TensorFlow uses static computational graphs, where the graph structure is defined and compiled before the actual computation takes place. This can lead to better performance for production-level ...

The derivative computation extends your graph, and you can see that when you view your graph in TensorBoard. This capability is not unique to TensorFlow, but it’s very nice to have.

At runtime, TensorFlow executes the computation graph using the parameters provided. Note that the behavior of the computation graph may change depending on the parameters provided. TensorFlow itself ...

Announced today, TensorFlow is a set of free tools for machine learning applications. Or, in technical terms, “an open source software library for numerical computation using data flow graphs.” ...

We propose an extensible parallel training search space which describes parallel training schemes in a declarative fashion. We then implement a computation graph transformation compiler that can ...

Google today released TensorFlow Graph Neural Networks (TF-GNN) in alpha, a library designed to make it easier to work with graph structured data using TensorFlow, its machine learning framework.