We will demonstrate the usage of the classifier with notMNIST_small.tar.gz, which becomes the test set. For ongoing use of the classifier, you can source your own images and run them through a similar pipeline to test, not train.

You can create some 28x28 images yourself and place them into the test set for evaluation. You will be pleasantly surprised!

The practical issue with field usage is the heterogeneity of images in the wild. You may need to find images, crop them, downscale them, or perform a dozen other transformations. This all falls into the usage pipeline, which we discussed earlier.

Another technique to cover larger images, such as finding a letter on a page-sized image, is to slide a small window across the large image and feed every subsection of the image through the classifier.

We'll be taking our models into production in future chapters but, as a preview, one common setup is to move the trained model into a server on the cloud. The façade of the system might be a smartphone app that takes photos and sends them off for classification behind the scenes. In this case, we will wrap our entire program with a web service to accept incoming classification requests and programmatically respond to them. There are dozens of popular setups and we will explore several of them in Chapter 9, Cruise Control -Automation.