#! /usr/bin/python from ann import ANN ann = ANN() ann.loadData() inputSample = [0.0, 0.0, 0.2407407407407407, 0.79894179894179895, 0.0 , 0.0, 0.10317460317460314, 0.88095238095238093, 0.95767195767195767, 0.11528822055137844 , 0.010582010582010581, 0.74338624338624337, 0.64550264550264558, 0.8306878306878307, 0.35839598997493738 , 0.49470899470899465, 0.8306878306878307, 0.53968253968253976, 0.67460317460317465, 0.62155388471177941 , 0.75396825396825395, 0.19047619047619047, 0.0, 0.082010582010581978, 0.77694235588972438] ann.activate(inputSample) print ann.outputs
class ANN_Trainer:
def __init__(self, userid):
self.userid = userid
self.inputSamples = []
self.desiredOutputs = []
for s in models.Ann_samples.objects.filter(userid=userid):
self.inputSamples.append(eval(s.input))
self.desiredOutputs.append(eval(s.output))
self.learningRate = 0.1
self.init()
def __del__(self):
del self.inputSamples
del self.desiredOutputs
def init(self):
self.ann = ANN(self.userid)
if self.ann.loadData():
return
self.ann.init([25, 30, 20, 26])
def addData(self, inputSamples, desiredOutputs):
self.sum_errors_prev = [1000, 1000]
self.sum_errors_last = [1000, 1000]
self.inputSamples += inputSamples
self.desiredOutputs += desiredOutputs
def updateLearningRate(self, sum_errors, sum_errors_prev):
err = sum_errors[0]
err_prev = sum_errors_prev[0]
if err_prev == 0.0:
return
if err / err_prev > 1.04:
self.learningRate *= 0.7
if err < err_prev:
self.learningRate *= 1.05
if self.learningRate > 3.0:
self.learningRate = 0.1
def saveData(self, se, episode):
data = [se, self.sum_errors_last, self.learningRate, episode]
if not models.Ann_trainer_data.objects.filter(userid=self.userid):
models.Ann_trainer_data.objects.create(userid=self.userid, data=str(data))
else:
models.Ann_trainer_data.objects.filter(userid=self.userid).update(data=str(data))
del data
def loadData(self):
if not models.Ann_trainer_data.objects.filter(userid=self.userid):
return 0
data = eval(models.Ann_trainer_data.objects.get(userid=self.userid).data)
# del self.sum_errors_prev
# del self.sum_errors_last
# self.sum_errors_prev = data[0]
# self.sum_errors_last = data[1]
self.learningRate = data[2]
res = data[3]
del data
return res
def train(self, max_error):
self.sum_errors_prev = [1000, 1000]
self.sum_errors_last = [1000, 1000]
episode = self.loadData()
while True:
sum_errors = [0, 0]
for i in range(len(self.inputSamples)):
self.ann.activate(self.inputSamples[i])
err = self.ann.updateErrorGradients(self.desiredOutputs[i])
sum_errors[0] += err[0]
sum_errors[1] += err[1]
del err
if i < len(self.inputSamples) - 1:
self.ann.updateWeights(self.learningRate)
self.updateLearningRate(sum_errors, self.sum_errors_prev)
if not episode % 10:
if sum_errors[0] < self.sum_errors_last[0]:
self.ann.saveData()
# print "net data saved to file ..."
self.saveData(sum_errors, episode)
# print "trainer data saved to file ..."
self.sum_errors_last[0] = sum_errors[0]
self.sum_errors_last[1] = sum_errors[1]
# print sum_errors, self.learningRate
# print "episode =", episode
# print "---------------------------"
# if sum_errors[0] < 1.5:
if sum_errors[0] < max_error:
break
self.ann.updateWeights(self.learningRate)
del self.sum_errors_prev
self.sum_errors_prev = sum_errors
episode += 1
class ANN_Trainer: def __init__(self, userid): self.userid = userid self.inputSamples = [] self.desiredOutputs = [] for s in models.Ann_samples.objects.filter(userid=userid): self.inputSamples.append(eval(s.input)) self.desiredOutputs.append(eval(s.output)) self.learningRate = 0.1 self.init() def __del__(self): del self.inputSamples del self.desiredOutputs def init(self): self.ann = ANN(self.userid) if self.ann.loadData(): return self.ann.init([25, 30, 20, 26]) def addData(self, inputSamples, desiredOutputs): self.sum_errors_prev = [1000, 1000] self.sum_errors_last = [1000, 1000] self.inputSamples += inputSamples self.desiredOutputs += desiredOutputs def updateLearningRate(self, sum_errors, sum_errors_prev): err = sum_errors[0] err_prev = sum_errors_prev[0] if err_prev == 0.0: return if err / err_prev > 1.04: self.learningRate *= 0.7 if err < err_prev: self.learningRate *= 1.05 if self.learningRate > 3.0: self.learningRate = 0.1 def saveData(self, se, episode): data = [se, self.sum_errors_last, self.learningRate, episode] if not models.Ann_trainer_data.objects.filter(userid=self.userid): models.Ann_trainer_data.objects.create(userid=self.userid, data=str(data)) else: models.Ann_trainer_data.objects.filter(userid=self.userid).update(data=str(data)) del data def loadData(self): if not models.Ann_trainer_data.objects.filter(userid=self.userid): return 0 data = eval(models.Ann_trainer_data.objects.get(userid=self.userid).data) #del self.sum_errors_prev #del self.sum_errors_last #self.sum_errors_prev = data[0] #self.sum_errors_last = data[1] self.learningRate = data[2] res = data[3] del data return res def train(self, max_error): self.sum_errors_prev = [1000, 1000] self.sum_errors_last = [1000, 1000] episode = self.loadData() while True: sum_errors = [0, 0] for i in range(len(self.inputSamples)): self.ann.activate(self.inputSamples[i]) err = self.ann.updateErrorGradients(self.desiredOutputs[i]) sum_errors[0] += err[0] sum_errors[1] += err[1] del err if i < len(self.inputSamples) - 1: self.ann.updateWeights(self.learningRate) self.updateLearningRate(sum_errors, self.sum_errors_prev) if not episode % 10: if sum_errors[0] < self.sum_errors_last[0]: self.ann.saveData() #print "net data saved to file ..." self.saveData(sum_errors, episode) #print "trainer data saved to file ..." self.sum_errors_last[0] = sum_errors[0] self.sum_errors_last[1] = sum_errors[1] # print sum_errors, self.learningRate #print "episode =", episode #print "---------------------------" #if sum_errors[0] < 1.5: if sum_errors[0] < max_error: break self.ann.updateWeights(self.learningRate) del self.sum_errors_prev self.sum_errors_prev = sum_errors episode += 1
class ANN_Trainer: def __init__(self, userid): self.userid = userid self.inputSamples = [] self.desiredOutputs = [] for s in models.Ann_samples.objects.filter(userid=userid): self.inputSamples.append(eval(s.input)) self.desiredOutputs.append(eval(s.output)) self.init() def __del__(self): del self.inputSamples del self.desiredOutputs def init(self): self.ann = ANN(self.userid) if self.ann.loadData(): return self.ann.init([25, 30, 20, 26]) def addData(self, inputSamples, desiredOutputs): self.sum_errors_last = [1000, 1000] self.inputSamples += inputSamples self.desiredOutputs += desiredOutputs def saveData(self, se, episode): data = [se, episode] if not models.Ann_trainer_rp_data.objects.filter(userid=self.userid): models.Ann_trainer_rp_data.objects.create(userid=self.userid, data=str(data)) else: models.Ann_trainer_rp_data.objects.filter(userid=self.userid).update(data=str(data)) del data def loadData(self): return 0 if not models.Ann_trainer_rp_data.objects.filter(userid=self.userid): return 0 data = eval(models.Ann_trainer_rp_data.objects.get(userid=self.userid).data) #del self.sum_errors_last #self.sum_errors_last = data[0] res = data[1] del data return res def train(self, max_error): self.sum_errors_last = [1000, 1000] episode = self.loadData() while True: sum_errors = [0, 0] for i in range(len(self.inputSamples)): self.ann.activate(self.inputSamples[i]) err = self.ann.updateErrorGradients(self.desiredOutputs[i]) sum_errors[0] += err[0] sum_errors[1] += err[1] del err self.ann.updateWeights_slope() #print sum_errors[0] if not episode % 10: if sum_errors[0] < self.sum_errors_last[0]: self.ann.saveData() #print "net data saved to file ..." self.saveData(sum_errors, episode) #print "trainer data saved to file ..." self.sum_errors_last[0] = sum_errors[0] self.sum_errors_last[1] = sum_errors[1] #print sum_errors #print "episode =", episode #print "---------------------------" if sum_errors[0] < max_error: break self.ann.updateWeights() del sum_errors episode += 1
