Cannot reshape array of size 1 into shape [1,10] Please Help

[LeonJWD]

I am getting an Error when trying to train Deep Java Library neural Network with an Image folder Dataset
0

With this code:
`import ai.djl.Application;
import ai.djl.MalformedModelException;
import ai.djl.Model;
import ai.djl.basicdataset.cv.classification.ImageFolder;
import ai.djl.basicdataset.cv.classification.Mnist;
import ai.djl.basicmodelzoo.basic.Mlp;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.transform.Resize;
import ai.djl.modality.cv.transform.ToTensor;
import ai.djl.ndarray.types.Shape;
import ai.djl.nn.Activation;
import ai.djl.nn.Blocks;
import ai.djl.nn.SequentialBlock;
import ai.djl.nn.core.Linear;
import ai.djl.repository.Repository;
import ai.djl.training.DefaultTrainingConfig;
import ai.djl.training.EasyTrain;
import ai.djl.training.Trainer;
import ai.djl.training.TrainingConfig;
import ai.djl.training.dataset.RandomAccessDataset;
import ai.djl.training.evaluator.Accuracy;
import ai.djl.training.listener.TrainingListener;
import ai.djl.training.loss.Loss;
import ai.djl.training.optimizer.Optimizer;
import ai.djl.training.util.ProgressBar;
import ai.djl.translate.TranslateException;

import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;

public class Train {
public static void main(String[] args) throws IOException, TranslateException {

	Application application = Application.CV.IMAGE_CLASSIFICATION;
	long inputSize = 28*28;
	long outputSize = 10;
	int batchSize=1;
	Shape shape= new Shape(1,28*28);
	SequentialBlock block = new SequentialBlock()
			.add(Blocks.batchFlattenBlock(inputSize))
			.add(Linear.builder().setUnits(inputSize).build())
			.add(Activation::relu)
			.add(Linear.builder().setUnits(128).build())
			.add(Activation::sigmoid)
			.add(Linear.builder().setUnits(outputSize).build());
	Repository repository = Repository.newInstance("folder", Paths.get("src/main/java/Ressources"));

	RandomAccessDataset dataset = ImageFolder.builder()
			.setRepository(repository)
			.optFlag(Image.Flag.GRAYSCALE)
			.addTransform(new Resize( 28,28))
			.addTransform(new ToTensor())
			.setSampling(batchSize,true)
			.build();
	dataset.prepare(new ProgressBar());



	Mnist mnist = Mnist.builder()
			.setSampling(batchSize,true)
			.build();
	mnist.prepare();
	Model model = Model.newInstance("mlp");
	model.setBlock(new Mlp((int) inputSize, (int) outputSize, new int[] {128, 64}));
	//model.setBlock(block);
	TrainingConfig config = new DefaultTrainingConfig(Loss.l2Loss())
			.addEvaluator(new Accuracy())
			.optOptimizer(Optimizer.adadelta().build())
			.addTrainingListeners(TrainingListener.Defaults.logging());

	Trainer trainer = model.newTrainer(config);
	trainer.initialize(shape);
	int epoch = 2;
	EasyTrain.fit(trainer,epoch,dataset,null);
	EasyTrain.fit(trainer,epoch,mnist,null);

	Path modelDir = Paths.get("build/mlp");
	Files.createDirectories(modelDir);

	model.setProperty("Epoch", String.valueOf(epoch));

	model.save(modelDir, "mlp");
	try {
		Use.main(new String[]{});
	} catch (MalformedModelException e) {
		throw new RuntimeException(e);
	}


}

}
`
When running it the EasyTrain.fit function gives following error:

Exception in thread "main" ai.djl.engine.EngineException: MXNet engine call failed: MXNetError: Check failed: src.Size() == dst->Size() (1 vs. 10) : Cannot reshape array of size 1 into shape [1,10]
Stack trace:
File "C:\source\mxnet\src\operator\numpy\np_matrix_op.cc", line 144

at ai.djl.mxnet.jna.JnaUtils.checkCall(JnaUtils.java:1942)
at ai.djl.mxnet.jna.JnaUtils.imperativeInvoke(JnaUtils.java:521)
at ai.djl.mxnet.jna.FunctionInfo.invoke(FunctionInfo.java:75)
at ai.djl.mxnet.engine.MxNDManager.invoke(MxNDManager.java:382)
at ai.djl.mxnet.engine.MxNDManager.invoke(MxNDManager.java:400)
at ai.djl.mxnet.engine.MxNDArray.reshape(MxNDArray.java:1160)
at ai.djl.training.loss.L2Loss.evaluate(L2Loss.java:56)
at ai.djl.training.EasyTrain.trainSplit(EasyTrain.java:124)
at ai.djl.training.EasyTrain.trainBatch(EasyTrain.java:110)
at ai.djl.training.EasyTrain.fit(EasyTrain.java:58)
at Train.main(Train.java:77)

The dataset at least seemed to be set up correctly.

I tried reducing the batch size, but dst.size is allways 10 times src.size

My dataset are multiple 28*28 jpg images

Another les important problem I have is, that I am not able to get Mxnet to work with an Cuda 121 GPU