An example of gan implemented by DL4J

dl4j-gan-examples/README.md

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+An example of a simple gan implemented with DL4J
+
+          *****    ********        *****************
+    z ---- * G *----* G(z) * ------ * discriminator * ---- fake
+           *****    ********        *               *
+    x ----------------------------- ***************** ---- real

dl4j-gan-examples/pom.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>org.deeplearning4j</groupId>
+    <artifactId>dl4j-gan-examples</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+
+    <!-- Properties Section. Change ND4J versions here, if required -->
+    <properties>
+        <dl4j-master.version>1.0.0-beta7</dl4j-master.version>
+        <logback.version>1.2.3</logback.version>
+        <java.version>1.8</java.version>
+        <maven-shade-plugin.version>2.4.3</maven-shade-plugin.version>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    </properties>
+
+
+    <dependencies>
+        <dependency>
+            <groupId>org.deeplearning4j</groupId>
+            <artifactId>deeplearning4j-core</artifactId>
+            <version>${dl4j-master.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>org.nd4j</groupId>
+            <artifactId>nd4j-native</artifactId>
+            <version>${dl4j-master.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>ch.qos.logback</groupId>
+            <artifactId>logback-classic</artifactId>
+            <version>${logback.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+            <version>4.12</version>
+            <scope>compile</scope>
+        </dependency>
+    </dependencies>
+
+    <build>
+        <plugins>
+            <!-- Maven compiler plugin: compile for Java 8 -->
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>3.5.1</version>
+                <configuration>
+                    <source>${java.version}</source>
+                    <target>${java.version}</target>
+                </configuration>
+            </plugin>
+
+
+            <!--
+            Maven shade plugin configuration: this is required so that if you build a single JAR file (an "uber-jar")
+            it will contain all the required native libraries, and the backends will work correctly.
+            Used for example when running the following commants
+
+            mvn package
+            cd target
+            java -cp deeplearning4j-examples-1.0.0-beta-bin.jar org.deeplearning4j.LenetMnistExample
+            -->
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-shade-plugin</artifactId>
+                <version>${maven-shade-plugin.version}</version>
+                <configuration>
+                    <shadedArtifactAttached>true</shadedArtifactAttached>
+                    <shadedClassifierName>bin</shadedClassifierName>
+                    <createDependencyReducedPom>true</createDependencyReducedPom>
+                    <filters>
+                        <filter>
+                            <artifact>*:*</artifact>
+                            <excludes>
+                                <exclude>org/datanucleus/**</exclude>
+                                <exclude>META-INF/*.SF</exclude>
+                                <exclude>META-INF/*.DSA</exclude>
+                                <exclude>META-INF/*.RSA</exclude>
+                            </excludes>
+                        </filter>
+                    </filters>
+                </configuration>
+
+                <executions>
+                    <execution>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>shade</goal>
+                        </goals>
+                        <configuration>
+                            <transformers>
+                                <transformer
+                                        implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                                    <resource>reference.conf</resource>
+                                </transformer>
+                                <transformer
+                                        implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+                                <transformer
+                                        implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
+                                </transformer>
+                            </transformers>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+        </plugins>
+    </build>
+</project>

dl4j-gan-examples/src/main/java/org/deeplearning4j/ganexamples/MNISTVisualizer.java

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+package org.deeplearning4j.ganexamples;
+
+import org.nd4j.linalg.api.ndarray.INDArray;
+
+import javax.swing.*;
+import java.awt.*;
+import java.awt.image.BufferedImage;
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * @author zdl
+ */
+public class MNISTVisualizer {
+    private double imageScale;
+    private List<INDArray> digits;
+    private String title;
+    private int gridWidth;
+    private JFrame frame;
+
+    public MNISTVisualizer(double imageScale, String title) {
+        this(imageScale, title, 5);
+    }
+
+    public MNISTVisualizer(double imageScale, String title, int gridWidth) {
+        this.imageScale = imageScale;
+        this.title = title;
+        this.gridWidth = gridWidth;
+    }
+
+    public void visualize() {
+        if (null != frame) {
+            frame.dispose();
+        }
+        frame = new JFrame();
+        frame.setTitle(title);
+        frame.setSize(800, 600);
+        JPanel panel = new JPanel();
+        panel.setPreferredSize(new Dimension(800, 600));
+        panel.setLayout(new GridLayout(0, gridWidth));
+        List<JLabel> list = getComponents();
+        for (JLabel image : list) {
+            panel.add(image);
+        }
+
+        frame.add(panel);
+        frame.setVisible(true);
+        frame.pack();
+    }
+
+    public List<JLabel> getComponents() {
+        List<JLabel> images = new ArrayList<JLabel>();
+        for (INDArray arr : digits) {
+            BufferedImage bi = new BufferedImage(28, 28, BufferedImage.TYPE_BYTE_GRAY);
+            for (int i = 0; i < 784; i++) {
+                bi.getRaster().setSample(i % 28, i / 28, 0, (int) (255 * arr.getDouble(i)));
+            }
+            ImageIcon orig = new ImageIcon(bi);
+            Image imageScaled = orig.getImage().getScaledInstance((int) (imageScale * 28), (int) (imageScale * 28),
+                Image.SCALE_DEFAULT);
+            ImageIcon scaled = new ImageIcon(imageScaled);
+            images.add(new JLabel(scaled));
+        }
+        return images;
+    }
+
+    public List<INDArray> getDigits() {
+        return digits;
+    }
+
+    public void setDigits(List<INDArray> digits) {
+        this.digits = digits;
+    }
+
+}

dl4j-gan-examples/src/main/java/org/deeplearning4j/ganexamples/SimpleGan.java

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+package org.deeplearning4j.ganexamples;
+
+import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
+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.deeplearning4j.optimize.listeners.ScoreIterationListener;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.rng.distribution.impl.NormalDistribution;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.learning.config.RmsProp;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ *          *****    ********        *****************
+ *   z ---- * G *----* G(z) * ------ * discriminator * ---- fake
+ *          *****    ********        *               *
+ *   x ----------------------------- ***************** ---- real
+ *
+ * @author zdl
+ */
+public class SimpleGan {
+
+    public static void main(String[] args) throws Exception {
+
+        /**
+         *Build the discriminator
+         */
+        MultiLayerConfiguration discriminatorConf = new NeuralNetConfiguration.Builder().seed(12345)
+            .weightInit(WeightInit.XAVIER).updater(new RmsProp(0.001))
+            .list()
+            .layer(0, new DenseLayer.Builder().nIn(28 * 28).nOut(512).activation(Activation.RELU).build())
+            .layer(1, new DenseLayer.Builder().activation(Activation.RELU)
+                .nIn(512).nOut(256).build())
+            .layer(2, new DenseLayer.Builder().activation(Activation.RELU)
+                .nIn(256).nOut(128).build())
+            .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.XENT)
+                .activation(Activation.SIGMOID).nIn(128).nOut(1).build()).build();
+
+
+        MultiLayerConfiguration ganConf = new NeuralNetConfiguration.Builder().seed(12345)
+            .weightInit(WeightInit.XAVIER)
+            //generator
+            .updater(new RmsProp(0.001)).list()
+            .layer(0, new DenseLayer.Builder().nIn(20).nOut(256).activation(Activation.RELU).build())
+            .layer(1, new DenseLayer.Builder().activation(Activation.RELU)
+                .nIn(256).nOut(512).build())
+            .layer(2, new DenseLayer.Builder().activation(Activation.RELU)
+                .nIn(512).nOut(28 * 28).build())
+            //Freeze the discriminator parameter
+            .layer(3, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(new DenseLayer.Builder().nIn(28 * 28).nOut(512).activation(Activation.RELU).build()))
+            .layer(4, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(new DenseLayer.Builder().nIn(512).nOut(256).activation(Activation.RELU).build()))
+            .layer(5, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(new DenseLayer.Builder().nIn(256).nOut(128).activation(Activation.RELU).build()))
+            .layer(6, new org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop(new OutputLayer.Builder(LossFunctions.LossFunction.XENT)
+                .activation(Activation.SIGMOID).nIn(128).nOut(1).build())).build();
+
+
+        MultiLayerNetwork discriminatorNetwork = new MultiLayerNetwork(discriminatorConf);
+        discriminatorNetwork.init();
+        System.out.println(discriminatorNetwork.summary());
+        discriminatorNetwork.setListeners(new ScoreIterationListener(1));
+
+        MultiLayerNetwork ganNetwork = new MultiLayerNetwork(ganConf);
+        ganNetwork.init();
+        ganNetwork.setListeners(new ScoreIterationListener(1));
+        System.out.println(ganNetwork.summary());
+
+        DataSetIterator train = new MnistDataSetIterator(30, true, 12345);
+
+        INDArray labelD = Nd4j.vstack(Nd4j.ones(30, 1), Nd4j.zeros(30, 1));
+        INDArray labelG = Nd4j.ones(30, 1);
+        MNISTVisualizer mnistVisualizer = new MNISTVisualizer(1, "Gan");
+        for (int i = 1; i <= 100000; i++) {
+            if (!train.hasNext()) {
+                train.reset();
+            }
+            INDArray trueImage = train.next().getFeatures();
+            INDArray z = Nd4j.rand(new NormalDistribution(), new long[]{30, 20});
+            List<INDArray> ganFeedForward = ganNetwork.feedForward(z, false);
+            INDArray fakeImage = ganFeedForward.get(3);
+            INDArray trainDiscriminatorFeatures = Nd4j.vstack(trueImage, fakeImage);
+            //Training discriminator
+            discriminatorNetwork.fit(trainDiscriminatorFeatures, labelD);
+            copyDiscriminatorParam(discriminatorNetwork, ganNetwork);
+            //Training generator
+            ganNetwork.fit(z, labelG);
+            if (i % 1000 == 0) {
+                List<INDArray> indArrays = ganNetwork.feedForward(Nd4j.rand(new NormalDistribution(), new long[]{30, 20}), false);
+                List<INDArray> list = new ArrayList<>();
+                INDArray indArray = indArrays.get(3);
+                for (int j = 0; j < indArray.size(0); j++) {
+                    list.add(indArray.getRow(j));
+                }
+                mnistVisualizer.setDigits(list);
+                mnistVisualizer.visualize();
+            }
+        }
+    }
+
+    public static void copyDiscriminatorParam(MultiLayerNetwork discriminatorNetwork, MultiLayerNetwork ganNetwork) {
+        for (int i = 0; i <= 3; i++) {
+            ganNetwork.getLayer(i + 3).setParams(discriminatorNetwork.getLayer(i).params());
+        }
+    }
+}