Why Tensorflow Uses Computational Graphs?

TensorFlow uses directed graphs internally to represent computations, and they call this data flow graphs (or computational graphs). ... The edges correspond to data, or multidimensional arrays (so-called Tensors) that flow through the different operations. In other words, edges carry information from one node to another.

What is computational graph in TensorFlow?

What Are Computational Graphs? In TensorFlow, machine learning algorithms are represented as computational graphs. A computational graph is a type of directed graph where nodes describe operations, while edges represent the data (tensor) flowing between those operations.

Why are computational graphs useful?

A computational graph is a directed graph where the nodes correspond to operations or variables. Variables can feed their value into operations, and operations can feed their output into other operations. ... The concept of a computational graph becomes more useful once the computations become more complex.