public MLPNN(TrainingDataMethodsAndParameters <T> trainingDataMethodsAndParameters,
              NetWorkMethodsAndParameters <T, U> netWorkMethodsAndParameters, params int[] parameters) :
     base(netWorkMethodsAndParameters)
 {
     SampleSize                     = trainingDataMethodsAndParameters.sampleSize;                     // ini of sample size for gradien decent
     BatchSize                      = trainingDataMethodsAndParameters.batchSize;                      // ini the total batch size for gradient decent
     ValidSize                      = trainingDataMethodsAndParameters.validSize;                      // ini the verification batch  size for gradient decent
     ComputeGradient                = trainingDataMethodsAndParameters.computeGradient;                // comptutes gradient and the cost function
     GetInputOutput                 = trainingDataMethodsAndParameters.getInputOutput;                 // sets input output
     ActivationFunctions            = trainingDataMethodsAndParameters.activationFunctions;            //sets Activation function for hidden layer
     ActivationFunctionsOutputLayer = trainingDataMethodsAndParameters.activationFunctionsOutputLayer; //sets Activation function for hidden layer
     GenerateEmptyMatrix            = trainingDataMethodsAndParameters.generateEmptyMatrix;            // generates the Matrix
     epochNumber                    = trainingDataMethodsAndParameters.epochNumber;                    //pass epochnumber
     learningRate                   = trainingDataMethodsAndParameters.learningRate;                   // setting learning rate
     lTwoRegularization             = trainingDataMethodsAndParameters.lTwoRegularization;             // setting l2 regularization
     GetVerfication                 = trainingDataMethodsAndParameters.getVerfication;                 // verification function
     NNInputOutput                  = trainingDataMethodsAndParameters.nNInputOutput;                  // conveting float output into classification
     ValidationFunction             = trainingDataMethodsAndParameters.validationFunction;             // how we compare the true output with the computed output
     weightMatrix                   = new List <T>(NetworkSize.Length - 1);                            // initilaex the weigts
     biasMatrix                     = new List <U>(NetworkSize.Length - 1);                            //// initilaex the bias
     biasTempMatrix                 = new List <T>(NetworkSize.Length - 1);                            // initiate bias layers Matrix
     errorValues                    = new List <T>(NetworkSize.Length - 1);                            //// inti errors
     neuronOutputValues             = new List <T>(NetworkSize.Length - 1);                            // ini outputes
     neuronInputValues              = new List <T>(NetworkSize.Length - 1);                            //ini inputs
     this.parameters                = parameters;                                                      // optiontl parameters// Now not sure what is going to be here
     SetTheNetwrok();                                                                                  // intilaizng
 }
 // call cosntructor
 protected NNDataBase(NetWorkMethodsAndParameters <T, U> netWorkMethodsAndParameters) // make protected finally
 {
     GenerateWeights          = netWorkMethodsAndParameters.generateWeights;          // ini method for generating weights
     GenerateBias             = netWorkMethodsAndParameters.generateBias;             // ini method for generating bias
     MatMatMul                = netWorkMethodsAndParameters.matMatMul;                // ini method for Matrix x Matrix
     MatMatMulTranspose       = netWorkMethodsAndParameters.matMatMulTranspose;       // ini method for Matrix Transpose x Matrix
     MatMatMulSecondTranspose = netWorkMethodsAndParameters.matMatMulSecondTranspose; // ini method for Matrix x Matrix Transpose
     MatMatMulPointWise       = netWorkMethodsAndParameters.matMatMulPointWise;       // ini method for Matrix x Matrix PointWise
     MatVecMul                = netWorkMethodsAndParameters.matVecMul;                // ini method for Matrix x Vector
     MatMatSum                = netWorkMethodsAndParameters.matMatSum;                // ini method for Matrix + Matrix
     MatScalarMult            = netWorkMethodsAndParameters.matScalarMult;            // ini method mat x scalar
     VectScalarMult           = netWorkMethodsAndParameters.vectScalarMult;           // ini method vec x scalar
     PointWiseVecVecSum       = netWorkMethodsAndParameters.pointWiseVecVecSum;       // ini method for Shur product
     VecToMat           = netWorkMethodsAndParameters.vecToMat;                       // ini method for construction of Matrix with Vector
     NetworkSize        = netWorkMethodsAndParameters.networkSize;                    // number of layers, neurons
     GenerateUnitVector = netWorkMethodsAndParameters.GenerateUnitVector;             // generate unit vector
     TransposeMatrix    = netWorkMethodsAndParameters.TransposeMatrix;                // matrix Transpose
 }
 public static void SetUpNNParameters(out NetWorkMethodsAndParameters <Matrix <float>, Vector <float> > parameters)
 {
     parameters = new NetWorkMethodsAndParameters <Matrix <float>, Vector <float> >();// creaty empty;
 }
 public static void SetUpNNParameters(out NetWorkMethodsAndParameters <float [][], float []> parameters)
 {
     parameters = null;
 }
Ejemplo n.º 5
0
        static void Main(string[] args)
        {
            #region Setting up  binary data reader
            // ImageFileReader imageFileReader = new ImageFileReader(3 * 1024, 10); // COLOR IMAGES
            ImageFileReader imageFileReader = new ImageFileReader(28 * 28, 10, 16, 8); // MNIST DIGITS
            #endregion
            #region Initializng training Data
            int   sampleSize           = 50;                   // this is for test 10; // the Size of MiniBatch ( so called Sample)
            int   inputRows            = imageFileReader.Size; //10; // matrix sizes
            int   numberOfHiddenLayers = 1;                    // 1; // number of hidden layers
            int[] hiddenLayers         = new int[numberOfHiddenLayers];
            for (int i = 0; i < numberOfHiddenLayers; i++)
            {
                hiddenLayers[i] = inputRows / (int)Math.Pow(2, i + 1) / 3;          // defining number of hidden layers
            }
            int    outputRows               = imageFileReader.SizeOfClassification; // 1; // outputSizes // imageFileReader.SizeOfClassification
            int    parameterToPass          = 1;                                    // if positive, random Normal generated weights and matrices, or unifrom otherwise
            int    parameterSampleGenerator = 0;                                    // if positive, sample is fixed// gradient decent
            int    epochNumber              = 20;                                   // 300; // number of attempts
            double learningRate             = 2;                                    // learning rate
            int[]  NetWorkSize              = new int[2 + numberOfHiddenLayers];    // { rows, hiddenLayers outputRows }; // inti networkSize
            NetWorkSize[0] = inputRows;                                             // ini input layer
            NetWorkSize[1 + numberOfHiddenLayers] = outputRows;                     // ini output layer
            for (int i = 1; i < 1 + numberOfHiddenLayers; i++)
            {
                NetWorkSize[i] = hiddenLayers[i - 1]; // ini of NN architecture
            }
            NetWorkSize[1] = 30;                      //manual, comment after all
            #endregion
            #region SetUpTheNetWorkMethods
            NetWorkMethodsAndParameters <Matrix <float>, Vector <float> > parametersForNN =                                                                                           //iniParamtersMethod to Pass to a NN
                                                                                            new NetWorkMethodsAndParameters <Matrix <float>, Vector <float> >();                      // creaty empty
            TrainingDataMethodsAndParameters <Matrix <float> > methodsForNN =                                                                                                         //iniParamtersMethods to Pass to a Training
                                                                              new TrainingDataMethodsAndParameters <Matrix <float> >();                                               // create empty
            // ini for NN
            parametersForNN.generateWeights          = RandomGenerators.GenerateRandomMatrix;                                                                                         // pass weights generators.
            parametersForNN.generateBias             = RandomGenerators.GenerateRandomVector;                                                                                         // pass bias generators.
            parametersForNN.vecToMat                 = MathLibrary.VectorToMatrix;                                                                                                    // vectorToMatrix for biases
            parametersForNN.matMatSum                = MathLibrary.MatMatSum;                                                                                                         // sum matrix and matrix
            parametersForNN.matMatMul                = MathLibrary.MatMatMul;                                                                                                         // product matrix and matrix
            parametersForNN.matMatMulTranspose       = MathLibrary.MatMatMulTranspose;                                                                                                // product matrixT and matrix
            parametersForNN.matMatMulSecondTranspose = MathLibrary.MatMatMulSecondTranspose;                                                                                          // product matrix and matrixT
            parametersForNN.matMatMulPointWise       = MathLibrary.MatMatMulPointWise;                                                                                                // product matrix and matrix, PointWise
            parametersForNN.matScalarMult            = MathLibrary.MatScalarMul;                                                                                                      // matrix x scalar product delegate
            parametersForNN.matVecMul                = MathLibrary.MatVecMul;                                                                                                         // matrix x vector;
            parametersForNN.vectScalarMult           = MathLibrary.VecScalarMul;                                                                                                      // matrix x scalar product delegate
            parametersForNN.pointWiseVecVecSum       = MathLibrary.VectorVectorSum;                                                                                                   // vect + vect;
            parametersForNN.GenerateUnitVector       = MathLibrary.GenerateUnitVector;                                                                                                // generate unit vector
            parametersForNN.networkSize              = NetWorkSize;                                                                                                                   // passint networkSize
            parametersForNN.TransposeMatrix          = MathLibrary.TranposeMatrix;                                                                                                    // Matrix Transpose
            // ini methods for propagation and feeding
            methodsForNN.sampleSize          = sampleSize;                                                                                                                            // setting sampleSize
            methodsForNN.validSize           = imageFileReader.ValidSize;                                                                                                             // setting sampleSize
            methodsForNN.batchSize           = imageFileReader.BatchSize;                                                                                                             // BatchSize for now disabled
            methodsForNN.epochNumber         = epochNumber;                                                                                                                           // number of grad steps
            methodsForNN.learningRate        = learningRate;                                                                                                                          // learning rate
            methodsForNN.getVerfication      = imageFileReader.VerInOutSample;                                                                                                        //computing verification
            methodsForNN.validationFunction  = MathLibrary.ValidatePrediction;                                                                                                        // validator
            methodsForNN.nNInputOutput       = MathLibrary.GenerateMatrix;                                                                                                            // generate input rule
            methodsForNN.generateEmptyMatrix = MathLibrary.GenerateMatrix;                                                                                                            // null matrix generator
            methodsForNN.getInputOutput      = imageFileReader.InputSample;                                                                                                           // TrainingDataSet<Matrix<float>>.SetTrainingDataBenchmark; //setting InputOutput// imageFileReader.InputSample(sampleSize, null);
            methodsForNN.computeGradient     = CostFunction.GradientQuadratic;                                                                                                        // CostFunction.GradientEntropy;//  passing a gradient function // fix this issue tommorow
            ActivationFunctions.ReturnActivationFunction(out methodsForNN.activationFunctions, 0, "Sigmoid", "Sigmoid");                                                              // fixed // passing activation functions // hidden layers
            ActivationFunctions.ReturnActivationFunction(out methodsForNN.activationFunctionsOutputLayer, 1, "Sigmoid", "Sigmoid");                                                   // fixed // passing activation functions // output layers
            MLPNN <Matrix <float>, Vector <float> > myFullNN = new MLPNN <Matrix <float>, Vector <float> >(methodsForNN, parametersForNN, parameterToPass, parameterSampleGenerator); // iniFullNetwork to Test
            #endregion
            #region Optimization and Output
            myFullNN.Optimization();                                                                           // running optimization procedure // printes cost function
            Console.WriteLine($"Accuracy is {myFullNN.Accuracy(10000,imageFileReader.SizeOfVerification)} %"); // ok it is working or it seems so.
            #endregion
            //// print Input
            //PrintMatrix(myFullNN.ShowInputLayer(), inputRows, sampleSize, "\nPrint InputLayer");
            //// print ComputedOutput
            //PrintMatrix(myFullNN.ShowComputedOutputLayer(), outputRows, sampleSize, "\nPrint ComputedOutputLayer", 0);
            //// print TrueOutput
            //PrintMatrix(myFullNN.ShowOutputLayer(), outputRows, sampleSize, "\nPrint TrueOutputLayer");
            //// print RefinedComputedOutput
            //PrintMatrix(MathLibrary.GenerateMatrix(myFullNN.ShowComputedOutputLayer()), outputRows, sampleSize, "\nPrint ComputedOutputLayerModifiedByfunction");
            //Console.WriteLine($"The Number of CorrectAnswers is" +
            //    $" {(int)MathLibrary.ValidatePrediction(myFullNN.ShowOutputLayer(), MathLibrary.GenerateMatrix(myFullNN.ShowComputedOutputLayer()))}");

            //Console.WriteLine("\nPress Enter to Finish");
            //Console.ReadKey();
        }