#include <CscNeuralNetworkTensorFlow.h>

Inheritance diagram for conscience_core::ai::nn::CscNeuralNetworkTensorFlow:

Public Member Functions
	CscNeuralNetworkTensorFlow (CscNeuralNetworkType neuralNetworkType, NeuralNetworkHyperparameters neuralNetworkHyperparametersInit, TrainingHyperparameters trainingHyperparametersInit)

	CscNeuralNetworkTensorFlow (CscNeuralNetworkType neuralNetworkType, NeuralNetworkHyperparameters neuralNetworkHyperparametersInit, TrainingHyperparameters trainingHyperparametersInit, float weightsLoadedFromModel, float biasesLoadedFromModel, bool withCostFunction)

virtual	~CscNeuralNetworkTensorFlow ()

CscNeuralNetworkTensorFlow *	getDuplicata ()

vector< TF_Session * >	getSessions ()

CscNeuralNetworkBuilderTensorFlow *	getBuilder () const

CscNeuralNetworkPersistenceTensorFlow *	getPersistence () const

ResultTraining	trainNeuralNetwork (const int nbEpochs, const int nbMiniBatchs, DataSet trainingDataSet, DataSet validationDataSet, const bool debugMode=false) override
	Trains a neural network using the specified parameters and datasets. More...

float	testNeuralNetworkOnValidationDataSet (DataSet *validationDataSet, const bool nnNeverInitialized=false)
	Tests the neural network on the provided validation dataset and calculates the mean error. More...

vector< float >	executeWithoutCostFunction (TF_Session session, float *inputs)
	Executes the neural network without a cost function and returns the results. More...

Public Member Functions inherited from conscience_core::ai::nn::CscNeuralNetwork
	CscNeuralNetwork (CscNeuralNetworkType neuralNetworkType, NeuralNetworkHyperparameters neuralNetworkHyperparametersInit, TrainingHyperparameters trainingHyperparametersInit)

	CscNeuralNetwork (CscNeuralNetworkType neuralNetworkType, NeuralNetworkHyperparameters neuralNetworkHyperparametersInit, TrainingHyperparameters trainingHyperparametersInit, float weightsLoadedFromModel, float biasesLoadedFromModel)

virtual	~CscNeuralNetwork ()

CscNeuralNetworkType	getNeuralNetworkType () const

CscNeuralNetwork::NeuralNetworkHyperparameters *	getNeuralNetworkHyperparameters () const

CscNeuralNetwork::TrainingHyperparameters *	getTrainingHyperparameters () const

Static Public Member Functions
static CscNeuralNetworkTensorFlow *	loadModelFromDirectory (const fs::path &dirPath, bool forTraining)
	Loads a neural network model from a specified directory. More...

Static Public Member Functions inherited from conscience_core::ai::nn::CscNeuralNetwork
static float ***	adaptDataForMiniBatches (float **dataToTransform, const int minibatchSize, const int dataSetSize)
	Prepares the dataset for minibatch processing by dividing it into smaller batches. More...

static void	normalizeDataAutoRange (float **data, size_t size, int nbInputsOrOutputs, CscNormalizationType normalizationType)

static void	normalizeDataCustomRange0to1 (float *data, size_t size, int nbInputsOrOutputs, float minValue, float *maxValue)

static void	normalizeDataCustomRangeMinus1to1 (float *data, size_t size, int nbInputsOrOutputs, float minValue, float *maxValue)

static void	normalizeData (float **data, size_t size, int rowData, float newMin, float newMax, float minValue, float maxValue)

static void	deleteAllDataMiniBatches (float ***dataMiniBatches, const int nbMiniBatches, const int nbDataPerMinibatch)
	Frees the memory allocated for the mini-batches of data. More...

static void	initializeWeights (float weightsToInitialize, int size, CscInitializationWeightsStrategy strategy, int fanIn, int fanOut=0, float weightsLoaded=nullptr)
	Initializes the weights of the neural network according to a specified initialization strategy. More...

static void	initializeBias (float biasToInitialize, int size, CscInitializationBiasStrategy strategy, float biasLoaded=nullptr)

Additional Inherited Members
Protected Member Functions inherited from conscience_core::ai::nn::CscNeuralNetwork
void	shuffleMinibatchData (float *minibatchDataInputs, float *minibatchDataTargets, const int numberOfBatches, const int minibatchSize)
	Shuffles the data within each minibatch as well as between minibatches. More...

Static Protected Member Functions inherited from conscience_core::ai::nn::CscNeuralNetwork
static float	calculateStddevHe (int fanIn)

static float	calculateLimitGlorot (int fanIn, int fanOut)

static float	calculateStddevGlorot (int fanIn, int fanOut)

static float	calculateStddevLeCun (int fanIn)

static void	initNormal (float *weights, int size, mt19937 &gen, double stddev)

static void	initUniform (float *weights, int size, mt19937 &gen, float limit)

Protected Attributes inherited from conscience_core::ai::nn::CscNeuralNetwork
CscNeuralNetworkType	neuralNetworkType

TrainingHyperparameters *	trainingHyperparameters = nullptr

NeuralNetworkHyperparameters *	neuralNetworkHyperparameters = nullptr

unique_ptr< CscLogger >	logger

Constructor & Destructor Documentation

◆ CscNeuralNetworkTensorFlow() [1/2]

conscience_core::ai::nn::CscNeuralNetworkTensorFlow::CscNeuralNetworkTensorFlow	(	CscNeuralNetworkType	neuralNetworkType,
		NeuralNetworkHyperparameters *	neuralNetworkHyperparametersInit,
		TrainingHyperparameters *	trainingHyperparametersInit
	)

◆ CscNeuralNetworkTensorFlow() [2/2]

conscience_core::ai::nn::CscNeuralNetworkTensorFlow::CscNeuralNetworkTensorFlow	(	CscNeuralNetworkType	neuralNetworkType,
		NeuralNetworkHyperparameters *	neuralNetworkHyperparametersInit,
		TrainingHyperparameters *	trainingHyperparametersInit,
		float **	weightsLoadedFromModel,
		float **	biasesLoadedFromModel,
		bool	withCostFunction
	)

◆ ~CscNeuralNetworkTensorFlow()

conscience_core::ai::nn::CscNeuralNetworkTensorFlow::~CscNeuralNetworkTensorFlow ( )

virtual

Member Function Documentation

◆ executeWithoutCostFunction()

vector< float > conscience_core::ai::nn::CscNeuralNetworkTensorFlow::executeWithoutCostFunction	(	TF_Session *	session,
		float **	inputs
	)

Executes the neural network without a cost function and returns the results.

Parameters

session	Pointer to the TensorFlow session.
inputs	Pointer to the input data.

Returns: A vector of floats containing the results of the neural network execution.

This function initializes the neural network values and prepares the input data as a TensorFlow tensor. It then executes the graph without a cost function and retrieves the results. The results are converted into a vector of floats and returned. The function ensures proper cleanup of TensorFlow tensors used in the process.

◆ getBuilder()

CscNeuralNetworkBuilderTensorFlow * conscience_core::ai::nn::CscNeuralNetworkTensorFlow::getBuilder ( ) const

◆ getDuplicata()

CscNeuralNetworkTensorFlow * conscience_core::ai::nn::CscNeuralNetworkTensorFlow::getDuplicata ( )

◆ getPersistence()

CscNeuralNetworkPersistenceTensorFlow * conscience_core::ai::nn::CscNeuralNetworkTensorFlow::getPersistence ( ) const

◆ getSessions()

vector< TF_Session * > conscience_core::ai::nn::CscNeuralNetworkTensorFlow::getSessions ( )

◆ loadModelFromDirectory()

CscNeuralNetworkTensorFlow * conscience_core::ai::nn::CscNeuralNetworkTensorFlow::loadModelFromDirectory	(	const fs::path &	dirPath,
		bool	forTraining
	)

static

Loads a neural network model from a specified directory.

Parameters

filePath	The file path to the directory containing the model.
forTraining	Flag indicating if the model is to be loaded for training.

Returns: A pointer to the loaded CscNeuralNetworkTensorFlow object.

This function loads a neural network model from the specified directory. It utilizes the CscNeuralNetworkPersistenceTensorFlow class to retrieve all the necessary parameters (including training hyperparameters, neural network hyperparameters, weights, and biases). It then constructs a new CscNeuralNetworkTensorFlow object with these parameters and returns a pointer to it.

◆ testNeuralNetworkOnValidationDataSet()

float conscience_core::ai::nn::CscNeuralNetworkTensorFlow::testNeuralNetworkOnValidationDataSet	(	DataSet *	validationDataSet,
		const bool	nnNeverInitialized = `false`
	)

Tests the neural network on the provided validation dataset and calculates the mean error.

Parameters

validationDataSet	Pointer to the dataset used for validation.
nnNeverInitialized	Flag indicating if the neural network has never been initialized.

Returns: The mean error of the neural network on the validation dataset.

This function tests the neural network on a given validation dataset. It first checks that the number of data points per mini-batch does not exceed a predefined maximum. If the neural network has never been initialized (nnNeverInitialized is true), it initializes the weights and biases. The function then calculates and returns the mean error by executing the network on mini-batches of the validation dataset.

◆ trainNeuralNetwork()

CscNeuralNetwork::ResultTraining conscience_core::ai::nn::CscNeuralNetworkTensorFlow::trainNeuralNetwork	(	const int	nbEpochs,
		const int	nbMiniBatchs,
		DataSet *	trainingDataSet,
		DataSet *	validationDataSet,
		const bool	debugMode = `false`
	)

overridevirtual

Trains a neural network using the specified parameters and datasets.

Parameters

nbEpochs	The number of epochs for training.
nbMiniBatchs	The number of mini-batches to use for each epoch.
trainingDataSet	Pointer to the dataset used for training.
validationDataSet	Pointer to the dataset used for validation.
debugMode	Flag to enable or disable debug mode, which logs additional information.

Returns: A ResultTraining object containing details about the training process, such as the number of epochs performed, the epoch with the best validation result, the mean error on training data, and the best mean error on validation data.

The function first asserts that the number of data per mini-batch matches the expected value based on the training dataset size and number of mini-batches. It then initializes the training process, including session creation, weight and bias initialization, and optimizer-specific initializations. The main training loop iterates through the specified number of epochs, shuffling the training data, and executing training on mini-batches. During each epoch, the mean error on training and validation data is calculated and logged if debug mode is enabled. The function also includes an early stopping mechanism, which terminates training if there is no improvement in validation error for a specified number of epochs (patience). Finally, the function returns a ResultTraining object summarizing the training results.

Implements conscience_core::ai::nn::CscNeuralNetwork.

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

ArtificialIntelligence/CscNeuralNetworkTensorFlow.h
ArtificialIntelligence/CscNeuralNetworkTensorFlow.cpp

Conscience Core

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Constructor & Destructor Documentation

◆ CscNeuralNetworkTensorFlow() [1/2]

◆ CscNeuralNetworkTensorFlow() [2/2]

◆ ~CscNeuralNetworkTensorFlow()

Member Function Documentation

◆ executeWithoutCostFunction()

◆ getBuilder()

◆ getDuplicata()

◆ getPersistence()

◆ getSessions()

◆ loadModelFromDirectory()

◆ testNeuralNetworkOnValidationDataSet()

◆ trainNeuralNetwork()