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package org.nd4j.linalg.lossfunctions;
import org.apache.commons.math3.util.Pair;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.impl.*;
import org.nd4j.shade.jackson.annotation.JsonSubTypes;
import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
import java.io.Serializable;
/**
* Interface for loss functions
*/
@JsonTypeInfo(use = JsonTypeInfo.Id.NAME, include = JsonTypeInfo.As.WRAPPER_OBJECT)
@JsonSubTypes(value = {@JsonSubTypes.Type(value = LossBinaryXENT.class, name = "BinaryXENT"),
@JsonSubTypes.Type(value = LossCosineProximity.class, name = "CosineProximity"),
@JsonSubTypes.Type(value = LossHinge.class, name = "Hinge"),
@JsonSubTypes.Type(value = LossKLD.class, name = "KLD"),
@JsonSubTypes.Type(value = LossMAE.class, name = "MAE"),
@JsonSubTypes.Type(value = LossL1.class, name = "L1"),
@JsonSubTypes.Type(value = LossMAPE.class, name = "MAPE"),
@JsonSubTypes.Type(value = LossMCXENT.class, name = "MCXENT"),
@JsonSubTypes.Type(value = LossMSE.class, name = "MSE"),
@JsonSubTypes.Type(value = LossL2.class, name = "L2"),
@JsonSubTypes.Type(value = LossMSLE.class, name = "MSLE"),
@JsonSubTypes.Type(value = LossNegativeLogLikelihood.class, name = "NegativeLogLikelihood"),
@JsonSubTypes.Type(value = LossPoisson.class, name = "Poisson"),
@JsonSubTypes.Type(value = LossSquaredHinge.class, name = "SquaredHinge")})
public interface ILossFunction extends Serializable {
/**
* Compute the score (loss function value) for the given inputs.
* @param labels Label/expected preOutput
* @param preOutput Output of the model (neural network)
* @param activationFn Activation function that should be applied to preOutput
* @param mask Mask array; may be null
* @param average Whether the score should be averaged (divided by number of rows in labels/preOutput) or not @return Loss function value
*/
double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask, boolean average);
/**
* Compute the score (loss function value) for each example individually.
* For input [numExamples,nOut] returns scores as a column vector: [numExamples,1]
* @param labels Labels/expected output
* @param preOutput Output of the model (neural network)
* @param activationFn Activation function that should be applied to preOutput
* @param mask @return Loss function value for each example; column vector
*/
INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask);
/**
* Compute the gradient of the loss function with respect to the inputs: dL/dOutput
*
* @param labels Label/expected output
* @param preOutput Output of the model (neural network), before the activation function is applied
* @param activationFn Activation function that should be applied to preOutput
* @param mask Mask array; may be null
* @return Gradient dL/dPreOut
*/
INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask);
/**
* Compute both the score (loss function value) and gradient. This is equivalent to calling {@link #computeScore(INDArray, INDArray, IActivation, INDArray, boolean)}
* and {@link #computeGradient(INDArray, INDArray, IActivation, INDArray)} individually
*
* @param labels Label/expected output
* @param preOutput Output of the model (neural network)
* @param activationFn Activation function that should be applied to preOutput
* @param mask Mask array; may be null
* @param average Whether the score should be averaged (divided by number of rows in labels/output) or not
* @return The score (loss function value) and gradient
*/
//TODO: do we want to use the apache commons pair here?
Pair computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn,
INDArray mask, boolean average);
}
