Uses of Interface
org.deeplearning4j.nn.api.Model

Packages that use Model

Package	Description
org.deeplearning4j.earlystopping
org.deeplearning4j.earlystopping.listener
org.deeplearning4j.earlystopping.saver
org.deeplearning4j.earlystopping.scorecalc
org.deeplearning4j.earlystopping.scorecalc.base
org.deeplearning4j.earlystopping.trainer
org.deeplearning4j.nn.adapters
org.deeplearning4j.nn.api
org.deeplearning4j.nn.api.layers
org.deeplearning4j.nn.graph
org.deeplearning4j.nn.layers
org.deeplearning4j.nn.layers.convolution
org.deeplearning4j.nn.layers.convolution.subsampling
org.deeplearning4j.nn.layers.convolution.upsampling
org.deeplearning4j.nn.layers.feedforward
org.deeplearning4j.nn.layers.feedforward.autoencoder
org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.elementwise
org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.normalization
org.deeplearning4j.nn.layers.objdetect
org.deeplearning4j.nn.layers.ocnn
org.deeplearning4j.nn.layers.pooling
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.samediff
org.deeplearning4j.nn.layers.training
org.deeplearning4j.nn.layers.util
org.deeplearning4j.nn.layers.variational
org.deeplearning4j.nn.layers.wrapper
org.deeplearning4j.nn.multilayer
org.deeplearning4j.nn.updater
org.deeplearning4j.optimize
org.deeplearning4j.optimize.api
org.deeplearning4j.optimize.listeners
org.deeplearning4j.optimize.listeners.callbacks
org.deeplearning4j.optimize.solvers
org.deeplearning4j.optimize.solvers.accumulation
org.deeplearning4j.util

Uses of Model in org.deeplearning4j.earlystopping

Classes in org.deeplearning4j.earlystopping with type parameters of type Model

Modifier and Type	Class	Description
class	EarlyStoppingConfiguration<T extends Model>
static class	EarlyStoppingConfiguration.Builder<T extends Model>
interface	EarlyStoppingModelSaver<T extends Model>
class	EarlyStoppingResult<T extends Model>

Uses of Model in org.deeplearning4j.earlystopping.listener

Classes in org.deeplearning4j.earlystopping.listener with type parameters of type Model

Modifier and Type	Interface	Description
interface	EarlyStoppingListener<T extends Model>

Uses of Model in org.deeplearning4j.earlystopping.saver

Classes in org.deeplearning4j.earlystopping.saver with type parameters of type Model

Modifier and Type	Class	Description
class	InMemoryModelSaver<T extends Model>

Uses of Model in org.deeplearning4j.earlystopping.scorecalc

Classes in org.deeplearning4j.earlystopping.scorecalc with type parameters of type Model

Modifier and Type	Interface	Description
interface	ScoreCalculator<T extends Model>

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

Modifier and Type	Method	Description
protected INDArray[]	AutoencoderScoreCalculator.output(Model network, INDArray[] input, INDArray[] fMask, INDArray[] lMask)
protected INDArray	AutoencoderScoreCalculator.output(Model net, INDArray input, INDArray fMask, INDArray lMask)
protected INDArray[]	DataSetLossCalculator.output(Model network, INDArray[] input, INDArray[] fMask, INDArray[] lMask)
protected INDArray	DataSetLossCalculator.output(Model network, INDArray input, INDArray fMask, INDArray lMask)
protected INDArray[]	VAEReconErrorScoreCalculator.output(Model network, INDArray[] input, INDArray[] fMask, INDArray[] lMask)
protected INDArray	VAEReconErrorScoreCalculator.output(Model net, INDArray input, INDArray fMask, INDArray lMask)
protected INDArray[]	VAEReconProbScoreCalculator.output(Model network, INDArray[] input, INDArray[] fMask, INDArray[] lMask)
protected INDArray	VAEReconProbScoreCalculator.output(Model network, INDArray input, INDArray fMask, INDArray lMask)
protected double	AutoencoderScoreCalculator.scoreMinibatch(Model network, INDArray[] features, INDArray[] labels, INDArray[] fMask, INDArray[] lMask, INDArray[] output)
protected double	AutoencoderScoreCalculator.scoreMinibatch(Model network, INDArray features, INDArray labels, INDArray fMask, INDArray lMask, INDArray output)
protected double	DataSetLossCalculator.scoreMinibatch(Model network, INDArray[] features, INDArray[] labels, INDArray[] fMask, INDArray[] lMask, INDArray[] output)
protected double	VAEReconErrorScoreCalculator.scoreMinibatch(Model network, INDArray[] features, INDArray[] labels, INDArray[] fMask, INDArray[] lMask, INDArray[] output)
protected double	VAEReconErrorScoreCalculator.scoreMinibatch(Model network, INDArray features, INDArray labels, INDArray fMask, INDArray lMask, INDArray output)
protected double	VAEReconProbScoreCalculator.scoreMinibatch(Model network, INDArray[] features, INDArray[] labels, INDArray[] fMask, INDArray[] lMask, INDArray[] output)
protected double	VAEReconProbScoreCalculator.scoreMinibatch(Model net, INDArray features, INDArray labels, INDArray fMask, INDArray lMask, INDArray output)

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

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

Modifier and Type	Class	Description
class	BaseIEvaluationScoreCalculator<T extends Model,U extends IEvaluation>
class	BaseScoreCalculator<T extends Model>

Uses of Model in org.deeplearning4j.earlystopping.trainer

Classes in org.deeplearning4j.earlystopping.trainer with type parameters of type Model

Modifier and Type	Class	Description
class	BaseEarlyStoppingTrainer<T extends Model>
interface	IEarlyStoppingTrainer<T extends Model>

Fields in org.deeplearning4j.earlystopping.trainer declared as Model

Modifier and Type	Field	Description
protected T	BaseEarlyStoppingTrainer.model

Methods in org.deeplearning4j.earlystopping.trainer with parameters of type Model

Modifier and Type	Method	Description
protected void	BaseEarlyStoppingTrainer.triggerEpochListeners(boolean epochStart, Model model, int epochNum)

Uses of Model in org.deeplearning4j.nn.adapters

Methods in org.deeplearning4j.nn.adapters with parameters of type Model

Modifier and Type	Method	Description
List<DetectedObject>	YoloModelAdapter.apply(Model model, INDArray[] inputs, INDArray[] masks, INDArray[] labelsMasks)

Uses of Model in org.deeplearning4j.nn.api

Subinterfaces of Model in org.deeplearning4j.nn.api

Modifier and Type	Interface	Description
interface	Classifier
interface	Layer

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

Modifier and Type	Method	Description
T	ModelAdapter.apply(Model model, INDArray[] inputs, INDArray[] inputMasks, INDArray[] labelsMasks)	This method invokes model internally, and does convertion to T

Uses of Model in org.deeplearning4j.nn.api.layers

Subinterfaces of Model in org.deeplearning4j.nn.api.layers

Modifier and Type	Interface	Description
interface	IOutputLayer
interface	RecurrentLayer

Uses of Model in org.deeplearning4j.nn.graph

Classes in org.deeplearning4j.nn.graph that implement Model

Modifier and Type	Class	Description
class	ComputationGraph

Uses of Model in org.deeplearning4j.nn.layers

Classes in org.deeplearning4j.nn.layers that implement Model

Modifier and Type	Class	Description
class	AbstractLayer<LayerConfT extends Layer>	A layer with input and output, no parameters or gradients
class	ActivationLayer
class	BaseLayer<LayerConfT extends BaseLayer>	A layer with parameters
class	BaseOutputLayer<LayerConfT extends BaseOutputLayer>
class	BasePretrainNetwork<LayerConfT extends BasePretrainNetwork>
class	DropoutLayer
class	FrozenLayer
class	FrozenLayerWithBackprop	Frozen layer freezes parameters of the layer it wraps, but allows the backpropagation to continue.
class	LossLayer
class	OutputLayer
class	RepeatVector

Uses of Model in org.deeplearning4j.nn.layers.convolution

Classes in org.deeplearning4j.nn.layers.convolution that implement Model

Modifier and Type	Class	Description
class	Cnn3DLossLayer
class	CnnLossLayer
class	Convolution1DLayer
class	Convolution3DLayer
class	ConvolutionLayer
class	Cropping1DLayer
class	Cropping2DLayer
class	Cropping3DLayer
class	Deconvolution2DLayer
class	Deconvolution3DLayer
class	DepthwiseConvolution2DLayer
class	SeparableConvolution2DLayer
class	SpaceToBatch
class	SpaceToDepth
class	ZeroPadding1DLayer
class	ZeroPadding3DLayer
class	ZeroPaddingLayer

Uses of Model in org.deeplearning4j.nn.layers.convolution.subsampling

Classes in org.deeplearning4j.nn.layers.convolution.subsampling that implement Model

Modifier and Type	Class	Description
class	Subsampling1DLayer
class	Subsampling3DLayer
class	SubsamplingLayer

Uses of Model in org.deeplearning4j.nn.layers.convolution.upsampling

Classes in org.deeplearning4j.nn.layers.convolution.upsampling that implement Model

Modifier and Type	Class	Description
class	Upsampling1D
class	Upsampling2D
class	Upsampling3D

Uses of Model in org.deeplearning4j.nn.layers.feedforward

Classes in org.deeplearning4j.nn.layers.feedforward that implement Model

Modifier and Type	Class	Description
class	PReLU

Uses of Model in org.deeplearning4j.nn.layers.feedforward.autoencoder

Classes in org.deeplearning4j.nn.layers.feedforward.autoencoder that implement Model

Modifier and Type	Class	Description
class	AutoEncoder

Uses of Model in org.deeplearning4j.nn.layers.feedforward.dense

Classes in org.deeplearning4j.nn.layers.feedforward.dense that implement Model

Modifier and Type	Class	Description
class	DenseLayer

Uses of Model in org.deeplearning4j.nn.layers.feedforward.elementwise

Classes in org.deeplearning4j.nn.layers.feedforward.elementwise that implement Model

Modifier and Type	Class	Description
class	ElementWiseMultiplicationLayer

Uses of Model in org.deeplearning4j.nn.layers.feedforward.embedding

Classes in org.deeplearning4j.nn.layers.feedforward.embedding that implement Model

Modifier and Type	Class	Description
class	EmbeddingLayer
class	EmbeddingSequenceLayer

Uses of Model in org.deeplearning4j.nn.layers.normalization

Classes in org.deeplearning4j.nn.layers.normalization that implement Model

Modifier and Type	Class	Description
class	BatchNormalization
class	LocalResponseNormalization

Uses of Model in org.deeplearning4j.nn.layers.objdetect

Classes in org.deeplearning4j.nn.layers.objdetect that implement Model

Modifier and Type	Class	Description
class	Yolo2OutputLayer

Uses of Model in org.deeplearning4j.nn.layers.ocnn

Classes in org.deeplearning4j.nn.layers.ocnn that implement Model

Modifier and Type	Class	Description
class	OCNNOutputLayer

Uses of Model in org.deeplearning4j.nn.layers.pooling

Classes in org.deeplearning4j.nn.layers.pooling that implement Model

Modifier and Type	Class	Description
class	GlobalPoolingLayer

Uses of Model in org.deeplearning4j.nn.layers.recurrent

Classes in org.deeplearning4j.nn.layers.recurrent that implement Model

Modifier and Type	Class	Description
class	BaseRecurrentLayer<LayerConfT extends BaseRecurrentLayer>
class	BidirectionalLayer
class	GravesBidirectionalLSTM
class	GravesLSTM	Deprecated.
class	LastTimeStepLayer
class	LSTM
class	MaskZeroLayer
class	RnnLossLayer
class	RnnOutputLayer
class	SimpleRnn
class	TimeDistributedLayer

Uses of Model in org.deeplearning4j.nn.layers.samediff

Classes in org.deeplearning4j.nn.layers.samediff that implement Model

Modifier and Type	Class	Description
class	SameDiffLayer
class	SameDiffOutputLayer

Uses of Model in org.deeplearning4j.nn.layers.training

Classes in org.deeplearning4j.nn.layers.training that implement Model

Modifier and Type	Class	Description
class	CenterLossOutputLayer

Uses of Model in org.deeplearning4j.nn.layers.util

Classes in org.deeplearning4j.nn.layers.util that implement Model

Modifier and Type	Class	Description
class	MaskLayer

Uses of Model in org.deeplearning4j.nn.layers.variational

Classes in org.deeplearning4j.nn.layers.variational that implement Model

Modifier and Type	Class	Description
class	VariationalAutoencoder

Uses of Model in org.deeplearning4j.nn.layers.wrapper

Classes in org.deeplearning4j.nn.layers.wrapper that implement Model

Modifier and Type	Class	Description
class	BaseWrapperLayer

Uses of Model in org.deeplearning4j.nn.multilayer

Classes in org.deeplearning4j.nn.multilayer that implement Model

Modifier and Type	Class	Description
class	MultiLayerNetwork

Uses of Model in org.deeplearning4j.nn.updater

Classes in org.deeplearning4j.nn.updater with type parameters of type Model

Modifier and Type	Class	Description
class	BaseMultiLayerUpdater<T extends Model>

Fields in org.deeplearning4j.nn.updater declared as Model

Modifier and Type	Field	Description
protected T	BaseMultiLayerUpdater.network

Methods in org.deeplearning4j.nn.updater with parameters of type Model

Modifier and Type	Method	Description
static Updater	UpdaterCreator.getUpdater(Model layer)

Uses of Model in org.deeplearning4j.optimize

Methods in org.deeplearning4j.optimize with parameters of type Model

Modifier and Type	Method	Description
Solver.Builder	Solver.Builder.model(Model model)

Uses of Model in org.deeplearning4j.optimize.api

Methods in org.deeplearning4j.optimize.api with parameters of type Model

Modifier and Type	Method	Description
void	BaseTrainingListener.iterationDone(Model model, int iteration, int epoch)
abstract void	IterationListener.iterationDone(Model model, int iteration, int epoch)	Deprecated. Event listener for each iteration
void	TrainingListener.iterationDone(Model model, int iteration, int epoch)	Event listener for each iteration.
void	BaseTrainingListener.onBackwardPass(Model model)
void	TrainingListener.onBackwardPass(Model model)	Called once per iteration (backward pass) after gradients have been calculated, and updated Gradients are available via gradient().
void	BaseTrainingListener.onEpochEnd(Model model)
void	TrainingListener.onEpochEnd(Model model)	Called once at the end of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)
void	BaseTrainingListener.onEpochStart(Model model)
void	TrainingListener.onEpochStart(Model model)	Called once at the start of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)
void	BaseTrainingListener.onForwardPass(Model model, List<INDArray> activations)
void	BaseTrainingListener.onForwardPass(Model model, Map<String,INDArray> activations)
void	TrainingListener.onForwardPass(Model model, List<INDArray> activations)	Called once per iteration (forward pass) for activations (usually for a MultiLayerNetwork), only at training time
void	TrainingListener.onForwardPass(Model model, Map<String,INDArray> activations)	Called once per iteration (forward pass) for activations (usually for a ComputationGraph), only at training time
void	BaseTrainingListener.onGradientCalculation(Model model)
void	TrainingListener.onGradientCalculation(Model model)	Called once per iteration (backward pass) before the gradients are updated Gradients are available via gradient().
void	ConvexOptimizer.updateGradientAccordingToParams(Gradient gradient, Model model, int batchSize, LayerWorkspaceMgr workspaceMgr)	Update the gradient according to the configuration such as adagrad, momentum, and sparsity

Uses of Model in org.deeplearning4j.optimize.listeners

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

Modifier and Type	Method	Description
protected void	FailureTestingListener.call(FailureTestingListener.CallType callType, Model model)
protected static int	CheckpointListener.getEpoch(Model model)
protected static int	CheckpointListener.getIter(Model model)
protected static String	CheckpointListener.getModelType(Model model)
protected void	EvaluativeListener.invokeListener(Model model)
void	CheckpointListener.iterationDone(Model model, int iteration, int epoch)
void	CollectScoresIterationListener.iterationDone(Model model, int iteration, int epoch)
void	CollectScoresListener.iterationDone(Model model, int iteration, int epoch)
void	ComposableIterationListener.iterationDone(Model model, int iteration, int epoch)	Deprecated.
void	EvaluativeListener.iterationDone(Model model, int iteration, int epoch)	Event listener for each iteration
void	FailureTestingListener.iterationDone(Model model, int iteration, int epoch)
void	PerformanceListener.iterationDone(Model model, int iteration, int epoch)
void	ScoreIterationListener.iterationDone(Model model, int iteration, int epoch)
void	SleepyTrainingListener.iterationDone(Model model, int iteration, int epoch)
void	TimeIterationListener.iterationDone(Model model, int iteration, int epoch)
void	FailureTestingListener.onBackwardPass(Model model)
void	SleepyTrainingListener.onBackwardPass(Model model)
void	CheckpointListener.onEpochEnd(Model model)
void	EvaluativeListener.onEpochEnd(Model model)
void	FailureTestingListener.onEpochEnd(Model model)
void	SleepyTrainingListener.onEpochEnd(Model model)
void	EvaluativeListener.onEpochStart(Model model)
void	FailureTestingListener.onEpochStart(Model model)
void	SleepyTrainingListener.onEpochStart(Model model)
void	FailureTestingListener.onForwardPass(Model model, List<INDArray> activations)
void	FailureTestingListener.onForwardPass(Model model, Map<String,INDArray> activations)
void	SleepyTrainingListener.onForwardPass(Model model, List<INDArray> activations)
void	SleepyTrainingListener.onForwardPass(Model model, Map<String,INDArray> activations)
void	FailureTestingListener.onGradientCalculation(Model model)
void	SleepyTrainingListener.onGradientCalculation(Model model)
boolean	FailureTestingListener.And.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)
abstract boolean	FailureTestingListener.FailureTrigger.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)	If true: trigger the failure.
boolean	FailureTestingListener.HostNameTrigger.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)
boolean	FailureTestingListener.IterationEpochTrigger.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)
boolean	FailureTestingListener.Or.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)
boolean	FailureTestingListener.RandomProb.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)
boolean	FailureTestingListener.TimeSinceInitializedTrigger.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)
boolean	FailureTestingListener.UserNameTrigger.triggerFailure(FailureTestingListener.CallType callType, int iteration, int epoch, Model model)

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

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

Modifier and Type	Method	Description
void	EvaluationCallback.call(EvaluativeListener listener, Model model, long invocationsCount, IEvaluation[] evaluations)
void	ModelSavingCallback.call(EvaluativeListener listener, Model model, long invocationsCount, IEvaluation[] evaluations)
protected void	ModelSavingCallback.save(Model model, String filename)	This method saves model

Uses of Model in org.deeplearning4j.optimize.solvers

Fields in org.deeplearning4j.optimize.solvers declared as Model

Modifier and Type	Field	Description
protected Model	BaseOptimizer.model

Methods in org.deeplearning4j.optimize.solvers with parameters of type Model

Modifier and Type	Method	Description
static void	BaseOptimizer.applyConstraints(Model model)
static int	BaseOptimizer.getEpochCount(Model model)
static int	BaseOptimizer.getIterationCount(Model model)
static void	BaseOptimizer.incrementIterationCount(Model model, int incrementBy)
void	BaseOptimizer.updateGradientAccordingToParams(Gradient gradient, Model model, int batchSize, LayerWorkspaceMgr workspaceMgr)

Constructors in org.deeplearning4j.optimize.solvers with parameters of type Model

Constructor	Description
BackTrackLineSearch(Model optimizable, ConvexOptimizer optimizer)
BackTrackLineSearch(Model layer, StepFunction stepFunction, ConvexOptimizer optimizer)
BaseOptimizer(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<TrainingListener> trainingListeners, Model model)
ConjugateGradient(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<TrainingListener> trainingListeners, Model model)
LBFGS(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<TrainingListener> trainingListeners, Model model)
LineGradientDescent(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<TrainingListener> trainingListeners, Model model)
StochasticGradientDescent(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<TrainingListener> trainingListeners, Model model)

Uses of Model in org.deeplearning4j.optimize.solvers.accumulation

Methods in org.deeplearning4j.optimize.solvers.accumulation with parameters of type Model

Modifier and Type	Method	Description
static long	EncodedGradientsAccumulator.getOptimalBufferSize(Model model, int numWorkers, int queueSize)

Uses of Model in org.deeplearning4j.util

Methods in org.deeplearning4j.util with parameters of type Model

Modifier and Type	Method	Description
static String	CrashReportingUtil.generateMemoryStatus(Model net, int minibatch, InputType... inputTypes)	Generate memory/system report as a String, for the specified network.
static INDArray	NetworkUtils.output(Model model, INDArray input)	Currently supports MultiLayerNetwork and ComputationGraph models.
static Task	ModelSerializer.taskByModel(Model model)
static void	CrashReportingUtil.writeMemoryCrashDump(@NonNull Model net, @NonNull Throwable e)	Generate and write the crash dump to the crash dump root directory (by default, the working directory).
static void	ModelSerializer.writeModel(@NonNull Model model, @NonNull File file, boolean saveUpdater)	Write a model to a file
static void	ModelSerializer.writeModel(@NonNull Model model, @NonNull File file, boolean saveUpdater, DataNormalization dataNormalization)	Write a model to a file
static void	ModelSerializer.writeModel(@NonNull Model model, @NonNull OutputStream stream, boolean saveUpdater)	Write a model to an output stream
static void	ModelSerializer.writeModel(@NonNull Model model, @NonNull OutputStream stream, boolean saveUpdater, DataNormalization dataNormalization)	Write a model to an output stream
static void	ModelSerializer.writeModel(@NonNull Model model, @NonNull String path, boolean saveUpdater)	Write a model to a file path