org.deeplearning4j.nn.layers.normalization

Class LocalResponseNormalization

java.lang.Object

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

org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization

All Implemented Interfaces:

Serializable, Cloneable, Layer, Model, Trainable

public class LocalResponseNormalization
extends AbstractLayer<LocalResponseNormalization>

Deep neural net normalization approach normalizes activations between layers "brightness normalization" Used for nets like AlexNet

For a^i_{x,y} the activity of a neuron computed by applying kernel i at position (x,y) and applying ReLU nonlinearity, the response normalized activation b^i_{x,y} is given by:

x^2 = (a^j_{x,y})^2 unitScale = (k + alpha * sum_{j=max(0, i - n/2)}^{max(N-1, i + n/2)} (a^j_{x,y})^2 ) y = b^i_{x,y} = x * unitScale**-beta

gy = epsilon (aka deltas from previous layer) sumPart = sum(a^j_{x,y} * gb^j_{x,y}) gx = gy * unitScale**-beta - 2 * alpha * beta * sumPart/unitScale * a^i_{x,y}

Reference: http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf https://github.com/vlfeat/matconvnet/issues/10 Chainer

Created by nyghtowl on 10/29/15.

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 static org.slf4j.Logger	log

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

cacheMode, conf, dropoutApplied, epochCount, index, input, inputModificationAllowed, iterationCount, maskArray, maskState, preOutput, trainingListeners

Constructor Summary

Constructors

Constructor and Description
LocalResponseNormalization(NeuralNetConfiguration conf)
LocalResponseNormalization(NeuralNetConfiguration conf, INDArray input)

Method Summary

Modifier and Type	Method and Description
INDArray	activate(boolean training, LayerWorkspaceMgr workspaceMgr) Perform forward pass and return the activations array with the last set input
Pair<Gradient,INDArray>	backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) 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()
void	fit(INDArray input, LayerWorkspaceMgr workspaceMgr) Fit the model to the given data
LayerHelper	getHelper()
INDArray	getParam(String param) Get the parameter
boolean	isPretrainLayer() Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
INDArray	params() Returns the parameters of the neural network as a flattened row vector
void	setParams(INDArray params) Set the parameters for this model.
Layer.Type	type() Returns the layer type

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

activate, addListeners, allowInputModification, applyConstraints, applyDropOutIfNecessary, applyMask, assertInputSet, backpropDropOutIfPresent, batchSize, clear, computeGradientAndScore, conf, feedForwardMaskArray, fit, getConfig, getEpochCount, getGradientsViewArray, getIndex, getInput, getInputMiniBatchSize, getListeners, getMaskArray, getOptimizer, gradient, gradientAndScore, init, input, layerConf, layerId, numParams, numParams, paramTable, paramTable, score, setBackpropGradientsViewArray, setCacheMode, setConf, setEpochCount, setIndex, setInput, setInputMiniBatchSize, setListeners, setListeners, setMaskArray, setParam, setParams, setParamsViewArray, setParamTable, update, update

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

getIterationCount, setIterationCount

Field Detail

log

protected static final org.slf4j.Logger log

Constructor Detail

LocalResponseNormalization

public LocalResponseNormalization(NeuralNetConfiguration conf,
                                  INDArray input)

LocalResponseNormalization

public LocalResponseNormalization(NeuralNetConfiguration conf)

Method Detail

clone

public Layer clone()

Overrides:

clone in class Object

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<LocalResponseNormalization>

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<LocalResponseNormalization>

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<LocalResponseNormalization>

Returns:

fit

public void fit(INDArray input,
                LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Model

Fit the model to the given data

Specified by:

fit in interface Model

Overrides:

fit in class AbstractLayer<LocalResponseNormalization>

Parameters:

input - the data to fit the model to

backpropGradient

public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon,
                                                LayerWorkspaceMgr workspaceMgr)

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.

workspaceMgr - Workspace manager

Returns:

Pair where Gradient is gradient for this layer, INDArray is epsilon (activation gradient) 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() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the ArrayType.ACTIVATION_GRAD workspace via the workspace manager

activate

public INDArray activate(boolean training,
                         LayerWorkspaceMgr workspaceMgr)

Description copied from interface: Layer

Perform forward pass and return the activations array with the last set input

Parameters:

training - training or test mode

workspaceMgr - Workspace manager

Returns:

the activation (layer output) of the last specified input. Note that the returned array should be placed in the ArrayType.ACTIVATIONS workspace via the workspace manager

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()

getHelper

public LayerHelper getHelper()

Specified by:

getHelper in interface Layer

Overrides:

getHelper in class AbstractLayer<LocalResponseNormalization>

Returns:

Get the layer helper, if any

params

public 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

Specified by:

params in interface Trainable

Overrides:

params in class AbstractLayer<LocalResponseNormalization>

Returns:

the parameters of the neural network

getParam

public INDArray getParam(String param)

Description copied from interface: Model

Get the parameter

Specified by:

getParam in interface Model

Overrides:

getParam in class AbstractLayer<LocalResponseNormalization>

Parameters:

param - the key of the parameter

Returns:

the parameter vector/matrix with that particular key

setParams

public void setParams(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<LocalResponseNormalization>

Parameters:

params - the parameters for the model