diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-11-27 06:29:45 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-11-27 06:33:15 -0800 |
| commit | 191825e63f341a4e7777b85254f616e541000d5c (patch) | |
| tree | 55e7a384e6dcea2e154a5419b5dc05326fb20c8b | |
| parent | a264269f523467ac018708a647eab02c1f1010fe (diff) | |
Delete trailing whitespace
PiperOrigin-RevId: 177008504
75 files changed, 203 insertions, 203 deletions
diff --git a/RELEASE.md b/RELEASE.md index d8db1f7200..e04bd3fc50 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -494,7 +494,7 @@ answered questions, and were part of inspiring discussions. This release contains contributions from many people at Google, as well as: A. Besir Kurtulmus, Adal Chiriliuc, @akash, Alec-Desouza, Alex Rothberg, Alex -Sergeev, Alexander Heinecke, Allen Guo, Andreas Madsen, Ankesh Anand, Anton +Sergeev, Alexander Heinecke, Allen Guo, Andreas Madsen, Ankesh Anand, Anton Loss, @Aravind, @Arie, Ashutosh Das, AuréLien Geron, Bairen Yi, @bakunyo, Ben Visser, Brady Zhou, Calpa Liu, Changming Sun, Chih Cheng Liang, Christopher Berner, Clark Zinzow, @Conchylicultor, Dan Ellis, Dan J, Dan Jarvis, Daniel diff --git a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc index a574123d6b..96981534d5 100644 --- a/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc +++ b/tensorflow/compiler/xla/service/gpu/llvm_gpu_backend/gpu_backend_lib.cc @@ -77,7 +77,7 @@ static string GetLibdeviceFilename(const string& libdevice_dir_path, // Since CUDA 9.0, all GPU versions are included in a single file const char* unified_libdevice_filename = "libdevice.10.bc"; std::vector<string> unified_libdevice_files; - const tensorflow::Status status = + const tensorflow::Status status = tensorflow::Env::Default()->GetMatchingPaths( tensorflow::io::JoinPath(libdevice_dir_path, unified_libdevice_filename), &unified_libdevice_files); diff --git a/tensorflow/contrib/android/cmake/README.md b/tensorflow/contrib/android/cmake/README.md index 6f19b657fe..934b58c724 100644 --- a/tensorflow/contrib/android/cmake/README.md +++ b/tensorflow/contrib/android/cmake/README.md @@ -14,7 +14,7 @@ Add TensorFlow-Android-Inference as a dependency of your Android application ``` include ':TensorFlow-Android-Inference' -findProject(":TensorFlow-Android-Inference").projectDir = +findProject(":TensorFlow-Android-Inference").projectDir = new File("${/path/to/tensorflow_repo}/contrib/android/cmake") ``` diff --git a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java index 1f423a7a5b..dc5b9fb887 100644 --- a/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java +++ b/tensorflow/contrib/android/java/org/tensorflow/contrib/android/TensorFlowInferenceInterface.java @@ -160,7 +160,7 @@ public class TensorFlowInferenceInterface { throw new RuntimeException("Failed to load model from the input stream", e); } } - + /* * Construct a TensorFlowInferenceInterface with provided Graph * @@ -168,7 +168,7 @@ public class TensorFlowInferenceInterface { */ public TensorFlowInferenceInterface(Graph g) { prepareNativeRuntime(); - + // modelName is redundant here, here is for // avoiding error in initialization as modelName is marked final. this.modelName = ""; @@ -290,7 +290,7 @@ public class TensorFlowInferenceInterface { */ public void feed(String inputName, boolean[] src, long... dims) { byte[] b = new byte[src.length]; - + for (int i = 0; i < src.length; i++) { b[i] = src[i] ? (byte) 1 : (byte) 0; } diff --git a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc index b31b882fa1..e9b79a066d 100644 --- a/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc +++ b/tensorflow/contrib/cloud/kernels/bigquery_table_accessor_test.cc @@ -421,7 +421,7 @@ TEST_F(BigQueryTableAccessorTest, MultiplePagesTest) { TF_EXPECT_OK(accessor_->ReadRow(&row_id, &example)); EXPECT_EQ(3, row_id); EXPECT_TRUE(accessor_->Done()); - + Example expected_example; ASSERT_TRUE(protobuf::TextFormat::ParseFromString(kTestExampleProtoWithNulls, &expected_example)); diff --git a/tensorflow/contrib/cmake/tf_grappler.cmake b/tensorflow/contrib/cmake/tf_grappler.cmake index a7841c98e8..410490531a 100644 --- a/tensorflow/contrib/cmake/tf_grappler.cmake +++ b/tensorflow/contrib/cmake/tf_grappler.cmake @@ -23,7 +23,7 @@ file(GLOB tf_grappler_srcs "${tensorflow_source_dir}/tensorflow/python/grappler/model_analyzer.cc" "${tensorflow_source_dir}/tensorflow/python/grappler/model_analyzer.h" ) - + add_library(tf_grappler OBJECT ${tf_grappler_srcs}) add_dependencies(tf_grappler tf_core_cpu)
\ No newline at end of file diff --git a/tensorflow/contrib/cmake/tf_shared_lib.cmake b/tensorflow/contrib/cmake/tf_shared_lib.cmake index 3e3fe0cdfa..dcedabb333 100644 --- a/tensorflow/contrib/cmake/tf_shared_lib.cmake +++ b/tensorflow/contrib/cmake/tf_shared_lib.cmake @@ -45,7 +45,7 @@ if(WIN32) $<TARGET_FILE:tensorflow_static> $<TARGET_FILE:tf_protos_cc> ) - + set(tensorflow_deffile "${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_BUILD_TYPE}/tensorflow.def") set_source_files_properties(${tensorflow_deffile} PROPERTIES GENERATED TRUE) diff --git a/tensorflow/contrib/cmake/tf_stream_executor.cmake b/tensorflow/contrib/cmake/tf_stream_executor.cmake index 8d95f0d3e8..91ca33f4c4 100644 --- a/tensorflow/contrib/cmake/tf_stream_executor.cmake +++ b/tensorflow/contrib/cmake/tf_stream_executor.cmake @@ -61,18 +61,18 @@ file(GLOB tf_stream_executor_srcs "${tensorflow_source_dir}/tensorflow/stream_executor/platform/default/*.h" ) -if (tensorflow_ENABLE_GPU) +if (tensorflow_ENABLE_GPU) file(GLOB tf_stream_executor_gpu_srcs "${tensorflow_source_dir}/tensorflow/stream_executor/cuda/*.cc" ) list(APPEND tf_stream_executor_srcs ${tf_stream_executor_gpu_srcs}) -endif() +endif() #file(GLOB_RECURSE tf_stream_executor_test_srcs # "${tensorflow_source_dir}/tensorflow/stream_executor/*_test.cc" # "${tensorflow_source_dir}/tensorflow/stream_executor/*_test.h" #) -#list(REMOVE_ITEM tf_stream_executor_srcs ${tf_stream_executor_test_srcs}) +#list(REMOVE_ITEM tf_stream_executor_srcs ${tf_stream_executor_test_srcs}) if (NOT WIN32) set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -lgomp") diff --git a/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc b/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc index 2b67992138..f8b56ab1c5 100755 --- a/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc +++ b/tensorflow/contrib/image/ops/single_image_random_dot_stereograms_ops.cc @@ -40,7 +40,7 @@ REGISTER_OP("SingleImageRandomDotStereograms") .Doc(R"doc( Outputs a single image random dot stereogram for export via encode_PNG/JPG OP. -Given the 2-D tensor 'depth_values' with encoded Z values, this operation will +Given the 2-D tensor 'depth_values' with encoded Z values, this operation will encode 3-D data into a 2-D image. The output of this Op is suitable for the encode_PNG/JPG ops. Be careful with image compression as this may corrupt the encode 3-D data witin the image. @@ -68,14 +68,14 @@ with open('picture_out.png', 'wb') as f: f.write(png) ``` -depth_values: Z values of data to encode into 'output_data_window' window, +depth_values: Z values of data to encode into 'output_data_window' window, lower values are further away {0.0 floor(far), 1.0 ceiling(near) after normalization}, must be 2-D tensor hidden_surface_removal: Activate hidden surface removal convergence_dots_size: Black dot size in pixels to help view converge image, drawn on bottom of image dots_per_inch: Output device in dots/inch eye_separation: Separation between eyes in inches mu: Depth of field, Fraction of viewing distance (eg. 1/3 = .3333) -normalize: Normalize input data to [0.0, 1.0] +normalize: Normalize input data to [0.0, 1.0] normalize_max: Fix MAX value for Normalization - if < MIN, autoscale normalize_min: Fix MIN value for Normalization - if > MAX, autoscale border_level: Value of border depth 0.0 {far} to 1.0 {near} diff --git a/tensorflow/contrib/lite/g3doc/apis.md b/tensorflow/contrib/lite/g3doc/apis.md index 311fc69696..e8f5566f11 100644 --- a/tensorflow/contrib/lite/g3doc/apis.md +++ b/tensorflow/contrib/lite/g3doc/apis.md @@ -52,7 +52,7 @@ typedef enum { Failures can be easily verified with: ```c++ if (status != kTfLiteOk) { - // ... error handling here ... + // ... error handling here ... } ``` diff --git a/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml b/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml index ab7d3fd496..0a71dbd0e8 100644 --- a/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml +++ b/tensorflow/contrib/lite/java/demo/app/src/main/res/values/base-strings.xml @@ -19,12 +19,12 @@ <string name="app_name">TfLiteCameraDemo</string> <string name="intro_message"> <![CDATA[ - - + + This sample demonstrates the basic use of TfLite API. Check the source code to see how you can use TfLite for efficient, on-device inference with trained TensorFlow models. - - + + ]]> </string> </resources> diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md index 65bd60c12a..9345303ff1 100644 --- a/tensorflow/contrib/makefile/README.md +++ b/tensorflow/contrib/makefile/README.md @@ -16,17 +16,17 @@ This static library will not contain: - Python or other language bindings - GPU support - + You can target: - iOS - OS X (macOS) - Android - Raspberry-PI - + You will compile tensorflow and protobuf libraries that you can link into other applications. You will also compile the [benchmark](../../tools/benchmark/) application that will let you check your application. - + ## Before you start (all platforms) First, clone this TensorFlow repository. @@ -58,9 +58,9 @@ You should then be able to run the `build_all_linux.sh` script to compile: tensorflow/contrib/makefile/build_all_linux.sh ``` -This should compile a static library in -`tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a`, -and create an example executable at `tensorflow/contrib/makefile/gen/bin/benchmark`. +This should compile a static library in +`tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a`, +and create an example executable at `tensorflow/contrib/makefile/gen/bin/benchmark`. Get the graph file, if you have not already: @@ -201,7 +201,7 @@ library in a simple app. ### Building by hand This section covers each step of building. For all the code in one place, see -[build_all_ios.sh](build_all_ios.sh). +[build_all_ios.sh](build_all_ios.sh). If you have not already, you will need to download dependencies: @@ -232,7 +232,7 @@ make -f tensorflow/contrib/makefile/Makefile \ This creates a library in `tensorflow/contrib/makefile/gen/lib/libtensorflow-core.a` that you can link any -xcode project against. +xcode project against. To see TensorFlow running on iOS, the example Xcode project in [tensorflow/examples/ios](../../examples/ios/) shows how to use the static @@ -258,15 +258,15 @@ tensorflow/contrib/makefile/compile_ios_tensorflow.sh -f "-O3" -h tensorflow/con In XCode, you will need to use -force_load in the linker flags section of the build settings to pull in the global constructors that are used -to register ops and kernels. +to register ops and kernels. #### Optimization - + The `compile_ios_tensorflow.sh` script can take optional command-line arguments. The first argument will be passed as a C++ optimization flag and defaults to debug mode. If you are concerned about performance or are working on a release build, you would likely want a higher optimization setting, like so: - + ```bash compile_ios_tensorflow.sh -f "-Os" ``` @@ -330,7 +330,7 @@ what you need for your desired system. ## Dependency Management The Makefile loads in a list of dependencies stored in text files. These files -are generated from the main Bazel build by running +are generated from the main Bazel build by running `tensorflow/contrib/makefile/gen_file_lists.sh`. You'll need to re-run this i you make changes to the files that are included in the build. @@ -361,10 +361,10 @@ codebase can sometimes break the makefile build process. If you find that tests relying on this makefile are failing with a change you're involved in, here are some trouble-shooting steps: - - Try to reproduce the issue on your platform. If you're on Linux, running + - Try to reproduce the issue on your platform. If you're on Linux, running `make -f tensorflow/contrib/makefile/Makefile` should be enough to recreate most issues. For other platforms, see the sections earlier in this document. - + - The most common cause of breakages are files that have been added to the Bazel build scripts, but that the makefile isn't aware of. Typical symptoms of this include linker errors mentioning missing symbols or protobuf headers @@ -377,11 +377,11 @@ some trouble-shooting steps: `tensorflow/core/BUILD`, so if you change the wildcards there to include new files you'll need to also update `CORE_CC_ALL_SRCS` and `CORE_CC_EXCLUDE_SRCS` in the makefile. - + - Some of the supported platforms use clang instead of gcc as their compiler, so if you're hitting compile errors you may need to tweak your code to be more friendly to different compilers by avoiding gcc extensions or idioms. - + These are the most common reasons for makefile breakages, but it's also possible you may hit something unusual, like a platform incompatibility. For those, you'll need to see if you can reproduce the issue on that particular diff --git a/tensorflow/contrib/makefile/compile_ios_protobuf.sh b/tensorflow/contrib/makefile/compile_ios_protobuf.sh index 43e5809dd2..8fa2021363 100755 --- a/tensorflow/contrib/makefile/compile_ios_protobuf.sh +++ b/tensorflow/contrib/makefile/compile_ios_protobuf.sh @@ -270,7 +270,7 @@ case "$1" in echo "Unknown ARCH" exit 1 ;; -esac +esac } for build_element in "${build_targets[@]}" diff --git a/tensorflow/contrib/makefile/compile_nsync.sh b/tensorflow/contrib/makefile/compile_nsync.sh index 930e6b8dea..7927997678 100755 --- a/tensorflow/contrib/makefile/compile_nsync.sh +++ b/tensorflow/contrib/makefile/compile_nsync.sh @@ -28,7 +28,7 @@ usage="usage: $prog [-t linux|ios|android|macos|native] [-a architecture] [-v android_api_version] A script to build nsync for tensorflow. -This script can be run on Linux or MacOS host platforms, and can target +This script can be run on Linux or MacOS host platforms, and can target Linux, MacOS, iOS, or Android. Options: diff --git a/tensorflow/contrib/makefile/rename_protobuf.sh b/tensorflow/contrib/makefile/rename_protobuf.sh index b3bff2d503..8d52c1a169 100755 --- a/tensorflow/contrib/makefile/rename_protobuf.sh +++ b/tensorflow/contrib/makefile/rename_protobuf.sh @@ -38,7 +38,7 @@ # # Note that this script modifies the source code in-place, so once it's been run # it's no longer suitable for further manual modifications, since the difference -# with the top of tree will already be large. +# with the top of tree will already be large. mv tensorflow/contrib/makefile/downloads/protobuf/src/google/protobuf \ tensorflow/contrib/makefile/downloads/protobuf//src/google/protobuf3 @@ -71,7 +71,7 @@ sed -i '' 's%::google::protobuf;%google::protobuf3;%' \ # Fix up a couple of special build scripts that look for particular files. sed -i '' 's%src/google/protobuf/message.cc%src/google/protobuf3/message.cc%' \ - tensorflow/contrib/makefile/downloads/protobuf/configure.ac + tensorflow/contrib/makefile/downloads/protobuf/configure.ac sed -i '' 's%src/google/protobuf/stubs/common.h%src/google/protobuf3/stubs/common.h%' \ tensorflow/contrib/makefile/downloads/protobuf/autogen.sh diff --git a/tensorflow/contrib/metrics/README.md b/tensorflow/contrib/metrics/README.md index 247ebac5bb..e0f2d74fa3 100644 --- a/tensorflow/contrib/metrics/README.md +++ b/tensorflow/contrib/metrics/README.md @@ -4,7 +4,7 @@ Metrics are used in evaluation to assess the quality of a model. Most are "streaming" ops, meaning they create variables to accumulate a running total, -and return an update tensor to update these variables, and a value tensor to +and return an update tensor to update these variables, and a value tensor to read the accumulated value. Example: value, update_op = metrics.streaming_mean_squared_error( diff --git a/tensorflow/contrib/mpi/README.md b/tensorflow/contrib/mpi/README.md index b0d03d05a2..75cb823048 100644 --- a/tensorflow/contrib/mpi/README.md +++ b/tensorflow/contrib/mpi/README.md @@ -23,7 +23,7 @@ The following environment variables can be set to modify the behavior at runtime **MPI_DISABLED=[0,1]** -This environment variable allows you to disable the MPI path before launch (e.g. for performance or correctness testing). +This environment variable allows you to disable the MPI path before launch (e.g. for performance or correctness testing). **MPI_OPTIMAL_PATH=[0,1]** @@ -34,10 +34,10 @@ This path is disabled by default as it requires that the MPI library can directl ## Known problems -For certain complex neural nets the implementation sometimes crashes inside the MPI libraries. This seems to be related to memory allocations/routines that register the memory for the Infiniband transfers. (The crashes do not happen when all MPI processes are within the same physical machine). +For certain complex neural nets the implementation sometimes crashes inside the MPI libraries. This seems to be related to memory allocations/routines that register the memory for the Infiniband transfers. (The crashes do not happen when all MPI processes are within the same physical machine). **MVAPICH** -- The problem manifests itself with a segmentation fault inside a memory copy routine and during startup you will get the following warning: "WARNING: Error in initializing MVAPICH2 ptmalloc library. Continuing without InfiniBand registration cache support." +- The problem manifests itself with a segmentation fault inside a memory copy routine and during startup you will get the following warning: "WARNING: Error in initializing MVAPICH2 ptmalloc library. Continuing without InfiniBand registration cache support." **OpenMPI** - With OpenMPI corrupt data will be received resulting in an assertion or the MPI library will print an error and exit. The error is "Attempt to free memory that is still in use by an ongoing MPI communication. MPI job will now abort." @@ -58,11 +58,11 @@ Once a request has arrived from a remote process the request is forwarded to the * Receive tensor request The MPI thread will check if there are any incoming tensor request messages on the communication lines using MPI_Iprobe. Once a request has been received it will be passed on to the standard TensorFlow code and eventually will be placed on the sendQueue. -* Receive tensor +* Receive tensor At some point after a request has been sent the remote process will transmit the tensor. This tensor will be received and we look-up the callback that is associated with this tensor in our request table and execute the callback on the received data. -In the implementation all send operations are non-blocking, all probe operations are non-blocking and all receive-operations are blocking. The receive-operations are only executed after the probe has determined that there is something to receive. +In the implementation all send operations are non-blocking, all probe operations are non-blocking and all receive-operations are blocking. The receive-operations are only executed after the probe has determined that there is something to receive. The MPI processes identify each other using an MPI process ID. The TensorFlow gRPC processes identify each other using a name. During launch we create a mapping between the TensorFlow process name and the MPI process ID to allow the processes to communicate with the correct destinations when using MPI operations. diff --git a/tensorflow/contrib/pi_examples/README.md b/tensorflow/contrib/pi_examples/README.md index f550228083..177357bca6 100644 --- a/tensorflow/contrib/pi_examples/README.md +++ b/tensorflow/contrib/pi_examples/README.md @@ -13,7 +13,7 @@ sudo apt-get install -y libjpeg-dev ``` - To download the example model you'll need, run these commands: - + ```bash curl https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2015_stripped.zip \ -o /tmp/inception_dec_2015_stripped.zip diff --git a/tensorflow/contrib/pi_examples/camera/Makefile b/tensorflow/contrib/pi_examples/camera/Makefile index 578f1336f3..b354c03b6e 100644 --- a/tensorflow/contrib/pi_examples/camera/Makefile +++ b/tensorflow/contrib/pi_examples/camera/Makefile @@ -76,7 +76,7 @@ $(EXECUTABLE_NAME): $(EXECUTABLE_OBJS) $(TFLIBS) $(LIBFLAGS) $(LIB_PATH) $(LDFLAGS) $(LIBS) # Matches on C++ source files. -$(OBJDIR)%.o: %.cc +$(OBJDIR)%.o: %.cc @mkdir -p $(dir $@) $(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@ diff --git a/tensorflow/contrib/pi_examples/label_image/Makefile b/tensorflow/contrib/pi_examples/label_image/Makefile index 19652e581d..9d054a3133 100644 --- a/tensorflow/contrib/pi_examples/label_image/Makefile +++ b/tensorflow/contrib/pi_examples/label_image/Makefile @@ -75,7 +75,7 @@ $(EXECUTABLE_NAME): $(EXECUTABLE_OBJS) $(TFLIBS) $(LIBFLAGS) $(LIB_PATH) $(LDFLAGS) $(LIBS) # Matches on C++ source files. -$(OBJDIR)%.o: %.cc +$(OBJDIR)%.o: %.cc @mkdir -p $(dir $@) $(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@ diff --git a/tensorflow/contrib/pi_examples/label_image/label_image.cc b/tensorflow/contrib/pi_examples/label_image/label_image.cc index 7817cd0c64..0b18045789 100644 --- a/tensorflow/contrib/pi_examples/label_image/label_image.cc +++ b/tensorflow/contrib/pi_examples/label_image/label_image.cc @@ -89,7 +89,7 @@ Status LoadJpegFile(string file_name, std::vector<tensorflow::uint8>* data, FILE * infile; JSAMPARRAY buffer; int row_stride; - + if ((infile = fopen(file_name.c_str(), "rb")) == NULL) { LOG(ERROR) << "Can't open " << file_name; return tensorflow::errors::NotFound("JPEG file ", file_name, @@ -105,7 +105,7 @@ Status LoadJpegFile(string file_name, std::vector<tensorflow::uint8>* data, fclose(infile); return tensorflow::errors::Unknown("JPEG decoding failed"); } - + jpeg_create_decompress(&cinfo); jpeg_stdio_src(&cinfo, infile); jpeg_read_header(&cinfo, TRUE); @@ -119,14 +119,14 @@ Status LoadJpegFile(string file_name, std::vector<tensorflow::uint8>* data, buffer = (*cinfo.mem->alloc_sarray) ((j_common_ptr) &cinfo, JPOOL_IMAGE, row_stride, 1); while (cinfo.output_scanline < cinfo.output_height) { - tensorflow::uint8* row_address = &((*data)[cinfo.output_scanline * row_stride]); + tensorflow::uint8* row_address = &((*data)[cinfo.output_scanline * row_stride]); jpeg_read_scanlines(&cinfo, buffer, 1); memcpy(row_address, buffer[0], row_stride); } jpeg_finish_decompress(&cinfo); jpeg_destroy_decompress(&cinfo); - fclose(infile); + fclose(infile); return Status::OK(); } @@ -167,7 +167,7 @@ Status ReadTensorFromImageFile(string file_name, const int wanted_height, const int top_y_index = static_cast<int>(floorf(in_y)); const int bottom_y_index = std::min(static_cast<int>(ceilf(in_y)), (image_height - 1)); - const float y_lerp = in_y - top_y_index; + const float y_lerp = in_y - top_y_index; tensorflow::uint8* in_top_row = in + (top_y_index * image_rowlen); tensorflow::uint8* in_bottom_row = in + (bottom_y_index * image_rowlen); float *out_row = out + (y * wanted_width * wanted_channels); @@ -186,7 +186,7 @@ Status ReadTensorFromImageFile(string file_name, const int wanted_height, in_bottom_row + (right_x_index * wanted_channels); const float x_lerp = in_x - left_x_index; float *out_pixel = out_row + (x * wanted_channels); - for (int c = 0; c < wanted_channels; ++c) { + for (int c = 0; c < wanted_channels; ++c) { const float top_left((in_top_left_pixel[c] - input_mean) / input_std); const float top_right((in_top_right_pixel[c] - input_mean) / input_std); const float bottom_left((in_bottom_left_pixel[c] - input_mean) / input_std); @@ -198,7 +198,7 @@ Status ReadTensorFromImageFile(string file_name, const int wanted_height, } } } - + out_tensors->push_back(image_tensor); return Status::OK(); } diff --git a/tensorflow/contrib/quantize/README.md b/tensorflow/contrib/quantize/README.md index 782232e85f..40541729da 100644 --- a/tensorflow/contrib/quantize/README.md +++ b/tensorflow/contrib/quantize/README.md @@ -13,7 +13,7 @@ through estimator [2]. Note that during back propagation, the parameters are updated at high precision as this is needed to ensure sufficient precision in accumulating tiny adjustments to the parameters. However, for the forward pass, the parameters and activations are quantized to the desired lower precision. - +  ###Forward pass diff --git a/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc b/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc index 09b83e2af1..66aa293dc1 100644 --- a/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc +++ b/tensorflow/contrib/tensor_forest/hybrid/core/ops/stochastic_hard_routing_function_op.cc @@ -70,7 +70,7 @@ REGISTER_OP("StochasticHardRoutingFunction") return Status::OK(); }) .Doc(R"doc( - Samples a path for each instance in `input_data` and returns the + Samples a path for each instance in `input_data` and returns the probability of the path and the path taken. tree_depth: The depth of the decision tree. diff --git a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md index b174bb6af3..872474aee1 100644 --- a/tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md +++ b/tensorflow/contrib/timeseries/python/timeseries/state_space_models/g3doc/periodic_multires_derivation.md @@ -66,7 +66,7 @@ def make_eigval_mat_fn(to_power=1): if i == j: number = j // 2 + 1 powersign = ((j + 1) % 2) * 2 - 1 - return root_of_unity(matsize + 1, number=number, + return root_of_unity(matsize + 1, number=number, to_power=powersign*to_power) else: return 0 diff --git a/tensorflow/contrib/tpu/ops/outfeed_ops.cc b/tensorflow/contrib/tpu/ops/outfeed_ops.cc index ed5756cc54..5900c61a38 100644 --- a/tensorflow/contrib/tpu/ops/outfeed_ops.cc +++ b/tensorflow/contrib/tpu/ops/outfeed_ops.cc @@ -39,7 +39,7 @@ REGISTER_OP("OutfeedEnqueueTuple") .Doc(R"doc( An op which emits multiple Tensor values from an XLA computation. -inputs: A list of tensors that will be inserted into the outfeed queue as an +inputs: A list of tensors that will be inserted into the outfeed queue as an XLA tuple. )doc"); diff --git a/tensorflow/contrib/verbs/README.md b/tensorflow/contrib/verbs/README.md index dcb390b0a5..7c1c8ea459 100644 --- a/tensorflow/contrib/verbs/README.md +++ b/tensorflow/contrib/verbs/README.md @@ -38,7 +38,7 @@ The following improvements can be made in the future. First, conversion to Tenso * **RDMA channel:** Responsible for RDMA connection to a particular node. It manages multiple buffers. A channel has a callback table which stores all the callbacks for the requested tensors. * **RDMA buffer:** Responsible for sending or receiving data. It has a fixed size memory to store the data. It has a queue to store the pending jobs. There are three types of buffers, message buffer, ACK buffer and tensor buffer. A channel has two message buffers, two ack buffers and many tensor buffers. * **RDMA manager:** Manages the adapter and channels, including channel creation, channel setup via GRPC service, channel lookup, etc. -* **RDMA rendezvous manager:** manages multiple rdma rendezvous. +* **RDMA rendezvous manager:** manages multiple rdma rendezvous. * **RDMA rendezvous:** a derived class of BaseRemoteRendezvous. This class is the back end for "send" and "recv" ops. When the sendrecv_op wants to send or receive a tensor, it calls the rendezvous' "send" and "recv" functions respectively. Rendezvous are identified by "step_id", a random number, so that tensors for different iterations don't get mixed up. ### The SEND operation diff --git a/tensorflow/core/common_runtime/accumulate_n_optimizer.cc b/tensorflow/core/common_runtime/accumulate_n_optimizer.cc index 81cd44870e..a1e3b21e4f 100644 --- a/tensorflow/core/common_runtime/accumulate_n_optimizer.cc +++ b/tensorflow/core/common_runtime/accumulate_n_optimizer.cc @@ -35,7 +35,7 @@ Tensor make_zeros(const DataType& dtype, const TensorShapeProto& shape) { // Replaces occurrences of the "AccumulateNV2" stub operator with a graph of // lower-level ops. The graph is equivalent (modulo certain corner cases) // to the semantics of the original accumulate_n() Python op in math_ops.py. -// Implementing the op with a rewrite allows this new variant of accumulate_n +// Implementing the op with a rewrite allows this new variant of accumulate_n // to be differentiable. // // The binary code that generates AccumulateNV2 stub ops is located in a diff --git a/tensorflow/core/framework/bfloat16.cc b/tensorflow/core/framework/bfloat16.cc index a5ac0e1a8d..0efe43fde2 100644 --- a/tensorflow/core/framework/bfloat16.cc +++ b/tensorflow/core/framework/bfloat16.cc @@ -21,13 +21,13 @@ void FloatToBFloat16(const float* src, bfloat16* dst, int64 size) { const uint16_t* p = reinterpret_cast<const uint16_t*>(src); uint16_t* q = reinterpret_cast<uint16_t*>(dst); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - for (; size != 0; p += 2, q++, size--) { - *q = p[0]; - } + for (; size != 0; p += 2, q++, size--) { + *q = p[0]; + } #else - for (; size != 0; p += 2, q++, size--) { - *q = p[1]; - } + for (; size != 0; p += 2, q++, size--) { + *q = p[1]; + } #endif } @@ -35,15 +35,15 @@ void BFloat16ToFloat(const bfloat16* src, float* dst, int64 size) { const uint16_t* p = reinterpret_cast<const uint16_t*>(src); uint16_t* q = reinterpret_cast<uint16_t*>(dst); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - for (; size != 0; p++, q += 2, size--) { - q[0] = *p; - q[1] = 0; + for (; size != 0; p++, q += 2, size--) { + q[0] = *p; + q[1] = 0; + } +#else + for (; size != 0; p++, q += 2, size--) { + q[0] = 0; + q[1] = *p; } -#else - for (; size != 0; p++, q += 2, size--) { - q[0] = 0; - q[1] = *p; - } #endif } diff --git a/tensorflow/core/framework/bfloat16.h b/tensorflow/core/framework/bfloat16.h index b936e899d4..968c18bdd2 100644 --- a/tensorflow/core/framework/bfloat16.h +++ b/tensorflow/core/framework/bfloat16.h @@ -19,9 +19,9 @@ limitations under the License. #include "tensorflow/core/framework/numeric_types.h" #include "tensorflow/core/platform/types.h" -#if defined(PLATFORM_WINDOWS) -#include "tensorflow/core/platform/windows/cpu_info.h" -#endif +#if defined(PLATFORM_WINDOWS) +#include "tensorflow/core/platform/windows/cpu_info.h" +#endif // Compact 16-bit encoding of floating point numbers. This representation uses // 1 bit for the sign, 8 bits for the exponent and 7 bits for the mantissa. It diff --git a/tensorflow/core/kernels/cast_op.h b/tensorflow/core/kernels/cast_op.h index 7d3e0cbe3d..8fedf2c271 100644 --- a/tensorflow/core/kernels/cast_op.h +++ b/tensorflow/core/kernels/cast_op.h @@ -128,10 +128,10 @@ struct scalar_cast_op<::tensorflow::bfloat16, float> { float ret; uint16_t* p = reinterpret_cast<uint16_t*>(&ret); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - p[0] = a.value; - p[1] = 0; -#else - static_assert(::tensorflow::port::kLittleEndian, "Not a little endian system!"); + p[0] = a.value; + p[1] = 0; +#else + static_assert(::tensorflow::port::kLittleEndian, "Not a little endian system!"); p[0] = 0; p[1] = a.value; #endif diff --git a/tensorflow/core/kernels/diag_op.cc b/tensorflow/core/kernels/diag_op.cc index be862b82f1..86fa7dce36 100644 --- a/tensorflow/core/kernels/diag_op.cc +++ b/tensorflow/core/kernels/diag_op.cc @@ -108,7 +108,7 @@ class DiagPartOp : public OpKernel { }; // Implementation of the functor specialization for CPU. -// +// // According to the diagonal definition, // `output[i1,..., ik, i1,..., ik] = input[i1,..., ik]`, // @@ -116,7 +116,7 @@ class DiagPartOp : public OpKernel { // pointer can be represent by coordinate [i1,..., ik], // where `index = i1*(s2*...*sk) + i2*(s3*...*sk) +... + ik` // -// Let new_index is the offset of output's pointer with coordinate +// Let new_index is the offset of output's pointer with coordinate // [i1,..., ik, i1,..., ik], then we have // `new_index = i1*(s2*...sk*s1*...*sk) + i2*(s3*...*sk*s1*...*sk) +... + \ // ik*(s1*...*sk) + i1*(s2*...*sk) + i2*(s3*...*sk) +... + ik diff --git a/tensorflow/core/kernels/diag_op_gpu.cu.cc b/tensorflow/core/kernels/diag_op_gpu.cu.cc index 684f00ea61..d3c529d784 100644 --- a/tensorflow/core/kernels/diag_op_gpu.cu.cc +++ b/tensorflow/core/kernels/diag_op_gpu.cu.cc @@ -33,7 +33,7 @@ __global__ void DiagCudaKernel(const int num_threads, const T* in, T* out) { CUDA_1D_KERNEL_LOOP(index, num_threads) { - // Fill the diagonal elements or set to zero in other place. + // Fill the diagonal elements or set to zero in other place. if (index % (1 + size) == 0) { out[index] = in[index / (1 + size)]; } else { diff --git a/tensorflow/core/kernels/queue_ops.cc b/tensorflow/core/kernels/queue_ops.cc index d51dc4ecb0..17831b7437 100644 --- a/tensorflow/core/kernels/queue_ops.cc +++ b/tensorflow/core/kernels/queue_ops.cc @@ -429,7 +429,7 @@ class QueueIsClosedOp : public QueueOpKernel { public: explicit QueueIsClosedOp(OpKernelConstruction* context) : QueueOpKernel(context) {} - + protected: void ComputeAsync(OpKernelContext* ctx, QueueInterface* queue, DoneCallback callback) override { diff --git a/tensorflow/core/kernels/sparse_matmul_op_test.cc b/tensorflow/core/kernels/sparse_matmul_op_test.cc index a0c54805e2..f815ca9e34 100644 --- a/tensorflow/core/kernels/sparse_matmul_op_test.cc +++ b/tensorflow/core/kernels/sparse_matmul_op_test.cc @@ -284,12 +284,12 @@ class SparseMatmulOpTest : public ::testing::Test { uint16_t* data3_bfloat16_p = reinterpret_cast<uint16_t*>(data3_bfloat16) + i; #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - data3_p[1] = 0; - data3_bfloat16_p[0] = data3_p[0]; + data3_p[1] = 0; + data3_bfloat16_p[0] = data3_p[0]; #else - data3_p[0] = 0; - data3_bfloat16_p[0] = data3_p[1]; -#endif + data3_p[0] = 0; + data3_bfloat16_p[0] = data3_p[1]; +#endif } } diff --git a/tensorflow/core/kernels/xsmm_conv2d_test.cc b/tensorflow/core/kernels/xsmm_conv2d_test.cc index 381ea39b77..e294701246 100644 --- a/tensorflow/core/kernels/xsmm_conv2d_test.cc +++ b/tensorflow/core/kernels/xsmm_conv2d_test.cc @@ -73,7 +73,7 @@ LIBXSMM_INLINE void naive_copy_KCRS_to_RSCK(const float* kcrs, Tensor &rsck, in LIBXSMM_VLA_DECL(4, const float, input, kcrs, C, R, S); int r, s, c, k; auto output = rsck.flat<float>(); - + for ( r = 0; r < R; r++ ) { for ( s = 0; s < S; s++ ) { for ( c = 0; c < C; c++ ) { @@ -94,14 +94,14 @@ LIBXSMM_INLINE void zero_buf(float* buf, long size) { buf[i] = 0.0f; } } - + LIBXSMM_INLINE void copy_buf(Tensor &dst,float *src,long size) { long i; auto output = dst.flat<float>(); - for (i = 0; i < size; ++i) + for (i = 0; i < size; ++i) output(i) = src[i]; } - + LIBXSMM_INLINE void init_buf(float* buf, long size, int initPos, int initOne) { int i; @@ -110,7 +110,7 @@ LIBXSMM_INLINE void init_buf(float* buf, long size, int initPos, int initOne) buf[i] = (float)((initOne != 0) ? 1.0 : ((initPos != 0) ? drand48() : (0.05 - drand48()/10.0))); } } - + LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float* output, const float* filter) @@ -138,11 +138,11 @@ LIBXSMM_INLINE void naive_conv_fp(naive_conv_t* param, const float* input, float int stride_w = param->stride_w; /* loop counters */ int img, ofm, ifm, oj, oi, ij, ii, kj, ki; - + LIBXSMM_VLA_DECL(4, float, output_t, output + (pad_w_out * ofwp + pad_h_out), nOfm, ofhp, ofwp); LIBXSMM_VLA_DECL(4, const float, input_t, input + (pad_w_in * ifwp + pad_h_in), nIfm, ifhp, ifwp); LIBXSMM_VLA_DECL(4, const float, filter_t, filter, nIfm, kh, kw); - + for (img = 0; img < nImg; ++img) { for (ofm = 0; ofm < nOfm; ++ofm) { for (ifm = 0; ifm < nIfm; ++ifm) { @@ -172,7 +172,7 @@ void RunXsmmVsGeneric() {} class XsmmConv2DTest : public OpsTestBase { protected: void MakeOp(int stride) { - + TF_CHECK_OK(NodeDefBuilder("xsmm", "Conv2D") .Input(FakeInput(DT_FLOAT)) .Input(FakeInput(DT_FLOAT)) @@ -184,7 +184,7 @@ class XsmmConv2DTest : public OpsTestBase { TF_ASSERT_OK(InitOp()); } }; - + TEST_F(XsmmConv2DTest, Basic) { MakeOp(1); @@ -206,13 +206,13 @@ TEST_F(XsmmConv2DTest, Basic) { int stride_h = stride; int pad_h = pad; int pad_w = pad; - + int pad_h_in = pad_h; int pad_w_in = pad_w; - + int pad_h_out = 0; int pad_w_out = 0; - + /* deriving some values for naive code */ int ofh = (ifh + 2 * pad_h - kh) / stride_h + 1; int ofw = (ifw + 2 * pad_w - kw) / stride_w + 1; @@ -223,7 +223,7 @@ TEST_F(XsmmConv2DTest, Basic) { //Initialization of Filter and Image - + /* allocate data */ float *naive_input = (float*)libxsmm_aligned_scratch( nImg*nIfm*ifhp*ifwp*sizeof(float), 2097152); float *naive_output = (float*)libxsmm_aligned_scratch( nImg*nOfm*ofhp*ofwp*sizeof(float), 2097152); @@ -232,21 +232,21 @@ TEST_F(XsmmConv2DTest, Basic) { init_buf(naive_input, nImg*nIfm*ifhp*ifwp, 0, 0); zero_buf(naive_output, nImg*nOfm*ofhp*ofwp); init_buf(naive_filter, nOfm*nIfm*kh*kw, 0, 0); - + Tensor image(DT_FLOAT, {nImg, ifhp, ifwp, nIfm}); - - + + Tensor filter(DT_FLOAT, {kh,kw,nIfm,nOfm}); - + naive_copy_NCHW_to_NHWC(naive_input, image, nImg, ifhp, ifwp, nIfm); - naive_copy_KCRS_to_RSCK(naive_filter, filter, kh, kw, nIfm, nOfm); + naive_copy_KCRS_to_RSCK(naive_filter, filter, kh, kw, nIfm, nOfm); //Run naive convolution - + naive_conv_t naive_param; naive_param.nImg = nImg; @@ -274,8 +274,8 @@ TEST_F(XsmmConv2DTest, Basic) { naive_conv_fp(&naive_param, naive_input, naive_output, naive_filter); - - + + AddInputFromArray<float>(image.shape(), image.flat<float>()); AddInputFromArray<float>(filter.shape(), filter.flat<float>()); @@ -283,7 +283,7 @@ TEST_F(XsmmConv2DTest, Basic) { //Run Op (TF) TF_ASSERT_OK(RunOpKernel()); - + // Check the output. Tensor expected(DT_FLOAT, {nImg,ofhp,ofwp, nOfm}); naive_copy_NCHW_to_NHWC(naive_output, expected, nImg, ofhp, ofwp, nOfm); @@ -329,15 +329,15 @@ TEST(XsmmConv2DTest, Basic) { desc.fuse_ops = LIBXSMM_DNN_CONV_FUSE_NONE; desc.options = LIBXSMM_DNN_CONV_OPTION_NONE; desc.datatype = LIBXSMM_DNN_DATATYPE_F32; - + if (!CanUseXsmmConv2D(desc, data_format)) { return false; } - + auto input_ptr = input.template flat<float>().data(); auto filter_ptr = filter.template flat<float>().data(); auto output_ptr = output->template flat<float>().data(); - + bool success = functor::XsmmFwdConv2D<CPUDevice, float>()( ctx, desc, input_ptr, filter_ptr, output_ptr); return success; diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc index c3f8006415..13fbd2fa51 100644 --- a/tensorflow/core/ops/image_ops.cc +++ b/tensorflow/core/ops/image_ops.cc @@ -818,8 +818,8 @@ bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and height of the underlying image. -For example, if an image is 100 x 200 pixels (height x width) and the bounding -box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of +For example, if an image is 100 x 200 pixels (height x width) and the bounding +box is `[0.1, 0.2, 0.5, 0.9]`, the upper-left and bottom-right coordinates of the bounding box will be `(40, 10)` to `(100, 50)` (in (x,y) coordinates). Parts of the bounding box may fall outside the image. diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc index a242a13878..654e890b57 100644 --- a/tensorflow/core/ops/nn_ops.cc +++ b/tensorflow/core/ops/nn_ops.cc @@ -359,7 +359,7 @@ The size of 1D Tensors matches the dimension C of the 4D Tensors. y_backprop: A 4D Tensor for the gradient with respect to y. x: A 4D Tensor for input data. scale: A 1D Tensor for scaling factor, to scale the normalized x. -reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +reserve_space_1: When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation. @@ -407,7 +407,7 @@ The size of 1D Tensors matches the dimension C of the 4D Tensors. y_backprop: A 4D Tensor for the gradient with respect to y. x: A 4D Tensor for input data. scale: A 1D Tensor for scaling factor, to scale the normalized x. -reserve_space_1: When is_training is True, a 1D Tensor for the computed batch +reserve_space_1: When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation. diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl index caeed0aa4a..c63fb28ff9 100644 --- a/tensorflow/core/platform/default/build_config_root.bzl +++ b/tensorflow/core/platform/default/build_config_root.bzl @@ -28,7 +28,7 @@ def tf_additional_verbs_deps(): "//tensorflow:with_verbs_support": [ "//tensorflow/contrib/verbs:verbs_server_lib", "//tensorflow/contrib/verbs:grpc_verbs_client", - ], + ], "//conditions:default": [], }) diff --git a/tensorflow/core/profiler/README.md b/tensorflow/core/profiler/README.md index 8ca26fa5dc..9e628b1065 100644 --- a/tensorflow/core/profiler/README.md +++ b/tensorflow/core/profiler/README.md @@ -48,7 +48,7 @@ bazel-bin/tensorflow/python/profiler/profiler_ui \ # Create options to profile the time and memory information. builder = tf.profiler.ProfileOptionBuilder opts = builder(builder.time_and_memory()).order_by('micros').build() -# Create a profiling context, set constructor argument `trace_steps`, +# Create a profiling context, set constructor argument `trace_steps`, # `dump_steps` to empty for explicit control. with tf.contrib.tfprof.ProfileContext('/tmp/train_dir', trace_steps=[], diff --git a/tensorflow/docs_src/about/uses.md b/tensorflow/docs_src/about/uses.md index d41818e10c..8818177a28 100644 --- a/tensorflow/docs_src/about/uses.md +++ b/tensorflow/docs_src/about/uses.md @@ -5,7 +5,7 @@ This page highlights TensorFlow models in real world use. ## Model zoo -Please visit our collection of TensorFlow models in the +Please visit our collection of TensorFlow models in the [TensorFlow Zoo](https://github.com/tensorflow/models). If you have built a model with TensorFlow, please consider publishing it in diff --git a/tensorflow/docs_src/api_guides/python/nn.md b/tensorflow/docs_src/api_guides/python/nn.md index 75dbb04e7d..eb3b251099 100644 --- a/tensorflow/docs_src/api_guides/python/nn.md +++ b/tensorflow/docs_src/api_guides/python/nn.md @@ -73,7 +73,7 @@ The total padding applied along the height and width is computed as: pad_along_width = max(filter_width - strides[2], 0) else: pad_along_width = max(filter_width - (in_width % strides[2]), 0) - + Finally, the padding on the top, bottom, left and right are: pad_top = pad_along_height // 2 @@ -351,7 +351,7 @@ p_i = max(s\cdot (n_o - 1) + k - n_i, 0) \end{equation} Remember that, for `'SAME'` padding, -\\(n_o = \left \lceil{\frac{n_i}{s}}\right \rceil\\), as mentioned above. +\\(n_o = \left \lceil{\frac{n_i}{s}}\right \rceil\\), as mentioned above. We need to analyze in detail two cases: - \\(n_i \text{ mod } s = 0\\) diff --git a/tensorflow/docs_src/community/documentation.md b/tensorflow/docs_src/community/documentation.md index 77d4e0caec..003e0a25ec 100644 --- a/tensorflow/docs_src/community/documentation.md +++ b/tensorflow/docs_src/community/documentation.md @@ -10,10 +10,10 @@ particular, this document explains the following: You can view TensorFlow documentation on https://www.tensorflow.org, and you can view and edit the raw files on -[GitHub](https://www.tensorflow.org/code/tensorflow/docs_src/). +[GitHub](https://www.tensorflow.org/code/tensorflow/docs_src/). We're publishing our docs on GitHub so everybody can contribute. Whatever gets checked in to `tensorflow/docs_src` will be published soon after on -https://www.tensorflow.org. +https://www.tensorflow.org. Republishing TensorFlow documentation in different forms is absolutely allowed, but we are unlikely to accept other documentation formats (or the tooling to @@ -237,7 +237,7 @@ If a module is accidentally imported, it typically breaks the doc generator even if the doc generator succeeds, unwanted symbols may show up in the docs. Check the generated docs to make sure that all symbols that are documented are expected. If there are symbols that shouldn’t be there, you have the -following options for dealing with them: +following options for dealing with them: - Private symbols and imports - The `remove_undocumented` filter diff --git a/tensorflow/docs_src/community/style_guide.md b/tensorflow/docs_src/community/style_guide.md index 40a75a4736..a4c4e2674e 100644 --- a/tensorflow/docs_src/community/style_guide.md +++ b/tensorflow/docs_src/community/style_guide.md @@ -162,7 +162,7 @@ operation. it's present in the scope. * Layers that behave differently during training should take: - - `is_training`: `bool` indicator to conditionally choose different + - `is_training`: `bool` indicator to conditionally choose different computation paths (e.g. using `tf.cond`) during execution. Example: diff --git a/tensorflow/docs_src/community/welcome.md b/tensorflow/docs_src/community/welcome.md index 33740de5d5..a3abf25507 100644 --- a/tensorflow/docs_src/community/welcome.md +++ b/tensorflow/docs_src/community/welcome.md @@ -65,5 +65,5 @@ please read the following list carefully: [TensorFlow issues tracker](https://github.com/tensorflow/tensorflow/issues) on GitHub. For example, use the issue tracker to request a new operation in TensorFlow. - + diff --git a/tensorflow/docs_src/deploy/hadoop.md b/tensorflow/docs_src/deploy/hadoop.md index 7592cf828b..c4471562b9 100644 --- a/tensorflow/docs_src/deploy/hadoop.md +++ b/tensorflow/docs_src/deploy/hadoop.md @@ -32,8 +32,8 @@ be set: source ${HADOOP_HOME}/libexec/hadoop-config.sh ``` -* **LD_LIBRARY_PATH**: To include the path to libjvm.so, and optionally the path - to libhdfs.so if your Hadoop distribution does not install libhdfs.so in +* **LD_LIBRARY_PATH**: To include the path to libjvm.so, and optionally the path + to libhdfs.so if your Hadoop distribution does not install libhdfs.so in `$HADOOP_HDFS_HOME/lib/native`. On Linux: ```shell diff --git a/tensorflow/docs_src/extend/add_filesys.md b/tensorflow/docs_src/extend/add_filesys.md index ea3a6fe53a..44ba198998 100644 --- a/tensorflow/docs_src/extend/add_filesys.md +++ b/tensorflow/docs_src/extend/add_filesys.md @@ -32,7 +32,7 @@ Note that TensorFlow already includes many filesystem implementations, such as: Note: NFS filesystems often mount as a POSIX interface, and so standard TensorFlow can work on top of NFS-mounted remote filesystems. - + * HDFS - the Hadoop File System * GCS - Google Cloud Storage filesystem * A "memory-mapped-file" filesystem diff --git a/tensorflow/docs_src/extend/index.md b/tensorflow/docs_src/extend/index.md index 3f30b9a8c2..00b168c6be 100644 --- a/tensorflow/docs_src/extend/index.md +++ b/tensorflow/docs_src/extend/index.md @@ -20,7 +20,7 @@ TensorFlow: Python is currently the only language supported by TensorFlow's API stability promises. However, TensorFlow also provides functionality in C++, Java, and Go, -plus community support for [Haskell](https://github.com/tensorflow/haskell) and +plus community support for [Haskell](https://github.com/tensorflow/haskell) and [Rust](https://github.com/tensorflow/rust). If you'd like to create or develop TensorFlow features in a language other than these languages, read the following guide: diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md index be14ab4026..231108215a 100644 --- a/tensorflow/docs_src/get_started/get_started.md +++ b/tensorflow/docs_src/get_started/get_started.md @@ -330,8 +330,8 @@ 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 exactly the same as the aforementioned loss +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 diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index f7380bac8a..28b04bab95 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -51,15 +51,15 @@ must be installed on your system: <pre> $ <b>sudo apt-get install cuda-command-line-tools</b> </pre> - + and add its path to your `LD_LIBRARY_PATH` environment variable: - <pre> - $ <b>export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64</b> + <pre> + $ <b>export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64</b> </pre> For CUDA Toolkit <= 7.5 do: - + <pre> $ <b>sudo apt-get install libcupti-dev</b> </pre> diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index aa4ae6c876..dbc90e8112 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -143,7 +143,7 @@ The following NVIDIA <i>software</i> must be installed on your system: particularly the description of appending the appropriate pathname to your `LD_LIBRARY_PATH` environment variable. -Finally, you must also install `libcupti` which for Cuda Toolkit >= 8.0 you do via +Finally, you must also install `libcupti` which for Cuda Toolkit >= 8.0 you do via <pre> $ <b>sudo apt-get install cuda-command-line-tools</b> </pre> diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/docs_src/mobile/android_build.md index 030cd0d051..b5a1d5d7d1 100644 --- a/tensorflow/docs_src/mobile/android_build.md +++ b/tensorflow/docs_src/mobile/android_build.md @@ -66,7 +66,7 @@ them. ## Adding TensorFlow to your apps using Android Studio -To add TensorFlow to your own apps on Android, the simplest way is to add the +To add TensorFlow to your own apps on Android, the simplest way is to add the following lines to your Gradle build file: allprojects { @@ -74,7 +74,7 @@ following lines to your Gradle build file: jcenter() } } - + dependencies { compile 'org.tensorflow:tensorflow-android:+' } diff --git a/tensorflow/docs_src/mobile/index.md b/tensorflow/docs_src/mobile/index.md index 6bcd7d09d9..419ae7094a 100644 --- a/tensorflow/docs_src/mobile/index.md +++ b/tensorflow/docs_src/mobile/index.md @@ -2,7 +2,7 @@ TensorFlow was designed to be a good deep learning solution for mobile platforms. Currently we have two solutions for deploying machine learning -applications on mobile and embedded devices: +applications on mobile and embedded devices: @{$mobile/mobile_intro$TensorFlow for Mobile} and @{$mobile/tflite$TensorFlow Lite}. ## TensorFlow Lite versus TensorFlow Mobile diff --git a/tensorflow/docs_src/mobile/ios_build.md b/tensorflow/docs_src/mobile/ios_build.md index 2e6d3bf90e..a04655052f 100644 --- a/tensorflow/docs_src/mobile/ios_build.md +++ b/tensorflow/docs_src/mobile/ios_build.md @@ -24,7 +24,7 @@ If you'd like to add TensorFlow capabilities to your own app, do the following: - Open `YourProjectName.xcworkspace` and add your code. -- In your app's **Build Settings**, make sure to add `$(inherited)` to the +- In your app's **Build Settings**, make sure to add `$(inherited)` to the **Other Linker Flags**, and **Header Search Paths** sections. ## Running the Samples diff --git a/tensorflow/docs_src/mobile/optimizing.md b/tensorflow/docs_src/mobile/optimizing.md index 1da8be5689..d9e8875c38 100644 --- a/tensorflow/docs_src/mobile/optimizing.md +++ b/tensorflow/docs_src/mobile/optimizing.md @@ -57,7 +57,7 @@ get one inference every two seconds. Having this estimate helps you plan for what you’ll be able to realistically achieve on a device. If the model is using too many ops, then there are a lot of -opportunities to optimize the architecture to reduce that number. +opportunities to optimize the architecture to reduce that number. Advanced techniques include [SqueezeNet](https://arxiv.org/abs/1602.07360) and [MobileNet](https://arxiv.org/abs/1704.04861), which are architectures @@ -278,7 +278,7 @@ The run above was on your desktop, but the tool also works on Android, which is where it’s most useful for mobile development. Here’s an example command line to run it on a 64-bit ARM device: - bazel build -c opt --config=android_arm64 \ + bazel build -c opt --config=android_arm64 \ tensorflow/tools/benchmark:benchmark_model adb push bazel-bin/tensorflow/tools/benchmark/benchmark_model /data/local/tmp adb push /tmp/tensorflow_inception_graph.pb /data/local/tmp/ diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/docs_src/mobile/prepare_models.md index 8fc65be35a..360ee302aa 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/docs_src/mobile/prepare_models.md @@ -131,9 +131,9 @@ needs to understand which parts of the graph are actually needed, and which are artifacts of the training process, like summarization ops. Only ops that contribute to calculating the given output nodes will be kept. If you know how your graph is going to be used, these should just be the names of the nodes you -pass into `Session::Run()` as your fetch targets. The easiest way to find the +pass into `Session::Run()` as your fetch targets. The easiest way to find the node names is to inspect the Node objects while building your graph in python. -Inspecting your graph in TensorBoard is another simple way. You can get some +Inspecting your graph in TensorBoard is another simple way. You can get some suggestions on likely outputs by running the [`summarize_graph` tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms/README.md#inspecting-graphs). Because the output format for TensorFlow has changed over time, there are a @@ -164,7 +164,7 @@ The trickiest part of this process is figuring out the names of the nodes you want to use as inputs and outputs during inference. You'll need these anyway once you start to run inference, but you also need them here so that the transform can calculate which nodes are not needed on the inference-only -path. These may not be obvious from the training code. The easiest way to +path. These may not be obvious from the training code. The easiest way to determine the node name is to explore the graph with TensorBoard. Remember that mobile applications typically gather their data from sensors and @@ -187,9 +187,9 @@ output nodes. If you’ve just been given a frozen `GraphDef` file, and are not sure about the contents, try using the `summarize_graph` tool to print out information about the inputs and outputs it finds from the graph structure. Here’s an -example with the original Inception v3 file: +example with the original Inception v3 file: - bazel run tensorflow/tools/graph_transforms:summarize_graph -- + bazel run tensorflow/tools/graph_transforms:summarize_graph -- --in_graph=tensorflow_inception_graph.pb Once you have an idea of what the input and output nodes are, you can feed them @@ -259,7 +259,7 @@ on how to do this, and also see @{$mobile/optimizing#binary_size$Optimizing} for more on reducing your binary size. ### Locate the implementation - + Operations are broken into two parts. The first is the op definition, which declares the signature of the operation, which inputs, outputs, and attributes it has. These take up very little space, and so all are included by default. The @@ -267,7 +267,7 @@ implementations of the op computations are done in kernels, which live in the `tensorflow/core/kernels` folder. You need to compile the C++ file containing the kernel implementation of the op you need into the library. To figure out which file that is, you can search for the operation name in the source -files. +files. [Here’s an example search in github](https://github.com/search?utf8=%E2%9C%93&q=repo%3Atensorflow%2Ftensorflow+extension%3Acc+path%3Atensorflow%2Fcore%2Fkernels+REGISTER+Mul&type=Code&ref=searchresults). diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/docs_src/mobile/tflite/index.md index 59daa2fe25..49d93669a2 100644 --- a/tensorflow/docs_src/mobile/tflite/index.md +++ b/tensorflow/docs_src/mobile/tflite/index.md @@ -40,7 +40,7 @@ TensorFlow Lite provides an interface to leverage hardware acceleration, if available on the device. It does so via the Android Neural Networks library, released as part of Android O-MR1. -## Why do we need a new mobile-specific library? +## Why do we need a new mobile-specific library? Machine Learning is changing the computing paradigm, and we see an emerging trend of new use cases on mobile and embedded devices. Consumer expectations are @@ -67,7 +67,7 @@ There are several factors which are fueling interest in this domain: connected to a network. We believe the next wave of machine learning applications will have significant -processing on mobile and embedded devices. +processing on mobile and embedded devices. ## TensorFlow Lite developer preview highlights diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/programmers_guide/saved_model.md index 8731cae0d7..34e8e5faf5 100644 --- a/tensorflow/docs_src/programmers_guide/saved_model.md +++ b/tensorflow/docs_src/programmers_guide/saved_model.md @@ -160,7 +160,7 @@ Notes: ### Inspect variables in a checkpoint -We can quickly inspect variables in a checkpoint with the +We can quickly inspect variables in a checkpoint with the [`inspect_checkpoint`](https://www.tensorflow.org/code/tensorflow/python/tools/inspect_checkpoint.py) library. Continuing from the save/restore examples shown earlier: diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/programmers_guide/tensors.md index 88eb277e35..47d4db2a56 100644 --- a/tensorflow/docs_src/programmers_guide/tensors.md +++ b/tensorflow/docs_src/programmers_guide/tensors.md @@ -43,8 +43,8 @@ generating a random number. The **rank** of a `tf.Tensor` object is its number of dimensions. Synonyms for rank include **order** or **degree** or **n-dimension**. -Note that rank in TensorFlow is not the same as matrix rank in mathematics. -As the following table shows, each rank in TensorFlow corresponds to a +Note that rank in TensorFlow is not the same as matrix rank in mathematics. +As the following table shows, each rank in TensorFlow corresponds to a different mathematical entity: Rank | Math entity @@ -56,7 +56,7 @@ Rank | Math entity n | n-Tensor (you get the idea) -### Rank 0 +### Rank 0 The following snippet demonstrates creating a few rank 0 variables: @@ -108,7 +108,7 @@ my_image = tf.zeros([10, 299, 299, 3]) # batch x height x width x color ### Getting a `tf.Tensor` object's rank To determine the rank of a `tf.Tensor` object, call the `tf.rank` method. -For example, the following method programmatically determines the rank +For example, the following method programmatically determines the rank of the `tf.Tensor` defined in the previous section: ```python diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/programmers_guide/variables.md index f310b89380..16753c931f 100644 --- a/tensorflow/docs_src/programmers_guide/variables.md +++ b/tensorflow/docs_src/programmers_guide/variables.md @@ -37,7 +37,7 @@ You may optionally specify the `dtype` and initializer to `tf.get_variable`. For example: ``` python -my_int_variable = tf.get_variable("my_int_variable", [1, 2, 3], dtype=tf.int32, +my_int_variable = tf.get_variable("my_int_variable", [1, 2, 3], dtype=tf.int32, initializer=tf.zeros_initializer) ``` @@ -45,7 +45,7 @@ TensorFlow provides many convenient initializers. Alternatively, you may initialize a `tf.Variable` to have the value of a `tf.Tensor`. For example: ``` python -other_variable = tf.get_variable("other_variable", dtype=tf.int32, +other_variable = tf.get_variable("other_variable", dtype=tf.int32, initializer=tf.constant([23, 42])) ``` @@ -66,13 +66,13 @@ By default every `tf.Variable` gets placed in the following two collections: multiple devices, * `tf.GraphKeys.TRAINABLE_VARIABLES`--- variables for which TensorFlow will calculate gradients. - + If you don't want a variable to be trainable, add it to the `tf.GraphKeys.LOCAL_VARIABLES` collection instead. For example, the following snippet demonstrates how to add a variable named `my_local` to this collection: ``` python -my_local = tf.get_variable("my_local", shape=(), +my_local = tf.get_variable("my_local", shape=(), collections=[tf.GraphKeys.LOCAL_VARIABLES]) ``` @@ -80,8 +80,8 @@ Alternatively, you can specify `trainable=False` as an argument to `tf.get_variable`: ``` python -my_non_trainable = tf.get_variable("my_non_trainable", - shape=(), +my_non_trainable = tf.get_variable("my_non_trainable", + shape=(), trainable=False) ``` @@ -126,7 +126,7 @@ cluster_spec = { "ps": ["ps0:2222", "ps1:2222"], "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]} with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)): - v = tf.get_variable("v", shape=[20, 20]) # this variable is placed + v = tf.get_variable("v", shape=[20, 20]) # this variable is placed # in the parameter server # by the replica_device_setter ``` @@ -142,7 +142,7 @@ high-level frameworks such as `tf.contrib.slim`, `tf.estimator.Estimator` and Explicit initialization is otherwise useful because it allows you not to rerun potentially expensive initializers when reloading a model from a checkpoint as well as allowing determinism when randomly-initialized variables are shared in a -distributed setting. +distributed setting. To initialize all trainable variables in one go, before training starts, call `tf.global_variables_initializer()`. This function returns a single operation diff --git a/tensorflow/docs_src/tutorials/image_recognition.md b/tensorflow/docs_src/tutorials/image_recognition.md index ddb771700a..df13eabead 100644 --- a/tensorflow/docs_src/tutorials/image_recognition.md +++ b/tensorflow/docs_src/tutorials/image_recognition.md @@ -42,7 +42,7 @@ For example, here are the results from [AlexNet] classifying some images: To compare models, we examine how often the model fails to predict the correct answer as one of their top 5 guesses -- termed "top-5 error rate". [AlexNet] achieved by setting a top-5 error rate of 15.3% on the 2012 -validation data set; [Inception (GoogLeNet)] achieved 6.67%; +validation data set; [Inception (GoogLeNet)] achieved 6.67%; [BN-Inception-v2] achieved 4.9%; [Inception-v3] reaches 3.46%. > How well do humans do on ImageNet Challenge? There's a [blog post] by diff --git a/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java b/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java index eabc724f7f..07995febaf 100644 --- a/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java +++ b/tensorflow/examples/android/src/org/tensorflow/demo/Classifier.java @@ -100,7 +100,7 @@ public interface Classifier { List<Recognition> recognizeImage(Bitmap bitmap); void enableStatLogging(final boolean debug); - + String getStatString(); void close(); diff --git a/tensorflow/examples/ios/README.md b/tensorflow/examples/ios/README.md index 7d2eb870be..5bdaeb43ce 100644 --- a/tensorflow/examples/ios/README.md +++ b/tensorflow/examples/ios/README.md @@ -6,7 +6,7 @@ This folder contains examples of how to build applications for iOS devices using - You'll need Xcode 7.3 or later. - There are currently three examples: simple, benchmark, and camera. For now, - you can download the sample code by cloning the main tensorflow repository + you can download the sample code by cloning the main tensorflow repository (we are planning to make the samples available as a separate repository later). @@ -48,8 +48,8 @@ open tf_simple_example.xcworkspace # obs, not the .xcodeproj directory ### Troubleshooting - Make sure you use the TensorFlow-experimental pod (and not TensorFlow). - - - The TensorFlow-experimental pod is current about ~450MB. The reason it is + + - The TensorFlow-experimental pod is current about ~450MB. The reason it is so big is because we are bundling multiple platforms, and the pod includes all TensorFlow functionality (e.g. operations). The final app size after build is substantially smaller though (~25MB). Working with the complete @@ -91,7 +91,7 @@ target 'YourProjectName' open up the Xcode project in the `camera` subfolder. Once you build and run that, you should get a live camera view that you can point at objects to get real-time recognition results. - + ### Troubleshooting If you're hitting problems, here's a checklist of common things to investigate: diff --git a/tensorflow/examples/tutorials/deepdream/README.md b/tensorflow/examples/tutorials/deepdream/README.md index 3a715f6224..403e4b34f9 100644 --- a/tensorflow/examples/tutorials/deepdream/README.md +++ b/tensorflow/examples/tutorials/deepdream/README.md @@ -2,7 +2,7 @@ by [Alexander Mordvintsev](mailto:moralex@google.com) -This directory contains Jupyter notebook that demonstrates a number of Convolutional Neural Network +This directory contains Jupyter notebook that demonstrates a number of Convolutional Neural Network image generation techniques implemented with TensorFlow: - visualizing individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries) @@ -11,8 +11,8 @@ image generation techniques implemented with TensorFlow: - using Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost - generating DeepDream-like images with TensorFlow -You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes -embedded graph visualizations. You can still see them online +You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes +embedded graph visualizations. You can still see them online [using nbviewer](http://nbviewer.jupyter.org/github/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb) service. @@ -23,5 +23,5 @@ In order to run the notebook locally, the following dependencies must be install - NumPy - Jupyter Notebook -To open the notebook, run `ipython notebook` command in this directory, and +To open the notebook, run `ipython notebook` command in this directory, and select 'deepdream.ipynb' in the opened browser window. diff --git a/tensorflow/examples/udacity/README.md b/tensorflow/examples/udacity/README.md index 6faad294c2..f80c56d1c1 100644 --- a/tensorflow/examples/udacity/README.md +++ b/tensorflow/examples/udacity/README.md @@ -43,15 +43,15 @@ In addition, you may need to pass `--memory=8g` as an extra argument to `docker-machine` is a tool to provision and manage docker hosts, it supports multiple platform (ex. aws, gce, azure, virtualbox, ...). To create a new virtual machine locally with built-in docker engine, you can use docker-machine create -d virtualbox --virtualbox-memory 8196 tensorflow - + `-d` means the driver for the cloud platform, supported drivers listed [here](https://docs.docker.com/machine/drivers/). Here we use virtualbox to create a new virtual machine locally. `tensorflow` means the name of the virtual machine, feel free to use whatever you like. You can use docker-machine ip tensorflow - + to get the ip of the new virtual machine. To switch from default virtual machine to a new one (here we use tensorflow), type eval $(docker-machine env tensorflow) - + Note that `docker-machine env tensorflow` outputs some environment variables such like `DOCKER_HOST`. Then your docker client is now connected to the docker host in virtual machine `tensorflow` * **I'm getting a TLS connection error.** diff --git a/tensorflow/g3doc/README.txt b/tensorflow/g3doc/README.txt index 6eaf1e1bda..ed648f8b6b 100644 --- a/tensorflow/g3doc/README.txt +++ b/tensorflow/g3doc/README.txt @@ -7,7 +7,7 @@ Documentation (on Github, tensorflow.org, and anywhere else we decide to serve it from) is now generated from the files in tensorflow/docs_src/ (for tutorials and other guides) and TensorFlow source code (for the API reference pages). If you see a problem with -API reference, edit the code comments in the appropriate language. If you see a +API reference, edit the code comments in the appropriate language. If you see a problem with our other docs, edit the files in docs_src. To preview the results of your changes, or generate an offline copy of diff --git a/tensorflow/java/src/gen/perl/tftypes-runall.pl b/tensorflow/java/src/gen/perl/tftypes-runall.pl index a451ce92aa..65fe3b1506 100644 --- a/tensorflow/java/src/gen/perl/tftypes-runall.pl +++ b/tensorflow/java/src/gen/perl/tftypes-runall.pl @@ -1,13 +1,13 @@ #!/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. diff --git a/tensorflow/java/src/gen/perl/tftypes.pl b/tensorflow/java/src/gen/perl/tftypes.pl index 115723ac8a..c7c62e916f 100644 --- a/tensorflow/java/src/gen/perl/tftypes.pl +++ b/tensorflow/java/src/gen/perl/tftypes.pl @@ -1,13 +1,13 @@ #!/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. @@ -156,7 +156,7 @@ for (my $i = 1; $i <= $#info; $i++) { ." * String elements are sequences of bytes from the last array dimension.\n"; } - + my $intro = ($trank > 0) ? "Creates a rank-$trank tensor of {\@code $jtype} elements." : "Creates a scalar tensor containing a single {\@code $jtype} element."; diff --git a/tensorflow/java/src/gen/resources/Tensors.java.tmpl b/tensorflow/java/src/gen/resources/Tensors.java.tmpl index 98e1588559..e615524c8e 100644 --- a/tensorflow/java/src/gen/resources/Tensors.java.tmpl +++ b/tensorflow/java/src/gen/resources/Tensors.java.tmpl @@ -11,7 +11,7 @@ public final class Tensors { private Tensors() {} /** Creates a scalar String tensor using the default, UTF-8 encoding. - * + * * @param data The string to put into the new scalar tensor. */ public static Tensor<String> create(String data) { @@ -19,7 +19,7 @@ public final class Tensors { } /** Creates a scalar String tensor using a specified encoding. - * + * * @param charset The encoding from String to bytes. * @param data The string to put into the new scalar tensor. */ diff --git a/tensorflow/python/grappler/model_analyzer.i b/tensorflow/python/grappler/model_analyzer.i index d74bd37c63..726143a0bb 100644 --- a/tensorflow/python/grappler/model_analyzer.i +++ b/tensorflow/python/grappler/model_analyzer.i @@ -48,7 +48,7 @@ string GenerateModelReport(const tensorflow::MetaGraphDef& metagraph) { if (!item) { return "Error: failed to preprocess metagraph: check your log file for errors"; } - + string suffix; tensorflow::grappler::ModelAnalyzer analyzer(*item); diff --git a/tensorflow/stream_executor/cuda/cuda_platform.cc b/tensorflow/stream_executor/cuda/cuda_platform.cc index 874ac1ab65..3a73846148 100644 --- a/tensorflow/stream_executor/cuda/cuda_platform.cc +++ b/tensorflow/stream_executor/cuda/cuda_platform.cc @@ -197,7 +197,7 @@ void CudaPlatform::UnregisterTraceListener(TraceListener* listener) { static void InitializeCudaPlatform() { // Disabling leak checking, MultiPlatformManager does not destroy its // registered platforms. - + std::unique_ptr<cuda::CudaPlatform> platform(new cuda::CudaPlatform); SE_CHECK_OK(MultiPlatformManager::RegisterPlatform(std::move(platform))); } diff --git a/tensorflow/stream_executor/lib/static_threadlocal.h b/tensorflow/stream_executor/lib/static_threadlocal.h index 6e2bd0d455..02720cbd26 100644 --- a/tensorflow/stream_executor/lib/static_threadlocal.h +++ b/tensorflow/stream_executor/lib/static_threadlocal.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_STREAM_EXECUTOR_LIB_STATIC_THREADLOCAL_H_ #ifdef _MSC_VER -#define __thread __declspec(thread) +#define __thread __declspec(thread) #endif // For POD types in TLS mode, s_obj_VAR is the thread-local variable. diff --git a/tensorflow/tools/ci_build/README.md b/tensorflow/tools/ci_build/README.md index 202fcb9101..f2161b700a 100644 --- a/tensorflow/tools/ci_build/README.md +++ b/tensorflow/tools/ci_build/README.md @@ -67,10 +67,10 @@ this UI, to see the logs for a failed build: the build tool divided the target into multiple shards or ran the test multiple times. Each test log is specific to the shard, run, and attempt. To see a specific log: - + 1. Click on the log icon that is on the right next to the shard, run, and attempt number. - + 2. In the grid that appears on the right, click on the specific shard, run, and attempt to view its log. You can also type the desired shard, run, or attempt number in the field above its grid. diff --git a/tensorflow/tools/dist_test/scripts/dist_mnist_test.sh b/tensorflow/tools/dist_test/scripts/dist_mnist_test.sh index ea4906588d..e703e78531 100755 --- a/tensorflow/tools/dist_test/scripts/dist_mnist_test.sh +++ b/tensorflow/tools/dist_test/scripts/dist_mnist_test.sh @@ -43,7 +43,7 @@ # NOTES: # If you have the error "$'\r': command not found" # Please run the command below to remove trailing '\r' character that causes the error: -# sed -i 's/\r$//' dist_mnist_test.sh +# sed -i 's/\r$//' dist_mnist_test.sh # Configurations diff --git a/tensorflow/tools/docker/README.md b/tensorflow/tools/docker/README.md index e35c58ff80..f46c56e11a 100644 --- a/tensorflow/tools/docker/README.md +++ b/tensorflow/tools/docker/README.md @@ -41,7 +41,7 @@ Note: If you would have a problem running nvidia-docker you may try the old meth we have used. But it is not recommended. If you find a bug in nvidia-docker, please report it there and try using nvidia-docker as described above. - $ # The old, not recommended way to run docker with gpu support: + $ # The old, not recommended way to run docker with gpu support: $ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda.* | xargs -I{} echo '-v {}:{}') $ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') $ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow:latest-gpu diff --git a/tensorflow/tools/graph_transforms/README.md b/tensorflow/tools/graph_transforms/README.md index c7f7eca257..345d9eadb8 100644 --- a/tensorflow/tools/graph_transforms/README.md +++ b/tensorflow/tools/graph_transforms/README.md @@ -95,9 +95,9 @@ transforms to modify the graph with. The transforms are given as a list of names, and can each have arguments themselves. These transforms define the pipeline of modifications that are applied in order to produce the output. Sometimes you need some transforms to happen before others, and the ordering -within the list lets you specify which happen first. -Note that the optimization -`remove_nodes(op=Identity, op=CheckNumerics)` will break the model with control +within the list lets you specify which happen first. +Note that the optimization +`remove_nodes(op=Identity, op=CheckNumerics)` will break the model with control flow operations, such as `tf.cond`, `tf.map_fn`, and `tf.while`. ## Inspecting Graphs |