C++ NeuralNetwork class 沒有成員拓撲

Question

我在遵循以下指南時遇到了一些麻煩： https://www.geeksforgeeks.org/ml-neural-network-implementation-in-c-from-scratch/我已經用 vcpkg 安裝了 eigen 庫，它似乎是工作，因為它沒有給出錯誤。

代碼：

#pragma once
// NeuralNetwork.hpp 
#include <eigen3/Eigen/Eigen> 
#include <stdio.h> 
#include <vector> 

// use typedefs for future ease for changing data types like : float to double 
typedef float Scalar;
typedef Eigen::MatrixXf Matrix;
typedef Eigen::RowVectorXf RowVector;
typedef Eigen::VectorXf ColVector;

// neural network implementation class! 
class NeuralNetwork {
public:
    // constructor 
    NeuralNetwork(std::vector<unsigned int> topology, Scalar learningRate = Scalar(0.005));

    // function for forward propagation of data 
    void propagateForward(RowVector& input);

    // function for backward propagation of errors made by neurons 
    void propagateBackward(RowVector& output);

    // function to calculate errors made by neurons in each layer 
    void calcErrors(RowVector& output);

    // function to update the weights of connections 
    void updateWeights();

    // function to train the neural network give an array of data points 
    void train(std::vector<RowVector*> data);

    // storage objects for working of neural network 
    /*
          use pointers when using std::vector<Class> as std::vector<Class> calls destructor of
          Class as soon as it is pushed back! when we use pointers it can't do that, besides
          it also makes our neural network class less heavy!! It would be nice if you can use
          smart pointers instead of usual ones like this
        */
    std::vector<RowVector*> neuronLayers; // stores the different layers of out network 
    std::vector<RowVector*> cacheLayers; // stores the unactivated (activation fn not yet applied) values of layers 
    std::vector<RowVector*> deltas; // stores the error contribution of each neurons 
    std::vector<Matrix*> weights; // the connection weights itself 
    Scalar learningRate;
};



// constructor of neural network class 
NeuralNetwork::NeuralNetwork(std::vector<unsigned int> topology, Scalar learningRate)
{
    this->topology = topology; //<- ERROR HERE
    this->learningRate = learningRate;
    for (unsigned int i = 0; i < topology.size(); i++) {
        // initialze neuron layers 
        if (i == topology.size() - 1)
            neuronLayers.push_back(new RowVector(topology[i]));
        else
            neuronLayers.push_back(new RowVector(topology[i] + 1));

        // initialize cache and delta vectors 
        cacheLayers.push_back(new RowVector(neuronLayers.size()));
        deltas.push_back(new RowVector(neuronLayers.size()));

        // vector.back() gives the handle to recently added element 
        // coeffRef gives the reference of value at that place  
        // (using this as we are using pointers here) 
        if (i != topology.size() - 1) {
            neuronLayers.back()->coeffRef(topology[i]) = 1.0;
            cacheLayers.back()->coeffRef(topology[i]) = 1.0;
        }

        // initialze weights matrix 
        if (i > 0) {
            if (i != topology.size() - 1) {
                weights.push_back(new Matrix(topology[i - 1] + 1, topology[i] + 1));
                weights.back()->setRandom();
                weights.back()->col(topology[i]).setZero();
                weights.back()->coeffRef(topology[i - 1], topology[i]) = 1.0;
            }
            else {
                weights.push_back(new Matrix(topology[i - 1] + 1, topology[i]));
                weights.back()->setRandom();
            }
        }
    }
};



// constructor of neural network class 
NeuralNetwork::NeuralNetwork(std::vector<unsigned int> topology,Scalar learningRate)
{
    this->topology = topology;
    this->learningRate = learningRate;
    for (unsigned int i = 0; i < topology.size(); i++) {
        // initialze neuron layers 
        if (i == topology.size() - 1)
            neuronLayers.push_back(new RowVector(topology[i]));
        else
            neuronLayers.push_back(new RowVector(topology[i] + 1));

        // initialize cache and delta vectors 
        cacheLayers.push_back(new RowVector(neuronLayers.size()));
        deltas.push_back(new RowVector(neuronLayers.size()));

        // vector.back() gives the handle to recently added element 
        // coeffRef gives the reference of value at that place  
        // (using this as we are using pointers here) 
        if (i != topology.size() - 1) {
            neuronLayers.back()->coeffRef(topology[i]) = 1.0;
            cacheLayers.back()->coeffRef(topology[i]) = 1.0;
        }

        // initialze weights matrix 
        if (i > 0) {
            if (i != topology.size() - 1) {
                weights.push_back(new Matrix(topology[i - 1] + 1, topology[i] + 1));
                weights.back()->setRandom();
                weights.back()->col(topology[i]).setZero();
                weights.back()->coeffRef(topology[i - 1], topology[i]) = 1.0;
            }
            else {
                weights.push_back(new Matrix(topology[i - 1] + 1, topology[i]));
                weights.back()->setRandom();
            }
        }
    }
};

我得到的錯誤是：

class NeuralNetwork has no member "topology"

我在這里不知所措，我不明白為什么當“拓撲”實際上在構造函數中時它會給我這個錯誤。

Answer 1

正是它在錫上所說的，class 聲明中的成員列表：

class NeuralNetwork {
//...
    std::vector<RowVector*> neuronLayers; // stores the different layers of out network 
    std::vector<RowVector*> cacheLayers; // stores the unactivated (activation fn not yet applied) values of layers 
    std::vector<RowVector*> deltas; // stores the error contribution of each neurons 
    std::vector<Matrix*> weights; // the connection weights itself 
    Scalar learningRate;
};

不包含完全命名為topology的成員。 唯一的成員是：

• neuronLayers
• cacheLayers
• deltas
• weights
• learningRate