Uses of Interface
org.deeplearning4j.eval.IEvaluation

Packages that use IEvaluation

Package	Description
org.deeplearning4j.eval
org.deeplearning4j.nn.multilayer

Uses of IEvaluation in org.deeplearning4j.eval

Classes in org.deeplearning4j.eval with type parameters of type IEvaluation

Modifier and Type	Interface and Description
interface	IEvaluation<T extends IEvaluation> A general purpose interface for evaluating neural networks - methods are shared by implemetations such as Evaluation, RegressionEvaluation, ROC, ROCMultiClass

Classes in org.deeplearning4j.eval that implement IEvaluation

Modifier and Type	Class and Description
class	BaseEvaluation<T extends BaseEvaluation> BaseEvaluation implement common evaluation functionality (for time series, etc) for Evaluation, RegressionEvaluation, ROC, ROCMultiClass etc.
class	Evaluation Evaluation metrics: precision, recall, f1
class	RegressionEvaluation Evaluation method for the evaluation of regression algorithms. Provides the following metrics, for each column: - MSE: mean squared error - MAE: mean absolute error - RMSE: root mean squared error - RSE: relative squared error - correlation coefficient See for example: http://www.saedsayad.com/model_evaluation_r.htm For classification, see Evaluation
class	ROC ROC (Receiver Operating Characteristic) for binary classifiers, using the specified number of threshold steps.
class	ROCMultiClass ROC (Receiver Operating Characteristic) for multi-class classifiers, using the specified number of threshold steps.

Uses of IEvaluation in org.deeplearning4j.nn.multilayer

Methods in org.deeplearning4j.nn.multilayer with parameters of type IEvaluation

Modifier and Type	Method and Description
void	MultiLayerNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, IEvaluation evaluation) Perform evaluation using an arbitrary IEvaluation instance.