BaseWrapperLayer (deeplearning4j-nn 1.0.0-beta7 API)

java.lang.Object
- org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model, Trainable

Direct Known Subclasses:

FrozenLayer, FrozenLayerWithBackprop, LastTimeStepLayer, MaskZeroLayer, TimeDistributedLayer
```
public abstract class BaseWrapperLayer
extends Object
implements Layer
```
Abstract wrapper layer. The idea: this class passes through all methods to the underlying layer. Then, subclasses of BaseWrapperLayer can selectively override specific methods, rather than having to implement every single one of the passthrough methods in each subclass.

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 Layer underlying

Fields
Modifier and Type	Field and Description
`protected Layer`	`underlying`

Constructor Summary

Constructors
Constructor and Description

BaseWrapperLayer(@NonNull Layer underlying)

Constructors
Constructor and Description
`BaseWrapperLayer(@NonNull Layer underlying)`

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... listener)` 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
`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`	`close()`
`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
`TrainingConfig`	`getConfig()`
`int`	`getEpochCount()`
`INDArray`	`getGradientsViewArray()`
`LayerHelper`	`getHelper()`
`int`	`getIndex()` Get the layer index.
`int`	`getInputMiniBatchSize()` Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
`int`	`getIterationCount()`
`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
`Gradient`	`gradient()` Get the gradient.
`Pair<Gradient,Double>`	`gradientAndScore()` Get the gradient and score
`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)
`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
`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`	`setEpochCount(int epochCount)` Set the current epoch count (number of epochs passed ) for the layer/network
`void`	`setIndex(int index)` Set the layer index.
`void`	`setInput(INDArray input, LayerWorkspaceMgr workspaceMgr)` Set the layer input.
`void`	`setInputMiniBatchSize(int size)` Set current/last input mini-batch size. Used for score and gradient calculations.
`void`	`setIterationCount(int iterationCount)` Set the current iteration count (number of parameter updates) for the layer/network
`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

- Field Detail
  - underlying
```
protected Layer underlying
```
- Constructor Detail
  - BaseWrapperLayer
```
public BaseWrapperLayer(@NonNull
                        @NonNull Layer underlying)
```
- Method Detail
  - 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
  - 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
  - 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
  - addListeners
```
public void addListeners(TrainingListener... listener)
```
    Description copied from interface: Model
    
    This method ADDS additional TrainingListener to existing listeners
    
    Specified by:
    
    addListeners in interface Model
  - fit
```
public void fit()
```
    Description copied from interface: Model
    
    All models have a fit method
    
    Specified by:
    
    fit 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
  - 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
  - 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
  - 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
  - init
```
public void init()
```
    Description copied from interface: Model
    
    Init the model
    
    Specified by:
    
    init 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
  - 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
  - getIterationCount
```
public int getIterationCount()
```
    Specified by:
    
    getIterationCount in interface Layer
    
    Returns:
    
    The current iteration count (number of parameter updates) for the layer/network
  - getEpochCount
```
public int getEpochCount()
```
    Specified by:
    
    getEpochCount in interface Layer
    
    Returns:
    
    The current epoch count (number of training epochs passed) for the layer/network
  - setIterationCount
```
public void setIterationCount(int iterationCount)
```
    Description copied from interface: Layer
    
    Set the current iteration count (number of parameter updates) for the layer/network
    
    Specified by:
    
    setIterationCount in interface Layer
  - setEpochCount
```
public void setEpochCount(int epochCount)
```
    Description copied from interface: Layer
    
    Set the current epoch count (number of epochs passed ) for the layer/network
    
    Specified by:
    
    setEpochCount in interface Layer
  - setInput
```
public void setInput(INDArray input,
                     LayerWorkspaceMgr workspaceMgr)
```
    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
  - 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.
  - 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)
  - getHelper
```
public LayerHelper getHelper()
```
    Specified by:
    
    getHelper in interface Layer
    
    Returns:
    
    Get the layer helper, if any
  - getConfig
```
public TrainingConfig getConfig()
```
    Specified by:
    
    getConfig in interface Trainable
    
    Returns:
    
    Training configuration
  - 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)
  - close
```
public void close()
```
    Specified by:
    
    close in interface Model

Class BaseWrapperLayer

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

Field Detail

underlying

Constructor Detail

BaseWrapperLayer

Method Detail

setCacheMode

calcRegularizationScore

type

backpropGradient

activate

activate

getListeners

setListeners

addListeners

fit

update

update

score

computeGradientAndScore

params

numParams

numParams

setParams

setParamsViewArray

getGradientsViewArray

setBackpropGradientsViewArray

fit

gradient

gradientAndScore

batchSize

conf

setConf

input

getOptimizer

getParam

paramTable

paramTable

setParamTable

setParam

clear

applyConstraints

init

setListeners

setIndex

getIndex

getIterationCount

getEpochCount

setIterationCount

setEpochCount

setInput

setInputMiniBatchSize

getInputMiniBatchSize

setMaskArray

getMaskArray

isPretrainLayer

clearNoiseWeightParams

feedForwardMaskArray

allowInputModification

getHelper

getConfig

updaterDivideByMinibatch

close