cwNN.h/cpp/Makefile.am : Initial commit.

Makefile.am

@@ -4,6 +4,8 @@ libcwSRC =
 libcwHDR += src/libcw/cwCommon.h src/libcw/cwCommonImpl.h   src/libcw/cwMem.h   src/libcw/cwLog.h    src/libcw/cwUtility.h
 libcwSRC +=                      src/libcw/cwCommonImpl.cpp src/libcw/cwMem.cpp src/libcw/cwLog.cpp  src/libcw/cwUtility.cpp
 
+libcwHDR += src/libcw/cwMtx.h
+
 libcwHDR += src/libcw/cwFileSys.h   src/libcw/cwText.h   src/libcw/cwFile.h    src/libcw/cwTime.h   src/libcw/cwLex.h   src/libcw/cwNumericConvert.h 
 libcwSRC += src/libcw/cwFileSys.cpp src/libcw/cwText.cpp src/libcw/cwFile.cpp  src/libcw/cwTime.cpp src/libcw/cwLex.cpp
 
@@ -16,6 +18,8 @@ libcwSRC += src/libcw/cwThread.cpp  src/libcw/cwMutex.cpp src/libcw/cwThreadMach
 libcwHDR += src/libcw/cwMpScNbQueue.h src/libcw/cwSpScBuf.h   src/libcw/cwSpScQueueTmpl.h
 libcwSRC +=                           src/libcw/cwSpScBuf.cpp src/libcw/cwSpScQueueTmpl.cpp
 
+libcwHDR += src/libcw/cwSvg.h
+libcwSRC += src/libcw/cwSvg.cpp
 
 libcwHDR += src/libcw/cwWebSock.h   src/libcw/cwWebSockSvr.h    src/libcw/cwLib.h
 libcwSRC += src/libcw/cwWebSock.cpp src/libcw/cwWebSockSvr.cpp  src/libcw/cwLib.cpp
@@ -49,3 +53,5 @@ libcwHDR += src/libcw/cwMdns.h   src/libcw/cwEuCon.h   src/libcw/cwDnsSd.h   src
 libcwSRC += src/libcw/cwMdns.cpp src/libcw/cwEuCon.cpp src/libcw/cwDnsSd.cpp src/libcw/dns_sd/dns_sd.cpp src/libcw/dns_sd/dns_sd_print.cpp                                  src/libcw/dns_sd/fader.cpp   src/libcw/dns_sd/rpt.cpp
 
 
+libcwHDR += src/libcw/cwDataSets.h
+libcwSRC += src/libcw/cwDataSets.cpp

cwNN.cpp

@@ -0,0 +1,75 @@
+#include "cwCommon.h"
+#include "cwLog.h"
+#include "cwCommonImpl.h"
+#include "cwMem.h"
+#include "cwFile.h"
+#include "cwNN.h"
+
+
+namespace cw
+{
+  namespace nn
+  {
+    
+    template< typename R >
+    struct input_str
+    {
+      R*        x;
+      unsigned  dimN;
+      unsigned* dimV;
+    };
+    
+    typedef struct dense_str
+    {
+      unsigned  xN;   // count of neurons in src layer
+      unsigned  yN;   // count of neurons in this layer
+      
+      real_t*   wM;     // wM[ xN, yN ]  weight matrix
+      real_t*   bV;     // bV[ yN ]      bias vector
+
+
+      real_t*   yV;   // scaled input + bias
+      real_t*   aV;   // activation output
+      real_t*   dV;   // contribution to cost for each neurode
+      real_t*   gV;   // C gradient wrt weight at each neurode
+
+      
+    } dense_t;
+    
+    typedef struct layer_str
+    {
+    } layer_t;
+    
+    typedef struct nn_str
+    {
+      
+    } nn_t;
+
+
+    void _mtx_mul( R* z, R* m, R* x, unsigned mN, unsigned mM )
+    {
+    }
+
+    void _add( R* y, R* x, unsigned n )
+    {
+    }
+
+    void _activation( dense_t* l )
+    {
+    }
+
+    void _dense_forward( dense_t* l0, dense_t* l1 )
+    {
+      assert( l1->wM.dimV[1] == l0->yN );
+      assert( l1->wM.dimV[0] == l1->yN );
+      _mtx_mult( l1->zV, l1->wM.base, l0->aV, l0->yN, l1->yN );
+      _add( l1->zV, l1->bV, l1->yN );
+
+      _activation(l1)
+    }
+    
+  }
+}
+
+
+

cwNN.h

@@ -0,0 +1,62 @@
+#ifndef cwNN_H
+#define cwNN_H
+
+namespace cw
+{
+  namespace nn
+  {
+    typedef handle<struct nn_str> handle_t;
+
+    enum
+    {
+     kSigmoidActId,
+     kReluActId
+    };
+
+    enum
+    {
+     kInputLayerId,
+     kDenseLayerId,
+     kConv1DConvId
+    };
+
+    enum
+    {
+     kZeroInitId,
+     kUniformInitId,
+     kNormalInitId
+    };
+
+    typedef struct layer_args_str
+    {
+      unsigned        typeId;
+      unsigned        actId;
+      unsigned        weightInitId;
+      unsigned        biasInitId;
+      unsigned        dimN;
+      const unsigned* dimV;
+    } layer_args_t;
+
+    typedef struct network_args_str
+    {
+      layer_args_t* layers;
+      unsigned      layerN;
+    } network_args_t;
+
+
+    rc_t parse_args( const object_t& o, network_args_t& args );
+    
+    rc_t create(  handle_t& h, const network_args_t& args );
+    rc_t destroy( handle_t& h );
+
+    template< typename R >
+      rc_t train( handle_t h, unsigned epochN, unsigned batchN, const dataset<R>& trainDs );
+
+    template< typename R >
+      rc_t infer( handle_t h, const dataset<R>& ds );
+  }
+}
+
+
+#endif
+