org.deeplearning4j.nn.conf.ocnn

Class OCNNOutputLayer

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.BaseLayer

org.deeplearning4j.nn.conf.layers.FeedForwardLayer

org.deeplearning4j.nn.conf.layers.BaseOutputLayer

org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig

public class OCNNOutputLayer
extends BaseOutputLayer

An implementation of one class neural networks from: https://arxiv.org/pdf/1802.06360.pdf The one class neural network approach is an extension of the standard output layer with a single set of weights, an activation function, and a bias to: 2 sets of weights, a learnable "r" parameter that is held static 1 traditional set of weights. 1 additional weight matrix

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	OCNNOutputLayer.Builder

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

hasBias, legacyBatchScaledL2, lossFn

Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

nIn, nOut

Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer

activationFn, biasInit, biasUpdater, dist, gradientNormalization, gradientNormalizationThreshold, iUpdater, l1, l1Bias, l2, l2Bias, weightInit, weightNoise

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer

constraints, iDropout, layerName

Constructor Summary

Constructors

Constructor and Description
OCNNOutputLayer(int hiddenSize, double nu, IActivation activation, int windowSize, double initialRValue, boolean configureR)
OCNNOutputLayer(OCNNOutputLayer.Builder builder)

Method Summary

Modifier and Type	Method and Description
double	getL1ByParam(String paramName) Get the L1 coefficient for the given parameter.
double	getL2ByParam(String paramName) Get the L2 coefficient for the given parameter.
ILossFunction	getLossFn()
long	getNOut()
ParamInitializer	initializer()
Layer	instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams)

Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseOutputLayer

getMemoryReport, hasBias

Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

getOutputType, getPreProcessorForInputType, isPretrain, isPretrainParam, setNIn

Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer

clone, getGradientNormalization, getUpdaterByParam, resetLayerDefaultConfig

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer

initializeConstraints, setPretrain

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.deeplearning4j.nn.api.TrainingConfig

getGradientNormalizationThreshold, getLayerName

Constructor Detail

OCNNOutputLayer

public OCNNOutputLayer(OCNNOutputLayer.Builder builder)

OCNNOutputLayer

public OCNNOutputLayer(int hiddenSize,
                       double nu,
                       IActivation activation,
                       int windowSize,
                       double initialRValue,
                       boolean configureR)

Method Detail

getLossFn

public ILossFunction getLossFn()

instantiate

public Layer instantiate(NeuralNetConfiguration conf,
                         Collection<TrainingListener> trainingListeners,
                         int layerIndex,
                         INDArray layerParamsView,
                         boolean initializeParams)

Specified by:

instantiate in class Layer

getNOut

public long getNOut()

initializer

public ParamInitializer initializer()

Specified by:

initializer in class Layer

Returns:

The parameter initializer for this model

getL1ByParam

public double getL1ByParam(String paramName)

Description copied from class: Layer

Get the L1 coefficient for the given parameter. Different parameters may have different L1 values, even for a single .l1(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL1ByParam in interface TrainingConfig

Overrides:

getL1ByParam in class FeedForwardLayer

Parameters:

paramName - Parameter name

Returns:

L1 value for that parameter

getL2ByParam

public double getL2ByParam(String paramName)

Description copied from class: Layer

Get the L2 coefficient for the given parameter. Different parameters may have different L2 values, even for a single .l2(x) configuration. For example, biases generally aren't L1 regularized, even if weights are

Specified by:

getL2ByParam in interface TrainingConfig

Overrides:

getL2ByParam in class FeedForwardLayer

Parameters:

paramName - Parameter name

Returns:

L2 value for that parameter