Artificial Intelligence Now

Chapter 9. A Poet Does TensorFlow

Mike Loukides

After reading Pete Warden’s excellent “TensorFlow for poets”, I was impressed at how easy it seemed to build a working deep learning classifier. It was so simple that I had to try it myself.

I have a lot of photos around, mostly of birds and butterflies. So, I decided to build a simple butterfly classifier. I chose butterflies because I didn’t have as many photos to work with, and because they were already fairly well sorted. I didn’t want to spend hours sorting a thousand or so bird pictures. According to Pete, that’s the most laborious, time-consuming part of the process: getting your training data sorted.

Sorting was relatively easy: while I thought I’d need a database, or some CSV file tagging the photos by filename, I only needed a simple directory structure: a top-level directory named butterflies, with a directory underneath for each kind of butterfly I was classifying. Here’s what I ended up with:

# ls ../tf_files/butterflies/
Painted Lady black swallowtail monarch tiger swallowtail

Only four kinds of butterflies? Unfortunately, yes. While you don’t need thousands of images, you do need at least a dozen or so in each directory. If you don’t, you’ll get divide-by-zero errors that will make you pull your hair out. Pete’s code randomly separates the images you provide into a training set and a validation set. If either of those sets ends up empty, you’re in trouble. (Pete, thanks for your help understanding this!) I ended ...