/*
* Training: unsupervised learning of feedforward (unfolding) LDA by back propagation
*/
public static void TrainingBP_LDA(
SparseMatrix TrainData,
SparseMatrix TestData,
paramModel_t paramModel,
paramTrain_t paramTrain,
string ModelFile,
string ResultFile
)
{
// ---- Extract the parameters ----
// Model parameters
int nInput = paramModel.nInput;
int nHid = paramModel.nHid;
int nHidLayer = paramModel.nHidLayer;
int nOutput = paramModel.nOutput;
float eta = paramModel.eta;
float T_value = paramModel.T_value;
string OutputType = paramModel.OutputType;
float beta = paramModel.beta;
// Training parameters
int nEpoch = paramTrain.nEpoch;
float mu_Phi = paramTrain.mu_Phi;
float mu_U = paramTrain.mu_U;
int nTrain = paramTrain.nTrain;
float mu_Phi_ReduceFactor = paramTrain.mu_Phi_ReduceFactor;
string LearnRateSchedule = paramTrain.LearnRateSchedule;
int nSamplesPerDisplay = paramTrain.nSamplesPerDisplay;
int nEpochPerSave = paramTrain.nEpochPerSave;
int nEpochPerTest = paramTrain.nEpochPerTest;
int nEpochPerDump = paramTrain.nEpochPerDump;
// ---- Initialize the model ----
ModelInit_LDA_Feedforward(paramModel);
// ---- Initialize the training algorithm ----
Console.WriteLine("#################################################################");
Console.WriteLine("jvking version of BP-LDA: Mirror-Descent Back Propagation");
Console.WriteLine("#################################################################");
float TotLoss = 0.0f;
float TotCE = 0.0f;
double TotTime = 0.0f;
double TotTimeThisEpoch = 0.0f;
int TotSamples = 0;
int TotSamplesThisEpoch = 0;
double AvgnHidLayerEffective = 0.0;
int CntRunningAvg = 0;
int CntModelUpdate = 0;
DenseRowVector mu_phi_search = new DenseRowVector(nHid, mu_Phi);
DenseRowVector TestLoss_pool = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
DenseRowVector TestLoss_epoch = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
DenseRowVector TestLoss_time = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
int CountTest = 0;
DenseRowVector G_Phi_pool = new DenseRowVector(paramModel.nHidLayer);
DenseRowVector G_Phi_trunc_pool = new DenseRowVector(paramModel.nHidLayer, 0.0f);
DenseRowVector AdaGradSum = new DenseRowVector(nHid, 0.0f);
DenseRowVector TmpDenseRowVec = new DenseRowVector(nHid, 0.0f);
int[] SparsePatternGradPhi = null;
float nLearnLineSearch = 0.0f;
int[] IdxPerm = null;
int BatchSize_NormalBatch = paramTrain.BatchSize;
int BatchSize_tmp = paramTrain.BatchSize;
int nBatch = (int)Math.Ceiling(((float)nTrain) / ((float)BatchSize_NormalBatch));
DNNRun_t DNNRun_NormalBatch = new DNNRun_t(nHid, BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
DNNRun_t DNNRun_EndBatch = new DNNRun_t(nHid, nTrain - (nBatch - 1) * BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
DNNRun_t DNNRun = null;
Grad_t Grad = new Grad_t(nHid, nOutput, nInput, paramModel.nHidLayer, OutputType);
DenseMatrix TmpGradDense = new DenseMatrix(nInput, nHid);
DenseMatrix TmpMatDensePhi = new DenseMatrix(nInput, nHid);
paramModel_t paramModel_avg = new paramModel_t(paramModel);
Stopwatch stopWatch = new Stopwatch();
// ---- Compute the schedule of the learning rate
double[] stepsize_pool = null;
switch (LearnRateSchedule)
{
case "PreCompute":
stepsize_pool = PrecomputeLearningRateSchedule(nBatch, nEpoch, mu_Phi, mu_Phi / mu_Phi_ReduceFactor, 1e-8f);
break;
case "Constant":
stepsize_pool = new double[nEpoch];
for (int Idx = 0; Idx < nEpoch; Idx++)
{
stepsize_pool[Idx] = mu_Phi;
}
break;
default:
throw new Exception("Unknown type of LearnRateSchedule");
}
// Now start training.........................
for (int epoch = 0; epoch < nEpoch; epoch++)
{
TotSamplesThisEpoch = 0;
TotTimeThisEpoch = 0.0;
AvgnHidLayerEffective = 0.0;
// -- Set the batch size if there is schedule --
if (paramTrain.flag_BachSizeSchedule)
{
if (paramTrain.BachSizeSchedule.TryGetValue(epoch + 1, out BatchSize_tmp))
{
BatchSize_NormalBatch = BatchSize_tmp;
nBatch = (int)Math.Ceiling(((float)nTrain) / ((float)BatchSize_NormalBatch));
DNNRun_NormalBatch = new DNNRun_t(nHid, BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
DNNRun_EndBatch = new DNNRun_t(nHid, nTrain - (nBatch - 1) * BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
}
}
// -- Shuffle the data (generating shuffled index) --
IdxPerm = Statistics.RandPerm(nTrain);
// -- Reset the (MDA) inference step-sizes --
if (epoch > 0)
{
for (int Idx = 0; Idx < paramModel.nHidLayer; Idx++)
{
paramModel.T[Idx] = T_value;
}
}
// -- Take the learning rate for the current epoch --
mu_Phi = (float)stepsize_pool[epoch];
// -- Start this epoch --
Console.WriteLine("############## Epoch #{0}. BatchSize: {1} Learning Rate: {2} ##################", epoch + 1, BatchSize_NormalBatch, mu_Phi);
for (int IdxBatch = 0; IdxBatch < nBatch; IdxBatch++)
{
stopWatch.Start();
// Extract the batch
int BatchSize = 0;
if (IdxBatch < nBatch - 1)
{
BatchSize = BatchSize_NormalBatch;
DNNRun = DNNRun_NormalBatch;
}
else
{
BatchSize = nTrain - IdxBatch * BatchSize_NormalBatch;
DNNRun = DNNRun_EndBatch;
}
SparseMatrix Xt = new SparseMatrix(nInput, BatchSize);
SparseMatrix Dt = null;
int[] IdxSample = new int[BatchSize];
Array.Copy(IdxPerm, IdxBatch * BatchSize_NormalBatch, IdxSample, 0, BatchSize);
TrainData.GetColumns(Xt, IdxSample);
// Set the sparse pattern for the gradient
SparsePatternGradPhi = Xt.GetHorizontalUnionSparsePattern();
Grad.SetSparsePatternForAllGradPhi(SparsePatternGradPhi);
// Forward activation
LDA_Learn.ForwardActivation_LDA(Xt, DNNRun, paramModel, true);
// Back propagation
LDA_Learn.BackPropagation_LDA(Xt, Dt, DNNRun, paramModel, Grad);
// Compute the gradient and update the model (All gradients of Phi are accumulated into Grad.grad_Q_Phi)
MatrixOperation.ScalarDivideMatrix(Grad.grad_Q_Phi, (-1.0f) * ((beta - 1) / ((float)nTrain)), paramModel.Phi, true);
MatrixOperation.MatrixAddMatrix(Grad.grad_Q_Phi, Grad.grad_Q_TopPhi);
mu_phi_search.FillValue(mu_Phi);
// Different learning rate for different columns of Phi: Similar to AdaGrad but does not decay with time
++CntModelUpdate;
MatrixOperation.ElementwiseMatrixMultiplyMatrix(TmpMatDensePhi, Grad.grad_Q_Phi, Grad.grad_Q_Phi);
MatrixOperation.VerticalSumMatrix(TmpDenseRowVec, TmpMatDensePhi);
MatrixOperation.ScalarMultiplyVector(TmpDenseRowVec, 1.0f / ((float)nInput));
MatrixOperation.VectorSubtractVector(TmpDenseRowVec, AdaGradSum);
MatrixOperation.ScalarMultiplyVector(TmpDenseRowVec, 1.0f / CntModelUpdate);
MatrixOperation.VectorAddVector(AdaGradSum, TmpDenseRowVec);
MatrixOperation.ElementwiseSquareRoot(TmpDenseRowVec, AdaGradSum);
MatrixOperation.ScalarAddVector(TmpDenseRowVec, mu_Phi);
MatrixOperation.ElementwiseVectorDivideVector(mu_phi_search, mu_phi_search, TmpDenseRowVec);
nLearnLineSearch = SMD_Update(paramModel.Phi, Grad.grad_Q_Phi, mu_phi_search, eta);
// Running average of the model
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch) / 2.0f))
{
++CntRunningAvg;
MatrixOperation.MatrixSubtractMatrix(TmpMatDensePhi, paramModel.Phi, paramModel_avg.Phi);
MatrixOperation.ScalarMultiplyMatrix(TmpMatDensePhi, 1.0f / CntRunningAvg);
MatrixOperation.MatrixAddMatrix(paramModel_avg.Phi, TmpMatDensePhi);
}
// Display the result
TotCE += ComputeCrossEntropy(Xt, paramModel.Phi,DNNRun.theta_pool, DNNRun.nHidLayerEffective);
TotLoss = TotCE;
TotSamples += BatchSize;
TotSamplesThisEpoch += BatchSize;
AvgnHidLayerEffective = (((float)(TotSamplesThisEpoch-BatchSize))/((float)TotSamplesThisEpoch))*AvgnHidLayerEffective
+ (1.0/((float)TotSamplesThisEpoch))*( DNNRun.nHidLayerEffective.Sum());
stopWatch.Stop();
TimeSpan ts = stopWatch.Elapsed;
TotTime += ts.TotalSeconds;
TotTimeThisEpoch += ts.TotalSeconds;
stopWatch.Reset();
if (TotSamplesThisEpoch % nSamplesPerDisplay == 0)
{
// Display results
Console.WriteLine(
"* Ep#{0}/{1} Bat#{2}/{3}. Loss={4:F3}. CE={5:F3}. Speed={6} Samples/Sec.",
epoch + 1, nEpoch,
IdxBatch + 1, nBatch,
TotLoss / TotSamples, TotCE / TotSamples,
(int)((double)TotSamplesThisEpoch / TotTimeThisEpoch)
);
if (paramTrain.DebugLevel == DebugLevel_t.medium)
{
Console.WriteLine(
" muPhiMax={0} \n muPhiMin={1}",
mu_phi_search.VectorValue.Max(), mu_phi_search.VectorValue.Min()
);
Console.WriteLine();
}
if (paramTrain.DebugLevel == DebugLevel_t.high)
{
Console.WriteLine(
" muPhiMax={0} \n muPhiMin={1}",
mu_phi_search.VectorValue.Max(), mu_phi_search.VectorValue.Min()
);
Console.WriteLine(
" AvgnHidLayerEff={0:F1}. G_Phi={1:F3}.",
AvgnHidLayerEffective,
Grad.grad_Q_Phi.MaxAbsValue()
);
Console.WriteLine();
}
}
}
// -- Test --
if ((epoch + 1) % nEpochPerTest == 0)
{
TestLoss_epoch.VectorValue[(epoch + 1) / nEpochPerTest - 1] = epoch + 1;
TestLoss_time.VectorValue[(epoch + 1) / nEpochPerTest - 1] = (float)TotTime;
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch) / 2.0f))
{
TestLoss_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1] = Testing_BP_LDA(TestData, paramModel_avg, paramTrain.BatchSize_Test);
}
else
{
TestLoss_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1] = Testing_BP_LDA(TestData, paramModel, paramTrain.BatchSize_Test);
}
CountTest++;
}
// -- Save --
if ((epoch + 1) % nEpochPerSave == 0)
{
// Save model
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch) / 2.0f))
{
string PhiCol = null;
(new FileInfo(ResultFile + ".model.Phi")).Directory.Create();
StreamWriter FileSaveModel = new StreamWriter(ResultFile + ".model.Phi", false);
for (int IdxCol = 0; IdxCol < paramModel_avg.Phi.nCols; IdxCol++)
{
PhiCol = String.Join("\t", paramModel_avg.Phi.DenseMatrixValue[IdxCol].VectorValue);
FileSaveModel.WriteLine(PhiCol);
}
FileSaveModel.Close();
// Save the final learning curves
StreamWriter FileSavePerf = new StreamWriter(ResultFile + ".perf", false);
FileSavePerf.WriteLine(String.Join("\t", TestLoss_epoch.VectorValue));
FileSavePerf.WriteLine(String.Join("\t", TestLoss_time.VectorValue));
FileSavePerf.WriteLine(String.Join("\t", TestLoss_pool.VectorValue));
FileSavePerf.Close();
}
{
string PhiCol = null;
(new FileInfo(ResultFile + ".model.Phi")).Directory.Create();
StreamWriter FileSaveModel = new StreamWriter(ResultFile + ".model.Phi", false);
for (int IdxCol = 0; IdxCol < paramModel.Phi.nCols; IdxCol++)
{
PhiCol = String.Join("\t", paramModel.Phi.DenseMatrixValue[IdxCol].VectorValue);
FileSaveModel.WriteLine(PhiCol);
}
FileSaveModel.Close();
// Save the final learning curves
StreamWriter FileSavePerf = new StreamWriter(ResultFile + ".perf", false);
FileSavePerf.WriteLine(String.Join("\t", TestLoss_epoch.VectorValue));
FileSavePerf.WriteLine(String.Join("\t", TestLoss_time.VectorValue));
FileSavePerf.WriteLine(String.Join("\t", TestLoss_pool.VectorValue));
FileSavePerf.Close();
}
}
// -- Dump feature --
if (paramTrain.flag_DumpFeature && (epoch + 1) % nEpochPerDump == 0)
{
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch) / 2.0f))
{
DumpingFeature_BP_LDA(TrainData, paramModel_avg, paramTrain.BatchSize_Test, ResultFile + ".train.fea", "Train");
DumpingFeature_BP_LDA(TestData, paramModel_avg, paramTrain.BatchSize_Test, ResultFile + ".test.fea", "Test");
}
{
DumpingFeature_BP_LDA(TrainData, paramModel, paramTrain.BatchSize_Test, ResultFile + ".train.fea", "Train");
DumpingFeature_BP_LDA(TestData, paramModel, paramTrain.BatchSize_Test, ResultFile + ".test.fea", "Test");
}
}
}
}
/*
* Prediction: generating the output score
*/
public static void PredictingOutput_BP_sLDA(SparseMatrix TestData, paramModel_t paramModel, int BatchSize_normal, string ScoreFileName)
{
Console.WriteLine("----------------------------------------------------");
int nTest = TestData.nCols;
int nBatch = (int)Math.Ceiling(((float)nTest) / ((float)BatchSize_normal));
DNNRun_t DNNRun_NormalBatch = new DNNRun_t(paramModel.nHid, BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun_EndBatch = new DNNRun_t(paramModel.nHid, nTest - (nBatch - 1) * BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun = null;
int[] IdxSample_Tot = new int[nTest];
(new FileInfo(ScoreFileName)).Directory.Create();
StreamWriter ScoreFile = new StreamWriter(ScoreFileName);
for (int Idx = 0; Idx < nTest; Idx++)
{
IdxSample_Tot[Idx] = Idx;
}
// ---- Test in a batch-wise manner over the test data ----
for (int IdxBatch = 0; IdxBatch < nBatch; IdxBatch++)
{
// Extract the batch
int BatchSize;
if (IdxBatch < nBatch - 1)
{
BatchSize = BatchSize_normal;
DNNRun = DNNRun_NormalBatch;
}
else
{
BatchSize = nTest - IdxBatch * BatchSize_normal;
DNNRun = DNNRun_EndBatch;
}
SparseMatrix Xt = new SparseMatrix(paramModel.nInput, BatchSize);
SparseMatrix Dt = new SparseMatrix(paramModel.nOutput, BatchSize);
int[] IdxSample = new int[BatchSize];
Array.Copy(IdxSample_Tot, IdxBatch * BatchSize_normal, IdxSample, 0, BatchSize);
TestData.GetColumns(Xt, IdxSample);
// Forward activation
LDA_Learn.ForwardActivation_LDA(Xt, DNNRun, paramModel, false);
// Write the score into file
for (int IdxCol = 0; IdxCol < DNNRun.y.nCols; IdxCol++)
{
ScoreFile.WriteLine(String.Join("\t", DNNRun.y.DenseMatrixValue[IdxCol].VectorValue));
}
Console.Write(" Testing: Bat#{0}/{1}\r", (IdxBatch + 1), nBatch);
}
Console.WriteLine("----------------------------------------------------");
ScoreFile.Close();
}
/*
* Training: supervised learning of feedforward (unfolding) LDA by back propagation
*/
public static void TrainingBP_sLDA(
SparseMatrix TrainData,
SparseMatrix TrainLabel,
SparseMatrix TestData,
SparseMatrix TestLabel,
SparseMatrix ValidData,
SparseMatrix ValidLabel,
paramModel_t paramModel,
paramTrain_t paramTrain,
string ModelFile,
string ResultFile
)
{
Console.WriteLine("*****************************************************************");
Console.WriteLine("jvking version of BP-sLDA: Mirror-Descent Back Propagation");
Console.WriteLine("*****************************************************************");
// ---- Extract the parameters ----
// Model parameters
int nInput = paramModel.nInput;
int nHid = paramModel.nHid;
int nHidLayer = paramModel.nHidLayer;
int nOutput = paramModel.nOutput;
float eta = paramModel.eta;
float T_value = paramModel.T_value;
string OutputType = paramModel.OutputType;
float beta = paramModel.beta;
// Training parameters
int nEpoch = paramTrain.nEpoch;
float mu_Phi = paramTrain.mu_Phi;
float mu_U = paramTrain.mu_U;
int nTrain = paramTrain.nTrain;
float mu_ReduceFactor = paramTrain.mu_Phi_ReduceFactor;
string LearnRateSchedule = paramTrain.LearnRateSchedule;
int nSamplesPerDisplay = paramTrain.nSamplesPerDisplay;
int nEpochPerSave = paramTrain.nEpochPerSave;
int nEpochPerTest = paramTrain.nEpochPerTest;
int nEpochPerDump = paramTrain.nEpochPerDump;
// ---- Initialize the model ----
ModelInit_LDA_Feedforward(paramModel);
// ---- Initialize the training algorithm ----
float TotLoss = 0.0f;
float TotTrErr = 0.0f;
double TotTime = 0.0f;
double TotTimeThisEpoch = 0.0f;
int TotSamples = 0;
int TotSamplesThisEpoch = 0;
float CntRunningAvg = 0.0f;
float CntModelUpdate = 0.0f;
double AvgnHidLayerEffective = 0.0f;
DenseRowVector mu_phi_search = new DenseRowVector(nHid, mu_Phi);
DenseRowVector mu_U_search = new DenseRowVector(nHid, mu_U);
DenseRowVector AdaGradSum = new DenseRowVector(nHid, 0.0f);
DenseRowVector TmpDenseRowVec = new DenseRowVector(nHid, 0.0f);
DenseRowVector TestError_pool = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
DenseRowVector ValidError_pool = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
DenseRowVector TrainError_pool = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
DenseRowVector TrainLoss_pool = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
DenseRowVector TestError_epoch = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
DenseRowVector TestError_time = new DenseRowVector(nEpoch / nEpochPerTest, 0.0f);
int CountTest = 0;
float nLearnLineSearch = 0.0f;
int[] IdxPerm = null;
int BatchSize_NormalBatch = paramTrain.BatchSize;
int BatchSize_tmp = paramTrain.BatchSize;
int nBatch = (int)Math.Ceiling(((float)nTrain) / ((float)BatchSize_NormalBatch));
DNNRun_t DNNRun_NormalBatch = new DNNRun_t(nHid, BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
DNNRun_t DNNRun_EndBatch = new DNNRun_t(nHid, nTrain - (nBatch - 1) * BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
DNNRun_t DNNRun = null;
Grad_t Grad = new Grad_t(nHid, nOutput, nInput, paramModel.nHidLayer, OutputType);
SparseMatrix TmpGrad = new SparseMatrix(nInput, nHid, true);
DenseMatrix TmpMatDensePhi = new DenseMatrix(nInput, nHid);
DenseMatrix TmpMatDenseU = new DenseMatrix(nOutput, nHid);
paramModel_t paramModel_avg = new paramModel_t(paramModel);
Stopwatch stopWatch = new Stopwatch();
// ---- Compute the schedule of the learning rate
double[] stepsize_pool_Phi = null;
double[] stepsize_pool_U = null;
switch (LearnRateSchedule)
{
case "PreCompute":
stepsize_pool_Phi = PrecomputeLearningRateSchedule(nBatch, nEpoch, mu_Phi, mu_Phi / mu_ReduceFactor, 1e-8f);
stepsize_pool_U = PrecomputeLearningRateSchedule(nBatch, nEpoch, mu_U, mu_U / mu_ReduceFactor, 1e-8f);
break;
case "Constant":
stepsize_pool_Phi = new double[nEpoch];
stepsize_pool_U = new double[nEpoch];
for (int Idx = 0; Idx < nEpoch; Idx++)
{
stepsize_pool_Phi[Idx] = mu_Phi;
stepsize_pool_U[Idx] = mu_U;
}
break;
default:
throw new Exception("Unknown type of LearnRateSchedule");
}
// Now start training.........................
for (int epoch = 0; epoch < nEpoch; epoch++)
{
TotSamplesThisEpoch = 0;
TotTimeThisEpoch = 0.0;
AvgnHidLayerEffective = 0.0f;
// -- Set the batch size if there is schedule --
if (paramTrain.flag_BachSizeSchedule)
{
if (paramTrain.BachSizeSchedule.TryGetValue(epoch + 1, out BatchSize_tmp))
{
BatchSize_NormalBatch = BatchSize_tmp;
nBatch = (int)Math.Ceiling(((float)nTrain) / ((float)BatchSize_NormalBatch));
DNNRun_NormalBatch = new DNNRun_t(nHid, BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
DNNRun_EndBatch = new DNNRun_t(nHid, nTrain - (nBatch - 1) * BatchSize_NormalBatch, paramModel.nHidLayer, nOutput);
}
}
// -- Shuffle the data (generating shuffled index) --
IdxPerm = Statistics.RandPerm(nTrain);
// -- Reset the (MDA) inference step-sizes --
if (epoch > 0)
{
for (int Idx = 0; Idx < paramModel.nHidLayer; Idx++)
{
paramModel.T[Idx] = T_value;
}
}
// -- Take the learning rate for the current epoch --
mu_Phi = (float)stepsize_pool_Phi[epoch];
mu_U = (float)stepsize_pool_U[epoch];
// -- Start this epoch --
Console.WriteLine("############## Epoch #{0}. BatchSize: {1} Learning Rate: Phi:{2}, U:{3} ##################",
epoch + 1, BatchSize_NormalBatch, mu_Phi, mu_U);
for (int IdxBatch = 0; IdxBatch < nBatch; IdxBatch++)
{
stopWatch.Start();
// Extract the batch
int BatchSize = 0;
if (IdxBatch < nBatch - 1)
{
BatchSize = BatchSize_NormalBatch;
DNNRun = DNNRun_NormalBatch;
}
else
{
BatchSize = nTrain - IdxBatch * BatchSize_NormalBatch;
DNNRun = DNNRun_EndBatch;
}
SparseMatrix Xt = new SparseMatrix(nInput, BatchSize);
SparseMatrix Dt = new SparseMatrix(nOutput, BatchSize);
int[] IdxSample = new int[BatchSize];
Array.Copy(IdxPerm, IdxBatch * BatchSize_NormalBatch, IdxSample, 0, BatchSize);
TrainData.GetColumns(Xt, IdxSample);
TrainLabel.GetColumns(Dt, IdxSample);
// Forward activation
LDA_Learn.ForwardActivation_LDA(Xt, DNNRun, paramModel, true);
// Back propagation
LDA_Learn.BackPropagation_LDA(Xt, Dt, DNNRun, paramModel, Grad);
// Compute the gradient and update the model (All gradients of Phi are accumulated into Grad.grad_Q_Phi)
// (i) Update Phi
MatrixOperation.ScalarDivideMatrix(Grad.grad_Q_Phi, (-1.0f) * ((beta - 1) / ((float)nTrain)), paramModel.Phi, true);
mu_phi_search.FillValue(mu_Phi);
// Different learning rate for different columns of Phi: Similar to AdaGrad but does not decay with time
++CntModelUpdate;
MatrixOperation.ElementwiseMatrixMultiplyMatrix(TmpMatDensePhi, Grad.grad_Q_Phi, Grad.grad_Q_Phi);
MatrixOperation.VerticalSumMatrix(TmpDenseRowVec, TmpMatDensePhi);
MatrixOperation.ScalarMultiplyVector(TmpDenseRowVec, 1.0f / ((float)nInput));
MatrixOperation.VectorSubtractVector(TmpDenseRowVec, AdaGradSum);
MatrixOperation.ScalarMultiplyVector(TmpDenseRowVec, 1.0f / CntModelUpdate);
MatrixOperation.VectorAddVector(AdaGradSum, TmpDenseRowVec);
MatrixOperation.ElementwiseSquareRoot(TmpDenseRowVec, AdaGradSum);
MatrixOperation.ScalarAddVector(TmpDenseRowVec, mu_Phi);
MatrixOperation.ElementwiseVectorDivideVector(mu_phi_search, mu_phi_search, TmpDenseRowVec);
nLearnLineSearch = SMD_Update(paramModel.Phi, Grad.grad_Q_Phi, mu_phi_search, eta);
// (ii) Update U
MatrixOperation.ScalarMultiplyMatrix(Grad.grad_Q_U, (-1.0f) * mu_U);
MatrixOperation.MatrixAddMatrix(paramModel.U, Grad.grad_Q_U);
// (iii) Running average of the model
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch)/2.0f))
{
++CntRunningAvg;
MatrixOperation.MatrixSubtractMatrix(TmpMatDensePhi, paramModel.Phi, paramModel_avg.Phi);
MatrixOperation.MatrixSubtractMatrix(TmpMatDenseU, paramModel.U, paramModel_avg.U);
MatrixOperation.ScalarMultiplyMatrix(TmpMatDensePhi, 1.0f / CntRunningAvg);
MatrixOperation.ScalarMultiplyMatrix(TmpMatDenseU, 1.0f / CntRunningAvg);
MatrixOperation.MatrixAddMatrix(paramModel_avg.Phi, TmpMatDensePhi);
MatrixOperation.MatrixAddMatrix(paramModel_avg.U, TmpMatDenseU);
}
// Display the result
TotTrErr += 100 * ComputeNumberOfErrors(Dt, DNNRun.y);
TotLoss += ComputeSupervisedLoss(Dt, DNNRun.y, paramModel.OutputType);
TotSamples += BatchSize;
TotSamplesThisEpoch += BatchSize;
AvgnHidLayerEffective =
(((double)(TotSamplesThisEpoch - BatchSize)) / ((double)TotSamplesThisEpoch)) * AvgnHidLayerEffective
+
1.0 / ((double)TotSamplesThisEpoch) * DNNRun.nHidLayerEffective.Sum();
stopWatch.Stop();
TimeSpan ts = stopWatch.Elapsed;
TotTime += ts.TotalSeconds;
TotTimeThisEpoch += ts.TotalSeconds;
stopWatch.Reset();
if (TotSamplesThisEpoch % nSamplesPerDisplay == 0)
{
// Display results
Console.WriteLine(
"* Ep#{0}/{1} Bat#{2}/{3}. Loss={4:F3}. TrErr={5:F3}%. Speed={6} Samples/Sec.",
epoch + 1, nEpoch,
IdxBatch + 1, nBatch,
TotLoss / TotSamples, TotTrErr / TotSamples,
(int)((double)TotSamplesThisEpoch / TotTimeThisEpoch)
);
if (paramTrain.DebugLevel == DebugLevel_t.medium)
{
Console.WriteLine(
" muPhiMax={0} \n muPhiMin={1}",
mu_phi_search.VectorValue.Max(), mu_phi_search.VectorValue.Min()
);
Console.WriteLine();
}
if (paramTrain.DebugLevel == DebugLevel_t.high)
{
Console.WriteLine(
" muPhiMax={0} \n muPhiMin={1}",
mu_phi_search.VectorValue.Max(), mu_phi_search.VectorValue.Min()
);
float MaxAbsVal_Grad_Q_Phi = Grad.grad_Q_Phi.MaxAbsValue();
float MaxAbsVal_Grad_Q_U = Grad.grad_Q_U.MaxAbsValue();
Console.WriteLine(
" AvgnHidLayerEff={0:F1}. G_Phi={1:F3}. G_U={2:F3}",
AvgnHidLayerEffective,
MaxAbsVal_Grad_Q_Phi,
MaxAbsVal_Grad_Q_U
);
// Save the screen into a log file
(new FileInfo(ResultFile + ".log")).Directory.Create();
using (StreamWriter LogFile = File.AppendText(ResultFile + ".log"))
{
LogFile.WriteLine(
"- Ep#{0}/{1} Bat#{2}/{3}. Loss={4:F3}. TrErr={5:F3}%. Speed={6} Samples/Sec.",
epoch + 1, nEpoch,
IdxBatch + 1, nBatch,
TotLoss / TotSamples, TotTrErr / TotSamples,
(int)((double)TotSamplesThisEpoch / TotTimeThisEpoch)
);
LogFile.WriteLine(
" muPhiMax={0} \n muPhiMin={1}",
mu_phi_search.VectorValue.Max(), mu_phi_search.VectorValue.Min()
);
LogFile.WriteLine(
" AvgnHidLayerEff={0:F1}. G_Phi={1:F3}. G_U={2:F3}",
AvgnHidLayerEffective,
MaxAbsVal_Grad_Q_Phi,
MaxAbsVal_Grad_Q_U
);
Console.WriteLine();
}
Console.WriteLine();
}
}
}
// -- Test --
if ((epoch + 1) % nEpochPerTest == 0)
{
// Standard performance metric
TestError_epoch.VectorValue[(epoch + 1) / nEpochPerTest - 1] = epoch + 1;
TestError_time.VectorValue[(epoch + 1) / nEpochPerTest - 1] = (float)TotTime;
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch) / 2.0f))
{
if (paramTrain.flag_HasValidSet)
{
ValidError_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1]
= Testing_BP_sLDA(
ValidData,
ValidLabel,
paramModel_avg,
paramTrain.BatchSize_Test,
ResultFile + ".validscore",
"Validation Set"
);
}
TestError_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1]
= Testing_BP_sLDA(
TestData,
TestLabel,
paramModel_avg,
paramTrain.BatchSize_Test,
ResultFile + ".testscore",
"Test Set"
);
}
else
{
if (paramTrain.flag_HasValidSet)
{
ValidError_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1]
= Testing_BP_sLDA(
ValidData,
ValidLabel,
paramModel,
paramTrain.BatchSize_Test,
ResultFile + ".validscore",
"Validation Set"
);
}
TestError_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1]
= Testing_BP_sLDA(
TestData,
TestLabel,
paramModel,
paramTrain.BatchSize_Test,
ResultFile + ".testscore",
"Test Set"
);
}
TrainError_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1]
= TotTrErr / TotSamples;
TrainLoss_pool.VectorValue[(epoch + 1) / nEpochPerTest - 1]
= TotLoss / TotSamples;
// Performance metric evaluated using external evaluation tools, e.g., AUC, Top@K accuracy, etc.
if (paramTrain.flag_ExternalEval)
{
ExternalEvaluation(
paramTrain.ExternalEval,
ResultFile,
paramTrain.TestLabelFile,
epoch,
"Test Set"
);
if (paramTrain.flag_HasValidSet)
{
ExternalEvaluation(
paramTrain.ExternalEval,
ResultFile,
paramTrain.ValidLabelFile,
epoch,
"Validation Set"
);
}
}
CountTest++;
}
// -- Save --
if ((epoch + 1) % nEpochPerSave == 0)
{
// Save model
string PhiCol = null;
string UCol = null;
(new FileInfo(ResultFile + ".model.Phi")).Directory.Create();
string ModelName_Phi;
string ModelName_U;
if (paramTrain.flag_SaveAllModels)
{
ModelName_Phi = ResultFile + ".model.Phi" + ".iter" + (epoch + 1).ToString();
ModelName_U = ResultFile + ".model.U" + ".iter" + (epoch + 1).ToString();
}
else
{
ModelName_Phi = ResultFile + ".model.Phi";
ModelName_U = ResultFile + ".model.U";
}
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch) / 2.0f))
{
using (StreamWriter FileSaveModel_Phi = new StreamWriter(ModelName_Phi, false))
{
for (int IdxCol = 0; IdxCol < paramModel_avg.Phi.nCols; IdxCol++)
{
PhiCol = String.Join("\t", paramModel_avg.Phi.DenseMatrixValue[IdxCol].VectorValue);
FileSaveModel_Phi.WriteLine(PhiCol);
}
}
using (StreamWriter FileSaveModel_U = new StreamWriter(ModelName_U, false))
{
for (int IdxCol = 0; IdxCol < paramModel_avg.U.nCols; IdxCol++)
{
UCol = String.Join("\t", paramModel_avg.U.DenseMatrixValue[IdxCol].VectorValue);
FileSaveModel_U.WriteLine(UCol);
}
}
}
else
{
using (StreamWriter FileSaveModel_Phi = new StreamWriter(ModelName_Phi, false))
{
for (int IdxCol = 0; IdxCol < paramModel.Phi.nCols; IdxCol++)
{
PhiCol = String.Join("\t", paramModel.Phi.DenseMatrixValue[IdxCol].VectorValue);
FileSaveModel_Phi.WriteLine(PhiCol);
}
}
using (StreamWriter FileSaveModel_U = new StreamWriter(ModelName_U, false))
{
for (int IdxCol = 0; IdxCol < paramModel.U.nCols; IdxCol++)
{
UCol = String.Join("\t", paramModel.U.DenseMatrixValue[IdxCol].VectorValue);
FileSaveModel_U.WriteLine(UCol);
}
}
}
// Save the final learning curves
using (StreamWriter FileSavePerf = new StreamWriter(ResultFile + ".perf", false))
{
FileSavePerf.Write("Epoch:\t");
FileSavePerf.WriteLine(String.Join("\t", TestError_epoch.VectorValue));
FileSavePerf.Write("TrainTime:\t");
FileSavePerf.WriteLine(String.Join("\t", TestError_time.VectorValue));
if (paramTrain.flag_HasValidSet)
{
FileSavePerf.Write("Validation:\t");
FileSavePerf.WriteLine(String.Join("\t", ValidError_pool.VectorValue));
}
FileSavePerf.Write("Test:\t");
FileSavePerf.WriteLine(String.Join("\t", TestError_pool.VectorValue));
FileSavePerf.Write("TrainError:\t");
FileSavePerf.WriteLine(String.Join("\t", TrainError_pool.VectorValue));
FileSavePerf.Write("TrainLoss:\t");
FileSavePerf.WriteLine(String.Join("\t", TrainLoss_pool.VectorValue));
}
}
// -- Dump feature --
if (paramTrain.flag_DumpFeature && (epoch + 1) % nEpochPerDump == 0)
{
if (paramTrain.flag_RunningAvg && epoch >= (int)Math.Ceiling(((float)nEpoch) / 2.0f))
{
DumpingFeature_BP_LDA(TrainData, paramModel_avg, paramTrain.BatchSize_Test, ResultFile + ".train.fea", "Train");
DumpingFeature_BP_LDA(TestData, paramModel_avg, paramTrain.BatchSize_Test, ResultFile + ".test.fea", "Test");
if (paramTrain.flag_HasValidSet)
{
DumpingFeature_BP_LDA(ValidData, paramModel_avg, paramTrain.BatchSize_Test, ResultFile + ".valid.fea", "Validation");
}
}
{
DumpingFeature_BP_LDA(TrainData, paramModel, paramTrain.BatchSize_Test, ResultFile + ".train.fea", "Train");
DumpingFeature_BP_LDA(TestData, paramModel, paramTrain.BatchSize_Test, ResultFile + ".test.fea", "Test");
if (paramTrain.flag_HasValidSet)
{
DumpingFeature_BP_LDA(ValidData, paramModel, paramTrain.BatchSize_Test, ResultFile + ".valid.fea", "Validation");
}
}
}
}
}
public static float Testing_BP_sLDA(SparseMatrix TestData, SparseMatrix TestLabel, paramModel_t paramModel, int BatchSize_normal, string ScoreFileName, string EvalDataName)
{
Console.WriteLine("----------------------------------------------------");
int nTest = TestData.nCols;
int nBatch = (int)Math.Ceiling(((float)nTest) / ((float)BatchSize_normal));
float TestError = 0.0f;
DNNRun_t DNNRun_NormalBatch = new DNNRun_t(paramModel.nHid, BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun_EndBatch = new DNNRun_t(paramModel.nHid, nTest - (nBatch - 1) * BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun = null;
int[] IdxSample_Tot = new int[nTest];
(new FileInfo(ScoreFileName)).Directory.Create();
StreamWriter ScoreFile = new StreamWriter(ScoreFileName);
for (int Idx = 0; Idx < nTest; Idx++)
{
IdxSample_Tot[Idx] = Idx;
}
// ---- Test in a batch-wise manner over the test data ----
for (int IdxBatch = 0; IdxBatch < nBatch; IdxBatch++)
{
// Extract the batch
int BatchSize;
if (IdxBatch < nBatch - 1)
{
BatchSize = BatchSize_normal;
DNNRun = DNNRun_NormalBatch;
}
else
{
BatchSize = nTest - IdxBatch * BatchSize_normal;
DNNRun = DNNRun_EndBatch;
}
SparseMatrix Xt = new SparseMatrix(paramModel.nInput, BatchSize);
SparseMatrix Dt = new SparseMatrix(paramModel.nOutput, BatchSize);
int[] IdxSample = new int[BatchSize];
Array.Copy(IdxSample_Tot, IdxBatch * BatchSize_normal, IdxSample, 0, BatchSize);
TestData.GetColumns(Xt, IdxSample);
TestLabel.GetColumns(Dt, IdxSample);
// Forward activation
LDA_Learn.ForwardActivation_LDA(Xt, DNNRun, paramModel, false);
// Compute loss
switch (paramModel.OutputType)
{
case "softmaxCE":
TestError += ComputeNumberOfErrors(Dt, DNNRun.y);
break;
case "linearQuad":
TestError += ComputeSupervisedLoss(Dt, DNNRun.y, paramModel.OutputType);
break;
case "linearCE":
TestError += ComputeNumberOfErrors(Dt, DNNRun.y);
break;
default:
throw new Exception("Unknown OutputType.");
}
// Write the score into file
for (int IdxCol = 0; IdxCol < DNNRun.y.nCols; IdxCol++)
{
ScoreFile.WriteLine(String.Join("\t", DNNRun.y.DenseMatrixValue[IdxCol].VectorValue));
}
Console.Write(" Testing on " + EvalDataName + ": Bat#{0}/{1}\r", (IdxBatch + 1), nBatch);
}
switch (paramModel.OutputType)
{
case "softmaxCE":
TestError = 100 * TestError / nTest;
Console.WriteLine(" [" + EvalDataName + "]" + " TestError = {0}% ", TestError);
break;
case "linearQuad":
TestError = TestError / nTest;
Console.WriteLine(" [" + EvalDataName + "]" + " MSE = {0} ", TestError);
break;
case "linearCE":
TestError = 100 * TestError / nTest;
Console.WriteLine(" [" + EvalDataName + "]" + " TestError = {0}% ", TestError);
break;
default:
throw new Exception("Unknown OutputType.");
}
Console.WriteLine("----------------------------------------------------");
ScoreFile.Close();
return TestError;
}
/*
* Dumping features
*/
public static void DumpingFeature_BP_LDA(SparseMatrix InputData, paramModel_t paramModel, int BatchSize_normal, string FeatureFileName, string DataName)
{
Console.WriteLine("----------------------------------------------------");
int nTest = InputData.nCols;
int nBatch = (int)Math.Ceiling(((float)nTest) / ((float)BatchSize_normal));
DNNRun_t DNNRun_NormalBatch = new DNNRun_t(paramModel.nHid, BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun_EndBatch = new DNNRun_t(paramModel.nHid, nTest - (nBatch - 1) * BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun = null;
Console.Write(" Dumping feature ({0}): Bat#{1}/{2}\r", DataName, 0, nBatch);
int[] IdxSample_Tot = new int[nTest];
for (int Idx = 0; Idx < nTest; Idx++)
{
IdxSample_Tot[Idx] = Idx;
}
StreamWriter FeatureFile = new StreamWriter(FeatureFileName);
for (int IdxBatch = 0; IdxBatch < nBatch; IdxBatch++)
{
// Extract the batch
int BatchSize = 0;
if (IdxBatch < nBatch - 1)
{
BatchSize = BatchSize_normal;
DNNRun = DNNRun_NormalBatch;
}
else
{
BatchSize = nTest - IdxBatch * BatchSize_normal;
DNNRun = DNNRun_EndBatch;
}
SparseMatrix Xt = new SparseMatrix(paramModel.nInput, BatchSize);
int[] IdxSample = new int[BatchSize];
Array.Copy(IdxSample_Tot, IdxBatch * BatchSize_normal, IdxSample, 0, BatchSize);
InputData.GetColumns(Xt, IdxSample);
// Forward activation
LDA_Learn.ForwardActivation_LDA(Xt, DNNRun, paramModel, false);
// Dump the feature into file
for (int Idx = 0; Idx < BatchSize; Idx++)
{
FeatureFile.WriteLine(String.Join("\t", DNNRun.theta_pool[DNNRun.nHidLayerEffective[Idx] - 1].DenseMatrixValue[Idx].VectorValue));
}
Console.Write(" Dumping feature ({0}): Bat#{1}/{2}\r", DataName, (IdxBatch + 1), nBatch);
}
Console.Write("\n");
Console.WriteLine("----------------------------------------------------");
FeatureFile.Close();
}
/*
* Testing the model on the test data (Supervised learning). Compute the classification error (softmaxCE) or MSE (linearQuad).
*/
public static float Testing_BP_sLDA(SparseMatrix TestData, SparseMatrix TestLabel, paramModel_t paramModel)
{
Console.WriteLine("----------------------------------------------------");
Console.Write(" Testing: ");
DNNRun_t DNNRun = new DNNRun_t(paramModel.nHid, TestData.nCols, paramModel.nHidLayer, paramModel.nOutput);
ForwardActivation_LDA(TestData, DNNRun, paramModel, false);
int nTotError;
float TestError;
switch (paramModel.OutputType)
{
case "softmaxCE":
nTotError = ComputeNumberOfErrors(TestLabel, DNNRun.y);
TestError = 100 * ((float)nTotError) / ((float)TestLabel.nCols);
Console.WriteLine(" TestError = {0}%", TestError);
break;
case "linearQuad":
TestError = ComputeSupervisedLoss(TestLabel, DNNRun.y, paramModel.OutputType);
Console.WriteLine(" MSE = {0}", TestError);
break;
default:
throw new Exception("Unknown OutputType.");
}
Console.WriteLine("----------------------------------------------------");
return TestError;
}
public static float Testing_BP_LDA(SparseMatrix TestData, paramModel_t paramModel, int BatchSize_normal)
{
Console.WriteLine("----------------------------------------------------");
int nTest = TestData.nCols;
int nBatch = (int)Math.Ceiling(((float)nTest) / ((float)BatchSize_normal));
float NegLogLoss = 0.0f;
DNNRun_t DNNRun_NormalBatch = new DNNRun_t(paramModel.nHid, BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun_EndBatch = new DNNRun_t(paramModel.nHid, nTest - (nBatch - 1) * BatchSize_normal, paramModel.nHidLayer, paramModel.nOutput);
DNNRun_t DNNRun = null;
int[] IdxSample_Tot = new int[nTest];
for (int Idx = 0; Idx < nTest; Idx++)
{
IdxSample_Tot[Idx] = Idx;
}
for (int IdxBatch = 0; IdxBatch < nBatch; IdxBatch++)
{
// Extract the batch
int BatchSize = 0;
if (IdxBatch < nBatch - 1)
{
BatchSize = BatchSize_normal;
DNNRun = DNNRun_NormalBatch;
}
else
{
BatchSize = nTest - IdxBatch * BatchSize_normal;
DNNRun = DNNRun_EndBatch;
}
SparseMatrix Xt = new SparseMatrix(paramModel.nInput, BatchSize);
int[] IdxSample = new int[BatchSize];
Array.Copy(IdxSample_Tot, IdxBatch * BatchSize_normal, IdxSample, 0, BatchSize);
TestData.GetColumns(Xt, IdxSample);
// Forward activation
LDA_Learn.ForwardActivation_LDA(Xt, DNNRun, paramModel, false);
// Compute loss
NegLogLoss += ComputeCrossEntropy(Xt, paramModel.Phi, DNNRun.theta_pool, DNNRun.nHidLayerEffective);
Console.Write(" Testing: Bat#{0}/{1}\r", (IdxBatch + 1), nBatch);
}
NegLogLoss = NegLogLoss / ((float)nTest);
Console.WriteLine(" Testing: -LogLoss = {0}", NegLogLoss);
Console.WriteLine("----------------------------------------------------");
return NegLogLoss;
}
/*
* Testing the cross entropy on the test data (Unsupervised learning)
*/
public static float Testing_BP_LDA(SparseMatrix TestData, paramModel_t paramModel)
{
Console.WriteLine("----------------------------------------------------");
Console.Write(" Testing: ");
DNNRun_t DNNRun = new DNNRun_t(paramModel.nHid, TestData.nCols, paramModel.nHidLayer, paramModel.nOutput);
ForwardActivation_LDA(TestData, DNNRun, paramModel, false);
float NegLogLoss = ComputeCrossEntropy(TestData, paramModel.Phi, DNNRun.theta_pool, DNNRun.nHidLayerEffective);
NegLogLoss /= (float)TestData.nCols;
Console.WriteLine(" -LogLoss = {0}", NegLogLoss);
Console.WriteLine("----------------------------------------------------");
return NegLogLoss;
}
/*
* Back propagation of the unfolded LDA model (Mirror descent approach)
*/
// Implemented without atomic operation
public static void BackPropagation_LDA(SparseMatrix Xt, SparseMatrix Dt, DNNRun_t DNNRun, paramModel_t paramModel, Grad_t Grad)
{
// -------- Extract parameters --------
int nHid = paramModel.nHid;
int nHidLayer = paramModel.nHidLayer;
int nOutput = paramModel.nOutput;
float To = paramModel.To;
string OutputType = paramModel.OutputType;
int BatchSize = Xt.nCols;
int nInput = paramModel.nInput;
// -------- Back propagation --------
DenseMatrix grad_Q_po = new DenseMatrix(DNNRun.y);
SparseMatrix TmpSparseMat = new SparseMatrix(Xt);
SparseMatrix grad_Q_po_Sparse = new SparseMatrix(Xt);
DenseMatrix xi = new DenseMatrix(nHid, BatchSize);
DenseMatrix TmpDenseMat = new DenseMatrix(nHid, BatchSize);
DenseMatrix ThetaRatio = new DenseMatrix(nHid, BatchSize);
DenseRowVector TmpDenseRowVec = new DenseRowVector(BatchSize);
DenseMatrix tmp_theta_xi_b_T_OVER_theta_lm1_2 = new DenseMatrix(nHid, BatchSize);
SparseMatrix tmp_Xt_OVER_Phitheta = new SparseMatrix(Xt);
SparseMatrix tmp_Phi_theta_xi = new SparseMatrix(Xt);
Grad.grad_Q_Phi.ClearValue();
// ---- Offset of effective number of layers ----
int[] OffsetEffNumLayer = new int[BatchSize];
OffsetEffNumLayer[0] = 0;
int NumTotalLayer = DNNRun.nHidLayerEffective[0];
for (int IdxSample = 1; IdxSample < BatchSize; ++IdxSample)
{
OffsetEffNumLayer[IdxSample] = OffsetEffNumLayer[IdxSample - 1] + DNNRun.nHidLayerEffective[IdxSample-1];
NumTotalLayer += DNNRun.nHidLayerEffective[IdxSample];
}
// ---- Temporary variables that stores the intermediate results for computing the gradients ----
DenseMatrix tmp_theta_xi_pool = new DenseMatrix(nHid, NumTotalLayer, 0.0f);
DenseMatrix tmp_theta_xi = new DenseMatrix(nHid, BatchSize, 0.0f);
DenseMatrix theta_l_minus_one = new DenseMatrix(nHid, NumTotalLayer, 0.0f);
SparseMatrix tmp_Xt_OVER_Phitheta_pool = new SparseMatrix(nInput, NumTotalLayer);
SparseMatrix TmpSparseMat_pool = new SparseMatrix(nInput, NumTotalLayer);
int NumTotalNz = 0;
for (int IdxSample = 0; IdxSample < BatchSize; ++IdxSample)
{
int Layer_begin = OffsetEffNumLayer[IdxSample];
int Layer_end = Layer_begin + DNNRun.nHidLayerEffective[IdxSample];
SparseColumnVector[] tmp1 = tmp_Xt_OVER_Phitheta_pool.SparseColumnVectors;
SparseColumnVector[] tmp2 = TmpSparseMat_pool.SparseColumnVectors;
SparseColumnVector xt = Xt.SparseColumnVectors[IdxSample];
NumTotalNz += xt.nNonzero;
for (int IdxLayer = Layer_begin; IdxLayer < Layer_end; ++IdxLayer)
{
tmp1[IdxLayer] = new SparseColumnVector(xt);
tmp2[IdxLayer] = new SparseColumnVector(xt);
}
}
int[] SparsePatternGradPhi = Xt.GetHorizontalUnionSparsePattern();
SparseMatrix TmpGrad = new SparseMatrix(nInput, nHid, true);
TmpGrad.SetSparsePatternForAllColumn(SparsePatternGradPhi);
// ---- Compute grad Q wrt po if possible ----
switch (OutputType)
{
case "softmaxCE":
MatrixOperation.MatrixSubtractMatrix(grad_Q_po, Dt);
MatrixOperation.ScalarMultiplyMatrix(grad_Q_po, To);
Grad.grad_Q_U.ClearValue();
break;
case "linearQuad":
MatrixOperation.MatrixSubtractMatrix(grad_Q_po, Dt);
MatrixOperation.ScalarMultiplyMatrix(grad_Q_po, 2.0f);
Grad.grad_Q_U.ClearValue();
break;
case "unsupLDA":
Grad.grad_Q_TopPhi.SetAllValuesToZero();
break;
case "linearCE":
throw new Exception("linearCE is not implemented.");
default:
throw new Exception("Unknown OutputType");
}
Parallel.For(0, BatchSize, new ParallelOptions { MaxDegreeOfParallelism = MatrixOperation.MaxMultiThreadDegree }, IdxSample =>
{
// ***************************************************************************
// -------- Back propagation: top layer --------
switch (OutputType)
{
case "softmaxCE":
// ---- grad Q wrt pL (x_L) ----
MatrixOperation.MatrixTransposeMultiplyVector(
xi.DenseMatrixValue[IdxSample],
paramModel.U,
grad_Q_po.DenseMatrixValue[IdxSample]
);
MatrixOperation.ElementwiseVectorMultiplyVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[DNNRun.nHidLayerEffective[IdxSample] - 1].DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample]
);
TmpDenseRowVec.VectorValue[IdxSample] = TmpDenseMat.DenseMatrixValue[IdxSample].Sum();
MatrixOperation.ScalarAddVector(
xi.DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample],
TmpDenseRowVec.VectorValue[IdxSample] * (-1.0f)
);
break;
case "linearQuad":
// ---- grad Q wrt pL (x_L) ----
MatrixOperation.MatrixTransposeMultiplyVector(
xi.DenseMatrixValue[IdxSample],
paramModel.U,
grad_Q_po.DenseMatrixValue[IdxSample]
);
MatrixOperation.ElementwiseVectorMultiplyVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[DNNRun.nHidLayerEffective[IdxSample] - 1].DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample]
);
TmpDenseRowVec.VectorValue[IdxSample] = TmpDenseMat.DenseMatrixValue[IdxSample].Sum();
MatrixOperation.ScalarAddVector(
xi.DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample],
(-1.0f) * TmpDenseRowVec.VectorValue[IdxSample]
);
break;
case "unsupLDA":
// ---- grad Q wrt po ----
MatrixOperation.MatrixMultiplyVector(
grad_Q_po_Sparse.SparseColumnVectors[IdxSample],
paramModel.Phi,
DNNRun.theta_pool[DNNRun.nHidLayerEffective[IdxSample] - 1].DenseMatrixValue[IdxSample]
);
MatrixOperation.ElementwiseVectorDivideVector(
grad_Q_po_Sparse.SparseColumnVectors[IdxSample],
Xt.SparseColumnVectors[IdxSample],
grad_Q_po_Sparse.SparseColumnVectors[IdxSample]
);
// ---- grad Q wrt pL (x_L) ----
MatrixOperation.MatrixTransposeMultiplyVector(
xi.DenseMatrixValue[IdxSample],
paramModel.Phi,
grad_Q_po_Sparse.SparseColumnVectors[IdxSample]
);
MatrixOperation.ScalarMultiplyVector(
xi.DenseMatrixValue[IdxSample],
-1.0f
);
MatrixOperation.ElementwiseVectorMultiplyVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[DNNRun.nHidLayerEffective[IdxSample] - 1].DenseMatrixValue[IdxSample]
);
TmpDenseRowVec.VectorValue[IdxSample] = TmpDenseMat.DenseMatrixValue[IdxSample].Sum();
MatrixOperation.ScalarAddVector(
xi.DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample],
(-1.0f) * TmpDenseRowVec.VectorValue[IdxSample]
);
break;
case "linearCE":
throw new Exception("linearCE is not implemented.");
//break;
default:
throw new Exception("Unknown OutputType");
}
// ***************************************************************************
// -------- Back propagation: hidden layers --------
for (int IdxLayer = DNNRun.nHidLayerEffective[IdxSample] - 1; IdxLayer >= 0; IdxLayer--)
{
// ---- Compute the position in the temporary variable for the current layer at the current sample ----
int IdxTmpVar = OffsetEffNumLayer[IdxSample] + IdxLayer;
// ---- grad wrt b ---
// Not implemented at the moment. (Can be used to update the Dirichlet parameter automatically.)
// ---- Compute the intermediate variables ----
MatrixOperation.ElementwiseVectorMultiplyVector(
tmp_theta_xi_pool.DenseMatrixValue[IdxTmpVar],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample]
);
if (IdxLayer == 0)
{
MatrixOperation.ElementwiseVectorDivideVector(
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
tmp_theta_xi_pool.DenseMatrixValue[IdxTmpVar],
DNNRun.theta0.DenseMatrixValue[IdxSample]
);
}
else
{
MatrixOperation.ElementwiseVectorDivideVector(
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
tmp_theta_xi_pool.DenseMatrixValue[IdxTmpVar],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
}
if (IdxLayer == 0)
{
MatrixOperation.ElementwiseVectorDivideVector(
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
DNNRun.theta0.DenseMatrixValue[IdxSample]
);
}
else
{
MatrixOperation.ElementwiseVectorDivideVector(
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
}
MatrixOperation.ElementwiseVectorMultiplyVector(
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
paramModel.b
);
MatrixOperation.ScalarMultiplyVector(
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
DNNRun.T_pool.DenseMatrixValuePerRow[IdxLayer].VectorValue[IdxSample]
);
// Reset the elements to zero if theta_{l-1} is zero at these positions (mainly for alpha<1 case)
if (IdxLayer > 0)
{
MatrixOperation.ResetVectorSparsePattern(
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
}
// Continue to intermediate variable computation
if (IdxLayer == 0) // TmpSparseMat is Phitheta_lm1
{
MatrixOperation.MatrixMultiplyVector(
TmpSparseMat.SparseColumnVectors[IdxSample],
paramModel.Phi,
DNNRun.theta0.DenseMatrixValue[IdxSample]
);
}
else
{
MatrixOperation.MatrixMultiplyVector(
TmpSparseMat.SparseColumnVectors[IdxSample],
paramModel.Phi,
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
}
MatrixOperation.ElementwiseVectorDivideVector(
tmp_Xt_OVER_Phitheta_pool.SparseColumnVectors[IdxTmpVar],
Xt.SparseColumnVectors[IdxSample],
TmpSparseMat.SparseColumnVectors[IdxSample]
);
MatrixOperation.ElementwiseVectorDivideVector(
TmpSparseMat.SparseColumnVectors[IdxSample],
tmp_Xt_OVER_Phitheta_pool.SparseColumnVectors[IdxTmpVar],
TmpSparseMat.SparseColumnVectors[IdxSample]
); // TmpSparseMat is tmp_Xt_OVER_Phitheta2
MatrixOperation.MatrixMultiplyVector(
tmp_Phi_theta_xi.SparseColumnVectors[IdxSample],
paramModel.Phi,
tmp_theta_xi_pool.DenseMatrixValue[IdxTmpVar]
);
MatrixOperation.ElementwiseVectorMultiplyVector(
TmpSparseMat.SparseColumnVectors[IdxSample],
tmp_Phi_theta_xi.SparseColumnVectors[IdxSample]
); // TmpSparseMat is ( tmp_Phi_theta_xi.*tmp_Xt_OVER_Phitheta2 )
MatrixOperation.MatrixTransposeMultiplyVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
paramModel.Phi,
TmpSparseMat.SparseColumnVectors[IdxSample]
);
MatrixOperation.ScalarMultiplyVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
DNNRun.T_pool.DenseMatrixValuePerRow[IdxLayer].VectorValue[IdxSample]
); // TmpDenseMat is tmp_Tl_Phit_xtPhiTheta2_Phi_theta_xi
// ---- Compute the gradient wrt Phi ----
MatrixOperation.ScalarMultiplyVector(
tmp_Xt_OVER_Phitheta_pool.SparseColumnVectors[IdxTmpVar],
DNNRun.T_pool.DenseMatrixValuePerRow[IdxLayer].VectorValue[IdxSample]
);
MatrixOperation.ScalarMultiplyVector(
TmpSparseMat_pool.SparseColumnVectors[IdxTmpVar],
TmpSparseMat.SparseColumnVectors[IdxSample],
DNNRun.T_pool.DenseMatrixValuePerRow[IdxLayer].VectorValue[IdxSample]*(-1.0f)
);
if (IdxLayer == 0)
{
theta_l_minus_one.DenseMatrixValue[IdxTmpVar] = DNNRun.theta0.DenseMatrixValue[IdxSample];
}
else
{
theta_l_minus_one.DenseMatrixValue[IdxTmpVar] = DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample];
}
// ---- Compute xi_{l-1} via back propagation ----
if (IdxLayer > 0)
{
// Reset the elements to zero if theta_{l-1} is zero at these positions (mainly for alpha<1 case)
MatrixOperation.ElementwiseVectorDivideVector(
ThetaRatio.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
MatrixOperation.ResetVectorSparsePattern(
ThetaRatio.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
MatrixOperation.ElementwiseVectorMultiplyVector(
xi.DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample],
ThetaRatio.DenseMatrixValue[IdxSample]
);
// Compute xi_{l-1} now
MatrixOperation.VectorSubtractVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
xi.DenseMatrixValue[IdxSample],
TmpDenseMat.DenseMatrixValue[IdxSample]
);
MatrixOperation.VectorSubtractVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
TmpDenseMat.DenseMatrixValue[IdxSample],
tmp_theta_xi_b_T_OVER_theta_lm1_2.DenseMatrixValue[IdxSample]
);
MatrixOperation.ElementwiseVectorMultiplyVector(
tmp_theta_xi.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample],
TmpDenseMat.DenseMatrixValue[IdxSample]
); // tmp_theta_xi is tmp1 in matlab code
TmpDenseRowVec.VectorValue[IdxSample] = tmp_theta_xi.DenseMatrixValue[IdxSample].Sum();
MatrixOperation.ScalarAddVector(
xi.DenseMatrixValue[IdxSample],
TmpDenseMat.DenseMatrixValue[IdxSample],
TmpDenseRowVec.VectorValue[IdxSample] * (-1.0f)
);
}
}
});
// -------- Compute the gradients --------
// ---- Gradient with respect to U ----
DenseMatrix Theta_Top = new DenseMatrix(nHid, BatchSize);
for (int IdxSample = 0; IdxSample < BatchSize; ++IdxSample )
{
Theta_Top.DenseMatrixValue[IdxSample] = DNNRun.theta_pool[DNNRun.nHidLayerEffective[IdxSample] - 1].DenseMatrixValue[IdxSample];
}
switch (OutputType)
{
case "softmaxCE":
// ---- grad Q wrt U ----
MatrixOperation.MatrixMultiplyMatrixTranspose(Grad.grad_Q_U, grad_Q_po, Theta_Top);
MatrixOperation.ScalarMultiplyMatrix(Grad.grad_Q_U, (1.0f / (float)BatchSize));
break;
case "linearQuad":
// ---- grad Q wrt U ----
MatrixOperation.MatrixMultiplyMatrixTranspose(Grad.grad_Q_U, grad_Q_po, Theta_Top);
MatrixOperation.ScalarMultiplyMatrix(Grad.grad_Q_U, (1.0f / (float)BatchSize));
break;
case "unsupLDA":
// ---- grad Q wrt Phi on top ----
MatrixOperation.MatrixMultiplyMatrixTranspose(Grad.grad_Q_TopPhi, grad_Q_po_Sparse, Theta_Top, false);
MatrixOperation.ScalarMultiplyMatrix(Grad.grad_Q_TopPhi, Grad.grad_Q_TopPhi, (-1.0f / (float)BatchSize));
break;
case "linearCE":
throw new Exception("linearCE is not implemented.");
//break;
default:
throw new Exception("Unknown OutputType");
}
// ---- Gradient with respect to Phi ----
TmpGrad.SetAllValuesToZero();
MatrixOperation.MatrixMultiplyMatrixTranspose(TmpGrad, tmp_Xt_OVER_Phitheta_pool, tmp_theta_xi_pool, true);
MatrixOperation.MatrixMultiplyMatrixTranspose(TmpGrad, TmpSparseMat_pool, theta_l_minus_one, true);
MatrixOperation.ScalarMultiplyMatrix(TmpGrad, TmpGrad, (1.0f / (float)BatchSize));
MatrixOperation.MatrixAddMatrix(Grad.grad_Q_Phi, TmpGrad);
}
/*
* Forward activation of Latent Dirichlet Allocation model (Mirror descent approach)
*/
public static void ForwardActivation_LDA(SparseMatrix Xt, DNNRun_t DNNRun, paramModel_t paramModel, bool flag_IsTraining)
{
// -------- Extract parameters --------
int nHid = paramModel.nHid;
int nHidLayer = paramModel.nHidLayer;
float eta = paramModel.eta;
float T_value = paramModel.T_value;
string OutputType = paramModel.OutputType;
float To = paramModel.To;
int BatchSize = Xt.nCols;
// -------- Hidden activations --------
// ---- Reset the effective number of hidden layers (mainly for alpha<1 case) ----
Array.Clear(DNNRun.nHidLayerEffective,0,DNNRun.nHidLayerEffective.Length);
// ---- T is different over layers (adaptive step-size MDA) ----
DenseRowVector T = new DenseRowVector(BatchSize, T_value);
SparseMatrix Phitheta = new SparseMatrix(Xt);
DenseRowVector loss_pre = new DenseRowVector(BatchSize);
DenseRowVector loss_post = new DenseRowVector(BatchSize);
DenseRowVector loss_gap = new DenseRowVector(BatchSize);
DenseRowVector loss_gap_thresh = new DenseRowVector(BatchSize);
DenseRowVector gradproj = new DenseRowVector(BatchSize);
SparseMatrix TmpSparseMat = new SparseMatrix(Xt);
DenseMatrix TmpDenseMat = new DenseMatrix(nHid, BatchSize);
DenseMatrix LogTheta = new DenseMatrix(nHid, BatchSize);
DenseRowVector TmpDenseRowVec = new DenseRowVector(BatchSize);
DenseMatrix NegGrad = new DenseMatrix(nHid, BatchSize);
DenseMatrix LLR = new DenseMatrix(nHid, BatchSize);
//for (int IdxSample = 0; IdxSample < BatchSize; IdxSample++)
Parallel.For(0, BatchSize, new ParallelOptions { MaxDegreeOfParallelism = MatrixOperation.MaxMultiThreadDegree }, IdxSample =>
{
float KLDivergence = 0.0f;
// The forward activation for each data sample
for (int IdxLayer = 0; IdxLayer < nHidLayer; IdxLayer++)
{
// Compute the loss before unfolding the current layer
if (IdxLayer == 0)
{
MatrixOperation.MatrixMultiplyVector(
Phitheta.SparseColumnVectors[IdxSample],
paramModel.Phi,
DNNRun.theta0.DenseMatrixValue[IdxSample]
);
}
else
{
MatrixOperation.MatrixMultiplyVector(
Phitheta.SparseColumnVectors[IdxSample],
paramModel.Phi,
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
}
if (IdxLayer > 1)
{
loss_pre.VectorValue[IdxSample] = loss_post.VectorValue[IdxSample];
}
else
{
MatrixOperation.ScalarAddVector(TmpSparseMat.SparseColumnVectors[IdxSample], Phitheta.SparseColumnVectors[IdxSample], 1e-12f);
MatrixOperation.Log(TmpSparseMat.SparseColumnVectors[IdxSample]);
MatrixOperation.ElementwiseVectorMultiplyVector(TmpSparseMat.SparseColumnVectors[IdxSample], Xt.SparseColumnVectors[IdxSample]);
loss_pre.VectorValue[IdxSample] = (-1.0f)*TmpSparseMat.SparseColumnVectors[IdxSample].Sum();
if (IdxLayer == 0)
{
MatrixOperation.ScalarAddVector(TmpDenseMat.DenseMatrixValue[IdxSample], DNNRun.theta0.DenseMatrixValue[IdxSample], 1e-12f);
}
else
{
MatrixOperation.ScalarAddVector(TmpDenseMat.DenseMatrixValue[IdxSample], DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample], 1e-12f);
}
MatrixOperation.Log(TmpDenseMat.DenseMatrixValue[IdxSample]);
MatrixOperation.ElementwiseVectorMultiplyVector(TmpDenseMat.DenseMatrixValue[IdxSample], paramModel.b);
TmpDenseRowVec.VectorValue[IdxSample] = TmpDenseMat.DenseMatrixValue[IdxSample].Sum();
loss_pre.VectorValue[IdxSample] -= TmpDenseRowVec.VectorValue[IdxSample];
}
// Compute the hidden activation of the current layer
MatrixOperation.ScalarAddVector(TmpSparseMat.SparseColumnVectors[IdxSample], Phitheta.SparseColumnVectors[IdxSample], 1e-12f);
MatrixOperation.ElementwiseVectorDivideVector(
TmpSparseMat.SparseColumnVectors[IdxSample],
Xt.SparseColumnVectors[IdxSample],
TmpSparseMat.SparseColumnVectors[IdxSample]
);
MatrixOperation.MatrixTransposeMultiplyVector(
TmpDenseMat.DenseMatrixValue[IdxSample],
paramModel.Phi,
TmpSparseMat.SparseColumnVectors[IdxSample]
);
if (IdxLayer == 0)
{
MatrixOperation.ScalarAddVector(
NegGrad.DenseMatrixValue[IdxSample],
DNNRun.theta0.DenseMatrixValue[IdxSample],
1e-12f
);
}
else
{
MatrixOperation.ScalarAddVector(
NegGrad.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample],
1e-12f
);
}
MatrixOperation.ElementwiseVectorDivideVector(NegGrad.DenseMatrixValue[IdxSample], paramModel.b, NegGrad.DenseMatrixValue[IdxSample]);
MatrixOperation.VectorAddVector(NegGrad.DenseMatrixValue[IdxSample], TmpDenseMat.DenseMatrixValue[IdxSample]);
// Line search for the parameter T
if (paramModel.alpha >= 1)
{
T.VectorValue[IdxSample] *= (1.0f / eta);
} // only perform line search for alpha>=1 case (convex)
loss_post.VectorValue[IdxSample] = loss_pre.VectorValue[IdxSample];
if (IdxLayer == 0)
{
MatrixOperation.Log(LogTheta.DenseMatrixValue[IdxSample], DNNRun.theta0.DenseMatrixValue[IdxSample]);
}
else
{
MatrixOperation.Log(LogTheta.DenseMatrixValue[IdxSample], DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]);
}
while (true)
{
MatrixOperation.ScalarMultiplyVector(DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
NegGrad.DenseMatrixValue[IdxSample], T.VectorValue[IdxSample]);
MatrixOperation.VectorAddVector(DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
LogTheta.DenseMatrixValue[IdxSample]);
MatrixOperation.ScalarAddVector(DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
(-1.0f) * DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample].MaxValue());
MatrixOperation.Exp(DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample]);
MatrixOperation.ScalarMultiplyVector(DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
(1.0f / DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample].Sum()));
// Compute the loss after undfolding the current layer
MatrixOperation.MatrixMultiplyVector(Phitheta.SparseColumnVectors[IdxSample],
paramModel.Phi, DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample]);
MatrixOperation.Log(Phitheta.SparseColumnVectors[IdxSample]);
loss_post.VectorValue[IdxSample]
= (-1.0f) * MatrixOperation.InnerProduct(Xt.SparseColumnVectors[IdxSample], Phitheta.SparseColumnVectors[IdxSample]);
MatrixOperation.ScalarAddVector(TmpDenseMat.DenseMatrixValue[IdxSample], DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample], 1e-12f);
MatrixOperation.Log(TmpDenseMat.DenseMatrixValue[IdxSample]);
loss_post.VectorValue[IdxSample] -= MatrixOperation.InnerProduct(TmpDenseMat.DenseMatrixValue[IdxSample], paramModel.b);
if (IdxLayer == 0)
{
MatrixOperation.VectorSubtractVector(TmpDenseMat.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
DNNRun.theta0.DenseMatrixValue[IdxSample]);
}
else
{
MatrixOperation.VectorSubtractVector(TmpDenseMat.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]);
}
loss_gap.VectorValue[IdxSample] = loss_post.VectorValue[IdxSample] - loss_pre.VectorValue[IdxSample];
gradproj.VectorValue[IdxSample]
= (-1.0f) * MatrixOperation.InnerProduct(NegGrad.DenseMatrixValue[IdxSample],
TmpDenseMat.DenseMatrixValue[IdxSample]);
loss_gap_thresh.VectorValue[IdxSample] = gradproj.VectorValue[IdxSample]
+ (0.5f / T.VectorValue[IdxSample]) * (float)Math.Pow((double)TmpDenseMat.DenseMatrixValue[IdxSample].L1Norm(), 2.0);
if (loss_gap.VectorValue[IdxSample] > loss_gap_thresh.VectorValue[IdxSample] + 1e-12 && paramModel.alpha>=1)
{
T.VectorValue[IdxSample] *= eta;
} // Only perform line search for alpha>=1 case (convex)
else
{
DNNRun.T_pool.DenseMatrixValuePerRow[IdxLayer].VectorValue[IdxSample] = T.VectorValue[IdxSample];
break;
}
}
// Count the effective number of hidden layers
++DNNRun.nHidLayerEffective[IdxSample];
// stop MDA if termination condition holds
if (paramModel.flag_AdaptivenHidLayer)
{
if (IdxLayer == 0)
{
MatrixOperation.ElementwiseVectorDivideVector(
LLR.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
DNNRun.theta0.DenseMatrixValue[IdxSample]
);
MatrixOperation.Log(LLR.DenseMatrixValue[IdxSample]);
}
else
{
MatrixOperation.ElementwiseVectorDivideVector(
LLR.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer - 1].DenseMatrixValue[IdxSample]
);
MatrixOperation.Log(LLR.DenseMatrixValue[IdxSample]);
MatrixOperation.ResetVectorSparsePattern(
LLR.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample]
);
}
KLDivergence = MatrixOperation.InnerProduct(
LLR.DenseMatrixValue[IdxSample],
DNNRun.theta_pool[IdxLayer].DenseMatrixValue[IdxSample]
);
if (KLDivergence < 1e-12f)
{
break;
}
}
}
// ---- Generate output ----
switch (OutputType)
{
case "softmaxCE":
MatrixOperation.MatrixMultiplyVector(
DNNRun.y.DenseMatrixValue[IdxSample],
paramModel.U,
DNNRun.theta_pool[DNNRun.nHidLayerEffective[IdxSample] - 1].DenseMatrixValue[IdxSample]
);
MatrixOperation.ScalarAddVector(DNNRun.y.DenseMatrixValue[IdxSample], To);
TmpDenseRowVec.VectorValue[IdxSample] = DNNRun.y.DenseMatrixValue[IdxSample].MaxValue();
MatrixOperation.ScalarAddVector(DNNRun.y.DenseMatrixValue[IdxSample], (-1.0f) * TmpDenseRowVec.VectorValue[IdxSample]);
MatrixOperation.Exp(DNNRun.y.DenseMatrixValue[IdxSample]);
TmpDenseRowVec.VectorValue[IdxSample] = DNNRun.y.DenseMatrixValue[IdxSample].Sum();
MatrixOperation.ScalarMultiplyVector(DNNRun.y.DenseMatrixValue[IdxSample], (1.0f) / TmpDenseRowVec.VectorValue[IdxSample]);
break;
case "unsupLDA":
// Will not compute the reconstructed input at forward activation to save time during training.
break;
case "linearQuad":
MatrixOperation.MatrixMultiplyVector(
DNNRun.y.DenseMatrixValue[IdxSample],
paramModel.U,
DNNRun.theta_pool[DNNRun.nHidLayerEffective[IdxSample] - 1].DenseMatrixValue[IdxSample]
);
break;
case "linearCE":
throw new Exception("linearCE not implemented.");
default:
throw new Exception("Unknown OutputType.");
}
});
}
