After you’ve set up PyTorch, deep learning textbooks normally throw a bunch of jargon at you before doing anything interesting. I try to keep that to a minimum and work through an example, albeit one that can easily be expanded as you get more comfortable working with PyTorch. We use this example throughout the book to demonstrate how to debug a model (Chapter 7) or deploy it to production (Chapter 8).
What we’re going to construct from now until the end of Chapter 4 is an image classifier. Neural networks are commonly used as image classifiers; the network is given a picture and asked what is, to us, a simple question: “What is this?”
Let’s get started with building our PyTorch application.
Our Classification Problem
Here we build a simple classifier that can tell the difference between fish and cats. We’ll be iterating over the design and how we build our model to make it more and more accurate.
Figures 2-1 and 2-2 show a fish and a cat in all their glory. I’m not sure whether the fish has a name, but the cat is called Helvetica.
Let’s begin with a discussion of the traditional challenges involved in classification.
Figure 2-1. A fish!
Figure 2-2. Helvetica in a box
Traditional Challenges
How would you go about writing a program ...
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