MultiLayerConfiguration.Builder (deeplearning4j-nn 0.9.1 API)

java.lang.Object
- org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Direct Known Subclasses:

NeuralNetConfiguration.ListBuilder

Enclosing class:

MultiLayerConfiguration
```
public static class MultiLayerConfiguration.Builder
extends Object
```

Field Summary

Fields
Modifier and Type	Field and Description
`protected boolean`	`backprop`
`protected BackpropType`	`backpropType`
`protected CacheMode`	`cacheMode`
`protected List<NeuralNetConfiguration>`	`confs`
`protected double`	`dampingFactor`
`protected WorkspaceMode`	`inferenceWorkspaceMode`
`protected Map<Integer,InputPreProcessor>`	`inputPreProcessors`
`protected InputType`	`inputType`
`protected boolean`	`pretrain`
`protected int`	`tbpttBackLength`
`protected int`	`tbpttFwdLength`
`protected WorkspaceMode`	`trainingWorkspaceMode`

Constructor Summary

Constructors
Constructor and Description

Builder()

Constructors
Constructor and Description
`Builder()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`MultiLayerConfiguration.Builder`	`backprop(boolean backprop)` Whether to do back prop or not
`MultiLayerConfiguration.Builder`	`backpropType(BackpropType type)` The type of backprop.
`MultiLayerConfiguration`	`build()`
`MultiLayerConfiguration.Builder`	`cacheMode(CacheMode cacheMode)` This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)
`MultiLayerConfiguration.Builder`	`confs(List<NeuralNetConfiguration> confs)`
`MultiLayerConfiguration.Builder`	`inferenceWorkspaceMode(WorkspaceMode workspaceMode)` This method defines Workspace mode being used during inference: NONE: workspace won't be used SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops
`MultiLayerConfiguration.Builder`	`inputPreProcessor(Integer layer, InputPreProcessor processor)` Specify the processors.
`MultiLayerConfiguration.Builder`	`inputPreProcessors(Map<Integer,InputPreProcessor> processors)`
`MultiLayerConfiguration.Builder`	`pretrain(boolean pretrain)` Whether to do pre train or not
`MultiLayerConfiguration.Builder`	`setInputType(InputType inputType)`
`MultiLayerConfiguration.Builder`	`tBPTTBackwardLength(int backwardLength)` When doing truncated BPTT: how many steps of backward should we do? Only applicable when doing backpropType(BackpropType.TruncatedBPTT) This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
`MultiLayerConfiguration.Builder`	`tBPTTForwardLength(int forwardLength)` When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop? Only applicable when doing backpropType(BackpropType.TruncatedBPTT) Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller) Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
`MultiLayerConfiguration.Builder`	`tBPTTLength(int bpttLength)` When doing truncated BPTT: how many steps should we do? Only applicable when doing backpropType(BackpropType.TruncatedBPTT) See: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
`MultiLayerConfiguration.Builder`	`trainingWorkspaceMode(WorkspaceMode workspaceMode)` This method defines Workspace mode being used during training: NONE: workspace won't be used SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops

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

- Field Detail
  - confs
```
protected List<NeuralNetConfiguration> confs
```
  - dampingFactor
```
protected double dampingFactor
```
  - inputPreProcessors
```
protected Map<Integer,InputPreProcessor> inputPreProcessors
```
  - pretrain
```
protected boolean pretrain
```
  - backprop
```
protected boolean backprop
```
  - backpropType
```
protected BackpropType backpropType
```
  - tbpttFwdLength
```
protected int tbpttFwdLength
```
  - tbpttBackLength
```
protected int tbpttBackLength
```
  - inputType
```
protected InputType inputType
```
  - trainingWorkspaceMode
```
protected WorkspaceMode trainingWorkspaceMode
```
  - inferenceWorkspaceMode
```
protected WorkspaceMode inferenceWorkspaceMode
```
  - cacheMode
```
protected CacheMode cacheMode
```
- Constructor Detail
  - Builder
```
public Builder()
```
- Method Detail
  - inputPreProcessor
```
public MultiLayerConfiguration.Builder inputPreProcessor(Integer layer,
                                                         InputPreProcessor processor)
```
    Specify the processors. These are used at each layer for doing things like normalization and shaping of input.
    
    Parameters:
    
    processor - what to use to preProcess the data.
    
    Returns:
    
    builder pattern
  - inputPreProcessors
```
public MultiLayerConfiguration.Builder inputPreProcessors(Map<Integer,InputPreProcessor> processors)
```
  - backprop
```
public MultiLayerConfiguration.Builder backprop(boolean backprop)
```
    Whether to do back prop or not
    
    Parameters:
    
    backprop - whether to do back prop or not
    
    Returns:
  - trainingWorkspaceMode
```
public MultiLayerConfiguration.Builder trainingWorkspaceMode(@NonNull
                                                             WorkspaceMode workspaceMode)
```
    This method defines Workspace mode being used during training: NONE: workspace won't be used SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops
    
    Parameters:
    
    workspaceMode -
    
    Returns:
  - inferenceWorkspaceMode
```
public MultiLayerConfiguration.Builder inferenceWorkspaceMode(@NonNull
                                                              WorkspaceMode workspaceMode)
```
    This method defines Workspace mode being used during inference: NONE: workspace won't be used SINGLE: one workspace will be used during whole iteration loop SEPARATE: separate workspaces will be used for feedforward and backprop iteration loops
    
    Parameters:
    
    workspaceMode -
    
    Returns:
  - cacheMode
```
public MultiLayerConfiguration.Builder cacheMode(@NonNull
                                                 CacheMode cacheMode)
```
    This method defines how/if preOutput cache is handled: NONE: cache disabled (default value) HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)
    
    Parameters:
    
    cacheMode -
    
    Returns:
  - backpropType
```
public MultiLayerConfiguration.Builder backpropType(BackpropType type)
```
    The type of backprop. Default setting is used for most networks (MLP, CNN etc), but optionally truncated BPTT can be used for training recurrent neural networks. If using TruncatedBPTT make sure you set both tBPTTForwardLength() and tBPTTBackwardLength()
  - tBPTTLength
```
public MultiLayerConfiguration.Builder tBPTTLength(int bpttLength)
```
    When doing truncated BPTT: how many steps should we do?
    Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
    See: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
    
    Parameters:
    
    bpttLength - length > 0
  - tBPTTForwardLength
```
public MultiLayerConfiguration.Builder tBPTTForwardLength(int forwardLength)
```
    When doing truncated BPTT: how many steps of forward pass should we do before doing (truncated) backprop?
    Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
    Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but never smaller)
    Ideally your training data time series length should be divisible by this This is the k1 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
    
    Parameters:
    
    forwardLength - Forward length > 0, >= backwardLength
  - tBPTTBackwardLength
```
public MultiLayerConfiguration.Builder tBPTTBackwardLength(int backwardLength)
```
    When doing truncated BPTT: how many steps of backward should we do?
    Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
    This is the k2 parameter on pg23 of http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
    
    Parameters:
    
    backwardLength - <= forwardLength
  - pretrain
```
public MultiLayerConfiguration.Builder pretrain(boolean pretrain)
```
    Whether to do pre train or not
    
    Parameters:
    
    pretrain - whether to do pre train or not
    
    Returns:
    
    builder pattern
  - confs
```
public MultiLayerConfiguration.Builder confs(List<NeuralNetConfiguration> confs)
```
  - setInputType
```
public MultiLayerConfiguration.Builder setInputType(InputType inputType)
```
  - build
```
public MultiLayerConfiguration build()
```

Class MultiLayerConfiguration.Builder

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

confs

dampingFactor

inputPreProcessors

pretrain

backprop

backpropType

tbpttFwdLength

tbpttBackLength

inputType

trainingWorkspaceMode

inferenceWorkspaceMode

cacheMode

Constructor Detail

Builder

Method Detail

inputPreProcessor

inputPreProcessors

backprop

trainingWorkspaceMode

inferenceWorkspaceMode

cacheMode

backpropType

tBPTTLength

tBPTTForwardLength

tBPTTBackwardLength

pretrain

confs

setInputType

build