From cccccbf6cca94a3eaf813b4468453160e91c332b Mon Sep 17 00:00:00 2001
From: Joe Zhao <ztuowen@gmail.com>
Date: Mon, 14 Apr 2014 08:14:45 +0800
Subject: First commit

---
 src/TNetLib/.svn/text-base/Nnet.h.svn-base | 194 +++++++++++++++++++++++++++++
 1 file changed, 194 insertions(+)
 create mode 100644 src/TNetLib/.svn/text-base/Nnet.h.svn-base

(limited to 'src/TNetLib/.svn/text-base/Nnet.h.svn-base')

diff --git a/src/TNetLib/.svn/text-base/Nnet.h.svn-base b/src/TNetLib/.svn/text-base/Nnet.h.svn-base
new file mode 100644
index 0000000..12e2585
--- /dev/null
+++ b/src/TNetLib/.svn/text-base/Nnet.h.svn-base
@@ -0,0 +1,194 @@
+#ifndef _NETWORK_H_
+#define _NETWORK_H_
+
+#include "Component.h"
+#include "BiasedLinearity.h"
+#include "SharedLinearity.h"
+#include "Activation.h"
+
+#include "Vector.h"
+
+#include <vector>
+
+
+namespace TNet {
+
+class Network
+{
+//////////////////////////////////////
+// Typedefs
+typedef std::vector<Component*> LayeredType;
+  
+  //////////////////////////////////////
+  // Disable copy construction and assignment
+ private:
+  Network(Network&); 
+  Network& operator=(Network&);
+   
+ public:
+  // allow incomplete network creation
+  Network() 
+  { }
+
+  ~Network();
+
+  int Layers() const
+  { return mNnet.size(); }
+
+  Component& Layer(int i)
+  { return *mNnet[i]; }
+ 
+  const Component& Layer(int i) const
+  { return *mNnet[i]; }
+
+  /// Feedforward the data per blocks, this needs less memory, 
+  /// and allows to process very long files.
+  /// It does not trim the *_frm_ext, but uses it 
+  /// for concatenation of segments
+  void Feedforward(const Matrix<BaseFloat>& in, Matrix<BaseFloat>& out, 
+                   size_t start_frm_ext, size_t end_frm_ext);
+  /// forward the data to the output
+  void Propagate(const Matrix<BaseFloat>& in, Matrix<BaseFloat>& out);
+  /// backpropagate the error while calculating the gradient
+  void Backpropagate(const Matrix<BaseFloat>& globerr); 
+
+  /// accumulate the gradient from other networks
+  void AccuGradient(const Network& src, int thr, int thrN);
+  /// update weights, reset the accumulator
+  void Update(int thr, int thrN);
+  
+  Network* Clone(); ///< Clones the network
+
+  void ReadNetwork(const char* pSrc);     ///< read the network from file
+  void ReadNetwork(std::istream& rIn);    ///< read the network from stream
+  void WriteNetwork(const char* pDst);    ///< write network to file
+  void WriteNetwork(std::ostream& rOut);  ///< write network to stream
+
+  size_t GetNInputs() const; ///< Dimensionality of the input features
+  size_t GetNOutputs() const; ///< Dimensionality of the desired vectors
+
+  void SetLearnRate(BaseFloat learnRate); ///< set the learning rate value
+  BaseFloat GetLearnRate();  ///< get the learning rate value
+
+  void SetWeightcost(BaseFloat l2); ///< set the L2 regularization const
+
+  void ResetBunchsize(); ///< reset the frame counter (needed for L2 regularization
+  void AccuBunchsize(const Network& src); ///< accumulate frame counts in bunch (needed in L2 regularization
+
+ private:
+  /// Creates a component by reading from stream
+  Component* ComponentFactory(std::istream& In);
+  /// Dumps component into a stream
+  void ComponentDumper(std::ostream& rOut, Component& rComp);
+
+ private:
+  LayeredType mNnet; ///< container with the network layers
+
+};
+  
+
+//////////////////////////////////////////////////////////////////////////
+// INLINE FUNCTIONS 
+// Network::
+inline Network::~Network() {
+  //delete all the components
+  LayeredType::iterator it;
+  for(it=mNnet.begin(); it!=mNnet.end(); ++it) {
+    delete *it;
+  }
+}
+
+    
+inline size_t Network::GetNInputs() const {
+  assert(mNnet.size() > 0);
+  return mNnet.front()->GetNInputs();
+}
+
+
+inline size_t
+Network::
+GetNOutputs() const
+{
+  assert(mNnet.size() > 0);
+  return mNnet.back()->GetNOutputs();
+}
+
+
+
+inline void
+Network::
+SetLearnRate(BaseFloat learnRate)
+{
+  LayeredType::iterator it;
+  for(it=mNnet.begin(); it!=mNnet.end(); ++it) {
+    if((*it)->IsUpdatable()) {
+      dynamic_cast<UpdatableComponent*>(*it)->LearnRate(learnRate);
+    }
+  }
+}
+
+
+inline BaseFloat
+Network::
+GetLearnRate()
+{
+  //TODO - learn rates may differ layer to layer
+  assert(mNnet.size() > 0);
+  for(size_t i=0; i<mNnet.size(); i++) {
+    if(mNnet[i]->IsUpdatable()) {
+      return dynamic_cast<UpdatableComponent*>(mNnet[i])->LearnRate();
+    }
+  }
+  Error("No updatable NetComponents");
+  return -1;
+}
+
+
+inline void
+Network::
+SetWeightcost(BaseFloat l2)
+{
+  LayeredType::iterator it;
+  for(it=mNnet.begin(); it!=mNnet.end(); ++it) {
+    if((*it)->IsUpdatable()) {
+      dynamic_cast<UpdatableComponent*>(*it)->Weightcost(l2);
+    }
+  }
+}
+
+
+inline void 
+Network::
+ResetBunchsize()
+{
+  LayeredType::iterator it;
+  for(it=mNnet.begin(); it!=mNnet.end(); ++it) {
+    if((*it)->IsUpdatable()) {
+      dynamic_cast<UpdatableComponent*>(*it)->Bunchsize(0);
+    }
+  }
+}
+
+inline void
+Network::
+AccuBunchsize(const Network& src)
+{
+  assert(Layers() == src.Layers());
+  assert(Layers() > 0);
+
+  for(int i=0; i<Layers(); i++) {
+    if(Layer(i).IsUpdatable()) {
+      UpdatableComponent& tgt_comp = dynamic_cast<UpdatableComponent&>(Layer(i));
+      const UpdatableComponent& src_comp = dynamic_cast<const UpdatableComponent&>(src.Layer(i));
+      tgt_comp.Bunchsize(tgt_comp.Bunchsize()+src_comp.GetOutput().Rows());
+    }
+  }
+}
+
+  
+
+} //namespace
+
+#endif
+
+
-- 
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