Implementing unit tests

Testing code results in faster prototyping, more efficient debugging, faster changing, and makes it easier to share code. TensorFlow 2.0 provides the tf.test module and we will cover it in this recipe.

Getting ready

When programming a TensorFlow model, it helps 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 those changes do not break the model in unknown ways. In Python, the main test framework is unittest but TensorFlow provides its own test framework. In this recipe, we will create a custom layer class. We will implement a unit test to illustrate how to write it in TensorFlow.

How to do it...

1. First, we need to load the necessary libraries as follows:

   import tensorflow as tf
   import numpy as np
   

2. Then, we need to declare our custom gate that applies the function f(x) = a1 * x + b1:

   class MyCustomGate(tf...