Uses of Interface
deepnetts.data.MLDataItem

Packages that use MLDataItem

Package	Description
deepnetts.automl	Support for automatically building deep learning models using hyper-parameter search.
deepnetts.data	Data structures to store example data used for building machine learning models.
deepnetts.data.norm	Data normalization methods, used to scale data to specific range, in order to make them suitable for use by a neural network.
deepnetts.eval	Evaluation procedures for machine learning models, used to estimate how good models are performing when given new data that (that was not used for training).
deepnetts.net	Neural network architectures with their corresponding builders.
deepnetts.net.loss	Commonly used loss functions, which are used to calculate error during the training as a difference between predicted and target output.
deepnetts.net.train	Training algorithms and related utilities.

Uses of MLDataItem in deepnetts.automl

Methods in deepnetts.automl that return types with arguments of type MLDataItem

Modifier and Type	Method and Description
javax.visrec.ml.eval.Evaluator<NeuralNetwork,javax.visrec.ml.data.DataSet<? extends MLDataItem>>	HyperParameterSearch.getEvaluator()

Method parameters in deepnetts.automl with type arguments of type MLDataItem

Modifier and Type	Method and Description
HyperParameterSearch	HyperParameterSearch.evaluator(javax.visrec.ml.eval.Evaluator<NeuralNetwork,javax.visrec.ml.data.DataSet<? extends MLDataItem>> evaluator)

Uses of MLDataItem in deepnetts.data

Classes in deepnetts.data with type parameters of type MLDataItem

Modifier and Type	Class and Description
class	TabularDataSet<T extends MLDataItem> Basic data set with tabular data.

Classes in deepnetts.data that implement MLDataItem

Modifier and Type	Class and Description
class	ExampleImage Example image to train a deep learning model.
static class	TabularDataSet.Item Represents a basic data set item (single row) with input tensor and target vector in a data set.

Uses of MLDataItem in deepnetts.data.norm

Method parameters in deepnetts.data.norm with type arguments of type MLDataItem

Modifier and Type	Method and Description
void	DecimalScaler.apply(javax.visrec.ml.data.DataSet<MLDataItem> dataSet) Performs normalization on the given inputs.
void	MaxScaler.apply(javax.visrec.ml.data.DataSet<MLDataItem> dataSet) Performs normalization on the given inputs.
void	MinMaxScaler.apply(javax.visrec.ml.data.DataSet<MLDataItem> dataSet) Performs normalization on the given inputs.
void	RangeScaler.apply(javax.visrec.ml.data.DataSet<MLDataItem> dataSet) Performs normalization on the given inputs.
void	Standardizer.apply(javax.visrec.ml.data.DataSet<MLDataItem> dataSet)

Constructor parameters in deepnetts.data.norm with type arguments of type MLDataItem

Constructor and Description
DecimalScaler(javax.visrec.ml.data.DataSet<MLDataItem> dataSet) Creates a new instance of max normalizer initialized to max values in given data set.
MaxScaler(javax.visrec.ml.data.DataSet<MLDataItem> dataSet) Creates a new instance of max normalizer initialized to max values in given data set.
MinMaxScaler(javax.visrec.ml.data.DataSet<MLDataItem> dataSet) Creates a new instance of max normalizer initialized to max values in given data set.
Standardizer(javax.visrec.ml.data.DataSet<MLDataItem> dataSet)

Uses of MLDataItem in deepnetts.eval

Method parameters in deepnetts.eval with type arguments of type MLDataItem

Modifier and Type	Method and Description
ClassificationMetrics	ClassifierEvaluator.evaluate(NeuralNetwork neuralNet, javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet) Performs classifier evaluation and returns classification performance metrics.
javax.visrec.ml.eval.EvaluationMetrics	RegresionEvaluator.evaluate(NeuralNetwork neuralNet, javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet)
static ClassificationMetrics	Evaluators.evaluateClassifier(NeuralNetwork<?> neuralNet, javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet)
static RegressionMetrics	Evaluators.evaluateRegressor(NeuralNetwork<?> neuralNet, javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet) Evaluates specified neural network with test set, as a regression model and returns basic regression evauation metrics.

Uses of MLDataItem in deepnetts.net

Method parameters in deepnetts.net with type arguments of type MLDataItem

Modifier and Type	Method and Description
javax.visrec.ml.eval.EvaluationMetrics	NeuralNetwork.test(javax.visrec.ml.data.DataSet<? extends MLDataItem> testSet) Tests how good are predictions of this network using specified test set.
void	NeuralNetwork.train(javax.visrec.ml.data.DataSet<? extends MLDataItem> trainingSet) Trains the neural network using specified training set.

Uses of MLDataItem in deepnetts.net.loss

Method parameters in deepnetts.net.loss with type arguments of type MLDataItem

Modifier and Type	Method and Description
default float	LossFunction.valueFor(NeuralNetwork nnet, javax.visrec.ml.data.DataSet<? extends MLDataItem> dataSet) Calculates and returns loss function value for the given neural network and data set.

Uses of MLDataItem in deepnetts.net.train

Method parameters in deepnetts.net.train with type arguments of type MLDataItem

Modifier and Type	Method and Description
KFoldCrossValidation.Builder	KFoldCrossValidation.Builder.evaluator(javax.visrec.ml.eval.Evaluator<NeuralNetwork,javax.visrec.ml.data.DataSet<? extends MLDataItem>> evaluator)
void	BackpropagationTrainer.setTestSet(javax.visrec.ml.data.DataSet<MLDataItem> testSet) Test set is used after the training to estimate performance of the trained model and generalization ability with new data.
void	BackpropagationTrainer.train(javax.visrec.ml.data.DataSet<? extends MLDataItem> trainingSet) Runs training using specified training set.
void	Trainer.train(javax.visrec.ml.data.DataSet<? extends MLDataItem> trainingSet) Trains this model using specified training set.
void	BackpropagationTrainer.train(javax.visrec.ml.data.DataSet<MLDataItem> trainingSet, javax.visrec.ml.data.DataSet<MLDataItem> validationSet) Runs training using given training and validation sets.
void	BackpropagationTrainer.train(javax.visrec.ml.data.DataSet<MLDataItem> trainingSet, javax.visrec.ml.data.DataSet<MLDataItem> validationSet) Runs training using given training and validation sets.