tensorflow c++接口

1.编译
bazel build //tensorflow:libtensorflow_cc.so
出现linking of rule failed的问题，更改tensorflow版本1.4解决问题
bazel-bin/tensorflow/目录下会出现libtensorflow_cc.so文件
2.配置其他依赖
tensorflow/contrib/makefile目录下，执行build_all_linux.sh文件，成功后会在makefile目录下出现一个gen文件夹
出现./autogen.sh: 48: ./autogen.sh: autoreconf: not found
解决sudo apt-get install autoconf automake libtool
3.测试

#include <iostream>
#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/public/session.h>

using namespace std;
using namespace tensorflow;

int main(int argc, char **argv) {
  Session* session;
  Status status = NewSession(SessionOptions(), &session);
  if(!status.ok()){
    cout << status.ToString() << endl;
  }
  cout << "Session successfully created." << endl;

  return 0;
}

4.cmake

cmake_minimum_required(VERSION 2.6)
project(DeeperDepthPrediction)
set( CMAKE_CXX_FLAGS "-std=c++11" )

add_executable(FirstTry  FirstTry.cpp)

include_directories( /home/ai/Programs/TensorFlow
  /home/ai/Programs/TensorFlow/bazel-genfiles  
  /home/ai/Programs/TensorFlow/tensorflow/contrib/makefile/gen/protobuf/include
  /home/ai/Programs/TensorFlow/tensorflow/contrib/makefile/downloads/nsync/public
  /home/ai/Programs/TensorFlow/tensorflow/contrib/makefile/downloads/eigen
)

target_link_libraries( FirstTry 
  /home/ai/Programs/TensorFlow/bazel-bin/tensorflow/libtensorflow_cc.so
  /home/ai/Programs/TensorFlow/bazel-bin/tensorflow/libtensorflow_framework.so

)

5.常用语句
模型加载

GraphDef graphdef;
Status status_load =ReadBinaryProto(Env::Default(), fire_path, &graphdef);

创建会话

Session* session;
Status status = NewSession(SessionOptions(), &session);
Status status_creat = session->Create(graphdef);

输入Mat–tensor

Tensor inputImg(DT_FLOAT, TensorShape({*, *, *, *}));
auto inputImageMapped = inputImg.tensor<float, 4>();
for (int i=0; i< *; i++){
    Mat image = images[i];
    for(int y=0; y<*; y++){
        uchar* pixel = image.ptr<uchar>(y);
        for(int x=0; x<28; x++){
            inputImageMapped(i,y,x,0) = pixel[x] *1/255.0;
        }
    }
}

会话运行

Status status_run = session->Run({{"inputImg",inputImg}}, {"result1"},{}, &pred_tensor);
if(!status_run.ok())
    cout <<"run failed"<< endl;

输出tensor–Mat

Tensor result = pred_tensor[0];
auto result_map = result.tensor<float, 2>();
for(int y=0; y<*; y++){
    float* pixel = pred.ptr<float>(y);
    for(int x=0; x<*; x++){
        pixel[x] = result_map(y,x);
    }
}

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