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.trainer
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.feedforward.autoencoder
org.deeplearning4j.nn.layers.feedforward.dense
org.deeplearning4j.nn.layers.feedforward.embedding
org.deeplearning4j.nn.layers.feedforward.rbm
org.deeplearning4j.nn.layers.normalization
org.deeplearning4j.nn.layers.pooling
org.deeplearning4j.nn.layers.recurrent
org.deeplearning4j.nn.layers.training
org.deeplearning4j.nn.layers.variational
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 and Description
class	EarlyStoppingConfiguration<T extends Model> Early stopping configuration: Specifies the various configuration options for running training with early stopping. Users need to specify the following: (a) EarlyStoppingModelSaver: How models will be saved (to disk, to memory, etc) (Default: in memory) (b) Termination conditions: at least one termination condition must be specified (i) Iteration termination conditions: calculated once for each minibatch.
static class	EarlyStoppingConfiguration.Builder<T extends Model>
interface	EarlyStoppingModelSaver<T extends Model> Interface for saving MultiLayerNetworks learned during early stopping, and retrieving them again later
class	EarlyStoppingResult<T extends Model> EarlyStoppingResult: contains the results of the early stopping training, such as: - Why the training was terminated - Score vs.

Uses of Model in org.deeplearning4j.earlystopping.listener

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

Modifier and Type	Interface and Description
interface	EarlyStoppingListener<T extends Model> EarlyStoppingListener is a listener interface for conducting early stopping training.

Uses of Model in org.deeplearning4j.earlystopping.saver

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

Modifier and Type	Class and Description
class	InMemoryModelSaver<T extends Model> Save the best (and latest) models for early stopping training to memory for later retrieval Note: Assumes that network is cloneable via .clone() method

Uses of Model in org.deeplearning4j.earlystopping.scorecalc

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

Modifier and Type	Interface and Description
interface	ScoreCalculator<T extends Model> ScoreCalculator interface is used to calculate a score for a neural network.

Uses of Model in org.deeplearning4j.earlystopping.trainer

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

Modifier and Type	Class and Description
class	BaseEarlyStoppingTrainer<T extends Model> Base/abstract class for conducting early stopping training locally (single machine). Can be used to train a MultiLayerNetwork or a ComputationGraph via early stopping
interface	IEarlyStoppingTrainer<T extends Model> Interface for early stopping trainers

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

Modifier and Type	Field and Description
protected T	BaseEarlyStoppingTrainer.model

Uses of Model in org.deeplearning4j.nn.api

Subinterfaces of Model in org.deeplearning4j.nn.api

Modifier and Type	Interface and Description
interface	Classifier A classifier (this is for supervised learning)
interface	Layer Interface for a layer of a neural network.

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

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

Modifier and Type	Interface and Description
interface	IOutputLayer Interface for output layers (those that calculate gradients with respect to a labels array)
interface	RecurrentLayer Created by Alex on 28/08/2016.

Uses of Model in org.deeplearning4j.nn.graph

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

Modifier and Type	Class and Description
class	ComputationGraph A ComputationGraph network is a neural network with arbitrary (directed acyclic graph) connection structure.

Uses of Model in org.deeplearning4j.nn.layers

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

Modifier and Type	Class and Description
class	AbstractLayer<LayerConfT extends Layer> A layer with input and output, no parameters or gradients
class	ActivationLayer Activation Layer Used to apply activation on input and corresponding derivative on epsilon.
class	BaseLayer<LayerConfT extends BaseLayer> A layer with parameters
class	BaseOutputLayer<LayerConfT extends BaseOutputLayer> Output layer with different objective in co-occurrences for different objectives.
class	BasePretrainNetwork<LayerConfT extends BasePretrainNetwork> Baseline class for any Neural Network used as a layer in a deep network *
class	DropoutLayer Created by davekale on 12/7/16.
class	FrozenLayer For purposes of transfer learning A frozen layers wraps another dl4j layer within it.
class	LossLayer LossLayer is a flexible output "layer" that performs a loss function on an input without MLP logic.
class	OutputLayer Output layer with different objective incooccurrences for different objectives.

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

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

Modifier and Type	Class and Description
class	Convolution1DLayer 1D (temporal) convolutional layer.
class	ConvolutionLayer Convolution layer
class	ZeroPaddingLayer Zero padding layer for convolutional neural networks.

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 and Description
class	Subsampling1DLayer 1D (temporal) subsampling layer.
class	SubsamplingLayer Subsampling layer.

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 and Description
class	AutoEncoder 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 and Description
class	DenseLayer

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 and Description
class	EmbeddingLayer Embedding layer: feed-forward layer that expects single integers per example as input (class numbers, in range 0 to numClass-1) as input.

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

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

Modifier and Type	Class and Description
class	RBM Restricted Boltzmann Machine.

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

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

Modifier and Type	Class and Description
class	BatchNormalization Batch normalization layer.
class	LocalResponseNormalization Deep neural net normalization approach normalizes activations between layers "brightness normalization" Used for nets like AlexNet

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

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

Modifier and Type	Class and Description
class	GlobalPoolingLayer Global pooling layer - used to do pooling over time for RNNs, and 2d pooling for CNNs. Supports the following PoolingTypes: SUM, AVG, MAX, PNORM Global pooling layer can also handle mask arrays when dealing with variable length inputs.

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

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

Modifier and Type	Class and Description
class	BaseRecurrentLayer<LayerConfT extends BaseLayer>
class	GravesBidirectionalLSTM RNN tutorial: http://deeplearning4j.org/usingrnns.html READ THIS FIRST Bdirectional LSTM layer implementation.
class	GravesLSTM LSTM layer implementation.
class	LSTM LSTM layer implementation.
class	RnnOutputLayer Recurrent Neural Network Output Layer. Handles calculation of gradients etc for various objective functions. Functionally the same as OutputLayer, but handles output and label reshaping automatically. Input and output activations are same as other RNN layers: 3 dimensions with shape [miniBatchSize,nIn,timeSeriesLength] and [miniBatchSize,nOut,timeSeriesLength] respectively.

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

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

Modifier and Type	Class and Description
class	CenterLossOutputLayer Center loss is similar to triplet loss except that it enforces intraclass consistency and doesn't require feed forward of multiple examples.

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

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

Modifier and Type	Class and Description
class	VariationalAutoencoder Variational Autoencoder layer

Uses of Model in org.deeplearning4j.nn.multilayer

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

Modifier and Type	Class and Description
class	MultiLayerNetwork MultiLayerNetwork is a neural network with multiple layers in a stack, and usually an output layer.

Uses of Model in org.deeplearning4j.nn.updater

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

Modifier and Type	Class and Description
class	BaseMultiLayerUpdater<T extends Model> BaseMultiLayerUpdater - core functionality for applying updaters to MultiLayerNetwork and ComputationGraph.

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

Modifier and Type	Field and Description
protected T	BaseMultiLayerUpdater.network

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

Modifier and Type	Method and 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 and 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 and Description
void	IterationListener.iterationDone(Model model, int iteration) Event listener for each iteration
void	TrainingListener.onBackwardPass(Model model) Called once per iteration (backward pass) after gradients have been calculated, and updated Gradients are available via gradient().
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	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	TrainingListener.onForwardPass(Model model, List<org.nd4j.linalg.api.ndarray.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,org.nd4j.linalg.api.ndarray.INDArray> activations) Called once per iteration (forward pass) for activations (usually for a ComputationGraph), only at training time
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) 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 and Description
protected void	EvaluativeListener.invokeListener(Model model)
void	PerformanceListener.iterationDone(Model model, int iteration)
void	ScoreIterationListener.iterationDone(Model model, int iteration)
void	EvaluativeListener.iterationDone(Model model, int iteration) Event listener for each iteration
void	SleepyTrainingListener.iterationDone(Model model, int iteration)
void	ComposableIterationListener.iterationDone(Model model, int iteration)
void	ParamAndGradientIterationListener.iterationDone(Model model, int iteration)
void	TimeIterationListener.iterationDone(Model model, int iteration)
void	CollectScoresIterationListener.iterationDone(Model model, int iteration)
void	EvaluativeListener.onBackwardPass(Model model)
void	SleepyTrainingListener.onBackwardPass(Model model)
void	EvaluativeListener.onEpochEnd(Model model)
void	SleepyTrainingListener.onEpochEnd(Model model)
void	EvaluativeListener.onEpochStart(Model model)
void	SleepyTrainingListener.onEpochStart(Model model)
void	EvaluativeListener.onForwardPass(Model model, List<org.nd4j.linalg.api.ndarray.INDArray> activations)
void	SleepyTrainingListener.onForwardPass(Model model, List<org.nd4j.linalg.api.ndarray.INDArray> activations)
void	EvaluativeListener.onForwardPass(Model model, Map<String,org.nd4j.linalg.api.ndarray.INDArray> activations)
void	SleepyTrainingListener.onForwardPass(Model model, Map<String,org.nd4j.linalg.api.ndarray.INDArray> activations)
void	EvaluativeListener.onGradientCalculation(Model model)
void	SleepyTrainingListener.onGradientCalculation(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 and Description
void	ModelSavingCallback.call(EvaluativeListener listener, Model model, long invocationsCount, IEvaluation[] evaluations)
void	EvaluationCallback.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 and Description
protected Model	BaseOptimizer.model

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

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

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

Constructor and Description
BackTrackLineSearch(Model optimizable, ConvexOptimizer optimizer)
BackTrackLineSearch(Model layer, StepFunction stepFunction, ConvexOptimizer optimizer)
BaseOptimizer(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Collection<TerminationCondition> terminationConditions, Model model)
BaseOptimizer(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Model model)
ConjugateGradient(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Collection<TerminationCondition> terminationConditions, Model model)
ConjugateGradient(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Model model)
LBFGS(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Collection<TerminationCondition> terminationConditions, Model model)
LBFGS(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Model model)
LineGradientDescent(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Collection<TerminationCondition> terminationConditions, Model model)
LineGradientDescent(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Model model)
StochasticGradientDescent(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, Collection<TerminationCondition> terminationConditions, Model model)
StochasticGradientDescent(NeuralNetConfiguration conf, StepFunction stepFunction, Collection<IterationListener> iterationListeners, 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 and Description
static int	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 and Description
static org.nd4j.linalg.heartbeat.reports.Task	ModelSerializer.taskByModel(Model model)
static void	ModelSerializer.writeModel(Model model, File file, boolean saveUpdater) Write a model to a file
static void	ModelSerializer.writeModel(Model model, OutputStream stream, boolean saveUpdater) Write a model to an output stream
static void	ModelSerializer.writeModel(Model model, String path, boolean saveUpdater) Write a model to a file path