現在的深度學習框架一般都是基於 Python 來實現，構建、訓練、保存和調用模型都可以很容易地在 Python 下完成。但有時候，我們在實際應用這些模型的時候可能需要在其他編程語言下進行，本文將通過直接調用 TensorFlow 的 C/C++ 接口來導入 TensorFlow 預訓練好的模型。

1.環境配置 點此查看 C/C++ 接口的編譯

2. 導入預定義的圖和訓練好的參數值

    // set up your input paths
    const string pathToGraph = "/home/senius/python/c_python/test/model-10.meta";
    const string checkpointPath = "/home/senius/python/c_python/test/model-10";

    auto session = NewSession(SessionOptions()); //　創建會話
    if (session == nullptr)
    {
        throw runtime_error("Could not create Tensorflow session.");
    }

    Status status;

    // Read in the protobuf graph we exported
    MetaGraphDef graph_def;
    status = ReadBinaryProto(Env::Default(), pathToGraph, &graph_def);　 //　導入圖模型
    if (!status.ok())
    {
        throw runtime_error("Error reading graph definition from " + pathToGraph + ": " + status.ToString());
    }

    // Add the graph to the session
    status = session->Create(graph_def.graph_def());　 //　將圖模型加入到會話中
    if (!status.ok())
    {
        throw runtime_error("Error creating graph: " + status.ToString());
    }

    // Read weights from the saved checkpoint
    Tensor checkpointPathTensor(DT_STRING, TensorShape());
    checkpointPathTensor.scalar<std::string>()() = checkpointPath;　// 讀取預訓練好的權重
    status = session->Run({{graph_def.saver_def().filename_tensor_name(), checkpointPathTensor},}, {},
                          {graph_def.saver_def().restore_op_name()}, nullptr);
    if (!status.ok())
    {
        throw runtime_error("Error loading checkpoint from " + checkpointPath + ": " + status.ToString());
    }
 

３. 準備測試數據

    const string filename = "/home/senius/python/c_python/test/04t30t00.npy";

    //Read TXT data to array
    float Array[1681*41];
    ifstream is(filename);
    for (int i = 0; i < 1681*41; i++){
        is >> Array[i];
    }
    is.close();

    tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, 41, 41, 41, 1}));
    auto input_tensor_mapped = input_tensor.tensor<float, 5>();

    float *pdata = Array;

    // copying the data into the corresponding tensor
    for (int x = 0; x < 41; ++x)//depth
    {
        for (int y = 0; y < 41; ++y) {
            for (int z = 0; z < 41; ++z) {
                const float *source_value = pdata + x * 1681 + y * 41 + z;
                input_tensor_mapped(0, x, y, z, 0) = *source_value;
            }
        }
    }
 

• 本例中輸入數據是一個 [None, 41, 41, 41, 1] 的張量，我們需要先從 TXT 文件中讀出測試數據，然後正確地填充到張量中去。

４. 前向傳播得到預測值

    std::vector<tensorflow::Tensor> finalOutput;
    std::string InputName = "X"; // Your input placeholder‘s name
    std::string OutputName = "sigmoid"; // Your output tensor‘s name
    vector<std::pair<string, Tensor> > inputs;
    inputs.push_back(std::make_pair(InputName, input_tensor));

    // Fill input tensor with your input data
    session->Run(inputs, {OutputName}, {}, &finalOutput);

    auto output_y = finalOutput[0].scalar<float>();
    std::cout << output_y() << "\n";
 

• 通過給定輸入和輸出張量的名字，我們可以將測試數據傳入到模型中，然後進行前向傳播得到預測值。

5. 一些問題

• 本模型是在 TensorFlow 1.4 下訓練的，然後編譯 TensorFlow 1.4 的 C++ 接口可以正常調用模型，但若是想調用更高版本訓練好的模型，則會報錯，據出錯信息猜測可能是高版本的 TensorFlow 中添加了一些低版本沒有的函數，所以不能正常運行。
• 若是編譯高版本的 TensorFlow ，比如最新的 TensorFlow 1.11 的 C++ 接口，則無論是調用舊版本訓練的模型還是新版本訓練的模型都不能正常運行。出錯信息如下：Error loading checkpoint from /media/lab/data/yongsen/Tensorflow_test/test/model-40: Invalid argument: Session was not created with a graph before Run()!，網上暫時也查不到解決辦法，姑且先放在這裏。

6. 完整代碼

#include </home/senius/tensorflow-r1.4/bazel-genfiles/tensorflow/cc/ops/io_ops.h>
#include </home/senius/tensorflow-r1.4/bazel-genfiles/tensorflow/cc/ops/parsing_ops.h>
#include </home/senius/tensorflow-r1.4/bazel-genfiles/tensorflow/cc/ops/array_ops.h>
#include </home/senius/tensorflow-r1.4/bazel-genfiles/tensorflow/cc/ops/math_ops.h>
#include </home/senius/tensorflow-r1.4/bazel-genfiles/tensorflow/cc/ops/data_flow_ops.h>


#include <tensorflow/core/public/session.h>
#include <tensorflow/core/protobuf/meta_graph.pb.h>
#include <fstream>

using namespace std;
using namespace tensorflow;
using namespace tensorflow::ops;

int main()
{
    // set up your input paths
    const string pathToGraph = "/home/senius/python/c_python/test/model-10.meta";
    const string checkpointPath = "/home/senius/python/c_python/test/model-10";

    auto session = NewSession(SessionOptions());
    if (session == nullptr)
    {
        throw runtime_error("Could not create Tensorflow session.");
    }

    Status status;

    // Read in the protobuf graph we exported
    MetaGraphDef graph_def;
    status = ReadBinaryProto(Env::Default(), pathToGraph, &graph_def);
    if (!status.ok())
    {
        throw runtime_error("Error reading graph definition from " + pathToGraph + ": " + status.ToString());
    }

    // Add the graph to the session
    status = session->Create(graph_def.graph_def());
    if (!status.ok())
    {
        throw runtime_error("Error creating graph: " + status.ToString());
    }

    // Read weights from the saved checkpoint
    Tensor checkpointPathTensor(DT_STRING, TensorShape());
    checkpointPathTensor.scalar<std::string>()() = checkpointPath;
    status = session->Run({{graph_def.saver_def().filename_tensor_name(), checkpointPathTensor},}, {},
                          {graph_def.saver_def().restore_op_name()}, nullptr);
    if (!status.ok())
    {
        throw runtime_error("Error loading checkpoint from " + checkpointPath + ": " + status.ToString());
    }

    cout << 1 << endl;

    const string filename = "/home/senius/python/c_python/test/04t30t00.npy";

    //Read TXT data to array
    float Array[1681*41];
    ifstream is(filename);
    for (int i = 0; i < 1681*41; i++){
        is >> Array[i];
    }
    is.close();

    tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({1, 41, 41, 41, 1}));
    auto input_tensor_mapped = input_tensor.tensor<float, 5>();

    float *pdata = Array;

    // copying the data into the corresponding tensor
    for (int x = 0; x < 41; ++x)//depth
    {
        for (int y = 0; y < 41; ++y) {
            for (int z = 0; z < 41; ++z) {
                const float *source_value = pdata + x * 1681 + y * 41 + z;
//                input_tensor_mapped(0, x, y, z, 0) = *source_value;
                input_tensor_mapped(0, x, y, z, 0) = 1;
            }
        }
    }

    std::vector<tensorflow::Tensor> finalOutput;
    std::string InputName = "X"; // Your input placeholder‘s name
    std::string OutputName = "sigmoid"; // Your output placeholder‘s name
    vector<std::pair<string, Tensor> > inputs;
    inputs.push_back(std::make_pair(InputName, input_tensor));

    // Fill input tensor with your input data
    session->Run(inputs, {OutputName}, {}, &finalOutput);

    auto output_y = finalOutput[0].scalar<float>();
    std::cout << output_y() << "\n";


    return 0;
}

• Cmakelist　文件如下

cmake_minimum_required(VERSION 3.8)
project(Tensorflow_test)

set(CMAKE_CXX_STANDARD 11)

set(SOURCE_FILES main.cpp)


include_directories(
        /home/senius/tensorflow-r1.4
        /home/senius/tensorflow-r1.4/tensorflow/bazel-genfiles
        /home/senius/tensorflow-r1.4/tensorflow/contrib/makefile/gen/protobuf/include
        /home/senius/tensorflow-r1.4/tensorflow/contrib/makefile/gen/host_obj
        /home/senius/tensorflow-r1.4/tensorflow/contrib/makefile/gen/proto
        /home/senius/tensorflow-r1.4/tensorflow/contrib/makefile/downloads/nsync/public
        /home/senius/tensorflow-r1.4/tensorflow/contrib/makefile/downloads/eigen
        /home/senius/tensorflow-r1.4/bazel-out/local_linux-py3-opt/genfiles
)

add_executable(Tensorflow_test ${SOURCE_FILES})

target_link_libraries(Tensorflow_test
        /home/senius/tensorflow-r1.4/bazel-bin/tensorflow/libtensorflow_cc.so
        /home/senius/tensorflow-r1.4/bazel-bin/tensorflow/libtensorflow_framework.so
        )

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在 C/C++ 中使用 TensorFlow 預訓練好的模型—— 直接調用 Ｃ++ 接口實現