WeightedLossFunctionExample.java

package org.deeplearning4j.examples.misc;

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.impl.LossMCXENT;

/**
 * @author Alex Black
 */
public class WeightedLossFunctionExample {

    public static void main(String[] args) {


        /*
        Idea with a weighted loss function: it allows us to add a weight to the outputs.
        For example, if we have 3 classes, and we consider predictions of the 3rd class to be more important, we might use
        a weight array of [0.5,0.5,1.0]. This means that the first 2 outputs will contribute only half as much as they
        normally would to the loss/score.

        Note that the weights don't (and shouldn't necessarily) sum to 1.0 - and that a weight array of all 1s is equivalent
        to having no weight array at all.
        If the use case is dealing with class imbalance for classification, use smaller weights for frequently occurring
         classes, and 1.0 or larger weights for infrequently occurring classes.

        Training and the data pipelines when using weighted loss functions are identical to not using them, so this example
        shows only how to configure the weighting.
         */

        int numInputs = 4;
        int numClasses = 3;     //3 classes for classification

        //Create the weights array. Note that we have 3 output classes, therefore we have 3 weights
        INDArray weightsArray = Nd4j.create(new double[]{0.5, 0.5, 1.0});

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
            .activation(Activation.RELU)
            .weightInit(WeightInit.XAVIER)
            .learningRate(0.1)
            .list()
            .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(5)
                .build())
            .layer(1, new DenseLayer.Builder().nIn(5).nOut(5)
                .build())
            .layer(2, new OutputLayer.Builder()
                .lossFunction(new LossMCXENT(weightsArray))     // *** Weighted loss function configured here ***
                .activation(Activation.SOFTMAX)
                .nIn(5).nOut(numClasses).build())
            .backprop(true).pretrain(false)
            .build();

        //Initialize and use the model as before
        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
    }
}