org.deeplearning4j.nn.layers.convolution

Class ZeroPaddingLayer

java.lang.Object

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

org.deeplearning4j.nn.layers.convolution.ZeroPaddingLayer

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model

public class ZeroPaddingLayer
extends AbstractLayer<ZeroPaddingLayer>

Zero padding layer for convolutional neural networks. Allows padding to be done separately for top/bottom/left/right

Author:

Alex Black

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, index, input, iterationListeners, maskArray, maskState, preOutput

Constructor Summary

Constructors

Constructor and Description
ZeroPaddingLayer(NeuralNetConfiguration conf)

Method Summary

Modifier and Type	Method and Description
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
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
boolean	isPretrainLayer() Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)
org.nd4j.linalg.api.ndarray.INDArray	preOutput(boolean training)
Layer.Type	type() Returns the layer type

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

accumulateScore, activate, activate, activate, activate, activate, addListeners, applyDropOutIfNecessary, applyLearningRateScoreDecay, applyMask, batchSize, calcGradient, clear, computeGradientAndScore, conf, derivativeActivation, error, feedForwardMaskArray, fit, fit, getGradientsViewArray, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, getParam, gradient, gradientAndScore, init, initParams, input, iterate, layerConf, layerId, merge, numParams, numParams, params, paramTable, paramTable, preOutput, preOutput, preOutput, score, setBackpropGradientsViewArray, setCacheMode, setConf, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, setParam, setParams, setParams, setParamsViewArray, setParamTable, transpose, update, update, validateInput

Methods inherited from class java.lang.Object

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

Constructor Detail

ZeroPaddingLayer

public ZeroPaddingLayer(NeuralNetConfiguration conf)

Method Detail

preOutput

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

Specified by:

preOutput in class AbstractLayer<ZeroPaddingLayer>

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

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<ZeroPaddingLayer>

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

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

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

clone

public Layer clone()

Description copied from interface: Layer

Clone the layer

Specified by:

clone in interface Layer

Specified by:

clone in class AbstractLayer<ZeroPaddingLayer>

Returns:

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<ZeroPaddingLayer>

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.

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<ZeroPaddingLayer>

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.