Code error using MNIST example of deeplearning4j - deep-learning

Question

Help me please! I'm working on a project using deeplearning4j. The MNIST example works very well but I get an error with my dataset.
My data set has two outputs.
int height = 45;
int width = 800;
int channels = 1;
int rngseed = 123;
Random randNumGen = new Random(rngseed);
int batchSize = 128;
int outputNum = 2;
int numEpochs = 15;
File trainData = new File("C:/Users/JHP/Desktop/learningData/training");
File testData = new File("C:/Users/JHP/Desktop/learningData/testing");
FileSplit train = new FileSplit(trainData, NativeImageLoader.ALLOWED_FORMATS, randNumGen);
FileSplit test = new FileSplit(testData, NativeImageLoader.ALLOWED_FORMATS, randNumGen);
ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
ImageRecordReader recordReader = new ImageRecordReader(height, width, channels, labelMaker);
ImageRecordReader recordReader2 = new ImageRecordReader(height, width, channels, labelMaker);
recordReader.initialize(train);
recordReader2.initialize(test);
DataSetIterator dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, outputNum);
DataSetIterator testIter = new RecordReaderDataSetIterator(recordReader2, batchSize, 1, outputNum);
DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
System.out.println("Build model....");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(rngseed)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.iterations(1)
.learningRate(0.006)
.updater(Updater.NESTEROVS).momentum(0.9)
.regularization(true).l2(1e-4)
.list()
.layer(0, new DenseLayer.Builder()
.nIn(height * width)
.nOut(1000)
.activation(Activation.RELU)
.weightInit(WeightInit.XAVIER)
.build()
)
.layer(1, newOutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
.nIn(1000)
.nOut(outputNum)
.activation(Activation.SOFTMAX)
.weightInit(WeightInit.XAVIER)
.build()
)
.pretrain(false).backprop(true)
.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(new ScoreIterationListener(1));
System.out.println("Train model....");
for (int i = 0; i < numEpochs; i++) {
try {
model.fit(dataIter);
} catch (Exception e) {
System.out.println(e);
}
}
error is
org.deeplearning4j.exception.DL4JInvalidInputException: Input that is
not a matrix; expected matrix (rank 2), got rank 4 array with shape
[128, 1, 45, 800]

Answer 1

You're initializing the neural net wrong. If you look closer at every cnn example in the dl4j examples repo (hint: this is the canonical source of where you should be pulling code from, anything else will likely be invalid or out of date: https://github.com/deeplearning4j/dl4j-examples)
You'll notice in all of our examples we have an inputType config:
https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/LenetMnistExample.java#L114
There are various types you should be using you should never set nIn manually. Just nOut.
For mnist, we use convolutional flat and convert it in to a 4d dataset automaticall for you.
Mnist starts off as a flat vector, but a cnn only understands 3d data. We do that transition and reshape for you.