Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)
Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)
Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)
Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)
Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)
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.
Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)
Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)
Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)
Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)
Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)
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.
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Commits
fdfc646 Merge pull request #58581 from tensorflow-jenkins/version-numbers-2.10.1-4527
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dependabot[bot]
commented
1 year ago
Bumps tensorflow from 2.7.0 to 2.10.1.
Release notes
Sourced from tensorflow's releases.
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Changelog
Sourced from tensorflow's changelog.
... (truncated)
Commits
fdfc646Merge pull request #58581 from tensorflow-jenkins/version-numbers-2.10.1-4527319f094Update version numbers to 2.10.17c857b8Merge pull request #58475 from tensorflow-jenkins/relnotes-2.10.1-66496f133daUpdate RELEASE.md3982264Merge pull request #58573 from tensorflow/r2.10-f856d02e532f425d38Merge pull request #58571 from tensorflow/r2.10-7b174a0f2e4dbe4291Merge pull request #58569 from tensorflow/r2.10-216525144ee965517aMerge pull request #58565 from tensorflow/r2.10-af4a6a3c8b9c09738eMerge pull request #58564 from tensorflow/r2.10-9f03a9d3baf3da111cMerge pull request #58561 from tensorflow/r2.10-8310bf8dd18Dependabot 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)