org.deeplearning4j.nn.layers.convolution.upsampling

Class Upsampling2D

java.lang.Object

org.deeplearning4j.nn.layers.AbstractLayer<Upsampling2D>

org.deeplearning4j.nn.layers.convolution.upsampling.Upsampling2D

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model

Direct Known Subclasses:

Upsampling1D

public class Upsampling2D
extends AbstractLayer<Upsampling2D>

2D Upsampling layer.

Used for upsampling a 2D convolution

Author:

Max Pumperla

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

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

Constructor Summary

Constructors

Constructor and Description
Upsampling2D(NeuralNetConfiguration conf)
Upsampling2D(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.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
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.
void	clearNoiseWeightParams()
Layer	clone() Clone the layer
void	computeGradientAndScore() Update the score
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
protected int	getSize()
Gradient	gradient() Get the gradient.
boolean	isPretrainLayer() Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
void	iterate(org.nd4j.linalg.api.ndarray.INDArray input) iterate one iteration of the network
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)
org.nd4j.linalg.api.ndarray.INDArray	preOutput(boolean training, boolean forBackprop)
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, applyConstraints, applyDropOutIfNecessary, applyMask, batchSize, clear, conf, feedForwardMaskArray, getGradientsViewArray, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, gradientAndScore, init, initParams, input, layerConf, layerId, migrateInput, 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

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

getEpochCount, getIterationCount, setEpochCount, setIterationCount

Constructor Detail

Upsampling2D

public Upsampling2D(NeuralNetConfiguration conf)

Upsampling2D

public Upsampling2D(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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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

getSize

protected int getSize()

preOutput

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

Specified by:

preOutput in class AbstractLayer<Upsampling2D>

preOutput

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

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

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<Upsampling2D>

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<Upsampling2D>

Returns:

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

clearNoiseWeightParams

public void clearNoiseWeightParams()

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<Upsampling2D>

Parameters:

input - the input to iterate on

gradient

public Gradient gradient()

Description copied from interface: Model

Get the gradient. Note that this method will not calculate the gradient, it will rather return the gradient that has been computed before. For calculating the gradient, see Model.computeGradientAndScore() .

Specified by:

gradient in interface Model

Overrides:

gradient in class AbstractLayer<Upsampling2D>

Returns:

the gradient for this model, as calculated before

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

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<Upsampling2D>

Parameters:

params - the parameters for the model