Modifier and Type | Class and Description |
---|---|
class |
DataSet
A data transform (example/outcome pairs)
The outcomes are specifically for neural network encoding such that
any labels that are considered true are 1s.
|
Modifier and Type | Method and Description |
---|---|
DataSet |
DataSet.getRange(int from,
int to) |
Modifier and Type | Method and Description |
---|---|
DataSet |
DataSet.getRange(int from,
int to) |
Modifier and Type | Method and Description |
---|---|
void |
DataSetPreProcessor.preProcess(DataSet toPreProcess)
Pre process a dataset
|
static INDArray |
DataSetUtil.tailor2d(DataSet dataSet,
boolean areFeatures) |
static INDArray |
DataSetUtil.tailor3d2d(DataSet dataset,
boolean areFeatures)
Deprecated.
|
static INDArray |
DataSetUtil.tailor4d2d(DataSet dataset,
boolean areFeatures) |
Modifier and Type | Method and Description |
---|---|
void |
AbstractDataSetNormalizer.fit(DataSet dataSet)
Fit a dataset (only compute based on the statistics from this dataset)
|
void |
VGG16ImagePreProcessor.fit(DataSet dataSet)
Fit a dataset (only compute
based on the statistics from this dataset0
|
void |
ImagePreProcessingScaler.fit(DataSet dataSet)
Fit a dataset (only compute
based on the statistics from this dataset0
|
void |
AbstractDataSetNormalizer.preProcess(DataSet toPreProcess)
Pre process a dataset
|
void |
VGG16ImagePreProcessor.preProcess(DataSet toPreProcess) |
void |
DataNormalization.preProcess(DataSet toPreProcess) |
void |
ImageFlatteningDataSetPreProcessor.preProcess(DataSet toPreProcess) |
void |
ImagePreProcessingScaler.preProcess(DataSet toPreProcess) |
void |
AbstractDataSetNormalizer.revert(DataSet data)
Revert the data to what it was before transform
|
void |
VGG16ImagePreProcessor.revert(DataSet toRevert) |
void |
ImagePreProcessingScaler.revert(DataSet toRevert) |
void |
AbstractDataSetNormalizer.transform(DataSet toPreProcess)
Transform the given dataset
|
void |
VGG16ImagePreProcessor.transform(DataSet toPreProcess)
Transform the data
|
void |
ImagePreProcessingScaler.transform(DataSet toPreProcess)
Transform the data
|
Modifier and Type | Method and Description |
---|---|
void |
UnderSamplingByMaskingPreProcessor.preProcess(DataSet toPreProcess) |
Modifier and Type | Method and Description |
---|---|
NormalizerStats.Builder<S> |
NormalizerStats.Builder.addFeatures(DataSet dataSet) |
DistributionStats.Builder |
DistributionStats.Builder.addFeatures(DataSet dataSet)
Add the features of a DataSet to the statistics
|
MinMaxStats.Builder |
MinMaxStats.Builder.addFeatures(DataSet dataSet)
Add the features of a DataSet to the statistics
|
NormalizerStats.Builder<S> |
NormalizerStats.Builder.addLabels(DataSet dataSet)
Add the labels of a DataSet to the statistics
|
DistributionStats.Builder |
DistributionStats.Builder.addLabels(DataSet dataSet)
Add the labels of a DataSet to the statistics
|
MinMaxStats.Builder |
MinMaxStats.Builder.addLabels(DataSet dataSet)
Add the labels of a DataSet to the statistics
|
Modifier and Type | Method and Description |
---|---|
static Task |
TaskUtils.buildTask(DataSet dataSet) |
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