部分内容from: Tensorflow C++ 从训练到部署(1)：环境搭建
在之前的编译中，已经编译好了tensorflow_pkg相关的wheel。现在有一个需求，需要按照C++的代码进行模型加载和训练。查询资料后发现，需要重新编译一套TensorFlow支持的C++接口，主要是编译出来libtensorflow_cc.so和libtensorflow_framework.so这两个文件。
bazel build -c opt --copt=-mavx --copt=-msse4.2 --config=monolithic //tensorflow:libtensorflow_cc.so
bazel build -c opt --copt=-mavx --copt=-msse4.2 --config=monolithic //tensorflow:libtensorflow_framework.so像这种严格与机器相关的选项，虽然可以加快执行速度，但是在使用之前一定要查明自己的目标机器是否适合。
中间可能会遇到之前的一些问题，功查找https://www.cnblogs.com/jourluohua/p/9180709.html
编译完成后，安装第三方库
source tensorflow/contrib/makefile/build_all_linux.shsudo cp -r bazel-genfiles/ /usr/local/include/tf
sudo cp -r tensorflow/cc /usr/local/include/tf/tensorflow
sudo cp -r tensorflow/core /usr/local/include/tf/tensorflow
sudo cp -r third_party /usr/local/include/tf
sudo cp bazel-bin/tensorflow/libtensorflow_cc.so /usr/local/lib
sudo cp bazel-bin/tensorflow/libtensorflow_framework.so /usr/local/lib如果你使用的是C的接口，用的是libtensorflow.so库的话，需要拷贝tensorflow/c/相关的文件
之后是新建一个Python文件，去生成相关的pb文件（代码来源于他人代码，有修改）
#!/usr/bin/env python
import tensorflow.compat.v1 as tf
import numpy as np
with tf.Session() as sess:
a=tf.placeholder(tf.float32,shape=None, name='a')
b=tf.placeholder(tf.float32,shape=None, name='b')
c = tf.multiply(a, b, name='c')
sess.run(tf.global_variables_initializer())
tf.train.write_graph(sess.graph_def, 'model/', 'simple.pb', as_text=False)
res = sess.run(c, feed_dict={'a:0': 2.0, 'b:0': 3.0})
print("res = ", res)生成了model/simple.pb文件
写load_simple_net.cpp文件（代码来源于他人代码，有修改https://gitee.com/liuzc/tensorflow_cpp.git）
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"
using namespace tensorflow;
int main(int argc, char* argv[]) {
// Initialize a tensorflow session
Session* session;
Status status = NewSession(SessionOptions(), &session);
if (!status.ok()) {
std::cerr << status.ToString() << std::endl;
return 1;
} else {
std::cout << "Session created successfully" << std::endl;
}
if (argc != 2)
{
std::cerr << std::endl << "Usage: ./project path_to_graph.pb" << std::endl;
return 1;
}
// Load the protobuf graph
GraphDef graph_def;
std::string graph_path = argv[1];
status = ReadBinaryProto(Env::Default(), graph_path, &graph_def);
if (!status.ok()) {
std::cerr << status.ToString() << std::endl;
return 1;
} else {
std::cout << "Load graph protobuf successfully" << std::endl;
}
// Add the graph to the session
status = session->Create(graph_def);
if (!status.ok()) {
std::cerr << status.ToString() << std::endl;
return 1;
} else {
std::cout << "Add graph to session successfully" << std::endl;
}
// Setup inputs and outputs:
// Our graph doesn't require any inputs, since it specifies default values,
// but we'll change an input to demonstrate.
Tensor a(DT_FLOAT, TensorShape());
a.scalar<float>()() = 2.0;
Tensor b(DT_FLOAT, TensorShape());
b.scalar<float>()() = 3.0;
std::vector<std::pair<string, tensorflow::Tensor>> inputs = {
{ "a:0", a },
{ "b:0", b },
};
// The session will initialize the outputs
std::vector<tensorflow::Tensor> outputs;
// Run the session, evaluating our "c" operation from the graph
status = session->Run(inputs, {"c:0"}, {}, &outputs);
if (!status.ok()) {
std::cerr << status.ToString() << std::endl;
return 1;
} else {
std::cout << "Run session successfully" << std::endl;
}
// Grab the first output (we only evaluated one graph node: "c")
// and convert the node to a scalar representation.
auto output_c = outputs[0].scalar<float>();
// (There are similar methods for vectors and matrices here:
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/public/tensor.h)
// Print the results
std::cout << outputs[0].DebugString() << std::endl; // Tensor<type: float shape: [] values: 30>
std::cout << "Output value: " << output_c() << std::endl; // 30
// Free any resources used by the session
session->Close();
return 0;
}添加CMakeLists文件，目录结构变为
添加eigen库，这里的原因是TensorFlow默认的eigen库，里边实际上是不支持C++接口的，不信的人可以试下，里边的unsupported/CXX/Tensor里边自己include了自己，会导致递归死循环包含错误，因此需要自己添加eigen库，拷贝到当前目录eigen3文件夹下
修改CMakeLists文件，内容为
cmake_minimum_required(VERSION 3.5)
project(tensorflow_cpp)
set(CMAKE_CXX_STANDARD 11)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
set(TENSORFLOW_INCLUDES
/usr/local/include/tf/
/usr/local/include/tf/bazel-genfiles
/usr/local/include/tf/tensorflow/
/usr/local/include/tf/tensorflow/third-party
)
set(TENSORFLOW_LIBS
/usr/local/lib/libtensorflow_cc.so
/usr/local/lib/libtensorflow_framework.so
)
include_directories(
${TENSORFLOW_INCLUDES}
${PROJECT_SOURCE_DIR}/eigen3
)
add_executable(load_simple_net load_simple_net.cpp)
target_link_libraries(load_simple_net
${TENSORFLOW_LIBS}
${OpenCV_LIBS}
)新建build文件，进入该build文件中
cd ./build
cmake ..
make这里有一个非常重要的点，/usr/local/lib/libtensorflow_cc.so /usr/local/lib/libtensorflow_framework.so的顺序关系，如果顺序不对，会一直报
E tensorflow/core/common_runtime/session.cc:89] Not found: No session factory registered for the given session options: {target: "" config: } Registered factories are {}.
使用./load_simple_net ../model/simple.pb，按道理可以得到正确的值
Session created successfully
Load graph protobuf successfully
Add graph to session successfully
Run session successfully
Tensor<type: float shape: [] values: 6>
Output value: 6
参考资料：
https://medium.com/jim-fleming/loading-tensorflow-graphs-via-host-languages-be10fd81876f
https://medium.com/jim-fleming/loading-a-tensorflow-graph-with-the-c-api-4caaff88463f#.z4qeoyfb0
https://www.tensorflow.org/install/install_c
http://www.liuxiao.org/2018/08/ubuntu-tensorflow-c-%e4%bb%8e%e8%ae%ad%e7%bb%83%e5%88%b0%e9%a2%84%e6%b5%8b1%ef%bc%9a%e7%8e%af%e5%a2%83%e6%90%ad%e5%bb%ba/
https://blog.csdn.net/melissa_cjt/article/details/85983659
https://www.cnblogs.com/shouhuxianjian/p/9416934.html
来源：oschina
链接：https://my.oschina.net/u/4325773/blog/3339151