Modifier and Type | Method and Description |
---|---|
void |
BaseListener.activationAvailable(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation) |
void |
Listener.activationAvailable(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation)
Called when any activation becomes available.
|
void |
BaseEvaluationListener.activationAvailable(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation) |
void |
BaseEvaluationListener.activationAvailableEvaluations(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
String varName,
INDArray activation)
|
void |
BaseListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
void |
Listener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss)
Called at the end of every iteration, after all operations (including updating parameters) has been completed
|
void |
BaseListener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlMs) |
void |
Listener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlTimeMs)
Called at the start of every iteration (minibatch), before any operations have been executed
|
void |
BaseListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
INDArray[] outputs) |
void |
Listener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
INDArray[] outputs)
Called at the end of each operation execution
|
Modifier and Type | Method and Description |
---|---|
void |
CheckpointListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
Modifier and Type | Method and Description |
---|---|
void |
OpBenchmarkListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
INDArray[] outputs) |
void |
ArraySavingListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
INDArray[] outputs) |
Modifier and Type | Method and Description |
---|---|
void |
UIListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
void |
ScoreListener.iterationDone(SameDiff sd,
At at,
MultiDataSet dataSet,
Loss loss) |
void |
UIListener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlMs) |
void |
ScoreListener.iterationStart(SameDiff sd,
At at,
MultiDataSet data,
long etlMs) |
void |
UIListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
INDArray[] outputs) |
Modifier and Type | Method and Description |
---|---|
protected Map<String,INDArray> |
SameDiff.directExecHelper(Map<String,INDArray> placeholders,
At at,
MultiDataSet batch,
Collection<String> requiredActivations,
List<Listener> activeListeners,
String... outputs)
Do inference for the given variables for a single batch, with training information
|
History |
SameDiff.fit(MultiDataSet dataSet,
Listener... listeners)
Fit the SameDiff instance based on a single MultiDataSet (i.e., a single minibatch for one iteration).
Note that a TrainingConfig must be set via SameDiff.setTrainingConfig(TrainingConfig) before training can
be performed. |
Map<String,INDArray> |
SameDiff.output(MultiDataSet dataSet,
String... outputs)
Do a single batch inference on a network.
For example, if the variable to infer was called "softmax" you would use: |
Modifier and Type | Method and Description |
---|---|
OutputConfig |
OutputConfig.data(MultiDataSet data)
Set the data to use as input.
|
Modifier and Type | Method and Description |
---|---|
abstract T[] |
AbstractSession.getOutputs(O op,
AbstractSession.FrameIter outputFrameIter,
Set<AbstractSession.VarId> inputs,
Set<AbstractSession.VarId> allIterInputs,
Set<String> constAndPhInputs,
List<Listener> listeners,
At at,
MultiDataSet batch,
Set<String> allReqVariables)
Execute the op - calculate INDArrays, or shape info, etc
|
INDArray[] |
TrainingSession.getOutputs(SameDiffOp op,
AbstractSession.FrameIter outputFrameIter,
Set<AbstractSession.VarId> opInputs,
Set<AbstractSession.VarId> allIterInputs,
Set<String> constAndPhInputs,
List<Listener> listeners,
At at,
MultiDataSet batch,
Set<String> allReqVariables) |
INDArray[] |
InferenceSession.getOutputs(SameDiffOp op,
AbstractSession.FrameIter outputFrameIter,
Set<AbstractSession.VarId> opInputs,
Set<AbstractSession.VarId> allIterInputs,
Set<String> constAndPhInputs,
List<Listener> listeners,
At at,
MultiDataSet batch,
Set<String> allReqVariables) |
Map<String,T> |
AbstractSession.output(List<String> variables,
Map<String,T> placeholderValues,
MultiDataSet batch,
Collection<String> requiredActivations,
List<Listener> listeners,
At at)
Get the output of the session - i.e., perform inference/forward pass and return the autputs for the specified variables
|
Loss |
TrainingSession.trainingIteration(TrainingConfig config,
Map<String,INDArray> placeholders,
Set<String> paramsToTrain,
Map<String,GradientUpdater> updaters,
MultiDataSet batch,
List<String> lossVariables,
List<Listener> listeners,
At at)
Perform one iteration of training - i.e., do forward and backward passes, and update the parameters
|
Modifier and Type | Method and Description |
---|---|
void |
ActivationGradientCheckListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
INDArray[] outputs) |
Modifier and Type | Method and Description |
---|---|
void |
NonInplaceValidationListener.opExecution(SameDiff sd,
At at,
MultiDataSet batch,
SameDiffOp op,
INDArray[] outputs) |
Modifier and Type | Class and Description |
---|---|
class |
MultiDataSet
Implementation of
MultiDataSet |
Modifier and Type | Field and Description |
---|---|
protected MultiDataSet |
AsyncMultiDataSetIterator.nextElement |
protected MultiDataSet |
AsyncMultiDataSetIterator.terminator |
Modifier and Type | Field and Description |
---|---|
protected BlockingQueue<MultiDataSet> |
AsyncMultiDataSetIterator.buffer |
Modifier and Type | Method and Description |
---|---|
MultiDataSet |
AsyncMultiDataSetIterator.next()
Returns the next element in the iteration.
|
MultiDataSet |
AsyncMultiDataSetIterator.next(int num)
Like the standard next method but allows a
customizable number of examples returned
|
MultiDataSet |
DataSet.toMultiDataSet() |
Modifier and Type | Method and Description |
---|---|
List<MultiDataSet> |
MultiDataSet.asList() |
Modifier and Type | Method and Description |
---|---|
static MultiDataSet |
MultiDataSet.merge(Collection<? extends MultiDataSet> toMerge)
Merge a collection of MultiDataSet objects into a single MultiDataSet.
|
Constructor and Description |
---|
AsyncPrefetchThread(BlockingQueue<MultiDataSet> queue,
MultiDataSetIterator iterator,
MultiDataSet terminator,
int deviceId) |
Constructor and Description |
---|
AsyncMultiDataSetIterator(MultiDataSetIterator iterator,
int queueSize,
BlockingQueue<MultiDataSet> queue) |
AsyncMultiDataSetIterator(MultiDataSetIterator iterator,
int queueSize,
BlockingQueue<MultiDataSet> queue,
boolean useWorkspace) |
AsyncMultiDataSetIterator(MultiDataSetIterator iterator,
int queueSize,
BlockingQueue<MultiDataSet> queue,
boolean useWorkspace,
DataSetCallback callback) |
AsyncMultiDataSetIterator(MultiDataSetIterator iterator,
int queueSize,
BlockingQueue<MultiDataSet> queue,
boolean useWorkspace,
DataSetCallback callback,
Integer deviceId) |
AsyncPrefetchThread(BlockingQueue<MultiDataSet> queue,
MultiDataSetIterator iterator,
MultiDataSet terminator,
int deviceId) |
Modifier and Type | Method and Description |
---|---|
MultiDataSet |
SingletonMultiDataSetIterator.next() |
MultiDataSet |
MultiDataSetIteratorAdapter.next() |
MultiDataSet |
SingletonMultiDataSetIterator.next(int num) |
MultiDataSet |
MultiDataSetIteratorAdapter.next(int i) |
Constructor and Description |
---|
SingletonMultiDataSetIterator(MultiDataSet multiDataSet) |
Modifier and Type | Method and Description |
---|---|
MultiDataSet |
MultiDataSet.copy()
Clone the dataset
|
MultiDataSet |
DataSet.toMultiDataSet() |
Modifier and Type | Method and Description |
---|---|
List<MultiDataSet> |
MultiDataSet.asList()
SplitV the MultiDataSet into a list of individual examples.
|
Modifier and Type | Method and Description |
---|---|
void |
MultiDataSetPreProcessor.preProcess(MultiDataSet multiDataSet)
Preprocess the MultiDataSet
|
Modifier and Type | Method and Description |
---|---|
MultiDataSet |
TestMultiDataSetIterator.next() |
MultiDataSet |
MultiDataSetIterator.next(int num)
Fetch the next 'num' examples.
|
MultiDataSet |
TestMultiDataSetIterator.next(int num) |
MultiDataSet[] |
BlockMultiDataSetIterator.next(int maxDatasets)
This method tries to fetch specified number of DataSets and returns them
|
MultiDataSet |
ParallelMultiDataSetIterator.nextFor()
Returns next DataSet for attached consumer
|
MultiDataSet |
ParallelMultiDataSetIterator.nextFor(int consumer)
Returns next DataSet for given consumer
|
Constructor and Description |
---|
TestMultiDataSetIterator(int batch,
MultiDataSet... dataset)
Makes an iterator from the given datasets.
|
Modifier and Type | Method and Description |
---|---|
void |
MultiNormalizerHybrid.fit(MultiDataSet dataSet)
Fit a MultiDataSet (only compute based on the statistics from this dataset)
|
void |
ImageMultiPreProcessingScaler.fit(MultiDataSet dataSet) |
void |
AbstractMultiDataSetNormalizer.fit(MultiDataSet dataSet)
Fit a MultiDataSet (only compute based on the statistics from this
MultiDataSet ) |
void |
CompositeMultiDataSetPreProcessor.preProcess(MultiDataSet multiDataSet) |
void |
MultiNormalizerHybrid.preProcess(MultiDataSet data) |
void |
MultiDataNormalization.preProcess(MultiDataSet multiDataSet) |
void |
ImageMultiPreProcessingScaler.preProcess(MultiDataSet multiDataSet) |
void |
AbstractMultiDataSetNormalizer.preProcess(MultiDataSet toPreProcess)
Pre process a MultiDataSet
|
void |
MultiNormalizerHybrid.revert(MultiDataSet data)
Undo (revert) the normalization applied by this DataNormalization instance (arrays are modified in-place)
|
void |
ImageMultiPreProcessingScaler.revert(MultiDataSet toRevert) |
void |
AbstractMultiDataSetNormalizer.revert(MultiDataSet data)
Revert the data to what it was before transform
|
void |
MultiNormalizerHybrid.transform(MultiDataSet data)
Transform the dataset
|
void |
ImageMultiPreProcessingScaler.transform(MultiDataSet toPreProcess) |
void |
AbstractMultiDataSetNormalizer.transform(MultiDataSet toPreProcess) |
Modifier and Type | Method and Description |
---|---|
void |
UnderSamplingByMaskingMultiDataSetPreProcessor.preProcess(MultiDataSet multiDataSet) |
Modifier and Type | Method and Description |
---|---|
void |
DataSetCallback.call(MultiDataSet multiDataSet) |
void |
DefaultCallback.call(MultiDataSet multiDataSet) |
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