c++ - 用 C++ 训练 Keras 模块

Question

我正在尝试tf.keras.Sequential用 C++ 训练模型。使用 python 创建模型。我可以使用 tf c api 在 C++ 中加载。前向处理有效，但我无法训练模型。我看了这个：可以使用 TensorFlow C API 训练 Keras 模型吗？但我还是不明白。这是我的python文件

import tensorflow as tf

model = tf.keras.Sequential(
                [
                    tf.keras.layers.InputLayer(1),
                    tf.keras.layers.Dense(1),

                ])


model.compile(optimizer="adam", loss="mse")
model.save("x64/Debug/model_saved.tf", save_format="tf")

这是 C++ 文件

// CPP Keras.cpp : This file contains the 'main' function. Program execution begins and ends there.
//

#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include <tensorflow/c/c_api.h>

void NoOpDeallocator(void* data, size_t a, void* b) {}

int main()
{
     //********* Read model
    TF_Graph* Graph = TF_NewGraph();
    TF_Status* Status = TF_NewStatus();

    TF_SessionOptions* SessionOpts = TF_NewSessionOptions();
    TF_Buffer* RunOpts = NULL;

    const char* saved_model_dir = "D:/MyCode/AGreatYear2021/reposetories/AlphaZero/test/CPP Keras/x64/Debug/model_saved.tf";
    const char* tags = "serve"; // default model serving tag; can change in future
    int ntags = 1;

    TF_Session* Session = TF_LoadSessionFromSavedModel(SessionOpts, RunOpts, saved_model_dir, &tags, ntags, Graph, NULL, Status);
    if(TF_GetCode(Status) == TF_OK)
    {
        printf("TF_LoadSessionFromSavedModel OK\n");
    }
    else
    {
        printf("%s",TF_Message(Status));
    }

    //****** Get input tensor
    //TODO : need to use saved_model_cli to read saved_model arch
    int NumInputs = 1;
    TF_Output* Input = (TF_Output*)malloc(sizeof(TF_Output) * NumInputs);
    
    TF_Output t2 = { TF_GraphOperationByName(Graph, "StatefulPartitionedCall"), 0 };
    TF_Output t1 = { TF_GraphOperationByName(Graph, "serving_default_input_1"), 0 };
    if(t1.oper == NULL)
        printf("ERROR: Failed TF_GraphOperationByName serving_default_input_1\n");
    else
        printf("TF_GraphOperationByName serving_default_input_1 is OK\n");
    
    Input[0] = t1;

    int NumOutputs = 1;
    TF_Output* Output = (TF_Output*)malloc(sizeof(TF_Output) * NumOutputs);

    if (t2.oper == NULL)
        printf("ERROR: Failed TF_GraphOperationByName StatefulPartitionedCall\n");
    else
        printf("TF_GraphOperationByName StatefulPartitionedCall is OK\n");

    Output[0] = t2;
    //********* Allocate data for inputs & outputs
    TF_Tensor** InputValues = (TF_Tensor**)malloc(sizeof(TF_Tensor*) * NumInputs);
    TF_Tensor** OutputValues =(TF_Tensor**)malloc(sizeof(TF_Tensor*) * NumOutputs);

    int ndims = 2;
    int64_t dims[] = { 1,1 };
    float data[] = { 1 };
    int ndata = sizeof(float); // This is tricky, it number of bytes not number of element

    TF_Tensor* int_tensor = TF_NewTensor(TF_FLOAT, dims, ndims, data, ndata, &NoOpDeallocator, 0);
    if (int_tensor != NULL)
    {
        printf("TF_NewTensor is OK\n");
    }
    else
        printf("ERROR: Failed TF_NewTensor\n");
    TF_Tensor* out_tensor = TF_NewTensor(TF_FLOAT, dims, ndims, data, ndata, &NoOpDeallocator, 0);
    if (int_tensor != NULL)
    {
        printf("TF_NewTensor is OK\n");
    }
    else
        printf("ERROR: Failed TF_NewTensor\n");

    InputValues[0] = int_tensor;

    // //Run the Session
    TF_SessionRun(Session, NULL, Input, InputValues, NumInputs, Output, OutputValues, NumOutputs, NULL, 0, NULL, Status);

    if (TF_GetCode(Status) == TF_OK)
    {
        printf("Session is OK\n");
    }
    else
    {
        printf("%s", TF_Message(Status));
    }

    // //Free memory
    TF_DeleteGraph(Graph);
    TF_DeleteSession(Session, Status);
    TF_DeleteSessionOptions(SessionOpts);
    TF_DeleteStatus(Status);

    void* buff = TF_TensorData(OutputValues[0]);
    float* offsets = (float*)buff;
    printf("Result Tensor :\n");
    printf("%f\n", offsets[0]);
    return 0;
}

标签： c++tensorflowkerassequential