org.deeplearning4j.nn.layers.normalization

Class BatchNormalization

java.lang.Object

org.deeplearning4j.nn.layers.BaseLayer<BatchNormalization>

org.deeplearning4j.nn.layers.normalization.BatchNormalization

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Layer, Model

public class BatchNormalization
extends BaseLayer<BatchNormalization>

Batch normalization layer. Rerences: http://arxiv.org/pdf/1502.03167v3.pdf http://arxiv.org/pdf/1410.7455v8.pdf https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html tried this approach but results did not match https://cthorey.github.io/backpropagation/ ideal to apply this between linear and non-linear transformations in layers it follows

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 int	index
protected java.util.List<IterationListener>	listeners
protected static org.slf4j.Logger	log
protected org.nd4j.linalg.api.ndarray.INDArray	std
protected org.nd4j.linalg.api.ndarray.INDArray	xHat
protected org.nd4j.linalg.api.ndarray.INDArray	xMu

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

conf, dropoutApplied, dropoutMask, gradient, gradientsFlattened, gradientViews, input, iterationListeners, maskArray, maskState, optimizer, params, paramsFlattened, score, solver

Constructor Summary

Constructors

Constructor and Description
BatchNormalization(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	activate(org.nd4j.linalg.api.ndarray.INDArray input, Layer.TrainingMode training) Initialize the layer with the given input and return the activation for this layer given this input
org.nd4j.linalg.api.ndarray.INDArray	activate(Layer.TrainingMode training) Trigger an activation with the last specified input
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
Gradient	error(org.nd4j.linalg.api.ndarray.INDArray input) Calculate error with respect to the current layer.
void	fit(org.nd4j.linalg.api.ndarray.INDArray data) Fit the model to the given data
int	getIndex() Get the layer index.
java.util.Collection<IterationListener>	getListeners() Get the iteration listeners for this layer.
int[]	getShape(org.nd4j.linalg.api.ndarray.INDArray x)
Gradient	gradient() Calculate a gradient
boolean	isPretrainLayer() Returns true if the layer can be trained in an unsupervised/pretrain manner (VAE, RBMs etc)
void	merge(Layer layer, int batchSize) Averages the given logistic regression from a mini batch into this layer
org.nd4j.linalg.api.ndarray.INDArray	preOutput(org.nd4j.linalg.api.ndarray.INDArray x) Classify input
org.nd4j.linalg.api.ndarray.INDArray	preOutput(org.nd4j.linalg.api.ndarray.INDArray x, boolean training) Raw activations
org.nd4j.linalg.api.ndarray.INDArray	preOutput(org.nd4j.linalg.api.ndarray.INDArray x, Layer.TrainingMode training) Raw activations
void	setIndex(int index) Set the layer index.
void	setListeners(IterationListener... listeners) Set the iteration listeners for this layer.
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

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

accumulateScore, activate, activate, activate, activationMean, applyDropOutIfNecessary, applyLearningRateScoreDecay, applyMask, batchSize, clear, computeGradientAndScore, conf, createGradient, derivativeActivation, feedForwardMaskArray, fit, getInput, getInputMiniBatchSize, getMaskArray, getOptimizer, getParam, gradientAndScore, init, initParams, input, iterate, layerConf, layerNameAndIndex, numParams, numParams, params, paramTable, paramTable, preOutput, score, setBackpropGradientsViewArray, setConf, setInput, setInputMiniBatchSize, setListeners, setMaskArray, setParam, setParams, setParams, setParamsViewArray, setParamTable, setScoreWithZ, toString, update, update, validateInput

Methods inherited from class java.lang.Object

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

Field Detail

log

protected static final org.slf4j.Logger log

index

protected int index

listeners

protected java.util.List<IterationListener> listeners

std

protected org.nd4j.linalg.api.ndarray.INDArray std

xMu

protected org.nd4j.linalg.api.ndarray.INDArray xMu

xHat

protected org.nd4j.linalg.api.ndarray.INDArray xHat

Constructor Detail

BatchNormalization

public BatchNormalization(NeuralNetConfiguration conf)

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 BaseLayer<BatchNormalization>

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 BaseLayer<BatchNormalization>

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 BaseLayer<BatchNormalization>

Returns:

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 BaseLayer<BatchNormalization>

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 BaseLayer<BatchNormalization>

Parameters:

layerError - the layer error

Returns:

the gradient

backpropGradient

public 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

Specified by:

backpropGradient in interface Layer

Overrides:

backpropGradient in class BaseLayer<BatchNormalization>

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

merge

public void merge(Layer layer,
                  int batchSize)

Description copied from class: BaseLayer

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

Specified by:

merge in interface Layer

Overrides:

merge in class BaseLayer<BatchNormalization>

Parameters:

layer - the logistic regression layer to average into this layer

batchSize - the batch size

fit

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

Description copied from interface: Model

Fit the model to the given data

Specified by:

fit in interface Model

Overrides:

fit in class BaseLayer<BatchNormalization>

Parameters:

data - the data to fit the model to

activate

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

Description copied from interface: Layer

Trigger an activation with the last specified input

Specified by:

activate in interface Layer

Overrides:

activate in class BaseLayer<BatchNormalization>

Parameters:

training - training or test mode

Returns:

the activation of the last specified input

gradient

public Gradient gradient()

Description copied from interface: Model

Calculate a gradient

Specified by:

gradient in interface Model

Overrides:

gradient in class BaseLayer<BatchNormalization>

Returns:

the gradient for this model

preOutput

public org.nd4j.linalg.api.ndarray.INDArray preOutput(org.nd4j.linalg.api.ndarray.INDArray x)

Description copied from class: BaseLayer

Classify input

Specified by:

preOutput in interface Layer

Overrides:

preOutput in class BaseLayer<BatchNormalization>

Parameters:

x - the input (can either be a matrix or vector) If it's a matrix, each row is considered an example and associated rows are classified accordingly. Each row will be the likelihood of a label given that example

Returns:

a probability distribution for each row

preOutput

public org.nd4j.linalg.api.ndarray.INDArray preOutput(org.nd4j.linalg.api.ndarray.INDArray x,
                                                      Layer.TrainingMode training)

Description copied from interface: Layer

Raw activations

Specified by:

preOutput in interface Layer

Overrides:

preOutput in class BaseLayer<BatchNormalization>

Parameters:

x - the input to transform

Returns:

the raw activation for this layer

activate

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

Description copied from interface: Layer

Trigger an activation with the last specified input

Specified by:

activate in interface Layer

Overrides:

activate in class BaseLayer<BatchNormalization>

Parameters:

training - training or test mode

Returns:

the activation of the last specified input

activate

public org.nd4j.linalg.api.ndarray.INDArray activate(org.nd4j.linalg.api.ndarray.INDArray input,
                                                     Layer.TrainingMode training)

Description copied from interface: Layer

Initialize the layer with the given input and return the activation for this layer given this input

Specified by:

activate in interface Layer

Overrides:

activate in class BaseLayer<BatchNormalization>

Parameters:

input - the input to use

training - train or test mode

Returns:

preOutput

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

Description copied from interface: Layer

Raw activations

Specified by:

preOutput in interface Layer

Overrides:

preOutput in class BaseLayer<BatchNormalization>

Parameters:

x - the input to transform

Returns:

the raw activation 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 BaseLayer<BatchNormalization>

Returns:

the transposed layer

clone

public Layer clone()

Description copied from interface: Layer

Clone the layer

Specified by:

clone in interface Layer

Overrides:

clone in class BaseLayer<BatchNormalization>

Returns:

getListeners

public java.util.Collection<IterationListener> getListeners()

Description copied from interface: Layer

Get the iteration listeners for this layer.

Specified by:

getListeners in interface Layer

Overrides:

getListeners in class BaseLayer<BatchNormalization>

setListeners

public void setListeners(IterationListener... listeners)

Description copied from interface: Layer

Set the iteration listeners for this layer.

Specified by:

setListeners in interface Layer

Specified by:

setListeners in interface Model

Overrides:

setListeners in class BaseLayer<BatchNormalization>

setIndex

public void setIndex(int index)

Description copied from interface: Layer

Set the layer index.

Specified by:

setIndex in interface Layer

Overrides:

setIndex in class BaseLayer<BatchNormalization>

getIndex

public int getIndex()

Description copied from interface: Layer

Get the layer index.

Specified by:

getIndex in interface Layer

Overrides:

getIndex in class BaseLayer<BatchNormalization>

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

getShape

public int[] getShape(org.nd4j.linalg.api.ndarray.INDArray x)