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);
}
