Class ElementWiseMultiplicationLayer
- java.lang.Object
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- org.deeplearning4j.nn.layers.AbstractLayer<LayerConfT>
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- org.deeplearning4j.nn.layers.BaseLayer<ElementWiseMultiplicationLayer>
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- org.deeplearning4j.nn.layers.feedforward.elementwise.ElementWiseMultiplicationLayer
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- All Implemented Interfaces:
Serializable
,Cloneable
,Layer
,Model
,Trainable
public class ElementWiseMultiplicationLayer extends BaseLayer<ElementWiseMultiplicationLayer>
- See Also:
- Serialized Form
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Nested Class Summary
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Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.Layer
Layer.TrainingMode, Layer.Type
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Field Summary
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Fields inherited from class org.deeplearning4j.nn.layers.BaseLayer
gradient, gradientsFlattened, gradientViews, optimizer, params, paramsFlattened, score, solver, weightNoiseParams
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Fields inherited from class org.deeplearning4j.nn.layers.AbstractLayer
cacheMode, conf, dataType, dropoutApplied, epochCount, index, input, inputModificationAllowed, iterationCount, maskArray, maskState, preOutput, trainingListeners
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Constructor Summary
Constructors Constructor Description ElementWiseMultiplicationLayer(NeuralNetConfiguration conf, DataType dataType)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description Pair<Gradient,INDArray>
backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Calculate the gradient relative to the error in the next layerboolean
isPretrainLayer()
Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)INDArray
preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
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Methods inherited from class org.deeplearning4j.nn.layers.BaseLayer
activate, calcRegularizationScore, clear, clearNoiseWeightParams, clone, computeGradientAndScore, fit, fit, getGradientsViewArray, getOptimizer, getParam, getParamWithNoise, gradient, hasBias, hasLayerNorm, layerConf, numParams, params, paramTable, paramTable, preOutputWithPreNorm, score, setBackpropGradientsViewArray, setParam, setParams, setParams, setParamsViewArray, setParamTable, setScoreWithZ, toString, update, update
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Methods inherited from class org.deeplearning4j.nn.layers.AbstractLayer
activate, addListeners, allowInputModification, applyConstraints, applyDropOutIfNecessary, applyMask, assertInputSet, backpropDropOutIfPresent, batchSize, close, conf, feedForwardMaskArray, getConfig, getEpochCount, getHelper, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, gradientAndScore, init, input, layerId, numParams, setCacheMode, setConf, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, type, updaterDivideByMinibatch
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Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
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Methods inherited from interface org.deeplearning4j.nn.api.Layer
getIterationCount, setIterationCount
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Constructor Detail
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ElementWiseMultiplicationLayer
public ElementWiseMultiplicationLayer(NeuralNetConfiguration conf, DataType dataType)
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Method Detail
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backpropGradient
public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Description copied from interface:Layer
Calculate the gradient relative to the error in the next layer- Specified by:
backpropGradient
in interfaceLayer
- Overrides:
backpropGradient
in classBaseLayer<ElementWiseMultiplicationLayer>
- Parameters:
epsilon
- w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C is cost function a=sigma(z) is activation.workspaceMgr
- Workspace manager- Returns:
- Pair
where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD
workspace via the workspace manager
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isPretrainLayer
public boolean isPretrainLayer()
Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)- Returns:
- true if the layer can be pretrained (using fit(INDArray), false otherwise
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preOutput
public INDArray preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
- Overrides:
preOutput
in classBaseLayer<ElementWiseMultiplicationLayer>
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