VariationalAutoencoder (deeplearning4j-nn 1.0.0-beta5 API)

java.lang.Object
- org.deeplearning4j.nn.layers.variational.VariationalAutoencoder

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model, Trainable
```
public class VariationalAutoencoder
extends Object
implements Layer
```
Variational Autoencoder layer
See: Kingma & Welling, 2013: Auto-Encoding Variational Bayes - https://arxiv.org/abs/1312.6114
This implementation allows multiple encoder and decoder layers, the number and sizes of which can be set independently.
A note on scores during pretraining: This implementation minimizes the negative of the variational lower bound objective as described in Kingma & Welling; the mathematics in that paper is based on maximization of the variational lower bound instead. Thus, scores reported during pretraining in DL4J are the negative of the variational lower bound equation in the paper. The backpropagation and learning procedure is otherwise as described there.

Author:

Alex Black

See Also:

Serialized Form

Nested Class Summary
- Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.Layer
  Layer.TrainingMode, Layer.Type

Field Summary

Fields
Modifier and Type	Field and Description
`protected CacheMode`	`cacheMode`
`protected NeuralNetConfiguration`	`conf`
`protected org.nd4j.linalg.api.buffer.DataType`	`dataType`
`protected int[]`	`decoderLayerSizes`
`protected int[]`	`encoderLayerSizes`
`protected int`	`epochCount`
`protected Gradient`	`gradient`
`protected INDArray`	`gradientsFlattened`
`protected Map<String,INDArray>`	`gradientViews`
`protected int`	`index`
`protected INDArray`	`input`
`protected int`	`iterationCount`
`protected INDArray`	`maskArray`
`protected int`	`numSamples`
`protected ConvexOptimizer`	`optimizer`
`protected Map<String,INDArray>`	`params`
`protected INDArray`	`paramsFlattened`
`protected IActivation`	`pzxActivationFn`
`protected ReconstructionDistribution`	`reconstructionDistribution`
`protected double`	`score`
`protected Solver`	`solver`
`protected Collection<TrainingListener>`	`trainingListeners`
`protected Map<String,INDArray>`	`weightNoiseParams`
`protected boolean`	`zeroedPretrainParamGradients`

Constructor Summary

Constructors
Constructor and Description

VariationalAutoencoder(NeuralNetConfiguration conf, org.nd4j.linalg.api.buffer.DataType dataType)

Constructors
Constructor and Description
`VariationalAutoencoder(NeuralNetConfiguration conf, org.nd4j.linalg.api.buffer.DataType dataType)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`INDArray`	`activate(boolean training, LayerWorkspaceMgr workspaceMgr)` Perform forward pass and return the activations array with the last set input
`INDArray`	`activate(INDArray input, boolean training, LayerWorkspaceMgr workspaceMgr)` Perform forward pass and return the activations array with the specified input
`void`	`addListeners(TrainingListener... listeners)` This method ADDS additional TrainingListener to existing listeners
`void`	`allowInputModification(boolean allow)` A performance optimization: mark whether the layer is allowed to modify its input array in-place.
`void`	`applyConstraints(int iteration, int epoch)` Apply any constraints to the model
`void`	`assertInputSet(boolean backprop)`
`Pair<Gradient,INDArray>`	`backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)` Calculate the gradient relative to the error in the next layer
`int`	`batchSize()` The current inputs batch size
`double`	`calcRegularizationScore(boolean backpropParamsOnly)` Calculate the regularization component of the score, for the parameters in this layer For example, the L1, L2 and/or weight decay components of the loss function
`void`	`clear()` Clear input
`void`	`clearNoiseWeightParams()`
`void`	`computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)` Update the score
`NeuralNetConfiguration`	`conf()` The configuration for the neural network
`Pair<INDArray,MaskState>`	`feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState, int minibatchSize)` Feed forward the input mask array, setting in the layer as appropriate.
`void`	`fit()` All models have a fit method
`void`	`fit(INDArray data, LayerWorkspaceMgr workspaceMgr)` Fit the model to the given data
`INDArray`	`generateAtMeanGivenZ(INDArray latentSpaceValues)` Given a specified values for the latent space as input (latent space being z in p(z\|data)), generate output from P(x\|z), where x = E[P(x\|z)] i.e., return the mean value for the distribution P(x\|z)
`INDArray`	`generateRandomGivenZ(INDArray latentSpaceValues, LayerWorkspaceMgr workspaceMgr)` Given a specified values for the latent space as input (latent space being z in p(z\|data)), randomly generate output x, where x ~ P(x\|z)
`TrainingConfig`	`getConfig()`
`INDArray`	`getGradientsViewArray()`
`LayerHelper`	`getHelper()`
`int`	`getIndex()` Get the layer index.
`int`	`getInputMiniBatchSize()` Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
`Collection<TrainingListener>`	`getListeners()` Get the iteration listeners for this layer.
`INDArray`	`getMaskArray()`
`ConvexOptimizer`	`getOptimizer()` Returns this models optimizer
`INDArray`	`getParam(String param)` Get the parameter
`protected INDArray`	`getParamWithNoise(String param, boolean training, LayerWorkspaceMgr workspaceMgr)`
`Gradient`	`gradient()` Get the gradient.
`Pair<Gradient,Double>`	`gradientAndScore()` Get the gradient and score
`boolean`	`hasLossFunction()` Does the reconstruction distribution have a loss function (such as mean squared error) or is it a standard probabilistic reconstruction distribution?
`void`	`init()` Init the model
`INDArray`	`input()` The input/feature matrix for the model
`boolean`	`isPretrainLayer()` Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
`boolean`	`isPretrainParam(String param)`
`protected VariationalAutoencoder`	`layerConf()`
`protected String`	`layerId()`
`long`	`numParams()` the number of parameters for the model
`long`	`numParams(boolean backwards)` the number of parameters for the model
`INDArray`	`params()` Parameters of the model (if any)
`Map<String,INDArray>`	`paramTable()` The param table
`Map<String,INDArray>`	`paramTable(boolean backpropParamsOnly)` Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
`INDArray`	`preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)`
`INDArray`	`reconstructionError(INDArray data)` Return the reconstruction error for this variational autoencoder. NOTE (important): This method is used ONLY for VAEs that have a standard neural network loss function (i.e., an `ILossFunction` instance such as mean squared error) instead of using a probabilistic reconstruction distribution P(x\|z) for the reconstructions (as presented in the VAE architecture by Kingma and Welling). You can check if the VAE has a loss function using `hasLossFunction()` Consequently, the reconstruction error is a simple deterministic function (no Monte-Carlo sampling is required, unlike `reconstructionProbability(INDArray, int)` and `reconstructionLogProbability(INDArray, int)`)
`INDArray`	`reconstructionLogProbability(INDArray data, int numSamples)` Return the log reconstruction probability given the specified number of samples. See `reconstructionLogProbability(INDArray, int)` for more details
`INDArray`	`reconstructionProbability(INDArray data, int numSamples)` Calculate the reconstruction probability, as described in An & Cho, 2015 - "Variational Autoencoder based Anomaly Detection using Reconstruction Probability" (Algorithm 4) The authors describe it as follows: "This is essentially the probability of the data being generated from a given latent variable drawn from the approximate posterior distribution." Specifically, for each example x in the input, calculate p(x).
`double`	`score()` The score for the model
`void`	`setBackpropGradientsViewArray(INDArray gradients)` Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
`void`	`setCacheMode(CacheMode mode)` This method sets given CacheMode for current layer
`void`	`setConf(NeuralNetConfiguration conf)` Setter for the configuration
`void`	`setIndex(int index)` Set the layer index.
`void`	`setInput(INDArray input, LayerWorkspaceMgr layerWorkspaceMgr)` Set the layer input.
`void`	`setInputMiniBatchSize(int size)` Set current/last input mini-batch size. Used for score and gradient calculations.
`void`	`setListeners(Collection<TrainingListener> listeners)` Set the `TrainingListener`s for this model.
`void`	`setListeners(TrainingListener... listeners)` Set the `TrainingListener`s for this model.
`void`	`setMaskArray(INDArray maskArray)` Set the mask array.
`void`	`setParam(String key, INDArray val)` Set the parameter with a new ndarray
`void`	`setParams(INDArray params)` Set the parameters for this model.
`void`	`setParamsViewArray(INDArray params)` Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
`void`	`setParamTable(Map<String,INDArray> paramTable)` Setter for the param table
`Layer.Type`	`type()` Returns the layer type
`void`	`update(Gradient gradient)` Update layer weights and biases with gradient change
`void`	`update(INDArray gradient, String paramType)` Perform one update applying the gradient
`boolean`	`updaterDivideByMinibatch(String paramName)` DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size. However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.deeplearning4j.nn.api.Layer
getEpochCount, getIterationCount, setEpochCount, setIterationCount

- Field Detail
  - input
```
protected INDArray input
```
  - paramsFlattened
```
protected INDArray paramsFlattened
```
  - gradientsFlattened
```
protected INDArray gradientsFlattened
```
  - params
```
protected Map<String,INDArray> params
```
  - gradientViews
```
protected transient Map<String,INDArray> gradientViews
```
  - conf
```
protected NeuralNetConfiguration conf
```
  - score
```
protected double score
```
  - optimizer
```
protected ConvexOptimizer optimizer
```
  - gradient
```
protected Gradient gradient
```
  - trainingListeners
```
protected Collection<TrainingListener> trainingListeners
```
  - index
```
protected int index
```
  - maskArray
```
protected INDArray maskArray
```
  - solver
```
protected Solver solver
```
  - encoderLayerSizes
```
protected int[] encoderLayerSizes
```
  - decoderLayerSizes
```
protected int[] decoderLayerSizes
```
  - reconstructionDistribution
```
protected ReconstructionDistribution reconstructionDistribution
```
  - pzxActivationFn
```
protected IActivation pzxActivationFn
```
  - numSamples
```
protected int numSamples
```
  - cacheMode
```
protected CacheMode cacheMode
```
  - dataType
```
protected org.nd4j.linalg.api.buffer.DataType dataType
```
  - zeroedPretrainParamGradients
```
protected boolean zeroedPretrainParamGradients
```
  - weightNoiseParams
```
protected Map<String,INDArray> weightNoiseParams
```
  - iterationCount
```
protected int iterationCount
```
  - epochCount
```
protected int epochCount
```
- Constructor Detail
  - VariationalAutoencoder
```
public VariationalAutoencoder(NeuralNetConfiguration conf,
                              org.nd4j.linalg.api.buffer.DataType dataType)
```
- Method Detail
  - layerConf
```
protected VariationalAutoencoder layerConf()
```
  - setCacheMode
```
public void setCacheMode(CacheMode mode)
```
    Description copied from interface: Layer
    
    This method sets given CacheMode for current layer
    
    Specified by:
    
    setCacheMode in interface Layer
  - layerId
```
protected String layerId()
```
  - init
```
public void init()
```
    Init the model
    
    Specified by:
    
    init in interface Model
  - update
```
public void update(Gradient gradient)
```
    Description copied from interface: Model
    
    Update layer weights and biases with gradient change
    
    Specified by:
    
    update in interface Model
  - update
```
public void update(INDArray gradient,
                   String paramType)
```
    Description copied from interface: Model
    
    Perform one update applying the gradient
    
    Specified by:
    
    update in interface Model
    
    Parameters:
    
    gradient - the gradient to apply
  - score
```
public double score()
```
    Description copied from interface: Model
    
    The score for the model
    
    Specified by:
    
    score in interface Model
    
    Returns:
    
    the score for the model
  - getParamWithNoise
```
protected INDArray getParamWithNoise(String param,
                                     boolean training,
                                     LayerWorkspaceMgr workspaceMgr)
```
  - computeGradientAndScore
```
public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr)
```
    Description copied from interface: Model
    
    Update the score
    
    Specified by:
    
    computeGradientAndScore in interface Model
  - params
```
public INDArray params()
```
    Description copied from interface: Model
    
    Parameters of the model (if any)
    
    Specified by:
    
    params in interface Model
    
    Specified by:
    
    params in interface Trainable
    
    Returns:
    
    the parameters of the model
  - getConfig
```
public TrainingConfig getConfig()
```
    Specified by:
    
    getConfig in interface Trainable
    
    Returns:
    
    Training configuration
  - numParams
```
public long numParams()
```
    Description copied from interface: Model
    
    the number of parameters for the model
    
    Specified by:
    
    numParams in interface Model
    
    Specified by:
    
    numParams in interface Trainable
    
    Returns:
    
    the number of parameters for the model
  - numParams
```
public long numParams(boolean backwards)
```
    Description copied from interface: Model
    
    the number of parameters for the model
    
    Specified by:
    
    numParams in interface Model
    
    Returns:
    
    the number of parameters for the model
  - setParams
```
public void setParams(INDArray params)
```
    Description copied from interface: Model
    
    Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model
    
    Specified by:
    
    setParams in interface Model
    
    Parameters:
    
    params - the parameters for the model
  - setParamsViewArray
```
public void setParamsViewArray(INDArray params)
```
    Description copied from interface: Model
    
    Set the initial parameters array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
    
    Specified by:
    
    setParamsViewArray in interface Model
    
    Parameters:
    
    params - a 1 x nParams row vector that is a view of the larger (MLN/CG) parameters array
  - getGradientsViewArray
```
public INDArray getGradientsViewArray()
```
    Specified by:
    
    getGradientsViewArray in interface Model
    
    Specified by:
    
    getGradientsViewArray in interface Trainable
    
    Returns:
    
    1D gradients view array
  - setBackpropGradientsViewArray
```
public void setBackpropGradientsViewArray(INDArray gradients)
```
    Description copied from interface: Model
    
    Set the gradients array as a view of the full (backprop) network parameters NOTE: this is intended to be used internally in MultiLayerNetwork and ComputationGraph, not by users.
    
    Specified by:
    
    setBackpropGradientsViewArray in interface Model
    
    Parameters:
    
    gradients - a 1 x nParams row vector that is a view of the larger (MLN/CG) gradients array
  - fit
```
public void fit(INDArray data,
                LayerWorkspaceMgr workspaceMgr)
```
    Description copied from interface: Model
    
    Fit the model to the given data
    
    Specified by:
    
    fit in interface Model
    
    Parameters:
    
    data - the data to fit the model to
  - gradient
```
public Gradient gradient()
```
    Description copied from interface: Model
    
    Get the gradient. Note that this method will not calculate the gradient, it will rather return the gradient that has been computed before. For calculating the gradient, see Model.computeGradientAndScore(LayerWorkspaceMgr) } .
    
    Specified by:
    
    gradient in interface Model
    
    Returns:
    
    the gradient for this model, as calculated before
  - gradientAndScore
```
public Pair<Gradient,Double> gradientAndScore()
```
    Description copied from interface: Model
    
    Get the gradient and score
    
    Specified by:
    
    gradientAndScore in interface Model
    
    Returns:
    
    the gradient and score
  - batchSize
```
public int batchSize()
```
    Description copied from interface: Model
    
    The current inputs batch size
    
    Specified by:
    
    batchSize in interface Model
    
    Returns:
    
    the current inputs batch size
  - conf
```
public NeuralNetConfiguration conf()
```
    Description copied from interface: Model
    
    The configuration for the neural network
    
    Specified by:
    
    conf in interface Model
    
    Returns:
    
    the configuration for the neural network
  - setConf
```
public void setConf(NeuralNetConfiguration conf)
```
    Description copied from interface: Model
    
    Setter for the configuration
    
    Specified by:
    
    setConf in interface Model
  - input
```
public INDArray input()
```
    Description copied from interface: Model
    
    The input/feature matrix for the model
    
    Specified by:
    
    input in interface Model
    
    Returns:
    
    the input/feature matrix for the model
  - getOptimizer
```
public ConvexOptimizer getOptimizer()
```
    Description copied from interface: Model
    
    Returns this models optimizer
    
    Specified by:
    
    getOptimizer in interface Model
    
    Returns:
    
    this models optimizer
  - getParam
```
public INDArray getParam(String param)
```
    Description copied from interface: Model
    
    Get the parameter
    
    Specified by:
    
    getParam in interface Model
    
    Parameters:
    
    param - the key of the parameter
    
    Returns:
    
    the parameter vector/matrix with that particular key
  - paramTable
```
public Map<String,INDArray> paramTable()
```
    Description copied from interface: Model
    
    The param table
    
    Specified by:
    
    paramTable in interface Model
    
    Returns:
  - paramTable
```
public Map<String,INDArray> paramTable(boolean backpropParamsOnly)
```
    Description copied from interface: Model
    
    Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
    
    Specified by:
    
    paramTable in interface Model
    
    Specified by:
    
    paramTable in interface Trainable
    
    Parameters:
    
    backpropParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())
    
    Returns:
    
    Parameter table
  - updaterDivideByMinibatch
```
public boolean updaterDivideByMinibatch(String paramName)
```
    Description copied from interface: Trainable
    
    DL4J layers typically produce the sum of the gradients during the backward pass for each layer, and if required (if minibatch=true) then divide by the minibatch size.
    However, there are some exceptions, such as the batch norm mean/variance estimate parameters: these "gradients" are actually not gradients, but are updates to be applied directly to the parameter vector. Put another way, most gradients should be divided by the minibatch to get the average; some "gradients" are actually final updates already, and should not be divided by the minibatch size.
    
    Specified by:
    
    updaterDivideByMinibatch in interface Trainable
    
    Parameters:
    
    paramName - Name of the parameter
    
    Returns:
    
    True if gradients should be divided by minibatch (most params); false otherwise (edge cases like batch norm mean/variance estimates)
  - setParamTable
```
public void setParamTable(Map<String,INDArray> paramTable)
```
    Description copied from interface: Model
    
    Setter for the param table
    
    Specified by:
    
    setParamTable in interface Model
  - setParam
```
public void setParam(String key,
                     INDArray val)
```
    Description copied from interface: Model
    
    Set the parameter with a new ndarray
    
    Specified by:
    
    setParam in interface Model
    
    Parameters:
    
    key - the key to se t
    
    val - the new ndarray
  - clear
```
public void clear()
```
    Description copied from interface: Model
    
    Clear input
    
    Specified by:
    
    clear in interface Model
  - applyConstraints
```
public void applyConstraints(int iteration,
                             int epoch)
```
    Description copied from interface: Model
    
    Apply any constraints to the model
    
    Specified by:
    
    applyConstraints in interface Model
  - isPretrainParam
```
public boolean isPretrainParam(String param)
```
  - calcRegularizationScore
```
public double calcRegularizationScore(boolean backpropParamsOnly)
```
    Description copied from interface: Layer
    
    Calculate the regularization component of the score, for the parameters in this layer
    For example, the L1, L2 and/or weight decay components of the loss function
    
    Specified by:
    
    calcRegularizationScore in interface Layer
    
    Parameters:
    
    backpropParamsOnly - If true: calculate regularization score based on backprop params only. If false: calculate based on all params (including pretrain params, if any)
    
    Returns:
    
    the regularization score of
  - type
```
public Layer.Type type()
```
    Description copied from interface: Layer
    
    Returns the layer type
    
    Specified by:
    
    type in interface Layer
    
    Returns:
  - backpropGradient
```
public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon,
                                                LayerWorkspaceMgr workspaceMgr)
```
    Description copied from interface: Layer
    
    Calculate the gradient relative to the error in the next layer
    
    Specified by:
    
    backpropGradient in interface Layer
    
    Parameters:
    
    epsilon - w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C is cost function a=sigma(z) is activation.
    
    workspaceMgr - Workspace manager
    
    Returns:
    
    Pair where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD workspace via the workspace manager
  - preOutput
```
public INDArray preOutput(boolean training,
                          LayerWorkspaceMgr workspaceMgr)
```
  - activate
```
public INDArray activate(boolean training,
                         LayerWorkspaceMgr workspaceMgr)
```
    Description copied from interface: Layer
    
    Perform forward pass and return the activations array with the last set input
    
    Specified by:
    
    activate in interface Layer
    
    Parameters:
    
    training - training or test mode
    
    workspaceMgr - Workspace manager
    
    Returns:
    
    the activation (layer output) of the last specified input. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager
  - activate
```
public INDArray activate(INDArray input,
                         boolean training,
                         LayerWorkspaceMgr workspaceMgr)
```
    Description copied from interface: Layer
    
    Perform forward pass and return the activations array with the specified input
    
    Specified by:
    
    activate in interface Layer
    
    Parameters:
    
    input - the input to use
    
    training - train or test mode
    
    workspaceMgr - Workspace manager.
    
    Returns:
    
    Activations array. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager
  - getListeners
```
public Collection<TrainingListener> getListeners()
```
    Description copied from interface: Layer
    
    Get the iteration listeners for this layer.
    
    Specified by:
    
    getListeners in interface Layer
  - setListeners
```
public void setListeners(TrainingListener... listeners)
```
    Description copied from interface: Layer
    
    Set the TrainingListeners for this model. If any listeners have previously been set, they will be replaced by this method
    
    Specified by:
    
    setListeners in interface Layer
    
    Specified by:
    
    setListeners in interface Model
  - setListeners
```
public void setListeners(Collection<TrainingListener> listeners)
```
    Description copied from interface: Layer
    
    Set the TrainingListeners for this model. If any listeners have previously been set, they will be replaced by this method
    
    Specified by:
    
    setListeners in interface Layer
    
    Specified by:
    
    setListeners in interface Model
  - addListeners
```
public void addListeners(TrainingListener... listeners)
```
    This method ADDS additional TrainingListener to existing listeners
    
    Specified by:
    
    addListeners in interface Model
    
    Parameters:
    
    listeners -
  - setIndex
```
public void setIndex(int index)
```
    Description copied from interface: Layer
    
    Set the layer index.
    
    Specified by:
    
    setIndex in interface Layer
  - getIndex
```
public int getIndex()
```
    Description copied from interface: Layer
    
    Get the layer index.
    
    Specified by:
    
    getIndex in interface Layer
  - setInput
```
public void setInput(INDArray input,
                     LayerWorkspaceMgr layerWorkspaceMgr)
```
    Description copied from interface: Layer
    
    Set the layer input.
    
    Specified by:
    
    setInput in interface Layer
  - setInputMiniBatchSize
```
public void setInputMiniBatchSize(int size)
```
    Description copied from interface: Layer
    
    Set current/last input mini-batch size.
    Used for score and gradient calculations. Mini batch size may be different from getInput().size(0) due to reshaping operations - for example, when using RNNs with DenseLayer and OutputLayer. Called automatically during forward pass.
    
    Specified by:
    
    setInputMiniBatchSize in interface Layer
  - getInputMiniBatchSize
```
public int getInputMiniBatchSize()
```
    Description copied from interface: Layer
    
    Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
    
    Specified by:
    
    getInputMiniBatchSize in interface Layer
    
    See Also:
    
    Layer.setInputMiniBatchSize(int)
  - setMaskArray
```
public void setMaskArray(INDArray maskArray)
```
    Description copied from interface: Layer
    
    Set the mask array. Note: In general, Layer.feedForwardMaskArray(INDArray, MaskState, int) should be used in preference to this.
    
    Specified by:
    
    setMaskArray in interface Layer
    
    Parameters:
    
    maskArray - Mask array to set
  - getMaskArray
```
public INDArray getMaskArray()
```
    Specified by:
    
    getMaskArray in interface Layer
  - isPretrainLayer
```
public boolean isPretrainLayer()
```
    Description copied from interface: Layer
    
    Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
    
    Specified by:
    
    isPretrainLayer in interface Layer
    
    Returns:
    
    true if the layer can be pretrained (using fit(INDArray), false otherwise
  - clearNoiseWeightParams
```
public void clearNoiseWeightParams()
```
    Specified by:
    
    clearNoiseWeightParams in interface Layer
  - allowInputModification
```
public void allowInputModification(boolean allow)
```
    Description copied from interface: Layer
    
    A performance optimization: mark whether the layer is allowed to modify its input array in-place. In many cases, this is totally safe - in others, the input array will be shared by multiple layers, and hence it's not safe to modify the input array. This is usually used by ops such as dropout.
    
    Specified by:
    
    allowInputModification in interface Layer
    
    Parameters:
    
    allow - If true: the input array is safe to modify. If false: the input array should be copied before it is modified (i.e., in-place modifications are un-safe)
  - feedForwardMaskArray
```
public Pair<INDArray,MaskState> feedForwardMaskArray(INDArray maskArray,
                                                     MaskState currentMaskState,
                                                     int minibatchSize)
```
    Description copied from interface: Layer
    
    Feed forward the input mask array, setting in the layer as appropriate. This allows different layers to handle masks differently - for example, bidirectional RNNs and normal RNNs operate differently with masks (the former sets activations to 0 outside of the data present region (and keeps the mask active for future layers like dense layers), whereas normal RNNs don't zero out the activations/errors )instead relying on backpropagated error arrays to handle the variable length case.
    This is also used for example for networks that contain global pooling layers, arbitrary preprocessors, etc.
    
    Specified by:
    
    feedForwardMaskArray in interface Layer
    
    Parameters:
    
    maskArray - Mask array to set
    
    currentMaskState - Current state of the mask - see MaskState
    
    minibatchSize - Current minibatch size. Needs to be known as it cannot always be inferred from the activations array due to reshaping (such as a DenseLayer within a recurrent neural network)
    
    Returns:
    
    New mask array after this layer, along with the new mask state.
  - getHelper
```
public LayerHelper getHelper()
```
    Specified by:
    
    getHelper in interface Layer
    
    Returns:
    
    Get the layer helper, if any
  - fit
```
public void fit()
```
    Description copied from interface: Model
    
    All models have a fit method
    
    Specified by:
    
    fit in interface Model
  - reconstructionProbability
```
public INDArray reconstructionProbability(INDArray data,
                                          int numSamples)
```
    Calculate the reconstruction probability, as described in An & Cho, 2015 - "Variational Autoencoder based Anomaly Detection using Reconstruction Probability" (Algorithm 4)
    The authors describe it as follows: "This is essentially the probability of the data being generated from a given latent variable drawn from the approximate posterior distribution."
    
    Specifically, for each example x in the input, calculate p(x). Note however that p(x) is a stochastic (Monte-Carlo) estimate of the true p(x), based on the specified number of samples. More samples will produce a more accurate (lower variance) estimate of the true p(x) for the current model parameters.
    
    Internally uses reconstructionLogProbability(INDArray, int) for the actual implementation. That method may be more numerically stable in some cases.
    
    The returned array is a column vector of reconstruction probabilities, for each example. Thus, reconstruction probabilities can (and should, for efficiency) be calculated in a batched manner.
    
    Parameters:
    
    data - The data to calculate the reconstruction probability for
    
    numSamples - Number of samples with which to base the reconstruction probability on.
    
    Returns:
    
    Column vector of reconstruction probabilities for each example (shape: [numExamples,1])
  - reconstructionLogProbability
```
public INDArray reconstructionLogProbability(INDArray data,
                                             int numSamples)
```
    Return the log reconstruction probability given the specified number of samples.
    See reconstructionLogProbability(INDArray, int) for more details
    
    Parameters:
    
    data - The data to calculate the log reconstruction probability
    
    numSamples - Number of samples with which to base the reconstruction probability on.
    
    Returns:
    
    Column vector of reconstruction log probabilities for each example (shape: [numExamples,1])
  - generateAtMeanGivenZ
```
public INDArray generateAtMeanGivenZ(INDArray latentSpaceValues)
```
    Given a specified values for the latent space as input (latent space being z in p(z|data)), generate output from P(x|z), where x = E[P(x|z)]
    i.e., return the mean value for the distribution P(x|z)
    
    Parameters:
    
    latentSpaceValues - Values for the latent space. size(1) must equal nOut configuration parameter
    
    Returns:
    
    Sample of data: E[P(x|z)]
  - generateRandomGivenZ
```
public INDArray generateRandomGivenZ(INDArray latentSpaceValues,
                                     LayerWorkspaceMgr workspaceMgr)
```
    Given a specified values for the latent space as input (latent space being z in p(z|data)), randomly generate output x, where x ~ P(x|z)
    
    Parameters:
    
    latentSpaceValues - Values for the latent space. size(1) must equal nOut configuration parameter
    
    Returns:
    
    Sample of data: x ~ P(x|z)
  - hasLossFunction
```
public boolean hasLossFunction()
```
    Does the reconstruction distribution have a loss function (such as mean squared error) or is it a standard probabilistic reconstruction distribution?
  - reconstructionError
```
public INDArray reconstructionError(INDArray data)
```
    Return the reconstruction error for this variational autoencoder.
    NOTE (important): This method is used ONLY for VAEs that have a standard neural network loss function (i.e., an ILossFunction instance such as mean squared error) instead of using a probabilistic reconstruction distribution P(x|z) for the reconstructions (as presented in the VAE architecture by Kingma and Welling).
    You can check if the VAE has a loss function using hasLossFunction()
    Consequently, the reconstruction error is a simple deterministic function (no Monte-Carlo sampling is required, unlike reconstructionProbability(INDArray, int) and reconstructionLogProbability(INDArray, int))
    
    Parameters:
    
    data - The data to calculate the reconstruction error on
    
    Returns:
    
    Column vector of reconstruction errors for each example (shape: [numExamples,1])
  - assertInputSet
```
public void assertInputSet(boolean backprop)
```

Class VariationalAutoencoder

Nested Class Summary

Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.Layer

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

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

Field Detail

input

paramsFlattened

gradientsFlattened

params

gradientViews

conf

score

optimizer

gradient

trainingListeners

index

maskArray

solver

encoderLayerSizes

decoderLayerSizes

reconstructionDistribution

pzxActivationFn

numSamples

cacheMode

dataType

zeroedPretrainParamGradients

weightNoiseParams

iterationCount

epochCount

Constructor Detail

VariationalAutoencoder

Method Detail

layerConf

setCacheMode

layerId

init

update

update

score

getParamWithNoise

computeGradientAndScore

params

getConfig

numParams

numParams

setParams

setParamsViewArray

getGradientsViewArray

setBackpropGradientsViewArray

fit

gradient

gradientAndScore

batchSize

conf

setConf

input

getOptimizer

getParam

paramTable

paramTable

updaterDivideByMinibatch

setParamTable

setParam

clear

applyConstraints

isPretrainParam

calcRegularizationScore

type

backpropGradient

preOutput

activate

activate

getListeners

setListeners

setListeners

addListeners