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"
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"
#!/usr/bin/python # Construct a encoder.net like network from scratch from snns import krui,util krui.setLearnFunc('Std_Backpropagation') krui.setUpdateFunc('Topological_Order') krui.setUnitDefaults(1,0,krui.INPUT,0,1,'Act_Logistic','Out_Identity') print "Creating the network out of thin air" # build the input layer pos = [0,0,0] inputs = [] for i in range(1,9) : pos[0] = i num = krui.createDefaultUnit() inputs.append(num) krui.setUnitName(num,'Input_%i' % i) krui.setUnitPosition(num, pos) # hidden layer pos[1]=2 hidden = [] for i in range(1,4) : pos[0] = i + 3 num = krui.createDefaultUnit() hidden.append(num) krui.setUnitName(num,'Hidden_%i' % i) krui.setUnitTType(num,krui.HIDDEN) krui.setUnitPosition(num,pos) krui.setCurrentUnit(num)
#!/usr/bin/python # Construct a encoder.net like network from scratch from snns import krui, util krui.setLearnFunc('Std_Backpropagation') krui.setUpdateFunc('Topological_Order') krui.setUnitDefaults(1, 0, krui.INPUT, 0, 1, 'Act_Logistic', 'Out_Identity') print "Creating the network out of thin air" # build the input layer pos = [0, 0, 0] inputs = [] for i in range(1, 9): pos[0] = i num = krui.createDefaultUnit() inputs.append(num) krui.setUnitName(num, 'Input_%i' % i) krui.setUnitPosition(num, pos) # hidden layer pos[1] = 2 hidden = [] for i in range(1, 4): pos[0] = i + 3 num = krui.createDefaultUnit() hidden.append(num) krui.setUnitName(num, 'Hidden_%i' % i) krui.setUnitTType(num, krui.HIDDEN) krui.setUnitPosition(num, pos) krui.setCurrentUnit(num)
if self._ix >= self._blocks: raise StopIteration block = self.getBlock(self._ix) self._ix = self._ix + 1 return block # pomocne funkce def pix2real(p): return p * (2.0/255.0) - 1.0 def real2pix(r): return int(r * 128.0 + 127.5) krui.setLearnFunc('BackpropBatch') krui.setUpdateFunc('Topological_Order') krui.setUnitDefaults(1,0,krui.INPUT,0,1,'Act_TanH','Out_Identity') print "Nahrávám obrázek" im = image() im.load("beerfox2.rgb") print "Velikost:", len(im._data) print "Konstruuji sí»" vnejsi_vrstvy = 8 * 8 vnitrni_vrstva = 4 * 4 # vstupni vrstva 8x8 (64 neuronu) pos = [0,0,0] inputs = []