Key highlights of this version include support for gradient boosted trees estimators, new keras layers to speed up GRU and LSTM implementations and tfe.Network deprecation. It also includes improved functions for supporting data loading, text processing and pre-made estimators.
Tensorflow 1.9 major features and improvements
One should try the newly added tf.keras.layers.CuDNNGRU, used for a faster GRU implementation and tf.keras.layers.CuDNNLSTM layers, which allows faster LSTM implementation. Both these layers are backed by cuDNN( NVIDIA CUDA Deep Neural Network library (cuDNN)).
Also, the python interface for the TFLite Optimizing Converter has been expanded, and the command line interface (AKA: toco, tflite_convert) is once again included in the standard pip installation. The distributions.Bijector API in the TF version 1.9 also supports broadcasting for Bijectors with the new API changes.
Tensorflow 1.9 also includes improved data-loading and text processing with tf.decode_compressed, tf.string_strip, and Tf.strings.regex_full_match. It also has an added experimental support for new pre-made estimators like tf.contrib.estimator.BaselineEstimator, tf.contrib.estimator.RNNClassifier, tf.contrib.estimator.RNNEstimator.
This version includes two breaking changes. Firstly for opening up empty variable scopes one can replace variable_scope(”, …) by variable_scope(tf.get_variable_scope(), …), which is used to get the current scope of the variable.
And the second breakthrough change is, headers used for building custom ops have been moved to a different file path. From site-packages/external to site-packages/tensorflow/include/external.
Some bug fixes and other changes include:
- The tfe.Network has been deprecated
- Layered variable names have changed in the following conditions:
- Using tf.keras.layers with custom variable scopes.
- Using tf.layers in a subclassed tf.keras.Model class.
- Added the ability to pause recording operations for gradient computation via tf.GradientTape.stop_recording in the Eager execution and updated its documentation and introductory notebooks.
- Fixed an issue in which the TensorBoard Debugger Plugin, which could not handle total source file size exceeding gRPC message size limit (4 MB).
- Added GCS Configuration Ops and complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D.
- Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary.
- Prevents tf.gradients() from backpropagating through integer tensors.
- LinearOperator[1D,2D,3D]Circulant added to tensorflow.linalg.
To know more about the other changes, visit TensorFlow 1.9 release notes on GitHub.