static void Main(string[] args) { String positiveSamplesPath = "D:\\StromaSet\\S-114-HE_64\\crossvalidation\\stroma"; String negativeSamplesPath = "D:\\StromaSet\\S-114-HE_64\\crossvalidation\\not-stroma"; String outputPath = "D:\\StromaSet\\reconstructions"; String rbm0WeightsPath = "D:\\StromaSet\\weights\\RBM0_T1_769_500_139_0,04191353.weights"; String rbm1WeightsPath = "D:\\StromaSet\\weights\\RBM1_T1_500_75_375_0,07180008.weights"; String rbm2WeightsPath = "D:\\StromaSet\\weights\\RBM2_TOP_T3_76_40_16_0,1688143.weights"; int batchSize = 100; int patchWidth = 16; int patchHeight = 16; IBatchGenerator generator = new ScaleBatchGenerator(positiveSamplesPath, negativeSamplesPath); Matrix<float> rbm0Weights = WeightsHelper.loadWeights(rbm0WeightsPath); Matrix<float> rbm1Weights = WeightsHelper.loadWeights(rbm1WeightsPath); Matrix<float> rbm2Weights = WeightsHelper.loadWeights(rbm2WeightsPath); RBM rbm0 = new RBM(rbm0Weights, false); RBM rbm1 = new RBM(rbm1Weights, false); RBM rbm2 = new RBM(rbm2Weights, false); Matrix<float> batch = generator.nextBatch(batchSize, patchWidth, patchHeight); Matrix<float> rbm0Hidden = rbm0.getHidden(batch); Matrix<float> rbm1Hidden = rbm1.getHidden(rbm0Hidden); Matrix<float> rbm1HiddenWithEmptyLabels = MatrixHelper.addEmptyLabels(rbm1Hidden); Matrix<float> rbm2Hidden = rbm2.getHidden(rbm1HiddenWithEmptyLabels); Matrix<float> rbm2Visible = rbm2.getVisible(rbm2Hidden); Matrix<float> rbm2VisibleWithoutLabels = MatrixHelper.removeLabels(rbm2Visible); Matrix<float> rbm1Visible = rbm1.getVisible(rbm2VisibleWithoutLabels); Matrix<float> rbm0Visible = rbm0.getVisible(rbm1Visible); ImageHelper.persistOriginalAndReconstruction(patchWidth, patchHeight, batch, rbm0Visible, outputPath); Console.WriteLine("Image Reconstruction: " + RBMTrainer.reconstructionError(batch, rbm0Visible)); Console.WriteLine("Prediction Quality: " + RBMTrainer.predictionQuality(rbm2Visible)); Console.WriteLine("press key to exit: "); Console.ReadKey(); }
private static void classifyImage(ParseObject o, RBM rbm0, RBM rbm1, RBM rbm2) { Bitmap image = o.getImage(); LinkedList<float[]> scaledPatches = new LinkedList<float[]>(); int classWhite = 0; int classStroma = 0; int classNotStroma = 0; for (int y = 0; y < image.Height - patchHeight; y += scanIncrement) { for (int x = 0; x < image.Width - patchWidth; x += scanIncrement) { Bitmap subImage = image.Clone(new Rectangle(x, y, patchWidth, patchHeight), image.PixelFormat); float[] scaledPatch = ImageHelper.generateScaledPatch(subImage, scaleWidth, scaleHeight, whiteThreshold); if (scaledPatch == null) { ++classWhite; continue; } scaledPatches.AddLast(scaledPatch); } } if (scaledPatches.Count > 0) { int columnCount = scaleWidth * scaleHeight * 3 + 1; Matrix<float> batch = Matrix<float>.Build.Dense(scaledPatches.Count, columnCount); int row = 0; foreach (float[] scaledPatch in scaledPatches) { batch.SetRow(row++, scaledPatch); } Matrix<float> rbm0Hidden = rbm0.getHidden(batch); Matrix<float> rbm1Hidden = rbm1.getHidden(rbm0Hidden); Matrix<float> rbm1HiddenWithEmptyLabels = MatrixHelper.addEmptyLabels(rbm1Hidden); Matrix<float> rbm2Hidden = rbm2.getHidden(rbm1HiddenWithEmptyLabels); Matrix<float> rbm2Visible = rbm2.getVisible(rbm2Hidden); int lastColumn = rbm2Visible.ColumnCount - 1; for (int i = 0; i < rbm2Visible.RowCount; ++i) { if (rbm2Visible.At(i, lastColumn) > 0.5f) ++classStroma; else ++classNotStroma; } } float stroma = classStroma / (float)(classNotStroma + classWhite + classStroma); Boolean isStroma = stroma > classificationThreshold; Console.WriteLine("Is Stroma: " + isStroma + ", " + stroma); Console.WriteLine("Stroma: " + classStroma + ", NotStroma: " + classNotStroma + ", White: " + classWhite); o.setStroma(isStroma); o.setStromaRatio(stroma); }