如何为本地镜像配置DL4j

Question

我正在尝试使用DeepLearning4j对32x32图像进行从0到9的分类。我查找了许多示例和教程，但在将数据集拟合到网络时总是遇到一些异常。

我目前正在尝试使用带有ParentPathLabelGenerator和RecordReaderDataSetIterator的ImageRecordReader。

图像看起来加载得很好，但我在试穿的时候总是会碰到DL4JInvalidInputException。

        File parentDir = new File(dataPath);
        FileSplit filesInDir = new FileSplit(parentDir, NativeImageLoader.ALLOWED_FORMATS);
        ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();

        BalancedPathFilter pathFilter = new BalancedPathFilter(new Random(), labelMaker, 100);
        InputSplit[] filesInDirSplit = filesInDir.sample(pathFilter, 80, 20);
        InputSplit trainData = filesInDirSplit[0];
        InputSplit testData = filesInDirSplit[1];

        ImageRecordReader recordReader = new ImageRecordReader(numRows, numColumns, 3, labelMaker);
        recordReader.initialize(trainData);

        DataSetIterator dataIter = new RecordReaderDataSetIterator(recordReader, 1, 1, outputNum);

使用DenseLayer时：

Exception in thread "main" org.deeplearning4j.exception.DL4JInvalidInputException: Input that is not a matrix; expected matrix (rank 2), got rank 4 array with shape [1, 3, 32, 32]. Missing preprocessor or wrong input type? (layer name: layer0, layer index: 0, layer type: DenseLayer)

使用ConvolutionLayer时，错误发生在OutputLayer上：

Exception in thread "main" org.deeplearning4j.exception.DL4JInvalidInputException: Input that is not a matrix; expected matrix (rank 2), got rank 4 array with shape [1, 1000, 28, 28]. Missing preprocessor or wrong input type? (layer name: layer1, layer index: 1, layer type: OutputLayer)

是我加载图像的尝试不正确还是我的网络配置错误？

配置：

MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .list()
                .layer(0, new ConvolutionLayer.Builder()
                        .nIn(3) // Number of input datapoints.
                        .nOut(1000) // Number of output datapoints.
                        .activation(Activation.RELU) // Activation function.
                        .weightInit(WeightInit.XAVIER) // Weight initialization.
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nIn(1000)
                        .nOut(outputNum)
                        .activation(Activation.SOFTMAX)
                        .weightInit(WeightInit.XAVIER)
                        .build())
                .build();

Answer 1

最简单的方法是在定义网络时使用.setInputType配置选项。它将为您设置所有必要的预处理器，并计算所有正确的.nIn值。

再看一下这个示例https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/mnist/MnistClassifier.java#L156

当您使用.setInputType方式设置网络时，您根本不需要设置任何.nIn值-您仍然可以这样做，这在我链接的示例中很明显，但通常没有很好的理由这样做。