As mentioned earlier, TF 2.0 brings about default eager execution, which means that legacy TF 1.x custom training logic implementations based on a graph-based code flow are now obsolete. To implement such custom training logic in TF 2.0 with regard to eager execution, tf.GradientTape can be used. The purpose of tf.GradientTape is to record operations for automatic differentiation or for computing the gradient of an operation or computation with respect to its input variables. This is done by using tf.GradientTape as a context manager. TensorFlow records all operations executed in the context of tf.GradientTape onto a tape, which is then, along with the gradients, associated with those operations to compute the gradient of the recorded operation using reverse mode differentiation.
For example, the gradient of a simple cube operation can be calculated as follows...
