TensorFlow 1.9: Improved support for tf.keras and eager execution
TensorFlow 1.9 is here! So what does this latest update mean for the popular machine learning project? For starters, there’s an improved tf.keras beginner’s guide. For everyone else, there’s eager execution, improved GRU and LSTM implementation, and gradient boosted trees estimators.
TensorFlow 1.9 is here! The latest version of the popular machine learning project is generally available. So, now that eBay, Google, and Twitter all use TensorFlow, are you willing to give it a try? After all, it’s one of the hottest skills to have for freelancing or full time work.
It’s easier than ever with 1.9: new improvements and support makes switching over a breeze. This update focuses mostly on improvements for
tf.keras documentation, GRU and LSTM implementation, and more support for gradient boosted tree emulators. Plus, bug fixes and a few breaking changes!
Major updates in TensorFlow 1.9
tf.keras has updated documentation for getting started and a programmer’s guide. It’s also been updated to the Keras 2.1. API, with new layers
tf.keras.layers.CuDNNLSTM for developers to try. As for bugfixes, Keras code is now out of _impl folder and removes API files. Plus,
tf.keras.Model.save_weights now saves in TensorFlow format by default.
TensorFlow 1.9 offers more support to core feature columns and losses to gradient boosted trees estimators, making it easier to use the non-parametric statistical learning techniques for classification and regression.
TensorFlow 1.9 also offers improved data-loading and text processing with
tf.strings.regex_full_match. There’s also added experimental support for new pre-made Estimators.
Last but not least, the distributions.Bijector API now supports broadcasting for Bijectors with new API changes.
Breaking changes and bug fixes
There are two breaking changes in TensorFlow 1.9 to watch out for:
- When opening empty variable scopes, replace
- Headers for building custom ops have been moved! They are now in site-packages/tensorflow/include/externa
There are an awful lot of bugfixes in 1.9. Here are some of the big ones:
- Network has been deprecated.
- Layered variable names are now changed under the following conditions:
keras.layerswithcustom variable scopes.
- Using layers in a subclassed
tf.keras.Modelclass. See here for more details.
There’s a lot of changes for
tf.data, and more. Check the complete changelog for more information.
Getting TensorFlow 1.9
Interested in trying out the latest update yourself? Installing TensorFlow is easy. There are a number of installation guide available to help you get started. More information can be found on GitHub.