Скачать или смотреть 2.4 Graphs and Sessions: TensorFlow Computational Graph

The TensorFlow computational graph is a foundational concept that underpins TensorFlow's execution model, essential for effective model building and training. A computational graph is a directed graph comprising nodes representing operations and edges representing the flow of data (tensors) between these operations. To construct a computational graph, operations are defined, tensors are created to hold data and intermediate results, and connections between operations are specified. Visualization tools like TensorBoard enable users to visualize and comprehend the structure and data flow within the graph. Execution of the computational graph occurs within a tf.Session() context, where operations are executed, and output tensors are computed. The computational graph offers numerous benefits, including optimization for performance and memory usage, portability across environments, and aiding in debugging and understanding model behavior. Overall, mastering the construction, visualization, and execution of computational graphs empowers developers to efficiently develop and debug complex machine learning systems, leveraging TensorFlow's flexibility, optimization, and scalability.