| Commit message (Collapse) | Author | Age |
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of upcoming DistributionStrategy support in Estimator.
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New home: https://github.com/tensorflow/probability/tree/master/tensorflow_probability/python/mcmc
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- Add a xla.OpSharding field to the HloInstructionProto.
- Metatdata handling is tested.
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- Added `save_checkpoint_steps` attribute to `MonitoredTrainingSession`.
If both `save_checkpoint_steps` and `save_checkpoint_secs` are both `None` then default saver is disabled. Default is `save_checkpoint_secs=600`
- Added `test_save_checkpoint_steps`
- Updated golden file
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* Fix missing interpretation of document
* Rephrase the sentence of missing interpretation
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to be consistent with export_saved_model
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/ iterator stack.
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Also,
- Templatized ComputeAndCompareRX and CreateRXParameter so that they accept XlaBuilder and XlaOp.
- Clear data held by an XlaBuilder when Build() is called, otherwise errors will occur when the builder is reused.
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linux systems (#18006)
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`tf.distributions.Distribution`.
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Fixes: #16619
See the referred GitHub issue for details, but users want to be
able to skip certain nodes when searching for inf/nans, because
some nodes generate inf/nans even in nominal conditions.
This CL adds a new optional flag `--filter_exclude_node_names`
(or `-fenn` for short), which allows users to do exactly that,
by using a regex for node names.
RELNOTES: tfdbg CLI: Allow exclusion of nodes by regular expressions
during tensor filter-enabled Session runs: see the new flags
`--filter_exclude_node_names` (or `-fenn` for short).
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Requiring it
to happen before OpInit() is way too confusing for users.
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the presence of ops that modify their inputs in-place: Do not dedup nodes if the underlying buffers for their outputs may be passed to an in-place op.
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by tf.GradientTape.gradient() at this time.
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Branch 190479555
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extended BasicDecoder which for example returns a tf.contrib.seq2seq.AttentionWrapperState.
In this case the internal while-loop fails when trying to store an instance tf.contrib.seq2seq.AttentionWrapperState in the internal TensorArray.
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reads imagenet TFRecord files.
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function buffering resource so that we can start using it with re-initializable iterators.
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https://www.tensorflow.org/api_docs/python/tf/contrib/training/HParams to show that it allows '=' in the values.
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Add support for Maximum to TensorFlowLite.
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