tensorflow/python/training
have been moved to tensorflow/python/tracking
and tensorflow/python/checkpoint
. Please update your imports accordingly, the old files will be removed in Release 2.11.tf.keras.initializers
. Keras initializers will now use stateless random ops to generate random numbers.seed=None
), a random seed will be created and assigned at initializer creation (different initializer instances get different seeds).tf.lite
:
tf.keras
:
tf.keras.models.experimental.SharpnessAwareMinimization
. This class implements the sharpness-aware minimization technique, which boosts model performance on various tasks, e.g., ResNet on image classification.EinsumDense
layer moved from experimental to core. Its import path moved from tf.keras.layers.experimental.EinsumDense
to tf.keras.layers.EinsumDense
.tf.keras.utils.audio_dataset_from_directory
utility to easily generate audio classification datasets from directories of .wav
files.subset="both"
support in tf.keras.utils.image_dataset_from_directory
, tf.keras.utils.text_dataset_from_directory
, and audio_dataset_from_directory
, to be used with the validation_split
argument, for returning both dataset splits at once, as a tuple.tf.keras.utils.split_dataset
utility to split a Dataset
object or a list/tuple of arrays into two Dataset
objects (e.g. train/test).BackupAndRestore
callback for handling distributed training failures & restarts. The training state can now be restored at the exact epoch and step at which it was previously saved before failing.tf.keras.dtensor.experimental.optimizers.AdamW
. This optimizer is similar as the existing keras.optimizers.experimental.AdamW
, and works in the DTensor training use case.tf.data
:
inject_prefetch
, to tf.data.experimental.OptimizationOptions
. If it is set to True
, tf.data will now automatically add a prefetch
transformation to datasets that end in synchronous transformations. This enables data generation to be overlapped with data consumption. This may cause a small increase in memory usage due to buffering. To enable this behavior, set inject_prefetch=True
in tf.data.experimental.OptimizationOptions
.tf.data.Options.autotune.autotune_algorithm
: STAGE_BASED. If the autotune algorithm is set to STAGE_BASED, then it runs a new algorithm that can get the same performance with lower CPU/memory usage.tf.distribute
:
tf.distribute.experimental.PreemptionCheckpointHandler
to handle worker preemption/maintenance and cluster-wise consistent error reporting for tf.distribute.MultiWorkerMirroredStrategy
. Specifically, for the type of interruption with advance notice, it automatically saves a checkpoint, exits the program without raising an unrecoverable error, and restores the progress when training restarts.tf.math
:
tf.math.approx_max_k
and tf.math.approx_min_k
which are the optimized alternatives to tf.math.top_k
on TPU. The performance difference range from 8 to 100 times depending on the size of k. When running on CPU and GPU, a non-optimized XLA kernel is used.tf.vectorized_map
:
warn
. This parameter controls whether or not warnings will be printed when operations in the provided fn
fall back to a while loop.XLA:
New argument experimental_device_ordinal
in LogicalDeviceConfiguration
to control the order of logical devices. (GPU only)
tf.keras
:
tf.keras.callbacks.TensorBoard
callback, so that summaries logged automatically for model weights now include either a /histogram
or /image
suffix in their tag names, in order to prevent tag name collisions across summary types.When running on GPU (with cuDNN version 7.6.3 or later), tf.nn.depthwise_conv2d
backprop to filter
(and therefore also tf.keras.layers.DepthwiseConv2D
) now operate deterministically (and tf.errors.UnimplementedError
is no longer thrown) when op-determinism has been enabled via tf.config.experimental.enable_op_determinism
. This closes issue 47174.
tf.random
tf.random.experimental.stateless_shuffle
, a stateless version of tf.random.shuffle
.tensorflow::Code
and tensorflow::Status
will become aliases of respectively absl::StatusCode
and absl::Status
in some future release.tensorflow::OkStatus()
instead of tensorflow::Status::OK()
.Status
objects from tensorflow::error::Code
.tensorflow::errors::Code
fields. Accessing tensorflow::error::Code
fields is fine.tensorflow::errors:InvalidArgument
to create status using an error code without accessing it.tensorflow::errors::IsInvalidArgument
if needed.static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT)
or static_cast<int>(code)
for comparisons.tensorflow::StatusOr
will also become in the future alias to absl::StatusOr
, so use StatusOr::value
instead of StatusOr::ConsumeValueOrDie
.This release contains contributions from many people at Google, as well as:
, , , , ,
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.
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.
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.
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 tutorial 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 overridden 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
:
ShardedVariable
s (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
This releases introduces several vulnerability fixes:
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 releases introduces several vulnerability fixes:
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 releases introduces several vulnerability fixes:
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 issuetf.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 TRTEngineOp
s.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 punctuation.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 number). 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.Dataset
tf.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_mean
tf.math.segment_prod
tf.math.segment_sum
tf.math.unsorted_segment_mean
tf.math.unsorted_segment_prod
tf.math.unsorted_segment_sum
tf.math.unsorted_segment_sqrt
tf.nn.ctc_loss
(resolved, possibly in prior release, and confirmed with tests)tf.nn.sparse_softmax_crossentropy_with_logits
tf.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 FakeQuantWithMinMaxVarsPerChannelGradient
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++tf.compat.v1.nn.fused_batch_norm
backprop to offset
when is_training=False
tf.image.adjust_contrast
forwardtf.image.resize
with method=ResizeMethod.NEAREST
backproptf.linalg.svd
tf.math.bincount
tf.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.DebugNumericSummaryV2
tf.timestamp
. Throws FailedPrecondition
tf.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 deterministic 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.QuantizationDebugger
tf.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_sum
tf.math.segment_prod
tf.math.segment_mean
tf.math.unsorted_segment_sum
tf.math.unsorted_segment_prod
tf.math.unsorted_segment_sqrt
tf.math.unsorted_segment_mean
tf.gather
backproptf.convert_to_tensor
when fed with (sparse) tf.IndexedSlices
tf.nn.sparse_softmax_crossentropy_with_logits
tf.nn.ctc_loss
(resolved, possibly in prior release, and confirmed with tests)tf.data.Dataset
tf.scatter_nd
and other related scatter functions, such as tf.tensor_scatter_nd_update
TF_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=False
tf.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.DebugNumericSummaryV2
tf.Variable.scatter_add
(and other scatter methods, both on ref and resource variables)tf.linalg.svd
tf.nn.dilation2d
gradienttf.nn.max_pool_with_argmax
gradienttf.timestamp
. Throws FailedPrecondition
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++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_stats
tf.data.experimental.StatsAggregator
tf.data.experimental.StatsOptions.*
tf.data.experimental.bytes_produced_stats
tf.data.experimental.latency_stats
tf.data.experimental.MapVectorizationOptions.*
tf.data.experimental.OptimizationOptions.filter_with_random_uniform_fusion
tf.data.experimental.OptimizationOptions.hoist_random_uniform
tf.data.experimental.OptimizationOptions.map_vectorization
* tf.data.experimental.OptimizationOptions.reorder_data_discarding_ops
tf.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.addDelegate
setNumThreads
- Use Interpreter.Options.setNumThreads
tf.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 RaggedTensor
s (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 RaggedTensor
s (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 RaggedTensor
s (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 RaggedTensor
s (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 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' to
tf.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)SavedModel
s 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_TString
s.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.TensorSpec
s 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_scale
instead of opt.loss_scale()
.should_cast_variables
has been removed from tf.keras.mixed_precision.experimental.Policy
tf.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_policy
Layer.call
, AutoCastVariable
s will no longer be casted within MirroredStrategy.run
or ReplicaContext.merge_call
. This is because a thread local variable is used to determine whether AutoCastVariable
s 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
, AutoCastVariable
s 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 .value
s 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 SparseTensor
s (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_multiscale
Optimizer.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.LossScaleOptimizer
tf.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' or
TfLiteGpuDelegateOptionsV2::is_precision_loss_allowed`.DynamicBuffer::AddJoinedString()
will now add a separator if the first string to be joined is empty.TensorRT
session_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
operationstf.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.EagerTensor
s 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
, Rescaling
RandomCrop
, RandomFlip
, RandomTranslation
, RandomRotation
, RandomHeight
, RandomWidth
, RandomZoom
, RandomContrast
TextVectorization
layer, which handles string tokenization, n-gram generation, and token encodingTextVectorization
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 argmax
This 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.MultiWorkerMirroredStrategy
NCCL
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_uniform
random_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_poisson
tf.random.stateless_uniform
now supports unbounded sampling of int
types.tf.linalg.LinearOperatorTridiag
.LinearOperatorBlockLowerTriangular
tf.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 NaN
s.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.Optimizer
s. 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 = True
batch()
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_metrics
tf.compat.v1.estimator.inputs
instead of tf.estimator.inputs
tf.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.tan
tf.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.BaselineEstimator
tf.contrib.estimator.DNNLinearCombinedEstimator
with tf.estimator.DNNLinearCombinedEstimator
tf.contrib.estimator.DNNEstimator
with tf.estimator.DNNEstimator
tf.contrib.estimator.LinearEstimator
with tf.estimator.LinearEstimator
tf.contrib.estimator.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.Variable
s.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.BaselineEstimator
tf.contrib.estimator.DNNLinearCombinedEstimator
with tf.estimator.DNNLinearCombinedEstimator
tf.contrib.estimator.DNNEstimator
with tf.estimator.DNNEstimator
tf.contrib.estimator.LinearEstimator
with tf.estimator.LinearEstimator
tf.contrib.estimator.export_all_saved_models
and related should switch to tf.estimator.Estimator.experimental_export_all_saved_models
.regression_head
to 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_hook
This 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.strings
tf.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
:Estimator
s 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_patches
tf.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.tile
tf.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_max
float64
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_length
tf.contrib.tensorrt
that enables native TensorRT in TensorFlow.MaxPoolGradGrad
support for XLAtf.data
:tf.data.Dataset
tf.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.data
tf.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_dataset
tf.contrib.ndlstm
. You can find it externally at https://github.com/tmbarchive/tfndlstm.tf.contrib.bayesflow
to its own repo: tfp
TPUClusterResolver
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.distributions
tf.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 GEMMEstimator
s 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.Tensor
s.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 Tensor
s.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.
DNNClassifier
DNNRegressor
LinearClassifier
LinearRegressor
DNNLinearCombinedClassifier
DNNLinearCombinedRegressor
.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.MultivariateNormalTriL
tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev
replaced by tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale
tf.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/cmake
tf.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.slim
This 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/learn
contrib/linear_optimizer
contrib/tensor_forest
contrib/ctc
half
data typecontrib/
)TENSORFLOW_USE_EIGEN_THREADPOOL
definebool
-strictness: Tensors have to be explicitly compared to None
tf.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 metadataGraphDef
s 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.h
status.h
-> ../lib/core/status.h
tensor.h
-> ../framework/tensor.h
tensor_shape.h
-> ../framework/tensor_shape.h
partial_tensor_shape.h
-> ../framework/partial_tensor_shape.h
tensorflow_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).Variable
s 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.