Understanding classes
The classifier we're writing seeks to assign unknown images to a class. Classes can be of the following types:
- Feline versus canine
- Two versus seven
- Tumor versus normal
- Smiling versus frowning
In our case, we are considering each letter a class for a total of 10 classes. The training set will reveal 10 subfolders with thousands of images underneath each subfolder. The name of the subfolder is important as it is the label for each of the images. These details will be used by the pipeline to prepare data for TensorFlow.
Automating the training data setup
Ideally, we will want the entire process automated. This way, we can easily run the process end to end on any computer we use without having to carry around ancillary assets. This will be important later, as we will often develop on one computer (our development machine) and deploy on a different machine (our production server).
I have already written the code for this chapter, as well as all the other chapters; it is available...
