org.nd4j.linalg.lossfunctions.impl

Class LossL1

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.lossfunctions.impl.LossL1

All Implemented Interfaces:

Serializable, ILossFunction

Direct Known Subclasses:

LossMAE

public class LossL1
extends DifferentialFunction
implements ILossFunction

L1 loss function: i.e., sum of absolute errors, L = sum_i abs(predicted_i - actual_i) See also LossMAE for a mathematically similar loss function (MAE has division by N, where N is output size)

Author:

Susan Eraly

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected INDArray	weights

Fields inherited from class org.nd4j.autodiff.functions.DifferentialFunction

dimensions, extraArgs, inPlace, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
LossL1()
LossL1(INDArray weights) L1 loss function where each the output is (optionally) weighted/scaled by a flags scalar value.

Method Summary

Modifier and Type	Method and Description
INDArray	computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) Compute the gradient of the loss function with respect to the inputs: dL/dOutput
org.nd4j.linalg.primitives.Pair<Double,INDArray>	computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) Compute both the score (loss function value) and gradient.
double	computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average) Compute the score (loss function value) for the given inputs.
INDArray	computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) Compute the score (loss function value) for each example individually.
List<SDVariable>	doDiff(List<SDVariable> f1) The actual implementation for automatic differentiation.
void	initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map<String,OnnxProto3.AttributeProto> attributesForNode, OnnxProto3.GraphProto graph) Iniitialize the function from the given OnnxProto3.NodeProto
void	initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String,AttrValue> attributesForNode, GraphDef graph) Initialize the function from the given NodeDef
String	name() The opName of this function
String	onnxName() The opName of this function in onnx
String	opName() The name of the op
Op.Type	opType() The type of the op
SDVariable[]	outputVariables() Return the output variables for this differential function.
SDVariable[]	outputVariables(String baseName) Return the output functions for this differential function.
INDArray	scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask)
String	tensorflowName() The opName of this function tensorflow
String	toString()

Methods inherited from class org.nd4j.autodiff.functions.DifferentialFunction

arg, args, asProperties, attributeAdaptersForFunction, calculateOutputShape, configFieldName, diff, dup, equals, f, getValue, hashCode, hasPlaceHolderInputs, isConfigProperties, larg, mappingsForFunction, onnxNames, opNum, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setValueFor, tensorflowNames

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Field Detail

weights

protected final INDArray weights

Constructor Detail

LossL1

public LossL1()

LossL1

public LossL1(INDArray weights)

L1 loss function where each the output is (optionally) weighted/scaled by a flags scalar value. Note that the weights array must be a row vector, of length equal to the labels/output dimension 1 size. A weight vector of 1s should give identical results to no weight vector.

Parameters:

weights - Weights array (row vector). May be null.

Method Detail

scoreArray

public INDArray scoreArray(INDArray labels,
                           INDArray preOutput,
                           IActivation activationFn,
                           INDArray mask)

computeScore

public double computeScore(INDArray labels,
                           INDArray preOutput,
                           IActivation activationFn,
                           INDArray mask,
                           boolean average)

Description copied from interface: ILossFunction

Compute the score (loss function value) for the given inputs.

Specified by:

computeScore in interface ILossFunction

Parameters:

labels - Label/expected preOutput

preOutput - Output of the model (neural network)

activationFn - Activation function that should be applied to preOutput

mask - Mask array; may be null

average - Whether the score should be averaged (divided by number of rows in labels/preOutput) or not @return Loss function value

computeScoreArray

public INDArray computeScoreArray(INDArray labels,
                                  INDArray preOutput,
                                  IActivation activationFn,
                                  INDArray mask)

Description copied from interface: ILossFunction

Compute the score (loss function value) for each example individually. For input [numExamples,nOut] returns scores as a column vector: [numExamples,1]

Specified by:

computeScoreArray in interface ILossFunction

Parameters:

labels - Labels/expected output

preOutput - Output of the model (neural network)

activationFn - Activation function that should be applied to preOutput

mask - @return Loss function value for each example; column vector

computeGradient

public INDArray computeGradient(INDArray labels,
                                INDArray preOutput,
                                IActivation activationFn,
                                INDArray mask)

Description copied from interface: ILossFunction

Compute the gradient of the loss function with respect to the inputs: dL/dOutput

Specified by:

computeGradient in interface ILossFunction

Parameters:

labels - Label/expected output

preOutput - Output of the model (neural network), before the activation function is applied

activationFn - Activation function that should be applied to preOutput

mask - Mask array; may be null

Returns:

Gradient dL/dPreOut

computeGradientAndScore

public org.nd4j.linalg.primitives.Pair<Double,INDArray> computeGradientAndScore(INDArray labels,
                                                                                INDArray preOutput,
                                                                                IActivation activationFn,
                                                                                INDArray mask,
                                                                                boolean average)

Description copied from interface: ILossFunction

Compute both the score (loss function value) and gradient. This is equivalent to calling ILossFunction.computeScore(INDArray, INDArray, IActivation, INDArray, boolean) and ILossFunction.computeGradient(INDArray, INDArray, IActivation, INDArray) individually

Specified by:

computeGradientAndScore in interface ILossFunction

Parameters:

labels - Label/expected output

preOutput - Output of the model (neural network)

activationFn - Activation function that should be applied to preOutput

mask - Mask array; may be null

average - Whether the score should be averaged (divided by number of rows in labels/output) or not

Returns:

The score (loss function value) and gradient

name

public String name()

The opName of this function

Specified by:

name in interface ILossFunction

Returns:

toString

public String toString()

Overrides:

toString in class Object

outputVariables

public SDVariable[] outputVariables()

Description copied from class: DifferentialFunction

Return the output variables for this differential function. Note that this op *may* dynamically generate variable outputs.

Overrides:

outputVariables in class DifferentialFunction

Returns:

outputVariables

public SDVariable[] outputVariables(String baseName)

Description copied from class: DifferentialFunction

Return the output functions for this differential function.

Specified by:

outputVariables in class DifferentialFunction

Returns:

doDiff

public List<SDVariable> doDiff(List<SDVariable> f1)

Description copied from class: DifferentialFunction

The actual implementation for automatic differentiation.

Specified by:

doDiff in class DifferentialFunction

Returns:

opName

public String opName()

Description copied from class: DifferentialFunction

The name of the op

Overrides:

opName in class DifferentialFunction

Returns:

opType

public Op.Type opType()

Description copied from class: DifferentialFunction

The type of the op

Overrides:

opType in class DifferentialFunction

Returns:

initFromTensorFlow

public void initFromTensorFlow(NodeDef nodeDef,
                               SameDiff initWith,
                               Map<String,AttrValue> attributesForNode,
                               GraphDef graph)

Description copied from class: DifferentialFunction

Initialize the function from the given NodeDef

Specified by:

initFromTensorFlow in class DifferentialFunction

initFromOnnx

public void initFromOnnx(OnnxProto3.NodeProto node,
                         SameDiff initWith,
                         Map<String,OnnxProto3.AttributeProto> attributesForNode,
                         OnnxProto3.GraphProto graph)

Description copied from class: DifferentialFunction

Iniitialize the function from the given OnnxProto3.NodeProto

Specified by:

initFromOnnx in class DifferentialFunction

onnxName

public String onnxName()

Description copied from class: DifferentialFunction

The opName of this function in onnx

Specified by:

onnxName in class DifferentialFunction

Returns:

tensorflowName

public String tensorflowName()

Description copied from class: DifferentialFunction

The opName of this function tensorflow

Specified by:

tensorflowName in class DifferentialFunction

Returns: