TensorFlow - 异或实现

在本章中，我们将学习使用 TensorFlow 的 XOR 实现。在开始 TensorFlow 中的 XOR 实现之前，让我们先看看 XOR 表值。这将有助于我们理解加密和解密过程。

XOR密码加密方法基本上用于加密难以用暴力方法破解的数据，即通过生成与适当密钥匹配的随机加密密钥。

XOR Cipher 的实现原理是定义一个 XOR 加密密钥，然后用该密钥对用户尝试加密的指定字符串中的字符进行 XOR 运算。现在我们将重点关注使用 TensorFlow 的 XOR 实现，如下所述 -

#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}))

上面的代码行生成一个输出，如下面的屏幕截图所示 -