TensorFlow bitwise.bitwise_xor() 方法 – Python

TensorFlow bitwise.bitwise_xor()方法执行 bitwise_xor 操作，结果将设置那些在 a 和 b 中不同的位。该操作是在 a 和 b 的表示上完成的。该方法属于按位模块。

Syntax: tf.bitwise.bitwise_xor(a, b, name=None)

Arguments

• a: This must be a Tensor.It should be from the one of the following types: int8, int16, int32, int64, uint8, uint16, uint32, uint64.
• b: This should also be a Tensor, Type same as a.
• name: This is optional parameter and this is the name of the operation.

Return: It returns a Tensor having the same type as a and b.

让我们通过几个例子来看看这个概念：
示例 1：

# Importing the Tensorflow library 
import tensorflow as tf 
  
# A constant a and b 
a = tf.constant(43, dtype = tf.int32) 
b = tf.constant(5, dtype = tf.int32) 
  
# Applying the bitwise_xor function 
# storing the result in 'c' 
c = tf.bitwise.bitwise_xor(a, b) 
  
# Initiating a Tensorflow session 
with tf.Session() as sess:
    print("Input 1", a)
    print(sess.run(a))
  
    print("Input 2", b)
    print(sess.run(b))
  
    print("Output: ", c)
    print(sess.run(c))

输出：

Input 1 Tensor("Const_36:0", shape=(), dtype=int32)
43
Input 2 Tensor("Const_37:0", shape=(), dtype=int32)
5
Output:  Tensor("BitwiseXor_4:0", shape=(), dtype=int32)
46

示例 2：

# Importing the Tensorflow library 
import tensorflow as tf 
  
# A constant vector of size 2 
a = tf.constant([10, 6], dtype = tf.int32) 
b = tf.constant([12, 5], dtype = tf.int32) 
  
# Applying the bitwise_xor function 
# storing the result in 'c' 
c = tf.bitwise.bitwise_xor(a, b) 
  
# Initiating a Tensorflow session 
with tf.Session() as sess:
    print("Input 1", a)
    print(sess.run(a))
    print("Input 2", b)
    print(sess.run(b))
    print("Output: ", c)
    print(sess.run(c))

输出：

Input 1 Tensor("Const_34:0", shape=(2, ), dtype=int32)
[10  6]
Input 2 Tensor("Const_35:0", shape=(2, ), dtype=int32)
[12  5]
Output:  Tensor("BitwiseXor_3:0", shape=(2, ), dtype=int32)
[6 3]

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