org.deeplearning4j.dbn

Class CDBN

java.lang.Object

org.deeplearning4j.nn.BaseMultiLayerNetwork

org.deeplearning4j.dbn.DBN

org.deeplearning4j.dbn.CDBN

All Implemented Interfaces:

Serializable, Persistable

public class CDBN
extends DBN

Continuous Deep Belief Network. Uses a continuous RBM in the first layer

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CDBN.Builder

Field Summary

Fields inherited from class org.deeplearning4j.nn.BaseMultiLayerNetwork

errorTolerance, layers, learningRateUpdate

Constructor Summary

Constructors

Constructor and Description
CDBN()
CDBN(int n_ins, int[] hidden_layer_sizes, int n_outs, int n_layers, org.apache.commons.math3.random.RandomGenerator rng)
CDBN(int n_ins, int[] hidden_layer_sizes, int n_outs, int n_layers, org.apache.commons.math3.random.RandomGenerator rng, org.jblas.DoubleMatrix input, org.jblas.DoubleMatrix labels)

Method Summary

Methods

Modifier and Type	Method and Description
NeuralNetwork	createLayer(org.jblas.DoubleMatrix input, int nVisible, int nHidden, org.jblas.DoubleMatrix W, org.jblas.DoubleMatrix hBias, org.jblas.DoubleMatrix vBias, org.apache.commons.math3.random.RandomGenerator rng, int index) Creates a layer depending on the index.
NeuralNetwork[]	createNetworkLayers(int numLayers)

Methods inherited from class org.deeplearning4j.dbn.DBN

pretrain, pretrain, trainNetwork

Methods inherited from class org.deeplearning4j.nn.BaseMultiLayerNetwork

applyTransforms, asDecoder, backProp, backPropStep, clone, encode, fanIn, feedForward, finetune, finetune, getActivation, getColumnMeans, getColumnStds, getColumnSums, getDist, getErrorTolerance, getFanIn, getHiddenLayerSizes, getInput, getL2, getLabels, getLayers, getLearningRateUpdate, getLogLayer, getMomentum, getnIns, getnLayers, getnOuts, getOptimizer, getRenderWeightsEveryNEpochs, getRng, getSigmoidLayers, getSparsity, getWeightTransforms, initializeLayers, initializeNetwork, isForceNumEpochs, isShouldBackProp, isShouldInit, isToDecode, isUseRegularization, load, loadFromFile, merge, negativeLogLikelihood, predict, reconstruct, reconstruct, setActivation, setColumnMeans, setColumnStds, setColumnSums, setDist, setErrorTolerance, setFanIn, setForceNumEpochs, setHiddenLayerSizes, setInput, setL2, setLabels, setLayers, setLearningRateUpdate, setLogLayer, setMomentum, setnIns, setnLayers, setnOuts, setOptimizer, setRenderWeightsEveryNEpochs, setRng, setShouldBackProp, setShouldInit, setSigmoidLayers, setSparsity, setToDecode, setUseRegularization, setWeightTransforms, update, write

Methods inherited from class java.lang.Object

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

Constructor Detail

CDBN

public CDBN()

CDBN

public CDBN(int n_ins,
    int[] hidden_layer_sizes,
    int n_outs,
    int n_layers,
    org.apache.commons.math3.random.RandomGenerator rng,
    org.jblas.DoubleMatrix input,
    org.jblas.DoubleMatrix labels)

CDBN

public CDBN(int n_ins,
    int[] hidden_layer_sizes,
    int n_outs,
    int n_layers,
    org.apache.commons.math3.random.RandomGenerator rng)

Method Detail

createLayer

public NeuralNetwork createLayer(org.jblas.DoubleMatrix input,
                        int nVisible,
                        int nHidden,
                        org.jblas.DoubleMatrix W,
                        org.jblas.DoubleMatrix hBias,
                        org.jblas.DoubleMatrix vBias,
                        org.apache.commons.math3.random.RandomGenerator rng,
                        int index)

Description copied from class: BaseMultiLayerNetwork

Creates a layer depending on the index. The main reason this matters is for continuous variations such as the CDBN where the first layer needs to be an CRBM for continuous inputs. Please be sure to call super.initializeNetwork to handle the passing of baseline parameters such as fanin and rendering.

Overrides:

createLayer in class DBN

Parameters:

input - the input to the layer

nVisible - the number of visible inputs

nHidden - the number of hidden units

W - the weight vector

hBias - the hidden bias

vBias - the visible bias

rng - the rng to use (THiS IS IMPORTANT; YOU DO NOT WANT TO HAVE A MIS REFERENCED RNG OTHERWISE NUMBERS WILL BE MEANINGLESS)

index - the index of the layer

Returns:

a neural network layer such as RBM

createNetworkLayers

public NeuralNetwork[] createNetworkLayers(int numLayers)

Overrides:

createNetworkLayers in class DBN