Index (Deep Java Library 0.2.0 API specification)

A B C D E F G H I J K L M N O P Q R S T U V W X Z

A

abs() - Method in interface ai.djl.ndarray.NDArray: Returns the absolute value of this NDArray element-wise.
AbstractBlock - Class in ai.djl.nn: AbstractBlock is an abstract implementation of Block.
AbstractBlock() - Constructor for class ai.djl.nn.AbstractBlock
Accuracy - Class in ai.djl.training.metrics: Accuracy is a TrainingMetric that computes the accuracy score.
Accuracy(String, int, int) - Constructor for class ai.djl.training.metrics.Accuracy: Creates an accuracy metric.
Accuracy() - Constructor for class ai.djl.training.metrics.Accuracy: Creates an accuracy metric that computes accuracy across axis 1 along the 0th index.
Accuracy(String, int) - Constructor for class ai.djl.training.metrics.Accuracy: Creates an accuracy metric that computes accuracy across axis 1 along given index.
acos() - Method in interface ai.djl.ndarray.NDArray: Returns the inverse trigonometric cosine of this NDArray element-wise.
acosh() - Method in interface ai.djl.ndarray.NDArray: Returns the inverse hyperbolic cosine of this NDArray element-wise.
Activation - Class in ai.djl.nn: Utility class that provides activation functions and blocks.
activation - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
Adam - Class in ai.djl.training.optimizer: Adam is a generalization of the AdaGrad Optimizer.
Adam(Adam.Builder) - Constructor for class ai.djl.training.optimizer.Adam: Creates a new instance of Adam optimizer.
adam() - Static method in class ai.djl.training.optimizer.Optimizer: Returns a new instance of Adam.Builder that can build an Adam optimizer.
Adam.Builder - Class in ai.djl.training.optimizer: The Builder to construct an Adam object.
add(Number) - Method in interface ai.djl.ndarray.NDArray: Adds a number to this NDArray element-wise.
add(NDArray) - Method in interface ai.djl.ndarray.NDArray: Adds other NDArrays to this NDArray element-wise.
add(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Adds a number to the NDArray element-wise.
add(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Adds a NDArray to a number element-wise.
add(NDArray...) - Static method in class ai.djl.ndarray.NDArrays: Adds a NDArray to a NDArray element-wise.
add(Block) - Method in class ai.djl.nn.ParallelBlock: Adds the given Block to the block, which is one parallel branch.
add(Function<NDList, NDList>) - Method in class ai.djl.nn.ParallelBlock: Adds a LambdaBlock, that applies the given function, to the list of parallel branches.
add(Block) - Method in class ai.djl.nn.SequentialBlock: Adds the given Block to the block to be executed in order.
add(Function<NDList, NDList>) - Method in class ai.djl.nn.SequentialBlock: Adds a LambdaBlock that applies the given function to the sequence of blocks.
add(Transform) - Method in class ai.djl.translate.Pipeline: Adds the given Transform to the list of transforms to be applied on the input when the transform method is called on this object.
add(int, Transform) - Method in class ai.djl.translate.Pipeline: Adds the given Transform to the list of transforms to be applied on the NDArray at the given index in the input NDList.
add(String, Transform) - Method in class ai.djl.translate.Pipeline: Adds the given Transform to the list of transforms to be applied on the NDArray with the given key as name in the input NDList.
addAll(NDList) - Method in class ai.djl.ndarray.NDList: Appends all of the NDArrays in the specified NDList to the end of this NDList, in the order that they are returned by the specified NDList's iterator.
addAll(Shape) - Method in class ai.djl.ndarray.types.Shape: Joins a this shape with specified other shape.
addAll(Block...) - Method in class ai.djl.nn.ParallelBlock: Adds an array of blocks, each of which is a parallel branch.
addAll(Collection<Block>) - Method in class ai.djl.nn.ParallelBlock: Adds a Collection of blocks, each of which is a parallel branch.
addAll(Block...) - Method in class ai.djl.nn.SequentialBlock: Adds an array of blocks to be executed in sequence, in order.
addAll(Collection<Block>) - Method in class ai.djl.nn.SequentialBlock: Adds a Collection of blocks to be executed in sequence, in order.
addBooleanIndex(NDArray) - Method in class ai.djl.ndarray.index.NDIndex: Updates the NDIndex by appending a boolean NDArray.
addColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Adds a column vector (copy).
addCorrectInstances(long) - Method in class ai.djl.training.metrics.Accuracy: Add a number to the correct instances.
addi(Number) - Method in interface ai.djl.ndarray.NDArray: Adds a number to this NDArray element-wise in place.
addi(NDArray) - Method in interface ai.djl.ndarray.NDArray: Adds other NDArrays to this NDArray element-wise in place.
addi(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Adds a number to the NDArray element-wise in place.
addi(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Adds a NDArray to a number element-wise in place.
addi(NDArray...) - Static method in class ai.djl.ndarray.NDArrays: Adds all of the NDArrays together element-wise in place.
addiColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Adds a column vector in place.
addIndices(String) - Method in class ai.djl.ndarray.index.NDIndex: Updates the NDIndex by appending indices to the array.
addIndices(long...) - Method in class ai.djl.ndarray.index.NDIndex: Updates the NDIndex by appending indices as specified values on the NDArray.
addiRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Adds a row vector in place.
addMetric(Metric) - Method in class ai.djl.metric.Metrics: Adds a Metric to the collection.
addMetric(String, Number) - Method in class ai.djl.metric.Metrics: Adds a Metric given the metric's name and value.
addMetric(String, Number, String) - Method in class ai.djl.metric.Metrics: Adds a Metric given the metric's name, value, and unit.
addRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Adds a row vector (copy).
addSliceDim(long, long) - Method in class ai.djl.ndarray.index.NDIndex: Appends a new index to slice the dimension and returns a range of values.
addSliceDim(long, long, long) - Method in class ai.djl.ndarray.index.NDIndex: Appends a new index to slice the dimension and returns a range of values.
addTotalInstances(long) - Method in class ai.djl.training.metrics.Accuracy: Add a number to the total instances.
addTrainingMetric(TrainingMetric) - Method in class ai.djl.training.DefaultTrainingConfig: Adds a TrainingMetric that needs to be computed during training.
ai.djl - package ai.djl: Contains top level, framework-agnostic classes for both inference and training.
ai.djl.engine - package ai.djl.engine: Contains classes responsible for loading a deep learning framework.
ai.djl.inference - package ai.djl.inference: Contains classes to implement inference tasks.
ai.djl.metric - package ai.djl.metric: Contains classes to collect metrics information.
ai.djl.modality - package ai.djl.modality: Contains utility classes for each of the predefined modalities.
ai.djl.modality.cv - package ai.djl.modality.cv: Contains utility classes for computer vision tasks and image processing.
ai.djl.modality.cv.transform - package ai.djl.modality.cv.transform: Contains Transforms for working with Images.
ai.djl.modality.cv.util - package ai.djl.modality.cv.util: Contains utility classes for image manipulation.
ai.djl.ndarray - package ai.djl.ndarray: Contains classes and interfaces that define an n-dimensional array.
ai.djl.ndarray.index - package ai.djl.ndarray.index: Contains classes that help access NDArray's indices.
ai.djl.ndarray.types - package ai.djl.ndarray.types: Contains classes that define n-dimensional array data types.
ai.djl.nn - package ai.djl.nn: Contains classes to construct neural networks.
ai.djl.nn.convolutional - package ai.djl.nn.convolutional: Contains classes that define convolutional operations extending Convolution.
ai.djl.nn.core - package ai.djl.nn.core: Contains classes that define simple neural network operations.
ai.djl.nn.norm - package ai.djl.nn.norm: Contains classes that define normalizing neural network operations.
ai.djl.nn.pooling - package ai.djl.nn.pooling: Contains pooling neural network operations in Pool and helpers for it.
ai.djl.nn.recurrent - package ai.djl.nn.recurrent: Contains classes for recurrent neural network operations.
ai.djl.training - package ai.djl.training: Contains classes and implementations to train a neural network.
ai.djl.training.dataset - package ai.djl.training.dataset: Contains classes to download and prepare training and testing data.
ai.djl.training.initializer - package ai.djl.training.initializer: Contains classes for initializing a Block's Parameters.
ai.djl.training.loss - package ai.djl.training.loss: Contains classes for measuring the Loss of a model.
ai.djl.training.metrics - package ai.djl.training.metrics: Contains classes for monitoring the performance of models.
ai.djl.training.optimizer - package ai.djl.training.optimizer: Contains classes for optimizing a neural network Block.
ai.djl.training.optimizer.learningrate - package ai.djl.training.optimizer.learningrate: Contains classes for having a gradually changing learning rate.
ai.djl.training.util - package ai.djl.training.util: Contains utilities to use during training.
ai.djl.translate - package ai.djl.translate: Contains classes and interfaces that translate between java objects and NDArrays.
all() - Method in interface ai.djl.ndarray.NDArray: Returns true if all elements within this NDArray are non-zero or true.
allClose(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns true if two NDArrays are element-wise equal within a tolerance.
allClose(NDArray, double, double, boolean) - Method in interface ai.djl.ndarray.NDArray: Returns true if two NDArray are element-wise equal within a tolerance.
allClose(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns true if two NDArray are element-wise equal within a tolerance.
allClose(NDArray, NDArray, double, double, boolean) - Static method in class ai.djl.ndarray.NDArrays: Returns true if two NDArray are element-wise equal within a tolerance.
allocateDirect(int) - Method in interface ai.djl.ndarray.NDManager: Allocates a new engine specific direct byte buffer.
any() - Method in interface ai.djl.ndarray.NDArray: Returns true if any of the elements within this NDArray are non-zero or true.
arange(Number) - Method in interface ai.djl.ndarray.NDManager: Returns evenly spaced values starting from 0.
arange(Number, Number) - Method in interface ai.djl.ndarray.NDManager: Returns evenly spaced values within a given interval with step 1.
arange(Number, Number, Number) - Method in interface ai.djl.ndarray.NDManager: Returns evenly spaced values within a given interval.
arange(Number, Number, Number, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Returns evenly spaced values within a given interval.
argMax() - Method in interface ai.djl.ndarray.NDArray: Returns the indices of the maximum values into the flattened NDArray.
argMax(int) - Method in interface ai.djl.ndarray.NDArray: Returns the indices of the maximum values along given axis.
argMin() - Method in interface ai.djl.ndarray.NDArray: Returns the indices of the minimum values into the flattened NDArray.
argMin(int) - Method in interface ai.djl.ndarray.NDArray: Returns the indices of the minimum values along given axis.
argSort() - Method in interface ai.djl.ndarray.NDArray: Returns the indices that would sort this NDArray.
argSort(int) - Method in interface ai.djl.ndarray.NDArray: Returns the indices that would sort this NDArray given the axis.
argSort(int, boolean) - Method in interface ai.djl.ndarray.NDArray: Returns the indices that would sort this NDArray given the axis.
ArrayDataset - Class in ai.djl.training.dataset: ArrayDataset is an implementation of RandomAccessDataset that consist entirely of large NDArrays.
ArrayDataset(RandomAccessDataset.BaseBuilder<?>) - Constructor for class ai.djl.training.dataset.ArrayDataset: Creates a new instance of ArrayDataset with the arguments in ArrayDataset.Builder.
ArrayDataset.Builder - Class in ai.djl.training.dataset: The Builder to construct an ArrayDataset.
asDataType(ByteBuffer) - Method in enum ai.djl.ndarray.types.DataType: Converts a ByteBuffer to a buffer for this data type.
asin() - Method in interface ai.djl.ndarray.NDArray: Returns the inverse trigonometric sine of this NDArray element-wise.
asInDevice(Device, boolean) - Method in interface ai.djl.ndarray.NDArray: Moves this NDArray to a different Device.
asInDevice(Device, boolean) - Method in class ai.djl.ndarray.NDList: Converts all the NDArray in NDList to a different Device.
asinh() - Method in interface ai.djl.ndarray.NDArray: Returns the inverse hyperbolic sine of this NDArray element-wise.
asMatrix() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray into a 2-D Matrix.
asType(DataType, boolean) - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a different DataType.
atan() - Method in interface ai.djl.ndarray.NDArray: Returns the inverse trigonometric tangent of this NDArray element-wise.
atanh() - Method in interface ai.djl.ndarray.NDArray: Returns the inverse hyperbolic tangent of this NDArray element-wise.
attach(NDManager) - Method in interface ai.djl.ndarray.NDArray: Attaches this NDArray to the specified NDManager.
attach(NDManager) - Method in class ai.djl.ndarray.NDList: Attaches each ndarray in this list to the specified manager.
attach(String, AutoCloseable) - Method in interface ai.djl.ndarray.NDManager: Attaches a NDArray or NDManager to this NDManager.
attachGradient() - Method in interface ai.djl.ndarray.NDArray: Attaches a gradient NDArray to this NDArray and marks it so GradientCollector.backward(NDArray) can compute the gradient with respect to it.
avgPool(NDArray, Shape, Shape, Shape, PoolingConvention, boolean) - Static method in class ai.djl.nn.pooling.Pool: Performs Avg Pooling on the input.
avgPool(NDArray, Shape, Shape, Shape) - Static method in class ai.djl.nn.pooling.Pool: Performs Avg Pooling on the input.
axis - Variable in class ai.djl.training.metrics.Accuracy

B

backward(NDArray) - Method in interface ai.djl.training.GradientCollector: Calculate the gradient w.r.t previously marked variable (head).
BaseBuilder() - Constructor for class ai.djl.modality.cv.ImageTranslator.BaseBuilder
BaseBuilder() - Constructor for class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
BaseBuilder() - Constructor for class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
BasePredictor<I,O> - Class in ai.djl.inference: BasePredictor contains common code for implementations of the Predictor interface.
BasePredictor(Model, Translator<I, O>, boolean) - Constructor for class ai.djl.inference.BasePredictor: Creates a new instance of BasePredictor with the given Model and Translator.
Batch - Class in ai.djl.training.dataset: A Batch is used to hold multiple items (data and labels) from a Dataset.
Batch(NDManager, NDList, NDList) - Constructor for class ai.djl.training.dataset.Batch: Creates a new instance of Batch with the given manager, data and labels.
Batch(NDManager, NDList, NDList, Batchifier) - Constructor for class ai.djl.training.dataset.Batch: Creates a new instance of Batch with the given manager, data and labels.
batchFlattenBlock() - Static method in class ai.djl.nn.Blocks: Creates a Block whose forward function applies the batchFlatten method.
batchFlattenBlock(long) - Static method in class ai.djl.nn.Blocks: Creates a Block whose forward function applies the batchFlatten method.
batchFlattenBlock(long, long) - Static method in class ai.djl.nn.Blocks: Creates a Block whose forward function applies the batchFlatten method.
batchifier - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
batchifier - Variable in class ai.djl.training.dataset.RandomAccessDataset
Batchifier - Interface in ai.djl.translate: An interface that provides methods to convert an un-batched NDList into a batched NDList and vice versa.
batchify(NDList[]) - Method in interface ai.djl.translate.Batchifier: Converts an array of NDList into an NDList.
batchify(NDList[]) - Method in class ai.djl.translate.StackBatchifier: Converts an array of NDList into an NDList.
BatchNorm - Class in ai.djl.nn.norm: Normalizes a data batch by mean and variance, and applies a scale gamma as well as offset beta.
BatchNorm.Builder - Class in ai.djl.nn.norm: The Builder to construct a BatchNorm.
batchPredict(List<I>) - Method in class ai.djl.inference.BasePredictor: Predicts a batch for inference.
batchPredict(List<I>) - Method in interface ai.djl.inference.Predictor: Predicts a batch for inference.
BatchSampler - Class in ai.djl.training.dataset: BatchSampler is a Sampler that returns a single epoch over the data.
BatchSampler(Sampler.SubSampler, long) - Constructor for class ai.djl.training.dataset.BatchSampler: Creates a new instance of BatchSampler that samples from the given Sampler.SubSampler, and yields a mini-batch of indices.
BatchSampler(Sampler.SubSampler, long, boolean) - Constructor for class ai.djl.training.dataset.BatchSampler: Creates a new instance of BatchSampler that samples from the given Sampler.SubSampler, and yields a mini-batch of indices.
beforeInitialize(Shape[]) - Method in class ai.djl.nn.AbstractBlock: Performs any action necessary before initialization.
beforeInitialize(Shape[]) - Method in class ai.djl.nn.convolutional.Convolution: Performs any action necessary before initialization.
beforeInitialize(Shape[]) - Method in class ai.djl.nn.core.Linear: Performs any action necessary before initialization.
beforeInitialize(Shape[]) - Method in class ai.djl.nn.norm.BatchNorm: Performs any action necessary before initialization.
beforeInitialize(Shape[]) - Method in class ai.djl.nn.recurrent.GRU: Performs any action necessary before initialization.
beforeInitialize(Shape[]) - Method in class ai.djl.nn.recurrent.LSTM: Performs any action necessary before initialization.
beforeInitialize(Shape[]) - Method in class ai.djl.nn.recurrent.RNN: Performs any action necessary before initialization.
best() - Method in class ai.djl.modality.Classifications: Returns the most likely class for the classification.
bias - Variable in class ai.djl.nn.convolutional.Convolution
Block - Interface in ai.djl.nn: A Block is a composable unit that forms a neural network.
BlockList - Class in ai.djl.nn: Represents a set of names and Blocks.
BlockList() - Constructor for class ai.djl.nn.BlockList: Creates an empty BlockList.
BlockList(int) - Constructor for class ai.djl.nn.BlockList: Constructs an empty BlockList with the specified initial capacity.
BlockList(List<String>, List<Block>) - Constructor for class ai.djl.nn.BlockList: Constructs a BlockList containing the elements of the specified keys and values.
BlockList(List<Pair<String, Block>>) - Constructor for class ai.djl.nn.BlockList: Constructs a BlockList containing the elements of the specified list of Pairs.
BlockList(Map<String, Block>) - Constructor for class ai.djl.nn.BlockList: Constructs a BlockList containing the elements of the specified map.
Blocks - Class in ai.djl.nn: Utility class that provides some useful blocks.
booleanMask(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns portion of this NDArray given the index boolean NDArray along first axis.
booleanMask(NDArray, int) - Method in interface ai.djl.ndarray.NDArray: Returns portion of this NDArray given the index boolean NDArray along given axis.
booleanMask(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns portion of the NDArray given the index boolean NDArray along first axis.
booleanMask(NDArray, NDArray, int) - Static method in class ai.djl.ndarray.NDArrays: Returns portion of the NDArray given the index boolean NDArray along given axis.
BoundingBox - Interface in ai.djl.modality.cv: An interface representing a bounding box for the detected object.
BoundingBoxError - Class in ai.djl.training.metrics: BoundingBoxError is a TrainingMetric that computes the error in the prediction of bounding boxes in SingleShotDetection model.
BoundingBoxError(String) - Constructor for class ai.djl.training.metrics.BoundingBoxError: Creates an BoundingBoxError metric.
broadcast(Shape) - Method in interface ai.djl.ndarray.NDArray: Broadcasts this NDArray to be the given shape.
broadcast(long...) - Method in interface ai.djl.ndarray.NDArray: Broadcasts this NDArray to be the given shape.
BufferedImageUtils - Class in ai.djl.modality.cv.util: BufferedImageUtils is an image processing utility to load, reshape and convert images using BufferedImage.
build() - Method in class ai.djl.modality.cv.ImageClassificationTranslator.Builder: Builds the ImageClassificationTranslator with the provided data.
build() - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator.Builder: Builds the translator.
build() - Method in class ai.djl.modality.cv.MultiBoxDetection.Builder: Builds a MultiBoxDetection block.
build() - Method in class ai.djl.modality.cv.MultiBoxPrior.Builder: Builds a MultiBoxPrior block.
build() - Method in class ai.djl.modality.cv.MultiBoxTarget.Builder: Builds a MultiBoxTarget block.
build() - Method in class ai.djl.modality.cv.SimplePoseTranslator.Builder: Builds the translator.
build() - Method in class ai.djl.modality.cv.SingleShotDetectionTranslator.Builder: Builds the translator.
build() - Method in class ai.djl.nn.convolutional.Conv1D.Builder: Builds a Conv1D block.
build() - Method in class ai.djl.nn.convolutional.Conv2D.Builder: Builds a Conv2D block.
build() - Method in class ai.djl.nn.convolutional.Conv3D.Builder: Builds a Conv3D block.
build() - Method in class ai.djl.nn.core.Embedding.Builder: Builds the Embedding.
build() - Method in class ai.djl.nn.core.Linear.Builder: Returns the constructed Linear.
build() - Method in class ai.djl.nn.norm.BatchNorm.Builder: Builds a BatchNorm block.
build() - Method in class ai.djl.nn.norm.Dropout.Builder: Builds a Dropout block.
build() - Method in class ai.djl.nn.recurrent.GRU.Builder: Builds a GRU block.
build() - Method in class ai.djl.nn.recurrent.LSTM.Builder: Builds a LSTM block.
build() - Method in class ai.djl.nn.recurrent.RNN.Builder: Builds a RNN block.
build() - Method in class ai.djl.training.dataset.ArrayDataset.Builder: Builds a new instance of ArrayDataset with the specified data and labels.
build() - Method in class ai.djl.training.optimizer.Adam.Builder: Builds a Adam block.
build() - Method in class ai.djl.training.optimizer.learningrate.FactorTracker.Builder: Builds a FactorTracker block.
build() - Method in class ai.djl.training.optimizer.learningrate.MultiFactorTracker.Builder: Builds a MultiFactorTracker block.
build() - Method in class ai.djl.training.optimizer.Nag.Builder: Builds a Nag block.
build() - Method in class ai.djl.training.optimizer.Sgd.Builder: Builds a Sgd block.
Builder() - Constructor for class ai.djl.modality.cv.ImageClassificationTranslator.Builder
Builder() - Constructor for class ai.djl.modality.cv.InstanceSegmentationTranslator.Builder
Builder() - Constructor for class ai.djl.modality.cv.MultiBoxDetection.Builder
Builder() - Constructor for class ai.djl.modality.cv.MultiBoxPrior.Builder
Builder() - Constructor for class ai.djl.modality.cv.MultiBoxTarget.Builder
Builder() - Constructor for class ai.djl.modality.cv.SimplePoseTranslator.Builder
Builder() - Constructor for class ai.djl.modality.cv.SingleShotDetectionTranslator.Builder
Builder() - Constructor for class ai.djl.nn.convolutional.Conv1D.Builder: Creates a builder that can build a Conv1D block.
Builder() - Constructor for class ai.djl.nn.convolutional.Conv2D.Builder: Creates a builder that can build a Conv2D block.
Builder() - Constructor for class ai.djl.nn.convolutional.Conv3D.Builder: Creates a builder that can build a Conv3D block.
Builder() - Constructor for class ai.djl.nn.core.Embedding.Builder
Builder() - Constructor for class ai.djl.nn.core.Linear.Builder
Builder() - Constructor for class ai.djl.nn.norm.BatchNorm.Builder
Builder() - Constructor for class ai.djl.nn.norm.Dropout.Builder
Builder() - Constructor for class ai.djl.nn.recurrent.GRU.Builder
Builder() - Constructor for class ai.djl.nn.recurrent.LSTM.Builder
Builder() - Constructor for class ai.djl.nn.recurrent.RNN.Builder
Builder() - Constructor for class ai.djl.training.dataset.ArrayDataset.Builder
Builder() - Constructor for class ai.djl.training.optimizer.Adam.Builder
Builder() - Constructor for class ai.djl.training.optimizer.learningrate.FactorTracker.Builder
Builder() - Constructor for class ai.djl.training.optimizer.learningrate.MultiFactorTracker.Builder
Builder() - Constructor for class ai.djl.training.optimizer.Nag.Builder
Builder() - Constructor for class ai.djl.training.optimizer.Sgd.Builder

C

cast(DataType) - Method in interface ai.djl.Model: Casts the model to support a different precision level.
cast(DataType) - Method in class ai.djl.nn.AbstractBlock: Guaranteed to throw an exception.
cast(DataType) - Method in interface ai.djl.nn.Block: Guaranteed to throw an exception.
cbrt() - Method in interface ai.djl.ndarray.NDArray: Returns the cube-root of this NDArray element-wise.
ceil() - Method in interface ai.djl.ndarray.NDArray: Returns the ceiling of this NDArray element-wise.
CenterCrop - Class in ai.djl.modality.cv.transform: A Transform that crops the center of an image.
CenterCrop() - Constructor for class ai.djl.modality.cv.transform.CenterCrop: Creates a CenterCrop Transform that crops to size min(width, height).
CenterCrop(int, int) - Constructor for class ai.djl.modality.cv.transform.CenterCrop: Creates a CenterCrop Transform that crops the given width and height.
centerCrop(NDArray) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Crops an image to a square of size min(width, height).
centerCrop(NDArray, int, int) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Crops an image to a given width and height from the center of the image.
checkLabelShapes(NDArray, NDArray, boolean) - Method in class ai.djl.training.metrics.TrainingMetric: Checks if the two input NDArray have the same length or shape.
checkLabelShapes(NDArray, NDArray) - Method in class ai.djl.training.metrics.TrainingMetric: Checks the length of NDArrays.
Classification(String, double) - Constructor for class ai.djl.modality.Classifications.Classification: Constructs a single class result for a classification.
Classifications - Class in ai.djl.modality: Classifications is the container that stores the classification results for classification on a single input.
Classifications(List<String>, List<Double>) - Constructor for class ai.djl.modality.Classifications: Constructs a Classifications using a parallel list of classNames and probabilities.
Classifications(List<String>, NDArray) - Constructor for class ai.djl.modality.Classifications: Constructs a Classifications using list of classNames parallel to an NDArray of probabilities.
Classifications.Classification - Class in ai.djl.modality: A Classification stores the classification result for a single class on a single input.
classNames - Variable in class ai.djl.modality.Classifications
clear() - Method in class ai.djl.nn.AbstractBlock: Closes all the parameters of the block.
clear() - Method in interface ai.djl.nn.Block: Closes all the parameters of the block.
clip(Number, Number) - Method in interface ai.djl.ndarray.NDArray: Clips (limit) the values in this NDArray.
clipGrad - Variable in class ai.djl.training.optimizer.Optimizer
clipLstmState - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
close() - Method in class ai.djl.inference.BasePredictor
close() - Method in interface ai.djl.inference.Predictor
close() - Method in interface ai.djl.Model
close() - Method in interface ai.djl.ndarray.NDArray
close() - Method in class ai.djl.ndarray.NDList
close() - Method in interface ai.djl.ndarray.NDManager
close() - Method in class ai.djl.nn.Parameter
close() - Method in class ai.djl.training.dataset.Batch
close() - Method in interface ai.djl.training.GradientCollector
close() - Method in class ai.djl.training.LocalParameterServer
close() - Method in interface ai.djl.training.ParameterServer
close() - Method in interface ai.djl.training.Trainer
close() - Method in interface ai.djl.translate.TranslatorContext
columns() - Method in class ai.djl.ndarray.types.Shape: Returns the number of columns in the matrix.
CompositeLoss - Class in ai.djl.training.loss: CompositeLoss is an implementation of the Loss abstract class that can combine different Loss functions by adding the individual losses together.
CompositeLoss(Loss...) - Constructor for class ai.djl.training.loss.CompositeLoss: Creates a new instance of CompositeLoss that can combine the given Loss components.
concat(NDArray) - Method in interface ai.djl.ndarray.NDArray: Joins a NDArray along the first axis.
concat(NDArray, int) - Method in interface ai.djl.ndarray.NDArray: Joins a NDArray along an existing axis.
concat(NDList) - Static method in class ai.djl.ndarray.NDArrays: Joins a NDList along the first axis.
concat(NDList, int) - Static method in class ai.djl.ndarray.NDArrays: Joins a NDList along an existing axis.
contains(String) - Method in class ai.djl.ndarray.NDList: Returns true if this NDList contains an NDArray with the specified name.
contentEquals(Number) - Method in interface ai.djl.ndarray.NDArray: Returns true if all elements in this NDArray are equal to the Number.
contentEquals(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns true if all elements in this NDArray are equal to the other NDArray.
contentEquals(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns true if all elements in NDArray a are equal to NDArray b.
contentEquals(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns true if all elements in NDArray a are equal to NDArray b.
Conv1D - Class in ai.djl.nn.convolutional: Computes 1-D convolution on 3-D input.
Conv1D.Builder - Class in ai.djl.nn.convolutional: The Builder to construct a Conv1D type of Block.
Conv2D - Class in ai.djl.nn.convolutional: Computes 2-D convolution on 4-D input.
Conv2D.Builder - Class in ai.djl.nn.convolutional: The Builder to construct a Conv2D type of Block.
Conv3D - Class in ai.djl.nn.convolutional: Computes 3-D convolution on 5-D input.
Conv3D.Builder - Class in ai.djl.nn.convolutional: The Builder to construct a Conv3D type of Block.
Convolution - Class in ai.djl.nn.convolutional: Computes N-D convolution on (N+2)-D input.
Convolution(Convolution.ConvolutionBuilder<?>) - Constructor for class ai.djl.nn.convolutional.Convolution: Creates a Convolution object.
Convolution.ConvolutionBuilder<T extends Convolution.ConvolutionBuilder> - Class in ai.djl.nn.convolutional: A builder that can build any Convolution block.
ConvolutionBuilder() - Constructor for class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
copyTo(NDArray) - Method in interface ai.djl.ndarray.NDArray: Deep-copies the current NDArray to the one passed in.
cos() - Method in interface ai.djl.ndarray.NDArray: Returns the trigonometric cosine of this NDArray element-wise.
cosh() - Method in interface ai.djl.ndarray.NDArray: Returns the hyperbolic cosine of this NDArray element-wise.
countNonzero() - Method in interface ai.djl.ndarray.NDArray: Counts the number of non-zero values in this NDArray.
countNonzero(int) - Method in interface ai.djl.ndarray.NDArray: Counts the number of non-zero values in this NDArray along a given axis.
cpu() - Static method in class ai.djl.Device: Returns the default CPU Device.
cpu(int) - Static method in class ai.djl.Device: Returns a new instance of CPU Device with the specified deviceId.
create(Shape) - Method in interface ai.djl.ndarray.NDManager: Creates an uninitialized instance of DataType.FLOAT32 NDArray with specified Shape.
create(float) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a scalar NDArray.
create(int) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a scalar NDArray.
create(double) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a scalar NDArray.
create(long) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a scalar NDArray.
create(byte) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a scalar NDArray.
create(boolean) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a scalar NDArray.
create(float[]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 1D NDArray.
create(int[]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 1D NDArray.
create(double[]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 1D NDArray.
create(long[]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 1D NDArray.
create(byte[]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 1D NDArray.
create(boolean[]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 1D NDArray.
create(float[][]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 2D NDArray.
create(int[][]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 2D NDArray.
create(double[][]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 2D NDArray.
create(long[][]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 2-D NDArray.
create(byte[][]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 2-D NDArray.
create(boolean[][]) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a 2-D NDArray.
create(Buffer, Shape) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes a NDArray with specified Shape.
create(Shape, DataType) - Method in interface ai.djl.ndarray.NDManager: Creates an uninitialized instance of NDArray with specified Shape, and DataType.
create(Buffer, Shape, DataType) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes an instance of NDArray with specified Shape and DataType.
create(float[], Shape) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes an instance of NDArray with specified Shape and float array.
create(int[], Shape) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes an instance of NDArray with specified Shape and int array.
create(double[], Shape) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes an instance of NDArray with specified Shape and double array.
create(long[], Shape) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes an instance of NDArray with specified Shape and long array.
create(byte[], Shape) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes an instance of NDArray with specified Shape and byte array.
create(boolean[], Shape) - Method in interface ai.djl.ndarray.NDManager: Creates and initializes an instance of NDArray with specified Shape and boolean array.
create(Shape, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Creates an uninitialized instance of NDArray with specified Shape, DataType and Device.
createCSR(Buffer, long[], long[], Shape, Device) - Method in interface ai.djl.ndarray.NDManager: Creates a Compressed Sparse Row Storage (CSR) Format Matrix.
createCSR(Buffer, long[], long[], Shape) - Method in interface ai.djl.ndarray.NDManager: Creates a Compressed Sparse Row Storage (CSR) Format Matrix.
createMask(NDIndex) - Method in interface ai.djl.ndarray.NDArray: Returns a mask on whether each element matches the given index.
createMask(Predicate<Number>) - Method in interface ai.djl.ndarray.NDArray: Returns a mask on whether each element matches the given condition.
createRowSparse(Buffer, Shape, long[], Shape, Device) - Method in interface ai.djl.ndarray.NDManager: Stores the matrix in row sparse format.
createRowSparse(Buffer, Shape, long[], Shape) - Method in interface ai.djl.ndarray.NDManager: Stores the matrix in row sparse format.
Crop - Class in ai.djl.modality.cv.transform: A Transform that crops the image to a given location and size.
Crop(int, int, int, int) - Constructor for class ai.djl.modality.cv.transform.Crop: Creates a CenterCrop Transform.
crop(NDArray, int, int, int, int) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Crops an image with a given location and size.
cumSum() - Method in interface ai.djl.ndarray.NDArray: Returns the cumulative sum of the elements in the flattened NDArray.
cumSum(int) - Method in interface ai.djl.ndarray.NDArray: Return the cumulative sum of the elements along a given axis.
currentPoint() - Method in interface ai.djl.modality.cv.PathIterator: Returns the coordinates and type of the current path segment in the iteration.

D

data - Variable in class ai.djl.training.dataset.ArrayDataset
DataDesc - Class in ai.djl.ndarray.types: A data descriptor class that encapsulates information of a NDArray.
DataDesc(Shape) - Constructor for class ai.djl.ndarray.types.DataDesc: Constructs and initializes a DataDesc with specified Shape.
DataDesc(Shape, String) - Constructor for class ai.djl.ndarray.types.DataDesc: Constructs and initializes a DataDesc with specified Shape and name.
DataDesc(Shape, DataType) - Constructor for class ai.djl.ndarray.types.DataDesc: Constructs and initializes a DataDesc with specified Shape and DataType.
DataDesc(Shape, DataType, String) - Constructor for class ai.djl.ndarray.types.DataDesc: Constructs and initializes a DataDesc with specified Shape, DataType, name, Device and SparseFormat.
DataIterable - Class in ai.djl.training.dataset: DataIterable is a data loader that combines Dataset, Batchifier, Pipeline, and Sampler to provide an iterable over the given RandomAccessDataset.
DataIterable(RandomAccessDataset, NDManager, Sampler, Batchifier, Pipeline, Pipeline, ExecutorService, int, long, Device) - Constructor for class ai.djl.training.dataset.DataIterable: Creates a new instance of DataIterable with the given parameters.
Dataset - Interface in ai.djl.training.dataset: An interface to represent a dataset.
Dataset.Usage - Enum in ai.djl.training.dataset: An enum that indicates the mode - training, test or validation.
DataType - Enum in ai.djl.ndarray.types: An enum representing the underlying NDArray's data type.
DataType.Format - Enum in ai.djl.ndarray.types: The general data type format categories.
decode(DataInputStream) - Static method in class ai.djl.ndarray.types.Shape: Decodes the data in the given DataInputStream and converts it into the corresponding Shape object.
defaultDevice() - Static method in class ai.djl.Device: Returns the default context used in Engine.
defaultDevice() - Method in class ai.djl.engine.Engine: Returns the system's default device.
defaultIfNull(Device) - Static method in class ai.djl.Device: Returns the given device or the default if it is null.
defaultIfNull(Device, Device) - Static method in class ai.djl.Device: Returns the given device or the passed in default if it is null.
DefaultTrainingConfig - Class in ai.djl.training: DefaultTrainingConfig is an implementation of the TrainingConfig interface.
DefaultTrainingConfig(Initializer, Loss) - Constructor for class ai.djl.training.DefaultTrainingConfig: Creates an instance of DefaultTrainingConfig with the given Initializer.
describeInput() - Method in interface ai.djl.Model: Returns the input descriptor of the model.
describeInput() - Method in class ai.djl.nn.AbstractBlock: Returns a PairList of input names, and shapes.
describeInput() - Method in interface ai.djl.nn.Block: Returns a PairList of input names, and shapes.
describeInput() - Method in class ai.djl.nn.core.Linear: Returns a PairList of input names, and shapes.
describeOutput() - Method in interface ai.djl.Model: Returns the output descriptor of the model.
detach() - Method in interface ai.djl.ndarray.NDArray: Detaches the NDArray from current NDManager's lifecycle.
detach() - Method in class ai.djl.ndarray.NDList: Detaches each ndarray in this list from their current managers.
detach(String) - Method in interface ai.djl.ndarray.NDManager: Detaches a NDArray from this NDManager's lifecycle.
DetectedObject(String, double, BoundingBox) - Constructor for class ai.djl.modality.cv.DetectedObjects.DetectedObject: Constructs a bounding box with the given data.
DetectedObjects - Class in ai.djl.modality.cv: A class representing the detected objects results for a single image in an object detection case.
DetectedObjects(List<String>, List<Double>, List<BoundingBox>) - Constructor for class ai.djl.modality.cv.DetectedObjects: Constructs a DetectedObjects, usually during post-processing.
DetectedObjects.DetectedObject - Class in ai.djl.modality.cv: A DetectedObject represents a single potential detected Object for an image.
detection(NDList) - Method in class ai.djl.modality.cv.MultiBoxDetection: Converts multi-box detection predictions.
Device - Class in ai.djl: The Device class provides the specified assignment for CPU/GPU processing on the NDArray.
Device(String, int) - Constructor for class ai.djl.Device: Creates a Device with basic information.
device - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
device - Variable in class ai.djl.training.dataset.RandomAccessDataset
dilate - Variable in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
dilate - Variable in class ai.djl.nn.convolutional.Convolution
dimension() - Method in class ai.djl.ndarray.types.Shape: Returns the number of dimensions of this Shape.
div(Number) - Method in interface ai.djl.ndarray.NDArray: Divides this NDArray by a number element-wise.
div(NDArray) - Method in interface ai.djl.ndarray.NDArray: Divides this NDArray by the other NDArray element-wise.
div(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Divides the NDArray by a number element-wise.
div(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Divides a number by a NDArray element-wise.
div(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Divides a NDArray by a NDArray element-wise.
divColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Divides a column vector (copy).
divi(Number) - Method in interface ai.djl.ndarray.NDArray: Divides this NDArray by a number element-wise in place.
divi(NDArray) - Method in interface ai.djl.ndarray.NDArray: Divides this NDArray by the other NDArray element-wise in place.
divi(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Divides a number by a NDArray element-wise in place.
divi(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Divides a number by a NDArray element-wise.
divi(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Divides a NDArray by a NDArray element-wise.
diviColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Divides a column vector in place.
diviRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Divides a row vector in place.
divRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Divides a row vector (copy).
dot(NDArray) - Method in interface ai.djl.ndarray.NDArray: Dot product of this NDArray and the other NDArray.
dot(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Dot product of NDArray a and NDArray b.
draw(Graphics2D, int, int) - Method in interface ai.djl.modality.cv.BoundingBox: Draws the bounding box using the Graphics2D.
draw(Graphics2D, int, int) - Method in class ai.djl.modality.cv.Mask: Draws the bounding box using the Graphics2D.
draw(Graphics2D, int, int) - Method in class ai.djl.modality.cv.Rectangle: Draws the bounding box using the Graphics2D.
drawBoundingBoxes(BufferedImage, DetectedObjects) - Static method in class ai.djl.modality.cv.ImageVisualization: Draws the bounding boxes on an image.
drawJoints(BufferedImage, Joints) - Static method in class ai.djl.modality.cv.ImageVisualization: Draws all joints of a body on an image.
Dropout - Class in ai.djl.nn.norm: Applies dropout operation to an input array.
Dropout.Builder - Class in ai.djl.nn.norm: The Builder to construct a Dropout type of Block.
dropRate - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
dropRate - Variable in class ai.djl.nn.recurrent.RecurrentCell
duplicate() - Method in interface ai.djl.ndarray.NDArray: Returns a copy of this NDArray.
duplicate() - Method in class ai.djl.training.loss.CompositeLoss: Creates and returns a copy of this object.
duplicate() - Method in class ai.djl.training.loss.Loss: Creates and returns a copy of this object.
duplicate() - Method in class ai.djl.training.metrics.BoundingBoxError
duplicate() - Method in class ai.djl.training.metrics.TrainingMetric: Creates and returns a copy of this object.

E

elu(NDArray, float) - Static method in class ai.djl.nn.Activation: Applies ELU activation on the input NDArray.
elu(NDList, float) - Static method in class ai.djl.nn.Activation: Applies ELU(Exponential Linear Unit) activation on the input singleton NDList.
eluBlock(float) - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the ELU activation function in its forward function.
Embedding<T> - Class in ai.djl.nn.core: An Embedding block map a collection of items to 1-Dimensional representative NDArrays.
Embedding.Builder<T> - Class in ai.djl.nn.core: The Builder to construct a Embedding type of Block.
end() - Method in class ai.djl.training.util.ProgressBar
Engine - Class in ai.djl.engine: The Engine interface is the base of the provided implementation for DJL.
Engine() - Constructor for class ai.djl.engine.Engine
EngineException - Exception in ai.djl.engine: Thrown to indicate that a native error is raised from the underlying Engine.
EngineException(String) - Constructor for exception ai.djl.engine.EngineException: Constructs a new exception with the specified detail message.
EngineException(String, Throwable) - Constructor for exception ai.djl.engine.EngineException: Constructs a new exception with the specified detail message and cause.
EngineException(Throwable) - Constructor for exception ai.djl.engine.EngineException: Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) (which typically contains the class and detail message of cause).
EngineProvider - Interface in ai.djl.engine: The EngineProvider instance manufactures an Engine instance, which is available in the system.
eq(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Equals" comparison.
eq(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Equals" comparison.
eq(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Equals" comparison.
eq(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Equals" comparison.
eq(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Equals" comparison.
equals(Object) - Method in class ai.djl.Device
equals(Object) - Method in class ai.djl.ndarray.types.Shape
excludeBatchAxis(NDArray, int) - Method in class ai.djl.training.loss.Loss: Gets all axes except the batch axis because loss functions require reduction on all axes except the batch axis.
executor - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
executor - Variable in class ai.djl.training.dataset.RandomAccessDataset
exp() - Method in interface ai.djl.ndarray.NDArray: Returns the exponential value of this NDArray element-wise.
expandDims(int) - Method in interface ai.djl.ndarray.NDArray: Expands the Shape of a NDArray.
eye(int) - Method in interface ai.djl.ndarray.NDManager: Returns a 2-D array with ones on the diagonal and zeros elsewhere.
eye(int, int) - Method in interface ai.djl.ndarray.NDManager: Returns a 2-D array with ones on the diagonal and zeros elsewhere.
eye(int, int, int) - Method in interface ai.djl.ndarray.NDManager: Returns a 2-D array with ones on the diagonal and zeros elsewhere.
eye(int, int, int, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Returns a 2-D array with ones on the diagonal and zeros elsewhere.

F

FactorTracker - Class in ai.djl.training.optimizer.learningrate: FactorTracker is an implementation of LearningRateTracker which is updated by a multiplicative factor, at a constant interval of steps, until it reaches a specified stop value.
FactorTracker(FactorTracker.Builder) - Constructor for class ai.djl.training.optimizer.learningrate.FactorTracker: Creates a new instance of FactorTracker.
factorTracker() - Static method in class ai.djl.training.optimizer.learningrate.LearningRateTracker: Returns a new instance of FactorTracker.Builder that can build an FactorTracker.
FactorTracker.Builder - Class in ai.djl.training.optimizer.learningrate: The Builder to construct an FactorTracker object.
filterByLayoutType(Predicate<LayoutType>) - Method in class ai.djl.ndarray.types.Shape: Returns only the axes of the Shape whose layout types match the predicate.
fixedLearningRate(float) - Static method in class ai.djl.training.optimizer.learningrate.LearningRateTracker: Returns a new instance of MultiFactorTracker.
flag - Variable in class ai.djl.modality.cv.ImageTranslator.BaseBuilder
flatten() - Method in interface ai.djl.ndarray.NDArray: Flattens this NDArray into a 1-D NDArray in row-major order.
floor() - Method in interface ai.djl.ndarray.NDArray: Returns the floor of this NDArray element-wise.
forward(NDList) - Method in class ai.djl.inference.BasePredictor
forward(ParameterStore, NDList) - Method in interface ai.djl.nn.Block: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in interface ai.djl.nn.Block: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.convolutional.Convolution: Applies the operating function of the block once.
forward(ParameterStore, NDManager, T[][]) - Method in class ai.djl.nn.core.Embedding: Finds the embedding of items as a NDArray.
forward(ParameterStore, NDManager, T[]) - Method in class ai.djl.nn.core.Embedding: Finds the embedding of items as a NDArray.
forward(ParameterStore, NDManager, T) - Method in class ai.djl.nn.core.Embedding: Finds the embedding of an item as a NDArray.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.core.Embedding: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.core.Linear: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.core.Prelu: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.LambdaBlock: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.norm.BatchNorm: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.norm.Dropout: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.ParallelBlock: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.recurrent.GRU: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.recurrent.LSTM: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.recurrent.RNN: Applies the operating function of the block once.
forward(ParameterStore, NDList, PairList<String, Object>) - Method in class ai.djl.nn.SequentialBlock: Applies the operating function of the block once.
forward(NDList) - Method in interface ai.djl.training.Trainer: Applies the forward function of the model once on the given input NDList.
fromBuffer(Buffer) - Static method in enum ai.djl.ndarray.types.DataType: Returns the data type to use for a data buffer.
fromFile(Path) - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Loads the image from the specified file.
fromUrl(URL) - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Loads the image from the specified URL.
fromUrl(String) - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Loads the image from the specified URL.
fromValue(char) - Static method in enum ai.djl.ndarray.types.LayoutType: Converts the character to the matching layout type.
fromValue(String) - Static method in enum ai.djl.ndarray.types.LayoutType: Converts each character to the matching layout type.
fromValue(int) - Static method in enum ai.djl.ndarray.types.SparseFormat: Gets the SparseFormat from it's integer value.

G

gelu(NDArray) - Static method in class ai.djl.nn.Activation: Applies GELU(Gaussian Error Linear Unit) activation on the input NDArray.
gelu(NDList) - Static method in class ai.djl.nn.Activation: Applies GELU(Gaussian Error Linear Unit) activation on the input singleton NDList.
geluBlock() - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the GELU activation function in its forward function.
generateAnchorBoxes(NDArray) - Method in class ai.djl.modality.cv.MultiBoxPrior: Generates the anchorBoxes array in the input's manager and device.
get(String) - Method in class ai.djl.modality.Classifications: Returns the result for a particular class name.
get(int) - Method in class ai.djl.ndarray.index.NDIndex: Returns the index affecting the given dimension.
get(NDIndex) - Method in interface ai.djl.ndarray.NDArray: Returns a partial NDArray.
get(String) - Method in interface ai.djl.ndarray.NDArray: Returns a partial NDArray.
get(long...) - Method in interface ai.djl.ndarray.NDArray: Returns a partial NDArray.
get(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns a partial NDArray.
get(int) - Method in class ai.djl.ndarray.types.Shape: Returns the shape in the given dimension.
get(NDManager, long) - Method in class ai.djl.training.dataset.ArrayDataset: Gets the Record for the given index from the dataset.
get(NDManager, long) - Method in class ai.djl.training.dataset.RandomAccessDataset: Gets the Record for the given index from the dataset.
getArray() - Method in class ai.djl.nn.Parameter: Gets the values of this Parameter as an NDArray.
getArtifact(String, Function<InputStream, T>) - Method in interface ai.djl.Model: Attempts to load the artifact using the given function and cache it if the specified artifact is not already cached.
getArtifact(String) - Method in interface ai.djl.Model: Finds an artifact resource with a given name in the model.
getArtifactAsStream(String) - Method in interface ai.djl.Model: Finds an artifact resource with a given name in the model.
getArtifactNames() - Method in interface ai.djl.Model: Returns the artifact names associated with the model.
getAsFullSlice(Shape) - Method in class ai.djl.ndarray.index.NDIndex: Returns this index as a full slice if it can be represented as one.
getAttachment(String) - Method in interface ai.djl.translate.TranslatorContext: Returns value of attached key-value pair to context.
getBatchifier() - Method in interface ai.djl.translate.Translator: Gets the Batchifier.
getBatchSize() - Method in class ai.djl.training.DefaultTrainingConfig: Gets the batch size that must be used during training.
getBatchSize() - Method in interface ai.djl.training.TrainingConfig: Gets the batch size that must be used during training.
getBlock() - Method in interface ai.djl.Model: Gets the block from the Model.
getBoolean(long...) - Method in interface ai.djl.ndarray.NDArray: Returns a boolean element from this NDArray.
getBoundingBox() - Method in class ai.djl.modality.cv.DetectedObjects.DetectedObject: Returns the BoundingBox of the detected object.
getBounds() - Method in interface ai.djl.modality.cv.BoundingBox: Returns the bounding Rectangle of this BoundingBox.
getBounds() - Method in class ai.djl.modality.cv.Rectangle: Returns the bounding Rectangle of this BoundingBox.
getByte(long...) - Method in interface ai.djl.ndarray.NDArray: Returns an byte element from this NDArray.
getChildren() - Method in interface ai.djl.nn.Block: Returns a list of all the children of the block.
getChildren() - Method in class ai.djl.nn.ParallelBlock: Returns a list of all the children of the block.
getChildren() - Method in class ai.djl.nn.ParameterBlock: Returns a list of all the children of the block.
getChildren() - Method in class ai.djl.nn.SequentialBlock: Returns a list of all the children of the block.
getClassName() - Method in class ai.djl.modality.Classifications.Classification: Returns the class name.
getColumn(long) - Method in interface ai.djl.ndarray.Matrix: Returns the specified column.
getColumns(int...) - Method in interface ai.djl.ndarray.Matrix: Returns a new NDArray comprised of the specified columns only.
getConfidence() - Method in class ai.djl.modality.cv.Joints.Joint: Returns the confidence probability for the joint.
getData() - Method in class ai.djl.training.dataset.Batch: Gets the data of this Batch.
getData(NDManager) - Method in interface ai.djl.training.dataset.Dataset: Fetches an iterator that can iterate through the Dataset.
getData(NDManager) - Method in class ai.djl.training.dataset.RandomAccessDataset: Fetches an iterator that can iterate through the Dataset.
getData() - Method in class ai.djl.training.dataset.Record: Gets the data of this Record.
getDataType() - Method in interface ai.djl.Model: Returns the standard data type used within the model.
getDataType() - Method in interface ai.djl.ndarray.NDArray: Returns the DataType of this NDArray.
getDataType() - Method in class ai.djl.ndarray.types.DataDesc: Returns the DataType of the NDArray.
getDevice() - Method in interface ai.djl.ndarray.NDArray: Returns the Device of this NDArray.
getDevice() - Method in interface ai.djl.ndarray.NDManager: Returns the default Device of this NDManager.
getDeviceId() - Method in class ai.djl.Device: Returns the deviceId of the Device.
getDevices(int) - Static method in class ai.djl.Device: Returns an array of devices given the maximum number of GPUs to use.
getDevices() - Method in class ai.djl.training.DefaultTrainingConfig: Gets the Device that are available for computation.
getDevices() - Method in interface ai.djl.training.TrainingConfig: Gets the Device that are available for computation.
getDeviceType() - Method in class ai.djl.Device: Returns the device type of the Device.
getDirectParameters() - Method in interface ai.djl.nn.Block: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.convolutional.Convolution: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.core.Embedding: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.core.Linear: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.core.Prelu: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.LambdaBlock: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.norm.BatchNorm: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.norm.Dropout: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.ParallelBlock: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.recurrent.GRU: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.recurrent.LSTM: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.recurrent.RNN: Returns a list of all the direct parameters of the block.
getDirectParameters() - Method in class ai.djl.nn.SequentialBlock: Returns a list of all the direct parameters of the block.
getDouble(long...) - Method in interface ai.djl.ndarray.NDArray: Returns a double element from this NDArray.
getEncoded() - Method in class ai.djl.ndarray.types.Shape: Gets the byte array representation of this Shape for serialization.
getEngine(String) - Static method in class ai.djl.engine.Engine: Returns the Engine with the given name.
getEngine() - Method in interface ai.djl.engine.EngineProvider: Returns the instance of the Engine class EngineProvider should bind to.
getEngineName() - Method in class ai.djl.engine.Engine: Returns the name of the Engine.
getExpectedLayout() - Method in class ai.djl.nn.convolutional.Conv1D: Returns the expected layout of the input.
getExpectedLayout() - Method in class ai.djl.nn.convolutional.Conv2D: Returns the expected layout of the input.
getExpectedLayout() - Method in class ai.djl.nn.convolutional.Conv3D: Returns the expected layout of the input.
getExpectedLayout() - Method in class ai.djl.nn.convolutional.Convolution: Returns the expected layout of the input.
getFloat(long...) - Method in interface ai.djl.ndarray.NDArray: Returns a float element from this NDArray.
getFormat() - Method in enum ai.djl.ndarray.types.DataType: Returns the format of the data type.
getGpuCount() - Method in class ai.djl.engine.Engine: Returns the number of GPUs available in the system.
getGpuMemory(Device) - Method in class ai.djl.engine.Engine: Returns the MemoryUsage of the specified GPU device.
getGradient() - Method in interface ai.djl.ndarray.NDArray: Returns the gradient NDArray attached to this NDArray.
getHeight() - Method in class ai.djl.modality.cv.Rectangle: Returns the height of the Rectangle.
getId() - Method in class ai.djl.nn.Parameter: Gets the ID of this Parameter.
getIndex() - Method in class ai.djl.ndarray.index.NDIndexBooleans: Returns the mask binary NDArray.
getIndex() - Method in class ai.djl.ndarray.index.NDIndexFixed: Returns the dimension of the index.
getIndices() - Method in class ai.djl.ndarray.index.NDIndex: Returns the indices.
getInitializer() - Method in enum ai.djl.nn.ParameterType: Gets the Initializer of this ParameterType.
getInitializer() - Method in class ai.djl.training.DefaultTrainingConfig: Gets the Initializer to initialize the parameters of the model.
getInitializer() - Method in interface ai.djl.training.TrainingConfig: Gets the Initializer to initialize the parameters of the model.
getInstance() - Static method in class ai.djl.engine.Engine: Returns the default Engine.
getInt(long...) - Method in interface ai.djl.ndarray.NDArray: Returns an int element from this NDArray.
getJoints() - Method in class ai.djl.modality.cv.Joints: Gets the joints for the image.
getLabels() - Method in class ai.djl.training.dataset.Batch: Gets the labels corresponding to the data of this Batch.
getLabels() - Method in class ai.djl.training.dataset.Record: Gets the labels that correspond to the data of this Record.
getLayout() - Method in class ai.djl.ndarray.types.Shape: Returns the layout type for each axis in this shape.
getLayoutType(int) - Method in class ai.djl.ndarray.types.Shape: Returns the layout type in the given dimension.
getLeadingOnes() - Method in class ai.djl.ndarray.types.Shape: Returns the number of leading ones in the array shape.
getLong(long...) - Method in interface ai.djl.ndarray.NDArray: Returns a long element from this NDArray.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.CompositeLoss: Calculates loss between the label and prediction.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.HingeLoss: Calculates loss between the label and prediction.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.L1Loss: Calculates loss between the label and prediction.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.L2Loss: Calculates loss between the label and prediction.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.Loss: Calculates loss between the label and prediction.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.SigmoidBinaryCrossEntropyLoss: Calculates loss between the label and prediction.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.SingleShotDetectionLoss: Calculate loss between label and prediction.
getLoss(NDList, NDList) - Method in class ai.djl.training.loss.SoftmaxCrossEntropyLoss: Calculates loss between the label and prediction.
getLoss() - Method in interface ai.djl.training.Trainer: Gets the training Loss function of the trainer.
getLossFunction() - Method in class ai.djl.training.DefaultTrainingConfig: Gets the Loss function to compute the loss against.
getLossFunction() - Method in interface ai.djl.training.TrainingConfig: Gets the Loss function to compute the loss against.
getManager() - Method in interface ai.djl.ndarray.NDArray: Returns the NDManager used to create this NDArray.
getManager() - Method in class ai.djl.training.dataset.Batch: Gets the NDManager that is attached to this Batch.
getManager() - Method in interface ai.djl.training.Trainer: Gets the NDManager from the model.
getMax() - Method in class ai.djl.ndarray.index.NDIndexFullSlice: Returns the slice max for each axis.
getMax() - Method in class ai.djl.ndarray.index.NDIndexSlice: Returns the end of the range.
getMetric(String) - Method in class ai.djl.metric.Metrics: Returns all Metrics with the specified metric name.
getMetricName() - Method in class ai.djl.metric.Metric: Returns the name of the Metric.
getMetrics() - Method in interface ai.djl.translate.TranslatorContext: Returns the Metric tool to do benchmark.
getMin() - Method in class ai.djl.ndarray.index.NDIndexFullSlice: Returns the slice min for each axis.
getMin() - Method in class ai.djl.ndarray.index.NDIndexSlice: Returns the start of the range.
getModel() - Method in interface ai.djl.training.Trainer: Returns the model used to create this trainer.
getModel() - Method in interface ai.djl.translate.TranslatorContext: Returns the Model object to understand the input/output.
getName() - Method in interface ai.djl.Model: Gets the model name.
getName() - Method in interface ai.djl.ndarray.NDArray: Returns the name of this NDArray.
getName() - Method in class ai.djl.ndarray.types.DataDesc: Returns the name of the NDArray.
getName() - Method in class ai.djl.nn.Parameter: Gets the name of this Parameter.
getName() - Method in class ai.djl.training.metrics.TrainingMetric: Gets the name of this TrainingMetric.
getNDArrayInternal() - Method in interface ai.djl.ndarray.NDArray: Returns an internal representative of Native NDArray.
getNDManager() - Method in interface ai.djl.Model: Gets the NDManager from the model.
getNDManager() - Method in interface ai.djl.translate.TranslatorContext: Returns the NDManager to create NDArray.
getNewLearningRate(int) - Method in class ai.djl.training.optimizer.learningrate.FactorTracker: Fetches the value of the learning rate after the given number of steps/updates.
getNewLearningRate(int) - Method in class ai.djl.training.optimizer.learningrate.LearningRateTracker: Fetches the value of the learning rate after the given number of steps/updates.
getNewLearningRate(int) - Method in class ai.djl.training.optimizer.learningrate.MultiFactorTracker: Fetches the value of the learning rate after the given number of steps/updates.
getNumberOfObjects() - Method in class ai.djl.modality.cv.DetectedObjects: Returns the number of objects found in an image.
getNumOfBytes() - Method in enum ai.djl.ndarray.types.DataType: Returns the number of bytes for each element.
getOptimizer() - Method in class ai.djl.training.DefaultTrainingConfig: Gets the Optimizer to use during training.
getOptimizer() - Method in interface ai.djl.training.TrainingConfig: Gets the Optimizer to use during training.
getOutput(long, long, long, long) - Method in enum ai.djl.nn.pooling.PoolingConvention: Computes the size of the output in the given input dimension for this PoolingConvention.
getOutputShapes(NDManager, Shape[]) - Method in interface ai.djl.nn.Block: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.convolutional.Convolution: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.core.Embedding: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.core.Linear: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.core.Prelu: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.LambdaBlock: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.norm.BatchNorm: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.norm.Dropout: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.ParallelBlock: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.recurrent.GRU: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.recurrent.LSTM: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.recurrent.RNN: Returns the expected output shapes of the block for the specified input shapes.
getOutputShapes(NDManager, Shape[]) - Method in class ai.djl.nn.SequentialBlock: Returns the expected output shapes of the block for the specified input shapes.
getParameters() - Method in class ai.djl.nn.AbstractBlock: Returns a list of all the parameters of the block, including the parameters of its children fetched recursively.
getParameters() - Method in interface ai.djl.nn.Block: Returns a list of all the parameters of the block, including the parameters of its children fetched recursively.
getParameterShape(String, Shape[]) - Method in interface ai.djl.nn.Block: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.convolutional.Convolution: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.core.Embedding: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.core.Linear: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.core.Prelu: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.LambdaBlock: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.norm.BatchNorm: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.norm.Dropout: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.ParallelBlock: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.recurrent.GRU: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.recurrent.LSTM: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.recurrent.RNN: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParameterShape(String, Shape[]) - Method in class ai.djl.nn.SequentialBlock: Returns the shape of the specified direct parameter of this block given the shapes of the input to the block.
getParentManager() - Method in interface ai.djl.ndarray.NDManager: Returns the parent NDManager.
getPath() - Method in interface ai.djl.modality.cv.BoundingBox: Returns an iterator object that iterates along the BoundingBox boundary and provides access to the geometry of the BoundingBox outline.
getPath() - Method in class ai.djl.modality.cv.Rectangle: Returns an iterator object that iterates along the BoundingBox boundary and provides access to the geometry of the BoundingBox outline.
getPipeline() - Method in class ai.djl.modality.cv.ImageTranslator: Gets the Pipeline applied to the input.
getPipeline() - Method in interface ai.djl.translate.PreProcessor: Gets the Pipeline applied to the input.
getPoint() - Method in interface ai.djl.modality.cv.BoundingBox: Returns the top left point of the bounding box.
getPoint() - Method in class ai.djl.modality.cv.Rectangle: Returns the top left point of the bounding box.
getProbability() - Method in class ai.djl.modality.Classifications.Classification: Returns the probability.
getProbDist() - Method in class ai.djl.modality.cv.Mask: Returns the probability for each pixel.
getProperty(String) - Method in interface ai.djl.Model: Gets the property of the model based on property name.
getRank() - Method in class ai.djl.ndarray.index.NDIndex: Returns the number of dimensions specified in the Index.
getRank() - Method in class ai.djl.ndarray.index.NDIndexAll: Returns the number of dimensions occupied by this index element.
getRank() - Method in class ai.djl.ndarray.index.NDIndexBooleans: Returns the number of dimensions occupied by this index element.
getRank() - Method in interface ai.djl.ndarray.index.NDIndexElement: Returns the number of dimensions occupied by this index element.
getRank() - Method in class ai.djl.ndarray.index.NDIndexFixed: Returns the number of dimensions occupied by this index element.
getRank() - Method in class ai.djl.ndarray.index.NDIndexSlice: Returns the number of dimensions occupied by this index element.
getRow(long) - Method in interface ai.djl.ndarray.Matrix: Returns the specified row.
getRows(int...) - Method in interface ai.djl.ndarray.Matrix: Returns a new NDArray comprised of the specified rows only.
getSampler() - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Gets the Sampler for the dataset.
getScalar(long, long) - Method in interface ai.djl.ndarray.Matrix: Returns the element at the specified row/column.
getScalar(long...) - Method in interface ai.djl.ndarray.NDArray: Returns a scalar NDArray corresponding to a single element.
getShape() - Method in class ai.djl.ndarray.index.NDIndexFullSlice: Returns the slice shape without squeezing.
getShape() - Method in interface ai.djl.ndarray.NDArray: Returns the Shape of this NDArray.
getShape() - Method in class ai.djl.ndarray.types.DataDesc: Returns the Shape of the NDArray.
getShape() - Method in class ai.djl.ndarray.types.Shape: Returns the dimensions of the Shape.
getSparseFormat() - Method in interface ai.djl.ndarray.NDArray: Returns the SparseFormat of this NDArray.
getSqueezedShape() - Method in class ai.djl.ndarray.index.NDIndexFullSlice: Returns the slice shape with squeezing.
getStep() - Method in class ai.djl.ndarray.index.NDIndexFullSlice: Returns the slice step for each axis.
getStep() - Method in class ai.djl.ndarray.index.NDIndexSlice: Returns the step between each slice.
getStringLayout() - Method in class ai.djl.nn.convolutional.Conv1D: Returns the string representing the layout of the input.
getStringLayout() - Method in class ai.djl.nn.convolutional.Conv2D: Returns the string representing the layout of the input.
getStringLayout() - Method in class ai.djl.nn.convolutional.Conv3D: Returns the string representing the layout of the input.
getStringLayout() - Method in class ai.djl.nn.convolutional.Convolution: Returns the string representing the layout of the input.
getTimestamp() - Method in class ai.djl.metric.Metric: Returns the timestamp of the Metric.
getToSqueeze() - Method in class ai.djl.ndarray.index.NDIndexFullSlice: Returns the list of axis to squeeze.
getTrailingOnes() - Method in class ai.djl.ndarray.types.Shape: Returns the number of trailing ones in the array shape.
getTrainingMetric(Class<T>) - Method in interface ai.djl.training.Trainer: Gets the training TrainingMetric that is an instance of the given Class.
getTrainingMetrics() - Method in class ai.djl.training.DefaultTrainingConfig: Returns the list of TrainingMetric that should be computed during training.
getTrainingMetrics() - Method in interface ai.djl.training.TrainingConfig: Returns the list of TrainingMetric that should be computed during training.
getType() - Method in enum ai.djl.ndarray.types.SparseFormat: Returns the SparseFormat name.
getType() - Method in class ai.djl.nn.Parameter: Gets the type of this Parameter.
getType() - Method in enum ai.djl.training.GradReq: Gets the type of this GradReq.
getUid() - Method in interface ai.djl.ndarray.NDArray: Returns unique identifier of this NDArray.
getUint8(long...) - Method in interface ai.djl.ndarray.NDArray: Returns an integer element from this NDArray that represent unsigned integer with 8 bits.
getUnit() - Method in class ai.djl.metric.Metric: Returns the unit of the Metric.
getUnknownValueCount() - Method in class ai.djl.ndarray.types.Shape: Return the count of unknown value in this Shape.
getValidationLoss() - Method in interface ai.djl.training.Trainer: Gets the validation Loss function of the trainer.
getValidationMetric(Class<T>) - Method in interface ai.djl.training.Trainer: Gets the validation TrainingMetric that is an instance of the given Class.
getValue() - Method in class ai.djl.metric.Metric: Returns the value of the Metric.
getValue() - Method in enum ai.djl.ndarray.types.LayoutType: Returns the character representation of the layout type.
getValue() - Method in enum ai.djl.ndarray.types.SparseFormat: Returns the integer value of this SparseFormat.
getValue() - Method in enum ai.djl.training.GradReq: Gets the value of this GradType.
getValue() - Method in class ai.djl.training.loss.CompositeLoss: Calculates metric values.
getValue() - Method in class ai.djl.training.loss.Loss: Calculates metric values.
getValue() - Method in class ai.djl.training.metrics.Accuracy: Calculates metric values.
getValue() - Method in class ai.djl.training.metrics.BoundingBoxError: Calculates metric values.
getValue() - Method in class ai.djl.training.metrics.TrainingMetric: Calculates metric values.
getValue(Parameter, Device) - Method in class ai.djl.training.ParameterStore: Returns the value of a mirrored parameter on a device.
getVersion() - Method in class ai.djl.engine.Engine: Returns the version of the deep learning framework.
getVersion() - Method in class ai.djl.nn.convolutional.Conv1D: Gets the version of the Convolution block.
getVersion() - Method in class ai.djl.nn.convolutional.Conv2D: Gets the version of the Convolution block.
getVersion() - Method in class ai.djl.nn.convolutional.Conv3D: Gets the version of the Convolution block.
getVersion() - Method in class ai.djl.nn.convolutional.Convolution: Gets the version of the Convolution block.
getWeightDecay() - Method in class ai.djl.training.optimizer.Optimizer: Gets the value of weight decay.
getWidth() - Method in class ai.djl.modality.cv.Rectangle: Returns the width of the Rectangle.
getX() - Method in class ai.djl.modality.cv.Point: Returns the X coordinate of this Point in double precision.
getX() - Method in class ai.djl.modality.cv.Rectangle: Returns the left x-coordinate of the Rectangle.
getY() - Method in class ai.djl.modality.cv.Point: Returns the Y coordinate of this Point in double precision.
getY() - Method in class ai.djl.modality.cv.Rectangle: Returns the top y-coordinate of the Rectangle.
globalAvgPool(NDArray) - Static method in class ai.djl.nn.pooling.Pool: Performs Global Avg Pooling on the input.
globalLpPool(NDArray, int) - Static method in class ai.djl.nn.pooling.Pool: Performs Global LP Pooling on the input.
globalMaxPool(NDArray) - Static method in class ai.djl.nn.pooling.Pool: Performs Global Max Pooling on the input.
globalSumPool(NDArray) - Static method in class ai.djl.nn.pooling.Pool: Performs Global Sum Pooling on the input.
gpu() - Static method in class ai.djl.Device: Returns the default GPU Device.
gpu(int) - Static method in class ai.djl.Device: Returns a new instance of GPU Device with the specified deviceId.
GradientCollector - Interface in ai.djl.training: An interface that provides a mechanism to collect gradients during training.
GradReq - Enum in ai.djl.training: An enum that indicates whether gradient is required.
GRU - Class in ai.djl.nn.recurrent: Applies GRU (Gated Recurrent Unit) recurrent layer to input.
GRU.Builder - Class in ai.djl.nn.recurrent: The Builder to construct a GRU type of Block.
gt(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Greater" comparison.
gt(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Greater Than" comparison.
gt(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Greater Than" comparison.
gt(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Greater Than" comparison.
gt(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Greater Than" comparison.
gte(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Greater or equals" comparison.
gte(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Greater or equals" comparison.
gte(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Greater or equals" comparison.
gte(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Greater or equals" comparison.
gte(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Greater or equals" comparison.

H

hashCode() - Method in class ai.djl.Device
hashCode() - Method in class ai.djl.ndarray.types.Shape
hasMetric(String) - Method in class ai.djl.metric.Metrics: Returns true if the metrics object has a metric with the given name.
hasNext() - Method in interface ai.djl.modality.cv.PathIterator: Tests if the iteration is complete.
hasNext() - Method in class ai.djl.training.dataset.DataIterable
head() - Method in class ai.djl.ndarray.NDList: Returns the head index of the NDList.
head() - Method in class ai.djl.ndarray.types.Shape: Returns the head index of the shape.
HingeLoss - Class in ai.djl.training.loss: HingeLoss is a type of Loss.
HingeLoss(int, float, int) - Constructor for class ai.djl.training.loss.HingeLoss: Calculates Hinge loss.
HingeLoss() - Constructor for class ai.djl.training.loss.HingeLoss: Calculates Hinge loss.
hingeLoss() - Static method in class ai.djl.training.loss.Loss: Returns a new instance of HingeLoss with default arguments.
hingeLoss(int, float, int) - Static method in class ai.djl.training.loss.Loss: Returns a new instance of HingeLoss with the given arguments.

I

identityBlock() - Static method in class ai.djl.nn.Blocks: Creates a LambdaBlock that performs the identity function.
ImageClassificationTranslator - Class in ai.djl.modality.cv: A generic Translator for Image Classification tasks.
ImageClassificationTranslator(ImageClassificationTranslator.Builder) - Constructor for class ai.djl.modality.cv.ImageClassificationTranslator: Constructs an Image Classification using ImageClassificationTranslator.Builder.
ImageClassificationTranslator.Builder - Class in ai.djl.modality.cv: A Builder to construct a ImageClassificationTranslator.
ImageTranslator<T> - Class in ai.djl.modality.cv: Built-in Translator that provides default image pre-processing.
ImageTranslator(ImageTranslator.BaseBuilder<?>) - Constructor for class ai.djl.modality.cv.ImageTranslator: Constructs an ImageTranslator with the provided builder.
ImageTranslator.BaseBuilder<T extends ImageTranslator.BaseBuilder> - Class in ai.djl.modality.cv: A builder to extend for all classes extending the ImageTranslator.
ImageVisualization - Class in ai.djl.modality.cv: A Collection of utilities for visualizing the results of Computer Vision tasks.
includeBias - Variable in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
includeBias - Variable in class ai.djl.nn.convolutional.Convolution
increment(long) - Method in class ai.djl.training.util.ProgressBar
index - Variable in class ai.djl.training.metrics.Accuracy
init(String, NDArray[]) - Method in class ai.djl.training.LocalParameterServer: Initializes the ParameterStore for the given parameter.
init(String, NDArray[]) - Method in interface ai.djl.training.ParameterServer: Initializes the ParameterStore for the given parameter.
initialize(NDManager, DataType, Shape...) - Method in interface ai.djl.nn.Block: Initializes the parameters of the block.
initialize(NDManager, DataType, Shape...) - Method in class ai.djl.nn.ParallelBlock: Initializes the parameters of the block.
initialize(NDManager, DataType, Shape[]) - Method in class ai.djl.nn.Parameter: Initializes the parameter with the given NDManager, with given DataType for the given expected input shapes.
initialize(NDManager, DataType, Shape...) - Method in class ai.djl.nn.ParameterBlock: Initializes the parameters of the block.
initialize(NDManager, DataType, Shape...) - Method in class ai.djl.nn.SequentialBlock: Initializes the parameters of the block.
initialize(NDManager, Shape, DataType) - Method in interface ai.djl.training.initializer.Initializer: Initializes a single NDArray.
initialize(NDManager, Shape, DataType) - Method in class ai.djl.training.initializer.NormalInitializer: Initializes a single NDArray.
initialize(NDManager, Shape, DataType) - Method in class ai.djl.training.initializer.UniformInitializer: Initializes a single NDArray.
initialize(NDManager, Shape, DataType) - Method in class ai.djl.training.initializer.XavierInitializer: Initializes a single NDArray.
initialize(Shape...) - Method in interface ai.djl.training.Trainer: Initializes the Model that the Trainer is going to train.
Initializer - Interface in ai.djl.training.initializer: An interface representing an initialization method.
inputNames - Variable in class ai.djl.nn.AbstractBlock
inputShapes - Variable in class ai.djl.nn.AbstractBlock
insert(int, Transform) - Method in class ai.djl.translate.Pipeline: Inserts the given Transform to the list of transforms at the given position.
insert(int, int, Transform) - Method in class ai.djl.translate.Pipeline: Inserts the given Transform to the list of transforms at the given position to be applied on the NDArray at the given index in the input NDList.
insert(int, String, Transform) - Method in class ai.djl.translate.Pipeline: Inserts the given Transform to the list of transforms at the given position to be applied on the NDArray with the given name in the input NDList.
InstanceSegmentationTranslator - Class in ai.djl.modality.cv: A ImageTranslator that post-process the NDArray into DetectedObjects with boundaries at the detailed pixel level.
InstanceSegmentationTranslator(InstanceSegmentationTranslator.Builder) - Constructor for class ai.djl.modality.cv.InstanceSegmentationTranslator: Creates the Instance Segmentation translator from the given builder.
InstanceSegmentationTranslator.Builder - Class in ai.djl.modality.cv: The builder for Instance Segmentation translator.
invoke(String, NDArray[], NDArray[], PairList<String, ?>) - Method in interface ai.djl.ndarray.NDManager: An engine specific generic invocation to native operator.
invoke(String, NDList, PairList<String, ?>) - Method in interface ai.djl.ndarray.NDManager: An engine specific generic invocation to native operator.
isColumnVector() - Method in class ai.djl.ndarray.types.Shape: Returns true if this NDArray is a matrix and the number of columns is 1.
isEmpty() - Method in interface ai.djl.ndarray.NDArray: Returns true if this NDArray is special case: no-value NDArray.
isFloating() - Method in enum ai.djl.ndarray.types.DataType: Checks whether it is a floating data type.
isInfinite() - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray with value true where this NDArray's entries are infinite, or false where they are not infinite.
isInitialized() - Method in class ai.djl.nn.AbstractBlock: Returns a boolean whether the block is initialized.
isInitialized() - Method in interface ai.djl.nn.Block: Returns a boolean whether the block is initialized.
isInitialized() - Method in class ai.djl.nn.Parameter: Checks if this Parameter is initialized.
isInteger() - Method in enum ai.djl.ndarray.types.DataType: Checks whether it is an integer data type.
isLayoutKnown() - Method in class ai.djl.ndarray.types.Shape: Returns true if a layout is set.
isMatrix() - Method in class ai.djl.ndarray.types.Shape: Returns true if the NDArray is a matrix.
isNaN() - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray with value true where this NDArray's entries are NaN, or false where they are not NaN.
isRowVector() - Method in class ai.djl.ndarray.types.Shape: Returns true if this NDArray is a matrix and the number of rows is 1.
isScalar() - Method in interface ai.djl.ndarray.NDArray: Returns true if this NDArray is a scalar NDArray with empty Shape.
isScalar() - Method in class ai.djl.ndarray.types.Shape: Returns true if the NDArray is a scalar.
isSparse() - Method in interface ai.djl.ndarray.NDArray: Returns true if this NDArray is a SparseNDArray.
isSquare() - Method in class ai.djl.ndarray.types.Shape: Returns whether the matrix has the same rows and columns.
isVectorMatrix() - Method in class ai.djl.ndarray.types.Shape: Returns true if this NDArray is a vector matrix.
item(int) - Method in class ai.djl.modality.Classifications: Returns the item at a given index based on the order used to construct the Classifications.
item(int) - Method in class ai.djl.modality.cv.DetectedObjects: Returns the item at a given index based on the order used to construct the Classifications.
items() - Method in class ai.djl.modality.Classifications: Returns a classification item for each potential class for the input.
iterateDataset(Dataset) - Method in interface ai.djl.training.Trainer: Fetches an iterator that can iterate through the given Dataset.
iterator() - Method in class ai.djl.training.dataset.DataIterable

J

Joint(double, double, double) - Constructor for class ai.djl.modality.cv.Joints.Joint: Constructs a Joint with given data.
Joints - Class in ai.djl.modality.cv: A result of all joints found during Human Pose Estimation on a single image.
Joints(List<Joints.Joint>) - Constructor for class ai.djl.modality.cv.Joints: Constructs the Joints with the provided joints.
Joints.Joint - Class in ai.djl.modality.cv: A joint that was detected using Human Pose Estimation on an image.

K

kernel - Variable in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
kernel - Variable in class ai.djl.nn.convolutional.Convolution

L

L1Loss - Class in ai.djl.training.loss: L1Loss calculates L1 loss between label and prediction.
L1Loss(float, int) - Constructor for class ai.djl.training.loss.L1Loss: Calculates L1 Loss between the label and prediction, a.k.a.
L1Loss() - Constructor for class ai.djl.training.loss.L1Loss: Calculates L1 Loss between the label and prediction, a.k.a.
l1Loss() - Static method in class ai.djl.training.loss.Loss: Returns a new instance of L1Loss with default weight and batch axis.
l1Loss(float, int) - Static method in class ai.djl.training.loss.Loss: Returns a new instance of L1Loss with given weight and batch axis.
L2Loss - Class in ai.djl.training.loss: Calculates L2Loss between label and prediction, a.k.a.
L2Loss(float, int) - Constructor for class ai.djl.training.loss.L2Loss: Calculates L2Loss between the label and prediction, a.k.a.
L2Loss() - Constructor for class ai.djl.training.loss.L2Loss: Calculate L2Loss between the label and prediction, a.k.a.
l2Loss() - Static method in class ai.djl.training.loss.Loss: Returns a new instance of L2Loss with default weight and batch axis.
l2Loss(float, int) - Static method in class ai.djl.training.loss.Loss: Returns a new instance of L2Loss with given weight and batch axis.
labels - Variable in class ai.djl.training.dataset.ArrayDataset
LambdaBlock - Class in ai.djl.nn: LambdaBlock is a Block with no parameters or children.
LambdaBlock(Function<NDList, NDList>) - Constructor for class ai.djl.nn.LambdaBlock: Creates a LambdaBlock that can apply the specified function.
LayoutType - Enum in ai.djl.ndarray.types: An enum to represent the meaning of a particular axis in an NDArray.
leakyRelu(NDArray, float) - Static method in class ai.djl.nn.Activation: Applies Leaky ReLU activation on the input NDArray.
leakyRelu(NDList, float) - Static method in class ai.djl.nn.Activation: Applies Leaky ReLU activation on the input singleton NDList.
leakyReluBlock(float) - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the LeakyReLU activation function in its forward function.
LearningRateTracker - Class in ai.djl.training.optimizer.learningrate: A LearningRateTracker tracks the evolution of the learning rate through the training process.
LearningRateTracker.LrBaseBuilder<T extends LearningRateTracker.LrBaseBuilder> - Class in ai.djl.training.optimizer.learningrate: The Builder to construct a LearningRateTracker.
like() - Method in interface ai.djl.ndarray.NDArray: Returns an uninitialized NDArray with the same Shape, DataType and SparseFormat as the input NDArray.
Linear - Class in ai.djl.nn.core: A Linear block applies a linear transformation \(Y = XW^T + b\).
Linear.Builder - Class in ai.djl.nn.core: The Builder to construct a Linear type of Block.
linspace(Number, Number, int) - Method in interface ai.djl.ndarray.NDManager: Returns evenly spaced numbers over a specified interval.
linspace(Number, Number, int, boolean, Device) - Method in interface ai.djl.ndarray.NDManager: Returns evenly spaced numbers over a specified interval.
load(Path) - Method in interface ai.djl.Model: Loads the model from the modelPath.
load(Path, String) - Method in interface ai.djl.Model: Loads the model from the modelPath and the given name.
load(Path, String, Map<String, String>) - Method in interface ai.djl.Model: Loads the model from the modelPath with the name and options provided.
load(NDManager, DataInputStream) - Method in class ai.djl.nn.Parameter: Loads parameter NDArrays from InputStream.
loadParameters(NDManager, DataInputStream) - Method in interface ai.djl.nn.Block: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.convolutional.Convolution: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.core.Embedding: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.core.Linear: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.core.Prelu: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.LambdaBlock: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.norm.BatchNorm: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.norm.Dropout: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.ParallelBlock: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.recurrent.GRU: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.recurrent.LSTM: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.recurrent.RNN: Loads the parameters from the given input stream.
loadParameters(NDManager, DataInputStream) - Method in class ai.djl.nn.SequentialBlock: Loads the parameters from the given input stream.
LocalParameterServer - Class in ai.djl.training: LocalParameterServer is an implementation of the ParameterServer interface.
LocalParameterServer(Optimizer) - Constructor for class ai.djl.training.LocalParameterServer: Create a new instance of LocalParameterServer for the given optimizer.
log() - Method in interface ai.djl.ndarray.NDArray: Returns the natural logarithmic value of this NDArray element-wise.
log10() - Method in interface ai.djl.ndarray.NDArray: Returns the base 10 logarithm of this NDArray element-wise.
log2() - Method in interface ai.djl.ndarray.NDArray: Returns the base 2 logarithm of this NDArray element-wise.
logicalAnd(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the truth value of this NDArray AND the other NDArray element-wise.
logicalAnd(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the truth value of NDArray a AND NDArray b element-wise.
logicalNot() - Method in interface ai.djl.ndarray.NDArray: Computes the truth value of NOT this NDArray element-wise.
logicalOr(NDArray) - Method in interface ai.djl.ndarray.NDArray: Computes the truth value of this NDArray OR the other NDArray element-wise.
logicalOr(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Computes the truth value of NDArray a AND NDArray b element-wise.
logicalXor(NDArray) - Method in interface ai.djl.ndarray.NDArray: Computes the truth value of this NDArray XOR the other NDArray element-wise.
logicalXor(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Computes the truth value of NDArray a AND NDArray b element-wise.
Loss - Class in ai.djl.training.loss: Loss functions or Cost Functions to evaluate model predictions against true labels.
Loss(String) - Constructor for class ai.djl.training.loss.Loss: Base class for metric with abstract update methods.
lpPool(NDArray, Shape, Shape, Shape, PoolingConvention, int) - Static method in class ai.djl.nn.pooling.Pool: Performs LP Pooling on the input.
lpPool(NDArray, Shape, Shape, Shape, int) - Static method in class ai.djl.nn.pooling.Pool: Performs LP Pooling on the input.
LrBaseBuilder() - Constructor for class ai.djl.training.optimizer.learningrate.LearningRateTracker.LrBaseBuilder
LSTM - Class in ai.djl.nn.recurrent: Applies Long Short-Term Memory recurrent layer to input.
LSTM.Builder - Class in ai.djl.nn.recurrent: The Builder to construct a LSTM type of Block.
lstmStateClipMax - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
lstmStateClipMin - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
lt(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Less" comparison.
lt(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Less" comparison.
lt(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Less" comparison.
lt(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Less" comparison.
lt(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Less" comparison.
lte(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Less or equals" comparison.
lte(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Less or equals" comparison.
lte(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Less or equals" comparison.
lte(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Less or equals" comparison.
lte(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Less or equals" comparison.

M

MalformedModelException - Exception in ai.djl: Thrown to indicate Model parameters are not in expected format or are malformed.
MalformedModelException(String) - Constructor for exception ai.djl.MalformedModelException: Constructs a new exception with the specified detail message.
MalformedModelException(String, Throwable) - Constructor for exception ai.djl.MalformedModelException: Constructs a new exception with the specified detail message and cause.
MalformedModelException(Throwable) - Constructor for exception ai.djl.MalformedModelException: Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) which typically contains the class and detail message of cause.
manager - Variable in class ai.djl.inference.BasePredictor
map(Function<Pair<Long, LayoutType>, Pair<Long, LayoutType>>) - Method in class ai.djl.ndarray.types.Shape: Returns a mapped shape.
Mask - Class in ai.djl.modality.cv: A mask with a probability for each pixel within a bounding rectangle.
Mask(double, double, double, double, float[][]) - Constructor for class ai.djl.modality.cv.Mask: Constructs a Mask with the given data.
Matrix - Interface in ai.djl.ndarray: An interface representing a 2-dimensional matrix.
max() - Method in interface ai.djl.ndarray.NDArray: Returns the maximum of this NDArray.
max(int[]) - Method in interface ai.djl.ndarray.NDArray: Returns the maximum of this NDArray along given axes.
max(int[], boolean) - Method in interface ai.djl.ndarray.NDArray: Returns the maximum of this NDArray along given axes.
maximum(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the maximum of this NDArray and a number element-wise.
maximum(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the maximum of this NDArray and the other NDArray element-wise.
maximum(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the maximum of a NDArray and a number element-wise.
maximum(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the maximum of a number and a NDArray element-wise.
maximum(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the maximum of NDArray a and NDArray b element-wise.
maxIteration - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
maxPool(NDArray, Shape, Shape, Shape, PoolingConvention) - Static method in class ai.djl.nn.pooling.Pool: Performs Max Pooling on the input.
maxPool(NDArray, Shape, Shape, Shape) - Static method in class ai.djl.nn.pooling.Pool: Performs Max Pooling on the input.
mean(String) - Method in class ai.djl.metric.Metrics: Returns the average value of the specified metric.
mean() - Method in interface ai.djl.ndarray.NDArray: Returns the average of this NDArray.
mean(int[]) - Method in interface ai.djl.ndarray.NDArray: Returns the average of this NDArray along given axes.
mean(int[], boolean) - Method in interface ai.djl.ndarray.NDArray: Returns the average of this NDArray along given axes.
median() - Method in interface ai.djl.ndarray.NDArray: Returns median value for this NDArray.
median(int[]) - Method in interface ai.djl.ndarray.NDArray: Returns median value along given axes.
Metric - Class in ai.djl.metric: A class representing a single recorded Metric value.
Metric(String, Number) - Constructor for class ai.djl.metric.Metric: Constructs a Metric instance with the specified metricName and value.
Metric(String, Number, String) - Constructor for class ai.djl.metric.Metric: Constructs a Metric instance with the specified metricName, value, and unit.
Metrics - Class in ai.djl.metric: A collection of Metric objects organized by metric name.
Metrics() - Constructor for class ai.djl.metric.Metrics: Constructs an empty Metrics instance.
min() - Method in interface ai.djl.ndarray.NDArray: Returns the minimum of this NDArray.
min(int[]) - Method in interface ai.djl.ndarray.NDArray: Returns the minimum of this NDArray along given axes.
min(int[], boolean) - Method in interface ai.djl.ndarray.NDArray: Returns the minimum of this NDArray along given axes.
minimum(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the minimum of this NDArray and a number element-wise.
minimum(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the maximum of this NDArray and the other NDArray element-wise.
minimum(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the minimum of a NDArray and a number element-wise.
minimum(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the minimum of a number and a NDArray element-wise.
minimum(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the minimum of NDArray a and NDArray b element-wise.
mod(Number) - Method in interface ai.djl.ndarray.NDArray: Returns element-wise remainder of division.
mod(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns element-wise remainder of division.
mod(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns element-wise remainder of division.
mod(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns element-wise remainder of division.
mod(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns element-wise remainder of division.
mode - Variable in class ai.djl.nn.recurrent.RecurrentCell
model - Variable in class ai.djl.inference.BasePredictor
Model - Interface in ai.djl: A model is a collection of artifacts that is created by the training process.
ModelException - Exception in ai.djl: Thrown to indicate Model parameter or load exceptions parent to Model Exceptions.
ModelException(String) - Constructor for exception ai.djl.ModelException: Constructs a new exception with the specified detail message.
ModelException(String, Throwable) - Constructor for exception ai.djl.ModelException: Constructs a new exception with the specified detail message and cause.
ModelException(Throwable) - Constructor for exception ai.djl.ModelException: Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) which typically contains the class and detail message of cause.
modi(Number) - Method in interface ai.djl.ndarray.NDArray: Returns element-wise remainder of division in place.
modi(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns in place element-wise remainder of division in place.
modi(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns element-wise remainder of division in place.
modi(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns element-wise remainder of division in place.
modi(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns element-wise remainder of division.
mul(Number) - Method in interface ai.djl.ndarray.NDArray: Multiplies this NDArray by a number element-wise.
mul(NDArray) - Method in interface ai.djl.ndarray.NDArray: Multiplies this NDArray by other NDArrays element-wise.
mul(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Multiplies the NDArray by a number element-wise.
mul(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Multiplies a number by a NDArray element-wise.
mul(NDArray...) - Static method in class ai.djl.ndarray.NDArrays: Multiplies all of the NDArrays together element-wise.
mulColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Multiplies a column vector (copy).
muli(Number) - Method in interface ai.djl.ndarray.NDArray: Multiplies this NDArray by a number element-wise in place.
muli(NDArray) - Method in interface ai.djl.ndarray.NDArray: Multiplies this NDArray by other NDArrays element-wise in place.
muli(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Multiplies the NDArray by a number element-wise in place.
muli(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Multiplies a number by a NDArray element-wise.
muli(NDArray...) - Static method in class ai.djl.ndarray.NDArrays: Multiplies all of the NDArrays together element-wise in place.
muliColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Multiplies a column vector in place.
muliRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Multiplies a row vector in place.
mulRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Multiplies a row vector (copy).
MultiBoxDetection - Class in ai.djl.modality.cv: MultiBoxDetection is the class that takes the output of a multi-box detection model, and converts it into an NDList that contains the object detections.
MultiBoxDetection(MultiBoxDetection.Builder) - Constructor for class ai.djl.modality.cv.MultiBoxDetection: Creates a new instance of MultiBoxDetection with the arguments from the given MultiBoxDetection.Builder.
MultiBoxDetection.Builder - Class in ai.djl.modality.cv: The Builder to construct a MultiBoxDetection object.
MultiBoxPrior - Class in ai.djl.modality.cv: MultiBoxPrior is the class that generates anchor boxes that act as priors for object detection.
MultiBoxPrior(MultiBoxPrior.Builder) - Constructor for class ai.djl.modality.cv.MultiBoxPrior: Creates a new instance of MultiBoxPrior with the arguments from the given MultiBoxPrior.Builder.
MultiBoxPrior.Builder - Class in ai.djl.modality.cv: The Builder to construct a MultiBoxPrior object.
MultiBoxTarget - Class in ai.djl.modality.cv: MultiBoxTarget is the class that computes the training targets for training a Single Shot Detection (SSD) models.
MultiBoxTarget(MultiBoxTarget.Builder) - Constructor for class ai.djl.modality.cv.MultiBoxTarget: Creates a new instance of MultiBoxTarget with the arguments from the given MultiBoxTarget.Builder.
MultiBoxTarget.Builder - Class in ai.djl.modality.cv: The Builder to construct a MultiBoxTarget object.
multiFactorTracker() - Static method in class ai.djl.training.optimizer.learningrate.LearningRateTracker: Returns a new instance of MultiFactorTracker.Builder that can build an MultiFactorTracker.
MultiFactorTracker - Class in ai.djl.training.optimizer.learningrate: MultiFactorTracker is an implementation of LearningRateTracker which is updated by a multiplicative factor, at an uneven interval of steps, until it reaches a specified stop value.
MultiFactorTracker(MultiFactorTracker.Builder) - Constructor for class ai.djl.training.optimizer.learningrate.MultiFactorTracker: Creates a new instance of MultiFactorTracker.
MultiFactorTracker.Builder - Class in ai.djl.training.optimizer.learningrate: The Builder to construct an MultiFactorTracker object.

N

Nag - Class in ai.djl.training.optimizer: Nag is a Nesterov accelerated gradient optimizer.
Nag(Nag.Builder) - Constructor for class ai.djl.training.optimizer.Nag: Creates a new instance of Nag optimizer.
nag() - Static method in class ai.djl.training.optimizer.Optimizer: Returns a new instance of Nag.Builder that can build an Nag optimizer.
Nag.Builder - Class in ai.djl.training.optimizer: The Builder to construct an Nag object.
NDArray - Interface in ai.djl.ndarray: An interface representing an n-dimensional array.
NDArrays - Class in ai.djl.ndarray: This class contains various methods for manipulating NDArrays.
NDImageUtils - Class in ai.djl.modality.cv.util: NDImageUtils is an image processing utility to load, reshape, and convert images using NDArray images.
NDImageUtils.Flag - Enum in ai.djl.modality.cv.util: Flag indicates the color channel options for images.
NDIndex - Class in ai.djl.ndarray.index: The NDIndex allows you to specify a subset of an NDArray that can be used for fetching or updating.
NDIndex() - Constructor for class ai.djl.ndarray.index.NDIndex: Creates an empty NDIndex to append values to.
NDIndex(String) - Constructor for class ai.djl.ndarray.index.NDIndex: Creates a NDIndex given the index values.
NDIndex(long...) - Constructor for class ai.djl.ndarray.index.NDIndex: Creates an NDIndex with the given indices as specified values on the NDArray.
NDIndexAll - Class in ai.djl.ndarray.index: An NDIndexElement to return all values in a particular dimension.
NDIndexAll() - Constructor for class ai.djl.ndarray.index.NDIndexAll
NDIndexBooleans - Class in ai.djl.ndarray.index: An NDIndexElement to return values based on a mask binary NDArray.
NDIndexBooleans(NDArray) - Constructor for class ai.djl.ndarray.index.NDIndexBooleans: Constructs a NDIndexBooleans instance with specified mask binary NDArray.
NDIndexElement - Interface in ai.djl.ndarray.index: An index for particular dimensions created by NDIndex.
NDIndexFixed - Class in ai.djl.ndarray.index: An NDIndexElement that returns only a specific value in the corresponding dimension.
NDIndexFixed(long) - Constructor for class ai.djl.ndarray.index.NDIndexFixed: Constructs a NDIndexFixed instance with specified dimension.
NDIndexFullSlice - Class in ai.djl.ndarray.index: An index as a slice on all dimensions where some dimensions can be squeezed.
NDIndexSlice - Class in ai.djl.ndarray.index: An NDIndexElement that returns a range of values in the specified dimension.
NDIndexSlice(Long, Long, Long) - Constructor for class ai.djl.ndarray.index.NDIndexSlice: Constructs a NDIndexSlice instance with specified range and step.
NDList - Class in ai.djl.ndarray: An NDList represents a sequence of NDArrays with names.
NDList() - Constructor for class ai.djl.ndarray.NDList: Constructs an empty NDList.
NDList(int) - Constructor for class ai.djl.ndarray.NDList: Constructs an empty NDList with the specified initial capacity.
NDList(NDArray...) - Constructor for class ai.djl.ndarray.NDList: Constructs and initiates an NDList with the specified NDArrays.
NDList(Collection<NDArray>) - Constructor for class ai.djl.ndarray.NDList: Constructs and initiates an NDList with the specified NDArrays.
NDManager - Interface in ai.djl.ndarray: NDArray managers are used to create NDArrays (n-dimensional array on native engine).
neg() - Method in interface ai.djl.ndarray.NDArray: Returns the numerical negative NDArray element-wise.
negi() - Method in interface ai.djl.ndarray.NDArray: Returns the numerical negative NDArray element-wise in place.
neq(Number) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Not equals" comparison.
neq(NDArray) - Method in interface ai.djl.ndarray.NDArray: Returns the boolean NDArray for element-wise "Not equals" comparison.
neq(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Not equals" comparison.
neq(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Not equals" comparison.
neq(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns the boolean NDArray for element-wise "Not equals" comparison.
newBaseManager() - Method in class ai.djl.engine.Engine: Creates a new top-level NDManager.
newBaseManager(Device) - Method in class ai.djl.engine.Engine: Creates a new top-level NDManager with specified Device.
newBaseManager() - Static method in interface ai.djl.ndarray.NDManager: Creates a new top-level NDManager.
newBaseManager(Device) - Static method in interface ai.djl.ndarray.NDManager: Creates a new top-level NDManager with specified Device.
newGradientCollector() - Method in interface ai.djl.training.Trainer: Returns a new instance of GradientCollector.
newInstance() - Static method in interface ai.djl.Model: Creates an empty model instance.
newInstance(Device) - Static method in interface ai.djl.Model: Creates an empty model instance on the specified Device.
newModel(Device) - Method in class ai.djl.engine.Engine: Constructs a new model.
newPredictor(Translator<I, O>) - Method in interface ai.djl.Model: Creates a new Predictor based on the model.
newSubManager() - Method in interface ai.djl.ndarray.NDManager: Creates a child NDManager.
newSubManager(Device) - Method in interface ai.djl.ndarray.NDManager: Creates a child NDManager with specified default Device.
newTrainer(TrainingConfig) - Method in interface ai.djl.Model: Creates a new Trainer instance for a Model.
next() - Method in interface ai.djl.modality.cv.PathIterator: Moves the iterator to the next segment of the path forwards along the primary direction of traversal as long as there are more points in that direction.
next() - Method in class ai.djl.training.dataset.DataIterable
none() - Method in interface ai.djl.ndarray.NDArray: Returns true if none of the elements within this NDArray are non-zero or true.
nonzero() - Method in interface ai.djl.ndarray.NDArray: Returns the indices of elements that are non-zero.
NormalInitializer - Class in ai.djl.training.initializer: NormalInitializer initializes weights with random values sampled from a normal distribution with a mean of zero and standard deviation of sigma.
NormalInitializer() - Constructor for class ai.djl.training.initializer.NormalInitializer: Creates an instance of NormalInitializer with a default sigma of 0.01.
NormalInitializer(double) - Constructor for class ai.djl.training.initializer.NormalInitializer: Creates a Normal initializer.
Normalize - Class in ai.djl.modality.cv.transform: A Transform that normalizes an image NDArray of shape CHW or NCHW.
Normalize(float[], float[]) - Constructor for class ai.djl.modality.cv.transform.Normalize: Creates a Normalize Transform that normalizes.
normalize(NDArray, float, float) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Normalizes an image NDArray of shape CHW or NCHW with a single mean and standard deviation to apply to all channels.
normalize(NDArray, float[], float[]) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Normalizes an image NDArray of shape CHW or NCHW with mean and standard deviation.
numDimensions() - Method in class ai.djl.nn.convolutional.Conv1D: Returns the number of dimensions of the input.
numDimensions() - Method in class ai.djl.nn.convolutional.Conv2D: Returns the number of dimensions of the input.
numDimensions() - Method in class ai.djl.nn.convolutional.Conv3D: Returns the number of dimensions of the input.
numDimensions() - Method in class ai.djl.nn.convolutional.Convolution: Returns the number of dimensions of the input.
numFilters - Variable in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
numFilters - Variable in class ai.djl.nn.convolutional.Convolution
numGroups - Variable in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
numGroups - Variable in class ai.djl.nn.convolutional.Convolution
numStackedLayers - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
numStackedLayers - Variable in class ai.djl.nn.recurrent.RecurrentCell

O

onEpoch() - Method in interface ai.djl.training.TrainingListener: Listens to the end of an epoch during training.
ones(Shape, DataType) - Method in interface ai.djl.ndarray.NDManager: Creates an instance of NDArray with specified Shape filled with ones.
ones(Shape) - Method in interface ai.djl.ndarray.NDManager: Creates an instance of NDArray with specified Shape filled with ones.
ones(Shape, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Creates an instance of NDArray with specified Device, Shape, and DataType filled with ones.
ONES - Static variable in interface ai.djl.training.initializer.Initializer
onesLike() - Method in interface ai.djl.ndarray.NDArray: Returns an NDArray of ones with the same Shape, DataType and SparseFormat as the input NDArray.
onTrainingBatch() - Method in interface ai.djl.training.TrainingListener: Listens to the end of training one batch of data during training.
onValidationBatch() - Method in interface ai.djl.training.TrainingListener: Listens to the end of validating one batch of data during validation.
optAxis(int) - Method in class ai.djl.nn.norm.BatchNorm.Builder: Set the axis in which channel is specified.
optBackgroundId(int) - Method in class ai.djl.modality.cv.MultiBoxDetection.Builder: Sets the class ID for the background.
optBaseLearningRate(float) - Method in class ai.djl.training.optimizer.learningrate.LearningRateTracker.LrBaseBuilder: Sets the base learning rate.
optBatchier(Batchifier) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the Batchifier for the dataset.
optBeginNumUpdate(int) - Method in class ai.djl.training.optimizer.Optimizer.OptimizerBuilder: Sets the initial value of the number of updates.
optBeta1(float) - Method in class ai.djl.training.optimizer.Adam.Builder: Sets the decay rate for the first moment estimates.
optBeta2(float) - Method in class ai.djl.training.optimizer.Adam.Builder: Sets the decay rate for the second moment estimates.
optBias(boolean) - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Sets the optional parameter of whether to include a bias vector.
optBias(boolean) - Method in class ai.djl.nn.core.Linear.Builder: Sets the optional parameter that indicates whether to include a bias vector with default value of true.
optBidrectional(boolean) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the optional parameter that indicates whether to use bidirectional recurrent layers.
optCenter(boolean) - Method in class ai.djl.nn.norm.BatchNorm.Builder: If True, add offset of `beta` to normalized tensor.
optClip(boolean) - Method in class ai.djl.modality.cv.MultiBoxDetection.Builder: Sets the boolean parameter that indicates whether to clip out-of-boundary boxes.
optClip(boolean) - Method in class ai.djl.modality.cv.MultiBoxPrior.Builder: Sets the boolean parameter that indicates whether to clip out-of-boundary boxes.
optClipGrad(float) - Method in class ai.djl.training.optimizer.Optimizer.OptimizerBuilder: Sets the value of the \(clipGrad\).
optDataType(DataType) - Method in class ai.djl.nn.core.Embedding.Builder: Sets the data type of the embedding arrays (default is Float32).
optDevice(Device) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the Device.
optDilate(Shape) - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Sets the dilation along each dimension.
optDropRate(float) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the drop rate of the dropout on the outputs of each RNN layer, except the last layer.
optEpsilon(float) - Method in class ai.djl.nn.norm.BatchNorm.Builder: Sets the epsilon value to prevent division by 0.
optEpsilon(float) - Method in class ai.djl.training.optimizer.Adam.Builder: Sets \(epsilon\) - a small quantity for numerical stability.
optExcutor(ExecutorService, int) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the ExecutorService to spawn threads to fetch data.
optFactor(float) - Method in class ai.djl.training.optimizer.learningrate.FactorTracker.Builder: Sets the value of the multiplicative factor.
optFactor(float) - Method in class ai.djl.training.optimizer.learningrate.MultiFactorTracker.Builder: Set the value of the multiplicative factor.
optFlag(NDImageUtils.Flag) - Method in class ai.djl.modality.cv.ImageTranslator.BaseBuilder: Sets the optional NDImageUtils.Flag (default is NDImageUtils.Flag#COLOR).
optForceSuppress(boolean) - Method in class ai.djl.modality.cv.MultiBoxDetection.Builder: Sets the boolean parameter that indicates whether to suppress all detections regardless of class_id.
optIgnoreLabel(float) - Method in class ai.djl.modality.cv.MultiBoxTarget.Builder: Sets the label for ignored anchors.
Optimizer - Class in ai.djl.training.optimizer: An Optimizer updates the weight parameters to minimize the loss function.
Optimizer(Optimizer.OptimizerBuilder<?>) - Constructor for class ai.djl.training.optimizer.Optimizer: Creates a new instance of Optimizer.
Optimizer.OptimizerBuilder<T extends Optimizer.OptimizerBuilder> - Class in ai.djl.training.optimizer: The Builder to construct an Optimizer.
OptimizerBuilder() - Constructor for class ai.djl.training.optimizer.Optimizer.OptimizerBuilder
optIouThreshold(float) - Method in class ai.djl.modality.cv.MultiBoxTarget.Builder: Sets the anchor-GroundTruth overlap threshold to be regarded as a positive match.
optLabels(NDArray...) - Method in class ai.djl.training.dataset.ArrayDataset.Builder: Sets the labels for the data in the ArrayDataset.
optLearningRateTracker(LearningRateTracker) - Method in class ai.djl.training.optimizer.Adam.Builder: Sets the LearningRateTracker for this optimizer.
optLstmStateClipMin(float, float) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the minimum and maximum clip value of LSTM states.
optMaxEdge(int) - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator.Builder: Sets the maximum edge length of the rescaled image.
optMaxIteration(long) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the maximum number of iterations.
optMinNegativeSamples(int) - Method in class ai.djl.modality.cv.MultiBoxTarget.Builder: Sets the minimum number of negative samples.
optMomentum(float) - Method in class ai.djl.nn.norm.BatchNorm.Builder: Set the momentum for moving average.
optMomentum(float) - Method in class ai.djl.training.optimizer.Sgd.Builder: Sets the momentum for Sgd.
optNegativeMinigRatio(float) - Method in class ai.djl.modality.cv.MultiBoxTarget.Builder: Sets the max negative to positive samples ratio.
optNegativeMiningThreshold(float) - Method in class ai.djl.modality.cv.MultiBoxTarget.Builder: Sets the threshold used for negative mining.
optNmsThreashold(float) - Method in class ai.djl.modality.cv.MultiBoxDetection.Builder: Sets the non-maximum suppression(NMS) threshold.
optNmsTopK(int) - Method in class ai.djl.modality.cv.MultiBoxDetection.Builder: Sets the boolean parameter that indicates whether to clip out-of-boundary boxes.
optNumGroups(int) - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Sets the number of group partitions.
optOffsets(List<Float>) - Method in class ai.djl.modality.cv.MultiBoxPrior.Builder: Sets the value of the center-box offsets across \(x\) and \(y\) dimensions.
optPad(Shape) - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Sets the padding along each dimension.
optPipeline(Pipeline) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the Pipeline of Transform to be applied on the data.
optProbability(float) - Method in class ai.djl.nn.norm.Dropout.Builder: Sets the probability or the fraction of the input that gets dropped out during training time.
optRescaleSize(double, double) - Method in class ai.djl.modality.cv.SingleShotDetectionTranslator.Builder: Sets the optional rescale size.
optScale(boolean) - Method in class ai.djl.nn.norm.BatchNorm.Builder: If True, multiply result by `gamma`.
optSharedAxes(int[]) - Method in class ai.djl.nn.norm.Dropout.Builder: Sets the axes for variational dropout kernel.
optShortEdge(int) - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator.Builder: Sets the shorter edge length of the rescaled image.
optStateOutput(boolean) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the optional parameter that indicates whether to have the states as symbol outputs.
optSteps(List<Float>) - Method in class ai.djl.modality.cv.MultiBoxPrior.Builder: Sets the step across \(x\) and \(y\) dimensions.
optStopFactorLearningRate(float) - Method in class ai.djl.training.optimizer.learningrate.FactorTracker.Builder: Sets the stop value after which the learning rate should remain constant.
optStride(Shape) - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Sets the stride of the convolution.
optTargetPipeline(Pipeline) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the Pipeline of Transform to be applied on the labels.
optThreshold(float) - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator.Builder: Sets the threshold for prediction accuracy.
optThreshold(float) - Method in class ai.djl.modality.cv.MultiBoxDetection.Builder: Sets the threshold score for a detection to be a positive prediction.
optThreshold(float) - Method in class ai.djl.modality.cv.SimplePoseTranslator.Builder: Sets the threshold for prediction accuracy.
optThreshold(float) - Method in class ai.djl.modality.cv.SingleShotDetectionTranslator.Builder: Sets the threshold for prediction accuracy.
optUseDefault(boolean) - Method in class ai.djl.nn.core.Embedding.Builder: Sets whether to use a default embedding for undefined items (default true).
optWarmUpBeginLearningRate(float) - Method in class ai.djl.training.optimizer.learningrate.LearningRateTracker.LrBaseBuilder: Sets the value of the learning rate at the beginning of warm-up mode.
optWarmUpMode(WarmUpMode) - Method in class ai.djl.training.optimizer.learningrate.LearningRateTracker.LrBaseBuilder: Sets the WarmUpMode for the LearningRateTracker.
optWarmUpSteps(int) - Method in class ai.djl.training.optimizer.learningrate.LearningRateTracker.LrBaseBuilder: Sets the number of steps until the point the learning rate is updated in warm-up mode.
optWeightDecays(float) - Method in class ai.djl.training.optimizer.Optimizer.OptimizerBuilder: Sets the value of weight decay.

P

pad - Variable in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
pad - Variable in class ai.djl.nn.convolutional.Convolution
ParallelBlock - Class in ai.djl.nn: ParallelBlock is a Block whose children form a parallel branch in the network and are combined to produce a single output.
ParallelBlock(Function<List<NDList>, NDList>) - Constructor for class ai.djl.nn.ParallelBlock: Creates a parallel block whose branches are combined to form a single output by the given function.
ParallelBlock(Function<List<NDList>, NDList>, List<Block>) - Constructor for class ai.djl.nn.ParallelBlock: Creates a parallel block whose branches are formed by each block in the list of blocks, and are combined to form a single output by the given function.
Parameter - Class in ai.djl.nn: Parameter is a container class that holds a learnable parameter of a model.
Parameter(String, Block, ParameterType) - Constructor for class ai.djl.nn.Parameter: Creates a Parameter with the given name, and parameter type, and associated with the given Block.
Parameter(String, Block, ParameterType, boolean) - Constructor for class ai.djl.nn.Parameter: Creates a Parameter with the given name, and parameter type, and associated with the given Block.
ParameterBlock - Class in ai.djl.nn: ParameterBlock is an abstract implementation of Block.
ParameterBlock() - Constructor for class ai.djl.nn.ParameterBlock
ParameterList - Class in ai.djl.nn: Represents a set of names and Parameters.
ParameterList() - Constructor for class ai.djl.nn.ParameterList: Create an empty ParameterList.
ParameterList(int) - Constructor for class ai.djl.nn.ParameterList: Constructs an empty ParameterList with the specified initial capacity.
ParameterList(List<String>, List<Parameter>) - Constructor for class ai.djl.nn.ParameterList: Constructs a ParameterList containing the elements of the specified keys and values.
ParameterList(List<Pair<String, Parameter>>) - Constructor for class ai.djl.nn.ParameterList: Constructs a ParameterList containing the elements of the specified list of Pairs.
ParameterList(Map<String, Parameter>) - Constructor for class ai.djl.nn.ParameterList: Constructs a ParameterList containing the elements of the specified map.
ParameterServer - Interface in ai.djl.training: An interface for a key-value store to store parameters, and their corresponding gradients.
ParameterStore - Class in ai.djl.training: The ParameterStore contains a map from a parameter to the mirrors of it on other devices.
ParameterStore(NDManager, boolean) - Constructor for class ai.djl.training.ParameterStore: Constructs an empty ParameterStore.
ParameterType - Enum in ai.djl.nn: Enumerates the types of Parameter.
PathIterator - Interface in ai.djl.modality.cv: A sequence of points used to outline an object in an image.
percentile(String, int) - Method in class ai.djl.metric.Metrics: Returns a percentile Metric object for the specified metric name.
percentile(Number) - Method in interface ai.djl.ndarray.NDArray: Returns percentile for this NDArray.
percentile(Number, int[]) - Method in interface ai.djl.ndarray.NDArray: Returns median along given dimension(s).
pipeline - Variable in class ai.djl.modality.cv.ImageTranslator.BaseBuilder
pipeline - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
pipeline - Variable in class ai.djl.training.dataset.RandomAccessDataset
Pipeline - Class in ai.djl.translate: Pipeline allows applying multiple transforms on an input NDList.
Pipeline() - Constructor for class ai.djl.translate.Pipeline: Creates a new instance of Pipeline that has no Transform defined yet.
Pipeline(Transform...) - Constructor for class ai.djl.translate.Pipeline: Creates a new instance of Pipeline that can apply the given transforms on its input.
Point - Class in ai.djl.modality.cv: A point representing a location in (x,y) coordinate space, specified in double precision.
Point(double, double) - Constructor for class ai.djl.modality.cv.Point: Constructs and initializes a point at the specified (x,y) location in the coordinate space.
Pool - Class in ai.djl.nn.pooling: Utility class that provides Block and methods for different pooling functions.
PoolingConvention - Enum in ai.djl.nn.pooling: An enum that enumerates the different pooling conventions.
PostProcessor<O> - Interface in ai.djl.translate: An interface that provides post-processing functionality.
pow(Number) - Method in interface ai.djl.ndarray.NDArray: Takes the power of this NDArray with a number element-wise.
pow(NDArray) - Method in interface ai.djl.ndarray.NDArray: Takes the power of this NDArray with the other NDArray element-wise.
pow(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Takes the power of the NDArray with a number element-wise.
pow(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Takes the power of a number with a NDArray element-wise.
pow(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Takes the power of a NDArray with a NDArray element-wise.
powi(Number) - Method in interface ai.djl.ndarray.NDArray: Takes the power of this NDArray with a number element-wise in place.
powi(NDArray) - Method in interface ai.djl.ndarray.NDArray: Takes the power of this NDArray with the other NDArray element-wise in place.
powi(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Takes the power of the NDArray with a number element-wise in place.
powi(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Takes the power of a number with a NDArray element-wise in place.
powi(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Takes the power of a NDArray with a NDArray element-wise.
predict(I) - Method in class ai.djl.inference.BasePredictor: Predicts an item for inference.
predict(I) - Method in interface ai.djl.inference.Predictor: Predicts an item for inference.
Predictor<I,O> - Interface in ai.djl.inference: The Predictor interface provides model inference functionality.
prefetchNumber - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
prefetchNumber - Variable in class ai.djl.training.dataset.RandomAccessDataset
Prelu - Class in ai.djl.nn.core: Applies Leaky Parametric ReLU activation element-wise to the input.
Prelu() - Constructor for class ai.djl.nn.core.Prelu: Creates a Parametric ReLU Block.
preluBlock() - Static method in class ai.djl.nn.Activation: Returns a Prelu block.
PreProcessor<I> - Interface in ai.djl.translate: An interface that provides pre-processing functionality.
probabilities - Variable in class ai.djl.modality.Classifications
processInput(TranslatorContext, BufferedImage) - Method in class ai.djl.modality.cv.ImageTranslator: Processes the BufferedImage input and converts it to NDList.
processInput(TranslatorContext, BufferedImage) - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator: Processes the BufferedImage input and converts it to NDList.
processInput(TranslatorContext, I) - Method in interface ai.djl.translate.PreProcessor: Processes the input and converts it to NDList.
processOutput(TranslatorContext, NDList) - Method in class ai.djl.modality.cv.ImageClassificationTranslator: Processes the output NDList to the corresponding output object.
processOutput(TranslatorContext, NDList) - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator: Processes the output NDList to the corresponding output object.
processOutput(TranslatorContext, NDList) - Method in class ai.djl.modality.cv.SimplePoseTranslator: Processes the output NDList to the corresponding output object.
processOutput(TranslatorContext, NDList) - Method in class ai.djl.modality.cv.SingleShotDetectionTranslator: Processes the output NDList to the corresponding output object.
processOutput(TranslatorContext, NDList) - Method in interface ai.djl.translate.PostProcessor: Processes the output NDList to the corresponding output object.
prod() - Method in interface ai.djl.ndarray.NDArray: Returns the product of this NDArray.
prod(int[]) - Method in interface ai.djl.ndarray.NDArray: Returns the product of this NDArray elements over the given axes.
prod(int[], boolean) - Method in interface ai.djl.ndarray.NDArray: Returns the product of this NDArray elements over the given axes.
ProgressBar - Class in ai.djl.training.util: ProgressBar is an implementation of Progress.
ProgressBar() - Constructor for class ai.djl.training.util.ProgressBar: Creates an instance of ProgressBar with a maximum value of 1.
ProgressBar(String, long) - Constructor for class ai.djl.training.util.ProgressBar: Creates an instance of ProgressBar with the given maximum value, and displays the given message.
pull(String, NDArray[], int) - Method in class ai.djl.training.LocalParameterServer: Pulls the value of a key from Parameter Server to NDArrays.
pull(String, NDArray[], int) - Method in interface ai.djl.training.ParameterServer: Pulls the value of a key from Parameter Server to NDArrays.
push(String, NDArray[], int) - Method in class ai.djl.training.LocalParameterServer: Updates values of a key in Parameter Server.
push(String, NDArray[], int) - Method in interface ai.djl.training.ParameterServer: Updates values of a key in Parameter Server.
put(int, int, Number) - Method in interface ai.djl.ndarray.Matrix: Inserts the element at the specified index.
putColumn(int, NDArray) - Method in interface ai.djl.ndarray.Matrix: Inserts a column into this array.
putiColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Assigns a column vector in place.
putiRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Assigns a row vector to each row of this array in place.
putRow(long, NDArray) - Method in interface ai.djl.ndarray.Matrix: Inserts a row into this matrix.

Q

quantize() - Method in interface ai.djl.Model: Converts the model to use a lower precision quantized network.

R

RandomAccessDataset - Class in ai.djl.training.dataset: RandomAccessDataset represent the dataset that support random access reads.
RandomAccessDataset(RandomAccessDataset.BaseBuilder<?>) - Constructor for class ai.djl.training.dataset.RandomAccessDataset: Creates a new instance of RandomAccessDataset with the given necessary configurations.
RandomAccessDataset.BaseBuilder<T extends RandomAccessDataset.BaseBuilder> - Class in ai.djl.training.dataset: The Builder to construct a RandomAccessDataset.
randomColor() - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Returns a random color.
randomMultinomial(int, NDArray) - Method in interface ai.djl.ndarray.NDManager: Draw samples from a multinomial distribution.
randomMultinomial(int, NDArray, Shape) - Method in interface ai.djl.ndarray.NDManager: Draw samples from a multinomial distribution.
randomNormal(Shape) - Method in interface ai.djl.ndarray.NDManager: Draws random samples from a normal (Gaussian) distribution with mean 0 and standard deviation 1.
randomNormal(Shape, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Draws random samples from a normal (Gaussian) distribution with mean 0 and standard deviation 1.
randomNormal(Number, Number, Shape, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Draws random samples from a normal (Gaussian) distribution.
RandomSampler - Class in ai.djl.training.dataset: RandomSampler is an implementation of the Sampler.SubSampler interface.
RandomSampler() - Constructor for class ai.djl.training.dataset.RandomSampler: Creates a new instance of RandomSampler.
RandomSampler(int) - Constructor for class ai.djl.training.dataset.RandomSampler: Creates a new instance of RandomSampler with the given seed.
randomUniform(Number, Number, Shape) - Method in interface ai.djl.ndarray.NDManager: Draws samples from a uniform distribution.
randomUniform(Number, Number, Shape, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Draws samples from a uniform distribution.
rdivColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses the division of a column vector (copy).
rdiviColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses the divison of a column vector in place.
rdiviRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses division of a column vector in place.
rdivRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses division of a column vector (copy).
readFileToArray(NDManager, Path) - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Reads an image file and converts to NDArray.
readFileToArray(NDManager, Path, NDImageUtils.Flag) - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Reads an image file and converts to NDArray.
Record - Class in ai.djl.training.dataset: Record represents a single element of data and labels from Dataset.
Record(NDList, NDList) - Constructor for class ai.djl.training.dataset.Record: Creates a new instance of Record with a single element of data and its corresponding labels.
Rectangle - Class in ai.djl.modality.cv: A Rectangle specifies an area in a coordinate space that is enclosed by the Rectangle object's upper-left point Point in the coordinate space, its width, and its height.
Rectangle(double, double, double, double) - Constructor for class ai.djl.modality.cv.Rectangle: Constructs a new Rectangle whose upper-left corner is specified as (x,y) and whose width and height are specified by the arguments of the same name.
Rectangle(Point, double, double) - Constructor for class ai.djl.modality.cv.Rectangle: Constructs a new Rectangle whose upper-left corner is specified as coordinate point and whose width and height are specified by the arguments of the same name.
RecurrentCell - Class in ai.djl.nn.recurrent: Applies recurrent layers to input data.
RecurrentCell(RecurrentCell.BaseBuilder<?>) - Constructor for class ai.djl.nn.recurrent.RecurrentCell: Creates a RecurrentCell object.
RecurrentCell.BaseBuilder<T extends RecurrentCell.BaseBuilder> - Class in ai.djl.nn.recurrent: The Builder to construct a RecurrentCell type of Block.
relu(NDArray) - Static method in class ai.djl.nn.Activation: Applies ReLU activation on the input NDArray.
relu(NDList) - Static method in class ai.djl.nn.Activation: Applies ReLU activation on the input singleton NDList.
reluBlock() - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the ReLU activation function in its forward function.
remove(String) - Method in class ai.djl.ndarray.NDList: Removes the first occurrence of the specified element from this NDList if it is present.
removeLastBlock() - Method in class ai.djl.nn.SequentialBlock: Removes the Block added last from the sequence of blocks.
removeLastBlock() - Method in interface ai.djl.nn.SymbolBlock: Removes the last block in the symbolic graph.
repeat(long) - Method in interface ai.djl.ndarray.NDArray: Repeats element of this NDArray the number of times given repeats.
repeat(int, long) - Method in interface ai.djl.ndarray.NDArray: Repeats element of this NDArray the number of times given repeats along given axis.
repeat(long[]) - Method in interface ai.djl.ndarray.NDArray: Repeats element of this NDArray the number of times given repeats along each axis.
repeat(Shape) - Method in interface ai.djl.ndarray.NDArray: Repeats element of this NDArray to match the desired shape.
replaceLastBlock(Block) - Method in class ai.djl.nn.SequentialBlock: Replaces the Block last added from the sequence of blocks, and adds the given block.
requireGradient() - Method in class ai.djl.nn.Parameter: Returns whether this parameter needs gradients to be computed.
rescaleGrad - Variable in class ai.djl.training.optimizer.Optimizer
reset() - Method in class ai.djl.training.loss.CompositeLoss: Resets metric values.
reset() - Method in class ai.djl.training.loss.Loss: Resets metric values.
reset() - Method in class ai.djl.training.metrics.Accuracy: Resets metric values.
reset() - Method in class ai.djl.training.metrics.BoundingBoxError: Resets metric values.
reset() - Method in class ai.djl.training.metrics.TrainingMetric: Resets metric values.
reset(String, long) - Method in class ai.djl.training.util.ProgressBar
resetTrainingMetrics() - Method in interface ai.djl.training.Trainer: Resets each of the training metrics and loss to its respective initial value.
reshape(char, int, int) - Method in interface ai.djl.ndarray.Matrix: Reshapes the NDArray (it can't change the length of the NDArray).
reshape(long...) - Method in interface ai.djl.ndarray.NDArray: Reshapes this NDArray to the given Shape.
reshape(Shape) - Method in interface ai.djl.ndarray.NDArray: Reshapes this NDArray to the given Shape.
Resize - Class in ai.djl.modality.cv.transform: A Transform that resizes the image.
Resize(int) - Constructor for class ai.djl.modality.cv.transform.Resize: Creates a Resize Transform that resizes to the given size.
Resize(int, int) - Constructor for class ai.djl.modality.cv.transform.Resize: Creates a Resize Transform that resizes to the given width and height.
resize(NDArray, int) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Resizes an image to the given size.
resize(NDArray, int, int) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Resizes an image to the given width and height.
RNN - Class in ai.djl.nn.recurrent: Applies a single-gate recurrent layer to input.
RNN.Activation - Enum in ai.djl.nn.recurrent: An enum that enumerates the type of activation.
RNN.Builder - Class in ai.djl.nn.recurrent: The Builder to construct a RNN type of Block.
round() - Method in interface ai.djl.ndarray.NDArray: Returns the round of this NDArray element-wise.
rows() - Method in class ai.djl.ndarray.types.Shape: Returns the number of rows in the matrix.
rsubColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses subtraction of a column vector (copy).
rsubiColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses subtraction of a column vector in place.
rsubiRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses subtraction of a row vector in place.
rsubRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Reverses subtraction of a row vector (copy).

S

sample(RandomAccessDataset) - Method in class ai.djl.training.dataset.BatchSampler: Fetches an iterator that iterates through the given RandomAccessDataset in mini-batches of indices.
sample(RandomAccessDataset) - Method in class ai.djl.training.dataset.RandomSampler: Fetches an iterator that iterates through the indices of the given RandomAccessDataset.
sample(RandomAccessDataset) - Method in interface ai.djl.training.dataset.Sampler: Fetches an iterator that iterates through the given RandomAccessDataset in mini-batches of indices.
sample(RandomAccessDataset) - Method in interface ai.djl.training.dataset.Sampler.SubSampler: Fetches an iterator that iterates through the indices of the given RandomAccessDataset.
sample(RandomAccessDataset) - Method in class ai.djl.training.dataset.SequenceSampler: Fetches an iterator that iterates through the indices of the given RandomAccessDataset.
sampler - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
sampler - Variable in class ai.djl.training.dataset.RandomAccessDataset
Sampler - Interface in ai.djl.training.dataset: An interface for sampling data items from a RandomAccessDataset.
Sampler.SubSampler - Interface in ai.djl.training.dataset: An interface that samples a single data item at a time.
save(Path, String) - Method in interface ai.djl.Model: Saves the model to the specified modelPath with the name provided.
save(DataOutputStream) - Method in class ai.djl.nn.Parameter: Writes the parameter NDArrays to the given output stream.
saveParameters(DataOutputStream) - Method in interface ai.djl.nn.Block: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.convolutional.Convolution: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.core.Embedding: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.core.Linear: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.core.Prelu: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.LambdaBlock: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.norm.BatchNorm: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.norm.Dropout: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.ParallelBlock: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.recurrent.GRU: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.recurrent.LSTM: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.recurrent.RNN: Writes the parameters of the block to the given outputStream.
saveParameters(DataOutputStream) - Method in class ai.djl.nn.SequentialBlock: Writes the parameters of the block to the given outputStream.
self() - Method in class ai.djl.modality.cv.ImageClassificationTranslator.Builder
self() - Method in class ai.djl.modality.cv.ImageTranslator.BaseBuilder
self() - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator.Builder
self() - Method in class ai.djl.modality.cv.SimplePoseTranslator.Builder
self() - Method in class ai.djl.modality.cv.SingleShotDetectionTranslator.Builder
self() - Method in class ai.djl.nn.convolutional.Conv1D.Builder
self() - Method in class ai.djl.nn.convolutional.Conv2D.Builder
self() - Method in class ai.djl.nn.convolutional.Conv3D.Builder
self() - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
self() - Method in class ai.djl.nn.recurrent.GRU.Builder
self() - Method in class ai.djl.nn.recurrent.LSTM.Builder
self() - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
self() - Method in class ai.djl.nn.recurrent.RNN.Builder
self() - Method in class ai.djl.training.dataset.ArrayDataset.Builder: Returns this {code Builder} object.
self() - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Returns this {code Builder} object.
self() - Method in class ai.djl.training.optimizer.Adam.Builder
self() - Method in class ai.djl.training.optimizer.learningrate.FactorTracker.Builder
self() - Method in class ai.djl.training.optimizer.learningrate.LearningRateTracker.LrBaseBuilder
self() - Method in class ai.djl.training.optimizer.learningrate.MultiFactorTracker.Builder
self() - Method in class ai.djl.training.optimizer.Nag.Builder
self() - Method in class ai.djl.training.optimizer.Optimizer.OptimizerBuilder
self() - Method in class ai.djl.training.optimizer.Sgd.Builder
selu(NDArray) - Static method in class ai.djl.nn.Activation: Applies Scaled ELU activation on the input NDArray.
selu(NDList) - Static method in class ai.djl.nn.Activation: Applies Scaled ELU activation on the input singleton NDList.
seluBlock() - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the SELU activation function in its forward function.
SequenceSampler - Class in ai.djl.training.dataset: SequenceSampler is an implementation of the Sampler.SubSampler interface.
SequenceSampler() - Constructor for class ai.djl.training.dataset.SequenceSampler
SequentialBlock - Class in ai.djl.nn: SequentialBlock is a Block whose children form a chain of blocks with each child block feeding its output to the next.
SequentialBlock() - Constructor for class ai.djl.nn.SequentialBlock
set(Buffer) - Method in interface ai.djl.ndarray.NDArray: Sets this NDArray value from Buffer.
set(float[]) - Method in interface ai.djl.ndarray.NDArray: Sets this NDArray value from an array of floats.
set(int[]) - Method in interface ai.djl.ndarray.NDArray: Sets this NDArray value from an array of ints.
set(double[]) - Method in interface ai.djl.ndarray.NDArray: Sets this NDArray value from an array of doubles.
set(long[]) - Method in interface ai.djl.ndarray.NDArray: Sets this NDArray value from an array of longs.
set(byte[]) - Method in interface ai.djl.ndarray.NDArray: Sets this NDArray value from an array of bytes.
set(NDIndex, NDArray) - Method in interface ai.djl.ndarray.NDArray: Sets the specified index in this NDArray with the given values.
set(NDIndex, Number) - Method in interface ai.djl.ndarray.NDArray: Sets the specified index in this NDArray with the given value.
setActivation(RNN.Activation) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the activation for the RNN - ReLu or Tanh.
setArray(NDArray) - Method in class ai.djl.nn.Parameter: Sets the values of this Parameter.
setAttachment(String, Object) - Method in interface ai.djl.translate.TranslatorContext: Set a key-value pair of attachments.
setBatchSize(int) - Method in class ai.djl.training.DefaultTrainingConfig: Sets the size of a batch for training.
setBlock(Block) - Method in interface ai.djl.Model: Sets the block for the Model for training and inference.
setClasses(List<String>) - Method in class ai.djl.modality.cv.SingleShotDetectionTranslator.Builder: Sets the class list.
setData(NDArray...) - Method in class ai.djl.training.dataset.ArrayDataset.Builder: Sets the data as an NDArray for the ArrayDataset.
setDataType(DataType) - Method in interface ai.djl.Model: Sets the standard data type used within the model.
setDataType(DataType) - Method in class ai.djl.ndarray.types.DataDesc: Sets the DataType of the NDArray.
setDevices(Device[]) - Method in class ai.djl.training.DefaultTrainingConfig: Sets the array of Device available for training.
setEmbeddingSize(int) - Method in class ai.djl.nn.core.Embedding.Builder: Sets the size of the embeddings.
setInitializer(Initializer) - Method in class ai.djl.nn.AbstractBlock: Sets an Initializer to the block.
setInitializer(Initializer, String) - Method in class ai.djl.nn.AbstractBlock: Sets an Initializer to the specified direct parameter of the block, overriding the initializer of the parameter, if already set.
setInitializer(Initializer) - Method in interface ai.djl.nn.Block: Sets an Initializer to the block.
setInitializer(Initializer, String) - Method in interface ai.djl.nn.Block: Sets an Initializer to the specified direct parameter of the block, overriding the initializer of the parameter, if already set.
setInitializer(Initializer, boolean) - Method in class ai.djl.nn.Parameter: Sets the Initializer for this Parameter, if not already set.
setItems(Collection<T>) - Method in class ai.djl.nn.core.Embedding.Builder: Sets the collection of items that should feature embeddings.
setKernel(Shape) - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Sets the shape of the kernel.
setLearningRateTracker(LearningRateTracker) - Method in class ai.djl.training.optimizer.Nag.Builder: Sets the LearningRateTracker for this optimizer.
setLearningRateTracker(LearningRateTracker) - Method in class ai.djl.training.optimizer.Sgd.Builder: Sets the LearningRateTracker for this optimizer.
setMandatoryDataType(DataType) - Method in class ai.djl.nn.Parameter: Sets the mandatory data type for this Parameter.
setMetrics(Metrics) - Method in class ai.djl.inference.BasePredictor: Attaches a Metrics param to use for benchmark.
setMetrics(Metrics) - Method in interface ai.djl.inference.Predictor: Attaches a Metrics param to use for benchmark.
setMetrics(Metrics) - Method in interface ai.djl.training.Trainer: Attaches a Metrics param to use for benchmark.
setMomentum(float) - Method in class ai.djl.training.optimizer.Nag.Builder: Sets the momentum for Nag.
setName(String) - Method in interface ai.djl.ndarray.NDArray: Sets name of this NDArray.
setName(String) - Method in class ai.djl.ndarray.types.DataDesc: Sets the name of the NDArray.
setNumFilters(int) - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Sets the Required number of filters.
setNumStackedLayers(int) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the Required number of stacked layers.
setOptimizer(Optimizer) - Method in class ai.djl.training.DefaultTrainingConfig: Sets the Optimizer used during training.
setOutChannels(long) - Method in class ai.djl.nn.core.Linear.Builder: Sets the number of output channels.
setParameterServer(ParameterServer, Device[]) - Method in class ai.djl.training.ParameterStore: Sets the parameterServer used to apply updates to the parameters.
setPipeline(Pipeline) - Method in class ai.djl.modality.cv.ImageTranslator.BaseBuilder: Sets the Pipeline to use for pre-processing the image.
setProperty(String, String) - Method in interface ai.djl.Model: Sets a property to the model.
setRatios(List<Float>) - Method in class ai.djl.modality.cv.MultiBoxPrior.Builder: Sets the aspect ratios of the anchor boxes to be generated around each pixel.
setRescaleGrad(float) - Method in class ai.djl.training.optimizer.Optimizer.OptimizerBuilder: Sets the value used to rescale the gradient.
setSampling(long, boolean) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the Sampler with the given batch size.
setSampling(long, boolean, boolean) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the Sampler with the given batch size.
setSampling(Sampler) - Method in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder: Sets the Sampler for the dataset.
setScalar(NDIndex, Number) - Method in interface ai.djl.ndarray.NDArray: Sets the specified index in this NDArray with the given value.
setSequenceLength(boolean) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the optional parameter that indicates whether to include an extra input parameter sequence_length to specify variable length sequence.
setShape(Shape) - Method in class ai.djl.ndarray.types.DataDesc: Sets the Shape of the NDArray.
setSizes(List<Float>) - Method in class ai.djl.modality.cv.MultiBoxPrior.Builder: Sets the sizes of the anchor boxes to be generated around each pixel.
setStateSize(int) - Method in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder: Sets the Required size of the state for each layer.
setStep(int) - Method in class ai.djl.training.optimizer.learningrate.FactorTracker.Builder: Sets the number of steps after which the multiplicative factor must be applied once.
setSteps(int[]) - Method in class ai.djl.training.optimizer.learningrate.MultiFactorTracker.Builder: Sets an array of integers indicating when the learning rate should be changed, usually in an uneven interval of steps.
setSynsetArtifactName(String) - Method in class ai.djl.modality.cv.ImageClassificationTranslator.Builder: Sets the name of the synset file listing the potential classes for an image.
setSynsetArtifactName(String) - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator.Builder: Sets the name for the synset.
setSynsetArtifactName(String) - Method in class ai.djl.modality.cv.SingleShotDetectionTranslator.Builder: Sets the name for the synset.
setTrainingListener(TrainingListener) - Method in interface ai.djl.training.Trainer: Sets a TrainingListener to the Trainer.
sgd() - Static method in class ai.djl.training.optimizer.Optimizer: Returns a new instance of Sgd.Builder that can build an Sgd optimizer.
Sgd - Class in ai.djl.training.optimizer: Sgd is a Stochastic Gradient Descent (SDG) optimizer.
Sgd(Sgd.Builder) - Constructor for class ai.djl.training.optimizer.Sgd: Creates a new instance of Sgd.
Sgd.Builder - Class in ai.djl.training.optimizer: The Builder to construct an Sgd object.
Shape - Class in ai.djl.ndarray.types: A class that presents the NDArray's shape information.
Shape(long...) - Constructor for class ai.djl.ndarray.types.Shape: Constructs and initializes a Shape with specified dimension as {@code (long...
Shape(List<Long>) - Constructor for class ai.djl.ndarray.types.Shape: Constructs and initializes a Shape with specified dimension.
Shape(PairList<Long, LayoutType>) - Constructor for class ai.djl.ndarray.types.Shape: Constructs and initializes a Shape with specified shape and layout pairList.
Shape(long[], String) - Constructor for class ai.djl.ndarray.types.Shape: Constructs and initializes a Shape with specified dimension and layout.
Shape(long[], LayoutType[]) - Constructor for class ai.djl.ndarray.types.Shape: Constructs and initializes a Shape with specified dimension and layout.
shapeEquals(NDArray) - Method in interface ai.djl.ndarray.NDArray: Checks 2 NDArrays for equal shapes.
shapeEquals(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Checks 2 NDArrays for equal shapes.
sigmoid(NDArray) - Static method in class ai.djl.nn.Activation: Applies Sigmoid activation on the input NDArray.
sigmoid(NDList) - Static method in class ai.djl.nn.Activation: Applies Sigmoid activation on the input singleton NDList.
sigmoidBinaryCrossEntropyLoss() - Static method in class ai.djl.training.loss.Loss: Returns a new instance of SigmoidBinaryCrossEntropyLoss with default arguments.
sigmoidBinaryCrossEntropyLoss(float, int, boolean) - Static method in class ai.djl.training.loss.Loss: Returns a new instance of SigmoidBinaryCrossEntropyLoss with the given arguments.
SigmoidBinaryCrossEntropyLoss - Class in ai.djl.training.loss: SigmoidBinaryCrossEntropyLoss is a type of Loss.
SigmoidBinaryCrossEntropyLoss(float, int, boolean) - Constructor for class ai.djl.training.loss.SigmoidBinaryCrossEntropyLoss: Performs Sigmoid cross-entropy loss for binary classification.
SigmoidBinaryCrossEntropyLoss() - Constructor for class ai.djl.training.loss.SigmoidBinaryCrossEntropyLoss: Performs Sigmoid cross-entropy loss for binary classification.
sigmoidBlock() - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the Sigmoid activation function in its forward function.
SimplePoseTranslator - Class in ai.djl.modality.cv: A ImageTranslator that post-process the NDArray into human Joints.
SimplePoseTranslator(SimplePoseTranslator.Builder) - Constructor for class ai.djl.modality.cv.SimplePoseTranslator: Creates the Pose Estimation translator from the given builder.
SimplePoseTranslator.Builder - Class in ai.djl.modality.cv: The builder for Pose Estimation translator.
sin() - Method in interface ai.djl.ndarray.NDArray: Returns the trigonometric sine of this NDArray element-wise.
SingleShotDetectionAccuracy - Class in ai.djl.training.metrics: SingleShotDetectionAccuracy is an implementation of Accuracy.
SingleShotDetectionAccuracy(String) - Constructor for class ai.djl.training.metrics.SingleShotDetectionAccuracy: Creates a new instance of SingleShotDetectionAccuracy with the given name.
SingleShotDetectionLoss - Class in ai.djl.training.loss: SingleShotDetectionLoss is an implementation of Loss.
SingleShotDetectionLoss(String) - Constructor for class ai.djl.training.loss.SingleShotDetectionLoss: Base class for metric with abstract update methods.
SingleShotDetectionTranslator - Class in ai.djl.modality.cv: A ImageTranslator that post-process the NDArray into DetectedObjects with boundaries.
SingleShotDetectionTranslator(SingleShotDetectionTranslator.Builder) - Constructor for class ai.djl.modality.cv.SingleShotDetectionTranslator: Creates the SSD translator from the given builder.
SingleShotDetectionTranslator.Builder - Class in ai.djl.modality.cv: The builder for SSD translator.
singletonOrThrow() - Method in class ai.djl.ndarray.NDList: Returns the only element if this is a singleton NDList or throws an exception if multiple elements.
sinh() - Method in interface ai.djl.ndarray.NDArray: Returns the hyperbolic sine of this NDArray element-wise.
size(int) - Method in interface ai.djl.ndarray.NDArray: Returns the size of this NDArray along a given axis.
size() - Method in interface ai.djl.ndarray.NDArray: Returns the total number of elements in this NDArray.
size(int...) - Method in class ai.djl.ndarray.types.Shape: Returns the size of a specific dimension or several specific dimensions.
size() - Method in class ai.djl.ndarray.types.Shape: Returns the total size.
size() - Method in class ai.djl.training.dataset.ArrayDataset: Returns the size of this Dataset.
size() - Method in class ai.djl.training.dataset.RandomAccessDataset: Returns the size of this Dataset.
slice() - Method in interface ai.djl.ndarray.NDArray: Creates a new NDArray whose content is a copy of this NDArray's content.
slice(int) - Method in class ai.djl.ndarray.types.Shape: Creates a new Shape whose content is a slice of this shape.
slice(int, int) - Method in class ai.djl.ndarray.types.Shape: Creates a new Shape whose content is a slice of this shape.
softmax(int) - Method in interface ai.djl.ndarray.NDArray: Applies the softmax function along the given axis.
softmax(int, double) - Method in interface ai.djl.ndarray.NDArray: Applies the softmax function along the given axis.
softmax(int[]) - Method in interface ai.djl.ndarray.NDArray: Applies the softmax function along the given axes.
softmax(int[], double) - Method in interface ai.djl.ndarray.NDArray: Applies the softmax function along the given axes.
softmaxCrossEntropyLoss() - Static method in class ai.djl.training.loss.Loss: Returns a new instance of SoftmaxCrossEntropyLoss with default arguments.
softmaxCrossEntropyLoss(float, int, int, boolean, boolean) - Static method in class ai.djl.training.loss.Loss: Returns a new instance of SoftmaxCrossEntropyLoss with the given arguments.
SoftmaxCrossEntropyLoss - Class in ai.djl.training.loss: SoftmaxCrossEntropyLoss is a type of Loss that calculates the softmax cross entropy loss.
SoftmaxCrossEntropyLoss(float, int, int, boolean, boolean) - Constructor for class ai.djl.training.loss.SoftmaxCrossEntropyLoss: Creates a new instance of SoftmaxCrossEntropyLoss with the given parameters.
SoftmaxCrossEntropyLoss() - Constructor for class ai.djl.training.loss.SoftmaxCrossEntropyLoss: Creates a new instance of SoftmaxCrossEntropyLoss with default parameters.
softrelu(NDArray) - Static method in class ai.djl.nn.Activation: Applies soft ReLU activation on the input NDArray.
softrelu(NDList) - Static method in class ai.djl.nn.Activation: Applies soft ReLU activation on the input singleton NDList.
softreluBlock() - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the SoftReLU activation function in its forward function.
sort() - Method in interface ai.djl.ndarray.NDArray: Sorts the flattened NDArray.
sort(int) - Method in interface ai.djl.ndarray.NDArray: Sorts the flattened NDArray.
SparseFormat - Enum in ai.djl.ndarray.types: An enum representing Sparse matrix storage formats.
SparseNDArray - Interface in ai.djl.ndarray: An interface representing a Sparse NDArray.
split(int) - Method in interface ai.djl.ndarray.NDArray: Splits this NDArray into multiple subNDArrays given sections along first axis.
split(int[]) - Method in interface ai.djl.ndarray.NDArray: Splits this NDArray into multiple sub-NDArrays given indices along first axis.
split(int, int) - Method in interface ai.djl.ndarray.NDArray: Splits this NDArray into multiple subNDArrays given sections along the given axis.
split(int[], int) - Method in interface ai.djl.ndarray.NDArray: Splits this NDArray into multiple sub-NDArrays given indices along given axis.
split(Device[], boolean) - Method in class ai.djl.training.dataset.Batch: Splits the data and labels in the Batch across the given devices.
split(NDList, int, boolean) - Method in interface ai.djl.translate.Batchifier: Splits the given NDList into the given number of slices.
split(NDList, int, boolean) - Method in class ai.djl.translate.StackBatchifier: Splits the given NDList into the given number of slices.
square() - Method in interface ai.djl.ndarray.NDArray: Returns the square of this NDArray element-wise.
squeeze() - Method in interface ai.djl.ndarray.NDArray: Removes all singleton dimensions from this NDArray Shape.
squeeze(int) - Method in interface ai.djl.ndarray.NDArray: Removes a singleton dimension at the given axis.
squeeze(int[]) - Method in interface ai.djl.ndarray.NDArray: Removes singleton dimensions at the given axes.
stack(NDArray) - Method in interface ai.djl.ndarray.NDArray: Joins a NDArray along the first axis.
stack(NDArray, int) - Method in interface ai.djl.ndarray.NDArray: Joins a NDArray along a new axis.
stack(NDList) - Static method in class ai.djl.ndarray.NDArrays: Joins a sequence of NDArrays in NDList along the first axis.
stack(NDList, int) - Static method in class ai.djl.ndarray.NDArrays: Joins a sequence of NDArrays in NDList along a new axis.
STACK - Static variable in interface ai.djl.translate.Batchifier
StackBatchifier - Class in ai.djl.translate: StackBatchifier is used to merge a list of samples to form a mini-batch of NDArray(s).
StackBatchifier() - Constructor for class ai.djl.translate.StackBatchifier
start(long) - Method in class ai.djl.training.util.ProgressBar
stateOutputs - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
stateOutputs - Variable in class ai.djl.nn.recurrent.RecurrentCell
stateSize - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
stateSize - Variable in class ai.djl.nn.recurrent.RecurrentCell
step() - Method in interface ai.djl.training.Trainer: Updates all of the parameters of the model once.
stream() - Method in class ai.djl.ndarray.index.NDIndex: Returns a stream of the NDIndexElements.
stream() - Method in class ai.djl.ndarray.types.Shape: Returns a stream of the Shape.
stride - Variable in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder
stride - Variable in class ai.djl.nn.convolutional.Convolution
sub(Number) - Method in interface ai.djl.ndarray.NDArray: Subtracts a number from this NDArray element-wise.
sub(NDArray) - Method in interface ai.djl.ndarray.NDArray: Subtracts the other NDArray from this NDArray element-wise.
sub(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Subtracts a number from the NDArray element-wise.
sub(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Subtracts a NDArray from a number element-wise.
sub(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Subtracts a NDArray from a NDArray element-wise.
subColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Subtracts a column vector (copy).
subi(Number) - Method in interface ai.djl.ndarray.NDArray: Subtracts a number from this NDArray element-wise in place.
subi(NDArray) - Method in interface ai.djl.ndarray.NDArray: Subtracts the other NDArray from this NDArray element-wise in place.
subi(NDArray, Number) - Static method in class ai.djl.ndarray.NDArrays: Subtracts a number from the NDArray element-wise in place.
subi(Number, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Subtracts a NDArray from a number element-wise in place.
subi(NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Subtracts a NDArray from a NDArray element-wise in place.
subiColumnVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Subtracts a column vector in place.
subiRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Subtracts a row vector in place.
subNDList(int) - Method in class ai.djl.ndarray.NDList: Returns a view of the portion of this NDList between the specified fromIndex, inclusive, and to the end.
subRowVector(NDArray) - Method in interface ai.djl.ndarray.Matrix: Subtracts a row vector (copy).
sum() - Method in interface ai.djl.ndarray.NDArray: Returns the sum of this NDArray.
sum(int[]) - Method in interface ai.djl.ndarray.NDArray: Returns the minimum of this NDArray along given axes.
sum(int[], boolean) - Method in interface ai.djl.ndarray.NDArray: Returns the minimum of this NDArray along given axes.
sumPool(NDArray, Shape, Shape, Shape, PoolingConvention) - Static method in class ai.djl.nn.pooling.Pool: Performs Sum Pooling on the input.
sumPool(NDArray, Shape, Shape, Shape) - Static method in class ai.djl.nn.pooling.Pool: Performs Sum Pooling on the input.
swapAxes(int, int) - Method in interface ai.djl.ndarray.NDArray: Interchanges two axes of this NDArray.
swish(NDArray, float) - Static method in class ai.djl.nn.Activation: Applies Swish activation on the input NDArray.
swish(NDList, float) - Static method in class ai.djl.nn.Activation: Applies SWish activation on the input singleton NDList.
swishBlock(float) - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the Swish activation function in its forward function.
SymbolBlock - Interface in ai.djl.nn: SymbolBlock is a Block is used to load models that were exported directly from the engine in its native format.
sync() - Method in class ai.djl.training.ParameterStore: Synchronizes the values on all mirrors with the main parameter.

T

tan() - Method in interface ai.djl.ndarray.NDArray: Returns the trigonometric tangent of this NDArray element-wise.
tanh() - Method in interface ai.djl.ndarray.NDArray: Returns the hyperbolic tangent of this NDArray element-wise.
tanh(NDArray) - Static method in class ai.djl.nn.Activation: Applies Tanh activation on the input NDArray.
tanh(NDList) - Static method in class ai.djl.nn.Activation: Applies Tanh activation on the input singleton NDList.
tanhBlock() - Static method in class ai.djl.nn.Activation: Creates a LambdaBlock that applies the Tanh activation function in its forward function.
target(NDList) - Method in class ai.djl.modality.cv.MultiBoxTarget: Computes multi-box training targets.
targetPipeline - Variable in class ai.djl.training.dataset.RandomAccessDataset.BaseBuilder
targetPipeline - Variable in class ai.djl.training.dataset.RandomAccessDataset
tile(long) - Method in interface ai.djl.ndarray.NDArray: Constructs a NDArray by repeating this NDArray the number of times given repeats.
tile(int, long) - Method in interface ai.djl.ndarray.NDArray: Constructs a NDArray by repeating this NDArray the number of times given by repeats along given axis.
tile(long[]) - Method in interface ai.djl.ndarray.NDArray: Constructs a NDArray by repeating this NDArray the number of times given by repeats.
tile(Shape) - Method in interface ai.djl.ndarray.NDArray: Constructs a NDArray by repeating this NDArray the number of times to match the desired shape.
toArray() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a Number array based on its DataType.
toBooleanArray() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a boolean array.
toByteArray() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a byte array.
toByteBuffer() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a ByteBuffer.
toDegrees() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray from radians to degrees element-wise.
toDense() - Method in interface ai.djl.ndarray.NDArray: Returns a dense representation of the sparse NDArray.
toDoubleArray() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a double array.
toDoubleMatrix() - Method in interface ai.djl.ndarray.Matrix: Converts this NDArray to a 2d double matrix.
toFloatArray() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a float array.
toFloatMatrix() - Method in interface ai.djl.ndarray.Matrix: Converts this NDArray to a 2d float matrix.
toIntArray() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to an int array.
toIntMatrix() - Method in interface ai.djl.ndarray.Matrix: Converts this NDArray to a 2d int matrix.
toLayoutString() - Method in class ai.djl.ndarray.types.Shape: Returns the string layout type for each axis in this shape.
toLongArray() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a long array.
toLongMatrix() - Method in interface ai.djl.ndarray.Matrix: Converts this NDArray to a 2d long matrix.
toNDArray(NDManager, BufferedImage) - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Converts a BufferedImage to an RGB NDArray.
toNDArray(NDManager, BufferedImage, NDImageUtils.Flag) - Static method in class ai.djl.modality.cv.util.BufferedImageUtils: Converts a BufferedImage to an NDArray with designated color mode.
topK(int) - Method in class ai.djl.modality.Classifications: Returns a list of the top k best classes.
TopKAccuracy - Class in ai.djl.training.metrics: TopKAccuracy is a TrainingMetric that computes the accuracy of the top k predictions.
TopKAccuracy(String, int, int) - Constructor for class ai.djl.training.metrics.TopKAccuracy: Creates a TopKAccuracy instance.
TopKAccuracy(int, int) - Constructor for class ai.djl.training.metrics.TopKAccuracy: Creates an instance of TopKAccuracy metric that computes topK accuracy across axis 1 along the given index.
TopKAccuracy(int) - Constructor for class ai.djl.training.metrics.TopKAccuracy: Creates an instance of TopKAccuracy metric that computes topK accuracy across axis 1 along the 0th index.
toRadians() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray from degrees to radians element-wise.
toSparse(SparseFormat) - Method in interface ai.djl.ndarray.NDArray: Returns a sparse representation of NDArray.
toString() - Method in class ai.djl.Device
toString() - Method in class ai.djl.metric.Metric
toString() - Method in class ai.djl.modality.Classifications.Classification
toString() - Method in class ai.djl.modality.Classifications
toString() - Method in class ai.djl.modality.cv.DetectedObjects.DetectedObject
toString() - Method in class ai.djl.modality.cv.Joints.Joint
toString() - Method in class ai.djl.modality.cv.Joints
toString() - Method in class ai.djl.modality.cv.Rectangle
toString() - Method in class ai.djl.ndarray.NDList
toString() - Method in class ai.djl.ndarray.types.DataDesc
toString() - Method in enum ai.djl.ndarray.types.DataType
toString(LayoutType[]) - Static method in enum ai.djl.ndarray.types.LayoutType: Converts a layout type array to a string of the character representations.
toString() - Method in class ai.djl.ndarray.types.Shape
toString() - Method in class ai.djl.nn.LambdaBlock
toString() - Method in class ai.djl.nn.ParallelBlock
toString() - Method in class ai.djl.nn.ParameterBlock
toString() - Method in class ai.djl.nn.SequentialBlock
ToTensor - Class in ai.djl.modality.cv.transform: A Transform that converts an image NDArray from preprocessing format to Neural Network format.
ToTensor() - Constructor for class ai.djl.modality.cv.transform.ToTensor
toTensor(NDArray) - Static method in class ai.djl.modality.cv.util.NDImageUtils: Converts an image NDArray from preprocessing format to Neural Network format.
toUint8Array() - Method in interface ai.djl.ndarray.NDArray: Converts this NDArray to a uint8 array.
trace() - Method in interface ai.djl.ndarray.NDArray: Returns the sum along diagonals of this NDArray.
trace(int) - Method in interface ai.djl.ndarray.NDArray: Returns the sum along diagonals of this NDArray.
trace(int, int, int) - Method in interface ai.djl.ndarray.NDArray: Returns the sum along diagonals of this NDArray.
trainBatch(Batch) - Method in interface ai.djl.training.Trainer: Trains the model with one iteration of the given Batch of data.
Trainer - Interface in ai.djl.training: An interface that represents a single training iteration.
TrainingConfig - Interface in ai.djl.training: An interface that is responsible for holding the configuration required by Trainer.
TrainingDivergedException - Exception in ai.djl: Thrown to indicate when there is a divergence during Training.
TrainingDivergedException(String) - Constructor for exception ai.djl.TrainingDivergedException: Constructs a new exception with the specified detail message.
TrainingDivergedException(String, Throwable) - Constructor for exception ai.djl.TrainingDivergedException: Constructs a new exception with the specified detail message and cause.
TrainingDivergedException(Throwable) - Constructor for exception ai.djl.TrainingDivergedException: Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) which typically contains the class and detail message of cause.
TrainingListener - Interface in ai.djl.training: TrainingListener offers an interface that allows performing some actions when certain events have occurred in the Trainer.
TrainingMetric - Class in ai.djl.training.metrics: Base class for all training metrics.
TrainingMetric(String) - Constructor for class ai.djl.training.metrics.TrainingMetric: Creates a metric with abstract update methods.
transform(NDArray) - Method in class ai.djl.modality.cv.InstanceSegmentationTranslator: Applies the Transform to the given NDArray.
transform(NDArray) - Method in class ai.djl.modality.cv.transform.CenterCrop: Applies the Transform to the given NDArray.
transform(NDArray) - Method in class ai.djl.modality.cv.transform.Crop: Applies the Transform to the given NDArray.
transform(NDArray) - Method in class ai.djl.modality.cv.transform.Normalize: Applies the Transform to the given NDArray.
transform(NDArray) - Method in class ai.djl.modality.cv.transform.Resize: Applies the Transform to the given NDArray.
transform(NDArray) - Method in class ai.djl.modality.cv.transform.ToTensor: Applies the Transform to the given NDArray.
transform(NDList) - Method in class ai.djl.translate.Pipeline: Applies the transforms configured in this object on the input NDList.
Transform - Interface in ai.djl.translate: An interface to apply various transforms to the input.
transform(NDArray) - Method in interface ai.djl.translate.Transform: Applies the Transform to the given NDArray.
TranslateException - Exception in ai.djl.translate: Thrown to indicate that an error is raised during processing of the input or output.
TranslateException(String) - Constructor for exception ai.djl.translate.TranslateException: Constructs a new exception with the specified detail message.
TranslateException(String, Throwable) - Constructor for exception ai.djl.translate.TranslateException: Constructs a new exception with the specified detail message and cause.
TranslateException(Throwable) - Constructor for exception ai.djl.translate.TranslateException: Constructs a new exception with the specified cause and a detail message of (cause==null ? null : cause.toString()) which typically contains the class and detail message of cause.
Translator<I,O> - Interface in ai.djl.translate: The Translator interface provides model pre-processing and postprocessing functionality.
TranslatorContext - Interface in ai.djl.translate: The TranslatorContext interface provides a toolkit for pre-processing and postprocessing functionality.
transpose() - Method in interface ai.djl.ndarray.NDArray: Returns this NDArray with axes transposed.
transpose(int...) - Method in interface ai.djl.ndarray.NDArray: Returns this NDArray with given axes transposed.
trunc() - Method in interface ai.djl.ndarray.NDArray: Returns the truncated value of this NDArray element-wise.

U

unbatchify(NDList) - Method in interface ai.djl.translate.Batchifier: Reverses the batchify operation.
unbatchify(NDList) - Method in class ai.djl.translate.StackBatchifier: Reverses the batchify operation.
UniformInitializer - Class in ai.djl.training.initializer: UniformInitializer initializes weights with random values uniformly sampled from a given range.
UniformInitializer() - Constructor for class ai.djl.training.initializer.UniformInitializer: Creates an instance of UniformInitializer with a default scale of 0.07.
UniformInitializer(float) - Constructor for class ai.djl.training.initializer.UniformInitializer: Initializes a uniform initializer.
update(NDList, NDList) - Method in class ai.djl.training.loss.CompositeLoss: Updates the training metrics based on a NDList of labels and predictions.
update(NDList, NDList) - Method in class ai.djl.training.loss.Loss: Updates the training metrics based on a NDList of labels and predictions.
update(NDArray, NDArray) - Method in class ai.djl.training.metrics.Accuracy: Computes and updates the accuracy based on the labels and predictions.
update(NDList, NDList) - Method in class ai.djl.training.metrics.Accuracy: Computes and updates the training metrics based on the labels and predictions.
update(NDList, NDList) - Method in class ai.djl.training.metrics.BoundingBoxError: Computes and updates the detection bounding box prediction error based on NDList of labels and predictions.
update(NDList, NDList) - Method in class ai.djl.training.metrics.SingleShotDetectionAccuracy
update(NDArray, NDArray) - Method in class ai.djl.training.metrics.TopKAccuracy: Computes and updates the accuracy based on the labels and predictions.
update(NDList, NDList) - Method in class ai.djl.training.metrics.TrainingMetric: Computes and updates the training metrics based on the labels and predictions.
update(String, NDArray, NDArray) - Method in class ai.djl.training.optimizer.Adam: Updates the parameters according to the gradients.
update(String, NDArray, NDArray) - Method in class ai.djl.training.optimizer.Nag: Updates the parameters according to the gradients.
update(String, NDArray, NDArray) - Method in class ai.djl.training.optimizer.Optimizer: Updates the parameters according to the gradients.
update(String, NDArray, NDArray) - Method in class ai.djl.training.optimizer.Sgd: Updates the parameters according to the gradients.
update(long, String) - Method in class ai.djl.training.util.ProgressBar
updateAllParameters() - Method in class ai.djl.training.ParameterStore: Updates all the mirrored parameters.
updateCount(String) - Method in class ai.djl.training.optimizer.Optimizer
useBidirectional - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
useBidirectional - Variable in class ai.djl.nn.recurrent.RecurrentCell
useSequenceLength - Variable in class ai.djl.nn.recurrent.RecurrentCell.BaseBuilder
useSequenceLength - Variable in class ai.djl.nn.recurrent.RecurrentCell

V

validate() - Method in class ai.djl.nn.convolutional.Convolution.ConvolutionBuilder: Validates that the required arguments are set.
validateBatch(Batch) - Method in interface ai.djl.training.Trainer: Validates the given batch of data.
validateInputSize(NDList) - Method in class ai.djl.nn.recurrent.RecurrentCell
validateLayout(LayoutType[], LayoutType[]) - Static method in interface ai.djl.nn.Block: Validates that actual layout matches the expected layout.
valueOf(String) - Static method in enum ai.djl.modality.cv.util.NDImageUtils.Flag: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.ndarray.types.DataType.Format: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.ndarray.types.DataType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.ndarray.types.LayoutType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.ndarray.types.SparseFormat: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.nn.ParameterType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.nn.pooling.PoolingConvention: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.nn.recurrent.RNN.Activation: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.training.dataset.Dataset.Usage: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.training.GradReq: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.training.initializer.XavierInitializer.FactorType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.training.initializer.XavierInitializer.RandomType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.djl.training.optimizer.learningrate.WarmUpMode: Returns the enum constant of this type with the specified name.
values() - Static method in enum ai.djl.modality.cv.util.NDImageUtils.Flag: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.ndarray.types.DataType.Format: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.ndarray.types.DataType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.ndarray.types.LayoutType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.ndarray.types.SparseFormat: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.nn.ParameterType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.nn.pooling.PoolingConvention: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.nn.recurrent.RNN.Activation: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.training.dataset.Dataset.Usage: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.training.GradReq: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.training.initializer.XavierInitializer.FactorType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.training.initializer.XavierInitializer.RandomType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.djl.training.optimizer.learningrate.WarmUpMode: Returns an array containing the constants of this enum type, in the order they are declared.

W

waitToRead(NDList) - Method in class ai.djl.inference.BasePredictor
WarmUpMode - Enum in ai.djl.training.optimizer.learningrate: An enum that enumerates the types of warm-up modes for a LearningRateTracker.
weight - Variable in class ai.djl.nn.convolutional.Convolution
where(NDArray, NDArray, NDArray) - Static method in class ai.djl.ndarray.NDArrays: Returns elements chosen from the NDArray or the other NDArray depending on condition.
withDefaultState(Map<String, Map<Device, NDArray>>, String, Device, Function<String, NDArray>) - Method in class ai.djl.training.optimizer.Optimizer

X

XavierInitializer - Class in ai.djl.training.initializer: XavierInitializer is an Initializer that performs "Xavier" initialization for parameters.
XavierInitializer(XavierInitializer.RandomType, XavierInitializer.FactorType, double) - Constructor for class ai.djl.training.initializer.XavierInitializer: Initializes a Xavier initializer.
XavierInitializer() - Constructor for class ai.djl.training.initializer.XavierInitializer: Creates a new instance of XavierInitializer.
XavierInitializer.FactorType - Enum in ai.djl.training.initializer: Enum for different types of factor type.
XavierInitializer.RandomType - Enum in ai.djl.training.initializer: Enum for different types of random distributions.

Z

zeros(Shape) - Method in interface ai.djl.ndarray.NDManager: Creates an instance of NDArray with specified Shape filled with zeros.
zeros(Shape, DataType) - Method in interface ai.djl.ndarray.NDManager: Creates an instance of NDArray with specified Shape filled with zeros.
zeros(Shape, DataType, Device) - Method in interface ai.djl.ndarray.NDManager: Creates an instance of NDArray with specified Device, Shape, and DataType filled with zeros.
ZEROS - Static variable in interface ai.djl.training.initializer.Initializer
zerosLike() - Method in interface ai.djl.ndarray.NDArray: Returns an NDArray of zeros with the same Shape, DataType and SparseFormat as the input NDArray.

A B C D E F G H I J K L M N O P Q R S T U V W X Z