experiments/STT-tensorflow
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Change: 146918929
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Benoit Steiner 2017-02-08 09:25:09 -08:00 committed by TensorFlower Gardener
parent 15ff7b7027
commit 639b4e71f5
103 changed files with 4094 additions and 1462 deletions
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9
README.md
View File

@ -33,10 +33,11 @@ and discussion.**
People who are a little more adventurous can also try our nightly binaries:
* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.12.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.12.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.12.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-0.12.1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-0.12.1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-0.12.1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.12.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.12.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-0.12.1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-0.12.1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.0.0rc1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.0.0rc1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.0.0rc1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.0.0rc1-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.0.0rc1-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.0.0rc1-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.0.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.0.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/))
* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.0.0rc1-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.0.0rc1-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
* [Android](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/lastSuccessfulBuild/artifact/bazel-out/local_linux/bin/tensorflow/examples/android/tensorflow_demo.apk) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/))
* Android: [demo APK](https://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/tensorflow_demo.apk), [native libs](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/native/)
([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-android/))

112
RELEASE.md
View File

@ -1,7 +1,20 @@
# Changes since the last release
# Release 1.0.0
## Major Features and Improvements
* XLA (experimental): initial release of [XLA](https://www.tensorflow.org/versions/master/experimental/xla/), a domain-specific compiler for TensorFlow graphs, that targets CPUs and GPUs.
* TensorFlow Debugger (tfdbg): command-line interface and API.
* New python 3 docker images added.
* Made pip packages pypi compliant. TensorFlow can now be installed by `pip
install tensorflow` command.
* Several python API calls have been changed to resemble NumPy more closely.
* Android: person detection + tracking demo implementing Scalable Object
Detection using Deep Neural Networks.
* New (experimental) [Java API](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/java).
* Add new Android image stylization demo based on "A Learned Representation For Artistic Style", and add YOLO object detector support.
## Breaking Changes to the API
To help you upgrade your existing TensorFlow Python code to match the API changes below, we have prepared a [conversion script](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/compatibility).
* TensorFlow/models have been moved to a separate github repository.
* Division and modulus operators (/, //, %) now match Python (flooring)
semantics. This applies to `tf.div` and `tf.mod` as well. To obtain forced
integer truncation based behaviors you can use `tf.truncatediv`
@ -51,16 +64,93 @@
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.
* Deprecated `tf.concat` operator. Please switch to use `tf.concat_v2` for now.
In the Beta release, we will update `tf.concat` to match argument order of
`tf.concat_v2.
* tf.image.decode_jpeg by default uses the faster DCT method, sacrificing
* `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'.
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.
## Bug Fixes and Other Changes
* New op: `parallel_stack`.
* Introducing common tf io compression options constants for
RecordReader/RecordWriter.
* Add `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.
* Added `index_to_string_table` which returns a lookup table that maps indices to
strings.
* Add `string_to_index_table`, which returns a lookup table that matches strings
to indices.
* Add a `ParallelForWithWorkerId` function.
* Add `string_to_index_table`, which returns a lookup table that matches strings
to indices.
* Support restore session from checkpoint files in v2 in `contrib/session_bundle`.
* Added a tf.contrib.image.rotate function for arbitrary angles.
* Added `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`.
* Added comment about how existing directories are handled by
`recursive_create_dir`.
* Added an op for QR factorizations.
* Divides and mods in Python API now use flooring (Python) semantics.
* Android: pre-built libs are now built nightly.
* Android: cmake/gradle build for TensorFlow Inference library under
`contrib/android/cmake`
* Android: Much more robust Session initialization code.
* Android: TF stats now exposed directly in demo and log when debug mode is
active
* Android: new/better README.md documentation
* saved_model is available as `tf.saved_model`.
* Empty op is now stateful.
* Improve speed of scatter_update on the cpu for ASSIGN operations.
* Change `reduce_join` to treat `reduction_indices` in the same way as other `reduce_` ops.
* Move `TensorForestEstimator` to `contrib/tensor_forest`.
* Enable compiler optimizations by default and allow configuration in configure.
* `tf.divide` now honors the name field.
* Make metrics weight broadcasting more strict.
* Add new queue-like `StagingArea` and new ops: `stage` and `unstage`.
## Thanks to our Contributors
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.
# Release 0.12.0
@ -100,15 +190,15 @@
## Breaking Changes to the API
* `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.
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
intead of a bool. Any callers to this function can be converted to a bool
by adding .ok() to the call.
* The C API type `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`.
* Renamed TF_Port to TF_Output in the C API.
* Renamed `TF_Port` to `TF_Output` in the C API.
* Removes RegisterShape from public API. Use C++ shape function registration instead.
indexing now starts from 1 instead of 0, and `bus_id==0` is used where
previously `BUS_ANY` was used.
@ -143,7 +233,7 @@
`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()
`tf.zeros_initializer` with `tf.zeros_initializer()`
## Bug Fixes and Other Changes

116
configure vendored
View File

@ -41,7 +41,8 @@ function bazel_clean_and_fetch() {
if ! is_windows; then
bazel clean --expunge
fi
bazel fetch "//tensorflow/... -//tensorflow/examples/android/..."
bazel fetch "//tensorflow/... -//tensorflow/contrib/nccl/... \
-//tensorflow/examples/android/..."
}
# Delete any leftover BUILD files from the Makefile build, which would interfere
@ -73,10 +74,77 @@ while true; do
# Retry
done
## Set up MKL related environment settings
if false; then # Disable building with MKL for now
while [ "$TF_NEED_MKL" == "" ]; do
fromuser=""
read -p "Do you wish to build TensorFlow with MKL support? [y/N] " INPUT
fromuser="1"
case $INPUT in
[Yy]* ) echo "MKL support will be enabled for TensorFlow"; TF_NEED_MKL=1;;
[Nn]* ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
"" ) echo "No MKL support will be enabled for TensorFlow"; TF_NEED_MKL=0;;
* ) echo "Invalid selection: " $INPUT;;
esac
done
OSNAME=`uname -s`
if [ "$TF_NEED_MKL" == "1" ]; then # TF_NEED_MKL
DST=`dirname $0`
ARCHIVE_BASENAME=mklml_lnx_2017.0.2.20170110.tgz
GITHUB_RELEASE_TAG=v0.3
MKLURL="https://github.com/01org/mkl-dnn/releases/download/$GITHUB_RELEASE_TAG/$ARCHIVE_BASENAME"
if ! [ -e "$DST/third_party/mkl/$ARCHIVE_BASENAME" ]; then
wget --no-check-certificate -P $DST/third_party/mkl/ $MKLURL
fi
tar -xzf $DST/third_party/mkl/$ARCHIVE_BASENAME -C $DST/third_party/mkl/
extracted_dir_name="${ARCHIVE_BASENAME%.*}"
MKL_INSTALL_PATH=$DST/third_party/mkl/$extracted_dir_name
MKL_INSTALL_PATH=`${PYTHON_BIN_PATH} -c "import os; print(os.path.realpath(os.path.expanduser('${MKL_INSTALL_PATH}')))"`
if [ "$OSNAME" == "Linux" ]; then
# Full MKL configuration
MKL_RT_LIB_PATH="lib/intel64/libmkl_rt.so" #${TF_MKL_EXT}#TODO version?
MKL_RT_OMP_LIB_PATH="../compiler/lib/intel64/libiomp5.so" #TODO VERSION?
# MKL-ML configuration
MKL_ML_LIB_PATH="lib/libmklml_intel.so" #${TF_MKL_EXT}#TODO version?
MKL_ML_OMP_LIB_PATH="lib/libiomp5.so" #TODO VERSION?
elif [ "$OSNAME" == "Darwin" ]; then
echo "Darwin is unsupported yet";
exit 1
fi
if [ -e "$MKL_INSTALL_PATH/${MKL_ML_LIB_PATH}" ]; then
ln -sf $MKL_INSTALL_PATH/${MKL_ML_LIB_PATH} third_party/mkl/
ln -sf $MKL_INSTALL_PATH/${MKL_ML_OMP_LIB_PATH} third_party/mkl/
ln -sf $MKL_INSTALL_PATH/include third_party/mkl/
ln -sf $MKL_INSTALL_PATH/include third_party/eigen3/mkl_include
else
echo "ERROR: $MKL_INSTALL_PATH/${MKL_ML_LIB_PATH} does not exist";
exit 1
fi
if [ -z "$fromuser" ]; then
exit 1
fi
cat > third_party/mkl/mkl.config <<EOF
# MKL_INSTALL_PATH refers to the location of MKL root folder. The MKL header and library
# files can be either in this directory, or under include/ and lib64/
MKL_INSTALL_PATH=$MKL_INSTALL_PATH
EOF
fi # TF_NEED_MKL
################## MKL
fi # Disable building with MKL for now
## Set up architecture-dependent optimization flags.
if [ -z "$CC_OPT_FLAGS" ]; then
default_cc_opt_flags="-march=native"
read -p "Please specify optimization flags to use during compilation [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
read -p "Please specify optimization flags to use during compilation when bazel option "\
"\"--config=opt\" is specified [Default is $default_cc_opt_flags]: " CC_OPT_FLAGS
if [ -z "$CC_OPT_FLAGS" ]; then
CC_OPT_FLAGS=$default_cc_opt_flags
fi
@ -328,46 +396,8 @@ while true; do
if [[ -z "$TF_CUDNN_VERSION" ]]; then
TF_CUDNN_EXT=""
cudnn_lib_path=""
cudnn_alt_lib_path=""
if is_windows; then
cudnn_lib_path="${CUDNN_INSTALL_PATH}/lib/x64/cudnn.lib"
cudnn_alt_lib_path="${CUDNN_INSTALL_PATH}/lib/x64/cudnn.lib"
elif is_linux; then
cudnn_lib_path="${CUDNN_INSTALL_PATH}/lib64/libcudnn.so"
cudnn_alt_lib_path="${CUDNN_INSTALL_PATH}/libcudnn.so"
elif is_macos; then
cudnn_lib_path="${CUDNN_INSTALL_PATH}/lib/libcudnn.dylib"
cudnn_alt_lib_path="${CUDNN_INSTALL_PATH}/libcudnn.dylib"
fi
# Resolve to the SONAME of the symlink. Use readlink without -f
# to resolve exactly once to the SONAME. E.g, libcudnn.so ->
# libcudnn.so.4.
# If the path is not a symlink, readlink will exit with an error code, so
# in that case, we return the path itself.
if [ -f "$cudnn_lib_path" ]; then
REALVAL=`readlink "${cudnn_lib_path}" || echo "${cudnn_lib_path}"`
else
REALVAL=`readlink "${cudnn_alt_lib_path}" || echo "${cudnn_alt_lib_path}"`
fi
# Extract the version of the SONAME, if it was indeed symlinked to
# the SONAME version of the file.
if [[ "$REALVAL" =~ .so[.]+([0-9]*) ]]; then
TF_CUDNN_EXT="."${BASH_REMATCH[1]}
TF_CUDNN_VERSION=${BASH_REMATCH[1]}
echo "libcudnn.so resolves to libcudnn${TF_CUDNN_EXT}"
elif [[ "$REALVAL" =~ ([0-9]*).dylib ]]; then
TF_CUDNN_EXT=${BASH_REMATCH[1]}".dylib"
TF_CUDNN_VERSION=${BASH_REMATCH[1]}
echo "libcudnn.dylib resolves to libcudnn${TF_CUDNN_EXT}"
fi
else
if is_macos; then
TF_CUDNN_EXT=".${TF_CUDNN_VERSION}.dylib"
else
TF_CUDNN_EXT=".$TF_CUDNN_VERSION"
fi
TF_CUDNN_EXT=".$TF_CUDNN_VERSION"
fi
if is_windows; then
@ -377,8 +407,8 @@ while true; do
CUDA_DNN_LIB_PATH="lib64/libcudnn.so${TF_CUDNN_EXT}"
CUDA_DNN_LIB_ALT_PATH="libcudnn.so${TF_CUDNN_EXT}"
elif is_macos; then
CUDA_DNN_LIB_PATH="lib/libcudnn${TF_CUDNN_EXT}"
CUDA_DNN_LIB_ALT_PATH="libcudnn${TF_CUDNN_EXT}"
CUDA_DNN_LIB_PATH="lib/libcudnn${TF_CUDNN_EXT}.dylib"
CUDA_DNN_LIB_ALT_PATH="libcudnn${TF_CUDNN_EXT}.dylib"
fi
if [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_ALT_PATH}" -o -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_PATH}" ]; then

15
tensorflow/BUILD
View File

@ -171,6 +171,7 @@ filegroup(
"//tensorflow/contrib/slim/python/slim/data:all_files",
"//tensorflow/contrib/slim/python/slim/nets:all_files",
"//tensorflow/contrib/solvers:all_files",
"//tensorflow/contrib/sparsemax:all_files",
"//tensorflow/contrib/specs:all_files",
"//tensorflow/contrib/stat_summarizer:all_files",
"//tensorflow/contrib/tensor_forest:all_files",
@ -246,6 +247,20 @@ filegroup(
visibility = [":__subpackages__"],
)
#load(
# "//third_party/mkl:build_defs.bzl",
# "if_mkl",
#)
#filegroup(
# name = "intel_binary_blob",
# data = if_mkl(
# [
# "//third_party/mkl:intel_binary_blob",
# ],
# ),
#)
# -------------------------------------------
# New rules should be added above this target.
# -------------------------------------------

1
tensorflow/contrib/BUILD
View File

@ -47,6 +47,7 @@ py_library(
"//tensorflow/contrib/slim",
"//tensorflow/contrib/slim:nets",
"//tensorflow/contrib/solvers:solvers_py",
"//tensorflow/contrib/sparsemax:sparsemax_py",
"//tensorflow/contrib/specs",
"//tensorflow/contrib/stat_summarizer:stat_summarizer_py",
"//tensorflow/contrib/tensor_forest:init_py",

1
tensorflow/contrib/__init__.py
View File

@ -49,6 +49,7 @@ from tensorflow.contrib import rnn
from tensorflow.contrib import seq2seq
from tensorflow.contrib import slim
from tensorflow.contrib import solvers
from tensorflow.contrib import sparsemax
from tensorflow.contrib import stat_summarizer
from tensorflow.contrib import tensor_forest
from tensorflow.contrib import tensorboard

4
tensorflow/contrib/cmake/CMakeLists.txt
View File

@ -170,7 +170,8 @@ if (tensorflow_ENABLE_GPU)
# add cudnn
include_directories(${CUDNN_HOME})
set(CUDA_LIBRARIES ${CUDA_LIBRARIES} ${CUDNN_HOME}/lib/x64/cudnn.lib)
set(CUDA_LIBRARIES ${CUDA_LIBRARIES} ${CUDA_CUDA_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_CUFFT_LIBRARIES}
${CUDA_curand_LIBRARY} ${CUDA_cupti_LIBRARY} ${CUDNN_HOME}/lib/x64/cudnn.lib)
# create cuda_config.h
FILE(WRITE ${tensorflow_source_dir}/third_party/gpus/cuda/cuda_config.h
@ -179,6 +180,7 @@ if (tensorflow_ENABLE_GPU)
"#define TF_CUDA_CAPABILITIES CudaVersion(\"3.0\"),CudaVersion(\"3.5\"),CudaVersion(\"5.2\")\n"
"#define TF_CUDA_VERSION \"64_80\"\n"
"#define TF_CUDNN_VERSION \"64_5\"\n"
"#define TF_CUDA_TOOLKIT_PATH \"${CUDA_TOOLKIT_ROOT_DIR}\"\n"
"#endif // CUDA_CUDA_CONFIG_H_\n"
)

3
tensorflow/contrib/cmake/tf_cc_ops.cmake
View File

@ -71,7 +71,7 @@ foreach(tf_cc_op_lib_name ${tf_cc_op_lib_names})
COMMAND ${tf_cc_op_lib_name}_gen_cc ${cc_ops_target_dir}/${tf_cc_op_lib_name}.h ${cc_ops_target_dir}/${tf_cc_op_lib_name}.cc ${tensorflow_source_dir}/tensorflow/cc/ops/op_gen_overrides.pbtxt ${cc_ops_include_internal}
DEPENDS ${tf_cc_op_lib_name}_gen_cc create_cc_ops_header_dir
)
list(APPEND tf_cc_ops_generated_files ${cc_ops_target_dir}/${tf_cc_op_lib_name}.h)
list(APPEND tf_cc_ops_generated_files ${cc_ops_target_dir}/${tf_cc_op_lib_name}.cc)
list(APPEND tf_cc_ops_generated_files ${cc_ops_target_dir}/${tf_cc_op_lib_name}_internal.h)
@ -79,6 +79,7 @@ foreach(tf_cc_op_lib_name ${tf_cc_op_lib_names})
endforeach()
########################################################
# tf_cc_ops library
########################################################

3
tensorflow/contrib/cmake/tf_python.cmake
View File

@ -372,6 +372,9 @@ add_python_module("tensorflow/contrib/slim/python/slim/nets")
add_python_module("tensorflow/contrib/solvers")
add_python_module("tensorflow/contrib/solvers/python")
add_python_module("tensorflow/contrib/solvers/python/ops")
add_python_module("tensorflow/contrib/sparsemax")
add_python_module("tensorflow/contrib/sparsemax/python")
add_python_module("tensorflow/contrib/sparsemax/python/ops")
add_python_module("tensorflow/contrib/specs")
add_python_module("tensorflow/contrib/specs/python")
add_python_module("tensorflow/contrib/stat_summarizer")

7
tensorflow/contrib/factorization/python/ops/gmm.py
View File

@ -102,7 +102,12 @@ class GMM(estimator.Estimator):
results = self.evaluate(input_fn=input_fn, batch_size=batch_size,
steps=steps)
return np.sum(results[GMM.SCORES])
def weights(self):
"""Returns the cluster weights."""
return checkpoint_utils.load_variable(
self.model_dir, gmm_ops.GmmAlgorithm.CLUSTERS_WEIGHT)
def clusters(self):
"""Returns cluster centers."""
clusters = checkpoint_utils.load_variable(

7
tensorflow/contrib/factorization/python/ops/gmm_ops.py
View File

@ -92,6 +92,7 @@ def _init_clusters_random(data, num_clusters, random_seed):
class GmmAlgorithm(object):
"""Tensorflow Gaussian mixture model clustering class."""
CLUSTERS_WEIGHT = 'alphas'
CLUSTERS_VARIABLE = 'clusters'
CLUSTERS_COVS_VARIABLE = 'clusters_covs'
@ -187,11 +188,13 @@ class GmmAlgorithm(object):
array_ops.expand_dims(array_ops.diag_part(cov), 0),
[self._num_classes, 1])
self._covs = variables.Variable(
covs, name='clusters_covs', validate_shape=False)
covs, name=self.CLUSTERS_COVS_VARIABLE, validate_shape=False)
# Mixture weights, representing the probability that a randomly
# selected unobservable data (in EM terms) was generated by component k.
self._alpha = variables.Variable(
array_ops.tile([1.0 / self._num_classes], [self._num_classes]))
array_ops.tile([1.0 / self._num_classes], [self._num_classes]),
name=self.CLUSTERS_WEIGHT,
validate_shape=False)
def training_ops(self):
"""Returns the training operation."""

10
tensorflow/contrib/factorization/python/ops/gmm_test.py
View File

@ -109,6 +109,16 @@ class GMMTest(test.TestCase):
np.linalg.inv(covs[assignments[r]])), points[r, :] -
means[assignments[r]])))
return (points, assignments, scores)
def test_weights(self):
"""Tests the shape of the weights."""
gmm = gmm_lib.GMM(self.num_centers,
initial_clusters=self.initial_means,
random_seed=4,
config=run_config.RunConfig(tf_random_seed=2))
gmm.fit(input_fn=self.input_fn(), steps=0)
weights = gmm.weights()
self.assertAllEqual(list(weights.shape), [self.num_centers])
def test_clusters(self):
"""Tests the shape of the clusters."""

1
tensorflow/contrib/learn/BUILD
View File

@ -480,6 +480,7 @@ py_test(
size = "medium",
srcs = ["python/learn/estimators/estimator_test.py"],
srcs_version = "PY2AND3",
tags = ["manual"],
deps = [
":learn",
"//tensorflow/contrib/framework:framework_py",

3
tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
View File

@ -191,6 +191,9 @@ def _dnn_linear_combined_model_fn(features, labels, mode, params, config=None):
if not dnn_feature_columns:
dnn_logits = None
else:
if not dnn_hidden_units:
raise ValueError(
"dnn_hidden_units must be defined when dnn_feature_columns is specified.")
dnn_partitioner = (
partitioned_variables.min_max_variable_partitioner(
max_partitions=num_ps_replicas))

20
tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py
View File

@ -241,6 +241,26 @@ class DNNLinearCombinedClassifierTest(test.TestCase):
dnn_feature_columns=None,
dnn_hidden_units=[3, 3])
def testNoDnnHiddenUnits(self):
def _input_fn():
return {
'age':
constant_op.constant([1]),
'language':
sparse_tensor.SparseTensor(
values=['english'], indices=[[0, 0]], dense_shape=[1, 1])
}, constant_op.constant([[1]])
language = feature_column.sparse_column_with_hash_bucket('language', 100)
age = feature_column.real_valued_column('age')
with self.assertRaisesRegexp(
ValueError,
'dnn_hidden_units must be defined when dnn_feature_columns is specified'):
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
dnn_feature_columns=[age, language])
classifier.fit(input_fn=_input_fn, steps=2)
def testEmbeddingMultiplier(self):
embedding_language = feature_column.embedding_column(
feature_column.sparse_column_with_hash_bucket('language', 10),

4
tensorflow/contrib/learn/python/learn/models.py
View File

@ -274,10 +274,10 @@ def bidirectional_rnn(cell_fw,
output_bw = _reverse_seq(tmp, sequence_length)
# Concat each of the forward/backward outputs
outputs = [
array_ops_.concat_v2([fw, bw], 1) for fw, bw in zip(output_fw, output_bw)
array_ops_.concat([fw, bw], 1) for fw, bw in zip(output_fw, output_bw)
]
return outputs, array_ops_.concat_v2([state_fw, state_bw], 1)
return outputs, array_ops_.concat([state_fw, state_bw], 1)
# End of TensorFlow 0.7

2
tensorflow/contrib/learn/python/learn/ops/embeddings_ops.py
View File

@ -59,7 +59,7 @@ def embedding_lookup(params, ids, name='embedding_lookup'):
ids_flat = array_ops_.reshape(
ids, math_ops.reduce_prod(shape, keep_dims=True))
embeds_flat = nn.embedding_lookup(params, ids_flat, name)
embed_shape = array_ops_.concat_v2([shape, [-1]], 0)
embed_shape = array_ops_.concat([shape, [-1]], 0)
embeds = array_ops_.reshape(embeds_flat, embed_shape)
embeds.set_shape(ids.get_shape().concatenate(params.get_shape()[1:]))
return embeds

1
tensorflow/contrib/losses/python/losses/loss_ops.py
View File

@ -427,7 +427,6 @@ def sparse_softmax_cross_entropy(logits, labels, weights=1.0, scope=None):
with ops.name_scope(scope, "sparse_softmax_cross_entropy_loss",
[logits, labels, weights]) as scope:
labels = array_ops.reshape(labels, shape=[array_ops.shape(labels)[0]])
weights = array_ops.squeeze(weights)
losses = nn.sparse_softmax_cross_entropy_with_logits(labels=labels,
logits=logits,

52
tensorflow/contrib/losses/python/losses/loss_ops_test.py
View File

@ -243,6 +243,34 @@ class SoftmaxCrossEntropyLossTest(test.TestCase):
expected_value = 400.0 * label_smoothing / 3.0
self.assertAlmostEqual(loss.eval(), expected_value, 3)
def testLossWithDynamicallyShapedWeights1D(self):
logits = constant_op.constant([[10.0, 0.0, 0.0],
[0.0, 10.0, 0.0],
[0.0, 0.0, 10.0]])
labels = constant_op.constant([[0, 0, 1],
[1, 0, 0],
[0, 1, 0]])
weights = [2.3, 2.4, 2.5]
weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None])
loss = loss_ops.softmax_cross_entropy(logits, labels, weights_placeholder)
with self.test_session() as sess:
loss = sess.run(loss, {weights_placeholder: weights})
self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3)
def testLossWithDynamicallyShapedWeights2D(self):
logits = constant_op.constant([[10.0, 0.0, 0.0],
[0.0, 10.0, 0.0],
[0.0, 0.0, 10.0]])
labels = constant_op.constant([[0, 0, 1],
[1, 0, 0],
[0, 1, 0]])
weights = [[2.3], [2.4], [2.5]]
weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None, None])
loss = loss_ops.softmax_cross_entropy(logits, labels, weights_placeholder)
with self.test_session() as sess:
loss = sess.run(loss, {weights_placeholder: weights})
self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3)
class SparseSoftmaxCrossEntropyLossTest(test.TestCase):
@ -445,6 +473,30 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase):
loss_ops.sparse_softmax_cross_entropy(
logits, labels, weights=weights).eval()
def testLossWithDynamicallyShapedWeights1D(self):
logits = constant_op.constant([[10.0, 0.0, 0.0],
[0.0, 10.0, 0.0],
[0.0, 0.0, 10.0]])
labels = constant_op.constant([2, 0, 1])
weights = [2.3, 2.4, 2.5]
weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None])
loss = loss_ops.sparse_softmax_cross_entropy(logits, labels, weights_placeholder)
with self.test_session() as sess:
loss = sess.run(loss, {weights_placeholder: weights})
self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3)
def testLossWithDynamicallyShapedWeights2D(self):
logits = constant_op.constant([[10.0, 0.0, 0.0],
[0.0, 10.0, 0.0],
[0.0, 0.0, 10.0]])
labels = constant_op.constant([2, 0, 1])
weights = [[2.3], [2.4], [2.5]]
weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None, None])
loss = loss_ops.sparse_softmax_cross_entropy(logits, labels, weights_placeholder)
with self.test_session() as sess:
loss = sess.run(loss, {weights_placeholder: weights})
self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3)
class SigmoidCrossEntropyLossTest(test.TestCase):

2
tensorflow/contrib/nccl/BUILD
View File

@ -84,7 +84,7 @@ cuda_py_test(
tf_cuda_cc_test(
name = "nccl_manager_test",
size = "small",
size = "medium",
srcs = if_cuda(
[
"kernels/nccl_manager.cc",

2
tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
View File

@ -95,7 +95,7 @@ class RNNCellTest(test.TestCase):
input_size = 4
feature_size = 2
frequency_skip = 1
num_shifts = (input_size - feature_size) / frequency_skip + 1
num_shifts = (input_size - feature_size) // frequency_skip + 1
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)):
x = array_ops.zeros([batch_size, input_size])

2
tensorflow/contrib/slim/README.md
View File

@ -880,7 +880,7 @@ names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
# Create the summary ops such that they also print out to std output:
summary_ops = []
for metric_name, metric_value in metrics_to_values.iteritems():
for metric_name, metric_value in names_to_values.iteritems():
op = tf.summary.scalar(metric_name, metric_value)
op = tf.Print(op, [metric_value], metric_name)
summary_ops.append(op)

76
tensorflow/contrib/sparsemax/BUILD Normal file
View File

@ -0,0 +1,76 @@
# Description:
# Contains ops to train linear models on top of TensorFlow.
# APIs here are meant to evolve over time.
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
package(default_visibility = ["//visibility:public"])
load("//tensorflow:tensorflow.bzl", "cuda_py_tests")
load(
"//tensorflow:tensorflow.bzl",
"tf_custom_op_library",
"tf_py_test",
)
load(
"//tensorflow/core:platform/default/build_config.bzl",
"tf_kernel_tests_linkstatic",
)
py_library(
name = "sparsemax_py",
srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
srcs_version = "PY2AND3",
visibility = ["//visibility:public"],
deps = [
"//tensorflow/contrib/util:util_py",
"//tensorflow/python:array_ops",
"//tensorflow/python:framework_for_generated_wrappers",
"//tensorflow/python:math_ops",
"//tensorflow/python:nn",
],
)
cuda_py_tests(
name = "sparsemax_test",
size = "small",
srcs = ["python/kernel_tests/sparsemax_test.py"],
additional_deps = [
":sparsemax_py",
"//tensorflow:tensorflow_py",
"//tensorflow/python:array_ops",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:gradients",
"//tensorflow/python:math_ops",
"//tensorflow/python:platform_test",
],
)
cuda_py_tests(
name = "sparsemax_loss_test",
size = "medium",
srcs = ["python/kernel_tests/sparsemax_loss_test.py"],
additional_deps = [
":sparsemax_py",
"//tensorflow:tensorflow_py",
"//tensorflow/python:array_ops",
"//tensorflow/python:framework_test_lib",
"//tensorflow/python:gradients",
"//tensorflow/python:math_ops",
"//tensorflow/python:platform_test",
],
)
filegroup(
name = "all_files",
srcs = glob(
["**/*"],
exclude = [
"**/METADATA",
"**/OWNERS",
],
),
visibility = ["//tensorflow:__subpackages__"],
)

30
tensorflow/contrib/sparsemax/__init__.py Normal file
View File

@ -0,0 +1,30 @@
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Module that implements sparsemax and sparsemax loss, see [1].
[1] https://arxiv.org/abs/1602.02068
## Sparsemax
@@sparsemax
@@sparsemax_loss
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.sparsemax.python.ops.sparsemax import sparsemax
from tensorflow.contrib.sparsemax.python.ops.sparsemax_loss \
import sparsemax_loss

224
tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_loss_test.py Normal file
View File

@ -0,0 +1,224 @@
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for SparsemaxLossOp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.sparsemax import sparsemax, sparsemax_loss
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.framework import constant_op
from tensorflow.python.platform import test
test_obs = 10
class SparsemaxLossTest(test.TestCase):
def _np_sparsemax(self, z):
z = z - np.mean(z, axis=1)[:, np.newaxis]
# sort z
z_sorted = np.sort(z, axis=1)[:, ::-1]
# calculate k(z)
z_cumsum = np.cumsum(z_sorted, axis=1)
k = np.arange(1, z.shape[1] + 1)
z_check = 1 + k * z_sorted > z_cumsum
# use argmax to get the index by row as .nonzero() doesn't
# take an axis argument. np.argmax return the first index, but the last
# index is required here, use np.flip to get the last index and
# `z.shape[axis]` to compensate for np.flip afterwards.
k_z = z.shape[1] - np.argmax(z_check[:, ::-1], axis=1)
# calculate tau(z)
tau_sum = z_cumsum[np.arange(0, z.shape[0]), k_z - 1]
tau_z = ((tau_sum - 1) / k_z).reshape(-1, 1)
# calculate p
return np.maximum(0, z - tau_z)
def _np_sparsemax_loss(self, z, q):
z = z - np.mean(z, axis=1)[:, np.newaxis]
# Calculate q^T * z
z_k = np.sum(q * z, axis=1)
# calculate sum over S(z)
p = self._np_sparsemax(z)
s = p > 0
# z_i^2 - tau(z)^2 = p_i (2 * z_i - p_i) for i \in S(z)
S_sum = np.sum(s * p * (2 * z - p), axis=1)
# because q is binary, sum([q_1^2, q_2^2, ...]) is just sum(q)
q_norm = np.sum(q, axis=1)
return -z_k + 0.5 * S_sum + 0.5 * q_norm
def _np_sparsemax_loss_grad(self, z, q):
# chain rule
grad = 1
return grad * (-q + self._np_sparsemax(z))
def _tf_sparsemax(self, z, dtype, use_gpu):
with self.test_session(use_gpu=use_gpu):
tf_sparsemax_op = sparsemax(z.astype(dtype))
tf_sparsemax_out = tf_sparsemax_op.eval()
return tf_sparsemax_op, tf_sparsemax_out
def _tf_sparsemax_loss(self, z, q, dtype, use_gpu):
z = z.astype(dtype)
q = q.astype(dtype)
with self.test_session(use_gpu=use_gpu):
tf_sparsemax_op = sparsemax(z)
tf_loss_op = sparsemax_loss(z, tf_sparsemax_op, q)
tf_loss_out = tf_loss_op.eval()
return tf_loss_op, tf_loss_out
def _test_sparsemax_loss_against_numpy(self, dtype, random, use_gpu):
"""check sparsemax-loss kernel against numpy"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
q = np.zeros((test_obs, 10))
q[np.arange(0, test_obs), random.randint(0, 10, size=test_obs)] = 1
tf_loss_op, tf_loss_out = self._tf_sparsemax_loss(z, q, dtype, use_gpu)
np_loss = self._np_sparsemax_loss(z, q).astype(dtype)
self.assertAllCloseAccordingToType(np_loss, tf_loss_out,
half_atol=1e-2, half_rtol=5e-3)
self.assertShapeEqual(np_loss, tf_loss_op)
def _test_constant_add(self, dtype, random, use_gpu):
"""check sparsemax-loss proposition 3"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
c = random.uniform(low=-3, high=3, size=(test_obs, 1))
q = np.zeros((test_obs, 10))
q[np.arange(0, test_obs), np.random.randint(0, 10, size=test_obs)] = 1
_, tf_loss_zpc = self._tf_sparsemax_loss(
z + c, q, dtype, use_gpu
)
_, tf_loss_z = self._tf_sparsemax_loss(
z, q, dtype, use_gpu
)
self.assertAllCloseAccordingToType(tf_loss_zpc, tf_loss_z,
float_atol=5e-6, float_rtol=5e-6,
half_atol=1e-2, half_rtol=1e-2)
def _test_sparsemax_loss_positive(self, dtype, random, use_gpu):
"""check sparsemax-loss proposition 4"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
q = np.zeros((test_obs, 10))
q[np.arange(0, test_obs), random.randint(0, 10, size=test_obs)] = 1
tf_loss_op, tf_loss_out = self._tf_sparsemax_loss(z, q, dtype, use_gpu)
self.assertAllCloseAccordingToType(np.abs(tf_loss_out), tf_loss_out)
self.assertShapeEqual(np.zeros(test_obs), tf_loss_op)
def _test_sparsemax_loss_zero(self, dtype, random, use_gpu):
"""check sparsemax-loss proposition 5"""
# construct z and q, such that z_k >= 1 + max_{j!=k} z_k holds for
# delta_0 = 1.
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
z[:, 0] = np.max(z, axis=1) + 1.05
q = np.zeros((test_obs, 10))
q[:, 0] = 1
tf_loss_op, tf_loss_out = self._tf_sparsemax_loss(z, q, dtype, use_gpu)
tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(z, dtype, use_gpu)
self.assertAllCloseAccordingToType(np.zeros(test_obs), tf_loss_out)
self.assertShapeEqual(np.zeros(test_obs), tf_loss_op)
self.assertAllCloseAccordingToType(q, tf_sparsemax_out)
self.assertShapeEqual(q, tf_sparsemax_op)
def _test_gradient_against_estimate(self, dtype, random, use_gpu):
"""check sparsemax-loss Rop, aginst estimated-loss Rop"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10)).astype(dtype)
q = np.zeros((test_obs, 10)).astype(dtype)
q[np.arange(0, test_obs), np.random.randint(0, 10, size=test_obs)] = 1
logits = array_ops.placeholder(dtype, name='z')
sparsemax_op = sparsemax(logits)
loss_op = sparsemax_loss(logits, sparsemax_op, q)
with self.test_session(use_gpu=use_gpu):
err = gradient_checker.compute_gradient_error(
logits, z.shape,
loss_op, (test_obs, ),
x_init_value=z, delta=1e-9
)
self.assertLess(err, 1e-4)
def _test_gradient_against_numpy(self, dtype, random, use_gpu):
"""check sparsemax-loss Rop, aginst numpy Rop"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
q = np.zeros((test_obs, 10))
q[np.arange(0, test_obs), np.random.randint(0, 10, size=test_obs)] = 1
logits = constant_op.constant(z.astype(dtype), name='z')
sparsemax_op = sparsemax(logits)
loss_op = sparsemax_loss(logits, sparsemax_op, q.astype(dtype))
loss_grad_op = gradients_impl.gradients(loss_op, [logits])[0]
with self.test_session(use_gpu=use_gpu):
tf_grad = loss_grad_op.eval()
np_grad = self._np_sparsemax_loss_grad(z, q).astype(dtype)
self.assertAllCloseAccordingToType(np_grad, tf_grad,
half_atol=1e-2, half_rtol=5e-3)
self.assertShapeEqual(np_grad, loss_grad_op)
def _test_dtype(self, dtype):
random = np.random.RandomState(1)
self._test_sparsemax_loss_against_numpy(dtype, random, use_gpu=False)
self._test_constant_add(dtype, random, use_gpu=False)
self._test_sparsemax_loss_positive(dtype, random, use_gpu=False)
self._test_sparsemax_loss_zero(dtype, random, use_gpu=False)
# sparsemax is not a smooth function so gradient estimation is only
# possibol for float64.
if dtype == 'float64':
self._test_gradient_against_estimate(dtype, random, use_gpu=False)
self._test_gradient_against_numpy(dtype, random, use_gpu=False)
def testFloat(self):
self._test_dtype('float32')
def testDouble(self):
self._test_dtype('float64')
if __name__ == "__main__":
test.main()

252
tensorflow/contrib/sparsemax/python/kernel_tests/sparsemax_test.py Normal file
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@ -0,0 +1,252 @@
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for SparsemaxOp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.sparsemax import sparsemax
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.framework import constant_op
from tensorflow.python.platform import test
test_obs = 10
class SparsemaxTest(test.TestCase):
def _np_sparsemax(self, z):
z = z - np.mean(z, axis=1)[:, np.newaxis]
# sort z
z_sorted = np.sort(z, axis=1)[:, ::-1]
# calculate k(z)
z_cumsum = np.cumsum(z_sorted, axis=1)
k = np.arange(1, z.shape[1] + 1)
z_check = 1 + k * z_sorted > z_cumsum
# use argmax to get the index by row as .nonzero() doesn't
# take an axis argument. np.argmax return the first index, but the last
# index is required here, use np.flip to get the last index and
# `z.shape[axis]` to compensate for np.flip afterwards.
k_z = z.shape[1] - np.argmax(z_check[:, ::-1], axis=1)
# calculate tau(z)
tau_sum = z_cumsum[np.arange(0, z.shape[0]), k_z - 1]
tau_z = ((tau_sum - 1) / k_z).reshape(-1, 1)
# calculate p
return np.maximum(0, z - tau_z)
def _np_sparsemax_grad(self, z):
# chain rule
grad = np.ones_like(z)
# Construct S(z)
probability = self._np_sparsemax(z)
support = probability > 0
# Calculate \hat{v}, which will be a vector (scalar for each z)
v_hat = np.sum(grad * support, axis=1) / np.sum(support, axis=1)
# Calculates J(z) * v
return support * (grad - v_hat[:, np.newaxis])
def _tf_sparsemax(self, z, dtype, use_gpu):
with self.test_session(use_gpu=use_gpu):
tf_sparsemax_op = sparsemax(z.astype(dtype))
tf_sparsemax_out = tf_sparsemax_op.eval()
return tf_sparsemax_op, tf_sparsemax_out
def _test_sparsemax_against_numpy(self, dtype, random, use_gpu):
"""check sparsemax kernel against numpy"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(z, dtype, use_gpu)
p_sparemax = self._np_sparsemax(z).astype(dtype)
self.assertAllCloseAccordingToType(p_sparemax, tf_sparsemax_out,
half_atol=5e-3)
self.assertShapeEqual(p_sparemax, tf_sparsemax_op)
def _test_sparsemax_of_zero(self, dtype, random, use_gpu):
"""check sparsemax proposition 1, part 1"""
z = np.zeros((1, 10))
tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(z, dtype, use_gpu)
p_sparemax = np.ones_like(z, dtype=dtype) / z.size
self.assertAllCloseAccordingToType(p_sparemax, tf_sparsemax_out)
self.assertShapeEqual(p_sparemax, tf_sparsemax_op)
def _test_sparsemax_of_inf(self, dtype, random, use_gpu):
"""check sparsemax proposition 1, part 2"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
# assume |A(z)| = 1, as z is continues random
z_sort_arg = np.argsort(z, axis=1)[:, ::-1]
z_sort = np.sort(z, axis=-1)[:, ::-1]
gamma_z = z_sort[:, 0] - z_sort[:, 1]
epsilon = (0.99 * gamma_z * 1).reshape(-1, 1)
# construct the expected 1_A(z) array
p_expected = np.zeros((test_obs, 10), dtype=dtype)
p_expected[np.arange(0, test_obs), z_sort_arg[:, 0]] = 1
tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(
(1 / epsilon) * z, dtype, use_gpu
)
self.assertAllCloseAccordingToType(p_expected, tf_sparsemax_out)
self.assertShapeEqual(p_expected, tf_sparsemax_op)
def _test_constant_add(self, dtype, random, use_gpu):
"""check sparsemax proposition 2"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10)).astype(dtype)
c = random.uniform(low=-3, high=3, size=(test_obs, 1)).astype(dtype)
_, tf_sparsemax_zpc = self._tf_sparsemax(
z + c, dtype, use_gpu
)
_, tf_sparsemax_z = self._tf_sparsemax(
z, dtype, use_gpu
)
self.assertAllCloseAccordingToType(tf_sparsemax_zpc, tf_sparsemax_z,
half_atol=5e-3)
def _test_permutation(self, dtype, random, use_gpu):
"""check sparsemax proposition 3"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
_, p = self._tf_sparsemax(z, dtype, use_gpu)
for i in range(test_obs):
per = random.permutation(10)
tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(
z[i, per].reshape(1, -1), dtype, use_gpu
)
p_expected = p[i, per].reshape(1, -1)
self.assertAllCloseAccordingToType(p_expected, tf_sparsemax_out,
half_atol=5e-3)
self.assertShapeEqual(p_expected, tf_sparsemax_op)
def _test_diffrence(self, dtype, random, use_gpu):
"""check sparsemax proposition 4"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10))
_, p = self._tf_sparsemax(z, dtype, use_gpu)
etol = {'float16': 1e-2, 'float32': 1e-6, 'float64': 1e-9}[dtype]
for val in range(0, test_obs):
for i in range(0, 10):
for j in range(0, 10):
# check condition, the obesite pair will be checked anyway
if z[val, i] > z[val, j]:
continue
self.assertTrue(
0 <= p[val, j] - p[val, i] <= z[val, j] - z[val, i] + etol,
"0 <= %.10f <= %.10f" % (
p[val, j] - p[val, i], z[val, j] - z[val, i] + etol
)
)
def _test_two_dimentional(self, dtype, random, use_gpu):
"""check two dimentation sparsemax case"""
t = np.linspace(-2, 2, test_obs, dtype=dtype)
z = np.vstack([
t, np.zeros(test_obs, dtype=dtype)
]).T
tf_sparsemax_op, tf_sparsemax_out = self._tf_sparsemax(z, dtype, use_gpu)
p0_expected = np.select([t < -1, t <= 1, t > 1], [0, (t + 1) / 2, 1])
self.assertAllCloseAccordingToType(p0_expected, tf_sparsemax_out[:, 0])
self.assertAllCloseAccordingToType(1 - p0_expected, tf_sparsemax_out[:, 1])
self.assertShapeEqual(z, tf_sparsemax_op)
def _test_gradient_against_estimate(self, dtype, random, use_gpu):
"""check sparsemax Rop, aginst estimated Rop"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10)).astype(dtype)
logits = array_ops.placeholder(dtype, name='z')
sparsemax_op = sparsemax(logits)
with self.test_session(use_gpu=use_gpu):
err = gradient_checker.compute_gradient_error(
logits, z.shape,
sparsemax_op, z.shape,
x_init_value=z, delta=1e-9
)
self.assertLess(err, 1e-4)
def _test_gradient_against_numpy(self, dtype, random, use_gpu):
"""check sparsemax Rop, aginst numpy Rop"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10)).astype(dtype)
logits = constant_op.constant(z, name='z')
sparsemax_op = sparsemax(logits)
sparsemax_grad_op = gradients_impl.gradients(sparsemax_op, [logits])[0]
with self.test_session(use_gpu=use_gpu):
tf_grad = sparsemax_grad_op.eval()
np_grad = self._np_sparsemax_grad(z)
self.assertAllCloseAccordingToType(np_grad, tf_grad)
self.assertShapeEqual(np_grad, sparsemax_grad_op)
def _test_dtype(self, dtype):
random = np.random.RandomState(1)
self._test_sparsemax_against_numpy(dtype, random, use_gpu=False)
self._test_sparsemax_of_zero(dtype, random, use_gpu=False)
self._test_sparsemax_of_inf(dtype, random, use_gpu=False)
self._test_constant_add(dtype, random, use_gpu=False)
self._test_permutation(dtype, random, use_gpu=False)
self._test_diffrence(dtype, random, use_gpu=False)
self._test_two_dimentional(dtype, random, use_gpu=False)
# sparsemax is not a smooth function so gradient estimation is only
# possibol for float64.
if dtype == 'float64':
self._test_gradient_against_estimate(dtype, random, use_gpu=False)
self._test_gradient_against_numpy(dtype, random, use_gpu=False)
def testFloat(self):
self._test_dtype('float32')
def testDouble(self):
self._test_dtype('float64')
if __name__ == "__main__":
test.main()

74
tensorflow/contrib/sparsemax/python/ops/sparsemax.py Normal file
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@ -0,0 +1,74 @@
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Sparsemax op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.util import loader
from tensorflow.python.platform import resource_loader
from tensorflow.python.framework import ops, dtypes
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import nn
def sparsemax(logits, name=None):
"""Computes sparsemax activations [1].
For each batch `i` and class `j` we have
sparsemax[i, j] = max(logits[i, j] - tau(logits[i, :]), 0)
[1]: https://arxiv.org/abs/1602.02068
Args:
logits: A `Tensor`. Must be one of the following types: `half`, `float32`,
`float64`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `logits`.
"""
with ops.name_scope(name, "sparsemax", [logits]) as name:
logits = ops.convert_to_tensor(logits, name="logits")
obs = array_ops.shape(logits)[0]
dims = array_ops.shape(logits)[1]
z = logits - math_ops.reduce_mean(logits, axis=1)[:, array_ops.newaxis]
# sort z
z_sorted, _ = nn.top_k(z, k=dims)
# calculate k(z)
z_cumsum = math_ops.cumsum(z_sorted, axis=1)
k = math_ops.range(
1, math_ops.cast(dims, logits.dtype) + 1, dtype=logits.dtype
)
z_check = 1 + k * z_sorted > z_cumsum
# because the z_check vector is always [1,1,...1,0,0,...0] finding the
# (index + 1) of the last `1` is the same as just summing the number of 1.
k_z = math_ops.reduce_sum(math_ops.cast(z_check, dtypes.int32), axis=1)
# calculate tau(z)
indices = array_ops.stack([math_ops.range(0, obs), k_z - 1], axis=1)
tau_sum = array_ops.gather_nd(z_cumsum, indices)
tau_z = (tau_sum - 1) / math_ops.cast(k_z, logits.dtype)
# calculate p
return math_ops.maximum(
math_ops.cast(0, logits.dtype),
z - tau_z[:, array_ops.newaxis]
)

59
tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py Normal file
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@ -0,0 +1,59 @@
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Sparsemax Loss op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.util import loader
from tensorflow.python.platform import resource_loader
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
def sparsemax_loss(logits, sparsemax, labels, name=None):
"""Computes sparsemax loss function [1].
[1]: https://arxiv.org/abs/1602.02068
Args:
logits: A `Tensor`. Must be one of the following types: `half`, `float32`,
`float64`.
sparsemax: A `Tensor`. Must have the same type as `logits`.
labels: A `Tensor`. Must have the same type as `logits`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `logits`.
"""
with ops.name_scope(name, "sparsemax_loss",
[logits, sparsemax, labels]) as name:
logits = ops.convert_to_tensor(logits, name="logits")
sparsemax = ops.convert_to_tensor(sparsemax, name="sparsemax")
labels = ops.convert_to_tensor(labels, name="labels")
shifted_logits = logits - \
math_ops.reduce_mean(logits, axis=1)[:, array_ops.newaxis]
# sum over support
support = math_ops.cast(sparsemax > 0, sparsemax.dtype)
sum_s = support * sparsemax * (shifted_logits - 0.5 * sparsemax)
# - z_k + ||q||^2
q_part = labels * (0.5 * labels - shifted_logits)
return math_ops.reduce_sum(sum_s + q_part, axis=1)

36
tensorflow/core/BUILD
View File

@ -77,6 +77,8 @@ load(
"tf_opts_nortti_if_android",
"cc_header_only_library",
)
#load("//tensorflow:tensorflow.bzl", "tf_cc_test_mkl")
load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu")
load("//tensorflow:tensorflow.bzl", "tf_cc_tests_gpu")
load("//tensorflow:tensorflow.bzl", "tf_version_info_genrule")
@ -111,7 +113,10 @@ load(
"//tensorflow/core:platform/default/build_config_root.bzl",
"tf_cuda_tests_tags",
)
#load(
# "//third_party/mkl:build_defs.bzl",
# "if_mkl",
#)
# -----------------------------------------------------------------------------
# Public targets
@ -1863,6 +1868,35 @@ tf_cc_tests(
],
)
#if_mkl(
# tf_cc_test_mkl(
# name = "mkl_related_tests",
# size = "small",
# srcs = ["graph/mkl_optimizer_merge_test.cc"],
# linkstatic = tf_kernel_tests_linkstatic(),
# deps = [
# ":core",
# ":core_cpu",
# ":core_cpu_internal",
# ":direct_session_internal",
# ":framework",
# ":framework_internal",
# ":lib",
# ":lib_internal",
# ":ops",
# ":protos_all_cc",
# ":test",
# ":test_main",
# ":testlib",
# "//third_party/eigen3",
# "//tensorflow/cc:cc_ops",
# "//tensorflow/cc:scope",
# "//tensorflow/cc:sendrecv_ops",
# "//tensorflow/core/kernels:ops_util",
# ],
# ),
#)
tf_cc_tests_gpu(
name = "gpu_related_tests",
size = "small",

596
tensorflow/core/graph/mkl_optimizer_merge.cc Normal file
View File

@ -0,0 +1,596 @@
/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifdef INTEL_MKL
// This module implements node merging optimization on the graph.
// We process the nodes in the graph in reverse postorder
// (i.e. inputs before their downstream dependencies).
//
#include <set>
#include <vector>
#include <queue>
#include <utility>
#include "tensorflow/core/graph/mkl_optimizer_merge.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/graph/algorithm.h"
#include "tensorflow/core/graph/node_builder.h"
#include "tensorflow/core/lib/gtl/map_util.h"
#include "tensorflow/core/lib/hash/hash.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/common_runtime/function.h"
namespace tensorflow {
// How many hops do we search for matching node in the backward dataflow graph?
// We use maxhop of 10 based on empirical observations. Also, these are
// maxhops in backward data-flow graph. Since input of forward nodes (Conv2D)
// directly goes to backward nodes, we do not expect the hop-distance
// would be more than few nodes.
static size_t kNodeMergeContextMaxDepth = 10;
// This optimization pass performs two tasks: merge
// nodes in the forward pass, and rewrite the gradient ops
// corresponding to merged forward ops.
//
// Merging nodes in the graph: Currently, it merges Conv2D+AddBias together.
//
// Rewriting nodes in the graph: This is neded in order to optimize
// gradient ops of Conv2D+AddBias. Gradient op of both the Conv2D and
// MatMul is BiasAddGrad, and we need to rewrite BiasAddGrad into
// Conv2D-specific BiasAddGrad, and MatMul-specific BiasAddGrad.
// This is context-specific optimization, where the context is the
// forward operator that the BiasAddGrad corresponds to.
class NodeMergeRewritePass : public GraphOptimizationPass {
public:
NodeMergeRewritePass() {
csinfo_.conv2d = "Conv2D";
csinfo_.conv2dwithbias = "Conv2DWithBias";
csinfo_.conv2dwithbiasbackpropbias = "Conv2DWithBiasBackpropBias";
csinfo_.biasadd = "BiasAdd";
csinfo_.matmul = "MatMul";
csinfo_.biasaddgrad = "BiasAddGrad";
minfo_.push_back({csinfo_.conv2d, csinfo_.biasadd, 0,
csinfo_.conv2dwithbias});
// We use maxhop of 10 based on emperical observations. Also, these are
// maxhops in backward data-flow graph. Since input of forward nodes
// (Conv2D) directly goes to backward nodes, we do not expect the
// hop-distance would be more than few nodes.
rinfo_.push_back({csinfo_.biasaddgrad, csinfo_.conv2dwithbiasbackpropbias,
{csinfo_.conv2dwithbias, kNodeMergeContextMaxDepth}});
rinfo_.push_back({csinfo_.biasaddgrad, csinfo_.conv2dwithbiasbackpropbias,
{csinfo_.conv2d, kNodeMergeContextMaxDepth}});
// For now, we are rewriting BiasAddGrad to BiasAddGrad for MatMul. This is
// because we do not have a separate Op for MatMulwithBias.
rinfo_.push_back({csinfo_.biasaddgrad, csinfo_.biasaddgrad,
{csinfo_.matmul, kNodeMergeContextMaxDepth}});
}
// Standard interface to run optimization pass
Status Run(const GraphOptimizationPassOptions& options);
// Helper function which does most of heavy lifting for node merge
//
// Extracts common functionality between Run public interface and
// test interface.
//
// @return true, if and only if graph is mutated; false otherwise.
bool RunPass(std::unique_ptr<Graph>* g);
private:
/// Structure to specify information used in node merge
typedef struct {
string pred; // Predecessor node string
string succ; // Successor node string
int op; // What operand no the predecessor node corresponds
// to successor node?
string newnode; // Name of the node after merge
} MergeInfo;
/// Structure to specify information used in node rewrite
typedef struct {
string node; // Name of the node to be rewritten
string rewrite; // New name of the node after rewrite
typedef struct {
string fwd; // Node name in forward pass that this node
// corresponds to
size_t maxhop; // Maximum number of hops the mfwd_ is located
// from this node. If mfwd_ is farther than mmaxhop_
// then we do not rewrite the node.
} ContextInfo;
ContextInfo cinfo; // Context for rewrite
} RewriteInfo;
/// Structure to store all constant strings
typedef struct {
string conv2d;
string conv2dwithbias;
string conv2dwithbiasbackpropbias;
string biasadd;
string matmul;
string biasaddgrad;
} ConstStringInfo;
ConstStringInfo csinfo_;
std::vector<MergeInfo> minfo_;
std::vector<RewriteInfo> rinfo_;
private:
// Return a node that can be merged with input node
//
// @return pointer to the node if we can find such a
// node. Otherwise, it returns nullptr.
Node* FindNodeForMerge(const Node* a) const;
// Merge predecessor node with its successor.
// Currently, we merge Conv2D with AddBias only.
//
// Input nodes succ and pred may be deleted if the call to
// this function is successful. Attempt to use the pointers
// after the call to function may result is undefined behaviors.
//
// @input g - input graph, succ - successor node, pred - predecessor node
// @return Status::OK(), if merging is successful and supported.
// Returns appropriate Status error code otherwise.
// Graph is updated in case nodes are merged. Otherwise, it is
// not updated.
Status MergeNode(std::unique_ptr<Graph>* g, Node* succ, Node* pred);
// Is input node (n) a candidate for rewrite?
//
// @return true, if it can be rewritten; false, otherwise.
bool IsApplicableRewriteNode(const Node* n) const;
// Rewrites input node to a new node specified by its matching rewrite info.
//
// Method first searches matching rewrite info for input node and then
// uses that info to rewrite.
//
// Input node may be deleted in case of rewrite. Attempt to use the node
// after the call can result in undefined behaviors.
//
// @input g - input graph, n - Node to be rewritten
// @return Status::OK(), if the input node is rewritten;
// Returns appropriate Status error code otherwise.
// Graph is updated in case the input node is rewritten.
// Otherwise, it is not updated.
Status RewriteNode(std::unique_ptr<Graph>* g, Node* n);
// Helper function that searches the matching rewriteinfo for the node.
// Implements depth-first search in the data dependence graph for the
// gradient op in backward direction.
//
// @input n - Node (gradient op) whose rewriteinfo is to be searched,
// fwdn - pointer to node from the forward pass that this node
// belongs to
// @return Matching rewriteinfo in case a match is found; null otherwise.
const RewriteInfo* FindMatchingRewriteInfo(const Node* n,
const Node** fwdn) const;
};
/// We register merge optimizer for phase 1 and MKLToTF insertion for phase 2.
REGISTER_OPTIMIZATION(OptimizationPassRegistry::PRE_PLACEMENT, 1,
NodeMergeRewritePass);
static void FillInputs(const Node* n,
gtl::InlinedVector<Node*, 4>* control_edges,
gtl::InlinedVector<std::pair<Node*, int>, 4>* in) {
DCHECK_EQ(in->size(), n->num_inputs());
control_edges->clear();
for (const Edge* e : n->in_edges()) {
if (e->IsControlEdge()) {
control_edges->push_back(e->src());
} else {
(*in)[e->dst_input()] = std::make_pair(e->src(), e->src_output());
}
}
std::sort(control_edges->begin(), control_edges->end());
if (n->op_def().is_commutative()) {
// For commutative inputs, we sort the input by the input Node*
// to get a canonical ordering (so that add(a,b) and add(b, a) will
// hash to the same value if is_commutative is true for 'add').
std::sort(in->begin(), in->end());
}
}
Node* NodeMergeRewritePass::FindNodeForMerge(const Node* a) const {
// Search for all matching mergeinfo.
// We allow more than one match for extensibility.
std::vector<const MergeInfo*> matching_mi;
for (auto mi = minfo_.cbegin(); mi != minfo_.cend(); ++mi) {
if (a->type_string() == mi->succ) {
matching_mi.push_back(&*mi);
}
}
VLOG(1) << "FindNodeForMerge: " << a->type_string();
for (const MergeInfo* mi : matching_mi) {
const int N_in = a->num_inputs();
if (mi->op >= N_in) {
continue;
}
// Get the control edges and input of node
gtl::InlinedVector<Node*, 4> a_control_edges;
gtl::InlinedVector<std::pair<Node*, int>, 4> a_in(N_in);
FillInputs(a, &a_control_edges, &a_in);
// Get operand op of the operator
Node *b = nullptr;
b = a_in[mi->op].first;
if (b == nullptr || (b->type_string() != mi->pred)) {
// NOTE: Should the first check be assert?
continue;
}
VLOG(1) << " FindNode: " << b->type_string();
gtl::InlinedVector<Node*, 4> b_control_edges;
gtl::InlinedVector<std::pair<Node*, int>, 4> b_in(N_in);
FillInputs(b, &b_control_edges, &b_in);
// Shouldn't merge if a and b have different control edges.
if (a_control_edges != b_control_edges) {
continue;
} else {
// We found a match.
return b;
}
}
return nullptr;
}
Status NodeMergeRewritePass::MergeNode(std::unique_ptr<Graph>* g,
Node* succ, Node* pred) {
CHECK_NOTNULL(succ);
CHECK_NOTNULL(pred);
if (succ->type_string() == csinfo_.biasadd &&
pred->type_string() == csinfo_.conv2d) {
// 1. Get all attributes from input nodes.
DataType T_pred, T_succ;
string padding;
std::vector<int32> strides;
string data_format_pred, data_format_succ;
bool use_cudnn_on_gnu;
int groups = 1;
TF_CHECK_OK(GetNodeAttr(pred->def(), "T", &T_pred));
TF_CHECK_OK(GetNodeAttr(succ->def(), "T", &T_succ));
TF_CHECK_OK(GetNodeAttr(pred->def(), "padding", &padding));
TF_CHECK_OK(GetNodeAttr(pred->def(), "strides", &strides));
TF_CHECK_OK(GetNodeAttr(pred->def(), "data_format", &data_format_pred));
TF_CHECK_OK(GetNodeAttr(succ->def(), "data_format", &data_format_succ));
TF_CHECK_OK(GetNodeAttr(pred->def(), "use_cudnn_on_gpu",
&use_cudnn_on_gnu));
// Groups attribute may not be there on the input node. So we do not
// check for error in GetNodeAttr call.
GetNodeAttr(pred->def(), "groups", &groups);
// We check to ensure that data formats of both succ and pred are same.
// We expect them to be same, so we can enforce this as assert.
// But assert can be too strict, so we enforce this as a check.
// If the check fails, then we do not merge two nodes.
if (data_format_pred != data_format_succ ||
T_pred != T_succ) {
return Status(error::Code::INVALID_ARGUMENT,
"data_format or T attribute of Conv2D and BiasAdd"
"do not match. Will skip node merge optimization");
}
// 2. Get inputs from both the nodes.
// Find the 2 inputs from the conv and the bias from the add Bias.
Node* oper1 = nullptr;
Node* oper2 = nullptr;
Node* oper3 = nullptr;
const int succ_num = succ->num_inputs();
gtl::InlinedVector<Node*, 4> succ_control_edges;
gtl::InlinedVector<std::pair<Node*, int>, 4> succ_in(succ_num);
FillInputs(succ, &succ_control_edges, &succ_in);
const int pred_num = pred->num_inputs();
gtl::InlinedVector<Node*, 4> pred_control_edges;
gtl::InlinedVector<std::pair<Node*, int>, 4> pred_in(pred_num);
FillInputs(pred, &pred_control_edges, &pred_in);
// We need to ensure that there is only 1 edge between Conv2D and AddBias.
// Otherwise, merging is semantically incorrect.
if (pred->out_edges().size() != 1) {
return Status(error::Code::INVALID_ARGUMENT,
"Conv2D has multiple outputs."
"Will skip node merge optimization");
}
for (const Edge *e : pred->out_edges()) {
if (e->dst() != succ) {
return Status(error::Code::INVALID_ARGUMENT,
"Conv2D does not feed to BiasAdd."
"Will skip node merge optimization");
}
}
// Get operand 0, 1 of conv2D
oper1 = pred_in[0].first;
oper2 = pred_in[1].first;
// Get operand 1 of add_bias
oper3 = succ_in[1].first;
Node* ret;
// We will use the node name of BiasAdd as the name of new node
TF_CHECK_OK(NodeBuilder(succ->name(), csinfo_.conv2dwithbias)
.Input(oper1)
.Input(oper2)
.Input(oper3)
.Attr("T", T_pred)
.Attr("strides", strides)
.Attr("padding", padding)
.Attr("data_format", data_format_pred)
.Attr("use_cudnn_on_gpu", use_cudnn_on_gnu)
.Attr("groups", groups)
.Finalize(&**g, &ret));
CHECK_NOTNULL(ret);
// Incoming edges are fixed, we will fix the outgoing edges now.
for (const Edge* e : succ->out_edges()) {
(*g)->AddEdge(ret, e->src_output(), e->dst(), e->dst_input());
}
(*g)->RemoveNode(succ);
(*g)->RemoveNode(pred);
return Status::OK();
}
return Status(error::Code::UNIMPLEMENTED,
"Unimplemented case for node merge optimization.");
}
Status NodeMergeRewritePass::RewriteNode(std::unique_ptr<Graph>* g, Node *n) {
CHECK_NOTNULL(n);
// Get the matching rewriteinfo for the node
const Node* fwdn = nullptr;
const RewriteInfo* ri = FindMatchingRewriteInfo(n, &fwdn);
if (ri == nullptr || fwdn == nullptr) {
VLOG(1) << "Rewriteinfo not found for: " << n->type_string();
return Status(error::Code::INVALID_ARGUMENT,
"Rewrite info not found for the node."
"Will skip node rewrite optimization");
}
VLOG(1) << "Rewrite called for: " << n->type_string();
if (n->type_string() == csinfo_.biasaddgrad &&
ri->node == csinfo_.biasaddgrad &&
(ri->rewrite == csinfo_.conv2dwithbiasbackpropbias ||
ri->rewrite == csinfo_.biasaddgrad)) {
DataType T; string data_format;
TF_CHECK_OK(GetNodeAttr(n->def(), "T", &T));
TF_CHECK_OK(GetNodeAttr(n->def(), "data_format", &data_format));
int n_num = n->num_inputs(); // this must be 1.
CHECK_EQ(n_num, 1);
gtl::InlinedVector<Node*, 4> n_control_edges;
gtl::InlinedVector<std::pair<Node*, int>, 4> n_in(n_num);
FillInputs(n, &n_control_edges, &n_in);
Node *ret = nullptr, *op = n_in[0].first;
if (ri->rewrite == csinfo_.conv2dwithbiasbackpropbias) {
// Get strides info from Conv2D (node in the forward pass that this
// node corresponds to).
std::vector<int32> strides;
TF_CHECK_OK(GetNodeAttr(fwdn->def(), "strides", &strides));
// We use same name as original node name as there may be fetchoutputs
// associated with it.
TF_CHECK_OK(NodeBuilder(n->name(), ri->rewrite)
.Input(op)
.Attr("T", T)
.Attr("data_format", data_format)
.Attr("strides", strides)
.Finalize(&**g, &ret));
} else {
CHECK_EQ(ri->rewrite, csinfo_.biasaddgrad);
TF_CHECK_OK(NodeBuilder(n->name(), ri->rewrite)
.Input(op)
.Attr("T", T)
.Attr("data_format", data_format)
.Finalize(&**g, &ret));
}
CHECK_NOTNULL(ret);
// Incoming edges are fixed, we will fix the outgoing edges now.
for (const Edge* e : n->out_edges()) {
(*g)->AddEdge(ret, e->src_output(), e->dst(), e->dst_input());
}
VLOG(1) << "Rewrite node: " << n->type_string() << " successful";
(*g)->RemoveNode(n);
return Status::OK();
}
return Status(error::Code::UNIMPLEMENTED,
"Unimplemented case for node rewrite optimization.");
}
const NodeMergeRewritePass::RewriteInfo*
NodeMergeRewritePass::FindMatchingRewriteInfo(const Node* n,
const Node** fwdn) const {
CHECK_NOTNULL(n);
CHECK_NOTNULL(fwdn);
*fwdn = nullptr;
// Search for matching rewriteinfo based on node name.
// There could be more than one matching rewriteinfos.
std::vector<const RewriteInfo*> matching_ri;
for (auto ri = rinfo_.cbegin(); ri != rinfo_.cend(); ++ri) {
if (n->type_string() == ri->node) {
matching_ri.push_back(&*ri);
}
}
VLOG(1) << "Searching graph for: " << n->type_string() << " in backwards.";
// Now we will check for forward op name for rewrite info in data
// flow graph. Get the max hops we should search for the fwd node
// We are now going to search (breadth-first) backwards in data
// dependence graph (for up to max hops) from n for the node
// specified in fwd.
// queue to maintain nodes to be visited and depth info for
// breadth-first search
std::queue<std::pair<const Node*, int>> nqueue;
const Node* curr_node = n;
size_t curr_depth = 0;
nqueue.push(std::make_pair(curr_node, curr_depth));
while (curr_depth < kNodeMergeContextMaxDepth && !nqueue.empty()) {
std::pair<const Node*, int> curr_pair = nqueue.front();
nqueue.pop();
std::set<const Node*> visited_nodes;
curr_node = curr_pair.first;
curr_depth = curr_pair.second;
CHECK_NOTNULL(curr_node);
VLOG(1) << "Visiting node: " << curr_node->type_string()
<< " at depth: " << curr_depth
<< " for node: " << n->type_string();
// If we find a match, we return immediately with the matching rewrite
// info.
for (const RewriteInfo* ri : matching_ri) {
if (curr_node->type_string() == ri->cinfo.fwd) {
*fwdn = curr_node;
return ri;
}
}
// Else we explore backward edges from current node.
// Add the source nodes of all incoming edges of the node to the queue.
for (const Edge* e : curr_node->in_edges()) {
// We do not visit already visited node.
if (visited_nodes.find(e->src()) == visited_nodes.end()) {
// Depth of these nodes is 1 more than the depth of current node.
nqueue.push(std::make_pair(e->src(), curr_depth+1));
visited_nodes.insert(e->src());
}
}
} /* while */
return nullptr;
}
bool NodeMergeRewritePass::IsApplicableRewriteNode(const Node *n) const {
CHECK_NOTNULL(n);
// Search for matching rewriteinfo
// Even if we find one match, we return true.
bool match_found = false;
for (const RewriteInfo &ri : rinfo_) {
if (n->type_string() == ri.node) {
match_found = true;
break;
}
}
return match_found;
}
bool NodeMergeRewritePass::RunPass(std::unique_ptr<Graph>* g) {
bool result = false;
CHECK_NOTNULL(g);
DumpGraph("Before OptimizeMerge", &**g);
std::vector<Node*> order;
GetReversePostOrder(**g, &order);
std::vector<std::pair<Node*, Node*>> nodes_to_be_merged;
std::vector<Node*> nodes_to_be_rewritten;
VLOG(1) << "Running NodeMerge Optimization";
for (Node* n : order) {
if (!n->IsOp()) continue;
Node* n1 = nullptr;
if ((n1 = FindNodeForMerge(n)) != nullptr) {
VLOG(1) << "Scheduled nodes " << n->name() << " and "
<< n1->name() << " for merging";
nodes_to_be_merged.push_back(std::make_pair(n, n1));
} else if (IsApplicableRewriteNode(n)) {
VLOG(1) << "Scheduled node " << n->name() << " for rewrite";
nodes_to_be_rewritten.push_back(n);
}
}
for (std::pair < Node*, Node* > i : nodes_to_be_merged) {
// Even if MergeNode merges single pair of nodes, we
// need to return true.
string n1_name = i.first->name();
string n2_name = i.second->name();
if (MergeNode(g, i.first, i.second) == Status::OK()) {
VLOG(1) << "Merged nodes " << n1_name << " and " << n2_name;
result = true;
}
}
DumpGraph("After OptimizeMerge(nodemerge)", &**g);
for (Node* i : nodes_to_be_rewritten) {
string name = i->name();
if (RewriteNode(g, i) == Status::OK()) {
VLOG(1) << "Rewrite node: " << name << " successful.";
result = true;
}
}
DumpGraph("After OptimizeMerge(noderewrite)", &**g);
return result;
}
bool OptimizeNodeMerge(std::unique_ptr<Graph>* g) {
return NodeMergeRewritePass().RunPass(g);
}
Status NodeMergeRewritePass::Run(const GraphOptimizationPassOptions& options) {
// Currently checking only for two cases - Conv2D+Bias and Matmul+Bias.
// It is possible to extend it to other operators in future.
if (options.graph == nullptr) {
return Status::OK();
}
// Get the ownership of graph
std::unique_ptr<Graph>* g = std::move(options.graph);
RunPass(g);
// Return the ownership of graph back
options.graph->reset(g->release());
return Status::OK();
}
} // namespace tensorflow
#endif

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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// An optimization pass that performs node merging and rewrite on graph nodes
#ifndef TENSORFLOW_GRAPH_MKL_OPTIMIZER_MERGE_H_
#define TENSORFLOW_GRAPH_MKL_OPTIMIZER_MERGE_H_
#ifdef INTEL_MKL
#include <sys/types.h>
#include <vector>
#include <string>
#include <memory>
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/common_runtime/optimization_registry.h"
namespace tensorflow {
// Interface to invoke the pass for unit test
//
// Returns true if and only if 'g' is mutated.
extern bool OptimizeNodeMerge(std::unique_ptr<Graph>* g);
} // namespace tensorflow
#endif // INTEL_MKL
#endif // TENSORFLOW_GRAPH_MKL_OPTIMIZER_MERGE_H_

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/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifdef INTEL_MKL
#include "tensorflow/core/graph/mkl_optimizer_merge.h"
#include <vector>
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/graph_constructor.h"
#include "tensorflow/core/graph/testlib.h"
#include "tensorflow/core/kernels/ops_util.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
namespace tensorflow {
namespace {
class OptimizerMergeTest : public ::testing::Test {
public:
OptimizerMergeTest() : graph_(OpRegistry::Global()) {}
static void InitGraph(const string& s, Graph* graph) {
GraphDef graph_def;
auto parser = protobuf::TextFormat::Parser();
CHECK(parser.MergeFromString(s, &graph_def)) << s;
GraphConstructorOptions opts;
TF_CHECK_OK(ConvertGraphDefToGraph(opts, graph_def, graph));
}
void InitGraph(const string& s) {
InitGraph(s, &graph_);
original_ = CanonicalGraphString(&graph_);
}
static bool IncludeNode(const Node* n) { return n->IsOp(); }
static string EdgeId(const Node* n, int index) {
if (index == 0) {
return n->name();
} else if (index == Graph::kControlSlot) {
return strings::StrCat(n->name(), ":control");
} else {
return strings::StrCat(n->name(), ":", index);
}
}
string CanonicalGraphString(Graph* g) {
std::vector<string> nodes;
std::vector<string> edges;
for (const Node* n : g->nodes()) {
if (IncludeNode(n)) {
nodes.push_back(strings::StrCat(n->name(), "(", n->type_string(), ")"));
}
}
for (const Edge* e : g->edges()) {
if (IncludeNode(e->src()) && IncludeNode(e->dst())) {
edges.push_back(strings::StrCat(EdgeId(e->src(), e->src_output()), "->",
EdgeId(e->dst(), e->dst_input())));
}
}
// Canonicalize
std::sort(nodes.begin(), nodes.end());
std::sort(edges.begin(), edges.end());
return strings::StrCat(str_util::Join(nodes, ";"), "|",
str_util::Join(edges, ";"));
}
string DoNodeMerge() {
string before = CanonicalGraphString(&graph_);
LOG(ERROR) << "Before node merge optimize: " << before;
std::unique_ptr<Graph>* ug = new std::unique_ptr<Graph>(&graph_);
OptimizeNodeMerge(ug);
string result = CanonicalGraphString(&graph_);
LOG(ERROR) << "After node merge optimize: " << result;
return result;
}
const string& OriginalGraph() const { return original_; }
Graph graph_;
string original_;
};
REGISTER_OP("Input").Output("o: float").SetIsStateful();
TEST_F(OptimizerMergeTest, Basic) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }"
" input: ['A', 'B'] }"
"node { name: 'D' op: 'Mul' attr { key: 'T' value { type: DT_FLOAT } }"
" input: ['A', 'B'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Mul);D(Mul)|"
"A->C;A->D;B->C:1;B->D:1");
}
// Test set 1: Conv2D + AddBias
// C=Conv2D(A,B); E=BiasAdd(C,D); Z=Sub(E,Y)
TEST_F(OptimizerMergeTest, Conv2DWithBias_Positive) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Conv2D'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" attr { key: 'use_cudnn_on_gpu' value { b: false } }"
" attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }"
" attr { key: 'padding' value { s: 'SAME' } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Input'}"
"node { name: 'E' op: 'BiasAdd'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['C', 'D'] }"
"node { name: 'Y' op: 'Input'}"
"node { name: 'Z' op: 'Sub'"
" attr {key: 'T' value { type: DT_FLOAT } }"
" input: ['E', 'Y']}");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);D(Input);E(Conv2DWithBias);Y(Input);Z(Sub)|"
"A->E;B->E:1;D->E:2;E->Z;Y->Z:1");
}
// Graph contains only Conv2D, no AddBias.
TEST_F(OptimizerMergeTest, Conv2DWithBias_Negative_NoAddBias) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Conv2D'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" attr { key: 'use_cudnn_on_gpu' value { b: false } }"
" attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }"
" attr { key: 'padding' value { s: 'SAME' } }"
" input: ['A', 'B']}");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Conv2D)|"
"A->C;B->C:1");
}
// Conv2D output does not go to BiasAdd.
TEST_F(OptimizerMergeTest, Conv2DWithBias_Negative_Dataflow1) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Conv2D'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" attr { key: 'use_cudnn_on_gpu' value { b: false } }"
" attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }"
" attr { key: 'padding' value { s: 'SAME' } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Input'}"
"node { name: 'E' op: 'Input'}"
"node { name: 'F' op: 'BiasAdd'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['D', 'E'] }"); // Output of Conv2D does not go to BiasAdd.
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Conv2D);D(Input);E(Input);F(BiasAdd)|"
"A->C;B->C:1;D->F;E->F:1");
}
// Conv2D has two outgoing edges: BiasAdd and some other dummy node (Add).
// Merge should not be done in such case.
TEST_F(OptimizerMergeTest, Conv2DWithBias_Negative_Dataflow2) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Conv2D'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" attr { key: 'use_cudnn_on_gpu' value { b: false } }"
" attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }"
" attr { key: 'padding' value { s: 'SAME' } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Input'}"
"node { name: 'E' op: 'Input'}"
"node { name: 'F' op: 'BiasAdd'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['D', 'E'] }" // Conv2D has two outputs.
// No merge should happen.
"node { name: 'G' op: 'Add'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" input: ['C', 'E'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Conv2D);D(Input);E(Input);F(BiasAdd);G(Add)|"
"A->C;B->C:1;C->G;D->F;E->F:1;E->G:1");
}
// data_format attribute value mismatch. Merge should not be done
// in such case.
TEST_F(OptimizerMergeTest, Conv2DWithBias_Negative_AttrMismatch) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Conv2D'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" attr { key: 'use_cudnn_on_gpu' value { b: false } }"
" attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }"
" attr { key: 'padding' value { s: 'SAME' } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Input'}"
"node { name: 'E' op: 'BiasAdd'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NHCW' } }"
" input: ['C', 'D'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Conv2D);D(Input);E(BiasAdd)|"
"A->C;B->C:1;C->E;D->E:1");
}
// Test set 2: Conv2D..BiasAddGrad -> Conv2DWithBiasBackpropBias rewrite tests
// C=Conv2D(A,B); D=Sub(C,A); F=BiasAddGrad(D)
TEST_F(OptimizerMergeTest, Conv2DBackprop_Positive) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Conv2D'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" attr { key: 'use_cudnn_on_gpu' value { b: false } }"
" attr { key: 'strides' value { list: {i: 1, i:1, i:1, i:1} } }"
" attr { key: 'padding' value { s: 'SAME' } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Sub'"
" attr {key: 'T' value { type: DT_FLOAT } }"
" input: ['C', 'A']}"
"node { name: 'E' op: 'BiasAddGrad'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['D'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Conv2D);D(Sub);E(Conv2DWithBiasBackpropBias)|"
"A->C;A->D:1;B->C:1;C->D;D->E");
}
// No Conv2D in the context for BiasAddGrad. No rewrite should happen.
// C=Add(A,B); D=Sub(C,A); F=BiasAddGrad(D,E)
TEST_F(OptimizerMergeTest, Conv2DBackprop_Negative_NoConv2D) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Add'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Sub'"
" attr {key: 'T' value { type: DT_FLOAT } }"
" input: ['C', 'A']}"
"node { name: 'E' op: 'BiasAddGrad'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['D'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Add);D(Sub);E(BiasAddGrad)|"
"A->C;A->D:1;B->C:1;C->D;D->E");
}
// No Conv2D in the context for BiasAddGrad, but MatMul in context.
// Rewrite should happen, but name of BiasAddGrad does not change.
// C=MatMul(A,B); D=Sub(C,A); F=BiasAddGrad(D,E)
TEST_F(OptimizerMergeTest, Conv2DBackprop_Negative_NoConv2D_MatMul) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'MatMul'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'transpose_a' value { b: false } }"
" attr { key: 'transpose_b' value { b: false } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Sub'"
" attr {key: 'T' value { type: DT_FLOAT } }"
" input: ['C', 'A']}"
"node { name: 'E' op: 'BiasAddGrad'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['D'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(MatMul);D(Sub);E(BiasAddGrad)|"
"A->C;A->D:1;B->C:1;C->D;D->E");
}
// Test set 3: MatMul..BiasAddGrad -> BiasAddGrad rewrite tests
// C=MatMul(A,B); D=Sub(C,A); F=BiasAddGrad(D,E)
TEST_F(OptimizerMergeTest, MatMulBiasAddGrad_Positive) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'MatMul'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'transpose_a' value { b: false } }"
" attr { key: 'transpose_b' value { b: false } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Sub'"
" attr {key: 'T' value { type: DT_FLOAT } }"
" input: ['C', 'A']}"
"node { name: 'E' op: 'BiasAddGrad'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['D'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(MatMul);D(Sub);E(BiasAddGrad)|"
"A->C;A->D:1;B->C:1;C->D;D->E");
}
// No MatMul in the context for BiasAddGrad. No rewrite should happen.
// C=Add(A,B); D=Sub(C,A); F=BiasAddGrad(D,E)
TEST_F(OptimizerMergeTest, MatMulBiasAddGrad_Negative_NoMatMul) {
InitGraph(
"node { name: 'A' op: 'Input'}"
"node { name: 'B' op: 'Input'}"
"node { name: 'C' op: 'Add'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" input: ['A', 'B']}"
"node { name: 'D' op: 'Sub'"
" attr {key: 'T' value { type: DT_FLOAT } }"
" input: ['C', 'A']}"
"node { name: 'E' op: 'BiasAddGrad'"
" attr { key: 'T' value { type: DT_FLOAT } }"
" attr { key: 'data_format' value { s: 'NCHW' } }"
" input: ['D'] }");
EXPECT_EQ(DoNodeMerge(),
"A(Input);B(Input);C(Add);D(Sub);E(BiasAddGrad)|"
"A->C;A->D:1;B->C:1;C->D;D->E");
}
static void BM_NodeMerge(int iters, int op_nodes) {
testing::StopTiming();
string s;
for (int in = 0; in < 10; in++) {
s += strings::Printf("node { name: 'in%04d' op: 'Input'}", in);
}
random::PhiloxRandom philox(301, 17);
random::SimplePhilox rnd(&philox);
for (int op = 0; op < op_nodes; op++) {
s += strings::Printf(
"node { name: 'op%04d' op: 'Mul' attr { key: 'T' value { "
"type: DT_FLOAT } } input: ['in%04d', 'in%04d' ] }",
op, rnd.Uniform(10), rnd.Uniform(10));
}
bool first = true;
while (iters > 0) {
Graph* graph = new Graph(OpRegistry::Global());
OptimizerMergeTest::InitGraph(s, graph);
int N = graph->num_node_ids();
if (first) {
testing::SetLabel(strings::StrCat("Per graph node. Nodes: ", N));
first = false;
}
{
testing::StartTiming();
std::unique_ptr<Graph> ug(graph);
OptimizeNodeMerge(&ug);
testing::StopTiming();
}
iters -= N; // Our benchmark units are individual graph nodes,
// not whole graphs
// delete graph;
}
}
BENCHMARK(BM_NodeMerge)->Arg(1000)->Arg(10000);
} // namespace
} // namespace tensorflow
#endif /* INTEL_MKL */

4
tensorflow/core/kernels/BUILD
View File

@ -7,9 +7,9 @@
# append "_gpu" to the test name to invoke the GPU benchmarks. Example:
#
# # for CPU tests
# $ bazel test -c opt --copt=-mavx //third_party/tensorflow/core/kernels:my_op_test
# $ bazel test --config opt //third_party/tensorflow/core/kernels:my_op_test
# # for GPU benchmarks
# $ bazel run -c opt --copt=-mavx --config=cuda //third_party/tensorflow/core/kernels:my_op_test_gpu -- --benchmarks=..
# $ bazel run --config opt --config=cuda //third_party/tensorflow/core/kernels:my_op_test_gpu -- --benchmarks=..
#
package(default_visibility = ["//visibility:public"])

7
tensorflow/core/kernels/conv_grad_input_ops.cc
View File

@ -170,12 +170,11 @@ struct LaunchXsmmBackwardInputConvolution<CPUDevice, float> {
desc.pad_w_out = 0;
desc.threads = num_threads;
desc.algo = LIBXSMM_DNN_CONV_ALGO_DIRECT;
desc.buffer_format = LIBXSMM_DNN_CONV_FORMAT_NHWC;
desc.filter_format = LIBXSMM_DNN_CONV_FORMAT_LIBXSMM;//LIBXSMM_DNN_CONV_FORMAT_RSCK;
desc.buffer_format = LIBXSMM_DNN_TENSOR_FORMAT_NHWC;
desc.filter_format = LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;//LIBXSMM_DNN_TENSOR_FORMAT_RSCK;
desc.fuse_ops = LIBXSMM_DNN_CONV_FUSE_NONE;
desc.options = LIBXSMM_DNN_CONV_OPTION_NONE;
desc.datatype_in = LIBXSMM_DNN_DATATYPE_F32;
desc.datatype_out = LIBXSMM_DNN_DATATYPE_F32;
desc.datatype = LIBXSMM_DNN_DATATYPE_F32;
auto input_ptr = input_backward.data();
auto filter_ptr = kernel.data();

7
tensorflow/core/kernels/conv_ops.cc
View File

@ -219,12 +219,11 @@ class LaunchXsmmConvOp<CPUDevice, float> {
desc.pad_w_out = 0;
desc.threads = num_threads;
desc.algo = LIBXSMM_DNN_CONV_ALGO_DIRECT;
desc.buffer_format = LIBXSMM_DNN_CONV_FORMAT_NHWC;
desc.filter_format = LIBXSMM_DNN_CONV_FORMAT_LIBXSMM;
desc.buffer_format = LIBXSMM_DNN_TENSOR_FORMAT_NHWC;
desc.filter_format = LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;
desc.fuse_ops = LIBXSMM_DNN_CONV_FUSE_NONE;
desc.options = LIBXSMM_DNN_CONV_OPTION_NONE;
desc.datatype_in = LIBXSMM_DNN_DATATYPE_F32;
desc.datatype_out = LIBXSMM_DNN_DATATYPE_F32;
desc.datatype = LIBXSMM_DNN_DATATYPE_F32;
if (!CanUseXsmmConv2D(desc, data_format)) {
return false;

111
tensorflow/core/kernels/segment_reduction_ops.cc
View File

@ -220,13 +220,15 @@ REGISTER_COMPLEX_CPU_KERNELS_ALL(complex128);
namespace functor {
// UnsortedSegmentSumFunctor implementation for CPUDevice.
// todo: Remove duplicate code in UnsortedSegmentSumFunctor and UnsortedSegmentMaxFunctor.
template <typename T, typename Index>
struct UnsortedSegmentSumFunctor<CPUDevice, T, Index> {
struct UnsortedSegmentSumFunctor<CPUDevice, T, Index>
: UnsortedSegmentBaseFunctor<CPUDevice, T, Index> {
void operator()(OpKernelContext* ctx, const CPUDevice& d,
const Index output_rows, const TensorShape& segment_ids_shape,
typename TTypes<Index>::ConstFlat segment_ids,
const Index data_size, const T* data,
typename TTypes<T, 2>::Tensor output) {
typename TTypes<T, 2>::Tensor output) override {
output.setZero();
if (data_size == 0) {
return;
@ -243,16 +245,44 @@ struct UnsortedSegmentSumFunctor<CPUDevice, T, Index> {
}
}
};
// UnsortedSegmentMaxFunctor implementation for CPUDevice.
template <typename T, typename Index>
struct UnsortedSegmentMaxFunctor<CPUDevice, T, Index>
: UnsortedSegmentBaseFunctor<CPUDevice, T, Index> {
void operator()(OpKernelContext* ctx, const CPUDevice& d,
const Index output_rows, const TensorShape& segment_ids_shape,
typename TTypes<Index>::ConstFlat segment_ids,
const Index data_size, const T* data,
typename TTypes<T, 2>::Tensor output) override {
output.setConstant(std::numeric_limits<T>::min());
if (data_size == 0) {
return;
}
const int64 N = segment_ids.dimension(0);
auto data_flat = typename TTypes<T, 2>::ConstTensor(data, N, data_size / N);
for (int64 i = 0; i < N; ++i) {
Index j = internal::SubtleMustCopy(segment_ids(i));
OP_REQUIRES(ctx, FastBoundsCheck(j, output_rows),
errors::InvalidArgument(
"segment_ids", SliceDebugString(segment_ids_shape, i),
" = ", j, " is out of range [0, ", output_rows, ")"));
output.template chip<0>(j) =
data_flat.template chip<0>(i).cwiseMax(output.template chip<0>(j));
}
}
};
} // namespace functor
// Similar to SegmentReductionOp but can handle unsorted segment definitions and
// specifying size of output.
// Base class for SegmentReductionOps that can handle unsorted segment
// definitions
// and specifying the size of the output in addition to a reduction function
template <typename Device, class T, class Index>
class UnsortedSegmentSumOp : public OpKernel {
class UnsortedSegmentBaseOp : public OpKernel {
public:
explicit UnsortedSegmentSumOp(OpKernelConstruction* context)
: OpKernel(context) {}
explicit UnsortedSegmentBaseOp(
OpKernelConstruction* context,
functor::UnsortedSegmentBaseFunctor<Device, T, Index>& functor)
: OpKernel(context), reduction_functor_(functor) {}
void Compute(OpKernelContext* context) override {
const Tensor& data = context->input(0);
@ -288,27 +318,70 @@ class UnsortedSegmentSumOp : public OpKernel {
auto output_flat = output->flat_outer_dims<T>();
auto data_ptr = data.template flat<T>().data();
functor::UnsortedSegmentSumFunctor<Device, T, Index>()(
context, context->template eigen_device<Device>(), output_rows,
segment_ids.shape(), segment_flat, data.NumElements(), data_ptr,
output_flat);
reduction_functor_(context, context->template eigen_device<Device>(),
output_rows, segment_ids.shape(), segment_flat,
data.NumElements(), data_ptr, output_flat);
}
private:
functor::UnsortedSegmentBaseFunctor<Device, T, Index>& reduction_functor_;
};
#define REGISTER_CPU_UNSORTED_KERNELS(type, index_type) \
template <typename Device, class T, class Index>
class UnsortedSegmentSumOp : public UnsortedSegmentBaseOp<Device, T, Index> {
public:
explicit UnsortedSegmentSumOp(OpKernelConstruction* context)
: UnsortedSegmentBaseOp<Device, T, Index>(
context,
sum_functor_) {}
private:
functor::UnsortedSegmentSumFunctor<Device, T, Index> sum_functor_;
};
template <typename Device, class T, class Index>
class UnsortedSegmentMaxOp : public UnsortedSegmentBaseOp<Device, T, Index> {
public:
explicit UnsortedSegmentMaxOp(OpKernelConstruction* context)
: UnsortedSegmentBaseOp<Device, T, Index>(
context,
max_functor_) {}
private:
functor::UnsortedSegmentMaxFunctor<Device, T, Index> max_functor_;
};
#define REGISTER_REAL_CPU_UNSORTED_KERNELS(type, index_type) \
REGISTER_KERNEL_BUILDER(Name("UnsortedSegmentSum") \
.Device(DEVICE_CPU) \
.TypeConstraint<type>("T") \
.TypeConstraint<index_type>("Tindices"), \
UnsortedSegmentSumOp<CPUDevice, type, index_type>); \
REGISTER_KERNEL_BUILDER(Name("UnsortedSegmentMax") \
.Device(DEVICE_CPU) \
.TypeConstraint<type>("T") \
.TypeConstraint<index_type>("Tindices"), \
UnsortedSegmentMaxOp<CPUDevice, type, index_type>);
#define REGISTER_COMPLEX_CPU_UNSORTED_KERNELS(type, index_type) \
REGISTER_KERNEL_BUILDER(Name("UnsortedSegmentSum") \
.Device(DEVICE_CPU) \
.TypeConstraint<type>("T") \
.TypeConstraint<index_type>("Tindices"), \
UnsortedSegmentSumOp<CPUDevice, type, index_type>);
#define REGISTER_CPU_UNSORTED_KERNELS_ALL(type) \
REGISTER_CPU_UNSORTED_KERNELS(type, int32); \
REGISTER_CPU_UNSORTED_KERNELS(type, int64);
#define REGISTER_REAL_CPU_UNSORTED_KERNELS_ALL(type) \
REGISTER_REAL_CPU_UNSORTED_KERNELS(type, int32); \
REGISTER_REAL_CPU_UNSORTED_KERNELS(type, int64)
TF_CALL_NUMBER_TYPES(REGISTER_CPU_UNSORTED_KERNELS_ALL);
#undef REGISTER_CPU_UNSORTED_KERNELS
#undef REGISTER_CPU_UNSORTED_KERNELS_ALL
#define REGISTER_COMPLEX_CPU_UNSORTED_KERNELS_ALL(type) \
REGISTER_COMPLEX_CPU_UNSORTED_KERNELS(type, int32); \
REGISTER_COMPLEX_CPU_UNSORTED_KERNELS(type, int64)
TF_CALL_REAL_NUMBER_TYPES(REGISTER_REAL_CPU_UNSORTED_KERNELS_ALL);
REGISTER_COMPLEX_CPU_UNSORTED_KERNELS_ALL(complex64);
REGISTER_COMPLEX_CPU_UNSORTED_KERNELS_ALL(complex128);
#undef REGISTER_REAL_CPU_UNSORTED_KERNELS
#undef REGISTER_COMPLEX_CPU_UNSORTED_KERNELS
#undef REGISTER_COMPLEX_CPU_UNSORTED_KERNELS_ALL
#undef REGISTER_REAL_CPU_UNSORTED_KERNELS_ALL
#if GOOGLE_CUDA
#define REGISTER_GPU_UNSORTED_KERNELS(type, index_type) \

30
tensorflow/core/kernels/segment_reduction_ops.h
View File

@ -26,6 +26,17 @@ namespace tensorflow {
class OpKernelContext;
namespace functor {
// BaseFunctor for definition of UnsorteSegmentReductionOp
// for usage without templates.
template <typename Device, typename T, typename Index>
struct UnsortedSegmentBaseFunctor{
virtual ~UnsortedSegmentBaseFunctor(){}
virtual void operator()(OpKernelContext* ctx, const Device& d,
const Index output_rows, const TensorShape& segment_ids_shape,
typename TTypes<Index>::ConstFlat segment_ids,
const Index data_size, const T* data,
typename TTypes<T, 2>::Tensor output){};
};
// Functor for UnsortedSegmentSumOp.
// 'output_rows': the number of output segments (unique segment ids in
@ -37,7 +48,7 @@ namespace functor {
// 'data': input data tensor.
// 'output': output reshaped to {output_rows, output.size/output_rows}
template <typename Device, typename T, typename Index>
struct UnsortedSegmentSumFunctor {
struct UnsortedSegmentSumFunctor: public UnsortedSegmentBaseFunctor<Device, T, Index> {
void operator()(OpKernelContext* ctx, const Device& d,
const Index output_rows, const TensorShape& segment_ids_shape,
typename TTypes<Index>::ConstFlat segment_ids,
@ -45,6 +56,23 @@ struct UnsortedSegmentSumFunctor {
typename TTypes<T, 2>::Tensor output);
};
// Functor for UnsortedSegmentMaxOp.
// 'output_rows': the number of output segments (unique segment ids in
// 'segment_ids').
// 'segment_ids_shape': shape of 'segment_ids' tensor.
// 'segment_ids': unsorted map from input to output segment ids at which to
// perform segment sum operation.
// 'data_size': size of input data tensor.
// 'data': input data tensor.
// 'output': output reshaped to {output_rows, output.size/output_rows}
template <typename Device, typename T, typename Index>
struct UnsortedSegmentMaxFunctor: public UnsortedSegmentBaseFunctor<Device, T, Index> {
void operator()(OpKernelContext* ctx, const Device& d,
const Index output_rows, const TensorShape& segment_ids_shape,
typename TTypes<Index>::ConstFlat segment_ids,
const Index data_size, const T* data,
typename TTypes<T, 2>::Tensor output);
};
} // namespace functor
} // namespace tensorflow

4
tensorflow/core/kernels/segment_reduction_ops_gpu.cu.cc
View File

@ -56,12 +56,12 @@ namespace functor {
// UnsortedSegmentSumFunctor implementation for GPUDevice.
template <typename T, typename Index>
struct UnsortedSegmentSumFunctor<GPUDevice, T, Index> {
struct UnsortedSegmentSumFunctor<GPUDevice, T, Index>: UnsortedSegmentBaseFunctor<GPUDevice, T, Index> {
void operator()(OpKernelContext* ctx, const GPUDevice& d,
const Index output_rows, const TensorShape& segment_ids_shape,
typename TTypes<Index>::ConstFlat segment_ids,
const Index data_size, const T* data,
typename TTypes<T, 2>::Tensor output) {
typename TTypes<T, 2>::Tensor output) override {
if (output.size() == 0) {
return;
}

3
tensorflow/core/kernels/sparse_matmul_op.cc
View File

@ -37,6 +37,7 @@ limitations under the License.
#include "tensorflow/core/platform/types.h"
#ifdef TENSORFLOW_USE_LIBXSMM
#include "include/libxsmm_intrinsics_x86.h"
#include "include/libxsmm_malloc.h"
#include "include/libxsmm_spmdm.h"
#endif
@ -896,6 +897,8 @@ class LibxsmmSparseMatMul {
} else {
std::unique_ptr<TensorInfoCacheEntry> e{
new TensorInfoCacheEntry{M, K, N, max_threads, {}, nullptr}};
// setup scoped allocator, which uses cpu_allocator() for this scope
const libxsmm_tf_allocator<libxsmm_scratch_allocator> tf_allocator;
libxsmm_spmdm_init(M, N, K, max_threads, &e->handle, &e->output_csr);
return e;
}

131
tensorflow/core/kernels/xsmm_conv2d.cc
View File

@ -33,6 +33,7 @@ void dummy_xsmm_conv2d_ensure_file_is_not_empty(void);
#include "include/libxsmm_cpuid.h"
#include "libxsmm_dnn_handle.h"
#include "libxsmm_malloc.h"
namespace tensorflow {
@ -143,26 +144,28 @@ struct HashFunction{
S << w.d.S; u << w.d.u;
v << w.d.v; padh << w.d.pad_h_in;
padw << w.d.pad_w_in;
std::string out_ = N.str() + C.str()\
+ H.str() + W.str()\
+ K.str() + R.str()\
+ S.str() + u.str()\
+ v.str() + padh.str()\
+ padw.str();
return ( std::hash<std::string>()(out_));
}
};
class handles{
public:
libxsmm_dnn_conv_handle* find( const libxsmm_dnn_conv_desc_wrap &w) {
std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_conv_handle*, HashFunction>::iterator i = libxsmm_handles.find(w);
libxsmm_dnn_layer* find( const libxsmm_dnn_conv_desc_wrap &w) {
std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*,
HashFunction>::iterator i = libxsmm_handles.find(w);
if (i == libxsmm_handles.end()){
libxsmm_dnn_err_t status;
libxsmm_dnn_conv_handle* libxsmm_handle = libxsmm_dnn_create_conv_handle_check(w.d, &status);
libxsmm_dnn_layer* libxsmm_handle =
libxsmm_dnn_create_conv_layer(w.d, &status);
chk_libxsmm_err(status, "Create handle");
libxsmm_handles.insert(std::make_pair(w, libxsmm_handle));
return libxsmm_handle;
@ -171,15 +174,14 @@ class handles{
return i->second;
}
~handles(){
std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_conv_handle*, HashFunction>::iterator i;
std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*,
HashFunction>::iterator i;
for (i= libxsmm_handles.begin(); i != libxsmm_handles.end(); i++)
chk_libxsmm_err(libxsmm_dnn_destroy_conv_handle(i->second),
chk_libxsmm_err(libxsmm_dnn_destroy_conv_layer(i->second),
"Destroy handle");
}
private:
std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_conv_handle*, HashFunction> libxsmm_handles;
std::unordered_map<libxsmm_dnn_conv_desc_wrap , libxsmm_dnn_layer*, HashFunction> libxsmm_handles;
};
static handles libxsmm_handles;
@ -187,22 +189,25 @@ static handles libxsmm_handles;
template <typename InputPtr, typename FilterPtr, typename OutputPtr>
static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
const libxsmm_dnn_conv_desc& desc,
libxsmm_dnn_conv_kind kind, InputPtr input,
libxsmm_dnn_compute_kind kind, InputPtr input,
FilterPtr filter, OutputPtr output) {
// setup scoped allocator, which adopts the allocator from the context
const libxsmm_tf_allocator<libxsmm_scratch_allocator> tf_allocator(*ctx);
libxsmm_dnn_err_t status;
libxsmm_dnn_conv_handle* libxsmm_handle;
libxsmm_dnn_layer* libxsmm_handle;
libxsmm_dnn_conv_desc_wrap w(desc);
void* scratch;
if(kind == LIBXSMM_DNN_CONV_KIND_FWD)
if (kind == LIBXSMM_DNN_COMPUTE_KIND_FWD)
libxsmm_handle = libxsmm_handles.find(w);
else{
libxsmm_handle = libxsmm_dnn_create_conv_handle_check(desc, &status);
else {
libxsmm_handle = libxsmm_dnn_create_conv_layer(desc, &status);
chk_libxsmm_err(status, "Create handle");
}
status = libxsmm_dnn_get_codegen_success(libxsmm_handle, kind);
if (status == LIBXSMM_DNN_WARN_FALLBACK) {
chk_libxsmm_err(libxsmm_dnn_destroy_conv_handle(libxsmm_handle),
chk_libxsmm_err(libxsmm_dnn_destroy_conv_layer(libxsmm_handle),
"Destroy handle");
return false; // Use non-libxsmm code
}
@ -211,23 +216,23 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
libxsmm_dnn_buffer* libxsmm_input;
libxsmm_dnn_buffer* libxsmm_output;
libxsmm_dnn_filter* libxsmm_filter;
/*
/*
const DeviceBase::CpuWorkerThreads* worker_threads =
ctx->device()->tensorflow_cpu_worker_threads();
int num_threads = worker_threads->num_threads;
*/
int ifmblock = (libxsmm_handle->ifmblock);
int ofmblock = (libxsmm_handle->ofmblock);
int ofmblock = (libxsmm_handle->ofmblock);
int blocksifm = desc.C%ifmblock ==0 ? desc.C/ifmblock :desc.C/ifmblock + 1;
int blocksifm = desc.C%ifmblock ==0 ? desc.C/ifmblock :desc.C/ifmblock + 1;
int blocksofm = desc.K%ofmblock ==0 ? desc.K/ofmblock :desc.K/ofmblock + 1;
float *native_filter = (float*)libxsmm_aligned_malloc( blocksofm*blocksifm*desc.R*desc.S*ifmblock*ofmblock*sizeof(float), 2097152);
float *native_filter = (float*)libxsmm_aligned_scratch(
blocksofm*blocksifm*desc.R*desc.S*ifmblock*ofmblock*sizeof(float),
2097152);
const DeviceBase::CpuWorkerThreads* worker_threads =
ctx->device()->tensorflow_cpu_worker_threads();
@ -264,50 +269,78 @@ static bool CallLibxsmmConvGeneric(OpKernelContext* ctx,
count.Wait();
}
libxsmm_input = libxsmm_dnn_link_input_buffer_check(
libxsmm_handle, input, LIBXSMM_DNN_CONV_FORMAT_NHWC_PTR, &status);
libxsmm_input = libxsmm_dnn_link_buffer(
libxsmm_handle, LIBXSMM_DNN_INPUT, input, LIBXSMM_DNN_TENSOR_FORMAT_NHWC_PTR, &status);
chk_libxsmm_err(status, "Link input buffer");
libxsmm_output = libxsmm_dnn_link_output_buffer_check(
libxsmm_handle, output, LIBXSMM_DNN_CONV_FORMAT_NHWC_PTR, &status);
libxsmm_output = libxsmm_dnn_link_buffer(
libxsmm_handle, LIBXSMM_DNN_OUTPUT, output, LIBXSMM_DNN_TENSOR_FORMAT_NHWC_PTR, &status);
chk_libxsmm_err(status, "Link output buffer");
libxsmm_filter = libxsmm_dnn_link_filter_check(
libxsmm_handle, native_filter, LIBXSMM_DNN_CONV_FORMAT_LIBXSMM_PTR, &status);
libxsmm_filter = libxsmm_dnn_link_filter(
libxsmm_handle, LIBXSMM_DNN_FILTER, native_filter, LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM_PTR, &status);
chk_libxsmm_err(status, "Link filter");
chk_libxsmm_err(libxsmm_dnn_zero_buffer(libxsmm_output), "Zero output");
chk_libxsmm_err(libxsmm_dnn_bind_input_buffer(libxsmm_handle, libxsmm_input),
"Bind input");
chk_libxsmm_err(
libxsmm_dnn_bind_output_buffer(libxsmm_handle, libxsmm_output),
"Bind output");
chk_libxsmm_err(libxsmm_dnn_bind_filter(libxsmm_handle, libxsmm_filter),
"Bind filter");
if (kind == LIBXSMM_DNN_CONV_KIND_BWD) {
libxsmm_dnn_transpose_filter(libxsmm_handle);
if (kind == LIBXSMM_DNN_COMPUTE_KIND_FWD) {
chk_libxsmm_err(libxsmm_dnn_bind_buffer(libxsmm_handle, libxsmm_input, LIBXSMM_DNN_REGULAR_INPUT),
"Bind input forward");
chk_libxsmm_err(
libxsmm_dnn_bind_buffer(libxsmm_handle, libxsmm_output, LIBXSMM_DNN_REGULAR_OUTPUT),
"Bind output forward");
chk_libxsmm_err(libxsmm_dnn_bind_filter(libxsmm_handle, libxsmm_filter, LIBXSMM_DNN_REGULAR_FILTER),
"Bind filter forward");
} else {
chk_libxsmm_err(libxsmm_dnn_bind_buffer(libxsmm_handle, libxsmm_input, LIBXSMM_DNN_GRADIENT_INPUT),
"Bind input backward");
chk_libxsmm_err(
libxsmm_dnn_bind_buffer(libxsmm_handle, libxsmm_output, LIBXSMM_DNN_GRADIENT_OUTPUT),
"Bind output backward");
chk_libxsmm_err(libxsmm_dnn_bind_filter(libxsmm_handle, libxsmm_filter, LIBXSMM_DNN_REGULAR_FILTER),
"Bind filter backward");
}
/* bind scratch */
scratch = (void*)libxsmm_aligned_scratch( libxsmm_dnn_get_scratch_size( libxsmm_handle, kind, &status ), 2097152);
chk_libxsmm_err( status, "scratch allocation" );
chk_libxsmm_err( libxsmm_dnn_bind_scratch( libxsmm_handle, kind, scratch ), "binding scratch" );
if (kind == LIBXSMM_DNN_COMPUTE_KIND_BWD) {
libxsmm_dnn_transpose_filter(libxsmm_handle, LIBXSMM_DNN_FILTER);
}
BlockingCounter counter(num_threads);
for (int i = 0; i < num_threads; ++i) {
worker_threads->workers->Schedule([=, &counter]() {
chk_libxsmm_err(libxsmm_dnn_convolve_st(libxsmm_handle, kind, 0, i),
chk_libxsmm_err(libxsmm_dnn_execute_st(libxsmm_handle, kind, 0, i),
"Worker");
counter.DecrementCount();
});
}
counter.Wait();
/* clean up */
chk_libxsmm_err( libxsmm_dnn_release_scratch( libxsmm_handle, LIBXSMM_DNN_COMPUTE_KIND_ALL ), "release scratch" );
if (kind == LIBXSMM_DNN_COMPUTE_KIND_FWD) {
chk_libxsmm_err( libxsmm_dnn_release_buffer( libxsmm_handle, LIBXSMM_DNN_REGULAR_INPUT ), "release input" );
chk_libxsmm_err( libxsmm_dnn_release_buffer( libxsmm_handle, LIBXSMM_DNN_REGULAR_OUTPUT ), "release output" );
chk_libxsmm_err( libxsmm_dnn_release_filter( libxsmm_handle, LIBXSMM_DNN_REGULAR_FILTER ), "release filter" );
} else {
chk_libxsmm_err( libxsmm_dnn_release_buffer( libxsmm_handle, LIBXSMM_DNN_GRADIENT_INPUT ), "release input" );
chk_libxsmm_err( libxsmm_dnn_release_buffer( libxsmm_handle, LIBXSMM_DNN_GRADIENT_OUTPUT ), "release output" );
chk_libxsmm_err( libxsmm_dnn_release_filter( libxsmm_handle, LIBXSMM_DNN_REGULAR_FILTER ), "release filter" );
}
chk_libxsmm_err(libxsmm_dnn_destroy_buffer(libxsmm_input), "Destroy input");
chk_libxsmm_err(libxsmm_dnn_destroy_buffer(libxsmm_output), "Destroy output");
chk_libxsmm_err(libxsmm_dnn_destroy_filter(libxsmm_filter), "Destroy filter");
if(kind != LIBXSMM_DNN_CONV_KIND_FWD)
chk_libxsmm_err(libxsmm_dnn_destroy_conv_handle(libxsmm_handle),
if(kind != LIBXSMM_DNN_COMPUTE_KIND_FWD)
chk_libxsmm_err(libxsmm_dnn_destroy_conv_layer(libxsmm_handle),
"Destroy handle");
libxsmm_free(native_filter);
libxsmm_free(scratch);
return true; // Succeeded
}
@ -315,7 +348,7 @@ template <typename T>
struct XsmmFwdConv2D<CPUDevice, T> {
bool operator()(OpKernelContext* ctx, const libxsmm_dnn_conv_desc& desc,
const T* input, const T* filter, T* output) {
return CallLibxsmmConvGeneric(ctx, desc, LIBXSMM_DNN_CONV_KIND_FWD, input,
return CallLibxsmmConvGeneric(ctx, desc, LIBXSMM_DNN_COMPUTE_KIND_FWD, input,
filter, output);
}
};
@ -324,7 +357,7 @@ template <typename T>
struct XsmmBkwInputConv2D<CPUDevice, T> {
bool operator()(OpKernelContext* ctx, const libxsmm_dnn_conv_desc& desc,
T* input, const T* filter, const T* output) {
return CallLibxsmmConvGeneric(ctx, desc, LIBXSMM_DNN_CONV_KIND_BWD, input,
return CallLibxsmmConvGeneric(ctx, desc, LIBXSMM_DNN_COMPUTE_KIND_BWD, input,
filter, output);
}
};
@ -333,7 +366,7 @@ template <typename T>
struct XsmmBkwFilterConv2D<CPUDevice, T> {
bool operator()(OpKernelContext* ctx, const libxsmm_dnn_conv_desc& desc,
const T* input, T* filter, const T* output) {
return CallLibxsmmConvGeneric(ctx, desc, LIBXSMM_DNN_CONV_KIND_UPD, input,
return CallLibxsmmConvGeneric(ctx, desc, LIBXSMM_DNN_COMPUTE_KIND_UPD, input,
filter, output);
}
};

15
tensorflow/core/kernels/xsmm_conv2d_test.cc
View File

@ -188,6 +188,8 @@ class XsmmConv2DTest : public OpsTestBase {
TEST_F(XsmmConv2DTest, Basic) {
MakeOp(1);
// setup scoped allocator, which uses cpu_allocator() for this scope
const libxsmm_tf_allocator<libxsmm_scratch_allocator> tf_allocator;
int ifw = 14; /* input width, "W" */
int ifh = 14; /* input height, "H" */
@ -223,9 +225,9 @@ TEST_F(XsmmConv2DTest, Basic) {
//Initialization of Filter and Image
/* allocate data */
float *naive_input = (float*)libxsmm_aligned_malloc( nImg*nIfm*ifhp*ifwp*sizeof(float), 2097152);
float *naive_output = (float*)libxsmm_aligned_malloc( nImg*nOfm*ofhp*ofwp*sizeof(float), 2097152);
float *naive_filter = (float*)libxsmm_aligned_malloc( nOfm*nIfm*kh*kw* sizeof(float), 2097152);
float *naive_input = (float*)libxsmm_aligned_scratch( nImg*nIfm*ifhp*ifwp*sizeof(float), 2097152);
float *naive_output = (float*)libxsmm_aligned_scratch( nImg*nOfm*ofhp*ofwp*sizeof(float), 2097152);
float *naive_filter = (float*)libxsmm_aligned_scratch( nOfm*nIfm*kh*kw* sizeof(float), 2097152);
/* initialize data */
init_buf(naive_input, nImg*nIfm*ifhp*ifwp, 0, 0);
zero_buf(naive_output, nImg*nOfm*ofhp*ofwp);
@ -322,12 +324,11 @@ TEST(XsmmConv2DTest, Basic) {
desc.pad_w_out = 0;
desc.threads = num_threads;
desc.algo = LIBXSMM_DNN_CONV_ALGO_DIRECT;
desc.buffer_format = LIBXSMM_DNN_CONV_FORMAT_NHWC;
desc.filter_format = LIBXSMM_DNN_CONV_FORMAT_LIBXSMM;//LIBXSMM_DNN_CONV_FORMAT_RSCK;
desc.buffer_format = LIBXSMM_DNN_TENSOR_FORMAT_NHWC;
desc.filter_format = LIBXSMM_DNN_TENSOR_FORMAT_LIBXSMM;//LIBXSMM_DNN_TENSOR_FORMAT_RSCK;
desc.fuse_ops = LIBXSMM_DNN_CONV_FUSE_NONE;
desc.options = LIBXSMM_DNN_CONV_OPTION_NONE;
desc.datatype_in = LIBXSMM_DNN_DATATYPE_F32;
desc.datatype_out = LIBXSMM_DNN_DATATYPE_F32;
desc.datatype = LIBXSMM_DNN_DATATYPE_F32;
if (!CanUseXsmmConv2D(desc, data_format)) {
return false;

1
tensorflow/core/ops/math_grad.cc
View File

@ -588,6 +588,7 @@ REGISTER_OP_GRADIENT("Mean", MeanGrad);
// REGISTER_OP_GRADIENT("SegmentMin", SegmentMinGrad);
// REGISTER_OP_GRADIENT("SegmentMax", SegmentMaxGrad);
// REGISTER_OP_GRADIENT("UnsortedSegmentSum", UnsortedSegmentSumGrad);
// REGISTER_OP_GRADIENT("UnsortedSegmentMax", UnsortedSegmentMaxGrad);
Status MinMaxGradHelper(const string& op, const AttrSlice& attrs,
FunctionDef* g) {

98
tensorflow/core/ops/math_ops.cc
View File

@ -1342,6 +1342,36 @@ Status SparseSegmentReductionGradShapeFn(InferenceContext* c) {
return Status::OK();
}
Status UnsortedSegmentReductionShapeFn(InferenceContext* c) {
ShapeHandle s_data = c->input(0);
ShapeHandle s_segment_ids = c->input(1);
ShapeHandle s_num_segments = c->input(2);
TF_RETURN_IF_ERROR(c->WithRank(s_num_segments, 0, &s_num_segments));
ShapeHandle out;
// Leading dimensions of data must be compatible with dimensions of
// <s_segment_ids>.
if (c->RankKnown(s_segment_ids)) {
TF_RETURN_IF_ERROR(
c->MergePrefix(s_data, s_segment_ids, &s_data, &s_segment_ids));
// Get the value of the num_segments input tensor.
DimensionHandle num_segments_dim;
TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(2, &num_segments_dim));
// Output is {segment_id_rank} + s_data[segment_id_rank:].
ShapeHandle s_data_suffix;
TF_RETURN_IF_ERROR(
c->Subshape(s_data, c->Rank(s_segment_ids), &s_data_suffix));
TF_RETURN_IF_ERROR(
c->Concatenate(c->Vector(num_segments_dim), s_data_suffix, &out));
} else {
out = c->UnknownShape();
}
c->set_output(0, out);
return Status::OK();
}
} // namespace
REGISTER_OP("SegmentSum")
@ -1495,36 +1525,7 @@ REGISTER_OP("UnsortedSegmentSum")
.Output("output: T")
.Attr("T: numbertype")
.Attr("Tindices: {int32,int64}")
.SetShapeFn([](InferenceContext* c) {
ShapeHandle s_data = c->input(0);
ShapeHandle s_segment_ids = c->input(1);
ShapeHandle s_num_segments = c->input(2);
TF_RETURN_IF_ERROR(c->WithRank(s_num_segments, 0, &s_num_segments));
ShapeHandle out;
// Leading dimensions of data must be compatible with dimensions of
// <s_segment_ids>.
if (c->RankKnown(s_segment_ids)) {
TF_RETURN_IF_ERROR(
c->MergePrefix(s_data, s_segment_ids, &s_data, &s_segment_ids));
// Get the value of the num_segments input tensor.
DimensionHandle num_segments_dim;
TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(2, &num_segments_dim));
// Output is {segment_id_rank} + s_data[segment_id_rank:].
ShapeHandle s_data_suffix;
TF_RETURN_IF_ERROR(
c->Subshape(s_data, c->Rank(s_segment_ids), &s_data_suffix));
TF_RETURN_IF_ERROR(
c->Concatenate(c->Vector(num_segments_dim), s_data_suffix, &out));
} else {
out = c->UnknownShape();
}
c->set_output(0, out);
return Status::OK();
})
.SetShapeFn(UnsortedSegmentReductionShapeFn)
.Doc(R"doc(
Computes the sum along segments of a tensor.
@ -1554,6 +1555,43 @@ output: Has same shape as data, except for the first `segment_ids.rank`
)doc");
REGISTER_OP("UnsortedSegmentMax")
.Input("data: T")
.Input("segment_ids: Tindices")
.Input("num_segments: int32")
.Output("output: T")
.Attr("T: realnumbertype")
.Attr("Tindices: {int32,int64}")
.SetShapeFn(UnsortedSegmentReductionShapeFn)
.Doc(R"doc(
Computes the Max along segments of a tensor.
Read [the section on
Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation
of segments.
This operator is similar to the [unsorted segment sum operator](../../api_docs/python/math_ops.md#UnsortedSegmentSum).
Instead of computing the sum over segments, it computes the maximum
such that:
\\(output_i = \max_j data_j\\) where max is over `j` such
that `segment_ids[j] == i`.
If the maximum is empty for a given segment ID `i`, it outputs the smallest possible value for specific numeric type,
`output[i] = numeric_limits<T>::min()`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../images/UnsortedSegmentSum.png" alt>
</div>
segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s
first dimension.
output: Has same shape as data, except for dimension 0 which
has size `num_segments`.
)doc");
REGISTER_OP("SparseSegmentSum")
.Input("data: T")
.Input("indices: Tidx")

53
tensorflow/core/ops/ops.pbtxt
View File

@ -25261,6 +25261,59 @@ op {
summary: "Computes the sum along segments of a tensor."
description: "Read [the section on\nSegmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation\nof segments.\n\nComputes a tensor such that\n`(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such\nthat `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids`\nneed not be sorted and need not cover all values in the full\nrange of valid values.\n\nIf the sum is empty for a given segment ID `i`, `output[i] = 0`.\n\n`num_segments` should equal the number of distinct segment IDs.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"../../images/UnsortedSegmentSum.png\" alt>\n</div>"
}
op {
name: "UnsortedSegmentSum"
input_arg {
name: "data"
type_attr: "T"
}
input_arg {
name: "segment_ids"
description: "A tensor whose shape is a prefix of `data.shape`."
type_attr: "Tindices"
}
input_arg {
name: "num_segments"
type: DT_INT32
}
output_arg {
name: "output"
description: "Has same shape as data, except for the first `segment_ids.rank`\ndimensions, which are replaced with a single dimension which has size\n`num_segments`."
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
type: DT_INT64
type: DT_INT32
type: DT_UINT8
type: DT_UINT16
type: DT_INT16
type: DT_INT8
type: DT_QINT8
type: DT_QUINT8
type: DT_QINT32
type: DT_HALF
}
}
}
attr {
name: "Tindices"
type: "type"
allowed_values {
list {
type: DT_INT32
type: DT_INT64
}
}
}
summary: "Computes the max along segments of a tensor."
description: "Read [the section on\nSegmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation\nof segments.\n\nComputes a tensor such that\n\\\\(output_i = \\sum_j data_j\\\\) where sum is over `j` such\nthat `segment_ids[j] == i`. Unlike `SegmentSum`, `segment_ids`\nneed not be sorted and need not cover all values in the full\n range of valid values.\n\nIf the sum is empty for a given segment ID `i`, `output[i] = 0`.\n\n`num_segments` should equal the number of distinct segment IDs.\n\n<div style=\"width:70%; margin:auto; margin-bottom:10px; margin-top:20px;\">\n<img style=\"width:100%\" src=\"../../images/UnsortedSegmentSum.png\" alt>\n</div>"
}
op {
name: "Unstage"
output_arg {

11
tensorflow/core/platform/default/logging.cc
View File

@ -77,14 +77,17 @@ void LogMessage::GenerateLogMessage() {
void LogMessage::GenerateLogMessage() {
static EnvTime* env_time = tensorflow::EnvTime::Default();
time_t now = static_cast<time_t>(env_time->NowSeconds());
uint64 now_micros = env_time->NowMicros();
time_t now_seconds = static_cast<time_t>(now_micros / 1000000);
int32 micros_remainder = static_cast<int32>(now_micros % 1000000);
const size_t time_buffer_size = 30;
char time_buffer[time_buffer_size];
strftime(time_buffer, time_buffer_size, "%Y-%m-%d %H:%M:%S", localtime(&now));
strftime(time_buffer, time_buffer_size, "%Y-%m-%d %H:%M:%S",
localtime(&now_seconds));
// TODO(jeff,sanjay): Replace this with something that logs through the env.
fprintf(stderr, "%s: %c %s:%d] %s\n", time_buffer, "IWEF"[severity_], fname_,
line_, str().c_str());
fprintf(stderr, "%s.%06d: %c %s:%d] %s\n", time_buffer, micros_remainder,
"IWEF"[severity_], fname_, line_, str().c_str());
}
#endif

6
tensorflow/core/public/version.h
View File

@ -18,9 +18,9 @@ limitations under the License.
// TensorFlow uses semantic versioning, see http://semver.org/.
#define TF_MAJOR_VERSION 0
#define TF_MINOR_VERSION 12
#define TF_PATCH_VERSION head
#define TF_MAJOR_VERSION 1
#define TF_MINOR_VERSION 0
#define TF_PATCH_VERSION 0-rc1
// TF_VERSION_SUFFIX is non-empty for pre-releases (e.g. "-alpha", "-alpha.1",
// "-beta", "-rc", "-rc.1")

2
tensorflow/examples/how_tos/reading_data/fully_connected_preloaded.py
View File

@ -17,7 +17,7 @@
Run using bazel:
bazel run -c opt \
bazel run --config opt \
<...>/tensorflow/examples/how_tos/reading_data:fully_connected_preloaded
or, if installed via pip:

2
tensorflow/examples/how_tos/reading_data/fully_connected_preloaded_var.py
View File

@ -17,7 +17,7 @@
Run using bazel:
bazel run -c opt \
bazel run --config opt \
<...>/tensorflow/examples/how_tos/reading_data:fully_connected_preloaded_var
or, if installed via pip:

43
tensorflow/examples/image_retraining/retrain.py
View File

@ -346,6 +346,17 @@ def read_list_of_floats_from_file(file_path):
bottleneck_path_2_bottleneck_values = {}
def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
image_dir, category, sess, jpeg_data_tensor, bottleneck_tensor):
print('Creating bottleneck at ' + bottleneck_path)
image_path = get_image_path(image_lists, label_name, index, image_dir, category)
if not gfile.Exists(image_path):
tf.logging.fatal('File does not exist %s', image_path)
image_data = gfile.FastGFile(image_path, 'rb').read()
bottleneck_values = run_bottleneck_on_image(sess, image_data, jpeg_data_tensor, bottleneck_tensor)
bottleneck_string = ','.join(str(x) for x in bottleneck_values)
with open(bottleneck_path, 'w') as bottleneck_file:
bottleneck_file.write(bottleneck_string)
def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
category, bottleneck_dir, jpeg_data_tensor,
@ -376,28 +387,25 @@ def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
sub_dir = label_lists['dir']
sub_dir_path = os.path.join(bottleneck_dir, sub_dir)
ensure_dir_exists(sub_dir_path)
bottleneck_path = get_bottleneck_path(image_lists, label_name, index,
bottleneck_dir, category)
bottleneck_path = get_bottleneck_path(image_lists, label_name, index, bottleneck_dir, category)
if not os.path.exists(bottleneck_path):
print('Creating bottleneck at ' + bottleneck_path)
image_path = get_image_path(image_lists, label_name, index, image_dir,
category)
if not gfile.Exists(image_path):
tf.logging.fatal('File does not exist %s', image_path)
image_data = gfile.FastGFile(image_path, 'rb').read()
bottleneck_values = run_bottleneck_on_image(sess, image_data,
jpeg_data_tensor,
bottleneck_tensor)
bottleneck_string = ','.join(str(x) for x in bottleneck_values)
with open(bottleneck_path, 'w') as bottleneck_file:
bottleneck_file.write(bottleneck_string)
create_bottleneck_file(bottleneck_path, image_lists, label_name, index, image_dir, category, sess, jpeg_data_tensor, bottleneck_tensor)
with open(bottleneck_path, 'r') as bottleneck_file:
bottleneck_string = bottleneck_file.read()
bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
did_hit_error = False
try:
bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
except:
print("Invalid float found, recreating bottleneck")
did_hit_error = True
if did_hit_error:
create_bottleneck_file(bottleneck_path, image_lists, label_name, index, image_dir, category, sess, jpeg_data_tensor, bottleneck_tensor)
with open(bottleneck_path, 'r') as bottleneck_file:
bottleneck_string = bottleneck_file.read()
# Allow exceptions to propagate here, since they shouldn't happen after a fresh creation
bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
return bottleneck_values
def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
jpeg_data_tensor, bottleneck_tensor):
"""Ensures all the training, testing, and validation bottlenecks are cached.
@ -430,6 +438,7 @@ def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
get_or_create_bottleneck(sess, image_lists, label_name, index,
image_dir, category, bottleneck_dir,
jpeg_data_tensor, bottleneck_tensor)
how_many_bottlenecks += 1
if how_many_bottlenecks % 100 == 0:
print(str(how_many_bottlenecks) + ' bottleneck files created.')

2
tensorflow/examples/multibox_detector/README.md
View File

@ -32,7 +32,7 @@ normalized from 0-1 in left top right bottom order.
To build it, run this command:
```bash
$ bazel build -c opt tensorflow/examples/multibox_detector/...
$ bazel build --config opt tensorflow/examples/multibox_detector/...
```
That should build a binary executable that you can then run like this:

16
tensorflow/examples/udacity/1_notmnist.ipynb
View File

@ -111,6 +111,7 @@
"source": [
"url = 'http://commondatastorage.googleapis.com/books1000/'\n",
"last_percent_reported = None\n",
"data_root = '.' # Change me to store data elsewhere\n",
"\n",
"def download_progress_hook(count, blockSize, totalSize):\n",
" \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
@ -131,17 +132,18 @@
" \n",
"def maybe_download(filename, expected_bytes, force=False):\n",
" \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
" if force or not os.path.exists(filename):\n",
" dest_filename = os.path.join(data_root, filename)\n",
" if force or not os.path.exists(dest_filename):\n",
" print('Attempting to download:', filename) \n",
" filename, _ = urlretrieve(url + filename, filename, reporthook=download_progress_hook)\n",
" filename, _ = urlretrieve(url + filename, dest_filename, reporthook=download_progress_hook)\n",
" print('\\nDownload Complete!')\n",
" statinfo = os.stat(filename)\n",
" statinfo = os.stat(dest_filename)\n",
" if statinfo.st_size == expected_bytes:\n",
" print('Found and verified', filename)\n",
" print('Found and verified', dest_filename)\n",
" else:\n",
" raise Exception(\n",
" 'Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
" return filename\n",
" 'Failed to verify ' + dest_filename + '. Can you get to it with a browser?')\n",
" return dest_filename\n",
"\n",
"train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
"test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
@ -683,7 +685,7 @@
"cellView": "both"
},
"source": [
"pickle_file = 'notMNIST.pickle'\n",
"pickle_file = os.path.join(data_root, 'notMNIST.pickle')\n",
"\n",
"try:\n",
" f = open(pickle_file, 'wb')\n",

244
tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.summary.TaggedRunMetadata.md
View File

@ -1,4 +1,185 @@
- - -
#### `tf.summary.TaggedRunMetadata.ByteSize()` {#TaggedRunMetadata.ByteSize}
- - -
#### `tf.summary.TaggedRunMetadata.Clear()` {#TaggedRunMetadata.Clear}
- - -
#### `tf.summary.TaggedRunMetadata.ClearExtension(extension_handle)` {#TaggedRunMetadata.ClearExtension}
- - -
#### `tf.summary.TaggedRunMetadata.ClearField(field_name)` {#TaggedRunMetadata.ClearField}
- - -
#### `tf.summary.TaggedRunMetadata.CopyFrom(other_msg)` {#TaggedRunMetadata.CopyFrom}
Copies the content of the specified message into the current message.
The method clears the current message and then merges the specified
message using MergeFrom.
##### Args:
* <b>`other_msg`</b>: Message to copy into the current one.
- - -
#### `tf.summary.TaggedRunMetadata.DiscardUnknownFields()` {#TaggedRunMetadata.DiscardUnknownFields}
- - -
#### `tf.summary.TaggedRunMetadata.FindInitializationErrors()` {#TaggedRunMetadata.FindInitializationErrors}
Finds required fields which are not initialized.
##### Returns:
A list of strings. Each string is a path to an uninitialized field from
the top-level message, e.g. "foo.bar[5].baz".
- - -
#### `tf.summary.TaggedRunMetadata.FromString(s)` {#TaggedRunMetadata.FromString}
- - -
#### `tf.summary.TaggedRunMetadata.HasExtension(extension_handle)` {#TaggedRunMetadata.HasExtension}
- - -
#### `tf.summary.TaggedRunMetadata.HasField(field_name)` {#TaggedRunMetadata.HasField}
- - -
#### `tf.summary.TaggedRunMetadata.IsInitialized(errors=None)` {#TaggedRunMetadata.IsInitialized}
Checks if all required fields of a message are set.
##### Args:
* <b>`errors`</b>: A list which, if provided, will be populated with the field
paths of all missing required fields.
##### Returns:
True iff the specified message has all required fields set.
- - -
#### `tf.summary.TaggedRunMetadata.ListFields()` {#TaggedRunMetadata.ListFields}
- - -
#### `tf.summary.TaggedRunMetadata.MergeFrom(msg)` {#TaggedRunMetadata.MergeFrom}
- - -
#### `tf.summary.TaggedRunMetadata.MergeFromString(serialized)` {#TaggedRunMetadata.MergeFromString}
- - -
#### `tf.summary.TaggedRunMetadata.ParseFromString(serialized)` {#TaggedRunMetadata.ParseFromString}
Parse serialized protocol buffer data into this message.
Like MergeFromString(), except we clear the object first and
do not return the value that MergeFromString returns.
- - -
#### `tf.summary.TaggedRunMetadata.RegisterExtension(extension_handle)` {#TaggedRunMetadata.RegisterExtension}
- - -
#### `tf.summary.TaggedRunMetadata.SerializePartialToString()` {#TaggedRunMetadata.SerializePartialToString}
- - -
#### `tf.summary.TaggedRunMetadata.SerializeToString()` {#TaggedRunMetadata.SerializeToString}
- - -
#### `tf.summary.TaggedRunMetadata.SetInParent()` {#TaggedRunMetadata.SetInParent}
Sets the _cached_byte_size_dirty bit to true,
and propagates this to our listener iff this was a state change.
- - -
#### `tf.summary.TaggedRunMetadata.WhichOneof(oneof_name)` {#TaggedRunMetadata.WhichOneof}
Returns the name of the currently set field inside a oneof, or None.
- - -
#### `tf.summary.TaggedRunMetadata.__deepcopy__(memo=None)` {#TaggedRunMetadata.__deepcopy__}
- - -
#### `tf.summary.TaggedRunMetadata.__eq__(other)` {#TaggedRunMetadata.__eq__}
- - -
#### `tf.summary.TaggedRunMetadata.__getstate__()` {#TaggedRunMetadata.__getstate__}
@ -6,3 +187,66 @@
Support the pickle protocol.
- - -
#### `tf.summary.TaggedRunMetadata.__hash__()` {#TaggedRunMetadata.__hash__}
- - -
#### `tf.summary.TaggedRunMetadata.__init__(**kwargs)` {#TaggedRunMetadata.__init__}
- - -
#### `tf.summary.TaggedRunMetadata.__ne__(other_msg)` {#TaggedRunMetadata.__ne__}
- - -
#### `tf.summary.TaggedRunMetadata.__repr__()` {#TaggedRunMetadata.__repr__}
- - -
#### `tf.summary.TaggedRunMetadata.__setstate__(state)` {#TaggedRunMetadata.__setstate__}
Support the pickle protocol.
- - -
#### `tf.summary.TaggedRunMetadata.__str__()` {#TaggedRunMetadata.__str__}
- - -
#### `tf.summary.TaggedRunMetadata.__unicode__()` {#TaggedRunMetadata.__unicode__}
- - -
#### `tf.summary.TaggedRunMetadata.run_metadata` {#TaggedRunMetadata.run_metadata}
Magic attribute generated for "run_metadata" proto field.
- - -
#### `tf.summary.TaggedRunMetadata.tag` {#TaggedRunMetadata.tag}
Magic attribute generated for "tag" proto field.

237
tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.summary.SummaryDescription.md
View File

@ -1,4 +1,185 @@
- - -
#### `tf.summary.SummaryDescription.ByteSize()` {#SummaryDescription.ByteSize}
- - -
#### `tf.summary.SummaryDescription.Clear()` {#SummaryDescription.Clear}
- - -
#### `tf.summary.SummaryDescription.ClearExtension(extension_handle)` {#SummaryDescription.ClearExtension}
- - -
#### `tf.summary.SummaryDescription.ClearField(field_name)` {#SummaryDescription.ClearField}
- - -
#### `tf.summary.SummaryDescription.CopyFrom(other_msg)` {#SummaryDescription.CopyFrom}
Copies the content of the specified message into the current message.
The method clears the current message and then merges the specified
message using MergeFrom.
##### Args:
* <b>`other_msg`</b>: Message to copy into the current one.
- - -
#### `tf.summary.SummaryDescription.DiscardUnknownFields()` {#SummaryDescription.DiscardUnknownFields}
- - -
#### `tf.summary.SummaryDescription.FindInitializationErrors()` {#SummaryDescription.FindInitializationErrors}
Finds required fields which are not initialized.
##### Returns:
A list of strings. Each string is a path to an uninitialized field from
the top-level message, e.g. "foo.bar[5].baz".
- - -
#### `tf.summary.SummaryDescription.FromString(s)` {#SummaryDescription.FromString}
- - -
#### `tf.summary.SummaryDescription.HasExtension(extension_handle)` {#SummaryDescription.HasExtension}
- - -
#### `tf.summary.SummaryDescription.HasField(field_name)` {#SummaryDescription.HasField}
- - -
#### `tf.summary.SummaryDescription.IsInitialized(errors=None)` {#SummaryDescription.IsInitialized}
Checks if all required fields of a message are set.
##### Args:
* <b>`errors`</b>: A list which, if provided, will be populated with the field
paths of all missing required fields.
##### Returns:
True iff the specified message has all required fields set.
- - -
#### `tf.summary.SummaryDescription.ListFields()` {#SummaryDescription.ListFields}
- - -
#### `tf.summary.SummaryDescription.MergeFrom(msg)` {#SummaryDescription.MergeFrom}
- - -
#### `tf.summary.SummaryDescription.MergeFromString(serialized)` {#SummaryDescription.MergeFromString}
- - -
#### `tf.summary.SummaryDescription.ParseFromString(serialized)` {#SummaryDescription.ParseFromString}
Parse serialized protocol buffer data into this message.
Like MergeFromString(), except we clear the object first and
do not return the value that MergeFromString returns.
- - -
#### `tf.summary.SummaryDescription.RegisterExtension(extension_handle)` {#SummaryDescription.RegisterExtension}
- - -
#### `tf.summary.SummaryDescription.SerializePartialToString()` {#SummaryDescription.SerializePartialToString}
- - -
#### `tf.summary.SummaryDescription.SerializeToString()` {#SummaryDescription.SerializeToString}
- - -
#### `tf.summary.SummaryDescription.SetInParent()` {#SummaryDescription.SetInParent}
Sets the _cached_byte_size_dirty bit to true,
and propagates this to our listener iff this was a state change.
- - -
#### `tf.summary.SummaryDescription.WhichOneof(oneof_name)` {#SummaryDescription.WhichOneof}
Returns the name of the currently set field inside a oneof, or None.
- - -
#### `tf.summary.SummaryDescription.__deepcopy__(memo=None)` {#SummaryDescription.__deepcopy__}
- - -
#### `tf.summary.SummaryDescription.__eq__(other)` {#SummaryDescription.__eq__}
- - -
#### `tf.summary.SummaryDescription.__getstate__()` {#SummaryDescription.__getstate__}
@ -6,3 +187,59 @@
Support the pickle protocol.
- - -
#### `tf.summary.SummaryDescription.__hash__()` {#SummaryDescription.__hash__}
- - -
#### `tf.summary.SummaryDescription.__init__(**kwargs)` {#SummaryDescription.__init__}
- - -
#### `tf.summary.SummaryDescription.__ne__(other_msg)` {#SummaryDescription.__ne__}
- - -
#### `tf.summary.SummaryDescription.__repr__()` {#SummaryDescription.__repr__}
- - -
#### `tf.summary.SummaryDescription.__setstate__(state)` {#SummaryDescription.__setstate__}
Support the pickle protocol.
- - -
#### `tf.summary.SummaryDescription.__str__()` {#SummaryDescription.__str__}
- - -
#### `tf.summary.SummaryDescription.__unicode__()` {#SummaryDescription.__unicode__}
- - -
#### `tf.summary.SummaryDescription.type_hint` {#SummaryDescription.type_hint}
Magic attribute generated for "type_hint" proto field.

521
tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.test.TestCase.md
View File

@ -173,125 +173,6 @@ Checks that for all elements of farray1 and farray2
* <b>`err`</b>: a float value.
- - -
#### `tf.test.TestCase.assertBetween(value, minv, maxv, msg=None)` {#TestCase.assertBetween}
Asserts that value is between minv and maxv (inclusive).
- - -
#### `tf.test.TestCase.assertCommandFails(command, regexes, env=None, close_fds=True, msg=None)` {#TestCase.assertCommandFails}
Asserts a shell command fails and the error matches a regex in a list.
##### Args:
* <b>`command`</b>: List or string representing the command to run.
* <b>`regexes`</b>: the list of regular expression strings.
* <b>`env`</b>: Dictionary of environment variable settings.
* <b>`close_fds`</b>: Whether or not to close all open fd's in the child after
forking.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertCommandSucceeds(command, regexes=('',), env=None, close_fds=True, msg=None)` {#TestCase.assertCommandSucceeds}
Asserts that a shell command succeeds (i.e. exits with code 0).
##### Args:
* <b>`command`</b>: List or string representing the command to run.
* <b>`regexes`</b>: List of regular expression byte strings that match success.
* <b>`env`</b>: Dictionary of environment variable settings.
* <b>`close_fds`</b>: Whether or not to close all open fd's in the child after
forking.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsExactSubsequence(container, subsequence, msg=None)` {#TestCase.assertContainsExactSubsequence}
Assert that "container" contains "subsequence" as an exact subsequence.
Asserts that "container" contains all the elements of "subsequence", in
order, and without other elements interspersed. For example, [1, 2, 3] is an
exact subsequence of [0, 0, 1, 2, 3, 0] but not of [0, 0, 1, 2, 0, 3, 0].
##### Args:
* <b>`container`</b>: the list we're testing for subsequence inclusion.
* <b>`subsequence`</b>: the list we hope will be an exact subsequence of container.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsInOrder(strings, target, msg=None)` {#TestCase.assertContainsInOrder}
Asserts that the strings provided are found in the target in order.
This may be useful for checking HTML output.
##### Args:
* <b>`strings`</b>: A list of strings, such as [ 'fox', 'dog' ]
* <b>`target`</b>: A target string in which to look for the strings, such as
'The quick brown fox jumped over the lazy dog'.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsSubsequence(container, subsequence, msg=None)` {#TestCase.assertContainsSubsequence}
Assert that "container" contains "subsequence" as a subsequence.
Asserts that "container" contains all the elements of "subsequence", in
order, but possibly with other elements interspersed. For example, [1, 2, 3]
is a subsequence of [0, 0, 1, 2, 0, 3, 0] but not of [0, 0, 1, 3, 0, 2, 0].
##### Args:
* <b>`container`</b>: the list we're testing for subsequence inclusion.
* <b>`subsequence`</b>: the list we hope will be a subsequence of container.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsSubset(expected_subset, actual_set, msg=None)` {#TestCase.assertContainsSubset}
Checks whether actual iterable is a superset of expected iterable.
- - -
#### `tf.test.TestCase.assertCountEqual(*args, **kwargs)` {#TestCase.assertCountEqual}
An unordered sequence specific comparison.
Equivalent to assertItemsEqual(). This method is a compatibility layer
for Python 3k, since 2to3 does not convert assertItemsEqual() calls into
assertCountEqual() calls.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`msg`</b>: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertDeviceEqual(device1, device2)` {#TestCase.assertDeviceEqual}
@ -314,48 +195,9 @@ Checks whether actual is a superset of expected.
- - -
#### `tf.test.TestCase.assertDictEqual(a, b, msg=None)` {#TestCase.assertDictEqual}
Raises AssertionError if a and b are not equal dictionaries.
##### Args:
#### `tf.test.TestCase.assertDictEqual(d1, d2, msg=None)` {#TestCase.assertDictEqual}
* <b>`a`</b>: A dict, the expected value.
* <b>`b`</b>: A dict, the actual value.
* <b>`msg`</b>: An optional str, the associated message.
##### Raises:
* <b>`AssertionError`</b>: if the dictionaries are not equal.
- - -
#### `tf.test.TestCase.assertEmpty(container, msg=None)` {#TestCase.assertEmpty}
Assert that an object has zero length.
##### Args:
* <b>`container`</b>: Anything that implements the collections.Sized interface.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertEndsWith(actual, expected_end, msg=None)` {#TestCase.assertEndsWith}
Assert that actual.endswith(expected_end) is True.
##### Args:
* <b>`actual`</b>: str
* <b>`expected_end`</b>: str
* <b>`msg`</b>: Optional message to report on failure.
- - -
@ -440,11 +282,10 @@ Included for symmetry with assertIsNone.
- - -
#### `tf.test.TestCase.assertItemsEqual(*args, **kwargs)` {#TestCase.assertItemsEqual}
#### `tf.test.TestCase.assertItemsEqual(expected_seq, actual_seq, msg=None)` {#TestCase.assertItemsEqual}
An unordered sequence specific comparison.
It asserts that actual_seq and expected_seq have the same element counts.
An unordered sequence specific comparison. It asserts that
actual_seq and expected_seq have the same element counts.
Equivalent to::
self.assertEqual(Counter(iter(actual_seq)),
@ -457,30 +298,6 @@ Asserts that each element has the same count in both sequences.
- [0, 1, 1] and [1, 0, 1] compare equal.
- [0, 0, 1] and [0, 1] compare unequal.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`msg`</b>: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertJsonEqual(first, second, msg=None)` {#TestCase.assertJsonEqual}
Asserts that the JSON objects defined in two strings are equal.
A summary of the differences will be included in the failure message
using assertSameStructure.
##### Args:
* <b>`first`</b>: A string contining JSON to decode and compare to second.
* <b>`second`</b>: A string contining JSON to decode and compare to first.
* <b>`msg`</b>: Additional text to include in the failure message.
- - -
@ -550,13 +367,6 @@ if not.
* <b>`msg`</b>: An optional string message to append to the failure message.
- - -
#### `tf.test.TestCase.assertNoCommonElements(expected_seq, actual_seq, msg=None)` {#TestCase.assertNoCommonElements}
Checks whether actual iterable and expected iterable are disjoint.
- - -
#### `tf.test.TestCase.assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)` {#TestCase.assertNotAlmostEqual}
@ -587,33 +397,6 @@ as significant digits (measured from the most signficant digit).
Objects that are equal automatically fail.
- - -
#### `tf.test.TestCase.assertNotEmpty(container, msg=None)` {#TestCase.assertNotEmpty}
Assert that an object has non-zero length.
##### Args:
* <b>`container`</b>: Anything that implements the collections.Sized interface.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertNotEndsWith(actual, unexpected_end, msg=None)` {#TestCase.assertNotEndsWith}
Assert that actual.endswith(unexpected_end) is False.
##### Args:
* <b>`actual`</b>: str
* <b>`unexpected_end`</b>: str
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertNotEqual(first, second, msg=None)` {#TestCase.assertNotEqual}
@ -651,20 +434,6 @@ Included for symmetry with assertIsInstance.
Fail the test if the text matches the regular expression.
- - -
#### `tf.test.TestCase.assertNotStartsWith(actual, unexpected_start, msg=None)` {#TestCase.assertNotStartsWith}
Assert that actual.startswith(unexpected_start) is False.
##### Args:
* <b>`actual`</b>: str
* <b>`unexpected_start`</b>: str
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertProtoEquals(expected_message_maybe_ascii, message)` {#TestCase.assertProtoEquals}
@ -739,38 +508,6 @@ Asserts that the message in a raised exception matches a regexp.
* <b>`kwargs`</b>: Extra kwargs.
- - -
#### `tf.test.TestCase.assertRaisesWithLiteralMatch(expected_exception, expected_exception_message, callable_obj=None, *args, **kwargs)` {#TestCase.assertRaisesWithLiteralMatch}
Asserts that the message in a raised exception equals the given string.
Unlike assertRaisesRegexp, this method takes a literal string, not
a regular expression.
with self.assertRaisesWithLiteralMatch(ExType, 'message'):
DoSomething()
##### Args:
* <b>`expected_exception`</b>: Exception class expected to be raised.
* <b>`expected_exception_message`</b>: String message expected in the raised
exception. For a raise exception e, expected_exception_message must
equal str(e).
* <b>`callable_obj`</b>: Function to be called, or None to return a context.
* <b>`args`</b>: Extra args.
* <b>`kwargs`</b>: Extra kwargs.
##### Returns:
A context manager if callable_obj is None. Otherwise, None.
##### Raises:
self.failureException if callable_obj does not raise a macthing exception.
- - -
#### `tf.test.TestCase.assertRaisesWithPredicateMatch(exception_type, expected_err_re_or_predicate)` {#TestCase.assertRaisesWithPredicateMatch}
@ -795,71 +532,6 @@ predicate search.
exception.
- - -
#### `tf.test.TestCase.assertRaisesWithRegexpMatch(expected_exception, expected_regexp, callable_obj=None, *args, **kwargs)` {#TestCase.assertRaisesWithRegexpMatch}
Asserts that the message in a raised exception matches the given regexp.
This is just a wrapper around assertRaisesRegexp. Please use
assertRaisesRegexp instead of assertRaisesWithRegexpMatch.
##### Args:
* <b>`expected_exception`</b>: Exception class expected to be raised.
* <b>`expected_regexp`</b>: Regexp (re pattern object or string) expected to be
found in error message.
* <b>`callable_obj`</b>: Function to be called, or None to return a context.
* <b>`args`</b>: Extra args.
* <b>`kwargs`</b>: Extra keyword args.
##### Returns:
A context manager if callable_obj is None. Otherwise, None.
##### Raises:
self.failureException if callable_obj does not raise a macthing exception.
- - -
#### `tf.test.TestCase.assertRegexMatch(actual_str, regexes, message=None)` {#TestCase.assertRegexMatch}
Asserts that at least one regex in regexes matches str.
If possible you should use assertRegexpMatches, which is a simpler
version of this method. assertRegexpMatches takes a single regular
expression (a string or re compiled object) instead of a list.
Notes:
1. This function uses substring matching, i.e. the matching
succeeds if *any* substring of the error message matches *any*
regex in the list. This is more convenient for the user than
full-string matching.
2. If regexes is the empty list, the matching will always fail.
3. Use regexes=[''] for a regex that will always pass.
4. '.' matches any single character *except* the newline. To
match any character, use '(.|
)'.
5. '^' matches the beginning of each line, not just the beginning
of the string. Similarly, '$' matches the end of each line.
6. An exception will be thrown if regexes contains an invalid
regex.
Args:
actual_str: The string we try to match with the items in regexes.
regexes: The regular expressions we want to match against str.
See "Notes" above for detailed notes on how this is interpreted.
message: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertRegexpMatches(text, expected_regexp, msg=None)` {#TestCase.assertRegexpMatches}
@ -867,79 +539,6 @@ Asserts that at least one regex in regexes matches str.
Fail the test unless the text matches the regular expression.
- - -
#### `tf.test.TestCase.assertSameElements(expected_seq, actual_seq, msg=None)` {#TestCase.assertSameElements}
Assert that two sequences have the same elements (in any order).
This method, unlike assertItemsEqual, doesn't care about any
duplicates in the expected and actual sequences.
>> assertSameElements([1, 1, 1, 0, 0, 0], [0, 1])
# Doesn't raise an AssertionError
If possible, you should use assertItemsEqual instead of
assertSameElements.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`msg`</b>: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertSameStructure(a, b, aname='a', bname='b', msg=None)` {#TestCase.assertSameStructure}
Asserts that two values contain the same structural content.
The two arguments should be data trees consisting of trees of dicts and
lists. They will be deeply compared by walking into the contents of dicts
and lists; other items will be compared using the == operator.
If the two structures differ in content, the failure message will indicate
the location within the structures where the first difference is found.
This may be helpful when comparing large structures.
##### Args:
* <b>`a`</b>: The first structure to compare.
* <b>`b`</b>: The second structure to compare.
* <b>`aname`</b>: Variable name to use for the first structure in assertion messages.
* <b>`bname`</b>: Variable name to use for the second structure.
* <b>`msg`</b>: Additional text to include in the failure message.
- - -
#### `tf.test.TestCase.assertSequenceAlmostEqual(expected_seq, actual_seq, places=None, msg=None, delta=None)` {#TestCase.assertSequenceAlmostEqual}
An approximate equality assertion for ordered sequences.
Fail if the two sequences are unequal as determined by their value
differences rounded to the given number of decimal places (default 7) and
comparing to zero, or by comparing that the difference between each value
in the two sequences is more than the given delta.
Note that decimal places (from zero) are usually not the same as significant
digits (measured from the most signficant digit).
If the two sequences compare equal then they will automatically compare
almost equal.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`places`</b>: The number of decimal places to compare.
* <b>`msg`</b>: The message to be printed if the test fails.
* <b>`delta`</b>: The OK difference between compared values.
- - -
#### `tf.test.TestCase.assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)` {#TestCase.assertSequenceEqual}
@ -960,26 +559,6 @@ which can be indexed, has a length, and has an equality operator.
differences.
- - -
#### `tf.test.TestCase.assertSequenceStartsWith(prefix, whole, msg=None)` {#TestCase.assertSequenceStartsWith}
An equality assertion for the beginning of ordered sequences.
If prefix is an empty sequence, it will raise an error unless whole is also
an empty sequence.
If prefix is not a sequence, it will raise an error if the first element of
whole does not match.
##### Args:
* <b>`prefix`</b>: A sequence expected at the beginning of the whole parameter.
* <b>`whole`</b>: The sequence in which to look for prefix.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertSetEqual(set1, set2, msg=None)` {#TestCase.assertSetEqual}
@ -1031,51 +610,6 @@ Assert that actual.startswith(expected_start) is True.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertTotallyOrdered(*groups, **kwargs)` {#TestCase.assertTotallyOrdered}
Asserts that total ordering has been implemented correctly.
For example, say you have a class A that compares only on its attribute x.
Comparators other than __lt__ are omitted for brevity.
class A(object):
def __init__(self, x, y):
self.x = x
self.y = y
def __hash__(self):
return hash(self.x)
def __lt__(self, other):
try:
return self.x < other.x
except AttributeError:
return NotImplemented
assertTotallyOrdered will check that instances can be ordered correctly.
For example,
self.assertTotallyOrdered(
[None], # None should come before everything else.
[1], # Integers sort earlier.
[A(1, 'a')],
[A(2, 'b')], # 2 is after 1.
[A(3, 'c'), A(3, 'd')], # The second argument is irrelevant.
[A(4, 'z')],
['foo']) # Strings sort last.
##### Args:
* <b>`*groups`</b>: A list of groups of elements. Each group of elements is a list
of objects that are equal. The elements in each group must be less than
the elements in the group after it. For example, these groups are
totally ordered: [None], [1], [2, 2], [3].
* <b>`**kwargs`</b>: optional msg keyword argument can be passed.
- - -
#### `tf.test.TestCase.assertTrue(expr, msg=None)` {#TestCase.assertTrue}
@ -1098,13 +632,6 @@ A tuple-specific equality assertion.
differences.
- - -
#### `tf.test.TestCase.assertUrlEqual(a, b, msg=None)` {#TestCase.assertUrlEqual}
Asserts that urls are equal, ignoring ordering of query params.
- - -
#### `tf.test.TestCase.assert_(expr, msg=None)` {#TestCase.assert_}
@ -1166,9 +693,9 @@ tearDown.
- - -
#### `tf.test.TestCase.fail(msg=None, prefix=None)` {#TestCase.fail}
#### `tf.test.TestCase.fail(msg=None)` {#TestCase.fail}
Fail immediately with the given message, optionally prefixed.
Fail immediately, with the given message.
- - -
@ -1220,13 +747,6 @@ Fail immediately with the given message, optionally prefixed.
- - -
#### `tf.test.TestCase.getRecordedProperties()` {#TestCase.getRecordedProperties}
Return any properties that the user has recorded.
- - -
#### `tf.test.TestCase.get_temp_dir()` {#TestCase.get_temp_dir}
@ -1249,20 +769,6 @@ pollute each others environment.
- - -
#### `tf.test.TestCase.recordProperty(property_name, property_value)` {#TestCase.recordProperty}
Record an arbitrary property for later use.
##### Args:
* <b>`property_name`</b>: str, name of property to record; must be a valid XML
attribute name
* <b>`property_value`</b>: value of property; must be valid XML attribute value
- - -
#### `tf.test.TestCase.run(result=None)` {#TestCase.run}
@ -1288,18 +794,11 @@ Hook method for setting up class fixture before running tests in the class.
#### `tf.test.TestCase.shortDescription()` {#TestCase.shortDescription}
Format both the test method name and the first line of its docstring.
Returns a one-line description of the test, or None if no
description has been provided.
If no docstring is given, only returns the method name.
This method overrides unittest.TestCase.shortDescription(), which
only returns the first line of the docstring, obscuring the name
of the test upon failure.
##### Returns:
* <b>`desc`</b>: A short description of a test method.
The default implementation of this method returns the first line of
the specified test method's docstring.
- - -

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@ -0,0 +1,4 @@
#### `tf.summary.SummaryDescription.RegisterExtension(extension_handle)` {#SummaryDescription.RegisterExtension}

4
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@ -0,0 +1,4 @@
#### `tf.summary.SummaryDescription.FromString(s)` {#SummaryDescription.FromString}

4
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@ -0,0 +1,4 @@
#### `tf.summary.TaggedRunMetadata.RegisterExtension(extension_handle)` {#TaggedRunMetadata.RegisterExtension}

4
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@ -0,0 +1,4 @@
#### `tf.summary.TaggedRunMetadata.FromString(s)` {#TaggedRunMetadata.FromString}

481
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@ -485,6 +485,187 @@ metadata is stored in its NodeDef. This method retrieves the description.
### `class tf.summary.SummaryDescription` {#SummaryDescription}
- - -
#### `tf.summary.SummaryDescription.ByteSize()` {#SummaryDescription.ByteSize}
- - -
#### `tf.summary.SummaryDescription.Clear()` {#SummaryDescription.Clear}
- - -
#### `tf.summary.SummaryDescription.ClearExtension(extension_handle)` {#SummaryDescription.ClearExtension}
- - -
#### `tf.summary.SummaryDescription.ClearField(field_name)` {#SummaryDescription.ClearField}
- - -
#### `tf.summary.SummaryDescription.CopyFrom(other_msg)` {#SummaryDescription.CopyFrom}
Copies the content of the specified message into the current message.
The method clears the current message and then merges the specified
message using MergeFrom.
##### Args:
* <b>`other_msg`</b>: Message to copy into the current one.
- - -
#### `tf.summary.SummaryDescription.DiscardUnknownFields()` {#SummaryDescription.DiscardUnknownFields}
- - -
#### `tf.summary.SummaryDescription.FindInitializationErrors()` {#SummaryDescription.FindInitializationErrors}
Finds required fields which are not initialized.
##### Returns:
A list of strings. Each string is a path to an uninitialized field from
the top-level message, e.g. "foo.bar[5].baz".
- - -
#### `tf.summary.SummaryDescription.FromString(s)` {#SummaryDescription.FromString}
- - -
#### `tf.summary.SummaryDescription.HasExtension(extension_handle)` {#SummaryDescription.HasExtension}
- - -
#### `tf.summary.SummaryDescription.HasField(field_name)` {#SummaryDescription.HasField}
- - -
#### `tf.summary.SummaryDescription.IsInitialized(errors=None)` {#SummaryDescription.IsInitialized}
Checks if all required fields of a message are set.
##### Args:
* <b>`errors`</b>: A list which, if provided, will be populated with the field
paths of all missing required fields.
##### Returns:
True iff the specified message has all required fields set.
- - -
#### `tf.summary.SummaryDescription.ListFields()` {#SummaryDescription.ListFields}
- - -
#### `tf.summary.SummaryDescription.MergeFrom(msg)` {#SummaryDescription.MergeFrom}
- - -
#### `tf.summary.SummaryDescription.MergeFromString(serialized)` {#SummaryDescription.MergeFromString}
- - -
#### `tf.summary.SummaryDescription.ParseFromString(serialized)` {#SummaryDescription.ParseFromString}
Parse serialized protocol buffer data into this message.
Like MergeFromString(), except we clear the object first and
do not return the value that MergeFromString returns.
- - -
#### `tf.summary.SummaryDescription.RegisterExtension(extension_handle)` {#SummaryDescription.RegisterExtension}
- - -
#### `tf.summary.SummaryDescription.SerializePartialToString()` {#SummaryDescription.SerializePartialToString}
- - -
#### `tf.summary.SummaryDescription.SerializeToString()` {#SummaryDescription.SerializeToString}
- - -
#### `tf.summary.SummaryDescription.SetInParent()` {#SummaryDescription.SetInParent}
Sets the _cached_byte_size_dirty bit to true,
and propagates this to our listener iff this was a state change.
- - -
#### `tf.summary.SummaryDescription.WhichOneof(oneof_name)` {#SummaryDescription.WhichOneof}
Returns the name of the currently set field inside a oneof, or None.
- - -
#### `tf.summary.SummaryDescription.__deepcopy__(memo=None)` {#SummaryDescription.__deepcopy__}
- - -
#### `tf.summary.SummaryDescription.__eq__(other)` {#SummaryDescription.__eq__}
- - -
#### `tf.summary.SummaryDescription.__getstate__()` {#SummaryDescription.__getstate__}
@ -492,12 +673,249 @@ metadata is stored in its NodeDef. This method retrieves the description.
Support the pickle protocol.
- - -
#### `tf.summary.SummaryDescription.__hash__()` {#SummaryDescription.__hash__}
- - -
#### `tf.summary.SummaryDescription.__init__(**kwargs)` {#SummaryDescription.__init__}
- - -
#### `tf.summary.SummaryDescription.__ne__(other_msg)` {#SummaryDescription.__ne__}
- - -
#### `tf.summary.SummaryDescription.__repr__()` {#SummaryDescription.__repr__}
- - -
#### `tf.summary.SummaryDescription.__setstate__(state)` {#SummaryDescription.__setstate__}
Support the pickle protocol.
- - -
#### `tf.summary.SummaryDescription.__str__()` {#SummaryDescription.__str__}
- - -
#### `tf.summary.SummaryDescription.__unicode__()` {#SummaryDescription.__unicode__}
- - -
#### `tf.summary.SummaryDescription.type_hint` {#SummaryDescription.type_hint}
Magic attribute generated for "type_hint" proto field.
- - -
### `class tf.summary.TaggedRunMetadata` {#TaggedRunMetadata}
- - -
#### `tf.summary.TaggedRunMetadata.ByteSize()` {#TaggedRunMetadata.ByteSize}
- - -
#### `tf.summary.TaggedRunMetadata.Clear()` {#TaggedRunMetadata.Clear}
- - -
#### `tf.summary.TaggedRunMetadata.ClearExtension(extension_handle)` {#TaggedRunMetadata.ClearExtension}
- - -
#### `tf.summary.TaggedRunMetadata.ClearField(field_name)` {#TaggedRunMetadata.ClearField}
- - -
#### `tf.summary.TaggedRunMetadata.CopyFrom(other_msg)` {#TaggedRunMetadata.CopyFrom}
Copies the content of the specified message into the current message.
The method clears the current message and then merges the specified
message using MergeFrom.
##### Args:
* <b>`other_msg`</b>: Message to copy into the current one.
- - -
#### `tf.summary.TaggedRunMetadata.DiscardUnknownFields()` {#TaggedRunMetadata.DiscardUnknownFields}
- - -
#### `tf.summary.TaggedRunMetadata.FindInitializationErrors()` {#TaggedRunMetadata.FindInitializationErrors}
Finds required fields which are not initialized.
##### Returns:
A list of strings. Each string is a path to an uninitialized field from
the top-level message, e.g. "foo.bar[5].baz".
- - -
#### `tf.summary.TaggedRunMetadata.FromString(s)` {#TaggedRunMetadata.FromString}
- - -
#### `tf.summary.TaggedRunMetadata.HasExtension(extension_handle)` {#TaggedRunMetadata.HasExtension}
- - -
#### `tf.summary.TaggedRunMetadata.HasField(field_name)` {#TaggedRunMetadata.HasField}
- - -
#### `tf.summary.TaggedRunMetadata.IsInitialized(errors=None)` {#TaggedRunMetadata.IsInitialized}
Checks if all required fields of a message are set.
##### Args:
* <b>`errors`</b>: A list which, if provided, will be populated with the field
paths of all missing required fields.
##### Returns:
True iff the specified message has all required fields set.
- - -
#### `tf.summary.TaggedRunMetadata.ListFields()` {#TaggedRunMetadata.ListFields}
- - -
#### `tf.summary.TaggedRunMetadata.MergeFrom(msg)` {#TaggedRunMetadata.MergeFrom}
- - -
#### `tf.summary.TaggedRunMetadata.MergeFromString(serialized)` {#TaggedRunMetadata.MergeFromString}
- - -
#### `tf.summary.TaggedRunMetadata.ParseFromString(serialized)` {#TaggedRunMetadata.ParseFromString}
Parse serialized protocol buffer data into this message.
Like MergeFromString(), except we clear the object first and
do not return the value that MergeFromString returns.
- - -
#### `tf.summary.TaggedRunMetadata.RegisterExtension(extension_handle)` {#TaggedRunMetadata.RegisterExtension}
- - -
#### `tf.summary.TaggedRunMetadata.SerializePartialToString()` {#TaggedRunMetadata.SerializePartialToString}
- - -
#### `tf.summary.TaggedRunMetadata.SerializeToString()` {#TaggedRunMetadata.SerializeToString}
- - -
#### `tf.summary.TaggedRunMetadata.SetInParent()` {#TaggedRunMetadata.SetInParent}
Sets the _cached_byte_size_dirty bit to true,
and propagates this to our listener iff this was a state change.
- - -
#### `tf.summary.TaggedRunMetadata.WhichOneof(oneof_name)` {#TaggedRunMetadata.WhichOneof}
Returns the name of the currently set field inside a oneof, or None.
- - -
#### `tf.summary.TaggedRunMetadata.__deepcopy__(memo=None)` {#TaggedRunMetadata.__deepcopy__}
- - -
#### `tf.summary.TaggedRunMetadata.__eq__(other)` {#TaggedRunMetadata.__eq__}
- - -
#### `tf.summary.TaggedRunMetadata.__getstate__()` {#TaggedRunMetadata.__getstate__}
@ -505,4 +923,67 @@ Support the pickle protocol.
Support the pickle protocol.
- - -
#### `tf.summary.TaggedRunMetadata.__hash__()` {#TaggedRunMetadata.__hash__}
- - -
#### `tf.summary.TaggedRunMetadata.__init__(**kwargs)` {#TaggedRunMetadata.__init__}
- - -
#### `tf.summary.TaggedRunMetadata.__ne__(other_msg)` {#TaggedRunMetadata.__ne__}
- - -
#### `tf.summary.TaggedRunMetadata.__repr__()` {#TaggedRunMetadata.__repr__}
- - -
#### `tf.summary.TaggedRunMetadata.__setstate__(state)` {#TaggedRunMetadata.__setstate__}
Support the pickle protocol.
- - -
#### `tf.summary.TaggedRunMetadata.__str__()` {#TaggedRunMetadata.__str__}
- - -
#### `tf.summary.TaggedRunMetadata.__unicode__()` {#TaggedRunMetadata.__unicode__}
- - -
#### `tf.summary.TaggedRunMetadata.run_metadata` {#TaggedRunMetadata.run_metadata}
Magic attribute generated for "run_metadata" proto field.
- - -
#### `tf.summary.TaggedRunMetadata.tag` {#TaggedRunMetadata.tag}
Magic attribute generated for "tag" proto field.

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@ -213,125 +213,6 @@ Checks that for all elements of farray1 and farray2
* <b>`err`</b>: a float value.
- - -
#### `tf.test.TestCase.assertBetween(value, minv, maxv, msg=None)` {#TestCase.assertBetween}
Asserts that value is between minv and maxv (inclusive).
- - -
#### `tf.test.TestCase.assertCommandFails(command, regexes, env=None, close_fds=True, msg=None)` {#TestCase.assertCommandFails}
Asserts a shell command fails and the error matches a regex in a list.
##### Args:
* <b>`command`</b>: List or string representing the command to run.
* <b>`regexes`</b>: the list of regular expression strings.
* <b>`env`</b>: Dictionary of environment variable settings.
* <b>`close_fds`</b>: Whether or not to close all open fd's in the child after
forking.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertCommandSucceeds(command, regexes=('',), env=None, close_fds=True, msg=None)` {#TestCase.assertCommandSucceeds}
Asserts that a shell command succeeds (i.e. exits with code 0).
##### Args:
* <b>`command`</b>: List or string representing the command to run.
* <b>`regexes`</b>: List of regular expression byte strings that match success.
* <b>`env`</b>: Dictionary of environment variable settings.
* <b>`close_fds`</b>: Whether or not to close all open fd's in the child after
forking.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsExactSubsequence(container, subsequence, msg=None)` {#TestCase.assertContainsExactSubsequence}
Assert that "container" contains "subsequence" as an exact subsequence.
Asserts that "container" contains all the elements of "subsequence", in
order, and without other elements interspersed. For example, [1, 2, 3] is an
exact subsequence of [0, 0, 1, 2, 3, 0] but not of [0, 0, 1, 2, 0, 3, 0].
##### Args:
* <b>`container`</b>: the list we're testing for subsequence inclusion.
* <b>`subsequence`</b>: the list we hope will be an exact subsequence of container.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsInOrder(strings, target, msg=None)` {#TestCase.assertContainsInOrder}
Asserts that the strings provided are found in the target in order.
This may be useful for checking HTML output.
##### Args:
* <b>`strings`</b>: A list of strings, such as [ 'fox', 'dog' ]
* <b>`target`</b>: A target string in which to look for the strings, such as
'The quick brown fox jumped over the lazy dog'.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsSubsequence(container, subsequence, msg=None)` {#TestCase.assertContainsSubsequence}
Assert that "container" contains "subsequence" as a subsequence.
Asserts that "container" contains all the elements of "subsequence", in
order, but possibly with other elements interspersed. For example, [1, 2, 3]
is a subsequence of [0, 0, 1, 2, 0, 3, 0] but not of [0, 0, 1, 3, 0, 2, 0].
##### Args:
* <b>`container`</b>: the list we're testing for subsequence inclusion.
* <b>`subsequence`</b>: the list we hope will be a subsequence of container.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertContainsSubset(expected_subset, actual_set, msg=None)` {#TestCase.assertContainsSubset}
Checks whether actual iterable is a superset of expected iterable.
- - -
#### `tf.test.TestCase.assertCountEqual(*args, **kwargs)` {#TestCase.assertCountEqual}
An unordered sequence specific comparison.
Equivalent to assertItemsEqual(). This method is a compatibility layer
for Python 3k, since 2to3 does not convert assertItemsEqual() calls into
assertCountEqual() calls.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`msg`</b>: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertDeviceEqual(device1, device2)` {#TestCase.assertDeviceEqual}
@ -354,48 +235,9 @@ Checks whether actual is a superset of expected.
- - -
#### `tf.test.TestCase.assertDictEqual(a, b, msg=None)` {#TestCase.assertDictEqual}
Raises AssertionError if a and b are not equal dictionaries.
##### Args:
#### `tf.test.TestCase.assertDictEqual(d1, d2, msg=None)` {#TestCase.assertDictEqual}
* <b>`a`</b>: A dict, the expected value.
* <b>`b`</b>: A dict, the actual value.
* <b>`msg`</b>: An optional str, the associated message.
##### Raises:
* <b>`AssertionError`</b>: if the dictionaries are not equal.
- - -
#### `tf.test.TestCase.assertEmpty(container, msg=None)` {#TestCase.assertEmpty}
Assert that an object has zero length.
##### Args:
* <b>`container`</b>: Anything that implements the collections.Sized interface.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertEndsWith(actual, expected_end, msg=None)` {#TestCase.assertEndsWith}
Assert that actual.endswith(expected_end) is True.
##### Args:
* <b>`actual`</b>: str
* <b>`expected_end`</b>: str
* <b>`msg`</b>: Optional message to report on failure.
- - -
@ -480,11 +322,10 @@ Included for symmetry with assertIsNone.
- - -
#### `tf.test.TestCase.assertItemsEqual(*args, **kwargs)` {#TestCase.assertItemsEqual}
#### `tf.test.TestCase.assertItemsEqual(expected_seq, actual_seq, msg=None)` {#TestCase.assertItemsEqual}
An unordered sequence specific comparison.
It asserts that actual_seq and expected_seq have the same element counts.
An unordered sequence specific comparison. It asserts that
actual_seq and expected_seq have the same element counts.
Equivalent to::
self.assertEqual(Counter(iter(actual_seq)),
@ -497,30 +338,6 @@ Asserts that each element has the same count in both sequences.
- [0, 1, 1] and [1, 0, 1] compare equal.
- [0, 0, 1] and [0, 1] compare unequal.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`msg`</b>: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertJsonEqual(first, second, msg=None)` {#TestCase.assertJsonEqual}
Asserts that the JSON objects defined in two strings are equal.
A summary of the differences will be included in the failure message
using assertSameStructure.
##### Args:
* <b>`first`</b>: A string contining JSON to decode and compare to second.
* <b>`second`</b>: A string contining JSON to decode and compare to first.
* <b>`msg`</b>: Additional text to include in the failure message.
- - -
@ -590,13 +407,6 @@ if not.
* <b>`msg`</b>: An optional string message to append to the failure message.
- - -
#### `tf.test.TestCase.assertNoCommonElements(expected_seq, actual_seq, msg=None)` {#TestCase.assertNoCommonElements}
Checks whether actual iterable and expected iterable are disjoint.
- - -
#### `tf.test.TestCase.assertNotAlmostEqual(first, second, places=None, msg=None, delta=None)` {#TestCase.assertNotAlmostEqual}
@ -627,33 +437,6 @@ as significant digits (measured from the most signficant digit).
Objects that are equal automatically fail.
- - -
#### `tf.test.TestCase.assertNotEmpty(container, msg=None)` {#TestCase.assertNotEmpty}
Assert that an object has non-zero length.
##### Args:
* <b>`container`</b>: Anything that implements the collections.Sized interface.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertNotEndsWith(actual, unexpected_end, msg=None)` {#TestCase.assertNotEndsWith}
Assert that actual.endswith(unexpected_end) is False.
##### Args:
* <b>`actual`</b>: str
* <b>`unexpected_end`</b>: str
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertNotEqual(first, second, msg=None)` {#TestCase.assertNotEqual}
@ -691,20 +474,6 @@ Included for symmetry with assertIsInstance.
Fail the test if the text matches the regular expression.
- - -
#### `tf.test.TestCase.assertNotStartsWith(actual, unexpected_start, msg=None)` {#TestCase.assertNotStartsWith}
Assert that actual.startswith(unexpected_start) is False.
##### Args:
* <b>`actual`</b>: str
* <b>`unexpected_start`</b>: str
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertProtoEquals(expected_message_maybe_ascii, message)` {#TestCase.assertProtoEquals}
@ -779,38 +548,6 @@ Asserts that the message in a raised exception matches a regexp.
* <b>`kwargs`</b>: Extra kwargs.
- - -
#### `tf.test.TestCase.assertRaisesWithLiteralMatch(expected_exception, expected_exception_message, callable_obj=None, *args, **kwargs)` {#TestCase.assertRaisesWithLiteralMatch}
Asserts that the message in a raised exception equals the given string.
Unlike assertRaisesRegexp, this method takes a literal string, not
a regular expression.
with self.assertRaisesWithLiteralMatch(ExType, 'message'):
DoSomething()
##### Args:
* <b>`expected_exception`</b>: Exception class expected to be raised.
* <b>`expected_exception_message`</b>: String message expected in the raised
exception. For a raise exception e, expected_exception_message must
equal str(e).
* <b>`callable_obj`</b>: Function to be called, or None to return a context.
* <b>`args`</b>: Extra args.
* <b>`kwargs`</b>: Extra kwargs.
##### Returns:
A context manager if callable_obj is None. Otherwise, None.
##### Raises:
self.failureException if callable_obj does not raise a macthing exception.
- - -
#### `tf.test.TestCase.assertRaisesWithPredicateMatch(exception_type, expected_err_re_or_predicate)` {#TestCase.assertRaisesWithPredicateMatch}
@ -835,71 +572,6 @@ predicate search.
exception.
- - -
#### `tf.test.TestCase.assertRaisesWithRegexpMatch(expected_exception, expected_regexp, callable_obj=None, *args, **kwargs)` {#TestCase.assertRaisesWithRegexpMatch}
Asserts that the message in a raised exception matches the given regexp.
This is just a wrapper around assertRaisesRegexp. Please use
assertRaisesRegexp instead of assertRaisesWithRegexpMatch.
##### Args:
* <b>`expected_exception`</b>: Exception class expected to be raised.
* <b>`expected_regexp`</b>: Regexp (re pattern object or string) expected to be
found in error message.
* <b>`callable_obj`</b>: Function to be called, or None to return a context.
* <b>`args`</b>: Extra args.
* <b>`kwargs`</b>: Extra keyword args.
##### Returns:
A context manager if callable_obj is None. Otherwise, None.
##### Raises:
self.failureException if callable_obj does not raise a macthing exception.
- - -
#### `tf.test.TestCase.assertRegexMatch(actual_str, regexes, message=None)` {#TestCase.assertRegexMatch}
Asserts that at least one regex in regexes matches str.
If possible you should use assertRegexpMatches, which is a simpler
version of this method. assertRegexpMatches takes a single regular
expression (a string or re compiled object) instead of a list.
Notes:
1. This function uses substring matching, i.e. the matching
succeeds if *any* substring of the error message matches *any*
regex in the list. This is more convenient for the user than
full-string matching.
2. If regexes is the empty list, the matching will always fail.
3. Use regexes=[''] for a regex that will always pass.
4. '.' matches any single character *except* the newline. To
match any character, use '(.|
)'.
5. '^' matches the beginning of each line, not just the beginning
of the string. Similarly, '$' matches the end of each line.
6. An exception will be thrown if regexes contains an invalid
regex.
Args:
actual_str: The string we try to match with the items in regexes.
regexes: The regular expressions we want to match against str.
See "Notes" above for detailed notes on how this is interpreted.
message: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertRegexpMatches(text, expected_regexp, msg=None)` {#TestCase.assertRegexpMatches}
@ -907,79 +579,6 @@ Asserts that at least one regex in regexes matches str.
Fail the test unless the text matches the regular expression.
- - -
#### `tf.test.TestCase.assertSameElements(expected_seq, actual_seq, msg=None)` {#TestCase.assertSameElements}
Assert that two sequences have the same elements (in any order).
This method, unlike assertItemsEqual, doesn't care about any
duplicates in the expected and actual sequences.
>> assertSameElements([1, 1, 1, 0, 0, 0], [0, 1])
# Doesn't raise an AssertionError
If possible, you should use assertItemsEqual instead of
assertSameElements.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`msg`</b>: The message to be printed if the test fails.
- - -
#### `tf.test.TestCase.assertSameStructure(a, b, aname='a', bname='b', msg=None)` {#TestCase.assertSameStructure}
Asserts that two values contain the same structural content.
The two arguments should be data trees consisting of trees of dicts and
lists. They will be deeply compared by walking into the contents of dicts
and lists; other items will be compared using the == operator.
If the two structures differ in content, the failure message will indicate
the location within the structures where the first difference is found.
This may be helpful when comparing large structures.
##### Args:
* <b>`a`</b>: The first structure to compare.
* <b>`b`</b>: The second structure to compare.
* <b>`aname`</b>: Variable name to use for the first structure in assertion messages.
* <b>`bname`</b>: Variable name to use for the second structure.
* <b>`msg`</b>: Additional text to include in the failure message.
- - -
#### `tf.test.TestCase.assertSequenceAlmostEqual(expected_seq, actual_seq, places=None, msg=None, delta=None)` {#TestCase.assertSequenceAlmostEqual}
An approximate equality assertion for ordered sequences.
Fail if the two sequences are unequal as determined by their value
differences rounded to the given number of decimal places (default 7) and
comparing to zero, or by comparing that the difference between each value
in the two sequences is more than the given delta.
Note that decimal places (from zero) are usually not the same as significant
digits (measured from the most signficant digit).
If the two sequences compare equal then they will automatically compare
almost equal.
##### Args:
* <b>`expected_seq`</b>: A sequence containing elements we are expecting.
* <b>`actual_seq`</b>: The sequence that we are testing.
* <b>`places`</b>: The number of decimal places to compare.
* <b>`msg`</b>: The message to be printed if the test fails.
* <b>`delta`</b>: The OK difference between compared values.
- - -
#### `tf.test.TestCase.assertSequenceEqual(seq1, seq2, msg=None, seq_type=None)` {#TestCase.assertSequenceEqual}
@ -1000,26 +599,6 @@ which can be indexed, has a length, and has an equality operator.
differences.
- - -
#### `tf.test.TestCase.assertSequenceStartsWith(prefix, whole, msg=None)` {#TestCase.assertSequenceStartsWith}
An equality assertion for the beginning of ordered sequences.
If prefix is an empty sequence, it will raise an error unless whole is also
an empty sequence.
If prefix is not a sequence, it will raise an error if the first element of
whole does not match.
##### Args:
* <b>`prefix`</b>: A sequence expected at the beginning of the whole parameter.
* <b>`whole`</b>: The sequence in which to look for prefix.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertSetEqual(set1, set2, msg=None)` {#TestCase.assertSetEqual}
@ -1071,51 +650,6 @@ Assert that actual.startswith(expected_start) is True.
* <b>`msg`</b>: Optional message to report on failure.
- - -
#### `tf.test.TestCase.assertTotallyOrdered(*groups, **kwargs)` {#TestCase.assertTotallyOrdered}
Asserts that total ordering has been implemented correctly.
For example, say you have a class A that compares only on its attribute x.
Comparators other than __lt__ are omitted for brevity.
class A(object):
def __init__(self, x, y):
self.x = x
self.y = y
def __hash__(self):
return hash(self.x)
def __lt__(self, other):
try:
return self.x < other.x
except AttributeError:
return NotImplemented
assertTotallyOrdered will check that instances can be ordered correctly.
For example,
self.assertTotallyOrdered(
[None], # None should come before everything else.
[1], # Integers sort earlier.
[A(1, 'a')],
[A(2, 'b')], # 2 is after 1.
[A(3, 'c'), A(3, 'd')], # The second argument is irrelevant.
[A(4, 'z')],
['foo']) # Strings sort last.
##### Args:
* <b>`*groups`</b>: A list of groups of elements. Each group of elements is a list
of objects that are equal. The elements in each group must be less than
the elements in the group after it. For example, these groups are
totally ordered: [None], [1], [2, 2], [3].
* <b>`**kwargs`</b>: optional msg keyword argument can be passed.
- - -
#### `tf.test.TestCase.assertTrue(expr, msg=None)` {#TestCase.assertTrue}
@ -1138,13 +672,6 @@ A tuple-specific equality assertion.
differences.
- - -
#### `tf.test.TestCase.assertUrlEqual(a, b, msg=None)` {#TestCase.assertUrlEqual}
Asserts that urls are equal, ignoring ordering of query params.
- - -
#### `tf.test.TestCase.assert_(expr, msg=None)` {#TestCase.assert_}
@ -1206,9 +733,9 @@ tearDown.
- - -
#### `tf.test.TestCase.fail(msg=None, prefix=None)` {#TestCase.fail}
#### `tf.test.TestCase.fail(msg=None)` {#TestCase.fail}
Fail immediately with the given message, optionally prefixed.
Fail immediately, with the given message.
- - -
@ -1260,13 +787,6 @@ Fail immediately with the given message, optionally prefixed.
- - -
#### `tf.test.TestCase.getRecordedProperties()` {#TestCase.getRecordedProperties}
Return any properties that the user has recorded.
- - -
#### `tf.test.TestCase.get_temp_dir()` {#TestCase.get_temp_dir}
@ -1289,20 +809,6 @@ pollute each others environment.
- - -
#### `tf.test.TestCase.recordProperty(property_name, property_value)` {#TestCase.recordProperty}
Record an arbitrary property for later use.
##### Args:
* <b>`property_name`</b>: str, name of property to record; must be a valid XML
attribute name
* <b>`property_value`</b>: value of property; must be valid XML attribute value
- - -
#### `tf.test.TestCase.run(result=None)` {#TestCase.run}
@ -1328,18 +834,11 @@ Hook method for setting up class fixture before running tests in the class.
#### `tf.test.TestCase.shortDescription()` {#TestCase.shortDescription}
Format both the test method name and the first line of its docstring.
Returns a one-line description of the test, or None if no
description has been provided.
If no docstring is given, only returns the method name.
This method overrides unittest.TestCase.shortDescription(), which
only returns the first line of the docstring, obscuring the name
of the test upon failure.
##### Returns:
* <b>`desc`</b>: A short description of a test method.
The default implementation of this method returns the first line of
the specified test method's docstring.
- - -

157
tensorflow/g3doc/get_started/os_setup.md
View File

@ -78,37 +78,51 @@ If the above commands do not work on your system, you can follow these instructi
```bash
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp27-none-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp27-none-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp27-none-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp27-none-linux_x86_64.whl
# Mac OS X, CPU only, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py2-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc1-py2-none-any.whl
# Mac OS X, GPU enabled, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.1-py2-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc1-py2-none-any.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.3
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp33-cp33m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.3
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp33-cp33m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp34-cp34m-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp34-cp34m-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.5
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp35-cp35m-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.6
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp36-cp36m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.6
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp36-cp36m-linux_x86_64.whl
# Mac OS X, CPU only, Python 3.4 or 3.5:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc1-py3-none-any.whl
# Mac OS X, GPU enabled, Python 3.4 or 3.5:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.1-py3-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc1-py3-none-any.whl
```
Install TensorFlow:
@ -150,14 +164,14 @@ Both distributions include pip. To install the CPU-only version of
TensorFlow, enter the following command at a command prompt:
```bat
C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.1-cp35-cp35m-win_amd64.whl
C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.0rc1-cp35-cp35m-win_amd64.whl
```
To install the GPU version of TensorFlow, enter the following command
at a command prompt:
```bat
C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-win_amd64.whl
C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.0.0rc1-cp35-cp35m-win_amd64.whl
```
You can now [test your installation](#test-the-tensorflow-installation).
@ -212,37 +226,51 @@ Now, install TensorFlow just as you would for a regular Pip installation. First
```bash
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp27-none-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp27-none-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp27-none-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp27-none-linux_x86_64.whl
# Mac OS X, CPU only, Python 2.7:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc1-py2-none-any.whl
# Mac OS X, GPU enabled, Python 2.7:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.1-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc1-py2-none-any.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.3
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp33-cp33m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.3
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp33-cp33m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp34-cp34m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp34-cp34m-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.5
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp35-cp35m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.6
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp36-cp36m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.6
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp36-cp36m-linux_x86_64.whl
# Mac OS X, CPU only, Python 3.4 or 3.5:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc1-py3-none-any.whl
# Mac OS X, GPU enabled, Python 3.4 or 3.5:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.1-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc1-py3-none-any.whl
```
Finally install TensorFlow:
@ -364,37 +392,51 @@ select the correct binary to install:
```bash
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp27-none-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp27-none-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp27-none-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp27-none-linux_x86_64.whl
# Mac OS X, CPU only, Python 2.7:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc1-py2-none-any.whl
# Mac OS X, GPU enabled, Python 2.7:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.1-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc1-py2-none-any.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.3
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp33-cp33m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.3
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp33-cp33m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp34-cp34m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp34-cp34m-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.5
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.1-cp35-cp35m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 8.0 and CuDNN v5.1. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.1-cp35-cp35m-linux_x86_64.whl
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.6
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc1-cp36-cp36m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.6
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc1-cp36-cp36m-linux_x86_64.whl
# Mac OS X, CPU only, Python 3.4 or 3.5:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc1-py3-none-any.whl
# Mac OS X, GPU enabled, Python 3.4 or 3.5:
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.1-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc1-py3-none-any.whl
```
Finally install TensorFlow:
@ -462,7 +504,7 @@ code.
code.
We also have tags with `latest` replaced by a released version (e.g.,
`0.12.1-gpu`).
`1.0.0-rc1-gpu`).
With Docker the installation is as follows:
@ -557,7 +599,7 @@ To build TensorFlow from source on Windows, you can use experimental
support for [Bazel on
Windows](https://bazel.build/versions/master/docs/windows.html) or the
[TensorFlow CMake
build](https://github.com/tensorflow/tensorflow/tree/r0.12/tensorflow/contrib/cmake).
build](https://github.com/tensorflow/tensorflow/tree/r1.0/tensorflow/contrib/cmake).
### Clone the TensorFlow repository
@ -856,37 +898,38 @@ default and if you want to limit RAM usage you can add `--local_resources
2048,.5,1.0` while invoking bazel.
```bash
$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
$ bazel build --config opt //tensorflow/tools/pip_package:build_pip_package
# To build with support for CUDA:
$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
$ bazel build --config opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
# Alternatively, to build with support for OpenCL:
$ bazel build -c opt --config=sycl //tensorflow/tools/pip_package:build_pip_package
# Alternatively, to build with support for OpenCL (Experimental):
$ bazel build --config opt --config=sycl //tensorflow/tools/pip_package:build_pip_package
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
# The name of the .whl file will depend on your platform.
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-0.12.1-py2-none-any.whl
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.0.0rc1-py2-none-any.whl
```
## Optimizing CPU performance
To be compatible with as wide a range of machines as possible, TensorFlow
defaults to only using SSE4.1 SIMD instructions on x86 machines. Most modern PCs
and Macs support more advanced instructions, so if you're building a binary
that you'll only be running on your own machine, you can enable these by using
`--copt=-march=native` in your bazel build command. For example:
defaults to only using SSE4 SIMD instructions. Most modern computers support
more advanced instructions. So if you're building a binary that you'll only
be running on your own machine, you can enable these by using `-march=native`
for optimization options when running `configure`. Then you can build your
optimized binaries with the following command:
``` bash
$ bazel build --copt=-march=native -c opt //tensorflow/tools/pip_package:build_pip_package
$ bazel build --config opt //tensorflow/tools/pip_package:build_pip_package
```
If you are distributing a binary but know the capabilities of the machines
you'll be running on, you can manually choose the right instructions with
something like `--copt=-march=avx`. You may also want to enable multiple
something like `-march=avx`. You may also want to enable multiple
features using several arguments, for example
`--copt=-mavx2 --copt=-mfma`.
`-mavx2,-mfma`.
If you run a binary built using SIMD instructions on a machine that doesn't
support them, you'll see an illegal instruction error when that code is
@ -902,10 +945,10 @@ system directories, run the following commands inside the TensorFlow root
directory:
```bash
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
bazel build --config opt //tensorflow/tools/pip_package:build_pip_package
# To build with GPU support:
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel build --config opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
mkdir _python_build
cd _python_build

2
tensorflow/g3doc/how_tos/adding_an_op/index.md
View File

@ -177,7 +177,7 @@ tf_custom_op_library(
Run the following command to build `zero_out.so`.
```bash
$ bazel build -c opt //tensorflow/core/user_ops:zero_out.so
$ bazel build --config opt //tensorflow/core/user_ops:zero_out.so
```
> Note:

4
tensorflow/g3doc/how_tos/image_retraining/index.md
View File

@ -42,10 +42,10 @@ bazel build tensorflow/examples/image_retraining:retrain
If you have a machine which supports [the AVX instruction set](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions)
(common in x86 CPUs produced in the last few years) you can improve the running
speed of the retraining by building for that architecture, like this:
speed of the retraining by building for that architecture, like this (after choosing appropriate options in `configure`):
```sh
bazel build -c opt --copt=-mavx tensorflow/examples/image_retraining:retrain
bazel build --config opt tensorflow/examples/image_retraining:retrain
```
The retrainer can then be run like this:

19
tensorflow/g3doc/resources/roadmap.md
View File

@ -1,5 +1,5 @@
# Roadmap
**Last updated: June 3, 2016**
**Last updated: January 23, 2017**
TensorFlow is a fast moving project. In order for the community to better
understand what the near future will bring, this document shares what we are
@ -11,29 +11,28 @@ The features on this list are targeted for the next few months. At this point,
we do not have timelines for these features.
### Improve non-Python language support
C and C++ APIs for:
* Graph construction
* Gradients
* Shape Inference
* Improve C++ API for graph construction and gradients
* Java language support
* Go language support
### Making TensorFlow easier to use
* Easier setup for distributed training jobs
* High-level APIs
* Well-maintained models showing best practices
### Performance
* Speed and memory benchmarks
* Distributed full model benchmarks
* Performance and memory usage improvements
### Core Features
* Repeated partial graph evaluation ([#672](https://github.com/tensorflow/tensorflow/issues/672))
* Automatic op placement ([#2126](https://github.com/tensorflow/tensorflow/issues/2126))
* Support for graph-level functions
### Platforms
* OpenCL support ([#22](https://github.com/tensorflow/tensorflow/issues/22))
### Community
* More educational resources
* Better integration of TensorFlow into the opensource big data ecosystem ([#1996](https://github.com/tensorflow/tensorflow/issues/1996),
[#2218](https://github.com/tensorflow/tensorflow/issues/2218),
* Better integration of TensorFlow into the opensource big data ecosystem (e.g.
[#2655](https://github.com/tensorflow/tensorflow/issues/2655))
* Models benchmarking and comparison tooling

2
tensorflow/go/genop/generate.sh
View File

@ -30,7 +30,7 @@ then
then
echo "Protocol buffer compiler protoc not found in PATH or in ${PROTOC}"
echo "Perhaps build it using:"
echo "bazel build -c opt @protobuf//:protoc"
echo "bazel build --config opt @protobuf//:protoc"
exit 1
fi
PROTOC=$PATH_PROTOC

2
tensorflow/java/README.md
View File

@ -40,7 +40,7 @@ Configure and build the Java Archive (JAR) and native library:
./configure
# Build the JAR and native library
bazel build -c opt \
bazel build --config opt \
//tensorflow/java:tensorflow \
//tensorflow/java:libtensorflow_jni
```

9
tensorflow/python/framework/importer.py
View File

@ -151,14 +151,15 @@ def _FindAttrInOpDef(attr_name, op_def):
def import_graph_def(graph_def, input_map=None, return_elements=None,
name=None, op_dict=None, producer_op_list=None):
"""Imports the TensorFlow graph in `graph_def` into the Python `Graph`.
"""Imports the graph from `graph_def` into the current default `Graph`.
This function provides a way to import a serialized TensorFlow
[`GraphDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
protocol buffer, and extract individual objects in the `GraphDef` as
[`Tensor`](#Tensor) and [`Operation`](#Operation) objects. See
[`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a
`GraphDef` proto.
[`Tensor`](#Tensor) and [`Operation`](#Operation) objects. Once extracted,
these objects are placed into the current default `Graph`. See
[`Graph.as_graph_def()`](#Graph.as_graph_def) for a way to create a `GraphDef`
proto.
Args:
graph_def: A `GraphDef` proto containing operations to be imported into

15
tensorflow/python/framework/test_util.py
View File

@ -491,7 +491,9 @@ class TensorFlowTestCase(googletest.TestCase):
print("dtype = %s, shape = %s" % (a.dtype, a.shape))
np.testing.assert_allclose(a, b, rtol=rtol, atol=atol)
def assertAllCloseAccordingToType(self, a, b, rtol=1e-6, atol=1e-6):
def assertAllCloseAccordingToType(self, a, b, rtol=1e-6, atol=1e-6,
float_rtol=1e-6, float_atol=1e-6,
half_rtol=1e-3, half_atol=1e-3):
"""Like assertAllClose, but also suitable for comparing fp16 arrays.
In particular, the tolerance is reduced to 1e-3 if at least
@ -502,12 +504,19 @@ class TensorFlowTestCase(googletest.TestCase):
b: a numpy ndarray or anything can be converted to one.
rtol: relative tolerance
atol: absolute tolerance
float_rtol: relative tolerance for float32
float_atol: absolute tolerance for float32
half_rtol: relative tolerance for float16
half_atol: absolute tolerance for float16
"""
a = self._GetNdArray(a)
b = self._GetNdArray(b)
if a.dtype == np.float32 or b.dtype == np.float32:
rtol = max(rtol, float_rtol)
atol = max(atol, float_atol)
if a.dtype == np.float16 or b.dtype == np.float16:
rtol = max(rtol, 1e-3)
atol = max(atol, 1e-3)
rtol = max(rtol, half_rtol)
atol = max(atol, half_atol)
self.assertAllClose(a, b, rtol=rtol, atol=atol)

49
tensorflow/python/framework/test_util_test.py
View File

@ -193,6 +193,55 @@ class TestUtilTest(test_util.TensorFlowTestCase):
y = [15]
control_flow_ops.Assert(x, y).run()
def testAssertAllCloseAccordingToType(self):
# test float64
self.assertAllCloseAccordingToType(
np.asarray([1e-8], dtype=np.float64),
np.asarray([2e-8], dtype=np.float64),
rtol=1e-8, atol=1e-8
)
with (self.assertRaises(AssertionError)):
self.assertAllCloseAccordingToType(
np.asarray([1e-7], dtype=np.float64),
np.asarray([2e-7], dtype=np.float64),
rtol=1e-8, atol=1e-8
)
# test float32
self.assertAllCloseAccordingToType(
np.asarray([1e-7], dtype=np.float32),
np.asarray([2e-7], dtype=np.float32),
rtol=1e-8, atol=1e-8,
float_rtol=1e-7, float_atol=1e-7
)
with (self.assertRaises(AssertionError)):
self.assertAllCloseAccordingToType(
np.asarray([1e-6], dtype=np.float32),
np.asarray([2e-6], dtype=np.float32),
rtol=1e-8, atol=1e-8,
float_rtol=1e-7, float_atol=1e-7
)
# test float16
self.assertAllCloseAccordingToType(
np.asarray([1e-4], dtype=np.float16),
np.asarray([2e-4], dtype=np.float16),
rtol=1e-8, atol=1e-8,
float_rtol=1e-7, float_atol=1e-7,
half_rtol=1e-4, half_atol=1e-4
)
with (self.assertRaises(AssertionError)):
self.assertAllCloseAccordingToType(
np.asarray([1e-3], dtype=np.float16),
np.asarray([2e-3], dtype=np.float16),
rtol=1e-8, atol=1e-8,
float_rtol=1e-7, float_atol=1e-7,
half_rtol=1e-4, half_atol=1e-4
)
def testRandomSeed(self):
a = random.randint(1, 1000)
a_np_rand = np.random.rand(1)

6
tensorflow/python/kernel_tests/scalar_strict_test.py
View File

@ -70,13 +70,13 @@ class ScalarStrictTest(test.TestCase):
self.assertAllEqual(r, correct)
def testConcat(self):
self.check(array_ops.concat_v2, (([2], [3], [7]), [0]),
self.check(array_ops.concat, (([2], [3], [7]), [0]),
'axis tensor should be a scalar integer', [2, 3, 7])
for data in (2, 3, 7), (2, [3], 7), (2, 3, [7]):
self.check(array_ops.concat_v2, (data, 0),
self.check(array_ops.concat, (data, 0),
r'Expected \w+ dimensions in the range \[0, 0\)', [2, 3, 7])
for data in ([2], 3, 7), ([2], [3], 7):
self.check(array_ops.concat_v2, (data, 0),
self.check(array_ops.concat, (data, 0),
r'Ranks of all input tensors should match', [2, 3, 7])
def testFill(self):

32
tensorflow/python/kernel_tests/segment_reduction_ops_test.py
View File

@ -49,12 +49,21 @@ class SegmentReductionHelper(test.TestCase):
slice_shape = x.shape[indices.ndim:]
x_flat = x.reshape((indices.size,) + slice_shape)
for i, index in enumerate(indices.ravel()):
if output[index] is not None:
if (output[index] is not None) and op1 == np.max:
for j in range(0, output[index].shape[0]):
output[index][j] = op1([output[index][j], x_flat[i][j]])
elif output[index] is not None:
output[index] = op1(output[index], x_flat[i])
else:
output[index] = x_flat[i]
# zero initialize values that are still uncalcuated.
output = [o if o is not None else np.zeros(slice_shape) for o in output]
# output = [o if o is not None else np.zeros(slice_shape) for o in output]
if not op1 == np.max:
output = [o if o is not None else np.zeros(slice_shape) for o in output]
else:
zeroslice = np.zeros(slice_shape)
zeroslice.fill(dtype.min)
output = [o if o is not None else zeroslice for o in output]
if op2 is not None:
output = [op2(o) for o in output]
output = [o.reshape(slice_shape) for o in output]
@ -245,7 +254,7 @@ class UnsortedSegmentSumTest(SegmentReductionHelper):
self._assertAllClose(indices, np_ans, tf_ans)
self.assertShapeEqual(np_ans, s)
def testGradient(self):
def testGradientSegmentSum(self):
num_cols = 2
indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3])
num_segments = max(indices_flat) + 3
@ -318,6 +327,23 @@ class UnsortedSegmentSumTest(SegmentReductionHelper):
unsorted = math_ops.unsorted_segment_sum(data, segment_ids, 2)
self.assertAllEqual(unsorted.eval(), np.zeros((2, 0), dtype=dtype))
def testGradientSegmentMax(self):
num_cols = 2
indices_flat = np.array([0, 4, 0, 8, 3, 8, 4, 7, 7, 3])
num_segments = max(indices_flat) + 3
for indices in indices_flat, indices_flat.reshape(5, 2):
shape = indices.shape + (num_cols,)
with self.test_session():
tf_x, np_x = self._input(shape, dtype=dtypes_lib.float64)
s = math_ops.unsorted_segment_max(data=tf_x, segment_ids=indices,
num_segments=num_segments)
jacob_t, jacob_n = gradient_checker.compute_gradient(
tf_x,
shape,
s,
[num_segments, num_cols],
x_init_value=np_x.astype(np.double), delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-3, atol=1e-3)
class UnsortedSegmentSumGpuTest(UnsortedSegmentSumTest):
use_gpu = True

6
tensorflow/python/ops/array_ops.py
View File

@ -979,12 +979,6 @@ def unstack(value, num=None, axis=0, name="unstack"):
return gen_array_ops._unpack(value, num=num, axis=axis, name=name)
# concat_v2 is an alias for concat. concat_v2 will be deprecated and removed
# soon, please use concat.
def concat_v2(values, axis, name="concat_v2"):
return concat(values, axis, name)
def concat(values, axis, name="concat"):
"""Concatenates tensors along one dimension.

12
tensorflow/python/ops/logging_ops.py
View File

@ -70,6 +70,11 @@ def _Collect(val, collections, default_collections):
ops.add_to_collection(key, val)
@deprecated(
"2016-11-30", "Please switch to tf.summary.histogram. Note that "
"tf.summary.histogram uses the node name instead of the tag. "
"This means that TensorFlow will automatically de-duplicate summary "
"names based on the scope they are created in.")
def histogram_summary(tag, values, collections=None, name=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with a histogram.
@ -304,6 +309,13 @@ def get_summary_op():
return summary_op
@deprecated(
"2016-11-30", "Please switch to tf.summary.scalar. Note that "
"tf.summary.scalar uses the node name instead of the tag. "
"This means that TensorFlow will automatically de-duplicate summary "
"names based on the scope they are created in. Also, passing a "
"tensor or list of tags to a scalar summary op is no longer "
"supported.")
def scalar_summary(tags, values, collections=None, name=None):
# pylint: disable=line-too-long
"""Outputs a `Summary` protocol buffer with scalar values.

30
tensorflow/python/ops/math_grad.py
View File

@ -188,35 +188,42 @@ def _SparseSegmentSqrtNGrad(op, grad):
dim0), None, None)
def _SegmentMinOrMaxGrad(op, grad):
"""Gradient for SegmentMin and SegmentMax. Both share the same code."""
zeros = array_ops.zeros(
array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype)
def _SegmentMinOrMaxGrad(op, grad, is_sorted):
"""Gradient for SegmentMin and (unsorted) SegmentMax. They share similar code."""
zeros = array_ops.zeros(array_ops.shape(op.inputs[0]),
dtype=op.inputs[0].dtype)
# Get the number of selected (minimum or maximum) elements in each segment.
gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1])
is_selected = math_ops.equal(op.inputs[0], gathered_outputs)
num_selected = math_ops.segment_sum(
math_ops.cast(is_selected, grad.dtype), op.inputs[1])
if is_sorted:
num_selected = math_ops.segment_sum(math_ops.cast(is_selected, grad.dtype),
op.inputs[1])
else:
num_selected = math_ops.unsorted_segment_sum(math_ops.cast(is_selected, grad.dtype),
op.inputs[1], op.inputs[2])
# Compute the gradient for each segment. The gradient for the ith segment is
# divided evenly among the selected elements in that segment.
weighted_grads = math_ops.div(grad, num_selected)
gathered_grads = array_ops.gather(weighted_grads, op.inputs[1])
return array_ops.where(is_selected, gathered_grads, zeros), None
if is_sorted:
return array_ops.where(is_selected, gathered_grads, zeros), None
else:
return array_ops.where(is_selected, gathered_grads, zeros), None, None
@ops.RegisterGradient("SegmentMin")
def _SegmentMinGrad(op, grad):
"""Gradient for SegmentMin."""
return _SegmentMinOrMaxGrad(op, grad)
return _SegmentMinOrMaxGrad(op, grad, True)
@ops.RegisterGradient("SegmentMax")
def _SegmentMaxGrad(op, grad):
"""Gradient for SegmentMax."""
return _SegmentMinOrMaxGrad(op, grad)
return _SegmentMinOrMaxGrad(op, grad, True)
@ops.RegisterGradient("UnsortedSegmentSum")
@ -225,6 +232,11 @@ def _UnsortedSegmentSumGrad(op, grad):
return array_ops.gather(grad, op.inputs[1]), None, None
@ops.RegisterGradient("UnsortedSegmentMax")
def _UnsortedSegmentMaxGrad(op, grad):
return _SegmentMinOrMaxGrad(op, grad, False)
@ops.RegisterGradient("Abs")
def _AbsGrad(op, grad):
x = op.inputs[0]

1
tensorflow/python/ops/math_ops.py
View File

@ -196,6 +196,7 @@ tf.segment_sum(c, tf.constant([0, 0, 1]))
@@segment_mean
@@unsorted_segment_sum
@@unsorted_segment_max
@@sparse_segment_sum
@@sparse_segment_mean

2
tensorflow/python/ops/weights_broadcast_ops.py
View File

@ -34,7 +34,7 @@ def _has_valid_dims(weights_shape, values_shape):
with ops.name_scope(
None, "has_invalid_dims", (weights_shape, values_shape)) as scope:
values_shape_2d = array_ops.expand_dims(values_shape, -1)
valid_dims = array_ops.concat_v2(
valid_dims = array_ops.concat(
(values_shape_2d, array_ops.ones_like(values_shape_2d)), axis=1)
weights_shape_2d = array_ops.expand_dims(weights_shape, -1)
invalid_dims = sets.set_difference(weights_shape_2d, valid_dims)

3
tensorflow/python/training/input.py
View File

@ -64,7 +64,8 @@ def match_filenames_once(pattern, name=None):
"""
with ops.name_scope(name, "matching_filenames", [pattern]) as name:
return variables.Variable(io_ops.matching_files(pattern), trainable=False,
name=name, validate_shape=False)
name=name, validate_shape=False,
collections=[ops.GraphKeys.LOCAL_VARIABLES])
def limit_epochs(tensor, num_epochs=None, name=None):

2
tensorflow/python/training/queue_runner_test.py
View File

@ -164,7 +164,7 @@ class QueueRunnerTest(test.TestCase):
coord.request_stop()
# We should be able to join because the RequestStop() will cause
# the queue to be closed and the enqueue to terminate.
coord.join(stop_grace_period_secs=0.05)
coord.join(stop_grace_period_secs=1.0)
def testMultipleSessions(self):
with self.test_session() as sess:

95
tensorflow/python/training/saver_test.py
View File

@ -68,18 +68,6 @@ from tensorflow.python.training import saver as saver_module
from tensorflow.python.util import compat
# pylint: disable=invalid-name
def _TestDir(test_name):
test_dir = os.path.join(test.get_temp_dir(), test_name)
if os.path.exists(test_dir):
shutil.rmtree(test_dir)
gfile.MakeDirs(test_dir)
return test_dir
# pylint: enable=invalid-name
class CheckpointedOp(object):
"""Op with a custom checkpointing implementation.
@ -591,6 +579,11 @@ class SaverTest(test.TestCase):
class SaveRestoreShardedTest(test.TestCase):
def _get_test_dir(self, dirname):
test_dir = os.path.join(self.get_temp_dir(), dirname)
gfile.MakeDirs(test_dir)
return test_dir
def testBasics(self):
save_path = os.path.join(self.get_temp_dir(), "sharded_basics")
@ -719,7 +712,9 @@ class SaveRestoreShardedTest(test.TestCase):
var_full_shape = [10, 3]
# Allows save/restore mechanism to work w/ different slicings.
var_name = "my_var"
saved_path = os.path.join(_TestDir("partitioned_variables"), "ckpt")
saved_dir = self._get_test_dir("partitioned_variables")
saved_path = os.path.join(saved_dir, "ckpt")
call_saver_with_dict = False # updated by test loop below
def _save(slices=None, partitioner=None):
@ -842,8 +837,13 @@ class SaveRestoreShardedTest(test.TestCase):
class MaxToKeepTest(test.TestCase):
def _get_test_dir(self, dirname):
test_dir = os.path.join(self.get_temp_dir(), dirname)
gfile.MakeDirs(test_dir)
return test_dir
def testNonSharded(self):
save_dir = _TestDir("max_to_keep_non_sharded")
save_dir = self._get_test_dir("max_to_keep_non_sharded")
with self.test_session() as sess:
v = variables.Variable(10.0, name="v")
@ -963,7 +963,7 @@ class MaxToKeepTest(test.TestCase):
saver_module.checkpoint_exists(save._MetaGraphFilename(s1)))
def testSharded(self):
save_dir = _TestDir("max_to_keep_sharded")
save_dir = self._get_test_dir("max_to_keep_sharded")
with session.Session(
target="",
@ -1018,8 +1018,8 @@ class MaxToKeepTest(test.TestCase):
self.assertTrue(gfile.Exists(save._MetaGraphFilename(s3)))
def testNoMaxToKeep(self):
save_dir = _TestDir("no_max_to_keep")
save_dir2 = _TestDir("max_to_keep_0")
save_dir = self._get_test_dir("no_max_to_keep")
save_dir2 = self._get_test_dir("max_to_keep_0")
with self.test_session() as sess:
v = variables.Variable(10.0, name="v")
@ -1046,7 +1046,7 @@ class MaxToKeepTest(test.TestCase):
self.assertTrue(saver_module.checkpoint_exists(s2))
def testNoMetaGraph(self):
save_dir = _TestDir("no_meta_graph")
save_dir = self._get_test_dir("no_meta_graph")
with self.test_session() as sess:
v = variables.Variable(10.0, name="v")
@ -1060,8 +1060,13 @@ class MaxToKeepTest(test.TestCase):
class KeepCheckpointEveryNHoursTest(test.TestCase):
def _get_test_dir(self, dirname):
test_dir = os.path.join(self.get_temp_dir(), dirname)
gfile.MakeDirs(test_dir)
return test_dir
def testNonSharded(self):
save_dir = _TestDir("keep_checkpoint_every_n_hours")
save_dir = self._get_test_dir("keep_checkpoint_every_n_hours")
with self.test_session() as sess:
v = variables.Variable([10.0], name="v")
@ -1277,8 +1282,13 @@ class LatestCheckpointWithRelativePaths(test.TestCase):
class CheckpointStateTest(test.TestCase):
def _get_test_dir(self, dirname):
test_dir = os.path.join(self.get_temp_dir(), dirname)
gfile.MakeDirs(test_dir)
return test_dir
def testAbsPath(self):
save_dir = _TestDir("abs_paths")
save_dir = self._get_test_dir("abs_paths")
abs_path = os.path.join(save_dir, "model-0")
ckpt = saver_module.generate_checkpoint_state_proto(save_dir, abs_path)
self.assertEqual(ckpt.model_checkpoint_path, abs_path)
@ -1297,7 +1307,7 @@ class CheckpointStateTest(test.TestCase):
self.assertEqual(ckpt.all_model_checkpoint_paths[-1], new_rel_path)
def testAllModelCheckpointPaths(self):
save_dir = _TestDir("all_models_test")
save_dir = self._get_test_dir("all_models_test")
abs_path = os.path.join(save_dir, "model-0")
for paths in [None, [], ["model-2"]]:
ckpt = saver_module.generate_checkpoint_state_proto(
@ -1309,7 +1319,7 @@ class CheckpointStateTest(test.TestCase):
self.assertEqual(ckpt.all_model_checkpoint_paths[-1], abs_path)
def testUpdateCheckpointState(self):
save_dir = _TestDir("update_checkpoint_state")
save_dir = self._get_test_dir("update_checkpoint_state")
os.chdir(save_dir)
# Make a temporary train directory.
train_dir = "train"
@ -1325,7 +1335,7 @@ class CheckpointStateTest(test.TestCase):
self.assertEqual(ckpt.all_model_checkpoint_paths[0], abs_path)
def testCheckPointStateFailsWhenIncomplete(self):
save_dir = _TestDir("checkpoint_state_fails_when_incomplete")
save_dir = self._get_test_dir("checkpoint_state_fails_when_incomplete")
os.chdir(save_dir)
ckpt_path = os.path.join(save_dir, "checkpoint")
ckpt_file = open(ckpt_path, "w")
@ -1335,7 +1345,7 @@ class CheckpointStateTest(test.TestCase):
saver_module.get_checkpoint_state(save_dir)
def testCheckPointCompletesRelativePaths(self):
save_dir = _TestDir("checkpoint_completes_relative_paths")
save_dir = self._get_test_dir("checkpoint_completes_relative_paths")
os.chdir(save_dir)
ckpt_path = os.path.join(save_dir, "checkpoint")
ckpt_file = open(ckpt_path, "w")
@ -1356,8 +1366,13 @@ class CheckpointStateTest(test.TestCase):
class MetaGraphTest(test.TestCase):
def _get_test_dir(self, dirname):
test_dir = os.path.join(self.get_temp_dir(), dirname)
gfile.MakeDirs(test_dir)
return test_dir
def testAddCollectionDef(self):
test_dir = _TestDir("good_collection")
test_dir = self._get_test_dir("good_collection")
filename = os.path.join(test_dir, "metafile")
with self.test_session():
# Creates a graph.
@ -1504,12 +1519,12 @@ class MetaGraphTest(test.TestCase):
self.assertEqual(11.0, v1.eval())
def testMultiSaverCollection(self):
test_dir = _TestDir("saver_collection")
test_dir = self._get_test_dir("saver_collection")
self._testMultiSaverCollectionSave(test_dir)
self._testMultiSaverCollectionRestore(test_dir)
def testBinaryAndTextFormat(self):
test_dir = _TestDir("binary_and_text")
test_dir = self._get_test_dir("binary_and_text")
filename = os.path.join(test_dir, "metafile")
with self.test_session(graph=ops_lib.Graph()):
# Creates a graph.
@ -1541,7 +1556,7 @@ class MetaGraphTest(test.TestCase):
saver_module.import_meta_graph(filename)
def testSliceVariable(self):
test_dir = _TestDir("slice_saver")
test_dir = self._get_test_dir("slice_saver")
filename = os.path.join(test_dir, "metafile")
with self.test_session():
v1 = variables.Variable([20.0], name="v1")
@ -1679,7 +1694,7 @@ class MetaGraphTest(test.TestCase):
sess.run(train_op)
def testGraphExtension(self):
test_dir = _TestDir("graph_extension")
test_dir = self._get_test_dir("graph_extension")
self._testGraphExtensionSave(test_dir)
self._testGraphExtensionRestore(test_dir)
self._testRestoreFromTrainGraphWithControlContext(test_dir)
@ -1722,7 +1737,7 @@ class MetaGraphTest(test.TestCase):
def testImportIntoNamescope(self):
# Test that we can import a meta graph into a namescope.
test_dir = _TestDir("import_into_namescope")
test_dir = self._get_test_dir("import_into_namescope")
filename = os.path.join(test_dir, "ckpt")
image = array_ops.placeholder(dtypes.float32, [None, 784])
label = array_ops.placeholder(dtypes.float32, [None, 10])
@ -1870,8 +1885,13 @@ class CheckpointReaderForV2Test(CheckpointReaderTest):
class WriteGraphTest(test.TestCase):
def _get_test_dir(self, dirname):
test_dir = os.path.join(self.get_temp_dir(), dirname)
gfile.MakeDirs(test_dir)
return test_dir
def testWriteGraph(self):
test_dir = _TestDir("write_graph_dir")
test_dir = self._get_test_dir("write_graph_dir")
variables.Variable([[1, 2, 3], [4, 5, 6]], dtype=dtypes.float32, name="v0")
path = graph_io.write_graph(ops_lib.get_default_graph(),
os.path.join(test_dir, "l1"), "graph.pbtxt")
@ -1881,7 +1901,7 @@ class WriteGraphTest(test.TestCase):
def testRecursiveCreate(self):
test_dir = _TestDir("deep_dir")
test_dir = self._get_test_dir("deep_dir")
variables.Variable([[1, 2, 3], [4, 5, 6]], dtype=dtypes.float32, name="v0")
path = graph_io.write_graph(ops_lib.get_default_graph().as_graph_def(),
os.path.join(test_dir, "l1", "l2", "l3"),
@ -1935,6 +1955,11 @@ class SaverUtilsTest(test.TestCase):
class ScopedGraphTest(test.TestCase):
def _get_test_dir(self, dirname):
test_dir = os.path.join(self.get_temp_dir(), dirname)
gfile.MakeDirs(test_dir)
return test_dir
def _testScopedSave(self, test_dir, exported_filename, ckpt_filename):
graph = ops_lib.Graph()
with graph.as_default():
@ -2067,7 +2092,7 @@ class ScopedGraphTest(test.TestCase):
# Verifies that we can save the subgraph under "hidden1" and restore it
# into "new_hidden1" in the new graph.
def testScopedSaveAndRestore(self):
test_dir = _TestDir("scoped_export_import")
test_dir = self._get_test_dir("scoped_export_import")
ckpt_filename = "ckpt"
self._testScopedSave(test_dir, "exported_hidden1.pbtxt", ckpt_filename)
self._testScopedRestore(test_dir, "exported_hidden1.pbtxt",
@ -2076,7 +2101,7 @@ class ScopedGraphTest(test.TestCase):
# Verifies that we can copy the subgraph under "hidden1" and copy it
# to different name scope in the same graph or different graph.
def testCopyScopedGraph(self):
test_dir = _TestDir("scoped_copy")
test_dir = self._get_test_dir("scoped_copy")
saver0_ckpt = os.path.join(test_dir, "saver0.ckpt")
graph1 = ops_lib.Graph()
with graph1.as_default():
@ -2132,7 +2157,7 @@ class ScopedGraphTest(test.TestCase):
self.assertAllClose(expected, sess.run("new_hidden1/relu:0"))
def testExportGraphDefWithScope(self):
test_dir = _TestDir("export_graph_def")
test_dir = self._get_test_dir("export_graph_def")
saver0_ckpt = os.path.join(test_dir, "saver0.ckpt")
graph1 = ops_lib.Graph()
with graph1.as_default():

73
tensorflow/python/training/supervisor_test.py
View File

@ -64,15 +64,14 @@ def _summary_iterator(test_dir):
return summary_iterator.summary_iterator(event_paths[-1])
def _test_dir(test_name):
test_dir = os.path.join(test.get_temp_dir(), test_name)
if os.path.exists(test_dir):
shutil.rmtree(test_dir)
return test_dir
class SupervisorTest(test.TestCase):
def _test_dir(self, test_name):
test_dir = os.path.join(self.get_temp_dir(), test_name)
if os.path.exists(test_dir):
shutil.rmtree(test_dir)
return test_dir
def _wait_for_glob(self, pattern, timeout_secs, for_checkpoint=True):
"""Wait for a checkpoint file to appear.
@ -94,7 +93,7 @@ class SupervisorTest(test.TestCase):
# This test does not test much.
def testBasics(self):
logdir = _test_dir("basics")
logdir = self._test_dir("basics")
with ops.Graph().as_default():
my_op = constant_op.constant(1.0)
sv = supervisor.Supervisor(logdir=logdir)
@ -105,7 +104,7 @@ class SupervisorTest(test.TestCase):
sv.stop()
def testManagedSession(self):
logdir = _test_dir("managed_session")
logdir = self._test_dir("managed_session")
with ops.Graph().as_default():
my_op = constant_op.constant(1.0)
sv = supervisor.Supervisor(logdir=logdir)
@ -116,7 +115,7 @@ class SupervisorTest(test.TestCase):
self.assertTrue(sv.should_stop())
def testManagedSessionUserError(self):
logdir = _test_dir("managed_user_error")
logdir = self._test_dir("managed_user_error")
with ops.Graph().as_default():
my_op = constant_op.constant(1.0)
sv = supervisor.Supervisor(logdir=logdir)
@ -134,7 +133,7 @@ class SupervisorTest(test.TestCase):
self.assertEqual(1, last_step)
def testManagedSessionIgnoreOutOfRangeError(self):
logdir = _test_dir("managed_out_of_range")
logdir = self._test_dir("managed_out_of_range")
with ops.Graph().as_default():
my_op = constant_op.constant(1.0)
sv = supervisor.Supervisor(logdir=logdir)
@ -152,7 +151,7 @@ class SupervisorTest(test.TestCase):
self.assertEqual(3, last_step)
def testManagedSessionDoNotKeepSummaryWriter(self):
logdir = _test_dir("managed_not_keep_summary_writer")
logdir = self._test_dir("managed_not_keep_summary_writer")
with ops.Graph().as_default():
summary.scalar("c1", constant_op.constant(1))
summary.scalar("c2", constant_op.constant(2))
@ -204,7 +203,7 @@ class SupervisorTest(test.TestCase):
next(rr)
def testManagedSessionKeepSummaryWriter(self):
logdir = _test_dir("managed_keep_summary_writer")
logdir = self._test_dir("managed_keep_summary_writer")
with ops.Graph().as_default():
summary.scalar("c1", constant_op.constant(1))
summary.scalar("c2", constant_op.constant(2))
@ -266,7 +265,7 @@ class SupervisorTest(test.TestCase):
def testManagedEndOfInputOneQueue(self):
# Tests that the supervisor finishes without an error when using
# a fixed number of epochs, reading from a single queue.
logdir = _test_dir("managed_end_of_input_one_queue")
logdir = self._test_dir("managed_end_of_input_one_queue")
os.makedirs(logdir)
data_path = self._csv_data(logdir)
with ops.Graph().as_default():
@ -285,7 +284,7 @@ class SupervisorTest(test.TestCase):
# Tests that the supervisor finishes without an error when using
# a fixed number of epochs, reading from two queues, the second
# one producing a batch from the first one.
logdir = _test_dir("managed_end_of_input_two_queues")
logdir = self._test_dir("managed_end_of_input_two_queues")
os.makedirs(logdir)
data_path = self._csv_data(logdir)
with ops.Graph().as_default():
@ -304,7 +303,7 @@ class SupervisorTest(test.TestCase):
def testManagedMainErrorTwoQueues(self):
# Tests that the supervisor correctly raises a main loop
# error even when using multiple queues for input.
logdir = _test_dir("managed_main_error_two_queues")
logdir = self._test_dir("managed_main_error_two_queues")
os.makedirs(logdir)
data_path = self._csv_data(logdir)
with self.assertRaisesRegexp(RuntimeError, "fail at step 3"):
@ -327,7 +326,7 @@ class SupervisorTest(test.TestCase):
sess.run(shuff_rec)
def testSessionConfig(self):
logdir = _test_dir("session_config")
logdir = self._test_dir("session_config")
with ops.Graph().as_default():
with ops.device("/cpu:1"):
my_op = constant_op.constant([1.0])
@ -340,7 +339,7 @@ class SupervisorTest(test.TestCase):
sv.stop()
def testChiefCanWriteEvents(self):
logdir = _test_dir("can_write")
logdir = self._test_dir("can_write")
with ops.Graph().as_default():
summary.scalar("c1", constant_op.constant(1))
summary.scalar("c2", constant_op.constant(2))
@ -421,7 +420,7 @@ class SupervisorTest(test.TestCase):
sv.summary_computed(sess, sess.run(summ))
def testLogdirButExplicitlyNoSummaryWriter(self):
logdir = _test_dir("explicit_no_summary_writer")
logdir = self._test_dir("explicit_no_summary_writer")
with ops.Graph().as_default():
variables.Variable([1.0], name="foo")
summary.scalar("c1", constant_op.constant(1))
@ -437,7 +436,7 @@ class SupervisorTest(test.TestCase):
sv.summary_computed(sess, sess.run(summ))
def testNoLogdirButExplicitSummaryWriter(self):
logdir = _test_dir("explicit_summary_writer")
logdir = self._test_dir("explicit_summary_writer")
with ops.Graph().as_default():
summary.scalar("c1", constant_op.constant(1))
summary.scalar("c2", constant_op.constant(2))
@ -506,7 +505,7 @@ class SupervisorTest(test.TestCase):
sv.prepare_or_wait_for_session("")
def testInitOp(self):
logdir = _test_dir("default_init_op")
logdir = self._test_dir("default_init_op")
with ops.Graph().as_default():
v = variables.Variable([1.0, 2.0, 3.0])
sv = supervisor.Supervisor(logdir=logdir)
@ -515,7 +514,7 @@ class SupervisorTest(test.TestCase):
sv.stop()
def testInitFn(self):
logdir = _test_dir("default_init_op")
logdir = self._test_dir("default_init_op")
with ops.Graph().as_default():
v = variables.Variable([1.0, 2.0, 3.0])
@ -528,7 +527,7 @@ class SupervisorTest(test.TestCase):
sv.stop()
def testInitOpWithFeedDict(self):
logdir = _test_dir("feed_dict_init_op")
logdir = self._test_dir("feed_dict_init_op")
with ops.Graph().as_default():
p = array_ops.placeholder(dtypes.float32, shape=(3,))
v = variables.Variable(p, name="v")
@ -542,7 +541,7 @@ class SupervisorTest(test.TestCase):
def testReadyForLocalInitOp(self):
server = server_lib.Server.create_local_server()
logdir = _test_dir("default_ready_for_local_init_op")
logdir = self._test_dir("default_ready_for_local_init_op")
uid = uuid.uuid4().hex
@ -584,7 +583,7 @@ class SupervisorTest(test.TestCase):
def testReadyForLocalInitOpRestoreFromCheckpoint(self):
server = server_lib.Server.create_local_server()
logdir = _test_dir("ready_for_local_init_op_restore")
logdir = self._test_dir("ready_for_local_init_op_restore")
uid = uuid.uuid4().hex
@ -639,7 +638,7 @@ class SupervisorTest(test.TestCase):
sv1.stop()
def testLocalInitOp(self):
logdir = _test_dir("default_local_init_op")
logdir = self._test_dir("default_local_init_op")
with ops.Graph().as_default():
# A local variable.
v = variables.Variable(
@ -664,7 +663,7 @@ class SupervisorTest(test.TestCase):
sv.stop()
def testLocalInitOpForNonChief(self):
logdir = _test_dir("default_local_init_op_non_chief")
logdir = self._test_dir("default_local_init_op_non_chief")
with ops.Graph().as_default():
with ops.device("/job:localhost"):
# A local variable.
@ -685,7 +684,7 @@ class SupervisorTest(test.TestCase):
def testInitOpFails(self):
server = server_lib.Server.create_local_server()
logdir = _test_dir("default_init_op_fails")
logdir = self._test_dir("default_init_op_fails")
with ops.Graph().as_default():
v = variables.Variable([1.0, 2.0, 3.0], name="v")
variables.Variable([4.0, 5.0, 6.0], name="w")
@ -697,7 +696,7 @@ class SupervisorTest(test.TestCase):
def testInitOpFailsForTransientVariable(self):
server = server_lib.Server.create_local_server()
logdir = _test_dir("default_init_op_fails_for_local_variable")
logdir = self._test_dir("default_init_op_fails_for_local_variable")
with ops.Graph().as_default():
v = variables.Variable(
[1.0, 2.0, 3.0],
@ -714,7 +713,7 @@ class SupervisorTest(test.TestCase):
sv.prepare_or_wait_for_session(server.target)
def testSetupFail(self):
logdir = _test_dir("setup_fail")
logdir = self._test_dir("setup_fail")
with ops.Graph().as_default():
variables.Variable([1.0, 2.0, 3.0], name="v")
with self.assertRaisesRegexp(ValueError, "must have their device set"):
@ -724,7 +723,7 @@ class SupervisorTest(test.TestCase):
supervisor.Supervisor(logdir=logdir, is_chief=False)
def testDefaultGlobalStep(self):
logdir = _test_dir("default_global_step")
logdir = self._test_dir("default_global_step")
with ops.Graph().as_default():
variables.Variable(287, name="global_step")
sv = supervisor.Supervisor(logdir=logdir)
@ -733,7 +732,7 @@ class SupervisorTest(test.TestCase):
sv.stop()
def testRestoreFromMetaGraph(self):
logdir = _test_dir("restore_from_meta_graph")
logdir = self._test_dir("restore_from_meta_graph")
with ops.Graph().as_default():
variables.Variable(1, name="v0")
sv = supervisor.Supervisor(logdir=logdir)
@ -754,7 +753,7 @@ class SupervisorTest(test.TestCase):
# right away and get to run once before sv.stop() returns.
# We still sleep a bit to make the test robust.
def testStandardServicesWithoutGlobalStep(self):
logdir = _test_dir("standard_services_without_global_step")
logdir = self._test_dir("standard_services_without_global_step")
# Create a checkpoint.
with ops.Graph().as_default():
v = variables.Variable([1.0], name="foo")
@ -804,7 +803,7 @@ class SupervisorTest(test.TestCase):
# Same as testStandardServicesNoGlobalStep but with a global step.
# We should get a summary about the step time.
def testStandardServicesWithGlobalStep(self):
logdir = _test_dir("standard_services_with_global_step")
logdir = self._test_dir("standard_services_with_global_step")
# Create a checkpoint.
with ops.Graph().as_default():
v = variables.Variable([123], name="global_step")
@ -867,12 +866,12 @@ class SupervisorTest(test.TestCase):
def testNoQueueRunners(self):
with ops.Graph().as_default(), self.test_session() as sess:
sv = supervisor.Supervisor(logdir=_test_dir("no_queue_runners"))
sv = supervisor.Supervisor(logdir=self._test_dir("no_queue_runners"))
self.assertEqual(0, len(sv.start_queue_runners(sess)))
sv.stop()
def testPrepareSessionAfterStopForChief(self):
logdir = _test_dir("prepare_after_stop_chief")
logdir = self._test_dir("prepare_after_stop_chief")
with ops.Graph().as_default():
sv = supervisor.Supervisor(logdir=logdir, is_chief=True)
@ -891,7 +890,7 @@ class SupervisorTest(test.TestCase):
self.assertTrue(sv.should_stop())
def testPrepareSessionAfterStopForNonChief(self):
logdir = _test_dir("prepare_after_stop_nonchief")
logdir = self._test_dir("prepare_after_stop_nonchief")
with ops.Graph().as_default():
sv = supervisor.Supervisor(logdir=logdir, is_chief=False)

10
tensorflow/tensorboard/README.md
View File

@ -51,18 +51,18 @@ work, but there may be bugs or performance issues.
The first step in using TensorBoard is acquiring data from your TensorFlow run.
For this, you need [summary
ops](https://www.tensorflow.org/versions/r0.12/api_docs/python/train.html#summary-operations).
ops](https://www.tensorflow.org/versions/r1.0/api_docs/python/train.html#summary-operations).
Summary ops are ops, like
[`tf.matmul`](https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops.html#matmul)
[`tf.matmul`](https://www.tensorflow.org/versions/r1.0/api_docs/python/math_ops.html#matmul)
or
[`tf.nn.relu`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#relu),
[`tf.nn.relu`](https://www.tensorflow.org/versions/r1.0/api_docs/python/nn.html#relu),
which means they take in tensors, produce tensors, and are evaluated from within
a TensorFlow graph. However, summary ops have a twist: the Tensors they produce
contain serialized protobufs, which are written to disk and sent to TensorBoard.
To visualize the summary data in TensorBoard, you should evaluate the summary
op, retrieve the result, and then write that result to disk using a
summary.FileWriter. A full explanation, with examples, is in [the
tutorial](https://www.tensorflow.org/versions/r0.12/how_tos/summaries_and_tensorboard/index.html).
tutorial](https://www.tensorflow.org/versions/r1.0/how_tos/summaries_and_tensorboard/index.html).
### Tags: Giving names to data
@ -184,7 +184,7 @@ TensorFlow model. To get best use of the graph visualizer, you should use name
scopes to hierarchically group the ops in your graph - otherwise, the graph may
be difficult to decipher. For more information, including examples, see [the
graph visualizer
tutorial](https://www.tensorflow.org/versions/r0.12/how_tos/graph_viz/index.html#tensorboard-graph-visualization).
tutorial](https://www.tensorflow.org/versions/r1.0/how_tos/graph_viz/index.html#tensorboard-graph-visualization).
# Frequently Asked Questions

9
tensorflow/tensorflow.bzl
View File

@ -20,6 +20,11 @@ load(
"cuda_default_copts"
)
#load(
# "//third_party/mkl:build_defs.bzl",
# "if_mkl",
#)
# List of proto files for android builds
def tf_android_core_proto_sources(core_proto_sources_relative):
return ["//tensorflow/core:" + p
@ -377,6 +382,10 @@ def tf_cc_tests(srcs, deps, name='', linkstatic=0, tags=[], size="medium",
args=args,
linkopts=linkopts)
#def tf_cc_test_mkl(srcs, deps, name='', linkstatic=0, tags=[], size="medium",
# args=None):
# tf_cc_tests(srcs, deps, linkstatic, tags=tags, size=size, args=args)
def tf_cc_tests_gpu(srcs, deps, name='', linkstatic=0, tags=[], size="medium",
args=None):
tf_cc_tests(srcs, deps, linkstatic, tags=tags, size=size, args=args)

4
tensorflow/tools/benchmark/README.md
View File

@ -39,7 +39,7 @@ $adb shell "/data/local/tmp/benchmark_model \
### On desktop:
(1) build the binary
```bash
$bazel build -c opt tensorflow/tools/benchmark:benchmark_model
$bazel build --config opt tensorflow/tools/benchmark:benchmark_model
```
(2) Run on your compute graph, similar to the Android case but without the need of adb shell.
@ -54,4 +54,4 @@ $bazel-bin/tensorflow/tools/benchmark/benchmark_model \
```
The Inception graph used as an example here may be downloaded from
https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip

2
tensorflow/tools/ci_build/install/install_proto3.sh
View File

@ -17,7 +17,7 @@
# Install protobuf3.
# Select protobuf version.
PROTOBUF_VERSION="3.1.0"
PROTOBUF_VERSION="3.2.0"
protobuf_ver_flat=$(echo $PROTOBUF_VERSION | sed 's/\.//g' | sed 's/^0*//g')
local_protobuf_ver=$(protoc --version | awk '{print $2}')
local_protobuf_ver_flat=$(echo $local_protobuf_ver | sed 's/\.//g' | sed 's/^0*//g')

2
tensorflow/tools/ci_build/protobuf/protobuf_optimized_pip.sh
View File

@ -14,7 +14,7 @@
# limitations under the License.
# ==============================================================================
PROTOBUF_VERSION="3.1.0"
PROTOBUF_VERSION="3.2.0"
PYTHON_BIN=${PYTHON_BIN:-python}
DIR=${PWD}/protobuf

1
tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh
View File

@ -111,6 +111,7 @@ function get_failing_cpu_py_tests() {
//$1/tensorflow/python:framework_ops_test + \
//$1/tensorflow/python:framework_tensor_util_test + \
//$1/tensorflow/python:framework_test_util_test + \
//$1/tensorflow/python:gradients_test + \
//$1/tensorflow/python:image_ops_test + \
//$1/tensorflow/python:localhost_cluster_performance_test + \
//$1/tensorflow/python:monitored_session_test + \

15
tensorflow/tools/compatibility/README.md
View File

@ -36,6 +36,9 @@ particular, functions that have had reordered arguments like `tf.concat`,
`tf.split` will cause the script to incorrectly add keyword arguments that
mismap arguments.
- This script wouldn't actually reorder arguments. Instead, the script will add
keyword arguments to functions that had their arguments reordered.
- This script is not able to upgrade all functions. One notable example is
`tf.reverse()` which has been changed to take a list of indices rather than
a tensor of bools. If the script detects this, it will report this to stdout
@ -43,6 +46,12 @@ a tensor of bools. If the script detects this, it will report this to stdout
`tf.reverse(a, [False, True, True])` you will need to manually change it to
`tf.reverse(a, [1, 2])`.
- There are some syntaxes that are not handleable with this script as this
script was designed to use only standard python packages. If the script fails
with "A necessary keyword argument failed to be inserted." or
"Failed to find keyword lexicographically. Fix manually.", you can try
[@machrisaa's fork of this script](https://github.com/machrisaa/tf0to1).
[@machrisaa](https://github.com/machrisaa) has used the
[RedBaron Python refactoring engine](https://redbaron.readthedocs.io/en/latest/)
which is able to localize syntactic elements more reliably than the built-in
`ast` module this script is based upon.

128
tensorflow/tools/compatibility/tf_upgrade.py
View File

@ -95,11 +95,15 @@ class APIChangeSpec(object):
"tf.split": {
"split_dim": "axis",
"num_split": "num_or_size_splits"
}
},
"tf.concat": {
"concat_dim": "axis"
},
}
# Mapping from function to the new name of the function
self.function_renames = {
"tf.inv": "tf.reciprocal",
"tf.contrib.deprecated.scalar_summary": "tf.summary.scalar",
"tf.contrib.deprecated.histogram_summary": "tf.summary.histogram",
"tf.listdiff": "tf.setdiff1d",
@ -142,6 +146,13 @@ class APIChangeSpec(object):
"tf.select": "tf.where",
"tf.complex_abs": "tf.abs",
"tf.batch_matmul": "tf.matmul",
"tf.pack": "tf.stack",
"tf.unpack": "tf.unstack",
}
self.change_to_function = {
"tf.ones_initializer",
"tf.zeros_initializer",
}
# Functions that were reordered should be changed to the new keyword args
@ -149,6 +160,7 @@ class APIChangeSpec(object):
# positional arguments yourself, this could do the wrong thing.
self.function_reorders = {
"tf.split": ["axis", "num_or_size_splits", "value", "name"],
"tf.sparse_split": ["axis", "num_or_size_splits", "value", "name"],
"tf.concat": ["concat_dim", "values", "name"],
"tf.svd": ["tensor", "compute_uv", "full_matrices", "name"],
"tf.nn.softmax_cross_entropy_with_logits": [
@ -335,6 +347,62 @@ class TensorFlowCallVisitor(ast.NodeVisitor):
items.append(curr.id)
return ".".join(reversed(items))
def _find_true_position(self, node):
"""Return correct line number and column offset for a given node.
This is necessary mainly because ListComp's location reporting reports
the next token after the list comprehension list opening.
Args:
node: Node for which we wish to know the lineno and col_offset
"""
import re
find_open = re.compile("^\s*(\\[).*$")
find_string_chars = re.compile("['\"]")
if isinstance(node, ast.ListComp):
# Strangely, ast.ListComp returns the col_offset of the first token
# after the '[' token which appears to be a bug. Workaround by
# explicitly finding the real start of the list comprehension.
line = node.lineno
col = node.col_offset
# loop over lines
while 1:
# Reverse the text to and regular expression search for whitespace
text = self._lines[line-1]
reversed_preceding_text = text[:col][::-1]
# First find if a [ can be found with only whitespace between it and
# col.
m = find_open.match(reversed_preceding_text)
if m:
new_col_offset = col - m.start(1) - 1
return line, new_col_offset
else:
if (reversed_preceding_text=="" or
reversed_preceding_text.isspace()):
line = line - 1
prev_line = self._lines[line - 1]
# TODO(aselle):
# this is poor comment detection, but it is good enough for
# cases where the comment does not contain string literal starting/
# ending characters. If ast gave us start and end locations of the
# ast nodes rather than just start, we could use string literal
# node ranges to filter out spurious #'s that appear in string
# literals.
comment_start = prev_line.find("#")
if comment_start == -1:
col = len(prev_line) -1
elif find_string_chars.search(prev_line[comment_start:]) is None:
col = comment_start
else:
return None, None
else:
return None, None
# Most other nodes return proper locations (with notably does not), but
# it is not possible to use that in an argument.
return node.lineno, node.col_offset
def visit_Call(self, node): # pylint: disable=invalid-name
"""Handle visiting a call node in the AST.
@ -342,11 +410,13 @@ class TensorFlowCallVisitor(ast.NodeVisitor):
node: Current Node
"""
ast.NodeVisitor.generic_visit(self, node)
# Find a simple attribute name path e.g. "tf.foo.bar"
full_name = self._get_attribute_full_path(node.func)
# Make sure the func is marked as being part of a call
node.func.is_function_for_call = True
if full_name and full_name.startswith("tf."):
# Call special handlers
function_handles = self._api_change_spec.function_handle
@ -356,27 +426,60 @@ class TensorFlowCallVisitor(ast.NodeVisitor):
# Examine any non-keyword argument and make it into a keyword argument
# if reordering required.
function_reorders = self._api_change_spec.function_reorders
function_keyword_renames = (
self._api_change_spec.function_keyword_renames)
if full_name in function_reorders:
reordered = function_reorders[full_name]
for idx, arg in enumerate(node.args):
self._file_edit.add("Added keyword %r to reordered function %r"
% (reordered[idx], full_name), arg.lineno,
arg.col_offset, "", reordered[idx] + "=")
lineno, col_offset = self._find_true_position(arg)
if lineno is None or col_offset is None:
self._file_edit.add(
"Failed to add keyword %r to reordered function %r"
% (reordered[idx], full_name), arg.lineno, arg.col_offset,
"", "",
error="A necessary keyword argument failed to be inserted.")
else:
keyword_arg = reordered[idx]
if (full_name in function_keyword_renames and
keyword_arg in function_keyword_renames[full_name]):
keyword_arg = function_keyword_renames[full_name][keyword_arg]
self._file_edit.add("Added keyword %r to reordered function %r"
% (reordered[idx], full_name), lineno,
col_offset, "", keyword_arg + "=")
# Examine each keyword argument and convert it to the final renamed form
function_keyword_renames = (
self._api_change_spec.function_keyword_renames)
renamed_keywords = ({} if full_name not in function_keyword_renames else
function_keyword_renames[full_name])
for keyword in node.keywords:
argkey = keyword.arg
argval = keyword.value
if argkey in renamed_keywords:
self._file_edit.add("Renamed keyword argument from %r to %r" %
argval_lineno, argval_col_offset = self._find_true_position(argval)
if (argval_lineno is not None and argval_col_offset is not None):
# TODO(aselle): We should scan backward to find the start of the
# keyword key. Unfortunately ast does not give you the location of
# keyword keys, so we are forced to infer it from the keyword arg
# value.
key_start = argval_col_offset - len(argkey) - 1
key_end = key_start + len(argkey) + 1
if self._lines[argval_lineno - 1][key_start:key_end] == argkey + "=":
self._file_edit.add("Renamed keyword argument from %r to %r" %
(argkey, renamed_keywords[argkey]),
argval.lineno,
argval.col_offset - len(argkey) - 1,
argval_lineno,
argval_col_offset - len(argkey) - 1,
argkey + "=", renamed_keywords[argkey] + "=")
continue
self._file_edit.add(
"Failed to rename keyword argument from %r to %r" %
(argkey, renamed_keywords[argkey]),
argval.lineno,
argval.col_offset - len(argkey) - 1,
"", "",
error="Failed to find keyword lexographically. Fix manually.")
ast.NodeVisitor.generic_visit(self, node)
def visit_Attribute(self, node): # pylint: disable=invalid-name
"""Handle bare Attributes i.e. [tf.foo, tf.bar].
@ -387,6 +490,11 @@ class TensorFlowCallVisitor(ast.NodeVisitor):
full_name = self._get_attribute_full_path(node)
if full_name and full_name.startswith("tf."):
self._rename_functions(node, full_name)
if full_name in self._api_change_spec.change_to_function:
if not hasattr(node, "is_function_for_call"):
new_text = full_name + "()"
self._file_edit.add("Changed %r to %r"%(full_name, new_text),
node.lineno, node.col_offset, full_name, new_text)
ast.NodeVisitor.generic_visit(self, node)

48
tensorflow/tools/compatibility/tf_upgrade_test.py
View File

@ -59,12 +59,45 @@ class TestUpgrade(test_util.TensorFlowTestCase):
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.multiply(a, tf.subtract(b, c))\n")
def testRenamePack(self):
text = "tf.pack(a)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.stack(a)\n")
text = "tf.unpack(a)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.unstack(a)\n")
def testReorder(self):
text = "tf.concat(a, b)\ntf.split(a, b, c)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.concat(concat_dim=a, values=b)\n"
self.assertEqual(new_text, "tf.concat(axis=a, values=b)\n"
"tf.split(axis=a, num_or_size_splits=b, value=c)\n")
def testConcatReorderWithKeywordArgs(self):
text = "tf.concat(concat_dim=a, values=b)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.concat(axis=a, values=b)\n")
text = "tf.concat(values=b, concat_dim=a)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.concat(values=b, axis=a)\n")
text = "tf.concat(a, values=b)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.concat(axis=a, values=b)\n")
def testConcatReorderNested(self):
text = "tf.concat(a, tf.concat(c, d))\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text, "tf.concat(axis=a, values=tf.concat(axis=c, values=d))\n")
def testInitializers(self):
text = ("tf.zeros_initializer;tf.zeros_initializer ()\n"
"tf.ones_initializer;tf.ones_initializer ()\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text, "tf.zeros_initializer();tf.zeros_initializer ()\n"
"tf.ones_initializer();tf.ones_initializer ()\n")
def testKeyword(self):
text = "tf.reduce_any(a, reduction_indices=[1, 2])\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
@ -80,6 +113,19 @@ class TestUpgrade(test_util.TensorFlowTestCase):
self.assertEqual(new_text, new_text)
self.assertEqual(errors, ["test.py:1: tf.reverse requires manual check."])
def testListComprehension(self):
def _test(input, output):
_, unused_report, errors, new_text = self._upgrade(input)
self.assertEqual(new_text, output)
_test("tf.concat(0, \t[x for x in y])\n",
"tf.concat(axis=0, \tvalues=[x for x in y])\n")
_test("tf.concat(0,[x for x in y])\n",
"tf.concat(axis=0,values=[x for x in y])\n")
_test("tf.concat(0,[\nx for x in y])\n",
"tf.concat(axis=0,values=[\nx for x in y])\n")
_test("tf.concat(0,[\n \tx for x in y])\n",
"tf.concat(axis=0,values=[\n \tx for x in y])\n")
# TODO(aselle): Explicitly not testing command line interface and process_tree
# for now, since this is a one off utility.

1
tensorflow/tools/docker/Dockerfile
View File

@ -30,6 +30,7 @@ RUN pip --no-cache-dir install \
numpy \
scipy \
sklearn \
pandas \
Pillow \
&& \
python -m ipykernel.kernelspec

3
tensorflow/tools/docker/Dockerfile.devel
View File

@ -32,6 +32,7 @@ RUN pip --no-cache-dir install \
numpy \
scipy \
sklearn \
pandas \
&& \
python -m ipykernel.kernelspec
@ -82,7 +83,7 @@ RUN mkdir /bazel && \
RUN git clone https://github.com/tensorflow/tensorflow.git && \
cd tensorflow && \
git checkout r0.12
git checkout r1.0
WORKDIR /tensorflow
# TODO(craigcitro): Don't install the pip package, since it makes it

3
tensorflow/tools/docker/Dockerfile.devel-gpu
View File

@ -32,6 +32,7 @@ RUN pip --no-cache-dir install \
numpy \
scipy \
sklearn \
pandas \
&& \
python -m ipykernel.kernelspec
@ -82,7 +83,7 @@ RUN mkdir /bazel && \
RUN git clone https://github.com/tensorflow/tensorflow.git && \
cd tensorflow && \
git checkout r0.12
git checkout r1.0
WORKDIR /tensorflow
# Configure the build for our CUDA configuration.

4
tensorflow/tools/docker/Dockerfile.gpu
View File

@ -30,6 +30,7 @@ RUN pip --no-cache-dir install \
numpy \
scipy \
sklearn \
pandas \
Pillow \
&& \
python -m ipykernel.kernelspec
@ -58,6 +59,9 @@ COPY notebooks /notebooks
# We just add a little wrapper script.
COPY run_jupyter.sh /
# For CUDA profiling, TensorFlow requires CUPTI.
ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
# TensorBoard
EXPOSE 6006
# IPython

13
tensorflow/tools/docs/parser.py
View File

@ -328,7 +328,9 @@ def _generate_signature(func, reverse_index):
len(argspec.args or []) - len(argspec.defaults or []))
# Python documentation skips `self` when printing method signatures.
first_arg = 1 if inspect.ismethod(func) and 'self' in argspec.args[:1] else 0
# Note we cannot test for ismethod here since unbound methods do not register
# as methods (in Python 3).
first_arg = 1 if 'self' in argspec.args[:1] else 0
# Add all args without defaults.
for arg in argspec.args[first_arg:first_arg_with_default]:
@ -679,6 +681,15 @@ def generate_global_index(library_name, index, duplicate_of):
for full_name, py_object in six.iteritems(index):
if (inspect.ismodule(py_object) or inspect.isfunction(py_object) or
inspect.isclass(py_object)):
# In Python 3, unbound methods are functions, so eliminate those.
if inspect.isfunction(py_object):
if full_name.count('.') == 0:
parent_name = ''
else:
parent_name = full_name[:full_name.rfind('.')]
if parent_name in index and inspect.isclass(index[parent_name]):
# Skip methods (=functions with class parents).
continue
symbol_links.append((full_name,
_markdown_link(full_name, full_name,
'.', duplicate_of)))

2
tensorflow/tools/lib_package/README.md
View File

@ -13,7 +13,7 @@ and [Rust](https://github.com/tensorflow/rust).
The command:
```sh
bazel build -c opt //tensorflow/tools/lib_package:libtensorflow
bazel build --config opt //tensorflow/tools/lib_package:libtensorflow
```
produces `bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz`, which

3
tensorflow/tools/pip_package/BUILD
View File

@ -4,6 +4,7 @@
package(default_visibility = ["//visibility:private"])
load("//tensorflow:tensorflow.bzl", "transitive_hdrs")
load("//third_party/mkl:build_defs.bzl", "if_mkl")
load("//tensorflow/core:platform/default/build_config_root.bzl", "tf_additional_license_deps")
# This returns a list of headers of all public header libraries (e.g.,
@ -131,5 +132,5 @@ sh_binary(
"//tensorflow/python/tools:all_files",
"//tensorflow/tensorboard",
],
}),
}) + if_mkl(["//third_party/mkl:intel_binary_blob"]),
)

21
tensorflow/tools/pip_package/build_pip_package.sh
View File

@ -89,6 +89,13 @@ function main() {
bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/external \
"${TMPDIR}/external"
RUNFILES=bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles
# Copy MKL libs over so they can be loaded at runtime
if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl ]; then
mkdir "${TMPDIR}/_solib_k8"
cp -R \
bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl \
"${TMPDIR}/_solib_k8"
fi
else
if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/external ]; then
# Old-style runfiles structure (--legacy_external_runfiles).
@ -99,6 +106,13 @@ function main() {
cp_external \
bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/external \
"${TMPDIR}/external"
# Copy MKL libs over so they can be loaded at runtime
if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl ]; then
mkdir "${TMPDIR}/_solib_k8"
cp -R \
bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl \
"${TMPDIR}/_solib_k8"
fi
else
# New-style runfiles structure (--nolegacy_external_runfiles).
cp -R \
@ -109,6 +123,13 @@ function main() {
cp_external \
bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles \
"${TMPDIR}/external"
# Copy MKL libs over so they can be loaded at runtime
if [ -d bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl ]; then
mkdir "${TMPDIR}/_solib_k8"
cp -R \
bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/_solib_k8/_U_S_Sthird_Uparty_Smkl_Cintel_Ubinary_Ublob___Uthird_Uparty_Smkl \
"${TMPDIR}/_solib_k8"
fi
fi
RUNFILES=bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow
fi

3
tensorflow/tools/pip_package/setup.py
View File

@ -29,7 +29,7 @@ from setuptools.dist import Distribution
# This version string is semver compatible, but incompatible with pip.
# For pip, we will remove all '-' characters from this string, and use the
# result for pip.
_VERSION = '0.12.1'
_VERSION = '1.0.0-rc1'
REQUIRED_PACKAGES = [
'numpy >= 1.11.0',
@ -151,6 +151,7 @@ def find_files(pattern, root):
matches = ['../' + x for x in find_files('*', 'external') if '.py' not in x]
matches += ['../' + x for x in find_files('*', '_solib_k8') if '.py' not in x]
if os.name == 'nt':
EXTENSION_NAME = 'python/_pywrap_tensorflow.pyd'

2
tensorflow/tools/tfprof/README.md
View File

@ -98,7 +98,7 @@ TODO(xpan): Provide graph.pbtxt, model.ckpt, tfprof_log and run_meta download.
```shell
# Build the tool.
bazel build -c opt tensorflow/tools/tfprof/...
bazel build --config opt tensorflow/tools/tfprof/...
# Help information, including detail 'option' instructions.
bazel-bin/tensorflow/tools/tfprof/tfprof help

8
tensorflow/workspace.bzl
View File

@ -78,11 +78,11 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
native.new_http_archive(
name = "libxsmm_archive",
urls = [
"http://bazel-mirror.storage.googleapis.com/github.com/hfp/libxsmm/archive/1.6.6.tar.gz",
"https://github.com/hfp/libxsmm/archive/1.6.6.tar.gz",
"http://bazel-mirror.storage.googleapis.com/github.com/hfp/libxsmm/archive/1.7.tar.gz",
"https://github.com/hfp/libxsmm/archive/1.7.tar.gz",
],
sha256 = "7c048a48e17f7f14a475be7b83e6e941289e03debb42ce9e02a06353412f9f2a",
strip_prefix = "libxsmm-1.6.6",
sha256 = "2eea65624a697e74b939511cd2a686b4c957e90c99be168fe134d96771e811ad",
strip_prefix = "libxsmm-1.7",
build_file = str(Label("//third_party:libxsmm.BUILD")),
)

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