Three years after the initial version of TensorFlow debuted, developers are eagerly awaiting the release of TensorFlow 2.0 which promises to simplify the API and make model building that much easier.
More information on the upcoming TensorFlow 2.0 can be found in this post on Medium:
TensorFlow 2.0 will focus on simplicity and ease of use, featuring updates like:
Easy model building with Keras and eager execution.
Robust model deployment in production on any platform.
Powerful experimentation for research.
Simplifying the API by cleaning up deprecated APIs and reducing duplication.Over the last few years, we’ve added a number of components to TensorFlow. With TensorFlow 2.0, these will be packaged together into a comprehensive platform that supports machine learning workflows from training through deployment.
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There have been a number of versions and API iterations since we first open-sourced TensorFlow. With the rapid evolution of ML, the platform has grown enormously and now supports a diverse mix of users with a diverse mix of needs. With TensorFlow 2.0, we have an opportunity to clean up and modularize the platform based on semantic versioning.
Here are some of the larger changes coming:
Removal of queue runners in favor of tf.data.
Removal of graph collections.
Changes to how variables are treated.
Moving and renaming of API symbols.
