| Modifier and Type | Method and Description |
|---|---|
void |
SameDiff.evaluate(@NonNull DataSetIterator iterator,
@NonNull Map<String,IEvaluation> variableEvals,
Listener... listeners)
Evaluation for multiple-output networks.
See SameDiff.evaluate(MultiDataSetIterator, Map, Map, Listener[]). |
void |
SameDiff.evaluate(@NonNull DataSetIterator iterator,
@NonNull String outputVariable,
IEvaluation... evaluations)
|
void |
SameDiff.evaluate(@NonNull DataSetIterator iterator,
@NonNull String outputVariable,
@NonNull List<Listener> listeners,
IEvaluation... evaluations)
Evaluate the performance of a single variable's prediction.
For example, if the variable to evaluatate was called "softmax" you would use: |
void |
SameDiff.evaluateMultiple(DataSetIterator iterator,
Map<String,List<IEvaluation>> variableEvals,
Listener... listeners)
Evaluation for multiple output networks - one or more.
|
History |
SameDiff.fit(@NonNull DataSetIterator iter,
int numEpochs,
DataSetIterator validationIter,
int validationFrequency,
Listener... listeners)
Fit the SameDiff instance based on DataSetIterator for the specified number of epochs.
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. |
History |
SameDiff.fit(@NonNull DataSetIterator iter,
int numEpochs,
DataSetIterator validationIter,
int validationFrequency,
Listener... listeners)
Fit the SameDiff instance based on DataSetIterator for the specified number of epochs.
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. |
History |
SameDiff.fit(@NonNull DataSetIterator iter,
int numEpochs,
Listener... listeners)
See
SameDiff.fit(DataSetIterator, int, DataSetIterator, int, Listener...), does not preform validation. |
Map<String,INDArray> |
SameDiff.output(@NonNull DataSetIterator iterator,
@NonNull List<Listener> listeners,
String... outputs)
Do inference on a network with a single input.
For example, if the variable to infer was called "softmax" you would use: |
Map<String,INDArray> |
SameDiff.output(@NonNull DataSetIterator dataSet,
String... outputs)
|
List<Map<String,INDArray>> |
SameDiff.outputBatches(DataSetIterator iterator,
List<Listener> listeners,
String... outputs)
See
SameDiff.output(DataSetIterator, List, String...), but without the concatenation of batches. |
List<Map<String,INDArray>> |
SameDiff.outputBatches(DataSetIterator iterator,
String... outputs)
See
SameDiff.output(DataSetIterator, String...), but without the concatenation of batches. |
| Modifier and Type | Method and Description |
|---|---|
EvaluationConfig |
EvaluationConfig.data(@NonNull DataSetIterator data)
Set the data to evaluate on.
|
OutputConfig |
OutputConfig.data(@NonNull DataSetIterator data)
Set the data to use as input.
|
FitConfig |
FitConfig.train(@NonNull DataSetIterator trainingData)
Set the training data
|
FitConfig |
FitConfig.train(@NonNull DataSetIterator trainingData,
int epochs)
Set the training data and number of epochs
|
FitConfig |
FitConfig.validate(DataSetIterator validationData)
Set the validation data
|
FitConfig |
FitConfig.validate(DataSetIterator validationData,
int validationFrequency)
Set the validation data and frequency
|
| Modifier and Type | Class and Description |
|---|---|
class |
AsyncDataSetIterator |
class |
ExistingMiniBatchDataSetIterator |
class |
MiniBatchFileDataSetIterator |
class |
ViewIterator
Iterate over a dataset
with views
|
| Modifier and Type | Field and Description |
|---|---|
protected DataSetIterator |
AsyncDataSetIterator.backedIterator |
| Modifier and Type | Method and Description |
|---|---|
DataSetIterator |
DataSet.iterateWithMiniBatches() |
| Constructor and Description |
|---|
AsyncDataSetIterator(DataSetIterator baseIterator)
Create an Async iterator with the default queue size of 8
|
AsyncDataSetIterator(DataSetIterator baseIterator,
int queueSize) |
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) |
AsyncDataSetIterator(DataSetIterator baseIterator,
int queueSize,
boolean useWorkspace) |
AsyncDataSetIterator(DataSetIterator baseIterator,
int queueSize,
boolean useWorkspace,
DataSetCallback callback) |
AsyncDataSetIterator(DataSetIterator baseIterator,
int queueSize,
boolean useWorkspace,
Integer deviceId) |
AsyncPrefetchThread(@NonNull BlockingQueue<DataSet> queue,
@NonNull DataSetIterator iterator,
@NonNull DataSet terminator,
MemoryWorkspace workspace,
int deviceId) |
| Modifier and Type | Class and Description |
|---|---|
class |
SingletonDataSetIterator |
| Constructor and Description |
|---|
MultiDataSetIteratorAdapter(DataSetIterator iter) |
| Modifier and Type | Method and Description |
|---|---|
DataSetIterator |
DataSet.iterateWithMiniBatches()
Deprecated.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ParallelDataSetIterator |
| Modifier and Type | Class and Description |
|---|---|
class |
BaseDatasetIterator |
class |
CachingDataSetIterator |
class |
KFoldIterator |
class |
MultipleEpochsIterator
Deprecated.
|
class |
SamplingDataSetIterator
A wrapper for a dataset to sample from.
|
class |
TestDataSetIterator |
| Modifier and Type | Method and Description |
|---|---|
DataSetIterator |
DataSetIteratorFactory.create() |
| Modifier and Type | Method and Description |
|---|---|
void |
StandardScaler.fit(DataSetIterator iterator)
Deprecated.
Fit the given model
|
| Constructor and Description |
|---|
CachingDataSetIterator(DataSetIterator sourceIterator,
DataSetCache cache) |
CachingDataSetIterator(DataSetIterator sourceIterator,
DataSetCache cache,
String namespace) |
CachingDataSetIterator(DataSetIterator sourceIterator,
DataSetCache cache,
String namespace,
boolean allowPrefetching) |
MultipleEpochsIterator(int numPasses,
DataSetIterator iter)
Deprecated.
|
| Modifier and Type | Method and Description |
|---|---|
void |
AbstractDataSetNormalizer.fit(DataSetIterator iterator)
Fit the given model
|
void |
DataNormalization.fit(DataSetIterator iterator)
Iterates over a dataset
accumulating statistics for normalization
|
void |
ImagePreProcessingScaler.fit(DataSetIterator iterator)
Iterates over a dataset
accumulating statistics for normalization
|
void |
VGG16ImagePreProcessor.fit(DataSetIterator iterator)
Iterates over a dataset
accumulating statistics for normalization
|
| Modifier and Type | Method and Description |
|---|---|
static Task |
TaskUtils.buildTask(DataSetIterator dataSetIterator) |
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