org.nd4j.linalg.lossfunctions.impl

Class LossBinaryXENT

java.lang.Object

org.nd4j.autodiff.functions.DifferentialFunction

org.nd4j.linalg.lossfunctions.impl.LossBinaryXENT

All Implemented Interfaces:

Serializable, ILossFunction

public class LossBinaryXENT
extends DifferentialFunction
implements ILossFunction

Binary cross entropy loss function https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression Labels are assumed to take values 0 or 1

Author:

Susan Eraly

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_CLIPPING_EPSILON

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

dimensions, extraArgs, inPlace, sameDiff, scalarValue

Constructor Summary

Constructors

Constructor and Description
LossBinaryXENT()
LossBinaryXENT(double clipEps) Binary cross entropy where each the output is (optionally) weighted/scaled by a fixed scalar value.
LossBinaryXENT(double clipEps, INDArray weights) Binary cross entropy where each the output is (optionally) weighted/scaled by a fixed scalar value.
LossBinaryXENT(INDArray weights) Binary cross entropy where each the output is (optionally) weighted/scaled by a fixed 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.
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

DEFAULT_CLIPPING_EPSILON

public static final double DEFAULT_CLIPPING_EPSILON

See Also:

Constant Field Values

Constructor Detail

LossBinaryXENT

public LossBinaryXENT()

LossBinaryXENT

public LossBinaryXENT(INDArray weights)

Binary cross entropy where each the output is (optionally) weighted/scaled by a fixed 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.

LossBinaryXENT

public LossBinaryXENT(double clipEps)

Binary cross entropy where each the output is (optionally) weighted/scaled by a fixed 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:

clipEps - Epsilon value for clipping. Probabilities are clipped in range of [eps, 1-eps]. Default eps: 1e-5

LossBinaryXENT

public LossBinaryXENT(double clipEps,
                      INDArray weights)

Binary cross entropy where each the output is (optionally) weighted/scaled by a fixed 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:

clipEps - Epsilon value for clipping. Probabilities are clipped in range of [eps, 1-eps]. Default eps: 1e-5

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

Method Detail

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:

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:

toString

public String toString()

Overrides:

toString in class Object

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

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:

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: