First commit

1 files changed, 138 insertions, 0 deletions

diff --git a/src/TNetLib/Activation.cc b/src/TNetLib/Activation.cc
new file mode 100644
index 0000000..8e84190
--- /dev/null
+++ b/src/TNetLib/Activation.cc
@@ -0,0 +1,138 @@
+
+#include "Activation.h"
+
+
+namespace TNet {
+
+void Sigmoid::PropagateFnc(const BfMatrix& X, BfMatrix& Y) {
+  //Y = 1/(1+e^{-X})
+  for(size_t r=0; r<X.Rows(); r++) {
+    for(size_t c=0; c<X.Cols(); c++) {
+      Y(r,c) = 1.0f/(1.0f+exp(-X(r,c)));
+    }
+  }
+}
+
+
+void Sigmoid::BackpropagateFnc(const Matrix<BaseFloat>& X, Matrix<BaseFloat>& Y) {
+  const Matrix<BaseFloat>& out = GetOutput();
+  //Y = OUT*(1-OUT)*X //ODVOZENO
+  for(size_t r=0; r<X.Rows(); r++) {
+    for(size_t c=0; c<X.Cols(); c++) {
+      Y(r,c) = X(r,c)*out(r,c)*(1.0f-out(r,c));
+    }
+  }
+}
+
+
+
+void Softmax::PropagateFnc(const BfMatrix& X, BfMatrix& Y) {
+  //Y_j = e^X_j / sum_i(e^X_i)
+  //
+  //    e^(X_j+c) / sum_i(e^X_i+c)
+  //    = e^c.e^X_h / e^c.sum_i(e^X_i)
+  //    = e^X_j / sum_i(e^X_i)
+  //
+  size_t rows = X.Rows();
+  for(size_t i=0; i<rows; i++) {
+    BfSubVector y_i(Y[i]); //<< y_i gets pointer to i'th row of matrix Y
+    y_i.Copy(X[i]);
+    BaseFloat max = y_i.Max();
+    y_i.Subtract(max);
+    y_i.ApplyExp();
+    BaseFloat sum = y_i.Sum();
+    y_i.Scale(1.0f/sum);
+  }
+}
+
+
+void Softmax::BackpropagateFnc(const BfMatrix& X, BfMatrix& Y) {
+  //simply copy the error...,
+  Y.Copy(X);
+}
+
+
+void BlockSoftmax::ReadFromStream(std::istream& rIn) {
+  rIn >> mDim; 
+  mDimOffset.Init(mDim.Dim()+1);
+  
+  int off=0; 
+  for(int i=0; i<mDim.Dim(); i++) { 
+    mDimOffset[i]=off;
+    off += mDim[i];
+  }
+  mDimOffset[mDim.Dim()]=off;
+
+  if(off!=GetNOutputs()) {
+    KALDI_ERR << "Non-matching dimension of sum of softmaxes,"
+      << " the sum:" << off 
+      << " GetNOutputs:" << GetNOutputs();
+  }
+}
+
+void BlockSoftmax::WriteToStream(std::ostream& rOut) {
+  rOut << mDim;
+}
+
+
+
+
+void BlockSoftmax::PropagateFnc(const BfMatrix& X, BfMatrix& Y) {
+  //Y_j = e^X_j / sum_i(e^X_i)
+  //
+  //    e^(X_j+c) / sum_i(e^X_i+c)
+  //    = e^c.e^X_h / e^c.sum_i(e^X_i)
+  //    = e^X_j / sum_i(e^X_i)
+  //
+  size_t rows = X.Rows();
+  for(size_t i=0; i<rows; i++) {
+    BfSubVector y_i(Y[i]); //<< y_i gets pointer to i'th row of matrix Y
+    y_i.Copy(X[i]);
+    //BaseFloat max = y_i.Max();
+    //y_i.Subtract(max);
+    //y_i.ApplyExp();
+    //normalize separately on each softmax interval...
+    for(int j=0; j<mDim.Dim(); j++) {
+      BfSubVector y_i_smx_j(y_i.Range(mDimOffset[j],mDim[j]));
+      BaseFloat max = y_i_smx_j.Max();
+      y_i_smx_j.Subtract(max);
+      y_i_smx_j.ApplyExp();
+      BaseFloat sum = y_i_smx_j.Sum();
+      y_i_smx_j.Scale(1.0f/sum);
+    }
+  }
+
+//  X.CheckData("BlockSoftmax PropagateFnc X");
+//  Y.CheckData("BlockSoftmax PropagateFnc Y");
+}
+
+
+void BlockSoftmax::BackpropagateFnc(const BfMatrix& X, BfMatrix& Y) {
+  //set the output to zero
+  Y.Zero();
+  //copy only parts of the error
+  //from softmax intervals which sum up to 0.0, not 1.0
+  for(int i=0; i<X.Rows(); i++) {
+    for(int j=0; j<mDim.Dim(); j++) {
+      const BfSubVector x_i_smx_j(X[i].Range(mDimOffset[j],mDim[j]));
+      BaseFloat sum = x_i_smx_j.Sum();
+      if(sum > -0.1 && sum < 0.1) {
+        BfSubVector y_i_smx_j(Y[i].Range(mDimOffset[j],mDim[j]));
+        y_i_smx_j.Copy(x_i_smx_j);
+      } else if (sum > 0.9 && sum < 1.1) {
+        ; //do nothing
+      } else {
+        KALDI_ERR << "Invalid sum: " << sum;
+      }
+    }
+  }
+
+//  X.CheckData("BlockSoftmax BackpropagateFnc X");
+//  Y.CheckData("BlockSoftmax BackpropagateFnc Y");
+
+}
+
+
+
+} //namespace TNet
+