experiments/STT-tensorflow
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Change: 142074581
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Martin Wicke 2016-12-14 15:46:53 -08:00 committed by TensorFlower Gardener
parent 811629aed4
commit 2e4869af1a
121 changed files with 2586 additions and 2232 deletions
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8
README.md
View File

@ -33,10 +33,10 @@ 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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc0-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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-0.12.0rc1-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/))
#### *Try your first TensorFlow program*

4
RELEASE.md
View File

@ -61,9 +61,7 @@
acceleration. Known limitations include: It is not currently possible to load
a custom op library. The GCS and HDFS file systems are not currently
supported. The following ops are not currently implemented:
DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter,
DepthwiseConv2dNativeBackpropInput, Dequantize, Digamma, Erf, Erfc, Igamma,
Igammac, Lgamma, Polygamma, QuantizeAndDequantize, QuantizedAvgPool,
Dequantize, QuantizeAndDequantize, QuantizedAvgPool,
QuantizedBatchNomWithGlobalNormalization, QuantizedBiasAdd, QuantizedConcat,
QuantizedConv2D, QuantizedMatmul, QuantizedMaxPool,
QuantizeDownAndShrinkRange, QuantizedRelu, QuantizedRelu6, QuantizedReshape,

7
configure vendored
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@ -24,7 +24,8 @@ function bazel_clean_and_fetch() {
if ! is_windows; then
bazel clean --expunge
fi
bazel fetch //tensorflow/...
# TODO(https://github.com/bazelbuild/bazel/issues/2220) Remove the nested `bazel query`.
bazel fetch $(bazel query "//tensorflow/... -//tensorflow/examples/android/...")
}
## Set up python-related environment settings
@ -279,7 +280,7 @@ while true; do
TF_CUDNN_VERSION=${BASH_REMATCH[1]}
echo "libcudnn.so resolves to libcudnn${TF_CUDNN_EXT}"
elif [[ "$REALVAL" =~ ([0-9]*).dylib ]]; then
TF_CUDNN_EXT=${BASH_REMATCH[1]}".dylib"
TF_CUDNN_EXT="."${BASH_REMATCH[1]}".dylib"
TF_CUDNN_VERSION=${BASH_REMATCH[1]}
echo "libcudnn.dylib resolves to libcudnn${TF_CUDNN_EXT}"
fi
@ -435,7 +436,7 @@ while true; do
# Point to ComputeCpp root
if [ -z "$COMPUTECPP_TOOLKIT_PATH" ]; then
default_computecpp_toolkit_path=/usr/local/computecpp
read -p "Please specify the location where ComputeCpp $TF_OPENCL_VERSION is installed. Refer to README.md for more details. [Default is $default_computecpp_toolkit_path]: " COMPUTECPP_TOOLKIT_PATH
read -p "Please specify the location where ComputeCpp for SYCL $TF_OPENCL_VERSION is installed. [Default is $default_computecpp_toolkit_path]: " COMPUTECPP_TOOLKIT_PATH
fromuser="1"
if [ -z "$COMPUTECPP_TOOLKIT_PATH" ]; then
COMPUTECPP_TOOLKIT_PATH=$default_computecpp_toolkit_path

6
tensorflow/BUILD
View File

@ -50,6 +50,12 @@ config_setting(
visibility = ["//visibility:public"],
)
config_setting(
name = "no_tensorflow_py_deps",
values = {"define": "no_tensorflow_py_deps=true"},
visibility = ["//visibility:public"],
)
config_setting(
name = "ios",
values = {

30
tensorflow/contrib/cmake/README.md
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@ -13,12 +13,36 @@ Linux.
Current Status
--------------
CMake can be used to build TensorFlow on Windows. See the [getting started documentation](https://www.tensorflow.org/get_started/os_setup.html#pip-installation-on-windows)
for instructions on how to install a pre-built TensorFlow package on Windows.
### Current known limitations
* It is not possible to load a custom Op library.
* GCS and HDFS file systems are not supported.
* The following Ops are not currently implemented:
- Dequantize
- QuantizeAndDequantize
- QuantizedAvgPool
- QuantizedBatchNomWithGlobalNormalization
- QuantizedBiasAdd
- QuantizedConcat
- QuantizedConv2D
- QuantizedMatmul
- QuantizedMaxPoo
- QuantizeDownAndShrinkRange
- QuantizedRelu
- QuantizedRelu6
- QuantizedReshape
- QuantizeV2
- RequantizationRange
- Requantize
## Building with CMake
The CMake files in this directory can build the core TensorFlow runtime, an
example C++ binary, and a PIP package containing the runtime and Python
bindings.
Note: Windows support is in an **alpha** state, and we welcome your feedback.
### Pre-requisites
* CMake version 3.5 up to 3.6
@ -46,6 +70,8 @@ Note: Windows support is in an **alpha** state, and we welcome your feedback.
- [swigwin-3.0.10](http://www.swig.org/download.html)
- [NVidia CUDA Toolkit 8.0] (https://developer.nvidia.com/cuda-downloads)
- [NVidia CUDNN 5.1] (https://developer.nvidia.com/cudnn)
- [CMake 3.6](https://cmake.org/files/v3.6/cmake-3.6.3-win64-x64.msi)
* Ubuntu 14.04
- Makefile generator
- Docker 1.9.1 (for automated testing)

2
tensorflow/contrib/cmake/setup.py
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@ -26,7 +26,7 @@ from setuptools import find_packages, setup, Command
from setuptools.command.install import install as InstallCommandBase
from setuptools.dist import Distribution
_VERSION = '0.12.0-rc0-cmake-experimental'
_VERSION = '0.12.0-rc1-cmake-experimental'
REQUIRED_PACKAGES = [
'numpy >= 1.11.0',

2
tensorflow/contrib/cmake/tf_core_kernels.cmake
View File

@ -62,7 +62,7 @@ if(tensorflow_BUILD_CONTRIB_KERNELS)
"${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_gradient_op.cc"
"${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/unpack_path_op.cc"
"${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/hybrid/core/ops/utils.cc"
)
)
list(APPEND tf_core_kernels_srcs ${tf_contrib_kernels_srcs})
endif(tensorflow_BUILD_CONTRIB_KERNELS)

2
tensorflow/contrib/cmake/tf_core_ops.cmake
View File

@ -48,7 +48,7 @@ GENERATE_CONTRIB_OP_LIBRARY(cudnn_rnn "${tensorflow_source_dir}/tensorflow/contr
GENERATE_CONTRIB_OP_LIBRARY(factorization_clustering "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/clustering_ops.cc")
GENERATE_CONTRIB_OP_LIBRARY(factorization_factorization "${tensorflow_source_dir}/tensorflow/contrib/factorization/ops/factorization_ops.cc")
GENERATE_CONTRIB_OP_LIBRARY(framework_variable "${tensorflow_source_dir}/tensorflow/contrib/framework/ops/variable_ops.cc")
GENERATE_CONTRIB_OP_LIBRARY(tensor_forest "${tensorflow_source_dir}/tensorflow/contrib/tensor_forest/ops/tensor_forest_ops.cc")
########################################################
# tf_user_ops library

22
tensorflow/contrib/cmake/tf_python.cmake
View File

@ -188,6 +188,7 @@ add_python_module("tensorflow/python/lib")
add_python_module("tensorflow/python/lib/core")
add_python_module("tensorflow/python/lib/io")
add_python_module("tensorflow/python/ops")
add_python_module("tensorflow/python/ops/losses")
add_python_module("tensorflow/python/platform")
add_python_module("tensorflow/python/platform/default")
add_python_module("tensorflow/python/platform/summary")
@ -220,6 +221,7 @@ add_python_module("tensorflow/contrib/bayesflow/examples/reinforce_simple")
add_python_module("tensorflow/contrib/bayesflow/python")
add_python_module("tensorflow/contrib/bayesflow/python/kernel_tests")
add_python_module("tensorflow/contrib/bayesflow/python/ops")
add_python_module("tensorflow/contrib/compiler")
add_python_module("tensorflow/contrib/copy_graph")
add_python_module("tensorflow/contrib/copy_graph/python")
add_python_module("tensorflow/contrib/copy_graph/python/util")
@ -261,6 +263,12 @@ add_python_module("tensorflow/contrib/grid_rnn")
add_python_module("tensorflow/contrib/grid_rnn/python")
add_python_module("tensorflow/contrib/grid_rnn/python/kernel_tests")
add_python_module("tensorflow/contrib/grid_rnn/python/ops")
add_python_module("tensorflow/contrib/image")
add_python_module("tensorflow/contrib/image/python")
add_python_module("tensorflow/contrib/image/python/ops")
add_python_module("tensorflow/contrib/input_pipeline")
add_python_module("tensorflow/contrib/input_pipeline/python")
add_python_module("tensorflow/contrib/input_pipeline/python/ops")
add_python_module("tensorflow/contrib/integrate")
add_python_module("tensorflow/contrib/integrate/python")
add_python_module("tensorflow/contrib/integrate/python/ops")
@ -301,6 +309,7 @@ add_python_module("tensorflow/contrib/learn/python/learn/preprocessing/tests")
add_python_module("tensorflow/contrib/learn/python/learn/tests")
add_python_module("tensorflow/contrib/learn/python/learn/tests/dataframe")
add_python_module("tensorflow/contrib/learn/python/learn/utils")
add_python_module("tensorflow/contrib/legacy_seq2seq")
add_python_module("tensorflow/contrib/linalg")
add_python_module("tensorflow/contrib/linalg/python")
add_python_module("tensorflow/contrib/linalg/python/ops")
@ -392,6 +401,7 @@ add_python_module("tensorflow/contrib/training/python")
add_python_module("tensorflow/contrib/training/python/training")
add_python_module("tensorflow/contrib/util")
# Additional directories with no Python sources.
add_custom_command(TARGET tf_python_touchup_modules PRE_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory "${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/tensorboard/dist")
@ -423,6 +433,7 @@ set(tf_python_op_lib_names
)
function(GENERATE_PYTHON_OP_LIB tf_python_op_lib_name)
set(options SHAPE_FUNCTIONS_NOT_REQUIRED)
set(oneValueArgs DESTINATION)
set(multiValueArgs ADDITIONAL_LIBRARIES)
cmake_parse_arguments(GENERATE_PYTHON_OP_LIB
@ -432,7 +443,12 @@ function(GENERATE_PYTHON_OP_LIB tf_python_op_lib_name)
set(GENERATE_PYTHON_OP_LIB_DESTINATION
"${python_ops_target_dir}/gen_${tf_python_op_lib_name}.py")
endif()
if(GENERATE_PYTHON_OP_LIB_SHAPE_FUNCTIONS_NOT_REQUIRED)
set(require_shape_fn 0)
else()
set(require_shape_fn 1)
endif()
# Create a C++ executable that links in the appropriate op
# registrations and generates Python wrapper code based on the
# registered ops.
@ -453,7 +469,7 @@ function(GENERATE_PYTHON_OP_LIB tf_python_op_lib_name)
# containing the wrappers.
add_custom_command(
OUTPUT ${GENERATE_PYTHON_OP_LIB_DESTINATION}
COMMAND ${tf_python_op_lib_name}_gen_python @${tensorflow_source_dir}/tensorflow/python/ops/hidden_ops.txt 1 > ${GENERATE_PYTHON_OP_LIB_DESTINATION}
COMMAND ${tf_python_op_lib_name}_gen_python @${tensorflow_source_dir}/tensorflow/python/ops/hidden_ops.txt ${require_shape_fn} > ${GENERATE_PYTHON_OP_LIB_DESTINATION}
DEPENDS ${tf_python_op_lib_name}_gen_python
)
@ -496,6 +512,8 @@ GENERATE_PYTHON_OP_LIB("contrib_factorization_factorization_ops"
DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/factorization/python/ops/gen_factorization_ops.py)
GENERATE_PYTHON_OP_LIB("contrib_framework_variable_ops"
DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/framework/python/ops/gen_variable_ops.py)
GENERATE_PYTHON_OP_LIB("contrib_tensor_forest_ops"
DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/tf_python/tensorflow/contrib/tensor_forest/python/ops/gen_tensor_forest_ops.py)
add_custom_target(tf_python_ops SOURCES ${tf_python_ops_generated_files} ${PYTHON_PROTO_GENFILES})
add_dependencies(tf_python_ops tf_python_op_gen_main)

4
tensorflow/contrib/cmake/tf_tests.cmake
View File

@ -126,8 +126,9 @@ if (tensorflow_BUILD_PYTHON_TESTS)
set(tf_test_src_py_exclude
# generally not working
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/__init__.py"
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/benchmark_test.py"
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/benchmark_test.py"
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/resource_variable_ops_test.py"
"${tensorflow_source_dir}/tensorflow/python/saved_model/saved_model_test.py"
)
if (WIN32)
set(tf_test_src_py_exclude
@ -144,7 +145,6 @@ if (tensorflow_BUILD_PYTHON_TESTS)
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/string_to_number_op_test.py"
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/clip_ops_test.py"
# misc
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/cwise_ops_test.py"
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/variable_scope_test.py"
"${tensorflow_source_dir}/tensorflow/python/kernel_tests/reshape_op_test.py"
"${tensorflow_source_dir}/tensorflow/tensorboard/backend/server_test.py"

14
tensorflow/contrib/layers/python/layers/layers_test.py
View File

@ -532,7 +532,7 @@ class ConvolutionTest(tf.test.TestCase):
self.assertListEqual(list(eval_output.shape), expected_size_dynamic)
def testDynamicOutputSizeWithRateOneValidPaddingNCHW(self):
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
num_filters = 32
input_size = [5, 3, 9, 11]
expected_size = [None, num_filters, None, None]
@ -624,7 +624,7 @@ class Convolution2dTransposeTests(tf.test.TestCase):
def testOutputSizeWithStrideOneSamePaddingNCHW(self):
# `NCHW` data fomat is only supported for `GPU` device.
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True) as sess:
num_filters = 32
input_size = [5, 3, 10, 12]
@ -1782,7 +1782,7 @@ class BatchNormTest(tf.test.TestCase):
self._testNoneUpdatesCollections(False, data_format='NCHW')
def testNoneUpdatesCollectionsFusedNCHW(self):
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._testNoneUpdatesCollections(True, data_format='NCHW')
def testNoneUpdatesCollectionsFusedNHWC(self):
@ -1849,7 +1849,7 @@ class BatchNormTest(tf.test.TestCase):
self._testDelayedUpdateMovingVars(False, data_format='NCHW')
def testDelayedUpdateMovingVarsFusedNCHW(self):
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._testDelayedUpdateMovingVars(True, data_format='NCHW')
def testDelayedUpdateMovingVarsFusedNHWC(self):
@ -2037,7 +2037,7 @@ class BatchNormTest(tf.test.TestCase):
self._testIsTrainingVariable(False, data_format='NCHW')
def testIsTrainingVariableFusedNCHW(self):
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._testIsTrainingVariable(True, data_format='NCHW')
def testIsTrainingVariableFusedNHWC(self):
@ -2176,7 +2176,7 @@ class BatchNormTest(tf.test.TestCase):
self._testNoneUpdatesCollectionIsTrainingVariable(False, data_format='NCHW')
def testNoneUpdatesCollectionIsTrainingVariableFusedNCHW(self):
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._testNoneUpdatesCollectionIsTrainingVariable(
True, data_format='NCHW')
@ -2255,7 +2255,7 @@ class BatchNormTest(tf.test.TestCase):
self._testTrainMovingVars(False, data_format='NCHW')
def testTrainMovingVarsFusedNCHW(self):
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._testTrainMovingVars(True, data_format='NCHW')
def testTrainMovingVarsFusedNHWC(self):

1
tensorflow/contrib/learn/python/learn/estimators/estimator_test.py
View File

@ -90,7 +90,6 @@ def boston_eval_fn():
return tf.concat_v2([features, features], 0), tf.concat_v2([labels, labels],
0)
def extract(data, key):
if isinstance(data, dict):
assert key in data

3
tensorflow/contrib/learn/python/learn/export_strategy.py
View File

@ -13,7 +13,8 @@
# limitations under the License.
# ==============================================================================
"""Experiment class collecting information needed for a single training run."""
"""ExportStrategy class that provides strategies to export model so later it
can be used for TensorFlow serving."""
from __future__ import absolute_import
from __future__ import division

51
tensorflow/contrib/learn/python/learn/utils/checkpoints.py
View File

@ -1,51 +0,0 @@
# 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.
# ==============================================================================
"""Tools to work with checkpoints."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.framework import deprecated
from tensorflow.contrib.framework.python.framework import checkpoint_utils
@deprecated('2016-08-22', 'Please use tf.contrib.framework.load_checkpoint '
'instead')
def load_checkpoint(filepattern):
"""See `tf.contrib.framework.load_checkpoint`."""
return checkpoint_utils.load_checkpoint(filepattern)
@deprecated('2016-08-22', 'Please use tf.contrib.framework.load_variable '
'instead')
def load_variable(checkpoint_dir, name):
"""See `tf.contrib.framework.load_variable`."""
return checkpoint_utils.load_variable(checkpoint_dir, name)
@deprecated('2016-08-22', 'Please use tf.contrib.framework.list_variables '
'instead')
def list_variables(checkpoint_dir):
"""See `tf.contrib.framework.list_variables`."""
return checkpoint_utils.list_variables(checkpoint_dir)
@deprecated('2016-08-22', 'Please use tf.contrib.framework.init_from_checkpoint'
' instead')
def init_from_checkpoint(checkpoint_dir, assignment_map):
"""See `tf.contrib.framework.init_from_checkpoint`."""
checkpoint_utils.init_from_checkpoint(checkpoint_dir, assignment_map)

1
tensorflow/contrib/makefile/tf_op_files.txt
View File

@ -106,6 +106,7 @@ tensorflow/core/kernels/deep_conv2d.cc
tensorflow/core/kernels/xsmm_conv2d.cc
tensorflow/core/kernels/cwise_ops_common.cc
tensorflow/core/kernels/cwise_op_tanh.cc
tensorflow/core/kernels/cwise_op_pow.cc
tensorflow/core/kernels/cwise_op_sub.cc
tensorflow/core/kernels/cwise_op_squared_difference.cc
tensorflow/core/kernels/cwise_op_square.cc

2
tensorflow/contrib/metrics/BUILD
View File

@ -8,8 +8,6 @@ exports_files(["LICENSE"])
package(default_visibility = ["//tensorflow:__subpackages__"])
load("//tensorflow:tensorflow.bzl", "tf_py_test")
py_library(
name = "metrics_py",
srcs = [

41
tensorflow/contrib/slim/README.md
View File

@ -99,7 +99,7 @@ normal distribution, regularize it with an `l2_loss` and place it on the `CPU`,
one need only declare the following:
```python
weights = variables.variable('weights',
weights = slim.variable('weights',
shape=[10, 10, 3 , 3],
initializer=tf.truncated_normal_initializer(stddev=0.1),
regularizer=slim.l2_regularizer(0.05),
@ -361,11 +361,11 @@ One can also nest `arg_scopes` and use multiple operations in the same scope.
For example:
```python
with slim.arg_scope([slim.conv2d, slim.fully_connected],
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
with arg_scope([slim.conv2d], stride=1, padding='SAME'):
with slim.arg_scope([slim.conv2d], stride=1, padding='SAME'):
net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
net = slim.conv2d(net, 256, [5, 5],
weights_initializer=tf.truncated_normal_initializer(stddev=0.03),
@ -450,7 +450,7 @@ images, labels = ...
predictions = vgg.vgg16(images)
# Define the loss functions and get the total loss.
loss = losses.softmax_cross_entropy(predictions, labels)
loss = slim.losses.softmax_cross_entropy(predictions, labels)
```
In this example, we start by creating the model (using TF-Slim's VGG
@ -477,7 +477,7 @@ total_loss = slim.losses.get_total_loss(add_regularization_losses=False)
In this example, we have two losses which we add by calling
`slim.losses.softmax_cross_entropy` and `slim.losses.sum_of_squares`. We can
obtain the total loss by adding them together (`total_loss`) or by calling
`slim.losses.GetTotalLoss()`. How did this work?
`slim.losses.get_total_loss()`. How did this work?
When you create a loss function via TF-Slim, TF-Slim adds the loss to a
special TensorFlow collection of loss functions. This enables you to either
manage the total loss manually, or allow TF-Slim to manage them for you.
@ -566,11 +566,10 @@ vgg = tf.contrib.slim.nets.vgg
...
train_log_dir = ...
if not gfile.Exists(train_log_dir):
gfile.MakeDirs(train_log_dir)
if not tf.gfile.Exists(train_log_dir):
tf.gfile.MakeDirs(train_log_dir)
g = tf.Graph()
with g.as_default():
with tf.Graph().as_default():
# Set up the data loading:
images, labels = ...
@ -581,7 +580,7 @@ with g.as_default():
slim.losses.softmax_cross_entropy(predictions, labels)
total_loss = slim.losses.get_total_loss()
tf.summary.scalar('losses/total loss', total_loss)
tf.summary.scalar('losses/total_loss', total_loss)
# Specify the optimization scheme:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=.001)
@ -638,8 +637,8 @@ helper functions to select a subset of variables to restore:
```python
# Create some variables.
v1 = slim.variables.variable(name="v1", ...)
v2 = slim.variables.variable(name="nested/v2", ...)
v1 = slim.variable(name="v1", ...)
v2 = slim.variable(name="nested/v2", ...)
...
# Get list of variables to restore (which contains only 'v2'). These are all
@ -748,7 +747,7 @@ We define a metric to be a performance measure that is not a loss function
(losses are directly optimized during training), but which we are still
interested in for the purpose of evaluating our model.
For example, we might want to minimize log loss, but our metrics of interest
might be F1 score, or Intersection Over Union score (which are not
might be F1 score (test accuracy), or Intersection Over Union score (which are not
differentiable, and therefore cannot be used as losses).
TF-Slim provides a set of metric operations that makes evaluating models
@ -775,8 +774,8 @@ set (upon which the loss is computed), we'll assume we're using test data:
images, labels = LoadTestData(...)
predictions = MyModel(images)
mae_value_op, mae_update_op = slim.metrics.mean_absolute_error(predictions, labels)
mre_value_op, mre_update_op = slim.metrics.mean_relative_error(predictions, labels, labels)
mae_value_op, mae_update_op = slim.metrics.streaming_mean_absolute_error(predictions, labels)
mre_value_op, mre_update_op = slim.metrics.streaming_mean_relative_error(predictions, labels, labels)
pl_value_op, pl_update_op = slim.metrics.percentage_less(mean_relative_errors, 0.3)
```
@ -793,13 +792,13 @@ this, TF-Slim provides two convenience functions:
# Aggregates the value and update ops in two lists:
value_ops, update_ops = slim.metrics.aggregate_metrics(
slim.metrics.mean_absolute_error(predictions, labels),
slim.metrics.mean_squared_error(predictions, labels))
slim.metrics.streaming_mean_absolute_error(predictions, labels),
slim.metrics.streaming_mean_squared_error(predictions, labels))
# Aggregates the value and update ops in two dictionaries:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
"eval/mean_absolute_error": slim.metrics.mean_absolute_error(predictions, labels),
"eval/mean_squared_error": slim.metrics.mean_squared_error(predictions, labels),
"eval/mean_absolute_error": slim.metrics.streaming_mean_absolute_error(predictions, labels),
"eval/mean_squared_error": slim.metrics.streaming_mean_squared_error(predictions, labels),
})
```
@ -823,8 +822,8 @@ predictions = vgg.vgg_16(images)
# Choose the metrics to compute:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
"eval/mean_absolute_error": slim.metrics.mean_absolute_error(predictions, labels),
"eval/mean_squared_error": slim.metrics.mean_squared_error(predictions, labels),
"eval/mean_absolute_error": slim.metrics.streaming_mean_absolute_error(predictions, labels),
"eval/mean_squared_error": slim.metrics.streaming_mean_squared_error(predictions, labels),
})
# Evaluate the model using 1000 batches of data:

2
tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py
View File

@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for nets.inception_v1."""
"""Tests for nets.inception_v3."""
from __future__ import absolute_import
from __future__ import division

1
tensorflow/core/BUILD
View File

@ -2352,6 +2352,7 @@ filegroup(
# GIF data with optimization
"lib/gif/testdata/optimized.gif",
],
visibility = ["//visibility:public"],
)
filegroup(

6
tensorflow/core/common_runtime/sycl/sycl_allocator.cc
View File

@ -36,8 +36,10 @@ void SYCLAllocator::DeallocateRaw(void *ptr) {
}
void SYCLAllocator::EnterLameDuckMode() {
device_->deallocate_all();
device_ = nullptr;
if (device_) {
device_->deallocate_all();
device_ = nullptr;
}
}
} // namespace tensorflow

1
tensorflow/core/common_runtime/sycl/sycl_allocator.h
View File

@ -22,7 +22,6 @@ limitations under the License.
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/platform/types.h"
#define EIGEN_USE_SYCL
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
namespace tensorflow {

31
tensorflow/core/common_runtime/sycl/sycl_device.cc
View File

@ -23,11 +23,38 @@ limitations under the License.
namespace tensorflow {
static std::unordered_set<SYCLDevice*> live_devices;
void ShutdownSycl() {
for (auto device : live_devices) {
device->EnterLameDuckMode();
}
live_devices.clear();
}
bool first_time = true;
void SYCLDevice::RegisterDevice() {
if (first_time) {
first_time = false;
atexit(ShutdownSycl);
}
live_devices.insert(this);
}
SYCLDevice::~SYCLDevice() {
device_context_->Unref();
sycl_allocator_->EnterLameDuckMode();
delete sycl_device_;
delete sycl_queue_;
live_devices.erase(this);
}
void SYCLDevice::EnterLameDuckMode() {
sycl_allocator_->EnterLameDuckMode();
delete sycl_device_;
sycl_device_ = nullptr;
delete sycl_queue_;
sycl_queue_ = nullptr;
}
void SYCLDevice::Compute(OpKernel *op_kernel, OpKernelContext *context) {
@ -63,8 +90,8 @@ Status SYCLDevice::MakeTensorFromProto(const TensorProto &tensor_proto,
Tensor copy(GetAllocator(alloc_attrs), parsed.dtype(), parsed.shape());
device_context_->CopyCPUTensorToDevice(&parsed, this, &copy,
[&status](const Status &s) {
status = s;
});
status = s;
});
*tensor = copy;
}
return status;

7
tensorflow/core/common_runtime/sycl/sycl_device.h
View File

@ -20,8 +20,6 @@ limitations under the License.
#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_SYCL_SYCL_DEVICE_H_
#define TENSORFLOW_CORE_COMMON_RUNTIME_SYCL_SYCL_DEVICE_H_
#define EIGEN_USE_SYCL
#include "tensorflow/core/common_runtime/local_device.h"
#include "tensorflow/core/common_runtime/sycl/sycl_allocator.h"
#include "tensorflow/core/common_runtime/sycl/sycl_device_context.h"
@ -45,10 +43,13 @@ public:
sycl_allocator_(new SYCLAllocator(sycl_queue_)),
device_context_(new SYCLDeviceContext()) {
set_eigen_sycl_device(sycl_device_);
RegisterDevice();
}
~SYCLDevice() override;
void EnterLameDuckMode();
void Compute(OpKernel *op_kernel, OpKernelContext *context) override;
Allocator *GetAllocator(AllocatorAttributes attr) override;
Status MakeTensorFromProto(const TensorProto &tensor_proto,
@ -65,6 +66,8 @@ public:
}
private:
void RegisterDevice();
Allocator *cpu_allocator_; // owned
Eigen::QueueInterface* sycl_queue_; // owned
Eigen::SyclDevice* sycl_device_; // owned

1
tensorflow/core/common_runtime/sycl/sycl_device_context.cc
View File

@ -15,7 +15,6 @@ limitations under the License.
#if TENSORFLOW_USE_SYCL
#define EIGEN_USE_SYCL
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/common_runtime/sycl/sycl_device_context.h"

2
tensorflow/core/distributed_runtime/master_session.cc
View File

@ -438,7 +438,7 @@ static bool CopyIfNeeded(TensorProto* in, TensorProto* out) {
} else {
Tensor t(in->dtype());
if (!t.FromProto(cpu_allocator(), *in)) return false;
t.AsProtoField(out);
t.AsProtoTensorContent(out);
}
return true;
}

2
tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
View File

@ -377,7 +377,7 @@ class GrpcWorkerService : public AsyncServiceInterface {
recv->set_key(key);
// TODO(zhifengc): Deal with gpu -> cpu copy.
TensorProto* proto = recv->mutable_val();
val.AsProtoField(proto);
val.AsProtoTensorContent(proto);
}
}
delete collector;

7
tensorflow/core/framework/tensor.proto
View File

@ -28,8 +28,11 @@ message TensorProto {
// to represent a constant Tensor with a single value.
int32 version_number = 3;
// Serialized content from Tensor::AsProtoTensorContent(). This representation
// can be used for all tensor types.
// Serialized raw tensor content from either Tensor::AsProtoTensorContent or
// memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation
// can be used for all tensor types. The purpose of this representation is to
// reduce serialization overhead during RPC call by avoiding serialization of
// many repeated small items.
bytes tensor_content = 4;
// Type specific representations that make it easy to create tensor protos in

16
tensorflow/core/kernels/argmax_op.cc
View File

@ -57,19 +57,21 @@ class ArgOp : public OpKernel {
const int32 dim = internal::SubtleMustCopy(dimension.scalar<int32>()());
const int input_dims = input.dims();
OP_REQUIRES(context, dim >= 0, errors::InvalidArgument("dim must be >= 0"));
OP_REQUIRES(context, dim < input_dims,
errors::InvalidArgument("Minimum tensor rank: ", dim + 1,
" but got: ", input_dims));
int axis = dim < 0 ? dim + input_dims : dim;
OP_REQUIRES(context, axis >= 0 && axis < input_dims,
errors::InvalidArgument(
"Expected dimension in the range [", -input_dims, ", ",
input_dims, "), but got ", dim));
OP_REQUIRES(
context, input.dim_size(dim) > 0,
context, input.dim_size(axis) > 0,
errors::InvalidArgument("Reduction axis ", dim, " is empty in shape ",
input.shape().DebugString()));
TensorShape output_shape;
const TensorShape& input_shape = input.shape();
for (int d = 0; d < input_dims - 1; ++d) {
output_shape.AddDim(input_shape.dim_size((d < dim) ? d : d + 1));
output_shape.AddDim(input_shape.dim_size((d < axis) ? d : d + 1));
}
Tensor* output = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
@ -77,7 +79,7 @@ class ArgOp : public OpKernel {
#define HANDLE_DIM(NDIM) \
case NDIM: \
ArgFunctor::Reduce##NDIM(context->eigen_device<Device>(), \
input.tensor<T, NDIM>(), dim, \
input.tensor<T, NDIM>(), axis, \
output->tensor<int64, NDIM - 1>()); \
break;

37
tensorflow/core/kernels/constant_op.cc
View File

@ -16,9 +16,6 @@ limitations under the License.
// See docs in ../ops/array_ops.cc.
#define EIGEN_USE_THREADS
#if TENSORFLOW_USE_SYCL
#define EIGEN_USE_SYCL
#endif
#include "tensorflow/core/kernels/constant_op.h"
@ -116,6 +113,9 @@ REGISTER_KERNEL_BUILDER(Name("Const")
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
#ifdef TENSORFLOW_USE_SYCL
typedef Eigen::SyclDevice SYCLDevice;
#endif //TENSORFLOW_USE_SYCL
namespace functor {
@ -128,6 +128,17 @@ struct FillFunctor<CPUDevice, T> {
}
};
#ifdef TENSORFLOW_USE_SYCL
// Partial specialization of FillFunctor<Device=SYCLDevice, T>.
template <typename T>
struct FillFunctor<SYCLDevice, T> {
void operator()(const SYCLDevice& d, typename TTypes<T>::Flat out,
typename TTypes<T>::ConstScalar in) {
To32Bit(out).device(d) = To32Bit(out).constant(in());
}
};
#endif // TENSORFLOW_USE_SYCL
} // end namespace functor
template <typename Device, typename T>
@ -172,6 +183,17 @@ TF_CALL_ALL_TYPES(REGISTER_CPU_KERNEL);
REGISTER_KERNEL(CPU, quint8);
#undef REGISTER_CPU_KERNEL
#ifdef TENSORFLOW_USE_SYCL
REGISTER_KERNEL(SYCL, float)
REGISTER_KERNEL_BUILDER(Name("Fill")
.Device(DEVICE_SYCL)
.TypeConstraint<int32>("T")
.HostMemory("dims")
.HostMemory("value")
.HostMemory("output"),
FillOp<CPUDevice, int32>);
#endif // TENSORFLOW_USE_SYCL
#if GOOGLE_CUDA
REGISTER_KERNEL(GPU, Eigen::half);
REGISTER_KERNEL(GPU, float);
@ -220,6 +242,15 @@ class ZerosLikeOp : public OpKernel {
TF_CALL_POD_STRING_TYPES(REGISTER_CPU);
#undef REGISTER_CPU
#ifdef TENSORFLOW_USE_SYCL
REGISTER_KERNEL(float, SYCL);
REGISTER_KERNEL_BUILDER(Name("ZerosLike")
.Device(DEVICE_SYCL)
.TypeConstraint<int32>("T")
.HostMemory("y"),
ZerosLikeOp<CPUDevice, int32>);
#endif // TENSORFLOW_USE_SYCL
#if GOOGLE_CUDA
REGISTER_KERNEL(bool, GPU);
REGISTER_KERNEL(Eigen::half, GPU);

2
tensorflow/core/kernels/cwise_op_digamma.cc
View File

@ -16,12 +16,10 @@ limitations under the License.
#include "tensorflow/core/kernels/cwise_ops_common.h"
namespace tensorflow {
#if EIGEN_HAS_C99_MATH
REGISTER3(UnaryOp, CPU, "Digamma", functor::digamma, float, Eigen::half,
double);
#if GOOGLE_CUDA
REGISTER3(UnaryOp, GPU, "Digamma", functor::digamma, float, Eigen::half,
double);
#endif // GOOGLE_CUDA
#endif // EIGEN_HAS_C99_MATH
} // namespace tensorflow

1
tensorflow/core/kernels/cwise_op_div.cc
View File

@ -37,6 +37,7 @@ REGISTER5(BinaryOp, CPU, "RealDiv", functor::div, float, Eigen::half, double,
.TypeConstraint<TYPE>("T"), \
BinaryOp<SYCLDevice, functor::div<TYPE>>);
REGISTER_SYCL_KERNEL(float)
REGISTER_SYCL_KERNEL(int32)
#undef REGISTER_SYCL_KERNEL
#endif // TENSORFLOW_USE_SYCL
#if GOOGLE_CUDA

2
tensorflow/core/kernels/cwise_op_erf.cc
View File

@ -16,10 +16,8 @@ limitations under the License.
#include "tensorflow/core/kernels/cwise_ops_common.h"
namespace tensorflow {
#if EIGEN_HAS_C99_MATH
REGISTER3(UnaryOp, CPU, "Erf", functor::erf, float, Eigen::half, double);
#if GOOGLE_CUDA
REGISTER3(UnaryOp, GPU, "Erf", functor::erf, float, Eigen::half, double);
#endif // GOOGLE_CUDA
#endif // EIGEN_HAS_C99_MATH
} // namespace tensorflow

2
tensorflow/core/kernels/cwise_op_erfc.cc
View File

@ -16,10 +16,8 @@ limitations under the License.
#include "tensorflow/core/kernels/cwise_ops_common.h"
namespace tensorflow {
#if EIGEN_HAS_C99_MATH
REGISTER3(UnaryOp, CPU, "Erfc", functor::erfc, float, Eigen::half, double);
#if GOOGLE_CUDA
REGISTER3(UnaryOp, GPU, "Erfc", functor::erfc, float, Eigen::half, double);
#endif // GOOGLE_CUDA
#endif // EIGEN_HAS_C99_MATH
} // namespace tensorflow

2
tensorflow/core/kernels/cwise_op_igammas.cc
View File

@ -16,8 +16,6 @@ limitations under the License.
#include "tensorflow/core/kernels/cwise_ops_common.h"
namespace tensorflow {
#if EIGEN_HAS_C99_MATH
REGISTER2(BinaryOp, CPU, "Igamma", functor::igamma, float, double);
REGISTER2(BinaryOp, CPU, "Igammac", functor::igammac, float, double);
#endif // EIGEN_HAS_C99_MATH
} // namespace tensorflow

2
tensorflow/core/kernels/cwise_op_lgamma.cc
View File

@ -16,11 +16,9 @@ limitations under the License.
#include "tensorflow/core/kernels/cwise_ops_common.h"
namespace tensorflow {
#if EIGEN_HAS_C99_MATH
REGISTER3(UnaryOp, CPU, "Lgamma", functor::lgamma, float, Eigen::half, double);
#if GOOGLE_CUDA
REGISTER3(UnaryOp, GPU, "Lgamma", functor::lgamma, float, Eigen::half, double);
#endif
#endif // EIGEN_HAS_C99_MATH
} // namespace tensorflow

2
tensorflow/core/kernels/cwise_op_zeta.cc
View File

@ -17,7 +17,5 @@ limitations under the License.
namespace tensorflow {
REGISTER2(BinaryOp, CPU, "Zeta", functor::zeta, float, double);
#if EIGEN_HAS_C99_MATH
REGISTER2(BinaryOp, CPU, "Polygamma", functor::polygamma, float, double);
#endif // EIGEN_HAS_C99_MATH
} // namespace tensorflow

1
tensorflow/core/kernels/cwise_ops_sycl_common.h
View File

@ -20,7 +20,6 @@ limitations under the License.
#ifndef TENSORFLOW_CORE_KERNELS_CWISE_OPS_SYCL_COMMON_H_
#define TENSORFLOW_CORE_KERNELS_CWISE_OPS_SYCL_COMMON_H_
#define EIGEN_USE_SYCL
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/register_types.h"

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

@ -14,9 +14,6 @@ limitations under the License.
==============================================================================*/
#define EIGEN_USE_THREADS
#if TENSORFLOW_USE_SYCL
#define EIGEN_USE_SYCL
#endif
#include "tensorflow/core/kernels/dense_update_ops.h"
#include "tensorflow/core/framework/op_kernel.h"

14
tensorflow/core/kernels/dynamic_stitch_op.cc
View File

@ -165,6 +165,20 @@ class DynamicStitchOp : public OpKernel {
TF_CALL_POD_STRING_TYPES(REGISTER_DYNAMIC_STITCH);
#undef REGISTER_DYNAMIC_STITCH
#ifdef TENSORFLOW_USE_SYCL
#define REGISTER_DYNAMIC_STITCH_SYCL(type) \
REGISTER_KERNEL_BUILDER(Name("DynamicStitch") \
.Device(DEVICE_SYCL) \
.TypeConstraint<type>("T") \
.HostMemory("indices") \
.HostMemory("data") \
.HostMemory("merged"), \
DynamicStitchOp<type>)
TF_CALL_ALL_TYPES(REGISTER_DYNAMIC_STITCH_SYCL);
#undef REGISTER_DYNAMIC_STITCH_SYCL
#endif // TENSORFLOW_USE_SYCL
#if GOOGLE_CUDA
#define REGISTER_DYNAMIC_STITCH_GPU(type) \
REGISTER_KERNEL_BUILDER(Name("DynamicStitch") \

13
tensorflow/core/kernels/fill_functor.cc
View File

@ -52,5 +52,18 @@ DEFINE_SETZERO_CPU(complex64);
DEFINE_SETZERO_CPU(complex128);
#undef DEFINE_SETZERO_CPU
#ifdef TENSORFLOW_USE_SYCL
template <typename T>
void SetZeroFunctor<Eigen::SyclDevice, T>::operator()(
const Eigen::SyclDevice& d, typename TTypes<T>::Flat out) {
out.device(d) = out.constant(T(0));
}
#define DEFINE_SETZERO_SYCL(T) \
template struct SetZeroFunctor<Eigen::SyclDevice, T>;
DEFINE_SETZERO_SYCL(float);
#undef DEFINE_SETZERO_SYCL
#endif // TENSORFLOW_USE_SYCL
} // namespace functor
} // namespace tensorflow

9
tensorflow/core/kernels/fill_functor.h
View File

@ -45,6 +45,15 @@ struct SetZeroFunctor<Eigen::ThreadPoolDevice, T> {
typename TTypes<T>::Flat out);
};
#ifdef TENSORFLOW_USE_SYCL
// Partial specialization of SetZeroFunctor<Device=Eigen::SyclDevice, T>.
template <typename T>
struct SetZeroFunctor<Eigen::SyclDevice, T> {
void operator()(const Eigen::SyclDevice& d,
typename TTypes<T>::Flat out);
};
#endif // TENSORFLOW_USE_SYCL
template <>
struct SetZeroFunctor<Eigen::ThreadPoolDevice, string> {
void operator()(const Eigen::ThreadPoolDevice& d,

8
tensorflow/core/kernels/function_ops.cc
View File

@ -93,7 +93,7 @@ REGISTER_KERNEL_BUILDER(Name("_Retval").Device(DEVICE_CPU), RetvalOp);
Name("_Arg").Device(DEVICE_SYCL).TypeConstraint<type>("T"), ArgOp);
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER)
TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Arg")
.Device(DEVICE_GPU)
.Device(DEVICE_SYCL)
.HostMemory("output")
.TypeConstraint<int32>("T"),
ArgOp);
@ -104,7 +104,7 @@ TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Arg")
RetvalOp);
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER)
TF_CALL_bool(REGISTER) REGISTER_KERNEL_BUILDER(Name("_Retval")
.Device(DEVICE_GPU)
.Device(DEVICE_SYCL)
.HostMemory("input")
.TypeConstraint<int32>("T"),
RetvalOp);
@ -238,5 +238,9 @@ REGISTER_KERNEL_BUILDER(Name(kGradientOp).Device(DEVICE_CPU),
SymbolicGradientOp);
REGISTER_KERNEL_BUILDER(Name(kGradientOp).Device(DEVICE_GPU),
SymbolicGradientOp);
#if TENSORFLOW_USE_SYCL
REGISTER_KERNEL_BUILDER(Name(kGradientOp).Device(DEVICE_SYCL),
SymbolicGradientOp);
#endif // TENSORFLOW_USE_SYCL
} // namespace tensorflow

23
tensorflow/core/kernels/identity_op.cc
View File

@ -54,10 +54,29 @@ REGISTER_KERNEL_BUILDER(Name("RefIdentity").Device(DEVICE_CPU), IdentityOp);
IdentityOp)
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
REGISTER_SYCL_KERNEL(bfloat16);
#undef REGISTER_SYCL_KERNEL
#endif
#define REGISTER_SYCL_HOST_KERNEL(type) \
REGISTER_KERNEL_BUILDER(Name("Identity") \
.Device(DEVICE_SYCL) \
.HostMemory("input") \
.HostMemory("output") \
.TypeConstraint<type>("T"), \
IdentityOp); \
REGISTER_KERNEL_BUILDER(Name("RefIdentity") \
.Device(DEVICE_SYCL) \
.HostMemory("input") \
.HostMemory("output") \
.TypeConstraint<type>("T"), \
IdentityOp)
REGISTER_SYCL_HOST_KERNEL(int32);
REGISTER_SYCL_HOST_KERNEL(bool);
#undef REGISTER_SYCL_HOST_KERNEL
#endif // TENSORFLOW_USE_SYCL
#define REGISTER_GPU_KERNEL(type) \
REGISTER_KERNEL_BUILDER( \

21
tensorflow/core/kernels/reshape_op.cc
View File

@ -31,6 +31,27 @@ REGISTER_KERNEL_BUILDER(Name("Reshape").Device(DEVICE_CPU).HostMemory("shape"),
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_GPU_KERNEL);
#undef REGISTER_GPU_KERNEL
#ifdef TENSORFLOW_USE_SYCL
#define REGISTER_SYCL_KERNEL(type) \
REGISTER_KERNEL_BUILDER(Name("Reshape") \
.Device(DEVICE_SYCL) \
.HostMemory("shape") \
.TypeConstraint<type>("T") \
.TypeConstraint<int32>("Tshape"), \
ReshapeOp);
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
#undef REGISTER_SYCL_KERNEL
REGISTER_KERNEL_BUILDER(Name("Reshape")
.Device(DEVICE_SYCL)
.HostMemory("tensor")
.HostMemory("shape")
.HostMemory("output")
.TypeConstraint<int32>("T")
.TypeConstraint<int32>("Tshape"),
ReshapeOp);
#endif // TENSORFLOW_USE_SYCL
#if GOOGLE_CUDA
// A special GPU kernel for int32.
// TODO(b/25387198): Also enable int32 in device memory. This kernel

6
tensorflow/core/kernels/sequence_ops.cc
View File

@ -89,6 +89,12 @@ class RangeOp : public OpKernel {
#define REGISTER_CPU_KERNEL(T) REGISTER_KERNEL(DEVICE_CPU, T)
#define REGISTER_GPU_KERNEL(T) REGISTER_KERNEL(DEVICE_GPU, T)
#ifdef TENSORFLOW_USE_SYCL
#define REGISTER_SYCL_KERNEL(T) REGISTER_KERNEL(DEVICE_SYCL, T)
TF_CALL_float(REGISTER_SYCL_KERNEL);
TF_CALL_int32(REGISTER_SYCL_KERNEL);
TF_CALL_int64(REGISTER_SYCL_KERNEL);
#endif // TENSORFLOW_USE_SYCL
TF_CALL_float(REGISTER_CPU_KERNEL);
TF_CALL_double(REGISTER_CPU_KERNEL);

62
tensorflow/core/kernels/shape_ops.cc
View File

@ -63,6 +63,40 @@ REGISTER_KERNEL_BUILDER(Name("Shape")
.TypeConstraint<int64>("out_type"),
ShapeOp<int64>);
#ifdef TENSORFLOW_USE_SYCL
#define REGISTER_SYCL_KERNEL(type) \
REGISTER_KERNEL_BUILDER(Name("Shape") \
.Device(DEVICE_SYCL) \
.HostMemory("output") \
.TypeConstraint<int32>("out_type") \
.TypeConstraint<type>("T"), \
ShapeOp<int32>); \
REGISTER_KERNEL_BUILDER(Name("Shape") \
.Device(DEVICE_SYCL) \
.HostMemory("output") \
.TypeConstraint<int64>("out_type") \
.TypeConstraint<type>("T"), \
ShapeOp<int64>);
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_SYCL_KERNEL);
#undef REGISTER_SYCL_KERNEL
REGISTER_KERNEL_BUILDER(Name("Shape")
.Device(DEVICE_SYCL)
.HostMemory("input")
.HostMemory("output")
.TypeConstraint<int32>("T")
.TypeConstraint<int32>("out_type"),
ShapeOp<int32>);
REGISTER_KERNEL_BUILDER(Name("Shape")
.Device(DEVICE_SYCL)
.HostMemory("input")
.HostMemory("output")
.TypeConstraint<int32>("T")
.TypeConstraint<int64>("out_type"),
ShapeOp<int64>);
#endif // TENSORFLOW_USE_SYCL
#if GOOGLE_CUDA
#define REGISTER_GPU_KERNEL(type) \
REGISTER_KERNEL_BUILDER(Name("Shape") \
@ -193,6 +227,34 @@ class RankOp : public OpKernel {
REGISTER_KERNEL_BUILDER(Name("Rank").Device(DEVICE_CPU).HostMemory("output"),
RankOp);
#ifdef TENSORFLOW_USE_SYCL
#define REGISTER_SYCL_KERNEL(type) \
REGISTER_KERNEL_BUILDER(Name("Rank") \
.Device(DEVICE_SYCL) \
.TypeConstraint<type>("T") \
.HostMemory("output"), \
RankOp);
REGISTER_SYCL_KERNEL(float);
#undef REGISTER_SYCL_KERNEL
// A special GPU kernel for int32 and bool.
// TODO(b/25387198): Also enable int32 in device memory. This kernel
// registration requires all int32 inputs and outputs to be in host memory.
REGISTER_KERNEL_BUILDER(Name("Rank")
.Device(DEVICE_SYCL)
.TypeConstraint<int32>("T")
.HostMemory("input")
.HostMemory("output"),
RankOp);
REGISTER_KERNEL_BUILDER(Name("Rank")
.Device(DEVICE_SYCL)
.TypeConstraint<bool>("T")
.HostMemory("input")
.HostMemory("output"),
RankOp);
#endif // TENSORFLOW_USE_SYCL
#if GOOGLE_CUDA
#define REGISTER_GPU_KERNEL(type) \
REGISTER_KERNEL_BUILDER(Name("Rank") \

19
tensorflow/core/kernels/training_ops.cc
View File

@ -25,6 +25,7 @@ namespace tensorflow {
using CPUDevice = Eigen::ThreadPoolDevice;
using GPUDevice = Eigen::GpuDevice;
using SYCLDevice = Eigen::SyclDevice;
namespace {
template <class T>
@ -220,9 +221,9 @@ struct ApplyMomentum<CPUDevice, T> {
}
};
template <typename T>
struct ApplyAdam<CPUDevice, T> {
void operator()(const CPUDevice& d, typename TTypes<T>::Flat var,
template <typename Device, typename T>
struct ApplyAdamNonCuda {
void operator()(const Device& d, typename TTypes<T>::Flat var,
typename TTypes<T>::Flat m, typename TTypes<T>::Flat v,
typename TTypes<T>::ConstScalar beta1_power,
typename TTypes<T>::ConstScalar beta2_power,
@ -239,6 +240,12 @@ struct ApplyAdam<CPUDevice, T> {
}
};
template <typename T>
struct ApplyAdam<CPUDevice, T> : ApplyAdamNonCuda<CPUDevice, T> {};
template <typename T>
struct ApplyAdam<SYCLDevice, T> : ApplyAdamNonCuda<SYCLDevice, T> {};
template <typename T>
struct ApplyRMSProp<CPUDevice, T> {
void operator()(const CPUDevice& d, typename TTypes<T>::Flat var,
@ -2139,6 +2146,12 @@ TF_CALL_half(REGISTER_CPU_KERNELS);
TF_CALL_float(REGISTER_CPU_KERNELS);
TF_CALL_double(REGISTER_CPU_KERNELS);
#ifdef TENSORFLOW_USE_SYCL
#define REGISTER_SYCL_KERNELS(T) REGISTER_KERNELS(SYCL, T);
TF_CALL_float(REGISTER_SYCL_KERNELS);
#endif
#if GOOGLE_CUDA
// Forward declarations of the functor specializations for GPU.
namespace functor {

3
tensorflow/core/ops/math_grad_test.cc
View File

@ -388,6 +388,9 @@ class TestOp : public OpKernel {
void Compute(OpKernelContext* ctx) override { ctx->set_output(0, Tensor()); }
};
REGISTER_KERNEL_BUILDER(Name("TestOpWithNoGrad").Device(DEVICE_CPU), TestOp);
#ifdef TENSORFLOW_USE_SYCL
REGISTER_KERNEL_BUILDER(Name("TestOpWithNoGrad").Device(DEVICE_SYCL), TestOp);
#endif // TENSORFLOW_USE_SYCL
TEST_F(MathGradTest, Error_Reporting) {
auto x = test::AsTensor<float>({-3.f});

11
tensorflow/core/ops/math_ops.cc
View File

@ -1268,17 +1268,18 @@ Status ArgOpShape(shape_inference::InferenceContext* c) {
dimension_val = dim_t->scalar<int64>()();
}
if (dimension_val < 0 || dimension_val >= input_rank) {
int64 axis = dimension_val < 0 ? dimension_val + input_rank : dimension_val;
if (axis < 0 || axis >= input_rank) {
return errors::InvalidArgument("Dimension (", dimension_val,
") must be in the range [0, ", input_rank,
"), where ", input_rank, " is the ",
"number of dimensions in the input.");
") must be in the range [", -input_rank,
", ", input_rank, "), where ", input_rank,
" is the number of dimensions in the input.");
}
// Return the input shape without the dimension being reduced.
std::vector<DimensionHandle> dims;
for (int i = 0; i < input_rank; ++i) {
if (dimension_val != i) {
if (axis != i) {
dims.emplace_back(c->Dim(input_shape, i));
}
}

8
tensorflow/core/ops/math_ops_test.cc
View File

@ -450,11 +450,15 @@ TEST(MathOpsTest, ArgOps_ShapeFn) {
// Dimension value out of bounds
dimension = test::AsScalar(10);
op.input_tensors[1] = &dimension;
INFER_ERROR("must be in the range [0, 3)", op, "[2,3,4];[]");
INFER_ERROR("must be in the range [-3, 3)", op, "[2,3,4];[]");
dimension = test::AsScalar(-10);
op.input_tensors[1] = &dimension;
INFER_ERROR("must be in the range [0, 3)", op, "[2,3,4];[]");
INFER_ERROR("must be in the range [-3, 3)", op, "[2,3,4];[]");
dimension = test::AsScalar(-1);
op.input_tensors[1] = &dimension;
INFER_OK(op, "[2,3,4];[]", "[d0_0,d0_1]");
}
TEST(MathOpsTest, Betainc_ShapeFn) {

4
tensorflow/core/platform/cpu_info.h
View File

@ -16,6 +16,10 @@ limitations under the License.
#ifndef TENSORFLOW_PLATFORM_CPU_INFO_H_
#define TENSORFLOW_PLATFORM_CPU_INFO_H_
#if defined(PLATFORM_WINDOWS)
#include "tensorflow/core/platform/windows/cpu_info.h"
#endif
namespace tensorflow {
namespace port {

25
tensorflow/python/lib/core/status_helper.i → tensorflow/core/platform/windows/cpu_info.h
View File

@ -1,4 +1,4 @@
/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
/* 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.
@ -13,19 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// SWIG test helper for lib::tensorflow::Status
#ifndef TENSORFLOW_PLATFORM_WINDOWS_CPU_INFO_H_
#define TENSORFLOW_PLATFORM_WINDOWS_CPU_INFO_H_
%include "tensorflow/python/platform/base.i"
%import(module="tensorflow.python.pywrap_tensorflow") "tensorflow/python/lib/core/status.i"
// Byte order defines provided by gcc. MSVC doesn't define those so
// we define them here.
// We assume that all windows platform out there are little endian.
#define __ORDER_LITTLE_ENDIAN__ 0x4d2
#define __ORDER_BIG_ENDIAN__ 0x10e1
#define __BYTE_ORDER__ __ORDER_LITTLE_ENDIAN__
%inline %{
#include "tensorflow/core/lib/core/status.h"
tensorflow::Status NotOkay() {
return tensorflow::Status(tensorflow::error::INVALID_ARGUMENT, "Testing 1 2 3");
}
tensorflow::Status Okay() {
return tensorflow::Status();
}
%}
#endif // TENSORFLOW_PLATFORM_WINDOWS_CPU_INFO_H_

21
tensorflow/examples/tutorials/monitors/iris_monitors.py
View File

@ -21,6 +21,7 @@ import os
import numpy as np
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.metric_spec import MetricSpec
tf.logging.set_verbosity(tf.logging.INFO)
@ -65,6 +66,26 @@ def main(unused_argv):
# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
validation_metrics = {
"accuracy": MetricSpec(
metric_fn=tf.contrib.metrics.streaming_accuracy,
prediction_key="classes"),
"recall": MetricSpec(
metric_fn=tf.contrib.metrics.streaming_recall,
prediction_key="classes"),
"precision": MetricSpec(
metric_fn=tf.contrib.metrics.streaming_precision,
prediction_key="classes")
}
validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(
test_set.data,
test_set.target,
every_n_steps=50,
metrics=validation_metrics,
early_stopping_metric="loss",
early_stopping_metric_minimize=True,
early_stopping_rounds=200)
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(
feature_columns=feature_columns,

4
tensorflow/g3doc/api_docs/cc/ClassEnv.md
View File

@ -186,13 +186,13 @@ Renames file src to target. If target already exists, it will be replaced.
#### `virtual uint64 tensorflow::Env::NowMicros()=0` {#virtual_uint64_tensorflow_Env_NowMicros}
Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.
Returns the number of micro-seconds since the Unix epoch.
#### `virtual uint64 tensorflow::Env::NowSeconds()` {#virtual_uint64_tensorflow_Env_NowSeconds}
Returns the number of seconds since some fixed point in time. Only useful for computing deltas of time.
Returns the number of seconds since the Unix epoch.

2
tensorflow/g3doc/api_docs/cc/ClassEnvWrapper.md
View File

@ -50,7 +50,7 @@ Returns true if the path matches the given pattern. The wildcards allowed in pat
#### `uint64 tensorflow::EnvWrapper::NowMicros() override` {#uint64_tensorflow_EnvWrapper_NowMicros}
Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.
Returns the number of micro-seconds since the Unix epoch.

1
tensorflow/g3doc/api_docs/leftnav_files
View File

@ -14,7 +14,6 @@ python/sparse_ops.md
python/io_ops.md
python/python_io.md
python/nn.md
python/rnn_cell.md
python/client.md
python/train.md
python/histogram_ops.md

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.

2
tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.all_variables.md
View File

@ -2,7 +2,7 @@
See `tf.global_variables`. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-03-02.
THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02.
Instructions for updating:
Please use tf.global_variables instead.

17
tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.merge_all_summaries.md Normal file
View File

@ -0,0 +1,17 @@
### `tf.merge_all_summaries(*args, **kwargs)` {#merge_all_summaries}
Merges all summaries collected in the default graph. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.merge_all.
Args:
key: `GraphKey` used to collect the summaries. Defaults to
`GraphKeys.SUMMARIES`.
Returns:
If no summaries were collected, returns None. Otherwise returns a scalar
`Tensor` of type `string` containing the serialized `Summary` protocol
buffer resulting from the merging.

49
tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.image_summary.md Normal file
View File

@ -0,0 +1,49 @@
### `tf.image_summary(*args, **kwargs)` {#image_summary}
Outputs a `Summary` protocol buffer with images. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.image. 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. Also, the max_images argument was renamed to max_outputs.
The summary has up to `max_images` summary values containing images. The
images are built from `tensor` which must be 4-D with shape `[batch_size,
height, width, channels]` and where `channels` can be:
* 1: `tensor` is interpreted as Grayscale.
* 3: `tensor` is interpreted as RGB.
* 4: `tensor` is interpreted as RGBA.
The images have the same number of channels as the input tensor. For float
input, the values are normalized one image at a time to fit in the range
`[0, 255]`. `uint8` values are unchanged. The op uses two different
normalization algorithms:
* If the input values are all positive, they are rescaled so the largest one
is 255.
* If any input value is negative, the values are shifted so input value 0.0
is at 127. They are then rescaled so that either the smallest value is 0,
or the largest one is 255.
The `tag` argument is a scalar `Tensor` of type `string`. It is used to
build the `tag` of the summary values:
* If `max_images` is 1, the summary value tag is '*tag*/image'.
* If `max_images` is greater than 1, the summary value tags are
generated sequentially as '*tag*/image/0', '*tag*/image/1', etc.
Args:
tag: A scalar `Tensor` of type `string`. Used to build the `tag`
of the summary values.
tensor: A 4-D `uint8` or `float32` `Tensor` of shape `[batch_size, height,
width, channels]` where `channels` is 1, 3, or 4.
max_images: Max number of batch elements to generate images for.
collections: Optional list of ops.GraphKeys. The collections to add the
summary to. Defaults to [ops.GraphKeys.SUMMARIES]
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.

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@ -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.

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@ -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}
@ -1241,20 +761,6 @@ Return any properties that the user has recorded.
- - -
#### `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}
@ -1280,18 +786,11 @@ 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 +1,22 @@
### `tf.scalar_summary(*args, **kwargs)` {#scalar_summary}
Outputs a `Summary` protocol buffer with scalar values. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-30.
Instructions for updating:
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.
The input `tags` and `values` must have the same shape. The generated
summary has a summary value for each tag-value pair in `tags` and `values`.
Args:
tags: A `string` `Tensor`. Tags for the summaries.
values: A real numeric Tensor. Values for the summaries.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.

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

26
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@ -0,0 +1,26 @@
### `tf.histogram_summary(*args, **kwargs)` {#histogram_summary}
Outputs a `Summary` protocol buffer with a histogram. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-30.
Instructions for updating:
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 their scope.
The generated
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
has one summary value containing a histogram for `values`.
This op reports an `InvalidArgument` error if any value is not finite.
Args:
tag: A `string` `Tensor`. 0-D. Tag to use for the summary value.
values: A real numeric `Tensor`. Any shape. Values to use to
build the histogram.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.

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@ -0,0 +1,27 @@
### `tf.merge_summary(*args, **kwargs)` {#merge_summary}
Merges summaries. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.merge.
This op creates a
[`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
protocol buffer that contains the union of all the values in the input
summaries.
When the Op is run, it reports an `InvalidArgument` error if multiple values
in the summaries to merge use the same tag.
Args:
inputs: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer resulting from the merging.

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}

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@ -0,0 +1,207 @@
- - -
#### `tf.train.SummaryWriter.__init__(*args, **kwargs)` {#SummaryWriter.__init__}
Creates a `SummaryWriter` and an event file. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.FileWriter. The interface and behavior is the same; this is just a rename.
This class is deprecated, and should be replaced with tf.summary.FileWriter.
On construction the summary writer creates a new event file in `logdir`.
This event file will contain `Event` protocol buffers constructed when you
call one of the following functions: `add_summary()`, `add_session_log()`,
`add_event()`, or `add_graph()`.
If you pass a `Graph` to the constructor it is added to
the event file. (This is equivalent to calling `add_graph()` later).
TensorBoard will pick the graph from the file and display it graphically so
you can interactively explore the graph you built. You will usually pass
the graph from the session in which you launched it:
```python
...create a graph...
# Launch the graph in a session.
sess = tf.Session()
# Create a summary writer, add the 'graph' to the event file.
writer = tf.train.SummaryWriter(<some-directory>, sess.graph)
```
The other arguments to the constructor control the asynchronous writes to
the event file:
* `flush_secs`: How often, in seconds, to flush the added summaries
and events to disk.
* `max_queue`: Maximum number of summaries or events pending to be
written to disk before one of the 'add' calls block.
Args:
logdir: A string. Directory where event file will be written.
graph: A `Graph` object, such as `sess.graph`.
max_queue: Integer. Size of the queue for pending events and summaries.
flush_secs: Number. How often, in seconds, to flush the
pending events and summaries to disk.
graph_def: DEPRECATED: Use the `graph` argument instead.
- - -
#### `tf.train.SummaryWriter.add_event(event)` {#SummaryWriter.add_event}
Adds an event to the event file.
##### Args:
* <b>`event`</b>: An `Event` protocol buffer.
- - -
#### `tf.train.SummaryWriter.add_graph(graph, global_step=None, graph_def=None)` {#SummaryWriter.add_graph}
Adds a `Graph` to the event file.
The graph described by the protocol buffer will be displayed by
TensorBoard. Most users pass a graph in the constructor instead.
##### Args:
* <b>`graph`</b>: A `Graph` object, such as `sess.graph`.
* <b>`global_step`</b>: Number. Optional global step counter to record with the
graph.
* <b>`graph_def`</b>: DEPRECATED. Use the `graph` parameter instead.
##### Raises:
* <b>`ValueError`</b>: If both graph and graph_def are passed to the method.
- - -
#### `tf.train.SummaryWriter.add_meta_graph(meta_graph_def, global_step=None)` {#SummaryWriter.add_meta_graph}
Adds a `MetaGraphDef` to the event file.
The `MetaGraphDef` allows running the given graph via
`saver.import_meta_graph()`.
##### Args:
* <b>`meta_graph_def`</b>: A `MetaGraphDef` object, often as retured by
`saver.export_meta_graph()`.
* <b>`global_step`</b>: Number. Optional global step counter to record with the
graph.
##### Raises:
* <b>`TypeError`</b>: If both `meta_graph_def` is not an instance of `MetaGraphDef`.
- - -
#### `tf.train.SummaryWriter.add_run_metadata(run_metadata, tag, global_step=None)` {#SummaryWriter.add_run_metadata}
Adds a metadata information for a single session.run() call.
##### Args:
* <b>`run_metadata`</b>: A `RunMetadata` protobuf object.
* <b>`tag`</b>: The tag name for this metadata.
* <b>`global_step`</b>: Number. Optional global step counter to record with the
StepStats.
##### Raises:
* <b>`ValueError`</b>: If the provided tag was already used for this type of event.
- - -
#### `tf.train.SummaryWriter.add_session_log(session_log, global_step=None)` {#SummaryWriter.add_session_log}
Adds a `SessionLog` protocol buffer to the event file.
This method wraps the provided session in an `Event` protocol buffer
and adds it to the event file.
##### Args:
* <b>`session_log`</b>: A `SessionLog` protocol buffer.
* <b>`global_step`</b>: Number. Optional global step value to record with the
summary.
- - -
#### `tf.train.SummaryWriter.add_summary(summary, global_step=None)` {#SummaryWriter.add_summary}
Adds a `Summary` protocol buffer to the event file.
This method wraps the provided summary in an `Event` protocol buffer
and adds it to the event file.
You can pass the result of evaluating any summary op, using
[`Session.run()`](client.md#Session.run) or
[`Tensor.eval()`](framework.md#Tensor.eval), to this
function. Alternatively, you can pass a `tf.Summary` protocol
buffer that you populate with your own data. The latter is
commonly done to report evaluation results in event files.
##### Args:
* <b>`summary`</b>: A `Summary` protocol buffer, optionally serialized as a string.
* <b>`global_step`</b>: Number. Optional global step value to record with the
summary.
- - -
#### `tf.train.SummaryWriter.close()` {#SummaryWriter.close}
Flushes the event file to disk and close the file.
Call this method when you do not need the summary writer anymore.
- - -
#### `tf.train.SummaryWriter.flush()` {#SummaryWriter.flush}
Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.
- - -
#### `tf.train.SummaryWriter.get_logdir()` {#SummaryWriter.get_logdir}
Returns the directory where event file will be written.
- - -
#### `tf.train.SummaryWriter.reopen()` {#SummaryWriter.reopen}
Reopens the EventFileWriter.
Can be called after `close()` to add more events in the same directory.
The events will go into a new events file.
Does nothing if the EventFileWriter was not closed.

2
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@ -16,7 +16,7 @@ in the summaries to merge use the same tag.
* <b>`inputs`</b>: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
* <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
added to these collections. Defaults to `[]`.
* <b>`name`</b>: A name for the operation (optional).
##### Returns:

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@ -0,0 +1,37 @@
### `tf.audio_summary(*args, **kwargs)` {#audio_summary}
Outputs a `Summary` protocol buffer with audio. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.audio. 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.
The summary has up to `max_outputs` summary values containing audio. The
audio is built from `tensor` which must be 3-D with shape `[batch_size,
frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
assumed to be in the range of `[-1.0, 1.0]` with a sample rate of
`sample_rate`.
The `tag` argument is a scalar `Tensor` of type `string`. It is used to
build the `tag` of the summary values:
* If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
* If `max_outputs` is greater than 1, the summary value tags are
generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
Args:
tag: A scalar `Tensor` of type `string`. Used to build the `tag`
of the summary values.
tensor: A 3-D `float32` `Tensor` of shape `[batch_size, frames, channels]`
or a 2-D `float32` `Tensor` of shape `[batch_size, frames]`.
sample_rate: A Scalar `float32` `Tensor` indicating the sample rate of the
signal in hertz.
max_outputs: Max number of batch elements to generate audio for.
collections: Optional list of ops.GraphKeys. The collections to add the
summary to. Defaults to [ops.GraphKeys.SUMMARIES]
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`. The serialized `Summary` protocol
buffer.

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

2
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@ -3444,7 +3444,7 @@ device assignments have not changed.
See `tf.global_variables`. (deprecated)
THIS FUNCTION IS DEPRECATED. It will be removed after 2016-03-02.
THIS FUNCTION IS DEPRECATED. It will be removed after 2017-03-02.
Instructions for updating:
Please use tf.global_variables instead.

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@ -424,7 +424,7 @@ in the summaries to merge use the same tag.
* <b>`inputs`</b>: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
* <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
added to these collections. Defaults to `[]`.
* <b>`name`</b>: A name for the operation (optional).
##### Returns:
@ -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}
@ -1281,20 +801,6 @@ Return any properties that the user has recorded.
- - -
#### `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}
@ -1320,18 +826,11 @@ 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.
- - -

103
tensorflow/g3doc/get_started/os_setup.md
View File

@ -82,37 +82,37 @@ of the binary on Linux or Mac, you can follow these instructions:
```bash
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp27-none-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp27-none-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# 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-0.12.0rc0-cp27-none-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-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-0.12.0rc0-py2-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc1-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-0.12.0rc0-py2-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc1-py2-none-any.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# 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-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-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-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# 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-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-cp35-cp35m-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-0.12.0rc0-py3-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc1-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-0.12.0rc0-py3-none-any.whl
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc1-py3-none-any.whl
```
Install TensorFlow:
@ -136,24 +136,32 @@ You can now [test your installation](#test-the-tensorflow-installation).
### Pip installation on Windows
TensorFlow supports only 64-bit Python 3.5 on Windows. We have tested
the pip packages with the following distributions of Python:
TensorFlow supports only 64-bit Python 3.5 on Windows. We have tested the pip packages
with the following distributions of Python:
* [Python 3.5 from python.org](https://www.python.org/downloads/release/python-352/)
* [Python 3.5 from Anaconda](https://www.continuum.io/downloads#windows)
* [Python 3.5 from python.org](https://www.python.org/downloads/release/python-352/).
NOTE: TensorFlow requires `MSVCP140.DLL`, which may not be installed on your system.
If, when you `import tensorflow as tf`, you see an error about `No module named
"_pywrap_tensorflow"` and/or `DLL load failed`, check whether `MSVCP140.DLL` is in
your `%PATH%` and, if not, you should install the [Visual C++ 2015
redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=53587)
(x64 version).
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-0.12.0rc0-cp35-cp35m-win_amd64.whl
C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-0.12.0rc1-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-0.12.0rc0-cp35-cp35m-win_amd64.whl
C:\> pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-0.12.0rc1-cp35-cp35m-win_amd64.whl
```
You can now [test your installation](#test-the-tensorflow-installation).
@ -208,37 +216,37 @@ 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-0.12.0rc0-cp27-none-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp27-none-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# 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-0.12.0rc0-cp27-none-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-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-0.12.0rc0-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc1-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-0.12.0rc0-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc1-py2-none-any.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# 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-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-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-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# 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-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-cp35-cp35m-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-0.12.0rc0-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc1-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-0.12.0rc0-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc1-py3-none-any.whl
```
Finally install TensorFlow:
@ -360,37 +368,37 @@ 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-0.12.0rc0-cp27-none-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp27-none-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# 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-0.12.0rc0-cp27-none-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-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-0.12.0rc0-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc1-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-0.12.0rc0-py2-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc1-py2-none-any.whl
# Ubuntu/Linux 64-bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp34-cp34m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# 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-0.12.0rc0-cp34-cp34m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-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-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0rc1-cp35-cp35m-linux_x86_64.whl
# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# 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-0.12.0rc0-cp35-cp35m-linux_x86_64.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-0.12.0rc1-cp35-cp35m-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-0.12.0rc0-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.0rc1-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-0.12.0rc0-py3-none-any.whl
(tensorflow)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-0.12.0rc1-py3-none-any.whl
```
Finally install TensorFlow:
@ -458,7 +466,7 @@ code.
code.
We also have tags with `latest` replaced by a released version (e.g.,
`0.12.0-rc0-gpu`).
`0.12.0-rc1-gpu`).
With Docker the installation is as follows:
@ -860,7 +868,7 @@ $ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_pack
$ 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-0.12.0rc0-py2-none-any.whl
$ sudo pip install /tmp/tensorflow_pkg/tensorflow-0.12.0rc1-py2-none-any.whl
```
## Optimizing CPU performance
@ -1083,6 +1091,27 @@ Exception:
Solution: Add an `--ignore-installed` flag to the pip command.
#### Cannot remove entries from nonexistent file: easy-install.pth
If during a `pip` installation using an Anaconda Python distribution you encounter the error:
```
Cannot remove entries from nonexistent file <path-to-anaconda-instalation>/anaconda[version]/lib/site-packages/easy-install.pth
```
1. Upgrade setuptools:
`pip install --upgrade -I setuptools`
2. Install TensorFlow again adding `--ignore-installed` flag:
`pip install --ignore-installed --upgrade <tensorflow_url>`
Step #1 might already solve the problem, however if it still persists, execute step #2.
This issue occurs with new Anaconda installations when `pip` tries to remove `easy-install.pth`.
This file is not included in Anaconda packages, which causes the `pip` installation to fail.
### Linux issues

8
tensorflow/g3doc/how_tos/tool_developers/index.md
View File

@ -34,11 +34,7 @@ definitions. If you see a standalone TensorFlow file representing a model, it's
likely to contain a serialized version of one of these `GraphDef` objects
saved out by the protobuf code.
This generated code is used to save and load the GraphDef files from disk. A
good example to look at as we dig into this is
[graph_metrics.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/graph_metrics.py). This Python script takes a saved graph
definition, and analyzes the model to estimate performance and resource
statistics. The code that actually loads the model looks like this:
This generated code is used to save and load the GraphDef files from disk. The code that actually loads the model looks like this:
```python
graph_def = graph_pb2.GraphDef()
@ -67,7 +63,7 @@ There are actually two different formats that a ProtoBuf can be saved in.
TextFormat is a human-readable form, which makes it nice for debugging and
editing, but can get large when there's numerical data like weights stored in
it. You can see a small example of that in
[graph_run_run2.pbtxt](https://github.com/tensorflow/tensorflow/blob/ae3c8479f88da1cd5636b974f653f27755cb0034/tensorflow/tensorboard/components/tf_tensorboard/test/data/graph_run_run2.pbtxt).
[graph_run_run2.pbtxt](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/components/tf_tensorboard/test/data/graph_run_run2.pbtxt).
Binary format files are a lot smaller than their text equivalents, even though
they're not as readable for us. In this script, we ask the user to supply a

16
tensorflow/g3doc/resources/bib.md
View File

@ -69,3 +69,19 @@ Yuan Yu, and Xiaoqiang Zheng.
TensorFlow: Large-scale machine learning on heterogeneous systems,
2015. Software available from tensorflow.org.
```
If you use [TF.Learn](https://www.tensorflow.org/tutorials/tflearn/) in your research and would like to cite it, we suggest you cite the [whitepaper](https://arxiv.org/abs/1612.04251):
```
@article{tang2016tflearn,
title={TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning},
author={Tang, Yuan},
journal={arXiv preprint arXiv:1612.04251},
year={2016}
}
```
In textual form:
```
Tang, Yuan. "TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning." arXiv preprint arXiv:1612.04251 (2016).
```

7
tensorflow/g3doc/resources/index.md
View File

@ -1,12 +1,17 @@
# Additional Resources
## TensorFlow WhitePaper
## TensorFlow WhitePapers
Additional details about the TensorFlow programming model and the underlying
implementation can be found in our white paper:
* [TensorFlow: Large-scale machine learning on heterogeneous systems](http://download.tensorflow.org/paper/whitepaper2015.pdf)
A white paper is also available for [TF.Learn](https://www.tensorflow.org/tutorials/tflearn/):
* [TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning](https://arxiv.org/abs/1612.04251)
### Citation
If you use TensorFlow in your research and would like to cite the TensorFlow

8
tensorflow/g3doc/tutorials/estimators/index.md
View File

@ -152,6 +152,8 @@ def maybe_download():
print("Training data is downloaded to %s" % train_file_name)
if FLAGS.test_data:
test_file_name = FLAGS.test_data
else:
test_file = tempfile.NamedTemporaryFile(delete=False)
urllib.urlretrieve("http://download.tensorflow.org/data/abalone_test.csv", test_file.name)
test_file_name = test_file.name
@ -379,7 +381,7 @@ tf.contrib.layers provides the following convenience functions for constructing
fully connected layers:
* `relu(inputs, num_outputs)`. Create a layer of `num_outputs` nodes fully
connected to the previous layer `inputs` with a [ReLu activation
connected to the previous layer `inputs` with a [ReLU activation
function](https://en.wikipedia.org/wiki/Rectifier_\(neural_networks\))
([tf.nn.relu](../../api_docs/python/nn.md#relu)):
@ -388,7 +390,7 @@ fully connected layers:
```
* `relu6(inputs, num_outputs)`. Create a layer of `num_outputs` nodes fully
connected to the previous layer `hidden_layer` with a ReLu 6 activation
connected to the previous layer `hidden_layer` with a ReLU 6 activation
function ([tf.nn.relu6](../../api_docs/python/nn.md#relu6)):
```python
@ -448,7 +450,7 @@ def model_fn(features, targets, mode, params):
Here, because you'll be passing the abalone `Datasets` directly to `fit()`,
`evaluate()`, and `predict()` via `x` and `y` arguments, the input layer is the
`features` `Tensor` passed to the `model_fn`. The network contains two hidden
layers, each with 10 nodes and a ReLu activation function. The output layer
layers, each with 10 nodes and a ReLU activation function. The output layer
contains no activation function, and is
[reshaped](../../api_docs/python/array_ops.md#reshape) to a one-dimensional
tensor to capture the model's predictions, which are stored in

2
tensorflow/g3doc/tutorials/mnist/tf/index.md
View File

@ -98,7 +98,7 @@ The `inference()` function builds the graph as far as needed to
return the tensor that would contain the output predictions.
It takes the images placeholder as input and builds on top
of it a pair of fully connected layers with [ReLu](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) activation followed by a ten
of it a pair of fully connected layers with [ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)) activation followed by a ten
node linear layer specifying the output logits.
Each layer is created beneath a unique [`tf.name_scope`](../../../api_docs/python/framework.md#name_scope)

1
tensorflow/python/BUILD
View File

@ -13,6 +13,7 @@ exports_files(["LICENSE"])
load("//tensorflow:tensorflow.bzl", "if_not_windows")
load("//tensorflow:tensorflow.bzl", "tf_cuda_library")
load("//tensorflow:tensorflow.bzl", "tf_gen_op_wrapper_py")
load("//tensorflow:tensorflow.bzl", "py_test")
load("//tensorflow:tensorflow.bzl", "tf_py_test")
load("//tensorflow:tensorflow.bzl", "py_tests")
load("//tensorflow:tensorflow.bzl", "tf_py_wrap_cc")

1
tensorflow/python/client/device_lib.i
View File

@ -37,7 +37,6 @@ static std::vector<string> ListDevices(TF_Status* out_status) {
std::vector<std::unique_ptr<Device>> device_holder(devices.begin(), devices.end());
for (const Device* device : devices) {
const DeviceAttributes& attr = device->attributes();
string attr_serialized;

1
tensorflow/python/debug/BUILD
View File

@ -14,6 +14,7 @@ licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
load("//tensorflow:tensorflow.bzl", "cuda_py_test")
load("//tensorflow:tensorflow.bzl", "py_test")
py_library(
name = "debug_py",

6
tensorflow/python/framework/dtypes.py
View File

@ -59,6 +59,7 @@ class DType(object):
@@name
@@base_dtype
@@real_dtype
@@is_bool
@@is_floating
@@is_complex
@@is_integer
@ -141,6 +142,11 @@ class DType(object):
"""Returns a `types_pb2.DataType` enum value based on this `DType`."""
return self._type_enum
@property
def is_bool(self):
"""Returns whether this is a boolean data type"""
return self.base_dtype == bool
@property
def is_integer(self):
"""Returns whether this is a (non-quantized) integer type."""

2
tensorflow/python/kernel_tests/argmax_op_test.py
View File

@ -54,7 +54,7 @@ class ArgMaxTest(tf.test.TestCase):
x = np.asarray(100*np.random.randn(3, 2, 4, 5, 6), dtype=dtype)
# Check that argmin and argmax match numpy along all dimensions
for dim in range(5):
for dim in range(-5, 5):
self._testBothArg(tf.argmax, x, dim, x.argmax(dim))
self._testBothArg(tf.argmin, x, dim, x.argmin(dim))

4
tensorflow/python/kernel_tests/atrous_convolution_test.py
View File

@ -100,8 +100,8 @@ class AtrousConvolutionTest(tf.test.TestCase):
dilation_rate=[rate])
def testAtrousConvolutionNC(self):
if tf.test.is_gpu_available():
# "NCW" and "NCHW" formats are not currently supported on CPU.
if tf.test.is_gpu_available(cuda_only=True):
# "NCW" and "NCHW" formats are currently supported only on CUDA.
with self.test_session(use_gpu=True):
for padding in ["SAME", "VALID"]:
self._test_atrous_convolution(

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

@ -29,8 +29,8 @@ def GetTestConfigs():
all the valid test configs as tuples of data_format and use_gpu.
"""
test_configs = [("NHWC", False), ("NHWC", True)]
if tf.test.is_gpu_available():
# "NCHW" format is not currently supported on CPU.
if tf.test.is_gpu_available(cuda_only=True):
# "NCHW" format is currently only supported on CUDA.
test_configs += [("NCHW", True)]
return test_configs
@ -89,7 +89,7 @@ class BiasAddTest(tf.test.TestCase):
self._testBias(np_inputs, np_bias, use_gpu=False)
if np_inputs.dtype in [np.float16, np.float32, np.float64]:
self._testBias(np_inputs, np_bias, use_gpu=True)
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._testBiasNCHW(np_inputs, np_bias, use_gpu=True)
def testInputDims(self):

12
tensorflow/python/kernel_tests/conv2d_transpose_test.py
View File

@ -159,8 +159,8 @@ class Conv2DTransposeTest(tf.test.TestCase):
self.assertLess(err, err_tolerance)
def testConv2DTransposeSingleStrideNCHW(self):
# `NCHW` data fomat is only supported for `GPU` device.
if tf.test.is_gpu_available():
# `NCHW` data fomat is only supported for CUDA device.
if tf.test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
strides = [1, 1, 1, 1]
@ -192,8 +192,8 @@ class Conv2DTransposeTest(tf.test.TestCase):
self.assertAllClose(target, value[n, k, h, w])
def testConv2DTransposeSameNCHW(self):
# `NCHW` data fomat is only supported for `GPU` device.
if tf.test.is_gpu_available():
# `NCHW` data fomat is only supported for CUDA device.
if tf.test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
strides = [1, 1, 2, 2]
@ -226,8 +226,8 @@ class Conv2DTransposeTest(tf.test.TestCase):
self.assertAllClose(target, value[n, k, h, w])
def testConv2DTransposeValidNCHW(self):
# `NCHW` data fomat is only supported for `GPU` device.
if tf.test.is_gpu_available():
# `NCHW` data fomat is only supported for CUDA device.
if tf.test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
strides = [1, 1, 2, 2]

4
tensorflow/python/kernel_tests/pool_test.py
View File

@ -232,8 +232,8 @@ class PoolingTest(tf.test.TestCase):
strides=strides)
def testPoolNC(self):
if tf.test.is_gpu_available():
# "NC*" format is not currently supported on CPU.
if tf.test.is_gpu_available(cuda_only=True):
# "NC*" format is currently only supported on CUDA.
with self.test_session(use_gpu=True):
for padding in ["SAME", "VALID"]:
self._test(input_shape=[2, 2, 9],

770
tensorflow/python/kernel_tests/seq2seq_test.py
View File

@ -1,770 +0,0 @@
# 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.
# ==============================================================================
"""Tests for functional style sequence-to-sequence models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import random
import numpy as np
import tensorflow as tf
class Seq2SeqTest(tf.test.TestCase):
def testRNNDecoder(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
_, enc_state = tf.nn.rnn(
tf.nn.rnn_cell.GRUCell(2), inp, dtype=tf.float32)
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
cell = tf.nn.rnn_cell.OutputProjectionWrapper(
tf.nn.rnn_cell.GRUCell(2), 4)
dec, mem = tf.nn.seq2seq.rnn_decoder(dec_inp, enc_state, cell)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testBasicRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
cell = tf.nn.rnn_cell.OutputProjectionWrapper(
tf.nn.rnn_cell.GRUCell(2), 4)
dec, mem = tf.nn.seq2seq.basic_rnn_seq2seq(inp, dec_inp, cell)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testTiedRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
cell = tf.nn.rnn_cell.OutputProjectionWrapper(
tf.nn.rnn_cell.GRUCell(2), 4)
dec, mem = tf.nn.seq2seq.tied_rnn_seq2seq(inp, dec_inp, cell)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual(1, len(res))
self.assertEqual((2, 2), res[0].shape)
def testEmbeddingRNNDecoder(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
_, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
dec, mem = tf.nn.seq2seq.embedding_rnn_decoder(
dec_inp, enc_state, cell, num_symbols=4, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
res = sess.run([mem])
self.assertEqual(1, len(res))
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
def testEmbeddingRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)]
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
dec, mem = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
# Test with state_is_tuple=False.
with tf.variable_scope("no_tuple"):
cell1 = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
dec, mem = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell1, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 4), res[0].shape)
# Test externally provided output projection.
w = tf.get_variable("proj_w", [2, 5])
b = tf.get_variable("proj_b", [5])
with tf.variable_scope("proj_seq2seq"):
dec, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, output_projection=(w, b))
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [tf.constant(0, tf.int32, shape=[2]) for _ in range(3)]
with tf.variable_scope("other"):
d3, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2,
feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
tf.get_variable_scope().reuse_variables()
d1, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
d2, _ = tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
def testEmbeddingTiedRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)]
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
dec, mem = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
# Test when num_decoder_symbols is provided, the size of decoder output
# is num_decoder_symbols.
with tf.variable_scope("decoder_symbols_seq2seq"):
dec, mem = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_symbols=5, num_decoder_symbols=3,
embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 3), res[0].shape)
# Test externally provided output projection.
w = tf.get_variable("proj_w", [2, 5])
b = tf.get_variable("proj_b", [5])
with tf.variable_scope("proj_seq2seq"):
dec, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2,
output_projection=(w, b))
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [tf.constant(0, tf.int32, shape=[2])] * 3
with tf.variable_scope("other"):
d3, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp2, cell, num_symbols=5, embedding_size=2,
feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
tf.get_variable_scope().reuse_variables()
d1, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2,
feed_previous=True)
d2, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp2, cell, num_symbols=5, embedding_size=2,
feed_previous=True)
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
def testAttentionDecoder1(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = [tf.constant(0.5, shape=[2, 2])] * 2
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testAttentionDecoder2(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = [tf.constant(0.5, shape=[2, 2])] * 2
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4,
num_heads=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testDynamicAttentionDecoder1(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = tf.constant(0.5, shape=[2, 2, 2])
enc_outputs, enc_state = tf.nn.dynamic_rnn(cell, inp, dtype=tf.float32)
attn_states = enc_outputs
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testDynamicAttentionDecoder2(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.GRUCell(2)
inp = tf.constant(0.5, shape=[2, 2, 2])
enc_outputs, enc_state = tf.nn.dynamic_rnn(cell, inp, dtype=tf.float32)
attn_states = enc_outputs
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4,
num_heads=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testAttentionDecoderStateIsTuple(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell] * 2,
state_is_tuple=True)
inp = [tf.constant(0.5, shape=[2, 2])] * 2
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual(2, len(res[0]))
self.assertEqual((2, 2), res[0][0].c.shape)
self.assertEqual((2, 2), res[0][0].h.shape)
self.assertEqual((2, 2), res[0][1].c.shape)
self.assertEqual((2, 2), res[0][1].h.shape)
# pylint: disable=unused-variable,invalid-name
def testDynamicAttentionDecoderStateIsTuple(self):
with self.test_session() as sess:
with tf.variable_scope(
"root", initializer=tf.constant_initializer(0.5)):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
cell = tf.nn.rnn_cell.MultiRNNCell(cells=[cell] * 2,
state_is_tuple=True)
inp = tf.constant(0.5, shape=[2, 2, 2])
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3
dec, mem = tf.nn.seq2seq.attention_decoder(
dec_inp, enc_state,
attn_states, cell, output_size=4)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 4), res[0].shape)
res = sess.run([mem])
self.assertEqual(2, len(res[0]))
self.assertEqual((2, 2), res[0][0].c.shape)
self.assertEqual((2, 2), res[0][0].h.shape)
self.assertEqual((2, 2), res[0][1].c.shape)
self.assertEqual((2, 2), res[0][1].h.shape)
def testEmbeddingAttentionDecoder(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
inp = [tf.constant(0.5, shape=[2, 2])] * 2
cell = tf.nn.rnn_cell.GRUCell(2)
enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32)
attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size])
for e in enc_outputs])
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
dec, mem = tf.nn.seq2seq.embedding_attention_decoder(
dec_inp, enc_state, attn_states, cell, num_symbols=4,
embedding_size=2, output_size=3)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 3), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].shape)
def testEmbeddingAttentionSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)]
dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
dec, mem = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
# Test with state_is_tuple=False.
with tf.variable_scope("no_tuple"):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
dec, mem = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run([mem])
self.assertEqual((2, 4), res[0].shape)
# Test externally provided output projection.
w = tf.get_variable("proj_w", [2, 5])
b = tf.get_variable("proj_b", [5])
with tf.variable_scope("proj_seq2seq"):
dec, _ = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, output_projection=(w, b))
sess.run([tf.global_variables_initializer()])
res = sess.run(dec)
self.assertEqual(3, len(res))
self.assertEqual((2, 2), res[0].shape)
# Test that previous-feeding model ignores inputs after the first.
dec_inp2 = [tf.constant(0, tf.int32, shape=[2]) for _ in range(3)]
with tf.variable_scope("other"):
d3, _ = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2,
feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
tf.get_variable_scope().reuse_variables()
d1, _ = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
d2, _ = tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp2, cell, num_encoder_symbols=2,
num_decoder_symbols=5, embedding_size=2, feed_previous=True)
res1 = sess.run(d1)
res2 = sess.run(d2)
res3 = sess.run(d3)
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
def testOne2ManyRNNSeq2Seq(self):
with self.test_session() as sess:
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)]
dec_inp_dict = {}
dec_inp_dict["0"] = [
tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
dec_inp_dict["1"] = [
tf.constant(i, tf.int32, shape=[2]) for i in range(4)]
dec_symbols_dict = {"0": 5, "1": 6}
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
outputs_dict, state_dict = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict, cell, 2, dec_symbols_dict, embedding_size=2)
sess.run([tf.global_variables_initializer()])
res = sess.run(outputs_dict["0"])
self.assertEqual(3, len(res))
self.assertEqual((2, 5), res[0].shape)
res = sess.run(outputs_dict["1"])
self.assertEqual(4, len(res))
self.assertEqual((2, 6), res[0].shape)
res = sess.run([state_dict["0"]])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
res = sess.run([state_dict["1"]])
self.assertEqual((2, 2), res[0].c.shape)
self.assertEqual((2, 2), res[0].h.shape)
# Test that previous-feeding model ignores inputs after the first, i.e.
# dec_inp_dict2 has different inputs from dec_inp_dict after the first
# time-step.
dec_inp_dict2 = {}
dec_inp_dict2["0"] = [
tf.constant(0, tf.int32, shape=[2]) for _ in range(3)]
dec_inp_dict2["1"] = [
tf.constant(0, tf.int32, shape=[2]) for _ in range(4)]
with tf.variable_scope("other"):
outputs_dict3, _ = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict2, cell, 2, dec_symbols_dict,
embedding_size=2, feed_previous=tf.constant(True))
sess.run([tf.global_variables_initializer()])
tf.get_variable_scope().reuse_variables()
outputs_dict1, _ = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict, cell, 2, dec_symbols_dict,
embedding_size=2, feed_previous=True)
outputs_dict2, _ = tf.nn.seq2seq.one2many_rnn_seq2seq(
enc_inp, dec_inp_dict2, cell, 2, dec_symbols_dict,
embedding_size=2, feed_previous=True)
res1 = sess.run(outputs_dict1["0"])
res2 = sess.run(outputs_dict2["0"])
res3 = sess.run(outputs_dict3["0"])
self.assertAllClose(res1, res2)
self.assertAllClose(res1, res3)
def testSequenceLoss(self):
with self.test_session() as sess:
logits = [tf.constant(i + 0.5, shape=[2, 5]) for i in range(3)]
targets = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
weights = [tf.constant(1.0, shape=[2]) for i in range(3)]
average_loss_per_example = tf.nn.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=True,
average_across_batch=True)
res = sess.run(average_loss_per_example)
self.assertAllClose(1.60944, res)
average_loss_per_sequence = tf.nn.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=False,
average_across_batch=True)
res = sess.run(average_loss_per_sequence)
self.assertAllClose(4.828314, res)
total_loss = tf.nn.seq2seq.sequence_loss(
logits, targets, weights,
average_across_timesteps=False,
average_across_batch=False)
res = sess.run(total_loss)
self.assertAllClose(9.656628, res)
def testSequenceLossByExample(self):
with self.test_session() as sess:
output_classes = 5
logits = [tf.constant(i + 0.5, shape=[2, output_classes])
for i in range(3)]
targets = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)]
weights = [tf.constant(1.0, shape=[2]) for i in range(3)]
average_loss_per_example = tf.nn.seq2seq.sequence_loss_by_example(
logits, targets, weights,
average_across_timesteps=True)
res = sess.run(average_loss_per_example)
self.assertAllClose(np.asarray([1.609438, 1.609438]), res)
loss_per_sequence = tf.nn.seq2seq.sequence_loss_by_example(
logits, targets, weights,
average_across_timesteps=False)
res = sess.run(loss_per_sequence)
self.assertAllClose(np.asarray([4.828314, 4.828314]), res)
def testModelWithBucketsScopeAndLoss(self):
"""Test that variable scope reuse is not reset after model_with_buckets."""
classes = 10
buckets = [(4, 4), (8, 8)]
with self.test_session():
# Here comes a sample Seq2Seq model using GRU cells.
def SampleGRUSeq2Seq(enc_inp, dec_inp, weights, per_example_loss):
"""Example sequence-to-sequence model that uses GRU cells."""
def GRUSeq2Seq(enc_inp, dec_inp):
cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(24)] * 2,
state_is_tuple=True)
return tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=classes,
num_decoder_symbols=classes, embedding_size=24)
targets = [dec_inp[i+1] for i in range(len(dec_inp) - 1)] + [0]
return tf.nn.seq2seq.model_with_buckets(
enc_inp, dec_inp, targets, weights, buckets, GRUSeq2Seq,
per_example_loss=per_example_loss)
# Now we construct the copy model.
inp = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
out = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
weights = [tf.ones_like(inp[0], dtype=tf.float32) for _ in range(8)]
with tf.variable_scope("root"):
_, losses1 = SampleGRUSeq2Seq(inp, out, weights, per_example_loss=False)
# Now check that we did not accidentally set reuse.
self.assertEqual(False, tf.get_variable_scope().reuse)
# Construct one more model with per-example loss.
tf.get_variable_scope().reuse_variables()
_, losses2 = SampleGRUSeq2Seq(inp, out, weights, per_example_loss=True)
# First loss is scalar, the second one is a 1-dimensinal tensor.
self.assertEqual([], losses1[0].get_shape().as_list())
self.assertEqual([None], losses2[0].get_shape().as_list())
def testModelWithBuckets(self):
"""Larger tests that does full sequence-to-sequence model training."""
# We learn to copy 10 symbols in 2 buckets: length 4 and length 8.
classes = 10
buckets = [(4, 4), (8, 8)]
perplexities = [[], []] # Results for each bucket.
tf.set_random_seed(111)
random.seed(111)
np.random.seed(111)
with self.test_session() as sess:
# We use sampled softmax so we keep output projection separate.
w = tf.get_variable("proj_w", [24, classes])
w_t = tf.transpose(w)
b = tf.get_variable("proj_b", [classes])
# Here comes a sample Seq2Seq model using GRU cells.
def SampleGRUSeq2Seq(enc_inp, dec_inp, weights):
"""Example sequence-to-sequence model that uses GRU cells."""
def GRUSeq2Seq(enc_inp, dec_inp):
cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(24)] * 2,
state_is_tuple=True)
return tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols=classes,
num_decoder_symbols=classes, embedding_size=24,
output_projection=(w, b))
targets = [dec_inp[i+1] for i in range(len(dec_inp) - 1)] + [0]
def SampledLoss(labels, inputs):
labels = tf.reshape(labels, [-1, 1])
return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, 8, classes)
return tf.nn.seq2seq.model_with_buckets(
enc_inp, dec_inp, targets, weights, buckets, GRUSeq2Seq,
softmax_loss_function=SampledLoss)
# Now we construct the copy model.
batch_size = 8
inp = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
out = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)]
weights = [tf.ones_like(inp[0], dtype=tf.float32) for _ in range(8)]
with tf.variable_scope("root"):
_, losses = SampleGRUSeq2Seq(inp, out, weights)
updates = []
params = tf.global_variables()
optimizer = tf.train.AdamOptimizer(0.03, epsilon=1e-5)
for i in range(len(buckets)):
full_grads = tf.gradients(losses[i], params)
grads, _ = tf.clip_by_global_norm(full_grads, 30.0)
update = optimizer.apply_gradients(zip(grads, params))
updates.append(update)
sess.run([tf.global_variables_initializer()])
steps = 6
for _ in range(steps):
bucket = random.choice(np.arange(len(buckets)))
length = buckets[bucket][0]
i = [np.array([np.random.randint(9) + 1 for _ in range(batch_size)],
dtype=np.int32) for _ in range(length)]
# 0 is our "GO" symbol here.
o = [np.array([0] * batch_size, dtype=np.int32)] + i
feed = {}
for i1, i2, o1, o2 in zip(inp[:length], i[:length],
out[:length], o[:length]):
feed[i1.name] = i2
feed[o1.name] = o2
if length < 8: # For the 4-bucket, we need the 5th as target.
feed[out[length].name] = o[length]
res = sess.run([updates[bucket], losses[bucket]], feed)
perplexities[bucket].append(math.exp(float(res[1])))
for bucket in range(len(buckets)):
if len(perplexities[bucket]) > 1: # Assert that perplexity went down.
self.assertLess(perplexities[bucket][-1], perplexities[bucket][0])
def testModelWithBooleanFeedPrevious(self):
"""Test the model behavior when feed_previous is True.
For example, the following two cases have the same effect:
- Train `embedding_rnn_seq2seq` with `feed_previous=True`, which contains
a `embedding_rnn_decoder` with `feed_previous=True` and
`update_embedding_for_previous=True`. The decoder is fed with "<Go>"
and outputs "A, B, C".
- Train `embedding_rnn_seq2seq` with `feed_previous=False`. The decoder
is fed with "<Go>, A, B".
"""
num_encoder_symbols = 3
num_decoder_symbols = 5
batch_size = 2
num_enc_timesteps = 2
num_dec_timesteps = 3
def TestModel(seq2seq):
with self.test_session(graph=tf.Graph()) as sess:
tf.set_random_seed(111)
random.seed(111)
np.random.seed(111)
enc_inp = [tf.constant(i + 1, tf.int32, shape=[batch_size])
for i in range(num_enc_timesteps)]
dec_inp_fp_true = [tf.constant(i, tf.int32, shape=[batch_size])
for i in range(num_dec_timesteps)]
dec_inp_holder_fp_false = [tf.placeholder(tf.int32, shape=[batch_size])
for _ in range(num_dec_timesteps)]
targets = [tf.constant(i + 1, tf.int32, shape=[batch_size])
for i in range(num_dec_timesteps)]
weights = [tf.constant(1.0, shape=[batch_size])
for i in range(num_dec_timesteps)]
def ForwardBackward(enc_inp, dec_inp, feed_previous):
scope_name = "fp_{}".format(feed_previous)
with tf.variable_scope(scope_name):
dec_op, _ = seq2seq(enc_inp, dec_inp, feed_previous=feed_previous)
net_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope_name)
optimizer = tf.train.AdamOptimizer(0.03, epsilon=1e-5)
update_op = optimizer.minimize(
tf.nn.seq2seq.sequence_loss(dec_op, targets, weights),
var_list=net_variables)
return dec_op, update_op, net_variables
dec_op_fp_true, update_fp_true, variables_fp_true = ForwardBackward(
enc_inp, dec_inp_fp_true, feed_previous=True)
_, update_fp_false, variables_fp_false = ForwardBackward(
enc_inp, dec_inp_holder_fp_false, feed_previous=False)
sess.run(tf.global_variables_initializer())
# We only check consistencies between the variables existing in both
# the models with True and False feed_previous. Variables created by
# the loop_function in the model with True feed_previous are ignored.
v_false_name_dict = {v.name.split("/", 1)[-1]: v
for v in variables_fp_false}
matched_variables = [(v, v_false_name_dict[v.name.split("/", 1)[-1]])
for v in variables_fp_true]
for v_true, v_false in matched_variables:
sess.run(tf.assign(v_false, v_true))
# Take the symbols generated by the decoder with feed_previous=True as
# the true input symbols for the decoder with feed_previous=False.
dec_fp_true = sess.run(dec_op_fp_true)
output_symbols_fp_true = np.argmax(dec_fp_true, axis=2)
dec_inp_fp_false = np.vstack((dec_inp_fp_true[0].eval(),
output_symbols_fp_true[:-1]))
sess.run(update_fp_true)
sess.run(update_fp_false,
{holder: inp for holder, inp in zip(dec_inp_holder_fp_false,
dec_inp_fp_false)})
for v_true, v_false in matched_variables:
self.assertAllClose(v_true.eval(), v_false.eval())
def EmbeddingRNNSeq2SeqF(enc_inp, dec_inp, feed_previous):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
return tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols,
num_decoder_symbols, embedding_size=2, feed_previous=feed_previous)
def EmbeddingRNNSeq2SeqNoTupleF(enc_inp, dec_inp, feed_previous):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
return tf.nn.seq2seq.embedding_rnn_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols,
num_decoder_symbols, embedding_size=2, feed_previous=feed_previous)
def EmbeddingTiedRNNSeq2Seq(enc_inp, dec_inp, feed_previous):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
return tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_decoder_symbols, embedding_size=2,
feed_previous=feed_previous)
def EmbeddingTiedRNNSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
return tf.nn.seq2seq.embedding_tied_rnn_seq2seq(
enc_inp, dec_inp, cell, num_decoder_symbols, embedding_size=2,
feed_previous=feed_previous)
def EmbeddingAttentionSeq2Seq(enc_inp, dec_inp, feed_previous):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True)
return tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols,
num_decoder_symbols, embedding_size=2, feed_previous=feed_previous)
def EmbeddingAttentionSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous):
cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False)
return tf.nn.seq2seq.embedding_attention_seq2seq(
enc_inp, dec_inp, cell, num_encoder_symbols,
num_decoder_symbols, embedding_size=2, feed_previous=feed_previous)
for model in (EmbeddingRNNSeq2SeqF, EmbeddingRNNSeq2SeqNoTupleF,
EmbeddingTiedRNNSeq2Seq, EmbeddingTiedRNNSeq2SeqNoTuple,
EmbeddingAttentionSeq2Seq, EmbeddingAttentionSeq2SeqNoTuple):
TestModel(model)
if __name__ == "__main__":
tf.test.main()

37
tensorflow/python/kernel_tests/variable_scope_test.py
View File

@ -22,6 +22,7 @@ import numpy
import tensorflow as tf
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import variable_scope
@ -95,6 +96,21 @@ class VariableScopeTest(tf.test.TestCase):
with self.assertRaises(TypeError):
tf.get_variable("x", initializer={})
def testInitFromNonInitializer(self):
with self.test_session() as sess:
# Test various dtypes with zeros initializer as following:
types = [tf.int8, tf.uint8, tf.int16, tf.uint16, tf.int32, tf.int64,
tf.bool]
# Use different varibale_name to distinguish various dtypes
for (i, dtype) in enumerate(types):
x = tf.get_variable(name='x%d' % i, shape=(3, 4), dtype=dtype)
y = tf.get_variable(name='y%d' % i, shape=(3, 4), dtype=dtype,
initializer=init_ops.zeros_initializer(dtype=dtype))
tf.global_variables_initializer().run()
self.assertAllEqual(x.eval(), y.eval())
def testVarScopeCachingDevice(self):
with self.test_session():
caching_device = "/job:moo"
@ -672,6 +688,27 @@ def axis0_into3_partitioner(shape=None, **unused_kwargs):
class VariableScopeWithPartitioningTest(tf.test.TestCase):
def testInitFromNonInitializer(self):
with self.test_session() as sess:
# Test various dtypes with zeros initializer as following:
types = [tf.int8, tf.uint8, tf.int16, tf.uint16, tf.int32, tf.int64,
tf.bool]
# Use different varibale_name to distinguish various dtypes
for (i, dtype) in enumerate(types):
x = tf.get_variable(name='x%d' % i, shape=(3, 4), dtype=dtype,
partitioner=axis0_into2_partitioner)
y = tf.get_variable(name='y%d' % i, shape=(6, 4), dtype=dtype,
partitioner=axis0_into2_partitioner,
initializer=init_ops.zeros_initializer(dtype=dtype))
tf.global_variables_initializer().run()
# x and y would become var list after partition
val_x = sess.run(list(x))
val_y = sess.run(list(y))
self.assertAllEqual(val_x, val_y)
def testResultNameMatchesRequested(self):
with tf.variable_scope("scope0", partitioner=axis0_into2_partitioner):
v = tf.get_variable("name0", shape=(3, 1, 1))

8
tensorflow/python/kernel_tests/variables_test.py
View File

@ -366,6 +366,14 @@ class VariablesTestCase(tf.test.TestCase):
for i in v2.initializer.inputs:
self.assertEqual(expected_group_v2, i.op.colocation_groups())
def testLoad(self):
with self.test_session():
var = tf.Variable(np.zeros((5,5), np.float32))
tf.global_variables_initializer().run()
var.load(np.ones((5, 5), np.float32))
self.assertAllClose(np.ones((5, 5), np.float32), var.eval())
class IsInitializedTest(tf.test.TestCase):

3
tensorflow/python/ops/init_ops.py
View File

@ -66,7 +66,8 @@ def zeros_initializer(dtype=dtypes.float32):
"""Returns an initializer that generates tensors initialized to 0."""
def _initializer(shape, dtype=dtype, partition_info=None):
return constant_op.constant(0, dtype=dtype, shape=shape)
return constant_op.constant(False if dtype is dtypes.bool else 0,
dtype=dtype, shape=shape)
return _initializer

24
tensorflow/python/ops/nn_fused_batchnorm_test.py
View File

@ -139,66 +139,66 @@ class BatchNormalizationTest(tf.test.TestCase):
def testInference(self):
x_shape = [1, 1, 6, 1]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_inference(x_shape, [1], use_gpu=True, data_format='NHWC')
self._test_inference(x_shape, [1], use_gpu=True, data_format='NCHW')
self._test_inference(x_shape, [1], use_gpu=False, data_format='NHWC')
x_shape = [1, 1, 6, 2]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_inference(x_shape, [2], use_gpu=True, data_format='NHWC')
self._test_inference(x_shape, [2], use_gpu=False, data_format='NHWC')
x_shape = [1, 2, 1, 6]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_inference(x_shape, [2], use_gpu=True, data_format='NCHW')
x_shape = [27, 131, 127, 6]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_inference(x_shape, [131], use_gpu=True, data_format='NCHW')
self._test_inference(x_shape, [6], use_gpu=True, data_format='NHWC')
self._test_inference(x_shape, [6], use_gpu=False, data_format='NHWC')
def testTraining(self):
x_shape = [1, 1, 6, 1]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_training(x_shape, [1], use_gpu=True, data_format='NHWC')
self._test_training(x_shape, [1], use_gpu=True, data_format='NCHW')
self._test_training(x_shape, [1], use_gpu=False, data_format='NHWC')
x_shape = [1, 1, 6, 2]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_training(x_shape, [2], use_gpu=True, data_format='NHWC')
self._test_training(x_shape, [2], use_gpu=False, data_format='NHWC')
x_shape = [1, 2, 1, 6]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_training(x_shape, [2], use_gpu=True, data_format='NCHW')
x_shape = [27, 131, 127, 6]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_training(x_shape, [131], use_gpu=True, data_format='NCHW')
self._test_training(x_shape, [6], use_gpu=True, data_format='NHWC')
self._test_training(x_shape, [6], use_gpu=False, data_format='NHWC')
def testBatchNormGrad(self):
x_shape = [1, 1, 6, 1]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_gradient(x_shape, [1], use_gpu=True, data_format='NHWC')
self._test_gradient(x_shape, [1], use_gpu=True, data_format='NCHW')
self._test_gradient(x_shape, [1], use_gpu=False, data_format='NHWC')
x_shape = [1, 1, 6, 2]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_gradient(x_shape, [2], use_gpu=True, data_format='NHWC')
self._test_gradient(x_shape, [2], use_gpu=False, data_format='NHWC')
x_shape = [1, 2, 1, 6]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_gradient(x_shape, [2], use_gpu=True, data_format='NCHW')
x_shape = [7, 9, 13, 6]
if tf.test.is_gpu_available():
if tf.test.is_gpu_available(cuda_only=True):
self._test_gradient(x_shape, [9], use_gpu=True, data_format='NCHW')
self._test_gradient(x_shape, [6], use_gpu=True, data_format='NHWC')
self._test_gradient(x_shape, [6], use_gpu=False, data_format='NHWC')

47
tensorflow/python/ops/variable_scope.py
View File

@ -526,9 +526,14 @@ class _VariableStore(object):
var_full_name = "%s/part_%d" % (name, i)
with ops.name_scope(var_full_name + "/PartitionedInitializer"):
# Create the tensor to initialize the variable with default value.
if initializer is None:
init = init_ops.uniform_unit_scaling_initializer()
init_shape = var_shape
init, initializing_from_value = self._get_default_initializer(
name=name, shape=shape, dtype=dtype)
if initializing_from_value:
init_shape = None
else:
init_shape = var_shape
elif callable(initializer):
init = initializer
init_shape = var_shape
@ -653,9 +658,10 @@ class _VariableStore(object):
raise ValueError("Shape of a new variable (%s) must be fully defined, "
"but instead was %s." % (name, shape))
# Create the tensor to initialize the variable.
# Create the tensor to initialize the variable with default value.
if initializer is None:
initializer = init_ops.uniform_unit_scaling_initializer()
initializer, initializing_from_value = self._get_default_initializer(
name=name, shape=shape, dtype=dtype)
# Clear control dependencies while creating the initializer.
with ops.control_dependencies(None):
if initializing_from_value:
@ -692,6 +698,39 @@ class _VariableStore(object):
return v
# Initialize variable when no initializer provided
def _get_default_initializer(self, name, shape=None, dtype=dtypes.float32):
"""Provide a default initializer and a corresponding value.
Args:
name: see get_variable.
shape: see get_variable.
dtype: see get_variable.
Returns:
initializer and initializing_from_value. See get_variable above.
Raises:
ValueError: When giving unsupported dtype.
"""
# If dtype is DT_FLOAT, provide a uniform unit scaling initializer
if dtype.is_floating:
initializer = init_ops.uniform_unit_scaling_initializer()
initializing_from_value = False
# If dtype is DT_INT/DT_UINT, provide a default value `zero`
# If dtype is DT_BOOL, provide a default value `FALSE`
elif dtype.is_integer or dtype.is_unsigned or dtype.is_bool:
initializer = init_ops.zeros_initializer()(
shape=shape, dtype=dtype.base_dtype)
initializing_from_value = True
# NOTES:Do we need to support for handling DT_STRING and DT_COMPLEX here?
else:
raise ValueError("An initializer for variable %s of %s is required"
% (name, dtype.base_dtype))
return initializer, initializing_from_value
# To stop regularization, use this regularizer
def no_regularizer(_):
"""Use this function to prevent regularization of variables."""

42
tensorflow/python/ops/variables.py
View File

@ -650,6 +650,46 @@ class Variable(object):
"""
return state_ops.count_up_to(self._variable, limit=limit)
def load(self, value, session=None):
"""Load new value into this variable
Writes new value to variable's memory. Doesn't add ops to the graph.
This convenience method requires a session where the graph containing this
variable has been launched. If no session is passed, the default session is
used. See the [Session class](../../api_docs/python/client.md#Session) for
more information on launching a graph and on sessions.
```python
v = tf.Variable([1, 2])
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
# Usage passing the session explicitly.
v.load([2, 3], sess)
print(v.eval(sess)) # prints [2 3]
# Usage with the default session. The 'with' block
# above makes 'sess' the default session.
v.load([3, 4], sess)
print(v.eval()) # prints [3 4]
```
Args:
value: New variable value
session: The session to use to evaluate this variable. If
none, the default session is used.
Raises:
ValueError: Session is not passed and no default session
"""
session = session or ops.get_default_session()
if session is None:
raise ValueError(
"Either session argument should be provided or default session "
"should be established")
session.run(self._initializer_op, {self._initializer_op.inputs[1]: value})
# Conversion to tensor.
@staticmethod
def _TensorConversionFunction(v, dtype=None, name=None, as_ref=False): # pylint: disable=invalid-name
@ -1070,7 +1110,7 @@ def global_variables():
return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
@deprecated("2016-03-02", "Please use tf.global_variables instead.")
@deprecated("2017-03-02", "Please use tf.global_variables instead.")
def all_variables():
"""See `tf.global_variables`."""
return global_variables()

2
tensorflow/python/saved_model/BUILD
View File

@ -9,6 +9,8 @@ licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
load("//tensorflow:tensorflow.bzl", "py_test")
py_library(
name = "constants",
srcs = ["constants.py"],

2
tensorflow/python/summary/summary.py
View File

@ -273,7 +273,7 @@ def merge(inputs, collections=None, name=None):
inputs: A list of `string` `Tensor` objects containing serialized `Summary`
protocol buffers.
collections: Optional list of graph collections keys. The new summary op is
added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
added to these collections. Defaults to `[]`.
name: A name for the operation (optional).
Returns:

2
tensorflow/python/tools/BUILD
View File

@ -7,6 +7,8 @@ licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
load("//tensorflow:tensorflow.bzl", "py_test")
py_library(
name = "freeze_graph_lib",
srcs = ["freeze_graph.py"],

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