TensorFlow follows directed graph philosophy to set up computational models where mathematical operations are represented as nodes with each node supporting multiple input and output while the edges represent the communication of data between nodes. There are also edges known as control dependencies in TensorFlow that do not represent the data flow; rather the provide information related to control dependence such as node for the control dependence must finish processing before the destination node of control dependence starts executing.

An example TensorFlow graph for logistic regression scoring is shown in the following diagram:

The preceding figure illustrates the TensorFlow graph to score logistic regression with optimized weights:

The MatMul node performs matrix multiplication between input feature matrix X and optimized weight β. The constant C is then added to the output from the MatMul node. The output from Add is then transformed using the Sigmoid function to output Pr(y=1|X).