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Java example source code file (BaseOutputLayer.java)

This example Java source code file (BaseOutputLayer.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

baseoutputlayer, cannot, equivalent, evaluation, gradient, illegalstateexception, indarray, list, mse, override, pair, returns, todo, triple, util

The BaseOutputLayer.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://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.
 *
 */

package org.deeplearning4j.nn.layers;

import java.io.*;
import java.util.ArrayList;
import java.util.List;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.berkeley.Triple;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.Classifier;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.optimize.Solver;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.LossFunction;
import org.nd4j.linalg.api.ops.impl.transforms.SoftMax;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.lossfunctions.LossCalculation;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.util.FeatureUtil;
import org.nd4j.linalg.util.LinAlgExceptions;

import static org.nd4j.linalg.ops.transforms.Transforms.pow;
import static org.nd4j.linalg.ops.transforms.Transforms.sqrt;


/**
 * Output layer with different objective
 * incooccurrences for different objectives.
 * This includes classification as well as prediction
 * @author Adam Gibson
 *
 */
public abstract class BaseOutputLayer<LayerConfT extends org.deeplearning4j.nn.conf.layers.BaseOutputLayer>
        extends BaseLayer<LayerConfT> implements Serializable,Classifier {

    //current input and label matrices
    protected INDArray labels;

    private transient Solver solver;

    private double fullNetworkL1;
    private double fullNetworkL2;

    public BaseOutputLayer(NeuralNetConfiguration conf) {
        super(conf);
    }

    public BaseOutputLayer(NeuralNetConfiguration conf, INDArray input) {
        super(conf, input);
    }

    /** Compute score after labels and input have been set.
     * @param fullNetworkL1 L1 regularization term for the entire network
     * @param fullNetworkL2 L2 regularization term for the entire network
     * @param training whether score should be calculated at train or test time (this affects things like application of
     *                 dropout, etc)
     * @return score (loss function)
     */
    public double computeScore( double fullNetworkL1, double fullNetworkL2, boolean training) {
        if( input == null || labels == null )
            throw new IllegalStateException("Cannot calculate score without input and labels");
        this.fullNetworkL1 = fullNetworkL1;
        this.fullNetworkL2 = fullNetworkL2;
        INDArray preOut = preOutput2d(training);
        LossFunctions.LossFunction lf = ((org.deeplearning4j.nn.conf.layers.BaseOutputLayer)conf.getLayer()).getLossFunction();
        if ( (lf == LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD || lf == LossFunctions.LossFunction.MCXENT) && layerConf().getActivationFunction().equals("softmax")) {
            //special case: softmax + NLL or MCXENT: use log softmax to avoid numerical underflow
            setScore(null,preOut);
        } else {
            INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf().getLayer().getActivationFunction(), preOut));
            setScoreWithZ(output);
        }
        return score;
    }

    /**Compute the score for each example individually, after labels and input have been set.
     *
     * @param fullNetworkL1 L1 regularization term for the entire network (or, 0.0 to not include regularization)
     * @param fullNetworkL2 L2 regularization term for the entire network (or, 0.0 to not include regularization)
     * @return A column INDArray of shape [numExamples,1], where entry i is the score of the ith example
     */
    public INDArray computeScoreForExamples(double fullNetworkL1, double fullNetworkL2){
        if( input == null || labels == null )
            throw new IllegalStateException("Cannot calculate score without input and labels");
        INDArray preOut = preOutput2d(false);
        INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf().getLayer().getActivationFunction(), preOut.dup()));

        return LossCalculation.builder()
                .l1(fullNetworkL1).l2(fullNetworkL2)
                .labels(getLabels2d()).z(output)
                .preOut(preOut).activationFn(conf().getLayer().getActivationFunction())
                .lossFunction(layerConf().getLossFunction())
                .useRegularization(conf.isUseRegularization())
                .mask(maskArray).build().scoreExamples();
    }

    @Override
    public void computeGradientAndScore() {
        if(input == null || labels == null)
            return;

        INDArray preOut = preOutput2d(true);
        Triple<Gradient,INDArray,INDArray> triple = getGradientsAndDelta(preOut);
        this.gradient = triple.getFirst();
        setScore(triple.getThird(), preOut);
    }

    @Override
    protected void setScoreWithZ(INDArray z) {
        setScore(z, null);
    }

    private void setScore(INDArray z, INDArray preOut ){
        if (layerConf().getLossFunction() == LossFunctions.LossFunction.CUSTOM) {
            LossFunction create = Nd4j.getOpFactory().createLossFunction(layerConf().getCustomLossFunction(), input, z);
            create.exec();
            score = create.getFinalResult().doubleValue();
        }
        else {
            score = LossCalculation.builder()
                    .l1(fullNetworkL1).l2(fullNetworkL2)
                    .labels(getLabels2d()).z(z)
                    .preOut(preOut).activationFn(conf().getLayer().getActivationFunction())
                    .lossFunction(layerConf().getLossFunction())
                    .miniBatch(conf.isMiniBatch()).miniBatchSize(getInputMiniBatchSize())
                    .useRegularization(conf.isUseRegularization())
                    .mask(maskArray).build().score();
        }
    }

    @Override
    public Pair<Gradient, Double> gradientAndScore() {
        return new Pair<>(gradient(),score());
    }

    @Override
    public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon) {
        Triple<Gradient,INDArray,INDArray> triple = getGradientsAndDelta(preOutput2d(true));	//Returns Gradient and delta^(this), not Gradient and epsilon^(this-1)
        INDArray delta = triple.getSecond();

        INDArray epsilonNext = params.get(DefaultParamInitializer.WEIGHT_KEY).mmul(delta.transpose()).transpose();
        return new Pair<>(triple.getFirst(),epsilonNext);
    }

    /**
     * Gets the gradient from one training iteration
     * @return the gradient (bias and weight matrix)
     */
    @Override
    public Gradient gradient() {
        LinAlgExceptions.assertRows(input, getLabels2d());
        return gradient;

    }

    /** Returns tuple: {Gradient,Delta,Output} given preOut */
    private Triple<Gradient,INDArray,INDArray> getGradientsAndDelta(INDArray preOut) {
        INDArray output = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf().getLayer().getActivationFunction(), preOut.dup()));
        INDArray outSubLabels = output.sub(getLabels2d());
        Gradient gradient = new DefaultGradient();

        INDArray weightGradView = gradientViews.get(DefaultParamInitializer.WEIGHT_KEY);
        INDArray biasGradView = gradientViews.get(DefaultParamInitializer.BIAS_KEY);

        gradient.gradientForVariable().put(DefaultParamInitializer.WEIGHT_KEY,weightGradView);
        gradient.gradientForVariable().put(DefaultParamInitializer.BIAS_KEY,biasGradView);

        if(maskArray != null){
            //Masking on gradients. Mask values are 0 or 1. If 0: no output -> no error for this example
            outSubLabels.muliColumnVector(maskArray);
        }

        Triple<Gradient,INDArray,INDArray> triple;
        switch (layerConf().getLossFunction()) {
            case NEGATIVELOGLIKELIHOOD:
            case MCXENT:	//cross-entropy (multi-class, with one-hot encoding)
                Nd4j.gemm(input,outSubLabels,weightGradView,true,false,1.0,0.0);    //Equivalent to:  weightGradView.assign(input.transpose().mmul(outSubLabels));
                biasGradView.assign(outSubLabels.sum(0));   //TODO: do this without the assign
                triple = new Triple<>(gradient,outSubLabels,output);
                break;

            case XENT: // cross-entropy (single binary output variable)
                Nd4j.gemm(input, outSubLabels.div(output.mul(output.rsub(1))), weightGradView, true, false, 1.0, 0.0);  //Equivalent to:  weightGradView.assign(input.transpose().mmul(outSubLabels.div(output.mul(output.rsub(1)))));
                biasGradView.assign(outSubLabels.sum(0));    //TODO: do this without the assign
                triple = new Triple<>(gradient,outSubLabels,output);
                break;

            case MSE: // mean squared error
                INDArray delta = outSubLabels.mul(derivativeActivation(preOut));
                Nd4j.gemm(input,delta,weightGradView,true,false,1.0,0.0);   //Equivalent to:  weightGradView.assign(input.transpose().mmul(delta));
                biasGradView.assign(delta.sum(0));         //TODO: do this without the assign
                triple = new Triple<>(gradient,delta,output);
                break;

            case EXPLL: // exponential logarithmic
                Nd4j.gemm(input,labels.rsub(1).divi(output),weightGradView,true,false,1.0,0.0); //Equivalent to:  weightGradView.assign(input.transpose().mmul(labels.rsub(1).divi(output)));
                biasGradView.assign(outSubLabels.sum(0));   //TODO: do this without the assign
                triple = new Triple<>(gradient,outSubLabels,output);
                break;

            case RMSE_XENT: // root mean squared error cross entropy
                INDArray squaredrmseXentDiff = pow(outSubLabels, 2.0);
                INDArray sqrt = sqrt(squaredrmseXentDiff);
                Nd4j.gemm(input,sqrt,weightGradView,true,false,1.0,0.0);    //Equivalent to: weightGradView.assign(input.transpose().mmul(sqrt));
                biasGradView.assign(outSubLabels.sum(0));   //TODO: do this without the assign
                triple = new Triple<>(gradient,outSubLabels,output);
                break;

            case SQUARED_LOSS:
                Nd4j.gemm(input,outSubLabels.mul(outSubLabels),weightGradView,true,false,1.0,0.0);  //Equivalent to: weightGradView.assign(input.transpose().mmul(outSubLabels.mul(outSubLabels)));
                biasGradView.assign(outSubLabels.sum(0));   //TODO: do this without the assign
                triple = new Triple<>(gradient,outSubLabels,output);
                break;
            default:
                throw new IllegalStateException("Invalid loss function: " + layerConf().getLossFunction());
        }

        return triple;
    }


    @Override
    public INDArray activate(INDArray input, boolean training) {
        setInput(input);
        return output(training);
    }

    @Override
    public INDArray activate(INDArray input) {
        setInput(input);
        return output(true);
    }

    @Override
    public INDArray activate() {
        return output(false);
    }

    public  INDArray output(INDArray input, boolean training) {
        setInput(input);
        return output(training);
    }

    public  INDArray output(INDArray input) {
        setInput(input);
        return output(false);
    }

    /**
     * Classify input
     * @param training determines if its training
     * the input (can either be a matrix or vector)
     * If it's a matrix, each row is considered an example
     * and associated rows are classified accordingly.
     * Each row will be the likelihood of a label given that example
     * @return a probability distribution for each row
     */
    public  INDArray output(boolean training) {
        if(input == null)
            throw new IllegalArgumentException("No null input allowed");
        return super.activate(training);
    }


    /**
     * Sets the input and labels and returns a score for the prediction
     * wrt true labels
     *
     * @param data the data to score
     * @return the score for the given input,label pairs
     */
    public double f1Score(DataSet data) {
        return f1Score(data.getFeatureMatrix(), data.getLabels());
    }

    /**
     * Returns the f1 score for the given examples.
     * Think of this to be like a percentage right.
     * The higher the number the more it got right.
     * This is on a scale from 0 to 1.
     *
     * @param examples te the examples to classify (one example in each row)
     * @param labels   the true labels
     * @return the scores for each ndarray
     */
    public double f1Score(INDArray examples, INDArray labels) {
        Evaluation eval = new Evaluation();
        eval.eval(labels,labelProbabilities(examples));
        return  eval.f1();

    }

    /**
     * Returns the number of possible labels
     *
     * @return the number of possible labels for this classifier
     */
    @Override
    public int numLabels() {
        return labels.size(1);
    }

    @Override
    public void fit(DataSetIterator iter) {
        while(iter.hasNext())
            fit(iter.next());
    }

    /**
     * Returns the predictions for each example in the dataset
     * @param input the matrix to predict
     * @return the prediction for the dataset
     */
    @Override
    public int[] predict(INDArray input) {
        INDArray output = output(input);
        int[] ret = new int[input.rows()];
        for(int i = 0; i < ret.length; i++)
            ret[i] = Nd4j.getBlasWrapper().iamax(output.getRow(i));
        return ret;
    }

    /**
     * Return predicted label names
     *
     * @param dataSet to predict
     * @return the predicted labels for the dataSet
     */
    @Override
    public List<String> predict(DataSet dataSet) {
        int[] intRet = predict(dataSet.getFeatureMatrix());
        List<String> ret = new ArrayList<>();
        for(int i: intRet) {
            ret.add(i,dataSet.getLabelName(i));
        }
        return ret;
    }

    /**
     * Returns the probabilities for each label
     * for each example row wise
     *
     * @param examples the examples to classify (one example in each row)
     * @return the likelihoods of each example and each label
     */
    @Override
    public INDArray labelProbabilities(INDArray examples) {
        return output(examples);
    }

    /**
     * Fit the model
     *
     * @param input the examples to classify (one example in each row)
     * @param labels   the example labels(a binary outcome matrix)
     */
    @Override
    public void fit(INDArray input, INDArray labels) {
        setInput(input);
        setLabels(labels);
        applyDropOutIfNecessary(true);
        if( solver == null ){
            solver = new Solver.Builder()
                    .configure(conf())
                    .listeners(getListeners())
                    .model(this).build();
        }
        solver.optimize();
    }

    /**
     * Fit the model
     *
     * @param data the data to train on
     */
    @Override
    public void fit(DataSet data) {
        fit(data.getFeatureMatrix(), data.getLabels());
    }

    /**
     * Fit the model
     *
     * @param examples the examples to classify (one example in each row)
     * @param labels   the labels for each example (the number of labels must match
     */
    @Override
    public void fit(INDArray examples, int[] labels) {
        INDArray outcomeMatrix = FeatureUtil.toOutcomeMatrix(labels, numLabels());
        fit(examples,outcomeMatrix);

    }

    @Override
    public void clear() {
        super.clear();
        if(labels != null) {
            labels.data().destroy();
            labels = null;
        }
        solver = null;
    }

    /**
     * Fit the model to the given data
     *
     * @param data the data to fit the model to
     */
    @Override
    public void fit(INDArray data) {
        //no-op

    }

    @Override
    public void iterate(INDArray input) {
        throw new UnsupportedOperationException();
    }


    public  INDArray getLabels() {
        return labels;
    }

    public  void setLabels(INDArray labels) {
        this.labels = labels;
    }

    protected INDArray preOutput2d(boolean training){
        return preOutput(training);
    }

    protected INDArray output2d(INDArray input){
        return output(input);
    }

    protected INDArray getLabels2d(){
        if(labels.rank() > 2) {
            return labels.reshape(labels.size(2),labels.size(1));
        }
        return labels;
    }

}

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