TensorFlow 1.7.0-rc0

Release 1.7.0

Major Features And Improvements

• Eager mode is moving out of contrib, try tf.enable_eager_execution().
• Graph rewrites emulating fixed-point quantization compatible with TensorFlow Lite, supported by new tf.contrib.quantize package.
• Easily customize gradient computation with tf.custom_gradient.
• TensorBoard Debugger Plugin, the graphical user interface (GUI) of TensorFlow Debugger (tfdbg), is now in alpha.
• Experimental support for reading a sqlite database as a Dataset with new tf.contrib.data.SqlDataset.
• Distributed Mutex / CriticalSection added to tf.contrib.framework.CriticalSection.
• Better text processing with tf.regex_replace.
• Easy, efficient sequence input with tf.contrib.data.bucket_by_sequence_length

Bug Fixes and Other Changes

• Accelerated Linear Algebra (XLA):
  • Add MaxPoolGradGrad support for XLA
  • CSE pass from Tensorflow is now disabled in XLA.
• tf.data:
  • tf.data.Dataset
    • Add support for building C++ Dataset op kernels as external libraries, using the tf.load_op_library() mechanism.
    • Dataset.list_files() now shuffles its output by default.
    • Dataset.shuffle(..., seed=tf.constant(0, dtype=tf.int64)) now yields the same sequence of elements as Dataset.shuffle(..., seed=0).
  • Add num_parallel_reads argument to tf.data.TFRecordDataset.
• tf.contrib:
  • tf.contrib.bayesflow.halton_sequence now supports randomization.
  • Add support for scalars in tf.contrib.all_reduce.
  • Add effective_sample_size to tf.contrib.bayesflow.mcmc_diagnostics.
  • Add potential_scale_reduction to tf.contrib.bayesflow.mcmc_diagnostics.
  • Add BatchNormalization, Kumaraswamy bijectors.
  • Deprecate tf.contrib.learn. Please check contrib/learn/README.md for instructions on how to convert existing code.
  • tf.contrib.data
    • Remove deprecated tf.contrib.data.Dataset, tf.contrib.data.Iterator, tf.contrib.data.FixedLengthRecordDataset, tf.contrib.data.TextLineDataset, and tf.contrib.data.TFRecordDataset classes.
    • Added bucket_by_sequence_length, sliding_window_batch, and make_batched_features_dataset
  • Remove unmaintained tf.contrib.ndlstm. You can find it externally at https://github.com/tmbarchive/tfndlstm.
  • Moved most of tf.contrib.bayesflow to its own repo: tfp
• Other:
  • tf.py_func now reports the full stack trace if an exception occurs.
  • Integrate TPUClusterResolver with GKE's integration for Cloud TPUs.
  • Add a library for statistical testing of samplers.
  • Add Helpers to stream data from the GCE VM to a Cloud TPU.
  • Integrate ClusterResolvers with TPUEstimator.
  • Unify metropolis_hastings interface with HMC kernel.
  • Move LIBXSMM convolutions to a separate --define flag so that they are disabled by default.
  • Fix MomentumOptimizer lambda.
  • Reduce tfp.layers boilerplate via programmable docstrings.
  • Add auc_with_confidence_intervals, a method for computing the AUC and confidence interval with linearithmic time complexity.
  • regression_head now accepts customized link function, to satisfy the usage that user can define their own link function if the array_ops.identity does not meet the requirement.
  • Fix initialized_value and initial_value behaviors for ResourceVariables created from VariableDef protos.
  • Add TensorSpec to represent the specification of Tensors.
  • Constant folding pass is now deterministic.
  • Support float16 dtype in tf.linalg.*.
  • Add tf.estimator.export.TensorServingInputReceiver that allows tf.estimator.Estimator.export_savedmodel to pass raw tensors to model functions.

Thanks to our Contributors

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

4d55397500, Abe, Alistair Low, Andy Kernahan, Appledore, Ben, Ben Barsdell, Boris Pfahringer, Brad Wannow, Brett Koonce, Carl Thomé, cclauss, Chengzhi Chen, Chris Drake, Christopher Yeh, Clayne Robison, Codrut Grosu, Daniel Trebbien, Danny Goodman, David Goodwin, David Norman, Deron Eriksson, Donggeon Lim, Donny Viszneki, DosLin, DylanDmitri, Francisco Guerrero, Fred Reiss, gdh1995, Giuseppe, Glenn Weidner, gracehoney, Guozhong Zhuang, Haichen "Hc" Li, Harald Husum, harumitsu.nobuta, Henry Spivey, hsm207, Jekyll Song, Jerome, Jiongyan Zhang, jjsjann123, John Sungjin Park, Johnson145, JoshVarty, Julian Wolff, Jun Wang, June-One, Kamil Sindi, Kb Sriram, Kdavis-Mozilla, Kenji, lazypanda1, Liang-Chi Hsieh, Loo Rong Jie, Mahesh Bhosale, MandarJKulkarni, ManHyuk, Marcus Ong, Marshal Hayes, Martin Pool, matthieudelaro, mdfaijul, mholzel, Michael Zhou, Ming Li, Minmin Sun, Myungjoo Ham, MyungsungKwak, Naman Kamra, Peng Yu, Penghao Cen, Phil, Raghuraman-K, resec, Rohin Mohanadas, Sandeep N Gupta, Scott Tseng, seaotterman, Seo Sanghyeon, Sergei Lebedev, Ted Chang, terrytangyuan, Tim H, tkunic, Tod, vihanjain, Yan Facai (颜发才), Yin Li, Yong Tang, Yukun Chen, Yusuke Yamada

TensorFlow 1.6.0

Release 1.6.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
• Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.

Major Features And Improvements

• New Optimizer internal API for non-slot variables. Descendants of AdamOptimizer that access _beta[12]_power will need to be updated.
• tf.estimator.{FinalExporter,LatestExporter} now export stripped SavedModels. This improves forward compatibility of the SavedModel.
• FFT support added to XLA CPU/GPU.
• Android TF can now be built with CUDA acceleration on compatible Tegra devices (see contrib/makefile/README.md for more information)

Bug Fixes and Other Changes

• Documentation updates:
  • Added a second version of Getting Started, which is aimed at ML
    newcomers.
  • Clarified documentation on resize_images.align_corners parameter.
  • Additional documentation for TPUs.
• Google Cloud Storage (GCS):
  • Add client-side throttle.
  • Add a FlushCaches() method to the FileSystem interface, with an implementation for GcsFileSystem.
• Other:
  • Add tf.contrib.distributions.Kumaraswamy.
  • RetryingFileSystem::FlushCaches() calls the base FileSystem's FlushCaches().
  • Add auto_correlation to distributions.
  • Add tf.contrib.distributions.Autoregressive.
  • Add SeparableConv1D layer.
  • Add convolutional Flipout layers.
  • When both inputs of tf.matmul are bfloat16, it returns bfloat16, instead of float32.
  • Added tf.contrib.image.connected_components.
  • Add tf.contrib.framework.CriticalSection that allows atomic variable access.
  • Output variance over trees predictions for classifications tasks.
  • For pt and eval commands, allow writing tensor values to filesystem as numpy files.
  • gRPC: Propagate truncated errors (instead of returning gRPC internal error).
  • Augment parallel_interleave to support 2 kinds of prefetching.
  • Improved XLA support for C64-related ops log, pow, atan2, tanh.
  • Add probabilistic convolutional layers.

API Changes

• Introducing prepare_variance boolean with default setting to False for backward compatibility.
• Move layers_dense_variational_impl.py to layers_dense_variational.py.

Known Bugs

• Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
  CUDA_ILLEGAL_ADDRESS failures.

  Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
  and CUDA 9.1 sometimes does not properly compute the carry bit when
  decomposing 64-bit address calculations with large offsets (e.g. load [x + large_constant]) into 32-bit arithmetic in SASS.

  As a result, these versions of ptxas miscompile most XLA programs which use
  more than 4GB of temp memory. This results in garbage results and/or
  CUDA_ERROR_ILLEGAL_ADDRESS failures.

  A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a
  fix for CUDA 9.0.x. Until the fix is available, the only workaround is to
  downgrade to CUDA 8.0.x
  or disable XLA:GPU.

  TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
  CUDA; see e00ba24.

• The tensorboard command or module may appear to be missing after certain
  upgrade flows. This is due to pip package conflicts as a result of changing
  the TensorBoard package name. See the TensorBoard 1.6.0 release notes for a fix.

Thanks to our Contributors

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

4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman,
amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios,
Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian,
Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison,
Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien,
Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian,
dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu,
Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2,
ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse
Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan,
Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama,
Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta,
Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich,
Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla,
Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge
Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu,
Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat
Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri,
Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash
Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta,
Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck,
Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal,
Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang,
Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武

TensorFlow 1.6.0-rc1

Release 1.6.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
• Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.

Major Features And Improvements

• New Optimizer internal API for non-slot variables. Descendants of AdamOptimizer that access _beta[12]_power will need to be updated.
• tf.estimator.{FinalExporter,LatestExporter} now export stripped SavedModels. This improves forward compatibility of the SavedModel.
• FFT support added to XLA CPU/GPU.
• Android TF can now be built with CUDA acceleration on compatible Tegra devices (see contrib/makefile/README.md for more information)

Bug Fixes and Other Changes

• Documentation updates:
  • Added a second version of Getting Started, which is aimed at ML
    newcomers.
  • Clarified documentation on resize_images.align_corners parameter.
  • Additional documentation for TPUs.
• Google Cloud Storage (GCS):
  • Add client-side throttle.
  • Add a FlushCaches() method to the FileSystem interface, with an implementation for GcsFileSystem.
• Other:
  • Add tf.contrib.distributions.Kumaraswamy.
  • RetryingFileSystem::FlushCaches() calls the base FileSystem's FlushCaches().
  • Add auto_correlation to distributions.
  • Add tf.contrib.distributions.Autoregressive.
  • Add SeparableConv1D layer.
  • Add convolutional Flipout layers.
  • When both inputs of tf.matmul are bfloat16, it returns bfloat16, instead of float32.
  • Added tf.contrib.image.connected_components.
  • Add tf.contrib.framework.CriticalSection that allows atomic variable access.
  • Output variance over trees predictions for classifications tasks.
  • For pt and eval commands, allow writing tensor values to filesystem as numpy files.
  • gRPC: Propagate truncated errors (instead of returning gRPC internal error).
  • Augment parallel_interleave to support 2 kinds of prefetching.
  • Improved XLA support for C64-related ops log, pow, atan2, tanh.
  • Add probabilistic convolutional layers.

API Changes

• Introducing prepare_variance boolean with default setting to False for backward compatibility.
• Move layers_dense_variational_impl.py to layers_dense_variational.py.

Known Bugs

• Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
  CUDA_ILLEGAL_ADDRESS failures.

  Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
  and CUDA 9.1 sometimes does not properly compute the carry bit when
  decomposing 64-bit address calculations with large offsets (e.g. load [x + large_constant]) into 32-bit arithmetic in SASS.

  As a result, these versions of ptxas miscompile most XLA programs which use
  more than 4GB of temp memory. This results in garbage results and/or
  CUDA_ERROR_ILLEGAL_ADDRESS failures.

  A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a
  fix for CUDA 9.0.x. Until the fix is available, the only workaround is to
  downgrade to CUDA 8.0.x
  or disable XLA:GPU.

  TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
  CUDA; see e00ba24.

• The tensorboard command or module may appear to be missing after certain
  upgrade flows. This is due to pip package conflicts as a result of changing
  the TensorBoard package name. See the TensorBoard 1.6.0 release notes for a fix.

Thanks to our Contributors

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

4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman,
amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios,
Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian,
Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison,
Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien,
Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian,
dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu,
Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2,
ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse
Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan,
Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama,
Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta,
Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich,
Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla,
Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge
Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu,
Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat
Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri,
Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash
Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta,
Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck,
Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal,
Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang,
Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武

TensorFlow 1.6.0-rc0

Release 1.6.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
• Prebuilt binaries will use AVX instructions. This may break TF on older CPUs.

Major Features And Improvements

• tf.estimator.{FinalExporter,LatestExporter} now export stripped SavedModels. This improves forward compatibility of the SavedModel.
• FFT support added to XLA CPU/GPU.

Bug Fixes and Other Changes

• Documentation updates:
  • Added a second version of Getting Started, which is aimed at ML
    newcomers.
  • Clarified documentation on resize_images.align_corners parameter.
  • Additional documentation for TPUs.
• Google Cloud Storage (GCS):
  • Add client-side throttle.
  • Add a FlushCaches() method to the FileSystem interface, with an implementation for GcsFileSystem.
• Other:
  • New Optimizer internal API for non-slot variables. Descendants of AdamOptimizer that access _beta[12]_power will need to be updated.
  • Add tf.contrib.distributions.Kumaraswamy.
  • RetryingFileSystem::FlushCaches() calls the base FileSystem's FlushCaches().
  • Add auto_correlation to distributions.
  • Add tf.contrib.distributions.Autoregressive.
  • Add SeparableConv1D layer.
  • Add convolutional Flipout layers.
  • When both inputs of tf.matmul are bfloat16, it returns bfloat16, instead of float32.
  • Added tf.contrib.image.connected_components.
  • Add tf.contrib.framework.CriticalSection that allows atomic variable access.
  • Output variance over trees predictions for classifications tasks.
  • For pt and eval commands, allow writing tensor values to filesystem as numpy files.
  • gRPC: Propagate truncated errors (instead of returning gRPC internal error).
  • Augment parallel_interleave to support 2 kinds of prefetching.
  • Improved XLA support for C64-related ops log, pow, atan2, tanh.
  • Add probabilistic convolutional layers.

API Changes

• Introducing prepare_variance boolean with default setting to False for backward compatibility.
• Move layers_dense_variational_impl.py to layers_dense_variational.py.

Known Bugs

• Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results and/or
  CUDA_ILLEGAL_ADDRESS failures.

  Google discovered in mid-December 2017 that the PTX-to-SASS compiler in CUDA 9
  and CUDA 9.1 sometimes does not properly compute the carry bit when
  decomposing 64-bit address calculations with large offsets (e.g. load [x + large_constant]) into 32-bit arithmetic in SASS.

  As a result, these versions of ptxas miscompile most XLA programs which use
  more than 4GB of temp memory. This results in garbage results and/or
  CUDA_ERROR_ILLEGAL_ADDRESS failures.

  A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a
  fix for CUDA 9.0.x. Until the fix is available, the only workaround is to
  downgrade to CUDA 8.0.x
  or disable XLA:GPU.

  TensorFlow will print a warning if you use XLA:GPU with a known-bad version of
  CUDA; see e00ba24.

Thanks to our Contributors

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

4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman,
amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios,
Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian,
Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison,
Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien,
Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian,
dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu,
Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2,
ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse
Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan,
Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama,
Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta,
Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich,
Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla,
Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge
Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu,
Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat
Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri,
Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash
Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta,
Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck,
Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal,
Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang,
Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武

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.