Uses of Interface
org.deeplearning4j.eval.IEvaluation

Packages that use IEvaluation

Package	Description
org.deeplearning4j.earlystopping.scorecalc
org.deeplearning4j.earlystopping.scorecalc.base
org.deeplearning4j.eval
org.deeplearning4j.nn.api
org.deeplearning4j.nn.graph
org.deeplearning4j.nn.multilayer
org.deeplearning4j.optimize.listeners
org.deeplearning4j.optimize.listeners.callbacks

Uses of IEvaluation in org.deeplearning4j.earlystopping.scorecalc

Methods in org.deeplearning4j.earlystopping.scorecalc that return IEvaluation

Modifier and Type	Method and Description
protected IEvaluation	ROCScoreCalculator.newEval()

Methods in org.deeplearning4j.earlystopping.scorecalc with parameters of type IEvaluation

Modifier and Type	Method and Description
protected double	ROCScoreCalculator.finalScore(IEvaluation eval)

Uses of IEvaluation in org.deeplearning4j.earlystopping.scorecalc.base

Classes in org.deeplearning4j.earlystopping.scorecalc.base with type parameters of type IEvaluation

Modifier and Type	Class and Description
class	BaseIEvaluationScoreCalculator<T extends Model,U extends IEvaluation> Base score function based on an IEvaluation instance.

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, fBeta, accuracy, Matthews correlation coefficient, gMeasure - Top N accuracy (if using constructor Evaluation.Evaluation(List, int)) - Custom binary evaluation decision threshold (use constructor Evaluation.Evaluation(double) (default if not set is argmax / 0.5) - Custom cost array, using Evaluation.Evaluation(INDArray) or Evaluation.Evaluation(List, INDArray) for multi-class Note: Care should be taken when using the Evaluation class for binary classification metrics such as F1, precision, recall, etc.
class	EvaluationBinary EvaluationBinary: used for evaluating networks with binary classification outputs.
class	EvaluationCalibration EvaluationCalibration is an evaluation class designed to analyze the calibration of a classifier. It provides a number of tools for this purpose: - Counts of the number of labels and predictions for each class - Reliability diagram (or reliability curve) - Residual plot (histogram) - Histograms of probabilities, including probabilities for each class separately References: - Reliability diagram: see for example Niculescu-Mizil and Caruana 2005, Predicting Good Probabilities With Supervised Learning - Residual plot: see Wallace and Dahabreh 2012, Class Probability Estimates are Unreliable for Imbalanced Data (and How to Fix Them)
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 - PC: pearson correlation coefficient - R^2: coefficient of determination See for example: http://www.saedsayad.com/model_evaluation_r.htm For classification, see Evaluation
class	ROC ROC (Receiver Operating Characteristic) for binary classifiers. ROC has 2 modes of operation: (a) Thresholded (less memory) (b) Exact (default; use numSteps == 0 to set.
class	ROCBinary ROC (Receiver Operating Characteristic) for multi-task binary classifiers.
class	ROCMultiClass ROC (Receiver Operating Characteristic) for multi-class classifiers.

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

Modifier and Type	Method and Description
static <T extends IEvaluation> T	BaseEvaluation.fromJson(String json, Class<T> clazz)
static <T extends IEvaluation> T	BaseEvaluation.fromYaml(String yaml, Class<T> clazz)

Uses of IEvaluation in org.deeplearning4j.nn.api

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

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	NeuralNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) This method executes evaluation of the model against given iterator and evaluation implementations
<T extends IEvaluation> T[]	NeuralNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T... evaluations) This method executes evaluation of the model against given iterator and evaluation implementations

Methods in org.deeplearning4j.nn.api that return IEvaluation

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	NeuralNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) This method executes evaluation of the model against given iterator and evaluation implementations
<T extends IEvaluation> T[]	NeuralNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T... evaluations) This method executes evaluation of the model against given iterator and evaluation implementations

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

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	NeuralNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) This method executes evaluation of the model against given iterator and evaluation implementations
<T extends IEvaluation> T[]	NeuralNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T... evaluations) This method executes evaluation of the model against given iterator and evaluation implementations

Uses of IEvaluation in org.deeplearning4j.nn.graph

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

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	ComputationGraph.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) Perform evaluation on the given data (DataSetIterator) with the given IEvaluation instance
<T extends IEvaluation> T[]	ComputationGraph.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T... evaluations) Perform evaluation on the given data (MultiDataSetIterator) with the given IEvaluation instance

Methods in org.deeplearning4j.nn.graph that return IEvaluation

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	ComputationGraph.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) Perform evaluation on the given data (DataSetIterator) with the given IEvaluation instance
<T extends IEvaluation> T[]	ComputationGraph.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T... evaluations) Perform evaluation on the given data (MultiDataSetIterator) with the given IEvaluation instance

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

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	ComputationGraph.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) Perform evaluation on the given data (DataSetIterator) with the given IEvaluation instance
<T extends IEvaluation> T[]	ComputationGraph.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T... evaluations) Perform evaluation on the given data (MultiDataSetIterator) with the given IEvaluation instance

Uses of IEvaluation in org.deeplearning4j.nn.multilayer

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

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	MultiLayerNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) Perform evaluation using an arbitrary IEvaluation instance.
<T extends IEvaluation> T[]	MultiLayerNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T[] evaluations)

Methods in org.deeplearning4j.nn.multilayer that return IEvaluation

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	MultiLayerNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) Perform evaluation using an arbitrary IEvaluation instance.
<T extends IEvaluation> T[]	MultiLayerNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T[] evaluations)

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

Modifier and Type	Method and Description
<T extends IEvaluation> T[]	MultiLayerNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, T... evaluations) Perform evaluation using an arbitrary IEvaluation instance.
<T extends IEvaluation> T[]	MultiLayerNetwork.doEvaluation(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, T[] evaluations)

Uses of IEvaluation in org.deeplearning4j.optimize.listeners

Fields in org.deeplearning4j.optimize.listeners declared as IEvaluation

Modifier and Type	Field and Description
protected IEvaluation[]	EvaluativeListener.evaluations

Methods in org.deeplearning4j.optimize.listeners with parameters of type IEvaluation

Modifier and Type	Method and Description
protected void	EvaluativeListener.evalAtIndex(IEvaluation evaluation, org.nd4j.linalg.api.ndarray.INDArray[] labels, org.nd4j.linalg.api.ndarray.INDArray[] predictions, int index)

Constructors in org.deeplearning4j.optimize.listeners with parameters of type IEvaluation

Constructor and Description
EvaluativeListener(org.nd4j.linalg.dataset.DataSet dataSet, int frequency, InvocationType type, IEvaluation... evaluations)
EvaluativeListener(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, int frequency, IEvaluation... evaluations) Evaluation will be launched after each *frequency* iteration
EvaluativeListener(org.nd4j.linalg.dataset.api.iterator.DataSetIterator iterator, int frequency, InvocationType type, IEvaluation... evaluations)
EvaluativeListener(org.nd4j.linalg.dataset.MultiDataSet multiDataSet, int frequency, InvocationType type, IEvaluation... evaluations)
EvaluativeListener(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, int frequency, IEvaluation... evaluations) Evaluation will be launched after each *frequency* iteration
EvaluativeListener(org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator iterator, int frequency, InvocationType type, IEvaluation... evaluations)

Uses of IEvaluation in org.deeplearning4j.optimize.listeners.callbacks

Methods in org.deeplearning4j.optimize.listeners.callbacks with parameters of type IEvaluation

Modifier and Type	Method and Description
void	ModelSavingCallback.call(EvaluativeListener listener, Model model, long invocationsCount, IEvaluation[] evaluations)
void	EvaluationCallback.call(EvaluativeListener listener, Model model, long invocationsCount, IEvaluation[] evaluations)