AttributeError:“列表"对象没有属性"T" [英] AttributeError: 'list' object has no attribute 'T'

问题描述

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

I have been trying to implement a neural network in python that uses back propagation and keep getting the above error. How can I go about eliminating it. The code runs for one epoch without calculating the error in the system hence it is not able to back propagate the error across the network

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'

推荐答案

X是list.您可以通过键入type(X)看到它.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 is a list. You can see that by typing type(X). And lists do not have a transpose method. You want an array, so replace X = [0.4, 0.7] with:

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:

Oh and btw.: A transpose of X = np.array([0.4, 0.7]) will be the same as X:

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

对于所有具有一维尺寸的X都是如此.

This is true for all X with one dimension.

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