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| 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)
"g1")
.addLayer("g3",
new DenseLayer.Builder().nIn(512).nOut(28 * 28).activation(Activation.RELU)
"g2")
.addVertex("stack", new StackVertex(), "input2", "g3")
.addLayer("d1",
new DenseLayer.Builder().nIn(28 * 28).nOut(256).activation(Activation.RELU)
"stack")
.addLayer("d2",
new DenseLayer.Builder().nIn(256).nOut(128).activation(Activation.RELU)
"d1")
.addLayer("d3",
new DenseLayer.Builder().nIn(128).nOut(128).activation(Activation.RELU)
"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);
}
```
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：
```java
public class LoadGan {
|
