Tensorflow 简明教程

TensorFlow - XOR Implementation

在本章中，我们将了解使用 TensorFlow 的 XOR 实现。在开始使用 TensorFlow 中的 XOR 实现之前，让我们看看 XOR 表值。这将帮助我们理解加密和解密过程。

In this chapter, we will learn about the XOR implementation using TensorFlow. Before starting with XOR implementation in TensorFlow, let us see the XOR table values. This will help us understand encryption and decryption process.

A

B

A XOR B

0

0

0

0

1

1

1

0

1

1

1

0

XOR 密码加密方法基本上用来加密难以用蛮力方法（即通过生成与适当密钥匹配的随机加密密钥来）破解的数据。

XOR Cipher encryption method is basically used to encrypt data which is hard to crack with brute force method, i.e., by generating random encryption keys which match the appropriate key.

使用 XOR 密码实现的概念是定义一个 XOR 加密密钥，然后对指定字符串中的字符执行与该密钥的 XOR 操作，这是用户尝试进行加密的操作。现在，我们将专注于使用 TensorFlow 的 XOR 实现，如下所述：

The concept of implementation with XOR Cipher is to define a XOR encryption key and then perform XOR operation of the characters in the specified string with this key, which a user tries to encrypt. Now we will focus on XOR implementation using TensorFlow, which is mentioned below −

#Declaring necessary modules
import tensorflow as tf
import numpy as np
"""
A simple numpy implementation of a XOR gate to understand the backpropagation
algorithm
"""

x = tf.placeholder(tf.float64,shape = [4,2],name = "x")
#declaring a place holder for input x
y = tf.placeholder(tf.float64,shape = [4,1],name = "y")
#declaring a place holder for desired output y

m = np.shape(x)[0]#number of training examples
n = np.shape(x)[1]#number of features
hidden_s = 2 #number of nodes in the hidden layer
l_r = 1#learning rate initialization

theta1 = tf.cast(tf.Variable(tf.random_normal([3,hidden_s]),name = "theta1"),tf.float64)
theta2 = tf.cast(tf.Variable(tf.random_normal([hidden_s+1,1]),name = "theta2"),tf.float64)

#conducting forward propagation
a1 = tf.concat([np.c_[np.ones(x.shape[0])],x],1)
#the weights of the first layer are multiplied by the input of the first layer

z1 = tf.matmul(a1,theta1)
#the input of the second layer is the output of the first layer, passed through the
   activation function and column of biases is added

a2 = tf.concat([np.c_[np.ones(x.shape[0])],tf.sigmoid(z1)],1)
#the input of the second layer is multiplied by the weights

z3 = tf.matmul(a2,theta2)
#the output is passed through the activation function to obtain the final probability

h3 = tf.sigmoid(z3)
cost_func = -tf.reduce_sum(y*tf.log(h3)+(1-y)*tf.log(1-h3),axis = 1)

#built in tensorflow optimizer that conducts gradient descent using specified
   learning rate to obtain theta values

optimiser = tf.train.GradientDescentOptimizer(learning_rate = l_r).minimize(cost_func)

#setting required X and Y values to perform XOR operation
X = [[0,0],[0,1],[1,0],[1,1]]
Y = [[0],[1],[1],[0]]

#initializing all variables, creating a session and running a tensorflow session
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

#running gradient descent for each iteration and printing the hypothesis
   obtained using the updated theta values
for i in range(100000):
   sess.run(optimiser, feed_dict = {x:X,y:Y})#setting place holder values using feed_dict
   if i%100==0:
      print("Epoch:",i)
      print("Hyp:",sess.run(h3,feed_dict = {x:X,y:Y}))

上面这一行代码生成的输出如以下截图所示 −

The above line of code generates an output as shown in the screenshot below −

xor implementation using tensorflow