FlexNN::NeuralNetwork Class Reference

Class representing a neural network. More...

#include <FlexNN.h>

Public Member Functions
	NeuralNetwork (const std::vector< Layer > &layers)
	Constructor for the NeuralNetwork class.
void	train (const Eigen::MatrixXd &input, const Eigen::MatrixXd &target, double learningRate, int epochs)
	Train the neural network.
double	accuracy (const Eigen::MatrixXd &X, const Eigen::MatrixXd &Y)
	Calculate the accuracy of the neural network.
Eigen::MatrixXd	predict (const Eigen::MatrixXd &input)
	Predict the output for given input data.

Detailed Description

Class representing a neural network.

This class encapsulates the functionality of a neural network, including training, prediction, and accuracy calculation. It uses a vector of Layer objects to represent the structure of the network.

Constructor & Destructor Documentation

NeuralNetwork()

FlexNN::NeuralNetwork::NeuralNetwork ( const std::vector< Layer > &  layers )

inline

Constructor for the NeuralNetwork class.

Parameters

layers

A vector of Layer objects representing the layers of the neural network.

Member Function Documentation

accuracy()

double FlexNN::NeuralNetwork::accuracy	(	const Eigen::MatrixXd &	X,
		const Eigen::MatrixXd &	Y
	)

Calculate the accuracy of the neural network.

This method computes the accuracy of the neural network's predictions against the target data.

Parameters

X	The input data for prediction.
Y	The target output data for comparison.

Returns

The accuracy as a double value.

predict()

Eigen::MatrixXd FlexNN::NeuralNetwork::predict ( const Eigen::MatrixXd &  input )

inline

Predict the output for given input data.

This method performs a forward pass through the neural network to predict the output for the provided input data.

Parameters

input

The input data for prediction.

Returns

The predicted output as an Eigen::MatrixXd.

train()

void FlexNN::NeuralNetwork::train	(	const Eigen::MatrixXd &	input,
		const Eigen::MatrixXd &	target,
		double	learningRate,
		int	epochs
	)

Train the neural network.

This method trains the neural network using the provided input and target data. It performs forward and backward passes, updating weights based on the gradients.

Parameters

input	The input data for training.
target	The target output data for training.
learningRate	The learning rate for weight updates.
epochs	The number of training epochs.

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The documentation for this class was generated from the following files:

• include/FlexNN.h
• lib/FlexNN.cpp