Bump tensorflow from 1.1 to 1.12.2 in /docker

[dependabot]

Bumps tensorflow from 1.1 to 1.12.2.

Release notes

Sourced from tensorflow's releases.

TensorFlow 1.12.2

Release 1.12.2

Bug Fixes and Other Changes

• Fixes a potential security vulnerability where carefully crafted GIF images can produce a null pointer dereference during decoding

TensorFlow 1.12.0

Release 1.12.0

Major Features and Improvements

• Keras models can now be directly exported to the SavedModel format(tf.contrib.saved_model.save_keras_model()) and used with Tensorflow Serving.
• Keras models now support evaluating with a tf.data.Dataset.
• TensorFlow binaries are built with XLA support linked in by default.
• Ignite Dataset added to contrib/ignite that allows to work with Apache Ignite.

Bug Fixes and Other Changes

• tf.data:
  • tf.data users can now represent, get, and set options of TensorFlow input pipelines using tf.data.Options(), tf.data.Dataset.options(), and tf.data.Dataset.with_options() respectively.
  • New tf.data.Dataset.reduce() API allows users to reduce a finite dataset to a single element using a user-provided reduce function.
  • New 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.
  • All C++ code moves to the tensorflow::data namespace.
  • Add support for num_parallel_calls to tf.data.Dataset.interleave.
• tf.contrib:
  • Remove tf.contrib.linalg. tf.linalg should be used instead.
  • Replace any calls to 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
  • Deprecate, and replace by tf.data.experimental.
• Other:
  • Improved XLA stability and performance.
  • Fix single replica TensorBoard summary stats in Cloud ML Engine.
  • TPUEstimator: Initialize dataset iterators in parallel.
  • Keras on TPU model quality and bug fixes.
  • Instead of jemalloc, revert back to using system malloc since it simplifies build and has comparable performance.
  • Remove integer types from tf.nn.softplus and tf.nn.softsign OpDefs. This is a bugfix; these ops were never meant to support integers.
  • Allow subslicing Tensors with a single dimension.
  • Add option to calculate string length in Unicode characters
  • Add functionality to SubSlice a tensor.
  • Add searchsorted (ie lower/upper_bound) op.
  • Add model explainability to Boosted Trees.
  • Support negative positions for tf.substr
  • There was previously a bug in the bijector_impl where the _reduce_jacobian_det_over_event does not handle scalar ILDJ implementations properly.
  • In tf eager execution, allow re-entering a GradientTape context
  • Add tf_api_version flag. If --define=tf_api_version=2 flag is passed in, then bazel will build TensorFlow API version 2.0. Note that TensorFlow 2.0 is under active development and has no guarantees at this point.
  • Add additional compression options to TfRecordWriter
  • Performance improvements for regex full match operations.
  • Replace tf.GraphKeys.VARIABLES with tf.GraphKeys.GLOBAL_VARIABLES
  • Remove unused dynamic learning rate support.

Thanks to our Contributors

... (truncated)

Changelog

Sourced from tensorflow's changelog.

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, 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

• 6b63465 Merge pull request #27959 from tensorflow/update-release-notes-version
• e967833 Update header on release notes
• cf74798 Merge pull request #27958 from tensorflow/update-release-version
• 7fba173 Update version to 1.12.2
• 332f080 Merge pull request #27878 from tensorflow/windows-cpu
• c9fcc49 Fix windows build for CPU too
• 416b4a3 Merge pull request #27873 from tensorflow/more-bazel-incompatible-flags
• 3ebe165 Add --incompatible_disable_cc_toolchain_label_from_crosstool_proto=false flag
• 5ab9466 Reformat bazel invocation lines
• 446d393 Merge pull request #27870 from tensorflow/bazel-http-archive
• Additional commits viewable in compare view

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[dependabot]

Superseded by #56.