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.

TensorFlow does that for you, letting you move on to more interesting problems. The derivative computation extends your graph, and you can see that when you view your graph in TensorBoard.

While DeepMind’s original implementation uses an older TensorFlow 1.0 framework, which lacks compatibility with recent libraries, we adapt their architecture to TensorFlow 2, exploring the newly ...

XLA compiles a TensorFlow graph into a sequence of computation kernels generated specifically for the given model. Because these kernels are unique to the model, they can exploit model-specific ...

The configuration ran on a recent version of the TensorFlow. Leveraging large memory support on the server platform, the partners said they achieved scaling of more than 120 3.9-megapixel images per ...