Modifier and Type | Method and Description |
---|---|
History |
SameDiff.fit(@NonNull DataSet dataSet,
Listener... listeners)
Fit the SameDiff instance based on a single DataSet (i.e., a single minibatch for one iteration).
This method can only be used for singe input, single output SameDiff instances as DataSet only supports a single input and a single output. Note that a TrainingConfig must be set via SameDiff.setTrainingConfig(TrainingConfig) before training can
be performed. |
Map<String,INDArray> |
SameDiff.output(@NonNull DataSet dataSet,
String... outputs)
Do a single batch inference on a network with a single input.
For example, if the variable to infer was called "softmax" you would use: |
Modifier and Type | Field and Description |
---|---|
protected DataSet |
AsyncDataSetIterator.nextElement |
protected DataSet |
AsyncDataSetIterator.terminator |
Modifier and Type | Field and Description |
---|---|
protected BlockingQueue<DataSet> |
AsyncDataSetIterator.buffer |
Modifier and Type | Method and Description |
---|---|
DataSet |
DataSet.copy()
Clone the dataset
|
static DataSet |
DataSet.empty()
Returns a single dataset (all fields are null)
|
DataSet |
DataSet.filterBy(int[] labels)
Strips the data transform of all but the passed in labels
|
DataSet |
DataSet.get(int i)
Gets a copy of example i
|
DataSet |
DataSet.get(int[] i)
Gets a copy of example i
|
DataSet |
SplitTestAndTrain.getTest() |
DataSet |
SplitTestAndTrain.getTrain() |
static DataSet |
DataSet.merge(List<? extends DataSet> data)
Merge the list of datasets in to one list.
|
DataSet |
AsyncDataSetIterator.next()
Returns the next element in the iteration.
|
DataSet |
ExistingMiniBatchDataSetIterator.next() |
DataSet |
ViewIterator.next() |
DataSet |
MiniBatchFileDataSetIterator.next() |
DataSet |
AsyncDataSetIterator.next(int num)
Like the standard next method but allows a
customizable number of examples returned
|
DataSet |
ExistingMiniBatchDataSetIterator.next(int num) |
DataSet |
ViewIterator.next(int num) |
DataSet |
MiniBatchFileDataSetIterator.next(int num) |
DataSet |
DataSet.reshape(int rows,
int cols)
Reshapes the input in to the given rows and columns
|
DataSet |
DataSet.sample(int numSamples)
Sample without replacement and a random rng
|
DataSet |
DataSet.sample(int numSamples,
boolean withReplacement)
Sample a dataset numSamples times
|
DataSet |
DataSet.sample(int numSamples,
Random rng)
Sample without replacement
|
DataSet |
DataSet.sample(int numSamples,
Random rng,
boolean withReplacement)
Sample a dataset
|
Modifier and Type | Method and Description |
---|---|
List<DataSet> |
DataSet.asList() |
List<DataSet> |
DataSet.batchBy(int num)
Partitions a dataset in to mini batches where
each dataset in each list is of the specified number of examples
|
List<DataSet> |
DataSet.batchByNumLabels() |
List<DataSet> |
DataSet.dataSetBatches(int num)
Partitions the data transform by the specified number.
|
Iterator<DataSet> |
DataSet.iterator() |
List<DataSet> |
DataSet.sortAndBatchByNumLabels()
Sorts the dataset by label:
Splits the data transform such that examples are sorted by their labels.
|
Modifier and Type | Method and Description |
---|---|
void |
DataSet.addRow(DataSet d,
int i) |
void |
SplitTestAndTrain.setTest(DataSet test) |
void |
SplitTestAndTrain.setTrain(DataSet train) |
Constructor and Description |
---|
AsyncPrefetchThread(@NonNull BlockingQueue<DataSet> queue,
@NonNull DataSetIterator iterator,
@NonNull DataSet terminator,
MemoryWorkspace workspace,
int deviceId) |
MiniBatchFileDataSetIterator(DataSet baseData,
int batchSize) |
MiniBatchFileDataSetIterator(DataSet baseData,
int batchSize,
boolean delete) |
MiniBatchFileDataSetIterator(DataSet baseData,
int batchSize,
boolean delete,
File rootDir) |
SplitTestAndTrain(DataSet train,
DataSet test) |
ViewIterator(DataSet data,
int batchSize) |
Constructor and Description |
---|
AsyncDataSetIterator(DataSetIterator iterator,
int queueSize,
BlockingQueue<DataSet> queue)
Create an Async iterator with the default queue size of 8
|
AsyncDataSetIterator(DataSetIterator iterator,
int queueSize,
BlockingQueue<DataSet> queue,
boolean useWorkspace) |
AsyncDataSetIterator(DataSetIterator iterator,
int queueSize,
BlockingQueue<DataSet> queue,
boolean useWorkspace,
DataSetCallback callback) |
AsyncDataSetIterator(DataSetIterator iterator,
int queueSize,
BlockingQueue<DataSet> queue,
boolean useWorkspace,
DataSetCallback callback,
Integer deviceId) |
Modifier and Type | Method and Description |
---|---|
DataSet |
SingletonDataSetIterator.next() |
DataSet |
SingletonDataSetIterator.next(int num) |
Constructor and Description |
---|
SingletonDataSetIterator(DataSet multiDataSet) |
Modifier and Type | Method and Description |
---|---|
DataSet |
DataSet.copy()
Create a copy of the DataSet
|
DataSet |
DataSet.filterBy(int[] labels) |
DataSet |
DataSet.get(int i) |
DataSet |
DataSet.get(int[] i) |
DataSet |
DataSet.reshape(int rows,
int cols) |
DataSet |
DataSet.sample(int numSamples) |
DataSet |
DataSet.sample(int numSamples,
boolean withReplacement) |
DataSet |
DataSet.sample(int numSamples,
Random rng) |
DataSet |
DataSet.sample(int numSamples,
Random rng,
boolean withReplacement) |
Modifier and Type | Method and Description |
---|---|
List<DataSet> |
DataSet.asList()
Extract each example in the DataSet into its own DataSet object, and return all of them as a list
|
List<DataSet> |
DataSet.batchBy(int num) |
List<DataSet> |
DataSet.batchByNumLabels() |
List<DataSet> |
DataSet.dataSetBatches(int num)
Deprecated.
prefer
DataSet.batchBy(int) |
Iterator<DataSet> |
DataSet.iterator() |
List<DataSet> |
DataSet.sortAndBatchByNumLabels() |
Modifier and Type | Method and Description |
---|---|
void |
DataSet.addRow(DataSet d,
int i) |
Modifier and Type | Field and Description |
---|---|
protected DataSet |
KFoldIterator.allData |
protected DataSet |
KFoldIterator.test |
protected DataSet |
KFoldIterator.train |
Modifier and Type | Method and Description |
---|---|
DataSet |
KFoldIterator.next() |
DataSet |
MultipleEpochsIterator.next()
Deprecated.
Returns the next element in the iteration.
|
DataSet |
SamplingDataSetIterator.next() |
DataSet |
TestDataSetIterator.next() |
DataSet |
BaseDatasetIterator.next() |
DataSet |
CachingDataSetIterator.next() |
DataSet |
KFoldIterator.next(int num) |
DataSet |
DataSetIterator.next(int num)
Like the standard next method but allows a
customizable number of examples returned
|
DataSet |
MultipleEpochsIterator.next(int num)
Deprecated.
Like the standard next method but allows a
customizable number of examples returned
|
DataSet |
SamplingDataSetIterator.next(int num) |
DataSet |
TestDataSetIterator.next(int num) |
DataSet |
BaseDatasetIterator.next(int num) |
DataSet |
CachingDataSetIterator.next(int num) |
DataSet |
ParallelDataSetIterator.nextFor()
Returns next DataSet for attached consumer
|
DataSet |
ParallelDataSetIterator.nextFor(int consumer)
Returns next DataSet for given consumer
|
DataSet |
KFoldIterator.testFold() |
Modifier and Type | Method and Description |
---|---|
void |
StandardScaler.fit(DataSet dataSet)
Deprecated.
|
void |
StandardScaler.transform(DataSet dataSet)
Deprecated.
Transform the data
|
Constructor and Description |
---|
KFoldIterator(DataSet allData)
Create a k-fold cross-validation iterator given the dataset and k=10 train-test splits.
|
KFoldIterator(int k,
DataSet allData)
Create an iterator given the dataset with given k train-test splits
N number of samples are split into k batches.
|
SamplingDataSetIterator(DataSet sampleFrom,
int batchSize,
int totalNumberSamples) |
SamplingDataSetIterator(DataSet sampleFrom,
int batchSize,
int totalNumberSamples,
boolean replace) |
TestDataSetIterator(DataSet dataset)
This makes an iterator from the given dataset and batchsize
ONLY for use in tests in nd4j
Initializes with a default batch of 5
|
TestDataSetIterator(DataSet dataset,
int batch) |
Constructor and Description |
---|
TestDataSetIterator(List<DataSet> coll,
int batch) |
Modifier and Type | Method and Description |
---|---|
DataSet |
DataSetCache.get(String key) |
DataSet |
InMemoryDataSetCache.get(String key) |
DataSet |
InFileDataSetCache.get(String key) |
DataSet |
InFileAndMemoryDataSetCache.get(String key) |
Modifier and Type | Method and Description |
---|---|
void |
DataSetCache.put(String key,
DataSet dataSet) |
void |
InMemoryDataSetCache.put(String key,
DataSet dataSet) |
void |
InFileDataSetCache.put(String key,
DataSet dataSet) |
void |
InFileAndMemoryDataSetCache.put(String key,
DataSet dataSet) |
Modifier and Type | Field and Description |
---|---|
protected DataSet |
BaseDataFetcher.curr |
Modifier and Type | Method and Description |
---|---|
DataSet |
BaseDataFetcher.next() |
DataSet |
DataSetFetcher.next()
Returns the next data applyTransformToDestination
|
Modifier and Type | Method and Description |
---|---|
protected void |
BaseDataFetcher.initializeCurrFromList(List<DataSet> examples)
Initializes this data transform fetcher from the passed in datasets
|
Modifier and Type | Method and Description |
---|---|
static Task |
TaskUtils.buildTask(DataSet dataSet) |
Modifier and Type | Method and Description |
---|---|
void |
DataSetUtils.showDataSet(int mtLv,
String itemCode,
DataSet ds,
int in_Digits,
int ot_Digits,
int r_End_I,
int c_End_I)
showDataSet
public void showDataSet( int mtLv, String itemCode, DataSet ds, int in_Digits, int ot_Digits, int r_End_I, int c_End_I ) Shows content of DataSet. |
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