ComputationGraphConfiguration.GraphBuilder (deeplearning4j-nn 0.7.1 API)

java.lang.Object
- org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder

Enclosing class:: ComputationGraphConfiguration

public static class ComputationGraphConfiguration.GraphBuilder
extends Object

Field Summary

Fields
Modifier and Type	Field and Description
`protected boolean`	`backprop`
`protected BackpropType`	`backpropType`
`protected NeuralNetConfiguration.Builder`	`globalConfiguration`
`protected Map<String,InputPreProcessor>`	`inputPreProcessors`
`protected List<String>`	`networkInputs`
`protected List<InputType>`	`networkInputTypes`
`protected List<String>`	`networkOutputs`
`protected boolean`	`pretrain`
`protected int`	`tbpttBackLength`
`protected int`	`tbpttFwdLength`
`protected Map<String,List<String>>`	`vertexInputs` Key: graph node.
`protected Map<String,GraphVertex>`	`vertices`

Constructor Summary

Constructors
Constructor and Description

GraphBuilder(NeuralNetConfiguration.Builder globalConfiguration)

Constructors
Constructor and Description
`GraphBuilder(NeuralNetConfiguration.Builder globalConfiguration)`

Method Summary

All Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method and Description
`ComputationGraphConfiguration.GraphBuilder`	`addInputs(String... inputNames)` Specify the inputs to the network, and their associated labels.
`ComputationGraphConfiguration.GraphBuilder`	`addLayer(String layerName, Layer layer, InputPreProcessor preProcessor, String... layerInputs)` Add a layer and an `InputPreProcessor`, with the specified name and specified inputs.
`ComputationGraphConfiguration.GraphBuilder`	`addLayer(String layerName, Layer layer, String... layerInputs)` Add a layer, with no `InputPreProcessor`, with the specified name and specified inputs.
`ComputationGraphConfiguration.GraphBuilder`	`addVertex(String vertexName, GraphVertex vertex, String... vertexInputs)` Add a `GraphVertex` to the network configuration.
`ComputationGraphConfiguration.GraphBuilder`	`backprop(boolean backprop)` Whether to do back prop (standard supervised learning) or not
`ComputationGraphConfiguration.GraphBuilder`	`backpropType(BackpropType type)` The type of backprop.
`ComputationGraphConfiguration`	`build()` Create the ComputationGraphConfiguration from the Builder pattern
`ComputationGraphConfiguration.GraphBuilder`	`inputPreProcessor(String layer, InputPreProcessor processor)` Specify the processors for a given layer These are used at each layer for doing things like normalization and shaping of input. Note: preprocessors can also be defined using the `addLayer(String, Layer, InputPreProcessor, String...)` method.
`ComputationGraphConfiguration.GraphBuilder`	`pretrain(boolean pretrain)` Whether to do layerwise pre training or not
`ComputationGraphConfiguration.GraphBuilder`	`redistributeParams(boolean redistributeParams)` Deprecated. As of 0.6.0
`ComputationGraphConfiguration.GraphBuilder`	`setInputTypes(InputType... inputTypes)` Specify the types of inputs to the network, so that: (a) preprocessors can be automatically added, and (b) the nIns (input size) for each layer can be automatically calculated and set The order here is the same order as .addInputs().
`ComputationGraphConfiguration.GraphBuilder`	`setOutputs(String... outputNames)` Set the network output labels.
`ComputationGraphConfiguration.GraphBuilder`	`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
`ComputationGraphConfiguration.GraphBuilder`	`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

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

- Field Detail
  - vertices
```
protected Map<String,GraphVertex> vertices
```
  - vertexInputs
```
protected Map<String,List<String>> vertexInputs
```
    Key: graph node. Values: input to that node
  - networkInputs
```
protected List<String> networkInputs
```
  - networkInputTypes
```
protected List<InputType> networkInputTypes
```
  - networkOutputs
```
protected List<String> networkOutputs
```
  - pretrain
```
protected boolean pretrain
```
  - backprop
```
protected boolean backprop
```
  - backpropType
```
protected BackpropType backpropType
```
  - tbpttFwdLength
```
protected int tbpttFwdLength
```
  - tbpttBackLength
```
protected int tbpttBackLength
```
  - inputPreProcessors
```
protected Map<String,InputPreProcessor> inputPreProcessors
```
  - globalConfiguration
```
protected NeuralNetConfiguration.Builder globalConfiguration
```
- Constructor Detail
  - GraphBuilder
```
public GraphBuilder(NeuralNetConfiguration.Builder globalConfiguration)
```
- Method Detail
  - redistributeParams
```
@Deprecated
public ComputationGraphConfiguration.GraphBuilder redistributeParams(boolean redistributeParams)
```
    Deprecated. As of 0.6.0
  - inputPreProcessor
```
public ComputationGraphConfiguration.GraphBuilder inputPreProcessor(String layer,
                                                                    InputPreProcessor processor)
```
    Specify the processors for a given layer These are used at each layer for doing things like normalization and shaping of input.
    Note: preprocessors can also be defined using the addLayer(String, Layer, InputPreProcessor, String...) method.
    
    Parameters:
    
    layer - the name of the layer that this preprocessor will be used with
    
    processor - the preprocessor to use for the specified layer
  - backprop
```
public ComputationGraphConfiguration.GraphBuilder backprop(boolean backprop)
```
    Whether to do back prop (standard supervised learning) or not
    
    Parameters:
    
    backprop - whether to do back prop or not
  - pretrain
```
public ComputationGraphConfiguration.GraphBuilder pretrain(boolean pretrain)
```
    Whether to do layerwise pre training or not
    
    Parameters:
    
    pretrain - whether to do pre train or not
  - backpropType
```
public ComputationGraphConfiguration.GraphBuilder 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()
    
    Parameters:
    
    type - Type of backprop. Default: BackpropType.Standard
  - tBPTTForwardLength
```
public ComputationGraphConfiguration.GraphBuilder 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 ComputationGraphConfiguration.GraphBuilder 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
  - addLayer
```
public ComputationGraphConfiguration.GraphBuilder addLayer(String layerName,
                                                           Layer layer,
                                                           String... layerInputs)
```
    Add a layer, with no InputPreProcessor, with the specified name and specified inputs.
    
    Parameters:
    
    layerName - Name/label of the layer to add
    
    layer - The layer configuration
    
    layerInputs - Inputs to this layer (must be 1 or more). Inputs may be other layers, GraphVertex objects, on a combination of the two.
    
    See Also:
    
    addLayer(String, Layer, InputPreProcessor, String...)
  - addLayer
```
public ComputationGraphConfiguration.GraphBuilder addLayer(String layerName,
                                                           Layer layer,
                                                           InputPreProcessor preProcessor,
                                                           String... layerInputs)
```
    Add a layer and an InputPreProcessor, with the specified name and specified inputs.
    
    Parameters:
    
    layerName - Name/label of the layer to add
    
    layer - The layer configuration
    
    preProcessor - The InputPreProcessor to use with this layer.
    
    layerInputs - Inputs to this layer (must be 1 or more). Inputs may be other layers, GraphVertex objects, on a combination of the two.
  - addInputs
```
public ComputationGraphConfiguration.GraphBuilder addInputs(String... inputNames)
```
    Specify the inputs to the network, and their associated labels.
    
    Parameters:
    
    inputNames - The names of the inputs. This also defines their order
  - setInputTypes
```
public ComputationGraphConfiguration.GraphBuilder setInputTypes(InputType... inputTypes)
```
    Specify the types of inputs to the network, so that:
    (a) preprocessors can be automatically added, and
    (b) the nIns (input size) for each layer can be automatically calculated and set
    The order here is the same order as .addInputs(). Thus, if you do .addInputs("a","b") and .setInputTypes(InputType.feedForward(), InputType.convolutional(1,28,28)) then the input labelled "a" is a feed forward input, whereas the input labelled "b" in a CNN input, with 28x28x1 images as input.
    Note: Using setInputTypes is not always necessary, but can be especially helpful for example with CNNs such that the calculations on input/ouput sizes (width, height, depth, etc) don't need to be done manually.
    Note 2: If a preprocessor is manually added for a given layer, it will not be overridden by the automatic addition of preprocessors. Note 3: If a layer has an nIn set manually, this will not be overridden
  - setOutputs
```
public ComputationGraphConfiguration.GraphBuilder setOutputs(String... outputNames)
```
    Set the network output labels. These should be the names of the OutputLayer instances in the network
    
    Parameters:
    
    outputNames - The names of the output layers. This also defines their order.
  - addVertex
```
public ComputationGraphConfiguration.GraphBuilder addVertex(String vertexName,
                                                            GraphVertex vertex,
                                                            String... vertexInputs)
```
    Add a GraphVertex to the network configuration. A GraphVertex defines forward and backward pass methods, and can contain a LayerVertex, a ElementWiseVertex to do element-wise addition/subtraction, a MergeVertex to combine/concatenate the activations out of multiple layers or vertices, a SubsetVertex to select a subset of the activations out of another layer/GraphVertex.
    Custom GraphVertex objects (that extend the abstract GraphVertex class) may also be used.
    
    Parameters:
    
    vertexName - The name of the GraphVertex to add
    
    vertex - The GraphVertex to add
    
    vertexInputs - The inputs/activations to this GraphVertex
  - build
```
public ComputationGraphConfiguration build()
```
    Create the ComputationGraphConfiguration from the Builder pattern

Class ComputationGraphConfiguration.GraphBuilder

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

vertices

vertexInputs

networkInputs

networkInputTypes

networkOutputs

pretrain

backprop

backpropType

tbpttFwdLength

tbpttBackLength

inputPreProcessors

globalConfiguration

Constructor Detail

GraphBuilder

Method Detail

redistributeParams

inputPreProcessor

backprop

pretrain

backpropType

tBPTTForwardLength

tBPTTBackwardLength

addLayer

addLayer

addInputs

setInputTypes

setOutputs

addVertex

build