"Fossies" - the Fresh Open Source Software Archive 
"Fossies" - the Fresh Open Source Software Archive 
2.9.0_vs_2.9.1.A hint: This file contains one or more very long lines, so maybe it is better readable using the pure text view mode that shows the contents as wrapped lines within the browser window.
Add an upper bound for protobuf in setup.py
since protobuf after version 3.20 is currently incompatible
with TensorFlow. See https://github.com/tensorflow/tensorflow/issues/53234,
https://github.com/protocolbuffers/protobuf/issues/9954
and https://github.com/tensorflow/tensorflow/issues/56077.
_GLIBCXX_USE_CXX11_ABI=1. Downstream projects that
encounter std::__cxx11 or [abi:cxx11] linker
errors will need to adopt this compiler option. See the
GNU C++ Library docs on Dual ABI.tf.keras.mixed_precision.experimental API has been
removed. The non-experimental symbols under
tf.keras.mixed_precision have been available since
TensorFlow 2.4 and should be used instead.
tf.keras.mixed_precision symbols. E.g., replace
tf.keras.mixed_precision.experimental.global_policy with
tf.keras.mixed_precision.global_policy.tf.keras.mixed_precision.experimental.set_policy with
tf.keras.mixed_precision.set_global_policy. The
experimental symbol set_policy was renamed to
set_global_policy in the non-experimental API.LossScaleOptimizer(opt, "dynamic") with
LossScaleOptimizer(opt). If you pass anything other than
"dynamic" to the second argument, see (1) of the next
section."dynamic" to the
loss_scale argument (the second argument) of
LossScaleOptimizer:
loss_scale argument (the
second argument) of Policy:
Policy optionally took in a
tf.compat.v1.mixed_precision.LossScale in the constructor,
which defaulted to a dynamic loss scale for the
"mixed_float16" policy and no loss scale for other
policies. In Model.compile, if the model's policy had a
loss scale, the optimizer would be wrapped with a
LossScaleOptimizer. With the non-experimental
Policy, there is no loss scale associated with the
Policy, and Model.compile wraps the optimizer
with a LossScaleOptimizer if and only if the policy is a
"mixed_float16" policy. If you previously passed a
LossScale to the experimental Policy, consider
just removing it, as the default loss scaling behavior is usually what
you want. If you really want to customize the loss scaling behavior, you
can wrap your optimizer with a LossScaleOptimizer before
passing it to Model.compile.tf.keras.mixed_precision.experimental.get_layer_policy:
tf.keras.mixed_precision.experimental.get_layer_policy(layer)
with layer.dtype_policy.tf.mixed_precision.experimental.LossScale and its
subclasses have been removed from the TF2 namespace. This symbols were
very rarely used and were only useful in TF2 for use in the now-removed
tf.keras.mixed_precision.experimental API. The symbols are
still available under tf.compat.v1.mixed_precision.experimental_relax_shapes heuristic for
tf.function has been deprecated and replaced with
reduce_retracing which encompasses broader heuristics to
reduce the number of retraces (see below)tf.keras:
tf.keras.applications.resnet_rs models. This
includes the ResNetRS50, ResNetRS101,
ResNetRS152, ResNetRS200,
ResNetRS270, ResNetRS350 and
ResNetRS420 model architectures. The ResNetRS models are
based on the architecture described in Revisiting ResNets: Improved
Training and Scaling Strategiestf.keras.optimizers.experimental.Optimizer. The
reworked optimizer gives more control over different phases of optimizer
calls, and is easier to customize. We provide Adam, SGD, Adadelta,
AdaGrad and RMSprop optimizers based on
tf.keras.optimizers.experimental.Optimizer. Generally the
new optimizers work in the same way as the old ones, but support new
constructor arguments. In the future, the symbols
tf.keras.optimizers.Optimizer/Adam/etc will
point to the new optimizers, and the previous generation of optimizers
will be moved to
tf.keras.optimizers.legacy.Optimizer/Adam/etc.tf.keras.layers.UnitNormalization.tf.keras.regularizers.OrthogonalRegularizer, a
new regularizer that encourages orthogonality between the rows (or
columns) or a weight matrix.tf.keras.layers.RandomBrightness layer for image
preprocessing.tf.keras.utils.disable_interactive_logging() to write the
logs to ABSL logging. You can also use
tf.keras.utils.enable_interactive_logging() to change it
back to stdout, or
tf.keras.utils.is_interactive_logging_enabled() to check if
interactive logging is enabled.verbose argument of
Model.evaluate() and Model.predict() to
"auto", which defaults to verbose=1 for most
cases and defaults to verbose=2 when used with
ParameterServerStrategy or with interactive logging
disabled.jit_compile in Model.compile()
now applies to Model.evaluate() and
Model.predict(). Setting jit_compile=True in
compile() compiles the model's training, evaluation, and
inference steps to XLA.
Note that jit_compile=True may not necessarily work for all
models.tf.keras.dtensor
namespace. The APIs are still classified as experimental. You are
welcome to try it out. Please check the tutoral and guide on https://www.tensorflow.org/ for
more details about DTensor.tf.lite:
tf.math.argmin/tf.math.argmax for input
data type tf.bool on CPU.tf.nn.gelu op for output data type
tf.float32 and quantization on CPU.list_ops.tensor_list_set_item with
DynamicUpdateSlice.experimental_new_dynamic_range_quantizer in
tf.lite.TFLiteConverter to False to disable this changeexperimental_enable_resource_variables on
tf.lite.TFLiteConverter is now True by default and will be removed in
the future.tf.function:
tf.function can
now specify rules regarding when retracing needs to occur by
implementing the Tracing Protocol available through
tf.types.experimental.SupportsTracingProtocol.TypeSpec classes (as associated with
ExtensionTypes) also implement the Tracing Protocol which
can be overriden if necessary.reduce_retracing option also uses
the Tracing Protocol to proactively generate generalized traces similar
to experimental_relax_shapes (which has now been
deprecated).Unified eager and tf.function execution:
tf.function,
allowing for more consistent feature support in future releases.TF_RUN_EAGER_OP_AS_FUNCTION environment
variable in eager
context.tf.function itself is unaffected.tf.experimental.dtensor: Added DTensor, an extension
to TensorFlow for large-scale modeling with minimal changes to user
code. You are welcome to try it out, though be aware that the DTensor
API is experimental and up-to backward-incompatible changes. DTensor and
Keras integration is published under tf.keras.dtensor in
this release (refer to the tf.keras entry). The tutoral and
guide for DTensor will be published on https://www.tensorflow.org/.
Please stay tuned.
oneDNN CPU performance optimizations are available in Linux x86, Windows x86, and Linux aarch64 packages.
--config=mkl_aarch64)
package:
TF_ENABLE_ONEDNN_OPTS to
1 (enable) or 0 (disable) before running
TensorFlow. (The variable is checked during
import tensorflow.) To fall back to default settings, unset
the environment variable.tf.data:
tf.data.experimental.parse_example_dataset
when tf.io.RaggedFeatures would specify
value_key but no partitions. Before the fix,
setting value_key but no partitions would
result in the feature key being replaced by the value key, e.g.
{'value_key': <RaggedTensor>} instead of
{'key': <RaggedTensor>}. Now the correct feature key
will be used. This aligns the behavior of
tf.data.experimental.parse_example_dataset to match the
behavior of tf.io.parse_example.filter_parallelization, to
tf.data.experimental.OptimizationOptions. If it is set to
True, tf.data will run Filter transformation
with multiple threads. Its default value is False if not
specified.tf.keras:
ShardedVariables
(used for training with
tf.distribute.experimental.ParameterServerStrategy).tf.random:
tf.random.experimental.index_shuffle, for
shuffling a sequence without materializing the sequence in memory.tf.RaggedTensor:
tf.experimental.RowPartition, which encodes
how one dimension in a RaggedTensor relates to another, into the public
API.tf.experimental.DynamicRaggedShape, which
represents the shape of a RaggedTensor.saved_model_cli (CVE-2022-29216)TensorSummaryV2
to crash (CVE-2022-29193)QuantizeAndDequantizeV4Grad (CVE-2022-29192)DeleteSessionTensor (CVE-2022-29194)GetSessionTensor (CVE-2022-29191)StagePeek (CVE-2022-29195)UnsortedSegmentJoin (CVE-2022-29197)LoadAndRemapMatrix (CVE-2022-29199)SparseTensorToCSRSparseMatrix (CVE-2022-29198)LSTMBlockCell (CVE-2022-29200)Conv3DBackpropFilterV2 (CVE-2022-29196)CHECK failure in depthwise ops via overflows
(CVE-2021-41197)SparseTensorDenseAdd (CVE-2022-29206)QuantizedConv2D (CVE-2022-29201)SpaceToBatchND (CVE-2022-29203)EditDistance (CVE-2022-29208)Conv3DBackpropFilterV2 (CVE-2022-29204)tf.ragged.constant due to
lack of validation (CVE-2022-29202)tf.histogram_fixed_width is
called with NaN values (CVE-2022-29211)CHECK-failure based
denial of service (CVE-2022-29209)curl to 7.83.1 to handle (CVE-2022-22576,
(CVE-2022-27774,
(CVE-2022-27775,
(CVE-2022-27776,
(CVE-2022-27778,
(CVE-2022-27779,
(CVE-2022-27780,
(CVE-2022-27781,
(CVE-2022-27782
and (CVE-2022-30115zlib to 1.2.12 after
1.2.11 was pulled due to security
issueThis release contains contributions from many people at Google, as well as:
Aaron Debattista, Abel Soares Siqueira, Abhishek Varma, Andrei Ivanov, andreii, Andrew Goodbody, apeltop, Arnab Dutta, Ashiq Imran, Banikumar Maiti (Intel Aipg), Ben Greiner, Benjamin Peterson, bhack, Christopher Bate, chunduriv, Copybara-Service, DEKHTIARJonathan, Deven Desai, Duncan Riach, Eric Kunze, Everton Constantino, Faruk D, Fredrik Knutsson, gadagashwini, Gauri1 Deshpande, gtiHibGele, Guozhong Zhuang, Islem-Esi, Ivanov Viktor, Jason Furmanek, Jason Zaman, Jim, Jinzhe Zeng, John Laxson, Jonas Eschle, Jonas Eschle 'Mayou36, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, KaurkerDevourer, Koan-Sin Tan, kushanam, Laramie Leavitt, Li-Wen Chang, lipracer, Louis Sugy, Lu Teng, Mahmoud Abuzaina, Malcolm Slaney, Malik Shahzad Muzaffar, Marek Šuppa, Matt Conley, Michael Melesse, Milos Puzovic, mohantym, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Patrice Vignola, peterjc123, Philip Turner, Rajeshwar Reddy T, Robert Kalmar, Rodrigo Formigone, Rohit Santhanam, rui, Sachin Muradi, Saduf2019, sandip, Scott Leishman, Serge Panev, Shi,Guangyong, Srinivasan Narayanamoorthy, stanley, Steven I Reeves, stevenireeves, sushreebarsa, Tamas Bela Feher, Tao He, Thomas Schmeyer, Tiago Almeida, Trevor Morris, Uday Bondhugula, Uwe L. Korn, Varghese, Jojimon, Vishnuvardhan Janapati, William Muir, William Raveane, xutianming, Yasuhiro Matsumoto, Yimei Sun, Yong Tang, Yu Feng, Yuriy Chernyshov, zhaozheng09
tf.lite:
tf.raw_ops.Bucketize op on CPU.tf.where op for data types
tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.tf.random.normal op for output data type
tf.float32 on CPU.tf.random.uniform op for output data type
tf.float32 on CPU.tf.random.categorical op for output data type
tf.int64 on CPU.tensorflow.experimental.tensorrt:
conversion_params is now deprecated inside
TrtGraphConverterV2 in favor of direct arguments:
max_workspace_size_bytes, precision_mode,
minimum_segment_size, maximum_cached_engines,
use_calibration and
allow_build_at_runtime.save_gpu_specific_engines
to the .save() function inside
TrtGraphConverterV2. When False, the
.save() function won't save any TRT engines that have been
built. When True (default), the original behavior is
preserved.TrtGraphConverterV2 provides a new API called
.summary() which outputs a summary of the inference
converted by TF-TRT. It namely shows each TRTEngineOp with
their input(s)' and output(s)' shape and dtype. A detailed version of
the summary is available which prints additionally all the TensorFlow
OPs included in each of the TRTEngineOps.tf.tpu.experimental.embedding:
tf.tpu.experimental.embedding.FeatureConfig now takes
an additional argument output_shape which can specify the
shape of the output activation for the feature.tf.tpu.experimental.embedding.TPUEmbedding now has the
same behavior as
tf.tpu.experimental.embedding.serving_embedding_lookup
which can take arbitrary rank of dense and sparse tensor. For ragged
tensor, though the input tensor remains to be rank 2, the activations
now can be rank 2 or above by specifying the output shape in the feature
config or via the build method.Add tf.config.experimental.enable_op_determinism,
which makes TensorFlow ops run deterministically at the cost of
performance. Replaces the TF_DETERMINISTIC_OPS
environmental variable, which is now deprecated. The "Bug Fixes and
Other Changes" section lists more determinism-related changes.
(Since TF 2.7) Add PluggableDevice support to TensorFlow Profiler.
tf.data:
options.deterministic = False
would only modify one transformation to run non-deterministically,
leaving other transformations deterministic. The option will now apply
the same across all transformations.parallel_batch now becomes default if
not disabled by users, which will parallelize copying of batch
elements.TensorSliceDataset to identify
and handle inputs that are files. This enables creating hermetic
SavedModels when using datasets created from files.tf.lite:
Interpreter::SetNumThreads, in favor of
InterpreterBuilder::SetNumThreads.tf.keras:
tf.compat.v1.keras.utils.get_or_create_layer to
aid migration to TF2 by enabling tracking of nested keras models created
in TF1-style, when used with the
tf.compat.v1.keras.utils.track_tf1_style_variables
decorator.tf.keras.layers.experimental.preprocessing.HashedCrossing
layer which applies the hashing trick to the concatenation of crossed
scalar inputs. This provides a stateless way to try adding feature
crosses of integer or string data to a model.keras.layers.experimental.preprocessing.CategoryCrossing.
Users should migrate to the HashedCrossing layer or use
tf.sparse.cross/tf.ragged.cross directly.standardize and split
modes to TextVectorization:
standardize="lower" will lowercase inputs.standardize="string_punctuation" will remove all
puncuation.split="character" will split on every unicode
character.output_mode argument to the
Discretization and Hashing layers with the
same semantics as other preprocessing layers. All categorical
preprocessing layers now support output_mode.tf.keras.mixed_precision.Policy, unless constructed with
output_mode="int" in which case output will be
tf.int64. The output type of any preprocessing layer can be
controlled individually by passing a dtype argument to the
layer.tf.random.Generator for keras initializers and all RNG
code.tf.random.Generator in keras backend, which will be the new
backend for all the RNG in Keras. We plan to switch on the new code path
by default in tf 2.8, and the behavior change will likely to cause some
breakage on user side (eg if the test is checking against some golden
nubmer). These 3 APIs will allow user to disable and switch back to
legacy behavior if they prefer. In future (eg TF 2.10), we expect to
totally remove the legacy code path (stateful random Ops), and these 3
APIs will be removed as well.tf.keras.callbacks.experimental.BackupAndRestore is now
available as tf.keras.callbacks.BackupAndRestore. The
experimental endpoint is deprecated and will be removed in a future
release.tf.keras.experimental.SidecarEvaluator is now available
as tf.keras.utils.SidecarEvaluator. The experimental
endpoint is deprecated and will be removed in a future release.Model.train_step() is now customizable via overriding
Model.compute_metrics().Model.train_step()
is now customizable via overriding
Model.compute_loss().jit_compile added to Model.compile() on an
opt-in basis to compile the model's training step with XLA. Note that
jit_compile=True may not necessarily work for all
models.Deterministic Op Functionality:
tf.function(jit_compile=True)'s that use
Scatter.tf.data.Datasettf.convert_to_tensor when fed with
(sparse) tf.IndexedSlices (because it uses
tf.math.unsorted_segment_sum)tf.gather backprop (because
tf.convert_to_tensor reduces tf.gather's
(sparse) tf.IndexedSlices gradients into its dense
params input)tf.math.segment_meantf.math.segment_prodtf.math.segment_sumtf.math.unsorted_segment_meantf.math.unsorted_segment_prodtf.math.unsorted_segment_sumtf.math.unsorted_segment_sqrttf.nn.ctc_loss (resolved, possibly in
prior release, and confirmed with tests)tf.nn.sparse_softmax_crossentropy_with_logitstf.scatter_nd and other related
scatter functions, such as tf.tensor_scatter_nd_update, on
CPU (with significant performance penalty).tf.config.experimental.enable_op_determinism has been
called), an attempt to use the specified paths through the following ops
on a GPU will cause tf.errors.UnimplementedError (with an
understandable message), unless otherwise specified, to be thrown.
FakeQuantWithMinMaxVarsGradient and
FakeQuantWithMinMaxVarsPerChannelGradienttf.compat.v1.get_seed if the global random
seed has not yet been set (via tf.random.set_seed). Throws
RuntimeError from Python or InvalidArgument
from C++tf.compat.v1.nn.fused_batch_norm backprop
to offset when is_training=Falsetf.image.adjust_contrast forwardtf.image.resize with
method=ResizeMethod.NEAREST backproptf.linalg.svdtf.math.bincounttf.nn.depthwise_conv2d backprop to
filter when not using cuDNN convolutiontf.nn.dilation2d gradienttf.nn.max_pool_with_argmax gradienttf.raw_ops.DebugNumericSummary and
tf.raw_ops.DebugNumericSummaryV2tf.timestamp. Throws
FailedPreconditiontf.Variable.scatter_add (and other scatter
methods, both on ref and resource variables)tf.random module when the global random seed has not yet
been set (via tf.random.set_seed). Throws
RuntimeError from Python or InvalidArgument
from C++TensorFlow-oneDNN no longer supports explicit use
of oneDNN blocked tensor format, e.g., setting the environment
variable TF_ENABLE_MKL_NATIVE_FORMAT will not have any
effect.
TensorFlow has been validated on Windows Subsystem for Linux 2 (aka WSL 2) for both GPUs and CPUs.
Due to security issues (see section below), all boosted trees code has been deprecated. Users should switch to TensorFlow Decision Forests. TF's boosted trees code will be eliminated before the branch cut for TF 2.9 and will no longer be present since that release.
ReverseSequence (CVE-2022-21728)Dequantize (CVE-2022-21726)Dequantize (CVE-2022-21727)FractionalAvgPoolGrad (CVE-2022-21730)UnravelIndex
(CVE-2022-21729)ConcatV2
(CVE-2022-21731)ThreadPoolHandle (CVE-2022-21732)StringNGrams
(CVE-2022-21733)AddManySparseToTensorsMap
(CVE-2022-23568)CHECK-failures in
MapStage (CVE-2022-21734)FractionalMaxPool (CVE-2022-21735)CHECK-fails when building
invalid/overflowing tensor shapes (CVE-2022-23569)SparseTensorSliceDataset
(CVE-2022-21736)QuantizedMaxPool (CVE-2022-21739)SparseCountSparseOutput (CVE-2022-21738)SparseCountSparseOutput (CVE-2022-21740)BiasAndClamp in TFLite (CVE-2022-23557)tf.sparse.split to crash when axis is a tuple
(CVE-2021-41206)CHECK-fail when decoding resource handles from
proto (CVE-2022-23564)CHECK-fail with repeated AttrDef
(CVE-2022-23565)CHECK-fail when decoding invalid tensors from
proto (CVE-2022-23571)SpecializeType (CVE-2022-23574)AssignOp (CVE-2022-23573)OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)GetInitOp (CVE-2022-23577)CHECK-failures during Grappler's
IsSimplifiableReshape (CVE-2022-23581)CHECK-failures during Grappler's
SafeToRemoveIdentity (CVE-2022-23579)CHECK-failures in
TensorByteSize (CVE-2022-23582)CHECK-failures in binary ops due to type
confusion (CVE-2022-23583)DecodePng kernel (CVE-2022-23584)CHECK-fails in function.cc
(CVE-2022-23586)CHECK-fails due to attempting to build a
reference tensor (CVE-2022-23588)IsConstant (CVE-2022-23589)CHECK failure in constant folding (CVE-2021-41197)GraphDef (CVE-2022-23591)RunForwardTypeInference (CVE-2022-23592)StatusOr (CVE-2022-23590)simplifyBroadcast (MLIR) (CVE-2022-23593)BuildXlaCompilationCache (XLA) (CVE-2022-23595)icu to 69.1 to handle CVE-2020-10531This release contains contributions from many people at Google, as well as:
8bitmp3, Adam Lanicek, ag.ramesh, alesapin, Andrew Goodbody, annasuheyla, Ariel Elkin, Arnab Dutta, Ben Barsdell, bhack, cfRod, Chengji Yao, Christopher Bate, dan, Dan F-M, David Korczynski, DEKHTIARJonathan, dengzhiyuan, Deven Desai, Duncan Riach, Eli Osherovich, Ewout Ter Hoeven, ez2take, Faijul Amin, fo40225, Frederic Bastien, gadagashwini, Gauri1 Deshpande, Georgiy Manuilov, Guilherme De Lázari, Guozhong Zhuang, H1Gdev, homuler, Hongxu Jia, Jacky_Yin, jayfurmanek, jgehw, Jhalak Patel, Jinzhe Zeng, Johan Gunnarsson, Jonathan Dekhtiar, Kaixi Hou, Kanvi Khanna, Kevin Cheng, Koan-Sin Tan, Kruglov-Dmitry, Kun Lu, Lemo, Lequn Chen, long.chen, Louis Sugy, Mahmoud Abuzaina, Mao, Marius Brehler, Mark Harfouche, Martin Patz, Maxiwell S. Garcia, Meenakshi Venkataraman, Michael Melesse, Mrinal Tyagi, Måns Nilsson, Nathan John Sircombe, Nathan Luehr, Nilesh Agarwalla, Oktay Ozturk, Patrice Vignola, Pawel-Polyai, Rama Ketineni, Ramesh Sampath, Reza Rahimi, Rob Suderman, Robert Kalmar, Rohit Santhanam, Sachin Muradi, Saduf2019, Samuel Marks, Shi,Guangyong, Sidong-Wei, Srinivasan Narayanamoorthy, Srishti Srivastava, Steven I Reeves, stevenireeves, Supernovae, Tamas Bela Feher, Tao Xu, Thibaut Goetghebuer-Planchon, Thomas Schmeyer, tilakrayal, Valery Mironov, Victor Guo, Vignesh Kothapalli, Vishnuvardhan Janapati, wamuir, Wang,Quintin, William Muir, William Raveane, Yash Goel, Yimei Sun, Yong Tang, Yuduo Wu
This releases introduces several vulnerability fixes:
ReverseSequence (CVE-2022-21728)Dequantize (CVE-2022-21726)Dequantize (CVE-2022-21727)FractionalAvgPoolGrad (CVE-2022-21730)UnravelIndex
(CVE-2022-21729)ConcatV2
(CVE-2022-21731)ThreadPoolHandle (CVE-2022-21732)StringNGrams
(CVE-2022-21733)AddManySparseToTensorsMap
(CVE-2022-23568)CHECK-failures in
MapStage (CVE-2022-21734)FractionalMaxPool (CVE-2022-21735)CHECK-fails when building
invalid/overflowing tensor shapes (CVE-2022-23569)SparseTensorSliceDataset
(CVE-2022-21736)QuantizedMaxPool (CVE-2022-21739)SparseCountSparseOutput (CVE-2022-21738)SparseCountSparseOutput (CVE-2022-21740)BiasAndClamp in TFLite (CVE-2022-23557)tf.sparse.split to crash when axis is a tuple
(CVE-2021-41206)CHECK-fail when decoding resource handles from
proto (CVE-2022-23564)CHECK-fail with repeated AttrDef
(CVE-2022-23565)CHECK-fail when decoding invalid tensors from
proto (CVE-2022-23571)SpecializeType (CVE-2022-23574)AssignOp (CVE-2022-23573)OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)GetInitOp (CVE-2022-23577)CHECK-failures during Grappler's
IsSimplifiableReshape (CVE-2022-23581)CHECK-failures during Grappler's
SafeToRemoveIdentity (CVE-2022-23579)CHECK-failures in
TensorByteSize (CVE-2022-23582)CHECK-failures in binary ops due to type
confusion (CVE-2022-23583)DecodePng kernel (CVE-2022-23584)CHECK-fails in function.cc
(CVE-2022-23586)CHECK-fails due to attempting to build a
reference tensor (CVE-2022-23588)IsConstant (CVE-2022-23589)CHECK failure in constant folding (CVE-2021-41197)GraphDef (CVE-2022-23591)StatusOr (CVE-2022-23590)BuildXlaCompilationCache (XLA) (CVE-2022-23595)icu to 69.1 to handle CVE-2020-10531This releases introduces several vulnerability fixes:
ReverseSequence (CVE-2022-21728)Dequantize (CVE-2022-21726)Dequantize (CVE-2022-21727)FractionalAvgPoolGrad (CVE-2022-21730)UnravelIndex
(CVE-2022-21729)ConcatV2
(CVE-2022-21731)ThreadPoolHandle (CVE-2022-21732)StringNGrams
(CVE-2022-21733)AddManySparseToTensorsMap
(CVE-2022-23568)CHECK-failures in
MapStage (CVE-2022-21734)FractionalMaxPool (CVE-2022-21735)CHECK-fails when building
invalid/overflowing tensor shapes (CVE-2022-23569)SparseTensorSliceDataset
(CVE-2022-21736)QuantizedMaxPool (CVE-2022-21739)SparseCountSparseOutput (CVE-2022-21738)SparseCountSparseOutput (CVE-2022-21740)BiasAndClamp in TFLite (CVE-2022-23557)tf.sparse.split to crash when axis is a tuple
(CVE-2021-41206)CHECK-fail when decoding resource handles from
proto (CVE-2022-23564)CHECK-fail with repeated AttrDef
(CVE-2022-23565)CHECK-fail when decoding invalid tensors from
proto (CVE-2022-23571)SpecializeType (CVE-2022-23574)AssignOp (CVE-2022-23573)OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)GetInitOp (CVE-2022-23577)CHECK-failures during Grappler's
IsSimplifiableReshape (CVE-2022-23581)CHECK-failures during Grappler's
SafeToRemoveIdentity (CVE-2022-23579)CHECK-failures in
TensorByteSize (CVE-2022-23582)CHECK-failures in binary ops due to type
confusion (CVE-2022-23583)DecodePng kernel (CVE-2022-23584)CHECK-fails in function.cc
(CVE-2022-23586)CHECK-fails due to attempting to build a
reference tensor (CVE-2022-23588)IsConstant (CVE-2022-23589)CHECK failure in constant folding (CVE-2021-41197)GraphDef (CVE-2022-23591)BuildXlaCompilationCache (XLA) (CVE-2022-23595)icu to 69.1 to handle CVE-2020-10531This releases introduces several vulnerability fixes:
ReverseSequence (CVE-2022-21728)Dequantize (CVE-2022-21726)Dequantize (CVE-2022-21727)FractionalAvgPoolGrad (CVE-2022-21730)UnravelIndex
(CVE-2022-21729)ConcatV2
(CVE-2022-21731)ThreadPoolHandle (CVE-2022-21732)StringNGrams
(CVE-2022-21733)AddManySparseToTensorsMap
(CVE-2022-23568)CHECK-failures in
MapStage (CVE-2022-21734)FractionalMaxPool (CVE-2022-21735)CHECK-fails when building
invalid/overflowing tensor shapes (CVE-2022-23569)SparseTensorSliceDataset
(CVE-2022-21736)QuantizedMaxPool (CVE-2022-21739)SparseCountSparseOutput (CVE-2022-21738)SparseCountSparseOutput (CVE-2022-21740)BiasAndClamp in TFLite (CVE-2022-23557)tf.sparse.split to crash when axis is a tuple
(CVE-2021-41206)CHECK-fail when decoding resource handles from
proto (CVE-2022-23564)CHECK-fail with repeated AttrDef
(CVE-2022-23565)CHECK-fail when decoding invalid tensors from
proto (CVE-2022-23571)AssignOp (CVE-2022-23573)OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)GetInitOp (CVE-2022-23577)CHECK-failures during Grappler's
IsSimplifiableReshape (CVE-2022-23581)CHECK-failures during Grappler's
SafeToRemoveIdentity (CVE-2022-23579)CHECK-failures in
TensorByteSize (CVE-2022-23582)CHECK-failures in binary ops due to type
confusion (CVE-2022-23583)DecodePng kernel (CVE-2022-23584)CHECK-fails in function.cc
(CVE-2022-23586)CHECK-fails due to attempting to build a
reference tensor (CVE-2022-23588)IsConstant (CVE-2022-23589)CHECK failure in constant folding (CVE-2021-41197)GraphDef (CVE-2022-23591)icu to 69.1 to handle CVE-2020-10531tf.keras:
Model.fit(), Model.predict(),
and Model.evaluate() will no longer uprank input data of
shape (batch_size,) to become (batch_size, 1).
This enables Model subclasses to process scalar data in
their
train_step()/test_step()/predict_step()
methods.train_step()/test_step()/predict_step()
methods, e.g.
if x.shape.rank == 1: x = tf.expand_dims(x, axis=-1).
Functional models as well as Sequential models built with an explicit
input shape are not affected.Model.to_yaml() and
keras.models.model_from_yaml have been replaced to raise a
RuntimeError as they can be abused to cause arbitrary code
execution. It is recommended to use JSON serialization instead of YAML,
or, a better alternative, serialize to H5.LinearModel and WideDeepModel are moved to
the tf.compat.v1.keras.models. namespace
(tf.compat.v1.keras.models.LinearModel and
tf.compat.v1.keras.models.WideDeepModel), and their
experimental endpoints
(tf.keras.experimental.models.LinearModel and
tf.keras.experimental.models.WideDeepModel) are being
deprecated.tf.keras.initializers
classes. For any class constructed with a fixed seed, it will no longer
generate same value when invoked multiple times. Instead, it will return
different value, but a determinisitic sequence. This change will make
the initialize behavior align between v1 and v2.tf.lite:
SignatureDef table in schema to maximize
the parity with TF SavedModel's Signature concept.tflite::OpResolver::GetDelegates. The list
returned by TfLite's BuiltinOpResolver::GetDelegates is now
always empty. Instead, recommend using new method
tflite::OpResolver::GetDelegateCreators in order to achieve
lazy initialization on TfLite delegate instances.TF Core:
tf.Graph.get_name_scope() now always returns a string,
as documented. Previously, when called within
name_scope("") or name_scope(None) contexts,
it returned None; now it returns the empty string.tensorflow/core/ir/ contains a new MLIR-based Graph
dialect that is isomorphic to GraphDef and will be used to replace
GraphDef-based (e.g., Grappler) optimizations.attrs() function in shape
inference. All attributes should be queried by name now (rather than
range returned) to enable changing the underlying storage there.tf.quantize_and_dequantize_v4 (accidentally introduced
in TensorFlow 2.4): Use
tf.quantization.quantize_and_dequantize_v2 instead.tf.batch_mat_mul_v3 (accidentally introduced in
TensorFlow 2.6): Use tf.linalg.matmul instead.tf.sparse_segment_sum_grad (accidentally introduced in
TensorFlow 2.6): Use tf.raw_ops.SparseSegmentSumGrad
instead. Directly calling this op is typically not necessary, as it is
automatically used when computing the gradient of
tf.sparse.segment_sum.Modular File System Migration:
tensorflow-io python package should be installed for S3
and HDFS support with tensorflow.Improvements to the TensorFlow debugging experience:
This behavior can be disabled by calling
tf.debugging.disable_traceback_filtering(), and can be
re-enabled via tf.debugging.enable_traceback_filtering().
If you are debugging a TensorFlow-internal issue (e.g. to prepare a
TensorFlow PR), make sure to disable traceback filtering. You can check
whether this feature is currently enabled by calling
tf.debugging.is_traceback_filtering_enabled().
Note that this feature is only available with Python 3.7 or higher.
Layer.__call__(), by adding the full list of argument
values passed to the layer in every exception.Introduce the
tf.compat.v1.keras.utils.track_tf1_style_variables
decorator, which enables using large classes of tf1-style
variable_scope, get_variable, and
compat.v1.layer-based components from within TF2 models
running with TF2 behavior enabled.
tf.data:
tf.data service now supports auto-sharding. Users specify the
sharding policy with
tf.data.experimental.service.ShardingPolicy enum. It can be
one of OFF (equivalent to today's
"parallel_epochs" mode), DYNAMIC (equivalent
to today's "distributed_epoch" mode), or one of the static
sharding policies: FILE, DATA,
FILE_OR_DATA, or HINT (corresponding to values
of tf.data.experimental.AutoShardPolicy).
Static sharding (auto-sharding) requires the number of tf.data
service workers be fixed. Users need to specify the worker addresses in
tensorflow.data.experimental.DispatcherConfig.
tf.data.experimental.service.register_dataset now
accepts optional compression argument.
Keras:
tf.keras.layers.Conv now includes a public
convolution_op method. This method can be used to simplify
the implementation of Conv subclasses. There are two primary ways to use
this new method. The first is to use the method directly in your own
call method:
python class StandardizedConv2D(tf.keras.layers.Conv2D): def call(self, inputs): mean, var = tf.nn.moments(self.kernel, axes=[0, 1, 2], keepdims=True) return self.convolution_op(inputs, (self.kernel - mean) / tf.sqrt(var + 1e-10))
Alternatively, you can override convolution_op:
python class StandardizedConv2D(tf.keras.Layer): def convolution_op(self, inputs, kernel): mean, var = tf.nn.moments(kernel, axes=[0, 1, 2], keepdims=True) # Author code uses std + 1e-5 return super().convolution_op(inputs, (kernel - mean) / tf.sqrt(var + 1e-10))merge_state() method to
tf.keras.metrics.Metric for use in distributed
computations.sparse and ragged options to
tf.keras.layers.TextVectorization to allow for
SparseTensor and RaggedTensor outputs from the
layer.distribute.experimental.rpc package:
distribute.experimental.rpc package introduces APIs to create a GRPC based server to register tf.function methods and a GRPC client to invoke remote registered methods. RPC APIs are intended for multi-client setups i.e. server and clients are started in separate binaries independently.
Example usage to create server: ```python server = tf.distribute.experimental.rpc.Server.create("grpc", "127.0.0.1:1234") @tf.function(input_signature=[ tf.TensorSpec([], tf.int32), tf.TensorSpec([], dtypes.int32) ]) def _remote_multiply(a, b): return tf.math.multiply(a, b)
server.register("multiply", _remote_multiply) ```
Example usage to create client:
python client = tf.distribute.experimental.rpc.Client.create("grpc", address) a = tf.constant(2, dtype=tf.int32) b = tf.constant(3, dtype=tf.int32) result = client.multiply(a, b)
tf.lite:
experimental_from_jax to support
conversion from Jax models to TensorFlow Lite.tf.lite.QuantizationDebuggertf.function. This returns a callable object which
validates TFLite compatibility. If an incompatible operation is
encountered during execution, an exception will be raised with
information about the incompatible ops.Extension Types
tf.experimental.ExtensionType as its
base, and use type annotations to specify the type for each field. E.g.:
python class MaskedTensor(tf.experimental.ExtensionType): values: tf.Tensor mask: tf.Tensor
The tf.ExtensionType base class works similarly to typing.NamedTuple
and @dataclasses.dataclass
from the standard Python library.tf.add or
tf.concat) when they are applied to ExtensionType
values.BatchableExtensionType API can be used to define
extension types that support APIs that make use of batching, such as
tf.data.Dataset and tf.map_fn.alg to tf.random.stateless_*
functions to explicitly select the RNG algorithm.tf.nn.experimental.stateless_dropout, a stateless
version of tf.nn.dropout.tf.random.Generator now can be created inside the scope
of tf.distribute.experimental.ParameterServerStrategy and
tf.distribute.experimental.CentralStorageStrategy.tf.experimental.disable_functional_ops_lowering which
disables functional control flow op lowering optimization. This is
useful when executing within a portable runtime where control flow op
kernels may not be loaded due to selective registration.experimental_is_anonymous to
tf.lookup.StaticHashTable.__init__ to create the table in
anonymous mode. In this mode, the table resource can only be accessed
via resource handles (not resource names) and will be deleted
automatically when all resource handles pointing to it are gone.tf.data:
tf.data.experimental.at API which
provides random access for input pipelines that consist of
transformations that support random access. The initial set of
transformations that support random access includes:
tf.data.Dataset.from_tensor_slices,tf.data.Dataset.shuffle,
tf.data.Dataset.batch, tf.data.Dataset.shard,
tf.data.Dataset.map, and
tf.data.Dataset.range.tf.data.Options.experimental_deterministic API
to tf.data.Options.deterministic and deprecate the
experimental endpoint.tf.data.Options.experimental_optimization.autotune* to
a newly created tf.data.Options.autotune.* and remove
support for
tf.data.Options.experimental_optimization.autotune_buffers.tf.data.experimental.sample_from_datasets API
to tf.data.Dataset.sample_from_datasets and deprecate the
experimental endpoint.TF_GPU_ALLOCATOR=cuda_malloc_async that use
cudaMallocAsync from CUDA 11.2. This could become the default in the
future.tf.saved_model.SaveOptions to disable this.--input_examples inputs are now
restricted to python literals to avoid code injection.jit_compile=True are now deterministic).tf.saved_model.save:
TF_DETERMINISTIC_OPS to "true" or
"1"):
tf.math.segment_sumtf.math.segment_prodtf.math.segment_meantf.math.unsorted_segment_sumtf.math.unsorted_segment_prodtf.math.unsorted_segment_sqrttf.math.unsorted_segment_meantf.gather backproptf.convert_to_tensor when fed with (sparse)
tf.IndexedSlicestf.nn.sparse_softmax_crossentropy_with_logitstf.nn.ctc_loss (resolved, possibly in prior release,
and confirmed with tests)tf.data.Datasettf.scatter_nd and other related scatter functions, such
as tf.tensor_scatter_nd_updateTF_DETERMINISTIC_OPS is set to "true" or
"1"), an attempt to use the specified paths through the
following ops on a GPU will cause
tf.errors.UnimplementedError (with an understandable
message), unless otherwise specified, to be thrown.
tf.compat.v1.nn.fused_batch_norm backprop to
offset when is_training=Falsetf.image.adjust_contrast forwardtf.nn.depthwise_conv2d backprop to filter
when not using cuDNN convolutiontf.image.resize with
method=ResizeMethod.NEAREST backproptf.math.bincount - TODO: confirm exception addedtf.raw_ops.DebugNumericSummary and
tf.raw_ops.DebugNumericSummaryV2tf.Variable.scatter_add (and other scatter methods,
both on ref and resource variables)tf.linalg.svdtf.nn.dilation2d gradienttf.nn.max_pool_with_argmax gradienttf.timestamp. Throws
FailedPreconditiontf.random
module when the global random seed has not yet been set (via
tf.random.set_seed). Throws RuntimeError from
Python or InvalidArgument from C++tf.compat.v1.get_seed if the global random seed has not
yet been set (via tf.random.set_seed). Throws
RuntimeError from Python or InvalidArgument
from C++saved_model_cli (CVE-2021-41228)FusedBatchNorm kernels (CVE-2021-41223)ImmutableConst (CVE-2021-41227)SparseBinCount (CVE-2021-41226)SparseFillEmptyRows (CVE-2021-41224)SplitV (CVE-2021-41222)Cudnn* ops (CVE-2021-41221)Exit node is not
preceded by Enter op (CVE-2021-41217)tf.raw_ops.AllToAll
(CVE-2021-41218)CollectiveReduceV2 (CVE-2021-41220)nullptr reference
binding in sparse matrix multiplication (CVE-2021-41219)Transpose (CVE-2021-41216)tf.function objects (CVE-2021-41213)DeserializeSparse (CVE-2021-41215)nullptr in tf.ragged.cross (CVE-2021-41214)tf.ragged.cross (CVE-2021-41212)QuantizeV2 (CVE-2021-41211)tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)ParallelConcat (CVE-2021-41207)tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)EinsumHelper::ParseEquation (CVE-2021-41201)tf.range (CVE-2021-41202)tf.image.resize
when size is large (CVE-2021-41199)tf.tile when
tiling tensor is large (CVE-2021-41198)tf.summary.create_file_writer (CVE-2021-41200)CHECK-fail
in ops with large tensor shapes (CVE-2021-41197)max_pool3d when size argument is 0 or
negative (CVE-2021-41196)tf.math.segment_* operations (CVE-2021-41195)curl to 7.78.0 to handle CVE-2021-22922,
CVE-2021-22923,
CVE-2021-22924,
CVE-2021-22925,
and CVE-2021-22926.This release contains contributions from many people at Google, as well as:
8bitmp3, Abhilash Majumder, abhilash1910, AdeshChoudhar, Adrian Garcia Badaracco, Adrian Ratiu, ag.ramesh, Aleksandr Nikolaev, Alexander Bosch, Alexander Grund, Annie Tallund, Anush Elangovan, Artem Sokolovskii, azazhu, Balint Cristian, Bas Aarts, Ben Barsdell, bhack, cfRod, Cheney-Wang, Cheng Ren, Christopher Bate, collin, Danila Bespalov, David Datascientist, Deven Desai, Duncan Riach, Ehsan Kia, Ellie, Fan Du, fo40225, Frederic Bastien, fsx950223, Gauri1 Deshpande, geetachavan1, Guillaume Klein, guozhong.zhuang, helen, Håkon Sandsmark, japm48, jgehw, Jinzhe Zeng, Jonathan Dekhtiar, Kai Zhu, Kaixi Hou, Kanvi Khanna, Koan-Sin Tan, Koki Ibukuro, Kulin Seth, KumaTea, Kun-Lu, Lemo, lipracer, liuyuanqiang, Mahmoud Abuzaina, Marius Brehler, Maxiwell S. Garcia, mdfaijul, metarutaiga, Michal Szutenberg, nammbash, Neil Girdhar, Nishidha Panpaliya, Nyadla-Sys, Patrice Vignola, Peter Kasting, Philipp Hack, PINTO0309, Prateek Gupta, puneeshkhanna, Rahul Butani, Rajeshwar Reddy T, Reza Rahimi, RinozaJiffry, rmothukuru, Rohit Santhanam, Saduf2019, Samuel Marks, sclarkson, Sergii Khomenko, Sheng, Yang, Sidong-Wei, slowy07, Srinivasan Narayanamoorthy, Srishti Srivastava, stanley, Stella Alice Schlotter, Steven I Reeves, stevenireeves, svobora, Takayoshi Koizumi, Tamas Bela Feher, Thibaut Goetghebuer-Planchon, Trent Lo, Twice, Varghese, Jojimon, Vishnuvardhan Janapati, Wang Yanzhang, Wang,Quintin, William Muir, William Raveane, Yasir Modak, Yasuhiro Matsumoto, Yi Li, Yong Tang, zhaozheng09, Zhoulong Jiang, zzpmiracle
Fixes an issue where keras,
tensorflow_estimator and tensorboard were
missing proper upper bounds and resulted in broken installs after TF 2.7
release
This release introduces several vulnerability fixes:
saved_model_cli (CVE-2021-41228)FusedBatchNorm kernels (CVE-2021-41223)ImmutableConst (CVE-2021-41227)SparseBinCount (CVE-2021-41226)SparseFillEmptyRows (CVE-2021-41224)SplitV (CVE-2021-41222)Cudnn* ops (CVE-2021-41221)Exit node is not
preceded by Enter op (CVE-2021-41217)tf.raw_ops.AllToAll
(CVE-2021-41218)CollectiveReduceV2 (CVE-2021-41220)nullptr reference
binding in sparse matrix multiplication (CVE-2021-41219)Transpose (CVE-2021-41216)tf.function objects (CVE-2021-41213)DeserializeSparse (CVE-2021-41215)nullptr in tf.ragged.cross (CVE-2021-41214)tf.ragged.cross (CVE-2021-41212)QuantizeV2 (CVE-2021-41211)tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)ParallelConcat (CVE-2021-41207)tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)EinsumHelper::ParseEquation (CVE-2021-41201)tf.range (CVE-2021-41202)tf.image.resize
when size is large (CVE-2021-41199)tf.tile when
tiling tensor is large (CVE-2021-41198)tf.summary.create_file_writer (CVE-2021-41200)CHECK-fail
in ops with large tensor shapes (CVE-2021-41197)max_pool3d when size argument is 0 or
negative (CVE-2021-41196)tf.math.segment_* operations (CVE-2021-41195)curl to 7.78.0 to handle CVE-2021-22922,
CVE-2021-22923,
CVE-2021-22924,
CVE-2021-22925,
and CVE-2021-22926.tf.train.experimental.enable_mixed_precision_graph_rewrite
is removed, as the API only works in graph mode and is not customizable.
The function is still accessible under
tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite,
but it is recommended to use the Keras mixed
precision API instead.
tf.lite:
experimental.nn.dynamic_rnn,
experimental.nn.TfLiteRNNCell and
experimental.nn.TfLiteLSTMCell since they're no longer
supported. It's recommended to just use keras
lstm instead.tf.keras:
keras),
and its code has been moved to the GitHub repositorykeras-team/keras. The API
endpoints for tf.keras stay unchanged, but are now backed
by the keras PIP package. The existing code in
tensorflow/python/keras is a staled copy and will be removed in future
release (2.7). Please remove any imports to
tensorflow.python.keras and replace them with public
tf.keras API instead.Model.to_yaml() and
keras.models.model_from_yaml have been replaced to raise a
RuntimeError as they can be abused to cause arbitrary code
execution. It is recommended to use JSON serialization instead of YAML,
or, a better alternative, serialize to H5.tf.while_loop, which caused it to
execute sequentially, even when parallel_iterations>1,
has now been fixed. However, the increased parallelism may result in
increased memory use. Users who experience unwanted regressions should
reset their while_loop's parallel_iterations
value to 1, which is consistent with prior behavior.tf.keras:
keras), and its code has been moved to the GitHub
repository keras-team/keras. The API
endpoints for tf.keras stay unchanged, but are now backed
by the keras PIP package. All Keras-related PRs and issues
should now be directed to the GitHub repository. keras-team/keras.tf.keras.utils.experimental.DatasetCreator now takes an
optional tf.distribute.InputOptions for specific options
when used with distribution.tf.keras.experimental.SidecarEvaluator is now available
for a program intended to be run on an evaluator task, which is commonly
used to supplement a training cluster running with
tf.distribute.experimental.ParameterServerStrategy (see `https://www.tensorflow.org/tutorials/distribute/parameter_server_training).
It can also be used with single-worker training or other strategies. See
docstring for more info.tf.keras.layers.preprocessing.experimental to
tf.keras.layers.StringLookup and IntegerLookup default for
mask_token changed to None. This matches the
default masking behavior of Hashing and
Embedding layers. To keep existing behavior, pass
mask_token="" during layer creation."binary" output mode to
"multi_hot" for CategoryEncoding,
StringLookup, IntegerLookup, and
TextVectorization. Multi-hot encoding will no longer
automatically uprank rank 1 inputs, so these layers can now multi-hot
encode unbatched multi-dimensional samples."one_hot" for
CategoryEncoding, StringLookup,
IntegerLookup, which will encode each element in an input
batch individually, and automatically append a new output dimension if
necessary. Use this mode on rank 1 inputs for the old
"binary" behavior of one-hot encoding a batch of
scalars.Normalization will no longer automatically uprank rank
1 inputs, allowing normalization of unbatched multi-dimensional
samples.tf.lite:
experimental_enable_resource_variables on
tf.lite.TFLiteConverter to True. Note: mutable variables is only
available using from_saved_model in this release, support
for other methods is coming soon.tf.saved_model:
tf.saved_model.SaveOption(experimental_custom_gradients=True)
to enable this feature. The documentation in Advanced
autodiff has been updated.TF Core:
tf.config.experimental.reset_memory_stats to
reset the tracked peak memory returned by
tf.config.experimental.get_memory_info.tf.data:
target_workers param to
data_service_ops.from_dataset_id and
data_service_ops.distribute. Users can specify
"AUTO", "ANY", or "LOCAL" (case
insensitive). If "AUTO", tf.data service runtime decides
which workers to read from. If "ANY", TF workers read from
any tf.data service workers. If "LOCAL", TF workers will
only read from local in-processs tf.data service workers.
"AUTO" works well for most cases, while users can specify
other targets. For example, "LOCAL" would help avoid RPCs
and data copy if every TF worker colocates with a tf.data service
worker. Currently, "AUTO" reads from any tf.data service
workers to preserve existing behavior. The default value is
"AUTO".tf.lookup.experimental.MutableHashTable, which
provides a generic mutable hash table implementation.
tf.lookup.experimental.DenseHashTable this
offers lower overall memory usage, and a cleaner API. It does not
require specifying a delete_key and empty_key
that cannot be inserted into the table.perturb_singular to
tf.linalg.tridiagonal_solve that allows solving linear
systems with a numerically singular tridiagonal matrix, e.g. for use in
inverse iteration.tf.linalg.eigh_tridiagonal that computes the
eigenvalues of a Hermitian tridiagonal matrix.tf.constant now places its output on the current
default device.tf.saved_model.experimental.TrackableResource,
which allows the creation of custom wrapper objects for resource
tensors.tf.saved_model.LoadOptions
for details.SparseSegmentSumGrad to match the other
sparse segment gradient ops and avoid an extra gather operation that was
in the previous gradient implementation.internal_fragmentation_fraction, which controls when the
BFC Allocator needs to split an oversized chunk to satisfy an allocation
request.tf.get_current_name_scope() which returns the
current full name scope string that will be prepended to op names.tf.data:
tf.data.experimental.bucket_by_sequence_length API to
tf.data.Dataset.bucket_by_sequence_length and deprecating
the experimental endpoint.tf.data.experimental.get_single_element API
to tf.data.Dataset.get_single_element and deprecating the
experimental endpoint.tf.data.experimental.group_by_window API to
tf.data.Dataset.group_by_window and deprecating the
experimental endpoint.tf.data.experimental.RandomDataset API to
tf.data.Dataset.random and deprecating the experimental
endpoint.tf.data.experimental.scan API to
tf.data.Dataset.scan and deprecating the experimental
endpoint.tf.data.experimental.snapshot API to
tf.data.Dataset.shapshot and deprecating the experimental
endpoint.tf.data.experimental.take_while API to
tf.data.Dataset.take_while and deprecating the experimental
endpoint.tf.data.experimental.ThreadingOptions API to
tf.data.ThreadingOptions and deprecating the experimental
endpoint.tf.data.experimental.unique API to
tf.data.Dataset.unique and deprecating the experimental
endpoint.stop_on_empty_dataset parameter to
sample_from_datasets and choose_from_datasets.
Setting stop_on_empty_dataset=True will stop sampling if it
encounters an empty dataset. This preserves the sampling ratio
throughout training. The prior behavior was to continue sampling,
skipping over exhausted datasets, until all datasets are exhausted. By
default, the original behavior
(stop_on_empty_dataset=False) is preserved.tf.data.Options.experimental_statstf.data.experimental.StatsAggregatortf.data.experimental.StatsOptions.*tf.data.experimental.bytes_produced_statstf.data.experimental.latency_statstf.data.experimental.MapVectorizationOptions.*tf.data.experimental.OptimizationOptions.filter_with_random_uniform_fusiontf.data.experimental.OptimizationOptions.hoist_random_uniformtf.data.experimental.OptimizationOptions.map_vectorization
*
tf.data.experimental.OptimizationOptions.reorder_data_discarding_opstf.keras:
__getitem__ slicing in Keras Functional
APIs when the inputs are RaggedTensor objects.keepdims argument to all GlobalPooling
layers.include_preprocessing argument to
MobileNetV3 architectures to control the inclusion of
Rescaling layer in the model.force) to
make_(train|test|predict)_funtion methods to skip the
cached function and generate a new one. This is useful to regenerate in
a single call the compiled training function when any
.trainable attribute of any model's layer has changed.save_spec property which contains the
TensorSpec specs for calling the model. This spec is
automatically saved when the model is called for the first time.tf.linalg:
CompositeTensor as a base class to
LinearOperator.tf.lite:
framework_stable BUILD target, which links in
only the non-experimental TF Lite APIs.Interpreter methods:
modifyGraphWithDelegate - Use
Interpreter.Options.addDelegatesetNumThreads - Use
Interpreter.Options.setNumThreadstf.summary:
tf.summary.should_record_summaries() so it
correctly reflects when summaries will be written, even when
tf.summary.record_if() is not n effect, by returning True
tensor if default writer is present.TF_DETERMINISTIC_OPS to "true" or
"1"):
tf.nn.softmax_cross_entropy_with_logits. See PR 49178.tf.image.crop_and_resize. See PR 48905.tf.errors.UnimplementedError (with an understandable
message) to be thrown.
SparseDenseCwiseDiv
(CVE-2021-37636)CompressElement (CVE-2021-37637)RaggedTensorToTensor (CVE-2021-37638)ResourceScatterDiv (CVE-2021-37642)RaggedGather (CVE-2021-37641)std::abort raised from
TensorListReserve (CVE-2021-37644)MatrixDiagPartOp
(CVE-2021-37643)StringNGrams caused by
integer conversion (CVE-2021-37646)SparseTensorSliceDataset (CVE-2021-37647)SaveV2 inputs (CVE-2021-37648)UncompressElement
(CVE-2021-37649){Experimental,}DatasetToTFRecord (CVE-2021-37650)FractionalAvgPoolGrad
(CVE-2021-37651)ResourceGather (CVE-2021-37653)CHECK fail in
ResourceGather (CVE-2021-37654)ResourceScatterUpdate (CVE-2021-37655)RaggedTensorToSparse (CVE-2021-37656)MatrixDiagV* ops (CVE-2021-37657)MatrixSetDiagV* ops (CVE-2021-37658)QuantizeV2 (CVE-2021-37663)RaggedTensorToVariant (CVE-2021-37666)tf.raw_ops.UnravelIndex (CVE-2021-37668)UpperBound and
LowerBound (CVE-2021-37670)SdcaOptimizerV2 (CVE-2021-37672)CHECK-fail in MapStage (CVE-2021-37673)MaxPoolGrad (CVE-2021-37674)Dequantize (CVE-2021-37677)tf.map_fn with
RaggedTensors (CVE-2021-37679)Gather* implementations
(CVE-2021-37687)curl to 7.77.0 to handle CVE-2021-22876,
CVE-2021-22897,
CVE-2021-22898,
and CVE-2021-22901.This release contains contributions from many people at Google, as well as:
Aadhitya A, Abhilash Mahendrakar, Abhishek Varma, Abin Shahab, Adam Hillier, Aditya Kane, AdityaKane2001, ag.ramesh, Amogh Joshi, Armen Poghosov, armkevincheng, Avrosh K, Ayan Moitra, azazhu, Banikumar Maiti, Bas Aarts, bhack, Bhanu Prakash Bandaru Venkata, Billy Cao, Bohumir Zamecnik, Bradley Reece, CyanXu, Daniel Situnayake, David Pal, Ddavis-2015, DEKHTIARJonathan, Deven Desai, Duncan Riach, Edward, Eli Osherovich, Eugene Kuznetsov, europeanplaice, evelynmitchell, Evgeniy Polyakov, Felix Vollmer, Florentin Hennecker, François Chollet, Frederic Bastien, Fredrik Knutsson, Gabriele Macchi, Gaurav Shukla, Gauri1 Deshpande, geetachavan1, Georgiy Manuilov, H, Hengwen Tong, Henri Woodcock, Hiran Sarkar, Ilya Arzhannikov, Janghoo Lee, jdematos, Jens Meder, Jerry Shih, jgehw, Jim Fisher, Jingbei Li, Jiri Podivin, Joachim Gehweiler, Johannes Lade, Jonas I. Liechti, Jonas Liechti, Jonas Ohlsson, Jonathan Dekhtiar, Julian Gross, Kaixi Hou, Kevin Cheng, Koan-Sin Tan, Kulin Seth, linzewen, Liubov Batanina, luisleee, Lukas Geiger, Mahmoud Abuzaina, mathgaming, Matt Conley, Max H. Gerlach, mdfaijul, Mh Kwon, Michael Martis, Michal Szutenberg, Måns Nilsson, nammbash, Neil Girdhar, Nicholas Vadivelu, Nick Kreeger, Nirjas Jakilim, okyanusoz, Patrice Vignola, Patrik Laurell, Pedro Marques, Philipp Hack, Phillip Cloud, Piergiacomo De Marchi, Prashant Kumar, puneeshkhanna, pvarouktsis, QQ喵, Rajeshwar Reddy T, Rama Ketineni, Reza Rahimi, Robert Kalmar, rsun, Ryan Kuester, Saduf2019, Sean Morgan, Sean Moriarity, Shaochen Shi, Sheng, Yang, Shu Wang, Shuai Zhang, Soojeong, Stanley-Nod, Steven I Reeves, stevenireeves, Suraj Sudhir, Sven Mayer, Tamas Bela Feher, tashuang.zk, tcervi, Teng Lu, Thales Elero Cervi, Thibaut Goetghebuer-Planchon, Thomas Walther, Till Brychcy, Trent Lo, Uday Bondhugula, vishakha.agrawal, Vishnuvardhan Janapati, wamuir, Wenwen Ouyang, wenwu, Williard Joshua Jose, xiaohong1031, Xiaoming (Jason) Cui, Xinan Jiang, Yasir Modak, Yi Li, Yong Tang, zilinzhu, 박상준, 이장
This release introduces several vulnerability fixes:
saved_model_cli (CVE-2021-41228)FusedBatchNorm kernels (CVE-2021-41223)ImmutableConst (CVE-2021-41227)SparseBinCount (CVE-2021-41226)SparseFillEmptyRows (CVE-2021-41224)SplitV (CVE-2021-41222)Cudnn* ops (CVE-2021-41221)Exit node is not
preceded by Enter op (CVE-2021-41217)tf.raw_ops.AllToAll
(CVE-2021-41218)nullptr reference
binding in sparse matrix multiplication (CVE-2021-41219)Transpose (CVE-2021-41216)tf.function objects (CVE-2021-41213)DeserializeSparse (CVE-2021-41215)nullptr in tf.ragged.cross (CVE-2021-41214)tf.ragged.cross (CVE-2021-41212)tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)ParallelConcat (CVE-2021-41207)tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)EinsumHelper::ParseEquation (CVE-2021-41201)tf.range (CVE-2021-41202)tf.image.resize
when size is large (CVE-2021-41199)tf.tile when
tiling tensor is large (CVE-2021-41198)tf.summary.create_file_writer (CVE-2021-41200)CHECK-fail
in ops with large tensor shapes (CVE-2021-41197)max_pool3d when size argument is 0 or
negative (CVE-2021-41196)tf.math.segment_* operations (CVE-2021-41195)curl to 7.78.0 to handle CVE-2021-22922,
CVE-2021-22923,
CVE-2021-22924,
CVE-2021-22925,
and CVE-2021-22926.This release introduces several vulnerability fixes:
SparseDenseCwiseDiv
(CVE-2021-37636)CompressElement (CVE-2021-37637)RaggedTensorToTensor (CVE-2021-37638)ResourceScatterDiv (CVE-2021-37642)RaggedGather (CVE-2021-37641)std::abort raised from
TensorListReserve (CVE-2021-37644)MatrixDiagPartOp
(CVE-2021-37643)StringNGrams caused by
integer conversion (CVE-2021-37646)SparseTensorSliceDataset (CVE-2021-37647)SaveV2 inputs (CVE-2021-37648)UncompressElement
(CVE-2021-37649){Experimental,}DatasetToTFRecord (CVE-2021-37650)FractionalAvgPoolGrad
(CVE-2021-37651)ResourceGather (CVE-2021-37653)CHECK fail in
ResourceGather (CVE-2021-37654)ResourceScatterUpdate (CVE-2021-37655)RaggedTensorToSparse (CVE-2021-37656)MatrixDiagV* ops (CVE-2021-37657)MatrixSetDiagV* ops (CVE-2021-37658)QuantizeV2 (CVE-2021-37663)RaggedTensorToVariant (CVE-2021-37666)tf.raw_ops.UnravelIndex (CVE-2021-37668)UpperBound and
LowerBound (CVE-2021-37670)SdcaOptimizerV2 (CVE-2021-37672)CHECK-fail in MapStage (CVE-2021-37673)MaxPoolGrad (CVE-2021-37674)Dequantize (CVE-2021-37677)tf.map_fn with
RaggedTensors (CVE-2021-37679)Gather* implementations
(CVE-2021-37687)curl to 7.77.0 to handle CVE-2021-22876,
CVE-2021-22897,
CVE-2021-22898,
and CVE-2021-22901.This release introduces several vulnerability fixes:
saved_model_cli (CVE-2021-41228)FusedBatchNorm kernels (CVE-2021-41223)ImmutableConst (CVE-2021-41227)SparseBinCount (CVE-2021-41226)SparseFillEmptyRows (CVE-2021-41224)SplitV (CVE-2021-41222)Cudnn* ops (CVE-2021-41221)Exit node is not
preceded by Enter op (CVE-2021-41217)tf.raw_ops.AllToAll
(CVE-2021-41218)nullptr reference
binding in sparse matrix multiplication (CVE-2021-41219)Transpose (CVE-2021-41216)tf.function objects (CVE-2021-41213)DeserializeSparse (CVE-2021-41215)nullptr in tf.ragged.cross (CVE-2021-41214)tf.ragged.cross (CVE-2021-41212)tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)ParallelConcat (CVE-2021-41207)tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)EinsumHelper::ParseEquation (CVE-2021-41201)tf.range (CVE-2021-41202)tf.image.resize
when size is large (CVE-2021-41199)tf.tile when
tiling tensor is large (CVE-2021-41198)tf.summary.create_file_writer (CVE-2021-41200)CHECK-fail
in ops with large tensor shapes (CVE-2021-41197)max_pool3d when size argument is 0 or
negative (CVE-2021-41196)tf.math.segment_* operations (CVE-2021-41195)curl to 7.78.0 to handle CVE-2021-22922,
CVE-2021-22923,
CVE-2021-22924,
CVE-2021-22925,
and CVE-2021-22926.This release introduces several vulnerability fixes:
SparseDenseCwiseDiv
(CVE-2021-37636)CompressElement (CVE-2021-37637)RaggedTensorToTensor (CVE-2021-37638)ResourceScatterDiv (CVE-2021-37642)RaggedGather (CVE-2021-37641)std::abort raised from
TensorListReserve (CVE-2021-37644)MatrixDiagPartOp
(CVE-2021-37643)StringNGrams caused by
integer conversion (CVE-2021-37646)SparseTensorSliceDataset (CVE-2021-37647)SaveV2 inputs (CVE-2021-37648)UncompressElement
(CVE-2021-37649){Experimental,}DatasetToTFRecord (CVE-2021-37650)FractionalAvgPoolGrad
(CVE-2021-37651)ResourceGather (CVE-2021-37653)CHECK fail in
ResourceGather (CVE-2021-37654)ResourceScatterUpdate (CVE-2021-37655)RaggedTensorToSparse (CVE-2021-37656)MatrixDiagV* ops (CVE-2021-37657)MatrixSetDiagV* ops (CVE-2021-37658)QuantizeV2 (CVE-2021-37663)RaggedTensorToVariant (CVE-2021-37666)tf.raw_ops.UnravelIndex (CVE-2021-37668)UpperBound and
LowerBound (CVE-2021-37670)SdcaOptimizerV2 (CVE-2021-37672)CHECK-fail in MapStage (CVE-2021-37673)MaxPoolGrad (CVE-2021-37674)Dequantize (CVE-2021-37677)tf.map_fn with
RaggedTensors (CVE-2021-37679)Gather* implementations
(CVE-2021-37687)curl to 7.77.0 to handle CVE-2021-22876,
CVE-2021-22897,
CVE-2021-22898,
and CVE-2021-22901.This release introduces several vulnerability fixes:
SparseDenseCwiseDiv
(CVE-2021-37636)CompressElement (CVE-2021-37637)RaggedTensorToTensor (CVE-2021-37638)ResourceScatterDiv (CVE-2021-37642)RaggedGather (CVE-2021-37641)std::abort raised from
TensorListReserve (CVE-2021-37644)MatrixDiagPartOp
(CVE-2021-37643)StringNGrams caused by
integer conversion (CVE-2021-37646)SparseTensorSliceDataset (CVE-2021-37647)SaveV2 inputs (CVE-2021-37648)UncompressElement
(CVE-2021-37649){Experimental,}DatasetToTFRecord (CVE-2021-37650)FractionalAvgPoolGrad
(CVE-2021-37651)ResourceGather (CVE-2021-37653)CHECK fail in
ResourceGather (CVE-2021-37654)ResourceScatterUpdate (CVE-2021-37655)RaggedTensorToSparse (CVE-2021-37656)MatrixDiagV* ops (CVE-2021-37657)MatrixSetDiagV* ops (CVE-2021-37658)QuantizeV2 (CVE-2021-37663)RaggedTensorToVariant (CVE-2021-37666)tf.raw_ops.UnravelIndex (CVE-2021-37668)UpperBound and
LowerBound (CVE-2021-37670)SdcaOptimizerV2 (CVE-2021-37672)CHECK-fail in MapStage (CVE-2021-37673)MaxPoolGrad (CVE-2021-37674)Dequantize (CVE-2021-37677)tf.map_fn with
RaggedTensors (CVE-2021-37679)Gather* implementations
(CVE-2021-37687)curl to 7.77.0 to handle CVE-2021-22876,
CVE-2021-22897,
CVE-2021-22898,
and CVE-2021-22901.This release introduces several vulnerability fixes:
RaggedBinCount (CVE-2021-29512)RaggedBinCount (CVE-2021-29514)MatrixDiag* ops (CVE-2021-29515)Conv3D (CVE-2021-29517)CHECK-fail in SparseCross caused
by type confusion (CVE-2021-29519)SparseCountSparseOutput (CVE-2021-29521)Conv3DBackprop* (CVE-2021-29520)Conv3DBackprop* (CVE-2021-29522)CHECK-fail in
AddManySparseToTensorsMap (CVE-2021-29523)Conv2DBackpropFilter (CVE-2021-29524)Conv2DBackpropInput (CVE-2021-29525)Conv2D (CVE-2021-29526)QuantizedConv2D (CVE-2021-29527)QuantizedMul (CVE-2021-29528)SparseMatrixSparseCholesky (CVE-2021-29530)CHECK-fail in tf.raw_ops.EncodePng
(CVE-2021-29531)RaggedCross (CVE-2021-29532)CHECK-fail in DrawBoundingBoxes
(CVE-2021-29533)QuantizedMul (CVE-2021-29535)CHECK-fail in SparseConcat (CVE-2021-29534)QuantizedResizeBilinear
(CVE-2021-29537)QuantizedReshape (CVE-2021-29536)Conv2DBackpropFilter (CVE-2021-29538)Conv2DBackpropFilter
(CVE-2021-29540)StringNGrams (CVE-2021-29542)StringNGrams (CVE-2021-29541)CHECK-fail in
QuantizeAndDequantizeV4Grad (CVE-2021-29544)CHECK-fail in CTCGreedyDecoder (CVE-2021-29543)SparseTensorToCSRSparseMatrix (CVE-2021-29545)QuantizedBiasAdd (CVE-2021-29546)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29547)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29548)QuantizedAdd (CVE-2021-29549)FractionalAvgPool (CVE-2021-29550)MatrixTriangularSolve (CVE-2021-29551)QuantizeAndDequantizeV3 (CVE-2021-29553)CHECK-failure in
UnsortedSegmentJoin (CVE-2021-29552)DenseCountSparseOutput (CVE-2021-29554)FusedBatchNorm (CVE-2021-29555)SparseMatMul (CVE-2021-29557)Reverse (CVE-2021-29556)SparseSplit (CVE-2021-29558)RaggedTensorToTensor
(CVE-2021-29560)CHECK-fail in LoadAndRemapMatrix
(CVE-2021-29561)CHECK-fail in tf.raw_ops.IRFFT (CVE-2021-29562)CHECK-fail in tf.raw_ops.RFFT (CVE-2021-29563)EditDistance (CVE-2021-29564)SparseFillEmptyRows
(CVE-2021-29565)Dilation2DBackpropInput (CVE-2021-29566)ParameterizedTruncatedNormal (CVE-2021-29568)SparseDenseCwiseMul (CVE-2021-29567)MaxPoolGradWithArgmax (CVE-2021-29570)RequantizationRange
(CVE-2021-29569)DrawBoundingBoxesV2 (CVE-2021-29571)SdcaOptimizer
(CVE-2021-29572)tf.raw_ops.ReverseSequence (CVE-2021-29575)MaxPoolGradWithArgmax (CVE-2021-29573)MaxPool3DGradGrad (CVE-2021-29574)MaxPool3DGradGrad (CVE-2021-29576)AvgPool3DGrad (CVE-2021-29577)CHECK-fail in
FractionalMaxPoolGrad (CVE-2021-29580)FractionalAvgPoolGrad
(CVE-2021-29578)MaxPoolGrad (CVE-2021-29579)CTCBeamSearchDecoder (CVE-2021-29581)tf.raw_ops.Dequantize (CVE-2021-29582)CHECK-fail due to integer overflow (CVE-2021-29584)FusedBatchNorm (CVE-2021-29583)SpaceToDepth (CVE-2021-29587)GatherNd (CVE-2021-29589)TransposeConv (CVE-2021-29588)Minimum or Maximum (CVE-2021-29590)Reshape
operator (CVE-2021-29592)DepthToSpace (CVE-2021-29595)EmbeddingLookup (CVE-2021-29596)BatchToSpaceNd (CVE-2021-29593)SpaceToBatchNd (CVE-2021-29597)SVDF (CVE-2021-29598)Split (CVE-2021-29599)OneHot (CVE-2021-29600)DepthwiseConv (CVE-2021-29602)RaggedTensorToTensor (CVE-2021-29608)SparseAdd (CVE-2021-29609)SparseSparseMinimum (CVE-2021-29607)SparseReshape (CVE-2021-29611)QuantizeAndDequantizeV2 (CVE-2021-29610)BandedTriangularSolve
(CVE-2021-29612)tf.raw_ops.CTCLoss (CVE-2021-29613)tf.io.decode_raw (CVE-2021-29614)ParseAttrValue with nested
tensors (CVE-2021-29615)TrySimplify (CVE-2021-29616)tf.transpose with complex inputs (CVE-2021-29618)tf.strings.substr due to
CHECK-fail (CVE-2021-29617)tf.raw_ops.SparseCountSparseOutput
(CVE-2021-29619)tf.raw_ops.ImmutableConst (CVE-2021-29539)curl to 7.76.0 to handle CVE-2020-8169,
CVE-2020-8177,
CVE-2020-8231,
CVE-2020-8284,
CVE-2020-8285
and CVE-2020-8286.This release introduces several vulnerability fixes:
RaggedBinCount (CVE-2021-29512)RaggedBinCount (CVE-2021-29514)MatrixDiag* ops (CVE-2021-29515)Conv3D (CVE-2021-29517)CHECK-fail in SparseCross caused
by type confusion (CVE-2021-29519)SparseCountSparseOutput (CVE-2021-29521)Conv3DBackprop* (CVE-2021-29520)Conv3DBackprop* (CVE-2021-29522)CHECK-fail in
AddManySparseToTensorsMap (CVE-2021-29523)Conv2DBackpropFilter (CVE-2021-29524)Conv2DBackpropInput (CVE-2021-29525)Conv2D (CVE-2021-29526)QuantizedConv2D (CVE-2021-29527)QuantizedMul (CVE-2021-29528)SparseMatrixSparseCholesky (CVE-2021-29530)CHECK-fail in tf.raw_ops.EncodePng
(CVE-2021-29531)RaggedCross (CVE-2021-29532)CHECK-fail in DrawBoundingBoxes
(CVE-2021-29533)QuantizedMul (CVE-2021-29535)CHECK-fail in SparseConcat (CVE-2021-29534)QuantizedResizeBilinear
(CVE-2021-29537)QuantizedReshape (CVE-2021-29536)Conv2DBackpropFilter (CVE-2021-29538)Conv2DBackpropFilter
(CVE-2021-29540)StringNGrams (CVE-2021-29542)StringNGrams (CVE-2021-29541)CHECK-fail in
QuantizeAndDequantizeV4Grad (CVE-2021-29544)CHECK-fail in CTCGreedyDecoder (CVE-2021-29543)SparseTensorToCSRSparseMatrix (CVE-2021-29545)QuantizedBiasAdd (CVE-2021-29546)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29547)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29548)QuantizedAdd (CVE-2021-29549)FractionalAvgPool (CVE-2021-29550)MatrixTriangularSolve (CVE-2021-29551)QuantizeAndDequantizeV3 (CVE-2021-29553)CHECK-failure in
UnsortedSegmentJoin (CVE-2021-29552)DenseCountSparseOutput (CVE-2021-29554)FusedBatchNorm (CVE-2021-29555)SparseMatMul (CVE-2021-29557)Reverse (CVE-2021-29556)SparseSplit (CVE-2021-29558)RaggedTensorToTensor
(CVE-2021-29560)CHECK-fail in LoadAndRemapMatrix
(CVE-2021-29561)CHECK-fail in tf.raw_ops.IRFFT (CVE-2021-29562)CHECK-fail in tf.raw_ops.RFFT (CVE-2021-29563)EditDistance (CVE-2021-29564)SparseFillEmptyRows
(CVE-2021-29565)Dilation2DBackpropInput (CVE-2021-29566)ParameterizedTruncatedNormal (CVE-2021-29568)SparseDenseCwiseMul (CVE-2021-29567)MaxPoolGradWithArgmax (CVE-2021-29570)RequantizationRange
(CVE-2021-29569)DrawBoundingBoxesV2 (CVE-2021-29571)SdcaOptimizer
(CVE-2021-29572)tf.raw_ops.ReverseSequence (CVE-2021-29575)MaxPoolGradWithArgmax (CVE-2021-29573)MaxPool3DGradGrad (CVE-2021-29574)MaxPool3DGradGrad (CVE-2021-29576)AvgPool3DGrad (CVE-2021-29577)CHECK-fail in
FractionalMaxPoolGrad (CVE-2021-29580)FractionalAvgPoolGrad
(CVE-2021-29578)MaxPoolGrad (CVE-2021-29579)CTCBeamSearchDecoder (CVE-2021-29581)tf.raw_ops.Dequantize (CVE-2021-29582)CHECK-fail due to integer overflow (CVE-2021-29584)FusedBatchNorm (CVE-2021-29583)SpaceToDepth (CVE-2021-29587)GatherNd (CVE-2021-29589)TransposeConv (CVE-2021-29588)Minimum or Maximum (CVE-2021-29590)Reshape
operator (CVE-2021-29592)DepthToSpace (CVE-2021-29595)EmbeddingLookup (CVE-2021-29596)BatchToSpaceNd (CVE-2021-29593)SpaceToBatchNd (CVE-2021-29597)SVDF (CVE-2021-29598)Split (CVE-2021-29599)OneHot (CVE-2021-29600)DepthwiseConv (CVE-2021-29602)RaggedTensorToTensor (CVE-2021-29608)SparseAdd (CVE-2021-29609)SparseSparseMinimum (CVE-2021-29607)SparseReshape (CVE-2021-29611)QuantizeAndDequantizeV2 (CVE-2021-29610)BandedTriangularSolve
(CVE-2021-29612)tf.raw_ops.CTCLoss (CVE-2021-29613)tf.io.decode_raw (CVE-2021-29614)ParseAttrValue with nested
tensors (CVE-2021-29615)TrySimplify (CVE-2021-29616)tf.transpose with complex inputs (CVE-2021-29618)tf.strings.substr due to
CHECK-fail (CVE-2021-29617)tf.raw_ops.SparseCountSparseOutput
(CVE-2021-29619)tf.raw_ops.ImmutableConst (CVE-2021-29539)curl to 7.76.0 to handle CVE-2020-8169,
CVE-2020-8177,
CVE-2020-8231,
CVE-2020-8284,
CVE-2020-8285
and CVE-2020-8286.This release introduces several vulnerability fixes:
RaggedBinCount (CVE-2021-29512)RaggedBinCount (CVE-2021-29514)MatrixDiag* ops (CVE-2021-29515)Conv3D (CVE-2021-29517)CHECK-fail in SparseCross caused
by type confusion (CVE-2021-29519)SparseCountSparseOutput (CVE-2021-29521)Conv3DBackprop* (CVE-2021-29520)Conv3DBackprop* (CVE-2021-29522)CHECK-fail in
AddManySparseToTensorsMap (CVE-2021-29523)Conv2DBackpropFilter (CVE-2021-29524)Conv2DBackpropInput (CVE-2021-29525)Conv2D (CVE-2021-29526)QuantizedConv2D (CVE-2021-29527)QuantizedMul (CVE-2021-29528)SparseMatrixSparseCholesky (CVE-2021-29530)CHECK-fail in tf.raw_ops.EncodePng
(CVE-2021-29531)RaggedCross (CVE-2021-29532)CHECK-fail in DrawBoundingBoxes
(CVE-2021-29533)QuantizedMul (CVE-2021-29535)CHECK-fail in SparseConcat (CVE-2021-29534)QuantizedResizeBilinear
(CVE-2021-29537)QuantizedReshape (CVE-2021-29536)Conv2DBackpropFilter (CVE-2021-29538)Conv2DBackpropFilter
(CVE-2021-29540)StringNGrams (CVE-2021-29542)StringNGrams (CVE-2021-29541)CHECK-fail in
QuantizeAndDequantizeV4Grad (CVE-2021-29544)CHECK-fail in CTCGreedyDecoder (CVE-2021-29543)SparseTensorToCSRSparseMatrix (CVE-2021-29545)QuantizedBiasAdd (CVE-2021-29546)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29547)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29548)QuantizedAdd (CVE-2021-29549)FractionalAvgPool (CVE-2021-29550)MatrixTriangularSolve (CVE-2021-29551)QuantizeAndDequantizeV3 (CVE-2021-29553)CHECK-failure in
UnsortedSegmentJoin (CVE-2021-29552)DenseCountSparseOutput (CVE-2021-29554)FusedBatchNorm (CVE-2021-29555)SparseMatMul (CVE-2021-29557)Reverse (CVE-2021-29556)SparseSplit (CVE-2021-29558)RaggedTensorToTensor
(CVE-2021-29560)CHECK-fail in LoadAndRemapMatrix
(CVE-2021-29561)CHECK-fail in tf.raw_ops.IRFFT (CVE-2021-29562)CHECK-fail in tf.raw_ops.RFFT (CVE-2021-29563)EditDistance (CVE-2021-29564)SparseFillEmptyRows
(CVE-2021-29565)Dilation2DBackpropInput (CVE-2021-29566)ParameterizedTruncatedNormal (CVE-2021-29568)SparseDenseCwiseMul (CVE-2021-29567)MaxPoolGradWithArgmax (CVE-2021-29570)RequantizationRange
(CVE-2021-29569)DrawBoundingBoxesV2 (CVE-2021-29571)SdcaOptimizer
(CVE-2021-29572)tf.raw_ops.ReverseSequence (CVE-2021-29575)MaxPoolGradWithArgmax (CVE-2021-29573)MaxPool3DGradGrad (CVE-2021-29574)MaxPool3DGradGrad (CVE-2021-29576)AvgPool3DGrad (CVE-2021-29577)CHECK-fail in
FractionalMaxPoolGrad (CVE-2021-29580)FractionalAvgPoolGrad
(CVE-2021-29578)MaxPoolGrad (CVE-2021-29579)CTCBeamSearchDecoder (CVE-2021-29581)tf.raw_ops.Dequantize (CVE-2021-29582)CHECK-fail due to integer overflow (CVE-2021-29584)FusedBatchNorm (CVE-2021-29583)SpaceToDepth (CVE-2021-29587)GatherNd (CVE-2021-29589)TransposeConv (CVE-2021-29588)Minimum or Maximum (CVE-2021-29590)Reshape
operator (CVE-2021-29592)DepthToSpace (CVE-2021-29595)EmbeddingLookup (CVE-2021-29596)BatchToSpaceNd (CVE-2021-29593)SpaceToBatchNd (CVE-2021-29597)SVDF (CVE-2021-29598)Split (CVE-2021-29599)OneHot (CVE-2021-29600)DepthwiseConv (CVE-2021-29602)RaggedTensorToTensor (CVE-2021-29608)SparseAdd (CVE-2021-29609)SparseSparseMinimum (CVE-2021-29607)SparseReshape (CVE-2021-29611)QuantizeAndDequantizeV2 (CVE-2021-29610)BandedTriangularSolve
(CVE-2021-29612)tf.raw_ops.CTCLoss (CVE-2021-29613)tf.io.decode_raw (CVE-2021-29614)ParseAttrValue with nested
tensors (CVE-2021-29615)TrySimplify (CVE-2021-29616)tf.transpose with complex inputs (CVE-2021-29618)tf.strings.substr due to
CHECK-fail (CVE-2021-29617)tf.raw_ops.SparseCountSparseOutput
(CVE-2021-29619)tf.raw_ops.ImmutableConst (CVE-2021-29539)curl to 7.76.0 to handle CVE-2020-8169,
CVE-2020-8177,
CVE-2020-8231,
CVE-2020-8284,
CVE-2020-8285
and CVE-2020-8286.This release introduces several vulnerability fixes:
RaggedBinCount (CVE-2021-29512)RaggedBinCount (CVE-2021-29514)MatrixDiag* ops (CVE-2021-29515)Conv3D (CVE-2021-29517)CHECK-fail in SparseCross caused
by type confusion (CVE-2021-29519)SparseCountSparseOutput (CVE-2021-29521)Conv3DBackprop* (CVE-2021-29520)Conv3DBackprop* (CVE-2021-29522)CHECK-fail in
AddManySparseToTensorsMap (CVE-2021-29523)Conv2DBackpropFilter (CVE-2021-29524)Conv2DBackpropInput (CVE-2021-29525)Conv2D (CVE-2021-29526)QuantizedConv2D (CVE-2021-29527)QuantizedMul (CVE-2021-29528)SparseMatrixSparseCholesky (CVE-2021-29530)CHECK-fail in tf.raw_ops.EncodePng
(CVE-2021-29531)RaggedCross (CVE-2021-29532)CHECK-fail in DrawBoundingBoxes
(CVE-2021-29533)QuantizedMul (CVE-2021-29535)CHECK-fail in SparseConcat (CVE-2021-29534)QuantizedResizeBilinear
(CVE-2021-29537)QuantizedReshape (CVE-2021-29536)Conv2DBackpropFilter (CVE-2021-29538)Conv2DBackpropFilter
(CVE-2021-29540)StringNGrams (CVE-2021-29542)StringNGrams (CVE-2021-29541)CHECK-fail in
QuantizeAndDequantizeV4Grad (CVE-2021-29544)CHECK-fail in CTCGreedyDecoder (CVE-2021-29543)SparseTensorToCSRSparseMatrix (CVE-2021-29545)QuantizedBiasAdd (CVE-2021-29546)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29547)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29548)QuantizedAdd (CVE-2021-29549)FractionalAvgPool (CVE-2021-29550)MatrixTriangularSolve (CVE-2021-29551)QuantizeAndDequantizeV3 (CVE-2021-29553)CHECK-failure in
UnsortedSegmentJoin (CVE-2021-29552)DenseCountSparseOutput (CVE-2021-29554)FusedBatchNorm (CVE-2021-29555)SparseMatMul (CVE-2021-29557)Reverse (CVE-2021-29556)SparseSplit (CVE-2021-29558)RaggedTensorToTensor
(CVE-2021-29560)CHECK-fail in LoadAndRemapMatrix
(CVE-2021-29561)CHECK-fail in tf.raw_ops.IRFFT (CVE-2021-29562)CHECK-fail in tf.raw_ops.RFFT (CVE-2021-29563)EditDistance (CVE-2021-29564)SparseFillEmptyRows
(CVE-2021-29565)Dilation2DBackpropInput (CVE-2021-29566)ParameterizedTruncatedNormal (CVE-2021-29568)SparseDenseCwiseMul (CVE-2021-29567)MaxPoolGradWithArgmax (CVE-2021-29570)RequantizationRange
(CVE-2021-29569)DrawBoundingBoxesV2 (CVE-2021-29571)SdcaOptimizer
(CVE-2021-29572)tf.raw_ops.ReverseSequence (CVE-2021-29575)MaxPoolGradWithArgmax (CVE-2021-29573)MaxPool3DGradGrad (CVE-2021-29574)MaxPool3DGradGrad (CVE-2021-29576)AvgPool3DGrad (CVE-2021-29577)CHECK-fail in
FractionalMaxPoolGrad (CVE-2021-29580)FractionalAvgPoolGrad
(CVE-2021-29578)MaxPoolGrad (CVE-2021-29579)CTCBeamSearchDecoder (CVE-2021-29581)tf.raw_ops.Dequantize (CVE-2021-29582)CHECK-fail due to integer overflow (CVE-2021-29584)FusedBatchNorm (CVE-2021-29583)SpaceToDepth (CVE-2021-29587)GatherNd (CVE-2021-29589)TransposeConv (CVE-2021-29588)Minimum or Maximum (CVE-2021-29590)Reshape
operator (CVE-2021-29592)DepthToSpace (CVE-2021-29595)EmbeddingLookup (CVE-2021-29596)BatchToSpaceNd (CVE-2021-29593)SpaceToBatchNd (CVE-2021-29597)SVDF (CVE-2021-29598)Split (CVE-2021-29599)OneHot (CVE-2021-29600)DepthwiseConv (CVE-2021-29602)RaggedTensorToTensor (CVE-2021-29608)SparseAdd (CVE-2021-29609)SparseSparseMinimum (CVE-2021-29607)SparseReshape (CVE-2021-29611)QuantizeAndDequantizeV2 (CVE-2021-29610)BandedTriangularSolve
(CVE-2021-29612)tf.raw_ops.CTCLoss (CVE-2021-29613)tf.io.decode_raw (CVE-2021-29614)ParseAttrValue with nested
tensors (CVE-2021-29615)TrySimplify (CVE-2021-29616)tf.transpose with complex inputs (CVE-2021-29618)tf.strings.substr due to
CHECK-fail (CVE-2021-29617)tf.raw_ops.SparseCountSparseOutput
(CVE-2021-29619)tf.raw_ops.ImmutableConst (CVE-2021-29539)curl to 7.76.0 to handle CVE-2020-8169,
CVE-2020-8177,
CVE-2020-8231,
CVE-2020-8284,
CVE-2020-8285
and CVE-2020-8286.tf.data:
tf.data service now supports strict round-robin reads,
which is useful for synchronous training workloads where example sizes
vary. With strict round robin reads, users can guarantee that consumers
get similar-sized examples in the same step.compression=None to
tf.data.experimental.service.distribute(...).tf.data.Dataset.batch() now supports
num_parallel_calls and deterministic
arguments. num_parallel_calls is used to indicate that
multiple input batches should be computed in parallel. With
num_parallel_calls set, deterministic is used
to indicate that outputs can be obtained in the non-deterministic
order.tf.data.Dataset.options() are no
longer mutable.map. The debug mode can be enabled through
tf.data.experimental.enable_debug_mode().tf.lite
experimental_new_quantizer in
tf.lite.TFLiteConverter to False to disable this changetf.keras
tf.keras.metrics.AUC now support logit
predictions.Model.fit,
tf.keras.utils.experimental.DatasetCreator, which takes a
callable, dataset_fn. DatasetCreator is
intended to work across all tf.distribute strategies, and
is the only input type supported for Parameter Server strategy.tf.distribute
tf.distribute.experimental.ParameterServerStrategy now
supports training with Keras Model.fit when used with
DatasetCreator.tf.random.Generator under
tf.distribute.Strategy scopes is now allowed (except for
tf.distribute.experimental.CentralStorageStrategy and
tf.distribute.experimental.ParameterServerStrategy).
Different replicas will get different random-number streams.profile_data_directory to
EmbeddingConfigSpec in
_tpu_estimator_embedding.py. This allows embedding lookup
statistics gathered at runtime to be used in embedding layer
partitioning decisions.TF_ENABLE_ONEDNN_OPTS=1.TF_CPP_MIN_VLOG_LEVEL environment variable has been
renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly
describes its effect.tf.keras:
StringLookup added output_mode,
sparse, and pad_to_max_tokens arguments with
same semantics as TextVectorization.IntegerLookup added output_mode,
sparse, and pad_to_max_tokens arguments with
same semantics as TextVectorization. Renamed
max_values, oov_value and
mask_value to max_tokens,
oov_token and mask_token to align with
StringLookup and TextVectorization.TextVectorization default for
pad_to_max_tokens switched to False.CategoryEncoding no longer supports adapt,
IntegerLookup now supports equivalent functionality.
max_tokens argument renamed to
num_tokens.Discretization added num_bins argument for
learning bins boundaries through calling adapt on a
dataset. Renamed bins argument to
bin_boundaries for specifying bins without
adapt.model.load_weights now accepts paths to saved
models.tf.TypeSpecs.tf.keras.optimizers.schedules.CosineDecay
andtf.keras.optimizers.schedules.CosineDecayRestarts.tf.data:
tf.data.experimental.ExternalStatePolicy,
which can be used to control how external state should be handled during
dataset serialization or iterator checkpointing.tf.data.experimental.save to store the type
specification of the dataset elements. This avoids the need for
explicitly specifying the element_spec argument of
tf.data.experimental.load when loading the previously saved
dataset..element_spec property to
tf.data.DatasetSpec to access the inner spec. This can be
used to extract the structure of nested datasets.tf.data.experimental.AutoShardingPolicy.HINT which
can be used to provide hints to tf.distribute-based auto-sharding as to
where in the input pipeline to insert sharding transformations.tf.function and
GraphDef boundaries.XLA compilation:
tf.function(experimental_compile=True) has become a
stable API, renamed tf.function(jit_compile=True).strategy.run can now be annoted with
jit_compile=True.tf.distribute:
experimental_prefetch_to_device in
tf.distribute.InputOptions to
experimental_fetch_to_device to better reflect the
purpose.tf.lite:
tflite::Subgraph:
tensors() method and the non-const overload
of the nodes_and_registration() method, both of which were
previously documented as temporary and to be removed.
tensors() can be replaced by calling the
existing methods tensors_size() and
tensor(int).nodes_and_registration can be replaced by calling the
existing methods nodes_size() and context(),
and then calling the GetNodeAndRegistration method in the
TfLiteContext returned by context().Interpreter::UseNNAPI(bool) C++ API.
NnApiDelegate() and related delegate configuration
methods directly.TFLiteConverter.from_saved_model.RFFT2D as builtin op. (RFFT2D also
supports RFFTD.) Currently only supports float32
input.SLICE op.ReshapeV2.TFLiteConverter.from_saved_model.experimental_preserve_all_tensors to aid in debugging
conversion.tf.compat.v1.lite.experimental.get_potentially_supported_ops.
Use tf.lite.TFLiteConverter directly to check whether a
model is convertible.converter.target_spec._experimental_custom_op_registerers.
used in Python Interpreter API.TF Core:
tf.cond, tf.while_loop, and compositions like
tf.foldl) computed with tf.GradientTape inside
a tf.function.gradient_checker_v2.compute_gradients to be exactly
representable as a binary floating point numbers. This avoids poluting
gradient approximations needlessly, which is some cases leads to false
negatives in op gradient tests.tf.config.experimental.get_memory_info, returning
a dict with the current and peak memory usage. Deprecated
tf.config.experimental.get_memory_usage in favor of this
new function.tf.config.experimental.enable_tensor_float_32_execution to
control Tensor-Float-32 evaluation in RNNs.tf.summary:
tf.summary.graph allows manual write of TensorFlow
graph (tf.Graph or tf.compat.v1.GraphDef) as a
summary. This is not a replacement for the trace-based API.Set /d2ReducedOptimizeHugeFunctions by default for
Windows builds. This provides a big compile-time speedup, and
effectively raises the minimum supported MSVC version to 16.4 (current:
16.8).
TensorRT
session_config parameter for the
TF1-TRT converter TrtGraphConverter. Previously, we issued
a warning when the value of the parameter is not None.TrtGraphConverterV2 takes an
object of class TrtConversionParams as a parameter. Removed three
deprecated fields from this class:
rewriter_config_template, is_dynamic_op, and
max_batch_size. Previously, we issued a warning when the
value of rewriter_config_template is not None. We issued an
error when the value of is_dynamic_op is not True. We
didn't use the value for max_batch_size for building
TensorRT engines. Add parameters use_dynamic_shape to
enable dynamic shape support. The default is to disable dynamic shape
support. Add dynamic_shape_profile_strategy for selecting a
dynamic shape profile strategy. The default is profile strategy is
Range.TF XLA
MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED to
tf.config.experimental.mlir_bridge_rollout to enable a
"safe" mode. This runs the MLIR bridge only when an analysis of the
graph only when an analysis of the graph determines that it is safe to
run.MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED' totf.config.experimental.mlir_bridge_rollout`
to enable a fallback for the MLIR bridge in a "safe" mode. This runs the
MLIR bridge in a FallbackEnabled mode when an analysis of the graph
determines that the graph does not have unsupported features.Deterministic Op Functionality:
TF_DETERMINISTIC_OPS is
set to "true" or "1" (when op-determinism is
expected), an attempt to run the following ops on a GPU will throw
tf.errors.UnimplementedError (with an understandable
message) when data is a floating-point type, including
complex types (if supported): tf.math.segment_prod,
tf.math.segment_sum,
tf.math.unsorted_segment_mean,
tf.math.unsorted_segment_sqrt_n,
tf.math.unsorted_segment_prod,
tf.math.unsorted_segment_sum, and therefore also
tf.convert_to_tensor when value is of type
tf.IndexedSlices (such as in the back prop though
tf.gather into a dense embedding). See issue 39751
which this change addresses, but does not solve. This exception-throwing
behavior can be disabled by setting the environment variable
TF_DISABLE_SEGMENT_REDUCTION_OP_DETERMINISM_EXCEPTIONS to
"true" or "1". For more information about
these changes, see the description in pull request 47772.tf.sparse.sparse_dense_matmul introduced truly random noise
in the forward path for data of type tf.float32 but not for
data of type tf.float64 (for which there was no GPU
implementation). In this current release, GPU support for other
floating-point types (tf.float16, tf.float64,
tf.complex64, and tf.complex128) has been
added for this op. If you were relying on the determinism of the
tf.float64 CPU implementation being automatically selected
because of the absence of the tf.float64 GPU
implementation, you with either need to force the op to run on the CPU
or use a different data type.Security
RaggedBinCount (CVE-2021-29512)RaggedBinCount (CVE-2021-29514)MatrixDiag* ops (CVE-2021-29515)Conv3D (CVE-2021-29517)CHECK-fail in SparseCross caused
by type confusion (CVE-2021-29519)SparseCountSparseOutput (CVE-2021-29521)Conv3DBackprop* (CVE-2021-29520)Conv3DBackprop* (CVE-2021-29522)CHECK-fail in
AddManySparseToTensorsMap (CVE-2021-29523)Conv2DBackpropFilter (CVE-2021-29524)Conv2DBackpropInput (CVE-2021-29525)Conv2D (CVE-2021-29526)QuantizedConv2D (CVE-2021-29527)QuantizedMul (CVE-2021-29528)SparseMatrixSparseCholesky (CVE-2021-29530)CHECK-fail in tf.raw_ops.EncodePng
(CVE-2021-29531)RaggedCross (CVE-2021-29532)CHECK-fail in DrawBoundingBoxes
(CVE-2021-29533)QuantizedMul (CVE-2021-29535)CHECK-fail in SparseConcat (CVE-2021-29534)QuantizedResizeBilinear
(CVE-2021-29537)QuantizedReshape (CVE-2021-29536)Conv2DBackpropFilter (CVE-2021-29538)Conv2DBackpropFilter
(CVE-2021-29540)StringNGrams (CVE-2021-29542)StringNGrams (CVE-2021-29541)CHECK-fail in
QuantizeAndDequantizeV4Grad (CVE-2021-29544)CHECK-fail in CTCGreedyDecoder (CVE-2021-29543)SparseTensorToCSRSparseMatrix (CVE-2021-29545)QuantizedBiasAdd (CVE-2021-29546)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29547)QuantizedBatchNormWithGlobalNormalization (CVE-2021-29548)QuantizedAdd (CVE-2021-29549)FractionalAvgPool (CVE-2021-29550)MatrixTriangularSolve (CVE-2021-29551)QuantizeAndDequantizeV3 (CVE-2021-29553)CHECK-failure in
UnsortedSegmentJoin (CVE-2021-29552)DenseCountSparseOutput (CVE-2021-29554)FusedBatchNorm (CVE-2021-29555)SparseMatMul (CVE-2021-29557)Reverse (CVE-2021-29556)SparseSplit (CVE-2021-29558)RaggedTensorToTensor
(CVE-2021-29560)CHECK-fail in LoadAndRemapMatrix
(CVE-2021-29561)CHECK-fail in tf.raw_ops.IRFFT (CVE-2021-29562)CHECK-fail in tf.raw_ops.RFFT (CVE-2021-29563)EditDistance (CVE-2021-29564)SparseFillEmptyRows
(CVE-2021-29565)Dilation2DBackpropInput (CVE-2021-29566)ParameterizedTruncatedNormal (CVE-2021-29568)SparseDenseCwiseMul (CVE-2021-29567)MaxPoolGradWithArgmax (CVE-2021-29570)RequantizationRange
(CVE-2021-29569)DrawBoundingBoxesV2 (CVE-2021-29571)SdcaOptimizer
(CVE-2021-29572)tf.raw_ops.ReverseSequence (CVE-2021-29575)MaxPoolGradWithArgmax (CVE-2021-29573)MaxPool3DGradGrad (CVE-2021-29574)MaxPool3DGradGrad (CVE-2021-29576)AvgPool3DGrad (CVE-2021-29577)CHECK-fail in
FractionalMaxPoolGrad (CVE-2021-29580)FractionalAvgPoolGrad
(CVE-2021-29578)MaxPoolGrad (CVE-2021-29579)CTCBeamSearchDecoder (CVE-2021-29581)tf.raw_ops.Dequantize (CVE-2021-29582)CHECK-fail due to integer overflow (CVE-2021-29584)FusedBatchNorm (CVE-2021-29583)SpaceToDepth (CVE-2021-29587)GatherNd (CVE-2021-29589)TransposeConv (CVE-2021-29588)Minimum or Maximum (CVE-2021-29590)Reshape
operator (CVE-2021-29592)DepthToSpace (CVE-2021-29595)EmbeddingLookup (CVE-2021-29596)BatchToSpaceNd (CVE-2021-29593)SpaceToBatchNd (CVE-2021-29597)SVDF (CVE-2021-29598)Split (CVE-2021-29599)OneHot (CVE-2021-29600)DepthwiseConv (CVE-2021-29602)RaggedTensorToTensor (CVE-2021-29608)SparseAdd (CVE-2021-29609)SparseSparseMinimum (CVE-2021-29607)SparseReshape (CVE-2021-29611)QuantizeAndDequantizeV2 (CVE-2021-29610)BandedTriangularSolve
(CVE-2021-29612)tf.raw_ops.CTCLoss (CVE-2021-29613)tf.io.decode_raw (CVE-2021-29614)ParseAttrValue with nested
tensors (CVE-2021-29615)TrySimplify (CVE-2021-29616)tf.transpose with complex inputs (CVE-2021-29618)tf.strings.substr due to
CHECK-fail (CVE-2021-29617)tf.raw_ops.SparseCountSparseOutput
(CVE-2021-29619)tf.raw_ops.ImmutableConst (CVE-2021-29539)curl to 7.76.0 to handle CVE-2020-8169,
CVE-2020-8177,
CVE-2020-8231,
CVE-2020-8284,
CVE-2020-8285
and CVE-2020-8286.Other
show_debug_info to
mlir.convert_graph_def and
mlir.convert_function.--config=mkl_aarch64
build.This release contains contributions from many people at Google, as well as:
8bitmp3, Aaron S. Mondal, Abhilash Mahendrakar, Abhinav Upadhyay, Abhishek Kulkarni, Abolfazl Shahbazi, Adam Hillier, Aditya Kane, Ag Ramesh, ahmedsabie, Albert Villanova Del Moral, Aleksey Vitebskiy, Alex Hoffman, Alexander Bayandin, Alfie Edwards, Aman Kishore, Amogh Joshi, andreABbauer, Andrew Goodbody, Andrzej Pomirski, Artemiy Ryabinkov, Ashish Jha, ather, Ayan Moitra, Bairen Yi, Bart Ribbers, Bas Aarts, Behzad Abghari, Ben Arnao, Ben Barsdell, Benjamin Klimczak, bhack, Brendan Collins, Can Wang, Cheng Ren, Chris Leary, Chris Olivier, Clemens Giuliani, Cloud Han, Corey Cole, Cui, Yifeng, Cuong V. Nguyen, Daniel Moore, Dawid Wojciechowski, Ddavis-2015, Dean Wyatte, Denisa Roberts, dependabot[bot], Dmitry Volodin, Dominic Jack, Duncan Riach, dushuai, Elena Zhelezina, Eli Osherovich, Erik Smistad, ewsn1593, Felix Fent, fo40225, François Chollet, Frederic Bastien, Freedom" Koan-Sin Tan, fsx950223, ganand1, gbaned, Georgiy Manuilov, gerbauz, Guillaume Klein, Guozhong Zhuang, Harry Slatyer, Harsh188, henri, Henri Woodcock, Hiran Sarkar, Hollow Man, Håkon Sandsmark, I Wayan Dharmana, icysapphire, Ikko Ashimine, Jab Hofmeier, Jack Hessel, Jacob Valdez, Jakub Jatczak, James Bernardi, Jared Smolens, Jason Zaman, jedlimlx, Jenny Plunkett, Jens Elofsson, Jerry Shih, jgehw, Jia Fu Low, Jim Fisher, jpodivin, Julien Stephan, Jungsub Lim, Junha Park, Junhyuk So, justkw, Kaixi Hou, kashyapraval, Kasra Bigdeli, Kazuaki Ishizaki, Keith Mok, Kevin Cheng, kopytjuk, Kristian Hartikainen, ksood12345, Kulin Seth, kushanam, latyas, Lequn Chen, Leslie-Fang, Long M. Lưu, Lukas Geiger, machineko, Mahmoud Abuzaina, Manish, Mao Yunfei, Maozhou, Ge, Marcin Juszkiewicz, Marcin Owsiany, Marconi Jiang, Marcos Pereira, Maria Romanenko Vexlard, Maria Vexlard, Marius Brehler, marload, Martin Kubovčík, Matej, Mateusz Holenko, Maxiwell S. Garcia, Mazhar, mazharul, mbhuiyan, mdfaijul, Michael Gielda, Michael Kuchnik, Michal Szutenberg, Mikhail Stepanov, Milan Straka, Mitchel Humpherys, Mohamed Moselhy, Mohamed Nour Abouelseoud, Måns Bermell, Måns Nilsson, Nathan Luehr, Nico Jahn, Niroop Ammbashankar, Oceania2018, Omri Steiner, Orivej Desh, Oskar Flordal, oujiafan, Patrik Laurell, Paul B. Isaac'S, Paul Klinger, Pawel Piskorski, Pedro Marques, Phat Tran, Piotr Zierhoffer, piyushdatta, Pnikam-Cad, Prashant Kumar, Prateek Gupta, PratsBhatt, Pravin Karandikar, qqq.jq, QQ喵, Quintin, Rama Ketineni, ravikyram, Rehan Guha, rhdong, rmothukuru, Roger Cheng, Rohit Santhanam, rposts, Rsanthanam-Amd, rsun, Rsun-Bdti, Ryan Kuester, ryanking13, Saduf2019, Sami Kama, Samuel Marks, Scott Tseng, Sean Moriarity, Sergey Popov, Sergii Khomenko, Sheng, Yang, shwetaoj, Sidong-Wei, Simon Maurer, Simrit Kaur, Srini511, Srinivasan Narayanamoorthy, Stephan, Stephen Matthews, Sungmann Cho, Sunoru, Suraj Sudhir, Suraj Upadhyay, Taebum Kim, Takayoshi Koizumi, Tamas Bela Feher, Teng Lu, Thibaut Goetghebuer-Planchon, Tomwildenhain-Microsoft, Tony, Traun Leyden, Trent Lo, TVLIgnacy, Tzu-Wei Sung, vaibhav, Vignesh Kothapalli, Vikram Dattu, viktprog, Vinayaka Bandishti, Vincent Abriou, Vishakha Agrawal, Vivek Panyam, Vladimir Silyaev, Võ Văn Nghĩa, wamuir, Wang, Yanzhang, wangsiyu, Waqar Hameed, wxinix, Xiao Yang, xiaohong1031, Xiaoming (Jason) Cui, Xinan Jiang, Yair Ehrenwald, Yajush Vyas, Yasir Modak, Yimei Sun, Yong Tang, Yosshi999, youshenmebutuo, yqtianust, Yuan Tang, yuanbopeng, Yuriy Chernyshov, Yuta Fukasawa, Zachary Deane-Mayer, Zeno Gantner, Zhoulong Jiang, zhuyie, zilinzhu, 彭震东
tf.raw_ops.DataFormatVecPermute and
tf.raw_ops.DataFormatDimMap (CVE-2020-26267)tf.raw_ops.ImmutableConst (CVE-2020-26268CHECK-fail in LSTM with zero-length input (CVE-2020-26270)SavedModel (CVE-2020-26271)libjpeg-turbo to 2.0.5 to handle
CVE-2020-13790.junit to 4.13.1 to handle CVE-2020-15250.PCRE to 8.44 to handle CVE-2019-20838
and CVE-2020-14155.sqlite3 to 3.44.0 to keep in sync
with master branch.tf.raw_ops.DataFormatVecPermute and
tf.raw_ops.DataFormatDimMap (CVE-2020-26267)tf.raw_ops.ImmutableConst (CVE-2020-26268CHECK-fail in LSTM with zero-length input (CVE-2020-26270)SavedModel (CVE-2020-26271)SavedModels that
import functionslibjpeg-turbo to 2.0.5 to handle
CVE-2020-13790.junit to 4.13.1 to handle CVE-2020-15250.PCRE to 8.44 to handle CVE-2019-20838
and CVE-2020-14155.sqlite3 to 3.44.0 to keep in sync
with master branch.tf.raw_ops.DataFormatVecPermute and
tf.raw_ops.DataFormatDimMap (CVE-2020-26267)tf.raw_ops.ImmutableConst (CVE-2020-26268CHECK-fail in LSTM with zero-length input (CVE-2020-26270)SavedModel (CVE-2020-26271)libjpeg-turbo to 2.0.5 to handle
CVE-2020-13790.junit to 4.13.1 to handle CVE-2020-15250.PCRE to 8.44 to handle CVE-2019-20838
and CVE-2020-14155.sqlite3 to 3.44.0 to keep in sync
with master branch.Note that this is the last patch release for the TensorFlow 2.0.x series.
tf.raw_ops.DataFormatVecPermute and
tf.raw_ops.DataFormatDimMap (CVE-2020-26267)tf.raw_ops.ImmutableConst (CVE-2020-26268CHECK-fail in LSTM with zero-length input (CVE-2020-26270)SavedModel (CVE-2020-26271)libjpeg-turbo to 2.0.5 to handle
CVE-2020-13790.junit to 4.13.1 to handle CVE-2020-15250.PCRE to 8.44 to handle CVE-2019-20838
and CVE-2020-14155.sqlite3 to 3.44.0 to keep in sync
with master branch.Note that this is the last patch release for the TensorFlow 1.x series.
tf.raw_ops.DataFormatVecPermute and
tf.raw_ops.DataFormatDimMap (CVE-2020-26267)tf.raw_ops.ImmutableConst (CVE-2020-26268CHECK-fail in LSTM with zero-length input (CVE-2020-26270)SavedModel (CVE-2020-26271)libjpeg-turbo to 2.0.5 to handle
CVE-2020-13790.junit to 4.13.1 to handle CVE-2020-15250.PCRE to 8.44 to handle CVE-2019-20838
and CVE-2020-14155.sqlite3 to 3.44.0 to keep in sync
with master branch.## Major Features and Improvements
tf.distribute introduces experimental support for
asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy
API. Please see the tutorial
to learn more.
MultiWorkerMirroredStrategy
is now a stable API and is no longer considered experimental. Some of
the major improvements involve handling peer failure and many bug fixes.
Please check out the detailed tutorial on Multi-worker
training with Keras.
Introduces experimental support for a new module named tf.experimental.numpy
which is a NumPy-compatible API for writing TF programs. See the detailed guide to
learn more. Additional details below.
Adds Support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.
A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.
Keras mixed precision API tf.keras.mixed_precision
is no longer experimental and allows the use of 16-bit floating point
formats during training, improving performance by up to 3x on GPUs and
60% on TPUs. Please see below for additional details.
TensorFlow Profiler now supports profiling
MultiWorkerMirroredStrategy and tracing multiple workers
using the sampling
mode API.
TFLite Profiler for Android is available. See the detailed guide to learn more.
TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.
TF Core:
tf.config.experimental.enable_tensor_float_32_execution(False).tensorflow::tstring/TF_TStrings.TF_StringDecode,
TF_StringEncode, and TF_StringEncodedSize are
no longer relevant and have been removed; see
core/platform/ctstring.h for string access/modification in
C.tensorflow.python, tensorflow.core and
tensorflow.compiler modules are now hidden. These modules
are not part of TensorFlow public API.tf.raw_ops.Max and tf.raw_ops.Min no
longer accept inputs of type tf.complex64 or
tf.complex128, because the behavior of these ops is not
well defined for complex types.TF_XLA_FLAGS=--tf_xla_enable_xla_devices if you really need
them, but this flag will eventually be removed in subsequent
releases.tf.keras:
steps_per_execution argument in
model.compile() is no longer experimental; if you were
passing experimental_steps_per_execution, rename it to
steps_per_execution in your code. This argument controls
the number of batches to run during each tf.function call
when calling model.fit(). Running multiple batches inside a
single tf.function call can greatly improve performance on
TPUs or small models with a large Python overhead.isinstance(x, tf.Tensor) instead of
tf.is_tensor when checking Keras symbolic inputs/outputs
should switch to using tf.is_tensor.tensor.ref(), etc.) may break.get_concrete_function to
trace Keras symbolic inputs directly should switch to building matching
tf.TensorSpecs directly and tracing the
TensorSpec objects.tf.map_fn/tf.cond/tf.while_loop/control
flow as op layers and happens to work before TF 2.4. These will
explicitly be unsupported now. Converting these ops to Functional API op
layers was unreliable before TF 2.4, and prone to erroring
incomprehensibly or being silently buggy.tf.rank used to return a static or symbolic value
depending on if the input had a fully static shape or not. Now these ops
always return symbolic values.GradientTape on the actual
Tensors passed to the already-constructed model instead.tf.keras.Model layer
by layer and assumes layers only ever have one positional argument. This
assumption doesn't hold true before TF 2.4 either, but is more likely to
cause issues now.keras.backend.get_graph()
before building a functional model is no longer needed.Input objects
in a Functional model, and the shape of the data passed to that model.
You can fix this mismatch by either calling the model with
correctly-shaped data, or by relaxing Input shape
assumptions (note that you can pass shapes with None
entries for axes that are meant to be dynamic). You can also disable the
input checking entirely by setting
model.input_spec = None.tf.keras.mixed_precision.experimental. Note that it is now
recommended to use the non-experimental
tf.keras.mixed_precision API.AutoCastVariable.dtype now refers to the actual
variable dtype, not the dtype it will be casted to.tf.keras.layers.Embedding now outputs a float16 or bfloat16
tensor instead of a float32 tensor.tf.keras.mixed_precision.experimental.LossScaleOptimizer.loss_scale
is now a tensor, not a LossScale object. This means to get
a loss scale of a LossScaleOptimizer as a tensor, you must
now call opt.loss_scaleinstead of
opt.loss_scale().should_cast_variables has been removed
from tf.keras.mixed_precision.experimental.Policytf.mixed_precision.experimental.DynamicLossScale to
tf.keras.mixed_precision.experimental.LossScaleOptimizer,
the DynamicLossScale's multiplier must be 2.tf.mixed_precision.experimental.DynamicLossScale to
tf.keras.mixed_precision.experimental.LossScaleOptimizer,
the weights of the DynanmicLossScale are copied into the
LossScaleOptimizer instead of being reused. This means
modifying the weights of the DynamicLossScale will no
longer affect the weights of the LossScaleOptimizer, and vice
versa.tf.keras.mixed_precision.experimental.set_policyLayer.call, AutoCastVariables will no
longer be casted within MirroredStrategy.run or
ReplicaContext.merge_call. This is because a thread local
variable is used to determine whether AutoCastVariables are
casted, and those two functions run with a different thread. Note this
only applies if one of these two functions is called within
Layer.call; if one of those two functions calls
Layer.call, AutoCastVariables will still be
casted.tf.data:
tf.data.experimental.service.DispatchServer now takes a
config tuple instead of individual arguments. Usages should be updated
to
tf.data.experimental.service.DispatchServer(dispatcher_config).tf.data.experimental.service.WorkerServer now takes a
config tuple instead of individual arguments. Usages should be updated
to
tf.data.experimental.service.WorkerServer(worker_config).tf.distribute:
tf.distribute.Strategy.experimental_make_numpy_dataset.
Please use tf.data.Dataset.from_tensor_slices instead.experimental_hints in
tf.distribute.StrategyExtended.reduce_to,
tf.distribute.StrategyExtended.batch_reduce_to,
tf.distribute.ReplicaContext.all_reduce to
options.tf.distribute.experimental.CollectiveHints to
tf.distribute.experimental.CommunicationOptions.tf.distribute.experimental.CollectiveCommunication to
tf.distribute.experimental.CommunicationImplementation.tf.distribute.Strategy.experimental_distribute_datasets_from_function
to distribute_datasets_from_function as it is no longer
experimental.tf.distribute.Strategy.experimental_run_v2
method, which was deprecated in TF 2.2.tf.lite:
tf.quantization.quantize_and_dequantize_v2 has been
introduced, which updates the gradient definition for quantization which
is outside the range to be 0. To simulate the V1 the behavior of
tf.quantization.quantize_and_dequantize(...) use
tf.grad_pass_through(tf.quantization.quantize_and_dequantize_v2)(...).Building TensorFlow:
--copt=/experimental:preprocessor --host_copt=/experimental:preprocessor
(see .bazelrc for more details). Builds including
TensorFlow may fail with unexpected syntax errors if these flags are
absent. See also this
thread on SIG Build.tf.keras.mixed_precision
RMSprop.apply_gradients or
Nadam.apply_gradients outside a tf.function
does not work and will raise the AttributeError "Tensor.op is
meaningless when eager execution is enabled". See this issue
for details and a workaround.tf.experimental.numpy,
which is a NumPy-compatible API for writing TF programs. This module
provides class ndarray, which mimics the
ndarray class in NumPy, and wraps an immutable
tf.Tensor under the hood. A subset of NumPy functions (e.g.
numpy.add) are provided. Their inter-operation with TF
facilities is seamless in most cases. See tensorflow/python/ops/numpy_ops/README.md
for details of what operations are supported and what are the
differences from NumPy.tf.types.experimental.TensorLike is a new
Union type that can be used as type annotation for
variables representing a Tensor or a value that can be converted to
Tensor by tf.convert_to_tensor.tf.sparse.map_values to apply a function to the
.values of SparseTensor arguments.Tensor
(__and__, __or__, __xor__ and
__invert__ now support non-bool arguments and
apply the corresponding bitwise ops. bool arguments
continue to be supported and dispatch to logical ops. This brings them
more in line with Python and NumPy behavior.tf.SparseTensor.with_values. This returns a new
SparseTensor with the same sparsity pattern, but with new provided
values. It is similar to the with_values function of
RaggedTensor.StatelessCase op, and uses it if none of case
branches has stateful ops.tf.config.experimental.get_memory_usage to return
total memory usage of the device.RaggedTensorToVariant and
RaggedTensorFromVariant.tf.debugging:
tf.debugging.assert_shapes() now works on
SparseTensors (Fixes #36268).tf.config.experimental.enable_tensor_float_32_execution.tf.math:
tf.math.erfcinv, the inverse to
tf.math.erfc.tf.nn:
tf.nn.max_pool2d now supports explicit padding.tf.image:
tf.image.stateless_random_*
functions for each tf.image.random_* function. Added a new
op stateless_sample_distorted_bounding_box which is a
deterministic version of sample_distorted_bounding_box op.
Given the same seed, these stateless functions/ops produce the same
results independent of how many times the function is called, and
independent of global seed settings.tf.image.resize backprop CUDA
kernels for method=ResizeMethod.BILINEAR (the default
method). Enable by setting the environment variable
TF_DETERMINISTIC_OPS to "true" or
"1".tf.print:
tf.print() with OrderedDict
where if an OrderedDict didn't have the keys sorted, the
keys and values were not being printed in accordance with their correct
mapping.tf.train.Checkpoint:
root argument in the initialization,
which generates a checkpoint with a root object. This allows users to
create a Checkpoint object that is compatible with Keras
model.save_weights() and model.load_weights.
The checkpoint is also compatible with the checkpoint saved in the
variables/ folder in the SavedModel.save_path can be a path to a
SavedModel. The function will automatically find the checkpoint in the
SavedModel.tf.data:tf.data.experimental.service.register_dataset
and tf.data.experimental.service.from_dataset_id APIs to
enable one process to register a dataset with the tf.data service, and
another process to consume data from the dataset.work_dir when running your
dispatcher server and set dispatcher_fault_tolerance=True.
The dispatcher will store its state to work_dir, so that on
restart it can continue from its previous state after restart.work_dir must be accessible from workers. If the worker
fails to read from the work_dir, it falls back to using RPC
for dataset graph transfer.exclude_cols parameter to CsvDataset.
This parameter is the complement of select_cols; at most
one of these should be specified.take and shard to
happen earlier in the dataset when it is safe to do so. The optimization
can be disabled via the
experimental_optimization.reorder_data_discarding_ops
dataset option.tf.data.Options were previously immutable and can now
be overridden.tf.data.Dataset.from_generator now supports Ragged and
Sparse tensors with a new output_signature argument, which
allows from_generator to produce any type describable by a
tf.TypeSpec.tf.data.experimental.AUTOTUNE is now available in the
core API as tf.data.AUTOTUNE.tf.distribute:tf.distribute.experimental.ParameterServerStrategy:
tf.distribute.experimental.ParameterServerStrategy symbol
with a new class that is for parameter server training in TF2. Usage of
the old symbol, usually with Estimator API, should be
replaced with
[tf.compat.v1.distribute.experimental.ParameterServerStrategy].tf.distribute.experimental.coordinator.*
namespace, including the main API ClusterCoordinator for
coordinating the training cluster, the related data structure
RemoteValue and PerWorkerValue.MultiWorkerMirroredStrategy](https://www.tensorflow.org/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy)
is now a stable API and is no longer considered experimental. Some of
the major improvements involve handling peer failure and many bug fixes.
Please check out the detailed tutorial on Multi-worer
training with Keras.tf.distribute.Strategy.gather and
tf.distribute.ReplicaContext.all_gather APIs to support
gathering dense distributed values.tf.keras:tf.image.ssim_multiscaleOptimizer.minimize can now accept a loss
Tensor and a GradientTape as an alternative to
accepting a callable loss.beta hyperparameter to FTRL
optimizer classes (Keras and others) to match FTRL
paper.Optimizer.__init__ now accepts a
gradient_aggregator to allow for customization of how
gradients are aggregated across devices, as well as
gradients_transformers to allow for custom gradient
transformations (such as gradient clipping).Attention and AdditiveAttention layers,
the call() method now accepts a
return_attention_scores argument. When set to True, the
layer returns the attention scores as an additional output
argument.tf.metrics.log_cosh and
tf.metrics.logcosh API entrypoints with the same
implementation as their tf.losses equivalent.Model.evaluate
uses no cached data for evaluation, while Model.fit uses
cached data when validation_data arg is provided for better
performance.save_traces argument to model.save/
tf.keras.models.save_model which determines whether the
SavedModel format stores the Keras model/layer call functions. The
traced functions allow Keras to revive custom models and layers without
the original class definition, but if this isn't required the tracing
can be disabled with the added option.tf.keras.mixed_precision API is now
non-experimental. The non-experimental API differs from the experimental
API in several ways.
tf.keras.mixed_precision.Policy no longer takes in a
tf.mixed_precision. experimental.LossScale in the
constructor, and no longer has a LossScale associated with
it. Instead, Model.compile will automatically wrap the
optimizer with a LossScaleOptimizer using dynamic loss
scaling if Policy.name is "mixed_float16".tf.keras.mixed_precision.LossScaleOptimizer's
constructor takes in different arguments. In particular, it no longer
takes in a LossScale, and there is no longer a
LossScale associated with the
LossScaleOptimizer. Instead,
LossScaleOptimizer directly implements fixed or dynamic
loss scaling. See the documentation of tf.keras.mixed_precision.experimental.LossScaleOptimizer
for details on the differences between the experimental
LossScaleOptimizer and the new non-experimental
LossScaleOptimizer.tf.mixed_precision.experimental.LossScale and its
subclasses are deprecated, as all of its functionality now exists within
tf.keras.mixed_precision.LossScaleOptimizertf.lite:TFLiteConverter:
inference_input_type and
inference_output_type for full integer quantized models.
This allows users to modify the model input and output type to integer
types (tf.int8, tf.uint8) instead of
defaulting to float type (tf.float32).Interpreter.setUseNNAPI(boolean)
Java API. Use Interpreter.Options.setUseNNAPI instead.Interpreter::UseNNAPI(bool) C++ API. Use
NnApiDelegate() and related delegate configuration methods
directly.Interpreter::SetAllowFp16PrecisionForFp32(bool) C++ API.
Prefer controlling this via delegate options, e.g.
tflite::StatefulNnApiDelegate::Options::allow_fp16' orTfLiteGpuDelegateOptionsV2::is_precision_loss_allowed`.DynamicBuffer::AddJoinedString() will now add a
separator if the first string to be joined is empty.TensorRTsession_config parameter for
the TF1 converter is used or the rewrite_config_template
field in the TF2 converter parameter object is used.beta parameter of the FTRL
optimizer for TPU embeddings. Users of other TensorFlow platforms can
implement equivalent behavior by adjusting the l2
parameter.tf.function(experimental_compile=True) instead.tf.function.experimental_get_compiler_ir which
returns compiler IR (currently 'hlo' and 'optimized_hlo') for given
input for given function.tf.raw_ops.Switch, (CVE-2020-15190)SparseFillEmptyRowsGrad
RaggedCountSparseOutput and
SparseCountSparseOutput operations
tf.strings.as_string, (CVE-2020-15203)tf.raw_ops.StringNGrams, (CVE-2020-15205)SavedModel
validation, (CVE-2020-15206)tf.quantization.quantize_and_dequantize, (CVE-2020-15265)tf.raw_ops.DataFormatVecPermute and
tf.raw_ops.DataFormatDimMap which can cause uninitialized
memory access, read outside bounds of arrays, data corruption and
segmentation faults (CVE-2020-26267)tf.config.experimental.mlir_bridge_rollout which
will help us rollout the new MLIR TPU bridge.tf.experimental.register_filesystem_plugin to load
modular filesystem plugins from PythonThis release contains contributions from many people at Google as well as the following external contributors:
8bitmp3, aaa.jq, Abhineet Choudhary, Abolfazl Shahbazi, acxz, Adam Hillier, Adrian Garcia Badaracco, Ag Ramesh, ahmedsabie, Alan Anderson, Alexander Grund, Alexandre Lissy, Alexey Ivanov, Amedeo Cavallo, anencore94, Aniket Kumar Singh, Anthony Platanios, Ashwin Phadke, Balint Cristian, Basit Ayantunde, bbbboom, Ben Barsdell, Benjamin Chetioui, Benjamin Peterson, bhack, Bhanu Prakash Bandaru Venkata, Biagio Montaruli, Brent M. Spell, bubblebooy, bzhao, cfRod, Cheng Chen, Cheng(Kit) Chen, Chris Tessum, Christian, chuanqiw, codeadmin_peritiae, COTASPAR, CuiYifeng, danielknobe, danielyou0230, dannyfriar, daria, DarrenZhang01, Denisa Roberts, dependabot[bot], Deven Desai, Dmitry Volodin, Dmitry Zakharov, drebain, Duncan Riach, Eduard Feicho, Ehsan Toosi, Elena Zhelezina, emlaprise2358, Eugene Kuznetsov, Evaderan-Lab, Evgeniy Polyakov, Fausto Morales, Felix Johnny, fo40225, Frederic Bastien, Fredrik Knutsson, fsx950223, Gaurav Singh, Gauri1 Deshpande, George Grzegorz Pawelczak, gerbauz, Gianluca Baratti, Giorgio Arena, Gmc2, Guozhong Zhuang, Hannes Achleitner, Harirai, HarisWang, Harsh188, hedgehog91, Hemal Mamtora, Hideto Ueno, Hugh Ku, Ian Beauregard, Ilya Persky, jacco, Jakub Beránek, Jan Jongboom, Javier Montalt Tordera, Jens Elofsson, Jerry Shih, jerryyin, jgehw, Jinjing Zhou, jma, jmsmdy, Johan Nordström, John Poole, Jonah Kohn, Jonathan Dekhtiar, jpodivin, Jung Daun, Kai Katsumata, Kaixi Hou, Kamil Rakoczy, Kaustubh Maske Patil, Kazuaki Ishizaki, Kedar Sovani, Koan-Sin Tan, Koki Ibukuro, Krzysztof Laskowski, Kushagra Sharma, Kushan Ahmadian, Lakshay Tokas, Leicong Li, levinxo, Lukas Geiger, Maderator, Mahmoud Abuzaina, Mao Yunfei, Marius Brehler, markf, Martin Hwasser, Martin Kubovčík, Matt Conley, Matthias, mazharul, mdfaijul, Michael137, MichelBr, Mikhail Startsev, Milan Straka, Ml-0, Myung-Hyun Kim, Måns Nilsson, Nathan Luehr, ngc92, nikochiko, Niranjan Hasabnis, nyagato_00, Oceania2018, Oleg Guba, Ongun Kanat, OscarVanL, Patrik Laurell, Paul Tanger, Peter Sobot, Phil Pearl, PlusPlusUltra, Poedator, Prasad Nikam, Rahul-Kamat, Rajeshwar Reddy T, redwrasse, Rickard, Robert Szczepanski, Rohan Lekhwani, Sam Holt, Sami Kama, Samuel Holt, Sandeep Giri, sboshin, Sean Settle, settle, Sharada Shiddibhavi, Shawn Presser, ShengYang1, Shi,Guangyong, Shuxiang Gao, Sicong Li, Sidong-Wei, Srihari Humbarwadi, Srinivasan Narayanamoorthy, Steenu Johnson, Steven Clarkson, stjohnso98, Tamas Bela Feher, Tamas Nyiri, Tarandeep Singh, Teng Lu, Thibaut Goetghebuer-Planchon, Tim Bradley, Tomasz Strejczek, Tongzhou Wang, Torsten Rudolf, Trent Lo, Ty Mick, Tzu-Wei Sung, Varghese, Jojimon, Vignesh Kothapalli, Vishakha Agrawal, Vividha, Vladimir Menshakov, Vladimir Silyaev, VoVAllen, Võ Văn Nghĩa, wondertx, xiaohong1031, Xiaoming (Jason) Cui, Xinan Jiang, Yair Ehrenwald, Yasir Modak, Yasuhiro Matsumoto, Yimei Sun, Yiwen Li, Yixing, Yoav Ramon, Yong Tang, Yong Wu, yuanbopeng, Yunmo Koo, Zhangqiang, Zhou Peng, ZhuBaohe, zilinzhu, zmx
tf.raw_ops.Switch (CVE-2020-15190)SparseFillEmptyRowsGrad
(CVE-2020-15194,
CVE-2020-15195)RaggedCountSparseOutput and
SparseCountSparseOutput operations (CVE-2020-15196,
CVE-2020-15197,
CVE-2020-15198,
CVE-2020-15199,
CVE-2020-15200,
CVE-2020-15201)tf.strings.as_string (CVE-2020-15203)tf.raw_ops.StringNGrams (CVE-2020-15205)SavedModel
validation (CVE-2020-15206)sqlite3 to 3.33.00 to handle CVE-2020-15358.collections APIscipy dependency from setup.py
since TensorFlow does not need it to install the pip packagetf.raw_ops.Switch (CVE-2020-15190)SparseFillEmptyRowsGrad
(CVE-2020-15194,
CVE-2020-15195)tf.strings.as_string (CVE-2020-15203)tf.raw_ops.StringNGrams (CVE-2020-15205)SavedModel
validation (CVE-2020-15206)sqlite3 to 3.33.00 to handle CVE-2020-9327,
CVE-2020-11655,
CVE-2020-11656,
CVE-2020-13434,
CVE-2020-13435,
CVE-2020-13630,
CVE-2020-13631,
CVE-2020-13871,
and CVE-2020-15358.collections APIscipy dependency from setup.py
since TensorFlow does not need it to install the pip packagetf.raw_ops.Switch (CVE-2020-15190)SparseFillEmptyRowsGrad
(CVE-2020-15194,
CVE-2020-15195)tf.strings.as_string (CVE-2020-15203)tf.raw_ops.StringNGrams (CVE-2020-15205)SavedModel
validation (CVE-2020-15206)sqlite3 to 3.33.00 to handle CVE-2020-9327,
CVE-2020-11655,
CVE-2020-11656,
CVE-2020-13434,
CVE-2020-13435,
CVE-2020-13630,
CVE-2020-13631,
CVE-2020-13871,
and CVE-2020-15358.scipy dependency from setup.py
since TensorFlow does not need it to install the pip packagetf.raw_ops.Switch (CVE-2020-15190)SparseFillEmptyRowsGrad
(CVE-2020-15194,
CVE-2020-15195)tf.strings.as_string (CVE-2020-15203)tf.raw_ops.StringNGrams (CVE-2020-15205)SavedModel
validation (CVE-2020-15206)sqlite3 to 3.33.00 to handle CVE-2020-9327,
CVE-2020-11655,
CVE-2020-11656,
CVE-2020-13434,
CVE-2020-13435,
CVE-2020-13630,
CVE-2020-13631,
CVE-2020-13871,
and CVE-2020-15358.numpy to 1.18.5 to prevent ABI breakage when
compiling code that uses both NumPy and TensorFlow headers.tf.raw_ops.Switch (CVE-2020-15190)SparseFillEmptyRowsGrad
(CVE-2020-15194,
CVE-2020-15195)tf.strings.as_string (CVE-2020-15203)tf.raw_ops.StringNGrams (CVE-2020-15205)SavedModel
validation (CVE-2020-15206)sqlite3 to 3.33.00 to handle CVE-2020-9327,
CVE-2020-11655,
CVE-2020-11656,
CVE-2020-13434,
CVE-2020-13435,
CVE-2020-13630,
CVE-2020-13631,
CVE-2020-13871,
and CVE-2020-15358.max_seq_length in CuDNN
descriptor cache keynumpy to 1.18.5 to prevent ABI breakage when
compiling code that uses both NumPy and TensorFlow headers.tf.data adds two new mechanisms to solve input
pipeline bottlenecks and save resources:
In addition checkout the detailed guide for analyzing input pipeline performance with TF Profiler.
tf.distribute.TPUStrategy
is now a stable API and no longer considered experimental for
TensorFlow. (earlier
tf.distribute.experimental.TPUStrategy).
TF Profiler introduces two new tools: a memory profiler to visualize your model’s memory usage over time and a python tracer which allows you to trace python function calls in your model. Usability improvements include better diagnostic messages and profile options to customize the host and device trace verbosity level.
Introduces experimental support for Keras Preprocessing Layers
API (tf.keras.layers.experimental.preprocessing.*)
to handle data preprocessing operations, with support for composite
tensor inputs. Please see below for additional details on these
layers.
TFLite now properly supports dynamic shapes during conversion and inference. We’ve also added opt-in support on Android and iOS for XNNPACK, a highly optimized set of CPU kernels, as well as opt-in support for executing quantized models on the GPU.
Libtensorflow packages are available in GCS starting this release. We have also started to release a nightly version of these packages.
The experimental Python API tf.debugging.experimental.enable_dump_debug_info()
now allows you to instrument a TensorFlow program and dump debugging
information to a directory on the file system. The directory can be read
and visualized by a new interactive dashboard in TensorBoard 2.3 called
Debugger
V2, which reveals the details of the TensorFlow program including
graph structures, history of op executions at the Python (eager) and
intra-graph levels, the runtime dtype, shape, and numerical composition
of tensors, as well as their code locations.
tf.data
tf.data.IteratorBase::RestoreInternal,
IteratorBase::SaveInternal, and
DatasetBase::CheckExternalState become pure-virtual and
subclasses are now expected to provide an implementation.DatasetBase::IsStateful method is
removed in favor of DatasetBase::CheckExternalState.DatasetBase::MakeIterator and
MakeIteratorFromInputElement are removed.tensorflow::data::IteratorBase::SaveInternal and
tensorflow::data::IteratorBase::SaveInput has been extended
with SerializationContext argument to enable overriding the
default policy for the handling external state during iterator
checkpointing. This is not a backwards compatible change and all
subclasses of IteratorBase need to be updated
accordingly.tf.keras
BackupAndRestore callback for handling
distributed training failures & restarts. Please take a look at this
tutorial
for details on how to use the callback.tf.image.extract_glimpse has been updated to correctly
process the case where centered=False and
normalized=False. This is a breaking change as the output
is different from (incorrect) previous versions. Note this breaking
change only impacts tf.image.extract_glimpse and
tf.compat.v2.image.extract_glimpse API endpoints. The
behavior of tf.compat.v1.image.extract_glimpse does not
change. The behavior of existing C++ kernel ExtractGlimpse
does not change either, so saved models using
tf.raw_ops.ExtractGlimpse will not be impacted.tf.lite
tf2_behavior to 1 to enable V2 for early loading
cases.execute_fn_for_device function to dynamically
choose the implementation based on underlying device placement.reduce_logsumexp benchmark with experiment
compile.EagerTensors a meaningful __array__
implementation.tf.function/AutoGraph:
AutoGraph now includes into TensorFlow loops any
variables that are closed over by local functions. Previously, such
variables were sometimes incorrectly ignored.get_concrete_function method
of tf.function objects can now be called with arguments
consistent with the original arguments or type specs passed to
get_concrete_function. This calling convention is now the
preferred way to use concrete functions with nested values and composite
tensors. Please check the guide for
more details on concrete_ function.tf.function's
experimental_relax_shapes to handle composite tensors
appropriately.tf.function invocation, by removing redundant
list converter.tf.function will retrace when called with a different
variable instead of simply using the dtype &
shape.tf.function.tf.math:
argmin/argmax contract to
always return the smallest index for ties.tf.math.reduce_variance and
tf.math.reduce_std return correct computation for complex
types and no longer support integer types.tf.math.special.tf.divide now always returns a tensor to be consistent
with documentation and other APIs.tf.image:
tf.image.non_max_suppression_padded
with a new implementation that supports batched inputs, which is
considerably faster on TPUs and GPUs. Boxes with area=0 will be ignored.
Existing usage with single inputs should still work as before.tf.linalg
tf.linalg.banded_triangular_solve.tf.random:
tf.random.stateless_parameterized_truncated_normal.tf.ragged:
tf.ragged.cross and
tf.ragged.cross_hashed operations.tf.RaggedTensor:
RaggedTensor.to_tensor() now preserves static
shape.tf.strings.format() and tf.print() to
support RaggedTensors.tf.saved_model:
@tf.function from SavedModel no longer ignores args
after a RaggedTensor when selecting the concrete function
to run.tf.saved_model.LoadOptions with experimental_io_device
as arg with default value None to choose the I/O device for
loading models and weights.tf.saved_model.SaveOptions with experimental_io_device
as arg with default value None to choose the I/O device for
saving models and weights.TF_USE_CUDNN.tf.while_loop/tf.cond/tf.switch_case.tf.vectorized_map to support vectorizing
tf.while_loop and TensorList operations.tf.custom_gradient can now be applied to functions that
accept nested structures of tensors as inputs (instead of
just a list of tensors). Note that Python structures such as tuples and
lists now won't be treated as tensors, so if you still want them to be
treated that way, you need to wrap them with
tf.convert_to_tensor.DeviceIndex op.tf.gather to support
batch_dims and axis args.tf.map_fn to support RaggedTensors and
SparseTensors.tf.group. It is not useful in eager
mode.FTRL/FTRLV2
that can triggerred by multiply_linear_by_lr allowing a
learning rate of zero.tf.data:tf.data.experimental.dense_to_ragged_batch works
correctly with tuples.tf.data.experimental.dense_to_ragged_batch to output
variable ragged rank.tf.data.experimental.cardinality is now a method on
tf.data.Dataset.tf.data.Dataset now supports len(Dataset)
when the cardinality is finite.tf.distribute:tf.distribute.DistributedDataset
and tf.distribute.DistributedIterator
to distribute input data when using tf.distribute to scale
training on multiple devices.
get_next_as_optional
method for tf.distribute.DistributedIterator
class to return a tf.experimental.Optional instance that
contains the next value for all replicas or none instead of raising an
out of range error. Also see new guide on
input distribution..assign
in replica context to be more convenient, instead of having to use
Strategy.extended.update which was the previous way of
updating variables in this situation.tf.distribute.experimental.MultiWorkerMirroredStrategy
adds support for partial batches. Workers running out of data now
continue to participate in the training with empty inputs, instead of
raising an error. Learn more about partial
batches here.tf.distribute.experimental.MultiWorkerMirroredStrategy.strategy.reduce() inside
tf.function may raise exceptions when the values to reduce
are from loops or if-clauses.tf.distribute.MirroredStrategy
cannot be used together with
tf.distribute.experimental.MultiWorkerMirroredStrategy.tf.distribute.cluster_resolver.TPUClusterResolver.connect
API to simplify TPU initialization.tf.distribute.Strategy.gather and
tf.distribute.ReplicaContext.all_gather methods to gather
and concatenate tf.distribute.DistributedValues across
workers and devices.tf.keras:tf.keras.layers.experimental.preprocessing) to handle data
preprocessing operations such as categorical feature encoding, text
vectorization, data normalization, and data discretization (binning).
The newly added layers provide a replacement for the legacy feature
column API, and support composite tensor inputs.IntegerLookup & StringLookup: build an
index of categorical feature valuesCategoryEncoding: turn integer-encoded categories into
one-hot, multi-hot, or tf-idf encoded representationsCategoryCrossing: create new categorical features
representing co-occurrences of previous categorical feature valuesHashing: the hashing trick, for large-vocabulary
categorical featuresDiscretization: turn continuous numerical features into
categorical features by binning their valuesCenterCrop, RescalingRandomCrop, RandomFlip,
RandomTranslation, RandomRotation,
RandomHeight, RandomWidth,
RandomZoom, RandomContrastTextVectorization layer,
which handles string tokenization, n-gram generation, and token encoding
TextVectorization layer now accounts for the
mask_token as part of the vocabulary size when output_mode='int'. This
means that, if you have a max_tokens value of 5000, your output will
have 5000 unique values (not 5001 as before).TextVectorization.get_vocabulary() from byte
to string. Users who previously were calling 'decode' on
the output of this method should no longer need to do so.image_dataset_from_directory
is a utility based on tf.data.Dataset, meant to replace the
legacy ImageDataGenerator. It takes you from a structured
directory of images to a labeled dataset, in one function call. Note
that it doesn't perform image data augmentation (which is meant to be
done using preprocessing layers).text_dataset_from_directory
takes you from a structured directory of text files to a labeled
dataset, in one function call.timeseries_dataset_from_array
is a tf.data.Dataset-based replacement of the legacy
TimeseriesGenerator. It takes you from an array of
timeseries data to a dataset of shifting windows with their
targets.experimental_steps_per_execution
arg to model.compile to indicate the number of batches to
run per tf.function call. This can speed up Keras Models on
TPUs up to 3x.tf.keras.layers.Lambda layers to support
multi-argument lambdas, and keyword arguments when calling the
layer.BatchNormalization layer's
trainable property to act like standard python state when
it's used inside tf.functions (frozen at tracing time),
instead of acting like a pseudo-variable whose updates kind of
sometimes get reflected in already-traced tf.function
traces.Conv1DTranspose layer.SensitivitySpecificityBase
derived metrics. See the updated API docstrings for tf.keras.metrics.SensitivityAtSpecificity
and tf.keras.metrics.SpecificityAtSensitivty.tf.lite:inference_input_type and
inference_output_type flags in TF 2.x TFLiteConverter
(backward compatible with TF 1.x) to support integer (tf.int8, tf.uint8)
input and output types in post training full integer quantized
models.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8.Conv2D on x86.XNNPACK for
optimized CPU performance.XNNPACK for
optimized CPU performance.XNNPACK delegate automatically when the model has a
fp32 operation.StatefulNnApiDelegate::Options::max_number_delegated_partitions
to 3.NNAPI CPU and check
NNAPI Errno.NNAPI with target accelerator
specified with model containing Conv2d or FullyConnected or LSTM nodes
with quantized weights.ANEURALNETWORKS_BAD_DATA execution failures with
sum/max/min/reduce
operations with scalar inputs.int8 support for most hexagon ops.conv in
Hexagon delegate.BatchMatMul.half_pixel_centers with
ResizeNearestNeighbor.BatchToSpaceND.BroadcastSub, Maximum,
Minimum, Transpose and
BroadcastDiv.kTfLiteActRelu1 to
kTfLiteActReluN1To1.Buckettize, SparseCross and
BoostedTreesBucketize to the flex whitelist.ByteBuffer inputs with
graphs that have dynamic shapes.HardSwish.tf.sysconfig.get_build_info(). Returns a dict
that describes the build environment of the currently installed
TensorFlow package, e.g. the NVIDIA CUDA and NVIDIA CuDNN versions used
when TensorFlow was built.XStatVisitor::RefValue().FTRL with
multiply_linear_by_lr.gstpu.restartType in cloud tpu client.TFE_Py_Execute traceme.argmin and argmaxThis release contains contributions from many people at Google, as well as:
902449@58880@bigcat_chen@ASIC, Abdul Baseer Khan, Abhineet Choudhary, Abolfazl Shahbazi, Adam Hillier, ag.ramesh, Agoniii, Ajay P, Alex Hoffman, Alexander Bayandin, Alexander Grund, Alexandre Abadie, Alexey Rogachevskiy, amoitra, Andrew Stevens, Angus-Luo, Anshuman Tripathy, Anush Elangovan, Artem Mavrin, Ashutosh Hathidara, autoih, Ayushman Kumar, ayushmankumar7, Bairen Yi, Bas Aarts, Bastian Eichenberger, Ben Barsdell, bhack, Bharat Raghunathan, Biagio Montaruli, Bigcat-Himax, blueyi, Bryan Cutler, Byambaa, Carlos Hernandez-Vaquero, Chen Lei, Chris Knorowski, Christian Clauss, chuanqiw, CuiYifeng, Daniel Situnayake, Daria Zhuravleva, Dayananda-V, Deven Desai, Devi Sandeep Endluri, Dmitry Zakharov, Dominic Jack, Duncan Riach, Edgar Liberis, Ehsan Toosi, ekuznetsov139, Elena Zhelezina, Eugene Kuznetsov, Eugene Mikhantiev, Evgenii Zheltonozhskii, Fabio Di Domenico, Fausto Morales, Fei Sun, feihugis, Felix E. Klee, flyingcat, Frederic Bastien, Fredrik Knutsson, frreiss, fsx950223, ganler, Gaurav Singh, Georgios Pinitas, Gian Marco Iodice, Giorgio Arena, Giuseppe Rossini, Gregory Keith, Guozhong Zhuang, gurushantj, Hahn Anselm, Harald Husum, Harjyot Bagga, Hristo Vrigazov, Ilya Persky, Ir1d, Itamar Turner-Trauring, jacco, Jake Tae, Janosh Riebesell, Jason Zaman, jayanth, Jeff Daily, Jens Elofsson, Jinzhe Zeng, JLZ, Jonas Skog, Jonathan Dekhtiar, Josh Meyer, Joshua Chia, Judd, justkw, Kaixi Hou, Kam D Kasravi, Kamil Rakoczy, Karol Gugala, Kayou, Kazuaki Ishizaki, Keith Smiley, Khaled Besrour, Kilaru Yasaswi Sri Chandra Gandhi, Kim, Young Soo, Kristian Hartikainen, Kwabena W. Agyeman, Leslie-Fang, Leslie-Fang-Intel, Li, Guizi, Lukas Geiger, Lutz Roeder, M\U00E5Ns Nilsson, Mahmoud Abuzaina, Manish, Marcel Koester, Marcin Sielski, marload, Martin Jul, Matt Conley, mdfaijul, Meng, Peng, Meteorix, Michael Käufl, Michael137, Milan Straka, Mitchell Vitez, Ml-0, Mokke Meguru, Mshr-H, nammbash, Nathan Luehr, naumkin, Neeraj Bhadani, ngc92, Nick Morgan, nihui, Niranjan Hasabnis, Niranjan Yadla, Nishidha Panpaliya, Oceania2018, oclyke, Ouyang Jin, OverLordGoldDragon, Owen Lyke, Patrick Hemmer, Paul Andrey, Peng Sun, periannath, Phil Pearl, Prashant Dandriyal, Prashant Kumar, Rahul Huilgol, Rajan Singh, Rajeshwar Reddy T, rangjiaheng, Rishit Dagli, Rohan Reddy, rpalakkal, rposts, Ruan Kunliang, Rushabh Vasani, Ryohei Ikegami, Semun Lee, Seo-Inyoung, Sergey Mironov, Sharada Shiddibhavi, ShengYang1, Shraiysh Vaishay, Shunya Ueta, shwetaoj, Siyavash Najafzade, Srinivasan Narayanamoorthy, Stephan Uphoff, storypku, sunchenggen, sunway513, Sven-Hendrik Haase, Swapnil Parekh, Tamas Bela Feher, Teng Lu, tigertang, tomas, Tomohiro Ubukata, tongxuan.ltx, Tony Tonev, Tzu-Wei Huang, Téo Bouvard, Uday Bondhugula, Vaibhav Jade, Vijay Tadikamalla, Vikram Dattu, Vincent Abriou, Vishnuvardhan Janapati, Vo Van Nghia, VoVAllen, Will Battel, William D. Irons, wyzhao, Xiaoming (Jason) Cui, Xiaoquan Kong, Xinan Jiang, xutianming, Yair Ehrenwald, Yasir Modak, Yasuhiro Matsumoto, Yixing Fu, Yong Tang, Yuan Tang, zhaozheng09, Zilin Zhu, zilinzhu, 张志豪
sqlite3 to 3.31.01 to handle CVE-2019-19880,
CVE-2019-19244
and CVE-2019-19645curl to 7.69.1 to handle CVE-2019-15601libjpeg-turbo to 2.0.4 to handle
CVE-2018-19664,
CVE-2018-20330
and CVE-2019-139602.4.5 to handle CVE-2019-10099,
CVE-2018-17190
and CVE-2018-11770sqlite3 to 3.31.01 to handle CVE-2019-19880,
CVE-2019-19244
and CVE-2019-19645curl to 7.69.1 to handle CVE-2019-15601libjpeg-turbo to 2.0.4 to handle
CVE-2018-19664,
CVE-2018-20330
and CVE-2019-139602.4.5 to handle CVE-2019-10099,
CVE-2018-17190
and CVE-2018-11770sqlite3 to 3.31.01 to handle CVE-2019-19880,
CVE-2019-19244
and CVE-2019-19645curl to 7.69.1 to handle CVE-2019-15601libjpeg-turbo to 2.0.4 to handle
CVE-2018-19664,
CVE-2018-20330
and CVE-2019-139602.4.5 to handle CVE-2019-10099,
CVE-2018-17190
and CVE-2018-11770TensorFlow 2.2 discontinues support for Python 2, previously announced as following Python 2's EOL on January 1, 2020.
Coinciding with this change, new releases of TensorFlow's
Docker images provide Python 3 exclusively. Because all images now
use Python 3, Docker tags containing -py3 will no longer be
provided and existing -py3 tags like
latest-py3 will not be updated.
Replaced the scalar type for string tensors from
std::string to tensorflow::tstring which is
now ABI stable.
A new Profiler for TF 2 for CPU/GPU/TPU. It offers both device and host performance analysis, including input pipeline and TF Ops. Optimization advisory is provided whenever possible. Please see this tutorial and guide for usage guidelines.
Export C++ functions to Python using pybind11 as
opposed to SWIG as a part of our deprecation
of swig efforts.
tf.distribute:
BatchNormalization by
using the newly added
tf.keras.layers.experimental.SyncBatchNormalization layer.
This layer will sync BatchNormalization statistics every
step across all replicas taking part in sync training.tf.distribute.experimental.MultiWorkerMirroredStrategyNCCL to 2.5.7-1 for better
performance and performance tuning. Please see nccl
developer guide for more information on this.allreduce in float16. See
this example
usage.experimental_run_v2 method for distribution
strategies and renamed the method run as it is no longer
experimental.tf.keras:
Model.fit major improvements:
Model.fit by
overriding Model.train_step.Model.fit handles for you (distribution
strategies, callbacks, data formats, looping logic, etc)Model.train_step
for an example of what this function should look like. Same applies for
validation and inference via Model.test_step and
Model.predict_step.Model._saved_model_inputs_spec
attr now instead of relying on Model.inputs and
Model.input_names, which are no longer set for subclass
Models. This attr is set in eager, tf.function, and graph
modes. This gets rid of the need for users to manually call
Model._set_inputs when using Custom Training
Loops(CTLs).Model._standardize_user_data. Long-term, a
solution where the DataAdapter doesn't need to call the
Model is probably preferable.fused_batch_norm. You should see
significant performance improvements when using
fused_batch_norm in Eager mode.tf.lite:
XLA
tf.function with “compile or throw exception”
semantics on CPU and GPU.tf.keras:
tf.keras.applications the name of the "top" layer
has been standardized to "predictions". This is only a problem if your
code relies on the exact name of the layer.tf.py_function, tf.py_func and
tf.numpy_function.XLA_CPU and XLA_GPU devices
with this release.cc_experimental_shared_library.metrics,
metrics_names will now be available only after
training/evaluating the model on actual data for
functional models. metrics will now
include model loss and output
losses.loss_functions property has been removed from the
model. This was an undocumented property that was accidentally public
and has now been removed.tf.data:
autotune_algorithm from experimental
optimization options.tf.constant always creates CPU tensors irrespective of
the current device context.TensorHandles maintain a list of mirrors for any
copies to local or remote devices. This avoids any redundant copies due
to op execution.tf.Tensor & tf.Variable,
.experimental_ref() is no longer experimental and is
available as simply .ref().pfor/vectorized_map: Added support for vectorizing 56
more ops. Vectorizing tf.cond is also supported now.tf.while_loop emits
StatelessWhile op if cond and body functions
are stateless. This allows multiple gradients while ops to run in
parallel under distribution strategy.GradientTape in eager mode by auto-generating
list of op inputs/outputs which are unused and hence not cached for
gradient functions.back_prop=False in while_v2 but
mark it as deprecated.None in
data-dependent control flow.RaggedTensor.numpy().RaggedTensor.__getitem__ to preserve uniform
dimensions & allow indexing into uniform dimensions.tf.expand_dims to always insert the new
dimension as a non-ragged dimension.tf.embedding_lookup to use
partition_strategy and max_norm when
ids is ragged.batch_dims==rank(indices) in
tf.gather.tf.print.tf.distribute:
embedding_column with variable-length input
features for MultiWorkerMirroredStrategy.tf.keras:
experimental_aggregate_gradients argument to
tf.keras.optimizer.Optimizer.apply_gradients. This allows
custom gradient aggregation and processing aggregated gradients in
custom training loop.pathlib.Path paths for loading models via Keras
API.tf.function/AutoGraph:
ReplicaContext.merge_call,
Strategy.extended.update and
Strategy.extended.update_non_slot.tf.function. See the API docs for
tf.autograph.experimental.set_loop_options for additional
info.tf.function input arguments
to unlock more Grappler optimizations in TensorFlow 2.x.experimental_run_v2 in tf.function.RaggedTensors using a for loop
inside tf.function.tf.lite:
tf.lite C inference API out of
experimental into lite/c.NNAPI CPU / partial
acceleration on Android 10NNAPI CPU Fallback is disabled.tf.math.reciprocal1 op by lowering
to tf.div op.strided_slice.DEPTH_TO_SPACE to
NNAPI causing op not to be accelerated.NNAPI DelegateNNAPI delegate failure when an operand for
Maximum/Minimum operation is a scalar.NNAPI delegate failure when Axis input for reduce
operation is a scalar.NNAPI.tf.random:
random_uniformrandom_seed documentation improvement.RandomBinomial broadcasts and appends the sample shape
to the left rather than the right.tf.random.stateless_binomial,
tf.random.stateless_gamma,
tf.random.stateless_poissontf.random.stateless_uniform now supports unbounded
sampling of int types.tf.linalg.LinearOperatorTridiag.LinearOperatorBlockLowerTriangulartf.math.sobol_sample op.tf.math.xlog1py.tf.math.special.{dawsn,expi,fresnel_cos,fresnel_sin,spence}.tf.signal.TpuClusterResolver to move shared logic to a
separate pip package.saved_model_cli aot_compile_cpu allows you to compile
saved models to XLA header+object files and include them in your C++
programs.Igamma, Igammac for XLA.TF_DETERMINISTIC_OPS is set to "true" or "1". This extends
deterministic tf.nn.bias_add back-prop functionality (and
therefore also deterministic back-prop of bias-addition in Keras layers)
to include when XLA JIT compilation is enabled.TF_DETERMINISTIC_OPS or environment variable
TF_CUDNN_DETERMINISTIC is set to "true" or "1", in which
some layer configurations led to an exception with the message "No
algorithm worked!"_send traceme to allow
easier debugging.fastpathexecute.in-place.tensorflow/core:framework/*_pyclif rules to
tensorflow/core/framework:*_pyclif.This release contains contributions from many people at Google, as well as:
372046933, 8bitmp3, aaronhma, Abin Shahab, Aditya Patwardhan, Agoniii, Ahti Kitsik, Alan Yee, Albin Joy, Alex Hoffman, Alexander Grund, Alexandre E. Eichenberger, Amit Kumar Jaiswal, amoitra, Andrew Anderson, Angus-Luo, Anthony Barbier, Anton Kachatkou, Anuj Rawat, archis, Arpan-Dhatt, Arvind Sundararajan, Ashutosh Hathidara, autoih, Bairen Yi, Balint Cristian, Bas Aarts, BashirSbaiti, Basit Ayantunde, Ben Barsdell, Benjamin Gaillard, boron, Brett Koonce, Bryan Cutler, Christian Goll, Christian Sachs, Clayne Robison, comet, Daniel Falbel, Daria Zhuravleva, darsh8200, David Truby, Dayananda-V, deepakm, Denis Khalikov, Devansh Singh, Dheeraj R Reddy, Diederik Van Liere, Diego Caballero, Dominic Jack, dothinking, Douman, Drake Gens, Duncan Riach, Ehsan Toosi, ekuznetsov139, Elena Zhelezina, elzino, Ending2015a, Eric Schweitz, Erik Zettel, Ethan Saadia, Eugene Kuznetsov, Evgeniy Zheltonozhskiy, Ewout Ter Hoeven, exfalso, FAIJUL, Fangjun Kuang, Fei Hu, Frank Laub, Frederic Bastien, Fredrik Knutsson, frreiss, Frédéric Rechtenstein, fsx950223, Gaurav Singh, gbaned, George Grzegorz Pawelczak, George Sterpu, Gian Marco Iodice, Giorgio Arena, Hans Gaiser, Hans Pabst, Haoyu Wu, Harry Slatyer, hsahovic, Hugo, Hugo Sjöberg, IrinaM21, jacco, Jake Tae, Jean-Denis Lesage, Jean-Michel Gorius, Jeff Daily, Jens Elofsson, Jerry Shih, jerryyin, Jin Mingjian, Jinjing Zhou, JKIsaacLee, jojimonv, Jonathan Dekhtiar, Jose Ignacio Gomez, Joseph-Rance, Judd, Julian Gross, Kaixi Hou, Kaustubh Maske Patil, Keunwoo Choi, Kevin Hanselman, Khor Chean Wei, Kilaru Yasaswi Sri Chandra Gandhi, Koan-Sin Tan, Koki Ibukuro, Kristian Holsheimer, kurileo, Lakshay Tokas, Lee Netherton, leike666666, Leslie-Fang-Intel, Li, Guizi, LIUJIAN435, Lukas Geiger, Lyo Nguyen, madisetti, Maher Jendoubi, Mahmoud Abuzaina, Manuel Freiberger, Marcel Koester, Marco Jacopo Ferrarotti, Markus Franke, marload, Mbah-Javis, mbhuiyan, Meng Zhang, Michael Liao, MichaelKonobeev, Michal Tarnowski, Milan Straka, minoring, Mohamed Nour Abouelseoud, MoussaMM, Mrinal Jain, mrTsjolder, Måns Nilsson, Namrata Bhave, Nicholas Gao, Niels Ole Salscheider, nikochiko, Niranjan Hasabnis, Nishidha Panpaliya, nmostafa, Noah Trenaman, nuka137, Officium, Owen L - Sfe, Pallavi G, Paul Andrey, Peng Sun, Peng Wu, Phil Pearl, PhilipMay, pingsutw, Pooya Davoodi, PragmaTwice, pshiko, Qwerty71, R Gomathi, Rahul Huilgol, Richard Xiao, Rick Wierenga, Roberto Rosmaninho, ruchit2801, Rushabh Vasani, Sami, Sana Damani, Sarvesh Dubey, Sasan Jafarnejad, Sergii Khomenko, Shane Smiskol, Shaochen Shi, sharkdtu, Shawn Presser, ShengYang1, Shreyash Patodia, Shyam Sundar Dhanabalan, Siju Samuel, Somyajit Chakraborty Sam, Srihari Humbarwadi, srinivasan.narayanamoorthy, Srishti Yadav, Steph-En-M, Stephan Uphoff, Stephen Mugisha, SumanSudhir, Taehun Kim, Tamas Bela Feher, TengLu, Tetragramm, Thierry Herrmann, Tian Jin, tigertang, Tom Carchrae, Tom Forbes, Trent Lo, Victor Peng, vijayphoenix, Vincent Abriou, Vishal Bhola, Vishnuvardhan Janapati, vladbataev, VoVAllen, Wallyss Lima, Wen-Heng (Jack) Chung, wenxizhu, William D. Irons, William Zhang, Xiaoming (Jason) Cui, Xiaoquan Kong, Xinan Jiang, Yasir Modak, Yasuhiro Matsumoto, Yaxun (Sam) Liu, Yong Tang, Ytyt-Yt, yuan, Yuan Mingshuai, Yuan Tang, Yuki Ueda, Yusup, zhangshijin, zhuwenxi
tf.float16 value produces a segmentation fault (CVE-2020-5215)curl to 7.66.0 to handle CVE-2019-5482
and CVE-2019-5481sqlite3 to 3.30.01 to handle CVE-2019-19646,
CVE-2019-19645
and CVE-2019-16168tf.float16 value produces a segmentation fault (CVE-2020-5215)curl to 7.66.0 to handle CVE-2019-5482
and CVE-2019-5481sqlite3 to 3.30.01 to handle CVE-2019-19646,
CVE-2019-19645
and CVE-2019-16168TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support officially ends an January 1, 2020. As announced earlier, TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019.
tensorflow pip package now includes GPU support by
default (same as tensorflow-gpu) for both Linux and
Windows. This runs on machines with and without NVIDIA GPUs.
tensorflow-gpu is still available, and CPU-only packages
can be downloaded at tensorflow-cpu for users who are
concerned about package size.tensorflow Pip packages are now built with Visual Studio
2019 version 16.4 in order to take advantage of the new
/d2ReducedOptimizeHugeFunctions compiler flag. To use these
new packages, you must install "Microsoft Visual C++ Redistributable for
Visual Studio 2015, 2017 and 2019", available from Microsoft's website
here.
EIGEN_STRONG_INLINE can take over 48 hours to compile
without this flag. Refer to configure.py for more
information about EIGEN_STRONG_INLINE and
/d2ReducedOptimizeHugeFunctions.msvcp140.dll (old) or
msvcp140_1.dll (new), are missing on your machine,
import tensorflow will print a warning message.tensorflow pip package is built with CUDA 10.1 and
cuDNN 7.6.tf.keras
TextVectorization layer, which takes as
input raw strings and takes care of text standardization, tokenization,
n-gram generation, and vocabulary indexing. See this end-to-end
text classification example..compile .fit .evaluate
and .predict are allowed to be outside of the
DistributionStrategy scope, as long as the model was constructed inside
of a scope..compile,
.fit, .evaluate, and .predict is
available for Cloud TPUs, Cloud TPU, for all types of Keras models
(sequential, functional and subclassing models).tf.summary to be used more conveniently with Cloud
TPUs..fit, .evaluate,
.predict on TPU using numpy data, in addition to
tf.data.Dataset.tf.data
tf.data datasets +
DistributionStrategy for better performance. Note that the dataset also
behaves slightly differently, in that the rebatched dataset cardinality
will always be a multiple of the number of replicas.tf.data.Dataset now supports automatic data
distribution and sharding in distributed environments, including on TPU
pods.tf.data.Dataset can now be
tuned with 1.
tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA)
2.
tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)tf.debugging
tf.debugging.enable_check_numerics() and
tf.debugging.disable_check_numerics() to help debugging the
root causes of issues involving infinities and NaNs.tf.distribute
strategy.experimental_distribute_dataset,
strategy.experimental_distribute_datasets_from_function,
strategy.experimental_run_v2,
strategy.reduce.tf.distribute.experimental_set_strategy(), in addition to
strategy.scope().TensorRT
tf.experimental.tensorrt.Converter.TF_DETERMINISTIC_OPS has been
added. When set to "true" or "1", this environment variable makes
tf.nn.bias_add operate deterministically (i.e.
reproducibly), but currently only when XLA JIT compilation is
not enabled. Setting TF_DETERMINISTIC_OPS to
"true" or "1" also makes cuDNN convolution and max-pooling operate
deterministically. This makes Keras Conv*D and MaxPool*D layers operate
deterministically in both the forward and backward directions when
running on a CUDA-enabled GPU.Operation.traceback_with_start_lines for which
we know of no usages.id from tf.Tensor.__repr__() as
id is not useful other than internal debugging.tf.assert_* methods now raise assertions at
operation creation time if the input tensors' values are known at that
time, not during the session.run(). This only changes
behavior when the graph execution would have resulted in an error. When
this happens, a noop is returned and the input tensors are marked
non-feedable. In other words, if they are used as keys in
feed_dict argument to session.run(), an error
will be raised. Also, because some assert ops don't make it into the
graph, the graph structure changes. A different graph can result in
different per-op random seeds when they are not given explicitly (most
often).tf.config.list_logical_devices,
tf.config.list_physical_devices,
tf.config.get_visible_devices,
tf.config.set_visible_devices,
tf.config.get_logical_device_configuration,
tf.config.set_logical_device_configuration.tf.config.experimentalVirtualDeviceConfiguration has
been renamed to tf.config.LogicalDeviceConfiguration.tf.config.experimental_list_devices has been removed,
please use tf.config.list_logical_devices.tf.data
tf.data.experimental.parallel_interleave with
sloppy=True.tf.data.experimental.dense_to_ragged_batch().tf.data parsing ops to support
RaggedTensors.tf.distribute
tf.distribute.Strategy was used.tf.estimator
tf.estimator.CheckpointSaverHook to not
save the GraphDef.tf.keras
depthwise_conv2d in
tf.keras.backend.trainable_weights, non_trainable_weights, and
weights are explicitly deduplicated.model.load_weights now accepts
skip_mismatch as an argument. This was available in
external Keras, and has now been copied over to
tf.keras.Model.fit_generator,
Model.evaluate_generator,
Model.predict_generator, Model.train_on_batch,
Model.test_on_batch, and
Model.predict_on_batch methods now respect the
run_eagerly property, and will correctly run using
tf.function by default. Note that
Model.fit_generator, Model.evaluate_generator,
and Model.predict_generator are deprecated endpoints. They
are subsumed by Model.fit, Model.evaluate, and
Model.predict which now support generators and
Sequences.tf.lite
NMS ops in TFLite.narrow_range and axis to
quantize_v2 and dequantize ops.FusedBatchNormV3 in converter.errno-like field to NNAPI delegate
for detecting NNAPI errors for fallback behaviour.NNAPI Delegate to support detailed reason why
an operation is not accelerated.tf.tpu.experimental.initialize_tpu_system.RaggedTensor.merge_dims().uniform_row_length row-partitioning tensor to
RaggedTensor.shape arg to RaggedTensor.to_tensor;
Improve speed of RaggedTensor.to_tensor.tf.io.parse_sequence_example and
tf.io.parse_single_sequence_example now support ragged
features.while_v2 with variables in custom gradient.tf.cond and
tf.while_loop using LookupTable.vectorized_map failed on inputs with
unknown static shape.None now behaves as expected.tf.function(f)(),
tf.function(f).get_concrete_function and
tf.function(f).get_initialization_function
thread-safe.tf.identity to work with CompositeTensors (such
as SparseTensor)dtypes and zero-sized inputs to
Einsum Op and improved its performanceNCCL all-reduce inside
functions executing eagerly.RFFT, RFFT2D,
RFFT3D, IRFFT, IRFFT2D, and
IRFFT3D.pfor converter for
SelfAdjointEigV2.tf.math.ndtri and tf.math.erfinv.tf.config.experimental.enable_mlir_bridge to allow
using MLIR compiler bridge in eager model.tf.autodiff.ForwardAccumulator for forward-mode
autodiffLinearOperatorPermutation.tf.reduce_logsumexp.AUC metriczeros_like.None or types with
an __index__ method.tf.random.uniform microbenchmark._protogen suffix for proto library targets instead
of _cc_protogen suffix.swig to
pybind11.tf.device & MirroredStrategy now
supports passing in a tf.config.LogicalDevice.bazelversion file at the root of the project
directory.This release contains contributions from many people at Google, as well as:
8bitmp3, Aaron Ma, AbdüLhamit Yilmaz, Abhai Kollara, aflc, Ag Ramesh, Albert Z. Guo, Alex Torres, amoitra, Andrii Prymostka, angeliand, Anshuman Tripathy, Anthony Barbier, Anton Kachatkou, Anubh-V, Anuja Jakhade, Artem Ryabov, autoih, Bairen Yi, Bas Aarts, Basit Ayantunde, Ben Barsdell, Bhavani Subramanian, Brett Koonce, candy.dc, Captain-Pool, caster, cathy, Chong Yan, Choong Yin Thong, Clayne Robison, Colle, Dan Ganea, David Norman, David Refaeli, dengziming, Diego Caballero, Divyanshu, djshen, Douman, Duncan Riach, EFanZh, Elena Zhelezina, Eric Schweitz, Evgenii Zheltonozhskii, Fei Hu, fo40225, Fred Reiss, Frederic Bastien, Fredrik Knutsson, fsx950223, fwcore, George Grzegorz Pawelczak, George Sterpu, Gian Marco Iodice, Giorgio Arena, giuros01, Gomathi Ramamurthy, Guozhong Zhuang, Haifeng Jin, Haoyu Wu, HarikrishnanBalagopal, HJYOO, Huang Chen-Yi, Ilham Firdausi Putra, Imran Salam, Jared Nielsen, Jason Zaman, Jasper Vicenti, Jeff Daily, Jeff Poznanovic, Jens Elofsson, Jerry Shih, jerryyin, Jesper Dramsch, jim.meyer, Jongwon Lee, Jun Wan, Junyuan Xie, Kaixi Hou, kamalkraj, Kan Chen, Karthik Muthuraman, Keiji Ariyama, Kevin Rose, Kevin Wang, Koan-Sin Tan, kstuedem, Kwabena W. Agyeman, Lakshay Tokas, latyas, Leslie-Fang-Intel, Li, Guizi, Luciano Resende, Lukas Folle, Lukas Geiger, Mahmoud Abuzaina, Manuel Freiberger, Mark Ryan, Martin Mlostek, Masaki Kozuki, Matthew Bentham, Matthew Denton, mbhuiyan, mdfaijul, Muhwan Kim, Nagy Mostafa, nammbash, Nathan Luehr, Nathan Wells, Niranjan Hasabnis, Oleksii Volkovskyi, Olivier Moindrot, olramde, Ouyang Jin, OverLordGoldDragon, Pallavi G, Paul Andrey, Paul Wais, pkanwar23, Pooya Davoodi, Prabindh Sundareson, Rajeshwar Reddy T, Ralovich, Kristof, Refraction-Ray, Richard Barnes, richardbrks, Robert Herbig, Romeo Kienzler, Ryan Mccormick, saishruthi, Saket Khandelwal, Sami Kama, Sana Damani, Satoshi Tanaka, Sergey Mironov, Sergii Khomenko, Shahid, Shawn Presser, ShengYang1, Siddhartha Bagaria, Simon Plovyt, skeydan, srinivasan.narayanamoorthy, Stephen Mugisha, sunway513, Takeshi Watanabe, Taylor Jakobson, TengLu, TheMindVirus, ThisIsIsaac, Tim Gates, Timothy Liu, Tomer Gafner, Trent Lo, Trevor Hickey, Trevor Morris, vcarpani, Wei Wang, Wen-Heng (Jack) Chung, wenshuai, Wenshuai-Xiaomi, wenxizhu, william, William D. Irons, Xinan Jiang, Yannic, Yasir Modak, Yasuhiro Matsumoto, Yong Tang, Yongfeng Gu, Youwei Song, Zaccharie Ramzi, Zhang, Zhenyu Guo, 王振华 (Zhenhua Wang), 韩董, 이중건 Isaac Lee
This is the last 1.x release for TensorFlow. We do not expect to update the 1.x branch with features, although we will issue patch releases to fix vulnerabilities for at least one year.
tensorflow pip package will by default include GPU support
(same as tensorflow-gpu now) for the platforms we currently
have GPU support (Linux and Windows). It will work on machines with and
without Nvidia GPUs. tensorflow-gpu will still be
available, and CPU-only packages can be downloaded at
tensorflow-cpu for users who are concerned about package
size.compat.v2 module. It contains a copy of the 1.15 main
module (without contrib) in the compat.v1
module. TensorFlow 1.15 is able to emulate 2.0 behavior using the
enable_v2_behavior() function. This enables writing forward
compatible code: by explicitly importing either
tensorflow.compat.v1 or tensorflow.compat.v2,
you can ensure that your code works without modifications against an
installation of 1.15 or 2.0.tf.enable_control_flow_v2() and
tf.disable_control_flow_v2() for enabling/disabling v2
control flow.tf.enable_v2_behavior() and
TF2_BEHAVIOR=1.tf.function-decorated functions. AutoGraph is also applied
in functions used with tf.data, tf.distribute
and tf.keras APIS.enable_tensor_equality(), which switches the
behavior such that:
== and !=,
yielding a Boolean Tensor with element-wise comparison results. This
will be the default behavior in 2.0.tensorflow_core containing all the code (in the future it
will contain only the private implementation) and
tensorflow which is a virtual pip package doing forwarding
to tensorflow_core (and in the future will contain only the
public API of tensorflow). We don't expect this to be breaking, unless
you were importing directly from the implementation.constraint= and
.constraint with ResourceVariable.tf.keras:
OMP_NUM_THREADS is no longer used by the default Keras
config. To configure the number of threads, use
tf.config.threading APIs.tf.keras.model.save_model and model.save
now defaults to saving a TensorFlow SavedModel.keras.backend.resize_images (and consequently,
keras.layers.Upsampling2D) behavior has changed, a bug in
the resizing implementation was fixed.float32, and automatically cast
their inputs to the layer's dtype. If you had a model that used
float64, it will probably silently use float32
in TensorFlow2, and a warning will be issued that starts with Layer
"layer-name" is casting an input tensor from dtype float64 to the
layer's dtype of float32. To fix, either set the default dtype to
float64 with tf.keras.backend.set_floatx('float64'), or
pass dtype='float64' to each of the Layer constructors. See
tf.keras.layers.Layer for more information.tf.assert_* methods now raise assertions at
operation creation time (i.e. when this Python line executes) if the
input tensors' values are known at that time, not during the
session.run(). When this happens, a noop is returned and the input
tensors are marked non-feedable. In other words, if they are used as
keys in feed_dict argument to session.run(),
an error will be raised. Also, because some assert ops don't make it
into the graph, the graph structure changes. A different graph can
result in different per-op random seeds when they are not given
explicitly (most often).tf.estimator:
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.DenseFeatures
usability in TF2tf.data:
unbatch from experimental to core API.from_tensors
and from_tensor_slices and batching and unbatching of
nested datasets.tf.keras:
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.tf.saved_model.save now
saves the list of variables, trainable variables, regularization losses,
and the call function.tf.keras.experimental.export_saved_model and
tf.keras.experimental.function. Please use
tf.keras.models.save_model(..., save_format='tf') and
tf.keras.models.load_model instead.implementation=3 mode for
tf.keras.layers.LocallyConnected2D and
tf.keras.layers.LocallyConnected1D layers using
tf.SparseTensor to store weights, allowing a dramatic
speedup for large sparse models.experimental_run_tf_function flag by default. This flag
enables single training/eval/predict execution path. With this 1. All
input types are converted to Dataset. 2. When distribution
strategy is not specified this goes through the no-op distribution
strategy path. 3. Execution is wrapped in tf.function unless
run_eagerly=True is set in compile.batch_size argument is used when input
is dataset/generator/keras sequence.tf.lite
GATHER support to NN API delegate.QUANTIZE.QUANTIZED_16BIT_LSTM.cycle_length
argument of tf.data.Dataset.interleave to the number of
schedulable CPU cores.parallel_for: Add converter for
MatrixDiag.narrow_range attribute to
QuantizeAndDequantizeV2 and V3.tf.strings.unsorted_segment_join.topK_v2.TypeSpec classes.Head as public API.batch_dims case.tf.sparse.from_dense utility function.TensorFlowTestCase.ResizeInputTensor now works for all delegates.EXPAND_DIMS support to NN API delegate TEST:
expand_dims_testtf.cond emits a StatelessIf op if the branch functions
are stateless and do not touch any resources.tf.cond, tf.while and if and
while in AutoGraph now accept a nonscalar predicate if has
a single element. This does not affect non-V2 control flow.tf.while_loop emits a StatelessWhile op if the cond and
body functions are stateless and do not touch any resources.LogSoftMax.nested_value_rowids for ragged
tensors.tf.math.cumulative_logsumexp operation.tf.ragged.stack.AddNewInputConstantTensor.MemoryAllocation::MemoryAllocation().NNAPIDelegateKernel from
nnapi_delegate.ccFusedBatchNormV3 in converter.tf.gradients().This release contains contributions from many people at Google, as well as:
a6802739, Aaron Ma, Abdullah Selek, Abolfazl Shahbazi, Ag Ramesh, Albert Z. Guo, Albin Joy, Alex Itkes, Alex Sergeev, Alexander Pivovarov, Alexey Romanov, alhkad, Amit Srivastava, amoitra, Andrew Lihonosov, Andrii Prymostka, Anuj Rawat, Astropeak, Ayush Agrawal, Bairen Yi, Bas Aarts, Bastian Eichenberger, Ben Barsdell, Benjamin Peterson, bhack, Bharat Raghunathan, Bhavani Subramanian, Bryan Cutler, candy.dc, Cao Zongyan, Captain-Pool, Casper Da Costa-Luis, Chen Guoyin, Cheng Chang, chengchingwen, Chong Yan, Choong Yin Thong, Christopher Yeh, Clayne Robison, Coady, Patrick, Dan Ganea, David Norman, Denis Khalikov, Deven Desai, Diego Caballero, Duncan Dean, Duncan Riach, Dwight J Lyle, Eamon Ito-Fisher, eashtian3, EFanZh, ejot, Elroy Ashtian Jr, Eric Schweitz, Fangjun Kuang, Fei Hu, fo40225, formath, Fred Reiss, Frederic Bastien, Fredrik Knutsson, G. Hussain Chinoy, Gabriel, gehring, George Grzegorz Pawelczak, Gianluca Varisco, Gleb Popov, Greg Peatfield, Guillaume Klein, Gurpreet Singh, Gustavo Lima Chaves, haison, Haraldur TóMas HallgríMsson, HarikrishnanBalagopal, HåKon Sandsmark, I-Hong, Ilham Firdausi Putra, Imran Salam, Jason Zaman, Jason Zavaglia, jayhpark530, jefby, Jeff Daily, Jeffrey Poznanovic, Jekyll Lai, Jeroen BéDorf, Jerry Shih, jerryyin, jiakai, JiangXIAO, Joe Bowser, Joel Shapiro, Johan Gunnarsson, Jojimon Varghese, Joon, Josh Beal, Julian Niedermeier, Jun Wan, Junqin Zhang, Junyuan Xie, Justin Tunis, Kaixi Hou, Karl Lessard, Karthik Muthuraman, Kbhute-Ibm, khanhlvg, Koock Yoon, kstuedem, Kyuwon Kim, Lakshay Tokas, leike666666, leonard951, Leslie-Fang, Leslie-Fang-Intel, Li, Guizi, Lukas Folle, Lukas Geiger, Mahmoud Abuzaina, Manraj Singh Grover, Margaret Maynard-Reid, Mark Ryan, Matt Conley, Matthew Bentham, Matthew Denton, mbhuiyan, mdfaijul, Mei Jie, merturl, MichaelKonobeev, Michal W. Tarnowski, minds, mpppk, musikisomorphie, Nagy Mostafa, Nayana Thorat, Neil, Niels Ole Salscheider, Niklas SilfverströM, Niranjan Hasabnis, ocjosen, olramde, Pariksheet Pinjari, Patrick J. Lopresti, Patrik Gustavsson, per1234, PeterLee, Phan Van Nguyen Duc, Phillip Kravtsov, Pooya Davoodi, Pranav Marathe, Putra Manggala, Qingqing Cao, Rajeshwar Reddy T, Ramon ViñAs, Rasmus Diederichsen, Reuben Morais, richardbrks, robert, RonLek, Ryan Jiang, saishruthi, Saket Khandelwal, Saleem Abdulrasool, Sami Kama, Sana-Damani, Sergii Khomenko, Severen Redwood, Shubham Goyal, Sigrid Keydana, Siju Samuel, sleighsoft, smilu97, Son Tran, Srini511, srinivasan.narayanamoorthy, Sumesh Udayakumaran, Sungmann Cho, Tae-Hwan Jung, Taehoon Lee, Takeshi Watanabe, TengLu, terryky, TheMindVirus, ThisIsIsaac, Till Hoffmann, Timothy Liu, Tomer Gafner, Tongxuan Liu, Trent Lo, Trevor Morris, Uday Bondhugula, Vasileios Lioutas, vbvg2008, Vishnuvardhan Janapati, Vivek Suryamurthy, Wei Wang, Wen-Heng (Jack) Chung, wenxizhu, William D. Irons, winstonq, wyzhao, Xiaoming (Jason) Cui, Xinan Jiang, Xinping Wang, Yann-Yy, Yasir Modak, Yong Tang, Yongfeng Gu, Yuchen Ying, Yuxin Wu, zyeric, 王振华 (Zhenhua Wang)
TensorFlow 2.0 focuses on simplicity and ease of use, featuring updates like:
For details on best practices with 2.0, see the Effective 2.0 guide
For information on upgrading your existing TensorFlow 1.x models, please refer to our Upgrade and Migration guides. We have also released a collection of tutorials and getting started guides.
tf.data,
for building scalable input pipelines. Checkout guide
for additional details.tf.distribute.Strategy
API to distribute training with minimal code changes, yielding great
out-of-the-box performance. It supports distributed training with Keras
model.fit, as well as with custom training loops. Multi-GPU support is
available, along with experimental support for multi worker and Cloud
TPUs. Check out the guide
for more details.tf.Session
is discouraged, and replaced with by writing regular Python functions.
Using the tf.function decorator, such functions can be
turned into graphs which can be executed remotely, serialized, and
optimized for performance.tf.train.Optimizers and
tf.keras.Optimizers. Use tf.keras.Optimizers
for TF2.0. compute_gradients is removed as public API, use
GradientTape to compute gradients.tf.function-decorated functions. AutoGraph is also applied
in functions used with tf.data, tf.distribute and tf.keras APIs.tf.app,
tf.flags, and tf.logging in favor of absl-py.tf.global_variables_initializer and
tf.get_global_step.tf.enable_control_flow_v2() and
tf.disable_control_flow_v2() for enabling/disabling v2
control flow.tf.enable_v2_behavior() and
TF2_BEHAVIOR=1.__init__.py files.float16 for acceleration on Volta and Turing Tensor Cores.
This feature can be enabled by wrapping an optimizer class with
tf.train.experimental.enable_mixed_precision_graph_rewrite().TF_CUDNN_DETERMINISTIC.
Setting to "true" or "1" forces the selection of deterministic cuDNN
convolution and max-pooling algorithms. When this is enabled, the
algorithm selection procedure itself is also deterministic.Many backwards incompatible API changes have been made to clean up the APIs and make them more consistent.
Toolchains:
freeze_graph command line tool;
SavedModel should be used in place of frozen graphs.tf.contrib:
tf.contrib has been deprecated, and functionality has
been either migrated to the core TensorFlow API, to an ecosystem project
such as tensorflow/addons or
tensorflow/io, or
removed entirely.tf.contrib.timeseries dependency on TF
distributions.tf.estimator.experimental.* for apis in
early_stopping.py.tf.estimator:
tf.keras.optimizers instead
of the tf.compat.v1.train.Optimizers. If you do not pass in
an optimizer= arg or if you use a string, the premade
estimator will use the Keras optimizer. This is checkpoint breaking, as
the optimizers have separate variables. A checkpoint converter tool for
converting optimizers is included with the release, but if you want to
avoid any change, switch to the v1 version of the estimator:
tf.compat.v1.estimator.DNN/Linear/DNNLinearCombined*.SUM_OVER_BATCH_SIZE. To maintain previous default behavior,
please pass SUM as the loss aggregation method.input_layer_partitioner
arg in the API. If you have this arg, you will have to switch to
tf.compat.v1 canned Estimators.Estimator.export_savedmodel has been renamed to
export_saved_model.tf.compat.v1.Estimator.tf.feature_column.input_layer
has been deprecated in favor of
tf.keras.layers.DenseFeatures. v1 feature columns have
direct analogues in v2 except for shared_embedding_columns,
which are not cross-compatible with v1 and v2. Use
tf.feature_column.shared_embeddings instead.tf.keras:
OMP_NUM_THREADS is no longer used by the default Keras
config. To configure the number of threads, use
tf.config.threading APIs.tf.keras.model.save_model and model.save
now defaults to saving a TensorFlow SavedModel. HDF5 files are still
supported.tf.keras.experimental.export_saved_model and
tf.keras.experimental.function. Please use
tf.keras.models.save_model(..., save_format='tf') and
tf.keras.models.load_model instead.Layer <layer-name> is casting
an input tensor from dtype float64 to the layer's dtype of float32. To
fix, either set the default dtype to float64 with
tf.keras.backend.set_floatx('float64'), or pass
dtype='float64' to each of the Layer constructors. See
tf.keras.layers.Layer for more information.tf.lite:
lite.OpHint, lite.experimental,
and lite.constant from 2.0 API.Tensors are no longer hashable, but instead compare element-wise
with == and !=. Use
tf.compat.v1.disable_tensor_equality() to return to the
previous behavior.
Performing equality operations on Tensors or Variables with
incompatible shapes an exception is no longer thrown. Instead
__eq__ returns False and __ne__ returns
True.
Removed tf.string_split from v2 API.
Deprecated the use of constraint= and
.constraint with ResourceVariable.
Add UnifiedGRU as the new GRU implementation for
tf2.0. Change the default recurrent activation function for GRU from
hard_sigmoid to sigmoid, and
reset_after to True in 2.0. Historically recurrent
activation is hard_sigmoid since it is fast than 'sigmoid'.
With new unified backend between CPU and GPU mode, since the CuDNN
kernel is using sigmoid, we change the default for CPU mode to sigmoid
as well. With that, the default GRU will be compatible with both CPU and
GPU kernel. This will enable user with GPU to use CuDNN kernel by
default and get a 10x performance boost in training. Note that this is
checkpoint breaking change. If user want to use their 1.x pre-trained
checkpoint, please construct the layer with
GRU(recurrent_activation='hard_sigmoid', reset_after=False) to fallback
to 1.x behavior.
CUDNN_INSTALL_PATH,
TENSORRT_INSTALL_PATH, NCCL_INSTALL_PATH,
NCCL_HDR_PATH are deprecated. Use
TF_CUDA_PATHS instead which supports a comma-separated list
of base paths that are searched to find CUDA libraries and
headers.
Refer to our public project
status tracker and issues
tagged with 2.0 on GitHub for insight into recent
issues and development progress.
If you experience any snags when using TF 2.0, please let us know at the TF 2.0 Testing User Group. We have a support mailing list as well as weekly testing meetings, and would love to hear your migration feedback and questions.
tf.contrib:
tf.contrib.proto.* ops in tf.io
(they will exist in TF2)tf.data:
tf.data Dataset.tf.data.shuffle(..., reshuffle_each_iteration=True) and
cache() to work across different Python iterators for the
same dataset.experimental_numa_aware option from
tf.data.Options.num_parallel_reads and passing in a Dataset
containing filenames into TextLineDataset and
FixedLengthRecordDataset.cycle_length
argument of tf.data.Dataset.interleave to the number of
schedulable CPU cores.tf.data.experimental.enumerate_dataset to
core as tf.data.Dataset.enumerate.tf.data.experimental.unbatch to core as
tf.data.Dataset.unbatch.tf.data.Options().experimental_slack = Truebatch() and padded_batch(). This functionality
can be enabled through tf.data.Options().reduce.dataset node name as prefix instead of the
op name, to identify the component correctly in metrics, for pipelines
with repeated components.from_tensors().unbatch from experimental to core API.from_tensors
and from_tensor_slices and batching and unbatching of
nested datasets.tf.distribute:
tf.distribute.experimental.MultiWorkerMirroredStrategy
working in eager mode.MultiWorkerMirroredStrategy.run_eagerly and distribution strategy if there
are symbolic tensors added to the model using add_metric or
add_loss.tf.distribute.Strategy.AUTO for improving
reliability of loss scaling with distribution strategy and custom
training loops. AUTO indicates that the reduction option
will be determined by the usage context. For almost all cases this
defaults to SUM_OVER_BATCH_SIZE. When used in distribution
strategy scope, outside of built-in training loops such as
tf.keras compile and fit, we
expect reduction value to be 'None' or 'SUM'. Using other values will
raise an error.ncclAllReduce in Distribution
Strategy.tf.estimator:
tf.contrib.estimator.add_metrics with
tf.estimator.add_metricstf.compat.v1.estimator.inputs instead of
tf.estimator.inputstf.estimator.experimental.* for apis in early_s in
Estimatortf.train.Optimizers to
tf.keras.optimizers.tf.nn.compute_average_loss,
tf.nn.scale_regularization_loss.tf.keras:
model.save and tf.saved_model.save may now
save to the TensorFlow SavedModel format. The model can be restored
using tf.keras.models.load_model. HDF5 files are still
supported, and may be used by specifying save_format="h5"
when saving.metrics
argument in Keras compile.tf.keras.layers.AbstractRNNCell as the preferred
implementation for RNN cells in TF v2. User can use it to implement RNN
cells with custom behavior.fit/evaluate/predict execution to use
only a single unified path by default unless eager execution has been
explicitly disabled, regardless of input type. This unified path places
an eager-friendly training step inside of a tf.function.
With thisDataset.tf.function unless
run_eagerly=True is set in compile. The single path
execution code does not yet support all use cases. We fallback to the
existing v1 execution paths if your model contains the following:
sample_weight_mode in compileweighted_metrics in compileexperimental_run_tf_function=False in compile meanwhile. We
have seen couple of use cases where the model usage pattern is not as
expected and would not work with this change.set_session as compat.v1
only.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.keras.backend.resize_images (and consequently,
keras.layers.Upsampling2D) behavior has changed, a bug in
the resizing implementation was fixed.implementation=3 mode for
tf.keras.layers.LocallyConnected2D and
tf.keras.layers.LocallyConnected1D layers using
tf.SparseTensor to store weights, allowing a dramatic
speedup for large sparse models.batch_size argument is used when input
is dataset/generator/keras sequence.keras.backend.name_scope to use TF 2.0
name_scope.tf.losses = tf.keras.losses &
tf.metrics = tf.keras.metrics &
tf.initializers = tf.keras.initializers &
tf.optimizers = tf.keras.optimizers.cumsum and cumprod
keras backend functions.ValueError if an integer is passed to the
training APIs.model.fit() with MultiWorkerMirroredStrategy,
tutorial available.tf.distribute, Keras API is recommended
over estimator.steps_per_epoch and steps arguments are
supported with numpy arrays.tf.nn.compute_average_loss,
tf.nn.scale_regularization_loss.Layer apply and add_variable APIs are deprecated.add_update,
add_metric, add_loss, activity regularizers
are used inside of a control flow branch.AUTO: Indicates that the reduction option will be
determined by the usage context. For almost all cases this defaults to
SUM_OVER_BATCH_SIZE. When used with
tf.distribute.Strategy, outside of built-in training loops
such as tf.keras compile and fit,
we expect reduction value to be SUM or NONE.
Using AUTO in that case will raise an error.NONE: Weighted losses with one dimension reduced
(axis=-1, or axis specified by loss function). When this reduction type
used with built-in Keras training loops like
fit/evaluate, the unreduced vector loss is
passed to the optimizer but the reported loss will be a scalar
value.SUM: Scalar sum of weighted losses. 4.
SUM_OVER_BATCH_SIZE: Scalar SUM divided by
number of elements in losses. This reduction type is not supported when
used with tf.distribute.Strategy outside of built-in
training loops like tf.keras
compile/fit.compile API (strings and v1
losses) which are not instances of v2 Loss class in
LossWrapper class. => All losses will now use
SUM_OVER_BATCH_SIZE reduction as default.model.add_loss(symbolic_tensor) should work in ambient
eager.weighted prefix from weighted metric
names.AUCCurve and
AUCSummationMethod enums.add_update can now be passed a zero-arg callable in
order to support turning off the update when setting
trainable=False on a Layer of a Model compiled with
run_eagerly=True.norm_axis and params_axis with
axis.tf.lite:
COCO minivalQUANTIZE.GATHER support to NN API delegate.EXPAND_DIMS support to NN API delegate TEST.narrow_range attribute to QuantizeAndDequantizeV2
and V3.tflite_convert command line tool in
2.0.QUANTIZED_16BIT_LSTM.NNAPIDelegateKernel from
nnapi_delegate.ccTensorRT
TrtGraphConverterV2 API
for TensorRT conversion. TensorRT initialization arguments are now
passed wrapped in a named-tuple, TrtConversionParams,
rather than as separate arguments as in
TrtGraphConverter.converter.build() where
previously is_dynamic_op=False would be set.converter.convert() no longer returns a
tf.function. Now the function must be accessed from the
saved model.converter.calibrate() method has been removed. To
trigger calibration, a calibration_input_fn should be
provided to converter.convert().Other:
tf.gradients().gather_nd.ResourceVariable and Variable no longer
accepts constraint in the constructor, nor expose it as a
@property.SparseToDense op.image.resize in 2.0 now supports gradients for the new
resize kernels.image.resize now considers proper pixel centers and has
new kernels (incl. anti-aliasing).tf.image functions to remove duplicate "image"
where it is redundant.StringViewVariantWrapper.Fingerprint64Map op registrationtf.matmul.BatchMatMulV2.tf.math.cumulative_logsumexp operation.tf.einsum().nest.*
methods.strings.byte_split.tf.strings.split.tf.string_split and
tf.strings_split.tf.strings.split to support inputs with any
rank.tf.random.binomial.key and skip methods to
random.experimental.Generator.tf.function with basic support for
CompositeTensors arguments (such as SparseTensor and
RaggedTensor).parallel_for.pfor: add converters for Softmax,
LogSoftmax, IsNaN, All, Any, and MatrixSetDiag.parallel_for: add converters for LowerTriangularSolve
and Cholesky.parallel_for: add converters for
LogMatrixDeterminant and MatrixBandPart.parallel_for: Add converter for
MatrixDiag.parallel_for: Add converters for OneHot,
LowerBound, UpperBound.parallel_for: add converter for
BroadcastTo.pfor converter for Squeeze.RaggedTensor.placeholder().tf.squeeze.LinearOperator.solve to take a
LinearOperator.LinearOperatorCirculant.LinearOperatorHouseholder.TensorSpec support for CompositeTensors.tf.linalg.tridiagonal_solve op.tf.linalg.tridiagonal_solve.tf.linalg.tridiagonal_solve.tf.linalg.tridiagonal_mul op.tf.linalg.tridiagonal_matmul.LinearOperatorToeplitz.tf.ragged.boolean_mask.tf.switch_case added, which selects a branch_fn based
on a branch_index.trainable arg
of tf.Variable.EagerTensor now supports numpy buffer interface for
tensors.FullyConnected Op to 5.tf.strings.unsorted_segment_join.topK_v2.Head as public API.tf.sparse.from_dense utility function.TensorFlowTestCase.nested_value_rowids for ragged
tensors.tf.ragged.stack.ResizeInputTensor now works for all delegates.tf.cond emits a StatelessIf op if the branch functions
are stateless and do not touch any resources._TridiagonalSolveGrad.LogSoftMax.AddNewInputConstantTensor.tf.while_loop emits a StatelessWhile op if the cond and
body functions are stateless and do not touch any resources.tf.cond, tf.while and if and while in
AutoGraph now accept a nonscalar predicate if has a single element. This
does not affect non-V2 control flow.dynamic constructor argument in Layer and
Model, which should be set to True when using imperative
control flow in the call method.batch_dims argument to tf.gather.tf.gather is now correct when
axis=None and batch_dims<0.batch_dims case.tf.math.nextafter op.--define=tensorflow_mkldnn_contraction_kernel=0.tf.linspace(start, stop, num) now always uses "stop" as
last value (for num > 1)pooling_ops were removed.
Some users may need to add explicit dependencies on
:pooling_ops if they reference the operators from that
library.CompositeTensor base class.Tensor::UnsafeCopyFromInternal deprecated in favor
Tensor::BitcastFrom.map_vectorization optimization, reduce the degree of
parallelism in the vectorized map node.absl::string_view.dtype.as_datatype_enum instead of int(dtype)
to get the same result.LinearOperator.adjoint and
LinearOperator.H (alias).tf.CriticalSection.SignatureDef util functions have been deprecated.Fingerprint64Map to use aliasesadd_metric in the graph function
mode.precision_mode argument to
TrtGraphConverter is now case insensitive.This release contains contributions from many people at Google, as well as:
1e100, a6802739, 4d55397500, a6802739, Abdullah Selek, abenmao, Abolfazl Shahbazi, Adam Richter, Adam Weiss, Ag Ramesh, Alan Du, Albin Joy, Alex, Alex Itkes, Alex Sergeev, Alexander Pivovarov, Alexey Romanov, alhkad, Aman Patel, Amit, Amit Kumar Jaiswal, Amit Srivastava, amoitra, Andreas Eberle, Andrew Lihonosov, Andy Craze, Anshuman Tripathy, Anthony Hsu, Anthony Platanios, Anuj Rawat, arp95, Arpit Shah, Armen Poghosov, armenpoghosov, Astropeak, Ashwin Ramaswami, Arpit Shah, Augustina Ragwitz, Aurelien Geron, AuréLien Geron, avasid, aweers, awesomealex1, Ayush Agrawal, Bas Aarts, Bastian Eichenberger, Bairen Yi, Bayberry Z, Ben Barsdell, Benjamin Peterson, bhack, Bharat Raghunathan, Bhavani Subramanian, Bin Fan, blairhan, BléNesi Attila, Bodin-E, Brandon Carter, Bryan Cutler, candy.dc, Cao Zongyan, Casper Da Costa-Luis, Chao Liu, Chen Guoyin, chenchc, chengchingwen, chie8842, Christian Hansen, Christoph Boeddeker, Christopher Yeh, Clayne Robison, Coady, Patrick, crafet, csukuangfj, ctiijima, Dan Jarvis, Dan Lazewatsky, Daniel Ingram, Daniel Rasmussen, Daniel Salvadori, Dave Airlie, David Norman, Dayananda V, delock, Denis Khalikov, Deven Desai, Dheeraj Rajaram Reddy, Diego Caballero, dmitrievanthony, Donovan Ong, Drew Szurko, Duncan Dean, Duncan Riach, Dustin Neighly, Dwight J Lyle, Eamon Ito-Fisher, eashtian3, Edward Forgacs, EFanZh, ejot, Elroy Ashtian Jr, Eric Schweitz, Evgeniy Polyakov, Fangjun Kuang, Federico Martinez, Fei Hu, Felix Lemke, Filip Matzner, FlashTek, fo40225, formath, FrançOis Chollet, frreiss, Fred Reiss, Frederic Bastien, Fredrik Knutsson, G. Hussain Chinoy, Gabriel, Gautam, gehring, Geoffrey Irving, George Grzegorz Pawelczak, Grzegorz Pawelczak, George Sterpu, Gianluca Varisco, Gleb Popov, Greg Peatfield, Guillaume Klein, Gurpreet Singh, Gustavo Lima Chaves, Gyoung-Yoon Ryoo, haison, Hanton Yang, HanGuo97, Haraldur TóMas HallgríMsson, Hari Shankar, hehongliang, Heungsub Lee, Hoeseong Kim, Huan Li (李卓桓), HåKon Sandsmark, I-Hong, I-Hong Jhuo, Ilham Firdausi Putra, Ilango R, Imran Salam, Innovimax, Jacky Ko, Irene Dea, Ivan Habernal, Jakub Lipinski, Jacky, Jason Zaman, Jason Zavaglia, jayhpark530, jcf94, jefby, Jeff Daily, Jeff Poznanovic, Jeffrey Poznanovic, Jekyll Lai, jer, Jeroen BéDorf, jerryyin, jhalakp, jiakai, Jia Qingtong, Jiankang, JiangXIAO, Joe Bowser, Joe Q, Joe Quadrino, Joel Shapiro, Johan Gunnarsson, Jojimon Varghese, Jonas Rauber, Jonathan Kyl, Jonathan, Joon, Joppe Geluykens, Joseph Friedman, Josh Beal, jtressle, Julian Niedermeier, Junqin Zhang, Justin Dujardin, Justin Tunis, jwu, K. Hodges, kaixih, Kaixi Hou, kjopek, Karl Lessard, Karl Weinmeister, Karthik Muthuraman, Kashif Rasul, Kay Zhu, Kbhute-Ibm, KDR, Keno Fischer, Kevin Mader, khanhlvg, Kilaru Yasaswi Sri Chandra Gandhi, Koan-Sin Tan, Koock Yoon, kouml, ktaebum, Kyuwon Kim, Lakshay Tokas, Laurent Le Brun, leike666666, leonard951, Leslie-Fang, Letian Kang, Li, Guizi, Loo Rong Jie, Lucas Hendren, Lukas Folle, Lukas Geiger, Luke Han, luxupu, lvli, Ma, Guokai, Mahmoud Abuzaina, Maksym Kysylov, Mandar Deshpande, manhyuk, Manraj Singh Grover, Marco Gaido, Marek Drozdowski, Margaret Maynard-Reid, Mark Ryan, mars20, Mateusz Chudyk, Matt Conley, mbhuiyan, mdfaijul, Mei Jie, Melissa Grueter, merturl, MichaelKonobeev, Michael KäUfl, Michal W. Tarnowski, MickaëL Schoentgen, Miguel Morin, Mihail Salnikov, Mikalai Drabovich, Mike Arpaia, Mike Holcomb, minds, monklof, Moses Marin, mpppk, Mr. Metal, Mshr-H, musikisomorphie, nammbash, Natalia Gimelshein, Nathan Luehr, Nayana-Ibm, Nayana Thorat, neargye, Neeraj Pradhan, Nehal J Wani, Neil, Nick, Nick Lewycky, Niels Ole Salscheider, Niklas SilfverströM, Niranjan Hasabnis, Nuka-137, Nutti, ocjosen, olicht, omeir1, P Sudeepam, Paige Bailey, Palmer Lao, Pan Daoxin, Pariksheet Pinjari, Pasquale Minervini, Patrick J. Lopresti, Patrik Gustavsson, Pavel Akhtyamov, Pavel Samolysov, PENGWA, per1234, PeterLee, Phan Van Nguyen Duc, Philipp Jund, Phillip Kravtsov, Pooya Davoodi, Pranav Marathe, Putra Manggala, Qingqing Cao, R S Nikhil Krishna, Rajeshwar Reddy T, Ramon ViñAs, Rasmus Diederichsen, Reuben Morais, robert, Rohit Gupta, Roland Zimmermann, Roman Soldatow, RonLek, Ruizhe, Ryan Jiang, saishruthi, Saleem Abdulrasool, Samantha Andow, Sami Kama, Sana-Damani, Saurabh Deoras, sdamani, Sean Morgan, seanshpark, Sebastien Iooss, Serv-Inc, Severen Redwood, Shahzad Lone, Shashank Gupta, shashvat, Shashvat Chand Shahi, Shubham Goyal, Shashi, Sigrid Keydana, Siju, Siju Samuel, sleighsoft, smilu97, Snease-Abq, Son Tran, Spencer Schaber, sremedios, Srini511, srinivasan.narayanamoorthy, Steve Lang, Steve Nesae, Subin, Sumesh Udayakumaran, Sungmann Cho, sunway513, Supriya Rao, sxwang, Tae-Hwan Jung, Taehoon Lee, Takeo Sawada, Taylor Jakobson, Taylor Thornton, Ted Chang, TengLu, terryky, ThisIsIsaac, ThisIsPIRI, Thomas Deegan, Thomas Hagebols, tianyapiaozi, Till Hoffmann, Tim Zaman, tomguluson92, Tongxuan Liu, Trent Lo, Trevor Morris, TungJerry, Tyorden, Uday Bondhugula, v1incent, Vagif, Vasileios Lioutas, vbvg2008, vcarpani, Vijay Ravichandran, Vikram Tiwari,Viktor Gal, Vishwak Srinivasan, Vincent, Vishnuvardhan Janapati, Vitor-Alves, Vivek Suryamurthy, wangsiyu, wateryzephyr, WeberXie, Wei Wang, WeijieSun, Wen-Heng (Jack) Chung, wenxizhu, Will Battel, William D. Irons, winstonq, wyzhao, Xiaoming (Jason) Cui, Xiaoquan Kong, Xin, Xinping Wang, Yan Facai (颜发才), Yann-Yy, Yasir Modak, Yasuhiro Matsumoto, ymodak, Yong Tang, Yongfeng Gu, Younes Khoudli, Yuan Lin, Yuan (Terry) Tang, Yuchen Ying, Yves-Noel Weweler, zhangyujing, zjjott, zyeric, 王振华 (Zhenhua Wang), 黄鑫
AUTO for improving
reliability of loss scaling with distribution strategy and custom
training loops. AUTO indicates that the reduction option
will be determined by the usage context. For almost all cases this
defaults to SUM_OVER_BATCH_SIZE. When used in distribution
strategy scope, outside of built-in training loops such as
tf.keras compile and fit, we
expect reduction value to be 'None' or 'SUM'. Using other values will
raise an error.compile API (strings and v1
losses) which are not instances of v2 Loss class in
LossWrapper class. => All losses will now use
SUM_OVER_BATCH_SIZE reduction as default.run_eagerly and distribution strategy if there
are symbolic tensors added to the model using add_metric or
add_loss.ResourceVariable and Variable no longer
accepts constraint in the constructor, nor expose it as a
@property.map_vectorization optimization, reduce the degree of
parallelism in the vectorized map node.norm_axis and params_axis with
axis.clear_losses API to be able to clear losses at
the end of forward pass in a custom training loop in eager.metrics
param in Keras compile.cumsum and cumprod
keras backend functions.dynamic constructor argument in Layer and
Model, which should be set to True when using imperative control flow in
the call method.add_metric in the graph function
mode.add_update can now be passed a zero-arg callable in
order to support turning off the update when setting
trainable=False on a Layer of a Model compiled with
run_eagerly=True.weighted prefix from weighted metric
names.defun, providing an
escape hatch to continue using the legacy Defun.tensorflow_core and
tensorflow is just a virtual pip package. No code changes
are needed for projects using TensorFlow, the change is transparentThis release contains contributions from many people at Google, as well as:
1e100, 4d55397500, a6802739, abenmao, Adam Weiss, Ag Ramesh, Alan Du, Albin Joy, Alex, Aman Patel, Amit, Amit Kumar Jaiswal, Amit Srivastava, Andreas Eberle, Andy Craze, Anthony Platanios, Armen Poghosov, armenpoghosov, arp95, Arpit Shah, Ashwin Ramaswami, Aurelien Geron, AuréLien Geron, aweers, awesomealex1, Ayush Agrawal, Ben Barsdell, Bharat Raghunathan, Bhavani Subramanian, blairhan, BléNesi Attila, Brandon Carter, candy.dc, Chao Liu, chenchc, chie8842, Christian Hansen, Christian Sigg, Clayne Robison, crafet, csukuangfj, ctiijima, Dan Jarvis, Dan Lazewatsky, Daniel Ingram, Daniel Salvadori, Dave Airlie, David Norman, Dayananda V, Dayananda-V, delock, Denis Khalikov, Deven Desai, Dheeraj Rajaram Reddy, dmitrievanthony, Donovan Ong, Drew Szurko, Duncan Riach, Dustin Neighly, Edward Forgacs, EFanZh, Fei Hu, Felix Lemke, Filip Matzner, fo40225, frreiss, Gautam, gehring, Geoffrey Irving, Grzegorz George Pawelczak, Grzegorz Pawelczak, Gyoung-Yoon Ryoo, HanGuo97, Hanton Yang, Hari Shankar, hehongliang, Heungsub Lee, Hoeseong Kim, I-Hong Jhuo, Ilango R, Innovimax, Irene Dea, Jacky Ko, Jakub Lipinski, Jason Zaman, jcf94, Jeffrey Poznanovic, Jens Elofsson, Jeroen BéDorf, Jia Qingtong, Jiankang, Joe Q, Joe Quadrino, Joeran Beel, Jonas Rauber, Jonathan, Jonathan Kyl, Joppe Geluykens, Joseph Friedman, jtressle, jwu, K Yasaswi Sri Chandra Gandhi, K. Hodges, Kaixi Hou, Karl Lessard, Karl Weinmeister, Karthik Muthuraman, Kashif Rasul, KDR, Keno Fischer, Kevin Mader, kjopek, Koan-Sin Tan, kouml, ktaebum, Lakshay Tokas, Laurent Le Brun, Letian Kang, Li, Guizi, Loo Rong Jie, Lucas Hendren, Lukas Geiger, Luke Han, luxupu, Ma, Guokai, Mahmoud Abuzaina, Mandar Deshpande, manhyuk, Marco Gaido, Marek Drozdowski, Mark Collier, Mark Ryan, mars20, Mateusz Chudyk, Matt Conley, MattConley, mbhuiyan, mdfaijul, Melissa Grueter, Michael KäUfl, MickaëL Schoentgen, Miguel Morin, Mihail Salnikov, Mike Arpaia, Mike Holcomb, monklof, Moses Marin, Mshr-H, nammbash, Natalia Gimelshein, Nayana-Ibm, neargye, Neeraj Pradhan, Nehal J Wani, Nick, Niels Ole Salscheider, Niranjan Hasabnis, nlewycky, Nuka-137, Nutti, olicht, P Sudeepam, Palmer Lao, Pan Daoxin, Pariksheet Pinjari, Pavel Samolysov, PENGWA, Pooya Davoodi, R S Nikhil Krishna, Rohit Gupta, Roman Soldatow, rthadur, Ruizhe, Ryan Jiang, Samantha Andow, Sami Kama, Sana-Damani, Saurabh Deoras, sdamani, seanshpark, Sebastien Iooss, Serv-Inc, Shahzad Lone, Shashank Gupta, Shashi, shashvat, shashvatshahi1998, Siju, Siju Samuel, Snease-Abq, Spencer Schaber, sremedios, srinivasan.narayanamoorthy, Steve Lang, Steve Nesae, Sumesh Udayakumaran, Supriya Rao, Taylor Jakobson, Taylor Thornton, Ted Chang, ThisIsPIRI, Thomas Deegan, Thomas Hagebols, tianyapiaozi, Tim Zaman, tomguluson92, Tongxuan Liu, TungJerry, v1incent, Vagif, vcarpani, Vikram Tiwari, Vishwak Srinivasan, Vitor-Alves, wangsiyu, wateryzephyr, WeberXie, WeijieSun, Wen-Heng (Jack) Chung, wenxizhu, Will Battel, William D. Irons, wyzhao, Xin, Yasuhiro Matsumoto, ymodak, Yong Tang, Younes Khoudli, Yuan Lin, Yves-Noel Weweler, Zantares, zjjott, 卜居, 王振华 (Wang Zhenhua), 黄鑫
png_archive dependency to 1.6.37 to not be
affected by CVE-2019-7317, CVE-2018-13785, and CVE-2018-14048.sqlite dependency to 3.28.0 to not be affected
by CVE-2018-20506, CVE-2018-20346, and CVE-2018-20505.tf.lite and source code is now
under tensorflow/lite rather than
tensorflow/contrib/lite.tf.constant.gain argument of convolutional orthogonal
initializers (convolutional_delta_orthogonal,
convolutional_orthogonal_1D,
convolutional_orthogonal_2D,
convolutional_orthogonal_3D) have consistent behavior with
the tf.initializers.orthogonal initializer, i.e. scale the
output l2-norm by gain and NOT by sqrt(gain).
(Note that these functions are currently in tf.contrib
which is not guaranteed backward compatible).tf.acos, tf.acosh, tf.add,
tf.as_string, tf.asin, tf.asinh,
tf.atan, tf.atan2, tf.atanh,
tf.cos, tf.cosh, tf.equal,
tf.exp, tf.floor, tf.greater,
tf.greater_equal, tf.less,
tf.less_equal, tf.log, tf.logp1,
tf.logical_and, tf.logical_not,
tf.logical_or, tf.maximum,
tf.minimum, tf.not_equal, tf.sin,
tf.sinh, tf.tantf.data.Dataset.shard.saved_model.loader.load which is replaced by
saved_model.load and saved_model.main_op,
which will be replaced by saved_model.main_op in V2.Variable.count_up_to and
tf.count_up_to in favor of Dataset.range.confusion_matrix op as
tf.math.confusion_matrix instead of
tf.train.confusion_matrix.tf.dtypes. endpoint for every constant in
dtypes.py. Moving endpoints in versions.py to corresponding endpoints in
tf.sysconfig. and tf.version.. Moving all
constants under tf.saved_model submodules to
tf.saved_model module. New endpoints are added in V1 and V2
but existing endpoint removals are only applied in V2.tf.register_tensor_conversion_function.tf.contrib.saved_model.save_keras_model.LinearOperator.matmul now returns a new
LinearOperator.ignore_unknown argument to
parse_values which suppresses ValueError for unknown
hyperparameter types. Such * Add tf.linalg.matvec
convenience function.tf.einsum()raises ValueError for
unsupported equations like "ii->".tf.signal.dct and
tf.signal.idct.round_mode to QuantizeAndDequantizeV2
op to select rounding algorithm.unicode_encode, unicode_decode,
unicode_decode_with_offsets, unicode_split,
unicode_split_with_offset, and
unicode_transcode ops. Amongst other things, this Op adds
the ability to encode, decode, and transcode a variety of input text
encoding formats into the main Unicode encodings (UTF-8, UTF-16-BE,
UTF-32-BE)SpaceToDepth supports uint8 data type.tf.nn.safe_embedding_lookup_sparse,
tf.nn.sampled_softmax and tf.nn.nce_loss.
hyperparameter are ignored.tf.spectral into tf.signal for
TensorFlow 2.0.tensorflow/contrib/lite to
tensorflow/lite.tf.contrib:
rate argument, keep_prob
is deprecated.tf.contrib.estimator
were changed to tf.estimator:tf.contrib.estimator.BaselineEstimator with
tf.estimator.BaselineEstimatortf.contrib.estimator.DNNLinearCombinedEstimator with
tf.estimator.DNNLinearCombinedEstimatortf.contrib.estimator.DNNEstimator with
tf.estimator.DNNEstimatortf.contrib.estimator.LinearEstimator with
tf.estimator.LinearEstimatortf.contrib.estimator.InMemoryEvaluatorHook and
tf.estimator.experimental.InMemoryEvaluatorHook`.tf.contrib.estimator.make_stop_at_checkpoint_step_hook
with
tf.estimator.experimental.make_stop_at_checkpoint_step_hook.tf.contrib.signal to tf.signal
(preserving aliases in tf.contrib.signal).tf.contrib.estimator.export_all_saved_models
and related should switch to
tf.estimator.Estimator.experimental_export_all_saved_models.tf.data.experimental.StatsOptions(), to configure
options to collect statistics from tf.data.Dataset pipeline
using StatsAggregator. Add nested option,
experimental_stats (which takes a
tf.data.experimen tal.StatsOptions object), to
tf.data.Options. Deprecates
tf.data.experimental.set_stats_agregator.tf.data.experimental.OptimizationOptions(), to
configure options to enable tf.data performance
optimizations. Add nested option, experimental_optimization
(which takes a tf.data.experimental.OptimizationOptions
object), to tf.data.Options. Remove performance
optimization options from tf.data.Options, and add them
under tf.data.experimental.OptimizationOptions
instead.map_and_batch_fusion and
noop_elimination optimizations by default. They can be
disabled by configuring
tf.data.experimental.OptimizationOptions to set
map_and_batch = False or
noop_elimination = False respectively. To disable all
default optimizations, set
apply_default_optimizations = False.map_and_filter_fusion.tf.Variables.tf.data.Dataset.make_one_shot_iterator() in
V1, removed it from V2, and added
tf.compat.v1.data.make_one_shot_iterator()`.tf.data.Dataset.make_initializable_iterator()
in V1, removed it from V2, and added
tf.compat.v1.data.make_initializable_iterator().tf.data
transformations.tf.data.Dataset implementers: Added
tf.data.Dataset._element_structured property to replace
Dataset.output_{types,shapes,classes}.num_parallel_calls of
tf.data.Dataset.interleave and
tf.data.Dataset.map work in Eager mode.EVP_MD_CTX_destroy.:android_tensorflow_lib_selective_registration*
targets, use :android_tensorflow_lib_lite* targets
instead.RoundToEven function to
xla/client/lib/math.h.TF_XLA_DEBUG_OPTIONS_PASSTHROUGH set to "1" or "true"
allows the debug options passed within an XRTCompile op to be passed
directly to the XLA compilation backend. If such variable is not set
(service side), only a restricted set will be passed through.tf.contrib.estimator.BaselineEstimator with
tf.estimator.BaselineEstimatortf.contrib.estimator.DNNLinearCombinedEstimator with
tf.estimator.DNNLinearCombinedEstimatortf.contrib.estimator.DNNEstimator with
tf.estimator.DNNEstimatortf.contrib.estimator.LinearEstimator with
tf.estimator.LinearEstimatortf.contrib.estimator.export_all_saved_models
and related should switch to
tf.estimator.Estimator.experimental_export_all_saved_models.regression_head to the new 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_hookThis release contains contributions from many people at Google, as well as:
Abhinav Upadhyay, Ag Ramesh, akikaaa, Alexis Louis, Anders Huss, Andreas Madsen, Andrew Banchich, Andy Craze, Anton Dmitriev, Artem Malykh, Avijit-Nervana, Balint Cristian, Benjamin Tan Wei Hao, Bhavani Subramanian, Brendan Finan, Brian Nemsick, Bryan Cutler, By Shen, Cao Zongyan, Castiel, Chris Antaki, Christian Goll, Cibifang, Clayne Robison, Codrut Grosu, Cong Xu, Dalmo Cirne, Daniel Hunter, Dougal J. Sutherland, Edvard Fagerholm, EFanZh, Erik Smistad, Evgeniy Polyakov, Feiyang Chen, franklin5, Fred Reiss, Gautam, gehring, Geoffrey Irving, George Sterpu, Gitea, Grzegorz George Pawelczak, Guozhong Zhuang, himkt, Hoeseong Kim, Huan Li (李卓桓), HuiyangFei, hyunyoung, Isaac Burbank, jackonan, Jacky Ko, Jason Furmanek, Jason Zaman, Javier Luraschi, Jiang,Zhoulong, joaak, John Lin, Jonathan Wyatt Hoech, josephyearsley, Josh Gordon, Julian Niedermeier, Karl Lessard, Keno Fischer, lanhin, Leon Graser, leondgarse, Li, Guizi, Li, Yiqiang, lxl910915, Mahmoud Abuzaina, manhyuk, Marcela Morales Quispe, margaretmz, Matt Conley, Max Pumperla, mbhuiyan, mdfaijul, Meng, Peng, Michael, Michael Gielda, mrTsjolder, Muhammad Wildan, neargye, Nehal J Wani, NEWPLAN, Niranjan Hasabnis, Nutti, olicht, Pan Daoxin, Pedro Monreal, Peng Yu, pillarpond, Pooya Davoodi, qiezi, Rholais Lii, Richard Yu, Rin Arakaki, Roger Iyengar, sahilbadyal, Sami Kama, Sandip Giri, Scott Leishman, Serge Panev, Seunghoon Park, Shafi Dayatar, shengfuintel, Shimin Guo, Siju, silent567, Stefan Dyulgerov, steven, Tao Wei, Thor Johnsen, Tingbo Lu, tomguluson92, Tongxuan Liu, Trevor Morris, Ubuntu, Vadim Borisov, vanderliang, wangsiyu, Wen Yun, Wen-Heng (Jack) Chung, wenxizhu, William D. Irons, Xiaoming (Jason) Cui, Yan Facai (颜发才), Yanbo Liang, Yaniv Blumenfeld, Yash Gaurkar, Yicheng Fan, Yong Tang, Yongjoon Lee, Yuan (Terry) Tang, Yuxin Wu, zldrobit
tf.contrib.saved_model.save_keras_model()) and used
with Tensorflow Serving.tf.data.Dataset.tf.data.Options(),
tf.data.Dataset.options(), and
tf.data.Dataset.with_options() respectively.tf.data.Dataset.reduce() API allows users to reduce
a finite dataset to a single element using a user-provided reduce
function.tf.data.Dataset.window() API allows users to create
finite windows of input dataset; when combined with the
tf.data.Dataset.reduce() API, this allows users to
implement customized batching.tensorflow::data
namespace.num_parallel_calls to
tf.data.Dataset.interleave.tf.contrib:
tf.contrib.linalg. tf.linalg should
be used instead.tf.contrib.get_signature_def_by_key(metagraph_def, signature_def_key)
with meta_graph_def.signature_def[signature_def_key].
Catching a ValueError exception thrown by
tf.contrib.get_signature_def_by_key should be replaced by
catching a KeyError exception.tf.contrib.data
tf.nn.softplus and
tf.nn.softsign OpDefs. This is a bugfix; these ops were
never meant to support integers.tf.GraphKeys.GLOBAL_VARIABLES.This release contains contributions from many people at Google, as well as:
(David) Siu-Kei Muk, Ag Ramesh, Anton Dmitriev, Artem Sobolev, Avijit-Nervana, Bairen Yi, Bruno Goncalves, By Shen, candy.dc, Cheng Chen, Clayne Robison, coder3101, Dao Zhang, Elms, Fei Hu, feiquan, Geoffrey Irving, Guozhong Zhuang, hellcom, Hoeseong Kim, imsheridan, Jason Furmanek, Jason Zaman, Jenny Sahng, jiefangxuanyan, Johannes Bannhofer, Jonathan Homer, Koan-Sin Tan, kouml, Loo Rong Jie, Lukas Geiger, manipopopo, Ming Li, Moritz KröGer, Naurril, Niranjan Hasabnis, Pan Daoxin, Peng Yu, pengwa, rasmi, Roger Xin, Roland Fernandez, Sami Kama, Samuel Matzek, Sangjung Woo, Sergei Lebedev, Sergii Khomenko, shaohua, Shaohua Zhang, Shujian2015, Sunitha Kambhampati, tomguluson92, ViníCius Camargo, wangsiyu, weidankong, Wen-Heng (Jack) Chung, William D. Irons, Xin Jin, Yan Facai (颜发才), Yanbo Liang, Yash Katariya, Yong Tang, 在原佐为
fit, evaluate and predict
to distribute their model on multiple GPUs.RandomUniform,
RandomNormal, and TruncatedNormal initializers
have been changed to match those in external Keras.model.get_config() on a Sequential
model now returns a config dictionary (consistent with other Model
instances) instead of a list of configs for the underlying layers.num_parallel_parser_calls argument from
tf.contrib.data.make_csv_dataset(). [tf.data] Remove
num_parallel_parser_calls argument from
tf.contrib.data.make_csv_dataset().tf.data.Dataset.list_files() raises an exception at
initialization time if the argument matches no files.tf.contrib.data.reduce_dataset which can be used to
reduce a dataset to a single element.tf.contrib.data.sliding_window_batch.tf.contrib:
implementation argument to
tf.keras.layers.LocallyConnected2D and
tf.keras.layers.LocallyConnected1D. The new mode
(implementation=2) performs forward pass as a single dense
matrix multiplication, allowing dramatic speedups in certain scenarios
(but worse performance in others - see docstring). The option also
allows to use padding=same.TFDBG_DISK_BYTES_LIMIT
to allow adjustment of this upper limit.This release contains contributions from many people at Google, as well as:
Aapeli, adoda, Ag Ramesh, Amogh Mannekote, Andrew Gibiansky, Andy Craze, Anirudh Koul, Aurelien Geron, Avijit, Avijit-Nervana, Ben, Benjamin H. Myara, bhack, Brett Koonce, Cao Zongyan, cbockman, cheerss, Chikanaga Tomoyuki, Clayne Robison, cosine0, Cui Wei, Dan J, David, David Norman, Dmitry Klimenkov, Eliel Hojman, Florian Courtial, fo40225, formath, Geoffrey Irving, gracehoney, Grzegorz Pawelczak, Guoliang Hua, Guozhong Zhuang, Herman Zvonimir DošIlović, HuiyangFei, Jacker, Jan HüNnemeyer, Jason Taylor, Jason Zaman, Jesse, Jiang,Zhoulong, Jiawei Zhang, Jie, Joe Yearsley, Johannes Schmitz, Jon Perl, Jon Triebenbach, Jonathan, Jonathan Hseu, Jongmin Park, Justin Shenk, karl@kubx.ca, Kate Hodesdon, Kb Sriram, Keishi Hattori, Kenneth Blomqvist, Koan-Sin Tan, Li Liangbin, Li, Yiqiang, Loo Rong Jie, Madiyar, Mahmoud Abuzaina, Mark Ryan, Matt Dodge, mbhuiyan, melvinljy96, Miguel Mota, Nafis Sadat, Nathan Luehr, naurril, Nehal J Wani, Niall Moran, Niranjan Hasabnis, Nishidha Panpaliya, npow, olicht, Pei Zhang, Peng Wang (Simpeng), Peng Yu, Philipp Jund, Pradeep Banavara, Pratik Kalshetti, qwertWZ, Rakesh Chada, Randy West, Ray Kim, Rholais Lii, Robin Richtsfeld, Rodrigo Silveira, Ruizhi, Santosh Kumar, Seb Bro, Sergei Lebedev, sfujiwara, Shaba Abhiram, Shashi, SneakyFish5, Soila Kavulya, Stefan Dyulgerov, Steven Winston, Sunitha Kambhampati, Surry Shome, Taehoon Lee, Thor Johnsen, Tristan Rice, TShapinsky, tucan, tucan9389, Vicente Reyes, Vilmar-Hillow, Vitaly Lavrukhin, wangershi, weidan.kong, weidankong, Wen-Heng (Jack) Chung, William D. Irons, Wim Glenn, XFeiF, Yan Facai (颜发才), Yanbo Liang, Yong Tang, Yoshihiro Yamazaki, Yuan (Terry) Tang, Yuan, Man, zhaoyongke, ÁRon Ricardo Perez-Lopez, 张天启, 张晓飞
tf.keras:
tf.lite runtime now supports
complex64.tf.data.tf.estimator.train_and_evaluate which does not reload
checkpoints for evaluation.RunConfig now sets device_filters to restrict how
workers and PS can communicate. This can speed up training and ensure
clean shutdowns in some situations. But if you have jobs that require
communication between workers, you will have to set custom
session_options in your RunConfig.tf.contrib.distributions to Tensorflow Probability
(TFP). tf.contrib.distributions is now deprecated and
will be removed by the end of 2018.tf.debugging,
tf.dtypes,
tf.image,
tf.io,
tf.linalg,
tf.manip,
tf.math,
tf.quantization,
tf.stringstf.data:
tf.contrib.data.group_by_reducer() is now available via
the public API.tf.contrib.data.choose_from_datasets() is now available
via the public API.drop_remainder argument to
tf.data.Dataset.batch() and
tf.data.Dataset.padded_batch(), deprecating
tf.contrib.data.batch_and_drop_remainder() and
tf.contrib.data.padded_batch_and_drop_remainder().tf.estimator:
Estimators now use custom savers included in
EstimatorSpec scaffolds for saving SavedModels during
export.EstimatorSpec will now add a default prediction output
for export if no export_output is provided, eliminating the
need to explicitly include a PredictOutput object in the
model_fn for simple use-cases.DNNClassifier,
DNNRegressor, and DNNEstimator.synchronization and aggregation args
to get_variable(). These args will be used for distributed
variables.synchronization and aggregation args
to the layer add_weight() API. These args will be used for
distributed variables.tf.losses.* do not add to the global collection when
executing eagerly (to avoid leaking memory).tf.train.MonitoredTrainingSession().tf.contrib.rnn.tf.random_gamma with respect to the
alpha parameter.tf.igamma(a, x) and
tf.igammac(a, x) with respect to a.tf.spectral.idct(type=2|3).TimeDistributed.WALSComputePartialLhsAndRhsOp.tf.image namespace:
tf.image.extract_image_patchestf.debugging namespace:
tf.debugging.check_numerics,
tf.debugging.is_finite, tf.debugging.is_inf,
tf.debugging.is_nan.tf.dtypes namespace:
tf.dtypes.as_string.tf.io namespace:
tf.io.decode_base64, tf.io.decode_compressed,
tf.io.decode_json_example, tf.io.decode_raw,
tf.io.encode_base64, tf.io.matching_files,
tf.io.parse_tensor,
tf.io.read_file,tf.io.write_file`.tf.linalg.cross,
tf.linalg.tensor_diag (corresponds to
tf.diag), tf.linalg.tensor_diag_part
(corresponds to tf.diag_part).tf.manip.batch_to_space_nd,
tf.manip.gather_nd, tf.manip.reshape,
tf.manip.reverse, tf.manip.scatter_nd,
tf.manip.space_to_batch_nd, tf.manip.tiletf.math.acos,
tf.math.acosh, tf.math.add,
tf.math.asin, tf.math.asinh,
tf.math.atan, tf.math.atan2,
tf.math.atanh, tf.math.betainc,
tf.math.ceil, tf.math.cos,
tf.math.cosh, tf.math.digamma,
tf.math.equal, tf.math.erfc,
tf.math.exp, tf.math.expm1,
tf.math.floor, tf.math.greater,
tf.math.greater_equal, tf.math.igamma,
tf.math.igammac, tf.math.invert_permutation,
tf.math.less, tf.math.less_equal,
tf.math.lgamma, tf.math.log,
tf.math.log1p, tf.math.logical_and,
tf.math.logical_not, tf.math.logical_or,
tf.math.maximum, tf.math.minimum,
tf.math.not_equal, tf.math.polygamma,
tf.math.reciprocal, tf.math.rint,
tf.math.rsqrt, tf.math.segment_max,
tf.math.segment_mean, tf.math.segment_min,
tf.math.segment_prod, tf.math.segment_sum,
tf.math.sin, tf.math.sinh,
tf.math.softplus, tf.math.softsign,
tf.math.squared_difference, tf.math.tan,
tf.math.unsorted_segment_max,
tf.math.unsorted_segment_min,
tf.math.unsorted_segment_prod,
tf.math.unsorted_segment_sum,
tf.math.zeta.tf.quantization namespace:
tf.quantization.dequantize,
tf.quantization.fake_quant_with_min_max_args,
tf.quantization.fake_quant_with_min_max_args_gradient,
tf.quantization.fake_quant_with_min_max_vars,
tf.quantization.fake_quant_with_min_max_vars_gradient,
tf.quantization.fake_quant_with_min_max_vars_per_channel,
tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient.tf.strings.join
(corresponds to tf.string_join),
tf.strings.regex_replace, tf.strings.to_number
(corresponds to tf.string_to_number),
tf.strings.strip (corresponds to
tf.string_strip), tf.strings.substr,
tf.strings.to_hash_bucket (corresponds to
tf.string_to_hash_bucket),
tf.strings.to_hash_bucket_fast (corresponds to
tf.string_to_hash_bucket_fast),
tf.strings.to_hash_bucket_strong (corresponds to
tf.string_to_hash_bucket_strong).This release contains contributions from many people at Google, as well as:
Ag Ramesh, Alex Wiltschko, Alexander Pantyukhin, Amogh Mannekote, An Jiaoyang, Andrei Nigmatulin, Andrew Ginns, BjøRn Moholt, Brett Koonce, Chengzhi Chen, Chinmay Das, Christian Ertler, Christoph Boeddeker, Clayne Robison, Courtial Florian, ctiijima, Dan Douthit, Dan J, Dan Ringwalt, EFanZh, Emanuele Ballarin, eqy, Evgeniy Zheltonozhskiy, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, G K, gracehoney, Guillaume Klein, Guozhong Zhuang, Hsien-Yang Li, hsm207, ImSheridan, Jayaram Bobba, Jiandong Ruan, Jie, Joel Shor, Jonas Rauber, Jongmin Baek, jsawruk, Karan Kaw, Karl Lessard, karl@kubx.ca, Kb Sriram, KinmanLam, leiiwang, Li, Yiqiang, Loo Rong Jie, Mahmoud Abuzaina, Mahmoud Aslan, ManHyuk, Martin Patz, Martin Zeitler, mktozk, Mohammad Ashraf Bhuiyan, mrTsjolder, Naman Bhalla, Nick Felt, Nicolas Lopez, Niranjan Hasabnis, Nishidha Panpaliya, Nitish, nrstott, Nutti, Parag Jain, PeterLee, Philipp Jund, Rach L, Rafal Wojdyla, Roland Zimmermann, Sergei Lebedev, SneakyFish5, Soila Kavulya, Sriram Veturi, Steven Schmatz, Taehoon Lee, Tang, Wenyi, Taras Sereda, Ted Chang, Tim Zaman, Tristan Rice, tucan, vchigrin, Vikram Tiwari, Vincent, WeberXie, William D. Irons, Yan Facai (颜发才), Yong Tang, Yu Yi, Yuxin Wu, Zé ViníCius
tf.keras: New Keras-based get started,
and programmers
guide page.tf.keras to the Keras 2.1.6 API.tf.keras.layers.CuDNNGRU
and tf.keras.layers.CuDNNLSTM
layers. Try
it.toco, tflite_convert) is once
again included in the standard pip installation.variable_scope('', ...) by
variable_scope(tf.get_variable_scope(), ...).tfe.Network is deprecated. Please inherit from
tf.keras.Model.tf.keras.layers with custom variable scopes.tf.layers in a subclassed
tf.keras.Model class. See here
for more detailstf.data:
Dataset.from_generator() now accepts an
args list, in order to create nested generators.Dataset.list_files() now produces deterministic results
when shuffle=False or a seed is passed.tf.contrib.data.sample_from_datasets() and
tf.contrib.data.choose_from_datasets() make it easier to
sample or deterministically choose elements from multiple datasets.tf.contrib.data.make_csv_dataset() now supports line
breaks in quoted strings, and two infrequently used arguments
removed.DatasetBase::DebugString() is now
const.DatasetBase::MakeIterator() has been renamed to
DatasetBase::MakeIteratorInternal().IteratorBase::Initialize() method was added to
support raising errors during iterator construction.tf.GradientTape.stop_recording.tf.keras:
tf.keras.Model.save_weights now saves in TensorFlow
format by default.tf.keras.Model
training/eval methods.tf.contrib:
tf.contrib.framework.zero_initializer supports
ResourceVariable.MakeIterator to enable
propagating error status.tf.reduce_prod gradient for complex
dtypes.nn.embedding_lookup_sparse. This
helps to reduce RPC calls for looking up the embeddings when there are
repeated ids in the batch.tf.gradients() from backpropagating through
integer tensors.tensorflow.linalg.tf.train.Checkpoint for reading/writing
object-based checkpoints.This release contains contributions from many people at Google, as well as:
Abdullah Alrasheed, Achal Shah, Ad-530, ADiegoCAlonso, Aditya Yogi, Ag Ramesh, akindyakov, Andy Kernahan, Anya Petrova, Aurelien Geron, Ben, Ben Barsdell, Bhavani-Subramanian, braincodercn, Brett Koonce, Brian Nemsick, Brian Zier, Bryan Heden, candy.dc, cclauss, Clayne Robison, ctiijima, Dalmo Cirne, David Norman, David T.H. Kao, DosLin, ekelsen, Elson Rodriguez, Erik Smistad, Felix Abecassis, Fergal Cotter, fo40225, foo0x29a, Freedom" Koan-Sin Tan, FréDéRic Branchaud-Charron, gdh1995, Geoffrey Irving, Giuseppe, gracehoney, Guido Zuidhof, Guillaume Klein, Guozhong Zhuang, Haggai, Harald Husum, imsheridan, Ivan Zhang, Jan Zikes, Jayaram Bobba, Jesse Benson, Jesse Gumz, Jiajia Li, Jie, jinghuangintel, Jingwen, jjsjann123, Joe Yearsley, Joel Hestness, Joel Shor, josephyearsley, Junpeng Lao, Karol M. Langner, Kb Sriram, krantideep95, Krish Ravindranath, Letian Feng, Loo Rong Jie, Lukas Geiger, Maciej, Mahmoud Abuzaina, ManHyuk, Mark Ryan, mbhuiyan, Michal Turek, Mostafa Alaa, Myungsung Kwak, Nand Dalal, Nehal J Wani, Neil Tenenholtz, ngc92, Nicholas Nadeau, P.Eng., Avs, Niranjan Hasabnis, P-Hidringer, Paul Van Eck, Peng Yu, Qing Zhao, Qingying Chen, Quanlong, Rajendra Arora, Rholais Lii, rmanyari, Robin Richtsfeld, Russell Klopfer, Sagi, Sam Sendelbach, Sandeep N Gupta, Sandip Giri, Sarah Edkins, Scott Tseng, Sdalbsoo, Sergii Khomenko, Seungwoo Choi (Biggie), Seyed Majid Azimi, Shaoning Zeng, shengfuintel, Siu Kei, Muk, Smit Shilu, soonson, Stefan Schweter, Sukhwan Kim, Sunitha Kambhampati, Taehoon Lee, tamimaddari82, Tang, Wenyi, Ted Chang, u2takey, Utkarsh Upadhyay, Vadim Markovtsev, voegtlel, Wai Hon Law, wangsiyu, Wenhao Hu, wenhao.hu, William D. Irons, Yan Facai (颜发才), Yanbo Liang, Yihong Wang, Yilei (Dolee) Yang, Yong Tang, Yuan (Terry) Tang
tf.contrib.distribute.MirroredStrategy()
to tf.estimator.RunConfig() to run an Estimator model on
multiple GPUs on one machine.tf.contrib.data.prefetch_to_device(), which
supports prefetching to GPU memory.tf.contrib.bayesflow is moving out to it's own
repo.tf.contrib.{proto,rpc} to allow generic proto
parsing and RPC communication1.tf.data:
tf.contrib.data.prefetch_to_device, which enables
prefetching dataset elements to GPU memory.tf.contrib.data.AUTOTUNE, which allows the tf.data
runtime to automatically tune the prefetch buffer sizes based on your
system and environment.tf.contrib.data.make_csv_dataset for building
datasets of CSV files.for batch in dataset:). Both
Dataset.__iter__() and
Dataset.make_one_shot_iterator() can now be used to create
iterators when eager execution is enabled.with tf.device(“/gpu:0”)) (Fixes #14133)tf.GradientTape has moved out of contrib.tf.keras:
image/random_brightness,
sequence/TimeseriesGenerator, and
text/hashing_trick.tf.contrib:
tf.contrib.layers.recompute_grad works for explicit
gradient checkpointing on TPU.tf.contrib.framework.argsort.DNNBoostedTreeCombinedEstimator to work with core
versions of feature columns and losses.tf.contrib.image.sparse_image_warp,
tf.contrib.image.dense_image_warp, and
tf.contrib.image.interpolate_spline.tf.contrib.opt.MultitaskOptimizerWrapper
where types of tensors were mismatched.TF_C_API_GRAPH_CONSTRUCTION=0 in
this release. Future releases will remove the ability to disable this
change. Please file a
bug if you find yourself using this escape hatch.tf.distributions.Distribution.tf.scatter_min and tf.scatter_maxfloat64 support for Conv2d,
Conv2dBackpropInput, and
Conv2dBackpropFilter.float64 support for
AvgPool/AvgPoolGrad.tf.image.psnr, tf.image.ssim,
tf.image.ssim_multiscale,
tf.image.image_gradients,
tf.image.sobel_edges.1 The cancellation logic of the RPC op contains a concurrency error. A fix has been submitted to master and will be part of the next release.
This release contains contributions from many people at Google, as well as:
4d55397500, Aghasy, Alan Du, Alan Lee, Alan Yee, Alex Wiltschko, Animesh Karnewar, Ankit Gupta, Anton Matosov, Aris L, Ben Barsdell, Brent Yi, Brett Koonce, Carl Thomé, cbockman, Chikanaga Tomoyuki, Chris Tava, CéDric Deltheil, Dahan Gong, Dalmo Cirne, Daniel Erenrich, David Norman, DavidNorman, Edd Wilder-James, Fanjin Zeng, Felix Abecassis, fo40225, George Sterpu, Giovanni Terlingen, Gor Baghdasaryan, Guillaume Klein, Hanchen Li, Ilya Polenov, Jakub Kolodziejczyk, Jason Sadler, Jayaram Bobba, Jerry Liu, jinghuangintel, Jiongyan Zhang (张炯衍), Joel Shor, Jong Wook Kim, Julian Eisenschlos, Karl Lessard, Krish Ravindranath, Loo Rong Jie, Lukas Geiger, Luke Iwanski, Mahmoud Abuzaina, ManHyuk, Marvin Richter, Maximilian Mitchell, Mohammad Ashraf Bhuiyan, msofka, Mustafa Kasap, Nathan Burnham, Nathan Luehr, Naveen Marri, ngc92, nio1814, Oleg Zabluda, Ou Changkun, Panos Ipeirotis, Paul Van Eck, Peter Lee, Piotr Czapla, qjivy, Rholais Lii, Rodrigo Formigone, Russell Klopfer, ryantimjohn, Sang Han, SebastiáN RamíRez, shengfuintel, Siby Jose Plathottam, Silver Chan, Stanislaw Antol, Taehoon Lee, Tarang Chugh, Ted Chang, Thomas Bastiani, Xian Xu, Xiaoming (Jason) Cui, Yan Facai (颜发才), yaox12, Yashal Shakti Kanungo, Yong Tang, Yuan (Terry) Tang, Yuxin Wu, Ziyue(Louis) Lu
tf.enable_eager_execution().tf.contrib.quantize
package.tf.custom_gradient.Dataset with new
tf.contrib.data.SqlDataset.tf.contrib.framework.CriticalSection.tf.regex_replace.tf.contrib.data.bucket_by_sequence_lengthtf.contrib.tensorrt that enables
native TensorRT in TensorFlow.MaxPoolGradGrad support for XLAtf.data:
tf.data.Datasettf.load_op_library() mechanism.Dataset.list_files() now shuffles its output by
default.Dataset.shuffle(..., seed=tf.constant(0, dtype=tf.int64))
now yields the same sequence of elements as
Dataset.shuffle(..., seed=0).num_parallel_reads argument to
tf.data.TFRecordDataset.tf.contrib:
tf.contrib.bayesflow.halton_sequence now supports
randomization.tf.contrib.all_reduce.effective_sample_size to
tf.contrib.bayesflow.mcmc_diagnostics.potential_scale_reduction to
tf.contrib.bayesflow.mcmc_diagnostics.BatchNormalization, Kumaraswamy
bijectors.tf.contrib.learn. Please check
contrib/learn/README.md for instructions on how to convert existing
code.tf.contrib.datatf.contrib.data.Dataset,
tf.contrib.data.Iterator,
tf.contrib.data.FixedLengthRecordDataset,
tf.contrib.data.TextLineDataset, and
tf.contrib.data.TFRecordDataset classes.bucket_by_sequence_length,
sliding_window_batch, and
make_batched_features_datasettf.contrib.ndlstm. You can find it
externally at https://github.com/tmbarchive/tfndlstm.tf.contrib.bayesflow to its own repo:
tfpTPUClusterResolver with GKE's integration for
Cloud TPUs.MomentumOptimizer lambda.tfp.layers boilerplate via programmable
docstrings.auc_with_confidence_intervals, a method for
computing the AUC and confidence interval with linearithmic time
complexity.regression_head now accepts customized link function,
to satisfy the usage that user can define their own link function if the
array_ops.identity does not meet the requirement.initialized_value and initial_value
behaviors for ResourceVariables created from
VariableDef protos.float16 dtype in
tf.linalg.*.tf.estimator.export.TensorServingInputReceiver that
allows tf.estimator.Estimator.export_savedmodel to pass raw
tensors to model functions.This release contains contributions from many people at Google, as well as:
4d55397500, Abe, Alistair Low, Andy Kernahan, Appledore, Ben, Ben Barsdell, Boris Pfahringer, Brad Wannow, Brett Koonce, Carl Thomé, cclauss, Chengzhi Chen, Chris Drake, Christopher Yeh, Clayne Robison, Codrut Grosu, Daniel Trebbien, Danny Goodman, David Goodwin, David Norman, Deron Eriksson, Donggeon Lim, Donny Viszneki, DosLin, DylanDmitri, Francisco Guerrero, Fred Reiss, gdh1995, Giuseppe, Glenn Weidner, gracehoney, Guozhong Zhuang, Haichen "Hc" Li, Harald Husum, harumitsu.nobuta, Henry Spivey, hsm207, Jekyll Song, Jerome, Jiongyan Zhang, jjsjann123, John Sungjin Park, Johnson145, JoshVarty, Julian Wolff, Jun Wang, June-One, Kamil Sindi, Kb Sriram, Kdavis-Mozilla, Kenji, lazypanda1, Liang-Chi Hsieh, Loo Rong Jie, Mahesh Bhosale, MandarJKulkarni, ManHyuk, Marcus Ong, Marshal Hayes, Martin Pool, matthieudelaro, mdfaijul, mholzel, Michael Zhou, Ming Li, Minmin Sun, Myungjoo Ham, MyungsungKwak, Naman Kamra, Peng Yu, Penghao Cen, Phil, Raghuraman-K, resec, Rohin Mohanadas, Sandeep N Gupta, Scott Tseng, seaotterman, Seo Sanghyeon, Sergei Lebedev, Ted Chang, terrytangyuan, Tim H, tkunic, Tod, vihanjain, Yan Facai (颜发才), Yin Li, Yong Tang, Yukun Chen, Yusuke Yamada
tf.estimator.{FinalExporter,LatestExporter} now export
stripped SavedModels. This improves forward compatibility of the
SavedModel.resize_images.align_corners
parameter.FlushCaches() method to the FileSystem interface,
with an implementation for GcsFileSystem.tf.contrib.distributions.Kumaraswamy.RetryingFileSystem::FlushCaches() calls the base
FileSystem's FlushCaches().auto_correlation to distributions.tf.contrib.distributions.Autoregressive.tf.matmul are bfloat16, it returns
bfloat16, instead of float32.tf.contrib.image.connected_components.tf.contrib.framework.CriticalSection that allows
atomic variable access.pt and eval commands, allow writing
tensor values to filesystem as numpy files.parallel_interleave to support 2 kinds of
prefetching.prepare_variance boolean with default
setting to False for backward compatibility.layers_dense_variational_impl.py to
layers_dense_variational.py.Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results
and/or CUDA_ILLEGAL_ADDRESS failures.
Google discovered in mid-December 2017 that the PTX-to-SASS compiler
in CUDA 9 and CUDA 9.1 sometimes does not properly compute the carry bit
when decomposing 64-bit address calculations with large offsets (e.g.
load [x + large_constant]) into 32-bit arithmetic in
SASS.
As a result, these versions of ptxas miscompile most XLA
programs which use more than 4GB of temp memory. This results in garbage
results and/or CUDA_ERROR_ILLEGAL_ADDRESS failures.
A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a fix for CUDA 9.0.x. Until the fix is available, the only workaround is to downgrade to CUDA 8.0.x or disable XLA:GPU.
TensorFlow will print a warning if you use XLA:GPU with a known-bad version of CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.
This release contains contributions from many people at Google, as well as:
4d55397500, Ag Ramesh, Aiden Scandella, Akimasa Kimura, Alex Rothberg, Allen Goodman, amilioto, Andrei Costinescu, Andrei Nigmatulin, Anjum Sayed, Anthony Platanios, Anush Elangovan, Armando Fandango, Ashish Kumar Ram, Ashwini Shukla, Ben, Bhavani Subramanian, Brett Koonce, Carl Thomé, cclauss, Cesc, Changming Sun, Christoph Boeddeker, Clayne Robison, Clemens Schulz, Clint (Woonhyuk Baek), codrut3, Cole Gerdemann, Colin Raffel, Daniel Trebbien, Daniel Ylitalo, Daniel Zhang, Daniyar, Darjan Salaj, Dave Maclachlan, David Norman, Dong--Jian, dongsamb, dssgsra, Edward H, eladweiss, elilienstein, Eric Lilienstein, error.d, Eunji Jeong, fanlu, Florian Courtial, fo40225, Fred, Gregg Helt, Guozhong Zhuang, Hanchen Li, hsm207, hyunyoung2, ImSheridan, Ishant Mrinal Haloi, Jacky Ko, Jay Young, Jean Flaherty, Jerome, JerrikEph, Jesse Kinkead, jfaath, Jian Lin, jinghuangintel, Jiongyan Zhang, Joel Hestness, Joel Shor, Johnny Chan, Julian Niedermeier, Julian Wolff, JxKing, K-W-W, Karl Lessard, Kasper Marstal, Keiji Ariyama, Koan-Sin Tan, Loki Der Quaeler, Loo Rong Jie, Luke Schaefer, Lynn Jackson, ManHyuk, Matt Basta, Matt Smith, Matthew Schulkind, Michael, michaelkhan3, Miguel Piedrafita, Mikalai Drabovich, Mike Knapp, mjwen, mktozk, Mohamed Aly, Mohammad Ashraf Bhuiyan, Myungjoo Ham, Naman Bhalla, Namrata-Ibm, Nathan Luehr, nathansilberman, Netzeband, Niranjan Hasabnis, Omar Aflak, Ozge Yalcinkaya, Parth P Panchal, patrickzzy, Patryk Chrabaszcz, Paul Van Eck, Paweł Kapica, Peng Yu, Philip Yang, Pierre Blondeau, Po-Hsien Chu, powderluv, Puyu Wang, Rajendra Arora, Rasmus, Renat Idrisov, resec, Robin Richtsfeld, Ronald Eddy Jr, Sahil Singh, Sam Matzek, Sami Kama, sandipmgiri, Santiago Castro, Sayed Hadi Hashemi, Scott Tseng, Sergii Khomenko, Shahid, Shengpeng Liu, Shreyash Sharma, Shrinidhi Kl, Simone Cirillo, simsicon, Stanislav Levental, starsblinking, Stephen Lumenta, Steven Hickson, Su Tang, Taehoon Lee, Takuya Wakisaka, Ted Chang, Ted Ying, Tijmen Verhulsdonck, Timofey Kondrashov, vade, vaibhav, Valentin Khrulkov, vchigrin, Victor Costan, Viraj Navkal, Vivek Rane, wagonhelm, Yan Facai (颜发才), Yanbo Liang, Yaroslav Bulatov, yegord, Yong Tang, Yoni Tsafir, yordun, Yuan (Terry) Tang, Yuxin Wu, zhengdi, Zhengsheng Wei, 田传武
complex64 support to XLA compiler.bfloat support is now added to XLA infrastructure.ClusterSpec propagation work with XLA
devices.tf.contrib:
tf.contrib.distributions:tf.contrib.distributions.Autoregressive.tf.contrib.distributions QuadratureCompound
classes support batchtf.contrib.distributions.RelaxedOneHotCategorical
dtype from arguments.tf.contrib.distributions quadrature family
parameterized by quadrature_grid_and_prob vs
quadrature_degree.auto_correlation added to
tf.contrib.distributionstf.contrib.bayesflow.layers, a collection of
probabilistic (neural) layers.tf.contrib.bayesflow.halton_sequence.tf.contrib.data.make_saveable_from_iterator.tf.contrib.data.shuffle_and_repeat.tf.contrib.data.scan().tf.contrib.distributions.bijectors:tf.contrib.distributions.bijectors.MaskedAutoregressiveFlow.tf.contrib.distributions.bijectors.Permute.tf.contrib.distributions.bijectors.Gumbel.tf.contrib.distributions.bijectors.Reshape.streaming_precision_recall_at_equal_thresholds, a
method for computing streaming precision and recall with
O(num_thresholds + size of predictions) time and space
complexity.RunConfig default behavior to not set a random
seed, making random behavior independently random on distributed
workers. We expect this to generally improve training performance.
Models that do rely on determinism should set a random seed
explicitly.tf.flags with
absl.flags.CUBLAS_TENSOR_OP_MATH in fp16 GEMMEstimators save
checkpoints.tf2xla
bridge.SpaceToDepth and
DepthToSpace.mfcc_mel_filterbank.h
and mfcc.h to clarify that the input domain is squared
magnitude spectra and the weighting is done on linear magnitude spectra
(sqrt of inputs).tf.contrib.distributions docstring examples to
use tfd alias rather than ds,
bs.tf.distributions.bijectors.Bijector.tf.assert_equal no longer raises
ValueError. It now raises
InvalidArgumentError, as documented.import_meta_graph's handling of
partitioned variables when importing into a scope. WARNING: This may
break loading checkpoints of graphs with partitioned variables saved
after using import_meta_graph with a non-empty
import_scope argument.WorkerService.DeleteWorkerSession method to
the gRPC interface, to fix a memory leak. Ensure that your master and
worker servers are running the same version of TensorFlow to avoid
compatibility issues.log_det_jacobian to match
log_prob in TransformedDistribution.import_meta_graph's handling of
partitioned variables whentf.distributions.Multinomial doesn't underflow
in log_prob. Before this change, all partitions of an
integer variable were initialized with the shape of the unpartitioned
variable; after this change they are initialized correctly.DenseFlipout probabilistic layer.ignore_live_threads is available on train.
If set to True, it will ignore threads that remain running
when tearing down infrastructure after successfully completing training,
instead of throwing a RuntimeError.DenseVariational as simpler template for
other probabilistic layers.tf.data now supports tf.SparseTensor
components in dataset elements.Tensors.SparseSegmentReduction ops to have missing
segment IDs.Conv2D, Conv2DBackpropInput,
Conv2DBackpropFilter now supports arbitrary dilations with
GPU and cuDNNv6 support.Estimator now supports Dataset:
input_fn can return a Dataset instead of
Tensors.RevBlock, a memory-efficient implementation of
reversible residual layers.cross_entropy and kl_divergence to
tf.distributions.Distribution.tf.nn.softmax_cross_entropy_with_logits_v2 which
enables backprop w.r.t. the labels.ptxas to compile generated
PTX.BufferAssignment's protocol buffer dump is now
deterministic.DynamicStitch.quantile to
tf.distributions.TransformedDistribution.NCHW_VECT_C support for
tf.depth_to_space on GPU.NCHW_VECT_C support for
tf.space_to_depth on GPU.SqueezeDims attribute to Axis in
C++ API for Squeeze op.Stream::BlockHostUntilDone now returns Status rather
than bool.stochastic to
common and remove stochastic.Using XLA:GPU with CUDA 9 and CUDA 9.1 results in garbage results
and/or CUDA_ILLEGAL_ADDRESS failures.
Google discovered in mid-December 2017 that the PTX-to-SASS compiler
in CUDA 9 and CUDA 9.1 sometimes does not properly compute the carry bit
when decomposing 64-bit address calculations with large offsets (e.g.
load [x + large_constant]) into 32-bit arithmetic in
SASS.
As a result, these versions of ptxas miscompile most XLA
programs which use more than 4GB of temp memory. This results in garbage
results and/or CUDA_ERROR_ILLEGAL_ADDRESS failures.
A fix in CUDA 9.1.121 is expected in late February 2018. We do not expect a fix for CUDA 9.0.x. Until the fix is available, the only workaround is to downgrade to CUDA 8.0.x or disable XLA:GPU.
TensorFlow will print a warning if you use XLA:GPU with a known-bad version of CUDA; see e00ba24c4038e7644da417ddc639169b6ea59122.
This release contains contributions from many people at Google, as well as:
Adam Zahran, Ag Ramesh, Alan Lee, Alan Yee, Alex Sergeev, Alexander, Amir H. Jadidinejad, Amy, Anastasios Doumoulakis, Andrei Costinescu, Andrei Nigmatulin, Anthony Platanios, Anush Elangovan, arixlin, Armen Donigian, ArtëM Sobolev, Atlas7, Ben Barsdell, Bill Prin, Bo Wang, Brett Koonce, Cameron Thomas, Carl Thomé, Cem Eteke, cglewis, Changming Sun, Charles Shenton, Chi-Hung, Chris Donahue, Chris Filo Gorgolewski, Chris Hoyean Song, Chris Tava, Christian Grail, Christoph Boeddeker, cinqS, Clayne Robison, codrut3, concerttttt, CQY, Dan Becker, Dan Jarvis, Daniel Zhang, David Norman, dmaclach, Dmitry Trifonov, Donggeon Lim, dongpilYu, Dr. Kashif Rasul, Edd Wilder-James, Eric Lv, fcharras, Felix Abecassis, FirefoxMetzger, formath, FredZhang, Gaojin Cao, Gary Deer, Guenther Schmuelling, Hanchen Li, Hanmin Qin, hannesa2, hyunyoung2, Ilya Edrenkin, Jackson Kontny, Jan, Javier Luraschi, Jay Young, Jayaram Bobba, Jeff, Jeff Carpenter, Jeremy Sharpe, Jeroen BéDorf, Jimmy Jia, Jinze Bai, Jiongyan Zhang, Joe Castagneri, Johan Ju, Josh Varty, Julian Niedermeier, JxKing, Karl Lessard, Kb Sriram, Keven Wang, Koan-Sin Tan, Kyle Mills, lanhin, LevineHuang, Loki Der Quaeler, Loo Rong Jie, Luke Iwanski, LáSzló Csomor, Mahdi Abavisani, Mahmoud Abuzaina, ManHyuk, Marek ŠUppa, MathSquared, Mats Linander, Matt Wytock, Matthew Daley, Maximilian Bachl, mdymczyk, melvyniandrag, Michael Case, Mike Traynor, miqlas, Namrata-Ibm, Nathan Luehr, Nathan Van Doorn, Noa Ezra, Nolan Liu, Oleg Zabluda, opensourcemattress, Ouwen Huang, Paul Van Eck, peisong, Peng Yu, PinkySan, pks, powderluv, Qiao Hai-Jun, Qiao Longfei, Rajendra Arora, Ralph Tang, resec, Robin Richtsfeld, Rohan Varma, Ryohei Kuroki, SaintNazaire, Samuel He, Sandeep Dcunha, sandipmgiri, Sang Han, scott, Scott Mudge, Se-Won Kim, Simon Perkins, Simone Cirillo, Steffen Schmitz, Suvojit Manna, Sylvus, Taehoon Lee, Ted Chang, Thomas Deegan, Till Hoffmann, Tim, Toni Kunic, Toon Verstraelen, Tristan Rice, Urs KöSter, Utkarsh Upadhyay, Vish (Ishaya) Abrams, Winnie Tsang, Yan Chen, Yan Facai (颜发才), Yi Yang, Yong Tang, Youssef Hesham, Yuan (Terry) Tang, Zhengsheng Wei, zxcqwe4906, 张志豪, 田传武
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
LinearClassifier fix.tf.keras is now part of the core TensorFlow API.tf.data
is now part of the core TensorFlow API.
tf.contrib.data API,
see the README.Dataset.from_generator()
(for building an input pipeline from a Python generator), and the
Dataset.apply() method for applying custom transformation
functions.tf.contrib.data.batch_and_drop_remainder() and
tf.contrib.data.sloppy_interleave().train_and_evaluate for simple distributed
Estimator training.tf.spectral.dct for computing the DCT-II.tf.contrib.signal (with GPU and gradient support).import tensorflow for Windows DLL
issues.tf.depth_to_space on GPU.eval command to allow evaluation of arbitrary
Python/numpy expressions in tfdbg command-line interface. See Debugging TensorFlow
Programs for more details.has_inf_or_nan is now added to Session
wrappers and hooks by default. So there is no need for clients to call
.add_tensor_filter(tf_debug.has_inf_or_nan) anymore.contrib.distributions.GANEstimator opensource.Estimator.export_savedmodel() now includes all valid
serving signatures that can be constructed from the Serving Input
Receiver and all available ExportOutputs. For instance, a classifier may
provide regression- and prediction-flavored outputs, in addition to the
classification-flavored one. Building signatures from these allows TF
Serving to honor requests using the different APIs (Classify, Regress,
and Predict). Furthermore, serving_input_receiver_fn() may
now specify alternative subsets of nodes that may act as inputs. This
allows, for instance, producing a prediction signature for a classifier
that accepts raw Tensors instead of a serialized
tf.Example.tf.contrib.bayesflow.hmc.tf.contrib.distributions.MixtureSameFamily.Dataset.shuffle() always reshuffles after each
iteration by default.tf.contrib.bayesflow.metropolis_hastings.log_rate parameter to
tf.contrib.distributions.Poisson.tf.contrib.distributions.bijector API to handle
some non-injective transforms.Tensor<Integer>) for improved
type-safety (courtesy @andrewcmyers).tf.contrib) on Linux and OS Xtf.nn.rnn_cell.DropoutWrapper is now more careful about
dropping out LSTM states. Specifically, it no longer ever drops the
c (memory) state of an LSTMStateTuple. The new
behavior leads to proper dropout behavior for LSTMs and stacked LSTMs.
This bug fix follows recommendations from published literature, but is a
behavioral change. State dropout behavior may be customized via the new
dropout_state_filter_visitor argument.tf.contrib.training.python_input. The same
behavior, in a more flexible and reproducible package, is available via
the new tf.contrib.data.Dataset.from_generator method!tf.contrib.distributions.Affine incorrectly
computing log-det-jacobian.tf.random_gamma incorrectly handling non-batch,
scalar draws.tf.sysconfig.get_lib()).RunConfig default behavior to not set a random
seed, making random behavior independently random on distributed
workers. We expect this to generally improve training performance.
Models that do rely on determinism should set a random seed
explicitly.tf.contrib.data.rejection_resample() function has been
changed. It now returns a function that can be used as an argument to
Dataset.apply().tf.contrib.data.Iterator.from_dataset() method.
Use Dataset.make_initializable_iterator() instead.tf.contrib.data.Iterator.dispose_op().Dataset.from_generator() does not support
Unicode strings. You must convert any strings to bytes objects before
yielding them from the generator.This release contains contributions from many people at Google, as well as:
4d55397500, Abdullah Alrasheed, abenmao, Adam Salvail, Aditya Dhulipala, Ag Ramesh, Akimasa Kimura, Alan Du, Alan Yee, Alexander, Amit Kushwaha, Amy, Andrei Costinescu, Andrei Nigmatulin, Andrew Erlichson, Andrew Myers, Andrew Stepanov, Androbin, AngryPowman, Anish Shah, Anton Daitche, Artsiom Chapialiou, asdf2014, Aseem Raj Baranwal, Ash Hall, Bart Kiers, Batchu Venkat Vishal, ben, Ben Barsdell, Bill Piel, Carl Thomé, Catalin Voss, Changming Sun, Chengzhi Chen, Chi Zeng, Chris Antaki, Chris Donahue, Chris Oelmueller, Chris Tava, Clayne Robison, Codrut, Courtial Florian, Dalmo Cirne, Dan J, Darren Garvey, David Kristoffersson, David Norman, David RöThlisberger, DavidNorman, Dhruv, DimanNe, Dorokhov, Duncan Mac-Vicar P, EdwardDixon, EMCP, error.d, FAIJUL, Fan Xia, Francois Xavier, Fred Reiss, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Guenther Schmuelling, Guo Yejun (郭叶军), Hans Gaiser, HectorSVC, Hyungsuk Yoon, James Pruegsanusak, Jay Young, Jean Wanka, Jeff Carpenter, Jeremy Rutman, Jeroen BéDorf, Jett Jones, Jimmy Jia, jinghuangintel, jinze1994, JKurland, Joel Hestness, joetoth, John B Nelson, John Impallomeni, John Lawson, Jonas, Jonathan Dekhtiar, joshkyh, Jun Luan, Jun Mei, Kai Sasaki, Karl Lessard, karl@kubx.ca, Kb Sriram, Kenichi Ueno, Kevin Slagle, Kongsea, Lakshay Garg, lhlmgr, Lin Min, liu.guangcong, Loki Der Quaeler, Louie Helm, lucasmoura, Luke Iwanski, Lyndon White, Mahmoud Abuzaina, Marcel Puyat, Mark Aaron Shirley, Michele Colombo, MtDersvan, Namrata-Ibm, Nathan Luehr, Naurril, Nayana Thorat, Nicolas Lopez, Niranjan Hasabnis, Nolan Liu, Nouce, Oliver Hennigh, osdamv, Patrik Erdes, Patryk Chrabaszcz, Pavel Christof, Penghao Cen, postBG, Qingqing Cao, Qingying Chen, qjivy, Raphael, Rasmi, raymondxyang, Renze Yu, resec, Roffel, Ruben Vereecken, Ryohei Kuroki, sandipmgiri, Santiago Castro, Scott Kirkland, Sean Vig, Sebastian Raschka, Sebastian Weiss, Sergey Kolesnikov, Sergii Khomenko, Shahid, Shivam Kotwalia, Stuart Berg, Sumit Gouthaman, superzerg, Sven Mayer, tetris, Ti Zhou, Tiago Freitas Pereira, Tian Jin, Tomoaki Oiki, Vaibhav Sood, vfdev, Vivek Rane, Vladimir Moskva, wangqr, Weber Xie, Will Frey, Yan Facai (颜发才), yanivbl6, Yaroslav Bulatov, Yixing Lao, Yong Tang, youkaichao, Yuan (Terry) Tang, Yue Zhang, Yuxin Wu, Ziming Dong, ZxYuan, 黄璞
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
See also TensorBoard 0.1.4 release notes.
DNNClassifierDNNRegressorLinearClassifierLinearRegressorDNNLinearCombinedClassifierDNNLinearCombinedRegressor.import tensorflow now goes much faster.tf.gather.constant_values keyword argument to
tf.pad.Dataset.interleave transformation.ConcatenateDataset to concatenate two
datasets.Dataset.list_files API.-s
flag to command print_tensor or pt.print_feed or
pf command and clickable links in the curses UI.run -p command.tf.distributions.tf.where
and tf.nn.top_k.tf.contrib.seq2seq.tf.contrib.signal, a library for signal
processing primitives.tf.contrib.resampler, containing CPU and GPU ops
for differentiable resampling of images.tf.RewriterConfig was removed from the Python API after
being available in 1.2 release candidates (it was never in an actual
release). Graph rewriting is still available, just not as
tf.RewriterConfig. Instead add an explicit import.tf.contrib.data.Dataset APIs that
expect a nested structure. Lists are now converted to
tf.Tensor implicitly. You may need to change uses of lists
to tuples in existing code. In addition, dicts are now supported as a
nested structure.tf.contrib.metrics.{streaming_covariance,streaming_pearson_correlation}
modified to return nan when they have seen less or equal to 1 unit of
weight.strides and begin dtype mismatch
when slicing using int64 Tensor index in python.saved_model.utils now support SparseTensors
transparently.saver.restore.tf.spectral.rfft & tf.spectral.irfft.tf.layers.conv2d
when setting use_bias=True by 2x by using nn.bias_add.tf.summary ops to allow
controlling the tab name used in Tensorboard for organizing
summaries.tf.Session.make_callable.This release contains contributions from many people at Google, as well as:
4F2E4A2E, Adriano Carmezim, Adrià Arrufat, Alan Yee, Alex Lattas, Alex Rothberg, Alexandr Baranezky, Ali Siddiqui, Andreas Solleder, Andrei Costinescu, Andrew Hundt, Androbin, Andy Kernahan, Anish Shah, Anthony Platanios, Arvinds-Ds, b1rd, Baptiste Arnaud, Ben Mabey, Benedikt Linse, Beomsu Kim, Bo Wang, Boyuan Deng, Brett Koonce, Bruno Rosa, Carl Thomé, Changming Sun, Chase Roberts, Chirag Bhatia, Chris Antaki, Chris Hoyean Song, Chris Tava, Christos Nikolaou, Croath Liu, cxx, Czxck001, Daniel Ylitalo, Danny Goodman, Darren Garvey, David Brailovsky, David Norman, DavidNorman, davidpham87, ddurham2, Dhruv, DimanNe, Drew Hintz, Dustin Tran, Earthson Lu, ethiraj, Fabian Winnen, Fei Sun, Freedom" Koan-Sin Tan, Fritz Obermeyer, Gao, Xiang, Gautam, Guenther Schmuelling, Gyu-Ho Lee, Hauke Brammer, horance, Humanity123, J Alammar, Jayeol Chun, Jeroen BéDorf, Jianfei Wang, jiefangxuanyan, Jing Jun Yin, Joan Puigcerver, Joel Hestness, Johannes Mayer, John Lawson, Johnson145, Jon Malmaud, Jonathan Alvarez-Gutierrez, Juang, Yi-Lin, Julian Viereck, Kaarthik Sivashanmugam, Karl Lessard, karl@kubx.ca, Kevin Carbone, Kevin Van Der Burgt, Kongsea, ksellesk, lanhin, Lef Ioannidis, Liangliang He, Louis Tiao, Luke Iwanski, LáSzló Csomor, magixsno, Mahmoud Abuzaina, Marcel Hlopko, Mark Neumann, Maxwell Paul Brickner, mdfaijul, MichaëL Defferrard, Michał JastrzęBski, Michele Colombo, Mike Brodie, Mosnoi Ion, mouradmourafiq, myPrecious, Nayana Thorat, Neeraj Kashyap, Nelson Liu, Niranjan Hasabnis, Olivier Moindrot, orome, Pankaj Gupta, Paul Van Eck, peeyush18, Peng Yu, Pierre, preciousdp11, qjivy, Raingo, raoqiyu, ribx, Richard S. Imaoka, Rishabh Patel, Robert Walecki, Rockford Wei, Ryan Kung, Sahil Dua, Sandip Giri, Sayed Hadi Hashemi, sgt101, Shitian Ni, Shuolongbj, Siim PõDer, Simon Perkins, sj6077, SOLARIS, Spotlight0xff, Steffen Eberbach, Stephen Fox, superryanguo, Sven Mayer, Tapan Prakash, Tiago Morais Morgado, Till Hoffmann, Tj Rana, Vadim Markovtsev, vhasanov, Wei Wu, windead, Yan (Asta) Li, Yan Chen, Yann Henon, Yi Wang, Yong Tang, yorkie, Yuan (Terry) Tang, Yuxin Wu, zhengjiajin, zhongzyd, 黄璞
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
Python 3.6 support on Windows.
Added tf.layers.conv3d_transpose layer for spatio
temporal deconvolution.
Added tf.Session.make_callable(), which provides a
lower overhead means of running a similar step multiple times.
Added libverbs-based RDMA support to contrib (courtesy @junshi15 from Yahoo).
Bring tf.feature_column.* into the API.
Non-deprecated functionality from tf.contrib.layers.* is
moved to tf.feature_column.* with cosmetic
changes.
RNNCell objects now subclass
tf.layers.Layer. The strictness described in the TensorFlow
1.1 release is gone: The first time an RNNCell is used, it caches its
scope. All future uses of the RNNCell will reuse variables from that
same scope. This is a breaking change from the behavior of RNNCells in
TensorFlow versions <= 1.0.1. TensorFlow 1.1 had checks in place to
ensure old code works correctly with the new semantics; this version
allows more flexible uses of RNNCell but can lead to subtle errors if
using code meant for TensorFlow <= 1.0.1. For example, writing:
MultiRNNCell([lstm] * 5) will now build a 5-layer LSTM
stack where each layer shares the same parameters. To
get 5 layers each with their own parameters, write:
MultiRNNCell([LSTMCell(...) for _ in range(5)]). If at all
unsure, first test your code with TF 1.1; ensure it raises no errors,
and then upgrade to TF 1.2.
RNNCells' variable names have been renamed for consistency with Keras layers. Specifically, the previous variable names "weights" and "biases" have been changed to "kernel" and "bias", respectively. This may cause backward incompatibility with regard to your old checkpoints containing such RNN cells, in which case you can use the tool checkpoint_convert script to convert the variable names in your old checkpoints.
Many of the RNN functions and classes that were in the
tf.nn namespace before the 1.0 release and which were moved
to tf.contrib.rnn have now been moved back to the core
namespace. This includes RNNCell, LSTMCell,
GRUCell, and a number of other cells. These now reside in
tf.nn.rnn_cell (with aliases in tf.contrib.rnn
for backwards compatibility). The original tf.nn.rnn
function is now tf.nn.static_rnn, and the bidirectional
static and state saving static rnn functions are also now back in the
tf.nn namespace.
Notable exceptions are the EmbeddingWrapper,
InputProjectionWrapper and
OutputProjectionWrapper, which will slowly be moved to
deprecation in tf.contrib.rnn. These are inefficient
wrappers that should often be replaced by calling
embedding_lookup or layers.dense as pre- or
post- processing of the rnn. For RNN decoding, this functionality has
been replaced with an alternative API in
tf.contrib.seq2seq.
Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of optimized deep learning primitives: In addition to matrix multiplication and convolution, these building blocks include: Direct batched convolution Pooling: maximum, minimum, average Normalization: LRN, batch normalization Activation: rectified linear unit (ReLU) Data manipulation: multi-dimensional transposition (conversion), split, concat, sum and scale.
TensorForest Estimator now supports SavedModel export for serving.
Support client-provided ClusterSpec's and propagate them to all workers to enable the creation of dynamic TensorFlow clusters.
TensorFlow C library now available for Windows.
We released a new open-source version of TensorBoard.
SavedModel CLI
tool available to inspect and execute MetaGraph in SavedModel
Android releases of TensorFlow are now pushed to jcenter for easier integration into apps. See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/android/inference_interface/README.md for more details.
org.tensorflow.contrib.android.TensorFlowInferenceInterface
now throws exceptions where possible and has simplified method
signatures.tf.contrib.util.create_example.tf.contrib.image.tf.contrib.stateless for random ops with custom
seed control.tf.contrib.kernel_methods module with Ops and
estimators for primal (explicit) kernel methods in TensorFlow.Operation.get_attr on type attributes
returns the Python DType version of the type to match expected get_attr
documentation rather than the protobuf enum.categorical_column_with_vocabulary_file.reduction arg to losses.tf.placeholder can represent scalar shapes and
partially known.tf.summary.text for outputting text to
TensorBoard.tf.string_to_number now supports int64 and float64
outputs.This release contains contributions from many people at Google, as well as:
4F2E4A2E, Aaron Schumacher, Abhi Agg, admcrae, Adriano Carmezim, Adrià Arrufat, agramesh1, Akimitsu Seo, Alan Mosca, Alex Egg, Alex Rothberg, Alexander Heinecke, Alexander Matyasko, Alexandr Baranezky, Alexandre Caulier, Ali Siddiqui, Anand Venkat, Andrew Hundt, Androbin, Anmol Sharma, Arie, Arno Leist, Arron Cao, AuréLien Geron, Bairen Yi, Beomsu Kim, Carl Thomé, cfperez, Changming Sun, Corey Wharton, critiqjo, Dalei Li, Daniel Rasmussen, Daniel Trebbien, DaríO Hereñú, David Eng, David Norman, David Y. Zhang, Davy Song, ddurham2, Deepak Subburam, Dmytro Kyrychuk, Dominic Rossi, Dominik SchlöSser, Dustin Tran, Eduardo Pinho, Egil Martinsson, Elliot Saba, Eric Bigelow, Erik Smistad, Evan Klitzke, Fabrizio Milo, Falcon Dai, Fei Gao, FloopCZ, Fung Lam, Gautam, GBLin5566, Greg Peatfield, Gu Wang, Guenther Schmuelling, Hans Pabst, Harun Gunaydin, Huaizheng, Ido Shamay, Ikaro Silva, Ilya Edrenkin, Immexxx, James Mishra, Jamie Cooke, Jay Young, Jayaram Bobba, Jianfei Wang, jinghua2, Joey Meyer, John Maidens, Jonghoon Jin, Julian Villella, Jun Kim, Jun Shi, Junwei Pan, jyegerlehner, Karan Desai, Karel Van De Plassche, Kb Sriram, KhabarlakKonstantin, Koan-Sin Tan, krivard, Kwotsin, Leandro Gracia Gil, Li Chen, Liangliang He, Louie Helm, lspvic, Luiz Henrique Soares, LáSzló Csomor, Mark Wong, Mathew Wicks, Matthew Rahtz, Maxwell Paul Brickner, Michael Hofmann, Miguel Flores Ruiz De Eguino, MikeTam1021, Mortada Mehyar, Mycosynth, Namnamseo, Nate Harada, Neven Miculinic, Nghia Tran, Nick Lyu, Niranjan Hasabnis, Nishidha, Oleksii Kuchaiev, Oyesh Mann Singh, Panmari, Patrick, Paul Van Eck, Piyush Chaudhary, Quim Llimona, Raingo, Richard Davies, Ruben Vereecken, Sahit Chintalapudi, Sam Abrahams, Santiago Castro, Scott Sievert, Sean O'Keefe, Sebastian Schlecht, Shane, Shubhankar Deshpande, Spencer Schaber, Sunyeop Lee, t13m, td2014, Thomas H. P. Andersen, Toby Petty, Umang Mehta, Vadim Markovtsev, Valentin Iovene, Vincent Zhao, Vit Stepanovs, Vivek Rane, Vu Pham, wannabesrevenge, weipingpku, wuhaixutab, wydwww, Xiang Gao, Xiaolin Lin, xiaoyaozhuzi, Yaroslav Bulatov, Yi Liu, Yoshihiro Sugi, Yuan (Terry) Tang, Yuming Wang, Yuxin Wu, Zader Zheng, Zhaojun Zhang, zhengjiajin, ZhipengShen, Ziming Dong, zjj2wry
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
tf.spectral module. Moved existing FFT ops to
tf.spectral while keeping an alias in the old location
(tf.*).tf.spectral.tf.bincount function.RecordInput.tf.contrib.image.compose_transforms
function.tf.estimator.* into the API. Non-deprecated
functionality from tf.contrib.learn.Estimator is moved to
tf.estimator.Estimator with cosmetic changes.print_source / ps)invoke_stepper) now uses
intermediate tensor dumps. It also uses TensorHandles as
direct feeds during successive cont calls for improved
performance and reduced memory consumption.reuse=True.pmf, pdf,
log_pmf, log_pdf.bayesflow.special_math to distributions.tf.contrib.tensor_forest.python.tensor_forest.RandomForestDeviceAssigner
removed.tf.contrib.distributions.MultivariateNormalFull
replaced by
tf.contrib.distributions.MultivariateNormalTriL.tf.contrib.distributions.MultivariateNormalCholesky
replaced by
tf.contrib.distributions.MultivariateNormalTriLtf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev
replaced by
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScaletf.contrib.distributions.MultivariateNormalDiag
arguments changed from mu, diag_stddev to
log, scale_diag.tf.contrib.distributions.MultivariateNormalDiagPlusVDVT
removed.tf.contrib.distributions.MultivariateNormalDiagPlusLowRank
added.tf.contrib.layers.sparse_column_with_keys.tf.set_random_seed(0) to be deterministic for all
ops.tf.matching_files.LogMessage now includes a timestamp as beginning of a
message.StagingArea.sparse_matmul_op reenabled for Android builds.TF_GraphImportGraphDefWithReturnOutputs())tf.while_loops.This release contains contributions from many people at Google, as well as:
A. Besir Kurtulmus, Adal Chiriliuc, @akash, Alec-Desouza, Alex Rothberg, Alex Sergeev, Alexander Heinecke, Allen Guo, Andreas Madsen, Ankesh Anand, Anton Loss, @Aravind, @Arie, Ashutosh Das, AuréLien Geron, Bairen Yi, @bakunyo, Ben Visser, Brady Zhou, Calpa Liu, Changming Sun, Chih Cheng Liang, Christopher Berner, Clark Zinzow, @Conchylicultor, Dan Ellis, Dan J, Dan Jarvis, Daniel Ylitalo, Darren Garvey, David Norman, David Truong, @DavidNorman, Dimitar Pavlov, Dmitry Persiyanov, @Eddie, @elirex, Erfan Noury, Eron Wright, Evgeny Mazovetskiy, Fabrizio (Misto) Milo, @fanlu, Fisher Coder, Florian Courtial, Franck Dernoncourt, Gagan Goel, Gao, Xiang, @Gautam, Gefu Tang, @guilherme, @guschmue, Hannah Provenza, Hans Pabst, @hartb, Hsiao Yi, Huazuo Gao, Igor ChorążEwicz, Ivan Smirnov, Jakub Kolodziejczyk, Jason Gavris, Jason Morton, Jay Young, Jayaram Bobba, Jeremy Sawruk, Jiaming Liu, Jihun Choi, @jiqiu, Joan Thibault, John C F, Jojy George Varghese, Jon Malmaud, Julian Berman, Julian Niedermeier, Junpeng Lao, Kai Sasaki, @Kankroc, Karl Lessard, Kyle Bostelmann, @Lezcano, Li Yi, Luo Yun, @lurker, Mahmoud-Abuzaina, Mandeep Singh, Marek Kolodziej, Mark Szepieniec, Martial Hue, Medhat Omr, Memo Akten, Michael Gharbi, MichaëL Defferrard, Milan Straka, @MircoT, @mlucool, Muammar Ibn Faisal, Nayana Thorat, @nghiattran, Nicholas Connor, Nikolaas Steenbergen, Niraj Patel, Niranjan Hasabnis, @Panmari, Pavel Bulanov, Philip Pries Henningsen, Philipp Jund, @polonez, Prayag Verma, Rahul Kavi, Raphael Gontijo Lopes, @rasbt, Raven Iqqe, Reid Pryzant, Richard Shin, Rizwan Asif, Russell Kaplan, Ryo Asakura, RüDiger Busche, Saisai Shao, Sam Abrahams, @sanosay, Sean Papay, @seaotterman, @selay01, Shaurya Sharma, Sriram Narayanamoorthy, Stefano Probst, @taknevski, @tbonza, @teldridge11, Tim Anglade, Tomas Reimers, Tomer Gafner, Valentin Iovene, Vamsi Sripathi, Viktor Malyi, Vit Stepanovs, Vivek Rane, Vlad Firoiu, @wangg12, @will, Xiaoyu Tao, Yaroslav Bulatov, Yi Liu, Yuan (Terry) Tang, @Yufeng, Yuming Wang, Yuxin Wu, Zafar Takhirov, Ziming Dong
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
tf.core and tf.python modules from
the API. These were never intended to be exposed. Please use the same
objects through top-level tf module instead.pip install tensorflow command.To help you upgrade your existing TensorFlow Python code to match the API changes below, we have prepared a conversion script.
tf.div and tf.mod
as well. To obtain forced integer truncation based behaviors you can use
tf.truncatediv and tf.truncatemod. *
tf.divide() is now the recommended division function.
tf.div() will remain, but its semantics do not respond to
Python 3 or from future mechanisms. * tf.reverse() now
takes indices of axes to be reversed. E.g.
tf.reverse(a, [True, False, True]) must now be written as
tf.reverse(a, [0, 2]). tf.reverse_v2() will
remain until 1.0 final. * tf.mul, tf.sub and
tf.neg are deprecated in favor of tf.multiply,
tf.subtract and tf.negative. *
tf.pack and tf.unpack are deprecated in favor
of tf.stack and tf.unstack. *
TensorArray.pack and TensorArray.unpack are
getting deprecated in favor of TensorArray.stack and
TensorArray.unstack. * The following Python functions have
had their arguments changed to use axis when referring to
specific dimensions. We have kept the old keyword arguments for
compatibility currently, but we will be removing them well before the
final 1.0.tf.argmax: dimension becomes
axis * tf.argmin: dimension
becomes axis * tf.count_nonzero:
reduction_indices becomes axis *
tf.expand_dims: dim becomes axis
* tf.reduce_all: reduction_indices becomes
axis * tf.reduce_any:
reduction_indices becomes axis *
tf.reduce_join: reduction_indices becomes
axis * tf.reduce_logsumexp:
reduction_indices becomes axis *
tf.reduce_max: reduction_indices becomes
axis * tf.reduce_mean:
reduction_indices becomes axis *
tf.reduce_min: reduction_indices becomes
axis * tf.reduce_prod:
reduction_indices becomes axis *
tf.reduce_sum: reduction_indices becomes
axis * tf.reverse_sequence:
batch_dim becomes batch_axis,
seq_dim becomes seq_axis *
tf.sparse_concat: concat_dim becomes
axis * tf.sparse_reduce_sum:
reduction_axes becomes axis *
tf.sparse_reduce_sum_sparse: reduction_axes
becomes axis * tf.sparse_split:
split_dim becomes axis *
tf.listdiff has been renamed to tf.setdiff1d
to match NumPy naming. * tf.inv has been renamed to be
tf.reciprocal (component-wise reciprocal) to avoid
confusion with np.inv which is matrix inversion * tf.round
now uses banker's rounding (round to even) semantics to match NumPy. *
tf.split now takes arguments in a reversed order and with
different keywords. In particular, we now match NumPy order as
tf.split(value, num_or_size_splits, axis). *
tf.sparse_split now takes arguments in reversed order and
with different keywords. In particular we now match NumPy order as
tf.sparse_split(sp_input, num_split, axis). NOTE: we have
temporarily made tf.sparse_split require keyword arguments.
* tf.concat now takes arguments in reversed order and with
different keywords. In particular we now match NumPy order as
tf.concat(values, axis, name). *
tf.image.decode_jpeg by default uses the faster DCT method,
sacrificing a little fidelity for improved speed. One can revert to the
old behavior by specifying the attribute
dct_method='INTEGER_ACCURATE'. *
tf.complex_abs has been removed from the Python interface.
tf.abs supports complex tensors and should be used instead.
* In the C++ API (in tensorflow/cc), Input, Output, etc. have moved from
the tensorflow::ops namespace to tensorflow. *
Template.var_scope property renamed to
.variable_scope * SyncReplicasOptimizer is removed and
SyncReplicasOptimizerV2 renamed to SyncReplicasOptimizer. *
tf.zeros_initializer() and
tf.ones_initializer() now return a callable that must be
called with initializer arguments, in your code replace
tf.zeros_initializer with
tf.zeros_initializer(). * SparseTensor.shape
has been renamed to SparseTensor.dense_shape. Same for
SparseTensorValue.shape. * Replace tf.scalar_summary,
tf.histogram_summary, tf.audio_summary, tf.image_summary with
tf.summary.scalar, tf.summary.histogram, tf.summary.audio,
tf.summary.image, respectively. The new summary ops take name rather
than tag as their first argument, meaning summary ops now respect
TensorFlow name scopes. * Replace tf.train.SummaryWriter and
tf.train.SummaryWriterCache with tf.summary.FileWriter and
tf.summary.FileWriterCache. * Removes RegisterShape from public API. Use
C++ shape function registration instead. * Deprecated _ref
dtypes from the python API. * In the C++ API (in tensorflow/cc), Input,
Output, etc. have moved from the tensorflow::ops namespace to
tensorflow. * Change arg order for
{softmax,sparse_softmax,sigmoid}_cross_entropy_with_logits
to be (labels, predictions), and force use of named args. *
tf.nn.rnn_cell.* and most functions in tf.nn.rnn.* (with the exception
of dynamic_rnn and raw_rnn) are temporarily in tf.contrib.rnn. They will
be moved back into core for TF 1.2. *
tf.nn.sampled_softmax_loss and tf.nn.nce_loss
have both changed their API such that you need to switch the
inputs, labels to labels, inputs parameters. *
The shape keyword argument of the SparseTensor constructor
changes its name to dense_shape between Tensorflow 0.12 and
Tensorflow 1.0.parallel_stack.sparse_column_with_vocabulary_file, to specify a
feature column that transform string features to IDs, where the mapping
is defined by a vocabulary file.index_to_string_table which returns a lookup
table that maps indices to strings.string_to_index_table, which returns a lookup table
that matches strings to indices.ParallelForWithWorkerId function.string_to_index_table, which returns a lookup table
that matches strings to indices.contrib/session_bundle.tf.contrib.framework.filter_variables as a
convenience function to filter lists of variables based on regular
expressions.make_template() takes an optional
custom_getter_ param.recursive_create_dir.contrib/android/cmaketf.saved_model.reduce_join to treat
reduction_indices in the same way as other
reduce_ ops.TensorForestEstimator to
contrib/tensor_forest.tf.divide now honors the name field.StagingArea and new ops:
stage and unstage.This release contains contributions from many people at Google, as well as:
Aaron Hu, Abhishek Aggarwal, Adam Michael, Adriano Carmezim, @AfirSraftGarrier, Alexander Novikov, Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Hundt, Anish Shah, Anton Loss, @b0noI, @BoyuanJiang, Carl Thomé, Chad Kennedy, Comic Chang, Connor Braa, Daniel N. Lang, Daniel Trebbien, @danielgordon10, Darcy Liu, Darren Garvey, Dmitri Lapin, Eron Wright, Evan Cofer, Fabrizio Milo, Finbarr Timbers, Franck Dernoncourt, Garrett Smith, @guschmue, Hao Wei, Henrik Holst, Huazuo Gao, @Ian, @Issac, Jacob Israel, Jangsoo Park, Jin Kim, Jingtian Peng, John Pope, Kye Bostelmann, Liangliang He, Ling Zhang, Luheng He, Luke Iwanski, @lvli, Michael Basilyan, Mihir Patel, Mikalai Drabovich, Morten Just, @newge, Nick Butlin, Nishant Shukla, Pengfei Ni, Przemyslaw Tredak, @rasbt, @Ronny, Rudolf Rosa, @RustingSword, Sam Abrahams, Sam Putnam, @SeongAhJo, Shi Jiaxin, @skavulya, Steffen MüLler, @TheUSER123, @tiriplicamihai, @vhasanov, Victor Costan, Vit Stepanovs, Wangda Tan, Wenjian Huang, Xingdong Zuo, Yaroslav Bulatov, Yota Toyama, Yuan (Terry) Tang, Yuxin Wu
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
tf.train.Saver. Old V1 checkpoints continue to be readable;
controlled by the write_version argument,
tf.train.Saver now by default writes out in the new V2
format. It significantly reduces the peak memory required and latency
incurred during restore.matrix_solve_ls and
self_adjoint_eig.tf.contrib.integrate.odeint.tf.contrib.labeled_tensor.BusAdjacency enum replaced with a protocol buffer
DeviceLocality. PCI bus indexing now starts from 1 instead
of 0, and bus_id==0 is used where previously
BUS_ANY was used.Env::FileExists and FileSystem::FileExists
now return a tensorflow::Status instead of a bool. Any callers to this
function can be converted to a bool by adding .ok() to the call.TF_SessionWithGraph has been renamed to
TF_Session, indicating its preferred use in language
bindings for TensorFlow. What was previously TF_Session has
been renamed to TF_DeprecatedSession.TF_Port to TF_Output in the C
API.bus_id==0 is used where previously BUS_ANY was
used.tf.contrib.layers). This
means old checkpoints written using this code will not load after this
change without providing Saver a list of variable renames.
Examples of variable scope changes include RNN ->
rnn in tf.nn.rnn,
tf.nn.dynamic_rnn and moving from
Linear/Matrix -> weights and
Linear/Bias -> biases in most RNN
cells.SparseTensor.shape has been renamed to
SparseTensor.dense_shape. Same for
SparseTensorValue.shape.Env::FileExists and FileSystem::FileExists
now return a tensorflow::Status instead of a bool. Any
callers to this function can be converted to a bool by adding
.ok() to the call.TF_SessionWithGraph has been renamed to
TF_Session, indicating its preferred use in language
bindings for TensorFlow. What was previously TF_Session has
been renamed to TF_DeprecatedSession.TF_Port to TF_Output.TF_Tensor
objects provided to TF_Run, TF_SessionRun,
TF_SetAttrTensor etc.tf.image.per_image_whitening() to
tf.image.per_image_standardization()tf.summary
submodule.histogram_summary,
audio_summary, scalar_summary,
image_summary, merge_summary, and
merge_all_summaries.batch_* and regular version of linear algebra
and FFT ops. The regular op now handles batches as well. All
batch_* Python interfaces were removed.tf.all_variables, tf.VARIABLES and
tf.initialize_all_variables renamed to
tf.global_variables, tf.GLOBAL_VARIABLES and
tf.global_variables_initializer respectively.tf.zeros_initializer() and
tf.ones_initializer() now return a callable that must be
called with initializer arguments, in your code replace
tf.zeros_initializer with
tf.zeros_initializer()lgamma function.tf.sqrt handling of negative arguments.batch_matmul on
multi-core CPUs.matrix_set_diag,
matrix_diag_part and their gradients to work for
rectangular matrices.This release contains contributions from many people at Google, as well as:
@a7744hsc, Abhi Agg, @admcrae, Adriano Carmezim, Aki Sukegawa, Alex Kendall, Alexander Rosenberg Johansen, @amcrae, Amlan Kar, Andre Simpelo, Andreas Eberle, Andrew Hundt, Arnaud Lenglet, @b0noI, Balachander Ramachandran, Ben Barsdell, Ben Guidarelli, Benjamin Mularczyk, Burness Duan, @c0g, Changming Sun, @chanis, Corey Wharton, Dan J, Daniel Trebbien, Darren Garvey, David Brailovsky, David Jones, Di Zeng, @DjangoPeng, Dr. Kashif Rasul, @drag0, Fabrizio (Misto) Milo, FabríCio Ceschin, @fp, @Ghedeon, @guschmue, Gökçen Eraslan, Haosdent Huang, Haroen Viaene, Harold Cooper, Henrik Holst, @hoangmit, Ivan Ukhov, Javier Dehesa, Jingtian Peng, Jithin Odattu, Joan Pastor, Johan Mathe, Johannes Mayer, Jongwook Choi, Justus Schwabedal, Kai Wolf, Kamil Hryniewicz, Kamran Amini, Karen Brems, Karl Lattimer, @kborer, Ken Shirriff, Kevin Rose, Larissa Laich, Laurent Mazare, Leonard Lee, Liang-Chi Hsieh, Liangliang He, Luke Iwanski, Marek Kolodziej, Moustafa Alzantot, @MrQianjinsi, @nagachika, Neil Han, Nick Meehan, Niels Ole Salscheider, Nikhil Mishra, @nschuc, Ondrej Skopek, OndřEj Filip, @OscarDPan, Pablo Moyano, Przemyslaw Tredak, @qitaishui, @Quarazy, @raix852, Philipp Helo, Sam Abrahams, @SriramRamesh, Till Hoffmann, Tushar Soni, @tvn, @tyfkda, Uwe Schmidt, Victor Villas, Vit Stepanovs, Vladislav Gubarev, @wujingyue, Xuesong Yang, Yi Liu, Yilei Yang, @youyou3, Yuan (Terry) Tang, Yuming Wang, Zafar Takhirov, @zhongyuk, Ziming Dong, @guotong1988
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
tensorflow/contrib/cudnn_rnn.foo[1, 2:4, tf.newaxis, ..., :-3:-1, :] are now supported.
In addition we have preliminary (non-broadcasting) support for sliced
assignment to variables. In particular one can write
var[1:3].assign([1,11,111]).tf.op_scope and
tf.variable_op_scope in favor of a unified
tf.name_scope and tf.variable_scope. The new
argument order of tf.variable_scope is incompatible with
previous versions.core/util/tensor_bundle module: a module to
efficiently serialize/deserialize tensors to disk. Will be used in TF's
new checkpoint format.self_adjoint_eig or self_adjoint_eigvals.batch_* methods for most linear algebra and
FFT ops and promoted the non-batch version of the ops to handle batches
of matrices.TF_GraphGetTensorNumDims and
TF_GraphGetTensorShape.REGISTER_OP(...).SetShapeFn(...). Python shape inference
RegisterShape calls use the C++ shape functions with
common_shapes.call_cpp_shape_fn. A future release will
remove RegisterShape from python.tensorflow.__git_version__ now allows users to identify
the version of the code that TensorFlow was compiled with. We also have
tensorflow.__git_compiler__ which identifies the compiler
used to compile TensorFlow's core.batch_matmul.state_is_tuple=True. For a quick fix while transitioning
to the new default, simply pass the argument
state_is_tuple=False.uniform_unit_scaling_initializer() no longer takes a
full_shape arg, instead relying on the partition info
passed to the initializer function when it's called.node_def.proto instead of graph.proto.ops.NoGradient was renamed
ops.NotDifferentiable. ops.NoGradient will be
removed soon.dot.h / DotGraph was removed (it was an early analysis
tool prior to TensorBoard, no longer that useful). It remains in history
should someone find the code useful.This release contains contributions from many people at Google, as well as:
Abid K, @afshinrahimi, @AidanGG, Ajay Rao, Aki Sukegawa, Alex Rothberg, Alexander Rosenberg Johansen, Andrew Gibiansky, Andrew Thomas, @Appleholic, Bastiaan Quast, Ben Dilday, Bofu Chen, Brandon Amos, Bryon Gloden, Cissp®, @chanis, Chenyang Liu, Corey Wharton, Daeyun Shin, Daniel Julius Lasiman, Daniel Waterworth, Danijar Hafner, Darren Garvey, Denis Gorbachev, @DjangoPeng, Egor-Krivov, Elia Palme, Eric Platon, Fabrizio Milo, Gaetan Semet, Georg Nebehay, Gu Wang, Gustav Larsson, @haosdent, Harold Cooper, Hw-Zz, @ichuang, Igor Babuschkin, Igor Macedo Quintanilha, Ilya Edrenkin, @ironhead, Jakub Kolodziejczyk, Jennifer Guo, Jihun Choi, Jonas Rauber, Josh Bleecher Snyder, @jpangburn, Jules Gagnon-Marchand, Karen Brems, @kborer, Kirill Bobyrev, Laurent Mazare, Longqi Yang, Malith Yapa, Maniteja Nandana, Martin Englund, Matthias Winkelmann, @mecab, Mu-Ik Jeon, Nand Dalal, Niels Ole Salscheider, Nikhil Mishra, Park Jiin, Pieter De Rijk, @raix852, Ritwik Gupta, Sahil Sharma, Sangheum Hwang, @SergejsRk, Shinichiro Hamaji, Simon Denel, @Steve, @suiyuan2009, Tiago Jorge, Tijmen Tieleman, @tvn, @tyfkda, Wang Yang, Wei-Ting Kuo, Wenjian Huang, Yan Chen, @YenChenLin, Yuan (Terry) Tang, Yuncheng Li, Yunfeng Wang, Zack Polizzi, @zhongzyd, Ziming Dong, @perhapszzy
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
tf.contrib.slimThis release contains contributions from many people at Google, as well as:
Alex Rothberg, Andrew Royer, Austin Marshall, @BlackCoal, Bob Adolf, Brian Diesel, Charles-Emmanuel Dias, @chemelnucfin, Chris Lesniewski, Daeyun Shin, Daniel Rodriguez, Danijar Hafner, Darcy Liu, Kristinn R. Thórisson, Daniel Castro, Dmitry Savintsev, Kashif Rasul, Dylan Paiton, Emmanuel T. Odeke, Ernest Grzybowski, Gavin Sherry, Gideon Dresdner, Gregory King, Harold Cooper, @heinzbeinz, Henry Saputra, Huarong Huo, Huazuo Gao, Igor Babuschkin, Igor Macedo Quintanilha, Ivan Ukhov, James Fysh, Jan Wilken Dörrie, Jihun Choi, Johnny Lim, Jonathan Raiman, Justin Francis, @lilac, Li Yi, Marc Khoury, Marco Marchesi, Max Melnick, Micael Carvalho, @mikowals, Mostafa Gazar, Nico Galoppo, Nishant Agrawal, Petr Janda, Yuncheng Li, @raix852, Robert Rose, @Robin-des-Bois, Rohit Girdhar, Sam Abrahams, satok16, Sergey Kishchenko, Sharkd Tu, @shotat, Siddharth Agrawal, Simon Denel, @sono-bfio, SunYeop Lee, Thijs Vogels, @tobegit3hub, @Undo1, Wang Yang, Wenjian Huang, Yaroslav Bulatov, Yuan Tang, Yunfeng Wang, Ziming Dong
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
tf.nn.dynamic_rnn, tf.nn.rnn, and the classes
in tf.nn.rnn_cell).tf.nn.moments() now accepts a shift
argument. Shifting by a good estimate of the mean improves numerical
stability. Also changes the behavior of the shift argument
to tf.nn.sufficient_statistics().This release contains contributions from many people at Google, as well as:
Aaron Schumacher, Aidan Dang, Akihiko ITOH, Aki Sukegawa, Arbit Chen, Aziz Alto, Danijar Hafner, Erik Erwitt, Fabrizio Milo, Felix Maximilian Möller, Henry Saputra, Sung Kim, Igor Babuschkin, Jan Zikes, Jeremy Barnes, Jesper Steen Møller, Johannes Mayer, Justin Harris, Kashif Rasul, Kevin Robinson, Loo Rong Jie, Lucas Moura, Łukasz Bieniasz-Krzywiec, Mario Cho, Maxim Grechkin, Michael Heilman, Mostafa Rahmani, Mourad Mourafiq, @ninotoshi, Orion Reblitz-Richardson, Yuncheng Li, @raoqiyu, Robert DiPietro, Sam Abrahams, Sebastian Raschka, Siddharth Agrawal, @snakecharmer1024, Stephen Roller, Sung Kim, SunYeop Lee, Thijs Vogels, Till Hoffmann, Victor Melo, Ville Kallioniemi, Waleed Abdulla, Wenjian Huang, Yaroslav Bulatov, Yeison Rodriguez, Yuan Tang, Yuxin Wu, @zhongzyd, Ziming Dong, Zohar Jackson
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
contrib/learncontrib/linear_optimizercontrib/tensor_forestcontrib/ctchalf data typecontrib/)TENSORFLOW_USE_EIGEN_THREADPOOL definebool-strictness: Tensors have to be explicitly compared
to Nonetf.while_loop (deprecated
control_flow_ops.While)This release contains contributions from many people at Google, as well as:
Abhinav Upadhyay, Aggelos Avgerinos, Alan Wu, Alexander G. de G. Matthews, Aleksandr Yahnev, @amchercashin, Andy Kitchen, Aurelien Geron, Awni Hannun, @BanditCat, Bas Veeling, Cameron Chen, @cg31, Cheng-Lung Sung, Christopher Bonnett, Dan Becker, Dan Van Boxel, Daniel Golden, Danijar Hafner, Danny Goodman, Dave Decker, David Dao, David Kretch, Dongjoon Hyun, Dustin Dorroh, @e-lin, Eurico Doirado, Erik Erwitt, Fabrizio Milo, @gaohuazuo, Iblis Lin, Igor Babuschkin, Isaac Hodes, Isaac Turner, Iván Vallés, J Yegerlehner, Jack Zhang, James Wexler, Jan Zikes, Jay Young, Jeff Hodges, @jmtatsch, Johnny Lim, Jonas Meinertz Hansen, Kanit Wongsuphasawat, Kashif Rasul, Ken Shirriff, Kenneth Mitchner, Kenta Yonekura, Konrad Magnusson, Konstantin Lopuhin, @lahwran, @lekaha, @liyongsea, Lucas Adams, @makseq, Mandeep Singh, @manipopopo, Mark Amery, Memo Akten, Michael Heilman, Michael Peteuil, Nathan Daly, Nicolas Fauchereau, @ninotoshi, Olav Nymoen, @panmari, @papelita1234, Pedro Lopes, Pranav Sailesh Mani, RJ Ryan, Rob Culliton, Robert DiPietro, @ronrest, Sam Abrahams, Sarath Shekkizhar, Scott Graham, Sebastian Raschka, Sung Kim, Surya Bhupatiraju, Syed Ahmed, Till Hoffmann, @timsl, @urimend, @vesnica, Vlad Frolov, Vlad Zagorodniy, Wei-Ting Kuo, Wenjian Huang, William Dmitri Breaden Madden, Wladimir Schmidt, Yuan Tang, Yuwen Yan, Yuxin Wu, Yuya Kusakabe, @zhongzyd, @znah.
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
contrib/ directory for unsupported or
experimental features, including higher level layers
moduleMetaGraphDef which makes it easier to save graphs
with metadataGraphDefs to ensure
compatibilityBUILD files and cleaned up C++
headers*fft, *_matrix_solve)AdjustContrast kernel deprecated, new kernel
AdjustContrastv2 takes and outputs float only.
adjust_contrast now takes all data types.adjust_brightness's delta argument is now
always assumed to be in [0,1] (as is the norm for images in
floating point formats), independent of the data type of the input
image.min and
max inputs any more, casting safety is handled by
saturate_cast, which makes sure over- and underflows are
handled before casting to data types with smaller ranges.IsLegacyScalar and
IsLegacyVector are now gone from
TensorShapeUtils since TensorFlow is scalar strict within
Google (for example, the shape argument to tf.reshape can't
be a scalar anymore). The open source release was already scalar strict,
so outside Google IsScalar and IsVector are
exact replacements.tensorflow/core/public/:
env.h -> ../platform/env.hstatus.h -> ../lib/core/status.htensor.h -> ../framework/tensor.htensor_shape.h ->
../framework/tensor_shape.hpartial_tensor_shape.h ->
../framework/partial_tensor_shape.htensorflow_server.h deletedTensorShape::ShortDebugString has
been renamed to DebugString, and the previous
DebugString behavior is gone (it was needlessly verbose and
produced a confusing empty string for scalars).GraphOptions.skip_common_subexpression_elimination has
been removed. All graph optimizer options are now specified via
GraphOptions.OptimizerOptions.ASSERT_OK / EXPECT_OK macros conflicted
with external projects, so they were renamed TF_ASSERT_OK,
TF_EXPECT_OK. The existing macros are currently maintained
for short-term compatibility but will be removed.nn.rnn and the various
nn.seq2seq methods now return just the final state instead
of the list of all states.tf.scatter_update now no longer guarantees that
lexicographically largest index be used for update when duplicate
entries exist.tf.image.random_crop(image, [height, width]) is now
tf.random_crop(image, [height, width, depth]), and
tf.random_crop works for any rank (not just 3-D images).
The C++ RandomCrop op has been replaced with pure
Python.tf.test.GetTempDir and
tf.test.IsBuiltWithCuda to
tf.test.get_temp_dir and
tf.test.is_built_with_cuda for PEP-8 compatibility.parse_example's interface has changed, the old
interface is accessible in legacy_parse_example (same for
related functions).Variables are not added to the same collection
several times even if a list with duplicates is passed to the
constructor.list member of
AttrValue in constructed GraphDef messages for
empty lists. The serialization of some graphs will change, but the
change is both forwards and backwards compatible. It will break tests
that compare a generated GraphDef to a golden serialized
GraphDef (which is discouraged).This release contains contributions from many people at Google, as well as:
Akiomi Kamakura, Alex Vig, Alexander Rosenberg Johansen, Andre Cruz, Arun Ahuja, Bart Coppens, Bernardo Pires, Carl Vondrick, Cesar Salgado, Chen Yu, Christian Jauvin, Damien Aymeric, Dan Vanderkam, Denny Britz, Dongjoon Hyun, Eren Güven, Erik Erwitt, Fabrizio Milo, G. Hussain Chinoy, Jim Fleming, Joao Felipe Santos, Jonas Meinertz Hansen, Joshi Rekha, Julian Viereck, Keiji Ariyama, Kenton Lee, Krishna Sankar, Kristina Chodorow, Linchao Zhu, Lukas Krecan, Mark Borgerding, Mark Daoust, Moussa Taifi, Nathan Howell, Naveen Sundar Govindarajulu, Nick Sweeting, Niklas Riekenbrauck, Olivier Grisel, Patrick Christ, Povilas Liubauskas, Rainer Wasserfuhr, Romain Thouvenin, Sagan Bolliger, Sam Abrahams, Taehoon Kim, Timothy J Laurent, Vlad Zavidovych, Yangqing Jia, Yi-Lin Juang, Yuxin Wu, Zachary Lipton, Zero Chen, Alan Wu, @brchiu, @emmjaykay, @jalammar, @Mandar-Shinde, @nsipplswezey, @ninotoshi, @panmari, @prolearner and @rizzomichaelg.
We are also grateful to all who filed issues or helped resolve them, asked and answered questions, and were part of inspiring discussions.
Python 3.3+ support via changes to python codebase and ability to specify python version via ./configure.
Some improvements to GPU performance and memory usage: convnet benchmarks roughly equivalent with native cudnn v2 performance. Improvements mostly due to moving to 32-bit indices, faster shuffling kernels. More improvements to come in later releases.
Lots of fixes to documentation and tutorials, many contributed by the public.
271 closed issues on github issues.
tf.nn.fixed_unigram_candidate_sampler changed its
default 'distortion' attribute from 0.0 to 1.0. This was a bug in the
original release that is now fixed.
added DeterministicRandomTestTool to migration_utils.py. This is useful when you are migrating from TF 1.x to TF2 and need to make sure your computation is still happening correctly along the way. See the validating correctness migration guide for more info.
Initial release of TensorFlow.