Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Bump tensorflow from 1.3.0 to 1.12.2 #31

Closed
wants to merge 1 commit into from

Conversation

dependabot[bot]
Copy link
Contributor

@dependabot dependabot bot commented on behalf of github Nov 2, 2019

Bumps tensorflow from 1.3.0 to 1.12.2.

Release notes

Sourced from tensorflow's releases.

TensorFlow 1.12.2

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

TensorFlow 1.12.0

Release 1.12.0

Major Features and Improvements

  • Keras models can now be directly exported to the SavedModel format(tf.contrib.saved_model.save_keras_model()) and used with Tensorflow Serving.
  • Keras models now support evaluating with a tf.data.Dataset.
  • TensorFlow binaries are built with XLA support linked in by default.
  • Ignite Dataset added to contrib/ignite that allows to work with Apache Ignite.

Bug Fixes and Other Changes

  • tf.data:
    • tf.data users can now represent, get, and set options of TensorFlow input pipelines using tf.data.Options(), tf.data.Dataset.options(), and tf.data.Dataset.with_options() respectively.
    • New tf.data.Dataset.reduce() API allows users to reduce a finite dataset to a single element using a user-provided reduce function.
    • New tf.data.Dataset.window() API allows users to create finite windows of input dataset; when combined with the tf.data.Dataset.reduce() API, this allows users to implement customized batching.
    • All C++ code moves to the tensorflow::data namespace.
    • Add support for num_parallel_calls to tf.data.Dataset.interleave.
  • tf.contrib:
    • Remove tf.contrib.linalg. tf.linalg should be used instead.
    • Replace any calls to tf.contrib.get_signature_def_by_key(metagraph_def, signature_def_key) with meta_graph_def.signature_def[signature_def_key]. Catching a ValueError exception thrown by tf.contrib.get_signature_def_by_key should be replaced by catching a KeyError exception.
  • tf.contrib.data
    • Deprecate, and replace by tf.data.experimental.
  • Other:
    • Improved XLA stability and performance.
    • Fix single replica TensorBoard summary stats in Cloud ML Engine.
    • TPUEstimator: Initialize dataset iterators in parallel.
    • Keras on TPU model quality and bug fixes.
    • Instead of jemalloc, revert back to using system malloc since it simplifies build and has comparable performance.
    • Remove integer types from tf.nn.softplus and tf.nn.softsign OpDefs. This is a bugfix; these ops were never meant to support integers.
    • Allow subslicing Tensors with a single dimension.
    • Add option to calculate string length in Unicode characters
    • Add functionality to SubSlice a tensor.
    • Add searchsorted (ie lower/upper_bound) op.
    • Add model explainability to Boosted Trees.
    • Support negative positions for tf.substr
    • There was previously a bug in the bijector_impl where the _reduce_jacobian_det_over_event does not handle scalar ILDJ implementations properly.
    • In tf eager execution, allow re-entering a GradientTape context
    • Add tf_api_version flag. If --define=tf_api_version=2 flag is passed in, then bazel will build TensorFlow API version 2.0. Note that TensorFlow 2.0 is under active development and has no guarantees at this point.
    • Add additional compression options to TfRecordWriter
    • Performance improvements for regex full match operations.
    • Replace tf.GraphKeys.VARIABLES with tf.GraphKeys.GLOBAL_VARIABLES
    • Remove unused dynamic learning rate support.

Thanks to our Contributors

... (truncated)
Changelog

Sourced from tensorflow'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
  • 6b63465 Merge pull request #27959 from tensorflow/update-release-notes-version
  • e967833 Update header on release notes
  • cf74798 Merge pull request #27958 from tensorflow/update-release-version
  • 7fba173 Update version to 1.12.2
  • 332f080 Merge pull request #27878 from tensorflow/windows-cpu
  • c9fcc49 Fix windows build for CPU too
  • 416b4a3 Merge pull request #27873 from tensorflow/more-bazel-incompatible-flags
  • 3ebe165 Add --incompatible_disable_cc_toolchain_label_from_crosstool_proto=false flag
  • 5ab9466 Reformat bazel invocation lines
  • 446d393 Merge pull request #27870 from tensorflow/bazel-http-archive
  • Additional commits viewable in compare view

Dependabot compatibility score

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.


Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

  • @dependabot rebase will rebase this PR
  • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
  • @dependabot merge will merge this PR after your CI passes on it
  • @dependabot squash and merge will squash and merge this PR after your CI passes on it
  • @dependabot cancel merge will cancel a previously requested merge and block automerging
  • @dependabot reopen will reopen this PR if it is closed
  • @dependabot ignore this [patch|minor|major] version will close this PR and stop Dependabot creating any more for this minor/major version (unless you reopen the PR or upgrade to it yourself)
  • @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
  • @dependabot use these labels will set the current labels as the default for future PRs for this repo and language
  • @dependabot use these reviewers will set the current reviewers as the default for future PRs for this repo and language
  • @dependabot use these assignees will set the current assignees as the default for future PRs for this repo and language
  • @dependabot use this milestone will set the current milestone as the default for future PRs for this repo and language

You can disable automated security fix PRs for this repo from the Security Alerts page.

@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Nov 2, 2019
@dependabot @github
Copy link
Contributor Author

dependabot bot commented on behalf of github Dec 16, 2019

Superseded by #32.

@dependabot dependabot bot closed this Dec 16, 2019
@dependabot dependabot bot deleted the dependabot/pip/tensorflow-1.12.2 branch December 16, 2019 20:59
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
dependencies Pull requests that update a dependency file
Projects
None yet
Development

Successfully merging this pull request may close these issues.

0 participants