1 files changed, 123 insertions, 0 deletions
diff --git a/src/CuTNetLib/.svn/text-base/cuBiasedLinearity.cc.svn-base b/src/CuTNetLib/.svn/text-base/cuBiasedLinearity.cc.svn-base
new file mode 100644
index 0000000..b9ac137
--- /dev/null
+++ b/src/CuTNetLib/.svn/text-base/cuBiasedLinearity.cc.svn-base
@@ -0,0 +1,123 @@
+
+
+#include "cuBiasedLinearity.h"
+
+
+namespace TNet
+{
+
+  void 
+  CuBiasedLinearity::
+  PropagateFnc(const CuMatrix<BaseFloat>& X, CuMatrix<BaseFloat>& Y)
+  {
+    //Y.SetConst(0.0);
+    Y.AddScaledRow(1.0,mBias,0.0);
+    Y.Gemm('N','N', 1.0, X, mLinearity, 1.0);
+  }
+
+
+  void 
+  CuBiasedLinearity::
+  BackpropagateFnc(const CuMatrix<BaseFloat>& X, CuMatrix<BaseFloat>& Y)
+  {
+    //Y.SetConst(0.0);
+    Y.Gemm('N', 'T', 1.0, X, mLinearity, 0.0);
+  }
+
+  
+  void 
+  CuBiasedLinearity::
+  Update() 
+  {
+#if 0
+    //former implementation
+    BaseFloat N = static_cast<BaseFloat>(GetInput().Rows());
+
+    mLinearityCorrection.Gemm('T','N',-mLearningRate/N,GetInput(),GetErrorInput(),mMomentum);
+    mBiasCorrection.AddColSum(-mLearningRate/N,GetErrorInput(),mMomentum);
+
+    //regularization weight decay
+    mLinearityCorrection.AddScaled(-mLearningRate*mWeightcost,mLinearity,1.0);
+    
+    mLinearity.AddScaled(1.0,mLinearityCorrection,1.0);
+    mBias.AddScaled(1.0,mBiasCorrection,1.0);
+#endif
+
+#if 1
+    //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;
+
+    mLinearityCorrection.Gemm('T','N',1.0,GetInput(),GetErrorInput(),mMomentum);
+    mBiasCorrection.AddColSum(1.0,GetErrorInput(),mMomentum);
+
+    mLinearity.AddScaled(-mLearningRate/N,mLinearityCorrection,1.0);
+    mBias.AddScaled(-mLearningRate/N,mBiasCorrection,1.0);
+
+    //regularization weight decay (from actual weights only)
+    BaseFloat L2_decay = -mLearningRate*mWeightcost*(mGradDivFrm?1.0:GetInput().Rows());
+    mLinearity.AddScaled(L2_decay, mLinearity,1.0);
+#endif
+  }
+
+
+  void
+  CuBiasedLinearity::
+  ReadFromStream(std::istream& rIn)
+  {
+    //matrix is stored transposed as SNet does
+    BfMatrix transpose;
+    rIn >> transpose;
+    mLinearity.CopyFrom(BfMatrix(transpose, TRANS));
+    //biases stored normally
+    BfVector bias;
+    rIn >> bias;
+    mBias.CopyFrom(bias);
+
+    if(transpose.Cols()*transpose.Rows() == 0) {
+      Error("Missing linearity matrix in network file");
+    }
+    if(bias.Dim() == 0) {
+      Error("Missing bias vector in network file");
+    }
+    if(mLinearity.Cols() != GetNOutputs() || 
+       mLinearity.Rows() != GetNInputs() ||
+       mBias.Dim() != GetNOutputs()
+    ){
+      std::ostringstream os;
+      os << "Wrong dimensionalities of matrix/vector in network file\n"
+         << "Inputs:" << GetNInputs()
+         << "Outputs:" << GetNOutputs()
+         << "\n"
+         << "linearityCols:" << mLinearity.Cols()
+         << "linearityRows:" << mLinearity.Rows()
+         << "biasDims:" << mBias.Dim()
+         << "\n";
+      Error(os.str());
+    }
+  }
+
+   
+  void
+  CuBiasedLinearity::
+  WriteToStream(std::ostream& rOut)
+  {
+    //matrix is stored transposed as SNet does
+    BfMatrix tmp;
+    mLinearity.CopyTo(tmp);
+    BfMatrix transpose(tmp, TRANS);
+    rOut << transpose;
+    //biases stored normally
+    BfVector vec;
+    mBias.CopyTo(vec);
+    rOut << vec;
+    rOut << std::endl;
+  }
+
+ 
+} //namespace
+