TensorFlow is very unlike other programming languages. We first need to build a blueprint of whatever neural network we want to create. This is accomplished by dividing the program into two separate parts, namely, definition of the computational graph and its execution. At first, this might appear cumbersome to the conventional programmer, but it is this separation of the execution graph from the graph definition that gives TensorFlow its strength, that is, the ability to work on multiple platforms and parallel execution.

Computational graph: A computational graph is a network of nodes and edges. In this section, all the data to be used, in other words, tensor Objects (constants, variables, and placeholders) and all the computations to be performed, namely, Operation Objects (in short referred as ops), are defined. Each node can have zero or more inputs but only one output. Nodes in the network represent Objects (tensors and Operations), and edges represent the Tensors that flow between operations. The computation graph defines the blueprint of the neural network but Tensors in it have no value associated with them yet.

To build a computation graph we define all the constants, variables, and operations that we need to perform. Constants, variables, and placeholders will be dealt with in the next recipe. Mathematical operations will be dealt in detail in the recipe for matrix manipulations. Here, we describe the structure using a simple example of defining and executing a graph to add two vectors.

Execution of the graph: The execution of the graph is performed using Session Object. The Session Object encapsulates the environment in which tensor and Operation Objects are evaluated. This is the place where actual calculations and transfer of information from one layer to another takes place. The values of different tensor Objects are initialized, accessed, and saved in Session Object only. Up to now the tensor Objects were just abstract definitions, here they come to life.