How does tf.function() work?

The tf.function work involves examining the tracing process, compilation and optimization. Let’s explore the whole process:

• tf.function() works by tracing the Python function and creating a ConcreteFunction for each set of input shapes and dtypes. A ConcreteFunction is a callable graph that executes the traced operations.
• tf.function() takes the Python function as input and returns a tf.types.experimental.PolymorphicFunction, which is a Python callable that builds TensorFlow graphs from the Python function.
• When we call the PolymorphicFunction, it traces the Python function and creates a graph that represents the computation. The graph can be optimized by TensorFlow to improve performance and portability. The PolymorphicFunction can handle different types and shapes of inputs by creating multiple graphs, each specialized for a specific input signature. This is called polymorphism, and it allows us to use the same tf.function for different scenarios.
• We can inspect the graphs created by tf.function by using the get_concrete_function method, which returns a tf.types.experimental.ConcreteFunction. A ConcreteFunction is a single graph with a fixed input signature and output.
• We can also obtain the graph object directly by using the graph property of the ConcreteFunction. The graph object contains information about the nodes and edges of the graph, and can be used for debugging or visualization.

TensorFlow is a machine learning framework that has offered flexibility, scalability and performance for deep learning tasks. tf.function helps to optimize and accelerate computation by leveraging graph-based execution. In the article, we will cover the concept of tf.function in TensorFlow.