TensorFlow 1.9.0-rc2 releases!

2 min read

After the 1.9.0-rc0 release early last month, the TensorFlow team is out with another update 1.9.0-rc2, unveiling major features and updates. The new release includes latest improvements, bug fixes, and other changes.

Let’s have a look at the noteworthy features in TensorFlow 1.9.0-rc2:

Key features and improvements

Docs for tf.keras namely the new Keras-based ‘get started’ page and the programmers guide page have been updated.
Layers tf.keras.layers.CuDNNGRU and tf.keras.layers.CuDNNLSTM have been added.
Python interface for TFLite Optimizing Converter is expanded. Command line interface (AKA: toco, tflite_convert) is added in the standard pip installation again.
Data loading and text processing has improved with:

Tf.decode_compressed, tf.string_strip, and tf.strings.regex_full_match

Headers used for custom apps have moved from site-packages/external into site-packages/tensorflow/include/external.
On opening empty variable scopes; replace variable_scope(”, …) by variable_scope(tf.get_variable_scope(), …).

Bug Fixes

tfe.Network has now been deprecated. You can now inherit from tf.keras.Model.
The layered variable names have changed for the conditions mentioned below:
- using tf.keras.layers with custom variable scopes
- using tf.layers in a subclass of tf.keras.Model class.
In tf.data, DatasetBase::DebugString() method is now const. The tf.contrib.data.sample_from_datasets() API for randomly sampling from multiple datasets has now been added.
In tf.contrib, tf.contrib.data.choose_from_datasets() is added and tf.contrib.data.make_csv_dataset() will now support line breaks in quoted strings. From make_csv_dataset, two arguments were removed.The tf.contrib.framework.zero_initializer supports ResourceVariable. “Constrained_optimization” is added to tensorflow/contrib.

Other changes

GCS Configuration Ops has been added.
The signature of Makelterator has been changed to enable propagation of error status.
The bug in tf.reduce_prod gradient has been fixed for complex dtypes.
Benchmark for tf.scan has been updated in order to match ranges across eager and graph modes.
Optional args argument added to Dataset.from_generator().
Ids in nn.embedding_lookup_sparse have been made unique which helps reduce RPC calls that are made for looking up the embeddings in case there are repeated ids in the batch.
tf.train.Checkpoint is added for reading/writing object-based checkpoints.

To get more information on the new updates and features in the latest TensorFlow 1.9.0-rc2 release, check out the official release notes.