org.deeplearning4j.nn.layers.convolution.subsampling

Class SubsamplingLayer

java.lang.Object

org.deeplearning4j.nn.layers.AbstractLayer<SubsamplingLayer>

org.deeplearning4j.nn.layers.convolution.subsampling.SubsamplingLayer

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model

Direct Known Subclasses:

Subsampling1DLayer

public class SubsamplingLayer
extends AbstractLayer<SubsamplingLayer>

Subsampling layer. Used for downsampling a convolution

Author:

Adam Gibson

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.deeplearning4j.nn.api.Layer

Layer.TrainingMode, Layer.Type

Field Summary

Fields

Modifier and Type	Field and Description
protected ConvolutionMode	convolutionMode
protected SubsamplingHelper	helper

Fields inherited from class org.deeplearning4j.nn.layers.AbstractLayer

cacheMode, conf, dropoutApplied, dropoutMask, index, input, iterationListeners, maskArray, maskState, preOutput

Constructor Summary

Constructors

Constructor and Description
SubsamplingLayer(NeuralNetConfiguration conf)
SubsamplingLayer(NeuralNetConfiguration conf, org.nd4j.linalg.api.ndarray.INDArray input)

Method Summary

Modifier and Type	Method and Description
void	accumulateScore(double accum) Sets a rolling tally for the score.
org.nd4j.linalg.api.ndarray.INDArray	activate(boolean training) Trigger an activation with the last specified input
org.nd4j.linalg.api.ndarray.INDArray	activationMean() Calculate the mean representation for the activation for this layer
org.nd4j.linalg.primitives.Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray>	backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon) Calculate the gradient relative to the error in the next layer
Gradient	calcGradient(Gradient layerError, org.nd4j.linalg.api.ndarray.INDArray indArray) Calculate the gradient
double	calcL1(boolean backpropParamsOnly) Calculate the l1 regularization term 0.0 if regularization is not used.
double	calcL2(boolean backpropParamsOnly) Calculate the l2 regularization term 0.0 if regularization is not used.
Layer	clone() Clone the layer
void	computeGradientAndScore() Update the score
Gradient	error(org.nd4j.linalg.api.ndarray.INDArray input) Calculate error with respect to the current layer.
void	fit() All models have a fit method
void	fit(org.nd4j.linalg.api.ndarray.INDArray input) Fit the model to the given data
org.nd4j.linalg.api.ndarray.INDArray	getParam(String param) Get the parameter
Gradient	gradient() Calculate a gradient
boolean	isPretrainLayer() Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)
void	iterate(org.nd4j.linalg.api.ndarray.INDArray input) iterate one iteration of the network
void	merge(Layer layer, int batchSize) Averages the given logistic regression from a mini batch into this layer
int	numParams() The number of parameters for the model
org.nd4j.linalg.api.ndarray.INDArray	params() Returns the parameters of the neural network as a flattened row vector
org.nd4j.linalg.api.ndarray.INDArray	preOutput(boolean training)
double	score() The score for the model
void	setParams(org.nd4j.linalg.api.ndarray.INDArray params) Set the parameters for this model.
Layer	transpose() Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)
Layer.Type	type() Returns the layer type
void	update(org.nd4j.linalg.api.ndarray.INDArray gradient, String paramType) Perform one update applying the gradient

Methods inherited from class org.deeplearning4j.nn.layers.AbstractLayer

activate, activate, activate, activate, activate, addListeners, applyDropOutIfNecessary, applyLearningRateScoreDecay, applyMask, batchSize, clear, conf, derivativeActivation, feedForwardMaskArray, getGradientsViewArray, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, gradientAndScore, init, initParams, input, layerConf, layerId, numParams, paramTable, paramTable, preOutput, preOutput, preOutput, setBackpropGradientsViewArray, setCacheMode, setConf, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, setParam, setParams, setParamsViewArray, setParamTable, update, validateInput

Methods inherited from class java.lang.Object

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

Field Detail

helper

protected SubsamplingHelper helper

convolutionMode

protected ConvolutionMode convolutionMode

Constructor Detail

SubsamplingLayer

public SubsamplingLayer(NeuralNetConfiguration conf)

SubsamplingLayer

public SubsamplingLayer(NeuralNetConfiguration conf,
                        org.nd4j.linalg.api.ndarray.INDArray input)

Method Detail

calcL2

public double calcL2(boolean backpropParamsOnly)

Description copied from interface: Layer

Calculate the l2 regularization term
0.0 if regularization is not used. Or 0.5 * l2Coeff * l2Magnitude otherwise.
Note that this does not divide by mini-batch size

Specified by:

calcL2 in interface Layer

Overrides:

calcL2 in class AbstractLayer<SubsamplingLayer>

Parameters:

backpropParamsOnly - If true: calculate L2 based on backprop params only. If false: calculate based on all params (including pretrain params, if any)

Returns:

the l2 regularization term for this layer.

calcL1

public double calcL1(boolean backpropParamsOnly)

Description copied from interface: Layer

Calculate the l1 regularization term
0.0 if regularization is not used. Or l1Coeff * l1Magnitude otherwise.
Note that this does not divide by mini-batch size

Specified by:

calcL1 in interface Layer

Overrides:

calcL1 in class AbstractLayer<SubsamplingLayer>

Parameters:

backpropParamsOnly - If true: calculate L1 based on backprop params only. If false: calculate based on all params (including pretrain params, if any)

Returns:

the l1 regularization term for this layer.

type

public Layer.Type type()

Description copied from interface: Layer

Returns the layer type

Specified by:

type in interface Layer

Overrides:

type in class AbstractLayer<SubsamplingLayer>

Returns:

backpropGradient

public org.nd4j.linalg.primitives.Pair<Gradient,org.nd4j.linalg.api.ndarray.INDArray> backpropGradient(org.nd4j.linalg.api.ndarray.INDArray epsilon)

Description copied from interface: Layer

Calculate the gradient relative to the error in the next layer

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.

Returns:

Pair where Gradient is gradient for this layer, INDArray is epsilon 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() == (w^(L)*(delta^(L))^T)^T

activate

public org.nd4j.linalg.api.ndarray.INDArray activate(boolean training)

Description copied from interface: Layer

Trigger an activation with the last specified input

Parameters:

training - training or test mode

Returns:

the activation of the last specified input

error

public Gradient error(org.nd4j.linalg.api.ndarray.INDArray input)

Description copied from interface: Layer

Calculate error with respect to the current layer. This gradient will contain the error signal

Specified by:

error in interface Layer

Overrides:

error in class AbstractLayer<SubsamplingLayer>

Parameters:

input - the gradient for the forward layer If this is the final layer, it will start with the error from the output. This is on the user to initialize.

Returns:

the gradient wrt the parameters on the current layer

calcGradient

public Gradient calcGradient(Gradient layerError,
                             org.nd4j.linalg.api.ndarray.INDArray indArray)

Description copied from interface: Layer

Calculate the gradient

Specified by:

calcGradient in interface Layer

Overrides:

calcGradient in class AbstractLayer<SubsamplingLayer>

Parameters:

layerError - the layer error

Returns:

the gradient

merge

public void merge(Layer layer,
                  int batchSize)

Description copied from class: AbstractLayer

Averages the given logistic regression from a mini batch into this layer

Specified by:

merge in interface Layer

Overrides:

merge in class AbstractLayer<SubsamplingLayer>

Parameters:

layer - the logistic regression layer to average into this layer

batchSize - the batch size

activationMean

public org.nd4j.linalg.api.ndarray.INDArray activationMean()

Description copied from interface: Layer

Calculate the mean representation for the activation for this layer

Returns:

the activation mean for this layer

transpose

public Layer transpose()

Description copied from interface: Layer

Return a transposed copy of the weights/bias (this means reverse the number of inputs and outputs on the weights)

Specified by:

transpose in interface Layer

Overrides:

transpose in class AbstractLayer<SubsamplingLayer>

Returns:

the transposed layer

clone

public Layer clone()

Description copied from interface: Layer

Clone the layer

Specified by:

clone in interface Layer

Specified by:

clone in class AbstractLayer<SubsamplingLayer>

Returns:

isPretrainLayer

public boolean isPretrainLayer()

Description copied from interface: Layer

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

iterate

public void iterate(org.nd4j.linalg.api.ndarray.INDArray input)

Description copied from class: AbstractLayer

iterate one iteration of the network

Specified by:

iterate in interface Model

Overrides:

iterate in class AbstractLayer<SubsamplingLayer>

Parameters:

input - the input to iterate on

gradient

public Gradient gradient()

Description copied from interface: Model

Calculate a gradient

Specified by:

gradient in interface Model

Overrides:

gradient in class AbstractLayer<SubsamplingLayer>

Returns:

the gradient for this model

fit

public void fit()

Description copied from interface: Model

All models have a fit method

Specified by:

fit in interface Model

Overrides:

fit in class AbstractLayer<SubsamplingLayer>

numParams

public int numParams()

Description copied from class: AbstractLayer

The number of parameters for the model

Specified by:

numParams in interface Model

Overrides:

numParams in class AbstractLayer<SubsamplingLayer>

Returns:

the number of parameters for the model

fit

public void fit(org.nd4j.linalg.api.ndarray.INDArray input)

Description copied from interface: Model

Fit the model to the given data

Specified by:

fit in interface Model

Overrides:

fit in class AbstractLayer<SubsamplingLayer>

Parameters:

input - the data to fit the model to

computeGradientAndScore

public void computeGradientAndScore()

Description copied from interface: Model

Update the score

Specified by:

computeGradientAndScore in interface Model

Overrides:

computeGradientAndScore in class AbstractLayer<SubsamplingLayer>

score

public double score()

Description copied from interface: Model

The score for the model

Specified by:

score in interface Model

Overrides:

score in class AbstractLayer<SubsamplingLayer>

Returns:

the score for the model

accumulateScore

public void accumulateScore(double accum)

Description copied from interface: Model

Sets a rolling tally for the score. This is useful for mini batch learning when you are accumulating error across a dataset.

Specified by:

accumulateScore in interface Model

Overrides:

accumulateScore in class AbstractLayer<SubsamplingLayer>

Parameters:

accum - the amount to accum

update

public void update(org.nd4j.linalg.api.ndarray.INDArray gradient,
                   String paramType)

Description copied from interface: Model

Perform one update applying the gradient

Specified by:

update in interface Model

Overrides:

update in class AbstractLayer<SubsamplingLayer>

Parameters:

gradient - the gradient to apply

params

public org.nd4j.linalg.api.ndarray.INDArray params()

Description copied from class: AbstractLayer

Returns the parameters of the neural network as a flattened row vector

Specified by:

params in interface Model

Overrides:

params in class AbstractLayer<SubsamplingLayer>

Returns:

the parameters of the neural network

getParam

public org.nd4j.linalg.api.ndarray.INDArray getParam(String param)

Description copied from interface: Model

Get the parameter

Specified by:

getParam in interface Model

Overrides:

getParam in class AbstractLayer<SubsamplingLayer>

Parameters:

param - the key of the parameter

Returns:

the parameter vector/matrix with that particular key

setParams

public void setParams(org.nd4j.linalg.api.ndarray.INDArray params)

Description copied from interface: Model

Set the parameters for this model. This expects a linear ndarray which then be unpacked internally relative to the expected ordering of the model

Specified by:

setParams in interface Model

Overrides:

setParams in class AbstractLayer<SubsamplingLayer>

Parameters:

params - the parameters for the model

preOutput

public org.nd4j.linalg.api.ndarray.INDArray preOutput(boolean training)

Specified by:

preOutput in class AbstractLayer<SubsamplingLayer>