Package org.deeplearning4j.nn.layers
Class DropoutLayer
- java.lang.Object
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- org.deeplearning4j.nn.layers.AbstractLayer<LayerConfT>
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- org.deeplearning4j.nn.layers.BaseLayer<DropoutLayer>
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- org.deeplearning4j.nn.layers.DropoutLayer
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- All Implemented Interfaces:
Serializable
,Cloneable
,Layer
,Model
,Trainable
public class DropoutLayer extends BaseLayer<DropoutLayer>
- 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 DropoutLayer(NeuralNetConfiguration conf, DataType dataType)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description INDArray
activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Perform forward pass and return the activations array with the last set inputPair<Gradient,INDArray>
backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)
Calculate the gradient relative to the error in the next layerdouble
calcRegularizationScore(boolean backpropParamsOnly)
Calculate the regularization component of the score, for the parameters in this layer
For example, the L1, L2 and/or weight decay components of the loss functionvoid
fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
Fit the model to the given databoolean
isPretrainLayer()
Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)INDArray
params()
Returns the parameters of the neural network as a flattened row vectorLayer.Type
type()
Returns the layer type-
Methods inherited from class org.deeplearning4j.nn.layers.BaseLayer
clear, clearNoiseWeightParams, clone, computeGradientAndScore, fit, getGradientsViewArray, getOptimizer, getParam, getParamWithNoise, gradient, hasBias, hasLayerNorm, layerConf, numParams, paramTable, paramTable, preOutput, 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, 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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DropoutLayer
public DropoutLayer(NeuralNetConfiguration conf, DataType dataType)
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Method Detail
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calcRegularizationScore
public double calcRegularizationScore(boolean backpropParamsOnly)
Description copied from interface:Layer
Calculate the regularization component of the score, for the parameters in this layer
For example, the L1, L2 and/or weight decay components of the loss function- Specified by:
calcRegularizationScore
in interfaceLayer
- Overrides:
calcRegularizationScore
in classBaseLayer<DropoutLayer>
- Parameters:
backpropParamsOnly
- If true: calculate regularization score based on backprop params only. If false: calculate based on all params (including pretrain params, if any)- Returns:
- the regularization score of
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type
public Layer.Type type()
Description copied from interface:Layer
Returns the layer type- Specified by:
type
in interfaceLayer
- Overrides:
type
in classAbstractLayer<DropoutLayer>
- Returns:
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fit
public void fit(INDArray input, LayerWorkspaceMgr workspaceMgr)
Description copied from interface:Model
Fit the model to the given data- Specified by:
fit
in interfaceModel
- Overrides:
fit
in classBaseLayer<DropoutLayer>
- Parameters:
input
- the data to fit the model to
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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<DropoutLayer>
- 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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activate
public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr)
Description copied from interface:Layer
Perform forward pass and return the activations array with the last set input- Specified by:
activate
in interfaceLayer
- Overrides:
activate
in classBaseLayer<DropoutLayer>
- Parameters:
training
- training or test modeworkspaceMgr
- Workspace manager- Returns:
- the activation (layer output) of the last specified input. Note that the returned array should be placed
in the
ArrayType.ACTIVATIONS
workspace via the workspace manager
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isPretrainLayer
public boolean isPretrainLayer()
Description copied from interface:Layer
Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)- Returns:
- true if the layer can be pretrained (using fit(INDArray), false otherwise
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