The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure.
Major Features and Improvements
tf.lite
Added 16-bit and 64-bit float type support for built-in op cast.
The Python TF Lite Interpreter bindings now have an option experimental_disable_delegate_clustering to turn-off delegate clustering.
Added int16x8 support for the built-in op exp
Added int16x8 support for the built-in op mirror_pad
Added int16x8 support for the built-in ops space_to_batch_nd and batch_to_space_nd
Added 16-bit int type support for built-in op less, greater_than, equal
Added 8-bit and 16-bit support for floor_div and floor_mod.
Added 16-bit and 32-bit int support for the built-in op bitcast.
Added 8-bit/16-bit/32-bit int/uint support for the built-in op bitwise_xor
Added int16 indices support for built-in op gather and gather_nd.
Added 8-bit/16-bit/32-bit int/uint support for the built-in op right_shift
Added reference implementation for 16-bit int unquantized add.
Added reference implementation for 16-bit int and 32-bit unsigned int unquantized mul.
add_op supports broadcasting up to 6 dimensions.
Added 16-bit support for top_k.
tf.function
ConcreteFunction (tf.types.experimental.ConcreteFunction) as generated through get_concrete_function now performs holistic input validation similar to calling tf.function directly. This can cause breakages where existing calls pass Tensors with the wrong shape or omit certain non-Tensor arguments (including default values).
tf.nn
tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse now support ids and weights described by tf.RaggedTensors.
Added a new boolean argument allow_fast_lookup to tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse, which enables a simplified and typically faster lookup procedure.
tf.data
tf.data.Dataset.zip now supports Python-style zipping, i.e. Dataset.zip(a, b, c).
tf.data.Dataset.shuffle now supports tf.data.UNKNOWN_CARDINALITY When doing a "full shuffle" using dataset = dataset.shuffle(dataset.cardinality()). But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).
tf.math
tf.nn.top_k now supports specifying the output index type via parameter index_type. Supported types are tf.int16, tf.int32 (default), and tf.int64.
tf.SavedModel
Introduced class method tf.saved_model.experimental.Fingerprint.from_proto(proto), which can be used to construct a Fingerprint object directly from a protobuf.
The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure.
Major Features and Improvements
tf.lite
Added 16-bit and 64-bit float type support for built-in op cast.
The Python TF Lite Interpreter bindings now have an option experimental_disable_delegate_clustering to turn-off delegate clustering.
Added int16x8 support for the built-in op exp
Added int16x8 support for the built-in op mirror_pad
Added int16x8 support for the built-in ops space_to_batch_nd and batch_to_space_nd
Added 16-bit int type support for built-in op less, greater_than, equal
Added 8-bit and 16-bit support for floor_div and floor_mod.
Added 16-bit and 32-bit int support for the built-in op bitcast.
Added 8-bit/16-bit/32-bit int/uint support for the built-in op bitwise_xor
Added int16 indices support for built-in op gather and gather_nd.
Added 8-bit/16-bit/32-bit int/uint support for the built-in op right_shift
Added reference implementation for 16-bit int unquantized add.
Added reference implementation for 16-bit int and 32-bit unsigned int unquantized mul.
add_op supports broadcasting up to 6 dimensions.
Added 16-bit support for top_k.
tf.function
ConcreteFunction (tf.types.experimental.ConcreteFunction) as generated through get_concrete_function now performs holistic input validation similar to calling tf.function directly. This can cause breakages where existing calls pass Tensors with the wrong shape or omit certain non-Tensor arguments (including default values).
tf.nn
tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse now support ids and weights described by tf.RaggedTensors.
Added a new boolean argument allow_fast_lookup to tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse, which enables a simplified and typically faster lookup procedure.
tf.data
tf.data.Dataset.zip now supports Python-style zipping, i.e. Dataset.zip(a, b, c).
tf.data.Dataset.shuffle now supports tf.data.UNKNOWN_CARDINALITY When doing a "full shuffle" using dataset = dataset.shuffle(dataset.cardinality()). But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).
tf.math
tf.nn.top_k now supports specifying the output index type via parameter index_type. Supported types are tf.int16, tf.int32 (default), and tf.int64.
tf.SavedModel
Introduced class method tf.saved_model.experimental.Fingerprint.from_proto(proto), which can be used to construct a Fingerprint object directly from a protobuf.
Introduced member method tf.saved_model.experimental.Fingerprint.singleprint(), which provides a convenient way to uniquely identify a SavedModel.
... (truncated)
Commits
1cb1a03 updating release notes with security fixes (#61119)
bd4c381 Merge pull request #61102 from tensorflow/venkat-patch-123
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dependabot[bot]
commented
1 year ago
Bumps tensorflow from 2.11.0 to 2.13.0.
Release notes
Sourced from tensorflow's releases.
... (truncated)
Changelog
Sourced from tensorflow's changelog.
... (truncated)
Commits
1cb1a03updating release notes with security fixes (#61119)bd4c381Merge pull request #61102 from tensorflow/venkat-patch-1232a17745update estimator and keras versions71a2f7fMerge pull request #61097 from tensorflow-jenkins/version-numbers-2.13.0-11793e6e3ceUpdate version numbers to 2.13.06657f49Merge pull request #61075 from elfringham/limit_numpy90389e9Fix unit test failure caused by numpy update5b6abc8Merge pull request #60904 from tensorflow/venkat-patch-225763bc3Fix TPUExecute for TPU embedding operations. Create temporary device memory1c27a49Merge pull request #60888 from tensorflow/venkat-patch-16Dependabot 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)