Note: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.
Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourself steps. You can refer to the release notes of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.
This release also introduces several vulnerability fixes:
The tf.keras.optimizers.Optimizer base class now points to the new Keras optimizer, while the old optimizers have been moved to the tf.keras.optimizers.legacy namespace.
If you find your workflow failing due to this change, you may be facing one of the following issues:
Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizer.legacy.XXX (e.g. tf.keras.optimizer.legacy.Adam).
TF1 compatibility. The new optimizer, tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend migrating your workflow to TF2 for stable support and new features.
Old optimizer API not found. The new optimizer, tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
Learning rate schedule access. When using a tf.keras.optimizers.schedules.LearningRateSchedule, the new optimizer's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._learning_rate.
If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in the Keras GitHub repo.
Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls the optimizer to update different parts of the model in multiple stages, please call optimizer.build(model.trainable_variables) before the training loop.
Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.
The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example, tf.keras.optimizers.Adafactor) will only be implemented based on the new tf.keras.optimizers.Optimizer base class.
tensorflow/python/keras code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release. Please remove any import of tensorflow.python.keras and use the public API with from tensorflow import keras or import tensorflow as tf; tf.keras.
Note: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin.
Security vulnerability fixes will no longer be patched to this Tensorflow version. The latest Tensorflow version includes the security vulnerability fixes. You can update to the latest version (recommended) or patch security vulnerabilities yourself steps. You can refer to the release notes of the latest Tensorflow version for a list of newly fixed vulnerabilities. If you have any questions, please create a GitHub issue to let us know.
This release also introduces several vulnerability fixes:
tf.keras.optimizers.Optimizer now points to the new Keras optimizer, and
old optimizers have moved to the tf.keras.optimizers.legacy namespace.
If you find your workflow failing due to this change,
you may be facing one of the following issues:
Checkpoint loading failure. The new optimizer handles optimizer
state differently from the old optimizer, which simplies the logic of
checkpoint saving/loading, but at the cost of breaking checkpoint
backward compatibility in some cases. If you want to keep using an old
checkpoint, please change your optimizer to
tf.keras.optimizers.legacy.XXX (e.g.
tf.keras.optimizers.legacy.Adam).
TF1 compatibility. The new optimizer does not support TF1 any more,
so please use the legacy optimizer tf.keras.optimizer.legacy.XXX.
We highly recommend to migrate your workflow to TF2 for stable
support and new features.
API not found. The new optimizer has a different set of public APIs
from the old optimizer. These API changes are mostly related to
getting rid of slot variables and TF1 support. Please check the API
... (truncated)
Commits
a3e2c69 Merge pull request #60016 from tensorflow/fix-relnotes
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You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/guanshuicheng/invoice/network/alerts).
Bumps tensorflow from 2.5.1 to 2.11.1.
Release notes
Sourced from tensorflow's releases.
... (truncated)
Changelog
Sourced from tensorflow's changelog.
... (truncated)
Commits
a3e2c69Merge pull request #60016 from tensorflow/fix-relnotes13b85dcFix release notes48b18dbMerge pull request #60014 from tensorflow/disable-test-that-oomseea48f5Disable a test that results in OOM+segfaulta632584Merge pull request #60000 from tensorflow/venkat-patch-393dea7aUpdate RELEASE.mda2ba9f1Updating Release.md with Legal Language for Release Notesfae41c7Merge pull request #59998 from tensorflow/fix-bad-cherrypick-again2757416Fix bad cherrypickc78616fMerge pull request #59992 from tensorflow/fix-2.11-buildDependabot 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) You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/guanshuicheng/invoice/network/alerts).