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4 years ago OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version.
If you change your mind, just re-open this PR and I'll resolve any conflicts on it.
Bumps tensorflow from 1.12.0 to 1.15.0.
Release notes
*Sourced from [tensorflow's releases](https://github.com/tensorflow/tensorflow/releases).* > ## TensorFlow 1.15.0 > # Release 1.15.0 > 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. > > ## Major Features and Improvements > * As [announced](https://groups.google.com/a/tensorflow.org/forum/#!topic/developers/iRCt5m4qUz0), `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. > * TensorFlow 1.15 contains a complete implementation of the 2.0 API in its `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. > * `EagerTensor` now supports numpy buffer interface for tensors. > * Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()` for enabling/disabling v2 control flow. > * Enable v2 control flow as part of `tf.enable_v2_behavior()` and `TF2_BEHAVIOR=1`. > * AutoGraph translates Python control flow into TensorFlow expressions, allowing users to write regular Python inside `tf.function`-decorated functions. AutoGraph is also applied in functions used with `tf.data`, `tf.distribute` and `tf.keras` APIS. > * Adds `enable_tensor_equality()`, which switches the behavior such that: > * Tensors are no longer hashable. > * Tensors can be compared with `==` and `!=`, yielding a Boolean Tensor with element-wise comparison results. This will be the default behavior in 2.0. > * Auto Mixed-Precision graph optimizer simplifies converting models to `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()`. > * Add environment variable `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. > * TensorRT > * Migrate TensorRT conversion sources from contrib to compiler directory in preparation for TF 2.0. > * Add additional, user friendly `TrtGraphConverter` API for TensorRT conversion. > * Expand support for TensorFlow operators in TensorRT conversion (e.g. > `Gather`, `Slice`, `Pack`, `Unpack`, `ArgMin`, `ArgMax`,`DepthSpaceShuffle`). > * Support TensorFlow operator `CombinedNonMaxSuppression` in TensorRT conversion which > significantly accelerates object detection models. > > ## Breaking Changes > * Tensorflow code now produces 2 different pip packages: `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. > * TensorFlow 1.15 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow. > * Deprecated the use of `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. > * Layers now default to `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. > * Some `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). > > ## Bug Fixes and Other Changes > * `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`. > * Fix tests in canned estimators. > * Expose Head as public API. > * Fixes critical bugs that help with `DenseFeatures` usability in TF2 > * `tf.data`: > * Promoting `unbatch` from experimental to core API. > * Adding support for datasets as inputs to `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`. > * Saving a Keras Model using `tf.saved_model.save` now saves the list of variables, trainable variables, regularization losses, and the call function. > * Deprecated `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. > * Add an `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. > ... (truncated)Changelog
*Sourced from [tensorflow's changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md).* > # Release 1.15.0 > 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. > > ## Major Features and Improvements > * As [announced](https://groups.google.com/a/tensorflow.org/forum/#!topic/developers/iRCt5m4qUz0), `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. > * TensorFlow 1.15 contains a complete implementation of the 2.0 API in its `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. > * EagerTensor now supports numpy buffer interface for tensors. > * Add toggles `tf.enable_control_flow_v2()` and `tf.disable_control_flow_v2()` for enabling/disabling v2 control flow. > * Enable v2 control flow as part of `tf.enable_v2_behavior()` and `TF2_BEHAVIOR=1`. > * AutoGraph translates Python control flow into TensorFlow expressions, allowing users to write regular Python inside `tf.function`-decorated functions. AutoGraph is also applied in functions used with `tf.data`, `tf.distribute` and `tf.keras` APIS. > * Adds `enable_tensor_equality()`, which switches the behavior such that: > * Tensors are no longer hashable. > * Tensors can be compared with `==` and `!=`, yielding a Boolean Tensor with element-wise comparison results. This will be the default behavior in 2.0. > > ## Breaking Changes > * Tensorflow code now produces 2 different pip packages: `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. > * TensorFlow 1.15 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow. > * Deprecated the use of `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. > * Layers now default to `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. > * Some `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). > > ## Bug Fixes and Other Changes > * `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`. > * Fix tests in canned estimators. > * Expose Head as public API. > * Fixes critical bugs that help with `DenseFeatures` usability in TF2 > * `tf.data`: > * Promoting `unbatch` from experimental to core API. > * Adding support for datasets as inputs to `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`. > * Saving a Keras Model using `tf.saved_model.save` now saves the list of variables, trainable variables, regularization losses, and the call function. > * Deprecated `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. > * Add an `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. > * Enable the Keras compile API `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. > * Raise error if `batch_size` argument is used when input is dataset/generator/keras sequence. > * `tf.lite` > * Add `GATHER` support to NN API delegate. > * tflite object detection script has a debug mode. > * Add delegate support for `QUANTIZE`. > * Added evaluation script for COCO minival. > * Add delegate support for `QUANTIZED_16BIT_LSTM`. > * Converts hardswish subgraphs into atomic ops. > * Add support for defaulting the value of `cycle_length` argument of `tf.data.Dataset.interleave` to the number of schedulable CPU cores. > ... (truncated)Commits
- [`590d6ee`](https://github.com/tensorflow/tensorflow/commit/590d6eef7e91a6a7392c8ffffb7b58f2e0c8bc6b) Merge pull request [#31861](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/31861) from tensorflow-jenkins/relnotes-1.15.0rc0-16184 - [`b27ac43`](https://github.com/tensorflow/tensorflow/commit/b27ac431aa37cfeb9d5c35cc50081cdb6763a40e) Update RELEASE.md - [`07bf663`](https://github.com/tensorflow/tensorflow/commit/07bf6634f602757ef0b2106a92c519d09e80157e) Merge pull request [#33213](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/33213) from Intel-tensorflow/mkl-dnn-0.20.6 - [`46f50ff`](https://github.com/tensorflow/tensorflow/commit/46f50ff8a0f099269ac29573bc6ac09d1bc6cab7) Merge pull request [#33262](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/33262) from tensorflow/ggadde-1-15-cp2 - [`49c154e`](https://github.com/tensorflow/tensorflow/commit/49c154e17e9fdfe008f8b0b929d1a729e5939c51) Merge pull request [#33263](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/33263) from tensorflow/ggadde-1-15-final-version - [`a16adeb`](https://github.com/tensorflow/tensorflow/commit/a16adeb793b587a08958a72cbbf0d338e063a042) Update TensorFlow version to 1.15.0 in preparation for final relase. - [`8d71a87`](https://github.com/tensorflow/tensorflow/commit/8d71a87b0e3de6d07588f9139660a77271d12498) Add saving of loaded/trained compatibility models in test and fix a compatibi... - [`8c48aff`](https://github.com/tensorflow/tensorflow/commit/8c48affdf8ec0e5a9c5252f88e63aa5b97daf239) [Intel Mkl] Upgrading MKL-DNN to 0.20.6 to fix SGEMM regression - [`38ea9bb`](https://github.com/tensorflow/tensorflow/commit/38ea9bbfea423eb968fcc70bc454471277c9537c) Merge pull request [#33120](https://github-redirect.dependabot.com/tensorflow/tensorflow/issues/33120) from tensorflow/perf - [`a8ef0f5`](https://github.com/tensorflow/tensorflow/commit/a8ef0f5d3bff3fe6f46b821832a4e9073dd7c01d) Automated rollback of commit db7e43192d405973c6c50f6e60e831a198bb4a49 - Additional commits viewable in [compare view](https://github.com/tensorflow/tensorflow/compare/v1.12.0...v1.15.0)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
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