This release matches TensorFlow 2.5.0-rc0

This release matches TensorFlow 2.4.0

This release matches TensorFlow 2.4.0-rc0

This release matches TensorFlow

This release matches TensorFlow 2.3.0-rc0

Changes

• Refactor TpuClusterResolver.
• Estimator SavedModels now save resource variables by default.

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Refactor TpuClusterResolver.
• Estimator SavedModels now save resource variables by default.

Thanks to our Contributors

This release contains contributions from many people at Google.

This release is similar to 1.15.1 with the only addition of making early_stopping hook work again after TensorFlow released security patch 1.15.2

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Moving the checkpoint reader from swig to pybind11.

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Moving the checkpoint reader from swig to pybind11.

Thanks to our Contributors

This release contains contributions from many people at Google.

TensorFlow Estimator 2.0 has been released with a private import of a symbol from TensorFlow master. However, that symbol does not exist on TensorFlow 2.0, due to a race condition regarding branch cuts.

This release patches TensorFlow Estimator to resolve this symbol not found error.

Thanks to our Contributors

This release contains contributions from many people at Google.

Breaking changes

Both for Estimator and for main TensorFlow, tf.contrib has been deprecated, and functionality has been either migrated to the core TensorFlow API, to an ecosystem project such as https://www.github.com/tensorflow/addons or https://www.github.com/tensorflow/io, or removed entirely.

Thanks to our Contributors

This release contains contributions from many people at Google.

This release is the same as 1.15.0 but we needed a new release to update the version number inside setup.py

Changes

• tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.
• "Fix tests in canned estimators."
• Expose Head as public API.
• Fixes critical bugs that help with DenseFeatures usability in TF2

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.
• "Fix tests in canned estimators."
• Expose Head as public API.
• Fixes critical bugs that help with DenseFeatures usability in TF2

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
• Replace contrib references with tf.estimator.experimental.* for APIs in early_stopping.py
• Determining the “correct” value of the --iterations_per_loop for TPUEstimator or DistributionStrategy continues to be a challenge for our users. We propose dynamically tuning the --iterations_per_loop variable, specifically for using TPUEstimator in training mode, based on a user target TPU execution time. Users might specify a value such as: --iterations_per_loop=300s, which will result in roughly 300 seconds being spent on the TPU between host side operations.

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
• Replace contrib references with tf.estimator.experimental.* for APIs in early_stopping.py
• Determining the “correct” value of the --iterations_per_loop for TPUEstimator or DistributionStrategy continues to be a challenge for our users. We propose dynamically tuning the --iterations_per_loop variable, specifically for using TPUEstimator in training mode, based on a user target TPU execution time. Users might specify a value such as: --iterations_per_loop=300s, which will result in roughly 300 seconds being spent on the TPU between host side operations.

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
• Replace contrib references with tf.estimator.experimental.* for APIs in early_stopping.py
• Determining the “correct” value of the --iterations_per_loop for TPUEstimator or DistributionStrategy continues to be a challenge for our users. We propose dynamically tuning the --iterations_per_loop variable, specifically for using TPUEstimator in training mode, based on a user target TPU execution time. Users might specify a value such as: --iterations_per_loop=300s, which will result in roughly 300 seconds being spent on the TPU between host side operations.

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
• Replace contrib references with tf.estimator.experimental.* for apis in early_stopping.py

Thanks to our Contributors

This release contains contributions from many people at Google

Changes

• Replace all occurences of tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimator
• Replace all occurences of tf.contrib.estimator.DNNLinearCombinedEstimator with tf.estimator.DNNLinearCombinedEstimator
• Replace all occurrences of tf.contrib.estimator.DNNEstimator with tf.estimator.DNNEstimator
• Replace all occurrences of tf.contrib.estimator.LinearEstimator with tf.estimator.LinearEstimator
• Users of tf.contrib.estimator.export_all_saved_models and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models.
• Update regression_head to head API for Canned Estimator V2.
• Update multi_class_head to head API for Canned Estimator V2.
• Replace all occurences of tf.contrib.estimator.InMemoryEvaluatorHook and tf.contrib.estimator.make_stop_at_checkpoint_step_hook with tf.estimator.experimental.InMemoryEvaluatorHook and tf.estimator.experimental.make_stop_at_checkpoint_step_hook
• Migrate linear optimizer from contrib to core.

Thanks to our Contributors

This release contains contributions from many people at Google.

Changes

• Replace all occurences of tf.contrib.estimator.BaselineEstimator with tf.estimator.BaselineEstimator
• Replace all occurences of tf.contrib.estimator.DNNLinearCombinedEstimator with tf.estimator.DNNLinearCombinedEstimator
• Replace all occurrences of tf.contrib.estimator.DNNEstimator with tf.estimator.DNNEstimator
• Replace all occurrences of tf.contrib.estimator.LinearEstimator with tf.estimator.LinearEstimator
• Users of tf.contrib.estimator.export_all_saved_models and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models.
• Update regression_head to head API for Canned Estimator V2.
• Update multi_class_head to head API for Canned Estimator V2.
• Replace all occurences of tf.contrib.estimator.InMemoryEvaluatorHook and tf.contrib.estimator.make_stop_at_checkpoint_step_hook with tf.estimator.experimental.InMemoryEvaluatorHook and tf.estimator.experimental.make_stop_at_checkpoint_step_hook
• Migrate linear optimizer from contrib to core.

Thanks to our Contributors

This release contains contributions from many people at Google.