#include <Training.h>

Public Member Functions
	SupervisedLearningCycle (INeuralNetworkPtr model, ILabeledTrainingDataPtr trainingData, ILabeledTrainingDataPtr testData, LossFunctions costFunction, AZStd::size_t totalIterations, AZStd::size_t batchSize, float learningRate, float learningRateDecay, float earlyStopCost)

void	InitializeContexts ()

void	StartTraining ()

void	StopTraining ()

Public Attributes
AZStd::atomic< AZStd::size_t >	m_currentEpoch = 0

std::atomic< bool >	m_trainingComplete = true

AZ::ThreadSafeDeque< float >	m_testCosts

AZ::ThreadSafeDeque< float >	m_trainCosts

INeuralNetworkPtr	m_model

bool	m_shuffleTrainingData = true

TrainingDataView	m_trainData

TrainingDataView	m_testData

LossFunctions	m_costFunction = LossFunctions::MeanSquaredError

AZStd::size_t	m_totalIterations = 0

AZStd::size_t	m_batchSize = 0

float	m_learningRate = 0.0f

float	m_learningRateDecay = 0.0f

float	m_earlyStopCost = 0.0f

AZStd::size_t	m_currentIndex = 0

AZStd::unique_ptr< IInferenceContext >	m_inferenceContext

AZStd::unique_ptr< ITrainingContext >	m_trainingContext

Detailed Description

Performs a supervised learning training cycle. Supervised learning is a form of machine learning where a model is provided a set of training data with expected output Training then takes place in an iterative loop where the total error (cost, loss) of the model is minimized This differs from unsupervised learning, where the training data lacks any form of labeling (expected correct output), and the model is expected to learn the underlying structures of data on its own.

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

Gems/MachineLearning/Code/Source/Algorithms/Training.h

Public Member Functions

Public Attributes

Detailed Description