Iterators

Data iteration tools for loading into neural networks.

What is an iterator?

A dataset iterator allows for easy loading of data into neural networks and help organize batching, conversion, and masking. The iterators included in Eclipse Deeplearning4j help with either user-provided data, or automatic loading of common benchmarking datasets such as MNIST and IRIS.

Usage

For most use cases, initializing an iterator and passing a reference to a MultiLayerNetwork or ComputationGraph fit() method is all you need to begin a task for training:

MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();

// pass an MNIST data iterator that automatically fetches data
DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, rngSeed);
net.fit(mnistTrain);

Many other methods also accept iterators for tasks such as evaluation:

// passing directly to the neural network
DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, rngSeed);
net.eval(mnistTest);

// using an evaluation class
Evaluation eval = new Evaluation(10); //create an evaluation object with 10 possible classes
while(mnistTest.hasNext()){
    DataSet next = mnistTest.next();
    INDArray output = model.output(next.getFeatureMatrix()); //get the networks prediction
    eval.eval(next.getLabels(), output); //check the prediction against the true class
}

Available iterators

MnistDataSetIterator

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MNIST data set iterator - 60000 training digits, 10000 test digits, 10 classes. Digits have 28x28 pixels and 1 channel (grayscale). For futher details, see http://yann.lecun.com/exdb/mnist/

UciSequenceDataSetIterator

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UCI synthetic control chart time series dataset. This dataset is useful for classification of univariate time series with six categories: Normal, Cyclic, Increasing trend, Decreasing trend, Upward shift, Downward shift

Details: https://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series Data: https://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/synthetic_control.data Image: https://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/data.jpeg

UciSequenceDataSetIterator

public UciSequenceDataSetIterator(int batchSize)

Create an iterator for the training set, with the specified minibatch size. Randomized with RNG seed 123

param batchSize Minibatch size

Cifar10DataSetIterator

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CifarDataSetIterator is an iterator for CIFAR-10 dataset - 10 classes, with 32x32 images with 3 channels (RGB)

This fetcher uses a cached version of the CIFAR dataset which is converted to PNG images, see: https://pjreddie.com/projects/cifar-10-dataset-mirror/.

Cifar10DataSetIterator

public Cifar10DataSetIterator(int batchSize)

Create an iterator for the training set, with random iteration order (RNG seed fixed to 123)

param batchSize Minibatch size for the iterator

IrisDataSetIterator

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IrisDataSetIterator: An iterator for the well-known Iris dataset. 4 features, 3 label classes https://archive.ics.uci.edu/ml/datasets/Iris

IrisDataSetIterator

public IrisDataSetIterator()

next

public DataSet next()

IrisDataSetIterator handles traversing through the Iris Data Set.

see https://archive.ics.uci.edu/ml/datasets/Iris
param batch Batch size
param numExamples Total number of examples

LFWDataSetIterator

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LFW iterator - Labeled Faces from the Wild dataset See http://vis-www.cs.umass.edu/lfw/ 13233 images total, with 5749 classes.

LFWDataSetIterator

public LFWDataSetIterator(int batchSize, int numExamples, int[] imgDim, int numLabels, boolean useSubset,
                    PathLabelGenerator labelGenerator, boolean train, double splitTrainTest,
                    ImageTransform imageTransform, Random rng)

Create LFW data specific iterator

param batchSize the batch size of the examples
param numExamples the overall number of examples
param imgDim an array of height, width and channels
param numLabels the overall number of examples
param useSubset use a subset of the LFWDataSet
param labelGenerator path label generator to use
param train true if use train value
param splitTrainTest the percentage to split data for train and remainder goes to test
param imageTransform how to transform the image
param rng random number to lock in batch shuffling

TinyImageNetDataSetIterator

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Tiny ImageNet is a subset of the ImageNet database. TinyImageNet is the default course challenge for CS321n at Stanford University.

Tiny ImageNet has 200 classes, each consisting of 500 training images. Images are 64x64 pixels, RGB.

See: http://cs231n.stanford.edu/ and https://tiny-imagenet.herokuapp.com/

TinyImageNetDataSetIterator

public TinyImageNetDataSetIterator(int batchSize)

Create an iterator for the training set, with random iteration order (RNG seed fixed to 123)

param batchSize Minibatch size for the iterator

EmnistDataSetIterator

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EMNIST DataSetIterator

COMPLETE: Also known as 'ByClass' split. 814,255 examples total (train + test), 62 classes
MERGE: Also known as 'ByMerge' split. 814,255 examples total. 47 unbalanced classes. Combines lower and upper case characters (that are difficult to distinguish) into one class for each letter (instead of 2), for letters C, I, J, K, L, M, O, P, S, U, V, W, X, Y and Z
BALANCED: 131,600 examples total. 47 classes (equal number of examples in each class)
LETTERS: 145,600 examples total. 26 balanced classes
DIGITS: 280,000 examples total. 10 balanced classes

See: https://www.nist.gov/itl/iad/image-group/emnist-dataset and https://arxiv.org/abs/1702.05373

EmnistDataSetIterator

public EmnistDataSetIterator(Set dataSet, int batch, boolean train) throws IOException

EMNIST dataset has multiple different subsets. See {- link EmnistDataSetIterator} Javadoc for details.

numExamplesTrain

public static int numExamplesTrain(Set dataSet)

Create an EMNIST iterator with randomly shuffled data based on a specified RNG seed

param dataSet Dataset (subset) to return
param batchSize Batch size
param train If true: use training set. If false: use test set
param seed Random number generator seed

numExamplesTest

public static int numExamplesTest(Set dataSet)

Get the number of test examples for the specified subset

param dataSet Subset to get
return Number of examples for the specified subset

numLabels

public static int numLabels(Set dataSet)

Get the number of labels for the specified subset

param dataSet Subset to get
return Number of labels for the specified subset

isBalanced

public static boolean isBalanced(Set dataSet)

Get the labels as a character array

return Labels

RecordReaderDataSetIterator

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DataSet objects as well as producing minibatches from individual records.

Example 1: Image classification, batch size 32, 10 classes

rr.initialize(new FileSplit(new File("/path/to/directory")));

DataSetIterator iter = new RecordReaderDataSetIterator.Builder(rr, 32)
//Label index (first arg): Always value 1 when using ImageRecordReader. For CSV etc: use index of the column
//  that contains the label (should contain an integer value, 0 to nClasses-1 inclusive). Column indexes start
// at 0. Number of classes (second arg): number of label classes (i.e., 10 for MNIST - 10 digits)
.classification(1, nClasses)
.preProcessor(new ImagePreProcessingScaler())      //For normalization of image values 0-255 to 0-1
.build()
}

Example 2: Multi-output regression from CSV, batch size 128

rr.initialize(new FileSplit(new File("/path/to/myCsv.txt")));

DataSetIterator iter = new RecordReaderDataSetIterator.Builder(rr, 128)
//Specify the columns that the regression labels/targets appear in. Note that all other columns will be
// treated as features. Columns indexes start at 0
.regression(labelColFrom, labelColTo)
.build()
}

RecordReaderDataSetIterator

public RecordReaderDataSetIterator(RecordReader recordReader, int batchSize)

Constructor for classification, where: (a) the label index is assumed to be the very last Writable/column, and (b) the number of classes is inferred from RecordReader.getLabels() Note that if RecordReader.getLabels() returns null, no output labels will be produced

param recordReader Record reader to use as the source of data
param batchSize Minibatch size, for each call of .next()

setCollectMetaData

public void setCollectMetaData(boolean collectMetaData)

Main constructor for classification. This will convert the input class index (at position labelIndex, with integer values 0 to numPossibleLabels-1 inclusive) to the appropriate one-hot output/labels representation.

param recordReader RecordReader: provides the source of the data
param batchSize Batch size (number of examples) for the output DataSet objects
param labelIndex Index of the label Writable (usually an IntWritable), as obtained by recordReader.next()
param numPossibleLabels Number of classes (possible labels) for classification

loadFromMetaData

public DataSet loadFromMetaData(RecordMetaData recordMetaData) throws IOException

Load a single example to a DataSet, using the provided RecordMetaData. Note that it is more efficient to load multiple instances at once, using {- link #loadFromMetaData(List)}

param recordMetaData RecordMetaData to load from. Should have been produced by the given record reader
return DataSet with the specified example
throws IOException If an error occurs during loading of the data

loadFromMetaData

public DataSet loadFromMetaData(List<RecordMetaData> list) throws IOException

Load a multiple examples to a DataSet, using the provided RecordMetaData instances.

param list List of RecordMetaData instances to load from. Should have been produced by the record reader provided to the RecordReaderDataSetIterator constructor
return DataSet with the specified examples
throws IOException If an error occurs during loading of the data

writableConverter

public Builder writableConverter(WritableConverter converter)

Builder class for RecordReaderDataSetIterator

maxNumBatches

public Builder maxNumBatches(int maxNumBatches)

Optional argument, usually not used. If set, can be used to limit the maximum number of minibatches that will be returned (between resets). If not set, will always return as many minibatches as there is data available.

param maxNumBatches Maximum number of minibatches per epoch / reset

regression

public Builder regression(int labelIndex)

Use this for single output regression (i.e., 1 output/regression target)

param labelIndex Column index that contains the regression target (indexes start at 0)

regression

public Builder regression(int labelIndexFrom, int labelIndexTo)

Use this for multiple output regression (1 or more output/regression targets). Note that all regression targets must be contiguous (i.e., positions x to y, without gaps)

param labelIndexFrom Column index of the first regression target (indexes start at 0)
param labelIndexTo Column index of the last regression target (inclusive)

classification

public Builder classification(int labelIndex, int numClasses)

Use this for classification

param labelIndex Index that contains the label index. Column (indexes start from 0) be an integer value, and contain values 0 to numClasses-1
param numClasses Number of label classes (i.e., number of categories/classes in the dataset)

preProcessor

public Builder preProcessor(DataSetPreProcessor preProcessor)

Optional arg. Allows the preprocessor to be set

param preProcessor Preprocessor to use

collectMetaData

public Builder collectMetaData(boolean collectMetaData)

When set to true: metadata for the current examples will be present in the returned DataSet. Disabled by default.

param collectMetaData Whether metadata should be collected or not

RecordReaderMultiDataSetIterator

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The idea: generate multiple inputs and multiple outputs from one or more Sequence/RecordReaders. Inputs and outputs may be obtained from subsets of the RecordReader and SequenceRecordReaders columns (for examples, some inputs and outputs as different columns in the same record/sequence); it is also possible to mix different types of data (for example, using both RecordReaders and SequenceRecordReaders in the same RecordReaderMultiDataSetIterator). inputs and subsets.

RecordReaderMultiDataSetIterator

public RecordReaderMultiDataSetIterator build()

When dealing with time series data of different lengths, how should we align the input/labels time series? For equal length: use EQUAL_LENGTH For sequence classification: use ALIGN_END

loadFromMetaData

public MultiDataSet loadFromMetaData(RecordMetaData recordMetaData) throws IOException

Load a single example to a DataSet, using the provided RecordMetaData. Note that it is more efficient to load multiple instances at once, using {- link #loadFromMetaData(List)}

param recordMetaData RecordMetaData to load from. Should have been produced by the given record reader
return DataSet with the specified example
throws IOException If an error occurs during loading of the data

loadFromMetaData

public MultiDataSet loadFromMetaData(List<RecordMetaData> list) throws IOException

Load a multiple sequence examples to a DataSet, using the provided RecordMetaData instances.

param list List of RecordMetaData instances to load from. Should have been produced by the record reader provided to the SequenceRecordReaderDataSetIterator constructor
return DataSet with the specified examples
throws IOException If an error occurs during loading of the data

SequenceRecordReaderDataSetIterator

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Sequence record reader data set iterator. Given a record reader (and optionally another record reader for the labels) generate time series (sequence) data sets. Supports padding for one-to-many and many-to-one type data loading (i.e., with different number of inputs vs.

SequenceRecordReaderDataSetIterator

public SequenceRecordReaderDataSetIterator(SequenceRecordReader featuresReader, SequenceRecordReader labels,
                    int miniBatchSize, int numPossibleLabels)

Constructor where features and labels come from different RecordReaders (for example, different files), and labels are for classification.

param featuresReader SequenceRecordReader for the features
param labels Labels: assume single value per time step, where values are integers in the range 0 to numPossibleLables-1
param miniBatchSize Minibatch size for each call of next()
param numPossibleLabels Number of classes for the labels

hasNext

public boolean hasNext()

Constructor where features and labels come from different RecordReaders (for example, different files)

loadFromMetaData

public DataSet loadFromMetaData(RecordMetaData recordMetaData) throws IOException

Load a single sequence example to a DataSet, using the provided RecordMetaData. Note that it is more efficient to load multiple instances at once, using {- link #loadFromMetaData(List)}

param recordMetaData RecordMetaData to load from. Should have been produced by the given record reader
return DataSet with the specified example
throws IOException If an error occurs during loading of the data

loadFromMetaData

public DataSet loadFromMetaData(List<RecordMetaData> list) throws IOException

Load a multiple sequence examples to a DataSet, using the provided RecordMetaData instances.

param list List of RecordMetaData instances to load from. Should have been produced by the record reader provided to the SequenceRecordReaderDataSetIterator constructor
return DataSet with the specified examples
throws IOException If an error occurs during loading of the data

AsyncMultiDataSetIterator

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Async prefetching iterator wrapper for MultiDataSetIterator implementations This will asynchronously prefetch the specified number of minibatches from the underlying iterator. Also has the option (enabled by default for most constructors) to use a cyclical workspace to avoid creating INDArrays with off-heap memory that needs to be cleaned up by the JVM garbage collector.

Note that appropriate DL4J fit methods automatically utilize this iterator, so users don’t need to manually wrap their iterators when fitting a network

next

public MultiDataSet next(int num)

We want to ensure, that background thread will have the same thread->device affinity, as master thread

setPreProcessor

public void setPreProcessor(MultiDataSetPreProcessor preProcessor)

Set the preprocessor to be applied to each MultiDataSet, before each MultiDataSet is returned.

param preProcessor MultiDataSetPreProcessor. May be null.

resetSupported

public boolean resetSupported()

Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don’t

return true if reset method is supported; false otherwise

asyncSupported

public boolean asyncSupported()

Does this DataSetIterator support asynchronous prefetching of multiple DataSet objects? Most DataSetIterators do, but in some cases it may not make sense to wrap this iterator in an iterator that does asynchronous prefetching. For example, it would not make sense to use asynchronous prefetching for the following types of iterators: (a) Iterators that store their full contents in memory already (b) Iterators that re-use features/labels arrays (as future next() calls will overwrite past contents) (c) Iterators that already implement some level of asynchronous prefetching (d) Iterators that may return different data depending on when the next() method is called

return true if asynchronous prefetching from this iterator is OK; false if asynchronous prefetching should not be used with this iterator

reset

public void reset()

Resets the iterator back to the beginning

shutdown

public void shutdown()

We want to ensure, that background thread will have the same thread->device affinity, as master thread

hasNext

public boolean hasNext()

Returns {- code true} if the iteration has more elements. (In other words, returns {- code true} if {- link #next} would return an element rather than throwing an exception.)

return {- code true} if the iteration has more elements

next

public MultiDataSet next()

Returns the next element in the iteration.

return the next element in the iteration

remove

public void remove()

Removes from the underlying collection the last element returned by this iterator (optional operation). This method can be called only once per call to {- link #next}. The behavior of an iterator is unspecified if the underlying collection is modified while the iteration is in progress in any way other than by calling this method.

throws UnsupportedOperationException if the {- code remove} operation is not supported by this iterator
throws IllegalStateException if the {- code next} method has not yet been called, or the {- code remove} method has already been called after the last call to the {- code next} method
implSpec The default implementation throws an instance of {- link UnsupportedOperationException} and performs no other action.

IteratorDataSetIterator

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required to get the specified batch size.

Typically used in Spark training, but may be used elsewhere. NOTE: reset method is not supported here.

AsyncDataSetIterator

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Async prefetching iterator wrapper for DataSetIterator implementations. This will asynchronously prefetch the specified number of minibatches from the underlying iterator. Also has the option (enabled by default for most constructors) to use a cyclical workspace to avoid creating INDArrays with off-heap memory that needs to be cleaned up by the JVM garbage collector.

Note that appropriate DL4J fit methods automatically utilize this iterator, so users don’t need to manually wrap their iterators when fitting a network

AsyncDataSetIterator

public AsyncDataSetIterator(DataSetIterator baseIterator)

Create an Async iterator with the default queue size of 8

param baseIterator Underlying iterator to wrap and fetch asynchronously from

next

public DataSet next(int num)

Create an Async iterator with the default queue size of 8

param iterator Underlying iterator to wrap and fetch asynchronously from
param queue Queue size - number of iterators to

inputColumns

public int inputColumns()

Input columns for the dataset

return

totalOutcomes

public int totalOutcomes()

The number of labels for the dataset

return

resetSupported

public boolean resetSupported()

Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don’t

return true if reset method is supported; false otherwise

asyncSupported

public boolean asyncSupported()

return true if asynchronous prefetching from this iterator is OK; false if asynchronous prefetching should not be used with this iterator

reset

public void reset()

Resets the iterator back to the beginning

shutdown

public void shutdown()

We want to ensure, that background thread will have the same thread->device affinity, as master thread

batch

public int batch()

Batch size

return

setPreProcessor

public void setPreProcessor(DataSetPreProcessor preProcessor)

Set a pre processor

param preProcessor a pre processor to set

getPreProcessor

public DataSetPreProcessor getPreProcessor()

Returns preprocessors, if defined

return

hasNext

public boolean hasNext()

Get dataset iterator record reader labels

next

public DataSet next()

Returns the next element in the iteration.

return the next element in the iteration

remove

public void remove()

throws UnsupportedOperationException if the {- code remove} operation is not supported by this iterator
throws IllegalStateException if the {- code next} method has not yet been called, or the {- code remove} method has already been called after the last call to the {- code next} method
implSpec The default implementation throws an instance of {- link UnsupportedOperationException} and performs no other action.

DoublesDataSetIterator

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First value in pair is the features vector, second value in pair is the labels. Supports generating 2d features/labels only

DoublesDataSetIterator

public DoublesDataSetIterator(@NonNull Iterable<Pair<double[], double[]>> iterable, int batchSize)

param iterable Iterable to source data from
param batchSize Batch size for generated DataSet objects

IteratorMultiDataSetIterator

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required to get a specified batch size.

Typically used in Spark training, but may be used elsewhere. NOTE: reset method is not supported here.

SamplingDataSetIterator

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A wrapper for a dataset to sample from. This will randomly sample from the given dataset.

SamplingDataSetIterator

public SamplingDataSetIterator(DataSet sampleFrom, int batchSize, int totalNumberSamples)

INDArrayDataSetIterator

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First value in pair is the features vector, second value in pair is the labels.

INDArrayDataSetIterator

public INDArrayDataSetIterator(@NonNull Iterable<Pair<INDArray, INDArray>> iterable, int batchSize)

param iterable Iterable to source data from
param batchSize Batch size for generated DataSet objects

WorkspacesShieldDataSetIterator

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This iterator detaches/migrates DataSets coming out from backed DataSetIterator, thus providing “safe” DataSets. This is typically used for debugging and testing purposes, and should not be used in general by users

WorkspacesShieldDataSetIterator

public WorkspacesShieldDataSetIterator(@NonNull DataSetIterator iterator)

param iterator The underlying iterator to detach values from

MultiDataSetIteratorSplitter

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This iterator virtually splits given MultiDataSetIterator into Train and Test parts. I.e. you have 100000 examples. Your batch size is 32. That means you have 3125 total batches. With split ratio of 0.7 that will give you 2187 training batches, and 938 test batches.

PLEASE NOTE: You can’t use Test iterator twice in a row. Train iterator should be used before Test iterator use. PLEASE NOTE: You can’t use this iterator, if underlying iterator uses randomization/shuffle between epochs.

MultiDataSetIteratorSplitter

public MultiDataSetIteratorSplitter(@NonNull MultiDataSetIterator baseIterator, long totalBatches, double ratio)

param baseIterator
param totalBatches - total number of batches in underlying iterator. this value will be used to determine number of test/train batches
param ratio - this value will be used as splitter. should be between in range of 0.0 > X < 1.0. I.e. if value 0.7 is provided, then 70% of total examples will be used for training, and 30% of total examples will be used for testing

getTrainIterator

public MultiDataSetIterator getTrainIterator()

This method returns train iterator instance

return

next

public MultiDataSet next(int num)

This method returns test iterator instance

return

AsyncShieldDataSetIterator

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This wrapper takes your existing DataSetIterator implementation and prevents asynchronous prefetch This is mainly used for debugging purposes; generally an iterator that isn’t safe to asynchronously prefetch from

AsyncShieldDataSetIterator

public AsyncShieldDataSetIterator(@NonNull DataSetIterator iterator)

param iterator Iterator to wrop, to disable asynchronous prefetching for

next

public DataSet next(int num)

Like the standard next method but allows a customizable number of examples returned

param num the number of examples
return the next data applyTransformToDestination

inputColumns

public int inputColumns()

Input columns for the dataset

return

totalOutcomes

public int totalOutcomes()

The number of labels for the dataset

return

resetSupported

public boolean resetSupported()

Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don’t

return true if reset method is supported; false otherwise

asyncSupported

public boolean asyncSupported()

Does this DataSetIterator support asynchronous prefetching of multiple DataSet objects?

PLEASE NOTE: This iterator ALWAYS returns FALSE

return true if asynchronous prefetching from this iterator is OK; false if asynchronous prefetching should not be used with this iterator

reset

public void reset()

Resets the iterator back to the beginning

batch

public int batch()

Batch size

return

setPreProcessor

public void setPreProcessor(DataSetPreProcessor preProcessor)

Set a pre processor

param preProcessor a pre processor to set

getPreProcessor

public DataSetPreProcessor getPreProcessor()

Returns preprocessors, if defined

return

hasNext

public boolean hasNext()

Get dataset iterator record reader labels

next

public DataSet next()

Returns the next element in the iteration.

return the next element in the iteration

remove

public void remove()

throws UnsupportedOperationException if the {- code remove} operation is not supported by this iterator
throws IllegalStateException if the {- code next} method has not yet been called, or the {- code remove} method has already been called after the last call to the {- code next} method
implSpec The default implementation throws an instance of {- link UnsupportedOperationException} and performs no other action.

DummyBlockDataSetIterator

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This class provides baseline implementation of BlockDataSetIterator interface

BaseDatasetIterator

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Baseline implementation includes control over the data fetcher and some basic getters for metadata

AsyncShieldMultiDataSetIterator

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This wrapper takes your existing MultiDataSetIterator implementation and prevents asynchronous prefetch

next

public MultiDataSet next(int num)

Fetch the next ‘num’ examples. Similar to the next method, but returns a specified number of examples

param num Number of examples to fetch

setPreProcessor

public void setPreProcessor(MultiDataSetPreProcessor preProcessor)

Set the preprocessor to be applied to each MultiDataSet, before each MultiDataSet is returned.

param preProcessor MultiDataSetPreProcessor. May be null.

resetSupported

public boolean resetSupported()

Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don’t

return true if reset method is supported; false otherwise

asyncSupported

public boolean asyncSupported()

/ Does this DataSetIterator support asynchronous prefetching of multiple DataSet objects?

PLEASE NOTE: This iterator ALWAYS returns FALSE

return true if asynchronous prefetching from this iterator is OK; false if asynchronous prefetching should not be used with this iterator

reset

public void reset()

Resets the iterator back to the beginning

hasNext

public boolean hasNext()

Returns {- code true} if the iteration has more elements. (In other words, returns {- code true} if {- link #next} would return an element rather than throwing an exception.)

return {- code true} if the iteration has more elements

next

public MultiDataSet next()

Returns the next element in the iteration.

return the next element in the iteration

remove

public void remove()

throws UnsupportedOperationException if the {- code remove} operation is not supported by this iterator
throws IllegalStateException if the {- code next} method has not yet been called, or the {- code remove} method has already been called after the last call to the {- code next} method
implSpec The default implementation throws an instance of {- link UnsupportedOperationException} and performs no other action.

RandomMultiDataSetIterator

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RandomMultiDataSetIterator: Generates random values (or zeros, ones, integers, etc) according to some distribution. Note: This is typically used for testing, debugging and benchmarking purposes.

RandomMultiDataSetIterator

public RandomMultiDataSetIterator(int numMiniBatches, @NonNull List<Triple<long[], Character, Values>> features, @NonNull List<Triple<long[], Character, Values>> labels)

param numMiniBatches Number of minibatches per epoch
param features Each triple in the list specifies the shape, array order and type of values for the features arrays
param labels Each triple in the list specifies the shape, array order and type of values for the labels arrays

addFeatures

public Builder addFeatures(long[] shape, Values values)

param numMiniBatches Number of minibatches per epoch

addFeatures

public Builder addFeatures(long[] shape, char order, Values values)

Add a new features array to the iterator

param shape Shape of the features
param order Order (‘c’ or ‘f’) for the array
param values Values to fill the array with

addLabels

public Builder addLabels(long[] shape, Values values)

Add a new labels array to the iterator

param shape Shape of the features
param values Values to fill the array with

addLabels

public Builder addLabels(long[] shape, char order, Values values)

Add a new labels array to the iterator

param shape Shape of the features
param order Order (‘c’ or ‘f’) for the array
param values Values to fill the array with

generate

public static INDArray generate(long[] shape, Values values)

Generate a random array with the specified shape

param shape Shape of the array
param values Values to fill the array with
return Random array of specified shape + contents

generate

public static INDArray generate(long[] shape, char order, Values values)

Generate a random array with the specified shape and order

param shape Shape of the array
param order Order of array (‘c’ or ‘f’)
param values Values to fill the array with
return Random array of specified shape + contents

EarlyTerminationMultiDataSetIterator

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Builds an iterator that terminates once the number of minibatches returned with .next() is equal to a specified number. Note that a call to .next(num) is counted as a call to return a minibatch regardless of the value of num This essentially restricts the data to this specified number of minibatches.

EarlyTerminationMultiDataSetIterator

public EarlyTerminationMultiDataSetIterator(MultiDataSetIterator underlyingIterator, int terminationPoint)

Constructor takes the iterator to wrap and the number of minibatches after which the call to hasNext() will return false

param underlyingIterator, iterator to wrap
param terminationPoint, minibatches after which hasNext() will return false

ExistingDataSetIterator

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ExistingDataSetIterator

public ExistingDataSetIterator(@NonNull Iterator<DataSet> iterator)

Note that when using this constructor, resetting is not supported

param iterator Iterator to wrap

next

public DataSet next(int num)

Note that when using this constructor, resetting is not supported

param iterator Iterator to wrap
param labels String labels. May be null.

DummyBlockMultiDataSetIterator

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This class provides baseline implementation of BlockMultiDataSetIterator interface

EarlyTerminationDataSetIterator

[source]

EarlyTerminationDataSetIterator

public EarlyTerminationDataSetIterator(DataSetIterator underlyingIterator, int terminationPoint)

Constructor takes the iterator to wrap and the number of minibatches after which the call to hasNext() will return false

param underlyingIterator, iterator to wrap
param terminationPoint, minibatches after which hasNext() will return false

ReconstructionDataSetIterator

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Wraps a data set iterator setting the first (feature matrix) as the labels.

next

public DataSet next(int num)

Like the standard next method but allows a customizable number of examples returned

param num the number of examples
return the next data applyTransformToDestination

inputColumns

public int inputColumns()

Input columns for the dataset

return

totalOutcomes

public int totalOutcomes()

The number of labels for the dataset

return

reset

public void reset()

Resets the iterator back to the beginning

batch

public int batch()

Batch size

return

hasNext

public boolean hasNext()

Returns {- code true} if the iteration has more elements. (In other words, returns {- code true} if {- link #next} would return an element rather than throwing an exception.)

return {- code true} if the iteration has more elements

next

public DataSet next()

Returns the next element in the iteration.

return the next element in the iteration

remove

public void remove()

throws UnsupportedOperationException if the {- code remove} operation is not supported by this iterator
throws IllegalStateException if the {- code next} method has not yet been called, or the {- code remove} method has already been called after the last call to the {- code next} method

DataSetIteratorSplitter

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DataSetIteratorSplitter

public DataSetIteratorSplitter(@NonNull DataSetIterator baseIterator, long totalBatches, double ratio)

The only constructor

param baseIterator - iterator to be wrapped and split
param totalBatches - total batches in baseIterator
param ratio - train/test split ratio

getTrainIterator

public DataSetIterator getTrainIterator()

This method returns train iterator instance

return

next

public DataSet next(int i)

This method returns test iterator instance

return

JointMultiDataSetIterator

[source]

This dataset iterator combines multiple DataSetIterators into 1 MultiDataSetIterator. Values from each iterator are joined on a per-example basis - i.e., the values from each DataSet are combined as different feature arrays for a multi-input neural network. Labels can come from either one of the underlying DataSetIteartors only (if ‘outcome’ is >= 0) or from all iterators (if outcome is < 0)

JointMultiDataSetIterator

public JointMultiDataSetIterator(DataSetIterator... iterators)

param iterators Underlying iterators to wrap

next

public MultiDataSet next(int num)

param outcome Index to get the label from. If < 0, labels from all iterators will be used to create the final MultiDataSet
param iterators Underlying iterators to wrap

setPreProcessor

public void setPreProcessor(MultiDataSetPreProcessor preProcessor)

Set the preprocessor to be applied to each MultiDataSet, before each MultiDataSet is returned.

param preProcessor MultiDataSetPreProcessor. May be null.

getPreProcessor

public MultiDataSetPreProcessor getPreProcessor()

Get the {- link MultiDataSetPreProcessor}, if one has previously been set. Returns null if no preprocessor has been set

return Preprocessor

resetSupported

public boolean resetSupported()

Is resetting supported by this DataSetIterator? Many DataSetIterators do support resetting, but some don’t

return true if reset method is supported; false otherwise

asyncSupported

public boolean asyncSupported()

Does this MultiDataSetIterator support asynchronous prefetching of multiple MultiDataSet objects? Most MultiDataSetIterators do, but in some cases it may not make sense to wrap this iterator in an iterator that does asynchronous prefetching. For example, it would not make sense to use asynchronous prefetching for the following types of iterators: (a) Iterators that store their full contents in memory already (b) Iterators that re-use features/labels arrays (as future next() calls will overwrite past contents) (c) Iterators that already implement some level of asynchronous prefetching (d) Iterators that may return different data depending on when the next() method is called

return true if asynchronous prefetching from this iterator is OK; false if asynchronous prefetching should not be used with this iterator

reset

public void reset()

Resets the iterator back to the beginning

hasNext

public boolean hasNext()

Returns {- code true} if the iteration has more elements. (In other words, returns {- code true} if {- link #next} would return an element rather than throwing an exception.)

return {- code true} if the iteration has more elements

next

public MultiDataSet next()

Returns the next element in the iteration.

return the next element in the iteration

remove

public void remove()

PLEASE NOTE: This method is NOT implemented

throws UnsupportedOperationException if the {- code remove} operation is not supported by this iterator
throws IllegalStateException if the {- code next} method has not yet been called, or the {- code remove} method has already been called after the last call to the {- code next} method
implSpec The default implementation throws an instance of {- link UnsupportedOperationException} and performs no other action.

FloatsDataSetIterator

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First value in pair is the features vector, second value in pair is the labels. Supports generating 2d features/labels only

FloatsDataSetIterator

public FloatsDataSetIterator(@NonNull Iterable<Pair<float[], float[]>> iterable, int batchSize)

param iterable Iterable to source data from
param batchSize Batch size for generated DataSet objects

FileSplitDataSetIterator

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Simple iterator working with list of files. File to DataSet conversion will be handled via provided FileCallback implementation

FileSplitDataSetIterator

public FileSplitDataSetIterator(@NonNull List<File> files, @NonNull FileCallback callback)

param files List of files to iterate over
param callback Callback for loading the files

MultipleEpochsIterator

[source]

A dataset iterator for doing multiple passes over a dataset

Use MultiLayerNetwork/ComputationGraph.fit(DataSetIterator, int numEpochs) instead

next

public DataSet next(int num)

Like the standard next method but allows a customizable number of examples returned

param num the number of examples
return the next data applyTransformToDestination

inputColumns

public int inputColumns()

Input columns for the dataset

return

totalOutcomes

public int totalOutcomes()

The number of labels for the dataset

return

reset

public void reset()

Resets the iterator back to the beginning

batch

public int batch()

Batch size

return

hasNext

public boolean hasNext()

Returns {- code true} if the iteration has more elements. (In other words, returns {- code true} if {- link #next} would return an element rather than throwing an exception.)

return {- code true} if the iteration has more elements

remove

public void remove()

throws UnsupportedOperationException if the {- code remove} operation is not supported by this iterator
throws IllegalStateException if the {- code next} method has not yet been called, or the {- code remove} method has already been called after the last call to the {- code next} method

MultiDataSetWrapperIterator

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This class is simple wrapper that takes single-input MultiDataSets and converts them to DataSets on the fly

PLEASE NOTE: This only works if number of features/labels/masks is 1

MultiDataSetWrapperIterator

public MultiDataSetWrapperIterator(MultiDataSetIterator iterator)

param iterator Undelying iterator to wrap

RandomDataSetIterator

[source]

RandomDataSetIterator: Generates random values (or zeros, ones, integers, etc) according to some distribution. Note: This is typically used for testing, debugging and benchmarking purposes.

RandomDataSetIterator

public RandomDataSetIterator(int numMiniBatches, long[] featuresShape, long[] labelsShape, Values featureValues, Values labelValues)

param numMiniBatches Number of minibatches per epoch
param featuresShape Features shape
param labelsShape Labels shape
param featureValues Type of values for the features
param labelValues Type of values for the labels

MultiDataSetIteratorAdapter

[source]

Iterator that adapts a DataSetIterator to a MultiDataSetIterator

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