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TensorFlow 2.0 Alpha Released

Today the alpha 2.0 version of the popular open source machine learning platform TensorFlow was released on Github.

According to the official announcement of the release, the focus of TensorFlow 2.0 is on ease of use via new APIs and other features to make it much simpler for both beginners and experienced machine learning experts to use TensorFlow in their projects.

The creators have also previously commented about their desire to make TensorFlow "more effective" with this release, via changes like improved Python runtime integration, more consistency between the various APIs offered and the removal of some redundant APIs.

A detailed list of all the modules, classes and functions in TensorFlow 2.0 can be found here.

Two of the new features particularly promoted in the announcement are eager execution and @tf.function (and how they work together):

"One of the biggest changes is that TensorFlow is eager-first, which means ops are run immediately upon calling them. In TensorFlow 1.x, you might be familiar with first constructing a graph and then executing pieces of the graph via tf.Session.run(). TensorFlow 2.0 radically simplifies TensorFlow usage — the same great ops, now much easier to understand and use....

TensorFlow 2.0 uses Keras as a core developer experience. With 2.0, you can use Keras as you know it, building your models with the Sequential API, and then using compile and fit. All of these familiar tf.keras examples from tensorflow.org work out of the box in 2.0.

The announcement notes that while TensorFlow 2.0 is designed to be used with the Python-based open source deep learning library Keras, it's not required.

The company is offering "Getting Started for Beginners" and "Getting Started for Experts" tutorials for 2.0.

Being an alpha release, this current version of 2.0 is considered to be in "test drive" mode for developers. Still the creators have published a migration guide for transitioning TensorFlow 1code to TensorFlow 2 here.

About the Author

Becky Nagel is the vice president of Web & Digital Strategy for 1105's Converge360 Group, where she oversees the front-end Web team and deals with all aspects of digital strategy. She also serves as executive editor of the group's media Web sites, and you'll even find her byline on PureAI.com, the group's newest site for enterprise developers working with AI. She recently gave a talk at a leading technical publishers conference about how changes in Web technology may impact publishers' bottom lines. Follow her on twitter @beckynagel.

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