Computation Graph

What is Computation Graph? Computation Graph Explained.

A computation graph, also known as a computational graph or a directed acyclic graph (DAG), is a graphical representation of a mathematical expression or a computational process. It is commonly used in various fields, including machine learning, deep learning, and numerical computation.

In a computation graph, nodes represent mathematical operations or functions, and edges represent the flow of data between these operations. The graph is typically directed, meaning that the edges have a specific direction indicating the order in which the operations should be performed.

Each node in the graph takes input data, performs a specific computation, and produces an output. The inputs and outputs are usually multidimensional arrays, also known as tensors, in the context of machine learning and deep learning. The computation performed by each node can range from simple arithmetic operations, such as addition or multiplication, to complex mathematical functions or machine learning algorithms.

Computation graphs are particularly useful for optimizing and efficiently executing complex mathematical expressions or computational processes. By representing the computation as a graph, it becomes possible to analyze the dependencies between different operations, identify potential optimizations, and parallelize the computation when applicable.

During the process of backpropagation, which is crucial for training neural networks, computation graphs play a central role. By traversing the graph in the reverse direction, gradients can be efficiently computed and propagated through the network, enabling the optimization of the network’s parameters.

Overall, computation graphs provide a structured and visual way to represent complex computations, facilitate optimization and efficient execution, and are widely used in various computational domains, especially in the fields of machine learning and deep learning.