Package org.deeplearning4j.nn.conf.layers

Class Deconvolution3D

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.BaseLayer

org.deeplearning4j.nn.conf.layers.FeedForwardLayer

org.deeplearning4j.nn.conf.layers.ConvolutionLayer

org.deeplearning4j.nn.conf.layers.Deconvolution3D

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig

public class Deconvolution3D
extends ConvolutionLayer

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	Deconvolution3D.Builder

Nested classes/interfaces inherited from class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

ConvolutionLayer.AlgoMode, ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>>, ConvolutionLayer.BwdDataAlgo, ConvolutionLayer.BwdFilterAlgo, ConvolutionLayer.FwdAlgo

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

cnn2dDataFormat, convolutionMode, cudnnAlgoMode, cudnnAllowFallback, cudnnBwdDataAlgo, cudnnBwdFilterAlgo, cudnnFwdAlgo, dilation, hasBias, kernelSize, padding, stride

Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

nIn, nOut, timeDistributedFormat

Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer

activationFn, biasInit, biasUpdater, gainInit, gradientNormalization, gradientNormalizationThreshold, iUpdater, regularization, regularizationBias, weightInitFn, weightNoise

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer

constraints, iDropout, layerName

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	Deconvolution3D(Deconvolution3D.Builder builder)	Deconvolution3D layer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

Method Summary

Modifier and Type	Method	Description
Deconvolution3D	clone()
InputType	getOutputType(int layerIndex, InputType inputType)	For a given type of input to this layer, what is the type of the output?
InputPreProcessor	getPreProcessorForInputType(InputType inputType)	For the given type of input to this layer, what preprocessor (if any) is required? Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor
boolean	hasBias()
ParamInitializer	initializer()
Layer	instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)
void	setNIn(InputType inputType, boolean override)	Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

Methods inherited from class org.deeplearning4j.nn.conf.layers.ConvolutionLayer

getMemoryReport

Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer

isPretrainParam

Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer

getGradientNormalization, getRegularizationByParam, getUpdaterByParam, resetLayerDefaultConfig

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer

initializeConstraints, setDataType

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.deeplearning4j.nn.api.TrainingConfig

getGradientNormalizationThreshold, getLayerName

Constructor Detail

Deconvolution3D

protected Deconvolution3D(Deconvolution3D.Builder builder)

Deconvolution3D layer nIn in the input layer is the number of channels nOut is the number of filters to be used in the net or in other words the channels The builder specifies the filter/kernel size, the stride and padding The pooling layer takes the kernel size

Method Detail

hasBias

public boolean hasBias()

Overrides:

hasBias in class ConvolutionLayer

clone

public Deconvolution3D clone()

Overrides:

clone in class ConvolutionLayer

instantiate

public Layer instantiate(NeuralNetConfiguration conf,
                         Collection<TrainingListener> trainingListeners,
                         int layerIndex,
                         INDArray layerParamsView,
                         boolean initializeParams,
                         DataType networkDataType)

Overrides:

instantiate in class ConvolutionLayer

initializer

public ParamInitializer initializer()

Overrides:

initializer in class ConvolutionLayer

Returns:

The parameter initializer for this model

getPreProcessorForInputType

public InputPreProcessor getPreProcessorForInputType(InputType inputType)

Description copied from class: Layer

For the given type of input to this layer, what preprocessor (if any) is required?
Returns null if no preprocessor is required, otherwise returns an appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor

Overrides:

getPreProcessorForInputType in class ConvolutionLayer

Parameters:

inputType - InputType to this layer

Returns:

Null if no preprocessor is required, otherwise the type of preprocessor necessary for this layer/input combination

setNIn

public void setNIn(InputType inputType,
                   boolean override)

Description copied from class: Layer

Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

Overrides:

setNIn in class ConvolutionLayer

Parameters:

inputType - Input type for this layer

override - If false: only set the nIn value if it's not already set. If true: set it regardless of whether it's already set or not.

getOutputType

public InputType getOutputType(int layerIndex,
                               InputType inputType)

Description copied from class: Layer

For a given type of input to this layer, what is the type of the output?

Overrides:

getOutputType in class ConvolutionLayer

Parameters:

layerIndex - Index of the layer

inputType - Type of input for the layer

Returns:

Type of output from the layer