java.lang.Object
- org.deeplearning4j.nn.conf.layers.Layer.Builder<T>
- - org.deeplearning4j.nn.conf.layers.BaseLayer.Builder<T>

Direct Known Subclasses:: FeedForwardLayer.Builder

Enclosing class:: BaseLayer

public abstract static class BaseLayer.Builder<T extends BaseLayer.Builder<T>>
extends Layer.Builder<T>

Field Summary

Fields
Modifier and Type	Field	Description
`protected IActivation`	`activationFn`	Set the activation function for the layer.
`protected double`	`biasInit`	Bias initialization value, for layers with biases.
`protected IUpdater`	`biasUpdater`	Gradient updater configuration, for the biases only.
`protected double`	`gainInit`	Gain initialization value, for layers with Layer Normalization.
`protected GradientNormalization`	`gradientNormalization`	Gradient normalization strategy.
`protected double`	`gradientNormalizationThreshold`	Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue Not used otherwise. L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
`protected IUpdater`	`iupdater`	Gradient updater.
`protected List<Regularization>`	`regularization`	Regularization for the parameters (excluding biases).
`protected List<Regularization>`	`regularizationBias`	Regularization for the bias parameters only
`protected IWeightInit`	`weightInitFn`	Weight initialization scheme to use, for initial weight values
`protected IWeightNoise`	`weightNoise`	Set the weight noise (such as `DropConnect` and `WeightNoise`) for this layer

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder
allParamConstraints, biasConstraints, iDropout, layerName, weightConstraints

Constructor Summary

Constructors
Constructor Description

Builder()

Method Summary

All Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method	Description
`T`	`activation(Activation activation)`	Set the activation function for the layer, from an `Activation` enumeration value.
`T`	`activation(IActivation activationFunction)`	Set the activation function for the layer.
`T`	`biasInit(double biasInit)`	Bias initialization value, for layers with biases.
`T`	`biasUpdater(IUpdater biasUpdater)`	Gradient updater configuration, for the biases only.
`T`	`dist(Distribution dist)`	Deprecated.
`T`	`gainInit(double gainInit)`	Gain initialization value, for layers with Layer Normalization.
`T`	`gradientNormalization(GradientNormalization gradientNormalization)`	Gradient normalization strategy.
`T`	`gradientNormalizationThreshold(double threshold)`	Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue Not used otherwise. L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
`T`	`l1(double l1)`	L1 regularization coefficient (weights only).
`T`	`l1Bias(double l1Bias)`	L1 regularization coefficient for the bias.
`T`	`l2(double l2)`	L2 regularization coefficient (weights only).
`T`	`l2Bias(double l2Bias)`	L2 regularization coefficient for the bias.
`BaseLayer.Builder`	`regularization(List<Regularization> regularization)`	Set the regularization for the parameters (excluding biases) - for example `WeightDecay`
`BaseLayer.Builder`	`regularizationBias(List<Regularization> regularizationBias)`	Set the regularization for the biases only - for example `WeightDecay`
`T`	`updater(Updater updater)`	Deprecated.
`T`	`updater(IUpdater updater)`	Gradient updater.
`BaseLayer.Builder`	`weightDecay(double coefficient)`	Add weight decay regularization for the network parameters (excluding biases). This applies weight decay with multiplying the learning rate - see `WeightDecay` for more details.
`BaseLayer.Builder`	`weightDecay(double coefficient, boolean applyLR)`	Add weight decay regularization for the network parameters (excluding biases).
`BaseLayer.Builder`	`weightDecayBias(double coefficient)`	Weight decay for the biases only - see `weightDecay(double)` for more details.
`BaseLayer.Builder`	`weightDecayBias(double coefficient, boolean applyLR)`	Weight decay for the biases only - see `weightDecay(double)` for more details
`T`	`weightInit(Distribution distribution)`	Set weight initialization scheme to random sampling via the specified distribution.
`T`	`weightInit(IWeightInit weightInit)`	Weight initialization scheme to use, for initial weight values
`T`	`weightInit(WeightInit weightInit)`	Weight initialization scheme to use, for initial weight values
`T`	`weightNoise(IWeightNoise weightNoise)`	Set the weight noise (such as `DropConnect` and `WeightNoise`) for this layer

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder
build, constrainAllParameters, constrainBias, constrainWeights, dropOut, dropOut, name

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

- Field Detail
  - activationFn
```
protected IActivation activationFn
```
    Set the activation function for the layer. This overload can be used for custom IActivation instances
  - weightInitFn
```
protected IWeightInit weightInitFn
```
    Weight initialization scheme to use, for initial weight values
    
    See Also:
    
    IWeightInit
  - biasInit
```
protected double biasInit
```
    Bias initialization value, for layers with biases. Defaults to 0
  - gainInit
```
protected double gainInit
```
    Gain initialization value, for layers with Layer Normalization. Defaults to 1
  - regularization
```
protected List<Regularization> regularization
```
    Regularization for the parameters (excluding biases).
  - regularizationBias
```
protected List<Regularization> regularizationBias
```
    Regularization for the bias parameters only
  - iupdater
```
protected IUpdater iupdater
```
    Gradient updater. For example, Adam or Nesterovs
  - biasUpdater
```
protected IUpdater biasUpdater
```
    Gradient updater configuration, for the biases only. If not set, biases will use the updater as set by updater(IUpdater)
  - gradientNormalization
```
protected GradientNormalization gradientNormalization
```
    Gradient normalization strategy. Used to specify gradient renormalization, gradient clipping etc.
    
    See Also:
    
    GradientNormalization
  - gradientNormalizationThreshold
```
protected double gradientNormalizationThreshold
```
    Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
    Not used otherwise.
    L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
  - weightNoise
```
protected IWeightNoise weightNoise
```
    Set the weight noise (such as DropConnect and WeightNoise) for this layer
- Constructor Detail
  - Builder
```
public Builder()
```
- Method Detail
  - activation
```
public T activation(IActivation activationFunction)
```
    Set the activation function for the layer. This overload can be used for custom IActivation instances
    
    Parameters:
    
    activationFunction - Activation function to use for the layer
  - activation
```
public T activation(Activation activation)
```
    Set the activation function for the layer, from an Activation enumeration value.
    
    Parameters:
    
    activation - Activation function to use for the layer
  - weightInit
```
public T weightInit(IWeightInit weightInit)
```
    Weight initialization scheme to use, for initial weight values
    
    See Also:
    
    IWeightInit
  - weightInit
```
public T weightInit(WeightInit weightInit)
```
    Weight initialization scheme to use, for initial weight values
    
    See Also:
    
    WeightInit
  - weightInit
```
public T weightInit(Distribution distribution)
```
    Set weight initialization scheme to random sampling via the specified distribution. Equivalent to: .weightInit(new WeightInitDistribution(distribution))
    
    Parameters:
    
    distribution - Distribution to use for weight initialization
  - biasInit
```
public T biasInit(double biasInit)
```
    Bias initialization value, for layers with biases. Defaults to 0
    
    Parameters:
    
    biasInit - Value to use for initializing biases
  - gainInit
```
public T gainInit(double gainInit)
```
    Gain initialization value, for layers with Layer Normalization. Defaults to 1
    
    Parameters:
    
    gainInit - Value to use for initializing gain
  - dist
```
@Deprecated
public T dist(Distribution dist)
```
    Deprecated.
    
    Distribution to sample initial weights from. Equivalent to: .weightInit(new WeightInitDistribution(distribution))
  - l1
```
public T l1(double l1)
```
    L1 regularization coefficient (weights only). Use l1Bias(double) to configure the l1 regularization coefficient for the bias.
  - l2
```
public T l2(double l2)
```
    L2 regularization coefficient (weights only). Use l2Bias(double) to configure the l2 regularization coefficient for the bias.
    Note: Generally, WeightDecay (set via weightDecay(double,boolean) should be preferred to L2 regularization. See WeightDecay javadoc for further details.
  - l1Bias
```
public T l1Bias(double l1Bias)
```
    L1 regularization coefficient for the bias. Default: 0. See also l1(double)
  - l2Bias
```
public T l2Bias(double l2Bias)
```
    L2 regularization coefficient for the bias. Default: 0. See also l2(double)
    Note: Generally, WeightDecay (set via weightDecayBias(double,boolean) should be preferred to L2 regularization. See WeightDecay javadoc for further details.
  - weightDecay
```
public BaseLayer.Builder weightDecay(double coefficient)
```
    Add weight decay regularization for the network parameters (excluding biases).
    This applies weight decay with multiplying the learning rate - see WeightDecay for more details.
    
    Parameters:
    
    coefficient - Weight decay regularization coefficient
    
    See Also:
    
    weightDecay(double, boolean)
  - weightDecay
```
public BaseLayer.Builder weightDecay(double coefficient,
                                     boolean applyLR)
```
    Add weight decay regularization for the network parameters (excluding biases). See WeightDecay for more details.
    
    Parameters:
    
    coefficient - Weight decay regularization coefficient
    
    applyLR - Whether the learning rate should be multiplied in when performing weight decay updates. See WeightDecay for more details.
    
    See Also:
    
    weightDecay(double, boolean)
  - weightDecayBias
```
public BaseLayer.Builder weightDecayBias(double coefficient)
```
    Weight decay for the biases only - see weightDecay(double) for more details. This applies weight decay with multiplying the learning rate.
    
    Parameters:
    
    coefficient - Weight decay regularization coefficient
    
    See Also:
    
    weightDecayBias(double, boolean)
  - weightDecayBias
```
public BaseLayer.Builder weightDecayBias(double coefficient,
                                         boolean applyLR)
```
    Weight decay for the biases only - see weightDecay(double) for more details
    
    Parameters:
    
    coefficient - Weight decay regularization coefficient
  - regularization
```
public BaseLayer.Builder regularization(List<Regularization> regularization)
```
    Set the regularization for the parameters (excluding biases) - for example WeightDecay
    
    Parameters:
    
    regularization - Regularization to apply for the network parameters/weights (excluding biases)
  - regularizationBias
```
public BaseLayer.Builder regularizationBias(List<Regularization> regularizationBias)
```
    Set the regularization for the biases only - for example WeightDecay
    
    Parameters:
    
    regularizationBias - Regularization to apply for the network biases only
  - updater
```
@Deprecated
public T updater(Updater updater)
```
    Deprecated.
    
    Gradient updater. For example, SGD for standard stochastic gradient descent, NESTEROV for Nesterov momentum, RSMPROP for RMSProp, etc.
    
    See Also:
    
    Updater
  - updater
```
public T updater(IUpdater updater)
```
    Gradient updater. For example, Adam or Nesterovs
    
    Parameters:
    
    updater - Updater to use
  - biasUpdater
```
public T biasUpdater(IUpdater biasUpdater)
```
    Gradient updater configuration, for the biases only. If not set, biases will use the updater as set by updater(IUpdater)
    
    Parameters:
    
    biasUpdater - Updater to use for bias parameters
  - gradientNormalization
```
public T gradientNormalization(GradientNormalization gradientNormalization)
```
    Gradient normalization strategy. Used to specify gradient renormalization, gradient clipping etc.
    
    Parameters:
    
    gradientNormalization - Type of normalization to use. Defaults to None.
    
    See Also:
    
    GradientNormalization
  - gradientNormalizationThreshold
```
public T gradientNormalizationThreshold(double threshold)
```
    Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer, GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue
    Not used otherwise.
    L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.
  - weightNoise
```
public T weightNoise(IWeightNoise weightNoise)
```
    Set the weight noise (such as DropConnect and WeightNoise) for this layer
    
    Parameters:
    
    weightNoise - Weight noise instance to use

Class BaseLayer.Builder<T extends BaseLayer.Builder<T>>

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder

Constructor Summary

Method Summary

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder

Methods inherited from class java.lang.Object

Field Detail

activationFn

weightInitFn

biasInit

gainInit

regularization

regularizationBias

iupdater

biasUpdater

gradientNormalization

gradientNormalizationThreshold

weightNoise

Constructor Detail

Builder

Method Detail

activation

activation

weightInit

weightInit

weightInit

biasInit

gainInit

dist

l1

l2

l1Bias

l2Bias

weightDecay

weightDecay

weightDecayBias

weightDecayBias

regularization

regularizationBias

updater

updater

biasUpdater

gradientNormalization

gradientNormalizationThreshold

weightNoise