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. This way, every node in the graph defines a function of the variables.

A computation graph is the basic unit of computation in TensorFlow. A computation graph consists of nodes and edges. Each node represents an instance of tf. Operation , while each edge represents an instance of tf. Tensor that gets transferred between the nodes.

UNFOLDING COMPUTATIONAL GRAPHS. A computational graph is a way to formalize the structure of a set of computations, such as. those involved in mapping inputs and parameters to outputs and loss. The idea of unfolding a recursive or recurrent computation into a computational graph that has a.

Computational graphs: TensorFlow uses a static computational graph, while PyTorch employs a dynamic one. This impacts the flexibility and ease of debugging during model development. Usability: PyTorch is often considered more intuitive and user-friendly, especially for those new to deep learning.