How to implementing a neural network in C++

There are several benefits to using a neural network in C++. Because of its speed and aptitude to optimize memory allocation for complex computations, C++ provides improved execution capacity and revolutionized performance. Additionally, it enables efficient parallel computing paradigm execution, lower-level hardware access, and interfacing with hardware accelerators like GPUs. Accordingly, C++ may be used in embedded systems and robotics as well as other production-level applications that demand both stability and "real-time" operations. Additionally, the development of neural networks in C++ brings remarkable opportunities to modify the networks for specific needs and improves the understanding of algorithms.

Difference in implementation in C++ from Python

Neural network programming in C++ is not the same as neural network programming in Python. Here's the comparison of regarding neural network implementation in C++ vs Python:

Feature	C++	Python
Ease of Use	Complex and manual coding	Simple and quick prototyping with minimal code
Performance	High efficiency, great for real-time applications	Adequate for most tasks, with less performance control
Memory Management	Low-level control, manual management	Automated, handled by libraries
Library Support	Fewer libraries	Extensive library support (TensorFlow, PyTorch)
Community Support	Smaller community	Large, active community
Versatility	Ideal for high-performance and real-time applications	Versatile with modern libraries and easy deployment

How to implement a neural network

Using a neural network in C++ requires one to build a class and function to describe a network’s organization, such as neurons, which are the main units, layers, and the whole network. The section that follows deduces a C++ code of feedforward neural network presentation below.

Neuron class: You may define a class to represent a neuron in the neural network. Each neuron is described by its weights and activating function, resulting in an output value.
Layer class: Create a class that stands for the neuronal layer of the network. It will deal with the two processes, that is the passage of input through the layer and the computations carried out, including the output.
Neural network class: Declare a class to convoke a whole neural network system. We will have a class with multiple layers to perform forward pass through the network.

Neural network in C++

The C++ language's foundation for neural network design is made up of the programs listed below. The sequence gathers the classes of neurons housed in the network's tier, layers, and entire matrix; it also gathers the functions that compute the output of each neuron and carries out the network's pass. However, keep in mind that this is a very basic example, and "real-life" networks can require additional parts and adjustments.

Here’s a basic implementation of these concepts in C++:

#include <iostream>
#include <vector>
#include <cmath>
using namespace std;
// Activation function (sigmoid)
double sigmoid(double x) {
    return 1.0 / (1.0 + exp(-x));
}
// Neuron class
class Neuron {
private:
    vector<double> weights;
    double output;
public:
    Neuron(int numInputs) {
        // Initialize weights randomly
        for (int i = 0; i < numInputs; ++i) {
            weights.push_back((double)rand() / RAND_MAX);
        }
    }
    // Calculate output of the neuron
    double calculateOutput(const vector<double>& inputs) {
        double sum = 0.0;
        for (int i = 0; i < inputs.size(); ++i) {
            sum += inputs[i] * weights[i];
        }
        output = sigmoid(sum);
        return output;
    }
};
// Layer class
class Layer {
private:
    vector<Neuron> neurons;
public:
    Layer(int numNeurons, int numInputsPerNeuron) {
        // Initialize neurons
        for (int i = 0; i < numNeurons; ++i) {
            neurons.push_back(Neuron(numInputsPerNeuron));
        }
    }
    // Calculate outputs of all neurons in the layer
    vector<double> calculateOutputs(const vector<double>& inputs) {
        vector<double> outputs;
        for (int i = 0; i < neurons.size(); ++i) {
            outputs.push_back(neurons[i].calculateOutput(inputs));
        }
        return outputs;
    }
};
// NeuralNetwork class
class NeuralNetwork {
private:
    vector<Layer> layers;
public:
    NeuralNetwork(const vector<int>& layerSizes) {
        // Initialize layers
        for (int i = 0; i < layerSizes.size() - 1; ++i) {
            layers.push_back(Layer(layerSizes[i + 1], layerSizes[i]));
        }
    }
    // Forward pass through the network
    vector<double> forwardPass(const vector<double>& inputs) {
        vector<double> currentInputs = inputs;
        for (int i = 0; i < layers.size(); ++i) {
            currentInputs = layers[i].calculateOutputs(currentInputs);
        }
        return currentInputs;
    }
};
int main() {
    // Define network architecture (e.g., 2 input, 2 hidden, 1 output)
    vector<int> layerSizes = {2, 2, 1};
    NeuralNetwork nn(layerSizes);
    // Example input
    vector<double> input = {0.5, 0.3};
    // Perform forward pass
    vector<double> output = nn.forwardPass(input);
    // Print output
    cout << "Output: ";
    for (int i = 0; i < output.size(); ++i) {
        cout << output[i] << " ";
    }
    cout << endl;
    return 0;
}

Let's break the code written above:

Lines 8–10: This function implements the sigmoid activation function, which is one of the well-known non-linear functions and is applied to neural networks to transform linear models into non-linear ones. Since neural networks without activation functions can only represent linear data sets, the sigmoid activation function is applied to neural networks in order to introduce non-linearity.
Lines 13–23: Here, the class is the neuron, which is a part of the neural network. It is composed of both private members for parameters and results (Neuron output). The constructed neuron is assigned random values for weight through the use of an initializer.
Lines 26–33: This mechanism finds out the output of a neuron by doing a weighted sum of the inputs that it has and applies a sigmoid activation function to it.
Lines 37–56: These are the bypass neurons, which represent one of the network layer. It assumes the form of a network of neurons' vector. The constructor sets the width of the layer equal to a given number of neurons and each of the neurons has the input width which was provided.
Lines 49–55: This function calculates the outputs of all neurons in the layer, taking into consideration given inputs.
Lines 59–68: This class is designed to encapsulate a neural network structured as a vector of layers.
Lines 70–77: This algorithm is taken through all layers—starting from the inputs to the outputs- and is used to calculate the final outputs.
Lines 80–99: The main function does exactly what it instructs, to illustrate the use and creation of the neural network. It starts the network with some predefined architecture, passes the input dataset through the network to obtain the desired output, and then prints the result.

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