Open source contributors from TensorFlow Community has successfully released TensorFlow 1.10 loaded with numerous features, multiple bug fixes and improvements. Let’s have a look at the key improvements added to the TensorFlow framework.
New Features and Improvements:
- Runtime tf.lite now supports complex64
- tf.data gets Bigtable integration
- tf.estimator.train_and_evaluate enhanced with improved local run behaviour
- Added restriction support in RunConfig for speeding up training and clean shutdown assurance.
- Moved Distributions and Bijectors from tf.contrib.distributions to Tensorflow Probability (TFP)
- Added new endpoints like tf.debugging, tf.dtypes, tf.image, tf.io, tf.linalg, tf.manip, tf.math, tf.quantization, tf.strings
- tf.contrib.distributions deprecation in the process and to be removed by the end of year
- Dropping off official support for cmake
- Support to Bazel from TensorFlow 1.11 onwards
Bug Fixes and Miscellaneous Changes:
- tf.contrib.data.group_by_reducer() is now available via the public API
- Added drop_remainder argument to tf.data.Dataset.batch() and tf.data.Dataset.padded_batch()
- Custom savers for Estimator included in EstimatorSpec useful during export
- Supports sparse_combiner in canned Linear Estimators.
- Added batch normalization to DNNClassifier, DNNRegressor, and DNNEstimator.
- Added ranking support and center bias option for boosted trees.
You can visit TensorFlow official release page on Github to review the full release notes on the complete list of added features and changes.