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Published by geetachavan1 over 3 years ago
This release matches TensorFlow 2.5.0-rc0
Published by geetachavan1 almost 4 years ago
This release matches TensorFlow 2.4.0
Published by geetachavan1 almost 4 years ago
This release matches TensorFlow 2.4.0-rc0
Published by geetachavan1 about 4 years ago
This release matches TensorFlow
Published by geetachavan1 over 4 years ago
This release matches TensorFlow 2.3.0-rc0
Published by geetachavan1 over 4 years ago
This release contains contributions from many people at Google.
Published by geetachavan1 over 4 years ago
This release contains contributions from many people at Google.
Published by mihaimaruseac over 4 years ago
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
This release contains contributions from many people at Google.
Published by mihaimaruseac almost 5 years ago
This release contains contributions from many people at Google.
Published by mihaimaruseac almost 5 years ago
This release contains contributions from many people at Google.
Published by mihaimaruseac about 5 years ago
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.
This release contains contributions from many people at Google.
Published by mihaimaruseac about 5 years ago
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.
This release contains contributions from many people at Google.
Published by mihaimaruseac about 5 years ago
This release is the same as 1.15.0 but we needed a new release to update the version number inside setup.py
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
.This release contains contributions from many people at Google.
Published by mihaimaruseac about 5 years ago
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
.This release contains contributions from many people at Google.
Published by mihaimaruseac over 5 years ago
tf.compat.v1.estimator.inputs
instead of tf.estimator.inputs
contrib
references with tf.estimator.experimental.*
for APIs in early_stopping.py
--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.This release contains contributions from many people at Google.
Published by mihaimaruseac over 5 years ago
tf.compat.v1.estimator.inputs
instead of tf.estimator.inputs
contrib
references with tf.estimator.experimental.*
for APIs in early_stopping.py
--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.This release contains contributions from many people at Google.
Published by mihaimaruseac over 5 years ago
tf.compat.v1.estimator.inputs
instead of tf.estimator.inputs
contrib
references with tf.estimator.experimental.*
for APIs in early_stopping.py
--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.This release contains contributions from many people at Google.
Published by mihaimaruseac over 5 years ago
This release contains contributions from many people at Google
Published by mihaimaruseac over 5 years ago
tf.contrib.estimator.BaselineEstimator
with tf.estimator.BaselineEstimator
tf.contrib.estimator.DNNLinearCombinedEstimator
with tf.estimator.DNNLinearCombinedEstimator
tf.contrib.estimator.DNNEstimator
with tf.estimator.DNNEstimator
tf.contrib.estimator.LinearEstimator
with tf.estimator.LinearEstimator
tf.contrib.estimator.export_all_saved_models
and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models
.regression_head
to head API for Canned Estimator V2.multi_class_head
to head API for Canned Estimator V2.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
This release contains contributions from many people at Google.
Published by mihaimaruseac over 5 years ago
tf.contrib.estimator.BaselineEstimator
with tf.estimator.BaselineEstimator
tf.contrib.estimator.DNNLinearCombinedEstimator
with tf.estimator.DNNLinearCombinedEstimator
tf.contrib.estimator.DNNEstimator
with tf.estimator.DNNEstimator
tf.contrib.estimator.LinearEstimator
with tf.estimator.LinearEstimator
tf.contrib.estimator.export_all_saved_models
and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models
.regression_head
to head API for Canned Estimator V2.multi_class_head
to head API for Canned Estimator V2.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
This release contains contributions from many people at Google.