# Convolutional Autoencoder Example

**URL:** <https://community.konduit.ai/t/convolutional-autoencoder-example/771>\
**Category:** Showcase\
**Created:** [August 5, 2020, 9:31pm UTC](https://community.konduit.ai/t/convolutional-autoencoder-example/771 "2020-08-05T21:31:29Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![cagneymoreau](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/cagneymoreau/32/296_2.png) [@cagneymoreau](https://community.konduit.ai/u/cagneymoreau)\
**Post date:** [August 5, 2020, 9:31pm UTC](https://community.konduit.ai/t/convolutional-autoencoder-example/771/1 "2020-08-05T21:31:29Z")

</div>

My goal was to build a CNN autoencoder as I have never tried to build a CNN yet. I must be getting used to the api because it only took a single day. I used this dataset [http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)

It works basically as I expected but surprisingly well for my first attempt. I’m posting in case you have any corrections I could implement or If your looking for an example as I couldn’t find one myself of a CNNAE.

edit: I think I quoted it wrong but this is a single class file I pasted even though it looks all broken up. You should be able to do a copy and paste. Instantiate and call run with any image dataset.

![CNNAE results](https://canada1.discourse-cdn.com/flex035/uploads/konduit/original/1X/2db8a0744552c069b172a76ca8c717a97b9a448f.jpeg)

> package ML;
> 
> import nu.pattern.OpenCV;  
> import org.datavec.api.split.FileSplit;  
> import org.datavec.image.loader.Java2DNativeImageLoader;  
> import org.datavec.image.recordreader.ImageRecordReader;  
> import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;  
> import org.deeplearning4j.datasets.fetchers.DataSetType;  
> import org.deeplearning4j.datasets.iterator.impl.Cifar10DataSetIterator;  
> import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;  
> import org.deeplearning4j.nn.api.Model;  
> import org.deeplearning4j.nn.api.OptimizationAlgorithm;  
> import org.deeplearning4j.nn.conf.MultiLayerConfiguration;  
> import org.deeplearning4j.nn.conf.NeuralNetConfiguration;  
> import org.deeplearning4j.nn.conf.inputs.InputType;  
> import org.deeplearning4j.nn.conf.layers.\*;  
> import org.deeplearning4j.nn.conf.layers.variational.BernoulliReconstructionDistribution;  
> import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder;  
> import org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer;  
> import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;  
> import org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor;  
> import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;  
> import org.deeplearning4j.nn.weights.WeightInit;  
> import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;  
> import org.deeplearning4j.optimize.api.BaseTrainingListener;  
> import org.deeplearning4j.optimize.listeners.ScoreIterationListener;  
> import org.nd4j.linalg.activations.Activation;  
> import org.nd4j.linalg.api.buffer.DataType;  
> import org.nd4j.linalg.api.ndarray.INDArray;  
> 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.learning.config.AdaDelta;  
> import org.nd4j.linalg.learning.config.RmsProp;  
> import org.nd4j.linalg.lossfunctions.LossFunctions;  
> import org.opencv.core.Mat;  
> import org.opencv.imgcodecs.Imgcodecs;  
> import org.slf4j.Logger;  
> import org.slf4j.LoggerFactory;
> 
> import javax.swing._;  
> import java.awt._;  
> import java.awt.image.BufferedImage;  
> import java.io.File;  
> import java.util.ArrayList;  
> import java.util.List;
> 
> public class NewExp {
> 
> ```
> private static JFrame frame;
> private static JLabel label;
> 
> JLabel originalLaabel;
> JLabel resultLabel;
> Java2DNativeImageLoader imageLoader;
> Java2DNativeImageLoader imageLoaderTwo;
> 
> private static final Logger log = LoggerFactory.getLogger(NewExp.class);
> int height = 32;
> int width = 32;
> 
> ```
> 
> // String source = “D:\Downloads\img\_align\_celeba\output\”;  
> String source = “D:\Downloads\img\_align\_celeba\img\_align\_celeba\”;
> 
> ```
> int channels = 3;
> 
> public NewExp() {
> 
> OpenCV.loadLocally();
> String[] file = new File(source).list();
> 
> Mat src = Imgcodecs.imread(source + file[0]);
> System.out.println("FINAL \n Width: " + src.width() + "\n Height: " + src.height());
> 
> frame = new JFrame("Results");
> frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
> 
> imageLoader = new Java2DNativeImageLoader();
> imageLoaderTwo = new Java2DNativeImageLoader();
> originalLaabel = new JLabel();
> originalLaabel.setBounds(0,0, width * 2, height * 2);
> 
> resultLabel = new JLabel();
> resultLabel.setBounds(width * 2,0, width * 2, height * 2);
> 
> frame.add(originalLaabel);
> frame.add(resultLabel);
> frame.setSize(width * 3, height * 4 );
> frame.setLayout(null);
> frame.setVisible(true);
> 
> }
> 
> public void run()
> {
> 
> DataSetIterator iter = getDataSetIter();
> 
> MultiLayerNetwork net = getNet();
> 
> for (int i = 0; i < 100; i++) {
> 
> int count = 0;
> while (iter.hasNext()){
> 
> DataSet d = iter.next();
> 
> net.fit(d.getFeatures(), d.getFeatures());
> 
> if ((count % 100) == 0) {
> 
> BufferedImage bf = imageLoader.asBufferedImage(d.getFeatures());
> 
> List<INDArray> a = net.feedForwardToLayer(10, d.getFeatures());
> 
> BufferedImage bfO = imageLoaderTwo.asBufferedImage(a.get(a.size() - 1));
> 
> originalLaabel.setIcon(new ImageIcon(bf));
> resultLabel.setIcon(new ImageIcon(bfO));
> 
> System.out.println("");
> 
> }
> count++;
> }
> 
> }
> 
> }
> 
> public DataSetIterator getDataSetIter()
> {
> try{
> ImageRecordReader recordReader = new ImageRecordReader(height,width, 3);
> recordReader.initialize(new FileSplit(new File(source)));
> 
> DataSetIterator dataSetIterator = new RecordReaderDataSetIterator(recordReader, 1);
> 
> return dataSetIterator;
> 
> }catch (Exception e){
> e.printStackTrace();
> }
> 
> return null;
> }
> 
> private MultiLayerNetwork getNet()
> {
> int rngSeed = 123;
> int dimensions = 16;
> 
> //Neural net configuration
> Nd4j.getRandom().setSeed(rngSeed);
> MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
> .seed(rngSeed)
> .updater(new RmsProp(1e-3))
> .weightInit(WeightInit.XAVIER)
> .l2(1e-4)
> .list()
> 
> //encode
> .layer(0, new ConvolutionLayer.Builder().kernelSize(3,3).stride(1,1).activation(Activation.RELU).nIn(channels).nOut(32).build())
> //(32-3)/1 + 1 = 32x30x30
> //(64-3)/1 + 1 = 32x62x62
> .layer( new BatchNormalization())
> .layer(1, new SubsamplingLayer.Builder().kernelSize(2,2).stride(2,2).poolingType(SubsamplingLayer.PoolingType.MAX).build())
> //(30-2)/2 + 1 = 32x15x15
> //(62-3)/2 + 1 = 32x30x30
> .layer(2, new ConvolutionLayer.Builder().kernelSize(2,2).stride(1,1).activation(Activation.RELU).nIn(32).nOut(16).build())
> //(15-2)/1 + 1 = 16x14x14
> //(35-2)/1 + 1 = 16x29x29
> .layer(new BatchNormalization())
> .layer(3, new SubsamplingLayer.Builder().kernelSize(2,2).stride(2,2).poolingType(SubsamplingLayer.PoolingType.MAX).build())
> //(14-2)/2 + 1 = 16x7x7
> //(29-2)/2 + 1 = 16x15x15
> 
> //double stuff oreo center
> .layer(4, new DenseLayer.Builder().nIn(784).nOut(dimensions).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).build())
> .layer(5, new DenseLayer.Builder().nIn(dimensions).nOut(784).weightInit(WeightInit.XAVIER).activation(Activation.IDENTITY).build())
> 
> //decode
> .layer( 6, new Upsampling2D.Builder().size(2) .build())
> .layer( new BatchNormalization())
> .layer(7, new Deconvolution2D.Builder().kernelSize(2,2) .stride(1,1).nIn(16).nOut(32).activation(Activation.RELU).build())
> .layer(8, new Upsampling2D.Builder().size(2).build())
> .layer( new BatchNormalization())
> .layer(9, new Deconvolution2D.Builder().kernelSize(3,3).stride(1,1).activation(Activation.RELU).nIn(32).nOut(channels).build())
> 
> .layer(10, new CnnLossLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).build())
> //.layer(10, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(3072).nOut(3072).activation(Activation.IDENTITY).build())
> 
> // say hail marys here
> .inputPreProcessor(4, new CnnToFeedForwardPreProcessor(7,7,16))
> .inputPreProcessor(6, new FeedForwardToCnnPreProcessor(7,7,16))
> //.inputPreProcessor(10, new CnnToFeedForwardPreProcessor(32,32,3))
> .build();
> 
> MultiLayerNetwork net = new MultiLayerNetwork(conf);
> net.init();
> net.setListeners(new ScoreIterationListener(100));
> 
> return net;
> 
> }
> 
> ```
> 
> }