コード例 #1
0
ファイル: Form1.cs プロジェクト: adnanshaheen/neuralnetwork
        private void btnLoadMNIST_Click(object sender, EventArgs e)
        {
            try {
                /*
                 * Use the DataPoint[]
                 * Get double[][] from DataPoint[]
                 * array of array, should be easily converted to double[][]
                 */
                //String trainDir = "..\\..\\..\\..\\..\\..\\handouts\\data\\trainingAll60000";
                String trainDir = "..\\..\\..\\..\\..\\..\\handouts\\data\\train";
                //String testDir = "..\\..\\..\\..\\..\\..\\handouts\\data\\testAll10000";
                Stopwatch sw = new Stopwatch();
                sw.Start();
                DataPoint[] data      = ImageReader.ReadAllDataScaled(trainDir);
                double[][]  trainData = ImageReader.GetData(data);
                sw.Stop();
                MessageBox.Show("Time taken to read the trainer data " + sw.ElapsedMilliseconds.ToString());

                int[] layers = { 100, trainData[0].Count() };               // neurons in hidden layer, ouput layer
                sparse_encoder = new Network(trainData[0].Count(), layers); // # of inputs
                sparse_encoder.randomizeAll();
                sparse_encoder.LearningAlg.ErrorTreshold = 0.0001f;
                sparse_encoder.LearningAlg.MaxIteration  = 10000;

                //sparse_encoder = Network.load("sparse_encoder");
                sw.Restart();
                sparse_encoder.LearningAlg.Learn(trainData, trainData);
                sw.Stop();
                MessageBox.Show("Done training...Time taken " + sw.ElapsedMilliseconds.ToString());

                /* Save the auto encoder learn */
                sparse_encoder.save("sparse_encoder");

                double[][] expectedOutputs = ImageReader.ExpectedOutput(data);
                int[]      nnLayers        = { 100, expectedOutputs[0].Length }; // neurons in hidden layer, ouput layer
                nn = new Network(trainData[0].Count(), nnLayers);                // # of inputs

                /* No need to randmize, get weight and baise from sparse_encoder */
                for (int i = 0; i < sparse_encoder.layers[0].NumNeurons; i++)
                {
                    nn.layers[0].Neurons[i].weights = sparse_encoder.layers[0].Neurons[i].weights;
                    nn.layers[0].Neurons[i].Bias    = sparse_encoder.layers[0].Neurons[i].Bias;
                }
                //nn.randomizeAll();
                nn.LearningAlg.ErrorTreshold = 0.0001f;
                nn.LearningAlg.MaxIteration  = 10000;

                sw.Restart();
                nn.LearningAlg.Learn(trainData, expectedOutputs);
                sw.Stop();
                MessageBox.Show("Done training...Time taken " + sw.ElapsedMilliseconds.ToString());
                nn.save("nn_ae");
            }
            catch (Exception ex)
            {
                MessageBox.Show(ex.Message);
            }
        }
コード例 #2
0
ファイル: Form1.cs プロジェクト: adnanshaheen/neuralnetwork
        private void btnLoadPCA_Click(object sender, EventArgs e)
        {
            try {
                /*
                 * STEPS
                 * 1- Convert image to grayscale
                 * 2- Convert to 2-D image i.e. conversion to vector
                 */
                //String trainDir = "..\\..\\..\\..\\..\\..\\handouts\\data\\trainingAll60000";
                String trainDir = "..\\..\\..\\..\\..\\..\\handouts\\data\\train";
                //String testDir = "..\\..\\..\\..\\..\\..\\handouts\\data\\testAll10000";
                //string trainDir = "..\\..\\..\\..\\..\\..\\handouts\\AttDataSet\\ATTDataSet\\Training";
                Stopwatch sw = new Stopwatch();
                sw.Start();
                DataPoint[] data = ImageReader.ReadAllDataUnscaled(trainDir);
                //double[][] trainData = ImageReader.ReadAllData(trainDir);
                double[][] trainDataOrig = ImageReader.GetData(data);
                double[][] trainData     = PCA.Transpose(trainDataOrig, trainDataOrig[0].Length);

                sw.Stop();
                MessageBox.Show("Time taken to read the trainer data " + sw.ElapsedMilliseconds.ToString());

                /*
                 * STEPS:
                 * 3- Compute the mean vector of all test images
                 * 4- Subtract mean vector from each image
                 * 5- Compute covariant matrix of all test images
                 * pass the vector through the PCA
                 * then pass that data through NN
                 */
                sw.Restart();

                iMean = PCA.FindMean(trainData);
                PCA.SubMean(trainData, iMean);

                double[][] covariance = PCA.Covariance(trainData);

                /* Compute the eigan values (values are sorted) */
                PCALib.Matrix mapackMatrix = new PCALib.Matrix(covariance);
                PCALib.IEigenvalueDecomposition EigenVal = mapackMatrix.GetEigenvalueDecomposition();

                /* select the top 50 Eigen values */
                int top = 50;
#if DEBUG
                /*
                 * we don't need the eigen values
                 * because the eigen vactors are already
                 * calculated by mapack library
                 */
                double[] topEigen = new double[top];
                PCA.GetTopN(EigenVal.RealEigenvalues, topEigen, top);
#endif // DEBUG
                /* get Eigen vector */
                double[][] EigenVector = PCA.GetEigenVector(EigenVal.EigenvectorMatrix, top);

                /* multiply eigen vector with vector that has mean substracted */
                EigenFaceImage = PCA.Multiply(trainData, EigenVector);

                /* Project each image on to reduced top dimensional space */
                double[][] transposeInput     = PCA.Transpose(EigenFaceImage, EigenFaceImage[0].Length);
                double[][] transposeTrainData = PCA.Transpose(trainData, trainData[0].Length);
                projectionInput = PCA.Multiply(transposeTrainData, EigenFaceImage);
                sw.Stop();
                MessageBox.Show("Done PCA...Time taken " + sw.ElapsedMilliseconds.ToString());      // 256094 normal vs 211514 parallel

                double[][] image = PCA.ConvertToPixels(transposeInput);
                int        iNo   = 0;
                foreach (Control obj in groupbox1.Controls)
                {
                    if (obj is PictureBox)
                    {
                        obj.BackgroundImage = PCA.Draw(image, iNo++, trainDir);
                    }
                }

                double[][] expectedOutputs = ImageReader.ExpectedOutput(data);

                int[] layers = { 50, 10 };                           // neurons in hidden layer, ouput layer
                nn = new Network(projectionInput[0].Length, layers); // # of inputs
                nn.randomizeAll();
                nn.LearningAlg.ErrorTreshold = 0.0001f;
                nn.LearningAlg.MaxIteration  = 10000;

                sw.Restart();
                nn.LearningAlg.Learn(projectionInput, expectedOutputs);
                sw.Stop();
                MessageBox.Show("Done training...Time taken " + sw.ElapsedMilliseconds.ToString());
                nn.save("nn_pca");
            }
            catch (Exception ex)
            {
                MessageBox.Show(ex.Message);
            }
        }