TF 2.0 can be used in two main ways—using low-level APIs and using high-level APIs. To use the low-level APIs in TF 2.0, APIs such as tf.GradientTape and tf.function are implemented.
The code flow for writing low-level code is to define a forward pass inside of a function that takes the input data as an argument. This function is then annotated with the tf.function decorator in order to run it in graph mode along with all of its benefits. To record and get the gradients of the forward pass, both the decorator function and the loss function are run inside the tf.GradientTape context manager, from which gradients can be calculated and applied on the model variables.
Training code can also be written using the low-level APIs for tf.keras models by using tf.GradientTape. This is for when more control and customizability is needed over the default tf.keras.Model...
