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.

今天，TensorFlow 官方博客发布了 TensorFlow Graph Neural Networks（TensorFlow GNN）库 ，这个库使得用户在使用 TensorFlow 时能够轻松处理图结构数据。

雷锋网 (公众号：雷锋网)按：本文为雷锋字幕组编译的技术博客，原标题 Smart way to serialize/deserialize classes to/from Tensorflow graph ，作者为 Francesco ...

That library, TensorFlow, was developed by the Google Brain team over the past several years and released to open source in November 2015. TensorFlow does computation using data flow graphs.

TensorFlow data flow graphs TensorFlow supports machine learning, neural networks, and deep learning in the larger context of data flow graphs.

TensorFlow Hub encourages the publication and discovery of self-contained modular pieces of TensorFlow graphs for reuse across similar tasks.

“TensorFlow is a machine learning library that’s used across Google for applying deep learning to a lot of different areas,” says Rajat Mongo, a technical lead on the TensorFlow project, in a YouTube ...

There is no real middle ground when it comes to TensorFlow use cases. Most implementations take place either in a single node or at the drastic Google-scale, with few scalability stories in between.

Similarly, Google has released TensorFlow Graph Neural Networks, a library designed to make it easier to work with graph-structured data in its TensorFlow machine learning (ML) framework.

TensorFlow has become the most popular tool and framework for machine learning in a short span of time. It enjoys tremendous popularity among ML engineers and developers.