The APIs in TensorFlow 1.0 have changed in ways that are not all backward-compatible. That is, TensorFlow programs that worked on TensorFlow 0.x won't necessarily work on TensorFlow 1.x. These API changes have been made to ensure an internally-consistent API. In other words, Google does not have any plans to make TensorFlow backwards-breaking changes throughout the 1.x lifecycle.

In the latest TensorFlow 1.x version, Python APIs resemble NumPy more closely. This has made the current version more stable for array-based computation. Two experimental APIs for Java and GO have been introduced too. This is very good news for the Java and GO programmer.

A new tool called TensorFlow Debugger (tfdbg) has been introduced. This is a command-line interface and API for debugging live TensorFlow programs. A new Android demos (https://github.com/tensorflow/tensorflow/tree/r1.0/tensorflow/examples/android) for object detection and localization and camera-based image stylization have been made available.

Now the installation of TensorFlow can be done through an Anaconda and Docker image of TensorFlow. Finally and most importantly, a new domain-specific compiler for TensorFlow graphs targeting CPU and GPU computing has been introduced. This is called Accelerated Linear Algebra (XLA).