AbstractLayer (deeplearning4j-nn 0.9.1 API)

java.lang.Object
- org.deeplearning4j.nn.layers.AbstractLayer<LayerConfT>

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model

Direct Known Subclasses:

ActivationLayer, BaseLayer, GlobalPoolingLayer, LocalResponseNormalization, SubsamplingLayer, ZeroPaddingLayer
```
public abstract class AbstractLayer<LayerConfT extends Layer>
extends Object
implements Layer
```
A layer with input and output, no parameters or gradients

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 boolean`	`dropoutApplied`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`dropoutMask`
`protected int`	`index`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`input`
`protected Collection<IterationListener>`	`iterationListeners`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`maskArray`
`protected MaskState`	`maskState`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`preOutput`

Constructor Summary

Constructors
Constructor and Description

AbstractLayer(NeuralNetConfiguration conf)

AbstractLayer(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)

Constructors
Constructor and Description
`AbstractLayer(NeuralNetConfiguration conf)`
`AbstractLayer(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`void`	`accumulateScore(double accum)` Sets a rolling tally for the score.
`org.nd4j.linalg.api.ndarray.INDArray`	`activate()` Trigger an activation with the last specified input
`org.nd4j.linalg.api.ndarray.INDArray`	`activate(org.nd4j.linalg.api.ndarray.INDArray input)` Initialize the layer with the given input and return the activation for this layer given this input
`org.nd4j.linalg.api.ndarray.INDArray`	`activate(org.nd4j.linalg.api.ndarray.INDArray input, boolean training)` Initialize the layer with the given input and return the activation for this layer given this input
`org.nd4j.linalg.api.ndarray.INDArray`	`activate(org.nd4j.linalg.api.ndarray.INDArray input, Layer.TrainingMode training)` Initialize the layer with the given input and return the activation for this layer given this input
`org.nd4j.linalg.api.ndarray.INDArray`	`activate(Layer.TrainingMode training)` Trigger an activation with the last specified input
`void`	`addListeners(IterationListener... listeners)` This method ADDS additional IterationListener to existing listeners
`protected void`	`applyDropOutIfNecessary(boolean training)`
`void`	`applyLearningRateScoreDecay()` Update learningRate using for this model.
`protected void`	`applyMask(org.nd4j.linalg.api.ndarray.INDArray to)`
`int`	`batchSize()` The current inputs batch size
`Gradient`	`calcGradient(Gradient layerError, org.nd4j.linalg.api.ndarray.INDArray activation)` Calculate the gradient
`double`	`calcL1(boolean backpropParamsOnly)` Calculate the l1 regularization term 0.0 if regularization is not used.
`double`	`calcL2(boolean backpropParamsOnly)` Calculate the l2 regularization term 0.0 if regularization is not used.
`void`	`clear()` Clear input
`abstract Layer`	`clone()` Clone the layer
`void`	`computeGradientAndScore()` Update the score
`NeuralNetConfiguration`	`conf()` The configuration for the neural network
`org.nd4j.linalg.api.ndarray.INDArray`	`derivativeActivation(org.nd4j.linalg.api.ndarray.INDArray input)` Deprecated.
`Gradient`	`error(org.nd4j.linalg.api.ndarray.INDArray errorSignal)` Calculate error with respect to the current layer.
`org.nd4j.linalg.primitives.Pair<org.nd4j.linalg.api.ndarray.INDArray,MaskState>`	`feedForwardMaskArray(org.nd4j.linalg.api.ndarray.INDArray maskArray, MaskState currentMaskState, int minibatchSize)` Feed forward the input mask array, setting in in the layer as appropriate.
`void`	`fit()` All models have a fit method
`void`	`fit(org.nd4j.linalg.api.ndarray.INDArray input)` Fit the model to the given data
`org.nd4j.linalg.api.ndarray.INDArray`	`getGradientsViewArray()`
`int`	`getIndex()` Get the layer index.
`org.nd4j.linalg.api.ndarray.INDArray`	`getInput()`
`int`	`getInputMiniBatchSize()` Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
`Collection<IterationListener>`	`getListeners()` Get the iteration listeners for this layer.
`org.nd4j.linalg.api.ndarray.INDArray`	`getMaskArray()`
`ConvexOptimizer`	`getOptimizer()` Returns this models optimizer
`org.nd4j.linalg.api.ndarray.INDArray`	`getParam(String param)` Get the parameter
`Gradient`	`gradient()` Calculate a gradient
`org.nd4j.linalg.primitives.Pair<Gradient,Double>`	`gradientAndScore()` Get the gradient and score
`void`	`init()` Init the model
`void`	`initParams()` Initialize the parameters
`org.nd4j.linalg.api.ndarray.INDArray`	`input()` The input/feature matrix for the model
`void`	`iterate(org.nd4j.linalg.api.ndarray.INDArray input)` iterate one iteration of the network
`protected LayerConfT`	`layerConf()`
`protected String`	`layerId()`
`void`	`merge(Layer l, int batchSize)` Averages the given logistic regression from a mini batch into this layer
`int`	`numParams()` The number of parameters for the model
`int`	`numParams(boolean backwards)` the number of parameters for the model
`org.nd4j.linalg.api.ndarray.INDArray`	`params()` Returns the parameters of the neural network as a flattened row vector
`Map<String,org.nd4j.linalg.api.ndarray.INDArray>`	`paramTable()` The param table
`Map<String,org.nd4j.linalg.api.ndarray.INDArray>`	`paramTable(boolean backpropParamsOnly)` Table of parameters by key, for backprop For many models (dense layers, etc) - all parameters are backprop parameters
`abstract org.nd4j.linalg.api.ndarray.INDArray`	`preOutput(boolean training)`
`org.nd4j.linalg.api.ndarray.INDArray`	`preOutput(org.nd4j.linalg.api.ndarray.INDArray x)` Classify input
`org.nd4j.linalg.api.ndarray.INDArray`	`preOutput(org.nd4j.linalg.api.ndarray.INDArray x, boolean training)` Raw activations
`org.nd4j.linalg.api.ndarray.INDArray`	`preOutput(org.nd4j.linalg.api.ndarray.INDArray x, Layer.TrainingMode training)` Raw activations
`double`	`score()` The score for the model
`void`	`setBackpropGradientsViewArray(org.nd4j.linalg.api.ndarray.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(org.nd4j.linalg.api.ndarray.INDArray input)` Get the layer input.
`void`	`setInputMiniBatchSize(int size)` Set current/last input mini-batch size. Used for score and gradient calculations.
`void`	`setListeners(Collection<IterationListener> listeners)` Set the iteration listeners for this layer.
`void`	`setListeners(IterationListener... listeners)` Set the iteration listeners for this layer.
`void`	`setMaskArray(org.nd4j.linalg.api.ndarray.INDArray maskArray)` Set the mask array.
`void`	`setParam(String key, org.nd4j.linalg.api.ndarray.INDArray val)` Set the parameter with a new ndarray
`void`	`setParams(org.nd4j.linalg.api.ndarray.INDArray params)` Set the parameters for this model.
`protected void`	`setParams(org.nd4j.linalg.api.ndarray.INDArray params, char order)`
`void`	`setParamsViewArray(org.nd4j.linalg.api.ndarray.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,org.nd4j.linalg.api.ndarray.INDArray> paramTable)` Setter for the param table
`Layer`	`transpose()` Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
`Layer.Type`	`type()` Returns the layer type
`void`	`update(Gradient gradient)` Update layer weights and biases with gradient change
`void`	`update(org.nd4j.linalg.api.ndarray.INDArray gradient, String paramType)` Perform one update applying the gradient
`void`	`validateInput()` Validate the input

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.Layer
activate, activationMean, backpropGradient, isPretrainLayer

- Field Detail
  - input
```
protected org.nd4j.linalg.api.ndarray.INDArray input
```
  - preOutput
```
protected org.nd4j.linalg.api.ndarray.INDArray preOutput
```
  - conf
```
protected NeuralNetConfiguration conf
```
  - dropoutMask
```
protected org.nd4j.linalg.api.ndarray.INDArray dropoutMask
```
  - dropoutApplied
```
protected boolean dropoutApplied
```
  - iterationListeners
```
protected Collection<IterationListener> iterationListeners
```
  - index
```
protected int index
```
  - maskArray
```
protected org.nd4j.linalg.api.ndarray.INDArray maskArray
```
  - maskState
```
protected MaskState maskState
```
  - cacheMode
```
protected CacheMode cacheMode
```
- Constructor Detail
  - AbstractLayer
```
public AbstractLayer(NeuralNetConfiguration conf)
```
  - AbstractLayer
```
public AbstractLayer(NeuralNetConfiguration conf,
                     org.nd4j.linalg.api.ndarray.INDArray input)
```
- 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
  - layerConf
```
protected LayerConfT layerConf()
```
  - layerId
```
protected String layerId()
```
  - getInput
```
public org.nd4j.linalg.api.ndarray.INDArray getInput()
```
  - init
```
public void init()
```
    Init the model
    
    Specified by:
    
    init in interface Model
  - clone
```
public abstract Layer clone()
```
    Description copied from interface: Layer
    
    Clone the layer
    
    Specified by:
    
    clone in interface Layer
    
    Overrides:
    
    clone in class Object
    
    Returns:
  - setInput
```
public void setInput(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Description copied from interface: Layer
    
    Get the layer input.
    
    Specified by:
    
    setInput in interface Layer
  - getIndex
```
public int getIndex()
```
    Description copied from interface: Layer
    
    Get the layer index.
    
    Specified by:
    
    getIndex in interface Layer
  - setIndex
```
public void setIndex(int index)
```
    Description copied from interface: Layer
    
    Set the layer index.
    
    Specified by:
    
    setIndex in interface Layer
  - getListeners
```
public Collection<IterationListener> getListeners()
```
    Description copied from interface: Layer
    
    Get the iteration listeners for this layer.
    
    Specified by:
    
    getListeners in interface Layer
  - setListeners
```
public void setListeners(Collection<IterationListener> listeners)
```
    Description copied from interface: Layer
    
    Set the iteration listeners for this layer.
    
    Specified by:
    
    setListeners in interface Layer
    
    Specified by:
    
    setListeners in interface Model
  - addListeners
```
public void addListeners(IterationListener... listeners)
```
    This method ADDS additional IterationListener to existing listeners
    
    Specified by:
    
    addListeners in interface Model
    
    Parameters:
    
    listeners -
  - setListeners
```
public void setListeners(IterationListener... listeners)
```
    Description copied from interface: Layer
    
    Set the iteration listeners for this layer.
    
    Specified by:
    
    setListeners in interface Layer
    
    Specified by:
    
    setListeners in interface Model
  - error
```
public Gradient error(org.nd4j.linalg.api.ndarray.INDArray errorSignal)
```
    Description copied from interface: Layer
    
    Calculate error with respect to the current layer. This gradient will contain the error signal
    
    Specified by:
    
    error in interface Layer
    
    Parameters:
    
    errorSignal - the gradient for the forward layer If this is the final layer, it will start with the error from the output. This is on the user to initialize.
    
    Returns:
    
    the gradient wrt the parameters on the current layer
  - derivativeActivation
```
@Deprecated
public org.nd4j.linalg.api.ndarray.INDArray derivativeActivation(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Deprecated.
    
    Description copied from interface: Layer
    
    Take the derivative of the given input based on the activation
    
    Specified by:
    
    derivativeActivation in interface Layer
    
    Parameters:
    
    input - the input to take the derivative of
    
    Returns:
    
    the derivative of the action
  - calcGradient
```
public Gradient calcGradient(Gradient layerError,
                             org.nd4j.linalg.api.ndarray.INDArray activation)
```
    Description copied from interface: Layer
    
    Calculate the gradient
    
    Specified by:
    
    calcGradient in interface Layer
    
    Parameters:
    
    layerError - the layer error
    
    Returns:
    
    the gradient
  - computeGradientAndScore
```
public void computeGradientAndScore()
```
    Description copied from interface: Model
    
    Update the score
    
    Specified by:
    
    computeGradientAndScore in interface Model
  - preOutput
```
public org.nd4j.linalg.api.ndarray.INDArray preOutput(org.nd4j.linalg.api.ndarray.INDArray x,
                                                      Layer.TrainingMode training)
```
    Description copied from interface: Layer
    
    Raw activations
    
    Specified by:
    
    preOutput in interface Layer
    
    Parameters:
    
    x - the input to transform
    
    Returns:
    
    the raw activation for this layer
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate(Layer.TrainingMode training)
```
    Description copied from interface: Layer
    
    Trigger an activation with the last specified input
    
    Specified by:
    
    activate in interface Layer
    
    Parameters:
    
    training - training or test mode
    
    Returns:
    
    the activation of the last specified input
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate(org.nd4j.linalg.api.ndarray.INDArray input,
                                                     Layer.TrainingMode training)
```
    Description copied from interface: Layer
    
    Initialize the layer with the given input and return the activation for this layer given this input
    
    Specified by:
    
    activate in interface Layer
    
    Parameters:
    
    input - the input to use
    
    training - train or test mode
    
    Returns:
  - iterate
```
public void iterate(org.nd4j.linalg.api.ndarray.INDArray input)
```
    iterate one iteration of the network
    
    Specified by:
    
    iterate in interface Model
    
    Parameters:
    
    input - the input to iterate on
  - 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(org.nd4j.linalg.api.ndarray.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
  - getOptimizer
```
public ConvexOptimizer getOptimizer()
```
    Description copied from interface: Model
    
    Returns this models optimizer
    
    Specified by:
    
    getOptimizer in interface Model
    
    Returns:
    
    this models optimizer
  - setConf
```
public void setConf(NeuralNetConfiguration conf)
```
    Description copied from interface: Model
    
    Setter for the configuration
    
    Specified by:
    
    setConf in interface Model
  - params
```
public org.nd4j.linalg.api.ndarray.INDArray params()
```
    Returns the parameters of the neural network as a flattened row vector
    
    Specified by:
    
    params in interface Model
    
    Returns:
    
    the parameters of the neural network
  - getParam
```
public org.nd4j.linalg.api.ndarray.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
  - setParam
```
public void setParam(String key,
                     org.nd4j.linalg.api.ndarray.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
  - setParams
```
public void setParams(org.nd4j.linalg.api.ndarray.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
  - setParams
```
protected void setParams(org.nd4j.linalg.api.ndarray.INDArray params,
                         char order)
```
  - setParamsViewArray
```
public void setParamsViewArray(org.nd4j.linalg.api.ndarray.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 org.nd4j.linalg.api.ndarray.INDArray getGradientsViewArray()
```
    Specified by:
    
    getGradientsViewArray in interface Model
  - setBackpropGradientsViewArray
```
public void setBackpropGradientsViewArray(org.nd4j.linalg.api.ndarray.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
  - setParamTable
```
public void setParamTable(Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable)
```
    Description copied from interface: Model
    
    Setter for the param table
    
    Specified by:
    
    setParamTable in interface Model
  - initParams
```
public void initParams()
```
    Description copied from interface: Model
    
    Initialize the parameters
    
    Specified by:
    
    initParams in interface Model
  - paramTable
```
public Map<String,org.nd4j.linalg.api.ndarray.INDArray> paramTable()
```
    Description copied from interface: Model
    
    The param table
    
    Specified by:
    
    paramTable in interface Model
    
    Returns:
  - paramTable
```
public Map<String,org.nd4j.linalg.api.ndarray.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
    
    Parameters:
    
    backpropParamsOnly - If true, return backprop params only. If false: return all params (equivalent to paramsTable())
  - preOutput
```
public org.nd4j.linalg.api.ndarray.INDArray preOutput(org.nd4j.linalg.api.ndarray.INDArray x,
                                                      boolean training)
```
    Description copied from interface: Layer
    
    Raw activations
    
    Specified by:
    
    preOutput in interface Layer
    
    Parameters:
    
    x - the input to transform
    
    Returns:
    
    the raw activation for this layer
  - preOutput
```
public abstract org.nd4j.linalg.api.ndarray.INDArray preOutput(boolean training)
```
  - applyMask
```
protected void applyMask(org.nd4j.linalg.api.ndarray.INDArray to)
```
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Description copied from interface: Layer
    
    Initialize the layer with the given input and return the activation for this layer given this input
    
    Specified by:
    
    activate in interface Layer
    
    Parameters:
    
    input - the input to use
    
    Returns:
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate(org.nd4j.linalg.api.ndarray.INDArray input,
                                                     boolean training)
```
    Description copied from interface: Layer
    
    Initialize the layer with the given input and return the activation for this layer given this input
    
    Specified by:
    
    activate in interface Layer
    
    Parameters:
    
    input - the input to use
    
    training - train or test mode
    
    Returns:
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate()
```
    Description copied from interface: Layer
    
    Trigger an activation with the last specified input
    
    Specified by:
    
    activate in interface Layer
    
    Returns:
    
    the activation of the last specified input
  - preOutput
```
public org.nd4j.linalg.api.ndarray.INDArray preOutput(org.nd4j.linalg.api.ndarray.INDArray x)
```
    Classify input
    
    Specified by:
    
    preOutput in interface Layer
    
    Parameters:
    
    x - the input (can either be a matrix or vector) If it's a matrix, each row is considered an example and associated rows are classified accordingly. Each row will be the likelihood of a label given that example
    
    Returns:
    
    a probability distribution for each row
  - calcL2
```
public double calcL2(boolean backpropParamsOnly)
```
    Description copied from interface: Layer
    
    Calculate the l2 regularization term
    0.0 if regularization is not used. Or 0.5 * l2Coeff * l2Magnitude otherwise.
    Note that this does not divide by mini-batch size
    
    Specified by:
    
    calcL2 in interface Layer
    
    Parameters:
    
    backpropParamsOnly - If true: calculate L2 based on backprop params only. If false: calculate based on all params (including pretrain params, if any)
    
    Returns:
    
    the l2 regularization term for this layer.
  - calcL1
```
public double calcL1(boolean backpropParamsOnly)
```
    Description copied from interface: Layer
    
    Calculate the l1 regularization term
    0.0 if regularization is not used. Or l1Coeff * l1Magnitude otherwise.
    Note that this does not divide by mini-batch size
    
    Specified by:
    
    calcL1 in interface Layer
    
    Parameters:
    
    backpropParamsOnly - If true: calculate L1 based on backprop params only. If false: calculate based on all params (including pretrain params, if any)
    
    Returns:
    
    the l1 regularization term for this layer.
  - 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
  - clear
```
public void clear()
```
    Description copied from interface: Model
    
    Clear input
    
    Specified by:
    
    clear in interface Model
  - applyDropOutIfNecessary
```
protected void applyDropOutIfNecessary(boolean training)
```
  - merge
```
public void merge(Layer l,
                  int batchSize)
```
    Averages the given logistic regression from a mini batch into this layer
    
    Specified by:
    
    merge in interface Layer
    
    Parameters:
    
    l - the logistic regression layer to average into this layer
    
    batchSize - the batch size
  - type
```
public Layer.Type type()
```
    Description copied from interface: Layer
    
    Returns the layer type
    
    Specified by:
    
    type in interface Layer
    
    Returns:
  - numParams
```
public int numParams()
```
    The number of parameters for the model
    
    Specified by:
    
    numParams in interface Model
    
    Returns:
    
    the number of parameters for the model
  - numParams
```
public int 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
  - fit
```
public void fit(org.nd4j.linalg.api.ndarray.INDArray input)
```
    Description copied from interface: Model
    
    Fit the model to the given data
    
    Specified by:
    
    fit in interface Model
    
    Parameters:
    
    input - the data to fit the model to
  - gradientAndScore
```
public org.nd4j.linalg.primitives.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
  - input
```
public org.nd4j.linalg.api.ndarray.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
  - validateInput
```
public void validateInput()
```
    Description copied from interface: Model
    
    Validate the input
    
    Specified by:
    
    validateInput in interface Model
  - transpose
```
public Layer transpose()
```
    Description copied from interface: Layer
    
    Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
    
    Specified by:
    
    transpose in interface Layer
    
    Returns:
    
    the transposed 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)
  - applyLearningRateScoreDecay
```
public void applyLearningRateScoreDecay()
```
    Description copied from interface: Model
    
    Update learningRate using for this model. Use the learningRateScoreBasedDecay to adapt the score if the Eps termination condition is met
    
    Specified by:
    
    applyLearningRateScoreDecay in interface Model
  - setMaskArray
```
public void setMaskArray(org.nd4j.linalg.api.ndarray.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 org.nd4j.linalg.api.ndarray.INDArray getMaskArray()
```
    Specified by:
    
    getMaskArray in interface Layer
  - feedForwardMaskArray
```
public org.nd4j.linalg.primitives.Pair<org.nd4j.linalg.api.ndarray.INDArray,MaskState> feedForwardMaskArray(org.nd4j.linalg.api.ndarray.INDArray maskArray,
                                                                                                            MaskState currentMaskState,
                                                                                                            int minibatchSize)
```
    Description copied from interface: Layer
    
    Feed forward the input mask array, setting in 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.
  - gradient
```
public Gradient gradient()
```
    Description copied from interface: Model
    
    Calculate a gradient
    
    Specified by:
    
    gradient in interface Model
    
    Returns:
    
    the gradient for this model
  - fit
```
public void fit()
```
    Description copied from interface: Model
    
    All models have a fit method
    
    Specified by:
    
    fit in interface Model
  - 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
  - accumulateScore
```
public void accumulateScore(double accum)
```
    Description copied from interface: Model
    
    Sets a rolling tally for the score. This is useful for mini batch learning when you are accumulating error across a dataset.
    
    Specified by:
    
    accumulateScore in interface Model
    
    Parameters:
    
    accum - the amount to accum

Class AbstractLayer<LayerConfT extends Layer>

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

preOutput

conf

dropoutMask

dropoutApplied

iterationListeners

index

maskArray

maskState

cacheMode

Constructor Detail

AbstractLayer

AbstractLayer

Method Detail

setCacheMode

layerConf

layerId

getInput

init

clone

setInput

getIndex

setIndex

getListeners

setListeners

addListeners

setListeners

error

derivativeActivation

calcGradient

computeGradientAndScore

preOutput

activate

activate

iterate

update

update

getOptimizer

setConf

params

getParam

setParam

setParams

setParams

setParamsViewArray

getGradientsViewArray

setBackpropGradientsViewArray

setParamTable

initParams

paramTable

paramTable

preOutput

preOutput

applyMask

activate

activate

activate

preOutput

calcL2

calcL1

batchSize

conf

clear

applyDropOutIfNecessary

merge

type

numParams

numParams

fit

gradientAndScore

input