TensorFlow 1.5.0

Release 1.5.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9 and cuDNN 7.
• Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
  This may break TF on older CPUs.

Major Features And Improvements

• Eager execution
  preview version is now available.
• TensorFlow Lite
  dev preview is now available.
• CUDA 9 and cuDNN 7 support.
• Accelerated Linear Algebra (XLA):
  • Add complex64 support to XLA compiler.
  • bfloat support is now added to XLA infrastructure.
  • Make ClusterSpec propagation work with XLA devices.
  • Use a determinisitic executor to generate XLA graph.
• tf.contrib:
  • tf.contrib.distributions:
    • Add tf.contrib.distributions.Autoregressive.
    • Make tf.contrib.distributions QuadratureCompound classes support batch
    • Infer tf.contrib.distributions.RelaxedOneHotCategorical dtype from arguments.
    • Make tf.contrib.distributions quadrature family parameterized by
      quadrature_grid_and_prob vs quadrature_degree.
    • auto_correlation added to tf.contrib.distributions
  • Add tf.contrib.bayesflow.layers, a collection of probabilistic (neural) layers.
  • Add tf.contrib.bayesflow.halton_sequence.
  • Add tf.contrib.data.make_saveable_from_iterator.
  • Add tf.contrib.data.shuffle_and_repeat.
  • Add new custom transformation: tf.contrib.data.scan().
  • tf.contrib.distributions.bijectors:
    • Add tf.contrib.distributions.bijectors.MaskedAutoregressiveFlow.
    • Add tf.contrib.distributions.bijectors.Permute.
    • Add tf.contrib.distributions.bijectors.Gumbel.
    • Add tf.contrib.distributions.bijectors.Reshape.
    • Support shape inference (i.e., shapes containing -1) in the Reshape bijector.
• Add streaming_precision_recall_at_equal_thresholds, a method for computing
  streaming precision and recall with O(num_thresholds + size of predictions)
  time and space complexity.
• Change RunConfig default behavior to not set a random seed, making random
  behavior independently random on distributed workers. We expect this to
  generally improve training performance. Models that do rely on determinism
  should set a random seed explicitly.
• Replaced the implementation of tf.flags with absl.flags.
• Add support for CUBLAS_TENSOR_OP_MATH in fp16 GEMM
• Add support for CUDA on NVIDIA Tegra devices

Bug Fixes and Other Changes

• Documentation updates:
  • Clarified that you can only install TensorFlow on 64-bit machines.
  • Added a short doc explaining how Estimators save checkpoints.
  • Add documentation for ops supported by the tf2xla bridge.
  • Fix minor typos in the doc of SpaceToDepth and DepthToSpace.
  • Updated documentation comments in mfcc_mel_filterbank.h and mfcc.h to
    clarify that the input domain is squared magnitude spectra and the weighting
    is done on linear magnitude spectra (sqrt of inputs).
  • Change tf.contrib.distributions docstring examples to use tfd alias
    rather than ds, bs.
  • Fix docstring typos in tf.distributions.bijectors.Bijector.
  • tf.assert_equal no longer raises ValueError. It now raises
    InvalidArgumentError, as documented.
  • Update Getting Started docs and API intro.
• Google Cloud Storage (GCS):
  • Add userspace DNS caching for the GCS client.
  • Customize request timeouts for the GCS filesystem.
  • Improve GCS filesystem caching.
• Bug Fixes:
  • Fix bug where partitioned integer variables got their wrong shapes. Before
  • Fix correctness bug in CPU and GPU implementations of Adadelta.
  • Fix a bug in import_meta_graph's handling of partitioned variables when
    importing into a scope. WARNING: This may break loading checkpoints of
    graphs with partitioned variables saved after using import_meta_graph with
    a non-empty import_scope argument.
  • Fix bug in offline debugger which prevented viewing events.
  • Added the WorkerService.DeleteWorkerSession method to the gRPC interface,
    to fix a memory leak. Ensure that your master and worker servers are running
    the same version of TensorFlow to avoid compatibility issues.
  • Fix bug in peephole implementation of BlockLSTM cell.
  • Fix bug by casting dtype of log_det_jacobian to match log_prob in
    TransformedDistribution.
  • Fix a bug in import_meta_graph's handling of partitioned variables when
  • Ensure tf.distributions.Multinomial doesn't underflow in log_prob.
    Before this change, all partitions of an integer variable were initialized
    with the shape of the unpartitioned variable; after this change they are
    initialized correctly.
• Other:
  • Add necessary shape util support for bfloat16.
  • Add a way to run ops using a step function to MonitoredSession.
  • Add DenseFlipout probabilistic layer.
  • A new flag ignore_live_threads is available on train. If set to True, it
    will ignore threads that remain running when tearing down infrastructure
    after successfully completing training, instead of throwing a RuntimeError.
  • Restandardize DenseVariational as simpler template for other probabilistic
    layers.
  • tf.data now supports tf.SparseTensor components in dataset elements.
  • It is now possible to iterate over Tensors.
  • Allow SparseSegmentReduction ops to have missing segment IDs.
  • Modify custom export strategy to account for multidimensional sparse float
    splits.
  • Conv2D, Conv2DBackpropInput, Conv2DBackpropFilter now supports arbitrary
    dilations with GPU and cuDNNv6 support.
  • Estimator now supports Dataset: input_fn can return a Dataset
    instead of Tensors.
  • Add RevBlock, a memory-efficient implementation of reversible residual layers.
  • Reduce BFCAllocator internal fragmentation.
  • Add cross_entropy and kl_divergence to tf.distributions.Distribution.
  • Add tf.nn.softmax_cross_entropy_with_logits_v2 which enables backprop
    w.r.t. the labels.
  • GPU back-end now uses ptxas to compile generated PTX.
  • BufferAssignment's protocol buffer dump is now deterministic.
  • Change embedding op to use parallel version of DynamicStitch.
  • Add support for sparse multidimensional feature columns.
  • Speed up the case for sparse float columns that have only 1 value.
  • Allow sparse float splits to support multivalent feature columns.
  • Add quantile to tf.distributions.TransformedDistribution.
  • Add NCHW_VECT_C support for tf.depth_to_space on GPU.
  • Add NCHW_VECT_C support for tf.space_to_depth on GPU.

API Changes

• Rename SqueezeDims attribute to Axis in C++ API for Squeeze op.
• Stream::BlockHostUntilDone now returns Status rather than bool.
• Minor refactor: move stats files from stochastic to common and remove
  stochastic.

Thanks to our Contributors

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

Adam Zahran, Ag Ramesh, Alan Lee, Alan Yee, Alex Sergeev, Alexander, Amir H. Jadidinejad,
Amy, Anastasios Doumoulakis, Andrei Costinescu, Andrei Nigmatulin, Anthony Platanios,
Anush Elangovan, arixlin, Armen Donigian, ArtëM Sobolev, Atlas7, Ben Barsdell, Bill Prin,
Bo Wang, Brett Koonce, Cameron Thomas, Carl Thomé, Cem Eteke, cglewis, Changming Sun,
Charles Shenton, Chi-Hung, Chris Donahue, Chris Filo Gorgolewski, Chris Hoyean Song,
Chris Tava, Christian Grail, Christoph Boeddeker, cinqS, Clayne Robison, codrut3, concerttttt,
CQY, Dan Becker, Dan Jarvis, Daniel Zhang, David Norman, dmaclach, Dmitry Trifonov,
Donggeon Lim, dongpilYu, Dr. Kashif Rasul, Edd Wilder-James, Eric Lv, fcharras, Felix Abecassis,
FirefoxMetzger, formath, FredZhang, Gaojin Cao, Gary Deer, Guenther Schmuelling, Hanchen Li,
Hanmin Qin, hannesa2, hyunyoung2, Ilya Edrenkin, Jackson Kontny, Jan, Javier Luraschi,
Jay Young, Jayaram Bobba, Jeff, Jeff Carpenter, Jeremy Sharpe, Jeroen BéDorf, Jimmy Jia,
Jinze Bai, Jiongyan Zhang, Joe Castagneri, Johan Ju, Josh Varty, Julian Niedermeier,
JxKing, Karl Lessard, Kb Sriram, Keven Wang, Koan-Sin Tan, Kyle Mills, lanhin, LevineHuang,
Loki Der Quaeler, Loo Rong Jie, Luke Iwanski, LáSzló Csomor, Mahdi Abavisani, Mahmoud Abuzaina,
ManHyuk, Marek ŠUppa, MathSquared, Mats Linander, Matt Wytock, Matthew Daley, Maximilian Bachl,
mdymczyk, melvyniandrag, Michael Case, Mike Traynor, miqlas, Namrata-Ibm, Nathan Luehr,
Nathan Van Doorn, Noa Ezra, Nolan Liu, Oleg Zabluda, opensourcemattress, Ouwen Huang,
Paul Van Eck, peisong, Peng Yu, PinkySan, pks, powderluv, Qiao Hai-Jun, Qiao Longfei,
Rajendra Arora, Ralph Tang, resec, Robin Richtsfeld, Rohan Varma, Ryohei Kuroki, SaintNazaire,
Samuel He, Sandeep Dcunha, sandipmgiri, Sang Han, scott, Scott Mudge, Se-Won Kim, Simon Perkins,
Simone Cirillo, Steffen Schmitz, Suvojit Manna, Sylvus, Taehoon Lee, Ted Chang, Thomas Deegan,
Till Hoffmann, Tim, Toni Kunic, Toon Verstraelen, Tristan Rice, Urs KöSter, Utkarsh Upadhyay,
Vish (Ishaya) Abrams, Winnie Tsang, Yan Chen, Yan Facai (颜发才), Yi Yang, Yong Tang,
Youssef Hesham, Yuan (Terry) Tang, Zhengsheng Wei, zxcqwe4906, 张志豪, 田传武

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

TensorFlow 1.5.0-rc1

Release 1.5.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9 and cuDNN 7.
• Our Linux binaries are built using ubuntu 16 containers, potentially introducing glibc incompatibility issues with ubuntu 14.
• Starting from 1.6 release, our prebuilt binaries will use AVX instructions. This may break TF on older CPUs.

Major Features And Improvements

• Eager execution preview version is now available.
• TensorFlow Lite dev preview is now available.
• CUDA 9 and cuDNN 7 support.
• Accelerated Linear Algebra (XLA):
  • Add complex64 support to XLA compiler.
  • bfloat support is now added to XLA infrastructure.
  • Make ClusterSpec propagation work with XLA devices.
  • Use a determinisitic executor to generate XLA graph.
• tf.contrib:
  • tf.contrib.distributions:
    • Add tf.contrib.distributions.Autoregressive.
    • Make tf.contrib.distributions QuadratureCompound classes support batch
    • Infer tf.contrib.distributions.RelaxedOneHotCategorical dtype from arguments.
    • Make tf.contrib.distributions quadrature family parameterized by
      quadrature_grid_and_prob vs quadrature_degree.
    • auto_correlation added to tf.contrib.distributions
  • Add tf.contrib.bayesflow.layers, a collection of probabilistic (neural) layers.
  • Add tf.contrib.bayesflow.halton_sequence.
  • Add tf.contrib.data.make_saveable_from_iterator.
  • Add tf.contrib.data.shuffle_and_repeat.
  • Add new custom transformation: tf.contrib.data.scan().
  • tf.contrib.distributions.bijectors:
    • Add tf.contrib.distributions.bijectors.MaskedAutoregressiveFlow.
    • Add tf.contrib.distributions.bijectors.Permute.
    • Add tf.contrib.distributions.bijectors.Gumbel.
    • Add tf.contrib.distributions.bijectors.Reshape.
    • Support shape inference (i.e., shapes containing -1) in the Reshape bijector.
• Add streaming_precision_recall_at_equal_thresholds, a method for computing streaming precision and recall with O(num_thresholds + size of predictions) time and space complexity.
• Change RunConfig default behavior to not set a random seed, making random behavior independently random on distributed workers. We expect this to generally improve training performance. Models that do rely on determinism should set a random seed explicitly.
• Replaced the implementation of tf.flags with absl.flags.
• Add support for CUBLAS_TENSOR_OP_MATH in fp16 GEMM
• Add support for CUDA on NVIDIA Tegra devices

Bug Fixes and Other Changes

• Documentation updates:
  • Clarified that you can only install TensorFlow on 64-bit machines.
  • Added a short doc explaining how Estimators save checkpoints.
  • Add documentation for ops supported by the tf2xla bridge.
  • Fix minor typos in the doc of SpaceToDepth and DepthToSpace.
  • Updated documentation comments in mfcc_mel_filterbank.h and mfcc.h to clarify that the input domain is squared magnitude spectra and the weighting is done on linear magnitude spectra (sqrt of inputs).
  • Change tf.contrib.distributions docstring examples to use tfd alias rather than ds, bs.
  • Fix docstring typos in tf.distributions.bijectors.Bijector.
  • tf.assert_equal no longer raises ValueError. It now raises InvalidArgumentError, as documented.
  • Update Getting Started docs and API intro.
• Google Cloud Storage (GCS):
  • Add userspace DNS caching for the GCS client.
  • Customize request timeouts for the GCS filesystem.
  • Improve GCS filesystem caching.
• Bug Fixes:
  • Fix bug where partitioned integer variables got their wrong shapes. Before
  • Fix correctness bug in CPU and GPU implementations of Adadelta.
  • Fix a bug in import_meta_graph's handling of partitioned variables when importing into a scope. WARNING: This may break loading checkpoints of graphs with partitioned variables saved after using import_meta_graph with a non-empty import_scope argument.
  • Fix bug in offline debugger which prevented viewing events.
  • Added the WorkerService.DeleteWorkerSession method to the gRPC interface, to fix a memory leak. Ensure that your master and worker servers are running the same version of TensorFlow to avoid compatibility issues.
  • Fix bug in peephole implementation of BlockLSTM cell.
  • Fix bug by casting dtype of log_det_jacobian to match log_prob in TransformedDistribution.
  • Fix a bug in import_meta_graph's handling of partitioned variables when
  • Ensure tf.distributions.Multinomial doesn't underflow in log_prob. Before this change, all partitions of an integer variable were initialized with the shape of the unpartitioned variable; after this change they are initialized correctly.
• Other:
  • Add necessary shape util support for bfloat16.
  • Add a way to run ops using a step function to MonitoredSession.
  • Add DenseFlipout probabilistic layer.
  • A new flag ignore_live_threads is available on train. If set to True, it will ignore threads that remain running when tearing down infrastructure after successfully completing training, instead of throwing a RuntimeError.
  • Restandardize DenseVariational as simpler template for other probabilistic layers.
  • tf.data now supports tf.SparseTensor components in dataset elements.
  • It is now possible to iterate over Tensors.
  • Allow SparseSegmentReduction ops to have missing segment IDs.
  • Modify custom export strategy to account for multidimensional sparse float splits.
  • Conv2D, Conv2DBackpropInput, Conv2DBackpropFilter now supports arbitrary dilations with GPU and cuDNNv6 support.
  • Estimator now supports Dataset: input_fn can return a Dataset instead of Tensors.
  • Add RevBlock, a memory-efficient implementation of reversible residual layers.
  • Reduce BFCAllocator internal fragmentation.
  • Add cross_entropy and kl_divergence to tf.distributions.Distribution.
  • Add tf.nn.softmax_cross_entropy_with_logits_v2 which enables backprop w.r.t. the labels.
  • GPU back-end now uses ptxas to compile generated PTX.
  • BufferAssignment's protocol buffer dump is now deterministic.
  • Change embedding op to use parallel version of DynamicStitch.
  • Add support for sparse multidimensional feature columns.
  • Speed up the case for sparse float columns that have only 1 value.
  • Allow sparse float splits to support multivalent feature columns.
  • Add quantile to tf.distributions.TransformedDistribution.
  • Add NCHW_VECT_C support for tf.depth_to_space on GPU.
  • Add NCHW_VECT_C support for tf.space_to_depth on GPU.

API Changes

• Rename SqueezeDims attribute to Axis in C++ API for Squeeze op.
• Stream::BlockHostUntilDone now returns Status rather than bool.
• Minor refactor: move stats files from stochastic to common and remove
  stochastic.

Thanks to our Contributors

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

Adam Zahran, Ag Ramesh, Alan Lee, Alan Yee, Alex Sergeev, Alexander, Amir H. Jadidinejad,
Amy, Anastasios Doumoulakis, Andrei Costinescu, Andrei Nigmatulin, Anthony Platanios,
Anush Elangovan, arixlin, Armen Donigian, ArtëM Sobolev, Atlas7, Ben Barsdell, Bill Prin,
Bo Wang, Brett Koonce, Cameron Thomas, Carl Thomé, Cem Eteke, cglewis, Changming Sun,
Charles Shenton, Chi-Hung, Chris Donahue, Chris Filo Gorgolewski, Chris Hoyean Song,
Chris Tava, Christian Grail, Christoph Boeddeker, cinqS, Clayne Robison, codrut3, concerttttt,
CQY, Dan Becker, Dan Jarvis, Daniel Zhang, David Norman, dmaclach, Dmitry Trifonov,
Donggeon Lim, dongpilYu, Dr. Kashif Rasul, Edd Wilder-James, Eric Lv, fcharras, Felix Abecassis,
FirefoxMetzger, formath, FredZhang, Gaojin Cao, Gary Deer, Guenther Schmuelling, Hanchen Li,
Hanmin Qin, hannesa2, hyunyoung2, Ilya Edrenkin, Jackson Kontny, Jan, Javier Luraschi,
Jay Young, Jayaram Bobba, Jeff, Jeff Carpenter, Jeremy Sharpe, Jeroen BéDorf, Jimmy Jia,
Jinze Bai, Jiongyan Zhang, Joe Castagneri, Johan Ju, Josh Varty, Julian Niedermeier,
JxKing, Karl Lessard, Kb Sriram, Keven Wang, Koan-Sin Tan, Kyle Mills, lanhin, LevineHuang,
Loki Der Quaeler, Loo Rong Jie, Luke Iwanski, LáSzló Csomor, Mahdi Abavisani, Mahmoud Abuzaina,
ManHyuk, Marek ŠUppa, MathSquared, Mats Linander, Matt Wytock, Matthew Daley, Maximilian Bachl,
mdymczyk, melvyniandrag, Michael Case, Mike Traynor, miqlas, Namrata-Ibm, Nathan Luehr,
Nathan Van Doorn, Noa Ezra, Nolan Liu, Oleg Zabluda, opensourcemattress, Ouwen Huang,
Paul Van Eck, peisong, Peng Yu, PinkySan, pks, powderluv, Qiao Hai-Jun, Qiao Longfei,
Rajendra Arora, Ralph Tang, resec, Robin Richtsfeld, Rohan Varma, Ryohei Kuroki, SaintNazaire,
Samuel He, Sandeep Dcunha, sandipmgiri, Sang Han, scott, Scott Mudge, Se-Won Kim, Simon Perkins,
Simone Cirillo, Steffen Schmitz, Suvojit Manna, Sylvus, Taehoon Lee, Ted Chang, Thomas Deegan,
Till Hoffmann, Tim, Toni Kunic, Toon Verstraelen, Tristan Rice, Urs KöSter, Utkarsh Upadhyay,
Vish (Ishaya) Abrams, Winnie Tsang, Yan Chen, Yan Facai (颜发才), Yi Yang, Yong Tang,
Youssef Hesham, Yuan (Terry) Tang, Zhengsheng Wei, zxcqwe4906, 张志豪, 田传武

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

TensorFlow 1.5.0-rc0

Release 1.5.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9 and cuDNN 7.
• Our Linux binaries are built using ubuntu 16 containers, potentially
  introducing glibc incompatibility issues with ubuntu 14.
• Starting from 1.6 release, our prebuilt binaries will use AVX instructions.
  This may break TF on older CPUs.

Major Features And Improvements

• Eager execution
  preview version is now available.
• TensorFlow Lite
  dev preview is now available.
• CUDA 9 and cuDNN 7 support.

Bug Fixes and Other Changes

• auto_correlation added to tf.contrib.distributions.
• Add DenseFlipout probabilistic layer.
• Restandardize DenseVariational as simpler template for other probabilistic layers.
• Make tf.contrib.distributions QuadratureCompound classes support batch.
• Stream::BlockHostUntilDone now returns Status rather than bool.
• Customize request timeouts for the GCS filesystem.

Thanks to our Contributors

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

4d55397500, Abdullah Alrasheed, abenmao, Adam Salvail, Aditya Dhulipala, Ag Ramesh,
Akimasa Kimura, Alan Du, Alan Yee, Alexander, Amit Kushwaha, Amy, Andrei Costinescu,
Andrei Nigmatulin, Andrew Erlichson, Andrew Myers, Andrew Stepanov, Androbin, AngryPowman,
Anish Shah, Anton Daitche, Artsiom Chapialiou, asdf2014, Aseem Raj Baranwal, Ash Hall,
Bart Kiers, Batchu Venkat Vishal, ben, Ben Barsdell, Bill Piel, Carl Thomé, Catalin Voss,
Changming Sun, Chengzhi Chen, Chi Zeng, Chris Antaki, Chris Donahue, Chris Oelmueller,
Chris Tava, Clayne Robison, Codrut, Courtial Florian, Dalmo Cirne, Dan J, Darren Garvey,
David Kristoffersson, David Norman, David RöThlisberger, DavidNorman, Dhruv, DimanNe,
Dorokhov, Duncan Mac-Vicar P, EdwardDixon, EMCP, error.d, FAIJUL, Fan Xia,
Francois Xavier, Fred Reiss, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang,
Guenther Schmuelling, Guo Yejun (郭叶军), Hans Gaiser, HectorSVC, Hyungsuk Yoon,
James Pruegsanusak, Jay Young, Jean Wanka, Jeff Carpenter, Jeremy Rutman, Jeroen BéDorf,
Jett Jones, Jimmy Jia, jinghuangintel, jinze1994, JKurland, Joel Hestness, joetoth,
John B Nelson, John Impallomeni, John Lawson, Jonas, Jonathan Dekhtiar, joshkyh, Jun Luan,
Jun Mei, Kai Sasaki, Karl Lessard, karl@kubx.ca, Kb Sriram, Kenichi Ueno, Kevin Slagle,
Kongsea, Lakshay Garg, lhlmgr, Lin Min, liu.guangcong, Loki Der Quaeler, Louie Helm,
lucasmoura, Luke Iwanski, Lyndon White, Mahmoud Abuzaina, Marcel Puyat, Mark Aaron Shirley,
Michele Colombo, MtDersvan, Namrata-Ibm, Nathan Luehr, Naurril, Nayana Thorat, Nicolas Lopez,
Niranjan Hasabnis, Nolan Liu, Nouce, Oliver Hennigh, osdamv, Patrik Erdes,
Patryk Chrabaszcz, Pavel Christof, Penghao Cen, postBG, Qingqing Cao, Qingying Chen, qjivy,
Raphael, Rasmi, raymondxyang, Renze Yu, resec, Roffel, Ruben Vereecken, Ryohei Kuroki,
sandipmgiri, Santiago Castro, Scott Kirkland, Sean Vig, Sebastian Raschka, Sebastian Weiss,
Sergey Kolesnikov, Sergii Khomenko, Shahid, Shivam Kotwalia, Stuart Berg, Sumit Gouthaman,
superzerg, Sven Mayer, tetris, Ti Zhou, Tiago Freitas Pereira, Tian Jin, Tomoaki Oiki,
Vaibhav Sood, vfdev, Vivek Rane, Vladimir Moskva, wangqr, Weber Xie, Will Frey,
Yan Facai (颜发才), yanivbl6, Yaroslav Bulatov, Yixing Lao, Yong Tang, youkaichao,
Yuan (Terry) Tang, Yue Zhang, Yuxin Wu, Ziming Dong, ZxYuan, 黄璞

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

TensorFlow 1.4.1

Release 1.4.1

Bug Fixes and Other Changes

• LinearClassifier fix for CloudML Engine.
• NOTE: There is no Windows binary for 1.4.1. The only difference to 1.4.0 is the CloudML Engine fix, and since CloudML Engine only supports Linux, Windows is unaffected.

TensorFlow 1.4.0

Release 1.4.0

Major Features And Improvements

• tf.keras is now part of the core TensorFlow API.
• tf.data is now part of
  the core TensorFlow API.
  • The API is now subject to backwards compatibility guarantees.
  • For a guide to migrating from the tf.contrib.data API, see the
    README.
  • Major new features include Dataset.from_generator() (for building an input
    pipeline from a Python generator), and the Dataset.apply() method for
    applying custom transformation functions.
  • Several custom transformation functions have been added, including
    tf.contrib.data.batch_and_drop_remainder() and
    tf.contrib.data.sloppy_interleave().
• Add train_and_evaluate for simple distributed Estimator training.
• Add tf.spectral.dct for computing the DCT-II.
• Add Mel-Frequency Cepstral Coefficient support to tf.contrib.signal
  (with GPU and gradient support).
• Add a self-check on import tensorflow for Windows DLL issues.
• Add NCHW support to tf.depth_to_space on GPU.
• TensorFlow Debugger (tfdbg):
  • Add eval command to allow evaluation of arbitrary Python/numpy expressions
    in tfdbg command-line interface. See
    Debugging TensorFlow Programs
    for more details.
  • Usability improvement: The frequently used tensor filter has_inf_or_nan is
    now added to Session wrappers and hooks by default. So there is no need
    for clients to call .add_tensor_filter(tf_debug.has_inf_or_nan) anymore.
• SinhArcsinh (scalar) distribution added to contrib.distributions.
• Make GANEstimator opensource.
• Estimator.export_savedmodel() now includes all valid serving signatures
  that can be constructed from the Serving Input Receiver and all available
  ExportOutputs. For instance, a classifier may provide regression- and
  prediction-flavored outputs, in addition to the classification-flavored one.
  Building signatures from these allows TF Serving to honor requests using the
  different APIs (Classify, Regress, and Predict). Furthermore,
  serving_input_receiver_fn() may now specify alternative subsets of nodes
  that may act as inputs. This allows, for instance, producing a prediction
  signature for a classifier that accepts raw Tensors instead of a serialized
  tf.Example.
• Add tf.contrib.bayesflow.hmc.
• Add tf.contrib.distributions.MixtureSameFamily.
• Make Dataset.shuffle() always reshuffles after each iteration by default.
• Add tf.contrib.bayesflow.metropolis_hastings.
• Add log_rate parameter to tf.contrib.distributions.Poisson.
• Extend tf.contrib.distributions.bijector API to handle some non-injective
  transforms.
• Java:
  • Generics (e.g., Tensor<Integer>) for improved type-safety
    (courtesy @andrewcmyers).
  • Support for multi-dimensional string tensors.
  • Support loading of custom operations (e.g. many in tf.contrib) on Linux
    and OS X
• All our prebuilt binaries have been built with CUDA 8 and cuDNN 6.
  We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.

Bug Fixes and Other Changes

• tf.nn.rnn_cell.DropoutWrapper is now more careful about dropping out LSTM
  states. Specifically, it no longer ever drops the c (memory) state of an
  LSTMStateTuple. The new behavior leads to proper dropout behavior
  for LSTMs and stacked LSTMs. This bug fix follows recommendations from
  published literature, but is a behavioral change. State dropout behavior
  may be customized via the new dropout_state_filter_visitor argument.
• Removed tf.contrib.training.python_input. The same behavior, in a more
  flexible and reproducible package, is available via the new
  tf.contrib.data.Dataset.from_generator method!
• Fix tf.contrib.distributions.Affine incorrectly computing log-det-jacobian.
• Fix tf.random_gamma incorrectly handling non-batch, scalar draws.
• Resolved a race condition in TensorForest TreePredictionsV4Op.
• Google Cloud Storage file system, Amazon S3 file system, and Hadoop file
  system support are now default build options.
• Custom op libraries must link against libtensorflow_framework.so
  (installed at tf.sysconfig.get_lib()).
• Change RunConfig default behavior to not set a random seed, making random
  behavior independently random on distributed workers. We expect this to
  generally improve training performance. Models that do rely on determinism
  should set a random seed explicitly.

Breaking Changes to the API

• The signature of the tf.contrib.data.rejection_resample() function has been
  changed. It now returns a function that can be used as an argument to
  Dataset.apply().
• Remove tf.contrib.data.Iterator.from_dataset() method. Use
  Dataset.make_initializable_iterator() instead.
• Remove seldom used and unnecessary tf.contrib.data.Iterator.dispose_op().
• Reorder some TFGAN loss functions in a non-backwards compatible way.

Known Issues

• In Python 3, Dataset.from_generator() does not support Unicode strings.
  You must convert any strings to bytes objects before yielding them from
  the generator.

Thanks to our Contributors

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

4d55397500, Abdullah Alrasheed, abenmao, Adam Salvail, Aditya Dhulipala, Ag Ramesh,
Akimasa Kimura, Alan Du, Alan Yee, Alexander, Amit Kushwaha, Amy, Andrei Costinescu,
Andrei Nigmatulin, Andrew Erlichson, Andrew Myers, Andrew Stepanov, Androbin, AngryPowman,
Anish Shah, Anton Daitche, Artsiom Chapialiou, asdf2014, Aseem Raj Baranwal, Ash Hall,
Bart Kiers, Batchu Venkat Vishal, ben, Ben Barsdell, Bill Piel, Carl Thomé, Catalin Voss,
Changming Sun, Chengzhi Chen, Chi Zeng, Chris Antaki, Chris Donahue, Chris Oelmueller,
Chris Tava, Clayne Robison, Codrut, Courtial Florian, Dalmo Cirne, Dan J, Darren Garvey,
David Kristoffersson, David Norman, David RöThlisberger, DavidNorman, Dhruv, DimanNe,
Dorokhov, Duncan Mac-Vicar P, EdwardDixon, EMCP, error.d, FAIJUL, Fan Xia,
Francois Xavier, Fred Reiss, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang,
Guenther Schmuelling, Guo Yejun (郭叶军), Hans Gaiser, HectorSVC, Hyungsuk Yoon,
James Pruegsanusak, Jay Young, Jean Wanka, Jeff Carpenter, Jeremy Rutman, Jeroen BéDorf,
Jett Jones, Jimmy Jia, jinghuangintel, jinze1994, JKurland, Joel Hestness, joetoth,
John B Nelson, John Impallomeni, John Lawson, Jonas, Jonathan Dekhtiar, joshkyh, Jun Luan,
Jun Mei, Kai Sasaki, Karl Lessard, karl@kubx.ca, Kb Sriram, Kenichi Ueno, Kevin Slagle,
Kongsea, Lakshay Garg, lhlmgr, Lin Min, liu.guangcong, Loki Der Quaeler, Louie Helm,
lucasmoura, Luke Iwanski, Lyndon White, Mahmoud Abuzaina, Marcel Puyat, Mark Aaron Shirley,
Michele Colombo, MtDersvan, Namrata-Ibm, Nathan Luehr, Naurril, Nayana Thorat, Nicolas Lopez,
Niranjan Hasabnis, Nolan Liu, Nouce, Oliver Hennigh, osdamv, Patrik Erdes,
Patryk Chrabaszcz, Pavel Christof, Penghao Cen, postBG, Qingqing Cao, Qingying Chen, qjivy,
Raphael, Rasmi, raymondxyang, Renze Yu, resec, Roffel, Ruben Vereecken, Ryohei Kuroki,
sandipmgiri, Santiago Castro, Scott Kirkland, Sean Vig, Sebastian Raschka, Sebastian Weiss,
Sergey Kolesnikov, Sergii Khomenko, Shahid, Shivam Kotwalia, Stuart Berg, Sumit Gouthaman,
superzerg, Sven Mayer, tetris, Ti Zhou, Tiago Freitas Pereira, Tian Jin, Tomoaki Oiki,
Vaibhav Sood, vfdev, Vivek Rane, Vladimir Moskva, wangqr, Weber Xie, Will Frey,
Yan Facai (颜发才), yanivbl6, Yaroslav Bulatov, Yixing Lao, Yong Tang, youkaichao,
Yuan (Terry) Tang, Yue Zhang, Yuxin Wu, Ziming Dong, ZxYuan, 黄璞

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

TensorFlow 1.4.0-rc1

Release 1.4.0

Major Features And Improvements

• tf.keras is now part of the core TensorFlow API.
• tf.data is now part of the core TensorFlow API.
  • The API is now subject to backwards compatibility guarantees.
  • For a guide to migrating from the tf.contrib.data API, see the
    README.
  • Major new features include Dataset.from_generator() (for building an input
    pipeline from a Python generator), and the Dataset.apply() method for
    applying custom transformation functions.
  • Several custom transformation functions have been added, including
    tf.contrib.data.batch_and_drop_remainder() and
    tf.contrib.data.sloppy_interleave().
• Add train_and_evaluate for simple distributed Estimator training.
• Add tf.spectral.dct for computing the DCT-II.
• Add Mel-Frequency Cepstral Coefficient support to tf.contrib.signal
  (with GPU and gradient support).
• Add a self-check on import tensorflow for Windows DLL issues.
• Add NCHW support to tf.depth_to_space on GPU.
• TensorFlow Debugger (tfdbg):
  • Add eval command to allow evaluation of arbitrary Python/numpy expressions
    in tfdbg command-line interface. See
    Debugging TensorFlow Programs
    for more details.
  • Usability improvement: The frequently used tensor filter has_inf_or_nan is
    now added to Session wrappers and hooks by default. So there is no need
    for clients to call .add_tensor_filter(tf_debug.has_inf_or_nan) anymore.
• SinhArcsinh (scalar) distribution added to contrib.distributions.
• Make GANEstimator opensource.
• Estimator.export_savedmodel() now includes all valid serving signatures
  that can be constructed from the Serving Input Receiver and all available
  ExportOutputs. For instance, a classifier may provide regression- and
  prediction-flavored outputs, in addition to the classification-flavored one.
  Building signatures from these allows TF Serving to honor requests using the
  different APIs (Classify, Regress, and Predict). Furthermore,
  serving_input_receiver_fn() may now specify alternative subsets of nodes
  that may act as inputs. This allows, for instance, producing a prediction
  signature for a classifier that accepts raw Tensors instead of a serialized
  tf.Example.
• Add tf.contrib.bayesflow.hmc.
• Add tf.contrib.distributions.MixtureSameFamily.
• Make Dataset.shuffle() always reshuffles after each iteration by default.
• Add tf.contrib.bayesflow.metropolis_hastings.
• Add log_rate parameter to tf.contrib.distributions.Poisson.
• Extend tf.contrib.distributions.bijector API to handle some non-injective
  transforms.
• Java:
  • Generics (e.g., Tensor<Integer>) for improved type-safety
    (courtesy @andrewcmyers).
  • Support for multi-dimensional string tensors.
  • Support loading of custom operations (e.g. many in tf.contrib) on Linux
    and OS X
• All our prebuilt binaries have been built with CUDA 8 and cuDNN 6.
  We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.

Bug Fixes and Other Changes

• tf.nn.rnn_cell.DropoutWrapper is now more careful about dropping out LSTM
  states. Specifically, it no longer ever drops the c (memory) state of an
  LSTMStateTuple. The new behavior leads to proper dropout behavior
  for LSTMs and stacked LSTMs. This bug fix follows recommendations from
  published literature, but is a behavioral change. State dropout behavior
  may be customized via the new dropout_state_filter_visitor argument.
• Removed tf.contrib.training.python_input. The same behavior, in a more
  flexible and reproducible package, is available via the new
  tf.contrib.data.Dataset.from_generator method!
• Fix tf.contrib.distributions.Affine incorrectly computing log-det-jacobian.
• Fix tf.random_gamma incorrectly handling non-batch, scalar draws.
• Resolved a race condition in TensorForest TreePredictionsV4Op.
• Google Cloud Storage file system, Amazon S3 file system, and Hadoop file
  system support are now default build options.
• Custom op libraries must link against libtensorflow_framework.so
  (installed at tf.sysconfig.get_lib()).
• Change RunConfig default behavior to not set a random seed, making random
  behavior independently random on distributed workers. We expect this to
  generally improve training performance. Models that do rely on determinism
  should set a random seed explicitly.

Breaking Changes to the API

• The signature of the tf.contrib.data.rejection_resample() function has been
  changed. It now returns a function that can be used as an argument to
  Dataset.apply().
• Remove tf.contrib.data.Iterator.from_dataset() method. Use
  Dataset.make_initializable_iterator() instead.
• Remove seldom used and unnecessary tf.contrib.data.Iterator.dispose_op().
• Reorder some TFGAN loss functions in a non-backwards compatible way.

Known Issues

• In Python 3, Dataset.from_generator() does not support Unicode strings.
  You must convert any strings to bytes objects before yielding them from
  the generator.

Thanks to our Contributors

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

4d55397500, Abdullah Alrasheed, abenmao, Adam Salvail, Aditya Dhulipala, Ag Ramesh,
Akimasa Kimura, Alan Du, Alan Yee, Alexander, Amit Kushwaha, Amy, Andrei Costinescu,
Andrei Nigmatulin, Andrew Erlichson, Andrew Myers, Andrew Stepanov, Androbin, AngryPowman,
Anish Shah, Anton Daitche, Artsiom Chapialiou, asdf2014, Aseem Raj Baranwal, Ash Hall,
Bart Kiers, Batchu Venkat Vishal, ben, Ben Barsdell, Bill Piel, Carl Thomé, Catalin Voss,
Changming Sun, Chengzhi Chen, Chi Zeng, Chris Antaki, Chris Donahue, Chris Oelmueller,
Chris Tava, Clayne Robison, Codrut, Courtial Florian, Dalmo Cirne, Dan J, Darren Garvey,
David Kristoffersson, David Norman, David RöThlisberger, DavidNorman, Dhruv, DimanNe,
Dorokhov, Duncan Mac-Vicar P, EdwardDixon, EMCP, error.d, FAIJUL, Fan Xia,
Francois Xavier, Fred Reiss, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang,
Guenther Schmuelling, Guo Yejun (郭叶军), Hans Gaiser, HectorSVC, Hyungsuk Yoon,
James Pruegsanusak, Jay Young, Jean Wanka, Jeff Carpenter, Jeremy Rutman, Jeroen BéDorf,
Jett Jones, Jimmy Jia, jinghuangintel, jinze1994, JKurland, Joel Hestness, joetoth,
John B Nelson, John Impallomeni, John Lawson, Jonas, Jonathan Dekhtiar, joshkyh, Jun Luan,
Jun Mei, Kai Sasaki, Karl Lessard, karl@kubx.ca, Kb Sriram, Kenichi Ueno, Kevin Slagle,
Kongsea, Lakshay Garg, lhlmgr, Lin Min, liu.guangcong, Loki Der Quaeler, Louie Helm,
lucasmoura, Luke Iwanski, Lyndon White, Mahmoud Abuzaina, Marcel Puyat, Mark Aaron Shirley,
Michele Colombo, MtDersvan, Namrata-Ibm, Nathan Luehr, Naurril, Nayana Thorat, Nicolas Lopez,
Niranjan Hasabnis, Nolan Liu, Nouce, Oliver Hennigh, osdamv, Patrik Erdes,
Patryk Chrabaszcz, Pavel Christof, Penghao Cen, postBG, Qingqing Cao, Qingying Chen, qjivy,
Raphael, Rasmi, raymondxyang, Renze Yu, resec, Roffel, Ruben Vereecken, Ryohei Kuroki,
sandipmgiri, Santiago Castro, Scott Kirkland, Sean Vig, Sebastian Raschka, Sebastian Weiss,
Sergey Kolesnikov, Sergii Khomenko, Shahid, Shivam Kotwalia, Stuart Berg, Sumit Gouthaman,
superzerg, Sven Mayer, tetris, Ti Zhou, Tiago Freitas Pereira, Tian Jin, Tomoaki Oiki,
Vaibhav Sood, vfdev, Vivek Rane, Vladimir Moskva, wangqr, Weber Xie, Will Frey,
Yan Facai (颜发才), yanivbl6, Yaroslav Bulatov, Yixing Lao, Yong Tang, youkaichao,
Yuan (Terry) Tang, Yue Zhang, Yuxin Wu, Ziming Dong, ZxYuan, 黄璞

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

TensorFlow 1.4.0-rc0

Release 1.4.0

Major Features And Improvements

• tf.data is now part of the core TensorFlow API.
  • The API is now subject to backwards compatibility guarantees.
  • For a guide to migrating from the tf.contrib.data API, see the
    README.
  • Major new features include Dataset.from_generator() (for building an input
    pipeline from a Python generator), and the Dataset.apply() method for
    applying custom transformation functions.
  • Several custom transformation functions have been added, including
    tf.contrib.data.batch_and_drop_remainder() and
    tf.contrib.data.sloppy_interleave().
• Add train_and_evaluate for simple distributed Estimator training.
• Add tf.spectral.dct for computing the DCT-II.
• Add Mel-Frequency Cepstral Coefficient support to tf.contrib.signal
  (with GPU and gradient support).
• Add a self-check on import tensorflow for Windows DLL issues.
• Add NCHW support to tf.depth_to_space on GPU.
• SinhArcsinh (scalar) distribution added to contrib.distributions.
• Make GANEstimator opensource.
• Estimator.export_savedmodel() now includes all valid serving signatures
  that can be constructed from the Serving Input Receiver and all available
  ExportOutputs. For instance, a classifier may provide regression- and
  prediction-flavored outputs, in addition to the classification-flavored one.
  Building signatures from these allows TF Serving to honor requests using the
  different APIs (Classify, Regress, and Predict). Furthermore,
  serving_input_receiver_fn() may now specify alternative subsets of nodes
  that may act as inputs. This allows, for instance, producing a prediction
  signature for a classifier that accepts raw Tensors instead of a serialized
  tf.Example.
• Add tf.contrib.bayesflow.hmc.
• Add tf.contrib.distributions.MixtureSameFamily.
• Make Dataset.shuffle() always reshuffles after each iteration by default.
• Add tf.contrib.bayesflow.metropolis_hastings.
• Add log_rate parameter to tf.contrib.distributions.Poisson.
• Extend tf.contrib.distributions.bijector API to handle some non-injective
  transforms.
• Java:
  • Generics (e.g., Tensor<Integer>) for improved type-safety
    (courtesy @andrewcmyers).
  • Support for multi-dimensional string tensors.
  • Support loading of custom operations (e.g. many in tf.contrib) on Linux
    and OS X

Bug Fixes and Other Changes

• tf.nn.rnn_cell.DropoutWrapper is now more careful about dropping out LSTM
  states. Specifically, it no longer ever drops the c (memory) state of an
  LSTMStateTuple. The new behavior leads to proper dropout behavior
  for LSTMs and stacked LSTMs. This bug fix follows recommendations from
  published literature, but is a behavioral change. State dropout behavior
  may be customized via the new dropout_state_filter_visitor argument.
• Removed tf.contrib.training.python_input. The same behavior, in a more
  flexible and reproducible package, is available via the new
  tf.contrib.data.Dataset.from_generator method!
• Fix tf.contrib.distributions.Affine incorrectly computing log-det-jacobian.
• Fix tf.random_gamma incorrectly handling non-batch, scalar draws.
• Resolved a race condition in TensorForest TreePredictionsV4Op.
• Google Cloud Storage file system and Hadoop file system support are now
  default build options.

Breaking Changes to the API

• The signature of the tf.contrib.data.rejection_resample() function has been
  changed. It now returns a function that can be used as an argument to
  Dataset.apply().
• Remove tf.contrib.data.Iterator.from_dataset() method. Use
  Dataset.make_initializable_iterator() instead.
• Remove seldom used and unnecessary tf.contrib.data.Iterator.dispose_op().
• Reorder some TFGAN loss functions in a non-backwards compatible way.

Thanks to our Contributors

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

4d55397500, Abdullah Alrasheed, abenmao, Adam Salvail, Aditya Dhulipala, Ag Ramesh,
Akimasa Kimura, Alan Du, Alan Yee, Alexander, Amit Kushwaha, Amy, Andrei Costinescu,
Andrei Nigmatulin, Andrew Erlichson, Andrew Myers, Andrew Stepanov, Androbin, AngryPowman,
Anish Shah, Anton Daitche, Artsiom Chapialiou, asdf2014, Aseem Raj Baranwal, Ash Hall,
Bart Kiers, Batchu Venkat Vishal, ben, Ben Barsdell, Bill Piel, Carl Thomé, Catalin Voss,
Changming Sun, Chengzhi Chen, Chi Zeng, Chris Antaki, Chris Donahue, Chris Oelmueller,
Chris Tava, Clayne Robison, Codrut, Courtial Florian, Dalmo Cirne, Dan J, Darren Garvey,
David Kristoffersson, David Norman, David RöThlisberger, DavidNorman, Dhruv, DimanNe,
Dorokhov, Duncan Mac-Vicar P, EdwardDixon, EMCP, error.d, FAIJUL, Fan Xia,
Francois Xavier, Fred Reiss, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang,
Guenther Schmuelling, Guo Yejun (郭叶军), Hans Gaiser, HectorSVC, Hyungsuk Yoon,
James Pruegsanusak, Jay Young, Jean Wanka, Jeff Carpenter, Jeremy Rutman, Jeroen BéDorf,
Jett Jones, Jimmy Jia, jinghuangintel, jinze1994, JKurland, Joel Hestness, joetoth,
John B Nelson, John Impallomeni, John Lawson, Jonas, Jonathan Dekhtiar, joshkyh, Jun Luan,
Jun Mei, Kai Sasaki, Karl Lessard, karl@kubx.ca, Kb Sriram, Kenichi Ueno, Kevin Slagle,
Kongsea, Lakshay Garg, lhlmgr, Lin Min, liu.guangcong, Loki Der Quaeler, Louie Helm,
lucasmoura, Luke Iwanski, Lyndon White, Mahmoud Abuzaina, Marcel Puyat, Mark Aaron Shirley,
Michele Colombo, MtDersvan, Namrata-Ibm, Nathan Luehr, Naurril, Nayana Thorat, Nicolas Lopez,
Niranjan Hasabnis, Nolan Liu, Nouce, Oliver Hennigh, osdamv, Patrik Erdes,
Patryk Chrabaszcz, Pavel Christof, Penghao Cen, postBG, Qingqing Cao, Qingying Chen, qjivy,
Raphael, Rasmi, raymondxyang, Renze Yu, resec, Roffel, Ruben Vereecken, Ryohei Kuroki,
sandipmgiri, Santiago Castro, Scott Kirkland, Sean Vig, Sebastian Raschka, Sebastian Weiss,
Sergey Kolesnikov, Sergii Khomenko, Shahid, Shivam Kotwalia, Stuart Berg, Sumit Gouthaman,
superzerg, Sven Mayer, tetris, Ti Zhou, Tiago Freitas Pereira, Tian Jin, Tomoaki Oiki,
Vaibhav Sood, vfdev, Vivek Rane, Vladimir Moskva, wangqr, Weber Xie, Will Frey,
Yan Facai (颜发才), yanivbl6, Yaroslav Bulatov, Yixing Lao, Yong Tang, youkaichao,
Yuan (Terry) Tang, Yue Zhang, Yuxin Wu, Ziming Dong, ZxYuan, 黄璞

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

TensorFlow 1.3.1

Release 1.3.1

NOTE: TensorFlow 1.3.1 is a GitHub only release. The latest exported binaries are still version 1.3.0.

Bug Fixes and Other Changes

• Fixing the hash mismatch errors when building from source.

TensorFlow 1.3.0

Release 1.3.0

See also TensorBoard 0.1.4 release notes.

Major Features and Improvements

• Added canned estimators to Tensorflow library. List of added estimators:
  • DNNClassifier
  • DNNRegressor
  • LinearClassifier
  • LinearRegressor
  • DNNLinearCombinedClassifier
  • DNNLinearCombinedRegressor.
• All our prebuilt binaries have been built with cuDNN 6. We anticipate releasing TensorFlow 1.4 with cuDNN 7.
• import tensorflow now goes much faster.
• Adds a file cache to the GCS filesystem with configurable max staleness for file contents. This permits caching of file contents across close/open boundaries.
• Added an axis parameter to tf.gather.
• Added a constant_values keyword argument to tf.pad.
• Adds Dataset.interleave transformation.
• Add ConcatenateDataset to concatenate two datasets.
• Added Mobilenet support to TensorFlow for Poets training script.
• Adds a block cache to the GCS filesystem with configurable block size and count.
• SinhArcSinh bijector added.
• Added Dataset.list_files API.
• Introduces new operations and Python bindings for the Cloud TPU.
• Adding TensorFlow-iOS CocoaPod for symmetry with tensorflow-android.
• Introduces base implementations of ClusterResolvers.
• Unify memory representations of TensorShape and PartialTensorShape. As a consequence, tensors now have a maximum of 254 dimensions, not 255.
• Changed references to LIBXSMM to use version 1.8.1.
• TensorFlow Debugger (tfdbg):
  • Display summaries of numeric tensor values with the -s flag to command print_tensor or pt.
  • Display feed values with the print_feed or pf command and clickable links in the curses UI.
  • Runtime profiler at the op level and the Python source line level with the run -p command.
• Initial release of the statistical distribution library tf.distributions.
• GPU kernels and speed improvements for for unary tf.where and tf.nn.top_k.
• Monotonic Attention wrappers added to tf.contrib.seq2seq.
• Added tf.contrib.signal, a library for signal processing primitives.
• Added tf.contrib.resampler, containing CPU and GPU ops for differentiable resampling of images.

Breaking Changes to the API

• tf.RewriterConfig was removed from the Python API after being available in 1.2 release candidates (it was never in an actual release). Graph rewriting is still available, just not as tf.RewriterConfig. Instead add an explicit import.
• Breaking change to tf.contrib.data.Dataset APIs that expect a nested structure. Lists are now converted to tf.Tensor implicitly. You may need to change uses of lists to tuples in existing code. In addition, dicts are now supported as a nested structure.

Changes to contrib APIs

• Adds tf.contrib.nn.rank_sampled_softmax_loss, a sampled-softmax variant that can improve rank loss.
• tf.contrib.metrics.{streaming_covariance,streaming_pearson_correlation} modified to return nan when they have seen less or equal to 1 unit of weight.
• Adds time series models to contrib. See contrib/timeseries/README.md for details.
• Adds FULLY_CONNECTED Op to tensorflow/contrib/lite/schema.fbs

Known Issues

• Tensorflow_gpu compilation fails with Bazel 0.5.3.

Bug Fixes and Other Changes

• Fixes strides and begin dtype mismatch when slicing using int64 Tensor index in python.
• Improved convolution padding documentation.
• Add a tag constant, gpu, to present graph with GPU support.
• saved_model.utils now support SparseTensors transparently.
• A more efficient implementation of non-max suppression.
• Add support for the shrinkage-type L2 to FtrlOptimizer in addition to the online L2 it already supports.
• Fix negative variance in moments calculation.
• Expand UniqueOp Benchmark Tests to cover more collision cases.
• Improves stability of GCS filesystem on Mac.
• Add time estimation to HloCostAnalysis.
• Fixed the bug in Estimator that params in constructor was not a deepcopy of the user provided one. This bugs inadvertently enabled user to mutate the params after the creation of Estimator, leading to potentially undefined behavior.
• Added None check for save_path in saver.restore.
• Register devices under their legacy names in device_mgr to ease the transition to clusterspec-propagated configurations.
• VectorExponential added to distributions.
• Add a bitwise module with bitwise_and, bitwise_or, bitwise_xor, and invert functions.
• Add fixed-grid ODE integration routines.
• Allow passing bounds to ScipyOptimizerInterface.
• Correctness fixes for fft_length parameter to tf.spectral.rfft & tf.spectral.irfft.
• Exported model signatures using the 'predict' method will no longer have their input and output keys silently ignored and rewritten to 'inputs' and 'outputs'. If a model was exported with different names before 1.2, and is now served with tensorflow/serving, it will accept requests using 'inputs' and 'outputs'. Starting at 1.2, such a model will accept the keys specified during export. Therefore, inference requests using 'inputs' and 'outputs' may start to fail. To fix this, either update any inference clients to send requests with the actual input and output keys used by the trainer code, or conversely, update the trainer code to name the input and output Tensors 'inputs' and 'outputs', respectively. Signatures using the 'classify' and 'regress' methods are not affected by this change; they will continue to standardize their input and output keys as before.
• Add in-memory caching to the Dataset API.
• Set default end_of_sequence variable in datasets iterators to false.
• [Performance] Increase performance of tf.layers.con2d when setting use_bias=True by 2x by using nn.bias_add.
• Update iOS examples to use CocoaPods, and moved to tensorflow/examples/ios.
• Adds a family= attribute in tf.summary ops to allow controlling the tab name used in Tensorboard for organizing summaries.
• When GPU is configured, do not require --config=cuda, instead, automatically build for GPU if this is requested in the configure script.
• Fix incorrect sampling of small probabilities in CPU/GPU multinomial.
• Add a list_devices() API on sessions to list devices within a cluster. Additionally, this change augment the ListDevices master API to support specifying a session.
• Allow uses of over-parameterized separable convolution.
• TensorForest multi-regression bug fix.
• Framework now supports armv7, cocoapods.org now displays correct page.
• Script to create iOS framework for CocoaPods.
• Android releases of TensorFlow are now pushed to jcenter for easier integration into apps. See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md for more details.
• TensorFlow Debugger (tfdbg):
  • Fixed a bug that prevented tfdbg from functioning with multi-GPU setups.
  • Fixed a bug that prevented tfdbg from working with tf.Session.make_callable.

Thanks to our Contributors

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

4F2E4A2E, Adriano Carmezim, Adrià Arrufat, Alan Yee, Alex Lattas, Alex Rothberg,
Alexandr Baranezky, Ali Siddiqui, Andreas Solleder, Andrei Costinescu, Andrew Hundt,
Androbin, Andy Kernahan, Anish Shah, Anthony Platanios, Arvinds-Ds, b1rd, Baptiste
Arnaud, Ben Mabey, Benedikt Linse, Beomsu Kim, Bo Wang, Boyuan Deng, Brett Koonce,
Bruno Rosa, Carl Thomé, Changming Sun, Chase Roberts, Chirag Bhatia, Chris Antaki,
Chris Hoyean Song, Chris Tava, Christos Nikolaou, Croath Liu, cxx, Czxck001, Daniel
Ylitalo, Danny Goodman, Darren Garvey, David Brailovsky, David Norman, DavidNorman,
davidpham87, ddurham2, Dhruv, DimanNe, Drew Hintz, Dustin Tran, Earthson Lu, ethiraj,
Fabian Winnen, Fei Sun, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Gautam,
Guenther Schmuelling, Gyu-Ho Lee, Hauke Brammer, horance, Humanity123, J Alammar,
Jayeol Chun, Jeroen BéDorf, Jianfei Wang, jiefangxuanyan, Jing Jun Yin, Joan Puigcerver,
Joel Hestness, Johannes Mayer, John Lawson, Johnson145, Jon Malmaud, Jonathan Alvarez-Gutierrez,
Juang, Yi-Lin, Julian Viereck, Kaarthik Sivashanmugam, Karl Lessard, karl@kubx.ca, Kevin
Carbone, Kevin Van Der Burgt, Kongsea, ksellesk, lanhin, Lef Ioannidis, Liangliang He,
Louis Tiao, Luke Iwanski, LáSzló Csomor, magixsno, Mahmoud Abuzaina, Marcel Hlopko, Mark
Neumann, Maxwell Paul Brickner, mdfaijul, MichaëL Defferrard, Michał JastrzęBski, Michele
Colombo, Mike Brodie, Mosnoi Ion, mouradmourafiq, myPrecious, Nayana Thorat,
Neeraj Kashyap, Nelson Liu, Niranjan Hasabnis, Olivier Moindrot, orome, Pankaj Gupta, Paul
Van Eck, peeyush18, Peng Yu, Pierre, preciousdp11, qjivy, Raingo, raoqiyu, ribx, Richard S.
Imaoka, Rishabh Patel, Robert Walecki, Rockford Wei, Ryan Kung, Sahil Dua, Sandip Giri, Sayed
Hadi Hashemi, sgt101, Shitian Ni, Shuolongbj, Siim PõDer, Simon Perkins, sj6077, SOLARIS,
Spotlight0xff, Steffen Eberbach, Stephen Fox, superryanguo, Sven Mayer, Tapan Prakash,
Tiago Morais Morgado, Till Hoffmann, Tj Rana, Vadim Markovtsev, vhasanov, Wei Wu,
windead, Yan (Asta) Li, Yan Chen, Yann Henon, Yi Wang, Yong Tang, yorkie, Yuan (Terry)
Tang, Yuxin Wu, zhengjiajin, zhongzyd, 黄璞

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

TensorFlow 1.3.0-rc2

Release 1.3.0

Major Features and Improvements

• Added canned estimators to Tensorflow library. List of added estimators:
  • DNNClassifier
  • DNNRegressor
  • LinearClassifier
  • LinearRegressor
  • DNNLinearCombinedClassifier
  • DNNLinearCombinedRegressor.
• All our prebuilt binaries have been built with cuDNN 6.
• import tensorflow now goes much faster.
• Adds a file cache to the GCS filesystem with configurable max staleness for file contents. This permits caching of file contents across close/open boundaries.
• Added an axis parameter to tf.gather.
• Added a constant_values keyword argument to tf.pad.
• Adds Dataset.interleave transformation.
• Add ConcatenateDataset to concatenate two datasets.
• Added Mobilenet support to TensorFlow for Poets training script.
• Adds a block cache to the GCS filesystem with configurable block size and count.
• SinhArcSinh bijector added.
• Added Dataset.list_files API.
• Introduces new operations and Python bindings for the Cloud TPU.
• Adding TensorFlow-iOS CocoaPod for symmetry with tensorflow-android.
• Introduces base implementations of ClusterResolvers.
• Unify memory representations of TensorShape and PartialTensorShape. As a consequence, tensors now have a maximum of 254 dimensions, not 255.
• Changed references to LIBXSMM to use version 1.8.1.
• TensorFlow Debugger (tfdbg):
  • Display summaries of numeric tensor values with the -s flag to command print_tensor or pt.
  • Display feed values with the print_feed or pf command and clickable links in the curses UI.
  • Runtime profiler at the op level and the Python source line level with the run -p command.
• Initial release of the statistical distribution library tf.distributions.
• GPU kernels and speed improvements for for unary tf.where and tf.nn.top_k.
• Monotonic Attention wrappers added to tf.contrib.seq2seq.
• Added tf.contrib.signal, a library for signal processing primitives.
• Added tf.contrib.resampler, containing CPU and GPU ops for differentiable resampling of images.

Breaking Changes to the API

• tf.RewriterConfig was removed from the Python API after being available in 1.2 release candidates (it was never in an actual release). Graph rewriting is still available, just not as tf.RewriterConfig. Instead add an explicit import.
• Breaking change to tf.contrib.data.Dataset APIs that expect a nested structure. Lists are now converted to tf.Tensor implicitly. You may need to change uses of lists to tuples in existing code. In addition, dicts are now supported as a nested structure.

Changes to contrib APIs

• Adds tf.contrib.nn.rank_sampled_softmax_loss, a sampled-softmax variant that can improve rank loss.
• tf.contrib.metrics.{streaming_covariance,streaming_pearson_correlation} modified to return nan when they have seen less or equal to 1 unit of weight.
• Adds time series models to contrib. See contrib/timeseries/README.md for details.
• Adds FULLY_CONNECTED Op to tensorflow/contrib/lite/schema.fbs

Bug Fixes and Other Changes

• Fixes strides and begin dtype mismatch when slicing using int64 Tensor index in python.
• Improved convolution padding documentation.
• Add a tag constant, gpu, to present graph with GPU support.
• saved_model.utils now support SparseTensors transparently.
• A more efficient implementation of non-max suppression.
• Add support for the shrinkage-type L2 to FtrlOptimizer in addition to the online L2 it already supports.
• Fix negative variance in moments calculation.
• Expand UniqueOp Benchmark Tests to cover more collision cases.
• Improves stability of GCS filesystem on Mac.
• Add time estimation to HloCostAnalysis.
• Fixed the bug in Estimator that params in constructor was not a deepcopy of the user provided one. This bugs inadvertently enabled user to mutate the params after the creation of Estimator, leading to potentially undefined behavior.
• Added None check for save_path in saver.restore.
• Register devices under their legacy names in device_mgr to ease the transition to clusterspec-propagated configurations.
• VectorExponential added to distributions.
• Add a bitwise module with bitwise_and, bitwise_or, bitwise_xor, and invert functions.
• Add fixed-grid ODE integration routines.
• Allow passing bounds to ScipyOptimizerInterface.
• Correctness fixes for fft_length parameter to tf.spectral.rfft & tf.spectral.irfft.
• Exported model signatures using the 'predict' method will no longer have their input and output keys silently ignored and rewritten to 'inputs' and 'outputs'. If a model was exported with different names before 1.2, and is now served with tensorflow/serving, it will accept requests using 'inputs' and 'outputs'. Starting at 1.2, such a model will accept the keys specified during export. Therefore, inference requests using 'inputs' and 'outputs' may start to fail. To fix this, either update any inference clients to send requests with the actual input and output keys used by the trainer code, or conversely, update the trainer code to name the input and output Tensors 'inputs' and 'outputs', respectively. Signatures using the 'classify' and 'regress' methods are not affected by this change; they will continue to standardize their input and output keys as before.
• Add in-memory caching to the Dataset API.
• Set default end_of_sequence variable in datasets iterators to false.
• [Performance] Increase performance of tf.layers.con2d when setting use_bias=True by 2x by using nn.bias_add.
• Update iOS examples to use CocoaPods, and moved to tensorflow/examples/ios.
• Adds a family= attribute in tf.summary ops to allow controlling the tab name used in Tensorboard for organizing summaries.
• When GPU is configured, do not require --config=cuda, instead, automatically build for GPU if this is requested in the configure script.
• Fix incorrect sampling of small probabilities in CPU/GPU multinomial.
• Add a list_devices() API on sessions to list devices within a cluster. Additionally, this change augment the ListDevices master API to support specifying a session.
• Allow uses of over-parameterized separable convolution.
• TensorForest multi-regression bug fix.
• Framework now supports armv7, cocoapods.org now displays correct page.
• Script to create iOS framework for CocoaPods.
• Android releases of TensorFlow are now pushed to jcenter for easier integration into apps. See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md for more details.
• TensorFlow Debugger (tfdbg):
  • Fixed a bug that prevented tfdbg from functioning with multi-GPU setups.
  • Fixed a bug that prevented tfdbg from working with tf.Session.make_callable.

Thanks to our Contributors

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

4F2E4A2E, Adriano Carmezim, Adrià Arrufat, Alan Yee, Alex Lattas, Alex Rothberg,
Alexandr Baranezky, Ali Siddiqui, Andreas Solleder, Andrei Costinescu, Andrew Hundt,
Androbin, Andy Kernahan, Anish Shah, Anthony Platanios, Arvinds-Ds, b1rd, Baptiste
Arnaud, Ben Mabey, Benedikt Linse, Beomsu Kim, Bo Wang, Boyuan Deng, Brett Koonce,
Bruno Rosa, Carl Thomé, Changming Sun, Chase Roberts, Chirag Bhatia, Chris Antaki,
Chris Hoyean Song, Chris Tava, Christos Nikolaou, Croath Liu, cxx, Czxck001, Daniel
Ylitalo, Danny Goodman, Darren Garvey, David Brailovsky, David Norman, DavidNorman,
davidpham87, ddurham2, Dhruv, DimanNe, Drew Hintz, Dustin Tran, Earthson Lu, ethiraj,
Fabian Winnen, Fei Sun, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Gautam,
Guenther Schmuelling, Gyu-Ho Lee, Hauke Brammer, horance, Humanity123, J Alammar,
Jayeol Chun, Jeroen BéDorf, Jianfei Wang, jiefangxuanyan, Jing Jun Yin, Joan Puigcerver,
Joel Hestness, Johannes Mayer, John Lawson, Johnson145, Jon Malmaud, Jonathan Alvarez-Gutierrez,
Juang, Yi-Lin, Julian Viereck, Kaarthik Sivashanmugam, Karl Lessard, karl@kubx.ca, Kevin
Carbone, Kevin Van Der Burgt, Kongsea, ksellesk, lanhin, Lef Ioannidis, Liangliang He,
Louis Tiao, Luke Iwanski, LáSzló Csomor, magixsno, Mahmoud Abuzaina, Marcel Hlopko, Mark
Neumann, Maxwell Paul Brickner, mdfaijul, MichaëL Defferrard, Michał JastrzęBski, Michele
Colombo, Mike Brodie, Mosnoi Ion, mouradmourafiq, myPrecious, Nayana Thorat,
Neeraj Kashyap, Nelson Liu, Niranjan Hasabnis, Olivier Moindrot, orome, Pankaj Gupta, Paul
Van Eck, peeyush18, Peng Yu, Pierre, preciousdp11, qjivy, Raingo, raoqiyu, ribx, Richard S.
Imaoka, Rishabh Patel, Robert Walecki, Rockford Wei, Ryan Kung, Sahil Dua, Sandip Giri, Sayed
Hadi Hashemi, sgt101, Shitian Ni, Shuolongbj, Siim PõDer, Simon Perkins, sj6077, SOLARIS,
Spotlight0xff, Steffen Eberbach, Stephen Fox, superryanguo, Sven Mayer, Tapan Prakash,
Tiago Morais Morgado, Till Hoffmann, Tj Rana, Vadim Markovtsev, vhasanov, Wei Wu,
windead, Yan (Asta) Li, Yan Chen, Yann Henon, Yi Wang, Yong Tang, yorkie, Yuan (Terry)
Tang, Yuxin Wu, zhengjiajin, zhongzyd, 黄璞

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