreover, we took a quick tour of the inner workings of many
architectural elements built into these models. This is what we’ve covered:
• learning about transfer learning
• learning about ImageNet, ILSVRC, and the most popular architectures
developed to tackle it
• comparing the size, speed, and performance of these architectures
• loading the AlexNet model
• loading the model’s pre-trained weights
558 | Chapter 7: Transfer Learning