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diff --git a/src/CuTNetLib/.svn/text-base/cuDiscreteLinearity.cc.svn-base b/src/CuTNetLib/.svn/text-base/cuDiscreteLinearity.cc.svn-base
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+
+
+#include "cuDiscreteLinearity.h"
+#include "cumath.h"
+
+namespace TNet
+{
+
+  void 
+  CuDiscreteLinearity::
+  PropagateFnc(const CuMatrix<BaseFloat>& X, CuMatrix<BaseFloat>& Y)
+  {
+    //Y.SetConst(0.0);
+
+    //precopy bias
+    Y.AddScaledRow(1.0,mBias,0.0);
+
+    //mulitply with the matrices
+    int offset_in=0, offset_out=0;
+    for (int i=0; i<mNBlocks; i++) {
+      CuMath<BaseFloat>::OffsetGemm('N','N', 1.0, X, mLinearity[i], 1.0, Y, 
+                                    offset_in, 0, offset_out);
+      offset_in += mLinearity[i].Rows();
+      offset_out += mLinearity[i].Cols();
+    }
+  }
+
+
+  void 
+  CuDiscreteLinearity::
+  BackpropagateFnc(const CuMatrix<BaseFloat>& X, CuMatrix<BaseFloat>& Y)
+  {
+    //Y.SetConst(0.0);
+
+    int offset_in=0, offset_out=0;
+    for(int i=0; i<mNBlocks; i++) {
+      CuMath<BaseFloat>::OffsetGemm('N', 'T', 1.0, X, mLinearity[i], 0.0, Y,
+                                    offset_in, 0, offset_out);
+      offset_in += mLinearity[i].Cols();
+      offset_out += mLinearity[i].Rows();
+    }
+  }
+
+  
+  void 
+  CuDiscreteLinearity::
+  Update() 
+  {
+    //new implementation
+    BaseFloat N = 1; 
+    if(mGradDivFrm) {
+      N = static_cast<BaseFloat>(GetInput().Rows());
+    }
+    BaseFloat mmt_gain = static_cast<BaseFloat>(1.0/(1.0-mMomentum));
+    N *= mmt_gain; //compensate higher gradient estimates due to momentum 
+
+    //get gradients of discrete linearities
+    int offset_in=0, offset_out=0;
+    for(int i=0; i<mNBlocks; i++) {
+      CuMath<BaseFloat>::OffsetGemm('T','N',1.0,
+                        GetInput(),GetErrorInput(),
+                        mMomentum, mLinearityCorrection[i],
+                        offset_in,offset_out,0);
+      offset_in += mLinearity[i].Rows();
+      offset_out += mLinearity[i].Cols();
+    }
+    for(int i=0; i<mNBlocks; i++) {
+      //perform update 
+      mLinearity[i].AddScaled(-mLearningRate/N,mLinearityCorrection[i],1.0);
+      //regularization weight decay
+      mLinearity[i].AddScaled(-mLearningRate*mWeightcost,mLinearity[i],1.0);
+    }
+
+    //get gradient of bias
+    mBiasCorrection.AddColSum(1.0,GetErrorInput(),mMomentum);
+    //update biases
+    mBias.AddScaled(-mLearningRate/N,mBiasCorrection,1.0);
+  }
+
+
+  void
+  CuDiscreteLinearity::
+  ReadFromStream(std::istream& rIn)
+  {
+    rIn >> std::ws >> mNBlocks;
+    if(mNBlocks < 1) {
+      KALDI_ERR << "Bad number of blocks:" << mNBlocks;
+    }
+
+    mLinearity.resize(mNBlocks);
+    mLinearityCorrection.resize(mNBlocks);
+
+    int in_dim = 0, out_dim = 0;
+    for(int i=0; i<mNBlocks; i++) {
+      //matrix is stored transposed as SNet does
+      BfMatrix transpose;
+      rIn >> transpose;
+      mLinearity[i].CopyFrom(BfMatrix(transpose, TRANS));
+      
+      if(transpose.Cols()*transpose.Rows() == 0) {
+        Error("Missing linearity matrix in network file");
+      }
+      //allocate training buffers
+      mLinearityCorrection[i].Init(mLinearity[i].Rows(),mLinearity[i].Cols());
+      mLinearityCorrection[i].SetConst(0.0);
+
+      in_dim += transpose.Cols();
+      out_dim += transpose.Rows();
+    }
+    
+    //biases stored normally
+    BfVector bias;
+    rIn >> bias;
+    mBias.CopyFrom(bias);
+    if(bias.Dim() == 0) {
+      Error("Missing bias vector in network file");
+    }
+    mBiasCorrection.Init(mBias.Dim());
+    mBiasCorrection.SetConst(0.0);
+
+    if(out_dim != GetNOutputs() || 
+       in_dim != GetNInputs() ||
+       mBias.Dim() != GetNOutputs()
+    ){
+      std::ostringstream os;
+      os << "Wrong dimensionalities of matrix/vector in network file\n"
+         << "Inputs:" << GetNInputs()
+         << "Outputs:" << GetNOutputs()
+         << "\n"
+         << "linearityCols:" << in_dim
+         << "linearityRows:" << out_dim
+         << "biasDims:" << mBias.Dim()
+         << "\n";
+      Error(os.str());
+    }
+  }
+
+   
+  void
+  CuDiscreteLinearity::
+  WriteToStream(std::ostream& rOut)
+  {
+    rOut << mNBlocks << "\n";
+    for(int i=0; i< mNBlocks; i++) {
+      //matrix is stored transposed as SNet does
+      BfMatrix tmp;
+      mLinearity[i].CopyTo(tmp);
+      BfMatrix transpose(tmp, TRANS);
+      rOut << transpose;
+    }
+    //biases stored normally
+    BfVector vec;
+    mBias.CopyTo(vec);
+    rOut << vec;
+    rOut << std::endl;
+  }
+
+ 
+} //namespace
+