Implementing unit tests

Testing code results in faster prototyping, more efficient debugging, faster changing, and makes it easier to share code. There are easy ways to implement unit tests in TensorFlow that we will cover in this recipe.

Getting ready

When programming a TensorFlow model, it will help to have unit tests to check the functionality of the program. This helps us because, when we want to make changes to a program unit, tests will make sure that those changes do not break the model in unknown ways. In this recipe, we will create a simple CNN network that relies on the MNIST data. With it, we will implement three different types of unit test to illustrate how to write them in TensorFlow.

Note

Note that Python has a great testing library called Nose. TensorFlow also has built-in testing functions, and we will illustrate how these make it easier to test the value of tensor objects without having to evaluate the values in a session.

1. We start by loading the necessary libraries and formatting...