pypi intel-tensorflow 1.13.1
TensorFlow 1.13.1
on Python PyPI

latest releases: 2.14.0, 2.14.dev202334, 2.13.0...
5 years ago

Release 1.13.1

Major Features and Improvements

  • TensorFlow Lite has moved from contrib to core. This means that Python modules are under tf.lite and source code is now under tensorflow/lite rather than tensorflow/contrib/lite.
  • TensorFlow GPU binaries are now built against CUDA 10 and TensorRT 5.0.
  • Support for Python3.7 on all operating systems.
  • Moved NCCL to core.

Behavioral changes

  • Disallow conversion of python floating types to uint32/64 (matching behavior of other integer types) in tf.constant.
  • Make the gain argument of convolutional orthogonal initializers (convolutional_delta_orthogonal, convolutional_orthogonal_1D, convolutional_orthogonal_2D, convolutional_orthogonal_3D) have consistent behavior with the tf.initializers.orthogonal initializer, i.e. scale the output l2-norm by gain and NOT by sqrt(gain). (Note that these functions are currently in tf.contrib which is not guaranteed backward compatible).

Bug Fixes and Other Changes

  • Documentation
    • Update the doc with the details about the rounding mode used in quantize_and_dequantize_v2.
    • Clarify that tensorflow::port::InitMain() should be called before using the TensorFlow library. Programs failing to do this are not portable to all platforms.
  • Deprecations and Symbol renames.
    • Removing deprecations for the following endpoints: tf.acos, tf.acosh, tf.add, tf.as_string, tf.asin, tf.asinh, tf.atan, tf.atan2, tf.atanh, tf.cos, tf.cosh, tf.equal, tf.exp, tf.floor, tf.greater, tf.greater_equal, tf.less, tf.less_equal, tf.log, tf.logp1, tf.logical_and, tf.logical_not, tf.logical_or, tf.maximum, tf.minimum, tf.not_equal, tf.sin, tf.sinh, tf.tan
    • Deprecate tf.data.Dataset.shard.
    • Deprecate saved_model.loader.load which is replaced by saved_model.load and saved_model.main_op, which will be replaced by saved_model.main_op in V2.
    • Deprecate tf.QUANTIZED_DTYPES. The official new symbol is tf.dtypes.QUANTIZED_DTYPES.
    • Update sklearn imports for deprecated packages.
    • Deprecate Variable.count_up_to and tf.count_up_to in favor of Dataset.range.
    • Export confusion_matrix op as tf.math.confusion_matrix instead of tf.train.confusion_matrix.
    • Add tf.dtypes. endpoint for every constant in dtypes.py; moving endpoints in versions.py to corresponding endpoints in tf.sysconfig. and tf.version.; moving all constants under tf.saved_model submodules to tf.saved_model module. New endpoints are added in V1 and V2 but existing endpoint removals are only applied in V2.
    • Deprecates behavior where device assignment overrides collocation constraints inside a collocation context manager.
  • Keras & Python API
    • Add to Keras functionality analogous to tf.register_tensor_conversion_function.
    • Subclassed Keras models can now be saved through tf.contrib.saved_model.save_keras_model.
    • LinearOperator.matmul now returns a new LinearOperator.
  • New ops and improved op functionality
    • Add a Nearest Neighbor Resize op.
    • Add an ignore_unknown argument to parse_values which suppresses ValueError for unknown hyperparameter types. Such * Add tf.linalg.matvec convenience function.
    • tf.einsum()raises ValueError for unsupported equations like "ii->".
    • Add DCT-I and IDCT-I in tf.signal.dct and tf.signal.idct.
    • Add LU decomposition op.
    • Add quantile loss to gradient boosted trees in estimator.
    • Add round_mode to QuantizeAndDequantizeV2 op to select rounding algorithm.
    • Add unicode_encode, unicode_decode, unicode_decode_with_offsets, unicode_split, unicode_split_with_offset, and unicode_transcode ops. Amongst other things, this Op adds the ability to encode, decode, and transcode a variety of input text encoding formats into the main Unicode encodings (UTF-8, UTF-16-BE, UTF-32-BE)
    • Add "unit" attribute to the substr op, which allows obtaining the substring of a string containing unicode characters.
    • Broadcasting support for Ragged Tensors.
    • SpaceToDepth supports uint8 data type.
    • Support multi-label quantile regression in estimator.
    • We now use "div" as the default partition_strategy in tf.nn.safe_embedding_lookup_sparse, tf.nn.sampled_softmax and tf.nn.nce_loss.
      hyperparameter are ignored.
  • Performance
    • Improve performance of GPU cumsum/cumprod by up to 300x.
    • Added support for weight decay in most TPU embedding optimizers, including AdamW and MomentumW.
  • TensorFlow 2.0 Development
    • Add a command line tool to convert to TF2.0, tf_upgrade_v2
    • Merge tf.spectral into tf.signal for TensorFlow 2.0.
    • Change the default recurrent activation function for LSTM from 'hard_sigmoid' to 'sigmoid' in 2.0. Historically recurrent activation is 'hard_sigmoid' since it is fast than 'sigmoid'. With new unified backend between CPU and GPU mode, since the CuDNN kernel is using sigmoid, we change the default for CPU mode to sigmoid as well. With that, the default LSTM will be compatible with both CPU and GPU kernel. This will enable user with GPU to use CuDNN kernel by default and get a 10x performance boost in training. Note that this is checkpoint breaking change. If user want to use their 1.x pre-trained checkpoint, please construct the layer with LSTM(recurrent_activation='hard_sigmoid') to fallback to 1.x behavior.
  • TensorFlow Lite
    • Move from tensorflow/contrib/lite to tensorflow/lite.
    • Add experimental Java API for injecting TensorFlow Lite delegates
    • Add support for strings in TensorFlow Lite Java API.
  • tf.contrib:
    • Add Apache Ignite Filesystem plugin to support accessing Apache IGFS.
    • Dropout now takes rate argument, keep_prob is deprecated.
    • Estimator occurrences references tf.contrib.estimator were changed to tf.estimator:
      • tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimator
      • tf.contrib.estimator.DNNLinearCombinedEstimator with tf.estimator.DNNLinearCombinedEstimator
      • tf.contrib.estimator.DNNEstimator with tf.estimator.DNNEstimator
      • tf.contrib.estimator.LinearEstimator with tf.estimator.LinearEstimator
      • tf.contrib.estimator.InMemoryEvaluatorHook and tf.estimator.experimental.InMemoryEvaluatorHook`.
      • tf.contrib.estimator.make_stop_at_checkpoint_step_hook with tf.estimator.experimental.make_stop_at_checkpoint_step_hook.
    • Expose `tf.distribute.Strategy as the new name for tf.contrib.distribute.DistributionStrategy.
    • Migrate linear optimizer from contrib to core.
    • Move tf.contrib.signal to tf.signal (preserving aliases in tf.contrib.signal).
    • Users of tf.contrib.estimator.export_all_saved_models and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models.
  • tf.data:
    • Add tf.data.experimental.StatsOptions(), to configure options to collect statistics from tf.data.Dataset pipeline using StatsAggregator. Add nested option, experimental_stats (which takes a tf.data.experimen tal.StatsOptions object), to tf.data.Options. Deprecates tf.data.experimental.set_stats_agregator.
    • Performance optimizations:
      • Add tf.data.experimental.OptimizationOptions(), to configure options to enable tf.data performance optimizations. Add nested option, experimental_optimization (which takes a tf.data.experimental.OptimizationOptions object), to tf.data.Options. Remove performance optimization options from tf.data.Options, and add them under tf.data.experimental.OptimizationOptions instead.
      • Enable map_and_batch_fusion and noop_elimination optimizations by default. They can be disabled by configuring tf.data.experimental.OptimizationOptions to set map_and_batch = False or noop_elimination = False respectively. To disable all default optimizations, set apply_default_optimizations = False.
      • Support parallel map in map_and_filter_fusion.
      • Disable static optimizations for input pipelines that use non-resource tf.Variables.
    • Add NUMA-aware MapAndBatch dataset.
    • Deprecate tf.data.Dataset.make_one_shot_iterator() in V1, removed it from V2, and added tf.compat.v1.data.make_one_shot_iterator()`.
    • Deprecate tf.data.Dataset.make_initializable_iterator() in V1, removed it from V2, and added tf.compat.v1.data.make_initializable_iterator().
    • Enable nested dataset support in core tf.data transformations.
    • For tf.data.Dataset implementers: Added tf.data.Dataset._element_structured property to replace Dataset.output_{types,shapes,classes}.
    • Make num_parallel_calls of tf.data.Dataset.interleave and tf.data.Dataset.map work in Eager mode.
  • Toolchains
    • Fixed OpenSSL compatibility by avoiding EVP_MD_CTX_destroy.
    • Added bounds checking to printing deprecation warnings.
    • Upgraded CUDA dependency to 10.0
    • To build with Android NDK r14b, add "#include <linux/compiler.h>" to android-ndk-r14b/platforms/android-14/arch-*/usr/include/linux/futex.h
    • Removed :android_tensorflow_lib_selective_registration* targets, use :android_tensorflow_lib_lite* targets instead.
  • XLA
    • Move RoundToEven function to xla/client/lib/math.h.
    • A new environment variable TF_XLA_DEBUG_OPTIONS_PASSTHROUGH set to "1" or "true" allows the debug options passed within an XRTCompile op to be passed directly to the XLA compilation backend. If such variable is not set (service side), only a restricted set will be passed through.
    • Allow the XRTCompile op to return the ProgramShape resulted form the XLA compilation as a second return argument.
    • XLA HLO graphs can now be rendered as SVG/HTML.
  • Estimator
    • Replace all occurences of tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimator
    • Replace all occurences of tf.contrib.estimator.DNNLinearCombinedEstimator with tf.estimator.DNNLinearCombinedEstimator
    • Replace all occurrences of tf.contrib.estimator.DNNEstimator with tf.estimator.DNNEstimator
    • Replace all occurrences of tf.contrib.estimator.LinearEstimator with tf.estimator.LinearEstimator
    • Users of tf.contrib.estimator.export_all_saved_models and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models.
    • Update regression_head to the new Head API for Canned Estimator V2.
    • Switch multi_class_head to Head API for Canned Estimator V2.
    • Replace all occurences of tf.contrib.estimator.InMemoryEvaluatorHook and tf.contrib.estimator.make_stop_at_checkpoint_step_hook with tf.estimator.experimental.InMemoryEvaluatorHook and tf.estimator.experimental.make_stop_at_checkpoint_step_hook
    • Migrate linear optimizer from contrib to core.

Thanks to our Contributors

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

Abhinav Upadhyay, Ag Ramesh, akikaaa, Alexis Louis, Anders Huss, Andreas Madsen, Andrew Banchich, Andy Craze, Anton Dmitriev, Artem Malykh, Avijit-Nervana, Balint Cristian, Benjamin Tan Wei Hao, Bhavani Subramanian, Brendan Finan, Brian Nemsick, Bryan Cutler, By Shen, Cao Zongyan, Castiel, Chris Antaki, Christian Goll, Cibifang, Clayne Robison, Codrut Grosu, Cong Xu, Dalmo Cirne, Daniel Hunter, Dougal J. Sutherland, Edvard Fagerholm, EFanZh, Erik Smistad, Evgeniy Polyakov, Feiyang Chen, franklin5, Fred Reiss, Gautam, gehring, Geoffrey Irving, George Sterpu, Gitea, Grzegorz George Pawelczak, Guozhong Zhuang, himkt, Hoeseong Kim, Huan Li (李卓桓), HuiyangFei, hyunyoung, Isaac Burbank, jackonan, Jacky Ko, Jason Furmanek, Jason Zaman, Javier Luraschi, Jiang,Zhoulong, joaak, John Lin, Jonathan Wyatt Hoech, josephyearsley, Josh Gordon, Julian Niedermeier, Karl Lessard, Keno Fischer, lanhin, Leon Graser, leondgarse, Li, Guizi, Li, Yiqiang, lxl910915, Mahmoud Abuzaina, manhyuk, Marcela Morales Quispe, margaretmz, Matt Conley, Max Pumperla, mbhuiyan, mdfaijul, Meng, Peng, Michael, Michael Gielda, mrTsjolder, Muhammad Wildan, neargye, Nehal J Wani, NEWPLAN, Niranjan Hasabnis, Nutti, olicht, Pan Daoxin, Pedro Monreal, Peng Yu, pillarpond, Pooya Davoodi, qiezi, Rholais Lii, Richard Yu, Rin Arakaki, Roger Iyengar, sahilbadyal, Sami Kama, Sandip Giri, Scott Leishman, Serge Panev, Seunghoon Park, Shafi Dayatar, shengfuintel, Shimin Guo, Siju, silent567, Stefan Dyulgerov, steven, Tao Wei, Thor Johnsen, Tingbo Lu, tomguluson92, Tongxuan Liu, Trevor Morris, Ubuntu, Vadim Borisov, vanderliang, wangsiyu, Wen Yun, Wen-Heng (Jack) Chung, wenxizhu, William D. Irons, Xiaoming (Jason) Cui, Yan Facai (颜发才), Yanbo Liang, Yaniv Blumenfeld, Yash Gaurkar, Yicheng Fan, Yong Tang, Yongjoon Lee, Yuan (Terry) Tang, Yuxin Wu, zldrobit

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