"How to fix 'Cannot resolve method "迭代和getFeatureMatrix“'？

Question

" 我是神经网络和 DL4j 的新手，我想用 CSV 训练神经网络并构建线性回归。我该如何修复这些错误 "Cannot resolve method'.iterations and getFeatureMatrix()'"？

"Previously I'm tried to do that, but have another error in 'seed'".

import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
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.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.evaluation.classification.Evaluation;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.io.File;






public class Data {
    public static void main(String[] args) throws Exception {

参数：

        int seed = 3000;
        int batchSize = 200;
        double learningRate = 0.001;
        int nEpochs = 150;
        int numInputs = 2;
        int numOutputs = 2;
        int numHiddenNodes = 100;

加载数据：

        //load data train
        RecordReader rr = new CSVRecordReader();
        rr.initialize(new FileSplit(new File("train.csv")));
        DataSetIterator trainIter = new RecordReaderDataSetIterator(rr, batchSize, 0, 2);

        //load test data

        RecordReader rrTest = new CSVRecordReader();
        rr.initialize(new FileSplit(new File("test.csv")));


        DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest, batchSize, 0, 2);

网络配置：

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(1000)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .learningRate(learningRate)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .list()
                .layer(0, new DenseLayer.Builder()
                        .nIn(numInputs)
                        .nOut(numHiddenNodes)
                        .weightInit(WeightInit.XAVIER)
                        .activation(Activation.fromString("relu"))
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .weightInit(WeightInit.XAVIER)
                        .activation(Activation.fromString("softmax"))
                        .weightInit(WeightInit.XAVIER)
                        .nIn(numHiddenNodes)
                        .nOut(numOutputs)
                        .build()
                )
                .pretrain(false).backprop(true).build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(new ScoreIterationListener((15)));
        for (int n = 0; n < nEpochs; n++) {
            model.fit((trainIter));
            System.out.println(("--------------eval model"));
            Evaluation eval = new Evaluation(numOutputs);
            while (testIter.hasNext()) {
                DataSet t = testIter.next();
                INDArray features = getFeatureMatrix();
                INDArray lables = t.getLabels();
                INDArray predicted = model.output(features, false);
                eval.eval(lables, predicted);
            }
            System.out.println(eval.stats());
        }
    }
}

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Answer 1

首先你应该考虑使用更多 class（比如一个用于神经网络的定义，一个用于训练过程等，...）。只是一个最佳实践。

我不知道您使用的是哪个版本的 DL4J，但我们可以注意到 getFeatureMatrix() has been removed。还有一件事是，应该在 DataSet 对象上调用此函数，而不是像您看起来那样 "statically" 。（你应该做 t.getFeatureMatrix()）。

神经网络创建的iterations()函数也是一样的；这个函数has been removed since some DL4J releases. You can get more information about this function on this thread. Now you have to find an alternative to set up number of iteration, you can take a look at 。希望它能回答您的问题！

"How to fix 'Cannot resolve method "迭代和getFeatureMatrix“'？

"How to fix 'Cannot resolve method "iterations and getFeatureMatrix "'?

dl4j

参数：

加载数据：

网络配置：

日志