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Python | Tensorflow logical_and() method
  • Last Updated : 10 Dec, 2018

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_and() [alias tf.math.logical_and] provides support for the logical AND function in Tensorflow. It expects the input of bool type. The input types are tensor and if the tensors contains more than one element, an element-wise logical AND is computed,  $x AND y$ .

Syntax: tf.logical_and(x, y, name=None) or tf.math.logical_and(x, y, name=None)

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.


# 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 AND function and
# storing the result in 'c'
c = tf.logical_and(a, b, name ='logical_and')
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input type:', a)
    print('Input a:',
    print('Input b:',
    print('Return type:', c)


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_and:0", shape=(4, ), dtype=bool)
Output: [ True False False False]


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