public class ImageMultiPreProcessingScaler extends Object implements MultiDataNormalization
Constructor and Description |
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ImageMultiPreProcessingScaler(double a,
double b,
int[] featureIndices) |
ImageMultiPreProcessingScaler(double a,
double b,
int maxBits,
int[] featureIndices)
Preprocessor can take a range as minRange and maxRange
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ImageMultiPreProcessingScaler(int... featureIndices) |
Modifier and Type | Method and Description |
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void |
fit(MultiDataSet dataSet)
Fit a dataset (only compute based on the statistics from this dataset)
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void |
fit(MultiDataSetIterator iterator)
Iterates over a dataset
accumulating statistics for normalization
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NormalizerType |
getType()
Get the enum opType of this normalizer
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void |
preProcess(MultiDataSet multiDataSet)
Preprocess the MultiDataSet
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void |
revert(MultiDataSet toRevert)
Undo (revert) the normalization applied by this DataNormalization instance (arrays are modified in-place)
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void |
revertFeatures(INDArray[] features)
Undo (revert) the normalization applied by this DataNormalization instance to the specified features array
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void |
revertFeatures(INDArray[] features,
INDArray[] featuresMask)
Undo (revert) the normalization applied by this DataNormalization instance to the specified features array
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void |
revertLabels(INDArray[] labels)
Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array.
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void |
revertLabels(INDArray[] labels,
INDArray[] labelsMask)
Undo (revert) the normalization applied by this DataNormalization instance to the specified labels array.
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void |
transform(MultiDataSet toPreProcess)
Transform the dataset
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public ImageMultiPreProcessingScaler(int... featureIndices)
public ImageMultiPreProcessingScaler(double a, double b, int[] featureIndices)
public ImageMultiPreProcessingScaler(double a, double b, int maxBits, int[] featureIndices)
a,
- default = 0b,
- default = 1maxBits
- in the image, default = 8featureIndices
- Indices of feature arrays to process. If only one feature array is present,
this should always be 0public void fit(MultiDataSetIterator iterator)
MultiDataNormalization
fit
in interface MultiDataNormalization
iterator
- the iterator to use for
collecting statistics.public void preProcess(MultiDataSet multiDataSet)
MultiDataSetPreProcessor
preProcess
in interface MultiDataSetPreProcessor
preProcess
in interface MultiDataNormalization
public void revertFeatures(INDArray[] features, INDArray[] featuresMask)
MultiDataNormalization
revertFeatures
in interface MultiDataNormalization
features
- Features to revert the normalization onpublic void revertFeatures(INDArray[] features)
MultiDataNormalization
revertFeatures
in interface MultiDataNormalization
features
- Features to revert the normalization onpublic void revertLabels(INDArray[] labels, INDArray[] labelsMask)
MultiDataNormalization
#isFitLabel()
== false) then this is a no-op.
Can also be used to undo normalization for network output arrays, in the case of regression.revertLabels
in interface MultiDataNormalization
labels
- Labels array to revert the normalization onlabelsMask
- Labels mask array (may be null)public void revertLabels(INDArray[] labels)
MultiDataNormalization
#isFitLabel()
== false) then this is a no-op.
Can also be used to undo normalization for network output arrays, in the case of regression.revertLabels
in interface MultiDataNormalization
labels
- Labels array to revert the normalization onpublic void fit(MultiDataSet dataSet)
Normalizer
fit
in interface Normalizer<MultiDataSet>
dataSet
- the dataset to compute onpublic void transform(MultiDataSet toPreProcess)
Normalizer
transform
in interface Normalizer<MultiDataSet>
toPreProcess
- the dataset to re processpublic void revert(MultiDataSet toRevert)
Normalizer
revert
in interface Normalizer<MultiDataSet>
toRevert
- DataSet to revert the normalization onpublic NormalizerType getType()
Normalizer
getType
in interface Normalizer<MultiDataSet>
NormalizerSerializerStrategy.getSupportedType()
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