在我的博士课程中，我们被指派做一个神经元神经网络，用Python脚本计算OR，AND，XOR运算。我有一个非常奇怪的错误，在我的代码中让我发疯。
首先，我有一个Vector类：
class Vector3D: # Defines the Vector3D class
def __init__(self,bias,x,y): # Defines the variables for the Vector3D class
self.bias = bias
self.x = x
self.y = y
def __add__(self,other): # Defines the built-in "add" ("+") operation for Vector3D
return Vector3D(self.bias+other.bias,self.x+other.x,self.y+other.y)
def __mul__(self,other): # Defines the built-in "multipication" ("*") operation for Vector3D
if(isinstance(other,int)):
return Vector3D(self.bias * other, self.x * other, self.y * other)
else:
return Vector3D(self.bias * other.bias, self.x * other.x, self.y * other.y)
def __str__(self): # Defines the built-in string return value for Vector3D
return "Vector(%f,%f,%f)" % (self.bias, self.x, self.y)
def UpdateWeights(self,eta, targetOutput, currentOutput, valueX, valueY, valueBias): # Function for updating the weights
self.bias = self.bias + (eta * (targetOutput - currentOutput) * valueBias)
self.x = self.x + (eta * (targetOutput - currentOutput) * valueX)
self.y = self.y + (eta * (targetOutput - currentOutput) * valueY)
return Vector3D(self.bias,self.x, self.y)
def getX(self): # Function for getting the x value of a vector
return self.x
def getY(self): # Function for getting the y value of a vector
return self.y
def getBias(self): # Function for getting the bias value of a vector
return self.bias
其次，我有一个神经元课：
class Neuron: # Defines the Neuron class
def __init__(self, dataTable, eta, theta, targetArrayOr, targetArrayAnd, targetArrayXor): # Function for defining the variables for initialization
self.dataTable = dataTable
self.eta = eta
self.theta = theta
self.targetArrayOr = targetArrayOr
self.targetArrayAnd = targetArrayAnd
self.targetArrayXor = targetArrayXor
self.wVbias = random.uniform(-0.2, 0.2)
self.wVX = random.uniform(-0.2, 0.2)
self.wVY = random.uniform(-0.2, 0.2)
self.weightVector = Vector3D(self.wVbias,self.wVX,self.wVY)
self.weightVectorOr = Vector3D(0,0,0)
self.weightVectorAnd = Vector3D(0,0,0)
self.weightVectorXor = Vector3D(0,0,0)
def TrainForOr(self) : # Function training the weight vector for OR operation
iteration = 0 # Number of iterations
check = 0 # Initial value of the while loop
finalCheck = 200 # Final value of the while loop
targetReached = False # Boolean variable for if the target is reached
rowNb = 0 # Initial value of the index number in the data table
weightVector = self.weightVector # Initial weight vector
print(self.weightVector)
while check < finalCheck : # Makes sure that the entire loop runs 200 times for accuracy
while rowNb < len(self.dataTable) : # Makes sure every row is iterated
while targetReached == False:
D1dotW = DotProduct(self.dataTable[rowNb],weightVector) # Dot product of the input vector and the weight vector
if(D1dotW > self.theta):
currentOutput = 1
elif(D1dotW <= self.theta):
currentOutput = 0
if(currentOutput == self.targetArrayOr[rowNb]):
targetReached = True
else:
iteration = iteration + 1
print(self.weightVector)
weightVector = weightVector.UpdateWeights(self.eta,self.targetArrayOr[rowNb], currentOutput, self.dataTable[rowNb].getX(), self.dataTable[rowNb].getY(), self.dataTable[rowNb].getBias())
print(self.weightVector)
targetReached = False
targetReached = False
rowNb = rowNb + 1
check = check + 1
rowNb = 0
self.weightVectorOr = weightVector # Sets the OR weight vector
return "OR - Final weight vector is " + str(weightVector) + " " + "("+ str(iteration) + " iteration(s) )"
我还有AND和XOR的其他方法，但它们与上面的方法相同，只有很小的改动。
现在上面的代码＆＃34;工作＆＃34;作为＆＃34;错误＆＃34;是非常小的，不会改变最终结果。但我想了解它为什么会发生。
当我运行上面的代码段以及其余的GUI代码等时，我得到了控制台结果：
Vector(-0.051856,-0.099352,0.079270)
Vector(-0.051856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
Vector(-0.001856,-0.099352,0.079270)
这意味着初始self.weightVector正在更改行：
weightVector = weightVector.UpdateWeights(self.eta,self.targetArrayOr[rowNb], currentOutput, self.dataTable[rowNb].getX(), self.dataTable[rowNb].getY(), self.dataTable[rowNb].getBias())
我不明白这一点，因为我在self.weightVector方法中没有以任何方式更改UpdateWeights。
如果有人能解释为什么会发生这种情况，我们将不胜感激。
答案 0 :(得分：3)
看看这个方法：
def UpdateWeights(self,eta, targetOutput, currentOutput, valueX, valueY, valueBias):
self.bias = self.bias + (eta * (targetOutput - currentOutput) * valueBias)
self.x = self.x + (eta * (targetOutput - currentOutput) * valueX)
self.y = self.y + (eta * (targetOutput - currentOutput) * valueY)
return Vector3D(self.bias,self.x, self.y)
它不仅会返回一个新的Vector3D，还会修改自己（self)。之前你设置了：
weightVector = self.weightVector
因此调用weightVector.UpdateWeights将导致更改同一对象的self。
答案 1 :(得分：1)
UpdateWeights()方法修改了向量。这就是调用UpdateWeights()时向量发生变化的原因。
这是一个固定版本：
def UpdateWeights(self, eta, targetOutput, currentOutput,
valueX, valueY, valueBias):
"""Returns a new vector with updated weights."""
bias = self.bias + (eta * (targetOutput - currentOutput) * valueBias)
x = self.x + (eta * (targetOutput - currentOutput) * valueX)
y = self.y + (eta * (targetOutput - currentOutput) * valueY)
return Vector3D(bias, x, y)
P.S。另请注意，文档应该放在文档字符串中，而不是注释中。