util.registerFunction(outtestfunc, "outtestfunc", krui.OUT_FUNC, 0, 0)
util.registerFunction(outtestfunc2, "outtestfunc2", krui.OUT_FUNC, 0, 0)
util.registerFunction(acttest, "acttest", krui.ACT_FUNC, 0, 0)
util.registerFunction(actderivtest, "acttest", krui.ACT_DERIV_FUNC, 0, 0)
util.registerFunction(actderiv2test, "acttest", krui.ACT_2_DERIV_FUNC, 0, 0)
newnum = krui.getNoOfFunctions()
print "After adding:", newnum
for num in range(oldnum - 2, newnum + 1):
print "Function number", num, "Info:", krui.getFuncInfo(num)
print krui.loadNet('encoder.net')
for num in [1, 10, 19]:
krui.setUnitOutFunc(num, "outtestfunc")
for num in [2, 11, 18]:
krui.setUnitOutFunc(num, "outtestfunc2")
for num in [3, 9, 17]:
krui.setUnitActFunc(num, "acttest")
krui.loadNewPatterns('encoder.pat')
krui.DefTrainSubPat()
print "Learning one pattern"
krui.learnSinglePattern(1, (0.2, 0))
krui.setUnitDefaults(1.0, 0, krui.INPUT, 0, 1, "acttest", "outtestfunc")
newunit = krui.createDefaultUnit()
print "New unit:", newunit
print "Act func name:", krui.getUnitActFuncName(newunit)
krui.deleteUnitList(newunit)
krui.saveNet("tmp.net", "testnet")
print "finished"
print "Unit 1 has successor", succ[0],
print "with link strength", succ[1]
num = krui.getNextSuccUnit()[0]
while num :
print "Next successor:", num
num = krui.getNextSuccUnit()[0]
print "Deleting a few links"
krui.getFirstSuccUnit(9)
krui.deleteLink()
krui.getFirstSuccUnit(1)
krui.getNextSuccUnit()
krui.deleteAllInputLinks()
krui.getNextSuccUnit()
krui.deleteAllOutputLinks()
krui.saveNet('foo.net','Testnet')
print krui.getLearnFunc()
#krui.setLearnFunc('RBF-DDA')
print krui.getInitialisationFunc()
print krui.getUpdateFunc()
patset = krui.loadNewPatterns('encoder.pat')
print "Patternset", patset, "loaded."
print "Old pattern number:", krui.getPatternNo()
# fiddle around with some patterns
krui.setPatternNo(8)
krui.deletePattern()
krui.setPatternNo(5)
krui.modifyPattern()
krui.setPatternNo(7)
krui.showPattern(krui.OUTPUT_OUT)
krui.newPattern()
print "Unit 1 has successor", succ[0],
print "with link strength", succ[1]
num = krui.getNextSuccUnit()[0]
while num:
print "Next successor:", num
num = krui.getNextSuccUnit()[0]
print "Deleting a few links"
krui.getFirstSuccUnit(9)
krui.deleteLink()
krui.getFirstSuccUnit(1)
krui.getNextSuccUnit()
krui.deleteAllInputLinks()
krui.getNextSuccUnit()
krui.deleteAllOutputLinks()
krui.saveNet('foo.net', 'Testnet')
print krui.getLearnFunc()
#krui.setLearnFunc('RBF-DDA')
print krui.getInitialisationFunc()
print krui.getUpdateFunc()
patset = krui.loadNewPatterns('encoder.pat')
print "Patternset", patset, "loaded."
print "Old pattern number:", krui.getPatternNo()
# fiddle around with some patterns
krui.setPatternNo(8)
krui.deletePattern()
krui.setPatternNo(5)
krui.modifyPattern()
krui.setPatternNo(7)
krui.showPattern(krui.OUTPUT_OUT)
krui.newPattern()
util.registerFunction(outtestfunc2,"outtestfunc2",krui.OUT_FUNC,0,0)
util.registerFunction(acttest,"acttest",krui.ACT_FUNC,0,0)
util.registerFunction(actderivtest,"acttest",krui.ACT_DERIV_FUNC,0,0)
util.registerFunction(actderiv2test,"acttest",krui.ACT_2_DERIV_FUNC,0,0)
newnum = krui.getNoOfFunctions()
print "After adding:", newnum
for num in range(oldnum - 2, newnum + 1) :
print "Function number", num, "Info:",krui.getFuncInfo(num)
print krui.loadNet('encoder.net')
for num in [1,10,19] :
krui.setUnitOutFunc(num,"outtestfunc")
for num in [2,11,18] :
krui.setUnitOutFunc(num,"outtestfunc2")
for num in [3,9,17] :
krui.setUnitActFunc(num,"acttest")
krui.loadNewPatterns('encoder.pat')
krui.DefTrainSubPat()
print "Learning one pattern"
krui.learnSinglePattern(1,(0.2,0))
krui.setUnitDefaults(1.0,0,krui.INPUT,0,1,"acttest","outtestfunc")
newunit = krui.createDefaultUnit()
print "New unit:", newunit
print "Act func name:", krui.getUnitActFuncName(newunit)
krui.deleteUnitList(newunit)
krui.saveNet("tmp.net","testnet")
print "finished"
krui.setCurrentUnit(num)
for src in hidden:
krui.createLink(src, 0)
print "Creating patterns"
krui.deleteAllPatterns()
patset = krui.allocNewPatternSet()
for unum in range(8):
for curnum in range(8):
for row in inputs, outputs:
krui.setUnitActivation(row[curnum], curnum == unum)
krui.newPattern()
krui.initializeNet(-1, 1)
krui.shufflePatterns(1)
krui.DefTrainSubPat()
print "Training"
i = 0
while i < 50000:
res = krui.learnAllPatterns(0.2, 0)
if not i % 5000: print "Error in learning cycle %d:" % i, res[0]
i = i + 1
print "Writing pyencoder.pat"
krui.saveNewPatterns('pyencoder.pat', patset)
print "Writing pyencoder.net"
krui.saveNet('pyencoder.net', 'pyencoder')
krui.setCurrentUnit(num)
for src in hidden :
krui.createLink(src,0)
print "Creating patterns"
krui.deleteAllPatterns()
patset = krui.allocNewPatternSet()
for unum in range(8) :
for curnum in range(8) :
for row in inputs, outputs :
krui.setUnitActivation(row[curnum], curnum == unum)
krui.newPattern()
krui.initializeNet(-1,1)
krui.shufflePatterns(1)
krui.DefTrainSubPat()
print "Training"
i=0
while i < 50000 :
res = krui.learnAllPatterns(0.2,0)
if not i % 5000 : print "Error in learning cycle %d:" % i, res[0]
i = i + 1
print "Writing pyencoder.pat"
krui.saveNewPatterns('pyencoder.pat',patset)
print "Writing pyencoder.net"
krui.saveNet('pyencoder.net','pyencoder')
# druhá fáze (jemnìj¹í)
i=0
while i < pruch_2:
res = krui.learnAllPatterns(0.03, 0.1)
if not i % 100 : print "Fáze 2, chyba v cyklu %d:" % i, res[0]
i = i + 1
print "Rekonstruuji pùvodní obrázek"
im.blank()
for p in xrange(krui.getNoOfPatterns()):
krui.setPatternNo(p + 1)
krui.showPattern(1);
krui.updateNet()
block = []
for u in xrange(64 + 16 + 1, 64 * 2 + 16 + 1):
block.append(real2pix(krui.getUnitActivation(u)))
im.setBlock(p, block)
print "Zapisuji výsledný obrázek na disk"
im.store("compressed.rgb")
print "Vytváøím soubor vzorkù pro SNNS"
krui.saveNewPatterns('image.pat', patset)
print "Vytváøím soubor sítì pro SNNS"
krui.saveNet('image.net','image')
# konec
