Understanding Symbolic Tensors in TensorFlow and Keras

In the realm of machine learning, especially within the frameworks of TensorFlow and Keras, symbolic tensors play a pivotal role in constructing and understanding neural networks. Here, we delve into what symbolic tensors are, their importance, and how they can be leveraged in these popular libraries.

What are Symbolic Tensors?

Symbolic tensors are abstract representations of data that hold the structure and shape of a tensor without any actual data. They allow for defining operations and models in a way that's independent of the data itself. This abstraction is crucial for building computational graphs where operations can be strategically arranged before the actual data flows through during execution.

Key Characteristics of Symbolic Tensors:

• Shape Abstraction: Symbolic tensors define the dimensionality but do not contain any numerical data.
• Graph-Based Execution: They are used in constructing computation graphs, which are executed later with actual data.
• Differentiation Friendly: Operations on symbolic tensors facilitate automatic differentiation, a key aspect of training neural networks.

Symbolic vs. Eager Tensors

In TensorFlow, there are two modes of execution: eager execution and graph execution.

• Eager Execution: This mode evaluates operations immediately, providing immediate feedback. Here, tensors are dense and concrete, holding actual data values. Eager tensors facilitate intuitive debugging and interactive development.
• Graph Execution: This is where symbolic tensors come into play. Instead of executing operations immediately, operations are defined within a graph and executed upon data feeding.

Usage of Symbolic Tensors in TensorFlow and Keras

When using TensorFlow and Keras, most of the defining of models, especially using the Keras API, involves creating symbolic tensors. For instance, when defining a tf.keras.Model, the inputs and transformations are represented symbolically.

Example in Keras:

• Element-wise Operations: Like addition, subtraction, multiplication.
• Matrix Operations: Like dot products, transpositions, and convolutions.
• Advanced Features: Automatic differentiation and back-propagation, essential for deep learning.