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

Packages that use NeuralNetConfiguration.Builder

Package	Description
org.deeplearning4j.nn.conf

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(String activationFunction) Activation function / neuron non-linearity Typical values include: "relu" (rectified linear), "tanh", "sigmoid", "softmax", "hardtanh", "leakyrelu", "maxout", "softsign", "softplus"
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.adamMeanDecay(double adamMeanDecay) Mean decay rate for Adam updater.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.adamVarDecay(double adamVarDecay) Variance decay rate for Adam updater.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.biasInit(double biasInit) Constant for bias initialization.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.biasLearningRate(double biasLearningRate) Bias learning rate.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.clone()
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.convolutionMode(ConvolutionMode convolutionMode)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.dist(Distribution dist) Distribution to sample initial weights from.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.dropOut(double dropOut) Dropout probability.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.epsilon(double epsilon) Epsilon value for updaters: Adagrad and Adadelta.
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.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.iterations(int numIterations) Number of optimization iterations.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.l1(double l1) L1 regularization coefficient.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.l2(double l2) L2 regularization coefficient Use with .regularization(true)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.layer(Layer layer) Layer class.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.leakyreluAlpha(double leakyreluAlpha)
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.learningRate(double learningRate) Learning rate.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.learningRateDecayPolicy(LearningRatePolicy policy) Learning rate decay policy.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.learningRateSchedule(Map<Integer,Double> learningRateSchedule) Learning rate schedule.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.learningRateScoreBasedDecayRate(double lrScoreBasedDecay) Rate to decrease learningRate by when the score stops improving.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.lrPolicyDecayRate(double lrPolicyDecayRate) Set the decay rate for the learning rate decay policy.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.lrPolicyPower(double lrPolicyPower) Set the power used for learning rate inverse policy.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.lrPolicySteps(double lrPolicySteps) Set the number of steps used for learning decay rate steps policy.
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.momentum(double momentum) Momentum rate Used only when Updater is set to Updater.NESTEROVS
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.momentumAfter(Map<Integer,Double> momentumAfter) Momentum schedule.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.optimizationAlgo(OptimizationAlgorithm optimizationAlgo) Optimization algorithm to use.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.regularization(boolean useRegularization) Whether to use regularization (l1, l2, dropout, etc
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.rho(double rho) Ada delta coefficient
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.rmsDecay(double rmsDecay) Decay rate for RMSProp.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.seed(int seed) Random number generator seed.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.seed(long seed) Random number generator seed.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.stepFunction(StepFunction stepFunction) Step function to apply for back track line search.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.updater(Updater updater) Gradient updater.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.useDropConnect(boolean useDropConnect) Use drop connect: multiply the weight by a binomial sampling wrt the dropout probability.
NeuralNetConfiguration.Builder	NeuralNetConfiguration.Builder.weightInit(WeightInit weightInit) Weight initialization scheme.

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(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)