Python | Tensorflow logical_and() method
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,
.
Syntax: tf.logical_and(x, y, name=None) or tf.math.logical_and(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:
import tensorflow as tf
a = tf.constant([ True , False , True , False ], dtype = tf. bool )
b = tf.constant([ True , False , False , True ], dtype = tf. bool )
c = tf.logical_and(a, b, name = 'logical_and' )
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_and:0", shape=(4, ), dtype=bool)
Output: [ True False False False]
Last Updated :
10 Dec, 2018
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