如何通过c检查张量流图（协议缓冲区）的节点名称

我想检查加载图是否正确 .

我通过python保存学习的协议缓冲区文件 . 并且，我通过c加载协议缓冲区文件 .

但是当会话运行时我无法获得输出张量 .

我想输出并检查图形信息 .

Saveing code by python

with tf.Graph().as_default() as graph:
    input_data = tf.placeholder(tf.float32, shape=train_data.shape, name="input")
    keep_prob = tf.placeholder(tf.float32)

    answer = net.inference(input_data, units, io_data_dim, keep_prob, output_net=True)
    saver = tf.train.Saver()

    with tf.Session() as sess:

        # Load model file
        sess.run(tf.initialize_all_variables())
        ckpt = tf.train.get_checkpoint_state(ckpt_dir_name)
        if ckpt: # checkpoint is exist
            last_model = ckpt.model_checkpoint_path # last model path
            saver.restore(sess, last_model)
        else:
            print("There is no training network...")
            exit()

        # check the saveing graph
        for v in sess.graph.get_operations():
            print(v.name)

        graph_def = graph_util.convert_variables_to_constants(sess, graph.as_graph_def(), ['output_lay/output_lay'])

        tf.train.write_graph(graph_def, '.', pb_name, as_text=False)

Loading code by C++

Status LoadGraph(string graph_file_name, std::unique_ptr<tensorflow::Session>* session) {
    tensorflow::GraphDef graph_def;
    Status graph_status = ReadBinaryProto(tensorflow::Env::Default(), graph_file_name, &graph_def);
    if (!graph_status.ok())
    {
        return graph_status;
    }
    session->reset(tensorflow::NewSession(tensorflow::SessionOptions()));
    Status create_status = (*session)->Create(graph_def);
    if (!create_status.ok())
    {
        return create_status;
    }
    return Status::OK();
}

Runing code by c++

input_node_name =“输入”

output_node_name =“output_lay / output_lay”

Status Infer(std::unique_ptr<tensorflow::Session>* session,
    tensorflow::Tensor* input,
    string* input_node_name,
    string* output_node_name,
    tensorflow::Tensor* output)
{
    tensorflow::Tensor input_object = *input;

    // input
    // I am not confident here
    tensorflow::Tensor keep_prob(tensorflow::DT_FLOAT, tensorflow::TensorShape());
    keep_prob.scalar<float>()() = 1.0;


    std::vector<std::pair<string, tensorflow::Tensor>> inputs = {
        {"Placeholder", keep_prob},
        {*input_node_name, *input}
    };

    // ouput
    std::vector<tensorflow::Tensor> outputs;

    std::cout << "Runnning network..." << std::endl;

    Status result = (*session)->Run(
        inputs,
        {*output_node_name},
        {},
        &outputs
        );
    // output 0 (no reply?)
    std::cout << "outputs size " << outputs.size() << std::endl;

    if (!result.ok())
    {
        LOG(ERROR) << "Failure: " << result;
    }
    (*output) = outputs[0];
    //std::cout << "take first output" << std::endl;
    return result;
}

result 是

无效参数：不兼容的形状： [77,1,513,16] vs. [13780,1,513,16] [[节点：conv1 / dropout / mul = Mul [T = DT_FLOAT，_device = "/job:localhost/replica:0/task:0/cpu:0"]（conv1 / dropout / Div，conv1 / dropout / Floor）]]

ex）如果不使用cnn（3层感知器网络）无效参数：不兼容的形状： [77,600] vs. [10242,600] [[Node：hidden1 / dropout / mul = Mul [T = DT_FLOAT，_device = "/job:localhost/replica:0/task:0/cpu:0"]（hidden1 / dropout / Div，hidden1 /差/地板）]]

pickup of the output 1st python code

• 输入

• 占位符

• 重塑/形状

• 重塑

• conv1 / weights

• conv1 /偏见

• conv1 / Conv2D

• conv1 / MaxPool

• conv1 / Add

• conv1 / conv1

• conv1 / dropout / Shape

• conv1 / dropout / add

• conv1 / dropout / Floor

• conv1 / dropout / Div

• conv1 / dropout / mul

• conv2 / weights

• conv2 /偏见

• conv2 / Conv2D

• ......

• Reshape_1 / shape

• Reshape_1

• hidden1 / weights

• hidden1 /偏见

• hidden1 / MatMul

• hidden1 /添加

• hidden1 / hidden1

• hidden1 / dropout / add

• hidden1 / dropout / Floor

• hidden1 / dropout / Div

• hidden1 / dropout / mul

• hidden2 / weights

• ......

• output_lay / weights

• output_lay /偏差

• output_lay / MatMul

• output_lay / Add

• output_lay / output_lay