MultilayerConFigureTion返回DL4JinvalidInputeXception：输入大小

Question

我的CSVDATASET的结构

N,P,K,temperature,humidity,ph,rainfall,label
90,42,43,20.87974371,82.00274423,6.502985292000001,202.9355362,rice

是预测给定特征的最佳作物的建议。我的管道如下：

private static void dataLoad2() {
        try(RecordReader myReader = new CSVRecordReader(1,',')){
            myReader.initialize(

                    //reading file
                    new FileSplit(
                            //setting dir
                            new File("dir/to/cropSet.csv")));
            
            Schema dbSchema = new Schema.Builder()
                    .addColumnInteger("N")
                    .addColumnInteger("P")
                    .addColumnInteger("K")
                    .addColumnDouble("temperature")
                    .addColumnDouble("humidity")
                    .addColumnDouble("ph")
                    .addColumnDouble("rainfall")
                    .addColumnCategorical("label", "rice", "maize", "chickpea", "kidneybeans", "pigeonpeas","mothbeans", "mungbean", "blackgram", "lentil", "pomegranate","banana", "mango", "grapes", "watermelon", "muskmelon", "apple","orange", "papaya", "coconut", "cotton", "jute", "coffee")
                    .build();


            DataAnalysis analysis = AnalyzeLocal.analyze(dbSchema, myReader);

            TransformProcess transformer = new TransformProcess.Builder(dbSchema)
                    .convertToInteger("N" ).normalize("N",Normalize.MinMax,analysis)
                    .convertToInteger("P"  ).normalize("N",Normalize.MinMax,analysis)
                    .convertToInteger("K"  ).normalize("K", Normalize.MinMax,analysis )
                    .removeColumns("temperature"  )
                    .removeColumns("humidity"  )
                    .removeColumns("ph"  )
                    .removeColumns("rainfall"  )
                    .categoricalToInteger("label")
                    .build();

            Schema transSchema = transformer.getFinalSchema();

            RecordReader recordReader2 = new CSVRecordReader(1, ',');
            

            TransformProcessRecordReader tprr = new TransformProcessRecordReader(recordReader2,transformer);


            tprr.initialize( //reading file                                                                           );
                             new FileSplit(
                                     //setting dir
                                     new File("/Users/mac/Desktop/RTS_ML/src/main/resources/cropSet.csv")

                             ));



            RecordReaderDataSetIterator builderObj = new RecordReaderDataSetIterator.Builder(tprr,100)
                    .classification(transSchema.getIndexOfColumn("label"),23)
                            .build()

                    ;


            DataSet mutiData = builderObj.next();
            


            //Normalizing the data
            DataNormalization normalizerObj = new NormalizerStandardize();

            //Fit dataset to normalizer
            normalizerObj.fit(mutiData);

            //Perform normalization
            normalizerObj.transform(mutiData);

            //Splitting the dataset
            SplitTestAndTrain testAndTrain = mutiData.splitTestAndTrain(75);

            //train data
            DataSet train = testAndTrain.getTrain();

            //test data
            DataSet test = testAndTrain.getTest();

            cropNetwork( train,test,transSchema);

        }catch (FrameFilter.Exception e){
            out.println("Error: " + e.getLocalizedMessage());

        } catch (IOException | InterruptedException e) {
            throw new RuntimeException(e);
        }
        
    }

和模型：

private static void cropNetwork(DataSet training, DataSet testing, Schema nwdbSchema){
    int outputNum = 23;
    int numOfRows =training.numExamples();
    int numOfColumns = nwdbSchema.numColumns();


    MultiLayerConfiguration nnConfig = new NeuralNetConfiguration.Builder()
            .seed(0xC0FFEE)
            .weightInit(WeightInit.XAVIER)
            .activation(Activation.TANH)
            .updater(new Adam.Builder().learningRate(0.005).build())
            .l2(0.000316)
            .list()

            .layer(0,new DenseLayer.Builder().nIn( numOfRows * numOfColumns).nOut(24).activation(Activation.RELU)
                    .weightInit(WeightInit.XAVIER).build())
            .layer(1,new DenseLayer.Builder().nIn(numOfRows * numOfColumns).nOut(numOfRows * numOfColumns).activation(Activation.RELU)
                    .weightInit(WeightInit.XAVIER).build())
            .layer(2,new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nIn(numOfRows * numOfColumns).nOut(23)
                    .activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER).build())
            .setInputType(InputType.feedForward(numOfRows * numOfColumns))
            .build();







    // Initlizing model
    MultiLayerNetwork model2 = new MultiLayerNetwork(nnConfig);
    model2.init();

    //nnModel.setListeners( new ScoreIterationListener(100));

    // Training model
    model2.fit(training);

    try (INDArray modeOutput = model2.output(training.getFeatures(),false)) {
        Evaluation evalModel = new Evaluation(23);
        evalModel.eval(testing.getLabels(),modeOutput);
        out.println(modeOutput);
    }

}

此返回：

线程“主” org.deeplearning4j.exception.dl4jinvalidinputexception中的异常：输入大小（3列; shape = [75，3]图层输入大小（图层＃inputs = 300）（图层名称：layer0，图层索引：0，图层类型：denselayer） 请访问org.deeplearning4j.nn.layers.baselayer.preoutputwithprenorm（baselayer.java:317）

我知道这肯定与我的网络配置有关，但我的一部分感觉就像是管道。我在做什么？

Answer 1

对于InputType.FeedForward，仅应等于输入层中的列数或功能。

然后，这将设置其余图层以具有适当的输入数量。

每行都是一个示例。您无需告诉inputType，默认情况下，前面的行数始终会变化。