TensorFlow - 使用TensorFlow开发程序编译环境配置
flyfish
环境：Win7,Microsoft Visual Studio 2017 Community，平台x64 语言：C++
编译器配置如下
C\C++-》 常规-》附加包含目录 E:\lib\tensorflow E:\lib\tensorflow\tensorflow\contrib\cmake\build E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\zlib_archive E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\gif_archive\giflib-5.1.4 E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\png_archive E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\jpeg_archive E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\eigen_archive E:\lib\tensorflow\third_party\eigen3 E:\lib\tensorflow\tensorflow\contrib\cmake\build\gemmlowp\src\gemmlowp E:\lib\tensorflow\tensorflow\contrib\cmake\build\jsoncpp\src\jsoncpp E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\farmhash_archive E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\farmhash_archive\util E:\lib\tensorflow\tensorflow\contrib\cmake\build\external\highwayhash E:\lib\tensorflow\tensorflow\contrib\cmake\build\protobuf\src\protobuf\src E:\lib\tensorflow\tensorflow\contrib\cmake\build\grpc\src\grpc\include
C\C++-》预处理器-》预处理器定义
WIN32 _WINDOWS _DEBUG EIGEN_AVOID_STL_ARRAY NOMINMAX _WIN32_WINNT=0x0A00 LANG_CXX11 COMPILER_MSVC OS_WIN _MBCS WIN64 WIN32_LEAN_AND_MEAN NOGDI PLATFORM_WINDOWS TENSORFLOW_USE_EIGEN_THREADPOOL EIGEN_HAS_C99_MATH TF_COMPILE_LIBRARY CMAKE_INTDIR=”Debug”
链接器-》输入-》附加依赖项 有些lib是windows自己增加的
kernel32.lib user32.lib gdi32.lib winspool.lib shell32.lib ole32.lib oleaut32.lib uuid.lib comdlg32.lib advapi32.lib Debug\tf_protos_cc.lib zlib\install\lib\zlibstaticd.lib gif\install\lib\giflib.lib png\install\lib\libpng12_staticd.lib jpeg\install\lib\libjpeg.lib jsoncpp\src\jsoncpp\src\lib_json$(Configuration)\jsoncpp.lib farmhash\install\lib\farmhash.lib fft2d\src\lib\fft2d.lib highwayhash\install\lib\highwayhash.lib protobuf\src\protobuf$(Configuration)\libprotobufd.lib grpc\src\grpc$(Configuration)\grpc++_unsecure.lib grpc\src\grpc$(Configuration)\grpc_unsecure.lib grpc\src\grpc$(Configuration)\gpr.lib wsock32.lib ws2_32.lib shlwapi.lib
添加引用如下
使用TensorFlow自带代码示例
// MyTFTest.cpp : 定义控制台应用程序的入口点。 // #include "stdafx.h" #include <cstdio> #include <functional> #include <string> #include <vector> #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/graph/default_device.h" #include "tensorflow/core/graph/graph_def_builder.h" #include "tensorflow/core/lib/core/threadpool.h" #include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/init_main.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" #include "tensorflow/core/public/session.h" using tensorflow::string; using tensorflow::int32; namespace tensorflow { namespace example { struct Options { int num_concurrent_sessions = 1; // The number of concurrent sessions int num_concurrent_steps = 10; // The number of concurrent steps int num_iterations = 100; // Each step repeats this many times bool use_gpu = false; // Whether to use gpu in the training }; // A = [3 2; -1 0]; x = rand(2, 1); // We want to compute the largest eigenvalue for A. // repeat x = y / y.norm(); y = A * x; end GraphDef CreateGraphDef() { // TODO(jeff,opensource): This should really be a more interesting // computation. Maybe turn this into an mnist model instead? Scope root = Scope::NewRootScope(); using namespace ::tensorflow::ops; // NOLINT(build/namespaces) // A = [3 2; -1 0]. Using Const<float> means the result will be a // float tensor even though the initializer has integers. auto a = Const<float>(root, { { 3, 2 },{ -1, 0 } }); // x = [1.0; 1.0] auto x = Const(root.WithOpName("x"), { { 1.f },{ 1.f } }); // y = A * x auto y = MatMul(root.WithOpName("y"), a, x); // y2 = y.^2 auto y2 = Square(root, y); // y2_sum = sum(y2). Note that you can pass constants directly as // inputs. Sum() will automatically create a Const node to hold the // 0 value. auto y2_sum = Sum(root, y2, 0); // y_norm = sqrt(y2_sum) auto y_norm = Sqrt(root, y2_sum); // y_normalized = y ./ y_norm Div(root.WithOpName("y_normalized"), y, y_norm); GraphDef def; TF_CHECK_OK(root.ToGraphDef(&def)); return def; } string DebugString(const Tensor& x, const Tensor& y) { CHECK_EQ(x.NumElements(), 2); CHECK_EQ(y.NumElements(), 2); auto x_flat = x.flat<float>(); auto y_flat = y.flat<float>(); // Compute an estimate of the eigenvalue via // (x' A x) / (x' x) = (x' y) / (x' x) // and exploit the fact that x' x = 1 by assumption Eigen::Tensor<float, 0, Eigen::RowMajor> lambda = (x_flat * y_flat).sum(); return strings::Printf("lambda = %8.6f x = [%8.6f %8.6f] y = [%8.6f %8.6f]", lambda(), x_flat(0), x_flat(1), y_flat(0), y_flat(1)); } void ConcurrentSteps(const Options* opts, int session_index) { // Creates a session. SessionOptions options; std::unique_ptr<Session> session(NewSession(options)); GraphDef def = CreateGraphDef(); if (options.target.empty()) { graph::SetDefaultDevice(opts->use_gpu ? "/gpu:0" : "/cpu:0", &def); } TF_CHECK_OK(session->Create(def)); // Spawn M threads for M concurrent steps. const int M = opts->num_concurrent_steps; std::unique_ptr<thread::ThreadPool> step_threads( new thread::ThreadPool(Env::Default(), "trainer", M)); for (int step = 0; step < M; ++step) { step_threads->Schedule([&session, opts, session_index, step]() { // Randomly initialize the input. Tensor x(DT_FLOAT, TensorShape({ 2, 1 })); auto x_flat = x.flat<float>(); x_flat.setRandom(); Eigen::Tensor<float, 0, Eigen::RowMajor> inv_norm = x_flat.square().sum().sqrt().inverse(); x_flat = x_flat * inv_norm(); // Iterations. std::vector<Tensor> outputs; for (int iter = 0; iter < opts->num_iterations; ++iter) { outputs.clear(); TF_CHECK_OK( session->Run({ { "x", x } }, { "y:0", "y_normalized:0" }, {}, &outputs)); CHECK_EQ(size_t{ 2 }, outputs.size()); const Tensor& y = outputs[0]; const Tensor& y_norm = outputs[1]; // Print out lambda, x, and y. std::printf("d/d %s\n", session_index, step, DebugString(x, y).c_str()); // Copies y_normalized to x. x = y_norm; } }); } // Delete the threadpool, thus waiting for all threads to complete. step_threads.reset(nullptr); TF_CHECK_OK(session->Close()); } void ConcurrentSessions(const Options& opts) { // Spawn N threads for N concurrent sessions. const int N = opts.num_concurrent_sessions; // At the moment our Session implementation only allows // one concurrently computing Session on GPU. CHECK_EQ(1, N) << "Currently can only have one concurrent session."; thread::ThreadPool session_threads(Env::Default(), "trainer", N); for (int i = 0; i < N; ++i) { session_threads.Schedule(std::bind(&ConcurrentSteps, &opts, i)); } } } // end namespace example } // end namespace tensorflow namespace { bool ParseInt32Flag(tensorflow::StringPiece arg, tensorflow::StringPiece flag, int32* dst) { if (arg.Consume(flag) && arg.Consume("=")) { char extra; return (sscanf(arg.data(), "%d%c", dst, &extra) == 1); } return false; } bool ParseBoolFlag(tensorflow::StringPiece arg, tensorflow::StringPiece flag, bool* dst) { if (arg.Consume(flag)) { if (arg.empty()) { *dst = true; return true; } if (arg == "=true") { *dst = true; return true; } else if (arg == "=false") { *dst = false; return true; } } return false; } } // namespace int main(int argc, char* argv[]) { tensorflow::example::Options opts; std::vector<char*> unknown_flags; for (int i = 1; i < argc; ++i) { if (string(argv[i]) == "--") { while (i < argc) { unknown_flags.push_back(argv[i]); ++i; } break; } if (ParseInt32Flag(argv[i], "--num_concurrent_sessions", &opts.num_concurrent_sessions) || ParseInt32Flag(argv[i], "--num_concurrent_steps", &opts.num_concurrent_steps) || ParseInt32Flag(argv[i], "--num_iterations", &opts.num_iterations) || ParseBoolFlag(argv[i], "--use_gpu", &opts.use_gpu)) { continue; } fprintf(stderr, "Unknown flag: %s\n", argv[i]); return -1; } // Passthrough any unknown flags. int dst = 1; // Skip argv[0] for (char* f : unknown_flags) { argv[dst++] = f; } argv[dst++] = nullptr; argc = static_cast<int>(unknown_flags.size() + 1); tensorflow::port::InitMain(argv[0], &argc, &argv); tensorflow::example::ConcurrentSessions(opts); }代码执行
