DifferentialFunction (nd4j-api 1.0.0-M2 API)

java.lang.Object
- org.nd4j.autodiff.functions.DifferentialFunction

Direct Known Subclasses:: BaseOp, DynamicCustomOp

public abstract class DifferentialFunction
extends Object

Field Summary

Fields
Modifier and Type	Field and Description
`protected int[]`	`dimensions`
`protected Object[]`	`extraArgs`
`protected boolean`	`inPlace`
`protected String`	`ownName`
`protected boolean`	`ownNameSetWithDefault`
`protected SameDiff`	`sameDiff`
`protected INDArray`	`scalarValue`

Constructor Summary

Constructors
Constructor and Description
`DifferentialFunction()`
`DifferentialFunction(boolean sameDiff)`
`DifferentialFunction(SameDiff sameDiff, boolean inPlace, Object[] extraArgs)`
`DifferentialFunction(SameDiff sameDiff, boolean inPlace, SDVariable[] args)` Add the various arguments for this function
`DifferentialFunction(SameDiff sameDiff, NodeDef nodeDef, Map<String,AttrValue> attributesForNode, GraphDef graph)` Initialize the function from the given `NodeDef`
`DifferentialFunction(SameDiff sameDiff, Object[] extraArgs)`
`DifferentialFunction(SameDiff sameDiff, Onnx.NodeProto node, Map<String,Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph)` Initialize the function from the given `Onnx.NodeProto`
`DifferentialFunction(SameDiff sameDiff, SDVariable[] args)`

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`SDVariable`	`arg()` Return the first argument
`SDVariable`	`arg(int num)` Return the specified argument for this function
`String[]`	`argNames()`
`SDVariable[]`	`args()` Return the arguments for a given function
`Map<String,Map<String,AttributeAdapter>>`	`attributeAdaptersForFunction()` Returns the `AttributeAdapter` s for each of the possible ops for import (typically tensorflow and onnx) See `AttributeAdapter` for more information on what the adapter does.
`List<DataType>`	`calculateOutputDataTypes(List<DataType> dataTypes)` Calculate the data types for the output arrays.
`List<LongShapeDescriptor>`	`calculateOutputShape()` Calculate the output shape for this op
`List<LongShapeDescriptor>`	`calculateOutputShape(OpContext oc)`
`abstract void`	`clearArrays()` Clear the input and output INDArrays, if any are set
`String`	`configFieldName()` Returns the name of the field to be used for looking up field names.
`void`	`configureWithSameDiff(SameDiff sameDiff)`
`List<SDVariable>`	`diff(List<SDVariable> i_v1)` Perform automatic differentiation wrt the input variables
`abstract List<SDVariable>`	`doDiff(List<SDVariable> f1)` The actual implementation for automatic differentiation.
`DifferentialFunction`	`dup()` Duplicate this function
`boolean`	`equals(Object o)`
`protected Boolean`	`getBooleanFromProperty(String propertyName, Map<String,Object> properties)`
`protected Double`	`getDoubleValueFromProperty(String propertyName, Map<String,Object> properties)`
`INDArray`	`getInputArgument(int index)`
`protected Integer`	`getIntValueFromProperty(String propertyName, Map<String,Object> properties)`
`protected Long`	`getLongValueFromProperty(String propertyName, Map<String,Object> properties)`
`int`	`getNumOutputs()`
`protected String`	`getStringFromProperty(String propertyName, Map<String,Object> properties)`
`Object`	`getValue(Field property)` Get the value for a given property for this function
`int`	`hashCode()`
`abstract void`	`initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map<String,Onnx.AttributeProto> attributesForNode, Onnx.GraphProto graph)` Iniitialize the function from the given `Onnx.NodeProto`
`abstract void`	`initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph)` Initialize the function from the given `NodeDef`
`boolean`	`isConfigProperties()` Returns true if the fields for this class should be looked up from a configuration class.
`SDVariable`	`larg()` The left argument for this function
`Map<String,Map<String,PropertyMapping>>`	`mappingsForFunction()` Returns the mappings for a given function ( for tensorflow and onnx import mapping properties of this function).
`abstract String`	`onnxName()` The opName of this function in onnx
`String[]`	`onnxNames()` The opName of this function in onnx
`String`	`opName()` The name of the op
`int`	`opNum()` The number of the op (mainly for old legacy XYZ ops like `Op`)
`Op.Type`	`opType()` The type of the op
`List<SDVariable>`	`outputs()`
`SDVariable`	`outputVariable()`
`SDVariable[]`	`outputVariables()` Return the output variables for this differential function.
`abstract SDVariable[]`	`outputVariables(String baseName)` Return the output functions for this differential function.
`String[]`	`outputVariablesNames()`
`Map<String,Object>`	`propertiesForFunction()` Returns the properties for a given function
`SDVariable`	`rarg()` The right argument for this function.
`void`	`replaceArg(int i, SDVariable newArg)` Replace argument at the specfied index
`protected void`	`setInstanceId()`
`void`	`setPropertiesForFunction(Map<String,Object> properties)`
`void`	`setValueFor(Field target, Object value)` Set the value for this function.
`abstract String`	`tensorflowName()` The opName of this function tensorflow
`String[]`	`tensorflowNames()` The opName of this function tensorflow

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

Field Detail

sameDiff
```
protected SameDiff sameDiff
```

inPlace
```
protected boolean inPlace
```

scalarValue
```
protected INDArray scalarValue
```

dimensions
```
protected int[] dimensions
```

extraArgs
```
protected Object[] extraArgs
```

ownName
```
protected String ownName
```

ownNameSetWithDefault

protected boolean ownNameSetWithDefault

Constructor Detail

DifferentialFunction
```
public DifferentialFunction()
```

DifferentialFunction

public DifferentialFunction(boolean sameDiff)

DifferentialFunction

public DifferentialFunction(SameDiff sameDiff,
                            NodeDef nodeDef,
                            Map<String,AttrValue> attributesForNode,
                            GraphDef graph)

Initialize the function from the given NodeDef

Parameters:: nodeDef -

DifferentialFunction

public DifferentialFunction(SameDiff sameDiff,
                            Onnx.NodeProto node,
                            Map<String,Onnx.AttributeProto> attributesForNode,
                            Onnx.GraphProto graph)

Initialize the function from the given Onnx.NodeProto

Parameters:: node -

DifferentialFunction

public DifferentialFunction(SameDiff sameDiff,
                            boolean inPlace,
                            Object[] extraArgs)

Parameters:: sameDiff -; extraArgs -

DifferentialFunction

public DifferentialFunction(SameDiff sameDiff,
                            Object[] extraArgs)

Parameters:: sameDiff -; extraArgs -

DifferentialFunction

public DifferentialFunction(SameDiff sameDiff,
                            SDVariable[] args)

Parameters:: sameDiff -; args -

DifferentialFunction

public DifferentialFunction(SameDiff sameDiff,
                            boolean inPlace,
                            SDVariable[] args)

Add the various arguments for this function

Parameters:: sameDiff -; inPlace -; args -

Method Detail
- attributeAdaptersForFunction
```
public Map<String,Map<String,AttributeAdapter>> attributeAdaptersForFunction()
```
  Returns the AttributeAdapter s for each of the possible ops for import (typically tensorflow and onnx) See AttributeAdapter for more information on what the adapter does. Similar to mappingsForFunction(), the returned map contains a AttributeAdapter for each field name when one is present. (It is optional for one to exist)_
  
  Returns:
- mappingsForFunction
```
public Map<String,Map<String,PropertyMapping>> mappingsForFunction()
```
  Returns the mappings for a given function ( for tensorflow and onnx import mapping properties of this function). The mapping is indexed by field name. If the function has no properties, this returned map will be empty. Note that some functions have multiple names. This function returns a map indexed by each alias it has for a given name. These names include both onnx and tensorflow names (which might be 1 or more)
  
  Returns:
- propertiesForFunction
```
public Map<String,Object> propertiesForFunction()
```
  Returns the properties for a given function
  
  Returns:
- configureWithSameDiff
```
public void configureWithSameDiff(SameDiff sameDiff)
```
- setPropertiesForFunction
```
public void setPropertiesForFunction(Map<String,Object> properties)
```
- getBooleanFromProperty
```
protected Boolean getBooleanFromProperty(String propertyName,
                                         Map<String,Object> properties)
```
- getStringFromProperty
```
protected String getStringFromProperty(String propertyName,
                                       Map<String,Object> properties)
```
- getIntValueFromProperty
```
protected Integer getIntValueFromProperty(String propertyName,
                                          Map<String,Object> properties)
```
- getLongValueFromProperty
```
protected Long getLongValueFromProperty(String propertyName,
                                        Map<String,Object> properties)
```
- getDoubleValueFromProperty
```
protected Double getDoubleValueFromProperty(String propertyName,
                                            Map<String,Object> properties)
```
- getValue
```
public Object getValue(Field property)
```
  Get the value for a given property for this function
  
  Parameters:
  
  property - the property to get
  
  Returns:
  
  the value for the function if it exists
- setValueFor
```
public void setValueFor(Field target,
                        Object value)
```
  Set the value for this function. Note that if value is null an ND4JIllegalStateException will be thrown.
  
  Parameters:
  
  target - the target field
  
  value - the value to set
- isConfigProperties
```
public boolean isConfigProperties()
```
  Returns true if the fields for this class should be looked up from a configuration class.
  
  Returns:
- configFieldName
```
public String configFieldName()
```
  Returns the name of the field to be used for looking up field names. This should be used in conjunction with isConfigProperties() to facilitate mapping fields for model import.
  
  Returns:
- replaceArg
```
public void replaceArg(int i,
                       SDVariable newArg)
```
  Replace argument at the specfied index
  
  Parameters:
  
  i - the index
  
  newArg - the new argument
- outputVariables
```
public SDVariable[] outputVariables()
```
  Return the output variables for this differential function. Note that this op *may* dynamically generate variable outputs.
  
  Returns:
- outputVariable
```
public SDVariable outputVariable()
```
  Returns:
  
  The output variable, or the first output variable, if multiple outputs exist
- outputs
```
public List<SDVariable> outputs()
```
- outputVariablesNames
```
public String[] outputVariablesNames()
```
- outputVariables
```
public abstract SDVariable[] outputVariables(String baseName)
```
  Return the output functions for this differential function.
  
  Returns:
- doDiff
```
public abstract List<SDVariable> doDiff(List<SDVariable> f1)
```
  The actual implementation for automatic differentiation.
  
  Parameters:
  
  f1 -
  
  Returns:
- args
```
public SDVariable[] args()
```
  Return the arguments for a given function
  
  Returns:
  
  the arguments for a given function
- arg
```
public SDVariable arg(int num)
```
  Return the specified argument for this function
  
  Parameters:
  
  num - Number of the argument. Must be in range 0 to numArgs - 1 inclusive
  
  Returns:
  
  Specified argument
- argNames
```
public String[] argNames()
```
- arg
```
public SDVariable arg()
```
  Return the first argument
  
  Returns:
- diff
```
public List<SDVariable> diff(List<SDVariable> i_v1)
```
  Perform automatic differentiation wrt the input variables
  
  Parameters:
  
  i_v1 - the input variables
  
  Returns:
  
  the differentiated output wrt each input variable
- setInstanceId
```
protected void setInstanceId()
```
- opName
```
public String opName()
```
  The name of the op
  
  Returns:
- opType
```
public Op.Type opType()
```
  The type of the op
  
  Returns:
- opNum
```
public int opNum()
```
  The number of the op (mainly for old legacy XYZ ops like Op)
  
  Returns:
- getInputArgument
```
public INDArray getInputArgument(int index)
```
- initFromTensorFlow
```
public abstract void initFromTensorFlow(NodeDef nodeDef,
                                        SameDiff initWith,
                                        Map<String,AttrValue> attributesForNode,
                                        GraphDef graph)
```
  Initialize the function from the given NodeDef
  
  Parameters:
  
  nodeDef -
  
  initWith -
  
  attributesForNode -
  
  graph -
- initFromOnnx
```
public abstract void initFromOnnx(Onnx.NodeProto node,
                                  SameDiff initWith,
                                  Map<String,Onnx.AttributeProto> attributesForNode,
                                  Onnx.GraphProto graph)
```
  Iniitialize the function from the given Onnx.NodeProto
  
  Parameters:
  
  node -
  
  initWith -
  
  attributesForNode -
  
  graph -
- larg
```
public SDVariable larg()
```
  The left argument for this function
  
  Returns:
- rarg
```
public SDVariable rarg()
```
  The right argument for this function. Note that this assumes that there are 2 args for this function, if 2 are not set, it throws an ND4JIllegalStateException
  
  Returns:
- dup
```
public DifferentialFunction dup()
```
  Duplicate this function
  
  Returns:
- calculateOutputShape
```
public List<LongShapeDescriptor> calculateOutputShape()
```
  Calculate the output shape for this op
  
  Returns:
  
  List of output shape descriptors
- calculateOutputShape
```
public List<LongShapeDescriptor> calculateOutputShape(OpContext oc)
```
- calculateOutputDataTypes
```
public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes)
```
  Calculate the data types for the output arrays. Though datatypes can also be inferred from calculateOutputShape(), this method differs in that it does not require the input arrays to be populated. This is important as it allows us to do greedy datatype inference for the entire net - even if arrays are not available.
  
  Parameters:
  
  dataTypes - The data types of the inputs
  
  Returns:
  
  The data types of the outputs
- equals
```
public boolean equals(Object o)
```
  Overrides:
  
  equals in class Object
- hashCode
```
public int hashCode()
```
  Overrides:
  
  hashCode in class Object
- onnxNames
```
public String[] onnxNames()
```
  The opName of this function in onnx
  
  Returns:
- tensorflowNames
```
public String[] tensorflowNames()
```
  The opName of this function tensorflow
  
  Returns:
- onnxName
```
public abstract String onnxName()
```
  The opName of this function in onnx
  
  Returns:
- tensorflowName
```
public abstract String tensorflowName()
```
  The opName of this function tensorflow
  
  Returns:
- getNumOutputs
```
public int getNumOutputs()
```
- clearArrays
```
public abstract void clearArrays()
```
  Clear the input and output INDArrays, if any are set

Class DifferentialFunction

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

sameDiff

inPlace

scalarValue

dimensions

extraArgs

ownName

ownNameSetWithDefault

Constructor Detail

DifferentialFunction

DifferentialFunction

DifferentialFunction

DifferentialFunction

DifferentialFunction

DifferentialFunction

DifferentialFunction

DifferentialFunction

Method Detail

attributeAdaptersForFunction

mappingsForFunction

propertiesForFunction

configureWithSameDiff

setPropertiesForFunction

getBooleanFromProperty

getStringFromProperty

getIntValueFromProperty

getLongValueFromProperty

getDoubleValueFromProperty

getValue

setValueFor

isConfigProperties

configFieldName

replaceArg

outputVariables

outputVariable

outputs

outputVariablesNames

outputVariables

doDiff

args

arg

argNames

arg

diff

setInstanceId

opName

opType

opNum

getInputArgument

initFromTensorFlow

initFromOnnx

larg

rarg

dup

calculateOutputShape

calculateOutputShape

calculateOutputDataTypes

equals

hashCode

onnxNames

tensorflowNames

onnxName

tensorflowName

getNumOutputs

clearArrays