Question

我一直在尝试在python中使用反向传播来实现神经网络，并不断收到上述错误。我该如何消除它。该代码仅运行一个时期，而没有计算系统中的错误，因此它无法在网络上向后传播错误

import numpy as np 

 X = [0.4, 0.7]
    y = [0.1]
    class Neural_Network(object):
      def __init__(self):
        #parameters
        self.inputSize = 2
        self.outputSize = 1
        self.hiddenSize = 2

        #weights
        self.W1 = [[0.1, 0.4],
                   [0.2, -0.2]]  # (2x2) weight matrix from input to hidden layer
        self.W2 = np.array([0.2, -0.5])[np.newaxis]  # (2x1) weight matrix from hidden to output layer


      def forward(self, X):
        #forward propagation through our network
        self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
        self.z2 = self.sigmoid(self.z) # activation function
        self.z3 = np.dot(self.z2, self.W2.T) # dot product of hidden layer (z2) and second set of 3x1 weights
        o = self.sigmoid(self.z3) # final activation function
        return o

      def sigmoid(self, s):
        # activation function
        return 1/(1+np.exp(-s))

      def sigmoidPrime(self, s):
        #derivative of sigmoid
        return s * (1 - s)

      def backward(self, X, y, o):
        # backward propgate through the network
        self.o_error = y - o # error in output
        self.o_delta = self.o_error*self.sigmoidPrime(o) # applying derivative of sigmoid to error

        self.z2_error = self.o_delta.dot(self.W2) # z2 error: how much our hidden layer weights contributed to output error
        self.z2_delta = self.z2_error*self.sigmoidPrime(self.z2) # applying derivative of sigmoid to z2 error

        self.W1 += X.T.dot(self.z2_delta) # adjusting first set (input --> hidden) weights
        self.W2 += self.z2.T.dot(self.o_delta) # adjusting second set (hidden --> output) weights

      def train (self, X, y):
        o = self.forward(X)
        self.backward(X, y, o)

    NN = Neural_Network()
    for i in xrange(1000): # trains the NN 1,000 times
      print "Input: \n" + str(X)
      print "Actual Output: \n" + str(y)
      print "Predicted Output: \n" + str(NN.forward(X))
      print "Loss: \n" + str(np.mean(np.square(y - NN.forward(X)))) # mean sum squared loss
      print "\n"
      NN.train(X, y)

我得到的错误是

File "C:/Users/Aaa/AppData/Local/Temp/abc.py", line 43, in backward
    self.W1 += X.T.dot(self.z2_delta) # adjusting first set (input --> hidden) weights
AttributeError: 'list' object has no attribute 'T'

Answer 1

您使用的X是list。您应该使用numpy.array：

X = np.array([0.4, 0.7])

Answer 2

X是list。您可以通过键入type(X)来看到。并且列表没有转置方法。您需要一个数组，因此将X = [0.4, 0.7]替换为：

X = np.array([0.4, 0.7])

哦，顺便说一句：X = np.array([0.4, 0.7])的转置与X相同：

print(np.all(X.T == X))
# Out: True

对于所有一维的X都是如此。

AttributeError：“列表”对象没有属性“ T”

2 个答案: