ConvexOptimizer (deeplearning4j-nn 1.0.0-beta4 API)

All Superinterfaces:

Serializable

All Known Implementing Classes:

BaseOptimizer, ConjugateGradient, LBFGS, LineGradientDescent, StochasticGradientDescent
```
public interface ConvexOptimizer
extends Serializable
```
Convex optimizer.

Author:

Adam Gibson

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`int`	`batchSize()` The batch size for the optimizer
`ComputationGraphUpdater`	`getComputationGraphUpdater()`
`NeuralNetConfiguration`	`getConf()`
`GradientsAccumulator`	`getGradientsAccumulator()` This method returns GradientsAccumulator instance used in this optimizer.
`StepFunction`	`getStepFunction()` This method returns StepFunction defined within this Optimizer instance
`Updater`	`getUpdater()`
`Pair<Gradient,Double>`	`gradientAndScore(LayerWorkspaceMgr workspaceMgr)` The gradient and score for this optimizer
`boolean`	`optimize(LayerWorkspaceMgr workspaceMgr)` Calls optimize
`void`	`postStep(INDArray line)` After the step has been made, do an action
`void`	`preProcessLine()` Pre preProcess a line before an iteration
`double`	`score()` The score for the optimizer so far
`void`	`setBatchSize(int batchSize)` Set the batch size for the optimizer
`void`	`setGradientsAccumulator(GradientsAccumulator accumulator)` This method specifies GradientsAccumulator instance to be used for updates sharing across multiple models
`void`	`setListeners(Collection<TrainingListener> listeners)`
`void`	`setUpdater(Updater updater)`
`void`	`setUpdaterComputationGraph(ComputationGraphUpdater updater)`
`void`	`setupSearchState(Pair<Gradient,Double> pair)` Based on the gradient and score setup a search state
`void`	`updateGradientAccordingToParams(Gradient gradient, Model model, int batchSize, LayerWorkspaceMgr workspaceMgr)` Update the gradient according to the configuration such as adagrad, momentum, and sparsity

- Method Detail
  - score
```
double score()
```
    The score for the optimizer so far
    
    Returns:
    
    the score for this optimizer so far
  - getUpdater
```
Updater getUpdater()
```
  - getComputationGraphUpdater
```
ComputationGraphUpdater getComputationGraphUpdater()
```
  - setUpdater
```
void setUpdater(Updater updater)
```
  - setUpdaterComputationGraph
```
void setUpdaterComputationGraph(ComputationGraphUpdater updater)
```
  - setListeners
```
void setListeners(Collection<TrainingListener> listeners)
```
  - setGradientsAccumulator
```
void setGradientsAccumulator(GradientsAccumulator accumulator)
```
    This method specifies GradientsAccumulator instance to be used for updates sharing across multiple models
    
    Parameters:
    
    accumulator -
  - getStepFunction
```
StepFunction getStepFunction()
```
    This method returns StepFunction defined within this Optimizer instance
    
    Returns:
  - getGradientsAccumulator
```
GradientsAccumulator getGradientsAccumulator()
```
    This method returns GradientsAccumulator instance used in this optimizer. This method can return null.
    
    Returns:
  - getConf
```
NeuralNetConfiguration getConf()
```
  - gradientAndScore
```
Pair<Gradient,Double> gradientAndScore(LayerWorkspaceMgr workspaceMgr)
```
    The gradient and score for this optimizer
    
    Returns:
    
    the gradient and score for this optimizer
  - optimize
```
boolean optimize(LayerWorkspaceMgr workspaceMgr)
```
    Calls optimize
    
    Returns:
    
    whether the convex optimizer converted or not
  - batchSize
```
int batchSize()
```
    The batch size for the optimizer
    
    Returns:
  - setBatchSize
```
void setBatchSize(int batchSize)
```
    Set the batch size for the optimizer
    
    Parameters:
    
    batchSize -
  - preProcessLine
```
void preProcessLine()
```
    Pre preProcess a line before an iteration
  - postStep
```
void postStep(INDArray line)
```
    After the step has been made, do an action
    
    Parameters:
    
    line -
  - setupSearchState
```
void setupSearchState(Pair<Gradient,Double> pair)
```
    Based on the gradient and score setup a search state
    
    Parameters:
    
    pair - the gradient and score
  - updateGradientAccordingToParams
```
void updateGradientAccordingToParams(Gradient gradient,
                                     Model model,
                                     int batchSize,
                                     LayerWorkspaceMgr workspaceMgr)
```
    Update the gradient according to the configuration such as adagrad, momentum, and sparsity
    
    Parameters:
    
    gradient - the gradient to modify
    
    model - the model with the parameters to update
    
    batchSize - batchSize for update

Interface ConvexOptimizer

Method Summary

Method Detail

score

getUpdater

getComputationGraphUpdater

setUpdater

setUpdaterComputationGraph

setListeners

setGradientsAccumulator

getStepFunction

getGradientsAccumulator

getConf

gradientAndScore

optimize

batchSize

setBatchSize

preProcessLine

postStep

setupSearchState

updateGradientAccordingToParams