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之前，让我们看一下XOR表值.这将有助于我们了解加密和解密过程.
| A | B | A XOR B |
| 0 | 0 | 0 |
| 0 | 1 | 1 |
| 1 | 0 | 1 |
| 1 | 1 | 0 |
XOR密码加密方法主要用于加密数据使用强力方法很难破解，即通过生成与适当密钥匹配的随机加密密钥.
使用XOR密码实现的概念是定义XOR加密密钥然后执行使用此密钥对指定字符串中的字符进行XOR操作，用户尝试对其进行加密.现在我们将重点关注使用TensorFlow的XOR实现，下面提到 : 去;
#Declaring necessary modulesimport tensorflow as tfimport numpy as np"""A simple numpy implementation of a XOR gate to understand the backpropagationalgorithm"""x = tf.placeholder(tf.float64,shape = [4,2],name = "x")#declaring a place holder for input xy = tf.placeholder(tf.float64,shape = [4,1],name = "y")#declaring a place holder for desired output ym = np.shape(x)[0]#number of training examplesn = np.shape(x)[1]#number of featureshidden_s = 2 #number of nodes in the hidden layerl_r = 1#learning rate initializationtheta1 = 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 propagationa1 = 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 layerz1 = 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 addeda2 = tf.concat([np.c_[np.ones(x.shape[0])],tf.sigmoid(z1)],1)#the input of the second layer is multiplied by the weightsz3 = tf.matmul(a2,theta2)#the output is passed through the activation function to obtain the final probabilityh3 = 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 valuesoptimiser = tf.train.GradientDescentOptimizer(learning_rate = l_r).minimize(cost_func)#setting required X and Y values to perform XOR operationX = [[0,0],[0,1],[1,0],[1,1]]Y = [[0],[1],[1],[0]]#initializing all variables, creating a session and running a tensorflow sessioninit = 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 valuesfor 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}))以上行代码生成输出，如下面的屏幕截图所示 :
