Uses of Class
org.deeplearning4j.nn.conf.NeuralNetConfiguration.Builder

Packages that use NeuralNetConfiguration.Builder

Package	Description
org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.samediff
org.deeplearning4j.nn.transferlearning

Uses of NeuralNetConfiguration.Builder in org.deeplearning4j.nn.conf

Fields in org.deeplearning4j.nn.conf declared as NeuralNetConfiguration.Builder

Modifier and Type	Field and Description
protected NeuralNetConfiguration.Builder	ComputationGraphConfiguration.GraphBuilder.globalConfiguration

Methods in org.deeplearning4j.nn.conf that return NeuralNetConfiguration.Builder

Modifier and Type	Method and Description
NeuralNetConfiguration.Builder	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	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	NeuralNetConfiguration.Builder.biasInit(double biasInit) Constant for bias initialization.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.biasUpdater(IUpdater updater) Gradient updater configuration, for the biases only.
NeuralNetConfiguration.Builder	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)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.clone()
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.constrainAllParameters(LayerConstraint... constraints) Set constraints to be applied to all layers.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.constrainBias(LayerConstraint... constraints) Set constraints to be applied to all layers.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.constrainWeights(LayerConstraint... constraints) Set constraints to be applied to all layers.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.convolutionMode(ConvolutionMode convolutionMode) Sets the convolution mode for convolutional layers, which impacts padding and output sizes.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.cudnnAlgoMode(ConvolutionLayer.AlgoMode cudnnAlgoMode) Sets the cuDNN algo mode for convolutional layers, which impacts performance and memory usage of cuDNN.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.dataType(@NonNull DataType dataType) Set the DataType for the network parameters and activations.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.dist(Distribution dist) Deprecated. Use weightInit(Distribution)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.dropOut(double inputRetainProbability) Dropout probability.
NeuralNetConfiguration.Builder	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	NeuralNetConfiguration.Builder.gradientNormalization(GradientNormalization gradientNormalization) Gradient normalization strategy.
NeuralNetConfiguration.Builder	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.
NeuralNetConfiguration.Builder	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)
NeuralNetConfiguration.Builder	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	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	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	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	NeuralNetConfiguration.Builder.layer(Layer layer) Layer class.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.maxNumLineSearchIterations(int maxNumLineSearchIterations) Maximum number of line search iterations.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.miniBatch(boolean miniBatch) Process input as minibatch vs full dataset.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.minimize(boolean minimize) Objective function to minimize or maximize cost function Default set to minimize true.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.optimizationAlgo(OptimizationAlgorithm optimizationAlgo) Optimization algorithm to use.
NeuralNetConfiguration.Builder	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	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	NeuralNetConfiguration.Builder.seed(long seed) Random number generator seed.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.stepFunction(StepFunction stepFunction) Deprecated.
NeuralNetConfiguration.Builder	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)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.updater(IUpdater updater) Gradient updater configuration.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.updater(Updater updater) Deprecated. Use updater(IUpdater)
NeuralNetConfiguration.Builder	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	NeuralNetConfiguration.Builder.weightDecay(double coefficient, boolean applyLR) Add weight decay regularization for the network parameters (excluding biases).
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.weightDecayBias(double coefficient) Weight decay for the biases only - see weightDecay(double) for more details.
NeuralNetConfiguration.Builder	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	NeuralNetConfiguration.Builder.weightInit(Distribution distribution) Set weight initialization scheme to random sampling via the specified distribution.
NeuralNetConfiguration.Builder	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	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	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 in org.deeplearning4j.nn.conf that return types with arguments of type NeuralNetConfiguration.Builder

Modifier and Type	Method and Description
Map<Integer,NeuralNetConfiguration.Builder>	NeuralNetConfiguration.ListBuilder.getLayerwise()

Constructors in org.deeplearning4j.nn.conf with parameters of type NeuralNetConfiguration.Builder

Constructor and Description
GraphBuilder(ComputationGraphConfiguration newConf, NeuralNetConfiguration.Builder globalConfiguration)
GraphBuilder(NeuralNetConfiguration.Builder globalConfiguration)
ListBuilder(NeuralNetConfiguration.Builder globalConfig)
ListBuilder(NeuralNetConfiguration.Builder globalConfig, Map<Integer,NeuralNetConfiguration.Builder> layerMap)

Constructor parameters in org.deeplearning4j.nn.conf with type arguments of type NeuralNetConfiguration.Builder

Constructor and Description
ListBuilder(NeuralNetConfiguration.Builder globalConfig, Map<Integer,NeuralNetConfiguration.Builder> layerMap)

Uses of NeuralNetConfiguration.Builder in org.deeplearning4j.nn.conf.layers

Methods in org.deeplearning4j.nn.conf.layers with parameters of type NeuralNetConfiguration.Builder

Modifier and Type	Method and Description
void	LocallyConnected1D.applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig)
void	LocallyConnected2D.applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig)
void	RecurrentAttentionLayer.applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig)

Uses of NeuralNetConfiguration.Builder in org.deeplearning4j.nn.conf.layers.samediff

Methods in org.deeplearning4j.nn.conf.layers.samediff with parameters of type NeuralNetConfiguration.Builder

Modifier and Type	Method and Description
void	AbstractSameDiffLayer.applyGlobalConfig(NeuralNetConfiguration.Builder b)
void	SameDiffVertex.applyGlobalConfig(NeuralNetConfiguration.Builder b)
void	AbstractSameDiffLayer.applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig)
void	SameDiffVertex.applyGlobalConfigToLayer(NeuralNetConfiguration.Builder globalConfig)

Uses of NeuralNetConfiguration.Builder in org.deeplearning4j.nn.transferlearning

Methods in org.deeplearning4j.nn.transferlearning that return NeuralNetConfiguration.Builder

Modifier and Type	Method and Description
NeuralNetConfiguration.Builder	FineTuneConfiguration.appliedNeuralNetConfigurationBuilder()