[RESOLVED] SharpLearning neuralnetwork classification of 1D arrays

Hi,
I am trying to classify arrays of integers of size 202 (which are physical curves) into either 'Bad' or 'Good' labels.
My observations consist of 1608 pre-labelled columns.

I read the data from CSV file that I prepared in another application into Data-matrix (1608 over 202 2D array), and the labels into Labels_matrix (1608 1D array).
I did not know which parameters to put inside Weight, Height and Depth since my data is not images but rather 1D array of 202 integers.
I try to train the net by Learn method, but keep getting the following exception:

Quote:

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System.ArgumentOutOfRangeException
HResult=0x80131502
Message=The number of columns of a matrix must be positive.
Parameter name: columnCount
Source=MathNet.Numerics
StackTrace:
at MathNet.Numerics.LinearAlgebra.Storage.MatrixStorage`1..ctor(Int32 rowCount, Int32 columnCount) in MathNet.Numerics.LinearAlgebra.Storage\MatrixStorage.cs:line 48
at MathNet.Numerics.LinearAlgebra.Storage.DenseColumnMajorMatrixStorage`1..ctor(Int32 rows, Int32 columns) in MathNet.Numerics.LinearAlgebra.Storage\DenseColumnMajorMatrixStorage.cs:line 24
at MathNet.Numerics.LinearAlgebra.MatrixBuilder`1.Dense(Int32 rows, Int32 columns) in MathNet.Numerics.LinearAlgebra\MatrixBuilder.cs:line 136
at SharpLearning.Neural.Layers.Conv2DLayer.Initialize(Int32 inputWidth, Int32 inputHeight, Int32 inputDepth, Int32 batchSize, Initialization initializtion, Random random) in SharpLearning.Neural.Layers\Conv2DLayer.cs:line 144
at SharpLearning.Neural.NeuralNet.Initialize(Int32 batchSize, Random random) in SharpLearning.Neural\NeuralNet.cs:line 94
at SharpLearning.Neural.NeuralNetLearner.Learn(F64Matrix observations, Double[] targets, Int32[] indices, F64Matrix validationObservations, Double[] validationTargets) in SharpLearning.Neural\NeuralNetLearner.cs:line 126
at SharpLearning.Neural.NeuralNetLearner.Learn(F64Matrix observations, Double[] targets, Int32[] indices) in SharpLearning.Neural\NeuralNetLearner.cs:line 97
at SharpLearning.Neural.NeuralNetLearner.Learn(F64Matrix observations, Double[] targets) in SharpLearning.Neural\NeuralNetLearner.cs:line 92
at SharpLearning.Neural.Learners.ClassificationNeuralNetLearner.Learn(F64Matrix observations, Double[] targets) in SharpLearning.Neural.Learners\ClassificationNeuralNetLearner.cs:line 30
at TLDetect_ML.DataConvert.Main(String[] args) in C:\Gal\Visual Studio\TLDetect_ML\Program.cs:line 113

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The code I used after loading my data from the CSV file is as follows:

Code:

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var numberOfClasses = 2;

var net = new NeuralNet();
//net.Add(new InputLayer(width: 203, height: 1, depth: 1)); // MNIST data is 28x28x1.
net.Add(new InputLayer(width: 203, height: 1, depth: 1));
net.Add(new Conv2DLayer(filterWidth: 5, filterHeight: 5, filterCount: 32));
net.Add(new MaxPool2DLayer(poolWidth: 2, poolHeight: 2));
net.Add(new DropoutLayer(0.5));
net.Add(new DenseLayer(256, Activation.Relu));
net.Add(new DropoutLayer(0.5));
net.Add(new SoftMaxLayer(numberOfClasses));

F64Matrix matt = new F64Matrix(Data_matrix.Length, Data_matrix[1].Length);
matt = Data_matrix;

var learner = new ClassificationNeuralNetLearner(net, iterations: 10, batchSize: 1, loss: new AccuracyLoss());
var model = learner.Learn(matt, Labels_matrix);

var metric = new TotalErrorClassificationMetric<double>();
var predictions = model.Predict(Test_matrix);

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