Major Features and Improvements

• XLA (experimental): initial release of XLA, a domain-specific compiler for TensorFlow graphs, that targets CPUs and GPUs.
• TensorFlow Debugger (tfdbg): command-line interface and API.
• New python 3 docker images added.
• Made pip packages pypi compliant. TensorFlow can now be installed by pip install tensorflow command.
• Several python API calls have been changed to resemble NumPy more closely.
• New (experimental) Java API.
• Android: new person detection + tracking demo implementing "Scalable Object Detection using Deep Neural Networks" (with additional YOLO object detector support)
• Android: new camera-based image stylization demo based on "A Learned Representation For Artistic Style"

Breaking Changes to the API

To help you upgrade your existing TensorFlow Python code to match the API changes below, we have prepared a conversion script.

• TensorFlow/models have been moved to a separate github repository.
• Division and modulus operators (/, //, %) now match Python (flooring)
  semantics. This applies to tf.div and tf.mod as well. To obtain forced
  integer truncation based behaviors you can use tf.truncatediv
  and tf.truncatemod.
• tf.divide() is now the recommended division function. tf.div() will
  remain, but its semantics do not respond to Python 3 or from future
  mechanisms.
• tf.reverse() now takes indices of axes to be reversed. E.g.
  tf.reverse(a, [True, False, True]) must now be written as
  tf.reverse(a, [0, 2]). tf.reverse_v2() will remain until 1.0 final.
• tf.mul, tf.sub and tf.neg are deprecated in favor of tf.multiply,
  tf.subtract and tf.negative.
• tf.pack and tf.unpack are deprecated in favor of tf.stack and
  tf.unstack.
• TensorArray.pack and TensorArray.unpack are getting deprecated in favor of
  TensorArray.stack and TensorArray.unstack.
• The following Python functions have had their arguments changed to use axis
  when referring to specific dimensions. We have kept the old keyword arguments
  for compatibility currently, but we will be removing them well before the
  final 1.0.
  • tf.argmax: dimension becomes axis
  • tf.argmin: dimension becomes axis
  • tf.count_nonzero: reduction_indices becomes axis
  • tf.expand_dims: dim becomes axis
  • tf.reduce_all: reduction_indices becomes axis
  • tf.reduce_any: reduction_indices becomes axis
  • tf.reduce_join: reduction_indices becomes axis
  • tf.reduce_logsumexp: reduction_indices becomes axis
  • tf.reduce_max: reduction_indices becomes axis
  • tf.reduce_mean: reduction_indices becomes axis
  • tf.reduce_min: reduction_indices becomes axis
  • tf.reduce_prod: reduction_indices becomes axis
  • tf.reduce_sum: reduction_indices becomes axis
  • tf.reverse_sequence: batch_dim becomes batch_axis, seq_dim becomes seq_axis
  • tf.sparse_concat: concat_dim becomes axis
  • tf.sparse_reduce_sum: reduction_axes becomes axis
  • tf.sparse_reduce_sum_sparse: reduction_axes becomes axis
  • tf.sparse_split: split_dim becomes axis
• tf.listdiff has been renamed to tf.setdiff1d to match NumPy naming.
• tf.inv has been renamed to be tf.reciprocal (component-wise reciprocal)
  to avoid confusion with np.inv which is matrix inversion
• tf.round now uses banker's rounding (round to even) semantics to match NumPy.
• tf.split now takes arguments in a reversed order and with different
  keywords. In particular, we now match NumPy order as
  tf.split(value, num_or_size_splits, axis).
• tf.sparse_split now takes arguments in reversed order and with different
  keywords. In particular we now match NumPy order as
  tf.sparse_split(sp_input, num_split, axis). NOTE: we have temporarily
  made tf.sparse_split require keyword arguments.
• tf.concat now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as tf.concat(values, axis, name).
• tf.image.decode_jpeg by default uses the faster DCT method, sacrificing
  a little fidelity for improved speed. One can revert to the old
  behavior by specifying the attribute dct_method='INTEGER_ACCURATE'.
• tf.complex_abs has been removed from the Python interface. tf.abs
  supports complex tensors and should be used instead.
• Template.var_scope property renamed to .variable_scope
• SyncReplicasOptimizer is removed and SyncReplicasOptimizerV2 renamed to SyncReplicasOptimizer.
• tf.zeros_initializer() and tf.ones_initializer() now return a callable
  that must be called with initializer arguments, in your code replace
  tf.zeros_initializer with tf.zeros_initializer().
• SparseTensor.shape has been renamed to SparseTensor.dense_shape. Same for
  SparseTensorValue.shape.
• Replace tf.scalar_summary, tf.histogram_summary, tf.audio_summary, tf.image_summary with tf.summary.scalar, tf.summary.histogram, tf.summary.audio, tf.summary.image, respectively. The new summary ops take name rather than tag as their first argument, meaning summary ops now respect TensorFlow name scopes.
• Replace tf.train.SummaryWriter and tf.train.SummaryWriterCache with tf.summary.FileWriter and tf.summary.FileWriterCache.
• Removes RegisterShape from public API. Use C++ shape function registration
  instead.
• Deprecated _ref dtypes from the python API.
• In the C++ API (in tensorflow/cc), Input, Output, etc. have moved
  from the tensorflow::ops namespace to tensorflow.
• Change arg order for {softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits to be (labels, predictions), and force use of named args.
• tf.nn.rnn_cell.* and most functions in tf.nn.rnn.* (with the exception of dynamic_rnn and raw_rnn) are temporarily in tf.contrib.rnn. They will be moved back into core for TF 1.1.

Bug Fixes and Other Changes

• Numerous C++ API updates.
• New op: parallel_stack.
• Introducing common tf io compression options constants for
  RecordReader/RecordWriter.
• Add sparse_column_with_vocabulary_file, to specify a feature column that
  transform string features to IDs, where the mapping is defined by a vocabulary
  file.
• Added index_to_string_table which returns a lookup table that maps indices to
  strings.
• Add string_to_index_table, which returns a lookup table that matches strings
  to indices.
• Add a ParallelForWithWorkerId function.
• Add string_to_index_table, which returns a lookup table that matches strings
  to indices.
• Support restore session from checkpoint files in v2 in contrib/session_bundle.
• Added a tf.contrib.image.rotate function for arbitrary angles.
• Added tf.contrib.framework.filter_variables as a convenience function to
  filter lists of variables based on regular expressions.
• make_template() takes an optional custom_getter_ param.
• Added comment about how existing directories are handled by
  recursive_create_dir.
• Added an op for QR factorizations.
• Divides and mods in Python API now use flooring (Python) semantics.
• Android: pre-built libs are now built nightly.
• Android: cmake/gradle build for TensorFlow Inference library under
  contrib/android/cmake
• Android: Much more robust Session initialization code.
• Android: TF stats now exposed directly in demo and log when debug mode is
  active
• Android: new/better README.md documentation
• saved_model is available as tf.saved_model.
• Empty op is now stateful.
• Improve speed of scatter_update on the cpu for ASSIGN operations.
• Change reduce_join to treat reduction_indices in the same way as other reduce_ ops.
• Move TensorForestEstimator to contrib/tensor_forest.
• Enable compiler optimizations by default and allow configuration in configure.
• tf.divide now honors the name field.
• Make metrics weight broadcasting more strict.
• Add new queue-like StagingArea and new ops: stage and unstage.
• Enable inplace update ops for strings on CPU. Speed up string concat.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Aaron Hu, Abhishek Aggarwal, Adam Michael, Adriano Carmezim, @AfirSraftGarrier,
Alexander Novikov, Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Hundt,
Anish Shah, Anton Loss, @b0noI, @BoyuanJiang, Carl Thomé, Chad Kennedy, Comic
Chang, Connor Braa, Daniel N. Lang, Daniel Trebbien,
@danielgordon10, Darcy Liu, Darren Garvey, Dmitri Lapin, Eron Wright, Evan
Cofer, Fabrizio Milo, Finbarr Timbers, Franck Dernoncourt, Garrett Smith,
@guschmue, Hao Wei, Henrik Holst, Huazuo Gao, @ian, @issac, Jacob Israel,
Jangsoo Park, Jin Kim, Jingtian Peng, John Pope, Kye Bostelmann, Liangliang He,
Ling Zhang, Luheng He, Luke Iwanski, @lvli, Michael Basilyan, Mihir Patel,
Mikalai Drabovich, Morten Just, @newge, Nick Butlin, Nishant Shukla,
Pengfei Ni, Przemyslaw Tredak, @rasbt, @ronny, Rudolf Rosa, @RustingSword,
Sam Abrahams, Sam Putnam, @seongahjo, Shi Jiaxin, @skavulya, Steffen MüLler,
@TheUSER123, @tiriplicamihai, @vhasanov, Victor Costan, Vit Stepanovs,
Wangda Tan, Wenjian Huang, Xingdong Zuo, Yaroslav Bulatov, Yota Toyama,
Yuan (Terry) Tang, Yuxin Wu

We are also grateful to all who filed issues or helped resolve them, asked and
answered questions, and were part of inspiring discussions.