BaseLayer (deeplearning4j-core 0.5.0 API)

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

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model

Direct Known Subclasses:

ActivationLayer, BaseOutputLayer, BasePretrainNetwork, BaseRecurrentLayer, BatchNormalization, ConvolutionLayer, DenseLayer, EmbeddingLayer, LocalResponseNormalization, SubsamplingLayer
```
public abstract class BaseLayer<LayerConfT extends Layer>
extends Object
implements Layer
```
A layer with a bias and activation function

Author:

Adam Gibson

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 NeuralNetConfiguration`	`conf`
`protected boolean`	`dropoutApplied`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`dropoutMask`
`protected Gradient`	`gradient`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`gradientsFlattened`
`protected Map<String,org.nd4j.linalg.api.ndarray.INDArray>`	`gradientViews`
`protected int`	`index`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`input`
`protected Collection<IterationListener>`	`iterationListeners`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`maskArray`
`protected ConvexOptimizer`	`optimizer`
`protected Map<String,org.nd4j.linalg.api.ndarray.INDArray>`	`params`
`protected org.nd4j.linalg.api.ndarray.INDArray`	`paramsFlattened`
`protected double`	`score`
`protected Solver`	`solver`

Constructor Summary

Constructors
Constructor and Description

BaseLayer(NeuralNetConfiguration conf)

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

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

Method Summary

All Methods Instance Methods Concrete 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(boolean training)` 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
`org.nd4j.linalg.api.ndarray.INDArray`	`activationMean()` Calculate the mean representation for the activation for this layer
`protected void`	`applyDropOutIfNecessary(boolean training)`
`void`	`applyLearningRateScoreDecay()` Update learningRate using for this model.
`Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray>`	`backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon)` Calculate the gradient relative to the error in the next layer
`int`	`batchSize()` The current inputs batch size
`Gradient`	`calcGradient(Gradient layerError, org.nd4j.linalg.api.ndarray.INDArray activation)` Calculate the gradient
`double`	`calcL1()` Calculate the l1 regularization term 0.0 if regularization is not used.
`double`	`calcL2()` Calculate the l2 regularization term 0.0 if regularization is not used.
`void`	`clear()` Clear input
`Layer`	`clone()` Clone the layer
`void`	`computeGradientAndScore()` Update the score
`NeuralNetConfiguration`	`conf()` The configuration for the neural network
`protected Gradient`	`createGradient(org.nd4j.linalg.api.ndarray.INDArray... gradients)` Create a gradient list based on the passed in parameters.
`org.nd4j.linalg.api.ndarray.INDArray`	`derivativeActivation(org.nd4j.linalg.api.ndarray.INDArray input)` Take the derivative of the given input based on the activation
`Gradient`	`error(org.nd4j.linalg.api.ndarray.INDArray errorSignal)` Calculate error with respect to the current layer.
`void`	`fit()` All models have a fit method
`void`	`fit(org.nd4j.linalg.api.ndarray.INDArray input)` Fit the model to the given data
`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.
`ConvexOptimizer`	`getOptimizer()` Returns this models optimizer
`org.nd4j.linalg.api.ndarray.INDArray`	`getParam(String param)` Get the parameter
`Gradient`	`gradient()` Calculate a gradient
`Pair<Gradient,Double>`	`gradientAndScore()` Get the gradient and score
`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()`
`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
`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()` Objective function: the specified objective
`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`	`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)`
`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
`protected void`	`setScoreWithZ(org.nd4j.linalg.api.ndarray.INDArray z)`
`String`	`toString()`
`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)` Update layer weights and biases with gradient change
`void`	`validateInput()` Validate the input

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

- Field Detail
  - input
```
protected org.nd4j.linalg.api.ndarray.INDArray input
```
  - paramsFlattened
```
protected org.nd4j.linalg.api.ndarray.INDArray paramsFlattened
```
  - gradientsFlattened
```
protected org.nd4j.linalg.api.ndarray.INDArray gradientsFlattened
```
  - params
```
protected Map<String,org.nd4j.linalg.api.ndarray.INDArray> params
```
  - gradientViews
```
protected transient Map<String,org.nd4j.linalg.api.ndarray.INDArray> gradientViews
```
  - conf
```
protected NeuralNetConfiguration conf
```
  - dropoutMask
```
protected org.nd4j.linalg.api.ndarray.INDArray dropoutMask
```
  - dropoutApplied
```
protected boolean dropoutApplied
```
  - score
```
protected double score
```
  - optimizer
```
protected ConvexOptimizer optimizer
```
  - gradient
```
protected Gradient gradient
```
  - iterationListeners
```
protected Collection<IterationListener> iterationListeners
```
  - index
```
protected int index
```
  - maskArray
```
protected org.nd4j.linalg.api.ndarray.INDArray maskArray
```
  - solver
```
protected Solver solver
```
- Constructor Detail
  - BaseLayer
```
public BaseLayer(NeuralNetConfiguration conf)
```
  - BaseLayer
```
public BaseLayer(NeuralNetConfiguration conf,
                 org.nd4j.linalg.api.ndarray.INDArray input)
```
- Method Detail
  - layerConf
```
protected LayerConfT layerConf()
```
  - getInput
```
public org.nd4j.linalg.api.ndarray.INDArray getInput()
```
  - 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
  - setListeners
```
public void setListeners(IterationListener... listeners)
```
    Description copied from interface: Layer
    
    Set the iteration listeners for this layer.
    
    Specified by:
    
    setListeners in interface Layer
  - 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
```
public org.nd4j.linalg.api.ndarray.INDArray derivativeActivation(org.nd4j.linalg.api.ndarray.INDArray input)
```
    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
  - backpropGradient
```
public Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray> backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon)
```
    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.
    
    Returns:
    
    Pair where Gradient is gradient for this layer, INDArray is epsilon 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() == (w^(L)*(delta^(L))^T)^T
  - fit
```
public void fit()
```
    Description copied from interface: Model
    
    All models have a fit method
    
    Specified by:
    
    fit in interface Model
  - computeGradientAndScore
```
public void computeGradientAndScore()
```
    Description copied from interface: Model
    
    Update the score
    
    Specified by:
    
    computeGradientAndScore in interface Model
  - setScoreWithZ
```
protected void setScoreWithZ(org.nd4j.linalg.api.ndarray.INDArray z)
```
  - 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:
  - score
```
public double score()
```
    Objective function: the specified objective
    
    Specified by:
    
    score in interface Model
    
    Returns:
    
    the score for the objective
  - gradient
```
public Gradient gradient()
```
    Description copied from interface: Model
    
    Calculate a gradient
    
    Specified by:
    
    gradient in interface Model
    
    Returns:
    
    the gradient for this model
  - 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: Layer
    
    Update layer weights and biases with gradient change
    
    Specified by:
    
    update in interface Layer
  - update
```
public void update(org.nd4j.linalg.api.ndarray.INDArray gradient,
                   String paramType)
```
    Description copied from interface: Layer
    
    Update layer weights and biases with gradient change
    
    Specified by:
    
    update in interface Layer
    
    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
  - 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:
  - 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 org.nd4j.linalg.api.ndarray.INDArray preOutput(boolean training)
```
  - activate
```
public org.nd4j.linalg.api.ndarray.INDArray activate(boolean 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)
```
    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()
```
    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
    
    Returns:
    
    the l2 regularization term for this layer.
  - calcL1
```
public double calcL1()
```
    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
    
    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
  - activationMean
```
public org.nd4j.linalg.api.ndarray.INDArray activationMean()
```
    Description copied from interface: Layer
    
    Calculate the mean representation for the activation for this layer
    
    Specified by:
    
    activationMean in interface Layer
    
    Returns:
    
    the activation mean for this layer
  - 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
  - clone
```
public Layer clone()
```
    Description copied from interface: Layer
    
    Clone the layer
    
    Specified by:
    
    clone in interface Layer
    
    Overrides:
    
    clone in class Object
    
    Returns:
  - 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 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
  - createGradient
```
protected Gradient createGradient(org.nd4j.linalg.api.ndarray.INDArray... gradients)
```
    Create a gradient list based on the passed in parameters. Will throw an IllegalArgumentException if the number of gradient matrices isn't equal to the number of keys in the parameter list
    
    Parameters:
    
    gradients - the gradients to create from
    
    Returns:
    
    the create based on the passed in ndarrays
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - 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
  - 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
  - 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)
```
    Specified by:
    
    setMaskArray in interface Layer

Class BaseLayer<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

Field Detail

input

paramsFlattened

gradientsFlattened

params

gradientViews

conf

dropoutMask

dropoutApplied

score

optimizer

gradient

iterationListeners

index

maskArray

solver

Constructor Detail

BaseLayer

BaseLayer

Method Detail

layerConf

getInput

setInput

getIndex

setIndex

getListeners

setListeners

setListeners

error

derivativeActivation

calcGradient

backpropGradient

fit

computeGradientAndScore

setScoreWithZ

preOutput

activate

activate

score

gradient

iterate

update

update

getOptimizer

setConf

params

getParam

setParam

setParams

setParams

setParamsViewArray

setBackpropGradientsViewArray

setParamTable

initParams

paramTable

preOutput

preOutput

activate

activate

activate

activate

preOutput

calcL2

calcL1

batchSize

activationMean

conf

clear

applyDropOutIfNecessary

merge

clone

type

numParams