将mnist客户端重写为c++（arg[0]不是矩阵）

Question

我正在尝试将 mnist_client 的 python 代码重写为 c++。由于我是 tensorflow 和 TF 服务的新手，我遇到了一些困难。我浏览了教程和 c++ 客户端示例 (inception_client)。 Python mnist_client 没有任何问题，但是当我运行我的 c++ 客户端时，它给了我arg[0] is not a matrix

gRPC call return code: 3: In[0] is not a matrix 
 [[Node: MatMul = MatMul[T=DT_FLOAT, _output_shapes=[[?,10]], transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_x_0_0, Variable/read)]]

我按照教程中的方法训练了模型，并检查了我读取的 minst 数据是否正常。

从这里： tensorflow Invalid argument: In[0] is not a matrix , 我了解MatMul 至少需要二维数据。但是，我浏览了 inception_client 和 python mnist_client 的 c++ 代码，并且都将图像数据读入了 1-dim char 数组... 我在这里想念什么？

inception_client的代码：https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/inception_client.cc

任何帮助将不胜感激。 :)

class ServingClient{
public:
ServingClient(std::shared_ptr<Channel> channel) : stub_(PredictionService::NewStub(channel)){}

tensorflow::string callPredict( const tensorflow::string &model_name,
                                const tensorflow::string &model_signature,
                                const int num_tests){
PredictRequest request;
PredictResponse response;
ClientContext context;
int image_size;
int image_offset = 16;
int label_offset = 8;

request.mutable_model_spec()->set_name(model_name);
request.mutable_model_spec()->set_signature_name(model_signature);

google::protobuf::Map<tensorflow::string, tensorflow::TensorProto> &inputs = *request.mutable_inputs();

std::fstream imageFile("t10k-images-idx3-ubyte", std::ios::binary | std::ios::in);
std::fstream labelFile("t10k-labels-idx1-ubyte", std::ios::binary | std::ios::in);

labelFile.seekp(0);
imageFile.seekp(0);

uint32_t magic_number_images;
uint32_t nImages;
uint32_t magic_number_labels;
uint32_t rowsI =0;
uint32_t rowsL =0;
uint32_t colsI = 0;
uint32_t colsL = 0;


imageFile.read((char *)&magic_number_images, sizeof(magic_number_images));
imageFile.read((char *)&nImages, sizeof(nImages));
imageFile.read((char *)(&rowsI), sizeof(rowsI));
imageFile.read((char *)&colsI, sizeof(colsI));

image_size = ReverseInt(rowsI) * ReverseInt(colsI);

labelFile.read((char *)&magic_number_labels, sizeof(magic_number_labels));
labelFile.read((char *)&rowsL, sizeof(rowsL));

for(int i=0; i<num_tests; i++){
    tensorflow::TensorProto proto;

    labelFile.seekp(label_offset);
    imageFile.seekp(image_offset);

    //read mnist image
    char *img = new char[image_size]();
    char label = 0;
    imageFile.read((char *)img, image_size);

    image_offset += image_size;
    //read label
    labelFile.read(&label, 1);
    label_offset++;

    //predict
    proto.set_dtype(tensorflow::DataType::DT_STRING);
    proto.add_string_val(img, image_size);
    proto.mutable_tensor_shape()->add_dim()->set_size(1);
    inputs["images"] = proto;

    Status status = stub_->Predict(&context, request, &response);
    delete[] img;

    if(status.ok()){
    std::cout << "status OK." << std::endl;
    OutMap &map_outputs = *response.mutable_outputs();
    OutMap::iterator iter;
    int output_index = 0;

    for(iter = map_outputs.begin(); iter != map_outputs.end(); ++iter){
        tensorflow::TensorProto &result_tensor_proto = iter->second;
        tensorflow::Tensor tensor;
        //check if response converted succesfully 
        bool converted = tensor.FromProto(result_tensor_proto);
        if (converted) {
            std::cout << "the result tensor[" << output_index << "] is:" << std::endl
                        << tensor.SummarizeValue(10) << std::endl;
         } 
         else {
            std::cout << "the result tensor[" << output_index
                        << "] convert failed." << std::endl;
        }
        ++output_index;
                }
        }
    else{
        std::cout << "gRPC call return code: " << status.error_code() << ": "
            << status.error_message() << std::endl;
            }
        }
imageFile.close();
labelFile.close();
}

private:
    std::unique_ptr<PredictionService::Stub> stub_;

};

编辑 1： 我认为问题一定在于模型是如何创建的，以及客户端发送的数据是什么维度。 我使用提供的 python 程序来训练和导出设置尺寸的模型：

feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'], name='x')  # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

Answer 1

正如预期的那样，修复很明显。 所要做的就是添加另一个维度：

   proto.mutable_tensor_shape()->add_dim()->set_size(image_size);

获得[image_size,1] 形状。