SleepyTrainingListener (deeplearning4j-nn 1.0.0-beta API)

java.lang.Object
- org.deeplearning4j.optimize.api.BaseTrainingListener
- - org.deeplearning4j.optimize.listeners.SleepyTrainingListener

All Implemented Interfaces:

Serializable, TrainingListener
```
public class SleepyTrainingListener
extends BaseTrainingListener
implements Serializable
```
This TrainingListener implementation provides a way to "sleep" during specific Neural Network training phases. Suitable for debugging/testing purposes. PLEASE NOTE: All timers treat time values as milliseconds. PLEASE NOTE: Do not use it in production environment.

Author:

raver119@gmail.com

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class SleepyTrainingListener.SleepMode

static class SleepyTrainingListener.TimeMode

Nested Classes
Modifier and Type	Class and Description
`static class`	`SleepyTrainingListener.SleepMode`
`static class`	`SleepyTrainingListener.TimeMode`

Field Summary

Fields
Modifier and Type	Field and Description
`protected ThreadLocal<AtomicLong>`	`lastBP`
`protected ThreadLocal<AtomicLong>`	`lastEE`
`protected ThreadLocal<AtomicLong>`	`lastES`
`protected ThreadLocal<AtomicLong>`	`lastFF`
`protected ThreadLocal<AtomicLong>`	`lastIteration`
`protected SleepyTrainingListener.SleepMode`	`sleepMode`
`protected SleepyTrainingListener.TimeMode`	`timeMode`
`protected long`	`timerBP`
`protected long`	`timerEE`
`protected long`	`timerES`
`protected long`	`timerFF`
`protected long`	`timerIteration`

Constructor Summary

Constructors
Constructor and Description

SleepyTrainingListener()

Constructors
Constructor and Description
`SleepyTrainingListener()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and 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<org.nd4j.linalg.api.ndarray.INDArray> activations)` Called once per iteration (forward pass) for activations (usually for a `MultiLayerNetwork`), only at training time
`void`	`onForwardPass(Model model, Map<String,org.nd4j.linalg.api.ndarray.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()`.
`protected void`	`sleep(AtomicLong lastTime, long sleepTime)`
`protected void`	`sleep(long sleepTimeMs)`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - lastEE
```
protected final transient ThreadLocal<AtomicLong> lastEE
```
  - lastES
```
protected final transient ThreadLocal<AtomicLong> lastES
```
  - lastFF
```
protected final transient ThreadLocal<AtomicLong> lastFF
```
  - lastBP
```
protected final transient ThreadLocal<AtomicLong> lastBP
```
  - lastIteration
```
protected final transient ThreadLocal<AtomicLong> lastIteration
```
  - timerEE
```
protected long timerEE
```
  - timerES
```
protected long timerES
```
  - timerFF
```
protected long timerFF
```
  - timerBP
```
protected long timerBP
```
  - timerIteration
```
protected long timerIteration
```
  - sleepMode
```
protected SleepyTrainingListener.SleepMode sleepMode
```
  - timeMode
```
protected SleepyTrainingListener.TimeMode timeMode
```
- Constructor Detail
  - SleepyTrainingListener
```
public SleepyTrainingListener()
```
- Method Detail
  - sleep
```
protected void sleep(long sleepTimeMs)
```
  - sleep
```
protected void sleep(AtomicLong lastTime,
                     long sleepTime)
```
  - 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
    
    Overrides:
    
    onEpochStart in class BaseTrainingListener
  - 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
    
    Overrides:
    
    onEpochEnd in class BaseTrainingListener
  - onForwardPass
```
public void onForwardPass(Model model,
                          List<org.nd4j.linalg.api.ndarray.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
    
    Overrides:
    
    onForwardPass in class BaseTrainingListener
    
    Parameters:
    
    model - Model
    
    activations - Layer activations (including input)
  - onForwardPass
```
public void onForwardPass(Model model,
                          Map<String,org.nd4j.linalg.api.ndarray.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
    
    Overrides:
    
    onForwardPass in class BaseTrainingListener
    
    Parameters:
    
    model - Model
    
    activations - Layer activations (including input)
  - 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
    
    Overrides:
    
    iterationDone in class BaseTrainingListener
    
    Parameters:
    
    model - the model iterating
    
    iteration - the iteration
  - 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
    
    Overrides:
    
    onBackwardPass in class BaseTrainingListener
    
    Parameters:
    
    model - Model
  - 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
    
    Overrides:
    
    onGradientCalculation in class BaseTrainingListener
    
    Parameters:
    
    model - Model

Class SleepyTrainingListener

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

lastEE

lastES

lastFF

lastBP

lastIteration

timerEE

timerES

timerFF

timerBP

timerIteration

sleepMode

timeMode

Constructor Detail

SleepyTrainingListener

Method Detail

sleep

sleep

onEpochStart

onEpochEnd

onForwardPass

onForwardPass

iterationDone

onBackwardPass

onGradientCalculation