问题描述
我开始实现神经网络的后端,但陷入了python代码中。 下面是神经网络的代码。当我在要制作的应用程序之一中使用用户定义的类时,出现名称为attributeError的错误。请帮助我解决它。 我尝试了所有缩进语法,但没有任何效果。
class NeuralNetwork:
def __init__(self,layers,alpha=0.1):
self.W=[]
self.layers=layers
self.alpha=alpha
for i in np.arange(0,len(layers)-2):
w=np.random.randn(layers[i]+1,layers[i+1]+1)
self.W.append(w/np.sqrt(layers[i]))
w=np.random.randn(layers[-2]+1,layers[-1])
self.W.append(w/np.sqrt(layers[-2]))
def __repr__(self):
return "NeuralNetwork: {}".format("-".join(str(l) for l in self.layers ))
def sigmoid(self,x):
#compute and return sigmoid activation for a given input vlaue
return 1.0/(1+np.exp(-x))
def sigmoid_deriv(self,x):
return x*(1-x)
def fit(self,X,y,epochs=1000,displayUpdate=100):
X=np.c_[X,np.ones((X.shape[0]))]
for epoch in np.arange(0,epochs):
for(x,target) in zip(X,y):
self.fit_partial(x,target)
if epoch==0 or (epoch+1)% displayUpdate==0:
loss=self.calculate_loss(X,y)
print("[INFO] epoch={},loss={:.7f}".format(epoch+1,loss))
def fit_partial(self,x,y):
A=[np.atleast_2d(x)]
#FeedForward
for layer in np.arange(0,len(self.W)):
net=A[layer].dot(self.W[layer])
out=self.sigmoid(net)
A.append(out)
#Backward
error=A[-1]-y
D=[error.self.sigmoid_deriv(A[-1])]
for layer in np.arange(len(A)-2,-1):
delta=D[-1].dot(self.W[layer].T)
delta=delta*self.sigmoid_deriv(A[layer])
D.append(delta)
D=D[::-1]
for layer in np.arange(0,len(self.W)):
self.W[layer] += -self.alpha*A[layer].T.dot(D[layer])
def predict(self,addBias=True):
p=np.atleast_2d(X)
if addBias:
p=np.c_[p,np.ones((p.shape[0]))]
for layer in np.arange(0,len(self.W)):
p=self.sigmoid(np.dot(p,self.W[layer]))
return p
def calculate_loss(self,targets):
targets=np.atleast_2d(targets)
predictions=self.predict(X,addBias=False)
loss=0.5*np.sum((predictions-targets)**2)
return loss
下面是我在分类问题中使用定义的类后弹出的错误日志。
AttributeError Traceback (most recent call last)
<ipython-input-7-6d9ffad8d4e7> in <module>
1 nn=NeuralNetwork([2,2,1],alpha=0.5)
----> 2 nn.fit(X,epochs=20000)
3
4 for (x,y):
5 pred=nn.predict(x)[0][0]
D:\BackPropagation\neuralnetwork.ipynb in fit(self,epochs,displayUpdate)
42 " def sigmoid_deriv(self,x):\n",43 " return x*(1-x) \n",---> 44 " \n",45 " \n",46 " def fit(self,displayUpdate=100):\n",D:\BackPropagation\neuralnetwork.ipynb in fit_partial(self,y)
69 " \n",70 " \n",---> 71 " out=self.sigmoid(net)\n",72 " \n",73 " A.append(out)\n",**AttributeError: 'numpy.ndarray' object has no attribute 'self'**
解决方法
根据神经网络后端理论,必须将误差乘以上一层激活的S形,以得到增量的值,而不是其点积。这样的错误。自我将不会被识别。
更改以下代码
error.self.sigmoid_deriv(A[-1])
到
error*self.sigmoid_deriv(A[-1])