Revision 0:482cc6c25690, committed 2018-04-14

Comitter:

asyrofi

Date:

Sat Apr 14 04:32:05 2018 +0000

Commit message:

perlu bantuan;

Changed in this revision

diff -r 000000000000 -r 482cc6c25690 main.cpp
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/main.cpp	Sat Apr 14 04:32:05 2018 +0000
@@ -0,0 +1,255 @@
+#include <math.h>
+#include <mbed.h>
+Serial pc(USBTX, USBRX);
+const int PatternCount = 10;
+const int InputNodes = 7;
+const int HiddenNodes = 8;
+const int OutputNodes = 4;
+const float LearningRate = 0.3;
+const float Momentum = 0.9;
+const float InitialWeightMax = 0.5;
+const float Success = 0.0004;
+
+const byte Input[PatternCount][InputNodes] = {
+  { 1, 1, 1, 1, 1, 1, 0 },  // 0
+  { 0, 1, 1, 0, 0, 0, 0 },  // 1
+  { 1, 1, 0, 1, 1, 0, 1 },  // 2
+  { 1, 1, 1, 1, 0, 0, 1 },  // 3
+  { 0, 1, 1, 0, 0, 1, 1 },  // 4
+  { 1, 0, 1, 1, 0, 1, 1 },  // 5
+  { 0, 0, 1, 1, 1, 1, 1 },  // 6
+  { 1, 1, 1, 0, 0, 0, 0 },  // 7 
+  { 1, 1, 1, 1, 1, 1, 1 },  // 8
+  { 1, 1, 1, 0, 0, 1, 1 }   // 9
+}; 
+
+const byte Target[PatternCount][OutputNodes] = {
+  { 0, 0, 0, 0 },  
+  { 0, 0, 0, 1 }, 
+  { 0, 0, 1, 0 }, 
+  { 0, 0, 1, 1 }, 
+  { 0, 1, 0, 0 }, 
+  { 0, 1, 0, 1 }, 
+  { 0, 1, 1, 0 }, 
+  { 0, 1, 1, 1 }, 
+  { 1, 0, 0, 0 }, 
+  { 1, 0, 0, 1 } 
+};
+
+int i, j, p, q, r;
+int ReportEvery1000;
+int RandomizedIndex[PatternCount];
+long  TrainingCycle;
+float Rando;
+float Error;
+float Accum;
+
+
+float Hidden[HiddenNodes];
+float Output[OutputNodes];
+float HiddenWeights[InputNodes+1][HiddenNodes];
+float OutputWeights[HiddenNodes+1][OutputNodes];
+float HiddenDelta[HiddenNodes];
+float OutputDelta[OutputNodes];
+float ChangeHiddenWeights[InputNodes+1][HiddenNodes];
+float ChangeOutputWeights[HiddenNodes+1][OutputNodes];
+
+int main()
+{
+  pc.baud(9600);
+  randomSeed(analogRead(3));
+  ReportEvery1000 = 1;
+  for( p = 0 ; p < PatternCount ; p++ ) 
+  {    
+    RandomizedIndex[p] = p ;
+  }
+  
+  void loop ()
+  {
+
+
+
+    for( i = 0 ; i < HiddenNodes ; i++ ) 
+    {    
+        for( j = 0 ; j <= InputNodes ; j++ ) 
+        { 
+            ChangeHiddenWeights[j][i] = 0.0 ;
+            Rando = float(random(100))/100;
+            HiddenWeights[j][i] = 2.0 * ( Rando - 0.5 ) * InitialWeightMax ;
+        }
+    }
+
+    for( i = 0 ; i < OutputNodes ; i ++ ) 
+    {    
+        for( j = 0 ; j <= HiddenNodes ; j++ ) 
+        {
+            ChangeOutputWeights[j][i] = 0.0 ;  
+            Rando = float(random(100))/100;        
+            OutputWeights[j][i] = 2.0 * ( Rando - 0.5 ) * InitialWeightMax ;
+        }
+    }
+    pc.printf("Initial/Untrained Outputs: \n");
+    toTerminal();
+
+    for( TrainingCycle = 1 ; TrainingCycle < 2147483647 ; TrainingCycle++) 
+    {    
+
+        for( p = 0 ; p < PatternCount ; p++) 
+        {
+            q = random(PatternCount);
+            r = RandomizedIndex[p] ; 
+            RandomizedIndex[p] = RandomizedIndex[q] ; 
+            RandomizedIndex[q] = r ;
+        }
+        Error = 0.0 ;
+
+        for( q = 0 ; q < PatternCount ; q++ ) 
+        {    
+            p = RandomizedIndex[q];
+
+            for( i = 0 ; i < HiddenNodes ; i++ ) 
+            {    
+                Accum = HiddenWeights[InputNodes][i] ;
+                for( j = 0 ; j < InputNodes ; j++ ) 
+                {
+                    Accum += Input[p][j] * HiddenWeights[j][i] ;
+                }
+                Hidden[i] = 1.0/(1.0 + exp(-Accum)) ;
+            }
+
+
+            for( i = 0 ; i < OutputNodes ; i++ ) 
+            {    
+                Accum = OutputWeights[HiddenNodes][i] ;
+                for( j = 0 ; j < HiddenNodes ; j++ ) 
+                {
+                    Accum += Hidden[j] * OutputWeights[j][i] ;
+                }
+                Output[i] = 1.0/(1.0 + exp(-Accum)) ;   
+                OutputDelta[i] = (Target[p][i] - Output[i]) * Output[i] * (1.0 - Output[i]) ;   
+                Error += 0.5 * (Target[p][i] - Output[i]) * (Target[p][i] - Output[i]) ;
+            }
+
+
+            for( i = 0 ; i < HiddenNodes ; i++ ) 
+            {    
+                Accum = 0.0 ;
+                for( j = 0 ; j < OutputNodes ; j++ ) 
+                {
+                    Accum += OutputWeights[i][j] * OutputDelta[j] ;
+                }
+                HiddenDelta[i] = Accum * Hidden[i] * (1.0 - Hidden[i]) ;
+            }
+
+
+
+
+            for( i = 0 ; i < HiddenNodes ; i++ ) 
+            {     
+                ChangeHiddenWeights[InputNodes][i] = LearningRate * HiddenDelta[i] + Momentum * ChangeHiddenWeights[InputNodes][i] ;
+                HiddenWeights[InputNodes][i] += ChangeHiddenWeights[InputNodes][i] ;
+                for( j = 0 ; j < InputNodes ; j++ ) 
+                { 
+                    ChangeHiddenWeights[j][i] = LearningRate * Input[p][j] * HiddenDelta[i] + Momentum * ChangeHiddenWeights[j][i];
+                    HiddenWeights[j][i] += ChangeHiddenWeights[j][i] ;
+                }
+            }
+
+
+            for( i = 0 ; i < OutputNodes ; i ++ ) 
+            {    
+                ChangeOutputWeights[HiddenNodes][i] = LearningRate * OutputDelta[i] + Momentum * ChangeOutputWeights[HiddenNodes][i] ;
+                OutputWeights[HiddenNodes][i] += ChangeOutputWeights[HiddenNodes][i] ;
+                for( j = 0 ; j < HiddenNodes ; j++ ) 
+                {
+                    ChangeOutputWeights[j][i] = LearningRate * Hidden[j] * OutputDelta[i] + Momentum * ChangeOutputWeights[j][i] ;
+                    OutputWeights[j][i] += ChangeOutputWeights[j][i] ;
+                }
+            }
+     }
+
+
+    ReportEvery1000 = ReportEvery1000 - 1;
+    if (ReportEvery1000 == 0)
+    { 
+      pc.printf ("TrainingCycle: ");
+      pc.printf ("%i",(int16_t)TrainingCycle);
+      pc.printf ("  Error = ");
+      pc.printf ("%i\n",(int16_t)Error, 5);
+
+      toTerminal();
+
+      if (TrainingCycle==1)
+      {
+        ReportEvery1000 = 999;
+      }
+        else
+      {
+        ReportEvery1000 = 1000;
+      }
+    }    
+
+    if( Error < Success ) break ;  
+  } 
+    pc.printf ("TrainingCycle: ");
+    pc.printf ("%i",(int16_t)TrainingCycle);
+    pc.printf ("  Error = ");
+    pc.printf ("%i\n",(int16_t)Error, 5);
+
+    toTerminal();
+
+    pc.printf ("Training Set Solved!\n");
+    pc.printf ("--------\n");   
+    ReportEvery1000 = 1;
+    }
+}  
+
+void toTerminal()
+{
+
+  for( p = 0 ; p < PatternCount ; p++ ) { 
+    pc.printf ("  Training Pattern: ");
+    pc.printf ("%i\n",(int16_t)p);      
+    pc.printf ("  Input ");
+    for( i = 0 ; i < InputNodes ; i++ ) {
+      pc.printf ("%i,%i",(int16_t)Input[p][i],(int16_t)DEC);
+      pc.printft (" ");
+    }
+    pc.printf ("  Target ");
+    for( i = 0 ; i < OutputNodes ; i++ ) {
+      pc.printf ("%i,%i",(int16_t)Input[p][i],(int16_t)DEC);
+      Serial.print (" ");
+    }
+/******************************************************************
+* Compute hidden layer activations
+******************************************************************/
+
+    for( i = 0 ; i < HiddenNodes ; i++ ) {    
+      Accum = HiddenWeights[InputNodes][i] ;
+      for( j = 0 ; j < InputNodes ; j++ ) {
+        Accum += Input[p][j] * HiddenWeights[j][i] ;
+      }
+      Hidden[i] = 1.0/(1.0 + exp(-Accum)) ;
+    }
+
+/******************************************************************
+* Compute output layer activations and calculate errors
+******************************************************************/
+
+    for( i = 0 ; i < OutputNodes ; i++ ) {    
+      Accum = OutputWeights[HiddenNodes][i] ;
+      for( j = 0 ; j < HiddenNodes ; j++ ) {
+        Accum += Hidden[j] * OutputWeights[j][i] ;
+      }
+      Output[i] = 1.0/(1.0 + exp(-Accum)) ; 
+    }
+    pc.printf ("  Output ");
+    for( i = 0 ; i < OutputNodes ; i++ ) {       
+      pc.printf ("%i",Output[i], 5);
+      pc.printf (" ");
+    }
+  }
+
+
+}
+

diff -r 000000000000 -r 482cc6c25690 mbed.bld
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/mbed.bld	Sat Apr 14 04:32:05 2018 +0000
@@ -0,0 +1,1 @@
+http://mbed.org/users/mbed_official/code/mbed/builds/4f6c30876dfa
\ No newline at end of file