Bump tensorflow from 2.5.1 to 2.11.1 in /tests/models/tensorflow/mobilenet

Bumps tensorflow from 2.5.1 to 2.11.1.

Release notes

TensorFlow 2.11.1

Release 2.11.1

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:

Fixes an FPE in TFLite in conv kernel CVE-2023-27579

Fixes a double free in Fractional(Max/Avg)Pool CVE-2023-25801

Fixes a null dereference on ParallelConcat with XLA CVE-2023-25676

Fixes a segfault in Bincount with XLA CVE-2023-25675

Fixes an NPE in RandomShuffle with XLA enable CVE-2023-25674

Fixes an FPE in TensorListSplit with XLA CVE-2023-25673

Fixes segmentation fault in tfg-translate CVE-2023-25671

Fixes an NPE in QuantizedMatMulWithBiasAndDequantize CVE-2023-25670

Fixes an FPE in AvgPoolGrad with XLA CVE-2023-25669

Fixes a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation CVE-2023-25668

Fixes a segfault when opening multiframe gif CVE-2023-25667

Fixes an NPE in SparseSparseMaximum CVE-2023-25665

Fixes an FPE in AudioSpectrogram CVE-2023-25666

Fixes a heap-buffer-overflow in AvgPoolGrad CVE-2023-25664

Fixes a NPE in TensorArrayConcatV2 CVE-2023-25663

Fixes a Integer overflow in EditDistance CVE-2023-25662

Fixes a Seg fault in tf.raw_ops.Print CVE-2023-25660

Fixes a OOB read in DynamicStitch CVE-2023-25659

Fixes a OOB Read in GRUBlockCellGrad CVE-2023-25658

TensorFlow 2.11.0

Release 2.11.0

Breaking Changes

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.

Major Features and Improvements

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.11.1

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:

Fixes an FPE in TFLite in conv kernel CVE-2023-27579

Fixes a double free in Fractional(Max/Avg)Pool CVE-2023-25801

Fixes a null dereference on ParallelConcat with XLA CVE-2023-25676

Fixes a segfault in Bincount with XLA CVE-2023-25675

Fixes an NPE in RandomShuffle with XLA enable CVE-2023-25674

Fixes an FPE in TensorListSplit with XLA CVE-2023-25673

Fixes segmentation fault in tfg-translate CVE-2023-25671

Fixes an NPE in QuantizedMatMulWithBiasAndDequantize CVE-2023-25670

Fixes an FPE in AvgPoolGrad with XLA CVE-2023-25669

Fixes a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation CVE-2023-25668

Fixes a segfault when opening multiframe gif CVE-2023-25667

Fixes an NPE in SparseSparseMaximum CVE-2023-25665

Fixes an FPE in AudioSpectrogram CVE-2023-25666

Fixes a heap-buffer-overflow in AvgPoolGrad CVE-2023-25664

Fixes a NPE in TensorArrayConcatV2 CVE-2023-25663

Fixes a Integer overflow in EditDistance CVE-2023-25662

Fixes a Seg fault in tf.raw_ops.Print CVE-2023-25660

Fixes a OOB read in DynamicStitch CVE-2023-25659

Fixes a OOB Read in GRUBlockCellGrad CVE-2023-25658

Release 2.11.0

Breaking Changes

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
13b85dc Fix release notes
48b18db Merge pull request #60014 from tensorflow/disable-test-that-ooms
eea48f5 Disable a test that results in OOM+segfault
a632584 Merge pull request #60000 from tensorflow/venkat-patch-3
93dea7a Update RELEASE.md
a2ba9f1 Updating Release.md with Legal Language for Release Notes
fae41c7 Merge pull request #59998 from tensorflow/fix-bad-cherrypick-again
2757416 Fix bad cherrypick
c78616f Merge pull request #59992 from tensorflow/fix-2.11-build
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