Modifier and Type | Method and Description |
---|---|
static Pair<INDArray,INDArray> |
TimeSeriesWritableUtils.convertWritablesSequence(List<List<List<Writable>>> timeSeriesRecord)
Convert the writables
to a sequence (3d) data set,
and also return the
mask array (if necessary)
|
static Pair<INDArray,INDArray> |
TimeSeriesWritableUtils.convertWritablesSequence(List<List<List<Writable>>> list,
TimeSeriesWritableUtils.RecordDetails details)
Convert the writables
to a sequence (3d) data set,
and also return the
mask array (if necessary)
|
Modifier and Type | Method and Description |
---|---|
static Pair<Schema,ArrowWritableRecordBatch> |
ArrowConverter.readFromBytes(byte[] input)
Read a datavec schema and record set
from the given bytes (usually expected to be an arrow format file)
|
static Pair<Schema,ArrowWritableRecordBatch> |
ArrowConverter.readFromFile(File input)
Read a datavec schema and record set
from the given arrow file.
|
static Pair<Schema,ArrowWritableRecordBatch> |
ArrowConverter.readFromFile(FileInputStream input)
Read a datavec schema and record set
from the given arrow file.
|
Modifier and Type | Method and Description |
---|---|
List<Pair<Long,Long>> |
IndexToKey.initialize(org.apache.hadoop.io.MapFile.Reader[] readers,
Class<? extends org.apache.hadoop.io.Writable> valueClass)
Initialise the instance, and return the first and last record indexes (inclusive) for each reader
|
Modifier and Type | Method and Description |
---|---|
List<Pair<Long,Long>> |
LongIndexToKey.initialize(org.apache.hadoop.io.MapFile.Reader[] readers,
Class<? extends org.apache.hadoop.io.Writable> valueClass) |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,org.bytedeco.opencv.opencv_core.Mat> |
CifarLoader.convertMat(byte[] byteFeature) |
Modifier and Type | Field and Description |
---|---|
protected List<Pair<ImageTransform,Double>> |
PipelineImageTransform.imageTransforms |
protected List<Pair<ImageTransform,Double>> |
PipelineImageTransform.Builder.imageTransforms |
Constructor and Description |
---|
PipelineImageTransform(List<Pair<ImageTransform,Double>> transforms) |
PipelineImageTransform(List<Pair<ImageTransform,Double>> transforms,
boolean shuffle) |
PipelineImageTransform(long seed,
List<Pair<ImageTransform,Double>> transforms) |
PipelineImageTransform(long seed,
List<Pair<ImageTransform,Double>> transforms,
boolean shuffle) |
PipelineImageTransform(Random random,
long seed,
List<Pair<ImageTransform,Double>> transforms,
boolean shuffle) |
Modifier and Type | Method and Description |
---|---|
List<Writable> |
RecordReaderFunction.apply(Pair<String,InputStream> value) |
List<List<Writable>> |
SequenceRecordReaderFunction.apply(Pair<String,InputStream> value) |
Modifier and Type | Method and Description |
---|---|
Pair<Text,BytesWritable> |
FilesAsBytesFunction.apply(Pair<String,InputStream> in) |
Modifier and Type | Method and Description |
---|---|
Pair<Text,BytesWritable> |
FilesAsBytesFunction.apply(Pair<String,InputStream> in) |
List<Writable> |
RecordReaderBytesFunction.apply(Pair<Text,BytesWritable> v1) |
List<List<Writable>> |
SequenceRecordReaderBytesFunction.apply(Pair<Text,BytesWritable> v1) |
Modifier and Type | Method and Description |
---|---|
Pair<List<Writable>,List<Writable>> |
ExtractKeysFunction.apply(List<Writable> writables) |
Modifier and Type | Method and Description |
---|---|
List<List<Writable>> |
ExecuteJoinFromCoGroupFlatMapFunctionAdapter.call(Pair<List<Writable>,Pair<List<List<Writable>>,List<List<Writable>>>> t2) |
Modifier and Type | Method and Description |
---|---|
List<List<Writable>> |
ExecuteJoinFromCoGroupFlatMapFunctionAdapter.call(Pair<List<Writable>,Pair<List<List<Writable>>,List<List<Writable>>>> t2) |
Modifier and Type | Method and Description |
---|---|
Pair<Writable,List<Writable>> |
ColumnAsKeyPairFunction.apply(List<Writable> writables) |
Pair<Writable,Long> |
ColumnToKeyPairTransform.apply(List<Writable> list) |
Modifier and Type | Method and Description |
---|---|
Long |
SumLongsFunction2.apply(Pair<Long,Long> input) |
List<List<Writable>> |
SequenceMergeFunction.apply(Pair<T,Iterable<List<List<Writable>>>> t2) |
Modifier and Type | Method and Description |
---|---|
int |
Tuple2Comparator.compare(Pair<T,Long> o1,
Pair<T,Long> o2) |
int |
Tuple2Comparator.compare(Pair<T,Long> o1,
Pair<T,Long> o2) |
Modifier and Type | Method and Description |
---|---|
List<Writable> |
UnzipForCalculateSortedRankFunction.apply(Pair<Pair<Writable,List<Writable>>,Long> v1) |
Modifier and Type | Method and Description |
---|---|
List<Writable> |
UnzipForCalculateSortedRankFunction.apply(Pair<Pair<Writable,List<Writable>>,Long> v1) |
Modifier and Type | Method and Description |
---|---|
Pair<String,List<Writable>> |
MapToPairForReducerFunction.apply(List<Writable> writables) |
Modifier and Type | Method and Description |
---|---|
Pair<Writable,List<Writable>> |
LocalMapToPairByColumnFunction.apply(List<Writable> writables) |
Pair<List<Writable>,List<Writable>> |
LocalMapToPairByMultipleColumnsFunction.apply(List<Writable> writables) |
Modifier and Type | Method and Description |
---|---|
static Pair<String,MultiDimensionalMap<Integer,Integer,String>> |
ContextLabelRetriever.stringWithLabels(String sentence,
TokenizerFactory tokenizerFactory)
Returns a stripped sentence with the indices of words
with certain kinds of labels.
|
Modifier and Type | Method and Description |
---|---|
static Pair<Schema,org.apache.spark.api.java.JavaRDD<List<Writable>>> |
DataFrames.toRecords(org.apache.spark.sql.Dataset<org.apache.spark.sql.Row> dataFrame)
Create a compatible schema
and rdd for datavec
|
static Pair<Schema,org.apache.spark.api.java.JavaRDD<List<List<Writable>>>> |
DataFrames.toRecordsSequence(org.apache.spark.sql.Dataset<org.apache.spark.sql.Row> dataFrame)
Convert the given DataFrame to a sequence
Note: It is assumed here that the DataFrame has been created by DataFrames.toDataFrameSequence(Schema, JavaRDD) . |
Modifier and Type | Method and Description |
---|---|
Pair<Double,Long> |
CentersHolder.getCenterByMinDistance(Point point,
Distance distanceFunction) |
Pair<Cluster,Double> |
ClusterSet.nearestCluster(Point point) |
Modifier and Type | Method and Description |
---|---|
Pair<Double,INDArray> |
KDTree.nn(INDArray point)
Query for nearest neighbor.
|
static Pair<float[],float[]> |
HyperRect.point(INDArray vector) |
Modifier and Type | Method and Description |
---|---|
List<Pair<Float,INDArray>> |
KDTree.knn(INDArray point,
float distance) |
Constructor and Description |
---|
HyperRect(Pair<float[],float[]> ends) |
Modifier and Type | Method and Description |
---|---|
static List<Pair<Double,Integer>> |
RPUtils.queryAllWithDistances(INDArray toQuery,
INDArray X,
List<RPTree> trees,
int n,
String similarityFunction)
Query all trees using the given input and data
|
List<Pair<Double,Integer>> |
RPForest.queryWithDistances(INDArray query,
int numResults)
Query all with the distances
sorted by index
|
List<Pair<Double,Integer>> |
RPTree.queryWithDistances(INDArray query,
int numResults)
Query all with the distances
sorted by index
|
static List<Pair<Double,Integer>> |
RPUtils.sortCandidates(INDArray x,
INDArray X,
List<Integer> candidates,
String similarityFunction)
Get the sorted distances given the
query vector, input data, given the list of possible search candidates
|
Modifier and Type | Method and Description |
---|---|
Pair<INDArray[],INDArray[]> |
BertIterator.featurizeSentencePairs(List<Pair<String,String>> listOnlySentencePairs)
For use during inference.
|
Pair<INDArray[],INDArray[]> |
BertIterator.featurizeSentences(List<String> listOnlySentences)
For use during inference.
|
Pair<String,String> |
LabeledSentenceProvider.nextSentence() |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray[],INDArray[]> |
BertIterator.featurizeSentencePairs(List<Pair<String,String>> listOnlySentencePairs)
For use during inference.
|
Modifier and Type | Method and Description |
---|---|
Pair<List<String>,boolean[]> |
BertMaskedLMMasker.maskSequence(List<String> input,
String maskToken,
List<String> vocabWords) |
Pair<List<String>,boolean[]> |
BertSequenceMasker.maskSequence(List<String> input,
String maskToken,
List<String> vocabWords) |
Modifier and Type | Method and Description |
---|---|
Pair<String,String> |
CollectionLabeledSentenceProvider.nextSentence() |
Pair<String,String> |
FileLabeledSentenceProvider.nextSentence() |
Pair<String,String> |
LabelAwareConverter.nextSentence() |
Modifier and Type | Method and Description |
---|---|
static Pair<InMemoryLookupTable,VocabCache> |
WordVectorSerializer.loadTxt(@NonNull File file) |
static Pair<InMemoryLookupTable,VocabCache> |
WordVectorSerializer.loadTxt(@NonNull InputStream inputStream)
Loads an in memory cache from the given input stream (sets syn0 and the vocab).
|
Pair<VocabWord,float[]> |
WordVectorSerializer.Reader.next() |
Pair<VocabWord,float[]> |
WordVectorSerializer.BinaryReader.next() |
Pair<VocabWord,float[]> |
WordVectorSerializer.CSVReader.next() |
Modifier and Type | Method and Description |
---|---|
static Word2Vec |
WordVectorSerializer.fromPair(Pair<InMemoryLookupTable,VocabCache> pair)
Load word vectors from the given pair
|
Modifier and Type | Method and Description |
---|---|
Pair<String,Float> |
FastText.predictProbability(String text) |
Modifier and Type | Method and Description |
---|---|
Pair<String,INDArray> |
ParagraphVectors.InferenceCallable.call() |
Modifier and Type | Method and Description |
---|---|
Future<Pair<String,INDArray>> |
ParagraphVectors.inferVectorBatched(@NonNull LabelledDocument document)
This method implements batched inference, based on Java Future parallelism model.
|
Modifier and Type | Method and Description |
---|---|
static Tree |
TreeFactory.buildTree(org.cleartk.syntax.constituent.type.TreebankNode node,
Pair<String,MultiDimensionalMap<Integer,Integer,String>> labels,
List<String> possibleLabels)
Builds a tree recursively
adding the children as necessary
|
static Tree |
TreeFactory.toTree(org.cleartk.syntax.constituent.type.TreebankNode node,
Pair<String,MultiDimensionalMap<Integer,Integer,String>> labels)
Converts a treebank node to a tree
|
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
Layer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr)
Calculate the gradient relative to the error in the next layer
|
Pair<INDArray,MaskState> |
Layer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize)
Feed forward the input mask array, setting in the layer as appropriate.
|
Pair<Gradient,Double> |
Model.gradientAndScore()
Get the gradient and score
|
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
RecurrentLayer.tbpttBackpropGradient(INDArray epsilon,
int tbpttBackLength,
LayerWorkspaceMgr workspaceMgr)
Truncated BPTT equivalent of Layer.backpropGradient().
|
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,MaskState> |
InputPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,MaskState> |
AttentionVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,MaskState> |
LearnedSelfAttentionLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,MaskState> |
SameDiffLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
SameDiffVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,MaskState> |
BaseInputPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
Cnn3DToFeedForwardPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
CnnToFeedForwardPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
CnnToRnnPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
ComposableInputPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
FeedForwardToCnn3DPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
FeedForwardToCnnPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
FeedForwardToRnnPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
RnnToCnnPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
RnnToFeedForwardPreProcessor.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,Double> |
ComputationGraph.gradientAndScore() |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray[]> |
BaseWrapperVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
GraphVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr)
Do backward pass
|
Pair<INDArray,MaskState> |
BaseWrapperVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
GraphVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray[]> |
ElementWiseVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
InputVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
L2NormalizeVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
L2Vertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
LayerVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
MergeVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
PoolHelperVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
PreprocessorVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
ReshapeVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
ScaleVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
ShiftVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
StackVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
SubsetVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
UnstackVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<INDArray,MaskState> |
ElementWiseVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
InputVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
L2NormalizeVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
L2Vertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
LayerVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
MergeVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
PoolHelperVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
PreprocessorVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
ReshapeVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
ScaleVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
ShiftVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
StackVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
SubsetVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
UnstackVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray[]> |
DuplicateToTimeSeriesVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
LastTimeStepVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
ReverseTimeSeriesVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<INDArray,MaskState> |
DuplicateToTimeSeriesVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
LastTimeStepVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
ReverseTimeSeriesVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
ConvolutionHelper.backpropGradient(INDArray input,
INDArray weights,
INDArray bias,
INDArray delta,
int[] kernel,
int[] strides,
int[] pad,
INDArray biasGradView,
INDArray weightGradView,
IActivation afn,
ConvolutionLayer.AlgoMode mode,
ConvolutionLayer.BwdFilterAlgo bwdFilterAlgo,
ConvolutionLayer.BwdDataAlgo bwdDataAlgo,
ConvolutionMode convolutionMode,
int[] dilation,
CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Cnn3DLossLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
CnnLossLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Convolution1DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Convolution3DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
ConvolutionLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Cropping1DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Cropping2DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Cropping3DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Deconvolution2DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Deconvolution3DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
DepthwiseConvolution2DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
SeparableConvolution2DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
SpaceToBatch.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
SpaceToDepth.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
ZeroPadding1DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
ZeroPadding3DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
ZeroPaddingLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<INDArray,INDArray> |
Convolution1DLayer.causalConv1dForward() |
Pair<INDArray,MaskState> |
Cnn3DLossLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
CnnLossLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
Convolution1DLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
ConvolutionLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
protected Pair<INDArray,INDArray> |
Convolution1DLayer.preOutput(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<INDArray,INDArray> |
Convolution3DLayer.preOutput(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<INDArray,INDArray> |
ConvolutionLayer.preOutput(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr)
PreOutput method that also returns the im2col2d array (if being called for backprop), as this can be re-used
instead of being calculated again.
|
protected Pair<INDArray,INDArray> |
Deconvolution2DLayer.preOutput(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<INDArray,INDArray> |
DepthwiseConvolution2DLayer.preOutput(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<INDArray,INDArray> |
SeparableConvolution2DLayer.preOutput(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<INDArray,INDArray> |
Convolution1DLayer.preOutput4d(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<INDArray,INDArray> |
ConvolutionLayer.preOutput4d(boolean training,
boolean forBackprop,
LayerWorkspaceMgr workspaceMgr)
preOutput4d: Used so that ConvolutionLayer subclasses (such as Convolution1DLayer) can maintain their standard
non-4d preOutput method, while overriding this to return 4d activations (for use in backprop) without modifying
the public API
|
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
SubsamplingHelper.backpropGradient(INDArray input,
INDArray epsilon,
int[] kernel,
int[] strides,
int[] pad,
PoolingType poolingType,
ConvolutionMode convolutionMode,
int[] dilation,
CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Subsampling1DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Subsampling3DLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
SubsamplingLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<INDArray,MaskState> |
Subsampling1DLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
SubsamplingLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
Upsampling1D.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Upsampling2D.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
Upsampling3D.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
PReLU.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,INDArray> |
AutoEncoder.sampleHiddenGivenVisible(INDArray v) |
Pair<INDArray,INDArray> |
AutoEncoder.sampleVisibleGivenHidden(INDArray h) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
ElementWiseMultiplicationLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
EmbeddingLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
EmbeddingSequenceLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
MKLDNNLocalResponseNormalizationHelper.backpropGradient(INDArray input,
INDArray epsilon,
double k,
double n,
double alpha,
double beta,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
MKLDNNConvHelper.backpropGradient(INDArray input,
INDArray weights,
INDArray bias,
INDArray delta,
int[] kernel,
int[] strides,
int[] pad,
INDArray biasGradView,
INDArray weightGradView,
IActivation afn,
ConvolutionLayer.AlgoMode mode,
ConvolutionLayer.BwdFilterAlgo bwdFilterAlgo,
ConvolutionLayer.BwdDataAlgo bwdDataAlgo,
ConvolutionMode convolutionMode,
int[] dilation,
CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
MKLDNNSubsamplingHelper.backpropGradient(INDArray input,
INDArray epsilon,
int[] kernel,
int[] strides,
int[] pad,
PoolingType poolingType,
ConvolutionMode convolutionMode,
int[] dilation,
CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
MKLDNNBatchNormHelper.backpropGradient(INDArray input,
INDArray epsilon,
long[] shape,
INDArray gamma,
INDArray beta,
INDArray dGammaView,
INDArray dBetaView,
double eps,
CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
MKLDNNLSTMHelper.backpropGradient(NeuralNetConfiguration conf,
IActivation gateActivationFn,
INDArray input,
INDArray recurrentWeights,
INDArray inputWeights,
INDArray epsilon,
boolean truncatedBPTT,
int tbpttBackwardLength,
FwdPassReturn fwdPass,
boolean forwards,
String inputWeightKey,
String recurrentWeightKey,
String biasWeightKey,
Map<String,INDArray> gradientViews,
INDArray maskArray,
boolean hasPeepholeConnections,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
LocalResponseNormalizationHelper.backpropGradient(INDArray input,
INDArray epsilon,
double k,
double n,
double alpha,
double beta,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
BatchNormalizationHelper.backpropGradient(INDArray input,
INDArray epsilon,
long[] shape,
INDArray gamma,
INDArray beta,
INDArray dGammaView,
INDArray dBetaView,
double eps,
CNN2DFormat format,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
BatchNormalization.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
LocalResponseNormalization.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
Yolo2OutputLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,Double> |
Yolo2OutputLayer.gradientAndScore() |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
OCNNOutputLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Double,INDArray> |
OCNNOutputLayer.OCNNLossFunction.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
GlobalPoolingLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<INDArray,MaskState> |
GlobalPoolingLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
BidirectionalLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
GravesBidirectionalLSTM.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
GravesLSTM.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr)
Deprecated.
|
Pair<Gradient,INDArray> |
LastTimeStepLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
LSTM.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
MaskZeroLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
RnnLossLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
RnnOutputLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
SimpleRnn.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
TimeDistributedLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
LSTMHelper.backpropGradient(NeuralNetConfiguration conf,
IActivation gateActivationFn,
INDArray input,
INDArray recurrentWeights,
INDArray inputWeights,
INDArray epsilon,
boolean truncatedBPTT,
int tbpttBackwardLength,
FwdPassReturn fwdPass,
boolean forwards,
String inputWeightKey,
String recurrentWeightKey,
String biasWeightKey,
Map<String,INDArray> gradientViews,
INDArray maskArray,
boolean hasPeepholeConnections,
LayerWorkspaceMgr workspaceMgr) |
static Pair<Gradient,INDArray> |
LSTMHelpers.backpropGradientHelper(BaseRecurrentLayer layer,
NeuralNetConfiguration conf,
IActivation gateActivationFn,
INDArray input,
INDArray recurrentWeights,
INDArray inputWeights,
INDArray epsilon,
boolean truncatedBPTT,
int tbpttBackwardLength,
FwdPassReturn fwdPass,
boolean forwards,
String inputWeightKey,
String recurrentWeightKey,
String biasWeightKey,
Map<String,INDArray> gradientViews,
INDArray maskArray,
boolean hasPeepholeConnections,
LSTMHelper helper,
LayerWorkspaceMgr workspaceMgr,
boolean isHelperAllowFallback) |
Pair<INDArray,MaskState> |
BidirectionalLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
GravesBidirectionalLSTM.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
GravesLSTM.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize)
Deprecated.
|
Pair<INDArray,MaskState> |
LastTimeStepLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
LSTM.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
MaskZeroLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
RnnLossLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
RnnOutputLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
TimeDistributedLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<Gradient,Double> |
BidirectionalLayer.gradientAndScore() |
Pair<Gradient,INDArray> |
BidirectionalLayer.tbpttBackpropGradient(INDArray epsilon,
int tbpttBackLength,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
GravesBidirectionalLSTM.tbpttBackpropGradient(INDArray epsilon,
int tbpttBackwardLength,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
GravesLSTM.tbpttBackpropGradient(INDArray epsilon,
int tbpttBackwardLength,
LayerWorkspaceMgr workspaceMgr)
Deprecated.
|
Pair<Gradient,INDArray> |
LSTM.tbpttBackpropGradient(INDArray epsilon,
int tbpttBackwardLength,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
SimpleRnn.tbpttBackpropGradient(INDArray epsilon,
int tbpttBackLength,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
SameDiffLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray> |
SameDiffOutputLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,INDArray[]> |
SameDiffGraphVertex.doBackward(boolean tbptt,
LayerWorkspaceMgr workspaceMgr) |
Pair<INDArray,MaskState> |
SameDiffLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<INDArray,MaskState> |
SameDiffGraphVertex.feedForwardMaskArrays(INDArray[] maskArrays,
MaskState currentMaskState,
int minibatchSize) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
CenterLossOutputLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<Gradient,Double> |
CenterLossOutputLayer.gradientAndScore() |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
MaskLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
VariationalAutoencoder.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<INDArray,MaskState> |
VariationalAutoencoder.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<Gradient,Double> |
VariationalAutoencoder.gradientAndScore() |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
BaseWrapperLayer.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Pair<INDArray,MaskState> |
BaseWrapperLayer.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<Gradient,Double> |
BaseWrapperLayer.gradientAndScore() |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
TFOpLayerImpl.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,INDArray> |
TimeSeriesGenerator.next(int index) |
Modifier and Type | Method and Description |
---|---|
static Pair<Boolean,Double> |
KerasLayerUtils.getMaskingConfiguration(List<String> inboundLayerNames,
Map<String,? extends KerasLayer> previousLayers) |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,INDArray> |
MultiLayerNetwork.backpropGradient(INDArray epsilon,
LayerWorkspaceMgr workspaceMgr) |
protected Pair<Gradient,INDArray> |
MultiLayerNetwork.calcBackpropGradients(INDArray epsilon,
boolean withOutputLayer,
boolean tbptt,
boolean returnInputActGrad)
Calculate gradients and errors.
|
Pair<Gradient,INDArray> |
MultiLayerNetwork.calculateGradients(@NonNull INDArray features,
@NonNull INDArray label,
INDArray fMask,
INDArray labelMask)
Calculate parameter gradients and input activation gradients given the input and labels, and optionally mask arrays
|
Pair<INDArray,MaskState> |
MultiLayerNetwork.feedForwardMaskArray(INDArray maskArray,
MaskState currentMaskState,
int minibatchSize) |
Pair<Gradient,Double> |
MultiLayerNetwork.gradientAndScore() |
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,Double> |
ConvexOptimizer.gradientAndScore(LayerWorkspaceMgr workspaceMgr)
The gradient and score for this optimizer
|
Modifier and Type | Method and Description |
---|---|
void |
ConvexOptimizer.setupSearchState(Pair<Gradient,Double> pair)
Based on the gradient and score
setup a search state
|
Modifier and Type | Method and Description |
---|---|
Pair<Gradient,Double> |
BaseOptimizer.gradientAndScore(LayerWorkspaceMgr workspaceMgr) |
Modifier and Type | Method and Description |
---|---|
void |
BaseOptimizer.setupSearchState(Pair<Gradient,Double> pair)
Setup the initial search state
|
void |
LBFGS.setupSearchState(Pair<Gradient,Double> pair) |
Modifier and Type | Method and Description |
---|---|
List<Pair<INDArray[],INDArray[]>> |
InferenceObservable.getInputBatches()
Get input batches - and their associated input mask arrays, if any
Note that usually the returned list will be of size 1 - however, in the batched case, not all inputs can actually be batched (variable size inputs to fully convolutional net, for example). |
Modifier and Type | Method and Description |
---|---|
List<Pair<INDArray[],INDArray[]>> |
BasicInferenceObservable.getInputBatches() |
List<Pair<INDArray[],INDArray[]>> |
BatchedInferenceObservable.getInputBatches() |
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,Double> |
BarnesHutTsne.computeGaussianKernel(INDArray distances,
double beta,
int k)
Computes a gaussian kernel
given a vector of squared distance distances
|
protected Pair<Double,INDArray> |
BarnesHutTsne.gradient(INDArray p) |
Pair<Gradient,Double> |
BarnesHutTsne.gradientAndScore() |
Pair<Double,INDArray> |
Tsne.hBeta(INDArray d,
double beta)
Computes a gaussian kernel
given a vector of squared distance distances
|
Modifier and Type | Method and Description |
---|---|
Pair<Double,INDArray> |
ActorCriticLoss.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Modifier and Type | Method and Description |
---|---|
static <C> Pair<IDQN,C> |
DataManager.load(File file,
Class<C> cClass) |
static <C> Pair<IDQN,C> |
DataManager.load(InputStream is,
Class<C> cClass) |
static <C> Pair<IDQN,C> |
DataManager.load(String path,
Class<C> cClass) |
Modifier and Type | Method and Description |
---|---|
Pair<VocabWord,INDArray> |
MapToPairFunction.call(Map.Entry<VocabWord,INDArray> pair) |
Modifier and Type | Method and Description |
---|---|
void |
Word2VecPerformer.call(Pair<List<VocabWord>,AtomicLong> pair)
Deprecated.
|
void |
Word2VecPerformerVoid.call(Pair<List<VocabWord>,AtomicLong> pair)
Deprecated.
|
Modifier and Type | Method and Description |
---|---|
Pair<Sequence<T>,Long> |
CountFunction.call(Sequence<T> sequence) |
Pair<Sequence<T>,Long> |
ExtraCountFunction.call(Sequence<T> sequence) |
Modifier and Type | Method and Description |
---|---|
Pair<SharedTrainingResult,SparkTrainingStats> |
SharedTrainingWorker.getFinalResultNoDataWithStats() |
Pair<SharedTrainingResult,SparkTrainingStats> |
SharedTrainingWorker.getFinalResultWithStats(ComputationGraph graph) |
Pair<SharedTrainingResult,SparkTrainingStats> |
SharedTrainingWorker.getFinalResultWithStats(MultiLayerNetwork network) |
Pair<SharedTrainingResult,SparkTrainingStats> |
SharedTrainingWorker.processMinibatchWithStats(DataSet dataSet,
ComputationGraph graph,
boolean isLast) |
Pair<SharedTrainingResult,SparkTrainingStats> |
SharedTrainingWorker.processMinibatchWithStats(DataSet dataSet,
MultiLayerNetwork network,
boolean isLast) |
Pair<SharedTrainingResult,SparkTrainingStats> |
SharedTrainingWorker.processMinibatchWithStats(MultiDataSet dataSet,
ComputationGraph graph,
boolean isLast) |
Modifier and Type | Method and Description |
---|---|
Pair<List<String>,AtomicLong> |
UpdateWordFreqAccumulatorFunction.call(List<String> lstOfWords) |
Modifier and Type | Method and Description |
---|---|
org.apache.spark.api.java.JavaRDD<Pair<List<String>,AtomicLong>> |
TextPipeline.getSentenceWordsCountRDD() |
org.apache.spark.api.java.JavaRDD<Pair<List<String>,AtomicLong>> |
TextPipeline.updateAndReturnAccumulatorVal(org.apache.spark.api.java.JavaRDD<List<String>> tokenizedRDD) |
Modifier and Type | Method and Description |
---|---|
AtomicLong |
GetSentenceCountFunction.call(Pair<List<String>,AtomicLong> pair) |
List<VocabWord> |
WordsListToVocabWordsFunction.call(Pair<List<String>,AtomicLong> pair) |
Modifier and Type | Method and Description |
---|---|
Pair<List<T>,String> |
InvertedIndex.documentWithLabel(int index)
Returns a list of words for a document
and the associated label
|
Pair<List<T>,Collection<String>> |
InvertedIndex.documentWithLabels(int index)
Returns a list of words associated with the document
and the associated labels
|
Modifier and Type | Method and Description |
---|---|
void |
InvertedIndex.eachDocWithLabel(org.nd4j.shade.guava.base.Function<Pair<List<T>,String>,Void> func,
Executor exec)
Iterate over each document with a label
|
void |
InvertedIndex.eachDocWithLabels(org.nd4j.shade.guava.base.Function<Pair<List<T>,Collection<String>>,Void> func,
Executor exec)
Iterate over each document with a label
|
Modifier and Type | Method and Description |
---|---|
static Pair<String,MultiDimensionalMap<Integer,Integer,String>> |
ContextLabelRetriever.stringWithLabels(String sentence,
TokenizerFactory tokenizerFactory)
Returns a stripped sentence with the indices of words
with certain kinds of labels.
|
Modifier and Type | Method and Description |
---|---|
List<Pair<String,int[]>> |
StatsReport.getGarbageCollectionStats()
Get the garbage collection stats: Pair contains GC name and the delta count/time values
|
Modifier and Type | Method and Description |
---|---|
List<Pair<String,int[]>> |
SbeStatsReport.getGarbageCollectionStats() |
Modifier and Type | Method and Description |
---|---|
List<Pair<String,int[]>> |
JavaStatsReport.getGarbageCollectionStats() |
Modifier and Type | Method and Description |
---|---|
static Pair<INDArray,int[]> |
TimeSeriesUtils.pullLastTimeSteps(INDArray pullFrom,
INDArray mask)
Extract out the last time steps (2d array from 3d array input) accounting for the mask layer, if present.
|
static Pair<INDArray,int[]> |
TimeSeriesUtils.pullLastTimeSteps(INDArray pullFrom,
INDArray mask,
LayerWorkspaceMgr workspaceMgr,
ArrayType arrayType)
Extract out the last time steps (2d array from 3d array input) accounting for the mask layer, if present.
|
static Pair<ComputationGraph,Normalizer> |
ModelSerializer.restoreComputationGraphAndNormalizer(@NonNull File file,
boolean loadUpdater)
Restore a ComputationGraph and Normalizer (if present - null if not) from a File
|
static Pair<ComputationGraph,Normalizer> |
ModelSerializer.restoreComputationGraphAndNormalizer(@NonNull InputStream is,
boolean loadUpdater)
Restore a ComputationGraph and Normalizer (if present - null if not) from the InputStream.
|
static Pair<MultiLayerNetwork,Normalizer> |
ModelSerializer.restoreMultiLayerNetworkAndNormalizer(@NonNull File file,
boolean loadUpdater)
Restore a MultiLayerNetwork and Normalizer (if present - null if not) from a File
|
static Pair<MultiLayerNetwork,Normalizer> |
ModelSerializer.restoreMultiLayerNetworkAndNormalizer(@NonNull InputStream is,
boolean loadUpdater)
Restore a MultiLayerNetwork and Normalizer (if present - null if not) from the InputStream.
|
Modifier and Type | Method and Description |
---|---|
static Pair<String,String> |
NASNetHelper.normalA(ComputationGraphConfiguration.GraphBuilder graphBuilder,
int filters,
String blockId,
String inputX,
String inputP) |
static Pair<String,String> |
NASNetHelper.reductionA(ComputationGraphConfiguration.GraphBuilder graphBuilder,
int filters,
String blockId,
String inputX,
String inputP) |
Modifier and Type | Method and Description |
---|---|
static Pair<INDArray,ByteBuffer> |
AeronNDArraySerde.toArrayAndByteBuffer(org.agrona.DirectBuffer buffer,
int offset)
Create an ndarray
from the unsafe buffer.
|
Modifier and Type | Method and Description |
---|---|
Collection<Pair<Operands.NodeDescriptor,INDArray>> |
Operands.asCollection()
This method returns contents of this entity as collection of key->value pairs
|
Modifier and Type | Method and Description |
---|---|
static Pair<String,Integer> |
SameDiff.parseVariable(@NonNull String varName)
Note: INTENDED FOR DEVELOPER USE
This method extract base variable name and output index (if exists) from raw variable name. |
Modifier and Type | Method and Description |
---|---|
Pair<SameDiffOp,OpContext> |
InferenceSession.getAndParameterizeOp(String opName,
FrameIter frameIter,
Set<AbstractSession.VarId> opInputs,
Set<AbstractSession.VarId> allIterInputs,
Set<String> constAndPhInputs,
Map<String,INDArray> placeholderValues,
Set<String> allReqVariables) |
Modifier and Type | Method and Description |
---|---|
INDArray[] |
InferenceSession.getOutputs(Pair<SameDiffOp,OpContext> opPair,
FrameIter outputFrameIter,
Set<AbstractSession.VarId> opInputs,
Set<AbstractSession.VarId> allIterInputs,
Set<String> constAndPhInputs,
List<Listener> listeners,
At at,
MultiDataSet batch,
Set<String> allReqVariables) |
INDArray[] |
TrainingSession.getOutputs(Pair<SameDiffOp,OpContext> opPair,
FrameIter outputFrameIter,
Set<AbstractSession.VarId> opInputs,
Set<AbstractSession.VarId> allIterInputs,
Set<String> constAndPhInputs,
List<Listener> listeners,
At at,
MultiDataSet batch,
Set<String> allReqVariables) |
Modifier and Type | Method and Description |
---|---|
Pair<K,T> |
MultiDimensionalMap.Entry.getKey()
Returns the key corresponding to this entry.
|
Modifier and Type | Method and Description |
---|---|
Iterator<Pair<K,V>> |
MultiDimensionalSet.iterator()
Returns an iterator over the elements in this applyTransformToDestination.
|
Set<Pair<K,T>> |
MultiDimensionalMap.keySet()
Returns a
Set view of the keys contained in this map. |
Modifier and Type | Method and Description |
---|---|
boolean |
MultiDimensionalSet.add(Pair<K,V> kvPair)
Adds the specified element to this applyTransformToDestination if it is not already present
(optional operation).
|
V |
MultiDimensionalMap.put(Pair<K,T> key,
V value)
Associates the specified value with the specified key in this map
(optional operation).
|
Modifier and Type | Method and Description |
---|---|
boolean |
MultiDimensionalSet.addAll(Collection<? extends Pair<K,V>> c)
Adds all of the elements in the specified collection to this applyTransformToDestination if
they're not already present (optional operation).
|
void |
MultiDimensionalMap.putAll(Map<? extends Pair<K,T>,? extends V> m)
Copies all of the mappings from the specified map to this map
(optional operation).
|
Constructor and Description |
---|
MultiDimensionalMap(Map<Pair<K,T>,V> backedMap) |
MultiDimensionalSet(Set<Pair<K,V>> backedSet) |
Modifier and Type | Method and Description |
---|---|
static <K,V> Map<K,Pair<List<V>,List<V>>> |
FunctionalUtils.cogroup(List<Pair<K,V>> left,
List<Pair<K,V>> right)
For each key in left and right, cogroup returns the list of values
as a pair for each value present in left as well as right.
|
static <K,V> List<Pair<K,V>> |
FunctionalUtils.mapToPair(Map<K,V> map)
Convert a map with a set of entries of type K for key
and V for value in to a list of
Pair |
Modifier and Type | Method and Description |
---|---|
static <K,V> Map<K,Pair<List<V>,List<V>>> |
FunctionalUtils.cogroup(List<Pair<K,V>> left,
List<Pair<K,V>> right)
For each key in left and right, cogroup returns the list of values
as a pair for each value present in left as well as right.
|
static <K,V> Map<K,Pair<List<V>,List<V>>> |
FunctionalUtils.cogroup(List<Pair<K,V>> left,
List<Pair<K,V>> right)
For each key in left and right, cogroup returns the list of values
as a pair for each value present in left as well as right.
|
static <K,V> Map<K,List<V>> |
FunctionalUtils.groupByKey(List<Pair<K,V>> listInput)
Group the input pairs by the key of each pair.
|
Modifier and Type | Method and Description |
---|---|
Pair<F,S> |
CounterMap.argMax()
This method returns pair of elements with a max value
|
static <T,E> Pair<T,E> |
Pair.create(T key,
E value) |
static <T> Pair<T,T> |
Pair.fromArray(T[] arr) |
static <T,E> Pair<T,E> |
Pair.makePair(T key,
E value) |
static <T,E> Pair<T,E> |
Pair.of(T key,
E value) |
static <T,E> Pair<T,E> |
Pair.pairOf(T key,
E value) |
Modifier and Type | Method and Description |
---|---|
PriorityQueue<Pair<T,Double>> |
Counter.asPriorityQueue() |
PriorityQueue<Pair<T,Double>> |
Counter.asReversedPriorityQueue() |
Iterator<Pair<F,S>> |
CounterMap.getIterator()
This method returns Iterator of all first/second pairs stored in this counter
|
Modifier and Type | Method and Description |
---|---|
int |
Counter.PairComparator.compare(Pair<T,Double> o1,
Pair<T,Double> o2) |
int |
Counter.PairComparator.compare(Pair<T,Double> o1,
Pair<T,Double> o2) |
int |
Counter.ReversedPairComparator.compare(Pair<T,Double> o1,
Pair<T,Double> o2) |
int |
Counter.ReversedPairComparator.compare(Pair<T,Double> o1,
Pair<T,Double> o2) |
Modifier and Type | Method and Description |
---|---|
static Pair<INDArray,INDArray> |
EvaluationUtils.extractNonMaskedTimeSteps(INDArray labels,
INDArray predicted,
INDArray outputMask) |
Modifier and Type | Field and Description |
---|---|
protected Map<Pair<Integer,Integer>,List<Object>> |
Evaluation.confusionMatrixMetaData |
Modifier and Type | Method and Description |
---|---|
protected Pair<Integer,com.google.flatbuffers.FlatBufferBuilder> |
LogFileWriter.encodeStaticHeader(byte type)
Encode the header as a UIStaticInfoRecord instance for the specific
UIEventType |
Modifier and Type | Method and Description |
---|---|
List<Pair<UIEvent,com.google.flatbuffers.Table>> |
LogFileWriter.readEvents()
Read all of the events.
|
List<Pair<UIEvent,com.google.flatbuffers.Table>> |
LogFileWriter.readEvents(long startOffset)
Read all of the events starting at a specific file offset
|
Modifier and Type | Method and Description |
---|---|
Pair<INDArray,INDArray> |
IActivation.backprop(INDArray in,
INDArray epsilon)
Backpropagate the errors through the activation function, given input z and epsilon dL/da.
|
Modifier and Type | Method and Description |
---|---|
Pair<DataBuffer,long[]> |
BaseShapeInfoProvider.createShapeInformation(long[] shape,
char order,
DataType dataType)
This method creates shapeInformation buffer, based on shape & order being passed in
|
Pair<DataBuffer,long[]> |
ShapeInfoProvider.createShapeInformation(long[] shape,
char order,
DataType dataType)
This method creates long shapeInformation buffer, based on shape & order being passed in
|
Pair<DataBuffer,long[]> |
BaseShapeInfoProvider.createShapeInformation(long[] shape,
DataType dataType)
This method creates shapeInformation buffer, based on shape being passed in
|
Pair<DataBuffer,long[]> |
ShapeInfoProvider.createShapeInformation(long[] shape,
DataType dataType)
This method creates long shapeInformation buffer, based on shape being passed in
|
Pair<DataBuffer,long[]> |
BaseShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dataType,
boolean empty) |
Pair<DataBuffer,long[]> |
ShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dataType,
boolean empty)
This method creates long shapeInformation buffer, based on detailed shape info being passed in
|
Pair<DataBuffer,long[]> |
BaseShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
long extras) |
Pair<DataBuffer,long[]> |
ShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
long extras) |
Modifier and Type | Method and Description |
---|---|
Pair<Long,Long> |
OpContext.getRngStates()
This method returns RNG states, root first node second
|
Modifier and Type | Method and Description |
---|---|
Pair<DataBuffer,DataBuffer> |
TADManager.getTADOnlyShapeInfo(INDArray array,
int... dimension)
This method returns TAD shapeInfo and all offsets
for specified tensor and dimensions.
|
Modifier and Type | Method and Description |
---|---|
static Pair<INDArray,String> |
NDArrayCreationUtil.getPermutedWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getPermutedWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getReshapedWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getReshapedWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getTransposedMatrixWithShape(char ordering,
int rows,
int cols,
int seed,
DataType dataType) |
static Pair<INDArray,String> |
NDArrayCreationUtil.getTransposedMatrixWithShape(long rows,
long cols,
long seed,
DataType dataType) |
Modifier and Type | Method and Description |
---|---|
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dPermutedWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dReshapedWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dSubArraysWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dTensorAlongDimensionWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get3dTensorAlongDimensionWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get4dTensorAlongDimensionWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get5dTensorAlongDimensionWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get6dPermutedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get6dReshapedWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.get6dSubArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll3dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll3dTestArraysWithShape(long seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll4dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll4dTestArraysWithShape(int seed,
long[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll5dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAll6dTestArraysWithShape(int seed,
int[] shape,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAllTestMatricesWithShape(char ordering,
int rows,
int cols,
int seed,
DataType dataType)
Get an array of INDArrays (2d) all with the specified shape.
|
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getAllTestMatricesWithShape(long rows,
long cols,
long seed,
DataType dataType)
Get an array of INDArrays (2d) all with the specified shape.
|
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getSubMatricesWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getSubMatricesWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getTensorAlongDimensionMatricesWithShape(char ordering,
long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getTensorAlongDimensionMatricesWithShape(long rows,
long cols,
long seed,
DataType dataType) |
static List<Pair<INDArray,String>> |
NDArrayCreationUtil.getTestMatricesWithVaryingShapes(int rank,
char order,
DataType dataType)
Test utility to sweep shapes given a rank
Given a rank will generate random test matrices that will cover all cases of a shape with a '1' anywhere in the shape
as well a shape with random ints that are not 0 or 1
eg. rank 2: 1,1; 1,2; 2,1; 2,2; 3,4
Motivated by TADs that often hit bugs when a "1" occurs as the size of a dimension
|
Modifier and Type | Method and Description |
---|---|
Pair<DataBuffer,long[]> |
DirectShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
DataType dataType) |
Pair<DataBuffer,long[]> |
DirectShapeInfoProvider.createShapeInformation(long[] shape,
long[] stride,
long elementWiseStride,
char order,
long extras) |
Pair<DataBuffer,DataBuffer> |
CpuTADManager.getTADOnlyShapeInfo(INDArray array,
int[] dimension) |
Modifier and Type | Method and Description |
---|---|
Pair<Long,Long> |
CpuOpContext.getRngStates() |
Modifier and Type | Method and Description |
---|---|
static Pair<INDArray,INDArray> |
DataSetUtil.merge2d(@NonNull INDArray[][] arrays,
INDArray[][] masks,
int inOutIdx)
Merge the specified 2d arrays and masks.
|
static Pair<INDArray,INDArray> |
DataSetUtil.merge2d(INDArray[] arrays,
INDArray[] masks)
Merge the specified 2d arrays and masks.
|
static Pair<INDArray,INDArray> |
DataSetUtil.merge4d(INDArray[][] arrays,
INDArray[][] masks,
int inOutIdx)
Merge the specified 4d arrays and masks.
|
static Pair<INDArray,INDArray> |
DataSetUtil.merge4d(INDArray[] arrays,
INDArray[] masks)
Merge the specified 4d arrays and masks.
|
static Pair<INDArray[],INDArray[]> |
DataSetUtil.mergeFeatures(@NonNull INDArray[][] featuresToMerge,
INDArray[][] featureMasksToMerge)
Merge all of the features arrays into one minibatch.
|
static Pair<INDArray,INDArray> |
DataSetUtil.mergeFeatures(INDArray[][] featuresToMerge,
INDArray[][] featureMasksToMerge,
int inOutIdx)
Extract out the specified column, and merge the specified features and mask arrays (i.e., concatenate the examples)
|
static Pair<INDArray,INDArray> |
DataSetUtil.mergeFeatures(@NonNull INDArray[] featuresToMerge,
INDArray[] featureMasksToMerge)
Merge the specified features and mask arrays (i.e., concatenate the examples)
|
static Pair<INDArray,INDArray> |
DataSetUtil.mergeLabels(@NonNull INDArray[][] labelsToMerge,
INDArray[][] labelMasksToMerge,
int inOutIdx)
Extract out the specified column, and merge the specified label and label mask arrays
(i.e., concatenate the examples)
|
static Pair<INDArray,INDArray> |
DataSetUtil.mergeLabels(INDArray[] labelsToMerge,
INDArray[] labelMasksToMerge)
Merge the specified labels and label mask arrays (i.e., concatenate the examples)
|
static Pair<INDArray,INDArray> |
DataSetUtil.mergeTimeSeries(INDArray[][] arrays,
INDArray[][] masks,
int inOutIdx)
Merge the specified time series (3d) arrays and masks.
|
static Pair<INDArray,INDArray> |
DataSetUtil.mergeTimeSeries(INDArray[] arrays,
INDArray[] masks)
Merge the specified time series (3d) arrays and masks.
|
Modifier and Type | Method and Description |
---|---|
static INDArray |
Nd4j.createArrayFromShapeBuffer(DataBuffer data,
Pair<DataBuffer,long[]> shapeInfo)
Create array based in data buffer and shape info,
|
Modifier and Type | Method and Description |
---|---|
Pair<Double,INDArray> |
ILossFunction.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average)
Compute both the score (loss function value) and gradient.
|
Pair<Double,INDArray> |
SameDiffLoss.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average)
Compute both the score (loss function value) and gradient.
|
Modifier and Type | Method and Description |
---|---|
Pair<Double,INDArray> |
LossBinaryXENT.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossCosineProximity.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossFMeasure.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossHinge.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossKLD.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossL1.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossL2.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossMAPE.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossMCXENT.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossMixtureDensity.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossMSLE.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossMultiLabel.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossPoisson.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossSparseMCXENT.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossSquaredHinge.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Pair<Double,INDArray> |
LossWasserstein.computeGradientAndScore(INDArray labels,
INDArray preOutput,
IActivation activationFn,
INDArray mask,
boolean average) |
Modifier and Type | Method and Description |
---|---|
protected Pair<NodeRole,String> |
VoidParameterServer.getRole(@NonNull VoidConfiguration voidConfiguration,
@NonNull Collection<String> localIPs)
Deprecated.
This method checks for designated role, according to local IP addresses and configuration passed into method
|
Modifier and Type | Method and Description |
---|---|
Pair<Integer,Integer> |
ModelParameterServer.getStartPosition()
This method returns pair of integers: iteration number and epoch number
|
Modifier and Type | Method and Description |
---|---|
INDArray[] |
GraphInferenceGrpcClient.output(long graphId,
Pair<String,INDArray>... inputs)
This method sends inference request to the GraphServer instance, and returns result as array of INDArrays
|
INDArray[] |
GraphInferenceGrpcClient.output(Pair<String,INDArray>... inputs)
This method sends inference request to the GraphServer instance, and returns result as array of INDArrays
PLEASE NOTE: This call will be routed to default graph with id 0
|
Modifier and Type | Method and Description |
---|---|
protected static Pair<INDArray,ByteBuffer> |
BinarySerde.toArrayAndByteBuffer(ByteBuffer buffer,
int offset)
Create an ndarray and existing bytebuffer
|
Modifier and Type | Method and Description |
---|---|
static Pair<String,String> |
SystemInfo.inferVersion() |
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