Java DeepLearning4J IllegalArgumentException用于CNN和自定义矩阵

我有一个自定义的7（高度）和24（宽度）矩阵输入用于培训。输出是带有年龄（年轻、成熟、年老）的标签。 我想继续深入学习4J卷积神经网络

在建立了一个非常基本的卷积神经网络之后，第一个训练项目给出了以下错误，我不知道这是怎么回事

Exception in thread "main" java.lang.IllegalArgumentException: Invalid size index 2 wher it's >= rank 2
at org.nd4j.linalg.api.ndarray.BaseNDArray.size(BaseNDArray.java:4066)
at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.preOutput(ConvolutionLayer.java:192)
at org.deeplearning4j.nn.layers.convolution.ConvolutionLayer.activate(ConvolutionLayer.java:247)
at org.deeplearning4j.nn.graph.vertex.impl.LayerVertex.doForward(LayerVertex.java:88)
at org.deeplearning4j.nn.graph.ComputationGraph.feedForward(ComputationGraph.java:983)
at org.deeplearning4j.nn.graph.ComputationGraph.computeGradientAndScore(ComputationGraph.java:889)

我的DL4J代码

//Model Config here
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
    .seed(seed)
    .iterations(iterations)
    .regularization(true).l2(0.0005)
    .learningRate(0.01)//.biasLearningRate(0.02)
    //.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75)
    .weightInit(WeightInit.XAVIER)
    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
    .updater(Updater.NESTEROVS).momentum(0.9) 
    .list()
    .layer(0, new ConvolutionLayer.Builder(4, 1)
        //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
            .name("hzvt1")
        .nIn(nChannels)
        .stride(1, 1)
        .nOut(26)
        .activation("relu")//.activation("identity")
        .build())
    .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
        .nOut(outputNum)
        .activation("softmax")
        .build())  
    .setInputType(InputType.convolutional(nChannels,height,width))
    .backprop(true).pretrain(false);

//Model build here            
model.fit(wmTrain);MultiLayerConfiguration conf = builder.build();
model.fit(wmTrain);MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();            

//Training data creation here 
INDArray weekMatrix = Nd4j.ones(DLAgeGender.nChannels,DLAgeGender.height*DLAgeGender.width);       
double[] vector = new double[] { 0.0, 1.0, 0.0 };
INDArray intLabels = Nd4j.create(vector);
DataSet ds=new DataSet(weekMatrix,intLabels);
//Train the first item
model.fit(wmTrain);

我使用的是DL4J版本0.6、Java版本1.8、maven 3.3+

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我怀疑库中有一个bug。

在gitter支持的帮助下。我发现模型和输入不匹配。正确的工作代码如下所示

我希望这些DL4J错误/异常消息在下一个版本中更加清晰

log.info("Build model....");
System.out.println("Building model...");
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
        .seed(seed)
        .iterations(iterations)
        .regularization(true).l2(0.0005)
        .learningRate(0.01)//.biasLearningRate(0.02)
        //.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75)
        .weightInit(WeightInit.XAVIER)
        .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
        .updater(Updater.NESTEROVS).momentum(0.9)
        .list()
        .layer(0, new ConvolutionLayer.Builder(4, 1)
            //nIn and nOut specify depth. nIn here is the nChannels and nOut is the number of filters to be applied
            .name("hzvt1")
            .nIn(nChannels)
            .stride(1, 1)
            .nOut(26)
            .activation("relu")//.activation("identity")
            .build())
        .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
            .nOut(classes)
            .activation("softmax")
            .build())
        .setInputType(InputType.convolutional(height,width,nChannels))
        .backprop(true).pretrain(false);

//Model build here            
model.fit(wmTrain);MultiLayerConfiguration conf = builder.build();
model.fit(wmTrain);MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();            

//Training data creation here 
    INDArray weekMatrix = Nd4j.ones(new int[]{1,DLAgeGender.nChannels,DLAgeGender.height,DLAgeGender.width});
    INDArray intLabels;
    double[] vector = new double[] { 0.0, 1.0, };
    intLabels = Nd4j.create(vector);
DataSet ds=new DataSet(weekMatrix,intLabels);

log.info("Train model....");
model.setListeners(new ScoreIterationListener(1));
model.fit(wmTrain);
System.out.println("Data train OK.");