- Code: Select all Expand view
- #define INPUT_SIZE 2
#define HIDDEN_SIZE 2
#define OUTPUT_SIZE 1
#define LEARNING_RATE 0.5
function Main()
local aInput[ INPUT_SIZE ]
local aHidden[ HIDDEN_SIZE ]
local nOutput
local aWih[ INPUT_SIZE ][ HIDDEN_SIZE ] // weights from input to hidden layer
local aWho[ HIDDEN_SIZE ] // weights from hidden to output layer
local aData := { { 0, 0, 0 }, { 0, 1, 1 }, { 1, 0, 1 }, { 1, 1, 0 } } // Xor values
local nError := 1, nAt, nDelta, nHDelta
SET DECIMALS TO 9
for n = 1 to INPUT_SIZE
for m = 1 to HIDDEN_SIZE
aWih[ n, m ] = hb_Random()
next
next
for n = 1 to HIDDEN_SIZE
aWho[ n ] = hb_Random()
next
for n = 1 to 100000
nAt = hb_RandomInt( 1, 4 )
aInput[ 1 ] = aData[ nAt ][ 1 ]
aInput[ 2 ] = aData[ nAt ][ 2 ]
// feed forward
for m = 1 to HIDDEN_SIZE
aHidden[ m ] = 0
for p = 1 to INPUT_SIZE
aHidden[ m ] += aInput[ p ] * aWih[ p ][ m ]
next
aHidden[ m ] = 1 / ( 1 + Math_E() ^ -aHidden[ m ] )
next
nOutput = 0
for m = 1 to HIDDEN_SIZE
nOutput += aHidden[ m ] * aWho[ m ]
next
nOutput = 1 / ( 1 + Math_E() ^ -nOutput )
// backpropagation
nError = aData[ nAt ][ 3 ] - nOutput
nDelta = nError * nOutput * ( 1 - nOutput )
for m = 1 to HIDDEN_SIZE
aWho[ m ] += LEARNING_RATE * aHidden[ m ] * nDelta
next
for m = 1 to HIDDEN_SIZE
nHDelta = nDelta * aWho[ m ] * aHidden[ m ] * ( 1 - aHidden[ m ] )
for p = 1 to INPUT_SIZE
aWih[ p ][ m ] += LEARNING_RATE * aInput[ p ] * nHDelta
next
next
next
for n = 1 to 4 // test
aInput[ 1 ] = aData[ n ][ 1 ]
aInput[ 2 ] = aData[ n ][ 2 ]
for m = 1 to HIDDEN_SIZE
aHidden[ m ] = 0
for p = 1 to INPUT_SIZE
aHidden[ m ] += aInput[ p ] * aWih[ p ][ m ]
next
aHidden[ m ] = 1 / ( 1 + Math_E() ^ -aHidden[ m ] )
next
nOutput = 0
for m = 1 to HIDDEN_SIZE
nOutput += aHidden[ m ] * aWho[ m ]
next
nOutput = 1 / ( 1 + Math_E() ^ -nOutput )
? AllTrim( Str( aData[ n ][ 1 ] ) ), " XOR ", AllTrim( Str( aData[ n ][ 2 ] ) ), "=", nOutput
next
return nil
#pragma BEGINDUMP
#include <hbapi.h>
#include <math.h>
#ifndef M_E
#define M_E 2.71828182845904523536
#endif
HB_FUNC( MATH_E )
{
hb_retnd( M_E );
}
#pragma ENDDUMP
See the level of accuracy predicting the result values:
0 XOR 0 = 0.028447448
0 XOR 1 = 0.944000896
1 XOR 0 = 0.952882189
1 XOR 1 = 0.094916901
