Tensorflow bitwise.bitwise_xor()方法–Python
原文:https://www.geesforgeks.org/tensorflow-bitwise-bitwise_xor-method-python/
Tensorflow bitwise.bitwise_xor()方法执行 bitwise_xor 运算，结果将设置 a 和 b 中不同的那些位，运算是在 a 和 b 的表示上完成的，这个方法属于 bitwise module。
语法:
tf.bitwise.bitwise_xor(a, b, name=None)论据
- a: 这一定是张量。它应该来自以下类型之一:int8、int16、int32、int64、uint8、uint16、uint32、uint64。
- b: 这也应该是张量，类型和 a 一样
- 名称:这是可选参数，这是操作的名称。
Return: 它返回一个与 a 和 b 类型相同的张量。
Let’s see this concept with the help of few examples:Example 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
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]
