ANN神经网络——实现异或XOR (Python实现)

一、Introduction

Perceptron can represent AND,OR,NOT

用初中的线性规划问题理解

异或的里程碑意义

想学的通透，先学历史！

据说在人工神经网络（artificial neural network, ANN）发展初期，由于无法实现对多层神经网络（包括异或逻辑）的训练而造成了一场ANN危机，到最后BP算法的出现，才让训练带有隐藏层的多层神经网络成为可能。因此异或的实现在ANN的发展史是也是具有里程碑意义的。异或之所以重要，是因为它相对于其他逻辑关系，例如与（AND）, 或（OR）等，异或是线性不可分的。如下图：

要解决非线性可分问题，需考虑使用多层功能神经元. 例如下图中这个

简单的两层感知机就能解决异或问题。在图中，输出层与输入层之间的一

层神经元，被称为隐含层(hidden layer) ，隐含层和输出层神经元都是拥

有激活函数的功能神经元.

能解决异或问题的两层感知机

参考周志华老师西瓜书

---

二、Python 代码实现

异或肯定是不能通过一条直线区分的，因此单层网络无法实现异或，但两层（包含一个隐藏层）就可以了。

在实际应用中，异或门（Exclusive-OR gate, XOR gate）是数字逻辑中实现逻辑异或的逻辑门，这一函数能实现模为2的加法。因此，异或门可以实现计算机中的二进制加法。

可以有多种方法实现Xor功能，本代码采用的算法图示如下

将上图转化为神经网络层形式便于理解：

# ----------
#
# In this exercise, you will create a network of perceptrons that can represent
# the XOR function, using a network structure like those shown in the previous
# quizzes.
#
# You will need to do two things:
# First, create a network of perceptrons with the correct weights
# Second, define a procedure EvalNetwork() which takes in a list of inputs and
# outputs the value of this network.
#
# ----------

import numpy as np

class Perceptron:
    """
    This class models an artificial neuron with step activation function.
    """

    def __init__(self, weights = np.array([1]), threshold = 0):
        """
        Initialize weights and threshold based on input arguments. Note that no
        type-checking is being performed here for simplicity.
        """
        self.weights = weights
        self.threshold = threshold

    def activate(self, values):
        """
        Takes in @param values, a list of numbers equal to length of weights.
        @return the output of a threshold perceptron with given inputs based on
        perceptron weights and threshold.
        """

        # First calculate the strength with which the perceptron fires
        strength = np.dot(values,self.weights)

        # Then return 0 or 1 depending on strength compared to threshold
        return int(strength >= self.threshold)#this row changed by myself

# Part 1: Set up the perceptron network
Network = [

    # input layer, declare input layer perceptrons here
    [ Perceptron([1,0],1),Perceptron([1,1],2),Perceptron([0,1],1) ], \
    # output node, declare output layer perceptron here
    [ Perceptron([1, -2, 1],   1) ]

]
# Part 2: Define a procedure to compute the output of the network, given inputs
def EvalNetwork(inputValues, Network):
    """
    Takes in @param inputValues, a list of input values, and @param Network
    that specifies a perceptron network. @return the output of the Network for
    the given set of inputs.
    """

    # MY MAIN CODE HERE

    # Be sure your output value is a single number

    #Method1 :
    return Network[1][0].activate([p.activate(inputValues) for p in Network[0]])
    # p is an instance of Perceptron.
    # inner brackets -->input layer
    # Network[1][0] -->Perceptron([1, -2, 1],   1)  -- Only one element

    #Method2 :
    # OutputValue = inputValues
    # for layer in Network:
    #     OutputValue = map(lambda p:p.activate(OutputValue), layer)
    # return OutputValue
    ## but warning:this method return a list ,not a single number
    ## to review Python Grammar?

def test():
    """
    A few tests to make sure that the perceptron class performs as expected.
    """
    print "0 XOR 0 = 0?:", EvalNetwork(np.array([0,0]), Network)
    print "0 XOR 1 = 1?:", EvalNetwork(np.array([0,1]), Network)
    print "1 XOR 0 = 1?:", EvalNetwork(np.array([1,0]), Network)
    print "1 XOR 1 = 0?:", EvalNetwork(np.array([1,1]), Network)

if __name__ == "__main__":
    test()

---

OUTPUT:

Running test()...
0 XOR 0 = 0?: 0
0 XOR 1 = 1?: 1
1 XOR 0 = 1?: 1
1 XOR 1 = 0?: 0
All done!

---