TensorFlow Isuue

[Mohammed-Ryiad-Eiadeh]

I aimed to buld a regression model as follows:

// Read the data from the given path var data = new CSVLoader<>(new RegressionFactory()).loadDataSource(Paths.get("C:\Users\20187\Desktop\regression_data.csv"), "Output");

    // Divide it into training and testing parts
    var splitter = new TrainTestSplitter<>(data, 0.6, Trainer.DEFAULT_SEED);
    var trainPart = new MutableDataset<>(splitter.getTrain());
    var testPart = new MutableDataset<>(splitter.getTest());

    // Construct the NN graph using Tribuo and Tensorflow
    var nnGraph = new Graph();
    var operationObject = Ops.create(nnGraph); // this object is used
    var inputName = "g_input";
    var numberOfFeatures = trainPart.getFeatureMap().size();
    var nnInitializer = new Glorot<TFloat32>(/*Initializer distribution*/ VarianceScaling.DISTRIBUTION_DEFAULT,
            /*Initializer seed*/ Trainer.DEFAULT_SEED);

    // The input layer that we will feed our input features into (no calculation is conducted in this layer)
    var inputLayer = operationObject.withName(inputName).placeholder(TFloat32.class, Placeholder.shape(Shape.of(-1, numberOfFeatures)));

    // The output layer (20 -> 1) that contains 20 units
    var outputWeights = operationObject.variable(nnInitializer.call(operationObject,operationObject.array(20L, 1L), TFloat32.class));
    var outputBiases = operationObject.variable(operationObject.fill(operationObject.array(1), operationObject.constant(0.1f)));
    var sigmoid = operationObject.math.sigmoid(operationObject.math.add(operationObject.linalg.matMul(inputLayer, outputWeights), outputBiases));
    var outputLayer = operationObject.math.add(operationObject.linalg.matMul(sigmoid, outputWeights), outputBiases);

    // This name will be passed to our trainer
    var outputName = outputLayer.op().name();

    // Use Adam optimizer as our optimization algorithm
    var optimizer = GradientOptimiser.GRADIENT_DESCENT;
    var optimizerParameters = Map.of("learningRate",0.1f,"initialAccumulatorValue",0.01f);

    // Converting Features into Tensors with FeatureConverter
    var denseFeaturesConverter = new DenseFeatureConverter(inputName);

    // Converting Outputs into Tensors (and back again) with OutputConverter
    var outputConverter = new RegressorConverter();

    // Now we want to construct our tensorflow trainer
    var TF_Trainer = new TensorFlowTrainer<Regressor>(nnGraph,
            outputName,
            optimizer,
            optimizerParameters,
            denseFeaturesConverter,
            outputConverter,
            16,            /*training batch size*/
            20,                 /*number of epochs*/
            16,             /*test batch size*/
            -1);           /*disable logging of the loss vale*/

    // Now we close the original graph to free the associated native resources.
    // The TensorFlowTrainer keeps a copy of the GraphDef protobuf to rebuild it when necessary.
    // This process is suitable for memory management
    nnGraph.close();

    // Train the model
    var trainedModel = TF_Trainer.train(trainPart);

    // Evaluate the model
    var evaluator = new RegressionEvaluator().evaluate(trainedModel, testPart);
    System.out.println(evaluator.rmse());

And I get this exception when I run the file:

2023-10-03 00:06:27.953705: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Exception in thread "main" org.tensorflow.exceptions.TFInvalidArgumentException: Dimensions must be equal, but are 1 and 20 for '{{node MatMul}} = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false](g_input, Variable)' with input shapes: [?,1], [20,1]. at org.tensorflow.internal.c_api.AbstractTF_Status.throwExceptionIfNotOK(AbstractTF_Status.java:87) at org.tensorflow.GraphOperationBuilder.finish(GraphOperationBuilder.java:459) at org.tensorflow.GraphOperationBuilder.build(GraphOperationBuilder.java:100) at org.tensorflow.GraphOperationBuilder.build(GraphOperationBuilder.java:71) at org.tensorflow.op.linalg.MatMul.create(MatMul.java:96) at org.tensorflow.op.LinalgOps.matMul(LinalgOps.java:692) at Main.org.MainNN.main(MainNN.java:50)

Can you help me to fix theis complication?!