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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 onnx.OnnxProto3;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.ops.CustomOp;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.primitives.Pair;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.ActivationSoftmax;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.LogSoftMax;
import org.nd4j.linalg.api.ops.impl.transforms.TimesOneMinus;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.LossUtil;
import org.nd4j.linalg.lossfunctions.serde.RowVectorDeserializer;
import org.nd4j.linalg.lossfunctions.serde.RowVectorSerializer;
import org.nd4j.linalg.ops.transforms.Transforms;
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;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.List;
import java.util.Map;
/**
* 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
*/
@EqualsAndHashCode
@JsonInclude(JsonInclude.Include.NON_NULL)
@Getter @Setter
public class LossBinaryXENT extends DifferentialFunction implements ILossFunction {
public static final double DEFAULT_CLIPPING_EPSILON = 1e-5;
@JsonSerialize(using = RowVectorSerializer.class)
@JsonDeserialize(using = RowVectorDeserializer.class)
private final INDArray weights;
private double clipEps;
public LossBinaryXENT() {
this(null);
}
/**
* 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.
*
* @param weights Weights array (row vector). May be null.
*/
public LossBinaryXENT(INDArray weights) {
this(DEFAULT_CLIPPING_EPSILON, 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.
*
* @param clipEps Epsilon value for clipping. Probabilities are clipped in range of [eps, 1-eps]. Default eps: 1e-5
*/
public LossBinaryXENT(double clipEps){
this(clipEps, null);
}
/**
* 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.
*
* @param clipEps Epsilon value for clipping. Probabilities are clipped in range of [eps, 1-eps]. Default eps: 1e-5
* @param weights Weights array (row vector). May be null.
*/
public LossBinaryXENT(@JsonProperty("clipEps") double clipEps, @JsonProperty("weights") INDArray weights){
if (weights != null && !weights.isRowVector()) {
throw new IllegalArgumentException("Weights array must be a row vector");
}
if(clipEps < 0 || clipEps > 0.5){
throw new IllegalArgumentException("Invalid clipping epsilon value: epsilon should be >= 0 (but near zero)."
+ "Got: " + clipEps);
}
this.clipEps = clipEps;
this.weights = weights;
}
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());
}
INDArray scoreArr;
if (activationFn instanceof ActivationSoftmax) {
//Use LogSoftMax op to avoid numerical issues when calculating score
INDArray logsoftmax = Nd4j.getExecutioner().execAndReturn(new LogSoftMax(preOutput.dup()));
scoreArr = logsoftmax.muli(labels);
} else {
//INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(activationFn, preOutput.dup()));
INDArray output = activationFn.getActivation(preOutput.dup(), true);
if (clipEps > 0.0) {
CustomOp op = DynamicCustomOp.builder("clipbyvalue")
.addInputs(output)
.callInplace(true)
.addFloatingPointArguments(clipEps, 1.0-clipEps)
.build();
Nd4j.getExecutioner().exec(op);
}
scoreArr = Transforms.log(output, true).muli(labels);
INDArray secondTerm = output.rsubi(1);
Transforms.log(secondTerm, false);
secondTerm.muli(labels.rsub(1));
scoreArr.addi(secondTerm);
}
//Weighted loss function
if (weights != null) {
if (weights.length() != preOutput.size(1)) {
throw new IllegalStateException("Weights vector (length " + weights.length()
+ ") does not match output.size(1)=" + preOutput.size(1));
}
scoreArr.muliRowVector(weights);
}
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(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 output = activationFn.getActivation(preOutput.dup(), true);
if (clipEps > 0.0) {
CustomOp op = DynamicCustomOp.builder("clipbyvalue")
.addInputs(output)
.callInplace(true)
.addFloatingPointArguments(clipEps, 1.0-clipEps)
.build();
Nd4j.getExecutioner().exec(op);
}
INDArray numerator = output.sub(labels);
INDArray denominator = Nd4j.getExecutioner().execAndReturn(new TimesOneMinus(output)); // output * (1-output)
INDArray dLda = numerator.divi(denominator);
if (mask != null && LossUtil.isPerOutputMasking(dLda, mask)) {
//For *most* activation functions: we don't actually need to mask dL/da in addition to masking dL/dz later
//but: some, like softmax, require both (due to dL/dz_i being a function of dL/da_j, for i != j)
//We could add a special case for softmax (activationFn instanceof ActivationSoftmax) but that would be
// error prone - but buy us a tiny bit of performance
LossUtil.applyMask(dLda, mask);
}
INDArray grad = activationFn.backprop(preOutput, dLda).getFirst(); //TODO activation functions 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);
}
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 SDVariable[] outputVariables() {
return new SDVariable[0];
}
@Override
public SDVariable[] outputVariables(String baseName) {
return new SDVariable[0];
}
@Override
public List doDiff(List f1) {
return null;
}
@Override
public String toString() {
if (weights == null)
return "LossBinaryXENT()";
return "LossBinaryXENT(weights=" + weights + ")";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
}
@Override
public String opName() {
return "lossbinaryxent";
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public String onnxName() {
return "SigmoidCrossEntropy";
}
@Override
public String tensorflowName() {
return "SigmoidCrossEntropy";
}
}
