👉 TensorFlow Computation Graph (TF CG) is a fundamental concept in TensorFlow that represents the computational steps involved in executing a machine learning model. It visualizes these steps as a directed graph, where nodes represent operations (like matrix multiplications or activation functions) and edges represent the flow of data between these operations, typically in the form of tensors. This graph is constructed during the model building process and remains static once created, regardless of the input data. During inference or execution, TensorFlow traverses this graph, computing the output tensor by sequentially applying each operation in the order they are defined. This approach allows for efficient optimization, parallelization, and caching of intermediate results, significantly speeding up the training and inference processes. TF CG also supports automatic differentiation, enabling gradient-based optimization algorithms to efficiently compute gradients for backpropagation and model training.
