Class Convolution3DLayer
- java.lang.Object
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- org.deeplearning4j.nn.layers.AbstractLayer<LayerConfT>
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- org.deeplearning4j.nn.layers.BaseLayer<ConvolutionLayer>
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- org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
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- org.deeplearning4j.nn.layers.convolution.Convolution3DLayer
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- All Implemented Interfaces:
Serializable
,Cloneable
,Layer
,Model
,Trainable
public class Convolution3DLayer extends ConvolutionLayer
- 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.convolution.ConvolutionLayer
convolutionMode, CUDA_CNN_HELPER_CLASS_NAME, dummyBias, dummyBiasGrad, helper, helperCountFail, i2d
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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 Convolution3DLayer(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 layerprotected Pair<INDArray,INDArray>
preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
PreOutput method that also returns the im2col2d array (if being called for backprop), as this can be re-used instead of being calculated again.INDArray
preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
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Methods inherited from class org.deeplearning4j.nn.layers.convolution.ConvolutionLayer
activate, feedForwardMaskArray, fit, getHelper, hasBias, isPretrainLayer, preOutput4d, setParams, type, validateInputDepth, validateInputRank
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Methods inherited from class org.deeplearning4j.nn.layers.BaseLayer
calcRegularizationScore, clear, clearNoiseWeightParams, clone, computeGradientAndScore, fit, getGradientsViewArray, getOptimizer, getParam, getParamWithNoise, gradient, hasLayerNorm, layerConf, numParams, params, paramTable, paramTable, preOutputWithPreNorm, score, setBackpropGradientsViewArray, setParam, 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, getConfig, getEpochCount, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, gradientAndScore, init, input, layerId, numParams, setCacheMode, setConf, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, 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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Convolution3DLayer
public Convolution3DLayer(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 classConvolutionLayer
- 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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preOutput
public INDArray preOutput(boolean training, LayerWorkspaceMgr workspaceMgr)
- Overrides:
preOutput
in classBaseLayer<ConvolutionLayer>
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preOutput
protected Pair<INDArray,INDArray> preOutput(boolean training, boolean forBackprop, LayerWorkspaceMgr workspaceMgr)
Description copied from class:ConvolutionLayer
PreOutput method that also returns the im2col2d array (if being called for backprop), as this can be re-used instead of being calculated again.- Overrides:
preOutput
in classConvolutionLayer
- Parameters:
training
- Train or test time (impacts dropout)forBackprop
- If true: return the im2col2d array for re-use during backprop. False: return null for second pair entry. Note that it may still be null in the case of CuDNN and the like.- Returns:
- Pair of arrays: preOutput (activations) and optionally the im2col2d array
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