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🧠 Neural Network C++ Implementation Documentation

Table of Contents

  1. Introduction
  2. MathUtils Class
  3. Activation Functions
  4. Neural Network Configuration
  5. Neural Network Class

Introduction

The NeuralNetwork C++ implementation provides a flexible and customizable framework for creating and training feedforward neural networks. The implementation supports various activation functions, including sigmoid, hyperbolic tangent (tanh), rectified linear unit (ReLU), linear, and softmax. The neural network is designed to handle a configurable number of input, hidden, and output nodes.

MathUtils Class

The MathUtils class contains static methods for common mathematical operations used in neural networks. Currently, it provides methods for calculating the sigmoid and hyperbolic tangent functions.

Sigmoid Function

static double sigmoid(double x);

Hyperbolic Tangent Function

static double tanh(double x);

Activation Functions

The ActivationFunction enumeration defines the supported activation functions for the neural network. The available functions include:

  • TANH
  • SIGMOID
  • RELU
  • LINEAR
  • TANH_DERIVATIVE
  • SOFTMAX

Neural Network Configuration

The NeuralNetworkConfig struct encapsulates the configuration parameters for creating a neural network. These parameters include:

  • inputSize: Number of input nodes
  • hiddenSize: Number of hidden nodes
  • outputSize: Number of output nodes
  • learningRate: Learning rate for weight updates during training
  • activationFunction: Activation function for the hidden and output layers

Neural Network Class

The NeuralNetwork class encapsulates the functionality of a feedforward neural network.

Constructor

NeuralNetwork(const NeuralNetworkConfig& config, ActivationFunction activationFunction);
  • Parameters:
    • config: Configuration parameters for the neural network.
    • activationFunction: Activation function for hidden and output layers.

Activation Function

double activate(double x);
  • Parameters:
    • x: Input value to the activation function.
  • Returns:
    • The result of applying the specified activation function to the input.

Feedforward

std::vector<double> feedforward(const std::vector<double>& inputs);
  • Parameters:
    • inputs: Input values to the neural network.
  • Returns:
    • The output values of the neural network after a feedforward pass.

Backpropagation

void backpropagation(const std::vector<double>& inputs, const std::vector<double>& targets);
  • Parameters:
    • inputs: Input values to the neural network.
    • targets: Target output values for the given inputs.
  • Description:
    • Performs backpropagation to update the weights of the neural network.

Training

void train(const std::vector<std::pair<std::vector<double>, std::vector<double>>>& trainingData, int numberOfIterations);
  • Parameters:
    • trainingData: Training data in the form of input-output pairs.
    • numberOfIterations: Number of training iterations.
  • Description:
    • Trains the neural network using the provided training data.

Model Saving and Loading

void saveModel(const std::string& filePath);
  • Parameters:
    • filePath: Path to the file where the model will be saved.
  • Description:
    • Saves the neural network model to a file.
int loadModel(const std::string& filePath);
  • Parameters:
    • filePath: Path to the file from which the model will be loaded.
  • Returns:
    • Returns true if the model is successfully loaded, otherwise false.
  • Description:
    • Loads a previously saved neural network model from a file.

This C++ implementation provides a foundation for building and experimenting with neural networks, allowing users to customize the architecture, activation functions, and training process based on their specific needs.