Merge changes from github.

END_PUBLIC

---
Commit daa67ad17 authored by Jonathan Hseu<vomjom@vomjom.net>
Committed by Frank Chen<frankchn@gmail.com>:
Remove unittest import (#11596)

---
Commit 491beb74c authored by A. Unique TensorFlower<gardener@tensorflow.org>
Committed by TensorFlower Gardener<gardener@tensorflow.org>:
BEGIN_PUBLIC
Automated g4 rollback of changelist 162423171

PiperOrigin-RevId: 162541442

CODEOWNERS

@@ -1,8 +1,9 @@
 # Where component owners are known, add them here.
 
+tensorflow/core/platform/windows/* @mrry
+tensorflow/java/* @asimshankar
 tensorflow/tensorboard/* @jart @dandelionmane
 tensorflow/tools/docs/* @markdaoust
-tensorflow/java/* @asimshankar
 
 # contrib
 
@@ -46,5 +47,6 @@ tensorflow/contrib/stateless/* @girving
 tensorflow/contrib/tensor_forest/* @gilberthendry @thomascolthurst
 tensorflow/contrib/testing/* @dandelionmane
 tensorflow/contrib/timeseries/* @allenlavoie
+tensorflow/contrib/tpu/* @frankchn @saeta @jhseu
 tensorflow/contrib/training/* @joel-shor @ebrevdo
 tensorflow/contrib/util/* @sherrym

configure

@@ -26,7 +26,7 @@ function is_windows() {
 }
 
 function is_ppc64le() {
-  [[ "${uname -m}" == "ppc64le" ]]
+  [[ "$(uname -m)" == "ppc64le" ]]
 }
 
 function sed_in_place() {
@@ -298,7 +298,7 @@ fi # TF_NEED_MKL
 
 ## Set up architecture-dependent optimization flags.
 if [ -z "$CC_OPT_FLAGS" ]; then
-  if [ is_ppc64le ]; then
+  if is_ppc64le; then
     # gcc on ppc64le does not support -march, use mcpu instead
     default_cc_opt_flags="-mcpu=native"
   else
@@ -492,6 +492,8 @@ while true; do
   if [ -z "$TF_CUDA_VERSION" ]; then
     read -p "Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 8.0]: " TF_CUDA_VERSION
   fi
+  # Set default CUDA version if not set
+  TF_CUDA_VERSION=${TF_CUDA_VERSION:-8.0}
 
   fromuser=""
   if [ -z "$CUDA_TOOLKIT_PATH" ]; then
@@ -545,11 +547,7 @@ while true; do
   CUDA_TOOLKIT_PATH=""
 done
 
-# Set default CUDA version if not set
-if [ -z "$TF_CUDA_VERSION" ]; then
-  TF_CUDA_VERSION="8.0"
-  export TF_CUDA_VERSION
-fi
+export TF_CUDA_VERSION
 write_action_env_to_bazelrc "TF_CUDA_VERSION" "$TF_CUDA_VERSION"
 
 # Set up which gcc nvcc should use as the host compiler
@@ -587,6 +585,8 @@ while true; do
   if [ -z "$TF_CUDNN_VERSION" ]; then
     read -p "Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 6.0]: " TF_CUDNN_VERSION
   fi
+  # Set default CUDNN version if not set
+  TF_CUDNN_VERSION=${TF_CUDNN_VERSION:-6}
 
   fromuser=""
   if [ -z "$CUDNN_INSTALL_PATH" ]; then
@@ -659,11 +659,7 @@ while true; do
   CUDNN_INSTALL_PATH=""
 done
 
-# Set default CUDNN version if not set
-if [ -z "$TF_CUDNN_VERSION" ]; then
-  TF_CUDNN_VERSION="6"
-  export TF_CUDNN_VERSION
-fi
+export TF_CUDNN_VERSION
 write_action_env_to_bazelrc "TF_CUDNN_VERSION" "$TF_CUDNN_VERSION"
 
 # Configure the compute capabilities that TensorFlow builds for.

tensorflow/cc/framework/gradients.cc

@@ -65,7 +65,7 @@ class SymbolicGradientBuilder {
   // gradients for the node associated with `src`.
   Status BackpropAlongEdge(const Output& dst_grad, const Output& src);
 
-  // Adds a node to the graph (returned in`grad`) that sums the in-bound
+  // Adds a node to the graph (returned in `grad`) that sums the in-bound
   // gradients to `src` (if there are more than one).
   Status SumGradients(const Output& src, Output* grad);
 

tensorflow/compiler/tf2xla/xla_op_registry.h

@@ -45,9 +45,10 @@ extern const char* const DEVICE_XLA_CPU;
 extern const char* const DEVICE_XLA_GPU;
 
 constexpr std::array<DataType, 2> kIntTypes = {{DT_INT32, DT_INT64}};
-constexpr std::array<DataType, 2> kFloatTypes = {{DT_FLOAT, DT_DOUBLE}};
-constexpr std::array<DataType, 4> kNumericTypes = {
-    {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE}};
+constexpr std::array<DataType, 3> kFloatTypes = {
+    {DT_HALF, DT_FLOAT, DT_DOUBLE}};
+constexpr std::array<DataType, 5> kNumericTypes = {
+    {DT_INT32, DT_INT64, DT_HALF, DT_FLOAT, DT_DOUBLE}};
 
 constexpr std::array<DataType, 5> kCpuAllTypes = {
     {DT_INT32, DT_INT64, DT_FLOAT, DT_DOUBLE, DT_BOOL}};

tensorflow/compiler/xla/util.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/xla/util.h"
 
+#include <numeric>
 #include <stdarg.h>
 #include <numeric>
 

tensorflow/contrib/cluster_resolver/python/training/cluster_resolver.py

@@ -75,8 +75,8 @@ class UnionClusterResolver(ClusterResolver):
 
   This class performs a union given two or more existing ClusterResolvers. It
   merges the underlying ClusterResolvers, and returns one unified ClusterSpec
-  when as_cluster_spec is called. The details of the merge function is
-  documented in the as_cluster_spec function.
+  when cluster_spec is called. The details of the merge function is
+  documented in the cluster_spec function.
   """
 
   def __init__(self, *args):

tensorflow/contrib/cmake/README.md

@@ -225,7 +225,7 @@ Step-by-step Windows build
 
    * `-Dtensorflow_ENABLE_GPU=(ON|OFF)`. Defaults to `OFF`. Include
      GPU support. If GPU is enabled you need to install the CUDA 8.0 Toolkit and CUDNN 5.1.
-     CMake will expect the location of CUDNN in -DCUDNN_HOME=path_you_unziped_cudnn.
+     CMake will expect the location of CUDNN in -DCUDNN_HOME=path_you_unzipped_cudnn.
 
    * `-Dtensorflow_BUILD_CC_TESTS=(ON|OFF)`. Defaults to `OFF`. This builds cc unit tests.
      There are many of them and building will take a few hours.

tensorflow/contrib/framework/python/ops/variables.py

@@ -416,7 +416,7 @@ def get_unique_variable(var_op_name):
   for candidate in candidates:
     if candidate.op.name == var_op_name:
       return candidate
-  raise ValueError('Variable %s does not uniquely identify a variable',
+  raise ValueError('Variable %s does not uniquely identify a variable' %
                    var_op_name)
 
 
@@ -444,7 +444,7 @@ def assign_from_values(var_names_to_values):
     var_value = var_names_to_values[var_name]
     var = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, var_name)
     if not var:
-      raise ValueError('Variable %s wasnt found', var_name)
+      raise ValueError('Variable %s wasn\'t found' % var_name)
     elif len(var) > 1:
       # tf.get_collection is just a filter on the prefix: find the exact match:
       found = False
@@ -455,7 +455,7 @@ def assign_from_values(var_names_to_values):
           break
 
       if not found:
-        raise ValueError('Variable %s doesnt uniquely identify a variable',
+        raise ValueError('Variable %s doesn\'t uniquely identify a variable' %
                          var_name)
     else:
       var = var[0]

tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py

@@ -35,7 +35,7 @@ def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
 
   Args:
     sparse_tensor: a `SparseTensor`.
-    mask: a 1D boolean dense`Tensor` whose length is equal to the 0th dimension
+    mask: a 1D boolean dense `Tensor` whose length is equal to the 0th dimension
       of `sparse_tensor`.
     name: optional name for this operation.
   Returns:

tensorflow/contrib/learn/python/learn/estimators/head.py

@@ -25,8 +25,7 @@ import six
 from tensorflow.contrib import framework as framework_lib
 from tensorflow.contrib import layers as layers_lib
 from tensorflow.contrib import lookup as lookup_lib
-# TODO(ptucker): Use tf.losses and tf.metrics.
-from tensorflow.contrib import losses as losses_lib
+# TODO(ptucker): Use tf.metrics.
 from tensorflow.contrib import metrics as metrics_lib
 from tensorflow.contrib.learn.python.learn.estimators import constants
 from tensorflow.contrib.learn.python.learn.estimators import model_fn
@@ -44,6 +43,7 @@ from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import weights_broadcast_ops
+from tensorflow.python.ops.losses import losses as losses_lib
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.summary import summary
 from tensorflow.python.training import training
@@ -1212,7 +1212,8 @@ class _BinarySvmHead(_SingleHead):
       with ops.name_scope(None, "hinge_loss", (logits, labels)) as name:
         with ops.control_dependencies((_assert_labels_rank(labels),)):
           labels = array_ops.reshape(labels, shape=(-1, 1))
-        loss = losses_lib.hinge_loss(logits=logits, labels=labels, scope=name)
+        loss = losses_lib.hinge_loss(labels=labels, logits=logits, scope=name,
+                                     reduction=losses_lib.Reduction.NONE)
         return _compute_weighted_loss(loss, weights)
 
     super(_BinarySvmHead, self).__init__(

tensorflow/contrib/makefile/build_all_ios.sh

@@ -18,7 +18,7 @@ set -e
 
 # Make sure we're on OS X.
 if [[ $(uname) != "Darwin" ]]; then
-    echo "ERROR: This makefile build requires OS X, which the current system "\
+    echo "ERROR: This makefile build requires macOS, which the current system "\
     "is not."
     exit 1
 fi
@@ -37,7 +37,9 @@ rm -rf tensorflow/contrib/makefile/downloads
 #
 #    ld: -bind_at_load and -bitcode_bundle (Xcode setting ENABLE_BITCODE=YES) cannot be used together
 #
-export MACOSX_DEPLOYMENT_TARGET="10.10"
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
 
 # Pull down the required versions of the frameworks we need.
 tensorflow/contrib/makefile/download_dependencies.sh
@@ -48,6 +50,5 @@ tensorflow/contrib/makefile/compile_ios_protobuf.sh
 # Build the iOS TensorFlow libraries.
 tensorflow/contrib/makefile/compile_ios_tensorflow.sh "-O3"
 
-# Creates a static universal library in 
+# Creates a static universal library in
 # tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a
-

tensorflow/contrib/makefile/compile_ios_protobuf.sh

@@ -15,9 +15,12 @@
 # ==============================================================================
 # Builds protobuf 3 for iOS.
 
-set -x
 set -e
 
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
+
 SCRIPT_DIR=$(dirname $0)
 source "${SCRIPT_DIR}/build_helper.subr"
 

tensorflow/contrib/makefile/compile_ios_tensorflow.sh

@@ -31,6 +31,10 @@ function less_than_required_version() {
   )
 }
 
+if [[ -n MACOSX_DEPLOYMENT_TARGET ]]; then
+    export MACOSX_DEPLOYMENT_TARGET=$(sw_vers -productVersion)
+fi
+
 ACTUAL_XCODE_VERSION=$(xcodebuild -version | head -n 1 | sed 's/Xcode //')
 REQUIRED_XCODE_VERSION=7.3.0
 if less_than_required_version $ACTUAL_XCODE_VERSION 7 3 0
@@ -44,7 +48,7 @@ LIBDIR=${GENDIR}lib
 LIB_PREFIX=libtensorflow-core
 
 make -j"${JOB_COUNT}" -f tensorflow/contrib/makefile/Makefile \
-TARGET=IOS IOS_ARCH=ARMV7 LIB_NAME=${LIB_PREFIX}-armv7.a OPTFLAGS="$1" 
+TARGET=IOS IOS_ARCH=ARMV7 LIB_NAME=${LIB_PREFIX}-armv7.a OPTFLAGS="$1"
 if [ $? -ne 0 ]
 then
   echo "armv7 compilation failed."

tensorflow/contrib/signal/python/ops/reconstruction_ops.py

@@ -37,7 +37,7 @@ def _shuffle_to_front(input_tensor, k):
     k: A scalar `Tensor` specifying how many indices to shuffle.
 
   Returns:
-    A tranposed version of `input_tensor` with `k` indices shuffled to the
+    A transposed version of `input_tensor` with `k` indices shuffled to the
     front.
 
   Raises:

tensorflow/contrib/slim/python/slim/evaluation.py

@@ -81,7 +81,7 @@ more summaries and call the evaluation_loop method:
 
   # Evaluate every 10 minutes:
   slim.evaluation_loop(
-      master='',
+      '',
       checkpoint_dir,
       logdir,
       num_evals=num_evals,

tensorflow/contrib/tfprof/README.md

@@ -1,9 +1,9 @@
 # tfprof: TensorFlow Profiler and Beyond
 
 <h1>Please use `tf.profiler.xxx` instead of `tf.contrib.tfprof.xxx`</h1>
-<h1>Full Document in tensorflow/core/profiler/README.md<h1>
+<h1>Full Document in <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/profiler/README.md">tensorflow/core/profiler/README.md</a><h1>
 
-###Features
+### Features
 
 * Profile model architectures
   * parameters, tensor shapes, float operations, device placement, etc.
@@ -16,7 +16,7 @@
   * operation configuration check
   * distributed runtime check (Not OSS)
 
-###Interfaces
+### Interfaces
 
 * Python API
 * Command Line

tensorflow/contrib/timeseries/python/timeseries/model.py

@@ -353,7 +353,7 @@ class SequentialTimeSeriesModel(TimeSeriesModel):
 
     Args:
       current_times: A [batch size] Tensor of times for each observation.
-      current_values: A [batch size] Tensor of values for each observaiton.
+      current_values: A [batch size] Tensor of values for each observation.
       state: Model state, updated to current_times.
       predictions: The outputs of _prediction_step
     Returns:

tensorflow/contrib/timeseries/python/timeseries/state_space_models/state_space_model.py

@@ -391,7 +391,7 @@ class StateSpaceModel(model.SequentialTimeSeriesModel):
 
     Args:
       current_times: A [batch size] Tensor for times for each observation.
-      current_values: A [batch size] Tensor of values for each observaiton.
+      current_values: A [batch size] Tensor of values for each observation.
       state: A tuple of (mean, covariance, previous_times) having shapes
           mean; [batch size x state dimension]
           covariance; [batch size x state dimension x state dimension]

tensorflow/core/BUILD

@@ -65,7 +65,7 @@ load(
     "full_path",
     "if_android",
     "if_ios",
-    "if_x86",
+    "if_linux_x86_64",
     "if_not_mobile",
     "if_not_windows",
     "tf_copts",
@@ -1379,7 +1379,7 @@ cc_library(
     name = "lib_hash_crc32c_accelerate_internal",
     srcs = ["lib/hash/crc32c_accelerate.cc"],
     # -msse4.2 enables the use of crc32c compiler builtins.
-    copts = tf_copts() + if_x86(["-msse4.2"]),
+    copts = tf_copts() + if_linux_x86_64(["-msse4.2"]),
 )
 
 cc_library(

tensorflow/core/framework/register_types.h

@@ -183,4 +183,18 @@ limitations under the License.
 #define TF_CALL_QUANTIZED_TYPES(m) \
   TF_CALL_qint8(m) TF_CALL_quint8(m) TF_CALL_qint32(m)
 
+#ifdef TENSORFLOW_SYCL_NO_DOUBLE
+#define TF_CALL_SYCL_double(m)
+#else  // TENSORFLOW_SYCL_NO_DOUBLE
+#define TF_CALL_SYCL_double(m) TF_CALL_double(m)
+#endif // TENSORFLOW_SYCL_NO_DOUBLE
+
+#ifdef __ANDROID_TYPES_SLIM__
+#define TF_CALL_SYCL_NUMBER_TYPES(m)  TF_CALL_float(m)
+#else  // __ANDROID_TYPES_SLIM__
+#define TF_CALL_SYCL_NUMBER_TYPES(m)    \
+    TF_CALL_float(m)                    \
+    TF_CALL_SYCL_double(m)
+#endif // __ANDROID_TYPES_SLIM__
+
 #endif  // TENSORFLOW_FRAMEWORK_REGISTER_TYPES_H_

tensorflow/core/kernels/batch_dataset_op.cc

@@ -31,7 +31,7 @@ class BatchDatasetOp : public UnaryDatasetOpKernel {
 
   void MakeDataset(OpKernelContext* ctx, DatasetBase* input,
                    DatasetBase** output) override {
-    int64 batch_size;
+    int64 batch_size = 0;
     OP_REQUIRES_OK(ctx,
                    ParseScalarArgument<int64>(ctx, "batch_size", &batch_size));
     OP_REQUIRES(

tensorflow/core/kernels/cwise_op_add_1.cc

@@ -36,7 +36,9 @@ REGISTER_KERNEL_BUILDER(Name("Add")
 
 
 #if TENSORFLOW_USE_SYCL
-REGISTER2(BinaryOp, SYCL, "Add", functor::add, float, double);
+#define REGISTER_KERNEL(type) REGISTER(BinaryOp, SYCL, "Add", functor::add, type);
+TF_CALL_SYCL_NUMBER_TYPES(REGISTER_KERNEL);
+
 REGISTER_KERNEL_BUILDER(Name("Add")
                             .Device(DEVICE_SYCL)
                             .HostMemory("x")

tensorflow/core/platform/windows/env.cc

@@ -82,22 +82,42 @@ class WindowsEnv : public Env {
     return new StdThread(thread_options, name, fn);
   }
 
+  static VOID CALLBACK SchedClosureCallback(PTP_CALLBACK_INSTANCE Instance,
+                                            PVOID Context, PTP_WORK Work) {
+    CloseThreadpoolWork(Work);
+    std::function<void()>* f = (std::function<void()>*)Context;
+    (*f)();
+    delete f;
+  }
   void SchedClosure(std::function<void()> closure) override {
-    // TODO(b/27290852): Spawning a new thread here is wasteful, but
-    // needed to deal with the fact that many `closure` functions are
-    // blocking in the current codebase.
-    std::thread closure_thread(closure);
-    closure_thread.detach();
+    PTP_WORK work = CreateThreadpoolWork(
+        SchedClosureCallback, new std::function<void()>(std::move(closure)),
+        nullptr);
+    SubmitThreadpoolWork(work);
+  }
+
+  static VOID CALLBACK SchedClosureAfterCallback(PTP_CALLBACK_INSTANCE Instance,
+                                                 PVOID Context,
+                                                 PTP_TIMER Timer) {
+    CloseThreadpoolTimer(Timer);
+    std::function<void()>* f = (std::function<void()>*)Context;
+    (*f)();
+    delete f;
   }
 
   void SchedClosureAfter(int64 micros, std::function<void()> closure) override {
-    // TODO(b/27290852): Consuming a thread here is wasteful, but this
-    // code is (currently) only used in the case where a step fails
-    // (AbortStep). This could be replaced by a timer thread
-    SchedClosure([this, micros, closure]() {
-      SleepForMicroseconds(micros);
-      closure();
-    });
+    PTP_TIMER timer = CreateThreadpoolTimer(
+        SchedClosureAfterCallback,
+        new std::function<void()>(std::move(closure)), nullptr);
+    // in 100 nanosecond units
+    FILETIME FileDueTime;
+    ULARGE_INTEGER ulDueTime;
+    // Negative indicates the amount of time to wait is relative to the current
+    // time.
+    ulDueTime.QuadPart = (ULONGLONG) - (10 * micros);
+    FileDueTime.dwHighDateTime = ulDueTime.HighPart;
+    FileDueTime.dwLowDateTime = ulDueTime.LowPart;
+    SetThreadpoolTimer(timer, &FileDueTime, 0, 0);
   }
 
   Status LoadLibrary(const char *library_filename, void** handle) override {

tensorflow/core/profiler/g3doc/python_api.md

@@ -24,7 +24,7 @@ opts = ProfileOptionBuilder(
     ).build()
 param_stats = tf.profiler.profile(
     tf.get_default_graph(),
-    cmd='code'
+    cmd='code',
     options=opts)
 
 # param_stats can be tensorflow.tfprof.GraphNodeProto or

tensorflow/docs_src/about/roadmap.md

@@ -5,7 +5,7 @@ TensorFlow is a fast moving project. In order for the community to better
 understand what the near future will bring, this document shares what we are
 working on internally. Many of these features were requested by the community,
 and we welcome
-[contributions](https://github.com/tensorflow/tensorflow/labels/contributions%20welcome).
+[contributions](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome).
 
 The features on this list are targeted for the next few months. At this point,
 we do not have timelines for these features.

tensorflow/docs_src/extend/adding_an_op.md

@@ -155,7 +155,7 @@ REGISTER_KERNEL_BUILDER(Name("ZeroOut").Device(DEVICE_CPU), ZeroOutOp);
 ### Multi-threaded CPU kernels
 
 To write a multi-threaded CPU kernel, the Shard function in
-[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/framework/work_sharder.h)
+[`work_sharder.h`](https://www.tensorflow.org/code/tensorflow/core/util/work_sharder.h)
 can be used. This function shards a computation function across the
 threads configured to be used for intra-op threading (see
 intra_op_parallelism_threads in

tensorflow/docs_src/get_started/export.md

@@ -168,7 +168,7 @@ and so determine which request types will be honored.
 > the corresponding `ExportOutput` entry.  The inputs are always those provided
 > by the `serving_input_receiver_fn`.
 > An inference request may specify the head by name.  One head must be named
-> using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/saved_model/signature_constants.py)
+> using [`signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY`](https://www.tensorflow.org/code/tensorflow/python/saved_model/signature_constants.py)
 > indicating which signature will be served when an inference request does not
 > specify one.
 
@@ -295,4 +295,3 @@ the different Tensorflow Serving APIs, selecting the signature by key, etc. -->
 
 <!-- TODO(soergel): document ExportStrategy here once Experiment moves
 from contrib to core. -->
-

tensorflow/docs_src/get_started/get_started.md

@@ -32,8 +32,8 @@ tensor's **rank** is its number of dimensions. Here are some examples of
 tensors:
 
 ```python
-3 # a rank 0 tensor; this is a scalar with shape []
-[1., 2., 3.] # a rank 1 tensor; this is a vector with shape [3]
+3 # a rank 0 tensor; a scalar with shape []
+[1., 2., 3.] # a rank 1 tensor; a vector with shape [3]
 [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
 [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]
 ```
@@ -181,7 +181,7 @@ initial value:
 W = tf.Variable([.3], dtype=tf.float32)
 b = tf.Variable([-.3], dtype=tf.float32)
 x = tf.placeholder(tf.float32)
-linear_model = W * x + b
+linear_model = W*x + b
 ```
 
 Constants are initialized when you call `tf.constant`, and their value can never
@@ -302,7 +302,7 @@ W = tf.Variable([.3], dtype=tf.float32)
 b = tf.Variable([-.3], dtype=tf.float32)
 # Model input and output
 x = tf.placeholder(tf.float32)
-linear_model = W * x + b
+linear_model = W*x + b
 y = tf.placeholder(tf.float32)
 
 # loss
@@ -330,9 +330,9 @@ When run, it produces
 W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11
 ```
 
-Notice that the loss is a very small number (very close to zero). If you run this
-program, your loss may not be the exact same because the model is initialized
-with pseudorandom values.
+Notice that the loss is a very small number (very close to zero). If you run 
+this program, your loss may not be exactly the same as the aforementioned loss 
+because the model is initialized with pseudorandom values.
 
 This more complicated program can still be visualized in TensorBoard
 ![TensorBoard final model visualization](https://www.tensorflow.org/images/getting_started_final.png)
@@ -426,7 +426,7 @@ def model_fn(features, labels, mode):
   # Build a linear model and predict values
   W = tf.get_variable("W", [1], dtype=tf.float64)
   b = tf.get_variable("b", [1], dtype=tf.float64)
-  y = W * features['x'] + b
+  y = W*features['x'] + b
   # Loss sub-graph
   loss = tf.reduce_sum(tf.square(y - labels))
   # Training sub-graph

tensorflow/docs_src/get_started/summaries_and_tensorboard.md

@@ -17,7 +17,7 @@ TensorBoard is fully configured, it looks like this:
 </div>
 
 This tutorial is intended to get you started with simple TensorBoard usage.
-There are other resources available as well! The [TensorBoard README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md)
+There are other resources available as well! The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard)
 has a lot more information on TensorBoard usage, including tips & tricks, and
 debugging information.
 
@@ -216,5 +216,4 @@ corner. Each tab represents a set of serialized data that can be visualized.
 For in depth information on how to use the *graph* tab to visualize your graph,
 see @{$graph_viz$TensorBoard: Graph Visualization}.
 
-For more usage information on TensorBoard in general, see the [TensorBoard
-README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md).
+For more usage information on TensorBoard in general, see the [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard).

tensorflow/docs_src/programmers_guide/datasets.md

@@ -447,7 +447,7 @@ dataset = tf.contrib.data.Dataset.from_tensor_slices(filenames)
 # * Filter out lines beginning with "#" (comments).
 dataset = dataset.flat_map(
     lambda filename: (
-        tf.contrib.data.Dataset.TextLineDataset(filename)
+        tf.contrib.data.TextLineDataset(filename)
         .skip(1)
         .filter(lambda line: tf.not_equal(tf.substr(line, 0, 1), "#"))))
 ```

tensorflow/docs_src/tutorials/image_retraining.md

@@ -137,7 +137,7 @@ Once TensorBoard is running, navigate your web browser to `localhost:6006` to vi
 
 The script will log TensorBoard summaries to `/tmp/retrain_logs` by default. You can change the directory with the `--summaries_dir` flag.
 
-The [TensorBoard README](https://www.tensorflow.org/code/tensorflow/tensorboard/README.md) has a lot more information on TensorBoard usage, including tips & tricks, and debugging information.
+The [TensorBoard's GitHub](https://github.com/tensorflow/tensorboard) has a lot more information on TensorBoard usage, including tips & tricks, and debugging information.
 
 ## Using the Retrained Model
 
@@ -337,7 +337,7 @@ the (much larger) training set.
 
 By default the script uses a pretrained version of the Inception v3 model
 architecture. This is a good place to start because it provides high accuracy
-results, but if you intend to deploy your model on mobile devices or other 
+results, but if you intend to deploy your model on mobile devices or other
 resource-constrained environments you may want to trade off a little accuracy
 for much smaller file sizes or faster speeds. To help with that, the
 [retrain.py script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py)

tensorflow/docs_src/tutorials/wide_and_deep.md

@@ -87,7 +87,7 @@ education = tf.feature_column.categorical_column_with_vocabulary_list(
         "Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
         "Preschool", "12th"
     ])
-tf.feature_column.categorical_column_with_vocabulary_list(
+marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
     "marital_status", [
         "Married-civ-spouse", "Divorced", "Married-spouse-absent",
         "Never-married", "Separated", "Married-AF-spouse", "Widowed"

tensorflow/examples/tutorials/word2vec/word2vec_basic.py

@@ -253,7 +253,7 @@ try:
   from sklearn.manifold import TSNE
   import matplotlib.pyplot as plt
 
-  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
+  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
   plot_only = 500
   low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
   labels = [reverse_dictionary[i] for i in xrange(plot_only)]

tensorflow/examples/udacity/5_word2vec.ipynb

@@ -806,7 +806,7 @@
       "source": [
         "num_points = 400\n",
         "\n",
-        "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
+        "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
         "two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
       ],
       "outputs": [],

tensorflow/java/BUILD

@@ -22,7 +22,10 @@ java_library(
 # .aar. At some point, might make sense for a .aar rule here instead.
 filegroup(
     name = "java_sources",
-    srcs = glob(["src/main/java/org/tensorflow/*.java"]),
+    srcs = glob([
+        "src/main/java/org/tensorflow/*.java",
+        "src/main/java/org/tensorflow/types/*.java",
+    ]),
     visibility = [
         "//tensorflow/contrib/android:__pkg__",
         "//tensorflow/java:__pkg__",

tensorflow/java/src/gen/perl/tftypes-runall.pl

@@ -0,0 +1,40 @@
+#!/usr/bin/perl
+#
+# Copyright 2017 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.
+# =============================================================================
+
+my $script = $0;
+my $dir = `dirname $script`;
+chomp $dir;
+my $gen = "$dir/..";
+my $tfjavasrc = "$gen/..";
+my $rsrc = "$gen/resources";
+my $root = "$tfjavasrc/main/java";
+my $pkg = "$root/org/tensorflow";
+
+sub locchk {
+    (my $f) = @_;
+    if (! -r $f) {
+        print STDERR "Script tftypes-runall seems to be located in the wrong place (could not find $f)\n";
+        exit 1;
+    }
+}
+&locchk("$gen");
+&locchk("$tfjavasrc/gen");
+&locchk("$dir/tftypes.pl");
+&locchk("$rsrc/tftypes.csv");
+
+system("perl $dir/tftypes.pl -t $rsrc/tftypes.csv $pkg/types");
+# system("perl $dir/tftypes.pl -c $rsrc/tftypes.csv $rsrc/Tensors.java.tmpl > $pkg/op/Tensors.java");

tensorflow/java/src/gen/perl/tftypes.pl

@@ -0,0 +1,157 @@
+#!/usr/bin/perl
+#
+# Copyright 2017 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.
+# =============================================================================
+
+use strict;
+
+my $copyright =
+'/* Copyright 2017 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.
+==============================================================================*/
+';
+
+my $count;
+
+my $option = '-t', my $template;
+
+sub usage {
+    print "Usage: tftypes [-ctdT] <type desc file> <tmpl file>\n\n"
+         ."This script generates parts of various .java files that depend on which"
+         ."TensorFlow types are supported by the Java API and how much. For each"
+         ."such .java file, there is a .tmpl file in the same source directory in"
+         ."which the strings \@TYPEINFO\@ and \@IMPORTS\@ are replaced with"
+         ."appropriate Java code. Output code is sent to standard output.\n\n";
+
+    print "Modulo putting in the correct directory names, it can be invoked as follows:\n";
+    print "tftypes -c tftypes.csv Tensors.java.tmpl > Tensors.java\n";
+    print "tftypes -t tftypes.csv <dir>                                   [outputs files to dir]\n";
+}
+
+if ($ARGV[0] =~ m/^-/) {
+    $option = shift;
+}
+my $typedesc = shift;
+my $tmpl = shift;
+
+my $dirname;
+
+if ($option eq '-t') {
+    $dirname = $tmpl;
+}
+
+open (TMPL, "<$tmpl") || die "Cannot open $tmpl for reading\n";
+
+my $text = do { local $/; <TMPL> };
+
+my %jtypecount;
+
+my $typeinfo, my $imports;
+
+open (TYPEDESC, $typedesc);
+
+my @info = ([]);
+
+while (<TYPEDESC>) {
+    chomp;
+    my $line = $_;
+    if ($line =~ m/^TF type/) { next }
+    $line =~ s/\r$//;
+    (my $name, my $jtype, my $creat, my $default, my $desc) =
+        split /,/, $line, 5;
+    $desc =~ s/^ *//g;
+    $desc =~ s/ *$//g;
+    $jtypecount{$jtype}++;
+    if ($jtypecount{$jtype} > 1) {
+# currently allowing Java types to stand for more than one TF type, but
+# may want to revisit this.
+#       print STDERR "Ambiguous Java type for $name : $jtype\n";
+#       exit 1
+    }
+
+    push @info, [$name, $jtype, $creat, $default, $desc];
+}
+
+for (my $i = 1; $i <= $#info; $i++) {
+    (my $name, my $jtype, my $creat, my $default, my $desc) =
+        @{$info[$i]};
+    my $tfname = "TF".$name;
+    my $ucname = uc $name;
+
+    if ($option eq '-t') {
+        if ($jtype eq '') { next }
+        # Generate class declarations
+        # print STDERR "Creating $dirname/$tfname.java\n";
+        open (CLASSFILE, ">$dirname/$tfname.java") || die "Can't open $tfname.java";
+        print CLASSFILE $copyright;
+        print CLASSFILE "// GENERATED FILE. To update, edit tftypes.pl instead.\n\n";
+
+        my $fulldesc = $desc;
+        if (substr($desc, 0, 1) =~ m/^[aeoiu8]$/i) {
+            $fulldesc = "an $desc"
+        } else {
+            $fulldesc = "a $desc"
+        }
+        print CLASSFILE  "package org.tensorflow.types;\n\n"
+                        ."import org.tensorflow.DataType;\n\n";
+        print CLASSFILE  "/** Represents $fulldesc. */\n"
+                        ."public class $tfname implements TFType {\n"
+                        ."  private $tfname() {}\n"
+                        ."  static {\n"
+                        ."    Types.typeCodes.put($tfname.class, DataType.$ucname);\n"
+                        ."  }\n";
+        if ($default ne '') {
+            print CLASSFILE
+                         "  static {\n"
+                        ."    Types.scalars.put($tfname.class, $default);\n"
+                        ."  }\n";
+        }
+        print CLASSFILE  "}\n";
+        close(CLASSFILE);
+    } elsif ($option eq '-c') {
+      # Generate creator declarations for Tensors.java
+      if ($jtype ne '' && $creat eq 'y') {
+        for (my $brackets = ''; length $brackets <= 12; $brackets .= '[]') {
+            $typeinfo .=
+                "  public static Tensor<$tfname> create($jtype$brackets data) {\n"
+               ."    return Tensor.create(data, $tfname.class);\n"
+               ."  }\n";
+        }
+      }
+      if ($text =~ m/\b$tfname\b/ || $creat eq 'y') {
+            $imports .= "import org.tensorflow.types.$tfname;\n";
+      }
+    }
+}
+
+if ($option ne '-t') {
+  print "// GENERATED FILE. Edits to this file will be lost -- edit $tmpl instead.\n";
+
+  $text =~ s/\@TYPEINFO\@/$typeinfo/;
+  $text =~ s/\@IMPORTS\@/$imports/;
+
+  print $text;
+}

tensorflow/java/src/gen/resources/tftypes.csv

@@ -0,0 +1,21 @@
+TF type,Java type,Creator?,Zero value,Description
+Float,float,y,0f,32-bit single precision floating point number
+Double,double,y,0.0,64-bit double precision floating point number
+Int32,int,y,0,32-bit signed integer
+UInt8,byte,n,(byte)0,8-bit unsigned integer
+Int16,,n,(short)0,16-bit signed integer
+Int8,,n,(byte)0,8-bit signed integer
+String,byte,n,,arbitrary sequence of bytes
+Complex64,,n,,single-precision complex number
+Int64,long,y,0L,64-bit signed integer
+Bool,boolean,y,false,boolean
+QInt8,,n,,quantized int8
+QUInt8,,n,,quantized uint8
+QInt32,,n,,quantized int32
+BFloat16,,n,,float32 truncated to 16 bits. Only for cast ops.
+QInt16,,n,,quantized int16
+QUInt16,,n,,quantized uint16
+UInt16,,n,,16-bit unsigned integer
+Complex128,,n,,double-precision complex number
+Half,,n,,
+Resource,,n,,

tensorflow/java/src/main/java/org/tensorflow/DataType.java

@@ -53,12 +53,14 @@ public enum DataType {
   int c() {
     return value;
   }
+  
+  // Cached to avoid copying it
+  final private static DataType[] values = values();
 
   static DataType fromC(int c) {
-    for (DataType t : DataType.values()) {
-      if (t.c() == c) {
+    for (DataType t : values) {
+      if (t.value == c)
         return t;
-      }
     }
     throw new IllegalArgumentException(
         "DataType " + c + " is not recognized in Java (version " + TensorFlow.version() + ")");

tensorflow/java/src/main/java/org/tensorflow/types/TFBool.java

@@ -0,0 +1,30 @@
+/* Copyright 2017 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.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a boolean. */
+public class TFBool implements TFType {
+  private TFBool() {}
+  static {
+    Types.typeCodes.put(TFBool.class, DataType.BOOL);
+  }
+  static {
+    Types.scalars.put(TFBool.class, false);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/TFDouble.java

@@ -0,0 +1,30 @@
+/* Copyright 2017 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.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 64-bit double precision floating point number. */
+public class TFDouble implements TFType {
+  private TFDouble() {}
+  static {
+    Types.typeCodes.put(TFDouble.class, DataType.DOUBLE);
+  }
+  static {
+    Types.scalars.put(TFDouble.class, 0.0);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/TFFloat.java

@@ -0,0 +1,30 @@
+/* Copyright 2017 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.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 32-bit single precision floating point number. */
+public class TFFloat implements TFType {
+  private TFFloat() {}
+  static {
+    Types.typeCodes.put(TFFloat.class, DataType.FLOAT);
+  }
+  static {
+    Types.scalars.put(TFFloat.class, 0f);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/TFInt32.java

@@ -0,0 +1,30 @@
+/* Copyright 2017 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.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 32-bit signed integer. */
+public class TFInt32 implements TFType {
+  private TFInt32() {}
+  static {
+    Types.typeCodes.put(TFInt32.class, DataType.INT32);
+  }
+  static {
+    Types.scalars.put(TFInt32.class, 0);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/TFInt64.java

@@ -0,0 +1,30 @@
+/* Copyright 2017 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.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents a 64-bit signed integer. */
+public class TFInt64 implements TFType {
+  private TFInt64() {}
+  static {
+    Types.typeCodes.put(TFInt64.class, DataType.INT64);
+  }
+  static {
+    Types.scalars.put(TFInt64.class, 0L);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/TFString.java

@@ -0,0 +1,27 @@
+/* Copyright 2017 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.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents an arbitrary sequence of bytes. */
+public class TFString implements TFType {
+  private TFString() {}
+  static {
+    Types.typeCodes.put(TFString.class, DataType.STRING);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/TFType.java

@@ -0,0 +1,20 @@
+/* Copyright 2017 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.
+==============================================================================*/
+package org.tensorflow.types;
+
+/**
+ * A marker interface for classes representing TensorFlow types.
+ */
+public interface TFType {}

tensorflow/java/src/main/java/org/tensorflow/types/TFUInt8.java

@@ -0,0 +1,30 @@
+/* Copyright 2017 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.
+==============================================================================*/
+// GENERATED FILE. To update, edit tftypes.pl instead.
+
+package org.tensorflow.types;
+
+import org.tensorflow.DataType;
+
+/** Represents an 8-bit unsigned integer. */
+public class TFUInt8 implements TFType {
+  private TFUInt8() {}
+  static {
+    Types.typeCodes.put(TFUInt8.class, DataType.UINT8);
+  }
+  static {
+    Types.scalars.put(TFUInt8.class, (byte)0);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/Types.java

@@ -0,0 +1,52 @@
+/* Copyright 2017 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.
+==============================================================================*/
+package org.tensorflow.types;
+
+import java.util.HashMap;
+import java.util.Map;
+import org.tensorflow.DataType;
+
+/**
+ * Utility class for managing the representation of TensorFlow types as Java
+ * types. For each TensorFlow type (e.g., int32), there is a corresponding Java
+ * type (e.g., TFInt32) that represents it at compile time and a corresponding
+ * class object (e.g., TFInt32.class) that represents it at run time. There is
+ * also an enumeration value in DataType that can be used to represent the
+ * type, though that should rarely be required.
+ */
+public class Types {
+
+  private Types() {} // not instantiable
+
+  static final Map<Class<?>, DataType> typeCodes = new HashMap<>();
+
+  /** Returns the DataType value corresponding to a TensorFlow type class. */
+  public static DataType dataType(Class<? extends TFType> c) {
+    DataType dtype = typeCodes.get(c);
+    if (dtype == null) {
+      throw new IllegalArgumentException("" + c + " is not a TensorFlow type.");
+    }
+    return dtype;
+  }
+
+  static final Map<Class<?>, Object> scalars = new HashMap<>();
+
+  /** Returns the zero value of type described by {@code c}, or null if
+   *  the type (e.g., string) is not numeric and therefore has no zero value.
+   */
+  public static Object zeroValue(Class<? extends TFType> c) {
+    return scalars.get(c);
+  }
+}

tensorflow/java/src/main/java/org/tensorflow/types/package-info.java

@@ -0,0 +1,27 @@
+/* Copyright 2017 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.
+==============================================================================*/
+
+/**
+ * Defines classes that represent TensorFlow data types. For each possible data type
+ * that can be used in a tensor, there is a corresponding class in this package that
+ * is used to represent it. For example, the TensorFlow int32 type is represented by
+ * the type TFInt32 and by the class object TFInt32.class. The former is used to
+ * support compile-time checking of tensor data types and the latter is used for
+ * run-time checking of data types. All such classes implement the TFType interface.
+ * TensorFlow data types are also separately represented by the DataType enum, with
+ * one enum value per data type. The enum representation should rarely be needed, but
+ * the Types class can be used to obtain it from the class object representation.
+ */
+package org.tensorflow.types;

tensorflow/java/src/test/java/org/tensorflow/OperationTest.java

@@ -24,6 +24,7 @@ import static org.junit.Assert.fail;
 import java.util.Arrays;
 import java.util.HashSet;
 import java.util.Set;
+
 import org.junit.Test;
 import org.junit.runner.RunWith;
 import org.junit.runners.JUnit4;

tensorflow/python/debug/lib/debug_gradients.py

@@ -303,7 +303,7 @@ class GradientsDebugger(object):
     """Register the gradient tensor for an x-tensor.
 
     Args:
-      x_tensor_name: (`str`) the name of the the independent `tf.Tensor`, i.e.,
+      x_tensor_name: (`str`) the name of the independent `tf.Tensor`, i.e.,
         the tensor on the denominator of the differentiation.
       gradient_tensor: the gradient `tf.Tensor`.
     """

tensorflow/python/estimator/model_fn.py

@@ -78,7 +78,7 @@ class EstimatorSpec(
 
     Depending on the value of `mode`, different arguments are required. Namely
     * For `mode == ModeKeys.TRAIN`: required fields are `loss` and `train_op`.
-    * For `mode == ModeKeys.EVAL`: required field is`loss`.
+    * For `mode == ModeKeys.EVAL`: required field is `loss`.
     * For `mode == ModeKeys.PREDICT`: required fields are `predictions`.
 
     model_fn can populate all arguments independent of mode. In this case, some

tensorflow/python/framework/common_shapes.py

@@ -617,7 +617,7 @@ def call_cpp_shape_fn(op, require_shape_fn=True):
 
 def _call_cpp_shape_fn_impl(
     op, input_tensors_needed, input_tensors_as_shapes_needed, require_shape_fn):
-  """Core implementaton of call_cpp_shape_fn."""
+  """Core implementation of call_cpp_shape_fn."""
   graph_def_version = op.graph.graph_def_versions.producer
   node_def_str = op.node_def.SerializeToString()
 

tensorflow/python/kernel_tests/argmax_op_test.py

@@ -29,12 +29,12 @@ class ArgMaxTest(test.TestCase):
   def _testArg(self,
                method,
                x,
-               dimension,
+               axis,
                expected_values,
                use_gpu=False,
                expected_err_re=None):
     with self.test_session(use_gpu=use_gpu):
-      ans = method(x, dimension=dimension)
+      ans = method(x, axis=axis)
       if expected_err_re is None:
         tf_ans = ans.eval()
         # Defaults to int64 output.
@@ -48,27 +48,26 @@ class ArgMaxTest(test.TestCase):
   def _testBothArg(self,
                    method,
                    x,
-                   dimension,
+                   axis,
                    expected_values,
                    expected_err_re=None):
-    self._testArg(method, x, dimension, expected_values, True, expected_err_re)
-    self._testArg(method, x, dimension, expected_values, False, expected_err_re)
+    self._testArg(method, x, axis, expected_values, True, expected_err_re)
+    self._testArg(method, x, axis, expected_values, False, expected_err_re)
 
   def _testBasic(self, dtype):
     x = np.asarray(100 * np.random.randn(200), dtype=dtype)
 
-    # Check that argmin and argmax match numpy along the primary
-    # dimension
+    # Check that argmin and argmax match numpy along the primary axis
     self._testBothArg(math_ops.argmax, x, 0, x.argmax())
     self._testBothArg(math_ops.argmin, x, 0, x.argmin())
 
   def _testDim(self, dtype):
     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, 5):
-      self._testBothArg(math_ops.argmax, x, dim, x.argmax(dim))
-      self._testBothArg(math_ops.argmin, x, dim, x.argmin(dim))
+    # Check that argmin and argmax match numpy along all axes
+    for axis in range(-5, 5):
+      self._testBothArg(math_ops.argmax, x, axis, x.argmax(axis))
+      self._testBothArg(math_ops.argmin, x, axis, x.argmin(axis))
 
   def testFloat(self):
     self._testBasic(np.float32)
@@ -78,7 +77,7 @@ class ArgMaxTest(test.TestCase):
     x = np.asarray(100 * np.random.randn(200), dtype=np.float32)
     expected_values = x.argmax()
     with self.test_session(use_gpu=True):
-      ans = math_ops.argmax(x, dimension=0, output_type=dtypes.int32)
+      ans = math_ops.argmax(x, axis=0, output_type=dtypes.int32)
       tf_ans = ans.eval()
       self.assertEqual(np.int32, tf_ans.dtype)
       # The values are equal when comparing int32 to int64 because
@@ -86,7 +85,7 @@ class ArgMaxTest(test.TestCase):
       self.assertAllEqual(tf_ans, expected_values)
     expected_values = x.argmin()
     with self.test_session(use_gpu=True):
-      ans = math_ops.argmin(x, dimension=0, output_type=dtypes.int32)
+      ans = math_ops.argmin(x, axis=0, output_type=dtypes.int32)
       tf_ans = ans.eval()
       self.assertEqual(np.int32, tf_ans.dtype)
       self.assertAllEqual(tf_ans, expected_values)

tensorflow/python/layers/base.py

@@ -537,7 +537,7 @@ class Layer(object):
         if x.get_shape().ndims is None:
           raise ValueError('Input ' + str(input_index) + ' of layer ' +
                            self.name + ' is incompatible with the layer: '
-                           'its rank is undefined, by the layer requires a '
+                           'its rank is undefined, but the layer requires a '
                            'defined rank.')
 
       # Check ndim.

tensorflow/python/ops/data_flow_ops.py

@@ -512,8 +512,9 @@ class QueueBase(object):
     the given queue. Subsequent `enqueue` and `enqueue_many`
     operations will fail. Subsequent `dequeue` and `dequeue_many`
     operations will continue to succeed if sufficient elements remain
-    in the queue. Subsequent `dequeue` and `dequeue_many` operations
-    that would block will fail immediately.
+    in the queue. Subsequently dequeue and dequeue_many operations 
+    that would otherwise block waiting for more elements (if close 
+    hadn't been called) will now fail immediately.
 
     If `cancel_pending_enqueues` is `True`, all pending requests will also
     be canceled.

tensorflow/python/ops/sparse_ops.py

@@ -1110,7 +1110,7 @@ def sparse_merge(sp_ids, sp_values, vocab_size, name=None,
   Args:
     sp_ids: A single `SparseTensor` with `values` property of type `int32`
       or `int64` or a Python list of such `SparseTensor`s or a list thereof.
-    sp_values: A`SparseTensor` of any type.
+    sp_values: A `SparseTensor` of any type.
     vocab_size: A scalar `int64` Tensor (or Python int) containing the new size
       of the last dimension, `all(0 <= sp_ids.values < vocab_size)`.
       Or a list thereof with `all(0 <= sp_ids[i].values < vocab_size[i])` for

tensorflow/python/tools/freeze_graph.py

@@ -115,10 +115,15 @@ def freeze_graph_with_def_protos(
         output_node_names.split(","),
         variable_names_blacklist=variable_names_blacklist)
 
-  with gfile.GFile(output_graph, "wb") as f:
-    f.write(output_graph_def.SerializeToString())
+  # Write GraphDef to file if output path has been given.
+  if output_graph:
+    with gfile.GFile(output_graph, "wb") as f:
+      f.write(output_graph_def.SerializeToString())
+
   print("%d ops in the final graph." % len(output_graph_def.node))
 
+  return output_graph_def
+
 
 def _parse_input_graph_proto(input_graph, input_binary):
   """Parser input tensorflow graph into GraphDef proto."""

tensorflow/python/tools/import_pb_to_tensorboard.py

@@ -64,11 +64,13 @@ if __name__ == "__main__":
       "--model_dir",
       type=str,
       default="",
+      required=True,
       help="The location of the protobuf (\'pb\') model to visualize.")
   parser.add_argument(
       "--log_dir",
       type=str,
       default="",
+      required=True,
       help="The location for the Tensorboard log to begin visualization from.")
   FLAGS, unparsed = parser.parse_known_args()
   app.run(main=main, argv=[sys.argv[0]] + unparsed)

tensorflow/python/tools/optimize_for_inference_test.py

@@ -271,7 +271,8 @@ class OptimizeForInferenceTest(test.TestCase):
 
     for node in optimized_graph_def.node:
       self.assertNotEqual("Conv2D", node.op)
-      self.assertNotEqual("ResizeBilinear", node.op)
+      self.assertNotEqual("MirrorPad", node.op)
+      
 
   def testFusePadAndConv(self):
     with self.test_session() as sess:
@@ -299,7 +300,7 @@ class OptimizeForInferenceTest(test.TestCase):
 
     for node in optimized_graph_def.node:
       self.assertNotEqual("Conv2D", node.op)
-      self.assertNotEqual("MirrorPad", node.op)
+      self.assertNotEqual("ResizeBilinear", node.op)
 
 
 if __name__ == "__main__":

tensorflow/tensorflow.bzl

@@ -117,11 +117,9 @@ def if_not_windows(a):
   })
 
 
-def if_x86(a):
+def if_linux_x86_64(a):
   return select({
       clean_dep("//tensorflow:linux_x86_64"): a,
-      clean_dep("//tensorflow:windows"): a,
-      clean_dep("//tensorflow:windows_msvc"): a,
       "//conditions:default": [],
   })
 
@@ -152,7 +150,7 @@ def tf_copts():
       "-Wno-sign-compare",
       "-fno-exceptions",
   ]) + if_cuda(["-DGOOGLE_CUDA=1"]) + if_mkl(["-DINTEL_MKL=1", "-fopenmp",]) + if_android_arm(
-      ["-mfpu=neon"]) + if_x86(["-msse3"]) + select({
+      ["-mfpu=neon"]) + if_linux_x86_64(["-msse3"]) + select({
           clean_dep("//tensorflow:android"): [
               "-std=c++11",
               "-DTF_LEAN_BINARY",

tensorflow/tools/ci_build/ci_sanity.sh

@@ -418,6 +418,10 @@ do_pip_smoke_test() {
     "The pip smoke test failed."
 }
 
+do_code_link_check() {
+  tensorflow/tools/ci_build/code_link_check.sh
+}
+
 do_check_load_py_test() {
   BUILD_CMD="bazel build //tensorflow/tools/pip_package:check_load_py_test"
   ${BUILD_CMD}
@@ -431,8 +435,8 @@ do_check_load_py_test() {
 }
 
 # Supply all sanity step commands and descriptions
-SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test")
-SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test")
+SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_buildifier" "do_bazel_nobuild" "do_pip_package_licenses_check" "do_lib_package_licenses_check" "do_java_package_licenses_check" "do_pip_smoke_test" "do_check_load_py_test" "do_code_link_check")
+SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test" "Code Link Check: Check there are no broken links")
 
 INCREMENTAL_FLAG=""
 

tensorflow/tools/ci_build/code_link_check.sh

@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+# Copyright 2017 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.
+# ==============================================================================
+
+# please run this at root directory of tensorflow
+success=1
+
+for i in `grep -onI https://www.tensorflow.org/code/\[a-zA-Z0-9/._-\]\* -r tensorflow`
+do
+  filename=`echo $i|awk -F: '{print $1}'`
+  linenumber=`echo $i|awk -F: '{print $2}'`
+  target=`echo $i|awk -F: '{print $4}'|tail -c +27`
+
+  # skip files in tensorflow/models
+  if [[ $target == tensorflow_models/* ]] ; then
+    continue
+  fi
+
+  if [ ! -f $target ] && [ ! -d $target ]; then
+    success=0
+    echo Broken link $target at line $linenumber of file $filename
+  fi
+done
+
+if [ $success == 0 ]; then
+  echo Code link check fails.
+  exit 1
+fi
+
+echo Code link check success.

tensorflow/tools/ci_build/install/install_golang.sh

@@ -16,7 +16,7 @@
 
 set -ex
 
-GOLANG_URL="https://storage.googleapis.com/golang/go1.7.5.linux-amd64.tar.gz"
+GOLANG_URL="https://storage.googleapis.com/golang/go1.8.3.linux-amd64.tar.gz"
 
 sudo mkdir -p /usr/local
 wget -q -O - "${GOLANG_URL}" | sudo tar -C /usr/local -xz

tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh

@@ -60,7 +60,7 @@ reinstall_tensorflow_pip ${PIP_NAME}
 
 # Define no_tensorflow_py_deps=true so that every py_test has no deps anymore,
 # which will result testing system installed tensorflow
-# GPU tests are very flaky when running concurently, so set local_test_jobs=1
+# GPU tests are very flaky when running concurrently, so set local_test_jobs=1
 bazel test -c opt --config=win-cuda $BUILD_OPTS -k --test_output=errors \
   --define=no_tensorflow_py_deps=true --test_lang_filters=py \
   --test_tag_filters=-no_pip,-no_windows,-no_windows_gpu \

tensorflow/workspace.bzl

@@ -205,11 +205,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""):
   native.new_http_archive(
       name = "farmhash_archive",
       urls = [
-          "http://mirror.bazel.build/github.com/google/farmhash/archive/92e897b282426729f4724d91a637596c7e2fe28f.zip",
-          "https://github.com/google/farmhash/archive/92e897b282426729f4724d91a637596c7e2fe28f.zip",
+          "http://mirror.bazel.build/github.com/google/farmhash/archive/23eecfbe7e84ebf2e229bd02248f431c36e12f1a.zip",
+          "https://github.com/google/farmhash/archive/23eecfbe7e84ebf2e229bd02248f431c36e12f1a.zip",
       ],
-      sha256 = "4c626d1f306bda2c6804ab955892f803f5245f4dcaecb4979dc08b091256da54",
-      strip_prefix = "farmhash-92e897b282426729f4724d91a637596c7e2fe28f",
+      sha256 = "55215f8cd3ddbe9781f6fe5cc228731d6dcc8301b6191c6d420034c3fff1cb8d",
+      strip_prefix = "farmhash-23eecfbe7e84ebf2e229bd02248f431c36e12f1a",
       build_file = str(Label("//third_party:farmhash.BUILD")),
   )
 

tools/bazel.rc

@@ -11,6 +11,9 @@ build:mkl --define=using_mkl=true
 build:sycl --crosstool_top=@local_config_sycl//crosstool:toolchain
 build:sycl --define=using_sycl=true
 
+build:sycl_nodouble --crosstool_top=@local_config_sycl//crosstool:toolchain
+build:sycl_nodouble --define=using_sycl=true --cxxopt -DTENSORFLOW_SYCL_NO_DOUBLE
+
 build:sycl_asan --crosstool_top=@local_config_sycl//crosstool:toolchain
 build:sycl_asan --define=using_sycl=true --copt -fno-omit-frame-pointer --copt -fsanitize-coverage=3 --copt -DGPR_NO_DIRECT_SYSCALLS --linkopt -fPIC --linkopt -fsanitize=address