NeuralNetConfiguration.Builder (deeplearning4j-nn 1.0.0-beta6 API)

java.lang.Object
- org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

All Implemented Interfaces:

Cloneable

Enclosing class:

NeuralNetConfiguration
```
public static class NeuralNetConfiguration.Builder
extends Object
implements Cloneable
```
NeuralNetConfiguration builder, used as a starting point for creating a MultiLayerConfiguration or ComputationGraphConfiguration.
Note that values set here on the layer will be applied to all relevant layers - unless the value is overridden on a layer's configuration

Field Summary

Fields
Modifier and Type	Field and Description
`protected IActivation`	`activationFn`
`protected List<LayerConstraint>`	`allParamConstraints`
`protected List<LayerConstraint>`	`biasConstraints`
`protected double`	`biasInit`
`protected IUpdater`	`biasUpdater`
`protected CacheMode`	`cacheMode`
`protected ConvolutionMode`	`convolutionMode`
`protected ConvolutionLayer.AlgoMode`	`cudnnAlgoMode`
`protected org.nd4j.linalg.api.buffer.DataType`	`dataType`
`protected double`	`gainInit`
`protected GradientNormalization`	`gradientNormalization`
`protected double`	`gradientNormalizationThreshold`
`protected IDropout`	`idropOut`
`protected WorkspaceMode`	`inferenceWorkspaceMode`
`protected IUpdater`	`iUpdater`
`protected Layer`	`layer`
`protected int`	`maxNumLineSearchIterations`
`protected boolean`	`miniBatch`
`protected boolean`	`minimize`
`protected OptimizationAlgorithm`	`optimizationAlgo`
`protected List<Regularization>`	`regularization`
`protected List<Regularization>`	`regularizationBias`
`protected long`	`seed`
`protected boolean`	`setIWM`
`protected boolean`	`setTWM`
`protected StepFunction`	`stepFunction`
`protected WorkspaceMode`	`trainingWorkspaceMode`
`protected List<LayerConstraint>`	`weightConstraints`
`protected IWeightInit`	`weightInitFn`
`protected IWeightNoise`	`weightNoise`

Constructor Summary

Constructors
Constructor and Description

Builder()

Builder(NeuralNetConfiguration newConf)

Constructors
Constructor and Description
`Builder()`
`Builder(NeuralNetConfiguration newConf)`

Method Summary

All Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`NeuralNetConfiguration.Builder`	`activation(Activation activation)` Activation function / neuron non-linearity Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`activation(IActivation activationFunction)` Activation function / neuron non-linearity Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`biasInit(double biasInit)` Constant for bias initialization.
`NeuralNetConfiguration.Builder`	`biasUpdater(IUpdater updater)` Gradient updater configuration, for the biases only.
`NeuralNetConfiguration`	`build()` Return a configuration based on this builder
`NeuralNetConfiguration.Builder`	`cacheMode(CacheMode cacheMode)` This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)
`NeuralNetConfiguration.Builder`	`clone()`
`NeuralNetConfiguration.Builder`	`constrainAllParameters(LayerConstraint... constraints)` Set constraints to be applied to all layers.
`NeuralNetConfiguration.Builder`	`constrainBias(LayerConstraint... constraints)` Set constraints to be applied to all layers.
`NeuralNetConfiguration.Builder`	`constrainWeights(LayerConstraint... constraints)` Set constraints to be applied to all layers.
`NeuralNetConfiguration.Builder`	`convolutionMode(ConvolutionMode convolutionMode)` Sets the convolution mode for convolutional layers, which impacts padding and output sizes.
`NeuralNetConfiguration.Builder`	`cudnnAlgoMode(ConvolutionLayer.AlgoMode cudnnAlgoMode)` Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.
`NeuralNetConfiguration.Builder`	`dataType(org.nd4j.linalg.api.buffer.DataType dataType)` Set the DataType for the network parameters and activations.
`NeuralNetConfiguration.Builder`	`dist(Distribution dist)` Deprecated. Use `weightInit(Distribution)`
`NeuralNetConfiguration.Builder`	`dropOut(double inputRetainProbability)` Dropout probability.
`NeuralNetConfiguration.Builder`	`dropOut(IDropout dropout)` Set the dropout for all layers in this network Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`gradientNormalization(GradientNormalization gradientNormalization)` Gradient normalization strategy.
`NeuralNetConfiguration.Builder`	`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. Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`ComputationGraphConfiguration.GraphBuilder`	`graphBuilder()` Create a GraphBuilder (for creating a ComputationGraphConfiguration).
`NeuralNetConfiguration.Builder`	`inferenceWorkspaceMode(WorkspaceMode workspaceMode)` This method defines Workspace mode being used during inference: NONE: workspace won't be used ENABLED: workspaces will be used for inference (reduced memory and better performance)
`NeuralNetConfiguration.Builder`	`l1(double l1)` L1 regularization coefficient for the weights (excluding biases). Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`l1Bias(double l1Bias)` L1 regularization coefficient for the bias. Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`l2(double l2)` L2 regularization coefficient for the weights (excluding biases). Note: Generally, `WeightDecay` (set via `weightDecay(double)` should be preferred to L2 regularization.
`NeuralNetConfiguration.Builder`	`l2Bias(double l2Bias)` L2 regularization coefficient for the bias. Note: Generally, `WeightDecay` (set via `weightDecayBias(double,boolean)` should be preferred to L2 regularization.
`NeuralNetConfiguration.Builder`	`layer(Layer layer)` Layer class.
`NeuralNetConfiguration.ListBuilder`	`list()` Create a ListBuilder (for creating a MultiLayerConfiguration) Usage:
`NeuralNetConfiguration.ListBuilder`	`list(Layer... layers)` Create a ListBuilder (for creating a MultiLayerConfiguration) with the specified layers Usage:
`NeuralNetConfiguration.Builder`	`maxNumLineSearchIterations(int maxNumLineSearchIterations)` Maximum number of line search iterations.
`NeuralNetConfiguration.Builder`	`miniBatch(boolean miniBatch)` Process input as minibatch vs full dataset.
`NeuralNetConfiguration.Builder`	`minimize(boolean minimize)` Objective function to minimize or maximize cost function Default set to minimize true.
`NeuralNetConfiguration.Builder`	`optimizationAlgo(OptimizationAlgorithm optimizationAlgo)` Optimization algorithm to use.
`NeuralNetConfiguration.Builder`	`regularization(List<Regularization> regularization)` Set the regularization for the parameters (excluding biases) - for example `WeightDecay` Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`regularizationBias(List<Regularization> regularizationBias)` Set the regularization for the biases only - for example `WeightDecay` Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`seed(long seed)` Random number generator seed.
`NeuralNetConfiguration.Builder`	`stepFunction(StepFunction stepFunction)` Deprecated.
`NeuralNetConfiguration.Builder`	`trainingWorkspaceMode(WorkspaceMode workspaceMode)` This method defines Workspace mode being used during training: NONE: workspace won't be used ENABLED: workspaces will be used for training (reduced memory and better performance)
`NeuralNetConfiguration.Builder`	`updater(IUpdater updater)` Gradient updater configuration.
`NeuralNetConfiguration.Builder`	`updater(Updater updater)` Deprecated. Use `updater(IUpdater)`
`NeuralNetConfiguration.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. Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`weightDecay(double coefficient, boolean applyLR)` Add weight decay regularization for the network parameters (excluding biases).
`NeuralNetConfiguration.Builder`	`weightDecayBias(double coefficient)` Weight decay for the biases only - see `weightDecay(double)` for more details.
`NeuralNetConfiguration.Builder`	`weightDecayBias(double coefficient, boolean applyLR)` Weight decay for the biases only - see `weightDecay(double)` for more details Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`weightInit(Distribution distribution)` Set weight initialization scheme to random sampling via the specified distribution.
`NeuralNetConfiguration.Builder`	`weightInit(IWeightInit weightInit)` Weight initialization scheme to use, for initial weight values Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`weightInit(WeightInit weightInit)` Weight initialization scheme to use, for initial weight values Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.
`NeuralNetConfiguration.Builder`	`weightNoise(IWeightNoise weightNoise)` Set the weight noise (such as `DropConnect` and `WeightNoise`) for the layers in this network. Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer.

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

- Field Detail
  - activationFn
```
protected IActivation activationFn
```
  - weightInitFn
```
protected IWeightInit weightInitFn
```
  - biasInit
```
protected double biasInit
```
  - gainInit
```
protected double gainInit
```
  - regularization
```
protected List<Regularization> regularization
```
  - regularizationBias
```
protected List<Regularization> regularizationBias
```
  - idropOut
```
protected IDropout idropOut
```
  - weightNoise
```
protected IWeightNoise weightNoise
```
  - iUpdater
```
protected IUpdater iUpdater
```
  - biasUpdater
```
protected IUpdater biasUpdater
```
  - layer
```
protected Layer layer
```
  - miniBatch
```
protected boolean miniBatch
```
  - maxNumLineSearchIterations
```
protected int maxNumLineSearchIterations
```
  - seed
```
protected long seed
```
  - optimizationAlgo
```
protected OptimizationAlgorithm optimizationAlgo
```
  - stepFunction
```
protected StepFunction stepFunction
```
  - minimize
```
protected boolean minimize
```
  - gradientNormalization
```
protected GradientNormalization gradientNormalization
```
  - gradientNormalizationThreshold
```
protected double gradientNormalizationThreshold
```
  - allParamConstraints
```
protected List<LayerConstraint> allParamConstraints
```
  - weightConstraints
```
protected List<LayerConstraint> weightConstraints
```
  - biasConstraints
```
protected List<LayerConstraint> biasConstraints
```
  - trainingWorkspaceMode
```
protected WorkspaceMode trainingWorkspaceMode
```
  - inferenceWorkspaceMode
```
protected WorkspaceMode inferenceWorkspaceMode
```
  - setTWM
```
protected boolean setTWM
```
  - setIWM
```
protected boolean setIWM
```
  - cacheMode
```
protected CacheMode cacheMode
```
  - dataType
```
protected org.nd4j.linalg.api.buffer.DataType dataType
```
  - convolutionMode
```
protected ConvolutionMode convolutionMode
```
  - cudnnAlgoMode
```
protected ConvolutionLayer.AlgoMode cudnnAlgoMode
```
- Constructor Detail
  - Builder
```
public Builder()
```
  - Builder
```
public Builder(NeuralNetConfiguration newConf)
```
- Method Detail
  - miniBatch
```
public NeuralNetConfiguration.Builder miniBatch(boolean miniBatch)
```
    Process input as minibatch vs full dataset. Default set to true.
  - trainingWorkspaceMode
```
public NeuralNetConfiguration.Builder trainingWorkspaceMode(@NonNull
                                                            WorkspaceMode workspaceMode)
```
    This method defines Workspace mode being used during training:
    NONE: workspace won't be used
    ENABLED: workspaces will be used for training (reduced memory and better performance)
    
    Parameters:
    
    workspaceMode - Workspace mode for training
    
    Returns:
    
    Builder
  - inferenceWorkspaceMode
```
public NeuralNetConfiguration.Builder inferenceWorkspaceMode(@NonNull
                                                             WorkspaceMode workspaceMode)
```
    This method defines Workspace mode being used during inference:
    NONE: workspace won't be used
    ENABLED: workspaces will be used for inference (reduced memory and better performance)
    
    Parameters:
    
    workspaceMode - Workspace mode for inference
    
    Returns:
    
    Builder
  - cacheMode
```
public NeuralNetConfiguration.Builder cacheMode(@NonNull
                                                CacheMode cacheMode)
```
    This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)
    
    Parameters:
    
    cacheMode - Cache mode to use
    
    Returns:
    
    Builder
  - minimize
```
public NeuralNetConfiguration.Builder minimize(boolean minimize)
```
    Objective function to minimize or maximize cost function Default set to minimize true.
  - maxNumLineSearchIterations
```
public NeuralNetConfiguration.Builder maxNumLineSearchIterations(int maxNumLineSearchIterations)
```
    Maximum number of line search iterations. Only applies for line search optimizers: Line Search SGD, Conjugate Gradient, LBFGS is NOT applicable for standard SGD
    
    Parameters:
    
    maxNumLineSearchIterations - > 0
    
    Returns:
  - layer
```
public NeuralNetConfiguration.Builder layer(Layer layer)
```
    Layer class.
  - stepFunction
```
@Deprecated
public NeuralNetConfiguration.Builder stepFunction(StepFunction stepFunction)
```
    Deprecated.
    
    Step function to apply for back track line search. Only applies for line search optimizers: Line Search SGD, Conjugate Gradient, LBFGS Options: DefaultStepFunction (default), NegativeDefaultStepFunction GradientStepFunction (for SGD), NegativeGradientStepFunction
  - list
```
public NeuralNetConfiguration.ListBuilder list()
```
    Create a ListBuilder (for creating a MultiLayerConfiguration)
    Usage:
```
  .list()
 .layer(new DenseLayer.Builder()...build())
 ...
 .layer(new OutputLayer.Builder()...build())
 
 
```
  - list
```
public NeuralNetConfiguration.ListBuilder list(Layer... layers)
```
    Create a ListBuilder (for creating a MultiLayerConfiguration) with the specified layers
    Usage:
```
  .list(
      new DenseLayer.Builder()...build(),
      ...,
      new OutputLayer.Builder()...build())
 
 
```
    Parameters:
    
    layers - The layer configurations for the network
  - graphBuilder
```
public ComputationGraphConfiguration.GraphBuilder graphBuilder()
```
    Create a GraphBuilder (for creating a ComputationGraphConfiguration).
  - seed
```
public NeuralNetConfiguration.Builder seed(long seed)
```
    Random number generator seed. Used for reproducability between runs
  - optimizationAlgo
```
public NeuralNetConfiguration.Builder optimizationAlgo(OptimizationAlgorithm optimizationAlgo)
```
    Optimization algorithm to use. Most common: OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT
    
    Parameters:
    
    optimizationAlgo - Optimization algorithm to use when training
  - clone
```
public NeuralNetConfiguration.Builder clone()
```
    Overrides:
    
    clone in class Object
  - activation
```
public NeuralNetConfiguration.Builder activation(IActivation activationFunction)
```
    Activation function / neuron non-linearity
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    See Also:
    
    activation(Activation)
  - activation
```
public NeuralNetConfiguration.Builder activation(Activation activation)
```
    Activation function / neuron non-linearity
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
  - weightInit
```
public NeuralNetConfiguration.Builder weightInit(IWeightInit weightInit)
```
    Weight initialization scheme to use, for initial weight values Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    See Also:
    
    IWeightInit
  - weightInit
```
public NeuralNetConfiguration.Builder weightInit(WeightInit weightInit)
```
    Weight initialization scheme to use, for initial weight values Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    See Also:
    
    WeightInit
  - weightInit
```
public NeuralNetConfiguration.Builder weightInit(Distribution distribution)
```
    Set weight initialization scheme to random sampling via the specified distribution. Equivalent to: .weightInit(new WeightInitDistribution(distribution)) Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    distribution - Distribution to use for weight initialization
  - biasInit
```
public NeuralNetConfiguration.Builder biasInit(double biasInit)
```
    Constant for bias initialization. Default: 0.0
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    biasInit - Constant for bias initialization
  - dist
```
@Deprecated
public NeuralNetConfiguration.Builder dist(Distribution dist)
```
    Deprecated. Use weightInit(Distribution)
    
    Distribution to sample initial weights from. Equivalent to: .weightInit(new WeightInitDistribution(distribution)).
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    See Also:
    
    weightInit(Distribution)
  - l1
```
public NeuralNetConfiguration.Builder l1(double l1)
```
    L1 regularization coefficient for the weights (excluding biases).
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
  - l2
```
public NeuralNetConfiguration.Builder l2(double l2)
```
    L2 regularization coefficient for the weights (excluding biases).
    Note: Generally, WeightDecay (set via weightDecay(double) should be preferred to L2 regularization. See WeightDecay javadoc for further details.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    Note: L2 regularization and weight decay usually should not be used together; if any weight decay (or L2) has been added for the biases, these will be removed first.
    
    See Also:
    
    weightDecay(double, boolean)
  - l1Bias
```
public NeuralNetConfiguration.Builder l1Bias(double l1Bias)
```
    L1 regularization coefficient for the bias.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
  - l2Bias
```
public NeuralNetConfiguration.Builder l2Bias(double l2Bias)
```
    L2 regularization coefficient for the bias.
    Note: Generally, WeightDecay (set via weightDecayBias(double,boolean) should be preferred to L2 regularization. See WeightDecay javadoc for further details.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    Note: L2 regularization and weight decay usually should not be used together; if any weight decay (or L2) has been added for the biases, these will be removed first.
    
    See Also:
    
    weightDecayBias(double, boolean)
  - weightDecay
```
public NeuralNetConfiguration.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.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    coefficient - Weight decay regularization coefficient
    
    See Also:
    
    weightDecay(double, boolean)
  - weightDecay
```
public NeuralNetConfiguration.Builder weightDecay(double coefficient,
                                                  boolean applyLR)
```
    Add weight decay regularization for the network parameters (excluding biases). See WeightDecay for more details.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    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 NeuralNetConfiguration.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.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    coefficient - Weight decay regularization coefficient
    
    See Also:
    
    weightDecayBias(double, boolean)
  - weightDecayBias
```
public NeuralNetConfiguration.Builder weightDecayBias(double coefficient,
                                                      boolean applyLR)
```
    Weight decay for the biases only - see weightDecay(double) for more details
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    coefficient - Weight decay regularization coefficient
  - regularization
```
public NeuralNetConfiguration.Builder regularization(List<Regularization> regularization)
```
    Set the regularization for the parameters (excluding biases) - for example WeightDecay
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    regularization - Regularization to apply for the network parameters/weights (excluding biases)
  - regularizationBias
```
public NeuralNetConfiguration.Builder regularizationBias(List<Regularization> regularizationBias)
```
    Set the regularization for the biases only - for example WeightDecay
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    regularizationBias - Regularization to apply for the network biases only
  - dropOut
```
public NeuralNetConfiguration.Builder dropOut(double inputRetainProbability)
```
    Dropout probability. This is the probability of retaining each input activation value for a layer. dropOut(x) will keep an input activation with probability x, and set to 0 with probability 1-x.
    dropOut(0.0) is a special value / special case - when set to 0.0., dropout is disabled (not applied). Note that a dropout value of 1.0 is functionally equivalent to no dropout: i.e., 100% probability of retaining each input activation.
    
    Note 1: Dropout is applied at training time only - and is automatically not applied at test time (for evaluation, etc)
    Note 2: This sets the probability per-layer. Care should be taken when setting lower values for complex networks (too much information may be lost with aggressive (very low) dropout values).
    Note 3: Frequently, dropout is not applied to (or, has higher retain probability for) input (first layer) layers. Dropout is also often not applied to output layers. This needs to be handled MANUALLY by the user - set .dropout(0) on those layers when using global dropout setting.
    Note 4: Implementation detail (most users can ignore): DL4J uses inverted dropout, as described here: http://cs231n.github.io/neural-networks-2/
    
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    inputRetainProbability - Dropout probability (probability of retaining each input activation value for a layer)
    
    See Also:
    
    dropOut(IDropout)
  - dropOut
```
public NeuralNetConfiguration.Builder dropOut(IDropout dropout)
```
    Set the dropout for all layers in this network
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    dropout - Dropout, such as Dropout, GaussianDropout, GaussianNoise etc
    
    Returns:
  - weightNoise
```
public NeuralNetConfiguration.Builder weightNoise(IWeightNoise weightNoise)
```
    Set the weight noise (such as DropConnect and WeightNoise) for the layers in this network.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    weightNoise - Weight noise instance to use
  - updater
```
@Deprecated
public NeuralNetConfiguration.Builder updater(Updater updater)
```
    Deprecated. Use updater(IUpdater)
  - updater
```
public NeuralNetConfiguration.Builder updater(IUpdater updater)
```
    Gradient updater configuration. For example, Adam or Nesterovs
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    updater - Updater to use
  - biasUpdater
```
public NeuralNetConfiguration.Builder biasUpdater(IUpdater updater)
```
    Gradient updater configuration, for the biases only. If not set, biases will use the updater as set by updater(IUpdater)
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    updater - Updater to use for bias parameters
  - gradientNormalization
```
public NeuralNetConfiguration.Builder gradientNormalization(GradientNormalization gradientNormalization)
```
    Gradient normalization strategy. Used to specify gradient renormalization, gradient clipping etc. See GradientNormalization for details
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    gradientNormalization - Type of normalization to use. Defaults to None.
    
    See Also:
    
    GradientNormalization
  - gradientNormalizationThreshold
```
public NeuralNetConfiguration.Builder 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.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
  - convolutionMode
```
public NeuralNetConfiguration.Builder convolutionMode(ConvolutionMode convolutionMode)
```
    Sets the convolution mode for convolutional layers, which impacts padding and output sizes. See ConvolutionMode for details. Defaults to ConvolutionMode.TRUNCATE
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    convolutionMode - Convolution mode to use
  - cudnnAlgoMode
```
public NeuralNetConfiguration.Builder cudnnAlgoMode(ConvolutionLayer.AlgoMode cudnnAlgoMode)
```
    Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN. See ConvolutionLayer.AlgoMode for details. Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    cudnnAlgoMode - cuDNN algo mode to use
  - constrainAllParameters
```
public NeuralNetConfiguration.Builder constrainAllParameters(LayerConstraint... constraints)
```
    Set constraints to be applied to all layers. Default: no constraints.
    Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have been updated.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    constraints - Constraints to apply to all parameters of all layers
  - constrainBias
```
public NeuralNetConfiguration.Builder constrainBias(LayerConstraint... constraints)
```
    Set constraints to be applied to all layers. Default: no constraints.
    Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have been updated.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    constraints - Constraints to apply to all bias parameters of all layers
  - constrainWeights
```
public NeuralNetConfiguration.Builder constrainWeights(LayerConstraint... constraints)
```
    Set constraints to be applied to all layers. Default: no constraints.
    Constraints can be used to enforce certain conditions (non-negativity of parameters, max-norm regularization, etc). These constraints are applied at each iteration, after the parameters have been updated.
    Note: values set by this method will be applied to all applicable layers in the network, unless a different value is explicitly set on a given layer. In other words: values set via this method are used as the default value, and can be overridden on a per-layer basis.
    
    Parameters:
    
    constraints - Constraints to apply to all weight parameters of all layers
  - dataType
```
public NeuralNetConfiguration.Builder dataType(@NonNull
                                               org.nd4j.linalg.api.buffer.DataType dataType)
```
    Set the DataType for the network parameters and activations. Must be a floating point type: DataType.DOUBLE, DataType.FLOAT or DataType.HALF.
  - build
```
public NeuralNetConfiguration build()
```
    Return a configuration based on this builder
    
    Returns:

Class NeuralNetConfiguration.Builder

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

activationFn

weightInitFn

biasInit

gainInit

regularization

regularizationBias

idropOut

weightNoise

iUpdater

biasUpdater

layer

miniBatch

maxNumLineSearchIterations

seed

optimizationAlgo

stepFunction

minimize

gradientNormalization

gradientNormalizationThreshold

allParamConstraints

weightConstraints

biasConstraints

trainingWorkspaceMode

inferenceWorkspaceMode

setTWM

setIWM

cacheMode

dataType

convolutionMode

cudnnAlgoMode

Constructor Detail

Builder

Builder

Method Detail

miniBatch

trainingWorkspaceMode

inferenceWorkspaceMode

cacheMode

minimize

maxNumLineSearchIterations

layer

stepFunction

list

list

graphBuilder

seed

optimizationAlgo

clone

activation

activation

weightInit

weightInit

weightInit

biasInit

dist

l1

l2

l1Bias

l2Bias

weightDecay

weightDecay

weightDecayBias

weightDecayBias

regularization

regularizationBias

dropOut

dropOut

weightNoise

updater

updater

biasUpdater

gradientNormalization

gradientNormalizationThreshold

convolutionMode