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PiperOrigin-RevId: 167401527
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Martin Wicke 2017-09-02 19:21:45 -07:00 committed by TensorFlower Gardener
parent ddba1e0aad
commit d57572e996
33 changed files with 133 additions and 83 deletions
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8
README.md
View File

@ -36,7 +36,15 @@ and discussion, and please direct specific questions to [Stack Overflow](https:/
People who are a little more adventurous can also try our nightly binaries:
**Nightly pip packages**
* We are pleased to announce that TensorFlow now offers nightly pip packages
under the [tf-nightly](https://pypi.python.org/pypi/tf-nightly) project on pypi.
Simply run `pip install tf-nightly` in a clean environment to install the nightly
tensorflow build. We currently only support CPU-only packages on Linux and Mac.
GPU packages on all platforms and Windows CPU-only packages will arrive soon!
**Individual whl files**
* Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-1.3.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
* Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-1.3.0-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/))

2
RELEASE.md
View File

@ -46,7 +46,7 @@ See also [TensorBoard 0.1.4](https://github.com/tensorflow/tensorboard/releases/
* Display feed values with the `print_feed` or `pf` command and clickable links in the curses UI.
* Runtime profiler at the op level and the Python source line level with the `run -p` command.
* Initial release of the statistical distribution library `tf.distributions`.
* GPU kernels and speed improvements for for unary `tf.where` and `tf.nn.top_k`.
* GPU kernels and speed improvements for unary `tf.where` and `tf.nn.top_k`.
* Monotonic Attention wrappers added to `tf.contrib.seq2seq`.
* Added `tf.contrib.signal`, a library for signal processing primitives.
* Added `tf.contrib.resampler`, containing CPU and GPU ops for differentiable resampling of images.

8
WORKSPACE
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@ -2,11 +2,11 @@ workspace(name = "org_tensorflow")
http_archive(
name = "io_bazel_rules_closure",
sha256 = "bc41b80486413aaa551860fc37471dbc0666e1dbb5236fb6177cb83b0c105846",
strip_prefix = "rules_closure-dec425a4ff3faf09a56c85d082e4eed05d8ce38f",
sha256 = "25f5399f18d8bf9ce435f85c6bbf671ec4820bc4396b3022cc5dc4bc66303609",
strip_prefix = "rules_closure-0.4.2",
urls = [
"http://mirror.bazel.build/github.com/bazelbuild/rules_closure/archive/dec425a4ff3faf09a56c85d082e4eed05d8ce38f.tar.gz", # 2017-06-02
"https://github.com/bazelbuild/rules_closure/archive/dec425a4ff3faf09a56c85d082e4eed05d8ce38f.tar.gz",
"http://mirror.bazel.build/github.com/bazelbuild/rules_closure/archive/0.4.2.tar.gz", # 2017-08-29
"https://github.com/bazelbuild/rules_closure/archive/0.4.2.tar.gz",
],
)

10
configure.py
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@ -143,7 +143,7 @@ def run_shell(cmd, allow_non_zero=False):
def cygpath(path):
"""Convert path from posix to windows."""
return run_shell(['cygpath', '-m', path])
return os.path.abspath(path).replace('\\', '/')
def get_python_path(environ_cp, python_bin_path):
@ -196,7 +196,7 @@ def setup_python(environ_cp, bazel_version):
environ_cp['PYTHON_BIN_PATH'] = ''
# Convert python path to Windows style before checking lib and version
if is_cygwin():
if is_windows() or is_cygwin():
python_bin_path = cygpath(python_bin_path)
# Get PYTHON_LIB_PATH
@ -219,7 +219,7 @@ def setup_python(environ_cp, bazel_version):
python_major_version = get_python_major_version(python_bin_path)
# Convert python path to Windows style before writing into bazel.rc
if is_cygwin():
if is_windows() or is_cygwin():
python_lib_path = cygpath(python_lib_path)
# Set-up env variables used by python_configure.bzl
@ -600,7 +600,7 @@ def set_tf_cuda_version(environ_cp):
# Find out where the CUDA toolkit is installed
default_cuda_path = _DEFAULT_CUDA_PATH
if is_cygwin():
if is_windows() or is_cygwin():
default_cuda_path = cygpath(
environ_cp.get('CUDA_PATH', _DEFAULT_CUDA_PATH_WIN))
elif is_linux():
@ -660,7 +660,7 @@ def set_tf_cunn_version(environ_cp):
# unusable. Going through one more level of expansion to handle that.
cudnn_install_path = os.path.realpath(
os.path.expanduser(cudnn_install_path))
if is_cygwin():
if is_windows() or is_cygwin():
cudnn_install_path = cygpath(cudnn_install_path)
if is_windows():

8
tensorflow/cc/gradients/nn_grad.cc
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@ -95,6 +95,14 @@ Status SeluGradHelper(const Scope& scope, const Operation& op,
}
REGISTER_GRADIENT_OP("Selu", SeluGradHelper);
Status L2LossGrad(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
grad_outputs->push_back(Mul(scope, op.input(0), grad_inputs[0]));
return scope.status();
}
REGISTER_GRADIENT_OP("L2Loss", L2LossGrad);
Status BiasAddGradHelper(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {

8
tensorflow/cc/gradients/nn_grad_test.cc
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@ -122,6 +122,14 @@ TEST_F(NNGradTest, SeluGrad) {
RunTest(x, x_init_value, y, shape);
}
TEST_F(NNGradTest, L2LossGrad) {
TensorShape x_shape({5, 2});
TensorShape y_shape({1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
auto y = L2Loss(scope_, x);
RunTest(x, x_shape, y, y_shape);
}
TEST_F(NNGradTest, BiasAddGradHelper) {
TensorShape shape({4, 5});
TensorShape bias_shape({5});

2
tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc
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@ -389,7 +389,7 @@ StatusOr<string> CompileModuleToPtx(llvm::Module* module,
// Loop unrolling exposes more opportunities for SROA. Therefore, we run SROA
// again after the standard optimization passes [http://b/13329423].
// TODO(jingyue): SROA may further expose more optimization opportunities, such
// TODO(jingyue): SROA may further expose more optimization opportunities such
// as more precise alias analysis and more function inlining (SROA may change
// the inlining cost of a function). For now, running SROA already emits good
// enough code for the evaluated benchmarks. We may want to run more

2
tensorflow/contrib/cmake/tf_tests.cmake
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@ -82,6 +82,7 @@ function(AddTest)
set_tests_properties(${_AT_TARGET}
PROPERTIES ENVIRONMENT "TEST_TMPDIR=${tempdir};TEST_SRCDIR=${testdir}"
)
set_tests_properties(${_AT_TARGET} PROPERTIES TIMEOUT "600")
foreach(datafile ${_AT_DATA})
file(RELATIVE_PATH datafile_rel ${tensorflow_source_dir} ${datafile})
@ -117,6 +118,7 @@ function(AddPythonTests)
if (_AT_DEPENDS)
add_dependencies(${_AT_TARGET} ${_AT_DEPENDS})
endif()
set_tests_properties(${sourcefile} PROPERTIES TIMEOUT "600")
endforeach()
endfunction(AddPythonTests)

1
tensorflow/contrib/gdr/BUILD
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@ -62,6 +62,7 @@ tf_cuda_library(
}),
deps = [
":gdr_proto_cc",
"//tensorflow/core:core_cpu_internal",
"//tensorflow/core:framework",
"//tensorflow/core:gpu_runtime",
"//tensorflow/core:lib",

7
tensorflow/contrib/gdr/gdr_memory_manager.h
View File

@ -16,14 +16,9 @@ limitations under the License.
#ifndef GDR_MEMORY_MANAGER_H_
#define GDR_MEMORY_MANAGER_H_
#include "google/protobuf/any.pb.h"
#include "tensorflow/core/lib/core/status.h"
namespace google {
namespace protobuf {
class Any;
}
}
namespace tensorflow {
class Device;

1
tensorflow/contrib/layers/__init__.py
View File

@ -115,6 +115,7 @@ _allowed_symbols = ['bias_add',
'legacy_linear',
'legacy_relu',
'OPTIMIZER_CLS_NAMES',
'OPTIMIZER_SUMMARIES',
'regression_target',
'SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY',
'summaries']

5
tensorflow/contrib/layers/python/layers/optimizers.py
View File

@ -129,8 +129,9 @@ def optimize_loss(loss,
`None` to use all trainable variables.
name: The name for this operation is used to scope operations and summaries.
summaries: List of internal quantities to visualize on tensorboard. If not
set only the loss and the learning rate will be reported. The
complete list is in OPTIMIZER_SUMMARIES.
set, the loss, the learning rate, and the global norm of the
gradients will be reported. The complete list of possible values
is in OPTIMIZER_SUMMARIES.
colocate_gradients_with_ops: If True, try colocating gradients with the
corresponding op.
increment_global_step: Whether to increment `global_step`. If your model

24
tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py
View File

@ -28,7 +28,6 @@ import six
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import tf_logging as logging
@ -44,7 +43,7 @@ def _get_in_out_shape(x_shape, y_shape, n_classes, batch_size=None):
x_is_dict, y_is_dict = isinstance(
x_shape, dict), y_shape is not None and isinstance(y_shape, dict)
if y_is_dict and n_classes is not None:
assert (isinstance(n_classes, dict))
assert isinstance(n_classes, dict)
if batch_size is None:
batch_size = list(x_shape.values())[0][0] if x_is_dict else x_shape[0]
@ -322,10 +321,12 @@ class DataFeeder(object):
self._x = dict([(k, check_array(v, v.dtype)) for k, v in list(x.items())
]) if x_is_dict else check_array(x, x.dtype)
self._y = None if y is None else \
dict([(k, check_array(v, v.dtype)) for k, v in list(y.items())]) if x_is_dict else check_array(y, y.dtype)
self._y = None if y is None else (
dict([(k, check_array(v, v.dtype)) for k, v in list(y.items())])
if y_is_dict else check_array(y, y.dtype))
# self.n_classes is not None means we're converting raw target indices to one-hot.
# self.n_classes is not None means we're converting raw target indices
# to one-hot.
if n_classes is not None:
if not y_is_dict:
y_dtype = (np.int64
@ -344,12 +345,15 @@ class DataFeeder(object):
x_shape, y_shape, n_classes, batch_size)
# Input dtype matches dtype of x.
self._input_dtype = dict([(k, _check_dtype(v.dtype)) for k, v in list(self._x.items())]) if x_is_dict \
else _check_dtype(self._x.dtype)
self._input_dtype = (
dict([(k, _check_dtype(v.dtype)) for k, v in list(self._x.items())])
if x_is_dict else _check_dtype(self._x.dtype))
# note: self._output_dtype = np.float32 when y is None
self._output_dtype = dict([(k, _check_dtype(v.dtype)) for k, v in list(self._y.items())]) if y_is_dict \
else _check_dtype(self._y.dtype) if y is not None else np.float32
# self._output_dtype == np.float32 when y is None
self._output_dtype = (
dict([(k, _check_dtype(v.dtype)) for k, v in list(self._y.items())])
if y_is_dict else (
_check_dtype(self._y.dtype) if y is not None else np.float32))
# self.n_classes is None means we're passing in raw target indices
if n_classes is not None and y_is_dict:

12
tensorflow/contrib/makefile/Makefile
View File

@ -316,14 +316,14 @@ ifeq ($(TARGET),IOS)
IPHONESIMULATOR_SYSROOT := $(shell xcrun --sdk iphonesimulator \
--show-sdk-path)
IOS_SDK_VERSION := $(shell xcrun --sdk iphoneos --show-sdk-version)
MIN_SDK_VERSION := 8.0
MIN_SDK_VERSION := 9.0
# Override IOS_ARCH with ARMV7, ARMV7S, ARM64, or I386.
IOS_ARCH := X86_64
ifeq ($(IOS_ARCH),ARMV7)
CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
-arch armv7 \
-fembed-bitcode \
-D__thread= \
-D__thread=thread_local \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-mno-thumb \
@ -347,7 +347,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
-arch armv7s \
-fembed-bitcode \
-D__thread= \
-D__thread=thread_local \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-mno-thumb \
@ -371,7 +371,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
-arch arm64 \
-fembed-bitcode \
-D__thread= \
-D__thread=thread_local \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-DTF_LEAN_BINARY \
@ -395,7 +395,7 @@ ifeq ($(TARGET),IOS)
-arch i386 \
-mno-sse \
-fembed-bitcode \
-D__thread= \
-D__thread=thread_local \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-DTF_LEAN_BINARY \
@ -418,7 +418,7 @@ ifeq ($(TARGET),IOS)
CXXFLAGS += -mios-simulator-version-min=$(MIN_SDK_VERSION) \
-arch x86_64 \
-fembed-bitcode \
-D__thread= \
-D__thread=thread_local \
-DUSE_GEMM_FOR_CONV \
-Wno-c++11-narrowing \
-DTF_LEAN_BINARY \

3
tensorflow/contrib/makefile/README.md
View File

@ -201,7 +201,8 @@ tensorflow/contrib/makefile/compile_ios_protobuf.sh
Then, you will need to compile the nsync library for iOS:
```export HOST_NSYNC_LIB=`tensorflow/contrib/makefile/compile_nsync.sh`
```bash
export HOST_NSYNC_LIB=`tensorflow/contrib/makefile/compile_nsync.sh`
export TARGET_NSYNC_LIB=`tensorflow/contrib/makefile/compile_nsync.sh -t ios`
```

27
tensorflow/core/distributed_runtime/base_rendezvous_mgr.cc
View File

@ -35,14 +35,18 @@ limitations under the License.
namespace tensorflow {
static void StartAbortRendevous(Rendezvous* rendez, const Status& s) {
rendez->StartAbort(s);
rendez->Unref();
}
BaseRendezvousMgr::BaseRendezvousMgr(const WorkerEnv* worker_env)
: worker_env_(worker_env) {}
BaseRendezvousMgr::~BaseRendezvousMgr() {
for (auto& p : table_) {
BaseRemoteRendezvous* rendez = p.second;
rendez->StartAbort(errors::Aborted("Shutdown"));
rendez->Unref();
auto rendez = p.second;
StartAbortRendevous(rendez, errors::Aborted("Shutdown"));
}
}
@ -52,7 +56,7 @@ RemoteRendezvous* BaseRendezvousMgr::Find(int64 step_id) {
BaseRemoteRendezvous* BaseRendezvousMgr::FindOrCreate(int64 step_id) {
mutex_lock l(mu_);
Table::iterator iter = table_.find(step_id);
auto iter = table_.find(step_id);
if (iter == table_.end()) {
auto rr = Create(step_id, worker_env_);
iter = table_.insert({step_id, rr}).first;
@ -64,7 +68,7 @@ BaseRemoteRendezvous* BaseRendezvousMgr::FindOrCreate(int64 step_id) {
void BaseRendezvousMgr::RecvLocalAsync(int64 step_id,
const Rendezvous::ParsedKey& parsed,
Rendezvous::DoneCallback done) {
BaseRemoteRendezvous* rendez = FindOrCreate(step_id);
auto rendez = FindOrCreate(step_id);
using namespace std::placeholders;
Rendezvous::DoneCallback done_cb = std::bind(
[rendez](Rendezvous::DoneCallback done,
@ -101,15 +105,15 @@ void BaseRendezvousMgr::Cleanup(int64 step_id) {
Rendezvous* rendez = nullptr;
{
mutex_lock l(mu_);
Table::iterator iter = table_.find(step_id);
auto iter = table_.find(step_id);
if (iter != table_.end()) {
rendez = iter->second;
table_.erase(iter);
}
}
if (!rendez) return;
rendez->StartAbort(errors::Aborted("Cleanup ", step_id));
rendez->Unref();
if (rendez) {
StartAbortRendevous(rendez, errors::Aborted("Cleanup ", step_id));
}
}
void BaseRendezvousMgr::CleanupAll() {
@ -122,8 +126,7 @@ void BaseRendezvousMgr::CleanupAll() {
table_.clear();
}
for (auto rendez : rendezs) {
rendez->StartAbort(errors::Aborted("Shutdown"));
rendez->Unref();
StartAbortRendevous(rendez, errors::Aborted("Shutdown"));
}
}
@ -165,7 +168,7 @@ Status BaseRemoteRendezvous::Initialize(WorkerSession* session) {
session_ = session;
std::swap(deferred_calls, deferred_calls_);
}
for (DeferredCall& call : deferred_calls) {
for (auto& call : deferred_calls) {
RecvLocalAsyncInternal(call.parsed, std::move(call.done));
}
return Status::OK();

2
tensorflow/core/framework/op_kernel.h
View File

@ -310,7 +310,7 @@ class OpKernelConstruction {
FunctionLibraryRuntime* function_library() const { return flib_; }
// The GraphDef version whose behavior we should follow.
const int graph_def_version() const { return graph_def_version_; }
int graph_def_version() const { return graph_def_version_; }
// Helper routines for the OP_REQUIRES macros
void CtxFailure(Status s);

6
tensorflow/docs_src/install/install_linux.md
View File

@ -151,10 +151,10 @@ Take the following steps to install TensorFlow with Virtualenv:
(tensorflow)$ <b>pip install --upgrade tensorflow-gpu</b> # for Python 2.7 and GPU
(tensorflow)$ <b>pip3 install --upgrade tensorflow-gpu</b> # for Python 3.n and GPU</pre>
If the preceding command succeeds, skip Step 5. If the preceding
command fails, perform Step 5.
If the preceding command succeeds, skip Step 6. If the preceding
command fails, perform Step 6.
5. (Optional) If Step 4 failed (typically because you invoked a pip version
6. (Optional) If Step 5 failed (typically because you invoked a pip version
lower than 8.1), install TensorFlow in the active virtualenv environment
by issuing a command of the following format:

6
tensorflow/docs_src/install/install_windows.md
View File

@ -71,12 +71,14 @@ Use that package at your own risk.
## Installing with native pip
If the following version of Python is not installed on your machine,
If one of the following versions of Python is not installed on your machine,
install it now:
* [Python 3.5.x 64-bit from python.org](https://www.python.org/downloads/release/python-352/)
* [Python 3.6.x 64-bit from python.org](https://www.python.org/downloads/release/python-362/)
Note that Python 3.5.x comes with the pip3 package manager, which is the
-TensorFlow supports Python 3.5.x and 3.6.x on Windows.
Note that Python 3 comes with the pip3 package manager, which is the
program you'll use to install TensorFlow.
To install TensorFlow, start a terminal. Then issue the appropriate

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

@ -1,4 +1,4 @@
# Speech Commands Example
This is a basic speech recognition example. For more information, see the
tutorial at http://tensorflow.org/tutorials/audio_recognition.
tutorial at https://www.tensorflow.org/versions/master/tutorials/audio_recognition.

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

@ -2473,7 +2473,7 @@ class _IndicatorColumn(_DenseColumn,
weighted_column = sparse_ops.sparse_merge(
sp_ids=id_tensor,
sp_values=weight_tensor,
vocab_size=self._variable_shape[-1])
vocab_size=int(self._variable_shape[-1]))
return sparse_ops.sparse_tensor_to_dense(weighted_column)
dense_id_tensor = sparse_ops.sparse_tensor_to_dense(

14
tensorflow/python/feature_column/feature_column_test.py
View File

@ -3206,6 +3206,20 @@ class IndicatorColumnTest(test.TestCase):
with _initialized_session():
self.assertAllEqual([[0, 0, 1], [1, 0, 0]], indicator_tensor.eval())
def test_transform_with_weighted_column(self):
# Github issue 12557
ids = fc.categorical_column_with_vocabulary_list(
key='ids', vocabulary_list=('a', 'b', 'c'))
weights = fc.weighted_categorical_column(ids, 'weights')
indicator = fc.indicator_column(weights)
features = {
'ids': constant_op.constant(['c', 'b', 'a'], shape=(1, 3)),
'weights': constant_op.constant([2., 4., 6.], shape=(1, 3))
}
indicator_tensor = _transform_features(features, [indicator])[indicator]
with _initialized_session():
self.assertAllEqual([[6., 4., 2.]], indicator_tensor.eval())
def test_linear_model(self):
animal = fc.indicator_column(
fc.categorical_column_with_identity('animal', num_buckets=4))

4
tensorflow/stream_executor/device_description.h
View File

@ -82,7 +82,7 @@ class DeviceDescription {
// Returns the limit on the number of simultaneously resident blocks
// on a multiprocessor.
const uint64 blocks_per_core_limit() const { return blocks_per_core_limit_; }
uint64 blocks_per_core_limit() const { return blocks_per_core_limit_; }
// Returns the limit on the total number of threads that can be launched in a
// single block; i.e. the limit on x * y * z dimensions of a ThreadDim.
@ -141,7 +141,7 @@ class DeviceDescription {
uint64 device_memory_size() const { return device_memory_size_; }
// Returns the device's core clock rate in GHz.
const float clock_rate_ghz() const { return clock_rate_ghz_; }
float clock_rate_ghz() const { return clock_rate_ghz_; }
// Returns whether ECC is enabled.
bool ecc_enabled() const { return ecc_enabled_; }

2
tensorflow/stream_executor/kernel.h
View File

@ -302,7 +302,7 @@ class KernelArgIterator {
//
// Returns a default-constructed KernelArg if there is no next argument.
KernelArg next() {
KernelArg result;
KernelArg result = {};
if (!has_next()) {
return result;
} else if ((shmem_indices_iter_ != shmem_indices_end_) &&

5
tensorflow/tools/ci_build/update_version.py
View File

@ -276,8 +276,9 @@ def check_for_lingering_string(lingering_string):
"""Check for given lingering strings."""
formatted_string = lingering_string.replace(".", r"\.")
try:
linger_strs = subprocess.check_output(
['grep', '-rnoH', formatted_string, TF_SRC_DIR]).split("\n")
linger_str_output = subprocess.check_output(
["grep", "-rnoH", formatted_string, TF_SRC_DIR])
linger_strs = linger_str_output.decode("utf8").split("\n")
except subprocess.CalledProcessError:
linger_strs = []

1
tensorflow/tools/pip_package/BUILD
View File

@ -84,6 +84,7 @@ py_binary(
"//tensorflow/python/saved_model",
"//tensorflow/python:spectral_ops_test_util",
"//tensorflow/python/tools:tools_pip",
"//tensorflow/python/eager:eager_pip",
# These targets don't build on Windows yet. Exclude them for now.
# "//tensorflow/contrib/ndlstm",
# "//tensorflow/contrib/slim",

4
third_party/sqlite.BUILD vendored
View File

@ -2,9 +2,9 @@
# Sqlite3 library. Provides utilities for interacting
# with sqlite3 databases.
licenses(["notice"]) # BSD/MIT-like license
licenses(["unencumbered"]) # Public Domain
exports_files(["LICENSE"])
# exports_files(["LICENSE"])
cc_library(
name = "sqlite",