ComputationGraphConfiguration (deeplearning4j 1.0.0-beta7 API)

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

All Implemented Interfaces:

Serializable, Cloneable
```
public class ComputationGraphConfiguration
extends Object
implements Serializable, Cloneable
```
ComputationGraphConfiguration is a configuration object for neural networks with arbitrary connection structure. It is analogous to MultiLayerConfiguration, but allows considerably greater flexibility for the network architecture.
Specifically, the network architecture is a directed acyclic graph, where each vertex in the graph is a GraphVertex, which may for example be a layer or a vertex/object that defines arbitrary forward and backward pass functionality.
Note that the ComputationGraph may have an arbitrary number of inputs (multiple independent inputs, possibly of different types), and an arbitrary number of outputs (for example, multiple OutputLayer instances. Typical usage:
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()....graphBuilder()...build();

Author:

Alex Black

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class ComputationGraphConfiguration.GraphBuilder

Nested Classes
Modifier and Type	Class and Description
`static class`	`ComputationGraphConfiguration.GraphBuilder`

Field Summary

Fields
Modifier and Type	Field and Description
`protected BackpropType`	`backpropType`
`protected CacheMode`	`cacheMode`
`protected DataType`	`dataType`
`protected NeuralNetConfiguration`	`defaultConfiguration`
`protected int`	`epochCount`
`protected WorkspaceMode`	`inferenceWorkspaceMode`
`protected int`	`iterationCount`
`protected List<String>`	`networkInputs` List of inputs to the network, by name
`protected List<String>`	`networkOutputs` List of network outputs, by name
`protected int`	`tbpttBackLength`
`protected int`	`tbpttFwdLength`
`protected int[]`	`topologicalOrder`
`protected List<String>`	`topologicalOrderStr`
`protected WorkspaceMode`	`trainingWorkspaceMode`
`protected boolean`	`validateOutputLayerConfig`
`protected Map<String,List<String>>`	`vertexInputs`
`protected Map<String,GraphVertex>`	`vertices`

Constructor Summary

Constructors
Constructor and Description

ComputationGraphConfiguration()

Constructors
Constructor and Description
`ComputationGraphConfiguration()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addPreProcessors(InputType... inputTypes)` Add preprocessors automatically, given the specified types of inputs for the network.
`ComputationGraphConfiguration`	`clone()`
`static ComputationGraphConfiguration`	`fromJson(String json)` Create a computation graph configuration from json
`static ComputationGraphConfiguration`	`fromYaml(String json)` Create a neural net configuration from YAML
`Map<String,InputType>`	`getLayerActivationTypes(boolean addPreprocIfNecessary, InputType... inputTypes)` For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph.
`Map<String,InputType>`	`getLayerActivationTypes(InputType... inputTypes)` For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph.
`NetworkMemoryReport`	`getMemoryReport(InputType... inputTypes)` Get a `MemoryReport` for the given computation graph configuration.
`String`	`toJson()`
`String`	`toString()`
`String`	`toYaml()`
`void`	`validate()` Check the configuration, make sure it is valid
`void`	`validate(boolean allowDisconnected, boolean allowNoOutput)` Check the configuration, make sure it is valid

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

- Field Detail
  - vertices
```
protected Map<String,GraphVertex> vertices
```
  - vertexInputs
```
protected Map<String,List<String>> vertexInputs
```
  - trainingWorkspaceMode
```
protected WorkspaceMode trainingWorkspaceMode
```
  - inferenceWorkspaceMode
```
protected WorkspaceMode inferenceWorkspaceMode
```
  - cacheMode
```
protected CacheMode cacheMode
```
  - dataType
```
protected DataType dataType
```
  - validateOutputLayerConfig
```
protected boolean validateOutputLayerConfig
```
  - networkInputs
```
protected List<String> networkInputs
```
    List of inputs to the network, by name
  - networkOutputs
```
protected List<String> networkOutputs
```
    List of network outputs, by name
  - backpropType
```
protected BackpropType backpropType
```
  - tbpttFwdLength
```
protected int tbpttFwdLength
```
  - tbpttBackLength
```
protected int tbpttBackLength
```
  - defaultConfiguration
```
protected NeuralNetConfiguration defaultConfiguration
```
  - iterationCount
```
protected int iterationCount
```
  - epochCount
```
protected int epochCount
```
  - topologicalOrder
```
protected int[] topologicalOrder
```
  - topologicalOrderStr
```
protected List<String> topologicalOrderStr
```
- Constructor Detail
  - ComputationGraphConfiguration
```
public ComputationGraphConfiguration()
```
- Method Detail
  - toYaml
```
public String toYaml()
```
    Returns:
    
    YAML representation of configuration
  - fromYaml
```
public static ComputationGraphConfiguration fromYaml(String json)
```
    Create a neural net configuration from YAML
    
    Parameters:
    
    json - the neural net configuration from YAML
    
    Returns:
    
    ComputationGraphConfiguration
  - toJson
```
public String toJson()
```
    Returns:
    
    JSON representation of computation graph configuration
  - fromJson
```
public static ComputationGraphConfiguration fromJson(String json)
```
    Create a computation graph configuration from json
    
    Parameters:
    
    json - the neural net configuration from json
    
    Returns:
    
    ComputationGraphConfiguration
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - clone
```
public ComputationGraphConfiguration clone()
```
    Overrides:
    
    clone in class Object
  - validate
```
public void validate()
```
    Check the configuration, make sure it is valid
    
    Throws:
    
    IllegalStateException - if configuration is not valid
  - validate
```
public void validate(boolean allowDisconnected,
                     boolean allowNoOutput)
```
    Check the configuration, make sure it is valid
    
    Parameters:
    
    allowDisconnected - If true: don't throw an exception on vertices that are 'disconnected'. A disconnected vertex is one that is not an output, and doesn't connect to any other vertices. i.e., it's output activations don't go anywhere
    
    Throws:
    
    IllegalStateException - if configuration is not valid
  - addPreProcessors
```
public void addPreProcessors(InputType... inputTypes)
```
    Add preprocessors automatically, given the specified types of inputs for the network. Inputs are specified using the InputType class, in the same order in which the inputs were defined in the original configuration.
    For example, in a network with two inputs: a convolutional input (28x28x1 images) and feed forward inputs, use .addPreProcessors(InputType.convolutional(28,28,1),InputType.feedForward()).
    For the CNN->Dense and CNN->RNN transitions, the nIns on the Dense/RNN layers will also be added automatically. NOTE: This method will be called automatically when using the ComputationGraphConfiguration.GraphBuilder.setInputTypes(InputType...) functionality. See that method for details.
  - getLayerActivationTypes
```
public Map<String,InputType> getLayerActivationTypes(InputType... inputTypes)
```
    For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph. Note that this method will automatically add preprocessors if required, to handle (for example) the transition between CNN and dense layers.
    
    Parameters:
    
    inputTypes - Input types for the network
    
    Returns:
    
    A map of activation types for the graph (key: vertex name. value: type of activations out of that vertex)
  - getLayerActivationTypes
```
public Map<String,InputType> getLayerActivationTypes(boolean addPreprocIfNecessary,
                                                     InputType... inputTypes)
```
    For the given input shape/type for the network, return a map of activation sizes for each layer and vertex in the graph. Note that this method can also add preprocessors if required (to handle transitions between some layer types such as convolutional -> dense, for example)
    
    Parameters:
    
    addPreprocIfNecessary - If true: add any required preprocessors, in the process of calculating the layer activation sizes
    
    inputTypes - Input types for the network
    
    Returns:
    
    A map of activation types for the graph (key: vertex name. value: type of activations out of that vertex)
  - getMemoryReport
```
public NetworkMemoryReport getMemoryReport(InputType... inputTypes)
```
    Get a MemoryReport for the given computation graph configuration. This is used to estimate the memory requirements for the given network configuration and input
    
    Parameters:
    
    inputTypes - Input types for the network
    
    Returns:
    
    Memory report for the network

Class ComputationGraphConfiguration

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

vertices

vertexInputs

trainingWorkspaceMode

inferenceWorkspaceMode

cacheMode

dataType

validateOutputLayerConfig

networkInputs

networkOutputs

backpropType

tbpttFwdLength

tbpttBackLength

defaultConfiguration

iterationCount

epochCount

topologicalOrder

topologicalOrderStr

Constructor Detail

ComputationGraphConfiguration

Method Detail

toYaml

fromYaml

toJson

fromJson

toString

clone

validate

validate

addPreProcessors

getLayerActivationTypes

getLayerActivationTypes

getMemoryReport