org.deeplearning4j.arbiter

Class BaseNetworkSpace.Builder<T extends BaseNetworkSpace.Builder<T>>

java.lang.Object

org.deeplearning4j.arbiter.BaseNetworkSpace.Builder<T>

Direct Known Subclasses:

ComputationGraphSpace.Builder, MultiLayerSpace.Builder

Enclosing class:

BaseNetworkSpace<T>

protected abstract static class BaseNetworkSpace.Builder<T extends BaseNetworkSpace.Builder<T>>
extends Object

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	validateOutputLayerConfig

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	Builder()

Method Summary

Modifier and Type	Method and Description
T	activation(org.nd4j.linalg.activations.Activation activationFunction)
T	activation(ParameterSpace<org.nd4j.linalg.activations.Activation> activationFunction)
T	activationFn(ParameterSpace<org.nd4j.linalg.activations.IActivation> activationFunction)
T	backpropType(org.deeplearning4j.nn.conf.BackpropType backpropType)
T	backpropType(ParameterSpace<org.deeplearning4j.nn.conf.BackpropType> backpropType)
T	biasInit(double biasInit)
T	biasInit(ParameterSpace<Double> biasInit)
T	biasUpdater(org.nd4j.linalg.learning.config.IUpdater biasUpdater)
T	biasUpdater(ParameterSpace<org.nd4j.linalg.learning.config.IUpdater> biasUpdater)
abstract <E extends BaseNetworkSpace> E	build()
T	constrainAllParams(org.deeplearning4j.nn.api.layers.LayerConstraint... constraints)
T	constrainAllParams(ParameterSpace<List<org.deeplearning4j.nn.api.layers.LayerConstraint>> constraints)
T	constrainBias(org.deeplearning4j.nn.api.layers.LayerConstraint... constraints)
T	constrainBias(ParameterSpace<List<org.deeplearning4j.nn.api.layers.LayerConstraint>> constraints)
T	constrainWeights(org.deeplearning4j.nn.api.layers.LayerConstraint... constraints)
T	constrainWeights(ParameterSpace<List<org.deeplearning4j.nn.api.layers.LayerConstraint>> constraints)
T	convolutionMode(org.deeplearning4j.nn.conf.ConvolutionMode convolutionMode)
T	convolutionMode(ParameterSpace<org.deeplearning4j.nn.conf.ConvolutionMode> convolutionMode)
T	dist(org.deeplearning4j.nn.conf.distribution.Distribution dist)
T	dist(ParameterSpace<org.deeplearning4j.nn.conf.distribution.Distribution> dist)
T	dropOut(double dropout)
T	dropOut(ParameterSpace<Double> dropOut)
T	gradientNormalization(org.deeplearning4j.nn.conf.GradientNormalization gradientNormalization)
T	gradientNormalization(ParameterSpace<org.deeplearning4j.nn.conf.GradientNormalization> gradientNormalization)
T	gradientNormalizationThreshold(double threshold)
T	gradientNormalizationThreshold(ParameterSpace<Double> gradientNormalizationThreshold)
T	idropOut(org.deeplearning4j.nn.conf.dropout.IDropout idropOut)
T	idropOut(ParameterSpace<org.deeplearning4j.nn.conf.dropout.IDropout> idropOut)
T	l1(double l1)
T	l1(ParameterSpace<Double> l1)
T	l1Bias(double l1Bias)
T	l1Bias(ParameterSpace<Double> l1Bias)
T	l2(double l2)
T	l2(ParameterSpace<Double> l2)
T	l2Bias(double l2Bias)
T	l2Bias(ParameterSpace<Double> l2Bias)
T	maxNumLineSearchIterations(int maxNumLineSearchIterations)
T	maxNumLineSearchIterations(ParameterSpace<Integer> maxNumLineSearchIterations)
T	miniBatch(boolean minibatch)
T	miniBatch(ParameterSpace<Boolean> miniBatch)
T	minimize(boolean minimize)
T	minimize(ParameterSpace<Boolean> minimize)
T	numEpochs(int numEpochs) Fixed number of training epochs.
T	optimizationAlgo(org.deeplearning4j.nn.api.OptimizationAlgorithm optimizationAlgorithm)
T	optimizationAlgo(ParameterSpace<org.deeplearning4j.nn.api.OptimizationAlgorithm> parameterSpace)
T	seed(long seed)
T	stepFunction(ParameterSpace<org.deeplearning4j.nn.conf.stepfunctions.StepFunction> stepFunction)
T	stepFunction(org.deeplearning4j.nn.conf.stepfunctions.StepFunction stepFunction)
T	tbpttBwdLength(int tbpttBwdLength)
T	tbpttBwdLength(ParameterSpace<Integer> tbpttBwdLength)
T	tbpttFwdLength(int tbpttFwdLength)
T	tbpttFwdLength(ParameterSpace<Integer> tbpttFwdLength)
T	updater(org.nd4j.linalg.learning.config.IUpdater updater)
T	updater(ParameterSpace<org.nd4j.linalg.learning.config.IUpdater> updater)
T	validateOutputLayerConfig(boolean validate)
T	weightInit(ParameterSpace<org.deeplearning4j.nn.weights.WeightInit> weightInit)
T	weightInit(org.deeplearning4j.nn.weights.WeightInit weightInit)
T	weightNoise(org.deeplearning4j.nn.conf.weightnoise.IWeightNoise weightNoise)
T	weightNoise(ParameterSpace<org.deeplearning4j.nn.conf.weightnoise.IWeightNoise> weightNoise)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

validateOutputLayerConfig

protected boolean validateOutputLayerConfig

Constructor Detail

Builder

protected Builder()

Method Detail

seed

public T seed(long seed)

optimizationAlgo

public T optimizationAlgo(org.deeplearning4j.nn.api.OptimizationAlgorithm optimizationAlgorithm)

optimizationAlgo

public T optimizationAlgo(ParameterSpace<org.deeplearning4j.nn.api.OptimizationAlgorithm> parameterSpace)

activation

public T activation(org.nd4j.linalg.activations.Activation activationFunction)

activation

public T activation(ParameterSpace<org.nd4j.linalg.activations.Activation> activationFunction)

activationFn

public T activationFn(ParameterSpace<org.nd4j.linalg.activations.IActivation> activationFunction)

biasInit

public T biasInit(double biasInit)

biasInit

public T biasInit(ParameterSpace<Double> biasInit)

weightInit

public T weightInit(org.deeplearning4j.nn.weights.WeightInit weightInit)

weightInit

public T weightInit(ParameterSpace<org.deeplearning4j.nn.weights.WeightInit> weightInit)

dist

public T dist(org.deeplearning4j.nn.conf.distribution.Distribution dist)

dist

public T dist(ParameterSpace<org.deeplearning4j.nn.conf.distribution.Distribution> dist)

maxNumLineSearchIterations

public T maxNumLineSearchIterations(int maxNumLineSearchIterations)

maxNumLineSearchIterations

public T maxNumLineSearchIterations(ParameterSpace<Integer> maxNumLineSearchIterations)

miniBatch

public T miniBatch(boolean minibatch)

miniBatch

public T miniBatch(ParameterSpace<Boolean> miniBatch)

minimize

public T minimize(boolean minimize)

minimize

public T minimize(ParameterSpace<Boolean> minimize)

stepFunction

public T stepFunction(org.deeplearning4j.nn.conf.stepfunctions.StepFunction stepFunction)

stepFunction

public T stepFunction(ParameterSpace<org.deeplearning4j.nn.conf.stepfunctions.StepFunction> stepFunction)

l1

public T l1(double l1)

l1

public T l1(ParameterSpace<Double> l1)

l2

public T l2(double l2)

l2

public T l2(ParameterSpace<Double> l2)

l1Bias

public T l1Bias(double l1Bias)

l1Bias

public T l1Bias(ParameterSpace<Double> l1Bias)

l2Bias

public T l2Bias(double l2Bias)

l2Bias

public T l2Bias(ParameterSpace<Double> l2Bias)

updater

public T updater(org.nd4j.linalg.learning.config.IUpdater updater)

updater

public T updater(ParameterSpace<org.nd4j.linalg.learning.config.IUpdater> updater)

biasUpdater

public T biasUpdater(org.nd4j.linalg.learning.config.IUpdater biasUpdater)

biasUpdater

public T biasUpdater(ParameterSpace<org.nd4j.linalg.learning.config.IUpdater> biasUpdater)

weightNoise

public T weightNoise(org.deeplearning4j.nn.conf.weightnoise.IWeightNoise weightNoise)

weightNoise

public T weightNoise(ParameterSpace<org.deeplearning4j.nn.conf.weightnoise.IWeightNoise> weightNoise)

dropOut

public T dropOut(double dropout)

dropOut

public T dropOut(ParameterSpace<Double> dropOut)

idropOut

public T idropOut(org.deeplearning4j.nn.conf.dropout.IDropout idropOut)

idropOut

public T idropOut(ParameterSpace<org.deeplearning4j.nn.conf.dropout.IDropout> idropOut)

gradientNormalization

public T gradientNormalization(org.deeplearning4j.nn.conf.GradientNormalization gradientNormalization)

gradientNormalization

public T gradientNormalization(ParameterSpace<org.deeplearning4j.nn.conf.GradientNormalization> gradientNormalization)

gradientNormalizationThreshold

public T gradientNormalizationThreshold(double threshold)

gradientNormalizationThreshold

public T gradientNormalizationThreshold(ParameterSpace<Double> gradientNormalizationThreshold)

convolutionMode

public T convolutionMode(org.deeplearning4j.nn.conf.ConvolutionMode convolutionMode)

convolutionMode

public T convolutionMode(ParameterSpace<org.deeplearning4j.nn.conf.ConvolutionMode> convolutionMode)

backpropType

public T backpropType(org.deeplearning4j.nn.conf.BackpropType backpropType)

backpropType

public T backpropType(ParameterSpace<org.deeplearning4j.nn.conf.BackpropType> backpropType)

tbpttFwdLength

public T tbpttFwdLength(int tbpttFwdLength)

tbpttFwdLength

public T tbpttFwdLength(ParameterSpace<Integer> tbpttFwdLength)

tbpttBwdLength

public T tbpttBwdLength(int tbpttBwdLength)

tbpttBwdLength

public T tbpttBwdLength(ParameterSpace<Integer> tbpttBwdLength)

constrainWeights

public T constrainWeights(org.deeplearning4j.nn.api.layers.LayerConstraint... constraints)

constrainWeights

public T constrainWeights(ParameterSpace<List<org.deeplearning4j.nn.api.layers.LayerConstraint>> constraints)

constrainBias

public T constrainBias(org.deeplearning4j.nn.api.layers.LayerConstraint... constraints)

constrainBias

public T constrainBias(ParameterSpace<List<org.deeplearning4j.nn.api.layers.LayerConstraint>> constraints)

constrainAllParams

public T constrainAllParams(org.deeplearning4j.nn.api.layers.LayerConstraint... constraints)

constrainAllParams

public T constrainAllParams(ParameterSpace<List<org.deeplearning4j.nn.api.layers.LayerConstraint>> constraints)

validateOutputLayerConfig

public T validateOutputLayerConfig(boolean validate)

numEpochs

public T numEpochs(int numEpochs)

Fixed number of training epochs. Default: 1 Note if both EarlyStoppingConfiguration and number of epochs is present, early stopping will be used in preference.

build

public abstract <E extends BaseNetworkSpace> E build()