Tensorflow is an open-source machine learning library developed by Google. One of its applications is to develop deep neural networks.
The module tensorflow.math provides support for many basic logical operations. Function tf.logical_xor() [alias tf.math.logical_xor] provides support for the logical XOR function in Tensorflow. It expects the inputs of bool type. The input types are tensor and if the tensors contains more than one element, an element-wise logical XOR is computed, 
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Syntax: tf.logical_xor(x, y, name=None) or tf.math.logical_xor(x, y, name=None)
Parameters:
x: A Tensor of type bool.
y: A Tensor of type bool.
name (optional): The name for the operation.Return type: A Tensor of bool type with the same size as that of x or y.
Code:
# Importing the Tensorflow library import tensorflow as tf   # A constant vector of size 4 a = tf.constant([True, False, True, False], dtype = tf.bool) b = tf.constant([True, False, False, True], dtype = tf.bool)   # Applying the XOR function and # storing the result in 'c' c = tf.logical_xor(a, b, name ='logical_xor')   # Initiating a Tensorflow session with tf.Session() as sess:     print('Input type:', a)     print('Input a:', sess.run(a))     print('Input b:', sess.run(b))     print('Return type:', c)     print('Output:', sess.run(c)) |
Output:
Input type: Tensor("Const:0", shape=(4, ), dtype=bool)
Input a: [ True False True False]
Input b: [ True False False True]
Return type: Tensor("logical_xor:0", shape=(4, ), dtype=bool)
Output: [False False True True]
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