class ANN: def __init__(self, userid): self.inputLayer = InputLayer() self.hiddenLayers = [] self.outputLayer = OutputLayer() self.userid = userid self.outputs = [] def init(self, layers_data): self.layers_data = layers_data for i in range(1, len(layers_data) - 1): self.hiddenLayers.append(HiddenLayer()) self.hiddenLayers[-1].init(layers_data[i - 1] + 1, layers_data[i]) self.outputLayer.init(layers_data[-2] + 1, layers_data[-1]) """self.hiddenLayers[0].neurons[0].weights = [0.5, 0.4, -0.8] self.hiddenLayers[0].neurons[1].weights = [0.9, 1.0, 0.1] self.outputLayer.neurons[0].weights = [-1.2, 1.1, -0.3]""" self.outputs = [0 for i in range(layers_data[-1])] def activate(self, input_data): self.inputLayer.init(input_data) self.hiddenLayers[0].activate(self.inputLayer.data) for i in range(1, len(self.hiddenLayers)): self.hiddenLayers[i].activate(self.hiddenLayers[i - 1].outputs) self.outputLayer.activate(self.hiddenLayers[-1].outputs) self.outputs = self.outputLayer.outputs def updateErrorGradients(self, desiredOutputs): sum_errors = self.outputLayer.updateErrorGradients(desiredOutputs) self.hiddenLayers[-1].updateErrorGradients(self.outputLayer) for i in range(len(self.hiddenLayers) - 2, -1, -1): self.hiddenLayers[i].updateErrorGradients(self.hiddenLayers[i + 1]) return sum_errors def updateWeights(self, learningRate): self.hiddenLayers[0].updateWeights(self.inputLayer.data, learningRate) for i in range(1, len(self.hiddenLayers)): self.hiddenLayers[i].updateWeights(self.hiddenLayers[i - 1].outputs, learningRate) self.outputLayer.updateWeights(self.hiddenLayers[-1].outputs, learningRate) def saveData(self): data = [] data.append(self.layers_data) for h in self.hiddenLayers: for n in h.neurons: data.append(n.weights) for n in self.outputLayer.neurons: data.append(n.weights) if not models.Ann_net_data.objects.filter(userid=self.userid): models.Ann_net_data.objects.create(userid=self.userid, data=str(data)) else: models.Ann_net_data.objects.filter(userid=self.userid).update(data=str(data)) del data def loadData(self): if not models.Ann_net_data.objects.filter(userid=self.userid): return data = eval(models.Ann_net_data.objects.get(userid=self.userid).data) self.init(data[0]) index = 1 for h in self.hiddenLayers: for n in h.neurons: n.weights = data[index] index += 1 for n in self.outputLayer.neurons: n.weights = data[index] index += 1 del data return True def __str__(self): result = "" result += "----------------->input layer :\n" + str(self.inputLayer) + '\n' for h in self.hiddenLayers: result += "----------------->hidden layer :\n" + str(h) + '\n' result += "----------------->output layer :\n" + str(self.outputLayer) + '\n' result += "----------------->outputs :\n" + str(self.outputs) return result
class ANN:
def __init__(self, userid):
self.inputLayer = InputLayer()
self.hiddenLayers = []
self.outputLayer = OutputLayer()
self.userid = userid
self.outputs = []
def init(self, layers_data):
self.layers_data = layers_data
for i in range(1, len(layers_data) - 1):
self.hiddenLayers.append(HiddenLayer())
self.hiddenLayers[-1].init(layers_data[i - 1] + 1, layers_data[i])
self.outputLayer.init(layers_data[-2] + 1, layers_data[-1])
"""self.hiddenLayers[0].neurons[0].weights = [0.5, 0.4, -0.8]
self.hiddenLayers[0].neurons[1].weights = [0.9, 1.0, 0.1]
self.outputLayer.neurons[0].weights = [-1.2, 1.1, -0.3]"""
self.outputs = [0 for i in range(layers_data[-1])]
def activate(self, input_data):
self.inputLayer.init(input_data)
self.hiddenLayers[0].activate(self.inputLayer.data)
for i in range(1, len(self.hiddenLayers)):
self.hiddenLayers[i].activate(self.hiddenLayers[i - 1].outputs)
self.outputLayer.activate(self.hiddenLayers[-1].outputs)
self.outputs = self.outputLayer.outputs
def updateErrorGradients(self, desiredOutputs):
sum_errors = self.outputLayer.updateErrorGradients(desiredOutputs)
self.hiddenLayers[-1].updateErrorGradients(self.outputLayer)
for i in range(len(self.hiddenLayers) - 2, -1, -1):
self.hiddenLayers[i].updateErrorGradients(self.hiddenLayers[i + 1])
return sum_errors
def updateWeights_slope(self):
self.hiddenLayers[0].updateWeights_slope(self.inputLayer.data)
for i in range(1, len(self.hiddenLayers)):
self.hiddenLayers[i].updateWeights_slope(
self.hiddenLayers[i - 1].outputs)
self.outputLayer.updateWeights_slope(self.hiddenLayers[-1].outputs)
def updateWeights(self):
for i in range(len(self.hiddenLayers)):
self.hiddenLayers[i].updateWeights()
self.outputLayer.updateWeights()
def saveData(self):
data = []
data.append(self.layers_data)
for h in self.hiddenLayers:
for n in h.neurons:
data.append(n.weights)
for n in self.outputLayer.neurons:
data.append(n.weights)
if not models.Ann_net_data.objects.filter(userid=self.userid):
models.Ann_net_data.objects.create(userid=self.userid,
data=str(data))
else:
models.Ann_net_data.objects.filter(userid=self.userid).update(
data=str(data))
del data
return
### save neurons data
data = []
for h in self.hiddenLayers:
for n in h.neurons:
data.append(n.weights_slope_prev)
data.append(n.deltha)
for n in self.outputLayer.neurons:
data.append(n.weights_slope_prev)
data.append(n.deltha)
if not models.Ann_trainer_rp_data.objects.filter(userid=self.userid):
models.Ann_trainer_rp_data.objects.create(userid=self.userid,
nrndata=str(data))
else:
models.Ann_trainer_rp_data.objects.filter(
userid=self.userid).update(nrndata=str(data))
del data
def loadData(self):
return False
if not models.Ann_net_data.objects.filter(userid=self.userid):
return False
if not models.Ann_trainer_rp_data.objects.filter(userid=self.userid):
return False
### load net data
data = eval(models.Ann_net_data.objects.get(userid=self.userid).data)
self.init(data[0])
index = 1
for h in self.hiddenLayers:
for n in h.neurons:
n.weights = data[index]
index += 1
for n in self.outputLayer.neurons:
n.weights = data[index]
index += 1
del data
### load neurons data
data = eval(
models.Ann_trainer_rp_data.objects.get(userid=self.userid).nrndata)
index = 0
for h in self.hiddenLayers:
for n in h.neurons:
n.weights_slope_prev = data[index]
n.deltha = data[index + 1]
index += 2
for n in self.outputLayer.neurons:
n.weights_slope_prev = data[index]
n.deltha = data[index + 1]
index += 2
del data
return True
def __str__(self):
result = ""
result += "----------------->input layer :\n" + str(
self.inputLayer) + '\n'
for h in self.hiddenLayers:
result += "----------------->hidden layer :\n" + str(h) + '\n'
result += "----------------->output layer :\n" + str(
self.outputLayer) + '\n'
result += "----------------->outputs :\n" + str(self.outputs)
return result
class ANN: def __init__(self, userid): self.inputLayer = InputLayer() self.hiddenLayers = [] self.outputLayer = OutputLayer() self.userid = userid self.outputs = [] def init(self, layers_data): for i in range(1, len(layers_data) - 1): self.hiddenLayers.append(HiddenLayer()) self.hiddenLayers[-1].init(layers_data[i - 1] + 1, layers_data[i]) self.outputLayer.init(layers_data[-2] + 1, layers_data[-1]) """self.hiddenLayers[0].neurons[0].weights = [0.5, 0.4, -0.8] self.hiddenLayers[0].neurons[1].weights = [0.9, 1.0, 0.1] self.outputLayer.neurons[0].weights = [-1.2, 1.1, -0.3]""" self.outputs = [0 for i in range(layers_data[-1])] def activate(self, input_data): self.inputLayer.init(input_data) self.hiddenLayers[0].activate(self.inputLayer.data) for i in range(1, len(self.hiddenLayers)): self.hiddenLayers[i].activate(self.hiddenLayers[i - 1].outputs) self.outputLayer.activate(self.hiddenLayers[-1].outputs) self.outputs = self.outputLayer.outputs def loadData(self): if not models.Ann_net_data.objects.filter(userid=self.userid): return data = eval(models.Ann_net_data.objects.get(userid=self.userid).data) self.init(data[0]) index = 1 for h in self.hiddenLayers: for n in h.neurons: n.weights = data[index] index += 1 for n in self.outputLayer.neurons: n.weights = data[index] index += 1 del data def __str__(self): result = "" result += "----------------->input layer :\n" + str(self.inputLayer) + '\n' for h in self.hiddenLayers: result += "----------------->hidden layer :\n" + str(h) + '\n' result += "----------------->output layer :\n" + str(self.outputLayer) + '\n' result += "----------------->outputs :\n" + str(self.outputs) return result
