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Python | Tensorflow abs() method

  • Last Updated : 10 Nov, 2021

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 mathematical operations. Function tf.abs() [alias tf.math.abs] provides support for the absolute function in Tensorflow. It expects the input in form of complex numbers as $a+bi$ or floating point numbers. The input type is tensor and if the input contains more than one element, an element-wise absolute value is computed.
For a complex number $a+bi$ , the absolute value is computed as \sqrt{a^2+b^2}
For floating point numbers $a$ , the absolute value is computed as $a if $a>=0, else -a. $
 

Syntax: tf.abs(x, name=None) or tf.math.abs(x, name=None)
Parameters
x: A Tensor or SparseTensor of type float16, float32, float64, int32, int64, complex64 or complex128. 
name (optional): The name for the operation.
Return type: A Tensor or SparseTensor with the same size and type as that of x with absolute values. For complex64 or complex128 input, the returned Tensor will be of type float32 or float64, respectively. 
 

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Code #1: For Floating point numbers 
 



Python3




# Importing the Tensorflow library
import tensorflow as tf
 
# A constant vector of size 5
a = tf.constant([-0.5, -0.1, 0, 0.1, 0.5], dtype = tf.float32)
 
# Applying the abs function and
# storing the result in 'b'
b = tf.abs(a, name ='abs')
 
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input type:', a)
    print('Input:', sess.run(a))
    print('Return type:', b)
    print('Output:', sess.run(b))

Output: 
 

Input type: Tensor("Const:0", shape=(5, ), dtype=float32)
Input : [-0.5 -0.1  0.   0.1  0.5]
Return Type : Tensor("abs:0", shape=(5, ), dtype=float32)
Output : [0.5 0.1 0.  0.1 0.5]

 
Code #2: Visualization 
 

Python3




# Importing the Tensorflow library
import tensorflow as tf
 
# Importing the NumPy library
import numpy as np
 
# Importing the matplotlib.pyplot function
import matplotlib.pyplot as plt
 
# A vector of size 11 with values from -5 to 5
a = np.linspace(-5, 5, 11)
 
# Applying the absolute function and
# storing the result in 'b'
b = tf.abs(a, name ='abs')
 
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input:', a)
    print('Output:', sess.run(b))
    plt.plot(a, sess.run(b), color = 'red', marker = "o")
    plt.title("tensorflow.abs")
    plt.xlabel("X")
    plt.ylabel("Y")
 
    plt.show()

Output: 
 

Input: [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]
Output: [5. 4. 3. 2. 1. 0. 1. 2. 3. 4. 5.]

 

Code #3: For Complex Numbers 
 

Python3




# Importing the Tensorflow library
import tensorflow as tf
 
# A constant vector of size 2
a = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]],
                              dtype = tf.complex64)
 
# Applying the abs function and
# storing the result in 'b'
b = tf.abs(a, name ='abs')
 
# Initiating a Tensorflow session
with tf.Session() as sess:
    print('Input type:', a)
    print('Input:', sess.run(a))
    print('Return type:', b)
    print('Output:', sess.run(b))

Output: 
 

Input type: Tensor("Const_1:0", shape=(2, 1), dtype=complex64)
Input : [[-2.25+4.75j] [-3.25+5.75j]]
Return Type : Tensor("abs_1:0", shape=(2, 1), dtype=float32)
Output : [[5.255949 ] [6.6049223]]

 
 




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