Package org.deeplearning4j.nn.conf.layers

Class ConvolutionLayer

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

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig

Direct Known Subclasses:

Convolution1DLayer, Convolution2D, Convolution3D, Deconvolution2D, Deconvolution3D, DepthwiseConvolution2D, SeparableConvolution2D

public class ConvolutionLayer
extends FeedForwardLayer

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	ConvolutionLayer.AlgoMode	The "PREFER_FASTEST" mode will pick the fastest algorithm for the specified parameters from the ConvolutionLayer.FwdAlgo, ConvolutionLayer.BwdFilterAlgo, and ConvolutionLayer.BwdDataAlgo lists, but they may be very memory intensive, so if weird errors occur when using cuDNN, please try the "NO_WORKSPACE" mode.
static class	ConvolutionLayer.BaseConvBuilder<T extends ConvolutionLayer.BaseConvBuilder<T>>
static class	ConvolutionLayer.Builder
static class	ConvolutionLayer.BwdDataAlgo	The backward data algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
static class	ConvolutionLayer.BwdFilterAlgo	The backward filter algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".
static class	ConvolutionLayer.FwdAlgo	The forward algorithm to use when ConvolutionLayer.AlgoMode is set to "USER_SPECIFIED".

Field Summary

Fields

Modifier and Type	Field	Description
protected CNN2DFormat	cnn2dDataFormat
protected ConvolutionMode	convolutionMode
protected ConvolutionLayer.AlgoMode	cudnnAlgoMode	Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.
protected boolean	cudnnAllowFallback
protected ConvolutionLayer.BwdDataAlgo	cudnnBwdDataAlgo
protected ConvolutionLayer.BwdFilterAlgo	cudnnBwdFilterAlgo
protected ConvolutionLayer.FwdAlgo	cudnnFwdAlgo
protected int[]	dilation
protected boolean	hasBias
protected int[]	kernelSize
protected int[]	padding
protected int[]	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	ConvolutionLayer(ConvolutionLayer.BaseConvBuilder<?> builder)	ConvolutionLayer 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
ConvolutionLayer	clone()
LayerMemoryReport	getMemoryReport(InputType inputType)	This is a report of the estimated memory consumption for the given layer
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.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

Field Detail

hasBias

protected boolean hasBias

convolutionMode

protected ConvolutionMode convolutionMode

dilation

protected int[] dilation

kernelSize

protected int[] kernelSize

stride

protected int[] stride

padding

protected int[] padding

cudnnAllowFallback

protected boolean cudnnAllowFallback

cnn2dDataFormat

protected CNN2DFormat cnn2dDataFormat

cudnnAlgoMode

protected ConvolutionLayer.AlgoMode cudnnAlgoMode

Defaults to "PREFER_FASTEST", but "NO_WORKSPACE" uses less memory.

cudnnFwdAlgo

protected ConvolutionLayer.FwdAlgo cudnnFwdAlgo

cudnnBwdFilterAlgo

protected ConvolutionLayer.BwdFilterAlgo cudnnBwdFilterAlgo

cudnnBwdDataAlgo

protected ConvolutionLayer.BwdDataAlgo cudnnBwdDataAlgo

Constructor Detail

ConvolutionLayer

protected ConvolutionLayer(ConvolutionLayer.BaseConvBuilder<?> builder)

ConvolutionLayer 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()

clone

public ConvolutionLayer clone()

Overrides:

clone in class BaseLayer

instantiate

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

Specified by:

instantiate in class Layer

initializer

public ParamInitializer initializer()

Specified by:

initializer in class Layer

Returns:

The parameter initializer for this model

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 FeedForwardLayer

Parameters:

layerIndex - Index of the layer

inputType - Type of input for the layer

Returns:

Type of output from the layer

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 FeedForwardLayer

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.

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 FeedForwardLayer

Parameters:

inputType - InputType to this layer

Returns:

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

getMemoryReport

public LayerMemoryReport getMemoryReport(InputType inputType)

Description copied from class: Layer

This is a report of the estimated memory consumption for the given layer

Specified by:

getMemoryReport in class Layer

Parameters:

inputType - Input type to the layer. Memory consumption is often a function of the input type

Returns:

Memory report for the layer