我正在尝试使用deeplearning4j训练xor网络，但我认为我并没有真正了解如何使用数据集。
我想创建一个带有两个输入，两个隐藏神经元和一个输出神经元的NN。
这就是我所拥有的：
package org.deeplearning4j.examples.xor;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.distribution.UniformDistribution;
import org.deeplearning4j.nn.conf.layers.GravesLSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
public class XorExample {
public static void main(String[] args) {
INDArray input = Nd4j.zeros(4, 2);
INDArray labels = Nd4j.zeros(4, 1);
input.putScalar(new int[] { 0, 0 }, 0);
input.putScalar(new int[] { 0, 1 }, 0);
input.putScalar(new int[] { 1, 0 }, 1);
input.putScalar(new int[] { 1, 1 }, 0);
input.putScalar(new int[] { 2, 0 }, 0);
input.putScalar(new int[] { 2, 1 }, 1);
input.putScalar(new int[] { 3, 0 }, 1);
input.putScalar(new int[] { 3, 1 }, 1);
labels.putScalar(new int[] { 0, 0 }, 0);
labels.putScalar(new int[] { 1, 0 }, 1);
labels.putScalar(new int[] { 2, 0 }, 1);
labels.putScalar(new int[] { 3, 0 }, 0);
DataSet ds = new DataSet(input,labels);
//Set up network configuration:
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.learningRate(0.1)
.list(2)
.layer(0, new GravesLSTM.Builder().nIn(2).nOut(2)
.updater(Updater.RMSPROP)
.activation("tanh").weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(-0.08, 0.08)).build())
.layer(1, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation("softmax") //MCXENT + softmax for classification
.updater(Updater.RMSPROP)
.nIn(2).nOut(1).weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(-0.08, 0.08)).build())
.pretrain(false).backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new ScoreIterationListener(1));
//Print the number of parameters in the network (and for each layer)
Layer[] layers = net.getLayers();
int totalNumParams = 0;
for( int i=0; i<layers.length; i++ ){
int nParams = layers[i].numParams();
System.out.println("Number of parameters in layer " + i + ": " + nParams);
totalNumParams += nParams;
}
System.out.println("Total number of network parameters: " + totalNumParams);
net.fit(ds);
Evaluation eval = new Evaluation(3);
INDArray output = net.output(ds.getFeatureMatrix());
eval.eval(ds.getLabels(), output);
System.out.println(eval.stats());
}
}
输出看起来像那样
Mär 20, 2016 7:03:06 PM com.github.fommil.jni.JniLoader liberalLoad
INFORMATION: successfully loaded C:\Users\LuckyPC\AppData\Local\Temp\jniloader5209513403648831212netlib-native_system-win-x86_64.dll
Number of parameters in layer 0: 46
Number of parameters in layer 1: 3
Total number of network parameters: 49
o.d.o.s.BaseOptimizer - Objective function automatically set to minimize. Set stepFunction in neural net configuration to change default settings.
o.d.o.l.ScoreIterationListener - Score at iteration 0 is 0.6931495070457458
Exception in thread "main" java.lang.IllegalArgumentException: Unable to getFloat row of non 2d matrix
at org.nd4j.linalg.api.ndarray.BaseNDArray.getRow(BaseNDArray.java:3640)
at org.deeplearning4j.eval.Evaluation.eval(Evaluation.java:107)
at org.deeplearning4j.examples.xor.XorExample.main(XorExample.java:80)
答案 0 :(得分：7)
这是我提出的解决方案。
public static void main(String[] args) throws IOException, InterruptedException {
CSVDataSet dataSet = new CSVDataSet(new File("./train.csv"));
CSVDataSetIterator trainingSetIterator = new CSVDataSetIterator(dataSet, dataSet.size());
MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
.weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0, 1)).iterations(1150)
.learningRate(1).seed(1)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD)
.list(2)
.backprop(true).pretrain(false)
.layer(0, new DenseLayer.Builder().nIn(2).nOut(3).updater(Updater.SGD).build())
.layer(1, new OutputLayer.Builder().nIn(3).nOut(1).build()).build();
MultiLayerNetwork network = new MultiLayerNetwork(configuration);
network.setListeners(new HistogramIterationListener(10), new ScoreIterationListener(100));
network.init();
long start = System.currentTimeMillis();
network.fit(trainingSetIterator);
System.out.println(System.currentTimeMillis() - start);
try(DataOutputStream dos = new DataOutputStream(Files.newOutputStream(Paths.get("xor-coefficients.bin")))){
Nd4j.write(network.params(), dos);
}
FileUtils.write(new File("xor-network-conf.json"), network.getLayerWiseConfigurations().toJson());
}
测试：
MultiLayerConfiguration configuration = MultiLayerConfiguration.fromJson(FileUtils.readFileToString(new File("xor-network-conf.json")));
try (DataInputStream dis = new DataInputStream(new FileInputStream("xor-coefficients.bin"))) {
INDArray parameters = Nd4j.read(dis);
MultiLayerNetwork network = new MultiLayerNetwork(configuration, parameters);
network.init();
List<INDArray> inputs = ImmutableList.of(Nd4j.create(new double[]{1, 0}),
Nd4j.create(new double[]{0, 1}),
Nd4j.create(new double[]{1, 1}),
Nd4j.create(new double[]{0, 0}));
List<INDArray> networkResults = inputs.stream().map(network::output).collect(toList());
System.out.println(networkResults);
}
}
有训练数据：
0,1,1
1,0,1
1,1,0
0,0,0
答案 1 :(得分：3)
我相信直接来自他们的git存储库有一个XOR示例！
代码已有详细记录，您可以在此处找到存储库：https://github.com/deeplearning4j/dl4j-0.4-examples.git