Package org.deeplearning4j.optimize.api

Class BaseTrainingListener

java.lang.Object

org.deeplearning4j.optimize.api.BaseTrainingListener

All Implemented Interfaces:

TrainingListener

Direct Known Subclasses:

CheckpointListener, CollectScoresIterationListener, CollectScoresListener, ComposableIterationListener, EvaluativeListener, IterationListener, PerformanceListener, ScoreIterationListener, SleepyTrainingListener, TimeIterationListener

public abstract class BaseTrainingListener
extends Object
implements TrainingListener

Constructor Summary

Constructors

Constructor	Description
BaseTrainingListener()

Method Summary

Modifier and Type	Method	Description
void	iterationDone(Model model, int iteration, int epoch)	Event listener for each iteration.
void	onBackwardPass(Model model)	Called once per iteration (backward pass) after gradients have been calculated, and updated Gradients are available via Model.gradient().
void	onEpochEnd(Model model)	Called once at the end of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)
void	onEpochStart(Model model)	Called once at the start of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)
void	onForwardPass(Model model, List<INDArray> activations)	Called once per iteration (forward pass) for activations (usually for a MultiLayerNetwork), only at training time
void	onForwardPass(Model model, Map<String,INDArray> activations)	Called once per iteration (forward pass) for activations (usually for a ComputationGraph), only at training time
void	onGradientCalculation(Model model)	Called once per iteration (backward pass) before the gradients are updated Gradients are available via Model.gradient().

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

BaseTrainingListener

public BaseTrainingListener()

Method Detail

onEpochStart

public void onEpochStart(Model model)

Description copied from interface: TrainingListener

Called once at the start of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)

Specified by:

onEpochStart in interface TrainingListener

onEpochEnd

public void onEpochEnd(Model model)

Description copied from interface: TrainingListener

Called once at the end of each epoch, when using methods such as MultiLayerNetwork.fit(DataSetIterator), ComputationGraph.fit(DataSetIterator) or ComputationGraph.fit(MultiDataSetIterator)

Specified by:

onEpochEnd in interface TrainingListener

onForwardPass

public void onForwardPass(Model model,
                          List<INDArray> activations)

Description copied from interface: TrainingListener

Called once per iteration (forward pass) for activations (usually for a MultiLayerNetwork), only at training time

Specified by:

onForwardPass in interface TrainingListener

Parameters:

model - Model

activations - Layer activations (including input)

onForwardPass

public void onForwardPass(Model model,
                          Map<String,INDArray> activations)

Description copied from interface: TrainingListener

Called once per iteration (forward pass) for activations (usually for a ComputationGraph), only at training time

Specified by:

onForwardPass in interface TrainingListener

Parameters:

model - Model

activations - Layer activations (including input)

onGradientCalculation

public void onGradientCalculation(Model model)

Description copied from interface: TrainingListener

Called once per iteration (backward pass) before the gradients are updated Gradients are available via Model.gradient(). Note that gradients will likely be updated in-place - thus they should be copied or processed synchronously in this method.

For updates (gradients post learning rate/momentum/rmsprop etc) see TrainingListener.onBackwardPass(Model)

Specified by:

onGradientCalculation in interface TrainingListener

Parameters:

model - Model

onBackwardPass

public void onBackwardPass(Model model)

Description copied from interface: TrainingListener

Called once per iteration (backward pass) after gradients have been calculated, and updated Gradients are available via Model.gradient().

Unlike TrainingListener.onGradientCalculation(Model) the gradients at this point will be post-update, rather than raw (pre-update) gradients at that method call.

Specified by:

onBackwardPass in interface TrainingListener

Parameters:

model - Model

iterationDone

public void iterationDone(Model model,
                          int iteration,
                          int epoch)

Description copied from interface: TrainingListener

Event listener for each iteration. Called once, after each parameter update has ocurred while training the network

Specified by:

iterationDone in interface TrainingListener

Parameters:

model - the model iterating

iteration - the iteration