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TensorFlow is an open source machine learning framework for everyone.
Last release 2 days ago
22 Sep 2026
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gaps range from 8 days to 7 months
Nearly every release is documented
notes for 59 of the last 60 stable releases
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10 years old
139 releases · first in 2016
Fixing keras on Cloud TPUs. No new binaries will be built for Windows.
tf.keras:
tf.contrib.distributions is now deprecated and will be removed by the end of 2018.
tf.lite runtime now supports complex64.tf.data.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.tf.contrib.distributions to Tensorflow Probability (TFP). tf.contrib.distributions is now deprecated and will be removed by the end of 2018.tf.debugging, tf.dtypes, tf.image, tf.io, tf.linalg, tf.manip, tf.math, tf.quantization, tf.stringstf.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.drop_remainder argument to tf.data.Dataset.batch() and tf.data.Dataset.padded_batch(), deprecating tf.contrib.data.batch_and_drop_remainder() and tf.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.DNNClassifier, DNNRegressor, and DNNEstimator.synchronization and aggregation args to get_variable(). These args will be used for distributed variables.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).tf.train.MonitoredTrainingSession().tf.contrib.rnn.tf.random_gamma with respect to the alpha parameter.tf.igamma(a, x) and tf.igammac(a, x) with respect to a.tf.spectral.idct(type=2|3).TimeDistributed.WALSComputePartialLhsAndRhsOp.tf.image namespace: tf.image.extract_image_patchestf.debugging namespace: tf.debugging.check_numerics, tf.debugging.is_finite, tf.debugging.is_inf, tf.debugging.is_nan.tf.dtypes namespace: tf.dtypes.as_string.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`.tf.linalg.cross, tf.linalg.tensor_diag (corresponds to tf.diag), tf.linalg.tensor_diag_part (corresponds to tf.diag_part).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.tiletf.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.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.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).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, karl@kubx.ca, 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
One column per quarter.
tfe.Network is deprecated. Please inherit from tf.keras.Model.
tf.keras: New Keras-based get started and programmers guide page.tf.keras to the Keras 2.1.6 API.tf.keras.layers.CuDNNGRU and tf.keras.layers.CuDNNLSTM layers. Try it.toco, tflite_convert) is once again included in the standard pip installation.variable_scope('', ...) by variable_scope(tf.get_variable_scope(), ...).tfe.Network is deprecated. Please inherit from tf.keras.Model.
Layered variable names have changed in the following conditions:
tf.keras.layers with custom variable scopes.tf.layers in a subclassed tf.keras.Model class. See here for more detailstf.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.DatasetBase::DebugString() is now const.DatasetBase::MakeIterator() has been renamed to DatasetBase::MakeIteratorInternal().IteratorBase::Initialize() method was added to support raising errors during iterator construction.Eager Execution:
tf.GradientTape.stop_recording.tf.keras:
tf.keras.Model.save_weights now saves in TensorFlow format by default.tf.keras.Model training/eval methods.TensorFlow Debugger (tfdbg)
tf.contrib:
tf.contrib.framework.zero_initializer supports ResourceVariable.Other:
MakeIterator to enable propagating error status.tf.reduce_prod gradient for complex dtypes.nn.embedding_lookup_sparse. This helps to reduce RPC calls for looking up the embeddings when there are repeated ids in the batch.tf.gradients() from backpropagating through integer tensors.tensorflow.linalg.tf.train.Checkpoint for reading/writing object-based checkpoints.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
Can now pass tf.contrib.distribute.MirroredStrategy() to tf.estimator.RunConfig() to run an Estimator model on multiple GPUs on one machine.
tf.contrib.distribute.MirroredStrategy() to tf.estimator.RunConfig() to run an Estimator model on multiple GPUs on one machine.tf.contrib.data.prefetch_to_device(), which supports prefetching to GPU memory.tf.contrib.bayesflow is moving out to it's own repo.tf.contrib.{proto,rpc} to allow generic proto parsing and RPC communication<sup>1</sup>.tf.data:
tf.contrib.data.prefetch_to_device, which enables prefetching dataset elements to GPU memory.tf.contrib.data.AUTOTUNE, which allows the tf.data runtime to automatically tune the prefetch buffer sizes based on your system and environment.tf.contrib.data.make_csv_dataset for building datasets of CSV files.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.with tf.device(“/gpu:0”)) (Fixes #14133)tf.GradientTape has moved out of contrib.tf.keras:
image/random_brightness, sequence/TimeseriesGenerator, and text/hashing_trick.tf.contrib:
tf.contrib.layers.recompute_grad works for explicit gradient checkpointing on TPU.tf.contrib.framework.argsort.DNNBoostedTreeCombinedEstimator to work with core versions of feature columns and losses.tf.contrib.image.sparse_image_warp, tf.contrib.image.dense_image_warp, and tf.contrib.image.interpolate_spline.tf.contrib.opt.MultitaskOptimizerWrapper where types of tensors were mismatched.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.tf.distributions.Distribution.tf.scatter_min and tf.scatter_maxfloat64 support for Conv2d, Conv2dBackpropInput, and Conv2dBackpropFilter.float64 support for AvgPool/AvgPoolGrad.tf.image.psnr, tf.image.ssim, tf.image.ssim_multiscale, tf.image.image_gradients, tf.image.sobel_edges.<a name="rpc-issue"><sup>1</sup></a> 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.
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
Fixes the following potential security vulnerabilities:
Deprecate tf.contrib.learn. Please check contrib/learn/README.md for instructions on how to convert existing code.
tf.enable_eager_execution().tf.contrib.quantize package.tf.custom_gradient.Dataset with new tf.contrib.data.SqlDataset.tf.contrib.framework.CriticalSection.tf.regex_replace.tf.contrib.data.bucket_by_sequence_lengthtf.contrib.tensorrt that enables native TensorRT in TensorFlowMaxPoolGradGrad support for XLAtf.data:
tf.data.Dataset
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).num_parallel_reads argument to tf.data.TFRecordDataset.tf.contrib:
tf.contrib.bayesflow.halton_sequence now supports randomization.tf.contrib.all_reduce.effective_sample_size to tf.contrib.bayesflow.mcmc_diagnostics.potential_scale_reduction to tf.contrib.bayesflow.mcmc_diagnostics.BatchNormalization, Kumaraswamy bijectors.tf.contrib.learn. Please check contrib/learn/README.md for instructions on how to convert existing code.tf.contrib.data
tf.contrib.data.Dataset, tf.contrib.data.Iterator, tf.contrib.data.FixedLengthRecordDataset, tf.contrib.data.TextLineDataset, and tf.contrib.data.TFRecordDataset classes.bucket_by_sequence_length, sliding_window_batch, and make_batched_features_datasettf.contrib.ndlstm. You can find it externally at https://github.com/tmbarchive/tfndlstm.tf.contrib.bayesflow to its own repo: tfpTPUClusterResolver with GKE's integration for Cloud TPUs.MomentumOptimizer lambda.tfp.layers boilerplate via programmable docstrings.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.initialized_value and initial_value behaviors for ResourceVariables created from VariableDef protos.float16 dtype in tf.linalg.*.tf.estimator.export.TensorServingInputReceiver that allows tf.estimator.Estimator.export_savedmodel to pass raw tensors to model functions.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, Tim H, tkunic, Tod, vihanjain, Yan Facai (颜发才), Yin Li, Yong Tang, Yuan (Terry) Tang, Yukun Chen, Yusuke Yamada
Prebuilt binaries are now built against CUDA 9.0 and cuDNN 7.
tf.estimator.{FinalExporter,LatestExporter} now export stripped SavedModels. This improves forward compatibility of the SavedModel.resize_images.align_corners parameter.FlushCaches() method to the FileSystem interface, with an implementation for GcsFileSystem.tf.contrib.distributions.Kumaraswamy.RetryingFileSystem::FlushCaches() calls the base FileSystem's FlushCaches().tf.contrib.distributions.Autoregressive.tf.matmul are bfloat16, it returns bfloat16, instead of float32.tf.contrib.image.connected_components.tf.contrib.framework.CriticalSection that allows atomic variable access.pt and eval commands, allow writing tensor values to filesystem as numpy files.layers_dense_variational_impl.py to layers_dense_variational.py.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.
The tensorboard command or module may appear to be missing after certain
upgrade flows. This is due to pip package conflicts as a result of changing
the TensorBoard package name. See the TensorBoard 1.6.0 release notes for a fix.
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
Fixes a potential security vulnerability where on-the-fly changes to the dtype of a tensor reference may lead to undefined behavior.
Prebuilt binaries are now built against CUDA 9 and cuDNN 7.
complex64 support to XLA compiler.bfloat support is now added to XLA infrastructure.ClusterSpec propagation work with XLA devices.tf.contrib:
tf.contrib.distributions:
tf.contrib.distributions.Autoregressive.tf.contrib.distributions QuadratureCompound classes support batchtf.contrib.distributions.RelaxedOneHotCategorical dtype from arguments.tf.contrib.distributions quadrature family parameterized by
quadrature_grid_and_prob vs quadrature_degree.auto_correlation added to tf.contrib.distributionstf.contrib.bayesflow.layers, a collection of probabilistic (neural) layers.tf.contrib.bayesflow.halton_sequence.tf.contrib.data.make_saveable_from_iterator.tf.contrib.data.shuffle_and_repeat.tf.contrib.data.scan().tf.contrib.distributions.bijectors:
tf.contrib.distributions.bijectors.MaskedAutoregressiveFlow.tf.contrib.distributions.bijectors.Permute.tf.contrib.distributions.bijectors.Gumbel.tf.contrib.distributions.bijectors.Reshape.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.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.tf.flags with absl.flags.CUBLAS_TENSOR_OP_MATH in fp16 GEMMEstimators save checkpoints.tf2xla bridge.SpaceToDepth and DepthToSpace.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).tf.contrib.distributions docstring examples to use tfd alias
rather than ds, bs.tf.distributions.bijectors.Bijector.tf.assert_equal no longer raises ValueError. It now raises
InvalidArgumentError, as documented.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.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.log_det_jacobian to match log_prob in
TransformedDistribution.import_meta_graph's handling of partitioned variables whentf.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.DenseFlipout probabilistic layer.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.DenseVariational as simpler template for other probabilistic
layers.tf.data now supports tf.SparseTensor components in dataset elements.Tensors.SparseSegmentReduction ops to have missing segment IDs.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.RevBlock, a memory-efficient implementation of reversible residual layers.cross_entropy and kl_divergence to tf.distributions.Distribution.tf.nn.softmax_cross_entropy_with_logits_v2 which enables backprop
w.r.t. the labels.ptxas to compile generated PTX.BufferAssignment's protocol buffer dump is now deterministic.DynamicStitch.quantile to tf.distributions.TransformedDistribution.NCHW_VECT_C support for tf.depth_to_space on GPU.NCHW_VECT_C support for tf.space_to_depth on GPU.SqueezeDims attribute to Axis in C++ API for Squeeze op.Stream::BlockHostUntilDone now returns Status rather than bool.stochastic to common and remove
stochastic.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.
LinearClassifier fix for CloudML Engine.
LinearClassifier fix for CloudML Engine.tf.keras is now part of the core TensorFlow API.
tf.keras is now part of the core TensorFlow API.tf.data is now part of
the core TensorFlow API.
tf.contrib.data API, see the
README.Dataset.from_generator() (for building an input
pipeline from a Python generator), and the Dataset.apply() method for
applying custom transformation functions.tf.contrib.data.batch_and_drop_remainder() and
tf.contrib.data.sloppy_interleave().train_and_evaluate for simple distributed Estimator training.tf.spectral.dct for computing the DCT-II.tf.contrib.signal
(with GPU and gradient support).import tensorflow for Windows DLL issues.tf.depth_to_space on GPU.eval command to allow evaluation of arbitrary Python/numpy expressions
in tfdbg command-line interface. See
Debugging TensorFlow Programs
for more details.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.contrib.distributions.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.tf.contrib.bayesflow.hmc.tf.contrib.distributions.MixtureSameFamily.Dataset.shuffle() always reshuffles after each iteration by default.tf.contrib.bayesflow.metropolis_hastings.log_rate parameter to tf.contrib.distributions.Poisson.tf.contrib.distributions.bijector API to handle some non-injective
transforms.Tensor<Integer>) for improved type-safety
(courtesy @andrewcmyers).tf.contrib) on Linux
and OS Xtf.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.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!tf.contrib.distributions.Affine incorrectly computing log-det-jacobian.tf.random_gamma incorrectly handling non-batch, scalar draws.tf.sysconfig.get_lib()).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.tf.contrib.data.rejection_resample() function has been
changed. It now returns a function that can be used as an argument to
Dataset.apply().tf.contrib.data.Iterator.from_dataset() method. Use
Dataset.make_initializable_iterator() instead.tf.contrib.data.Iterator.dispose_op().Dataset.from_generator() does not support Unicode strings.
You must convert any strings to bytes objects before yielding them from
the generator.This release contains contributions from many people at Google, as well as:
4d55397500, Abdullah Alrasheed, abenmao, Adam Salvail, Aditya Dhulipala, Ag Ramesh, Akimasa Kimura, Alan Du, Alan Yee, Alexander, Amit Kushwaha, Amy, Andrei Costinescu, Andrei Nigmatulin, Andrew Erlichson, Andrew Myers, Andrew Stepanov, Androbin, AngryPowman, Anish Shah, Anton Daitche, Artsiom Chapialiou, asdf2014, Aseem Raj Baranwal, Ash Hall, Bart Kiers, Batchu Venkat Vishal, ben, Ben Barsdell, Bill Piel, Carl Thomé, Catalin Voss, Changming Sun, Chengzhi Chen, Chi Zeng, Chris Antaki, Chris Donahue, Chris Oelmueller, Chris Tava, Clayne Robison, Codrut, Courtial Florian, Dalmo Cirne, Dan J, Darren Garvey, David Kristoffersson, David Norman, David RöThlisberger, DavidNorman, Dhruv, DimanNe, Dorokhov, Duncan Mac-Vicar P, EdwardDixon, EMCP, error.d, FAIJUL, Fan Xia, Francois Xavier, Fred Reiss, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Guenther Schmuelling, Guo Yejun (郭叶军), Hans Gaiser, HectorSVC, Hyungsuk Yoon, James Pruegsanusak, Jay Young, Jean Wanka, Jeff Carpenter, Jeremy Rutman, Jeroen BéDorf, Jett Jones, Jimmy Jia, jinghuangintel, jinze1994, JKurland, Joel Hestness, joetoth, John B Nelson, John Impallomeni, John Lawson, Jonas, Jonathan Dekhtiar, joshkyh, Jun Luan, Jun Mei, Kai Sasaki, Karl Lessard, karl@kubx.ca, Kb Sriram, Kenichi Ueno, Kevin Slagle, Kongsea, Lakshay Garg, lhlmgr, Lin Min, liu.guangcong, Loki Der Quaeler, Louie Helm, lucasmoura, Luke Iwanski, Lyndon White, Mahmoud Abuzaina, Marcel Puyat, Mark Aaron Shirley, Michele Colombo, MtDersvan, Namrata-Ibm, Nathan Luehr, Naurril, Nayana Thorat, Nicolas Lopez, Niranjan Hasabnis, Nolan Liu, Nouce, Oliver Hennigh, osdamv, Patrik Erdes, Patryk Chrabaszcz, Pavel Christof, Penghao Cen, postBG, Qingqing Cao, Qingying Chen, qjivy, Raphael, Rasmi, raymondxyang, Renze Yu, resec, Roffel, Ruben Vereecken, Ryohei Kuroki, sandipmgiri, Santiago Castro, Scott Kirkland, Sean Vig, Sebastian Raschka, Sebastian Weiss, Sergey Kolesnikov, Sergii Khomenko, Shahid, Shivam Kotwalia, Stuart Berg, Sumit Gouthaman, superzerg, Sven Mayer, tetris, Ti Zhou, Tiago Freitas Pereira, Tian Jin, Tomoaki Oiki, Vaibhav Sood, vfdev, Vivek Rane, Vladimir Moskva, wangqr, Weber Xie, Will Frey, Yan Facai (颜发才), yanivbl6, Yaroslav Bulatov, Yixing Lao, Yong Tang, youkaichao, Yuan (Terry) Tang, Yue Zhang, Yuxin Wu, Ziming Dong, ZxYuan, 黄璞
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
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 chang…
See also TensorBoard 0.1.4 release notes.
DNNClassifierDNNRegressorLinearClassifierLinearRegressorDNNLinearCombinedClassifierDNNLinearCombinedRegressor.import tensorflow now goes much faster.tf.gather.constant_values keyword argument to tf.pad.Dataset.interleave transformation.ConcatenateDataset to concatenate two datasets.Dataset.list_files API.-s flag to command print_tensor or pt.print_feed or pf command and clickable links in the curses UI.run -p command.tf.distributions.tf.where and tf.nn.top_k.tf.contrib.seq2seq.tf.contrib.signal, a library for signal processing primitives.tf.contrib.resampler, containing CPU and GPU ops for differentiable resampling of images.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.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.tf.contrib.metrics.{streaming_covariance,streaming_pearson_correlation} modified to return nan when they have seen less or equal to 1 unit of weight.strides and begin dtype mismatch when slicing using int64 Tensor index in python.saved_model.utils now support SparseTensors transparently.saver.restore.tf.spectral.rfft & tf.spectral.irfft.tf.layers.con2d when setting use_bias=True by 2x by using nn.bias_add.tf.summary ops to allow controlling the tab name used in Tensorboard for organizing summaries.tf.Session.make_callable.This release contains contributions from many people at Google, as well as:
4F2E4A2E, Adriano Carmezim, Adrià Arrufat, Alan Yee, Alex Lattas, Alex Rothberg, Alexandr Baranezky, Ali Siddiqui, Andreas Solleder, Andrei Costinescu, Andrew Hundt, Androbin, Andy Kernahan, Anish Shah, Anthony Platanios, Arvinds-Ds, b1rd, Baptiste Arnaud, Ben Mabey, Benedikt Linse, Beomsu Kim, Bo Wang, Boyuan Deng, Brett Koonce, Bruno Rosa, Carl Thomé, Changming Sun, Chase Roberts, Chirag Bhatia, Chris Antaki, Chris Hoyean Song, Chris Tava, Christos Nikolaou, Croath Liu, cxx, Czxck001, Daniel Ylitalo, Danny Goodman, Darren Garvey, David Brailovsky, David Norman, DavidNorman, davidpham87, ddurham2, Dhruv, DimanNe, Drew Hintz, Dustin Tran, Earthson Lu, ethiraj, Fabian Winnen, Fei Sun, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Gautam, Guenther Schmuelling, Gyu-Ho Lee, Hauke Brammer, horance, Humanity123, J Alammar, Jayeol Chun, Jeroen BéDorf, Jianfei Wang, jiefangxuanyan, Jing Jun Yin, Joan Puigcerver, Joel Hestness, Johannes Mayer, John Lawson, Johnson145, Jon Malmaud, Jonathan Alvarez-Gutierrez, Juang, Yi-Lin, Julian Viereck, Kaarthik Sivashanmugam, Karl Lessard, karl@kubx.ca, Kevin Carbone, Kevin Van Der Burgt, Kongsea, ksellesk, lanhin, Lef Ioannidis, Liangliang He, Louis Tiao, Luke Iwanski, LáSzló Csomor, magixsno, Mahmoud Abuzaina, Marcel Hlopko, Mark Neumann, Maxwell Paul Brickner, mdfaijul, MichaëL Defferrard, Michał JastrzęBski, Michele Colombo, Mike Brodie, Mosnoi Ion, mouradmourafiq, myPrecious, Nayana Thorat, Neeraj Kashyap, Nelson Liu, Niranjan Hasabnis, Olivier Moindrot, orome, Pankaj Gupta, Paul Van Eck, peeyush18, Peng Yu, Pierre, preciousdp11, qjivy, Raingo, raoqiyu, ribx, Richard S. Imaoka, Rishabh Patel, Robert Walecki, Rockford Wei, Ryan Kung, Sahil Dua, Sandip Giri, Sayed Hadi Hashemi, sgt101, Shitian Ni, Shuolongbj, Siim PõDer, Simon Perkins, sj6077, SOLARIS, Spotlight0xff, Steffen Eberbach, Stephen Fox, superryanguo, Sven Mayer, Tapan Prakash, Tiago Morais Morgado, Till Hoffmann, Tj Rana, Vadim Markovtsev, vhasanov, Wei Wu, windead, Yan (Asta) Li, Yan Chen, Yann Henon, Yi Wang, Yong Tang, yorkie, Yuan (Terry) Tang, Yuxin Wu, zhengjiajin, zhongzyd, 黄璞
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
Updating markdown version required to >= 2.6.8.
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 versio…
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 ibverbs-based RDMA support to contrib (courtesy @junshi15 from Yahoo).
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.
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.
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.
org.tensorflow.contrib.android.TensorFlowInferenceInterface now throws exceptions where possible and has simplified method signatures.tf.contrib.util.create_example.tf.contrib.image.tf.contrib.stateless for random ops with custom seed control.tf.contrib.kernel_methods module with Ops and estimators for primal
(explicit) kernel methods in TensorFlow.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.categorical_column_with_vocabulary_file.reduction arg to losses.tf.placeholder can represent scalar shapes and partially known.tf.summary.text for outputting text to TensorBoard.tf.string_to_number now supports int64 and float64 outputs.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.
Bring tf.estimator.* into the API. Non-deprecated functionality from tf.contrib.learn.Estimator is moved to tf.estimator.Estimator with cosmetic chang…
tf.spectral module. Moved existing FFT ops to tf.spectral while
keeping an alias in the old location (tf.*).tf.spectral.tf.bincount function.RecordInput.tf.contrib.image.compose_transforms function.tf.estimator.* into the API. Non-deprecated functionality from tf.contrib.learn.Estimator is moved to tf.estimator.Estimator with cosmetic changes.print_source / ps)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.reuse=True.pmf, pdf, log_pmf, log_pdf.bayesflow.special_math to distributions.tf.contrib.tensor_forest.python.tensor_forest.RandomForestDeviceAssigner removed.tf.contrib.distributions.MultivariateNormalFull replaced by tf.contrib.distributions.MultivariateNormalTriL.tf.contrib.distributions.MultivariateNormalCholesky replaced by tf.contrib.distributions.MultivariateNormalTriLtf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev replaced
by tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScaletf.contrib.distributions.MultivariateNormalDiag arguments changed from mu, diag_stddev to log, scale_diag.tf.contrib.distributions.MultivariateNormalDiagPlusVDVT removed.tf.contrib.distributions.MultivariateNormalDiagPlusLowRank added.tf.contrib.layers.sparse_column_with_keys.tf.set_random_seed(0) to be deterministic for all ops.tf.matching_files.LogMessage now includes a timestamp as beginning of a message.StagingArea.sparse_matmul_op reenabled for Android builds.TF_GraphImportGraphDefWithReturnOutputs())tf.while_loops.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.
Change GraphConstructor to not increase the version when importing, but instead take the min of all versions.
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.tf.mul, tf.sub and tf.neg are deprecated in favor of tf.multiply, tf.subtract and tf.negative.
pip install tensorflow command.To help you upgrade your existing TensorFlow Python code to match the API changes below, we have prepared a conversion script.
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(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.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 axistf.argmin: dimension becomes axistf.count_nonzero: reduction_indices becomes axistf.expand_dims: dim becomes axistf.reduce_all: reduction_indices becomes axistf.reduce_any: reduction_indices becomes axistf.reduce_join: reduction_indices becomes axistf.reduce_logsumexp: reduction_indices becomes axistf.reduce_max: reduction_indices becomes axistf.reduce_mean: reduction_indices becomes axistf.reduce_min: reduction_indices becomes axistf.reduce_prod: reduction_indices becomes axistf.reduce_sum: reduction_indices becomes axistf.reverse_sequence: batch_dim becomes batch_axis, seq_dim becomes seq_axistf.sparse_concat: concat_dim becomes axistf.sparse_reduce_sum: reduction_axes becomes axistf.sparse_reduce_sum_sparse: reduction_axes becomes axistf.sparse_split: split_dim becomes axistf.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 inversiontf.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.var_scope property renamed to .variable_scopetf.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._ref dtypes from the python API.{softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits to be (labels, predictions), and force use of named args.parallel_stack.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.index_to_string_table which returns a lookup table that maps indices to
strings.string_to_index_table, which returns a lookup table that matches strings
to indices.ParallelForWithWorkerId function.string_to_index_table, which returns a lookup table that matches strings
to indices.contrib/session_bundle.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.recursive_create_dir.contrib/android/cmaketf.saved_model.reduce_join to treat reduction_indices in the same way as other reduce_ ops.TensorForestEstimator to contrib/tensor_forest.tf.divide now honors the name field.StagingArea and new ops: stage and unstage.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.
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