static void Main(string[] args)
{
String positiveSamplesPath = "D:\\StromaSet\\S-114-HE_64\\training\\stroma";
String negativeSamplesPath = "D:\\StromaSet\\S-114-HE_64\\training\\not-stroma";
String rbmSavePath = "D:\\StromaSet\\weights";
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;
Random random = new Random();
int rbm0Visible = patchWidth * patchHeight * 3 + 1;
int rbm0Hidden = 500;
int rbm1Visible = rbm0Hidden;
int rbm1Hidden = 75;
int rbm2Visible = rbm1Hidden + 1;
int rbm2Hidden = 40;
IBatchGenerator generator = new ScaleBatchGenerator(positiveSamplesPath, negativeSamplesPath);
//Matrix<float> rbm0Weights = WeightsHelper.generateWeights(rbm0Visible, rbm0Hidden, random);
Matrix<float> rbm0Weights = WeightsHelper.loadWeights(rbm0WeightsPath);
//Matrix<float> rbm1Weights = WeightsHelper.generateWeights(rbm1Visible, rbm1Hidden, random);
Matrix<float> rbm1Weights = WeightsHelper.loadWeights(rbm1WeightsPath);
//Matrix<float> rbm2Weights = WeightsHelper.generateWeights(rbm2Visible, rbm2Hidden, random);
Matrix<float> rbm2Weights = WeightsHelper.loadWeights(rbm2WeightsPath);
RBM rbm0 = new RBM(rbm0Weights, false);
RBM rbm1 = new RBM(rbm1Weights, false);
RBM rbm2 = new RBM(rbm2Weights, false);
//RBMTrainer.IRBMInput rbm0Input = new RBM0Input(generator, batchSize, patchWidth, patchHeight);
//RBMTrainer.trainRBM(rbm0, rbm0Input, 0.01f, 100000, 100, rbmSavePath, "RBM0_LABEL_T1", rbm0Visible, rbm0Hidden);
//RBMTrainer.IRBMInput rbm1Input = new RBM1Input(generator, batchSize, patchWidth, patchHeight, rbm0);
//RBMTrainer.trainRBM(rbm1, rbm1Input, 0.01f, 2000000, 1000, rbmSavePath, "RBM1_LABELS_T2", rbm1Visible, rbm1Hidden);
RBMTrainer.IRBMInput rbm2Input = new RBM2Input(generator, batchSize, patchWidth, patchHeight, rbm0, rbm1);
RBMTrainer.trainRBM(rbm2, rbm2Input, 0.03f, 1000000, 1000, rbmSavePath, "RBM2_TOP_T3", rbm2Visible, rbm2Hidden);
}
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();
}
public static void trainRBM(RBM rbm, IRBMInput input, float learningRate, int epochs, int saveInterval, String saveDir, String trainingName, int visibleLayer, int hiddenLayer)
{
input.generateInput();
Matrix<float> currentInput = input.getInput();
float minError = float.MaxValue;
Matrix<float> minWeights = null;
float error = float.MaxValue;
int repeat = epochs / saveInterval;
for (int i = 0; i < repeat; ++i)
{
for (int j = 0; j < saveInterval; ++j)
{
Thread thread = new Thread(input.generateInput);
thread.Start();
error = rbm.train(currentInput, learningRate);
Console.WriteLine(trainingName + "; Epoche: " + (i * saveInterval + j) + "; Error: " + error);
if (error < minError)
{
minError = error;
minWeights = rbm.getWeights();
}
thread.Join();
currentInput = input.getInput();
}
// save best weights from last interval
String outputFile = saveDir + "\\" + trainingName + "_" + visibleLayer + "_" + hiddenLayer + "_" + i + "_" + minError + ".weights";
WeightsHelper.saveWeights(minWeights, outputFile);
minError = float.MaxValue;
Console.WriteLine("weights saved");
}
}
public RBM2Input(IBatchGenerator generator, int batchSize, int patchWidth, int patchHeight, RBM rbm0, RBM rbm1)
{
this.generator = generator;
this.batchSize = batchSize;
this.patchHeight = patchWidth;
this.patchWidth = patchWidth;
this.rbm0 = rbm0;
this.rbm1 = rbm1;
}
static void Main(string[] args)
{
InOut io = new InOut(args);
RBM rbm0 = new RBM(io.getRBM0Weights(), false);
RBM rbm1 = new RBM(io.getRBM1Weights(), false);
RBM rbm2 = new RBM(io.getRBM2Weights(), false);
LinkedList<ParseObject> objects = io.getParseObjects();
foreach (ParseObject o in objects)
{
classifyImage(o, rbm0, rbm1, rbm2);
}
io.writeOuput();
}
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);
}
