RELEASE.md

Release 1.10.0

Major Features And Improvements

• The tf.lite runtime now supports complex64.
• Initial Bigtable integration for tf.data.
• Improved local run behavior in tf.estimator.train_and_evaluate which does not reload checkpoints for evaluation.
• RunConfig now sets device_filters to restrict how workers and PS can communicate. This can speed up training and ensure clean shutdowns in some situations. But if you have jobs that require communication between workers, you will have to set custom session_options in your RunConfig.
• Moved Distributions and Bijectors from tf.contrib.distributions to Tensorflow Probability (TFP). tf.contrib.distributions is now deprecated and will be removed by the end of 2018.
• Adding new endpoints for existing tensorflow symbols. These endpoints are going to be the preferred endpoints going forward and may replace some of the existing endpoints in the future. See below for the complete list. New symbols have been added to the following modules: tf.debugging, tf.dtypes, tf.image, tf.io, tf.linalg, tf.manip, tf.math, tf.quantization, tf.strings

Breaking Changes

• Prebuilt binaries are now (as of TensorFlow 1.10) built against NCCL 2.2 and no longer include NCCL in the binary install. TensorFlow usage with multiple GPUs and NCCL requires upgrade to NCCL 2.2. See updated install guides: Installing TensorFlow on Ubuntu and Install TensorFlow from Sources.
• Starting from TensorFlow 1.11, Windows builds will use Bazel. Therefore, we will drop official support for cmake.

Bug Fixes and Other Changes

• tf.data:
  • tf.contrib.data.group_by_reducer() is now available via the public API.
  • tf.contrib.data.choose_from_datasets() is now available via the public API.
  • Adding drop_remainder argument to tf.data.Dataset.batch() and tf.data.Dataset.padded_batch(), deprecating tf.contrib.data.batch_and_drop_remainder()andtf.contrib.data.padded_batch_and_drop_remainder()`.
• tf.estimator:
  • Estimators now use custom savers included in EstimatorSpec scaffolds for saving SavedModels during export.
  • EstimatorSpec will now add a default prediction output for export if no export_output is provided, eliminating the need to explicitly include a PredictOutput object in the model_fn for simple use-cases.
  • Support sparse_combiner in canned Linear Estimators.
  • Added batch normalization to DNNClassifier, DNNRegressor, and DNNEstimator.
  • Adding ranking support for boosted trees.
  • Adding center bias option for boosted trees.
• Add synchronization and aggregation args to get_variable(). These args will be used for distributed variables.
• Add synchronization and aggregation args to the layer add_weight() API. These args will be used for distributed variables.
• tf.losses.* do not add to the global collection when executing eagerly (to avoid leaking memory).
• Support different summary and checkpoint directories in tf.train.MonitoredTrainingSession().
• Added IndRNN, IndyGRU, and IndyLSTM cells to tf.contrib.rnn.
• Add safe static factory functions for SparseTensor and convert all CHECKs to DCHECKs. Using the constructor directly is unsafe and deprecated.
• Make the Bigtable client connection pool configurable & increase the default # of connections for performance.
• Added derivative of tf.random_gamma with respect to the alpha parameter.
• Added derivative of tf.igamma(a, x) and tf.igammac(a, x) with respect to a.
• Modified Bessel functions of order zero and one.
• Add FillTriangular Bijector to create triangular matrices.
• Added support for Type III DCT, and tf.spectral.idct(type=2|3).
• Correctly handle CuDNN RNN weight loaded when nest in TimeDistributed.
• Adding per-element weight support for WALSComputePartialLhsAndRhsOp.
• ZerosLike and OnesLike ops treated as constants by Graph Transform Tool.
• Gamma distribution and the derived distributions (Beta, Dirichlet, Student's t, inverse Gamma) now fully reparameterized.
• Java: Experimental wrapper classes to make graph generation easier. Thanks @karllessard and @kbsriram
• Build & link in secure gRPC components (switch from the insecure grpc dependency to secure grpc dependency).
• Adding new endpoints for existing tensorflow symbols. These endpoints are going to be the preferred endpoints going forward and may replace some of the existing endpoints in the future. List of new endpoints:
  • New endpoints in tf.image namespace: tf.image.extract_image_patches
  • New endpoints in tf.debugging namespace: tf.debugging.check_numerics, tf.debugging.is_finite, tf.debugging.is_inf, tf.debugging.is_nan.
  • New endpoints in tf.dtypes namespace: tf.dtypes.as_string.
  • New endpoints in tf.io namespace: tf.io.decode_base64, tf.io.decode_compressed, tf.io.decode_json_example, tf.io.decode_raw, tf.io.encode_base64, tf.io.matching_files, tf.io.parse_tensor, tf.io.read_file, tf.io.write_file`.
  • New endpoints in tf.linalg namespace: tf.linalg.cross, tf.linalg.tensor_diag (corresponds to tf.diag), tf.linalg.tensor_diag_part (corresponds to tf.diag_part).
  • New endpoints in tf.manip namespace: tf.manip.batch_to_space_nd, tf.manip.gather_nd, tf.manip.reshape, tf.manip.reverse, tf.manip.scatter_nd, tf.manip.space_to_batch_nd, tf.manip.tile
  • New endpoints in tf.math namespace: tf.math.acos, tf.math.acosh, tf.math.add, tf.math.asin, tf.math.asinh, tf.math.atan, tf.math.atan2, tf.math.atanh, tf.math.betainc, tf.math.ceil, tf.math.cos, tf.math.cosh, tf.math.digamma, tf.math.equal, tf.math.erfc, tf.math.exp, tf.math.expm1, tf.math.floor, tf.math.greater, tf.math.greater_equal, tf.math.igamma, tf.math.igammac, tf.math.invert_permutation, tf.math.less, tf.math.less_equal, tf.math.lgamma, tf.math.log, tf.math.log1p, tf.math.logical_and, tf.math.logical_not, tf.math.logical_or, tf.math.maximum, tf.math.minimum, tf.math.not_equal, tf.math.polygamma, tf.math.reciprocal, tf.math.rint, tf.math.rsqrt, tf.math.segment_max, tf.math.segment_mean, tf.math.segment_min, tf.math.segment_prod, tf.math.segment_sum, tf.math.sin, tf.math.sinh, tf.math.softplus, tf.math.softsign, tf.math.squared_difference, tf.math.tan, tf.math.unsorted_segment_max, tf.math.unsorted_segment_min, tf.math.unsorted_segment_prod, tf.math.unsorted_segment_sum, tf.math.zeta.
  • New endpoints in tf.quantization namespace: tf.quantization.dequantize, tf.quantization.fake_quant_with_min_max_args, tf.quantization.fake_quant_with_min_max_args_gradient, tf.quantization.fake_quant_with_min_max_vars, tf.quantization.fake_quant_with_min_max_vars_gradient, tf.quantization.fake_quant_with_min_max_vars_per_channel, tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient.
  • New endpoints in tf.strings namespace: tf.strings.join (corresponds to tf.string_join), tf.strings.regex_replace, tf.strings.to_number (corresponds to tf.string_to_number), tf.strings.strip (corresponds to tf.string_strip), tf.strings.substr, tf.strings.to_hash_bucket (corresponds to tf.string_to_hash_bucket), tf.strings.to_hash_bucket_fast (corresponds to tf.string_to_hash_bucket_fast), tf.strings.to_hash_bucket_strong (corresponds to tf.string_to_hash_bucket_strong).

Thanks to our Contributors

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

Ag Ramesh, Alex Wiltschko, Alexander Pantyukhin, Amogh Mannekote, An Jiaoyang, Andrei Nigmatulin, Andrew Ginns, BjøRn Moholt, Brett Koonce, Chengzhi Chen, Chinmay Das, Christian Ertler, Christoph Boeddeker, Clayne Robison, Courtial Florian, ctiijima, Dan Douthit, Dan J, Dan Ringwalt, EFanZh, Emanuele Ballarin, eqy, Evgeniy Zheltonozhskiy, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, G K, gracehoney, Guillaume Klein, Guozhong Zhuang, Hsien-Yang Li, hsm207, ImSheridan, Jayaram Bobba, Jiandong Ruan, Jie, Joel Shor, Jonas Rauber, Jongmin Baek, jsawruk, Karan Kaw, Karl Lessard, [email protected], Kb Sriram, KinmanLam, leiiwang, Li, Yiqiang, Loo Rong Jie, Mahmoud Abuzaina, Mahmoud Aslan, ManHyuk, Martin Patz, Martin Zeitler, mktozk, Mohammad Ashraf Bhuiyan, mrTsjolder, Naman Bhalla, Nick Felt, Nicolas Lopez, Niranjan Hasabnis, Nishidha Panpaliya, Nitish, nrstott, Nutti, Parag Jain, PeterLee, Philipp Jund, Rach L, Rafal Wojdyla, Roland Zimmermann, Sergei Lebedev, SneakyFish5, Soila Kavulya, Sriram Veturi, Steven Schmatz, Taehoon Lee, Tang, Wenyi, Taras Sereda, Ted Chang, Tim Zaman, Tristan Rice, tucan, vchigrin, Vikram Tiwari, Vincent, WeberXie, William D. Irons, Yan Facai (颜发才), Yong Tang, Yu Yi, Yuxin Wu, Zé ViníCius

Release 1.9.0

Major Features And Improvements

• Updated docs for tf.keras: New Keras-based get started, and programmers guide page.
• Update tf.keras to the Keras 2.1.6 API.
• Added tf.keras.layers.CuDNNGRU and tf.keras.layers.CuDNNLSTM layers. Try it.
• Adding support of core feature columns and losses to gradient boosted trees estimators.
• The python interface for the TFLite Optimizing Converter has been expanded, and the command line interface (AKA: toco, tflite_convert) is once again included in the standard pip installation.
• Improved data-loading and text processing with:
  • tf.decode_compressed
  • tf.string_strip
  • tf.strings.regex_full_match
• Added experimental support for new pre-made Estimators:
  • tf.contrib.estimator.BaselineEstimator
  • tf.contrib.estimator.RNNClassifier
  • tf.contrib.estimator.RNNEstimator
• The distributions.Bijector API supports broadcasting for Bijectors with new API changes.

Breaking Changes

• If you're opening empty variable scopes; replace variable_scope('', ...) by variable_scope(tf.get_variable_scope(), ...).
• Headers used for building custom ops have been moved from site-packages/external into site-packages/tensorflow/include/external.

Bug Fixes and Other Changes

• tfe.Network is deprecated. Please inherit from tf.keras.Model.
• Layered variable names have changed in the following conditions:
  • Using tf.keras.layers with custom variable scopes.
  • Using tf.layers in a subclassed tf.keras.Model class. See here for more details
• tf.data:
  • Dataset.from_generator() now accepts an args list, in order to create nested generators.
  • Dataset.list_files() now produces determinstic results when shuffle=False or a seed is passed.
  • tf.contrib.data.sample_from_datasets() and tf.contrib.data.choose_from_datasets() make it easier to sample or deterministically choose elements from multiple datasets.
  • tf.contrib.data.make_csv_dataset() now supports line breaks in quoted strings, and two infrequently used arguments removed.
  • (C++) DatasetBase::DebugString() is now const.
  • (C++) DatasetBase::MakeIterator() has been renamed to DatasetBase::MakeIteratorInternal().
  • (C++) IteratorBase::Initialize() method was added to support raising errors during iterator construction.
• Eager Execution:
  • Added the ability to pause recording operations for gradient computation via tf.GradientTape.stop_recording.
  • Updated documentation, introductory notebooks.
• tf.keras:
  • Move Keras code out of _impl folder and remove API files.
  • tf.keras.Model.save_weights now saves in TensorFlow format by default.
  • Enable dataset iterators to be passed to tf.keras.Model training/eval methods.
• TensorFlow Debugger (tfdbg) CLI: fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB).
• tf.contrib:
  • tf.contrib.framework.zero_initializer supports ResourceVariable.
  • Adding "constrained_optimization" to tensorflow/contrib.
• Other:
  • Add GCS Configuration Ops.
  • Changing signature of MakeIterator to enable propagating error status.
  • KL divergence for two Dirichlet distributions.
  • More consistent GcsFileSystem behavior for certain reads past EOF.
  • Update benchmark for tf.scan to match ranges across eager and graph modes.
  • Fixed bug in tf.reduce_prod gradient for complex dtypes.
  • Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)").
  • Benchmark for tf.scan in graph and eager modes.
  • Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D.
  • Making ids unique in nn.embedding_lookup_sparse. This helps to reduce RPC calls for looking up the embeddings when there are repeated ids in the batch.
  • Support indicator column in boosted trees.
  • Prevent tf.gradients() from backpropagating through integer tensors.
  • LinearOperator[1D,2D,3D]Circulant added to tensorflow.linalg.
  • Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary.
  • Added tf.train.Checkpoint for reading/writing object-based checkpoints.
  • Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product.
  • Allow LinearOperator to broadcast.
  • SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other.

Thanks to our Contributors

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

Abdullah Alrasheed, Achal Shah, Ad-530, ADiegoCAlonso, Aditya Yogi, Ag Ramesh, akindyakov, Andy Kernahan, Anya Petrova, Aurelien Geron, Ben, Ben Barsdell, Bhavani-Subramanian, braincodercn, Brett Koonce, Brian Nemsick, Brian Zier, Bryan Heden, candy.dc, cclauss, Clayne Robison, ctiijima, Dalmo Cirne, David Norman, David T.H. Kao, DosLin, ekelsen, Elson Rodriguez, Erik Smistad, Felix Abecassis, Fergal Cotter, fo40225, foo0x29a, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, gdh1995, Geoffrey Irving, Giuseppe, gracehoney, Guido Zuidhof, Guillaume Klein, Guozhong Zhuang, Haggai, Harald Husum, imsheridan, Ivan Zhang, Jan Zikes, Jayaram Bobba, Jesse Benson, Jesse Gumz, Jiajia Li, Jie, jinghuangintel, Jingwen, jjsjann123, Joe Yearsley, Joel Hestness, Joel Shor, josephyearsley, Junpeng Lao, Karol M. Langner, Kb Sriram, krantideep95, Krish Ravindranath, Letian Feng, Loo Rong Jie, Lukas Geiger, Maciej, Mahmoud Abuzaina, ManHyuk, Mark Ryan, mbhuiyan, Michal Turek, Mostafa Alaa, Myungsung Kwak, Nand Dalal, Nehal J Wani, Neil Tenenholtz, ngc92, Nicholas Nadeau, P.Eng., Avs, Niranjan Hasabnis, P-Hidringer, Paul Van Eck, Peng Yu, Qing Zhao, Qingying Chen, Quanlong, Rajendra Arora, Rholais Lii, rmanyari, Robin Richtsfeld, Russell Klopfer, Sagi, Sam Sendelbach, Sandeep N Gupta, Sandip Giri, Sarah Edkins, Scott Tseng, Sdalbsoo, Sergii Khomenko, Seungwoo Choi (Biggie), Seyed Majid Azimi, Shaoning Zeng, shengfuintel, Siu Kei, Muk, Smit Shilu, soonson, Stefan Schweter, Sukhwan Kim, Sunitha Kambhampati, Taehoon Lee, tamimaddari82, Tang, Wenyi, Ted Chang, u2takey, Utkarsh Upadhyay, Vadim Markovtsev, voegtlel, Wai Hon Law, wangsiyu, Wenhao Hu, wenhao.hu, William D. Irons, Yan Facai (颜发才), Yanbo Liang, Yihong Wang, Yilei (Dolee) Yang, Yong Tang, Yuan (Terry) Tang

Release 1.8.0

Major Features And Improvements

• Can now pass tf.contrib.distribute.MirroredStrategy() to tf.estimator.RunConfig() to run an Estimator model on multiple GPUs on one machine.
• Add tf.contrib.data.prefetch_to_device(), which supports prefetching to GPU memory.
• Added Gradient Boosted Trees as pre-made Estimators: BoostedTreesClassifier, BoostedTreesRegressor.
• Add 3rd generation pipeline config for Cloud TPUs which improves performance and usability.
• tf.contrib.bayesflow is moving out to it's own repo.
• Added tf.contrib.{proto,rpc} to allow generic proto parsing and RPC communication¹.

Bug Fixes and Other Changes

• tf.data:
  • Add tf.contrib.data.prefetch_to_device, which enables prefetching dataset elements to GPU memory.
  • Add tf.contrib.data.AUTOTUNE, which allows the tf.data runtime to automatically tune the prefetch buffer sizes based on your system and environment.
  • Add tf.contrib.data.make_csv_dataset for building datasets of CSV files.
• Eager Execution:
  • With eager execution Datasets can now be used as standard python iterators (for batch in dataset:). Both Dataset.__iter__() and Dataset.make_one_shot_iterator() can now be used to create iterators when eager execution is enabled.
  • Automatic device placement has been enabled (i.e., use a GPU if available automatically, without requiring an explicit with tf.device(“/gpu:0”)) (Fixes #14133)
  • tf.GradientTape has moved out of contrib.
• tf.keras:
  • Added the fashion mnist dataset.
  • New data preprocessing functions: image/random_brightness, sequence/TimeseriesGenerator, and text/hashing_trick.
• Accelerated Linear Algebra (XLA):
  • Select and scatter in reference util and evaluator now use lexicographical order to break ties.
• TensorFlow Debugger (tfdbg) CLI:
  • During tensor-filter operations, allow exclusion of nodes by regular expressions.
  • Fix spurious background colors in some text terminals.
• tf.contrib:
  • Add meta-distribution BatchReshape which reshapes batch dimensions.
  • tf.contrib.layers.recompute_grad works for explicit gradient checkpointing on TPU.
  • Add tf.contrib.framework.argsort.
  • Allow DNNBoostedTreeCombinedEstimator to work with core versions of feature columns and losses.
  • Add non-linear image warping ops: tf.contrib.image.sparse_image_warp, tf.contrib.image.dense_image_warp, and tf.contrib.image.interpolate_spline.
  • Fix bug in tf.contrib.opt.MultitaskOptimizerWrapper where types of tensors were mismatched.
• Other:
  • Low-level graph construction now calls the TensorFlow C API. This change should be invisible to most users, but can be disabled by setting the environment variable TF_C_API_GRAPH_CONSTRUCTION=0 in this release. Future releases will remove the ability to disable this change. Please file a bug if you find yourself using this escape hatch.
  • Add description of shapes and a pointer to tutorial notebook in tf.distributions.Distribution.
  • Update scatter operations:
    • Add tf.scatter_min and tf.scatter_max
    • Extend scatter operations to work with a scalar update parameter.
  • Move cuDNN RNN ops to core for use in TensorFlow codebase only.
  • Add float64 support for Conv2d, Conv2dBackpropInput, and Conv2dBackpropFilter.
  • Add float64 support for AvgPool/AvgPoolGrad.
  • Make graph name scope thread local so that they work correctly in multi-threaded environments.
  • Update nsync synchronization library to avoid slow primitives on Linux.
  • Removed need to put nsync/public on C include path when building custom ops.
  • Add tf.image.psnr, tf.image.ssim, tf.image.ssim_multiscale, tf.image.image_gradients, tf.image.sobel_edges.
  • Add links to https://js.tensorflow.org.
  • Fix non-uniformity of orthogonal matrices.
  • Fix bug where multi-image Estimator eval summaries were not displayed correctly.

¹ The cancellation logic of the RPC op contains a concurrency error. A fix has been submitted to master and will be part of the next release.

Thanks to our Contributors

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

4d55397500, Aghasy, Alan Du, Alan Lee, Alan Yee, Alex Wiltschko, Animesh Karnewar, Ankit Gupta, Anton Matosov, Aris L, Ben Barsdell, Brent Yi, Brett Koonce, Carl Thomé, cbockman, Chikanaga Tomoyuki, Chris Tava, CéDric Deltheil, Dahan Gong, Dalmo Cirne, Daniel Erenrich, David Norman, DavidNorman, Edd Wilder-James, Fanjin Zeng, Felix Abecassis, fo40225, George Sterpu, Giovanni Terlingen, Gor Baghdasaryan, Guillaume Klein, Hanchen Li, Ilya Polenov, Jakub Kolodziejczyk, Jason Sadler, Jayaram Bobba, Jerry Liu, jinghuangintel, Jiongyan Zhang (张炯衍), Joel Shor, Jong Wook Kim, Julian Eisenschlos, Karl Lessard, Krish Ravindranath, Loo Rong Jie, Lukas Geiger, Luke Iwanski, Mahmoud Abuzaina, ManHyuk, Marvin Richter, Maximilian Mitchell, Mohammad Ashraf Bhuiyan, msofka, Mustafa Kasap, Nathan Burnham, Nathan Luehr, Naveen Marri, ngc92, nio1814, Oleg Zabluda, Ou Changkun, Panos Ipeirotis, Paul Van Eck, Peter Lee, Piotr Czapla, qjivy, Rholais Lii, Rodrigo Formigone, Russell Klopfer, ryantimjohn, Sang Han, SebastiáN RamíRez, shengfuintel, Siby Jose Plathottam, Silver Chan, Stanislaw Antol, Taehoon Lee, Tarang Chugh, Ted Chang, Thomas Bastiani, Xian Xu, Xiaoming (Jason) Cui, Yan Facai (颜发才), yaox12, Yashal Shakti Kanungo, Yong Tang, Yuan (Terry) Tang, Yuxin Wu, Ziyue(Louis) Lu

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
• Initial support for tf.contrib.tensorrt that enables native TensorRT in TensorFlow.

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.

Deprecations

• TensorFlow 1.7 may be the last time we support Cuda versions below 8.0. Starting with TensorFlow 1.8 release, 8.0 will be the minimum supported version.
• TensorFlow 1.7 may be the last time we support cuDNN versions below 6.0. Starting with TensorFlow 1.8 release, 6.0 will be the minimum supported version.

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

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.

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 e00ba24c4038e7644da417ddc639169b6ea59122.

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

Release 1.5.0

Breaking Changes

• Prebuilt binaries are now built against CUDA 9.0 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 9.0 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 deterministic 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.

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 e00ba24c4038e7644da417ddc639169b6ea59122.

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.

Release 1.4.1

Bug Fixes and Other Changes

• LinearClassifier fix.

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, [email protected], 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.

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 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.conv2d 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, [email protected], 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.

Release 1.2.1

Bug Fixes and Other Changes

• Updating markdown version required to >= 2.6.8.
• Support tensors as dropout rates again, by removing the min(max(..))

Release 1.2.0

Major Features and Improvements

• Python 3.6 support on Windows.

• Added tf.layers.conv3d_transpose layer for spatio temporal deconvolution.

• Added tf.Session.make_callable(), which provides a lower overhead means of running a similar step multiple times.

• Added libverbs-based RDMA support to contrib (courtesy @junshi15 from Yahoo).

• Bring tf.feature_column.* into the API. Non-deprecated functionality from tf.contrib.layers.* is moved to tf.feature_column.* with cosmetic changes.

• RNNCell objects now subclass tf.layers.Layer. The strictness described in the TensorFlow 1.1 release is gone: The first time an RNNCell is used, it caches its scope. All future uses of the RNNCell will reuse variables from that same scope. This is a breaking change from the behavior of RNNCells in TensorFlow versions <= 1.0.1. TensorFlow 1.1 had checks in place to ensure old code works correctly with the new semantics; this version allows more flexible uses of RNNCell but can lead to subtle errors if using code meant for TensorFlow <= 1.0.1. For example, writing: MultiRNNCell([lstm] * 5) will now build a 5-layer LSTM stack where each layer shares the same parameters. To get 5 layers each with their own parameters, write: MultiRNNCell([LSTMCell(...) for _ in range(5)]). If at all unsure, first test your code with TF 1.1; ensure it raises no errors, and then upgrade to TF 1.2.

• RNNCells' variable names have been renamed for consistency with Keras layers. Specifically, the previous variable names "weights" and "biases" have been changed to "kernel" and "bias", respectively. This may cause backward incompatibility with regard to your old checkpoints containing such RNN cells, in which case you can use the tool checkpoint_convert script to convert the variable names in your old checkpoints.

• Many of the RNN functions and classes that were in the tf.nn namespace before the 1.0 release and which were moved to tf.contrib.rnn have now been moved back to the core namespace. This includes RNNCell, LSTMCell, GRUCell, and a number of other cells. These now reside in tf.nn.rnn_cell (with aliases in tf.contrib.rnn for backwards compatibility). The original tf.nn.rnn function is now tf.nn.static_rnn, and the bidirectional static and state saving static rnn functions are also now back in the tf.nn namespace.

  Notable exceptions are the EmbeddingWrapper, InputProjectionWrapper and OutputProjectionWrapper, which will slowly be moved to deprecation in tf.contrib.rnn. These are inefficient wrappers that should often be replaced by calling embedding_lookup or layers.dense as pre- or post- processing of the rnn. For RNN decoding, this functionality has been replaced with an alternative API in tf.contrib.seq2seq.

• Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of optimized deep learning primitives: In addition to matrix multiplication and convolution, these building blocks include: Direct batched convolution Pooling: maximum, minimum, average Normalization: LRN, batch normalization Activation: rectified linear unit (ReLU) Data manipulation: multi-dimensional transposition (conversion), split, concat, sum and scale.

• TensorForest Estimator now supports SavedModel export for serving.

• Support client-provided ClusterSpec's and propagate them to all workers to enable the creation of dynamic TensorFlow clusters.

• TensorFlow C library now available for Windows.

• We released a new open-source version of TensorBoard.

• SavedModel CLI tool available to inspect and execute MetaGraph in SavedModel

• 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.

Deprecations

• TensorFlow 1.2 may be the last time we build with cuDNN 5.1. Starting with TensorFlow 1.3, we will try to build all our prebuilt binaries with cuDNN 6.0. While we will try to keep our source code compatible with cuDNN 5.1, it will be best effort.

Breaking Changes to the API

• org.tensorflow.contrib.android.TensorFlowInferenceInterface now throws exceptions where possible and has simplified method signatures.

Changes to contrib APIs

• Added tf.contrib.util.create_example.
• Added bilinear interpolation to tf.contrib.image.
• Add tf.contrib.stateless for random ops with custom seed control.
• MultivariateNormalFullCovariance added to contrib/distributions/
• tensorflow/contrib/rnn undergoes RNN cell variable renaming for consistency with Keras layers. Specifically, the previous variable names "weights" and "biases" are changed to "kernel" and "bias", respectively. This may cause backward incompatibility with regard to your old checkpoints containing such RNN cells, in which case you can use the checkpoint_convert script to convert the variable names in your old checkpoints.
• Added tf.contrib.kernel_methods module with Ops and estimators for primal (explicit) kernel methods in TensorFlow.

Bug Fixes and Other Changes

• In python, Operation.get_attr on type attributes returns the Python DType version of the type to match expected get_attr documentation rather than the protobuf enum.
• tensorflow/contrib/rnn undergoes RNN cell variable renaming for consistency with Keras layers. Specifically, the previous variable names "weights" and "biases" are changed to "kernel" and "bias", respectively.
• Changed MIN_SDK version to 8.0 when building iOS libraries.
• Fixed LIBXSMM integration.
• Make decode_jpeg/decode_png/decode_gif handle all formats, since users frequently try to decode an image as the wrong type.
• Improve implicit broadcasting lowering.
• Improving stability of GCS/BigQuery clients by a faster retrying of stale transmissions.
• Remove OpKernelConstruction::op_def() as part of minimizing proto dependencies.
• VectorLaplaceDiag distribution added.
• Android demo no longer requires libtensorflow_demo.so to run (libtensorflow_inference.so still required)
• Added categorical_column_with_vocabulary_file.
• Introduce ops for batching/unbatching tensors across Session::Run() calls.
• Add tf.log_sigmoid(x) = tf.log(tf.sigmoid(x)) = -tf.nn.softplus(-x).
• Changed hooks lists to immutable tuples, and now allow any iterable for the associated arguments.
• Introduce TFDecorator.
• Added an Mfcc op for speech feature generation.
• Improved DirectSession::Run() overhead and error checking. Feeding a value of the wrong type will now synchronously raise an INVALID_ARGUMENT error instead of asynchronously raising an INTERNAL error. Code that depends on the (undefined) behavior when feeding a tensor of the wrong type may need to be updated.
• Added unreduced NONE, and reduced MEAN options for losses. Removed "WEIGHTED_" prefix from other Reduction constants.
• assertAllClose now handles dicts.
• Added Gmock matcher for HloInstructions.
• Add var name to errors on variable restore.
• Added an AudioSpectrogram op for audio feature generation.
• Added reduction arg to losses.
• tf.placeholder can represent scalar shapes and partially known.
• Remove estimator_spec(mode) argument.
• Added an AudioSpectrogram op for audio feature generation.
• TensorBoard disables all runs by default if there are more than 40 runs.
• Removed old doc generator code.
• GCS file system integration now supports domain buckets, e.g gs://bucket.domain.com/path.
• Add tf.summary.text for outputting text to TensorBoard.
• The "run" command of tfdbg's command-line interface now supports filtering of tensors by node name, op type and tensor dtype.
• tf.string_to_number now supports int64 and float64 outputs.

Thanks to our Contributors

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

4F2E4A2E, Aaron Schumacher, Abhi Agg, admcrae, Adriano Carmezim, Adrià Arrufat, agramesh1, Akimitsu Seo, Alan Mosca, Alex Egg, Alex Rothberg, Alexander Heinecke, Alexander Matyasko, Alexandr Baranezky, Alexandre Caulier, Ali Siddiqui, Anand Venkat, Andrew Hundt, Androbin, Anmol Sharma, Arie, Arno Leist, Arron Cao, AuréLien Geron, Bairen Yi, Beomsu Kim, Carl Thomé, cfperez, Changming Sun, Corey Wharton, critiqjo, Dalei Li, Daniel Rasmussen, Daniel Trebbien, DaríO Hereñú, David Eng, David Norman, David Y. Zhang, Davy Song, ddurham2, Deepak Subburam, Dmytro Kyrychuk, Dominic Rossi, Dominik SchlöSser, Dustin Tran, Eduardo Pinho, Egil Martinsson, Elliot Saba, Eric Bigelow, Erik Smistad, Evan Klitzke, Fabrizio Milo, Falcon Dai, Fei Gao, FloopCZ, Fung Lam, Gautam, GBLin5566, Greg Peatfield, Gu Wang, Guenther Schmuelling, Hans Pabst, Harun Gunaydin, Huaizheng, Ido Shamay, Ikaro Silva, Ilya Edrenkin, Immexxx, James Mishra, Jamie Cooke, Jay Young, Jayaram Bobba, Jianfei Wang, jinghua2, Joey Meyer, John Maidens, Jonghoon Jin, Julian Villella, Jun Kim, Jun Shi, Junwei Pan, jyegerlehner, Karan Desai, Karel Van De Plassche, Kb Sriram, KhabarlakKonstantin, Koan-Sin Tan, krivard, Kwotsin, Leandro Gracia Gil, Li Chen, Liangliang He, Louie Helm, lspvic, Luiz Henrique Soares, LáSzló Csomor, Mark Wong, Mathew Wicks, Matthew Rahtz, Maxwell Paul Brickner, Michael Hofmann, Miguel Flores Ruiz De Eguino, MikeTam1021, Mortada Mehyar, Mycosynth, Namnamseo, Nate Harada, Neven Miculinic, Nghia Tran, Nick Lyu, Niranjan Hasabnis, Nishidha, Oleksii Kuchaiev, Oyesh Mann Singh, Panmari, Patrick, Paul Van Eck, Piyush Chaudhary, Quim Llimona, Raingo, Richard Davies, Ruben Vereecken, Sahit Chintalapudi, Sam Abrahams, Santiago Castro, Scott Sievert, Sean O'Keefe, Sebastian Schlecht, Shane, Shubhankar Deshpande, Spencer Schaber, Sunyeop Lee, t13m, td2014, Thomas H. P. Andersen, Toby Petty, Umang Mehta, Vadim Markovtsev, Valentin Iovene, Vincent Zhao, Vit Stepanovs, Vivek Rane, Vu Pham, wannabesrevenge, weipingpku, wuhaixutab, wydwww, Xiang Gao, Xiaolin Lin, xiaoyaozhuzi, Yaroslav Bulatov, Yi Liu, Yoshihiro Sugi, Yuan (Terry) Tang, Yuming Wang, Yuxin Wu, Zader Zheng, Zhaojun Zhang, zhengjiajin, ZhipengShen, Ziming Dong, zjj2wry

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

Release 1.1.0

Major Features and Improvements

• Added Java API support for Windows.
• Added tf.spectral module. Moved existing FFT ops to tf.spectral while keeping an alias in the old location (tf.*).
• Added 1D, 2D and 3D Fourier transform ops for real signals to tf.spectral.
• Added a tf.bincount function.
• Added Keras 2 API to contrib.
• Added a new lightweight queue-like object - RecordInput.
• Added tf.contrib.image.compose_transforms function.
• Bring tf.estimator.* into the API. Non-deprecated functionality from tf.contrib.learn.Estimator is moved to tf.estimator.Estimator with cosmetic changes.
• Docker images: TF images on gcr.io and Docker Hub are upgraded to ubuntu:16.04.
• Added the following features to TensorFlow Debugger (tfdbg):
  • Ability to inspect Python source file against TF ops and tensors (command print_source / ps)
  • New navigation bar in Curses-based UI
  • NodeStepper (command invoke_stepper) now uses intermediate tensor dumps. It also uses TensorHandles as direct feeds during successive cont calls for improved performance and reduced memory consumption.
• Initial release of installation guides for Java, C, and Go.
• Added Text Dashboard to TensorBoard.

Deprecations

• TensorFlow 1.1.0 will be the last time we release a binary with Mac GPU support. Going forward, we will stop testing on Mac GPU systems. We continue to welcome patches that maintain Mac GPU support, and we will try to keep the Mac GPU build working.

Changes to contrib APIs

• The behavior of RNNCells is now stricter due to the transition towards making RNNCells act more like Keras layers.
  • If an RNNCell is used twice in two different variable scopes, an error is raised describing how to avoid this behavior.
  • If an RNNCell is used in a variable scope with existing conflicting variables, an error is raised showing that the RNNCell must be constructed with argument reuse=True.
• Deprecated contrib/distributions pmf, pdf, log_pmf, log_pdf.
• Moved bayesflow.special_math to distributions.
• tf.contrib.tensor_forest.python.tensor_forest.RandomForestDeviceAssigner removed.
• Changed some MVN classes and parameters:
  • tf.contrib.distributions.MultivariateNormalFull replaced by tf.contrib.distributions.MultivariateNormalTriL.
  • tf.contrib.distributions.MultivariateNormalCholesky replaced by tf.contrib.distributions.MultivariateNormalTriL
  • tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev replaced by tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale
  • tf.contrib.distributions.MultivariateNormalDiag arguments changed from mu, diag_stddev to log, scale_diag.
  • tf.contrib.distributions.MultivariateNormalDiagPlusVDVT removed.
  • tf.contrib.distributions.MultivariateNormalDiagPlusLowRank added.

Bug Fixes and Other Changes

• Java: Support for loading models exported using the SavedModel API (courtesy @EronWright).
• Go: Added support for incremental graph execution.
• Fix a bug in the WALS solver when single-threaded.
• Added support for integer sparse feature values in tf.contrib.layers.sparse_column_with_keys.
• Fixed tf.set_random_seed(0) to be deterministic for all ops.
• Stability improvements for the GCS file system support.
• Improved TensorForest performance.
• Added support for multiple filename globs in tf.matching_files.
• LogMessage now includes a timestamp as beginning of a message.
• Added MultiBox person detector example standalone binary.
• Android demo: Makefile build functionality added to build.gradle to fully support building TensorFlow demo in Android on Windows.
• Android demo: read MultiBox priors from txt file rather than protobuf.
• Added colocation constraints to StagingArea.
• sparse_matmul_op reenabled for Android builds.
• Restrict weights rank to be the same as the broadcast target, to avoid ambiguity on broadcast rules.
• Upgraded libxsmm to 1.7.1 and applied other changes for performance and memory usage.
• Fixed bfloat16 integration of LIBXSMM sparse mat-mul.
• Improved performance and reduce memory usage by allowing ops to forward input buffers to output buffers and perform computations in-place.
• Improved the performance of CPU assignment for strings.
• Speed up matrix * vector multiplication and matrix * matrix with unknown shapes.
• C API: Graph imports now support input remapping, control dependencies, and returning imported nodes (see TF_GraphImportGraphDefWithReturnOutputs())
• Multiple C++ API updates.
• Multiple TensorBoard updates including:
  • Users can now view image summaries at various sampled steps (instead of just the last step).
  • Bugs involving switching runs as well as the image dashboard are fixed.
  • Removed data download links from TensorBoard.
  • TensorBoard uses a relative data directory, for easier embedding.
  • TensorBoard automatically ignores outliers for domain calculation, and formats proportional values consistently.
• Multiple tfdbg bug fixes:
  • Fixed Windows compatibility issues.
  • Command history now persists across runs.
  • Bug fix in graph validation related to tf.while_loops.
• Java Maven fixes for bugs with Windows installation.
• Backport fixes and improvements from external keras.
• Keras config file handling fix.

Thanks to our Contributors

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

A. Besir Kurtulmus, Adal Chiriliuc, @akash, Alec-Desouza, Alex Rothberg, Alex Sergeev, Alexander Heinecke, Allen Guo, Andreas Madsen, Ankesh Anand, Anton Loss, @Aravind, @Arie, Ashutosh Das, AuréLien Geron, Bairen Yi, @bakunyo, Ben Visser, Brady Zhou, Calpa Liu, Changming Sun, Chih Cheng Liang, Christopher Berner, Clark Zinzow, @Conchylicultor, Dan Ellis, Dan J, Dan Jarvis, Daniel Ylitalo, Darren Garvey, David Norman, David Truong, @DavidNorman, Dimitar Pavlov, Dmitry Persiyanov, @Eddie, @elirex, Erfan Noury, Eron Wright, Evgeny Mazovetskiy, Fabrizio (Misto) Milo, @fanlu, Fisher Coder, Florian Courtial, Franck Dernoncourt, Gagan Goel, Gao, Xiang, @Gautam, Gefu Tang, @guilherme, @guschmue, Hannah Provenza, Hans Pabst, @hartb, Hsiao Yi, Huazuo Gao, Igor ChorążEwicz, Ivan Smirnov, Jakub Kolodziejczyk, Jason Gavris, Jason Morton, Jay Young, Jayaram Bobba, Jeremy Sawruk, Jiaming Liu, Jihun Choi, @jiqiu, Joan Thibault, John C F, Jojy George Varghese, Jon Malmaud, Julian Berman, Julian Niedermeier, Junpeng Lao, Kai Sasaki, @Kankroc, Karl Lessard, Kyle Bostelmann, @Lezcano, Li Yi, Luo Yun, @lurker, Mahmoud-Abuzaina, Mandeep Singh, Marek Kolodziej, Mark Szepieniec, Martial Hue, Medhat Omr, Memo Akten, Michael Gharbi, MichaëL Defferrard, Milan Straka, @MircoT, @mlucool, Muammar Ibn Faisal, Nayana Thorat, @nghiattran, Nicholas Connor, Nikolaas Steenbergen, Niraj Patel, Niranjan Hasabnis, @Panmari, Pavel Bulanov, Philip Pries Henningsen, Philipp Jund, @polonez, Prayag Verma, Rahul Kavi, Raphael Gontijo Lopes, @rasbt, Raven Iqqe, Reid Pryzant, Richard Shin, Rizwan Asif, Russell Kaplan, Ryo Asakura, RüDiger Busche, Saisai Shao, Sam Abrahams, @sanosay, Sean Papay, @seaotterman, @selay01, Shaurya Sharma, Sriram Narayanamoorthy, Stefano Probst, @taknevski, @tbonza, @teldridge11, Tim Anglade, Tomas Reimers, Tomer Gafner, Valentin Iovene, Vamsi Sripathi, Viktor Malyi, Vit Stepanovs, Vivek Rane, Vlad Firoiu, @wangg12, @will, Xiaoyu Tao, Yaroslav Bulatov, Yi Liu, Yuan (Terry) Tang, @Yufeng, Yuming Wang, Yuxin Wu, Zafar Takhirov, Ziming Dong

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

Release 1.0.1

Bug Fixes and Other Changes

• Change GraphConstructor to not increase the version when importing, but instead take the min of all versions.
• Google Cloud Storage fixes.
• Removed tf.core and tf.python modules from the API. These were never intended to be exposed. Please use the same objects through top-level tf module instead.

Release 1.0.0

Major Features and Improvements

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

Breaking Changes to the API

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

• TensorFlow/models have been moved to a separate github repository.
• Division and modulus operators (/, //, %) now match Python (flooring) semantics. This applies to tf.div and tf.mod as well. To obtain forced integer truncation based behaviors you can use tf.truncatediv and tf.truncatemod.
• tf.divide() is now the recommended division function. tf.div() will remain, but its semantics do not respond to Python 3 or from future mechanisms.
• tf.reverse() now takes indices of axes to be reversed. E.g. tf.reverse(a, [True, False, True]) must now be written as tf.reverse(a, [0, 2]). tf.reverse_v2() will remain until 1.0 final.
• tf.mul, tf.sub and tf.neg are deprecated in favor of tf.multiply, tf.subtract and tf.negative.
• tf.pack and tf.unpack are deprecated in favor of tf.stack and tf.unstack.
• TensorArray.pack and TensorArray.unpack are getting deprecated in favor of TensorArray.stack and TensorArray.unstack.
• The following Python functions have had their arguments changed to use axis when referring to specific dimensions. We have kept the old keyword arguments for compatibility currently, but we will be removing them well before the final 1.0.
  • tf.argmax: dimension becomes axis
  • tf.argmin: dimension becomes axis
  • tf.count_nonzero: reduction_indices becomes axis
  • tf.expand_dims: dim becomes axis
  • tf.reduce_all: reduction_indices becomes axis
  • tf.reduce_any: reduction_indices becomes axis
  • tf.reduce_join: reduction_indices becomes axis
  • tf.reduce_logsumexp: reduction_indices becomes axis
  • tf.reduce_max: reduction_indices becomes axis
  • tf.reduce_mean: reduction_indices becomes axis
  • tf.reduce_min: reduction_indices becomes axis
  • tf.reduce_prod: reduction_indices becomes axis
  • tf.reduce_sum: reduction_indices becomes axis
  • tf.reverse_sequence: batch_dim becomes batch_axis, seq_dim becomes seq_axis
  • tf.sparse_concat: concat_dim becomes axis
  • tf.sparse_reduce_sum: reduction_axes becomes axis
  • tf.sparse_reduce_sum_sparse: reduction_axes becomes axis
  • tf.sparse_split: split_dim becomes axis
• tf.listdiff has been renamed to tf.setdiff1d to match NumPy naming.
• tf.inv has been renamed to be tf.reciprocal (component-wise reciprocal) to avoid confusion with np.inv which is matrix inversion
• tf.round now uses banker's rounding (round to even) semantics to match NumPy.
• tf.split now takes arguments in a reversed order and with different keywords. In particular, we now match NumPy order as tf.split(value, num_or_size_splits, axis).
• tf.sparse_split now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as tf.sparse_split(sp_input, num_split, axis). NOTE: we have temporarily made tf.sparse_split require keyword arguments.
• tf.concat now takes arguments in reversed order and with different keywords. In particular we now match NumPy order as tf.concat(values, axis, name).
• tf.image.decode_jpeg by default uses the faster DCT method, sacrificing a little fidelity for improved speed. One can revert to the old behavior by specifying the attribute dct_method='INTEGER_ACCURATE'.
• tf.complex_abs has been removed from the Python interface. tf.abs supports complex tensors and should be used instead.
• In the C++ API (in tensorflow/cc), Input, Output, etc. have moved from the tensorflow::ops namespace to tensorflow.
• Template.var_scope property renamed to .variable_scope
• SyncReplicasOptimizer is removed and SyncReplicasOptimizerV2 renamed to SyncReplicasOptimizer.
• tf.zeros_initializer() and tf.ones_initializer() now return a callable that must be called with initializer arguments, in your code replace tf.zeros_initializer with tf.zeros_initializer().
• SparseTensor.shape has been renamed to SparseTensor.dense_shape. Same for SparseTensorValue.shape.
• Replace tf.scalar_summary, tf.histogram_summary, tf.audio_summary, tf.image_summary with tf.summary.scalar, tf.summary.histogram, tf.summary.audio, tf.summary.image, respectively. The new summary ops take name rather than tag as their first argument, meaning summary ops now respect TensorFlow name scopes.
• Replace tf.train.SummaryWriter and tf.train.SummaryWriterCache with tf.summary.FileWriter and tf.summary.FileWriterCache.
• Removes RegisterShape from public API. Use C++ shape function registration instead.
• Deprecated _ref dtypes from the python API.
• In the C++ API (in tensorflow/cc), Input, Output, etc. have moved from the tensorflow::ops namespace to tensorflow.
• Change arg order for {softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits to be (labels, predictions), and force use of named args.
• tf.nn.rnn_cell.* and most functions in tf.nn.rnn.* (with the exception of dynamic_rnn and raw_rnn) are temporarily in tf.contrib.rnn. They will be moved back into core for TF 1.2.
• tf.nn.sampled_softmax_loss and tf.nn.nce_loss have both changed their API such that you need to switch the inputs, labels to labels, inputs parameters.
• The shape keyword argument of the SparseTensor constructor changes its name to dense_shape between Tensorflow 0.12 and Tensorflow 1.0.

Bug Fixes and Other Changes

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

Thanks to our Contributors

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

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

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

Release 0.12.0

Major Features and Improvements

• TensorFlow now builds and runs on Microsoft Windows (tested on Windows 10, Windows 7, and Windows Server 2016). Supported languages include Python (via a pip package) and C++. CUDA 8.0 and cuDNN 5.1 are supported for GPU acceleration. Known limitations include: It is not currently possible to load a custom op library. The GCS and HDFS file systems are not currently supported. The following ops are not currently implemented: Dequantize, QuantizeAndDequantize, QuantizedAvgPool, QuantizedBatchNomWithGlobalNormalization, QuantizedBiasAdd, QuantizedConcat, QuantizedConv2D, QuantizedMatmul, QuantizedMaxPool, QuantizeDownAndShrinkRange, QuantizedRelu, QuantizedRelu6, QuantizedReshape, QuantizeV2, RequantizationRange, and Requantize.
• Go: Experimental API in Go to create and execute graphs (https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go)
• New checkpoint format becomes the default in tf.train.Saver. Old V1 checkpoints continue to be readable; controlled by the write_version argument, tf.train.Saver now by default writes out in the new V2 format. It significantly reduces the peak memory required and latency incurred during restore.
• Added a new library for library of matrix-free (iterative) solvers for linear equations, linear least-squares, eigenvalues and singular values in tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization, conjugate gradients and CGLS.
• Added gradients for matrix_solve_ls and self_adjoint_eig.
• Large cleanup to add second order gradient for ops with C++ gradients and improve existing gradients such that most ops can now be differentiated multiple times.
• Added a solver for ordinary differential equations, tf.contrib.integrate.odeint.
• New contrib module for tensors with named axes, tf.contrib.labeled_tensor.
• Visualization of embeddings in TensorBoard.

Breaking Changes to the API

• BusAdjacency enum replaced with a protocol buffer DeviceLocality. PCI bus indexing now starts from 1 instead of 0, and bus_id==0 is used where previously BUS_ANY was used.
• Env::FileExists and FileSystem::FileExists now return a tensorflow::Status instead of a bool. Any callers to this function can be converted to a bool by adding .ok() to the call.
• The C API type TF_SessionWithGraph has been renamed to TF_Session, indicating its preferred use in language bindings for TensorFlow. What was previously TF_Session has been renamed to TF_DeprecatedSession.
• Renamed TF_Port to TF_Output in the C API.
• Removes RegisterShape from public API. Use C++ shape function registration instead. indexing now starts from 1 instead of 0, and bus_id==0 is used where previously BUS_ANY was used.
• Most RNN cells and RNN functions now use different variable scopes to be consistent with layers (tf.contrib.layers). This means old checkpoints written using this code will not load after this change without providing Saver a list of variable renames. Examples of variable scope changes include RNN -> rnn in tf.nn.rnn, tf.nn.dynamic_rnn and moving from Linear/Matrix -> weights and Linear/Bias -> biases in most RNN cells.
• Deprecated tf.select op. tf.where should be used instead.
• SparseTensor.shape has been renamed to SparseTensor.dense_shape. Same for SparseTensorValue.shape.
• Env::FileExists and FileSystem::FileExists now return a tensorflow::Status instead of a bool. Any callers to this function can be converted to a bool by adding .ok() to the call.
• C API: Type TF_SessionWithGraph has been renamed to TF_Session, indicating its preferred use in language bindings for TensorFlow. What was previously TF_Session has been renamed to TF_DeprecatedSession.
• C API: Renamed TF_Port to TF_Output.
• C API: The caller retains ownership of TF_Tensor objects provided to TF_Run, TF_SessionRun, TF_SetAttrTensor etc.
• Renamed tf.image.per_image_whitening() to tf.image.per_image_standardization()
• Move Summary protobuf constructors to tf.summary submodule.
• Deprecate histogram_summary, audio_summary, scalar_summary, image_summary, merge_summary, and merge_all_summaries.
• Combined batch_* and regular version of linear algebra and FFT ops. The regular op now handles batches as well. All batch_* Python interfaces were removed.
• tf.all_variables, tf.VARIABLES and tf.initialize_all_variables renamed to tf.global_variables, tf.GLOBAL_VARIABLES and tf.global_variables_initializer respectively.
• tf.zeros_initializer() and tf.ones_initializer() now return a callable that must be called with initializer arguments, in your code replace tf.zeros_initializer with tf.zeros_initializer()

Bug Fixes and Other Changes

• Use threadsafe version of lgamma function.
• Fix tf.sqrt handling of negative arguments.
• Fixed bug causing incorrect number of threads to be used for multi-threaded benchmarks.
• Performance optimizations for batch_matmul on multi-core CPUs.
• Improve trace, matrix_set_diag, matrix_diag_part and their gradients to work for rectangular matrices.
• Support for SVD of complex valued matrices.

Thanks to our Contributors

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

@a7744hsc, Abhi Agg, @admcrae, Adriano Carmezim, Aki Sukegawa, Alex Kendall, Alexander Rosenberg Johansen, @amcrae, Amlan Kar, Andre Simpelo, Andreas Eberle, Andrew Hundt, Arnaud Lenglet, @b0noI, Balachander Ramachandran, Ben Barsdell, Ben Guidarelli, Benjamin Mularczyk, Burness Duan, @c0g, Changming Sun, @chanis, Corey Wharton, Dan J, Daniel Trebbien, Darren Garvey, David Brailovsky, David Jones, Di Zeng, @DjangoPeng, Dr. Kashif Rasul, @drag0, Fabrizio (Misto) Milo, FabríCio Ceschin, @fp, @Ghedeon, @guschmue, Gökçen Eraslan, Haosdent Huang, Haroen Viaene, Harold Cooper, Henrik Holst, @hoangmit, Ivan Ukhov, Javier Dehesa, Jingtian Peng, Jithin Odattu, Joan Pastor, Johan Mathe, Johannes Mayer, Jongwook Choi, Justus Schwabedal, Kai Wolf, Kamil Hryniewicz, Kamran Amini, Karen Brems, Karl Lattimer, @kborer, Ken Shirriff, Kevin Rose, Larissa Laich, Laurent Mazare, Leonard Lee, Liang-Chi Hsieh, Liangliang He, Luke Iwanski, Marek Kolodziej, Moustafa Alzantot, @MrQianjinsi, @nagachika, Neil Han, Nick Meehan, Niels Ole Salscheider, Nikhil Mishra, @nschuc, Ondrej Skopek, OndřEj Filip, @OscarDPan, Pablo Moyano, Przemyslaw Tredak, @qitaishui, @Quarazy, @raix852, Philipp Helo, Sam Abrahams, @SriramRamesh, Till Hoffmann, Tushar Soni, @tvn, @tyfkda, Uwe Schmidt, Victor Villas, Vit Stepanovs, Vladislav Gubarev, @wujingyue, Xuesong Yang, Yi Liu, Yilei Yang, @youyou3, Yuan (Terry) Tang, Yuming Wang, Zafar Takhirov, @zhongyuk, Ziming Dong, @guotong1988

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

Release 0.11.0

Major Features and Improvements

• CUDA 8 support.
• cuDNN 5 support.
• HDFS Support.
• Adds Fused LSTM support via cuDNN 5 in tensorflow/contrib/cudnn_rnn.
• Improved support for NumPy style basic slicing including non-1 strides, ellipses, newaxis, and negative indices. For example complicated expressions like foo[1, 2:4, tf.newaxis, ..., :-3:-1, :] are now supported. In addition we have preliminary (non-broadcasting) support for sliced assignment to variables. In particular one can write var[1:3].assign([1,11,111]).
• Deprecated tf.op_scope and tf.variable_op_scope in favor of a unified tf.name_scope and tf.variable_scope. The new argument order of tf.variable_scope is incompatible with previous versions.
• Introducing core/util/tensor_bundle module: a module to efficiently serialize/deserialize tensors to disk. Will be used in TF's new checkpoint format.
• Added tf.svd for computing the singular value decomposition (SVD) of dense matrices or batches of matrices (CPU only).
• Added gradients for eigenvalues and eigenvectors computed using self_adjoint_eig or self_adjoint_eigvals.
• Eliminated batch_* methods for most linear algebra and FFT ops and promoted the non-batch version of the ops to handle batches of matrices.
• Tracing/timeline support for distributed runtime (no GPU profiler yet).
• C API gives access to inferred shapes with TF_GraphGetTensorNumDims and TF_GraphGetTensorShape.
• Shape functions for core ops have moved to C++ via REGISTER_OP(...).SetShapeFn(...). Python shape inference RegisterShape calls use the C++ shape functions with common_shapes.call_cpp_shape_fn. A future release will remove RegisterShape from python.

Bug Fixes and Other Changes

• Documentation now includes operator overloads on Tensor and Variable.
• tensorflow.__git_version__ now allows users to identify the version of the code that TensorFlow was compiled with. We also have tensorflow.__git_compiler__ which identifies the compiler used to compile TensorFlow's core.
• Improved multi-threaded performance of batch_matmul.
• LSTMCell, BasicLSTMCell, and MultiRNNCell constructors now default to state_is_tuple=True. For a quick fix while transitioning to the new default, simply pass the argument state_is_tuple=False.
• DeviceFactory's AddDevices and CreateDevices functions now return a Status instead of void.
• Int32 elements of list(type) arguments are no longer placed in host memory by default. If necessary, a list(type) argument to a kernel can be placed in host memory using a HostMemory annotation.
• uniform_unit_scaling_initializer() no longer takes a full_shape arg, instead relying on the partition info passed to the initializer function when it's called.
• The NodeDef protocol message is now defined in its own file node_def.proto instead of graph.proto.
• ops.NoGradient was renamed ops.NotDifferentiable. ops.NoGradient will be removed soon.
• dot.h / DotGraph was removed (it was an early analysis tool prior to TensorBoard, no longer that useful). It remains in history should someone find the code useful.
• re2 / regexp.h was removed from being a public interface of TF. Should users need regular expressions, they should depend on the RE2 library directly rather than via TensorFlow.

Thanks to our Contributors

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

Abid K, @afshinrahimi, @AidanGG, Ajay Rao, Aki Sukegawa, Alex Rothberg, Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Thomas, @Appleholic, Bastiaan Quast, Ben Dilday, Bofu Chen, Brandon Amos, Bryon Gloden, Cissp®, @chanis, Chenyang Liu, Corey Wharton, Daeyun Shin, Daniel Julius Lasiman, Daniel Waterworth, Danijar Hafner, Darren Garvey, Denis Gorbachev, @DjangoPeng, Egor-Krivov, Elia Palme, Eric Platon, Fabrizio Milo, Gaetan Semet, Georg Nebehay, Gu Wang, Gustav Larsson, @haosdent, Harold Cooper, Hw-Zz, @ichuang, Igor Babuschkin, Igor Macedo Quintanilha, Ilya Edrenkin, @ironhead, Jakub Kolodziejczyk, Jennifer Guo, Jihun Choi, Jonas Rauber, Josh Bleecher Snyder, @jpangburn, Jules Gagnon-Marchand, Karen Brems, @kborer, Kirill Bobyrev, Laurent Mazare, Longqi Yang, Malith Yapa, Maniteja Nandana, Martin Englund, Matthias Winkelmann, @mecab, Mu-Ik Jeon, Nand Dalal, Niels Ole Salscheider, Nikhil Mishra, Park Jiin, Pieter De Rijk, @raix852, Ritwik Gupta, Sahil Sharma, Sangheum Hwang, @SergejsRk, Shinichiro Hamaji, Simon Denel, @Steve, @suiyuan2009, Tiago Jorge, Tijmen Tieleman, @tvn, @tyfkda, Wang Yang, Wei-Ting Kuo, Wenjian Huang, Yan Chen, @YenChenLin, Yuan (Terry) Tang, Yuncheng Li, Yunfeng Wang, Zack Polizzi, @zhongzyd, Ziming Dong, @perhapszzy

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

Release 0.10.0

Major Features and Improvements

• Added support for C++ shape inference
• Added graph-construction C API
• Major revision to the graph-construction C++ API
• Support makefile build for iOS
• Added Mac GPU support
• Full version of TF-Slim available as tf.contrib.slim
• Added k-Means clustering and WALS matrix factorization

Bug Fixes and Other Changes

• Allow gradient computation for scalar values.
• Performance improvements for gRPC
• Improved support for fp16
• New high-level ops in tf.contrib.{layers,metrics}
• New features for TensorBoard, such as shape display, exponential smoothing
• Faster and more stable Google Cloud Storage (GCS) filesystem support
• Support for zlib compression and decompression for TFRecordReader and TFRecordWriter
• Support for reading (animated) GIFs
• Improved support for SparseTensor
• Added support for more probability distributions (Dirichlet, Beta, Bernoulli, etc.)
• Added Python interfaces to reset resource containers.
• Many bugfixes and performance improvements
• Many documentation fixes

Thanks to our Contributors

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

Alex Rothberg, Andrew Royer, Austin Marshall, @BlackCoal, Bob Adolf, Brian Diesel, Charles-Emmanuel Dias, @chemelnucfin, Chris Lesniewski, Daeyun Shin, Daniel Rodriguez, Danijar Hafner, Darcy Liu, Kristinn R. Thórisson, Daniel Castro, Dmitry Savintsev, Kashif Rasul, Dylan Paiton, Emmanuel T. Odeke, Ernest Grzybowski, Gavin Sherry, Gideon Dresdner, Gregory King, Harold Cooper, @heinzbeinz, Henry Saputra, Huarong Huo, Huazuo Gao, Igor Babuschkin, Igor Macedo Quintanilha, Ivan Ukhov, James Fysh, Jan Wilken Dörrie, Jihun Choi, Johnny Lim, Jonathan Raiman, Justin Francis, @lilac, Li Yi, Marc Khoury, Marco Marchesi, Max Melnick, Micael Carvalho, @mikowals, Mostafa Gazar, Nico Galoppo, Nishant Agrawal, Petr Janda, Yuncheng Li, @raix852, Robert Rose, @Robin-des-Bois, Rohit Girdhar, Sam Abrahams, satok16, Sergey Kishchenko, Sharkd Tu, @shotat, Siddharth Agrawal, Simon Denel, @sono-bfio, SunYeop Lee, Thijs Vogels, @tobegit3hub, @Undo1, Wang Yang, Wenjian Huang, Yaroslav Bulatov, Yuan Tang, Yunfeng Wang, Ziming Dong

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

Release 0.9.0

Major Features and Improvements

• Python 3.5 support and binaries
• Added iOS support
• Added support for processing on GPUs on MacOS
• Added makefile for better cross-platform build support (C API only)
• fp16 support and improved complex128 support for many ops
• Higher level functionality in contrib.{layers,losses,metrics,learn}
• More features to Tensorboard
• Improved support for string embedding and sparse features
• The RNN api is finally "official" (see, e.g., tf.nn.dynamic_rnn, tf.nn.rnn, and the classes in tf.nn.rnn_cell).
• TensorBoard now has an Audio Dashboard, with associated audio summaries.

Bug Fixes and Other Changes

• Turned on CuDNN Autotune.
• Added support for using third-party Python optimization algorithms (contrib.opt).
• Google Cloud Storage filesystem support.
• HDF5 support
• Add support for 3d convolutions and pooling.
• Update gRPC release to 0.14.
• Eigen version upgrade.
• Switch to eigen thread pool
• tf.nn.moments() now accepts a shift argument. Shifting by a good estimate of the mean improves numerical stability. Also changes the behavior of the shift argument to tf.nn.sufficient_statistics().
• Performance improvements
• Many bugfixes
• Many documentation fixes
• TensorBoard fixes: graphs with only one data point, Nan values, reload button and auto-reload, tooltips in scalar charts, run filtering, stable colors
• Tensorboard graph visualizer now supports run metadata. Clicking on nodes while viewing a stats for a particular run will show runtime statistics, such as memory or compute usage. Unused nodes will be faded out.

Thanks to our Contributors

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

Aaron Schumacher, Aidan Dang, Akihiko ITOH, Aki Sukegawa, Arbit Chen, Aziz Alto, Danijar Hafner, Erik Erwitt, Fabrizio Milo, Felix Maximilian Möller, Henry Saputra, Sung Kim, Igor Babuschkin, Jan Zikes, Jeremy Barnes, Jesper Steen Møller, Johannes Mayer, Justin Harris, Kashif Rasul, Kevin Robinson, Loo Rong Jie, Lucas Moura, Łukasz Bieniasz-Krzywiec, Mario Cho, Maxim Grechkin, Michael Heilman, Mostafa Rahmani, Mourad Mourafiq, @ninotoshi, Orion Reblitz-Richardson, Yuncheng Li, @raoqiyu, Robert DiPietro, Sam Abrahams, Sebastian Raschka, Siddharth Agrawal, @snakecharmer1024, Stephen Roller, Sung Kim, SunYeop Lee, Thijs Vogels, Till Hoffmann, Victor Melo, Ville Kallioniemi, Waleed Abdulla, Wenjian Huang, Yaroslav Bulatov, Yeison Rodriguez, Yuan Tang, Yuxin Wu, @zhongzyd, Ziming Dong, Zohar Jackson

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

Release 0.8.0

Major Features and Improvements

• Added a distributed runtime using GRPC
• Move skflow to contrib/learn
• Better linear optimizer in contrib/linear_optimizer
• Random forest implementation in contrib/tensor_forest
• CTC loss and decoders in contrib/ctc
• Basic support for half data type
• Better support for loading user ops (see examples in contrib/)
• Allow use of (non-blocking) Eigen threadpool with TENSORFLOW_USE_EIGEN_THREADPOOL define
• Add an extension mechanism for adding network file system support
• TensorBoard displays metadata stats (running time, memory usage and device used) and tensor shapes

Bug Fixes and Other Changes

• Utility for inspecting checkpoints
• Basic tracing and timeline support
• Allow building against cuDNN 5 (not incl. RNN/LSTM support)
• Added instructions and binaries for ProtoBuf library with fast serialization and without 64MB limit
• Added special functions
• bool-strictness: Tensors have to be explicitly compared to None
• Shape strictness: all fed values must have a shape that is compatible with the tensor they are replacing
• Exposed tf.while_loop (deprecated control_flow_ops.While)
• run() now takes RunOptions and RunMetadata, which enable timing stats
• Fixed lots of potential overflow problems in op kernels
• Various performance improvements, especially for RNNs and convolutions
• Many bugfixes
• Nightly builds, tutorial tests, many test improvements
• New examples: transfer learning and deepdream ipython notebook
• Added tutorials, many documentation fixes.

Thanks to our Contributors

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

Abhinav Upadhyay, Aggelos Avgerinos, Alan Wu, Alexander G. de G. Matthews, Aleksandr Yahnev, @amchercashin, Andy Kitchen, Aurelien Geron, Awni Hannun, @BanditCat, Bas Veeling, Cameron Chen, @cg31, Cheng-Lung Sung, Christopher Bonnett, Dan Becker, Dan Van Boxel, Daniel Golden, Danijar Hafner, Danny Goodman, Dave Decker, David Dao, David Kretch, Dongjoon Hyun, Dustin Dorroh, @e-lin, Eurico Doirado, Erik Erwitt, Fabrizio Milo, @gaohuazuo, Iblis Lin, Igor Babuschkin, Isaac Hodes, Isaac Turner, Iván Vallés, J Yegerlehner, Jack Zhang, James Wexler, Jan Zikes, Jay Young, Jeff Hodges, @jmtatsch, Johnny Lim, Jonas Meinertz Hansen, Kanit Wongsuphasawat, Kashif Rasul, Ken Shirriff, Kenneth Mitchner, Kenta Yonekura, Konrad Magnusson, Konstantin Lopuhin, @lahwran, @lekaha, @liyongsea, Lucas Adams, @makseq, Mandeep Singh, @manipopopo, Mark Amery, Memo Akten, Michael Heilman, Michael Peteuil, Nathan Daly, Nicolas Fauchereau, @ninotoshi, Olav Nymoen, @panmari, @papelita1234, Pedro Lopes, Pranav Sailesh Mani, RJ Ryan, Rob Culliton, Robert DiPietro, @ronrest, Sam Abrahams, Sarath Shekkizhar, Scott Graham, Sebastian Raschka, Sung Kim, Surya Bhupatiraju, Syed Ahmed, Till Hoffmann, @timsl, @urimend, @vesnica, Vlad Frolov, Vlad Zagorodniy, Wei-Ting Kuo, Wenjian Huang, William Dmitri Breaden Madden, Wladimir Schmidt, Yuan Tang, Yuwen Yan, Yuxin Wu, Yuya Kusakabe, @zhongzyd, @znah.

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

Release 0.7.1

Bug Fixes and Other Changes

• Added gfile.Open and gfile.Copy, used by input_data.py.
• Fixed Saver bug when MakeDirs tried to create empty directory.
• GPU Pip wheels are built with cuda 7.5 and cudnn-v4, making them required for the binary releases. Lower versions of cuda/cudnn can be supported by installing from sources and setting the options during ./configure
• Fix dataset encoding example for Python3 (@danijar)
• Fix PIP installation by not packaging protobuf as part of wheel, require protobuf 3.0.0b2.
• Fix Mac pip installation of numpy by requiring pip >= 1.10.1.
• Improvements and fixes to Docker image.

Release 0.7.0

Major Features and Improvements

• Allow using any installed Cuda >= 7.0 and cuDNN >= R2, and add support for cuDNN R4
• Added a contrib/ directory for unsupported or experimental features, including higher level layers module
• Added an easy way to add and dynamically load user-defined ops
• Built out a good suite of tests, things should break less!
• Added MetaGraphDef which makes it easier to save graphs with metadata
• Added assignments for "Deep Learning with TensorFlow" udacity course

Bug Fixes and Other Changes

• Added a versioning framework for GraphDefs to ensure compatibility
• Enforced Python 3 compatibility
• Internal changes now show up as sensibly separated commits
• Open-sourced the doc generator
• Un-fork Eigen
• Simplified the BUILD files and cleaned up C++ headers
• TensorFlow can now be used as a submodule in another bazel build
• New ops (e.g., *fft, *_matrix_solve)
• Support for more data types in many ops
• Performance improvements
• Various bugfixes
• Documentation fixes and improvements

Breaking Changes to the API

• AdjustContrast kernel deprecated, new kernel AdjustContrastv2 takes and outputs float only. adjust_contrast now takes all data types.
• adjust_brightness's delta argument is now always assumed to be in [0,1] (as is the norm for images in floating point formats), independent of the data type of the input image.
• The image processing ops do not take min and max inputs any more, casting safety is handled by saturate_cast, which makes sure over- and underflows are handled before casting to data types with smaller ranges.
• For C++ API users: IsLegacyScalar and IsLegacyVector are now gone from TensorShapeUtils since TensorFlow is scalar strict within Google (for example, the shape argument to tf.reshape can't be a scalar anymore). The open source release was already scalar strict, so outside Google IsScalar and IsVector are exact replacements.
• The following files are being removed from tensorflow/core/public/:
  • env.h -> ../platform/env.h
  • status.h -> ../lib/core/status.h
  • tensor.h -> ../framework/tensor.h
  • tensor_shape.h -> ../framework/tensor_shape.h
  • partial_tensor_shape.h -> ../framework/partial_tensor_shape.h
  • tensorflow_server.h deleted
• For C++ API users: TensorShape::ShortDebugString has been renamed to DebugString, and the previous DebugString behavior is gone (it was needlessly verbose and produced a confusing empty string for scalars).
• GraphOptions.skip_common_subexpression_elimination has been removed. All graph optimizer options are now specified via GraphOptions.OptimizerOptions.
• ASSERT_OK / EXPECT_OK macros conflicted with external projects, so they were renamed TF_ASSERT_OK, TF_EXPECT_OK. The existing macros are currently maintained for short-term compatibility but will be removed.
• The non-public nn.rnn and the various nn.seq2seq methods now return just the final state instead of the list of all states.
• tf.scatter_update now no longer guarantees that lexicographically largest index be used for update when duplicate entries exist.
• tf.image.random_crop(image, [height, width]) is now tf.random_crop(image, [height, width, depth]), and tf.random_crop works for any rank (not just 3-D images). The C++ RandomCrop op has been replaced with pure Python.
• Renamed tf.test.GetTempDir and tf.test.IsBuiltWithCuda to tf.test.get_temp_dir and tf.test.is_built_with_cuda for PEP-8 compatibility.
• parse_example's interface has changed, the old interface is accessible in legacy_parse_example (same for related functions).
• New Variables are not added to the same collection several times even if a list with duplicates is passed to the constructor.
• The Python API will now properly set the list member of AttrValue in constructed GraphDef messages for empty lists. The serialization of some graphs will change, but the change is both forwards and backwards compatible. It will break tests that compare a generated GraphDef to a golden serialized GraphDef (which is discouraged).

Thanks to our Contributors

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

Akiomi Kamakura, Alex Vig, Alexander Rosenberg Johansen, Andre Cruz, Arun Ahuja, Bart Coppens, Bernardo Pires, Carl Vondrick, Cesar Salgado, Chen Yu, Christian Jauvin, Damien Aymeric, Dan Vanderkam, Denny Britz, Dongjoon Hyun, Eren Güven, Erik Erwitt, Fabrizio Milo, G. Hussain Chinoy, Jim Fleming, Joao Felipe Santos, Jonas Meinertz Hansen, Joshi Rekha, Julian Viereck, Keiji Ariyama, Kenton Lee, Krishna Sankar, Kristina Chodorow, Linchao Zhu, Lukas Krecan, Mark Borgerding, Mark Daoust, Moussa Taifi, Nathan Howell, Naveen Sundar Govindarajulu, Nick Sweeting, Niklas Riekenbrauck, Olivier Grisel, Patrick Christ, Povilas Liubauskas, Rainer Wasserfuhr, Romain Thouvenin, Sagan Bolliger, Sam Abrahams, Taehoon Kim, Timothy J Laurent, Vlad Zavidovych, Yangqing Jia, Yi-Lin Juang, Yuxin Wu, Zachary Lipton, Zero Chen, Alan Wu, @brchiu, @emmjaykay, @jalammar, @Mandar-Shinde, @nsipplswezey, @ninotoshi, @panmari, @prolearner and @rizzomichaelg.

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

Release 0.6.0

Major Features and Improvements

• Python 3.3+ support via changes to python codebase and ability to specify python version via ./configure.

• Some improvements to GPU performance and memory usage: convnet benchmarks roughly equivalent with native cudnn v2 performance. Improvements mostly due to moving to 32-bit indices, faster shuffling kernels. More improvements to come in later releases.

Bug Fixes

• Lots of fixes to documentation and tutorials, many contributed by the public.

• 271 closed issues on github issues.

Backwards-Incompatible Changes

• tf.nn.fixed_unigram_candidate_sampler changed its default 'distortion' attribute from 0.0 to 1.0. This was a bug in the original release that is now fixed.

Release 0.5.0

Initial release of TensorFlow.