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Java example source code file (AutoEncoder.java)

This example Java source code file (AutoEncoder.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

autoencoder, indarray, override, pair

The AutoEncoder.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.feedforward.autoencoder;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.layers.BasePretrainNetwork;
import org.deeplearning4j.nn.params.PretrainParamInitializer;
import org.deeplearning4j.util.Dropout;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

/**
 *  Autoencoder.
 * Add Gaussian noise to input and learn
 * a reconstruction function.
 *
 * @author Adam Gibson
 *
 */
public class AutoEncoder extends BasePretrainNetwork<org.deeplearning4j.nn.conf.layers.AutoEncoder>  {

    private static final long serialVersionUID = -6445530486350763837L;

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

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

    @Override
    public Pair<INDArray, INDArray> sampleHiddenGivenVisible(
            INDArray v) {
        setInput(v);
        INDArray ret = encode(true);
        return new Pair<>(ret,ret);
    }

    @Override
    public Pair<INDArray, INDArray> sampleVisibleGivenHidden(
            INDArray h) {
        INDArray ret = decode(h);
        return new Pair<>(ret,ret);
    }

    // Encode
    public INDArray encode(boolean training) {
        if(conf.getLayer().getDropOut() > 0 && training) {
            Dropout.applyDropout(input, conf.getLayer().getDropOut());
        }

        INDArray W = getParam(PretrainParamInitializer.WEIGHT_KEY);
        INDArray hBias = getParam(PretrainParamInitializer.BIAS_KEY);

        INDArray preAct = input.mmul(W).addiRowVector(hBias);

        INDArray ret = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf.getLayer().getActivationFunction(), preAct));

        return ret;
    }

    // Decode
    public INDArray decode(INDArray y) {
        INDArray W = getParam(PretrainParamInitializer.WEIGHT_KEY);
        INDArray vBias = getParam(PretrainParamInitializer.VISIBLE_BIAS_KEY);
        INDArray preAct = y.mmul(W.transposei());
        preAct.addiRowVector(vBias);
        return Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf.getLayer().getActivationFunction(), preAct));

    }

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

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

    @Override
    public INDArray activate(boolean training) {
        return decode(encode(training));
    }

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

    @Override
    public void computeGradientAndScore() {
        INDArray W = getParam(PretrainParamInitializer.WEIGHT_KEY);

        double corruptionLevel = layerConf().getCorruptionLevel();

        INDArray corruptedX = corruptionLevel > 0 ? getCorruptedInput(input, corruptionLevel) : input;
        setInput(corruptedX);
        INDArray y = encode(true);

        INDArray z = decode(y);
        INDArray visibleLoss =  input.sub(z);
        INDArray hiddenLoss = layerConf().getSparsity() == 0 ? visibleLoss.mmul(W).muli(y).muli(y.rsub(1)) :
                visibleLoss.mmul(W).muli(y).muli(y.add(-layerConf().getSparsity()));


        INDArray wGradient = corruptedX.transposei().mmul(hiddenLoss).addi(visibleLoss.transposei().mmul(y));

        INDArray hBiasGradient = hiddenLoss.sum(0);
        INDArray vBiasGradient = visibleLoss.sum(0);

        gradient = createGradient(wGradient, vBiasGradient, hBiasGradient);
        setScoreWithZ(z);

    }


}

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