tf.Session.make_callable(), which provides a lower overhead means of running a similar step multiple times.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.
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.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.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.SparseTensor constructor changes its name to dense_shape between Tensorflow 0.12 and Tensorflow 1.0.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.
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.matrix_solve_ls and self_adjoint_eig.tf.contrib.integrate.odeint.tf.contrib.labeled_tensor.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.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.TF_Port to TF_Output in the C API.bus_id==0 is used where previously BUS_ANY was used.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.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.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.TF_Port to TF_Output.TF_Tensor objects provided to TF_Run, TF_SessionRun, TF_SetAttrTensor etc.tf.image.per_image_whitening() to tf.image.per_image_standardization()tf.summary submodule.histogram_summary, audio_summary, scalar_summary, image_summary, merge_summary, and merge_all_summaries.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()lgamma function.tf.sqrt handling of negative arguments.batch_matmul on multi-core CPUs.matrix_set_diag, matrix_diag_part and their gradients to work for rectangular matrices.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.
tensorflow/contrib/cudnn_rnn.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]).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.core/util/tensor_bundle module: a module to efficiently serialize/deserialize tensors to disk. Will be used in TF's new checkpoint format.self_adjoint_eig or self_adjoint_eigvals.batch_* methods for most linear algebra and FFT ops and promoted the non-batch version of the ops to handle batches of matrices.TF_GraphGetTensorNumDims and TF_GraphGetTensorShape.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.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.batch_matmul.state_is_tuple=True. For a quick fix while transitioning to the new default, simply pass the argument state_is_tuple=False.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.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.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.
tf.contrib.slimThis 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.
tf.nn.dynamic_rnn, tf.nn.rnn, and the classes in tf.nn.rnn_cell).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().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.
contrib/learncontrib/linear_optimizercontrib/tensor_forestcontrib/ctchalf data typecontrib/)TENSORFLOW_USE_EIGEN_THREADPOOL definebool-strictness: Tensors have to be explicitly compared to Nonetf.while_loop (deprecated control_flow_ops.While)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.
contrib/ directory for unsupported or experimental features, including higher level layers moduleMetaGraphDef which makes it easier to save graphs with metadataGraphDefs to ensure compatibilityBUILD files and cleaned up C++ headers*fft, *_matrix_solve)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.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.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.tensorflow/core/public/:env.h -> ../platform/env.hstatus.h -> ../lib/core/status.htensor.h -> ../framework/tensor.htensor_shape.h -> ../framework/tensor_shape.hpartial_tensor_shape.h -> ../framework/partial_tensor_shape.htensorflow_server.h deletedTensorShape::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.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.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).Variables are not added to the same collection several times even if a list with duplicates is passed to the constructor.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).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.
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.
Lots of fixes to documentation and tutorials, many contributed by the public.
271 closed issues on github issues.
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.Initial release of TensorFlow.