DenseLayer (index=9, name=ffn0) nIn=0, nOut=3072; nIn and nOut must be > 0

[whaile-off]

Help I have error in my code

public class Main {

private static final Logger log = LoggerFactory.getLogger(Main.class);

private static final int batchSize = 15;

public static void main(String[] args) throws Exception {
    long startTime = System.currentTimeMillis();

    File file = new File("C:\\Users\\whail\\IdeaProjects\\DLRecognizer\\deeplearning\\src\\main\\resources");

    File modelDir = new File(file.getPath() + "/model");

    // create directory
    if (!modelDir.exists()) { //noinspection ResultOfMethodCallIgnored
        modelDir.mkdirs();
    }

    //create model
    ComputationGraph model = createModel();

    //construct the iterator
    MultiDataSetIterator trainMulIterator = new MultiRecordDataSetIterator(batchSize, "6");
    MultiDataSetIterator testMulIterator = new MultiRecordDataSetIterator(batchSize, "test");

    //fit
    model.setListeners(new ScoreIterationListener(10), new EvaluativeListener(testMulIterator, 1, InvocationType.EPOCH_END));

    log.info("Эпоха запущена");
    model.fit(trainMulIterator, 4);

    //save
    model.save(new File(modelDir.getPath() + "/model.zip"), true);
    long endTime = System.currentTimeMillis();

    System.out.println("=============run time===================== " + (endTime - startTime));

    System.out.println("=====eval model=====test==================");
    modelPredict(model, testMulIterator);
}

private static ComputationGraph createModel() {

    ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder()
            .seed(123)
            .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
            .l2(1e-3)
            .updater(new Adam(1e-3))
            .weightInit( WeightInit.XAVIER_UNIFORM)
            .graphBuilder()
            .addInputs("trainFeatures")
            .setInputTypes(InputType.convolutional(50, 130, 3))
            .setOutputs("out1", "out2", "out3", "out4", "out5", "out6")
            .addLayer("cnn1",  new ConvolutionLayer.Builder(new int[]{5, 5}, new int[]{1, 1}, new int[]{0, 0})
                    .nIn(1).nOut(48).activation( Activation.RELU).build(), "trainFeatures")
            .addLayer("maxpool1",  new SubsamplingLayer.Builder(PoolingType.MAX, new int[]{2,2}, new int[]{2, 2}, new int[]{0, 0})
                    .build(), "cnn1")
            .addLayer("cnn2",  new ConvolutionLayer.Builder(new int[]{5, 5}, new int[]{1, 1}, new int[]{0, 0})
                    .nOut(64).activation( Activation.RELU).build(), "maxpool1")
            .addLayer("maxpool2",  new SubsamplingLayer.Builder(PoolingType.MAX, new int[]{2,1}, new int[]{2, 1}, new int[]{0, 0})
                    .build(), "cnn2")
            .addLayer("cnn3",  new ConvolutionLayer.Builder(new int[]{3, 3}, new int[]{1, 1}, new int[]{0, 0})
                    .nOut(128).activation( Activation.RELU).build(), "maxpool2")
            .addLayer("maxpool3",  new SubsamplingLayer.Builder(PoolingType.MAX, new int[]{2,2}, new int[]{2, 2}, new int[]{0, 0})
                    .build(), "cnn3")
            .addLayer("cnn4",  new ConvolutionLayer.Builder(new int[]{4, 4}, new int[]{1, 1}, new int[]{0, 0})
                    .nOut(256).activation( Activation.RELU).build(), "maxpool3")
            .addLayer("maxpool4",  new SubsamplingLayer.Builder(PoolingType.MAX, new int[]{2,2}, new int[]{2, 2}, new int[]{0, 0})
                    .build(), "cnn4")
            .addLayer("ffn0",  new DenseLayer.Builder().nOut(3072)
                    .build(), "maxpool4")
            .addLayer("ffn1",  new DenseLayer.Builder().nOut(3072)
                    .build(), "ffn0")
            .addLayer("out1", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    .nOut(10).activation(Activation.SOFTMAX).build(), "ffn1")
            .addLayer("out2", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    .nOut(10).activation(Activation.SOFTMAX).build(), "ffn1")
            .addLayer("out3", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    .nOut(10).activation(Activation.SOFTMAX).build(), "ffn1")
            .addLayer("out4", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    .nOut(10).activation(Activation.SOFTMAX).build(), "ffn1")
            .addLayer("out5", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    .nOut(10).activation(Activation.SOFTMAX).build(), "ffn1")
            .addLayer("out6", new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    .nOut(10).activation(Activation.SOFTMAX).build(), "ffn1")
            .build();

    // Construct and initialize model
    ComputationGraph model = new ComputationGraph(config);
    model.init();

    return model;
}

private static void modelPredict(ComputationGraph model, MultiDataSetIterator iterator) {
    int sumCount = 0;
    int correctCount = 0;

    while (iterator.hasNext()) {
        MultiDataSet mds = iterator.next();
        INDArray[]  output = model.output(mds.getFeatures());
        INDArray[] labels = mds.getLabels();
        int dataNum = Math.min(batchSize, output[0].rows());
        for (int dataIndex = 0;  dataIndex < dataNum; dataIndex ++) {
            StringBuilder reLabel = new StringBuilder();
            StringBuilder peLabel = new StringBuilder();
            INDArray preOutput;
            INDArray realLabel;
            for (int digit = 0; digit < 6; digit ++) {
                preOutput = output[digit].getRow(dataIndex);
                peLabel.append(Nd4j.argMax(preOutput).getInt(0));
                realLabel = labels[digit].getRow(dataIndex);
                reLabel.append(Nd4j.argMax(realLabel).getInt(0));
            }
            boolean equals = peLabel.toString().equals(reLabel.toString());
            if (equals) {
                correctCount ++;
            }
            sumCount ++;
            log.info("real image {}  prediction {} status {}", reLabel.toString(), peLabel.toString(), equals);
        }
    }
    iterator.reset();
    System.out.println("validate result : sum count =" + sumCount + " correct count=" + correctCount );
}

}

Error:
Exception in thread "main" org.deeplearning4j.exception.DL4JInvalidConfigException: DenseLayer (index=9, name=ffn0) nIn=0, nOut=3072; nIn and nOut must be > 0
at org.deeplearning4j.nn.conf.layers.LayerValidation.assertNInNOutSet(LayerValidation.java:55)
at org.deeplearning4j.nn.conf.layers.DenseLayer.instantiate(DenseLayer.java:58)
at org.deeplearning4j.nn.conf.graph.LayerVertex.instantiate(LayerVertex.java:106)
at org.deeplearning4j.nn.graph.ComputationGraph.init(ComputationGraph.java:581)
at org.deeplearning4j.nn.graph.ComputationGraph.init(ComputationGraph.java:442)
at org.mishaneyt.dlrecognizer.Main.createModel(Main.java:125)
at org.mishaneyt.dlrecognizer.Main.main(Main.java:55)

[agibsonccc]

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