Merge pull request #30544 from tensorflow:mm-fix-release-notes
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RELEASE.md
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RELEASE.md
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# Release 1.14.0
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## Major Features and Improvements
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* This is the first 1.x release containing the compat.v2 module. This module
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is required to allow libraries to publish code which works in both 1.x and
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2.x. After this release, no backwards incompatible changes are allowed in
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the 2.0 Python API.
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* Turn on MKL-DNN contraction kernels by default. MKL-DNN dynamically
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dispatches the best kernel implementation based on CPU vector architecture.
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To disable them, build with --define=tensorflow_mkldnn_contraction_kernel=0.
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## Behavioral changes
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* Set default loss reduction as `AUTO` for improving reliability of loss
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scaling with distribution strategy and custom training loops. `AUTO`
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indicates that the reduction option will be determined by the usage context.
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For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. When used in
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distribution strategy scope, outside of built-in training loops such as
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`tf.keras` `compile` and `fit`, we expect reduction value to be 'None' or
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'SUM'. Using other values will raise an error.
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* Wraps losses passed to the `compile` API (strings and v1 losses) which are
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not instances of v2 `Loss` class in `LossWrapper` class. => All losses will
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now use `SUM_OVER_BATCH_SIZE` reduction as default.
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* Disable `run_eagerly` and distribution strategy if there are symbolic
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tensors added to the model using `add_metric` or `add_loss`.
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* tf.linspace(start, stop, num) now always uses "stop" as last value (for
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num > 1)
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* `ResourceVariable` and `Variable` no longer accepts `constraint` in the
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constructor, nor expose it as a @property.
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* The behavior of tf.gather is now correct when axis=None and batch_dims<0.
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* Only create a GCS directory object if the object does not already exist.
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* In `map_vectorization` optimization, reduce the degree of parallelism in the
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vectorized map node.
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* Bug fix: loss and gradients should now more reliably be correctly scaled
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w.r.t. the global batch size when using a tf.distribute.Strategy.
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* Updating cosine similarity loss - removed the negate sign from cosine
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similarity.
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* DType is no longer convertible to an int. Use dtype.as_datatype_enum instead
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of int(dtype) to get the same result.
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* Changed default for gradient accumulation for TPU embeddings to true.
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* Callbacks now log values in eager mode when a deferred build model is used.
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* Transitive dependencies on :pooling_ops were removed. Some users may need to
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add explicit dependencies on :pooling_ops if they reference the operators
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from that library.
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## Bug Fixes and Other Changes
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* Documentation
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* Deprecations and Symbol renames.
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* Remove unused StringViewVariantWrapper
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* Delete unused Fingerprint64Map op registration
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* SignatureDef util functions have been deprecated.
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* Renamed tf.image functions to remove duplicate "image" where it is
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redundant.
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* tf.keras.experimental.export renamed to
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tf.keras.experimental.export_saved_model
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* Standardize the LayerNormalization API by replacing the args `norm_axis`
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and `params_axis` with `axis`.
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* Tensor::UnsafeCopyFromInternal deprecated in favor Tensor::BitcastFrom
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* Keras & Python API
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* Add v2 module aliases for:
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* tf.initializers => tf.keras.initializers
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* tf.losses => tf.keras.losses & tf.metrics => tf.keras.metrics
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* tf.optimizers => tf.keras.optimizers
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* Add tf.keras.layers.AbstractRNNCell as the preferred implementation of
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RNN cell for TF v2. User can use it to implement RNN cell with custom
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behavior.
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* Adding `clear_losses` API to be able to clear losses at the end of
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forward pass in a custom training loop in eager.
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* Add support for passing list of lists to the `metrics` param in Keras
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`compile`.
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* Added top-k to precision and recall to keras metrics.
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* Adding public APIs for `cumsum` and `cumprod` keras backend functions.
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* Fix: model.add_loss(symbolic_tensor) should work in ambient eager.
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* Add name argument to tf.string_split and tf.strings_split
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* Minor change to SavedModels exported from Keras using
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tf.keras.experimental.export. (SignatureDef key for evaluation mode is
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now "eval" instead of "test"). This will be reverted back to "test" in
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the near future.
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* Updates binary cross entropy logic in Keras when input is probabilities.
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Instead of converting probabilities to logits, we are using the cross
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entropy formula for probabilities.
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* Raw TensorFlow functions can now be used in conjunction with the Keras
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Functional API during model creation. This obviates the need for users
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to create Lambda layers in most cases when using the Functional API.
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Like Lambda layers, TensorFlow functions that result in Variable
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creation or assign ops are not supported.
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* Keras training and validation curves are shown on the same plot.
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* Introduce `dynamic` constructor argument in Layer and Model, which
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should be set to True when using imperative control flow in the `call`
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method.
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* Removing of dtype in the constructor of initializers and partition_info
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in call.
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* New ops and improved op functionality
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* Add OpKernels for some stateless maps
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* Add v2 APIs for AUCCurve and AUCSummationMethod
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enums. #tf-metrics-convergence
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* Add tf.math.nextafter op.
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* Add CompositeTensor base class.
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* Add tf.linalg.tridiagonal_solve op.
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* Add opkernel templates for common table operations.
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* Added support for TFLite in TensorFlow 2.0.
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* Adds summary trace API for collecting graph and profile information.
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* Add batch_dims argument to tf.gather.
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* Add support for `add_metric` in the graph function mode.
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* Add C++ Gradient for BatchMatMulV2.
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* Added tf.random.binomial
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* Added gradient for SparseToDense op.
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* Add legacy string flat hash map op kernels
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* Add a ragged size op and register it to the op dispatcher
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* Add broadcasting support to tf.matmul.
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* Add ellipsis (...) support for tf.einsum()
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* Added LinearOperator.adjoint and LinearOperator.H (alias).
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* Added GPU implementation of tf.linalg.tridiagonal_solve.
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* Added strings.byte_split
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* Add RaggedTensor.placeholder()
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* Add a new "result_type" parameter to tf.strings.split
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* `add_update` can now be passed a zero-arg callable in order to support
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turning off the update when setting `trainable=False` on a Layer of a
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Model compiled with `run_eagerly=True`.
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* Add variant wrapper for absl::string_view
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* Add expand_composites argument to all nest.* methods.
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* Add pfor converter for Squeeze.
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* Bug fix for tf.tile gradient
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* Expose CriticalSection in core as tf.CriticalSection.
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* Update Fingerprint64Map to use aliases
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* ResourceVariable support for gather_nd.
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* ResourceVariable's gather op supports batch dimensions.
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* Variadic reduce is supported on CPU
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* Extend tf.function with basic support for CompositeTensors arguments
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(such as SparseTensor and RaggedTensor).
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* Add templates and interfaces for creating lookup tables
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* Post-training quantization tool supports quantizing weights shared by
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multiple operations. The models made with versions of this tool will use
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INT8 types for weights and will only be executable interpreters from
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this version onwards.
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* Malformed gif images could result in an access out of bounds in the
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color palette of the frame. This has been fixed now
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* image.resize now considers proper pixel centers and has new kernels
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(incl. anti-aliasing).
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* Performance
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* Turn on MKL-DNN contraction kernels by default. MKL-DNN dynamically
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dispatches the best kernel implementation based on CPU vector
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architecture. To disable them, build with
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--define=tensorflow_mkldnn_contraction_kernel=0.
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* Support for multi-host ncclAllReduce in Distribution Strategy.
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* Expose a flag that allows the number of threads to vary across Python
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benchmarks.
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* TensorFlow 2.0 Development
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* Add v2 sparse categorical crossentropy metric.
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* Allow non-Tensors through v2 losses.
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* Add UnifiedGRU as the new GRU implementation for tf2.0. Change the
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default recurrent activation function for GRU from 'hard_sigmoid' to
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'sigmoid', and 'reset_after' to True in 2.0. Historically recurrent
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activation is 'hard_sigmoid' since it is fast than 'sigmoid'. With new
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unified backend between CPU and GPU mode, since the CuDNN kernel is
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using sigmoid, we change the default for CPU mode to sigmoid as well.
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With that, the default GRU will be compatible with both CPU and GPU
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kernel. This will enable user with GPU to use CuDNN kernel by default
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and get a 10x performance boost in training. Note that this is
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checkpoint breaking change. If user want to use their 1.x pre-trained
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checkpoint, please construct the layer with
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GRU(recurrent_activation='hard_sigmoid', reset_after=False) to fallback
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to 1.x behavior.
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* TF 2.0 - Update metric name to always reflect what the user has given in
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compile. Affects following cases 1. When name is given as
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'accuracy'/'crossentropy' 2. When an aliased function name is used eg.
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'mse' 3. Removing the `weighted` prefix from weighted metric names.
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* Begin adding Go wrapper for C Eager API
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* image.resize in 2.0 now supports gradients for the new resize kernels.
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* removed tf.string_split from v2 API
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* Expose tf.contrib.proto.* ops in tf.io (they will exist in TF2)
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* "Updates the TFLiteConverter API in 2.0. Changes from_concrete_function
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to from_concrete_functions."
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* Enable tf.distribute.experimental.MultiWorkerMirroredStrategy working in
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eager mode.
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* Support both binary and -1/1 label input in v2 hinge and squared hinge
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losses.
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* TensorFlow Lite
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* "Adds support for tflite_convert in 2.0."
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* "Remove lite.OpHint, lite.experimental, and lite.constant from 2.0 API."
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* tf.contrib
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* Added Neural Turing Implementation as described in
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https://arxiv.org/abs/1807.08518.
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* Remove tf.contrib.timeseries dependency on TF distributions.
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* tf.data
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* Add num_parallel_reads and passing in a Dataset containing filenames
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into TextLineDataset and FixedLengthRecordDataset
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* Going forward we operate in TF 2.0, this change is part of the effort to
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slowly converting XYZDataset to DatasetV2 type which is the official
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version going to be used in TF 2.0 and motivated by some compatibility
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issue found, _BigtableXYZDataset (of type DatasetV2) does not implement
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the _as_variant_tensor() of DatasetV1, when moving contrib.bigtable to
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tensorflow_io. Converting into DatasetV2 removes the overheads to
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maintain V1 while we are moving into TF 2.0.
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* Add dataset ops to the graph (or create kernels in Eager execution)
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during the python Dataset object creation instead doing it during
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Iterator creation time.
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* Add support for TensorArrays to tf.data Dataset.
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* Switching tf.data functions to use `defun`, providing an escape hatch to
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continue using the legacy `Defun`.
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* Toolchains
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* CUDNN_INSTALL_PATH, TENSORRT_INSTALL_PATH, NCCL_INSTALL_PATH,
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NCCL_HDR_PATH are deprecated. Use TF_CUDA_PATHS instead which supports a
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comma-separated list of base paths that are searched to find CUDA
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libraries and headers.
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* TF code now resides in `tensorflow_core` and `tensorflow` is just a
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virtual pip package. No code changes are needed for projects using
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TensorFlow, the change is transparent
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* XLA
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* XLA HLO graphs can be inspected with interactive_graphviz tool now.
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* Estimator
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* Use tf.compat.v1.estimator.inputs instead of tf.estimator.inputs
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* Replace contrib references with tf.estimator.experimental.* for apis in
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early_stopping.py
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## Thanks to our Contributors
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This release contains contributions from many people at Google, as well as:
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1e100, 4d55397500, a6802739, abenmao, Adam Weiss, Ag Ramesh, Alan Du, Albin Joy,
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Alex, Aman Patel, Amit, Amit Kumar Jaiswal, Amit Srivastava, Andreas Eberle,
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Andy Craze, Anthony Platanios, Armen Poghosov, armenpoghosov, arp95, Arpit Shah,
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Ashwin Ramaswami, Aurelien Geron, AuréLien Geron, aweers, awesomealex1, Ayush
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Agrawal, Ben Barsdell, Bharat Raghunathan, Bhavani Subramanian, blairhan,
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BléNesi Attila, Brandon Carter, candy.dc, Chao Liu, chenchc, chie8842, Christian
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Hansen, Christian Sigg, Clayne Robison, crafet, csukuangfj, ctiijima, Dan
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Jarvis, Dan Lazewatsky, Daniel Ingram, Daniel Salvadori, Dave Airlie, David
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Norman, Dayananda V, Dayananda-V, delock, Denis Khalikov, Deven Desai, Dheeraj
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Rajaram Reddy, dmitrievanthony, Donovan Ong, Drew Szurko, Duncan Riach, Dustin
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Neighly, Edward Forgacs, EFanZh, Fei Hu, Felix Lemke, Filip Matzner, fo40225,
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frreiss, Gautam, gehring, Geoffrey Irving, Grzegorz George Pawelczak, Grzegorz
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Pawelczak, Gyoung-Yoon Ryoo, HanGuo97, Hanton Yang, Hari Shankar, hehongliang,
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Heungsub Lee, Hoeseong Kim, I-Hong Jhuo, Ilango R, Innovimax, Irene Dea, Jacky
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Ko, Jakub Lipinski, Jason Zaman, jcf94, Jeffrey Poznanovic, Jens Elofsson,
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Jeroen BéDorf, Jia Qingtong, Jiankang, Joe Q, Joe Quadrino, Joeran Beel, Jonas
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Rauber, Jonathan, Jonathan Kyl, Joppe Geluykens, Joseph Friedman, jtressle, jwu,
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K Yasaswi Sri Chandra Gandhi, K. Hodges, Kaixi Hou, Karl Lessard, Karl
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Weinmeister, Karthik Muthuraman, Kashif Rasul, KDR, Keno Fischer, Kevin Mader,
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kjopek, Koan-Sin Tan, kouml, ktaebum, Lakshay Tokas, Laurent Le Brun, Letian
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Kang, Li, Guizi, Loo Rong Jie, Lucas Hendren, Lukas Geiger, Luke Han, luxupu,
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Ma, Guokai, Mahmoud Abuzaina, Mandar Deshpande, manhyuk, Marco Gaido, Marek
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Drozdowski, Mark Collier, Mark Ryan, mars20, Mateusz Chudyk, Matt Conley,
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MattConley, mbhuiyan, mdfaijul, Melissa Grueter, Michael KäUfl, MickaëL
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Schoentgen, Miguel Morin, Mihail Salnikov, Mike Arpaia, Mike Holcomb, monklof,
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Moses Marin, Mshr-H, nammbash, Natalia Gimelshein, Nayana-Ibm, neargye, Neeraj
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Pradhan, Nehal J Wani, Nick, Niels Ole Salscheider, Niranjan Hasabnis, nlewycky,
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Nuka-137, Nutti, olicht, P Sudeepam, Palmer Lao, Pan Daoxin, Pariksheet Pinjari,
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Pavel Samolysov, PENGWA, Pooya Davoodi, R S Nikhil Krishna, Rohit Gupta, Roman
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Soldatow, rthadur, Ruizhe, Ryan Jiang, Samantha Andow, Sami Kama, Sana-Damani,
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Saurabh Deoras, sdamani, seanshpark, Sebastien Iooss, Serv-Inc, Shahzad Lone,
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Shashank Gupta, Shashi, shashvat, shashvatshahi1998, Siju, Siju Samuel,
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Snease-Abq, Spencer Schaber, sremedios, srinivasan.narayanamoorthy, Steve Lang,
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Steve Nesae, Sumesh Udayakumaran, Supriya Rao, Taylor Jakobson, Taylor Thornton,
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Ted Chang, ThisIsPIRI, Thomas Deegan, Thomas Hagebols, tianyapiaozi, Tim Zaman,
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tomguluson92, Tongxuan Liu, TungJerry, v1incent, Vagif, vcarpani, Vikram Tiwari,
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Vishwak Srinivasan, Vitor-Alves, wangsiyu, wateryzephyr, WeberXie, WeijieSun,
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Wen-Heng (Jack) Chung, wenxizhu, Will Battel, William D. Irons, wyzhao, Xin,
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Yasuhiro Matsumoto, ymodak, Yong Tang, Younes Khoudli, Yuan Lin, Yves-Noel
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Weweler, Zantares, zjjott, 卜居, 王振华 (Wang Zhenhua), 黄鑫
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# Release 1.12.3
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## Bug Fixes and Other Changes
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* Updates `png_archive` dependency to 1.6.37 to not be affected by
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CVE-2019-7317, CVE-2018-13785, and CVE-2018-14048.
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* Updates `sqlite` depenency to 3.28.0 to not be affected by CVE-2018-20506,
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CVE-2018-20346, and CVE-2018-20505.
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# Release 1.12.2
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## Bug Fixes and Other Changes
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