Package org.nd4j.linalg.dataset.api.preprocessor

Class ImagePreProcessingScaler

java.lang.Object

org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler

All Implemented Interfaces:

Serializable, DataSetPreProcessor, DataNormalization, Normalizer<DataSet>

public class ImagePreProcessingScaler
extends Object
implements DataNormalization

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor	Description
ImagePreProcessingScaler()
ImagePreProcessingScaler(double a, double b)
ImagePreProcessingScaler(double a, double b, int maxBits)	Preprocessor can take a range as minRange and maxRange

Method Summary

Modifier and Type	Method	Description
void	fit(DataSet dataSet)	Fit a dataset (only compute based on the statistics from this dataset0
void	fit(DataSetIterator iterator)	Iterates over a dataset accumulating statistics for normalization
void	fitLabel(boolean fitLabels)	Flag to specify if the labels/outputs in the dataset should be also normalized.
NormalizerType	getType()	Get the enum opType of this normalizer
boolean	isFitLabel()	Whether normalization for the labels is also enabled.
void	preProcess(INDArray features)
void	preProcess(DataSet toPreProcess)	Pre process a dataset
void	revert(DataSet toRevert)	Undo (revert) the normalization applied by this DataNormalization instance (arrays are modified in-place)
void	revertFeatures(INDArray features)	Undo (revert) the normalization applied by this DataNormalization instance to the specified features array
void	revertFeatures(INDArray features, INDArray featuresMask)	Undo (revert) the normalization applied by this DataNormalization instance to the specified features array
void	revertLabels(INDArray labels)	Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array.
void	revertLabels(INDArray labels, INDArray labelsMask)	Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array.
void	transform(INDArray features)	Transform the dataset
void	transform(INDArray features, INDArray featuresMask)	Transform the features, with an optional mask array
void	transform(DataSet toPreProcess)	Transform the data
void	transformLabel(INDArray label)	Transform the labels.
void	transformLabel(INDArray labels, INDArray labelsMask)	Transform the labels.

Methods inherited from class java.lang.Object

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

Constructor Detail

ImagePreProcessingScaler

public ImagePreProcessingScaler()

ImagePreProcessingScaler

public ImagePreProcessingScaler(double a,
                                double b)

ImagePreProcessingScaler

public ImagePreProcessingScaler(double a,
                                double b,
                                int maxBits)

Preprocessor can take a range as minRange and maxRange

Parameters:

a - , default = 0

b - , default = 1

maxBits - in the image, default = 8

Method Detail

fit

public void fit(DataSet dataSet)

Fit a dataset (only compute based on the statistics from this dataset0

Specified by:

fit in interface Normalizer<DataSet>

Parameters:

dataSet - the dataset to compute on

fit

public void fit(DataSetIterator iterator)

Iterates over a dataset accumulating statistics for normalization

Specified by:

fit in interface DataNormalization

Parameters:

iterator - the iterator to use for collecting statistics.

preProcess

public void preProcess(DataSet toPreProcess)

Description copied from interface: DataSetPreProcessor

Pre process a dataset

Specified by:

preProcess in interface DataNormalization

Specified by:

preProcess in interface DataSetPreProcessor

Parameters:

toPreProcess - the data set to pre process

preProcess

public void preProcess(INDArray features)

transform

public void transform(DataSet toPreProcess)

Transform the data

Specified by:

transform in interface Normalizer<DataSet>

Parameters:

toPreProcess - the dataset to transform

transform

public void transform(INDArray features)

Description copied from interface: DataNormalization

Transform the dataset

Specified by:

transform in interface DataNormalization

Parameters:

features - the features to pre process

transform

public void transform(INDArray features,
                      INDArray featuresMask)

Description copied from interface: DataNormalization

Transform the features, with an optional mask array

Specified by:

transform in interface DataNormalization

Parameters:

features - the features to pre process

featuresMask - the mask array to pre process

transformLabel

public void transformLabel(INDArray label)

Description copied from interface: DataNormalization

Transform the labels. If DataNormalization.isFitLabel() == false, this is a no-op

Specified by:

transformLabel in interface DataNormalization

transformLabel

public void transformLabel(INDArray labels,
                           INDArray labelsMask)

Description copied from interface: DataNormalization

Transform the labels. If DataNormalization.isFitLabel() == false, this is a no-op

Specified by:

transformLabel in interface DataNormalization

revert

public void revert(DataSet toRevert)

Description copied from interface: Normalizer

Undo (revert) the normalization applied by this DataNormalization instance (arrays are modified in-place)

Specified by:

revert in interface Normalizer<DataSet>

Parameters:

toRevert - DataSet to revert the normalization on

getType

public NormalizerType getType()

Description copied from interface: Normalizer

Get the enum opType of this normalizer

Specified by:

getType in interface Normalizer<DataSet>

Returns:

the opType

See Also:

NormalizerSerializerStrategy.getSupportedType()

revertFeatures

public void revertFeatures(INDArray features)

Description copied from interface: DataNormalization

Undo (revert) the normalization applied by this DataNormalization instance to the specified features array

Specified by:

revertFeatures in interface DataNormalization

Parameters:

features - Features to revert the normalization on

revertFeatures

public void revertFeatures(INDArray features,
                           INDArray featuresMask)

Description copied from interface: DataNormalization

Undo (revert) the normalization applied by this DataNormalization instance to the specified features array

Specified by:

revertFeatures in interface DataNormalization

Parameters:

features - Features to revert the normalization on

revertLabels

public void revertLabels(INDArray labels)

Description copied from interface: DataNormalization

Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array. If labels normalization is disabled (i.e., #isFitLabels() == false) then this is a no-op. Can also be used to undo normalization for network output arrays, in the case of regression.

Specified by:

revertLabels in interface DataNormalization

Parameters:

labels - Labels array to revert the normalization on

revertLabels

public void revertLabels(INDArray labels,
                         INDArray labelsMask)

Description copied from interface: DataNormalization

Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array. If labels normalization is disabled (i.e., #isFitLabels() == false) then this is a no-op. Can also be used to undo normalization for network output arrays, in the case of regression.

Specified by:

revertLabels in interface DataNormalization

Parameters:

labels - Labels array to revert the normalization on

labelsMask - Labels mask array (may be null)

fitLabel

public void fitLabel(boolean fitLabels)

Description copied from interface: DataNormalization

Flag to specify if the labels/outputs in the dataset should be also normalized. Default value is usually false.

Specified by:

fitLabel in interface DataNormalization

isFitLabel

public boolean isFitLabel()

Description copied from interface: DataNormalization

Whether normalization for the labels is also enabled. Most commonly used for regression, not classification.

Specified by:

isFitLabel in interface DataNormalization

Returns:

True if labels will be