search

kjappelbaum / pyepal

Multiobjective active learning with tunable accuracy/efficiency tradeoff and clear stopping criterion.
Apache License 2.0
38 stars 5 forks source link

chore(deps-dev): update tensorflow requirement from <2.9,>=2.5 to >=2.5,<2.11 #245

Closed dependabot[bot] closed 2 years ago

dependabot[bot] commented 2 years ago

Updates the requirements on tensorflow to permit the latest version.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.10.0

Release 2.10.0

Breaking Changes

  • Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
  • Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
  • tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
  • RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
    • Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
    • An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

Deprecations

  • The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
    • Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
    • Stop constructing Status objects from tensorflow::error::Code.
    • One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
      • Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
      • Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
      • In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
  • tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.

Major Features and Improvements

  • tf.lite:

    • New operations supported:
      • tflite SelectV2 now supports 5D.
      • tf.einsum is supported with multiple unknown shapes.
      • tf.unsortedsegmentprod op is supported.
      • tf.unsortedsegmentmax op is supported.
      • tf.unsortedsegmentsum op is supported.
    • Updates to existing operations:
      • tfl.scatter_nd now supports I1 for update arg.
    • Upgrade Flatbuffers v2.0.5 from v1.12.0

  • tf.keras:

    • EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
    • Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
    • Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.
    • Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
    • Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
    • Added tf.keras.dtensor.experimental.optimizers.AdamW. This optimizer is similar as the existing keras.optimizers.experimental.AdamW, and works in the DTensor training use case.
    • Improved masking support for tf.keras.layers.MultiHeadAttention.
      • Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
      • Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.10.0

Breaking Changes

  • Causal attention in keras.layers.Attention and keras.layers.AdditiveAttention is now specified in the call() method via the use_causal_mask argument (rather than in the constructor), for consistency with other layers.
  • Some files in tensorflow/python/training have been moved to tensorflow/python/tracking and tensorflow/python/checkpoint. Please update your imports accordingly, the old files will be removed in Release 2.11.
  • tf.keras.optimizers.experimental.Optimizer will graduate in Release 2.11, which means tf.keras.optimizers.Optimizer will be an alias of tf.keras.optimizers.experimental.Optimizer. The current tf.keras.optimizers.Optimizer will continue to be supported as tf.keras.optimizers.legacy.Optimizer, e.g.,tf.keras.optimizers.legacy.Adam. Most users won't be affected by this change, but please check the API doc if any API used in your workflow is changed or deprecated, and make adaptions. If you decide to keep using the old optimizer, please explicitly change your optimizer to tf.keras.optimizers.legacy.Optimizer.
  • RNG behavior change for tf.keras.initializers. Keras initializers will now use stateless random ops to generate random numbers.
    • Both seeded and unseeded initializers will always generate the same values every time they are called (for a given variable shape). For unseeded initializers (seed=None), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).
    • An unseeded initializer will raise a warning if it is reused (called) multiple times. This is because it would produce the same values each time, which may not be intended.

Deprecations

  • The C++ tensorflow::Code and tensorflow::Status will become aliases of respectively absl::StatusCode and absl::Status in some future release.
    • Use tensorflow::OkStatus() instead of tensorflow::Status::OK().
    • Stop constructing Status objects from tensorflow::error::Code.
    • One MUST NOT access tensorflow::errors::Code fields. Accessing tensorflow::error::Code fields is fine.
      • Use the constructors such as tensorflow::errors:InvalidArgument to create status using an error code without accessing it.
      • Use the free functions such as tensorflow::errors::IsInvalidArgument if needed.
      • In the last resort, use e.g.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT) or static_cast<int>(code) for comparisons.
  • tensorflow::StatusOr will also become in the future alias to absl::StatusOr, so use StatusOr::value instead of StatusOr::ConsumeValueOrDie.

Major Features and Improvements

  • tf.lite:

    • New operations supported:
      • tflite SelectV2 now supports 5D.
      • tf.einsum is supported with multiple unknown shapes.
      • tf.unsortedsegmentprod op is supported.
      • tf.unsortedsegmentmax op is supported.
      • tf.unsortedsegmentsum op is supported.
    • Updates to existing operations:
      • tfl.scatter_nd now supports I1 for update arg.
    • Upgrade Flatbuffers v2.0.5 from v1.12.0

  • tf.keras:

    • EinsumDense layer is moved from experimental to core. Its import path is moved from tf.keras.layers.experimental.EinsumDense to tf.keras.layers.EinsumDense.
    • Added tf.keras.utils.audio_dataset_from_directory utility to easily generate audio classification datasets from directories of .wav files.
    • Added subset="both" support in tf.keras.utils.image_dataset_from_directory,tf.keras.utils.text_dataset_from_directory, and audio_dataset_from_directory, to be used with the validation_split argument, for returning both dataset splits at once, as a tuple.
    • Added tf.keras.utils.split_dataset utility to split a Dataset object or a list/tuple of arrays into two Dataset objects (e.g. train/test).
    • Added step granularity to BackupAndRestore callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.
    • Added tf.keras.dtensor.experimental.optimizers.AdamW. This optimizer is similar as the existing keras.optimizers.experimental.AdamW, and works in the DTensor training use case.
    • Improved masking support for tf.keras.layers.MultiHeadAttention.
      • Implicit masks for query, key and value inputs will automatically be used to compute a correct attention mask for the layer. These padding masks will be combined with any attention_mask passed in directly when calling the layer. This can be used with tf.keras.layers.Embedding with mask_zero=True to automatically infer a correct padding mask.
      • Added a use_causal_mask call time arugment to the layer. Passing use_causal_mask=True will compute a causal attention mask, and optionally combine it with any attention_mask passed in directly when calling the layer.
    • Added ignore_class argument in the loss SparseCategoricalCrossentropy and metrics IoU and MeanIoU, to specify a class index to be ignored during loss/metric computation (e.g. a background/void class).
    • Added tf.keras.models.experimental.SharpnessAwareMinimization. This class implements the sharpness-aware minimization technique, which boosts model performance on various tasks, e.g., ResNet on image classification.

... (truncated)

Commits
  • 359c3cd Merge pull request #57609 from tensorflow/vinila21-patch-6
  • 724308f Update estimator and keras version in TF 2.10 branch for 2.10.0.
  • 203b333 Merge pull request #57608 from tensorflow-jenkins/version-numbers-2.10.0-28960
  • cd950ff Update version numbers to 2.10.0
  • 9b13e9e Merge pull request #57510 from tensorflow/vinila21-patch-1
  • ba47bc7 Update release notes with security updates
  • f082fa9 Merge pull request #57464 from tensorflow/r2.10-b5f6fbfba76
  • 60ed7ce Re-enable testTensorListReserveWithNonScalarNumElements to work with mlir as ...
  • 23cb0d3 Merge pull request #57460 from tensorflow/revert-57075-r2.10-e9863e9a9cb
  • f419a41 Revert "r2.10 cherry-pick: e9863e9a9cb "Fix tf.raw_ops.EmptyTensorList vulner...
  • Additional commits viewable in compare view


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 close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor 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)
kjappelbaum commented 2 years ago

will give it a manual look.

dependabot[bot] commented 2 years ago

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version. You can also ignore all major, minor, or patch releases for a dependency by adding an ignore condition with the desired update_types to your config file.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.