如何用Deeplearning4j实现GAN
一、Gan的思想
Gan的核心所做的事情是在解决一个argminmax的问题，公式：
1、求解一个Discriminator，可以最大尺度的丈量Generator 产生的数据和真实数据之间的分布距离
2、求解一个Generator，可以最大程度减小产生数据和真实数据之间的距离
gan的原始公式如下：
实际上，我们不可能真求期望，只能sample出data来近似求解，于是，公式变成如下：
于是，求解V的最大值，变成了一个二分类问题，变成了求交叉熵的最小值。
二、代码
public class Gan {
static double lr = 0.01;
public static void main(String[] args) throws Exception {
final NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder().updater(new Sgd(lr))
.weightInit(WeightInit.XAVIER);
final GraphBuilder graphBuilder = builder.graphBuilder().backpropType(BackpropType.Standard)
.addInputs("input1", "input2")
.addLayer("g1",
new DenseLayer.Builder().nIn(10).nOut(128).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build(),
"input1")
.addLayer("g2",
new DenseLayer.Builder().nIn(128).nOut(512).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build(),
"g1")
.addLayer("g3",
new DenseLayer.Builder().nIn(512).nOut(28 * 28).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build(),
"g2")
.addVertex("stack", new StackVertex(), "input2", "g3")
.addLayer("d1",
new DenseLayer.Builder().nIn(28 * 28).nOut(256).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build(),
"stack")
.addLayer("d2",
new DenseLayer.Builder().nIn(256).nOut(128).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build(),
"d1")
.addLayer("d3",
new DenseLayer.Builder().nIn(128).nOut(128).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build(),
"d2")
.addLayer("out", new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(128).nOut(1)
.activation(Activation.SIGMOID).build(), "d3")
.setOutputs("out");
ComputationGraph net = new ComputationGraph(graphBuilder.build());
net.init();
System.out.println(net.summary());
UIServer uiServer = UIServer.getInstance();
StatsStorage statsStorage = new InMemoryStatsStorage();
uiServer.attach(statsStorage);
net.setListeners(new ScoreIterationListener(100));
net.getLayers();
DataSetIterator train = new MnistDataSetIterator(30, true, 12345);
INDArray labelD = Nd4j.vstack(Nd4j.ones(30, 1), Nd4j.zeros(30, 1));
INDArray labelG = Nd4j.ones(60, 1);
for (int i = 1; i <= 100000; i++) {
if (!train.hasNext()) {
train.reset();
}
INDArray trueExp = train.next().getFeatures();
INDArray z = Nd4j.rand(new long[] { 30, 10 }, new NormalDistribution());
MultiDataSet dataSetD = new org.nd4j.linalg.dataset.MultiDataSet(new INDArray[] { z, trueExp },
new INDArray[] { labelD });
for(int m=0;m<10;m++){
trainD(net, dataSetD);
}
z = Nd4j.rand(new long[] { 30, 10 }, new NormalDistribution());
MultiDataSet dataSetG = new org.nd4j.linalg.dataset.MultiDataSet(new INDArray[] { z, trueExp },
new INDArray[] { labelG });
trainG(net, dataSetG);
if (i % 10000 == 0) {
net.save(new File("E:/gan.zip"), true);
}
}
}
public static void trainD(ComputationGraph net, MultiDataSet dataSet) {
net.setLearningRate("g1", 0);
net.setLearningRate("g2", 0);
net.setLearningRate("g3", 0);
net.setLearningRate("d1", lr);
net.setLearningRate("d2", lr);
net.setLearningRate("d3", lr);
net.setLearningRate("out", lr);
net.fit(dataSet);
}
public static void trainG(ComputationGraph net, MultiDataSet dataSet) {
net.setLearningRate("g1", lr);
net.setLearningRate("g2", lr);
net.setLearningRate("g3", lr);
net.setLearningRate("d1", 0);
net.setLearningRate("d2", 0);
net.setLearningRate("d3", 0);
net.setLearningRate("out", 0);
net.fit(dataSet);
}
}说明：
1、dl4j并没有提供像keras那样冻结某些层参数的方法，这里采用设置learningrate为0的方法，来冻结某些层的参数
2、这个的更新器，用的是sgd，不能用其他的（比方说Adam、Rmsprop），因为这些自适应更新器会考虑前面batch的梯度作为本次更新的梯度，达不到不更新参数的目的
3、这里用了StackVertex，沿着第一维合并张量，也就是合并真实数据样本和Generator产生的数据样本，共同训练Discriminator
4、训练过程中多次update Discriminator的参数，以便量出最大距离，让后更新Generator一次
5、进行10w次迭代
三、Generator生成手写数字
加载训练好的模型，随机从NormalDistribution取出一些噪音数据，丢给模型，经过feedForward，取出最后一层Generator的激活值，便是我们想要的结果，代码如下：
public class LoadGan {
public static void main(String[] args) throws Exception {
ComputationGraph restored = ComputationGraph.load(new File("E:/gan.zip"), true);
DataSetIterator train = new MnistDataSetIterator(30, true, 12345);
INDArray trueExp = train.next().getFeatures();
Map<String, INDArray> map = restored.feedForward(
new INDArray[] { Nd4j.rand(new long[] { 50, 10 }, new NormalDistribution()), trueExp }, false);
INDArray indArray = map.get("g3");// .reshape(20,28,28);
List<INDArray> list = new ArrayList<>();
for (int j = 0; j < indArray.size(0); j++) {
list.add(indArray.getRow(j));
}
MNISTVisualizer bestVisualizer = new MNISTVisualizer(1, list, "Gan");
bestVisualizer.visualize();
}
public static class MNISTVisualizer {
private double imageScale;
private List<INDArray> digits; // Digits (as row vectors), one per
// INDArray
private String title;
private int gridWidth;
public MNISTVisualizer(double imageScale, List<INDArray> digits, String title) {
this(imageScale, digits, title, 5);
}
public MNISTVisualizer(double imageScale, List<INDArray> digits, String title, int gridWidth) {
this.imageScale = imageScale;
this.digits = digits;
this.title = title;
this.gridWidth = gridWidth;
}
public void visualize() {
JFrame frame = new JFrame();
frame.setTitle(title);
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
JPanel panel = new JPanel();
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<>();
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;
}
}
}实际效果，还算比较清晰
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