Bump libtensorflow from 1.12.0 to 1.13.1

[dependabot-preview]

Bumps libtensorflow from 1.12.0 to 1.13.1.

Release notes

Sourced from libtensorflow's releases.

TensorFlow 1.13.1

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

... (truncated)

Changelog

Sourced from libtensorflow's changelog.

Release 1.12.2

Bug Fixes and Other Changes

• Fixes a potential security vulnerability where carefully crafted GIF images
  can produce a null pointer dereference during decoding.

Release 1.13.0

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.

... (truncated)

Commits

• 6612da8 Merge pull request #26101 from gunan/r1.13
• 078bcf8 Update tensorboard dependency to 1.13.x
• e38f835 Bump TF version to 1.13.1
• bade323 Update package version to 1.13.0.
• 7b8469e Fix serialization naming for BatchNorm.
• c865ec5 Merge pull request #25769 from aselle/cp-20190214b
• 42debe3 Merge pull request #25770 from aselle/cp-20190214
• 88fa1f3 Only convert BackpropFilterConv to depthwise convolution if format is NHWC.
• 5d41621 [Intel MKL] Fix incorrect way to dump optimized graph
• 07a6b9e Merge pull request #25725 from aselle/cp-20190213
• Additional commits viewable in compare view

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