Deeplearning4J-神经网络结构

Question

在过去的几天里，我开始使用deeplearning4j库，我遇到了一个问题。

我的测试和输入数据由25个二进制值组成。训练集包含40行。网络有4个输出值。我的目标是训练网络有尽可能少的错误。

我的神经网络配置：

private static final int SEED = 123;
private static final int ITERATIONS = 1;
private static final int NUMBER_OF_INPUT_NODES = 25; 
private static final int NUMBER_OF_OUTPUT_NODES = 4; 
private static final int EPOCHS = 10;

public static MultiLayerNetwork getNeuralNetwork() {
    StatsStorage storage = configureUI();
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(SEED).iterations(ITERATIONS).learningRate(1e-1)
            .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
            .weightInit(WeightInit.RELU).updater(Updater.ADADELTA).list()
            .layer(0, new DenseLayer.Builder().nIn(NUMBER_OF_INPUT_NODES).nOut(60)
                    .activation(Activation.RELU).build())
            .layer(1, new DenseLayer.Builder().nIn(60).nOut(50)
                    .activation(Activation.RELU).build())
            .layer(2, new DenseLayer.Builder().nIn(50).nOut(50)
                    .activation(Activation.RELU).build())
            .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(50).nOut(NUMBER_OF_OUTPUT_NODES)
                    .activation(Activation.SOFTMAX).build()).backprop(true).build();

    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.setListeners(new StatsListener(storage), new ScoreIterationListener(1));
    DataSetIterator iterator = new ListDataSetIterator(createTrainingSet());
    for (int i = 0; i < EPOCHS; i++) {
        network.fit(iterator);
    }
    return network;
}

我会非常感激任何帮助。问候，

Answer 1

方法1：

似乎这是SOFTMAX激活的预期行为。这来自PredictGenderTest示例：

INDArray predicted = model.output(features);
//System.out.println("output : " + predicted);
if (predicted.getDouble(0) > predicted.getDouble(1))
   gender.setText("Female");
else if (predicted.getDouble(0) < predicted.getDouble(1))
   gender.setText("Male");

如果您想评估模型，使用此模式可能更容易：

Evaluation eval = new Evaluation(numOutputs);
while(testIter.hasNext()){
   DataSet t = testIter.next();
   INDArray features = t.getFeatureMatrix();
   INDArray labels = t.getLabels();
   INDArray predicted = network.output(features, false);
   eval.eval(labels, predicted);
}
System.out.println(eval.stats());

不要使用model.output，而是使用predict：

ArrayList labels=（ArrayList )model.predict（verifyData）；