Class RnnLossLayer.Builder
- java.lang.Object
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- org.deeplearning4j.nn.conf.layers.Layer.Builder<T>
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- org.deeplearning4j.nn.conf.layers.BaseLayer.Builder<T>
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- org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder<T>
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- org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder<RnnLossLayer.Builder>
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- org.deeplearning4j.nn.conf.layers.RnnLossLayer.Builder
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- Enclosing class:
- RnnLossLayer
public static class RnnLossLayer.Builder extends BaseOutputLayer.Builder<RnnLossLayer.Builder>
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Field Summary
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Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
lossFn
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Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nIn, nOut
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Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
activationFn, biasInit, biasUpdater, gainInit, gradientNormalization, gradientNormalizationThreshold, iupdater, regularization, regularizationBias, weightInitFn, weightNoise
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Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder
allParamConstraints, biasConstraints, iDropout, layerName, weightConstraints
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Constructor Summary
Constructors Constructor Description Builder()Builder(ILossFunction lossFunction)Builder(LossFunctions.LossFunction lossFunction)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description RnnLossLayerbuild()RnnLossLayer.BuilderdataFormat(RNNFormat rnnDataFormat)RnnLossLayer.BuildernIn(int nIn)Number of inputs for the layer (usually the size of the last layer).RnnLossLayer.BuildernOut(int nOut)Number of outputs - used to set the layer size (number of units/nodes for the current layer).voidsetNIn(long nIn)voidsetNOut(long nOut)-
Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseOutputLayer.Builder
hasBias, lossFunction, lossFunction
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Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer.Builder
nIn, nOut, units
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Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer.Builder
activation, activation, biasInit, biasUpdater, dist, gainInit, gradientNormalization, gradientNormalizationThreshold, l1, l1Bias, l2, l2Bias, regularization, regularizationBias, updater, updater, weightDecay, weightDecay, weightDecayBias, weightDecayBias, weightInit, weightInit, weightInit, weightNoise
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Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer.Builder
constrainAllParameters, constrainBias, constrainWeights, dropOut, dropOut, name
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Constructor Detail
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Builder
public Builder()
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Builder
public Builder(LossFunctions.LossFunction lossFunction)
- Parameters:
lossFunction- Loss function for the loss layer
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Builder
public Builder(ILossFunction lossFunction)
- Parameters:
lossFunction- Loss function for the loss layer
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Method Detail
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nIn
public RnnLossLayer.Builder nIn(int nIn)
Description copied from class:FeedForwardLayer.BuilderNumber of inputs for the layer (usually the size of the last layer).
Note that for Convolutional layers, this is the input channels, otherwise is the previous layer size.- Overrides:
nInin classFeedForwardLayer.Builder<RnnLossLayer.Builder>- Parameters:
nIn- Number of inputs for the layer
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nOut
public RnnLossLayer.Builder nOut(int nOut)
Description copied from class:FeedForwardLayer.BuilderNumber of outputs - used to set the layer size (number of units/nodes for the current layer). Note that this is equivalent toFeedForwardLayer.Builder.units(int)- Overrides:
nOutin classFeedForwardLayer.Builder<RnnLossLayer.Builder>- Parameters:
nOut- Number of outputs / layer size
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setNIn
public void setNIn(long nIn)
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setNOut
public void setNOut(long nOut)
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dataFormat
public RnnLossLayer.Builder dataFormat(RNNFormat rnnDataFormat)
- Parameters:
rnnDataFormat- Data format expected by the layer. NCW = [miniBatchSize, size, timeSeriesLength], NWC = [miniBatchSize, timeSeriesLength, size]. Defaults to NCW.
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build
public RnnLossLayer build()
- Specified by:
buildin classLayer.Builder<RnnLossLayer.Builder>
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