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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.lossfunctions.impl;
import lombok.EqualsAndHashCode;
import lombok.Getter;
import lombok.Setter;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.ActivationSoftmax;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.BooleanIndexing;
import org.nd4j.linalg.indexing.conditions.Conditions;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;
import org.nd4j.linalg.ops.transforms.Transforms;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.serde.jackson.shaded.NDArrayTextDeSerializer;
import org.nd4j.serde.jackson.shaded.NDArrayTextSerializer;
import org.nd4j.shade.jackson.annotation.JsonInclude;
import org.nd4j.shade.jackson.annotation.JsonProperty;
import org.nd4j.shade.jackson.databind.annotation.JsonDeserialize;
import org.nd4j.shade.jackson.databind.annotation.JsonSerialize;
/**
*
* Multi-Class Cross Entropy loss function:
* L = sum_i actual_i * log( predicted_i )
*
* @author Alex Black, Susan Eraly
* @see LossNegativeLogLikelihood
*/
@EqualsAndHashCode
@JsonInclude(JsonInclude.Include.NON_NULL)
@Getter @Setter
public class LossMCXENT implements ILossFunction {
private static final double DEFAULT_SOFTMAX_CLIPPING_EPSILON = 1e-10;
@JsonSerialize(using = NDArrayTextSerializer.class)
@JsonDeserialize(using = NDArrayTextDeSerializer.class)
private INDArray weights;
private double softmaxClipEps;
public LossMCXENT() {
this(null);
}
/**
* Multi-Class Cross Entropy 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.
*
* @param weights Weights array (row vector). May be null.
*/
public LossMCXENT(INDArray weights) {
this(DEFAULT_SOFTMAX_CLIPPING_EPSILON, weights);
}
/**
* Multi-Class Cross Entropy loss function 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.
*
* @param weights Weights array (row vector). May be null.
*/
public LossMCXENT(@JsonProperty("softmaxClipEps") double softmaxClipEps, @JsonProperty("weights") INDArray weights) {
if (weights != null && !weights.isRowVector()) {
throw new IllegalArgumentException("Weights array must be a row vector");
}
if(softmaxClipEps < 0 || softmaxClipEps > 0.5){
throw new IllegalArgumentException("Invalid clipping epsilon: epsilon should be >= 0 (but near zero). Got: "
+ softmaxClipEps);
}
this.weights = weights;
this.softmaxClipEps = softmaxClipEps;
}
private INDArray scoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
INDArray output = activationFn.getActivation(preOutput.dup(), true);
if(activationFn instanceof ActivationSoftmax && softmaxClipEps > 0.0){
BooleanIndexing.replaceWhere(output, softmaxClipEps, Conditions.lessThan(softmaxClipEps));
BooleanIndexing.replaceWhere(output, 1.0-softmaxClipEps, Conditions.greaterThan(1.0-softmaxClipEps));
}
INDArray scoreArr = Transforms.log(output, false).muli(labels);
//Weighted loss function
if (weights != null) {
if (weights.length() != scoreArr.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + preOutput.size(1));
}
scoreArr.muliRowVector(weights.castTo(scoreArr.dataType()));
}
if (mask != null) {
LossUtil.applyMask(scoreArr, mask);
}
return scoreArr;
}
@Override
public double computeScore(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask,
boolean average) {
INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);
double score = -scoreArr.sumNumber().doubleValue();
if (average) {
score /= scoreArr.size(0);
}
return score;
}
@Override
public INDArray computeScoreArray(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
INDArray scoreArr = scoreArray(labels, preOutput, activationFn, mask);
return scoreArr.sum(true,1).muli(-1);
}
@Override
public INDArray computeGradient(INDArray labels, INDArray preOutput, IActivation activationFn, INDArray mask) {
if(!labels.equalShapes(preOutput)){
Preconditions.throwEx("Labels and preOutput must have equal shapes: got shapes %s vs %s", labels.shape(), preOutput.shape());
}
INDArray grad;
INDArray output = activationFn.getActivation(preOutput.dup(), true);
labels = labels.castTo(preOutput.dataType()); //No-op if already correct dtype
if (activationFn instanceof ActivationSoftmax) {
if (mask != null && LossUtil.isPerOutputMasking(output, mask)) {
throw new UnsupportedOperationException("Per output masking for MCXENT + softmax: not supported");
}
//Weighted loss function
if (weights != null) {
if (weights.length() != output.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + output.size(1));
}
INDArray temp = labels.mulRowVector(weights);
INDArray col = temp.sum(true,1);
grad = output.mulColumnVector(col).sub(temp);
} else {
grad = output.subi(labels);
}
} else {
INDArray dLda = output.rdivi(labels).negi();
grad = activationFn.backprop(preOutput, dLda).getFirst(); //TODO activation function with weights
//Weighted loss function
if (weights != null) {
if (weights.length() != output.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + output.size(1));
}
grad.muliRowVector(weights.castTo(grad.dataType()));
}
}
//Loss function with masking
if (mask != null) {
LossUtil.applyMask(grad, mask);
}
return grad;
}
@Override
public Pair computeGradientAndScore(INDArray labels, INDArray preOutput, IActivation activationFn,
INDArray mask, boolean average) {
//TODO: probably a more efficient way to do this...
return new Pair<>(computeScore(labels, preOutput, activationFn, mask, average),
computeGradient(labels, preOutput, activationFn, mask));
}
/**
* The opName of this function
*
* @return
*/
@Override
public String name() {
return toString();
}
@Override
public String toString() {
if (weights == null)
return "LossMCXENT()";
return "LossMCXENT(weights=" + weights + ")";
}
}
