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 
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 , the absolute value is computed as .
For floating point numbers , the absolute value is computed as
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.
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]
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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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