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

Enclosing class:: NeuralNetConfiguration

public static class NeuralNetConfiguration.ListBuilder
extends MultiLayerConfiguration.Builder

Fluent interface for building a list of configurations

Nested Class Summary

Nested Classes
Modifier and Type Class Description

class NeuralNetConfiguration.ListBuilder.InputTypeBuilder
Helper class for setting input types

Field Summary
- Fields inherited from class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
  backpropType, cacheMode, confs, dampingFactor, dataType, inferenceWorkspaceMode, inputPreProcessors, inputType, overrideNinUponBuild, tbpttBackLength, tbpttFwdLength, trainingWorkspaceMode, validateOutputConfig, validateTbpttConfig

Constructor Summary

Constructors
Constructor	Description
`ListBuilder(NeuralNetConfiguration.Builder globalConfig)`
`ListBuilder(NeuralNetConfiguration.Builder globalConfig, Map<Integer,NeuralNetConfiguration.Builder> layerMap)`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`MultiLayerConfiguration.Builder`	`backpropType(@NonNull BackpropType type)`	The type of backprop.
`MultiLayerConfiguration`	`build()`	Build the multi layer network based on this neural network and overr ridden parameters
`NeuralNetConfiguration.ListBuilder`	`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)
`NeuralNetConfiguration.ListBuilder`	`confs(List<NeuralNetConfiguration> confs)`
`NeuralNetConfiguration.ListBuilder`	`dataType(@NonNull DataType dataType)`	Set the DataType for the network parameters and activations for all layers in the network.
`protected void`	`finalize()`
`List<InputType>`	`getLayerActivationTypes()`	For the (perhaps partially constructed) network configuration, return a list of activation sizes for each layer in the network. Note: To use this method, the network input type must have been set using `setInputType(InputType)` first
`Map<Integer,NeuralNetConfiguration.Builder>`	`getLayerwise()`
`NeuralNetConfiguration.ListBuilder`	`inputPreProcessor(Integer layer, InputPreProcessor processor)`	Specify the processors.
`NeuralNetConfiguration.ListBuilder`	`inputPreProcessors(Map<Integer,InputPreProcessor> processors)`
`NeuralNetConfiguration.ListBuilder.InputTypeBuilder`	`inputType()`	A convenience method for setting input types: note that for example .inputType().convolutional(h,w,d) is equivalent to .setInputType(InputType.convolutional(h,w,d))
`NeuralNetConfiguration.ListBuilder`	`layer(int ind, @NonNull Layer layer)`
`NeuralNetConfiguration.ListBuilder`	`layer(Layer layer)`
`NeuralNetConfiguration.ListBuilder`	`overrideNinUponBuild(boolean overrideNinUponBuild)`	Whether to over ride the nIn configuration forcibly upon construction.
`NeuralNetConfiguration.ListBuilder`	`setInputType(InputType inputType)`
`NeuralNetConfiguration.ListBuilder`	`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
`NeuralNetConfiguration.ListBuilder`	`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
`NeuralNetConfiguration.ListBuilder`	`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
`NeuralNetConfiguration.ListBuilder`	`validateOutputLayerConfig(boolean validate)`	Enabled by default.
`NeuralNetConfiguration.ListBuilder`	`validateTbpttConfig(boolean validate)`	Enabled by default.

Methods inherited from class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder
inferenceWorkspaceMode, trainingWorkspaceMode

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

- Constructor Detail
  - ListBuilder
```
public ListBuilder(NeuralNetConfiguration.Builder globalConfig,
                   Map<Integer,NeuralNetConfiguration.Builder> layerMap)
```
  - ListBuilder
```
public ListBuilder(NeuralNetConfiguration.Builder globalConfig)
```
- Method Detail
  - layer
```
public NeuralNetConfiguration.ListBuilder layer(int ind,
                                                @NonNull
                                                @NonNull Layer layer)
```
  - layer
```
public NeuralNetConfiguration.ListBuilder layer(Layer layer)
```
  - getLayerwise
```
public Map<Integer,NeuralNetConfiguration.Builder> getLayerwise()
```
  - overrideNinUponBuild
```
public NeuralNetConfiguration.ListBuilder overrideNinUponBuild(boolean overrideNinUponBuild)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    Whether to over ride the nIn configuration forcibly upon construction. Default value is true
    
    Overrides:
    
    overrideNinUponBuild in class MultiLayerConfiguration.Builder
    
    Parameters:
    
    overrideNinUponBuild - Whether to over ride the nIn configuration forcibly upon construction.
    
    Returns:
    
    builder pattern
  - inputPreProcessor
```
public NeuralNetConfiguration.ListBuilder inputPreProcessor(Integer layer,
                                                            InputPreProcessor processor)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    Specify the processors. These are used at each layer for doing things like normalization and shaping of input.
    
    Overrides:
    
    inputPreProcessor in class MultiLayerConfiguration.Builder
    
    processor - what to use to preProcess the data.
    
    Returns:
    
    builder pattern
  - inputPreProcessors
```
public NeuralNetConfiguration.ListBuilder inputPreProcessors(Map<Integer,InputPreProcessor> processors)
```
    Overrides:
    
    inputPreProcessors in class MultiLayerConfiguration.Builder
  - cacheMode
```
public NeuralNetConfiguration.ListBuilder cacheMode(@NonNull
                                                    @NonNull CacheMode cacheMode)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    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)
    
    Overrides:
    
    cacheMode in class MultiLayerConfiguration.Builder
    
    Returns:
  - backpropType
```
public MultiLayerConfiguration.Builder backpropType(@NonNull
                                                    @NonNull BackpropType type)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    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()
    
    Overrides:
    
    backpropType in class MultiLayerConfiguration.Builder
  - tBPTTLength
```
public NeuralNetConfiguration.ListBuilder tBPTTLength(int bpttLength)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    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
    
    Overrides:
    
    tBPTTLength in class MultiLayerConfiguration.Builder
    
    Parameters:
    
    bpttLength - length > 0
  - tBPTTForwardLength
```
public NeuralNetConfiguration.ListBuilder tBPTTForwardLength(int forwardLength)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    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
    
    Overrides:
    
    tBPTTForwardLength in class MultiLayerConfiguration.Builder
    
    Parameters:
    
    forwardLength - Forward length > 0, >= backwardLength
  - tBPTTBackwardLength
```
public NeuralNetConfiguration.ListBuilder tBPTTBackwardLength(int backwardLength)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    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
    
    Overrides:
    
    tBPTTBackwardLength in class MultiLayerConfiguration.Builder
    
    Parameters:
    
    backwardLength - <= forwardLength
  - confs
```
public NeuralNetConfiguration.ListBuilder confs(List<NeuralNetConfiguration> confs)
```
    Overrides:
    
    confs in class MultiLayerConfiguration.Builder
  - validateOutputLayerConfig
```
public NeuralNetConfiguration.ListBuilder validateOutputLayerConfig(boolean validate)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    Enabled by default. If enabled, the output layer configuration will be validated, to throw an exception on likely invalid outputs - such as softmax + nOut=1, or LossMCXENT + Tanh.
    If disabled (false) no output layer validation will be performed.
    Disabling this validation is not recommended, as the configurations that fail validation usually will not be able to learn correctly. However, the option to disable this validation is provided for advanced users when creating non-standard architectures.
    
    Overrides:
    
    validateOutputLayerConfig in class MultiLayerConfiguration.Builder
    
    Parameters:
    
    validate - If true: validate output layer configuration. False: don't validate
  - validateTbpttConfig
```
public NeuralNetConfiguration.ListBuilder validateTbpttConfig(boolean validate)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    Enabled by default. If enabled, an exception will be throw when using the (invalid) combination of truncated backpropagation through time (TBPTT) with either a GlobalPoolingLayer or LastTimeStepLayer.
    It is possible to disable this validation to allow what is almost certainly an invalid configuration to be used, however this is not recommended.
    
    Overrides:
    
    validateTbpttConfig in class MultiLayerConfiguration.Builder
    
    Parameters:
    
    validate - Whether TBPTT validation should be performed
  - dataType
```
public NeuralNetConfiguration.ListBuilder dataType(@NonNull
                                                   @NonNull DataType dataType)
```
    Description copied from class: MultiLayerConfiguration.Builder
    
    Set the DataType for the network parameters and activations for all layers in the network. Default: Float
    
    Overrides:
    
    dataType in class MultiLayerConfiguration.Builder
    
    Parameters:
    
    dataType - Datatype to use for parameters and activations
  - finalize
```
protected void finalize()
                 throws Throwable
```
    Overrides:
    
    finalize in class Object
    
    Throws:
    
    Throwable
  - setInputType
```
public NeuralNetConfiguration.ListBuilder setInputType(InputType inputType)
```
    Overrides:
    
    setInputType in class MultiLayerConfiguration.Builder
  - inputType
```
public NeuralNetConfiguration.ListBuilder.InputTypeBuilder inputType()
```
    A convenience method for setting input types: note that for example .inputType().convolutional(h,w,d) is equivalent to .setInputType(InputType.convolutional(h,w,d))
  - getLayerActivationTypes
```
public List<InputType> getLayerActivationTypes()
```
    For the (perhaps partially constructed) network configuration, return a list of activation sizes for each layer in the network.
    Note: To use this method, the network input type must have been set using setInputType(InputType) first
    
    Returns:
    
    A list of activation types for the network, indexed by layer number
  - build
```
public MultiLayerConfiguration build()
```
    Build the multi layer network based on this neural network and overr ridden parameters
    
    Overrides:
    
    build in class MultiLayerConfiguration.Builder
    
    Returns:
    
    the configuration to build

Class NeuralNetConfiguration.ListBuilder

Nested Class Summary

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Constructor Summary

Method Summary

Methods inherited from class org.deeplearning4j.nn.conf.MultiLayerConfiguration.Builder

Methods inherited from class java.lang.Object

Constructor Detail

ListBuilder

ListBuilder

Method Detail

layer

layer

getLayerwise

overrideNinUponBuild

inputPreProcessor

inputPreProcessors

cacheMode

backpropType

tBPTTLength

tBPTTForwardLength

tBPTTBackwardLength

confs

validateOutputLayerConfig

validateTbpttConfig

dataType

finalize

setInputType

inputType

getLayerActivationTypes

build